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(UK) Proposal for amendments to the Consolidated Resolution R.E.3

Read more about (UK) Proposal for amendments to the Consolidated Resolution R.E.3

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Proposal for amendments to Annex 7 of Consolidation Resolution of the Construction of Vehicles (R.E.3)

The text reproduced below is based on the document ECE/TRANS/WP.29/78/Rev.7. The modifications to that text are indicated in blue bold for new characters and ~~blue strikethrough~~ for deleted characters.

I. Proposal

Annex 7., amend to read:

Annex 7

Provisions on Software Identification Numbers

I1. Introduction

UN Regulation No. 156 ~~[15…]~~ on uniform provisions concerning the approval of vehicles with regards to software update and software updates management system is defining "RX Software Identification Number (RXSWIN)" that means a dedicated identifier, defined by the vehicle manufacturer, representing information about the type approval relevant software of the Electronic Control System contributing to the UN Regulation No. X type approval relevant characteristics of the vehicle.

In order to make use of RXSWIN, relevant UN Regulations ~~can~~ shall refer~~, by incorporation,~~ to this annex to introduce relevant definitions and provisions. ~~as follow:~~

II2. Definitions

For the purpose of this Consolidated Resolution and the UN Regulations referring to this annex:

2.1. "Rx [X] Software Identification Number (R[X]SWIN)" means a dedicated identifier, defined by the vehicle manufacturer, representing information about the type approval relevant software of the Electronic Control System contributing to the UN Regulation No. [X] type approval relevant characteristics of the vehicle. Where [X] is the number of the UN Regulation that is referring to the provisions of this Annex.

2.2. "Electronic Control System" means a combination of units, designed to co-operate in the production of the stated vehicle control function by electronic data processing. Such systems, often controlled by software, are built from discrete functional components such as sensors, electronic control units and actuators and connected by transmission links. They may include mechanical, electro-pneumatic or electro-hydraulic elements. "The System", referred to herein, is the one for which type approval is being sought.

2.3. "Software" means the part of an Electronic Control System that consists of digital data and instructions.

2.4. "Software Update Management System" means a systematic approach defining organizational processes and procedures to comply with the requirements for delivery of software updates.

~~III~~3. Requirements for software identification number

For the purpose of this Consolidated Resolution and the UN Regulations referring to this annex:

~~3.1. For the purpose of ensuring the software of the System can be identified, an RXSWIN may be implemented by the vehicle manufacturer.~~

~~3.2. If the manufacturer implements an RXSWIN, the following shall apply:~~

~~3.2.1. The vehicle manufacturer shall have a valid approval according to UN Regulation No. XXX [Software Update Process Regulation].~~

Note - as an alternative to the above paragraph:

3.1. The vehicle manufacturer shall have a Software Update Management System (SUMS) which, together with the vehicle type, shall comply with the technical requirements of the original series or later of UN Regulation No. 156.

3.1.1. For the purpose of ensuring that the software relevant to the system can be identified, an R[X]SWIN shall be used. The R[X]SWIN may be held on the vehicle or, if the R[X]SWIN is not held on the vehicle, the manufacturer shall declare the software version(s) of the vehicle or single ECUs with the connection to the relevant type approvals to the Approval Authority.

3.2.2. The vehicle manufacturer shall provide the following information to the Approval Authority ~~in the communication form of this Regulation (the Regulation referring to this annex)~~:

(a) The R[X]SWIN and, in the case where the R[X]SWIN is not held on the vehicle, the related software version(s);

(b) How to read the R[X]SWIN or software version(s) in the case where the R[X]SWIN is not held on the vehicle. ;

(c) Details on how to access the information from the auditable register for all software versions relevant to the R[X]SWIN.

3.2.3. The ~~vehicle manufacturer may~~ Approval Authority shall provide, in the communication form of the ~~related~~ UN Regulation that is referring to this Annex, a list of the relevant parameters that will allow the identification of those vehicles that can be updated with the software represented by the R[X]SWIN. The information provided shall be declared by the vehicle manufacturer ~~and may not be verified by an Approval Authority~~.

3.2.4. The vehicle manufacturer may obtain a new vehicle approval for the purpose of differentiating software versions intended to be used on vehicles already registered in the market from the software versions that are used on new vehicles. This may cover the situations where type approval regulations are updated or hardware changes are made to vehicles in series production. ~~In agreement with the testing agency duplication of tests shall be avoided where possible.~~

~~IV. Production definitely discontinued and RxSWIN~~

4.1. If the holder of the approval completely ceases to manufacture a type of vehicle approved in accordance with this Regulation (the related Regulation referring to this annex), he shall so inform the authority which granted the approval. Upon receiving the relevant communication that authority shall inform thereof the other Contracting Parties to the 1958 Agreement applying this Regulation (the related Regulation referring to this annex) by means of a communication form conforming to the model in Annex [Communication form] to this the related Regulation.

~~4.2. The production is not considered definitely discontinued if the vehicle manufacturer intends to obtain further approvals for software updates for vehicles already registered in the market.~~

~~V. Necessary insertion in the Communication Form relevant to RxSWIN~~

~~Note~~ : The communication form of the related Regulation referring to this annex shall include the mention Production definitively discontinued for such a case and shall include additional information regarding RXSWIN as follow (and marked in bold):

4. Amendments to the relevant UN Regulations

Note: The following paragraphs shall be integrated into or amended in the related Regulation referring to this annex.

In the paragraph titled “Definitions” the following sub-paragraph is inserted to read:

“x.x. For the definitions with regard to Software Identification Number, refer to the Consolidated Resolution on the Construction of Vehicles (R.E.3), Annex 7, paragraph 2.”

In the paragraph titled "Requirements” the following sub-paragraph is inserted to read:

“x.x. With regard to Software Identification Numbers, the requirements of the Consolidated Resolution on the Construction of Vehicles (R.E.3), Annex 7., paragraph 3., shall apply.”

In the paragraph titled "Product definitely discontinued” the following sub-paragraph is inserted to read:

“x.x. The production is not considered definitely discontinued if the vehicle manufacturer intends to obtain further approvals for software updates for vehicles already registered in the market.”

The Annex titled “Communication” is amended to read:

“

Communication

…

Additional information regarding R[number of this Regulation]SWIN:

R[number of this Regulation]SWIN:

Is the R[number of this Regulation]SWIN held on the vehicle: Yes/No

Information on how to read the R[number of this Regulation]SWIN, or the relevant software version(s) in the case where the R[number of this Regulation]SWIN is not held on the vehicle:

Description on how to access the information from the auditable register of all software versions relevant to the R[number of this Regulation]SWIN:

If applicable, list the relevant parameters that will allow the identification of those vehicles that can be updated with the software represented by the R[number of this Regulation]SWIN under the item above:

~~Communication form~~

~~Communication~~

~~(Maximum format: A4 (210 x 297 mm))~~

~~( issued by : Name of administration: ...................................... ...................................... ...................................... )~~

~~( 1 )[footnoteRef:2]~~ [2: Distinguishing number of the country which has granted/extended/refused/withdrawn approval (see approval provisions in the Regulation).]

~~Concerning:[footnoteRef:3] Approval granted~~ [3: Strike out what does not apply.]

~~Approval extended~~

~~Approval withdrawn with effect from dd/mm/yyyy~~

~~Approval refused~~

~~Production definitively discontinued~~

~~of a vehicle type, pursuant to UN Regulation No. [this Regulation]~~

~~Approval No.:~~

~~Extension No.:~~

~~Reason for extension:~~

~~(…)~~

~~Additional information regarding RXSWIN:~~

~~Information on how to read the RXSWIN or software version(s) in case the RXSWIN is not held on the vehicle:~~

~~If applicable, list the relevant parameters that will allow the identification of those vehicles that can be updated with the software represented by the RXSWIN under the item above:~~

II. Justification

The reason to implement the reference to RE.7 into the system regulations is to allow for the relevant software(s) for that approval to be identified in service. Currently the use of RXSWIN is at the discretion of the manufacturer because R.156 does not mandate its use and since there are no requirements on the vehicle type in R.156 if an RXSWIN is not used there would be no means to identify the relevant software.

The current wording of RE.3 maintains that optionality which limits the capability of authorities to identify software should an RXSWIN not be used. This was a flaw that was identified in R.157 that originally used the text from RE.3 but was subsequently changed to require an RXSWIN. Changing the wording in RE.3 to require an RXSWIN does not mandate it in all circumstances, it would be only for those system regulations that refer to it. This is the activity that WP.29 has requested the GRs to undertake; to identify which UN Regulations are where there needs to be the capability to identify the relevant software(s). Manufacturers can currently use RXSWIN in an optional, harmonised way without reference to RE.3 as all the provisions relating to it are contained in R.156. Therefore, there is no benefit in update and referencing the text in RE.3 unless it is to mandate its use.

It should be noted that RXSWIN is simply a unique identifier and does not need to be in the format: R79A̱ḆC̱ḎE̱, as an example. Consequently, the software identification number of the ECU or component could be used as the RXSWIN. However, it is appreciated that it is not likely since system approvals will tend to use many ECUs and software. Nevertheless, this is dealt with through utilising the fact that the RXSWIN can be recorded off-board the vehicle. In this instance the unique identifier of the RXSWIN acts as a reference to all the relevant software version(s) on the vehicle but that the manufacturer has to provide in the auditable register the list of the software version(s) under that RXSWIN. If the manufacturer is employing a certifiable SUMS then this process should already be in place as they will need to be able to identify the software relevant to a particular approval on any vehicle approved to R.156.

This proposal here is utilising the amendments made in supplement 2 to UN Regulation No.157 where it was clarified that an RXSWIN should always be used. Amendments are also made to the communication file so that relevant information about the RXSWIN is clearly available.

Submitted by the expert from the United Kingdom of Great Britian and Northern Ireland.

Informal document GRVA-18-27 18th GRVA, 22-26 January 2024

Provisional agenda item 5(a)

Proposal for amendments to Annex 7 of Consolidation Resolution of the Construction of Vehicles (R.E.3)

The text reproduced below is based on the document ECE/TRANS/WP.29/78/Rev.7. The modifications to that text are indicated in blue bold for new characters and blue strikethrough for deleted characters.

I. Proposal

Annex 7., amend to read:

Annex 7

Provisions on Software Identification Numbers

I1. Introduction

UN Regulation No. 156 [15…] on uniform provisions concerning the approval of vehicles with regards to software update and software updates management system is defining "RX Software Identification Number (RXSWIN)" that means a dedicated identifier, defined by the vehicle manufacturer, representing information about the type approval relevant software of the Electronic Control System contributing to the UN Regulation No. X type approval relevant characteristics of the vehicle.

In order to make use of RXSWIN, relevant UN Regulations can shall refer, by incorporation, to this annex to introduce relevant definitions and provisions. as follow:

II2. Definitions

For the purpose of this Consolidated Resolution and the UN Regulations referring to this annex:

2.1. "Rx[X] Software Identification Number (R[X]SWIN)" means a dedicated identifier, defined by the vehicle manufacturer, representing information about the type approval relevant software of the Electronic Control System contributing to the UN Regulation No. [X] type approval relevant characteristics of the vehicle. Where [X] is the number of the UN Regulation that is referring to the provisions of this Annex.

2.2. "Electronic Control System" means a combination of units, designed to co- operate in the production of the stated vehicle control function by electronic

data processing. Such systems, often controlled by software, are built from discrete functional components such as sensors, electronic control units and actuators and connected by transmission links. They may include mechanical, electro-pneumatic or electro-hydraulic elements. "The System", referred to herein, is the one for which type approval is being sought.

2.3. "Software" means the part of an Electronic Control System that consists of digital data and instructions.

2.4. "Software Update Management System" means a systematic approach defining organizational processes and procedures to comply with the requirements for delivery of software updates.

III3. Requirements for software identification number

For the purpose of this Consolidated Resolution and the UN Regulations referring to this annex:

3.1. For the purpose of ensuring the software of the System can be identified, an RXSWIN may be implemented by the vehicle manufacturer.

3.2. If the manufacturer implements an RXSWIN, the following shall apply:

3.2.1. The vehicle manufacturer shall have a valid approval according to UN Regulation No. XXX [Software Update Process Regulation].

Note - as an alternative to the above paragraph:

3.2.2. The vehicle manufacturer shall provide the following information to the Approval Authority in the communication form of this Regulation (the Regulation referring to this annex):

(a) The R[X]SWIN and, in the case where the R[X]SWIN is not held on the vehicle, the related software version(s);

(b) How to read the R[X]SWIN or software version(s) in the case where the R[X]SWIN is not held on the vehicle.;

(c) Details on how to access the information from the auditable register for all software versions relevant to the R[X]SWIN.

3.2.3. The vehicle manufacturer may Approval Authority shall provide, in the communication form of the related UN Regulation that is referring to this Annex, a list of the relevant parameters that will allow the identification of those vehicles that can be updated with the software represented by the

R[X]SWIN. The information provided shall be declared by the vehicle manufacturer and may not be verified by an Approval Authority.

3.2.4. The vehicle manufacturer may obtain a new vehicle approval for the purpose of differentiating software versions intended to be used on vehicles already registered in the market from the software versions that are used on new vehicles. This may cover the situations where type approval regulations are updated or hardware changes are made to vehicles in series production. In agreement with the testing agency duplication of tests shall be avoided where possible.

IV. Production definitely discontinued and RxSWIN

4.2. The production is not considered definitely discontinued if the vehicle manufacturer intends to obtain further approvals for software updates for vehicles already registered in the market.

V. Necessary insertion in the Communication Form relevant to RxSWIN

Note: The communication form of the related Regulation referring to this annex shall include the mention Production definitively discontinued for such a case and shall include additional information regarding RXSWIN as follow (and marked in bold):

4. Amendments to the relevant UN Regulations

Note: The following paragraphs shall be integrated into or amended in the related Regulation referring to this annex.

In the paragraph titled “Definitions” the following sub-paragraph is inserted to read:

“x.x. For the definitions with regard to Software Identification Number, refer to the Consolidated Resolution on the Construction of Vehicles (R.E.3), Annex 7, paragraph 2.”

In the paragraph titled "Requirements” the following sub-paragraph is inserted to read:

“x.x. With regard to Software Identification Numbers, the requirements of the Consolidated Resolution on the Construction of Vehicles (R.E.3), Annex 7., paragraph 3., shall apply.”

In the paragraph titled "Product definitely discontinued” the following sub-paragraph is inserted to read:

“x.x. The production is not considered definitely discontinued if the vehicle manufacturer intends to obtain further approvals for software updates for vehicles already registered in the market.”

The Annex titled “Communication” is amended to read:

“

Communication

…

Additional information regarding R[number of this Regulation]SWIN:

R[number of this Regulation]SWIN: ..................................................................................

Is the R[number of this Regulation]SWIN held on the vehicle: Yes/No

Information on how to read the R[number of this Regulation]SWIN, or the relevant software version(s) in the case where the R[number of this Regulation]SWIN is not held on the vehicle: ...............................................................................................................

Description on how to access the information from the auditable register of all software versions relevant to the R[number of this Regulation]SWIN: ..........................

Communication form

Communication

(Maximum format: A4 (210 x 297 mm))

Concerning:2 Approval granted Approval extended Approval withdrawn with effect from dd/mm/yyyy Approval refused Production definitively discontinued

of a vehicle type, pursuant to UN Regulation No. [this Regulation]

Approval No.: ..........................................................................................................................

Extension No.: .........................................................................................................................

Reason for extension: ..............................................................................................................

(…)

Additional information regarding RXSWIN:

Information on how to read the RXSWIN or software version(s) in case the RXSWIN is not held on the vehicle: ................................................................................................................

If applicable, list the relevant parameters that will allow the identification of those vehicles that can be updated with the software represented by the RXSWIN under the item above: ..

1 Distinguishing number of the country which has granted/extended/refused/withdrawn approval (see approval provisions in the Regulation).

2 Strike out what does not apply.

issued by: Name of administration: ...................................... ...................................... ......................................

II. Justification

It should be noted that RXSWIN is simply a unique identifier and does not need to be in the format: R79A̱ḆC̱ḎE̱, as an example. Consequently, the software identification number of the ECU or component could be used as the RXSWIN. However, it is appreciated that it is not likely since system approvals will tend to use many ECUs and software. Nevertheless, this is dealt with through utilising the fact that the RXSWIN can be recorded off-board the vehicle. In this instance the unique identifier of the RXSWIN acts as a reference to all the relevant software version(s) on the vehicle but that the manufacturer has to provide in the auditable register the list of the software version(s) under that RXSWIN. If the manufacturer is employing a certifiable SUMS then this process should already be in place as they will need to be able to identify the software relevant to a particular approval on any vehicle approved to R.156.

Market Forecast Tables 2023

Read more about Market Forecast Tables 2023

These tables show forest products production and trade forecasts for 2023 and 2024. 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 2023 and 2024

Table 15 - North America: Summary table of market forecasts for 2023 and 2024

Source: UNECE Committee on Forests and the Forest Industry , November 2023, 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.

In contrast to years prior to 2020, data are shown only for countries providing forecasts. Sub-regional totals thus reflect only the reporting countries of the subregion.

Confidential data have not been included. Please inform secretariat in case you notice any confidential data which might have been included inadvertently.

Wherever the forecast data is incomplete, then data is repeated to avoid skewing.

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.

Consumption figures are the sum of production and national imports minus national exports. Softwood = coniferous, hardwood = non-coniferous. United Kingdom production figures for OSB is secretariat estimate.

Uzbekistan – data extrapolated by the Secretariat based on national data for the first eight months 2023.

Poland - The trade turnover is based on data that includes the estimated value of trade turnover by entities exempt from the reporting obligation. These trade turnover figures are estimated at 3%. Roundwood: sawlogs and veneer logs and pulpwood and wood fuel - with removals from trees and shrubs outside the forest, including forest chips, with stump. Residues - production excluding recovered wood.

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.

Table1


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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	6,141	4,978	4,978	10,104	8,588	8,588	1,784	1,270	1,270	5,747	4,880	4,880	Autriche
Cyprus	33	34	34	1	1	1	32	33	33	0	0	0	Chypre
Czech Republic	2,965	2,343	2,470	4,720	3,776	4,040	583	414	350	2,338	1,847	1,920	République tchèque
Estonia	2,068	1,550	1,550	1,725	1,500	1,500	1,209	700	700	866	650	650	Estonie
Finland	2,938	2,420	2,420	11,200	10,300	10,400	305	20	20	8,567	7,900	8,000	Finlande
France	8,633	8,750	8,800	7,168	7,200	7,300	2,350	2,450	2,400	885	900	900	France
Germany	17,294	14,900	13,300	24,309	21,400	19,800	4,146	2,700	3,000	11,162	9,200	9,500	Allemagne
Hungary	788	902	918	85	96	86	717	821	842	14	15	11	Hongrie
Italy	4,790	4,302	4,302	400	400	400	4,608	4,157	4,157	217	255	255	Italie
Latvia	1,025	950	950	3,102	3,000	3,000	829	750	750	2,906	2,800	2,800	Lettonie
Luxembourg	71	122	122	39	39	39	43	91	91	11	8	8	Luxembourg
Malta	7	9	9	0	0	0	7	9	9	0	0	0	Malte
Montenegro	30	30	29	118	115	112	10	9	7	98	94	90	Monténégro
Netherlands	2,259	2,088	2,029	115	115	115	2,659	2,473	2,399	515	500	485	Pays-Bas
Poland	4,631	4,630	4,800	4,144	4,100	4,200	1,219	1,240	1,300	732	710	700	Pologne
Portugal	696	686	685	807	815	820	130	130	125	242	259	260	Portugal
Serbia	367	361	383	91	95	98	281	270	290	5	4	5	Serbie
Slovakia	847	810	860	1,430	1,360	1,400	480	450	460	1,063	1,000	1,000	Slovaquie
Slovenia	665	670	660	983	990	980	530	530	530	848	850	850	Slovénie
Spain	4,029	4,001	4,001	3,006	3,189	3,189	1,166	956	956	143	144	144	Espagne
Sweden	5,709	5,050	5,650	18,870	18,400	18,300	587	500	450	13,748	13,850	13,100	Suède
Switzerland	1,271	1,300	1,325	1,186	1,200	1,210	300	310	320	215	210	205	Suisse
United Kingdom	8,663	8,125	8,214	3,108	2,860	2,860	5,719	5,385	5,474	165	120	120	Royaume-Uni
Total Europe	75,919	69,011	68,490	96,712	89,540	88,439	29,694	25,668	25,934	50,487	46,197	45,883	Total Europe
Uzbekistan	2,256	1,498	1,498	0	0	0	2,256	1,498	1,498	0	0	0	Ouzbékistan
Total EECCA													Total EOCAC
Canada a	3,707	2,691	2,242	36,398	33,228	31,331	891	988	948	33,581	31,525	30,037	Canada a
United States a	87,925	87,155	88,151	64,039	64,178	64,399	26,202	25,492	26,149	2,316	2,515	2,397	Etats-Unis a
Total North America	91,632	89,846	90,393	100,437	97,406	95,730	27,093	26,480	27,097	35,898	34,040	32,434	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

