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JQ2022FIN

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JFSQ2022 Country Replies Finland

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Guidelines

Dear Correspondent, Thank you for contributing to the Joint Forest Sector Questionnaire (JFSQ). Before filling in the worksheets, please read these guidelines. Please use only this questionnaire to report your data. Use this questionnaire also to revise any historical data - fill in the correct year and your name on the cover page. The total number of sheets to be filled in is seven core sheets (green tabs - to be validated by Eurostat) plus three for ITTO (brown tabs - not validated by Eurostat). Four sheets containing cross-references are included at the end. The flat file is for Eurostat for validation purposes, please do not change any cells here. Also, please do not add / delete rows in any of the sheets, because this will affect the functioning of the flatfile. Put all your data into one Excel file. If you send some data in later, give your file a new version number and date (see A.1. below) and notify us of the changes with respect to the previous version. Only send us completely filled-in sheets, highlighting the changes in yellow. Do not delete worksheets. Each sheet has a working area for your input. Most sheets have checking cells and tables. Each working area has white cells and shaded green cells. Eurostat has highlighted the variables it considers most important for its publications - please fill those in as a priority. When you submit a revision, please highlight changes in yellow and explain them in the appropriate 'Note' column, but please fill in all the cells that were filled in previously. Please use flags and notes (see A.6 below). This information is important for Eurostat. A. General recommendations A.1 Please use eDAMIS to send your questionnaire to Eurostat. Choose the correct domain ("FOREST_A_A") and the correct reference year (for this data collection: 2022). A.2 Fill in the JFSQ quality report each year. A.3 The cover page is for your contact details, which are automatically copied to the other worksheets • Check your country code • If necessary change the reference year as appropriate - the previous year will appear automatically If you distribute worksheets to various experts, they can each put their contact details into the sheets. It will then be your job to put all the information together again and to verify the checking tables, since some of them will not work as designed in isolation. A.4 Look at the unit of measurement to be used for each item and report in this unit if possible, using the conversion factors on the last page of the JFSQ definitions. Please report the monetary values in the same unit for both reporting years. Only report data or modify cells in the working areas. Please do not delete checking areas or checking sheets. • Look at the checking areas and make the necessary corrections to your data to remove all warnings (see the specific recommendations) before sending in your data. Fill in real zeros '0' in the worksheets if there is no production or trade. Empty cells will be interpreted as 'Data not available'. • There are counters at the bottom of the tables to indicate the number of cells left to be filled in. Report all data with at least three decimals. Do not use a separator for thousands; for the decimal point, please use the one set up by default. A.5 Report numbers only. If data are confidential, please provide them if possible, appropriately flagged (see A.6). • Eurostat has a right to all confidential data necessary for its work. It has an obligation to use such data only in aggregates and to respect all the legal obligations. • If you cannot provide confidential data, a good option is to send in your own estimate flagged as a national estimate '9'. • As a last resort, leave the cell empty, flag it and write a note indicating data sources and links. Checking tables contain formulae to sum up the totals for sub-items. A.6 Flag cells and write notes as appropriate. Flags should be entered in the 'Flag' columns and notes in the 'Note' columns for the appropriate year and item. The flags to use are: • 3 for break in time series, see metadata (please explain in the notes and in the quality report the reasons) • 4 for definition differs, see metadata (please explain in the notes and in the quality report the reasons) • 5 for repeating the data of a previous year • 6 for confidential data • 7 for provisional data • 9 for national estimate B Specific recommendations B.1 Sheet 'Removals over bark' is for volumes of wood products measured over bark. General over bark/under bark conversion factors are calculated automatically. • Should you use different conversion factor(s) please delete the ones provided and insert your own • If you only have under bark data, please leave this worksheet empty, but revise the table with the conversion factors. • Unchanged conversion factors will be considered revised. A checking table verifies that sums of sub-items agree with the totals. B.2 Checking tables on worksheets improve data quality, verifying that: • The sum of the sub-items equals the total. • The sum of 'of which' items is not larger than the total. All cells in a checking table should be zero or empty. If this is not the case, please check your numbers for the sub-items and totals. The checking table indicates the difference, so if you see a negative value, you will have to decide which number should be increased by that amount. The only exception is when no data are entered due to confidentiality. B.3 Worksheets 'JQ2' contains a checking table for apparent consumption. Apparent consumption = Production + Imports – Exports. It should be positive or nil. If this is not the case, the cell will change colour and indicate the difference. • Please correct the data in the sheets until checking results are positive or nil. One solution is to increase production. • If the data are correct but apparent consumption is still negative, please explain why in the 'Note' column provided in the apparent consumption checking table. B.4 Sheets 'JQ2', 'ECE-EU Species' and 'EU1' on trade have checking tables to verify data consistency. Both quantity and value must be present. When something is missing, messages or coloured cells appear in the checking tables. Please correct your data until all warnings disappear. The meaning of the messages is: • 0: both value and quantity are zero – all is well, there is no trade • ZERO Q: value is reported, quantity is zero - please correct • ZERO V: quantity is reported, value is zero - please correct • REPORT: both quantity and value are blank - please fill in • NO Q: blank cell for quantity – please fill in • NO V: blank cell for value – please fill in Please enter even very small numbers to resolve problems, using as many decimal places as necessary. If there is no way to correct the problem, please write an explanation in the 'Note' column. If there is no trade for a product, please enter 0 for both quantity and value. Thank you for collecting data for the JFSQ, Eurostat's Forestry Team

JFSQ quality report

Joint Forest Sector Questionnaire Quality Report
		Quality information	Country reply
1	Contact
	Country name	Country name	FI
	Contact organisation	Contact organisation	NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791
	Contact name	Contact name
	Contact email address	Contact email address
2	Changes to previous year
	Necessity of update	Are there any changes to the quality report of the last data collection?	NO
		If yes, please provide details below.
3	Statistical processing
	Overview of the source data	Please provide an overview of the sources used to produce JFSQ data.	Data is produced mainly based on statistics of Natural Resources Insitute of Finland and Customs of Finland
		Do you use a dedicated survey (of the industry, of households, of forest owners, etc.)?	NO
		If yes, please provide details (e.g., who are the respondents, what is its frequency?).
		Do you use forestry statistics?	YES
		If yes, please provide details.	Details are given in the following answers.
		Do you use national forest inventory?	NO
		If yes, please provide details.
		Do you use national PRODCOM data compiled according to the CPA classification?	NO
		If yes, please provide details (which products, units, etc.).
		Do you use any other national production statistics?	NO
		If yes, please provide details.
		Do you use data collected by associations of industry?	YES
		If yes, please provide details.	Production data of forest production (6 - 12.4.) is collected and provided by Finnish Forest Industries.
		Do you collect data from direct contacts with manufacturing companies?	YES
		If yes, please provide details.	Data on wood consumption on the members of Finnish Forest Industries is collected from Finnish Forest Industries. Information for other forest industry companies is collected directly by the Natural Resources Institute Finland.
		Do you use estimates of roundwood use (in manufacturing)?	NO
		If yes, please provide details.
		Do you use national trade data?	YES
		If yes, please provide details.	All trade data is taken from Finnish Customs’ statistical database https://tulli.fi/en/statistics/uljas-statistical-database
		Do you use felling reports?	YES
		If yes, please provide details.	Basic industrial roundwood data is obtained from the annually compiled statistics on commercial fellings. Currently, the statistics cover nearly all logs and pulpwood felled in Finland. The volume of roundwood sawn for private use by non-industrial private forest owners is added to these figures. It has been determined by means of small-scale sawmill surveys conducted every 10 years. The most recent small-scale sawmill survey included a mailed questionnaire to identify the consumption of wood at small sawmills in 2008–2010. Data on industrial roundwood felling is collected annually through stratified sampling, including all largest wood buyers. As the forest industry is highly centralised in Finland, the companies included in the data collection process for the annual statistics have covered more than 95 per cent of Finland’s total industrial roundwood felling volumes in recent years. In addition, forest industry companies separately report their own felling volumes and those of their forest owner companies, and Metsähallitus reports felling volumes in state-owned forests. The most recent small-scale sawmill survey was targeted at sawmill companies and contractors that consume at most 10,000 cubic metres of wood per year. The survey identified roughly 1,200 small sawmills that accounted for a little more than three per cent of all wood consumed by the sawmill industry in 2008–2010. Total energy wood removals consist of roundwood consumed as fuelwood in small-scale housing, as well as domestic roundwood harvested for energy generation at heat and power plants, not included in industrial roundwood removals. Volumes of fuelwood consumed in small-scale housing (detached houses, farms and free-time residences) have been identified from wood users through sample surveys conducted nearly every ten years. The most recent survey of the consumption of fuelwood in detached houses was targeted at the 2016/2017 heating season, and its data was collected through a questionnaire mailed to a total of 10,000 residents or homeowners based on stratified sampling.
		Do you use forestry companies' accounting network?	NO
		If yes, please provide details.
		Do you use administrative data (e.g. tax records, business registers)?	NO
		If yes, please provide details.
		Do you use data from national accounts?	NO
		If yes, please provide details (e.g. for which data, from which account tables?).
		Do you use SBS (Structural business statistics)?	NO
		If yes, please provide details (e.g. for which data?).
		Do you use other environmental accounts?	NO
		If yes, please provide details.
		Do you use other statistics (e.g. agriculture statistics)?	NO
		If yes, please specify them.
		Do you use any other sources?	NO
		If yes, please specify them.
	Methodological issues	Are there any pending classification or measurement issues?	NO
		If yes, please specify them.
	Data validation	Do you check the quality of the data collected to compile JFSQ?	NO
		If yes, please explain the quality assurance procedure.
		Do you compare JFSQ data with different data sources or do you perform other cross-checks?	NO
		If yes, please explain your approach.
		Do you have validation rules and other plausibility checks for the outputs of your JFSQ data compilation process?	YES
		If yes, please briefly describe them.	Only those included in the JFSQ Excel sheet.
4	Relevance
	User needs	Please provide references to the relevance of JFSQ at national level e.g. main users, national indicator sets, quantitative policy targets etc.
5	Coherence and comparability
	Coherence - cross domain	Do you compare the JFSQ results with business, energy and agricultural and foreign trade statistics?	YES
		It not, please explain.	All trade data is taken from Finnish Customs’ statistical database. Energy data is compared with Energy statistics of Statistics Finland. Figures are the same.
		Do you cross-check the JFSQ data with the results of European Forest Accounts?	NO
		If yes, please indicate for which reporting items, and comments on the discrepancies observed, if any. It not, please explain.	Finland has only resported to Tables B1 and B2 in the EFA.
	Coherence - internal	Are there any other consistency issues related to your JFSQ data?	NO
		If yes, please explain them.
6	Accessibility and clarity
	Publications	Do you disseminate JFSQ data nationally (e.g. in news releases or other documents)?	NO
		If yes, please provide URLs and/or the reference to the relevant publications.
	Online database	Do you publish your JFSQ accounts in an online data base?	NO
		If yes, please provide URLs.
	Documentation on methodology	Did you prepare a description of your national JFSQ methodology or metadata?	NO
		If yes, please provide URLs.
	Quality documentation	Do you have national quality documentation?	NO
		If yes, please provide URLs.
7	Other comments
	Other comments	Please provide any further feedback you might have on the quality of the reported data, sources and methods used and/or Eurostat's validation and quality report templates.

Cover

Joint Forest Sector Questionnaire
			2022
	DATA INPUT FILE
		Correspondent country:			FI
		Reference year:			2022	Fill in the year
Name of person responsible for reply:

Official address (in full):				NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791

	Telephone:
	Fax:
	E-mail:

Removals over bark

Country:

Date:

Name of Official responsible for reply:

Check Table

Official Address (in full):

NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791

FOREST SECTOR QUESTIONNAIRE

EU JQ1 OB

Telephone:

Discrepancies

Removals

E-mail:

Please verify, if there's an error!

Year 1

Year 2

Flag

Note

Product

Unit

2021

2022

2021

2022

2021

2022

Product

Unit

2021

2022

Code

Quantity

Code

Quantity

ROUNDWOOD REMOVALS OVERBARK

All 2021 data is final

All 2022 data is final

ROUNDWOOD REMOVALS OVERBARK

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ob

76347.955

75112.063

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ob

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ob

10278.023

10825.926

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ob

1.1.C

Coniferous

1000 m3ob

4956.317

5320.816

1.1.C

Coniferous

1000 m3ob

1.1.NC

Non-Coniferous

1000 m3ob

5321.706

5505.11

1.1.NC

Non-Coniferous

1000 m3ob

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ob

66069.932

64286.137

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ob

1.2.C

Coniferous

1000 m3ob

55458.191

54067.555

1.2.C

Coniferous

1000 m3ob

1.2.NC

Non-Coniferous

1000 m3ob

10611.741

10218.582

1.2.NC

Non-Coniferous

1000 m3ob

1.2.NC.T

of which: Tropical

1000 m3ob

1.2.NC.T

of which: Tropical

1000 m3ob

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ob

29327.617

28888.148

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ob

1.2.1.C

Coniferous

1000 m3ob

28168.411

27716.23

1.2.1.C

Coniferous

1000 m3ob

1.2.1.NC

Non-Coniferous

1000 m3ob

1159.206

1171.918

1.2.1.NC

Non-Coniferous

1000 m3ob

1.2.2

PULPWOOD, ROUND AND SPLIT

1000 m3ob

36742.315

35397.989

1.2.2

PULPWOOD, ROUND AND SPLIT

1000 m3ob

1.2.2.C

Coniferous

1000 m3ob

27289.78

26351.325

1.2.2.C

Coniferous

1000 m3ob

1.2.2.NC

Non-Coniferous

1000 m3ob

9452.535

9046.664

1.2.2.NC

Non-Coniferous

1000 m3ob

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ob

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ob

1.2.3.C

Coniferous

1000 m3ob

1.2.3.C

Coniferous

1000 m3ob

1.2.3.NC

Non-Coniferous

1000 m3ob

1.2.3.NC

Non-Coniferous

1000 m3ob

To fill:

Product

Unit

2021

2022

Code

OVERBARK/UNDERBARK CONVERSION FACTORS

ROUNDWOOD (WOOD IN THE ROUGH)

m3/m3

1.144

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

m3/m3

1.153

1.1.C

Coniferous

m3/m3

1.153

1.1.NC

Non-Coniferous

m3/m3

1.153

1.2

INDUSTRIAL ROUNDWOOD

m3/m3

1.100

1.2.C

Coniferous

m3/m3

1.200

1.2.NC

Non-Coniferous

m3/m3

1.157

1.2.NC.T

of which: Tropical

m3/m3

ERROR:#DIV/0!

1.2.1

SAWLOGS AND VENEER LOGS

m3/m3

1.124

1.2.1.C

Coniferous

m3/m3

1.124

1.2.1.NC

Non-Coniferous

m3/m3

1.100

1.2.2

PULPWOOD, ROUND AND SPLIT

m3/m3

1.200

1.2.2.C

Coniferous

m3/m3

1.158

1.2.2.NC

Non-Coniferous

m3/m3

1.100

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

m3/m3

1.200

1.2.3.C

Coniferous

m3/m3

ERROR:#DIV/0!

1.2.3.NC

Non-Coniferous

m3/m3

1.100

JQ1 Production

Country:

Date:

Name of Official responsible for reply:

Official Address (in full):

FOREST SECTOR QUESTIONNAIRE JQ1

NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791

Industrial Roundwood Balance

PRIMARY PRODUCTS

Telephone:

This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!

Discrepancies

Removals and Production

E-mail:

test for good numbers, missing number, bad number, negative number

Year 1

Year 2

Flag

Note

Product

Unit

2021

2022

2021

2022

2021

2022

Product

Unit

2021

2022

2021

2022

% change

Conversion factors

Code

Quantity

Code

Quantity

Roundwood

Industrial roundwood availability

63,030

249,192

295%

m3 of wood in m3 or t of product

ALL REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)

Recovered wood used in particle board

-7%

Solid wood equivalent

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ub

66713.896538

65637.339725

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ub

Solid Wood Demand

agglomerate production

365

360

-2%

2.4

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ub

8911.045941

9386.077842

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ub

Sawnwood production

11,966

11,273

-6%

1.1.C

Coniferous

1000 m3ub

4297.126839

4613.147472

1.1.C

Coniferous

1000 m3ub

veneer production

170

184

1.1.NC

Non-Coniferous

1000 m3ub

4613.919102

4772.93037

1.1.NC

Non-Coniferous

1000 m3ub

plywood production

1,130

1,110

-2%

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ub

57802.850597

56251.261883

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ub

particle board production (incl OSB)

-7%

1.58

1.2.C

Coniferous

1000 m3ub

48628.868117

47415.890085

1.2.C

Coniferous

1000 m3ub

fibreboard production

-6%

1.8

1.2.NC

Non-Coniferous

1000 m3ub

9173.98248

8835.371798

1.2.NC

Non-Coniferous

1000 m3ub

mechanical/semi-chemical pulp production

2,640

2,840

2.5

1.2.NC.T

of which: Tropical

1000 m3ub

1.2.NC.T

of which: Tropical

1000 m3ub

chemical pulp production

8,320

7,680

-8%

4.9

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ub

26093.095192

25701.545062

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ub

dissolving pulp production

missing data

5.7

1.2.1.C

Coniferous

1000 m3ub

25067.197882

24664.397632

1.2.1.C

Coniferous

1000 m3ub

Availability

Solid Wood Demand

missing data

1.2.1.NC

Non-Coniferous

1000 m3ub

1025.89731

1037.14743

1.2.1.NC

Non-Coniferous

1000 m3ub

Difference (roundwood-demand)

missing data

positive = surplus

1.2.2

PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)

1000 m3ub

31709.755405

30549.716821

1.2.2

PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)

1000 m3ub

gap (demand/availability)

missing data

Negative number means not enough roundwood available

1.2.2.C

Coniferous

1000 m3ub

23561.670235

22751.492453

1.2.2.C

Coniferous

1000 m3ub

Positive number means more roundwood available than demanded

1.2.2.NC

Non-Coniferous

1000 m3ub

8148.08517

7798.224368

1.2.2.NC

Non-Coniferous

1000 m3ub

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ub

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ub

1.2.3.C

Coniferous

1000 m3ub

1.2.3.C

Coniferous

1000 m3ub

% of particle board that is from recovered wood

35%

1.2.3.NC

Non-Coniferous

1000 m3ub

1.2.3.NC

Non-Coniferous

1000 m3ub

share of agglomerates produced from industrial roundwood residues

100%

PRODUCTION

usable industrial roundwood - amount of roundwood that is used, remainder leaves industry

98.5%

WOOD CHARCOAL

1000 t

Data not available

WOOD CHARCOAL

1000 t

WOOD CHIPS, PARTICLES AND RESIDUES

1000 m3

15128.864

14375.6525

Data revised.

WOOD CHIPS, PARTICLES AND RESIDUES

1000 m3

3.1

WOOD CHIPS AND PARTICLES

1000 m3

9653.442

9302.541

Data revised.

3.1

WOOD CHIPS AND PARTICLES

1000 m3

3.2

WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)

1000 m3

5475.422

5073.1115

Data revised.

3.2

WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)

1000 m3

3.2.1

of which: Sawdust

1000 m3

3067.501

2912.517

Data revised.

3.2.1

of which: Sawdust

1000 m3

RECOVERED POST-CONSUMER WOOD

1000 t

551.688148

518.849848

RECOVERED POST-CONSUMER WOOD

1000 t

WOOD PELLETS AND OTHER AGGLOMERATES

1000 t

365.186

359.629

WOOD PELLETS AND OTHER AGGLOMERATES

1000 t

5.1

WOOD PELLETS

1000 t

365.186

359.629

5.1 includes also 5.2

5.1

WOOD PELLETS

1000 t

5.2

OTHER AGGLOMERATES

1000 t

5.2 included in 5.1

5.2

OTHER AGGLOMERATES

1000 t

SAWNWOOD (INCLUDING SLEEPERS)

1000 m3

11966

11273

SAWNWOOD (INCLUDING SLEEPERS)

1000 m3

6.C

Coniferous

1000 m3

11900

11200

6.C

Coniferous

1000 m3

6.NC

Non-Coniferous

1000 m3

Data revised

6.NC

Non-Coniferous

1000 m3

6.NC.T

of which: Tropical

1000 m3

6.NC.T

of which: Tropical

1000 m3

VENEER SHEETS

1000 m3

170

184

VENEER SHEETS

1000 m3

7.C

Coniferous

1000 m3

Data not available

7.C

Coniferous

1000 m3

7.NC

Non-Coniferous

1000 m3

Data not available

7.NC

Non-Coniferous

1000 m3

7.NC.T

of which: Tropical

1000 m3

7.NC.T

of which: Tropical

1000 m3

WOOD-BASED PANELS

1000 m3

1233

1206

WOOD-BASED PANELS

1000 m3

8.1

PLYWOOD

1000 m3

1130

1110

8.1

PLYWOOD

1000 m3

8.1.C

Coniferous

1000 m3

Data not available

8.1.C

Coniferous

1000 m3

8.1.NC

Non-Coniferous

1000 m3

Data not available

8.1.NC

Non-Coniferous

1000 m3

8.1.NC.T

of which: Tropical

1000 m3

8.1.NC.T

of which: Tropical

1000 m3

8.1.1

of which: Laminated Veneer Lumber (LVL)

1000 m3

Data not available

8.1.1

of which: Laminated Veneer Lumber (LVL)

1000 m3

8.1.1.C

Coniferous

1000 m3

Data not available

8.1.1.C

Coniferous

1000 m3

8.1.1.NC

Non-Coniferous

1000 m3

Data not available

8.1.1.NC

Non-Coniferous

1000 m3

8.1.1.NC.T

of which: Tropical

1000 m3

Data not available

8.1.1.NC.T

of which: Tropical

1000 m3

8.2

PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD

1000 m3

Confidential estimate

8.2

PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD

1000 m3

8.2.1

of which: ORIENTED STRAND BOARD (OSB)

1000 m3

Confidential estimate

8.2.1

of which: ORIENTED STRAND BOARD (OSB)

1000 m3

8.3

FIBREBOARD

1000 m3

Confidential estimate

8.3

FIBREBOARD

1000 m3

8.3.1

HARDBOARD

1000 m3

Confidential estimate

8.3.1

HARDBOARD

1000 m3

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

1000 m3

Confidential estimate

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

1000 m3

8.3.3

OTHER FIBREBOARD

1000 m3

Confidential estimate

8.3.3

OTHER FIBREBOARD

1000 m3

WOOD PULP

1000 t

10960

10520

WOOD PULP

1000 t

9.1

MECHANICAL AND SEMI-CHEMICAL WOOD PULP

1000 t

2640

2840

9.1

MECHANICAL AND SEMI-CHEMICAL WOOD PULP

1000 t

9.2

CHEMICAL WOOD PULP

1000 t

8320

7680

9.2

CHEMICAL WOOD PULP

1000 t

9.2.1

SULPHATE PULP

1000 t

Data not available

9.2.1

SULPHATE PULP

1000 t

9.2.1.1

of which: BLEACHED

1000 t

Data not available

9.2.1.1

of which: BLEACHED

1000 t

9.2.2

SULPHITE PULP

1000 t

Data not available

9.2.2

SULPHITE PULP

1000 t

9.3

DISSOLVING GRADES

1000 t

Data not available

9.3

DISSOLVING GRADES

1000 t

OTHER PULP

1000 t

Data not available

OTHER PULP

1000 t

10.1

PULP FROM FIBRES OTHER THAN WOOD

1000 t

Data not available

10.1

PULP FROM FIBRES OTHER THAN WOOD

1000 t

10.2

RECOVERED FIBRE PULP

1000 t

Data not available

10.2

RECOVERED FIBRE PULP

1000 t

RECOVERED PAPER

1000 t

460

450

RECOVERED PAPER

1000 t

PAPER AND PAPERBOARD

1000 t

8660

7210

PAPER AND PAPERBOARD

1000 t

12.1

GRAPHIC PAPERS

1000 t

3250

2160

12.1

GRAPHIC PAPERS

1000 t

12.1.1

NEWSPRINT

1000 t

Data not available

12.1.1

NEWSPRINT

1000 t

12.1.2

UNCOATED MECHANICAL

1000 t

Data not available

12.1.2

UNCOATED MECHANICAL

1000 t

12.1.3

UNCOATED WOODFREE

1000 t

Data not available

12.1.3

UNCOATED WOODFREE

1000 t

12.1.4

COATED PAPERS

1000 t

Data not available

12.1.4

COATED PAPERS

1000 t

12.2

HOUSEHOLD AND SANITARY PAPERS

1000 t

12.2 Included in 12.4

12.2

HOUSEHOLD AND SANITARY PAPERS

1000 t

12.3

PACKAGING MATERIALS

1000 t

4220

4150

12.3

PACKAGING MATERIALS

1000 t

12.3.1

CASE MATERIALS

1000 t

Data not available

12.3.1

CASE MATERIALS

1000 t

12.3.2

CARTONBOARD

1000 t

Data not available

12.3.2

CARTONBOARD

1000 t

12.3.3

WRAPPING PAPERS

1000 t

Data not available

12.3.3

WRAPPING PAPERS

1000 t

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

1000 t

Data not available

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

1000 t

12.4

OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)

1000 t

1190

900

12.4 Includes also 12.2 Revised data.

12.4 Includes also 12.2

12.4

OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)

1000 t

GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1

1000 m3

Data not available

GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1

1000 m3

15.1

GLULAM

1000 m3

Data not available

15.1

GLULAM

1000 m3

15.2

CROSS-LAMINATED TIMBER (CLT or X-LAM)

1000 m3

Data not available

15.2

CROSS-LAMINATED TIMBER (CLT or X-LAM)

1000 m3

I BEAMS (I-JOISTS)1

1000 t

Data not available

I BEAMS (I-JOISTS)1

1000 t

1 Glulam, CLT and I Beams are classified as secondary wood products but for ease of reporting are included here

To fill:

m3ub = cubic metres solid volume underbark (i.e. excluding bark)

m3 = cubic metres solid volume

t = metric tonnes

https://www.fao.org/3/cb8216en/cb8216en.pdf

JQ2 Trade

			61	62	61	62	91	92	91	92
		FOREST SECTOR QUESTIONNAIRE JQ2				Country:	FI		Date:																																							0	both VALUE and quantity reported ZERO
						Name of Official responsible for reply:																																										ZERO Q	quantity ZERO when VALUE is reported									INTRA-EU	The difference might be caused by Intra-EU trade
		PRIMARY PRODUCTS				Official Address (in full):		NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791																														This table highlights discrepancies between production and trade. For any negative number, indicating greater net exports than production, please verify your data!										ZERO V	Value ZERO when quantity is reported									CHECK
		Trade				Telephone:			Fax:	0																		This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!																		ZERO CHECK 1 - if no value please CHECK		NO Q	no quantity reported						ZERO CHECK 2 - if no value in Zero Check 1
						E-mail:																																			Country:	FI						NO V	no value reported			Treshold:	2		verifies whether the JQ2 figures refers only to intra-EU trade
	Value must always be in 1000 NAC (national currency)										Flag	Flag	Flag	Flag	Flag	Flag	Flag	Flag	Note	Note	Note	Note	Note	Note	Note	Note		Trade		Discrepancies																		REPORT	no figures reported
Product		Unit of	I M P O R T				E X P O R T				Import				Export				Import				Export				Product			I M P O R T				E X P O R T				Product			Apparent Consumption		Related Notes		Product		Value per	I M P O R T		E X P O R T		Column1	Column2	Product		Value per	I M P O R T		E X P O R T
code	Product	quantity	2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		code			2021		2022		2021		2022		code			2021	2022	2021	2022	code	Product	unit	2021	2022	2021	2022	IMPORT	EXPORT	code	Product	unit	2021	2022	2021	2022
			Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value				Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	6441.4119312	293666.602	3016.863488	247565.144	1119.09896	95639.977	1804.1389536	150573.545									All 2021 trade data is final		All 2022 trade data is provisional		All 2021 trade data is final		All 2022 trade data is provisional		1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK	1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	72,036	66,850			1	ROUNDWOOD (WOOD IN THE ROUGH)	NAC/m3	46	82	85	83	ACCEPT	ACCEPT	1	ROUNDWOOD (WOOD IN THE ROUGH)	NAC/m3
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	143.3679312	6981.578	137.513488	10733.085	48.57396	1895.276	101.3669536	4506.406																	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	9,006	9,422			1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	NAC/m3	49	78	39	44	ACCEPT	ACCEPT	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	NAC/m3
1.1.C	Coniferous	1000 m3ub	110.3252016	3868.409	119.9084032	7847.962	46.34872	1683.405	95.530528	3724.801																	1.1.C	Coniferous	1000 m3ub									1.1.C	Coniferous	1000 m3ub	4,361	4,638			1.1.C	Coniferous	NAC/m3	35	65	36	39	ACCEPT	ACCEPT	1.1.C	Coniferous	NAC/m3
1.1.NC	Non-Coniferous	1000 m3ub	33.0427296	3113.169	17.6050848	2885.123	2.22524	211.871	5.8364256	781.605																	1.1.NC	Non-Coniferous	1000 m3ub									1.1.NC	Non-Coniferous	1000 m3ub	4,645	4,785			1.1.NC	Non-Coniferous	NAC/m3	94	164	95	134	ACCEPT	ACCEPT	1.1.NC	Non-Coniferous	NAC/m3
1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	6298.044	286685.024	2879.35	236832.059	1070.525	93744.701	1702.772	146067.139																	1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK	1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	63,030	57,428			1.2	INDUSTRIAL ROUNDWOOD	NAC/m3	46	82	88	86	ACCEPT	ACCEPT	1.2	INDUSTRIAL ROUNDWOOD	NAC/m3
1.2.C	Coniferous	1000 m3ub	1467.83	75470.83	1295.643	97428.224	965.99	87673.586	1348.069	121347.463																	1.2.C	Coniferous	1000 m3ub									1.2.C	Coniferous	1000 m3ub	49,131	47,363			1.2.C	Coniferous	NAC/m3	51	75	91	90	ACCEPT	ACCEPT	1.2.C	Coniferous	NAC/m3
1.2.NC	Non-Coniferous	1000 m3ub	4830.214	211214.194	1583.707	139403.835	104.535	6071.115	354.703	24719.676																	1.2.NC	Non-Coniferous	1000 m3ub									1.2.NC	Non-Coniferous	1000 m3ub	13,900	10,064			1.2.NC	Non-Coniferous	NAC/m3	44	88	58	70	CHECK	ACCEPT	1.2.NC	Non-Coniferous	NAC/mt
1.2.NC.T	of which: Tropical1	1000 m3ub	0.004	35.988	0	0	0.022	44.446	0.011	43.563																	1.2.NC.T	of which: Tropical1	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK	1.2.NC.T	of which: Tropical1	1000 m3ub	-0	-0			1.2.NC.T	of which: Tropical	NAC/m3	8997	0	2020	3960	CHECK	ACCEPT	1.2.NC.T	of which: Tropical	1000 m3
2	WOOD CHARCOAL	1000 t	5.387026	3879.27	4.46745	3800.494	0.216576	165.646	0.148072	125.963																	2	WOOD CHARCOAL	1000 t									2	WOOD CHARCOAL	1000 t	5	4			2	WOOD CHARCOAL	NAC / t	720	851	765	851	ACCEPT	ACCEPT	2	WOOD CHARCOAL	1000 m3
3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	4659.5614608872	181243.825	1888.6792726599	119638.479	147.8582434382	6219.096	180.6978344648	9479.228																	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	19,641	16,084			3	WOOD CHIPS, PARTICLES AND RESIDUES	NAC/m3	39	63	42	52	ACCEPT	ACCEPT	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3
3.1	WOOD CHIPS AND PARTICLES	1000 m3	4406.6012759725	175138.267	1711.5280427522	112735.709	147.8323861676	6207.611	180.4398007829	9428.826																	3.1	WOOD CHIPS AND PARTICLES	1000 m3									3.1	WOOD CHIPS AND PARTICLES	1000 m3	13,912	10,834			3.1	WOOD CHIPS AND PARTICLES	NAC/m3	40	66	42	52	ACCEPT	ACCEPT	3.1	WOOD CHIPS AND PARTICLES	1000 mt
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	252.9601849147	6105.558	177.1512299077	6902.77	0.0258572705	11.485	0.2580336819	50.402																	3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3									3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	5,728	5,250			3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	NAC/m3	24	39	444	195	ACCEPT	CHECK	3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 mt
3.2.1	of which: Sawdust	1000 m3	252.9601849147	6105.558	177.1512299077	6902.77	0.0258572705	11.485	0.2580336819	50.402																	3.2.1	of which: Sawdust	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	3.2.1	of which: Sawdust	1000 m3	3,320	3,089			3.2.1	of which: Sawdust	NAC/m3	24	39	444	195	ACCEPT	CHECK
4	RECOVERED POST-CONSUMER WOOD	1000 t	252.137623	8058.262	180.006317	7362.999	0.366993	82.127	0.003484	1.098																	4	RECOVERED POST-CONSUMER WOOD	1000 t									4	RECOVERED POST-CONSUMER WOOD	1000 t	803	699			4	RECOVERED POST-CONSUMER WOOD	NAC / t	32	41	224	315	ACCEPT	ACCEPT	4	RECOVERED POST-CONSUMER WOOD	1000 mt
5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	238.384096	25763.472	207.58023	49696.796	20.520467	2201.435	33.782289	4655.498																	5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	583	533			5	WOOD PELLETS AND OTHER AGGLOMERATES	NAC / t	108	239	107	138	CHECK	ACCEPT	5	WOOD PELLETS AND OTHER AGGLOMERATES	NAC/m3
5.1	WOOD PELLETS	1000 t	196.126738	23113.719	195.644846	46038.066	12.525848	1575.689	18.117054	2618.033																	5.1	WOOD PELLETS	1000 t									5.1	WOOD PELLETS	1000 t	549	537			5.1	WOOD PELLETS	NAC / t	118	235	126	145	ACCEPT	ACCEPT	5.1	WOOD PELLETS	NAC/m3
5.2	OTHER AGGLOMERATES	1000 t	42.257358	2649.753	11.935384	3658.73	7.994619	625.746	15.665235	2037.465																	5.2	OTHER AGGLOMERATES	1000 t									5.2	OTHER AGGLOMERATES	1000 t	34	-4			5.2	OTHER AGGLOMERATES	NAC / t	63	307	78	130	CHECK	ACCEPT	5.2	OTHER AGGLOMERATES	NAC/m3
6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	577.897	160397.533	333.764	118555.537	8735.857	2572713.492	8576.479	2595922.152																	6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	3,808	3,030			6	SAWNWOOD (INCLUDING SLEEPERS)	NAC/m3	278	355	295	303	ACCEPT	ACCEPT	6	SAWNWOOD (INCLUDING SLEEPERS)	NAC/m3
6.C	Coniferous	1000 m3	547.269	133705.357	300.017	81521.236	8715.693	2562670.729	8553.927	2583504.78																	6.C	Coniferous	1000 m3									6.C	Coniferous	1000 m3	3,732	2,946			6.C	Coniferous	NAC/m3	244	272	294	302	ACCEPT	ACCEPT	6.C	Coniferous	NAC/m3
6.NC	Non-Coniferous	1000 m3	30.628	26692.176	33.747	37034.301	20.164	10042.763	22.552	12417.372																	6.NC	Non-Coniferous	1000 m3									6.NC	Non-Coniferous	1000 m3	76	84			6.NC	Non-Coniferous	NAC/m3	871	1097	498	551	ACCEPT	ACCEPT	6.NC	Non-Coniferous	NAC/m3
6.NC.T	of which: Tropical1	1000 m3	4.799	6122.931	7.883	9793.476	3.945	3631.587	3.644	2674.858																	6.NC.T	of which: Tropical1	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	6.NC.T	of which: Tropical1	1000 m3	1	4			6.NC.T	of which: Tropical	NAC/m3	1276	1242	921	734	ACCEPT	ACCEPT	6.NC.T	of which: Tropical	NAC/m3
7	VENEER SHEETS	1000 m3	9.084	6004.03	11.241	12467.008	171.347	54117.482	175.414	62350.77																	7	VENEER SHEETS	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	7	VENEER SHEETS	1000 m3	8	20			7	VENEER SHEETS	NAC/m3	661	1109	316	355	ACCEPT	ACCEPT	7	VENEER SHEETS	NAC/m3
7.C	Coniferous	1000 m3	0.085	410.688	0.299	1153.298	55.369	28758.383	50.548	30111.972																	7.C	Coniferous	1000 m3									7.C	Coniferous	1000 m3	-55	-50			7.C	Coniferous	NAC/m3	4832	3857	519	596	ACCEPT	ACCEPT	7.C	Coniferous	NAC/m3
7.NC	Non-Coniferous	1000 m3	8.999	5593.342	10.942	11313.71	115.978	25359.099	124.866	32238.798																	7.NC	Non-Coniferous	1000 m3									7.NC	Non-Coniferous	1000 m3	-107	-114			7.NC	Non-Coniferous	NAC/m3	622	1034	219	258	ACCEPT	ACCEPT	7.NC	Non-Coniferous	NAC/m3
7.NC.T	of which: Tropical	1000 m3	2.768	1101.939	2.966	1229.243	0.022	7.375	0.021	13.934																	7.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	7.NC.T	of which: Tropical	1000 m3	3	3			7.NC.T	of which: Tropical	NAC/m3	398	414	335	664	ACCEPT	ACCEPT	7.NC.T	of which: Tropical	NAC/m3
8	WOOD-BASED PANELS	1000 m3	417.37084125	175899.783	371.335	192097.504	1031.42633431	581443.567	971.748	717996.825																	8	WOOD-BASED PANELS	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8	WOOD-BASED PANELS	1000 m3	619	606			8	WOOD-BASED PANELS	NAC/m3	421	517	564	739	ACCEPT	ACCEPT	8	WOOD-BASED PANELS	NAC/m3
8.1	PLYWOOD	1000 m3	121.649	72689.783	87.188	64362.659	955.493	548258.478	900.051	676945.177																	8.1	PLYWOOD	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.1	PLYWOOD	1000 m3	296	297			8.1	PLYWOOD	NAC/m3	598	738	574	752	ACCEPT	ACCEPT	8.1	PLYWOOD	NAC/m3
8.1.C	Coniferous	1000 m3	19.241	10121.059	29.691	18590.006	673.568	303765.837	658.085	395667.91																	8.1.C	Coniferous	1000 m3									8.1.C	Coniferous	1000 m3	-654	-628			8.1.C	Coniferous	NAC/m3	526	626	451	601	ACCEPT	ACCEPT	8.1.C	Coniferous	NAC/m3
8.1.NC	Non-Coniferous	1000 m3	102.408	62568.724	57.497	45772.653	281.925	244492.641	241.966	281277.267																	8.1.NC	Non-Coniferous	1000 m3									8.1.NC	Non-Coniferous	1000 m3	-180	-184			8.1.NC	Non-Coniferous	NAC/m3	611	796	867	1162	ACCEPT	ACCEPT	8.1.NC	Non-Coniferous	NAC/m3
8.1.NC.T	of which: Tropical	1000 m3	0.815	2234.051	1.453	2231.742	0.206	760.631	0.227	704.976																	8.1.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.1.NC.T	of which: Tropical	1000 m3	1	1			8.1.NC.T	of which: Tropical	NAC/m3	2741	1536	3692	3106	ACCEPT	ACCEPT	8.1.NC.T	of which: Tropical	NAC/m3
8.1.1	of which: Laminated Veneer Lumber (LVL)	1000 m3			1.131	758.341			255.195	178297.826																	8.1.1	of which: Laminated Veneer Lumber (LVL)	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.1.1	of which: Laminated Veneer Lumber (LVL)	1000 m3	0	-254			8.1.1	of which: Laminated Veneer Lumber (LVL)	NAC/m3	REPORT	671	REPORT	699	CHECK	CHECK
8.1.1.C	Coniferous	1000 m3			0.959	602.542			247.832	173230.415																	8.1.1.C	Coniferous	1000 m3									8.1.1.C	Coniferous	1000 m3	0	-247			8.1.1.C	Coniferous	NAC/m3	REPORT	628	REPORT	699	CHECK	CHECK
8.1.1.NC	Non-Coniferous	1000 m3			0.172	155.799			7.363	5067.411																	8.1.1.NC	Non-Coniferous	1000 m3									8.1.1.NC	Non-Coniferous	1000 m3	0	-7			8.1.1.NC	Non-Coniferous	NAC/m3	REPORT	906	REPORT	688	CHECK	CHECK
8.1.1.NC.T	of which: Tropical	1000 m3			0.13	116.367			0.013	5.486																	8.1.1.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.1.1.NC.T	of which: Tropical	1000 m3	0	0			8.1.1.NC.T	of which: Tropical	NAC/m3	REPORT	895	REPORT	422	CHECK	CHECK
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3	128.66	43990.064	143.59	58980.217	29.69	9081.163	26.138	10907.108																	8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3									8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3	153	167			8.2	PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD	NAC/m3	342	411	306	417	ACCEPT	ACCEPT	8.2	PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD	NAC/m3
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	47.339	17410.628	56.485	21916.432	0.066	37.501	0.262	130.106																	8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	47	56			8.2.1	of which: ORIENTED STRANDBOARD (OSB)	NAC/m3	368	388	568	497	ACCEPT	ACCEPT	8.2.1	of which: ORIENTED STRANDBOARD (OSB)	NAC/m3
8.3	FIBREBOARD	1000 m3	167.06184125	59219.936	140.557	68754.628	46.24333431	24103.926	45.559	30144.54																	8.3	FIBREBOARD	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.3	FIBREBOARD	1000 m3	170	141			8.3	FIBREBOARD	NAC/m3	354	489	521	662	ACCEPT	ACCEPT	8.3	FIBREBOARD	NAC/m3
8.3.1	HARDBOARD	1000 m3	25.539	13141.73	20.569	12406.415	38.744	18772.382	41.32	26497.636																	8.3.1	HARDBOARD	1000 m3									8.3.1	HARDBOARD	1000 m3	36	25			8.3.1	HARDBOARD	NAC/m3	515	603	485	641	ACCEPT	ACCEPT	8.3.1	HARDBOARD	NAC/mt
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	108.8481	41088.161	86.32	50008.467	6.765022	5148.926	3.864	3574.976																	8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3									8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	102	82			8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	NAC/m3	377	579	761	925	ACCEPT	ACCEPT	8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	NAC/mt
8.3.3	OTHER FIBREBOARD	1000 m3	32.67474125	4990.045	33.668	6339.746	0.73431231	182.618	0.375	71.928																	8.3.3	OTHER FIBREBOARD	1000 m3									8.3.3	OTHER FIBREBOARD	1000 m3	32	33			8.3.3	OTHER FIBREBOARD	NAC/m3	153	188	249	192	ACCEPT	ACCEPT	8.3.3	OTHER FIBREBOARD	NAC/mt
9	WOOD PULP	1000 t	149.786251	88130.274	259.268315	201833.572	4475.258316	2585364.618	3963.438065	2864560.147																	9	WOOD PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	9	WOOD PULP	1000 t	6,635	6,816			9	WOOD PULP	NAC/t	588	778	578	723	ACCEPT	ACCEPT	9	WOOD PULP	NAC/mt
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	9.461724	3808.316	1.464619	744.174	352.734527	125149.538	332.362574	146048.33																	9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t									9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	2,297	2,509			9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	NAC/t	402	508	355	439	ACCEPT	ACCEPT	9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	NAC/mt
9.2	CHEMICAL WOOD PULP	1000 t	133.567665	77215.652	252.70262	193294.934	3822.016216	2242119.846	3624.86731	2714599.918																	9.2	CHEMICAL WOOD PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	9.2	CHEMICAL WOOD PULP	1000 t	4,632	4,308			9.2	CHEMICAL WOOD PULP	NAC/t	578	765	587	749	ACCEPT	ACCEPT	9.2	CHEMICAL WOOD PULP	NAC/mt
9.2.1	SULPHATE PULP	1000 t	130.696765	74345.615	249.751563	189212.985	3821.785512	2241923.336	3624.855019	2714562.091																	9.2.1	SULPHATE PULP	1000 t									9.2.1	SULPHATE PULP	1000 t	-3,691	-3,375			9.2.1	SULPHATE PULP	NAC/t	569	758	587	749	ACCEPT	ACCEPT	9.2.1	SULPHATE PULP	NAC/mt
9.2.1.1	of which: BLEACHED	1000 t	106.675607	63033.354	230.324236	179968.459	3654.358161	2146493.961	3408.22975	2586405.662																	9.2.1.1	of which: BLEACHED	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	9.2.1.1	of which: BLEACHED	1000 t	-3,548	-3,178			9.2.1.1	of which: BLEACHED	NAC/t	591	781	587	759	ACCEPT	ACCEPT	9.2.1.1	of which: BLEACHED	NAC/mt
9.2.2	SULPHITE PULP	1000 t	2.8709	2870.037	2.951057	4081.949	0.230704	196.51	0.012291	37.827																	9.2.2	SULPHITE PULP	1000 t									9.2.2	SULPHITE PULP	1000 t	3	3			9.2.2	SULPHITE PULP	NAC/t	1000	1383	852	3078	ACCEPT	CHECK	9.2.2	SULPHITE PULP	NAC/mt
9.3	DISSOLVING GRADES	1000 t	6.756862	7106.306	5.101076	7794.464	300.507573	218095.234	6.208181	3911.899																	9.3	DISSOLVING GRADES	1000 t									9.3	DISSOLVING GRADES	1000 t	-294	-1			9.3	DISSOLVING GRADES	NAC/t	1052	1528	726	630	ACCEPT	ACCEPT	9.3	DISSOLVING GRADES	NAC/mt
10	OTHER PULP	1000 t	2.948363	3684.133	4.894641	9133.463	0.051138	100.665	0.051817	139.052																	10	OTHER PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	10	OTHER PULP	1000 t	3	5			10	OTHER PULP	NAC/t	1250	1866	1968	2684	ACCEPT	ACCEPT	10	OTHER PULP	NAC/mt
10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	2.006406	3259.202	3.226039	8289.658	0.04168	92.084	0.047876	133.599																	10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t									10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	2	3			10.1	PULP FROM FIBRES OTHER THAN WOOD	NAC/t	1624	2570	2209	2791	ACCEPT	ACCEPT	10.1	PULP FROM FIBRES OTHER THAN WOOD	NAC/mt
10.2	RECOVERED FIBRE PULP	1000 t	0.941957	424.931	1.668602	843.805	0.009458	8.581	0.003941	5.453																	10.2	RECOVERED FIBRE PULP	1000 t									10.2	RECOVERED FIBRE PULP	1000 t	1	2			10.2	RECOVERED FIBRE PULP	NAC/t	451	506	907	1384	ACCEPT	ACCEPT	10.2	RECOVERED FIBRE PULP	NAC/mt
11	RECOVERED PAPER	1000 t	67.288019	13466.324	90.917188	18617.767	147.020301	20680.507	118.79845	21354.181																	11	RECOVERED PAPER	1000 t									11	RECOVERED PAPER	1000 t	380	422			11	RECOVERED PAPER	NAC/t	200	205	141	180	ACCEPT	ACCEPT	11	RECOVERED PAPER	NAC/mt
12	PAPER AND PAPERBOARD	1000 t	353.444831	291438.162	336.978375	352365.101	8388.559061	6262823.479	7025.372693	7141189.447																	12	PAPER AND PAPERBOARD	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	12	PAPER AND PAPERBOARD	1000 t	625	522			12	PAPER AND PAPERBOARD	NAC/t	825	1046	747	1016	ACCEPT	ACCEPT	12	PAPER AND PAPERBOARD	NAC/mt
12.1	GRAPHIC PAPERS	1000 t	70.972866	53402.649	91.184311	86729.658	3615.227156	2285472.386	2450.045059	2392360.202																	12.1	GRAPHIC PAPERS	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	12.1	GRAPHIC PAPERS	1000 t	-294	-199			12.1	GRAPHIC PAPERS	NAC/t	752	951	632	976	ACCEPT	ACCEPT	12.1	GRAPHIC PAPERS	NAC/mt
12.1.1	NEWSPRINT	1000 t	34.201265	13839.671	49.81562	33008.338	84.974454	37260.569	54.184912	40655.481																	12.1.1	NEWSPRINT	1000 t									12.1.1	NEWSPRINT	1000 t	-51	-4			12.1.1	NEWSPRINT	NAC/t	405	663	438	750	ACCEPT	ACCEPT	12.1.1	NEWSPRINT	NAC/mt
12.1.2	UNCOATED MECHANICAL	1000 t	3.620554	8073.357	5.325906	5106.216	426.621941	217670.314	310.064246	239409.604																	12.1.2	UNCOATED MECHANICAL	1000 t									12.1.2	UNCOATED MECHANICAL	1000 t	-423	-305			12.1.2	UNCOATED MECHANICAL	NAC/t	2230	959	510	772	CHECK	ACCEPT	12.1.2	UNCOATED MECHANICAL	NAC/mt
12.1.3	UNCOATED WOODFREE	1000 t	15.183061	15760.885	20.955766	30159.886	665.908512	452556.243	298.19805	357469.176																	12.1.3	UNCOATED WOODFREE	1000 t									12.1.3	UNCOATED WOODFREE	1000 t	-651	-277			12.1.3	UNCOATED WOODFREE	NAC/t	1038	1439	680	1199	ACCEPT	ACCEPT	12.1.3	UNCOATED WOODFREE	NAC/mt
12.1.4	COATED PAPERS	1000 t	17.967986	15728.736	15.087019	18455.218	2437.722249	1577985.26	1787.597851	1754825.941																	12.1.4	COATED PAPERS	1000 t									12.1.4	COATED PAPERS	1000 t	-2,420	-1,773			12.1.4	COATED PAPERS	NAC/t	875	1223	647	982	ACCEPT	ACCEPT	12.1.4	COATED PAPERS	NAC/mt
12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	1.869663	3413.688	3.028118	7185.223	19.487219	19099.163	19.510029	26754.411																	12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t									12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	-18	-16			12.2	HOUSEHOLD AND SANITARY PAPERS	NAC/t	1826	2373	980	1371	ACCEPT	ACCEPT	12.2	HOUSEHOLD AND SANITARY PAPERS	NAC/mt
12.3	PACKAGING MATERIALS	1000 t	278.890943	228962.157	239.589195	246096.477	4602.562778	3851504.021	4426.55091	4597186.507																	12.3	PACKAGING MATERIALS	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	12.3	PACKAGING MATERIALS	1000 t	-104	-37			12.3	PACKAGING MATERIALS	NAC/t	821	1027	837	1039	ACCEPT	ACCEPT	12.3	PACKAGING MATERIALS	NAC/mt
12.3.1	CASE MATERIALS	1000 t	153.727223	82115.166	137.141741	94649.701	1129.462709	699066.62	1144.124699	927143.504																	12.3.1	CASE MATERIALS	1000 t									12.3.1	CASE MATERIALS	1000 t	-976	-1,007			12.3.1	CASE MATERIALS	NAC/t	534	690	619	810	ACCEPT	ACCEPT	12.3.1	CASE MATERIALS	NAC/mt
12.3.2	CARTONBOARD	1000 t	85.740341	104588.381	65.375788	101785.248	2809.244407	2504501.191	2743.358156	2933137.885																	12.3.2	CARTONBOARD	1000 t									12.3.2	CARTONBOARD	1000 t	-2,724	-2,678			12.3.2	CARTONBOARD	NAC/t	1220	1557	892	1069	ACCEPT	ACCEPT	12.3.2	CARTONBOARD	NAC/mt
12.3.3	WRAPPING PAPERS	1000 t	33.472912	37961.457	31.012207	43709.311	490.703256	527039.689	354.440979	577020.059																	12.3.3	WRAPPING PAPERS	1000 t									12.3.3	WRAPPING PAPERS	1000 t	-457	-323			12.3.3	WRAPPING PAPERS	NAC/t	1134	1409	1074	1628	ACCEPT	ACCEPT	12.3.3	WRAPPING PAPERS	NAC/mt
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	5.950467	4297.153	6.059459	5952.217	173.152406	120896.521	184.627076	159885.059																	12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t									12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	-167	-179			12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	NAC/t	722	982	698	866	ACCEPT	ACCEPT	12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	NAC/mt
12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t	1.711359	5659.668	3.176751	12353.743	151.281908	106747.909	129.266695	124888.327																	12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t									12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t	1,040	774			12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	NAC/t	3307	3889	706	966	ACCEPT	ACCEPT	12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	NAC/mt
15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)2	1000 m3			17048.27857	15968.626			325517.76413	307751.85																	15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1	1000 m3	0	-308,469			15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1	NAC/m3	REPORT	1	REPORT	1	CHECK	CHECK
15.1	GLULAM	1000 m3			16,010	14,693			324,168	307,335																	15.1	GLULAM	1000 m3									15.1	GLULAM	1000 m3	0	-308,158			15.1	GLULAM	NAC/m3	REPORT	1	REPORT	1	CHECK	CHECK
15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	1000 m3			1,039	1,276			1,350	417																	15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	1000 m3									15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	1000 m3	0	-311			15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	NAC/m3	REPORT	1	REPORT	0	CHECK	CHECK
16	I BEAMS (I-JOISTS)2	1000 t			0	0			0	0																	16	I BEAMS (I-JOISTS)1	1000 t									16	I BEAMS (I-JOISTS)1	1000 t	0	0			16	I BEAMS (I-JOISTS)1	NAC/t	REPORT	0	REPORT	0	CHECK	CHECK
1 Please include the non-coniferous non-tropical species exported by tropical countries or imported from tropical countries.
2 Glulam, CLT and I Beams are classified as secondary wood products but for ease of reporting are included here
		To fill:	8	8	0	0	8	8	0	0
	m3 = cubic metres solid volume
	m3ub = cubic metres solid volume underbark (i.e. excluding bark)
	t = metric tonnes

https://www.fao.org/3/cb8216en/cb8216en.pdf

JQ3 Secondary PP Trade

			62	91	91
			Country:	FI	Date:
			Name of Official responsible for reply:
			Official Address (in full):
	FOREST SECTOR QUESTIONNAIRE JQ3		NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791
	SECONDARY PROCESSED PRODUCTS		Telephone/Fax:		0
	Trade		E-mail:
																This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!
	Value must always be in 1000 NAC (national currency)																Discrepancies
	Eurozone countries may use the old national currency, but only in both years					Flag	Flag	Flag	Flag	Note	Note	Note	Note
Product	Product	I M P O R T V A L U E		E X P O R T V A L U E		Import		Export		Import		Export			Product	Product	I M P O R T V A L U E		E X P O R T V A L U E
code		2021	2022	2021	2022	2021	2022	2021	2022	2021	2022	2021	2022		Code		2021	2022	2021	2022
13	SECONDARY WOOD PRODUCTS	534499.489	670520.674	658300.669	451069.817					All 2021 trade data is final	All 2022 trade data is provisional			All 2021 trade data is final		All 2022 trade data is provisional		OK	OK	OK
13.1	FURTHER PROCESSED SAWNWOOD	20964.006	32056.698	89393.713	85316.078										13.1	FURTHER PROCESSED SAWNWOOD	OK	OK	OK	OK
13.1.C	Coniferous	6258.563	8518.928	88306.284	83042.391										13.1.C	Coniferous
13.1.NC	Non-coniferous	14705.443	23537.77	1087.429	2273.687										13.1.NC	Non-coniferous
13.1.NC.T	of which: Tropical	857.1	1111.77	245.127	333.043										13.1.NC.T	of which: Tropical	OK	OK	OK	OK
13.2	WOODEN WRAPPING AND PACKAGING MATERIAL	27149.167	51324.488	37818.3	44568.362										13.2	WOODEN WRAPPING AND PACKAGING MATERIAL
13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE	11472.481	14821.198	3721.698	4147.039										13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD1	99967.865	103066.5	322661.542	60808.489										13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD1
13.5	WOODEN FURNITURE	313708.585	388616.33	124995.913	168964.113										13.5	WOODEN FURNITURE
13.6	PREFABRICATED BUILDINGS OF WOOD	38953.984	50799.014	72160.507	78873.529										13.6	PREFABRICATED BUILDINGS OF WOOD
13.7	OTHER MANUFACTURED WOOD PRODUCTS	22283.401	29836.446	7548.996	8392.207										13.7	OTHER MANUFACTURED WOOD PRODUCTS
14	SECONDARY PAPER PRODUCTS	268050.301	344900.497	418404.098	527262.188										14	SECONDARY PAPER PRODUCTS	OK	OK	OK	OK
14.1	COMPOSITE PAPER AND PAPERBOARD	3306.512	4349.658	25448.2	31025.603										14.1	COMPOSITE PAPER AND PAPERBOARD
14.2	SPECIAL COATED PAPER AND PULP PRODUCTS	41631.265	56206.666	129446.136	135473.469										14.2	SPECIAL COATED PAPER AND PULP PRODUCTS
14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE	41702.484	59442.782	88645.041	118856.039										14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE
14.4	PACKAGING CARTONS, BOXES ETC.	96085.55	114189.539	28996.441	39438.192										14.4	PACKAGING CARTONS, BOXES ETC.
14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	85324.49	110711.852	145868.28	202468.885										14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	OK	OK	OK	OK
14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE	1524.718	3412.258	61.692	83.024										14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE
14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP	12271.442	18391.09	1835.692	2641.871										14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP
14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE	8682.616	9134.027	684.109	670.182										14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE
1 In February 2023 this definition was updated to exclude Glulam, Cross-Laminated Timber and I-Beams which are now distinct items in the JFSQ (15.1, 15.2 and 16). This change was made to reflect the update of HS2022.
	To fill:	0	0	0	0

ECE-EU Species


							Country:	FI		Date:
							Name of Official responsible for reply:

		FOREST SECTOR QUESTIONNAIRE ECE/EU Species Trade					Official Address (in full):																														Check Table
							NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791																																		0	both VALUE and quantity reported ZERO
			Trade in Roundwood and Sawnwood by species				Telephone:			Fax:	0																		DISCREPANCIES												ZERO Q	quantity ZERO when VALUE is reported
							E-mail:																																		ZERO V	Value ZERO when quantity is reported
																													Checks whether the sum of subitems is bigger than the total										Zero check - if no value please CHECK		NO Q	no quantity reported
		Value must always be in 1000 NAC ( national currency)																																							NO V	no value reported			Treshold:	2
		Eurozone countries may use the old national currency, but only in both years			1000NAC								Flag	Flag	Flag	Flag	Flag	Flag	Flag	Flag	Note	Note	Note	Note	Note	Note	Note	Note													REPORT	no figures reported
					I M P O R T				E X P O R T				Import				Export				Import				Export				I M P O R T				E X P O R T							Value per	I M P O R T		E X P O R T		Unit price check
Product	Classification	Classification		Unit of	2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		Classification	Classification		unit	2021	2022	2021	2022	IMPORT	EXPORT
Code	HS2022	CN2022	Product	Quantity	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	HS2022	CN2022	Product
1.2.C	4403.11/21/22/23/24/25/26		Industrial Roundwood, Coniferous	1000 m3ub	1467.83	75470.83	1295.643	97428.224	965.99	87673.586	1348.069	121347.463									All 2021 trade data is final		All 2022 trade data is provisional						OK	OK	OK	OK	OK	OK	OK	OK	4403.11/21/22/23/24/25/26		Industrial Roundwood, Coniferous	NAC/m3	51	75	91	90	ACCEPT	ACCEPT							PRODUCTION		I M P O R T				E X P O R T
	4403.21/22		of which: Pine (Pinus spp.)	1000 m3ub	686.269	36373.917	671.034	49306.505	694.107	62994.886	927.625	82970.746																	OK	OK	OK	OK	OK	OK	OK	OK	4403.21/22		of which: Pine (Pinus spp.)	NAC/m3	53	73	91	89	ACCEPT	ACCEPT	Product	Classification	Classification		Unit of		2021	2022	2021		2022		2021		2022
		4403 21 10	sawlogs and veneer logs	1000 m3ub	63.772	4335.156	41.352	2779.369	285.006	20162.139	345.272	27389.877																										4403 21 10	sawlogs and veneer logs	NAC/m3	68	67	71	79	ACCEPT	ACCEPT	Code	HS2022	CN2022	Product	Quantity		Quantity	Quantity	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
		4403 21 90 4403 22 00	pulpwood and other industrial roundwood	1000 m3ub	622.497	32038.761	629.682	46527.136	409.101	42832.747	582.353	55580.869																										4403 21 90 4403 22 00	pulpwood and other industrial roundwood	NAC/m3	51	74	105	95	ACCEPT	ACCEPT	1	4401.11/12 44.03		Roundwood production	1000 m3	JQ1	66,714	65,637
	4403.23/24		of which: Fir/Spruce (Abies spp., Picea spp.)	1000 m3ub	781.552	39096.855	624.463	48083.009	236.479	12842.714	385.541	23676.924																	OK	OK	OK	OK	OK	OK	OK	OK	4403.23/24		of which: Fir/Spruce (Abies spp., Picea spp.)	NAC/m3	50	77	54	61	ACCEPT	ACCEPT						EU2	66713.896538	65637.339725
		4403 23 10	sawlogs and veneer logs	1000 m3ub	100.449	7125.368	82.672	6392.221	6.069	469.169	78.217	6422.1																										4403 23 10	sawlogs and veneer logs	NAC/m3	71	77	77	82	ACCEPT	ACCEPT						dif	0	0
		4403 23 90 4403 24 00	pulpwood and other industrial roundwood	1000 m3ub	681.103	31971.487	541.791	41690.788	230.41	12373.545	307.324	17254.824																										4403 23 90 4403 24 00	pulpwood and other industrial roundwood	NAC/m3	47	77	54	56	ACCEPT	ACCEPT	1.2.C	4403.11/21/22/23/24/25/26		Industrial Roundwood (wood in the rough), Coniferous	1000 m3	JQ2			1,468	75,471	1,296	97,428	966	87,674	1,348	121,347
1.2.NC	4403.12/41/42/49/91/93/94 4403.95/96/97/98/99		Industrial Roundwood, Non-Coniferous	1000 m3ub	4830.214	211214.194	1583.707	139403.835	104.535	6071.115	354.703	24719.676																	OK	OK	OK	OK	OK	OK	OK	OK	4403.12/41/42/49/91/93/94 4403.95/96/97/98/99		Industrial Roundwood, Non-Coniferous	NAC/m3	44	88	58	70	CHECK	ACCEPT						ECE/EU			1,468	75,471	1,296	97,428	966	87,674	1,348	121,347
	ex4403.12 4403.91		of which: Oak (Quercus spp.)	1000 m3ub	0.006	19.265	0.009	12.888	0	0	0	0																									ex4403.12 4403.91		of which: Oak (Quercus spp.)	NAC/m3	3211	1432	0	0	CHECK	CHECK						dif			0	0	0	0	0	0	0	0
	ex4403.12 4403.93/94		of which: Beech (Fagus spp.)	1000 m3ub	0	0	0.001	0.047	0	0	0	0																									ex4403.12 4403.93/94		of which: Beech (Fagus spp.)	NAC/m3	0	47	0	0	CHECK	CHECK	1.2.NC	4403.12/41/42/49/91/93/94/95/96/97/98/99		Industrial Roundwood (wood in the rough), Non-Coniferous	1000 m3	JQ2			4,830	211,214	1,584	139,404	105	6,071	355	24,720
	ex4403.12 4403.95/96		of which: Birch (Betula spp.)	1000 m3ub	4646.04	203879.254	1387.98	115355.221	98.837	5502.646	345.028	24070.048																	OK	OK	OK	OK	OK	OK	OK	OK	ex4403.12 4403.95/96		of which: Birch (Betula spp.)	NAC/m3	44	83	56	70	ACCEPT	ACCEPT						ECE/EU			4,830	211,214	1,584	139,404	105	6,071	355	24,720
		4403 95 10	sawlogs and veneer logs	1000 m3ub	174.086	14448.668	32.162	3076.514	0	0	0.746	86.923																										4403 95 10	sawlogs and veneer logs	NAC/m3	83	96	0	117	ACCEPT	CHECK						dif			0	0	0	0	0	0	0	0
		ex4403 12 00 4403 95 90 4403 96 00	pulpwood and other industrial roundwood	1000 m3ub	4471.954	189430.586	1355.818	112278.707	98.837	5502.646	344.282	23983.125																										ex4403 12 00 4403 95 90 4403 96 00	pulpwood and other industrial roundwood	NAC/m3	42	83	56	70	ACCEPT	ACCEPT	6.C	4406.11/91 4407.11/12/13/14/19		Sawnwood, Coniferous	1000 m3	JQ2			547	133,705	300	81,521	8,716	2,562,671	8,554	2,583,505
	ex4403.12 4403.97		of which: Poplar/Aspen (Populus spp.)	1000 m3ub	178.776	6965.015	79.616	4354.178	0.078	3.573	1.583	119.875																									ex4403.12 4403.97		of which: Poplar/Aspen (Populus spp.)	NAC/m3	39	55	46	76	ACCEPT	ACCEPT						ECE/EU			547	133,705	302	82,200	8,716	2,562,671	8,563	2,585,605
	ex4403.12 4403.98		of which: Eucalyptus (Eucalyptus spp.)	1000 m3ub	0	0	106.983	19070.564	0	0	0	0																									ex4403.12 4403.98		of which: Eucalyptus (Eucalyptus spp.)	NAC/m3	0	178	0	0	CHECK	CHECK						dif			0	0	-2	-679	0	0	-9	-2,100
6.C	4406.11/91 4407.11/12/13/14/19		Sawnwood, Coniferous	1000 m3	547.269	133705.357	301.635	82200.408	8715.693	2562670.729	8563.032	2585604.845																	OK	OK	OK	OK	OK	OK	OK	OK	4406.11/91 4407.11/12/13/14/19		Sawnwood, Coniferous	NAC/m3	244	273	294	302	ACCEPT	ACCEPT	6.NC	4406.12/92 4407.21/22/23/25/26/27/28/29/91/92/93/94/95/96/97/99		Sawnwood, Non-coniferous	1000 m3	JQ2			31	26,692	34	37,034	20	10,043	23	12,417
	4407.11 ex4407.13 ex4406.11/91		of which: Pine (Pinus spp.)	1000 m3	173.841	42264.517	95.894	28248.233	4345.373	1241655.375	4211.018	1245029.07																									4407.11 ex4407.13 ex4406.11/91		of which: Pine (Pinus spp.)	NAC/m3	243	295	286	296	ACCEPT	ACCEPT						ECE/EU			31	26,692	34	37,034	20	10,043	23	12,417
	4407.12 ex4407.13/14 ex4406.11/91		of which: Fir/Spruce (Abies spp., Picea spp.)	1000 m3	343.467	81287.748	185.119	44486.216	4369.292	1320485.99	4339.221	1335876.828																									4407.12 ex4407.13/14 ex4406.11/91		of which: Fir/Spruce (Abies spp., Picea spp.)	NAC/m3	237	240	302	308	ACCEPT	ACCEPT						dif			0	0	0	0	0	0	0	0
6.NC	4406.12/92 4407.21/22/23/25/26/27/28/29/ 91/92/93/94/95/96/97/99		Sawnwood, Non-coniferous	1000 m3	30.628	26692.176	33.747	37034.301	20.164	10042.763	22.552	12417.372																	OK	OK	OK	OK	OK	OK	OK	OK	4406.12/92 4407.21/22/23/25/26/27/28/29/ 91/92/93/94/95/96/97/99		Sawnwood, Non-coniferous	NAC/m3	871	1097	498	551	ACCEPT	ACCEPT
	ex4406.12/92 4407.91		of which: Oak (Quercus spp.)	1000 m3	6.472	8907.654	6.439	11972.37	0.051	56.276	0.055	101.726																									ex4406.12/92 4407.91		of which: Oak (Quercus spp.)	NAC/m3	1376	1859	1103	1850	ACCEPT	ACCEPT
	ex4406.12/92 4407.92		of which: Beech (Fagus spp.)	1000 m3	0.204	83.759	0.295	183.037	0.108	0.89	0.001	0.14																									ex4406.12/92 4407.92		of which: Beech (Fagus spp.)	NAC/m3	411	620	8	140	ACCEPT	CHECK
	ex4406.12/92 4407.93		of which: Maple (Acer spp.)	1000 m3	0.005	3.018	0.011	14.818	0.003	0.611	0	0																									ex4406.12/92 4407.93		of which: Maple (Acer spp.)	NAC/m3	604	1347	204	0	CHECK	CHECK
	ex4406.12/92 4407.94		of which: Cherry (Prunus spp.)	1000 m3	0	0	0	0	0	0	0	0																									ex4406.12/92 4407.94		of which: Cherry (Prunus spp.)	NAC/m3	0	0	0	0	CHECK	CHECK
	ex4406.12/92 4407.95		of which: Ash (Fraxinus spp.)	1000 m3	1.151	1157.702	1.009	1154.04	0.031	36.203	0.014	63.489																									ex4406.12/92 4407.95		of which: Ash (Fraxinus spp.)	NAC/m3	1006	1144	1168	4535	ACCEPT	CHECK
	ex4406.12/92 4407.96		of which: Birch (Betula spp.)	1000 m3	5.595	1906.527	2.929	1298.471	13.23	4504.734	16.19	7613.448																									ex4406.12/92 4407.96		of which: Birch (Betula spp.)	NAC/m3	341	443	340	470	ACCEPT	ACCEPT
	ex4406.12/92 4407.97		of which: Poplar/Aspen (Populus spp.)	1000 m3	2.032	1384.234	2.411	2240.963	0.806	878.586	0.503	665.988																									ex4406.12/92 4407.97		of which: Poplar/Aspen (Populus spp.)	NAC/m3	681	929	1090	1324	ACCEPT	ACCEPT
Light blue cells are requested only for EU members using the Combined Nomenclature to fill in - other countries are welcome to do so if their trade classification nomenclature permits
Please note that information on tropical species trade is requested in questionnaire ITTO2 for ITTO member countries
"ex" codes indicate that only part of that trade classication code is used				To fill:	0	0	0	0	0	0	0	0
m3ub = cubic metres underbark (i.e. excluding bark)
Please complete each cell if possible with
data (numerical value)
or " " for not available
or "0" for zero data

EU1 ExtraEU Trade

FOREST SECTOR QUESTIONNAIRE

Country:

Date:

both VALUE and quantity reported ZERO

EU1

Name of Official responsible for reply:

ZERO Q

quantity ZERO when VALUE is reported

Official Address (in full):

NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791

ZERO V

Value ZERO when quantity is reported

Trade with countries outside EU

Telephone:

Fax:

JQ2/EU1 comparison

Zero check - if no value please CHECK

NO Q

no quantity reported

Value must always be in 1000 NAC (national currency)

E-mail:

JQ2>=EU1

NO V

no value reported

Treshold:

Eurozone countries may use the old national currency, but only in both years

1000 NAC

Flag

Note

Trade

Discrepancies

REPORT

no figures reported

Product

Unit of

I M P O R T

E X P O R T

Import

Export

Import

Export

I M P O R T

E X P O R T

Product

I M P O R T

E X P O R T

Product

Value per

I M P O R T

E X P O R T

Column1

Column2

code

Product

quantity

2021

2022

2021

2022

2021

2022

2021

2022

2021

2022

2021

2022

2021

2022

2021

2022

code

2021

2022

2021

2022

code

Product

unit

2021

2022

2021

2022

IMPORT

EXPORT

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ub

4632.1810304

200775.163

647.991304

44464.517

122.847456

24347.109

118.1195504

26807.3549999999

All 2021 trade data is final

All 2022 trade data is provisional

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ub

ROUNDWOOD (WOOD IN THE ROUGH)

NAC/ m3

198

227

CHECK

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ub

26.9300304

1973.489

10.485304

1188.589

2.41145

364.565

5.7735504

785.67299

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ub

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

NAC/ m3

113

151

136

CHECK

1.1.C

Coniferous

1000 m3ub

1.2177952

32.543

0.0688352

1.41

0.470944

169.188

0.1180304

32.9319

1.1.C

Coniferous

1000 m3ub

1.1.C

Coniferous

NAC/ m3

359

279

CHECK

1.1.NC

Non-Coniferous

1000 m3ub

25.7122352

1940.946

10.416468

1187.179

1.9405

195.377

5.65552

752.741

1.1.NC

Non-Coniferous

1000 m3ub

1.1.NC

Non-Coniferous

NAC/ m3

114

101

133

CHECK

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ub

4605.251

198801.674

637.506

43275.928

120.436

23982.544

112.346

26021.682

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ub

1.2

INDUSTRIAL ROUNDWOOD

NAC/ m3

199

232

CHECK

1.2.C

Coniferous

1000 m3ub

526.032

25315.738

32.3619999

2602.499

120.41

23937.586

112.128

25952.538

1.2.C

Coniferous

1000 m3ub

1.2.C

Coniferous

NAC/ m3

199

231

CHECK

1.2.NC

Non-Coniferous

1000 m3ub

4079.219

173485.936

605.144

40673.429

0.026

44.958

0.218

69.144

1.2.NC

Non-Coniferous

1000 m3ub

1.2.NC

Non-Coniferous

NAC/ m3

1729

317

CHECK

1.2.NC.T

of which: Tropical

1000 m3ub

0.001

2.523

1.2.NC.T

of which: Tropical

1000 m3ub

1.2.NC.T

of which: Tropical

NAC/ m3

2523

WOOD CHARCOAL

1000 t

0.905714

628.495

0.636313

670.804

0.011792

9.794

0.003005

5.247

WOOD CHARCOAL

1000 t

WOOD CHARCOAL

NAC/ t

694

1054

831

1746

CHECK

WOOD CHIPS, PARTICLES AND RESIDUES

1000 m3

3667.039102045

130804.5

904.3199759537

49771.199

1.0920649674

216.912

0.7415880773

185.628

WOOD CHIPS, PARTICLES AND RESIDUES

1000 m3

WOOD CHIPS, PARTICLES AND RESIDUES

NAC/ m3

199

250

CHECK

3.1

WOOD CHIPS AND PARTICLES

1000 m3

3432.3309720813

126114.285

792.0731560471

47707.562

1.085030833

215.395

0.7196137266

171.642

3.1

WOOD CHIPS AND PARTICLES

1000 m3

3.1

WOOD CHIPS AND PARTICLES

NAC/ m3

199

239

CHECK

3.2

WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)

1000 m3

234.7081299636

4690.215

112.2468199066

2063.637

0.0070341337

1.517

0.0219743507

13.986

3.2

WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)

1000 m3

3.2

WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)

NAC/ m3

216

636

CHECK

3.2.1

of which: Sawdust

1000 m3

234.7081299636

4690.215

112.2468199066

2063.637

0.0070341337

1.517

0.0219743507

13.986

3.2.1

of which: Sawdust

1000 m3

3.2.1

of which: Sawdust

NAC/ m3

216

636

CHECK

RECOVERED POST-CONSUMER WOOD

1000 t

107.674844

3037.098

88.951814

2624.226

0.000023

1.129

0.000007

0.094

RECOVERED POST-CONSUMER WOOD

1000 t

RECOVERED POST-CONSUMER WOOD

NAC/ t

49087

13429

CHECK

WOOD PELLETS AND OTHER AGGLOMERATES

1000 t

136.674405

15329.851

79.025201

10967.148

6.325493

747.988

3.58228

401.474

WOOD PELLETS AND OTHER AGGLOMERATES

1000 t

WOOD PELLETS AND OTHER AGGLOMERATES

NAC/ t

112

139

118

112

CHECK

5.1

WOOD PELLETS

1000 t

122.12422

14331.793

73.523322

9992.408

5.431038

686.093

0.852014

187.165

5.1

WOOD PELLETS

1000 t

5.1

WOOD PELLETS

NAC/ t

117

136

126

220

CHECK

5.2

OTHER AGGLOMERATES

1000 t

14.550185

998.058

5.501879

974.74

0.894455

61.895

2.7302

214.309

5.2

OTHER AGGLOMERATES

1000 t

5.2

OTHER AGGLOMERATES

NAC/ t

177

CHECK

SAWNWOOD (INCLUDING SLEEPERS)

1000 m3

537.883

134058.716

287.537

79413.384

5678.565

1597002.536

5622.784

1578660.209

SAWNWOOD (INCLUDING SLEEPERS)

1000 m3

SAWNWOOD (INCLUDING SLEEPERS)

NAC/ m3

249

276

281

CHECK

6.C

Coniferous

1000 m3

526.432

126047.799

278.043

70282.92

5671.814

1593752.138

5615.47

1574688.687

6.C

Coniferous

1000 m3

6.C

Coniferous

NAC/ m3

239

253

281

280

CHECK

6.NC

Non-Coniferous

1000 m3

11.451

8010.917

9.494

9130.464

6.751

3250.398

7.314

3971.522

6.NC

Non-Coniferous

1000 m3

6.NC

Non-Coniferous

NAC/ m3

700

962

481

543

CHECK

6.NC.T

of which: Tropical

1000 m3

3.411

3005.844

4.403

3943.904

0.696

831.038

0.5

613.4

6.NC.T

of which: Tropical

1000 m3

6.NC.T

of which: Tropical

NAC/ m3

881

896

1194

1227

CHECK

VENEER SHEETS

1000 m3

4.894

1677.262

0.658

452.626

15.005

7770.043

15.087

9122.69

VENEER SHEETS

1000 m3

VENEER SHEETS

NAC/ m3

343

688

518

605

CHECK

7.C

Coniferous

1000 m3

0.003

0.557

0.028

61.667

14.604

7388.562

14.908

8767.848

7.C

Coniferous

1000 m3

7.C

Coniferous

NAC/ m3

186

2202

506

588

CHECK

7.NC

Non-Coniferous

1000 m3

4.891

1676.705

0.63

390.9589999999

0.401

381.481

0.179

354.842

7.NC

Non-Coniferous

1000 m3

7.NC

Non-Coniferous

NAC/ m3

343

621

951

1982

CHECK

7.NC.T

of which: Tropical

1000 m3

0.056

37.769

0.003

1.529

7.NC.T

of which: Tropical

1000 m3

7.NC.T

of which: Tropical

NAC/ m3

674

510

CHECK

WOOD-BASED PANELS

1000 m3

153.296038

67648.7080000002

87.854

47624.8099999998

375.94763031

213032.802

355.219

268643.421

WOOD-BASED PANELS

1000 m3

WOOD-BASED PANELS

NAC/ m3

441

542

567

756

CHECK

8.1

PLYWOOD

1000 m3

95.951

52445.989

56.51

36149.256

351.039

200954.313

333.549

255194.522

8.1

PLYWOOD

1000 m3

8.1

PLYWOOD

NAC/ m3

547

640

572

765

CHECK

8.1.C

Coniferous

1000 m3

12.17

5068.51

21.6

12355.156

279.992

132045.611

261.959

171768.172

8.1.C

Coniferous

1000 m3

8.1.C

Coniferous

NAC/ m3

416

572

472

656

CHECK

8.1.NC

Non-Coniferous

1000 m3

83.781

47377.479

34.91

23794.1

71.047

68908.702

71.59

83426.3499999999

8.1.NC

Non-Coniferous

1000 m3

8.1.NC

Non-Coniferous

NAC/ m3

565

682

970

1165

CHECK

8.1.NC.T

of which: Tropical

1000 m3

0.424

658.473

0.521

556.527

0.015

65.546

0.019

37.185

8.1.NC.T

of which: Tropical

1000 m3

8.1.NC.T

of which: Tropical

NAC/ m3

1553

1068

4370

1957

CHECK

8.1.1

of which: Laminated Veneer Lumber (LVL)

1000 m3

1.083

686.557

166.998

118189.298

8.1.1

of which: Laminated Veneer Lumber (LVL)

1000 m3

8.1.1

of which: Laminated Veneer Lumber (LVL)

NAC/ m3

REPORT

634

REPORT

708

CHECK

8.1.1.C

Coniferous

1000 m3

0.944

583.183

159.648

113127.373

8.1.1.C

Coniferous

1000 m3

8.1.1.C

Coniferous

NAC/ m3

REPORT

618

REPORT

709

CHECK

8.1.1.NC

Non-Coniferous

1000 m3

0.139

103.374

7.35

5061.925

8.1.1.NC

Non-Coniferous

1000 m3

8.1.1.NC

Non-Coniferous

NAC/ m3

REPORT

744

REPORT

689

CHECK

8.1.1.NC.T

of which: Tropical

1000 m3

0.097

63.942

8.1.1.NC.T

of which: Tropical

1000 m3

8.1.1.NC.T

of which: Tropical

NAC/ m3

REPORT

659

REPORT

CHECK

8.2

PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD

1000 m3

33.769

10374.404

12.546

3859.246999999

4.416

1485.227

4.701

1999.107

8.2

PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD

1000 m3

8.2

PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD

NAC/ m3

307

308

336

425

CHECK

8.2.1

of which: ORIENTED STRANDBOARD (OSB)

1000 m3

24.338

8377.785

9.12

2968.144

0.058

34.723

0.052

35.936

8.2.1

of which: ORIENTED STRANDBOARD (OSB)

1000 m3

8.2.1

of which: ORIENTED STRANDBOARD (OSB)

NAC/ m3

344

325

599

691

CHECK

8.3

FIBREBOARD

1000 m3

23.576038

4828.314999999

18.798

7616.307

20.49263031

10593.262

16.969

11449.792

8.3

FIBREBOARD

1000 m3

8.3

FIBREBOARD

NAC/ m3

205

405

517

675

CHECK

8.3.1

HARDBOARD

1000 m3

3.003

620.5099999999

2.169

1580.1

19.638

10140.48

16.442

10816.724

8.3.1

HARDBOARD

1000 m3

8.3.1

HARDBOARD

NAC/ m3

207

728

516

658

CHECK

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

1000 m3

12.697038

3153.616999999

8.855

4752

0.251318

294.265

0.442

611.655

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

1000 m3

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

NAC/ m3

248

537

1171

1384

CHECK

8.3.3

OTHER FIBREBOARD

1000 m3

7.876

1054.188

7.774

1284.199

0.60331231

158.517

0.085

21.413

8.3.3

OTHER FIBREBOARD

1000 m3

8.3.3

OTHER FIBREBOARD

NAC/ m3

134

165

263

252

CHECK

WOOD PULP

1000 t

75.579625

42344.11

152.548643

119882.77

2698.505274

1663694.632

2182.232221

1680717.275

WOOD PULP

1000 t

WOOD PULP

NAC/ t

560

786

617

770

CHECK

9.1

MECHANICAL AND SEMI-CHEMICAL WOOD PULP

1000 t

9.379279

3723.791

1.08849

480.475

28.508353

11178.401

0.974496

648.479

9.1

MECHANICAL AND SEMI-CHEMICAL WOOD PULP

1000 t

9.1

MECHANICAL AND SEMI-CHEMICAL WOOD PULP

NAC/ t

397

441

392

665

CHECK

9.2

CHEMICAL WOOD PULP

1000 t

59.793123

31846.523

146.659434

111978.657

2381.217967

1440622.081

2181.257545

1680068.718

9.2

CHEMICAL WOOD PULP

1000 t

9.2

CHEMICAL WOOD PULP

NAC/ t

533

764

605

770

CHECK

9.2.1

SULPHATE PULP

1000 t

59.785468

31837.081

146.636642

111956.668

2381.215957

1440597.471

2181.257445

1680068.664

9.2.1

SULPHATE PULP

1000 t

9.2.1

SULPHATE PULP

NAC/ t

533

763

605

770

CHECK

9.2.1.1

of which: BLEACHED

1000 t

50.720967

26421.695

142.854296

109494.065

2324.309347

1410541.753

2130.491751

1649516.76

9.2.1.1

of which: BLEACHED

1000 t

9.2.1.1

of which: BLEACHED

NAC/ t

521

766

607

774

CHECK

9.2.2

SULPHITE PULP

1000 t

0.007655

9.442

0.022792

21.989

0.00201

24.61

0.0001

0.054

9.2.2

SULPHITE PULP

1000 t

9.2.2

SULPHITE PULP

NAC/ t

1233

965

12244

540

CHECK

9.3

DISSOLVING GRADES

1000 t

6.407223

6773.796

4.800719

7423.638

288.778954

211894.15

0.00018

0.078

9.3

DISSOLVING GRADES

1000 t

9.3

DISSOLVING GRADES

NAC/ t

1057

1546

734

433

CHECK

OTHER PULP

1000 t

1.732147

2796.565

2.911001

7540.701

0.00977

10.261

0.003951

5.766

OTHER PULP

1000 t

OTHER PULP

NAC/ t

1615

2590

1050

1459

CHECK

10.1

PULP FROM FIBRES OTHER THAN WOOD

1000 t

1.731135

2793.358

2.90837

7534.745

0.000312

1.68

0.000072

0.363

10.1

PULP FROM FIBRES OTHER THAN WOOD

1000 t

10.1

PULP FROM FIBRES OTHER THAN WOOD

NAC/ t

1614

2591

5385

5042

CHECK

10.2

RECOVERED FIBRE PULP

1000 t

0.001012

3.207

0.002631

5.956

0.003879

5.403

10.2

RECOVERED FIBRE PULP

1000 t

10.2

RECOVERED FIBRE PULP

NAC/ t

3169

2264

1393

CHECK

RECOVERED PAPER

1000 t

6.283317

1384.743

5.570908

1494.103

8.547572

1111.306

3.212947

203.206

RECOVERED PAPER

1000 t

RECOVERED PAPER

NAC/ t

220

268

130

CHECK

PAPER AND PAPERBOARD

1000 t

43.666213

38735.298

28.157066

37975.8709999998

4201.382115

3110413.166

3376.025074

3520682.489

PAPER AND PAPERBOARD

1000 t

PAPER AND PAPERBOARD

NAC/ t

887

1349

740

1043

CHECK

12.1

GRAPHIC PAPERS

1000 t

21.33188

15514.37

11.5137379999

11026.502

1807.062392

1121889.264

1231.896031

1246315.256

12.1

GRAPHIC PAPERS

1000 t

12.1

GRAPHIC PAPERS

NAC/ t

727

958

621

1012

CHECK

12.1.1

NEWSPRINT

1000 t

20.154

7717.043

10.0322

6031.812

41.938827

17515.168

17.530157

12648.172

12.1.1

NEWSPRINT

1000 t

12.1.1

NEWSPRINT

NAC/ t

383

601

418

722

CHECK

12.1.2

UNCOATED MECHANICAL

1000 t

0.718056

5754.984

0.208663

376.024

272.759231

135403.925

193.314849

148640.549

12.1.2

UNCOATED MECHANICAL

1000 t

12.1.2

UNCOATED MECHANICAL

NAC/ t

8015

1802

496

769

CHECK

12.1.3

UNCOATED WOODFREE

1000 t

0.283416

1314.839

0.770856

3539.972

354.378963

237940.541

160.464113

190848.41

12.1.3

UNCOATED WOODFREE

1000 t

12.1.3

UNCOATED WOODFREE

NAC/ t

4639

4592

671

1189

CHECK

12.1.4

COATED PAPERS

1000 t

0.17635

727.504

0.501947

1078.694

1137.985371

731029.63

860.586912

894178.125000001

12.1.4

COATED PAPERS

1000 t

12.1.4

COATED PAPERS

NAC/ t

4125

2149

642

1039

CHECK

12.2

HOUSEHOLD AND SANITARY PAPERS

1000 t

0.030545

130.987

0.019015

103.6739

1.715313

1937.757

1.007324

1380.491

12.2

HOUSEHOLD AND SANITARY PAPERS

1000 t

12.2

HOUSEHOLD AND SANITARY PAPERS

NAC/ t

4288

5452

1130

1370

CHECK

12.3

PACKAGING MATERIALS

1000 t

22.26475

22775.539

16.15753

22343.201

2342.062617

1944684.231

2100.97858

2226647.329

12.3

PACKAGING MATERIALS

1000 t

12.3

PACKAGING MATERIALS

NAC/ t

1023

1383

830

1060

CHECK

12.3.1

CASE MATERIALS

1000 t

6.33245

6178.599

4.627524

5842.008

566.964676

348124.623

569.155051

458412.021

12.3.1

CASE MATERIALS

1000 t

12.3.1

CASE MATERIALS

NAC/ t

976

1262

614

805

CHECK

12.3.2

CARTONBOARD

1000 t

9.38452

9207.267

7.594489

10561.933

1513.737003

1330296.103

1308.442922

1414379.487

12.3.2

CARTONBOARD

1000 t

12.3.2

CARTONBOARD

NAC/ t

981

1391

879

1081

CHECK

12.3.3

WRAPPING PAPERS

1000 t

3.503352

5457.358

2.646659

4778.984

227.606276

240989.339

184.081194

313884.739

12.3.3

WRAPPING PAPERS

1000 t

12.3.3

WRAPPING PAPERS

NAC/ t

1558

1806

1059

1705

CHECK

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

1000 t

3.044424

1932.315

1.288858

1160.276

33.754662

25274.166

39.299413

39971.082

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

1000 t

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

NAC/ t

635

900

749

1017

CHECK

12.4

OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)

1000 t

0.039023

314.402

0.466783

4502.494

50.541793

41901.914

42.143139

46339.413

12.4

OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)

1000 t

12.4

OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)

NAC/ t

8057

9646

829

1100

CHECK

GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1

1000 m3

154.74485

143.781

301106.4212

284711.034

GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1

1000 m3

GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1

NAC/ m3

REPORT

CHECK

15.1

GLULAM

1000 m3

154.74485

143.781

301106.4212

284711.034

15.1

GLULAM

1000 m3

15.1

GLULAM

NAC/ m3

REPORT

CHECK

15.2

CROSS-LAMINATED TIMBER (CLT or X-LAM)

1000 m3

-0

15.2

CROSS-LAMINATED TIMBER (CLT or X-LAM)

1000 m3

15.2

CROSS-LAMINATED TIMBER (CLT or X-LAM)

NAC/ m3

REPORT

CHECK

I BEAMS (I-JOISTS)1

1000 t

I BEAMS (I-JOISTS)1

1000 t

I BEAMS (I-JOISTS)1

NAC/ t

REPORT

CHECK

To fill:

EU2 Removals

Country:

Date:

Name of Official responsible for reply:

Official Address (in full):

NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791

Phone/Fax:

E-mail:

FOREST SECTOR QUESTIONNAIRE

EU2

Removals by type of ownership

Discrepancies

Product code

Ownership

Flag

Note

Product code

Ownership

Unit

2021

2022

2021

2022

2021

2022

Unit

2021

2022

Quantity

ROUNDWOOD REMOVALS (under bark)

ROUNDWOOD REMOVALS

ROUNDWOOD

1000 m3

66713.896538

65637.339725

All 2021 data is final

All 2022 data is final

ROUNDWOOD

1000 m3

1.C

Coniferous

1000 m3

52925.994956

52029.037557

1.C

Coniferous

1000 m3

1.NC

Non-coniferous

1000 m3

13787.901582

13608.302168

1.NC

Non-coniferous

1000 m3

State forests

1000 m3

5483.46744028

5242.481822352

All data of subgroups are confidential.

State forests

1000 m3

Coniferous

1000 m3

4867.0951786452

4597.2976464245

All data of subgroups are confidential.

Coniferous

1000 m3

Non-coniferous

1000 m3

616.3722616348

645.1841759275

All data of subgroups are confidential.

Non-coniferous

1000 m3

Other publicly owned forests

1000 m3

Other publicly owned forests are included in Private forests.

Other publicly owned forests

1000 m3

Coniferous

1000 m3

Other publicly owned forests are included in Private forests.

Coniferous

1000 m3

Non-coniferous

1000 m3

Other publicly owned forests are included in Private forests.

Non-coniferous

1000 m3

Private forest

1000 m3

61230.42909772

60394.857902648

Private forest includes also Other publicly owned forests. Data revised. Data confidential.

Private forest

1000 m3

Coniferous

1000 m3

48058.8997773548

47431.7399105755

Private forest includes also Other publicly owned forests. Data revised. Data confidential.

Coniferous

1000 m3

Non-coniferous

1000 m3

13171.5293203652

12963.1179920725

Private forest includes also Other publicly owned forests. Data revised. Data confidential.

Non-coniferous

1000 m3

To fill:

Note:

–

Ownership categories correspond to those of the TBFRA.

State forests: Forests owned by national, state and regional governments, or government-owned corporations; Crown forests.

Other publicly owned forests: Forests belonging to cities, municipalities, villages and communes.

Private forests: Forests owned by individuals, co-operatives, enterprises and industries and other private institutions.

–

The unit should be solid cubic metres, under bark.

ITTO1-Estimates

				Country:	FI	Date:
				Name of Official responsible for reply:

				Official Address (in full):		NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791
	ITTO1
				Telephone:		Fax:	0
	FOREST SECTOR QUESTIONNAIRE			E-mail:
	Production and Trade Estimates for	2023
	Specify Currency and Unit of Value (e.g.:1000 US $):			__________

Product		Unit of	Production	Imports		Exports
Code	Product	quantity	Quantity	Quantity	Value	Quantity	Value

1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub
1.2.C	Coniferous	1000 m3ub
1.2.NC	Non-Coniferous	1000 m3ub
1.2.NC.T	of which: Tropical1	1000 m3ub
6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3
6.C	Coniferous	1000 m3
6.NC	Non-Coniferous	1000 m3
6.NC.T	of which: Tropical1	1000 m3
7	VENEER SHEETS	1000 m3
7.C	Coniferous	1000 m3
7.NC	Non-Coniferous	1000 m3
7.NC.T	of which: Tropical	1000 m3
8.1	PLYWOOD	1000 m3
8.1.C	Coniferous	1000 m3
8.1.NC	Non-Coniferous	1000 m3
8.1.NC.T	of which: Tropical	1000 m3
1 Please include the non-coniferous non-tropical species exported by tropical countries or imported from tropical countries.

	m3 = cubic metres solid volume
	m3ub = cubic metres solid volume underbark (i.e. excluding bark)

ITTO2-Species


						Country:	FI	Date:
		ITTO2				Name of Official responsible for reply:

						Official Address (in full):		NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791
		FOREST SECTOR QUESTIONNAIRE
		Trade in Tropical Species				Telephone:		Fax:	0
						E-mail:
	Specify Currency and Unit of Value (e.g.:1000 US $):			____________

				I M P O R T				E X P O R T
Product	Classifications			2021		2022		2021		2022
	HS2022/HS2017/HS2012/HS2007	Scientific Name	Local/Trade Name	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
				(1000 m3)		(1000 m3)		(1000 m3)		(1000 m3)
1.2.NC.T	HS2022:
Industrial Roundwood, Tropical	ex4403.12 4403.41/42/49
	HS2017:
	ex4403.12 4403.41/49
	HS2012/2007:
	ex4403.10 4403.41/49 ex4403.99
6.NC.T	HS2022:
Sawnwood, Tropical	ex4406.12/92 4407.21/22/23/25/26/27/28/29
	HS2017:
	ex4406.12/92 4407.21/22/25/26/27/28/29
	HS2012/2007:
	ex4406.10/90 4407.21/22/25/26/27/28/30
7.NC.T	HS2022:
Veneer Sheets, Tropical	4408.31/39
	HS2017:
	4408.31/39
	HS2012/2007:
	4408.31/39 ex4408.90
8.1.NC.T	HS2022:
Plywood, Tropical	4412.31/41/51/91
	HS2017:
	4412.31 ex4412.94/99
	HS2012/2007:
	4412.31 ex4412.32/94/99
Note: List the major species traded in each category. Use additional sheet if more species are to be explicitly reported. For tropical plywood, identify by face veneer if composed of more than one species.

ITTO3-Miscellaneous

Country:

Date:

Name of Official responsible for reply:

Official Address (in full):

FOREST SECTOR QUESTIONNAIRE ITTO3

Miscellaneous Items

Telephone:

Fax:

(use additional paper if necessary)

E-mail:

Please enter current import tariff rates applied to tropical and non-tropical timber products. If available, please provide tariffs by the relevant customs classification category. If tariff levels have been reported in previous years, enter changes only. (Logs = JQ code 1.2, Sawn = JQ code 6, Veneer = JQ code 7, and Plywood = JQ code 8.1)

Current import tariff

Logs

Tropical:

Sawn

Tropical:

Veneer

Tropical:

Plywood

Tropical:

Non-Tropical:

Comments (if any):

Please comment on any quotas, incentives, disincentives, tariff/non-tariff barriers or other related factors which now or in future will significantly affect your production and trade of tropical timber products.

Please elaborate on any short or medium term plans for expanding capacity for (further) processing of tropical timber products in your country.

Please indicate any trends or changes expected in the species composition of your trade. How important are lesser-used tropical timber species and/or minor tropical forest products?

Please indicate trends in domestic building activity, housing starts, mortgage/interest rates, substitution of non-tropical wood and/or non-wood products for tropical timbers, and any other domestic factors having a significant impact on tropical timber consumption in your country.

Please indicate the extent of foreign involvement in your timber sector (e.g. number and nationalities of concessionaires/mill (joint) owners, area of forest allocated, scale of investment, etc.).

Please provide details of any relevant forest law enforcement activities (e.g. legislation, fines, arrests, etc.) in your country in the past year.

Please indicate the current extent of forest plantations in your country (ha), annual establishment rate (ha/yr) and proportion of industrial roundwood production from plantations.

TS-OB

Min:

80%

Max:

120%

Notes

JQ1

Country

Flow

Unit

Product

2017

2018

2019

2020

2021

2022

17/18

18/19

19/20

20/21

21/21

21/22

2017

2018

2019

2020

P.OB

1000 m3

ERROR:#REF!

P.OB

1000 m3

1_C

ERROR:#REF!

P.OB

1000 m3

1_NC

ERROR:#REF!

P.OB

1000 m3

1_1

ERROR:#REF!

P.OB

1000 m3

1_1_C

ERROR:#REF!

P.OB

1000 m3

1_1_NC

ERROR:#REF!

P.OB

1000 m3

1_2

ERROR:#REF!

P.OB

1000 m3

1_2_C

ERROR:#REF!

P.OB

1000 m3

1_2_NC

ERROR:#REF!

P.OB

1000 m3

1_2_1

ERROR:#REF!

P.OB

1000 m3

1_2_1_C

ERROR:#REF!

P.OB

1000 m3

1_2_1_NC

ERROR:#REF!

P.OB

1000 m3

1_2_2

ERROR:#REF!

P.OB

1000 m3

1_2_2_C

ERROR:#REF!

P.OB

1000 m3

1_2_2_NC

ERROR:#REF!

P.OB

1000 m3

1_2_3

ERROR:#REF!

P.OB

1000 m3

1_2_3_C

ERROR:#REF!

P.OB

1000 m3

1_2_3_NC

ERROR:#REF!

TS-JQ1

Min:

80%

Max:

120%

Notes

JQ1

Country

Flow

Unit

Product

2017

2018

2019

2020

2021

2022

17/18

18/19

19/20

20/21

21/21

21/22

2017

2018

2019

2020

1000 m3

ERROR:#REF!

1000 m3

1_C

ERROR:#REF!

1000 m3

1_NC

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

1000 m3

1_1_C

ERROR:#REF!

1000 m3

1_1_NC

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

1000 m3

1_2_1

ERROR:#REF!

1000 m3

1_2_1_C

ERROR:#REF!

1000 m3

1_2_1_NC

ERROR:#REF!

1000 m3

1_2_2

ERROR:#REF!

1000 m3

1_2_2_C

ERROR:#REF!

1000 m3

1_2_2_NC

ERROR:#REF!

1000 m3

1_2_3

ERROR:#REF!

1000 m3

1_2_3_C

ERROR:#REF!

1000 m3

1_2_3_NC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

TS-JQ2

Min:

80%

Max:

120%

Notes

JQ2

Country

Flow

Unit

Product

2017

2018

2019

2020

2021

2022

17/18

18/19

19/20

20/21

21/21

21/22

2017

2018

2019

2020

1000 m3

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

€

1000 NAC

1_1

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

€

1000 NAC

1_1

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

€

1000 NAC

1_2

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

€

1000 NAC

1_2

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

€

1000 NAC

1_2_C

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

€

1000 NAC

1_2_C

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

€

1000 NAC

1_2_NC

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

€

1000 NAC

1_2_NC

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

1000 m3

1_2_NC_T

ERROR:#REF!

€

1000 NAC

1_2_NC_T

ERROR:#REF!

1000 m3

1_2_NC_T

ERROR:#REF!

1000 m3

1_2_NC_T

ERROR:#REF!

€

1000 NAC

1_2_NC_T

ERROR:#REF!

1000 m3

1_2_NC_T

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 m3

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

€

1000 NAC

3_1

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

€

1000 NAC

3_1

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

€

1000 NAC

3_2

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

€

1000 NAC

3_2

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

€

1000 NAC

4_1

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

€

1000 NAC

4_1

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

€

1000 NAC

4_2

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

€

1000 NAC

4_2

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

1000 m3

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

€

1000 NAC

5_C

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

€

1000 NAC

5_C

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

€

1000 NAC

5_NC

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

€

1000 NAC

5_NC

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

€

1000 NAC

5_NC_T

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

€

1000 NAC

5_NC_T

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

1000 m3

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

€

1000 NAC

6_1

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

€

1000 NAC

6_1

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

€

1000 NAC

6_1_C

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

€

1000 NAC

6_1_C

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

€

1000 NAC

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

€

1000 NAC

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

€

1000 NAC

6_1_NC_T

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

€

1000 NAC

6_1_NC_T

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

€

1000 NAC

6_2

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

€

1000 NAC

6_2

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

€

1000 NAC

6_2_C

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

€

1000 NAC

6_2_C

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

€

1000 NAC

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

€

1000 NAC

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

€

1000 NAC

6_2_NC_T

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

€

1000 NAC

6_2_NC_T

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

€

1000 NAC

6_3

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

€

1000 NAC

6_3

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

€

1000 NAC

6_3_1

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

€

1000 NAC

6_3_1

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

€

1000 NAC

6_4

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

€

1000 NAC

6_4

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

€

1000 NAC

6_4_1

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

€

1000 NAC

6_4_1

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

€

1000 NAC

6_4_2

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

€

1000 NAC

6_4_2

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

€

1000 NAC

6_4_3

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

€

1000 NAC

6_4_3

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

€

1000 NAC

7_1

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

€

1000 NAC

7_1

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

€

1000 NAC

7_2

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

€

1000 NAC

7_2

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

€

1000 NAC

7_3

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

€

1000 NAC

7_3

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

€

1000 NAC

7_3_1

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

€

1000 NAC

7_3_1

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

€

1000 NAC

7_3_2

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

€

1000 NAC

7_3_2

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

€

1000 NAC

7_3_3

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

€

1000 NAC

7_3_3

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

€

1000 NAC

7_3_4

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

€

1000 NAC

7_3_4

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

€

1000 NAC

7_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

€

1000 NAC

7_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

€

1000 NAC

8_1

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

€

1000 NAC

8_1

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

€

1000 NAC

8_2

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

€

1000 NAC

8_2

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

€

1000 NAC

10_1

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

€

1000 NAC

10_1

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

€

1000 NAC

10_1_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

€

1000 NAC

10_1_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

€

1000 NAC

10_1_2

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

€

1000 NAC

10_1_2

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

€

1000 NAC

10_1_3

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

€

1000 NAC

10_1_3

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

€

1000 NAC

10_1_4

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

€

1000 NAC

10_1_4

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

€

1000 NAC

10_2

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

€

1000 NAC

10_2

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

€

1000 NAC

10_3

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

€

1000 NAC

10_3

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

€

1000 NAC

10_3_1

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

€

1000 NAC

10_3_1

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

€

1000 NAC

10_3_2

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

€

1000 NAC

10_3_2

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

€

1000 NAC

10_3_3

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

€

1000 NAC

10_3_3

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

€

1000 NAC

10_3_4

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

€

1000 NAC

10_3_4

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

€

1000 NAC

10_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

€

1000 NAC

10_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

TS-JQ3

Min:

80%

Max:

120%

Notes

JQ3

Country

Flow

Unit

Product

2017

2018

2019

2020

2021

2022

17/18

18/19

19/20

20/21

21/21

21/22

2017

2018

2019

2020

1000 NAC

11_1

ERROR:#REF!

1000 NAC

11_1_C

ERROR:#REF!

1000 NAC

11_1_NC

ERROR:#REF!

1000 NAC

11_1_NC_T

ERROR:#REF!

1000 NAC

11_2

ERROR:#REF!

1000 NAC

11_3

ERROR:#REF!

1000 NAC

11_4

ERROR:#REF!

1000 NAC

11_5

ERROR:#REF!

1000 NAC

11_6

ERROR:#REF!

1000 NAC

11_7

ERROR:#REF!

1000 NAC

11_7_1

ERROR:#REF!

1000 NAC

12_1

ERROR:#REF!

1000 NAC

12_2

ERROR:#REF!

1000 NAC

12_3

ERROR:#REF!

1000 NAC

12_4

ERROR:#REF!

1000 NAC

12_5

ERROR:#REF!

1000 NAC

12_6

ERROR:#REF!

1000 NAC

12_6_1

ERROR:#REF!

1000 NAC

12_6_2

ERROR:#REF!

1000 NAC

12_6_3

ERROR:#REF!

1000 NAC

12_7

ERROR:#REF!

1000 NAC

12_7_1

ERROR:#REF!

1000 NAC

12_7_2

ERROR:#REF!

1000 NAC

12_7_3

ERROR:#REF!

1000 NAC

11_1

ERROR:#REF!

1000 NAC

11_1_C

ERROR:#REF!

1000 NAC

11_1_NC

ERROR:#REF!

1000 NAC

11_1_NC_T

ERROR:#REF!

1000 NAC

11_2

ERROR:#REF!

1000 NAC

11_3

ERROR:#REF!

1000 NAC

11_4

ERROR:#REF!

1000 NAC

11_5

ERROR:#REF!

1000 NAC

11_6

ERROR:#REF!

1000 NAC

11_7

ERROR:#REF!

1000 NAC

11_7_1

ERROR:#REF!

1000 NAC

12_1

ERROR:#REF!

1000 NAC

12_2

ERROR:#REF!

1000 NAC

12_3

ERROR:#REF!

1000 NAC

12_4

ERROR:#REF!

1000 NAC

12_5

ERROR:#REF!

1000 NAC

12_6

ERROR:#REF!

1000 NAC

12_6_1

ERROR:#REF!

1000 NAC

12_6_2

ERROR:#REF!

1000 NAC

12_6_3

ERROR:#REF!

TS-ECEEU

Min:

80%

Max:

120%

Notes

ECEEU

Country

Flow

Unit

Product

2017

2018

2019

2020

2021

2022

17/18

18/19

19/20

20/21

21/21

21/22

2017

2018

2019

2020

1000 m3

ST_1_2_C

ERROR:#REF!

€

1000 NAC

ST_1_2_C

ERROR:#REF!

1000 m3

ST_1_2_C

ERROR:#REF!

1000 m3

ST_1_2_C

ERROR:#REF!

€

1000 NAC

ST_1_2_C

ERROR:#REF!

1000 m3

ST_1_2_C

ERROR:#REF!

1000 m3

ST_1_2_C_1

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1

ERROR:#REF!

1000 m3

ST_1_2_C_1

ERROR:#REF!

1000 m3

ST_1_2_C_1

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1

ERROR:#REF!

1000 m3

ST_1_2_C_1

ERROR:#REF!

1000 m3

ST_1_2_C_1_1

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1_1

ERROR:#REF!

1000 m3

ST_1_2_C_1_1

ERROR:#REF!

1000 m3

ST_1_2_C_1_1

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1_1

ERROR:#REF!

1000 m3

ST_1_2_C_1_1

ERROR:#REF!

1000 m3

ST_1_2_C_1_2

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1_2

ERROR:#REF!

1000 m3

ST_1_2_C_1_2

ERROR:#REF!

1000 m3

ST_1_2_C_1_2

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1_2

ERROR:#REF!

1000 m3

ST_1_2_C_1_2

ERROR:#REF!

1000 m3

ST_1_2_C_1_3

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1_3

ERROR:#REF!

1000 m3

ST_1_2_C_1_3

ERROR:#REF!

1000 m3

ST_1_2_C_1_3

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1_3

ERROR:#REF!

1000 m3

ST_1_2_C_1_3

ERROR:#REF!

1000 m3

ST_1_2_C_2

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2

ERROR:#REF!

1000 m3

ST_1_2_C_2

ERROR:#REF!

1000 m3

ST_1_2_C_2

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2

ERROR:#REF!

1000 m3

ST_1_2_C_2

ERROR:#REF!

1000 m3

ST_1_2_C_2_1

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2_1

ERROR:#REF!

1000 m3

ST_1_2_C_2_1

ERROR:#REF!

1000 m3

ST_1_2_C_2_1

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2_1

ERROR:#REF!

1000 m3

ST_1_2_C_2_1

ERROR:#REF!

1000 m3

ST_1_2_C_2_2

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2_2

ERROR:#REF!

1000 m3

ST_1_2_C_2_2

ERROR:#REF!

1000 m3

ST_1_2_C_2_2

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2_2

ERROR:#REF!

1000 m3

ST_1_2_C_2_2

ERROR:#REF!

1000 m3

ST_1_2_C_2_3

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2_3

ERROR:#REF!

1000 m3

ST_1_2_C_2_3

ERROR:#REF!

1000 m3

ST_1_2_C_2_3

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2_3

ERROR:#REF!

1000 m3

ST_1_2_C_2_3

ERROR:#REF!

1000 m3

ST_1_2_C_3

ERROR:#REF!

€

1000 NAC

ST_1_2_C_3

ERROR:#REF!

1000 m3

ST_1_2_C_3

ERROR:#REF!

1000 m3

ST_1_2_C_3

ERROR:#REF!

€

1000 NAC

ST_1_2_C_3

ERROR:#REF!

1000 m3

ST_1_2_C_3

ERROR:#REF!

1000 m3

ST_1_2_NC

ERROR:#REF!

€

1000 NAC

ST_1_2_NC

ERROR:#REF!

1000 m3

ST_1_2_NC

ERROR:#REF!

1000 m3

ST_1_2_NC

ERROR:#REF!

€

1000 NAC

ST_1_2_NC

ERROR:#REF!

1000 m3

ST_1_2_NC

ERROR:#REF!

1000 m3

ST_1_2_NC_1

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1_1

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_1_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1_1

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_1_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1_2

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_1_2

ERROR:#REF!

1000 m3

ST_1_2_NC_1_2

ERROR:#REF!

1000 m3

ST_1_2_NC_1_2

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_1_2

ERROR:#REF!

1000 m3

ST_1_2_NC_1_2

ERROR:#REF!

1000 m3

ST_1_2_NC_1_3

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_1_3

ERROR:#REF!

1000 m3

ST_1_2_NC_1_3

ERROR:#REF!

1000 m3

ST_1_2_NC_1_3

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_1_3

ERROR:#REF!

1000 m3

ST_1_2_NC_1_3

ERROR:#REF!

1000 m3

ST_1_2_NC_2

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2_1

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_2_1

ERROR:#REF!

1000 m3

ST_1_2_NC_2_1

ERROR:#REF!

1000 m3

ST_1_2_NC_2_1

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_2_1

ERROR:#REF!

1000 m3

ST_1_2_NC_2_1

ERROR:#REF!

1000 m3

ST_1_2_NC_2_2

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_2_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2_2

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_2_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2_3

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_2_3

ERROR:#REF!

1000 m3

ST_1_2_NC_2_3

ERROR:#REF!

1000 m3

ST_1_2_NC_2_3

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_2_3

ERROR:#REF!

1000 m3

ST_1_2_NC_2_3

ERROR:#REF!

1000 m3

ST_1_2_NC_3

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_3

ERROR:#REF!

1000 m3

ST_1_2_NC_3

ERROR:#REF!

1000 m3

ST_1_2_NC_3

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_3

ERROR:#REF!

1000 m3

ST_1_2_NC_3

ERROR:#REF!

1000 m3

ST_1_2_NC_4

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_4

ERROR:#REF!

1000 m3

ST_1_2_NC_4

ERROR:#REF!

1000 m3

ST_1_2_NC_4

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_4

ERROR:#REF!

1000 m3

ST_1_2_NC_4

ERROR:#REF!

1000 m3

ST_1_2_NC_5

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_5

ERROR:#REF!

1000 m3

ST_1_2_NC_5

ERROR:#REF!

1000 m3

ST_1_2_NC_5

ERROR:#REF!

€

1000 NAC

ST_1_2_NC_5

ERROR:#REF!

1000 m3

ST_1_2_NC_5

ERROR:#REF!

1000 m3

ST_5_C

ERROR:#REF!

€

1000 NAC

ST_5_C

ERROR:#REF!

1000 m3

ST_5_C

ERROR:#REF!

1000 m3

ST_5_C

ERROR:#REF!

€

1000 NAC

ST_5_C

ERROR:#REF!

1000 m3

ST_5_C

ERROR:#REF!

1000 m3

ST_5_C_1

ERROR:#REF!

€

1000 NAC

ST_5_C_1

ERROR:#REF!

1000 m3

ST_5_C_1

ERROR:#REF!

1000 m3

ST_5_C_1

ERROR:#REF!

€

1000 NAC

ST_5_C_1

ERROR:#REF!

1000 m3

ST_5_C_1

ERROR:#REF!

1000 m3

ST_5_C_2

ERROR:#REF!

€

1000 NAC

ST_5_C_2

ERROR:#REF!

1000 m3

ST_5_C_2

ERROR:#REF!

1000 m3

ST_5_C_2

ERROR:#REF!

€

1000 NAC

ST_5_C_2

ERROR:#REF!

1000 m3

ST_5_C_2

ERROR:#REF!

1000 m3

ST_5_NC

ERROR:#REF!

€

1000 NAC

ST_5_NC

ERROR:#REF!

1000 m3

ST_5_NC

ERROR:#REF!

1000 m3

ST_5_NC

ERROR:#REF!

€

1000 NAC

ST_5_NC

ERROR:#REF!

1000 m3

ST_5_NC

ERROR:#REF!

1000 m3

ST_5_NC_1

ERROR:#REF!

€

1000 NAC

ST_5_NC_1

ERROR:#REF!

1000 m3

ST_5_NC_1

ERROR:#REF!

1000 m3

ST_5_NC_1

ERROR:#REF!

€

1000 NAC

ST_5_NC_1

ERROR:#REF!

1000 m3

ST_5_NC_1

ERROR:#REF!

1000 m3

ST_5_NC_2

ERROR:#REF!

€

1000 NAC

ST_5_NC_2

ERROR:#REF!

1000 m3

ST_5_NC_2

ERROR:#REF!

1000 m3

ST_5_NC_2

ERROR:#REF!

€

1000 NAC

ST_5_NC_2

ERROR:#REF!

1000 m3

ST_5_NC_2

ERROR:#REF!

1000 m3

ST_5_NC_3

ERROR:#REF!

€

1000 NAC

ST_5_NC_3

ERROR:#REF!

1000 m3

ST_5_NC_3

ERROR:#REF!

1000 m3

ST_5_NC_3

ERROR:#REF!

€

1000 NAC

ST_5_NC_3

ERROR:#REF!

1000 m3

ST_5_NC_3

ERROR:#REF!

1000 m3

ST_5_NC_4

ERROR:#REF!

€

1000 NAC

ST_5_NC_4

ERROR:#REF!

1000 m3

ST_5_NC_4

ERROR:#REF!

1000 m3

ST_5_NC_4

ERROR:#REF!

€

1000 NAC

ST_5_NC_4

ERROR:#REF!

1000 m3

ST_5_NC_4

ERROR:#REF!

1000 m3

ST_5_NC_5

ERROR:#REF!

€

1000 NAC

ST_5_NC_5

ERROR:#REF!

1000 m3

ST_5_NC_5

ERROR:#REF!

1000 m3

ST_5_NC_5

ERROR:#REF!

€

1000 NAC

ST_5_NC_5

ERROR:#REF!

1000 m3

ST_5_NC_5

ERROR:#REF!

1000 m3

ST_5_NC_6

ERROR:#REF!

€

1000 NAC

ST_5_NC_6

ERROR:#REF!

1000 m3

ST_5_NC_6

ERROR:#REF!

1000 m3

ST_5_NC_6

ERROR:#REF!

€

1000 NAC

ST_5_NC_6

ERROR:#REF!

1000 m3

ST_5_NC_6

ERROR:#REF!

1000 m3

ST_5_NC_7

ERROR:#REF!

€

1000 NAC

ST_5_NC_7

ERROR:#REF!

1000 m3

ST_5_NC_7

ERROR:#REF!

1000 m3

ST_5_NC_7

ERROR:#REF!

€

1000 NAC

ST_5_NC_7

ERROR:#REF!

1000 m3

ST_5_NC_7

ERROR:#REF!

TS-EU1

Min:

80%

Max:

120%

Notes

EU1

Country

Flow

Unit

Product

2017

2018

2019

2020

2021

2022

17/18

18/19

19/20

20/21

21/21

21/22

2017

2018

2019

2020

EX_M

1000 m3

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

EX_M

1000 m3

1_1

ERROR:#REF!

€

EX_M

1000 NAC

1_1

ERROR:#REF!

EX_M

1000 m3

1_1

ERROR:#REF!

EX_X

1000 m3

1_1

ERROR:#REF!

€

EX_X

1000 NAC

1_1

ERROR:#REF!

EX_X

1000 m3

1_1

ERROR:#REF!

EX_M

1000 m3

1_2

ERROR:#REF!

€

EX_M

1000 NAC

1_2

ERROR:#REF!

EX_M

1000 m3

1_2

ERROR:#REF!

EX_X

1000 m3

1_2

ERROR:#REF!

€

EX_X

1000 NAC

1_2

ERROR:#REF!

EX_X

1000 m3

1_2

ERROR:#REF!

EX_M

1000 m3

1_2_C

ERROR:#REF!

€

EX_M

1000 NAC

1_2_C

ERROR:#REF!

EX_M

1000 m3

1_2_C

ERROR:#REF!

EX_X

1000 m3

1_2_C

ERROR:#REF!

€

EX_X

1000 NAC

1_2_C

ERROR:#REF!

EX_X

1000 m3

1_2_C

ERROR:#REF!

EX_M

1000 m3

1_2_NC

ERROR:#REF!

€

EX_M

1000 NAC

1_2_NC

ERROR:#REF!

EX_M

1000 m3

1_2_NC

ERROR:#REF!

EX_X

1000 m3

1_2_NC

ERROR:#REF!

€

EX_X

1000 NAC

1_2_NC

ERROR:#REF!

EX_X

1000 m3

1_2_NC

ERROR:#REF!

EX_M

1000 m3

1_2_NC_T

ERROR:#REF!

€

EX_M

1000 NAC

1_2_NC_T

ERROR:#REF!

EX_M

1000 m3

1_2_NC_T

ERROR:#REF!

EX_X

1000 m3

1_2_NC_T

ERROR:#REF!

€

EX_X

1000 NAC

1_2_NC_T

ERROR:#REF!

EX_X

1000 m3

1_2_NC_T

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

EX_M

1000 m3

3_1

ERROR:#REF!

€

EX_M

1000 NAC

3_1

ERROR:#REF!

EX_M

1000 m3

3_1

ERROR:#REF!

EX_X

1000 m3

3_1

ERROR:#REF!

€

EX_X

1000 NAC

3_1

ERROR:#REF!

EX_X

1000 m3

3_1

ERROR:#REF!

EX_M

1000 m3

3_2

ERROR:#REF!

€

EX_M

1000 NAC

3_2

ERROR:#REF!

EX_M

1000 m3

3_2

ERROR:#REF!

EX_X

1000 m3

3_2

ERROR:#REF!

€

EX_X

1000 NAC

3_2

ERROR:#REF!

EX_X

1000 m3

3_2

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

4_1

ERROR:#REF!

€

EX_M

1000 NAC

4_1

ERROR:#REF!

EX_M

1000 mt

4_1

ERROR:#REF!

EX_X

1000 mt

4_1

ERROR:#REF!

€

EX_X

1000 NAC

4_1

ERROR:#REF!

EX_X

1000 mt

4_1

ERROR:#REF!

EX_M

1000 mt

4_2

ERROR:#REF!

€

EX_M

1000 NAC

4_2

ERROR:#REF!

EX_M

1000 mt

4_2

ERROR:#REF!

EX_X

1000 mt

4_2

ERROR:#REF!

€

EX_X

1000 NAC

4_2

ERROR:#REF!

EX_X

1000 mt

4_2

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

EX_M

1000 m3

5_C

ERROR:#REF!

€

EX_M

1000 NAC

5_C

ERROR:#REF!

EX_M

1000 m3

5_C

ERROR:#REF!

EX_X

1000 m3

5_C

ERROR:#REF!

€

EX_X

1000 NAC

5_C

ERROR:#REF!

EX_X

1000 m3

5_C

ERROR:#REF!

EX_M

1000 m3

5_NC

ERROR:#REF!

€

EX_M

1000 NAC

5_NC

ERROR:#REF!

EX_M

1000 m3

5_NC

ERROR:#REF!

EX_X

1000 m3

5_NC

ERROR:#REF!

€

EX_X

1000 NAC

5_NC

ERROR:#REF!

EX_X

1000 m3

5_NC

ERROR:#REF!

EX_M

1000 m3

5_NC_T

ERROR:#REF!

€

EX_M

1000 NAC

5_NC_T

ERROR:#REF!

EX_M

1000 m3

5_NC_T

ERROR:#REF!

EX_X

1000 m3

5_NC_T

ERROR:#REF!

€

EX_X

1000 NAC

5_NC_T

ERROR:#REF!

EX_X

1000 m3

5_NC_T

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

EX_M

1000 m3

6_1

ERROR:#REF!

€

EX_M

1000 NAC

6_1

ERROR:#REF!

EX_M

1000 m3

6_1

ERROR:#REF!

EX_X

1000 m3

6_1

ERROR:#REF!

€

EX_X

1000 NAC

6_1

ERROR:#REF!

EX_X

1000 m3

6_1

ERROR:#REF!

EX_M

1000 m3

6_1_C

ERROR:#REF!

€

EX_M

1000 NAC

6_1_C

ERROR:#REF!

EX_M

1000 m3

6_1_C

ERROR:#REF!

EX_X

1000 m3

6_1_C

ERROR:#REF!

€

EX_X

1000 NAC

6_1_C

ERROR:#REF!

EX_X

1000 m3

6_1_C

ERROR:#REF!

EX_M

1000 m3

6_1_NC

ERROR:#REF!

€

EX_M

1000 NAC

6_1_NC

ERROR:#REF!

EX_M

1000 m3

6_1_NC

ERROR:#REF!

EX_X

1000 m3

6_1_NC

ERROR:#REF!

€

EX_X

1000 NAC

6_1_NC

ERROR:#REF!

EX_X

1000 m3

6_1_NC

ERROR:#REF!

EX_M

1000 m3

6_1_NC_T

ERROR:#REF!

€

EX_M

1000 NAC

6_1_NC_T

ERROR:#REF!

EX_M

1000 m3

6_1_NC_T

ERROR:#REF!

EX_X

1000 m3

6_1_NC_T

ERROR:#REF!

€

EX_X

1000 NAC

6_1_NC_T

ERROR:#REF!

EX_X

1000 m3

6_1_NC_T

ERROR:#REF!

EX_M

1000 m3

6_2

ERROR:#REF!

€

EX_M

1000 NAC

6_2

ERROR:#REF!

EX_M

1000 m3

6_2

ERROR:#REF!

EX_X

1000 m3

6_2

ERROR:#REF!

€

EX_X

1000 NAC

6_2

ERROR:#REF!

EX_X

1000 m3

6_2

ERROR:#REF!

EX_M

1000 m3

6_2_C

ERROR:#REF!

€

EX_M

1000 NAC

6_2_C

ERROR:#REF!

EX_M

1000 m3

6_2_C

ERROR:#REF!

EX_X

1000 m3

6_2_C

ERROR:#REF!

€

EX_X

1000 NAC

6_2_C

ERROR:#REF!

EX_X

1000 m3

6_2_C

ERROR:#REF!

EX_M

1000 m3

6_2_NC

ERROR:#REF!

€

EX_M

1000 NAC

6_2_NC

ERROR:#REF!

EX_M

1000 m3

6_2_NC

ERROR:#REF!

EX_X

1000 m3

6_2_NC

ERROR:#REF!

€

EX_X

1000 NAC

6_2_NC

ERROR:#REF!

EX_X

1000 m3

6_2_NC

ERROR:#REF!

EX_M

1000 m3

6_2_NC_T

ERROR:#REF!

€

EX_M

1000 NAC

6_2_NC_T

ERROR:#REF!

EX_M

1000 m3

6_2_NC_T

ERROR:#REF!

EX_X

1000 m3

6_2_NC_T

ERROR:#REF!

€

EX_X

1000 NAC

6_2_NC_T

ERROR:#REF!

EX_X

1000 m3

6_2_NC_T

ERROR:#REF!

EX_M

1000 m3

6_3

ERROR:#REF!

€

EX_M

1000 NAC

6_3

ERROR:#REF!

EX_M

1000 m3

6_3

ERROR:#REF!

EX_X

1000 m3

6_3

ERROR:#REF!

€

EX_X

1000 NAC

6_3

ERROR:#REF!

EX_X

1000 m3

6_3

ERROR:#REF!

EX_M

1000 m3

6_3_1

ERROR:#REF!

€

EX_M

1000 NAC

6_3_1

ERROR:#REF!

EX_M

1000 m3

6_3_1

ERROR:#REF!

EX_X

1000 m3

6_3_1

ERROR:#REF!

€

EX_X

1000 NAC

6_3_1

ERROR:#REF!

EX_X

1000 m3

6_3_1

ERROR:#REF!

EX_M

1000 m3

6_4

ERROR:#REF!

€

EX_M

1000 NAC

6_4

ERROR:#REF!

EX_M

1000 m3

6_4

ERROR:#REF!

EX_X

1000 m3

6_4

ERROR:#REF!

€

EX_X

1000 NAC

6_4

ERROR:#REF!

EX_X

1000 m3

6_4

ERROR:#REF!

EX_M

1000 m3

6_4_1

ERROR:#REF!

€

EX_M

1000 NAC

6_4_1

ERROR:#REF!

EX_M

1000 m3

6_4_1

ERROR:#REF!

EX_X

1000 m3

6_4_1

ERROR:#REF!

€

EX_X

1000 NAC

6_4_1

ERROR:#REF!

EX_X

1000 m3

6_4_1

ERROR:#REF!

EX_M

1000 m3

6_4_2

ERROR:#REF!

€

EX_M

1000 NAC

6_4_2

ERROR:#REF!

EX_M

1000 m3

6_4_2

ERROR:#REF!

EX_X

1000 m3

6_4_2

ERROR:#REF!

€

EX_X

1000 NAC

6_4_2

ERROR:#REF!

EX_X

1000 m3

6_4_2

ERROR:#REF!

EX_M

1000 m3

6_4_3

ERROR:#REF!

€

EX_M

1000 NAC

6_4_3

ERROR:#REF!

EX_M

1000 m3

6_4_3

ERROR:#REF!

EX_X

1000 m3

6_4_3

ERROR:#REF!

€

EX_X

1000 NAC

6_4_3

ERROR:#REF!

EX_X

1000 m3

6_4_3

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

7_1

ERROR:#REF!

€

EX_M

1000 NAC

7_1

ERROR:#REF!

EX_M

1000 mt

7_1

ERROR:#REF!

EX_X

1000 mt

7_1

ERROR:#REF!

€

EX_X

1000 NAC

7_1

ERROR:#REF!

EX_X

1000 mt

7_1

ERROR:#REF!

EX_M

1000 mt

7_2

ERROR:#REF!

€

EX_M

1000 NAC

7_2

ERROR:#REF!

EX_M

1000 mt

7_2

ERROR:#REF!

EX_X

1000 mt

7_2

ERROR:#REF!

€

EX_X

1000 NAC

7_2

ERROR:#REF!

EX_X

1000 mt

7_2

ERROR:#REF!

EX_M

1000 mt

7_3

ERROR:#REF!

€

EX_M

1000 NAC

7_3

ERROR:#REF!

EX_M

1000 mt

7_3

ERROR:#REF!

EX_X

1000 mt

7_3

ERROR:#REF!

€

EX_X

1000 NAC

7_3

ERROR:#REF!

EX_X

1000 mt

7_3

ERROR:#REF!

EX_M

1000 mt

7_3_1

ERROR:#REF!

€

EX_M

1000 NAC

7_3_1

ERROR:#REF!

EX_M

1000 mt

7_3_1

ERROR:#REF!

EX_X

1000 mt

7_3_1

ERROR:#REF!

€

EX_X

1000 NAC

7_3_1

ERROR:#REF!

EX_X

1000 mt

7_3_1

ERROR:#REF!

EX_M

1000 mt

7_3_2

ERROR:#REF!

€

EX_M

1000 NAC

7_3_2

ERROR:#REF!

EX_M

1000 mt

7_3_2

ERROR:#REF!

EX_X

1000 mt

7_3_2

ERROR:#REF!

€

EX_X

1000 NAC

7_3_2

ERROR:#REF!

EX_X

1000 mt

7_3_2

ERROR:#REF!

EX_M

1000 mt

7_3_3

ERROR:#REF!

€

EX_M

1000 NAC

7_3_3

ERROR:#REF!

EX_M

1000 mt

7_3_3

ERROR:#REF!

EX_X

1000 mt

7_3_3

ERROR:#REF!

€

EX_X

1000 NAC

7_3_3

ERROR:#REF!

EX_X

1000 mt

7_3_3

ERROR:#REF!

EX_M

1000 mt

7_3_4

ERROR:#REF!

€

EX_M

1000 NAC

7_3_4

ERROR:#REF!

EX_M

1000 mt

7_3_4

ERROR:#REF!

EX_X

1000 mt

7_3_4

ERROR:#REF!

€

EX_X

1000 NAC

7_3_4

ERROR:#REF!

EX_X

1000 mt

7_3_4

ERROR:#REF!

EX_M

1000 mt

7_4

ERROR:#REF!

€

EX_M

1000 NAC

7_4

ERROR:#REF!

EX_M

1000 mt

7_4

ERROR:#REF!

EX_X

1000 mt

7_4

ERROR:#REF!

€

EX_X

1000 NAC

7_4

ERROR:#REF!

EX_X

1000 mt

7_4

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

8_1

ERROR:#REF!

€

EX_M

1000 NAC

8_1

ERROR:#REF!

EX_M

1000 mt

8_1

ERROR:#REF!

EX_X

1000 mt

8_1

ERROR:#REF!

€

EX_X

1000 NAC

8_1

ERROR:#REF!

EX_X

1000 mt

8_1

ERROR:#REF!

EX_M

1000 mt

8_2

ERROR:#REF!

€

EX_M

1000 NAC

8_2

ERROR:#REF!

EX_M

1000 mt

8_2

ERROR:#REF!

EX_X

1000 mt

8_2

ERROR:#REF!

€

EX_X

1000 NAC

8_2

ERROR:#REF!

EX_X

1000 mt

8_2

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

10_1

ERROR:#REF!

€

EX_M

1000 NAC

10_1

ERROR:#REF!

EX_M

1000 mt

10_1

ERROR:#REF!

EX_X

1000 mt

10_1

ERROR:#REF!

€

EX_X

1000 NAC

10_1

ERROR:#REF!

EX_X

1000 mt

10_1

ERROR:#REF!

EX_M

1000 mt

10_1_1

ERROR:#REF!

€

EX_M

1000 NAC

10_1_1

ERROR:#REF!

EX_M

1000 mt

10_1_1

ERROR:#REF!

EX_X

1000 mt

10_1_1

ERROR:#REF!

€

EX_X

1000 NAC

10_1_1

ERROR:#REF!

EX_X

1000 mt

10_1_1

ERROR:#REF!

EX_M

1000 mt

10_1_2

ERROR:#REF!

€

EX_M

1000 NAC

10_1_2

ERROR:#REF!

EX_M

1000 mt

10_1_2

ERROR:#REF!

EX_X

1000 mt

10_1_2

ERROR:#REF!

€

EX_X

1000 NAC

10_1_2

ERROR:#REF!

EX_X

1000 mt

10_1_2

ERROR:#REF!

EX_M

1000 mt

10_1_3

ERROR:#REF!

€

EX_M

1000 NAC

10_1_3

ERROR:#REF!

EX_M

1000 mt

10_1_3

ERROR:#REF!

EX_X

1000 mt

10_1_3

ERROR:#REF!

€

EX_X

1000 NAC

10_1_3

ERROR:#REF!

EX_X

1000 mt

10_1_3

ERROR:#REF!

EX_M

1000 mt

10_1_4

ERROR:#REF!

€

EX_M

1000 NAC

10_1_4

ERROR:#REF!

EX_M

1000 mt

10_1_4

ERROR:#REF!

EX_X

1000 mt

10_1_4

ERROR:#REF!

€

EX_X

1000 NAC

10_1_4

ERROR:#REF!

EX_X

1000 mt

10_1_4

ERROR:#REF!

EX_M

1000 mt

10_2

ERROR:#REF!

€

EX_M

1000 NAC

10_2

ERROR:#REF!

EX_M

1000 mt

10_2

ERROR:#REF!

EX_X

1000 mt

10_2

ERROR:#REF!

€

EX_X

1000 NAC

10_2

ERROR:#REF!

EX_X

1000 mt

10_2

ERROR:#REF!

EX_M

1000 mt

10_3

ERROR:#REF!

€

EX_M

1000 NAC

10_3

ERROR:#REF!

EX_M

1000 mt

10_3

ERROR:#REF!

EX_X

1000 mt

10_3

ERROR:#REF!

€

EX_X

1000 NAC

10_3

ERROR:#REF!

EX_X

1000 mt

10_3

ERROR:#REF!

EX_M

1000 mt

10_3_1

ERROR:#REF!

€

EX_M

1000 NAC

10_3_1

ERROR:#REF!

EX_M

1000 mt

10_3_1

ERROR:#REF!

EX_X

1000 mt

10_3_1

ERROR:#REF!

€

EX_X

1000 NAC

10_3_1

ERROR:#REF!

EX_X

1000 mt

10_3_1

ERROR:#REF!

EX_M

1000 mt

10_3_2

ERROR:#REF!

€

EX_M

1000 NAC

10_3_2

ERROR:#REF!

EX_M

1000 mt

10_3_2

ERROR:#REF!

EX_X

1000 mt

10_3_2

ERROR:#REF!

€

EX_X

1000 NAC

10_3_2

ERROR:#REF!

EX_X

1000 mt

10_3_2

ERROR:#REF!

EX_M

1000 mt

10_3_3

ERROR:#REF!

€

EX_M

1000 NAC

10_3_3

ERROR:#REF!

EX_M

1000 mt

10_3_3

ERROR:#REF!

EX_X

1000 mt

10_3_3

ERROR:#REF!

€

EX_X

1000 NAC

10_3_3

ERROR:#REF!

EX_X

1000 mt

10_3_3

ERROR:#REF!

EX_M

1000 mt

10_3_4

ERROR:#REF!

€

EX_M

1000 NAC

10_3_4

ERROR:#REF!

EX_M

1000 mt

10_3_4

ERROR:#REF!

EX_X

1000 mt

10_3_4

ERROR:#REF!

€

EX_X

1000 NAC

10_3_4

ERROR:#REF!

EX_X

1000 mt

10_3_4

ERROR:#REF!

EX_M

1000 mt

10_4

ERROR:#REF!

€

EX_M

1000 NAC

10_4

ERROR:#REF!

EX_M

1000 mt

10_4

ERROR:#REF!

EX_X

1000 mt

10_4

ERROR:#REF!

€

EX_X

1000 NAC

10_4

ERROR:#REF!

EX_X

1000 mt

10_4

ERROR:#REF!

TS-EU2

Min:

80%

Max:

120%

Notes

EU2

Country

Flow

Unit

Product

2017

2018

2019

2020

2021

2022

17/18

18/19

19/20

20/21

21/21

21/22

2017

2018

2019

2020

1000 m3

EU2_1

ERROR:#REF!

1000 m3

EU2_1_C

ERROR:#REF!

1000 m3

EU2_1_NC

ERROR:#REF!

1000 m3

EU2_1_1

ERROR:#REF!

1000 m3

EU2_1_1_C

ERROR:#REF!

1000 m3

EU2_1_1_NC

ERROR:#REF!

1000 m3

EU2_1_2

ERROR:#REF!

1000 m3

EU2_1_2_C

ERROR:#REF!

1000 m3

EU2_1_2_NC

ERROR:#REF!

1000 m3

EU2_1_3

ERROR:#REF!

1000 m3

EU2_1_3_C

ERROR:#REF!

1000 m3

EU2_1_3_NC

ERROR:#REF!

Annex1 | JQ1-Corres.



			Last updated in 2016
		FOREST SECTOR QUESTIONNAIRE JQ1 (Supp. 1)
		PRIMARY PRODUCTS
		Removals and Production
		CORRESPONDENCES to CPC Ver.2.1
		Central Product Classification Version 2.1 (CPC Ver. 2.1)
Product	Product
Code

REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
1	ROUNDWOOD (WOOD IN THE ROUGH)	031
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	0313
1.1.C	Coniferous	03131
1.1.NC	Non-Coniferous	03132
1.2	INDUSTRIAL ROUNDWOOD	0311 0312
1.2.C	Coniferous	0311
1.2.NC	Non-Coniferous	0312
1.2.NC.T	of which: Tropical	ex0312
1.2.1	SAWLOGS AND VENEER LOGS	ex03110 ex03120
1.2.1.C	Coniferous	ex03110
1.2.1.NC	Non-Coniferous	ex03120
1.2.2	PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)	ex03110 ex03120
1.2.2.C	Coniferous	ex03110
1.2.2.NC	Non-Coniferous	ex03120
1.2.3	OTHER INDUSTRIAL ROUNDWOOD	ex03110 ex03120
1.2.3.C	Coniferous	ex03110
1.2.3.NC	Non-Coniferous	ex03120
PRODUCTION
2	WOOD CHARCOAL	ex34510
3	WOOD CHIPS, PARTICLES AND RESIDUES	ex31230 ex39283
3.1	WOOD CHIPS AND PARTICLES	ex31230
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	ex39283
4	RECOVERED POST-CONSUMER WOOD	ex39283
5	WOOD PELLETS AND OTHER AGGLOMERATES	39281 39282
5.1	WOOD PELLETS	39281
5.2	OTHER AGGLOMERATES	39282
6	SAWNWOOD (INCLUDING SLEEPERS)	311 3132
6.C	Coniferous	31101 ex31109 ex3132
6.NC	Non-Coniferous	31102 ex31109 ex3132
6.NC.T	of which: Tropical	ex31102 ex31109 ex3132
7	VENEER SHEETS	3151
7.C	Coniferous	31511
7.NC	Non-Coniferous	31512
7.NC.T	of which: Tropical	ex31512
8	WOOD-BASED PANELS	3141 3142 3143 3144
8.1	PLYWOOD	3141 3142
8.1.C	Coniferous	31411 31421
8.1.NC	Non-Coniferous	31412 31422
8.1.NC.T	of which: Tropical	ex31412 ex31422
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) and SIMILAR BOARD	3143
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	31432
8.3	FIBREBOARD	3144
8.3.1	HARDBOARD	31442
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	31441
8.3.3	OTHER FIBREBOARD	31449
9	WOOD PULP	32111 32112 ex32113
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	ex32113
9.2	CHEMICAL WOOD PULP	32112
9.2.1	SULPHATE PULP	ex32112
9.2.1.1	of which: BLEACHED	ex32112
9.2.2	SULPHITE PULP	ex32112
9.3	DISSOLVING GRADES	32111
10	OTHER PULP	ex32113
10.1	PULP FROM FIBRES OTHER THAN WOOD	ex32113
10.2	RECOVERED FIBRE PULP	ex32113
11	RECOVERED PAPER	3924
12	PAPER AND PAPERBOARD	3212 3213 32142 32143 ex32149 32151 32198 ex32199
12.1	GRAPHIC PAPERS	3212 ex32143 ex32149
12.1.1	NEWSPRINT	32121
12.1.2	UNCOATED MECHANICAL	ex32122 ex32129
12.1.3	UNCOATED WOODFREE	32122 ex32129
12.1.4	COATED PAPERS	ex32143 ex32149
12.2	HOUSEHOLD AND SANITARY PAPERS	32131
12.3	PACKAGING MATERIALS	32132 ex32133 32134 32135 ex32136 ex32137 32142 32151 ex32143 ex32149
12.3.1	CASE MATERIALS	32132 32134 32135 ex32136
12.3.2	CARTONBOARD	ex32133 ex32136 ex32143 ex32149
12.3.3	WRAPPING PAPERS	ex32133 ex32136 ex32137 32142 32151
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	ex32136
12.4	OTHER PAPER AND PAPERBOARD N.E.S.	ex32149 ex32133 ex32136 ex32137 32198 ex32199
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the CPC Ver.2.1 code is applicable.
For instance "ex31512" under product 7.NC.T means that only a part of CPC Ver.2.1 code 31512 refers to non-coniferous tropical veneer sheets.
In CPC, if only 3 or 4 digits are shown, then all sub-codes at lower degrees of aggregation are included (for example, 0313 includes 03131 and 03132).

Annex2 | JQ2-Corres.




			FOREST SECTOR QUESTIONNAIRE JQ2 (Supp. 1)
			PRIMARY PRODUCTS
			Trade
			CORRESPONDENCES to HS2022, HS2017, HS2012 and SITC Rev.4
			C l a s s i f i c a t i o n s
Product	Product
Code		HS2022	HS2017	HS2012	SITC Rev.4

1	ROUNDWOOD (WOOD IN THE ROUGH)	4401.11/12 44.03	4401.11/12 44.03	4401.10 44.03	245.01 247
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	4401.11/12	4401.11/12	4401.10	245.01
1.1.C	Coniferous	4401.11	4401.11	ex4401.10	ex245.01
1.1.NC	Non-Coniferous	4401.12	4401.12	ex4401.10	ex245.01
1.2	INDUSTRIAL ROUNDWOOD	44.03	44.03	44.03	247
1.2.C	Coniferous	4403.11/21/22/23/24/25/26	4403.11/21/22/23/24/25/26	ex4403.10 4403.20	ex247.3 247.4
1.2.NC	Non-Coniferous	4403.12/41/42/49/91/93/94/95/96/97/98/99	4403.12/41/49/91/93/94/95/96/97/98/99	ex4403.10 4403.41/49/91/92/99	ex247.3 247.5 247.9
1.2.NC.T	of which: Tropical1	ex4403.12 4403.41/42/49	4403.41/49	ex4403.10 4403.41/49 ex4403.99	ex247.3 247.5 ex247.9
2	WOOD CHARCOAL	4402.90	4402.90	4402.90	ex245.02
3	WOOD CHIPS, PARTICLES AND RESIDUES	4401.21/22 4401.41 ex4401.49	4401.21/22 ex4401.40	4401.21/22 ex4401.39	246.1 ex246.2
3.1	WOOD CHIPS AND PARTICLES	4401.21/22	4401.21/22	4401.21/22	246.1
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	4401.41 ex4401.49++	ex4401.40++	ex4401.39	ex246.2
3.2.1	of which: Sawdust	4401.41	ex4401.40++	ex4401.39	ex246.2
4	RECOVERED POST-CONSUMER WOOD	ex4401.49++	ex4401.40++	ex4401.39	ex246.2
5	WOOD PELLETS AND OTHER AGGLOMERATES	4401.31/32/39	4401.31/39	4401.31 ex4401.39	ex246.2
5.1	WOOD PELLETS	4401.31	4401.31	4401.31	ex246.2
5.2	OTHER AGGLOMERATES	4401.32/39	4401.39	ex4401.39	ex246.2
6	SAWNWOOD (INCLUDING SLEEPERS)	44.06 44.07	44.06 44.07	44.06 44.07	248.1 248.2 248.4
6.C	Coniferous	4406.11/91 4407.11/12/13/14/19	4406.11/91 4407.11/12/19	ex4406.10/90 4407.10	ex248.11 ex248.19 248.2
6.NC	Non-Coniferous	4406.12/92 4407.21/22/23/25/26/27/28/29/91/92/93/94/95/96/97/99	4406.12/92 4407.21/22/25/26/27/28/29/91/92/93/94/95/96/97/99	ex4406.10/90 4407.21/22/25/26/27/28/29/91/92/93/94/95/99	ex248.11 ex248.19 248.4
6.NC.T	of which: Tropical1	ex4406.12/92 4407.21/22/23/25/26/27/28/29	4407.21/22/25/26/27/28/29	ex4406.10/90 4407.21/22/25/26/27/28/29 ex4407.99	ex248.11 ex248.19 ex248.4
7	VENEER SHEETS	44.08	44.08	44.08	634.1
7.C	Coniferous	4408.10	4408.10	4408.10	634.11
7.NC	Non-Coniferous	4408.31/39/90	4408.31/39/90	4408.31/39/90	634.12
7.NC.T	of which: Tropical	4408.31/39	4408.31/39	4408.31/39 ex4408.90	ex634.12
8	WOOD-BASED PANELS	44.10 44.11 4412.31/33/34/39/41/42/49/51/52/59/91/92/99	44.10 44.11 4412.31/33/34/39/94/99	44.10 44.11 4412.31/32/39/94/99	634.22/23/31/33/39 634.5
8.1	PLYWOOD	4412.31/33/34/39/41/42/49/51/52/59/91/92/99	4412.31/33/34/39/94/99	4412.31/32/39/94/99	634.31/33/39
8.1.C	Coniferous	4412.39/49/59/99	4412.39 ex4412.94 ex4412.99	4412.39 ex4412.94 ex.4412.99	ex634.31 ex634.33 ex634.39
8.1.NC	Non-Coniferous	4412.33/34/42/52/92	4412.31/33/34 ex4412.94 ex4412.99	4412.31/32 ex4412.94 ex4412.99	ex634.31 ex634.33 ex634.39
8.1.NC.T	of which: Tropical	4412.31/41/51/91	4412.31 ex4412.94 ex4412.99	4412.31 ex4412.32 ex4412.94 ex4412.99	ex634.31 ex634.33 ex634.39
8.1.1	of which: Laminated Veneer Lumber (LVL)	4412.41/42/49	ex4412.99	ex4412.99	ex634.39
8.1.1.C	Coniferous	4412.49	ex4412.99	ex4412.99	ex634.39
8.1.1.NC	Non-Coniferous	4412.41/42	ex4412.99	ex4412.99	ex634.39
8.1.1.NC.T	of which: Tropical	4412.41	ex4412.99	ex4412.99	ex634.39
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) and SIMILAR BOARD	44.10	44.10	44.10	634.22/23
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	4410.12	4410.12	4410.12	ex634.22
8.3	FIBREBOARD	44.11	44.11	44.11	634.5
8.3.1	HARDBOARD	4411.92	4411.92	4411.92	ex634.54 ex634.55
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	4411.12/13 ex4411.14*	4411.12/13 ex4411.14*	4411.12/13 ex4411.14*	ex634.54 ex634.55
8.3.3	OTHER FIBREBOARD	ex4411.14* 4411.93/94	ex4411.14* 4411.93/94	ex4411.14 4411.93/94	ex634.54 ex634.55
9	WOOD PULP	47.01/02/03/04/05	47.01/02/03/04/05	47.01/02/03/04/05	251.2 251.3 251.4 251.5 251.6 251.91
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	47.01 47.05	47.01 47.05	47.01 47.05	251.2 251.91
9.2	CHEMICAL WOOD PULP	47.03 47.04	47.03 47.04	47.03 47.04	251.4 251.5 251.6
9.2.1	SULPHATE PULP	47.03	47.03	47.03	251.4 251.5
9.2.1.1	of which: BLEACHED	4703.21/29	4703.21/29	4703.21/29	251.5
9.2.2	SULPHITE PULP	47.04	47.04	47.04	251.6
9.3	DISSOLVING GRADES	47.02	47.02	47.02	251.3
10	OTHER PULP	47.06	47.06	47.06	251.92
10.1	PULP FROM FIBRES OTHER THAN WOOD	4706.10/30/91/92/93	4706.10/30/91/92/93	4706.10/30/91/92/93	ex251.92
10.2	RECOVERED FIBRE PULP	4706.20	4706.20	4706.20	ex251.92
11	RECOVERED PAPER	47.07	47.07	47.07	251.1
12	PAPER AND PAPERBOARD	48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13	48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13	48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13	641.1 641.2 641.3 641.4 641.5 641.62/63/64/69/71/72/74/75/76/77/93 642.41
12.1	GRAPHIC PAPERS	48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29	48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29	48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29	641.1 641.21/22/26/29 641.3
12.1.1	NEWSPRINT	48.01	48.01	48.01	641.1
12.1.2	UNCOATED MECHANICAL	4802.61/62/69	4802.61/62/69	4802.61/62/69	641.29
12.1.3	UNCOATED WOODFREE	4802.10/20/54/55/56/57/58	4802.10/20/54/55/56/57/58	4802.10/20/54/55/56/57/58	641.21/22/26
12.1.4	COATED PAPERS	48.09 4810.13/14/19/22/29	48.09 4810.13/14/19/22/29	48.09 4810.13/14/19/22/29	641.3
12.2	HOUSEHOLD AND SANITARY PAPERS	48.03	48.03	48.03	641.63
12.3	PACKAGING MATERIALS	4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59	4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59	4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59	641.41/42/46 ex641.47 641.48/51/52 ex641.53 641.54/59/62/64/69/71/72/74/75/76/77
12.3.1	CASE MATERIALS	4804.11/19 4805.11/12/19/24/25/91	4804.11/19 4805.11/12/19/24/25/91	4804.11/19 4805.11/12/19/24/25/91	641.41/51/54 ex641.59
12.3.2	CARTONBOARD	4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59	4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59	4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59	ex641.47 641.48 ex641.59 641.75/76 ex641.77 641.71/72
12.3.3	WRAPPING PAPERS	4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99	4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99	4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99	641.42/46/52 ex641.53 641.62/64/69/74 ex641.77
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	4805.93	4805.93	4805.93	ex641.59
12.4	OTHER PAPER AND PAPERBOARD N.E.S.	4802.40 4804.41 4805.40/50 4806.30 48.12 48.13	4802.40 4804.41 4805.40/50 4806.30 48.12 48.13	4802.40 4804.41 4805.40/50 4806.30 48.12 48.13	641.24 ex641.47 641.56 ex641.53 641.55/93 642.41
15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)2	4418.81/82	ex4418.60	ex4418.60	ex635.39
15.1	GLULAM	4418.81	ex4418.60	ex4418.60	ex635.39
15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	4418.82	ex4418.60	ex4418.60	ex635.39
16	I BEAMS (I-JOISTS)2	4418.83	ex4418.60	ex4418.60	ex635.39
1Please include the non-coniferous non-tropical species exported by tropical countries or imported from tropical countries.
2 Glulam, CLT and I Beams are classified as secondary wood products but for ease of reporting are included in JQ1 and JQ2
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the HS2012/HS2017/HS2022 or SITC Rev.4 code is applicable.
For instance "ex4401.49" under product 3.2 means that only a part of HS2022 code 4401.49 refers to wood residues coming from wood processing (the other part coded under 4401.49 is recovered post-consumer wood).
++ Please use your judgement or, as a default, assign half of 4401.49 to item 3.2 and half to item 4 (note different quantity units)
In SITC Rev.4, if only 4 digits are shown, then all sub-headings at lower degrees of aggregation are included (for example, 634.1 includes 634.11 and 634.12).
* Please assign the trade data for HS code 4411.14 to product 8.3.2 (MDF/HDF) and 8.3.3 (other fibreboard) if it is possible to do this in national statistics. If not, please assign all the trade data to item 8.3.2 as in most cases MDF/HDF will represent the large majority of trade.

Annex3 | JQ3-Corres.



			FOREST SECTOR QUESTIONNAIRE JQ3 (Supp. 1)
			SECONDARY PROCESSED PRODUCTS
			Trade
			CORRESPONDENCES to HS 2022, HS2017, HS2012 and SITC Rev.4
		C l a s s i f i c a t i o n s
Product	Product
Code		HS2022	HS2017	HS2012	SITC Rev.4

13	SECONDARY WOOD PRODUCTS
13.1	FURTHER PROCESSED SAWNWOOD	4409.10/22/29	4409.10/22/29	4409.10/29	248.3 248.5
13.1.C	Coniferous	4409.10	4409.10	4409.10	248.3
13.1.NC	Non-coniferous	4409.22/29	4409.22/29	4409.29	248.5
13.1.NC.T	of which: Tropical	4409.22	4409.22	ex4409.29	ex248.5
13.2	WOODEN WRAPPING AND PACKAGING MATERIAL	44.15/16	44.15/16	44.15/16	635.1 635.2
13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE	44.14 4419.20 4419.90 44.20	44.14 4419.90 44.20	44.14 ex4419.00 44.20	635.41 ex635.42 635.49
13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD1	4418.11/19/21/29/30/40/50/74/75/79/89/92/99	4418.10/20/40/50/60/74/75/79/99	4418.10/20/40/50/60 ex4418.71 ex4418.72 ex4418.79 ex4418.90	635.31/32/33 ex635.34 ex635.39
13.5	WOODEN FURNITURE	9401.31/41 9401.61/69/91 9403.30/40/50/60/91	9401.61/69 ex9401.90 9403.30/40/50/60 ex9403.90	9401.61/69 ex9401.90 9403.30/40/50/60 ex9403.90	821.16 ex821.19 821.51/53/55/59 ex821.8
13.6	PREFABRICATED BUILDINGS OF WOOD	9406.10	9406.10	ex94.06	ex811.0
13.7	OTHER MANUFACTURED WOOD PRODUCTS	44.04/05/13/17 4421.10/20/99	44.04/05/13/17 4421.10/99	44.04/05/13/17 4421.10 ex4421.90	634.21/91/93 635.91 ex635.99
14	SECONDARY PAPER PRODUCTS
14.1	COMPOSITE PAPER AND PAPERBOARD	48.07	48.07	48.07	641.92
14.2	SPECIAL COATED PAPER AND PULP PRODUCTS	4811.10/41/49/60/90	4811.10/41/49/60/90	4811.10/41/49/60/90	641.73/78/79
14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE	48.18	48.18	48.18	642.43/94
14.4	PACKAGING CARTONS, BOXES ETC.	48.19	48.19	48.19	642.1
14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	48.14/16/17/20/21/22/23	48.14/16/17/20/21/22/23	48.14/16/17/20/21/22/23	641.94 642.2 642.3 642.42/45/91/93/99 892.81
14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE	ex4823.90	ex4823.90	ex4823.90	ex642.99
14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP	4823.70	4823.70	4823.70	ex642.99
14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE	4823.20	4823.20	4823.20	642.45
1 In February 2023 this definition was updated to exclude Glulam, Cross-Laminated Timber and I-Beams which are now distinct items in the JFSQ (15.1, 15.2 and 16).
This change was made to reflect the update of HS2022.
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the HS2012/HS2017/2022 or SITC Rev.4 code is applicable.
For instance "ex811.00" under "Prefabricated buildings of wood" means that only a part of SITC code 811.00 refers to buildings prefabricated from wood, as that code does not distinguish between the materials buildings were prefabricated from.
In SITC Rev.4, if only 4 digits are shown, then all subheadings at lower degrees of aggregation are included (for example, 892.2 includes 892.21 and 892.29).

Annex4 |JQ2-JQ3-Corres.

JQ Product code	Nomenclature	HS Code	Remarks on HS codes
1	HS2002	440110		Annex 4 does not include HS2022 codes
1	HS2002	4403
1	HS2007	440110
1	HS2007	4403
1	HS2012	440110
1	HS2012	4403
1	HS2017	440111
1	HS2017	440112
1	HS2017	4403
1.1	HS2002	440110
1.1	HS2007	440110
1.1	HS2012	440110
1.1	HS2017	440111
1.1	HS2017	440112
1.1C	HS2002	440110	Only some part of it
1.1C	HS2007	440110	Only some part of it
1.1C	HS2012	440110	Only some part of it
1.1C	HS2017	440111
1.1NC	HS2002	440110	Only some part of it
1.1NC	HS2007	440110	Only some part of it
1.1NC	HS2012	440110	Only some part of it
1.1NC	HS2017	440112
1.2	HS2002	4403
1.2	HS2007	4403
1.2	HS2012	4403
1.2	HS2017	4403
1.2.C	HS2002	440310	Only some part of it
1.2.C	HS2002	440320
1.2.C	HS2007	440310	Only some part of it
1.2.C	HS2007	440320
1.2.C	HS2012	440310	Only some part of it
1.2.C	HS2012	440320
1.2.C	HS2017	440311
1.2.C	HS2017	440321
1.2.C	HS2017	440322
1.2.C	HS2017	440323
1.2.C	HS2017	440324
1.2.C	HS2017	440325
1.2.C	HS2017	440326
1.2.NC	HS2002	440310	Only some part of it
1.2.NC	HS2002	440341
1.2.NC	HS2002	440349
1.2.NC	HS2002	440391
1.2.NC	HS2002	440392
1.2.NC	HS2002	440399
1.2.NC	HS2007	440310	Only some part of it
1.2.NC	HS2007	440341
1.2.NC	HS2007	440349
1.2.NC	HS2007	440391
1.2.NC	HS2007	440392
1.2.NC	HS2007	440399
1.2.NC	HS2012	440310	Only some part of it
1.2.NC	HS2012	440341
1.2.NC	HS2012	440349
1.2.NC	HS2012	440391
1.2.NC	HS2012	440392
1.2.NC	HS2012	440399
1.2.NC	HS2017	440312
1.2.NC	HS2017	440341
1.2.NC	HS2017	440349
1.2.NC	HS2017	440391
1.2.NC	HS2017	440393
1.2.NC	HS2017	440394
1.2.NC	HS2017	440395
1.2.NC	HS2017	440396
1.2.NC	HS2017	440397
1.2.NC	HS2017	440398
1.2.NC	HS2017	440399
1.2.NC.T	HS2002	440310	Only some part of it
1.2.NC.T	HS2002	440341
1.2.NC.T	HS2002	440349
1.2.NC.T	HS2002	440399	Only some part of it
1.2.NC.T	HS2007	440310	Only some part of it
1.2.NC.T	HS2007	440341
1.2.NC.T	HS2007	440349
1.2.NC.T	HS2007	440399	Only some part of it
1.2.NC.T	HS2012	440310	Only some part of it
1.2.NC.T	HS2012	440341
1.2.NC.T	HS2012	440349
1.2.NC.T	HS2012	440399	Only some part of it
1.2.NC.T	HS2017	440312	Only some part of it
1.2.NC.T	HS2017	440341
1.2.NC.T	HS2017	440349
2	HS2002	440200	Only some part of it
2	HS2007	440290
2	HS2012	440290
2	HS2017	440290
3	HS2002	440121
3	HS2002	440122
3	HS2002	440130	Only some part of it
3	HS2007	440121
3	HS2007	440122
3	HS2007	440130	Only some part of it
3	HS2012	440121
3	HS2012	440122
3	HS2012	440139	Only some part of it
3	HS2017	440121
3	HS2017	440122
3	HS2017	440140
3.1	HS2002	440121
3.1	HS2002	440122
3.1	HS2007	440121
3.1	HS2007	440122
3.1	HS2012	440121
3.1	HS2012	440122
3.1	HS2017	440121
3.1	HS2017	440122
3.2	HS2002	440130	Only some part of it
3.2	HS2012	440130	Only some part of it
3.2	HS2012	440139	Only some part of it
3.2	HS2017	440140	Only some part of it
4	HS2002	440130	Only some part of it
4	HS2007	440130	Only some part of it
4	HS2012	440139	Only some part of it
4	HS2017	440140	Only some part of it
5	HS2002	440130	Only some part of it
5	HS2007	440130	Only some part of it
5	HS2012	440131
5	HS2012	440139	Only some part of it
5	HS2017	440131
5	HS2017	440139
5.1	HS2002	440130	Only some part of it
5.1	HS2007	440130	Only some part of it
5.1	HS2012	440131
5.1	HS2017	440131
5.2	HS2002	440130	Only some part of it
5.2	HS2007	440130	Only some part of it
5.2	HS2012	440139	Only some part of it
5.2	HS2017	440139
6	HS2002	4406
6	HS2002	4407
6	HS2007	4406
6	HS2007	4407
6	HS2012	4406
6	HS2012	4407
6	HS2017	4406
6	HS2017	4407
6.C	HS2002	440610	Only some part of it
6.C	HS2002	440690	Only some part of it
6.C	HS2002	440710
6.C	HS2007	440610	Only some part of it
6.C	HS2007	440690	Only some part of it
6.C	HS2007	440710
6.C	HS2012	440610	Only some part of it
6.C	HS2012	440690	Only some part of it
6.C	HS2012	440710
6.C	HS2017	440611
6.C	HS2017	440691
6.C	HS2017	440711
6.C	HS2017	440712
6.C	HS2017	440719
6.NC	HS2002	440610	Only some part of it
6.NC	HS2002	440690	Only some part of it
6.NC	HS2002	440724
6.NC	HS2002	440725
6.NC	HS2002	440726
6.NC	HS2002	440729
6.NC	HS2002	440791
6.NC	HS2002	440792
6.NC	HS2002	440799
6.NC	HS2007	440610	Only some part of it
6.NC	HS2007	440690	Only some part of it
6.NC	HS2007	440721
6.NC	HS2007	440722
6.NC	HS2007	440725
6.NC	HS2007	440726
6.NC	HS2007	440727
6.NC	HS2007	440728
6.NC	HS2007	440729
6.NC	HS2007	440791
6.NC	HS2007	440792
6.NC	HS2007	440793
6.NC	HS2007	440794
6.NC	HS2007	440795
6.NC	HS2007	440799
6.NC	HS2012	440610	Only some part of it
6.NC	HS2012	440690	Only some part of it
6.NC	HS2012	440721
6.NC	HS2012	440722
6.NC	HS2012	440725
6.NC	HS2012	440726
6.NC	HS2012	440727
6.NC	HS2012	440728
6.NC	HS2012	440729
6.NC	HS2012	440791
6.NC	HS2012	440792
6.NC	HS2012	440793
6.NC	HS2012	440794
6.NC	HS2012	440795
6.NC	HS2012	440799
6.NC	HS2017	4406.12
6.NC	HS2017	4406.92
6.NC	HS2017	4407.21
6.NC	HS2017	4407.22
6.NC	HS2017	4407.25
6.NC	HS2017	4407.26
6.NC	HS2017	4407.27
6.NC	HS2017	4407.28
6.NC	HS2017	4407.29
6.NC	HS2017	4407.91
6.NC	HS2017	4407.92
6.NC	HS2017	4407.93
6.NC	HS2017	4407.94
6.NC	HS2017	4407.95
6.NC	HS2017	4407.96
6.NC	HS2017	4407.97
6.NC	HS2017	4407.99
6.NC.T	HS2002	440610	Only some part of it
6.NC.T	HS2002	440690	Only some part of it
6.NC.T	HS2002	440724
6.NC.T	HS2002	440725
6.NC.T	HS2002	440726
6.NC.T	HS2002	440729
6.NC.T	HS2002	440799	Only some part of it
6.NC.T	HS2007	440610	Only some part of it
6.NC.T	HS2007	440690	Only some part of it
6.NC.T	HS2007	440721
6.NC.T	HS2007	440722
6.NC.T	HS2007	440725
6.NC.T	HS2007	440726
6.NC.T	HS2007	440727
6.NC.T	HS2007	440728
6.NC.T	HS2007	440729
6.NC.T	HS2007	440799	Only some part of it
6.NC.T	HS2012	440610	Only some part of it
6.NC.T	HS2012	440690	Only some part of it
6.NC.T	HS2012	440721
6.NC.T	HS2012	440722
6.NC.T	HS2012	440725
6.NC.T	HS2012	440726
6.NC.T	HS2012	440727
6.NC.T	HS2012	440728
6.NC.T	HS2012	440729
6.NC.T	HS2012	440799	Only some part of it
6.NC.T	HS2017	440612	Only some part of it
6.NC.T	HS2017	440692	Only some part of it
6.NC.T	HS2017	440721
6.NC.T	HS2017	440722
6.NC.T	HS2017	440725
6.NC.T	HS2017	440726
6.NC.T	HS2017	440727
6.NC.T	HS2017	440728
6.NC.T	HS2017	440729
7	HS2002	4408
7	HS2007	4408
7	HS2012	4408
7	HS2017	4408
7.C	HS2002	440810
7.C	HS2007	440810
7.C	HS2012	440810
7.C	HS2017	440810
7.NC	HS2002	440831
7.NC	HS2002	440839
7.NC	HS2002	440890
7.NC	HS2007	440831
7.NC	HS2007	440839
7.NC	HS2007	440890
7.NC	HS2012	440831
7.NC	HS2012	440839
7.NC	HS2012	440890
7.NC	HS2017	440831
7.NC	HS2017	440839
7.NC	HS2017	440890
7.NC.T	HS2002	440831
7.NC.T	HS2002	440839
7.NC.T	HS2002	440890	Only some part of it
7.NC.T	HS2007	440831
7.NC.T	HS2007	440839
7.NC.T	HS2007	440890	Only some part of it
7.NC.T	HS2012	440831
7.NC.T	HS2012	440839
7.NC.T	HS2012	440890	Only some part of it
7.NC.T	HS2017	440831
7.NC.T	HS2017	440839
8	HS2002	4410
8	HS2002	4411
8	HS2002	441213
8	HS2002	441214
8	HS2002	441219
8	HS2002	441299	Only some part of it
8	HS2007	4410
8	HS2007	4411
8	HS2007	441231
8	HS2007	441232
8	HS2007	441239
8	HS2007	441294
8	HS2007	441299
8	HS2012	4410
8	HS2012	4411
8	HS2012	441231
8	HS2012	441232
8	HS2012	441239
8	HS2012	441294
8	HS2012	441299
8	HS2017	4410
8	HS2017	4411
8	HS2017	441231
8	HS2017	441233
8	HS2017	441234
8	HS2017	441239
8	HS2017	441294
8	HS2017	441299
8.1	HS2002	441213
8.1	HS2002	441214
8.1	HS2002	441219
8.1	HS2002	441299	Only some part of it
8.1	HS2007	441231
8.1	HS2007	441232
8.1	HS2007	441239
8.1	HS2007	441294
8.1	HS2007	441299
8.1	HS2012	441231
8.1	HS2012	441232
8.1	HS2012	441239
8.1	HS2012	441294
8.1	HS2012	441299
8.1	HS2017	441231
8.1	HS2017	441233
8.1	HS2017	441234
8.1	HS2017	441239
8.1	HS2017	441294
8.1	HS2017	441299
8.1.C	HS2002	441219
8.1.C	HS2002	441299	Only some part of it
8.1.C	HS2007	441239
8.1.C	HS2007	441294	Only some part of it
8.1.C	HS2007	441299	Only some part of it
8.1.C	HS2012	441239
8.1.C	HS2012	441294	Only some part of it
8.1.C	HS2012	441299	Only some part of it
8.1.C	HS2017	441239
8.1.C	HS2017	441294	Only some part of it
8.1.C	HS2017	441299	Only some part of it
8.1.NC	HS2002	441213
8.1.NC	HS2002	441214
8.1.NC	HS2002	441299	Only some part of it
8.1.NC	HS2007	441231
8.1.NC	HS2007	441232
8.1.NC	HS2007	441294	Only some part of it
8.1.NC	HS2007	441299	Only some part of it
8.1.NC	HS2012	441231
8.1.NC	HS2012	441232
8.1.NC	HS2012	441294	Only some part of it
8.1.NC	HS2012	441299	Only some part of it
8.1.NC	HS2017	441231
8.1.NC	HS2017	441233
8.1.NC	HS2017	441234
8.1.NC	HS2017	441294	Only some part of it
8.1.NC	HS2017	441299	Only some part of it
8.1.NC.T	HS2002	441213
8.1.NC.T	HS2002	441214	Only some part of it
8.1.NC.T	HS2002	441299	Only some part of it
8.1.NC.T	HS2007	441231
8.1.NC.T	HS2007	441232	Only some part of it
8.1.NC.T	HS2007	441294	Only some part of it
8.1.NC.T	HS2007	441299	Only some part of it
8.1.NC.T	HS2012	441231
8.1.NC.T	HS2012	441232	Only some part of it
8.1.NC.T	HS2012	441294	Only some part of it
8.1.NC.T	HS2012	441299	Only some part of it
8.1.NC.T	HS2017	441231
8.1.NC.T	HS2017	441294	Only some part of it
8.1.NC.T	HS2017	441299	Only some part of it
8.2	HS2002	4410
8.2	HS2007	4410
8.2	HS2012	4410
8.2	HS2017	4410
8.2.1	HS2002	441021	Only some part of it
8.2.1	HS2002	441029	Only some part of it
8.2.1	HS2007	441012
8.2.1	HS2012	441012
8.2.1	HS2017	441012
8.3	HS2002	4411
8.3	HS2007	4411
8.3	HS2012	4411
8.3	HS2017	4411
8.3.1	HS2002	441111	Only some part of it
8.3.1	HS2002	441119	Only some part of it
8.3.1	HS2007	441192
8.3.1	HS2012	441192
8.3.1	HS2017	441192
8.3.2	HS2002	441111	Only some part of it
8.3.2	HS2002	441119	Only some part of it
8.3.2	HS2002	441121	Only some part of it
8.3.2	HS2002	441129	Only some part of it
8.3.2	HS2007	441112
8.3.2	HS2007	441113
8.3.2	HS2007	441114	Only some part of it
8.3.2	HS2012	441112
8.3.2	HS2012	441113
8.3.2	HS2012	441114	Only some part of it
8.3.2	HS2017	441112
8.3.2	HS2017	441113
8.3.2	HS2017	441114	Only some part of it
8.3.3	HS2002	441131
8.3.3	HS2002	441139
8.3.3	HS2002	441191
8.3.3	HS2002	441199
8.3.3	HS2007	441114	Only some part of it
8.3.3	HS2007	441193
8.3.3	HS2007	441194
8.3.3	HS2012	441114	Only some part of it
8.3.3	HS2012	441193
8.3.3	HS2012	441194
8.3.3	HS2017	441114	Only some part of it
8.3.3	HS2017	441193
8.3.3	HS2017	441194
9	HS2002	4701
9	HS2002	4702
9	HS2002	4703
9	HS2002	4704
9	HS2002	4705
9	HS2007	4701
9	HS2007	4702
9	HS2007	4703
9	HS2007	4704
9	HS2007	4705
9	HS2012	4701
9	HS2012	4702
9	HS2012	4703
9	HS2012	4704
9	HS2012	4705
9	HS2017	4701
9	HS2017	4702
9	HS2017	4703
9	HS2017	4704
9	HS2017	4705
9.1	HS2002	4701
9.1	HS2002	4705
9.1	HS2007	4701
9.1	HS2007	4705
9.1	HS2012	4701
9.1	HS2012	4705
9.1	HS2017	4701
9.1	HS2017	4705
9.2	HS2002	4703
9.2	HS2002	4704
9.2	HS2007	4703
9.2	HS2007	4704
9.2	HS2012	4703
9.2	HS2012	4704
9.2	HS2017	4703
9.2	HS2017	4704
9.2.1	HS2002	4703
9.2.1	HS2007	4703
9.2.1	HS2012	4703
9.2.1	HS2017	4703
9.2.1.1	HS2002	470321
9.2.1.1	HS2002	470329
9.2.1.1	HS2007	470321
9.2.1.1	HS2007	470329
9.2.1.1	HS2012	470321
9.2.1.1	HS2012	470329
9.2.1.1	HS2017	470321
9.2.1.1	HS2017	470329
9.2.2	HS2002	4704
9.2.2	HS2007	4704
9.2.2	HS2012	4704
9.2.2	HS2017	4704
9.3	HS2002	4702
9.3	HS2007	4702
9.3	HS2012	4702
9.3	HS2017	4702
10	HS2002	4706
10	HS2007	4706
10	HS2012	4706
10	HS2017	4706
10.1	HS2002	470610
10.1	HS2002	470691
10.1	HS2002	470692
10.1	HS2002	470693
10.1	HS2007	470610
10.1	HS2007	470630
10.1	HS2007	470691
10.1	HS2007	470692
10.1	HS2007	470693
10.1	HS2012	470610
10.1	HS2012	470630
10.1	HS2012	470691
10.1	HS2012	470692
10.1	HS2012	470693
10.1	HS2017	470610
10.1	HS2017	470630
10.1	HS2017	470691
10.1	HS2017	470692
10.1	HS2017	470693
10.2	HS2002	470620
10.2	HS2007	470620
10.2	HS2012	470620
10.2	HS2017	470620
11	HS2002	4707
11	HS2007	4707
11	HS2012	4707
11	HS2017	4707
12	HS2002	4801
12	HS2002	4802
12	HS2002	4803
12	HS2002	4804
12	HS2002	4805
12	HS2002	4806
12	HS2002	4808
12	HS2002	4809
12	HS2002	4810
12	HS2002	481151
12	HS2002	481159
12	HS2002	4812
12	HS2002	4813
12	HS2007	4801
12	HS2007	4802
12	HS2007	4803
12	HS2007	4804
12	HS2007	4805
12	HS2007	4806
12	HS2007	4808
12	HS2007	4809
12	HS2007	4810
12	HS2007	481151
12	HS2007	481159
12	HS2007	4812
12	HS2007	4813
12	HS2012	4801
12	HS2012	4802
12	HS2012	4803
12	HS2012	4804
12	HS2012	4805
12	HS2012	4806
12	HS2012	4808
12	HS2012	4809
12	HS2012	4810
12	HS2012	481151
12	HS2012	481159
12	HS2012	4812
12	HS2012	4813
12	HS2017	4801
12	HS2017	4802
12	HS2017	4803
12	HS2017	4804
12	HS2017	4805
12	HS2017	4806
12	HS2017	4808
12	HS2017	4809
12	HS2017	4810
12	HS2017	481151
12	HS2017	481159
12	HS2017	4812
12	HS2017	4813
12.1	HS2002	4801
12.1	HS2002	480210
12.1	HS2002	480220
12.1	HS2002	480254
12.1	HS2002	480255
12.1	HS2002	480256
12.1	HS2002	480257
12.1	HS2002	480258
12.1	HS2002	480261
12.1	HS2002	480262
12.1	HS2002	480269
12.1	HS2002	4809
12.1	HS2002	481013
12.1	HS2002	481014
12.1	HS2002	481019
12.1	HS2002	481022
12.1	HS2002	481029
12.1	HS2007	4801
12.1	HS2007	480210
12.1	HS2007	480220
12.1	HS2007	480254
12.1	HS2007	480255
12.1	HS2007	480256
12.1	HS2007	480257
12.1	HS2007	480258
12.1	HS2007	480261
12.1	HS2007	480262
12.1	HS2007	480269
12.1	HS2007	4809
12.1	HS2007	481013
12.1	HS2007	481014
12.1	HS2007	481019
12.1	HS2007	481022
12.1	HS2007	481029
12.1	HS2012	4801
12.1	HS2012	480210
12.1	HS2012	480220
12.1	HS2012	480254
12.1	HS2012	480255
12.1	HS2012	480256
12.1	HS2012	480257
12.1	HS2012	480258
12.1	HS2012	480261
12.1	HS2012	480262
12.1	HS2012	480269
12.1	HS2012	4809
12.1	HS2012	481013
12.1	HS2012	481014
12.1	HS2012	481019
12.1	HS2012	481022
12.1	HS2012	481029
12.1	HS2017	4801
12.1	HS2017	480210
12.1	HS2017	480220
12.1	HS2017	480254
12.1	HS2017	480255
12.1	HS2017	480256
12.1	HS2017	480257
12.1	HS2017	480258
12.1	HS2017	480261
12.1	HS2017	480262
12.1	HS2017	480269
12.1	HS2017	4809
12.1	HS2017	481013
12.1	HS2017	481014
12.1	HS2017	481019
12.1	HS2017	481022
12.1	HS2017	481029
12.1.1	HS2002	4801
12.1.1	HS2007	4801
12.1.1	HS2012	4801
12.1.1	HS2017	4801
12.1.2	HS2002	480261
12.1.2	HS2002	480262
12.1.2	HS2002	480269
12.1.2	HS2007	480261
12.1.2	HS2007	480262
12.1.2	HS2007	480269
12.1.2	HS2012	480261
12.1.2	HS2012	480262
12.1.2	HS2012	480269
12.1.2	HS2017	480261
12.1.2	HS2017	480262
12.1.2	HS2017	480269
12.1.3	HS2002	480210
12.1.3	HS2002	480220
12.1.3	HS2002	480254
12.1.3	HS2002	480255
12.1.3	HS2002	480256
12.1.3	HS2002	480257
12.1.3	HS2002	480258
12.1.3	HS2007	480210
12.1.3	HS2007	480220
12.1.3	HS2007	480254
12.1.3	HS2007	480255
12.1.3	HS2007	480256
12.1.3	HS2007	480257
12.1.3	HS2007	480258
12.1.3	HS2012	480210
12.1.3	HS2012	480220
12.1.3	HS2012	480254
12.1.3	HS2012	480255
12.1.3	HS2012	480256
12.1.3	HS2012	480257
12.1.3	HS2012	480258
12.1.3	HS2017	480210
12.1.3	HS2017	480220
12.1.3	HS2017	480254
12.1.3	HS2017	480255
12.1.3	HS2017	480256
12.1.3	HS2017	480257
12.1.3	HS2017	480258
12.1.4	HS2002	4809
12.1.4	HS2002	481013
12.1.4	HS2002	481014
12.1.4	HS2002	481019
12.1.4	HS2002	481022
12.1.4	HS2002	481029
12.1.4	HS2007	4809
12.1.4	HS2007	481013
12.1.4	HS2007	481014
12.1.4	HS2007	481019
12.1.4	HS2007	481022
12.1.4	HS2007	481029
12.1.4	HS2012	4809
12.1.4	HS2012	481013
12.1.4	HS2012	481014
12.1.4	HS2012	481019
12.1.4	HS2012	481022
12.1.4	HS2012	481029
12.1.4	HS2017	4809
12.1.4	HS2017	481013
12.1.4	HS2017	481014
12.1.4	HS2017	481019
12.1.4	HS2017	481022
12.1.4	HS2017	481029
12.2	HS2002	4803
12.2	HS2007	4803
12.2	HS2012	4803
12.2	HS2017	4803
12.3	HS2002	480411
12.3	HS2002	480419
12.3	HS2002	480421
12.3	HS2002	480429
12.3	HS2002	480431
12.3	HS2002	480439
12.3	HS2002	480442
12.3	HS2002	480449
12.3	HS2002	480451
12.3	HS2002	480452
12.3	HS2002	480459
12.3	HS2002	480511
12.3	HS2002	480512
12.3	HS2002	480519
12.3	HS2002	480524
12.3	HS2002	480525
12.3	HS2002	480530
12.3	HS2002	480591
12.3	HS2002	480592
12.3	HS2002	480593
12.3	HS2002	480610
12.3	HS2002	480620
12.3	HS2002	480640
12.3	HS2002	4808
12.3	HS2002	481031
12.3	HS2002	481032
12.3	HS2002	481039
12.3	HS2002	481092
12.3	HS2002	481099
12.3	HS2002	481151
12.3	HS2002	481159
12.3	HS2007	480411
12.3	HS2007	480419
12.3	HS2007	480421
12.3	HS2007	480429
12.3	HS2007	480431
12.3	HS2007	480439
12.3	HS2007	480442
12.3	HS2007	480449
12.3	HS2007	480451
12.3	HS2007	480452
12.3	HS2007	480459
12.3	HS2007	480511
12.3	HS2007	480512
12.3	HS2007	480519
12.3	HS2007	480524
12.3	HS2007	480525
12.3	HS2007	480530
12.3	HS2007	480591
12.3	HS2007	480592
12.3	HS2007	480593
12.3	HS2007	480610
12.3	HS2007	480620
12.3	HS2007	480640
12.3	HS2007	4808
12.3	HS2007	481031
12.3	HS2007	481032
12.3	HS2007	481039
12.3	HS2007	481092
12.3	HS2007	481099
12.3	HS2007	481151
12.3	HS2007	481159
12.3	HS2012	480411
12.3	HS2012	480419
12.3	HS2012	480421
12.3	HS2012	480429
12.3	HS2012	480431
12.3	HS2012	480439
12.3	HS2012	480442
12.3	HS2012	480449
12.3	HS2012	480451
12.3	HS2012	480452
12.3	HS2012	480459
12.3	HS2012	480511
12.3	HS2012	480512
12.3	HS2012	480519
12.3	HS2012	480524
12.3	HS2012	480525
12.3	HS2012	480530
12.3	HS2012	480591
12.3	HS2012	480592
12.3	HS2012	480593
12.3	HS2012	480610
12.3	HS2012	480620
12.3	HS2012	480640
12.3	HS2012	4808
12.3	HS2012	481031
12.3	HS2012	481032
12.3	HS2012	481039
12.3	HS2012	481092
12.3	HS2012	481099
12.3	HS2012	481151
12.3	HS2012	481159
12.3	HS2017	480411
12.3	HS2017	480419
12.3	HS2017	480421
12.3	HS2017	480429
12.3	HS2017	480431
12.3	HS2017	480439
12.3	HS2017	480442
12.3	HS2017	480449
12.3	HS2017	480451
12.3	HS2017	480452
12.3	HS2017	480459
12.3	HS2017	480511
12.3	HS2017	480512
12.3	HS2017	480519
12.3	HS2017	480524
12.3	HS2017	480525
12.3	HS2017	480530
12.3	HS2017	480591
12.3	HS2017	480592
12.3	HS2017	480593
12.3	HS2017	480610
12.3	HS2017	480620
12.3	HS2017	480640
12.3	HS2017	4808
12.3	HS2017	481031
12.3	HS2017	481032
12.3	HS2017	481039
12.3	HS2017	481092
12.3	HS2017	481099
12.3	HS2017	481151
12.3	HS2017	481159
12.3.1	HS2002	480411
12.3.1	HS2002	480419
12.3.1	HS2002	480511
12.3.1	HS2002	480512
12.3.1	HS2002	480519
12.3.1	HS2002	480524
12.3.1	HS2002	480525
12.3.1	HS2002	480591
12.3.1	HS2007	480411
12.3.1	HS2007	480419
12.3.1	HS2007	480511
12.3.1	HS2007	480512
12.3.1	HS2007	480519
12.3.1	HS2007	480524
12.3.1	HS2007	480525
12.3.1	HS2007	480591
12.3.1	HS2012	480411
12.3.1	HS2012	480419
12.3.1	HS2012	480511
12.3.1	HS2012	480512
12.3.1	HS2012	480519
12.3.1	HS2012	480524
12.3.1	HS2012	480525
12.3.1	HS2012	480591
12.3.2	HS2002	480442
12.3.2	HS2002	480449
12.3.2	HS2002	480451
12.3.2	HS2002	480452
12.3.2	HS2002	480459
12.3.2	HS2002	480592
12.3.2	HS2002	481032
12.3.2	HS2002	481039
12.3.2	HS2002	481092
12.3.2	HS2002	481151
12.3.2	HS2002	481159
12.3.2	HS2007	480442
12.3.2	HS2007	480449
12.3.2	HS2007	480451
12.3.2	HS2007	480452
12.3.2	HS2007	480459
12.3.2	HS2007	480592
12.3.2	HS2007	481032
12.3.2	HS2007	481039
12.3.2	HS2007	481092
12.3.2	HS2007	481151
12.3.2	HS2007	481159
12.3.2	HS2012	480442
12.3.2	HS2012	480449
12.3.2	HS2012	480451
12.3.2	HS2012	480452
12.3.2	HS2012	480459
12.3.2	HS2012	480592
12.3.2	HS2012	481032
12.3.2	HS2012	481039
12.3.2	HS2012	481092
12.3.2	HS2012	481151
12.3.2	HS2012	481159
12.3.2	HS2017	480442
12.3.2	HS2017	480449
12.3.2	HS2017	480451
12.3.2	HS2017	480452
12.3.2	HS2017	480459
12.3.2	HS2017	480592
12.3.2	HS2017	481032
12.3.2	HS2017	481039
12.3.2	HS2017	481092
12.3.2	HS2017	481151
12.3.2	HS2017	481159
12.3.3	HS2002	480421
12.3.3	HS2002	480429
12.3.3	HS2002	480431
12.3.3	HS2002	480439
12.3.3	HS2002	480530
12.3.3	HS2002	480610
12.3.3	HS2002	480620
12.3.3	HS2002	480640
12.3.3	HS2002	4808
12.3.3	HS2002	481031
12.3.3	HS2002	481099
12.3.3	HS2007	480421
12.3.3	HS2007	480429
12.3.3	HS2007	480431
12.3.3	HS2007	480439
12.3.3	HS2007	480530
12.3.3	HS2007	480610
12.3.3	HS2007	480620
12.3.3	HS2007	480640
12.3.3	HS2007	4808
12.3.3	HS2007	481031
12.3.3	HS2007	481099
12.3.3	HS2012	480421
12.3.3	HS2012	480429
12.3.3	HS2012	480431
12.3.3	HS2012	480439
12.3.3	HS2012	480530
12.3.3	HS2012	480610
12.3.3	HS2012	480620
12.3.3	HS2012	480640
12.3.3	HS2012	4808
12.3.3	HS2012	481031
12.3.3	HS2012	481099
12.3.3	HS2017	480421
12.3.3	HS2017	480429
12.3.3	HS2017	480431
12.3.3	HS2017	480439
12.3.3	HS2017	480530
12.3.3	HS2017	480610
12.3.3	HS2017	480620
12.3.3	HS2017	480640
12.3.3	HS2017	4808
12.3.3	HS2017	481031
12.3.3	HS2017	481099
12.3.4	HS2002	480593
12.3.4	HS2007	480593
12.3.4	HS2012	480593
12.3.4	HS2017	480593
12.4	HS2002	480240
12.4	HS2002	480441
12.4	HS2002	480540
12.4	HS2002	480550
12.4	HS2002	480630
12.4	HS2002	4812
12.4	HS2002	4813
12.4	HS2007	480240
12.4	HS2007	480441
12.4	HS2007	480540
12.4	HS2007	480550
12.4	HS2007	480630
12.4	HS2007	4812
12.4	HS2007	4813
12.4	HS2012	480240
12.4	HS2012	480441
12.4	HS2012	480540
12.4	HS2012	480550
12.4	HS2012	480630
12.4	HS2012	4812
12.4	HS2012	4813
12.4	HS2017	480240
12.4	HS2017	480441
12.4	HS2017	480540
12.4	HS2017	480550
12.4	HS2017	480630
12.4	HS2017	4812
12.4	HS2017	4813
13.1	HS2002	440910
13.1	HS2002	440920	Only some part of it
13.1	HS2007	440910
13.1	HS2007	440929
13.1	HS2012	440910
13.1	HS2012	440929
13.1	HS2017	440910
13.1	HS2017	440922
13.1	HS2017	440929
13.1.C	HS2002	440910
13.1.C	HS2007	440910
13.1.C	HS2012	440910
13.1.C	HS2017	440910
13.1.NC	HS2002	440920	Only some part of it
13.1.NC	HS2007	440929
13.1.NC	HS2012	440929
13.1.NC	HS2017	440922
13.1.NC	HS2017	440929
13.1.NC.T	HS2002	440920	Only some part of it
13.1.NC.T	HS2007	440929	Only some part of it
13.1.NC.T	HS2012	440929	Only some part of it
13.1.NC.T	HS2017	440922
13.2	HS2002	4415
13.2	HS2002	4416
13.2	HS2007	4415
13.2	HS2007	4416
13.2	HS2012	4415
13.2	HS2012	4416
13.2	HS2017	4415
13.2	HS2017	4416
13.3	HS2002	4414
13.3	HS2002	4419	Only some part of it
13.3	HS2002	4420
13.3	HS2007	4414
13.3	HS2007	4419	Only some part of it
13.3	HS2007	4420
13.3	HS2012	4414
13.3	HS2012	4419	Only some part of it
13.3	HS2012	4420
13.3	HS2017	4414
13.3	HS2017	441990
13.3	HS2017	4420
13.4	HS2002	441810
13.4	HS2002	441820
13.4	HS2002	441830
13.4	HS2002	441840
13.4	HS2002	441850
13.4	HS2002	441890	Only some part of it
13.4	HS2007	441810
13.4	HS2007	481820
13.4	HS2007	441840
13.4	HS2007	441850
13.4	HS2007	441860
13.4	HS2007	441871	Only some part of it
13.4	HS2007	441872	Only some part of it
13.4	HS2007	441879	Only some part of it
13.4	HS2007	441890	Only some part of it
13.4	HS2012	441810
13.4	HS2012	441820
13.4	HS2012	441840
13.4	HS2012	441850
13.4	HS2012	441860
13.4	HS2012	441871	Only some part of it
13.4	HS2012	441872	Only some part of it
13.4	HS2012	441879	Only some part of it
13.4	HS2012	441890	Only some part of it
13.4	HS2017	441810
13.4	HS2017	441820
13.4	HS2017	441840
13.4	HS2017	441850
13.4	HS2017	441860
13.4	HS2017	441874
13.4	HS2017	441875
13.4	HS2017	441879
13.4	HS2017	441899
13.5	HS2002	940161
13.5	HS2002	940169
13.5	HS2002	940190	Only some part of it
13.5	HS2002	940330
13.5	HS2002	940340
13.5	HS2002	940350
13.5	HS2002	940360
13.5	HS2002	940390	Only some part of it
13.5	HS2007	940161
13.5	HS2007	940169
13.5	HS2007	940190	Only some part of it
13.5	HS2007	940330
13.5	HS2007	940340
13.5	HS2007	940350
13.5	HS2007	940360
13.5	HS2007	940390	Only some part of it
13.5	HS2012	940161
13.5	HS2012	940169
13.5	HS2012	940190	Only some part of it
13.5	HS2012	940330
13.5	HS2012	940340
13.5	HS2012	940350
13.5	HS2012	940360
13.5	HS2012	940390	Only some part of it
13.5	HS2017	940161
13.5	HS2017	940169
13.5	HS2017	940190	Only some part of it
13.5	HS2017	940330
13.5	HS2017	940340
13.5	HS2017	940350
13.5	HS2017	940360
13.5	HS2017	940390	Only some part of it
13.6	HS2002	9406	Only some part of it
13.6	HS2007	9406	Only some part of it
13.6	HS2012	9406	Only some part of it
13.6	HS2017	940610
13.7	HS2002	4404
13.7	HS2002	4405
13.7	HS2002	4413
13.7	HS2002	4417
13.7	HS2002	442110
13.7	HS2002	442190	Only some part of it
13.7	HS2007	4404
13.7	HS2007	4405
13.7	HS2007	4413
13.7	HS2007	4417
13.7	HS2007	442110
13.7	HS2007	442190	Only some part of it
13.7	HS2012	4404
13.7	HS2012	4405
13.7	HS2012	4413
13.7	HS2012	4417
13.7	HS2012	442110
13.7	HS2012	442190	Only some part of it
13.7	HS2017	4404
13.7	HS2017	4405
13.7	HS2017	4413
13.7	HS2017	4417
13.7	HS2017	442110
13.7	HS2017	442199
14.1	HS2002	4807
14.1	HS2007	4807
14.1	HS2012	4807
14.1	HS2017	4807
14.2	HS2002	481110
14.2	HS2002	481141
14.2	HS2002	481149
14.2	HS2002	481160
14.2	HS2002	481190
14.2	HS2007	481110
14.2	HS2007	481141
14.2	HS2007	481149
14.2	HS2007	481160
14.2	HS2007	481190
14.2	HS2012	481110
14.2	HS2012	481141
14.2	HS2012	481149
14.2	HS2012	481160
14.2	HS2012	481190
14.2	HS2017	481110
14.2	HS2017	481141
14.2	HS2017	481149
14.2	HS2017	481160
14.2	HS2017	481190
14.3	HS2002	4818
14.3	HS2007	4818
14.3	HS2012	4818
14.3	HS2017	4818
14.4	HS2002	4819
14.4	HS2007	4819
14.4	HS2012	4819
14.4	HS2017	4819
14.5	HS2002	4814
14.5	HS2002	4816
14.5	HS2002	4817
14.5	HS2002	4820
14.5	HS2002	4821
14.5	HS2002	4822
14.5	HS2002	4823
14.5	HS2007	4814
14.5	HS2007	4816
14.5	HS2007	4817
14.5	HS2007	4820
14.5	HS2007	4821
14.5	HS2007	4822
14.5	HS2007	4823
14.5	HS2012	4814
14.5	HS2012	4816
14.5	HS2012	4817
14.5	HS2012	4820
14.5	HS2012	4821
14.5	HS2012	4822
14.5	HS2012	4823
14.5	HS2017	4814
14.5	HS2017	4816
14.5	HS2017	4817
14.5	HS2017	4820
14.5	HS2017	4821
14.5	HS2017	4822
14.5	HS2017	4823
14.5.1	HS2002	482390	Only some part of it
14.5.1	HS2007	482390	Only some part of it
14.5.1	HS2012	482390	Only some part of it
14.5.1	HS2017	482390	Only some part of it
14.5.2	HS2002	482370
14.5.2	HS2007	482370
14.5.2	HS2012	482370
14.5.2	HS2017	482370
14.5.3	HS2002	482320
14.5.3	HS2007	482320
14.5.3	HS2012	482320
14.5.3	HS2017	482320
12.6	HS2002	482110	Only some part of it
12.6	HS2002	482190	Only some part of it
12.6	HS2002	482210	Only some part of it
12.6	HS2002	482290	Only some part of it
12.6	HS2002	482312	Only some part of it
12.6	HS2002	482319	Only some part of it
12.6	HS2002	482320	Only some part of it
12.6	HS2002	482340	Only some part of it
12.6	HS2002	482360	Only some part of it
12.6	HS2002	482370	Only some part of it
12.6	HS2002	482390	Only some part of it
12.6	HS2002	480210	Only some part of it
12.6	HS2002	480220	Only some part of it
12.6	HS2002	480230	Only some part of it
12.6	HS2002	480240	Only some part of it
12.6	HS2002	480254	Only some part of it
12.6	HS2002	480255	Only some part of it
12.6	HS2002	480256	Only some part of it
12.6	HS2002	480257	Only some part of it
12.6	HS2002	480258	Only some part of it
12.6	HS2002	480261	Only some part of it
12.6	HS2002	480262	Only some part of it
12.6	HS2002	480269	Only some part of it
12.6	HS2002	481013	Only some part of it
12.6	HS2002	481014	Only some part of it
12.6	HS2002	481019	Only some part of it
12.6	HS2002	481022	Only some part of it
12.6	HS2002	481029	Only some part of it
12.6	HS2002	481031	Only some part of it
12.6	HS2002	481032	Only some part of it
12.6	HS2002	481039	Only some part of it
12.6	HS2002	481092	Only some part of it
12.6	HS2002	481099	Only some part of it
12.6	HS2007	481410
12.6	HS2007	481420
12.6	HS2007	481490
12.6	HS2007	481710
12.6	HS2007	481720
12.6	HS2007	481730
12.6	HS2007	482010
12.6	HS2007	482020
12.6	HS2007	482030
12.6	HS2007	482040
12.6	HS2007	482050
12.6	HS2007	482090
12.6	HS2007	482110
12.6	HS2007	482190
12.6	HS2007	482210
12.6	HS2007	482290
12.6	HS2007	482320
12.6	HS2007	482340
12.6	HS2007	482361
12.6	HS2007	482369
12.6	HS2007	482370
12.6	HS2007	482390
12.6	HS2012	481420
12.6	HS2012	481490
12.6	HS2012	481710
12.6	HS2012	481720
12.6	HS2012	481730
12.6	HS2012	482020
12.6	HS2012	482030
12.6	HS2012	482040
12.6	HS2012	482050
12.6	HS2012	482090
12.6	HS2012	482110
12.6	HS2012	482190
12.6	HS2012	482210
12.6	HS2012	482290
12.6	HS2012	482320
12.6	HS2012	482340
12.6	HS2012	482361
12.6	HS2012	482369
12.6	HS2012	482370
12.6	HS2012	482390
12.6.1	HS2002	480210	Only some part of it
12.6.1	HS2002	480220	Only some part of it
12.6.1	HS2002	480230	Only some part of it
12.6.1	HS2002	480240	Only some part of it
12.6.1	HS2002	480254	Only some part of it
12.6.1	HS2002	480255	Only some part of it
12.6.1	HS2002	480256	Only some part of it
12.6.1	HS2002	480257	Only some part of it
12.6.1	HS2002	480258	Only some part of it
12.6.1	HS2002	480261	Only some part of it
12.6.1	HS2002	480262	Only some part of it
12.6.1	HS2002	480269	Only some part of it
12.6.1	HS2002	481013	Only some part of it
12.6.1	HS2002	481014	Only some part of it
12.6.1	HS2002	481019	Only some part of it
12.6.1	HS2002	481022	Only some part of it
12.6.1	HS2002	481029	Only some part of it
12.6.1	HS2002	481031	Only some part of it
12.6.1	HS2002	481032	Only some part of it
12.6.1	HS2002	481039	Only some part of it
12.6.1	HS2002	481092	Only some part of it
12.6.1	HS2002	481099	Only some part of it
12.6.1	HS2002	482390	Only some part of it
12.6.1	HS2007	482390	Only some part of it
12.6.1	HS2012	482390	Only some part of it
12.6.2	HS2002	482370
12.6.2	HS2007	482370
12.6.2	HS2012	482370
12.6.3	HS2002	482320
12.6.3	HS2007	482320
12.6.3	HS2012	482320

Conversion factors

JFSQ

JOINT FOREST SECTOR QUESTIONNAIRE

Conversion Factors

NOTE THESE ARE ONLY GENERAL FACTORS. IT WOULD BE PREFERABLE TO USE SPECIES- OR COUNTRY-SPECIFIC FACTORS

Multiply the quantity expressed in units on the right side of "per" with the factor to get the value expressed in units on left side of "per".

Items in BOLD RED text were added to the JFSQ in February 2023

Product Code

Product

JFSQ Quantity Unit

Results from UNECE/FAO/ITTO 2020 publication "Forest Product Conversion Factors"

UNECE/FAO Engineered Wood Products Questionnaire (last revised 2020)

Results from UNECE/FAO 2009 Conversion Factors Questionnaire (median)

FAO and UNECE Statistical Publications (Pre-2009)

volume to weight

volume/weight of finished product to volume of roundwood

Notes to Results

volume to weight

Notes to Results

volume to weight

volume/weight of finished product to volume of roundwood

Notes to Results

volume to weight

volume to area

volume/weight of finished product to volume of roundwood

m3 per MT

Roundwood equivalent

m3 per MT

Roundwood equivalent

m3 per MT

m3 per m2

Roundwood

equivalent

Europe

NA**

EECCA**

Europe

NA**

EECCA**

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3 ub

1.1

WOOD FUEL, INCLUDING WOOD FOR CHARCOAL

1000 m3 ub

1.38

1.1.C

Coniferous

1000 m3 ub

1.64

typical shipping weight

Green = 1.12

Based on 891 kg/m3 green, basic density of .41, and 20% moisture seasoned

1.60

1000 m3 ub

Seasoned = 1.82

Based on 407 kg/m3 dry, assuming 20% moisture

1.1.NC

Non-Coniferous

1000 m3 ub

1.11

typical shipping weight

Green=1.05

Based on 1137 kg/m3 green, specific gravity of .55, and 20% moisture seasoned

1.33

1000 m3 ub

Seasoned=1.43

1.2

INDUSTRIAL ROUNDWOOD

1000 m3 ub

1.2.C

Coniferous

1000 m3 ub

1.11

1.08

1.27

Averaged pulp and log

1.10

Based on 50/50 ratio of share of logs/pulpwood in industrial roundwood

1.2.C.Fir

Fir (and Spruce)

1000 m3 ub

1.21

Austrian Energy Agency, 2009. weighted by share of standing inventory of European speices (57% spruce, 10% silver fir and remaining species)

1.2.C.Pine

Pine

1000 m3 ub

1.08

Austrian Energy Agency, 2009, weighted 25% Scots Pine, 2% maritime pine, 2% black pine and remaining species

1.2.NC

Non-Coniferous

1000 m3 ub

0.98

1.02

1.15

0.91

Based on 50/50 ratio of share of logs/pulpwood in industrial roundwood

1.2.NC.T

of which:Tropical

1000 m3 ub

AFRICA=1.31, ASIA=0.956, LA. AM= 0.847, World=1.12

Source: Fonseca "Measurement of Roundwood" 2005, ITTO Annual Review 2007, table 3-2-a Species weight averaged using m3/tonne from Fonseca 2005 and volume exported by species from each region as shown in ITTO 2007 (assumes that bark is removed)

1.37

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3 ub

1.04

0.96

1.12

Averaged C & NC

1.05

Based on 950 kg/m3 green. Bark is included in weight but not in volume.

1.2.1.C

Coniferous

1000 m3 ub

1.10

1.00

1.19

1.07

Based on 935 kg/m3 green. Bark is included in weight but not in volume.

1.43

1.2.1.NC

Non-Coniferous

1000 m3 ub

0.97

0.92

1.04

0.91

Based on 1093 kg/m3 green. Bark is included in weight but not in volume.

1.25

1.2.NC.Beech

Beech

1000 m3 ub

0.92

Austrian Energy Agency, 2009

1.2.NC.Birch

Birch

1000 m3 ub

0.88

Austrian Energy Agency, 2009

1.2.NC.Eucalyptus

Eucalyptus

1000 m3 ub

0.77

ATIBT, 1982

1.2.NC.Oak

Oak

1000 m3 ub

0.88

Austrian Energy Agency, 2009

1.2.NC.Poplar

Poplar

1000 m3 ub

1.06

Austrian Energy Agency, 2009

1.2.2

PULPWOOD (ROUND & SPLIT)

1000 m3 ub

1.05

1.14

1.30

Averaged C & NC

1.08

Based on 930 kg/m3 green. Bark is included in weight but not in volume.

1.48

1.2.2.C

Coniferous

1000 m3 ub

1.11

1.16

1.35

1.12

Based on 891 kg/m3 green. Bark is included in weight but not in volume.

1.54

1.2.2.NC

Non-Coniferous

1000 m3 ub

0.98

1.11

1.25

0.91

Based on 1095 kg/m3 green. Bark is included in weight but not in volume.

1.33

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3 ub

1.07

1.33

1.2.3.C

Coniferous

1000 m3 ub

1.11

1.16

1.35

used pulpwood data

1.12

same as 1.2.2.C

1.43

1.2.3.NC

Non-Coniferous

1000 m3 ub

0.98

1.11

1.25

0.91

same as 1.2.2.NC

1.25

WOOD CHARCOAL

1000 MT

6 m3rw/tonne

5.35

Does not include the use of any of the wood fiber to generate the heat to make (add about 30% if inputted wood fiber used to provide heat)

6.00

WOOD CHIPS, PARTICLES AND RESIDUES

1000 m3

3.1

WOOD CHIPS AND PARTICLES

1000 m3

1.205

1.07

1.21

1.08

m3 /MT = green swe per odmt / avg delivered tonne/odmt, rwe= +1%

softwood=1.19

1.205

Based on swe/odmt of 2.41 and avg delivered mt / odmt of 2.0 in solid m3

1.60

1000 m3

hardwood = 1.05

1.123

Based on swe/odmt of 2.01 and avg delivered mt / odmt of 1.79 in solid m3

1000 m3

Woodchip, Green swe to oven-dry tonne m3/odmt

mix = 1.15

3.2

WOOD RESIDUES

1000 m3

1.205

1.07

1.21

1.08

Based on wood chips

Green=1.15

Based on wood chips

1.50

1000 m3

2.12

2.07

Seasoned = 2.12

2.07

Assumption for seasoned is based on average basic density of .42 from questionnaire and assumes 15% moisture content

3.2.1

of which: SAWDUST

1000 m3

1.205

1.07

1.21

1.08

Based on wood chips

RECOVERED POST-CONSUMER WOOD

1000 mt

Delivered MT (12-20% atmospheric moisture). Convert to dry weight for energy purposes (multiply by 0.88 - 0.80)

WOOD PELLETS AND OTHER AGGLOMERATES

1000 MT

5.1

WOOD PELLETS

1000 MT

1.54

1.45

1.54

1.51

1.44

nodata

m3/ton - bulk density, loose volume, 5-10% mcw- Equivalent - solid wood imput to bulk m3 pellets

1.51

1.44

Bulk (loose) volume, 5-10% moisture

5.2

OTHER AGGLOMERATES

1000 MT

1.12

nodata

2.32

nodata

m3/ton - Pressed logs and briquettes, bulk density, loose volume. Equivalent - m3rw/odmt

1.31

2.29

roundwood equivalent is m3rw/odmt, volume to weight is bulk (loose volume)

SAWNWOOD

1000 m3

1.6 / 1.82*

6.C

Coniferous

1000 m3

1.202

1.69

1.62

1.85

m3/ton - Average Sawnwood shipping weight. Equivalent - Sawnwood green rough

Green=1.202

RoughGreen=1.67

Green sawnwood based on basic density of .94, less bark (11%)

1.82

1000 m3

1.82

1.72

Nodata

1.69

2.05

Sawnwood dry rough

Dry = 1.99

RoughDry=1.99

Dry sawnwood weight based on basic density of .42, 4% shrinkage and 15% moisture content

1000 m3

2.26

2.08

nodata

Sawnwood dry planed

PlanedDry=2.13

6.C.Fir

Fir and Spruce

1000 m3

2.16

Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight). Weighted ratio of standing inventory.

6.C.Pine

Pine

1000 m3

1.72

Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight). Weighted ratio of standing inventory.

6.NC

Non-Coniferous

1000 m3

1.04

1.89

1.79

nodata

Sawnwood green rough

Green=1.04

RoughGreen=1.86

Green sawnwood based on basic density of 1.09, less bark (12%)

1.43

1000 m3

1.43

nodata

2.01

1.92

nodata

m3/ton - Average Sawnwood shipping weight. Equivalent - Sawnwood green rough

Seasoned=1.50

RoughDry=2.01

Dry sawnwood weight based on basic density of .55, 5% shrinkage and 15% moisture content

1000 m3

3.25

3.38

nodata

Sawnwood dry planed

PlanedDry=2.81

6.NC.Ash

Ash

1000 m3

1.47

Wood Database (wood-database.com). Air-dry.

6.NC.Beech

Beech

1000 m3

1.42

Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).

6.NC.Birch

Birch

1000 m3

1.47

Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).

6.NC.Cherry

Cherry

1000 m3

1.62

Giordano, 1976, Tecnologia del legno. Air-dry. Prunus avium.

6.NC.Maple

Maple

1000 m3

1.35

Giordano, 1976, Tecnologia del legno. Air-dry

6.NC.Oak

Oak

1000 m3

1.38

Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).

6.NC.Poplar

Poplar

1000 m3

2.29

Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).

6.NC.T

of which:Tropical

1000 m3

1.38

Based on FP Conversion Factors (2019), Asia (720 kg / m3)

VENEER SHEETS

1000 m3

1.33

0.0025

1.9*

7.C

Coniferous

1000 m3

1.05

1.95

1.5

Green veneer based on the ratio from the old conversion factors

Green=1.20

1.5***

Green veneer based on basic density of .94, less bark (11%)

0.003

1000 m3

1.8

nodata

2.08

1.6

nodata

m3/ton - Average panel shipping weight; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product

Seasoned=2.06

1.6***

Dry veneer weight based on basic density of .42, 9% shrinkage and 5% moisture content

7.NC

Non-Coniferous

1000 m3

1.15

nodata

2.11

1.89

Green veneer based on the ratio from the old conversion factors

Green=1.04

1.5***

Green veneer based on basic density of 1.09, less bark (11%)

0.001

1000 m3

1.7

nodata

2.25

nodata

m3/ton - Average panel shipping weight; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product

Seasoned=1.53

1.6***

Dry veneer weight based on basic density of .55, 11.5% shrinkage and 5% moisture content

7.NC.T

of which:Tropical

1000 m3

WOOD-BASED PANELS

1000 m3

1.6

8.1

PLYWOOD

1000 m3

1.54

0.105

2.3*

8,1.C

Coniferous

1000 m3

1.67

Nodata

2.16

1.92

nodata

1.69

2.12

dried, sanded, peeled

0.0165***

8.1.NC

Non-Coniferous

1000 m3

1.54

Nodata

2.54

2.14

nodata

1.54

1.92

dried, sanded, sliced

0.0215***

8.1.NC.T

of which:Tropical

1000 m3

8.1.1

of which: LAMINATED VENEER LUMBER

1000 m3

1.69

Same as coniferous plywood

8.1.1.C

Coniferous

1000 m3

1.69

Same as coniferous plywood

8.1.1.NC

Non-Coniferous

1000 m3

no data

8.1.1.NC.T

of which:Tropical

1000 m3

no data

8.2

PARTICLE BOARD (including OSB)

1000 m3

1.54

8.2x

PARTICLE BOARD (excluding OSB)

1000 m3

1.54

Nodata

1.51

1.54

nodata

m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product.

1.53

1.50

0.018***

8.2.1

of which: OSB

1000 m3

1.64

Nodata

1.72

1.63

nodata

m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product.

1.67

1.63

0.018***

8.3

FIBREBOARD

1000 m3

nodata

m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product.

8.3.1

HARDBOARD

1000 m3

1.06

Nodata

2.2

1.77

nodata

m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product.

1.06

1.93

solid wood per m3 of product

1.05

0.005
Alex McCusker: Alex McCusker: 0.003 per Conversion Factors Study

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

1000 m3

1.35

Nodata

1.80

1.53

nodata

m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product.

1.37

1.70

solid wood per m3 of product

2.00

0.016

8.3.3

OTHER FIBREBOARD

1000 m3

3.85

Nodata

0.68

0.71

nodata

m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product.

3.44

0.71

solid wood per m3 of product, mostly insulating board

4.00

0.025

WOOD PULP

1000 MT

3.7

nodata

3.76

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

3.86

3.37

9.1

MECHANICAL AND SEMI-CHEMICAL

1000 MT

2.59

2.45

2.94

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

2.60

air-dried metric ton (mechanical 2.50, semi-chemical 2.70)

9..2

CHEMICAL

1000 MT

4.80

4.29

4.10

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.90

9.2.1

SULPHATE

1000 MT

4.50

nodata

4.60

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.57

air-dried metric ton (unbleached 4.63, bleached 4.50)

9.2.1.1

of which: bleached

1000 MT

4.50

nodata

4.90

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.50

air-dried metric ton

9.2.2

SULPHITE

1000 MT

4.73

nodata

4.15

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.83

air-dried metric ton (unbleached 4.64 and bleached 5.01)

9.3

DISSOLVING GRADES

1000 MT

4.46

nodata

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

5.65

air-dried metric ton

OTHER PULP

1000 MT

nodata

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

10.1

PULP FROM FIBRES OTHER THAN WOOD

1000 MT

nodata

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

10.2

RECOVERED FIBRE PULP

1000 MT

nodata

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

RECOVERED PAPER

1000 MT

nodata

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

1.28 MT in per MT out

PAPER AND PAPERBOARD

1000 MT

3.85

nodata

4.15

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

3.6

3.37

12.1

GRAPHIC PAPERS

1000 MT

nodata

12.1.1

NEWSPRINT

1000 MT

2.80

2.50

3.15

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

2.80

air-dried metric ton

12.1.2

UNCOATED MECHANICAL

1000 MT

3.50

nodata

4.00

3.50

air-dried metric ton

12.1.3

UNCOATED WOODFREE

1000 MT

nodata

12.1.4

COATED PAPERS

1000 MT

3.50

nodata

4.00

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

3.95

air-dried metric ton

12.2

SANITARY AND HOUSEHOLD PAPERS

1000 MT

4.60

nodata

4.20

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.90

air-dried metric ton

12.3

PACKAGING MATERIALS

1000 MT

3.25

nodata

4.30

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

3.25

air-dried metric ton

12.3.1

CASE MATERIALS

1000 MT

4.20

nodata

4.00

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.20

air-dried metric ton

12.3.2

CARTONBOARD

1000 MT

4.00

nodata

4.30

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.00

air-dried metric ton

12.3.3

WRAPPING PAPERS

1000 MT

4.10

nodata

4.40

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.10

air-dried metric ton

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

1000 MT

4.00

nodata

3.30

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.00

air-dried metric ton

12.4

OTHER PAPER AND PAPERBOARD N.E.S

1000 MT

3.48

nodata

3.30

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

3.48

air-dried metric ton

GLULAM AND CROSS-LAMINATED TIMBER

1000 m3

15.1

GLULAM

1000 m3

1.69

same as coniferous plywood

15.2

CROSS-LAMINATED TIMBER

1000 m3

2.00

I-BEAMS

1000 MT

1.68

222 linear meters per MT

For inverse relationships divide 1 by the factor given, e.g. to convert m3 of wood charcoal to mt divide 1 by m3/mt factor of 6 = 0.167

Notes:

Forest Measures

MT = metric tonnes (1000 kg)

Unit

m3/unit

m3 = cubic meters (solid volume)

1000 board feet (sawlogs)

4.53****

**** = obsolete - more recent figures would be:

m2 = square meters

1000 board feet (sawnwood - nominal)

2.36

for Oregon, Washington State, Alaska (west of Cascades), South East United States (Doyle region): 6.3

(s) = solid volume

1000 board feet (sawnwood - actual)

1.69

Inland Western North America, Great Lakes (North America), Eastern Canada: 5.7

1000 square feet (1/8 inch thickness)

0.295

Northeast United States Int 1/4": 5

Unit Conversion

cord

3.625

1 inch = 25.4 millimetres

cord (pulpwood)

2.55

1 square foot = 0.0929 square metre

cord (wood fuel)

2.12

1 pound = 0.454 kilograms

cubic foot

0.02832

1 short ton (2000 pounds) = 0.9072 metric ton

cubic foot (stacked)

0.01841

1 long ton (2240 pounds) = 1.016 metric ton

cunit

2.83

Bold = FAO published figure

fathom

6.1164

hoppus cubic foot

0.0222

* = ITTO

hoppus super(ficial) foot

0.00185

hoppus ton (50 hoppus cubic feet)

1.11

** NA = North America; EECCA = Eastern Europe, Caucasus and Central Asia

Petrograd Standard

4.672

stere

*** = Conversion Factor Study, US figures, rotary for conifer and sliced for non-conifer

stere (pulpwood)

0.72

stere (wood fuel)

0.65

Fonseca "Measurement of Roundwood" 2005. Estimated by Matt Fonseca based on regional knowledge of the scaling methods and timber types

prepared February 2004

updated 2007 with RWE factors

updated 2009 with provisional results of forest products conversion factors study

updated 2011 with results of forest products conversion factors study (DP49)

updated 2023 with results of 2019 UNECE/FAO/ITTO study - https://www.fao.org/documents/card/en/c/ca7952en

Flatfile

geo	stk_flow	time	prod_wd	treespec	unit	obs_value	obs_flag
FI	PRD	2021	RW_OB	TOTAL	THS_M3	76347.955
FI	PRD	2021	RW_FW_OB	TOTAL	THS_M3	10278.023
FI	PRD	2021	RW_FW_OB	CONIF	THS_M3	4956.317
FI	PRD	2021	RW_FW_OB	NCONIF	THS_M3	5321.706
FI	PRD	2021	RW_IN_OB	TOTAL	THS_M3	66069.932
FI	PRD	2021	RW_IN_OB	CONIF	THS_M3	55458.191
FI	PRD	2021	RW_IN_OB	NCONIF	THS_M3	10611.741
FI	PRD	2021	RW_IN_OB	NC_TRO	THS_M3	0
FI	PRD	2021	RW_IN_LG_OB	TOTAL	THS_M3	29327.617
FI	PRD	2021	RW_IN_LG_OB	CONIF	THS_M3	28168.411
FI	PRD	2021	RW_IN_LG_OB	NCONIF	THS_M3	1159.206
FI	PRD	2021	RW_IN_PW_OB	TOTAL	THS_M3	36742.315
FI	PRD	2021	RW_IN_PW_OB	CONIF	THS_M3	27289.78
FI	PRD	2021	RW_IN_PW_OB	NCONIF	THS_M3	9452.535
FI	PRD	2021	RW_IN_O_OB	TOTAL	THS_M3	0
FI	PRD	2021	RW_IN_O_OB	CONIF	THS_M3	0
FI	PRD	2021	RW_IN_O_OB	NCONIF	THS_M3	0
FI	PRD	2022	RW_OB	TOTAL	THS_M3	75112.063
FI	PRD	2022	RW_FW_OB	TOTAL	THS_M3	10825.926
FI	PRD	2022	RW_FW_OB	CONIF	THS_M3	5320.816
FI	PRD	2022	RW_FW_OB	NCONIF	THS_M3	5505.11
FI	PRD	2022	RW_IN_OB	TOTAL	THS_M3	64286.137
FI	PRD	2022	RW_IN_OB	CONIF	THS_M3	54067.555
FI	PRD	2022	RW_IN_OB	NCONIF	THS_M3	10218.582
FI	PRD	2022	RW_IN_OB	NC_TRO	THS_M3	0
FI	PRD	2022	RW_IN_LG_OB	TOTAL	THS_M3	28888.148
FI	PRD	2022	RW_IN_LG_OB	CONIF	THS_M3	27716.23
FI	PRD	2022	RW_IN_LG_OB	NCONIF	THS_M3	1171.918
FI	PRD	2022	RW_IN_PW_OB	TOTAL	THS_M3	35397.989
FI	PRD	2022	RW_IN_PW_OB	CONIF	THS_M3	26351.325
FI	PRD	2022	RW_IN_PW_OB	NCONIF	THS_M3	9046.664
FI	PRD	2022	RW_IN_O_OB	TOTAL	THS_M3	0
FI	PRD	2022	RW_IN_O_OB	CONIF	THS_M3	0
FI	PRD	2022	RW_IN_O_OB	NCONIF	THS_M3	0
FI	PRD	2021	RW	TOTAL	THS_M3	66713.896538
FI	PRD	2021	RW_FW	TOTAL	THS_M3	8911.045941
FI	PRD	2021	RW_FW	CONIF	THS_M3	4297.126839
FI	PRD	2021	RW_FW	NCONIF	THS_M3	4613.919102
FI	PRD	2021	RW_IN	TOTAL	THS_M3	57802.850597
FI	PRD	2021	RW_IN	CONIF	THS_M3	48628.868117
FI	PRD	2021	RW_IN	NCONIF	THS_M3	9173.98248
FI	PRD	2021	RW_IN	NC_TRO	THS_M3	0
FI	PRD	2021	RW_IN_LG	TOTAL	THS_M3	26093.095192
FI	PRD	2021	RW_IN_LG	CONIF	THS_M3	25067.197882
FI	PRD	2021	RW_IN_LG	NCONIF	THS_M3	1025.89731
FI	PRD	2021	RW_IN_PW	TOTAL	THS_M3	31709.755405
FI	PRD	2021	RW_IN_PW	CONIF	THS_M3	23561.670235
FI	PRD	2021	RW_IN_PW	NCONIF	THS_M3	8148.08517
FI	PRD	2021	RW_IN_O	TOTAL	THS_M3	0
FI	PRD	2021	RW_IN_O	CONIF	THS_M3	0
FI	PRD	2021	RW_IN_O	NCONIF	THS_M3	0
FI	PRD	2021	CHA	TOTAL	THS_T
FI	PRD	2021	CHP_RES	TOTAL	THS_M3	15128.864
FI	PRD	2021	CHP	TOTAL	THS_M3	9653.442
FI	PRD	2021	RES	TOTAL	THS_M3	5475.422
FI	PRD	2021	RES_SWD	TOTAL	THS_M3	3067.501
FI	PRD	2021	RCW	TOTAL	THS_T	551.688148
FI	PRD	2021	PEL_AGG	TOTAL	THS_T	365.186
FI	PRD	2021	PEL	TOTAL	THS_T	365.186
FI	PRD	2021	AGG	TOTAL	THS_T	0
FI	PRD	2021	SN	TOTAL	THS_M3	11966
FI	PRD	2021	SN	CONIF	THS_M3	11900
FI	PRD	2021	SN	NCONIF	THS_M3	66
FI	PRD	2021	SN	NC_TRO	THS_M3	0
FI	PRD	2021	PN_VN	TOTAL	THS_M3	170	9
FI	PRD	2021	PN_VN	CONIF	THS_M3
FI	PRD	2021	PN_VN	NCONIF	THS_M3
FI	PRD	2021	PN_VN	NC_TRO	THS_M3	0	9
FI	PRD	2021	PN	TOTAL	THS_M3	1233	6
FI	PRD	2021	PN_PY	TOTAL	THS_M3	1130
FI	PRD	2021	PN_PY	CONIF	THS_M3
FI	PRD	2021	PN_PY	NCONIF	THS_M3
FI	PRD	2021	PN_PY	NC_TRO	THS_M3	0
FI	PRD	2021	PN_PY_LVL	TOTAL	THS_M3
FI	PRD	2021	PN_PY_LVL	CONIF	THS_M3
FI	PRD	2021	PN_PY_LVL	NCONIF	THS_M3
FI	PRD	2021	PN_PY_LVL	NC_TRO	THS_M3
FI	PRD	2021	PN_PB	TOTAL	THS_M3	54	6
FI	PRD	2021	PN_PB_OSB	TOTAL	THS_M3	0	6
FI	PRD	2021	PN_FB	TOTAL	THS_M3	49	6
FI	PRD	2021	PN_FB_HB	TOTAL	THS_M3	49	6
FI	PRD	2021	PN_FB_MDF	TOTAL	THS_M3	0	6
FI	PRD	2021	PN_FB_O	TOTAL	THS_M3	0	6
FI	PRD	2021	PL	TOTAL	THS_T	10960
FI	PRD	2021	PL_MC_SCH	TOTAL	THS_T	2640
FI	PRD	2021	PL_CH	TOTAL	THS_T	8320
FI	PRD	2021	PL_CH_SA	TOTAL	THS_T
FI	PRD	2021	PL_CH_SAB	TOTAL	THS_T
FI	PRD	2021	PL_CH_SI	TOTAL	THS_T
FI	PRD	2021	PL_DS	TOTAL	THS_T
FI	PRD	2021	PLO	TOTAL	THS_T
FI	PRD	2021	PLO_NW	TOTAL	THS_T
FI	PRD	2021	PLO_RC	TOTAL	THS_T
FI	PRD	2021	RCP	TOTAL	THS_T	460
FI	PRD	2021	PP	TOTAL	THS_T	8660
FI	PRD	2021	PP_GR	TOTAL	THS_T	3250
FI	PRD	2021	PP_GR_NP	TOTAL	THS_T
FI	PRD	2021	PP_GR_MC	TOTAL	THS_T
FI	PRD	2021	PP_GR_NW	TOTAL	THS_T
FI	PRD	2021	PP_GR_CO	TOTAL	THS_T
FI	PRD	2021	PP_HS	TOTAL	THS_T
FI	PRD	2021	PP_PK	TOTAL	THS_T	4220
FI	PRD	2021	PP_PK_CS	TOTAL	THS_T
FI	PRD	2021	PP_PK_CB	TOTAL	THS_T
FI	PRD	2021	PP_PK_WR	TOTAL	THS_T
FI	PRD	2021	PP_PK_O	TOTAL	THS_T
FI	PRD	2021	PP_O	TOTAL	THS_T	1190
FI	PRD	2021	GLT_CLT	TOTAL	THS_M3
FI	PRD	2021	GLT	TOTAL	THS_M3
FI	PRD	2021	CLT	TOTAL	THS_M3
FI	PRD	2021	I_BEAMS	TOTAL	THS_T
FI	PRD	2022	RW	TOTAL	THS_M3	65637.339725
FI	PRD	2022	RW_FW	TOTAL	THS_M3	9386.077842
FI	PRD	2022	RW_FW	CONIF	THS_M3	4613.147472
FI	PRD	2022	RW_FW	NCONIF	THS_M3	4772.93037
FI	PRD	2022	RW_IN	TOTAL	THS_M3	56251.261883
FI	PRD	2022	RW_IN	CONIF	THS_M3	47415.890085
FI	PRD	2022	RW_IN	NCONIF	THS_M3	8835.371798
FI	PRD	2022	RW_IN	NC_TRO	THS_M3	0
FI	PRD	2022	RW_IN_LG	TOTAL	THS_M3	25701.545062
FI	PRD	2022	RW_IN_LG	CONIF	THS_M3	24664.397632
FI	PRD	2022	RW_IN_LG	NCONIF	THS_M3	1037.14743
FI	PRD	2022	RW_IN_PW	TOTAL	THS_M3	30549.716821
FI	PRD	2022	RW_IN_PW	CONIF	THS_M3	22751.492453
FI	PRD	2022	RW_IN_PW	NCONIF	THS_M3	7798.224368
FI	PRD	2022	RW_IN_O	TOTAL	THS_M3	0
FI	PRD	2022	RW_IN_O	CONIF	THS_M3	0
FI	PRD	2022	RW_IN_O	NCONIF	THS_M3	0
FI	PRD	2022	CHA	TOTAL	THS_T
FI	PRD	2022	CHP_RES	TOTAL	THS_M3	14375.6525	7
FI	PRD	2022	CHP	TOTAL	THS_M3	9302.541	7
FI	PRD	2022	RES	TOTAL	THS_M3	5073.1115	7
FI	PRD	2022	RES_SWD	TOTAL	THS_M3	2912.517	7
FI	PRD	2022	RCW	TOTAL	THS_T	518.849848	7
FI	PRD	2022	PEL_AGG	TOTAL	THS_T	359.629	7
FI	PRD	2022	PEL	TOTAL	THS_T	359.629	7
FI	PRD	2022	AGG	TOTAL	THS_T	0	7
FI	PRD	2022	SN	TOTAL	THS_M3	11273
FI	PRD	2022	SN	CONIF	THS_M3	11200
FI	PRD	2022	SN	NCONIF	THS_M3	73
FI	PRD	2022	SN	NC_TRO	THS_M3	0
FI	PRD	2022	PN_VN	TOTAL	THS_M3	184	9
FI	PRD	2022	PN_VN	CONIF	THS_M3
FI	PRD	2022	PN_VN	NCONIF	THS_M3
FI	PRD	2022	PN_VN	NC_TRO	THS_M3	0	9
FI	PRD	2022	PN	TOTAL	THS_M3	1206	6
FI	PRD	2022	PN_PY	TOTAL	THS_M3	1110
FI	PRD	2022	PN_PY	CONIF	THS_M3
FI	PRD	2022	PN_PY	NCONIF	THS_M3
FI	PRD	2022	PN_PY	NC_TRO	THS_M3	0
FI	PRD	2022	PN_PY_LVL	TOTAL	THS_M3
FI	PRD	2022	PN_PY_LVL	CONIF	THS_M3
FI	PRD	2022	PN_PY_LVL	NCONIF	THS_M3
FI	PRD	2022	PN_PY_LVL	NC_TRO	THS_M3
FI	PRD	2022	PN_PB	TOTAL	THS_M3	50	6
FI	PRD	2022	PN_PB_OSB	TOTAL	THS_M3	0	6
FI	PRD	2022	PN_FB	TOTAL	THS_M3	46	6
FI	PRD	2022	PN_FB_HB	TOTAL	THS_M3	46	6
FI	PRD	2022	PN_FB_MDF	TOTAL	THS_M3	0	6
FI	PRD	2022	PN_FB_O	TOTAL	THS_M3	0	6
FI	PRD	2022	PL	TOTAL	THS_T	10520
FI	PRD	2022	PL_MC_SCH	TOTAL	THS_T	2840
FI	PRD	2022	PL_CH	TOTAL	THS_T	7680
FI	PRD	2022	PL_CH_SA	TOTAL	THS_T
FI	PRD	2022	PL_CH_SAB	TOTAL	THS_T
FI	PRD	2022	PL_CH_SI	TOTAL	THS_T
FI	PRD	2022	PL_DS	TOTAL	THS_T
FI	PRD	2022	PLO	TOTAL	THS_T
FI	PRD	2022	PLO_NW	TOTAL	THS_T
FI	PRD	2022	PLO_RC	TOTAL	THS_T
FI	PRD	2022	RCP	TOTAL	THS_T	450
FI	PRD	2022	PP	TOTAL	THS_T	7210
FI	PRD	2022	PP_GR	TOTAL	THS_T	2160
FI	PRD	2022	PP_GR_NP	TOTAL	THS_T
FI	PRD	2022	PP_GR_MC	TOTAL	THS_T
FI	PRD	2022	PP_GR_NW	TOTAL	THS_T
FI	PRD	2022	PP_GR_CO	TOTAL	THS_T
FI	PRD	2022	PP_HS	TOTAL	THS_T
FI	PRD	2022	PP_PK	TOTAL	THS_T	4150
FI	PRD	2022	PP_PK_CS	TOTAL	THS_T
FI	PRD	2022	PP_PK_CB	TOTAL	THS_T
FI	PRD	2022	PP_PK_WR	TOTAL	THS_T
FI	PRD	2022	PP_PK_O	TOTAL	THS_T
FI	PRD	2022	PP_O	TOTAL	THS_T	900
FI	PRD	2022	GLT_CLT	TOTAL	THS_M3
FI	PRD	2022	GLT	TOTAL	THS_M3
FI	PRD	2022	CLT	TOTAL	THS_M3
FI	PRD	2022	I_BEAMS	TOTAL	THS_T
FI	IMP	2021	RW	TOTAL	THS_M3	6441.4119312
FI	IMP	2021	RW_FW	TOTAL	THS_M3	143.3679312
FI	IMP	2021	RW_FW	CONIF	THS_M3	110.3252016
FI	IMP	2021	RW_FW	NCONIF	THS_M3	33.0427296
FI	IMP	2021	RW_IN	TOTAL	THS_M3	6298.044
FI	IMP	2021	RW_IN	CONIF	THS_M3	1467.83
FI	IMP	2021	RW_IN	NCONIF	THS_M3	4830.214
FI	IMP	2021	RW_IN	NC_TRO	THS_M3	0.004
FI	IMP	2021	CHA	TOTAL	THS_T	5.387026
FI	IMP	2021	CHP_RES	TOTAL	THS_M3	4659.5614608872
FI	IMP	2021	CHP	TOTAL	THS_M3	4406.6012759725
FI	IMP	2021	RES	TOTAL	THS_M3	252.9601849147
FI	IMP	2021	RES_SWD	TOTAL	THS_M3	252.9601849147
FI	IMP	2021	RCW	TOTAL	THS_T	252.137623
FI	IMP	2021	PEL_AGG	TOTAL	THS_T	238.384096
FI	IMP	2021	PEL	TOTAL	THS_T	196.126738
FI	IMP	2021	AGG	TOTAL	THS_T	42.257358
FI	IMP	2021	SN	TOTAL	THS_M3	577.897
FI	IMP	2021	SN	CONIF	THS_M3	547.269
FI	IMP	2021	SN	NCONIF	THS_M3	30.628
FI	IMP	2021	SN	NC_TRO	THS_M3	4.799
FI	IMP	2021	PN_VN	TOTAL	THS_M3	9.084
FI	IMP	2021	PN_VN	CONIF	THS_M3	0.085
FI	IMP	2021	PN_VN	NCONIF	THS_M3	8.999
FI	IMP	2021	PN_VN	NC_TRO	THS_M3	2.768
FI	IMP	2021	PN	TOTAL	THS_M3	417.37084125
FI	IMP	2021	PN_PY	TOTAL	THS_M3	121.649
FI	IMP	2021	PN_PY	CONIF	THS_M3	19.241
FI	IMP	2021	PN_PY	NCONIF	THS_M3	102.408
FI	IMP	2021	PN_PY	NC_TRO	THS_M3	0.815
FI	IMP	2021	PN_PY_LVL	TOTAL	THS_M3
FI	IMP	2021	PN_PY_LVL	CONIF	THS_M3
FI	IMP	2021	PN_PY_LVL	NCONIF	THS_M3
FI	IMP	2021	PN_PY_LVL	NC_TRO	THS_M3
FI	IMP	2021	PN_PB	TOTAL	THS_M3	128.66
FI	IMP	2021	PN_PB_OSB	TOTAL	THS_M3	47.339
FI	IMP	2021	PN_FB	TOTAL	THS_M3	167.06184125
FI	IMP	2021	PN_FB_HB	TOTAL	THS_M3	25.539
FI	IMP	2021	PN_FB_MDF	TOTAL	THS_M3	108.8481
FI	IMP	2021	PN_FB_O	TOTAL	THS_M3	32.67474125
FI	IMP	2021	PL	TOTAL	THS_T	149.786251
FI	IMP	2021	PL_MC_SCH	TOTAL	THS_T	9.461724
FI	IMP	2021	PL_CH	TOTAL	THS_T	133.567665
FI	IMP	2021	PL_CH_SA	TOTAL	THS_T	130.696765
FI	IMP	2021	PL_CH_SAB	TOTAL	THS_T	106.675607
FI	IMP	2021	PL_CH_SI	TOTAL	THS_T	2.8709
FI	IMP	2021	PL_DS	TOTAL	THS_T	6.756862
FI	IMP	2021	PLO	TOTAL	THS_T	2.948363
FI	IMP	2021	PLO_NW	TOTAL	THS_T	2.006406
FI	IMP	2021	PLO_RC	TOTAL	THS_T	0.941957
FI	IMP	2021	RCP	TOTAL	THS_T	67.288019
FI	IMP	2021	PP	TOTAL	THS_T	353.444831
FI	IMP	2021	PP_GR	TOTAL	THS_T	70.972866
FI	IMP	2021	PP_GR_NP	TOTAL	THS_T	34.201265
FI	IMP	2021	PP_GR_MC	TOTAL	THS_T	3.620554
FI	IMP	2021	PP_GR_NW	TOTAL	THS_T	15.183061
FI	IMP	2021	PP_GR_CO	TOTAL	THS_T	17.967986
FI	IMP	2021	PP_HS	TOTAL	THS_T	1.869663
FI	IMP	2021	PP_PK	TOTAL	THS_T	278.890943
FI	IMP	2021	PP_PK_CS	TOTAL	THS_T	153.727223
FI	IMP	2021	PP_PK_CB	TOTAL	THS_T	85.740341
FI	IMP	2021	PP_PK_WR	TOTAL	THS_T	33.472912
FI	IMP	2021	PP_PK_O	TOTAL	THS_T	5.950467
FI	IMP	2021	PP_O	TOTAL	THS_T	1.711359
FI	IMP	2021	GLT_CLT	TOTAL	THS_M3
FI	IMP	2021	GLT	TOTAL	THS_M3
FI	IMP	2021	CLT	TOTAL	THS_M3
FI	IMP	2021	I_BEAMS	TOTAL	THS_T
FI	IMP	2021	RW	TOTAL	THS_NAC	293666.602
FI	IMP	2021	RW_FW	TOTAL	THS_NAC	6981.578
FI	IMP	2021	RW_FW	CONIF	THS_NAC	3868.409
FI	IMP	2021	RW_FW	NCONIF	THS_NAC	3113.169
FI	IMP	2021	RW_IN	TOTAL	THS_NAC	286685.024
FI	IMP	2021	RW_IN	CONIF	THS_NAC	75470.83
FI	IMP	2021	RW_IN	NCONIF	THS_NAC	211214.194
FI	IMP	2021	RW_IN	NC_TRO	THS_NAC	35.988
FI	IMP	2021	CHA	TOTAL	THS_NAC	3879.27
FI	IMP	2021	CHP_RES	TOTAL	THS_NAC	181243.825
FI	IMP	2021	CHP	TOTAL	THS_NAC	175138.267
FI	IMP	2021	RES	TOTAL	THS_NAC	6105.558
FI	IMP	2021	RES_SWD	TOTAL	THS_NAC	6105.558
FI	IMP	2021	RCW	TOTAL	THS_NAC	8058.262
FI	IMP	2021	PEL_AGG	TOTAL	THS_NAC	25763.472
FI	IMP	2021	PEL	TOTAL	THS_NAC	23113.719
FI	IMP	2021	AGG	TOTAL	THS_NAC	2649.753
FI	IMP	2021	SN	TOTAL	THS_NAC	160397.533
FI	IMP	2021	SN	CONIF	THS_NAC	133705.357
FI	IMP	2021	SN	NCONIF	THS_NAC	26692.176
FI	IMP	2021	SN	NC_TRO	THS_NAC	6122.931
FI	IMP	2021	PN_VN	TOTAL	THS_NAC	6004.03
FI	IMP	2021	PN_VN	CONIF	THS_NAC	410.688
FI	IMP	2021	PN_VN	NCONIF	THS_NAC	5593.342
FI	IMP	2021	PN_VN	NC_TRO	THS_NAC	1101.939
FI	IMP	2021	PN	TOTAL	THS_NAC	175899.783
FI	IMP	2021	PN_PY	TOTAL	THS_NAC	72689.783
FI	IMP	2021	PN_PY	CONIF	THS_NAC	10121.059
FI	IMP	2021	PN_PY	NCONIF	THS_NAC	62568.724
FI	IMP	2021	PN_PY	NC_TRO	THS_NAC	2234.051
FI	IMP	2021	PN_PY_LVL	TOTAL	THS_NAC
FI	IMP	2021	PN_PY_LVL	CONIF	THS_NAC
FI	IMP	2021	PN_PY_LVL	NCONIF	THS_NAC
FI	IMP	2021	PN_PY_LVL	NC_TRO	THS_NAC
FI	IMP	2021	PN_PB	TOTAL	THS_NAC	43990.064
FI	IMP	2021	PN_PB_OSB	TOTAL	THS_NAC	17410.628
FI	IMP	2021	PN_FB	TOTAL	THS_NAC	59219.936
FI	IMP	2021	PN_FB_HB	TOTAL	THS_NAC	13141.73
FI	IMP	2021	PN_FB_MDF	TOTAL	THS_NAC	41088.161
FI	IMP	2021	PN_FB_O	TOTAL	THS_NAC	4990.045
FI	IMP	2021	PL	TOTAL	THS_NAC	88130.274
FI	IMP	2021	PL_MC_SCH	TOTAL	THS_NAC	3808.316
FI	IMP	2021	PL_CH	TOTAL	THS_NAC	77215.652
FI	IMP	2021	PL_CH_SA	TOTAL	THS_NAC	74345.615
FI	IMP	2021	PL_CH_SAB	TOTAL	THS_NAC	63033.354
FI	IMP	2021	PL_CH_SI	TOTAL	THS_NAC	2870.037
FI	IMP	2021	PL_DS	TOTAL	THS_NAC	7106.306
FI	IMP	2021	PLO	TOTAL	THS_NAC	3684.133
FI	IMP	2021	PLO_NW	TOTAL	THS_NAC	3259.202
FI	IMP	2021	PLO_RC	TOTAL	THS_NAC	424.931
FI	IMP	2021	RCP	TOTAL	THS_NAC	13466.324
FI	IMP	2021	PP	TOTAL	THS_NAC	291438.162
FI	IMP	2021	PP_GR	TOTAL	THS_NAC	53402.649
FI	IMP	2021	PP_GR_NP	TOTAL	THS_NAC	13839.671
FI	IMP	2021	PP_GR_MC	TOTAL	THS_NAC	8073.357
FI	IMP	2021	PP_GR_NW	TOTAL	THS_NAC	15760.885
FI	IMP	2021	PP_GR_CO	TOTAL	THS_NAC	15728.736
FI	IMP	2021	PP_HS	TOTAL	THS_NAC	3413.688
FI	IMP	2021	PP_PK	TOTAL	THS_NAC	228962.157
FI	IMP	2021	PP_PK_CS	TOTAL	THS_NAC	82115.166
FI	IMP	2021	PP_PK_CB	TOTAL	THS_NAC	104588.381
FI	IMP	2021	PP_PK_WR	TOTAL	THS_NAC	37961.457
FI	IMP	2021	PP_PK_O	TOTAL	THS_NAC	4297.153
FI	IMP	2021	PP_O	TOTAL	THS_NAC	5659.668
FI	IMP	2021	GLT_CLT	TOTAL	THS_NAC
FI	IMP	2021	GLT	TOTAL	THS_NAC
FI	IMP	2021	CLT	TOTAL	THS_NAC
FI	IMP	2021	I_BEAMS	TOTAL	THS_NAC
FI	IMP	2022	RW	TOTAL	THS_M3	3016.863488
FI	IMP	2022	RW_FW	TOTAL	THS_M3	137.513488
FI	IMP	2022	RW_FW	CONIF	THS_M3	119.9084032
FI	IMP	2022	RW_FW	NCONIF	THS_M3	17.6050848
FI	IMP	2022	RW_IN	TOTAL	THS_M3	2879.35
FI	IMP	2022	RW_IN	CONIF	THS_M3	1295.643
FI	IMP	2022	RW_IN	NCONIF	THS_M3	1583.707
FI	IMP	2022	RW_IN	NC_TRO	THS_M3	0
FI	IMP	2022	CHA	TOTAL	THS_T	4.46745
FI	IMP	2022	CHP_RES	TOTAL	THS_M3	1888.6792726599
FI	IMP	2022	CHP	TOTAL	THS_M3	1711.5280427522
FI	IMP	2022	RES	TOTAL	THS_M3	177.1512299077
FI	IMP	2022	RES_SWD	TOTAL	THS_M3	177.1512299077
FI	IMP	2022	RCW	TOTAL	THS_T	180.006317
FI	IMP	2022	PEL_AGG	TOTAL	THS_T	207.58023
FI	IMP	2022	PEL	TOTAL	THS_T	195.644846
FI	IMP	2022	AGG	TOTAL	THS_T	11.935384
FI	IMP	2022	SN	TOTAL	THS_M3	333.764
FI	IMP	2022	SN	CONIF	THS_M3	300.017
FI	IMP	2022	SN	NCONIF	THS_M3	33.747
FI	IMP	2022	SN	NC_TRO	THS_M3	7.883
FI	IMP	2022	PN_VN	TOTAL	THS_M3	11.241
FI	IMP	2022	PN_VN	CONIF	THS_M3	0.299
FI	IMP	2022	PN_VN	NCONIF	THS_M3	10.942
FI	IMP	2022	PN_VN	NC_TRO	THS_M3	2.966
FI	IMP	2022	PN	TOTAL	THS_M3	371.335
FI	IMP	2022	PN_PY	TOTAL	THS_M3	87.188
FI	IMP	2022	PN_PY	CONIF	THS_M3	29.691
FI	IMP	2022	PN_PY	NCONIF	THS_M3	57.497
FI	IMP	2022	PN_PY	NC_TRO	THS_M3	1.453
FI	IMP	2022	PN_PY_LVL	TOTAL	THS_M3	1.131
FI	IMP	2022	PN_PY_LVL	CONIF	THS_M3	0.959
FI	IMP	2022	PN_PY_LVL	NCONIF	THS_M3	0.172
FI	IMP	2022	PN_PY_LVL	NC_TRO	THS_M3	0.13
FI	IMP	2022	PN_PB	TOTAL	THS_M3	143.59
FI	IMP	2022	PN_PB_OSB	TOTAL	THS_M3	56.485
FI	IMP	2022	PN_FB	TOTAL	THS_M3	140.557
FI	IMP	2022	PN_FB_HB	TOTAL	THS_M3	20.569
FI	IMP	2022	PN_FB_MDF	TOTAL	THS_M3	86.32
FI	IMP	2022	PN_FB_O	TOTAL	THS_M3	33.668
FI	IMP	2022	PL	TOTAL	THS_T	259.268315
FI	IMP	2022	PL_MC_SCH	TOTAL	THS_T	1.464619
FI	IMP	2022	PL_CH	TOTAL	THS_T	252.70262
FI	IMP	2022	PL_CH_SA	TOTAL	THS_T	249.751563
FI	IMP	2022	PL_CH_SAB	TOTAL	THS_T	230.324236
FI	IMP	2022	PL_CH_SI	TOTAL	THS_T	2.951057
FI	IMP	2022	PL_DS	TOTAL	THS_T	5.101076
FI	IMP	2022	PLO	TOTAL	THS_T	4.894641
FI	IMP	2022	PLO_NW	TOTAL	THS_T	3.226039
FI	IMP	2022	PLO_RC	TOTAL	THS_T	1.668602
FI	IMP	2022	RCP	TOTAL	THS_T	90.917188
FI	IMP	2022	PP	TOTAL	THS_T	336.978375
FI	IMP	2022	PP_GR	TOTAL	THS_T	91.184311
FI	IMP	2022	PP_GR_NP	TOTAL	THS_T	49.81562
FI	IMP	2022	PP_GR_MC	TOTAL	THS_T	5.325906
FI	IMP	2022	PP_GR_NW	TOTAL	THS_T	20.955766
FI	IMP	2022	PP_GR_CO	TOTAL	THS_T	15.087019
FI	IMP	2022	PP_HS	TOTAL	THS_T	3.028118
FI	IMP	2022	PP_PK	TOTAL	THS_T	239.589195
FI	IMP	2022	PP_PK_CS	TOTAL	THS_T	137.141741
FI	IMP	2022	PP_PK_CB	TOTAL	THS_T	65.375788
FI	IMP	2022	PP_PK_WR	TOTAL	THS_T	31.012207
FI	IMP	2022	PP_PK_O	TOTAL	THS_T	6.059459
FI	IMP	2022	PP_O	TOTAL	THS_T	3.176751
FI	IMP	2022	GLT_CLT	TOTAL	THS_M3	17048.27857
FI	IMP	2022	GLT	TOTAL	THS_M3	16009.62857
FI	IMP	2022	CLT	TOTAL	THS_M3	1038.65
FI	IMP	2022	I_BEAMS	TOTAL	THS_T	0
FI	IMP	2022	RW	TOTAL	THS_NAC	247565.144
FI	IMP	2022	RW_FW	TOTAL	THS_NAC	10733.085
FI	IMP	2022	RW_FW	CONIF	THS_NAC	7847.962
FI	IMP	2022	RW_FW	NCONIF	THS_NAC	2885.123
FI	IMP	2022	RW_IN	TOTAL	THS_NAC	236832.059
FI	IMP	2022	RW_IN	CONIF	THS_NAC	97428.224
FI	IMP	2022	RW_IN	NCONIF	THS_NAC	139403.835
FI	IMP	2022	RW_IN	NC_TRO	THS_NAC	0
FI	IMP	2022	CHA	TOTAL	THS_NAC	3800.494
FI	IMP	2022	CHP_RES	TOTAL	THS_NAC	119638.479
FI	IMP	2022	CHP	TOTAL	THS_NAC	112735.709
FI	IMP	2022	RES	TOTAL	THS_NAC	6902.77
FI	IMP	2022	RES_SWD	TOTAL	THS_NAC	6902.77
FI	IMP	2022	RCW	TOTAL	THS_NAC	7362.999
FI	IMP	2022	PEL_AGG	TOTAL	THS_NAC	49696.796
FI	IMP	2022	PEL	TOTAL	THS_NAC	46038.066
FI	IMP	2022	AGG	TOTAL	THS_NAC	3658.73
FI	IMP	2022	SN	TOTAL	THS_NAC	118555.537
FI	IMP	2022	SN	CONIF	THS_NAC	81521.236
FI	IMP	2022	SN	NCONIF	THS_NAC	37034.301
FI	IMP	2022	SN	NC_TRO	THS_NAC	9793.476
FI	IMP	2022	PN_VN	TOTAL	THS_NAC	12467.008
FI	IMP	2022	PN_VN	CONIF	THS_NAC	1153.298
FI	IMP	2022	PN_VN	NCONIF	THS_NAC	11313.71
FI	IMP	2022	PN_VN	NC_TRO	THS_NAC	1229.243
FI	IMP	2022	PN	TOTAL	THS_NAC	192097.504
FI	IMP	2022	PN_PY	TOTAL	THS_NAC	64362.659
FI	IMP	2022	PN_PY	CONIF	THS_NAC	18590.006
FI	IMP	2022	PN_PY	NCONIF	THS_NAC	45772.653
FI	IMP	2022	PN_PY	NC_TRO	THS_NAC	2231.742
FI	IMP	2022	PN_PY_LVL	TOTAL	THS_NAC	758.341
FI	IMP	2022	PN_PY_LVL	CONIF	THS_NAC	602.542
FI	IMP	2022	PN_PY_LVL	NCONIF	THS_NAC	155.799
FI	IMP	2022	PN_PY_LVL	NC_TRO	THS_NAC	116.367
FI	IMP	2022	PN_PB	TOTAL	THS_NAC	58980.217
FI	IMP	2022	PN_PB_OSB	TOTAL	THS_NAC	21916.432
FI	IMP	2022	PN_FB	TOTAL	THS_NAC	68754.628
FI	IMP	2022	PN_FB_HB	TOTAL	THS_NAC	12406.415
FI	IMP	2022	PN_FB_MDF	TOTAL	THS_NAC	50008.467
FI	IMP	2022	PN_FB_O	TOTAL	THS_NAC	6339.746
FI	IMP	2022	PL	TOTAL	THS_NAC	201833.572
FI	IMP	2022	PL_MC_SCH	TOTAL	THS_NAC	744.174
FI	IMP	2022	PL_CH	TOTAL	THS_NAC	193294.934
FI	IMP	2022	PL_CH_SA	TOTAL	THS_NAC	189212.985
FI	IMP	2022	PL_CH_SAB	TOTAL	THS_NAC	179968.459
FI	IMP	2022	PL_CH_SI	TOTAL	THS_NAC	4081.949
FI	IMP	2022	PL_DS	TOTAL	THS_NAC	7794.464
FI	IMP	2022	PLO	TOTAL	THS_NAC	9133.463
FI	IMP	2022	PLO_NW	TOTAL	THS_NAC	8289.658
FI	IMP	2022	PLO_RC	TOTAL	THS_NAC	843.805
FI	IMP	2022	RCP	TOTAL	THS_NAC	18617.767
FI	IMP	2022	PP	TOTAL	THS_NAC	352365.101
FI	IMP	2022	PP_GR	TOTAL	THS_NAC	86729.658
FI	IMP	2022	PP_GR_NP	TOTAL	THS_NAC	33008.338
FI	IMP	2022	PP_GR_MC	TOTAL	THS_NAC	5106.216
FI	IMP	2022	PP_GR_NW	TOTAL	THS_NAC	30159.886
FI	IMP	2022	PP_GR_CO	TOTAL	THS_NAC	18455.218
FI	IMP	2022	PP_HS	TOTAL	THS_NAC	7185.223
FI	IMP	2022	PP_PK	TOTAL	THS_NAC	246096.477
FI	IMP	2022	PP_PK_CS	TOTAL	THS_NAC	94649.701
FI	IMP	2022	PP_PK_CB	TOTAL	THS_NAC	101785.248
FI	IMP	2022	PP_PK_WR	TOTAL	THS_NAC	43709.311
FI	IMP	2022	PP_PK_O	TOTAL	THS_NAC	5952.217
FI	IMP	2022	PP_O	TOTAL	THS_NAC	12353.743
FI	IMP	2022	GLT_CLT	TOTAL	THS_NAC	15968.626
FI	IMP	2022	GLT	TOTAL	THS_NAC	14692.674
FI	IMP	2022	CLT	TOTAL	THS_NAC	1275.952
FI	IMP	2022	I_BEAMS	TOTAL	THS_NAC	0
FI	EXP	2021	RW	TOTAL	THS_M3	1119.09896
FI	EXP	2021	RW_FW	TOTAL	THS_M3	48.57396
FI	EXP	2021	RW_FW	CONIF	THS_M3	46.34872
FI	EXP	2021	RW_FW	NCONIF	THS_M3	2.22524
FI	EXP	2021	RW_IN	TOTAL	THS_M3	1070.525
FI	EXP	2021	RW_IN	CONIF	THS_M3	965.99
FI	EXP	2021	RW_IN	NCONIF	THS_M3	104.535
FI	EXP	2021	RW_IN	NC_TRO	THS_M3	0.022
FI	EXP	2021	CHA	TOTAL	THS_T	0.216576
FI	EXP	2021	CHP_RES	TOTAL	THS_M3	147.8582434382
FI	EXP	2021	CHP	TOTAL	THS_M3	147.8323861676
FI	EXP	2021	RES	TOTAL	THS_M3	0.0258572705
FI	EXP	2021	RES_SWD	TOTAL	THS_M3	0.0258572705
FI	EXP	2021	RCW	TOTAL	THS_T	0.366993
FI	EXP	2021	PEL_AGG	TOTAL	THS_T	20.520467
FI	EXP	2021	PEL	TOTAL	THS_T	12.525848
FI	EXP	2021	AGG	TOTAL	THS_T	7.994619
FI	EXP	2021	SN	TOTAL	THS_M3	8735.857
FI	EXP	2021	SN	CONIF	THS_M3	8715.693
FI	EXP	2021	SN	NCONIF	THS_M3	20.164
FI	EXP	2021	SN	NC_TRO	THS_M3	3.945
FI	EXP	2021	PN_VN	TOTAL	THS_M3	171.347
FI	EXP	2021	PN_VN	CONIF	THS_M3	55.369
FI	EXP	2021	PN_VN	NCONIF	THS_M3	115.978
FI	EXP	2021	PN_VN	NC_TRO	THS_M3	0.022
FI	EXP	2021	PN	TOTAL	THS_M3	1031.42633431
FI	EXP	2021	PN_PY	TOTAL	THS_M3	955.493
FI	EXP	2021	PN_PY	CONIF	THS_M3	673.568
FI	EXP	2021	PN_PY	NCONIF	THS_M3	281.925
FI	EXP	2021	PN_PY	NC_TRO	THS_M3	0.206
FI	EXP	2021	PN_PY_LVL	TOTAL	THS_M3
FI	EXP	2021	PN_PY_LVL	CONIF	THS_M3
FI	EXP	2021	PN_PY_LVL	NCONIF	THS_M3
FI	EXP	2021	PN_PY_LVL	NC_TRO	THS_M3
FI	EXP	2021	PN_PB	TOTAL	THS_M3	29.69
FI	EXP	2021	PN_PB_OSB	TOTAL	THS_M3	0.066
FI	EXP	2021	PN_FB	TOTAL	THS_M3	46.24333431
FI	EXP	2021	PN_FB_HB	TOTAL	THS_M3	38.744
FI	EXP	2021	PN_FB_MDF	TOTAL	THS_M3	6.765022
FI	EXP	2021	PN_FB_O	TOTAL	THS_M3	0.73431231
FI	EXP	2021	PL	TOTAL	THS_T	4475.258316
FI	EXP	2021	PL_MC_SCH	TOTAL	THS_T	352.734527
FI	EXP	2021	PL_CH	TOTAL	THS_T	3822.016216
FI	EXP	2021	PL_CH_SA	TOTAL	THS_T	3821.785512
FI	EXP	2021	PL_CH_SAB	TOTAL	THS_T	3654.358161
FI	EXP	2021	PL_CH_SI	TOTAL	THS_T	0.230704
FI	EXP	2021	PL_DS	TOTAL	THS_T	300.507573
FI	EXP	2021	PLO	TOTAL	THS_T	0.051138
FI	EXP	2021	PLO_NW	TOTAL	THS_T	0.04168
FI	EXP	2021	PLO_RC	TOTAL	THS_T	0.009458
FI	EXP	2021	RCP	TOTAL	THS_T	147.020301
FI	EXP	2021	PP	TOTAL	THS_T	8388.559061
FI	EXP	2021	PP_GR	TOTAL	THS_T	3615.227156
FI	EXP	2021	PP_GR_NP	TOTAL	THS_T	84.974454
FI	EXP	2021	PP_GR_MC	TOTAL	THS_T	426.621941
FI	EXP	2021	PP_GR_NW	TOTAL	THS_T	665.908512
FI	EXP	2021	PP_GR_CO	TOTAL	THS_T	2437.722249
FI	EXP	2021	PP_HS	TOTAL	THS_T	19.487219
FI	EXP	2021	PP_PK	TOTAL	THS_T	4602.562778
FI	EXP	2021	PP_PK_CS	TOTAL	THS_T	1129.462709
FI	EXP	2021	PP_PK_CB	TOTAL	THS_T	2809.244407
FI	EXP	2021	PP_PK_WR	TOTAL	THS_T	490.703256
FI	EXP	2021	PP_PK_O	TOTAL	THS_T	173.152406
FI	EXP	2021	PP_O	TOTAL	THS_T	151.281908
FI	EXP	2021	GLT_CLT	TOTAL	THS_M3
FI	EXP	2021	GLT	TOTAL	THS_M3
FI	EXP	2021	CLT	TOTAL	THS_M3
FI	EXP	2021	I_BEAMS	TOTAL	THS_T
FI	EXP	2021	RW	TOTAL	THS_NAC	95639.977
FI	EXP	2021	RW_FW	TOTAL	THS_NAC	1895.276
FI	EXP	2021	RW_FW	CONIF	THS_NAC	1683.405
FI	EXP	2021	RW_FW	NCONIF	THS_NAC	211.871
FI	EXP	2021	RW_IN	TOTAL	THS_NAC	93744.701
FI	EXP	2021	RW_IN	CONIF	THS_NAC	87673.586
FI	EXP	2021	RW_IN	NCONIF	THS_NAC	6071.115
FI	EXP	2021	RW_IN	NC_TRO	THS_NAC	44.446
FI	EXP	2021	CHA	TOTAL	THS_NAC	165.646
FI	EXP	2021	CHP_RES	TOTAL	THS_NAC	6219.096
FI	EXP	2021	CHP	TOTAL	THS_NAC	6207.611
FI	EXP	2021	RES	TOTAL	THS_NAC	11.485
FI	EXP	2021	RES_SWD	TOTAL	THS_NAC	11.485
FI	EXP	2021	RCW	TOTAL	THS_NAC	82.127
FI	EXP	2021	PEL_AGG	TOTAL	THS_NAC	2201.435
FI	EXP	2021	PEL	TOTAL	THS_NAC	1575.689
FI	EXP	2021	AGG	TOTAL	THS_NAC	625.746
FI	EXP	2021	SN	TOTAL	THS_NAC	2572713.492
FI	EXP	2021	SN	CONIF	THS_NAC	2562670.729
FI	EXP	2021	SN	NCONIF	THS_NAC	10042.763
FI	EXP	2021	SN	NC_TRO	THS_NAC	3631.587
FI	EXP	2021	PN_VN	TOTAL	THS_NAC	54117.482
FI	EXP	2021	PN_VN	CONIF	THS_NAC	28758.383
FI	EXP	2021	PN_VN	NCONIF	THS_NAC	25359.099
FI	EXP	2021	PN_VN	NC_TRO	THS_NAC	7.375
FI	EXP	2021	PN	TOTAL	THS_NAC	581443.567
FI	EXP	2021	PN_PY	TOTAL	THS_NAC	548258.478
FI	EXP	2021	PN_PY	CONIF	THS_NAC	303765.837
FI	EXP	2021	PN_PY	NCONIF	THS_NAC	244492.641
FI	EXP	2021	PN_PY	NC_TRO	THS_NAC	760.631
FI	EXP	2021	PN_PY_LVL	TOTAL	THS_NAC
FI	EXP	2021	PN_PY_LVL	CONIF	THS_NAC
FI	EXP	2021	PN_PY_LVL	NCONIF	THS_NAC
FI	EXP	2021	PN_PY_LVL	NC_TRO	THS_NAC
FI	EXP	2021	PN_PB	TOTAL	THS_NAC	9081.163
FI	EXP	2021	PN_PB_OSB	TOTAL	THS_NAC	37.501
FI	EXP	2021	PN_FB	TOTAL	THS_NAC	24103.926
FI	EXP	2021	PN_FB_HB	TOTAL	THS_NAC	18772.382
FI	EXP	2021	PN_FB_MDF	TOTAL	THS_NAC	5148.926
FI	EXP	2021	PN_FB_O	TOTAL	THS_NAC	182.618
FI	EXP	2021	PL	TOTAL	THS_NAC	2585364.618
FI	EXP	2021	PL_MC_SCH	TOTAL	THS_NAC	125149.538
FI	EXP	2021	PL_CH	TOTAL	THS_NAC	2242119.846
FI	EXP	2021	PL_CH_SA	TOTAL	THS_NAC	2241923.336
FI	EXP	2021	PL_CH_SAB	TOTAL	THS_NAC	2146493.961
FI	EXP	2021	PL_CH_SI	TOTAL	THS_NAC	196.51
FI	EXP	2021	PL_DS	TOTAL	THS_NAC	218095.234
FI	EXP	2021	PLO	TOTAL	THS_NAC	100.665
FI	EXP	2021	PLO_NW	TOTAL	THS_NAC	92.084
FI	EXP	2021	PLO_RC	TOTAL	THS_NAC	8.581
FI	EXP	2021	RCP	TOTAL	THS_NAC	20680.507
FI	EXP	2021	PP	TOTAL	THS_NAC	6262823.479
FI	EXP	2021	PP_GR	TOTAL	THS_NAC	2285472.386
FI	EXP	2021	PP_GR_NP	TOTAL	THS_NAC	37260.569
FI	EXP	2021	PP_GR_MC	TOTAL	THS_NAC	217670.314
FI	EXP	2021	PP_GR_NW	TOTAL	THS_NAC	452556.243
FI	EXP	2021	PP_GR_CO	TOTAL	THS_NAC	1577985.26
FI	EXP	2021	PP_HS	TOTAL	THS_NAC	19099.163
FI	EXP	2021	PP_PK	TOTAL	THS_NAC	3851504.021
FI	EXP	2021	PP_PK_CS	TOTAL	THS_NAC	699066.62
FI	EXP	2021	PP_PK_CB	TOTAL	THS_NAC	2504501.191
FI	EXP	2021	PP_PK_WR	TOTAL	THS_NAC	527039.689
FI	EXP	2021	PP_PK_O	TOTAL	THS_NAC	120896.521
FI	EXP	2021	PP_O	TOTAL	THS_NAC	106747.909
FI	EXP	2021	GLT_CLT	TOTAL	THS_NAC
FI	EXP	2021	GLT	TOTAL	THS_NAC
FI	EXP	2021	CLT	TOTAL	THS_NAC
FI	EXP	2021	I_BEAMS	TOTAL	THS_NAC
FI	EXP	2022	RW	TOTAL	THS_M3	1804.1389536
FI	EXP	2022	RW_FW	TOTAL	THS_M3	101.3669536
FI	EXP	2022	RW_FW	CONIF	THS_M3	95.530528
FI	EXP	2022	RW_FW	NCONIF	THS_M3	5.8364256
FI	EXP	2022	RW_IN	TOTAL	THS_M3	1702.772
FI	EXP	2022	RW_IN	CONIF	THS_M3	1348.069
FI	EXP	2022	RW_IN	NCONIF	THS_M3	354.703
FI	EXP	2022	RW_IN	NC_TRO	THS_M3	0.011
FI	EXP	2022	CHA	TOTAL	THS_T	0.148072
FI	EXP	2022	CHP_RES	TOTAL	THS_M3	180.6978344648
FI	EXP	2022	CHP	TOTAL	THS_M3	180.4398007829
FI	EXP	2022	RES	TOTAL	THS_M3	0.2580336819
FI	EXP	2022	RES_SWD	TOTAL	THS_M3	0.2580336819
FI	EXP	2022	RCW	TOTAL	THS_T	0.003484
FI	EXP	2022	PEL_AGG	TOTAL	THS_T	33.782289
FI	EXP	2022	PEL	TOTAL	THS_T	18.117054
FI	EXP	2022	AGG	TOTAL	THS_T	15.665235
FI	EXP	2022	SN	TOTAL	THS_M3	8576.479
FI	EXP	2022	SN	CONIF	THS_M3	8553.927
FI	EXP	2022	SN	NCONIF	THS_M3	22.552
FI	EXP	2022	SN	NC_TRO	THS_M3	3.644
FI	EXP	2022	PN_VN	TOTAL	THS_M3	175.414
FI	EXP	2022	PN_VN	CONIF	THS_M3	50.548
FI	EXP	2022	PN_VN	NCONIF	THS_M3	124.866
FI	EXP	2022	PN_VN	NC_TRO	THS_M3	0.021
FI	EXP	2022	PN	TOTAL	THS_M3	971.748
FI	EXP	2022	PN_PY	TOTAL	THS_M3	900.051
FI	EXP	2022	PN_PY	CONIF	THS_M3	658.085
FI	EXP	2022	PN_PY	NCONIF	THS_M3	241.966
FI	EXP	2022	PN_PY	NC_TRO	THS_M3	0.227
FI	EXP	2022	PN_PY_LVL	TOTAL	THS_M3	255.195
FI	EXP	2022	PN_PY_LVL	CONIF	THS_M3	247.832
FI	EXP	2022	PN_PY_LVL	NCONIF	THS_M3	7.363
FI	EXP	2022	PN_PY_LVL	NC_TRO	THS_M3	0.013
FI	EXP	2022	PN_PB	TOTAL	THS_M3	26.138
FI	EXP	2022	PN_PB_OSB	TOTAL	THS_M3	0.262
FI	EXP	2022	PN_FB	TOTAL	THS_M3	45.559
FI	EXP	2022	PN_FB_HB	TOTAL	THS_M3	41.32
FI	EXP	2022	PN_FB_MDF	TOTAL	THS_M3	3.864
FI	EXP	2022	PN_FB_O	TOTAL	THS_M3	0.375
FI	EXP	2022	PL	TOTAL	THS_T	3963.438065
FI	EXP	2022	PL_MC_SCH	TOTAL	THS_T	332.362574
FI	EXP	2022	PL_CH	TOTAL	THS_T	3624.86731
FI	EXP	2022	PL_CH_SA	TOTAL	THS_T	3624.855019
FI	EXP	2022	PL_CH_SAB	TOTAL	THS_T	3408.22975
FI	EXP	2022	PL_CH_SI	TOTAL	THS_T	0.012291
FI	EXP	2022	PL_DS	TOTAL	THS_T	6.208181
FI	EXP	2022	PLO	TOTAL	THS_T	0.051817
FI	EXP	2022	PLO_NW	TOTAL	THS_T	0.047876
FI	EXP	2022	PLO_RC	TOTAL	THS_T	0.003941
FI	EXP	2022	RCP	TOTAL	THS_T	118.79845
FI	EXP	2022	PP	TOTAL	THS_T	7025.372693
FI	EXP	2022	PP_GR	TOTAL	THS_T	2450.045059
FI	EXP	2022	PP_GR_NP	TOTAL	THS_T	54.184912
FI	EXP	2022	PP_GR_MC	TOTAL	THS_T	310.064246
FI	EXP	2022	PP_GR_NW	TOTAL	THS_T	298.19805
FI	EXP	2022	PP_GR_CO	TOTAL	THS_T	1787.597851
FI	EXP	2022	PP_HS	TOTAL	THS_T	19.510029
FI	EXP	2022	PP_PK	TOTAL	THS_T	4426.55091
FI	EXP	2022	PP_PK_CS	TOTAL	THS_T	1144.124699
FI	EXP	2022	PP_PK_CB	TOTAL	THS_T	2743.358156
FI	EXP	2022	PP_PK_WR	TOTAL	THS_T	354.440979
FI	EXP	2022	PP_PK_O	TOTAL	THS_T	184.627076
FI	EXP	2022	PP_O	TOTAL	THS_T	129.266695
FI	EXP	2022	GLT_CLT	TOTAL	THS_M3	325517.76413
FI	EXP	2022	GLT	TOTAL	THS_M3	324167.81813
FI	EXP	2022	CLT	TOTAL	THS_M3	1349.946
FI	EXP	2022	I_BEAMS	TOTAL	THS_T	0
FI	EXP	2022	RW	TOTAL	THS_NAC	150573.545
FI	EXP	2022	RW_FW	TOTAL	THS_NAC	4506.406
FI	EXP	2022	RW_FW	CONIF	THS_NAC	3724.801
FI	EXP	2022	RW_FW	NCONIF	THS_NAC	781.605
FI	EXP	2022	RW_IN	TOTAL	THS_NAC	146067.139
FI	EXP	2022	RW_IN	CONIF	THS_NAC	121347.463
FI	EXP	2022	RW_IN	NCONIF	THS_NAC	24719.676
FI	EXP	2022	RW_IN	NC_TRO	THS_NAC	43.563
FI	EXP	2022	CHA	TOTAL	THS_NAC	125.963
FI	EXP	2022	CHP_RES	TOTAL	THS_NAC	9479.228
FI	EXP	2022	CHP	TOTAL	THS_NAC	9428.826
FI	EXP	2022	RES	TOTAL	THS_NAC	50.402
FI	EXP	2022	RES_SWD	TOTAL	THS_NAC	50.402
FI	EXP	2022	RCW	TOTAL	THS_NAC	1.098
FI	EXP	2022	PEL_AGG	TOTAL	THS_NAC	4655.498
FI	EXP	2022	PEL	TOTAL	THS_NAC	2618.033
FI	EXP	2022	AGG	TOTAL	THS_NAC	2037.465
FI	EXP	2022	SN	TOTAL	THS_NAC	2595922.152
FI	EXP	2022	SN	CONIF	THS_NAC	2583504.78
FI	EXP	2022	SN	NCONIF	THS_NAC	12417.372
FI	EXP	2022	SN	NC_TRO	THS_NAC	2674.858
FI	EXP	2022	PN_VN	TOTAL	THS_NAC	62350.77
FI	EXP	2022	PN_VN	CONIF	THS_NAC	30111.972
FI	EXP	2022	PN_VN	NCONIF	THS_NAC	32238.798
FI	EXP	2022	PN_VN	NC_TRO	THS_NAC	13.934
FI	EXP	2022	PN	TOTAL	THS_NAC	717996.825
FI	EXP	2022	PN_PY	TOTAL	THS_NAC	676945.177
FI	EXP	2022	PN_PY	CONIF	THS_NAC	395667.91
FI	EXP	2022	PN_PY	NCONIF	THS_NAC	281277.267
FI	EXP	2022	PN_PY	NC_TRO	THS_NAC	704.976
FI	EXP	2022	PN_PY_LVL	TOTAL	THS_NAC	178297.826
FI	EXP	2022	PN_PY_LVL	CONIF	THS_NAC	173230.415
FI	EXP	2022	PN_PY_LVL	NCONIF	THS_NAC	5067.411
FI	EXP	2022	PN_PY_LVL	NC_TRO	THS_NAC	5.486
FI	EXP	2022	PN_PB	TOTAL	THS_NAC	10907.108
FI	EXP	2022	PN_PB_OSB	TOTAL	THS_NAC	130.106
FI	EXP	2022	PN_FB	TOTAL	THS_NAC	30144.54
FI	EXP	2022	PN_FB_HB	TOTAL	THS_NAC	26497.636
FI	EXP	2022	PN_FB_MDF	TOTAL	THS_NAC	3574.976
FI	EXP	2022	PN_FB_O	TOTAL	THS_NAC	71.928
FI	EXP	2022	PL	TOTAL	THS_NAC	2864560.147
FI	EXP	2022	PL_MC_SCH	TOTAL	THS_NAC	146048.33
FI	EXP	2022	PL_CH	TOTAL	THS_NAC	2714599.918
FI	EXP	2022	PL_CH_SA	TOTAL	THS_NAC	2714562.091
FI	EXP	2022	PL_CH_SAB	TOTAL	THS_NAC	2586405.662
FI	EXP	2022	PL_CH_SI	TOTAL	THS_NAC	37.827
FI	EXP	2022	PL_DS	TOTAL	THS_NAC	3911.899
FI	EXP	2022	PLO	TOTAL	THS_NAC	139.052
FI	EXP	2022	PLO_NW	TOTAL	THS_NAC	133.599
FI	EXP	2022	PLO_RC	TOTAL	THS_NAC	5.453
FI	EXP	2022	RCP	TOTAL	THS_NAC	21354.181
FI	EXP	2022	PP	TOTAL	THS_NAC	7141189.447
FI	EXP	2022	PP_GR	TOTAL	THS_NAC	2392360.202
FI	EXP	2022	PP_GR_NP	TOTAL	THS_NAC	40655.481
FI	EXP	2022	PP_GR_MC	TOTAL	THS_NAC	239409.604
FI	EXP	2022	PP_GR_NW	TOTAL	THS_NAC	357469.176
FI	EXP	2022	PP_GR_CO	TOTAL	THS_NAC	1754825.941
FI	EXP	2022	PP_HS	TOTAL	THS_NAC	26754.411
FI	EXP	2022	PP_PK	TOTAL	THS_NAC	4597186.507
FI	EXP	2022	PP_PK_CS	TOTAL	THS_NAC	927143.504
FI	EXP	2022	PP_PK_CB	TOTAL	THS_NAC	2933137.885
FI	EXP	2022	PP_PK_WR	TOTAL	THS_NAC	577020.059
FI	EXP	2022	PP_PK_O	TOTAL	THS_NAC	159885.059
FI	EXP	2022	PP_O	TOTAL	THS_NAC	124888.327
FI	EXP	2022	GLT_CLT	TOTAL	THS_NAC	307751.85
FI	EXP	2022	GLT	TOTAL	THS_NAC	307334.732
FI	EXP	2022	CLT	TOTAL	THS_NAC	417.118
FI	EXP	2022	I_BEAMS	TOTAL	THS_NAC	0
FI	IMP_XEU	2021	RW	TOTAL	THS_M3	4632.1810304
FI	IMP_XEU	2021	RW_FW	TOTAL	THS_M3	26.9300304
FI	IMP_XEU	2021	RW_FW	CONIF	THS_M3	1.2177952
FI	IMP_XEU	2021	RW_FW	NCONIF	THS_M3	25.7122352
FI	IMP_XEU	2021	RW_IN	TOTAL	THS_M3	4605.251
FI	IMP_XEU	2021	RW_IN	CONIF	THS_M3	526.032
FI	IMP_XEU	2021	RW_IN	NCONIF	THS_M3	4079.219
FI	IMP_XEU	2021	RW_IN	NC_TRO	THS_M3	0.001
FI	IMP_XEU	2021	CHA	TOTAL	THS_T	0.905714
FI	IMP_XEU	2021	CHP_RES	TOTAL	THS_M3	3667.039102045
FI	IMP_XEU	2021	CHP	TOTAL	THS_M3	3432.3309720813
FI	IMP_XEU	2021	RES	TOTAL	THS_M3	234.7081299636
FI	IMP_XEU	2021	RES_SWD	TOTAL	THS_M3	234.7081299636
FI	IMP_XEU	2021	RCW	TOTAL	THS_T	107.674844
FI	IMP_XEU	2021	PEL_AGG	TOTAL	THS_T	136.674405
FI	IMP_XEU	2021	PEL	TOTAL	THS_T	122.12422
FI	IMP_XEU	2021	AGG	TOTAL	THS_T	14.550185
FI	IMP_XEU	2021	SN	TOTAL	THS_M3	537.883
FI	IMP_XEU	2021	SN	CONIF	THS_M3	526.432
FI	IMP_XEU	2021	SN	NCONIF	THS_M3	11.451
FI	IMP_XEU	2021	SN	NC_TRO	THS_M3	3.411
FI	IMP_XEU	2021	PN_VN	TOTAL	THS_M3	4.894
FI	IMP_XEU	2021	PN_VN	CONIF	THS_M3	0.003
FI	IMP_XEU	2021	PN_VN	NCONIF	THS_M3	4.891
FI	IMP_XEU	2021	PN_VN	NC_TRO	THS_M3	0.056
FI	IMP_XEU	2021	PN	TOTAL	THS_M3	153.296038
FI	IMP_XEU	2021	PN_PY	TOTAL	THS_M3	95.951
FI	IMP_XEU	2021	PN_PY	CONIF	THS_M3	12.17
FI	IMP_XEU	2021	PN_PY	NCONIF	THS_M3	83.781
FI	IMP_XEU	2021	PN_PY	NC_TRO	THS_M3	0.424
FI	IMP_XEU	2021	PN_PY_LVL	TOTAL	THS_M3
FI	IMP_XEU	2021	PN_PY_LVL	CONIF	THS_M3
FI	IMP_XEU	2021	PN_PY_LVL	NCONIF	THS_M3
FI	IMP_XEU	2021	PN_PY_LVL	NC_TRO	THS_M3
FI	IMP_XEU	2021	PN_PB	TOTAL	THS_M3	33.769
FI	IMP_XEU	2021	PN_PB_OSB	TOTAL	THS_M3	24.338
FI	IMP_XEU	2021	PN_FB	TOTAL	THS_M3	23.576038
FI	IMP_XEU	2021	PN_FB_HB	TOTAL	THS_M3	3.003
FI	IMP_XEU	2021	PN_FB_MDF	TOTAL	THS_M3	12.697038
FI	IMP_XEU	2021	PN_FB_O	TOTAL	THS_M3	7.876
FI	IMP_XEU	2021	PL	TOTAL	THS_T	75.579625
FI	IMP_XEU	2021	PL_MC_SCH	TOTAL	THS_T	9.379279
FI	IMP_XEU	2021	PL_CH	TOTAL	THS_T	59.793123
FI	IMP_XEU	2021	PL_CH_SA	TOTAL	THS_T	59.785468
FI	IMP_XEU	2021	PL_CH_SAB	TOTAL	THS_T	50.720967
FI	IMP_XEU	2021	PL_CH_SI	TOTAL	THS_T	0.007655
FI	IMP_XEU	2021	PL_DS	TOTAL	THS_T	6.407223
FI	IMP_XEU	2021	PLO	TOTAL	THS_T	1.732147
FI	IMP_XEU	2021	PLO_NW	TOTAL	THS_T	1.731135
FI	IMP_XEU	2021	PLO_RC	TOTAL	THS_T	0.001012
FI	IMP_XEU	2021	RCP	TOTAL	THS_T	6.283317
FI	IMP_XEU	2021	PP	TOTAL	THS_T	43.666213
FI	IMP_XEU	2021	PP_GR	TOTAL	THS_T	21.33188
FI	IMP_XEU	2021	PP_GR_NP	TOTAL	THS_T	20.154
FI	IMP_XEU	2021	PP_GR_MC	TOTAL	THS_T	0.718056
FI	IMP_XEU	2021	PP_GR_NW	TOTAL	THS_T	0.283416
FI	IMP_XEU	2021	PP_GR_CO	TOTAL	THS_T	0.17635
FI	IMP_XEU	2021	PP_HS	TOTAL	THS_T	0.030545
FI	IMP_XEU	2021	PP_PK	TOTAL	THS_T	22.26475
FI	IMP_XEU	2021	PP_PK_CS	TOTAL	THS_T	6.33245
FI	IMP_XEU	2021	PP_PK_CB	TOTAL	THS_T	9.38452
FI	IMP_XEU	2021	PP_PK_WR	TOTAL	THS_T	3.503352
FI	IMP_XEU	2021	PP_PK_O	TOTAL	THS_T	3.044424
FI	IMP_XEU	2021	PP_O	TOTAL	THS_T	0.039023
FI	IMP_XEU	2021	GLT_CLT	TOTAL	THS_M3
FI	IMP_XEU	2021	GLT	TOTAL	THS_M3
FI	IMP_XEU	2021	CLT	TOTAL	THS_M3
FI	IMP_XEU	2021	I_BEAMS	TOTAL	THS_T
FI	IMP_XEU	2021	RW	TOTAL	THS_NAC	200775.163
FI	IMP_XEU	2021	RW_FW	TOTAL	THS_NAC	1973.489
FI	IMP_XEU	2021	RW_FW	CONIF	THS_NAC	32.543
FI	IMP_XEU	2021	RW_FW	NCONIF	THS_NAC	1940.946
FI	IMP_XEU	2021	RW_IN	TOTAL	THS_NAC	198801.674
FI	IMP_XEU	2021	RW_IN	CONIF	THS_NAC	25315.738
FI	IMP_XEU	2021	RW_IN	NCONIF	THS_NAC	173485.936
FI	IMP_XEU	2021	RW_IN	NC_TRO	THS_NAC	2.523
FI	IMP_XEU	2021	CHA	TOTAL	THS_NAC	628.495
FI	IMP_XEU	2021	CHP_RES	TOTAL	THS_NAC	130804.5
FI	IMP_XEU	2021	CHP	TOTAL	THS_NAC	126114.285
FI	IMP_XEU	2021	RES	TOTAL	THS_NAC	4690.215
FI	IMP_XEU	2021	RES_SWD	TOTAL	THS_NAC	4690.215
FI	IMP_XEU	2021	RCW	TOTAL	THS_NAC	3037.098
FI	IMP_XEU	2021	PEL_AGG	TOTAL	THS_NAC	15329.851
FI	IMP_XEU	2021	PEL	TOTAL	THS_NAC	14331.793
FI	IMP_XEU	2021	AGG	TOTAL	THS_NAC	998.058
FI	IMP_XEU	2021	SN	TOTAL	THS_NAC	134058.716
FI	IMP_XEU	2021	SN	CONIF	THS_NAC	126047.799
FI	IMP_XEU	2021	SN	NCONIF	THS_NAC	8010.917
FI	IMP_XEU	2021	SN	NC_TRO	THS_NAC	3005.844
FI	IMP_XEU	2021	PN_VN	TOTAL	THS_NAC	1677.262
FI	IMP_XEU	2021	PN_VN	CONIF	THS_NAC	0.557
FI	IMP_XEU	2021	PN_VN	NCONIF	THS_NAC	1676.705
FI	IMP_XEU	2021	PN_VN	NC_TRO	THS_NAC	37.769
FI	IMP_XEU	2021	PN	TOTAL	THS_NAC	67648.7080000002
FI	IMP_XEU	2021	PN_PY	TOTAL	THS_NAC	52445.989
FI	IMP_XEU	2021	PN_PY	CONIF	THS_NAC	5068.51
FI	IMP_XEU	2021	PN_PY	NCONIF	THS_NAC	47377.479
FI	IMP_XEU	2021	PN_PY	NC_TRO	THS_NAC	658.473
FI	IMP_XEU	2021	PN_PY_LVL	TOTAL	THS_NAC
FI	IMP_XEU	2021	PN_PY_LVL	CONIF	THS_NAC
FI	IMP_XEU	2021	PN_PY_LVL	NCONIF	THS_NAC
FI	IMP_XEU	2021	PN_PY_LVL	NC_TRO	THS_NAC
FI	IMP_XEU	2021	PN_PB	TOTAL	THS_NAC	10374.404
FI	IMP_XEU	2021	PN_PB_OSB	TOTAL	THS_NAC	8377.785
FI	IMP_XEU	2021	PN_FB	TOTAL	THS_NAC	4828.314999999
FI	IMP_XEU	2021	PN_FB_HB	TOTAL	THS_NAC	620.5099999999
FI	IMP_XEU	2021	PN_FB_MDF	TOTAL	THS_NAC	3153.616999999
FI	IMP_XEU	2021	PN_FB_O	TOTAL	THS_NAC	1054.188
FI	IMP_XEU	2021	PL	TOTAL	THS_NAC	42344.11
FI	IMP_XEU	2021	PL_MC_SCH	TOTAL	THS_NAC	3723.791
FI	IMP_XEU	2021	PL_CH	TOTAL	THS_NAC	31846.523
FI	IMP_XEU	2021	PL_CH_SA	TOTAL	THS_NAC	31837.081
FI	IMP_XEU	2021	PL_CH_SAB	TOTAL	THS_NAC	26421.695
FI	IMP_XEU	2021	PL_CH_SI	TOTAL	THS_NAC	9.442
FI	IMP_XEU	2021	PL_DS	TOTAL	THS_NAC	6773.796
FI	IMP_XEU	2021	PLO	TOTAL	THS_NAC	2796.565
FI	IMP_XEU	2021	PLO_NW	TOTAL	THS_NAC	2793.358
FI	IMP_XEU	2021	PLO_RC	TOTAL	THS_NAC	3.207
FI	IMP_XEU	2021	RCP	TOTAL	THS_NAC	1384.743
FI	IMP_XEU	2021	PP	TOTAL	THS_NAC	38735.298
FI	IMP_XEU	2021	PP_GR	TOTAL	THS_NAC	15514.37
FI	IMP_XEU	2021	PP_GR_NP	TOTAL	THS_NAC	7717.043
FI	IMP_XEU	2021	PP_GR_MC	TOTAL	THS_NAC	5754.984
FI	IMP_XEU	2021	PP_GR_NW	TOTAL	THS_NAC	1314.839
FI	IMP_XEU	2021	PP_GR_CO	TOTAL	THS_NAC	727.504
FI	IMP_XEU	2021	PP_HS	TOTAL	THS_NAC	130.987
FI	IMP_XEU	2021	PP_PK	TOTAL	THS_NAC	22775.539
FI	IMP_XEU	2021	PP_PK_CS	TOTAL	THS_NAC	6178.599
FI	IMP_XEU	2021	PP_PK_CB	TOTAL	THS_NAC	9207.267
FI	IMP_XEU	2021	PP_PK_WR	TOTAL	THS_NAC	5457.358
FI	IMP_XEU	2021	PP_PK_O	TOTAL	THS_NAC	1932.315
FI	IMP_XEU	2021	PP_O	TOTAL	THS_NAC	314.402
FI	IMP_XEU	2021	GLT_CLT	TOTAL	THS_NAC
FI	IMP_XEU	2021	GLT	TOTAL	THS_NAC
FI	IMP_XEU	2021	CLT	TOTAL	THS_NAC
FI	IMP_XEU	2021	I_BEAMS	TOTAL	THS_NAC
FI	IMP_XEU	2022	RW	TOTAL	THS_M3	647.991304
FI	IMP_XEU	2022	RW_FW	TOTAL	THS_M3	10.485304
FI	IMP_XEU	2022	RW_FW	CONIF	THS_M3	0.0688352
FI	IMP_XEU	2022	RW_FW	NCONIF	THS_M3	10.416468
FI	IMP_XEU	2022	RW_IN	TOTAL	THS_M3	637.506
FI	IMP_XEU	2022	RW_IN	CONIF	THS_M3	32.3619999
FI	IMP_XEU	2022	RW_IN	NCONIF	THS_M3	605.144
FI	IMP_XEU	2022	RW_IN	NC_TRO	THS_M3	0
FI	IMP_XEU	2022	CHA	TOTAL	THS_T	0.636313
FI	IMP_XEU	2022	CHP_RES	TOTAL	THS_M3	904.3199759537
FI	IMP_XEU	2022	CHP	TOTAL	THS_M3	792.0731560471
FI	IMP_XEU	2022	RES	TOTAL	THS_M3	112.2468199066
FI	IMP_XEU	2022	RES_SWD	TOTAL	THS_M3	112.2468199066
FI	IMP_XEU	2022	RCW	TOTAL	THS_T	88.951814
FI	IMP_XEU	2022	PEL_AGG	TOTAL	THS_T	79.025201
FI	IMP_XEU	2022	PEL	TOTAL	THS_T	73.523322
FI	IMP_XEU	2022	AGG	TOTAL	THS_T	5.501879
FI	IMP_XEU	2022	SN	TOTAL	THS_M3	287.537
FI	IMP_XEU	2022	SN	CONIF	THS_M3	278.043
FI	IMP_XEU	2022	SN	NCONIF	THS_M3	9.494
FI	IMP_XEU	2022	SN	NC_TRO	THS_M3	4.403
FI	IMP_XEU	2022	PN_VN	TOTAL	THS_M3	0.658
FI	IMP_XEU	2022	PN_VN	CONIF	THS_M3	0.028
FI	IMP_XEU	2022	PN_VN	NCONIF	THS_M3	0.63
FI	IMP_XEU	2022	PN_VN	NC_TRO	THS_M3	0.003
FI	IMP_XEU	2022	PN	TOTAL	THS_M3	87.854
FI	IMP_XEU	2022	PN_PY	TOTAL	THS_M3	56.51
FI	IMP_XEU	2022	PN_PY	CONIF	THS_M3	21.6
FI	IMP_XEU	2022	PN_PY	NCONIF	THS_M3	34.91
FI	IMP_XEU	2022	PN_PY	NC_TRO	THS_M3	0.521
FI	IMP_XEU	2022	PN_PY_LVL	TOTAL	THS_M3	1.083
FI	IMP_XEU	2022	PN_PY_LVL	CONIF	THS_M3	0.944
FI	IMP_XEU	2022	PN_PY_LVL	NCONIF	THS_M3	0.139
FI	IMP_XEU	2022	PN_PY_LVL	NC_TRO	THS_M3	0.097
FI	IMP_XEU	2022	PN_PB	TOTAL	THS_M3	12.546
FI	IMP_XEU	2022	PN_PB_OSB	TOTAL	THS_M3	9.12
FI	IMP_XEU	2022	PN_FB	TOTAL	THS_M3	18.798
FI	IMP_XEU	2022	PN_FB_HB	TOTAL	THS_M3	2.169
FI	IMP_XEU	2022	PN_FB_MDF	TOTAL	THS_M3	8.855
FI	IMP_XEU	2022	PN_FB_O	TOTAL	THS_M3	7.774
FI	IMP_XEU	2022	PL	TOTAL	THS_T	152.548643
FI	IMP_XEU	2022	PL_MC_SCH	TOTAL	THS_T	1.08849
FI	IMP_XEU	2022	PL_CH	TOTAL	THS_T	146.659434
FI	IMP_XEU	2022	PL_CH_SA	TOTAL	THS_T	146.636642
FI	IMP_XEU	2022	PL_CH_SAB	TOTAL	THS_T	142.854296
FI	IMP_XEU	2022	PL_CH_SI	TOTAL	THS_T	0.022792
FI	IMP_XEU	2022	PL_DS	TOTAL	THS_T	4.800719
FI	IMP_XEU	2022	PLO	TOTAL	THS_T	2.911001
FI	IMP_XEU	2022	PLO_NW	TOTAL	THS_T	2.90837
FI	IMP_XEU	2022	PLO_RC	TOTAL	THS_T	0.002631
FI	IMP_XEU	2022	RCP	TOTAL	THS_T	5.570908
FI	IMP_XEU	2022	PP	TOTAL	THS_T	28.157066
FI	IMP_XEU	2022	PP_GR	TOTAL	THS_T	11.5137379999
FI	IMP_XEU	2022	PP_GR_NP	TOTAL	THS_T	10.0322
FI	IMP_XEU	2022	PP_GR_MC	TOTAL	THS_T	0.208663
FI	IMP_XEU	2022	PP_GR_NW	TOTAL	THS_T	0.770856
FI	IMP_XEU	2022	PP_GR_CO	TOTAL	THS_T	0.501947
FI	IMP_XEU	2022	PP_HS	TOTAL	THS_T	0.019015
FI	IMP_XEU	2022	PP_PK	TOTAL	THS_T	16.15753
FI	IMP_XEU	2022	PP_PK_CS	TOTAL	THS_T	4.627524
FI	IMP_XEU	2022	PP_PK_CB	TOTAL	THS_T	7.594489
FI	IMP_XEU	2022	PP_PK_WR	TOTAL	THS_T	2.646659
FI	IMP_XEU	2022	PP_PK_O	TOTAL	THS_T	1.288858
FI	IMP_XEU	2022	PP_O	TOTAL	THS_T	0.466783
FI	IMP_XEU	2022	GLT_CLT	TOTAL	THS_M3	154.74485
FI	IMP_XEU	2022	GLT	TOTAL	THS_M3	154.74485
FI	IMP_XEU	2022	CLT	TOTAL	THS_M3	0
FI	IMP_XEU	2022	I_BEAMS	TOTAL	THS_T	0
FI	IMP_XEU	2022	RW	TOTAL	THS_NAC	44464.517
FI	IMP_XEU	2022	RW_FW	TOTAL	THS_NAC	1188.589
FI	IMP_XEU	2022	RW_FW	CONIF	THS_NAC	1.41
FI	IMP_XEU	2022	RW_FW	NCONIF	THS_NAC	1187.179
FI	IMP_XEU	2022	RW_IN	TOTAL	THS_NAC	43275.928
FI	IMP_XEU	2022	RW_IN	CONIF	THS_NAC	2602.499
FI	IMP_XEU	2022	RW_IN	NCONIF	THS_NAC	40673.429
FI	IMP_XEU	2022	RW_IN	NC_TRO	THS_NAC	0
FI	IMP_XEU	2022	CHA	TOTAL	THS_NAC	670.804
FI	IMP_XEU	2022	CHP_RES	TOTAL	THS_NAC	49771.199
FI	IMP_XEU	2022	CHP	TOTAL	THS_NAC	47707.562
FI	IMP_XEU	2022	RES	TOTAL	THS_NAC	2063.637
FI	IMP_XEU	2022	RES_SWD	TOTAL	THS_NAC	2063.637
FI	IMP_XEU	2022	RCW	TOTAL	THS_NAC	2624.226
FI	IMP_XEU	2022	PEL_AGG	TOTAL	THS_NAC	10967.148
FI	IMP_XEU	2022	PEL	TOTAL	THS_NAC	9992.408
FI	IMP_XEU	2022	AGG	TOTAL	THS_NAC	974.74
FI	IMP_XEU	2022	SN	TOTAL	THS_NAC	79413.384
FI	IMP_XEU	2022	SN	CONIF	THS_NAC	70282.92
FI	IMP_XEU	2022	SN	NCONIF	THS_NAC	9130.464
FI	IMP_XEU	2022	SN	NC_TRO	THS_NAC	3943.904
FI	IMP_XEU	2022	PN_VN	TOTAL	THS_NAC	452.626
FI	IMP_XEU	2022	PN_VN	CONIF	THS_NAC	61.667
FI	IMP_XEU	2022	PN_VN	NCONIF	THS_NAC	390.9589999999
FI	IMP_XEU	2022	PN_VN	NC_TRO	THS_NAC	1.529
FI	IMP_XEU	2022	PN	TOTAL	THS_NAC	47624.8099999998
FI	IMP_XEU	2022	PN_PY	TOTAL	THS_NAC	36149.256
FI	IMP_XEU	2022	PN_PY	CONIF	THS_NAC	12355.156
FI	IMP_XEU	2022	PN_PY	NCONIF	THS_NAC	23794.1
FI	IMP_XEU	2022	PN_PY	NC_TRO	THS_NAC	556.527
FI	IMP_XEU	2022	PN_PY_LVL	TOTAL	THS_NAC	686.557
FI	IMP_XEU	2022	PN_PY_LVL	CONIF	THS_NAC	583.183
FI	IMP_XEU	2022	PN_PY_LVL	NCONIF	THS_NAC	103.374
FI	IMP_XEU	2022	PN_PY_LVL	NC_TRO	THS_NAC	63.942
FI	IMP_XEU	2022	PN_PB	TOTAL	THS_NAC	3859.246999999
FI	IMP_XEU	2022	PN_PB_OSB	TOTAL	THS_NAC	2968.144
FI	IMP_XEU	2022	PN_FB	TOTAL	THS_NAC	7616.307
FI	IMP_XEU	2022	PN_FB_HB	TOTAL	THS_NAC	1580.1
FI	IMP_XEU	2022	PN_FB_MDF	TOTAL	THS_NAC	4752
FI	IMP_XEU	2022	PN_FB_O	TOTAL	THS_NAC	1284.199
FI	IMP_XEU	2022	PL	TOTAL	THS_NAC	119882.77
FI	IMP_XEU	2022	PL_MC_SCH	TOTAL	THS_NAC	480.475
FI	IMP_XEU	2022	PL_CH	TOTAL	THS_NAC	111978.657
FI	IMP_XEU	2022	PL_CH_SA	TOTAL	THS_NAC	111956.668
FI	IMP_XEU	2022	PL_CH_SAB	TOTAL	THS_NAC	109494.065
FI	IMP_XEU	2022	PL_CH_SI	TOTAL	THS_NAC	21.989
FI	IMP_XEU	2022	PL_DS	TOTAL	THS_NAC	7423.638
FI	IMP_XEU	2022	PLO	TOTAL	THS_NAC	7540.701
FI	IMP_XEU	2022	PLO_NW	TOTAL	THS_NAC	7534.745
FI	IMP_XEU	2022	PLO_RC	TOTAL	THS_NAC	5.956
FI	IMP_XEU	2022	RCP	TOTAL	THS_NAC	1494.103
FI	IMP_XEU	2022	PP	TOTAL	THS_NAC	37975.8709999998
FI	IMP_XEU	2022	PP_GR	TOTAL	THS_NAC	11026.502
FI	IMP_XEU	2022	PP_GR_NP	TOTAL	THS_NAC	6031.812
FI	IMP_XEU	2022	PP_GR_MC	TOTAL	THS_NAC	376.024
FI	IMP_XEU	2022	PP_GR_NW	TOTAL	THS_NAC	3539.972
FI	IMP_XEU	2022	PP_GR_CO	TOTAL	THS_NAC	1078.694
FI	IMP_XEU	2022	PP_HS	TOTAL	THS_NAC	103.6739
FI	IMP_XEU	2022	PP_PK	TOTAL	THS_NAC	22343.201
FI	IMP_XEU	2022	PP_PK_CS	TOTAL	THS_NAC	5842.008
FI	IMP_XEU	2022	PP_PK_CB	TOTAL	THS_NAC	10561.933
FI	IMP_XEU	2022	PP_PK_WR	TOTAL	THS_NAC	4778.984
FI	IMP_XEU	2022	PP_PK_O	TOTAL	THS_NAC	1160.276
FI	IMP_XEU	2022	PP_O	TOTAL	THS_NAC	4502.494
FI	IMP_XEU	2022	GLT_CLT	TOTAL	THS_NAC	143.781
FI	IMP_XEU	2022	GLT	TOTAL	THS_NAC	143.781
FI	IMP_XEU	2022	CLT	TOTAL	THS_NAC	0
FI	IMP_XEU	2022	I_BEAMS	TOTAL	THS_NAC	0
FI	EXP_XEU	2021	RW	TOTAL	THS_M3	122.847456
FI	EXP_XEU	2021	RW_FW	TOTAL	THS_M3	2.41145
FI	EXP_XEU	2021	RW_FW	CONIF	THS_M3	0.470944
FI	EXP_XEU	2021	RW_FW	NCONIF	THS_M3	1.9405
FI	EXP_XEU	2021	RW_IN	TOTAL	THS_M3	120.436
FI	EXP_XEU	2021	RW_IN	CONIF	THS_M3	120.41
FI	EXP_XEU	2021	RW_IN	NCONIF	THS_M3	0.026
FI	EXP_XEU	2021	RW_IN	NC_TRO	THS_M3	0
FI	EXP_XEU	2021	CHA	TOTAL	THS_T	0.011792
FI	EXP_XEU	2021	CHP_RES	TOTAL	THS_M3	1.0920649674
FI	EXP_XEU	2021	CHP	TOTAL	THS_M3	1.085030833
FI	EXP_XEU	2021	RES	TOTAL	THS_M3	0.0070341337
FI	EXP_XEU	2021	RES_SWD	TOTAL	THS_M3	0.0070341337
FI	EXP_XEU	2021	RCW	TOTAL	THS_T	0.000023
FI	EXP_XEU	2021	PEL_AGG	TOTAL	THS_T	6.325493
FI	EXP_XEU	2021	PEL	TOTAL	THS_T	5.431038
FI	EXP_XEU	2021	AGG	TOTAL	THS_T	0.894455
FI	EXP_XEU	2021	SN	TOTAL	THS_M3	5678.565
FI	EXP_XEU	2021	SN	CONIF	THS_M3	5671.814
FI	EXP_XEU	2021	SN	NCONIF	THS_M3	6.751
FI	EXP_XEU	2021	SN	NC_TRO	THS_M3	0.696
FI	EXP_XEU	2021	PN_VN	TOTAL	THS_M3	15.005
FI	EXP_XEU	2021	PN_VN	CONIF	THS_M3	14.604
FI	EXP_XEU	2021	PN_VN	NCONIF	THS_M3	0.401
FI	EXP_XEU	2021	PN_VN	NC_TRO	THS_M3	0
FI	EXP_XEU	2021	PN	TOTAL	THS_M3	375.94763031
FI	EXP_XEU	2021	PN_PY	TOTAL	THS_M3	351.039
FI	EXP_XEU	2021	PN_PY	CONIF	THS_M3	279.992
FI	EXP_XEU	2021	PN_PY	NCONIF	THS_M3	71.047
FI	EXP_XEU	2021	PN_PY	NC_TRO	THS_M3	0.015
FI	EXP_XEU	2021	PN_PY_LVL	TOTAL	THS_M3
FI	EXP_XEU	2021	PN_PY_LVL	CONIF	THS_M3
FI	EXP_XEU	2021	PN_PY_LVL	NCONIF	THS_M3
FI	EXP_XEU	2021	PN_PY_LVL	NC_TRO	THS_M3
FI	EXP_XEU	2021	PN_PB	TOTAL	THS_M3	4.416
FI	EXP_XEU	2021	PN_PB_OSB	TOTAL	THS_M3	0.058
FI	EXP_XEU	2021	PN_FB	TOTAL	THS_M3	20.49263031
FI	EXP_XEU	2021	PN_FB_HB	TOTAL	THS_M3	19.638
FI	EXP_XEU	2021	PN_FB_MDF	TOTAL	THS_M3	0.251318
FI	EXP_XEU	2021	PN_FB_O	TOTAL	THS_M3	0.60331231
FI	EXP_XEU	2021	PL	TOTAL	THS_T	2698.505274
FI	EXP_XEU	2021	PL_MC_SCH	TOTAL	THS_T	28.508353
FI	EXP_XEU	2021	PL_CH	TOTAL	THS_T	2381.217967
FI	EXP_XEU	2021	PL_CH_SA	TOTAL	THS_T	2381.215957
FI	EXP_XEU	2021	PL_CH_SAB	TOTAL	THS_T	2324.309347
FI	EXP_XEU	2021	PL_CH_SI	TOTAL	THS_T	0.00201
FI	EXP_XEU	2021	PL_DS	TOTAL	THS_T	288.778954
FI	EXP_XEU	2021	PLO	TOTAL	THS_T	0.00977
FI	EXP_XEU	2021	PLO_NW	TOTAL	THS_T	0.000312
FI	EXP_XEU	2021	PLO_RC	TOTAL	THS_T	0
FI	EXP_XEU	2021	RCP	TOTAL	THS_T	8.547572
FI	EXP_XEU	2021	PP	TOTAL	THS_T	4201.382115
FI	EXP_XEU	2021	PP_GR	TOTAL	THS_T	1807.062392
FI	EXP_XEU	2021	PP_GR_NP	TOTAL	THS_T	41.938827
FI	EXP_XEU	2021	PP_GR_MC	TOTAL	THS_T	272.759231
FI	EXP_XEU	2021	PP_GR_NW	TOTAL	THS_T	354.378963
FI	EXP_XEU	2021	PP_GR_CO	TOTAL	THS_T	1137.985371
FI	EXP_XEU	2021	PP_HS	TOTAL	THS_T	1.715313
FI	EXP_XEU	2021	PP_PK	TOTAL	THS_T	2342.062617
FI	EXP_XEU	2021	PP_PK_CS	TOTAL	THS_T	566.964676
FI	EXP_XEU	2021	PP_PK_CB	TOTAL	THS_T	1513.737003
FI	EXP_XEU	2021	PP_PK_WR	TOTAL	THS_T	227.606276
FI	EXP_XEU	2021	PP_PK_O	TOTAL	THS_T	33.754662
FI	EXP_XEU	2021	PP_O	TOTAL	THS_T	50.541793
FI	EXP_XEU	2021	GLT_CLT	TOTAL	THS_M3
FI	EXP_XEU	2021	GLT	TOTAL	THS_M3
FI	EXP_XEU	2021	CLT	TOTAL	THS_M3
FI	EXP_XEU	2021	I_BEAMS	TOTAL	THS_T
FI	EXP_XEU	2021	RW	TOTAL	THS_NAC	24347.109
FI	EXP_XEU	2021	RW_FW	TOTAL	THS_NAC	364.565
FI	EXP_XEU	2021	RW_FW	CONIF	THS_NAC	169.188
FI	EXP_XEU	2021	RW_FW	NCONIF	THS_NAC	195.377
FI	EXP_XEU	2021	RW_IN	TOTAL	THS_NAC	23982.544
FI	EXP_XEU	2021	RW_IN	CONIF	THS_NAC	23937.586
FI	EXP_XEU	2021	RW_IN	NCONIF	THS_NAC	44.958
FI	EXP_XEU	2021	RW_IN	NC_TRO	THS_NAC	0
FI	EXP_XEU	2021	CHA	TOTAL	THS_NAC	9.794
FI	EXP_XEU	2021	CHP_RES	TOTAL	THS_NAC	216.912
FI	EXP_XEU	2021	CHP	TOTAL	THS_NAC	215.395
FI	EXP_XEU	2021	RES	TOTAL	THS_NAC	1.517
FI	EXP_XEU	2021	RES_SWD	TOTAL	THS_NAC	1.517
FI	EXP_XEU	2021	RCW	TOTAL	THS_NAC	1.129
FI	EXP_XEU	2021	PEL_AGG	TOTAL	THS_NAC	747.988
FI	EXP_XEU	2021	PEL	TOTAL	THS_NAC	686.093
FI	EXP_XEU	2021	AGG	TOTAL	THS_NAC	61.895
FI	EXP_XEU	2021	SN	TOTAL	THS_NAC	1597002.536
FI	EXP_XEU	2021	SN	CONIF	THS_NAC	1593752.138
FI	EXP_XEU	2021	SN	NCONIF	THS_NAC	3250.398
FI	EXP_XEU	2021	SN	NC_TRO	THS_NAC	831.038
FI	EXP_XEU	2021	PN_VN	TOTAL	THS_NAC	7770.043
FI	EXP_XEU	2021	PN_VN	CONIF	THS_NAC	7388.562
FI	EXP_XEU	2021	PN_VN	NCONIF	THS_NAC	381.481
FI	EXP_XEU	2021	PN_VN	NC_TRO	THS_NAC	0
FI	EXP_XEU	2021	PN	TOTAL	THS_NAC	213032.802
FI	EXP_XEU	2021	PN_PY	TOTAL	THS_NAC	200954.313
FI	EXP_XEU	2021	PN_PY	CONIF	THS_NAC	132045.611
FI	EXP_XEU	2021	PN_PY	NCONIF	THS_NAC	68908.702
FI	EXP_XEU	2021	PN_PY	NC_TRO	THS_NAC	65.546
FI	EXP_XEU	2021	PN_PY_LVL	TOTAL	THS_NAC
FI	EXP_XEU	2021	PN_PY_LVL	CONIF	THS_NAC
FI	EXP_XEU	2021	PN_PY_LVL	NCONIF	THS_NAC
FI	EXP_XEU	2021	PN_PY_LVL	NC_TRO	THS_NAC
FI	EXP_XEU	2021	PN_PB	TOTAL	THS_NAC	1485.227
FI	EXP_XEU	2021	PN_PB_OSB	TOTAL	THS_NAC	34.723
FI	EXP_XEU	2021	PN_FB	TOTAL	THS_NAC	10593.262
FI	EXP_XEU	2021	PN_FB_HB	TOTAL	THS_NAC	10140.48
FI	EXP_XEU	2021	PN_FB_MDF	TOTAL	THS_NAC	294.265
FI	EXP_XEU	2021	PN_FB_O	TOTAL	THS_NAC	158.517
FI	EXP_XEU	2021	PL	TOTAL	THS_NAC	1663694.632
FI	EXP_XEU	2021	PL_MC_SCH	TOTAL	THS_NAC	11178.401
FI	EXP_XEU	2021	PL_CH	TOTAL	THS_NAC	1440622.081
FI	EXP_XEU	2021	PL_CH_SA	TOTAL	THS_NAC	1440597.471
FI	EXP_XEU	2021	PL_CH_SAB	TOTAL	THS_NAC	1410541.753
FI	EXP_XEU	2021	PL_CH_SI	TOTAL	THS_NAC	24.61
FI	EXP_XEU	2021	PL_DS	TOTAL	THS_NAC	211894.15
FI	EXP_XEU	2021	PLO	TOTAL	THS_NAC	10.261
FI	EXP_XEU	2021	PLO_NW	TOTAL	THS_NAC	1.68
FI	EXP_XEU	2021	PLO_RC	TOTAL	THS_NAC	0
FI	EXP_XEU	2021	RCP	TOTAL	THS_NAC	1111.306
FI	EXP_XEU	2021	PP	TOTAL	THS_NAC	3110413.166
FI	EXP_XEU	2021	PP_GR	TOTAL	THS_NAC	1121889.264
FI	EXP_XEU	2021	PP_GR_NP	TOTAL	THS_NAC	17515.168
FI	EXP_XEU	2021	PP_GR_MC	TOTAL	THS_NAC	135403.925
FI	EXP_XEU	2021	PP_GR_NW	TOTAL	THS_NAC	237940.541
FI	EXP_XEU	2021	PP_GR_CO	TOTAL	THS_NAC	731029.63
FI	EXP_XEU	2021	PP_HS	TOTAL	THS_NAC	1937.757
FI	EXP_XEU	2021	PP_PK	TOTAL	THS_NAC	1944684.231
FI	EXP_XEU	2021	PP_PK_CS	TOTAL	THS_NAC	348124.623
FI	EXP_XEU	2021	PP_PK_CB	TOTAL	THS_NAC	1330296.103
FI	EXP_XEU	2021	PP_PK_WR	TOTAL	THS_NAC	240989.339
FI	EXP_XEU	2021	PP_PK_O	TOTAL	THS_NAC	25274.166
FI	EXP_XEU	2021	PP_O	TOTAL	THS_NAC	41901.914
FI	EXP_XEU	2021	GLT_CLT	TOTAL	THS_NAC
FI	EXP_XEU	2021	GLT	TOTAL	THS_NAC
FI	EXP_XEU	2021	CLT	TOTAL	THS_NAC
FI	EXP_XEU	2021	I_BEAMS	TOTAL	THS_NAC
FI	EXP_XEU	2022	RW	TOTAL	THS_M3	118.1195504
FI	EXP_XEU	2022	RW_FW	TOTAL	THS_M3	5.7735504
FI	EXP_XEU	2022	RW_FW	CONIF	THS_M3	0.1180304
FI	EXP_XEU	2022	RW_FW	NCONIF	THS_M3	5.65552
FI	EXP_XEU	2022	RW_IN	TOTAL	THS_M3	112.346
FI	EXP_XEU	2022	RW_IN	CONIF	THS_M3	112.128
FI	EXP_XEU	2022	RW_IN	NCONIF	THS_M3	0.218
FI	EXP_XEU	2022	RW_IN	NC_TRO	THS_M3	0
FI	EXP_XEU	2022	CHA	TOTAL	THS_T	0.003005
FI	EXP_XEU	2022	CHP_RES	TOTAL	THS_M3	0.7415880773
FI	EXP_XEU	2022	CHP	TOTAL	THS_M3	0.7196137266
FI	EXP_XEU	2022	RES	TOTAL	THS_M3	0.0219743507
FI	EXP_XEU	2022	RES_SWD	TOTAL	THS_M3	0.0219743507
FI	EXP_XEU	2022	RCW	TOTAL	THS_T	0.000007
FI	EXP_XEU	2022	PEL_AGG	TOTAL	THS_T	3.58228
FI	EXP_XEU	2022	PEL	TOTAL	THS_T	0.852014
FI	EXP_XEU	2022	AGG	TOTAL	THS_T	2.7302
FI	EXP_XEU	2022	SN	TOTAL	THS_M3	5622.784
FI	EXP_XEU	2022	SN	CONIF	THS_M3	5615.47
FI	EXP_XEU	2022	SN	NCONIF	THS_M3	7.314
FI	EXP_XEU	2022	SN	NC_TRO	THS_M3	0.5
FI	EXP_XEU	2022	PN_VN	TOTAL	THS_M3	15.087
FI	EXP_XEU	2022	PN_VN	CONIF	THS_M3	14.908
FI	EXP_XEU	2022	PN_VN	NCONIF	THS_M3	0.179
FI	EXP_XEU	2022	PN_VN	NC_TRO	THS_M3	0
FI	EXP_XEU	2022	PN	TOTAL	THS_M3	355.219
FI	EXP_XEU	2022	PN_PY	TOTAL	THS_M3	333.549
FI	EXP_XEU	2022	PN_PY	CONIF	THS_M3	261.959
FI	EXP_XEU	2022	PN_PY	NCONIF	THS_M3	71.59
FI	EXP_XEU	2022	PN_PY	NC_TRO	THS_M3	0.019
FI	EXP_XEU	2022	PN_PY_LVL	TOTAL	THS_M3	166.998
FI	EXP_XEU	2022	PN_PY_LVL	CONIF	THS_M3	159.648
FI	EXP_XEU	2022	PN_PY_LVL	NCONIF	THS_M3	7.35
FI	EXP_XEU	2022	PN_PY_LVL	NC_TRO	THS_M3	0
FI	EXP_XEU	2022	PN_PB	TOTAL	THS_M3	4.701
FI	EXP_XEU	2022	PN_PB_OSB	TOTAL	THS_M3	0.052
FI	EXP_XEU	2022	PN_FB	TOTAL	THS_M3	16.969
FI	EXP_XEU	2022	PN_FB_HB	TOTAL	THS_M3	16.442
FI	EXP_XEU	2022	PN_FB_MDF	TOTAL	THS_M3	0.442
FI	EXP_XEU	2022	PN_FB_O	TOTAL	THS_M3	0.085
FI	EXP_XEU	2022	PL	TOTAL	THS_T	2182.232221
FI	EXP_XEU	2022	PL_MC_SCH	TOTAL	THS_T	0.974496
FI	EXP_XEU	2022	PL_CH	TOTAL	THS_T	2181.257545
FI	EXP_XEU	2022	PL_CH_SA	TOTAL	THS_T	2181.257445
FI	EXP_XEU	2022	PL_CH_SAB	TOTAL	THS_T	2130.491751
FI	EXP_XEU	2022	PL_CH_SI	TOTAL	THS_T	0.0001
FI	EXP_XEU	2022	PL_DS	TOTAL	THS_T	0.00018
FI	EXP_XEU	2022	PLO	TOTAL	THS_T	0.003951
FI	EXP_XEU	2022	PLO_NW	TOTAL	THS_T	0.000072
FI	EXP_XEU	2022	PLO_RC	TOTAL	THS_T	0.003879
FI	EXP_XEU	2022	RCP	TOTAL	THS_T	3.212947
FI	EXP_XEU	2022	PP	TOTAL	THS_T	3376.025074
FI	EXP_XEU	2022	PP_GR	TOTAL	THS_T	1231.896031
FI	EXP_XEU	2022	PP_GR_NP	TOTAL	THS_T	17.530157
FI	EXP_XEU	2022	PP_GR_MC	TOTAL	THS_T	193.314849
FI	EXP_XEU	2022	PP_GR_NW	TOTAL	THS_T	160.464113
FI	EXP_XEU	2022	PP_GR_CO	TOTAL	THS_T	860.586912
FI	EXP_XEU	2022	PP_HS	TOTAL	THS_T	1.007324
FI	EXP_XEU	2022	PP_PK	TOTAL	THS_T	2100.97858
FI	EXP_XEU	2022	PP_PK_CS	TOTAL	THS_T	569.155051
FI	EXP_XEU	2022	PP_PK_CB	TOTAL	THS_T	1308.442922
FI	EXP_XEU	2022	PP_PK_WR	TOTAL	THS_T	184.081194
FI	EXP_XEU	2022	PP_PK_O	TOTAL	THS_T	39.299413
FI	EXP_XEU	2022	PP_O	TOTAL	THS_T	42.143139
FI	EXP_XEU	2022	GLT_CLT	TOTAL	THS_M3	301106.4212
FI	EXP_XEU	2022	GLT	TOTAL	THS_M3	301106.4212
FI	EXP_XEU	2022	CLT	TOTAL	THS_M3	-0
FI	EXP_XEU	2022	I_BEAMS	TOTAL	THS_T	0
FI	EXP_XEU	2022	RW	TOTAL	THS_NAC	26807.3549999999
FI	EXP_XEU	2022	RW_FW	TOTAL	THS_NAC	785.67299
FI	EXP_XEU	2022	RW_FW	CONIF	THS_NAC	32.9319
FI	EXP_XEU	2022	RW_FW	NCONIF	THS_NAC	752.741
FI	EXP_XEU	2022	RW_IN	TOTAL	THS_NAC	26021.682
FI	EXP_XEU	2022	RW_IN	CONIF	THS_NAC	25952.538
FI	EXP_XEU	2022	RW_IN	NCONIF	THS_NAC	69.144
FI	EXP_XEU	2022	RW_IN	NC_TRO	THS_NAC	0
FI	EXP_XEU	2022	CHA	TOTAL	THS_NAC	5.247
FI	EXP_XEU	2022	CHP_RES	TOTAL	THS_NAC	185.628
FI	EXP_XEU	2022	CHP	TOTAL	THS_NAC	171.642
FI	EXP_XEU	2022	RES	TOTAL	THS_NAC	13.986
FI	EXP_XEU	2022	RES_SWD	TOTAL	THS_NAC	13.986
FI	EXP_XEU	2022	RCW	TOTAL	THS_NAC	0.094
FI	EXP_XEU	2022	PEL_AGG	TOTAL	THS_NAC	401.474
FI	EXP_XEU	2022	PEL	TOTAL	THS_NAC	187.165
FI	EXP_XEU	2022	AGG	TOTAL	THS_NAC	214.309
FI	EXP_XEU	2022	SN	TOTAL	THS_NAC	1578660.209
FI	EXP_XEU	2022	SN	CONIF	THS_NAC	1574688.687
FI	EXP_XEU	2022	SN	NCONIF	THS_NAC	3971.522
FI	EXP_XEU	2022	SN	NC_TRO	THS_NAC	613.4
FI	EXP_XEU	2022	PN_VN	TOTAL	THS_NAC	9122.69
FI	EXP_XEU	2022	PN_VN	CONIF	THS_NAC	8767.848
FI	EXP_XEU	2022	PN_VN	NCONIF	THS_NAC	354.842
FI	EXP_XEU	2022	PN_VN	NC_TRO	THS_NAC	0
FI	EXP_XEU	2022	PN	TOTAL	THS_NAC	268643.421
FI	EXP_XEU	2022	PN_PY	TOTAL	THS_NAC	255194.522
FI	EXP_XEU	2022	PN_PY	CONIF	THS_NAC	171768.172
FI	EXP_XEU	2022	PN_PY	NCONIF	THS_NAC	83426.3499999999
FI	EXP_XEU	2022	PN_PY	NC_TRO	THS_NAC	37.185
FI	EXP_XEU	2022	PN_PY_LVL	TOTAL	THS_NAC	118189.298
FI	EXP_XEU	2022	PN_PY_LVL	CONIF	THS_NAC	113127.373
FI	EXP_XEU	2022	PN_PY_LVL	NCONIF	THS_NAC	5061.925
FI	EXP_XEU	2022	PN_PY_LVL	NC_TRO	THS_NAC	0
FI	EXP_XEU	2022	PN_PB	TOTAL	THS_NAC	1999.107
FI	EXP_XEU	2022	PN_PB_OSB	TOTAL	THS_NAC	35.936
FI	EXP_XEU	2022	PN_FB	TOTAL	THS_NAC	11449.792
FI	EXP_XEU	2022	PN_FB_HB	TOTAL	THS_NAC	10816.724
FI	EXP_XEU	2022	PN_FB_MDF	TOTAL	THS_NAC	611.655
FI	EXP_XEU	2022	PN_FB_O	TOTAL	THS_NAC	21.413
FI	EXP_XEU	2022	PL	TOTAL	THS_NAC	1680717.275
FI	EXP_XEU	2022	PL_MC_SCH	TOTAL	THS_NAC	648.479
FI	EXP_XEU	2022	PL_CH	TOTAL	THS_NAC	1680068.718
FI	EXP_XEU	2022	PL_CH_SA	TOTAL	THS_NAC	1680068.664
FI	EXP_XEU	2022	PL_CH_SAB	TOTAL	THS_NAC	1649516.76
FI	EXP_XEU	2022	PL_CH_SI	TOTAL	THS_NAC	0.054
FI	EXP_XEU	2022	PL_DS	TOTAL	THS_NAC	0.078
FI	EXP_XEU	2022	PLO	TOTAL	THS_NAC	5.766
FI	EXP_XEU	2022	PLO_NW	TOTAL	THS_NAC	0.363
FI	EXP_XEU	2022	PLO_RC	TOTAL	THS_NAC	5.403
FI	EXP_XEU	2022	RCP	TOTAL	THS_NAC	203.206
FI	EXP_XEU	2022	PP	TOTAL	THS_NAC	3520682.489
FI	EXP_XEU	2022	PP_GR	TOTAL	THS_NAC	1246315.256
FI	EXP_XEU	2022	PP_GR_NP	TOTAL	THS_NAC	12648.172
FI	EXP_XEU	2022	PP_GR_MC	TOTAL	THS_NAC	148640.549
FI	EXP_XEU	2022	PP_GR_NW	TOTAL	THS_NAC	190848.41
FI	EXP_XEU	2022	PP_GR_CO	TOTAL	THS_NAC	894178.125000001
FI	EXP_XEU	2022	PP_HS	TOTAL	THS_NAC	1380.491
FI	EXP_XEU	2022	PP_PK	TOTAL	THS_NAC	2226647.329
FI	EXP_XEU	2022	PP_PK_CS	TOTAL	THS_NAC	458412.021
FI	EXP_XEU	2022	PP_PK_CB	TOTAL	THS_NAC	1414379.487
FI	EXP_XEU	2022	PP_PK_WR	TOTAL	THS_NAC	313884.739
FI	EXP_XEU	2022	PP_PK_O	TOTAL	THS_NAC	39971.082
FI	EXP_XEU	2022	PP_O	TOTAL	THS_NAC	46339.413
FI	EXP_XEU	2022	GLT_CLT	TOTAL	THS_NAC	284711.034
FI	EXP_XEU	2022	GLT	TOTAL	THS_NAC	284711.034
FI	EXP_XEU	2022	CLT	TOTAL	THS_NAC	-0
FI	EXP_XEU	2022	I_BEAMS	TOTAL	THS_NAC	0
FI	IMP	2021	SW	TOTAL	THS_NAC	534499.489
FI	IMP	2021	SW_SN	TOTAL	THS_NAC	20964.006
FI	IMP	2021	SW_SN	CONIF	THS_NAC	6258.563
FI	IMP	2021	SW_SN	NCONIF	THS_NAC	14705.443
FI	IMP	2021	SW_SN	NC_TRO	THS_NAC	857.1
FI	IMP	2021	SW_WR	TOTAL	THS_NAC	27149.167
FI	IMP	2021	SW_DM	TOTAL	THS_NAC	11472.481
FI	IMP	2021	SW_JN	TOTAL	THS_NAC	99967.865
FI	IMP	2021	SW_FU	TOTAL	THS_NAC	313708.585
FI	IMP	2021	SW_BL_W	TOTAL	THS_NAC	38953.984
FI	IMP	2021	SW_O	TOTAL	THS_NAC	22283.401
FI	IMP	2021	SP	TOTAL	THS_NAC	268050.301
FI	IMP	2021	SP_CM	TOTAL	THS_NAC	3306.512
FI	IMP	2021	SP_SCO	TOTAL	THS_NAC	41631.265
FI	IMP	2021	SP_HS	TOTAL	THS_NAC	41702.484
FI	IMP	2021	SP_PK	TOTAL	THS_NAC	96085.55
FI	IMP	2021	SP_O	TOTAL	THS_NAC	85324.49
FI	IMP	2021	SP_O_PR	TOTAL	THS_NAC	1524.718
FI	IMP	2021	SP_O_AR	TOTAL	THS_NAC	12271.442
FI	IMP	2021	SP_O_FL	TOTAL	THS_NAC	8682.616
FI	IMP	2022	SW	TOTAL	THS_NAC	670520.674
FI	IMP	2022	SW_SN	TOTAL	THS_NAC	32056.698
FI	IMP	2022	SW_SN	CONIF	THS_NAC	8518.928
FI	IMP	2022	SW_SN	NCONIF	THS_NAC	23537.77
FI	IMP	2022	SW_SN	NC_TRO	THS_NAC	1111.77
FI	IMP	2022	SW_WR	TOTAL	THS_NAC	51324.488
FI	IMP	2022	SW_DM	TOTAL	THS_NAC	14821.198
FI	IMP	2022	SW_JN	TOTAL	THS_NAC	103066.5
FI	IMP	2022	SW_FU	TOTAL	THS_NAC	388616.33
FI	IMP	2022	SW_BL_W	TOTAL	THS_NAC	50799.014
FI	IMP	2022	SW_O	TOTAL	THS_NAC	29836.446
FI	IMP	2022	SP	TOTAL	THS_NAC	344900.497
FI	IMP	2022	SP_CM	TOTAL	THS_NAC	4349.658
FI	IMP	2022	SP_SCO	TOTAL	THS_NAC	56206.666
FI	IMP	2022	SP_HS	TOTAL	THS_NAC	59442.782
FI	IMP	2022	SP_PK	TOTAL	THS_NAC	114189.539
FI	IMP	2022	SP_O	TOTAL	THS_NAC	110711.852
FI	IMP	2022	SP_O_PR	TOTAL	THS_NAC	3412.258
FI	IMP	2022	SP_O_AR	TOTAL	THS_NAC	18391.09
FI	IMP	2022	SP_O_FL	TOTAL	THS_NAC	9134.027
FI	EXP	2021	SW	TOTAL	THS_NAC	658300.669
FI	EXP	2021	SW_SN	TOTAL	THS_NAC	89393.713
FI	EXP	2021	SW_SN	CONIF	THS_NAC	88306.284
FI	EXP	2021	SW_SN	NCONIF	THS_NAC	1087.429
FI	EXP	2021	SW_SN	NC_TRO	THS_NAC	245.127
FI	EXP	2021	SW_WR	TOTAL	THS_NAC	37818.3
FI	EXP	2021	SW_DM	TOTAL	THS_NAC	3721.698
FI	EXP	2021	SW_JN	TOTAL	THS_NAC	322661.542
FI	EXP	2021	SW_FU	TOTAL	THS_NAC	124995.913
FI	EXP	2021	SW_BL_W	TOTAL	THS_NAC	72160.507
FI	EXP	2021	SW_O	TOTAL	THS_NAC	7548.996
FI	EXP	2021	SP	TOTAL	THS_NAC	418404.098
FI	EXP	2021	SP_CM	TOTAL	THS_NAC	25448.2
FI	EXP	2021	SP_SCO	TOTAL	THS_NAC	129446.136
FI	EXP	2021	SP_HS	TOTAL	THS_NAC	88645.041
FI	EXP	2021	SP_PK	TOTAL	THS_NAC	28996.441
FI	EXP	2021	SP_O	TOTAL	THS_NAC	145868.28
FI	EXP	2021	SP_O_PR	TOTAL	THS_NAC	61.692
FI	EXP	2021	SP_O_AR	TOTAL	THS_NAC	1835.692
FI	EXP	2021	SP_O_FL	TOTAL	THS_NAC	684.109
FI	EXP	2022	SW	TOTAL	THS_NAC	451069.817
FI	EXP	2022	SW_SN	TOTAL	THS_NAC	85316.078
FI	EXP	2022	SW_SN	CONIF	THS_NAC	83042.391
FI	EXP	2022	SW_SN	NCONIF	THS_NAC	2273.687
FI	EXP	2022	SW_SN	NC_TRO	THS_NAC	333.043
FI	EXP	2022	SW_WR	TOTAL	THS_NAC	44568.362
FI	EXP	2022	SW_DM	TOTAL	THS_NAC	4147.039
FI	EXP	2022	SW_JN	TOTAL	THS_NAC	60808.489
FI	EXP	2022	SW_FU	TOTAL	THS_NAC	168964.113
FI	EXP	2022	SW_BL_W	TOTAL	THS_NAC	78873.529
FI	EXP	2022	SW_O	TOTAL	THS_NAC	8392.207
FI	EXP	2022	SP	TOTAL	THS_NAC	527262.188
FI	EXP	2022	SP_CM	TOTAL	THS_NAC	31025.603
FI	EXP	2022	SP_SCO	TOTAL	THS_NAC	135473.469
FI	EXP	2022	SP_HS	TOTAL	THS_NAC	118856.039
FI	EXP	2022	SP_PK	TOTAL	THS_NAC	39438.192
FI	EXP	2022	SP_O	TOTAL	THS_NAC	202468.885
FI	EXP	2022	SP_O_PR	TOTAL	THS_NAC	83.024
FI	EXP	2022	SP_O_AR	TOTAL	THS_NAC	2641.871
FI	EXP	2022	SP_O_FL	TOTAL	THS_NAC	670.182
FI	IMP	2021	ST_1_2	CONIF	THS_M3	1467.83
FI	IMP	2021	ST_1_2	C_PIN	THS_M3	686.269
FI	IMP	2021	ST_1_2_1	C_PIN	THS_M3	63.772
FI	IMP	2021	ST_1_2_2	C_PIN	THS_M3	622.497
FI	IMP	2021	ST_1_2	C_FIR	THS_M3	781.552
FI	IMP	2021	ST_1_2_1	C_FIR	THS_M3	100.449
FI	IMP	2021	ST_1_2_2	C_FIR	THS_M3	681.103
FI	IMP	2021	ST_1_2	NCONIF	THS_M3	4830.214
FI	IMP	2021	ST_1_2	NC_OAK	THS_M3	0.006
FI	IMP	2021	ST_1_2	NC_BEE	THS_M3	0
FI	IMP	2021	ST_1_2	NC_BIR	THS_M3	4646.04
FI	IMP	2021	ST_1_2_1	NC_BIR	THS_M3	174.086
FI	IMP	2021	ST_1_2_2	NC_BIR	THS_M3	4471.954
FI	IMP	2021	ST_1_2	NC_POP	THS_M3	178.776
FI	IMP	2021	ST_1_2	NC_EUC	THS_M3	0
FI	IMP	2021	ST_6	CONIF	THS_M3	547.269
FI	IMP	2021	ST_6	C_PIN	THS_M3	173.841
FI	IMP	2021	ST_6	C_FIR	THS_M3	343.467
FI	IMP	2021	ST_6	NCONIF	THS_M3	30.628
FI	IMP	2021	ST_6	NC_OAK	THS_M3	6.472
FI	IMP	2021	ST_6	NC_BEE	THS_M3	0.204
FI	IMP	2021	ST_6	NC_MAP	THS_M3	0.005
FI	IMP	2021	ST_6	NC_CHE	THS_M3	0
FI	IMP	2021	ST_6	NC_ASH	THS_M3	1.151
FI	IMP	2021	ST_6	NC_BIR	THS_M3	5.595
FI	IMP	2021	ST_6	NC_POP	THS_M3	2.032
FI	IMP	2021	ST_1_2	CONIF	THS_NAC	75470.83
FI	IMP	2021	ST_1_2	C_PIN	THS_NAC	36373.917
FI	IMP	2021	ST_1_2_1	C_PIN	THS_NAC	4335.156
FI	IMP	2021	ST_1_2_2	C_PIN	THS_NAC	32038.761
FI	IMP	2021	ST_1_2	C_FIR	THS_NAC	39096.855
FI	IMP	2021	ST_1_2_1	C_FIR	THS_NAC	7125.368
FI	IMP	2021	ST_1_2_2	C_FIR	THS_NAC	31971.487
FI	IMP	2021	ST_1_2	NCONIF	THS_NAC	211214.194
FI	IMP	2021	ST_1_2	NC_OAK	THS_NAC	19.265
FI	IMP	2021	ST_1_2	NC_BEE	THS_NAC	0
FI	IMP	2021	ST_1_2	NC_BIR	THS_NAC	203879.254
FI	IMP	2021	ST_1_2_1	NC_BIR	THS_NAC	14448.668
FI	IMP	2021	ST_1_2_2	NC_BIR	THS_NAC	189430.586
FI	IMP	2021	ST_1_2	NC_POP	THS_NAC	6965.015
FI	IMP	2021	ST_1_2	NC_EUC	THS_NAC	0
FI	IMP	2021	ST_6	CONIF	THS_NAC	133705.357
FI	IMP	2021	ST_6	C_PIN	THS_NAC	42264.517
FI	IMP	2021	ST_6	C_FIR	THS_NAC	81287.748
FI	IMP	2021	ST_6	NCONIF	THS_NAC	26692.176
FI	IMP	2021	ST_6	NC_OAK	THS_NAC	8907.654
FI	IMP	2021	ST_6	NC_BEE	THS_NAC	83.759
FI	IMP	2021	ST_6	NC_MAP	THS_NAC	3.018
FI	IMP	2021	ST_6	NC_CHE	THS_NAC	0
FI	IMP	2021	ST_6	NC_ASH	THS_NAC	1157.702
FI	IMP	2021	ST_6	NC_BIR	THS_NAC	1906.527
FI	IMP	2021	ST_6	NC_POP	THS_NAC	1384.234
FI	IMP	2022	ST_1_2	CONIF	THS_M3	1295.643
FI	IMP	2022	ST_1_2	C_PIN	THS_M3	671.034
FI	IMP	2022	ST_1_2_1	C_PIN	THS_M3	41.352
FI	IMP	2022	ST_1_2_2	C_PIN	THS_M3	629.682
FI	IMP	2022	ST_1_2	C_FIR	THS_M3	624.463
FI	IMP	2022	ST_1_2_1	C_FIR	THS_M3	82.672
FI	IMP	2022	ST_1_2_2	C_FIR	THS_M3	541.791
FI	IMP	2022	ST_1_2	NCONIF	THS_M3	1583.707
FI	IMP	2022	ST_1_2	NC_OAK	THS_M3	0.009
FI	IMP	2022	ST_1_2	NC_BEE	THS_M3	0.001
FI	IMP	2022	ST_1_2	NC_BIR	THS_M3	1387.98
FI	IMP	2022	ST_1_2_1	NC_BIR	THS_M3	32.162
FI	IMP	2022	ST_1_2_2	NC_BIR	THS_M3	1355.818
FI	IMP	2022	ST_1_2	NC_POP	THS_M3	79.616
FI	IMP	2022	ST_1_2	NC_EUC	THS_M3	106.983
FI	IMP	2022	ST_6	CONIF	THS_M3	301.635
FI	IMP	2022	ST_6	C_PIN	THS_M3	95.894
FI	IMP	2022	ST_6	C_FIR	THS_M3	185.119
FI	IMP	2022	ST_6	NCONIF	THS_M3	33.747
FI	IMP	2022	ST_6	NC_OAK	THS_M3	6.439
FI	IMP	2022	ST_6	NC_BEE	THS_M3	0.295
FI	IMP	2022	ST_6	NC_MAP	THS_M3	0.011
FI	IMP	2022	ST_6	NC_CHE	THS_M3	0
FI	IMP	2022	ST_6	NC_ASH	THS_M3	1.009
FI	IMP	2022	ST_6	NC_BIR	THS_M3	2.929
FI	IMP	2022	ST_6	NC_POP	THS_M3	2.411
FI	IMP	2022	ST_1_2	CONIF	THS_NAC	97428.224
FI	IMP	2022	ST_1_2	C_PIN	THS_NAC	49306.505
FI	IMP	2022	ST_1_2_1	C_PIN	THS_NAC	2779.369
FI	IMP	2022	ST_1_2_2	C_PIN	THS_NAC	46527.136
FI	IMP	2022	ST_1_2	C_FIR	THS_NAC	48083.009
FI	IMP	2022	ST_1_2_1	C_FIR	THS_NAC	6392.221
FI	IMP	2022	ST_1_2_2	C_FIR	THS_NAC	41690.788
FI	IMP	2022	ST_1_2	NCONIF	THS_NAC	139403.835
FI	IMP	2022	ST_1_2	NC_OAK	THS_NAC	12.888
FI	IMP	2022	ST_1_2	NC_BEE	THS_NAC	0.047
FI	IMP	2022	ST_1_2	NC_BIR	THS_NAC	115355.221
FI	IMP	2022	ST_1_2_1	NC_BIR	THS_NAC	3076.514
FI	IMP	2022	ST_1_2_2	NC_BIR	THS_NAC	112278.707
FI	IMP	2022	ST_1_2	NC_POP	THS_NAC	4354.178
FI	IMP	2022	ST_1_2	NC_EUC	THS_NAC	19070.564
FI	IMP	2022	ST_6	CONIF	THS_NAC	82200.408
FI	IMP	2022	ST_6	C_PIN	THS_NAC	28248.233
FI	IMP	2022	ST_6	C_FIR	THS_NAC	44486.216
FI	IMP	2022	ST_6	NCONIF	THS_NAC	37034.301
FI	IMP	2022	ST_6	NC_OAK	THS_NAC	11972.37
FI	IMP	2022	ST_6	NC_BEE	THS_NAC	183.037
FI	IMP	2022	ST_6	NC_MAP	THS_NAC	14.818
FI	IMP	2022	ST_6	NC_CHE	THS_NAC	0
FI	IMP	2022	ST_6	NC_ASH	THS_NAC	1154.04
FI	IMP	2022	ST_6	NC_BIR	THS_NAC	1298.471
FI	IMP	2022	ST_6	NC_POP	THS_NAC	2240.963
FI	EXP	2021	ST_1_2	CONIF	THS_M3	965.99
FI	EXP	2021	ST_1_2	C_PIN	THS_M3	694.107
FI	EXP	2021	ST_1_2_1	C_PIN	THS_M3	285.006
FI	EXP	2021	ST_1_2_2	C_PIN	THS_M3	409.101
FI	EXP	2021	ST_1_2	C_FIR	THS_M3	236.479
FI	EXP	2021	ST_1_2_1	C_FIR	THS_M3	6.069
FI	EXP	2021	ST_1_2_2	C_FIR	THS_M3	230.41
FI	EXP	2021	ST_1_2	NCONIF	THS_M3	104.535
FI	EXP	2021	ST_1_2	NC_OAK	THS_M3	0
FI	EXP	2021	ST_1_2	NC_BEE	THS_M3	0
FI	EXP	2021	ST_1_2	NC_BIR	THS_M3	98.837
FI	EXP	2021	ST_1_2_1	NC_BIR	THS_M3	0
FI	EXP	2021	ST_1_2_2	NC_BIR	THS_M3	98.837
FI	EXP	2021	ST_1_2	NC_POP	THS_M3	0.078
FI	EXP	2021	ST_1_2	NC_EUC	THS_M3	0
FI	EXP	2021	ST_6	CONIF	THS_M3	8715.693
FI	EXP	2021	ST_6	C_PIN	THS_M3	4345.373
FI	EXP	2021	ST_6	C_FIR	THS_M3	4369.292
FI	EXP	2021	ST_6	NCONIF	THS_M3	20.164
FI	EXP	2021	ST_6	NC_OAK	THS_M3	0.051
FI	EXP	2021	ST_6	NC_BEE	THS_M3	0.108
FI	EXP	2021	ST_6	NC_MAP	THS_M3	0.003
FI	EXP	2021	ST_6	NC_CHE	THS_M3	0
FI	EXP	2021	ST_6	NC_ASH	THS_M3	0.031
FI	EXP	2021	ST_6	NC_BIR	THS_M3	13.23
FI	EXP	2021	ST_6	NC_POP	THS_M3	0.806
FI	EXP	2021	ST_1_2	CONIF	THS_NAC	87673.586
FI	EXP	2021	ST_1_2	C_PIN	THS_NAC	62994.886
FI	EXP	2021	ST_1_2_1	C_PIN	THS_NAC	20162.139
FI	EXP	2021	ST_1_2_2	C_PIN	THS_NAC	42832.747
FI	EXP	2021	ST_1_2	C_FIR	THS_NAC	12842.714
FI	EXP	2021	ST_1_2_1	C_FIR	THS_NAC	469.169
FI	EXP	2021	ST_1_2_2	C_FIR	THS_NAC	12373.545
FI	EXP	2021	ST_1_2	NCONIF	THS_NAC	6071.115
FI	EXP	2021	ST_1_2	NC_OAK	THS_NAC	0
FI	EXP	2021	ST_1_2	NC_BEE	THS_NAC	0
FI	EXP	2021	ST_1_2	NC_BIR	THS_NAC	5502.646
FI	EXP	2021	ST_1_2_1	NC_BIR	THS_NAC	0
FI	EXP	2021	ST_1_2_2	NC_BIR	THS_NAC	5502.646
FI	EXP	2021	ST_1_2	NC_POP	THS_NAC	3.573
FI	EXP	2021	ST_1_2	NC_EUC	THS_NAC	0
FI	EXP	2021	ST_6	CONIF	THS_NAC	2562670.729
FI	EXP	2021	ST_6	C_PIN	THS_NAC	1241655.375
FI	EXP	2021	ST_6	C_FIR	THS_NAC	1320485.99
FI	EXP	2021	ST_6	NCONIF	THS_NAC	10042.763
FI	EXP	2021	ST_6	NC_OAK	THS_NAC	56.276
FI	EXP	2021	ST_6	NC_BEE	THS_NAC	0.89
FI	EXP	2021	ST_6	NC_MAP	THS_NAC	0.611
FI	EXP	2021	ST_6	NC_CHE	THS_NAC	0
FI	EXP	2021	ST_6	NC_ASH	THS_NAC	36.203
FI	EXP	2021	ST_6	NC_BIR	THS_NAC	4504.734
FI	EXP	2021	ST_6	NC_POP	THS_NAC	878.586
FI	EXP	2022	ST_1_2	CONIF	THS_M3	1348.069
FI	EXP	2022	ST_1_2	C_PIN	THS_M3	927.625
FI	EXP	2022	ST_1_2_1	C_PIN	THS_M3	345.272
FI	EXP	2022	ST_1_2_2	C_PIN	THS_M3	582.353
FI	EXP	2022	ST_1_2	C_FIR	THS_M3	385.541
FI	EXP	2022	ST_1_2_1	C_FIR	THS_M3	78.217
FI	EXP	2022	ST_1_2_2	C_FIR	THS_M3	307.324
FI	EXP	2022	ST_1_2	NCONIF	THS_M3	354.703
FI	EXP	2022	ST_1_2	NC_OAK	THS_M3	0
FI	EXP	2022	ST_1_2	NC_BEE	THS_M3	0
FI	EXP	2022	ST_1_2	NC_BIR	THS_M3	345.028
FI	EXP	2022	ST_1_2_1	NC_BIR	THS_M3	0.746
FI	EXP	2022	ST_1_2_2	NC_BIR	THS_M3	344.282
FI	EXP	2022	ST_1_2	NC_POP	THS_M3	1.583
FI	EXP	2022	ST_1_2	NC_EUC	THS_M3	0
FI	EXP	2022	ST_6	CONIF	THS_M3	8563.032
FI	EXP	2022	ST_6	C_PIN	THS_M3	4211.018
FI	EXP	2022	ST_6	C_FIR	THS_M3	4339.221
FI	EXP	2022	ST_6	NCONIF	THS_M3	22.552
FI	EXP	2022	ST_6	NC_OAK	THS_M3	0.055
FI	EXP	2022	ST_6	NC_BEE	THS_M3	0.001
FI	EXP	2022	ST_6	NC_MAP	THS_M3	0
FI	EXP	2022	ST_6	NC_CHE	THS_M3	0
FI	EXP	2022	ST_6	NC_ASH	THS_M3	0.014
FI	EXP	2022	ST_6	NC_BIR	THS_M3	16.19
FI	EXP	2022	ST_6	NC_POP	THS_M3	0.503
FI	EXP	2022	ST_1_2	CONIF	THS_NAC	121347.463
FI	EXP	2022	ST_1_2	C_PIN	THS_NAC	82970.746
FI	EXP	2022	ST_1_2_1	C_PIN	THS_NAC	27389.877
FI	EXP	2022	ST_1_2_2	C_PIN	THS_NAC	55580.869
FI	EXP	2022	ST_1_2	C_FIR	THS_NAC	23676.924
FI	EXP	2022	ST_1_2_1	C_FIR	THS_NAC	6422.1
FI	EXP	2022	ST_1_2_2	C_FIR	THS_NAC	17254.824
FI	EXP	2022	ST_1_2	NCONIF	THS_NAC	24719.676
FI	EXP	2022	ST_1_2	NC_OAK	THS_NAC	0
FI	EXP	2022	ST_1_2	NC_BEE	THS_NAC	0
FI	EXP	2022	ST_1_2	NC_BIR	THS_NAC	24070.048
FI	EXP	2022	ST_1_2_1	NC_BIR	THS_NAC	86.923
FI	EXP	2022	ST_1_2_2	NC_BIR	THS_NAC	23983.125
FI	EXP	2022	ST_1_2	NC_POP	THS_NAC	119.875
FI	EXP	2022	ST_1_2	NC_EUC	THS_NAC	0
FI	EXP	2022	ST_6	CONIF	THS_NAC	2585604.845
FI	EXP	2022	ST_6	C_PIN	THS_NAC	1245029.07
FI	EXP	2022	ST_6	C_FIR	THS_NAC	1335876.828
FI	EXP	2022	ST_6	NCONIF	THS_NAC	12417.372
FI	EXP	2022	ST_6	NC_OAK	THS_NAC	101.726
FI	EXP	2022	ST_6	NC_BEE	THS_NAC	0.14
FI	EXP	2022	ST_6	NC_MAP	THS_NAC	0
FI	EXP	2022	ST_6	NC_CHE	THS_NAC	0
FI	EXP	2022	ST_6	NC_ASH	THS_NAC	63.489
FI	EXP	2022	ST_6	NC_BIR	THS_NAC	7613.448
FI	EXP	2022	ST_6	NC_POP	THS_NAC	665.988
FI	PRD	2021	EU2_1	TOTAL	THS_M3	66713.896538
FI	PRD	2021	EU2_1	CONIF	THS_M3	52925.994956
FI	PRD	2021	EU2_1	NCONIF	THS_M3	13787.901582
FI	PRD	2021	EU2_1_1	TOTAL	THS_M3	5483.46744028	6
FI	PRD	2021	EU2_1_1	CONIF	THS_M3	4867.0951786452	6
FI	PRD	2021	EU2_1_1	NCONIF	THS_M3	616.3722616348	6
FI	PRD	2021	EU2_1_2	TOTAL	THS_M3
FI	PRD	2021	EU2_1_2	CONIF	THS_M3
FI	PRD	2021	EU2_1_2	NCONIF	THS_M3
FI	PRD	2021	EU2_1_3	TOTAL	THS_M3	61230.42909772	6
FI	PRD	2021	EU2_1_3	CONIF	THS_M3	48058.8997773548	6
FI	PRD	2021	EU2_1_3	NCONIF	THS_M3	13171.5293203652	6
FI	PRD	2022	EU2_1	TOTAL	THS_M3	65637.339725
FI	PRD	2022	EU2_1	CONIF	THS_M3	52029.037557
FI	PRD	2022	EU2_1	NCONIF	THS_M3	13608.302168
FI	PRD	2022	EU2_1_1	TOTAL	THS_M3	5242.481822352	6
FI	PRD	2022	EU2_1_1	CONIF	THS_M3	4597.2976464245	6
FI	PRD	2022	EU2_1_1	NCONIF	THS_M3	645.1841759275	6
FI	PRD	2022	EU2_1_2	TOTAL	THS_M3
FI	PRD	2022	EU2_1_2	CONIF	THS_M3
FI	PRD	2022	EU2_1_2	NCONIF	THS_M3
FI	PRD	2022	EU2_1_3	TOTAL	THS_M3	60394.857902648	6
FI	PRD	2022	EU2_1_3	CONIF	THS_M3	47431.7399105755	6
FI	PRD	2022	EU2_1_3	NCONIF	THS_M3	12963.1179920725	6