Table2


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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	310	222	222	238	202	202	217	140	140	145	120	120	Autriche
Cyprus	11	7	7	0	0	0	11	7	7	0	0	0	Chypre
Czech Republic	324	245	240	222	167	175	136	103	105	34	24	40	République tchèque
Estonia	232	125	125	175	125	125	147	60	60	90	60	60	Estonie
Finland	84	44	44	73	40	40	34	24	24	23	20	20	Finlande
France	1,124	1,140	1,150	1,446	1,300	1,400	264	420	350	586	580	600	France
Germany	693	650	650	997	800	800	395	300	300	699	450	450	Allemagne
Hungary	258	150	131	414	343	342	45	38	30	200	231	241	Hongrie
Italy	798	776	776	500	500	500	637	578	578	339	302	302	Italie
Latvia	5	105	105	720	800	800	54	55	55	769	750	750	Lettonie
Luxembourg	96	98	98	39	39	39	64	65	65	7	6	6	Luxembourg
Malta	7	8	9	0	0	0	7	8	9	0	0	0	Malte
Montenegro	11	8	10	39	35	34	2	1	1	30	28	25	Monténégro
Netherlands	238	213	203	34	34	34	314	289	279	110	110	110	Pays-Bas
Poland	495	470	500	487	450	460	267	270	300	259	250	260	Pologne
Portugal	369	295	290	182	185	190	287	200	190	100	90	90	Portugal
Serbia	172	215	225	343	370	385	64	60	70	235	215	230	Serbie
Slovakia	235	240	275	385	400	420	55	50	55	205	210	200	Slovaquie
Slovenia	106	145	145	143	145	145	83	80	80	121	80	80	Slovénie
Spain	425	467	467	302	321	321	175	193	193	53	47	47	Espagne
Sweden	142	140	140	100	100	100	83	80	80	41	40	40	Suède
Switzerland	78	79	81	52	53	54	50	51	52	24	25	25	Suisse
United Kingdom	807	810	810	37	40	40	787	790	790	17	20	20	Royaume-Uni
Total Europe	7,019	6,652	6,703	6,928	6,449	6,606	4,177	3,862	3,813	4,086	3,658	3,716	Total Europe
Uzbekistan	228	208	208	195	195	195	33	16	16	0	3	3	Ouzbékistan
Total EECCA													Total EOCAC
Canada	1,208	1,324	1,242	859	893	815	793	826	738	444	395	311	Canada
United States	14,647	14,835	15,217	17,637	17,827	18,214	798	805	820	3,788	3,797	3,817	Etats-Unis
Total North America	15,855	16,159	16,459	18,496	18,720	19,029	1,591	1,631	1,558	4,231	4,192	4,128	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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	306	219	219	238	202	202	213	136	136	144	119	119	Autriche
Cyprus	9	5	5	0	0	0	8	5	5	0	0	0	Chypre
Czech Republic	307	229	223	222	167	175	119	86	88	34	24	40	République tchèque
Estonia	230	122	122	175	125	125	142	56	56	87	59	59	Estonie
Finland	80	40	40	73	40	40	26	16	16	19	16	16	Finlande
France	960	988	988	1,420	1,285	1,375	123	280	210	583	577	597	France
Germany	664	630	630	997	800	800	315	240	240	649	410	410	Allemagne
Hungary	257	147	127	414	343	342	43	35	26	200	230	241	Hongrie
Italy	819	791	791	495	495	495	476	423	423	152	127	127	Italie
Latvia	5	105	105	720	800	800	54	55	55	769	750	750	Lettonie
Luxembourg	92	96	96	39	39	39	60	63	63	7	6	6	Luxembourg
Malta	6	7	8	0	0	0	6	7	8	0	0	0	Malte
Montenegro	11	8	10	39	35	34	2	1	1	30	28	25	Monténégro
Netherlands	89	80	77	27	27	27	117	108	105	55	55	55	Pays-Bas
Poland	484	459	488	487	450	460	254	257	286	257	248	258	Pologne
Portugal	319	272	268	170	172	178	180	150	140	31	50	50	Portugal
Serbia	167	211	220	342	369	384	59	57	66	234	215	230	Serbie
Slovakia	235	240	275	385	400	420	55	50	55	205	210	200	Slovaquie
Slovenia	104	143	143	143	145	145	81	78	78	120	80	80	Slovénie
Spain	383	417	417	300	318	318	128	142	142	45	43	43	Espagne
Sweden	142	139	139	100	100	100	83	79	79	41	40	40	Suède
Switzerland	69	70	72	49	50	51	44	45	46	24	25	25	Suisse
United Kingdom	716	720	720	37	40	40	693	700	700	14	20	20	Royaume-Uni
Total Europe	6,453	6,138	6,183	6,872	6,402	6,550	3,281	3,069	3,025	3,700	3,334	3,392	Total Europe
Uzbekistan	227	207	207	195	195	195	33	15	15	0	3	3	Ouzbékistan
Total EECCA													Total EOCAC
Canada	1,191	1,316	1,236	859	893	815	762	805	715	430	382	294	Canada
United States	14,379	14,578	14,957	17,637	17,827	18,214	523	529	544	3,782	3,778	3,801	Etats-Unis
Total North America	15,569	15,893	16,193	18,496	18,720	19,029	1,285	1,334	1,259	4,212	4,160	4,095	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
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
	Austria	3	3	3	0	0	0	4	4	4	1	1	1	Autriche
	Bulgaria	0	0	0	0	0	0	0	0	0	0	0	0	Bulgarie
	Cyprus	3	2	2	0	0	0	3	2	2	0	0	0	Chypre
	Czech Republic	17	17	17	0	0	0	17	17	17	0	0	0	République tchèque
	Estonia	2	3	3	0	0	0	5	4	4	3	1	1	Estonie
	Finland	4	4	4	0	0	0	8	8	8	4	4	4	Finlande
	France	164	152	162	26	15	25	141	140	140	3	3	3	France
	Germany	29	20	20	0	0	0	79	60	60	50	40	40	Allemagne
	Hungary	2	3	4	0	0	0	2	4	4	0	0	0	Hongrie
	Italy	-21	-15	-15	5	5	5	161	154	154	187	175	175	Italie
	Luxembourg	4	2	2	0	0	0	4	2	2	0	0	0	Luxembourg
	Malta	1	1	1	0	0	0	1	1	1	0	0	0	Malte
	Netherlands	149	133	126	7	7	7	197	181	174	55	55	55	Pays-Bas
	Poland	10	11	12	0	0	0	12	13	14	2	2	2	Pologne
	Portugal	50	23	22	12	13	12	107	50	50	69	40	40	Portugal
	Serbia	5	4	5	1	1	1	5	3	4	1	0	0	Serbie
	Slovenia	2	2	2	0	0	0	2	2	2	0	0	0	Slovénie
	Spain	42	49	49	2	2	2	47	50	50	7	4	4	Espagne
	Sweden	1	1	1	0	0	0	1	1	1	0	0	0	Suède
	Switzerland	9	9	9	3	3	3	6	6	6	0	0	0	Suisse
	United Kingdom	91	90	90	0	0	0	94	90	90	3	0	0	Royaume-Uni
	Total Europe	566	515	519	56	46	55	896	793	788	386	324	324	Total Europe
	Canada	17	8	7	0	0	0	31	21	23	14	13	16	Canada
	United States	269	257	260	0	0	0	275	276	276	6	19	16	Etats-Unis
	Total North America	286	266	266	0	0	0	305	297	299	20	31	32	Total Amérique du Nord

Table 3


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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	74	39	39	8	8	8	83	45	45	17	14	14	Autriche
Cyprus	1	1	1	0	0	0	1	1	1	0	0	0	Chypre
Czech Republic	28	28	27	28	16	17	58	53	50	58	41	40	République tchèque
Estonia	111	125	125	105	110	110	87	95	95	82	80	80	Estonie
Finland	27	21	21	190	160	160	12	10	10	175	149	149	Finlande
France	366	366	366	157	157	157	273	273	273	64	64	64	France
Germany	157	143	125	110	105	105	99	78	70	52	40	50	Allemagne
Hungary	23	25	20	13	18	13	39	39	39	28	31	32	Hongrie
Italy	344	308	308	107	107	107	274	234	234	37	33	33	Italie
Latvia	105	105	105	40	50	50	140	140	140	75	85	85	Lettonie
Luxembourg	1	0	0	0	0	0	1	0	0	0	0	0	Luxembourg
Malta	1	2	3	0	0	0	1	2	3	0	0	0	Malte
Netherlands	15	13	13	0	0	0	17	15	15	3	3	3	Pays-Bas
Poland	121	121	129	45	42	45	92	94	98	16	15	14	Pologne
Portugal	12	20	35	20	30	25	38	40	50	46	50	40	Portugal
Serbia	4	4	5	30	28	30	8	6	8	34	30	33	Serbie
Slovakia	17	25	25	21	25	25	27	30	30	31	30	30	Slovaquie
Slovenia	9	8	9	28	27	25	13	14	14	32	33	30	Slovénie
Spain	122	92	92	40	36	36	127	90	90	45	34	34	Espagne
Sweden	32	31	31	60	50	50	19	10	10	47	29	29	Suède
Switzerland	3	3	3	0	0	0	4	4	4	1	1	1	Suisse
United Kingdom	6	10	10	0	0	0	7	10	10	1	0	0	Royaume-Uni
Total Europe	1,577	1,490	1,491	1,002	969	962	1,419	1,283	1,288	843	762	760	Total Europe
Uzbekistan	4	4	4	3	3	3	2	1	1	0	0	0	Ouzbékistan
Total EECCA												0	Total EOCAC
Canada	204	262	267	581	581	581	212	218	230	590	537	544	Canada
United States	2,643	2,670	2,699	2,284	2,306	2,329	652	658	664	293	294	294	Etats-Unis
Total North America	2,847	2,932	2,966	2,866	2,887	2,910	864	876	894	883	831	838	Total Amérique du Nord
Note: Definition of veneers excludes domestic use for plywood.
La définition des placages exclus la conversion directe en contreplaqué.

Table 4


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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	19	15	15	131	155	155	183	150	150	296	290	290	Autriche
Cyprus	14	15	15	0	0	0	14	15	15	0	0	0	Chypre
Czech Republic	193	116	123	240	236	238	230	115	115	277	235	230	République tchèque
Estonia	145	50	50	200	210	210	151	50	50	205	210	210	Estonie
Finland	297	240	240	1,110	940	940	87	60	60	900	760	760	Finlande
France	589	583	583	253	270	270	476	452	452	140	139	139	France
Germany	1,073	1,154	840	85	80	80	1,319	1,281	1,000	330	207	240	Allemagne
Hungary	136	110	107	60	61	63	138	138	138	62	90	94	Hongrie
Italy	602	537	537	288	290	290	525	442	442	211	195	195	Italie
Latvia	92	55	55	331	300	300	94	95	95	333	340	340	Lettonie
Luxembourg	33	29	29	0	0	0	33	29	29	0	0	0	Luxembourg
Malta	10	11	12	0	0	0	10	11	12	0	0	0	Malte
Montenegro	2	2	2	1	1	1	2	2	2	1	1	1	Monténégro
Netherlands	488	457	441	0	0	0	586	551	529	98	94	88	Pays-Bas
Poland	650	640	670	539	515	530	468	475	480	357	350	340	Pologne
Portugal	154	180	166	103	100	110	95	110	100	44	30	44	Portugal
Serbia	40	36	38	19	18	19	34	30	33	13	12	14	Serbie
Slovakia	67	63	63	153	150	150	59	59	59	146	146	146	Slovaquie
Slovenia	49	50	58	94	90	98	26	30	30	71	70	70	Slovénie
Spain	231	326	326	462	416	416	132	117	117	363	207	207	Espagne
Sweden	278	160	160	90	90	90	236	120	120	48	50	50	Suède
Switzerland	206	206	206	7	7	7	203	203	203	4	4	4	Suisse
United Kingdom	1,254	1,180	1,180	0	0	0	1,320	1,250	1,250	66	70	70	Royaume-Uni
Total Europe	6,623	6,215	5,916	4,166	3,930	3,967	6,422	5,786	5,482	3,965	3,501	3,532	Total Europe
Uzbekistan	62	46	46	0	0	0	63	47	47	0	0	0	Ouzbékistan
Total EECCA												0	Total EOCAC
Canada	2,174	2,028	2,123	1,604	1,557	1,526	1,224	1,058	1,241	654	587	644	Canada
United States	14,742	14,890	15,188	9,254	9,345	9,528	6,259	6,317	6,436	771	772	776	Etats-Unis
Total North America	16,916	16,918	17,311	10,858	10,902	11,054	7,483	7,375	7,677	1,425	1,359	1,420	Total Amérique du Nord

Table 5


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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	951	630	630	2,280	2,170	2,170	313	355	355	1,642	1,895	1,895	Autriche
Cyprus	49	46	46	0	0	0	49	46	46	0	0	0	Chypre
Czech Republic	793	811	835	962	866	910	530	484	485	699	538	560	République tchèque
Estonia	123	67	67	90	0	0	77	68	68	44	2	1	Estonie
Finland	113	75	75	54	54	54	85	44	44	26	23	23	Finlande
France	2,224	2,148	2,148	3,177	3,094	3,094	299	355	355	1,253	1,301	1,301	France
Germany	5,572	5,220	4,970	5,526	5,195	5,020	1,970	1,934	1,900	1,924	1,909	1,950	Allemagne
Hungary	408	384	379	447	428	438	264	282	272	303	326	331	Hongrie
Italy	3,070	2,813	2,813	2,646	2,500	2,500	956	821	821	532	508	508	Italie
Latvia	52	85	85	306	300	300	69	25	25	322	240	240	Lettonie
Luxembourg	20	12	12	0	0	0	21	13	13	1	1	1	Luxembourg
Malta	10	11	11	0	0	0	10	11	11	0	0	0	Malte
Montenegro	32	33	34	0	0	0	32	33	34	0	0	0	Monténégro
Netherlands	464	440	432	0	0	0	514	488	479	50	48	47	Pays-Bas
Poland	6,501	6,450	6,740	5,227	5,150	5,450	2,173	2,180	2,200	899	880	910	Pologne
Portugal	537	473	514	766	750	760	281	300	290	510	577	536	Portugal
Serbia	373	351	371	219	210	220	196	184	198	42	43	47	Serbie
Slovakia	352	343	340	676	675	675	148	140	137	473	473	472	Slovaquie
Slovenia	137	110	110	0	0	0	143	114	114	6	4	4	Slovénie
Spain	2,392	2,213	2,213	2,566	2,310	2,310	626	621	621	800	718	718	Espagne
Sweden	1,055	868	868	636	600	600	475	335	335	57	67	67	Suède
Switzerland	281	286	286	420	425	425	141	141	141	280	280	280	Suisse
United Kingdom	2,606	2,542	2,542	2,012	1,982	1,982	648	610	610	55	50	50	Royaume-Uni
Total Europe	28,115	26,410	26,521	28,012	26,710	26,908	10,021	9,584	9,555	9,917	9,883	9,942	Total Europe
Uzbekistan	880	542	542	252	252	252	654	317	317	26	27	27	Ouzbékistan
Total EECCA												27	Total EOCAC
Canada	1,466	1,886	1,894	1,625	2,032	2,012	552	504	491	710	650	609	Canada
United States	5,196	5,565	5,562	4,488	4,552	4,534	1,193	1,465	1,487	485	452	459	Etats-Unis
Total North America	6,663	7,451	7,456	6,113	6,584	6,546	1,745	1,969	1,978	1,195	1,102	1,068	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


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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	205	135	135	0	0	0	212	140	140	7	5	5	Autriche
Cyprus	11	14	14	0	0	0	11	14	14	0	0	0	Chypre
Czech Republic	380	342	350	689	620	655	126	113	115	435	392	420	République tchèque
Estonia	55	32	32	0	0	0	55	32	32	1	0	0	Estonie
Finland	56	56	56	0	0	0	56	56	56	0	0	0	Finlande
France	427	522	522	302	406	406	222	165	165	96	49	49	France
Germany	1,316	1,238	1,130	1,164	1,105	1,080	679	669	600	526	536	550	Allemagne
Hungary	133	147	152	379	419	443	56	60	59	302	331	350	Hongrie
Italy	346	287	287	100	100	100	346	274	274	100	87	87	Italie
Latvia	196	165	165	674	650	650	76	75	75	554	560	560	Lettonie
Luxembourg	110	135	135	338	338	338	6	14	14	234	217	217	Luxembourg
Montenegro	2	2	2	0	0	0	2	2	2	0	0	0	Monténégro
Netherlands	222	222	227	0	0	0	286	286	292	64	64	65	Pays-Bas
Poland	655	650	760	647	650	750	302	320	350	294	320	340	Pologne
Portugal	46	37	41	0	0	0	50	40	45	4	3	4	Portugal
Serbia	40	35	41	0	0	0	41	36	42	1	1	1	Serbie
Slovakia	48	58	60	0	0	0	48	60	63	1	3	3	Slovaquie
Slovenia	31	24	24	0	0	0	33	26	26	2	2	2	Slovénie
Spain	26	15	15	3	3	3	35	33	33	12	20	20	Espagne
Sweden	94	92	92	0	0	0	97	95	95	3	3	3	Suède
Switzerland	95	95	95	0	0	0	96	96	96	1	1	1	Suisse
United Kingdom	773	758	758	598	598	598	365	350	350	190	190	190	Royaume-Uni
Total Europe	5,268	5,060	5,092	4,894	4,888	5,023	3,200	2,956	2,938	2,826	2,784	2,868	Total Europe
Uzbekistan	7	5	5	0	0	0	7	5	5	0	0	0	Ouzbékistan
Total EECCA												0	Total EOCAC
Canada	1,546	1,253	1,153	7,270	6,820	6,798	82	65	61	5,806	5,632	5,706	Canada
United States	19,658	19,834	20,197	13,592	13,783	14,059	6,198	6,236	6,326	132	185	188	Etats-Unis
Total North America	21,204	21,087	21,350	20,862	20,603	20,857	6,280	6,301	6,387	5,938	5,817	5,894	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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	421	386	386	470	395	395	331	308	308	381	316	316	Autriche
Cyprus	20	15	16	0	0	0	20	15	16	0	0	0	Chypre
Czech Republic	328	276	280	41	41	42	438	347	360	151	112	122	République tchèque
Estonia	70	46	47	75	40	40	65	46	47	70	40	40	Estonie
Finland	139	105	105	44	44	44	141	102	102	46	41	41	Finlande
France	828	915	915	1,238	1,035	1,035	721	772	772	1,130	892	892	France
Germany	3,791	3,437	3,325	5,194	4,900	4,800	1,590	1,543	1,470	2,993	3,006	2,945	Allemagne
Hungary	9	-17	-13	21	0	0	204	235	244	215	253	258	Hongrie
Italy	1,862	1,661	1,661	827	818	818	1,281	974	974	245	131	131	Italie
Latvia	60	50	40	48	50	50	62	65	65	50	65	75	Lettonie
Luxembourg	100	90	90	147	147	147	34	19	19	80	76	76	Luxembourg
Malta	6	7	7	0	0	0	6	7	7	0	0	0	Malte
Montenegro	32	32	33	0	0	0	32	32	33	0	0	0	Monténégro
Netherlands	332	310	296	29	29	29	465	431	412	162	150	145	Pays-Bas
Poland	3,808	3,765	4,020	4,960	4,920	5,080	590	585	630	1,743	1,740	1,690	Pologne
Portugal	534	485	529	526	520	560	338	315	335	330	350	366	Portugal
Serbia	74	74	88	19	20	22	71	73	88	16	19	22	Serbie
Slovakia	210	218	223	0	0	0	248	256	262	39	38	39	Slovaquie
Slovenia	24	15	15	132	120	125	28	25	30	136	130	140	Slovénie
Spain	920	894	894	1,430	1,287	1,287	462	355	355	972	748	748	Espagne
Sweden	301	260	260	0	0	0	425	360	360	124	100	100	Suède
Switzerland	238	238	238	97	97	97	308	308	308	167	167	167	Suisse
United Kingdom	1,692	1,630	1,630	856	850	850	895	840	840	60	60	60	Royaume-Uni
Total Europe	15,799	14,892	15,085	16,153	15,313	15,421	8,755	8,013	8,037	9,110	8,434	8,373	Total Europe
Uzbekistan	1,092	809	809	47	47	47	1,057	771	771	13	9	9	Ouzbékistan
Total EECCA													Total EOCAC
Canada	1,236	1,183	1,181	1,277	1,288	1,299	818	628	605	859	733	723	Canada
United States	8,684	8,749	8,888	6,362	6,420	6,571	3,359	3,289	3,310	1,038	960	993	Etats-Unis
Total North America	9,920	9,932	10,069	7,639	7,708	7,870	4,177	3,917	3,915	1,896	1,693	1,716	Total Amérique du Nord

Table 6a


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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	29	28	28	54	43	43	18	16	16	43	32	32	Autriche
Cyprus	2	1	2	0	0	0	2	1	2	0	0	0	Chypre
Czech Republic	43	45	45	0	0	0	61	59	60	18	14	15	République tchèque
Estonia	23	15	19	0	0	0	30	16	20	7	1	1	Estonie
Finland	23	21	21	44	44	44	21	15	15	41	38	38	Finlande
France	55	55	55	221	221	221	207	207	207	373	373	373	France
Germany	176	183	165	0	0	0	200	203	180	23	20	15	Allemagne
Hungary	27	41	45	2	0	0	65	81	85	40	40	40	Hongrie
Italy	280	280	280	16	16	16	283	283	283	19	19	19	Italie
Latvia	1	5	5	15	15	15	18	20	20	32	30	30	Lettonie
Luxembourg	-31	-12	-12	0	0	0	3	8	8	34	20	20	Luxembourg
Montenegro	1	1	1	0	0	0	1	1	1	0	0	0	Monténégro
Netherlands	44	41	39	0	0	0	63	58	56	19	17	17	Pays-Bas
Poland	-179	-120	-50	80	80	80	88	100	120	347	300	250	Pologne
Portugal	50	30	39	0	0	0	61	40	50	11	10	11	Portugal
Serbia	39	35	38	19	20	22	33	31	34	13	16	18	Serbie
Slovakia	21	20	21	0	0	0	21	21	22	1	1	1	Slovaquie
Slovenia	-1	0	1	0	0	0	4	2	4	4	2	3	Slovénie
Spain	17	15	15	32	29	29	46	46	46	61	60	60	Espagne
Sweden	47	30	30	0	0	0	116	110	110	70	80	80	Suède
Switzerland	19	19	19	0	0	0	24	24	24	5	5	5	Suisse
United Kingdom	101	90	90	0	0	0	110	100	100	9	10	10	Royaume-Uni
Total Europe	787	822	895	482	468	470	1,474	1,441	1,463	1,169	1,087	1,037	Total Europe
Uzbekistan	89	50	50	0	0	0	90	50	50	0	0	0	Ouzbékistan
Total EECCA													Total EOCAC
Canada	33	47	42	90	90	90	52	27	28	109	70	76	Canada
United States	481	509	514	437	504	509	259	255	258	215	250	253	Etats-Unis
Total North America	514	556	556	527	594	599	311	282	286	324	320	329	Total Amérique du Nord

Table 6b


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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	260	230	230	416	351	351	177	160	160	333	281	281	Autriche
Cyprus	16	12	12	0	0	0	16	12	12	0	0	0	Chypre
Czech Republic	199	157	160	41	41	42	180	135	140	22	19	22	République tchèque
Estonia	18	21	18	0	0	0	33	28	25	15	7	7	Estonie
Finland	82	67	67	0	0	0	86	70	70	4	3	3	Finlande
France	708	794	794	954	751	751	337	388	388	583	345	345	France
Germany	1,870	1,728	1,720	3,792	3,700	3,650	424	395	370	2,345	2,367	2,300	Allemagne
Hungary	-39	-65	-62	0	0	0	136	148	156	175	213	218	Hongrie
Italy	1,501	1,299	1,299	809	800	800	913	606	606	221	107	107	Italie
Latvia	52	40	30	33	35	35	22	25	25	2	20	30	Lettonie
Luxembourg	128	98	98	147	147	147	27	7	7	46	56	56	Luxembourg
Malta	5	5	5	0	0	0	5	5	5	0	0	0	Malte
Montenegro	31	31	32	0	0	0	31	31	32	0	0	0	Monténégro
Netherlands	220	205	196	0	0	0	361	336	322	141	131	126	Pays-Bas
Poland	3,066	3,020	3,130	3,052	3,030	3,100	470	450	470	456	460	440	Pologne
Portugal	447	440	465	494	500	530	257	260	265	305	320	330	Portugal
Serbia	31	35	46	0	0	0	34	38	50	3	3	4	Serbie
Slovakia	135	135	135	0	0	0	170	170	170	35	35	35	Slovaquie
Slovenia	24	15	14	132	120	125	24	23	26	131	128	137	Slovénie
Spain	835	821	821	1,334	1,201	1,201	397	302	302	897	682	682	Espagne
Sweden	254	225	225	0	0	0	284	230	230	30	5	5	Suède
Switzerland	24	24	24	97	97	97	88	88	88	161	161	161	Suisse
United Kingdom	1,553	1,510	1,510	856	850	850	739	700	700	42	40	40	Royaume-Uni
Total Europe	11,419	10,847	10,969	12,157	11,623	11,679	5,210	4,606	4,618	5,948	5,382	5,328	Total Europe
Uzbekistan	671	513	513	46	46	46	629	469	469	3	2	2	Ouzbékistan
Total EECCA													Total EOCAC
Canada	1,053	999	1,005	1,087	1,098	1,109	608	472	449	641	570	553	Canada
United States	5,156	5,228	5,226	2,746	2,778	2,786	2,939	2,874	2,866	529	424	426	Etats-Unis
Total North America	6,209	6,227	6,231	3,833	3,876	3,895	3,547	3,346	3,315	1,170	994	979	Total Amérique du Nord