The Making of Hedonic Index Numbers, Finland

Read more about The Making of Hedonic Index Numbers, Finland

Languages and translations

English

The Making of Hedonic Index Numbers Ville Auno, Henri Luomaranta-Helmivuo, Hannele Markkanen, Satu Montonen, Kristiina Nieminen, Antti Suoperä

Presenter: Satu Montonen Meeting of the Group of Experts on Consumer Price Indices 07 - 09 June 2023, Geneva

Content 1. Background 2. Data and data pre-processing 3. Steps of the process for producing the hedonic price index 4. Results 5. Conclusions

1 June, 2023 Statistics Finland | [email protected]

1. Background • Previously, the price index for second-hand cars was calculated by Autovista Group for the purpose of CPI

• From the beginning of 2023, Statistics Finland has done the calculation itself

• The same second-hand car is not sold every month, so it is impossible to follow the price of the same car over time

• In this study, we combine hedonic quality adjusting and traditional index calculation

• In Finland, the same method is used for the prices of houses as well as for the rents of offices and shops

1 June, 2023 Statistics Finland | [email protected]

2. Data and data pre-processing • Data is received on a daily basis from one major selling portal for second-hand cars in Finland

• Only the latest sales announcement of the month is considered

• The sales announcement data is supplemented with additional characteristics information from the vehicle register data from Finnish Transport and Communications Agency

• The monthly data contains approximately 75 000 individual sales announcements of second-hand cars

• For index calculation purposes, only the following are taken into account: - Second-hand cars with ”sold”-status purchased from car dealers - Passenger cars - Cars aged between one and twenty years - Cars with price greater than 2000 euros - Mileage needs to be less than one million kilometers

1 June, 2023 Statistics Finland | [email protected]

3. Steps of the process for producing the hedonic price index

Definition and estimation of price

model incl. statistical tests

Aggregation and Oaxaca-

decomposition

Index calculation

1 June, 2023 Statistics Finland | [email protected]

3.1 Definition and estimation of price model 1/5

• The price model is semilogarithmic:

𝑙𝑙𝑙𝑙𝑙𝑙 𝑝𝑝𝑖𝑖𝑖𝑖 = 𝛼𝛼01𝑖𝑖 + ⋯+ 𝛼𝛼0𝑘𝑘1𝑖𝑖 + 𝑥𝑥𝑥𝑖𝑖𝑖𝑖𝛽𝛽𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖,

where 𝑝𝑝 is the unit price of a second-hand car, parameters 𝛼𝛼 represent stratum effects and term 𝜀𝜀 is random error term

• The unknown parameters 𝛽𝛽 and 𝛼𝛼 are estimated using the ordinary least squares method (OLS)

The explanatory variables used in the price model

1 June, 2023 Statistics Finland | [email protected]

Variable Description

𝑥𝑥1 Gearbox type: If automatic 𝑥𝑥1 = 1, else 𝑥𝑥1 = 0.

𝑥𝑥2 Towing hook: If towing hook 𝑥𝑥2 = 1, else 𝑥𝑥2 = 0.

𝑥𝑥3 Service history: If service history is available 𝑥𝑥3 = 1, else 𝑥𝑥3 = 0.

𝑥𝑥4 Cruise control: If cruise control 𝑥𝑥4 = 1, else 𝑥𝑥4 = 0.

𝑥𝑥5 Selling time of a car, months.

𝑥𝑥6 = 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠(𝑥𝑥5) Square root of the selling time of a car.

𝑥𝑥7 Age of a car, years.

𝑥𝑥8 = 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠(𝑥𝑥7) Square root of the age of a car.

𝑥𝑥9 Mileage (ten thousand).

𝑥𝑥10 = 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠(𝑥𝑥9) Square root of mileage.

𝑥𝑥11 Power/Weight ratio of a car.

𝑥𝑥12 = 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠(𝑥𝑥11 ) Square root of Power/Weight of a car.

3.1 Definition and estimation of price model 2/5 • We define several hierarchical partitions of second-hand cars (homogenous stratums)

• Using the F-test, we select the suitable partition: model 6

1 June, 2023 Statistics Finland | [email protected]

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

No categori-

zation

Size of a car

Size of a car × Make

Size of a car × Make ×

Model

Size of a car × Make × Model × Driving

Power

Size of a car × Make × Model × Driving Power × Type of a car

Model 1 vs 2

Model 2 vs 3

Model 3 vs 4

Model 5 vs 4

Model 6 vs 5

Test statistic 11896 1872 711 36.8 10.7

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
	No categori-zation	Size of a car	Size of a car × Make	Size of a car × Make × Model	Size of a car × Make × Model × Driving Power	Size of a car × Make × Model × Driving Power × Type of a car
		Model 1 vs 2	Model 2 vs 3	Model 3 vs 4	Model 5 vs 4	Model 6 vs 5
Test statistic		11896	1872	711	36.8	10.7

3.1 Definition and estimation of price model 3/5 • We define several classifications of price models

• Using the F-test, we select the suitable classification of price model: model 8

1 June, 2023 Statistics Finland | [email protected]

Model 6 Model 7 Model 8

No heterogeneity Size of a car Size of a car × Make

Model 7 vs 6

Model 8 vs 7

Test statistic 206.5 45

	Model 6	Model 7	Model 8
	No heterogeneity	Size of a car	Size of a car × Make
		Model 7 vs 6	Model 8 vs 7
Test statistic		206.5	45

3.1 Definition and estimation of price model 4/5 • The price model is estimated for each year

• Estimation results for model 8 - Selling time of a car has little effect on price - Age of a car and mileage have a negative effect

on price - Power/Weight ratio of a car has a positive

effect on price

1 June, 2023 Statistics Finland | [email protected]

Year 2020 2021 Number of observations 287936 269663 Number of equations 72 74 Number of stratums/categories 1594 1691 Degrees of freedom 285478 267084 SSE 5401.6405077 4908.43633 R2 0.9645034599 0.9675392005 RMSE 0.1375550427 0.1355650208

2020 2021 Constant 9.9126394001 9.8211262087 If automatic gearbox 𝑥𝑥1 = 1, else 𝑥𝑥1 =0 0.0902673948 0.0923941505 If towing hook 𝑥𝑥2 = 1, else 𝑥𝑥2 = 0 0.0118209506 0.0113174535 If service history is available 𝑥𝑥3 = 1, else 𝑥𝑥3 = 0 -0.010492392 -0.008856039 If cruise control 𝑥𝑥4 = 1, else 𝑥𝑥4 = 0 0.017682513 0.0190084745 Selling time of a car, 𝑥𝑥5 -0.000386744 0.0036841099 𝑥𝑥6 = 𝑥𝑥5

1/2 0.0054383443 -0.012634214 Age of a car, 𝑥𝑥7 -0.138809764 -0.135251635 𝑥𝑥8 = 𝑥𝑥7

1/2 0.2915511757 0.2950576677 Mileage, 𝑥𝑥9 -0.033047764 -0.033221364 𝑥𝑥10 = 𝑥𝑥9

1/2 0.0180405738 0.026330353 Power/Weight ratio of a car, 𝑥𝑥11 12.089654612 9.8976375615 𝑥𝑥12 = 𝑥𝑥11

1/2 -2.549090343 -1.520907481

Year	2020	2021
Number of observations	287936	269663
Number of equations	72	74
Number of stratums/categories	1594	1691
Degrees of freedom	285478	267084
SSE	5401.6405077	4908.43633
R2	0.9645034599	0.9675392005
RMSE	0.1375550427	0.1355650208

	2020	2021
Constant	9.9126394001	9.8211262087
If automatic gearbox , else 0	0.0902673948	0.0923941505
If towing hook , else	0.0118209506	0.0113174535
If service history is available , else	-0.010492392	-0.008856039
If cruise control , else	0.017682513	0.0190084745
Selling time of a car,	-0.000386744	0.0036841099
	0.0054383443	-0.012634214
Age of a car,	-0.138809764	-0.135251635
	0.2915511757	0.2950576677
Mileage,	-0.033047764	-0.033221364
	0.0180405738	0.026330353
Power/Weight ratio of a car,	12.089654612	9.8976375615
	-2.549090343	-1.520907481

3.1 Definition and estimation of price model 5/5 The price effect of selling time (months) on the average log-prices in year 2020 and 2021 (red line)

1 June, 2023 Statistics Finland | [email protected]

The price effect of mileage (ten thousand) on the average log-prices in year 2020 and 2021 (red line)

The price effect of power/weight ratio (kW/kg) on the average log-prices in year 2020 and 2021 (red line)

The price effect of age (years) on the average log-prices in year 2020 and 2021 (red line)

3.2 Aggregation and Oaxaca-decomposition • We aggregate price models from observations into stratums of the partition

• We test unweighted geometric and arithmetic averages in aggregation

• The quality adjusting is performed using decomposition introduced by Oaxaca (1973) - The decomposition splits the actual average price change into quality corrections and quality adjusted price changes

for any stratum

(1) Price-ratio = Quality corrections + Quality adjusted price change conditional on �𝒙𝒙′𝑘𝑘𝑖𝑖

A = QC + QA

• The equation (1) can be represented as

𝑙𝑙𝑙𝑙𝑙𝑙 ⁄�̅�𝑝𝑘𝑘𝑖𝑖 �̅�𝑝𝑘𝑘0 = 𝑙𝑙𝑙𝑙𝑙𝑙 ⁄�𝑝𝑝𝑘𝑘𝑖𝑖 �̅�𝑝𝑘𝑘0 + 𝑙𝑙𝑙𝑙𝑙𝑙 ⁄�̅�𝑝𝑘𝑘𝑖𝑖 �𝑝𝑝𝑘𝑘𝑖𝑖 ,

where 𝑙𝑙𝑙𝑙𝑙𝑙 �̅�𝑝𝑘𝑘𝑖𝑖 is the average price for the current month, 𝑙𝑙𝑙𝑙𝑙𝑙 �̅�𝑝𝑘𝑘0 is the average price for the base period and

𝑙𝑙𝑙𝑙𝑙𝑙 �𝑝𝑝𝑘𝑘𝑖𝑖 = �𝛼𝛼𝑘𝑘0 + �𝒙𝒙′𝑘𝑘𝑖𝑖�𝜷𝜷𝑗𝑗0 is the current month's estimated price using the base period valuation of characteristics �𝜷𝜷𝑗𝑗0

• The price model estimates used are always from the base period 1 June, 2023 Statistics Finland | [email protected]

3.3 Index calculation • The averaged stratum-level price decompositions are summed up to COICOP7-level using weights 𝑤𝑤𝑘𝑘,𝑓𝑓 of

index number formula 𝑓𝑓

𝑒𝑒𝑥𝑥𝑝𝑝 ∑𝑘𝑘 𝑤𝑤𝑘𝑘,𝑓𝑓 𝑙𝑙𝑙𝑙𝑙𝑙 ⁄�̅�𝑝𝑘𝑘𝑖𝑖 �̅�𝑝𝑘𝑘0 = 𝑃𝑃𝑓𝑓,𝐴𝐴 ⁄𝑖𝑖 0 is the price index for actual average prices (A)

𝑒𝑒𝑥𝑥𝑝𝑝 ∑𝑘𝑘 𝑤𝑤𝑘𝑘,𝑓𝑓 𝑙𝑙𝑙𝑙𝑙𝑙 ⁄�𝑝𝑝𝑘𝑘𝑖𝑖 �̅�𝑝𝑘𝑘0 = 𝑃𝑃𝑓𝑓,𝑄𝑄𝑄𝑄 ⁄𝑖𝑖 0 is the price index for quality corrections (QC)

𝑒𝑒𝑥𝑥𝑝𝑝 ∑𝑘𝑘 𝑤𝑤𝑘𝑘,𝑓𝑓 𝑙𝑙𝑙𝑙𝑙𝑙 ⁄�̅�𝑝𝑘𝑘𝑖𝑖 �𝑝𝑝𝑘𝑘𝑖𝑖 = 𝑃𝑃𝑓𝑓,𝑄𝑄𝐴𝐴 ⁄𝑖𝑖 0 is price index for quality adjusted price changes (QA)

that satisfy the following equation

𝑃𝑃𝑓𝑓,𝐴𝐴 ⁄𝑖𝑖 0 = 𝑃𝑃𝑓𝑓,𝑄𝑄𝑄𝑄

⁄𝑖𝑖 0 � 𝑃𝑃𝑓𝑓,𝑄𝑄𝐴𝐴 ⁄𝑖𝑖 0

• In our case the base period is a previous year normalized as an average month - We use the flexible basket approach

• We test different index number formulas

1 June, 2023 Statistics Finland | [email protected]

4. Results 1/3 • Index series for actual average prices for ‘Small cars’ make ‘Honda’. Indices based on geometric are dotted

lines and arithmetic are solid lines

• Basic formulas are contingently biased, deviating from each other

• Price ratios using unweighted arithmetic or geometric average prices are closely related

1 June, 2023 Statistics Finland | [email protected]

4. Results 2/3 • Hedonic index series for actual arithmetic average prices (A), quality adjusted prices (QA) and quality

corrections (Qc_x)

• Age of a car (x7) and mileage (x9) have a negative effect on actual average prices - Sold cars are older and more driven in the current period

• Index series for actual prices must be corrected upwards, which is index series for quality adjusted prices

1 June, 2023 Statistics Finland | [email protected]

4. Results 3/3

• The differences between the series are due to the data source, regression model variables, index formula and strategy

1 June, 2023 Statistics Finland | [email protected]

Things to consider when designing a hedonic application (HICP Manual) • How many and which quality-related variables to include in the regression equation: Our model has 12 variables

(slide 6)

• Whether to use another (finer or coarser) stratification when estimating the regression coefficients than when computing the index: We use a coarser stratification for estimation (slide 8)

• How frequently to re-estimate the regression coefficients: We re-estimate every year

• Whether to weight the prices when estimating the regression coefficients: We use equal weights

• Which function form to use; semi-logarithmic, double-logarithmic or other: Our model is semi-logarithmic (slide 6)

• Whether valid or spurious results are obtained: Statistical inference leads to selection of the best price models. Estimators of the price models are the best linear unbiased estimates (BLUE)

• Whether the method improves the accuracy of the index so much that it outweighs the often relatively high cost for design work and for collection of quality-related data: Yes, see slide 14

1 June, 2023 Statistics Finland | [email protected]

5. Conclusions • Our proposal for producing a hedonic price index is as follows:

1. Use suitable partition in estimation of price models

2. Aggregate price models into stratum-level by using arithmetic average

- Arithmetic average is more interpretable than geometric average

3. Form price decompositions for stratums (Oaxaca)

4. Aggregate stratum-level price decompositions into COICOP-level using Törnqvist formula and base strategy with a flexible basket, that is free of chain drift

• This method is widely used in Statistics Finland

1 June, 2023 Statistics Finland | [email protected]

Thank You!

Satu Montonen [email protected]

The Making of Hedonic Index Numbers
Content
1. Background
2. Data and data pre-processing
3. Steps of the process for producing the hedonic price index
3.1 Definition and estimation of price model 1/5
3.1 Definition and estimation of price model 2/5
3.1 Definition and estimation of price model 3/5
3.1 Definition and estimation of price model 4/5
3.1 Definition and estimation of price model 5/5
3.2 Aggregation and Oaxaca-decomposition�
3.3 Index calculation
4. Results 1/3
4. Results 2/3
4. Results 3/3
Things to consider when designing a hedonic application (HICP Manual)
5. Conclusions
Thank You!

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The Making of Hedonic Index Numbers, Finland

Read more about The Making of Hedonic Index Numbers, Finland

This study combines heterogeneously behaving cross-sectional regressions and hedonic quality adjusting in traditional index number framework. The approach provides a transparent mathematical representation of quality correction and quality adjustment of price changes in elementary aggregates. We propose an alternative to the standard Griliches-type time-dummy hedonic approach, which in the sense of index number theory is more interpretable and mathematically transparent between actual average price changes, quality correction and quality adjustment.

Languages and translations

English

The Making of Hedonic Index Numbers

Auno, Ville, Statistics Finland

Luomaranta-Helmivuo, Henri, Statistics Finland

Markkanen, Hannele, Statistics Finland

Montonen, Satu, Statistics Finland

Nieminen, Kristiina, Statistics Finland

Suoperä, Antti, Statistics Finland

Abstract This study combines heterogeneously behaving cross-sectional regressions and hedonic quality adjusting in

traditional index number framework. The approach provides a transparent mathematical representation of

quality correction and quality adjustment of price changes in elementary aggregates. We propose an

alternative to the standard Griliches-type time-dummy hedonic approach, which in the sense of index number

theory is more interpretable and mathematically transparent between actual average price changes, quality

correction and quality adjustment.

In the first stage, the problem of heterogeneously behaving cross-sectional models is handled using the

principle of hierarchical, ‘nested’, price models. The price models are formulated by combining the proper

partition of observations (categorization of observations) and the proper classification of observations into

the most homogeneously behaving subgroups (heterogeneous between subgroups) using standard statistical

inference. These are achieved using the FE-models (fixed effects) familiar to economists. In the second

stage, the estimated price models are aggregated from observation level into the level of partition (i.e., into

stratums), where the so-called Oaxaca decompositions are computed. This decomposition, although not

unambiguous, consistently divides the actual price change into quality corrections and quality adjusted price

change for each stratum. We show what is the ideal selection of decompositions based on the algebraic

properties of the OLS method. In the third stage, the stratum level decompositions are aggregated into higher

levels similarly as in a traditional index number calculation where ‘a weighted-by-economic-importance’-

variable takes a central role. We use several basic and excellent index number formulas. The study ends in

empirical application of used cars in Finland.

Keywords

Partition, Unit Value, Logarithmic Representations, Index Number Formulas, Hedonic Method, FE-Model,

OLS Method, Unbiased, Price Aggregation, Oaxaca Decomposition, Logarithmic mean, Conditional and

Unconditional mean.

1 Introduction

In traditional index number theory direct price-links are based on comparisons 0 → t, t = 1, 2, …, for

commodities comparable in quality. Practically this means measurement of price changes from commodity

prices having a unique code e.g. GTIN-identifier. This traditional method fits nicely for e.g. daily products

but not generally. In most cases, like clothes, shoes, mobile phones, TV, home electronics etc., bilateral

price-linking is not possible because of quality change. This property makes bilateral strategies less useful

leading to indices being contingently biased caused by quality changes of quality characteristics. This

happens for example for prices of houses and used cars. For that Bailey, Muth and Nourse (1963) developed

a repeat-sales model (see, Case and Shiller,1989; Quigley, 1995) using a model based (or the stochastic)

approach to measure changes of prices. These repeat-sales models are problematic, because they can capture

a tiny fraction of the data because each transacted ‘commodity’, for example apartment or used car, appears

rarely more than once in the data in a short time span. Another well-known model-based approach is the

Griliches (1971) time-dummy hedonic method or the WTPD-model (Diewert and Fox, 2018, pp.15), which

cover the entire data and resolve the comparability issue using hedonic quality adjusting. These methods

suffer from several problems, but most importantly they are not connected any way with traditional index

number theory (see Koev, 2003; Suoperä, Luomaranta, Nieminen and Markkanen. 2021; Kaila, Luomaranta

& Suoperä, 2022). Therefore, these hedonic methods are abandoned in this study.

The focus of the study is to show ‘How hedonic quality adjusting, and traditional index number theory may

be combined using familiar regression analysis and its algebraic properties transparently?’. The work builds

on two earlier papers (Koev, 2003; Suoperä, 2006; see also Vartia, Suoperä & Vuorio, 2021; Suoperä &

Auno, 2021; Suoperä, Luomaranta, Nieminen and Markkanen. 2021; Kaila, Luomaranta & Suoperä, 2022)

which address most of issues based on hedonic approach to index numbers. The main idea is that because

effective matched pairs method or bilateral price-linking is not possible, the price-linking should be done for

some coarse but the most homogeneous grouping of observations. We do this using econometric approach

where price models include two-dimensional heterogeneity: ‘intercept’ or ‘categorical heterogeneity’ that

arise from a detailed partition and ‘slope coefficient heterogeneity’ from different OLS regressions in several

heterogeneously behaving subgroups (Suoperä and Vartia, 2011). In statistical textbooks this modelling is a

well-known Fixed Effects (FE) model (Hsiao, 1986, s.29-32).

The process consists of three steps. In the first step, we define several hierarchical ‘nested’ FE price models

and use statistical inference, that is the estimation of heterogeneously behaving price models and testing

equality between them. Statistical inference helps us to identify the data generating process of prices and

leads to selection of the best price models, that is the combination of the classification of price models and

their partitions. Estimators of the price models are the best linear unbiased estimates (BLUE). In second step,

we aggregate price models from observations into stratums of the partition. This will be done while

satisfying the basic algebraic properties of the OLS method. Then the quality adjusting is performed using

decomposition introduced by Oaxaca (1973). Even the decomposition is not unambiguous, it splits the true

average price change consistently into quality changes and quality adjusted price changes for any stratum in

question. In third step, we apply traditional index number theory for stratum level aggregates of the

decomposition. We analyze two stratum aggregates and their decompositions – unweighted arithmetic and

geometric averages. We perform our analysis of index numbers using several basic (Laspeyres (L), log-

Laspeyres (l), Log-Paasche (p), Paasche (P)) and excellent index number formulas (Törnqvist (T),

Montgomery-Vartia (MV), Sato-Vartia (SV), Fisher (F)).

The structure of the study is as follows. In chapter 2 we present the data, basic concepts and notations. In

chapter 3 we present several nested partitions and combine them with heterogeneously behaving cross-

sectional regressions. Theoretical methods are presented by their empirical counterparts. In chapter 4 we

derive stratum aggregates and their Oaxaca decompositions. In chapter 5 we apply index number methods to

our stratum aggregates and show some graphical figures comparing different basic and excellent index

numbers. Chapter 6 concludes.

2 Data, Basic Concepts and Notation

2.1 Data

Data is received on a daily basis from one major selling portal for second-hand cars in Finland. The received

data contains the sales announcements updated on the previous day. When daily announcements are

compiled as monthly data, only the latest sales announcement of the month is considered. The sales

announcement data is then supplemented with additional characteristics information from the vehicle register

data from Finnish Transport and Communications Agency. If the weight or the power of the car are

unavailable from abovementioned sources, they are imputed. The monthly data contains approximately

75 000 individual sales announcements of second-hand cars.

For index calculation purposes, only second-hand cars with ”sold”-status purchased from car dealers are

taken into account. Second-hand cars aged between one and twenty years are taken into index calculation.

Cars with price less than 2000 euros are excluded since they are not considered representative. Vans and

recreational vehicles are deleted from index calculation data. Cars with outliers or clearly incorrect

information in the categorical variables (such as mileage over one million kilometers, weight under 750

kilograms or over 3000 kilograms and power under 20 kilowatts or over 600 kilowatts) are also removed.

Also, cars with mileage under one kilometer are deleted since they are not considered as second-hand cars.

2.2 Basic Concepts

Price is defined as car specific unit value measuring price of a car. In this study, the unit prices are in

logarithmic scale, log-euros. All other variables are measured by their typical units of measurement, e.g. age

of the car in years, selling time of the cars in months, and mileage in kilometers. Non-linearity is taken into

account by calculating square roots of those explanatory variables that are not dummy variables. In short,

our price model is specified as semilogarithmic.

2.3 Notation

The notations in this study are two-fold. First, in observation level we use typical econometric notation

because we use model-based price analysis. Aggregation of variables (i.e., dependent, independent) from

observations into strata (i.e., into index commodities or stratum aggregates) connect notations also into

traditional notations of index number theory. The most important concepts are:

Observation level:

Commodities: 𝑎1, 𝑎2, … , 𝑎𝑛𝑡 are transacted used cars in period t.

Time periods: t = 0, 1, 2, … are the compared months.

Quantity: 𝑞𝑖 𝑡 = 𝑞𝑖𝑡 = 1 for 𝑎𝑖 in period t.

Unit value or unit price: 𝑝𝑖 𝑡 = 𝑣𝑖

𝑡 𝑞𝑖 𝑡⁄ or 𝑝𝑖𝑡 = 𝑣𝑖𝑡 𝑞𝑖𝑡⁄ is the unit price of a used car 𝑎𝑖 in period t

Value: 𝑣𝑖 𝑡 = 𝑣𝑖𝑡 = 𝑞𝑖𝑡𝑝𝑖𝑡 is the value of a used car 𝑎𝑖 in period t.

Total value: 𝑉𝑡 = ∑ 𝑣𝑖 𝑡

𝑖 = ∑ 𝑣𝑖𝑡𝑖 is the total value of all used cars in period t.

Total quantity: 𝑄𝑡 = ∑ 𝑞𝑖 𝑡

𝑖 = ∑ 𝑞𝑖𝑡𝑖 is the total quantity of all used cars in period t.

Explanatory variables in regressions: 𝒙𝑖𝑡 = (𝑥𝑖𝑡1 …𝑥𝑖𝑡𝑘)′ is a k-vector of observed characteristics in period t.

Stratum level (i.e., elementary aggregates, for example conditional averages):

Price relatives: �̅�𝑘 𝑡/0

= �̅�𝑘𝑡 �̅�𝑘0⁄ is the price relative of averaged unit prices for stratum k from period 0 to t.

Quantity relatives: 𝑞𝑘 𝑡/0

= 𝑞𝑘𝑡 𝑞𝑘0⁄ is the quantity relative for stratum k from period 0 to t.

Value relatives: 𝑣𝑘 𝑡/0

= 𝑣𝑘𝑡 𝑣𝑘0⁄ is the value relative for stratum k from period 0 to t.

Value shares: 𝑤𝑘𝑡 = 𝑣𝑘𝑡 ∑ 𝑣𝑘𝑡𝑘⁄ is the value share for stratum k in period t.

Explanatory variables in regressions: �̅�𝑘𝑡 = (�̅�𝑡1 … �̅�𝑡𝑘)′ is a k-vector of averaged characteristics for stratum

k in period t.

3 The Regression Analysis

We underline the importance of the analysis of heterogeneous micro behaviors that includes two main

sources of heterogeneity – intercept or categorical heterogeneity (problem of partition) and slope

heterogeneity from different OLS regressions. Inadequate partition or inadequate classification of price

models, or both, lead to biased estimates of the OLS regressions caused by omitted relevant variables. We

analyze this problem using several hierarchical partitions of observations and several classifications of the

nested OLS regressions.

Partition means for most statisticians the classification of statistical units into most ‘homogenous’ disjoint

stratums. ‘Homogeneous groupings’ are not easy to come by. In this study, we use statistical inference to

solve problem of partition. The same principle is used also in the decision-making of the classification of

price models. Together they make possible to control quality differences of the characteristic’s variables, that

is 𝒙𝑖𝑡 = (𝑥𝑖𝑡1 …𝑥𝑖𝑡𝑘)′, inside stratum k and time periods t ≠ t’.

We proceed similarly as in Suoperä and Vartia (2011) – we make partition of transacted used cars and then

apply regression analysis for some subgroup of stratums included in partition. We combine them into fixed-

effects dummy-variable approach (Hsiao, 1986, s.29-32). We show that regression analysis combined with

the partition is operational especially in construction of hedonic index numbers (Koev, 2003; Suoperä, 2006;

see also Vartia, Suoperä & Vuorio, 2021; Suoperä & Auno, 2021; Suoperä, Luomaranta, Nieminen and

Markkanen. 2021; Kaila, Luomaranta & Suoperä, 2022).

We give simple examples how to make hierarchical ‘competing price models’ that combine

intercept/categorical and slope heterogeneity into the FE models. We also show how to select the best price

model for our hedonic quality adjusting using simple statistical inference for these ‘nested models’. Following

Table shows two sources of heterogeneity for used cars.

Table 3.1: Two heterogeneity effects on price levels and price differences.

Intercept/categorical heterogeneity

Partition 1 Partition 2 Partition 3 Partition 4 Partition 5 Partition 6

partition

Size of a

car

Size of a

car × Make

Size of a car ×

Make × Model

Size of a car × Make ×

Model × Driving

Power

Size of a car × Make ×

Model × Driving Power ×

Type of a car

Slope heterogeneity categories

Naive Typical Good or ‘Best’

No heterogeneity Size of a car Size of a car × Make

Size of a car-indicator is formed with internationally used segment-variable which classify cars into standard,

SUV1- and MPV2- cars according to seven size categories from M to F. We group them into following four

size categories: {M, A, B} (‘Small), {C} (Normal), {D} (Big) and {E, F} (Maximum), which each includes

their SUV- and MPV-models. SUV- and MPV-models are included into categorization by separate indicators,

that are formed using ‘Make’ and ‘Model’ information. ‘Make’-indicator classifies cars into, e.g. ‘Audi’,

‘BMW’, ‘Ford’ and its ‘Model’ into e.g. ‘A4’, ‘Series-5’, ‘Focus’. Indicator ‘Driving Power’ classify cars into

five categories: Diesel, Electric, Hybrid, Gasoline and Others. ‘Type of a car’-indicator into estate and other

1 SUV=sport utility vehicle 2 MPV=multi-purpose vehicle

type cars. All indicators and their cartesian product, i.e. ‘×’ in Table 3.1, form partition of disjoint sets with

union of all observations.

In Table 3.1, we define six competing partitions and three different specification of slope heterogeneity. We

proceed using following three steps: In first step, we combine ‘naïve’ model with all five partitions, estimate

them separately and test the equality between them hierarchically (i.e., Partition 1 vs. Partition 2, Partition 2

vs. Partition 3, …). This step concludes the best partition in statistical sense. In second step, a naïve model is

replaced by four equations based on ‘Size of a car’ categories, which are combined with the best partition

selected in the first step. Price model from steps one and two are ‘nested models’ (certain linear restrictions on

model two leads into model one) and their equality may be tested using standard F-statistics. This test is a

measure of the loss of fit those results from imposing a linear restriction on price models of step two (see

Greene, 1997, p. 343-344, 657). In third step, we estimate about 70 equations based on size and make of a car

that are combined with the best partition selected in step one and two. The price models selected in each step

(i.e., step one, two and three) are nested hierarchical models and their equality may be tested using the same

F-test as before (see example: Suoperä and Vartia, 2011, p.21).

3.1 The Price Model for Heterogeneously Behaving Cross-sections

We start the analysis using the standard linearly additive price model in its most general representation:

(1) 𝑦𝑖𝑗𝑡 = ∑ 𝑖𝑖𝑘𝑡𝛼𝑘𝑡 𝐾𝑗

𝑘=1 + 𝒙′ 𝑖𝑗𝑡𝜷𝑗𝑡 + 𝜀𝑖𝑗𝑡,

where the dependent variable 𝑦𝑖𝑗𝑡 = log(𝑝𝑖𝑗𝑡) is a log-price for statistical unit i belonging into equation j in

time period t. 𝒙𝑖𝑗𝑡 is a E-dimensional vector of explanatory variables for equation j in time period t. 𝜷𝑗𝑡 is

a E-dimensional vector of parameters presenting of mean changes in the log-prices y from a unit changes of

x. The explanatory variables are measured in their original units of measurements meaning that equation (1)

is specified as semilogarithmic. Each equations includes 𝐾𝑗 categorical indicator or dummy variables (i.e.,

size of a car, make, model, driving power, type of a car) 𝑖𝑖𝑘𝑡 that gets value 1 if belongs into certain category

otherwise 0. The categorical variables form the partition of observations for any equation j.

The price model is defined in its most general form because the sources of heterogeneity may be easily

presented. Using simple algebra, the equation (1) may be represented as a sum of representative and

deviation behaviors (heterogeneity effects):

(2) 𝑦𝑖𝑗𝑡 = �̅�𝑡 + 𝒙′ 𝑖𝑗𝑡�̅�𝑡 + ∑ 𝑖𝑖𝑘𝑡(𝛼𝑘𝑡

𝐾 𝑘=1 − �̅�𝑡) + 𝒙′

𝑖𝑗𝑡(𝜷𝑗𝑡 − �̅�𝑡) + 𝜀𝑖𝑗𝑡,

where the representative behavior is �̅�𝑡 + 𝒙′ 𝑖𝑗𝑡�̅�𝑡 and two sources of heterogeneity behaviors, that are

categorial ∑ 𝑖𝑖𝑘𝑡(𝛼𝑘𝑡 𝐾 𝑘=1 − �̅�𝑡), k = 1,…, K (number of categories/stratums) and behavioral heterogeneity

𝒙′ 𝑖𝑗𝑡(𝜷𝑗𝑡 − �̅�𝑡). Interpretation of these two terms is presented in Vartia, (1979, 2008a); Suoperä and Vartia

(2011, pp.6) and may be noted simply as

Categorial: 𝑖𝑖𝑘𝑡(𝛼𝑘𝑡 − �̅�𝑡) = 𝑐𝑖𝑘𝑡, for k = 1,…, K and

Behavioral: 𝒙′ 𝑖𝑗𝑡(𝜷𝑗𝑡 − �̅�𝑡) = 𝒃𝑖𝑗𝑡 for j = 1,…, J.

Before empirical solution of (2) we put all things together using deterministic mathematics and matrix

notations for equation (2), that is

(3a) 𝒚𝑡 = 𝑿𝑡𝜷𝑡 ∗ + 𝑯𝑡𝟏𝑡+𝜺𝑡, where 𝑯𝑡 = [ 𝑪𝑡 𝑩𝑡]

or more compactly as

(3b) 𝒚𝑡 = 𝒁𝑡𝝓𝑡+𝜺𝑡, where 𝒁𝑡 = [𝑿𝑡 𝑯𝑡 ] and 𝝓𝑡 = (𝜷𝑡 ∗′ 𝟏′

𝑡 )′, where 𝜷𝑡 ∗′

= (𝛼𝑡 𝜷𝑡)′

𝒚𝑡 is 𝑁𝑡-vector of log-prices, 𝑿𝑡 is (𝑁𝑡 ∗ (𝐸 + 1))-matrix having unity vector in the first column (constant)

and rest columns are the E explanatory variables. 𝑯𝑡 matrix includes two heterogeneity matrices - 𝑪𝑡 is (𝑁𝑡 ∗ 𝐾))-matrix including categorial heterogeneity covariates and 𝑩𝑡 is (𝑁𝑡 ∗ 𝐸)-matrix including behavioral

slope heterogeneity covariates, that is

[

𝑦1𝑡

⋮ 𝑦𝑁𝑡𝑡

] , 𝑿𝑡 = [ 1 ⋮ 1

𝑥11𝑡 ⋯ 𝑥1𝐸𝑡

⋮ … ⋮ 𝑥𝑁𝑡1𝑡 ⋯ 𝑥𝑁𝑡𝐸𝑡

] , 𝑪𝑡 =

[ 𝒄1𝑡 𝟎 … 0 𝒄2𝑡 𝟎

⋮ ⋱ 𝟎 …

𝟎

… ⋮ ⋱ 𝟎

𝟎 𝒄𝐾𝑡] , 𝑩𝑡 = [

𝒃11𝑡 ⋯ 𝒃1𝐸𝑡

⋮ … ⋮ 𝒃𝐽1𝑡 ⋯ 𝒃𝐽𝐸𝑡

]

It is true that the estimation of equation (2) and (3) is impossible or at least difficult. Next, we show how it

can be done using the OLS method. Looking carefully, the analysis from (1) to (3), one may understand our

idea - the method reproduces separately specified price equations exactly in the observation level, but now in

the mean-deviation re-parameterized form (3). The first part of it consists of the common behavior described

by the mean parameter part of the equation and the second part the heterogeneity effects described by the

covariates.

3.2 The OLS solution for Heterogeneously Behaving Cross-sections

The price models (1) are familiar Fixed Effects models (FE) (Hsiao, 1986, s.29-32) that we specify as

semilogarithmic. The price equations for log-prices are specified as non-linear with respect to age of a car

(years), mileage (ten thousand), power/weight ratio of a car and selling time (months). All explanatory

variables of eq. (1) are listed in Table 3.2.

Table 3.2: The exogenous variables used in the price models for used cars in Finland.

Variable Description

Categorical variables Size of a car × Make × Model × Driving Power × Type of a car or some special cases of

these categorial variables (see Table 3.1). The size of a car is determined using

international segment-variable:

Small cars: Segment = {'A', 'A_SUV', 'B', 'B_MPV', 'B_SUV', 'M'}

Normal cars: Segment = {'C', 'C_SUV', 'C_MPV'}

Big cars: Segment = {'D', 'D_SUV', 'D_MPV'}

Maximum size cars: Segment = {'E', 'E_MPV', 'E_SUV', 'F'}

𝑥1 Gearbox type: If automatic 𝑥1 = 1, else 𝑥1 = 0.

𝑥2 Towing hook: If towing hook 𝑥2 = 1, else 𝑥2 = 0.

𝑥3 Service history: If service history is available 𝑥3 = 1, else 𝑥3 = 0.

𝑥4 Cruise control: If cruise control 𝑥4 = 1, else 𝑥4 = 0.

𝑥5 Selling time of a car, months.

𝑥6 = 𝑠𝑞𝑟𝑡(𝑥5) Square root of the selling time of a car.

𝑥7 Age of a car, years.

𝑥8 = 𝑠𝑞𝑟𝑡(𝑥7) Square root of the age of a car.

𝑥9 Mileage (ten thousand).

𝑥10 = 𝑠𝑞𝑟𝑡(𝑥9) Square root of mileage.

𝑥11 Power/Weight ratio of a car.

𝑥12 = 𝑠𝑞𝑟𝑡(𝑥11) Square root of Power/Weight of a car.

It is assumed, that 𝐸(𝜀𝑖𝑗𝑡|𝒙 ′ 𝑖𝑗𝑡) = 0 and 𝑉𝑎𝑟(𝜀𝑖𝑗𝑡|𝒙

′ 𝑖𝑗𝑡) = 𝜎𝑗𝑡

2< ∞ and the error covariance matrices are

diagonal for all j =1,…, J (number of equations) . Practically this means that the OLS estimation assumes

homoscedastic, uncorrelated model errors with zero mean for all equations - normality of the model errors is

not necessary for parameter estimation. According to the Frisch, Waugh and Lovell -theorem (Davidson &

MacKinnon, 1993), the OLS –estimation of the slopes can always be carried out via categorially centralized

variables. The constant term for category/stratum k is estimated by forcing the regression plane through the

point of averages, that is

�̂�𝑗𝑡 = [∑ ∑ (𝒙𝑖𝑘𝑗𝑡 − �̅�𝑘𝑗𝑡)𝑘 (𝒙𝑖𝑘𝑗𝑡 − �̅�𝑘𝑗𝑡) ′

𝑖 ] −1

∑ ∑ (𝒙𝑖𝑘𝑗𝑡 − �̅�𝑘𝑗𝑡)(𝑦𝑖𝑘𝑗𝑡 − �̅�𝑘𝑗𝑡)𝑘𝑖

�̂�𝑘𝑡 = �̅�𝑘𝑗𝑡 − �̅�′ 𝑘𝑗𝑡�̂�𝑗𝑡, k ∈ j

This method is computationally extremely effective especially when partition includes hundreds/thousands

of categories/strata (see Suoperä & Vartia, 2011). After estimation of (1) for all j we may construct equations

(2) and (3) and estimate them using the OLS method. These estimated models, based on the mean-deviation

re-parameterization, are mathematically exactly equal in all arguments compared with the price equation (1)

together taken – even the residuals are equal observation by observation. This is a known result mentioned

shortly e.g., by Balestra and Nerlove in their introduction in Matyás and Sevestre (1996). They just simply

state that the total sum of squares of one large seemingly unrelated regression model (SUR) reduces to the

sum of squares summed over the equations. This means, that the separately estimated price equations by the

OLS method are in fact equivalent to one large SUR estimation with diagonal covariance matrix. Therefore,

minimizing the sum of squared residual first in the equation level is equivalent to the minimizing all of them

at the same time in the mean-deviation re-parameterized form for all observations as a whole. So, the

estimation of the price equation (3) reproduces exactly the average OLS-estimates and the unity coefficients

(i.e., 1̂𝑡 = 1𝑡) for the covariances. The re-parameterization has a more central goal – the model (3) can be

used to estimate the variance-covariance matrix for the estimates of the model (2) or (3). We end our analysis

and show the variance-covariance matrix for the estimator of the model (3) by the OLS method. We know

that the slope coefficients or linear estimator 𝝓𝑡 is a linear function of disturbances. When we have no

stochastic 𝒁𝑡, that is 𝐸(𝜺𝑡|𝒁𝑡) = 𝟎, regardless of the distribution of 𝜺𝑡, the OLS estimator �̂�𝑡 is a best linear,

unbiased estimator of 𝝓𝑡 and its variance-covariance estimator is

(4) 𝑉𝑎𝑟(�̂�𝑡) = 𝜎𝑡 2(𝒁𝑡′𝒁𝑡)

−1, where

= 𝜎𝑡 2 [

(𝑿𝑡 ′𝑿𝒕)

−1(𝑰𝒕 + 𝑿𝑡 ′𝑯𝒕𝑹𝒕𝑯𝑡

′𝑿𝒕(𝑿𝑡 ′𝑿𝒕)

−1) (𝑿𝑡 ′𝑿𝒕)

−1𝑿𝑡 ′𝑯𝒕𝑹𝒕

−𝑹𝒕𝑯𝑡 ′𝑿𝒕(𝑿𝑡

′𝑿𝒕) −1 (𝑿𝑡

′𝑿𝒕 − 𝑿𝑡 ′𝑯𝒕(𝑯𝑡

′𝑯𝒕) −𝟏𝑯𝑡

′𝑿𝒕) −1]

where 𝑹𝒕 = (𝑯𝑡 ′𝑯𝒕 − 𝑯𝑡

′𝑿𝒕(𝑿𝑡 ′𝑿𝒕)

−𝟏𝑿𝑡 ′𝑯𝒕)

−𝟏 . This is a new result by which we may look at not only

significance of parameters of representative behavior but also significance of any single heterogeneity

variables, categorial and behavioral covariates that otherwise is impossible. We show some important

properties of (4) when significant categorial or/and behavioral heterogeneity components are deleted. The

whole mathematical and statistical story of this chapter is shown in Suoperä and Vartia (2011).

3.3 Statistical inference of Price Models

Now we turn into empirical analysis where we use statistical inference in selection of the best price model

for hedonic quality adjusting. We proceed above mathematical/statistical analysis in spirit of the Table 3.1:

First, we make statistical inference about partition/categorization of observations restricting behavioral slope

heterogeneity 𝜷𝑗𝑡 = �̅�𝑡 for all j. We get four hierarchical tests about five different partitions and select the

best one. Second, we relax the restriction 𝜷𝑗𝑡 = �̅�𝑡 and estimate price models according to three slope

heterogeneity categories using the best partition/categorization selected in the first stage. We get two

hierarchical tests about three different slope heterogeneity modelling and select the best one.

The statistical inference - estimation and hypothesis testing - is based on the OLS estimation and hypothesis

test on the well-known loss of fit test. We already know that the OLS estimator �̂�𝑡 is a best linear, unbiased

estimator of 𝝓𝑡 that is chosen to minimize the sum of squared errors, SSE. Because the coefficient of

determination 𝑅2 equals with 1 – SSE/SST, where the SST = ∑ (𝑦𝑖𝑡 − �̅�𝑡) 2

𝑖 , the OLS estimator is in fact

selected to maximize 𝑅2. This is the reason for our test – loss of fit.

Now we go back to Table 3.1 and give necessary statistics for testing equality of price models, that is,

number of observations (𝑁𝑡), categories (𝑘), equations (J), restrictions (R), decrees of freedom of free

model (𝐷𝑓𝑡) and the sum of squared errors (SSE). Our tests are based of hierarchic nested price models

meaning that the models are nested with each other so that they can be obtained from each other by imposing

suitable linear restrictions on parameters. Our test is

𝐹~ {(𝑆𝑆𝐸0 − 𝑆𝑆𝐸1)/𝑅} (𝑆𝑆𝐸1 𝐷𝑓𝑡⁄ )⁄

where 𝑆𝑆𝐸0 is the sum of squared errors of the restricted model, 𝑆𝑆𝐸1 is the sum of squared errors of the free

model, 𝐷𝑓𝑡 is the degrees of freedom of the free model and R is the number of linear restrictions. When the

degrees of freedom for free model becomes large the F-statistics reduced into 𝜒𝑅 2-test, where R corresponds

number of linear restrictions (see Greene 1997, p. 344 and p. 657). For example, a 1% critical value of 𝜒60 2 =

1.46 and becomes closer to one when R > 60. Table 3.3 shows necessary statistics for nested price models

results for testing the significance of additional partition.

Table 3.3: Testing the hypothesis of the categorial and behavioral homogeneity using hierarchical nested price

models in year 2022.

Intercept/categorical heterogeneity

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

categori-

zation

Size of a

car

Size of a

car ×

Make

Size of a car

× Make ×

Model

Size of a car × Make

× Model × Driving

Power

Size of a car × Make ×

Model × Driving Power

× Type of a car

𝑁𝑡 269663 269663 269663 269663 269663 269663

𝑘 1 4 103 516 1189 1691

J 1 1 1 1 1 1

Parameters 12 12 12 12 12 12

SSE 26886 22855 13545 6476 5928 5812

Model 1

vs 2

Model 2

vs 3

Model 3

vs 4

Model 5

vs 4

Model 6

vs 5

Test statistic 11896 1872 711 36.8 10.7

Slope heterogeneity categories

Model 6 ‘Naïve’ Model 7 ‘Typical’ Model 8 ‘Good or Best’

No heterogeneity Size of a car Size of a car × Make

𝑁𝑡 269663 269663 269663

𝑘 1691 1691 1691

J 1 4 74

Parameters 12 48 888

SSE 5812 5605 4908

Model 7

vs 6

Model 8

vs 7

Test statistic 206.5 45

Table 3.4: Estimation results for model 7 and 8.

Model 8 Model 8 Model 7 Model 7

Year 2020 2021 2020 2021

Number of observations 287936 269663 287936 269663

Number of equations 72 74 4 4

Number of stratums/categories 1594 1691 1594 1691

Degrees of freedom 285478 267084 286294 267924

SSE 5401.6405077 4908.43633 6096.4446791 5604.5913163

R2 0.9645034599 0.9675392005 0.9600517847 0.9630515476

RMSE 0.1375550427 0.1355650208 0.1459258378 0.1446325907

Constant 9.9126394001 9.8211262087 9.6028720349 9.6497628502

(0.0125144633) (0.0118472501) (0.0132567809) (0.0126661687)

If automatic gearbox 𝑥1 = 1, else

𝑥1 =0 0.0902673948 0.0923941505 0.0935819809 0.0986927146

(0.0006280357) (0.0006591217) (0.0006661904) (0.0007021883)

If towing hook 𝑥2 = 1, else 𝑥2 = 0 0.0118209506 0.0113174535 0.0101699236 0.010722559

(0.0005717011) (0.0005829585) (0.0006070502) (0.0006220419)

If service history is available 𝑥3 = 1,

else 𝑥3 = 0 -0.010492392 -0.008856039 -0.009808606 -0.009576151

(0.0006760757) (0.0006586455) (0.0007173066) (0.0007027478)

If cruise control 𝑥4 = 1, else 𝑥4 = 0 0.017682513 0.0190084745 0.0159907088 0.0161078885

(0.0006925544) (0.0006978619) (0.0007368138) (0.0007456235)

Selling time of a car, 𝑥5 -0.000386744 0.0036841099 -0.000090959 0.0045121389

(0.0008966894) (0.0004936162) (0.0009512569) (0.0005266493)

𝑥6 = 𝑥5 1/2

0.0054383443 -0.012634214 0.0047894653 -0.015270555

(0.0030867169) (0.0019822649) (0.0032745562) (0.0021148394)

Age of a car, 𝑥7 -0.138809764 -0.135251635 -0.144926668 -0.140582936

(0.0004627876) (0.0004667166) (0.0004908363) (0.0004980448)

𝑥8 = 𝑥7 1/2

0.2915511757 0.2950576677 0.3143214419 0.312142484

(0.0027085731) (0.0027962898) (0.0028720215) (0.0029842413)

Mileage, 𝑥9 -0.033047764 -0.033221364 -0.029542445 -0.03080527

(0.0001519705) (0.0001555112) (0.0001611581) (0.000165791)

𝑥10 = 𝑥9 1/2

0.0180405738 0.026330353 -0.001833825 0.0129272658

(0.0011911371) (0.0012313394) (0.0012646921) (0.0013158057)

Power/Weight ratio of a car, 𝑥11 12.089654612 9.8976375615 9.3307834547 9.2220969294

(0.1461774499) (0.1356128354) (0.1550967155) (0.1448799132)

𝑥12 = 𝑥11 1/2

-2.549090343 -1.520907481 -0.631702081 -0.671611542

(0.083681855) (0.0786039287) (0.0887347781) (0.0840297929)

HE(𝑐𝑘𝑡), Categorial heterogeneity 1 1 1 1

(0.0009034596) (0.0009179413) (0.0009825843) (0.0010226122)

HE(𝒃𝑗𝑡), Behavioral heterogeneity 1 1 1 1

(0.0009001723) (0.0009163475) (0.0014366229) (0.0015525605)

Parameters for heterogeneity components - HE(𝑐𝑘𝑡) and HE(𝒃𝑗𝑡), - are presented by unity parameter. This

operation is allowed, because all elements of the (k + E)-vector of covariates will estimate into ones and

linear combinations of k- and E-vectors of ones may present by single unity.

Some notes about the Table 3.3 and estimation of equations (1) and (3):

1. A typical FE model is inadequate (model with detailed categories, no slope heterogeneity) and leads

into biased estimates and biased quality adjusting in hedonic index numbers. Statistical inference for

equations (1) to (3) suggest using most detailed categorial heterogeneity (1691 categories) and slope

heterogeneity based on categorization of size of a car and make (74 equations). We call this model as

heterogeneously behaving FE model.

2. All parameters for explanatory variables in estimation of all j equations (1) will not estimate to

statistically significant parameters. We do not exclude these variables because insignificant variables

have no systematic significant effects on log-prices and on hedonic quality adjusting (estimation

efficiency from exclusion of variables is minimal when decrees of freedom in estimation are large).

3. Statistically and mathematically a single equation (3) coincides precisely the set of J equations –

simply saying (3) is precise representation of the set of J equations (1), but now we may derive

variance-covariance estimator for �̂�𝑡, which is a new result.

4. Equation (3) is mathematically equal with (being different representation of (1)) the set of J equations

in (1), where �̂�𝑡 = (�̂�𝑡 , �̂�𝑡 ′ , �̂�𝑡

′)′. This means: First, that parameters for representative behavior �̂�𝑡 , �̂�𝑡 ′

are necessarily weighted averages (relative shares as weights) of �̂�𝑘𝑡, �̂�𝑗𝑡 ′ . Second, that parameters for

the covariates (𝑐𝑖𝑘𝑡, 𝒃𝑖𝑗𝑡) must estimate into (k + E)-vector of ones.

5. Estimation of (4) enables us to evaluate standard errors for any parameter of �̂�𝑡 – we may estimate

separate t-statistic for each categorial variable (separate 1691 test for the partition) and for each

behavioral covariate variable (here 12) to find significance ones. All behavioral covariate variables

may be analyzed in isolation to find ‘winners’ and ‘losers’ compared with average representative

behavior. This is fine property of (3) and (4), but hard to derive otherwise for heterogeneously

behaving cross-sections (heterogeneously behaving slopes).

6. According to the variance-covariance estimator (4) – one may, by exclusion of behavioral

heterogeneity, lead to more efficient estimation of parameters, but omitting relevant variables

(covariates) leads to estimates being efficient but biased.

Interpretation of estimation results in Table 3.4 are familiar to most statisticians but we repeat them here.

Estimate of four first indicator-type x-variables, accessories, directly itself tells their effect on log-prices. In

equation (1) (or (3)) log-prices are specified for rest of the x-variables as non-linear with respect to selling

time (𝑥5), age (𝑥7), mileage (𝑥9) and power/weight ratio (𝑥11) and additional interpretations are needed. We

do this applying partial derivates for the equation (3) with respect to 𝑥𝑒-variables where e = 5, 7, 9, 11; that

is for example for 𝑥5 (other x-variables similarly)

𝜕𝑦𝑖𝑡 𝜕𝑥𝑖5𝑡⁄ = 𝜕𝒁𝑡𝝓𝑡 𝜕𝑥𝑖5𝑡⁄ = �̂�𝑗5𝑡 + 0.5 ∗ �̂�𝑗6𝑡/𝑥𝑖6𝑡 1/2

, for all i ∈ j

These partial derivates are evaluated for all observations i and variable 𝑥𝑒. We sort these partial derivates

according to 𝑥𝑒-variables and classify them equidistantly into ordered cohorts. Then we average derivatives

cohort by cohort and calculate cumulative sums of them. The results are presented in Figures 3.1 to 3.4 for

the 𝑥𝑒-variables where e = 5, 7, 9, 11. The approach takes account slope heterogeneity of ‘size of a car ×

Make’-categorization and partial derivates are evaluated at realized points of 𝑥𝑒-variables so that we have

together more than million partial derivates. The method is transparently interpreted and is based on standard

economics.

Figure 3.1: The price effect of selling time (months) Figure 3.2: The price effect of age (years) on the

on the average log-prices in year 2020 and 2021. average log-prices in year 2020 and 2021.

Figure 3.3: The price effect of mileage (ten thousand) Figure 3.4: The price effect of power/weight ratio

on the average log-prices in year 2020 and 2021. (kW/kg) on the average log-prices in year 2020 and

2021.

Figures tell us: Selling time (𝑥5), age (𝑥7) and mileage (𝑥9) behave almost similarly for the years 2020 and

2021 but power/weight ratio (𝑥11) not. This is caused by new markets for “plug hybrids” and fully electric

cars that are still developing and find more stable practices – it seems that the price effects from high

power/weight cars will be declined in time.

We have analyzed the first part of hedonic method – the data generating process of log-prices in

heterogeneously behaving gross-sections. Next step in this study continues into the hedonic quality adjusting.

4 Combining Regression Analysis and Index Numbers

Classical index calculation is based on bilateral price-links between commodities being comparable in

quality – prices and quantities are measured for the same set of commodities and outlets. This means that the

price modelling in chapter 3 is unnecessary for bilateral price-links because measured quality characteristics

𝒙𝑖0 = 𝒙𝑖𝑡 for all 0, t and quality adjusting is not needed. In our case of used cars 𝒙𝑖0 ≠ 𝒙𝑖𝑡 and quality

adjusting is necessary. Some notes about our price modelling in Chapter 3 combined with quality adjusting

must be done. First, our price modelling is based on optimal solution, the best linear unbiased estimator

(BLUE) under homoscedastic errors for heterogeneously behaving cross-sections. Second, this optimal

solution does not only include slope heterogeneity but also optimal solution for partition of observations. The

optimal OLS solution does not restrict into the correct size BLU estimates, but other optimal solutions may

produce aggregating observations into category/stratum level. These optimal algebraic properties of the OLS

are

1. The residuals sum up to zero for all category/stratum.

2. The conditional average equals with unconditional average for all category/stratum.

3. The regression hyperplane passes through the means of dependent and independent variables.

These three properties lead us into the optimal unbiased estimates of unconditional and conditional averages

meaning that they both are estimated into the correct size without systematic errors. In our empirical analysis

we use two averages – unweighted geometric and arithmetic averages. The aggregation rule for unweighted

geometric average is trivial and is presented in most statistical and econometric textbooks. The conditional

arithmetic average is more complicated and is presented first in Suoperä (2006, Annex 5, pp.31) and later in

Vartia, Suoperä and Vuorio (2019), Suoperä and Vuorio (2019): Suoperä and Auno (2021) and Kaila,

Luomaranta and Suoperä (2023). Both averages are unbiased and based on transparent algebra being

consistent in aggregation, even aggregation for arithmetic averages are not independent of units of

measurement. Our hedonic quality adjusting is based on these conditional and unconditional averages

together with a well-known decomposition developed by Oaxaca (1973). Because our price modelling is

applied for previous year data, our construction of hedonic index numbers, based on the Oaxaca

decomposition, is based on the base strategy which is free of chain drift.

We rely on: First the BLU estimates decided by statistical inference, second, unbiased conditional and

unconditional averages, third, mathematically consistent and transparent Oaxaca decomposition even it is not

unambiguous, four, consistent aggregation rules, fifth, drift free construction strategy of indices that are

based on hedonic quality adjusting. A well-known time-dummy hedonic regression (see Summers (1973);

Rao (2004)) or its weighted version in the sense of Diewert and Fox (2018) have little to do with above

mentioned properties – first their link with the traditional index number theory is missing and second the

weighted version of Diewert and Fox (2018) leads to parameter estimates whose statistical properties are

unknown. We show transparently how these shortcomings may be corrected using well-known basic

statistics, consistent aggregation clauses, some algebra, hedonic quality adjusting and several index number

formulas and of course unbiased estimators. In our view these are preferable for statistical offices, since the

methods are transparent, minimizes modeling assumptions, and are consistent with index number tradition.

Our analysis herein follows the tradition of Koev (2003); Suoperä (2004, 2006); Vartia, Suoperä & Vuorio

(2021): Suoperä & Auno (2021); Kaila, Luomaranta and Suoperä (2023).

Our focus in the study is three-fold: In the first step, we aggregate estimated equations from observations

into category level, stratums. In the second step we make for category/stratum aggregates and their

econometric relations a well-known decomposition introduced by Oaxaca (1973). The last step is similar as

traditional index numbers – the averaged category/stratum-level price decompositions are summed up using

weights of index number formulas, that is ‘weights-by-economic-importance’-variable. We analyze two sets

of index number formulae. The first set is based on formulas using old or new weights (asymmetrical

weights) and are called as a basic set of index numbers (old weights: Laspeyres (L), Log-Laspeyres (l) and

new weights: Log-Paasche (p) and Paasche (P)). The second set of index numbers include four formulae

using symmetrical weights: Montgomery-Vartia (MV), Törnqvist (T), Fisher (F) and Sato-Vartia (SV). We

call these index number formulae as excellent. For the fundamental analysis of these index number formulae

see Vartia & Suoperä, 2018. The analysis therein is in logarithmic form.

4.1 Algebra of Price-Ratio Decompositions

We simplify our analysis into two-time case, the base period (t = 0, a previous year) and the observation

month of a current year (t) analyzing only one stratum 𝐴𝑘 belonging into equation j. We use vector notations

for our conditional and unconditional average prices and calculate the difference between two price models

(0, t) in spirit of Oaxaca. The algebra for unweighted arithmetic average is based on logarithmic mean, L,

developed by Leo Törnqvist (1935, p. 35) (see also Y. Vartia, 1976; L. Törnqvist, P. Vartia and Y. Vartia,

1985, p. 44). We use logarithmic mean for aggregation of observations for unweighted arithmetic average

(see Suoperä, 2006, pp.31). The algebra is presented here only for unweighted geometric average and its

difference but is analogously presented also for unweighted arithmetic average in log-form (see Suoperä

(2006, pp.31). We show first necessary weights in aggregation of unconditional and conditional averages and

then their Oaxaca decompositions for estimated price models, that is (𝑛𝑘 is number of observations in

stratum k)

Table 4.1: Important statistics for hedonic quality adjusting for category/stratum k.

Statistics Unweighted geometric average Unweighted arithmetic average

Weights 𝑤𝑖𝑘𝑡 =

𝑛𝑘 , ∀𝑖 ∈ 𝐴𝑘 𝑤𝑖𝑘𝑡 =

𝐿(𝑝𝑖𝑘𝑡 ,1 )

𝐿(∑ 𝑝𝑖𝑘𝑡𝑖 , 𝑛𝑘) , ∀𝑖 ∈ 𝐴𝑘 ,

𝐿 means logarithmic mean

Unconditional �̅�𝑘𝑡 = ∏ 𝑝𝑖𝑘𝑡 𝑤𝑖𝑘𝑡 =𝑒𝑥𝑝{∑ 𝑤𝑖𝑘𝑡𝑙𝑜𝑔(𝑝𝑖𝑘𝑡)𝑖 } �̅�𝑘𝑡=𝑒𝑥𝑝{∑ 𝑤𝑖𝑘𝑡𝑙𝑜𝑔(𝑝𝑖𝑘𝑡)𝑖 } ≡

𝑛𝑘 ∑ 𝑝𝑖𝑘𝑡𝑖

Conditional 𝑙𝑜𝑔(�̅�𝑘𝑡) = �̂�𝑘𝑡 + 𝒙′ 𝑘𝑡�̂�𝑗𝑡 𝑙𝑜𝑔(�̅�𝑘𝑡) = �̂�𝑘𝑡

∗ + 𝒙𝑘𝑡 ′ �̂�𝑗𝑡 ,

where 𝒙𝑘𝑡 ′ = ∑ 𝑤𝑖𝑘𝑡𝒙

′ 𝑖𝑘𝑡𝑖

Oaxaca decomposition:

(5a) 𝑙𝑜𝑔(�̅�𝑘𝑡) − 𝑙𝑜𝑔(�̅�𝑘𝑜) = �̂�𝑘𝑡 + �̅�′ 𝑘𝑡�̂�𝑗𝑡 − �̂�𝑘0 + �̅�′

𝑘0�̂�𝑗0 ↔

(5b) 𝑙𝑜𝑔(�̅�𝑘𝑡 �̅�𝑘0⁄ ) = {(�̂�𝑘0 + �̅�′ 𝑘𝑡�̂�𝑗0) − (�̂�𝑘0 + �̅�′

𝑘0�̂�𝑗0)} + {( �̂�𝑘𝑡 + �̅�′ 𝑘𝑡�̂�𝑗𝑡) − (�̂�𝑘0 + �̅�′

𝑘𝑡�̂�𝑗0)} ↔

(5c) Price-ratio = {Quality Corrections } + {Quality Adjusted Price Change conditional on �̅�′ 𝑘𝑡}.

Table 4.1 and equations (5a) to (5c) reveals what we have spoken about - our transparent simple algebra

using optimal unbiased statistics. First, both averages satisfy three basic algebraic properties of the OLS

method without systematic errors. Second, the slope estimates are BLUE under homoscedastic errors. Third,

both averages are unbiased and consistent in aggregation. Fourth, the Oaxaca decomposition in (5b) is

consistent and surprisingly the most optimal for our empirical application. Fifth, true price-ratio of averaged

prices is decomposed into two parts: quality corrections and quality adjusted price change with comparable

in quality, that is �̅�′ 𝑘𝑡. Sixth, the Oaxaca decomposition in (5b) tell that the OLS estimation is necessary to

apply only for time period 0 because unconditional and conditional averages equal for any category/stratum k

because of algebraic property of OLS.

Using unconditional and conditional averages in suitable manner, the equations (5) may represent by simple

logarithmic price ratios as

(6) 𝑙𝑜𝑔(�̅�𝑘𝑡 �̅�𝑘0⁄ ) = 𝑙𝑜𝑔(�̃�𝑘𝑡 �̅�𝑘0⁄ ) + 𝑙𝑜𝑔(�̅�𝑘𝑡 �̃�𝑘𝑡⁄ ), ∀ 𝑘, 0, 𝑡

It is very simple and holds as an identity. On the left, we have the price-ratio of actual average prices. On the

right, the first term is quality correction (QC) estimated using the base period valuation of characteristics

(i.e., 𝑙𝑜𝑔(�̃�𝑘𝑡) = �̂�𝑘0 + �̅�′ 𝑘𝑡�̂�𝑗0 and 𝑙𝑜𝑔(�̅�𝑘0) = �̂�𝑘0 + �̅�′

𝑘0�̂�𝑗0) and the second term is quality adjusted

(QA) price change (i.e., 𝑙𝑜𝑔(�̅�𝑘𝑡) = �̂�𝑘𝑡 + �̅�′ 𝑘𝑡�̂�𝑗𝑡 and (𝑙𝑜𝑔(�̃�𝑘𝑡) = �̂�𝑘0 + �̅�′

𝑘𝑡�̂�𝑗0) estimated using the

base period valuation of characteristics (�̂�𝑗0) with characteristics being comparable in quality (i.e., �̅�′ 𝑘𝑡, for

all k and t). We construct the equation (6) for unweighted arithmetic and geometric averages.

4.2 Index Number Formulas

In price modelling all used cars are grouped together to form K categories, 𝐴𝑘 , 𝑘 = 1,… , 𝐾, which define our

partition of observations, that is 𝐴 = 𝐴1 ∪ 𝐴2 ∪ …𝐴𝐾, where different 𝐴𝑘 categories are disjoint. Previous

chapter ends our analysis into equation (6), where logarithmic price ratio of true actual averages (A) is

decomposed into log-price ratios for quality corrections (QC) and quality adjusted (QA) price change. This is

done for all categories, for which we define an index number formulas. We use a simple notation here for an

index number

𝑃𝑓 𝑡 0⁄

= 𝑃𝑓(�̅�0, 𝒒0, �̅�𝑡, 𝒒𝑡),

where �̅�0 and �̅�𝑡 are K-vector of average prices (geometric or arithmetic) and 𝒒0 and 𝒒𝑡 K-vector of

corresponding quantities of sold cars. We define above price index for equation (6), that is

(7a) 𝑒𝑥𝑝{∑ 𝑤𝑘,𝑓𝑘 𝑙𝑜𝑔(�̅�𝑘𝑡 �̅�𝑘0⁄ )} = 𝑒𝑥𝑝{∑ 𝑤𝑘,𝑓𝑘 𝑙𝑜𝑔(�̃�𝑘𝑡 �̅�𝑘0⁄ ) + ∑ 𝑤𝑘,𝑓𝑘 𝑙𝑜𝑔(�̅�𝑘𝑡 �̃�𝑘𝑡⁄ )} ↔

(7b) 𝑃𝑓,𝐴 𝑡 0⁄

= 𝑃𝑓,𝑄𝐶 𝑡 0⁄

∙ 𝑃𝑓,𝑄𝐴 𝑡 0⁄

The left side is the price index for average prices (A) for formula f for price-link from base period 0 to the

period t. The first term in the right side is the price index for quality corrections (QC) and the last term price

index for quality adjusted price changes (QA). Weights in equation (7a) for formulas are presented in Table

4.2.

Table 4.2: Weights for index number formulae (logarithmic forms).

Basic formulae, see Vartia & Suoperä, 2017, 2018, 𝐿 means logarithmic mean, see Vartia, 1976a, p. 128

Symbol and name of formula Weights of the formula

Laspeyres, f = L 𝑤𝑘,𝑓 = 𝑤𝑘,𝐿

0 = 𝐿(�̅�

𝑘𝑡 𝑞

𝑘0 , �̅�

𝑘0 𝑞

𝑘0 )

𝐿(∑ �̅� 𝑘𝑡

𝑞 𝑘0𝑘 , ∑ �̅�

𝑘0 𝑞

𝑘0𝑘 )

log-Laspeyres, f = LL 𝑤𝑘,𝑓 = 𝑤𝑘,𝑙 0 = 𝑣𝑘

0 𝑉0⁄

log-Paasche, f = LP 𝑤𝑘,𝑓 = 𝑤𝑘,𝑝 𝑡 = 𝑣𝑘

𝑡 𝑉𝑡⁄

Paasche, f = P 𝑤𝑘,𝑓 = 𝑤𝑘,𝑃

𝑡 = 𝐿(�̅�

𝑘𝑡 𝑞

𝑘𝑡 , �̅�

𝑘0 𝑞

𝑘𝑡 )

𝐿(∑ �̅� 𝑘𝑡

𝑞 𝑘𝑡𝑘 , ∑ �̅�

𝑘0 𝑞

𝑘𝑡𝑘 )

Excellent formula, see Vartia & Suoperä, 2017, 2018), 𝐿 means logarithmic mean, see Vartia, 1976

Törnqvist, f = T 𝑤𝑘,𝑓 = �̅�𝑘,𝑇 = 0.5 · (𝑤𝑘,𝑙 0 + 𝑤𝑘,𝑝

𝑡 )

Sato-Vartia, f = SV 𝑤𝑘,𝑓 = �̅�𝑘,𝑆𝑉 =

𝐿(𝑤𝑘 𝑡 , 𝑤𝑘

∑𝐿(𝑤𝑘 𝑡 , 𝑤𝑘

Montgomery-Vartia, f = MV 𝑤𝑘,𝑓 = �̅�𝑘,𝑀𝑉 =

𝐿(�̅� 𝑘𝑡

𝑞 𝑘𝑡 , �̅�

𝑘0 𝑞

𝑘0 )

𝐿(∑ �̅� 𝑘𝑡

𝑞 𝑘𝑡𝑘 , ∑ �̅�

𝑘0 𝑞

𝑘0𝑘 )

Fisher, f = F 𝑤𝑘,𝑓 = �̅�𝑘,𝐹 = 0.5 · (𝑤𝑘,𝐿 0 + 𝑤𝑘,𝑃

𝑡 )

Some notes are necessary:

1. We define price-link form 0 → t meaning that we use the base strategy that is free of the chain drift.

The base period is a previous year normalized as an average month and t a month of a current year.

2. Our aggregation means here always ‘a weighted-by-economic-importance’-variable familiar to index

numbers, i.e., weighting by 𝑤𝑘,𝑓.

3. Price index is based on transparent and familiar traditional theory of index numbers.

4. Quality corrections can be decomposed for E dimensional x variable-by-variable such that 𝑃𝑓,𝑄𝐶 𝑡 0⁄

𝑃𝑓,𝑄𝐶,𝑥1

𝑡 0⁄ ∙ 𝑃𝑓,𝑄𝐶,𝑥2

𝑡 0⁄ ∙ … ∙ 𝑃𝑓,𝑄𝐶,𝑥𝐸

𝑡 0⁄ holds as an identity.

5. We may construct index series not only for average prices (true averages and quality adjusted) but

also for any single quality corrections or any combinations of them consistently.

6. We use ‘a flexible basket’-approach that states ‘when the expenditure on a category tends to zero,

then its effect on the index should vanish’ (Pursiainen, 2006, pp32). We make comparison’s only for

categories having expenditures for both 0 and t.

In Table 4.2 we gather all information that is necessary for calculation of hedonic price indices for equations

(7). We analyze all index number formulae in logarithmic form, including Laspeyres, Paasche and Fisher

(see Vartia, 1976, p.128). The aggregation of price changes or their decompositions in (6) and (7) are much

simpler in additive form using ‘log’s’ – as in (7), they may simply transform back to indices. In empirical

part we use two set of formulas – basic and excellent.

5 Empirical Results for Category Averages and Hedonic Index Numbers

The empirical results for price models are presented in chapter three. Now we proceed into empirical analyze

of elementary aggregates, unweighted geometric and arithmetic averages, and their index number solutions

based on Oaxaca decompositions. First, we show which average (arithmetic or geometric) should be selected

as average statistics of relative change and second, does the formula matter.

5.1 Arithmetic or Geometric Average as Mean Statistic

Table 5.1 shows how much arithmetic and geometric averages deviate in aggregate level.

Table 5.1: Arithmetic and geometric average prices (Euro) in year 2020, 2021 and 2022.

Year Arithmetic average Geometric average

2020 15416 11990

2021 17214 13622

2022 18742 14280

Average prices are estimated from category averages using their frequencies as weights (i.e., relative shares).

Averages deviate substantially being about 30 log-%. For more expensive makes and models the difference

become even bigger indicating that geometric average is poor as official statistic as averages.

5.2 Arithmetic or Geometric Average as Statistic of Relative Change

We get back to equation (6) and show how closely relative changes of arithmetic and geometric averages are

related. First, we regress relative change of arithmetic averages on relative changes of geometric averages

(left side of eq. (6)). Second, we do the same for relative changes of quality adjusted average prices (second

term right hand in eq. (6)). The model is the simplest regression

𝑦𝑘𝑡 = 𝜌 ∙ 𝑥𝑘𝑡 + 𝜀𝑘𝑡,

where 𝑦𝑘𝑡stands for relative changes of arithmetic averages and 𝑥 for relative changes of geometric averages

for price-links 0 → t and categories k = 1,… , K. Similar equation are applied also for corresponding relative

changes of quality adjusted price changes. The estimator for 𝜌 is also nicely interpreted as

�̂� = 𝑟(𝑦, 𝑥) ∙ 𝑠𝑦

𝑠𝑥

When the standard deviations of x and y are closely related, the estimator �̂� practically equals to correlation

coefficient between x and y. In both OLS estimation we have 17935 observations (total number of categories

in years 2020, 2021 and 2022) from price ratios and Table 5.2 presents the results.

Table 5.2: Linear relation between price ratios of arithmetic and geometric averages.

𝑠𝑦 𝑠𝑥 �̂� 𝑟𝑥𝑦 𝑅2 Actual price ratio left side of (6) 0,103 0,106 0,966 0,995 0,991

Quality adjusted price ratio, second right term of (6) 0,182 0,179 0,9998 0,986 0,973

Empirical results show that price ratios using unweighted arithmetic or geometric average prices are very

closely related. Both 95 % fit plots for y include complete linear dependence meaning that statistically the

choice between arithmetic or geometric average have no matter. The correlation coefficient tells the same

story – they are close to one. Quite amazingly, although the arithmetic and geometric average prices deviate

largely (see Table 5.1), their price ratios go ‘hand-to-hand’ – at least statistically. Next, we analyze

differences between these averages using index numbers.

5.3 Does Formula and Average matter in Index Compilation?

All index numbers and index series are based on base strategy, where the base period is a previous year

normalized as an average month and the observation period is a month of a current year. The strategy is free

of chain drift. Our empirical analyze turns into two questions - ‘Does the formula matter in index

compilation?’ and ‘Does the average matter in index compilation?’. We compare two sets of formulas, the

basic and excellent (Vartia and Suoperä, 2017, see Table 4.2 and eq. (7)). All formulas are examined in log-

form. In this study our basic formulas are Laspeyres (L), log-Laspeyres (LL), Paasche (P) and log-Paasche

(LP). L and LL formulas use asymmetric old weights and formulas P and LP new ones. The second set of

formulas – excellent ones – uses symmetrical weights and are Fisher (F), Törnqvist (T), Montgomery-Vartia

(MV) and Sato-Vartia (SV) (see Vartia & Suoperä, 2017, 2018). The following graphs show why they are

excellent.

The Figures 5.1-5.4 present all that is needed to make decisions about the formula and the average used in

index compilation. Index series in Figures 5.2 and 5.4 are made using arithmetic and geometric average price

ratios. Index series based on arithmetic and geometric averages deviate seriously but excellent formulas go

‘hand-in-hand’ for both index series (both index series includes four excellent formulas). Our empirical

results in previous chapter show that price changes based on arithmetic and geometric average prices are

statistically almost ‘equal’ (95 % fit plots for y includes complete linear dependence) and correlation

between them was 𝑟𝑥𝑦 = 0.995. Simple econometric modelling concludes: ‘statistically the choice between

arithmetic or geometric average have no matter’.

Figure 5.1: Index series for actual average prices Figure 5.2: Index series for actual average prices

for ‘Small Cars’ make ‘Honda’. Basic formulas: for ‘Small Cars’ make ‘Honda’. Excellent

indices based on geometric are dotted and formulas: indices based on geometric are dotted

arithmetic solid lines. and arithmetic solid lines.

Figure 5.3: Index series for actual average Figure 5.4: Index series for actual average

prices for ‘Small Cars’ make ‘MB’. Basic prices for ‘Small Cars’ make ‘MB’. Excellent

formulas: indices based on geometric are dotted formulas: indices based on geometric are dotted

and arithmetic solid lines. and arithmetic solid lines.

Figures 5.2 and 5.4 tell that because of contingent nature of data, the index series based on arithmetic and

geometric averages may occasionally seriously deviate. Statistically they are almost equal but not

mathematically. Our selection for price concept of average statistic and index compilation is more

interpretable using arithmetic average (see also Table 5.1). In Figure 5.1 and 5.3 we see that basic formulas

are contingently biased (see Vartia and Suoperä, 2017, 2018) deviating seriously from each other. Basic

formulas for complete data should never be used.

5.4 Hedonic Index Numbers for Used Cars in Finland

Next, we aggregate decomposition in equation (7) from K-category into total using only excellent formulas.

In our empirical analysis excellent formulas are very closely related. This happens because all excellent

formulas are quadratic approximations of Fisher for small changes in log-prices and log-quantities (Vartia

and Suoperä, 2017, 2018, pp. 17-21). This seems to happen here also quite closely for moderate changes of

log-prices and log-quantities. The same happens extremely closely for quality adjusted indices (solid lines in

Figure 5.5).

Figure 5.5: Hedonic index series for actual average Figure 5.6: Hedonic index series for quality correc-

prices (arithmetic) and quality adjusted prices for tions for quality characteristics x (T_Qc = all) by excellent formulas F, T, MV and SV (solid lines). Törnqvist formula.

Figures 5.5 and 5.6 must be looked at together: For any excellent formula (F, T, MV and SV) difference

between index series for actual average prices and quality adjusted prices equals with total quality correction.

The most part the difference is explained by quality corrections of age of a car (𝑥7) and mileage (𝑥9) – sold

cars are simply older and more driven at observation period. Other quality corrections

(𝑥1, 𝑥2, 𝑥3, 𝑥4, 𝑥5 and 𝑥11) have minor role (index series close to one in Figure 5.6). The Figures 5.5 and 5.6

together are graphical presentation of equation (7b) for Törnqvist ideal formula, that is 𝑃𝑇,𝐴 𝑡 0⁄

= 𝑃𝑇,𝑄𝐶 𝑡 0⁄

∙ 𝑃𝑇,𝑄𝐴 𝑡 0⁄

6 Conclusion

We show, using statistical inference, how two sources of heterogeneity – categorial and behavioral – may be

chosen hierarchically for the best price models for hedonic quality adjusting. By this statistical inference we

empirically decide first the ‘best’ partition of observations and second the ‘best’ categorization of behavioral

‘beta’ heterogeneity. The decision-making leads us into the optimal best linear unbiased estimates, BLUE,

for fixed categorical and beta effects.

We combine the BLU estimates with consistent aggregation rules and get unbiased parametric presentations

for categorical averages. These K-categorical averages - arithmetic and geometric – both satisfy the well-

known algebraic properties the OLS method being also unbiased and optimal for making of hedonic index

numbers. The price modelling ends to aggregation of relations from observations into K-category level with

these averages.

Oaxaca decomposition divides changes of actual average (arithmetic or geometric) price ratios into two

parts: first, quality correction of quality characteristics and second, quality adjusted price changes. In the

Oaxaca decomposition the base period is the previous year normalized as an average month. This enables us

to use base strategy which is free of chain drift.

For the base strategy we select ‘flexible basket approach’ to verify the principle of Pursiainen that states

‘when the expenditure on a category tends to zero, then its effect on the index should vanish (Pursiainen,

2006, pp32). In we combine heterogeneously behaving cross-sections with classical index number theory.

This representation of ‘index numbers’ makes it possible to control quality changes of quality characteristics

and remove quality differences from unbiased actual average price ratios.

The making of hedonic index numbers, we use two set of formulas, the basic and excellent ones. We show

that basic formulas using asymmetric weighting, are contingently biased and should not be used. Excellent

formulas in the study uses symmetrical weighting giving excellent results. Using symmetric weights of these

excellent formulas satisfies the principle of ‘a weighted-by-economic-importance’-variable optimally being

mathematically transparent. According to the study, any excellent formula with arithmetic average can be

selected for official statistics.

References:

Bailey M. J., Muth, R. F. and Nourse, H. O. ‘A Regression Model for Real Estate Price Index

Construction’., JASA, vol. 58, 933-942, 1963.

Case, K. E. and Shiller, R. J. ‘Efficiency of the Market for Single Family Homes’, American Economic

Review, vol. 79, 125-137, 1989.

Davidson & MacKinnon ‘Estimation and Inference in Econometrics’, New York, Oxford University Press,

1993.

Diewert E. and Fox K. ‘Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data’

2018

Greene, W. “Econometric Analysis”, Prentice-Hall International, Inc. (third ed.), 1997.

Griliches Z. ‘Hedonic Price Indices for Automobiles: An Econometric Analysis

of Quality Change’, Zvi Griliches (ed.) Price Indexes ad Quality Changes, 55-97, 1971.

Hsiao, C. ‘Analysis of Panel Data’., Cambridge University Press, 1986.

Kaila, J., Luomaranta, H. and Suoperä, A. Hedonic Price Index Number for Blocks of Flats and Terraced

Houses in Finland’, 2023 (http://www.stat.fi/meta/menetelmakehitystyo/index_en.html).

Koev, E. ‘Combining Classification and Hedonic Quality Adjustment in Constructing a House Price Index’,

Licentiate thesis, Helsinki, 2003.

Koev, E. & Suoperä A. ’Pientalokiinteistöjen (omakotitalojen ja rakentamattomien pientalotonttien)

hintaindeksit 1985=100’, Helsinki, 2002. (in Finnish, Statistics Finland).

Matyás, L. and Sevestre, P., Eds. “The Econometrics of Panel Data: Handbook of Theory and Applications,

2nd ed. Dordrecht: Kluwer-Nijoff, 1996.

Oaxaca, R. ‘Male-Female Wage Differentials in Urban Labour Markets’, International Economic Review,

14, pp. 693-709, 1973.

Practical Guide on Multilateral Methods in the HICP (2020, WTPD-model), EuroStat.

Pursiainen, H. ‘Consistent Aggregation Methods and Index Number Theory’, 2005.

Quigley, R. ‘A Simple Hybrid Model for Estimating Real Estate Price Indexes’, Journal of Housing

Economics vol. 4, p. 1-12, 1995.

Rao, D.S. P. ‘On the Equivalence of the Weighted Country Product Dummy (CPD) Method and the Rao

System for Multilateral Price Comparisons’, Review of Income and Wealth 51:4, 2005, 571-580.

Summers R. ‘International Comparisons with Incomplete Data", Review of Income and Wealth 29:1, 1973,

pp. 1-16.

Suoperä, A. ’Some new perspectives on price aggregation and hedonic index methods: Empirical

application to rents of office and shop premises’, 2004, 2006 (unpublished, Statistics Finland).

http://www.stat.fi/meta/menetelmakehitystyo/index_en.html

Suoperä A. & Auno V. ‘Hedonic Index Numbers for Rents of Office and Shop Premises in Finland’, 2021

(https://www.researchgate.net/publication/350460207_Hedonic_Index_Numbers_for_Rents_of_Office_and_

Shop_Premises_in_Finland).

Suoperä, A., Nieminen, K., Montonen, S. and Markkanen H. “Comparing Basic Averages, Index

Numbers and Hedonic Methods as Price Change Statistic”, 2021

(http://www.stat.fi/meta/menetelmakehitystyo/index_en.html).

Suoperä, A. & Vartia, Y. ‘Analysis and Synthesis of Wage Determination in Heterogeneous Cross-

sections’, Discussion Paper No. 331, 2011.

Vartia, Y., Suoperä, A. and Vuorio, J. ’Hedonic Price Index Number for New Blocks of Flats and

Terraced Houses in Finland’, 2021 (http://www.stat.fi/meta/menetelmakehitystyo/index_en.html).

Vartia, Y. ‘Relative Changes and Index Numbers’, Ser. A4, Helsinki, Research Institute of

Finnish Economy, 1976.

Vartia, Y. ‘Ideal Log-Change Index Numbers’, Scandinavian Journal of Statistics., 3, pp. 121-

126,1976.

Vartia, Y. ’Kvadraattisten mikroyhtälöiden aggregoinnista’, ETLA, Discussion Papers no. 25,1979.

Vartia, Y. & Suoperä, A. “Index number theory and construction of CPI for complete micro data”, 2017

(http://www.stat.fi/meta/menetelmakehitystyo/index_en.html).

Vartia, Y. & Suoperä, A. “Contingently biased, permanently biased and excellent index numbers for

complete micro data”, 2018 (http://www.stat.fi/meta/menetelmakehitystyo/index_en.html).

Vartia, Y. and Vartia, P. ‘Descriptive Index Number Theory and the Bank of Finland Currency

Index’, Scandinavian Journal of Economics, vol. 3, pp. 352 . 364, 1985.

Törnqvist, L. ‘A Memorandum Concerning the Calculation of Bank of Finland Consumption

Price Index’, unpublished memo, Bank of Finland, 1935.

Törnqvist, L. ’Levnadskostnadsindexerna i Finland och Sverige, Deras Tillförlitlighet och

Jämförbarhet’, Ekonomiska Samfundets Tidskrift, vol. 37, 1–35, 1936.

Törnqvist, L. & Vartia, P. & Vartia, Y. ‘How Should Relative Changes be Measured’? The

American Statistician, Vol. 39, No. 1. pp. 43 - 46, 1985.

https://www.researchgate.net/publication/350460207_Hedonic_Index_Numbers_for_Rents_of_Office_and_Shop_Premises_in_Finland

http://www.stat.fi/meta/menetelmakehitystyo/index_en.html

The Making of Hedonic Index Numbers, Finland

Read more about The Making of Hedonic Index Numbers, Finland

Languages and translations

English

The Making of Hedonic Index Numbers Ville Auno, Henri Luomaranta-Helmivuo, Hannele Markkanen, Satu Montonen, Kristiina Nieminen, Antti Suoperä

Presenter: Satu Montonen Meeting of the Group of Experts on Consumer Price Indices 07 - 09 June 2023, Geneva

Content 1. Background 2. Data and data pre-processing 3. Steps of the process for producing the hedonic price index 4. Results 5. Conclusions

17 May, 2023 Statistics Finland | [email protected]

1. Background • Previously, the price index for second-hand cars was calculated by Autovista Group for the purpose of CPI

• From the beginning of 2023, Statistics Finland has done the calculation itself

• The same second-hand car is not sold every month, so it is impossible to follow the price of the same car over time

• In this study, we combine hedonic quality adjusting and traditional index calculation

• In Finland, the same method is used for the prices of houses as well as for the rents of offices and shops

17 May, 2023 Statistics Finland | [email protected]

2. Data and data pre-processing • Data is received on a daily basis from one major selling portal for second-hand cars in Finland

• Only the latest sales announcement of the month is considered

• The sales announcement data is supplemented with additional characteristics information from the vehicle register data from Finnish Transport and Communications Agency

• The monthly data contains approximately 75 000 individual sales announcements of second-hand cars

17 May, 2023 Statistics Finland | [email protected]

3. Steps of the process for producing the hedonic price index

Definition and estimation of price

model incl. statistical tests

Aggregation and Oaxaca-

decomposition

Index calculation

17 May, 2023 Statistics Finland | [email protected]

3.1 Definition and estimation of price model 1/5

• The price model is semilogarithmic:

𝑙𝑙𝑙𝑙𝑙𝑙 𝑝𝑝𝑖𝑖𝑖𝑖 = 𝛼𝛼01𝑖𝑖 + ⋯+ 𝛼𝛼0𝑘𝑘1𝑖𝑖 + 𝑥𝑥𝑥𝑖𝑖𝑖𝑖𝛽𝛽𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖,

where 𝑝𝑝 is the unit price of a second-hand car, parameters 𝛼𝛼 represent stratum effects and term 𝜀𝜀 is random error term

• The unknown parameters 𝛽𝛽 and 𝛼𝛼 are estimated using the ordinary least squares method (OLS)

The explanatory variables used in the price model

17 May, 2023 Statistics Finland | [email protected]

Variable Description

𝑥𝑥1 Gearbox type: If automatic 𝑥𝑥1 = 1, else 𝑥𝑥1 = 0.

𝑥𝑥2 Towing hook: If towing hook 𝑥𝑥2 = 1, else 𝑥𝑥2 = 0.

𝑥𝑥3 Service history: If service history is available 𝑥𝑥3 = 1, else 𝑥𝑥3 = 0.

𝑥𝑥4 Cruise control: If cruise control 𝑥𝑥4 = 1, else 𝑥𝑥4 = 0.

𝑥𝑥5 Selling time of a car, months.

𝑥𝑥6 = 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠(𝑥𝑥5) Square root of the selling time of a car.

𝑥𝑥7 Age of a car, years.

𝑥𝑥8 = 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠(𝑥𝑥7) Square root of the age of a car.

𝑥𝑥9 Mileage (ten thousand).

𝑥𝑥10 = 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠(𝑥𝑥9) Square root of mileage.

𝑥𝑥11 Power/Weight ratio of a car.

𝑥𝑥12 = 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠(𝑥𝑥11 ) Square root of Power/Weight of a car.

3.1 Definition and estimation of price model 2/5 • We define several hierarchical partitions of second-hand cars (homogenous stratums)

• Using the F-test, we select the suitable partition: model 6

17 May, 2023 Statistics Finland | [email protected]

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

No categori-

zation

Size of a car

Size of a car × Make

Size of a car × Make ×

Model

Size of a car × Make × Model × Driving

Power

Size of a car × Make × Model × Driving Power × Type of a car

Model 1 vs 2

Model 2 vs 3

Model 3 vs 4

Model 5 vs 4

Model 6 vs 5

Test statistic 11896 1872 711 36.8 10.7

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
	No categori-zation	Size of a car	Size of a car × Make	Size of a car × Make × Model	Size of a car × Make × Model × Driving Power	Size of a car × Make × Model × Driving Power × Type of a car
		Model 1 vs 2	Model 2 vs 3	Model 3 vs 4	Model 5 vs 4	Model 6 vs 5
Test statistic		11896	1872	711	36.8	10.7

3.1 Definition and estimation of price model 3/5 • We define several classifications of price models

• Using the F-test, we select the suitable classification of price model: model 8

17 May, 2023 Statistics Finland | [email protected]

Model 6 Model 7 Model 8

No heterogeneity Size of a car Size of a car × Make

Model 7 vs 6

Model 8 vs 7

Test statistic 206.5 45

	Model 6	Model 7	Model 8
	No heterogeneity	Size of a car	Size of a car × Make
		Model 7 vs 6	Model 8 vs 7
Test statistic		206.5	45

3.1 Definition and estimation of price model 4/5 • The price model is estimated for each year

• Estimation results for model 8 - Selling time of a car has little effect on price - Age of a car and mileage have a negative effect

on price - Power/Weight ratio of a car has a positive

effect on price

17 May, 2023 Statistics Finland | [email protected]

1/2 0.0054383443 -0.012634214 Age of a car, 𝑥𝑥7 -0.138809764 -0.135251635 𝑥𝑥8 = 𝑥𝑥7

1/2 0.2915511757 0.2950576677 Mileage, 𝑥𝑥9 -0.033047764 -0.033221364 𝑥𝑥10 = 𝑥𝑥9

1/2 0.0180405738 0.026330353 Power/Weight ratio of a car, 𝑥𝑥11 12.089654612 9.8976375615 𝑥𝑥12 = 𝑥𝑥11

1/2 -2.549090343 -1.520907481

Year	2020	2021
Number of observations	287936	269663
Number of equations	72	74
Number of stratums/categories	1594	1691
Degrees of freedom	285478	267084
SSE	5401.6405077	4908.43633
R2	0.9645034599	0.9675392005
RMSE	0.1375550427	0.1355650208

	2020	2021
Constant	9.9126394001	9.8211262087
If automatic gearbox , else 0	0.0902673948	0.0923941505
If towing hook , else	0.0118209506	0.0113174535
If service history is available , else	-0.010492392	-0.008856039
If cruise control , else	0.017682513	0.0190084745
Selling time of a car,	-0.000386744	0.0036841099
	0.0054383443	-0.012634214
Age of a car,	-0.138809764	-0.135251635
	0.2915511757	0.2950576677
Mileage,	-0.033047764	-0.033221364
	0.0180405738	0.026330353
Power/Weight ratio of a car,	12.089654612	9.8976375615
	-2.549090343	-1.520907481

3.1 Definition and estimation of price model 5/5 The price effect of selling time (months) on the average log-prices in year 2020 and 2021

17 May, 2023 Statistics Finland | [email protected]

The price effect of mileage (ten thousand) on the average log-prices in year 2020 and 2021

The price effect of power/weight ratio (kW/kg) on the average log-prices in year 2020 and 2021

3.2 Aggregation and Oaxaca-decomposition • We aggregate price models from observations into stratums of the partition

• We test unweighted geometric and arithmetic averages in aggregation

for any stratum

(1) Price-ratio = Quality corrections + Quality adjusted price change conditional on �𝒙𝒙′𝑘𝑘𝑖𝑖

A = QC + QA

• The equation (1) can be represented as

where 𝑙𝑙𝑙𝑙𝑙𝑙 �̅�𝑝𝑘𝑘𝑖𝑖 is the average price for the current month, 𝑙𝑙𝑙𝑙𝑙𝑙 �̅�𝑝𝑘𝑘0 is the average price for the base period and

• The price model estimates used are always from the base period 17 May, 2023 Statistics Finland | [email protected]

3.3 Index calculation • The averaged stratum-level price decompositions are summed up to COICOP7-level using weights 𝑤𝑤𝑘𝑘,𝑓𝑓 of

index number formula 𝑓𝑓

𝑒𝑒𝑥𝑥𝑝𝑝 ∑𝑘𝑘 𝑤𝑤𝑘𝑘,𝑓𝑓 𝑙𝑙𝑙𝑙𝑙𝑙 ⁄�̅�𝑝𝑘𝑘𝑖𝑖 �̅�𝑝𝑘𝑘0 = 𝑃𝑃𝑓𝑓,𝐴𝐴 ⁄𝑖𝑖 0 is the price index for average prices (A)

that satisfy the following equation

𝑃𝑃𝑓𝑓,𝐴𝐴 ⁄𝑖𝑖 0 = 𝑃𝑃𝑓𝑓,𝑄𝑄𝑄𝑄

⁄𝑖𝑖 0 � 𝑃𝑃𝑓𝑓,𝑄𝑄𝐴𝐴 ⁄𝑖𝑖 0

• In our case the base period is a previous year normalized as an average month - We use the flexible basket approach

• We test different index number formulas

17 May, 2023 Statistics Finland | [email protected]

4. Results 1/3 • Index series for actual average prices for ‘Small cars’ make ‘Honda’. Indices based on geometric are dotted

lines and arithmetic are solid lines

• Basic formulas are contingently biased, deviating from each other

• Price ratios using unweighted arithmetic or geometric average prices are closely related

17 May, 2023 Statistics Finland | [email protected]

4. Results 2/3 • Hedonic index series for actual arithmetic average prices (A), quality adjusted prices (QA) and quality

corrections (Qc_x)

• Age of a car (x7) and mileage (x9) have a negative effect on actual average prices - Sold cars are older and more driven in the current period

• Index series for actual prices must be corrected upwards, which is index series for quality adjusted prices

17 May, 2023 Statistics Finland | [email protected]

4. Results 3/3

• The differences between the series are due to the data source, regression model variables, index formula and strategy

17 May, 2023 Statistics Finland | [email protected]

Things to consider when designing a hedonic application (HICP Manual) • How many and which quality-related variables to include in the regression equation: Our model has 12 variables

(slide 6)

• Whether to use another (finer or coarser) stratification when estimating the regression coefficients than when computing the index: We use a coarser stratification for estimation (slide 8)

• How frequently to re-estimate the regression coefficients: We re-estimate every year

• Whether to weight the prices when estimating the regression coefficients: We use equal weights

• Which function form to use; semi-logarithmic, double-logarithmic or other: Our model is semi-logarithmic (slide 6)

• Whether valid or spurious results are obtained: Statistical inference leads to selection of the best price models. Estimators of the price models are the best linear unbiased estimates (BLUE)

• Whether the method improves the accuracy of the index so much that it outweighs the often relatively high cost for design work and for collection of quality-related data: Yes, see slide 14

17 May, 2023 Statistics Finland | [email protected]

5. Conclusions • Our proposal for producing a hedonic price index is as follows:

1. Use suitable partition in estimation of price models

2. Aggregate price models into stratum-level by using arithmetic average - Arithmetic average is more interpretable than geometric average

1. Form price decompositions for stratums (Oaxaca)

2. Aggregate stratum-level price decompositions into COICOP-level using Törnqvist formula and base strategy with a flexible basket, that is free of chain drift

• This method is widely used in Statistics Finland

17 May, 2023 Statistics Finland | [email protected]

Thank You!

Satu Montonen [email protected]

The Making of Hedonic Index Numbers
Content
1. Background
2. Data and data pre-processing
3. Steps of the process for producing the hedonic price index
3.1 Definition and estimation of price model 1/5
3.1 Definition and estimation of price model 2/5
3.1 Definition and estimation of price model 3/5
3.1 Definition and estimation of price model 4/5
3.1 Definition and estimation of price model 5/5
3.2 Aggregation and Oaxaca-decomposition�
3.3 Index calculation
4. Results 1/3
4. Results 2/3
4. Results 3/3
Things to consider when designing a hedonic application (HICP Manual)
5. Conclusions
Thank You!

The web-based survey on gender-based violence in Finland – experiences and results (Finland)

Read more about The web-based survey on gender-based violence in Finland – experiences and results (Finland)

Languages and translations

English

The web-based survey on gender-based violence in Finland – experiences and results Marjut Pietiläinen 10−12 May, 2023, UNECE Meeting of the

Gender Statistics Experts

Presentation prepared by Marjut Pietiläinen, Henna Attila,

Miina Keski-Petäjä, Laura Lipasti, Kimmo Haapakangas &

Juhani Saari

Content

11 May, 2023 Statistics Finland | [email protected]

Background information

Data collection

Key results

Take away tips

The Finnish GBV survey

• Statistics Finland conducted the survey to meet

- Istanbul convention’s & international recommendations

- National information needs

• The Finnish GBV project 11/2019–04/2023

- Project, steering and expert groups

• Offers internationally comparable data at EU level

- Implementation of the EU-GBV Survey

- Prevalence and different forms of inter-personal violence, especially against women.

• Funding: Eurostat grant, Statistics Finland & 3 ministries.

• Nationally, the survey brings also information on violence against men and young women.

• Included national questions on:

- Covid-19, honor-based violence, feelings of fear, use of shelters, forced marriages and attitudes towards genital mutilation

11 May, 2023 Statistics Finland | [email protected]

Data collection

• Survey name: Safety and well-being in Finland 2021

• Preparing the survey (questionnaire, respondent materials), about 8 months

• Data collection in fall 2021

• Self administrative web questionnaire

• Sample size 25,000, including

- 15,000 women aged 18 to 74

- 5,000 women aged 16 to 17

- 5,000 men aged 18 to 74

• Register-based sample represents the whole population

• Response rate 31 %

• Motivational calls by Statistics Finland’s interviewers

- trained to face possible victims of violence

11 May, 2023 Statistics Finland | [email protected]

Gender-based violence in Finland – key results

11 May, 2023 Statistics Finland | [email protected]

In general, how common do you think intimate partner violence against women / men is in your country?

• Women consider gender-

based violence more

common than men

• Women rated violence

against women as far more

prevalent than men.

• Women also viewed

violence against men as

more common than men.

11 May, 2023 Statistics Finland | [email protected]

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Men

Women

Violence against men

Men

Women

Violence against women

Very common Fairly common Not very common Not common at all

Source: Preliminary results of the Finnish GBV survey 2021, Statistics Finland

Prevalence of experiences on intimate partner violence

• 50% of women and 42% of men aged 18–74 had

experiences of psychological intimate partner violence

in their lifetime

• 33% of women and 17% of men aged 18–74

had experiences of physical intimate partner violence

in their lifetime

• 10% of women and 2% of men aged 18–74

had experiences of sexual intimate partner violence in

their lifetime

➢ When measuring this type of prevalence, one act of

violence is enough.

11 May, 2023 Statistics Finland | [email protected]

Source: Preliminary results of the Finnish GBV survey 2021

0 10 20 30 40 50 60

Total

Sexual

Physical

Psychological

Experiences of intimate partner violence, %

Women Men

Non-partner physical violence

• In adulthood, 30% of men and

17% of women experienced

physical violence

➢ Different distribution than

in IP violence

• 83% of perpetrators of

physical violence are men

• Victim woman and

perpetrator man: 81%

• Victim man and perpetrator

man: 85%

• Place of occurrence (last

episode)

• Private space 29%

• Semi-public space 30%

• Public space 24%

Results in line with the statistics on offences and coercive measures.

11 May, 2023 Statistics Finland | [email protected]

0 10 20 30 40 50 60

Total

Previous 12 months

1-5 years

More than 5 years

Experiences of non-partner physical violence, %

Women Men

Source: Preliminary results of the Finnish GBV survey 2021, Statistics Finland

Lifetime experiences of sexual violence

0 20 40 60 80 100

Total

55-74 years

35-54

16-34Age

Lifetime experiences of sexual violence, %

Women Men

0.4

0 20 40 60 80 100

Total

Other sexual violence

Attempted rape

Rape

Type of sexual violence, %

Women Men

11 May, 2023 Statistics Finland | [email protected]

Source: Preliminary results of the Finnish GBV survey 2021, Statistics Finland

Reporting gender-based violence • Intimate partner violence (physical, sexual +

threatening) reported to:

- Victim support services 6%

- Health and social services 17%

- Close person 46%

- Police 9%

• Non-partner violence (physical, sexual +

threatening) reported to:

- Victim support services 2%

- Health and social services 9%

- Close person 24%

- Police 7%

11 May, 2023 Statistics Finland | [email protected]

0.3

0 20 40 60 80 100

Victim support services

Health and social services

Close person

Police

Non-partner violence

Victim support services

Health and social services

Close person

Police

Intimate partner violence

Women Men

Source: Preliminary results of the Finnish GBV survey 2021, Statistics Finland

Experiences of the survey project

• Strong cooperation and commitment, co-financing

- National interests (national sample & national

questions)

• Filling the data gap (needs and requirements),

brings much-needed information

• Synergies with other projects on the topic

- Costs of domestic violence in Finland (LAKU)

- The impact of the Covid-19 crisis on gender equality

in Finland

• Data for researchers available later in 2023

• A complex topic: how to communicate the results

• Finnish participation in the GBV project required

many efforts

• Project group simultaneously involved in several

surveys and statistical productions

• Coding of the questionnaire and forming the data

sets took more time than expected

- Translations and national adaptation

• Web questioning method

- suitable during COVID-19

- Cost efficient

- Lower response rate (motivation calls)

- A paper option was considered but turned out to be

too complicated and risky for data conformity

• International guidelines, support and comparability

11 May, 2023 Statistics Finland | [email protected]

http://urn.fi/URN:ISBN:978-952-383-317-3

Take away tips

11 May, 2023 Statistics Finland | [email protected]

Highlight nationally the importance of information and

international obligations

Cooperate and get key stakeholders

involved in the project

Allocate sufficient time for the process

Follow the guidelines

Take the sensitivity of the topic into

account

Report and utilize the national survey results and bring up

the national peculiarities

Compare perceptions and experiences of

gender-based violence

Do not forget researchers and use

of the data also in the future

Once conducted — easier and less expensive in the future!

Thank you

11 May, 2023 Statistics Finland | [email protected]

More information available:

https://tilastokeskus.fi/tup/sukupuolistunut-vakivalta/index.html

marjut.pietilainen(at)stat.fi

henna.attila(at)stat.fi

miina.keski-petaja(at)stat.fi

kimmo.haapakangas(at)stat.fi

laura.lipasti(at)stat.fi

juhani.saari(at)stat.fi

https://tilastokeskus.fi/tup/sukupuolistunut-vakivalta/index.html

mailto:[email protected]

Gender-in-trade variables through linking enterprise and employment data in Finland

Read more about Gender-in-trade variables through linking enterprise and employment data in Finland

Languages and translations

English

Gender in Trade - variables through linking enterprise and employment data

Kasperi Lavikainen 9.5.2023 UN Workshop on Gender

Statistics

• International trade

- International trade in goods

(ITGS) collected by the Finnish

Customs

- International trade in services

(ITS) survey

- Reconciled international trade

in goods and services in

national accounts/balance of

payments

• Other business statistics

- Structural business statistics

(SBS) covering turnover, value-

added, employment

- OFATS: affiliates abroad

- IFATS: Foreign affiliates in

domestic economy

- Enterprises' research and

development -survey

- Business demography covering

enterprise age

- And others… • Employment statistics

- FOLK: a collection of

employment-related datasets

compiled for Statistics Finland’s

Research Services platform

- Data is derived from

administrative registers

- Structural earnings statistics

- Income register from Tax

Authority

• Miscellaneous registers:

- Self-assessed taxes from Tax

Administration

- VAT Information Exchange

System data from Tax

Administration

What sort of sources are available to us in Finland? (not a comprehensive list!)

4 May, 2023 Statistics Finland | [email protected]

What is published currently regarding gender in international trade? • Only one experimental statistics: Trade in Value-added (TiVA)

- There are a few TiVA initiatives, the most commonly known is arguably OECD’s TiVA. The one referred here is a national

variant developed together with OECD that utilizes microdata in combination with macro-level statistics.

• Methodologically advanced in the sense that it includes estimates on exports’ indirect employment effects

(jobs created through exporting firms’ backward linkages). However, in terms of linking employment and

enterprise data, not very advanced.

- Each employee is allocated to one primary job according to the last week of the year. No special consideration for part -

time workers.

- There might also be distortions due to differences in macro/micro-level data and the methodology used.

• Additional breakdowns in the publication: industry, size class, foreign ownership, educational attainment for

employees

• Based on the microdata it would be possible to calculate e.g. wage differentials across employee

characteristics (gender, educational attainment, age, occupation, nationality) and enterprise characteristics

(industry, size class, foreign ownership).

- There have been some statistical exercises doing just that in the past few years, but so far no regular compilation of

statistics. 4 May, 2023 Statistics Finland | [email protected]

https://pxdata.stat.fi/PxWeb/pxweb/en/Kokeelliset_tilastot/Kokeelliset_tilastot__tiva/

Some examples from TiVA

4 May, 2023 Statistics Finland | [email protected]

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

Females Males

Number of employees (in full-time equivalents) in exports and domestic production by gender in 2020

Employment in domestic production

Employment embodied in indirect exports

Employment embodied in direct exports

19 %

23 %

25 %

16 %

27 %

35 %

16 %

24 %

44 %

34 %

0 50,000 100,000

28 Manufacture of machinery and equipment n.e.c.

17 Manufacture of paper and paper products

62, 63 Computer and information service activities

24 Manufacture of basic metals

26 Manufacture of computer, electronic and optical products

20 Manufacture of chemicals and chemical products

16 Manufacture of wood and of products of wood and cork, except furniture;…

27 Manufacture of electrical equipment

10-12 Food industry etc.

46 Wholesale trade, except of motor vehicles and motorcycles

Ten largest industries by number of employees embodied in their exports (directly and indirectly) in 2020, percentage refers to the female share

Females Males

Some examples from TiVA

4 May, 2023 Statistics Finland | [email protected]

32 %;

28 %

30 %

26 %

0 100,000 200,000 300,000

Micro firms, 0-<10 employees

Small firms, 10-<50 employees

Medium-sized firms, 50-<250 employees

Large firms, 250- employees

Employment dependent on exports by enterprise size class and gender in 2020, percentage refers to the female share

Females Males

32 %

28 %

30 %

26 %

0 200,000 400,000

Domestic enterprise

Domestic multinational enterprise

Foreign multinational enterprise

Other

Employment dependent on exports by firm ownership and gender in 2020, percentage refers to the female share

Females Males

What challenges have we encountered? • In principle, linking should be simple enough.

- Statistics Finland can access register-based data and survey data that covers both people and enterprises.

- Everybody in Finland has an identity number and every legal unit in business register has an identifier.

- And we have information on which enterprises/legal units employ which people.

• However, linking has proven to be more complicated than anticipated.

• Should an employee working in multiple enterprises be counted once or twice, thrice etc.?

- If you want to include an employee only once, how do you define the primary employer? Highest wage sum? Longest

duration of employment for the year in question? Employment during last week of the year?

• Part-time employees? Some sources don’t include this information, but it should be addressed ideally.

• Different data sources are constructed for different reasons -> which might not suit your needs.

- For example, the most promising register-based data on wages doesn’t cover entrepreneurs (probably because their

taxation is handled differently from wage-earners).

• Identifying the problems and their solutions is not easy coming from a business statistics background.

Communication between employment and business statisticians is needed.

4 May, 2023 Statistics Finland | [email protected]

What challenges have we encountered?

• Business statistics are not necessarily aligned either. They are often based on surveys and samples.

Additionally, there are conceptual differences.

• For example, when estimating employment effects of international trade based on ratio of exports/imports of

to turnover/costs we noticed quite a few issues:

- Pricing of products might be different in customs-based trade in goods (ITGS) compared to sales/turnover in structural

business statistics (SBS), global production is not covered in ITGS, some investment-related imports are not reported

as costs in SBS, turnover/sales might not be reported in calendar years…

• The problems mentioned here and before are not gender-specific. But because linking employees to

enterprises is a general problem, it affects and potentially distorts gender-specific indicators alongside other

employee characteristics. Even if this linking works perfectly, gender-specific indicators will be affected by

differences in business statistics if trade-related indicators need to be combined with other business

statistics.

• In the future, it would be useful to investigate how extensive these issues are and if they could be alleviated

somehow.

4 May, 2023 Statistics Finland | [email protected]

In conclusion, we are still in the early days of investigating the

topic. Thank you!

Kasperi Lavikainen

[email protected]

Russian

Переменные показатели гендерных аспектов торговли, получаемые посредством связывания данных о предприятиях с данными о занятости

Каспери Лавикайнен, 9.5.2023

Рабочее совещание ООН по гендерной статистике

• Международная торговля

- Международная торговля

товарами (ITGS), данные

собирает финская таможня

- Обследование международной

торговли услугами (ITS)

- Согласованные показатели

международной торговли

товарами и услугами в

национальных

счетах/платежном балансе

• Прочая статистика

коммерческих предприятий

- Структурная бизнес-статистика

(SBS), охватывающая объемы

реализации, добавленную

стоимость, занятость

- ОФАТС: филиалы за рубежом

- IFATS: филиалы иностранных

компаний в отечественной

экономике

- Исследования и разработки

предприятий – обследование

- Демография бизнеса,

охватывающая возраст

предприятия

- И другие источники…

• Статистика занятости

- FOLK:набор наборов данных о

занятости, составленный для

платформы исследовательских

услуг Статистического

управления Финляндии.

- Данные поступают из

административных

реестров

- Статистика структуры доходов

- Регистр доходов от налогового

органа

• Различные реестры:

- Самоначисленные налоги –

данные от налоговой

администрации

- Данные системы обмена

информацией по НДС от

налоговой администрации

Какие источники данных доступны нам в Финляндии? (неполный список!)

8 May, 2023 Statistics Finland | [email protected]

Какая информация о гендерных аспектах международной торговли публикуется в настоящее время? • Только один источник экспериментальной статистики: Trade in statistics (Торговля в статистических данных).

• Передовой в методологическом плане, в том смысле, что он включает оценки косвенного влияния экспорта

на занятость (рабочие места, созданные за счет обратных связей экспортирующих фирм). Тем не менее, с

точки зрения увязки данных о занятости с данными о предприятиях, он не является очень передовым .

- Каждый сотрудник соотносится с одним основным рабочим местом по состоянию на последнюю неделю года. Нет

учета работающих неполный рабочий день.

- Также могут быть искажения из-за различий в данных макро/микроуровня и используемой методологии.

• Дополнительные категории разбивки в публикуемых данных: отрасль, размерный класс, иностранная

собственность, уровень образования сотрудников.

• На основе микроданных можно было бы рассчитать, например, различия в заработной плате в зависимости

от различных характеристик работников (пол, уровень образования, возраст, профессия, национальность) и

характеристик предприятия (отрасль, размерный класс, иностранная собственность).

- За последние несколько лет были проведены некоторые статистические мероприятия, направленные на решение

именно этой задачи, но регулярной компиляции статистических данных до сих пор не проводится.

8 May, 2023 Statistics Finland | [email protected]

https://pxdata.stat.fi/PxWeb/pxweb/en/Kokeelliset_tilastot/Kokeelliset_tilastot__tiva/

Некоторые примеры показателей торговли, измеряемой по добавленной стоимости

8 May, 2023 Statistics Finland | [email protected]

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

Females Males

Количество занятых (в пересчете на полный рабочий день) в экспорте и внутреннем производстве в разбивке по полу в 2020 г.

Employment in domestic production

Employment embodied in indirect exports

Employment embodied in direct exports

19 %

23 %

25 %

16 %

27 %

35 %

16 %

24 %

44 %

34 %

0 50,000 100,000

28 Manufacture of machinery and equipment n.e.c.

17 Manufacture of paper and paper products

62, 63 Computer and information service activities

24 Manufacture of basic metals

26 Manufacture of computer, electronic and optical products

20 Manufacture of chemicals and chemical products

16 Manufacture of wood and of products of wood and cork, except furniture;…

27 Manufacture of electrical equipment

10-12 Food industry etc.

46 Wholesale trade, except of motor vehicles and motorcycles

Десять крупнейших отраслей по количеству работников, занятых в их экспорте (прямо и косвенно) в 2020 г., процент относится к доле женщин

Females MalesЖенщины Мужчины

Занятые во внутреннем производстве

Занятые в косвенном импорте

Занятые в прямом импорте

28 Производство машин и

оборудования

17 Производство бумаги и бумажной

продукции

62, 63 Компьютерные и информационные услуги

62 Производство базовых металлов

26 Производство компьютеров, электронной и

оптической продукции

20 Производство химических веществ и

химической продукции

16 Производство древесины и продукции из

древесины и пробки, за исключением мебели…

27 Производство электрооборудования

10-12 Пищевая промышленность и пр.

46 Оптовая торговля, кроме торговли моторными

транспортными средствами и мотоциклами

Некоторые примеры показателей торговли, измеряемой по добавленной стоимости

8 May, 2023 Statistics Finland | [email protected]

32 %;

28 %

30 %

26 %

0 100,000 200,000 300,000

Micro firms, 0-<10 employees

Small firms, 10-<50 employees

Medium-sized firms, 50-<250 employees

Large firms, 250- employees

Занятость в зависимости от экспорта в разбивке по размеру предприятия и полу в 2020 г., процент относится к доле женщин

Females Males

32 %

28 %

30 %

26 %

0 200,000 400,000

Domestic enterprise

Domestic multinational enterprise

Foreign multinational enterprise

Other

Занятость в зависимости от экспорта в разбивке по типу собственности и гендерному признаку в 2020 г., процент относится к доле женщин

Females MalesЖенщины ЖенщиныМужчины Мужчины

Отечественное предприятие

Отечественная транснациональная

компания

Иностранная транснациональная

компания

Другое

Крупные фирмы, 250+ сотрудников

Фирмы среднего размера,

50 - <250 сотрудников

Фирмы малого размера,

10 - <50 сотрудников

Микрофирмы,

0 - <10 сотрудников

С какими трудностями мы столкнулись? • В принципе, связывание данных должно быть достаточно простой задачей .

- Статистическое управление Финляндии может получить доступ к данным реестров и данным обследований, которые охватывают как

людей, так и предприятия

- У каждого лица в Финляндии есть идентификационный номер, и каждая юридическая единица, внесенная в бизнес -реестр, имеет

идентификатор.

- У нас также есть информация о том, на каких предприятиях/юридических единицах работают какие люди .

• Однако связывание оказалось более сложным, чем предполагалось.

• Следует ли учитывать работника, работающего на нескольких предприятиях, один раз, дважды, трижды и т. д.?

- Если вы хотите включить работника только один раз, как определить основного работодателя? Максимальную сумму заработной

платы? Максимальную продолжительность работы в рассматриваемом году? Занятость в последнюю неделю года?

• Сотрудники, работающие на условиях неполной занятости? Некоторые источники не включают эту информацию, но в

идеале ее надо учитывать.

• Различные источники данных создаются по разным причинам -> которые могут не соответствовать вашим потребностям.

- Например, наиболее многообещающие данные о заработной плате из реестров не охватывают предпринимателей (вероятно, потому,

что их налогообложение отличается от налогообложения наемных работников).

• Определить проблемы и способы их решения нелегко, исходя из опыта бизнес -статистики. Необходимо взаимодействие

между специалистами, занимающимися вопросами статистики занятости и бизнес -статистики.

8 May, 2023 Statistics Finland | [email protected]

С какими трудностями мы столкнулись?

• Бизнес-статистика также не обязательно носит согласованный характер. Она часто разрабатывается на

основе обследований и выборок. Кроме того, существуют концептуальные различия.

• Например, при оценке влияния международной торговли на занятость на основе отношения

экспорта/импорта к обороту/затратам мы отметили довольно много проблем:

- Цены на продукты в торговле товарами, осуществляющейся через таможню (ITGS), могут отличаться от данных

продаж/объема реализации в структурной бизнес-статистике (SBS), ITGS не охватывает глобальное производство,

некоторые виды импорта, связанные с инвестициями, не отражаются в качестве затрат в SBS, объем

реализации/продажи могут не указываться в отчетности за календарный год…

• Проблемы, упомянутые здесь и выше, не носят гендерного характера. Но, поскольку привязка

сотрудников к предприятиям является общей проблемой, она влияет на гендерные показатели наряду с

другими характеристиками сотрудников и может привести к их искажению. Даже если эта привязка

работает идеально, различия в бизнес-статистике будут влиять на гендерные показатели, если показатели

торговли необходимо будет комбинировать с другими данными бизнес-статистиками.

• В будущем было бы полезно выяснить, насколько масштабны эти проблемы и можно ли их как -нибудь

уменьшить.

8 May, 2023 Statistics Finland | [email protected]

В заключение, хотелось бы отметить, что мы все еще находимся на

ранннем этапе исследования этой темы. Спасибо за внимание!

Каспери Лавикайнен

[email protected]

Trade in value added indicators - improved timeliness and new information on global value chains, Finland

Read more about Trade in value added indicators - improved timeliness and new information on global value chains, Finland

Languages and translations

English

Trade in value added indicators – improved timeliness and new information on global value chains Kristian Taskinen, 25 April 2023

UNECE: Group of Experts on National Accounts

Co-operation between OECD and Statistics Finland

• Strong policy need for more timely and more granular statistics on global value chains (GVC) in Finland

• Statistics Finland and OECD co-project in 2019-2020 was financed by domestic stakeholders

• Build on previous extensive development work by the Nordic statistical institutes (micro-data linking, MDL) and

OECD-WTO (Trade in Value Added, TiVA)

• Objectives of the development

- Preliminary TiVA-indicators for Finland with t+18 months delay (OECD schedule t+30 months)

- New classifications for existing indicators (firm heterogeneity: size, productivity, role in GVC)

- New indicators on employment effects of GVCs (jobs dependent directly or indirectly on trade, professions supported

by trade, skills, gender aspects)

- Set up processes for regular annual production

• Globalisation in Finland: Granular insights into the impact on businesses and employment

• Experimental statistics: Trade in value added (only StatFin database tables in English)

https://www.stat.fi/tup/julkaisut/tiedostot/julkaisuluettelo/ykau_202000_2020_23449_net.pdf

https://pxdata.stat.fi/PxWeb/pxweb/en/Kokeelliset_tilastot/Kokeelliset_tilastot__tiva/

Finland’s gross exports and domestic value added content in 2021

Indirect domestic value added content of Finland’s gross exports in 2021, by source industry

Origin of value added embodied in Finland’s gross exports in 2021

Largest export industries in Finland in 2021 by origin of value added of gross exports (m EUR)

Shares of exports by firm size in 2021

Employment indicators 2020

Finland’s trade dependence on China 2013-2021

Thank you!

Market Forecast Tables 2022

Read more about Market Forecast Tables 2022

These tables show forest products production and trade forecasts for 2022 and 2023. These cover roundwood (logs, pulpwood and fuel wood), sawnwood (coniferous and non-coniferous), wood-based panels (plywood, particle board, OSB and fibreboard), pulp, paper and wood pellets. The forecast data are provided by national correspondents and approved at the meeting of the Committee on Forests and the Forest Industry.

Languages and translations

English

List of tables

List of Tables and Notes

Table 1 - Sawn Softwood

Table 2 - Sawn Hardwood (total)

Table 2a - Sawn Hardwood (temperate)

Table 2b - Sawn Hardwood (tropical)

Table 3 - Veneer Sheets

Table 4 - Plywood

Table 5 - Particle Board (excluding OSB)

Table 5a - Oriented Strand Board

Table 6 - Fibreboard

Table 6a - Hardboard

Table 6b - MDF/HDF

Table 6c - Other Fibreboard

Table 7 - Wood Pulp

Table 8 - Paper and Paperboard

Table 9 - Removals of wood in the rough

Table 9a - Removals of wood in the rough (softwood)

Table 9b - Removals of wood in the rough (hardwood)

Table 10 - Softwood sawlogs

Table 11 - Hardwood sawlogs

Table 11a - Hardwood logs (temperate)

Table 11b - Hardwood logs (tropical)

Table 12 - Pulpwood

Table 12a - Pulpwood (softwood)

Table 12b - Pulpwood (hardwood)

Table 12c - Wood Residues, Chips and Particles

Table 13 - Wood Pellets

Table 14 - Europe: Summary table of market forecasts for 2022 and 2023

Table 15 - North America: Summary table of market forecasts for 2022 and 2023

Source: UNECE Committee on Forests and the Forest Industry , November 2022, http://www.unece.org/forests/fpm/timbercommittee.html

Notes: Data in italics are estimated by the secretariat. EECCA is Eastern Europe, Caucasus and Central Asia.

Data for the two latest years are forecasts.

In contrast to previous years, data are shown only for countries providing forecasts. Sub-regional totals are only for reporting countries.

Data are shown only for countries providing forecasts. Sub-regional totals thus reflect only the reporting countries of the subregion. No sub-regional forecast is provided for "Eastern Europe, Caucasus and Central Asia" due to lack of information provided by countries in this sub-region.

Germany – Pellets consumption is an estimated consumption as reported by Pellet Federation. There is a difference between reported consumption and apparent consumption of 242,000 metric tonnes and 214,000 metric tonnes, respectively. For 2022 and 2023, this difference is additionally stored at newly installed plants, i.e. sold but not yet consumed.

Slovenia trade figures are lower than actual as they do not include estimates for non-recorded trade with other EU countries.

Polish trade data exclude non-reporters (estimated at 1-3% of total). Residues exclude recovered wood. Polish sawnwood data exclude shop lumber. Wood pulp production is in metric tonnes, not air-dried, and excludes recovered fibre pulp. Wood pellets production data includes briquettes and non-wood based material.

United Kingdom production figures for OSB is secretariat estimate.

Softwood = coniferous, hardwood = non-coniferous

For tables 1-13, data in italics are secretariat estimates or repeated data. All other data are from national sources and are of course estimates for the current and future year.

Countries with nil, missing or confidential data for all years on a table are not shown.

Table 1

5.C
		TABLE 1
			SAWN SOFTWOOD						SCIAGES CONIFERES
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	6,547	6,469	6,400	10,582	10,370	10,300	1,911	2,045	2,000	5,947	5,947	5,900	Autriche
	Bulgaria	Bulgaria	528	...	...	638	...	...	25	...	...	135	...	...	Bulgarie
	Cyprus	Cyprus	32	33	32	2	1	1	31	32	31	0	0	0	Chypre
	Czech Republic	Czech Republic	3,250	3,249	3,272	5,015	5,144	5,279	526	555	560	2,291	2,450	2,567	République tchèque
	Estonia	Estonia	2,296	1,910	1,910	1,600	1,600	1,600	1,699	1,360	1,360	1,003	1,050	1,050	Estonie
	Finland	Finland	3,731	3,650	3,570	11,900	11,750	12,200	547	350	70	8,716	8,450	8,700	Finlande
	Germany	Germany	20,104	19,800	19,500	25,313	25,300	25,000	5,700	5,000	4,500	10,909	10,500	10,000	Allemagne
	Latvia	Latvia	1,968	1,500	1,300	3,641	3,500	3,300	1,463	900	700	3,136	2,900	2,700	Lettonie
	Luxembourg	Luxembourg	56	43	43	39	39	39	28	5	5	12	1	1	Luxembourg
	Malta	Malta	7	8	9	0	0	0	7	8	9	0	0	0	Malte
	Netherlands	Netherlands	3,036	2,905	2,850	110	100	100	3,408	3,276	3,226	481	470	475	Pays-Bas
	Poland	Poland	4,857	4,750	4,900	4,583	4,500	4,650	1,239	1,250	1,300	965	1,000	1,050	Pologne
	Portugal	Portugal	632	730	665	817	850	840	121	130	125	306	250	300	Portugal
	Serbia	Serbia	379	400	422	99	110	120	295	300	310	15	10	8	Serbie
	Slovakia	Slovakia	563	650	675	1,302	1,300	1,325	324	350	350	1,063	1,000	1,000	Slovaquie
	Slovenia	Slovenia	627	600	550	904	1,000	970	563	500	500	840	900	920	Slovénie
	Sweden	Sweden	6,954	6,450	5,300	19,000	18,500	17,500	514	450	300	12,560	12,500	12,500	Suède
	Switzerland	Switzerland	1,245	1,275	1,315	1,150	1,180	1,220	280	275	270	185	180	175	Suisse
	UK	United Kingdom	10,960	8,920	9,410	3,574	3,010	3,400	7,623	6,150	6,250	237	240	240	Royaume-Uni
		Total Europe	67,771	63,342	62,124	90,268	88,255	87,844	26,303	22,936	21,866	48,800	47,848	47,586	Total Europe
	Canada	Canada a	19,841	18,893	24,156	55,842	52,183	50,290	1,030	752	745	37,031	34,041	26,878	Canada a
	United States	United States a	88,263	88,484	89,272	63,417	64,178	64,820	26,931	26,270	26,533	2,085	1,963	2,081	Etats-Unis a
		Total North America	108,104	107,378	113,428	119,259	116,361	115,109	27,961	27,021	27,277	39,116	36,005	28,959	Total Amérique du Nord
			a converted from nominal to actual size using factor of 0.72						a convertis du dimension nominale au véritable avec une facteur du 0.72

Table 2

5.NC
		TABLE 2
			SAWN HARDWOOD (total)						SCIAGES NON-CONIFERES (total)
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	186	215	220	182	178	170	177	210	220	173	173	170	Autriche
	Bulgaria	Bulgaria	71	...	...	79	...	...	22	...	...	30	...	...	Bulgarie
	Cyprus	Cyprus	6	7	6	0	0	0	6	7	6	0	0	0	Chypre
	Czech Republic	Czech Republic	416	427	430	145	147	151	338	340	344	67	60	65	République tchèque
	Estonia	Estonia	230	150	150	150	100	100	177	140	140	97	90	90	Estonie
	Finland	Finland	64	55	55	54	50	50	30	25	25	20	20	20	Finlande
	Germany	Germany	786	760	700	1,061	1,060	1,000	459	400	400	735	700	700	Allemagne
	Latvia	Latvia	234	160	150	797	850	750	75	60	50	638	750	650	Lettonie
	Luxembourg	Luxembourg	38	51	51	39	39	39	18	12	12	19	0	0	Luxembourg
	Malta	Malta	7	7	8	0	0	0	7	7	8	0	0	0	Malte
	Netherlands	Netherlands	309	301	283	38	40	40	343	331	308	72	70	65	Pays-Bas
	Poland	Poland	512	500	540	486	460	510	312	350	380	286	310	350	Pologne
	Portugal	Portugal	224	220	225	148	160	150	106	90	100	31	30	25	Portugal
	Serbia	Serbia	185	197	200	353	382	390	103	95	100	271	280	290	Serbie
	Slovakia	Slovakia	225	325	350	350	375	400	52	100	100	177	150	150	Slovaquie
	Slovenia	Slovenia	121	55	80	140	125	130	99	100	100	118	170	150	Slovénie
	Sweden	Sweden	111	110	110	100	100	100	50	45	45	39	35	35	Suède
	Switzerland	Switzerland	80	85	90	55	60	65	40	40	40	15	15	15	Suisse
	UK	United Kingdom	534	540	540	37	40	40	536	540	540	39	40	40	Royaume-Uni
		Total Europe	4,338	4,166	4,188	4,215	4,166	4,085	2,950	2,892	2,918	2,826	2,893	2,815	Total Europe
	Canada	Canada	1,208	1,229	1,116	880	813	714	798	894	779	470	478	377	Canada
	United States	United States	14,348	15,065	14,707	17,326	17,607	17,467	717	1,040	878	3,695	3,581	3,638	Etats-Unis
		Total North America	15,556	16,295	15,823	18,206	18,420	18,181	1,514	1,934	1,658	4,165	4,059	4,015	Total Amérique du Nord

Table 2a

	TABLE 2a
		SAWN HARDWOOD (temperate)						SCIAGES NON-CONIFERES (zone tempérée)
	Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
							1000 m3
		Apparent Consumption						Imports			Exports
	Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
		2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
Austria	Austria	182	211	216	182	178	170	172	205	215	172	172	169	Autriche
Bulgaria	Bulgaria	70	...	...	79	...	...	21	...	...	30	...	...	Bulgarie
Cyprus	Cyprus	4	3	2	0	0	0	4	3	2	0	0	0	Chypre
Czech Republic	Czech Republic	409	421	424	145	147	151	329	331	335	65	57	62	République tchèque
Estonia	Estonia	227	149	149	150	100	100	172	136	136	94	87	87	Estonie
Finland	Finland	63	54	54	54	50	50	25	20	20	16	16	16	Finlande
Germany	Germany	747	718	658	1,059	1,058	998	385	325	325	698	665	665	Allemagne
Luxembourg	Luxembourg	27	49	49	39	39	39	6	10	10	19	0	0	Luxembourg
Malta	Malta	6	6	7	0	0	0	6	6	7	0	0	0	Malte
Netherlands	Netherlands	166	160	142	31	32	32	184	177	154	49	49	45	Pays-Bas
Poland	Poland	498	485	524	486	459	509	295	333	362	283	307	347	Pologne
Portugal	Portugal	222	190	197	136	150	137	74	50	70	-12	10	10	Portugal
Serbia	Serbia	184	196	199	352	381	389	103	95	100	271	280	290	Serbie
Slovenia	Slovenia	119	52	77	140	125	130	96	97	97	118	170	150	Slovénie
Sweden	Sweden	111	109	109	100	100	100	49	44	44	37	35	35	Suède
Switzerland	Switzerland	71	76	81	52	57	62	34	34	34	15	15	15	Suisse
UK	United Kingdom	458	460	460	37	40	40	456	460	460	36	40	40	Royaume-Uni
	Total Europe	3,563	3,340	3,349	3,042	2,917	2,908	2,411	2,326	2,372	1,890	1,903	1,931	Total Europe
Canada	Canada	1,202	1,214	1,107	880	813	714	781	865	753	459	464	360	Canada
United States	United States	14,162	14,835	14,498	17,326	17,607	17,467	491	773	632	3,656	3,545	3,600	Etats-Unis
	Total North America	15,364	16,049	15,605	18,206	18,420	18,181	1,272	1,638	1,385	4,115	4,009	3,960	Total Amérique du Nord

Table 2b

5.NC.T
		TABLE 2b
			SAWN HARDWOOD (tropical)						SCIAGES NON-CONIFERES (tropicale)
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	4	4	4	0	0	0	5	5	5	1	1	1	Autriche
	Bulgaria	Bulgaria	1	...	...	0	...	...	1	...	...	0	...	...	Bulgarie
	Cyprus	Cyprus	2	4	4	0	0	0	2	4	4	0	0	0	Chypre
	Czech Republic	Czech Republic	7	6	6	0	0	0	9	9	9	2	3	3	République tchèque
	Estonia	Estonia	3	1	1	0	0	0	5	4	4	2	3	3	Estonie
	Finland	Finland	1	1	1	0	0	0	5	5	5	4	4	4	Finlande
	Germany	Germany	39	42	42	2	2	2	74	75	75	37	35	35	Allemagne
	Luxembourg	Luxembourg	12	2	2	0	0	0	12	2	2	0	0	0	Luxembourg
	Malta	Malta	1	1	1	0	0	0	1	1	1	0	0	0	Malte
	Netherlands	Netherlands	143	141	141	8	8	8	159	154	153	23	21	20	Pays-Bas
	Poland	Poland	14	15	16	0	1	1	17	17	18	3	3	3	Pologne
	Portugal	Portugal	2	30	28	12	10	13	32	40	30	43	20	15	Portugal
	Serbia	Serbia	1	1	1	1	1	1	0	0	0	0	0	0	Serbie
	Slovenia	Slovenia	2	3	3	0	0	0	3	3	3	0	0	0	Slovénie
	Sweden	Sweden	-0	1	1	0	0	0	1	1	1	1	0	0	Suède
	Switzerland	Switzerland	9	9	9	3	3	3	6	6	6	0	0	0	Suisse
	UK	United Kingdom	76	80	80	0	0	0	79	80	80	3	0	0	Royaume-Uni
		Total Europe	316	341	339	26	25	28	412	406	396	122	90	84	Total Europe
	Canada	Canada	6	16	9	0	0	0	16	29	26	11	14	17	Canada
	United States	United States	186	230	208	0	0	0	226	267	246	39	36	38	Etats-Unis
		Total North America	192	246	217	0	0	0	242	296	273	50	50	55	Total Amérique du Nord

Table 3

6.1x
		TABLE 3
			VENEER SHEETS						FEUILLES DE PLACAGE
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	59	56	56	8	7	7	70	65	65	18	16	16	Autriche
	Bulgaria	Bulgaria	31	...	...	18	...	...	24	...	...	10	...	...	Bulgarie
	Cyprus	Cyprus	1	1	1	0	0	0	1	1	1	0	0	0	Chypre
	Czech Republic	Czech Republic	11	20	21	30	32	33	41	50	54	60	62	66	République tchèque
	Estonia	Estonia	103	140	140	110	140	140	85	86	90	92	86	90	Estonie
	Finland	Finland	8	8	7	170	184	178	9	9	9	171	185	180	Finlande
	Germany	Germany	167	165	160	116	115	110	111	110	110	59	60	60	Allemagne
	Latvia	Latvia	155	85	55	42	45	45	154	110	50	41	70	40	Lettonie
	Luxembourg	Luxembourg	1	0	0	0	0	0	1	0	0	0	0	0	Luxembourg
	Malta	Malta	1	1	2	0	0	0	1	1	2	0	0	0	Malte
	Netherlands	Netherlands	34	34	34	0	0	0	41	41	41	7	7	7	Pays-Bas
	Poland	Poland	146	145	150	46	42	44	121	125	130	21	22	24	Pologne
	Portugal	Portugal	-71	12	3	21	22	23	37	30	30	130	40	50	Portugal
	Serbia	Serbia	19	18	23	27	22	25	13	14	15	21	18	17	Serbie
	Slovakia	Slovakia	16	25	25	29	30	30	21	20	20	34	25	25	Slovaquie
	Slovenia	Slovenia	8	4	5	23	24	21	14	14	14	29	34	30	Slovénie
	Sweden	Sweden	30	25	20	60	55	50	17	20	15	47	50	45	Suède
	Switzerland	Switzerland	3	3	3	0	0	0	4	4	4	1	1	1	Suisse
	UK	United Kingdom	14	10	10	0	0	0	14	10	10	0	0	0	Royaume-Uni
		Total Europe	737	752	715	700	718	706	779	710	660	742	676	651	Total Europe
	Canada	Canada	144	181	173	581	565	565	183	222	230	620	607	622	Canada
	United States	United States	2,675	2,784	2,730	2,284	2,284	2,284	671	759	715	281	258	269	Etats-Unis
		Total North America	2,819	2,965	2,903	2,866	2,849	2,849	854	981	945	901	865	891	Total Amérique du Nord
		Note: Definition of veneers now includes all production (including converted directly to plywood). However most replies here continue to exclude the part going to plywood.
		La définition des placages comprend maintenant toute la production (y compris la conversion directe en contreplaqué).
		Cependant, la plupart des réponses continuent d'exclure la partie destinée au contreplaqué.

Table 4

6.2x
		TABLE 4
			PLYWOOD						CONTREPLAQUES
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	94	85	80	184	180	170	267	205	200	357	300	290	Autriche
	Bulgaria	Bulgaria	65	...	...	37	...	...	66	...	...	38	...	...	Bulgarie
	Cyprus	Cyprus	13	15	14	0	0	0	13	15	14	0	0	0	Chypre
	Czech Republic	Czech Republic	201	199	201	260	262	263	186	188	187	245	251	249	République tchèque
	Estonia	Estonia	102	100	100	190	180	180	118	110	110	206	190	190	Estonie
	Finland	Finland	296	280	285	1,130	1,120	1,120	121	110	100	955	950	935	Finlande
	Germany	Germany	1,185	1,170	1,170	103	100	100	1,464	1,450	1,450	382	380	380	Allemagne
	Latvia	Latvia	68	30	30	310	300	250	98	60	30	340	330	250	Lettonie
	Luxembourg	Luxembourg	11	2	2	0	0	0	12	2	2	1	0	0	Luxembourg
	Malta	Malta	10	11	11	0	0	0	10	11	11	0	0	0	Malte
	Netherlands	Netherlands	600	580	565	0	0	0	695	670	650	95	90	85	Pays-Bas
	Poland	Poland	773	770	790	543	540	550	604	620	650	374	390	410	Pologne
	Portugal	Portugal	215	215	200	126	110	100	116	130	120	27	25	20	Portugal
	Serbia	Serbia	41	43	48	15	14	17	30	32	34	4	3	3	Serbie
	Slovakia	Slovakia	232	320	345	307	375	400	65	70	70	140	125	125	Slovaquie
	Slovenia	Slovenia	79	66	68	102	96	98	57	50	50	80	80	80	Slovénie
	Sweden	Sweden	260	245	245	101	90	90	206	200	200	47	45	45	Suède
	Switzerland	Switzerland	209	214	220	7	7	8	205	210	215	3	3	3	Suisse
	UK	United Kingdom	1,486	1,490	1,490	0	0	0	1,541	1,540	1,540	55	50	50	Royaume-Uni
		Total Europe	5,940	5,835	5,864	3,415	3,374	3,346	5,874	5,673	5,633	3,349	3,212	3,115	Total Europe
	Canada	Canada	2,485	2,288	2,490	1,698	1,644	1,639	1,421	1,144	1,406	634	500	555	Canada
	United States	United States	17,031	17,295	17,163	9,705	9,895	9,800	8,086	8,163	8,124	759	762	761	Etats-Unis
		Total North America	19,516	19,583	19,653	11,403	11,539	11,439	9,507	9,306	9,530	1,393	1,263	1,316	Total Amérique du Nord

Table 5

6.3xPB
		TABLE 5
			PARTICLE BOARD (excluding OSB)						PANNEAUX DE PARTICULES (ne comprennent pas l'OSB)
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	868	776	766	2,550	2,350	2,300	361	370	360	2,043	1,944	1,894	Autriche
	Bulgaria	Bulgaria	487	...	...	773	...	...	118	...	...	403	...	...	Bulgarie
	Cyprus	Cyprus	47	42	41	0	0	0	47	42	41	0	0	0	Chypre
	Czech Republic	Czech Republic	739	688	671	965	945	930	578	598	577	804	855	836	République tchèque
	Estonia	Estonia	182	155	155	210	130	130	76	85	85	103	60	60	Estonie
	Finland	Finland	107	119	119	54	50	50	83	93	93	30	24	24	Finlande
	Germany	Germany	6,015	5,970	5,870	6,036	6,020	5,920	2,142	2,100	2,050	2,162	2,150	2,100	Allemagne
	Latvia	Latvia	139	120	180	350	300	300	53	70	80	264	250	200	Lettonie
	Luxembourg	Luxembourg	15	3	3	0	0	0	16	4	4	2	1	1	Luxembourg
	Malta	Malta	10	10	11	0	0	0	10	10	11	0	0	0	Malte
	Netherlands	Netherlands	446	430	430	0	0	0	520	500	500	74	70	70	Pays-Bas
	Poland	Poland	7,601	7,700	7,740	6,333	6,370	6,370	2,093	2,150	2,220	824	820	850	Pologne
	Portugal	Portugal	451	527	427	743	730	720	313	295	304	605	498	597	Portugal
	Serbia	Serbia	420	417	422	272	230	235	208	235	240	60	48	53	Serbie
	Slovakia	Slovakia	182	220	215	608	625	625	143	140	135	568	545	545	Slovaquie
	Slovenia	Slovenia	155	155	147	0	0	0	162	163	154	6	8	7	Slovénie
	Sweden	Sweden	971	985	975	561	550	550	506	520	510	97	85	85	Suède
	Switzerland	Switzerland	280	300	320	380	390	400	125	130	135	225	220	215	Suisse
	UK	United Kingdom	2,664	2,242	2,242	2,090	1,722	1,722	638	600	600	65	80	80	Royaume-Uni
		Total Europe	21,780	20,859	20,734	21,926	20,412	20,252	8,189	8,105	8,099	8,336	7,658	7,617	Total Europe
	Canada	Canada	1,487	1,594	1,591	1,647	1,724	1,686	593	594	586	754	724	681	Canada
	United States	United States	5,111	7,189	5,725	4,136	4,220	3,874	1,462	3,144	2,159	488	175	309	Etats-Unis
		Total North America	6,597	8,783	7,316	5,783	5,944	5,560	2,056	3,738	2,745	1,241	899	989	Total Amérique du Nord
		Data are calculated by subtracting OSB from the particleboard/OSB total - les données sont calculées en soustrayant les OSB du total des panneaux de particules et OSB.

Table 5a

6.3.1
		TABLE 5a
			ORIENTED STRAND BOARD (OSB)						PANNEAUX STRUCTURAUX ORIENTES (OSB)
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	186	224	214	0	0	0	192	230	220	6	6	6	Autriche
	Bulgaria	Bulgaria	175	...	...	252	...	...	8	...	...	85	...	...	Bulgarie
	Cyprus	Cyprus	15	18	17	0	0	0	15	18	17	0	0	0	Chypre
	Czech Republic	Czech Republic	355	360	366	745	770	795	127	132	135	517	542	564	République tchèque
	Estonia	Estonia	44	45	45	0	0	0	44	45	45	1	0	0	Estonie
	Finland	Finland	47	47	47	0	0	0	47	47	47	0	0	0	Finlande
	Germany	Germany	1,473	1,480	1,480	1,282	1,280	1,280	746	750	750	555	550	550	Allemagne
	Latvia	Latvia	211	160	100	700	600	600	73	60	50	562	500	550	Lettonie
	Luxembourg	Luxembourg	117	265	265	338	338	338	7	1	1	229	74	74	Luxembourg
	Netherlands	Netherlands	192	185	185	0	0	0	208	200	200	16	15	15	Pays-Bas
	Poland	Poland	802	800	860	827	830	880	316	350	380	341	380	400	Pologne
	Portugal	Portugal	31	33	33	0	0	0	34	35	36	3	2	3	Portugal
	Serbia	Serbia	44	53	58	0	0	0	46	55	60	2	2	2	Serbie
	Slovakia	Slovakia	91	90	95	0	0	0	94	95	100	3	5	5	Slovaquie
	Slovenia	Slovenia	33	35	34	0	0	0	36	37	36	2	2	2	Slovénie
	Sweden	Sweden	116	95	95	0	0	0	121	100	100	5	5	5	Suède
	Switzerland	Switzerland	90	90	90	0	0	0	90	90	90	0	0	0	Suisse
	UK	United Kingdom	925	868	868	598	598	598	461	440	440	133	170	170	Royaume-Uni
		Total Europe	4,948	4,848	4,852	4,741	4,416	4,491	2,665	2,685	2,707	2,459	2,253	2,346	Total Europe
	Canada	Canada	1,618	1,589	1,570	7,240	7,581	7,646	124	72	72	5,746	6,064	6,147	Canada
	United States	United States	19,804	20,091	20,381	13,839	14,040	14,243	6,147	6,236	6,326	182	185	188	Etats-Unis
		Total North America	21,422	21,680	21,951	21,079	21,621	21,889	6,271	6,308	6,398	5,928	6,249	6,335	Total Amérique du Nord

Table 6

	TABLE 6
		FIBREBOARD						PANNEAUX DE FIBRES
	Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
							1000 m3
		Apparent Consumption						Imports			Exports
	Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
		2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
Austria	Austria	504	469	445	690	570	545	370	340	330	556	441	430	Autriche
Bulgaria	Bulgaria	106	...	...	75	...	...	118	...	...	87	...	...	Bulgarie
Cyprus	Cyprus	15	16	15	0	0	0	15	16	15	0	0	0	Chypre
Czech Republic	Czech Republic	462	470	481	45	46	47	574	590	609	157	166	175	République tchèque
Estonia	Estonia	74	68	68	80	70	70	77	69	69	83	71	71	Estonie
Finland	Finland	162	165	165	46	46	46	164	161	161	48	41	41	Finlande
Germany	Germany	4,401	4,425	4,335	6,105	6,100	6,000	1,944	1,940	1,865	3,648	3,615	3,530	Allemagne
Latvia	Latvia	19	11	6	37	35	20	69	57	47	87	81	61	Lettonie
Luxembourg	Luxembourg	13	128	128	147	147	147	19	5	5	153	24	24	Luxembourg
Malta	Malta	...	...	...	...	...	...	...	...	...	...	...	...	Malte
Netherlands	Netherlands	454	436	436	29	29	29	572	550	550	147	143	143	Pays-Bas
Poland	Poland	4,398	4,650	4,770	5,750	5,850	6,050	912	970	990	2,264	2,170	2,270	Pologne
Portugal	Portugal	488	510	500	555	550	540	336	335	340	404	375	380	Portugal
Serbia	Serbia	123	151	163	21	18	20	143	168	181	41	35	38	Serbie
Slovakia	Slovakia	248	239	239	0	0	0	275	265	265	27	26	26	Slovaquie
Slovenia	Slovenia	26	30	30	136	135	135	57	55	55	167	160	160	Slovénie
Sweden	Sweden	308	317	293	0	0	0	391	395	365	84	78	72	Suède
Switzerland	Switzerland	292	302	312	205	210	215	266	266	266	179	174	169	Suisse
UK	United Kingdom	1,807	1,780	1,710	798	900	850	1,080	950	930	72	70	70	Royaume-Uni
	Total Europe	13,901	14,168	14,097	14,719	14,706	14,714	7,385	7,132	7,043	8,203	7,670	7,660	Total Europe
Canada	Canada	1,492	1,348	1,355	1,349	1,395	1,395	1,009	889	885	866	936	924	Canada
United States	United States	9,727	10,134	9,985	7,560	7,691	7,663	3,008	3,190	3,123	841	747	801	Etats-Unis
	Total North America	11,219	11,482	11,340	8,909	9,086	9,058	4,017	4,079	4,007	1,707	1,683	1,725	Total Amérique du Nord

Table 6a

6.4.1
		TABLE 6a
			HARDBOARD						PANNEAUX DURS
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	32	36	34	75	48	45	17	20	19	60	32	30	Autriche
	Bulgaria	Bulgaria	45	...	...	51	...	...	40	...	...	46	...	...	Bulgarie
	Cyprus	Cyprus	1	1	1	0	0	0	1	1	1	0	0	0	Chypre
	Czech Republic	Czech Republic	146	146	147	0	0	0	167	168	170	21	22	23	République tchèque
	Estonia	Estonia	27	24	24	0	0	0	34	30	30	7	6	6	Estonie
	Finland	Finland	26	30	30	46	46	46	21	20	20	41	36	36	Finlande
	Germany	Germany	213	210	210	0	0	0	242	240	240	29	30	30	Allemagne
	Latvia	Latvia	11	10	5	0	0	0	23	20	15	12	10	10	Lettonie
	Luxembourg	Luxembourg	-71	-9	-9	0	0	0	2	1	1	73	10	10	Luxembourg
	Netherlands	Netherlands	44	35	35	0	0	0	66	55	55	22	20	20	Pays-Bas
	Poland	Poland	-212	10	10	76	100	100	139	180	180	427	270	270	Pologne
	Portugal	Portugal	44	20	30	12	0	0	42	30	40	10	10	10	Portugal
	Serbia	Serbia	33	38	41	21	18	20	31	35	37	19	15	16	Serbie
	Slovakia	Slovakia	17	20	20	0	0	0	22	25	25	5	5	5	Slovaquie
	Slovenia	Slovenia	1	0	0	0	0	0	8	6	6	7	6	6	Slovénie
	Sweden	Sweden	75	87	78	0	0	0	88	100	90	14	13	12	Suède
	Switzerland	Switzerland	13	13	13	0	0	0	21	21	21	8	8	8	Suisse
	UK	United Kingdom	101	100	100	0	0	0	111	110	110	11	10	10	Royaume-Uni
		Total Europe	544	771	769	281	212	211	1,075	1,062	1,060	812	503	502	Total Europe
	Canada	Canada	46	42	36	90	90	90	68	60	66	112	108	120	Canada
	United States	United States	503	509	514	499	504	509	252	255	258	248	250	253	Etats-Unis
		Total North America	549	551	550	589	594	599	320	315	324	360	358	373	Total Amérique du Nord

Table 6b

6.4.2
		TABLE 6b
			MDF/HDF
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	316	283	265	615	522	500	192	166	161	491	405	396	Autriche
	Bulgaria	Bulgaria	58	...	...	24	...	...	75	...	...	41	...	...	Bulgarie
	Cyprus	Cyprus	13	13	12	0	0	0	13	13	12	0	0	0	Chypre
	Czech Republic	Czech Republic	248	249	251	45	46	47	243	250	258	40	47	54	République tchèque
	Estonia	Estonia	21	20	20	0	0	0	38	35	35	17	15	15	Estonie
	Finland	Finland	117	116	116	0	0	0	124	121	121	7	5	5	Finlande
	Germany	Germany	2,385	2,425	2,400	4,693	4,700	4,650	625	625	600	2,932	2,900	2,850	Allemagne
	Latvia	Latvia	1	0	0	37	35	20	28	25	20	64	60	40	Lettonie
	Luxembourg	Luxembourg	80	136	136	147	147	147	13	3	3	80	14	14	Luxembourg
	Malta	Malta	5	5	5	0	0	0	5	5	5	0	0	0	Malte
	Netherlands	Netherlands	291	285	285	0	0	0	408	400	400	117	115	115	Pays-Bas
	Poland	Poland	3,533	3,560	3,630	3,542	3,600	3,700	743	760	780	752	800	850	Pologne
	Portugal	Portugal	456	485	465	535	550	540	280	285	280	359	350	355	Portugal
	Serbia	Serbia	88	110	118	0	0	0	110	130	140	22	20	22	Serbie
	Slovakia	Slovakia	162	150	150	0	0	0	183	170	170	22	20	20	Slovaquie
	Slovenia	Slovenia	20	29	29	136	135	135	39	43	43	155	149	149	Slovénie
	Sweden	Sweden	213	210	200	0	0	0	272	265	250	59	55	50	Suède
	Switzerland	Switzerland	105	110	115	205	210	215	70	65	60	170	165	160	Suisse
	UK	United Kingdom	1,622	1,600	1,530	798	900	850	878	750	730	54	50	50	Royaume-Uni
		Total Europe	9,734	9,786	9,727	10,776	10,845	10,804	4,341	4,111	4,068	5,383	5,170	5,145	Total Europe
	Canada	Canada	1,301	1,135	1,153	1,159	1,205	1,205	780	648	639	637	718	691	Canada
	United States	United States	6,012	6,042	6,073	3,882	3,901	3,921	2,552	2,565	2,578	422	424	426	Etats-Unis
		Total North America	7,313	7,177	7,226	5,041	5,106	5,126	3,332	3,213	3,217	1,059	1,142	1,117	Total Amérique du Nord

Table 6c

6.4.3
		TABLE 6c
			OTHER FIBREBOARD						AUTRES PANNEAUX DE FIBRES
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	157	150	146	0	0	0	162	154	150	5	4	4	Autriche
	Bulgaria	Bulgaria	3	...	...	0	...	...	3	...	...	0	...	...	Bulgarie
	Cyprus	Cyprus	1	2	2	0	0	0	1	2	2	0	0	0	Chypre
	Czech Republic	Czech Republic	68	75	83	0	0	0	164	172	181	96	97	98	République tchèque
	Estonia	Estonia	26	24	24	80	70	70	5	4	4	59	50	50	Estonie
	Finland	Finland	19	19	19	0	0	0	20	20	20	0	0	0	Finlande
	Germany	Germany	1,803	1,790	1,725	1,412	1,400	1,350	1,078	1,075	1,025	686	685	650	Allemagne
	Latvia	Latvia	7	1	1	0	0	0	18	12	12	11	11	11	Lettonie
	Luxembourg	Luxembourg	4	1	1	0	0	0	4	1	1	0	0	0	Luxembourg
	Malta	Malta	1	1	2	0	0	0	1	1	2	0	0	0	Malte
	Netherlands	Netherlands	119	116	116	29	29	29	98	95	95	8	8	8	Pays-Bas
	Poland	Poland	1,077	1,080	1,130	2,132	2,150	2,250	30	30	30	1,085	1,100	1,150	Pologne
	Portugal	Portugal	-12	5	5	8	0	0	15	20	20	35	15	15	Portugal
	Serbia	Serbia	2	3	4	0	0	0	2	3	4	0	0	0	Serbie
	Slovakia	Slovakia	70	69	69	0	0	0	70	70	70	0	1	1	Slovaquie
	Slovenia	Slovenia	5	1	1	0	0	0	10	6	6	5	5	5	Slovénie
	Sweden	Sweden	20	20	15	0	0	0	31	30	25	11	10	10	Suède
	Switzerland	Switzerland	174	179	184	0	0	0	175	180	185	1	1	1	Suisse
	UK	United Kingdom	84	80	80	0	0	0	91	90	90	7	10	10	Royaume-Uni
		Total Europe	3,628	3,616	3,607	3,661	3,649	3,699	1,976	1,965	1,922	2,009	1,997	2,013	Total Europe
	Canada	Canada	144	171	166	100	100	100	162	181	180	117	110	114	Canada
	United States	United States	3,212	3,583	3,398	3,179	3,286	3,233	204	370	287	171	73	122	Etats-Unis
		Total North America	3,357	3,754	3,564	3,279	3,386	3,333	366	551	467	288	183	236	Total Amérique du Nord

Table 7

7.x
		TABLE 7
			WOOD PULP						PATE DE BOIS
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 mt
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	2,261	2,229	2,235	2,004	1,970	1,980	578	577	577	321	318	322	Autriche
	Bulgaria	Bulgaria	125	...	...	210	...	...	7	...	...	92	...	...	Bulgarie
	Czech Republic	Czech Republic	803	806	806	614	640	660	310	300	289	121	134	143	République tchèque
	Estonia	Estonia	84	140	140	260	200	200	49	50	50	225	110	110	Estonie
	Finland	Finland a	6,625	5,760	6,280	10,950	9,360	10,520	150	220	220	4,475	3,820	4,460	Finlande a
	Germany	Germany	5,622	5,685	5,715	2,327	2,390	2,420	4,451	4,400	4,400	1,156	1,105	1,105	Allemagne
	Latvia	Latvia	2	6	2	14	10	10	2	2	2	14	6	10	Lettonie
	Netherlands	Netherlands	929	987	987	37	37	37	2,167	2,150	2,150	1,274	1,200	1,200	Pays-Bas
	Poland	Poland	2,767	2,750	2,790	1,749	1,720	1,750	1,194	1,220	1,250	177	190	210	Pologne
	Portugal	Portugal	1,660	1,640	1,645	2,809	2,750	2,800	141	140	145	1,290	1,250	1,300	Portugal
	Serbia	Serbia	75	76	79	0	0	0	76	77	80	1	1	1	Serbie
	Slovakia	Slovakia	680	685	695	769	775	800	160	160	170	248	250	275	Slovaquie
	Slovenia	Slovenia	331	309	309	86	82	82	250	230	230	5	3	3	Slovénie
	Sweden	Sweden	8,146	8,250	8,400	11,701	11,950	12,150	602	600	600	4,157	4,300	4,350	Suède
	Switzerland	Switzerland	160	160	160	70	70	70	90	90	90	0	0	0	Suisse
	UK	United Kingdom	984	990	...	220	220	...	766	780	790	2	10	10	Royaume-Uni
		Total Europe	31,255	30,473	30,243	33,820	32,174	33,479	10,993	10,996	11,043	13,558	12,697	13,499	Total Europe
	Canada	Canada	7,265	6,097	6,156	14,886	13,861	13,468	1,095	950	1,185	8,717	8,714	8,497	Canada
	United States	United States	48,100	48,274	48,187	49,685	49,859	49,772	6,036	6,036	6,036	7,621	7,621	7,621	Etats-Unis
		Total North America	55,365	54,372	54,344	64,571	63,720	63,240	7,131	6,986	7,221	16,337	16,335	16,118	Total Amérique du Nord
			a imports exclude dissolving pulp						a les importations excluent pâte à dissoudre

Table 8

10.x
		TABLE 8
			PAPER AND PAPERBOARD						PAPIERS ET CARTONS
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 mt
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	2,334	2,340	2,315	5,065	5,100	5,080	1,296	1,310	1,300	4,028	4,070	4,065	Autriche
	Bulgaria	Bulgaria	573	...	...	394	...	...	358	...	...	180	...	...	Bulgarie
	Cyprus	Cyprus	44	48	46	0	0	0	44	48	46	0	0	0	Chypre
	Czech Republic	Czech Republic	1,618	1,602	1,604	901	906	909	1,623	1,604	1,600	906	908	905	République tchèque
	Estonia	Estonia	126	150	150	70	90	90	138	130	130	83	70	70	Estonie
	Finland	Finland	636	590	620	8,660	7,450	8,150	361	350	350	8,385	7,210	7,880	Finlande
	Germany	Germany	18,980	18,500	18,400	23,123	22,800	22,700	10,009	9,800	9,800	14,152	14,100	14,100	Allemagne
	Latvia	Latvia	174	182	182	28	30	30	186	200	200	40	48	48	Lettonie
	Luxembourg	Luxembourg	31	8	8	0	0	0	38	8	8	7	0	0	Luxembourg
	Malta	Malta	26	27	27	0	0	0	26	27	27	0	0	0	Malte
	Netherlands	Netherlands	2,869	2,890	2,890	2,942	2,950	2,950	2,268	2,260	2,260	2,341	2,320	2,320	Pays-Bas
	Poland	Poland	8,002	8,100	8,150	5,324	5,450	5,550	5,233	5,300	5,400	2,556	2,650	2,800	Pologne
	Portugal	Portugal	1,245	1,250	1,290	2,247	2,200	2,240	928	850	900	1,931	1,800	1,850	Portugal
	Serbia	Serbia	760	780	785	535	520	525	462	470	480	237	210	220	Serbie
	Slovakia	Slovakia	554	600	600	1,019	975	1,000	474	450	475	939	825	875	Slovaquie
	Slovenia	Slovenia	491	435	440	635	605	590	435	420	420	579	590	570	Slovénie
	Sweden	Sweden	704	950	950	8,924	8,700	8,850	897	750	750	9,117	8,500	8,650	Suède
	Switzerland	Switzerland	1,050	1,055	1,060	1,170	1,175	1,180	610	600	590	730	720	710	Suisse
	UK	United Kingdom	7,482	7,430	7,450	3,640	3,530	3,650	4,589	4,660	4,550	747	760	750	Royaume-Uni
		Total Europe	47,697	46,937	46,967	64,677	62,481	63,494	29,977	29,237	29,286	46,957	44,781	45,813	Total Europe
	Canada	Canada	4,940	4,796	4,930	8,787	8,436	8,436	2,424	2,567	2,538	6,272	6,207	6,045	Canada
	United States	United States	65,622	68,268	66,945	67,476	70,196	68,836	8,223	8,555	8,389	10,077	10,483	10,280	Etats-Unis
		Total North America	70,561	73,064	71,874	76,263	78,632	77,272	10,647	11,122	10,927	16,348	16,690	16,325	Total Amérique du Nord

Table 9

	TABLE 9
			REMOVALS OF WOOD IN THE ROUGH									QUANTITES ENLEVEES DE BOIS BRUT
			TOTAL									TOTAL
	1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
	Country		Industrial wood - Bois industriels												Wood fuel c Bois de chauffage c						Pays
			Total			Logs			Pulpwood a			Other b						Total
						Grumes			Bois de trituration a			Autre b
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
Austria	Austria		13,521	13,873	13,650	10,420	10,607	10,300	3,101	3,266	3,350	0	0	0	4,900	5,263	5,400	18,420	19,136	19,050	Autriche
Bulgaria	Bulgaria		3,172	...	...	1,524	...	...	1,606	...	...	42	...	...	2,357	...	...	5,529	...	...	Bulgarie
Cyprus	Cyprus		...	...	...	...	...	...	...	...	...	0	0	0	7	7	8	...	...	...	Chypre
Czech Republic	Czech Republic		25,146	21,122	20,576	17,739	15,858	14,572	7,294	5,149	5,887	113	115	117	5,110	4,933	4,536	30,256	26,055	25,112	République tchèque
Estonia	Estonia		6,520	6,317	6,317	4,145	4,060	4,060	2,323	2,200	2,200	52	57	57	4,148	4,100	4,100	10,667	10,417	10,417	Estonie
Finland	Finland		58,036	55,847	58,540	26,292	24,618	24,713	31,744	31,229	33,827	0	0	0	8,868	8,868	8,868	66,904	64,715	67,408	Finlande
Germany	Germany		59,187	57,179	54,270	47,403	44,256	42,085	11,624	12,765	12,027	161	158	158	23,224	23,900	24,100	82,411	81,079	78,370	Allemagne
Latvia	Latvia		13,003	12,650	12,550	7,827	7,400	7,300	3,986	4,100	4,100	1,190	1,150	1,150	2,940	3,150	3,200	15,943	15,800	15,750	Lettonie
Luxembourg	Luxembourg		217	332	197	39	86	144	84	160	38	94	86	15	46	73	45	262	405	242	Luxembourg
Netherlands	Netherlands		648	653	653	210	214	214	394	395	395	43	44	44	2,362	2,350	2,350	3,010	3,003	3,003	Pays-Bas
Poland	Poland		38,587	40,300	41,530	18,508	19,300	19,950	19,471	20,410	21,000	608	590	580	4,519	4,450	4,350	43,106	44,750	45,880	Pologne
Portugal	Portugal		12,136	12,240	12,155	2,147	2,190	2,220	9,659	9,700	9,600	331	350	335	1,762	1,830	1,780	13,899	14,070	13,935	Portugal
Serbia	Serbia		1,646	1,586	1,630	1,176	1,166	1,185	307	280	295	163	140	150	6,251	6,950	7,010	7,897	8,536	8,640	Serbie
Slovakia	Slovakia		7,170	7,475	7,590	4,243	4,335	4,400	2,893	3,100	3,150	34	40	40	495	550	610	7,665	8,025	8,200	Slovaquie
Slovenia	Slovenia		2,673	3,078	2,995	1,977	2,210	2,130	648	825	825	48	43	40	1,043	1,200	1,260	3,716	4,278	4,255	Slovénie
Sweden	Sweden		71,400	71,400	70,400	39,300	37,800	36,000	31,800	33,300	34,100	300	300	300	5,400	5,400	5,400	76,800	76,800	75,800	Suède
Switzerland	Switzerland		3,003	3,088	3,163	2,450	2,550	2,610	550	535	550	3	3	3	1,980	2,030	2,100	4,983	5,118	5,263	Suisse
UK	United Kingdom		8,716	7,660	8,410	6,354	5,360	6,060	1,898	1,900	1,900	463	400	450	2,184	2,180	2,180	10,899	9,840	10,590	Royaume-Uni
	Total Europe		324,781	314,800	314,626	191,753	182,010	177,943	129,382	129,314	133,244	3,646	3,476	3,439	77,596	77,234	77,297	402,369	392,027	391,915	Total Europe
Canada	Canada		138,131	135,303	135,303	120,741	117,995	117,995	15,239	15,040	15,040	2,152	2,268	2,268	1,472	1,567	1,567	139,603	136,869	136,869	Canada
United States	United States		382,956	386,045	384,500	183,473	184,966	184,219	185,734	187,318	186,526	13,749	13,762	13,755	71,111	71,127	71,119	454,066	457,172	455,619	Etats-Unis
	Total North America		521,087	521,348	519,803	304,213	302,961	302,214	200,973	202,358	201,566	15,901	16,030	16,023	72,582	72,693	72,685	593,669	594,041	592,488	Total Amérique du Nord
		a	Pulpwood, round and split, as well as chips and particles produced directly								a	Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées
			therefrom and used as pulpwood									directement à partir des rondins et quartiers et utilisées comme bois de trituration
		b	Pitprops, poles, piling, posts etc.								b	Bois de mine, poteaux, pilotis, piquets etc.
		c	Including chips and particles produced from wood in the rough and								c	Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées
			used for energy purposes									à des fins energétiques

Table 9a

1.2.3.C
		TABLE 9a
				REMOVALS OF WOOD IN THE ROUGH									QUANTITES ENLEVEES DE BOIS BRUT
				SOFTWOOD									CONIFERES
		1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
		Country		Industrial wood - Bois industriels												Wood fuel c Bois de chauffage c						Pays
				Total			Logs			Pulpwood a			Other b						Total
							Grumes			Bois de trituration a			Autre b
				2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria		12,671	12,947	12,700	10,139	10,315	10,000	2,531	2,632	2,700	0	0	0	2,993	3,158	3,200	15,663	16,105	15,900	Autriche
	Bulgaria	Bulgaria		2,228	...	...	1,178	...	...	1,019	...	...	31	...	...	608	...	...	2,836	...	...	Bulgarie
	Cyprus	Cyprus		...	...	...	2	2	2	...	...	...	0	0	0	6	6	7	...	...	...	Chypre
	Czech Republic	Czech Republic		24,251	20,470	19,943	17,301	15,480	14,210	6,841	4,880	5,621	109	110	112	4,463	4,365	3,965	28,714	24,835	23,908	République tchèque
	Estonia	Estonia		4,447	4,330	4,330	3,268	3,200	3,200	1,152	1,100	1,100	27	30	30	1,431	1,400	1,400	5,878	5,730	5,730	Estonie
	Finland	Finland		48,840	46,602	48,616	25,247	23,457	23,590	23,593	23,145	25,026	0	0	0	4,279	4,279	4,279	53,119	50,881	52,895	Finlande
	Germany	Germany		55,270	53,354	50,415	44,611	41,447	39,283	10,505	11,757	10,982	153	150	150	9,265	9,600	9,800	64,534	62,954	60,215	Allemagne
	Latvia	Latvia		8,661	8,350	8,250	5,975	5,600	5,500	2,036	2,100	2,100	650	650	650	315	350	400	8,976	8,700	8,650	Lettonie
	Luxembourg	Luxembourg		156	169	143	27	51	122	35	32	6	94	86	15	24	30	11	180	199	154	Luxembourg
	Netherlands	Netherlands		452	449	449	154	154	154	263	260	260	35	35	35	451	450	450	903	899	899	Pays-Bas
	Poland	Poland		31,131	32,500	33,350	15,698	16,370	16,900	14,861	15,570	15,900	572	560	550	2,189	2,150	2,100	33,320	34,650	35,450	Pologne
	Portugal	Portugal		3,352	3,440	3,455	1,851	1,900	1,970	1,370	1,400	1,350	131	140	135	445	480	450	3,797	3,920	3,905	Portugal
	Serbia	Serbia		319	315	335	202	210	220	76	70	75	41	35	40	129	150	160	448	465	495	Serbie
	Slovakia	Slovakia		3,678	3,815	3,830	2,724	2,735	2,750	928	1,050	1,050	26	30	30	223	250	285	3,901	4,065	4,115	Slovaquie
	Slovenia	Slovenia		1,790	1,978	1,888	1,510	1,680	1,600	262	285	275	18	13	13	106	150	160	1,896	2,128	2,048	Slovénie
	Sweden	Sweden		64,850	64,650	63,450	39,100	37,600	35,800	25,600	26,900	27,500	150	150	150	2,700	2,700	2,700	67,550	67,350	66,150	Suède
	Switzerland	Switzerland		2,602	2,652	2,712	2,224	2,300	2,350	376	350	360	2	2	2	834	880	900	3,436	3,532	3,612	Suisse
	UK	United Kingdom		8,608	7,550	8,300	6,298	5,300	6,000	1,895	1,900	1,900	415	350	400	1,571	1,570	1,570	10,179	9,120	9,870	Royaume-Uni
		Total Europe		273,305	263,571	262,166	177,509	167,801	163,651	93,345	93,431	96,205	2,454	2,341	2,312	32,032	31,968	31,837	305,330	295,533	293,995	Total Europe
	Canada	Canada		113,236	110,975	110,975	108,690	106,633	106,633	4,232	3,975	3,975	314	367	367	659	724	724	113,895	111,700	111,700	Canada
	United States	United States		306,264	307,884	307,074	150,702	151,554	151,128	143,462	144,219	143,840	12,100	12,111	12,106	33,760	33,770	33,765	340,023	341,654	340,839	Etats-Unis
		Total North America		419,499	418,859	418,049	259,392	258,187	257,761	147,694	148,194	147,816	12,414	12,478	12,472	34,419	34,495	34,489	453,918	453,354	452,538	Total Amérique du Nord
			a	Pulpwood, round and split, as well as chips and particles produced directly								a	Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées
				therefrom and used as pulpwood									directement à partir des rondins et quartiers et utilisées comme bois de trituration
			b	Pitprops, poles, piling, posts etc.								b	Bois de mine, poteaux, pilotis, piquets etc.
			c	Including chips and particles produced from wood in the rough and								c	Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées
				used for energy purposes									à des fins energétiques

Table 9b

1.2.3.NC
		TABLE 9b
				REMOVALS OF WOOD IN THE ROUGH									QUANTITES ENLEVEES DE BOIS BRUT
				HARDWOOD									NON-CONIFERES
		1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
		Country		Industrial wood - Bois industriels												Wood fuel c Bois de chauffage c						Pays
				Total			Logs			Pulpwood a			Other b						Total
							Grumes			Bois de trituration a			Autre b
				2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria		850	926	950	280	292	300	570	634	650	0	0	0	1,907	2,105	2,200	2,757	3,031	3,150	Autriche
	Bulgaria	Bulgaria		944	...	...	346	...	...	587	...	...	11	...	...	1,749	...	...	2,693	...	...	Bulgarie
	Cyprus	Cyprus		...	...	...	...	...	...	...	...	...	0	0	0	1	1	1	...	...	...	Chypre
	Czech Republic	Czech Republic		895	652	633	438	378	362	453	269	266	4	5	5	647	568	571	1,542	1,220	1,204	République tchèque
	Estonia	Estonia		2,073	1,987	1,987	877	860	860	1,171	1,100	1,100	25	27	27	2,717	2,700	2,700	4,789	4,687	4,687	Estonie
	Finland	Finland		9,196	9,245	9,924	1,045	1,161	1,123	8,151	8,084	8,801	0	0	0	4,589	4,589	4,589	13,785	13,834	14,513	Finlande
	Germany	Germany		3,918	3,824	3,855	2,792	2,809	2,802	1,119	1,008	1,045	8	8	8	13,959	14,300	14,300	17,877	18,124	18,155	Allemagne
	Latvia	Latvia		4,342	4,300	4,300	1,852	1,800	1,800	1,950	2,000	2,000	540	500	500	2,625	2,800	2,800	6,967	7,100	7,100	Lettonie
	Luxembourg	Luxembourg		61	163	54	12	35	22	49	128	32	0	0	0	22	43	34	83	206	89	Luxembourg
	Netherlands	Netherlands		196	204	204	57	60	60	131	135	135	9	9	9	1,912	1,900	1,900	2,108	2,104	2,104	Pays-Bas
	Poland	Poland		7,456	7,800	8,180	2,810	2,930	3,050	4,610	4,840	5,100	36	30	30	2,330	2,300	2,250	9,787	10,100	10,430	Pologne
	Portugal	Portugal		8,784	8,800	8,700	296	290	250	8,289	8,300	8,250	200	210	200	1,318	1,350	1,330	10,102	10,150	10,030	Portugal
	Serbia	Serbia		1,327	1,271	1,295	974	956	965	231	210	220	122	105	110	6,122	6,800	6,850	7,449	8,071	8,145	Serbie
	Slovakia	Slovakia		3,492	3,660	3,760	1,519	1,600	1,650	1,965	2,050	2,100	8	10	10	272	300	325	3,764	3,960	4,085	Slovaquie
	Slovenia	Slovenia		883	1,100	1,107	467	530	530	386	540	550	30	30	27	937	1,050	1,100	1,820	2,150	2,207	Slovénie
	Sweden	Sweden		6,550	6,750	6,950	200	200	200	6,200	6,400	6,600	150	150	150	2,700	2,700	2,700	9,250	9,450	9,650	Suède
	Switzerland	Switzerland		401	436	451	226	250	260	174	185	190	1	1	1	1,146	1,150	1,200	1,547	1,586	1,651	Suisse
	UK	United Kingdom		108	110	110	56	60	60	3	0	0	48	50	50	613	610	610	720	720	720	Royaume-Uni
		Total Europe		51,476	51,228	52,460	14,247	14,211	14,294	36,038	35,883	37,039	1,191	1,135	1,127	45,564	45,266	45,460	97,039	96,493	97,919	Total Europe
	Canada	Canada		24,896	24,328	24,328	12,051	11,361	11,361	11,007	11,065	11,065	1,838	1,901	1,901	812	842	842	25,708	25,170	25,170	Canada
	United States	United States		76,692	78,161	77,427	32,771	33,412	33,091	42,272	43,099	42,685	1,649	1,651	1,650	37,351	37,356	37,354	114,043	115,517	114,780	Etats-Unis
		Total North America		101,588	102,489	101,754	44,822	44,773	44,453	53,279	54,164	53,750	3,487	3,552	3,551	38,163	38,199	38,196	139,751	140,687	139,950	Total Amérique du Nord
			a	Pulpwood, round and split, as well as chips and particles produced directly								a	Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées
				therefrom and used as pulpwood									directement à partir des rondins et quartiers et utilisées comme bois de trituration
			b	Pitprops, poles, piling, posts etc.								b	Bois de mine, poteaux, pilotis, piquets etc.
			c	Including chips and particles produced from wood in the rough and								c	Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées
				used for energy purposes									à des fins energétiques

Table 10

1.2.1.C
		TABLE 10
				SOFTWOOD SAWLOGS						GRUMES DE SCIAGES DES CONIFERES
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
									1000 m3
				Apparent Consumption a						Imports			Exports
		Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
				2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria		17,589	16,095	16,500	10,139	10,315	10,000	8,044	6,660	7,000	594	880	500	Autriche
	Bulgaria	Bulgaria		1,173	...	...	1,178	...	...	0	...	...	5	...	...	Bulgarie
	Cyprus	Cyprus		2	2	2	2	2	2	0	0	0	0	0	0	Chypre
	Czech Republic	Czech Republic		8,801	8,617	7,916	17,301	15,480	14,210	750	663	660	9,250	7,526	6,954	République tchèque
	Estonia	Estonia		3,640	3,730	3,730	3,268	3,200	3,200	455	600	600	83	70	70	Estonie
	Finland	Finland		25,080	23,224	23,365	25,247	23,457	23,590	165	78	86	332	311	311	Finlande
	Germany	Germany		39,795	39,077	38,613	44,611	41,447	39,283	3,190	3,300	3,600	8,006	5,670	4,270	Allemagne
	Latvia	Latvia		6,786	6,350	6,100	5,975	5,600	5,500	1,088	1,100	900	277	350	300	Lettonie
	Luxembourg	Luxembourg		393	90	161	27	51	122	609	164	164	243	125	125	Luxembourg
	Netherlands	Netherlands		176	169	169	154	154	154	87	80	80	65	65	65	Pays-Bas
	Poland	Poland		14,868	15,470	16,000	15,698	16,370	16,900	1,090	1,150	1,200	1,920	2,050	2,100	Pologne
	Portugal	Portugal		1,971	1,990	2,075	1,851	1,900	1,970	150	130	140	30	40	35	Portugal
	Serbia	Serbia		226	220	233	202	210	220	28	12	15	4	2	2	Serbie
	Slovakia	Slovakia		3,057	3,235	3,250	2,724	2,735	2,750	1,049	900	900	716	400	400	Slovaquie
	Slovenia	Slovenia		1,511	1,740	1,620	1,510	1,680	1,600	287	320	300	286	260	280	Slovénie
	Sweden	Sweden		39,240	37,680	35,880	39,100	37,600	35,800	880	1,010	1,010	740	930	930	Suède
	Switzerland	Switzerland		1,935	2,045	2,105	2,224	2,300	2,350	52	55	55	341	310	300	Suisse
	UK	United Kingdom		6,515	5,510	6,200	6,298	5,300	6,000	359	360	360	142	150	160	Royaume-Uni
		Total Europe		172,758	165,244	163,919	177,509	167,801	163,651	18,283	16,582	17,070	23,033	19,139	16,802	Total Europe
	Canada	Canada		104,025	102,730	102,894	108,690	106,633	106,633	2,221	1,489	1,245	6,887	5,392	4,984	Canada
	United States	United States		142,644	143,443	143,043	150,702	151,554	151,128	278	280	279	8,336	8,391	8,364	Etats-Unis
		Total North America		246,668	246,173	245,937	259,392	258,187	257,761	2,500	1,769	1,524	15,223	13,784	13,348	Total Amérique du Nord
			a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 11

1.2.1.NC
		TABLE 11
				HARDWOOD SAWLOGS (total)						GRUMES DE SCIAGES DES NON-CONIFERES
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
									1000 m3
				Apparent Consumption a						Imports			Exports
		Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
				2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023		Country	Consumption			Production			Imports			Exports			Country
	Austria	Austria		398	362	370	280	292	300	162	140	120	45	70	50	Autriche	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Bulgaria	Bulgaria		346	...	...	346	...	...	0	...	...	0	...	...	Bulgarie	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Czech Republic	Czech Republic		305	239	218	438	378	362	132	135	138	265	274	282	République tchèque	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Estonia	Estonia		901	885	885	877	860	860	48	45	45	23	20	20	Estonie	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Finland	Finland		1,218	1,193	1,123	1,045	1,161	1,123	173	32	0	0	0	0	Finlande	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Germany	Germany		2,174	2,346	2,348	2,792	2,809	2,802	110	111	120	727	574	574	Allemagne	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Latvia	Latvia		1,570	1,470	1,520	1,852	1,800	1,800	27	60	50	309	390	330	Lettonie	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Luxembourg	Luxembourg		199	139	126	12	35	22	209	111	111	22	7	7	Luxembourg	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Netherlands	Netherlands		62	70	70	57	60	60	65	70	70	59	60	60	Pays-Bas	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Poland	Poland		2,740	2,860	3,050	2,810	2,930	3,050	80	80	...	150	150	...	Pologne	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Portugal	Portugal		406	380	350	296	290	250	140	120	130	30	30	30	Portugal	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Serbia	Serbia		954	951	955	974	956	965	30	15	20	50	20	30	Serbie	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Slovakia	Slovakia		1,658	1,700	1,750	1,519	1,600	1,650	562	500	500	423	400	400	Slovaquie	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Slovenia	Slovenia		263	242	260	467	530	530	43	42	40	248	330	310	Slovénie	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Sweden	Sweden		226	226	226	200	200	200	26	26	26	0	0	0	Suède	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Switzerland	Switzerland		104	130	150	226	250	260	27	35	40	149	155	150	Suisse	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	UK	United Kingdom		67	80	80	56	60	60	15	20	20	4	0	0	Royaume-Uni	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
		Total Europe		13,592	13,273	13,481	14,247	14,211	14,294	1,849	1,542	1,430	2,504	2,480	2,243	Total Europe	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	Canada	Canada		13,122	12,288	12,248	12,051	11,361	11,361	1,145	1,018	969	75	92	83	Canada	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
	United States	United States		30,814	31,416	31,115	32,771	33,412	33,091	151	154	153	2,109	2,150	2,129	Etats-Unis	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
		Total North America		43,935	43,704	43,363	44,822	44,773	44,453	1,297	1,173	1,122	2,183	2,241	2,212	Total Amérique du Nord	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!	ERROR:#REF!
			a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 11a

	TABLE 11a
			HARDWOOD LOGS (temperate)						GRUMES DE NON-CONIFERES (zone tempérée)
	Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption a						Imports			Exports
	Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
Austria	Austria		280	292	300	280	292	300	...	...	...	...	...	...	Autriche
Bulgaria	Bulgaria		346	...	...	346	...	...	...	...	...	...	...	...	Bulgarie
Czech Republic	Czech Republic		303	237	216	438	378	362	130	133	136	265	274	282	République tchèque
Estonia	Estonia		877	860	860	877	860	860	...	...	...	...	...	...	Estonie
Finland	Finland		1,045	1,161	1,123	1,045	1,161	1,123	...	...	...	...	...	...	Finlande
Germany	Germany		2,168	2,341	2,343	2,792	2,809	2,802	98	101	110	722	569	569	Allemagne
Latvia	Latvia		1,852	1,800	1,800	1,852	1,800	1,800	...	...	...	...	...	...	Lettonie
Luxembourg	Luxembourg		191	139	22	12	35	22	201	111	...	22	7	...	Luxembourg
Netherlands	Netherlands		53	62	62	57	60	60	50	55	55	53	53	53	Pays-Bas
Poland	Poland		2,739	2,858	3,050	2,810	2,930	3,050	78	78	...	150	150	...	Pologne
Portugal	Portugal		382	355	325	296	290	250	110	90	100	24	25	25	Portugal
Serbia	Serbia		953	950	954	974	956	965	29	14	19	50	20	30	Serbie
Slovakia	Slovakia		1,519	1,600	1,650	1,519	1,600	1,650	...	...	...	...	...	...	Slovaquie
Slovenia	Slovenia		262	241	259	467	530	530	42	41	39	248	330	310	Slovénie
Sweden	Sweden		200	200	200	200	200	200	...	...	...	...	...	...	Suède
Switzerland	Switzerland		226	250	260	226	250	260	...	...	...	...	...	...	Suisse
UK	United Kingdom		66	80	80	56	60	60	14	20	20	4	0	0	Royaume-Uni
	Total Europe		13,462	13,426	13,504	14,247	14,211	14,294	753	643	479	1,538	1,428	1,269	Total Europe
Canada	Canada		12,051	11,361	11,361	12,051	11,361	11,361	...	...	...	...	...	...	Canada
United States	United States		30,813	31,415	31,114	32,771	33,412	33,091	150	152	151	2,108	2,149	2,128	Etats-Unis
	Total North America		42,864	42,777	42,475	44,822	44,773	44,453	150	152	151	2,108	2,149	2,128	Total Amérique du Nord
		a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 11b

1.2.1.NC.T
		TABLE 11b
			HARDWOOD LOGS (tropical)						GRUMES DE NON-CONIFERES (tropicale)
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Net Trade						Imports			Exports
		Country	Commerce Net			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Czech Republic	Czech Republic	-2	-2	-2				2	2	2	0	0	0	République tchèque
	Germany	Germany	-7	-5	-5				11	10	10	5	5	5	Allemagne
	Luxembourg	Luxembourg	-8	0	...				8	0	...	0	0	...	Luxembourg
	Netherlands	Netherlands	-9	-8	-8				15	15	15	6	7	7	Pays-Bas
	Poland	Poland	-1	-2	-2				2	2	2	0	0	0	Pologne
	Portugal	Portugal	-24	-25	-25				30	30	30	6	5	5	Portugal
	Serbia	Serbia	-1	-1	-1				1	1	1	0	0	0	Serbie
	Slovenia	Slovenia	-1	-1	-1				1	1	1	0	0	0	Slovénie
	UK	United Kingdom	-1	0	0				1	0	0	0	0	0	Royaume-Uni
		Total Europe	-54	-44	-44				71	61	61	17	17	17	Total Europe
	United States	United States	-1	-1	-1				2	2	2	1	1	1	Etats-Unis
		Total North America	-1	-1	-1				2	2	2	1	1	1	Total Amérique du Nord

Table 12

	TABLE 12
			PULPWOOD (total)						BOIS DE TRITURATION (total)
	Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption a						Imports			Exports
	Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
Austria	Austria		14,154	13,876	13,900	10,568	10,416	10,450	4,953	4,650	4,500	1,367	1,190	1,050	Autriche
Bulgaria	Bulgaria		1,639	...	...	1,699	...	...	27	...	...	87	...	...	Bulgarie
Cyprus	Cyprus		...	...	...	...	...	...	...	...	...	...	...	...	Chypre
Czech Republic	Czech Republic		6,155	4,306	4,709	9,027	6,877	7,621	475	469	475	3,347	3,040	3,387	République tchèque
Estonia	Estonia		3,575	3,600	3,600	6,323	6,100	6,100	268	240	240	3,015	2,740	2,740	Estonie
Finland	Finland		57,262	49,139	51,233	47,052	46,586	49,737	11,200	3,364	2,307	990	811	811	Finlande
Germany	Germany		27,289	28,098	28,031	28,327	29,465	28,527	3,819	3,509	3,680	4,858	4,876	4,176	Allemagne
Latvia	Latvia		4,879	5,025	5,180	8,296	8,600	8,800	1,417	875	780	4,834	4,450	4,400	Lettonie
Luxembourg	Luxembourg		605	681	559	605	681	559	...	...	...	...	...	...	Luxembourg
Malta	Malta		…	…	…	…	…	…	...	...	...	...	...	...	Malte
Netherlands	Netherlands		1,566	1,565	1,565	1,365	1,365	1,365	644	680	680	443	480	480	Pays-Bas
Poland	Poland		31,755	32,925	31,800	29,682	30,910	31,800	3,947	3,810	...	1,874	1,795	...	Pologne
Portugal	Portugal		13,642	12,870	12,780	11,483	11,620	11,500	2,833	1,815	1,850	674	565	570	Portugal
Serbia	Serbia		812	840	890	800	830	875	15	12	17	3	2	2	Serbie
Slovakia	Slovakia		3,849	4,000	4,100	4,043	4,250	4,350	1,043	1,000	1,000	1,237	1,250	1,250	Slovaquie
Slovenia	Slovenia		1,129	1,019	1,345	2,008	2,275	2,325	653	674	680	1,532	1,930	1,660	Slovénie
Sweden	Sweden		61,355	61,618	61,918	55,300	55,800	56,100	6,858	6,774	6,774	803	956	956	Suède
Switzerland	Switzerland		1,773	1,773	1,793	1,340	1,345	1,370	623	613	613	190	185	190	Suisse
UK	United Kingdom		5,304	5,040	5,280	5,020	4,770	5,010	384	380	380	101	110	110	Royaume-Uni
	Total Europe		236,743	226,375	228,683	222,939	221,890	226,489	39,158	28,865	23,976	25,354	24,380	21,782	Total Europe
Canada	Canada		40,927	37,948	37,855	38,095	36,525	36,525	3,722	2,250	2,204	890	827	873	Canada
United States	United States		240,634	243,316	241,975	246,219	249,015	247,617	264	268	266	5,849	5,966	5,908	Etats-Unis
	Total North America		281,560	281,264	279,830	284,314	285,540	284,142	3,986	2,517	2,469	6,740	6,794	6,781	Total Amérique du Nord
		Includes wood residues, chips and particles for all purposes							Comprend les dechets de bois, plaquettes et particules pour toute utilisation
		a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 12a

1.2.2.C
		TABLE 12a
				PULPWOOD LOGS (ROUND AND SPLIT)						BOIS DE TRITURATION (RONDINS ET QUARTIERS)
				Softwood						Conifères
		1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
				Apparent Consumption a						Imports			Exports
		Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
				2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria		4,145	4,212	4,300	2,531	2,632	2,700	1,973	1,850	1,800	360	270	200	Autriche
	Bulgaria	Bulgaria		1,000	...	...	1,019	...	...	15	...	...	34	...	...	Bulgarie
	Czech Republic	Czech Republic		4,111	2,444	2,838	6,841	4,880	5,621	270	263	265	3,000	2,699	3,048	République tchèque
	Estonia	Estonia		533	560	560	1,152	1,100	1,100	32	40	40	650	580	580	Estonie
	Finland	Finland		24,151	23,246	25,181	23,593	23,145	25,026	1,294	754	808	736	653	653	Finlande
	Germany	Germany		10,697	11,527	11,552	10,505	11,757	10,982	2,523	2,200	2,400	2,331	2,430	1,830	Allemagne
	Latvia	Latvia		1,714	1,850	1,750	2,036	2,100	2,100	473	400	350	795	650	700	Lettonie
	Luxembourg	Luxembourg		35	32	6	35	32	6	...	...	...	...	...	...	Luxembourg
	Netherlands	Netherlands		195	180	180	263	260	260	113	110	110	182	190	190	Pays-Bas
	Poland	Poland		14,706	15,470	15,900	14,861	15,570	15,900	1,174	1,200	1,250	1,329	1,300	1,250	Pologne
	Portugal	Portugal		1,402	1,420	1,380	1,370	1,400	1,350	75	65	70	43	45	40	Portugal
	Serbia	Serbia		76	70	75	76	70	75	0	0	0	0	0	0	Serbie
	Slovakia	Slovakia		843	900	900	928	1,050	1,050	645	600	600	730	750	750	Slovaquie
	Slovenia	Slovenia		288	325	315	262	285	275	264	270	270	239	230	230	Slovénie
	Sweden	Sweden		28,302	29,632	30,232	25,600	26,900	27,500	3,110	3,255	3,255	408	523	523	Suède
	Switzerland	Switzerland		306	280	290	376	350	360	20	20	20	90	90	90	Suisse
	UK	United Kingdom		2,085	2,080	2,080	1,895	1,900	1,900	213	210	210	23	30	30	Royaume-Uni
		Total Europe		94,588	94,228	97,539	93,345	93,431	96,205	12,194	11,237	11,448	10,950	10,440	10,114	Total Europe
	Canada	Canada		5,139	4,236	4,204	4,232	3,975	3,975	961	297	273	54	36	45	Canada
	United States	United States		143,467	144,224	143,845	143,462	144,219	143,840	5	5	5	0	0	0	Etats-Unis
		Total North America		148,606	148,460	148,049	147,694	148,194	147,816	966	302	278	54	36	45	Total Amérique du Nord
			a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 12b

1.2.2.NC
		TABLE 12b
				PULPWOOD LOGS (ROUND AND SPLIT)						BOIS DE TRITURATION (RONDINS ET QUARTIERS)
				Hardwood						Non-conifères
		1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
				Apparent Consumption a						Imports			Exports
		Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
				2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria		1,182	1,214	1,200	570	634	650	688	700	650	75	120	100	Autriche
	Bulgaria	Bulgaria		583	...	...	587	...	...	5	...	...	9	...	...	Bulgarie
	Czech Republic	Czech Republic		365	187	190	453	269	266	2	9	8	90	91	84	République tchèque
	Estonia	Estonia		336	370	370	1,171	1,100	1,100	139	130	130	974	860	860	Estonie
	Finland	Finland		12,721	9,117	9,428	8,151	8,084	8,801	4,661	1,085	679	91	52	52	Finlande
	Germany	Germany		1,111	1,021	1,079	1,119	1,008	1,045	261	259	280	269	246	246	Allemagne
	Latvia	Latvia		432	375	480	1,950	2,000	2,000	166	175	180	1,684	1,800	1,700	Lettonie
	Luxembourg	Luxembourg		49	128	32	49	128	32	...	...	...	...	...	...	Luxembourg
	Netherlands	Netherlands		62	65	65	131	135	135	19	20	20	89	90	90	Pays-Bas
	Poland	Poland		5,095	5,325	5,100	4,610	4,840	5,100	560	560	...	75	75	...	Pologne
	Portugal	Portugal		8,939	8,930	8,890	8,289	8,300	8,250	1,000	950	970	350	320	330	Portugal
	Serbia	Serbia		230	210	220	231	210	220	0	0	0	1	0	0	Serbie
	Slovakia	Slovakia		1,909	2,000	2,050	1,965	2,050	2,100	91	100	100	147	150	150	Slovaquie
	Slovenia	Slovenia		131	154	160	386	540	550	117	114	110	372	500	500	Slovénie
	Sweden	Sweden		8,485	8,486	8,686	6,200	6,400	6,600	2,313	2,119	2,119	28	33	33	Suède
	Switzerland	Switzerland		137	148	153	174	185	190	3	3	3	40	40	40	Suisse
	UK	United Kingdom		54	50	50	3	0	0	52	50	50	1	0	0	Royaume-Uni
		Total Europe		41,820	37,780	38,153	36,038	35,883	37,039	10,077	6,274	5,299	4,294	4,377	4,185	Total Europe
	Canada	Canada		10,804	10,827	10,819	11,007	11,065	11,065	46	47	39	249	284	285	Canada
	United States	United States		42,259	43,085	42,672	42,272	43,099	42,685	42	42	42	55	56	56	Etats-Unis
		Total North America		53,063	53,912	53,491	53,279	54,164	53,750	88	89	81	304	340	340	Total Amérique du Nord
			a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 12c

3
		TABLE 12c
			WOOD RESIDUES, CHIPS AND PARTICLES						DECHETS DE BOIS, PLAQUETTES ET PARTICULES
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 m3
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	8,827	8,450	8,400	7,467	7,150	7,100	2,292	2,100	2,050	931	800	750	Autriche
	Bulgaria	Bulgaria	56	...	...	93	...	...	7	...	...	44	...	...	Bulgarie
	Cyprus	Cyprus	8	10	10	8	9	9	1	1	1	0	0	0	Chypre
	Czech Republic	Czech Republic	1,679	1,675	1,681	1,733	1,728	1,734	203	197	202	257	250	255	République tchèque
	Estonia	Estonia	2,706	2,670	2,670	4,000	3,900	3,900	96	70	70	1,390	1,300	1,300	Estonie
	Finland	Finland	20,390	16,776	16,624	15,308	15,357	15,910	5,245	1,525	820	163	106	106	Finlande
	Germany	Germany	15,481	15,550	15,400	16,703	16,700	16,500	1,036	1,050	1,000	2,258	2,200	2,100	Allemagne
	Latvia	Latvia	2,733	2,800	2,950	4,310	4,500	4,700	778	300	250	2,355	2,000	2,000	Lettonie
	Luxembourg	Luxembourg	680	517	517	521	521	521	283	17	17	124	21	21	Luxembourg
	Malta	Malta	2	2	3	0	0	0	2	2	3	0	0	0	Malte
	Netherlands	Netherlands	1,310	1,320	1,320	971	970	970	512	550	550	173	200	200	Pays-Bas
	Poland	Poland	11,954	12,130	12,400	10,211	10,500	10,800	2,213	2,050	2,000	469	420	400	Pologne
	Portugal	Portugal	3,301	2,520	2,510	1,824	1,920	1,900	1,758	800	810	281	200	200	Portugal
	Serbia	Serbia	506	560	595	493	550	580	15	12	17	2	2	2	Serbie
	Slovakia	Slovakia	1,097	1,100	1,150	1,150	1,150	1,200	307	300	300	360	350	350	Slovaquie
	Slovenia	Slovenia	710	540	870	1,360	1,450	1,500	272	290	300	922	1,200	930	Slovénie
	Sweden	Sweden	24,568	23,500	23,000	23,500	22,500	22,000	1,435	1,400	1,400	367	400	400	Suède
	Switzerland	Switzerland	1,330	1,345	1,350	790	810	820	600	590	590	60	55	60	Suisse
	UK	United Kingdom	3,164	2,910	3,150	3,122	2,870	3,110	119	120	120	77	80	80	Royaume-Uni
		Total Europe	100,505	94,375	94,600	93,564	92,585	93,254	17,174	11,374	10,500	10,234	9,584	9,154	Total Europe
	Canada	Canada	24,984	22,884	22,832	22,856	21,485	21,485	2,716	1,906	1,891	587	507	544	Canada
	United States	United States	54,907	56,007	55,457	60,485	61,697	61,091	216	221	219	5,794	5,910	5,852	Etats-Unis
		Total North America	79,892	78,891	78,290	83,341	83,182	82,576	2,932	2,127	2,110	6,382	6,417	6,396	Total Amérique du Nord

Table 13

4.1x
		TABLE 13
			WOOD PELLETS						GRANULES DE BOIS
		Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
								1000 mt
			Apparent Consumption						Imports			Exports
		Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
			2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
	Austria	Austria	1,144	1,270	1,410	1,607	1,720	2,060	413	328	400	875	778	1,050	Autriche
	Bulgaria	Bulgaria	202	...	...	216	...	...	132	...	...	146	...	...	Bulgarie
	Cyprus	Cyprus	4	3	3	0	0	0	4	3	3	0	0	0	Chypre
	Czech Republic	Czech Republic	126	199	201	490	503	517	39	35	37	403	339	353	République tchèque
	Estonia	Estonia	67	70	70	1,600	1,550	1,550	26	20	20	1,559	1,500	1,500	Estonie
	Finland	Finland	552	479	456	365	375	380	196	110	80	9	6	4	Finlande
	Germany	Germany	2,932	3,200	3,400	3,353	3,600	3,800	392	450	500	813	850	900	Allemagne
	Latvia	Latvia	221	280	100	2,138	2,200	2,000	592	380	400	2,509	2,300	2,300	Lettonie
	Luxembourg	Luxembourg	49	63	63	63	63	63	13	4	4	28	4	4	Luxembourg
	Malta	Malta	1	1	1	0	0	0	1	1	1	0	0	0	Malte
	Netherlands	Netherlands	2,449	2,457	2,457	307	315	315	2,297	2,297	2,297	155	155	155	Pays-Bas
	Poland	Poland	1,169	1,220	1,330	1,594	1,620	1,680	269	280	300	694	680	650	Pologne
	Portugal	Portugal	224	270	265	731	860	800	3	10	15	510	600	550	Portugal
	Serbia	Serbia	497	430	485	468	420	460	84	60	80	55	50	55	Serbie
	Slovakia	Slovakia	19	145	195	310	325	350	46	45	45	337	225	200	Slovaquie
	Slovenia	Slovenia	111	112	150	149	162	170	166	120	150	204	170	170	Slovénie
	Sweden	Sweden	1,771	1,985	1,985	1,900	2,100	2,100	154	235	235	282	350	350	Suède
	Switzerland	Switzerland	350	355	360	275	285	295	75	70	65	0	0	0	Suisse
	UK	United Kingdom	9,430	9,450	9,450	304	320	320	9,128	9,130	9,130	2	0	0	Royaume-Uni
		Total Europe	21,318	21,989	22,381	15,870	16,418	16,860	14,030	13,578	13,762	8,582	8,007	8,241	Total Europe
	Canada	Canada	706	761	548	3,830	4,131	4,131	29	33	35	3,153	3,402	3,618	Canada
	United States	United States	1,122	1,136	1,129	8,449	8,557	8,503	196	198	197	7,523	7,619	7,571	Etats-Unis
		Total North America	1,828	1,898	1,677	12,279	12,688	12,634	225	231	232	10,676	11,021	11,189	Total Amérique du Nord

Table 14

3+4
	TABLE 14
	Europe: Summary table of market forecasts for 2022 and 2023
	Europe: Tableau récapitulatif des prévisions du marché pour 2022 et 2023
	Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
		million m3 (pulp, paper and pellets million m.t. - pâte de bois, papiers et cartons, et granulés en millions de tonnes métriques)
		Apparent Consumption
		Consommation Apparente			Production			Imports - Importations			Exports - Exportations
		2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
		actual	forecasts		actual	forecasts		actual	forecasts		actual	forecasts
		réels	prévisions		réels	prévisions		réels	prévisions		réels	prévisions
	Sawn softwood	67.77	63.34	62.12	90.27	88.25	87.84	26.30	22.94	21.87	48.80	47.85	47.59	Sciages conifères
	Softwood logs a	172.76	165.24	163.92	177.51	167.80	163.65	18.28	16.58	17.07	23.03	19.14	16.80	Grumes de conifères a
	Sawn hardwood	4.34	4.17	4.19	4.22	4.17	4.09	2.95	2.89	2.92	2.83	2.89	2.82	Sciages non-conifères
	– temperate zone b	3.56	3.34	3.35	3.04	2.92	2.91	2.41	2.33	2.37	1.89	1.90	1.93	– zone tempérée b
	– tropical zone b	0.32	0.34	0.34	0.03	0.02	0.03	0.41	0.41	0.40	0.12	0.09	0.08	– zone tropicale b
	Hardwood logs a	13.59	13.27	13.48	14.25	14.21	14.29	1.85	1.54	1.43	2.50	2.48	2.24	Grumes de non-conifères a
	– temperate zone b	13.46	13.43	13.50	14.25	14.21	14.29	0.75	0.64	0.48	1.54	1.43	1.27	– zone tempérée b
	– tropical zone b	0.05	0.04	0.04				0.07	0.06	0.06	0.02	0.02	0.02	– zone tropicale b
	Veneer sheets	0.74	0.75	0.72	0.70	0.72	0.71	0.78	0.71	0.66	0.74	0.68	0.65	Feuilles de placage
	Plywood	5.94	5.84	5.86	3.41	3.37	3.35	5.87	5.67	5.63	3.35	3.21	3.12	Contreplaqués
	Particle board (excluding OSB)	21.78	20.86	20.73	21.93	20.41	20.25	8.19	8.10	8.10	8.34	7.66	7.62	Pann. de particules (sauf OSB)
	OSB	4.95	4.85	4.85	4.74	4.42	4.49	2.67	2.69	2.71	2.46	2.25	2.35	OSB
	Fibreboard	13.90	14.17	14.10	14.72	14.71	14.71	7.39	7.13	7.04	8.20	7.67	7.66	Panneaux de fibres
	– Hardboard	0.54	0.77	0.77	0.28	0.21	0.21	1.07	1.06	1.06	0.81	0.50	0.50	– Durs
	– MDF	9.73	9.79	9.73	10.78	10.85	10.80	4.34	4.11	4.07	5.38	5.17	5.15	– MDF
	– Other board	3.63	3.62	3.61	3.66	3.65	3.70	1.98	1.96	1.92	2.01	2.00	2.01	– Autres panneaux
	Pulpwood a	236.74	226.37	228.68	222.94	221.89	226.49	39.16	28.87	23.98	25.35	24.38	21.78	Bois de trituration a
	– Pulp logs	136.41	132.01	135.69	129.38	129.31	133.24	22.27	17.51	16.75	15.24	14.82	14.30	– Bois ronds de trituration
	– softwood	94.59	94.23	97.54	93.34	93.43	96.20	12.19	11.24	11.45	10.95	10.44	10.11	– conifères
	– hardwood	41.82	37.78	38.15	36.04	35.88	37.04	10.08	6.27	5.30	4.29	4.38	4.19	– non-conifères
	– Residues, chips and particles	100.50	94.37	94.60	93.56	92.59	93.25	17.17	11.37	10.50	10.23	9.58	9.15	– Déchets, plaquettes et part.
	Wood pulp	31.26	30.47	31.02	33.82	32.17	33.48	10.99	11.00	11.04	13.56	12.70	13.50	Pâte de bois
	Paper and paperboard	47.70	46.94	46.97	64.68	62.48	63.49	29.98	29.24	29.29	46.96	44.78	45.81	Papiers et cartons
	Wood Pellets	21.32	21.99	22.38	15.87	16.42	16.86	14.03	13.58	13.76	8.58	8.01	8.24	Granulés de bois
	a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fourni des données sur le commerce
	b Trade figures by zone do not equal the total as some countries cannot provide data for both zones							b Les chiffres du commerce par zone ne correspondent pas aux totaux
								en raison du fait que certains pays ne peuvent les différencier.

Table 15

3+4
	TABLE 15
	North America: Summary table of market forecasts for 2022 and 2023
	Amérique du Nord: Tableau récapitulatif des prévisions du marché pour 2022 et 2023
	Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
		million m3 (pulp, paper and pellets million m.t. - pâte de bois, papiers et cartons, et granulés en millions de tonnes métriques)
		Apparent Consumption
		Consommation Apparente			Production			Imports - Importations			Exports - Exportations
		2021	2022	2023	2021	2022	2023	2021	2022	2023	2021	2022	2023
		actual	forecasts		actual	forecasts		actual	forecasts		actual	forecasts
		réels	prévisions		réels	prévisions		réels	prévisions		réels	prévisions
	Sawn softwood	108.10	107.38	113.43	119.26	116.36	115.11	27.96	27.02	27.28	39.12	36.00	28.96	Sciages conifères
	Softwood logs	246.67	246.17	245.94	259.39	258.19	257.76	2.50	1.77	1.52	15.22	13.78	13.35	Grumes de conifères
	Sawn hardwood	15.56	16.29	15.82	18.21	18.42	18.18	1.51	1.93	1.66	4.17	4.06	4.02	Sciages non-conifères
	– temperate zone	15.36	16.05	15.61	18.21	18.42	18.18	1.27	1.64	1.38	4.11	4.01	3.96	– zone tempérée
	– tropical zone	0.19	0.25	0.22	0.00	0.00	0.00	0.24	0.30	0.27	0.05	0.05	0.06	– zone tropicale
	Hardwood logs	43.94	43.70	43.36	44.82	44.77	44.45	1.30	1.17	1.12	2.18	2.24	2.21	Grumes de non-conifères
	– temperate zone	42.86	42.78	42.48	44.82	44.77	44.45	0.15	0.15	0.15	2.11	2.15	2.13	– zone tempérée
	– tropical zone	0.00	0.00	0.00				0.00	0.00	0.00	0.00	0.00	0.00	– zone tropicale
	Veneer sheets	2.82	2.97	2.90	2.87	2.85	2.85	0.85	0.98	0.95	0.90	0.86	0.89	Feuilles de placage
	Plywood	19.52	19.58	19.65	11.40	11.54	11.44	9.51	9.31	9.53	1.39	1.26	1.32	Contreplaqués
	Particle board (excluding OSB)	6.60	8.78	7.32	5.78	5.94	5.56	2.06	3.74	2.74	1.24	0.90	0.99	Pann. de particules (sauf OSB)
	OSB	21.42	21.68	21.95	21.08	21.62	21.89	6.27	6.31	6.40	5.93	6.25	6.34	OSB
	Fibreboard	11.22	11.48	11.34	8.91	9.09	9.06	4.02	4.08	4.01	1.71	1.68	1.73	Panneaux de fibres
	– Hardboard	0.55	0.55	0.55	0.59	0.59	0.60	0.32	0.32	0.32	0.36	0.36	0.37	– Durs
	– MDF	7.31	7.18	7.23	5.04	5.11	5.13	3.33	3.21	3.22	1.06	1.14	1.12	– MDF
	– Other board	3.36	3.75	3.56	3.28	3.39	3.33	0.37	0.55	0.47	0.29	0.18	0.24	– Autres panneaux
	Pulpwood	281.56	281.26	279.83	284.31	285.54	284.14	3.99	2.52	2.47	6.74	6.79	6.78	Bois de trituration
	– Pulp logs	201.67	202.37	201.54	200.97	202.36	201.57	1.05	0.39	0.36	0.36	0.38	0.39	– Bois ronds de trituration
	– softwood	148.61	148.46	148.05	147.69	148.19	147.82	0.97	0.30	0.28	0.05	0.04	0.04	– conifères
	– hardwood	53.06	53.91	53.49	53.28	54.16	53.75	0.09	0.09	0.08	0.30	0.34	0.34	– non-conifères
	– Residues, chips and particles	79.89	78.89	78.29	83.34	83.18	82.58	2.93	2.13	2.11	6.38	6.42	6.40	– Déchets, plaquettes et part.
	Wood pulp	55.37	54.37	54.34	64.57	63.72	63.24	7.13	6.99	7.22	16.34	16.33	16.12	Pâte de bois
	Paper and paperboard	70.56	73.06	71.87	76.26	78.63	77.27	10.65	11.12	10.93	16.35	16.69	16.32	Papiers et cartons
	Wood pellets	1.83	1.90	1.68	12.28	12.69	12.63	0.23	0.23	0.23	10.68	11.02	11.19	Granulés de bois
															printed on 16/12

Joint Forest Sector Questionnaire - 2020 - National Reply - Finland

Read more about Joint Forest Sector Questionnaire - 2020 - National Reply - Finland

Reply as received from country.

Languages and translations

English

Guidelines

Dear Correspondent, Thank you for contributing to the Joint Forest Sector Questionnaire (JFSQ). Before filling in the worksheets, please read these guidelines. Please use only this questionnaire to report your data. The 2020 JFSQ contains no pre-filling. Use this questionnaire also to revise any historical data - fill in the correct year and your name on the cover page. The worksheet 'LAM & CHIPS' covers data on Glulam and X-lam. This sheet has a yellow drop-down cell where you can choose your unit of measurement. The total number of sheets to be filled in is eight core sheets (green tabs - to be validated by Eurostat) plus three for ITTO (brown tabs - not validated by Eurostat). Four sheets containing cross-references are included at the end. The flat file is for Eurostat for validation purposes, please do not change any cells here. Put all your data into one Excel file. If you send some data in later, give your file a new version number and date (see A.1. below) and notify us of the changes with respect to the previous version. Only send us completely filled-in sheets, highlighting the changes in yellow. Do not delete worksheets. Each sheet has a working area for your input. Most sheets have checking cells and tables. Each working area has white cells and shaded cells. Eurostat has highlighted the variables it considers most important for its publications - please fill those in as a priority. When you submit a revision, please highlight changes in yellow and explain them in the appropriate 'Note' column. Please use flags and notes (see A.6 below). This information is important for Eurostat. A. General recommendations A.1 Please use eDAMIS to send your questionnaire to Eurostat. Choose the correct domain ("FOREST_A_A") and the correct reference year (for this data collection: 2020). A.2 Fill in the JFSQ quality report if you haven't done so yet. If you already sent the quality report to Eurostat before, please fill it in only if major changes had happened to its content. A.3 The cover page is for your contact details, which are automatically copied to the other worksheets • Check your country code • If necessary change the reference year [red cell] as appropriate - the previous year will appear automatically If you distribute worksheets to various experts, they can each put their contact details into the sheets. It will then be your job to put all the information together again and to verify the checking tables, since some of them will not work as designed in isolation. A.4 Look at the unit of measurement to be used for each item and report in this unit if possible, using the conversion factors on the last page of the JFSQ definitions. Please report the monetary values in the same unit for both reporting years. Only report data or modify cells in the working areas. Please do not delete checking areas or checking sheets. • Look at the checking areas and make the necessary corrections to your data to remove all warnings (see the specific recommendations) before sending in your data. Fill in real zeroes '0' in the worksheets if there is no production or trade. Empty cells will be interpreted as 'Data not available'. • There are counters at the bottom of the tables to indicate the number of cells left to be filled in and the number of cells filled with text. Report all data with at least three decimals. Do not use a separator for thousands; for the decimal point, please use the one set up by default. A.5 Report numbers only. If data are confidential, please provide them if possible, appropriately flagged (see A.6). • Eurostat has a right to all confidential data necessary for its work. It has an obligation to use such data only in aggregates and to respect all the legal obligations. • If you cannot provide confidential data, a good option is to send in your own estimate flagged as a national estimate '9'. • As a last resort, leave the cell empty, flag it and write a note indicating data sources and links. Checking tables contain formulae to sum up the totals for sub-items. A.6 Flag cells and write notes as appropriate. Flags should be entered in the 'Flag' columns and notes in the 'Note' columns for the appropriate year and item. The flags to use are: • 5 for repeating the data of a previous year • 6 for confidential data • 7 for provisional data • 9 for national estimate B Specific recommendations B.1 Sheet 'Removals over bark' is for volumes of wood products measured over bark. General over bark/under bark conversion factors are calculated automatically. • Should you use different conversion factor(s) please delete the ones provided and insert your own • If you only have under bark data, please leave this worksheet empty, but revise the table with the conversion factors. • Unchanged conversion factors will be considered revised A checking table verifies that sums of sub-items agree with the totals B.2 Checking tables on worksheets improve data quality, verifying that: • The sum of the sub-items equals the total • The sum of 'of which' items is not larger than the total All cells in a checking table should be zero or empty. If this is not the case, please check your numbers for the sub-items and totals. The checking table indicates the difference, so if you see a negative value, you will have to decide which number should be increased by that amount. The only exception is when no data are entered due to confidentiality. B.3 Worksheets 'JQ2' and 'EU1' contain a checking table for apparent consumption and for unit values. Apparent consumption = Production + Imports – Exports. It should be positive or nil. If this is not the case, the cell will change colour and indicate the difference. • Please correct the data in the sheets until checking results are positive or nil. One solution is to increase production • If the data are correct but apparent consumption is still negative, please explain why in the 'Note' column provided in the apparent consumption checking table. B.4 Sheets 'JQ2', 'ECE-EU Species' and 'EU1' on trade have checking tables to verify data consistency. Both quantity and value must be present. When something is missing, messages or coloured cells appear in the checking tables. Please correct your data until all warnings disappear. The meaning of the messages is: • 0: both value and quantity are zero – all is well, there is no trade • ZERO Q: value is reported, quantity is zero - please correct • ZERO V: quantity is reported, value is zero - please correct • REPORT: both quantity and value are blank - please fill in • NO Q: blank cell for quantity – please fill in • NO V: blank cell for value – please fill in Please enter even very small numbers to resolve problems, using as many decimal places as necessary. If there is no way to correct the problem, please write an explanation in the 'Note' column. If there is no trade for a product, please enter 0 for both quantity and value. Thank you for collecting data for the JFSQ. Eurostat's Forestry Team

JFSQ quality report

Joint Forest Sector Questionnaire Quality Report
		Quality information	Country reply
1	Contact
	Country name	Country name
	Contact organisation	Contact organisation
	Contact name	Contact name
	Contact email address	Contact email address
2	Changes to previous year
	Necessity of update	Are there any changes to the quality report of the last data collection?	Please select YES or NO
		If yes, please provide details below.
3	Statistical processing
	Overview of the source data	Please provide an overview of the sources used to produce JFSQ data.
		Do you use a dedicated survey (of the industry, of households, of forest owners, etc.)?	Please select YES or NO
		If yes, please provide details (e.g., who are the respondents, what is its frequency?).
		Do you use forestry statistics?	Please select YES or NO
		If yes, please provide details.
		Do you use national forest inventory?	Please select YES or NO
		If yes, please provide details.
		Do you use national PRODCOM data compiled according to the CPA classification?	Please select YES or NO
		If yes, please provide details (which products, units, etc.).
		Do you use any other national production statistics?	Please select YES or NO
		If yes, please provide details.
		Do you use data collected by associations of industry?	Please select YES or NO
		If yes, please provide details.
		Do you collect data from direct contacts with manufacturing companies?	Please select YES or NO
		If yes, please provide details.
		Do you use estimates of roundwood use (in manufacturing)?	Please select YES or NO
		If yes, please provide details.
		Do you use national trade data?	Please select YES or NO
		If yes, please provide details.
		Do you use felling reports?	Please select YES or NO
		If yes, please provide details.
		Do you use forestry companies' accounting network?	Please select YES or NO
		If yes, please provide details.
		Do you use administrative data (e.g. tax records, business registers)?	Please select YES or NO
		If yes, please provide details.
		Do you use data from national accounts?	Please select YES or NO
		If yes, please provide details (e.g. for which data, from which account tables?).
		Do you use SBS (Structural business statistics)?	Please select YES or NO
		If yes, please provide details (e.g. for which data?).
		Do you use other environmental accounts?	Please select YES or NO
		If yes, please provide details.
		Do you use other statistics (e.g. agriculture statistics)?	Please select YES or NO
		If yes, please specify them.
		Do you use any other sources?	Please select YES or NO
		If yes, please specify them.
	Methodological issues	Are there any pending classification or measurement issues?	Please select YES or NO
		If yes, please specify them.
	Data validation	Do you check the quality of the data collected to compile JFSQ?	Please select YES or NO
		If yes, please explain the quality assurance procedure.
		Do you compare JFSQ data with different data sources or do you perform other cross-checks?	Please select YES or NO
		If yes, please explain your approach.
		Do you have validation rules and other plausibility checks for the outputs of your JFSQ data compilation process?	Please select YES or NO
		If yes, please briefly describe them.
4	Relevance
	User needs	Please provide references to the relevance of JFSQ at national level e.g. main users, national indicator sets, quantitative policy targets etc.
5	Coherence and comparability
	Coherence - cross domain	Do you compare the JFSQ results with business, energy and agricultural and foreign trade statistics?	Please select YES or NO
		It not, please explain.
		Do you compare the JFSQ results with business, energy and agricultural and foreign trade statistics?	Please select YES or NO
		It not, please explain.
		Do you cross-check the JFSQ data with the results of European Forest Accounts?	Please select YES or NO
		If yes, please indicate for which reporting items, and comments on the discrepancies observed, if any. It not, please explain.
	Coherence - internal	Are there any other consistency issues related to your JFSQ data?	Please select YES or NO
		If yes, please explain them.
6	Accessibility and clarity
	Publications	Do you disseminate JFSQ data nationally (e.g. in news releases or other documents)?	Please select YES or NO
		If yes, please provide URLs and/or the reference to the relevant publications.
	Online database	Do you publish your JFSQ accounts in an online data base?	Please select YES or NO
		If yes, please provide URLs.
	Documentation on methodology	Did you prepare a description of your national JFSQ methodology or metadata?	Please select YES or NO
		If yes, please provide URLs.
	Quality documentation	Do you have national quality documentation?	Please select YES or NO
		If yes, please provide URLs.
7	Other comments
	Other comments	Please provide any further feedback you might have on the quality of the reported data, sources and methods used and/or Eurostat's validation and quality report templates.

Cover

Joint Forest Sector Questionnaire
			2020
	DATA INPUT FILE
		Correspondent country:			FI
		Reference year:			2020	Fill in the year
Name of person responsible for reply:

Official address (in full):				NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791

	Telephone:
	Fax:
	E-mail:

Removals over bark

Country:

Date:

Name of Official responsible for reply:

Check Table

Official Address (in full):

EU JQ1 OB

NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791

FOREST SECTOR QUESTIONNAIRE

Telephone:

Discrepancies

Removals

E-mail:

Please verify, if there's an error!

Year 1

Year 2

Flag

Note

Product

Unit

2019

2020

2019

2020

2019

2020

Product

Unit

2019

2020

Code

Quantity

Code

Quantity

ROUNDWOOD REMOVALS OVERBARK

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ob

72926.627

68975.656

2019 revised

All data provisional

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ob

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ob

9242.481

10307.971

2019 revised

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ob

1.1.C

Coniferous

1000 m3ob

4286.094

4985.504

2019 revised

1.1.C

Coniferous

1000 m3ob

1.1.NC

Non-Coniferous

1000 m3ob

4956.386

5322.467

2019 revised

1.1.NC

Non-Coniferous

1000 m3ob

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ob

63684.146

58667.685

2019 revised

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ob

1.2.C

Coniferous

1000 m3ob

52727.583

49087.512

2019 revised

1.2.C

Coniferous

1000 m3ob

1.2.NC

Non-Coniferous

1000 m3ob

10956.563

9580.173

2019 revised

1.2.NC

Non-Coniferous

1000 m3ob

1.2.NC.T

of which: Tropical

1000 m3ob

2019 revised

1.2.NC.T

of which: Tropical

1000 m3ob

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ob

26092.2

25045.098

2019 revised

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ob

1.2.1.C

Coniferous

1000 m3ob

24970.182

24065.887

2019 revised

1.2.1.C

Coniferous

1000 m3ob

1.2.1.NC

Non-Coniferous

1000 m3ob

1122.018

979.211

2019 revised

1.2.1.NC

Non-Coniferous

1000 m3ob

1.2.2

PULPWOOD, ROUND AND SPLIT

1000 m3ob

37591.946

33622.587

2019 revised

1.2.2

PULPWOOD, ROUND AND SPLIT

1000 m3ob

1.2.2.C

Coniferous

1000 m3ob

27757.401

25021.625

2019 revised

1.2.2.C

Coniferous

1000 m3ob

1.2.2.NC

Non-Coniferous

1000 m3ob

9834.545

8600.962

2019 revised

1.2.2.NC

Non-Coniferous

1000 m3ob

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ob

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ob

1.2.3.C

Coniferous

1000 m3ob

1.2.3.C

Coniferous

1000 m3ob

1.2.3.NC

Non-Coniferous

1000 m3ob

1.2.3.NC

Non-Coniferous

1000 m3ob

To fill:

Text:

Product

Unit

2019

2020

Code

OVERBARK/UNDERBARK CONVERSION FACTORS

ROUNDWOOD (WOOD IN THE ROUGH)

m3/m3

1.145

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

m3/m3

1.153

1.1.C

Coniferous

m3/m3

1.153

1.1.NC

Non-Coniferous

m3/m3

1.153

1.2

INDUSTRIAL ROUNDWOOD

m3/m3

1.100

1.2.C

Coniferous

m3/m3

1.200

1.2.NC

Non-Coniferous

m3/m3

1.157

1.2.NC.T

of which: Tropical

m3/m3

ERROR:#DIV/0!

1.2.1

SAWLOGS AND VENEER LOGS

m3/m3

1.124

1.2.1.C

Coniferous

m3/m3

1.124

1.2.1.NC

Non-Coniferous

m3/m3

1.100

1.2.2

PULPWOOD, ROUND AND SPLIT

m3/m3

1.200

1.2.2.C

Coniferous

m3/m3

1.158

1.2.2.NC

Non-Coniferous

m3/m3

1.100

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

m3/m3

1.200

1.2.3.C

Coniferous

m3/m3

ERROR:#DIV/0!

1.2.3.NC

Non-Coniferous

m3/m3

1.100

JQ1 Production

Country:

Date:

Name of Official responsible for reply:

Official Address (in full):

FOREST SECTOR QUESTIONNAIRE JQ1

NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791

Industrial Roundwood Balance

PRIMARY PRODUCTS

Telephone:

This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!

Discrepancies

Removals and Production

E-mail:

test for good numbers, missing number, bad number, negative number

Year -1

Year

Flag

Note

Product

Unit

2019

2020

2019

2020

2019

2020

Product

Unit

2019

2020

2019

2020

% change

Conversion factors

Code

Quantity

Code

Quantity

Roundwood

Industrial roundwood availability
McCusker 14/6/07: McCusker 14/6/07: minus 1.2.3 (other ind. RW) production

60,532

280,754

364%

m3 of wood in m3 or t of product

REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)

Recovered wood used in particle board

missing data

Solid wood equivalent

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ub

63666.864

60233.268

2019 revised

All data final

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ub

Solid Wood Demand

agglomerate production

363

322

-11%

2.4

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ub

8013.230

8937.012

2019 revised

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ub

Sawnwood production

11,390

10,916

-4%

1.1.C

Coniferous

1000 m3ub

3716.043

4322.432

2019 revised

1.1.C

Coniferous

1000 m3ub

veneer production

Missing data

missing data

1.1.NC

Non-Coniferous

1000 m3ub

4297.187

4614.580

2019 revised

1.1.NC

Non-Coniferous

1000 m3ub

plywood production

1,090

990

-9%

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ub

55653.633

51296.256

2019 revised

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ub

particle board production (incl OSB)

missing data

1.58

1.2.C

Coniferous

1000 m3ub

46183.270

43015.625

2019 revised

1.2.C

Coniferous

1000 m3ub

fibreboard production

missing data

1.8

1.2.NC

Non-Coniferous

1000 m3ub

9470.364

8280.631

2019 revised

1.2.NC

Non-Coniferous

1000 m3ub

mechanical/semi-chemical pulp production

3,280

2,840

-13%

2.5

1.2.NC.T

of which: Tropical

1000 m3ub

1.2.NC.T

of which: Tropical

1000 m3ub

chemical pulp production

8,320

7,680

-8%

4.9

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ub

23211.271

22279.273

2019 revised

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ub

dissolving pulp production

missing data

5.7

1.2.1.C

Coniferous

1000 m3ub

22218.285

21412.672

2019 revised

1.2.1.C

Coniferous

1000 m3ub

Availability

Solid Wood Demand

missing data

1.2.1.NC

Non-Coniferous

1000 m3ub

992.986

866.602

2019 revised

1.2.1.NC

Non-Coniferous

1000 m3ub

Difference (roundwood-demand)

missing data

positive = surplus

1.2.2

PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)

1000 m3ub

32442.363

29016.982

2019 revised

1.2.2

PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)

1000 m3ub

gap (demand/availability)

missing data

Negative number means not enough roundwood available

1.2.2.C

Coniferous

1000 m3ub

23964.985

21602.953

2019 revised

1.2.2.C

Coniferous

1000 m3ub

Positive number means more roundwood available than demanded

1.2.2.NC

Non-Coniferous

1000 m3ub

8477.378

7414.029

2019 revised

1.2.2.NC

Non-Coniferous

1000 m3ub

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ub

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ub

1.2.3.C

Coniferous

1000 m3ub

1.2.3.C

Coniferous

1000 m3ub

% of particle board that is from recovered wood

35%

1.2.3.NC

Non-Coniferous

1000 m3ub

1.2.3.NC

Non-Coniferous

1000 m3ub

share of agglomerates produced from industrial roundwood residues

100%

PRODUCTION

usable industrial roundwood - amount of roundwood that is used, remainder leaves industry

98.5%

WOOD CHARCOAL

1000 t

WOOD CHARCOAL

1000 t

WOOD CHIPS, PARTICLES AND RESIDUES

1000 m3

14072.6885202963

13099.3735081481

WOOD CHIPS, PARTICLES AND RESIDUES

1000 m3

3.1

WOOD CHIPS AND PARTICLES

1000 m3

9121.031324

8554.700562

Revised_3.1

3.1

WOOD CHIPS AND PARTICLES

1000 m3

3.2

WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)

1000 m3

4951.6571962963

4544.6729461482

Revised_3.2

3.2

WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)

1000 m3

RECOVERED POST-CONSUMER WOOD

1000 t

458.213735734

439.45795829

Recovered wood used for energy production

RECOVERED POST-CONSUMER WOOD

1000 t

WOOD PELLETS AND OTHER AGGLOMERATES

1000 t

362.527

322.09

WOOD PELLETS AND OTHER AGGLOMERATES

1000 t

5.1

WOOD PELLETS

1000 t

362.527

322.09

5.1 includes also 5.2

5.1

WOOD PELLETS

1000 t

5.2

OTHER AGGLOMERATES

1000 t

5.2 included in 5.1

5.2

OTHER AGGLOMERATES

1000 t

SAWNWOOD (INCLUDING SLEEPERS)

1000 m3

11390

10916

SAWNWOOD (INCLUDING SLEEPERS)

1000 m3

6.C

Coniferous

1000 m3

11360

10880

6.C

Coniferous

1000 m3

6.NC

Non-Coniferous

1000 m3

6.NC

Non-Coniferous

1000 m3

6.NC.T

of which: Tropical

1000 m3

6.NC.T

of which: Tropical

1000 m3

VENEER SHEETS

1000 m3

Data not available

VENEER SHEETS

1000 m3

7.C

Coniferous

1000 m3

Data not available

7.C

Coniferous

1000 m3

7.NC

Non-Coniferous

1000 m3

Data not available

7.NC

Non-Coniferous

1000 m3

7.NC.T

of which: Tropical

1000 m3

Data not available

7.NC.T

of which: Tropical

1000 m3

WOOD-BASED PANELS

1000 m3

Data not available

WOOD-BASED PANELS

1000 m3

Error

8.1

PLYWOOD

1000 m3

1090

990

All data not available

8.1

PLYWOOD

1000 m3

8.1.C

Coniferous

1000 m3

800

990

8.1.C

Coniferous

1000 m3

8.1.NC

Non-Coniferous

1000 m3

290

Data not available

8.1.NC

Non-Coniferous

1000 m3

8.1.NC.T

of which: Tropical

1000 m3

Data not available

8.1.NC.T

of which: Tropical

1000 m3

8.2

PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD

1000 m3

Data not available

8.2

PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD

1000 m3

8.2.1

of which: ORIENTED STRAND BOARD (OSB)

1000 m3

Data not available

8.2.1

of which: ORIENTED STRAND BOARD (OSB)

1000 m3

8.3

FIBREBOARD

1000 m3

Data not available

8.3

FIBREBOARD

1000 m3

8.3.1

HARDBOARD

1000 m3

Data not available

8.3.1

HARDBOARD

1000 m3

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

1000 m3

Data not available

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

1000 m3

8.3.3

OTHER FIBREBOARD

1000 m3

Data not available

8.3.3

OTHER FIBREBOARD

1000 m3

WOOD PULP

1000 t

11600

10520

WOOD PULP

1000 t

9.1

MECHANICAL AND SEMI-CHEMICAL WOOD PULP

1000 t

3280

2840

9.1

MECHANICAL AND SEMI-CHEMICAL WOOD PULP

1000 t

9.2

CHEMICAL WOOD PULP

1000 t

8320

7680

9.2

CHEMICAL WOOD PULP

1000 t

9.2.1

SULPHATE PULP

1000 t

7330

6680

Includes only sulphate bld

9.2.1

SULPHATE PULP

1000 t

9.2.1.1

of which: BLEACHED

1000 t

7330

6680

9.2.1.1

of which: BLEACHED

1000 t

9.2.2

SULPHITE PULP

1000 t

990

1000

Includes sulphate unbld + dissolving pulp

9.2.2

SULPHITE PULP

1000 t

9.3

DISSOLVING GRADES

1000 t

Data not available

9.3

DISSOLVING GRADES

1000 t

OTHER PULP

1000 t

Data not available

OTHER PULP

1000 t

10.1

PULP FROM FIBRES OTHER THAN WOOD

1000 t

Data not available

10.1

PULP FROM FIBRES OTHER THAN WOOD

1000 t

10.2

RECOVERED FIBRE PULP

1000 t

Data not available

10.2

RECOVERED FIBRE PULP

1000 t

RECOVERED PAPER

1000 t

620

570

RECOVERED PAPER

1000 t

PAPER AND PAPERBOARD

1000 t

9720

8210

Revised_12

PAPER AND PAPERBOARD

1000 t

12.1

GRAPHIC PAPERS

1000 t

4880

3410

Revised_12.1

12.1

GRAPHIC PAPERS

1000 t

12.1.1

NEWSPRINT

1000 t

Data not available

12.1.1

NEWSPRINT

1000 t

12.1.2

UNCOATED MECHANICAL

1000 t

Data not available

12.1.2

UNCOATED MECHANICAL

1000 t

12.1.3

UNCOATED WOODFREE

1000 t

Data not available

12.1.3

UNCOATED WOODFREE

1000 t

12.1.4

COATED PAPERS

1000 t

Data not available

12.1.4

COATED PAPERS

1000 t

12.2

HOUSEHOLD AND SANITARY PAPERS

1000 t

12.2 Included in 12.4

12.2

HOUSEHOLD AND SANITARY PAPERS

1000 t

12.3

PACKAGING MATERIALS

1000 t

4440

4400

12.3

PACKAGING MATERIALS

1000 t

12.3.1

CASE MATERIALS

1000 t

Data not available

12.3.1

CASE MATERIALS

1000 t

12.3.2

CARTONBOARD

1000 t

Data not available

12.3.2

CARTONBOARD

1000 t

12.3.3

WRAPPING PAPERS

1000 t

Data not available

12.3.3

WRAPPING PAPERS

1000 t

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

1000 t

Data not available

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

1000 t

12.4

OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)

1000 t

400

12.4 Includes also 12.2

12.4

OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)

1000 t

m3ub = cubic metres solid volume underbark (i.e. excluding bark)

m3ub = cubic metres underbark (i.e. excluding bark)

Aggregates directly reported:

t = metric tonnes

Mechanical wood pulp, semi-chemical wood pulp and pulp from fibres other than wood 9.1+10.1

1000 t

3280.000

2840.000

To fill:

Text:

m3 = cubic metres solid volume

t = metric tonnes

JQ2 Trade

			61	62	61	62	91	92	91	92
		FOREST SECTOR QUESTIONNAIRE JQ2				Country:	FI		Date:																																							0	both VALUE and quantity reported ZERO
						Name of Official responsible for reply:		0																																								ZERO Q	quantity ZERO when VALUE is reported									INTRA-EU	The difference might be caused by Intra-EU trade
		PRIMARY PRODUCTS				Official Address (in full):		NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791																														This table highlights discrepancies between production and trade. For any negative number, indicating greater net exports than production, please verify your data!										ZERO V	Value ZERO when quantity is reported									CHECK
		Trade				Telephone:	0		Fax:	0																		This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!																		ZERO CHECK 1 - if no value please CHECK		NO Q	no quantity reported						ZERO CHECK 2 - if no value in Zero Check 1
	Value must always be in 1000 NAC (national currency)					E-mail:	0				Flag	Flag	Flag	Flag	Flag	Flag	Flag	Flag	Note	Note	Note	Note	Note	Note	Note	Note															Country:	FI						NO V	no value reported			Treshold:	2		verifies whether the JQ2 figures refers only to intra-EU trade
	Specify Currency and Unit of Value (e.g.:1000 US $):			1000 NAC		changed from version 1 by more than 1%																						Trade		Discrepancies																		REPORT	no figures reported
Product		Unit of	I M P O R T				E X P O R T				Import				Export				Import				Export				Product			I M P O R T				E X P O R T				Product			Apparent Consumption		Related Notes		Product		Value per	I M P O R T		E X P O R T		Column1	Column2	Product		Value per	I M P O R T		E X P O R T
code	Product	quantity	2019		2020		2019		2020		2019		2020		2019		2020		2019		2020		2019		2020		code			2019		2020		2019		2020		code			2019	2020	2019	2020	code	Product	unit	2019	2020	2019	2020	IMPORT	EXPORT	code	Product	unit	2019	2020	2019	2020
			Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value				Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	6324.417	321010.688	6462.9467952	291631.505	1447.303	91283.897	1264.3725312	97105.372											All data final	All data final			All data final	All data provisional	1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK	1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	68,544	289,960			1	ROUNDWOOD (WOOD IN THE ROUGH)	NAC/m3	51	45	63	77	ACCEPT	ACCEPT	1	ROUNDWOOD (WOOD IN THE ROUGH)	NAC/m3
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	89.404	3529.284	188.8327952	7511.495	90.922	3259.928	101.1475312	4062.402																	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	8,012	9,206			1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	NAC/m3	39	40	36	40	ACCEPT	ACCEPT	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	NAC/m3
1.1.C	Coniferous	1000 m3ub	5.047	142.634	162.1980672	5735.272	88.468	3012.913	98.9487088	3841.195																	1.1.C	Coniferous	1000 m3ub									1.1.C	Coniferous	1000 m3ub	3,633	1,452			1.1.C	Coniferous	NAC/m3	28	35	34	39	ACCEPT	ACCEPT	1.1.C	Coniferous	NAC/m3
1.1.NC	Non-Coniferous	1000 m3ub	84.357	3386.65	26.634728	1776.223	2.454	247.015	2.1988224	221.207																	1.1.NC	Non-Coniferous	1000 m3ub									1.1.NC	Non-Coniferous	1000 m3ub	4,379	7,754			1.1.NC	Non-Coniferous	NAC/m3	40	67	101	101	ACCEPT	ACCEPT	1.1.NC	Non-Coniferous	NAC/m3
1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	6235.013	317481.404	6274.114	284120.01	1356.381	88023.969	1163.225	93042.97																	1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK	1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	60,532	280,754			1.2	INDUSTRIAL ROUNDWOOD	NAC/m3	51	45	65	80	ACCEPT	ACCEPT	1.2	INDUSTRIAL ROUNDWOOD	NAC/m3
1.2.C	Coniferous	1000 m3ub	1744.746	108160.001	1533.846	81813.353	1174.095	78647.438	1095.264	88588.893																	1.2.C	Coniferous	1000 m3ub									1.2.C	Coniferous	1000 m3ub	46,754	72,528			1.2.C	Coniferous	NAC/m3	62	53	67	81	ACCEPT	ACCEPT	1.2.C	Coniferous	NAC/m3
1.2.NC	Non-Coniferous	1000 m3ub	4490.267	209321.403	4740.268	202306.657	182.286	9376.531	67.961	4454.077																	1.2.NC	Non-Coniferous	1000 m3ub									1.2.NC	Non-Coniferous	1000 m3ub	13,778	208,226			1.2.NC	Non-Coniferous	NAC/mt	47	43	51	66	ACCEPT	ACCEPT	1.2.NC	Non-Coniferous	NAC/mt
1.2.NC.T	of which: Tropical	1000 m3ub	0.004	22.308	0.003	50.978	0	0	0	0																	1.2.NC.T	of which: Tropical	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK	1.2.NC.T	of which: Tropical	1000 m3ub	0	22			1.2.NC.T	of which: Tropical	1000 m3	5577	16993	0	0	CHECK	ACCEPT	1.2.NC.T	of which: Tropical	1000 m3
2	WOOD CHARCOAL	1000 t	5.045	3230.965	4.783424	3279.706	0.526	368.809	0.304589	231.986																	2	WOOD CHARCOAL	1000 t									2	WOOD CHARCOAL	1000 t	5	2,862			2	WOOD CHARCOAL	1000 m3	640	686	701	762	ACCEPT	ACCEPT	2	WOOD CHARCOAL	1000 m3
3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	4146.75	169940.001	4673.5853990368	183501.93	237.932	11005.185	208.2333460768	9954.06																	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	17,982	172,034			3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	41	39	46	48	ACCEPT	ACCEPT	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3
3.1	WOOD CHIPS AND PARTICLES	1000 m3	3954.08	165840.307	4403.5627082493	177276.918	186.042	9365.661	189.1778133037	9321.967																	3.1	WOOD CHIPS AND PARTICLES	1000 m3									3.1	WOOD CHIPS AND PARTICLES	1000 m3	12,889	165,029			3.1	WOOD CHIPS AND PARTICLES	1000 mt	42	40	50	49	ACCEPT	ACCEPT	3.1	WOOD CHIPS AND PARTICLES	1000 mt
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	192.67	4099.694	270.0226907876	6225.012	51.89	1639.524	19.0555327731	632.093																	3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3									3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	5,092	7,005			3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 mt	21	23	32	33	ACCEPT	ACCEPT	3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 mt
4	RECOVERED POST-CONSUMER WOOD	1000 t	206.913	6435.113	399.1911638854	7332.401	0.519	32.273	0.173942903	23.648																	4	RECOVERED POST-CONSUMER WOOD	1000 t									4	RECOVERED POST-CONSUMER WOOD	1000 t	665	6,842			4	RECOVERED POST-CONSUMER WOOD	1000 mt	31	18	62	136	ACCEPT	CHECK	4	RECOVERED POST-CONSUMER WOOD	1000 mt
5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	116.223	15222.288	131.3331819619	16667.732	57.183	7027.23	12.7173898398	1596.717																	5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	422	8,517			5	WOOD PELLETS AND OTHER AGGLOMERATES	NAC/m3	131	127	123	126	ACCEPT	ACCEPT	5	WOOD PELLETS AND OTHER AGGLOMERATES	NAC/m3
5.1	WOOD PELLETS	1000 t	100.474	13260.607	97.2537869565	13992.757	29.849	4491.92	6.2604043478	881.972																	5.1	WOOD PELLETS	1000 t									5.1	WOOD PELLETS	1000 t	433	9,091			5.1	WOOD PELLETS	NAC/m3	132	144	150	141	ACCEPT	ACCEPT	5.1	WOOD PELLETS	NAC/m3
5.2	OTHER AGGLOMERATES	1000 t	15.749	1961.681	34.0793950054	2674.975	27.334	2535.31	6.456985492	714.745																	5.2	OTHER AGGLOMERATES	1000 t									5.2	OTHER AGGLOMERATES	1000 t	-12	-574			5.2	OTHER AGGLOMERATES	NAC/m3	125	78	93	111	ACCEPT	ACCEPT	5.2	OTHER AGGLOMERATES	NAC/m3
6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	593.354	115899.181	600.101	114244.426	8966.745	1729827.823	8217.919	1557546.684																	6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	3,017	-1,603,013			6	SAWNWOOD (INCLUDING SLEEPERS)	NAC/m3	195	190	193	190	ACCEPT	ACCEPT	6	SAWNWOOD (INCLUDING SLEEPERS)	NAC/m3
6.C	Coniferous	1000 m3	559.995	95658.068	569.549	91739.1	8951.761	1722057.473	8197.932	1548568.236																	6.C	Coniferous	1000 m3									6.C	Coniferous	1000 m3	2,968	-1,615,519			6.C	Coniferous	NAC/m3	171	161	192	189	ACCEPT	ACCEPT	6.C	Coniferous	NAC/m3
6.NC	Non-Coniferous	1000 m3	33.359	20241.113	30.552	22505.326	14.984	7770.35	19.987	8978.448																	6.NC	Non-Coniferous	1000 m3									6.NC	Non-Coniferous	1000 m3	48	12,507			6.NC	Non-Coniferous	NAC/m3	607	737	519	449	ACCEPT	ACCEPT	6.NC	Non-Coniferous	NAC/m3
6.NC.T	of which: Tropical	1000 m3	3.032	3385.065	4.303	5140.586	3.782	3682.354	3.106	2787.737																	6.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	6.NC.T	of which: Tropical	1000 m3	-1	-297			6.NC.T	of which: Tropical	NAC/m3	1116	1195	974	898	ACCEPT	ACCEPT	6.NC.T	of which: Tropical	NAC/m3
7	VENEER SHEETS	1000 m3	7.67	6737.564	6.764	5226.286	143.785	47068.249	146.053	44419.527																	7	VENEER SHEETS	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	7	VENEER SHEETS	1000 m3	-136	-40,331			7	VENEER SHEETS	NAC/m3	878	773	327	304	ACCEPT	ACCEPT	7	VENEER SHEETS	NAC/m3
7.C	Coniferous	1000 m3	0.126	119.218	0.165	336.775	53.511	27170.22	42.749	21668.895																	7.C	Coniferous	1000 m3									7.C	Coniferous	1000 m3	-53	-27,051			7.C	Coniferous	NAC/m3	946	2041	508	507	CHECK	ACCEPT	7.C	Coniferous	NAC/m3
7.NC	Non-Coniferous	1000 m3	7.544	6618.346	6.599	4889.511	90.274	19898.029	103.304	22750.632																	7.NC	Non-Coniferous	1000 m3									7.NC	Non-Coniferous	1000 m3	-83	-13,280			7.NC	Non-Coniferous	NAC/m3	877	741	220	220	ACCEPT	ACCEPT	7.NC	Non-Coniferous	NAC/m3
7.NC.T	of which: Tropical	1000 m3	0.574	738.355	1.365	719.536	0.033	9.938	0.088	18.972																	7.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	7.NC.T	of which: Tropical	1000 m3	1	728			7.NC.T	of which: Tropical	NAC/m3	1286	527	301	216	CHECK	ACCEPT	7.NC.T	of which: Tropical	NAC/m3
8	WOOD-BASED PANELS	1000 m3	382.724	140077.529	408.82109284	140562.989	985.891	527573.97	890.206437624	458735.34																	8	WOOD-BASED PANELS	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8	WOOD-BASED PANELS	1000 m3	-603	-387,496			8	WOOD-BASED PANELS	NAC/m3	366	344	535	515	ACCEPT	ACCEPT	8	WOOD-BASED PANELS	NAC/m3
8.1	PLYWOOD	1000 m3	118.431	55864.216	127.825	56652.512	918.471	503238.488	828.478	435724.773																	8.1	PLYWOOD	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.1	PLYWOOD	1000 m3	290	-446,384			8.1	PLYWOOD	NAC/m3	472	443	548	526	ACCEPT	ACCEPT	8.1	PLYWOOD	NAC/m3
8.1.C	Coniferous	1000 m3	30.972	12583.152	29.77	11988.996	626.25	258986.657	572.938	232676.2																	8.1.C	Coniferous	1000 m3									8.1.C	Coniferous	1000 m3	205	-245,414			8.1.C	Coniferous	NAC/m3	406	403	414	406	ACCEPT	ACCEPT	8.1.C	Coniferous	NAC/m3
8.1.NC	Non-Coniferous	1000 m3	87.459	43281.064	98.055	44663.516	292.221	244251.831	255.54	203048.573																	8.1.NC	Non-Coniferous	1000 m3									8.1.NC	Non-Coniferous	1000 m3	85	-200,971			8.1.NC	Non-Coniferous	NAC/m3	495	455	836	795	ACCEPT	ACCEPT	8.1.NC	Non-Coniferous	NAC/m3
8.1.NC.T	of which: Tropical	1000 m3	1.025	1922.459	0.874	1673.144	0.235	772.764	0.172	752.855																	8.1.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.1.NC.T	of which: Tropical	1000 m3	1	1,150			8.1.NC.T	of which: Tropical	NAC/m3	1876	1914	3288	4377	ACCEPT	ACCEPT	8.1.NC.T	of which: Tropical	NAC/m3
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3	119.991	34921.951	124.667	34251.958	20.713	7021.995	20.191	6438.063																	8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3									8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3	99	27,900			8.2	PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD	NAC/m3	291	275	339	319	ACCEPT	ACCEPT	8.2	PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD	NAC/m3
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	43.609	11538.724	48.709	11317.002	0.152	58.275	0.14	52.526																	8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	43	11,480			8.2.1	of which: ORIENTED STRANDBOARD (OSB)	NAC/m3	265	232	383	375	ACCEPT	ACCEPT	8.2.1	of which: ORIENTED STRANDBOARD (OSB)	NAC/m3
8.3	FIBREBOARD	1000 m3	144.302	49291.362	156.32909284	49658.519	46.707	17313.487	41.537437624	16572.504																	8.3	FIBREBOARD	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.3	FIBREBOARD	1000 m3	98	31,978			8.3	FIBREBOARD	NAC/m3	342	318	371	399	ACCEPT	ACCEPT	8.3	FIBREBOARD	NAC/m3
8.3.1	HARDBOARD	1000 m3	21.85	14888.692	21.08868648	13327.597	44.487	15644.996	36.996201624	13569.105																	8.3.1	HARDBOARD	1000 m3									8.3.1	HARDBOARD	1000 m3	-23	-756			8.3.1	HARDBOARD	NAC/mt	681	632	352	367	ACCEPT	ACCEPT	8.3.1	HARDBOARD	NAC/mt
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	103.619	30075.799	115.53034	31682.477	2.121	1647.336	4.447856	2975.664																	8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3									8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	101	28,428			8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	NAC/mt	290	274	777	669	ACCEPT	ACCEPT	8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	NAC/mt
8.3.3	OTHER FIBREBOARD	1000 m3	18.833	4326.871	19.71006636	4648.445	0.099	21.155	0.09338	27.735																	8.3.3	OTHER FIBREBOARD	1000 m3									8.3.3	OTHER FIBREBOARD	1000 m3	19	4,306			8.3.3	OTHER FIBREBOARD	NAC/mt	230	236	214	297	ACCEPT	ACCEPT	8.3.3	OTHER FIBREBOARD	NAC/mt
9	WOOD PULP	1000 t	347.307	203860.148	223.540716	102115.612	4518.595	2371208.54	4333.00452	1873276.62																	9	WOOD PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	9	WOOD PULP	1000 t	7,429	-2,156,828			9	WOOD PULP	NAC/mt	587	457	525	432	ACCEPT	ACCEPT	9	WOOD PULP	NAC/mt
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	5.466	1781.802	9.326338	2739.415	290.23	101694.552	393.15077	131703.67																	9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t									9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	2,995	-97,073			9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	NAC/mt	326	294	350	335	ACCEPT	ACCEPT	9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	NAC/mt
9.2	CHEMICAL WOOD PULP	1000 t	335.505	194816.673	207.438895	92007.728	4072.034	2173529.261	3737.369139	1643317.871																	9.2	CHEMICAL WOOD PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	9.2	CHEMICAL WOOD PULP	1000 t	4,583	-1,971,033			9.2	CHEMICAL WOOD PULP	NAC/mt	581	444	534	440	ACCEPT	ACCEPT	9.2	CHEMICAL WOOD PULP	NAC/mt
9.2.1	SULPHATE PULP	1000 t	333.248	192460.224	204.64003	89098.258	4071.779	2173380.998	3737.35834	1643269.14																	9.2.1	SULPHATE PULP	1000 t									9.2.1	SULPHATE PULP	1000 t	3,591	-1,974,241			9.2.1	SULPHATE PULP	NAC/mt	578	435	534	440	ACCEPT	ACCEPT	9.2.1	SULPHATE PULP	NAC/mt
9.2.1.1	of which: BLEACHED	1000 t	316.921	184329.582	178.103486	77381.526	4032.888	2155684.868	3697.896644	1628216.409																	9.2.1.1	of which: BLEACHED	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	9.2.1.1	of which: BLEACHED	1000 t	3,614	-1,964,675			9.2.1.1	of which: BLEACHED	NAC/mt	582	434	535	440	ACCEPT	ACCEPT	9.2.1.1	of which: BLEACHED	NAC/mt
9.2.2	SULPHITE PULP	1000 t	2.256	2356.449	2.798865	2909.47	0.025	58.241	0.010799	48.731																	9.2.2	SULPHITE PULP	1000 t									9.2.2	SULPHITE PULP	1000 t	992	3,298			9.2.2	SULPHITE PULP	NAC/mt	1045	1040	2330	4513	ACCEPT	ACCEPT	9.2.2	SULPHITE PULP	NAC/mt
9.3	DISSOLVING GRADES	1000 t	6.336	7261.673	6.775483	7368.469	156.331	95984.727	202.484611	98255.079																	9.3	DISSOLVING GRADES	1000 t									9.3	DISSOLVING GRADES	1000 t	-150	-88,723			9.3	DISSOLVING GRADES	NAC/mt	1146	1088	614	485	ACCEPT	ACCEPT	9.3	DISSOLVING GRADES	NAC/mt
10	OTHER PULP	1000 t	3.986	4859.066	2.951548	3004.669	0.052	41.206	0.041865	90.057																	10	OTHER PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	10	OTHER PULP	1000 t	4	4,818			10	OTHER PULP	NAC/mt	1219	1018	792	2151	ACCEPT	CHECK	10	OTHER PULP	NAC/mt
10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	3.097	4479.918	1.852972	2527.708	0.006	12.685	0.040329	88.506																	10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t									10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	3	4,467			10.1	PULP FROM FIBRES OTHER THAN WOOD	NAC/mt	1447	1364	2114	2195	ACCEPT	ACCEPT	10.1	PULP FROM FIBRES OTHER THAN WOOD	NAC/mt
10.2	RECOVERED FIBRE PULP	1000 t	0.889	379.148	1.098576	476.961	0.046	28.521	0.001536	1.551																	10.2	RECOVERED FIBRE PULP	1000 t									10.2	RECOVERED FIBRE PULP	1000 t	1	351			10.2	RECOVERED FIBRE PULP	NAC/mt	426	434	620	1010	ACCEPT	ACCEPT	10.2	RECOVERED FIBRE PULP	NAC/mt
11	RECOVERED PAPER	1000 t	114.896	19189.653	67.844011	11891.779	65.696	11663.794	100.489093	13394.885																	11	RECOVERED PAPER	1000 t									11	RECOVERED PAPER	1000 t	669	8,096			11	RECOVERED PAPER	NAC/mt	167	175	178	133	ACCEPT	ACCEPT	11	RECOVERED PAPER	NAC/mt
12	PAPER AND PAPERBOARD	1000 t	305.619	262392.643	322.699703	256668.741	9293.771	6903100.156	7831.573797	5591404.598																	12	PAPER AND PAPERBOARD	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	12	PAPER AND PAPERBOARD	1000 t	732	-6,632,498			12	PAPER AND PAPERBOARD	NAC/mt	859	795	743	714	ACCEPT	ACCEPT	12	PAPER AND PAPERBOARD	NAC/mt
12.1	GRAPHIC PAPERS	1000 t	54.163	47367.809	63.642434	47831.971	5057.563	3405995.969	3621.974779	2236158.413																	12.1	GRAPHIC PAPERS	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	12.1	GRAPHIC PAPERS	1000 t	-123	-3,355,218			12.1	GRAPHIC PAPERS	NAC/mt	875	752	673	617	ACCEPT	ACCEPT	12.1	GRAPHIC PAPERS	NAC/mt
12.1.1	NEWSPRINT	1000 t	23.163	11942.757	28.428837	12655.759	236.364	118953.006	161.75712	68882.711																	12.1.1	NEWSPRINT	1000 t									12.1.1	NEWSPRINT	1000 t	-213	-107,010			12.1.1	NEWSPRINT	NAC/mt	516	445	503	426	ACCEPT	ACCEPT	12.1.1	NEWSPRINT	NAC/mt
12.1.2	UNCOATED MECHANICAL	1000 t	2.742	2607.385	4.191579	3349.603	673.614	388731.68	426.191838	224616.104																	12.1.2	UNCOATED MECHANICAL	1000 t									12.1.2	UNCOATED MECHANICAL	1000 t	-671	-386,124			12.1.2	UNCOATED MECHANICAL	NAC/mt	951	799	577	527	ACCEPT	ACCEPT	12.1.2	UNCOATED MECHANICAL	NAC/mt
12.1.3	UNCOATED WOODFREE	1000 t	13.057	16580.849	13.01368	15199.157	861.6	645163.236	670.571491	454990.954																	12.1.3	UNCOATED WOODFREE	1000 t									12.1.3	UNCOATED WOODFREE	1000 t	-849	-628,582			12.1.3	UNCOATED WOODFREE	NAC/mt	1270	1168	749	679	ACCEPT	ACCEPT	12.1.3	UNCOATED WOODFREE	NAC/mt
12.1.4	COATED PAPERS	1000 t	15.2	16236.818	18.008338	16627.452	3285.985	2253148.047	2363.45433	1487668.644																	12.1.4	COATED PAPERS	1000 t									12.1.4	COATED PAPERS	1000 t	-3,271	-2,236,911			12.1.4	COATED PAPERS	NAC/mt	1068	923	686	629	ACCEPT	ACCEPT	12.1.4	COATED PAPERS	NAC/mt
12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	1.321	10361.178	1.332357	2815.806	29.844	30019.242	23.915193	21526.537																	12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t									12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	-29	-19,658			12.2	HOUSEHOLD AND SANITARY PAPERS	NAC/mt	7843	2113	1006	900	CHECK	ACCEPT	12.2	HOUSEHOLD AND SANITARY PAPERS	NAC/mt
12.3	PACKAGING MATERIALS	1000 t	248.803	200433.715	256.059929	200626.621	4070.87	3364947.931	4039.958067	3225833.022																	12.3	PACKAGING MATERIALS	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	12.3	PACKAGING MATERIALS	1000 t	618	-3,160,114			12.3	PACKAGING MATERIALS	NAC/mt	806	784	827	798	ACCEPT	ACCEPT	12.3	PACKAGING MATERIALS	NAC/mt
12.3.1	CASE MATERIALS	1000 t	159.004	87846.793	154.690622	76515.447	870.098	486467.143	871.457005	444677.772																	12.3.1	CASE MATERIALS	1000 t									12.3.1	CASE MATERIALS	1000 t	-711	-398,620			12.3.1	CASE MATERIALS	NAC/mt	552	495	559	510	ACCEPT	ACCEPT	12.3.1	CASE MATERIALS	NAC/mt
12.3.2	CARTONBOARD	1000 t	58.549	79744.569	65.797991	89668.358	2583.153	2280623.196	2558.143119	2226073.231																	12.3.2	CARTONBOARD	1000 t									12.3.2	CARTONBOARD	1000 t	-2,525	-2,200,879			12.3.2	CARTONBOARD	NAC/mt	1362	1363	883	870	ACCEPT	ACCEPT	12.3.2	CARTONBOARD	NAC/mt
12.3.3	WRAPPING PAPERS	1000 t	25.858	29513.763	30.005657	31261.871	470.578	500134.099	455.209835	451663.495																	12.3.3	WRAPPING PAPERS	1000 t									12.3.3	WRAPPING PAPERS	1000 t	-445	-470,620			12.3.3	WRAPPING PAPERS	NAC/mt	1141	1042	1063	992	ACCEPT	ACCEPT	12.3.3	WRAPPING PAPERS	NAC/mt
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	5.392	3328.59	5.565659	3180.945	147.04	97723.493	155.148108	103418.524																	12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t									12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	-142	-94,395			12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	NAC/mt	617	572	665	667	ACCEPT	ACCEPT	12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	NAC/mt
12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t	1.332	4229.941	1.664983	5394.343	135.494	102137.014	145.725758	107886.626																	12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t									12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t	266	-97,507			12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	NAC/mt	3176	3240	754	740	ACCEPT	ACCEPT	12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	NAC/mt
	m3ub = cubic metres solid volume underbark (i.e. excluding bark)
	Mechanical wood pulp, semi-chemical wood pulp and pulp from fibres other than wood 9.1+10.1	1000 t	8.563	6261.720	11.179	5267.123	290.236	101707.237	393.191	131792.176
		To fill:	0	0	0	0	0	0	0	0
		Text:	0	0	0	0	0	0	0	0

JQ3 Secondary PP Trade

			62	91	91
			Country:	FI	Date:
			Name of Official responsible for reply:		0											Specify Currency and Unit of Value (e.g.:1000 US $):	_____________________
			Official Address (in full):
	FOREST SECTOR QUESTIONNAIRE JQ3		NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791
	SECONDARY PROCESSED PRODUCTS		Telephone/Fax:	0	0
	Trade		E-mail:	0
															This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!
	Value must always be in 1000 NAC (national currency)		changes from version 1			Flag	Flag	Flag	Flag	Note	Note	Note	Note			Discrepancies
	Eurozone countries may use the old national currency, but only in both years
Product	Product	I M P O R T V A L U E		E X P O R T V A L U E		Import		Export		Import		Export		Product	Product	I M P O R T V A L U E		E X P O R T V A L U E
code		2019	2020	2019	2020	2019	2020	2019	2020	2019	2020	2019	2020	Code		2019	2020	2019	2020
13	SECONDARY WOOD PRODUCTS	490464.782	480984.132	458395.644	438703.836						All data final		All data final	13	SECONDARY WOOD PRODUCTS	OK	OK	OK	OK
13.1	FURTHER PROCESSED SAWNWOOD	16484.225	17549.117	50291.808	54807.581									13.1	FURTHER PROCESSED SAWNWOOD	OK	OK	OK	OK
13.1.C	Coniferous	4629.282	4967.088	49365.804	54236.275									13.1.C	Coniferous
13.1.NC	Non-coniferous	11854.943	12582.029	926.004	571.306									13.1.NC	Non-coniferous
13.1.NC.T	of which: Tropical	0	880.666	0	74.437									13.1.NC.T	of which: Tropical	OK	OK	OK	OK
13.2	WOODEN WRAPPING AND PACKAGING MATERIAL	24631.825	21744.963	28022.547	25444.418									13.2	WOODEN WRAPPING AND PACKAGING MATERIAL
13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE	10207.512	9141.429	2365.111	2716.33									13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD	82911.774	84077.777	197444.334	199125.03									13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD
13.5	WOODEN FURNITURE	276643.236	278222.857	131559.51	104421.839									13.5	WOODEN FURNITURE
13.6	PREFABRICATED BUILDINGS OF WOOD	66175.673	50161.157	45044.353	48042.373									13.6	PREFABRICATED BUILDINGS OF WOOD
13.7	OTHER MANUFACTURED WOOD PRODUCTS	13410.537	20086.832	3667.981	4146.265									13.7	OTHER MANUFACTURED WOOD PRODUCTS
14	SECONDARY PAPER PRODUCTS	247880.675	256537.943	414673.567	381313.586									14	SECONDARY PAPER PRODUCTS	OK	OK	OK	OK
14.1	COMPOSITE PAPER AND PAPERBOARD	2793.921	3735.091	28566.963	23528.184									14.1	COMPOSITE PAPER AND PAPERBOARD
14.2	SPECIAL COATED PAPER AND PULP PRODUCTS	40855.885	38010.035	114869.987	112283.379									14.2	SPECIAL COATED PAPER AND PULP PRODUCTS
14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE	41626.249	44375.201	103509.703	89733.997									14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE
14.4	PACKAGING CARTONS, BOXES ETC.	79974.08	90356.241	21557.071	21465.705									14.4	PACKAGING CARTONS, BOXES ETC.
14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	82630.54	80061.375	146169.843	134302.321									14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	OK	OK	OK	OK
14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE	11444.697	1135.469	92588.552	102.471									14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE
14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP	10101.78	11022.031	679.371	561.494									14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP
14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE	8527.939	9076.939	560.32	683.612									14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE

	To fill:	0	0	0	0
	Text:	0	0	0	0

LAM & CHIPS

Unit of quantity:

Please select

Year

Product

Flow

Year

App. Cons.