Table 6c


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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	131	128	128	0	0	0	136	132	132	4	3	3	Autriche
Cyprus	2	2	2	0	0	0	3	2	2	0	0	0	Chypre
Czech Republic	86	74	75	0	0	0	197	154	160	111	80	85	République tchèque
Estonia	29	10	10	75	40	40	3	2	2	49	32	32	Estonie
Finland	33	17	17	0	0	0	34	17	17	0	0	0	Finlande
France	65	66	66	63	63	63	177	177	177	174	174	174	France
Germany	1,745	1,526	1,440	1,402	1,200	1,150	966	945	920	624	619	630	Allemagne
Hungary	21	7	4	19	0	0	3	7	4	0	0	0	Hongrie
Italy	82	82	82	3	3	3	85	85	85	6	6	6	Italie
Latvia	7	5	5	0	0	0	23	20	20	16	15	15	Lettonie
Luxembourg	4	4	4	0	0	0	4	4	4	0	0	0	Luxembourg
Malta	1	2	2	0	0	0	1	2	2	0	0	0	Malte
Netherlands	68	64	61	29	29	29	41	37	34	2	2	2	Pays-Bas
Poland	920	865	940	1,828	1,810	1,900	33	35	40	940	980	1,000	Pologne
Portugal	37	15	25	32	20	30	20	15	20	15	20	25	Portugal
Serbia	4	4	4	0	0	0	4	4	4	0	0	0	Serbie
Slovakia	54	63	67	0	0	0	57	65	70	3	2	3	Slovaquie
Slovenia	0	0	0	0	0	0	0	0	0	0	0	0	Slovénie
Spain	69	59	59	64	58	58	20	7	7	15	6	6	Espagne
Sweden	0	5	5	0	0	0	25	20	20	24	15	15	Suède
Switzerland	195	195	195	0	0	0	196	196	196	1	1	1	Suisse
United Kingdom	38	30	30	0	0	0	47	40	40	9	10	10	Royaume-Uni
Total Europe	3,592	3,223	3,221	3,514	3,222	3,272	2,071	1,965	1,956	1,993	1,965	2,007	Total Europe
Uzbekistan	331	246	246	2	2	2	339	252	252	10	7	7	Ouzbékistan
Total EECCA													Total EOCAC
Canada	150	137	134	100	100	100	158	129	128	108	92	94	Canada
United States	3,047	3,012	3,148	3,179	3,138	3,276	161	160	186	294	286	314	Etats-Unis
Total North America	3,196	3,149	3,282	3,279	3,238	3,376	319	289	314	402	378	408	Total Amérique du Nord

Table 7


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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	2,209	1,950	2,030	1,977	1,700	1,800	630	610	630	399	360	400	Autriche
Czech Republic	847	688	700	640	525	540	324	259	260	117	96	100	République tchèque
Estonia	70	75	80	227	180	180	42	50	50	199	155	150	Estonie
Finland a	5,468	4,483	4,614	9,200	8,690	9,360	355	150	150	4,087	4,357	4,896	Finlande a
France	2,898	2,420	2,500	1,666	1,300	1,350	1,715	1,450	1,500	483	330	350	France
Germany	5,092	4,600	5,000	2,172	1,850	2,000	4,173	3,900	4,200	1,253	1,150	1,200	Allemagne
Hungary	205	206	214	66	77	87	141	133	131	3	3	4	Hongrie
Italy	3,466	3,466	3,466	223	223	223	3,536	3,536	3,536	293	293	293	Italie
Latvia	7	7	7	12	13	13	7	7	7	12	13	13	Lettonie
Netherlands	443	442	442	37	37	37	1,717	1,717	1,717	1,312	1,312	1,312	Pays-Bas
Poland	2,836	2,830	2,930	1,729	1,710	1,750	1,291	1,300	1,320	183	180	140	Pologne
Portugal	1,757	1,735	1,760	2,869	2,870	2,870	140	145	150	1,252	1,280	1,260	Portugal
Serbia	82	88	92	0	0	0	82	88	92	0	0	0	Serbie
Slovakia	700	700	715	692	700	725	173	170	170	166	170	180	Slovaquie
Slovenia	322	321	316	73	63	68	249	260	250	1	2	2	Slovénie
Spain	1,520	1,328	1,328	1,120	1,120	1,120	1,176	976	976	775	768	768	Espagne
Sweden	8,438	7,600	7,950	11,631	10,900	11,400	641	600	600	3,834	3,900	4,050	Suède
Switzerland	188	188	188	87	87	87	101	101	101	0	0	0	Suisse
United Kingdom	1,057	940	950	220	200	200	838	740	750	1	0	0	Royaume-Uni
Total Europe	37,604	34,067	35,282	34,641	32,244	33,809	17,333	16,193	16,590	14,369	14,369	15,118	Total Europe
Uzbekistan	38	28	28	1	1	1	37	28	28	0	0	0	Ouzbékistan
Total EECCA													Total EOCAC
Canada	6,007	5,851	5,616	14,200	13,102	12,638	472	582	640	8,665	7,833	7,662	Canada
United States	39,787	42,269	42,815	40,822	41,230	41,478	6,948	7,643	8,254	7,983	6,603	6,917	Etats-Unis
Total North America	45,794	48,121	48,431	55,022	54,332	54,116	7,420	8,224	8,894	16,648	14,436	14,579	Total Amérique du Nord
	a imports exclude dissolving pulp						a les importations excluent pâte à dissoudre

Table 8


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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	2,133	1,750	2,050	4,633	3,500	4,000	1,231	1,050	1,150	3,730	2,800	3,100	Autriche
Cyprus	56	48	48	0	0	0	56	48	48	0	0	0	Chypre
Czech Republic	1,467	1,234	1,258	938	769	785	1,531	1,286	1,312	1,002	822	838	République tchèque
Estonia	120	111	111	57	35	35	123	102	102	59	26	26	Estonie
Finland	514	475	460	7,200	5,990	6,150	333	275	280	7,019	5,790	5,970	Finlande
France	8,272	7,290	7,400	7,092	6,240	6,600	4,845	4,650	4,600	3,665	3,600	3,800	France
Germany	17,836	14,600	17,000	21,612	17,500	21,000	9,302	8,000	9,500	13,078	10,900	13,500	Allemagne
Hungary	1,213	1,167	1,212	1,057	1,003	1,034	877	892	898	720	727	721	Hongrie
Italy	11,390	11,390	11,390	8,696	8,696	8,696	5,800	5,800	5,800	3,106	3,106	3,106	Italie
Latvia	168	175	175	29	30	30	173	180	180	33	35	35	Lettonie
Luxembourg	26	14	14	0	0	0	27	15	15	1	1	1	Luxembourg
Malta	26	27	28	0	0	0	26	27	28	0	0	0	Malte
Netherlands	2,814	2,760	2,760	2,884	2,827	2,827	2,180	2,096	2,096	2,250	2,163	2,163	Pays-Bas
Poland	7,532	7,400	7,550	5,237	5,130	5,250	4,869	4,870	4,950	2,574	2,600	2,650	Pologne
Portugal	1,090	1,200	1,240	2,123	2,200	2,240	948	940	945	1,981	1,940	1,945	Portugal
Serbia	790	762	778	483	481	490	514	490	500	207	209	212	Serbie
Slovakia	565	575	600	967	975	1,000	457	450	475	859	850	875	Slovaquie
Slovenia	412	390	390	591	500	500	367	390	390	545	500	500	Slovénie
Spain	7,060	6,778	6,778	6,355	6,355	6,355	2,997	2,577	2,577	2,291	2,154	2,154	Espagne
Sweden	834	700	750	8,531	7,300	8,100	894	700	750	8,591	7,300	8,100	Suède
Switzerland	1,020	1,015	1,010	1,160	1,155	1,150	640	635	630	780	775	770	Suisse
United Kingdom	7,420	6,280	6,440	3,460	3,190	3,250	5,015	4,150	4,250	1,055	1,060	1,060	Royaume-Uni
Total Europe	72,758	66,140	69,442	83,103	73,876	79,492	43,204	39,623	41,476	53,549	47,358	51,526	Total Europe
Uzbekistan	335	297	297	142	142	142	214	171	171	21	17	17	Ouzbékistan
Total EECCA													Total EOCAC
Canada	5,505	6,069	6,231	9,094	9,124	9,155	2,516	2,242	2,235	6,105	5,298	5,159	Canada
United States	64,243	62,896	63,029	65,959	64,476	64,476	8,202	8,180	8,159	9,917	9,761	9,606	Etats-Unis
Total North America	69,748	68,964	69,260	75,053	73,600	73,631	10,718	10,423	10,395	16,023	15,059	14,765	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
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		13,935	11,716	12,225	10,711	8,904	9,338	3,223	2,812	2,887	0	0	0	5,424	5,115	5,234	19,359	16,831	17,459	Autriche
Cyprus		3	2	2	2	2	2	0	0	0	0	0	0	11	9	8	14	11	10	Chypre
Czech Republic		20,708	15,535	14,897	14,635	10,617	10,106	5,965	4,804	4,675	108	113	115	4,405	3,965	3,900	25,113	19,499	18,797	République tchèque
Estonia		6,474	6,401	6,401	4,276	4,200	4,200	2,148	2,150	2,150	51	51	51	4,066	3,800	3,800	10,541	10,201	10,201	Estonie
Finland		56,246	53,397	55,435	25,699	22,749	23,412	30,547	30,648	32,023	0	0	0	9,340	9,340	9,340	65,586	62,737	64,775	Finlande
France		25,648	25,270	25,070	17,198	17,200	17,300	7,891	7,500	7,200	559	570	570	24,173	24,500	25,600	49,821	49,770	50,670	France
Germany		56,534	53,930	49,630	44,756	41,200	39,500	11,644	12,600	10,000	135	130	130	22,338	22,700	22,700	78,872	76,630	72,330	Allemagne
Hungary		2,901	2,881	2,881	1,410	1,374	1,399	912	995	1,008	579	512	475	3,626	3,284	3,397	6,527	6,165	6,278	Hongrie
Italy		2,838	3,540	3,540	1,890	1,890	1,890	316	1,018	1,018	632	632	632	10,839	10,839	10,839	13,677	14,379	14,379	Italie
Latvia		12,491	12,150	12,350	7,603	7,250	7,450	3,868	3,800	3,800	1,020	1,100	1,100	2,936	3,000	3,000	15,427	15,150	15,350	Lettonie
Luxembourg		231	197	193	147	144	133	56	38	38	27	15	22	40	45	43	271	242	235	Luxembourg
Montenegro		751	697	678	515	492	487	201	198	186	35	7	5	194	193	190	945	890	868	Monténégro
Netherlands		614	599	589	221	220	215	352	340	335	41	39	39	2,382	2,380	2,385	2,996	2,979	2,974	Pays-Bas
Poland		38,735	39,880	40,850	18,533	18,800	19,150	19,350	20,100	20,550	852	980	1,150	6,958	7,420	7,750	45,693	47,300	48,600	Pologne
Portugal		12,235	12,330	12,190	2,038	2,040	2,060	9,799	9,850	9,700	399	440	430	2,383	2,380	2,300	14,619	14,710	14,490	Portugal
Serbia		1,478	1,520	1,561	1,077	1,104	1,130	265	275	283	136	141	148	6,574	6,646	6,760	8,052	8,166	8,321	Serbie
Slovakia		6,827	6,820	6,880	4,130	4,080	4,100	2,672	2,710	2,750	25	30	30	609	610	650	7,435	7,430	7,530	Slovaquie
Slovenia		2,928	3,752	3,482	2,184	2,780	2,600	698	920	830	45	52	52	1,149	1,290	1,270	4,076	5,042	4,752	Slovénie
Spain		14,366	15,244	15,244	4,150	4,404	4,404	9,813	10,413	10,413	403	427	427	3,555	3,772	3,772	17,921	19,016	19,016	Espagne
Sweden		71,165	69,076	69,310	38,280	37,480	37,080	32,585	31,296	31,930	300	300	300	6,000	6,016	6,016	77,165	75,092	75,326	Suède
Switzerland		3,011	3,082	3,142	2,555	2,625	2,680	444	445	450	12	12	12	1,938	2,000	2,025	4,949	5,082	5,167	Suisse
United Kingdom		7,604	7,193	7,193	5,509	5,236	5,236	1,646	1,529	1,529	448	428	428	2,184	2,184	2,184	9,788	9,377	9,377	Royaume-Uni
Total Europe		357,723	345,212	343,742	207,519	194,791	193,872	144,397	144,441	143,754	5,807	5,980	6,116	121,124	121,488	123,163	478,847	466,699	466,905	Total Europe
Canada		142,131	140,499	140,499	124,900	123,350	123,350	15,040	14,864	14,864	2,190	2,285	2,285	1,683	1,908	1,908	143,814	142,407	142,407	Canada
United States		382,544	384,963	388,611	186,157	188,221	191,211	182,650	182,996	183,637	13,737	13,746	13,763	76,230	76,240	76,278	458,774	461,203	464,889	Etats-Unis
Total North America		524,675	525,462	529,110	311,057	311,571	314,561	197,690	197,861	198,501	15,927	16,031	16,048	77,913	78,148	78,186	602,587	603,610	607,296	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


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
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		12,958	10,873	11,338	10,382	8,638	9,038	2,576	2,235	2,300	0	0	0	3,248	3,069	3,140	16,206	13,942	14,478	Autriche
Cyprus		2	2	2	2	2	2	0	0	0	0	0	0	10	8	7	12	10	9	Chypre
Czech Republic		19,440	14,455	13,825	14,019	10,094	9,589	5,316	4,253	4,125	105	109	111	3,610	3,249	3,200	23,050	17,704	17,025	République tchèque
Estonia		4,023	3,927	3,927	3,118	3,000	3,000	878	900	900	26	27	27	1,486	1,400	1,400	5,509	5,327	5,327	Estonie
Finland		47,408	45,464	47,590	24,662	21,700	22,351	22,746	23,764	25,239	0	0	0	4,593	4,593	4,593	52,001	50,057	52,183	Finlande
France		17,300	17,070	16,770	12,491	12,500	12,500	4,559	4,300	4,000	250	270	270	2,417	2,500	2,600	19,717	19,570	19,370	France
Germany		52,425	50,120	46,120	41,761	38,500	37,000	10,541	11,500	9,000	123	120	120	8,834	9,200	9,200	61,259	59,320	55,320	Allemagne
Hungary		688	759	743	175	201	208	411	488	481	102	70	53	383	294	333	1,071	1,053	1,076	Hongrie
Italy		1,797	2,502	2,502	1,169	1,169	1,169	148	853	853	480	480	480	1,180	1,180	1,180	2,977	3,682	3,682	Italie
Latvia		8,253	7,900	8,100	5,873	5,500	5,700	1,850	1,800	1,800	530	600	600	298	300	300	8,551	8,200	8,400	Lettonie
Luxembourg		162	143	145	124	122	115	10	6	8	27	15	22	17	11	12	178	154	158	Luxembourg
Montenegro		573	553	537	372	352	349	201	198	186	0	3	2	66	65	63	639	618	600	Monténégro
Netherlands		449	440	430	173	170	165	244	240	235	32	30	30	457	450	450	906	890	880	Pays-Bas
Poland		31,941	32,800	33,470	15,775	16,000	16,250	15,411	15,950	16,250	754	850	970	3,627	3,820	3,950	35,568	36,620	37,420	Pologne
Portugal		3,045	3,210	3,150	1,682	1,710	1,700	1,213	1,350	1,300	150	150	150	996	990	980	4,041	4,200	4,130	Portugal
Serbia		279	290	301	178	184	190	66	70	73	35	36	38	141	146	160	420	436	461	Serbie
Slovakia		3,325	3,160	3,120	2,559	2,430	2,400	748	710	700	18	20	20	259	260	275	3,584	3,420	3,395	Slovaquie
Slovenia		1,966	2,586	2,386	1,687	2,150	2,000	275	430	380	4	6	6	191	240	220	2,157	2,826	2,606	Slovénie
Spain		7,435	7,889	7,889	3,420	3,629	3,629	3,754	3,984	3,984	261	277	277	2,243	2,380	2,380	9,678	10,269	10,269	Espagne
Sweden		64,603	62,760	62,873	38,100	37,300	36,900	26,353	25,310	25,823	150	150	150	3,000	3,008	3,008	67,603	65,768	65,881	Suède
Switzerland		2,578	2,639	2,689	2,290	2,350	2,400	279	280	280	9	9	9	769	770	775	3,347	3,409	3,464	Suisse
United Kingdom		7,486	7,076	7,076	5,453	5,180	5,180	1,633	1,516	1,516	400	380	380	1,571	1,571	1,571	9,058	8,647	8,647	Royaume-Uni
Total Europe		288,136	276,619	274,984	185,467	172,881	171,836	99,212	100,136	99,433	3,458	3,602	3,715	39,396	39,504	39,798	327,533	316,123	314,781	Total Europe
Canada		114,659	112,907	112,907	110,046	108,424	108,424	4,229	4,021	4,021	384	462	462	806	946	946	115,465	113,853	113,853	Canada
United States		306,119	309,360	313,639	152,799	154,479	156,695	141,226	142,779	144,827	12,094	12,102	12,117	37,619	37,609	37,606	343,738	346,969	351,245	Etats-Unis
Total North America		420,778	422,267	426,546	262,845	262,903	265,119	145,455	146,800	148,848	12,478	12,564	12,579	38,425	38,555	38,552	459,203	460,822	465,098	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


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
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		977	843	887	329	266	300	647	577	587	0	0	0	2,176	2,046	2,094	3,153	2,889	2,981	Autriche
Cyprus		0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	Chypre
Czech Republic		1,268	1,079	1,071	616	524	517	649	552	550	3	4	4	795	716	700	2,063	1,795	1,771	République tchèque
Estonia		2,452	2,474	2,474	1,158	1,200	1,200	1,270	1,250	1,250	24	24	24	2,580	2,400	2,400	5,032	4,874	4,874	Estonie
Finland		8,838	7,933	7,845	1,037	1,049	1,061	7,801	6,884	6,784	0	0	0	4,747	4,747	4,747	13,585	12,680	12,592	Finlande
France		8,348	8,200	8,300	4,707	4,700	4,800	3,332	3,200	3,200	309	300	300	21,756	22,000	23,000	30,104	30,200	31,300	France
Germany		4,110	3,810	3,510	2,995	2,700	2,500	1,103	1,100	1,000	12	10	10	13,504	13,500	13,500	17,613	17,310	17,010	Allemagne
Hungary		2,213	2,122	2,138	1,234	1,173	1,191	502	507	526	477	442	421	3,244	2,990	3,064	5,456	5,112	5,202	Hongrie
Italy		1,041	1,038	1,038	721	721	721	168	166	166	152	152	152	9,659	9,659	9,659	10,700	10,697	10,697	Italie
Latvia		4,238	4,250	4,250	1,730	1,750	1,750	2,018	2,000	2,000	490	500	500	2,638	2,700	2,700	6,876	6,950	6,950	Lettonie
Luxembourg		69	54	47	23	22	18	46	32	30	0	0	0	23	34	30	92	89	78	Luxembourg
Montenegro		178	144	141	143	140	138	0	0	0	35	4	3	128	128	127	306	272	268	Monténégro
Netherlands		165	159	159	48	50	50	108	100	100	9	9	9	1,925	1,930	1,935	2,090	2,089	2,094	Pays-Bas
Poland		6,794	7,080	7,380	2,757	2,800	2,900	3,939	4,150	4,300	98	130	180	3,331	3,600	3,800	10,125	10,680	11,180	Pologne
Portugal		9,190	9,120	9,040	356	330	360	8,586	8,500	8,400	249	290	280	1,387	1,390	1,320	10,578	10,510	10,360	Portugal
Serbia		1,199	1,230	1,260	899	920	940	199	205	210	101	105	110	6,433	6,500	6,600	7,632	7,730	7,860	Serbie
Slovakia		3,502	3,660	3,760	1,570	1,650	1,700	1,924	2,000	2,050	8	10	10	350	350	375	3,851	4,010	4,135	Slovaquie
Slovenia		962	1,166	1,096	497	630	600	424	490	450	41	46	46	957	1,050	1,050	1,919	2,216	2,146	Slovénie
Spain		6,931	7,354	7,354	730	775	775	6,059	6,429	6,429	142	151	151	1,312	1,392	1,392	8,243	8,746	8,746	Espagne
Sweden		6,562	6,316	6,437	180	180	180	6,232	5,986	6,107	150	150	150	3,000	3,008	3,008	9,562	9,324	9,445	Suède
Switzerland		433	443	453	265	275	280	165	165	170	3	3	3	1,169	1,230	1,250	1,602	1,673	1,703	Suisse
United Kingdom		118	117	117	56	56	56	13	13	13	48	48	48	613	613	613	730	730	730	Royaume-Uni
Total Europe		69,587	68,593	68,759	22,052	21,910	22,036	45,185	44,305	44,322	2,350	2,377	2,401	81,728	81,984	83,365	151,314	150,576	152,124	Total Europe
Canada		27,472	27,592	27,592	14,854	14,926	14,926	10,812	10,843	10,843	1,806	1,823	1,823	877	961	961	28,349	28,554	28,554	Canada
United States		76,425	75,603	74,972	33,358	33,742	34,516	41,424	40,217	38,810	1,643	1,644	1,646	38,611	38,631	38,672	115,036	114,234	113,644	Etats-Unis
Total North America		103,897	103,196	102,564	48,212	48,668	49,442	52,236	51,060	49,653	3,449	3,467	3,469	39,488	39,592	39,633	143,385	142,788	142,197	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