Unit price

Production

Total Export

Total Import

Extra-EU Export

Extra-EU Import

TOT EXP

TOT IMP

X-EU EXP

X-EU IMP

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1000 NAC

Please select

1000 NAC

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1000 NAC

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1000 NAC

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2019

Glulam

2019

ERROR:#DIV/0!

2020

ERROR:#DIV/0!

2019

X-lam

2019

ERROR:#DIV/0!

2020

ERROR:#DIV/0!

Definitions: Glulam: Builders' carpentry also includes glue-laminated timber (glulam), which is a structural timber product obtained by gluing together a number of wood laminations having their grain essentially parallel. Laminations of curved members are arranged so that the plane of each lamination is at 90 degrees to the plane of the applied load; thus, laminations of a straight gluman beam are laid flat. [from HS 4418, Builders' joinery and carpentry of wood, including cellular wood panels, assembled flooring panels, shingles and shakes] X-lam: Panels consisting of laths of roughly sawn wood, assembled with glue in order to facilitate transport or later working. [from HS4421, Other articles of wood]

ECE-EU Species


							Country:	FI		Date:
							Name of Official responsible for reply:			0																			DISCREPANCIES

			FOREST SECTOR QUESTIONNAIRE ECE/EU Species Trade				Official Address (in full):																						Checks								Check Table
							NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791																																		0	both VALUE and quantity reported ZERO
			Trade in Roundwood and Sawnwood by species				Telephone:	0		Fax:	0																		- checks whether the sum of subitems is bigger than the total												ZERO Q	quantity ZERO when VALUE is reported
							E-mail:	0																																	ZERO V	Value ZERO when quantity is reported
																																							Zero check - if no value please CHECK		NO Q	no quantity reported
			Value must always be in 1000 NAC ( national currency)										Flag	Flag	Flag	Flag	Flag	Flag	Flag	Flag	Note	Note	Note	Note	Note	Note	Note	Note													NO V	no value reported			Treshold:	2
			Eurozone countries may use the old national currency, but only in both years		1000NAC																																				REPORT	no figures reported
					I M P O R T				E X P O R T				Import				Export				Import				Export				I M P O R T				E X P O R T							Value per	I M P O R T		E X P O R T		Unit price check
Product	Classification	Classification		Unit of	2019		2020		2019		2020		2019		2020		2019		2020		2019		2020		2019		2020		2019		2020		2019		2020		Classification	Classification		unit	2019	2020	2019	2020	IMPORT	EXPORT
Code	HS2017	CN2017	Product	Quantity	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	HS2007	CN2007	Product
1.2.C	4403.11/21/22/23/24/25/26		Industrial Roundwood, Coniferous	1000 m3ub	1744.746	108160.001	1533.846	81813.353	1174.095	78647.438	1095.264	88588.893											All data final	All data final			All data final	All data final	OK	OK	OK	OK	OK	OK	OK	OK	4403.11/21/22/23/24/25/26		0	NAC/m3	62	53	67	81	ACCEPT	ACCEPT							PRODUCTION		I M P O R T				E X P O R T
	4403.23/24		Fir/Spruce (Abies spp., Picea spp.)	1000 m3ub	899.653	54507.895	876.994	46127.133	311.19	17358.044	242.898	12852.72																	OK	OK	OK	OK	OK	OK	OK	OK	4403.23/24		Fir/Spruce (Abies spp., Picea spp.)	NAC/m3	61	53	56	53	ACCEPT	ACCEPT	Product	Classification	Classification		Unit of		2019	2020	2019		2020		2019		2020
		4403 23 10	sawlogs and veneer logs	1000 m3ub	191.014	13134.127	199.137	12989.835	38.474	2553.481	0.616	42.136																										4403 23 10	sawlogs and veneer logs (Abies alba, Picea abies)	NAC/m3	69	65	66	68	ACCEPT	ACCEPT	Code	HS2007	CN2007	Product	Quantity		Quantity	Quantity	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
		4403 23 90 4403 24 00	pulpwood and other industrial roundwood	1000 m3ub	708.639	41373.768	677.857	33137.298	272.716	14804.563	242.282	12810.584																										4403 23 90 4403 24 00	pulpwood and other industrial roundwood (Abies alba, Picea abies)	NAC/m3	58	49	54	53	ACCEPT	ACCEPT	1	4401.11/12 44.03		Roundwood production	1000 m3	JQ1	63,667	60,233
	4403.21/22		Pine (Pinus spp.)	1000 m3ub	845.092	53650.981	656.8	35671.599	862.905	61289.394	788.772	61654.163																	OK	OK	OK	OK	OK	OK	OK	OK	4403.21/22		Pine (Pinus spp.)	NAC/m3	63	54	71	78	ACCEPT	ACCEPT						EU2	63666.863634	60233.267515
		4403 21 10	sawlogs and veneer logs	1000 m3ub	87.748	5865.131	121.169	7734.159	427.574	26026.701	295.825	18949.39																										4403 21 10	sawlogs and veneer logs (Pinus sylvestris)	NAC/m3	67	64	61	64	ACCEPT	ACCEPT						dif	0	0
		4403 21 90 4403 22 00	pulpwood and other industrial roundwood	1000 m3ub	757.344	47785.85	535.631	27937.44	435.331	35262.693	492.947	42704.773																										4403 21 90 4403 22 00	pulpwood and other industrial roundwood (Pinus sylvestris)	NAC/m3	63	52	81	87	ACCEPT	ACCEPT	1.2.C	4403.11/21/22/23/24/25/26		Industrial Roundwood (wood in the rough), Coniferous	1000 m3	JQ2			1,745	108,160	1,534	81,813	1,174	78,647	1,095	88,589
																																																				ECE/EU			1,745	108,160	1,534	81,813	1,174	78,647	1,095	88,589
			Please note the deleted rows, do not use this area.																																																	dif			0	0	0	0	0	0	0	0
																																															1.2.NC	4403.12/41/49/91/93/94/95/96/97/98/99		Industrial Roundwood (wood in the rough), Non-Coniferous	1000 m3	JQ2			4,490	209,321	4,740	202,307	182	9,377	68	4,454
1.2.NC	4403.12/41/49/91/93/94 4403.95/96/97/98/99		Industrial Roundwood, Non-Coniferous	1000 m3ub	4490.267	209321.403	4740.268	202306.657	182.286	9376.531	67.961	4454.077																	OK	OK	OK	OK	OK	OK	OK	OK	4403.12/41/49/91/93/94 4403.95/96/97/98/99		0	NAC/m3	47	43	51	66	ACCEPT	ACCEPT						ECE/EU			4,490	209,321	4,740	202,307	182	9,377	68	4,454
	4403.91		of which: Oak (Quercus spp.)	1000 m3ub	0.031	29.364	0.028	25.258	0	0	0	0					6	6																			4403.91		of which: Oak (Quercus spp.)	NAC/m3	947	902	0	0	ACCEPT	CHECK						dif			0	0	0	0	0	0	0	0
	4403.93/94		of which: Beech (Fagus spp.)	1000 m3ub	0	0	0	0	0	0	0	0					6	6																			4403.93/94		of which: Beech (Fagus spp.)	NAC/m3	0	0	0	0	CHECK	CHECK	6.C	4406.11/91 4407.11/12/19		Sawnwood, Coniferous	1000 m3	JQ2			560	95,658	570	91,739	8,952	1,722,057	8,198	1,548,568
	4403.95/96		of which: Birch (Betula spp.)	1000 m3ub	4198.432	197235.438	4464.919	191377.665	182.204	9346.196	44.225	2158.997																	OK	OK	OK	OK	OK	OK	OK	OK	4403.95/96		of which: Birch (Betula spp.)	NAC/m3	47	43	51	49	ACCEPT	ACCEPT						ECE/EU			560	95,658	570	91,739	8,952	1,722,057	8,198	1,548,568
		4403 95 10	sawlogs and veneer logs	1000 m3ub	81.355	5833.633	103.235	7109.042	1.802	127.048	0	0																										4403 95 10	sawlogs and veneer logs	NAC/m3	72	69	71	0	ACCEPT	CHECK						dif			0	0	0	0	0	0	0	0
		4403 95 90 4403 96 00	pulpwood and other industrial roundwood	1000 m3ub	4117.077	191401.805	4361.684	184268.623	180.402	9219.148	44.225	2158.997																										4403 95 90 4403 96 00	pulpwood and other industrial roundwood	NAC/m3	46	42	51	49	ACCEPT	ACCEPT	6.NC	4406.12/92 4407.21/22/25/26/27/28/29/91/92/93/94/95/96/97/99		Sawnwood, Non-coniferous	1000 m3	JQ2			33	20,241	31	22,505	15	7,770	20	8,978
	4403.97		of which: Poplar/Aspen (Populus spp.)	1000 m3ub	284.022	11493.481	275.308	10832.305	0	0	0	0																									4403.97		of which: Poplar/Aspen (Populus spp.)	NAC/m3	40	39	0	0	ACCEPT	CHECK						ECE/EU			33	20,241	31	22,505	15	7,770	20	8,978
	4403.98		of which: Eucalyptus (Eucalyptus spp.)	1000 m3ub	0.021	2.924	0.001	0.023	0	0	0	0																									4403.98		of which: Eucalyptus (Eucalyptus spp.)	NAC/m3	139	23	0	0	CHECK	CHECK						dif			0	0	0	0	0	0	0	0
6.C	4406.11/91 4407.11/12/19		Sawnwood, Coniferous	1000 m3	559.995	95658.068	569.549	91739.1	8951.761	1722057.473	8197.932	1548568.236																	OK	OK	OK	OK	OK	OK	OK	OK	4406.11/91 4407.11/12/19		Sawnwood, Coniferous	NAC/m3	171	161	192	189	ACCEPT	ACCEPT
	4407.12		of which: Fir/Spruce (Abies spp., Picea spp.)	1000 m3	345.514	54820.656	347.424	51645.313	4499.321	896652.84	4093.365	798586.322																									4407.12		of which: Fir/Spruce (Abies spp., Picea spp.)	NAC/m3	159	149	199	195	ACCEPT	ACCEPT
	4407.11		of which: Pine (Pinus spp.)	1000 m3	196.514	34332.632	188.644	29451.877	4451.625	825203.242	4104.345	749919.196																									4407.11		of which: Pine (Pinus spp.)	NAC/m3	175	156	185	183	ACCEPT	ACCEPT							OK	OK
6.NC	4406.12/92 4407.21/22/25/26/27/28/29/ 91/92/93/94/95/96/97/99		Sawnwood, Non-coniferous	1000 m3	33.359	20241.113	30.552	22505.326	14.984	7770.35	19.987	8978.448																	OK	OK	OK	OK	OK	OK	OK	OK	4406.12/92 4407.21/22/25/26/27/28/29/ 91/92/93/94/95/96/97/99		Sawnwood, Non-coniferous	NAC/m3	607	737	519	449	ACCEPT	ACCEPT									OK	OK	OK	OK	OK	OK	OK	OK
	4407.91		of which: Oak (Quercus spp.)	1000 m3	11.927	6004.712	6.545	6568.341	0.012	35.293	0.007	4.261																									4407.91		of which: Oak (Quercus spp.)	NAC/m3	503	1004	2941	609	ACCEPT	CHECK									OK	OK	OK	OK	OK	OK	OK	OK
	4407.92		of which: Beech (Fagus spp.)	1000 m3	0.204	77.887	0.232	69.31	0	0	0.001	0.317																									4407.92		of which: Beech (Fagus spp.)	NAC/m3	382	299	0	317	ACCEPT	CHECK									OK	OK	OK	OK	OK	OK	OK	OK
	4407.93		of which: Maple (Acer spp.)	1000 m3	0.005	3.67	0.004	3.063	0	0	0	0																									4407.93		of which: Maple (Acer spp.)	NAC/m3	734	766	0	0	ACCEPT	CHECK									OK	OK	OK	OK	OK	OK	OK	OK
	4407.94		of which: Cherry (Prunus spp.)	1000 m3	0	0	0	0	0.007	10.063	0	0																									4407.94		of which: Cherry (Prunus spp.)	NAC/m3	0	0	1438	0	CHECK	CHECK
	4407.95		of which: Ash (Fraxinus spp.)	1000 m3	0.933	861.402	1.117	1015.602	0.034	31.39	0.148	136.227																									4407.95		of which: Ash (Fraxinus spp.)	NAC/m3	923	909	923	920	ACCEPT	ACCEPT
	4407.97		of which: Poplar/Aspen (Populus spp.)	1000 m3	0.326	256.776	0.7	500.729	0.124	98.35	0.071	72.925																									4407.97		of which: Poplar/Aspen (Populus spp.)	NAC/m3	788	715	793	1027	ACCEPT	ACCEPT
	4407.96		of which: Birch (Betula spp.)	1000 m3	16.932	9651.601	4.984	1802.47	11.025	3912.9	0.445	291.167																									4407.96		of which: Birch (Betula spp.)	NAC/m3	570	362	355	654	ACCEPT	ACCEPT
Light blue cells are requested only for EU members using the Combined Nomenclature to fill in - other countries are welcome to do so if their trade classification nomenclature permits
Please note that information on tropical species trade is requested in questionnaire ITTO2 for ITTO member countries				To fill:	3	3	3	3	3	3	3	3
m3ub = cubic metres underbark (i.e. excluding bark)				Text:	0	0	0	0	0	0	0	0

EU1 ExtraEU Trade

				EU1		Country:	FI		Date:																		JQ2/EU1 comparison																						0	both VALUE and quantity reported ZERO
						Name of Official responsible for reply:		0																																									ZERO Q	quantity ZERO when VALUE is reported
				FOREST SECTOR QUESTIONNAIRE		Official Address (in full):		NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791																			JQ2>=EU1																						ZERO V	Value ZERO when quantity is reported
				Trade with countries outside EU		Telephone:	0		Fax:	0																										Please verify if there's an error!											Zero check - if no value please CHECK		NO Q	no quantity reported
	Value must always be in 1000 NAC (national currency)					E-mail:	0				Flag	Flag	Flag	Flag	Flag	Flag	Flag	Flag	Note	Note	Note	Note	Note	Note	Note	Note																							NO V	no value reported			Treshold:	2
	Eurozone countries may use the old national currency, but only in both years			1000NAC																																Trade		Discrepancies											REPORT	no figures reported
Product		Unit of	I M P O R T				E X P O R T				Import				Export				Import				Export				I M P O R T				E X P O R T				Product			I M P O R T				E X P O R T				Product		Value per	I M P O R T		E X P O R T		Column1	Column2
code	Product	quantity	2019		2020		2019		2020		2019		2020		2019		2020		2019		2020		2019		2020		2019		2020		2019		2020		code			2019		2020		2019		2020		code	Product	unit	2019	2020	2019	2020	IMPORT	EXPORT
			Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value				Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	4600.545	215138.487	4779.8890704	207306.873	50.552232	7465.56	124.7548352	27806.336											All data final	All data final			All data final	Alla data provisional	OK	OK	OK	OK	OK	OK	OK	OK	1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK	1	ROUNDWOOD (WOOD IN THE ROUGH)	NAC/m3	47	43	148	223	CHECK	CHECK
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	86.923	3113.503	27.5190704	1545.456	3.247232	412.393	2.7048352	329.588																	OK	OK	OK	OK	OK	OK	OK	OK	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	NAC/m3	36	56	127	122	CHECK	CHECK
1.1.C	Coniferous	1000 m3ub	4.993	132.639	4.023864	114.923	0.8641728	176.829	0.51172	109.4																	OK	OK	OK	OK	OK	OK	OK	OK	1.1.C	Coniferous	1000 m3ub									1.1.C	Coniferous	NAC/m3	27	29	205	214	CHECK	CHECK
1.1.NC	Non-Coniferous	1000 m3ub	81.93	2980.864	23.4952064	1430.533	2.3830592	235.564	2.1931152	220.188																	OK	OK	OK	OK	OK	OK	OK	OK	1.1.NC	Non-Coniferous	1000 m3ub									1.1.NC	Non-Coniferous	NAC/m3	36	61	99	100	ACCEPT	CHECK
1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	4513.622	212024.984	4752.37	205761.417	47.305	7053.167	122.05	27476.748																	OK	OK	OK	OK	OK	OK	OK	OK	1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK	1.2	INDUSTRIAL ROUNDWOOD	NAC/m3	47	43	149	225	CHECK	CHECK
1.2.C	Coniferous	1000 m3ub	759.564	43005.318	702.496	36371.755	47.223	7022.822	122.05	27476.742																	OK	OK	OK	OK	OK	OK	OK	OK	1.2.C	Coniferous	1000 m3ub									1.2.C	Coniferous	NAC/m3	57	52	149	225	CHECK	CHECK
1.2.NC	Non-Coniferous	1000 m3ub	3754.058	169019.666	4049.874	169389.662	0.082	30.345	0	0															6	6	OK	OK	OK	OK	OK	OK	OK	OK	1.2.NC	Non-Coniferous	1000 m3ub									1.2.NC	Non-Coniferous	NAC/m3	45	42	370	0	CHECK	ACCEPT
1.2.NC.T	of which: Tropical	1000 m3ub	0	0	0	0	0	0	0	0																	OK	OK	OK	OK	OK	OK	OK	OK	1.2.NC.T	of which: Tropical	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK	1.2.NC.T	of which: Tropical	NAC/m3	0	0	0	0	ACCEPT	ACCEPT
2	WOOD CHARCOAL	1000 t	2.159	1150.237	1.093679	636.454	0.013539	14.358	0.028802	38.125																	OK	OK	OK	OK	OK	OK	OK	OK	2	WOOD CHARCOAL	1000 t									2	WOOD CHARCOAL	NAC/t	533	582	1060	1324	ACCEPT	CHECK
3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	3207.92	119777.56	3817.4956922622	139868.823	50.471	3450.551	27.1987175572	1823.901																	OK	OK	OK	OK	OK	OK	OK	OK	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	3	WOOD CHIPS, PARTICLES AND RESIDUES	NAC/m3	37	37	68	67	ACCEPT	CHECK
3.1	WOOD CHIPS AND PARTICLES	1000 m3	3015.256	115681.97	3555.8749473267	134059.012	50.463	3448.831	27.1631932309	1812.88																	OK	OK	OK	OK	OK	OK	OK	OK	3.1	WOOD CHIPS AND PARTICLES	1000 m3									3.1	WOOD CHIPS AND PARTICLES	NAC/m3	38	38	68	67	ACCEPT	CHECK
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	192.663	4095.59	261.6207449355	5809.811	0.008	1.72	0.0355243263	11.021																	OK	OK	OK	OK	OK	OK	OK	OK	3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3									3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	NAC/m3	21	22	215	310	CHECK	CHECK
4	RECOVERED POST-CONSUMER WOOD	1000 t	116.307	3784.27	195.5649352041	3350.259	0.0004	0.448	0.0048846105	0.002																	OK	OK	OK	OK	OK	OK	OK	OK	4	RECOVERED POST-CONSUMER WOOD	1000 t									4	RECOVERED POST-CONSUMER WOOD	NAC/t	33	17	1120	0	CHECK	ACCEPT
5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	87.285	9947.821	97.5782928884	12720.341	0.066	16.842	0.1386259239	45.746																	OK	OK	OK	OK	OK	OK	OK	OK	5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	5	WOOD PELLETS AND OTHER AGGLOMERATES	NAC/t	114	130	255	330	ACCEPT	CHECK
5.1	WOOD PELLETS	1000 t	74.365	9077.742	85.7766417391	11877.187	0.05	12.659	0.1179965217	31.238																	OK	OK	OK	OK	OK	OK	OK	OK	5.1	WOOD PELLETS	1000 t									5.1	WOOD PELLETS	NAC/t	122	138	253	265	ACCEPT	CHECK
5.2	OTHER AGGLOMERATES	1000 t	12.92	870.079	11.8016511492	843.154	0.016	4.183	0.0206294022	14.508																	OK	OK	OK	OK	OK	OK	OK	OK	5.2	OTHER AGGLOMERATES	1000 t									5.2	OTHER AGGLOMERATES	NAC/t	67	71	261	703	CHECK	CHECK
6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	556.306	96482.268	559.885	91930.514	5701.386	1012374.37	5792.237	1034830.99																	OK	OK	OK	OK	OK	OK	OK	OK	6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	6	SAWNWOOD (INCLUDING SLEEPERS)	NAC/m3	173	164	178	179	ACCEPT	CHECK
6.C	Coniferous	1000 m3	546.187	90367.986	549.2	84940.31	5698.973	1010103.938	5787.887	1032566.787																	OK	OK	OK	OK	OK	OK	OK	OK	6.C	Coniferous	1000 m3									6.C	Coniferous	NAC/m3	165	155	177	178	ACCEPT	CHECK
6.NC	Non-Coniferous	1000 m3	10.119	6114.282	10.685	6990.204	2.413	2270.432	4.35	2264.203																	OK	OK	OK	OK	OK	OK	OK	OK	6.NC	Non-Coniferous	1000 m3									6.NC	Non-Coniferous	NAC/m3	604	654	941	521	ACCEPT	CHECK
6.NC.T	of which: Tropical	1000 m3	1.931	1571.148	3.253	2602.392	0.56	1096.596	0.56	575.3																	OK	OK	OK	OK	OK	OK	OK	OK	6.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	6.NC.T	of which: Tropical	NAC/m3	814	800	1958	1027	CHECK	CHECK
7	VENEER SHEETS	1000 m3	4.316	1560.041	3.378	1257.641	15.254	8002.415	11.407	5769.777																	OK	OK	OK	OK	OK	OK	OK	OK	7	VENEER SHEETS	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	7	VENEER SHEETS	NAC/m3	361	372	525	506	ACCEPT	CHECK
7.C	Coniferous	1000 m3	0.001	0.389	0.053	28.407	14.212	7041.104	10.794	5318.044																	OK	OK	OK	OK	OK	OK	OK	OK	7.C	Coniferous	1000 m3									7.C	Coniferous	NAC/m3	389	536	495	493	ACCEPT	CHECK
7.NC	Non-Coniferous	1000 m3	4.315	1559.652	3.325	1229.234	1.042	961.311	0.613	451.733																	OK	OK	OK	OK	OK	OK	OK	OK	7.NC	Non-Coniferous	1000 m3									7.NC	Non-Coniferous	NAC/m3	361	370	923	737	CHECK	CHECK
7.NC.T	of which: Tropical	1000 m3	0.047	22.612	0.001	1.078	0.025	5.333	0	0																	OK	OK	OK	OK	OK	OK	OK	OK	7.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	7.NC.T	of which: Tropical	NAC/m3	481	1078	213	0	CHECK	ACCEPT
8	WOOD-BASED PANELS	1000 m3	125.533	45324.985	140.610178004	45542.6470000001	200.933	120783.183	333.165923922	175822.453																	OK	OK	OK	OK	OK	OK	OK	OK	8	WOOD-BASED PANELS	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8	WOOD-BASED PANELS	NAC/m3	361	324	601	528	ACCEPT	CHECK
8.1	PLYWOOD	1000 m3	91.905	37515.02	93.934	35800.93	192.953	117963.15	312.863	168673.984																	OK	OK	OK	OK	OK	OK	OK	OK	8.1	PLYWOOD	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.1	PLYWOOD	NAC/m3	408	381	611	539	ACCEPT	CHECK
8.1.C	Coniferous	1000 m3	24.605	8799.67	17.406	5406.519	133.911	62854.23	227.545	97383.396																	OK	OK	OK	OK	OK	OK	OK	OK	8.1.C	Coniferous	1000 m3									8.1.C	Coniferous	NAC/m3	358	311	469	428	ACCEPT	CHECK
8.1.NC	Non-Coniferous	1000 m3	67.3	28715.35	76.528	30394.411	59.022	55104.411	85.318	71290.588																	OK	OK	OK	OK	OK	OK	OK	OK	8.1.NC	Non-Coniferous	1000 m3									8.1.NC	Non-Coniferous	NAC/m3	427	397	934	836	CHECK	CHECK
8.1.NC.T	of which: Tropical	1000 m3	0.66	801.829	0.426	585.95	0.016	14.243	0.025	46.183																	OK	OK	OK	OK	OK	OK	OK	OK	8.1.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.1.NC.T	of which: Tropical	NAC/m3	1215	1375	890	1847	ACCEPT	CHECK
8.2	PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD	1000 m3	21.338	5190.271	28.827	6184.808	3.827	1277.397	4.278	1376.303																	OK	OK	OK	OK	OK	OK	OK	OK	8.2	PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD	1000 m3									8.2	PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD	NAC/m3	243	215	334	322	ACCEPT	CHECK
8.2.1	of which: ORIENTED STRANDBOARD (OSB)	1000 m3	15.062	3772.193	20.11	4297.326	0.122	49.429	0.134	51.093																	OK	OK	OK	OK	OK	OK	OK	OK	8.2.1	of which: ORIENTED STRANDBOARD (OSB)	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.2.1	of which: ORIENTED STRANDBOARD (OSB)	NAC/m3	250	214	405	381	ACCEPT	CHECK
8.3	FIBREBOARD	1000 m3	12.29	2619.694	17.849178004	3556.909	4.153	1542.636	16.024923922	5772.166																	OK	OK	OK	OK	OK	OK	OK	OK	8.3	FIBREBOARD	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.3	FIBREBOARD	NAC/m3	213	199	371	360	ACCEPT	CHECK
8.3.1	HARDBOARD	1000 m3	1.418	363.402	1.928767494	572.906	3.896	1402.084	15.567404922	5501.703																	OK	OK	OK	OK	OK	OK	OK	OK	8.3.1	HARDBOARD	1000 m3									8.3.1	HARDBOARD	NAC/m3	256	297	360	353	ACCEPT	CHECK
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	8.613	1671.548	12.477838	2213.651	0.16	120.388	0.383914	260.201																	OK	OK	OK	OK	OK	OK	OK	OK	8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3									8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	NAC/m3	194	177	752	678	CHECK	CHECK
8.3.3	OTHER FIBREBOARD	1000 m3	2.259	584.744	3.44257251	770.352	0.097	20.164	0.073605	10.262																	OK	OK	OK	OK	OK	OK	OK	OK	8.3.3	OTHER FIBREBOARD	1000 m3									8.3.3	OTHER FIBREBOARD	NAC/m3	259	224	208	139	ACCEPT	CHECK
9	WOOD PULP	1000 t	309.815	183761.815	163.773718	72038.613	2467.13	1247452.351	2600.269016	1141977.162																	OK	OK	OK	OK	OK	OK	OK	OK	9	WOOD PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	9	WOOD PULP	NAC/t	593	440	506	439	ACCEPT	CHECK
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	4.734	1413.078	8.98928	2571.062	16.911	5964.702	32.052959	10778.634																	OK	OK	OK	OK	OK	OK	OK	OK	9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t									9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	NAC/t	298	286	353	336	ACCEPT	CHECK
9.2	CHEMICAL WOOD PULP	1000 t	299.146	175511.995	148.251212	62334.563	2304.699	1148877.679	2369.693821	1034239.729																	OK	OK	OK	OK	OK	OK	OK	OK	9.2	CHEMICAL WOOD PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	9.2	CHEMICAL WOOD PULP	NAC/t	587	420	498	436	ACCEPT	CHECK
9.2.1	SULPHATE PULP	1000 t	298.388	174763.12	148.248247	62327.515	2304.698	1148865.659	2369.69177	1034215.316																	OK	OK	OK	OK	OK	OK	OK	OK	9.2.1	SULPHATE PULP	1000 t									9.2.1	SULPHATE PULP	NAC/t	586	420	498	436	ACCEPT	CHECK
9.2.1.1	of which: BLEACHED	1000 t	290.003	169931.053	140.70266	58929.55	2271.356	1133774.265	2338.873058	1022998.378																	OK	OK	OK	OK	OK	OK	OK	OK	9.2.1.1	of which: BLEACHED	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	9.2.1.1	of which: BLEACHED	NAC/t	586	419	499	437	ACCEPT	CHECK
9.2.2	SULPHITE PULP	1000 t	0.758	748.875	0.002965	7.048	0.001	12.015	0.002051	24.413																	OK	OK	OK	OK	OK	OK	OK	OK	9.2.2	SULPHITE PULP	1000 t									9.2.2	SULPHITE PULP	NAC/t	988	2377	12015	11903	CHECK	CHECK
9.3	DISSOLVING GRADES	1000 t	5.935	6836.742	6.533226	7132.988	145.52	92609.97	198.522236	96958.799																	OK	OK	OK	OK	OK	OK	OK	OK	9.3	DISSOLVING GRADES	1000 t									9.3	DISSOLVING GRADES	NAC/t	1152	1092	636	488	ACCEPT	CHECK
10	OTHER PULP	1000 t	2.861	4081.468	1.504697	2111.271	0.03202	20.636	0.001545	1.584																	OK	OK	OK	OK	OK	OK	OK	OK	10	OTHER PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	10	OTHER PULP	NAC/t	1427	1403	644	1025	CHECK	CHECK
10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	2.844	4073.737	1.504615	2110.61	0.00002	0.003	0.000009	0.033																	OK	OK	OK	OK	OK	OK	OK	OK	10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t									10.1	PULP FROM FIBRES OTHER THAN WOOD	NAC/t	1432	1403	150	3667	CHECK	CHECK
10.2	RECOVERED FIBRE PULP	1000 t	0.017	7.731	0.000082	0.661	0.032	20.633	0	0																	OK	OK	OK	OK	OK	OK	OK	OK	10.2	RECOVERED FIBRE PULP	1000 t									10.2	RECOVERED FIBRE PULP	NAC/t	455	8061	645	0	CHECK	ACCEPT
11	RECOVERED PAPER	1000 t	25.566	4727.268	5.381368	908.09	5.422	1616.692	21.295382	2691.453																	OK	OK	OK	OK	OK	OK	OK	OK	11	RECOVERED PAPER	1000 t									11	RECOVERED PAPER	NAC/t	185	169	298	126	ACCEPT	CHECK
12	PAPER AND PAPERBOARD	1000 t	28.943	24488.719	40.439078	39082.5370000002	4355.782	3177326.095	4144.039261	2911636.631																	OK	OK	OK	OK	OK	OK	OK	OK	12	PAPER AND PAPERBOARD	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	12	PAPER AND PAPERBOARD	NAC/t	846	966	729	703	ACCEPT	CHECK
12.1	GRAPHIC PAPERS	1000 t	15.869	9467.534	15.889501	8527.344	2392.802	1577990.1	1940.696578	1177098.539																	OK	OK	OK	OK	OK	OK	OK	OK	12.1	GRAPHIC PAPERS	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	12.1	GRAPHIC PAPERS	NAC/t	597	537	659	607	ACCEPT	CHECK
12.1.1	NEWSPRINT	1000 t	15.153	7484.645	14.863112	5698.682	58.489	29086.682	50.702795	20019.603																	OK	OK	OK	OK	OK	OK	OK	OK	12.1.1	NEWSPRINT	1000 t									12.1.1	NEWSPRINT	NAC/t	494	383	497	395	ACCEPT	CHECK
12.1.2	UNCOATED MECHANICAL	1000 t	0.125	157.683	0.087437	161.731	667.622	384804.756	421.049165	221434.863																	OK	OK	OK	OK	OK	OK	OK	OK	12.1.2	UNCOATED MECHANICAL	1000 t									12.1.2	UNCOATED MECHANICAL	NAC/t	1261	1850	576	526	CHECK	CHECK
12.1.3	UNCOATED WOODFREE	1000 t	0.231	737.261	0.468146	1731.259	318.218	221581.859	368.171181	244597.385																	OK	OK	OK	OK	OK	OK	OK	OK	12.1.3	UNCOATED WOODFREE	1000 t									12.1.3	UNCOATED WOODFREE	NAC/t	3192	3698	696	664	CHECK	CHECK
12.1.4	COATED PAPERS	1000 t	0.36	1087.945	0.470806	935.672	1348.473	942516.803	1100.773437	691046.687999999																	OK	OK	OK	OK	OK	OK	OK	OK	12.1.4	COATED PAPERS	1000 t									12.1.4	COATED PAPERS	NAC/t	3022	1987	699	628	CHECK	CHECK
12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	0.056	177.612	0.019211	93.025	1.905	2059.235	2.064192	2148.807																	OK	OK	OK	OK	OK	OK	OK	OK	12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t									12.2	HOUSEHOLD AND SANITARY PAPERS	NAC/t	3172	4842	1081	1041	CHECK	CHECK
12.3	PACKAGING MATERIALS	1000 t	13.01	14679.008	24.47081	29954.154	1914.64	1551888.413	2150.484673	1683965.651																	OK	OK	OK	OK	OK	OK	OK	OK	12.3	PACKAGING MATERIALS	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	12.3	PACKAGING MATERIALS	NAC/t	1128	1224	811	783	ACCEPT	CHECK
12.3.1	CASE MATERIALS	1000 t	3.215	2368.01	6.438244	11626.157	477.498	258192.142	525.563931	258341.588																	OK	OK	OK	OK	OK	OK	OK	OK	12.3.1	CASE MATERIALS	1000 t									12.3.1	CASE MATERIALS	NAC/t	737	1806	541	492	CHECK	CHECK
12.3.2	CARTONBOARD	1000 t	5.251	9006.921	10.753031	13421.349	1237.374	1079129.135	1387.730971	1191143.066																	OK	OK	OK	OK	OK	OK	OK	OK	12.3.2	CARTONBOARD	1000 t									12.3.2	CARTONBOARD	NAC/t	1715	1248	872	858	ACCEPT	CHECK
12.3.3	WRAPPING PAPERS	1000 t	2.303	1985.024	3.649434	3097.072	173.611	192880.069	211.836386	215112.278																	OK	OK	OK	OK	OK	OK	OK	OK	12.3.3	WRAPPING PAPERS	1000 t									12.3.3	WRAPPING PAPERS	NAC/t	862	849	1111	1015	ACCEPT	CHECK
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	2.241	1319.053	3.630101	1809.576	26.156	21687.067	25.353385	19368.719																	OK	OK	OK	OK	OK	OK	OK	OK	12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t									12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	NAC/t	589	498	829	764	ACCEPT	CHECK
12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t	0.008	164.565	0.059556	508.014	46.435	45388.347	50.793818	48423.634																	OK	OK	OK	OK	OK	OK	OK	OK	12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t									12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	NAC/t	20571	8530	977	953	CHECK	CHECK
	Mechanical wood pulp, semi-chemical wood pulp and pulp from fibres other than wood 9.1+10.1	1000 t	7.578	5486.815	10.494	4681.672	16.911	5964.705	32.053	10778.667
		To fill:	0	0	0	0	0	0	0	0
		Text:	0	0	0	0	0	0	0	0

EU2 Removals

Country:

Date:

Name of Official responsible for reply:

Official Address (in full):

Check Table

NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791

Phone/Fax:

E-mail:

EU2

Please verify if there's an error!

FOREST SECTOR QUESTIONNAIRE

Removals by type of ownership

Discrepancies

Flag

Note

Product code

Ownership

Product code

Ownership

Unit

2019

2020

2019

2020

2019

2020

Unit

2019

2020

Quantity

ROUNDWOOD REMOVALS (under bark)

ROUNDWOOD REMOVALS

ROUNDWOOD

1000 m3

63666.864

60233.268

2019 revised

ROUNDWOOD

1000 m3

1.C

Coniferous

1000 m3

49899.313

47338.057

2019 revised

1.C

Coniferous

1000 m3

1.NC

Non-coniferous

1000 m3

13767.550

12895.211

2019 revised

1.NC

Non-coniferous

1000 m3

State forests

1000 m3

5390.387

5172.931

State forests

1000 m3

Coniferous

1000 m3

4708.085

4543.213

Coniferous

1000 m3

Non-coniferous

1000 m3

682.302

629.719

Non-coniferous

1000 m3

Other publicly owned forests

1000 m3

Other publicly owned forests are included in Private forests.

Other publicly owned forests

1000 m3

Coniferous

1000 m3

Other publicly owned forests are included in Private forests.

Coniferous

1000 m3

Non-coniferous

1000 m3

Other publicly owned forests are included in Private forests.

Non-coniferous

1000 m3

Private forest

1000 m3

58276.477

55060.336

2019 revised. Private forest includes also Other publicly owned forests.

Private forest includes also Other publicly owned forests.

Private forest

1000 m3

Coniferous

1000 m3

45191.228

42794.844

2019 revised. Private forest includes also Other publicly owned forests.

Private forest includes also Other publicly owned forests.

Coniferous

1000 m3

Non-coniferous

1000 m3

13085.249

12265.492

2019 revised. Private forest includes also Other publicly owned forests.

Private forest includes also Other publicly owned forests.

Non-coniferous

1000 m3

To fill:

Text:

Note:

–

Ownership categories correspond to those of the TBFRA.

State forests: Forests owned by national, state and regional governments, or government-owned corporations; Crown forests.

Other publicly owned forests: Forests belonging to cities, municipalities, villages and communes.

Private forests: Forests owned by individuals, co-operatives, enterprises and industries and other private institutions.

–

The unit should be solid cubic metres, under bark.

ITTO1-Estimates

				Country:	FI	Date:
				Name of Official responsible for reply:		0

				Official Address (in full):		NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791
	ITTO1
				Telephone:	0	Fax:	0
	FOREST SECTOR QUESTIONNAIRE			E-mail:		0
	Production and Trade Estimates for	2021
	Value must always be in 1000 NAC (national currency)			1000 NAC

Product		Unit of	Production	Imports		Exports
Code	Product	quantity	Quantity	Quantity	Value	Quantity	Value

1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub
1.2.C	Coniferous	1000 m3ub
1.2.NC	Non-Coniferous	1000 m3ub
1.2.NC.T	of which: Tropical	1000 m3ub
6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3
6.C	Coniferous	1000 m3
6.NC	Non-Coniferous	1000 m3
6.NC.T	of which: Tropical	1000 m3
7	VENEER SHEETS	1000 m3
7.C	Coniferous	1000 m3
7.NC	Non-Coniferous	1000 m3
7.NC.T	of which: Tropical	1000 m3
8.1	PLYWOOD	1000 m3
8.1.C	Coniferous	1000 m3
8.1.NC	Non-Coniferous	1000 m3
8.1.NC.T	of which: Tropical	1000 m3
	m3 = cubic metres solid volume
	m3ub = cubic metres solid volume underbark (i.e. excluding bark)

ITTO2-Species


						Country:	FI	Date:
		ITTO2				Name of Official responsible for reply:		0

						Official Address (in full):		NATURAL RESOURCES INSTITUTE FINLAND (LUKE), Statistical Services, PO Box 2, FI-00791
		FOREST SECTOR QUESTIONNAIRE
		Trade in Tropical Species				Telephone:	0	Fax:	0
						E-mail:		0
	Value must always be in 1000 NAC (national currency)			1000 NAC

				I M P O R T				E X P O R T
Product	Classifications			2019		2020		2019		2020
	HS2007/HS2002/HS96	Scientific Name	Local/Trade Name	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
				(1000 m3)		(1000 m3)		(1000 m3)		(1000 m3)
1.2.NC.T	HS2017:	Total		0	22	0.003	50.978			0.000	0.000
Industrial Roundwood, Tropical	ex4403.12 4403.41/49	440341		0	0	0.000	0.000
	HS2012/2007:	440349		0	22	0.003	50.978
	ex4403.10 4403.41/49 ex4403.99
6.NC.T	HS2017:	Total		3	3,385	3.935	5,140.586	3.766	3,682.354	3.095	2,787.737
Sawnwood, Tropical	ex4406.12/92 4407.21/22/25/26/27/28/29	440721		1	978	0.578	785.536	0.000	0.000	0.000	0.000
		440722		0	103	0.082	316.563	0.001	5.634	0.002	11.140
	HS2012/2007:	440725		0	0	0.000	0.000	0.000	0.000	0.000	0.000
	ex4406.10/90 4407.21/22/25/26/27/28/30	440726		0	43	0.000	0.000	0.000	0.000	0.000	0.000
		440727		0	12	0.011	9.901	0.003	6.725	0.000	0.000
		440728		0	57	0.016	18.403	0.011	103.580	0.002	3.500
		440729		2	2,191	3.248	4,010.183	3.751	3,566.415	3.091	2,773.097
7.NC.T	HS2017:	Total		1	738	1.365	719.536	0.033	9.938	0.088	18.972
Veneer Sheets, Tropical	4408.31/39	440831		0	0	0.003	1.634	0.004	2.611	0.000	0.000
	HS2012/2007:	440839		1	738	1.362	717.902	0.029	7.327	0.088	18.972
	4408.31/39 ex4408.90
8.1.NC.T	HS2017:	Total		1	1,922	0.685	1,673.144	0.166	772.764	0.140	752.855
Plywood, Tropical	4412.31 ex4412.94/99	441231		1	1,922	0.685	1,673.144	0.166	772.764	0.140	752.855
	HS2012/2007:
	4412.31 ex4412.32/94/99
Note: List the major species traded in each category. Use additional sheet if more species are to be explicitly reported. For tropical plywood, identify by face veneer if composed of more than one species.

ITTO3-Miscellaneous

Country:

Date:

Name of Official responsible for reply:

Official Address (in full):

FOREST SECTOR QUESTIONNAIRE ITTO3

Miscellaneous Items

Telephone:

Fax:

(use additional paper if necessary)

E-mail:

Current import tariff

Logs

Tropical:

Sawn

Tropical:

Veneer

Tropical:

Plywood

Tropical:

Non-Tropical:

Comments (if any):

Please elaborate on any short or medium term plans for expanding capacity for (further) processing of tropical timber products in your country.

Please indicate any trends or changes expected in the species composition of your trade. How important are lesser-used tropical timber species and/or minor tropical forest products?

Please indicate the extent of foreign involvement in your timber sector (e.g. number and nationalities of concessionaires/mill (joint) owners, area of forest allocated, scale of investment, etc.).

Please provide details of any relevant forest law enforcement activities (e.g. legislation, fines, arrests, etc.) in your country in the past year.

Please indicate the current extent of forest plantations in your country (ha), annual establishment rate (ha/yr) and proportion of industrial roundwood production from plantations.

TS-OB

Min:

80%

Max:

120%

Notes

JQ1

Country

Flow

Unit

Product

2015

2016

2017

2018

2019

2020

15/16

16/17

17/18

18/19

19/19

19/20

2015

2016

2017

2018

P.OB

1000 m3

ERROR:#N/A

72926.627

68975.656

94.58%

P.OB

1000 m3

1_C

ERROR:#N/A

9242.481

10307.971

111.53%

P.OB

1000 m3

1_NC

ERROR:#N/A

4286.094

4985.504

116.32%

P.OB

1000 m3

1_1

ERROR:#N/A

4956.386

5322.467

107.39%

P.OB

1000 m3

1_1_C

ERROR:#N/A

63684.146

58667.685

92.12%

P.OB

1000 m3

1_1_NC

ERROR:#N/A

52727.583

49087.512

93.10%

P.OB

1000 m3

1_2

ERROR:#N/A

10956.563

9580.173

87.44%

P.OB

1000 m3

1_2_C

ERROR:#N/A

0.000

P.OB

1000 m3

1_2_NC

ERROR:#N/A

26092.200

25045.098

95.99%

P.OB

1000 m3

1_2_1

ERROR:#N/A

24970.182

24065.887

96.38%

P.OB

1000 m3

1_2_1_C

ERROR:#N/A

1122.018

979.211

87.27%

P.OB

1000 m3

1_2_1_NC

ERROR:#N/A

37591.946

33622.587

89.44%

P.OB

1000 m3

1_2_2

ERROR:#N/A

27757.401

25021.625

90.14%

P.OB

1000 m3

1_2_2_C

ERROR:#N/A

9834.545

8600.962

87.46%

P.OB

1000 m3

1_2_2_NC

ERROR:#N/A

0.000

P.OB

1000 m3

1_2_3

ERROR:#N/A

0.000

P.OB

1000 m3

1_2_3_C

ERROR:#N/A

0.000

P.OB

1000 m3

1_2_3_NC

ERROR:#N/A

ERROR:#REF!

TS-JQ1

Min:

80%

Max:

120%

Notes

JQ1

Country

Flow

Unit

Product

2015

2016

2017

2018

2019

2020

15/16

16/17

17/18

18/19

19/19

19/20

2015

2016

2017

2018

1000 m3

ERROR:#N/A

63666.864

60233.268

94.61%

1000 m3

1_C

ERROR:#N/A

8013.230

8937.012

111.53%

1000 m3

1_NC

ERROR:#N/A

3716.043

4322.432

116.32%

1000 m3

1_1

ERROR:#N/A

4297.187

4614.580

107.39%

1000 m3

1_1_C

ERROR:#N/A

55653.633

51296.256

92.17%

1000 m3

1_1_NC

ERROR:#N/A

46183.270

43015.625

93.14%

1000 m3

1_2

ERROR:#N/A

9470.364

8280.631

87.44%

1000 m3

1_2_C

ERROR:#N/A

0.000

1000 m3

1_2_NC

ERROR:#N/A

23211.271

22279.273

95.98%

1000 m3

1_2_1

ERROR:#N/A

22218.285

21412.672

96.37%

1000 m3

1_2_1_C

ERROR:#N/A

992.986

866.602

87.27%

1000 m3

1_2_1_NC

ERROR:#N/A

32442.363

29016.982

89.44%

1000 m3

1_2_2

ERROR:#N/A

23964.985

21602.953

90.14%

1000 m3

1_2_2_C

ERROR:#N/A

8477.378

7414.029

87.46%

1000 m3

1_2_2_NC

ERROR:#N/A

0.000

1000 m3

1_2_3

ERROR:#N/A

0.000

1000 m3

1_2_3_C

ERROR:#N/A

0.000

1000 m3

1_2_3_NC

ERROR:#N/A

0.000

1000 mt

ERROR:#N/A

14072.689

13099.374

93.08%

1000 m3

ERROR:#N/A

9121.031

8554.701

93.79%

1000 m3

3_1

ERROR:#N/A

4951.657

4544.673

91.78%

1000 m3

3_2

ERROR:#N/A

458.214

439.458

95.91%

1000 mt

ERROR:#N/A

362.527

322.090

88.85%

1000 mt

4_1

ERROR:#N/A

362.527

322.090

88.85%

1000 mt

4_2

ERROR:#N/A

0.000

1000 m3

ERROR:#N/A

11390.000

10916.000

95.84%

1000 m3

5_C

ERROR:#N/A

11360.000

10880.000

95.77%

1000 m3

5_NC

ERROR:#N/A

30.000

36.000

120.00%

1000 m3

5_NC_T

ERROR:#N/A

0.000

1000 m3

ERROR:#N/A

0.000

1000 m3

6_1

ERROR:#N/A

0.000

1000 m3

6_1_C

ERROR:#N/A

0.000

1000 m3

6_1_NC

ERROR:#N/A

0.000

1000 m3

6_1_NC_T

ERROR:#N/A

0.000

1000 m3

6_2

ERROR:#N/A

1090.000

990.000

90.83%

1000 m3

6_2_C

ERROR:#N/A

800.000

990.000

123.75%

1000 m3

6_2_NC

ERROR:#N/A

290.000

0.000

0.00%

1000 m3

6_2_NC_T

ERROR:#N/A

0.000

1000 m3

6_3

ERROR:#N/A

0.000

1000 m3

6_3_1

ERROR:#N/A

0.000

1000 m3

6_4

ERROR:#N/A

0.000

1000 m3

6_4_1

ERROR:#N/A

0.000

1000 m3

6_4_2

ERROR:#N/A

0.000

1000 m3

6_4_3

ERROR:#N/A

0.000

1000 mt

ERROR:#N/A

11600.000

10520.000

90.69%

1000 mt

7_1

ERROR:#N/A

3280.000

2840.000

86.59%

1000 mt

7_2

ERROR:#N/A

8320.000

7680.000

92.31%

1000 mt

7_3

ERROR:#N/A

7330.000

6680.000

91.13%

1000 mt

7_3_1

ERROR:#N/A

7330.000

6680.000

91.13%

1000 mt

7_3_2

ERROR:#N/A

990.000

1000.000

101.01%

1000 mt

7_3_3

ERROR:#N/A

0.000

1000 mt

7_3_4

ERROR:#N/A

0.000

1000 mt

7_4

ERROR:#N/A

0.000

1000 mt

ERROR:#N/A

0.000

1000 mt

8_1

ERROR:#N/A

620.000

570.000

91.94%

1000 mt

8_2

ERROR:#N/A

9720.000

8210.000

84.47%

1000 mt

ERROR:#N/A

4880.000

3410.000

69.88%

1000 mt

ERROR:#N/A

0.000

1000 mt

10_1

ERROR:#N/A

0.000

1000 mt

10_1_1

ERROR:#N/A

0.000

1000 mt

10_1_2

ERROR:#N/A

0.000

1000 mt

10_1_3

ERROR:#N/A

0.000

1000 mt

10_1_4

ERROR:#N/A

4440.000

4400.000

99.10%

1000 mt

10_2

ERROR:#N/A

0.000

1000 mt

10_3

ERROR:#N/A

0.000

1000 mt

10_3_1

ERROR:#N/A

0.000

1000 mt

10_3_2

ERROR:#N/A

0.000

1000 mt

10_3_3

ERROR:#N/A

400.000

100.00%

1000 mt

10_3_4

ERROR:#N/A

0.000

1000 mt

10_4

ERROR:#N/A

0.000

TS-JQ2

Min:

80%

Max:

120%

Notes

JQ2

Country

Flow

Unit

Product

2015

2016

2017

2018

2019

2020

15/16

16/17

17/18

18/19

19/19

19/20

2015

2016

2017

2018

1000 m3

ERROR:#N/A

6324.417

6462.947

102.19%

€

1000 NAC

ERROR:#N/A

321010.688

291631.505

90.85%

1000 m3

ERROR:#DIV/0!

ERROR:#N/A

50.757

45.124

88.90%

1000 m3

ERROR:#N/A

1447.303

1264.373

87.36%

€

1000 NAC

ERROR:#N/A

91283.897

97105.372

106.38%

1000 m3

ERROR:#DIV/0!

ERROR:#N/A

63.072

76.801

121.77%

1000 m3

1_1

ERROR:#N/A

89.404

188.833

211.21%

€

1000 NAC

1_1

ERROR:#N/A

3529.284

7511.495

212.83%

1000 m3

1_1

ERROR:#DIV/0!

ERROR:#N/A

39.476

39.779

100.77%

1000 m3

1_1

ERROR:#N/A

90.922

101.148

111.25%

€

1000 NAC

1_1

ERROR:#N/A

3259.928

4062.402

124.62%

1000 m3

1_1

ERROR:#DIV/0!

ERROR:#N/A

35.854

40.163

112.02%

1000 m3

1_2

ERROR:#N/A

5.047

162.198

3213.75%

€

1000 NAC

1_2

ERROR:#N/A

142.634

5735.272

4020.97%

1000 m3

1_2

ERROR:#DIV/0!

ERROR:#N/A

28.261

35.360

125.12%

1000 m3

1_2

ERROR:#N/A

88.468

98.949

111.85%

€

1000 NAC

1_2

ERROR:#N/A

3012.913

3841.195

127.49%

1000 m3

1_2

ERROR:#DIV/0!

ERROR:#N/A

34.057

38.820

113.99%

1000 m3

1_2_C

ERROR:#N/A

84.357

26.635

31.57%

€

1000 NAC

1_2_C

ERROR:#N/A

3386.650

1776.223

52.45%

1000 m3

1_2_C

ERROR:#DIV/0!

ERROR:#N/A

40.147

66.688

166.11%

1000 m3

1_2_C

ERROR:#N/A

2.454

2.199

89.60%

€

1000 NAC

1_2_C

ERROR:#N/A

247.015

221.207

89.55%

1000 m3

1_2_C

ERROR:#DIV/0!

ERROR:#N/A

100.658

100.602

99.94%

1000 m3

1_2_NC

ERROR:#N/A

6235.013

6274.114

100.63%

€

1000 NAC

1_2_NC

ERROR:#N/A

317481.404

284120.010

89.49%

1000 m3

1_2_NC

ERROR:#DIV/0!

ERROR:#N/A

50.919

45.284

88.93%

1000 m3

1_2_NC

ERROR:#N/A

1356.381

1163.225

85.76%

€

1000 NAC

1_2_NC

ERROR:#N/A

88023.969

93042.970

105.70%

1000 m3

1_2_NC

ERROR:#DIV/0!

ERROR:#N/A

64.896

79.987

123.25%

1000 m3

1_2_NC_T

ERROR:#N/A

1744.746

1533.846

87.91%

€

1000 NAC

1_2_NC_T

ERROR:#N/A

108160.001

81813.353

75.64%

1000 m3

1_2_NC_T

ERROR:#DIV/0!

ERROR:#N/A

61.992

53.339

86.04%

1000 m3

1_2_NC_T

ERROR:#N/A

1174.095

1095.264

93.29%

€

1000 NAC

1_2_NC_T

ERROR:#N/A

78647.438

88588.893

112.64%

1000 m3

1_2_NC_T

ERROR:#DIV/0!

ERROR:#N/A

66.986

80.884

120.75%

1000 mt

ERROR:#N/A

4490.267

4740.268

105.57%

€

1000 NAC

ERROR:#N/A

209321.403

202306.657

96.65%

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

46.617

42.678

91.55%

1000 mt

ERROR:#N/A

182.286

67.961

37.28%

€

1000 NAC

ERROR:#N/A

9376.531

4454.077

47.50%

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

51.439

65.539

127.41%

1000 m3

ERROR:#N/A

0.004

0.003

75.00%

€

1000 NAC

ERROR:#N/A

22.308

50.978

228.52%

1000 m3

ERROR:#DIV/0!

ERROR:#N/A

5577.000

16992.667

304.69%

1000 m3

ERROR:#N/A

0.000

€

1000 NAC

ERROR:#N/A

0.000

1000 m3

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

3_1

ERROR:#N/A

5.045

4.783

94.82%

€

1000 NAC

3_1

ERROR:#N/A

3230.965

3279.706

101.51%

1000 m3

3_1

ERROR:#DIV/0!

ERROR:#N/A

640.429

685.640

107.06%

1000 m3

3_1

ERROR:#N/A

0.526

0.305

57.91%

€

1000 NAC

3_1

ERROR:#N/A

368.809

231.986

62.90%

1000 m3

3_1

ERROR:#DIV/0!

ERROR:#N/A

701.158

761.636

108.63%

1000 m3

3_2

ERROR:#N/A

4146.750

4673.585

112.70%

€

1000 NAC

3_2

ERROR:#N/A

169940.001

183501.930

107.98%

1000 m3

3_2

ERROR:#DIV/0!

ERROR:#N/A

40.981

39.264

95.81%

1000 m3

3_2

ERROR:#N/A

237.932

208.233

87.52%

€

1000 NAC

3_2

ERROR:#N/A

11005.185

9954.060

90.45%

1000 m3

3_2

ERROR:#DIV/0!

ERROR:#N/A

46.253

47.802

103.35%

1000 mt

ERROR:#N/A

3954.080

4403.563

111.37%

€

1000 NAC

ERROR:#N/A

165840.307

177276.918

106.90%

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

41.942

40.258

95.98%

1000 mt

ERROR:#N/A

186.042

189.178

101.69%

€

1000 NAC

ERROR:#N/A

9365.661

9321.967

99.53%

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

50.342

49.276

97.88%

1000 mt

4_1

ERROR:#N/A

192.670

270.023

140.15%

€

1000 NAC

4_1

ERROR:#N/A

4099.694

6225.012

151.84%

1000 mt

4_1

ERROR:#DIV/0!

ERROR:#N/A

21.278

23.054

108.34%

1000 mt

4_1

ERROR:#N/A

51.890

19.056

36.72%

€

1000 NAC

4_1

ERROR:#N/A

1639.524

632.093

38.55%

1000 mt

4_1

ERROR:#DIV/0!

ERROR:#N/A

31.596

33.171

104.98%

1000 mt

4_2

ERROR:#N/A

206.913

399.191

192.93%

€

1000 NAC

4_2

ERROR:#N/A

6435.113

7332.401

113.94%

1000 mt

4_2

ERROR:#DIV/0!

ERROR:#N/A

31.101

18.368

59.06%

1000 mt

4_2

ERROR:#N/A

0.519

0.174

33.52%

€

1000 NAC

4_2

ERROR:#N/A

32.273

23.648

73.27%

1000 mt

4_2

ERROR:#DIV/0!

ERROR:#N/A

62.183

135.953

218.63%

1000 m3

ERROR:#N/A

116.223

131.333

113.00%

€

1000 NAC

ERROR:#N/A

15222.288

16667.732

109.50%

1000 m3

ERROR:#DIV/0!

ERROR:#N/A

130.975

126.912

96.90%

1000 m3

ERROR:#N/A

57.183

12.717

22.24%

€

1000 NAC

ERROR:#N/A

7027.230

1596.717

22.72%

1000 m3

ERROR:#DIV/0!

ERROR:#N/A

122.890

125.554

102.17%

1000 m3

5_C

ERROR:#N/A

100.474

97.254

96.79%

€

1000 NAC

5_C

ERROR:#N/A

13260.607

13992.757

105.52%

1000 m3

5_C

ERROR:#DIV/0!

ERROR:#N/A

131.980

143.879

109.02%

1000 m3

5_C

ERROR:#N/A

29.849

6.260

20.97%

€

1000 NAC

5_C

ERROR:#N/A

4491.920

881.972

19.63%

1000 m3

5_C

ERROR:#DIV/0!

ERROR:#N/A

150.488

140.881

93.62%

1000 m3

5_NC

ERROR:#N/A

15.749

34.079

216.39%

€

1000 NAC

5_NC

ERROR:#N/A

1961.681

2674.975

136.36%

1000 m3

5_NC

ERROR:#DIV/0!

ERROR:#N/A

124.559

78.492

63.02%

1000 m3

5_NC

ERROR:#N/A

27.334

6.457

23.62%

€

1000 NAC

5_NC

ERROR:#N/A

2535.310

714.745

28.19%

1000 m3

5_NC

ERROR:#DIV/0!

ERROR:#N/A

92.753

110.693

119.34%

1000 m3

5_NC_T

ERROR:#N/A

593.354

600.101

101.14%

€

1000 NAC

5_NC_T

ERROR:#N/A

115899.181

114244.426

98.57%

1000 m3

5_NC_T

ERROR:#DIV/0!

ERROR:#N/A

195.329

190.375

97.46%

1000 m3

5_NC_T

ERROR:#N/A

8966.745

8217.919

91.65%

€

1000 NAC

5_NC_T

ERROR:#N/A

1729827.823

1557546.684

90.04%

1000 m3

5_NC_T

ERROR:#DIV/0!

ERROR:#N/A

192.916

189.531

98.25%

1000 m3

ERROR:#N/A

559.995

569.549

101.71%

€

1000 NAC

ERROR:#N/A

95658.068

91739.100

95.90%

1000 m3

ERROR:#DIV/0!

ERROR:#N/A

170.820

161.073

94.29%

1000 m3

ERROR:#N/A

8951.761

8197.932

91.58%

€

1000 NAC

ERROR:#N/A

1722057.473

1548568.236

89.93%

1000 m3

ERROR:#DIV/0!

ERROR:#N/A

192.371

188.897

98.19%

1000 m3

6_1

ERROR:#N/A

33.359

30.552

91.59%

€

1000 NAC

6_1

ERROR:#N/A

20241.113

22505.326

111.19%

1000 m3

6_1

ERROR:#DIV/0!

ERROR:#N/A

606.766

736.624

121.40%

1000 m3

6_1

ERROR:#N/A

14.984

19.987

133.39%

€

1000 NAC

6_1

ERROR:#N/A

7770.350

8978.448

115.55%

1000 m3

6_1

ERROR:#DIV/0!

ERROR:#N/A

518.576

449.214

86.62%

1000 m3

6_1_C

ERROR:#N/A

3.032

4.303

141.92%

€

1000 NAC

6_1_C

ERROR:#N/A

3385.065

5140.586

151.86%

1000 m3

6_1_C

ERROR:#DIV/0!

ERROR:#N/A

1116.446

1194.652

107.00%

1000 m3

6_1_C

ERROR:#N/A

3.782

3.106

82.13%

€

1000 NAC

6_1_C

ERROR:#N/A

3682.354

2787.737

75.71%

1000 m3

6_1_C

ERROR:#DIV/0!

ERROR:#N/A

973.653

897.533

92.18%

1000 m3

6_1_NC

ERROR:#N/A

7.670

6.764

88.19%

€

1000 NAC

6_1_NC

ERROR:#N/A

6737.564

5226.286

77.57%

1000 m3

6_1_NC

ERROR:#DIV/0!

ERROR:#N/A

878.431

772.662

87.96%

1000 m3

6_1_NC

ERROR:#N/A

143.785

146.053

101.58%

€

1000 NAC

6_1_NC

ERROR:#N/A

47068.249

44419.527

94.37%

1000 m3

6_1_NC

ERROR:#DIV/0!

ERROR:#N/A

327.352

304.133

92.91%

1000 m3

6_1_NC_T

ERROR:#N/A

0.126

0.165

130.95%

€

1000 NAC

6_1_NC_T

ERROR:#N/A

119.218

336.775

282.49%

1000 m3

6_1_NC_T

ERROR:#DIV/0!

ERROR:#N/A

946.175

2041.061

215.72%

1000 m3

6_1_NC_T

ERROR:#N/A

53.511

42.749

79.89%

€

1000 NAC

6_1_NC_T

ERROR:#N/A

27170.220

21668.895

79.75%

1000 m3

6_1_NC_T

ERROR:#DIV/0!

ERROR:#N/A

507.750

506.887

99.83%

1000 m3

6_2

ERROR:#N/A

7.544

6.599

87.47%

€

1000 NAC

6_2

ERROR:#N/A

6618.346

4889.511

73.88%

1000 m3

6_2

ERROR:#DIV/0!

ERROR:#N/A

877.299

740.947

84.46%

1000 m3

6_2

ERROR:#N/A

90.274

103.304

114.43%

€

1000 NAC

6_2

ERROR:#N/A

19898.029

22750.632

114.34%

1000 m3

6_2

ERROR:#DIV/0!

ERROR:#N/A

220.418

220.230

99.91%

1000 m3

6_2_C

ERROR:#N/A

0.574

1.365

237.80%

€

1000 NAC

6_2_C

ERROR:#N/A

738.355

719.536

97.45%

1000 m3

6_2_C

ERROR:#DIV/0!

ERROR:#N/A

1286.333

527.133

40.98%

1000 m3

6_2_C

ERROR:#N/A

0.033

0.088

266.67%

€

1000 NAC

6_2_C

ERROR:#N/A

9.938

18.972

190.90%

1000 m3

6_2_C

ERROR:#DIV/0!

ERROR:#N/A

301.152

215.591

71.59%

1000 m3

6_2_NC

ERROR:#N/A

382.724

408.821

106.82%

€

1000 NAC

6_2_NC

ERROR:#N/A

140077.529

140562.989

100.35%

1000 m3

6_2_NC

ERROR:#DIV/0!

ERROR:#N/A

366.001

343.825

93.94%

1000 m3

6_2_NC

ERROR:#N/A

985.891

890.206

90.29%

€

1000 NAC

6_2_NC

ERROR:#N/A

527573.970

458735.340

86.95%

1000 m3

6_2_NC

ERROR:#DIV/0!

ERROR:#N/A

535.124

515.313

96.30%

1000 m3

6_2_NC_T

ERROR:#N/A

118.431

127.825

107.93%

€

1000 NAC

6_2_NC_T

ERROR:#N/A

55864.216

56652.512

101.41%

1000 m3

6_2_NC_T

ERROR:#DIV/0!

ERROR:#N/A

471.703

443.204

93.96%

1000 m3

6_2_NC_T

ERROR:#N/A

918.471

828.478

90.20%

€

1000 NAC

6_2_NC_T

ERROR:#N/A

503238.488

435724.773

86.58%

1000 m3

6_2_NC_T

ERROR:#DIV/0!

ERROR:#N/A

547.909

525.934

95.99%

1000 m3

6_3

ERROR:#N/A

30.972

29.770

96.12%

€

1000 NAC

6_3

ERROR:#N/A

12583.152

11988.996

95.28%

1000 m3

6_3

ERROR:#DIV/0!

ERROR:#N/A

406.275

402.721

99.13%

1000 m3

6_3

ERROR:#N/A

626.250

572.938

91.49%

€

1000 NAC

6_3

ERROR:#N/A

258986.657

232676.200

89.84%

1000 m3

6_3

ERROR:#DIV/0!

ERROR:#N/A

413.552

406.111

98.20%

1000 m3

6_3_1

ERROR:#N/A

87.459

98.055

112.12%

€

1000 NAC

6_3_1

ERROR:#N/A

43281.064

44663.516

103.19%

1000 m3

6_3_1

ERROR:#DIV/0!

ERROR:#N/A

494.873

455.495

92.04%

1000 m3

6_3_1

ERROR:#N/A

292.221

255.540

87.45%

€

1000 NAC

6_3_1

ERROR:#N/A

244251.831

203048.573

83.13%

1000 m3

6_3_1

ERROR:#DIV/0!

ERROR:#N/A

835.846

794.586

95.06%

1000 m3

6_4

ERROR:#N/A

1.025

0.874

85.27%

€

1000 NAC

6_4

ERROR:#N/A

1922.459

1673.144

87.03%

1000 m3

6_4

ERROR:#DIV/0!

ERROR:#N/A

1875.570

1914.352

102.07%

1000 m3

6_4

ERROR:#N/A

0.235

0.172

73.19%

€

1000 NAC

6_4

ERROR:#N/A

772.764

752.855

97.42%

1000 m3

6_4

ERROR:#DIV/0!

ERROR:#N/A

3288.357

4377.064

133.11%

1000 m3

6_4_1

ERROR:#N/A

119.991

124.667

103.90%

€

1000 NAC

6_4_1

ERROR:#N/A

34921.951

34251.958

98.08%

1000 m3

6_4_1

ERROR:#DIV/0!

ERROR:#N/A

291.038

274.748

94.40%

1000 m3

6_4_1

ERROR:#N/A

20.713

20.191

97.48%

€

1000 NAC

6_4_1

ERROR:#N/A

7021.995

6438.063

91.68%

1000 m3

6_4_1

ERROR:#DIV/0!

ERROR:#N/A

339.014

318.858

94.05%

1000 m3

6_4_2

ERROR:#N/A

43.609

48.709

111.69%

€

1000 NAC

6_4_2

ERROR:#N/A

11538.724

11317.002

98.08%

1000 m3

6_4_2

ERROR:#DIV/0!

ERROR:#N/A

264.595

232.339

87.81%

1000 m3

6_4_2

ERROR:#N/A

0.152

0.140

92.11%

€

1000 NAC

6_4_2

ERROR:#N/A

58.275

52.526

90.13%

1000 m3

6_4_2

ERROR:#DIV/0!

ERROR:#N/A

383.388

375.186

97.86%

1000 m3

6_4_3

ERROR:#N/A

144.302

156.329

108.33%

€

1000 NAC

6_4_3

ERROR:#N/A

49291.362

49658.519

100.74%

1000 m3

6_4_3

ERROR:#DIV/0!

ERROR:#N/A

341.585

317.654

92.99%

1000 m3

6_4_3

ERROR:#N/A

46.707

41.537

88.93%

€

1000 NAC

6_4_3

ERROR:#N/A

17313.487

16572.504

95.72%

1000 m3

6_4_3

ERROR:#DIV/0!

ERROR:#N/A

370.683

398.978

107.63%

1000 mt

ERROR:#N/A

21.850

21.089

96.52%

€

1000 NAC

ERROR:#N/A

14888.692

13327.597

89.51%

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

681.405

631.979

92.75%

1000 mt

ERROR:#N/A

44.487

36.996

83.16%

€

1000 NAC

ERROR:#N/A

15644.996

13569.105

86.73%

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

351.676

366.770

104.29%

1000 mt

7_1

ERROR:#N/A

103.619

115.530

111.50%

€

1000 NAC

7_1

ERROR:#N/A

30075.799

31682.477

105.34%

1000 mt

7_1

ERROR:#DIV/0!

ERROR:#N/A

290.254

274.235

94.48%

1000 mt

7_1

ERROR:#N/A

2.121

4.448

209.71%

€

1000 NAC

7_1

ERROR:#N/A

1647.336

2975.664

180.63%

1000 mt

7_1

ERROR:#DIV/0!

ERROR:#N/A

776.679

669.011

86.14%

1000 mt

7_2

ERROR:#N/A

18.833

19.710

104.66%

€

1000 NAC

7_2

ERROR:#N/A

4326.871

4648.445

107.43%

1000 mt

7_2

ERROR:#DIV/0!

ERROR:#N/A

229.749

235.841

102.65%

1000 mt

7_2

ERROR:#N/A

0.099

0.093

94.32%

€

1000 NAC

7_2

ERROR:#N/A

21.155

27.735

131.10%

1000 mt

7_2

ERROR:#DIV/0!

ERROR:#N/A

213.687

297.012

138.99%

1000 mt

7_3

ERROR:#N/A

347.307

223.541

64.36%

€

1000 NAC

7_3

ERROR:#N/A

203860.148

102115.612

50.09%

1000 mt

7_3

ERROR:#DIV/0!

ERROR:#N/A

586.974

456.810

77.82%

1000 mt

7_3

ERROR:#N/A

4518.595

4333.005

95.89%

€

1000 NAC

7_3

ERROR:#N/A

2371208.540

1873276.620

79.00%

1000 mt

7_3

ERROR:#DIV/0!

ERROR:#N/A

524.767

432.327

82.38%

1000 mt

7_3_1

ERROR:#N/A

5.466

9.326

170.62%

€

1000 NAC

7_3_1

ERROR:#N/A

1781.802

2739.415

153.74%

1000 mt

7_3_1

ERROR:#DIV/0!

ERROR:#N/A

325.979

293.729

90.11%

1000 mt

7_3_1

ERROR:#N/A

290.230

393.151

135.46%

€

1000 NAC

7_3_1

ERROR:#N/A

101694.552

131703.670

129.51%

1000 mt

7_3_1

ERROR:#DIV/0!

ERROR:#N/A

350.393

334.995

95.61%

1000 mt

7_3_2

ERROR:#N/A

335.505

207.439

61.83%

€

1000 NAC

7_3_2

ERROR:#N/A

194816.673

92007.728

47.23%

1000 mt

7_3_2

ERROR:#DIV/0!

ERROR:#N/A

580.667

443.541

76.38%

1000 mt

7_3_2

ERROR:#N/A

4072.034

3737.369

91.78%

€

1000 NAC

7_3_2

ERROR:#N/A

2173529.261

1643317.871

75.61%

1000 mt

7_3_2

ERROR:#DIV/0!

ERROR:#N/A

533.770

439.699

82.38%

1000 mt

7_3_3

ERROR:#N/A

333.248

204.640

61.41%

€

1000 NAC

7_3_3

ERROR:#N/A

192460.224

89098.258

46.29%

1000 mt

7_3_3

ERROR:#DIV/0!

ERROR:#N/A

577.529

435.390

75.39%

1000 mt

7_3_3

ERROR:#N/A

4071.779

3737.358

91.79%

€

1000 NAC

7_3_3

ERROR:#N/A

2173380.998

1643269.140

75.61%

1000 mt

7_3_3

ERROR:#DIV/0!

ERROR:#N/A

533.767

439.687

82.37%

1000 mt

7_3_4

ERROR:#N/A

316.921

178.103

56.20%

€

1000 NAC

7_3_4

ERROR:#N/A

184329.582

77381.526

41.98%

1000 mt

7_3_4

ERROR:#DIV/0!

ERROR:#N/A

581.626

434.475

74.70%

1000 mt

7_3_4

ERROR:#N/A

4032.888

3697.897

91.69%

€

1000 NAC

7_3_4

ERROR:#N/A

2155684.868

1628216.409

75.53%

1000 mt

7_3_4

ERROR:#DIV/0!

ERROR:#N/A

534.526

440.309

82.37%

1000 mt

7_4

ERROR:#N/A

2.256

2.799

124.06%

€

1000 NAC

7_4

ERROR:#N/A

2356.449

2909.470

123.47%

1000 mt

7_4

ERROR:#DIV/0!

ERROR:#N/A

1044.525

1039.518

99.52%

1000 mt

7_4

ERROR:#N/A

0.025

0.011

43.20%

€

1000 NAC

7_4

ERROR:#N/A

58.241

48.731

83.67%

1000 mt

7_4

ERROR:#DIV/0!

ERROR:#N/A

2329.640

4512.547

193.70%

1000 mt

ERROR:#N/A

6.336

6.775

106.94%

€

1000 NAC

ERROR:#N/A

7261.673

7368.469

101.47%

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

1146.097

1087.519

94.89%

1000 mt

ERROR:#N/A

156.331

202.485

129.52%

€

1000 NAC

ERROR:#N/A

95984.727

98255.079

102.37%

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

613.984

485.247

79.03%

1000 mt

8_1

ERROR:#N/A

3.986

2.952

74.05%

€

1000 NAC

8_1

ERROR:#N/A

4859.066

3004.669

61.84%

1000 mt

8_1

ERROR:#DIV/0!

ERROR:#N/A

1219.033

1017.998

83.51%

1000 mt

8_1

ERROR:#N/A

0.052

0.042

80.51%

€

1000 NAC

8_1

ERROR:#N/A

41.206

90.057

218.55%

1000 mt

8_1

ERROR:#DIV/0!

ERROR:#N/A

792.423

2151.129

271.46%

1000 mt

8_2

ERROR:#N/A

3.097

1.853

59.83%

€

1000 NAC

8_2

ERROR:#N/A

4479.918

2527.708

56.42%

1000 mt

8_2

ERROR:#DIV/0!

ERROR:#N/A

1446.535

1364.137

94.30%

1000 mt

8_2

ERROR:#N/A

0.006

0.040

672.15%

€

1000 NAC

8_2

ERROR:#N/A

12.685

88.506

697.72%

1000 mt

8_2

ERROR:#DIV/0!

ERROR:#N/A

2114.167

2194.599

103.80%

1000 mt

ERROR:#N/A

0.889

1.099

123.57%

€

1000 NAC

ERROR:#N/A

379.148

476.961

125.80%

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

426.488

434.163

101.80%

1000 mt

ERROR:#N/A

0.046

0.002

3.34%

€

1000 NAC

ERROR:#N/A

28.521

1.551

5.44%

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

620.022

1009.766

162.86%

1000 mt

ERROR:#N/A

114.896

67.844

59.05%

€

1000 NAC

ERROR:#N/A

19189.653

11891.779

61.97%

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

167.018

175.281

104.95%

1000 mt

ERROR:#N/A

65.696

100.489

152.96%

€

1000 NAC

ERROR:#N/A

11663.794

13394.885

114.84%

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

177.542

133.297

75.08%

1000 mt

10_1

ERROR:#N/A

305.619

322.700

105.59%

€

1000 NAC

10_1

ERROR:#N/A

262392.643

256668.741

97.82%

1000 mt

10_1

ERROR:#DIV/0!

ERROR:#N/A

858.561

795.380

92.64%

1000 mt

10_1

ERROR:#N/A

9293.771

7831.574

84.27%

€

1000 NAC

10_1

ERROR:#N/A

6903100.156

5591404.598

81.00%

1000 mt

10_1

ERROR:#DIV/0!

ERROR:#N/A

742.766

713.957

96.12%

1000 mt

10_1_1

ERROR:#N/A

54.163

63.642

117.50%

€

1000 NAC

10_1_1

ERROR:#N/A

47367.809

47831.971

100.98%

1000 mt

10_1_1

ERROR:#DIV/0!

ERROR:#N/A

874.542

751.574

85.94%

1000 mt

10_1_1

ERROR:#N/A

5057.563

3621.975

71.62%

€

1000 NAC

10_1_1

ERROR:#N/A

3405995.969

2236158.413

65.65%

1000 mt

10_1_1

ERROR:#DIV/0!

ERROR:#N/A

673.446

617.387

91.68%

1000 mt

10_1_2

ERROR:#N/A

23.163

28.429

122.73%

€

1000 NAC

10_1_2

ERROR:#N/A

11942.757

12655.759

105.97%

1000 mt

10_1_2

ERROR:#DIV/0!

ERROR:#N/A

515.596

445.173

86.34%

1000 mt

10_1_2

ERROR:#N/A

236.364

161.757

68.44%

€

1000 NAC

10_1_2

ERROR:#N/A

118953.006

68882.711

57.91%

1000 mt

10_1_2

ERROR:#DIV/0!

ERROR:#N/A

503.262

425.840

84.62%

1000 mt

10_1_3

ERROR:#N/A

2.742

4.192

152.87%

€

1000 NAC

10_1_3

ERROR:#N/A

2607.385

3349.603

128.47%

1000 mt

10_1_3

ERROR:#DIV/0!

ERROR:#N/A

950.906

799.127

84.04%

1000 mt

10_1_3

ERROR:#N/A

673.614

426.192

63.27%

€

1000 NAC

10_1_3

ERROR:#N/A

388731.680

224616.104

57.78%

1000 mt

10_1_3

ERROR:#DIV/0!

ERROR:#N/A

577.084

527.031

91.33%

1000 mt

10_1_4

ERROR:#N/A

13.057

13.014

99.67%

€

1000 NAC

10_1_4

ERROR:#N/A

16580.849

15199.157

91.67%

1000 mt

10_1_4

ERROR:#DIV/0!

ERROR:#N/A

1269.882

1167.937

91.97%

1000 mt

10_1_4

ERROR:#N/A

861.600

670.571

77.83%

€

1000 NAC

10_1_4

ERROR:#N/A

645163.236

454990.954

70.52%

1000 mt

10_1_4

ERROR:#DIV/0!

ERROR:#N/A

748.797

678.512

90.61%

1000 mt

10_2

ERROR:#N/A

15.200

18.008

118.48%

€

1000 NAC

10_2

ERROR:#N/A

16236.818

16627.452

102.41%

1000 mt

10_2

ERROR:#DIV/0!

ERROR:#N/A

1068.212

923.320

86.44%

1000 mt

10_2

ERROR:#N/A

3285.985

2363.454

71.93%

€

1000 NAC

10_2

ERROR:#N/A

2253148.047

1487668.644

66.03%

1000 mt

10_2

ERROR:#DIV/0!

ERROR:#N/A

685.684

629.447

91.80%

1000 mt

10_3

ERROR:#N/A

1.321

1.332

100.86%

€

1000 NAC

10_3

ERROR:#N/A

10361.178

2815.806

27.18%

1000 mt

10_3

ERROR:#DIV/0!

ERROR:#N/A

7843.435

2113.402

26.94%

1000 mt

10_3

ERROR:#N/A

29.844

23.915

80.13%

€

1000 NAC

10_3

ERROR:#N/A

30019.242

21526.537

71.71%

1000 mt

10_3

ERROR:#DIV/0!

ERROR:#N/A

1005.872

900.120

89.49%

1000 mt

10_3_1

ERROR:#N/A

248.803

256.060

102.92%

€

1000 NAC

10_3_1

ERROR:#N/A

200433.715

200626.621

100.10%

1000 mt

10_3_1

ERROR:#DIV/0!

ERROR:#N/A

805.592

783.514

97.26%

1000 mt

10_3_1

ERROR:#N/A

4070.870

4039.958

99.24%

€

1000 NAC

10_3_1

ERROR:#N/A

3364947.931

3225833.022

95.87%

1000 mt

10_3_1

ERROR:#DIV/0!

ERROR:#N/A

826.592

798.482

96.60%

1000 mt

10_3_2

ERROR:#N/A

159.004

154.691

97.29%

€

1000 NAC

10_3_2

ERROR:#N/A

87846.793

76515.447

87.10%

1000 mt

10_3_2

ERROR:#DIV/0!

ERROR:#N/A

552.482

494.635

89.53%

1000 mt

10_3_2

ERROR:#N/A

870.098

871.457

100.16%

€

1000 NAC

10_3_2

ERROR:#N/A

486467.143

444677.772

91.41%

1000 mt

10_3_2

ERROR:#DIV/0!

ERROR:#N/A

559.095

510.269

91.27%

1000 mt

10_3_3

ERROR:#N/A

58.549

65.798

112.38%

€

1000 NAC

10_3_3

ERROR:#N/A

79744.569

89668.358

112.44%

1000 mt

10_3_3

ERROR:#DIV/0!

ERROR:#N/A

1362.014

1362.783

100.06%

1000 mt

10_3_3

ERROR:#N/A

2583.153

2558.143

99.03%

€

1000 NAC

10_3_3

ERROR:#N/A

2280623.196

2226073.231

97.61%

1000 mt

10_3_3

ERROR:#DIV/0!

ERROR:#N/A

882.884

870.191

98.56%

1000 mt

10_3_4

ERROR:#N/A

25.858

30.006

116.04%

€

1000 NAC

10_3_4

ERROR:#N/A

29513.763

31261.871

105.92%

1000 mt

10_3_4

ERROR:#DIV/0!

ERROR:#N/A

1141.378

1041.866

91.28%

1000 mt

10_3_4

ERROR:#N/A

470.578

455.210

96.73%

€

1000 NAC

10_3_4

ERROR:#N/A

500134.099

451663.495

90.31%

1000 mt

10_3_4

ERROR:#DIV/0!

ERROR:#N/A

1062.808

992.209

93.36%

1000 mt

10_4

ERROR:#N/A

5.392

5.566

103.22%

€

1000 NAC

10_4

ERROR:#N/A

3328.590

3180.945

95.56%

1000 mt

10_4

ERROR:#DIV/0!

ERROR:#N/A

617.320

571.531

92.58%

1000 mt

10_4

ERROR:#N/A

147.040

155.148

105.51%

€

1000 NAC

10_4

ERROR:#N/A

97723.493

103418.524

105.83%

1000 mt

10_4

ERROR:#DIV/0!

ERROR:#N/A

664.605

666.579

100.30%

TS-JQ3

Min:

80%

Max:

120%

Notes

JQ3

Country

Flow

Unit

Product

2015

2016

2017

2018

2019

2020

15/16

16/17

17/18

18/19

19/19

19/20

2015

2016

2017

2018

1000 NAC

11_1

ERROR:#N/A

1000 NAC

11_1_C

ERROR:#N/A

1000 NAC

11_1_NC

ERROR:#N/A

1000 NAC

11_1_NC_T

ERROR:#N/A

1000 NAC

11_2

ERROR:#N/A

1000 NAC

11_3

ERROR:#N/A

1000 NAC

11_4

ERROR:#N/A

1000 NAC

11_5

ERROR:#N/A

1000 NAC

11_6

ERROR:#N/A

1000 NAC

11_7

ERROR:#N/A

1000 NAC

11_7_1

ERROR:#N/A

1000 NAC

12_1

ERROR:#N/A

1000 NAC

12_2

ERROR:#N/A

1000 NAC

12_3

ERROR:#N/A

1000 NAC

12_4

ERROR:#N/A

1000 NAC

12_5

ERROR:#N/A

1000 NAC

12_6

ERROR:#N/A

1000 NAC

12_6_1

ERROR:#N/A

1000 NAC

12_6_2

ERROR:#N/A

1000 NAC

12_6_3

ERROR:#N/A

1000 NAC

12_7

ERROR:#N/A

1000 NAC

12_7_1

ERROR:#N/A

1000 NAC

12_7_2

ERROR:#N/A

1000 NAC

12_7_3

ERROR:#N/A

1000 NAC

11_1

ERROR:#N/A

1000 NAC

11_1_C

ERROR:#N/A

1000 NAC

11_1_NC

ERROR:#N/A

1000 NAC

11_1_NC_T

ERROR:#N/A

1000 NAC

11_2

ERROR:#N/A

1000 NAC

11_3

ERROR:#N/A

1000 NAC

11_4

ERROR:#N/A

1000 NAC

11_5

ERROR:#N/A

1000 NAC

11_6

ERROR:#N/A

1000 NAC

11_7

ERROR:#N/A

1000 NAC

11_7_1

ERROR:#N/A

1000 NAC

12_1

ERROR:#N/A

1000 NAC

12_2

ERROR:#N/A

1000 NAC

12_3

ERROR:#N/A

1000 NAC

12_4

ERROR:#N/A

1000 NAC

12_5

ERROR:#N/A

1000 NAC

12_6

ERROR:#N/A

1000 NAC

12_6_1

ERROR:#N/A

1000 NAC

12_6_2

ERROR:#N/A

1000 NAC

12_6_3

ERROR:#N/A

TS-ECEEU

Min:

80%

Max:

120%

Notes

ECEEU

Country

Flow

Unit

Product

2015

2016

2017

2018

2019

2020

15/16

16/17

17/18

18/19

19/19

19/20

2015

2016

2017

2018

1000 m3

ST_1_2_C

ERROR:#N/A

1744.746

1533.846

87.91%

€

1000 NAC

ST_1_2_C

ERROR:#N/A

108160.001

81813.353

75.64%

1000 m3

ST_1_2_C

ERROR:#DIV/0!

ERROR:#N/A

61.992

53.339

86.04%

1000 m3

ST_1_2_C

ERROR:#N/A

1174.095

1095.264

93.29%

€

1000 NAC

ST_1_2_C

ERROR:#N/A

78647.438

88588.893

112.64%

1000 m3

ST_1_2_C

ERROR:#DIV/0!

ERROR:#N/A

66.986

80.884

120.75%

1000 m3

ST_1_2_C_1

ERROR:#N/A

899.653

876.994

97.48%

€

1000 NAC

ST_1_2_C_1

ERROR:#N/A

54507.895

46127.133

84.62%

1000 m3

ST_1_2_C_1

ERROR:#DIV/0!

ERROR:#N/A

60.588

52.597

86.81%

1000 m3

ST_1_2_C_1

ERROR:#N/A

311.190

242.898

78.05%

€

1000 NAC

ST_1_2_C_1

ERROR:#N/A

17358.044

12852.720

74.04%

1000 m3

ST_1_2_C_1

ERROR:#DIV/0!

ERROR:#N/A

55.780

52.914

94.86%

1000 m3

ST_1_2_C_1_1

ERROR:#N/A

191.014

199.137

104.25%

€

1000 NAC

ST_1_2_C_1_1

ERROR:#N/A

13134.127

12989.835

98.90%

1000 m3

ST_1_2_C_1_1

ERROR:#DIV/0!

ERROR:#N/A

68.760

65.231

94.87%

1000 m3

ST_1_2_C_1_1

ERROR:#N/A

38.474

0.616

1.60%

€

1000 NAC

ST_1_2_C_1_1

ERROR:#N/A

2553.481

42.136

1.65%

1000 m3

ST_1_2_C_1_1

ERROR:#DIV/0!

ERROR:#N/A

66.369

68.403

103.06%

1000 m3

ST_1_2_C_1_2

ERROR:#N/A

87.748

121.169

138.09%

€

1000 NAC

ST_1_2_C_1_2

ERROR:#N/A

5865.131

7734.159

131.87%

1000 m3

ST_1_2_C_1_2

ERROR:#DIV/0!

ERROR:#N/A

66.841

63.830

95.50%

1000 m3

ST_1_2_C_1_2

ERROR:#N/A

427.574

295.825

69.19%

€

1000 NAC

ST_1_2_C_1_2

ERROR:#N/A

26026.701

18949.390

72.81%

1000 m3

ST_1_2_C_1_2

ERROR:#DIV/0!

ERROR:#N/A

60.871

64.056

105.23%

1000 m3

ST_1_2_C_1_3

ERROR:#N/A

0.000

€

1000 NAC

ST_1_2_C_1_3

ERROR:#N/A

0.000

1000 m3

ST_1_2_C_1_3

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_1_2_C_1_3

ERROR:#N/A

0.000

€

1000 NAC

ST_1_2_C_1_3

ERROR:#N/A

0.000

1000 m3

ST_1_2_C_1_3

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_1_2_C_2

ERROR:#N/A

845.092

656.800

77.72%

€

1000 NAC

ST_1_2_C_2

ERROR:#N/A

53650.981

35671.599

66.49%

1000 m3

ST_1_2_C_2

ERROR:#DIV/0!

ERROR:#N/A

63.485

54.311

85.55%

1000 m3

ST_1_2_C_2

ERROR:#N/A

862.905

788.772

91.41%

€

1000 NAC

ST_1_2_C_2

ERROR:#N/A

61289.394

61654.163

100.60%

1000 m3

ST_1_2_C_2

ERROR:#DIV/0!

ERROR:#N/A

71.027

78.165

110.05%

1000 m3

ST_1_2_C_2_1

ERROR:#N/A

708.639

677.857

95.66%

€

1000 NAC

ST_1_2_C_2_1

ERROR:#N/A

41373.768

33137.298

80.09%

1000 m3

ST_1_2_C_2_1

ERROR:#DIV/0!

ERROR:#N/A

58.385

48.885

83.73%

1000 m3

ST_1_2_C_2_1

ERROR:#N/A

272.716

242.282

88.84%

€

1000 NAC

ST_1_2_C_2_1

ERROR:#N/A

14804.563

12810.584

86.53%

1000 m3

ST_1_2_C_2_1

ERROR:#DIV/0!

ERROR:#N/A

54.286

52.875

97.40%

1000 m3

ST_1_2_C_2_2

ERROR:#N/A

757.344

535.631

70.72%

€

1000 NAC

ST_1_2_C_2_2

ERROR:#N/A

47785.850

27937.440

58.46%

1000 m3

ST_1_2_C_2_2

ERROR:#DIV/0!

ERROR:#N/A

63.097

52.158

82.66%

1000 m3

ST_1_2_C_2_2

ERROR:#N/A

435.331

492.947

113.23%

€

1000 NAC

ST_1_2_C_2_2

ERROR:#N/A

35262.693

42704.773

121.10%

1000 m3

ST_1_2_C_2_2

ERROR:#DIV/0!

ERROR:#N/A

81.002

86.632

106.95%

1000 m3

ST_1_2_C_2_3

ERROR:#N/A

0.000

€

1000 NAC

ST_1_2_C_2_3

ERROR:#N/A

0.000

1000 m3

ST_1_2_C_2_3

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_1_2_C_2_3

ERROR:#N/A

0.000

€

1000 NAC

ST_1_2_C_2_3

ERROR:#N/A

0.000

1000 m3

ST_1_2_C_2_3

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_1_2_C_3

ERROR:#N/A

0.000

€

1000 NAC

ST_1_2_C_3

ERROR:#N/A

0.000

1000 m3

ST_1_2_C_3

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_1_2_C_3

ERROR:#N/A

0.000

€

1000 NAC

ST_1_2_C_3

ERROR:#N/A

0.000

1000 m3

ST_1_2_C_3

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_1_2_NC

ERROR:#N/A

4490.267

4740.268

105.57%

€

1000 NAC

ST_1_2_NC

ERROR:#N/A

209321.403

202306.657

96.65%

1000 m3

ST_1_2_NC

ERROR:#DIV/0!

ERROR:#N/A

46.617

42.678

91.55%

1000 m3

ST_1_2_NC

ERROR:#N/A

182.286

67.961

37.28%

€

1000 NAC

ST_1_2_NC

ERROR:#N/A

9376.531

4454.077

47.50%

1000 m3

ST_1_2_NC

ERROR:#DIV/0!

ERROR:#N/A

51.439

65.539

127.41%

1000 m3

ST_1_2_NC_1

ERROR:#N/A

0.031

0.028

90.32%

€

1000 NAC

ST_1_2_NC_1

ERROR:#N/A

29.364

25.258

86.02%

1000 m3

ST_1_2_NC_1

ERROR:#DIV/0!

ERROR:#N/A

947.226

902.071

95.23%

1000 m3

ST_1_2_NC_1

ERROR:#N/A

0.000

€

1000 NAC

ST_1_2_NC_1

ERROR:#N/A

0.000

1000 m3

ST_1_2_NC_1

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_1_2_NC_1_1

ERROR:#N/A

0.000

€

1000 NAC

ST_1_2_NC_1_1

ERROR:#N/A

0.000

1000 m3

ST_1_2_NC_1_1

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_1_2_NC_1_1

ERROR:#N/A

0.000

€

1000 NAC

ST_1_2_NC_1_1

ERROR:#N/A

0.000

1000 m3

ST_1_2_NC_1_1

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_1_2_NC_1_2

ERROR:#N/A

4117.077

4361.684

105.94%

€

1000 NAC

ST_1_2_NC_1_2

ERROR:#N/A

191401.805

184268.623

96.27%

1000 m3

ST_1_2_NC_1_2

ERROR:#DIV/0!

ERROR:#N/A

46.490

42.247

90.87%

1000 m3

ST_1_2_NC_1_2

ERROR:#N/A

180.402

44.225

24.51%

€

1000 NAC

ST_1_2_NC_1_2

ERROR:#N/A

9219.148

2158.997

23.42%

1000 m3

ST_1_2_NC_1_2

ERROR:#DIV/0!

ERROR:#N/A

51.103

48.818

95.53%

1000 m3

ST_1_2_NC_1_3

ERROR:#N/A

559.995

569.549

101.71%

€

1000 NAC

ST_1_2_NC_1_3

ERROR:#N/A

95658.068

91739.100

95.90%

1000 m3

ST_1_2_NC_1_3

ERROR:#DIV/0!

ERROR:#N/A

170.820

161.073

94.29%

1000 m3

ST_1_2_NC_1_3

ERROR:#N/A

8951.761

8197.932

91.58%

€

1000 NAC

ST_1_2_NC_1_3

ERROR:#N/A

1722057.473

1548568.236

89.93%

1000 m3

ST_1_2_NC_1_3

ERROR:#DIV/0!

ERROR:#N/A

192.371

188.897

98.19%

1000 m3

ST_1_2_NC_2

ERROR:#N/A

81.355

103.235

126.89%

€

1000 NAC

ST_1_2_NC_2

ERROR:#N/A

5833.633

7109.042

121.86%

1000 m3

ST_1_2_NC_2

ERROR:#DIV/0!

ERROR:#N/A

71.706

68.863

96.03%

1000 m3

ST_1_2_NC_2

ERROR:#N/A

1.802

0.000

0.00%

€

1000 NAC

ST_1_2_NC_2

ERROR:#N/A

127.048

0.000

0.00%

1000 m3

ST_1_2_NC_2

ERROR:#DIV/0!

ERROR:#N/A

70.504

ERROR:#DIV/0!

1000 m3

ST_1_2_NC_2_1

ERROR:#N/A

4198.432

4464.919

106.35%

€

1000 NAC

ST_1_2_NC_2_1

ERROR:#N/A

197235.438

191377.665

97.03%

1000 m3

ST_1_2_NC_2_1

ERROR:#DIV/0!

ERROR:#N/A

46.978

42.863

91.24%

1000 m3

ST_1_2_NC_2_1

ERROR:#N/A

182.204

44.225

24.27%

€

1000 NAC

ST_1_2_NC_2_1

ERROR:#N/A

9346.196

2158.997

23.10%

1000 m3

ST_1_2_NC_2_1

ERROR:#DIV/0!

ERROR:#N/A

51.295

48.818

95.17%

1000 m3

ST_1_2_NC_2_2

ERROR:#N/A

284.022

275.308

96.93%

€

1000 NAC

ST_1_2_NC_2_2

ERROR:#N/A

11493.481

10832.305

94.25%

1000 m3

ST_1_2_NC_2_2

ERROR:#DIV/0!

ERROR:#N/A

40.467

39.346

97.23%

1000 m3

ST_1_2_NC_2_2

ERROR:#N/A

0.000

€

1000 NAC

ST_1_2_NC_2_2

ERROR:#N/A

0.000

1000 m3

ST_1_2_NC_2_2

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_1_2_NC_2_3

ERROR:#N/A

345.514

347.424

100.55%

€

1000 NAC

ST_1_2_NC_2_3

ERROR:#N/A

54820.656

51645.313

94.21%

1000 m3

ST_1_2_NC_2_3

ERROR:#DIV/0!

ERROR:#N/A

158.664

148.652

93.69%

1000 m3

ST_1_2_NC_2_3

ERROR:#N/A

4499.321

4093.365

90.98%

€

1000 NAC

ST_1_2_NC_2_3

ERROR:#N/A

896652.840

798586.322

89.06%

1000 m3

ST_1_2_NC_2_3

ERROR:#DIV/0!

ERROR:#N/A

199.286

195.093

97.90%

1000 m3

ST_1_2_NC_3

ERROR:#N/A

0.021

0.001

4.76%

€

1000 NAC

ST_1_2_NC_3

ERROR:#N/A

2.924

0.023

0.79%

1000 m3

ST_1_2_NC_3

ERROR:#DIV/0!

ERROR:#N/A

139.238

23.000

16.52%

1000 m3

ST_1_2_NC_3

ERROR:#N/A

0.000

€

1000 NAC

ST_1_2_NC_3

ERROR:#N/A

0.000

1000 m3

ST_1_2_NC_3

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_1_2_NC_4

ERROR:#N/A

196.514

188.644

96.00%

€

1000 NAC

ST_1_2_NC_4

ERROR:#N/A

34332.632

29451.877

85.78%

1000 m3

ST_1_2_NC_4

ERROR:#DIV/0!

ERROR:#N/A

174.708

156.124

89.36%

1000 m3

ST_1_2_NC_4

ERROR:#N/A

4451.625

4104.345

92.20%

€

1000 NAC

ST_1_2_NC_4

ERROR:#N/A

825203.242

749919.196

90.88%

1000 m3

ST_1_2_NC_4

ERROR:#DIV/0!

ERROR:#N/A

185.371

182.713

98.57%

1000 m3

ST_1_2_NC_5

ERROR:#N/A

33.359

30.552

91.59%

€

1000 NAC

ST_1_2_NC_5

ERROR:#N/A

20241.113

22505.326

111.19%

1000 m3

ST_1_2_NC_5

ERROR:#DIV/0!

ERROR:#N/A

606.766

736.624

121.40%

1000 m3

ST_1_2_NC_5

ERROR:#N/A

14.984

19.987

133.39%

€

1000 NAC

ST_1_2_NC_5

ERROR:#N/A

7770.350

8978.448

115.55%

1000 m3

ST_1_2_NC_5

ERROR:#DIV/0!

ERROR:#N/A

518.576

449.214

86.62%

1000 m3

ST_5_C

ERROR:#N/A

11.927

6.545

54.88%

€

1000 NAC

ST_5_C

ERROR:#N/A

6004.712

6568.341

109.39%

1000 m3

ST_5_C

ERROR:#DIV/0!

ERROR:#N/A

503.455

1003.566

199.34%

1000 m3

ST_5_C

ERROR:#N/A

0.012

0.007

58.33%

€

1000 NAC

ST_5_C

ERROR:#N/A

35.293

4.261

12.07%

1000 m3

ST_5_C

ERROR:#DIV/0!

ERROR:#N/A

2941.083

608.714

20.70%

1000 m3

ST_5_C_1

ERROR:#N/A

0.204

0.232

113.73%

€

1000 NAC

ST_5_C_1

ERROR:#N/A

77.887

69.310

88.99%

1000 m3

ST_5_C_1

ERROR:#DIV/0!

ERROR:#N/A

381.799

298.750

78.25%

1000 m3

ST_5_C_1

ERROR:#N/A

0.000

0.001

€

1000 NAC

ST_5_C_1

ERROR:#N/A

0.000

0.317

1000 m3

ST_5_C_1

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

317.000

1000 m3

ST_5_C_2

ERROR:#N/A

0.005

0.004

80.00%

€

1000 NAC

ST_5_C_2

ERROR:#N/A

3.670

3.063

83.46%

1000 m3

ST_5_C_2

ERROR:#DIV/0!

ERROR:#N/A

734.000

765.750

104.33%

1000 m3

ST_5_C_2

ERROR:#N/A

0.000

€

1000 NAC

ST_5_C_2

ERROR:#N/A

0.000

1000 m3

ST_5_C_2

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_5_NC

ERROR:#N/A

0.000

€

1000 NAC

ST_5_NC

ERROR:#N/A

0.000

1000 m3

ST_5_NC

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_5_NC

ERROR:#N/A

0.007

0.000

0.00%

€

1000 NAC

ST_5_NC

ERROR:#N/A

10.063

0.000

0.00%

1000 m3

ST_5_NC

ERROR:#DIV/0!

ERROR:#N/A

1437.571

ERROR:#DIV/0!

1000 m3

ST_5_NC_1

ERROR:#N/A

0.933

1.117

119.72%

€

1000 NAC

ST_5_NC_1

ERROR:#N/A

861.402

1015.602

117.90%

1000 m3

ST_5_NC_1

ERROR:#DIV/0!

ERROR:#N/A

923.260

909.223

98.48%

1000 m3

ST_5_NC_1

ERROR:#N/A

0.034

0.148

435.29%

€

1000 NAC

ST_5_NC_1

ERROR:#N/A

31.390

136.227

433.98%

1000 m3

ST_5_NC_1

ERROR:#DIV/0!

ERROR:#N/A

923.235

920.453

99.70%

1000 m3

ST_5_NC_2

ERROR:#N/A

0.326

0.700

214.72%

€

1000 NAC

ST_5_NC_2

ERROR:#N/A

256.776

500.729

195.01%

1000 m3

ST_5_NC_2

ERROR:#DIV/0!

ERROR:#N/A

787.656

715.327

90.82%

1000 m3

ST_5_NC_2

ERROR:#N/A

0.124

0.071

57.26%

€

1000 NAC

ST_5_NC_2

ERROR:#N/A

98.350

72.925

74.15%

1000 m3

ST_5_NC_2

ERROR:#DIV/0!

ERROR:#N/A

793.145

1027.113

129.50%

1000 m3

ST_5_NC_3

ERROR:#N/A

16.932

4.984

29.44%

€

1000 NAC

ST_5_NC_3

ERROR:#N/A

9651.601

1802.470

18.68%

1000 m3

ST_5_NC_3

ERROR:#DIV/0!

ERROR:#N/A

570.021

361.651

63.45%

1000 m3

ST_5_NC_3

ERROR:#N/A

11.025

0.445

4.04%

€

1000 NAC

ST_5_NC_3

ERROR:#N/A

3912.900

291.167

7.44%

1000 m3

ST_5_NC_3

ERROR:#DIV/0!

ERROR:#N/A

354.912

654.308

184.36%

1000 m3

ST_5_NC_4

ERROR:#N/A

0.000

€

1000 NAC

ST_5_NC_4

ERROR:#N/A

0.000

1000 m3

ST_5_NC_4

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_5_NC_4

ERROR:#N/A

0.000

€

1000 NAC

ST_5_NC_4

ERROR:#N/A

0.000

1000 m3

ST_5_NC_4

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_5_NC_5

ERROR:#N/A

0.000

€

1000 NAC

ST_5_NC_5

ERROR:#N/A

0.000

1000 m3

ST_5_NC_5

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_5_NC_5

ERROR:#N/A

0.000

€

1000 NAC

ST_5_NC_5

ERROR:#N/A

0.000

1000 m3

ST_5_NC_5

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_5_NC_6

ERROR:#N/A

3.000

100.00%

€

1000 NAC

ST_5_NC_6

ERROR:#N/A

3.000

100.00%

1000 m3

ST_5_NC_6

ERROR:#DIV/0!

ERROR:#N/A

1.000

100.00%

1000 m3

ST_5_NC_6

ERROR:#N/A

3.000

100.00%

€

1000 NAC

ST_5_NC_6

ERROR:#N/A

3.000

100.00%

1000 m3

ST_5_NC_6

ERROR:#DIV/0!

ERROR:#N/A

1.000

100.00%

1000 m3

ST_5_NC_7

ERROR:#N/A

0.000

€

1000 NAC

ST_5_NC_7

ERROR:#N/A

0.000

1000 m3

ST_5_NC_7

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_5_NC_7

ERROR:#N/A

0.000

€

1000 NAC

ST_5_NC_7

ERROR:#N/A

0.000

1000 m3

ST_5_NC_7

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

TS-EU1

Min:

80%

Max:

120%

Notes

EU1

Country

Flow

Unit

Product

2015

2016

2017

2018

2019

2020

15/16

16/17

17/18

18/19

19/19

19/20

2015

2016

2017

2018

EX_M

1000 m3

ERROR:#N/A

4600.545

4779.889

103.90%

€

EX_M

1000 NAC

ERROR:#N/A

215138.487

207306.873

96.36%

EX_M

1000 m3

ERROR:#DIV/0!

ERROR:#N/A

46.764

43.371

92.74%

EX_X

1000 m3

ERROR:#N/A

50.552

124.755

246.78%

€

EX_X

1000 NAC

ERROR:#N/A

7465.560

27806.336

372.46%

EX_X

1000 m3

ERROR:#DIV/0!

ERROR:#N/A

147.680

222.888

150.93%

EX_M

1000 m3

1_1

ERROR:#N/A

86.923

27.519

31.66%

€

EX_M

1000 NAC

1_1

ERROR:#N/A

3113.503

1545.456

49.64%

EX_M

1000 m3

1_1

ERROR:#DIV/0!

ERROR:#N/A

35.819

56.159

156.79%

EX_X

1000 m3

1_1

ERROR:#N/A

3.247

2.705

83.30%

€

EX_X

1000 NAC

1_1

ERROR:#N/A

412.393

329.588

79.92%

EX_X

1000 m3

1_1

ERROR:#DIV/0!

ERROR:#N/A

126.998

121.851

95.95%

EX_M

1000 m3

1_2

ERROR:#N/A

4.993

4.024

80.59%

€

EX_M

1000 NAC

1_2

ERROR:#N/A

132.639

114.923

86.64%

EX_M

1000 m3

1_2

ERROR:#DIV/0!

ERROR:#N/A

26.565

28.560

107.51%

EX_X

1000 m3

1_2

ERROR:#N/A

0.864

0.512

59.22%

€

EX_X

1000 NAC

1_2

ERROR:#N/A

176.829

109.400

61.87%

EX_X

1000 m3

1_2

ERROR:#DIV/0!

ERROR:#N/A

204.622

213.789

104.48%

EX_M

1000 m3

1_2_C

ERROR:#N/A

81.930

23.495

28.68%

€

EX_M

1000 NAC

1_2_C

ERROR:#N/A

2980.864

1430.533

47.99%

EX_M

1000 m3

1_2_C

ERROR:#DIV/0!

ERROR:#N/A

36.383

60.886

167.35%

EX_X

1000 m3

1_2_C

ERROR:#N/A

2.383

2.193

92.03%

€

EX_X

1000 NAC

1_2_C

ERROR:#N/A

235.564

220.188

93.47%

EX_X

1000 m3

1_2_C

ERROR:#DIV/0!

ERROR:#N/A

98.849

100.400

101.57%

EX_M

1000 m3

1_2_NC

ERROR:#N/A

4513.622

4752.370

105.29%

€

EX_M

1000 NAC

1_2_NC

ERROR:#N/A

212024.984

205761.417

97.05%

EX_M

1000 m3

1_2_NC

ERROR:#DIV/0!

ERROR:#N/A

46.974

43.297

92.17%

EX_X

1000 m3

1_2_NC

ERROR:#N/A

47.305

122.050

258.01%

€

EX_X

1000 NAC

1_2_NC

ERROR:#N/A

7053.167

27476.748

389.57%

EX_X

1000 m3

1_2_NC

ERROR:#DIV/0!

ERROR:#N/A

149.100

225.127

150.99%

EX_M

1000 m3

1_2_NC_T

ERROR:#N/A

759.564

702.496

92.49%

€

EX_M

1000 NAC

1_2_NC_T

ERROR:#N/A

43005.318

36371.755

84.58%

EX_M

1000 m3

1_2_NC_T

ERROR:#DIV/0!

ERROR:#N/A

56.618

51.775

91.45%

EX_X

1000 m3

1_2_NC_T

ERROR:#N/A

47.223

122.050

258.45%

€

EX_X

1000 NAC

1_2_NC_T

ERROR:#N/A

7022.822

27476.742

391.25%

EX_X

1000 m3

1_2_NC_T

ERROR:#DIV/0!

ERROR:#N/A

148.716

225.127

151.38%

EX_M

1000 mt

ERROR:#N/A

3754.058

4049.874

107.88%

€

EX_M

1000 NAC

ERROR:#N/A

169019.666

169389.662

100.22%

EX_M

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

45.023

41.826

92.90%

EX_X

1000 mt

ERROR:#N/A

0.082

0.000

0.00%

€

EX_X

1000 NAC

ERROR:#N/A

30.345

0.000

0.00%

EX_X

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

370.061

ERROR:#DIV/0!

EX_M

1000 m3

ERROR:#N/A

0.000

€

EX_M

1000 NAC

ERROR:#N/A

0.000

EX_M

1000 m3

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

EX_X

1000 m3

ERROR:#N/A

0.000

€

EX_X

1000 NAC

ERROR:#N/A

0.000

EX_X

1000 m3

ERROR:#DIV/0!

ERROR:#N/A

ERROR:#DIV/0!

EX_M

1000 m3

3_1

ERROR:#N/A

2.159

1.094

50.66%

€

EX_M

1000 NAC

3_1

ERROR:#N/A

1150.237

636.454

55.33%

EX_M

1000 m3

3_1

ERROR:#DIV/0!

ERROR:#N/A

532.764

581.939

109.23%

EX_X

1000 m3

3_1

ERROR:#N/A

0.014

0.029

212.73%

€

EX_X

1000 NAC

3_1

ERROR:#N/A

14.358

38.125

265.53%

EX_X

1000 m3

3_1

ERROR:#DIV/0!

ERROR:#N/A

1060.492

1323.693

124.82%

EX_M

1000 m3

3_2

ERROR:#N/A

3207.920

3817.496

119.00%

€

EX_M

1000 NAC

3_2

ERROR:#N/A

119777.560

139868.823

116.77%

EX_M

1000 m3

3_2

ERROR:#DIV/0!

ERROR:#N/A

37.338

36.639

98.13%

EX_X

1000 m3

3_2

ERROR:#N/A

50.471

27.199

53.89%

€

EX_X

1000 NAC

3_2

ERROR:#N/A

3450.551

1823.901

52.86%

EX_X

1000 m3

3_2

ERROR:#DIV/0!

ERROR:#N/A

68.367

67.058

98.09%

EX_M

1000 mt

ERROR:#N/A

3015.256

3555.875

117.93%

€

EX_M

1000 NAC

ERROR:#N/A

115681.970

134059.012

115.89%

EX_M

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

38.366

37.701

98.27%

EX_X

1000 mt

ERROR:#N/A

50.463

27.163

53.83%

€

EX_X

1000 NAC

ERROR:#N/A

3448.831

1812.880

52.57%

EX_X

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

68.344

66.740

97.65%

EX_M

1000 mt

4_1

ERROR:#N/A

192.663

261.621

135.79%

€

EX_M

1000 NAC

4_1

ERROR:#N/A

4095.590

5809.811

141.86%

EX_M

1000 mt

4_1

ERROR:#DIV/0!

ERROR:#N/A

21.258

22.207

104.47%

EX_X

1000 mt

4_1

ERROR:#N/A

0.008

0.036

444.05%

€

EX_X

1000 NAC

4_1

ERROR:#N/A

1.720

11.021

640.76%

EX_X

1000 mt

4_1

ERROR:#DIV/0!

ERROR:#N/A

215.000

310.238

144.30%

EX_M

1000 mt

4_2

ERROR:#N/A

116.307

195.565

168.15%

€

EX_M

1000 NAC

4_2

ERROR:#N/A

3784.270

3350.259

88.53%

EX_M

1000 mt

4_2

ERROR:#DIV/0!

ERROR:#N/A

32.537

17.131

52.65%

EX_X

1000 mt

4_2

ERROR:#N/A

0.000

0.005

1221.15%

€

EX_X

1000 NAC

4_2

ERROR:#N/A

0.448

0.002

0.45%

EX_X

1000 mt

4_2

ERROR:#DIV/0!

ERROR:#N/A

1120.000

0.409

0.04%

EX_M

1000 m3

ERROR:#N/A

87.285

97.578

111.79%

€

EX_M

1000 NAC

ERROR:#N/A

9947.821

12720.341

127.87%

EX_M

1000 m3

ERROR:#DIV/0!

ERROR:#N/A

113.969

130.360

114.38%

EX_X

1000 m3

ERROR:#N/A

0.066

0.139

210.04%

€

EX_X

1000 NAC

ERROR:#N/A

16.842

45.746

271.62%

EX_X

1000 m3

ERROR:#DIV/0!

ERROR:#N/A

255.182

329.996

129.32%

EX_M

1000 m3

5_C

ERROR:#N/A

74.365

85.777

115.35%

€

EX_M

1000 NAC

5_C

ERROR:#N/A

9077.742

11877.187

130.84%

EX_M

1000 m3

5_C

ERROR:#DIV/0!

ERROR:#N/A

122.070

138.466

113.43%

EX_X

1000 m3

5_C

ERROR:#N/A

0.050

0.118

235.99%

€

EX_X

1000 NAC

5_C

ERROR:#N/A

12.659

31.238

246.77%

EX_X

1000 m3

5_C

ERROR:#DIV/0!

ERROR:#N/A

253.180

264.737

104.56%

EX_M

1000 m3

5_NC

ERROR:#N/A

12.920

11.802

91.34%

€

EX_M

1000 NAC

5_NC

ERROR:#N/A

870.079

843.154

96.91%

EX_M

1000 m3

5_NC

ERROR:#DIV/0!

ERROR:#N/A

67.344

71.444

106.09%

EX_X

1000 m3

5_NC

ERROR:#N/A

0.016

0.021

128.93%

€

EX_X

1000 NAC

5_NC

ERROR:#N/A

4.183

14.508

346.83%

EX_X

1000 m3

5_NC

ERROR:#DIV/0!

ERROR:#N/A

261.438

703.268

269.00%

EX_M

1000 m3

5_NC_T

ERROR:#N/A

556.306

559.885

100.64%

€

EX_M

1000 NAC

5_NC_T

ERROR:#N/A

96482.268

91930.514

95.28%

EX_M

1000 m3

5_NC_T

ERROR:#DIV/0!

ERROR:#N/A

173.434

164.195

94.67%

EX_X

1000 m3

5_NC_T

ERROR:#N/A

5701.386

5792.237

101.59%

€

EX_X

1000 NAC

5_NC_T

ERROR:#N/A

1012374.370

1034830.990

102.22%

EX_X

1000 m3

5_NC_T

ERROR:#DIV/0!

ERROR:#N/A

177.566

178.658

100.61%

EX_M

1000 m3

ERROR:#N/A

546.187

549.200

100.55%

€

EX_M

1000 NAC

ERROR:#N/A

90367.986

84940.310

93.99%

EX_M

1000 m3

ERROR:#DIV/0!

ERROR:#N/A

165.452

154.662

93.48%

EX_X

1000 m3

ERROR:#N/A

5698.973

5787.887

101.56%

€

EX_X

1000 NAC

ERROR:#N/A

1010103.938

1032566.787

102.22%

EX_X

1000 m3

ERROR:#DIV/0!

ERROR:#N/A

177.243

178.401

100.65%

EX_M

1000 m3

6_1

ERROR:#N/A

10.119

10.685

105.59%

€

EX_M

1000 NAC

6_1

ERROR:#N/A

6114.282

6990.204

114.33%

EX_M

1000 m3

6_1

ERROR:#DIV/0!

ERROR:#N/A

604.238

654.207

108.27%

EX_X

1000 m3

6_1

ERROR:#N/A

2.413

4.350

180.27%

€

EX_X

1000 NAC

6_1

ERROR:#N/A

2270.432

2264.203

99.73%

EX_X

1000 m3

6_1

ERROR:#DIV/0!

ERROR:#N/A

940.917

520.506

55.32%

EX_M

1000 m3

6_1_C

ERROR:#N/A

1.931

3.253

168.46%

€

EX_M

1000 NAC

6_1_C

ERROR:#N/A

1571.148

2602.392

165.64%

EX_M

1000 m3

6_1_C

ERROR:#DIV/0!

ERROR:#N/A

813.645

799.998

98.32%

EX_X

1000 m3

6_1_C

ERROR:#N/A

0.560

100.00%

€

EX_X

1000 NAC

6_1_C

ERROR:#N/A

1096.596

575.300

52.46%

EX_X

1000 m3

6_1_C

ERROR:#DIV/0!

ERROR:#N/A

1958.207

1027.321

52.46%

EX_M

1000 m3

6_1_NC

ERROR:#N/A

4.316

3.378

78.27%

€

EX_M

1000 NAC

6_1_NC

ERROR:#N/A

1560.041

1257.641

80.62%

EX_M

1000 m3

6_1_NC

ERROR:#DIV/0!

ERROR:#N/A

361.455

372.303

103.00%

EX_X

1000 m3

6_1_NC

ERROR:#N/A

15.254

11.407

74.78%

€

EX_X

1000 NAC

6_1_NC

ERROR:#N/A

8002.415

5769.777

72.10%

EX_X

1000 m3

6_1_NC

ERROR:#DIV/0!

ERROR:#N/A

524.611

505.810

96.42%

EX_M

1000 m3

6_1_NC_T

ERROR:#N/A

0.001

0.053

5300.00%

€

EX_M

1000 NAC

6_1_NC_T

ERROR:#N/A

0.389

28.407

7302.57%

EX_M

1000 m3

6_1_NC_T

ERROR:#DIV/0!

ERROR:#N/A

389.000

535.981

137.78%

EX_X

1000 m3

6_1_NC_T

ERROR:#N/A

14.212

10.794

75.95%

€

EX_X

1000 NAC

6_1_NC_T

ERROR:#N/A

7041.104

5318.044

75.53%

EX_X

1000 m3

6_1_NC_T

ERROR:#DIV/0!

ERROR:#N/A

495.434

492.685

99.45%

EX_M

1000 m3

6_2

ERROR:#N/A

4.315

3.325

77.06%

€

EX_M

1000 NAC

6_2

ERROR:#N/A

1559.652

1229.234

78.81%

EX_M

1000 m3

6_2

ERROR:#DIV/0!

ERROR:#N/A

361.449

369.694

102.28%

EX_X

1000 m3

6_2

ERROR:#N/A

1.042

0.613

58.83%

€

EX_X

1000 NAC

6_2

ERROR:#N/A

961.311

451.733

46.99%

EX_X

1000 m3

6_2

ERROR:#DIV/0!

ERROR:#N/A

922.563

736.922

79.88%

EX_M

1000 m3

6_2_C

ERROR:#N/A

0.047

0.001

2.13%

€

EX_M

1000 NAC

6_2_C

ERROR:#N/A

22.612

1.078

4.77%

EX_M

1000 m3

6_2_C

ERROR:#DIV/0!

ERROR:#N/A

481.106

1078.000

224.07%

EX_X

1000 m3

6_2_C

ERROR:#N/A

0.025

0.000

0.00%

€

EX_X

1000 NAC

6_2_C

ERROR:#N/A

5.333

0.000

0.00%

EX_X

1000 m3

6_2_C

ERROR:#DIV/0!

ERROR:#N/A

213.320

ERROR:#DIV/0!

EX_M

1000 m3

6_2_NC

ERROR:#N/A

125.533

140.610

112.01%

€

EX_M

1000 NAC

6_2_NC

ERROR:#N/A

45324.985

45542.647

100.48%

EX_M

1000 m3

6_2_NC

ERROR:#DIV/0!

ERROR:#N/A

361.060

323.893

89.71%

EX_X

1000 m3

6_2_NC

ERROR:#N/A

200.933

333.166

165.81%

€

EX_X

1000 NAC

6_2_NC

ERROR:#N/A

120783.183

175822.453

145.57%

EX_X

1000 m3

6_2_NC

ERROR:#DIV/0!

ERROR:#N/A

601.112

527.732

87.79%

EX_M

1000 m3

6_2_NC_T

ERROR:#N/A

91.905

93.934

102.21%

€

EX_M

1000 NAC

6_2_NC_T

ERROR:#N/A

37515.020

35800.930

95.43%

EX_M

1000 m3

6_2_NC_T

ERROR:#DIV/0!

ERROR:#N/A

408.193

381.129

93.37%

EX_X

1000 m3

6_2_NC_T

ERROR:#N/A

192.953

312.863

162.14%

€

EX_X

1000 NAC

6_2_NC_T

ERROR:#N/A

117963.150

168673.984

142.99%

EX_X

1000 m3

6_2_NC_T

ERROR:#DIV/0!

ERROR:#N/A

611.357

539.130

88.19%

EX_M

1000 m3

6_3

ERROR:#N/A

24.605

17.406

70.74%

€

EX_M

1000 NAC

6_3

ERROR:#N/A

8799.670

5406.519

61.44%

EX_M

1000 m3

6_3

ERROR:#DIV/0!

ERROR:#N/A

357.637

310.612

86.85%

EX_X

1000 m3

6_3

ERROR:#N/A

133.911

227.545

169.92%

€

EX_X

1000 NAC

6_3

ERROR:#N/A

62854.230

97383.396

154.94%

EX_X

1000 m3

6_3

ERROR:#DIV/0!

ERROR:#N/A

469.373

427.974

91.18%

EX_M

1000 m3

6_3_1

ERROR:#N/A

67.300

76.528

113.71%

€

EX_M

1000 NAC

6_3_1

ERROR:#N/A

28715.350

30394.411

105.85%

EX_M

1000 m3

6_3_1

ERROR:#DIV/0!

ERROR:#N/A

426.677

397.167

93.08%

EX_X

1000 m3

6_3_1

ERROR:#N/A

59.022

85.318

144.55%

€

EX_X

1000 NAC

6_3_1

ERROR:#N/A

55104.411

71290.588

129.37%

EX_X

1000 m3

6_3_1

ERROR:#DIV/0!

ERROR:#N/A

933.625

835.587

89.50%

EX_M

1000 m3

6_4

ERROR:#N/A

0.660

0.426

64.55%

€

EX_M

1000 NAC

6_4

ERROR:#N/A

801.829

585.950

73.08%

EX_M

1000 m3

6_4

ERROR:#DIV/0!

ERROR:#N/A

1214.892

1375.469

113.22%

EX_X

1000 m3

6_4

ERROR:#N/A

0.016

0.025

156.25%

€

EX_X

1000 NAC

6_4

ERROR:#N/A

14.243

46.183

324.25%

EX_X

1000 m3

6_4

ERROR:#DIV/0!

ERROR:#N/A

890.188

1847.320

207.52%

EX_M

1000 m3

6_4_1

ERROR:#N/A

21.338

28.827

135.10%

€

EX_M

1000 NAC

6_4_1

ERROR:#N/A

5190.271

6184.808

119.16%

EX_M

1000 m3

6_4_1

ERROR:#DIV/0!

ERROR:#N/A

243.241

214.549

88.20%

EX_X

1000 m3

6_4_1

ERROR:#N/A

3.827

4.278

111.78%

€

EX_X

1000 NAC

6_4_1

ERROR:#N/A

1277.397

1376.303

107.74%

EX_X

1000 m3

6_4_1

ERROR:#DIV/0!

ERROR:#N/A

333.785

321.716

96.38%

EX_M

1000 m3

6_4_2

ERROR:#N/A

15.062

20.110

133.51%

€

EX_M

1000 NAC

6_4_2

ERROR:#N/A

3772.193

4297.326

113.92%

EX_M

1000 m3

6_4_2

ERROR:#DIV/0!

ERROR:#N/A

250.444

213.691

85.32%

EX_X

1000 m3

6_4_2

ERROR:#N/A

0.122

0.134

109.84%

€

EX_X

1000 NAC

6_4_2

ERROR:#N/A

49.429

51.093

103.37%

EX_X

1000 m3

6_4_2

ERROR:#DIV/0!

ERROR:#N/A

405.156

381.291

94.11%

EX_M

1000 m3

6_4_3

ERROR:#N/A

12.290

17.849

145.23%

€

EX_M

1000 NAC

6_4_3

ERROR:#N/A

2619.694

3556.909

135.78%

EX_M

1000 m3

6_4_3

ERROR:#DIV/0!

ERROR:#N/A

213.157

199.276

93.49%

EX_X

1000 m3

6_4_3

ERROR:#N/A

4.153

16.025

385.86%

€

EX_X

1000 NAC

6_4_3

ERROR:#N/A

1542.636

5772.166

374.18%

EX_X

1000 m3

6_4_3

ERROR:#DIV/0!

ERROR:#N/A

371.451

360.199

96.97%

EX_M

1000 mt

ERROR:#N/A

1.418

1.929

136.02%

€

EX_M

1000 NAC

ERROR:#N/A

363.402

572.906

157.65%

EX_M

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

256.278

297.032

115.90%

EX_X

1000 mt

ERROR:#N/A

3.896

15.567

399.57%

€

EX_X

1000 NAC

ERROR:#N/A

1402.084

5501.703

392.39%

EX_X

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

359.878

353.412

98.20%

EX_M

1000 mt

7_1

ERROR:#N/A

8.613

12.478

144.87%

€

EX_M

1000 NAC

7_1

ERROR:#N/A

1671.548

2213.651

132.43%

EX_M

1000 mt

7_1

ERROR:#DIV/0!

ERROR:#N/A

194.073

177.407

91.41%

EX_X

1000 mt

7_1

ERROR:#N/A

0.160

0.384

239.95%

€

EX_X

1000 NAC

7_1

ERROR:#N/A

120.388

260.201

216.14%

EX_X

1000 mt

7_1

ERROR:#DIV/0!

ERROR:#N/A

752.425

677.759

90.08%

EX_M

1000 mt

7_2

ERROR:#N/A

2.259

3.443

152.39%

€

EX_M

1000 NAC

7_2

ERROR:#N/A

584.744

770.352

131.74%

EX_M

1000 mt

7_2

ERROR:#DIV/0!

ERROR:#N/A

258.851

223.772

86.45%

EX_X

1000 mt

7_2

ERROR:#N/A

0.097

0.074

75.88%

€

EX_X

1000 NAC

7_2

ERROR:#N/A

20.164

10.262

50.89%

EX_X

1000 mt

7_2

ERROR:#DIV/0!

ERROR:#N/A

207.876

139.420

67.07%

EX_M

1000 mt

7_3

ERROR:#N/A

309.815

163.774

52.86%

€

EX_M

1000 NAC

7_3

ERROR:#N/A

183761.815

72038.613

39.20%

EX_M

1000 mt

7_3

ERROR:#DIV/0!

ERROR:#N/A

593.134

439.867

74.16%

EX_X

1000 mt

7_3

ERROR:#N/A

2467.130

2600.269

105.40%

€

EX_X

1000 NAC

7_3

ERROR:#N/A

1247452.351

1141977.162

91.54%

EX_X

1000 mt

7_3

ERROR:#DIV/0!

ERROR:#N/A

505.629

439.177

86.86%

EX_M

1000 mt

7_3_1

ERROR:#N/A

4.734

8.989

189.89%

€

EX_M

1000 NAC

7_3_1

ERROR:#N/A

1413.078

2571.062

181.95%

EX_M

1000 mt

7_3_1

ERROR:#DIV/0!

ERROR:#N/A

298.496

286.014

95.82%

EX_X

1000 mt

7_3_1

ERROR:#N/A

16.911

32.053

189.54%

€

EX_X

1000 NAC

7_3_1

ERROR:#N/A

5964.702

10778.634

180.71%

EX_X

1000 mt

7_3_1

ERROR:#DIV/0!

ERROR:#N/A

352.711

336.276

95.34%

EX_M

1000 mt

7_3_2

ERROR:#N/A

299.146

148.251

49.56%

€

EX_M

1000 NAC

7_3_2

ERROR:#N/A

175511.995

62334.563

35.52%

EX_M

1000 mt

7_3_2

ERROR:#DIV/0!

ERROR:#N/A

586.710

420.466

71.66%

EX_X

1000 mt

7_3_2

ERROR:#N/A

2304.699

2369.694

102.82%

€

EX_X

1000 NAC

7_3_2

ERROR:#N/A

1148877.679

1034239.729

90.02%

EX_X

1000 mt

7_3_2

ERROR:#DIV/0!

ERROR:#N/A

498.494

436.444

87.55%

EX_M

1000 mt

7_3_3

ERROR:#N/A

298.388

148.248

49.68%

€

EX_M

1000 NAC

7_3_3

ERROR:#N/A

174763.120

62327.515

35.66%

EX_M

1000 mt

7_3_3

ERROR:#DIV/0!

ERROR:#N/A

585.691

420.427

71.78%

EX_X

1000 mt

7_3_3

ERROR:#N/A

2304.698

2369.692

102.82%

€

EX_X

1000 NAC

7_3_3

ERROR:#N/A

1148865.659

1034215.316

90.02%

EX_X

1000 mt

7_3_3

ERROR:#DIV/0!

ERROR:#N/A

498.489

436.435

87.55%

EX_M

1000 mt

7_3_4

ERROR:#N/A

290.003

140.703

48.52%

€

EX_M

1000 NAC

7_3_4

ERROR:#N/A

169931.053

58929.550

34.68%

EX_M

1000 mt

7_3_4

ERROR:#DIV/0!

ERROR:#N/A

585.963

418.823

71.48%

EX_X

1000 mt

7_3_4

ERROR:#N/A

2271.356

2338.873

102.97%

€

EX_X

1000 NAC

7_3_4

ERROR:#N/A

1133774.265

1022998.378

90.23%

EX_X

1000 mt

7_3_4

ERROR:#DIV/0!

ERROR:#N/A

499.162

437.389

87.62%

EX_M

1000 mt

7_4

ERROR:#N/A

0.758

0.003

0.39%

€

EX_M

1000 NAC

7_4

ERROR:#N/A

748.875

7.048

0.94%

EX_M

1000 mt

7_4

ERROR:#DIV/0!

ERROR:#N/A

987.962

2377.066

240.60%

EX_X

1000 mt

7_4

ERROR:#N/A

0.001

0.002

205.10%

€

EX_X

1000 NAC

7_4

ERROR:#N/A

12.015

24.413

203.19%

EX_X

1000 mt

7_4

ERROR:#DIV/0!

ERROR:#N/A

12015.000

11902.974

99.07%

EX_M

1000 mt

ERROR:#N/A

5.935

6.533

110.08%

€

EX_M

1000 NAC

ERROR:#N/A

6836.742

7132.988

104.33%

EX_M

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

1151.936

1091.802

94.78%

EX_X

1000 mt

ERROR:#N/A

145.520

198.522

136.42%

€

EX_X

1000 NAC

ERROR:#N/A

92609.970

96958.799

104.70%

EX_X

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

636.407

488.403

76.74%

EX_M

1000 mt

8_1

ERROR:#N/A

2.861

1.505

52.59%

€

EX_M

1000 NAC

8_1

ERROR:#N/A

4081.468

2111.271

51.73%

EX_M

1000 mt

8_1

ERROR:#DIV/0!

ERROR:#N/A

1426.588

1403.120

98.35%

EX_X

1000 mt

8_1

ERROR:#N/A

0.032

0.002

4.83%

€

EX_X

1000 NAC

8_1

ERROR:#N/A

20.636

1.584

7.68%

EX_X

1000 mt

8_1

ERROR:#DIV/0!

ERROR:#N/A

644.472

1025.243

159.08%

EX_M

1000 mt

8_2

ERROR:#N/A

2.844

1.505

52.90%

€

EX_M

1000 NAC

8_2

ERROR:#N/A

4073.737

2110.610

51.81%

EX_M

1000 mt

8_2

ERROR:#DIV/0!

ERROR:#N/A

1432.397

1402.758

97.93%

EX_X

1000 mt

8_2

ERROR:#N/A

0.000

45.00%

€

EX_X

1000 NAC

8_2

ERROR:#N/A

0.003

0.033

1100.00%

EX_X

1000 mt

8_2

ERROR:#DIV/0!

ERROR:#N/A

150.000

3666.667

2444.44%

EX_M

1000 mt

ERROR:#N/A

0.017

0.000

0.48%

€

EX_M

1000 NAC

ERROR:#N/A

7.731

0.661

8.55%

EX_M

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

454.765

8060.976

1772.56%

EX_X

1000 mt

ERROR:#N/A

0.032

0.000

0.00%

€

EX_X

1000 NAC

ERROR:#N/A

20.633

0.000

0.00%

EX_X

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

644.781

ERROR:#DIV/0!

EX_M

1000 mt

ERROR:#N/A

25.566

5.381

21.05%

€

EX_M

1000 NAC

ERROR:#N/A

4727.268

908.090

19.21%

EX_M

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

184.904

168.747

91.26%

EX_X

1000 mt

ERROR:#N/A

5.422

21.295

392.76%

€

EX_X

1000 NAC

ERROR:#N/A

1616.692

2691.453

166.48%

EX_X

1000 mt

ERROR:#DIV/0!

ERROR:#N/A

298.173

126.387

42.39%

EX_M

1000 mt

10_1

ERROR:#N/A

28.943

40.439

139.72%

€

EX_M

1000 NAC

10_1

ERROR:#N/A

24488.719

39082.537

159.59%

EX_M

1000 mt

10_1

ERROR:#DIV/0!

ERROR:#N/A

846.102

966.455

114.22%

EX_X

1000 mt

10_1

ERROR:#N/A

4355.782

4144.039

95.14%

€

EX_X

1000 NAC

10_1

ERROR:#N/A

3177326.095

2911636.631

91.64%

EX_X

1000 mt

10_1

ERROR:#DIV/0!

ERROR:#N/A

729.450

702.608

96.32%

EX_M

1000 mt

10_1_1

ERROR:#N/A

15.869

15.890

100.13%

€

EX_M

1000 NAC

10_1_1

ERROR:#N/A

9467.534

8527.344

90.07%

EX_M

1000 mt

10_1_1

ERROR:#DIV/0!

ERROR:#N/A

596.606

536.665

89.95%

EX_X

1000 mt

10_1_1

ERROR:#N/A

2392.802

1940.697

81.11%

€

EX_X

1000 NAC

10_1_1

ERROR:#N/A

1577990.100

1177098.539

74.59%

EX_X

1000 mt

10_1_1

ERROR:#DIV/0!

ERROR:#N/A

659.474

606.534

91.97%

EX_M

1000 mt

10_1_2

ERROR:#N/A

15.153

14.863

98.09%

€

EX_M

1000 NAC

10_1_2

ERROR:#N/A

7484.645

5698.682

76.14%

EX_M

1000 mt

10_1_2

ERROR:#DIV/0!

ERROR:#N/A

493.938

383.411

77.62%

EX_X

1000 mt

10_1_2

ERROR:#N/A

58.489

50.703

86.69%

€

EX_X

1000 NAC

10_1_2

ERROR:#N/A

29086.682

20019.603

68.83%

EX_X

1000 mt

10_1_2

ERROR:#DIV/0!

ERROR:#N/A

497.302

394.842

79.40%

EX_M

1000 mt

10_1_3

ERROR:#N/A

0.125

0.087

69.95%

€

EX_M

1000 NAC

10_1_3

ERROR:#N/A

157.683

161.731

102.57%

EX_M

1000 mt

10_1_3

ERROR:#DIV/0!

ERROR:#N/A

1261.464

1849.686

146.63%

EX_X

1000 mt

10_1_3

ERROR:#N/A

667.622

421.049

63.07%

€

EX_X

1000 NAC

10_1_3

ERROR:#N/A

384804.756

221434.863

57.54%

EX_X

1000 mt

10_1_3

ERROR:#DIV/0!

ERROR:#N/A

576.381

525.912

91.24%

EX_M

1000 mt

10_1_4

ERROR:#N/A

0.231

0.468

202.66%

€

EX_M

1000 NAC

10_1_4

ERROR:#N/A

737.261

1731.259

234.82%

EX_M

1000 mt

10_1_4

ERROR:#DIV/0!

ERROR:#N/A

3191.606

3698.118

115.87%

EX_X

1000 mt

10_1_4

ERROR:#N/A

318.218

368.171

115.70%

€

EX_X

1000 NAC

10_1_4

ERROR:#N/A

221581.859

244597.385

110.39%

EX_X

1000 mt

10_1_4

ERROR:#DIV/0!

ERROR:#N/A

696.321

664.358

95.41%

EX_M

1000 mt

10_2

ERROR:#N/A

0.360

0.471

130.78%

€

EX_M

1000 NAC

10_2

ERROR:#N/A

1087.945

935.672

86.00%

EX_M

1000 mt

10_2

ERROR:#DIV/0!

ERROR:#N/A

3022.069

1987.383

65.76%

EX_X

1000 mt

10_2

ERROR:#N/A

1348.473

1100.773

81.63%

€

EX_X

1000 NAC

10_2

ERROR:#N/A

942516.803

691046.688

73.32%

EX_X

1000 mt

10_2

ERROR:#DIV/0!

ERROR:#N/A

698.951

627.783

89.82%

EX_M

1000 mt

10_3

ERROR:#N/A

0.056

0.019

34.31%

€

EX_M

1000 NAC

10_3

ERROR:#N/A

177.612

93.025

52.38%

EX_M

1000 mt

10_3

ERROR:#DIV/0!

ERROR:#N/A

3171.643

4842.278

152.67%

EX_X

1000 mt

10_3

ERROR:#N/A

1.905

2.064

108.36%

€

EX_X

1000 NAC

10_3

ERROR:#N/A

2059.235

2148.807

104.35%

EX_X

1000 mt

10_3

ERROR:#DIV/0!

ERROR:#N/A

1080.963

1040.992

96.30%

EX_M

1000 mt

10_3_1

ERROR:#N/A

13.010

24.471

188.09%

€

EX_M

1000 NAC

10_3_1

ERROR:#N/A

14679.008

29954.154

204.06%

EX_M

1000 mt

10_3_1

ERROR:#DIV/0!

ERROR:#N/A

1128.287

1224.077

108.49%

EX_X

1000 mt

10_3_1

ERROR:#N/A

1914.640

2150.485

112.32%

€

EX_X

1000 NAC

10_3_1

ERROR:#N/A

1551888.413

1683965.651

108.51%

EX_X

1000 mt

10_3_1

ERROR:#DIV/0!

ERROR:#N/A

810.538

783.063

96.61%

EX_M

1000 mt

10_3_2

ERROR:#N/A

3.215

6.438

200.26%

€

EX_M

1000 NAC

10_3_2

ERROR:#N/A

2368.010

11626.157

490.97%

EX_M

1000 mt

10_3_2

ERROR:#DIV/0!

ERROR:#N/A

736.551

1805.796

245.17%

EX_X

1000 mt

10_3_2

ERROR:#N/A

477.498

525.564

110.07%

€

EX_X

1000 NAC

10_3_2

ERROR:#N/A

258192.142

258341.588

100.06%

EX_X

1000 mt

10_3_2

ERROR:#DIV/0!

ERROR:#N/A

540.719

491.551

90.91%

EX_M

1000 mt

10_3_3

ERROR:#N/A

5.251

10.753

204.78%

€

EX_M

1000 NAC

10_3_3

ERROR:#N/A

9006.921

13421.349

149.01%

EX_M

1000 mt

10_3_3

ERROR:#DIV/0!

ERROR:#N/A

1715.277

1248.146

72.77%

EX_X

1000 mt

10_3_3

ERROR:#N/A

1237.374

1387.731

112.15%

€

EX_X

1000 NAC

10_3_3

ERROR:#N/A

1079129.135

1191143.066

110.38%

EX_X

1000 mt

10_3_3

ERROR:#DIV/0!

ERROR:#N/A

872.112

858.339

98.42%

EX_M

1000 mt

10_3_4

ERROR:#N/A

2.303

3.649

158.46%

€

EX_M

1000 NAC

10_3_4

ERROR:#N/A

1985.024

3097.072

156.02%

EX_M

1000 mt

10_3_4

ERROR:#DIV/0!

ERROR:#N/A

861.930

848.644

98.46%

EX_X

1000 mt

10_3_4

ERROR:#N/A

173.611

211.836

122.02%

€

EX_X

1000 NAC

10_3_4

ERROR:#N/A

192880.069

215112.278

111.53%

EX_X

1000 mt

10_3_4

ERROR:#DIV/0!

ERROR:#N/A

1110.990

1015.464

91.40%

EX_M

1000 mt

10_4

ERROR:#N/A

2.241

3.630

161.99%

€

EX_M

1000 NAC

10_4

ERROR:#N/A

1319.053

1809.576

137.19%

EX_M

1000 mt

10_4

ERROR:#DIV/0!

ERROR:#N/A

588.600

498.492

84.69%

EX_X

1000 mt

10_4

ERROR:#N/A

26.156

25.353

96.93%

€

EX_X

1000 NAC

10_4

ERROR:#N/A

21687.067

19368.719

89.31%

EX_X

1000 mt

10_4

ERROR:#DIV/0!

ERROR:#N/A

829.143

763.950

92.14%

TS-EU2

Min:

80%

Max:

120%

Notes

EU2

Country

Flow

Unit

Product

2015

2016

2017

2018

2019

2020

15/16

16/17

17/18

18/19

19/19

19/20

2015

2016

2017

2018

1000 m3

EU2_1

ERROR:#N/A

63666.863634

60233.267515

94.61%

1000 m3

EU2_1_C

ERROR:#N/A

49899.313252

47338.05678

94.87%

1000 m3

EU2_1_NC

ERROR:#N/A

13767.550382

12895.210735

93.66%

1000 m3

EU2_1_1

ERROR:#N/A

5390.3869212

5172.93148052

95.97%

1000 m3

EU2_1_1_C

ERROR:#N/A

4708.0853649233

4543.2125252053

96.50%

1000 m3

EU2_1_1_NC

ERROR:#N/A

682.3015562767

629.7189553147

92.29%

1000 m3

EU2_1_2

ERROR:#N/A

1000 m3

EU2_1_2_C

ERROR:#N/A

1000 m3

EU2_1_2_NC

ERROR:#N/A

1000 m3

EU2_1_3

ERROR:#N/A

58276.4767128

55060.33603448

94.48%

1000 m3

EU2_1_3_C

ERROR:#N/A

45191.2278870767

42794.8442547947

94.70%

1000 m3

EU2_1_3_NC

ERROR:#N/A

13085.2488257233

12265.4917796853

93.74%

Annex1 | JQ1-Corres.




		FOREST SECTOR QUESTIONNAIRE JQ1 (Supp. 1)
		PRIMARY PRODUCTS
		Removals and Production
		CORRESPONDENCES to CPC Ver.2.1
		Central Product Classification Version 2.1 (CPC Ver. 2.1)
Product	Product
Code

REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
1	ROUNDWOOD (WOOD IN THE ROUGH)	031
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	0313
1.1.C	Coniferous	03131
1.1.NC	Non-Coniferous	03132
1.2	INDUSTRIAL ROUNDWOOD	0311 0312
1.2.C	Coniferous	0311
1.2.NC	Non-Coniferous	0312
1.2.NC.T	of which: Tropical	ex0312
1.2.1	SAWLOGS AND VENEER LOGS	ex03110 ex03120
1.2.1.C	Coniferous	ex03110
1.2.1.NC	Non-Coniferous	ex03120
1.2.2	PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)	ex03110 ex03120
1.2.2.C	Coniferous	ex03110
1.2.2.NC	Non-Coniferous	ex03120
1.2.3	OTHER INDUSTRIAL ROUNDWOOD	ex03110 ex03120
1.2.3.C	Coniferous	ex03110
1.2.3.NC	Non-Coniferous	ex03120
PRODUCTION
2	WOOD CHARCOAL	ex34510
3	WOOD CHIPS, PARTICLES AND RESIDUES	ex31230 ex39283
3.1	WOOD CHIPS AND PARTICLES	ex31230
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	ex39283
4	RECOVERED POST-CONSUMER WOOD	ex39283
5	WOOD PELLETS AND OTHER AGGLOMERATES	39281 39282
5.1	WOOD PELLETS	39281
5.2	OTHER AGGLOMERATES	39282
6	SAWNWOOD (INCLUDING SLEEPERS)	311 3132
6.C	Coniferous	31101 ex31109 ex3132
6.NC	Non-Coniferous	31102 ex31109 ex3132
6.NC.T	of which: Tropical	ex31102 ex31109 ex3132
7	VENEER SHEETS	3151
7.C	Coniferous	31511
7.NC	Non-Coniferous	31512
7.NC.T	of which: Tropical	ex31512
8	WOOD-BASED PANELS	3141 3142 3143 3144
8.1	PLYWOOD	3141 3142
8.1.C	Coniferous	31411 31421
8.1.NC	Non-Coniferous	31412 31422
8.1.NC.T	of which: Tropical	ex31412 ex31422
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) and SIMILAR BOARD	3143
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	31432
8.3	FIBREBOARD	3144
8.3.1	HARDBOARD	31442
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	31441
8.3.3	OTHER FIBREBOARD	31449
9	WOOD PULP	32111 32112 ex32113
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	ex32113
9.2	CHEMICAL WOOD PULP	32112
9.2.1	SULPHATE PULP	ex32112
9.2.1.1	of which: BLEACHED	ex32112
9.2.2	SULPHITE PULP	ex32112
9.3	DISSOLVING GRADES	32111
10	OTHER PULP	ex32113
10.1	PULP FROM FIBRES OTHER THAN WOOD	ex32113
10.2	RECOVERED FIBRE PULP	ex32113
11	RECOVERED PAPER	3924
12	PAPER AND PAPERBOARD	3212 3213 32142 32143 ex32149 32151 32198 ex32199
12.1	GRAPHIC PAPERS	3212 ex32143 ex32149
12.1.1	NEWSPRINT	32121
12.1.2	UNCOATED MECHANICAL	ex32122 ex32129
12.1.3	UNCOATED WOODFREE	32122 ex32129
12.1.4	COATED PAPERS	ex32143 ex32149
12.2	HOUSEHOLD AND SANITARY PAPERS	32131
12.3	PACKAGING MATERIALS	32132 ex32133 32134 32135 ex32136 ex32137 32142 32151 ex32143 ex32149
12.3.1	CASE MATERIALS	32132 32134 32135 ex32136
12.3.2	CARTONBOARD	ex32133 ex32136 ex32143 ex32149
12.3.3	WRAPPING PAPERS	ex32133 ex32136 ex32137 32142 32151
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	ex32136
12.4	OTHER PAPER AND PAPERBOARD N.E.S.	ex32149 ex32133 ex32136 ex32137 32198 ex32199
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the CPC Ver.2.1 code is applicable.
For instance "ex31512" under product 7.NC.T means that only a part of CPC Ver.2.1 code 31512 refers to non-coniferous tropical veneer sheets.
In CPC, if only 3 or 4 digits are shown, then all sub-codes at lower degrees of aggregation are included (for example, 0313 includes 03131 and 03132).

Annex2 | JQ2-Corres.




		FOREST SECTOR QUESTIONNAIRE JQ2 (Supp. 1)
		PRIMARY PRODUCTS
		Trade
		CORRESPONDENCES to HS2017, HS2012 and SITC Rev.4
		C l a s s i f i c a t i o n s
Product	Product
Code		HS2017	HS2012	SITC Rev.4

1	ROUNDWOOD (WOOD IN THE ROUGH)	4401.11/12 44.03	4401.10 44.03	245.01 247
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	4401.11/12	4401.10	245.01
1.1.C	Coniferous	4401.11	ex4401.10	ex245.01
1.1.NC	Non-Coniferous	4401.12	ex4401.10	ex245.01
1.2	INDUSTRIAL ROUNDWOOD	44.03	44.03	247
1.2.C	Coniferous	4403.11/21/22/23/24/25/26	ex4403.10 4403.20	ex247.3 247.4
1.2.NC	Non-Coniferous	4403.12/41/49/91/93/94/95/96/97/98/99	ex4403.10 4403.41/49/91/92/99	ex247.3 247.5 247.9
1.2.NC.T	of which: Tropical	ex4403.12 4403.41/49	ex4403.10 4403.41/49 ex4403.99	ex247.3 247.5 ex247.9
2	WOOD CHARCOAL	4402.90	4402.90	ex245.02
3	WOOD CHIPS, PARTICLES AND RESIDUES	4401.21/22 ex4401.40	4401.21/22 ex4401.39	246.1 ex246.2
3.1	WOOD CHIPS AND PARTICLES	4401.21/22	4401.21/22	246.1
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	ex4401.40	ex4401.39	ex246.2
4	RECOVERED POST-CONSUMER WOOD	ex4401.40	ex4401.39	ex246.2
5	WOOD PELLETS AND OTHER AGGLOMERATES	4401.31/39	4401.31 ex4401.39	ex246.2
5.1	WOOD PELLETS	4401.31	4401.31	ex246.2
5.2	OTHER AGGLOMERATES	4401.39	ex4401.39	ex246.2
6	SAWNWOOD (INCLUDING SLEEPERS)	44.06 44.07	44.06 44.07	248.1 248.2 248.4
6.C	Coniferous	4406.11/91 4407.11/12/19	ex4406.10/90 4407.10	ex248.11 ex248.19 248.2
6.NC	Non-Coniferous	4406.12/92 4407.21/22/25/26/27/28/29/91/92/93/94/95/96/97/99	ex4406.10/90 4407.21/22/25/26/27/28/29/91/92/93/94/95/99	ex248.11 ex248.19 248.4
6.NC.T	of which: Tropical	ex4406.12/92 4407.21/22/25/26/27/28/29	ex4406.10/90 4407.21/22/25/26/27/28/29 ex4407.99	ex248.11 ex248.19 ex248.4
7	VENEER SHEETS	44.08	44.08	634.1
7.C	Coniferous	4408.10	4408.10	634.11
7.NC	Non-Coniferous	4408.31/39/90	4408.31/39/90	634.12
7.NC.T	of which: Tropical	4408.31/39	4408.31/39 ex4408.90	ex634.12
8	WOOD-BASED PANELS	44.10 44.11 4412.31/33/34/39/94/99	44.10 44.11 4412.31/32/39/94/99	634.22/23/31/33/39 634.5
8.1	PLYWOOD	4412.31/33/34/39/94/99	4412.31/32/39/94/99	634.31/33/39
8.1.C	Coniferous	4412.39 ex4412.94 ex4412.99	4412.39 ex4412.94 ex.4412.99	ex634.31 ex634.33 ex634.39
8.1.NC	Non-Coniferous	4412.31/33/34 ex4412.94 ex4412.99	4412.31/32 ex4412.94 ex4412.99	ex634.31 ex634.33 ex634.39
8.1.NC.T	of which: Tropical	4412.31 ex4412.94 ex4412.99	4412.31 ex4412.32 ex4412.94 ex4412.99	ex634.31 ex634.33 ex634.39
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) and SIMILAR BOARD	44.10	44.10	634.22/23
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	4410.12	4410.12	ex634.22
8.3	FIBREBOARD	44.11	44.11	634.5
8.3.1	HARDBOARD	4411.92	4411.92	ex634.54 ex634.55
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	4411.12/13 ex4411.14*	4411.12/13 ex4411.14*	ex634.54 ex634.55
8.3.3	OTHER FIBREBOARD	ex4411.14 4411.93/94	ex4411.14 4411.93/94	ex634.54 ex634.55
9	WOOD PULP	47.01/02/03/04/05	47.01/02/03/04/05	251.2 251.3 251.4 251.5 251.6 251.91
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	47.01 47.05	47.01 47.05	251.2 251.91
9.2	CHEMICAL WOOD PULP	47.03 47.04	47.03 47.04	251.4 251.5 251.6
9.2.1	SULPHATE PULP	47.03	47.03	251.4 251.5
9.2.1.1	of which: BLEACHED	4703.21/29	4703.21/29	251.5
9.2.2	SULPHITE PULP	47.04	47.04	251.6
9.3	DISSOLVING GRADES	47.02	47.02	251.3
10	OTHER PULP	47.06	47.06	251.92
10.1	PULP FROM FIBRES OTHER THAN WOOD	4706.10/30/91/92/93	4706.10/30/91/92/93	ex251.92
10.2	RECOVERED FIBRE PULP	4706.20	4706.20	ex251.92
11	RECOVERED PAPER	47.07	47.07	251.1
12	PAPER AND PAPERBOARD	48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13	48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13	641.1 641.2 641.3 641.4 641.5 641.62/63/64/69/71/72/74/75/76/77/93 642.41
12.1	GRAPHIC PAPERS	48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29	48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29	641.1 641.21/22/26/29 641.3
12.1.1	NEWSPRINT	48.01	48.01	641.1
12.1.2	UNCOATED MECHANICAL	4802.61/62/69	4802.61/62/69	641.29
12.1.3	UNCOATED WOODFREE	4802.10/20/54/55/56/57/58	4802.10/20/54/55/56/57/58	641.21/22/26
12.1.4	COATED PAPERS	48.09 4810.13/14/19/22/29	48.09 4810.13/14/19/22/29	641.3
12.2	HOUSEHOLD AND SANITARY PAPERS	48.03	48.03	641.63
12.3	PACKAGING MATERIALS	4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59	4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59	641.41/42/46 ex641.47 641.48/51/52 ex641.53 641.54/59/62/64/69/71/72/74/75/76/77
12.3.1	CASE MATERIALS	4804.11/19 4805.11/12/19/24/25/91	4804.11/19 4805.11/12/19/24/25/91	641.41/51/54 ex641.59
12.3.2	CARTONBOARD	4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59	4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59	ex641.47 641.48 ex641.59 641.75/76 ex641.77 641.71/72
12.3.3	WRAPPING PAPERS	4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99	4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99	641.42/46/52 ex641.53 641.62/64/69/74 ex641.77
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	4805.93	4805.93	ex641.59
12.4	OTHER PAPER AND PAPERBOARD N.E.S.	4802.40 4804.41 4805.40/50 4806.30 48.12 48.13	4802.40 4804.41 4805.40/50 4806.30 48.12 48.13	641.24 ex641.47 641.56 ex641.53 641.55/93 642.41
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the HS2012/HS2017 or SITC Rev.4 code is applicable.
For instance "ex4401.40" under product 3.2 means that only a part of HS2017 code 4401.40 refers to wood residues coming from wood processing (the other part coded under 4401.40 is recovered post-consumer wood).
In SITC Rev.4, if only 4 digits are shown, then all sub-headings at lower degrees of aggregation are included (for example, 634.1 includes 634.11 and 634.12).
* - Please assign the trade data for HS code 4411.14 to product 8.3.2 (MDF/HDF) and 8.3.3 (other fibreboard) if it is possible to do this in national statistics. If not, please assign all the trade data to item 8.3.2 as in most cases MDF/HDF will represent the large majority of trade.