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
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		16,101	13,943	13,638	10,382	8,638	9,038	6,664	5,710	5,000	945	405	400	Autriche
Cyprus		2	2	2	2	2	2	0	0	0	0	0	0	Chypre
Czech Republic		8,002	6,511	6,962	14,019	10,094	9,589	411	596	715	6,428	4,178	3,343	République tchèque
Estonia		3,533	3,270	3,270	3,118	3,000	3,000	522	450	450	107	180	180	Estonie
Finland		24,310	21,336	21,991	24,662	21,700	22,351	127	79	83	479	443	443	Finlande
France		12,053	12,120	12,120	12,491	12,500	12,500	335	360	360	773	740	740	France
Germany		39,391	35,800	34,900	41,761	38,500	37,000	3,300	3,000	3,100	5,670	5,700	5,200	Allemagne
Hungary		175	201	208	175	201	208	0	0	0	0	0	0	Hongrie
Italy		1,645	1,396	1,396	1,169	1,169	1,169	580	457	457	104	230	230	Italie
Latvia		6,471	5,830	6,200	5,873	5,500	5,700	1,147	900	900	549	570	400	Lettonie
Luxembourg		465	403	396	124	122	115	693	424	424	352	143	143	Luxembourg
Montenegro		382	361	357	372	352	349	10	9	8	0	0	0	Monténégro
Netherlands		133	145	145	173	170	165	77	80	80	117	105	100	Pays-Bas
Poland		14,243	14,500	14,800	15,775	16,000	16,250	1,245	1,400	1,550	2,777	2,900	3,000	Pologne
Portugal		1,880	1,905	1,900	1,682	1,710	1,700	241	230	240	43	35	40	Portugal
Serbia		188	187	194	178	184	190	12	9	12	2	6	8	Serbie
Slovakia		3,059	3,030	3,100	2,559	2,430	2,400	900	950	1,000	400	350	300	Slovaquie
Slovenia		1,643	1,650	1,630	1,687	2,150	2,000	239	150	180	283	650	550	Slovénie
Spain		3,223	3,307	3,307	3,420	3,629	3,629	240	185	185	437	507	507	Espagne
Sweden		38,103	37,725	37,325	38,100	37,300	36,900	964	1,128	1,128	961	703	703	Suède
Switzerland		2,035	2,100	2,155	2,290	2,350	2,400	55	60	65	310	310	310	Suisse
United Kingdom		5,810	5,538	5,538	5,453	5,180	5,180	457	457	457	99	99	99	Royaume-Uni
Total Europe		182,849	171,260	171,534	185,467	172,881	171,836	18,218	16,634	16,394	20,836	18,255	16,696	Total Europe
Canada		105,870	103,492	103,916	110,046	108,424	108,424	1,346	1,402	1,309	5,522	6,333	5,816	Canada
United States		148,043	150,509	153,391	152,799	154,479	156,695	586	570	555	5,342	4,540	3,859	Etats-Unis
Total North America		253,913	254,001	257,307	262,845	262,903	265,119	1,931	1,972	1,864	10,863	10,873	9,675	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


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
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		406	311	300	329	266	300	134	90	50	57	45	50	Autriche
Czech Republic		544	457	447	616	524	517	144	120	125	216	186	195	République tchèque
Estonia		1,187	1,244	1,244	1,158	1,200	1,200	46	60	60	16	16	16	Estonie
Finland		1,068	1,041	1,061	1,037	1,049	1,061	32	1	9	1	9	9	Finlande
France		3,453	4,020	4,120	4,707	4,700	4,800	116	120	120	1,370	800	800	France
Germany		2,532	2,290	2,130	2,995	2,700	2,500	111	110	110	574	520	480	Allemagne
Hungary		1,234	1,173	1,191	1,234	1,173	1,191	0	0	0	0	0	0	Hongrie
Italy		2,088	1,718	1,718	721	721	721	1,413	1,055	1,055	47	59	59	Italie
Latvia		1,221	1,190	1,410	1,730	1,750	1,750	87	40	60	596	600	400	Lettonie
Luxembourg		226	148	144	23	22	18	221	160	160	18	34	34	Luxembourg
Montenegro		143	140	138	143	140	138	0	0	0	0	0	0	Monténégro
Netherlands		54	60	60	48	50	50	54	60	60	48	50	50	Pays-Bas
Poland		2,687	2,730	2,830	2,757	2,800	2,900	80	80	80	150	150	150	Pologne
Portugal		997	885	925	356	330	360	663	580	590	22	25	25	Portugal
Serbia		894	922	946	899	920	940	15	20	28	20	18	22	Serbie
Slovakia		1,670	1,700	1,750	1,570	1,650	1,700	500	450	450	400	400	400	Slovaquie
Slovenia		281	290	280	497	630	600	31	30	30	247	370	350	Slovénie
Spain		833	854	854	730	775	775	164	174	174	61	94	94	Espagne
Sweden		217	217	217	180	180	180	37	37	37	0	0	0	Suède
Switzerland		145	155	160	265	275	280	35	40	40	155	160	160	Suisse
United Kingdom		78	77	77	56	56	56	26	26	26	5	5	5	Royaume-Uni
Total Europe		21,959	21,622	22,002	22,052	21,910	22,036	3,910	3,253	3,265	4,003	3,541	3,299	Total Europe
Canada		15,890	15,923	15,895	14,854	14,926	14,926	1,106	1,060	1,027	70	64	59	Canada
United States		31,550	32,311	33,431	33,358	33,742	34,516	221	156	156	2,028	1,587	1,241	Etats-Unis
Total North America		47,441	48,234	49,326	48,212	48,668	49,442	1,327	1,216	1,183	2,098	1,650	1,300	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 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
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		406	311	300	329	266	300	134	90	50	57	45	50	Autriche
Czech Republic		544	457	447	616	524	517	144	120	125	216	186	195	République tchèque
Estonia		1,187	1,244	1,244	1,158	1,200	1,200	46	60	60	16	16	16	Estonie
Finland		1,068	1,041	1,061	1,037	1,049	1,061	32	1	9	1	9	9	Finlande
France		3,412	3,978	4,078	4,707	4,700	4,800	72	75	75	1,367	797	797	France
Germany		2,527	2,285	2,125	2,995	2,700	2,500	101	100	100	569	515	475	Allemagne
Hungary		1,234	1,173	1,191	1,234	1,173	1,191	0	0	0	0	0	0	Hongrie
Italy		2,068	1,729	1,729	721	721	721	1,389	1,047	1,047	42	39	39	Italie
Latvia		1,221	1,190	1,410	1,730	1,750	1,750	87	40	60	596	600	400	Lettonie
Luxembourg		226	148	144	23	22	18	221	160	160	18	34	34	Luxembourg
Montenegro		143	140	138	143	140	138	0	0	0	0	0	0	Monténégro
Netherlands		46	55	55	48	50	50	42	50	50	44	45	45	Pays-Bas
Poland		2,685	2,727	2,827	2,757	2,800	2,900	78	77	77	150	150	150	Pologne
Portugal		981	870	912	356	330	360	642	560	571	17	20	19	Portugal
Serbia		893	921	945	899	920	940	14	19	27	20	18	22	Serbie
Slovakia		1,670	1,700	1,750	1,570	1,650	1,700	500	450	450	400	400	400	Slovaquie
Slovenia		280	290	280	497	630	600	30	30	30	247	370	350	Slovénie
Spain		827	847	847	730	775	775	158	167	167	61	94	94	Espagne
Sweden		217	217	217	180	180	180	37	37	37	0	0	0	Suède
Switzerland		145	155	160	265	275	280	35	40	40	155	160	160	Suisse
United Kingdom		76	75	75	56	56	56	24	24	24	5	5	5	Royaume-Uni
Total Europe		21,857	21,553	21,935	22,052	21,910	22,036	3,786	3,146	3,158	3,980	3,503	3,260	Total Europe
Canada		15,890	15,923	15,895	14,854	14,926	14,926	1,106	1,060	1,027	70	64	59	Canada
United States		31,549	32,308	33,429	33,358	33,742	34,516	219	152	154	2,027	1,586	1,240	Etats-Unis
Total North America		47,440	48,231	49,324	48,212	48,668	49,442	1,325	1,212	1,181	2,097	1,649	1,299	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


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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
France	-41	-42	-42				44	45	45	3	3	3	France
Germany	-5	-5	-5				10	10	10	5	5	5	Allemagne
Italy	-20	11	11				25	9	9	4	20	20	Italie
Netherlands	-8	-5	-5				12	10	10	4	5	5	Pays-Bas
Poland	-2	-3	-3				2	3	3	0	0	0	Pologne
Portugal	-16	-15	-13				21	20	19	5	5	6	Portugal
Serbia	-1	-1	-1				1	1	1	0	0	0	Serbie
Slovenia	-1	-0	-0				1	0	1	0	0	0	Slovénie
Spain	-6	-7	-7				6	7	7	0	0	0	Espagne
United Kingdom	-2	-2	-2				2	2	2	0	0	0	Royaume-Uni
Total Europe	-102	-69	-67				124	107	106	22	38	39	Total Europe
United States	-1	-3	-1				2	4	2	1	1	1	Etats-Unis
Total North America	-1	-3	-1				2	4	2	1	1	1	Total Amérique du Nord

Table12


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
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		13,844	12,627	12,592	11,047	9,212	9,287	3,676	4,070	4,020	879	655	715	Autriche
Cyprus		8	9	10	7	8	9	1	1	1	0	0	0	Chypre
Czech Republic		5,559	5,135	5,154	7,664	6,164	6,130	1,270	1,146	1,162	3,375	2,175	2,138	République tchèque
Estonia		3,117	2,380	2,435	6,548	6,550	6,550	256	330	285	3,687	4,500	4,400	Estonie
Finland		48,404	47,241	49,358	44,923	44,026	45,568	5,037	4,969	5,545	1,556	1,755	1,755	Finlande
France		24,495	24,350	24,050	24,257	24,000	23,700	2,527	2,600	2,600	2,289	2,250	2,250	France
Germany		26,555	26,580	23,090	27,936	27,100	23,500	4,474	3,870	3,770	5,855	4,390	4,180	Allemagne
Hungary		2,122	2,017	2,065	2,049	1,984	2,023	112	73	82	39	39	39	Hongrie
Italy		4,508	5,210	5,210	3,916	4,618	4,618	1,288	1,288	1,288	696	696	696	Italie
Latvia		5,540	5,150	5,150	9,484	8,800	8,800	1,084	950	950	5,028	4,600	4,600	Lettonie
Luxembourg		583	589	589	577	559	559	182	130	130	176	100	100	Luxembourg
Malta		2	3	3	0	0	0	2	3	3	0	0	0	Malte
Montenegro		245	241	227	245	241	227	0	0	0	0	0	0	Monténégro
Netherlands		604	1,100	1,095	1,267	1,240	1,230	289	100	105	952	240	240	Pays-Bas
Poland		35,250	36,265	37,135	33,531	34,600	35,450	3,652	3,660	3,710	1,933	1,995	2,025	Pologne
Portugal		15,954	15,330	15,365	11,664	11,720	11,590	4,657	4,000	4,140	368	390	365	Portugal
Serbia		981	1,007	1,045	967	1,000	1,033	15	8	13	1	1	1	Serbie
Slovakia		3,634	3,650	3,760	3,821	3,860	3,950	1,023	1,030	1,050	1,210	1,240	1,240	Slovaquie
Slovenia		926	770	790	2,058	2,280	2,230	625	490	530	1,757	2,000	1,970	Slovénie
Spain		13,959	14,358	14,358	14,383	15,261	15,261	1,435	1,564	1,564	1,859	2,467	2,467	Espagne
Sweden		55,632	54,193	54,727	50,015	48,196	48,730	7,036	7,750	7,750	1,419	1,753	1,753	Suède
Switzerland		1,823	1,824	1,829	1,216	1,217	1,222	795	795	795	188	188	188	Suisse
United Kingdom		4,590	4,471	4,471	4,293	4,175	4,175	406	405	405	109	109	109	Royaume-Uni
Total Europe		268,336	264,500	264,508	261,870	256,811	255,841	39,843	39,232	39,898	33,377	31,543	31,231	Total Europe
Canada		37,044	35,822	35,734	35,326	32,985	32,975	2,578	3,462	3,467	860	625	708	Canada
United States		238,450	239,587	240,850	244,912	246,110	247,536	348	324	308	6,809	6,848	6,994	Etats-Unis
Total North America		275,495	275,409	276,585	280,238	279,096	280,511	2,926	3,786	3,776	7,670	7,473	7,702	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


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
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		3,681	3,895	3,850	2,576	2,235	2,300	1,312	1,750	1,700	206	90	150	Autriche
Czech Republic		3,927	3,744	3,675	5,316	4,253	4,125	811	811	830	2,200	1,320	1,280	République tchèque
Estonia		476	245	245	878	900	900	56	45	45	458	700	700	Estonie
Finland		22,913	24,189	25,835	22,746	23,764	25,239	1,163	1,410	1,581	996	985	985	Finlande
France		4,689	4,400	4,100	4,559	4,300	4,000	608	550	550	478	450	450	France
Germany		10,311	11,900	9,500	10,541	11,500	9,000	2,200	2,100	2,000	2,430	1,700	1,500	Allemagne
Hungary		411	488	481	411	488	481	0	0	0	0	0	0	Hongrie
Italy		148	853	853	148	853	853	0	0	0	0	0	0	Italie
Latvia		1,775	1,700	1,700	1,850	1,800	1,800	374	400	400	449	500	500	Lettonie
Luxembourg		-16	-18	-16	10	6	8	9	3	3	35	27	27	Luxembourg
Montenegro		201	198	186	201	198	186	0	0	0	0	0	0	Monténégro
Netherlands		146	150	145	244	240	235	70	80	85	168	170	175	Pays-Bas
Poland		15,378	15,900	16,300	15,411	15,950	16,250	1,428	1,500	1,650	1,462	1,550	1,600	Pologne
Portugal		1,323	1,430	1,375	1,213	1,350	1,300	122	100	90	12	20	15	Portugal
Serbia		66	70	74	66	70	73	0	0	1	0	0	0	Serbie
Slovakia		598	600	610	748	710	700	600	630	650	750	740	740	Slovaquie
Slovenia		264	200	220	275	430	380	268	170	200	278	400	360	Slovénie
Spain		3,369	3,467	3,467	3,754	3,984	3,984	179	138	138	564	655	655	Espagne
Sweden		28,513	27,431	27,944	26,353	25,310	25,823	3,114	3,269	3,269	954	1,148	1,148	Suède
Switzerland		209	210	210	279	280	280	20	20	20	90	90	90	Suisse
United Kingdom		1,894	1,776	1,776	1,633	1,516	1,516	291	291	291	31	31	31	Royaume-Uni
Total Europe		100,275	102,827	102,530	99,212	100,136	99,433	12,625	13,267	13,503	11,562	10,576	10,406	Total Europe
Canada		4,531	4,347	4,410	4,229	4,021	4,021	324	336	401	22	10	12	Canada
United States		141,231	142,785	144,831	141,226	142,779	144,827	5	6	4	0	0	0	Etats-Unis
Total North America		145,762	147,132	149,241	145,455	146,800	148,848	329	341	405	22	10	12	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


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
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		1,217	997	1,007	647	577	587	668	500	500	98	80	80	Autriche
Czech Republic		450	380	384	649	552	550	3	2	2	202	174	168	République tchèque
Estonia		363	200	250	1,270	1,250	1,250	154	250	200	1,060	1,300	1,200	Estonie
Finland		8,997	7,940	8,052	7,801	6,884	6,784	1,550	1,633	1,845	354	577	577	Finlande
France		2,386	2,250	2,250	3,332	3,200	3,200	43	50	50	989	1,000	1,000	France
Germany		1,116	1,180	1,090	1,103	1,100	1,000	259	270	270	246	190	180	Allemagne
Hungary		502	507	526	502	507	526	0	0	0	0	0	0	Hongrie
Italy		168	166	166	168	166	166	0	0	0	0	0	0	Italie
Latvia		172	200	200	2,018	2,000	2,000	244	100	100	2,090	1,900	1,900	Lettonie
Luxembourg		77	71	69	46	32	30	36	48	48	5	9	9	Luxembourg
Netherlands		62	50	55	108	100	100	21	20	20	67	70	65	Pays-Bas
Poland		4,424	4,635	4,785	3,939	4,150	4,300	560	560	560	75	75	75	Pologne
Portugal		10,495	10,300	10,260	8,586	8,500	8,400	2,100	2,000	2,050	191	200	190	Portugal
Serbia		199	205	210	199	205	210	0	0	0	0	0	0	Serbie
Slovakia		1,874	1,950	2,000	1,924	2,000	2,050	100	100	100	150	150	150	Slovaquie
Slovenia		137	120	130	424	490	450	84	80	90	371	450	410	Slovénie
Spain		5,422	5,288	5,288	6,059	6,429	6,429	269	291	291	906	1,432	1,432	Espagne
Sweden		8,517	8,412	8,533	6,232	5,986	6,107	2,313	2,481	2,481	28	55	55	Suède
Switzerland		128	128	133	165	165	170	3	3	3	40	40	40	Suisse
United Kingdom		23	22	22	13	13	13	18	18	18	9	9	9	Royaume-Uni
Total Europe		46,729	45,001	45,410	45,185	44,305	44,322	8,426	8,406	8,628	6,881	7,711	7,540	Total Europe
Canada		10,554	10,654	10,644	10,812	10,843	10,843	38	36	30	296	225	228	Canada
United States		41,407	40,200	38,795	41,424	40,217	38,810	58	32	18	75	50	33	Etats-Unis
Total North America		51,961	50,854	49,439	52,236	51,060	49,653	96	68	48	371	275	261	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


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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	8,945	7,735	7,735	7,824	6,400	6,400	1,696	1,820	1,820	575	485	485	Autriche
Cyprus	8	9	10	7	8	9	1	1	1	0	0	0	Chypre
Czech Republic	1,182	1,011	1,094	1,699	1,359	1,454	456	333	330	973	681	690	République tchèque
Estonia	2,278	1,935	1,940	4,400	4,400	4,400	47	35	40	2,169	2,500	2,500	Estonie
Finland	16,494	15,112	15,471	14,376	13,378	13,545	2,324	1,926	2,119	206	193	193	Finlande
France	17,420	17,700	17,700	16,366	16,500	16,500	1,876	2,000	2,000	822	800	800	France
Germany	15,128	13,500	12,500	16,292	14,500	13,500	2,015	1,500	1,500	3,179	2,500	2,500	Allemagne
Hungary	1,209	1,022	1,057	1,137	989	1,015	112	73	82	39	39	39	Hongrie
Italy	4,192	4,192	4,192	3,600	3,600	3,600	1,288	1,288	1,288	696	696	696	Italie
Latvia	3,593	3,250	3,250	5,616	5,000	5,000	466	450	450	2,489	2,200	2,200	Lettonie
Luxembourg	522	536	536	521	521	521	137	79	79	136	64	64	Luxembourg
Malta	2	3	3	0	0	0	2	3	3	0	0	0	Malte
Montenegro	44	43	41	44	43	41	0	0	0	0	0	0	Monténégro
Netherlands	396	900	895	915	900	895	198	0	0	717	0	0	Pays-Bas
Poland	15,448	15,730	16,050	14,181	14,500	14,900	1,664	1,600	1,500	396	370	350	Pologne
Portugal	4,136	3,600	3,730	1,865	1,870	1,890	2,435	1,900	2,000	165	170	160	Portugal
Serbia	716	732	761	702	725	750	15	8	12	1	1	1	Serbie
Slovakia	1,162	1,100	1,150	1,149	1,150	1,200	323	300	300	310	350	350	Slovaquie
Slovenia	525	450	440	1,360	1,360	1,400	273	240	240	1,107	1,150	1,200	Slovénie
Spain	5,169	5,603	5,603	4,570	4,849	4,849	987	1,135	1,135	388	380	380	Espagne
Sweden	18,602	18,350	18,250	17,430	16,900	16,800	1,609	2,000	2,000	437	550	550	Suède
Switzerland	1,486	1,486	1,486	772	772	772	772	772	772	58	58	58	Suisse
United Kingdom	2,673	2,673	2,673	2,646	2,646	2,646	96	96	96	69	69	69	Royaume-Uni
Total Europe	121,332	116,673	116,568	117,472	112,370	112,087	18,793	17,559	17,767	14,933	13,256	13,285	Total Europe
Canada	21,959	20,821	20,680	20,285	18,121	18,111	2,216	3,090	3,037	542	390	467	Canada
United States	55,812	56,602	57,224	62,262	63,114	63,899	285	286	286	6,734	6,798	6,961	Etats-Unis
Total North America	77,771	77,423	77,904	82,547	81,235	82,010	2,500	3,376	3,323	7,277	7,188	7,428	Total Amérique du Nord

Table 13


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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	1,290	1,497	1,450	1,691	1,938	2,050	344	309	300	745	750	900	Autriche
Cyprus	8	5	5	0	0	0	8	5	5	0	0	0	Chypre
Czech Republic	234	215	225	540	459	482	38	38	40	344	282	296	République tchèque
Estonia	284	300	230	1,650	1,350	1,300	12	50	30	1,378	1,100	1,100	Estonie
Finland	530	541	562	360	380	405	188	163	160	18	2	3	Finlande
France	2,735	3,260	3,660	2,050	2,250	2,450	775	1,100	1,300	90	90	90	France
Germany	3,328	3,540	3,720	3,569	3,700	3,900	443	480	420	684	640	600	Allemagne
Hungary	63	44	50	62	43	49	11	13	12	11	12	12	Hongrie
Italy	2,359	2,359	2,359	450	450	450	1,916	1,916	1,916	7	7	7	Italie
Latvia	621	750	750	1,980	2,000	2,000	326	350	350	1,685	1,600	1,600	Lettonie
Luxembourg	61	72	72	63	63	63	17	11	11	19	2	2	Luxembourg
Malta	1	1	1	0	0	0	1	1	1	0	0	0	Malte
Montenegro	18	25	26	83	84	84	0	0	0	65	59	58	Monténégro
Netherlands	5,354	5,354	5,354	268	268	268	5,551	5,551	5,551	465	465	465	Pays-Bas
Poland	842	920	1,100	1,152	1,200	1,350	366	370	380	677	650	630	Pologne
Portugal	228	225	220	747	740	735	4	5	5	523	520	520	Portugal
Serbia	478	460	485	418	450	480	83	70	80	23	60	75	Serbie
Slovakia	22	175	175	390	450	450	47	75	75	415	350	350	Slovaquie
Slovenia	125	155	150	164	175	180	126	120	130	165	140	160	Slovénie
Spain	867	907	907	1,007	1,007	1,007	65	46	46	206	146	146	Espagne
Sweden	1,776	1,800	1,850	1,809	1,750	1,800	199	210	210	232	160	160	Suède
Switzerland	410	415	420	330	335	340	80	80	80	0	0	0	Suisse
United Kingdom	7,819	7,830	7,830	327	330	330	7,516	7,520	7,520	23	20	20	Royaume-Uni
Total Europe	29,451	30,850	31,601	19,110	19,422	20,173	18,114	18,482	18,621	7,774	7,055	7,194	Total Europe
Canada	368	420	179	3,830	3,830	3,830	31	52	56	3,493	3,462	3,707	Canada
United States	761	273	152	9,544	9,744	9,948	194	174	155	8,977	9,644	9,951	Etats-Unis
Total North America	1,129	694	331	13,374	13,574	13,778	225	226	211	12,470	13,106	13,659	Total Amérique du Nord

Table 14


TABLE 14
Europe: Summary table of market forecasts for 2023 and 2024
Europe: Tableau récapitulatif des prévisions du marché pour 2023 et 2024
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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
	actual	forecasts		actual	forecasts		actual	forecasts		actual	forecasts
	réels	prévisions		réels	prévisions		réels	prévisions		réels	prévisions
Sawn softwood	75.92	69.01	68.49	96.71	89.54	88.44	29.69	25.67	25.93	50.49	46.20	45.88	Sciages conifères
Softwood logs a	182.85	171.26	171.53	185.47	172.88	171.84	18.22	16.63	16.39	20.84	18.25	16.70	Grumes de conifères a
Sawn hardwood	7.02	6.65	6.70	6.93	6.45	6.61	4.18	3.86	3.81	4.09	3.66	3.72	Sciages non-conifères
– temperate zone b	6.45	6.14	6.18	6.87	6.40	6.55	3.28	3.07	3.02	3.70	3.33	3.39	– zone tempérée b
– tropical zone b	0.57	0.51	0.52	0.06	0.05	0.06	0.90	0.79	0.79	0.39	0.32	0.32	– zone tropicale b
Hardwood logs a	21.96	21.62	22.00	22.05	21.91	22.04	3.91	3.25	3.26	4.00	3.54	3.30	Grumes de non-conifères a
– temperate zone b	21.86	21.55	21.93	22.05	21.91	22.04	3.79	3.15	3.16	3.98	3.50	3.26	– zone tempérée b
– tropical zone b	0.10	0.07	0.07				0.12	0.11	0.11	0.02	0.04	0.04	– zone tropicale b
Veneer sheets	1.58	1.49	1.49	1.00	0.97	0.96	1.42	1.28	1.29	0.84	0.76	0.76	Feuilles de placage
Plywood	6.62	6.21	5.92	4.17	3.93	3.97	6.42	5.79	5.48	3.96	3.50	3.53	Contreplaqués
Particle board (excluding OSB)	28.12	26.41	26.52	28.01	26.71	26.91	10.02	9.58	9.55	9.92	9.88	9.94	Pann. de particules (sauf OSB)
OSB	5.27	5.06	5.09	4.89	4.89	5.02	3.20	2.96	2.94	2.83	2.78	2.87	OSB
Fibreboard	15.80	14.89	15.09	16.15	15.31	15.42	8.76	8.01	8.04	9.11	8.43	8.37	Panneaux de fibres
– Hardboard	0.79	0.82	0.90	0.48	0.47	0.47	1.47	1.44	1.46	1.17	1.09	1.04	– Durs
– MDF	11.42	10.85	10.97	12.16	11.62	11.68	5.21	4.61	4.62	5.95	5.38	5.33	– MDF
– Other board	3.59	3.22	3.22	3.51	3.22	3.27	2.07	1.97	1.96	1.99	1.96	2.01	– Autres panneaux
Pulpwood a	268.34	264.50	264.51	261.87	256.81	255.84	39.84	39.23	39.90	33.38	31.54	31.23	Bois de trituration a
– Pulp logs	147.00	147.83	147.94	144.40	144.44	143.75	21.05	21.67	22.13	18.44	18.29	17.95	– Bois ronds de trituration
– softwood	100.28	102.83	102.53	99.21	100.14	99.43	12.63	13.27	13.50	11.56	10.58	10.41	– conifères
– hardwood	46.73	45.00	45.41	45.18	44.31	44.32	8.43	8.41	8.63	6.88	7.71	7.54	– non-conifères
– Residues, chips and particles	121.33	116.67	116.57	117.47	112.37	112.09	18.79	17.56	17.77	14.93	13.26	13.29	– Déchets, plaquettes et part.
Wood pulp	37.60	34.07	35.28	34.64	32.24	33.81	17.33	16.19	16.59	14.37	14.37	15.12	Pâte de bois
Paper and paperboard	72.76	66.14	69.44	83.10	73.88	79.49	43.20	39.62	41.48	53.55	47.36	51.53	Papiers et cartons
Wood Pellets	29.45	30.85	31.60	19.11	19.42	20.17	18.11	18.48	18.62	7.77	7.05	7.19	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


TABLE 15
North America: Summary table of market forecasts for 2023 and 2024
Amérique du Nord: Tableau récapitulatif des prévisions du marché pour 2023 et 2024
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
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
	actual	forecasts		actual	forecasts		actual	forecasts		actual	forecasts
	réels	prévisions		réels	prévisions		réels	prévisions		réels	prévisions
Sawn softwood	91.63	89.85	90.39	100.44	97.41	95.73	27.09	26.48	27.10	35.90	34.04	32.43	Sciages conifères
Softwood logs	253.91	254.00	257.31	262.84	262.90	265.12	1.93	1.97	1.86	10.86	10.87	9.68	Grumes de conifères
Sawn hardwood	15.85	16.16	16.46	18.50	18.72	19.03	1.59	1.63	1.56	4.23	4.19	4.13	Sciages non-conifères
– temperate zone	15.57	15.89	16.19	18.50	18.72	19.03	1.29	1.33	1.26	4.21	4.16	4.10	– zone tempérée
– tropical zone	0.29	0.27	0.27	0.00	0.00	0.00	0.31	0.30	0.30	0.02	0.03	0.03	– zone tropicale
Hardwood logs	47.44	48.23	49.33	48.21	48.67	49.44	1.33	1.22	1.18	2.10	1.65	1.30	Grumes de non-conifères
– temperate zone	47.44	48.23	49.32	48.21	48.67	49.44	1.32	1.21	1.18	2.10	1.65	1.30	– 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	2.85	2.93	2.97	2.87	2.89	2.91	0.86	0.88	0.89	0.88	0.83	0.84	Feuilles de placage
Plywood	16.92	16.92	17.31	10.86	10.90	11.05	7.48	7.37	7.68	1.43	1.36	1.42	Contreplaqués
Particle board (excluding OSB)	6.66	7.45	7.46	6.11	6.58	6.55	1.75	1.97	1.98	1.19	1.10	1.07	Pann. de particules (sauf OSB)
OSB	21.20	21.09	21.35	20.86	20.60	20.86	6.28	6.30	6.39	5.94	5.82	5.89	OSB
Fibreboard	9.92	9.93	10.07	7.64	7.71	7.87	4.18	3.92	3.92	1.90	1.69	1.72	Panneaux de fibres
– Hardboard	0.51	0.56	0.56	0.53	0.59	0.60	0.31	0.28	0.29	0.32	0.32	0.33	– Durs
– MDF	6.21	6.23	6.23	3.83	3.88	3.89	3.55	3.35	3.32	1.17	0.99	0.98	– MDF
– Other board	3.20	3.15	3.28	3.28	3.24	3.38	0.32	0.29	0.31	0.40	0.38	0.41	– Autres panneaux
Pulpwood	275.49	275.41	276.58	280.24	279.10	280.51	2.93	3.79	3.78	7.67	7.47	7.70	Bois de trituration
– Pulp logs	197.72	197.99	198.68	197.69	197.86	198.50	0.43	0.41	0.45	0.39	0.28	0.27	– Bois ronds de trituration
– softwood	145.76	147.13	149.24	145.45	146.80	148.85	0.33	0.34	0.41	0.02	0.01	0.01	– conifères
– hardwood	51.96	50.85	49.44	52.24	51.06	49.65	0.10	0.07	0.05	0.37	0.27	0.26	– non-conifères
– Residues, chips and particles	77.77	77.42	77.90	82.55	81.23	82.01	2.50	3.38	3.32	7.28	7.19	7.43	– Déchets, plaquettes et part.
Wood pulp	45.79	48.12	48.43	55.02	54.33	54.12	7.42	8.22	8.89	16.65	14.44	14.58	Pâte de bois
Paper and paperboard	69.75	68.96	69.26	75.05	73.60	73.63	10.72	10.42	10.39	16.02	15.06	14.77	Papiers et cartons
Wood pellets	1.13	0.69	0.33	13.37	13.57	13.78	0.23	0.23	0.21	12.47	13.11	13.66	Granulés de bois

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 2023 and 2024 Table 15 - North America: Summary table of market forecasts for 2023 and 2024

Source: UNECE Committee on Forests and the Forest Industry , November 2023, 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.

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.

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.

Uzbekistan – data extrapolated by the Secretariat based on national data for the first eight months 2023. Poland - The trade turnover is based on data that includes the estimated value of trade turnover by entities exempt from the reporting obligation. These trade turnover figures are estimated at 3%. Roundwood: sawlogs and veneer logs and pulpwood and wood fuel - with removals from trees and shrubs outside the forest, including forest chips, with stump. Residues - production excluding recovered wood.

In contrast to years prior to 2020, data are shown only for countries providing forecasts. Sub-regional totals thus reflect only the reporting countries of the subreg Confidential data have not been included. Please inform secretariat in case you notice any confidential data which might have been included inadvertently.

Wherever the forecast data is incomplete, then data is repeated to avoid skewing.

Countries with nil, missing or confidential data for all years on a table are not shown. Consumption figures are the sum of production and national imports minus national exports. Softwood = coniferous, hardwood = non-coniferous. United Kingdom production figures for OSB is secretariat estimate.

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 6,141 4,978 4,978 10,104 8,588 8,588 1,784 1,270 1,270 5,747 4,880 4,880 Autriche Cyprus 33 34 34 1 1 1 32 33 33 0 0 0 Chypre Czech Republic 2,965 2,343 2,470 4,720 3,776 4,040 583 414 350 2,338 1,847 1,920 République tchèque Estonia 2,068 1,550 1,550 1,725 1,500 1,500 1,209 700 700 866 650 650 Estonie Finland 2,938 2,420 2,420 11,200 10,300 10,400 305 20 20 8,567 7,900 8,000 Finlande France 8,633 8,750 8,800 7,168 7,200 7,300 2,350 2,450 2,400 885 900 900 France Germany 17,294 14,900 13,300 24,309 21,400 19,800 4,146 2,700 3,000 11,162 9,200 9,500 Allemagne Hungary 788 902 918 85 96 86 717 821 842 14 15 11 Hongrie Italy 4,790 4,302 4,302 400 400 400 4,608 4,157 4,157 217 255 255 Italie Latvia 1,025 950 950 3,102 3,000 3,000 829 750 750 2,906 2,800 2,800 Lettonie Luxembourg 71 122 122 39 39 39 43 91 91 11 8 8 Luxembourg Malta 7 9 9 0 0 0 7 9 9 0 0 0 Malte Montenegro 30 30 29 118 115 112 10 9 7 98 94 90 Monténégro Netherlands 2,259 2,088 2,029 115 115 115 2,659 2,473 2,399 515 500 485 Pays-Bas Poland 4,631 4,630 4,800 4,144 4,100 4,200 1,219 1,240 1,300 732 710 700 Pologne Portugal 696 686 685 807 815 820 130 130 125 242 259 260 Portugal Serbia 367 361 383 91 95 98 281 270 290 5 4 5 Serbie Slovakia 847 810 860 1,430 1,360 1,400 480 450 460 1,063 1,000 1,000 Slovaquie Slovenia 665 670 660 983 990 980 530 530 530 848 850 850 Slovénie Spain 4,029 4,001 4,001 3,006 3,189 3,189 1,166 956 956 143 144 144 Espagne Sweden 5,709 5,050 5,650 18,870 18,400 18,300 587 500 450 13,748 13,850 13,100 Suède Switzerland 1,271 1,300 1,325 1,186 1,200 1,210 300 310 320 215 210 205 Suisse United Kingdom 8,663 8,125 8,214 3,108 2,860 2,860 5,719 5,385 5,474 165 120 120 Royaume-Uni Total Europe 75,919 69,011 68,490 96,712 89,540 88,439 29,694 25,668 25,934 50,487 46,197 45,883 Total Europe Uzbekistan 2,256 1,498 1,498 0 0 0 2,256 1,498 1,498 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada a 3,707 2,691 2,242 36,398 33,228 31,331 891 988 948 33,581 31,525 30,037 Canada a

United States a 87,925 87,155 88,151 64,039 64,178 64,399 26,202 25,492 26,149 2,316 2,515 2,397 Etats-Unis a

Total North America 91,632 89,846 90,393 100,437 97,406 95,730 27,093 26,480 27,097 35,898 34,040 32,434 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

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 310 222 222 238 202 202 217 140 140 145 120 120 Autriche Cyprus 11 7 7 0 0 0 11 7 7 0 0 0 Chypre Czech Republic 324 245 240 222 167 175 136 103 105 34 24 40 République tchèque Estonia 232 125 125 175 125 125 147 60 60 90 60 60 Estonie Finland 84 44 44 73 40 40 34 24 24 23 20 20 Finlande France 1,124 1,140 1,150 1,446 1,300 1,400 264 420 350 586 580 600 France Germany 693 650 650 997 800 800 395 300 300 699 450 450 Allemagne Hungary 258 150 131 414 343 342 45 38 30 200 231 241 Hongrie Italy 798 776 776 500 500 500 637 578 578 339 302 302 Italie Latvia 5 105 105 720 800 800 54 55 55 769 750 750 Lettonie Luxembourg 96 98 98 39 39 39 64 65 65 7 6 6 Luxembourg Malta 7 8 9 0 0 0 7 8 9 0 0 0 Malte Montenegro 11 8 10 39 35 34 2 1 1 30 28 25 Monténégro Netherlands 238 213 203 34 34 34 314 289 279 110 110 110 Pays-Bas Poland 495 470 500 487 450 460 267 270 300 259 250 260 Pologne Portugal 369 295 290 182 185 190 287 200 190 100 90 90 Portugal Serbia 172 215 225 343 370 385 64 60 70 235 215 230 Serbie Slovakia 235 240 275 385 400 420 55 50 55 205 210 200 Slovaquie Slovenia 106 145 145 143 145 145 83 80 80 121 80 80 Slovénie Spain 425 467 467 302 321 321 175 193 193 53 47 47 Espagne Sweden 142 140 140 100 100 100 83 80 80 41 40 40 Suède Switzerland 78 79 81 52 53 54 50 51 52 24 25 25 Suisse United Kingdom 807 810 810 37 40 40 787 790 790 17 20 20 Royaume-Uni Total Europe 7,019 6,652 6,703 6,928 6,449 6,606 4,177 3,862 3,813 4,086 3,658 3,716 Total Europe Uzbekistan 228 208 208 195 195 195 33 16 16 0 3 3 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 1,208 1,324 1,242 859 893 815 793 826 738 444 395 311 Canada United States 14,647 14,835 15,217 17,637 17,827 18,214 798 805 820 3,788 3,797 3,817 Etats-Unis Total North America 15,855 16,159 16,459 18,496 18,720 19,029 1,591 1,631 1,558 4,231 4,192 4,128 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 306 219 219 238 202 202 213 136 136 144 119 119 Autriche Cyprus 9 5 5 0 0 0 8 5 5 0 0 0 Chypre Czech Republic 307 229 223 222 167 175 119 86 88 34 24 40 République tchèque Estonia 230 122 122 175 125 125 142 56 56 87 59 59 Estonie Finland 80 40 40 73 40 40 26 16 16 19 16 16 Finlande France 960 988 988 1,420 1,285 1,375 123 280 210 583 577 597 France Germany 664 630 630 997 800 800 315 240 240 649 410 410 Allemagne Hungary 257 147 127 414 343 342 43 35 26 200 230 241 Hongrie Italy 819 791 791 495 495 495 476 423 423 152 127 127 Italie Latvia 5 105 105 720 800 800 54 55 55 769 750 750 Lettonie Luxembourg 92 96 96 39 39 39 60 63 63 7 6 6 Luxembourg Malta 6 7 8 0 0 0 6 7 8 0 0 0 Malte Montenegro 11 8 10 39 35 34 2 1 1 30 28 25 Monténégro Netherlands 89 80 77 27 27 27 117 108 105 55 55 55 Pays-Bas Poland 484 459 488 487 450 460 254 257 286 257 248 258 Pologne Portugal 319 272 268 170 172 178 180 150 140 31 50 50 Portugal Serbia 167 211 220 342 369 384 59 57 66 234 215 230 Serbie Slovakia 235 240 275 385 400 420 55 50 55 205 210 200 Slovaquie Slovenia 104 143 143 143 145 145 81 78 78 120 80 80 Slovénie Spain 383 417 417 300 318 318 128 142 142 45 43 43 Espagne Sweden 142 139 139 100 100 100 83 79 79 41 40 40 Suède Switzerland 69 70 72 49 50 51 44 45 46 24 25 25 Suisse United Kingdom 716 720 720 37 40 40 693 700 700 14 20 20 Royaume-Uni Total Europe 6,453 6,138 6,183 6,872 6,402 6,550 3,281 3,069 3,025 3,700 3,334 3,392 Total Europe Uzbekistan 227 207 207 195 195 195 33 15 15 0 3 3 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 1,191 1,316 1,236 859 893 815 762 805 715 430 382 294 Canada United States 14,379 14,578 14,957 17,637 17,827 18,214 523 529 544 3,782 3,778 3,801 Etats-Unis Total North America 15,569 15,893 16,193 18,496 18,720 19,029 1,285 1,334 1,259 4,212 4,160 4,095 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 3 3 3 0 0 0 4 4 4 1 1 1 Autriche Bulgaria 0 0 0 0 0 0 0 0 0 0 0 0 Bulgarie Cyprus 3 2 2 0 0 0 3 2 2 0 0 0 Chypre Czech Republic 17 17 17 0 0 0 17 17 17 0 0 0 République tchèque Estonia 2 3 3 0 0 0 5 4 4 3 1 1 Estonie Finland 4 4 4 0 0 0 8 8 8 4 4 4 Finlande France 164 152 162 26 15 25 141 140 140 3 3 3 France Germany 29 20 20 0 0 0 79 60 60 50 40 40 Allemagne Hungary 2 3 4 0 0 0 2 4 4 0 0 0 Hongrie Italy -21 -15 -15 5 5 5 161 154 154 187 175 175 Italie Luxembourg 4 2 2 0 0 0 4 2 2 0 0 0 Luxembourg Malta 1 1 1 0 0 0 1 1 1 0 0 0 Malte Netherlands 149 133 126 7 7 7 197 181 174 55 55 55 Pays-Bas Poland 10 11 12 0 0 0 12 13 14 2 2 2 Pologne Portugal 50 23 22 12 13 12 107 50 50 69 40 40 Portugal Serbia 5 4 5 1 1 1 5 3 4 1 0 0 Serbie Slovenia 2 2 2 0 0 0 2 2 2 0 0 0 Slovénie Spain 42 49 49 2 2 2 47 50 50 7 4 4 Espagne Sweden 1 1 1 0 0 0 1 1 1 0 0 0 Suède Switzerland 9 9 9 3 3 3 6 6 6 0 0 0 Suisse United Kingdom 91 90 90 0 0 0 94 90 90 3 0 0 Royaume-Uni Total Europe 566 515 519 56 46 55 896 793 788 386 324 324 Total Europe Canada 17 8 7 0 0 0 31 21 23 14 13 16 Canada United States 269 257 260 0 0 0 275 276 276 6 19 16 Etats-Unis Total North America 286 266 266 0 0 0 305 297 299 20 31 32 Total Amérique du Nord

1000 m3

Apparent Consumption Country Consommation Apparente Production Imports - Importations

TABLE 2b SAWN HARDWOOD (tropical) SCIAGES NON-CONIFERES (tropicale)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

Exports - Exportations Pays

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 74 39 39 8 8 8 83 45 45 17 14 14 Autriche Cyprus 1 1 1 0 0 0 1 1 1 0 0 0 Chypre Czech Republic 28 28 27 28 16 17 58 53 50 58 41 40 République tchèque Estonia 111 125 125 105 110 110 87 95 95 82 80 80 Estonie Finland 27 21 21 190 160 160 12 10 10 175 149 149 Finlande France 366 366 366 157 157 157 273 273 273 64 64 64 France Germany 157 143 125 110 105 105 99 78 70 52 40 50 Allemagne Hungary 23 25 20 13 18 13 39 39 39 28 31 32 Hongrie Italy 344 308 308 107 107 107 274 234 234 37 33 33 Italie Latvia 105 105 105 40 50 50 140 140 140 75 85 85 Lettonie Luxembourg 1 0 0 0 0 0 1 0 0 0 0 0 Luxembourg Malta 1 2 3 0 0 0 1 2 3 0 0 0 Malte Netherlands 15 13 13 0 0 0 17 15 15 3 3 3 Pays-Bas Poland 121 121 129 45 42 45 92 94 98 16 15 14 Pologne Portugal 12 20 35 20 30 25 38 40 50 46 50 40 Portugal Serbia 4 4 5 30 28 30 8 6 8 34 30 33 Serbie Slovakia 17 25 25 21 25 25 27 30 30 31 30 30 Slovaquie Slovenia 9 8 9 28 27 25 13 14 14 32 33 30 Slovénie Spain 122 92 92 40 36 36 127 90 90 45 34 34 Espagne Sweden 32 31 31 60 50 50 19 10 10 47 29 29 Suède Switzerland 3 3 3 0 0 0 4 4 4 1 1 1 Suisse United Kingdom 6 10 10 0 0 0 7 10 10 1 0 0 Royaume-Uni Total Europe 1,577 1,490 1,491 1,002 969 962 1,419 1,283 1,288 843 762 760 Total Europe Uzbekistan 4 4 4 3 3 3 2 1 1 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 0 Total EOCAC Canada 204 262 267 581 581 581 212 218 230 590 537 544 Canada United States 2,643 2,670 2,699 2,284 2,306 2,329 652 658 664 293 294 294 Etats-Unis Total North America 2,847 2,932 2,966 2,866 2,887 2,910 864 876 894 883 831 838 Total Amérique du Nord Note: Definition of veneers excludes domestic use for plywood. La définition des placages exclus la conversion directe en contreplaqué.