Annex3 | JQ3-Corres.



		FOREST SECTOR QUESTIONNAIRE JQ3 (Supp. 1)
		SECONDARY PROCESSED PRODUCTS
		Trade
		CORRESPONDENCES to HS2017, HS2012 and SITC Rev.4
		C l a s s i f i c a t i o n s
Product	Product
Code		HS2017	HS2012	SITC Rev.4

13	SECONDARY WOOD PRODUCTS
13.1	FURTHER PROCESSED SAWNWOOD	4409.10/22/29	4409.10/29	248.3 248.5
13.1.C	Coniferous	4409.10	4409.10	248.3
13.1.NC	Non-coniferous	4409.22/29	4409.29	248.5
13.1.NC.T	of which: Tropical	4409.22	ex4409.29	ex248.5
13.2	WOODEN WRAPPING AND PACKAGING MATERIAL	44.15/16	44.15/16	635.1 635.2
13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE	44.14 4419.90 44.20	44.14 ex4419.00 44.20	635.41 ex635.42 635.49
13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD	4418.10/20/40/50/60/74/75/79/99	4418.10/20/40/50/60 ex4418.71 ex4418.72 ex4418.79 ex4418.90	635.31/32/33 ex635.34 ex635.39
13.5	WOODEN FURNITURE	9401.61/69 ex9401.90 9403.30/40/50/60 ex9403.90	9401.61/69 ex9401.90 9403.30/40/50/60 ex9403.90	821.16 ex821.19 821.51/53/55/59 ex821.8
13.6	PREFABRICATED BUILDINGS OF WOOD	9406.10	ex94.06	ex811.0
13.7	OTHER MANUFACTURED WOOD PRODUCTS	44.04/05/13/17 4421.10/99	44.04/05/13/17 4421.10 ex4421.90	634.21/91/93 635.91 ex635.99
14	SECONDARY PAPER PRODUCTS
14.1	COMPOSITE PAPER AND PAPERBOARD	48.07	48.07	641.92
14.2	SPECIAL COATED PAPER AND PULP PRODUCTS	4811.10/41/49/60/90	4811.10/41/49/60/90	641.73/78/79
14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE	48.18	48.18	642.43/94
14.4	PACKAGING CARTONS, BOXES ETC.	48.19	48.19	642.1
14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	48.14/16/17/20/21/22/23	48.14/16/17/20/21/22/23	641.94 642.2 642.3 642.42/45/91/93/99 892.81
14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE	ex4823.90	ex4823.90	ex642.99
14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP	4823.70	4823.70	ex642.99
14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE	4823.20	4823.20	642.45
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the HS2012/HS2017 or SITC Rev.4 code is applicable.
For instance "ex811.00" under "Prefabricated buildings of wood" means that only a part of SITC code 811.00 refers to buildings prefabricated from wood, as that code does not distinguish between the materials buildings were prefabricated from.
In SITC Rev.4, if only 4 digits are shown, then all subheadings at lower degrees of aggregation are included (for example, 892.2 includes 892.21 and 892.29).

SentData

Country	Flow	Year	Unit	Product	Conc	Data value
FI	P	2019	1000 m3	1	FI_P_2019_1000 m3_1	63666.863634	JQ1
FI	P	2019	1000 m3	1_C	FI_P_2019_1000 m3_1_C	8013.23016
FI	P	2019	1000 m3	1_NC	FI_P_2019_1000 m3_1_NC	3716.043498
FI	P	2019	1000 m3	1_1	FI_P_2019_1000 m3_1_1	4297.186662
FI	P	2019	1000 m3	1_1_C	FI_P_2019_1000 m3_1_1_C	55653.633474
FI	P	2019	1000 m3	1_1_NC	FI_P_2019_1000 m3_1_1_NC	46183.269754
FI	P	2019	1000 m3	1_2	FI_P_2019_1000 m3_1_2	9470.36372
FI	P	2019	1000 m3	1_2_C	FI_P_2019_1000 m3_1_2_C	0
FI	P	2019	1000 m3	1_2_NC	FI_P_2019_1000 m3_1_2_NC	23211.270942
FI	P	2019	1000 m3	1_2_1	FI_P_2019_1000 m3_1_2_1	22218.285012
FI	P	2019	1000 m3	1_2_1_C	FI_P_2019_1000 m3_1_2_1_C	992.98593
FI	P	2019	1000 m3	1_2_1_NC	FI_P_2019_1000 m3_1_2_1_NC	32442.362532
FI	P	2019	1000 m3	1_2_2	FI_P_2019_1000 m3_1_2_2	23964.984742
FI	P	2019	1000 m3	1_2_2_C	FI_P_2019_1000 m3_1_2_2_C	8477.37779
FI	P	2019	1000 m3	1_2_2_NC	FI_P_2019_1000 m3_1_2_2_NC	0
FI	P	2019	1000 m3	1_2_3	FI_P_2019_1000 m3_1_2_3	0
FI	P	2019	1000 m3	1_2_3_C	FI_P_2019_1000 m3_1_2_3_C	0
FI	P	2019	1000 m3	1_2_3_NC	FI_P_2019_1000 m3_1_2_3_NC	0
FI	P	2019	1000 mt	2	FI_P_2019_1000 mt_2	14072.6885202963
FI	P	2019	1000 m3	3	FI_P_2019_1000 m3_3	9121.031324
FI	P	2019	1000 m3	3_1	FI_P_2019_1000 m3_3_1	4951.6571962963
FI	P	2019	1000 m3	3_2	FI_P_2019_1000 m3_3_2	458.213735734
FI	P	2019	1000 mt	4	FI_P_2019_1000 mt_4	362.527
FI	P	2019	1000 mt	4_1	FI_P_2019_1000 mt_4_1	362.527
FI	P	2019	1000 mt	4_2	FI_P_2019_1000 mt_4_2	0
FI	P	2019	1000 m3	5	FI_P_2019_1000 m3_5	11390
FI	P	2019	1000 m3	5_C	FI_P_2019_1000 m3_5_C	11360
FI	P	2019	1000 m3	5_NC	FI_P_2019_1000 m3_5_NC	30
FI	P	2019	1000 m3	5_NC_T	FI_P_2019_1000 m3_5_NC_T	0
FI	P	2019	1000 m3	6	FI_P_2019_1000 m3_6	0
FI	P	2019	1000 m3	6_1	FI_P_2019_1000 m3_6_1	0
FI	P	2019	1000 m3	6_1_C	FI_P_2019_1000 m3_6_1_C	0
FI	P	2019	1000 m3	6_1_NC	FI_P_2019_1000 m3_6_1_NC	0
FI	P	2019	1000 m3	6_1_NC_T	FI_P_2019_1000 m3_6_1_NC_T	0
FI	P	2019	1000 m3	6_2	FI_P_2019_1000 m3_6_2	1090
FI	P	2019	1000 m3	6_2_C	FI_P_2019_1000 m3_6_2_C	800
FI	P	2019	1000 m3	6_2_NC	FI_P_2019_1000 m3_6_2_NC	290
FI	P	2019	1000 m3	6_2_NC_T	FI_P_2019_1000 m3_6_2_NC_T	0
FI	P	2019	1000 m3	6_3	FI_P_2019_1000 m3_6_3	0
FI	P	2019	1000 m3	6_3_1	FI_P_2019_1000 m3_6_3_1	0
FI	P	2019	1000 m3	6_4	FI_P_2019_1000 m3_6_4	0
FI	P	2019	1000 m3	6_4_1	FI_P_2019_1000 m3_6_4_1	0
FI	P	2019	1000 m3	6_4_2	FI_P_2019_1000 m3_6_4_2	0
FI	P	2019	1000 m3	6_4_3	FI_P_2019_1000 m3_6_4_3	0
FI	P	2019	1000 mt	7	FI_P_2019_1000 mt_7	11600
FI	P	2019	1000 mt	7_1	FI_P_2019_1000 mt_7_1	3280
FI	P	2019	1000 mt	7_2	FI_P_2019_1000 mt_7_2	8320
FI	P	2019	1000 mt	7_3	FI_P_2019_1000 mt_7_3	7330
FI	P	2019	1000 mt	7_3_1	FI_P_2019_1000 mt_7_3_1	7330
FI	P	2019	1000 mt	7_3_2	FI_P_2019_1000 mt_7_3_2	990
FI	P	2019	1000 mt	7_3_3	FI_P_2019_1000 mt_7_3_3	0
FI	P	2019	1000 mt	7_3_4	FI_P_2019_1000 mt_7_3_4	0
FI	P	2019	1000 mt	7_4	FI_P_2019_1000 mt_7_4	0
FI	P	2019	1000 mt	8	FI_P_2019_1000 mt_8	0
FI	P	2019	1000 mt	8_1	FI_P_2019_1000 mt_8_1	620
FI	P	2019	1000 mt	8_2	FI_P_2019_1000 mt_8_2	9720
FI	P	2019	1000 mt	9	FI_P_2019_1000 mt_9	4880
FI	P	2019	1000 mt	10	FI_P_2019_1000 mt_10	0
FI	P	2019	1000 mt	10_1	FI_P_2019_1000 mt_10_1	0
FI	P	2019	1000 mt	10_1_1	FI_P_2019_1000 mt_10_1_1	0
FI	P	2019	1000 mt	10_1_2	FI_P_2019_1000 mt_10_1_2	0
FI	P	2019	1000 mt	10_1_3	FI_P_2019_1000 mt_10_1_3	0
FI	P	2019	1000 mt	10_1_4	FI_P_2019_1000 mt_10_1_4	4440
FI	P	2019	1000 mt	10_2	FI_P_2019_1000 mt_10_2	0
FI	P	2019	1000 mt	10_3	FI_P_2019_1000 mt_10_3	0
FI	P	2019	1000 mt	10_3_1	FI_P_2019_1000 mt_10_3_1	0
FI	P	2019	1000 mt	10_3_2	FI_P_2019_1000 mt_10_3_2	0
FI	P	2019	1000 mt	10_3_3	FI_P_2019_1000 mt_10_3_3	400
FI	P	2019	1000 mt	10_3_4	FI_P_2019_1000 mt_10_3_4	0
FI	P	2019	1000 mt	10_4	FI_P_2019_1000 mt_10_4	0
FI	P	2020	1000 m3	1	FI_P_2020_1000 m3_1	60233.267515
FI	P	2020	1000 m3	1_C	FI_P_2020_1000 m3_1_C	8937.011724
FI	P	2020	1000 m3	1_NC	FI_P_2020_1000 m3_1_NC	4322.431968
FI	P	2020	1000 m3	1_1	FI_P_2020_1000 m3_1_1	4614.579756
FI	P	2020	1000 m3	1_1_C	FI_P_2020_1000 m3_1_1_C	51296.255791
FI	P	2020	1000 m3	1_1_NC	FI_P_2020_1000 m3_1_1_NC	43015.624812
FI	P	2020	1000 m3	1_2	FI_P_2020_1000 m3_1_2	8280.630979
FI	P	2020	1000 m3	1_2_C	FI_P_2020_1000 m3_1_2_C	0
FI	P	2020	1000 m3	1_2_NC	FI_P_2020_1000 m3_1_2_NC	22279.273315
FI	P	2020	1000 m3	1_2_1	FI_P_2020_1000 m3_1_2_1	21412.67158
FI	P	2020	1000 m3	1_2_1_C	FI_P_2020_1000 m3_1_2_1_C	866.601735
FI	P	2020	1000 m3	1_2_1_NC	FI_P_2020_1000 m3_1_2_1_NC	29016.982476
FI	P	2020	1000 m3	1_2_2	FI_P_2020_1000 m3_1_2_2	21602.953232
FI	P	2020	1000 m3	1_2_2_C	FI_P_2020_1000 m3_1_2_2_C	7414.029244
FI	P	2020	1000 m3	1_2_2_NC	FI_P_2020_1000 m3_1_2_2_NC	0
FI	P	2020	1000 m3	1_2_3	FI_P_2020_1000 m3_1_2_3	0
FI	P	2020	1000 m3	1_2_3_C	FI_P_2020_1000 m3_1_2_3_C	0
FI	P	2020	1000 m3	1_2_3_NC	FI_P_2020_1000 m3_1_2_3_NC	0
FI	P	2020	1000 mt	2	FI_P_2020_1000 mt_2	13099.3735081481
FI	P	2020	1000 m3	3	FI_P_2020_1000 m3_3	8554.700562
FI	P	2020	1000 m3	3_1	FI_P_2020_1000 m3_3_1	4544.6729461482
FI	P	2020	1000 m3	3_2	FI_P_2020_1000 m3_3_2	439.45795829
FI	P	2020	1000 mt	4	FI_P_2020_1000 mt_4	322.09
FI	P	2020	1000 mt	4_1	FI_P_2020_1000 mt_4_1	322.09
FI	P	2020	1000 mt	4_2	FI_P_2020_1000 mt_4_2	0
FI	P	2020	1000 m3	5	FI_P_2020_1000 m3_5	10916
FI	P	2020	1000 m3	5_C	FI_P_2020_1000 m3_5_C	10880
FI	P	2020	1000 m3	5_NC	FI_P_2020_1000 m3_5_NC	36
FI	P	2020	1000 m3	5_NC_T	FI_P_2020_1000 m3_5_NC_T	0
FI	P	2020	1000 m3	6	FI_P_2020_1000 m3_6	0
FI	P	2020	1000 m3	6_1	FI_P_2020_1000 m3_6_1	0
FI	P	2020	1000 m3	6_1_C	FI_P_2020_1000 m3_6_1_C	0
FI	P	2020	1000 m3	6_1_NC	FI_P_2020_1000 m3_6_1_NC	0
FI	P	2020	1000 m3	6_1_NC_T	FI_P_2020_1000 m3_6_1_NC_T	0
FI	P	2020	1000 m3	6_2	FI_P_2020_1000 m3_6_2	990
FI	P	2020	1000 m3	6_2_C	FI_P_2020_1000 m3_6_2_C	990
FI	P	2020	1000 m3	6_2_NC	FI_P_2020_1000 m3_6_2_NC	0
FI	P	2020	1000 m3	6_2_NC_T	FI_P_2020_1000 m3_6_2_NC_T	0
FI	P	2020	1000 m3	6_3	FI_P_2020_1000 m3_6_3	0
FI	P	2020	1000 m3	6_3_1	FI_P_2020_1000 m3_6_3_1	0
FI	P	2020	1000 m3	6_4	FI_P_2020_1000 m3_6_4	0
FI	P	2020	1000 m3	6_4_1	FI_P_2020_1000 m3_6_4_1	0
FI	P	2020	1000 m3	6_4_2	FI_P_2020_1000 m3_6_4_2	0
FI	P	2020	1000 m3	6_4_3	FI_P_2020_1000 m3_6_4_3	0
FI	P	2020	1000 mt	7	FI_P_2020_1000 mt_7	10520
FI	P	2020	1000 mt	7_1	FI_P_2020_1000 mt_7_1	2840
FI	P	2020	1000 mt	7_2	FI_P_2020_1000 mt_7_2	7680
FI	P	2020	1000 mt	7_3	FI_P_2020_1000 mt_7_3	6680
FI	P	2020	1000 mt	7_3_1	FI_P_2020_1000 mt_7_3_1	6680
FI	P	2020	1000 mt	7_3_2	FI_P_2020_1000 mt_7_3_2	1000
FI	P	2020	1000 mt	7_3_3	FI_P_2020_1000 mt_7_3_3	0
FI	P	2020	1000 mt	7_3_4	FI_P_2020_1000 mt_7_3_4	0
FI	P	2020	1000 mt	7_4	FI_P_2020_1000 mt_7_4	0
FI	P	2020	1000 mt	8	FI_P_2020_1000 mt_8	0
FI	P	2020	1000 mt	8_1	FI_P_2020_1000 mt_8_1	570
FI	P	2020	1000 mt	8_2	FI_P_2020_1000 mt_8_2	8210
FI	P	2020	1000 mt	9	FI_P_2020_1000 mt_9	3410
FI	P	2020	1000 mt	10	FI_P_2020_1000 mt_10	0
FI	P	2020	1000 mt	10_1	FI_P_2020_1000 mt_10_1	0
FI	P	2020	1000 mt	10_1_1	FI_P_2020_1000 mt_10_1_1	0
FI	P	2020	1000 mt	10_1_2	FI_P_2020_1000 mt_10_1_2	0
FI	P	2020	1000 mt	10_1_3	FI_P_2020_1000 mt_10_1_3	0
FI	P	2020	1000 mt	10_1_4	FI_P_2020_1000 mt_10_1_4	4400
FI	P	2020	1000 mt	10_2	FI_P_2020_1000 mt_10_2	0
FI	P	2020	1000 mt	10_3	FI_P_2020_1000 mt_10_3	0
FI	P	2020	1000 mt	10_3_1	FI_P_2020_1000 mt_10_3_1	0
FI	P	2020	1000 mt	10_3_2	FI_P_2020_1000 mt_10_3_2	0
FI	P	2020	1000 mt	10_3_3	FI_P_2020_1000 mt_10_3_3	400
FI	P	2020	1000 mt	10_3_4	FI_P_2020_1000 mt_10_3_4	0
FI	P	2020	1000 mt	10_4	FI_P_2020_1000 mt_10_4	0
FI	M	2019	1000 m3	1	FI_M_2019_1000 m3_1	6324.417	JQ2
FI	M	2019	1000 m3	1_1	FI_M_2019_1000 m3_1_1	89.404
FI	M	2019	1000 m3	1_2	FI_M_2019_1000 m3_1_2	5.047
FI	M	2019	1000 m3	1_2_C	FI_M_2019_1000 m3_1_2_C	84.357
FI	M	2019	1000 m3	1_2_NC	FI_M_2019_1000 m3_1_2_NC	6235.013
FI	M	2019	1000 m3	1_2_NC_T	FI_M_2019_1000 m3_1_2_NC_T	1744.746
FI	M	2019	1000 mt	2	FI_M_2019_1000 mt_2	4490.267
FI	M	2019	1000 m3	3	FI_M_2019_1000 m3_3	0.004
FI	M	2019	1000 m3	3_1	FI_M_2019_1000 m3_3_1	5.045
FI	M	2019	1000 m3	3_2	FI_M_2019_1000 m3_3_2	4146.75
FI	M	2019	1000 mt	4	FI_M_2019_1000 mt_4	3954.08
FI	M	2019	1000 mt	4_1	FI_M_2019_1000 mt_4_1	192.67
FI	M	2019	1000 mt	4_2	FI_M_2019_1000 mt_4_2	206.913
FI	M	2019	1000 m3	5	FI_M_2019_1000 m3_5	116.223
FI	M	2019	1000 m3	5_C	FI_M_2019_1000 m3_5_C	100.474
FI	M	2019	1000 m3	5_NC	FI_M_2019_1000 m3_5_NC	15.749
FI	M	2019	1000 m3	5_NC_T	FI_M_2019_1000 m3_5_NC_T	593.354
FI	M	2019	1000 m3	6	FI_M_2019_1000 m3_6	559.995
FI	M	2019	1000 m3	6_1	FI_M_2019_1000 m3_6_1	33.359
FI	M	2019	1000 m3	6_1_C	FI_M_2019_1000 m3_6_1_C	3.032
FI	M	2019	1000 m3	6_1_NC	FI_M_2019_1000 m3_6_1_NC	7.67
FI	M	2019	1000 m3	6_1_NC_T	FI_M_2019_1000 m3_6_1_NC_T	0.126
FI	M	2019	1000 m3	6_2	FI_M_2019_1000 m3_6_2	7.544
FI	M	2019	1000 m3	6_2_C	FI_M_2019_1000 m3_6_2_C	0.574
FI	M	2019	1000 m3	6_2_NC	FI_M_2019_1000 m3_6_2_NC	382.724
FI	M	2019	1000 m3	6_2_NC_T	FI_M_2019_1000 m3_6_2_NC_T	118.431
FI	M	2019	1000 m3	6_3	FI_M_2019_1000 m3_6_3	30.972
FI	M	2019	1000 m3	6_3_1	FI_M_2019_1000 m3_6_3_1	87.459
FI	M	2019	1000 m3	6_4	FI_M_2019_1000 m3_6_4	1.025
FI	M	2019	1000 m3	6_4_1	FI_M_2019_1000 m3_6_4_1	119.991
FI	M	2019	1000 m3	6_4_2	FI_M_2019_1000 m3_6_4_2	43.609
FI	M	2019	1000 m3	6_4_3	FI_M_2019_1000 m3_6_4_3	144.302
FI	M	2019	1000 mt	7	FI_M_2019_1000 mt_7	21.85
FI	M	2019	1000 mt	7_1	FI_M_2019_1000 mt_7_1	103.619
FI	M	2019	1000 mt	7_2	FI_M_2019_1000 mt_7_2	18.833
FI	M	2019	1000 mt	7_3	FI_M_2019_1000 mt_7_3	347.307
FI	M	2019	1000 mt	7_3_1	FI_M_2019_1000 mt_7_3_1	5.466
FI	M	2019	1000 mt	7_3_2	FI_M_2019_1000 mt_7_3_2	335.505
FI	M	2019	1000 mt	7_3_3	FI_M_2019_1000 mt_7_3_3	333.248
FI	M	2019	1000 mt	7_3_4	FI_M_2019_1000 mt_7_3_4	316.921
FI	M	2019	1000 mt	7_4	FI_M_2019_1000 mt_7_4	2.256
FI	M	2019	1000 mt	8	FI_M_2019_1000 mt_8	6.336
FI	M	2019	1000 mt	8_1	FI_M_2019_1000 mt_8_1	3.986
FI	M	2019	1000 mt	8_2	FI_M_2019_1000 mt_8_2	3.097
FI	M	2019	1000 mt	9	FI_M_2019_1000 mt_9	0.889
FI	M	2019	1000 mt	10	FI_M_2019_1000 mt_10	114.896
FI	M	2019	1000 mt	10_1	FI_M_2019_1000 mt_10_1	305.619
FI	M	2019	1000 mt	10_1_1	FI_M_2019_1000 mt_10_1_1	54.163
FI	M	2019	1000 mt	10_1_2	FI_M_2019_1000 mt_10_1_2	23.163
FI	M	2019	1000 mt	10_1_3	FI_M_2019_1000 mt_10_1_3	2.742
FI	M	2019	1000 mt	10_1_4	FI_M_2019_1000 mt_10_1_4	13.057
FI	M	2019	1000 mt	10_2	FI_M_2019_1000 mt_10_2	15.2
FI	M	2019	1000 mt	10_3	FI_M_2019_1000 mt_10_3	1.321
FI	M	2019	1000 mt	10_3_1	FI_M_2019_1000 mt_10_3_1	248.803
FI	M	2019	1000 mt	10_3_2	FI_M_2019_1000 mt_10_3_2	159.004
FI	M	2019	1000 mt	10_3_3	FI_M_2019_1000 mt_10_3_3	58.549
FI	M	2019	1000 mt	10_3_4	FI_M_2019_1000 mt_10_3_4	25.858
FI	M	2019	1000 mt	10_4	FI_M_2019_1000 mt_10_4	5.392
FI	M	2019	1000 NAC	1	FI_M_2019_1000 NAC_1	321010.688
FI	M	2019	1000 NAC	1_1	FI_M_2019_1000 NAC_1_1	3529.284
FI	M	2019	1000 NAC	1_2	FI_M_2019_1000 NAC_1_2	142.634
FI	M	2019	1000 NAC	1_2_C	FI_M_2019_1000 NAC_1_2_C	3386.65
FI	M	2019	1000 NAC	1_2_NC	FI_M_2019_1000 NAC_1_2_NC	317481.404
FI	M	2019	1000 NAC	1_2_NC_T	FI_M_2019_1000 NAC_1_2_NC_T	108160.001
FI	M	2019	1000 NAC	2	FI_M_2019_1000 NAC_2	209321.403
FI	M	2019	1000 NAC	3	FI_M_2019_1000 NAC_3	22.308
FI	M	2019	1000 NAC	3_1	FI_M_2019_1000 NAC_3_1	3230.965
FI	M	2019	1000 NAC	3_2	FI_M_2019_1000 NAC_3_2	169940.001
FI	M	2019	1000 NAC	4	FI_M_2019_1000 NAC_4	165840.307
FI	M	2019	1000 NAC	4_1	FI_M_2019_1000 NAC_4_1	4099.694
FI	M	2019	1000 NAC	4_2	FI_M_2019_1000 NAC_4_2	6435.113
FI	M	2019	1000 NAC	5	FI_M_2019_1000 NAC_5	15222.288
FI	M	2019	1000 NAC	5_C	FI_M_2019_1000 NAC_5_C	13260.607
FI	M	2019	1000 NAC	5_NC	FI_M_2019_1000 NAC_5_NC	1961.681
FI	M	2019	1000 NAC	5_NC_T	FI_M_2019_1000 NAC_5_NC_T	115899.181
FI	M	2019	1000 NAC	6	FI_M_2019_1000 NAC_6	95658.068
FI	M	2019	1000 NAC	6_1	FI_M_2019_1000 NAC_6_1	20241.113
FI	M	2019	1000 NAC	6_1_C	FI_M_2019_1000 NAC_6_1_C	3385.065
FI	M	2019	1000 NAC	6_1_NC	FI_M_2019_1000 NAC_6_1_NC	6737.564
FI	M	2019	1000 NAC	6_1_NC_T	FI_M_2019_1000 NAC_6_1_NC_T	119.218
FI	M	2019	1000 NAC	6_2	FI_M_2019_1000 NAC_6_2	6618.346
FI	M	2019	1000 NAC	6_2_C	FI_M_2019_1000 NAC_6_2_C	738.355
FI	M	2019	1000 NAC	6_2_NC	FI_M_2019_1000 NAC_6_2_NC	140077.529
FI	M	2019	1000 NAC	6_2_NC_T	FI_M_2019_1000 NAC_6_2_NC_T	55864.216
FI	M	2019	1000 NAC	6_3	FI_M_2019_1000 NAC_6_3	12583.152
FI	M	2019	1000 NAC	6_3_1	FI_M_2019_1000 NAC_6_3_1	43281.064
FI	M	2019	1000 NAC	6_4	FI_M_2019_1000 NAC_6_4	1922.459
FI	M	2019	1000 NAC	6_4_1	FI_M_2019_1000 NAC_6_4_1	34921.951
FI	M	2019	1000 NAC	6_4_2	FI_M_2019_1000 NAC_6_4_2	11538.724
FI	M	2019	1000 NAC	6_4_3	FI_M_2019_1000 NAC_6_4_3	49291.362
FI	M	2019	1000 NAC	7	FI_M_2019_1000 NAC_7	14888.692
FI	M	2019	1000 NAC	7_1	FI_M_2019_1000 NAC_7_1	30075.799
FI	M	2019	1000 NAC	7_2	FI_M_2019_1000 NAC_7_2	4326.871
FI	M	2019	1000 NAC	7_3	FI_M_2019_1000 NAC_7_3	203860.148
FI	M	2019	1000 NAC	7_3_1	FI_M_2019_1000 NAC_7_3_1	1781.802
FI	M	2019	1000 NAC	7_3_2	FI_M_2019_1000 NAC_7_3_2	194816.673
FI	M	2019	1000 NAC	7_3_3	FI_M_2019_1000 NAC_7_3_3	192460.224
FI	M	2019	1000 NAC	7_3_4	FI_M_2019_1000 NAC_7_3_4	184329.582
FI	M	2019	1000 NAC	7_4	FI_M_2019_1000 NAC_7_4	2356.449
FI	M	2019	1000 NAC	8	FI_M_2019_1000 NAC_8	7261.673
FI	M	2019	1000 NAC	8_1	FI_M_2019_1000 NAC_8_1	4859.066
FI	M	2019	1000 NAC	8_2	FI_M_2019_1000 NAC_8_2	4479.918
FI	M	2019	1000 NAC	9	FI_M_2019_1000 NAC_9	379.148
FI	M	2019	1000 NAC	10	FI_M_2019_1000 NAC_10	19189.653
FI	M	2019	1000 NAC	10_1	FI_M_2019_1000 NAC_10_1	262392.643
FI	M	2019	1000 NAC	10_1_1	FI_M_2019_1000 NAC_10_1_1	47367.809
FI	M	2019	1000 NAC	10_1_2	FI_M_2019_1000 NAC_10_1_2	11942.757
FI	M	2019	1000 NAC	10_1_3	FI_M_2019_1000 NAC_10_1_3	2607.385
FI	M	2019	1000 NAC	10_1_4	FI_M_2019_1000 NAC_10_1_4	16580.849
FI	M	2019	1000 NAC	10_2	FI_M_2019_1000 NAC_10_2	16236.818
FI	M	2019	1000 NAC	10_3	FI_M_2019_1000 NAC_10_3	10361.178
FI	M	2019	1000 NAC	10_3_1	FI_M_2019_1000 NAC_10_3_1	200433.715
FI	M	2019	1000 NAC	10_3_2	FI_M_2019_1000 NAC_10_3_2	87846.793
FI	M	2019	1000 NAC	10_3_3	FI_M_2019_1000 NAC_10_3_3	79744.569
FI	M	2019	1000 NAC	10_3_4	FI_M_2019_1000 NAC_10_3_4	29513.763
FI	M	2019	1000 NAC	10_4	FI_M_2019_1000 NAC_10_4	3328.59
FI	M	2020	1000 m3	1	FI_M_2020_1000 m3_1	6462.9467952
FI	M	2020	1000 m3	1_1	FI_M_2020_1000 m3_1_1	188.8327952
FI	M	2020	1000 m3	1_2	FI_M_2020_1000 m3_1_2	162.1980672
FI	M	2020	1000 m3	1_2_C	FI_M_2020_1000 m3_1_2_C	26.634728
FI	M	2020	1000 m3	1_2_NC	FI_M_2020_1000 m3_1_2_NC	6274.114
FI	M	2020	1000 m3	1_2_NC_T	FI_M_2020_1000 m3_1_2_NC_T	1533.846
FI	M	2020	1000 mt	2	FI_M_2020_1000 mt_2	4740.268
FI	M	2020	1000 m3	3	FI_M_2020_1000 m3_3	0.003
FI	M	2020	1000 m3	3_1	FI_M_2020_1000 m3_3_1	4.783424
FI	M	2020	1000 m3	3_2	FI_M_2020_1000 m3_3_2	4673.5853990368
FI	M	2020	1000 mt	4	FI_M_2020_1000 mt_4	4403.5627082493
FI	M	2020	1000 mt	4_1	FI_M_2020_1000 mt_4_1	270.0226907876
FI	M	2020	1000 mt	4_2	FI_M_2020_1000 mt_4_2	399.1911638854
FI	M	2020	1000 m3	5	FI_M_2020_1000 m3_5	131.3331819619
FI	M	2020	1000 m3	5_C	FI_M_2020_1000 m3_5_C	97.2537869565
FI	M	2020	1000 m3	5_NC	FI_M_2020_1000 m3_5_NC	34.0793950054
FI	M	2020	1000 m3	5_NC_T	FI_M_2020_1000 m3_5_NC_T	600.101
FI	M	2020	1000 m3	6	FI_M_2020_1000 m3_6	569.549
FI	M	2020	1000 m3	6_1	FI_M_2020_1000 m3_6_1	30.552
FI	M	2020	1000 m3	6_1_C	FI_M_2020_1000 m3_6_1_C	4.303
FI	M	2020	1000 m3	6_1_NC	FI_M_2020_1000 m3_6_1_NC	6.764
FI	M	2020	1000 m3	6_1_NC_T	FI_M_2020_1000 m3_6_1_NC_T	0.165
FI	M	2020	1000 m3	6_2	FI_M_2020_1000 m3_6_2	6.599
FI	M	2020	1000 m3	6_2_C	FI_M_2020_1000 m3_6_2_C	1.365
FI	M	2020	1000 m3	6_2_NC	FI_M_2020_1000 m3_6_2_NC	408.82109284
FI	M	2020	1000 m3	6_2_NC_T	FI_M_2020_1000 m3_6_2_NC_T	127.825
FI	M	2020	1000 m3	6_3	FI_M_2020_1000 m3_6_3	29.77
FI	M	2020	1000 m3	6_3_1	FI_M_2020_1000 m3_6_3_1	98.055
FI	M	2020	1000 m3	6_4	FI_M_2020_1000 m3_6_4	0.874
FI	M	2020	1000 m3	6_4_1	FI_M_2020_1000 m3_6_4_1	124.667
FI	M	2020	1000 m3	6_4_2	FI_M_2020_1000 m3_6_4_2	48.709
FI	M	2020	1000 m3	6_4_3	FI_M_2020_1000 m3_6_4_3	156.32909284
FI	M	2020	1000 mt	7	FI_M_2020_1000 mt_7	21.08868648
FI	M	2020	1000 mt	7_1	FI_M_2020_1000 mt_7_1	115.53034
FI	M	2020	1000 mt	7_2	FI_M_2020_1000 mt_7_2	19.71006636
FI	M	2020	1000 mt	7_3	FI_M_2020_1000 mt_7_3	223.540716
FI	M	2020	1000 mt	7_3_1	FI_M_2020_1000 mt_7_3_1	9.326338
FI	M	2020	1000 mt	7_3_2	FI_M_2020_1000 mt_7_3_2	207.438895
FI	M	2020	1000 mt	7_3_3	FI_M_2020_1000 mt_7_3_3	204.64003
FI	M	2020	1000 mt	7_3_4	FI_M_2020_1000 mt_7_3_4	178.103486
FI	M	2020	1000 mt	7_4	FI_M_2020_1000 mt_7_4	2.798865
FI	M	2020	1000 mt	8	FI_M_2020_1000 mt_8	6.775483
FI	M	2020	1000 mt	8_1	FI_M_2020_1000 mt_8_1	2.951548
FI	M	2020	1000 mt	8_2	FI_M_2020_1000 mt_8_2	1.852972
FI	M	2020	1000 mt	9	FI_M_2020_1000 mt_9	1.098576
FI	M	2020	1000 mt	10	FI_M_2020_1000 mt_10	67.844011
FI	M	2020	1000 mt	10_1	FI_M_2020_1000 mt_10_1	322.699703
FI	M	2020	1000 mt	10_1_1	FI_M_2020_1000 mt_10_1_1	63.642434
FI	M	2020	1000 mt	10_1_2	FI_M_2020_1000 mt_10_1_2	28.428837
FI	M	2020	1000 mt	10_1_3	FI_M_2020_1000 mt_10_1_3	4.191579
FI	M	2020	1000 mt	10_1_4	FI_M_2020_1000 mt_10_1_4	13.01368
FI	M	2020	1000 mt	10_2	FI_M_2020_1000 mt_10_2	18.008338
FI	M	2020	1000 mt	10_3	FI_M_2020_1000 mt_10_3	1.332357
FI	M	2020	1000 mt	10_3_1	FI_M_2020_1000 mt_10_3_1	256.059929
FI	M	2020	1000 mt	10_3_2	FI_M_2020_1000 mt_10_3_2	154.690622
FI	M	2020	1000 mt	10_3_3	FI_M_2020_1000 mt_10_3_3	65.797991
FI	M	2020	1000 mt	10_3_4	FI_M_2020_1000 mt_10_3_4	30.005657
FI	M	2020	1000 mt	10_4	FI_M_2020_1000 mt_10_4	5.565659
FI	M	2020	1000 NAC	1	FI_M_2020_1000 NAC_1	291631.505
FI	M	2020	1000 NAC	1_1	FI_M_2020_1000 NAC_1_1	7511.495
FI	M	2020	1000 NAC	1_2	FI_M_2020_1000 NAC_1_2	5735.272
FI	M	2020	1000 NAC	1_2_C	FI_M_2020_1000 NAC_1_2_C	1776.223
FI	M	2020	1000 NAC	1_2_NC	FI_M_2020_1000 NAC_1_2_NC	284120.01
FI	M	2020	1000 NAC	1_2_NC_T	FI_M_2020_1000 NAC_1_2_NC_T	81813.353
FI	M	2020	1000 NAC	2	FI_M_2020_1000 NAC_2	202306.657
FI	M	2020	1000 NAC	3	FI_M_2020_1000 NAC_3	50.978
FI	M	2020	1000 NAC	3_1	FI_M_2020_1000 NAC_3_1	3279.706
FI	M	2020	1000 NAC	3_2	FI_M_2020_1000 NAC_3_2	183501.93
FI	M	2020	1000 NAC	4	FI_M_2020_1000 NAC_4	177276.918
FI	M	2020	1000 NAC	4_1	FI_M_2020_1000 NAC_4_1	6225.012
FI	M	2020	1000 NAC	4_2	FI_M_2020_1000 NAC_4_2	7332.401
FI	M	2020	1000 NAC	5	FI_M_2020_1000 NAC_5	16667.732
FI	M	2020	1000 NAC	5_C	FI_M_2020_1000 NAC_5_C	13992.757
FI	M	2020	1000 NAC	5_NC	FI_M_2020_1000 NAC_5_NC	2674.975
FI	M	2020	1000 NAC	5_NC_T	FI_M_2020_1000 NAC_5_NC_T	114244.426
FI	M	2020	1000 NAC	6	FI_M_2020_1000 NAC_6	91739.1
FI	M	2020	1000 NAC	6_1	FI_M_2020_1000 NAC_6_1	22505.326
FI	M	2020	1000 NAC	6_1_C	FI_M_2020_1000 NAC_6_1_C	5140.586
FI	M	2020	1000 NAC	6_1_NC	FI_M_2020_1000 NAC_6_1_NC	5226.286
FI	M	2020	1000 NAC	6_1_NC_T	FI_M_2020_1000 NAC_6_1_NC_T	336.775
FI	M	2020	1000 NAC	6_2	FI_M_2020_1000 NAC_6_2	4889.511
FI	M	2020	1000 NAC	6_2_C	FI_M_2020_1000 NAC_6_2_C	719.536
FI	M	2020	1000 NAC	6_2_NC	FI_M_2020_1000 NAC_6_2_NC	140562.989
FI	M	2020	1000 NAC	6_2_NC_T	FI_M_2020_1000 NAC_6_2_NC_T	56652.512
FI	M	2020	1000 NAC	6_3	FI_M_2020_1000 NAC_6_3	11988.996
FI	M	2020	1000 NAC	6_3_1	FI_M_2020_1000 NAC_6_3_1	44663.516
FI	M	2020	1000 NAC	6_4	FI_M_2020_1000 NAC_6_4	1673.144
FI	M	2020	1000 NAC	6_4_1	FI_M_2020_1000 NAC_6_4_1	34251.958
FI	M	2020	1000 NAC	6_4_2	FI_M_2020_1000 NAC_6_4_2	11317.002
FI	M	2020	1000 NAC	6_4_3	FI_M_2020_1000 NAC_6_4_3	49658.519
FI	M	2020	1000 NAC	7	FI_M_2020_1000 NAC_7	13327.597
FI	M	2020	1000 NAC	7_1	FI_M_2020_1000 NAC_7_1	31682.477
FI	M	2020	1000 NAC	7_2	FI_M_2020_1000 NAC_7_2	4648.445
FI	M	2020	1000 NAC	7_3	FI_M_2020_1000 NAC_7_3	102115.612
FI	M	2020	1000 NAC	7_3_1	FI_M_2020_1000 NAC_7_3_1	2739.415
FI	M	2020	1000 NAC	7_3_2	FI_M_2020_1000 NAC_7_3_2	92007.728
FI	M	2020	1000 NAC	7_3_3	FI_M_2020_1000 NAC_7_3_3	89098.258
FI	M	2020	1000 NAC	7_3_4	FI_M_2020_1000 NAC_7_3_4	77381.526
FI	M	2020	1000 NAC	7_4	FI_M_2020_1000 NAC_7_4	2909.47
FI	M	2020	1000 NAC	8	FI_M_2020_1000 NAC_8	7368.469
FI	M	2020	1000 NAC	8_1	FI_M_2020_1000 NAC_8_1	3004.669
FI	M	2020	1000 NAC	8_2	FI_M_2020_1000 NAC_8_2	2527.708
FI	M	2020	1000 NAC	9	FI_M_2020_1000 NAC_9	476.961
FI	M	2020	1000 NAC	10	FI_M_2020_1000 NAC_10	11891.779
FI	M	2020	1000 NAC	10_1	FI_M_2020_1000 NAC_10_1	256668.741
FI	M	2020	1000 NAC	10_1_1	FI_M_2020_1000 NAC_10_1_1	47831.971
FI	M	2020	1000 NAC	10_1_2	FI_M_2020_1000 NAC_10_1_2	12655.759
FI	M	2020	1000 NAC	10_1_3	FI_M_2020_1000 NAC_10_1_3	3349.603
FI	M	2020	1000 NAC	10_1_4	FI_M_2020_1000 NAC_10_1_4	15199.157
FI	M	2020	1000 NAC	10_2	FI_M_2020_1000 NAC_10_2	16627.452
FI	M	2020	1000 NAC	10_3	FI_M_2020_1000 NAC_10_3	2815.806
FI	M	2020	1000 NAC	10_3_1	FI_M_2020_1000 NAC_10_3_1	200626.621
FI	M	2020	1000 NAC	10_3_2	FI_M_2020_1000 NAC_10_3_2	76515.447
FI	M	2020	1000 NAC	10_3_3	FI_M_2020_1000 NAC_10_3_3	89668.358
FI	M	2020	1000 NAC	10_3_4	FI_M_2020_1000 NAC_10_3_4	31261.871
FI	M	2020	1000 NAC	10_4	FI_M_2020_1000 NAC_10_4	3180.945
FI	X	2019	1000 m3	1	FI_X_2019_1000 m3_1	1447.303
FI	X	2019	1000 m3	1_1	FI_X_2019_1000 m3_1_1	90.922
FI	X	2019	1000 m3	1_2	FI_X_2019_1000 m3_1_2	88.468
FI	X	2019	1000 m3	1_2_C	FI_X_2019_1000 m3_1_2_C	2.454
FI	X	2019	1000 m3	1_2_NC	FI_X_2019_1000 m3_1_2_NC	1356.381
FI	X	2019	1000 m3	1_2_NC_T	FI_X_2019_1000 m3_1_2_NC_T	1174.095
FI	X	2019	1000 mt	2	FI_X_2019_1000 mt_2	182.286
FI	X	2019	1000 m3	3	FI_X_2019_1000 m3_3	0
FI	X	2019	1000 m3	3_1	FI_X_2019_1000 m3_3_1	0.526
FI	X	2019	1000 m3	3_2	FI_X_2019_1000 m3_3_2	237.932
FI	X	2019	1000 mt	4	FI_X_2019_1000 mt_4	186.042
FI	X	2019	1000 mt	4_1	FI_X_2019_1000 mt_4_1	51.89
FI	X	2019	1000 mt	4_2	FI_X_2019_1000 mt_4_2	0.519
FI	X	2019	1000 m3	5	FI_X_2019_1000 m3_5	57.183
FI	X	2019	1000 m3	5_C	FI_X_2019_1000 m3_5_C	29.849
FI	X	2019	1000 m3	5_NC	FI_X_2019_1000 m3_5_NC	27.334
FI	X	2019	1000 m3	5_NC_T	FI_X_2019_1000 m3_5_NC_T	8966.745
FI	X	2019	1000 m3	6	FI_X_2019_1000 m3_6	8951.761
FI	X	2019	1000 m3	6_1	FI_X_2019_1000 m3_6_1	14.984
FI	X	2019	1000 m3	6_1_C	FI_X_2019_1000 m3_6_1_C	3.782
FI	X	2019	1000 m3	6_1_NC	FI_X_2019_1000 m3_6_1_NC	143.785
FI	X	2019	1000 m3	6_1_NC_T	FI_X_2019_1000 m3_6_1_NC_T	53.511
FI	X	2019	1000 m3	6_2	FI_X_2019_1000 m3_6_2	90.274
FI	X	2019	1000 m3	6_2_C	FI_X_2019_1000 m3_6_2_C	0.033
FI	X	2019	1000 m3	6_2_NC	FI_X_2019_1000 m3_6_2_NC	985.891
FI	X	2019	1000 m3	6_2_NC_T	FI_X_2019_1000 m3_6_2_NC_T	918.471
FI	X	2019	1000 m3	6_3	FI_X_2019_1000 m3_6_3	626.25
FI	X	2019	1000 m3	6_3_1	FI_X_2019_1000 m3_6_3_1	292.221
FI	X	2019	1000 m3	6_4	FI_X_2019_1000 m3_6_4	0.235
FI	X	2019	1000 m3	6_4_1	FI_X_2019_1000 m3_6_4_1	20.713
FI	X	2019	1000 m3	6_4_2	FI_X_2019_1000 m3_6_4_2	0.152
FI	X	2019	1000 m3	6_4_3	FI_X_2019_1000 m3_6_4_3	46.707
FI	X	2019	1000 mt	7	FI_X_2019_1000 mt_7	44.487
FI	X	2019	1000 mt	7_1	FI_X_2019_1000 mt_7_1	2.121
FI	X	2019	1000 mt	7_2	FI_X_2019_1000 mt_7_2	0.099
FI	X	2019	1000 mt	7_3	FI_X_2019_1000 mt_7_3	4518.595
FI	X	2019	1000 mt	7_3_1	FI_X_2019_1000 mt_7_3_1	290.23
FI	X	2019	1000 mt	7_3_2	FI_X_2019_1000 mt_7_3_2	4072.034
FI	X	2019	1000 mt	7_3_3	FI_X_2019_1000 mt_7_3_3	4071.779
FI	X	2019	1000 mt	7_3_4	FI_X_2019_1000 mt_7_3_4	4032.888
FI	X	2019	1000 mt	7_4	FI_X_2019_1000 mt_7_4	0.025
FI	X	2019	1000 mt	8	FI_X_2019_1000 mt_8	156.331
FI	X	2019	1000 mt	8_1	FI_X_2019_1000 mt_8_1	0.052
FI	X	2019	1000 mt	8_2	FI_X_2019_1000 mt_8_2	0.006
FI	X	2019	1000 mt	9	FI_X_2019_1000 mt_9	0.046
FI	X	2019	1000 mt	10	FI_X_2019_1000 mt_10	65.696
FI	X	2019	1000 mt	10_1	FI_X_2019_1000 mt_10_1	9293.771
FI	X	2019	1000 mt	10_1_1	FI_X_2019_1000 mt_10_1_1	5057.563
FI	X	2019	1000 mt	10_1_2	FI_X_2019_1000 mt_10_1_2	236.364
FI	X	2019	1000 mt	10_1_3	FI_X_2019_1000 mt_10_1_3	673.614
FI	X	2019	1000 mt	10_1_4	FI_X_2019_1000 mt_10_1_4	861.6
FI	X	2019	1000 mt	10_2	FI_X_2019_1000 mt_10_2	3285.985
FI	X	2019	1000 mt	10_3	FI_X_2019_1000 mt_10_3	29.844
FI	X	2019	1000 mt	10_3_1	FI_X_2019_1000 mt_10_3_1	4070.87
FI	X	2019	1000 mt	10_3_2	FI_X_2019_1000 mt_10_3_2	870.098
FI	X	2019	1000 mt	10_3_3	FI_X_2019_1000 mt_10_3_3	2583.153
FI	X	2019	1000 mt	10_3_4	FI_X_2019_1000 mt_10_3_4	470.578
FI	X	2019	1000 mt	10_4	FI_X_2019_1000 mt_10_4	147.04
FI	X	2019	1000 NAC	1	FI_X_2019_1000 NAC_1	91283.897
FI	X	2019	1000 NAC	1_1	FI_X_2019_1000 NAC_1_1	3259.928
FI	X	2019	1000 NAC	1_2	FI_X_2019_1000 NAC_1_2	3012.913
FI	X	2019	1000 NAC	1_2_C	FI_X_2019_1000 NAC_1_2_C	247.015
FI	X	2019	1000 NAC	1_2_NC	FI_X_2019_1000 NAC_1_2_NC	88023.969
FI	X	2019	1000 NAC	1_2_NC_T	FI_X_2019_1000 NAC_1_2_NC_T	78647.438
FI	X	2019	1000 NAC	2	FI_X_2019_1000 NAC_2	9376.531
FI	X	2019	1000 NAC	3	FI_X_2019_1000 NAC_3	0
FI	X	2019	1000 NAC	3_1	FI_X_2019_1000 NAC_3_1	368.809
FI	X	2019	1000 NAC	3_2	FI_X_2019_1000 NAC_3_2	11005.185
FI	X	2019	1000 NAC	4	FI_X_2019_1000 NAC_4	9365.661
FI	X	2019	1000 NAC	4_1	FI_X_2019_1000 NAC_4_1	1639.524
FI	X	2019	1000 NAC	4_2	FI_X_2019_1000 NAC_4_2	32.273
FI	X	2019	1000 NAC	5	FI_X_2019_1000 NAC_5	7027.23
FI	X	2019	1000 NAC	5_C	FI_X_2019_1000 NAC_5_C	4491.92
FI	X	2019	1000 NAC	5_NC	FI_X_2019_1000 NAC_5_NC	2535.31
FI	X	2019	1000 NAC	5_NC_T	FI_X_2019_1000 NAC_5_NC_T	1729827.823
FI	X	2019	1000 NAC	6	FI_X_2019_1000 NAC_6	1722057.473
FI	X	2019	1000 NAC	6_1	FI_X_2019_1000 NAC_6_1	7770.35
FI	X	2019	1000 NAC	6_1_C	FI_X_2019_1000 NAC_6_1_C	3682.354
FI	X	2019	1000 NAC	6_1_NC	FI_X_2019_1000 NAC_6_1_NC	47068.249
FI	X	2019	1000 NAC	6_1_NC_T	FI_X_2019_1000 NAC_6_1_NC_T	27170.22
FI	X	2019	1000 NAC	6_2	FI_X_2019_1000 NAC_6_2	19898.029
FI	X	2019	1000 NAC	6_2_C	FI_X_2019_1000 NAC_6_2_C	9.938
FI	X	2019	1000 NAC	6_2_NC	FI_X_2019_1000 NAC_6_2_NC	527573.97
FI	X	2019	1000 NAC	6_2_NC_T	FI_X_2019_1000 NAC_6_2_NC_T	503238.488
FI	X	2019	1000 NAC	6_3	FI_X_2019_1000 NAC_6_3	258986.657
FI	X	2019	1000 NAC	6_3_1	FI_X_2019_1000 NAC_6_3_1	244251.831
FI	X	2019	1000 NAC	6_4	FI_X_2019_1000 NAC_6_4	772.764
FI	X	2019	1000 NAC	6_4_1	FI_X_2019_1000 NAC_6_4_1	7021.995
FI	X	2019	1000 NAC	6_4_2	FI_X_2019_1000 NAC_6_4_2	58.275
FI	X	2019	1000 NAC	6_4_3	FI_X_2019_1000 NAC_6_4_3	17313.487
FI	X	2019	1000 NAC	7	FI_X_2019_1000 NAC_7	15644.996
FI	X	2019	1000 NAC	7_1	FI_X_2019_1000 NAC_7_1	1647.336
FI	X	2019	1000 NAC	7_2	FI_X_2019_1000 NAC_7_2	21.155
FI	X	2019	1000 NAC	7_3	FI_X_2019_1000 NAC_7_3	2371208.54
FI	X	2019	1000 NAC	7_3_1	FI_X_2019_1000 NAC_7_3_1	101694.552
FI	X	2019	1000 NAC	7_3_2	FI_X_2019_1000 NAC_7_3_2	2173529.261
FI	X	2019	1000 NAC	7_3_3	FI_X_2019_1000 NAC_7_3_3	2173380.998
FI	X	2019	1000 NAC	7_3_4	FI_X_2019_1000 NAC_7_3_4	2155684.868
FI	X	2019	1000 NAC	7_4	FI_X_2019_1000 NAC_7_4	58.241
FI	X	2019	1000 NAC	8	FI_X_2019_1000 NAC_8	95984.727
FI	X	2019	1000 NAC	8_1	FI_X_2019_1000 NAC_8_1	41.206
FI	X	2019	1000 NAC	8_2	FI_X_2019_1000 NAC_8_2	12.685
FI	X	2019	1000 NAC	9	FI_X_2019_1000 NAC_9	28.521
FI	X	2019	1000 NAC	10	FI_X_2019_1000 NAC_10	11663.794
FI	X	2019	1000 NAC	10_1	FI_X_2019_1000 NAC_10_1	6903100.156
FI	X	2019	1000 NAC	10_1_1	FI_X_2019_1000 NAC_10_1_1	3405995.969
FI	X	2019	1000 NAC	10_1_2	FI_X_2019_1000 NAC_10_1_2	118953.006
FI	X	2019	1000 NAC	10_1_3	FI_X_2019_1000 NAC_10_1_3	388731.68
FI	X	2019	1000 NAC	10_1_4	FI_X_2019_1000 NAC_10_1_4	645163.236
FI	X	2019	1000 NAC	10_2	FI_X_2019_1000 NAC_10_2	2253148.047
FI	X	2019	1000 NAC	10_3	FI_X_2019_1000 NAC_10_3	30019.242
FI	X	2019	1000 NAC	10_3_1	FI_X_2019_1000 NAC_10_3_1	3364947.931
FI	X	2019	1000 NAC	10_3_2	FI_X_2019_1000 NAC_10_3_2	486467.143
FI	X	2019	1000 NAC	10_3_3	FI_X_2019_1000 NAC_10_3_3	2280623.196
FI	X	2019	1000 NAC	10_3_4	FI_X_2019_1000 NAC_10_3_4	500134.099
FI	X	2019	1000 NAC	10_4	FI_X_2019_1000 NAC_10_4	97723.493
FI	X	2020	1000 m3	1	FI_X_2020_1000 m3_1	1264.3725312
FI	X	2020	1000 m3	1_1	FI_X_2020_1000 m3_1_1	101.1475312
FI	X	2020	1000 m3	1_2	FI_X_2020_1000 m3_1_2	98.9487088
FI	X	2020	1000 m3	1_2_C	FI_X_2020_1000 m3_1_2_C	2.1988224
FI	X	2020	1000 m3	1_2_NC	FI_X_2020_1000 m3_1_2_NC	1163.225
FI	X	2020	1000 m3	1_2_NC_T	FI_X_2020_1000 m3_1_2_NC_T	1095.264
FI	X	2020	1000 mt	2	FI_X_2020_1000 mt_2	67.961
FI	X	2020	1000 m3	3	FI_X_2020_1000 m3_3	0
FI	X	2020	1000 m3	3_1	FI_X_2020_1000 m3_3_1	0.304589
FI	X	2020	1000 m3	3_2	FI_X_2020_1000 m3_3_2	208.2333460768
FI	X	2020	1000 mt	4	FI_X_2020_1000 mt_4	189.1778133037
FI	X	2020	1000 mt	4_1	FI_X_2020_1000 mt_4_1	19.0555327731
FI	X	2020	1000 mt	4_2	FI_X_2020_1000 mt_4_2	0.173942903
FI	X	2020	1000 m3	5	FI_X_2020_1000 m3_5	12.7173898398
FI	X	2020	1000 m3	5_C	FI_X_2020_1000 m3_5_C	6.2604043478
FI	X	2020	1000 m3	5_NC	FI_X_2020_1000 m3_5_NC	6.456985492
FI	X	2020	1000 m3	5_NC_T	FI_X_2020_1000 m3_5_NC_T	8217.919
FI	X	2020	1000 m3	6	FI_X_2020_1000 m3_6	8197.932
FI	X	2020	1000 m3	6_1	FI_X_2020_1000 m3_6_1	19.987
FI	X	2020	1000 m3	6_1_C	FI_X_2020_1000 m3_6_1_C	3.106
FI	X	2020	1000 m3	6_1_NC	FI_X_2020_1000 m3_6_1_NC	146.053
FI	X	2020	1000 m3	6_1_NC_T	FI_X_2020_1000 m3_6_1_NC_T	42.749
FI	X	2020	1000 m3	6_2	FI_X_2020_1000 m3_6_2	103.304
FI	X	2020	1000 m3	6_2_C	FI_X_2020_1000 m3_6_2_C	0.088
FI	X	2020	1000 m3	6_2_NC	FI_X_2020_1000 m3_6_2_NC	890.206437624
FI	X	2020	1000 m3	6_2_NC_T	FI_X_2020_1000 m3_6_2_NC_T	828.478
FI	X	2020	1000 m3	6_3	FI_X_2020_1000 m3_6_3	572.938
FI	X	2020	1000 m3	6_3_1	FI_X_2020_1000 m3_6_3_1	255.54
FI	X	2020	1000 m3	6_4	FI_X_2020_1000 m3_6_4	0.172
FI	X	2020	1000 m3	6_4_1	FI_X_2020_1000 m3_6_4_1	20.191
FI	X	2020	1000 m3	6_4_2	FI_X_2020_1000 m3_6_4_2	0.14
FI	X	2020	1000 m3	6_4_3	FI_X_2020_1000 m3_6_4_3	41.537437624
FI	X	2020	1000 mt	7	FI_X_2020_1000 mt_7	36.996201624
FI	X	2020	1000 mt	7_1	FI_X_2020_1000 mt_7_1	4.447856
FI	X	2020	1000 mt	7_2	FI_X_2020_1000 mt_7_2	0.09338
FI	X	2020	1000 mt	7_3	FI_X_2020_1000 mt_7_3	4333.00452
FI	X	2020	1000 mt	7_3_1	FI_X_2020_1000 mt_7_3_1	393.15077
FI	X	2020	1000 mt	7_3_2	FI_X_2020_1000 mt_7_3_2	3737.369139
FI	X	2020	1000 mt	7_3_3	FI_X_2020_1000 mt_7_3_3	3737.35834
FI	X	2020	1000 mt	7_3_4	FI_X_2020_1000 mt_7_3_4	3697.896644
FI	X	2020	1000 mt	7_4	FI_X_2020_1000 mt_7_4	0.010799
FI	X	2020	1000 mt	8	FI_X_2020_1000 mt_8	202.484611
FI	X	2020	1000 mt	8_1	FI_X_2020_1000 mt_8_1	0.041865
FI	X	2020	1000 mt	8_2	FI_X_2020_1000 mt_8_2	0.040329
FI	X	2020	1000 mt	9	FI_X_2020_1000 mt_9	0.001536
FI	X	2020	1000 mt	10	FI_X_2020_1000 mt_10	100.489093
FI	X	2020	1000 mt	10_1	FI_X_2020_1000 mt_10_1	7831.573797
FI	X	2020	1000 mt	10_1_1	FI_X_2020_1000 mt_10_1_1	3621.974779
FI	X	2020	1000 mt	10_1_2	FI_X_2020_1000 mt_10_1_2	161.75712
FI	X	2020	1000 mt	10_1_3	FI_X_2020_1000 mt_10_1_3	426.191838
FI	X	2020	1000 mt	10_1_4	FI_X_2020_1000 mt_10_1_4	670.571491
FI	X	2020	1000 mt	10_2	FI_X_2020_1000 mt_10_2	2363.45433
FI	X	2020	1000 mt	10_3	FI_X_2020_1000 mt_10_3	23.915193
FI	X	2020	1000 mt	10_3_1	FI_X_2020_1000 mt_10_3_1	4039.958067
FI	X	2020	1000 mt	10_3_2	FI_X_2020_1000 mt_10_3_2	871.457005
FI	X	2020	1000 mt	10_3_3	FI_X_2020_1000 mt_10_3_3	2558.143119
FI	X	2020	1000 mt	10_3_4	FI_X_2020_1000 mt_10_3_4	455.209835
FI	X	2020	1000 mt	10_4	FI_X_2020_1000 mt_10_4	155.148108
FI	X	2020	1000 NAC	1	FI_X_2020_1000 NAC_1	97105.372
FI	X	2020	1000 NAC	1_1	FI_X_2020_1000 NAC_1_1	4062.402
FI	X	2020	1000 NAC	1_2	FI_X_2020_1000 NAC_1_2	3841.195
FI	X	2020	1000 NAC	1_2_C	FI_X_2020_1000 NAC_1_2_C	221.207
FI	X	2020	1000 NAC	1_2_NC	FI_X_2020_1000 NAC_1_2_NC	93042.97
FI	X	2020	1000 NAC	1_2_NC_T	FI_X_2020_1000 NAC_1_2_NC_T	88588.893
FI	X	2020	1000 NAC	2	FI_X_2020_1000 NAC_2	4454.077
FI	X	2020	1000 NAC	3	FI_X_2020_1000 NAC_3	0
FI	X	2020	1000 NAC	3_1	FI_X_2020_1000 NAC_3_1	231.986
FI	X	2020	1000 NAC	3_2	FI_X_2020_1000 NAC_3_2	9954.06
FI	X	2020	1000 NAC	4	FI_X_2020_1000 NAC_4	9321.967
FI	X	2020	1000 NAC	4_1	FI_X_2020_1000 NAC_4_1	632.093
FI	X	2020	1000 NAC	4_2	FI_X_2020_1000 NAC_4_2	23.648
FI	X	2020	1000 NAC	5	FI_X_2020_1000 NAC_5	1596.717
FI	X	2020	1000 NAC	5_C	FI_X_2020_1000 NAC_5_C	881.972
FI	X	2020	1000 NAC	5_NC	FI_X_2020_1000 NAC_5_NC	714.745
FI	X	2020	1000 NAC	5_NC_T	FI_X_2020_1000 NAC_5_NC_T	1557546.684
FI	X	2020	1000 NAC	6	FI_X_2020_1000 NAC_6	1548568.236
FI	X	2020	1000 NAC	6_1	FI_X_2020_1000 NAC_6_1	8978.448
FI	X	2020	1000 NAC	6_1_C	FI_X_2020_1000 NAC_6_1_C	2787.737
FI	X	2020	1000 NAC	6_1_NC	FI_X_2020_1000 NAC_6_1_NC	44419.527
FI	X	2020	1000 NAC	6_1_NC_T	FI_X_2020_1000 NAC_6_1_NC_T	21668.895
FI	X	2020	1000 NAC	6_2	FI_X_2020_1000 NAC_6_2	22750.632
FI	X	2020	1000 NAC	6_2_C	FI_X_2020_1000 NAC_6_2_C	18.972
FI	X	2020	1000 NAC	6_2_NC	FI_X_2020_1000 NAC_6_2_NC	458735.34
FI	X	2020	1000 NAC	6_2_NC_T	FI_X_2020_1000 NAC_6_2_NC_T	435724.773
FI	X	2020	1000 NAC	6_3	FI_X_2020_1000 NAC_6_3	232676.2
FI	X	2020	1000 NAC	6_3_1	FI_X_2020_1000 NAC_6_3_1	203048.573
FI	X	2020	1000 NAC	6_4	FI_X_2020_1000 NAC_6_4	752.855
FI	X	2020	1000 NAC	6_4_1	FI_X_2020_1000 NAC_6_4_1	6438.063
FI	X	2020	1000 NAC	6_4_2	FI_X_2020_1000 NAC_6_4_2	52.526
FI	X	2020	1000 NAC	6_4_3	FI_X_2020_1000 NAC_6_4_3	16572.504
FI	X	2020	1000 NAC	7	FI_X_2020_1000 NAC_7	13569.105
FI	X	2020	1000 NAC	7_1	FI_X_2020_1000 NAC_7_1	2975.664
FI	X	2020	1000 NAC	7_2	FI_X_2020_1000 NAC_7_2	27.735
FI	X	2020	1000 NAC	7_3	FI_X_2020_1000 NAC_7_3	1873276.62
FI	X	2020	1000 NAC	7_3_1	FI_X_2020_1000 NAC_7_3_1	131703.67
FI	X	2020	1000 NAC	7_3_2	FI_X_2020_1000 NAC_7_3_2	1643317.871
FI	X	2020	1000 NAC	7_3_3	FI_X_2020_1000 NAC_7_3_3	1643269.14
FI	X	2020	1000 NAC	7_3_4	FI_X_2020_1000 NAC_7_3_4	1628216.409
FI	X	2020	1000 NAC	7_4	FI_X_2020_1000 NAC_7_4	48.731
FI	X	2020	1000 NAC	8	FI_X_2020_1000 NAC_8	98255.079
FI	X	2020	1000 NAC	8_1	FI_X_2020_1000 NAC_8_1	90.057
FI	X	2020	1000 NAC	8_2	FI_X_2020_1000 NAC_8_2	88.506
FI	X	2020	1000 NAC	9	FI_X_2020_1000 NAC_9	1.551
FI	X	2020	1000 NAC	10	FI_X_2020_1000 NAC_10	13394.885
FI	X	2020	1000 NAC	10_1	FI_X_2020_1000 NAC_10_1	5591404.598
FI	X	2020	1000 NAC	10_1_1	FI_X_2020_1000 NAC_10_1_1	2236158.413
FI	X	2020	1000 NAC	10_1_2	FI_X_2020_1000 NAC_10_1_2	68882.711
FI	X	2020	1000 NAC	10_1_3	FI_X_2020_1000 NAC_10_1_3	224616.104
FI	X	2020	1000 NAC	10_1_4	FI_X_2020_1000 NAC_10_1_4	454990.954
FI	X	2020	1000 NAC	10_2	FI_X_2020_1000 NAC_10_2	1487668.644
FI	X	2020	1000 NAC	10_3	FI_X_2020_1000 NAC_10_3	21526.537
FI	X	2020	1000 NAC	10_3_1	FI_X_2020_1000 NAC_10_3_1	3225833.022
FI	X	2020	1000 NAC	10_3_2	FI_X_2020_1000 NAC_10_3_2	444677.772
FI	X	2020	1000 NAC	10_3_3	FI_X_2020_1000 NAC_10_3_3	2226073.231
FI	X	2020	1000 NAC	10_3_4	FI_X_2020_1000 NAC_10_3_4	451663.495
FI	X	2020	1000 NAC	10_4	FI_X_2020_1000 NAC_10_4	103418.524
FI	M	490464.782	1000 NAC	11_1	FI_M_490464,782_1000 NAC_11_1	4629.282	JQ3
FI	M	490464.782	1000 NAC	11_1_C	FI_M_490464,782_1000 NAC_11_1_C	11854.943
FI	M	490464.782	1000 NAC	11_1_NC	FI_M_490464,782_1000 NAC_11_1_NC	0
FI	M	490464.782	1000 NAC	11_1_NC_T	FI_M_490464,782_1000 NAC_11_1_NC_T	24631.825
FI	M	490464.782	1000 NAC	11_2	FI_M_490464,782_1000 NAC_11_2	10207.512
FI	M	490464.782	1000 NAC	11_3	FI_M_490464,782_1000 NAC_11_3	82911.774
FI	M	490464.782	1000 NAC	11_4	FI_M_490464,782_1000 NAC_11_4	276643.236
FI	M	490464.782	1000 NAC	11_5	FI_M_490464,782_1000 NAC_11_5	66175.673
FI	M	490464.782	1000 NAC	11_6	FI_M_490464,782_1000 NAC_11_6	13410.537
FI	M	490464.782	1000 NAC	11_7	FI_M_490464,782_1000 NAC_11_7	247880.675
FI	M	490464.782	1000 NAC	11_7_1	FI_M_490464,782_1000 NAC_11_7_1	2793.921
FI	M	490464.782	1000 NAC	12_1	FI_M_490464,782_1000 NAC_12_1	41626.249
FI	M	490464.782	1000 NAC	12_2	FI_M_490464,782_1000 NAC_12_2	79974.08
FI	M	490464.782	1000 NAC	12_3	FI_M_490464,782_1000 NAC_12_3	82630.54
FI	M	490464.782	1000 NAC	12_4	FI_M_490464,782_1000 NAC_12_4	11444.697
FI	M	490464.782	1000 NAC	12_5	FI_M_490464,782_1000 NAC_12_5	10101.78
FI	M	490464.782	1000 NAC	12_6	FI_M_490464,782_1000 NAC_12_6	8527.939
FI	M	490464.782	1000 NAC	12_6_1	FI_M_490464,782_1000 NAC_12_6_1	0
FI	M	490464.782	1000 NAC	12_6_2	FI_M_490464,782_1000 NAC_12_6_2	0
FI	M	490464.782	1000 NAC	12_6_3	FI_M_490464,782_1000 NAC_12_6_3	0
FI	M	480984.132	1000 NAC	11_1	FI_M_480984,132_1000 NAC_11_1	4967.088
FI	M	480984.132	1000 NAC	11_1_C	FI_M_480984,132_1000 NAC_11_1_C	12582.029
FI	M	480984.132	1000 NAC	11_1_NC	FI_M_480984,132_1000 NAC_11_1_NC	880.666
FI	M	480984.132	1000 NAC	11_1_NC_T	FI_M_480984,132_1000 NAC_11_1_NC_T	21744.963
FI	M	480984.132	1000 NAC	11_2	FI_M_480984,132_1000 NAC_11_2	9141.429
FI	M	480984.132	1000 NAC	11_3	FI_M_480984,132_1000 NAC_11_3	84077.777
FI	M	480984.132	1000 NAC	11_4	FI_M_480984,132_1000 NAC_11_4	278222.857
FI	M	480984.132	1000 NAC	11_5	FI_M_480984,132_1000 NAC_11_5	50161.157
FI	M	480984.132	1000 NAC	11_6	FI_M_480984,132_1000 NAC_11_6	20086.832
FI	M	480984.132	1000 NAC	11_7	FI_M_480984,132_1000 NAC_11_7	256537.943
FI	M	480984.132	1000 NAC	11_7_1	FI_M_480984,132_1000 NAC_11_7_1	3735.091
FI	M	480984.132	1000 NAC	12_1	FI_M_480984,132_1000 NAC_12_1	44375.201
FI	M	480984.132	1000 NAC	12_2	FI_M_480984,132_1000 NAC_12_2	90356.241
FI	M	480984.132	1000 NAC	12_3	FI_M_480984,132_1000 NAC_12_3	80061.375
FI	M	480984.132	1000 NAC	12_4	FI_M_480984,132_1000 NAC_12_4	1135.469
FI	M	480984.132	1000 NAC	12_5	FI_M_480984,132_1000 NAC_12_5	11022.031
FI	M	480984.132	1000 NAC	12_6	FI_M_480984,132_1000 NAC_12_6	9076.939
FI	M	480984.132	1000 NAC	12_6_1	FI_M_480984,132_1000 NAC_12_6_1	0
FI	M	480984.132	1000 NAC	12_6_2	FI_M_480984,132_1000 NAC_12_6_2	0
FI	M	480984.132	1000 NAC	12_6_3	FI_M_480984,132_1000 NAC_12_6_3	0
FI	X	490464.782	1000 NAC	11_1	FI_X_490464,782_1000 NAC_11_1	49365.804
FI	X	490464.782	1000 NAC	11_1_C	FI_X_490464,782_1000 NAC_11_1_C	926.004
FI	X	490464.782	1000 NAC	11_1_NC	FI_X_490464,782_1000 NAC_11_1_NC	0
FI	X	490464.782	1000 NAC	11_1_NC_T	FI_X_490464,782_1000 NAC_11_1_NC_T	28022.547
FI	X	490464.782	1000 NAC	11_2	FI_X_490464,782_1000 NAC_11_2	2365.111
FI	X	490464.782	1000 NAC	11_3	FI_X_490464,782_1000 NAC_11_3	197444.334
FI	X	490464.782	1000 NAC	11_4	FI_X_490464,782_1000 NAC_11_4	131559.51
FI	X	490464.782	1000 NAC	11_5	FI_X_490464,782_1000 NAC_11_5	45044.353
FI	X	490464.782	1000 NAC	11_6	FI_X_490464,782_1000 NAC_11_6	3667.981
FI	X	490464.782	1000 NAC	11_7	FI_X_490464,782_1000 NAC_11_7	414673.567
FI	X	490464.782	1000 NAC	11_7_1	FI_X_490464,782_1000 NAC_11_7_1	28566.963
FI	X	490464.782	1000 NAC	12_1	FI_X_490464,782_1000 NAC_12_1	103509.703
FI	X	490464.782	1000 NAC	12_2	FI_X_490464,782_1000 NAC_12_2	21557.071
FI	X	490464.782	1000 NAC	12_3	FI_X_490464,782_1000 NAC_12_3	146169.843
FI	X	490464.782	1000 NAC	12_4	FI_X_490464,782_1000 NAC_12_4	92588.552
FI	X	490464.782	1000 NAC	12_5	FI_X_490464,782_1000 NAC_12_5	679.371
FI	X	490464.782	1000 NAC	12_6	FI_X_490464,782_1000 NAC_12_6	560.32
FI	X	490464.782	1000 NAC	12_6_1	FI_X_490464,782_1000 NAC_12_6_1	0
FI	X	490464.782	1000 NAC	12_6_2	FI_X_490464,782_1000 NAC_12_6_2	0
FI	X	490464.782	1000 NAC	12_6_3	FI_X_490464,782_1000 NAC_12_6_3	0
FI	X	480984.132	1000 NAC	11_1	FI_X_480984,132_1000 NAC_11_1	54236.275
FI	X	480984.132	1000 NAC	11_1_C	FI_X_480984,132_1000 NAC_11_1_C	571.306
FI	X	480984.132	1000 NAC	11_1_NC	FI_X_480984,132_1000 NAC_11_1_NC	74.437
FI	X	480984.132	1000 NAC	11_1_NC_T	FI_X_480984,132_1000 NAC_11_1_NC_T	25444.418
FI	X	480984.132	1000 NAC	11_2	FI_X_480984,132_1000 NAC_11_2	2716.33
FI	X	480984.132	1000 NAC	11_3	FI_X_480984,132_1000 NAC_11_3	199125.03
FI	X	480984.132	1000 NAC	11_4	FI_X_480984,132_1000 NAC_11_4	104421.839
FI	X	480984.132	1000 NAC	11_5	FI_X_480984,132_1000 NAC_11_5	48042.373
FI	X	480984.132	1000 NAC	11_6	FI_X_480984,132_1000 NAC_11_6	4146.265
FI	X	480984.132	1000 NAC	11_7	FI_X_480984,132_1000 NAC_11_7	381313.586
FI	X	480984.132	1000 NAC	11_7_1	FI_X_480984,132_1000 NAC_11_7_1	23528.184
FI	X	480984.132	1000 NAC	12_1	FI_X_480984,132_1000 NAC_12_1	89733.997
FI	X	480984.132	1000 NAC	12_2	FI_X_480984,132_1000 NAC_12_2	21465.705
FI	X	480984.132	1000 NAC	12_3	FI_X_480984,132_1000 NAC_12_3	134302.321
FI	X	480984.132	1000 NAC	12_4	FI_X_480984,132_1000 NAC_12_4	102.471
FI	X	480984.132	1000 NAC	12_5	FI_X_480984,132_1000 NAC_12_5	561.494
FI	X	480984.132	1000 NAC	12_6	FI_X_480984,132_1000 NAC_12_6	683.612
FI	X	480984.132	1000 NAC	12_6_1	FI_X_480984,132_1000 NAC_12_6_1	0
FI	X	480984.132	1000 NAC	12_6_2	FI_X_480984,132_1000 NAC_12_6_2	0
FI	X	480984.132	1000 NAC	12_6_3	FI_X_480984,132_1000 NAC_12_6_3	0
FI	M	2019	1000 m3	ST_1_2_C	FI_M_2019_1000 m3_ST_1_2_C	1744.746	ECEEU
FI	M	2019	1000 m3	ST_1_2_C_1	FI_M_2019_1000 m3_ST_1_2_C_1	899.653
FI	M	2019	1000 m3	ST_1_2_C_1_1	FI_M_2019_1000 m3_ST_1_2_C_1_1	191.014
FI	M	2019	1000 m3	ST_1_2_C_2_1	FI_M_2019_1000 m3_ST_1_2_C_2_1	708.639
FI	M	2019	1000 m3	ST_1_2_C_2	FI_M_2019_1000 m3_ST_1_2_C_2	845.092
FI	M	2019	1000 m3	ST_1_2_C_1_2	FI_M_2019_1000 m3_ST_1_2_C_1_2	87.748
FI	M	2019	1000 m3	ST_1_2_C_2_2	FI_M_2019_1000 m3_ST_1_2_C_2_2	757.344
FI	M	2019	1000 m3	ST_1_2_C_3	FI_M_2019_1000 m3_ST_1_2_C_3	0
FI	M	2019	1000 m3	ST_1_2_C_1_3	FI_M_2019_1000 m3_ST_1_2_C_1_3	0
FI	M	2019	1000 m3	ST_1_2_C_2_3	FI_M_2019_1000 m3_ST_1_2_C_2_3	0
FI	M	2019	1000 m3	ST_1_2_NC	FI_M_2019_1000 m3_ST_1_2_NC	4490.267
FI	M	2019	1000 m3	ST_1_2_NC_1	FI_M_2019_1000 m3_ST_1_2_NC_1	0.031
FI	M	2019	1000 m3	ST_1_2_NC_1_1	FI_M_2019_1000 m3_ST_1_2_NC_1_1	0
FI	M	2019	1000 m3	ST_1_2_NC_2_1	FI_M_2019_1000 m3_ST_1_2_NC_2_1	4198.432
FI	M	2019	1000 m3	ST_1_2_NC_2	FI_M_2019_1000 m3_ST_1_2_NC_2	81.355
FI	M	2019	1000 m3	ST_1_2_NC_1_2	FI_M_2019_1000 m3_ST_1_2_NC_1_2	4117.077
FI	M	2019	1000 m3	ST_1_2_NC_2_2	FI_M_2019_1000 m3_ST_1_2_NC_2_2	284.022
FI	M	2019	1000 m3	ST_1_2_NC_3	FI_M_2019_1000 m3_ST_1_2_NC_3	0.021
FI	M	2019	1000 m3	ST_1_2_NC_1_3	FI_M_2019_1000 m3_ST_1_2_NC_1_3	559.995
FI	M	2019	1000 m3	ST_1_2_NC_2_3	FI_M_2019_1000 m3_ST_1_2_NC_2_3	345.514
FI	M	2019	1000 m3	ST_1_2_NC_4	FI_M_2019_1000 m3_ST_1_2_NC_4	196.514
FI	M	2019	1000 m3	ST_1_2_NC_5	FI_M_2019_1000 m3_ST_1_2_NC_5	33.359
FI	M	2019	1000 m3	ST_5_C	FI_M_2019_1000 m3_ST_5_C	11.927
FI	M	2019	1000 m3	ST_5_C_1	FI_M_2019_1000 m3_ST_5_C_1	0.204
FI	M	2019	1000 m3	ST_5_C_2	FI_M_2019_1000 m3_ST_5_C_2	0.005
FI	M	2019	1000 m3	ST_5_NC	FI_M_2019_1000 m3_ST_5_NC	0
FI	M	2019	1000 m3	ST_5_NC_1	FI_M_2019_1000 m3_ST_5_NC_1	0.933
FI	M	2019	1000 m3	ST_5_NC_2	FI_M_2019_1000 m3_ST_5_NC_2	0.326
FI	M	2019	1000 m3	ST_5_NC_3	FI_M_2019_1000 m3_ST_5_NC_3	16.932
FI	M	2019	1000 m3	ST_5_NC_4	FI_M_2019_1000 m3_ST_5_NC_4	0
FI	M	2019	1000 m3	ST_5_NC_5	FI_M_2019_1000 m3_ST_5_NC_5	0
FI	M	2019	1000 m3	ST_5_NC_6	FI_M_2019_1000 m3_ST_5_NC_6	3
FI	M	2019	1000 m3	ST_5_NC_7	FI_M_2019_1000 m3_ST_5_NC_7	0
FI	M	2019	1000 NAC	ST_1_2_C	FI_M_2019_1000 NAC_ST_1_2_C	108160.001
FI	M	2019	1000 NAC	ST_1_2_C_1	FI_M_2019_1000 NAC_ST_1_2_C_1	54507.895
FI	M	2019	1000 NAC	ST_1_2_C_1_1	FI_M_2019_1000 NAC_ST_1_2_C_1_1	13134.127
FI	M	2019	1000 NAC	ST_1_2_C_2_1	FI_M_2019_1000 NAC_ST_1_2_C_2_1	41373.768
FI	M	2019	1000 NAC	ST_1_2_C_2	FI_M_2019_1000 NAC_ST_1_2_C_2	53650.981
FI	M	2019	1000 NAC	ST_1_2_C_1_2	FI_M_2019_1000 NAC_ST_1_2_C_1_2	5865.131
FI	M	2019	1000 NAC	ST_1_2_C_2_2	FI_M_2019_1000 NAC_ST_1_2_C_2_2	47785.85
FI	M	2019	1000 NAC	ST_1_2_C_3	FI_M_2019_1000 NAC_ST_1_2_C_3	0
FI	M	2019	1000 NAC	ST_1_2_C_1_3	FI_M_2019_1000 NAC_ST_1_2_C_1_3	0
FI	M	2019	1000 NAC	ST_1_2_C_2_3	FI_M_2019_1000 NAC_ST_1_2_C_2_3	0
FI	M	2019	1000 NAC	ST_1_2_NC	FI_M_2019_1000 NAC_ST_1_2_NC	209321.403
FI	M	2019	1000 NAC	ST_1_2_NC_1	FI_M_2019_1000 NAC_ST_1_2_NC_1	29.364
FI	M	2019	1000 NAC	ST_1_2_NC_1_1	FI_M_2019_1000 NAC_ST_1_2_NC_1_1	0
FI	M	2019	1000 NAC	ST_1_2_NC_2_1	FI_M_2019_1000 NAC_ST_1_2_NC_2_1	197235.438
FI	M	2019	1000 NAC	ST_1_2_NC_2	FI_M_2019_1000 NAC_ST_1_2_NC_2	5833.633
FI	M	2019	1000 NAC	ST_1_2_NC_1_2	FI_M_2019_1000 NAC_ST_1_2_NC_1_2	191401.805
FI	M	2019	1000 NAC	ST_1_2_NC_2_2	FI_M_2019_1000 NAC_ST_1_2_NC_2_2	11493.481
FI	M	2019	1000 NAC	ST_1_2_NC_3	FI_M_2019_1000 NAC_ST_1_2_NC_3	2.924
FI	M	2019	1000 NAC	ST_1_2_NC_1_3	FI_M_2019_1000 NAC_ST_1_2_NC_1_3	95658.068
FI	M	2019	1000 NAC	ST_1_2_NC_2_3	FI_M_2019_1000 NAC_ST_1_2_NC_2_3	54820.656
FI	M	2019	1000 NAC	ST_1_2_NC_4	FI_M_2019_1000 NAC_ST_1_2_NC_4	34332.632
FI	M	2019	1000 NAC	ST_1_2_NC_5	FI_M_2019_1000 NAC_ST_1_2_NC_5	20241.113
FI	M	2019	1000 NAC	ST_5_C	FI_M_2019_1000 NAC_ST_5_C	6004.712
FI	M	2019	1000 NAC	ST_5_C_1	FI_M_2019_1000 NAC_ST_5_C_1	77.887
FI	M	2019	1000 NAC	ST_5_C_2	FI_M_2019_1000 NAC_ST_5_C_2	3.67
FI	M	2019	1000 NAC	ST_5_NC	FI_M_2019_1000 NAC_ST_5_NC	0
FI	M	2019	1000 NAC	ST_5_NC_1	FI_M_2019_1000 NAC_ST_5_NC_1	861.402
FI	M	2019	1000 NAC	ST_5_NC_2	FI_M_2019_1000 NAC_ST_5_NC_2	256.776
FI	M	2019	1000 NAC	ST_5_NC_3	FI_M_2019_1000 NAC_ST_5_NC_3	9651.601
FI	M	2019	1000 NAC	ST_5_NC_4	FI_M_2019_1000 NAC_ST_5_NC_4	0
FI	M	2019	1000 NAC	ST_5_NC_5	FI_M_2019_1000 NAC_ST_5_NC_5	0
FI	M	2019	1000 NAC	ST_5_NC_6	FI_M_2019_1000 NAC_ST_5_NC_6	3
FI	M	2019	1000 NAC	ST_5_NC_7	FI_M_2019_1000 NAC_ST_5_NC_7	0
FI	M	2020	1000 m3	ST_1_2_C	FI_M_2020_1000 m3_ST_1_2_C	1533.846
FI	M	2020	1000 m3	ST_1_2_C_1	FI_M_2020_1000 m3_ST_1_2_C_1	876.994
FI	M	2020	1000 m3	ST_1_2_C_1_1	FI_M_2020_1000 m3_ST_1_2_C_1_1	199.137
FI	M	2020	1000 m3	ST_1_2_C_2_1	FI_M_2020_1000 m3_ST_1_2_C_2_1	677.857
FI	M	2020	1000 m3	ST_1_2_C_2	FI_M_2020_1000 m3_ST_1_2_C_2	656.8
FI	M	2020	1000 m3	ST_1_2_C_1_2	FI_M_2020_1000 m3_ST_1_2_C_1_2	121.169
FI	M	2020	1000 m3	ST_1_2_C_2_2	FI_M_2020_1000 m3_ST_1_2_C_2_2	535.631
FI	M	2020	1000 m3	ST_1_2_C_3	FI_M_2020_1000 m3_ST_1_2_C_3	0
FI	M	2020	1000 m3	ST_1_2_C_1_3	FI_M_2020_1000 m3_ST_1_2_C_1_3	0
FI	M	2020	1000 m3	ST_1_2_C_2_3	FI_M_2020_1000 m3_ST_1_2_C_2_3	0
FI	M	2020	1000 m3	ST_1_2_NC	FI_M_2020_1000 m3_ST_1_2_NC	4740.268
FI	M	2020	1000 m3	ST_1_2_NC_1	FI_M_2020_1000 m3_ST_1_2_NC_1	0.028
FI	M	2020	1000 m3	ST_1_2_NC_1_1	FI_M_2020_1000 m3_ST_1_2_NC_1_1	0
FI	M	2020	1000 m3	ST_1_2_NC_2_1	FI_M_2020_1000 m3_ST_1_2_NC_2_1	4464.919
FI	M	2020	1000 m3	ST_1_2_NC_2	FI_M_2020_1000 m3_ST_1_2_NC_2	103.235
FI	M	2020	1000 m3	ST_1_2_NC_1_2	FI_M_2020_1000 m3_ST_1_2_NC_1_2	4361.684
FI	M	2020	1000 m3	ST_1_2_NC_2_2	FI_M_2020_1000 m3_ST_1_2_NC_2_2	275.308
FI	M	2020	1000 m3	ST_1_2_NC_3	FI_M_2020_1000 m3_ST_1_2_NC_3	0.001
FI	M	2020	1000 m3	ST_1_2_NC_1_3	FI_M_2020_1000 m3_ST_1_2_NC_1_3	569.549
FI	M	2020	1000 m3	ST_1_2_NC_2_3	FI_M_2020_1000 m3_ST_1_2_NC_2_3	347.424
FI	M	2020	1000 m3	ST_1_2_NC_4	FI_M_2020_1000 m3_ST_1_2_NC_4	188.644
FI	M	2020	1000 m3	ST_1_2_NC_5	FI_M_2020_1000 m3_ST_1_2_NC_5	30.552
FI	M	2020	1000 m3	ST_5_C	FI_M_2020_1000 m3_ST_5_C	6.545
FI	M	2020	1000 m3	ST_5_C_1	FI_M_2020_1000 m3_ST_5_C_1	0.232
FI	M	2020	1000 m3	ST_5_C_2	FI_M_2020_1000 m3_ST_5_C_2	0.004
FI	M	2020	1000 m3	ST_5_NC	FI_M_2020_1000 m3_ST_5_NC	0
FI	M	2020	1000 m3	ST_5_NC_1	FI_M_2020_1000 m3_ST_5_NC_1	1.117
FI	M	2020	1000 m3	ST_5_NC_2	FI_M_2020_1000 m3_ST_5_NC_2	0.7
FI	M	2020	1000 m3	ST_5_NC_3	FI_M_2020_1000 m3_ST_5_NC_3	4.984
FI	M	2020	1000 m3	ST_5_NC_4	FI_M_2020_1000 m3_ST_5_NC_4	0
FI	M	2020	1000 m3	ST_5_NC_5	FI_M_2020_1000 m3_ST_5_NC_5	0
FI	M	2020	1000 m3	ST_5_NC_6	FI_M_2020_1000 m3_ST_5_NC_6	3
FI	M	2020	1000 m3	ST_5_NC_7	FI_M_2020_1000 m3_ST_5_NC_7	0
FI	M	2020	1000 NAC	ST_1_2_C	FI_M_2020_1000 NAC_ST_1_2_C	81813.353
FI	M	2020	1000 NAC	ST_1_2_C_1	FI_M_2020_1000 NAC_ST_1_2_C_1	46127.133
FI	M	2020	1000 NAC	ST_1_2_C_1_1	FI_M_2020_1000 NAC_ST_1_2_C_1_1	12989.835
FI	M	2020	1000 NAC	ST_1_2_C_2_1	FI_M_2020_1000 NAC_ST_1_2_C_2_1	33137.298
FI	M	2020	1000 NAC	ST_1_2_C_2	FI_M_2020_1000 NAC_ST_1_2_C_2	35671.599
FI	M	2020	1000 NAC	ST_1_2_C_1_2	FI_M_2020_1000 NAC_ST_1_2_C_1_2	7734.159
FI	M	2020	1000 NAC	ST_1_2_C_2_2	FI_M_2020_1000 NAC_ST_1_2_C_2_2	27937.44
FI	M	2020	1000 NAC	ST_1_2_C_3	FI_M_2020_1000 NAC_ST_1_2_C_3	0
FI	M	2020	1000 NAC	ST_1_2_C_1_3	FI_M_2020_1000 NAC_ST_1_2_C_1_3	0
FI	M	2020	1000 NAC	ST_1_2_C_2_3	FI_M_2020_1000 NAC_ST_1_2_C_2_3	0
FI	M	2020	1000 NAC	ST_1_2_NC	FI_M_2020_1000 NAC_ST_1_2_NC	202306.657
FI	M	2020	1000 NAC	ST_1_2_NC_1	FI_M_2020_1000 NAC_ST_1_2_NC_1	25.258
FI	M	2020	1000 NAC	ST_1_2_NC_1_1	FI_M_2020_1000 NAC_ST_1_2_NC_1_1	0
FI	M	2020	1000 NAC	ST_1_2_NC_2_1	FI_M_2020_1000 NAC_ST_1_2_NC_2_1	191377.665
FI	M	2020	1000 NAC	ST_1_2_NC_2	FI_M_2020_1000 NAC_ST_1_2_NC_2	7109.042
FI	M	2020	1000 NAC	ST_1_2_NC_1_2	FI_M_2020_1000 NAC_ST_1_2_NC_1_2	184268.623
FI	M	2020	1000 NAC	ST_1_2_NC_2_2	FI_M_2020_1000 NAC_ST_1_2_NC_2_2	10832.305
FI	M	2020	1000 NAC	ST_1_2_NC_3	FI_M_2020_1000 NAC_ST_1_2_NC_3	0.023
FI	M	2020	1000 NAC	ST_1_2_NC_1_3	FI_M_2020_1000 NAC_ST_1_2_NC_1_3	91739.1
FI	M	2020	1000 NAC	ST_1_2_NC_2_3	FI_M_2020_1000 NAC_ST_1_2_NC_2_3	51645.313
FI	M	2020	1000 NAC	ST_1_2_NC_4	FI_M_2020_1000 NAC_ST_1_2_NC_4	29451.877
FI	M	2020	1000 NAC	ST_1_2_NC_5	FI_M_2020_1000 NAC_ST_1_2_NC_5	22505.326
FI	M	2020	1000 NAC	ST_5_C	FI_M_2020_1000 NAC_ST_5_C	6568.341
FI	M	2020	1000 NAC	ST_5_C_1	FI_M_2020_1000 NAC_ST_5_C_1	69.31
FI	M	2020	1000 NAC	ST_5_C_2	FI_M_2020_1000 NAC_ST_5_C_2	3.063
FI	M	2020	1000 NAC	ST_5_NC	FI_M_2020_1000 NAC_ST_5_NC	0
FI	M	2020	1000 NAC	ST_5_NC_1	FI_M_2020_1000 NAC_ST_5_NC_1	1015.602
FI	M	2020	1000 NAC	ST_5_NC_2	FI_M_2020_1000 NAC_ST_5_NC_2	500.729
FI	M	2020	1000 NAC	ST_5_NC_3	FI_M_2020_1000 NAC_ST_5_NC_3	1802.47
FI	M	2020	1000 NAC	ST_5_NC_4	FI_M_2020_1000 NAC_ST_5_NC_4	0
FI	M	2020	1000 NAC	ST_5_NC_5	FI_M_2020_1000 NAC_ST_5_NC_5	0
FI	M	2020	1000 NAC	ST_5_NC_6	FI_M_2020_1000 NAC_ST_5_NC_6	3
FI	M	2020	1000 NAC	ST_5_NC_7	FI_M_2020_1000 NAC_ST_5_NC_7	0
FI	X	2019	1000 m3	ST_1_2_C	FI_X_2019_1000 m3_ST_1_2_C	1174.095
FI	X	2019	1000 m3	ST_1_2_C_1	FI_X_2019_1000 m3_ST_1_2_C_1	311.19
FI	X	2019	1000 m3	ST_1_2_C_1_1	FI_X_2019_1000 m3_ST_1_2_C_1_1	38.474
FI	X	2019	1000 m3	ST_1_2_C_2_1	FI_X_2019_1000 m3_ST_1_2_C_2_1	272.716
FI	X	2019	1000 m3	ST_1_2_C_2	FI_X_2019_1000 m3_ST_1_2_C_2	862.905
FI	X	2019	1000 m3	ST_1_2_C_1_2	FI_X_2019_1000 m3_ST_1_2_C_1_2	427.574
FI	X	2019	1000 m3	ST_1_2_C_2_2	FI_X_2019_1000 m3_ST_1_2_C_2_2	435.331
FI	X	2019	1000 m3	ST_1_2_C_3	FI_X_2019_1000 m3_ST_1_2_C_3	0
FI	X	2019	1000 m3	ST_1_2_C_1_3	FI_X_2019_1000 m3_ST_1_2_C_1_3	0
FI	X	2019	1000 m3	ST_1_2_C_2_3	FI_X_2019_1000 m3_ST_1_2_C_2_3	0
FI	X	2019	1000 m3	ST_1_2_NC	FI_X_2019_1000 m3_ST_1_2_NC	182.286
FI	X	2019	1000 m3	ST_1_2_NC_1	FI_X_2019_1000 m3_ST_1_2_NC_1	0
FI	X	2019	1000 m3	ST_1_2_NC_1_1	FI_X_2019_1000 m3_ST_1_2_NC_1_1	0
FI	X	2019	1000 m3	ST_1_2_NC_2_1	FI_X_2019_1000 m3_ST_1_2_NC_2_1	182.204
FI	X	2019	1000 m3	ST_1_2_NC_2	FI_X_2019_1000 m3_ST_1_2_NC_2	1.802
FI	X	2019	1000 m3	ST_1_2_NC_1_2	FI_X_2019_1000 m3_ST_1_2_NC_1_2	180.402
FI	X	2019	1000 m3	ST_1_2_NC_2_2	FI_X_2019_1000 m3_ST_1_2_NC_2_2	0
FI	X	2019	1000 m3	ST_1_2_NC_3	FI_X_2019_1000 m3_ST_1_2_NC_3	0
FI	X	2019	1000 m3	ST_1_2_NC_1_3	FI_X_2019_1000 m3_ST_1_2_NC_1_3	8951.761
FI	X	2019	1000 m3	ST_1_2_NC_2_3	FI_X_2019_1000 m3_ST_1_2_NC_2_3	4499.321
FI	X	2019	1000 m3	ST_1_2_NC_4	FI_X_2019_1000 m3_ST_1_2_NC_4	4451.625
FI	X	2019	1000 m3	ST_1_2_NC_5	FI_X_2019_1000 m3_ST_1_2_NC_5	14.984
FI	X	2019	1000 m3	ST_5_C	FI_X_2019_1000 m3_ST_5_C	0.012
FI	X	2019	1000 m3	ST_5_C_1	FI_X_2019_1000 m3_ST_5_C_1	0
FI	X	2019	1000 m3	ST_5_C_2	FI_X_2019_1000 m3_ST_5_C_2	0
FI	X	2019	1000 m3	ST_5_NC	FI_X_2019_1000 m3_ST_5_NC	0.007
FI	X	2019	1000 m3	ST_5_NC_1	FI_X_2019_1000 m3_ST_5_NC_1	0.034
FI	X	2019	1000 m3	ST_5_NC_2	FI_X_2019_1000 m3_ST_5_NC_2	0.124
FI	X	2019	1000 m3	ST_5_NC_3	FI_X_2019_1000 m3_ST_5_NC_3	11.025
FI	X	2019	1000 m3	ST_5_NC_4	FI_X_2019_1000 m3_ST_5_NC_4	0
FI	X	2019	1000 m3	ST_5_NC_5	FI_X_2019_1000 m3_ST_5_NC_5	0
FI	X	2019	1000 m3	ST_5_NC_6	FI_X_2019_1000 m3_ST_5_NC_6	3
FI	X	2019	1000 m3	ST_5_NC_7	FI_X_2019_1000 m3_ST_5_NC_7	0
FI	X	2019	1000 NAC	ST_1_2_C	FI_X_2019_1000 NAC_ST_1_2_C	78647.438
FI	X	2019	1000 NAC	ST_1_2_C_1	FI_X_2019_1000 NAC_ST_1_2_C_1	17358.044
FI	X	2019	1000 NAC	ST_1_2_C_1_1	FI_X_2019_1000 NAC_ST_1_2_C_1_1	2553.481
FI	X	2019	1000 NAC	ST_1_2_C_2_1	FI_X_2019_1000 NAC_ST_1_2_C_2_1	14804.563
FI	X	2019	1000 NAC	ST_1_2_C_2	FI_X_2019_1000 NAC_ST_1_2_C_2	61289.394
FI	X	2019	1000 NAC	ST_1_2_C_1_2	FI_X_2019_1000 NAC_ST_1_2_C_1_2	26026.701
FI	X	2019	1000 NAC	ST_1_2_C_2_2	FI_X_2019_1000 NAC_ST_1_2_C_2_2	35262.693
FI	X	2019	1000 NAC	ST_1_2_C_3	FI_X_2019_1000 NAC_ST_1_2_C_3	0
FI	X	2019	1000 NAC	ST_1_2_C_1_3	FI_X_2019_1000 NAC_ST_1_2_C_1_3	0
FI	X	2019	1000 NAC	ST_1_2_C_2_3	FI_X_2019_1000 NAC_ST_1_2_C_2_3	0
FI	X	2019	1000 NAC	ST_1_2_NC	FI_X_2019_1000 NAC_ST_1_2_NC	9376.531
FI	X	2019	1000 NAC	ST_1_2_NC_1	FI_X_2019_1000 NAC_ST_1_2_NC_1	0
FI	X	2019	1000 NAC	ST_1_2_NC_1_1	FI_X_2019_1000 NAC_ST_1_2_NC_1_1	0
FI	X	2019	1000 NAC	ST_1_2_NC_2_1	FI_X_2019_1000 NAC_ST_1_2_NC_2_1	9346.196
FI	X	2019	1000 NAC	ST_1_2_NC_2	FI_X_2019_1000 NAC_ST_1_2_NC_2	127.048
FI	X	2019	1000 NAC	ST_1_2_NC_1_2	FI_X_2019_1000 NAC_ST_1_2_NC_1_2	9219.148
FI	X	2019	1000 NAC	ST_1_2_NC_2_2	FI_X_2019_1000 NAC_ST_1_2_NC_2_2	0
FI	X	2019	1000 NAC	ST_1_2_NC_3	FI_X_2019_1000 NAC_ST_1_2_NC_3	0
FI	X	2019	1000 NAC	ST_1_2_NC_1_3	FI_X_2019_1000 NAC_ST_1_2_NC_1_3	1722057.473
FI	X	2019	1000 NAC	ST_1_2_NC_2_3	FI_X_2019_1000 NAC_ST_1_2_NC_2_3	896652.84
FI	X	2019	1000 NAC	ST_1_2_NC_4	FI_X_2019_1000 NAC_ST_1_2_NC_4	825203.242
FI	X	2019	1000 NAC	ST_1_2_NC_5	FI_X_2019_1000 NAC_ST_1_2_NC_5	7770.35
FI	X	2019	1000 NAC	ST_5_C	FI_X_2019_1000 NAC_ST_5_C	35.293
FI	X	2019	1000 NAC	ST_5_C_1	FI_X_2019_1000 NAC_ST_5_C_1	0
FI	X	2019	1000 NAC	ST_5_C_2	FI_X_2019_1000 NAC_ST_5_C_2	0
FI	X	2019	1000 NAC	ST_5_NC	FI_X_2019_1000 NAC_ST_5_NC	10.063
FI	X	2019	1000 NAC	ST_5_NC_1	FI_X_2019_1000 NAC_ST_5_NC_1	31.39
FI	X	2019	1000 NAC	ST_5_NC_2	FI_X_2019_1000 NAC_ST_5_NC_2	98.35
FI	X	2019	1000 NAC	ST_5_NC_3	FI_X_2019_1000 NAC_ST_5_NC_3	3912.9
FI	X	2019	1000 NAC	ST_5_NC_4	FI_X_2019_1000 NAC_ST_5_NC_4	0
FI	X	2019	1000 NAC	ST_5_NC_5	FI_X_2019_1000 NAC_ST_5_NC_5	0
FI	X	2019	1000 NAC	ST_5_NC_6	FI_X_2019_1000 NAC_ST_5_NC_6	3
FI	X	2019	1000 NAC	ST_5_NC_7	FI_X_2019_1000 NAC_ST_5_NC_7	0
FI	X	2020	1000 m3	ST_1_2_C	FI_X_2020_1000 m3_ST_1_2_C	1095.264
FI	X	2020	1000 m3	ST_1_2_C_1	FI_X_2020_1000 m3_ST_1_2_C_1	242.898
FI	X	2020	1000 m3	ST_1_2_C_1_1	FI_X_2020_1000 m3_ST_1_2_C_1_1	0.616
FI	X	2020	1000 m3	ST_1_2_C_2_1	FI_X_2020_1000 m3_ST_1_2_C_2_1	242.282
FI	X	2020	1000 m3	ST_1_2_C_2	FI_X_2020_1000 m3_ST_1_2_C_2	788.772
FI	X	2020	1000 m3	ST_1_2_C_1_2	FI_X_2020_1000 m3_ST_1_2_C_1_2	295.825
FI	X	2020	1000 m3	ST_1_2_C_2_2	FI_X_2020_1000 m3_ST_1_2_C_2_2	492.947
FI	X	2020	1000 m3	ST_1_2_C_3	FI_X_2020_1000 m3_ST_1_2_C_3	0
FI	X	2020	1000 m3	ST_1_2_C_1_3	FI_X_2020_1000 m3_ST_1_2_C_1_3	0
FI	X	2020	1000 m3	ST_1_2_C_2_3	FI_X_2020_1000 m3_ST_1_2_C_2_3	0
FI	X	2020	1000 m3	ST_1_2_NC	FI_X_2020_1000 m3_ST_1_2_NC	67.961
FI	X	2020	1000 m3	ST_1_2_NC_1	FI_X_2020_1000 m3_ST_1_2_NC_1	0
FI	X	2020	1000 m3	ST_1_2_NC_1_1	FI_X_2020_1000 m3_ST_1_2_NC_1_1	0
FI	X	2020	1000 m3	ST_1_2_NC_2_1	FI_X_2020_1000 m3_ST_1_2_NC_2_1	44.225
FI	X	2020	1000 m3	ST_1_2_NC_2	FI_X_2020_1000 m3_ST_1_2_NC_2	0
FI	X	2020	1000 m3	ST_1_2_NC_1_2	FI_X_2020_1000 m3_ST_1_2_NC_1_2	44.225
FI	X	2020	1000 m3	ST_1_2_NC_2_2	FI_X_2020_1000 m3_ST_1_2_NC_2_2	0
FI	X	2020	1000 m3	ST_1_2_NC_3	FI_X_2020_1000 m3_ST_1_2_NC_3	0
FI	X	2020	1000 m3	ST_1_2_NC_1_3	FI_X_2020_1000 m3_ST_1_2_NC_1_3	8197.932
FI	X	2020	1000 m3	ST_1_2_NC_2_3	FI_X_2020_1000 m3_ST_1_2_NC_2_3	4093.365
FI	X	2020	1000 m3	ST_1_2_NC_4	FI_X_2020_1000 m3_ST_1_2_NC_4	4104.345
FI	X	2020	1000 m3	ST_1_2_NC_5	FI_X_2020_1000 m3_ST_1_2_NC_5	19.987
FI	X	2020	1000 m3	ST_5_C	FI_X_2020_1000 m3_ST_5_C	0.007
FI	X	2020	1000 m3	ST_5_C_1	FI_X_2020_1000 m3_ST_5_C_1	0.001
FI	X	2020	1000 m3	ST_5_C_2	FI_X_2020_1000 m3_ST_5_C_2	0
FI	X	2020	1000 m3	ST_5_NC	FI_X_2020_1000 m3_ST_5_NC	0
FI	X	2020	1000 m3	ST_5_NC_1	FI_X_2020_1000 m3_ST_5_NC_1	0.148
FI	X	2020	1000 m3	ST_5_NC_2	FI_X_2020_1000 m3_ST_5_NC_2	0.071
FI	X	2020	1000 m3	ST_5_NC_3	FI_X_2020_1000 m3_ST_5_NC_3	0.445
FI	X	2020	1000 m3	ST_5_NC_4	FI_X_2020_1000 m3_ST_5_NC_4	0
FI	X	2020	1000 m3	ST_5_NC_5	FI_X_2020_1000 m3_ST_5_NC_5	0
FI	X	2020	1000 m3	ST_5_NC_6	FI_X_2020_1000 m3_ST_5_NC_6	3
FI	X	2020	1000 m3	ST_5_NC_7	FI_X_2020_1000 m3_ST_5_NC_7	0
FI	X	2020	1000 NAC	ST_1_2_C	FI_X_2020_1000 NAC_ST_1_2_C	88588.893
FI	X	2020	1000 NAC	ST_1_2_C_1	FI_X_2020_1000 NAC_ST_1_2_C_1	12852.72
FI	X	2020	1000 NAC	ST_1_2_C_1_1	FI_X_2020_1000 NAC_ST_1_2_C_1_1	42.136
FI	X	2020	1000 NAC	ST_1_2_C_2_1	FI_X_2020_1000 NAC_ST_1_2_C_2_1	12810.584
FI	X	2020	1000 NAC	ST_1_2_C_2	FI_X_2020_1000 NAC_ST_1_2_C_2	61654.163
FI	X	2020	1000 NAC	ST_1_2_C_1_2	FI_X_2020_1000 NAC_ST_1_2_C_1_2	18949.39
FI	X	2020	1000 NAC	ST_1_2_C_2_2	FI_X_2020_1000 NAC_ST_1_2_C_2_2	42704.773
FI	X	2020	1000 NAC	ST_1_2_C_3	FI_X_2020_1000 NAC_ST_1_2_C_3	0
FI	X	2020	1000 NAC	ST_1_2_C_1_3	FI_X_2020_1000 NAC_ST_1_2_C_1_3	0
FI	X	2020	1000 NAC	ST_1_2_C_2_3	FI_X_2020_1000 NAC_ST_1_2_C_2_3	0
FI	X	2020	1000 NAC	ST_1_2_NC	FI_X_2020_1000 NAC_ST_1_2_NC	4454.077
FI	X	2020	1000 NAC	ST_1_2_NC_1	FI_X_2020_1000 NAC_ST_1_2_NC_1	0
FI	X	2020	1000 NAC	ST_1_2_NC_1_1	FI_X_2020_1000 NAC_ST_1_2_NC_1_1	0
FI	X	2020	1000 NAC	ST_1_2_NC_2_1	FI_X_2020_1000 NAC_ST_1_2_NC_2_1	2158.997
FI	X	2020	1000 NAC	ST_1_2_NC_2	FI_X_2020_1000 NAC_ST_1_2_NC_2	0
FI	X	2020	1000 NAC	ST_1_2_NC_1_2	FI_X_2020_1000 NAC_ST_1_2_NC_1_2	2158.997
FI	X	2020	1000 NAC	ST_1_2_NC_2_2	FI_X_2020_1000 NAC_ST_1_2_NC_2_2	0
FI	X	2020	1000 NAC	ST_1_2_NC_3	FI_X_2020_1000 NAC_ST_1_2_NC_3	0
FI	X	2020	1000 NAC	ST_1_2_NC_1_3	FI_X_2020_1000 NAC_ST_1_2_NC_1_3	1548568.236
FI	X	2020	1000 NAC	ST_1_2_NC_2_3	FI_X_2020_1000 NAC_ST_1_2_NC_2_3	798586.322
FI	X	2020	1000 NAC	ST_1_2_NC_4	FI_X_2020_1000 NAC_ST_1_2_NC_4	749919.196
FI	X	2020	1000 NAC	ST_1_2_NC_5	FI_X_2020_1000 NAC_ST_1_2_NC_5	8978.448
FI	X	2020	1000 NAC	ST_5_C	FI_X_2020_1000 NAC_ST_5_C	4.261
FI	X	2020	1000 NAC	ST_5_C_1	FI_X_2020_1000 NAC_ST_5_C_1	0.317
FI	X	2020	1000 NAC	ST_5_C_2	FI_X_2020_1000 NAC_ST_5_C_2	0
FI	X	2020	1000 NAC	ST_5_NC	FI_X_2020_1000 NAC_ST_5_NC	0
FI	X	2020	1000 NAC	ST_5_NC_1	FI_X_2020_1000 NAC_ST_5_NC_1	136.227
FI	X	2020	1000 NAC	ST_5_NC_2	FI_X_2020_1000 NAC_ST_5_NC_2	72.925
FI	X	2020	1000 NAC	ST_5_NC_3	FI_X_2020_1000 NAC_ST_5_NC_3	291.167
FI	X	2020	1000 NAC	ST_5_NC_4	FI_X_2020_1000 NAC_ST_5_NC_4	0
FI	X	2020	1000 NAC	ST_5_NC_5	FI_X_2020_1000 NAC_ST_5_NC_5	0
FI	X	2020	1000 NAC	ST_5_NC_6	FI_X_2020_1000 NAC_ST_5_NC_6	3
FI	X	2020	1000 NAC	ST_5_NC_7	FI_X_2020_1000 NAC_ST_5_NC_7	0
FI	EX_M	2019	1000 m3	1	FI_EX_M_2019_1000 m3_1	4600.545	EU1
FI	EX_M	2019	1000 m3	1_1	FI_EX_M_2019_1000 m3_1_1	86.923
FI	EX_M	2019	1000 m3	1_2	FI_EX_M_2019_1000 m3_1_2	4.993
FI	EX_M	2019	1000 m3	1_2_C	FI_EX_M_2019_1000 m3_1_2_C	81.93
FI	EX_M	2019	1000 m3	1_2_NC	FI_EX_M_2019_1000 m3_1_2_NC	4513.622
FI	EX_M	2019	1000 m3	1_2_NC_T	FI_EX_M_2019_1000 m3_1_2_NC_T	759.564
FI	EX_M	2019	1000 mt	2	FI_EX_M_2019_1000 mt_2	3754.058
FI	EX_M	2019	1000 m3	3	FI_EX_M_2019_1000 m3_3	0
FI	EX_M	2019	1000 m3	3_1	FI_EX_M_2019_1000 m3_3_1	2.159
FI	EX_M	2019	1000 m3	3_2	FI_EX_M_2019_1000 m3_3_2	3207.92
FI	EX_M	2019	1000 mt	4	FI_EX_M_2019_1000 mt_4	3015.256
FI	EX_M	2019	1000 mt	4_1	FI_EX_M_2019_1000 mt_4_1	192.663
FI	EX_M	2019	1000 mt	4_2	FI_EX_M_2019_1000 mt_4_2	116.307
FI	EX_M	2019	1000 m3	5	FI_EX_M_2019_1000 m3_5	87.285
FI	EX_M	2019	1000 m3	5_C	FI_EX_M_2019_1000 m3_5_C	74.365
FI	EX_M	2019	1000 m3	5_NC	FI_EX_M_2019_1000 m3_5_NC	12.92
FI	EX_M	2019	1000 m3	5_NC_T	FI_EX_M_2019_1000 m3_5_NC_T	556.306
FI	EX_M	2019	1000 m3	6	FI_EX_M_2019_1000 m3_6	546.187
FI	EX_M	2019	1000 m3	6_1	FI_EX_M_2019_1000 m3_6_1	10.119
FI	EX_M	2019	1000 m3	6_1_C	FI_EX_M_2019_1000 m3_6_1_C	1.931
FI	EX_M	2019	1000 m3	6_1_NC	FI_EX_M_2019_1000 m3_6_1_NC	4.316
FI	EX_M	2019	1000 m3	6_1_NC_T	FI_EX_M_2019_1000 m3_6_1_NC_T	0.001
FI	EX_M	2019	1000 m3	6_2	FI_EX_M_2019_1000 m3_6_2	4.315
FI	EX_M	2019	1000 m3	6_2_C	FI_EX_M_2019_1000 m3_6_2_C	0.047
FI	EX_M	2019	1000 m3	6_2_NC	FI_EX_M_2019_1000 m3_6_2_NC	125.533
FI	EX_M	2019	1000 m3	6_2_NC_T	FI_EX_M_2019_1000 m3_6_2_NC_T	91.905
FI	EX_M	2019	1000 m3	6_3	FI_EX_M_2019_1000 m3_6_3	24.605
FI	EX_M	2019	1000 m3	6_3_1	FI_EX_M_2019_1000 m3_6_3_1	67.3
FI	EX_M	2019	1000 m3	6_4	FI_EX_M_2019_1000 m3_6_4	0.66
FI	EX_M	2019	1000 m3	6_4_1	FI_EX_M_2019_1000 m3_6_4_1	21.338
FI	EX_M	2019	1000 m3	6_4_2	FI_EX_M_2019_1000 m3_6_4_2	15.062
FI	EX_M	2019	1000 m3	6_4_3	FI_EX_M_2019_1000 m3_6_4_3	12.29
FI	EX_M	2019	1000 mt	7	FI_EX_M_2019_1000 mt_7	1.418
FI	EX_M	2019	1000 mt	7_1	FI_EX_M_2019_1000 mt_7_1	8.613
FI	EX_M	2019	1000 mt	7_2	FI_EX_M_2019_1000 mt_7_2	2.259
FI	EX_M	2019	1000 mt	7_3	FI_EX_M_2019_1000 mt_7_3	309.815
FI	EX_M	2019	1000 mt	7_3_1	FI_EX_M_2019_1000 mt_7_3_1	4.734
FI	EX_M	2019	1000 mt	7_3_2	FI_EX_M_2019_1000 mt_7_3_2	299.146
FI	EX_M	2019	1000 mt	7_3_3	FI_EX_M_2019_1000 mt_7_3_3	298.388
FI	EX_M	2019	1000 mt	7_3_4	FI_EX_M_2019_1000 mt_7_3_4	290.003
FI	EX_M	2019	1000 mt	7_4	FI_EX_M_2019_1000 mt_7_4	0.758
FI	EX_M	2019	1000 mt	8	FI_EX_M_2019_1000 mt_8	5.935
FI	EX_M	2019	1000 mt	8_1	FI_EX_M_2019_1000 mt_8_1	2.861
FI	EX_M	2019	1000 mt	8_2	FI_EX_M_2019_1000 mt_8_2	2.844
FI	EX_M	2019	1000 mt	9	FI_EX_M_2019_1000 mt_9	0.017
FI	EX_M	2019	1000 mt	10	FI_EX_M_2019_1000 mt_10	25.566
FI	EX_M	2019	1000 mt	10_1	FI_EX_M_2019_1000 mt_10_1	28.943
FI	EX_M	2019	1000 mt	10_1_1	FI_EX_M_2019_1000 mt_10_1_1	15.869
FI	EX_M	2019	1000 mt	10_1_2	FI_EX_M_2019_1000 mt_10_1_2	15.153
FI	EX_M	2019	1000 mt	10_1_3	FI_EX_M_2019_1000 mt_10_1_3	0.125
FI	EX_M	2019	1000 mt	10_1_4	FI_EX_M_2019_1000 mt_10_1_4	0.231
FI	EX_M	2019	1000 mt	10_2	FI_EX_M_2019_1000 mt_10_2	0.36
FI	EX_M	2019	1000 mt	10_3	FI_EX_M_2019_1000 mt_10_3	0.056
FI	EX_M	2019	1000 mt	10_3_1	FI_EX_M_2019_1000 mt_10_3_1	13.01
FI	EX_M	2019	1000 mt	10_3_2	FI_EX_M_2019_1000 mt_10_3_2	3.215
FI	EX_M	2019	1000 mt	10_3_3	FI_EX_M_2019_1000 mt_10_3_3	5.251
FI	EX_M	2019	1000 mt	10_3_4	FI_EX_M_2019_1000 mt_10_3_4	2.303
FI	EX_M	2019	1000 mt	10_4	FI_EX_M_2019_1000 mt_10_4	2.241
FI	EX_M	2019	1000 NAC	1	FI_EX_M_2019_1000 NAC_1	215138.487
FI	EX_M	2019	1000 NAC	1_1	FI_EX_M_2019_1000 NAC_1_1	3113.503
FI	EX_M	2019	1000 NAC	1_2	FI_EX_M_2019_1000 NAC_1_2	132.639
FI	EX_M	2019	1000 NAC	1_2_C	FI_EX_M_2019_1000 NAC_1_2_C	2980.864
FI	EX_M	2019	1000 NAC	1_2_NC	FI_EX_M_2019_1000 NAC_1_2_NC	212024.984
FI	EX_M	2019	1000 NAC	1_2_NC_T	FI_EX_M_2019_1000 NAC_1_2_NC_T	43005.318
FI	EX_M	2019	1000 NAC	2	FI_EX_M_2019_1000 NAC_2	169019.666
FI	EX_M	2019	1000 NAC	3	FI_EX_M_2019_1000 NAC_3	0
FI	EX_M	2019	1000 NAC	3_1	FI_EX_M_2019_1000 NAC_3_1	1150.237
FI	EX_M	2019	1000 NAC	3_2	FI_EX_M_2019_1000 NAC_3_2	119777.56
FI	EX_M	2019	1000 NAC	4	FI_EX_M_2019_1000 NAC_4	115681.97
FI	EX_M	2019	1000 NAC	4_1	FI_EX_M_2019_1000 NAC_4_1	4095.59
FI	EX_M	2019	1000 NAC	4_2	FI_EX_M_2019_1000 NAC_4_2	3784.27
FI	EX_M	2019	1000 NAC	5	FI_EX_M_2019_1000 NAC_5	9947.821
FI	EX_M	2019	1000 NAC	5_C	FI_EX_M_2019_1000 NAC_5_C	9077.742
FI	EX_M	2019	1000 NAC	5_NC	FI_EX_M_2019_1000 NAC_5_NC	870.079
FI	EX_M	2019	1000 NAC	5_NC_T	FI_EX_M_2019_1000 NAC_5_NC_T	96482.268
FI	EX_M	2019	1000 NAC	6	FI_EX_M_2019_1000 NAC_6	90367.986
FI	EX_M	2019	1000 NAC	6_1	FI_EX_M_2019_1000 NAC_6_1	6114.282
FI	EX_M	2019	1000 NAC	6_1_C	FI_EX_M_2019_1000 NAC_6_1_C	1571.148
FI	EX_M	2019	1000 NAC	6_1_NC	FI_EX_M_2019_1000 NAC_6_1_NC	1560.041
FI	EX_M	2019	1000 NAC	6_1_NC_T	FI_EX_M_2019_1000 NAC_6_1_NC_T	0.389
FI	EX_M	2019	1000 NAC	6_2	FI_EX_M_2019_1000 NAC_6_2	1559.652
FI	EX_M	2019	1000 NAC	6_2_C	FI_EX_M_2019_1000 NAC_6_2_C	22.612
FI	EX_M	2019	1000 NAC	6_2_NC	FI_EX_M_2019_1000 NAC_6_2_NC	45324.985
FI	EX_M	2019	1000 NAC	6_2_NC_T	FI_EX_M_2019_1000 NAC_6_2_NC_T	37515.02
FI	EX_M	2019	1000 NAC	6_3	FI_EX_M_2019_1000 NAC_6_3	8799.67
FI	EX_M	2019	1000 NAC	6_3_1	FI_EX_M_2019_1000 NAC_6_3_1	28715.35
FI	EX_M	2019	1000 NAC	6_4	FI_EX_M_2019_1000 NAC_6_4	801.829
FI	EX_M	2019	1000 NAC	6_4_1	FI_EX_M_2019_1000 NAC_6_4_1	5190.271
FI	EX_M	2019	1000 NAC	6_4_2	FI_EX_M_2019_1000 NAC_6_4_2	3772.193
FI	EX_M	2019	1000 NAC	6_4_3	FI_EX_M_2019_1000 NAC_6_4_3	2619.694
FI	EX_M	2019	1000 NAC	7	FI_EX_M_2019_1000 NAC_7	363.402
FI	EX_M	2019	1000 NAC	7_1	FI_EX_M_2019_1000 NAC_7_1	1671.548
FI	EX_M	2019	1000 NAC	7_2	FI_EX_M_2019_1000 NAC_7_2	584.744
FI	EX_M	2019	1000 NAC	7_3	FI_EX_M_2019_1000 NAC_7_3	183761.815
FI	EX_M	2019	1000 NAC	7_3_1	FI_EX_M_2019_1000 NAC_7_3_1	1413.078
FI	EX_M	2019	1000 NAC	7_3_2	FI_EX_M_2019_1000 NAC_7_3_2	175511.995
FI	EX_M	2019	1000 NAC	7_3_3	FI_EX_M_2019_1000 NAC_7_3_3	174763.12
FI	EX_M	2019	1000 NAC	7_3_4	FI_EX_M_2019_1000 NAC_7_3_4	169931.053
FI	EX_M	2019	1000 NAC	7_4	FI_EX_M_2019_1000 NAC_7_4	748.875
FI	EX_M	2019	1000 NAC	8	FI_EX_M_2019_1000 NAC_8	6836.742
FI	EX_M	2019	1000 NAC	8_1	FI_EX_M_2019_1000 NAC_8_1	4081.468
FI	EX_M	2019	1000 NAC	8_2	FI_EX_M_2019_1000 NAC_8_2	4073.737
FI	EX_M	2019	1000 NAC	9	FI_EX_M_2019_1000 NAC_9	7.731
FI	EX_M	2019	1000 NAC	10	FI_EX_M_2019_1000 NAC_10	4727.268
FI	EX_M	2019	1000 NAC	10_1	FI_EX_M_2019_1000 NAC_10_1	24488.719
FI	EX_M	2019	1000 NAC	10_1_1	FI_EX_M_2019_1000 NAC_10_1_1	9467.534
FI	EX_M	2019	1000 NAC	10_1_2	FI_EX_M_2019_1000 NAC_10_1_2	7484.645
FI	EX_M	2019	1000 NAC	10_1_3	FI_EX_M_2019_1000 NAC_10_1_3	157.683
FI	EX_M	2019	1000 NAC	10_1_4	FI_EX_M_2019_1000 NAC_10_1_4	737.261
FI	EX_M	2019	1000 NAC	10_2	FI_EX_M_2019_1000 NAC_10_2	1087.945
FI	EX_M	2019	1000 NAC	10_3	FI_EX_M_2019_1000 NAC_10_3	177.612
FI	EX_M	2019	1000 NAC	10_3_1	FI_EX_M_2019_1000 NAC_10_3_1	14679.008
FI	EX_M	2019	1000 NAC	10_3_2	FI_EX_M_2019_1000 NAC_10_3_2	2368.01
FI	EX_M	2019	1000 NAC	10_3_3	FI_EX_M_2019_1000 NAC_10_3_3	9006.921
FI	EX_M	2019	1000 NAC	10_3_4	FI_EX_M_2019_1000 NAC_10_3_4	1985.024
FI	EX_M	2019	1000 NAC	10_4	FI_EX_M_2019_1000 NAC_10_4	1319.053
FI	EX_M	2020	1000 m3	1	FI_EX_M_2020_1000 m3_1	4779.8890704
FI	EX_M	2020	1000 m3	1_1	FI_EX_M_2020_1000 m3_1_1	27.5190704
FI	EX_M	2020	1000 m3	1_2	FI_EX_M_2020_1000 m3_1_2	4.023864
FI	EX_M	2020	1000 m3	1_2_C	FI_EX_M_2020_1000 m3_1_2_C	23.4952064
FI	EX_M	2020	1000 m3	1_2_NC	FI_EX_M_2020_1000 m3_1_2_NC	4752.37
FI	EX_M	2020	1000 m3	1_2_NC_T	FI_EX_M_2020_1000 m3_1_2_NC_T	702.496
FI	EX_M	2020	1000 mt	2	FI_EX_M_2020_1000 mt_2	4049.874
FI	EX_M	2020	1000 m3	3	FI_EX_M_2020_1000 m3_3	0
FI	EX_M	2020	1000 m3	3_1	FI_EX_M_2020_1000 m3_3_1	1.093679
FI	EX_M	2020	1000 m3	3_2	FI_EX_M_2020_1000 m3_3_2	3817.4956922622
FI	EX_M	2020	1000 mt	4	FI_EX_M_2020_1000 mt_4	3555.8749473267
FI	EX_M	2020	1000 mt	4_1	FI_EX_M_2020_1000 mt_4_1	261.6207449355
FI	EX_M	2020	1000 mt	4_2	FI_EX_M_2020_1000 mt_4_2	195.5649352041
FI	EX_M	2020	1000 m3	5	FI_EX_M_2020_1000 m3_5	97.5782928884
FI	EX_M	2020	1000 m3	5_C	FI_EX_M_2020_1000 m3_5_C	85.7766417391
FI	EX_M	2020	1000 m3	5_NC	FI_EX_M_2020_1000 m3_5_NC	11.8016511492
FI	EX_M	2020	1000 m3	5_NC_T	FI_EX_M_2020_1000 m3_5_NC_T	559.885
FI	EX_M	2020	1000 m3	6	FI_EX_M_2020_1000 m3_6	549.2
FI	EX_M	2020	1000 m3	6_1	FI_EX_M_2020_1000 m3_6_1	10.685
FI	EX_M	2020	1000 m3	6_1_C	FI_EX_M_2020_1000 m3_6_1_C	3.253
FI	EX_M	2020	1000 m3	6_1_NC	FI_EX_M_2020_1000 m3_6_1_NC	3.378
FI	EX_M	2020	1000 m3	6_1_NC_T	FI_EX_M_2020_1000 m3_6_1_NC_T	0.053
FI	EX_M	2020	1000 m3	6_2	FI_EX_M_2020_1000 m3_6_2	3.325
FI	EX_M	2020	1000 m3	6_2_C	FI_EX_M_2020_1000 m3_6_2_C	0.001
FI	EX_M	2020	1000 m3	6_2_NC	FI_EX_M_2020_1000 m3_6_2_NC	140.610178004
FI	EX_M	2020	1000 m3	6_2_NC_T	FI_EX_M_2020_1000 m3_6_2_NC_T	93.934
FI	EX_M	2020	1000 m3	6_3	FI_EX_M_2020_1000 m3_6_3	17.406
FI	EX_M	2020	1000 m3	6_3_1	FI_EX_M_2020_1000 m3_6_3_1	76.528
FI	EX_M	2020	1000 m3	6_4	FI_EX_M_2020_1000 m3_6_4	0.426
FI	EX_M	2020	1000 m3	6_4_1	FI_EX_M_2020_1000 m3_6_4_1	28.827
FI	EX_M	2020	1000 m3	6_4_2	FI_EX_M_2020_1000 m3_6_4_2	20.11
FI	EX_M	2020	1000 m3	6_4_3	FI_EX_M_2020_1000 m3_6_4_3	17.849178004
FI	EX_M	2020	1000 mt	7	FI_EX_M_2020_1000 mt_7	1.928767494
FI	EX_M	2020	1000 mt	7_1	FI_EX_M_2020_1000 mt_7_1	12.477838
FI	EX_M	2020	1000 mt	7_2	FI_EX_M_2020_1000 mt_7_2	3.44257251
FI	EX_M	2020	1000 mt	7_3	FI_EX_M_2020_1000 mt_7_3	163.773718
FI	EX_M	2020	1000 mt	7_3_1	FI_EX_M_2020_1000 mt_7_3_1	8.98928
FI	EX_M	2020	1000 mt	7_3_2	FI_EX_M_2020_1000 mt_7_3_2	148.251212
FI	EX_M	2020	1000 mt	7_3_3	FI_EX_M_2020_1000 mt_7_3_3	148.248247
FI	EX_M	2020	1000 mt	7_3_4	FI_EX_M_2020_1000 mt_7_3_4	140.70266
FI	EX_M	2020	1000 mt	7_4	FI_EX_M_2020_1000 mt_7_4	0.002965
FI	EX_M	2020	1000 mt	8	FI_EX_M_2020_1000 mt_8	6.533226
FI	EX_M	2020	1000 mt	8_1	FI_EX_M_2020_1000 mt_8_1	1.504697
FI	EX_M	2020	1000 mt	8_2	FI_EX_M_2020_1000 mt_8_2	1.504615
FI	EX_M	2020	1000 mt	9	FI_EX_M_2020_1000 mt_9	0.000082
FI	EX_M	2020	1000 mt	10	FI_EX_M_2020_1000 mt_10	5.381368
FI	EX_M	2020	1000 mt	10_1	FI_EX_M_2020_1000 mt_10_1	40.439078
FI	EX_M	2020	1000 mt	10_1_1	FI_EX_M_2020_1000 mt_10_1_1	15.889501
FI	EX_M	2020	1000 mt	10_1_2	FI_EX_M_2020_1000 mt_10_1_2	14.863112
FI	EX_M	2020	1000 mt	10_1_3	FI_EX_M_2020_1000 mt_10_1_3	0.087437
FI	EX_M	2020	1000 mt	10_1_4	FI_EX_M_2020_1000 mt_10_1_4	0.468146
FI	EX_M	2020	1000 mt	10_2	FI_EX_M_2020_1000 mt_10_2	0.470806
FI	EX_M	2020	1000 mt	10_3	FI_EX_M_2020_1000 mt_10_3	0.019211
FI	EX_M	2020	1000 mt	10_3_1	FI_EX_M_2020_1000 mt_10_3_1	24.47081
FI	EX_M	2020	1000 mt	10_3_2	FI_EX_M_2020_1000 mt_10_3_2	6.438244
FI	EX_M	2020	1000 mt	10_3_3	FI_EX_M_2020_1000 mt_10_3_3	10.753031
FI	EX_M	2020	1000 mt	10_3_4	FI_EX_M_2020_1000 mt_10_3_4	3.649434
FI	EX_M	2020	1000 mt	10_4	FI_EX_M_2020_1000 mt_10_4	3.630101
FI	EX_M	2020	1000 NAC	1	FI_EX_M_2020_1000 NAC_1	207306.873
FI	EX_M	2020	1000 NAC	1_1	FI_EX_M_2020_1000 NAC_1_1	1545.456
FI	EX_M	2020	1000 NAC	1_2	FI_EX_M_2020_1000 NAC_1_2	114.923
FI	EX_M	2020	1000 NAC	1_2_C	FI_EX_M_2020_1000 NAC_1_2_C	1430.533
FI	EX_M	2020	1000 NAC	1_2_NC	FI_EX_M_2020_1000 NAC_1_2_NC	205761.417
FI	EX_M	2020	1000 NAC	1_2_NC_T	FI_EX_M_2020_1000 NAC_1_2_NC_T	36371.755
FI	EX_M	2020	1000 NAC	2	FI_EX_M_2020_1000 NAC_2	169389.662
FI	EX_M	2020	1000 NAC	3	FI_EX_M_2020_1000 NAC_3	0
FI	EX_M	2020	1000 NAC	3_1	FI_EX_M_2020_1000 NAC_3_1	636.454
FI	EX_M	2020	1000 NAC	3_2	FI_EX_M_2020_1000 NAC_3_2	139868.823
FI	EX_M	2020	1000 NAC	4	FI_EX_M_2020_1000 NAC_4	134059.012
FI	EX_M	2020	1000 NAC	4_1	FI_EX_M_2020_1000 NAC_4_1	5809.811
FI	EX_M	2020	1000 NAC	4_2	FI_EX_M_2020_1000 NAC_4_2	3350.259
FI	EX_M	2020	1000 NAC	5	FI_EX_M_2020_1000 NAC_5	12720.341
FI	EX_M	2020	1000 NAC	5_C	FI_EX_M_2020_1000 NAC_5_C	11877.187
FI	EX_M	2020	1000 NAC	5_NC	FI_EX_M_2020_1000 NAC_5_NC	843.154
FI	EX_M	2020	1000 NAC	5_NC_T	FI_EX_M_2020_1000 NAC_5_NC_T	91930.514
FI	EX_M	2020	1000 NAC	6	FI_EX_M_2020_1000 NAC_6	84940.31
FI	EX_M	2020	1000 NAC	6_1	FI_EX_M_2020_1000 NAC_6_1	6990.204
FI	EX_M	2020	1000 NAC	6_1_C	FI_EX_M_2020_1000 NAC_6_1_C	2602.392
FI	EX_M	2020	1000 NAC	6_1_NC	FI_EX_M_2020_1000 NAC_6_1_NC	1257.641
FI	EX_M	2020	1000 NAC	6_1_NC_T	FI_EX_M_2020_1000 NAC_6_1_NC_T	28.407
FI	EX_M	2020	1000 NAC	6_2	FI_EX_M_2020_1000 NAC_6_2	1229.234
FI	EX_M	2020	1000 NAC	6_2_C	FI_EX_M_2020_1000 NAC_6_2_C	1.078
FI	EX_M	2020	1000 NAC	6_2_NC	FI_EX_M_2020_1000 NAC_6_2_NC	45542.6470000001
FI	EX_M	2020	1000 NAC	6_2_NC_T	FI_EX_M_2020_1000 NAC_6_2_NC_T	35800.93
FI	EX_M	2020	1000 NAC	6_3	FI_EX_M_2020_1000 NAC_6_3	5406.519
FI	EX_M	2020	1000 NAC	6_3_1	FI_EX_M_2020_1000 NAC_6_3_1	30394.411
FI	EX_M	2020	1000 NAC	6_4	FI_EX_M_2020_1000 NAC_6_4	585.95
FI	EX_M	2020	1000 NAC	6_4_1	FI_EX_M_2020_1000 NAC_6_4_1	6184.808
FI	EX_M	2020	1000 NAC	6_4_2	FI_EX_M_2020_1000 NAC_6_4_2	4297.326
FI	EX_M	2020	1000 NAC	6_4_3	FI_EX_M_2020_1000 NAC_6_4_3	3556.909
FI	EX_M	2020	1000 NAC	7	FI_EX_M_2020_1000 NAC_7	572.906
FI	EX_M	2020	1000 NAC	7_1	FI_EX_M_2020_1000 NAC_7_1	2213.651
FI	EX_M	2020	1000 NAC	7_2	FI_EX_M_2020_1000 NAC_7_2	770.352
FI	EX_M	2020	1000 NAC	7_3	FI_EX_M_2020_1000 NAC_7_3	72038.613
FI	EX_M	2020	1000 NAC	7_3_1	FI_EX_M_2020_1000 NAC_7_3_1	2571.062
FI	EX_M	2020	1000 NAC	7_3_2	FI_EX_M_2020_1000 NAC_7_3_2	62334.563
FI	EX_M	2020	1000 NAC	7_3_3	FI_EX_M_2020_1000 NAC_7_3_3	62327.515
FI	EX_M	2020	1000 NAC	7_3_4	FI_EX_M_2020_1000 NAC_7_3_4	58929.55
FI	EX_M	2020	1000 NAC	7_4	FI_EX_M_2020_1000 NAC_7_4	7.048
FI	EX_M	2020	1000 NAC	8	FI_EX_M_2020_1000 NAC_8	7132.988
FI	EX_M	2020	1000 NAC	8_1	FI_EX_M_2020_1000 NAC_8_1	2111.271
FI	EX_M	2020	1000 NAC	8_2	FI_EX_M_2020_1000 NAC_8_2	2110.61
FI	EX_M	2020	1000 NAC	9	FI_EX_M_2020_1000 NAC_9	0.661
FI	EX_M	2020	1000 NAC	10	FI_EX_M_2020_1000 NAC_10	908.09
FI	EX_M	2020	1000 NAC	10_1	FI_EX_M_2020_1000 NAC_10_1	39082.5370000002
FI	EX_M	2020	1000 NAC	10_1_1	FI_EX_M_2020_1000 NAC_10_1_1	8527.344
FI	EX_M	2020	1000 NAC	10_1_2	FI_EX_M_2020_1000 NAC_10_1_2	5698.682
FI	EX_M	2020	1000 NAC	10_1_3	FI_EX_M_2020_1000 NAC_10_1_3	161.731
FI	EX_M	2020	1000 NAC	10_1_4	FI_EX_M_2020_1000 NAC_10_1_4	1731.259
FI	EX_M	2020	1000 NAC	10_2	FI_EX_M_2020_1000 NAC_10_2	935.672
FI	EX_M	2020	1000 NAC	10_3	FI_EX_M_2020_1000 NAC_10_3	93.025
FI	EX_M	2020	1000 NAC	10_3_1	FI_EX_M_2020_1000 NAC_10_3_1	29954.154
FI	EX_M	2020	1000 NAC	10_3_2	FI_EX_M_2020_1000 NAC_10_3_2	11626.157
FI	EX_M	2020	1000 NAC	10_3_3	FI_EX_M_2020_1000 NAC_10_3_3	13421.349
FI	EX_M	2020	1000 NAC	10_3_4	FI_EX_M_2020_1000 NAC_10_3_4	3097.072
FI	EX_M	2020	1000 NAC	10_4	FI_EX_M_2020_1000 NAC_10_4	1809.576
FI	EX_X	2019	1000 m3	1	FI_EX_X_2019_1000 m3_1	50.552232
FI	EX_X	2019	1000 m3	1_1	FI_EX_X_2019_1000 m3_1_1	3.247232
FI	EX_X	2019	1000 m3	1_2	FI_EX_X_2019_1000 m3_1_2	0.8641728
FI	EX_X	2019	1000 m3	1_2_C	FI_EX_X_2019_1000 m3_1_2_C	2.3830592
FI	EX_X	2019	1000 m3	1_2_NC	FI_EX_X_2019_1000 m3_1_2_NC	47.305
FI	EX_X	2019	1000 m3	1_2_NC_T	FI_EX_X_2019_1000 m3_1_2_NC_T	47.223
FI	EX_X	2019	1000 mt	2	FI_EX_X_2019_1000 mt_2	0.082
FI	EX_X	2019	1000 m3	3	FI_EX_X_2019_1000 m3_3	0
FI	EX_X	2019	1000 m3	3_1	FI_EX_X_2019_1000 m3_3_1	0.013539
FI	EX_X	2019	1000 m3	3_2	FI_EX_X_2019_1000 m3_3_2	50.471
FI	EX_X	2019	1000 mt	4	FI_EX_X_2019_1000 mt_4	50.463
FI	EX_X	2019	1000 mt	4_1	FI_EX_X_2019_1000 mt_4_1	0.008
FI	EX_X	2019	1000 mt	4_2	FI_EX_X_2019_1000 mt_4_2	0.0004
FI	EX_X	2019	1000 m3	5	FI_EX_X_2019_1000 m3_5	0.066
FI	EX_X	2019	1000 m3	5_C	FI_EX_X_2019_1000 m3_5_C	0.05
FI	EX_X	2019	1000 m3	5_NC	FI_EX_X_2019_1000 m3_5_NC	0.016
FI	EX_X	2019	1000 m3	5_NC_T	FI_EX_X_2019_1000 m3_5_NC_T	5701.386
FI	EX_X	2019	1000 m3	6	FI_EX_X_2019_1000 m3_6	5698.973
FI	EX_X	2019	1000 m3	6_1	FI_EX_X_2019_1000 m3_6_1	2.413
FI	EX_X	2019	1000 m3	6_1_C	FI_EX_X_2019_1000 m3_6_1_C	0.56
FI	EX_X	2019	1000 m3	6_1_NC	FI_EX_X_2019_1000 m3_6_1_NC	15.254
FI	EX_X	2019	1000 m3	6_1_NC_T	FI_EX_X_2019_1000 m3_6_1_NC_T	14.212
FI	EX_X	2019	1000 m3	6_2	FI_EX_X_2019_1000 m3_6_2	1.042
FI	EX_X	2019	1000 m3	6_2_C	FI_EX_X_2019_1000 m3_6_2_C	0.025
FI	EX_X	2019	1000 m3	6_2_NC	FI_EX_X_2019_1000 m3_6_2_NC	200.933
FI	EX_X	2019	1000 m3	6_2_NC_T	FI_EX_X_2019_1000 m3_6_2_NC_T	192.953
FI	EX_X	2019	1000 m3	6_3	FI_EX_X_2019_1000 m3_6_3	133.911
FI	EX_X	2019	1000 m3	6_3_1	FI_EX_X_2019_1000 m3_6_3_1	59.022
FI	EX_X	2019	1000 m3	6_4	FI_EX_X_2019_1000 m3_6_4	0.016
FI	EX_X	2019	1000 m3	6_4_1	FI_EX_X_2019_1000 m3_6_4_1	3.827
FI	EX_X	2019	1000 m3	6_4_2	FI_EX_X_2019_1000 m3_6_4_2	0.122
FI	EX_X	2019	1000 m3	6_4_3	FI_EX_X_2019_1000 m3_6_4_3	4.153
FI	EX_X	2019	1000 mt	7	FI_EX_X_2019_1000 mt_7	3.896
FI	EX_X	2019	1000 mt	7_1	FI_EX_X_2019_1000 mt_7_1	0.16
FI	EX_X	2019	1000 mt	7_2	FI_EX_X_2019_1000 mt_7_2	0.097
FI	EX_X	2019	1000 mt	7_3	FI_EX_X_2019_1000 mt_7_3	2467.13
FI	EX_X	2019	1000 mt	7_3_1	FI_EX_X_2019_1000 mt_7_3_1	16.911
FI	EX_X	2019	1000 mt	7_3_2	FI_EX_X_2019_1000 mt_7_3_2	2304.699
FI	EX_X	2019	1000 mt	7_3_3	FI_EX_X_2019_1000 mt_7_3_3	2304.698
FI	EX_X	2019	1000 mt	7_3_4	FI_EX_X_2019_1000 mt_7_3_4	2271.356
FI	EX_X	2019	1000 mt	7_4	FI_EX_X_2019_1000 mt_7_4	0.001
FI	EX_X	2019	1000 mt	8	FI_EX_X_2019_1000 mt_8	145.52
FI	EX_X	2019	1000 mt	8_1	FI_EX_X_2019_1000 mt_8_1	0.03202
FI	EX_X	2019	1000 mt	8_2	FI_EX_X_2019_1000 mt_8_2	0.00002
FI	EX_X	2019	1000 mt	9	FI_EX_X_2019_1000 mt_9	0.032
FI	EX_X	2019	1000 mt	10	FI_EX_X_2019_1000 mt_10	5.422
FI	EX_X	2019	1000 mt	10_1	FI_EX_X_2019_1000 mt_10_1	4355.782
FI	EX_X	2019	1000 mt	10_1_1	FI_EX_X_2019_1000 mt_10_1_1	2392.802
FI	EX_X	2019	1000 mt	10_1_2	FI_EX_X_2019_1000 mt_10_1_2	58.489
FI	EX_X	2019	1000 mt	10_1_3	FI_EX_X_2019_1000 mt_10_1_3	667.622
FI	EX_X	2019	1000 mt	10_1_4	FI_EX_X_2019_1000 mt_10_1_4	318.218
FI	EX_X	2019	1000 mt	10_2	FI_EX_X_2019_1000 mt_10_2	1348.473
FI	EX_X	2019	1000 mt	10_3	FI_EX_X_2019_1000 mt_10_3	1.905
FI	EX_X	2019	1000 mt	10_3_1	FI_EX_X_2019_1000 mt_10_3_1	1914.64
FI	EX_X	2019	1000 mt	10_3_2	FI_EX_X_2019_1000 mt_10_3_2	477.498
FI	EX_X	2019	1000 mt	10_3_3	FI_EX_X_2019_1000 mt_10_3_3	1237.374
FI	EX_X	2019	1000 mt	10_3_4	FI_EX_X_2019_1000 mt_10_3_4	173.611
FI	EX_X	2019	1000 mt	10_4	FI_EX_X_2019_1000 mt_10_4	26.156
FI	EX_X	2019	1000 NAC	1	FI_EX_X_2019_1000 NAC_1	7465.56
FI	EX_X	2019	1000 NAC	1_1	FI_EX_X_2019_1000 NAC_1_1	412.393
FI	EX_X	2019	1000 NAC	1_2	FI_EX_X_2019_1000 NAC_1_2	176.829
FI	EX_X	2019	1000 NAC	1_2_C	FI_EX_X_2019_1000 NAC_1_2_C	235.564
FI	EX_X	2019	1000 NAC	1_2_NC	FI_EX_X_2019_1000 NAC_1_2_NC	7053.167
FI	EX_X	2019	1000 NAC	1_2_NC_T	FI_EX_X_2019_1000 NAC_1_2_NC_T	7022.822
FI	EX_X	2019	1000 NAC	2	FI_EX_X_2019_1000 NAC_2	30.345
FI	EX_X	2019	1000 NAC	3	FI_EX_X_2019_1000 NAC_3	0
FI	EX_X	2019	1000 NAC	3_1	FI_EX_X_2019_1000 NAC_3_1	14.358
FI	EX_X	2019	1000 NAC	3_2	FI_EX_X_2019_1000 NAC_3_2	3450.551
FI	EX_X	2019	1000 NAC	4	FI_EX_X_2019_1000 NAC_4	3448.831
FI	EX_X	2019	1000 NAC	4_1	FI_EX_X_2019_1000 NAC_4_1	1.72
FI	EX_X	2019	1000 NAC	4_2	FI_EX_X_2019_1000 NAC_4_2	0.448
FI	EX_X	2019	1000 NAC	5	FI_EX_X_2019_1000 NAC_5	16.842
FI	EX_X	2019	1000 NAC	5_C	FI_EX_X_2019_1000 NAC_5_C	12.659
FI	EX_X	2019	1000 NAC	5_NC	FI_EX_X_2019_1000 NAC_5_NC	4.183
FI	EX_X	2019	1000 NAC	5_NC_T	FI_EX_X_2019_1000 NAC_5_NC_T	1012374.37
FI	EX_X	2019	1000 NAC	6	FI_EX_X_2019_1000 NAC_6	1010103.938
FI	EX_X	2019	1000 NAC	6_1	FI_EX_X_2019_1000 NAC_6_1	2270.432
FI	EX_X	2019	1000 NAC	6_1_C	FI_EX_X_2019_1000 NAC_6_1_C	1096.596
FI	EX_X	2019	1000 NAC	6_1_NC	FI_EX_X_2019_1000 NAC_6_1_NC	8002.415
FI	EX_X	2019	1000 NAC	6_1_NC_T	FI_EX_X_2019_1000 NAC_6_1_NC_T	7041.104
FI	EX_X	2019	1000 NAC	6_2	FI_EX_X_2019_1000 NAC_6_2	961.311
FI	EX_X	2019	1000 NAC	6_2_C	FI_EX_X_2019_1000 NAC_6_2_C	5.333
FI	EX_X	2019	1000 NAC	6_2_NC	FI_EX_X_2019_1000 NAC_6_2_NC	120783.183
FI	EX_X	2019	1000 NAC	6_2_NC_T	FI_EX_X_2019_1000 NAC_6_2_NC_T	117963.15
FI	EX_X	2019	1000 NAC	6_3	FI_EX_X_2019_1000 NAC_6_3	62854.23
FI	EX_X	2019	1000 NAC	6_3_1	FI_EX_X_2019_1000 NAC_6_3_1	55104.411
FI	EX_X	2019	1000 NAC	6_4	FI_EX_X_2019_1000 NAC_6_4	14.243
FI	EX_X	2019	1000 NAC	6_4_1	FI_EX_X_2019_1000 NAC_6_4_1	1277.397
FI	EX_X	2019	1000 NAC	6_4_2	FI_EX_X_2019_1000 NAC_6_4_2	49.429
FI	EX_X	2019	1000 NAC	6_4_3	FI_EX_X_2019_1000 NAC_6_4_3	1542.636
FI	EX_X	2019	1000 NAC	7	FI_EX_X_2019_1000 NAC_7	1402.084
FI	EX_X	2019	1000 NAC	7_1	FI_EX_X_2019_1000 NAC_7_1	120.388
FI	EX_X	2019	1000 NAC	7_2	FI_EX_X_2019_1000 NAC_7_2	20.164
FI	EX_X	2019	1000 NAC	7_3	FI_EX_X_2019_1000 NAC_7_3	1247452.351
FI	EX_X	2019	1000 NAC	7_3_1	FI_EX_X_2019_1000 NAC_7_3_1	5964.702
FI	EX_X	2019	1000 NAC	7_3_2	FI_EX_X_2019_1000 NAC_7_3_2	1148877.679
FI	EX_X	2019	1000 NAC	7_3_3	FI_EX_X_2019_1000 NAC_7_3_3	1148865.659
FI	EX_X	2019	1000 NAC	7_3_4	FI_EX_X_2019_1000 NAC_7_3_4	1133774.265
FI	EX_X	2019	1000 NAC	7_4	FI_EX_X_2019_1000 NAC_7_4	12.015
FI	EX_X	2019	1000 NAC	8	FI_EX_X_2019_1000 NAC_8	92609.97
FI	EX_X	2019	1000 NAC	8_1	FI_EX_X_2019_1000 NAC_8_1	20.636
FI	EX_X	2019	1000 NAC	8_2	FI_EX_X_2019_1000 NAC_8_2	0.003
FI	EX_X	2019	1000 NAC	9	FI_EX_X_2019_1000 NAC_9	20.633
FI	EX_X	2019	1000 NAC	10	FI_EX_X_2019_1000 NAC_10	1616.692
FI	EX_X	2019	1000 NAC	10_1	FI_EX_X_2019_1000 NAC_10_1	3177326.095
FI	EX_X	2019	1000 NAC	10_1_1	FI_EX_X_2019_1000 NAC_10_1_1	1577990.1
FI	EX_X	2019	1000 NAC	10_1_2	FI_EX_X_2019_1000 NAC_10_1_2	29086.682
FI	EX_X	2019	1000 NAC	10_1_3	FI_EX_X_2019_1000 NAC_10_1_3	384804.756
FI	EX_X	2019	1000 NAC	10_1_4	FI_EX_X_2019_1000 NAC_10_1_4	221581.859
FI	EX_X	2019	1000 NAC	10_2	FI_EX_X_2019_1000 NAC_10_2	942516.803
FI	EX_X	2019	1000 NAC	10_3	FI_EX_X_2019_1000 NAC_10_3	2059.235
FI	EX_X	2019	1000 NAC	10_3_1	FI_EX_X_2019_1000 NAC_10_3_1	1551888.413
FI	EX_X	2019	1000 NAC	10_3_2	FI_EX_X_2019_1000 NAC_10_3_2	258192.142
FI	EX_X	2019	1000 NAC	10_3_3	FI_EX_X_2019_1000 NAC_10_3_3	1079129.135
FI	EX_X	2019	1000 NAC	10_3_4	FI_EX_X_2019_1000 NAC_10_3_4	192880.069
FI	EX_X	2019	1000 NAC	10_4	FI_EX_X_2019_1000 NAC_10_4	21687.067
FI	EX_X	2020	1000 m3	1	FI_EX_X_2020_1000 m3_1	124.7548352
FI	EX_X	2020	1000 m3	1_1	FI_EX_X_2020_1000 m3_1_1	2.7048352
FI	EX_X	2020	1000 m3	1_2	FI_EX_X_2020_1000 m3_1_2	0.51172
FI	EX_X	2020	1000 m3	1_2_C	FI_EX_X_2020_1000 m3_1_2_C	2.1931152
FI	EX_X	2020	1000 m3	1_2_NC	FI_EX_X_2020_1000 m3_1_2_NC	122.05
FI	EX_X	2020	1000 m3	1_2_NC_T	FI_EX_X_2020_1000 m3_1_2_NC_T	122.05
FI	EX_X	2020	1000 mt	2	FI_EX_X_2020_1000 mt_2	0
FI	EX_X	2020	1000 m3	3	FI_EX_X_2020_1000 m3_3	0
FI	EX_X	2020	1000 m3	3_1	FI_EX_X_2020_1000 m3_3_1	0.028802
FI	EX_X	2020	1000 m3	3_2	FI_EX_X_2020_1000 m3_3_2	27.1987175572
FI	EX_X	2020	1000 mt	4	FI_EX_X_2020_1000 mt_4	27.1631932309
FI	EX_X	2020	1000 mt	4_1	FI_EX_X_2020_1000 mt_4_1	0.0355243263
FI	EX_X	2020	1000 mt	4_2	FI_EX_X_2020_1000 mt_4_2	0.0048846105
FI	EX_X	2020	1000 m3	5	FI_EX_X_2020_1000 m3_5	0.1386259239
FI	EX_X	2020	1000 m3	5_C	FI_EX_X_2020_1000 m3_5_C	0.1179965217
FI	EX_X	2020	1000 m3	5_NC	FI_EX_X_2020_1000 m3_5_NC	0.0206294022
FI	EX_X	2020	1000 m3	5_NC_T	FI_EX_X_2020_1000 m3_5_NC_T	5792.237
FI	EX_X	2020	1000 m3	6	FI_EX_X_2020_1000 m3_6	5787.887
FI	EX_X	2020	1000 m3	6_1	FI_EX_X_2020_1000 m3_6_1	4.35
FI	EX_X	2020	1000 m3	6_1_C	FI_EX_X_2020_1000 m3_6_1_C	0.56
FI	EX_X	2020	1000 m3	6_1_NC	FI_EX_X_2020_1000 m3_6_1_NC	11.407
FI	EX_X	2020	1000 m3	6_1_NC_T	FI_EX_X_2020_1000 m3_6_1_NC_T	10.794
FI	EX_X	2020	1000 m3	6_2	FI_EX_X_2020_1000 m3_6_2	0.613
FI	EX_X	2020	1000 m3	6_2_C	FI_EX_X_2020_1000 m3_6_2_C	0
FI	EX_X	2020	1000 m3	6_2_NC	FI_EX_X_2020_1000 m3_6_2_NC	333.165923922
FI	EX_X	2020	1000 m3	6_2_NC_T	FI_EX_X_2020_1000 m3_6_2_NC_T	312.863
FI	EX_X	2020	1000 m3	6_3	FI_EX_X_2020_1000 m3_6_3	227.545
FI	EX_X	2020	1000 m3	6_3_1	FI_EX_X_2020_1000 m3_6_3_1	85.318
FI	EX_X	2020	1000 m3	6_4	FI_EX_X_2020_1000 m3_6_4	0.025
FI	EX_X	2020	1000 m3	6_4_1	FI_EX_X_2020_1000 m3_6_4_1	4.278
FI	EX_X	2020	1000 m3	6_4_2	FI_EX_X_2020_1000 m3_6_4_2	0.134
FI	EX_X	2020	1000 m3	6_4_3	FI_EX_X_2020_1000 m3_6_4_3	16.024923922
FI	EX_X	2020	1000 mt	7	FI_EX_X_2020_1000 mt_7	15.567404922
FI	EX_X	2020	1000 mt	7_1	FI_EX_X_2020_1000 mt_7_1	0.383914
FI	EX_X	2020	1000 mt	7_2	FI_EX_X_2020_1000 mt_7_2	0.073605
FI	EX_X	2020	1000 mt	7_3	FI_EX_X_2020_1000 mt_7_3	2600.269016
FI	EX_X	2020	1000 mt	7_3_1	FI_EX_X_2020_1000 mt_7_3_1	32.052959
FI	EX_X	2020	1000 mt	7_3_2	FI_EX_X_2020_1000 mt_7_3_2	2369.693821
FI	EX_X	2020	1000 mt	7_3_3	FI_EX_X_2020_1000 mt_7_3_3	2369.69177
FI	EX_X	2020	1000 mt	7_3_4	FI_EX_X_2020_1000 mt_7_3_4	2338.873058
FI	EX_X	2020	1000 mt	7_4	FI_EX_X_2020_1000 mt_7_4	0.002051
FI	EX_X	2020	1000 mt	8	FI_EX_X_2020_1000 mt_8	198.522236
FI	EX_X	2020	1000 mt	8_1	FI_EX_X_2020_1000 mt_8_1	0.001545
FI	EX_X	2020	1000 mt	8_2	FI_EX_X_2020_1000 mt_8_2	0.000009
FI	EX_X	2020	1000 mt	9	FI_EX_X_2020_1000 mt_9	0
FI	EX_X	2020	1000 mt	10	FI_EX_X_2020_1000 mt_10	21.295382
FI	EX_X	2020	1000 mt	10_1	FI_EX_X_2020_1000 mt_10_1	4144.039261
FI	EX_X	2020	1000 mt	10_1_1	FI_EX_X_2020_1000 mt_10_1_1	1940.696578
FI	EX_X	2020	1000 mt	10_1_2	FI_EX_X_2020_1000 mt_10_1_2	50.702795
FI	EX_X	2020	1000 mt	10_1_3	FI_EX_X_2020_1000 mt_10_1_3	421.049165
FI	EX_X	2020	1000 mt	10_1_4	FI_EX_X_2020_1000 mt_10_1_4	368.171181
FI	EX_X	2020	1000 mt	10_2	FI_EX_X_2020_1000 mt_10_2	1100.773437
FI	EX_X	2020	1000 mt	10_3	FI_EX_X_2020_1000 mt_10_3	2.064192
FI	EX_X	2020	1000 mt	10_3_1	FI_EX_X_2020_1000 mt_10_3_1	2150.484673
FI	EX_X	2020	1000 mt	10_3_2	FI_EX_X_2020_1000 mt_10_3_2	525.563931
FI	EX_X	2020	1000 mt	10_3_3	FI_EX_X_2020_1000 mt_10_3_3	1387.730971
FI	EX_X	2020	1000 mt	10_3_4	FI_EX_X_2020_1000 mt_10_3_4	211.836386
FI	EX_X	2020	1000 mt	10_4	FI_EX_X_2020_1000 mt_10_4	25.353385
FI	EX_X	2020	1000 NAC	1	FI_EX_X_2020_1000 NAC_1	27806.336
FI	EX_X	2020	1000 NAC	1_1	FI_EX_X_2020_1000 NAC_1_1	329.588
FI	EX_X	2020	1000 NAC	1_2	FI_EX_X_2020_1000 NAC_1_2	109.4
FI	EX_X	2020	1000 NAC	1_2_C	FI_EX_X_2020_1000 NAC_1_2_C	220.188
FI	EX_X	2020	1000 NAC	1_2_NC	FI_EX_X_2020_1000 NAC_1_2_NC	27476.748
FI	EX_X	2020	1000 NAC	1_2_NC_T	FI_EX_X_2020_1000 NAC_1_2_NC_T	27476.742
FI	EX_X	2020	1000 NAC	2	FI_EX_X_2020_1000 NAC_2	0
FI	EX_X	2020	1000 NAC	3	FI_EX_X_2020_1000 NAC_3	0
FI	EX_X	2020	1000 NAC	3_1	FI_EX_X_2020_1000 NAC_3_1	38.125
FI	EX_X	2020	1000 NAC	3_2	FI_EX_X_2020_1000 NAC_3_2	1823.901
FI	EX_X	2020	1000 NAC	4	FI_EX_X_2020_1000 NAC_4	1812.88
FI	EX_X	2020	1000 NAC	4_1	FI_EX_X_2020_1000 NAC_4_1	11.021
FI	EX_X	2020	1000 NAC	4_2	FI_EX_X_2020_1000 NAC_4_2	0.002
FI	EX_X	2020	1000 NAC	5	FI_EX_X_2020_1000 NAC_5	45.746
FI	EX_X	2020	1000 NAC	5_C	FI_EX_X_2020_1000 NAC_5_C	31.238
FI	EX_X	2020	1000 NAC	5_NC	FI_EX_X_2020_1000 NAC_5_NC	14.508
FI	EX_X	2020	1000 NAC	5_NC_T	FI_EX_X_2020_1000 NAC_5_NC_T	1034830.99
FI	EX_X	2020	1000 NAC	6	FI_EX_X_2020_1000 NAC_6	1032566.787
FI	EX_X	2020	1000 NAC	6_1	FI_EX_X_2020_1000 NAC_6_1	2264.203
FI	EX_X	2020	1000 NAC	6_1_C	FI_EX_X_2020_1000 NAC_6_1_C	575.3
FI	EX_X	2020	1000 NAC	6_1_NC	FI_EX_X_2020_1000 NAC_6_1_NC	5769.777
FI	EX_X	2020	1000 NAC	6_1_NC_T	FI_EX_X_2020_1000 NAC_6_1_NC_T	5318.044
FI	EX_X	2020	1000 NAC	6_2	FI_EX_X_2020_1000 NAC_6_2	451.733
FI	EX_X	2020	1000 NAC	6_2_C	FI_EX_X_2020_1000 NAC_6_2_C	0
FI	EX_X	2020	1000 NAC	6_2_NC	FI_EX_X_2020_1000 NAC_6_2_NC	175822.453
FI	EX_X	2020	1000 NAC	6_2_NC_T	FI_EX_X_2020_1000 NAC_6_2_NC_T	168673.984
FI	EX_X	2020	1000 NAC	6_3	FI_EX_X_2020_1000 NAC_6_3	97383.396
FI	EX_X	2020	1000 NAC	6_3_1	FI_EX_X_2020_1000 NAC_6_3_1	71290.588
FI	EX_X	2020	1000 NAC	6_4	FI_EX_X_2020_1000 NAC_6_4	46.183
FI	EX_X	2020	1000 NAC	6_4_1	FI_EX_X_2020_1000 NAC_6_4_1	1376.303
FI	EX_X	2020	1000 NAC	6_4_2	FI_EX_X_2020_1000 NAC_6_4_2	51.093
FI	EX_X	2020	1000 NAC	6_4_3	FI_EX_X_2020_1000 NAC_6_4_3	5772.166
FI	EX_X	2020	1000 NAC	7	FI_EX_X_2020_1000 NAC_7	5501.703
FI	EX_X	2020	1000 NAC	7_1	FI_EX_X_2020_1000 NAC_7_1	260.201
FI	EX_X	2020	1000 NAC	7_2	FI_EX_X_2020_1000 NAC_7_2	10.262
FI	EX_X	2020	1000 NAC	7_3	FI_EX_X_2020_1000 NAC_7_3	1141977.162
FI	EX_X	2020	1000 NAC	7_3_1	FI_EX_X_2020_1000 NAC_7_3_1	10778.634
FI	EX_X	2020	1000 NAC	7_3_2	FI_EX_X_2020_1000 NAC_7_3_2	1034239.729
FI	EX_X	2020	1000 NAC	7_3_3	FI_EX_X_2020_1000 NAC_7_3_3	1034215.316
FI	EX_X	2020	1000 NAC	7_3_4	FI_EX_X_2020_1000 NAC_7_3_4	1022998.378
FI	EX_X	2020	1000 NAC	7_4	FI_EX_X_2020_1000 NAC_7_4	24.413
FI	EX_X	2020	1000 NAC	8	FI_EX_X_2020_1000 NAC_8	96958.799
FI	EX_X	2020	1000 NAC	8_1	FI_EX_X_2020_1000 NAC_8_1	1.584
FI	EX_X	2020	1000 NAC	8_2	FI_EX_X_2020_1000 NAC_8_2	0.033
FI	EX_X	2020	1000 NAC	9	FI_EX_X_2020_1000 NAC_9	0
FI	EX_X	2020	1000 NAC	10	FI_EX_X_2020_1000 NAC_10	2691.453
FI	EX_X	2020	1000 NAC	10_1	FI_EX_X_2020_1000 NAC_10_1	2911636.631
FI	EX_X	2020	1000 NAC	10_1_1	FI_EX_X_2020_1000 NAC_10_1_1	1177098.539
FI	EX_X	2020	1000 NAC	10_1_2	FI_EX_X_2020_1000 NAC_10_1_2	20019.603
FI	EX_X	2020	1000 NAC	10_1_3	FI_EX_X_2020_1000 NAC_10_1_3	221434.863
FI	EX_X	2020	1000 NAC	10_1_4	FI_EX_X_2020_1000 NAC_10_1_4	244597.385
FI	EX_X	2020	1000 NAC	10_2	FI_EX_X_2020_1000 NAC_10_2	691046.687999999
FI	EX_X	2020	1000 NAC	10_3	FI_EX_X_2020_1000 NAC_10_3	2148.807
FI	EX_X	2020	1000 NAC	10_3_1	FI_EX_X_2020_1000 NAC_10_3_1	1683965.651
FI	EX_X	2020	1000 NAC	10_3_2	FI_EX_X_2020_1000 NAC_10_3_2	258341.588
FI	EX_X	2020	1000 NAC	10_3_3	FI_EX_X_2020_1000 NAC_10_3_3	1191143.066
FI	EX_X	2020	1000 NAC	10_3_4	FI_EX_X_2020_1000 NAC_10_3_4	215112.278
FI	EX_X	2020	1000 NAC	10_4	FI_EX_X_2020_1000 NAC_10_4	19368.719
FI	P	2019	1000 m3	EU2_1	FI_P_2019_1000 m3_EU2_1	63666.863634	EU2
FI	P	2019	1000 m3	EU2_1_C	FI_P_2019_1000 m3_EU2_1_C	49899.313252
FI	P	2019	1000 m3	EU2_1_NC	FI_P_2019_1000 m3_EU2_1_NC	13767.550382
FI	P	2019	1000 m3	EU2_1_1	FI_P_2019_1000 m3_EU2_1_1	5390.3869212
FI	P	2019	1000 m3	EU2_1_1_C	FI_P_2019_1000 m3_EU2_1_1_C	4708.0853649233
FI	P	2019	1000 m3	EU2_1_1_NC	FI_P_2019_1000 m3_EU2_1_1_NC	682.3015562767
FI	P	2019	1000 m3	EU2_1_2	FI_P_2019_1000 m3_EU2_1_2	0
FI	P	2019	1000 m3	EU2_1_2_C	FI_P_2019_1000 m3_EU2_1_2_C	0
FI	P	2019	1000 m3	EU2_1_2_NC	FI_P_2019_1000 m3_EU2_1_2_NC	0
FI	P	2019	1000 m3	EU2_1_3	FI_P_2019_1000 m3_EU2_1_3	58276.4767128
FI	P	2019	1000 m3	EU2_1_3_C	FI_P_2019_1000 m3_EU2_1_3_C	45191.2278870767
FI	P	2019	1000 m3	EU2_1_3_NC	FI_P_2019_1000 m3_EU2_1_3_NC	13085.2488257233
FI	P	2020	1000 m3	EU2_1	FI_P_2020_1000 m3_EU2_1	60233.267515
FI	P	2020	1000 m3	EU2_1_C	FI_P_2020_1000 m3_EU2_1_C	47338.05678
FI	P	2020	1000 m3	EU2_1_NC	FI_P_2020_1000 m3_EU2_1_NC	12895.210735
FI	P	2020	1000 m3	EU2_1_1	FI_P_2020_1000 m3_EU2_1_1	5172.93148052
FI	P	2020	1000 m3	EU2_1_1_C	FI_P_2020_1000 m3_EU2_1_1_C	4543.2125252053
FI	P	2020	1000 m3	EU2_1_1_NC	FI_P_2020_1000 m3_EU2_1_1_NC	629.7189553147
FI	P	2020	1000 m3	EU2_1_2	FI_P_2020_1000 m3_EU2_1_2	0
FI	P	2020	1000 m3	EU2_1_2_C	FI_P_2020_1000 m3_EU2_1_2_C	0
FI	P	2020	1000 m3	EU2_1_2_NC	FI_P_2020_1000 m3_EU2_1_2_NC	0
FI	P	2020	1000 m3	EU2_1_3	FI_P_2020_1000 m3_EU2_1_3	55060.33603448
FI	P	2020	1000 m3	EU2_1_3_C	FI_P_2020_1000 m3_EU2_1_3_C	42794.8442547947
FI	P	2020	1000 m3	EU2_1_3_NC	FI_P_2020_1000 m3_EU2_1_3_NC	12265.4917796853
FI	P.OB	2019	1000 m3	1	FI_P.OB_2019_1000 m3_1	72926.627	OB
FI	P.OB	2019	1000 m3	1_C	FI_P.OB_2019_1000 m3_1_C	9242.481
FI	P.OB	2019	1000 m3	1_NC	FI_P.OB_2019_1000 m3_1_NC	4286.094
FI	P.OB	2019	1000 m3	1_1	FI_P.OB_2019_1000 m3_1_1	4956.386
FI	P.OB	2019	1000 m3	1_1_C	FI_P.OB_2019_1000 m3_1_1_C	63684.146
FI	P.OB	2019	1000 m3	1_1_NC	FI_P.OB_2019_1000 m3_1_1_NC	52727.583
FI	P.OB	2019	1000 m3	1_2	FI_P.OB_2019_1000 m3_1_2	10956.563
FI	P.OB	2019	1000 m3	1_2_C	FI_P.OB_2019_1000 m3_1_2_C	0
FI	P.OB	2019	1000 m3	1_2_NC	FI_P.OB_2019_1000 m3_1_2_NC	26092.2
FI	P.OB	2019	1000 m3	1_2_1	FI_P.OB_2019_1000 m3_1_2_1	24970.182
FI	P.OB	2019	1000 m3	1_2_1_C	FI_P.OB_2019_1000 m3_1_2_1_C	1122.018
FI	P.OB	2019	1000 m3	1_2_1_NC	FI_P.OB_2019_1000 m3_1_2_1_NC	37591.946
FI	P.OB	2019	1000 m3	1_2_2	FI_P.OB_2019_1000 m3_1_2_2	27757.401
FI	P.OB	2019	1000 m3	1_2_2_C	FI_P.OB_2019_1000 m3_1_2_2_C	9834.545
FI	P.OB	2019	1000 m3	1_2_2_NC	FI_P.OB_2019_1000 m3_1_2_2_NC	0
FI	P.OB	2019	1000 m3	1_2_3	FI_P.OB_2019_1000 m3_1_2_3	0
FI	P.OB	2019	1000 m3	1_2_3_C	FI_P.OB_2019_1000 m3_1_2_3_C	0
FI	P.OB	2019	1000 m3	1_2_3_NC	FI_P.OB_2019_1000 m3_1_2_3_NC	ERROR:#REF!
FI	P.OB	2020	1000 m3	1	FI_P.OB_2020_1000 m3_1	68975.656
FI	P.OB	2020	1000 m3	1_C	FI_P.OB_2020_1000 m3_1_C	10307.971
FI	P.OB	2020	1000 m3	1_NC	FI_P.OB_2020_1000 m3_1_NC	4985.504
FI	P.OB	2020	1000 m3	1_1	FI_P.OB_2020_1000 m3_1_1	5322.467
FI	P.OB	2020	1000 m3	1_1_C	FI_P.OB_2020_1000 m3_1_1_C	58667.685
FI	P.OB	2020	1000 m3	1_1_NC	FI_P.OB_2020_1000 m3_1_1_NC	49087.512
FI	P.OB	2020	1000 m3	1_2	FI_P.OB_2020_1000 m3_1_2	9580.173
FI	P.OB	2020	1000 m3	1_2_C	FI_P.OB_2020_1000 m3_1_2_C	0
FI	P.OB	2020	1000 m3	1_2_NC	FI_P.OB_2020_1000 m3_1_2_NC	25045.098
FI	P.OB	2020	1000 m3	1_2_1	FI_P.OB_2020_1000 m3_1_2_1	24065.887
FI	P.OB	2020	1000 m3	1_2_1_C	FI_P.OB_2020_1000 m3_1_2_1_C	979.211
FI	P.OB	2020	1000 m3	1_2_1_NC	FI_P.OB_2020_1000 m3_1_2_1_NC	33622.587
FI	P.OB	2020	1000 m3	1_2_2	FI_P.OB_2020_1000 m3_1_2_2	25021.625
FI	P.OB	2020	1000 m3	1_2_2_C	FI_P.OB_2020_1000 m3_1_2_2_C	8600.962
FI	P.OB	2020	1000 m3	1_2_2_NC	FI_P.OB_2020_1000 m3_1_2_2_NC	0
FI	P.OB	2020	1000 m3	1_2_3	FI_P.OB_2020_1000 m3_1_2_3	0
FI	P.OB	2020	1000 m3	1_2_3_C	FI_P.OB_2020_1000 m3_1_2_3_C	0
FI	P.OB	2020	1000 m3	1_2_3_NC	FI_P.OB_2020_1000 m3_1_2_3_NC	ERROR:#REF!