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 19 15 15 131 155 155 183 150 150 296 290 290 Autriche Cyprus 14 15 15 0 0 0 14 15 15 0 0 0 Chypre Czech Republic 193 116 123 240 236 238 230 115 115 277 235 230 République tchèque Estonia 145 50 50 200 210 210 151 50 50 205 210 210 Estonie Finland 297 240 240 1,110 940 940 87 60 60 900 760 760 Finlande France 589 583 583 253 270 270 476 452 452 140 139 139 France Germany 1,073 1,154 840 85 80 80 1,319 1,281 1,000 330 207 240 Allemagne Hungary 136 110 107 60 61 63 138 138 138 62 90 94 Hongrie Italy 602 537 537 288 290 290 525 442 442 211 195 195 Italie Latvia 92 55 55 331 300 300 94 95 95 333 340 340 Lettonie Luxembourg 33 29 29 0 0 0 33 29 29 0 0 0 Luxembourg Malta 10 11 12 0 0 0 10 11 12 0 0 0 Malte Montenegro 2 2 2 1 1 1 2 2 2 1 1 1 Monténégro Netherlands 488 457 441 0 0 0 586 551 529 98 94 88 Pays-Bas Poland 650 640 670 539 515 530 468 475 480 357 350 340 Pologne Portugal 154 180 166 103 100 110 95 110 100 44 30 44 Portugal Serbia 40 36 38 19 18 19 34 30 33 13 12 14 Serbie Slovakia 67 63 63 153 150 150 59 59 59 146 146 146 Slovaquie Slovenia 49 50 58 94 90 98 26 30 30 71 70 70 Slovénie Spain 231 326 326 462 416 416 132 117 117 363 207 207 Espagne Sweden 278 160 160 90 90 90 236 120 120 48 50 50 Suède Switzerland 206 206 206 7 7 7 203 203 203 4 4 4 Suisse United Kingdom 1,254 1,180 1,180 0 0 0 1,320 1,250 1,250 66 70 70 Royaume-Uni Total Europe 6,623 6,215 5,916 4,166 3,930 3,967 6,422 5,786 5,482 3,965 3,501 3,532 Total Europe Uzbekistan 62 46 46 0 0 0 63 47 47 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 0 Total EOCAC Canada 2,174 2,028 2,123 1,604 1,557 1,526 1,224 1,058 1,241 654 587 644 Canada United States 14,742 14,890 15,188 9,254 9,345 9,528 6,259 6,317 6,436 771 772 776 Etats-Unis Total North America 16,916 16,918 17,311 10,858 10,902 11,054 7,483 7,375 7,677 1,425 1,359 1,420 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 951 630 630 2,280 2,170 2,170 313 355 355 1,642 1,895 1,895 Autriche Cyprus 49 46 46 0 0 0 49 46 46 0 0 0 Chypre Czech Republic 793 811 835 962 866 910 530 484 485 699 538 560 République tchèque Estonia 123 67 67 90 0 0 77 68 68 44 2 1 Estonie Finland 113 75 75 54 54 54 85 44 44 26 23 23 Finlande France 2,224 2,148 2,148 3,177 3,094 3,094 299 355 355 1,253 1,301 1,301 France Germany 5,572 5,220 4,970 5,526 5,195 5,020 1,970 1,934 1,900 1,924 1,909 1,950 Allemagne Hungary 408 384 379 447 428 438 264 282 272 303 326 331 Hongrie Italy 3,070 2,813 2,813 2,646 2,500 2,500 956 821 821 532 508 508 Italie Latvia 52 85 85 306 300 300 69 25 25 322 240 240 Lettonie Luxembourg 20 12 12 0 0 0 21 13 13 1 1 1 Luxembourg Malta 10 11 11 0 0 0 10 11 11 0 0 0 Malte Montenegro 32 33 34 0 0 0 32 33 34 0 0 0 Monténégro Netherlands 464 440 432 0 0 0 514 488 479 50 48 47 Pays-Bas Poland 6,501 6,450 6,740 5,227 5,150 5,450 2,173 2,180 2,200 899 880 910 Pologne Portugal 537 473 514 766 750 760 281 300 290 510 577 536 Portugal Serbia 373 351 371 219 210 220 196 184 198 42 43 47 Serbie Slovakia 352 343 340 676 675 675 148 140 137 473 473 472 Slovaquie Slovenia 137 110 110 0 0 0 143 114 114 6 4 4 Slovénie Spain 2,392 2,213 2,213 2,566 2,310 2,310 626 621 621 800 718 718 Espagne Sweden 1,055 868 868 636 600 600 475 335 335 57 67 67 Suède Switzerland 281 286 286 420 425 425 141 141 141 280 280 280 Suisse United Kingdom 2,606 2,542 2,542 2,012 1,982 1,982 648 610 610 55 50 50 Royaume-Uni Total Europe 28,115 26,410 26,521 28,012 26,710 26,908 10,021 9,584 9,555 9,917 9,883 9,942 Total Europe Uzbekistan 880 542 542 252 252 252 654 317 317 26 27 27 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 27 Total EOCAC Canada 1,466 1,886 1,894 1,625 2,032 2,012 552 504 491 710 650 609 Canada United States 5,196 5,565 5,562 4,488 4,552 4,534 1,193 1,465 1,487 485 452 459 Etats-Unis Total North America 6,663 7,451 7,456 6,113 6,584 6,546 1,745 1,969 1,978 1,195 1,102 1,068 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.

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 205 135 135 0 0 0 212 140 140 7 5 5 Autriche Cyprus 11 14 14 0 0 0 11 14 14 0 0 0 Chypre Czech Republic 380 342 350 689 620 655 126 113 115 435 392 420 République tchèque Estonia 55 32 32 0 0 0 55 32 32 1 0 0 Estonie Finland 56 56 56 0 0 0 56 56 56 0 0 0 Finlande France 427 522 522 302 406 406 222 165 165 96 49 49 France Germany 1,316 1,238 1,130 1,164 1,105 1,080 679 669 600 526 536 550 Allemagne Hungary 133 147 152 379 419 443 56 60 59 302 331 350 Hongrie Italy 346 287 287 100 100 100 346 274 274 100 87 87 Italie Latvia 196 165 165 674 650 650 76 75 75 554 560 560 Lettonie Luxembourg 110 135 135 338 338 338 6 14 14 234 217 217 Luxembourg Montenegro 2 2 2 0 0 0 2 2 2 0 0 0 Monténégro Netherlands 222 222 227 0 0 0 286 286 292 64 64 65 Pays-Bas Poland 655 650 760 647 650 750 302 320 350 294 320 340 Pologne Portugal 46 37 41 0 0 0 50 40 45 4 3 4 Portugal Serbia 40 35 41 0 0 0 41 36 42 1 1 1 Serbie Slovakia 48 58 60 0 0 0 48 60 63 1 3 3 Slovaquie Slovenia 31 24 24 0 0 0 33 26 26 2 2 2 Slovénie Spain 26 15 15 3 3 3 35 33 33 12 20 20 Espagne Sweden 94 92 92 0 0 0 97 95 95 3 3 3 Suède Switzerland 95 95 95 0 0 0 96 96 96 1 1 1 Suisse United Kingdom 773 758 758 598 598 598 365 350 350 190 190 190 Royaume-Uni Total Europe 5,268 5,060 5,092 4,894 4,888 5,023 3,200 2,956 2,938 2,826 2,784 2,868 Total Europe Uzbekistan 7 5 5 0 0 0 7 5 5 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 0 Total EOCAC Canada 1,546 1,253 1,153 7,270 6,820 6,798 82 65 61 5,806 5,632 5,706 Canada United States 19,658 19,834 20,197 13,592 13,783 14,059 6,198 6,236 6,326 132 185 188 Etats-Unis Total North America 21,204 21,087 21,350 20,862 20,603 20,857 6,280 6,301 6,387 5,938 5,817 5,894 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 421 386 386 470 395 395 331 308 308 381 316 316 Autriche Cyprus 20 15 16 0 0 0 20 15 16 0 0 0 Chypre Czech Republic 328 276 280 41 41 42 438 347 360 151 112 122 République tchèque Estonia 70 46 47 75 40 40 65 46 47 70 40 40 Estonie Finland 139 105 105 44 44 44 141 102 102 46 41 41 Finlande France 828 915 915 1,238 1,035 1,035 721 772 772 1,130 892 892 France Germany 3,791 3,437 3,325 5,194 4,900 4,800 1,590 1,543 1,470 2,993 3,006 2,945 Allemagne Hungary 9 -17 -13 21 0 0 204 235 244 215 253 258 Hongrie Italy 1,862 1,661 1,661 827 818 818 1,281 974 974 245 131 131 Italie Latvia 60 50 40 48 50 50 62 65 65 50 65 75 Lettonie Luxembourg 100 90 90 147 147 147 34 19 19 80 76 76 Luxembourg Malta 6 7 7 0 0 0 6 7 7 0 0 0 Malte Montenegro 32 32 33 0 0 0 32 32 33 0 0 0 Monténégro Netherlands 332 310 296 29 29 29 465 431 412 162 150 145 Pays-Bas Poland 3,808 3,765 4,020 4,960 4,920 5,080 590 585 630 1,743 1,740 1,690 Pologne Portugal 534 485 529 526 520 560 338 315 335 330 350 366 Portugal Serbia 74 74 88 19 20 22 71 73 88 16 19 22 Serbie Slovakia 210 218 223 0 0 0 248 256 262 39 38 39 Slovaquie Slovenia 24 15 15 132 120 125 28 25 30 136 130 140 Slovénie Spain 920 894 894 1,430 1,287 1,287 462 355 355 972 748 748 Espagne Sweden 301 260 260 0 0 0 425 360 360 124 100 100 Suède Switzerland 238 238 238 97 97 97 308 308 308 167 167 167 Suisse United Kingdom 1,692 1,630 1,630 856 850 850 895 840 840 60 60 60 Royaume-Uni Total Europe 15,799 14,892 15,085 16,153 15,313 15,421 8,755 8,013 8,037 9,110 8,434 8,373 Total Europe Uzbekistan 1,092 809 809 47 47 47 1,057 771 771 13 9 9 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 1,236 1,183 1,181 1,277 1,288 1,299 818 628 605 859 733 723 Canada United States 8,684 8,749 8,888 6,362 6,420 6,571 3,359 3,289 3,310 1,038 960 993 Etats-Unis Total North America 9,920 9,932 10,069 7,639 7,708 7,870 4,177 3,917 3,915 1,896 1,693 1,716 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 29 28 28 54 43 43 18 16 16 43 32 32 Autriche Cyprus 2 1 2 0 0 0 2 1 2 0 0 0 Chypre Czech Republic 43 45 45 0 0 0 61 59 60 18 14 15 République tchèque Estonia 23 15 19 0 0 0 30 16 20 7 1 1 Estonie Finland 23 21 21 44 44 44 21 15 15 41 38 38 Finlande France 55 55 55 221 221 221 207 207 207 373 373 373 France Germany 176 183 165 0 0 0 200 203 180 23 20 15 Allemagne Hungary 27 41 45 2 0 0 65 81 85 40 40 40 Hongrie Italy 280 280 280 16 16 16 283 283 283 19 19 19 Italie Latvia 1 5 5 15 15 15 18 20 20 32 30 30 Lettonie Luxembourg -31 -12 -12 0 0 0 3 8 8 34 20 20 Luxembourg Montenegro 1 1 1 0 0 0 1 1 1 0 0 0 Monténégro Netherlands 44 41 39 0 0 0 63 58 56 19 17 17 Pays-Bas Poland -179 -120 -50 80 80 80 88 100 120 347 300 250 Pologne Portugal 50 30 39 0 0 0 61 40 50 11 10 11 Portugal Serbia 39 35 38 19 20 22 33 31 34 13 16 18 Serbie Slovakia 21 20 21 0 0 0 21 21 22 1 1 1 Slovaquie Slovenia -1 0 1 0 0 0 4 2 4 4 2 3 Slovénie Spain 17 15 15 32 29 29 46 46 46 61 60 60 Espagne Sweden 47 30 30 0 0 0 116 110 110 70 80 80 Suède Switzerland 19 19 19 0 0 0 24 24 24 5 5 5 Suisse United Kingdom 101 90 90 0 0 0 110 100 100 9 10 10 Royaume-Uni Total Europe 787 822 895 482 468 470 1,474 1,441 1,463 1,169 1,087 1,037 Total Europe Uzbekistan 89 50 50 0 0 0 90 50 50 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 33 47 42 90 90 90 52 27 28 109 70 76 Canada United States 481 509 514 437 504 509 259 255 258 215 250 253 Etats-Unis Total North America 514 556 556 527 594 599 311 282 286 324 320 329 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 260 230 230 416 351 351 177 160 160 333 281 281 Autriche Cyprus 16 12 12 0 0 0 16 12 12 0 0 0 Chypre Czech Republic 199 157 160 41 41 42 180 135 140 22 19 22 République tchèque Estonia 18 21 18 0 0 0 33 28 25 15 7 7 Estonie Finland 82 67 67 0 0 0 86 70 70 4 3 3 Finlande France 708 794 794 954 751 751 337 388 388 583 345 345 France Germany 1,870 1,728 1,720 3,792 3,700 3,650 424 395 370 2,345 2,367 2,300 Allemagne Hungary -39 -65 -62 0 0 0 136 148 156 175 213 218 Hongrie Italy 1,501 1,299 1,299 809 800 800 913 606 606 221 107 107 Italie Latvia 52 40 30 33 35 35 22 25 25 2 20 30 Lettonie Luxembourg 128 98 98 147 147 147 27 7 7 46 56 56 Luxembourg Malta 5 5 5 0 0 0 5 5 5 0 0 0 Malte Montenegro 31 31 32 0 0 0 31 31 32 0 0 0 Monténégro Netherlands 220 205 196 0 0 0 361 336 322 141 131 126 Pays-Bas Poland 3,066 3,020 3,130 3,052 3,030 3,100 470 450 470 456 460 440 Pologne Portugal 447 440 465 494 500 530 257 260 265 305 320 330 Portugal Serbia 31 35 46 0 0 0 34 38 50 3 3 4 Serbie Slovakia 135 135 135 0 0 0 170 170 170 35 35 35 Slovaquie Slovenia 24 15 14 132 120 125 24 23 26 131 128 137 Slovénie Spain 835 821 821 1,334 1,201 1,201 397 302 302 897 682 682 Espagne Sweden 254 225 225 0 0 0 284 230 230 30 5 5 Suède Switzerland 24 24 24 97 97 97 88 88 88 161 161 161 Suisse United Kingdom 1,553 1,510 1,510 856 850 850 739 700 700 42 40 40 Royaume-Uni Total Europe 11,419 10,847 10,969 12,157 11,623 11,679 5,210 4,606 4,618 5,948 5,382 5,328 Total Europe Uzbekistan 671 513 513 46 46 46 629 469 469 3 2 2 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 1,053 999 1,005 1,087 1,098 1,109 608 472 449 641 570 553 Canada United States 5,156 5,228 5,226 2,746 2,778 2,786 2,939 2,874 2,866 529 424 426 Etats-Unis Total North America 6,209 6,227 6,231 3,833 3,876 3,895 3,547 3,346 3,315 1,170 994 979 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 131 128 128 0 0 0 136 132 132 4 3 3 Autriche Cyprus 2 2 2 0 0 0 3 2 2 0 0 0 Chypre Czech Republic 86 74 75 0 0 0 197 154 160 111 80 85 République tchèque Estonia 29 10 10 75 40 40 3 2 2 49 32 32 Estonie Finland 33 17 17 0 0 0 34 17 17 0 0 0 Finlande France 65 66 66 63 63 63 177 177 177 174 174 174 France Germany 1,745 1,526 1,440 1,402 1,200 1,150 966 945 920 624 619 630 Allemagne Hungary 21 7 4 19 0 0 3 7 4 0 0 0 Hongrie Italy 82 82 82 3 3 3 85 85 85 6 6 6 Italie Latvia 7 5 5 0 0 0 23 20 20 16 15 15 Lettonie Luxembourg 4 4 4 0 0 0 4 4 4 0 0 0 Luxembourg Malta 1 2 2 0 0 0 1 2 2 0 0 0 Malte Netherlands 68 64 61 29 29 29 41 37 34 2 2 2 Pays-Bas Poland 920 865 940 1,828 1,810 1,900 33 35 40 940 980 1,000 Pologne Portugal 37 15 25 32 20 30 20 15 20 15 20 25 Portugal Serbia 4 4 4 0 0 0 4 4 4 0 0 0 Serbie Slovakia 54 63 67 0 0 0 57 65 70 3 2 3 Slovaquie Slovenia 0 0 0 0 0 0 0 0 0 0 0 0 Slovénie Spain 69 59 59 64 58 58 20 7 7 15 6 6 Espagne Sweden 0 5 5 0 0 0 25 20 20 24 15 15 Suède Switzerland 195 195 195 0 0 0 196 196 196 1 1 1 Suisse United Kingdom 38 30 30 0 0 0 47 40 40 9 10 10 Royaume-Uni Total Europe 3,592 3,223 3,221 3,514 3,222 3,272 2,071 1,965 1,956 1,993 1,965 2,007 Total Europe Uzbekistan 331 246 246 2 2 2 339 252 252 10 7 7 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 150 137 134 100 100 100 158 129 128 108 92 94 Canada United States 3,047 3,012 3,148 3,179 3,138 3,276 161 160 186 294 286 314 Etats-Unis Total North America 3,196 3,149 3,282 3,279 3,238 3,376 319 289 314 402 378 408 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 2,209 1,950 2,030 1,977 1,700 1,800 630 610 630 399 360 400 Autriche Czech Republic 847 688 700 640 525 540 324 259 260 117 96 100 République tchèque Estonia 70 75 80 227 180 180 42 50 50 199 155 150 Estonie Finland a 5,468 4,483 4,614 9,200 8,690 9,360 355 150 150 4,087 4,357 4,896 Finlande a

France 2,898 2,420 2,500 1,666 1,300 1,350 1,715 1,450 1,500 483 330 350 France Germany 5,092 4,600 5,000 2,172 1,850 2,000 4,173 3,900 4,200 1,253 1,150 1,200 Allemagne Hungary 205 206 214 66 77 87 141 133 131 3 3 4 Hongrie Italy 3,466 3,466 3,466 223 223 223 3,536 3,536 3,536 293 293 293 Italie Latvia 7 7 7 12 13 13 7 7 7 12 13 13 Lettonie Netherlands 443 442 442 37 37 37 1,717 1,717 1,717 1,312 1,312 1,312 Pays-Bas Poland 2,836 2,830 2,930 1,729 1,710 1,750 1,291 1,300 1,320 183 180 140 Pologne Portugal 1,757 1,735 1,760 2,869 2,870 2,870 140 145 150 1,252 1,280 1,260 Portugal Serbia 82 88 92 0 0 0 82 88 92 0 0 0 Serbie Slovakia 700 700 715 692 700 725 173 170 170 166 170 180 Slovaquie Slovenia 322 321 316 73 63 68 249 260 250 1 2 2 Slovénie Spain 1,520 1,328 1,328 1,120 1,120 1,120 1,176 976 976 775 768 768 Espagne Sweden 8,438 7,600 7,950 11,631 10,900 11,400 641 600 600 3,834 3,900 4,050 Suède Switzerland 188 188 188 87 87 87 101 101 101 0 0 0 Suisse United Kingdom 1,057 940 950 220 200 200 838 740 750 1 0 0 Royaume-Uni Total Europe 37,604 34,067 35,282 34,641 32,244 33,809 17,333 16,193 16,590 14,369 14,369 15,118 Total Europe Uzbekistan 38 28 28 1 1 1 37 28 28 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 6,007 5,851 5,616 14,200 13,102 12,638 472 582 640 8,665 7,833 7,662 Canada United States 39,787 42,269 42,815 40,822 41,230 41,478 6,948 7,643 8,254 7,983 6,603 6,917 Etats-Unis Total North America 45,794 48,121 48,431 55,022 54,332 54,116 7,420 8,224 8,894 16,648 14,436 14,579 Total Amérique du Nord