Database

Follow UNECE

Country	Flow	Year	Unit	Product	conc
FI	P	2015	1000 m3	1	FI_P_2015_1000 m3_1
FI	P	2015	1000 m3	1_C	FI_P_2015_1000 m3_1_C
FI	P	2015	1000 m3	1_NC	FI_P_2015_1000 m3_1_NC
FI	P	2015	1000 m3	1_1	FI_P_2015_1000 m3_1_1
FI	P	2015	1000 m3	1_1_C	FI_P_2015_1000 m3_1_1_C
FI	P	2015	1000 m3	1_1_NC	FI_P_2015_1000 m3_1_1_NC
FI	P	2015	1000 m3	1_2	FI_P_2015_1000 m3_1_2
FI	P	2015	1000 m3	1_2_C	FI_P_2015_1000 m3_1_2_C
FI	P	2015	1000 m3	1_2_NC	FI_P_2015_1000 m3_1_2_NC
FI	P	2015	1000 m3	1_2_1	FI_P_2015_1000 m3_1_2_1
FI	P	2015	1000 m3	1_2_1_C	FI_P_2015_1000 m3_1_2_1_C
FI	P	2015	1000 m3	1_2_1_NC	FI_P_2015_1000 m3_1_2_1_NC
FI	P	2015	1000 m3	1_2_2	FI_P_2015_1000 m3_1_2_2
FI	P	2015	1000 m3	1_2_2_C	FI_P_2015_1000 m3_1_2_2_C
FI	P	2015	1000 m3	1_2_2_NC	FI_P_2015_1000 m3_1_2_2_NC
FI	P	2015	1000 m3	1_2_3	FI_P_2015_1000 m3_1_2_3
FI	P	2015	1000 m3	1_2_3_C	FI_P_2015_1000 m3_1_2_3_C
FI	P	2015	1000 m3	1_2_3_NC	FI_P_2015_1000 m3_1_2_3_NC
FI	P	2015	1000 mt	2	FI_P_2015_1000 mt_2
FI	P	2015	1000 m3	3	FI_P_2015_1000 m3_3
FI	P	2015	1000 m3	3_1	FI_P_2015_1000 m3_3_1
FI	P	2015	1000 m3	3_2	FI_P_2015_1000 m3_3_2
FI	P	2015	1000 mt	4	FI_P_2015_1000 mt_4
FI	P	2015	1000 mt	4_1	FI_P_2015_1000 mt_4_1
FI	P	2015	1000 mt	4_2	FI_P_2015_1000 mt_4_2
FI	P	2015	1000 m3	5	FI_P_2015_1000 m3_5
FI	P	2015	1000 m3	5_C	FI_P_2015_1000 m3_5_C
FI	P	2015	1000 m3	5_NC	FI_P_2015_1000 m3_5_NC
FI	P	2015	1000 m3	5_NC_T	FI_P_2015_1000 m3_5_NC_T
FI	P	2015	1000 m3	6	FI_P_2015_1000 m3_6
FI	P	2015	1000 m3	6_1	FI_P_2015_1000 m3_6_1
FI	P	2015	1000 m3	6_1_C	FI_P_2015_1000 m3_6_1_C
FI	P	2015	1000 m3	6_1_NC	FI_P_2015_1000 m3_6_1_NC
FI	P	2015	1000 m3	6_1_NC_T	FI_P_2015_1000 m3_6_1_NC_T
FI	P	2015	1000 m3	6_2	FI_P_2015_1000 m3_6_2
FI	P	2015	1000 m3	6_2_C	FI_P_2015_1000 m3_6_2_C
FI	P	2015	1000 m3	6_2_NC	FI_P_2015_1000 m3_6_2_NC
FI	P	2015	1000 m3	6_2_NC_T	FI_P_2015_1000 m3_6_2_NC_T
FI	P	2015	1000 m3	6_3	FI_P_2015_1000 m3_6_3
FI	P	2015	1000 m3	6_3_1	FI_P_2015_1000 m3_6_3_1
FI	P	2015	1000 m3	6_4	FI_P_2015_1000 m3_6_4
FI	P	2015	1000 m3	6_4_1	FI_P_2015_1000 m3_6_4_1
FI	P	2015	1000 m3	6_4_2	FI_P_2015_1000 m3_6_4_2
FI	P	2015	1000 m3	6_4_3	FI_P_2015_1000 m3_6_4_3
FI	P	2015	1000 mt	7	FI_P_2015_1000 mt_7
FI	P	2015	1000 mt	7_1	FI_P_2015_1000 mt_7_1
FI	P	2015	1000 mt	7_2	FI_P_2015_1000 mt_7_2
FI	P	2015	1000 mt	7_3	FI_P_2015_1000 mt_7_3
FI	P	2015	1000 mt	7_3_1	FI_P_2015_1000 mt_7_3_1
FI	P	2015	1000 mt	7_3_2	FI_P_2015_1000 mt_7_3_2
FI	P	2015	1000 mt	7_3_3	FI_P_2015_1000 mt_7_3_3
FI	P	2015	1000 mt	7_3_4	FI_P_2015_1000 mt_7_3_4
FI	P	2015	1000 mt	7_4	FI_P_2015_1000 mt_7_4
FI	P	2015	1000 mt	8	FI_P_2015_1000 mt_8
FI	P	2015	1000 mt	8_1	FI_P_2015_1000 mt_8_1
FI	P	2015	1000 mt	8_2	FI_P_2015_1000 mt_8_2
FI	P	2015	1000 mt	9	FI_P_2015_1000 mt_9
FI	P	2015	1000 mt	10	FI_P_2015_1000 mt_10
FI	P	2015	1000 mt	10_1	FI_P_2015_1000 mt_10_1
FI	P	2015	1000 mt	10_1_1	FI_P_2015_1000 mt_10_1_1
FI	P	2015	1000 mt	10_1_2	FI_P_2015_1000 mt_10_1_2
FI	P	2015	1000 mt	10_1_3	FI_P_2015_1000 mt_10_1_3
FI	P	2015	1000 mt	10_1_4	FI_P_2015_1000 mt_10_1_4
FI	P	2015	1000 mt	10_2	FI_P_2015_1000 mt_10_2
FI	P	2015	1000 mt	10_3	FI_P_2015_1000 mt_10_3
FI	P	2015	1000 mt	10_3_1	FI_P_2015_1000 mt_10_3_1
FI	P	2015	1000 mt	10_3_2	FI_P_2015_1000 mt_10_3_2
FI	P	2015	1000 mt	10_3_3	FI_P_2015_1000 mt_10_3_3
FI	P	2015	1000 mt	10_3_4	FI_P_2015_1000 mt_10_3_4
FI	P	2015	1000 mt	10_4	FI_P_2015_1000 mt_10_4
FI	M	2015	1000 m3	1	FI_M_2015_1000 m3_1
FI	M	2015	1000 m3	1_1	FI_M_2015_1000 m3_1_1
FI	M	2015	1000 m3	1_2	FI_M_2015_1000 m3_1_2
FI	M	2015	1000 m3	1_2_C	FI_M_2015_1000 m3_1_2_C
FI	M	2015	1000 m3	1_2_NC	FI_M_2015_1000 m3_1_2_NC
FI	M	2015	1000 m3	1_2_NC_T	FI_M_2015_1000 m3_1_2_NC_T
FI	M	2015	1000 mt	2	FI_M_2015_1000 mt_2
FI	M	2015	1000 m3	3	FI_M_2015_1000 m3_3
FI	M	2015	1000 m3	3_1	FI_M_2015_1000 m3_3_1
FI	M	2015	1000 m3	3_2	FI_M_2015_1000 m3_3_2
FI	M	2015	1000 mt	4	FI_M_2015_1000 mt_4
FI	M	2015	1000 mt	4_1	FI_M_2015_1000 mt_4_1
FI	M	2015	1000 mt	4_2	FI_M_2015_1000 mt_4_2
FI	M	2015	1000 m3	5	FI_M_2015_1000 m3_5
FI	M	2015	1000 m3	5_C	FI_M_2015_1000 m3_5_C
FI	M	2015	1000 m3	5_NC	FI_M_2015_1000 m3_5_NC
FI	M	2015	1000 m3	5_NC_T	FI_M_2015_1000 m3_5_NC_T
FI	M	2015	1000 m3	6	FI_M_2015_1000 m3_6
FI	M	2015	1000 m3	6_1	FI_M_2015_1000 m3_6_1
FI	M	2015	1000 m3	6_1_C	FI_M_2015_1000 m3_6_1_C
FI	M	2015	1000 m3	6_1_NC	FI_M_2015_1000 m3_6_1_NC
FI	M	2015	1000 m3	6_1_NC_T	FI_M_2015_1000 m3_6_1_NC_T
FI	M	2015	1000 m3	6_2	FI_M_2015_1000 m3_6_2
FI	M	2015	1000 m3	6_2_C	FI_M_2015_1000 m3_6_2_C
FI	M	2015	1000 m3	6_2_NC	FI_M_2015_1000 m3_6_2_NC
FI	M	2015	1000 m3	6_2_NC_T	FI_M_2015_1000 m3_6_2_NC_T
FI	M	2015	1000 m3	6_3	FI_M_2015_1000 m3_6_3
FI	M	2015	1000 m3	6_3_1	FI_M_2015_1000 m3_6_3_1
FI	M	2015	1000 m3	6_4	FI_M_2015_1000 m3_6_4
FI	M	2015	1000 m3	6_4_1	FI_M_2015_1000 m3_6_4_1
FI	M	2015	1000 m3	6_4_2	FI_M_2015_1000 m3_6_4_2
FI	M	2015	1000 m3	6_4_3	FI_M_2015_1000 m3_6_4_3
FI	M	2015	1000 mt	7	FI_M_2015_1000 mt_7
FI	M	2015	1000 mt	7_1	FI_M_2015_1000 mt_7_1
FI	M	2015	1000 mt	7_2	FI_M_2015_1000 mt_7_2
FI	M	2015	1000 mt	7_3	FI_M_2015_1000 mt_7_3
FI	M	2015	1000 mt	7_3_1	FI_M_2015_1000 mt_7_3_1
FI	M	2015	1000 mt	7_3_2	FI_M_2015_1000 mt_7_3_2
FI	M	2015	1000 mt	7_3_3	FI_M_2015_1000 mt_7_3_3
FI	M	2015	1000 mt	7_3_4	FI_M_2015_1000 mt_7_3_4
FI	M	2015	1000 mt	7_4	FI_M_2015_1000 mt_7_4
FI	M	2015	1000 mt	8	FI_M_2015_1000 mt_8
FI	M	2015	1000 mt	8_1	FI_M_2015_1000 mt_8_1
FI	M	2015	1000 mt	8_2	FI_M_2015_1000 mt_8_2
FI	M	2015	1000 mt	9	FI_M_2015_1000 mt_9
FI	M	2015	1000 mt	10	FI_M_2015_1000 mt_10
FI	M	2015	1000 mt	10_1	FI_M_2015_1000 mt_10_1
FI	M	2015	1000 mt	10_1_1	FI_M_2015_1000 mt_10_1_1
FI	M	2015	1000 mt	10_1_2	FI_M_2015_1000 mt_10_1_2
FI	M	2015	1000 mt	10_1_3	FI_M_2015_1000 mt_10_1_3
FI	M	2015	1000 mt	10_1_4	FI_M_2015_1000 mt_10_1_4
FI	M	2015	1000 mt	10_2	FI_M_2015_1000 mt_10_2
FI	M	2015	1000 mt	10_3	FI_M_2015_1000 mt_10_3
FI	M	2015	1000 mt	10_3_1	FI_M_2015_1000 mt_10_3_1
FI	M	2015	1000 mt	10_3_2	FI_M_2015_1000 mt_10_3_2
FI	M	2015	1000 mt	10_3_3	FI_M_2015_1000 mt_10_3_3
FI	M	2015	1000 mt	10_3_4	FI_M_2015_1000 mt_10_3_4
FI	M	2015	1000 mt	10_4	FI_M_2015_1000 mt_10_4
FI	M	2015	1000 NAC	1	FI_M_2015_1000 NAC_1
FI	M	2015	1000 NAC	1_1	FI_M_2015_1000 NAC_1_1
FI	M	2015	1000 NAC	1_2	FI_M_2015_1000 NAC_1_2
FI	M	2015	1000 NAC	1_2_C	FI_M_2015_1000 NAC_1_2_C
FI	M	2015	1000 NAC	1_2_NC	FI_M_2015_1000 NAC_1_2_NC
FI	M	2015	1000 NAC	1_2_NC_T	FI_M_2015_1000 NAC_1_2_NC_T
FI	M	2015	1000 NAC	2	FI_M_2015_1000 NAC_2
FI	M	2015	1000 NAC	3	FI_M_2015_1000 NAC_3
FI	M	2015	1000 NAC	3_1	FI_M_2015_1000 NAC_3_1
FI	M	2015	1000 NAC	3_2	FI_M_2015_1000 NAC_3_2
FI	M	2015	1000 NAC	4	FI_M_2015_1000 NAC_4
FI	M	2015	1000 NAC	4_1	FI_M_2015_1000 NAC_4_1
FI	M	2015	1000 NAC	4_2	FI_M_2015_1000 NAC_4_2
FI	M	2015	1000 NAC	5	FI_M_2015_1000 NAC_5
FI	M	2015	1000 NAC	5_C	FI_M_2015_1000 NAC_5_C
FI	M	2015	1000 NAC	5_NC	FI_M_2015_1000 NAC_5_NC
FI	M	2015	1000 NAC	5_NC_T	FI_M_2015_1000 NAC_5_NC_T
FI	M	2015	1000 NAC	6	FI_M_2015_1000 NAC_6
FI	M	2015	1000 NAC	6_1	FI_M_2015_1000 NAC_6_1
FI	M	2015	1000 NAC	6_1_C	FI_M_2015_1000 NAC_6_1_C
FI	M	2015	1000 NAC	6_1_NC	FI_M_2015_1000 NAC_6_1_NC
FI	M	2015	1000 NAC	6_1_NC_T	FI_M_2015_1000 NAC_6_1_NC_T
FI	M	2015	1000 NAC	6_2	FI_M_2015_1000 NAC_6_2
FI	M	2015	1000 NAC	6_2_C	FI_M_2015_1000 NAC_6_2_C
FI	M	2015	1000 NAC	6_2_NC	FI_M_2015_1000 NAC_6_2_NC
FI	M	2015	1000 NAC	6_2_NC_T	FI_M_2015_1000 NAC_6_2_NC_T
FI	M	2015	1000 NAC	6_3	FI_M_2015_1000 NAC_6_3
FI	M	2015	1000 NAC	6_3_1	FI_M_2015_1000 NAC_6_3_1
FI	M	2015	1000 NAC	6_4	FI_M_2015_1000 NAC_6_4
FI	M	2015	1000 NAC	6_4_1	FI_M_2015_1000 NAC_6_4_1
FI	M	2015	1000 NAC	6_4_2	FI_M_2015_1000 NAC_6_4_2
FI	M	2015	1000 NAC	6_4_3	FI_M_2015_1000 NAC_6_4_3
FI	M	2015	1000 NAC	7	FI_M_2015_1000 NAC_7
FI	M	2015	1000 NAC	7_1	FI_M_2015_1000 NAC_7_1
FI	M	2015	1000 NAC	7_2	FI_M_2015_1000 NAC_7_2
FI	M	2015	1000 NAC	7_3	FI_M_2015_1000 NAC_7_3
FI	M	2015	1000 NAC	7_3_1	FI_M_2015_1000 NAC_7_3_1
FI	M	2015	1000 NAC	7_3_2	FI_M_2015_1000 NAC_7_3_2
FI	M	2015	1000 NAC	7_3_3	FI_M_2015_1000 NAC_7_3_3
FI	M	2015	1000 NAC	7_3_4	FI_M_2015_1000 NAC_7_3_4
FI	M	2015	1000 NAC	7_4	FI_M_2015_1000 NAC_7_4
FI	M	2015	1000 NAC	8	FI_M_2015_1000 NAC_8
FI	M	2015	1000 NAC	8_1	FI_M_2015_1000 NAC_8_1
FI	M	2015	1000 NAC	8_2	FI_M_2015_1000 NAC_8_2
FI	M	2015	1000 NAC	9	FI_M_2015_1000 NAC_9
FI	M	2015	1000 NAC	10	FI_M_2015_1000 NAC_10
FI	M	2015	1000 NAC	10_1	FI_M_2015_1000 NAC_10_1
FI	M	2015	1000 NAC	10_1_1	FI_M_2015_1000 NAC_10_1_1
FI	M	2015	1000 NAC	10_1_2	FI_M_2015_1000 NAC_10_1_2
FI	M	2015	1000 NAC	10_1_3	FI_M_2015_1000 NAC_10_1_3
FI	M	2015	1000 NAC	10_1_4	FI_M_2015_1000 NAC_10_1_4
FI	M	2015	1000 NAC	10_2	FI_M_2015_1000 NAC_10_2
FI	M	2015	1000 NAC	10_3	FI_M_2015_1000 NAC_10_3
FI	M	2015	1000 NAC	10_3_1	FI_M_2015_1000 NAC_10_3_1
FI	M	2015	1000 NAC	10_3_2	FI_M_2015_1000 NAC_10_3_2
FI	M	2015	1000 NAC	10_3_3	FI_M_2015_1000 NAC_10_3_3
FI	M	2015	1000 NAC	10_3_4	FI_M_2015_1000 NAC_10_3_4
FI	M	2015	1000 NAC	10_4	FI_M_2015_1000 NAC_10_4
FI	X	2015	1000 m3	1	FI_X_2015_1000 m3_1
FI	X	2015	1000 m3	1_1	FI_X_2015_1000 m3_1_1
FI	X	2015	1000 m3	1_2	FI_X_2015_1000 m3_1_2
FI	X	2015	1000 m3	1_2_C	FI_X_2015_1000 m3_1_2_C
FI	X	2015	1000 m3	1_2_NC	FI_X_2015_1000 m3_1_2_NC
FI	X	2015	1000 m3	1_2_NC_T	FI_X_2015_1000 m3_1_2_NC_T
FI	X	2015	1000 mt	2	FI_X_2015_1000 mt_2
FI	X	2015	1000 m3	3	FI_X_2015_1000 m3_3
FI	X	2015	1000 m3	3_1	FI_X_2015_1000 m3_3_1
FI	X	2015	1000 m3	3_2	FI_X_2015_1000 m3_3_2
FI	X	2015	1000 mt	4	FI_X_2015_1000 mt_4
FI	X	2015	1000 mt	4_1	FI_X_2015_1000 mt_4_1
FI	X	2015	1000 mt	4_2	FI_X_2015_1000 mt_4_2
FI	X	2015	1000 m3	5	FI_X_2015_1000 m3_5
FI	X	2015	1000 m3	5_C	FI_X_2015_1000 m3_5_C
FI	X	2015	1000 m3	5_NC	FI_X_2015_1000 m3_5_NC
FI	X	2015	1000 m3	5_NC_T	FI_X_2015_1000 m3_5_NC_T
FI	X	2015	1000 m3	6	FI_X_2015_1000 m3_6
FI	X	2015	1000 m3	6_1	FI_X_2015_1000 m3_6_1
FI	X	2015	1000 m3	6_1_C	FI_X_2015_1000 m3_6_1_C
FI	X	2015	1000 m3	6_1_NC	FI_X_2015_1000 m3_6_1_NC
FI	X	2015	1000 m3	6_1_NC_T	FI_X_2015_1000 m3_6_1_NC_T
FI	X	2015	1000 m3	6_2	FI_X_2015_1000 m3_6_2
FI	X	2015	1000 m3	6_2_C	FI_X_2015_1000 m3_6_2_C
FI	X	2015	1000 m3	6_2_NC	FI_X_2015_1000 m3_6_2_NC
FI	X	2015	1000 m3	6_2_NC_T	FI_X_2015_1000 m3_6_2_NC_T
FI	X	2015	1000 m3	6_3	FI_X_2015_1000 m3_6_3
FI	X	2015	1000 m3	6_3_1	FI_X_2015_1000 m3_6_3_1
FI	X	2015	1000 m3	6_4	FI_X_2015_1000 m3_6_4
FI	X	2015	1000 m3	6_4_1	FI_X_2015_1000 m3_6_4_1
FI	X	2015	1000 m3	6_4_2	FI_X_2015_1000 m3_6_4_2
FI	X	2015	1000 m3	6_4_3	FI_X_2015_1000 m3_6_4_3
FI	X	2015	1000 mt	7	FI_X_2015_1000 mt_7
FI	X	2015	1000 mt	7_1	FI_X_2015_1000 mt_7_1
FI	X	2015	1000 mt	7_2	FI_X_2015_1000 mt_7_2
FI	X	2015	1000 mt	7_3	FI_X_2015_1000 mt_7_3
FI	X	2015	1000 mt	7_3_1	FI_X_2015_1000 mt_7_3_1
FI	X	2015	1000 mt	7_3_2	FI_X_2015_1000 mt_7_3_2
FI	X	2015	1000 mt	7_3_3	FI_X_2015_1000 mt_7_3_3
FI	X	2015	1000 mt	7_3_4	FI_X_2015_1000 mt_7_3_4
FI	X	2015	1000 mt	7_4	FI_X_2015_1000 mt_7_4
FI	X	2015	1000 mt	8	FI_X_2015_1000 mt_8
FI	X	2015	1000 mt	8_1	FI_X_2015_1000 mt_8_1
FI	X	2015	1000 mt	8_2	FI_X_2015_1000 mt_8_2
FI	X	2015	1000 mt	9	FI_X_2015_1000 mt_9
FI	X	2015	1000 mt	10	FI_X_2015_1000 mt_10
FI	X	2015	1000 mt	10_1	FI_X_2015_1000 mt_10_1
FI	X	2015	1000 mt	10_1_1	FI_X_2015_1000 mt_10_1_1
FI	X	2015	1000 mt	10_1_2	FI_X_2015_1000 mt_10_1_2
FI	X	2015	1000 mt	10_1_3	FI_X_2015_1000 mt_10_1_3
FI	X	2015	1000 mt	10_1_4	FI_X_2015_1000 mt_10_1_4
FI	X	2015	1000 mt	10_2	FI_X_2015_1000 mt_10_2
FI	X	2015	1000 mt	10_3	FI_X_2015_1000 mt_10_3
FI	X	2015	1000 mt	10_3_1	FI_X_2015_1000 mt_10_3_1
FI	X	2015	1000 mt	10_3_2	FI_X_2015_1000 mt_10_3_2
FI	X	2015	1000 mt	10_3_3	FI_X_2015_1000 mt_10_3_3
FI	X	2015	1000 mt	10_3_4	FI_X_2015_1000 mt_10_3_4
FI	X	2015	1000 mt	10_4	FI_X_2015_1000 mt_10_4
FI	X	2015	1000 NAC	1	FI_X_2015_1000 NAC_1
FI	X	2015	1000 NAC	1_1	FI_X_2015_1000 NAC_1_1
FI	X	2015	1000 NAC	1_2	FI_X_2015_1000 NAC_1_2
FI	X	2015	1000 NAC	1_2_C	FI_X_2015_1000 NAC_1_2_C
FI	X	2015	1000 NAC	1_2_NC	FI_X_2015_1000 NAC_1_2_NC
FI	X	2015	1000 NAC	1_2_NC_T	FI_X_2015_1000 NAC_1_2_NC_T
FI	X	2015	1000 NAC	2	FI_X_2015_1000 NAC_2
FI	X	2015	1000 NAC	3	FI_X_2015_1000 NAC_3
FI	X	2015	1000 NAC	3_1	FI_X_2015_1000 NAC_3_1
FI	X	2015	1000 NAC	3_2	FI_X_2015_1000 NAC_3_2
FI	X	2015	1000 NAC	4	FI_X_2015_1000 NAC_4
FI	X	2015	1000 NAC	4_1	FI_X_2015_1000 NAC_4_1
FI	X	2015	1000 NAC	4_2	FI_X_2015_1000 NAC_4_2
FI	X	2015	1000 NAC	5	FI_X_2015_1000 NAC_5
FI	X	2015	1000 NAC	5_C	FI_X_2015_1000 NAC_5_C
FI	X	2015	1000 NAC	5_NC	FI_X_2015_1000 NAC_5_NC
FI	X	2015	1000 NAC	5_NC_T	FI_X_2015_1000 NAC_5_NC_T
FI	X	2015	1000 NAC	6	FI_X_2015_1000 NAC_6
FI	X	2015	1000 NAC	6_1	FI_X_2015_1000 NAC_6_1
FI	X	2015	1000 NAC	6_1_C	FI_X_2015_1000 NAC_6_1_C
FI	X	2015	1000 NAC	6_1_NC	FI_X_2015_1000 NAC_6_1_NC
FI	X	2015	1000 NAC	6_1_NC_T	FI_X_2015_1000 NAC_6_1_NC_T
FI	X	2015	1000 NAC	6_2	FI_X_2015_1000 NAC_6_2
FI	X	2015	1000 NAC	6_2_C	FI_X_2015_1000 NAC_6_2_C
FI	X	2015	1000 NAC	6_2_NC	FI_X_2015_1000 NAC_6_2_NC
FI	X	2015	1000 NAC	6_2_NC_T	FI_X_2015_1000 NAC_6_2_NC_T
FI	X	2015	1000 NAC	6_3	FI_X_2015_1000 NAC_6_3
FI	X	2015	1000 NAC	6_3_1	FI_X_2015_1000 NAC_6_3_1
FI	X	2015	1000 NAC	6_4	FI_X_2015_1000 NAC_6_4
FI	X	2015	1000 NAC	6_4_1	FI_X_2015_1000 NAC_6_4_1
FI	X	2015	1000 NAC	6_4_2	FI_X_2015_1000 NAC_6_4_2
FI	X	2015	1000 NAC	6_4_3	FI_X_2015_1000 NAC_6_4_3
FI	X	2015	1000 NAC	7	FI_X_2015_1000 NAC_7
FI	X	2015	1000 NAC	7_1	FI_X_2015_1000 NAC_7_1
FI	X	2015	1000 NAC	7_2	FI_X_2015_1000 NAC_7_2
FI	X	2015	1000 NAC	7_3	FI_X_2015_1000 NAC_7_3
FI	X	2015	1000 NAC	7_3_1	FI_X_2015_1000 NAC_7_3_1
FI	X	2015	1000 NAC	7_3_2	FI_X_2015_1000 NAC_7_3_2
FI	X	2015	1000 NAC	7_3_3	FI_X_2015_1000 NAC_7_3_3
FI	X	2015	1000 NAC	7_3_4	FI_X_2015_1000 NAC_7_3_4
FI	X	2015	1000 NAC	7_4	FI_X_2015_1000 NAC_7_4
FI	X	2015	1000 NAC	8	FI_X_2015_1000 NAC_8
FI	X	2015	1000 NAC	8_1	FI_X_2015_1000 NAC_8_1
FI	X	2015	1000 NAC	8_2	FI_X_2015_1000 NAC_8_2
FI	X	2015	1000 NAC	9	FI_X_2015_1000 NAC_9
FI	X	2015	1000 NAC	10	FI_X_2015_1000 NAC_10
FI	X	2015	1000 NAC	10_1	FI_X_2015_1000 NAC_10_1
FI	X	2015	1000 NAC	10_1_1	FI_X_2015_1000 NAC_10_1_1
FI	X	2015	1000 NAC	10_1_2	FI_X_2015_1000 NAC_10_1_2
FI	X	2015	1000 NAC	10_1_3	FI_X_2015_1000 NAC_10_1_3
FI	X	2015	1000 NAC	10_1_4	FI_X_2015_1000 NAC_10_1_4
FI	X	2015	1000 NAC	10_2	FI_X_2015_1000 NAC_10_2
FI	X	2015	1000 NAC	10_3	FI_X_2015_1000 NAC_10_3
FI	X	2015	1000 NAC	10_3_1	FI_X_2015_1000 NAC_10_3_1
FI	X	2015	1000 NAC	10_3_2	FI_X_2015_1000 NAC_10_3_2
FI	X	2015	1000 NAC	10_3_3	FI_X_2015_1000 NAC_10_3_3
FI	X	2015	1000 NAC	10_3_4	FI_X_2015_1000 NAC_10_3_4
FI	X	2015	1000 NAC	10_4	FI_X_2015_1000 NAC_10_4
FI	M	2015	1000 NAC	11_1	FI_M_2015_1000 NAC_11_1
FI	M	2015	1000 NAC	11_1_C	FI_M_2015_1000 NAC_11_1_C
FI	M	2015	1000 NAC	11_1_NC	FI_M_2015_1000 NAC_11_1_NC
FI	M	2015	1000 NAC	11_1_NC_T	FI_M_2015_1000 NAC_11_1_NC_T
FI	M	2015	1000 NAC	11_2	FI_M_2015_1000 NAC_11_2
FI	M	2015	1000 NAC	11_3	FI_M_2015_1000 NAC_11_3
FI	M	2015	1000 NAC	11_4	FI_M_2015_1000 NAC_11_4
FI	M	2015	1000 NAC	11_5	FI_M_2015_1000 NAC_11_5
FI	M	2015	1000 NAC	11_6	FI_M_2015_1000 NAC_11_6
FI	M	2015	1000 NAC	11_7	FI_M_2015_1000 NAC_11_7
FI	M	2015	1000 NAC	11_7_1	FI_M_2015_1000 NAC_11_7_1
FI	M	2015	1000 NAC	12_1	FI_M_2015_1000 NAC_12_1
FI	M	2015	1000 NAC	12_2	FI_M_2015_1000 NAC_12_2
FI	M	2015	1000 NAC	12_3	FI_M_2015_1000 NAC_12_3
FI	M	2015	1000 NAC	12_4	FI_M_2015_1000 NAC_12_4
FI	M	2015	1000 NAC	12_5	FI_M_2015_1000 NAC_12_5
FI	M	2015	1000 NAC	12_6	FI_M_2015_1000 NAC_12_6
FI	M	2015	1000 NAC	12_6_1	FI_M_2015_1000 NAC_12_6_1
FI	M	2015	1000 NAC	12_6_2	FI_M_2015_1000 NAC_12_6_2
FI	M	2015	1000 NAC	12_6_3	FI_M_2015_1000 NAC_12_6_3
FI	M	2015	1000 NAC	12_7	FI_M_2015_1000 NAC_12_7
FI	M	2015	1000 NAC	12_7_1	FI_M_2015_1000 NAC_12_7_1
FI	M	2015	1000 NAC	12_7_2	FI_M_2015_1000 NAC_12_7_2
FI	M	2015	1000 NAC	12_7_3	FI_M_2015_1000 NAC_12_7_3
FI	X	2015	1000 NAC	11_1	FI_X_2015_1000 NAC_11_1
FI	X	2015	1000 NAC	11_1_C	FI_X_2015_1000 NAC_11_1_C
FI	X	2015	1000 NAC	11_1_NC	FI_X_2015_1000 NAC_11_1_NC
FI	X	2015	1000 NAC	11_1_NC_T	FI_X_2015_1000 NAC_11_1_NC_T
FI	X	2015	1000 NAC	11_2	FI_X_2015_1000 NAC_11_2
FI	X	2015	1000 NAC	11_3	FI_X_2015_1000 NAC_11_3
FI	X	2015	1000 NAC	11_4	FI_X_2015_1000 NAC_11_4
FI	X	2015	1000 NAC	11_5	FI_X_2015_1000 NAC_11_5
FI	X	2015	1000 NAC	11_6	FI_X_2015_1000 NAC_11_6
FI	X	2015	1000 NAC	11_7	FI_X_2015_1000 NAC_11_7
FI	X	2015	1000 NAC	11_7_1	FI_X_2015_1000 NAC_11_7_1
FI	X	2015	1000 NAC	12_1	FI_X_2015_1000 NAC_12_1
FI	X	2015	1000 NAC	12_2	FI_X_2015_1000 NAC_12_2
FI	X	2015	1000 NAC	12_3	FI_X_2015_1000 NAC_12_3
FI	X	2015	1000 NAC	12_4	FI_X_2015_1000 NAC_12_4
FI	X	2015	1000 NAC	12_5	FI_X_2015_1000 NAC_12_5
FI	X	2015	1000 NAC	12_6	FI_X_2015_1000 NAC_12_6
FI	X	2015	1000 NAC	12_6_1	FI_X_2015_1000 NAC_12_6_1
FI	X	2015	1000 NAC	12_6_2	FI_X_2015_1000 NAC_12_6_2
FI	X	2015	1000 NAC	12_6_3	FI_X_2015_1000 NAC_12_6_3
FI	X	2015	1000 NAC	12_7	FI_X_2015_1000 NAC_12_7
FI	X	2015	1000 NAC	12_7_1	FI_X_2015_1000 NAC_12_7_1
FI	X	2015	1000 NAC	12_7_2	FI_X_2015_1000 NAC_12_7_2
FI	X	2015	1000 NAC	12_7_3	FI_X_2015_1000 NAC_12_7_3
FI	M	2015	1000 m3	ST_1_2_C	FI_M_2015_1000 m3_ST_1_2_C
FI	M	2015	1000 m3	ST_1_2_C_1	FI_M_2015_1000 m3_ST_1_2_C_1
FI	M	2015	1000 m3	ST_1_2_C_1_1	FI_M_2015_1000 m3_ST_1_2_C_1_1
FI	M	2015	1000 m3	ST_1_2_C_2_1	FI_M_2015_1000 m3_ST_1_2_C_2_1
FI	M	2015	1000 m3	ST_1_2_C_2	FI_M_2015_1000 m3_ST_1_2_C_2
FI	M	2015	1000 m3	ST_1_2_C_1_2	FI_M_2015_1000 m3_ST_1_2_C_1_2
FI	M	2015	1000 m3	ST_1_2_C_2_2	FI_M_2015_1000 m3_ST_1_2_C_2_2
FI	M	2015	1000 m3	ST_1_2_C_3	FI_M_2015_1000 m3_ST_1_2_C_3
FI	M	2015	1000 m3	ST_1_2_C_1_3	FI_M_2015_1000 m3_ST_1_2_C_1_3
FI	M	2015	1000 m3	ST_1_2_C_2_3	FI_M_2015_1000 m3_ST_1_2_C_2_3
FI	M	2015	1000 m3	ST_1_2_NC	FI_M_2015_1000 m3_ST_1_2_NC
FI	M	2015	1000 m3	ST_1_2_NC_1	FI_M_2015_1000 m3_ST_1_2_NC_1
FI	M	2015	1000 m3	ST_1_2_NC_1_1	FI_M_2015_1000 m3_ST_1_2_NC_1_1
FI	M	2015	1000 m3	ST_1_2_NC_2_1	FI_M_2015_1000 m3_ST_1_2_NC_2_1
FI	M	2015	1000 m3	ST_1_2_NC_2	FI_M_2015_1000 m3_ST_1_2_NC_2
FI	M	2015	1000 m3	ST_1_2_NC_1_2	FI_M_2015_1000 m3_ST_1_2_NC_1_2
FI	M	2015	1000 m3	ST_1_2_NC_2_2	FI_M_2015_1000 m3_ST_1_2_NC_2_2
FI	M	2015	1000 m3	ST_1_2_NC_3	FI_M_2015_1000 m3_ST_1_2_NC_3
FI	M	2015	1000 m3	ST_1_2_NC_1_3	FI_M_2015_1000 m3_ST_1_2_NC_1_3
FI	M	2015	1000 m3	ST_1_2_NC_2_3	FI_M_2015_1000 m3_ST_1_2_NC_2_3
FI	M	2015	1000 m3	ST_1_2_NC_4	FI_M_2015_1000 m3_ST_1_2_NC_4
FI	M	2015	1000 m3	ST_1_2_NC_5	FI_M_2015_1000 m3_ST_1_2_NC_5
FI	M	2015	1000 m3	ST_5_C	FI_M_2015_1000 m3_ST_5_C
FI	M	2015	1000 m3	ST_5_C_1	FI_M_2015_1000 m3_ST_5_C_1
FI	M	2015	1000 m3	ST_5_C_2	FI_M_2015_1000 m3_ST_5_C_2
FI	M	2015	1000 m3	ST_5_NC	FI_M_2015_1000 m3_ST_5_NC
FI	M	2015	1000 m3	ST_5_NC_1	FI_M_2015_1000 m3_ST_5_NC_1
FI	M	2015	1000 m3	ST_5_NC_2	FI_M_2015_1000 m3_ST_5_NC_2
FI	M	2015	1000 m3	ST_5_NC_3	FI_M_2015_1000 m3_ST_5_NC_3
FI	M	2015	1000 m3	ST_5_NC_4	FI_M_2015_1000 m3_ST_5_NC_4
FI	M	2015	1000 m3	ST_5_NC_5	FI_M_2015_1000 m3_ST_5_NC_5
FI	M	2015	1000 m3	ST_5_NC_6	FI_M_2015_1000 m3_ST_5_NC_6
FI	M	2015	1000 m3	ST_5_NC_7	FI_M_2015_1000 m3_ST_5_NC_7
FI	M	2015	1000 NAC	ST_1_2_C	FI_M_2015_1000 NAC_ST_1_2_C
FI	M	2015	1000 NAC	ST_1_2_C_1	FI_M_2015_1000 NAC_ST_1_2_C_1
FI	M	2015	1000 NAC	ST_1_2_C_1_1	FI_M_2015_1000 NAC_ST_1_2_C_1_1
FI	M	2015	1000 NAC	ST_1_2_C_2_1	FI_M_2015_1000 NAC_ST_1_2_C_2_1
FI	M	2015	1000 NAC	ST_1_2_C_2	FI_M_2015_1000 NAC_ST_1_2_C_2
FI	M	2015	1000 NAC	ST_1_2_C_1_2	FI_M_2015_1000 NAC_ST_1_2_C_1_2
FI	M	2015	1000 NAC	ST_1_2_C_2_2	FI_M_2015_1000 NAC_ST_1_2_C_2_2
FI	M	2015	1000 NAC	ST_1_2_C_3	FI_M_2015_1000 NAC_ST_1_2_C_3
FI	M	2015	1000 NAC	ST_1_2_C_1_3	FI_M_2015_1000 NAC_ST_1_2_C_1_3
FI	M	2015	1000 NAC	ST_1_2_C_2_3	FI_M_2015_1000 NAC_ST_1_2_C_2_3
FI	M	2015	1000 NAC	ST_1_2_NC	FI_M_2015_1000 NAC_ST_1_2_NC
FI	M	2015	1000 NAC	ST_1_2_NC_1	FI_M_2015_1000 NAC_ST_1_2_NC_1
FI	M	2015	1000 NAC	ST_1_2_NC_1_1	FI_M_2015_1000 NAC_ST_1_2_NC_1_1
FI	M	2015	1000 NAC	ST_1_2_NC_2_1	FI_M_2015_1000 NAC_ST_1_2_NC_2_1
FI	M	2015	1000 NAC	ST_1_2_NC_2	FI_M_2015_1000 NAC_ST_1_2_NC_2
FI	M	2015	1000 NAC	ST_1_2_NC_1_2	FI_M_2015_1000 NAC_ST_1_2_NC_1_2
FI	M	2015	1000 NAC	ST_1_2_NC_2_2	FI_M_2015_1000 NAC_ST_1_2_NC_2_2
FI	M	2015	1000 NAC	ST_1_2_NC_3	FI_M_2015_1000 NAC_ST_1_2_NC_3
FI	M	2015	1000 NAC	ST_1_2_NC_1_3	FI_M_2015_1000 NAC_ST_1_2_NC_1_3
FI	M	2015	1000 NAC	ST_1_2_NC_2_3	FI_M_2015_1000 NAC_ST_1_2_NC_2_3
FI	M	2015	1000 NAC	ST_1_2_NC_4	FI_M_2015_1000 NAC_ST_1_2_NC_4
FI	M	2015	1000 NAC	ST_1_2_NC_5	FI_M_2015_1000 NAC_ST_1_2_NC_5
FI	M	2015	1000 NAC	ST_5_C	FI_M_2015_1000 NAC_ST_5_C
FI	M	2015	1000 NAC	ST_5_C_1	FI_M_2015_1000 NAC_ST_5_C_1
FI	M	2015	1000 NAC	ST_5_C_2	FI_M_2015_1000 NAC_ST_5_C_2
FI	M	2015	1000 NAC	ST_5_NC	FI_M_2015_1000 NAC_ST_5_NC
FI	M	2015	1000 NAC	ST_5_NC_1	FI_M_2015_1000 NAC_ST_5_NC_1
FI	M	2015	1000 NAC	ST_5_NC_2	FI_M_2015_1000 NAC_ST_5_NC_2
FI	M	2015	1000 NAC	ST_5_NC_3	FI_M_2015_1000 NAC_ST_5_NC_3
FI	M	2015	1000 NAC	ST_5_NC_4	FI_M_2015_1000 NAC_ST_5_NC_4
FI	M	2015	1000 NAC	ST_5_NC_5	FI_M_2015_1000 NAC_ST_5_NC_5
FI	M	2015	1000 NAC	ST_5_NC_6	FI_M_2015_1000 NAC_ST_5_NC_6
FI	M	2015	1000 NAC	ST_5_NC_7	FI_M_2015_1000 NAC_ST_5_NC_7
FI	X	2015	1000 m3	ST_1_2_C	FI_X_2015_1000 m3_ST_1_2_C
FI	X	2015	1000 m3	ST_1_2_C_1	FI_X_2015_1000 m3_ST_1_2_C_1
FI	X	2015	1000 m3	ST_1_2_C_1_1	FI_X_2015_1000 m3_ST_1_2_C_1_1
FI	X	2015	1000 m3	ST_1_2_C_2_1	FI_X_2015_1000 m3_ST_1_2_C_2_1
FI	X	2015	1000 m3	ST_1_2_C_2	FI_X_2015_1000 m3_ST_1_2_C_2
FI	X	2015	1000 m3	ST_1_2_C_1_2	FI_X_2015_1000 m3_ST_1_2_C_1_2
FI	X	2015	1000 m3	ST_1_2_C_2_2	FI_X_2015_1000 m3_ST_1_2_C_2_2
FI	X	2015	1000 m3	ST_1_2_C_3	FI_X_2015_1000 m3_ST_1_2_C_3
FI	X	2015	1000 m3	ST_1_2_C_1_3	FI_X_2015_1000 m3_ST_1_2_C_1_3
FI	X	2015	1000 m3	ST_1_2_C_2_3	FI_X_2015_1000 m3_ST_1_2_C_2_3
FI	X	2015	1000 m3	ST_1_2_NC	FI_X_2015_1000 m3_ST_1_2_NC
FI	X	2015	1000 m3	ST_1_2_NC_1	FI_X_2015_1000 m3_ST_1_2_NC_1
FI	X	2015	1000 m3	ST_1_2_NC_1_1	FI_X_2015_1000 m3_ST_1_2_NC_1_1
FI	X	2015	1000 m3	ST_1_2_NC_2_1	FI_X_2015_1000 m3_ST_1_2_NC_2_1
FI	X	2015	1000 m3	ST_1_2_NC_2	FI_X_2015_1000 m3_ST_1_2_NC_2
FI	X	2015	1000 m3	ST_1_2_NC_1_2	FI_X_2015_1000 m3_ST_1_2_NC_1_2
FI	X	2015	1000 m3	ST_1_2_NC_2_2	FI_X_2015_1000 m3_ST_1_2_NC_2_2
FI	X	2015	1000 m3	ST_1_2_NC_3	FI_X_2015_1000 m3_ST_1_2_NC_3
FI	X	2015	1000 m3	ST_1_2_NC_1_3	FI_X_2015_1000 m3_ST_1_2_NC_1_3
FI	X	2015	1000 m3	ST_1_2_NC_2_3	FI_X_2015_1000 m3_ST_1_2_NC_2_3
FI	X	2015	1000 m3	ST_1_2_NC_4	FI_X_2015_1000 m3_ST_1_2_NC_4
FI	X	2015	1000 m3	ST_1_2_NC_5	FI_X_2015_1000 m3_ST_1_2_NC_5
FI	X	2015	1000 m3	ST_5_C	FI_X_2015_1000 m3_ST_5_C
FI	X	2015	1000 m3	ST_5_C_1	FI_X_2015_1000 m3_ST_5_C_1
FI	X	2015	1000 m3	ST_5_C_2	FI_X_2015_1000 m3_ST_5_C_2
FI	X	2015	1000 m3	ST_5_NC	FI_X_2015_1000 m3_ST_5_NC
FI	X	2015	1000 m3	ST_5_NC_1	FI_X_2015_1000 m3_ST_5_NC_1
FI	X	2015	1000 m3	ST_5_NC_2	FI_X_2015_1000 m3_ST_5_NC_2
FI	X	2015	1000 m3	ST_5_NC_3	FI_X_2015_1000 m3_ST_5_NC_3
FI	X	2015	1000 m3	ST_5_NC_4	FI_X_2015_1000 m3_ST_5_NC_4
FI	X	2015	1000 m3	ST_5_NC_5	FI_X_2015_1000 m3_ST_5_NC_5
FI	X	2015	1000 m3	ST_5_NC_6	FI_X_2015_1000 m3_ST_5_NC_6
FI	X	2015	1000 m3	ST_5_NC_7	FI_X_2015_1000 m3_ST_5_NC_7
FI	X	2015	1000 NAC	ST_1_2_C	FI_X_2015_1000 NAC_ST_1_2_C
FI	X	2015	1000 NAC	ST_1_2_C_1	FI_X_2015_1000 NAC_ST_1_2_C_1
FI	X	2015	1000 NAC	ST_1_2_C_1_1	FI_X_2015_1000 NAC_ST_1_2_C_1_1
FI	X	2015	1000 NAC	ST_1_2_C_2_1	FI_X_2015_1000 NAC_ST_1_2_C_2_1
FI	X	2015	1000 NAC	ST_1_2_C_2	FI_X_2015_1000 NAC_ST_1_2_C_2
FI	X	2015	1000 NAC	ST_1_2_C_1_2	FI_X_2015_1000 NAC_ST_1_2_C_1_2
FI	X	2015	1000 NAC	ST_1_2_C_2_2	FI_X_2015_1000 NAC_ST_1_2_C_2_2
FI	X	2015	1000 NAC	ST_1_2_C_3	FI_X_2015_1000 NAC_ST_1_2_C_3
FI	X	2015	1000 NAC	ST_1_2_C_1_3	FI_X_2015_1000 NAC_ST_1_2_C_1_3
FI	X	2015	1000 NAC	ST_1_2_C_2_3	FI_X_2015_1000 NAC_ST_1_2_C_2_3
FI	X	2015	1000 NAC	ST_1_2_NC	FI_X_2015_1000 NAC_ST_1_2_NC
FI	X	2015	1000 NAC	ST_1_2_NC_1	FI_X_2015_1000 NAC_ST_1_2_NC_1
FI	X	2015	1000 NAC	ST_1_2_NC_1_1	FI_X_2015_1000 NAC_ST_1_2_NC_1_1
FI	X	2015	1000 NAC	ST_1_2_NC_2_1	FI_X_2015_1000 NAC_ST_1_2_NC_2_1
FI	X	2015	1000 NAC	ST_1_2_NC_2	FI_X_2015_1000 NAC_ST_1_2_NC_2
FI	X	2015	1000 NAC	ST_1_2_NC_1_2	FI_X_2015_1000 NAC_ST_1_2_NC_1_2
FI	X	2015	1000 NAC	ST_1_2_NC_2_2	FI_X_2015_1000 NAC_ST_1_2_NC_2_2
FI	X	2015	1000 NAC	ST_1_2_NC_3	FI_X_2015_1000 NAC_ST_1_2_NC_3
FI	X	2015	1000 NAC	ST_1_2_NC_1_3	FI_X_2015_1000 NAC_ST_1_2_NC_1_3
FI	X	2015	1000 NAC	ST_1_2_NC_2_3	FI_X_2015_1000 NAC_ST_1_2_NC_2_3
FI	X	2015	1000 NAC	ST_1_2_NC_4	FI_X_2015_1000 NAC_ST_1_2_NC_4
FI	X	2015	1000 NAC	ST_1_2_NC_5	FI_X_2015_1000 NAC_ST_1_2_NC_5
FI	X	2015	1000 NAC	ST_5_C	FI_X_2015_1000 NAC_ST_5_C
FI	X	2015	1000 NAC	ST_5_C_1	FI_X_2015_1000 NAC_ST_5_C_1
FI	X	2015	1000 NAC	ST_5_C_2	FI_X_2015_1000 NAC_ST_5_C_2
FI	X	2015	1000 NAC	ST_5_NC	FI_X_2015_1000 NAC_ST_5_NC
FI	X	2015	1000 NAC	ST_5_NC_1	FI_X_2015_1000 NAC_ST_5_NC_1
FI	X	2015	1000 NAC	ST_5_NC_2	FI_X_2015_1000 NAC_ST_5_NC_2
FI	X	2015	1000 NAC	ST_5_NC_3	FI_X_2015_1000 NAC_ST_5_NC_3
FI	X	2015	1000 NAC	ST_5_NC_4	FI_X_2015_1000 NAC_ST_5_NC_4
FI	X	2015	1000 NAC	ST_5_NC_5	FI_X_2015_1000 NAC_ST_5_NC_5
FI	X	2015	1000 NAC	ST_5_NC_6	FI_X_2015_1000 NAC_ST_5_NC_6
FI	X	2015	1000 NAC	ST_5_NC_7	FI_X_2015_1000 NAC_ST_5_NC_7
FI	EX_M	2015	1000 m3	1	FI_EX_M_2015_1000 m3_1
FI	EX_M	2015	1000 m3	1_1	FI_EX_M_2015_1000 m3_1_1
FI	EX_M	2015	1000 m3	1_2	FI_EX_M_2015_1000 m3_1_2
FI	EX_M	2015	1000 m3	1_2_C	FI_EX_M_2015_1000 m3_1_2_C
FI	EX_M	2015	1000 m3	1_2_NC	FI_EX_M_2015_1000 m3_1_2_NC
FI	EX_M	2015	1000 m3	1_2_NC_T	FI_EX_M_2015_1000 m3_1_2_NC_T
FI	EX_M	2015	1000 mt	2	FI_EX_M_2015_1000 mt_2
FI	EX_M	2015	1000 m3	3	FI_EX_M_2015_1000 m3_3
FI	EX_M	2015	1000 m3	3_1	FI_EX_M_2015_1000 m3_3_1
FI	EX_M	2015	1000 m3	3_2	FI_EX_M_2015_1000 m3_3_2
FI	EX_M	2015	1000 mt	4	FI_EX_M_2015_1000 mt_4
FI	EX_M	2015	1000 mt	4_1	FI_EX_M_2015_1000 mt_4_1
FI	EX_M	2015	1000 mt	4_2	FI_EX_M_2015_1000 mt_4_2
FI	EX_M	2015	1000 m3	5	FI_EX_M_2015_1000 m3_5
FI	EX_M	2015	1000 m3	5_C	FI_EX_M_2015_1000 m3_5_C
FI	EX_M	2015	1000 m3	5_NC	FI_EX_M_2015_1000 m3_5_NC
FI	EX_M	2015	1000 m3	5_NC_T	FI_EX_M_2015_1000 m3_5_NC_T
FI	EX_M	2015	1000 m3	6	FI_EX_M_2015_1000 m3_6
FI	EX_M	2015	1000 m3	6_1	FI_EX_M_2015_1000 m3_6_1
FI	EX_M	2015	1000 m3	6_1_C	FI_EX_M_2015_1000 m3_6_1_C
FI	EX_M	2015	1000 m3	6_1_NC	FI_EX_M_2015_1000 m3_6_1_NC
FI	EX_M	2015	1000 m3	6_1_NC_T	FI_EX_M_2015_1000 m3_6_1_NC_T
FI	EX_M	2015	1000 m3	6_2	FI_EX_M_2015_1000 m3_6_2
FI	EX_M	2015	1000 m3	6_2_C	FI_EX_M_2015_1000 m3_6_2_C
FI	EX_M	2015	1000 m3	6_2_NC	FI_EX_M_2015_1000 m3_6_2_NC
FI	EX_M	2015	1000 m3	6_2_NC_T	FI_EX_M_2015_1000 m3_6_2_NC_T
FI	EX_M	2015	1000 m3	6_3	FI_EX_M_2015_1000 m3_6_3
FI	EX_M	2015	1000 m3	6_3_1	FI_EX_M_2015_1000 m3_6_3_1
FI	EX_M	2015	1000 m3	6_4	FI_EX_M_2015_1000 m3_6_4
FI	EX_M	2015	1000 m3	6_4_1	FI_EX_M_2015_1000 m3_6_4_1
FI	EX_M	2015	1000 m3	6_4_2	FI_EX_M_2015_1000 m3_6_4_2
FI	EX_M	2015	1000 m3	6_4_3	FI_EX_M_2015_1000 m3_6_4_3
FI	EX_M	2015	1000 mt	7	FI_EX_M_2015_1000 mt_7
FI	EX_M	2015	1000 mt	7_1	FI_EX_M_2015_1000 mt_7_1
FI	EX_M	2015	1000 mt	7_2	FI_EX_M_2015_1000 mt_7_2
FI	EX_M	2015	1000 mt	7_3	FI_EX_M_2015_1000 mt_7_3
FI	EX_M	2015	1000 mt	7_3_1	FI_EX_M_2015_1000 mt_7_3_1
FI	EX_M	2015	1000 mt	7_3_2	FI_EX_M_2015_1000 mt_7_3_2
FI	EX_M	2015	1000 mt	7_3_3	FI_EX_M_2015_1000 mt_7_3_3
FI	EX_M	2015	1000 mt	7_3_4	FI_EX_M_2015_1000 mt_7_3_4
FI	EX_M	2015	1000 mt	7_4	FI_EX_M_2015_1000 mt_7_4
FI	EX_M	2015	1000 mt	8	FI_EX_M_2015_1000 mt_8
FI	EX_M	2015	1000 mt	8_1	FI_EX_M_2015_1000 mt_8_1
FI	EX_M	2015	1000 mt	8_2	FI_EX_M_2015_1000 mt_8_2
FI	EX_M	2015	1000 mt	9	FI_EX_M_2015_1000 mt_9
FI	EX_M	2015	1000 mt	10	FI_EX_M_2015_1000 mt_10
FI	EX_M	2015	1000 mt	10_1	FI_EX_M_2015_1000 mt_10_1
FI	EX_M	2015	1000 mt	10_1_1	FI_EX_M_2015_1000 mt_10_1_1
FI	EX_M	2015	1000 mt	10_1_2	FI_EX_M_2015_1000 mt_10_1_2
FI	EX_M	2015	1000 mt	10_1_3	FI_EX_M_2015_1000 mt_10_1_3
FI	EX_M	2015	1000 mt	10_1_4	FI_EX_M_2015_1000 mt_10_1_4
FI	EX_M	2015	1000 mt	10_2	FI_EX_M_2015_1000 mt_10_2
FI	EX_M	2015	1000 mt	10_3	FI_EX_M_2015_1000 mt_10_3
FI	EX_M	2015	1000 mt	10_3_1	FI_EX_M_2015_1000 mt_10_3_1
FI	EX_M	2015	1000 mt	10_3_2	FI_EX_M_2015_1000 mt_10_3_2
FI	EX_M	2015	1000 mt	10_3_3	FI_EX_M_2015_1000 mt_10_3_3
FI	EX_M	2015	1000 mt	10_3_4	FI_EX_M_2015_1000 mt_10_3_4
FI	EX_M	2015	1000 mt	10_4	FI_EX_M_2015_1000 mt_10_4
FI	EX_M	2015	1000 NAC	1	FI_EX_M_2015_1000 NAC_1
FI	EX_M	2015	1000 NAC	1_1	FI_EX_M_2015_1000 NAC_1_1
FI	EX_M	2015	1000 NAC	1_2	FI_EX_M_2015_1000 NAC_1_2
FI	EX_M	2015	1000 NAC	1_2_C	FI_EX_M_2015_1000 NAC_1_2_C
FI	EX_M	2015	1000 NAC	1_2_NC	FI_EX_M_2015_1000 NAC_1_2_NC
FI	EX_M	2015	1000 NAC	1_2_NC_T	FI_EX_M_2015_1000 NAC_1_2_NC_T
FI	EX_M	2015	1000 NAC	2	FI_EX_M_2015_1000 NAC_2
FI	EX_M	2015	1000 NAC	3	FI_EX_M_2015_1000 NAC_3
FI	EX_M	2015	1000 NAC	3_1	FI_EX_M_2015_1000 NAC_3_1
FI	EX_M	2015	1000 NAC	3_2	FI_EX_M_2015_1000 NAC_3_2
FI	EX_M	2015	1000 NAC	4	FI_EX_M_2015_1000 NAC_4
FI	EX_M	2015	1000 NAC	4_1	FI_EX_M_2015_1000 NAC_4_1
FI	EX_M	2015	1000 NAC	4_2	FI_EX_M_2015_1000 NAC_4_2
FI	EX_M	2015	1000 NAC	5	FI_EX_M_2015_1000 NAC_5
FI	EX_M	2015	1000 NAC	5_C	FI_EX_M_2015_1000 NAC_5_C
FI	EX_M	2015	1000 NAC	5_NC	FI_EX_M_2015_1000 NAC_5_NC
FI	EX_M	2015	1000 NAC	5_NC_T	FI_EX_M_2015_1000 NAC_5_NC_T
FI	EX_M	2015	1000 NAC	6	FI_EX_M_2015_1000 NAC_6
FI	EX_M	2015	1000 NAC	6_1	FI_EX_M_2015_1000 NAC_6_1
FI	EX_M	2015	1000 NAC	6_1_C	FI_EX_M_2015_1000 NAC_6_1_C
FI	EX_M	2015	1000 NAC	6_1_NC	FI_EX_M_2015_1000 NAC_6_1_NC
FI	EX_M	2015	1000 NAC	6_1_NC_T	FI_EX_M_2015_1000 NAC_6_1_NC_T
FI	EX_M	2015	1000 NAC	6_2	FI_EX_M_2015_1000 NAC_6_2
FI	EX_M	2015	1000 NAC	6_2_C	FI_EX_M_2015_1000 NAC_6_2_C
FI	EX_M	2015	1000 NAC	6_2_NC	FI_EX_M_2015_1000 NAC_6_2_NC
FI	EX_M	2015	1000 NAC	6_2_NC_T	FI_EX_M_2015_1000 NAC_6_2_NC_T
FI	EX_M	2015	1000 NAC	6_3	FI_EX_M_2015_1000 NAC_6_3
FI	EX_M	2015	1000 NAC	6_3_1	FI_EX_M_2015_1000 NAC_6_3_1
FI	EX_M	2015	1000 NAC	6_4	FI_EX_M_2015_1000 NAC_6_4
FI	EX_M	2015	1000 NAC	6_4_1	FI_EX_M_2015_1000 NAC_6_4_1
FI	EX_M	2015	1000 NAC	6_4_2	FI_EX_M_2015_1000 NAC_6_4_2
FI	EX_M	2015	1000 NAC	6_4_3	FI_EX_M_2015_1000 NAC_6_4_3
FI	EX_M	2015	1000 NAC	7	FI_EX_M_2015_1000 NAC_7
FI	EX_M	2015	1000 NAC	7_1	FI_EX_M_2015_1000 NAC_7_1
FI	EX_M	2015	1000 NAC	7_2	FI_EX_M_2015_1000 NAC_7_2
FI	EX_M	2015	1000 NAC	7_3	FI_EX_M_2015_1000 NAC_7_3
FI	EX_M	2015	1000 NAC	7_3_1	FI_EX_M_2015_1000 NAC_7_3_1
FI	EX_M	2015	1000 NAC	7_3_2	FI_EX_M_2015_1000 NAC_7_3_2
FI	EX_M	2015	1000 NAC	7_3_3	FI_EX_M_2015_1000 NAC_7_3_3
FI	EX_M	2015	1000 NAC	7_3_4	FI_EX_M_2015_1000 NAC_7_3_4
FI	EX_M	2015	1000 NAC	7_4	FI_EX_M_2015_1000 NAC_7_4
FI	EX_M	2015	1000 NAC	8	FI_EX_M_2015_1000 NAC_8
FI	EX_M	2015	1000 NAC	8_1	FI_EX_M_2015_1000 NAC_8_1
FI	EX_M	2015	1000 NAC	8_2	FI_EX_M_2015_1000 NAC_8_2
FI	EX_M	2015	1000 NAC	9	FI_EX_M_2015_1000 NAC_9
FI	EX_M	2015	1000 NAC	10	FI_EX_M_2015_1000 NAC_10
FI	EX_M	2015	1000 NAC	10_1	FI_EX_M_2015_1000 NAC_10_1
FI	EX_M	2015	1000 NAC	10_1_1	FI_EX_M_2015_1000 NAC_10_1_1
FI	EX_M	2015	1000 NAC	10_1_2	FI_EX_M_2015_1000 NAC_10_1_2
FI	EX_M	2015	1000 NAC	10_1_3	FI_EX_M_2015_1000 NAC_10_1_3
FI	EX_M	2015	1000 NAC	10_1_4	FI_EX_M_2015_1000 NAC_10_1_4
FI	EX_M	2015	1000 NAC	10_2	FI_EX_M_2015_1000 NAC_10_2
FI	EX_M	2015	1000 NAC	10_3	FI_EX_M_2015_1000 NAC_10_3
FI	EX_M	2015	1000 NAC	10_3_1	FI_EX_M_2015_1000 NAC_10_3_1
FI	EX_M	2015	1000 NAC	10_3_2	FI_EX_M_2015_1000 NAC_10_3_2
FI	EX_M	2015	1000 NAC	10_3_3	FI_EX_M_2015_1000 NAC_10_3_3
FI	EX_M	2015	1000 NAC	10_3_4	FI_EX_M_2015_1000 NAC_10_3_4
FI	EX_M	2015	1000 NAC	10_4	FI_EX_M_2015_1000 NAC_10_4
FI	EX_X	2015	1000 m3	1	FI_EX_X_2015_1000 m3_1
FI	EX_X	2015	1000 m3	1_1	FI_EX_X_2015_1000 m3_1_1
FI	EX_X	2015	1000 m3	1_2	FI_EX_X_2015_1000 m3_1_2
FI	EX_X	2015	1000 m3	1_2_C	FI_EX_X_2015_1000 m3_1_2_C
FI	EX_X	2015	1000 m3	1_2_NC	FI_EX_X_2015_1000 m3_1_2_NC
FI	EX_X	2015	1000 m3	1_2_NC_T	FI_EX_X_2015_1000 m3_1_2_NC_T
FI	EX_X	2015	1000 mt	2	FI_EX_X_2015_1000 mt_2
FI	EX_X	2015	1000 m3	3	FI_EX_X_2015_1000 m3_3
FI	EX_X	2015	1000 m3	3_1	FI_EX_X_2015_1000 m3_3_1
FI	EX_X	2015	1000 m3	3_2	FI_EX_X_2015_1000 m3_3_2
FI	EX_X	2015	1000 mt	4	FI_EX_X_2015_1000 mt_4
FI	EX_X	2015	1000 mt	4_1	FI_EX_X_2015_1000 mt_4_1
FI	EX_X	2015	1000 mt	4_2	FI_EX_X_2015_1000 mt_4_2
FI	EX_X	2015	1000 m3	5	FI_EX_X_2015_1000 m3_5
FI	EX_X	2015	1000 m3	5_C	FI_EX_X_2015_1000 m3_5_C
FI	EX_X	2015	1000 m3	5_NC	FI_EX_X_2015_1000 m3_5_NC
FI	EX_X	2015	1000 m3	5_NC_T	FI_EX_X_2015_1000 m3_5_NC_T
FI	EX_X	2015	1000 m3	6	FI_EX_X_2015_1000 m3_6
FI	EX_X	2015	1000 m3	6_1	FI_EX_X_2015_1000 m3_6_1
FI	EX_X	2015	1000 m3	6_1_C	FI_EX_X_2015_1000 m3_6_1_C
FI	EX_X	2015	1000 m3	6_1_NC	FI_EX_X_2015_1000 m3_6_1_NC
FI	EX_X	2015	1000 m3	6_1_NC_T	FI_EX_X_2015_1000 m3_6_1_NC_T
FI	EX_X	2015	1000 m3	6_2	FI_EX_X_2015_1000 m3_6_2
FI	EX_X	2015	1000 m3	6_2_C	FI_EX_X_2015_1000 m3_6_2_C
FI	EX_X	2015	1000 m3	6_2_NC	FI_EX_X_2015_1000 m3_6_2_NC
FI	EX_X	2015	1000 m3	6_2_NC_T	FI_EX_X_2015_1000 m3_6_2_NC_T
FI	EX_X	2015	1000 m3	6_3	FI_EX_X_2015_1000 m3_6_3
FI	EX_X	2015	1000 m3	6_3_1	FI_EX_X_2015_1000 m3_6_3_1
FI	EX_X	2015	1000 m3	6_4	FI_EX_X_2015_1000 m3_6_4
FI	EX_X	2015	1000 m3	6_4_1	FI_EX_X_2015_1000 m3_6_4_1
FI	EX_X	2015	1000 m3	6_4_2	FI_EX_X_2015_1000 m3_6_4_2
FI	EX_X	2015	1000 m3	6_4_3	FI_EX_X_2015_1000 m3_6_4_3
FI	EX_X	2015	1000 mt	7	FI_EX_X_2015_1000 mt_7
FI	EX_X	2015	1000 mt	7_1	FI_EX_X_2015_1000 mt_7_1
FI	EX_X	2015	1000 mt	7_2	FI_EX_X_2015_1000 mt_7_2
FI	EX_X	2015	1000 mt	7_3	FI_EX_X_2015_1000 mt_7_3
FI	EX_X	2015	1000 mt	7_3_1	FI_EX_X_2015_1000 mt_7_3_1
FI	EX_X	2015	1000 mt	7_3_2	FI_EX_X_2015_1000 mt_7_3_2
FI	EX_X	2015	1000 mt	7_3_3	FI_EX_X_2015_1000 mt_7_3_3
FI	EX_X	2015	1000 mt	7_3_4	FI_EX_X_2015_1000 mt_7_3_4
FI	EX_X	2015	1000 mt	7_4	FI_EX_X_2015_1000 mt_7_4
FI	EX_X	2015	1000 mt	8	FI_EX_X_2015_1000 mt_8
FI	EX_X	2015	1000 mt	8_1	FI_EX_X_2015_1000 mt_8_1
FI	EX_X	2015	1000 mt	8_2	FI_EX_X_2015_1000 mt_8_2
FI	EX_X	2015	1000 mt	9	FI_EX_X_2015_1000 mt_9
FI	EX_X	2015	1000 mt	10	FI_EX_X_2015_1000 mt_10
FI	EX_X	2015	1000 mt	10_1	FI_EX_X_2015_1000 mt_10_1
FI	EX_X	2015	1000 mt	10_1_1	FI_EX_X_2015_1000 mt_10_1_1
FI	EX_X	2015	1000 mt	10_1_2	FI_EX_X_2015_1000 mt_10_1_2
FI	EX_X	2015	1000 mt	10_1_3	FI_EX_X_2015_1000 mt_10_1_3
FI	EX_X	2015	1000 mt	10_1_4	FI_EX_X_2015_1000 mt_10_1_4
FI	EX_X	2015	1000 mt	10_2	FI_EX_X_2015_1000 mt_10_2
FI	EX_X	2015	1000 mt	10_3	FI_EX_X_2015_1000 mt_10_3
FI	EX_X	2015	1000 mt	10_3_1	FI_EX_X_2015_1000 mt_10_3_1
FI	EX_X	2015	1000 mt	10_3_2	FI_EX_X_2015_1000 mt_10_3_2
FI	EX_X	2015	1000 mt	10_3_3	FI_EX_X_2015_1000 mt_10_3_3
FI	EX_X	2015	1000 mt	10_3_4	FI_EX_X_2015_1000 mt_10_3_4
FI	EX_X	2015	1000 mt	10_4	FI_EX_X_2015_1000 mt_10_4
FI	EX_X	2015	1000 NAC	1	FI_EX_X_2015_1000 NAC_1
FI	EX_X	2015	1000 NAC	1_1	FI_EX_X_2015_1000 NAC_1_1
FI	EX_X	2015	1000 NAC	1_2	FI_EX_X_2015_1000 NAC_1_2
FI	EX_X	2015	1000 NAC	1_2_C	FI_EX_X_2015_1000 NAC_1_2_C
FI	EX_X	2015	1000 NAC	1_2_NC	FI_EX_X_2015_1000 NAC_1_2_NC
FI	EX_X	2015	1000 NAC	1_2_NC_T	FI_EX_X_2015_1000 NAC_1_2_NC_T
FI	EX_X	2015	1000 NAC	2	FI_EX_X_2015_1000 NAC_2
FI	EX_X	2015	1000 NAC	3	FI_EX_X_2015_1000 NAC_3
FI	EX_X	2015	1000 NAC	3_1	FI_EX_X_2015_1000 NAC_3_1
FI	EX_X	2015	1000 NAC	3_2	FI_EX_X_2015_1000 NAC_3_2
FI	EX_X	2015	1000 NAC	4	FI_EX_X_2015_1000 NAC_4
FI	EX_X	2015	1000 NAC	4_1	FI_EX_X_2015_1000 NAC_4_1
FI	EX_X	2015	1000 NAC	4_2	FI_EX_X_2015_1000 NAC_4_2
FI	EX_X	2015	1000 NAC	5	FI_EX_X_2015_1000 NAC_5
FI	EX_X	2015	1000 NAC	5_C	FI_EX_X_2015_1000 NAC_5_C
FI	EX_X	2015	1000 NAC	5_NC	FI_EX_X_2015_1000 NAC_5_NC
FI	EX_X	2015	1000 NAC	5_NC_T	FI_EX_X_2015_1000 NAC_5_NC_T
FI	EX_X	2015	1000 NAC	6	FI_EX_X_2015_1000 NAC_6
FI	EX_X	2015	1000 NAC	6_1	FI_EX_X_2015_1000 NAC_6_1
FI	EX_X	2015	1000 NAC	6_1_C	FI_EX_X_2015_1000 NAC_6_1_C
FI	EX_X	2015	1000 NAC	6_1_NC	FI_EX_X_2015_1000 NAC_6_1_NC
FI	EX_X	2015	1000 NAC	6_1_NC_T	FI_EX_X_2015_1000 NAC_6_1_NC_T
FI	EX_X	2015	1000 NAC	6_2	FI_EX_X_2015_1000 NAC_6_2
FI	EX_X	2015	1000 NAC	6_2_C	FI_EX_X_2015_1000 NAC_6_2_C
FI	EX_X	2015	1000 NAC	6_2_NC	FI_EX_X_2015_1000 NAC_6_2_NC
FI	EX_X	2015	1000 NAC	6_2_NC_T	FI_EX_X_2015_1000 NAC_6_2_NC_T
FI	EX_X	2015	1000 NAC	6_3	FI_EX_X_2015_1000 NAC_6_3
FI	EX_X	2015	1000 NAC	6_3_1	FI_EX_X_2015_1000 NAC_6_3_1
FI	EX_X	2015	1000 NAC	6_4	FI_EX_X_2015_1000 NAC_6_4
FI	EX_X	2015	1000 NAC	6_4_1	FI_EX_X_2015_1000 NAC_6_4_1
FI	EX_X	2015	1000 NAC	6_4_2	FI_EX_X_2015_1000 NAC_6_4_2
FI	EX_X	2015	1000 NAC	6_4_3	FI_EX_X_2015_1000 NAC_6_4_3
FI	EX_X	2015	1000 NAC	7	FI_EX_X_2015_1000 NAC_7
FI	EX_X	2015	1000 NAC	7_1	FI_EX_X_2015_1000 NAC_7_1
FI	EX_X	2015	1000 NAC	7_2	FI_EX_X_2015_1000 NAC_7_2
FI	EX_X	2015	1000 NAC	7_3	FI_EX_X_2015_1000 NAC_7_3
FI	EX_X	2015	1000 NAC	7_3_1	FI_EX_X_2015_1000 NAC_7_3_1
FI	EX_X	2015	1000 NAC	7_3_2	FI_EX_X_2015_1000 NAC_7_3_2
FI	EX_X	2015	1000 NAC	7_3_3	FI_EX_X_2015_1000 NAC_7_3_3
FI	EX_X	2015	1000 NAC	7_3_4	FI_EX_X_2015_1000 NAC_7_3_4
FI	EX_X	2015	1000 NAC	7_4	FI_EX_X_2015_1000 NAC_7_4
FI	EX_X	2015	1000 NAC	8	FI_EX_X_2015_1000 NAC_8
FI	EX_X	2015	1000 NAC	8_1	FI_EX_X_2015_1000 NAC_8_1
FI	EX_X	2015	1000 NAC	8_2	FI_EX_X_2015_1000 NAC_8_2
FI	EX_X	2015	1000 NAC	9	FI_EX_X_2015_1000 NAC_9
FI	EX_X	2015	1000 NAC	10	FI_EX_X_2015_1000 NAC_10
FI	EX_X	2015	1000 NAC	10_1	FI_EX_X_2015_1000 NAC_10_1
FI	EX_X	2015	1000 NAC	10_1_1	FI_EX_X_2015_1000 NAC_10_1_1
FI	EX_X	2015	1000 NAC	10_1_2	FI_EX_X_2015_1000 NAC_10_1_2
FI	EX_X	2015	1000 NAC	10_1_3	FI_EX_X_2015_1000 NAC_10_1_3
FI	EX_X	2015	1000 NAC	10_1_4	FI_EX_X_2015_1000 NAC_10_1_4
FI	EX_X	2015	1000 NAC	10_2	FI_EX_X_2015_1000 NAC_10_2
FI	EX_X	2015	1000 NAC	10_3	FI_EX_X_2015_1000 NAC_10_3
FI	EX_X	2015	1000 NAC	10_3_1	FI_EX_X_2015_1000 NAC_10_3_1
FI	EX_X	2015	1000 NAC	10_3_2	FI_EX_X_2015_1000 NAC_10_3_2
FI	EX_X	2015	1000 NAC	10_3_3	FI_EX_X_2015_1000 NAC_10_3_3
FI	EX_X	2015	1000 NAC	10_3_4	FI_EX_X_2015_1000 NAC_10_3_4
FI	EX_X	2015	1000 NAC	10_4	FI_EX_X_2015_1000 NAC_10_4
FI	P	2015	1000 m3	EU2_1	FI_P_2015_1000 m3_EU2_1
FI	P	2015	1000 m3	EU2_1_C	FI_P_2015_1000 m3_EU2_1_C
FI	P	2015	1000 m3	EU2_1_NC	FI_P_2015_1000 m3_EU2_1_NC
FI	P	2015	1000 m3	EU2_1_1	FI_P_2015_1000 m3_EU2_1_1
FI	P	2015	1000 m3	EU2_1_1_C	FI_P_2015_1000 m3_EU2_1_1_C
FI	P	2015	1000 m3	EU2_1_1_NC	FI_P_2015_1000 m3_EU2_1_1_NC
FI	P	2015	1000 m3	EU2_1_2	FI_P_2015_1000 m3_EU2_1_2
FI	P	2015	1000 m3	EU2_1_2_C	FI_P_2015_1000 m3_EU2_1_2_C
FI	P	2015	1000 m3	EU2_1_2_NC	FI_P_2015_1000 m3_EU2_1_2_NC
FI	P	2015	1000 m3	EU2_1_3	FI_P_2015_1000 m3_EU2_1_3
FI	P	2015	1000 m3	EU2_1_3_C	FI_P_2015_1000 m3_EU2_1_3_C
FI	P	2015	1000 m3	EU2_1_3_NC	FI_P_2015_1000 m3_EU2_1_3_NC
FI	P.OB	2015	1000 m3	1	FI_P.OB_2015_1000 m3_1
FI	P.OB	2015	1000 m3	1_C	FI_P.OB_2015_1000 m3_1_C
FI	P.OB	2015	1000 m3	1_NC	FI_P.OB_2015_1000 m3_1_NC
FI	P.OB	2015	1000 m3	1_1	FI_P.OB_2015_1000 m3_1_1
FI	P.OB	2015	1000 m3	1_1_C	FI_P.OB_2015_1000 m3_1_1_C
FI	P.OB	2015	1000 m3	1_1_NC	FI_P.OB_2015_1000 m3_1_1_NC
FI	P.OB	2015	1000 m3	1_2	FI_P.OB_2015_1000 m3_1_2
FI	P.OB	2015	1000 m3	1_2_C	FI_P.OB_2015_1000 m3_1_2_C
FI	P.OB	2015	1000 m3	1_2_NC	FI_P.OB_2015_1000 m3_1_2_NC
FI	P.OB	2015	1000 m3	1_2_1	FI_P.OB_2015_1000 m3_1_2_1
FI	P.OB	2015	1000 m3	1_2_1_C	FI_P.OB_2015_1000 m3_1_2_1_C
FI	P.OB	2015	1000 m3	1_2_1_NC	FI_P.OB_2015_1000 m3_1_2_1_NC
FI	P.OB	2015	1000 m3	1_2_2	FI_P.OB_2015_1000 m3_1_2_2
FI	P.OB	2015	1000 m3	1_2_2_C	FI_P.OB_2015_1000 m3_1_2_2_C
FI	P.OB	2015	1000 m3	1_2_2_NC	FI_P.OB_2015_1000 m3_1_2_2_NC
FI	P.OB	2015	1000 m3	1_2_3	FI_P.OB_2015_1000 m3_1_2_3
FI	P.OB	2015	1000 m3	1_2_3_C	FI_P.OB_2015_1000 m3_1_2_3_C
FI	P.OB	2015	1000 m3	1_2_3_NC	FI_P.OB_2015_1000 m3_1_2_3_NC
FI	P	2014	1000 m3	1	FI_P_2014_1000 m3_1
FI	P	2014	1000 m3	1_C	FI_P_2014_1000 m3_1_C
FI	P	2014	1000 m3	1_NC	FI_P_2014_1000 m3_1_NC
FI	P	2014	1000 m3	1_1	FI_P_2014_1000 m3_1_1
FI	P	2014	1000 m3	1_1_C	FI_P_2014_1000 m3_1_1_C
FI	P	2014	1000 m3	1_1_NC	FI_P_2014_1000 m3_1_1_NC
FI	P	2014	1000 m3	1_2	FI_P_2014_1000 m3_1_2
FI	P	2014	1000 m3	1_2_C	FI_P_2014_1000 m3_1_2_C
FI	P	2014	1000 m3	1_2_NC	FI_P_2014_1000 m3_1_2_NC
FI	P	2014	1000 m3	1_2_1	FI_P_2014_1000 m3_1_2_1
FI	P	2014	1000 m3	1_2_1_C	FI_P_2014_1000 m3_1_2_1_C
FI	P	2014	1000 m3	1_2_1_NC	FI_P_2014_1000 m3_1_2_1_NC
FI	P	2014	1000 m3	1_2_2	FI_P_2014_1000 m3_1_2_2
FI	P	2014	1000 m3	1_2_2_C	FI_P_2014_1000 m3_1_2_2_C
FI	P	2014	1000 m3	1_2_2_NC	FI_P_2014_1000 m3_1_2_2_NC
FI	P	2014	1000 m3	1_2_3	FI_P_2014_1000 m3_1_2_3
FI	P	2014	1000 m3	1_2_3_C	FI_P_2014_1000 m3_1_2_3_C
FI	P	2014	1000 m3	1_2_3_NC	FI_P_2014_1000 m3_1_2_3_NC
FI	P	2014	1000 mt	2	FI_P_2014_1000 mt_2
FI	P	2014	1000 m3	3	FI_P_2014_1000 m3_3
FI	P	2014	1000 m3	3_1	FI_P_2014_1000 m3_3_1
FI	P	2014	1000 m3	3_2	FI_P_2014_1000 m3_3_2
FI	P	2014	1000 mt	4	FI_P_2014_1000 mt_4
FI	P	2014	1000 mt	4_1	FI_P_2014_1000 mt_4_1
FI	P	2014	1000 mt	4_2	FI_P_2014_1000 mt_4_2
FI	P	2014	1000 m3	5	FI_P_2014_1000 m3_5
FI	P	2014	1000 m3	5_C	FI_P_2014_1000 m3_5_C
FI	P	2014	1000 m3	5_NC	FI_P_2014_1000 m3_5_NC
FI	P	2014	1000 m3	5_NC_T	FI_P_2014_1000 m3_5_NC_T
FI	P	2014	1000 m3	6	FI_P_2014_1000 m3_6
FI	P	2014	1000 m3	6_1	FI_P_2014_1000 m3_6_1
FI	P	2014	1000 m3	6_1_C	FI_P_2014_1000 m3_6_1_C
FI	P	2014	1000 m3	6_1_NC	FI_P_2014_1000 m3_6_1_NC
FI	P	2014	1000 m3	6_1_NC_T	FI_P_2014_1000 m3_6_1_NC_T
FI	P	2014	1000 m3	6_2	FI_P_2014_1000 m3_6_2
FI	P	2014	1000 m3	6_2_C	FI_P_2014_1000 m3_6_2_C
FI	P	2014	1000 m3	6_2_NC	FI_P_2014_1000 m3_6_2_NC
FI	P	2014	1000 m3	6_2_NC_T	FI_P_2014_1000 m3_6_2_NC_T
FI	P	2014	1000 m3	6_3	FI_P_2014_1000 m3_6_3
FI	P	2014	1000 m3	6_3_1	FI_P_2014_1000 m3_6_3_1
FI	P	2014	1000 m3	6_4	FI_P_2014_1000 m3_6_4
FI	P	2014	1000 m3	6_4_1	FI_P_2014_1000 m3_6_4_1
FI	P	2014	1000 m3	6_4_2	FI_P_2014_1000 m3_6_4_2
FI	P	2014	1000 m3	6_4_3	FI_P_2014_1000 m3_6_4_3
FI	P	2014	1000 mt	7	FI_P_2014_1000 mt_7
FI	P	2014	1000 mt	7_1	FI_P_2014_1000 mt_7_1
FI	P	2014	1000 mt	7_2	FI_P_2014_1000 mt_7_2
FI	P	2014	1000 mt	7_3	FI_P_2014_1000 mt_7_3
FI	P	2014	1000 mt	7_3_1	FI_P_2014_1000 mt_7_3_1
FI	P	2014	1000 mt	7_3_2	FI_P_2014_1000 mt_7_3_2
FI	P	2014	1000 mt	7_3_3	FI_P_2014_1000 mt_7_3_3
FI	P	2014	1000 mt	7_3_4	FI_P_2014_1000 mt_7_3_4
FI	P	2014	1000 mt	7_4	FI_P_2014_1000 mt_7_4
FI	P	2014	1000 mt	8	FI_P_2014_1000 mt_8
FI	P	2014	1000 mt	8_1	FI_P_2014_1000 mt_8_1
FI	P	2014	1000 mt	8_2	FI_P_2014_1000 mt_8_2
FI	P	2014	1000 mt	9	FI_P_2014_1000 mt_9
FI	P	2014	1000 mt	10	FI_P_2014_1000 mt_10
FI	P	2014	1000 mt	10_1	FI_P_2014_1000 mt_10_1
FI	P	2014	1000 mt	10_1_1	FI_P_2014_1000 mt_10_1_1
FI	P	2014	1000 mt	10_1_2	FI_P_2014_1000 mt_10_1_2
FI	P	2014	1000 mt	10_1_3	FI_P_2014_1000 mt_10_1_3
FI	P	2014	1000 mt	10_1_4	FI_P_2014_1000 mt_10_1_4
FI	P	2014	1000 mt	10_2	FI_P_2014_1000 mt_10_2
FI	P	2014	1000 mt	10_3	FI_P_2014_1000 mt_10_3
FI	P	2014	1000 mt	10_3_1	FI_P_2014_1000 mt_10_3_1
FI	P	2014	1000 mt	10_3_2	FI_P_2014_1000 mt_10_3_2
FI	P	2014	1000 mt	10_3_3	FI_P_2014_1000 mt_10_3_3
FI	P	2014	1000 mt	10_3_4	FI_P_2014_1000 mt_10_3_4
FI	P	2014	1000 mt	10_4	FI_P_2014_1000 mt_10_4
FI	M	2014	1000 m3	1	FI_M_2014_1000 m3_1
FI	M	2014	1000 m3	1_1	FI_M_2014_1000 m3_1_1
FI	M	2014	1000 m3	1_2	FI_M_2014_1000 m3_1_2
FI	M	2014	1000 m3	1_2_C	FI_M_2014_1000 m3_1_2_C
FI	M	2014	1000 m3	1_2_NC	FI_M_2014_1000 m3_1_2_NC
FI	M	2014	1000 m3	1_2_NC_T	FI_M_2014_1000 m3_1_2_NC_T
FI	M	2014	1000 mt	2	FI_M_2014_1000 mt_2
FI	M	2014	1000 m3	3	FI_M_2014_1000 m3_3
FI	M	2014	1000 m3	3_1	FI_M_2014_1000 m3_3_1
FI	M	2014	1000 m3	3_2	FI_M_2014_1000 m3_3_2
FI	M	2014	1000 mt	4	FI_M_2014_1000 mt_4
FI	M	2014	1000 mt	4_1	FI_M_2014_1000 mt_4_1
FI	M	2014	1000 mt	4_2	FI_M_2014_1000 mt_4_2
FI	M	2014	1000 m3	5	FI_M_2014_1000 m3_5
FI	M	2014	1000 m3	5_C	FI_M_2014_1000 m3_5_C
FI	M	2014	1000 m3	5_NC	FI_M_2014_1000 m3_5_NC
FI	M	2014	1000 m3	5_NC_T	FI_M_2014_1000 m3_5_NC_T
FI	M	2014	1000 m3	6	FI_M_2014_1000 m3_6
FI	M	2014	1000 m3	6_1	FI_M_2014_1000 m3_6_1
FI	M	2014	1000 m3	6_1_C	FI_M_2014_1000 m3_6_1_C
FI	M	2014	1000 m3	6_1_NC	FI_M_2014_1000 m3_6_1_NC
FI	M	2014	1000 m3	6_1_NC_T	FI_M_2014_1000 m3_6_1_NC_T
FI	M	2014	1000 m3	6_2	FI_M_2014_1000 m3_6_2
FI	M	2014	1000 m3	6_2_C	FI_M_2014_1000 m3_6_2_C
FI	M	2014	1000 m3	6_2_NC	FI_M_2014_1000 m3_6_2_NC
FI	M	2014	1000 m3	6_2_NC_T	FI_M_2014_1000 m3_6_2_NC_T
FI	M	2014	1000 m3	6_3	FI_M_2014_1000 m3_6_3
FI	M	2014	1000 m3	6_3_1	FI_M_2014_1000 m3_6_3_1
FI	M	2014	1000 m3	6_4	FI_M_2014_1000 m3_6_4
FI	M	2014	1000 m3	6_4_1	FI_M_2014_1000 m3_6_4_1
FI	M	2014	1000 m3	6_4_2	FI_M_2014_1000 m3_6_4_2
FI	M	2014	1000 m3	6_4_3	FI_M_2014_1000 m3_6_4_3
FI	M	2014	1000 mt	7	FI_M_2014_1000 mt_7
FI	M	2014	1000 mt	7_1	FI_M_2014_1000 mt_7_1
FI	M	2014	1000 mt	7_2	FI_M_2014_1000 mt_7_2
FI	M	2014	1000 mt	7_3	FI_M_2014_1000 mt_7_3
FI	M	2014	1000 mt	7_3_1	FI_M_2014_1000 mt_7_3_1
FI	M	2014	1000 mt	7_3_2	FI_M_2014_1000 mt_7_3_2
FI	M	2014	1000 mt	7_3_3	FI_M_2014_1000 mt_7_3_3
FI	M	2014	1000 mt	7_3_4	FI_M_2014_1000 mt_7_3_4
FI	M	2014	1000 mt	7_4	FI_M_2014_1000 mt_7_4
FI	M	2014	1000 mt	8	FI_M_2014_1000 mt_8
FI	M	2014	1000 mt	8_1	FI_M_2014_1000 mt_8_1
FI	M	2014	1000 mt	8_2	FI_M_2014_1000 mt_8_2
FI	M	2014	1000 mt	9	FI_M_2014_1000 mt_9
FI	M	2014	1000 mt	10	FI_M_2014_1000 mt_10
FI	M	2014	1000 mt	10_1	FI_M_2014_1000 mt_10_1
FI	M	2014	1000 mt	10_1_1	FI_M_2014_1000 mt_10_1_1
FI	M	2014	1000 mt	10_1_2	FI_M_2014_1000 mt_10_1_2
FI	M	2014	1000 mt	10_1_3	FI_M_2014_1000 mt_10_1_3
FI	M	2014	1000 mt	10_1_4	FI_M_2014_1000 mt_10_1_4
FI	M	2014	1000 mt	10_2	FI_M_2014_1000 mt_10_2
FI	M	2014	1000 mt	10_3	FI_M_2014_1000 mt_10_3
FI	M	2014	1000 mt	10_3_1	FI_M_2014_1000 mt_10_3_1
FI	M	2014	1000 mt	10_3_2	FI_M_2014_1000 mt_10_3_2
FI	M	2014	1000 mt	10_3_3	FI_M_2014_1000 mt_10_3_3
FI	M	2014	1000 mt	10_3_4	FI_M_2014_1000 mt_10_3_4
FI	M	2014	1000 mt	10_4	FI_M_2014_1000 mt_10_4
FI	M	2014	1000 NAC	1	FI_M_2014_1000 NAC_1
FI	M	2014	1000 NAC	1_1	FI_M_2014_1000 NAC_1_1
FI	M	2014	1000 NAC	1_2	FI_M_2014_1000 NAC_1_2
FI	M	2014	1000 NAC	1_2_C	FI_M_2014_1000 NAC_1_2_C
FI	M	2014	1000 NAC	1_2_NC	FI_M_2014_1000 NAC_1_2_NC
FI	M	2014	1000 NAC	1_2_NC_T	FI_M_2014_1000 NAC_1_2_NC_T
FI	M	2014	1000 NAC	2	FI_M_2014_1000 NAC_2
FI	M	2014	1000 NAC	3	FI_M_2014_1000 NAC_3
FI	M	2014	1000 NAC	3_1	FI_M_2014_1000 NAC_3_1
FI	M	2014	1000 NAC	3_2	FI_M_2014_1000 NAC_3_2
FI	M	2014	1000 NAC	4	FI_M_2014_1000 NAC_4
FI	M	2014	1000 NAC	4_1	FI_M_2014_1000 NAC_4_1
FI	M	2014	1000 NAC	4_2	FI_M_2014_1000 NAC_4_2
FI	M	2014	1000 NAC	5	FI_M_2014_1000 NAC_5
FI	M	2014	1000 NAC	5_C	FI_M_2014_1000 NAC_5_C
FI	M	2014	1000 NAC	5_NC	FI_M_2014_1000 NAC_5_NC
FI	M	2014	1000 NAC	5_NC_T	FI_M_2014_1000 NAC_5_NC_T
FI	M	2014	1000 NAC	6	FI_M_2014_1000 NAC_6
FI	M	2014	1000 NAC	6_1	FI_M_2014_1000 NAC_6_1
FI	M	2014	1000 NAC	6_1_C	FI_M_2014_1000 NAC_6_1_C
FI	M	2014	1000 NAC	6_1_NC	FI_M_2014_1000 NAC_6_1_NC
FI	M	2014	1000 NAC	6_1_NC_T	FI_M_2014_1000 NAC_6_1_NC_T
FI	M	2014	1000 NAC	6_2	FI_M_2014_1000 NAC_6_2
FI	M	2014	1000 NAC	6_2_C	FI_M_2014_1000 NAC_6_2_C
FI	M	2014	1000 NAC	6_2_NC	FI_M_2014_1000 NAC_6_2_NC
FI	M	2014	1000 NAC	6_2_NC_T	FI_M_2014_1000 NAC_6_2_NC_T
FI	M	2014	1000 NAC	6_3	FI_M_2014_1000 NAC_6_3
FI	M	2014	1000 NAC	6_3_1	FI_M_2014_1000 NAC_6_3_1
FI	M	2014	1000 NAC	6_4	FI_M_2014_1000 NAC_6_4
FI	M	2014	1000 NAC	6_4_1	FI_M_2014_1000 NAC_6_4_1
FI	M	2014	1000 NAC	6_4_2	FI_M_2014_1000 NAC_6_4_2
FI	M	2014	1000 NAC	6_4_3	FI_M_2014_1000 NAC_6_4_3
FI	M	2014	1000 NAC	7	FI_M_2014_1000 NAC_7
FI	M	2014	1000 NAC	7_1	FI_M_2014_1000 NAC_7_1
FI	M	2014	1000 NAC	7_2	FI_M_2014_1000 NAC_7_2
FI	M	2014	1000 NAC	7_3	FI_M_2014_1000 NAC_7_3
FI	M	2014	1000 NAC	7_3_1	FI_M_2014_1000 NAC_7_3_1
FI	M	2014	1000 NAC	7_3_2	FI_M_2014_1000 NAC_7_3_2
FI	M	2014	1000 NAC	7_3_3	FI_M_2014_1000 NAC_7_3_3
FI	M	2014	1000 NAC	7_3_4	FI_M_2014_1000 NAC_7_3_4
FI	M	2014	1000 NAC	7_4	FI_M_2014_1000 NAC_7_4
FI	M	2014	1000 NAC	8	FI_M_2014_1000 NAC_8
FI	M	2014	1000 NAC	8_1	FI_M_2014_1000 NAC_8_1
FI	M	2014	1000 NAC	8_2	FI_M_2014_1000 NAC_8_2
FI	M	2014	1000 NAC	9	FI_M_2014_1000 NAC_9
FI	M	2014	1000 NAC	10	FI_M_2014_1000 NAC_10
FI	M	2014	1000 NAC	10_1	FI_M_2014_1000 NAC_10_1
FI	M	2014	1000 NAC	10_1_1	FI_M_2014_1000 NAC_10_1_1
FI	M	2014	1000 NAC	10_1_2	FI_M_2014_1000 NAC_10_1_2
FI	M	2014	1000 NAC	10_1_3	FI_M_2014_1000 NAC_10_1_3
FI	M	2014	1000 NAC	10_1_4	FI_M_2014_1000 NAC_10_1_4
FI	M	2014	1000 NAC	10_2	FI_M_2014_1000 NAC_10_2
FI	M	2014	1000 NAC	10_3	FI_M_2014_1000 NAC_10_3
FI	M	2014	1000 NAC	10_3_1	FI_M_2014_1000 NAC_10_3_1
FI	M	2014	1000 NAC	10_3_2	FI_M_2014_1000 NAC_10_3_2
FI	M	2014	1000 NAC	10_3_3	FI_M_2014_1000 NAC_10_3_3
FI	M	2014	1000 NAC	10_3_4	FI_M_2014_1000 NAC_10_3_4
FI	M	2014	1000 NAC	10_4	FI_M_2014_1000 NAC_10_4
FI	X	2014	1000 m3	1	FI_X_2014_1000 m3_1
FI	X	2014	1000 m3	1_1	FI_X_2014_1000 m3_1_1
FI	X	2014	1000 m3	1_2	FI_X_2014_1000 m3_1_2
FI	X	2014	1000 m3	1_2_C	FI_X_2014_1000 m3_1_2_C
FI	X	2014	1000 m3	1_2_NC	FI_X_2014_1000 m3_1_2_NC
FI	X	2014	1000 m3	1_2_NC_T	FI_X_2014_1000 m3_1_2_NC_T
FI	X	2014	1000 mt	2	FI_X_2014_1000 mt_2
FI	X	2014	1000 m3	3	FI_X_2014_1000 m3_3
FI	X	2014	1000 m3	3_1	FI_X_2014_1000 m3_3_1
FI	X	2014	1000 m3	3_2	FI_X_2014_1000 m3_3_2
FI	X	2014	1000 mt	4	FI_X_2014_1000 mt_4
FI	X	2014	1000 mt	4_1	FI_X_2014_1000 mt_4_1
FI	X	2014	1000 mt	4_2	FI_X_2014_1000 mt_4_2
FI	X	2014	1000 m3	5	FI_X_2014_1000 m3_5
FI	X	2014	1000 m3	5_C	FI_X_2014_1000 m3_5_C
FI	X	2014	1000 m3	5_NC	FI_X_2014_1000 m3_5_NC
FI	X	2014	1000 m3	5_NC_T	FI_X_2014_1000 m3_5_NC_T
FI	X	2014	1000 m3	6	FI_X_2014_1000 m3_6
FI	X	2014	1000 m3	6_1	FI_X_2014_1000 m3_6_1
FI	X	2014	1000 m3	6_1_C	FI_X_2014_1000 m3_6_1_C
FI	X	2014	1000 m3	6_1_NC	FI_X_2014_1000 m3_6_1_NC
FI	X	2014	1000 m3	6_1_NC_T	FI_X_2014_1000 m3_6_1_NC_T
FI	X	2014	1000 m3	6_2	FI_X_2014_1000 m3_6_2
FI	X	2014	1000 m3	6_2_C	FI_X_2014_1000 m3_6_2_C
FI	X	2014	1000 m3	6_2_NC	FI_X_2014_1000 m3_6_2_NC
FI	X	2014	1000 m3	6_2_NC_T	FI_X_2014_1000 m3_6_2_NC_T
FI	X	2014	1000 m3	6_3	FI_X_2014_1000 m3_6_3
FI	X	2014	1000 m3	6_3_1	FI_X_2014_1000 m3_6_3_1
FI	X	2014	1000 m3	6_4	FI_X_2014_1000 m3_6_4
FI	X	2014	1000 m3	6_4_1	FI_X_2014_1000 m3_6_4_1
FI	X	2014	1000 m3	6_4_2	FI_X_2014_1000 m3_6_4_2
FI	X	2014	1000 m3	6_4_3	FI_X_2014_1000 m3_6_4_3
FI	X	2014	1000 mt	7	FI_X_2014_1000 mt_7
FI	X	2014	1000 mt	7_1	FI_X_2014_1000 mt_7_1
FI	X	2014	1000 mt	7_2	FI_X_2014_1000 mt_7_2
FI	X	2014	1000 mt	7_3	FI_X_2014_1000 mt_7_3
FI	X	2014	1000 mt	7_3_1	FI_X_2014_1000 mt_7_3_1
FI	X	2014	1000 mt	7_3_2	FI_X_2014_1000 mt_7_3_2
FI	X	2014	1000 mt	7_3_3	FI_X_2014_1000 mt_7_3_3
FI	X	2014	1000 mt	7_3_4	FI_X_2014_1000 mt_7_3_4
FI	X	2014	1000 mt	7_4	FI_X_2014_1000 mt_7_4
FI	X	2014	1000 mt	8	FI_X_2014_1000 mt_8
FI	X	2014	1000 mt	8_1	FI_X_2014_1000 mt_8_1
FI	X	2014	1000 mt	8_2	FI_X_2014_1000 mt_8_2
FI	X	2014	1000 mt	9	FI_X_2014_1000 mt_9
FI	X	2014	1000 mt	10	FI_X_2014_1000 mt_10
FI	X	2014	1000 mt	10_1	FI_X_2014_1000 mt_10_1
FI	X	2014	1000 mt	10_1_1	FI_X_2014_1000 mt_10_1_1
FI	X	2014	1000 mt	10_1_2	FI_X_2014_1000 mt_10_1_2
FI	X	2014	1000 mt	10_1_3	FI_X_2014_1000 mt_10_1_3
FI	X	2014	1000 mt	10_1_4	FI_X_2014_1000 mt_10_1_4
FI	X	2014	1000 mt	10_2	FI_X_2014_1000 mt_10_2
FI	X	2014	1000 mt	10_3	FI_X_2014_1000 mt_10_3
FI	X	2014	1000 mt	10_3_1	FI_X_2014_1000 mt_10_3_1
FI	X	2014	1000 mt	10_3_2	FI_X_2014_1000 mt_10_3_2
FI	X	2014	1000 mt	10_3_3	FI_X_2014_1000 mt_10_3_3
FI	X	2014	1000 mt	10_3_4	FI_X_2014_1000 mt_10_3_4
FI	X	2014	1000 mt	10_4	FI_X_2014_1000 mt_10_4
FI	X	2014	1000 NAC	1	FI_X_2014_1000 NAC_1
FI	X	2014	1000 NAC	1_1	FI_X_2014_1000 NAC_1_1
FI	X	2014	1000 NAC	1_2	FI_X_2014_1000 NAC_1_2
FI	X	2014	1000 NAC	1_2_C	FI_X_2014_1000 NAC_1_2_C
FI	X	2014	1000 NAC	1_2_NC	FI_X_2014_1000 NAC_1_2_NC
FI	X	2014	1000 NAC	1_2_NC_T	FI_X_2014_1000 NAC_1_2_NC_T
FI	X	2014	1000 NAC	2	FI_X_2014_1000 NAC_2
FI	X	2014	1000 NAC	3	FI_X_2014_1000 NAC_3
FI	X	2014	1000 NAC	3_1	FI_X_2014_1000 NAC_3_1
FI	X	2014	1000 NAC	3_2	FI_X_2014_1000 NAC_3_2
FI	X	2014	1000 NAC	4	FI_X_2014_1000 NAC_4
FI	X	2014	1000 NAC	4_1	FI_X_2014_1000 NAC_4_1
FI	X	2014	1000 NAC	4_2	FI_X_2014_1000 NAC_4_2
FI	X	2014	1000 NAC	5	FI_X_2014_1000 NAC_5
FI	X	2014	1000 NAC	5_C	FI_X_2014_1000 NAC_5_C
FI	X	2014	1000 NAC	5_NC	FI_X_2014_1000 NAC_5_NC
FI	X	2014	1000 NAC	5_NC_T	FI_X_2014_1000 NAC_5_NC_T
FI	X	2014	1000 NAC	6	FI_X_2014_1000 NAC_6
FI	X	2014	1000 NAC	6_1	FI_X_2014_1000 NAC_6_1
FI	X	2014	1000 NAC	6_1_C	FI_X_2014_1000 NAC_6_1_C
FI	X	2014	1000 NAC	6_1_NC	FI_X_2014_1000 NAC_6_1_NC
FI	X	2014	1000 NAC	6_1_NC_T	FI_X_2014_1000 NAC_6_1_NC_T
FI	X	2014	1000 NAC	6_2	FI_X_2014_1000 NAC_6_2
FI	X	2014	1000 NAC	6_2_C	FI_X_2014_1000 NAC_6_2_C
FI	X	2014	1000 NAC	6_2_NC	FI_X_2014_1000 NAC_6_2_NC
FI	X	2014	1000 NAC	6_2_NC_T	FI_X_2014_1000 NAC_6_2_NC_T
FI	X	2014	1000 NAC	6_3	FI_X_2014_1000 NAC_6_3
FI	X	2014	1000 NAC	6_3_1	FI_X_2014_1000 NAC_6_3_1
FI	X	2014	1000 NAC	6_4	FI_X_2014_1000 NAC_6_4
FI	X	2014	1000 NAC	6_4_1	FI_X_2014_1000 NAC_6_4_1
FI	X	2014	1000 NAC	6_4_2	FI_X_2014_1000 NAC_6_4_2
FI	X	2014	1000 NAC	6_4_3	FI_X_2014_1000 NAC_6_4_3
FI	X	2014	1000 NAC	7	FI_X_2014_1000 NAC_7
FI	X	2014	1000 NAC	7_1	FI_X_2014_1000 NAC_7_1
FI	X	2014	1000 NAC	7_2	FI_X_2014_1000 NAC_7_2
FI	X	2014	1000 NAC	7_3	FI_X_2014_1000 NAC_7_3
FI	X	2014	1000 NAC	7_3_1	FI_X_2014_1000 NAC_7_3_1
FI	X	2014	1000 NAC	7_3_2	FI_X_2014_1000 NAC_7_3_2
FI	X	2014	1000 NAC	7_3_3	FI_X_2014_1000 NAC_7_3_3
FI	X	2014	1000 NAC	7_3_4	FI_X_2014_1000 NAC_7_3_4
FI	X	2014	1000 NAC	7_4	FI_X_2014_1000 NAC_7_4
FI	X	2014	1000 NAC	8	FI_X_2014_1000 NAC_8
FI	X	2014	1000 NAC	8_1	FI_X_2014_1000 NAC_8_1
FI	X	2014	1000 NAC	8_2	FI_X_2014_1000 NAC_8_2
FI	X	2014	1000 NAC	9	FI_X_2014_1000 NAC_9
FI	X	2014	1000 NAC	10	FI_X_2014_1000 NAC_10
FI	X	2014	1000 NAC	10_1	FI_X_2014_1000 NAC_10_1
FI	X	2014	1000 NAC	10_1_1	FI_X_2014_1000 NAC_10_1_1
FI	X	2014	1000 NAC	10_1_2	FI_X_2014_1000 NAC_10_1_2
FI	X	2014	1000 NAC	10_1_3	FI_X_2014_1000 NAC_10_1_3
FI	X	2014	1000 NAC	10_1_4	FI_X_2014_1000 NAC_10_1_4
FI	X	2014	1000 NAC	10_2	FI_X_2014_1000 NAC_10_2
FI	X	2014	1000 NAC	10_3	FI_X_2014_1000 NAC_10_3
FI	X	2014	1000 NAC	10_3_1	FI_X_2014_1000 NAC_10_3_1
FI	X	2014	1000 NAC	10_3_2	FI_X_2014_1000 NAC_10_3_2
FI	X	2014	1000 NAC	10_3_3	FI_X_2014_1000 NAC_10_3_3
FI	X	2014	1000 NAC	10_3_4	FI_X_2014_1000 NAC_10_3_4
FI	X	2014	1000 NAC	10_4	FI_X_2014_1000 NAC_10_4
FI	M	2014	1000 NAC	11_1	FI_M_2014_1000 NAC_11_1
FI	M	2014	1000 NAC	11_1_C	FI_M_2014_1000 NAC_11_1_C
FI	M	2014	1000 NAC	11_1_NC	FI_M_2014_1000 NAC_11_1_NC
FI	M	2014	1000 NAC	11_1_NC_T	FI_M_2014_1000 NAC_11_1_NC_T
FI	M	2014	1000 NAC	11_2	FI_M_2014_1000 NAC_11_2
FI	M	2014	1000 NAC	11_3	FI_M_2014_1000 NAC_11_3
FI	M	2014	1000 NAC	11_4	FI_M_2014_1000 NAC_11_4
FI	M	2014	1000 NAC	11_5	FI_M_2014_1000 NAC_11_5
FI	M	2014	1000 NAC	11_6	FI_M_2014_1000 NAC_11_6
FI	M	2014	1000 NAC	11_7	FI_M_2014_1000 NAC_11_7
FI	M	2014	1000 NAC	11_7_1	FI_M_2014_1000 NAC_11_7_1
FI	M	2014	1000 NAC	12_1	FI_M_2014_1000 NAC_12_1
FI	M	2014	1000 NAC	12_2	FI_M_2014_1000 NAC_12_2
FI	M	2014	1000 NAC	12_3	FI_M_2014_1000 NAC_12_3
FI	M	2014	1000 NAC	12_4	FI_M_2014_1000 NAC_12_4
FI	M	2014	1000 NAC	12_5	FI_M_2014_1000 NAC_12_5
FI	M	2014	1000 NAC	12_6	FI_M_2014_1000 NAC_12_6
FI	M	2014	1000 NAC	12_6_1	FI_M_2014_1000 NAC_12_6_1
FI	M	2014	1000 NAC	12_6_2	FI_M_2014_1000 NAC_12_6_2
FI	M	2014	1000 NAC	12_6_3	FI_M_2014_1000 NAC_12_6_3
FI	M	2014	1000 NAC	12_7	FI_M_2014_1000 NAC_12_7
FI	M	2014	1000 NAC	12_7_1	FI_M_2014_1000 NAC_12_7_1
FI	M	2014	1000 NAC	12_7_2	FI_M_2014_1000 NAC_12_7_2
FI	M	2014	1000 NAC	12_7_3	FI_M_2014_1000 NAC_12_7_3
FI	X	2014	1000 NAC	11_1	FI_X_2014_1000 NAC_11_1
FI	X	2014	1000 NAC	11_1_C	FI_X_2014_1000 NAC_11_1_C
FI	X	2014	1000 NAC	11_1_NC	FI_X_2014_1000 NAC_11_1_NC
FI	X	2014	1000 NAC	11_1_NC_T	FI_X_2014_1000 NAC_11_1_NC_T
FI	X	2014	1000 NAC	11_2	FI_X_2014_1000 NAC_11_2
FI	X	2014	1000 NAC	11_3	FI_X_2014_1000 NAC_11_3
FI	X	2014	1000 NAC	11_4	FI_X_2014_1000 NAC_11_4
FI	X	2014	1000 NAC	11_5	FI_X_2014_1000 NAC_11_5
FI	X	2014	1000 NAC	11_6	FI_X_2014_1000 NAC_11_6
FI	X	2014	1000 NAC	11_7	FI_X_2014_1000 NAC_11_7
FI	X	2014	1000 NAC	11_7_1	FI_X_2014_1000 NAC_11_7_1
FI	X	2014	1000 NAC	12_1	FI_X_2014_1000 NAC_12_1
FI	X	2014	1000 NAC	12_2	FI_X_2014_1000 NAC_12_2
FI	X	2014	1000 NAC	12_3	FI_X_2014_1000 NAC_12_3
FI	X	2014	1000 NAC	12_4	FI_X_2014_1000 NAC_12_4
FI	X	2014	1000 NAC	12_5	FI_X_2014_1000 NAC_12_5
FI	X	2014	1000 NAC	12_6	FI_X_2014_1000 NAC_12_6
FI	X	2014	1000 NAC	12_6_1	FI_X_2014_1000 NAC_12_6_1
FI	X	2014	1000 NAC	12_6_2	FI_X_2014_1000 NAC_12_6_2
FI	X	2014	1000 NAC	12_6_3	FI_X_2014_1000 NAC_12_6_3
FI	X	2014	1000 NAC	12_7	FI_X_2014_1000 NAC_12_7
FI	X	2014	1000 NAC	12_7_1	FI_X_2014_1000 NAC_12_7_1
FI	X	2014	1000 NAC	12_7_2	FI_X_2014_1000 NAC_12_7_2
FI	X	2014	1000 NAC	12_7_3	FI_X_2014_1000 NAC_12_7_3
FI	M	2014	1000 m3	ST_1_2_C	FI_M_2014_1000 m3_ST_1_2_C
FI	M	2014	1000 m3	ST_1_2_C_1	FI_M_2014_1000 m3_ST_1_2_C_1
FI	M	2014	1000 m3	ST_1_2_C_1_1	FI_M_2014_1000 m3_ST_1_2_C_1_1
FI	M	2014	1000 m3	ST_1_2_C_2_1	FI_M_2014_1000 m3_ST_1_2_C_2_1
FI	M	2014	1000 m3	ST_1_2_C_2	FI_M_2014_1000 m3_ST_1_2_C_2
FI	M	2014	1000 m3	ST_1_2_C_1_2	FI_M_2014_1000 m3_ST_1_2_C_1_2
FI	M	2014	1000 m3	ST_1_2_C_2_2	FI_M_2014_1000 m3_ST_1_2_C_2_2
FI	M	2014	1000 m3	ST_1_2_C_3	FI_M_2014_1000 m3_ST_1_2_C_3
FI	M	2014	1000 m3	ST_1_2_C_1_3	FI_M_2014_1000 m3_ST_1_2_C_1_3
FI	M	2014	1000 m3	ST_1_2_C_2_3	FI_M_2014_1000 m3_ST_1_2_C_2_3
FI	M	2014	1000 m3	ST_1_2_NC	FI_M_2014_1000 m3_ST_1_2_NC
FI	M	2014	1000 m3	ST_1_2_NC_1	FI_M_2014_1000 m3_ST_1_2_NC_1
FI	M	2014	1000 m3	ST_1_2_NC_1_1	FI_M_2014_1000 m3_ST_1_2_NC_1_1
FI	M	2014	1000 m3	ST_1_2_NC_2_1	FI_M_2014_1000 m3_ST_1_2_NC_2_1
FI	M	2014	1000 m3	ST_1_2_NC_2	FI_M_2014_1000 m3_ST_1_2_NC_2
FI	M	2014	1000 m3	ST_1_2_NC_1_2	FI_M_2014_1000 m3_ST_1_2_NC_1_2
FI	M	2014	1000 m3	ST_1_2_NC_2_2	FI_M_2014_1000 m3_ST_1_2_NC_2_2
FI	M	2014	1000 m3	ST_1_2_NC_3	FI_M_2014_1000 m3_ST_1_2_NC_3
FI	M	2014	1000 m3	ST_1_2_NC_1_3	FI_M_2014_1000 m3_ST_1_2_NC_1_3
FI	M	2014	1000 m3	ST_1_2_NC_2_3	FI_M_2014_1000 m3_ST_1_2_NC_2_3
FI	M	2014	1000 m3	ST_1_2_NC_4	FI_M_2014_1000 m3_ST_1_2_NC_4
FI	M	2014	1000 m3	ST_1_2_NC_5	FI_M_2014_1000 m3_ST_1_2_NC_5
FI	M	2014	1000 m3	ST_5_C	FI_M_2014_1000 m3_ST_5_C
FI	M	2014	1000 m3	ST_5_C_1	FI_M_2014_1000 m3_ST_5_C_1
FI	M	2014	1000 m3	ST_5_C_2	FI_M_2014_1000 m3_ST_5_C_2
FI	M	2014	1000 m3	ST_5_NC	FI_M_2014_1000 m3_ST_5_NC
FI	M	2014	1000 m3	ST_5_NC_1	FI_M_2014_1000 m3_ST_5_NC_1
FI	M	2014	1000 m3	ST_5_NC_2	FI_M_2014_1000 m3_ST_5_NC_2
FI	M	2014	1000 m3	ST_5_NC_3	FI_M_2014_1000 m3_ST_5_NC_3
FI	M	2014	1000 m3	ST_5_NC_4	FI_M_2014_1000 m3_ST_5_NC_4
FI	M	2014	1000 m3	ST_5_NC_5	FI_M_2014_1000 m3_ST_5_NC_5
FI	M	2014	1000 m3	ST_5_NC_6	FI_M_2014_1000 m3_ST_5_NC_6
FI	M	2014	1000 m3	ST_5_NC_7	FI_M_2014_1000 m3_ST_5_NC_7
FI	M	2014	1000 NAC	ST_1_2_C	FI_M_2014_1000 NAC_ST_1_2_C
FI	M	2014	1000 NAC	ST_1_2_C_1	FI_M_2014_1000 NAC_ST_1_2_C_1
FI	M	2014	1000 NAC	ST_1_2_C_1_1	FI_M_2014_1000 NAC_ST_1_2_C_1_1
FI	M	2014	1000 NAC	ST_1_2_C_2_1	FI_M_2014_1000 NAC_ST_1_2_C_2_1
FI	M	2014	1000 NAC	ST_1_2_C_2	FI_M_2014_1000 NAC_ST_1_2_C_2
FI	M	2014	1000 NAC	ST_1_2_C_1_2	FI_M_2014_1000 NAC_ST_1_2_C_1_2
FI	M	2014	1000 NAC	ST_1_2_C_2_2	FI_M_2014_1000 NAC_ST_1_2_C_2_2
FI	M	2014	1000 NAC	ST_1_2_C_3	FI_M_2014_1000 NAC_ST_1_2_C_3
FI	M	2014	1000 NAC	ST_1_2_C_1_3	FI_M_2014_1000 NAC_ST_1_2_C_1_3
FI	M	2014	1000 NAC	ST_1_2_C_2_3	FI_M_2014_1000 NAC_ST_1_2_C_2_3
FI	M	2014	1000 NAC	ST_1_2_NC	FI_M_2014_1000 NAC_ST_1_2_NC
FI	M	2014	1000 NAC	ST_1_2_NC_1	FI_M_2014_1000 NAC_ST_1_2_NC_1
FI	M	2014	1000 NAC	ST_1_2_NC_1_1	FI_M_2014_1000 NAC_ST_1_2_NC_1_1
FI	M	2014	1000 NAC	ST_1_2_NC_2_1	FI_M_2014_1000 NAC_ST_1_2_NC_2_1
FI	M	2014	1000 NAC	ST_1_2_NC_2	FI_M_2014_1000 NAC_ST_1_2_NC_2
FI	M	2014	1000 NAC	ST_1_2_NC_1_2	FI_M_2014_1000 NAC_ST_1_2_NC_1_2
FI	M	2014	1000 NAC	ST_1_2_NC_2_2	FI_M_2014_1000 NAC_ST_1_2_NC_2_2
FI	M	2014	1000 NAC	ST_1_2_NC_3	FI_M_2014_1000 NAC_ST_1_2_NC_3
FI	M	2014	1000 NAC	ST_1_2_NC_1_3	FI_M_2014_1000 NAC_ST_1_2_NC_1_3
FI	M	2014	1000 NAC	ST_1_2_NC_2_3	FI_M_2014_1000 NAC_ST_1_2_NC_2_3
FI	M	2014	1000 NAC	ST_1_2_NC_4	FI_M_2014_1000 NAC_ST_1_2_NC_4
FI	M	2014	1000 NAC	ST_1_2_NC_5	FI_M_2014_1000 NAC_ST_1_2_NC_5
FI	M	2014	1000 NAC	ST_5_C	FI_M_2014_1000 NAC_ST_5_C
FI	M	2014	1000 NAC	ST_5_C_1	FI_M_2014_1000 NAC_ST_5_C_1
FI	M	2014	1000 NAC	ST_5_C_2	FI_M_2014_1000 NAC_ST_5_C_2
FI	M	2014	1000 NAC	ST_5_NC	FI_M_2014_1000 NAC_ST_5_NC
FI	M	2014	1000 NAC	ST_5_NC_1	FI_M_2014_1000 NAC_ST_5_NC_1
FI	M	2014	1000 NAC	ST_5_NC_2	FI_M_2014_1000 NAC_ST_5_NC_2
FI	M	2014	1000 NAC	ST_5_NC_3	FI_M_2014_1000 NAC_ST_5_NC_3
FI	M	2014	1000 NAC	ST_5_NC_4	FI_M_2014_1000 NAC_ST_5_NC_4
FI	M	2014	1000 NAC	ST_5_NC_5	FI_M_2014_1000 NAC_ST_5_NC_5
FI	M	2014	1000 NAC	ST_5_NC_6	FI_M_2014_1000 NAC_ST_5_NC_6
FI	M	2014	1000 NAC	ST_5_NC_7	FI_M_2014_1000 NAC_ST_5_NC_7
FI	X	2014	1000 m3	ST_1_2_C	FI_X_2014_1000 m3_ST_1_2_C
FI	X	2014	1000 m3	ST_1_2_C_1	FI_X_2014_1000 m3_ST_1_2_C_1
FI	X	2014	1000 m3	ST_1_2_C_1_1	FI_X_2014_1000 m3_ST_1_2_C_1_1
FI	X	2014	1000 m3	ST_1_2_C_2_1	FI_X_2014_1000 m3_ST_1_2_C_2_1
FI	X	2014	1000 m3	ST_1_2_C_2	FI_X_2014_1000 m3_ST_1_2_C_2
FI	X	2014	1000 m3	ST_1_2_C_1_2	FI_X_2014_1000 m3_ST_1_2_C_1_2
FI	X	2014	1000 m3	ST_1_2_C_2_2	FI_X_2014_1000 m3_ST_1_2_C_2_2
FI	X	2014	1000 m3	ST_1_2_C_3	FI_X_2014_1000 m3_ST_1_2_C_3
FI	X	2014	1000 m3	ST_1_2_C_1_3	FI_X_2014_1000 m3_ST_1_2_C_1_3
FI	X	2014	1000 m3	ST_1_2_C_2_3	FI_X_2014_1000 m3_ST_1_2_C_2_3
FI	X	2014	1000 m3	ST_1_2_NC	FI_X_2014_1000 m3_ST_1_2_NC
FI	X	2014	1000 m3	ST_1_2_NC_1	FI_X_2014_1000 m3_ST_1_2_NC_1
FI	X	2014	1000 m3	ST_1_2_NC_1_1	FI_X_2014_1000 m3_ST_1_2_NC_1_1
FI	X	2014	1000 m3	ST_1_2_NC_2_1	FI_X_2014_1000 m3_ST_1_2_NC_2_1
FI	X	2014	1000 m3	ST_1_2_NC_2	FI_X_2014_1000 m3_ST_1_2_NC_2
FI	X	2014	1000 m3	ST_1_2_NC_1_2	FI_X_2014_1000 m3_ST_1_2_NC_1_2
FI	X	2014	1000 m3	ST_1_2_NC_2_2	FI_X_2014_1000 m3_ST_1_2_NC_2_2
FI	X	2014	1000 m3	ST_1_2_NC_3	FI_X_2014_1000 m3_ST_1_2_NC_3
FI	X	2014	1000 m3	ST_1_2_NC_1_3	FI_X_2014_1000 m3_ST_1_2_NC_1_3
FI	X	2014	1000 m3	ST_1_2_NC_2_3	FI_X_2014_1000 m3_ST_1_2_NC_2_3
FI	X	2014	1000 m3	ST_1_2_NC_4	FI_X_2014_1000 m3_ST_1_2_NC_4
FI	X	2014	1000 m3	ST_1_2_NC_5	FI_X_2014_1000 m3_ST_1_2_NC_5
FI	X	2014	1000 m3	ST_5_C	FI_X_2014_1000 m3_ST_5_C
FI	X	2014	1000 m3	ST_5_C_1	FI_X_2014_1000 m3_ST_5_C_1
FI	X	2014	1000 m3	ST_5_C_2	FI_X_2014_1000 m3_ST_5_C_2
FI	X	2014	1000 m3	ST_5_NC	FI_X_2014_1000 m3_ST_5_NC
FI	X	2014	1000 m3	ST_5_NC_1	FI_X_2014_1000 m3_ST_5_NC_1
FI	X	2014	1000 m3	ST_5_NC_2	FI_X_2014_1000 m3_ST_5_NC_2
FI	X	2014	1000 m3	ST_5_NC_3	FI_X_2014_1000 m3_ST_5_NC_3
FI	X	2014	1000 m3	ST_5_NC_4	FI_X_2014_1000 m3_ST_5_NC_4
FI	X	2014	1000 m3	ST_5_NC_5	FI_X_2014_1000 m3_ST_5_NC_5
FI	X	2014	1000 m3	ST_5_NC_6	FI_X_2014_1000 m3_ST_5_NC_6
FI	X	2014	1000 m3	ST_5_NC_7	FI_X_2014_1000 m3_ST_5_NC_7
FI	X	2014	1000 NAC	ST_1_2_C	FI_X_2014_1000 NAC_ST_1_2_C
FI	X	2014	1000 NAC	ST_1_2_C_1	FI_X_2014_1000 NAC_ST_1_2_C_1
FI	X	2014	1000 NAC	ST_1_2_C_1_1	FI_X_2014_1000 NAC_ST_1_2_C_1_1
FI	X	2014	1000 NAC	ST_1_2_C_2_1	FI_X_2014_1000 NAC_ST_1_2_C_2_1
FI	X	2014	1000 NAC	ST_1_2_C_2	FI_X_2014_1000 NAC_ST_1_2_C_2
FI	X	2014	1000 NAC	ST_1_2_C_1_2	FI_X_2014_1000 NAC_ST_1_2_C_1_2
FI	X	2014	1000 NAC	ST_1_2_C_2_2	FI_X_2014_1000 NAC_ST_1_2_C_2_2
FI	X	2014	1000 NAC	ST_1_2_C_3	FI_X_2014_1000 NAC_ST_1_2_C_3
FI	X	2014	1000 NAC	ST_1_2_C_1_3	FI_X_2014_1000 NAC_ST_1_2_C_1_3
FI	X	2014	1000 NAC	ST_1_2_C_2_3	FI_X_2014_1000 NAC_ST_1_2_C_2_3
FI	X	2014	1000 NAC	ST_1_2_NC	FI_X_2014_1000 NAC_ST_1_2_NC
FI	X	2014	1000 NAC	ST_1_2_NC_1	FI_X_2014_1000 NAC_ST_1_2_NC_1
FI	X	2014	1000 NAC	ST_1_2_NC_1_1	FI_X_2014_1000 NAC_ST_1_2_NC_1_1
FI	X	2014	1000 NAC	ST_1_2_NC_2_1	FI_X_2014_1000 NAC_ST_1_2_NC_2_1
FI	X	2014	1000 NAC	ST_1_2_NC_2	FI_X_2014_1000 NAC_ST_1_2_NC_2
FI	X	2014	1000 NAC	ST_1_2_NC_1_2	FI_X_2014_1000 NAC_ST_1_2_NC_1_2
FI	X	2014	1000 NAC	ST_1_2_NC_2_2	FI_X_2014_1000 NAC_ST_1_2_NC_2_2
FI	X	2014	1000 NAC	ST_1_2_NC_3	FI_X_2014_1000 NAC_ST_1_2_NC_3
FI	X	2014	1000 NAC	ST_1_2_NC_1_3	FI_X_2014_1000 NAC_ST_1_2_NC_1_3
FI	X	2014	1000 NAC	ST_1_2_NC_2_3	FI_X_2014_1000 NAC_ST_1_2_NC_2_3
FI	X	2014	1000 NAC	ST_1_2_NC_4	FI_X_2014_1000 NAC_ST_1_2_NC_4
FI	X	2014	1000 NAC	ST_1_2_NC_5	FI_X_2014_1000 NAC_ST_1_2_NC_5
FI	X	2014	1000 NAC	ST_5_C	FI_X_2014_1000 NAC_ST_5_C
FI	X	2014	1000 NAC	ST_5_C_1	FI_X_2014_1000 NAC_ST_5_C_1
FI	X	2014	1000 NAC	ST_5_C_2	FI_X_2014_1000 NAC_ST_5_C_2
FI	X	2014	1000 NAC	ST_5_NC	FI_X_2014_1000 NAC_ST_5_NC
FI	X	2014	1000 NAC	ST_5_NC_1	FI_X_2014_1000 NAC_ST_5_NC_1
FI	X	2014	1000 NAC	ST_5_NC_2	FI_X_2014_1000 NAC_ST_5_NC_2
FI	X	2014	1000 NAC	ST_5_NC_3	FI_X_2014_1000 NAC_ST_5_NC_3
FI	X	2014	1000 NAC	ST_5_NC_4	FI_X_2014_1000 NAC_ST_5_NC_4
FI	X	2014	1000 NAC	ST_5_NC_5	FI_X_2014_1000 NAC_ST_5_NC_5
FI	X	2014	1000 NAC	ST_5_NC_6	FI_X_2014_1000 NAC_ST_5_NC_6
FI	X	2014	1000 NAC	ST_5_NC_7	FI_X_2014_1000 NAC_ST_5_NC_7
FI	EX_M	2014	1000 m3	1	FI_EX_M_2014_1000 m3_1
FI	EX_M	2014	1000 m3	1_1	FI_EX_M_2014_1000 m3_1_1
FI	EX_M	2014	1000 m3	1_2	FI_EX_M_2014_1000 m3_1_2
FI	EX_M	2014	1000 m3	1_2_C	FI_EX_M_2014_1000 m3_1_2_C
FI	EX_M	2014	1000 m3	1_2_NC	FI_EX_M_2014_1000 m3_1_2_NC
FI	EX_M	2014	1000 m3	1_2_NC_T	FI_EX_M_2014_1000 m3_1_2_NC_T
FI	EX_M	2014	1000 mt	2	FI_EX_M_2014_1000 mt_2
FI	EX_M	2014	1000 m3	3	FI_EX_M_2014_1000 m3_3
FI	EX_M	2014	1000 m3	3_1	FI_EX_M_2014_1000 m3_3_1
FI	EX_M	2014	1000 m3	3_2	FI_EX_M_2014_1000 m3_3_2
FI	EX_M	2014	1000 mt	4	FI_EX_M_2014_1000 mt_4
FI	EX_M	2014	1000 mt	4_1	FI_EX_M_2014_1000 mt_4_1
FI	EX_M	2014	1000 mt	4_2	FI_EX_M_2014_1000 mt_4_2
FI	EX_M	2014	1000 m3	5	FI_EX_M_2014_1000 m3_5
FI	EX_M	2014	1000 m3	5_C	FI_EX_M_2014_1000 m3_5_C
FI	EX_M	2014	1000 m3	5_NC	FI_EX_M_2014_1000 m3_5_NC
FI	EX_M	2014	1000 m3	5_NC_T	FI_EX_M_2014_1000 m3_5_NC_T
FI	EX_M	2014	1000 m3	6	FI_EX_M_2014_1000 m3_6
FI	EX_M	2014	1000 m3	6_1	FI_EX_M_2014_1000 m3_6_1
FI	EX_M	2014	1000 m3	6_1_C	FI_EX_M_2014_1000 m3_6_1_C
FI	EX_M	2014	1000 m3	6_1_NC	FI_EX_M_2014_1000 m3_6_1_NC
FI	EX_M	2014	1000 m3	6_1_NC_T	FI_EX_M_2014_1000 m3_6_1_NC_T
FI	EX_M	2014	1000 m3	6_2	FI_EX_M_2014_1000 m3_6_2
FI	EX_M	2014	1000 m3	6_2_C	FI_EX_M_2014_1000 m3_6_2_C
FI	EX_M	2014	1000 m3	6_2_NC	FI_EX_M_2014_1000 m3_6_2_NC
FI	EX_M	2014	1000 m3	6_2_NC_T	FI_EX_M_2014_1000 m3_6_2_NC_T
FI	EX_M	2014	1000 m3	6_3	FI_EX_M_2014_1000 m3_6_3
FI	EX_M	2014	1000 m3	6_3_1	FI_EX_M_2014_1000 m3_6_3_1
FI	EX_M	2014	1000 m3	6_4	FI_EX_M_2014_1000 m3_6_4
FI	EX_M	2014	1000 m3	6_4_1	FI_EX_M_2014_1000 m3_6_4_1
FI	EX_M	2014	1000 m3	6_4_2	FI_EX_M_2014_1000 m3_6_4_2
FI	EX_M	2014	1000 m3	6_4_3	FI_EX_M_2014_1000 m3_6_4_3
FI	EX_M	2014	1000 mt	7	FI_EX_M_2014_1000 mt_7
FI	EX_M	2014	1000 mt	7_1	FI_EX_M_2014_1000 mt_7_1
FI	EX_M	2014	1000 mt	7_2	FI_EX_M_2014_1000 mt_7_2
FI	EX_M	2014	1000 mt	7_3	FI_EX_M_2014_1000 mt_7_3
FI	EX_M	2014	1000 mt	7_3_1	FI_EX_M_2014_1000 mt_7_3_1
FI	EX_M	2014	1000 mt	7_3_2	FI_EX_M_2014_1000 mt_7_3_2
FI	EX_M	2014	1000 mt	7_3_3	FI_EX_M_2014_1000 mt_7_3_3
FI	EX_M	2014	1000 mt	7_3_4	FI_EX_M_2014_1000 mt_7_3_4
FI	EX_M	2014	1000 mt	7_4	FI_EX_M_2014_1000 mt_7_4
FI	EX_M	2014	1000 mt	8	FI_EX_M_2014_1000 mt_8
FI	EX_M	2014	1000 mt	8_1	FI_EX_M_2014_1000 mt_8_1
FI	EX_M	2014	1000 mt	8_2	FI_EX_M_2014_1000 mt_8_2
FI	EX_M	2014	1000 mt	9	FI_EX_M_2014_1000 mt_9
FI	EX_M	2014	1000 mt	10	FI_EX_M_2014_1000 mt_10
FI	EX_M	2014	1000 mt	10_1	FI_EX_M_2014_1000 mt_10_1
FI	EX_M	2014	1000 mt	10_1_1	FI_EX_M_2014_1000 mt_10_1_1
FI	EX_M	2014	1000 mt	10_1_2	FI_EX_M_2014_1000 mt_10_1_2
FI	EX_M	2014	1000 mt	10_1_3	FI_EX_M_2014_1000 mt_10_1_3
FI	EX_M	2014	1000 mt	10_1_4	FI_EX_M_2014_1000 mt_10_1_4
FI	EX_M	2014	1000 mt	10_2	FI_EX_M_2014_1000 mt_10_2
FI	EX_M	2014	1000 mt	10_3	FI_EX_M_2014_1000 mt_10_3
FI	EX_M	2014	1000 mt	10_3_1	FI_EX_M_2014_1000 mt_10_3_1
FI	EX_M	2014	1000 mt	10_3_2	FI_EX_M_2014_1000 mt_10_3_2
FI	EX_M	2014	1000 mt	10_3_3	FI_EX_M_2014_1000 mt_10_3_3
FI	EX_M	2014	1000 mt	10_3_4	FI_EX_M_2014_1000 mt_10_3_4
FI	EX_M	2014	1000 mt	10_4	FI_EX_M_2014_1000 mt_10_4
FI	EX_M	2014	1000 NAC	1	FI_EX_M_2014_1000 NAC_1
FI	EX_M	2014	1000 NAC	1_1	FI_EX_M_2014_1000 NAC_1_1
FI	EX_M	2014	1000 NAC	1_2	FI_EX_M_2014_1000 NAC_1_2
FI	EX_M	2014	1000 NAC	1_2_C	FI_EX_M_2014_1000 NAC_1_2_C
FI	EX_M	2014	1000 NAC	1_2_NC	FI_EX_M_2014_1000 NAC_1_2_NC
FI	EX_M	2014	1000 NAC	1_2_NC_T	FI_EX_M_2014_1000 NAC_1_2_NC_T
FI	EX_M	2014	1000 NAC	2	FI_EX_M_2014_1000 NAC_2
FI	EX_M	2014	1000 NAC	3	FI_EX_M_2014_1000 NAC_3
FI	EX_M	2014	1000 NAC	3_1	FI_EX_M_2014_1000 NAC_3_1
FI	EX_M	2014	1000 NAC	3_2	FI_EX_M_2014_1000 NAC_3_2
FI	EX_M	2014	1000 NAC	4	FI_EX_M_2014_1000 NAC_4
FI	EX_M	2014	1000 NAC	4_1	FI_EX_M_2014_1000 NAC_4_1
FI	EX_M	2014	1000 NAC	4_2	FI_EX_M_2014_1000 NAC_4_2
FI	EX_M	2014	1000 NAC	5	FI_EX_M_2014_1000 NAC_5
FI	EX_M	2014	1000 NAC	5_C	FI_EX_M_2014_1000 NAC_5_C
FI	EX_M	2014	1000 NAC	5_NC	FI_EX_M_2014_1000 NAC_5_NC
FI	EX_M	2014	1000 NAC	5_NC_T	FI_EX_M_2014_1000 NAC_5_NC_T
FI	EX_M	2014	1000 NAC	6	FI_EX_M_2014_1000 NAC_6
FI	EX_M	2014	1000 NAC	6_1	FI_EX_M_2014_1000 NAC_6_1
FI	EX_M	2014	1000 NAC	6_1_C	FI_EX_M_2014_1000 NAC_6_1_C
FI	EX_M	2014	1000 NAC	6_1_NC	FI_EX_M_2014_1000 NAC_6_1_NC
FI	EX_M	2014	1000 NAC	6_1_NC_T	FI_EX_M_2014_1000 NAC_6_1_NC_T
FI	EX_M	2014	1000 NAC	6_2	FI_EX_M_2014_1000 NAC_6_2
FI	EX_M	2014	1000 NAC	6_2_C	FI_EX_M_2014_1000 NAC_6_2_C
FI	EX_M	2014	1000 NAC	6_2_NC	FI_EX_M_2014_1000 NAC_6_2_NC
FI	EX_M	2014	1000 NAC	6_2_NC_T	FI_EX_M_2014_1000 NAC_6_2_NC_T
FI	EX_M	2014	1000 NAC	6_3	FI_EX_M_2014_1000 NAC_6_3
FI	EX_M	2014	1000 NAC	6_3_1	FI_EX_M_2014_1000 NAC_6_3_1
FI	EX_M	2014	1000 NAC	6_4	FI_EX_M_2014_1000 NAC_6_4
FI	EX_M	2014	1000 NAC	6_4_1	FI_EX_M_2014_1000 NAC_6_4_1
FI	EX_M	2014	1000 NAC	6_4_2	FI_EX_M_2014_1000 NAC_6_4_2
FI	EX_M	2014	1000 NAC	6_4_3	FI_EX_M_2014_1000 NAC_6_4_3
FI	EX_M	2014	1000 NAC	7	FI_EX_M_2014_1000 NAC_7
FI	EX_M	2014	1000 NAC	7_1	FI_EX_M_2014_1000 NAC_7_1
FI	EX_M	2014	1000 NAC	7_2	FI_EX_M_2014_1000 NAC_7_2
FI	EX_M	2014	1000 NAC	7_3	FI_EX_M_2014_1000 NAC_7_3
FI	EX_M	2014	1000 NAC	7_3_1	FI_EX_M_2014_1000 NAC_7_3_1
FI	EX_M	2014	1000 NAC	7_3_2	FI_EX_M_2014_1000 NAC_7_3_2
FI	EX_M	2014	1000 NAC	7_3_3	FI_EX_M_2014_1000 NAC_7_3_3
FI	EX_M	2014	1000 NAC	7_3_4	FI_EX_M_2014_1000 NAC_7_3_4
FI	EX_M	2014	1000 NAC	7_4	FI_EX_M_2014_1000 NAC_7_4
FI	EX_M	2014	1000 NAC	8	FI_EX_M_2014_1000 NAC_8
FI	EX_M	2014	1000 NAC	8_1	FI_EX_M_2014_1000 NAC_8_1
FI	EX_M	2014	1000 NAC	8_2	FI_EX_M_2014_1000 NAC_8_2
FI	EX_M	2014	1000 NAC	9	FI_EX_M_2014_1000 NAC_9
FI	EX_M	2014	1000 NAC	10	FI_EX_M_2014_1000 NAC_10
FI	EX_M	2014	1000 NAC	10_1	FI_EX_M_2014_1000 NAC_10_1
FI	EX_M	2014	1000 NAC	10_1_1	FI_EX_M_2014_1000 NAC_10_1_1
FI	EX_M	2014	1000 NAC	10_1_2	FI_EX_M_2014_1000 NAC_10_1_2
FI	EX_M	2014	1000 NAC	10_1_3	FI_EX_M_2014_1000 NAC_10_1_3
FI	EX_M	2014	1000 NAC	10_1_4	FI_EX_M_2014_1000 NAC_10_1_4
FI	EX_M	2014	1000 NAC	10_2	FI_EX_M_2014_1000 NAC_10_2
FI	EX_M	2014	1000 NAC	10_3	FI_EX_M_2014_1000 NAC_10_3
FI	EX_M	2014	1000 NAC	10_3_1	FI_EX_M_2014_1000 NAC_10_3_1
FI	EX_M	2014	1000 NAC	10_3_2	FI_EX_M_2014_1000 NAC_10_3_2
FI	EX_M	2014	1000 NAC	10_3_3	FI_EX_M_2014_1000 NAC_10_3_3
FI	EX_M	2014	1000 NAC	10_3_4	FI_EX_M_2014_1000 NAC_10_3_4
FI	EX_M	2014	1000 NAC	10_4	FI_EX_M_2014_1000 NAC_10_4
FI	EX_X	2014	1000 m3	1	FI_EX_X_2014_1000 m3_1
FI	EX_X	2014	1000 m3	1_1	FI_EX_X_2014_1000 m3_1_1
FI	EX_X	2014	1000 m3	1_2	FI_EX_X_2014_1000 m3_1_2
FI	EX_X	2014	1000 m3	1_2_C	FI_EX_X_2014_1000 m3_1_2_C
FI	EX_X	2014	1000 m3	1_2_NC	FI_EX_X_2014_1000 m3_1_2_NC
FI	EX_X	2014	1000 m3	1_2_NC_T	FI_EX_X_2014_1000 m3_1_2_NC_T
FI	EX_X	2014	1000 mt	2	FI_EX_X_2014_1000 mt_2
FI	EX_X	2014	1000 m3	3	FI_EX_X_2014_1000 m3_3
FI	EX_X	2014	1000 m3	3_1	FI_EX_X_2014_1000 m3_3_1
FI	EX_X	2014	1000 m3	3_2	FI_EX_X_2014_1000 m3_3_2
FI	EX_X	2014	1000 mt	4	FI_EX_X_2014_1000 mt_4
FI	EX_X	2014	1000 mt	4_1	FI_EX_X_2014_1000 mt_4_1
FI	EX_X	2014	1000 mt	4_2	FI_EX_X_2014_1000 mt_4_2