a imports exclude dissolving pulp a les importations excluent pâte à dissoudre

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 2,133 1,750 2,050 4,633 3,500 4,000 1,231 1,050 1,150 3,730 2,800 3,100 Autriche Cyprus 56 48 48 0 0 0 56 48 48 0 0 0 Chypre Czech Republic 1,467 1,234 1,258 938 769 785 1,531 1,286 1,312 1,002 822 838 République tchèque Estonia 120 111 111 57 35 35 123 102 102 59 26 26 Estonie Finland 514 475 460 7,200 5,990 6,150 333 275 280 7,019 5,790 5,970 Finlande France 8,272 7,290 7,400 7,092 6,240 6,600 4,845 4,650 4,600 3,665 3,600 3,800 France Germany 17,836 14,600 17,000 21,612 17,500 21,000 9,302 8,000 9,500 13,078 10,900 13,500 Allemagne Hungary 1,213 1,167 1,212 1,057 1,003 1,034 877 892 898 720 727 721 Hongrie Italy 11,390 11,390 11,390 8,696 8,696 8,696 5,800 5,800 5,800 3,106 3,106 3,106 Italie Latvia 168 175 175 29 30 30 173 180 180 33 35 35 Lettonie Luxembourg 26 14 14 0 0 0 27 15 15 1 1 1 Luxembourg Malta 26 27 28 0 0 0 26 27 28 0 0 0 Malte Netherlands 2,814 2,760 2,760 2,884 2,827 2,827 2,180 2,096 2,096 2,250 2,163 2,163 Pays-Bas Poland 7,532 7,400 7,550 5,237 5,130 5,250 4,869 4,870 4,950 2,574 2,600 2,650 Pologne Portugal 1,090 1,200 1,240 2,123 2,200 2,240 948 940 945 1,981 1,940 1,945 Portugal Serbia 790 762 778 483 481 490 514 490 500 207 209 212 Serbie Slovakia 565 575 600 967 975 1,000 457 450 475 859 850 875 Slovaquie Slovenia 412 390 390 591 500 500 367 390 390 545 500 500 Slovénie Spain 7,060 6,778 6,778 6,355 6,355 6,355 2,997 2,577 2,577 2,291 2,154 2,154 Espagne Sweden 834 700 750 8,531 7,300 8,100 894 700 750 8,591 7,300 8,100 Suède Switzerland 1,020 1,015 1,010 1,160 1,155 1,150 640 635 630 780 775 770 Suisse United Kingdom 7,420 6,280 6,440 3,460 3,190 3,250 5,015 4,150 4,250 1,055 1,060 1,060 Royaume-Uni Total Europe 72,758 66,140 69,442 83,103 73,876 79,492 43,204 39,623 41,476 53,549 47,358 51,526 Total Europe Uzbekistan 335 297 297 142 142 142 214 171 171 21 17 17 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 5,505 6,069 6,231 9,094 9,124 9,155 2,516 2,242 2,235 6,105 5,298 5,159 Canada United States 64,243 62,896 63,029 65,959 64,476 64,476 8,202 8,180 8,159 9,917 9,761 9,606 Etats-Unis Total North America 69,748 68,964 69,260 75,053 73,600 73,631 10,718 10,423 10,395 16,023 15,059 14,765 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 13,935 11,716 12,225 10,711 8,904 9,338 3,223 2,812 2,887 0 0 0 5,424 5,115 5,234 19,359 16,831 17,459 Autriche Cyprus 3 2 2 2 2 2 0 0 0 0 0 0 11 9 8 14 11 10 Chypre Czech Republic 20,708 15,535 14,897 14,635 10,617 10,106 5,965 4,804 4,675 108 113 115 4,405 3,965 3,900 25,113 19,499 18,797 République tchèque Estonia 6,474 6,401 6,401 4,276 4,200 4,200 2,148 2,150 2,150 51 51 51 4,066 3,800 3,800 10,541 10,201 10,201 Estonie Finland 56,246 53,397 55,435 25,699 22,749 23,412 30,547 30,648 32,023 0 0 0 9,340 9,340 9,340 65,586 62,737 64,775 Finlande France 25,648 25,270 25,070 17,198 17,200 17,300 7,891 7,500 7,200 559 570 570 24,173 24,500 25,600 49,821 49,770 50,670 France Germany 56,534 53,930 49,630 44,756 41,200 39,500 11,644 12,600 10,000 135 130 130 22,338 22,700 22,700 78,872 76,630 72,330 Allemagne Hungary 2,901 2,881 2,881 1,410 1,374 1,399 912 995 1,008 579 512 475 3,626 3,284 3,397 6,527 6,165 6,278 Hongrie Italy 2,838 3,540 3,540 1,890 1,890 1,890 316 1,018 1,018 632 632 632 10,839 10,839 10,839 13,677 14,379 14,379 Italie Latvia 12,491 12,150 12,350 7,603 7,250 7,450 3,868 3,800 3,800 1,020 1,100 1,100 2,936 3,000 3,000 15,427 15,150 15,350 Lettonie Luxembourg 231 197 193 147 144 133 56 38 38 27 15 22 40 45 43 271 242 235 Luxembourg Montenegro 751 697 678 515 492 487 201 198 186 35 7 5 194 193 190 945 890 868 Monténégro Netherlands 614 599 589 221 220 215 352 340 335 41 39 39 2,382 2,380 2,385 2,996 2,979 2,974 Pays-Bas Poland 38,735 39,880 40,850 18,533 18,800 19,150 19,350 20,100 20,550 852 980 1,150 6,958 7,420 7,750 45,693 47,300 48,600 Pologne Portugal 12,235 12,330 12,190 2,038 2,040 2,060 9,799 9,850 9,700 399 440 430 2,383 2,380 2,300 14,619 14,710 14,490 Portugal Serbia 1,478 1,520 1,561 1,077 1,104 1,130 265 275 283 136 141 148 6,574 6,646 6,760 8,052 8,166 8,321 Serbie Slovakia 6,827 6,820 6,880 4,130 4,080 4,100 2,672 2,710 2,750 25 30 30 609 610 650 7,435 7,430 7,530 Slovaquie Slovenia 2,928 3,752 3,482 2,184 2,780 2,600 698 920 830 45 52 52 1,149 1,290 1,270 4,076 5,042 4,752 Slovénie Spain 14,366 15,244 15,244 4,150 4,404 4,404 9,813 10,413 10,413 403 427 427 3,555 3,772 3,772 17,921 19,016 19,016 Espagne Sweden 71,165 69,076 69,310 38,280 37,480 37,080 32,585 31,296 31,930 300 300 300 6,000 6,016 6,016 77,165 75,092 75,326 Suède Switzerland 3,011 3,082 3,142 2,555 2,625 2,680 444 445 450 12 12 12 1,938 2,000 2,025 4,949 5,082 5,167 Suisse United Kingdom 7,604 7,193 7,193 5,509 5,236 5,236 1,646 1,529 1,529 448 428 428 2,184 2,184 2,184 9,788 9,377 9,377 Royaume-Uni Total Europe 357,723 345,212 343,742 207,519 194,791 193,872 144,397 144,441 143,754 5,807 5,980 6,116 121,124 121,488 123,163 478,847 466,699 466,905 Total Europe Canada 142,131 140,499 140,499 124,900 123,350 123,350 15,040 14,864 14,864 2,190 2,285 2,285 1,683 1,908 1,908 143,814 142,407 142,407 Canada United States 382,544 384,963 388,611 186,157 188,221 191,211 182,650 182,996 183,637 13,737 13,746 13,763 76,230 76,240 76,278 458,774 461,203 464,889 Etats-Unis Total North America 524,675 525,462 529,110 311,057 311,571 314,561 197,690 197,861 198,501 15,927 16,031 16,048 77,913 78,148 78,186 602,587 603,610 607,296 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

Total Logs Pulpwood a Other b Total Grumes Bois de trituration a Autre bCountry

Industrial wood - Bois industriels

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

Wood fuel c

Bois de chauffage c Pays

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 12,958 10,873 11,338 10,382 8,638 9,038 2,576 2,235 2,300 0 0 0 3,248 3,069 3,140 16,206 13,942 14,478 Autriche Cyprus 2 2 2 2 2 2 0 0 0 0 0 0 10 8 7 12 10 9 Chypre Czech Republic 19,440 14,455 13,825 14,019 10,094 9,589 5,316 4,253 4,125 105 109 111 3,610 3,249 3,200 23,050 17,704 17,025 République tchèque Estonia 4,023 3,927 3,927 3,118 3,000 3,000 878 900 900 26 27 27 1,486 1,400 1,400 5,509 5,327 5,327 Estonie Finland 47,408 45,464 47,590 24,662 21,700 22,351 22,746 23,764 25,239 0 0 0 4,593 4,593 4,593 52,001 50,057 52,183 Finlande France 17,300 17,070 16,770 12,491 12,500 12,500 4,559 4,300 4,000 250 270 270 2,417 2,500 2,600 19,717 19,570 19,370 France Germany 52,425 50,120 46,120 41,761 38,500 37,000 10,541 11,500 9,000 123 120 120 8,834 9,200 9,200 61,259 59,320 55,320 Allemagne Hungary 688 759 743 175 201 208 411 488 481 102 70 53 383 294 333 1,071 1,053 1,076 Hongrie Italy 1,797 2,502 2,502 1,169 1,169 1,169 148 853 853 480 480 480 1,180 1,180 1,180 2,977 3,682 3,682 Italie Latvia 8,253 7,900 8,100 5,873 5,500 5,700 1,850 1,800 1,800 530 600 600 298 300 300 8,551 8,200 8,400 Lettonie Luxembourg 162 143 145 124 122 115 10 6 8 27 15 22 17 11 12 178 154 158 Luxembourg Montenegro 573 553 537 372 352 349 201 198 186 0 3 2 66 65 63 639 618 600 Monténégro Netherlands 449 440 430 173 170 165 244 240 235 32 30 30 457 450 450 906 890 880 Pays-Bas Poland 31,941 32,800 33,470 15,775 16,000 16,250 15,411 15,950 16,250 754 850 970 3,627 3,820 3,950 35,568 36,620 37,420 Pologne Portugal 3,045 3,210 3,150 1,682 1,710 1,700 1,213 1,350 1,300 150 150 150 996 990 980 4,041 4,200 4,130 Portugal Serbia 279 290 301 178 184 190 66 70 73 35 36 38 141 146 160 420 436 461 Serbie Slovakia 3,325 3,160 3,120 2,559 2,430 2,400 748 710 700 18 20 20 259 260 275 3,584 3,420 3,395 Slovaquie Slovenia 1,966 2,586 2,386 1,687 2,150 2,000 275 430 380 4 6 6 191 240 220 2,157 2,826 2,606 Slovénie Spain 7,435 7,889 7,889 3,420 3,629 3,629 3,754 3,984 3,984 261 277 277 2,243 2,380 2,380 9,678 10,269 10,269 Espagne Sweden 64,603 62,760 62,873 38,100 37,300 36,900 26,353 25,310 25,823 150 150 150 3,000 3,008 3,008 67,603 65,768 65,881 Suède Switzerland 2,578 2,639 2,689 2,290 2,350 2,400 279 280 280 9 9 9 769 770 775 3,347 3,409 3,464 Suisse United Kingdom 7,486 7,076 7,076 5,453 5,180 5,180 1,633 1,516 1,516 400 380 380 1,571 1,571 1,571 9,058 8,647 8,647 Royaume-Uni Total Europe 288,136 276,619 274,984 185,467 172,881 171,836 99,212 100,136 99,433 3,458 3,602 3,715 39,396 39,504 39,798 327,533 316,123 314,781 Total Europe Canada 114,659 112,907 112,907 110,046 108,424 108,424 4,229 4,021 4,021 384 462 462 806 946 946 115,465 113,853 113,853 Canada United States 306,119 309,360 313,639 152,799 154,479 156,695 141,226 142,779 144,827 12,094 12,102 12,117 37,619 37,609 37,606 343,738 346,969 351,245 Etats-Unis Total North America 420,778 422,267 426,546 262,845 262,903 265,119 145,455 146,800 148,848 12,478 12,564 12,579 38,425 38,555 38,552 459,203 460,822 465,098 Total Amérique du Nord

used for energy purposes à des fins energétiques

Total Logs Pulpwood a Other b Total Grumes Bois de trituration a Autre bCountry

Industrial wood - Bois industriels

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

Wood fuel c

Bois de chauffage c Pays

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 977 843 887 329 266 300 647 577 587 0 0 0 2,176 2,046 2,094 3,153 2,889 2,981 Autriche Cyprus 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 Chypre Czech Republic 1,268 1,079 1,071 616 524 517 649 552 550 3 4 4 795 716 700 2,063 1,795 1,771 République tchèque Estonia 2,452 2,474 2,474 1,158 1,200 1,200 1,270 1,250 1,250 24 24 24 2,580 2,400 2,400 5,032 4,874 4,874 Estonie Finland 8,838 7,933 7,845 1,037 1,049 1,061 7,801 6,884 6,784 0 0 0 4,747 4,747 4,747 13,585 12,680 12,592 Finlande France 8,348 8,200 8,300 4,707 4,700 4,800 3,332 3,200 3,200 309 300 300 21,756 22,000 23,000 30,104 30,200 31,300 France Germany 4,110 3,810 3,510 2,995 2,700 2,500 1,103 1,100 1,000 12 10 10 13,504 13,500 13,500 17,613 17,310 17,010 Allemagne Hungary 2,213 2,122 2,138 1,234 1,173 1,191 502 507 526 477 442 421 3,244 2,990 3,064 5,456 5,112 5,202 Hongrie Italy 1,041 1,038 1,038 721 721 721 168 166 166 152 152 152 9,659 9,659 9,659 10,700 10,697 10,697 Italie Latvia 4,238 4,250 4,250 1,730 1,750 1,750 2,018 2,000 2,000 490 500 500 2,638 2,700 2,700 6,876 6,950 6,950 Lettonie Luxembourg 69 54 47 23 22 18 46 32 30 0 0 0 23 34 30 92 89 78 Luxembourg Montenegro 178 144 141 143 140 138 0 0 0 35 4 3 128 128 127 306 272 268 Monténégro Netherlands 165 159 159 48 50 50 108 100 100 9 9 9 1,925 1,930 1,935 2,090 2,089 2,094 Pays-Bas Poland 6,794 7,080 7,380 2,757 2,800 2,900 3,939 4,150 4,300 98 130 180 3,331 3,600 3,800 10,125 10,680 11,180 Pologne Portugal 9,190 9,120 9,040 356 330 360 8,586 8,500 8,400 249 290 280 1,387 1,390 1,320 10,578 10,510 10,360 Portugal Serbia 1,199 1,230 1,260 899 920 940 199 205 210 101 105 110 6,433 6,500 6,600 7,632 7,730 7,860 Serbie Slovakia 3,502 3,660 3,760 1,570 1,650 1,700 1,924 2,000 2,050 8 10 10 350 350 375 3,851 4,010 4,135 Slovaquie Slovenia 962 1,166 1,096 497 630 600 424 490 450 41 46 46 957 1,050 1,050 1,919 2,216 2,146 Slovénie Spain 6,931 7,354 7,354 730 775 775 6,059 6,429 6,429 142 151 151 1,312 1,392 1,392 8,243 8,746 8,746 Espagne Sweden 6,562 6,316 6,437 180 180 180 6,232 5,986 6,107 150 150 150 3,000 3,008 3,008 9,562 9,324 9,445 Suède Switzerland 433 443 453 265 275 280 165 165 170 3 3 3 1,169 1,230 1,250 1,602 1,673 1,703 Suisse United Kingdom 118 117 117 56 56 56 13 13 13 48 48 48 613 613 613 730 730 730 Royaume-Uni Total Europe 69,587 68,593 68,759 22,052 21,910 22,036 45,185 44,305 44,322 2,350 2,377 2,401 81,728 81,984 83,365 151,314 150,576 152,124 Total Europe Canada 27,472 27,592 27,592 14,854 14,926 14,926 10,812 10,843 10,843 1,806 1,823 1,823 877 961 961 28,349 28,554 28,554 Canada United States 76,425 75,603 74,972 33,358 33,742 34,516 41,424 40,217 38,810 1,643 1,644 1,646 38,611 38,631 38,672 115,036 114,234 113,644 Etats-Unis Total North America 103,897 103,196 102,564 48,212 48,668 49,442 52,236 51,060 49,653 3,449 3,467 3,469 39,488 39,592 39,633 143,385 142,788 142,197 Total Amérique du Nord

used for energy purposes à des fins energétiques

Total Logs Pulpwood a Other b Total Grumes Bois de trituration a Autre bCountry

Industrial wood - Bois industriels

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

Wood fuel c

Bois de chauffage c Pays

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 16,101 13,943 13,638 10,382 8,638 9,038 6,664 5,710 5,000 945 405 400 Autriche Cyprus 2 2 2 2 2 2 0 0 0 0 0 0 Chypre Czech Republic 8,002 6,511 6,962 14,019 10,094 9,589 411 596 715 6,428 4,178 3,343 République tchèque Estonia 3,533 3,270 3,270 3,118 3,000 3,000 522 450 450 107 180 180 Estonie Finland 24,310 21,336 21,991 24,662 21,700 22,351 127 79 83 479 443 443 Finlande France 12,053 12,120 12,120 12,491 12,500 12,500 335 360 360 773 740 740 France Germany 39,391 35,800 34,900 41,761 38,500 37,000 3,300 3,000 3,100 5,670 5,700 5,200 Allemagne Hungary 175 201 208 175 201 208 0 0 0 0 0 0 Hongrie Italy 1,645 1,396 1,396 1,169 1,169 1,169 580 457 457 104 230 230 Italie Latvia 6,471 5,830 6,200 5,873 5,500 5,700 1,147 900 900 549 570 400 Lettonie Luxembourg 465 403 396 124 122 115 693 424 424 352 143 143 Luxembourg Montenegro 382 361 357 372 352 349 10 9 8 0 0 0 Monténégro Netherlands 133 145 145 173 170 165 77 80 80 117 105 100 Pays-Bas Poland 14,243 14,500 14,800 15,775 16,000 16,250 1,245 1,400 1,550 2,777 2,900 3,000 Pologne Portugal 1,880 1,905 1,900 1,682 1,710 1,700 241 230 240 43 35 40 Portugal Serbia 188 187 194 178 184 190 12 9 12 2 6 8 Serbie Slovakia 3,059 3,030 3,100 2,559 2,430 2,400 900 950 1,000 400 350 300 Slovaquie Slovenia 1,643 1,650 1,630 1,687 2,150 2,000 239 150 180 283 650 550 Slovénie Spain 3,223 3,307 3,307 3,420 3,629 3,629 240 185 185 437 507 507 Espagne Sweden 38,103 37,725 37,325 38,100 37,300 36,900 964 1,128 1,128 961 703 703 Suède Switzerland 2,035 2,100 2,155 2,290 2,350 2,400 55 60 65 310 310 310 Suisse United Kingdom 5,810 5,538 5,538 5,453 5,180 5,180 457 457 457 99 99 99 Royaume-Uni Total Europe 182,849 171,260 171,534 185,467 172,881 171,836 18,218 16,634 16,394 20,836 18,255 16,696 Total Europe Canada 105,870 103,492 103,916 110,046 108,424 108,424 1,346 1,402 1,309 5,522 6,333 5,816 Canada United States 148,043 150,509 153,391 152,799 154,479 156,695 586 570 555 5,342 4,540 3,859 Etats-Unis Total North America 253,913 254,001 257,307 262,845 262,903 265,119 1,931 1,972 1,864 10,863 10,873 9,675 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

Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 406 311 300 329 266 300 134 90 50 57 45 50 Autriche Czech Republic 544 457 447 616 524 517 144 120 125 216 186 195 République tchèque Estonia 1,187 1,244 1,244 1,158 1,200 1,200 46 60 60 16 16 16 Estonie Finland 1,068 1,041 1,061 1,037 1,049 1,061 32 1 9 1 9 9 Finlande France 3,453 4,020 4,120 4,707 4,700 4,800 116 120 120 1,370 800 800 France Germany 2,532 2,290 2,130 2,995 2,700 2,500 111 110 110 574 520 480 Allemagne Hungary 1,234 1,173 1,191 1,234 1,173 1,191 0 0 0 0 0 0 Hongrie Italy 2,088 1,718 1,718 721 721 721 1,413 1,055 1,055 47 59 59 Italie Latvia 1,221 1,190 1,410 1,730 1,750 1,750 87 40 60 596 600 400 Lettonie Luxembourg 226 148 144 23 22 18 221 160 160 18 34 34 Luxembourg Montenegro 143 140 138 143 140 138 0 0 0 0 0 0 Monténégro Netherlands 54 60 60 48 50 50 54 60 60 48 50 50 Pays-Bas Poland 2,687 2,730 2,830 2,757 2,800 2,900 80 80 80 150 150 150 Pologne Portugal 997 885 925 356 330 360 663 580 590 22 25 25 Portugal Serbia 894 922 946 899 920 940 15 20 28 20 18 22 Serbie Slovakia 1,670 1,700 1,750 1,570 1,650 1,700 500 450 450 400 400 400 Slovaquie Slovenia 281 290 280 497 630 600 31 30 30 247 370 350 Slovénie Spain 833 854 854 730 775 775 164 174 174 61 94 94 Espagne Sweden 217 217 217 180 180 180 37 37 37 0 0 0 Suède Switzerland 145 155 160 265 275 280 35 40 40 155 160 160 Suisse United Kingdom 78 77 77 56 56 56 26 26 26 5 5 5 Royaume-Uni Total Europe 21,959 21,622 22,002 22,052 21,910 22,036 3,910 3,253 3,265 4,003 3,541 3,299 Total Europe Canada 15,890 15,923 15,895 14,854 14,926 14,926 1,106 1,060 1,027 70 64 59 Canada United States 31,550 32,311 33,431 33,358 33,742 34,516 221 156 156 2,028 1,587 1,241 Etats-Unis Total North America 47,441 48,234 49,326 48,212 48,668 49,442 1,327 1,216 1,183 2,098 1,650 1,300 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

Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 406 311 300 329 266 300 134 90 50 57 45 50 Autriche Czech Republic 544 457 447 616 524 517 144 120 125 216 186 195 République tchèque Estonia 1,187 1,244 1,244 1,158 1,200 1,200 46 60 60 16 16 16 Estonie Finland 1,068 1,041 1,061 1,037 1,049 1,061 32 1 9 1 9 9 Finlande France 3,412 3,978 4,078 4,707 4,700 4,800 72 75 75 1,367 797 797 France Germany 2,527 2,285 2,125 2,995 2,700 2,500 101 100 100 569 515 475 Allemagne Hungary 1,234 1,173 1,191 1,234 1,173 1,191 0 0 0 0 0 0 Hongrie Italy 2,068 1,729 1,729 721 721 721 1,389 1,047 1,047 42 39 39 Italie Latvia 1,221 1,190 1,410 1,730 1,750 1,750 87 40 60 596 600 400 Lettonie Luxembourg 226 148 144 23 22 18 221 160 160 18 34 34 Luxembourg Montenegro 143 140 138 143 140 138 0 0 0 0 0 0 Monténégro Netherlands 46 55 55 48 50 50 42 50 50 44 45 45 Pays-Bas Poland 2,685 2,727 2,827 2,757 2,800 2,900 78 77 77 150 150 150 Pologne Portugal 981 870 912 356 330 360 642 560 571 17 20 19 Portugal Serbia 893 921 945 899 920 940 14 19 27 20 18 22 Serbie Slovakia 1,670 1,700 1,750 1,570 1,650 1,700 500 450 450 400 400 400 Slovaquie Slovenia 280 290 280 497 630 600 30 30 30 247 370 350 Slovénie Spain 827 847 847 730 775 775 158 167 167 61 94 94 Espagne Sweden 217 217 217 180 180 180 37 37 37 0 0 0 Suède Switzerland 145 155 160 265 275 280 35 40 40 155 160 160 Suisse United Kingdom 76 75 75 56 56 56 24 24 24 5 5 5 Royaume-Uni Total Europe 21,857 21,553 21,935 22,052 21,910 22,036 3,786 3,146 3,158 3,980 3,503 3,260 Total Europe Canada 15,890 15,923 15,895 14,854 14,926 14,926 1,106 1,060 1,027 70 64 59 Canada United States 31,549 32,308 33,429 33,358 33,742 34,516 219 152 154 2,027 1,586 1,240 Etats-Unis Total North America 47,440 48,231 49,324 48,212 48,668 49,442 1,325 1,212 1,181 2,097 1,649 1,299 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

Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 France -41 -42 -42 44 45 45 3 3 3 France Germany -5 -5 -5 10 10 10 5 5 5 Allemagne Italy -20 11 11 25 9 9 4 20 20 Italie Netherlands -8 -5 -5 12 10 10 4 5 5 Pays-Bas Poland -2 -3 -3 2 3 3 0 0 0 Pologne Portugal -16 -15 -13 21 20 19 5 5 6 Portugal Serbia -1 -1 -1 1 1 1 0 0 0 Serbie Slovenia -1 0 0 1 0 1 0 0 0 Slovénie Spain -6 -7 -7 6 7 7 0 0 0 Espagne United Kingdom -2 -2 -2 2 2 2 0 0 0 Royaume-Uni Total Europe -102 -69 -67 124 107 106 22 38 39 Total Europe United States -1 -3 -1 2 4 2 1 1 1 Etats-Unis Total North America -1 -3 -1 2 4 2 1 1 1 Total Amérique du Nord