FI	EX_X	2014	1000 m3	5	FI_EX_X_2014_1000 m3_5
FI	EX_X	2014	1000 m3	5_C	FI_EX_X_2014_1000 m3_5_C
FI	EX_X	2014	1000 m3	5_NC	FI_EX_X_2014_1000 m3_5_NC
FI	EX_X	2014	1000 m3	5_NC_T	FI_EX_X_2014_1000 m3_5_NC_T
FI	EX_X	2014	1000 m3	6	FI_EX_X_2014_1000 m3_6
FI	EX_X	2014	1000 m3	6_1	FI_EX_X_2014_1000 m3_6_1
FI	EX_X	2014	1000 m3	6_1_C	FI_EX_X_2014_1000 m3_6_1_C
FI	EX_X	2014	1000 m3	6_1_NC	FI_EX_X_2014_1000 m3_6_1_NC
FI	EX_X	2014	1000 m3	6_1_NC_T	FI_EX_X_2014_1000 m3_6_1_NC_T
FI	EX_X	2014	1000 m3	6_2	FI_EX_X_2014_1000 m3_6_2
FI	EX_X	2014	1000 m3	6_2_C	FI_EX_X_2014_1000 m3_6_2_C
FI	EX_X	2014	1000 m3	6_2_NC	FI_EX_X_2014_1000 m3_6_2_NC
FI	EX_X	2014	1000 m3	6_2_NC_T	FI_EX_X_2014_1000 m3_6_2_NC_T
FI	EX_X	2014	1000 m3	6_3	FI_EX_X_2014_1000 m3_6_3
FI	EX_X	2014	1000 m3	6_3_1	FI_EX_X_2014_1000 m3_6_3_1
FI	EX_X	2014	1000 m3	6_4	FI_EX_X_2014_1000 m3_6_4
FI	EX_X	2014	1000 m3	6_4_1	FI_EX_X_2014_1000 m3_6_4_1
FI	EX_X	2014	1000 m3	6_4_2	FI_EX_X_2014_1000 m3_6_4_2
FI	EX_X	2014	1000 m3	6_4_3	FI_EX_X_2014_1000 m3_6_4_3
FI	EX_X	2014	1000 mt	7	FI_EX_X_2014_1000 mt_7
FI	EX_X	2014	1000 mt	7_1	FI_EX_X_2014_1000 mt_7_1
FI	EX_X	2014	1000 mt	7_2	FI_EX_X_2014_1000 mt_7_2
FI	EX_X	2014	1000 mt	7_3	FI_EX_X_2014_1000 mt_7_3
FI	EX_X	2014	1000 mt	7_3_1	FI_EX_X_2014_1000 mt_7_3_1
FI	EX_X	2014	1000 mt	7_3_2	FI_EX_X_2014_1000 mt_7_3_2
FI	EX_X	2014	1000 mt	7_3_3	FI_EX_X_2014_1000 mt_7_3_3
FI	EX_X	2014	1000 mt	7_3_4	FI_EX_X_2014_1000 mt_7_3_4
FI	EX_X	2014	1000 mt	7_4	FI_EX_X_2014_1000 mt_7_4
FI	EX_X	2014	1000 mt	8	FI_EX_X_2014_1000 mt_8
FI	EX_X	2014	1000 mt	8_1	FI_EX_X_2014_1000 mt_8_1
FI	EX_X	2014	1000 mt	8_2	FI_EX_X_2014_1000 mt_8_2
FI	EX_X	2014	1000 mt	9	FI_EX_X_2014_1000 mt_9
FI	EX_X	2014	1000 mt	10	FI_EX_X_2014_1000 mt_10
FI	EX_X	2014	1000 mt	10_1	FI_EX_X_2014_1000 mt_10_1
FI	EX_X	2014	1000 mt	10_1_1	FI_EX_X_2014_1000 mt_10_1_1
FI	EX_X	2014	1000 mt	10_1_2	FI_EX_X_2014_1000 mt_10_1_2
FI	EX_X	2014	1000 mt	10_1_3	FI_EX_X_2014_1000 mt_10_1_3
FI	EX_X	2014	1000 mt	10_1_4	FI_EX_X_2014_1000 mt_10_1_4
FI	EX_X	2014	1000 mt	10_2	FI_EX_X_2014_1000 mt_10_2
FI	EX_X	2014	1000 mt	10_3	FI_EX_X_2014_1000 mt_10_3
FI	EX_X	2014	1000 mt	10_3_1	FI_EX_X_2014_1000 mt_10_3_1
FI	EX_X	2014	1000 mt	10_3_2	FI_EX_X_2014_1000 mt_10_3_2
FI	EX_X	2014	1000 mt	10_3_3	FI_EX_X_2014_1000 mt_10_3_3
FI	EX_X	2014	1000 mt	10_3_4	FI_EX_X_2014_1000 mt_10_3_4
FI	EX_X	2014	1000 mt	10_4	FI_EX_X_2014_1000 mt_10_4
FI	EX_X	2014	1000 NAC	1	FI_EX_X_2014_1000 NAC_1
FI	EX_X	2014	1000 NAC	1_1	FI_EX_X_2014_1000 NAC_1_1
FI	EX_X	2014	1000 NAC	1_2	FI_EX_X_2014_1000 NAC_1_2
FI	EX_X	2014	1000 NAC	1_2_C	FI_EX_X_2014_1000 NAC_1_2_C
FI	EX_X	2014	1000 NAC	1_2_NC	FI_EX_X_2014_1000 NAC_1_2_NC
FI	EX_X	2014	1000 NAC	1_2_NC_T	FI_EX_X_2014_1000 NAC_1_2_NC_T
FI	EX_X	2014	1000 NAC	2	FI_EX_X_2014_1000 NAC_2
FI	EX_X	2014	1000 NAC	3	FI_EX_X_2014_1000 NAC_3
FI	EX_X	2014	1000 NAC	3_1	FI_EX_X_2014_1000 NAC_3_1
FI	EX_X	2014	1000 NAC	3_2	FI_EX_X_2014_1000 NAC_3_2
FI	EX_X	2014	1000 NAC	4	FI_EX_X_2014_1000 NAC_4
FI	EX_X	2014	1000 NAC	4_1	FI_EX_X_2014_1000 NAC_4_1
FI	EX_X	2014	1000 NAC	4_2	FI_EX_X_2014_1000 NAC_4_2
FI	EX_X	2014	1000 NAC	5	FI_EX_X_2014_1000 NAC_5
FI	EX_X	2014	1000 NAC	5_C	FI_EX_X_2014_1000 NAC_5_C
FI	EX_X	2014	1000 NAC	5_NC	FI_EX_X_2014_1000 NAC_5_NC
FI	EX_X	2014	1000 NAC	5_NC_T	FI_EX_X_2014_1000 NAC_5_NC_T
FI	EX_X	2014	1000 NAC	6	FI_EX_X_2014_1000 NAC_6
FI	EX_X	2014	1000 NAC	6_1	FI_EX_X_2014_1000 NAC_6_1
FI	EX_X	2014	1000 NAC	6_1_C	FI_EX_X_2014_1000 NAC_6_1_C
FI	EX_X	2014	1000 NAC	6_1_NC	FI_EX_X_2014_1000 NAC_6_1_NC
FI	EX_X	2014	1000 NAC	6_1_NC_T	FI_EX_X_2014_1000 NAC_6_1_NC_T
FI	EX_X	2014	1000 NAC	6_2	FI_EX_X_2014_1000 NAC_6_2
FI	EX_X	2014	1000 NAC	6_2_C	FI_EX_X_2014_1000 NAC_6_2_C
FI	EX_X	2014	1000 NAC	6_2_NC	FI_EX_X_2014_1000 NAC_6_2_NC
FI	EX_X	2014	1000 NAC	6_2_NC_T	FI_EX_X_2014_1000 NAC_6_2_NC_T
FI	EX_X	2014	1000 NAC	6_3	FI_EX_X_2014_1000 NAC_6_3
FI	EX_X	2014	1000 NAC	6_3_1	FI_EX_X_2014_1000 NAC_6_3_1
FI	EX_X	2014	1000 NAC	6_4	FI_EX_X_2014_1000 NAC_6_4
FI	EX_X	2014	1000 NAC	6_4_1	FI_EX_X_2014_1000 NAC_6_4_1
FI	EX_X	2014	1000 NAC	6_4_2	FI_EX_X_2014_1000 NAC_6_4_2
FI	EX_X	2014	1000 NAC	6_4_3	FI_EX_X_2014_1000 NAC_6_4_3
FI	EX_X	2014	1000 NAC	7	FI_EX_X_2014_1000 NAC_7
FI	EX_X	2014	1000 NAC	7_1	FI_EX_X_2014_1000 NAC_7_1
FI	EX_X	2014	1000 NAC	7_2	FI_EX_X_2014_1000 NAC_7_2
FI	EX_X	2014	1000 NAC	7_3	FI_EX_X_2014_1000 NAC_7_3
FI	EX_X	2014	1000 NAC	7_3_1	FI_EX_X_2014_1000 NAC_7_3_1
FI	EX_X	2014	1000 NAC	7_3_2	FI_EX_X_2014_1000 NAC_7_3_2
FI	EX_X	2014	1000 NAC	7_3_3	FI_EX_X_2014_1000 NAC_7_3_3
FI	EX_X	2014	1000 NAC	7_3_4	FI_EX_X_2014_1000 NAC_7_3_4
FI	EX_X	2014	1000 NAC	7_4	FI_EX_X_2014_1000 NAC_7_4
FI	EX_X	2014	1000 NAC	8	FI_EX_X_2014_1000 NAC_8
FI	EX_X	2014	1000 NAC	8_1	FI_EX_X_2014_1000 NAC_8_1
FI	EX_X	2014	1000 NAC	8_2	FI_EX_X_2014_1000 NAC_8_2
FI	EX_X	2014	1000 NAC	9	FI_EX_X_2014_1000 NAC_9
FI	EX_X	2014	1000 NAC	10	FI_EX_X_2014_1000 NAC_10
FI	EX_X	2014	1000 NAC	10_1	FI_EX_X_2014_1000 NAC_10_1
FI	EX_X	2014	1000 NAC	10_1_1	FI_EX_X_2014_1000 NAC_10_1_1
FI	EX_X	2014	1000 NAC	10_1_2	FI_EX_X_2014_1000 NAC_10_1_2
FI	EX_X	2014	1000 NAC	10_1_3	FI_EX_X_2014_1000 NAC_10_1_3
FI	EX_X	2014	1000 NAC	10_1_4	FI_EX_X_2014_1000 NAC_10_1_4
FI	EX_X	2014	1000 NAC	10_2	FI_EX_X_2014_1000 NAC_10_2
FI	EX_X	2014	1000 NAC	10_3	FI_EX_X_2014_1000 NAC_10_3
FI	EX_X	2014	1000 NAC	10_3_1	FI_EX_X_2014_1000 NAC_10_3_1
FI	EX_X	2014	1000 NAC	10_3_2	FI_EX_X_2014_1000 NAC_10_3_2
FI	EX_X	2014	1000 NAC	10_3_3	FI_EX_X_2014_1000 NAC_10_3_3
FI	EX_X	2014	1000 NAC	10_3_4	FI_EX_X_2014_1000 NAC_10_3_4
FI	EX_X	2014	1000 NAC	10_4	FI_EX_X_2014_1000 NAC_10_4
FI	P	2014	1000 m3	EU2_1	FI_P_2014_1000 m3_EU2_1
FI	P	2014	1000 m3	EU2_1_C	FI_P_2014_1000 m3_EU2_1_C
FI	P	2014	1000 m3	EU2_1_NC	FI_P_2014_1000 m3_EU2_1_NC
FI	P	2014	1000 m3	EU2_1_1	FI_P_2014_1000 m3_EU2_1_1
FI	P	2014	1000 m3	EU2_1_1_C	FI_P_2014_1000 m3_EU2_1_1_C
FI	P	2014	1000 m3	EU2_1_1_NC	FI_P_2014_1000 m3_EU2_1_1_NC
FI	P	2014	1000 m3	EU2_1_2	FI_P_2014_1000 m3_EU2_1_2
FI	P	2014	1000 m3	EU2_1_2_C	FI_P_2014_1000 m3_EU2_1_2_C
FI	P	2014	1000 m3	EU2_1_2_NC	FI_P_2014_1000 m3_EU2_1_2_NC
FI	P	2014	1000 m3	EU2_1_3	FI_P_2014_1000 m3_EU2_1_3
FI	P	2014	1000 m3	EU2_1_3_C	FI_P_2014_1000 m3_EU2_1_3_C
FI	P	2014	1000 m3	EU2_1_3_NC	FI_P_2014_1000 m3_EU2_1_3_NC
FI	P.OB	2014	1000 m3	1	FI_P.OB_2014_1000 m3_1
FI	P.OB	2014	1000 m3	1_C	FI_P.OB_2014_1000 m3_1_C
FI	P.OB	2014	1000 m3	1_NC	FI_P.OB_2014_1000 m3_1_NC
FI	P.OB	2014	1000 m3	1_1	FI_P.OB_2014_1000 m3_1_1
FI	P.OB	2014	1000 m3	1_1_C	FI_P.OB_2014_1000 m3_1_1_C
FI	P.OB	2014	1000 m3	1_1_NC	FI_P.OB_2014_1000 m3_1_1_NC
FI	P.OB	2014	1000 m3	1_2	FI_P.OB_2014_1000 m3_1_2
FI	P.OB	2014	1000 m3	1_2_C	FI_P.OB_2014_1000 m3_1_2_C
FI	P.OB	2014	1000 m3	1_2_NC	FI_P.OB_2014_1000 m3_1_2_NC
FI	P.OB	2014	1000 m3	1_2_1	FI_P.OB_2014_1000 m3_1_2_1
FI	P.OB	2014	1000 m3	1_2_1_C	FI_P.OB_2014_1000 m3_1_2_1_C
FI	P.OB	2014	1000 m3	1_2_1_NC	FI_P.OB_2014_1000 m3_1_2_1_NC
FI	P.OB	2014	1000 m3	1_2_2	FI_P.OB_2014_1000 m3_1_2_2
FI	P.OB	2014	1000 m3	1_2_2_C	FI_P.OB_2014_1000 m3_1_2_2_C
FI	P.OB	2014	1000 m3	1_2_2_NC	FI_P.OB_2014_1000 m3_1_2_2_NC
FI	P.OB	2014	1000 m3	1_2_3	FI_P.OB_2014_1000 m3_1_2_3
FI	P.OB	2014	1000 m3	1_2_3_C	FI_P.OB_2014_1000 m3_1_2_3_C
FI	P.OB	2014	1000 m3	1_2_3_NC	FI_P.OB_2014_1000 m3_1_2_3_NC
FI	P	2013	1000 m3	1	FI_P_2013_1000 m3_1
FI	P	2013	1000 m3	1_C	FI_P_2013_1000 m3_1_C
FI	P	2013	1000 m3	1_NC	FI_P_2013_1000 m3_1_NC
FI	P	2013	1000 m3	1_1	FI_P_2013_1000 m3_1_1
FI	P	2013	1000 m3	1_1_C	FI_P_2013_1000 m3_1_1_C
FI	P	2013	1000 m3	1_1_NC	FI_P_2013_1000 m3_1_1_NC
FI	P	2013	1000 m3	1_2	FI_P_2013_1000 m3_1_2
FI	P	2013	1000 m3	1_2_C	FI_P_2013_1000 m3_1_2_C
FI	P	2013	1000 m3	1_2_NC	FI_P_2013_1000 m3_1_2_NC
FI	P	2013	1000 m3	1_2_1	FI_P_2013_1000 m3_1_2_1
FI	P	2013	1000 m3	1_2_1_C	FI_P_2013_1000 m3_1_2_1_C
FI	P	2013	1000 m3	1_2_1_NC	FI_P_2013_1000 m3_1_2_1_NC
FI	P	2013	1000 m3	1_2_2	FI_P_2013_1000 m3_1_2_2
FI	P	2013	1000 m3	1_2_2_C	FI_P_2013_1000 m3_1_2_2_C
FI	P	2013	1000 m3	1_2_2_NC	FI_P_2013_1000 m3_1_2_2_NC
FI	P	2013	1000 m3	1_2_3	FI_P_2013_1000 m3_1_2_3
FI	P	2013	1000 m3	1_2_3_C	FI_P_2013_1000 m3_1_2_3_C
FI	P	2013	1000 m3	1_2_3_NC	FI_P_2013_1000 m3_1_2_3_NC
FI	P	2013	1000 mt	2	FI_P_2013_1000 mt_2
FI	P	2013	1000 m3	3	FI_P_2013_1000 m3_3
FI	P	2013	1000 m3	3_1	FI_P_2013_1000 m3_3_1
FI	P	2013	1000 m3	3_2	FI_P_2013_1000 m3_3_2
FI	P	2013	1000 mt	4	FI_P_2013_1000 mt_4
FI	P	2013	1000 mt	4_1	FI_P_2013_1000 mt_4_1
FI	P	2013	1000 mt	4_2	FI_P_2013_1000 mt_4_2
FI	P	2013	1000 m3	5	FI_P_2013_1000 m3_5
FI	P	2013	1000 m3	5_C	FI_P_2013_1000 m3_5_C
FI	P	2013	1000 m3	5_NC	FI_P_2013_1000 m3_5_NC
FI	P	2013	1000 m3	5_NC_T	FI_P_2013_1000 m3_5_NC_T
FI	P	2013	1000 m3	6	FI_P_2013_1000 m3_6
FI	P	2013	1000 m3	6_1	FI_P_2013_1000 m3_6_1
FI	P	2013	1000 m3	6_1_C	FI_P_2013_1000 m3_6_1_C
FI	P	2013	1000 m3	6_1_NC	FI_P_2013_1000 m3_6_1_NC
FI	P	2013	1000 m3	6_1_NC_T	FI_P_2013_1000 m3_6_1_NC_T
FI	P	2013	1000 m3	6_2	FI_P_2013_1000 m3_6_2
FI	P	2013	1000 m3	6_2_C	FI_P_2013_1000 m3_6_2_C
FI	P	2013	1000 m3	6_2_NC	FI_P_2013_1000 m3_6_2_NC
FI	P	2013	1000 m3	6_2_NC_T	FI_P_2013_1000 m3_6_2_NC_T
FI	P	2013	1000 m3	6_3	FI_P_2013_1000 m3_6_3
FI	P	2013	1000 m3	6_3_1	FI_P_2013_1000 m3_6_3_1
FI	P	2013	1000 m3	6_4	FI_P_2013_1000 m3_6_4
FI	P	2013	1000 m3	6_4_1	FI_P_2013_1000 m3_6_4_1
FI	P	2013	1000 m3	6_4_2	FI_P_2013_1000 m3_6_4_2
FI	P	2013	1000 m3	6_4_3	FI_P_2013_1000 m3_6_4_3
FI	P	2013	1000 mt	7	FI_P_2013_1000 mt_7
FI	P	2013	1000 mt	7_1	FI_P_2013_1000 mt_7_1
FI	P	2013	1000 mt	7_2	FI_P_2013_1000 mt_7_2
FI	P	2013	1000 mt	7_3	FI_P_2013_1000 mt_7_3
FI	P	2013	1000 mt	7_3_1	FI_P_2013_1000 mt_7_3_1
FI	P	2013	1000 mt	7_3_2	FI_P_2013_1000 mt_7_3_2
FI	P	2013	1000 mt	7_3_3	FI_P_2013_1000 mt_7_3_3
FI	P	2013	1000 mt	7_3_4	FI_P_2013_1000 mt_7_3_4
FI	P	2013	1000 mt	7_4	FI_P_2013_1000 mt_7_4
FI	P	2013	1000 mt	8	FI_P_2013_1000 mt_8
FI	P	2013	1000 mt	8_1	FI_P_2013_1000 mt_8_1
FI	P	2013	1000 mt	8_2	FI_P_2013_1000 mt_8_2
FI	P	2013	1000 mt	9	FI_P_2013_1000 mt_9
FI	P	2013	1000 mt	10	FI_P_2013_1000 mt_10
FI	P	2013	1000 mt	10_1	FI_P_2013_1000 mt_10_1
FI	P	2013	1000 mt	10_1_1	FI_P_2013_1000 mt_10_1_1
FI	P	2013	1000 mt	10_1_2	FI_P_2013_1000 mt_10_1_2
FI	P	2013	1000 mt	10_1_3	FI_P_2013_1000 mt_10_1_3
FI	P	2013	1000 mt	10_1_4	FI_P_2013_1000 mt_10_1_4
FI	P	2013	1000 mt	10_2	FI_P_2013_1000 mt_10_2
FI	P	2013	1000 mt	10_3	FI_P_2013_1000 mt_10_3
FI	P	2013	1000 mt	10_3_1	FI_P_2013_1000 mt_10_3_1
FI	P	2013	1000 mt	10_3_2	FI_P_2013_1000 mt_10_3_2
FI	P	2013	1000 mt	10_3_3	FI_P_2013_1000 mt_10_3_3
FI	P	2013	1000 mt	10_3_4	FI_P_2013_1000 mt_10_3_4
FI	P	2013	1000 mt	10_4	FI_P_2013_1000 mt_10_4
FI	M	2013	1000 m3	1	FI_M_2013_1000 m3_1
FI	M	2013	1000 m3	1_1	FI_M_2013_1000 m3_1_1
FI	M	2013	1000 m3	1_2	FI_M_2013_1000 m3_1_2
FI	M	2013	1000 m3	1_2_C	FI_M_2013_1000 m3_1_2_C
FI	M	2013	1000 m3	1_2_NC	FI_M_2013_1000 m3_1_2_NC
FI	M	2013	1000 m3	1_2_NC_T	FI_M_2013_1000 m3_1_2_NC_T
FI	M	2013	1000 mt	2	FI_M_2013_1000 mt_2
FI	M	2013	1000 m3	3	FI_M_2013_1000 m3_3
FI	M	2013	1000 m3	3_1	FI_M_2013_1000 m3_3_1
FI	M	2013	1000 m3	3_2	FI_M_2013_1000 m3_3_2
FI	M	2013	1000 mt	4	FI_M_2013_1000 mt_4
FI	M	2013	1000 mt	4_1	FI_M_2013_1000 mt_4_1
FI	M	2013	1000 mt	4_2	FI_M_2013_1000 mt_4_2
FI	M	2013	1000 m3	5	FI_M_2013_1000 m3_5
FI	M	2013	1000 m3	5_C	FI_M_2013_1000 m3_5_C
FI	M	2013	1000 m3	5_NC	FI_M_2013_1000 m3_5_NC
FI	M	2013	1000 m3	5_NC_T	FI_M_2013_1000 m3_5_NC_T
FI	M	2013	1000 m3	6	FI_M_2013_1000 m3_6
FI	M	2013	1000 m3	6_1	FI_M_2013_1000 m3_6_1
FI	M	2013	1000 m3	6_1_C	FI_M_2013_1000 m3_6_1_C
FI	M	2013	1000 m3	6_1_NC	FI_M_2013_1000 m3_6_1_NC
FI	M	2013	1000 m3	6_1_NC_T	FI_M_2013_1000 m3_6_1_NC_T
FI	M	2013	1000 m3	6_2	FI_M_2013_1000 m3_6_2
FI	M	2013	1000 m3	6_2_C	FI_M_2013_1000 m3_6_2_C
FI	M	2013	1000 m3	6_2_NC	FI_M_2013_1000 m3_6_2_NC
FI	M	2013	1000 m3	6_2_NC_T	FI_M_2013_1000 m3_6_2_NC_T
FI	M	2013	1000 m3	6_3	FI_M_2013_1000 m3_6_3
FI	M	2013	1000 m3	6_3_1	FI_M_2013_1000 m3_6_3_1
FI	M	2013	1000 m3	6_4	FI_M_2013_1000 m3_6_4
FI	M	2013	1000 m3	6_4_1	FI_M_2013_1000 m3_6_4_1
FI	M	2013	1000 m3	6_4_2	FI_M_2013_1000 m3_6_4_2
FI	M	2013	1000 m3	6_4_3	FI_M_2013_1000 m3_6_4_3
FI	M	2013	1000 mt	7	FI_M_2013_1000 mt_7
FI	M	2013	1000 mt	7_1	FI_M_2013_1000 mt_7_1
FI	M	2013	1000 mt	7_2	FI_M_2013_1000 mt_7_2
FI	M	2013	1000 mt	7_3	FI_M_2013_1000 mt_7_3
FI	M	2013	1000 mt	7_3_1	FI_M_2013_1000 mt_7_3_1
FI	M	2013	1000 mt	7_3_2	FI_M_2013_1000 mt_7_3_2
FI	M	2013	1000 mt	7_3_3	FI_M_2013_1000 mt_7_3_3
FI	M	2013	1000 mt	7_3_4	FI_M_2013_1000 mt_7_3_4
FI	M	2013	1000 mt	7_4	FI_M_2013_1000 mt_7_4
FI	M	2013	1000 mt	8	FI_M_2013_1000 mt_8
FI	M	2013	1000 mt	8_1	FI_M_2013_1000 mt_8_1
FI	M	2013	1000 mt	8_2	FI_M_2013_1000 mt_8_2
FI	M	2013	1000 mt	9	FI_M_2013_1000 mt_9
FI	M	2013	1000 mt	10	FI_M_2013_1000 mt_10
FI	M	2013	1000 mt	10_1	FI_M_2013_1000 mt_10_1
FI	M	2013	1000 mt	10_1_1	FI_M_2013_1000 mt_10_1_1
FI	M	2013	1000 mt	10_1_2	FI_M_2013_1000 mt_10_1_2
FI	M	2013	1000 mt	10_1_3	FI_M_2013_1000 mt_10_1_3
FI	M	2013	1000 mt	10_1_4	FI_M_2013_1000 mt_10_1_4
FI	M	2013	1000 mt	10_2	FI_M_2013_1000 mt_10_2
FI	M	2013	1000 mt	10_3	FI_M_2013_1000 mt_10_3
FI	M	2013	1000 mt	10_3_1	FI_M_2013_1000 mt_10_3_1
FI	M	2013	1000 mt	10_3_2	FI_M_2013_1000 mt_10_3_2
FI	M	2013	1000 mt	10_3_3	FI_M_2013_1000 mt_10_3_3
FI	M	2013	1000 mt	10_3_4	FI_M_2013_1000 mt_10_3_4
FI	M	2013	1000 mt	10_4	FI_M_2013_1000 mt_10_4
FI	M	2013	1000 NAC	1	FI_M_2013_1000 NAC_1
FI	M	2013	1000 NAC	1_1	FI_M_2013_1000 NAC_1_1
FI	M	2013	1000 NAC	1_2	FI_M_2013_1000 NAC_1_2
FI	M	2013	1000 NAC	1_2_C	FI_M_2013_1000 NAC_1_2_C
FI	M	2013	1000 NAC	1_2_NC	FI_M_2013_1000 NAC_1_2_NC
FI	M	2013	1000 NAC	1_2_NC_T	FI_M_2013_1000 NAC_1_2_NC_T
FI	M	2013	1000 NAC	2	FI_M_2013_1000 NAC_2
FI	M	2013	1000 NAC	3	FI_M_2013_1000 NAC_3
FI	M	2013	1000 NAC	3_1	FI_M_2013_1000 NAC_3_1
FI	M	2013	1000 NAC	3_2	FI_M_2013_1000 NAC_3_2
FI	M	2013	1000 NAC	4	FI_M_2013_1000 NAC_4
FI	M	2013	1000 NAC	4_1	FI_M_2013_1000 NAC_4_1
FI	M	2013	1000 NAC	4_2	FI_M_2013_1000 NAC_4_2
FI	M	2013	1000 NAC	5	FI_M_2013_1000 NAC_5
FI	M	2013	1000 NAC	5_C	FI_M_2013_1000 NAC_5_C
FI	M	2013	1000 NAC	5_NC	FI_M_2013_1000 NAC_5_NC
FI	M	2013	1000 NAC	5_NC_T	FI_M_2013_1000 NAC_5_NC_T
FI	M	2013	1000 NAC	6	FI_M_2013_1000 NAC_6
FI	M	2013	1000 NAC	6_1	FI_M_2013_1000 NAC_6_1
FI	M	2013	1000 NAC	6_1_C	FI_M_2013_1000 NAC_6_1_C
FI	M	2013	1000 NAC	6_1_NC	FI_M_2013_1000 NAC_6_1_NC
FI	M	2013	1000 NAC	6_1_NC_T	FI_M_2013_1000 NAC_6_1_NC_T
FI	M	2013	1000 NAC	6_2	FI_M_2013_1000 NAC_6_2
FI	M	2013	1000 NAC	6_2_C	FI_M_2013_1000 NAC_6_2_C
FI	M	2013	1000 NAC	6_2_NC	FI_M_2013_1000 NAC_6_2_NC
FI	M	2013	1000 NAC	6_2_NC_T	FI_M_2013_1000 NAC_6_2_NC_T
FI	M	2013	1000 NAC	6_3	FI_M_2013_1000 NAC_6_3
FI	M	2013	1000 NAC	6_3_1	FI_M_2013_1000 NAC_6_3_1
FI	M	2013	1000 NAC	6_4	FI_M_2013_1000 NAC_6_4
FI	M	2013	1000 NAC	6_4_1	FI_M_2013_1000 NAC_6_4_1
FI	M	2013	1000 NAC	6_4_2	FI_M_2013_1000 NAC_6_4_2
FI	M	2013	1000 NAC	6_4_3	FI_M_2013_1000 NAC_6_4_3
FI	M	2013	1000 NAC	7	FI_M_2013_1000 NAC_7
FI	M	2013	1000 NAC	7_1	FI_M_2013_1000 NAC_7_1
FI	M	2013	1000 NAC	7_2	FI_M_2013_1000 NAC_7_2
FI	M	2013	1000 NAC	7_3	FI_M_2013_1000 NAC_7_3
FI	M	2013	1000 NAC	7_3_1	FI_M_2013_1000 NAC_7_3_1
FI	M	2013	1000 NAC	7_3_2	FI_M_2013_1000 NAC_7_3_2
FI	M	2013	1000 NAC	7_3_3	FI_M_2013_1000 NAC_7_3_3
FI	M	2013	1000 NAC	7_3_4	FI_M_2013_1000 NAC_7_3_4
FI	M	2013	1000 NAC	7_4	FI_M_2013_1000 NAC_7_4
FI	M	2013	1000 NAC	8	FI_M_2013_1000 NAC_8
FI	M	2013	1000 NAC	8_1	FI_M_2013_1000 NAC_8_1
FI	M	2013	1000 NAC	8_2	FI_M_2013_1000 NAC_8_2
FI	M	2013	1000 NAC	9	FI_M_2013_1000 NAC_9
FI	M	2013	1000 NAC	10	FI_M_2013_1000 NAC_10
FI	M	2013	1000 NAC	10_1	FI_M_2013_1000 NAC_10_1
FI	M	2013	1000 NAC	10_1_1	FI_M_2013_1000 NAC_10_1_1
FI	M	2013	1000 NAC	10_1_2	FI_M_2013_1000 NAC_10_1_2
FI	M	2013	1000 NAC	10_1_3	FI_M_2013_1000 NAC_10_1_3
FI	M	2013	1000 NAC	10_1_4	FI_M_2013_1000 NAC_10_1_4
FI	M	2013	1000 NAC	10_2	FI_M_2013_1000 NAC_10_2
FI	M	2013	1000 NAC	10_3	FI_M_2013_1000 NAC_10_3
FI	M	2013	1000 NAC	10_3_1	FI_M_2013_1000 NAC_10_3_1
FI	M	2013	1000 NAC	10_3_2	FI_M_2013_1000 NAC_10_3_2
FI	M	2013	1000 NAC	10_3_3	FI_M_2013_1000 NAC_10_3_3
FI	M	2013	1000 NAC	10_3_4	FI_M_2013_1000 NAC_10_3_4
FI	M	2013	1000 NAC	10_4	FI_M_2013_1000 NAC_10_4
FI	X	2013	1000 m3	1	FI_X_2013_1000 m3_1
FI	X	2013	1000 m3	1_1	FI_X_2013_1000 m3_1_1
FI	X	2013	1000 m3	1_2	FI_X_2013_1000 m3_1_2
FI	X	2013	1000 m3	1_2_C	FI_X_2013_1000 m3_1_2_C
FI	X	2013	1000 m3	1_2_NC	FI_X_2013_1000 m3_1_2_NC
FI	X	2013	1000 m3	1_2_NC_T	FI_X_2013_1000 m3_1_2_NC_T
FI	X	2013	1000 mt	2	FI_X_2013_1000 mt_2
FI	X	2013	1000 m3	3	FI_X_2013_1000 m3_3
FI	X	2013	1000 m3	3_1	FI_X_2013_1000 m3_3_1
FI	X	2013	1000 m3	3_2	FI_X_2013_1000 m3_3_2
FI	X	2013	1000 mt	4	FI_X_2013_1000 mt_4
FI	X	2013	1000 mt	4_1	FI_X_2013_1000 mt_4_1
FI	X	2013	1000 mt	4_2	FI_X_2013_1000 mt_4_2
FI	X	2013	1000 m3	5	FI_X_2013_1000 m3_5
FI	X	2013	1000 m3	5_C	FI_X_2013_1000 m3_5_C
FI	X	2013	1000 m3	5_NC	FI_X_2013_1000 m3_5_NC
FI	X	2013	1000 m3	5_NC_T	FI_X_2013_1000 m3_5_NC_T
FI	X	2013	1000 m3	6	FI_X_2013_1000 m3_6
FI	X	2013	1000 m3	6_1	FI_X_2013_1000 m3_6_1
FI	X	2013	1000 m3	6_1_C	FI_X_2013_1000 m3_6_1_C
FI	X	2013	1000 m3	6_1_NC	FI_X_2013_1000 m3_6_1_NC
FI	X	2013	1000 m3	6_1_NC_T	FI_X_2013_1000 m3_6_1_NC_T
FI	X	2013	1000 m3	6_2	FI_X_2013_1000 m3_6_2
FI	X	2013	1000 m3	6_2_C	FI_X_2013_1000 m3_6_2_C
FI	X	2013	1000 m3	6_2_NC	FI_X_2013_1000 m3_6_2_NC
FI	X	2013	1000 m3	6_2_NC_T	FI_X_2013_1000 m3_6_2_NC_T
FI	X	2013	1000 m3	6_3	FI_X_2013_1000 m3_6_3
FI	X	2013	1000 m3	6_3_1	FI_X_2013_1000 m3_6_3_1
FI	X	2013	1000 m3	6_4	FI_X_2013_1000 m3_6_4
FI	X	2013	1000 m3	6_4_1	FI_X_2013_1000 m3_6_4_1
FI	X	2013	1000 m3	6_4_2	FI_X_2013_1000 m3_6_4_2
FI	X	2013	1000 m3	6_4_3	FI_X_2013_1000 m3_6_4_3
FI	X	2013	1000 mt	7	FI_X_2013_1000 mt_7
FI	X	2013	1000 mt	7_1	FI_X_2013_1000 mt_7_1
FI	X	2013	1000 mt	7_2	FI_X_2013_1000 mt_7_2
FI	X	2013	1000 mt	7_3	FI_X_2013_1000 mt_7_3
FI	X	2013	1000 mt	7_3_1	FI_X_2013_1000 mt_7_3_1
FI	X	2013	1000 mt	7_3_2	FI_X_2013_1000 mt_7_3_2
FI	X	2013	1000 mt	7_3_3	FI_X_2013_1000 mt_7_3_3
FI	X	2013	1000 mt	7_3_4	FI_X_2013_1000 mt_7_3_4
FI	X	2013	1000 mt	7_4	FI_X_2013_1000 mt_7_4
FI	X	2013	1000 mt	8	FI_X_2013_1000 mt_8
FI	X	2013	1000 mt	8_1	FI_X_2013_1000 mt_8_1
FI	X	2013	1000 mt	8_2	FI_X_2013_1000 mt_8_2
FI	X	2013	1000 mt	9	FI_X_2013_1000 mt_9
FI	X	2013	1000 mt	10	FI_X_2013_1000 mt_10
FI	X	2013	1000 mt	10_1	FI_X_2013_1000 mt_10_1
FI	X	2013	1000 mt	10_1_1	FI_X_2013_1000 mt_10_1_1
FI	X	2013	1000 mt	10_1_2	FI_X_2013_1000 mt_10_1_2
FI	X	2013	1000 mt	10_1_3	FI_X_2013_1000 mt_10_1_3
FI	X	2013	1000 mt	10_1_4	FI_X_2013_1000 mt_10_1_4
FI	X	2013	1000 mt	10_2	FI_X_2013_1000 mt_10_2
FI	X	2013	1000 mt	10_3	FI_X_2013_1000 mt_10_3
FI	X	2013	1000 mt	10_3_1	FI_X_2013_1000 mt_10_3_1
FI	X	2013	1000 mt	10_3_2	FI_X_2013_1000 mt_10_3_2
FI	X	2013	1000 mt	10_3_3	FI_X_2013_1000 mt_10_3_3
FI	X	2013	1000 mt	10_3_4	FI_X_2013_1000 mt_10_3_4
FI	X	2013	1000 mt	10_4	FI_X_2013_1000 mt_10_4
FI	X	2013	1000 NAC	1	FI_X_2013_1000 NAC_1
FI	X	2013	1000 NAC	1_1	FI_X_2013_1000 NAC_1_1
FI	X	2013	1000 NAC	1_2	FI_X_2013_1000 NAC_1_2
FI	X	2013	1000 NAC	1_2_C	FI_X_2013_1000 NAC_1_2_C
FI	X	2013	1000 NAC	1_2_NC	FI_X_2013_1000 NAC_1_2_NC
FI	X	2013	1000 NAC	1_2_NC_T	FI_X_2013_1000 NAC_1_2_NC_T
FI	X	2013	1000 NAC	2	FI_X_2013_1000 NAC_2
FI	X	2013	1000 NAC	3	FI_X_2013_1000 NAC_3
FI	X	2013	1000 NAC	3_1	FI_X_2013_1000 NAC_3_1
FI	X	2013	1000 NAC	3_2	FI_X_2013_1000 NAC_3_2
FI	X	2013	1000 NAC	4	FI_X_2013_1000 NAC_4
FI	X	2013	1000 NAC	4_1	FI_X_2013_1000 NAC_4_1
FI	X	2013	1000 NAC	4_2	FI_X_2013_1000 NAC_4_2
FI	X	2013	1000 NAC	5	FI_X_2013_1000 NAC_5
FI	X	2013	1000 NAC	5_C	FI_X_2013_1000 NAC_5_C
FI	X	2013	1000 NAC	5_NC	FI_X_2013_1000 NAC_5_NC
FI	X	2013	1000 NAC	5_NC_T	FI_X_2013_1000 NAC_5_NC_T
FI	X	2013	1000 NAC	6	FI_X_2013_1000 NAC_6
FI	X	2013	1000 NAC	6_1	FI_X_2013_1000 NAC_6_1
FI	X	2013	1000 NAC	6_1_C	FI_X_2013_1000 NAC_6_1_C
FI	X	2013	1000 NAC	6_1_NC	FI_X_2013_1000 NAC_6_1_NC
FI	X	2013	1000 NAC	6_1_NC_T	FI_X_2013_1000 NAC_6_1_NC_T
FI	X	2013	1000 NAC	6_2	FI_X_2013_1000 NAC_6_2
FI	X	2013	1000 NAC	6_2_C	FI_X_2013_1000 NAC_6_2_C
FI	X	2013	1000 NAC	6_2_NC	FI_X_2013_1000 NAC_6_2_NC
FI	X	2013	1000 NAC	6_2_NC_T	FI_X_2013_1000 NAC_6_2_NC_T
FI	X	2013	1000 NAC	6_3	FI_X_2013_1000 NAC_6_3
FI	X	2013	1000 NAC	6_3_1	FI_X_2013_1000 NAC_6_3_1
FI	X	2013	1000 NAC	6_4	FI_X_2013_1000 NAC_6_4
FI	X	2013	1000 NAC	6_4_1	FI_X_2013_1000 NAC_6_4_1
FI	X	2013	1000 NAC	6_4_2	FI_X_2013_1000 NAC_6_4_2
FI	X	2013	1000 NAC	6_4_3	FI_X_2013_1000 NAC_6_4_3
FI	X	2013	1000 NAC	7	FI_X_2013_1000 NAC_7
FI	X	2013	1000 NAC	7_1	FI_X_2013_1000 NAC_7_1
FI	X	2013	1000 NAC	7_2	FI_X_2013_1000 NAC_7_2
FI	X	2013	1000 NAC	7_3	FI_X_2013_1000 NAC_7_3
FI	X	2013	1000 NAC	7_3_1	FI_X_2013_1000 NAC_7_3_1
FI	X	2013	1000 NAC	7_3_2	FI_X_2013_1000 NAC_7_3_2
FI	X	2013	1000 NAC	7_3_3	FI_X_2013_1000 NAC_7_3_3
FI	X	2013	1000 NAC	7_3_4	FI_X_2013_1000 NAC_7_3_4
FI	X	2013	1000 NAC	7_4	FI_X_2013_1000 NAC_7_4
FI	X	2013	1000 NAC	8	FI_X_2013_1000 NAC_8
FI	X	2013	1000 NAC	8_1	FI_X_2013_1000 NAC_8_1
FI	X	2013	1000 NAC	8_2	FI_X_2013_1000 NAC_8_2
FI	X	2013	1000 NAC	9	FI_X_2013_1000 NAC_9
FI	X	2013	1000 NAC	10	FI_X_2013_1000 NAC_10
FI	X	2013	1000 NAC	10_1	FI_X_2013_1000 NAC_10_1
FI	X	2013	1000 NAC	10_1_1	FI_X_2013_1000 NAC_10_1_1
FI	X	2013	1000 NAC	10_1_2	FI_X_2013_1000 NAC_10_1_2
FI	X	2013	1000 NAC	10_1_3	FI_X_2013_1000 NAC_10_1_3
FI	X	2013	1000 NAC	10_1_4	FI_X_2013_1000 NAC_10_1_4
FI	X	2013	1000 NAC	10_2	FI_X_2013_1000 NAC_10_2
FI	X	2013	1000 NAC	10_3	FI_X_2013_1000 NAC_10_3
FI	X	2013	1000 NAC	10_3_1	FI_X_2013_1000 NAC_10_3_1
FI	X	2013	1000 NAC	10_3_2	FI_X_2013_1000 NAC_10_3_2
FI	X	2013	1000 NAC	10_3_3	FI_X_2013_1000 NAC_10_3_3
FI	X	2013	1000 NAC	10_3_4	FI_X_2013_1000 NAC_10_3_4
FI	X	2013	1000 NAC	10_4	FI_X_2013_1000 NAC_10_4
FI	M	2013	1000 NAC	11_1	FI_M_2013_1000 NAC_11_1
FI	M	2013	1000 NAC	11_1_C	FI_M_2013_1000 NAC_11_1_C
FI	M	2013	1000 NAC	11_1_NC	FI_M_2013_1000 NAC_11_1_NC
FI	M	2013	1000 NAC	11_1_NC_T	FI_M_2013_1000 NAC_11_1_NC_T
FI	M	2013	1000 NAC	11_2	FI_M_2013_1000 NAC_11_2
FI	M	2013	1000 NAC	11_3	FI_M_2013_1000 NAC_11_3
FI	M	2013	1000 NAC	11_4	FI_M_2013_1000 NAC_11_4
FI	M	2013	1000 NAC	11_5	FI_M_2013_1000 NAC_11_5
FI	M	2013	1000 NAC	11_6	FI_M_2013_1000 NAC_11_6
FI	M	2013	1000 NAC	11_7	FI_M_2013_1000 NAC_11_7
FI	M	2013	1000 NAC	11_7_1	FI_M_2013_1000 NAC_11_7_1
FI	M	2013	1000 NAC	12_1	FI_M_2013_1000 NAC_12_1
FI	M	2013	1000 NAC	12_2	FI_M_2013_1000 NAC_12_2
FI	M	2013	1000 NAC	12_3	FI_M_2013_1000 NAC_12_3
FI	M	2013	1000 NAC	12_4	FI_M_2013_1000 NAC_12_4
FI	M	2013	1000 NAC	12_5	FI_M_2013_1000 NAC_12_5
FI	M	2013	1000 NAC	12_6	FI_M_2013_1000 NAC_12_6
FI	M	2013	1000 NAC	12_6_1	FI_M_2013_1000 NAC_12_6_1
FI	M	2013	1000 NAC	12_6_2	FI_M_2013_1000 NAC_12_6_2
FI	M	2013	1000 NAC	12_6_3	FI_M_2013_1000 NAC_12_6_3
FI	M	2013	1000 NAC	12_7	FI_M_2013_1000 NAC_12_7
FI	M	2013	1000 NAC	12_7_1	FI_M_2013_1000 NAC_12_7_1
FI	M	2013	1000 NAC	12_7_2	FI_M_2013_1000 NAC_12_7_2
FI	M	2013	1000 NAC	12_7_3	FI_M_2013_1000 NAC_12_7_3
FI	X	2013	1000 NAC	11_1	FI_X_2013_1000 NAC_11_1
FI	X	2013	1000 NAC	11_1_C	FI_X_2013_1000 NAC_11_1_C
FI	X	2013	1000 NAC	11_1_NC	FI_X_2013_1000 NAC_11_1_NC
FI	X	2013	1000 NAC	11_1_NC_T	FI_X_2013_1000 NAC_11_1_NC_T
FI	X	2013	1000 NAC	11_2	FI_X_2013_1000 NAC_11_2
FI	X	2013	1000 NAC	11_3	FI_X_2013_1000 NAC_11_3
FI	X	2013	1000 NAC	11_4	FI_X_2013_1000 NAC_11_4
FI	X	2013	1000 NAC	11_5	FI_X_2013_1000 NAC_11_5
FI	X	2013	1000 NAC	11_6	FI_X_2013_1000 NAC_11_6
FI	X	2013	1000 NAC	11_7	FI_X_2013_1000 NAC_11_7
FI	X	2013	1000 NAC	11_7_1	FI_X_2013_1000 NAC_11_7_1
FI	X	2013	1000 NAC	12_1	FI_X_2013_1000 NAC_12_1
FI	X	2013	1000 NAC	12_2	FI_X_2013_1000 NAC_12_2
FI	X	2013	1000 NAC	12_3	FI_X_2013_1000 NAC_12_3
FI	X	2013	1000 NAC	12_4	FI_X_2013_1000 NAC_12_4
FI	X	2013	1000 NAC	12_5	FI_X_2013_1000 NAC_12_5
FI	X	2013	1000 NAC	12_6	FI_X_2013_1000 NAC_12_6
FI	X	2013	1000 NAC	12_6_1	FI_X_2013_1000 NAC_12_6_1
FI	X	2013	1000 NAC	12_6_2	FI_X_2013_1000 NAC_12_6_2
FI	X	2013	1000 NAC	12_6_3	FI_X_2013_1000 NAC_12_6_3
FI	X	2013	1000 NAC	12_7	FI_X_2013_1000 NAC_12_7
FI	X	2013	1000 NAC	12_7_1	FI_X_2013_1000 NAC_12_7_1
FI	X	2013	1000 NAC	12_7_2	FI_X_2013_1000 NAC_12_7_2
FI	X	2013	1000 NAC	12_7_3	FI_X_2013_1000 NAC_12_7_3
FI	M	2013	1000 m3	ST_1_2_C	FI_M_2013_1000 m3_ST_1_2_C
FI	M	2013	1000 m3	ST_1_2_C_1	FI_M_2013_1000 m3_ST_1_2_C_1
FI	M	2013	1000 m3	ST_1_2_C_1_1	FI_M_2013_1000 m3_ST_1_2_C_1_1
FI	M	2013	1000 m3	ST_1_2_C_2_1	FI_M_2013_1000 m3_ST_1_2_C_2_1
FI	M	2013	1000 m3	ST_1_2_C_2	FI_M_2013_1000 m3_ST_1_2_C_2
FI	M	2013	1000 m3	ST_1_2_C_1_2	FI_M_2013_1000 m3_ST_1_2_C_1_2
FI	M	2013	1000 m3	ST_1_2_C_2_2	FI_M_2013_1000 m3_ST_1_2_C_2_2
FI	M	2013	1000 m3	ST_1_2_C_3	FI_M_2013_1000 m3_ST_1_2_C_3
FI	M	2013	1000 m3	ST_1_2_C_1_3	FI_M_2013_1000 m3_ST_1_2_C_1_3
FI	M	2013	1000 m3	ST_1_2_C_2_3	FI_M_2013_1000 m3_ST_1_2_C_2_3
FI	M	2013	1000 m3	ST_1_2_NC	FI_M_2013_1000 m3_ST_1_2_NC
FI	M	2013	1000 m3	ST_1_2_NC_1	FI_M_2013_1000 m3_ST_1_2_NC_1
FI	M	2013	1000 m3	ST_1_2_NC_1_1	FI_M_2013_1000 m3_ST_1_2_NC_1_1
FI	M	2013	1000 m3	ST_1_2_NC_2_1	FI_M_2013_1000 m3_ST_1_2_NC_2_1
FI	M	2013	1000 m3	ST_1_2_NC_2	FI_M_2013_1000 m3_ST_1_2_NC_2
FI	M	2013	1000 m3	ST_1_2_NC_1_2	FI_M_2013_1000 m3_ST_1_2_NC_1_2
FI	M	2013	1000 m3	ST_1_2_NC_2_2	FI_M_2013_1000 m3_ST_1_2_NC_2_2
FI	M	2013	1000 m3	ST_1_2_NC_3	FI_M_2013_1000 m3_ST_1_2_NC_3
FI	M	2013	1000 m3	ST_1_2_NC_1_3	FI_M_2013_1000 m3_ST_1_2_NC_1_3
FI	M	2013	1000 m3	ST_1_2_NC_2_3	FI_M_2013_1000 m3_ST_1_2_NC_2_3
FI	M	2013	1000 m3	ST_1_2_NC_4	FI_M_2013_1000 m3_ST_1_2_NC_4
FI	M	2013	1000 m3	ST_1_2_NC_5	FI_M_2013_1000 m3_ST_1_2_NC_5
FI	M	2013	1000 m3	ST_5_C	FI_M_2013_1000 m3_ST_5_C
FI	M	2013	1000 m3	ST_5_C_1	FI_M_2013_1000 m3_ST_5_C_1
FI	M	2013	1000 m3	ST_5_C_2	FI_M_2013_1000 m3_ST_5_C_2
FI	M	2013	1000 m3	ST_5_NC	FI_M_2013_1000 m3_ST_5_NC
FI	M	2013	1000 m3	ST_5_NC_1	FI_M_2013_1000 m3_ST_5_NC_1
FI	M	2013	1000 m3	ST_5_NC_2	FI_M_2013_1000 m3_ST_5_NC_2
FI	M	2013	1000 m3	ST_5_NC_3	FI_M_2013_1000 m3_ST_5_NC_3
FI	M	2013	1000 m3	ST_5_NC_4	FI_M_2013_1000 m3_ST_5_NC_4
FI	M	2013	1000 m3	ST_5_NC_5	FI_M_2013_1000 m3_ST_5_NC_5
FI	M	2013	1000 m3	ST_5_NC_6	FI_M_2013_1000 m3_ST_5_NC_6
FI	M	2013	1000 m3	ST_5_NC_7	FI_M_2013_1000 m3_ST_5_NC_7
FI	M	2013	1000 NAC	ST_1_2_C	FI_M_2013_1000 NAC_ST_1_2_C
FI	M	2013	1000 NAC	ST_1_2_C_1	FI_M_2013_1000 NAC_ST_1_2_C_1
FI	M	2013	1000 NAC	ST_1_2_C_1_1	FI_M_2013_1000 NAC_ST_1_2_C_1_1
FI	M	2013	1000 NAC	ST_1_2_C_2_1	FI_M_2013_1000 NAC_ST_1_2_C_2_1
FI	M	2013	1000 NAC	ST_1_2_C_2	FI_M_2013_1000 NAC_ST_1_2_C_2
FI	M	2013	1000 NAC	ST_1_2_C_1_2	FI_M_2013_1000 NAC_ST_1_2_C_1_2
FI	M	2013	1000 NAC	ST_1_2_C_2_2	FI_M_2013_1000 NAC_ST_1_2_C_2_2
FI	M	2013	1000 NAC	ST_1_2_C_3	FI_M_2013_1000 NAC_ST_1_2_C_3
FI	M	2013	1000 NAC	ST_1_2_C_1_3	FI_M_2013_1000 NAC_ST_1_2_C_1_3
FI	M	2013	1000 NAC	ST_1_2_C_2_3	FI_M_2013_1000 NAC_ST_1_2_C_2_3
FI	M	2013	1000 NAC	ST_1_2_NC	FI_M_2013_1000 NAC_ST_1_2_NC
FI	M	2013	1000 NAC	ST_1_2_NC_1	FI_M_2013_1000 NAC_ST_1_2_NC_1
FI	M	2013	1000 NAC	ST_1_2_NC_1_1	FI_M_2013_1000 NAC_ST_1_2_NC_1_1
FI	M	2013	1000 NAC	ST_1_2_NC_2_1	FI_M_2013_1000 NAC_ST_1_2_NC_2_1
FI	M	2013	1000 NAC	ST_1_2_NC_2	FI_M_2013_1000 NAC_ST_1_2_NC_2
FI	M	2013	1000 NAC	ST_1_2_NC_1_2	FI_M_2013_1000 NAC_ST_1_2_NC_1_2
FI	M	2013	1000 NAC	ST_1_2_NC_2_2	FI_M_2013_1000 NAC_ST_1_2_NC_2_2
FI	M	2013	1000 NAC	ST_1_2_NC_3	FI_M_2013_1000 NAC_ST_1_2_NC_3
FI	M	2013	1000 NAC	ST_1_2_NC_1_3	FI_M_2013_1000 NAC_ST_1_2_NC_1_3
FI	M	2013	1000 NAC	ST_1_2_NC_2_3	FI_M_2013_1000 NAC_ST_1_2_NC_2_3
FI	M	2013	1000 NAC	ST_1_2_NC_4	FI_M_2013_1000 NAC_ST_1_2_NC_4
FI	M	2013	1000 NAC	ST_1_2_NC_5	FI_M_2013_1000 NAC_ST_1_2_NC_5
FI	M	2013	1000 NAC	ST_5_C	FI_M_2013_1000 NAC_ST_5_C
FI	M	2013	1000 NAC	ST_5_C_1	FI_M_2013_1000 NAC_ST_5_C_1
FI	M	2013	1000 NAC	ST_5_C_2	FI_M_2013_1000 NAC_ST_5_C_2
FI	M	2013	1000 NAC	ST_5_NC	FI_M_2013_1000 NAC_ST_5_NC
FI	M	2013	1000 NAC	ST_5_NC_1	FI_M_2013_1000 NAC_ST_5_NC_1
FI	M	2013	1000 NAC	ST_5_NC_2	FI_M_2013_1000 NAC_ST_5_NC_2
FI	M	2013	1000 NAC	ST_5_NC_3	FI_M_2013_1000 NAC_ST_5_NC_3
FI	M	2013	1000 NAC	ST_5_NC_4	FI_M_2013_1000 NAC_ST_5_NC_4
FI	M	2013	1000 NAC	ST_5_NC_5	FI_M_2013_1000 NAC_ST_5_NC_5
FI	M	2013	1000 NAC	ST_5_NC_6	FI_M_2013_1000 NAC_ST_5_NC_6
FI	M	2013	1000 NAC	ST_5_NC_7	FI_M_2013_1000 NAC_ST_5_NC_7
FI	X	2013	1000 m3	ST_1_2_C	FI_X_2013_1000 m3_ST_1_2_C
FI	X	2013	1000 m3	ST_1_2_C_1	FI_X_2013_1000 m3_ST_1_2_C_1
FI	X	2013	1000 m3	ST_1_2_C_1_1	FI_X_2013_1000 m3_ST_1_2_C_1_1
FI	X	2013	1000 m3	ST_1_2_C_2_1	FI_X_2013_1000 m3_ST_1_2_C_2_1
FI	X	2013	1000 m3	ST_1_2_C_2	FI_X_2013_1000 m3_ST_1_2_C_2
FI	X	2013	1000 m3	ST_1_2_C_1_2	FI_X_2013_1000 m3_ST_1_2_C_1_2
FI	X	2013	1000 m3	ST_1_2_C_2_2	FI_X_2013_1000 m3_ST_1_2_C_2_2
FI	X	2013	1000 m3	ST_1_2_C_3	FI_X_2013_1000 m3_ST_1_2_C_3
FI	X	2013	1000 m3	ST_1_2_C_1_3	FI_X_2013_1000 m3_ST_1_2_C_1_3
FI	X	2013	1000 m3	ST_1_2_C_2_3	FI_X_2013_1000 m3_ST_1_2_C_2_3
FI	X	2013	1000 m3	ST_1_2_NC	FI_X_2013_1000 m3_ST_1_2_NC
FI	X	2013	1000 m3	ST_1_2_NC_1	FI_X_2013_1000 m3_ST_1_2_NC_1
FI	X	2013	1000 m3	ST_1_2_NC_1_1	FI_X_2013_1000 m3_ST_1_2_NC_1_1
FI	X	2013	1000 m3	ST_1_2_NC_2_1	FI_X_2013_1000 m3_ST_1_2_NC_2_1
FI	X	2013	1000 m3	ST_1_2_NC_2	FI_X_2013_1000 m3_ST_1_2_NC_2
FI	X	2013	1000 m3	ST_1_2_NC_1_2	FI_X_2013_1000 m3_ST_1_2_NC_1_2
FI	X	2013	1000 m3	ST_1_2_NC_2_2	FI_X_2013_1000 m3_ST_1_2_NC_2_2
FI	X	2013	1000 m3	ST_1_2_NC_3	FI_X_2013_1000 m3_ST_1_2_NC_3
FI	X	2013	1000 m3	ST_1_2_NC_1_3	FI_X_2013_1000 m3_ST_1_2_NC_1_3
FI	X	2013	1000 m3	ST_1_2_NC_2_3	FI_X_2013_1000 m3_ST_1_2_NC_2_3
FI	X	2013	1000 m3	ST_1_2_NC_4	FI_X_2013_1000 m3_ST_1_2_NC_4
FI	X	2013	1000 m3	ST_1_2_NC_5	FI_X_2013_1000 m3_ST_1_2_NC_5
FI	X	2013	1000 m3	ST_5_C	FI_X_2013_1000 m3_ST_5_C
FI	X	2013	1000 m3	ST_5_C_1	FI_X_2013_1000 m3_ST_5_C_1
FI	X	2013	1000 m3	ST_5_C_2	FI_X_2013_1000 m3_ST_5_C_2
FI	X	2013	1000 m3	ST_5_NC	FI_X_2013_1000 m3_ST_5_NC
FI	X	2013	1000 m3	ST_5_NC_1	FI_X_2013_1000 m3_ST_5_NC_1
FI	X	2013	1000 m3	ST_5_NC_2	FI_X_2013_1000 m3_ST_5_NC_2
FI	X	2013	1000 m3	ST_5_NC_3	FI_X_2013_1000 m3_ST_5_NC_3
FI	X	2013	1000 m3	ST_5_NC_4	FI_X_2013_1000 m3_ST_5_NC_4
FI	X	2013	1000 m3	ST_5_NC_5	FI_X_2013_1000 m3_ST_5_NC_5
FI	X	2013	1000 m3	ST_5_NC_6	FI_X_2013_1000 m3_ST_5_NC_6
FI	X	2013	1000 m3	ST_5_NC_7	FI_X_2013_1000 m3_ST_5_NC_7
FI	X	2013	1000 NAC	ST_1_2_C	FI_X_2013_1000 NAC_ST_1_2_C
FI	X	2013	1000 NAC	ST_1_2_C_1	FI_X_2013_1000 NAC_ST_1_2_C_1
FI	X	2013	1000 NAC	ST_1_2_C_1_1	FI_X_2013_1000 NAC_ST_1_2_C_1_1
FI	X	2013	1000 NAC	ST_1_2_C_2_1	FI_X_2013_1000 NAC_ST_1_2_C_2_1
FI	X	2013	1000 NAC	ST_1_2_C_2	FI_X_2013_1000 NAC_ST_1_2_C_2
FI	X	2013	1000 NAC	ST_1_2_C_1_2	FI_X_2013_1000 NAC_ST_1_2_C_1_2
FI	X	2013	1000 NAC	ST_1_2_C_2_2	FI_X_2013_1000 NAC_ST_1_2_C_2_2
FI	X	2013	1000 NAC	ST_1_2_C_3	FI_X_2013_1000 NAC_ST_1_2_C_3
FI	X	2013	1000 NAC	ST_1_2_C_1_3	FI_X_2013_1000 NAC_ST_1_2_C_1_3
FI	X	2013	1000 NAC	ST_1_2_C_2_3	FI_X_2013_1000 NAC_ST_1_2_C_2_3
FI	X	2013	1000 NAC	ST_1_2_NC	FI_X_2013_1000 NAC_ST_1_2_NC
FI	X	2013	1000 NAC	ST_1_2_NC_1	FI_X_2013_1000 NAC_ST_1_2_NC_1
FI	X	2013	1000 NAC	ST_1_2_NC_1_1	FI_X_2013_1000 NAC_ST_1_2_NC_1_1
FI	X	2013	1000 NAC	ST_1_2_NC_2_1	FI_X_2013_1000 NAC_ST_1_2_NC_2_1
FI	X	2013	1000 NAC	ST_1_2_NC_2	FI_X_2013_1000 NAC_ST_1_2_NC_2
FI	X	2013	1000 NAC	ST_1_2_NC_1_2	FI_X_2013_1000 NAC_ST_1_2_NC_1_2
FI	X	2013	1000 NAC	ST_1_2_NC_2_2	FI_X_2013_1000 NAC_ST_1_2_NC_2_2
FI	X	2013	1000 NAC	ST_1_2_NC_3	FI_X_2013_1000 NAC_ST_1_2_NC_3
FI	X	2013	1000 NAC	ST_1_2_NC_1_3	FI_X_2013_1000 NAC_ST_1_2_NC_1_3
FI	X	2013	1000 NAC	ST_1_2_NC_2_3	FI_X_2013_1000 NAC_ST_1_2_NC_2_3
FI	X	2013	1000 NAC	ST_1_2_NC_4	FI_X_2013_1000 NAC_ST_1_2_NC_4
FI	X	2013	1000 NAC	ST_1_2_NC_5	FI_X_2013_1000 NAC_ST_1_2_NC_5
FI	X	2013	1000 NAC	ST_5_C	FI_X_2013_1000 NAC_ST_5_C
FI	X	2013	1000 NAC	ST_5_C_1	FI_X_2013_1000 NAC_ST_5_C_1
FI	X	2013	1000 NAC	ST_5_C_2	FI_X_2013_1000 NAC_ST_5_C_2
FI	X	2013	1000 NAC	ST_5_NC	FI_X_2013_1000 NAC_ST_5_NC
FI	X	2013	1000 NAC	ST_5_NC_1	FI_X_2013_1000 NAC_ST_5_NC_1
FI	X	2013	1000 NAC	ST_5_NC_2	FI_X_2013_1000 NAC_ST_5_NC_2
FI	X	2013	1000 NAC	ST_5_NC_3	FI_X_2013_1000 NAC_ST_5_NC_3
FI	X	2013	1000 NAC	ST_5_NC_4	FI_X_2013_1000 NAC_ST_5_NC_4
FI	X	2013	1000 NAC	ST_5_NC_5	FI_X_2013_1000 NAC_ST_5_NC_5
FI	X	2013	1000 NAC	ST_5_NC_6	FI_X_2013_1000 NAC_ST_5_NC_6
FI	X	2013	1000 NAC	ST_5_NC_7	FI_X_2013_1000 NAC_ST_5_NC_7
FI	EX_M	2013	1000 m3	1	FI_EX_M_2013_1000 m3_1
FI	EX_M	2013	1000 m3	1_1	FI_EX_M_2013_1000 m3_1_1
FI	EX_M	2013	1000 m3	1_2	FI_EX_M_2013_1000 m3_1_2
FI	EX_M	2013	1000 m3	1_2_C	FI_EX_M_2013_1000 m3_1_2_C
FI	EX_M	2013	1000 m3	1_2_NC	FI_EX_M_2013_1000 m3_1_2_NC
FI	EX_M	2013	1000 m3	1_2_NC_T	FI_EX_M_2013_1000 m3_1_2_NC_T
FI	EX_M	2013	1000 mt	2	FI_EX_M_2013_1000 mt_2
FI	EX_M	2013	1000 m3	3	FI_EX_M_2013_1000 m3_3
FI	EX_M	2013	1000 m3	3_1	FI_EX_M_2013_1000 m3_3_1
FI	EX_M	2013	1000 m3	3_2	FI_EX_M_2013_1000 m3_3_2
FI	EX_M	2013	1000 mt	4	FI_EX_M_2013_1000 mt_4
FI	EX_M	2013	1000 mt	4_1	FI_EX_M_2013_1000 mt_4_1
FI	EX_M	2013	1000 mt	4_2	FI_EX_M_2013_1000 mt_4_2
FI	EX_M	2013	1000 m3	5	FI_EX_M_2013_1000 m3_5
FI	EX_M	2013	1000 m3	5_C	FI_EX_M_2013_1000 m3_5_C
FI	EX_M	2013	1000 m3	5_NC	FI_EX_M_2013_1000 m3_5_NC
FI	EX_M	2013	1000 m3	5_NC_T	FI_EX_M_2013_1000 m3_5_NC_T
FI	EX_M	2013	1000 m3	6	FI_EX_M_2013_1000 m3_6
FI	EX_M	2013	1000 m3	6_1	FI_EX_M_2013_1000 m3_6_1
FI	EX_M	2013	1000 m3	6_1_C	FI_EX_M_2013_1000 m3_6_1_C
FI	EX_M	2013	1000 m3	6_1_NC	FI_EX_M_2013_1000 m3_6_1_NC
FI	EX_M	2013	1000 m3	6_1_NC_T	FI_EX_M_2013_1000 m3_6_1_NC_T
FI	EX_M	2013	1000 m3	6_2	FI_EX_M_2013_1000 m3_6_2
FI	EX_M	2013	1000 m3	6_2_C	FI_EX_M_2013_1000 m3_6_2_C
FI	EX_M	2013	1000 m3	6_2_NC	FI_EX_M_2013_1000 m3_6_2_NC
FI	EX_M	2013	1000 m3	6_2_NC_T	FI_EX_M_2013_1000 m3_6_2_NC_T
FI	EX_M	2013	1000 m3	6_3	FI_EX_M_2013_1000 m3_6_3
FI	EX_M	2013	1000 m3	6_3_1	FI_EX_M_2013_1000 m3_6_3_1
FI	EX_M	2013	1000 m3	6_4	FI_EX_M_2013_1000 m3_6_4
FI	EX_M	2013	1000 m3	6_4_1	FI_EX_M_2013_1000 m3_6_4_1
FI	EX_M	2013	1000 m3	6_4_2	FI_EX_M_2013_1000 m3_6_4_2
FI	EX_M	2013	1000 m3	6_4_3	FI_EX_M_2013_1000 m3_6_4_3
FI	EX_M	2013	1000 mt	7	FI_EX_M_2013_1000 mt_7
FI	EX_M	2013	1000 mt	7_1	FI_EX_M_2013_1000 mt_7_1
FI	EX_M	2013	1000 mt	7_2	FI_EX_M_2013_1000 mt_7_2
FI	EX_M	2013	1000 mt	7_3	FI_EX_M_2013_1000 mt_7_3
FI	EX_M	2013	1000 mt	7_3_1	FI_EX_M_2013_1000 mt_7_3_1
FI	EX_M	2013	1000 mt	7_3_2	FI_EX_M_2013_1000 mt_7_3_2
FI	EX_M	2013	1000 mt	7_3_3	FI_EX_M_2013_1000 mt_7_3_3
FI	EX_M	2013	1000 mt	7_3_4	FI_EX_M_2013_1000 mt_7_3_4
FI	EX_M	2013	1000 mt	7_4	FI_EX_M_2013_1000 mt_7_4
FI	EX_M	2013	1000 mt	8	FI_EX_M_2013_1000 mt_8
FI	EX_M	2013	1000 mt	8_1	FI_EX_M_2013_1000 mt_8_1
FI	EX_M	2013	1000 mt	8_2	FI_EX_M_2013_1000 mt_8_2
FI	EX_M	2013	1000 mt	9	FI_EX_M_2013_1000 mt_9
FI	EX_M	2013	1000 mt	10	FI_EX_M_2013_1000 mt_10
FI	EX_M	2013	1000 mt	10_1	FI_EX_M_2013_1000 mt_10_1
FI	EX_M	2013	1000 mt	10_1_1	FI_EX_M_2013_1000 mt_10_1_1
FI	EX_M	2013	1000 mt	10_1_2	FI_EX_M_2013_1000 mt_10_1_2
FI	EX_M	2013	1000 mt	10_1_3	FI_EX_M_2013_1000 mt_10_1_3
FI	EX_M	2013	1000 mt	10_1_4	FI_EX_M_2013_1000 mt_10_1_4
FI	EX_M	2013	1000 mt	10_2	FI_EX_M_2013_1000 mt_10_2
FI	EX_M	2013	1000 mt	10_3	FI_EX_M_2013_1000 mt_10_3
FI	EX_M	2013	1000 mt	10_3_1	FI_EX_M_2013_1000 mt_10_3_1
FI	EX_M	2013	1000 mt	10_3_2	FI_EX_M_2013_1000 mt_10_3_2
FI	EX_M	2013	1000 mt	10_3_3	FI_EX_M_2013_1000 mt_10_3_3
FI	EX_M	2013	1000 mt	10_3_4	FI_EX_M_2013_1000 mt_10_3_4
FI	EX_M	2013	1000 mt	10_4	FI_EX_M_2013_1000 mt_10_4
FI	EX_M	2013	1000 NAC	1	FI_EX_M_2013_1000 NAC_1
FI	EX_M	2013	1000 NAC	1_1	FI_EX_M_2013_1000 NAC_1_1
FI	EX_M	2013	1000 NAC	1_2	FI_EX_M_2013_1000 NAC_1_2
FI	EX_M	2013	1000 NAC	1_2_C	FI_EX_M_2013_1000 NAC_1_2_C
FI	EX_M	2013	1000 NAC	1_2_NC	FI_EX_M_2013_1000 NAC_1_2_NC
FI	EX_M	2013	1000 NAC	1_2_NC_T	FI_EX_M_2013_1000 NAC_1_2_NC_T
FI	EX_M	2013	1000 NAC	2	FI_EX_M_2013_1000 NAC_2
FI	EX_M	2013	1000 NAC	3	FI_EX_M_2013_1000 NAC_3
FI	EX_M	2013	1000 NAC	3_1	FI_EX_M_2013_1000 NAC_3_1
FI	EX_M	2013	1000 NAC	3_2	FI_EX_M_2013_1000 NAC_3_2
FI	EX_M	2013	1000 NAC	4	FI_EX_M_2013_1000 NAC_4
FI	EX_M	2013	1000 NAC	4_1	FI_EX_M_2013_1000 NAC_4_1
FI	EX_M	2013	1000 NAC	4_2	FI_EX_M_2013_1000 NAC_4_2
FI	EX_M	2013	1000 NAC	5	FI_EX_M_2013_1000 NAC_5
FI	EX_M	2013	1000 NAC	5_C	FI_EX_M_2013_1000 NAC_5_C
FI	EX_M	2013	1000 NAC	5_NC	FI_EX_M_2013_1000 NAC_5_NC
FI	EX_M	2013	1000 NAC	5_NC_T	FI_EX_M_2013_1000 NAC_5_NC_T
FI	EX_M	2013	1000 NAC	6	FI_EX_M_2013_1000 NAC_6
FI	EX_M	2013	1000 NAC	6_1	FI_EX_M_2013_1000 NAC_6_1
FI	EX_M	2013	1000 NAC	6_1_C	FI_EX_M_2013_1000 NAC_6_1_C
FI	EX_M	2013	1000 NAC	6_1_NC	FI_EX_M_2013_1000 NAC_6_1_NC
FI	EX_M	2013	1000 NAC	6_1_NC_T	FI_EX_M_2013_1000 NAC_6_1_NC_T
FI	EX_M	2013	1000 NAC	6_2	FI_EX_M_2013_1000 NAC_6_2
FI	EX_M	2013	1000 NAC	6_2_C	FI_EX_M_2013_1000 NAC_6_2_C
FI	EX_M	2013	1000 NAC	6_2_NC	FI_EX_M_2013_1000 NAC_6_2_NC
FI	EX_M	2013	1000 NAC	6_2_NC_T	FI_EX_M_2013_1000 NAC_6_2_NC_T
FI	EX_M	2013	1000 NAC	6_3	FI_EX_M_2013_1000 NAC_6_3
FI	EX_M	2013	1000 NAC	6_3_1	FI_EX_M_2013_1000 NAC_6_3_1
FI	EX_M	2013	1000 NAC	6_4	FI_EX_M_2013_1000 NAC_6_4
FI	EX_M	2013	1000 NAC	6_4_1	FI_EX_M_2013_1000 NAC_6_4_1
FI	EX_M	2013	1000 NAC	6_4_2	FI_EX_M_2013_1000 NAC_6_4_2
FI	EX_M	2013	1000 NAC	6_4_3	FI_EX_M_2013_1000 NAC_6_4_3
FI	EX_M	2013	1000 NAC	7	FI_EX_M_2013_1000 NAC_7
FI	EX_M	2013	1000 NAC	7_1	FI_EX_M_2013_1000 NAC_7_1
FI	EX_M	2013	1000 NAC	7_2	FI_EX_M_2013_1000 NAC_7_2
FI	EX_M	2013	1000 NAC	7_3	FI_EX_M_2013_1000 NAC_7_3
FI	EX_M	2013	1000 NAC	7_3_1	FI_EX_M_2013_1000 NAC_7_3_1
FI	EX_M	2013	1000 NAC	7_3_2	FI_EX_M_2013_1000 NAC_7_3_2
FI	EX_M	2013	1000 NAC	7_3_3	FI_EX_M_2013_1000 NAC_7_3_3
FI	EX_M	2013	1000 NAC	7_3_4	FI_EX_M_2013_1000 NAC_7_3_4
FI	EX_M	2013	1000 NAC	7_4	FI_EX_M_2013_1000 NAC_7_4
FI	EX_M	2013	1000 NAC	8	FI_EX_M_2013_1000 NAC_8
FI	EX_M	2013	1000 NAC	8_1	FI_EX_M_2013_1000 NAC_8_1
FI	EX_M	2013	1000 NAC	8_2	FI_EX_M_2013_1000 NAC_8_2
FI	EX_M	2013	1000 NAC	9	FI_EX_M_2013_1000 NAC_9
FI	EX_M	2013	1000 NAC	10	FI_EX_M_2013_1000 NAC_10
FI	EX_M	2013	1000 NAC	10_1	FI_EX_M_2013_1000 NAC_10_1
FI	EX_M	2013	1000 NAC	10_1_1	FI_EX_M_2013_1000 NAC_10_1_1
FI	EX_M	2013	1000 NAC	10_1_2	FI_EX_M_2013_1000 NAC_10_1_2
FI	EX_M	2013	1000 NAC	10_1_3	FI_EX_M_2013_1000 NAC_10_1_3
FI	EX_M	2013	1000 NAC	10_1_4	FI_EX_M_2013_1000 NAC_10_1_4
FI	EX_M	2013	1000 NAC	10_2	FI_EX_M_2013_1000 NAC_10_2
FI	EX_M	2013	1000 NAC	10_3	FI_EX_M_2013_1000 NAC_10_3
FI	EX_M	2013	1000 NAC	10_3_1	FI_EX_M_2013_1000 NAC_10_3_1
FI	EX_M	2013	1000 NAC	10_3_2	FI_EX_M_2013_1000 NAC_10_3_2
FI	EX_M	2013	1000 NAC	10_3_3	FI_EX_M_2013_1000 NAC_10_3_3
FI	EX_M	2013	1000 NAC	10_3_4	FI_EX_M_2013_1000 NAC_10_3_4
FI	EX_M	2013	1000 NAC	10_4	FI_EX_M_2013_1000 NAC_10_4
FI	EX_X	2013	1000 m3	1	FI_EX_X_2013_1000 m3_1
FI	EX_X	2013	1000 m3	1_1	FI_EX_X_2013_1000 m3_1_1
FI	EX_X	2013	1000 m3	1_2	FI_EX_X_2013_1000 m3_1_2
FI	EX_X	2013	1000 m3	1_2_C	FI_EX_X_2013_1000 m3_1_2_C
FI	EX_X	2013	1000 m3	1_2_NC	FI_EX_X_2013_1000 m3_1_2_NC
FI	EX_X	2013	1000 m3	1_2_NC_T	FI_EX_X_2013_1000 m3_1_2_NC_T
FI	EX_X	2013	1000 mt	2	FI_EX_X_2013_1000 mt_2
FI	EX_X	2013	1000 m3	3	FI_EX_X_2013_1000 m3_3
FI	EX_X	2013	1000 m3	3_1	FI_EX_X_2013_1000 m3_3_1
FI	EX_X	2013	1000 m3	3_2	FI_EX_X_2013_1000 m3_3_2
FI	EX_X	2013	1000 mt	4	FI_EX_X_2013_1000 mt_4
FI	EX_X	2013	1000 mt	4_1	FI_EX_X_2013_1000 mt_4_1
FI	EX_X	2013	1000 mt	4_2	FI_EX_X_2013_1000 mt_4_2
FI	EX_X	2013	1000 m3	5	FI_EX_X_2013_1000 m3_5
FI	EX_X	2013	1000 m3	5_C	FI_EX_X_2013_1000 m3_5_C
FI	EX_X	2013	1000 m3	5_NC	FI_EX_X_2013_1000 m3_5_NC
FI	EX_X	2013	1000 m3	5_NC_T	FI_EX_X_2013_1000 m3_5_NC_T
FI	EX_X	2013	1000 m3	6	FI_EX_X_2013_1000 m3_6
FI	EX_X	2013	1000 m3	6_1	FI_EX_X_2013_1000 m3_6_1
FI	EX_X	2013	1000 m3	6_1_C	FI_EX_X_2013_1000 m3_6_1_C
FI	EX_X	2013	1000 m3	6_1_NC	FI_EX_X_2013_1000 m3_6_1_NC
FI	EX_X	2013	1000 m3	6_1_NC_T	FI_EX_X_2013_1000 m3_6_1_NC_T
FI	EX_X	2013	1000 m3	6_2	FI_EX_X_2013_1000 m3_6_2
FI	EX_X	2013	1000 m3	6_2_C	FI_EX_X_2013_1000 m3_6_2_C
FI	EX_X	2013	1000 m3	6_2_NC	FI_EX_X_2013_1000 m3_6_2_NC
FI	EX_X	2013	1000 m3	6_2_NC_T	FI_EX_X_2013_1000 m3_6_2_NC_T
FI	EX_X	2013	1000 m3	6_3	FI_EX_X_2013_1000 m3_6_3
FI	EX_X	2013	1000 m3	6_3_1	FI_EX_X_2013_1000 m3_6_3_1
FI	EX_X	2013	1000 m3	6_4	FI_EX_X_2013_1000 m3_6_4
FI	EX_X	2013	1000 m3	6_4_1	FI_EX_X_2013_1000 m3_6_4_1
FI	EX_X	2013	1000 m3	6_4_2	FI_EX_X_2013_1000 m3_6_4_2
FI	EX_X	2013	1000 m3	6_4_3	FI_EX_X_2013_1000 m3_6_4_3
FI	EX_X	2013	1000 mt	7	FI_EX_X_2013_1000 mt_7
FI	EX_X	2013	1000 mt	7_1	FI_EX_X_2013_1000 mt_7_1
FI	EX_X	2013	1000 mt	7_2	FI_EX_X_2013_1000 mt_7_2
FI	EX_X	2013	1000 mt	7_3	FI_EX_X_2013_1000 mt_7_3
FI	EX_X	2013	1000 mt	7_3_1	FI_EX_X_2013_1000 mt_7_3_1
FI	EX_X	2013	1000 mt	7_3_2	FI_EX_X_2013_1000 mt_7_3_2
FI	EX_X	2013	1000 mt	7_3_3	FI_EX_X_2013_1000 mt_7_3_3
FI	EX_X	2013	1000 mt	7_3_4	FI_EX_X_2013_1000 mt_7_3_4
FI	EX_X	2013	1000 mt	7_4	FI_EX_X_2013_1000 mt_7_4
FI	EX_X	2013	1000 mt	8	FI_EX_X_2013_1000 mt_8
FI	EX_X	2013	1000 mt	8_1	FI_EX_X_2013_1000 mt_8_1
FI	EX_X	2013	1000 mt	8_2	FI_EX_X_2013_1000 mt_8_2
FI	EX_X	2013	1000 mt	9	FI_EX_X_2013_1000 mt_9
FI	EX_X	2013	1000 mt	10	FI_EX_X_2013_1000 mt_10
FI	EX_X	2013	1000 mt	10_1	FI_EX_X_2013_1000 mt_10_1
FI	EX_X	2013	1000 mt	10_1_1	FI_EX_X_2013_1000 mt_10_1_1
FI	EX_X	2013	1000 mt	10_1_2	FI_EX_X_2013_1000 mt_10_1_2
FI	EX_X	2013	1000 mt	10_1_3	FI_EX_X_2013_1000 mt_10_1_3
FI	EX_X	2013	1000 mt	10_1_4	FI_EX_X_2013_1000 mt_10_1_4
FI	EX_X	2013	1000 mt	10_2	FI_EX_X_2013_1000 mt_10_2
FI	EX_X	2013	1000 mt	10_3	FI_EX_X_2013_1000 mt_10_3
FI	EX_X	2013	1000 mt	10_3_1	FI_EX_X_2013_1000 mt_10_3_1
FI	EX_X	2013	1000 mt	10_3_2	FI_EX_X_2013_1000 mt_10_3_2
FI	EX_X	2013	1000 mt	10_3_3	FI_EX_X_2013_1000 mt_10_3_3
FI	EX_X	2013	1000 mt	10_3_4	FI_EX_X_2013_1000 mt_10_3_4
FI	EX_X	2013	1000 mt	10_4	FI_EX_X_2013_1000 mt_10_4
FI	EX_X	2013	1000 NAC	1	FI_EX_X_2013_1000 NAC_1
FI	EX_X	2013	1000 NAC	1_1	FI_EX_X_2013_1000 NAC_1_1
FI	EX_X	2013	1000 NAC	1_2	FI_EX_X_2013_1000 NAC_1_2
FI	EX_X	2013	1000 NAC	1_2_C	FI_EX_X_2013_1000 NAC_1_2_C
FI	EX_X	2013	1000 NAC	1_2_NC	FI_EX_X_2013_1000 NAC_1_2_NC
FI	EX_X	2013	1000 NAC	1_2_NC_T	FI_EX_X_2013_1000 NAC_1_2_NC_T
FI	EX_X	2013	1000 NAC	2	FI_EX_X_2013_1000 NAC_2
FI	EX_X	2013	1000 NAC	3	FI_EX_X_2013_1000 NAC_3
FI	EX_X	2013	1000 NAC	3_1	FI_EX_X_2013_1000 NAC_3_1
FI	EX_X	2013	1000 NAC	3_2	FI_EX_X_2013_1000 NAC_3_2
FI	EX_X	2013	1000 NAC	4	FI_EX_X_2013_1000 NAC_4
FI	EX_X	2013	1000 NAC	4_1	FI_EX_X_2013_1000 NAC_4_1
FI	EX_X	2013	1000 NAC	4_2	FI_EX_X_2013_1000 NAC_4_2
FI	EX_X	2013	1000 NAC	5	FI_EX_X_2013_1000 NAC_5
FI	EX_X	2013	1000 NAC	5_C	FI_EX_X_2013_1000 NAC_5_C
FI	EX_X	2013	1000 NAC	5_NC	FI_EX_X_2013_1000 NAC_5_NC
FI	EX_X	2013	1000 NAC	5_NC_T	FI_EX_X_2013_1000 NAC_5_NC_T
FI	EX_X	2013	1000 NAC	6	FI_EX_X_2013_1000 NAC_6
FI	EX_X	2013	1000 NAC	6_1	FI_EX_X_2013_1000 NAC_6_1
FI	EX_X	2013	1000 NAC	6_1_C	FI_EX_X_2013_1000 NAC_6_1_C
FI	EX_X	2013	1000 NAC	6_1_NC	FI_EX_X_2013_1000 NAC_6_1_NC
FI	EX_X	2013	1000 NAC	6_1_NC_T	FI_EX_X_2013_1000 NAC_6_1_NC_T
FI	EX_X	2013	1000 NAC	6_2	FI_EX_X_2013_1000 NAC_6_2
FI	EX_X	2013	1000 NAC	6_2_C	FI_EX_X_2013_1000 NAC_6_2_C
FI	EX_X	2013	1000 NAC	6_2_NC	FI_EX_X_2013_1000 NAC_6_2_NC
FI	EX_X	2013	1000 NAC	6_2_NC_T	FI_EX_X_2013_1000 NAC_6_2_NC_T
FI	EX_X	2013	1000 NAC	6_3	FI_EX_X_2013_1000 NAC_6_3
FI	EX_X	2013	1000 NAC	6_3_1	FI_EX_X_2013_1000 NAC_6_3_1
FI	EX_X	2013	1000 NAC	6_4	FI_EX_X_2013_1000 NAC_6_4
FI	EX_X	2013	1000 NAC	6_4_1	FI_EX_X_2013_1000 NAC_6_4_1
FI	EX_X	2013	1000 NAC	6_4_2	FI_EX_X_2013_1000 NAC_6_4_2
FI	EX_X	2013	1000 NAC	6_4_3	FI_EX_X_2013_1000 NAC_6_4_3
FI	EX_X	2013	1000 NAC	7	FI_EX_X_2013_1000 NAC_7
FI	EX_X	2013	1000 NAC	7_1	FI_EX_X_2013_1000 NAC_7_1
FI	EX_X	2013	1000 NAC	7_2	FI_EX_X_2013_1000 NAC_7_2
FI	EX_X	2013	1000 NAC	7_3	FI_EX_X_2013_1000 NAC_7_3
FI	EX_X	2013	1000 NAC	7_3_1	FI_EX_X_2013_1000 NAC_7_3_1
FI	EX_X	2013	1000 NAC	7_3_2	FI_EX_X_2013_1000 NAC_7_3_2
FI	EX_X	2013	1000 NAC	7_3_3	FI_EX_X_2013_1000 NAC_7_3_3
FI	EX_X	2013	1000 NAC	7_3_4	FI_EX_X_2013_1000 NAC_7_3_4
FI	EX_X	2013	1000 NAC	7_4	FI_EX_X_2013_1000 NAC_7_4
FI	EX_X	2013	1000 NAC	8	FI_EX_X_2013_1000 NAC_8
FI	EX_X	2013	1000 NAC	8_1	FI_EX_X_2013_1000 NAC_8_1
FI	EX_X	2013	1000 NAC	8_2	FI_EX_X_2013_1000 NAC_8_2
FI	EX_X	2013	1000 NAC	9	FI_EX_X_2013_1000 NAC_9
FI	EX_X	2013	1000 NAC	10	FI_EX_X_2013_1000 NAC_10
FI	EX_X	2013	1000 NAC	10_1	FI_EX_X_2013_1000 NAC_10_1
FI	EX_X	2013	1000 NAC	10_1_1	FI_EX_X_2013_1000 NAC_10_1_1
FI	EX_X	2013	1000 NAC	10_1_2	FI_EX_X_2013_1000 NAC_10_1_2
FI	EX_X	2013	1000 NAC	10_1_3	FI_EX_X_2013_1000 NAC_10_1_3
FI	EX_X	2013	1000 NAC	10_1_4	FI_EX_X_2013_1000 NAC_10_1_4
FI	EX_X	2013	1000 NAC	10_2	FI_EX_X_2013_1000 NAC_10_2
FI	EX_X	2013	1000 NAC	10_3	FI_EX_X_2013_1000 NAC_10_3
FI	EX_X	2013	1000 NAC	10_3_1	FI_EX_X_2013_1000 NAC_10_3_1
FI	EX_X	2013	1000 NAC	10_3_2	FI_EX_X_2013_1000 NAC_10_3_2
FI	EX_X	2013	1000 NAC	10_3_3	FI_EX_X_2013_1000 NAC_10_3_3
FI	EX_X	2013	1000 NAC	10_3_4	FI_EX_X_2013_1000 NAC_10_3_4
FI	EX_X	2013	1000 NAC	10_4	FI_EX_X_2013_1000 NAC_10_4
FI	P	2013	1000 m3	EU2_1	FI_P_2013_1000 m3_EU2_1
FI	P	2013	1000 m3	EU2_1_C	FI_P_2013_1000 m3_EU2_1_C
FI	P	2013	1000 m3	EU2_1_NC	FI_P_2013_1000 m3_EU2_1_NC
FI	P	2013	1000 m3	EU2_1_1	FI_P_2013_1000 m3_EU2_1_1
FI	P	2013	1000 m3	EU2_1_1_C	FI_P_2013_1000 m3_EU2_1_1_C
FI	P	2013	1000 m3	EU2_1_1_NC	FI_P_2013_1000 m3_EU2_1_1_NC
FI	P	2013	1000 m3	EU2_1_2	FI_P_2013_1000 m3_EU2_1_2
FI	P	2013	1000 m3	EU2_1_2_C	FI_P_2013_1000 m3_EU2_1_2_C
FI	P	2013	1000 m3	EU2_1_2_NC	FI_P_2013_1000 m3_EU2_1_2_NC
FI	P	2013	1000 m3	EU2_1_3	FI_P_2013_1000 m3_EU2_1_3
FI	P	2013	1000 m3	EU2_1_3_C	FI_P_2013_1000 m3_EU2_1_3_C
FI	P	2013	1000 m3	EU2_1_3_NC	FI_P_2013_1000 m3_EU2_1_3_NC
FI	P.OB	2013	1000 m3	1	FI_P.OB_2013_1000 m3_1
FI	P.OB	2013	1000 m3	1_C	FI_P.OB_2013_1000 m3_1_C
FI	P.OB	2013	1000 m3	1_NC	FI_P.OB_2013_1000 m3_1_NC
FI	P.OB	2013	1000 m3	1_1	FI_P.OB_2013_1000 m3_1_1
FI	P.OB	2013	1000 m3	1_1_C	FI_P.OB_2013_1000 m3_1_1_C
FI	P.OB	2013	1000 m3	1_1_NC	FI_P.OB_2013_1000 m3_1_1_NC
FI	P.OB	2013	1000 m3	1_2	FI_P.OB_2013_1000 m3_1_2
FI	P.OB	2013	1000 m3	1_2_C	FI_P.OB_2013_1000 m3_1_2_C
FI	P.OB	2013	1000 m3	1_2_NC	FI_P.OB_2013_1000 m3_1_2_NC
FI	P.OB	2013	1000 m3	1_2_1	FI_P.OB_2013_1000 m3_1_2_1
FI	P.OB	2013	1000 m3	1_2_1_C	FI_P.OB_2013_1000 m3_1_2_1_C
FI	P.OB	2013	1000 m3	1_2_1_NC	FI_P.OB_2013_1000 m3_1_2_1_NC
FI	P.OB	2013	1000 m3	1_2_2	FI_P.OB_2013_1000 m3_1_2_2
FI	P.OB	2013	1000 m3	1_2_2_C	FI_P.OB_2013_1000 m3_1_2_2_C
FI	P.OB	2013	1000 m3	1_2_2_NC	FI_P.OB_2013_1000 m3_1_2_2_NC
FI	P.OB	2013	1000 m3	1_2_3	FI_P.OB_2013_1000 m3_1_2_3
FI	P.OB	2013	1000 m3	1_2_3_C	FI_P.OB_2013_1000 m3_1_2_3_C
FI	P.OB	2013	1000 m3	1_2_3_NC	FI_P.OB_2013_1000 m3_1_2_3_NC
FI	P	2012	1000 m3	1	FI_P_2012_1000 m3_1
FI	P	2012	1000 m3	1_C	FI_P_2012_1000 m3_1_C
FI	P	2012	1000 m3	1_NC	FI_P_2012_1000 m3_1_NC
FI	P	2012	1000 m3	1_1	FI_P_2012_1000 m3_1_1
FI	P	2012	1000 m3	1_1_C	FI_P_2012_1000 m3_1_1_C
FI	P	2012	1000 m3	1_1_NC	FI_P_2012_1000 m3_1_1_NC
FI	P	2012	1000 m3	1_2	FI_P_2012_1000 m3_1_2
FI	P	2012	1000 m3	1_2_C	FI_P_2012_1000 m3_1_2_C
FI	P	2012	1000 m3	1_2_NC	FI_P_2012_1000 m3_1_2_NC
FI	P	2012	1000 m3	1_2_1	FI_P_2012_1000 m3_1_2_1
FI	P	2012	1000 m3	1_2_1_C	FI_P_2012_1000 m3_1_2_1_C
FI	P	2012	1000 m3	1_2_1_NC	FI_P_2012_1000 m3_1_2_1_NC
FI	P	2012	1000 m3	1_2_2	FI_P_2012_1000 m3_1_2_2
FI	P	2012	1000 m3	1_2_2_C	FI_P_2012_1000 m3_1_2_2_C
FI	P	2012	1000 m3	1_2_2_NC	FI_P_2012_1000 m3_1_2_2_NC
FI	P	2012	1000 m3	1_2_3	FI_P_2012_1000 m3_1_2_3
FI	P	2012	1000 m3	1_2_3_C	FI_P_2012_1000 m3_1_2_3_C
FI	P	2012	1000 m3	1_2_3_NC	FI_P_2012_1000 m3_1_2_3_NC
FI	P	2012	1000 mt	2	FI_P_2012_1000 mt_2
FI	P	2012	1000 m3	3	FI_P_2012_1000 m3_3
FI	P	2012	1000 m3	3_1	FI_P_2012_1000 m3_3_1
FI	P	2012	1000 m3	3_2	FI_P_2012_1000 m3_3_2
FI	P	2012	1000 mt	4	FI_P_2012_1000 mt_4
FI	P	2012	1000 mt	4_1	FI_P_2012_1000 mt_4_1
FI	P	2012	1000 mt	4_2	FI_P_2012_1000 mt_4_2
FI	P	2012	1000 m3	5	FI_P_2012_1000 m3_5
FI	P	2012	1000 m3	5_C	FI_P_2012_1000 m3_5_C
FI	P	2012	1000 m3	5_NC	FI_P_2012_1000 m3_5_NC
FI	P	2012	1000 m3	5_NC_T	FI_P_2012_1000 m3_5_NC_T
FI	P	2012	1000 m3	6	FI_P_2012_1000 m3_6
FI	P	2012	1000 m3	6_1	FI_P_2012_1000 m3_6_1
FI	P	2012	1000 m3	6_1_C	FI_P_2012_1000 m3_6_1_C
FI	P	2012	1000 m3	6_1_NC	FI_P_2012_1000 m3_6_1_NC
FI	P	2012	1000 m3	6_1_NC_T	FI_P_2012_1000 m3_6_1_NC_T
FI	P	2012	1000 m3	6_2	FI_P_2012_1000 m3_6_2
FI	P	2012	1000 m3	6_2_C	FI_P_2012_1000 m3_6_2_C
FI	P	2012	1000 m3	6_2_NC	FI_P_2012_1000 m3_6_2_NC
FI	P	2012	1000 m3	6_2_NC_T	FI_P_2012_1000 m3_6_2_NC_T
FI	P	2012	1000 m3	6_3	FI_P_2012_1000 m3_6_3
FI	P	2012	1000 m3	6_3_1	FI_P_2012_1000 m3_6_3_1
FI	P	2012	1000 m3	6_4	FI_P_2012_1000 m3_6_4
FI	P	2012	1000 m3	6_4_1	FI_P_2012_1000 m3_6_4_1
FI	P	2012	1000 m3	6_4_2	FI_P_2012_1000 m3_6_4_2
FI	P	2012	1000 m3	6_4_3	FI_P_2012_1000 m3_6_4_3
FI	P	2012	1000 mt	7	FI_P_2012_1000 mt_7
FI	P	2012	1000 mt	7_1	FI_P_2012_1000 mt_7_1
FI	P	2012	1000 mt	7_2	FI_P_2012_1000 mt_7_2
FI	P	2012	1000 mt	7_3	FI_P_2012_1000 mt_7_3
FI	P	2012	1000 mt	7_3_1	FI_P_2012_1000 mt_7_3_1
FI	P	2012	1000 mt	7_3_2	FI_P_2012_1000 mt_7_3_2
FI	P	2012	1000 mt	7_3_3	FI_P_2012_1000 mt_7_3_3
FI	P	2012	1000 mt	7_3_4	FI_P_2012_1000 mt_7_3_4
FI	P	2012	1000 mt	7_4	FI_P_2012_1000 mt_7_4
FI	P	2012	1000 mt	8	FI_P_2012_1000 mt_8
FI	P	2012	1000 mt	8_1	FI_P_2012_1000 mt_8_1
FI	P	2012	1000 mt	8_2	FI_P_2012_1000 mt_8_2
FI	P	2012	1000 mt	9	FI_P_2012_1000 mt_9
FI	P	2012	1000 mt	10	FI_P_2012_1000 mt_10
FI	P	2012	1000 mt	10_1	FI_P_2012_1000 mt_10_1
FI	P	2012	1000 mt	10_1_1	FI_P_2012_1000 mt_10_1_1
FI	P	2012	1000 mt	10_1_2	FI_P_2012_1000 mt_10_1_2
FI	P	2012	1000 mt	10_1_3	FI_P_2012_1000 mt_10_1_3
FI	P	2012	1000 mt	10_1_4	FI_P_2012_1000 mt_10_1_4
FI	P	2012	1000 mt	10_2	FI_P_2012_1000 mt_10_2
FI	P	2012	1000 mt	10_3	FI_P_2012_1000 mt_10_3
FI	P	2012	1000 mt	10_3_1	FI_P_2012_1000 mt_10_3_1
FI	P	2012	1000 mt	10_3_2	FI_P_2012_1000 mt_10_3_2
FI	P	2012	1000 mt	10_3_3	FI_P_2012_1000 mt_10_3_3
FI	P	2012	1000 mt	10_3_4	FI_P_2012_1000 mt_10_3_4
FI	P	2012	1000 mt	10_4	FI_P_2012_1000 mt_10_4
FI	M	2012	1000 m3	1	FI_M_2012_1000 m3_1
FI	M	2012	1000 m3	1_1	FI_M_2012_1000 m3_1_1
FI	M	2012	1000 m3	1_2	FI_M_2012_1000 m3_1_2
FI	M	2012	1000 m3	1_2_C	FI_M_2012_1000 m3_1_2_C
FI	M	2012	1000 m3	1_2_NC	FI_M_2012_1000 m3_1_2_NC
FI	M	2012	1000 m3	1_2_NC_T	FI_M_2012_1000 m3_1_2_NC_T
FI	M	2012	1000 mt	2	FI_M_2012_1000 mt_2
FI	M	2012	1000 m3	3	FI_M_2012_1000 m3_3
FI	M	2012	1000 m3	3_1	FI_M_2012_1000 m3_3_1
FI	M	2012	1000 m3	3_2	FI_M_2012_1000 m3_3_2
FI	M	2012	1000 mt	4	FI_M_2012_1000 mt_4
FI	M	2012	1000 mt	4_1	FI_M_2012_1000 mt_4_1
FI	M	2012	1000 mt	4_2	FI_M_2012_1000 mt_4_2
FI	M	2012	1000 m3	5	FI_M_2012_1000 m3_5
FI	M	2012	1000 m3	5_C	FI_M_2012_1000 m3_5_C
FI	M	2012	1000 m3	5_NC	FI_M_2012_1000 m3_5_NC
FI	M	2012	1000 m3	5_NC_T	FI_M_2012_1000 m3_5_NC_T
FI	M	2012	1000 m3	6	FI_M_2012_1000 m3_6
FI	M	2012	1000 m3	6_1	FI_M_2012_1000 m3_6_1
FI	M	2012	1000 m3	6_1_C	FI_M_2012_1000 m3_6_1_C
FI	M	2012	1000 m3	6_1_NC	FI_M_2012_1000 m3_6_1_NC
FI	M	2012	1000 m3	6_1_NC_T	FI_M_2012_1000 m3_6_1_NC_T
FI	M	2012	1000 m3	6_2	FI_M_2012_1000 m3_6_2
FI	M	2012	1000 m3	6_2_C	FI_M_2012_1000 m3_6_2_C
FI	M	2012	1000 m3	6_2_NC	FI_M_2012_1000 m3_6_2_NC
FI	M	2012	1000 m3	6_2_NC_T	FI_M_2012_1000 m3_6_2_NC_T
FI	M	2012	1000 m3	6_3	FI_M_2012_1000 m3_6_3
FI	M	2012	1000 m3	6_3_1	FI_M_2012_1000 m3_6_3_1
FI	M	2012	1000 m3	6_4	FI_M_2012_1000 m3_6_4
FI	M	2012	1000 m3	6_4_1	FI_M_2012_1000 m3_6_4_1
FI	M	2012	1000 m3	6_4_2	FI_M_2012_1000 m3_6_4_2
FI	M	2012	1000 m3	6_4_3	FI_M_2012_1000 m3_6_4_3
FI	M	2012	1000 mt	7	FI_M_2012_1000 mt_7
FI	M	2012	1000 mt	7_1	FI_M_2012_1000 mt_7_1
FI	M	2012	1000 mt	7_2	FI_M_2012_1000 mt_7_2
FI	M	2012	1000 mt	7_3	FI_M_2012_1000 mt_7_3
FI	M	2012	1000 mt	7_3_1	FI_M_2012_1000 mt_7_3_1
FI	M	2012	1000 mt	7_3_2	FI_M_2012_1000 mt_7_3_2
FI	M	2012	1000 mt	7_3_3	FI_M_2012_1000 mt_7_3_3
FI	M	2012	1000 mt	7_3_4	FI_M_2012_1000 mt_7_3_4
FI	M	2012	1000 mt	7_4	FI_M_2012_1000 mt_7_4
FI	M	2012	1000 mt	8	FI_M_2012_1000 mt_8
FI	M	2012	1000 mt	8_1	FI_M_2012_1000 mt_8_1
FI	M	2012	1000 mt	8_2	FI_M_2012_1000 mt_8_2
FI	M	2012	1000 mt	9	FI_M_2012_1000 mt_9
FI	M	2012	1000 mt	10	FI_M_2012_1000 mt_10
FI	M	2012	1000 mt	10_1	FI_M_2012_1000 mt_10_1
FI	M	2012	1000 mt	10_1_1	FI_M_2012_1000 mt_10_1_1
FI	M	2012	1000 mt	10_1_2	FI_M_2012_1000 mt_10_1_2
FI	M	2012	1000 mt	10_1_3	FI_M_2012_1000 mt_10_1_3
FI	M	2012	1000 mt	10_1_4	FI_M_2012_1000 mt_10_1_4
FI	M	2012	1000 mt	10_2	FI_M_2012_1000 mt_10_2
FI	M	2012	1000 mt	10_3	FI_M_2012_1000 mt_10_3
FI	M	2012	1000 mt	10_3_1	FI_M_2012_1000 mt_10_3_1
FI	M	2012	1000 mt	10_3_2	FI_M_2012_1000 mt_10_3_2
FI	M	2012	1000 mt	10_3_3	FI_M_2012_1000 mt_10_3_3
FI	M	2012	1000 mt	10_3_4	FI_M_2012_1000 mt_10_3_4
FI	M	2012	1000 mt	10_4	FI_M_2012_1000 mt_10_4
FI	M	2012	1000 NAC	1	FI_M_2012_1000 NAC_1
FI	M	2012	1000 NAC	1_1	FI_M_2012_1000 NAC_1_1
FI	M	2012	1000 NAC	1_2	FI_M_2012_1000 NAC_1_2
FI	M	2012	1000 NAC	1_2_C	FI_M_2012_1000 NAC_1_2_C
FI	M	2012	1000 NAC	1_2_NC	FI_M_2012_1000 NAC_1_2_NC
FI	M	2012	1000 NAC	1_2_NC_T	FI_M_2012_1000 NAC_1_2_NC_T
FI	M	2012	1000 NAC	2	FI_M_2012_1000 NAC_2
FI	M	2012	1000 NAC	3	FI_M_2012_1000 NAC_3
FI	M	2012	1000 NAC	3_1	FI_M_2012_1000 NAC_3_1
FI	M	2012	1000 NAC	3_2	FI_M_2012_1000 NAC_3_2
FI	M	2012	1000 NAC	4	FI_M_2012_1000 NAC_4
FI	M	2012	1000 NAC	4_1	FI_M_2012_1000 NAC_4_1
FI	M	2012	1000 NAC	4_2	FI_M_2012_1000 NAC_4_2
FI	M	2012	1000 NAC	5	FI_M_2012_1000 NAC_5
FI	M	2012	1000 NAC	5_C	FI_M_2012_1000 NAC_5_C
FI	M	2012	1000 NAC	5_NC	FI_M_2012_1000 NAC_5_NC
FI	M	2012	1000 NAC	5_NC_T	FI_M_2012_1000 NAC_5_NC_T
FI	M	2012	1000 NAC	6	FI_M_2012_1000 NAC_6
FI	M	2012	1000 NAC	6_1	FI_M_2012_1000 NAC_6_1
FI	M	2012	1000 NAC	6_1_C	FI_M_2012_1000 NAC_6_1_C
FI	M	2012	1000 NAC	6_1_NC	FI_M_2012_1000 NAC_6_1_NC
FI	M	2012	1000 NAC	6_1_NC_T	FI_M_2012_1000 NAC_6_1_NC_T
FI	M	2012	1000 NAC	6_2	FI_M_2012_1000 NAC_6_2
FI	M	2012	1000 NAC	6_2_C	FI_M_2012_1000 NAC_6_2_C
FI	M	2012	1000 NAC	6_2_NC	FI_M_2012_1000 NAC_6_2_NC
FI	M	2012	1000 NAC	6_2_NC_T	FI_M_2012_1000 NAC_6_2_NC_T
FI	M	2012	1000 NAC	6_3	FI_M_2012_1000 NAC_6_3
FI	M	2012	1000 NAC	6_3_1	FI_M_2012_1000 NAC_6_3_1
FI	M	2012	1000 NAC	6_4	FI_M_2012_1000 NAC_6_4
FI	M	2012	1000 NAC	6_4_1	FI_M_2012_1000 NAC_6_4_1
FI	M	2012	1000 NAC	6_4_2	FI_M_2012_1000 NAC_6_4_2
FI	M	2012	1000 NAC	6_4_3	FI_M_2012_1000 NAC_6_4_3
FI	M	2012	1000 NAC	7	FI_M_2012_1000 NAC_7
FI	M	2012	1000 NAC	7_1	FI_M_2012_1000 NAC_7_1
FI	M	2012	1000 NAC	7_2	FI_M_2012_1000 NAC_7_2
FI	M	2012	1000 NAC	7_3	FI_M_2012_1000 NAC_7_3
FI	M	2012	1000 NAC	7_3_1	FI_M_2012_1000 NAC_7_3_1
FI	M	2012	1000 NAC	7_3_2	FI_M_2012_1000 NAC_7_3_2
FI	M	2012	1000 NAC	7_3_3	FI_M_2012_1000 NAC_7_3_3
FI	M	2012	1000 NAC	7_3_4	FI_M_2012_1000 NAC_7_3_4
FI	M	2012	1000 NAC	7_4	FI_M_2012_1000 NAC_7_4
FI	M	2012	1000 NAC	8	FI_M_2012_1000 NAC_8
FI	M	2012	1000 NAC	8_1	FI_M_2012_1000 NAC_8_1
FI	M	2012	1000 NAC	8_2	FI_M_2012_1000 NAC_8_2
FI	M	2012	1000 NAC	9	FI_M_2012_1000 NAC_9
FI	M	2012	1000 NAC	10	FI_M_2012_1000 NAC_10
FI	M	2012	1000 NAC	10_1	FI_M_2012_1000 NAC_10_1
FI	M	2012	1000 NAC	10_1_1	FI_M_2012_1000 NAC_10_1_1
FI	M	2012	1000 NAC	10_1_2	FI_M_2012_1000 NAC_10_1_2
FI	M	2012	1000 NAC	10_1_3	FI_M_2012_1000 NAC_10_1_3
FI	M	2012	1000 NAC	10_1_4	FI_M_2012_1000 NAC_10_1_4
FI	M	2012	1000 NAC	10_2	FI_M_2012_1000 NAC_10_2
FI	M	2012	1000 NAC	10_3	FI_M_2012_1000 NAC_10_3
FI	M	2012	1000 NAC	10_3_1	FI_M_2012_1000 NAC_10_3_1
FI	M	2012	1000 NAC	10_3_2	FI_M_2012_1000 NAC_10_3_2
FI	M	2012	1000 NAC	10_3_3	FI_M_2012_1000 NAC_10_3_3
FI	M	2012	1000 NAC	10_3_4	FI_M_2012_1000 NAC_10_3_4
FI	M	2012	1000 NAC	10_4	FI_M_2012_1000 NAC_10_4
FI	X	2012	1000 m3	1	FI_X_2012_1000 m3_1
FI	X	2012	1000 m3	1_1	FI_X_2012_1000 m3_1_1
FI	X	2012	1000 m3	1_2	FI_X_2012_1000 m3_1_2
FI	X	2012	1000 m3	1_2_C	FI_X_2012_1000 m3_1_2_C
FI	X	2012	1000 m3	1_2_NC	FI_X_2012_1000 m3_1_2_NC
FI	X	2012	1000 m3	1_2_NC_T	FI_X_2012_1000 m3_1_2_NC_T
FI	X	2012	1000 mt	2	FI_X_2012_1000 mt_2
FI	X	2012	1000 m3	3	FI_X_2012_1000 m3_3
FI	X	2012	1000 m3	3_1	FI_X_2012_1000 m3_3_1
FI	X	2012	1000 m3	3_2	FI_X_2012_1000 m3_3_2
FI	X	2012	1000 mt	4	FI_X_2012_1000 mt_4
FI	X	2012	1000 mt	4_1	FI_X_2012_1000 mt_4_1
FI	X	2012	1000 mt	4_2	FI_X_2012_1000 mt_4_2
FI	X	2012	1000 m3	5	FI_X_2012_1000 m3_5
FI	X	2012	1000 m3	5_C	FI_X_2012_1000 m3_5_C
FI	X	2012	1000 m3	5_NC	FI_X_2012_1000 m3_5_NC
FI	X	2012	1000 m3	5_NC_T	FI_X_2012_1000 m3_5_NC_T
FI	X	2012	1000 m3	6	FI_X_2012_1000 m3_6
FI	X	2012	1000 m3	6_1	FI_X_2012_1000 m3_6_1
FI	X	2012	1000 m3	6_1_C	FI_X_2012_1000 m3_6_1_C
FI	X	2012	1000 m3	6_1_NC	FI_X_2012_1000 m3_6_1_NC
FI	X	2012	1000 m3	6_1_NC_T	FI_X_2012_1000 m3_6_1_NC_T
FI	X	2012	1000 m3	6_2	FI_X_2012_1000 m3_6_2
FI	X	2012	1000 m3	6_2_C	FI_X_2012_1000 m3_6_2_C
FI	X	2012	1000 m3	6_2_NC	FI_X_2012_1000 m3_6_2_NC
FI	X	2012	1000 m3	6_2_NC_T	FI_X_2012_1000 m3_6_2_NC_T
FI	X	2012	1000 m3	6_3	FI_X_2012_1000 m3_6_3
FI	X	2012	1000 m3	6_3_1	FI_X_2012_1000 m3_6_3_1
FI	X	2012	1000 m3	6_4	FI_X_2012_1000 m3_6_4
FI	X	2012	1000 m3	6_4_1	FI_X_2012_1000 m3_6_4_1
FI	X	2012	1000 m3	6_4_2	FI_X_2012_1000 m3_6_4_2
FI	X	2012	1000 m3	6_4_3	FI_X_2012_1000 m3_6_4_3
FI	X	2012	1000 mt	7	FI_X_2012_1000 mt_7
FI	X	2012	1000 mt	7_1	FI_X_2012_1000 mt_7_1
FI	X	2012	1000 mt	7_2	FI_X_2012_1000 mt_7_2
FI	X	2012	1000 mt	7_3	FI_X_2012_1000 mt_7_3
FI	X	2012	1000 mt	7_3_1	FI_X_2012_1000 mt_7_3_1
FI	X	2012	1000 mt	7_3_2	FI_X_2012_1000 mt_7_3_2
FI	X	2012	1000 mt	7_3_3	FI_X_2012_1000 mt_7_3_3
FI	X	2012	1000 mt	7_3_4	FI_X_2012_1000 mt_7_3_4
FI	X	2012	1000 mt	7_4	FI_X_2012_1000 mt_7_4
FI	X	2012	1000 mt	8	FI_X_2012_1000 mt_8
FI	X	2012	1000 mt	8_1	FI_X_2012_1000 mt_8_1
FI	X	2012	1000 mt	8_2	FI_X_2012_1000 mt_8_2
FI	X	2012	1000 mt	9	FI_X_2012_1000 mt_9
FI	X	2012	1000 mt	10	FI_X_2012_1000 mt_10
FI	X	2012	1000 mt	10_1	FI_X_2012_1000 mt_10_1
FI	X	2012	1000 mt	10_1_1	FI_X_2012_1000 mt_10_1_1
FI	X	2012	1000 mt	10_1_2	FI_X_2012_1000 mt_10_1_2
FI	X	2012	1000 mt	10_1_3	FI_X_2012_1000 mt_10_1_3
FI	X	2012	1000 mt	10_1_4	FI_X_2012_1000 mt_10_1_4
FI	X	2012	1000 mt	10_2	FI_X_2012_1000 mt_10_2
FI	X	2012	1000 mt	10_3	FI_X_2012_1000 mt_10_3
FI	X	2012	1000 mt	10_3_1	FI_X_2012_1000 mt_10_3_1
FI	X	2012	1000 mt	10_3_2	FI_X_2012_1000 mt_10_3_2
FI	X	2012	1000 mt	10_3_3	FI_X_2012_1000 mt_10_3_3
FI	X	2012	1000 mt	10_3_4	FI_X_2012_1000 mt_10_3_4
FI	X	2012	1000 mt	10_4	FI_X_2012_1000 mt_10_4
FI	X	2012	1000 NAC	1	FI_X_2012_1000 NAC_1
FI	X	2012	1000 NAC	1_1	FI_X_2012_1000 NAC_1_1
FI	X	2012	1000 NAC	1_2	FI_X_2012_1000 NAC_1_2
FI	X	2012	1000 NAC	1_2_C	FI_X_2012_1000 NAC_1_2_C
FI	X	2012	1000 NAC	1_2_NC	FI_X_2012_1000 NAC_1_2_NC
FI	X	2012	1000 NAC	1_2_NC_T	FI_X_2012_1000 NAC_1_2_NC_T
FI	X	2012	1000 NAC	2	FI_X_2012_1000 NAC_2
FI	X	2012	1000 NAC	3	FI_X_2012_1000 NAC_3
FI	X	2012	1000 NAC	3_1	FI_X_2012_1000 NAC_3_1
FI	X	2012	1000 NAC	3_2	FI_X_2012_1000 NAC_3_2
FI	X	2012	1000 NAC	4	FI_X_2012_1000 NAC_4
FI	X	2012	1000 NAC	4_1	FI_X_2012_1000 NAC_4_1
FI	X	2012	1000 NAC	4_2	FI_X_2012_1000 NAC_4_2
FI	X	2012	1000 NAC	5	FI_X_2012_1000 NAC_5
FI	X	2012	1000 NAC	5_C	FI_X_2012_1000 NAC_5_C
FI	X	2012	1000 NAC	5_NC	FI_X_2012_1000 NAC_5_NC
FI	X	2012	1000 NAC	5_NC_T	FI_X_2012_1000 NAC_5_NC_T
FI	X	2012	1000 NAC	6	FI_X_2012_1000 NAC_6
FI	X	2012	1000 NAC	6_1	FI_X_2012_1000 NAC_6_1
FI	X	2012	1000 NAC	6_1_C	FI_X_2012_1000 NAC_6_1_C
FI	X	2012	1000 NAC	6_1_NC	FI_X_2012_1000 NAC_6_1_NC
FI	X	2012	1000 NAC	6_1_NC_T	FI_X_2012_1000 NAC_6_1_NC_T
FI	X	2012	1000 NAC	6_2	FI_X_2012_1000 NAC_6_2
FI	X	2012	1000 NAC	6_2_C	FI_X_2012_1000 NAC_6_2_C
FI	X	2012	1000 NAC	6_2_NC	FI_X_2012_1000 NAC_6_2_NC
FI	X	2012	1000 NAC	6_2_NC_T	FI_X_2012_1000 NAC_6_2_NC_T
FI	X	2012	1000 NAC	6_3	FI_X_2012_1000 NAC_6_3
FI	X	2012	1000 NAC	6_3_1	FI_X_2012_1000 NAC_6_3_1
FI	X	2012	1000 NAC	6_4	FI_X_2012_1000 NAC_6_4
FI	X	2012	1000 NAC	6_4_1	FI_X_2012_1000 NAC_6_4_1
FI	X	2012	1000 NAC	6_4_2	FI_X_2012_1000 NAC_6_4_2
FI	X	2012	1000 NAC	6_4_3	FI_X_2012_1000 NAC_6_4_3
FI	X	2012	1000 NAC	7	FI_X_2012_1000 NAC_7
FI	X	2012	1000 NAC	7_1	FI_X_2012_1000 NAC_7_1
FI	X	2012	1000 NAC	7_2	FI_X_2012_1000 NAC_7_2
FI	X	2012	1000 NAC	7_3	FI_X_2012_1000 NAC_7_3
FI	X	2012	1000 NAC	7_3_1	FI_X_2012_1000 NAC_7_3_1
FI	X	2012	1000 NAC	7_3_2	FI_X_2012_1000 NAC_7_3_2
FI	X	2012	1000 NAC	7_3_3	FI_X_2012_1000 NAC_7_3_3
FI	X	2012	1000 NAC	7_3_4	FI_X_2012_1000 NAC_7_3_4
FI	X	2012	1000 NAC	7_4	FI_X_2012_1000 NAC_7_4
FI	X	2012	1000 NAC	8	FI_X_2012_1000 NAC_8
FI	X	2012	1000 NAC	8_1	FI_X_2012_1000 NAC_8_1
FI	X	2012	1000 NAC	8_2	FI_X_2012_1000 NAC_8_2
FI	X	2012	1000 NAC	9	FI_X_2012_1000 NAC_9
FI	X	2012	1000 NAC	10	FI_X_2012_1000 NAC_10
FI	X	2012	1000 NAC	10_1	FI_X_2012_1000 NAC_10_1
FI	X	2012	1000 NAC	10_1_1	FI_X_2012_1000 NAC_10_1_1
FI	X	2012	1000 NAC	10_1_2	FI_X_2012_1000 NAC_10_1_2
FI	X	2012	1000 NAC	10_1_3	FI_X_2012_1000 NAC_10_1_3
FI	X	2012	1000 NAC	10_1_4	FI_X_2012_1000 NAC_10_1_4
FI	X	2012	1000 NAC	10_2	FI_X_2012_1000 NAC_10_2
FI	X	2012	1000 NAC	10_3	FI_X_2012_1000 NAC_10_3
FI	X	2012	1000 NAC	10_3_1	FI_X_2012_1000 NAC_10_3_1
FI	X	2012	1000 NAC	10_3_2	FI_X_2012_1000 NAC_10_3_2
FI	X	2012	1000 NAC	10_3_3	FI_X_2012_1000 NAC_10_3_3
FI	X	2012	1000 NAC	10_3_4	FI_X_2012_1000 NAC_10_3_4
FI	X	2012	1000 NAC	10_4	FI_X_2012_1000 NAC_10_4
FI	M	2012	1000 NAC	11_1	FI_M_2012_1000 NAC_11_1
FI	M	2012	1000 NAC	11_1_C	FI_M_2012_1000 NAC_11_1_C
FI	M	2012	1000 NAC	11_1_NC	FI_M_2012_1000 NAC_11_1_NC
FI	M	2012	1000 NAC	11_1_NC_T	FI_M_2012_1000 NAC_11_1_NC_T
FI	M	2012	1000 NAC	11_2	FI_M_2012_1000 NAC_11_2
FI	M	2012	1000 NAC	11_3	FI_M_2012_1000 NAC_11_3
FI	M	2012	1000 NAC	11_4	FI_M_2012_1000 NAC_11_4
FI	M	2012	1000 NAC	11_5	FI_M_2012_1000 NAC_11_5
FI	M	2012	1000 NAC	11_6	FI_M_2012_1000 NAC_11_6
FI	M	2012	1000 NAC	11_7	FI_M_2012_1000 NAC_11_7
FI	M	2012	1000 NAC	11_7_1	FI_M_2012_1000 NAC_11_7_1
FI	M	2012	1000 NAC	12_1	FI_M_2012_1000 NAC_12_1
FI	M	2012	1000 NAC	12_2	FI_M_2012_1000 NAC_12_2
FI	M	2012	1000 NAC	12_3	FI_M_2012_1000 NAC_12_3
FI	M	2012	1000 NAC	12_4	FI_M_2012_1000 NAC_12_4
FI	M	2012	1000 NAC	12_5	FI_M_2012_1000 NAC_12_5
FI	M	2012	1000 NAC	12_6	FI_M_2012_1000 NAC_12_6
FI	M	2012	1000 NAC	12_6_1	FI_M_2012_1000 NAC_12_6_1
FI	M	2012	1000 NAC	12_6_2	FI_M_2012_1000 NAC_12_6_2
FI	M	2012	1000 NAC	12_6_3	FI_M_2012_1000 NAC_12_6_3
FI	M	2012	1000 NAC	12_7	FI_M_2012_1000 NAC_12_7
FI	M	2012	1000 NAC	12_7_1	FI_M_2012_1000 NAC_12_7_1
FI	M	2012	1000 NAC	12_7_2	FI_M_2012_1000 NAC_12_7_2
FI	M	2012	1000 NAC	12_7_3	FI_M_2012_1000 NAC_12_7_3
FI	X	2012	1000 NAC	11_1	FI_X_2012_1000 NAC_11_1
FI	X	2012	1000 NAC	11_1_C	FI_X_2012_1000 NAC_11_1_C
FI	X	2012	1000 NAC	11_1_NC	FI_X_2012_1000 NAC_11_1_NC
FI	X	2012	1000 NAC	11_1_NC_T	FI_X_2012_1000 NAC_11_1_NC_T
FI	X	2012	1000 NAC	11_2	FI_X_2012_1000 NAC_11_2
FI	X	2012	1000 NAC	11_3	FI_X_2012_1000 NAC_11_3
FI	X	2012	1000 NAC	11_4	FI_X_2012_1000 NAC_11_4
FI	X	2012	1000 NAC	11_5	FI_X_2012_1000 NAC_11_5
FI	X	2012	1000 NAC	11_6	FI_X_2012_1000 NAC_11_6
FI	X	2012	1000 NAC	11_7	FI_X_2012_1000 NAC_11_7
FI	X	2012	1000 NAC	11_7_1	FI_X_2012_1000 NAC_11_7_1
FI	X	2012	1000 NAC	12_1	FI_X_2012_1000 NAC_12_1
FI	X	2012	1000 NAC	12_2	FI_X_2012_1000 NAC_12_2
FI	X	2012	1000 NAC	12_3	FI_X_2012_1000 NAC_12_3
FI	X	2012	1000 NAC	12_4	FI_X_2012_1000 NAC_12_4
FI	X	2012	1000 NAC	12_5	FI_X_2012_1000 NAC_12_5
FI	X	2012	1000 NAC	12_6	FI_X_2012_1000 NAC_12_6
FI	X	2012	1000 NAC	12_6_1	FI_X_2012_1000 NAC_12_6_1
FI	X	2012	1000 NAC	12_6_2	FI_X_2012_1000 NAC_12_6_2
FI	X	2012	1000 NAC	12_6_3	FI_X_2012_1000 NAC_12_6_3
FI	X	2012	1000 NAC	12_7	FI_X_2012_1000 NAC_12_7
FI	X	2012	1000 NAC	12_7_1	FI_X_2012_1000 NAC_12_7_1
FI	X	2012	1000 NAC	12_7_2	FI_X_2012_1000 NAC_12_7_2
FI	X	2012	1000 NAC	12_7_3	FI_X_2012_1000 NAC_12_7_3
FI	M	2012	1000 m3	ST_1_2_C	FI_M_2012_1000 m3_ST_1_2_C
FI	M	2012	1000 m3	ST_1_2_C_1	FI_M_2012_1000 m3_ST_1_2_C_1
FI	M	2012	1000 m3	ST_1_2_C_1_1	FI_M_2012_1000 m3_ST_1_2_C_1_1
FI	M	2012	1000 m3	ST_1_2_C_2_1	FI_M_2012_1000 m3_ST_1_2_C_2_1
FI	M	2012	1000 m3	ST_1_2_C_2	FI_M_2012_1000 m3_ST_1_2_C_2
FI	M	2012	1000 m3	ST_1_2_C_1_2	FI_M_2012_1000 m3_ST_1_2_C_1_2
FI	M	2012	1000 m3	ST_1_2_C_2_2	FI_M_2012_1000 m3_ST_1_2_C_2_2
FI	M	2012	1000 m3	ST_1_2_C_3	FI_M_2012_1000 m3_ST_1_2_C_3
FI	M	2012	1000 m3	ST_1_2_C_1_3	FI_M_2012_1000 m3_ST_1_2_C_1_3
FI	M	2012	1000 m3	ST_1_2_C_2_3	FI_M_2012_1000 m3_ST_1_2_C_2_3
FI	M	2012	1000 m3	ST_1_2_NC	FI_M_2012_1000 m3_ST_1_2_NC
FI	M	2012	1000 m3	ST_1_2_NC_1	FI_M_2012_1000 m3_ST_1_2_NC_1
FI	M	2012	1000 m3	ST_1_2_NC_1_1	FI_M_2012_1000 m3_ST_1_2_NC_1_1
FI	M	2012	1000 m3	ST_1_2_NC_2_1	FI_M_2012_1000 m3_ST_1_2_NC_2_1
FI	M	2012	1000 m3	ST_1_2_NC_2	FI_M_2012_1000 m3_ST_1_2_NC_2
FI	M	2012	1000 m3	ST_1_2_NC_1_2	FI_M_2012_1000 m3_ST_1_2_NC_1_2
FI	M	2012	1000 m3	ST_1_2_NC_2_2	FI_M_2012_1000 m3_ST_1_2_NC_2_2
FI	M	2012	1000 m3	ST_1_2_NC_3	FI_M_2012_1000 m3_ST_1_2_NC_3
FI	M	2012	1000 m3	ST_1_2_NC_1_3	FI_M_2012_1000 m3_ST_1_2_NC_1_3
FI	M	2012	1000 m3	ST_1_2_NC_2_3	FI_M_2012_1000 m3_ST_1_2_NC_2_3
FI	M	2012	1000 m3	ST_1_2_NC_4	FI_M_2012_1000 m3_ST_1_2_NC_4
FI	M	2012	1000 m3	ST_1_2_NC_5	FI_M_2012_1000 m3_ST_1_2_NC_5
FI	M	2012	1000 m3	ST_5_C	FI_M_2012_1000 m3_ST_5_C
FI	M	2012	1000 m3	ST_5_C_1	FI_M_2012_1000 m3_ST_5_C_1
FI	M	2012	1000 m3	ST_5_C_2	FI_M_2012_1000 m3_ST_5_C_2
FI	M	2012	1000 m3	ST_5_NC	FI_M_2012_1000 m3_ST_5_NC
FI	M	2012	1000 m3	ST_5_NC_1	FI_M_2012_1000 m3_ST_5_NC_1
FI	M	2012	1000 m3	ST_5_NC_2	FI_M_2012_1000 m3_ST_5_NC_2
FI	M	2012	1000 m3	ST_5_NC_3	FI_M_2012_1000 m3_ST_5_NC_3
FI	M	2012	1000 m3	ST_5_NC_4	FI_M_2012_1000 m3_ST_5_NC_4
FI	M	2012	1000 m3	ST_5_NC_5	FI_M_2012_1000 m3_ST_5_NC_5
FI	M	2012	1000 m3	ST_5_NC_6	FI_M_2012_1000 m3_ST_5_NC_6
FI	M	2012	1000 m3	ST_5_NC_7	FI_M_2012_1000 m3_ST_5_NC_7
FI	M	2012	1000 NAC	ST_1_2_C	FI_M_2012_1000 NAC_ST_1_2_C
FI	M	2012	1000 NAC	ST_1_2_C_1	FI_M_2012_1000 NAC_ST_1_2_C_1
FI	M	2012	1000 NAC	ST_1_2_C_1_1	FI_M_2012_1000 NAC_ST_1_2_C_1_1
FI	M	2012	1000 NAC	ST_1_2_C_2_1	FI_M_2012_1000 NAC_ST_1_2_C_2_1
FI	M	2012	1000 NAC	ST_1_2_C_2	FI_M_2012_1000 NAC_ST_1_2_C_2
FI	M	2012	1000 NAC	ST_1_2_C_1_2	FI_M_2012_1000 NAC_ST_1_2_C_1_2
FI	M	2012	1000 NAC	ST_1_2_C_2_2	FI_M_2012_1000 NAC_ST_1_2_C_2_2
FI	M	2012	1000 NAC	ST_1_2_C_3	FI_M_2012_1000 NAC_ST_1_2_C_3
FI	M	2012	1000 NAC	ST_1_2_C_1_3	FI_M_2012_1000 NAC_ST_1_2_C_1_3
FI	M	2012	1000 NAC	ST_1_2_C_2_3	FI_M_2012_1000 NAC_ST_1_2_C_2_3
FI	M	2012	1000 NAC	ST_1_2_NC	FI_M_2012_1000 NAC_ST_1_2_NC
FI	M	2012	1000 NAC	ST_1_2_NC_1	FI_M_2012_1000 NAC_ST_1_2_NC_1
FI	M	2012	1000 NAC	ST_1_2_NC_1_1	FI_M_2012_1000 NAC_ST_1_2_NC_1_1
FI	M	2012	1000 NAC	ST_1_2_NC_2_1	FI_M_2012_1000 NAC_ST_1_2_NC_2_1
FI	M	2012	1000 NAC	ST_1_2_NC_2	FI_M_2012_1000 NAC_ST_1_2_NC_2
FI	M	2012	1000 NAC	ST_1_2_NC_1_2	FI_M_2012_1000 NAC_ST_1_2_NC_1_2
FI	M	2012	1000 NAC	ST_1_2_NC_2_2	FI_M_2012_1000 NAC_ST_1_2_NC_2_2
FI	M	2012	1000 NAC	ST_1_2_NC_3	FI_M_2012_1000 NAC_ST_1_2_NC_3
FI	M	2012	1000 NAC	ST_1_2_NC_1_3	FI_M_2012_1000 NAC_ST_1_2_NC_1_3
FI	M	2012	1000 NAC	ST_1_2_NC_2_3	FI_M_2012_1000 NAC_ST_1_2_NC_2_3
FI	M	2012	1000 NAC	ST_1_2_NC_4	FI_M_2012_1000 NAC_ST_1_2_NC_4
FI	M	2012	1000 NAC	ST_1_2_NC_5	FI_M_2012_1000 NAC_ST_1_2_NC_5
FI	M	2012	1000 NAC	ST_5_C	FI_M_2012_1000 NAC_ST_5_C
FI	M	2012	1000 NAC	ST_5_C_1	FI_M_2012_1000 NAC_ST_5_C_1
FI	M	2012	1000 NAC	ST_5_C_2	FI_M_2012_1000 NAC_ST_5_C_2
FI	M	2012	1000 NAC	ST_5_NC	FI_M_2012_1000 NAC_ST_5_NC
FI	M	2012	1000 NAC	ST_5_NC_1	FI_M_2012_1000 NAC_ST_5_NC_1
FI	M	2012	1000 NAC	ST_5_NC_2	FI_M_2012_1000 NAC_ST_5_NC_2
FI	M	2012	1000 NAC	ST_5_NC_3	FI_M_2012_1000 NAC_ST_5_NC_3
FI	M	2012	1000 NAC	ST_5_NC_4	FI_M_2012_1000 NAC_ST_5_NC_4
FI	M	2012	1000 NAC	ST_5_NC_5	FI_M_2012_1000 NAC_ST_5_NC_5
FI	M	2012	1000 NAC	ST_5_NC_6	FI_M_2012_1000 NAC_ST_5_NC_6
FI	M	2012	1000 NAC	ST_5_NC_7	FI_M_2012_1000 NAC_ST_5_NC_7
FI	X	2012	1000 m3	ST_1_2_C	FI_X_2012_1000 m3_ST_1_2_C
FI	X	2012	1000 m3	ST_1_2_C_1	FI_X_2012_1000 m3_ST_1_2_C_1
FI	X	2012	1000 m3	ST_1_2_C_1_1	FI_X_2012_1000 m3_ST_1_2_C_1_1
FI	X	2012	1000 m3	ST_1_2_C_2_1	FI_X_2012_1000 m3_ST_1_2_C_2_1
FI	X	2012	1000 m3	ST_1_2_C_2	FI_X_2012_1000 m3_ST_1_2_C_2
FI	X	2012	1000 m3	ST_1_2_C_1_2	FI_X_2012_1000 m3_ST_1_2_C_1_2
FI	X	2012	1000 m3	ST_1_2_C_2_2	FI_X_2012_1000 m3_ST_1_2_C_2_2
FI	X	2012	1000 m3	ST_1_2_C_3	FI_X_2012_1000 m3_ST_1_2_C_3
FI	X	2012	1000 m3	ST_1_2_C_1_3	FI_X_2012_1000 m3_ST_1_2_C_1_3
FI	X	2012	1000 m3	ST_1_2_C_2_3	FI_X_2012_1000 m3_ST_1_2_C_2_3
FI	X	2012	1000 m3	ST_1_2_NC	FI_X_2012_1000 m3_ST_1_2_NC
FI	X	2012	1000 m3	ST_1_2_NC_1	FI_X_2012_1000 m3_ST_1_2_NC_1
FI	X	2012	1000 m3	ST_1_2_NC_1_1	FI_X_2012_1000 m3_ST_1_2_NC_1_1
FI	X	2012	1000 m3	ST_1_2_NC_2_1	FI_X_2012_1000 m3_ST_1_2_NC_2_1
FI	X	2012	1000 m3	ST_1_2_NC_2	FI_X_2012_1000 m3_ST_1_2_NC_2
FI	X	2012	1000 m3	ST_1_2_NC_1_2	FI_X_2012_1000 m3_ST_1_2_NC_1_2
FI	X	2012	1000 m3	ST_1_2_NC_2_2	FI_X_2012_1000 m3_ST_1_2_NC_2_2
FI	X	2012	1000 m3	ST_1_2_NC_3	FI_X_2012_1000 m3_ST_1_2_NC_3
FI	X	2012	1000 m3	ST_1_2_NC_1_3	FI_X_2012_1000 m3_ST_1_2_NC_1_3
FI	X	2012	1000 m3	ST_1_2_NC_2_3	FI_X_2012_1000 m3_ST_1_2_NC_2_3
FI	X	2012	1000 m3	ST_1_2_NC_4	FI_X_2012_1000 m3_ST_1_2_NC_4
FI	X	2012	1000 m3	ST_1_2_NC_5	FI_X_2012_1000 m3_ST_1_2_NC_5
FI	X	2012	1000 m3	ST_5_C	FI_X_2012_1000 m3_ST_5_C
FI	X	2012	1000 m3	ST_5_C_1	FI_X_2012_1000 m3_ST_5_C_1
FI	X	2012	1000 m3	ST_5_C_2	FI_X_2012_1000 m3_ST_5_C_2
FI	X	2012	1000 m3	ST_5_NC	FI_X_2012_1000 m3_ST_5_NC
FI	X	2012	1000 m3	ST_5_NC_1	FI_X_2012_1000 m3_ST_5_NC_1
FI	X	2012	1000 m3	ST_5_NC_2	FI_X_2012_1000 m3_ST_5_NC_2
FI	X	2012	1000 m3	ST_5_NC_3	FI_X_2012_1000 m3_ST_5_NC_3
FI	X	2012	1000 m3	ST_5_NC_4	FI_X_2012_1000 m3_ST_5_NC_4
FI	X	2012	1000 m3	ST_5_NC_5	FI_X_2012_1000 m3_ST_5_NC_5
FI	X	2012	1000 m3	ST_5_NC_6	FI_X_2012_1000 m3_ST_5_NC_6
FI	X	2012	1000 m3	ST_5_NC_7	FI_X_2012_1000 m3_ST_5_NC_7
FI	X	2012	1000 NAC	ST_1_2_C	FI_X_2012_1000 NAC_ST_1_2_C
FI	X	2012	1000 NAC	ST_1_2_C_1	FI_X_2012_1000 NAC_ST_1_2_C_1
FI	X	2012	1000 NAC	ST_1_2_C_1_1	FI_X_2012_1000 NAC_ST_1_2_C_1_1
FI	X	2012	1000 NAC	ST_1_2_C_2_1	FI_X_2012_1000 NAC_ST_1_2_C_2_1
FI	X	2012	1000 NAC	ST_1_2_C_2	FI_X_2012_1000 NAC_ST_1_2_C_2
FI	X	2012	1000 NAC	ST_1_2_C_1_2	FI_X_2012_1000 NAC_ST_1_2_C_1_2
FI	X	2012	1000 NAC	ST_1_2_C_2_2	FI_X_2012_1000 NAC_ST_1_2_C_2_2
FI	X	2012	1000 NAC	ST_1_2_C_3	FI_X_2012_1000 NAC_ST_1_2_C_3
FI	X	2012	1000 NAC	ST_1_2_C_1_3	FI_X_2012_1000 NAC_ST_1_2_C_1_3
FI	X	2012	1000 NAC	ST_1_2_C_2_3	FI_X_2012_1000 NAC_ST_1_2_C_2_3
FI	X	2012	1000 NAC	ST_1_2_NC	FI_X_2012_1000 NAC_ST_1_2_NC
FI	X	2012	1000 NAC	ST_1_2_NC_1	FI_X_2012_1000 NAC_ST_1_2_NC_1
FI	X	2012	1000 NAC	ST_1_2_NC_1_1	FI_X_2012_1000 NAC_ST_1_2_NC_1_1
FI	X	2012	1000 NAC	ST_1_2_NC_2_1	FI_X_2012_1000 NAC_ST_1_2_NC_2_1
FI	X	2012	1000 NAC	ST_1_2_NC_2	FI_X_2012_1000 NAC_ST_1_2_NC_2
FI	X	2012	1000 NAC	ST_1_2_NC_1_2	FI_X_2012_1000 NAC_ST_1_2_NC_1_2
FI	X	2012	1000 NAC	ST_1_2_NC_2_2	FI_X_2012_1000 NAC_ST_1_2_NC_2_2
FI	X	2012	1000 NAC	ST_1_2_NC_3	FI_X_2012_1000 NAC_ST_1_2_NC_3
FI	X	2012	1000 NAC	ST_1_2_NC_1_3	FI_X_2012_1000 NAC_ST_1_2_NC_1_3
FI	X	2012	1000 NAC	ST_1_2_NC_2_3	FI_X_2012_1000 NAC_ST_1_2_NC_2_3
FI	X	2012	1000 NAC	ST_1_2_NC_4	FI_X_2012_1000 NAC_ST_1_2_NC_4
FI	X	2012	1000 NAC	ST_1_2_NC_5	FI_X_2012_1000 NAC_ST_1_2_NC_5
FI	X	2012	1000 NAC	ST_5_C	FI_X_2012_1000 NAC_ST_5_C
FI	X	2012	1000 NAC	ST_5_C_1	FI_X_2012_1000 NAC_ST_5_C_1
FI	X	2012	1000 NAC	ST_5_C_2	FI_X_2012_1000 NAC_ST_5_C_2
FI	X	2012	1000 NAC	ST_5_NC	FI_X_2012_1000 NAC_ST_5_NC
FI	X	2012	1000 NAC	ST_5_NC_1	FI_X_2012_1000 NAC_ST_5_NC_1
FI	X	2012	1000 NAC	ST_5_NC_2	FI_X_2012_1000 NAC_ST_5_NC_2
FI	X	2012