Country Commerce Net Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 13,844 12,627 12,592 11,047 9,212 9,287 3,676 4,070 4,020 879 655 715 Autriche Cyprus 8 9 10 7 8 9 1 1 1 0 0 0 Chypre Czech Republic 5,559 5,135 5,154 7,664 6,164 6,130 1,270 1,146 1,162 3,375 2,175 2,138 République tchèque Estonia 3,117 2,380 2,435 6,548 6,550 6,550 256 330 285 3,687 4,500 4,400 Estonie Finland 48,404 47,241 49,358 44,923 44,026 45,568 5,037 4,969 5,545 1,556 1,755 1,755 Finlande France 24,495 24,350 24,050 24,257 24,000 23,700 2,527 2,600 2,600 2,289 2,250 2,250 France Germany 26,555 26,580 23,090 27,936 27,100 23,500 4,474 3,870 3,770 5,855 4,390 4,180 Allemagne Hungary 2,122 2,017 2,065 2,049 1,984 2,023 112 73 82 39 39 39 Hongrie Italy 4,508 5,210 5,210 3,916 4,618 4,618 1,288 1,288 1,288 696 696 696 Italie Latvia 5,540 5,150 5,150 9,484 8,800 8,800 1,084 950 950 5,028 4,600 4,600 Lettonie Luxembourg 583 589 589 577 559 559 182 130 130 176 100 100 Luxembourg Malta 2 3 3 0 0 0 2 3 3 0 0 0 Malte Montenegro 245 241 227 245 241 227 0 0 0 0 0 0 Monténégro Netherlands 604 1,100 1,095 1,267 1,240 1,230 289 100 105 952 240 240 Pays-Bas Poland 35,250 36,265 37,135 33,531 34,600 35,450 3,652 3,660 3,710 1,933 1,995 2,025 Pologne Portugal 15,954 15,330 15,365 11,664 11,720 11,590 4,657 4,000 4,140 368 390 365 Portugal Serbia 981 1,007 1,045 967 1,000 1,033 15 8 13 1 1 1 Serbie Slovakia 3,634 3,650 3,760 3,821 3,860 3,950 1,023 1,030 1,050 1,210 1,240 1,240 Slovaquie Slovenia 926 770 790 2,058 2,280 2,230 625 490 530 1,757 2,000 1,970 Slovénie Spain 13,959 14,358 14,358 14,383 15,261 15,261 1,435 1,564 1,564 1,859 2,467 2,467 Espagne Sweden 55,632 54,193 54,727 50,015 48,196 48,730 7,036 7,750 7,750 1,419 1,753 1,753 Suède Switzerland 1,823 1,824 1,829 1,216 1,217 1,222 795 795 795 188 188 188 Suisse United Kingdom 4,590 4,471 4,471 4,293 4,175 4,175 406 405 405 109 109 109 Royaume-Uni Total Europe 268,336 264,500 264,508 261,870 256,811 255,841 39,843 39,232 39,898 33,377 31,543 31,231 Total Europe Canada 37,044 35,822 35,734 35,326 32,985 32,975 2,578 3,462 3,467 860 625 708 Canada United States 238,450 239,587 240,850 244,912 246,110 247,536 348 324 308 6,809 6,848 6,994 Etats-Unis Total North America 275,495 275,409 276,585 280,238 279,096 280,511 2,926 3,786 3,776 7,670 7,473 7,702 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

Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 3,681 3,895 3,850 2,576 2,235 2,300 1,312 1,750 1,700 206 90 150 Autriche Czech Republic 3,927 3,744 3,675 5,316 4,253 4,125 811 811 830 2,200 1,320 1,280 République tchèque Estonia 476 245 245 878 900 900 56 45 45 458 700 700 Estonie Finland 22,913 24,189 25,835 22,746 23,764 25,239 1,163 1,410 1,581 996 985 985 Finlande France 4,689 4,400 4,100 4,559 4,300 4,000 608 550 550 478 450 450 France Germany 10,311 11,900 9,500 10,541 11,500 9,000 2,200 2,100 2,000 2,430 1,700 1,500 Allemagne Hungary 411 488 481 411 488 481 0 0 0 0 0 0 Hongrie Italy 148 853 853 148 853 853 0 0 0 0 0 0 Italie Latvia 1,775 1,700 1,700 1,850 1,800 1,800 374 400 400 449 500 500 Lettonie Luxembourg -16 -18 -16 10 6 8 9 3 3 35 27 27 Luxembourg Montenegro 201 198 186 201 198 186 0 0 0 0 0 0 Monténégro Netherlands 146 150 145 244 240 235 70 80 85 168 170 175 Pays-Bas Poland 15,378 15,900 16,300 15,411 15,950 16,250 1,428 1,500 1,650 1,462 1,550 1,600 Pologne Portugal 1,323 1,430 1,375 1,213 1,350 1,300 122 100 90 12 20 15 Portugal Serbia 66 70 74 66 70 73 0 0 1 0 0 0 Serbie Slovakia 598 600 610 748 710 700 600 630 650 750 740 740 Slovaquie Slovenia 264 200 220 275 430 380 268 170 200 278 400 360 Slovénie Spain 3,369 3,467 3,467 3,754 3,984 3,984 179 138 138 564 655 655 Espagne Sweden 28,513 27,431 27,944 26,353 25,310 25,823 3,114 3,269 3,269 954 1,148 1,148 Suède Switzerland 209 210 210 279 280 280 20 20 20 90 90 90 Suisse United Kingdom 1,894 1,776 1,776 1,633 1,516 1,516 291 291 291 31 31 31 Royaume-Uni Total Europe 100,275 102,827 102,530 99,212 100,136 99,433 12,625 13,267 13,503 11,562 10,576 10,406 Total Europe Canada 4,531 4,347 4,410 4,229 4,021 4,021 324 336 401 22 10 12 Canada United States 141,231 142,785 144,831 141,226 142,779 144,827 5 6 4 0 0 0 Etats-Unis Total North America 145,762 147,132 149,241 145,455 146,800 148,848 329 341 405 22 10 12 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

Pays Apparent Consumption a

Country Consommation Apparente a Production Imports - Importations Exports - Exportations

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

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 1,217 997 1,007 647 577 587 668 500 500 98 80 80 Autriche Czech Republic 450 380 384 649 552 550 3 2 2 202 174 168 République tchèque Estonia 363 200 250 1,270 1,250 1,250 154 250 200 1,060 1,300 1,200 Estonie Finland 8,997 7,940 8,052 7,801 6,884 6,784 1,550 1,633 1,845 354 577 577 Finlande France 2,386 2,250 2,250 3,332 3,200 3,200 43 50 50 989 1,000 1,000 France Germany 1,116 1,180 1,090 1,103 1,100 1,000 259 270 270 246 190 180 Allemagne Hungary 502 507 526 502 507 526 0 0 0 0 0 0 Hongrie Italy 168 166 166 168 166 166 0 0 0 0 0 0 Italie Latvia 172 200 200 2,018 2,000 2,000 244 100 100 2,090 1,900 1,900 Lettonie Luxembourg 77 71 69 46 32 30 36 48 48 5 9 9 Luxembourg Netherlands 62 50 55 108 100 100 21 20 20 67 70 65 Pays-Bas Poland 4,424 4,635 4,785 3,939 4,150 4,300 560 560 560 75 75 75 Pologne Portugal 10,495 10,300 10,260 8,586 8,500 8,400 2,100 2,000 2,050 191 200 190 Portugal Serbia 199 205 210 199 205 210 0 0 0 0 0 0 Serbie Slovakia 1,874 1,950 2,000 1,924 2,000 2,050 100 100 100 150 150 150 Slovaquie Slovenia 137 120 130 424 490 450 84 80 90 371 450 410 Slovénie Spain 5,422 5,288 5,288 6,059 6,429 6,429 269 291 291 906 1,432 1,432 Espagne Sweden 8,517 8,412 8,533 6,232 5,986 6,107 2,313 2,481 2,481 28 55 55 Suède Switzerland 128 128 133 165 165 170 3 3 3 40 40 40 Suisse United Kingdom 23 22 22 13 13 13 18 18 18 9 9 9 Royaume-Uni Total Europe 46,729 45,001 45,410 45,185 44,305 44,322 8,426 8,406 8,628 6,881 7,711 7,540 Total Europe Canada 10,554 10,654 10,644 10,812 10,843 10,843 38 36 30 296 225 228 Canada United States 41,407 40,200 38,795 41,424 40,217 38,810 58 32 18 75 50 33 Etats-Unis Total North America 51,961 50,854 49,439 52,236 51,060 49,653 96 68 48 371 275 261 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

Pays Apparent Consumption a

Country Consommation Apparente a Production Imports - Importations Exports - Exportations

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

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 8,945 7,735 7,735 7,824 6,400 6,400 1,696 1,820 1,820 575 485 485 Autriche Cyprus 8 9 10 7 8 9 1 1 1 0 0 0 Chypre Czech Republic 1,182 1,011 1,094 1,699 1,359 1,454 456 333 330 973 681 690 République tchèque Estonia 2,278 1,935 1,940 4,400 4,400 4,400 47 35 40 2,169 2,500 2,500 Estonie Finland 16,494 15,112 15,471 14,376 13,378 13,545 2,324 1,926 2,119 206 193 193 Finlande France 17,420 17,700 17,700 16,366 16,500 16,500 1,876 2,000 2,000 822 800 800 France Germany 15,128 13,500 12,500 16,292 14,500 13,500 2,015 1,500 1,500 3,179 2,500 2,500 Allemagne Hungary 1,209 1,022 1,057 1,137 989 1,015 112 73 82 39 39 39 Hongrie Italy 4,192 4,192 4,192 3,600 3,600 3,600 1,288 1,288 1,288 696 696 696 Italie Latvia 3,593 3,250 3,250 5,616 5,000 5,000 466 450 450 2,489 2,200 2,200 Lettonie Luxembourg 522 536 536 521 521 521 137 79 79 136 64 64 Luxembourg Malta 2 3 3 0 0 0 2 3 3 0 0 0 Malte Montenegro 44 43 41 44 43 41 0 0 0 0 0 0 Monténégro Netherlands 396 900 895 915 900 895 198 0 0 717 0 0 Pays-Bas Poland 15,448 15,730 16,050 14,181 14,500 14,900 1,664 1,600 1,500 396 370 350 Pologne Portugal 4,136 3,600 3,730 1,865 1,870 1,890 2,435 1,900 2,000 165 170 160 Portugal Serbia 716 732 761 702 725 750 15 8 12 1 1 1 Serbie Slovakia 1,162 1,100 1,150 1,149 1,150 1,200 323 300 300 310 350 350 Slovaquie Slovenia 525 450 440 1,360 1,360 1,400 273 240 240 1,107 1,150 1,200 Slovénie Spain 5,169 5,603 5,603 4,570 4,849 4,849 987 1,135 1,135 388 380 380 Espagne Sweden 18,602 18,350 18,250 17,430 16,900 16,800 1,609 2,000 2,000 437 550 550 Suède Switzerland 1,486 1,486 1,486 772 772 772 772 772 772 58 58 58 Suisse United Kingdom 2,673 2,673 2,673 2,646 2,646 2,646 96 96 96 69 69 69 Royaume-Uni Total Europe 121,332 116,673 116,568 117,472 112,370 112,087 18,793 17,559 17,767 14,933 13,256 13,285 Total Europe Canada 21,959 20,821 20,680 20,285 18,121 18,111 2,216 3,090 3,037 542 390 467 Canada United States 55,812 56,602 57,224 62,262 63,114 63,899 285 286 286 6,734 6,798 6,961 Etats-Unis Total North America 77,771 77,423 77,904 82,547 81,235 82,010 2,500 3,376 3,323 7,277 7,188 7,428 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 1,290 1,497 1,450 1,691 1,938 2,050 344 309 300 745 750 900 Autriche Cyprus 8 5 5 0 0 0 8 5 5 0 0 0 Chypre Czech Republic 234 215 225 540 459 482 38 38 40 344 282 296 République tchèque Estonia 284 300 230 1,650 1,350 1,300 12 50 30 1,378 1,100 1,100 Estonie Finland 530 541 562 360 380 405 188 163 160 18 2 3 Finlande France 2,735 3,260 3,660 2,050 2,250 2,450 775 1,100 1,300 90 90 90 France Germany 3,328 3,540 3,720 3,569 3,700 3,900 443 480 420 684 640 600 Allemagne Hungary 63 44 50 62 43 49 11 13 12 11 12 12 Hongrie Italy 2,359 2,359 2,359 450 450 450 1,916 1,916 1,916 7 7 7 Italie Latvia 621 750 750 1,980 2,000 2,000 326 350 350 1,685 1,600 1,600 Lettonie Luxembourg 61 72 72 63 63 63 17 11 11 19 2 2 Luxembourg Malta 1 1 1 0 0 0 1 1 1 0 0 0 Malte Montenegro 18 25 26 83 84 84 0 0 0 65 59 58 Monténégro Netherlands 5,354 5,354 5,354 268 268 268 5,551 5,551 5,551 465 465 465 Pays-Bas Poland 842 920 1,100 1,152 1,200 1,350 366 370 380 677 650 630 Pologne Portugal 228 225 220 747 740 735 4 5 5 523 520 520 Portugal Serbia 478 460 485 418 450 480 83 70 80 23 60 75 Serbie Slovakia 22 175 175 390 450 450 47 75 75 415 350 350 Slovaquie Slovenia 125 155 150 164 175 180 126 120 130 165 140 160 Slovénie Spain 867 907 907 1,007 1,007 1,007 65 46 46 206 146 146 Espagne Sweden 1,776 1,800 1,850 1,809 1,750 1,800 199 210 210 232 160 160 Suède Switzerland 410 415 420 330 335 340 80 80 80 0 0 0 Suisse United Kingdom 7,819 7,830 7,830 327 330 330 7,516 7,520 7,520 23 20 20 Royaume-Uni Total Europe 29,451 30,850 31,601 19,110 19,422 20,173 18,114 18,482 18,621 7,774 7,055 7,194 Total Europe Canada 368 420 179 3,830 3,830 3,830 31 52 56 3,493 3,462 3,707 Canada United States 761 273 152 9,544 9,744 9,948 194 174 155 8,977 9,644 9,951 Etats-Unis Total North America 1,129 694 331 13,374 13,574 13,778 225 226 211 12,470 13,106 13,659 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

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

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 actual actual actual actual réels réels réels réels

Sawn softwood 75.92 69.01 68.49 96.71 89.54 88.44 29.69 25.67 25.93 50.49 46.20 45.88 Sciages conifères

Softwood logs a 182.85 171.26 171.53 185.47 172.88 171.84 18.22 16.63 16.39 20.84 18.25 16.70 Grumes de conifères a

Sawn hardwood 7.02 6.65 6.70 6.93 6.45 6.61 4.18 3.86 3.81 4.09 3.66 3.72 Sciages non-conifères

– temperate zone b 6.45 6.14 6.18 6.87 6.40 6.55 3.28 3.07 3.02 3.70 3.33 3.39 – zone tempérée b

– tropical zone b 0.57 0.51 0.52 0.06 0.05 0.06 0.90 0.79 0.79 0.39 0.32 0.32 – zone tropicale b

Hardwood logs a 21.96 21.62 22.00 22.05 21.91 22.04 3.91 3.25 3.26 4.00 3.54 3.30 Grumes de non-conifères a

– temperate zone b 21.86 21.55 21.93 22.05 21.91 22.04 3.79 3.15 3.16 3.98 3.50 3.26 – zone tempérée b

– tropical zone b 0.10 0.07 0.07 0.12 0.11 0.11 0.02 0.04 0.04 – zone tropicale b

Veneer sheets 1.58 1.49 1.49 1.00 0.97 0.96 1.42 1.28 1.29 0.84 0.76 0.76 Feuilles de placage

Plywood 6.62 6.21 5.92 4.17 3.93 3.97 6.42 5.79 5.48 3.96 3.50 3.53 Contreplaqués

Particle board (excluding OSB) 28.12 26.41 26.52 28.01 26.71 26.91 10.02 9.58 9.55 9.92 9.88 9.94 Pann. de particules (sauf OSB)

OSB 5.27 5.06 5.09 4.89 4.89 5.02 3.20 2.96 2.94 2.83 2.78 2.87 OSB

Fibreboard 15.80 14.89 15.09 16.15 15.31 15.42 8.76 8.01 8.04 9.11 8.43 8.37 Panneaux de fibres

– Hardboard 0.79 0.82 0.90 0.48 0.47 0.47 1.47 1.44 1.46 1.17 1.09 1.04 – Durs

– MDF 11.42 10.85 10.97 12.16 11.62 11.68 5.21 4.61 4.62 5.95 5.38 5.33 – MDF

– Other board 3.59 3.22 3.22 3.51 3.22 3.27 2.07 1.97 1.96 1.99 1.96 2.01 – Autres panneaux Pulpwood a 268.34 264.50 264.51 261.87 256.81 255.84 39.84 39.23 39.90 33.38 31.54 31.23 Bois de trituration a

– Pulp logs 147.00 147.83 147.94 144.40 144.44 143.75 21.05 21.67 22.13 18.44 18.29 17.95 – Bois ronds de trituration

– softwood 100.28 102.83 102.53 99.21 100.14 99.43 12.63 13.27 13.50 11.56 10.58 10.41 – conifères

– hardwood 46.73 45.00 45.41 45.18 44.31 44.32 8.43 8.41 8.63 6.88 7.71 7.54 – non-conifères

– Residues, chips and particles 121.33 116.67 116.57 117.47 112.37 112.09 18.79 17.56 17.77 14.93 13.26 13.29 – Déchets, plaquettes et part. Wood pulp 37.60 34.07 35.28 34.64 32.24 33.81 17.33 16.19 16.59 14.37 14.37 15.12 Pâte de bois

Paper and paperboard 72.76 66.14 69.44 83.10 73.88 79.49 43.20 39.62 41.48 53.55 47.36 51.53 Papiers et cartons

Wood Pellets 29.45 30.85 31.60 19.11 19.42 20.17 18.11 18.48 18.62 7.77 7.05 7.19 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 14

Europe: Summary table of market forecasts for 2023 and 2024

Europe: Tableau récapitulatif des prévisions du marché pour 2023 et 2024 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

forecasts forecasts forecasts forecasts prévisions prévisions prévisions prévisions

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 actual actual actual actual réels réels réels réels

Sawn softwood 91.63 89.85 90.39 100.44 97.41 95.73 27.09 26.48 27.10 35.90 34.04 32.43 Sciages conifères

Softwood logs 253.91 254.00 257.31 262.84 262.90 265.12 1.93 1.97 1.86 10.86 10.87 9.68 Grumes de conifères

Sawn hardwood 15.85 16.16 16.46 18.50 18.72 19.03 1.59 1.63 1.56 4.23 4.19 4.13 Sciages non-conifères

– temperate zone 15.57 15.89 16.19 18.50 18.72 19.03 1.29 1.33 1.26 4.21 4.16 4.10 – zone tempérée

– tropical zone 0.29 0.27 0.27 0.00 0.00 0.00 0.31 0.30 0.30 0.02 0.03 0.03 – zone tropicale

Hardwood logs 47.44 48.23 49.33 48.21 48.67 49.44 1.33 1.22 1.18 2.10 1.65 1.30 Grumes de non-conifères

– temperate zone 47.44 48.23 49.32 48.21 48.67 49.44 1.32 1.21 1.18 2.10 1.65 1.30 – 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 2.85 2.93 2.97 2.87 2.89 2.91 0.86 0.88 0.89 0.88 0.83 0.84 Feuilles de placage

Plywood 16.92 16.92 17.31 10.86 10.90 11.05 7.48 7.37 7.68 1.43 1.36 1.42 Contreplaqués

Particle board (excluding OSB) 6.66 7.45 7.46 6.11 6.58 6.55 1.75 1.97 1.98 1.19 1.10 1.07 Pann. de particules (sauf OSB)

OSB 21.20 21.09 21.35 20.86 20.60 20.86 6.28 6.30 6.39 5.94 5.82 5.89 OSB

Fibreboard 9.92 9.93 10.07 7.64 7.71 7.87 4.18 3.92 3.92 1.90 1.69 1.72 Panneaux de fibres

– Hardboard 0.51 0.56 0.56 0.53 0.59 0.60 0.31 0.28 0.29 0.32 0.32 0.33 – Durs

– MDF 6.21 6.23 6.23 3.83 3.88 3.89 3.55 3.35 3.32 1.17 0.99 0.98 – MDF

– Other board 3.20 3.15 3.28 3.28 3.24 3.38 0.32 0.29 0.31 0.40 0.38 0.41 – Autres panneaux Pulpwood 275.49 275.41 276.58 280.24 279.10 280.51 2.93 3.79 3.78 7.67 7.47 7.70 Bois de trituration

– Pulp logs 197.72 197.99 198.68 197.69 197.86 198.50 0.43 0.41 0.45 0.39 0.28 0.27 – Bois ronds de trituration

– softwood 145.76 147.13 149.24 145.45 146.80 148.85 0.33 0.34 0.41 0.02 0.01 0.01 – conifères

– hardwood 51.96 50.85 49.44 52.24 51.06 49.65 0.10 0.07 0.05 0.37 0.27 0.26 – non-conifères

– Residues, chips and particles 77.77 77.42 77.90 82.55 81.23 82.01 2.50 3.38 3.32 7.28 7.19 7.43 – Déchets, plaquettes et part. Wood pulp 45.79 48.12 48.43 55.02 54.33 54.12 7.42 8.22 8.89 16.65 14.44 14.58 Pâte de bois

Paper and paperboard 69.75 68.96 69.26 75.05 73.60 73.63 10.72 10.42 10.39 16.02 15.06 14.77 Papiers et cartons

Wood pellets 1.13 0.69 0.33 13.37 13.57 13.78 0.23 0.23 0.21 12.47 13.11 13.66 Granulés de bois

TABLE 15

North America: Summary table of market forecasts for 2023 and 2024

Amérique du Nord: Tableau récapitulatif des prévisions du marché pour 2023 et 2024 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

forecasts forecasts forecasts forecasts prévisions prévisions prévisions prévisions

List of tables
Table1
Table2
Table 2a
Table 2b
Table 3
Table 4
Table 5
Table 5a
Table 6
Table 6a
Table 6b
Table 6c
Table 7
Table 8
Table 9
Table 9a
Table 9b
Table 10
Table 11
Table 11a
Table 11b
Table12
Table 12a
Table 12b
Table 12c
Table 13
Table 14
Table 15

JQ2022GBR

Read more about JQ2022GBR

JFSQ Country Replies United Kingdom

Languages and translations

English

Cover

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

Official address (in full):				Northern Research Station, Roslin, Midlothian, EH25 9SY

	Telephone:
	Fax:
	E-mail:

Manual

	The UNECE manual for the JFSQ for 2022 data is available on the UNECE website:
	https://unece.org/forestry-timber/documents/2023/04/informal-documents/jfsq-2022-data-manual
	The definitions for the JFSQ for 2022 data are available on the UNECE website:
	https://unece.org/forestry-timber/documents/2023/04/informal-documents/jfsq-2022-data-definitions

https://unece.org/forestry-timber/documents/2023/04/informal-documents/jfsq-2022-data-definitions https://unece.org/forestry-timber/documents/2023/04/informal-documents/jfsq-2022-data-manual

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

JQ1 Production

Country:

United Kingdom

Date: 23-04-11

Name of Official responsible for reply:

Official Address (in full):

FOREST SECTOR QUESTIONNAIRE JQ1

Northern Research Station

Roslin, Midlothian, EH25 9SY

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

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
McCusker 14/6/07: McCusker 14/6/07: minus 1.2.3 (other ind. RW) production

8,723

7,807

-11%

m3 of wood in m3 or t of product

ALL 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

10899.28

9787.66

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ub

Solid Wood Demand

agglomerate production

304

327

2.4

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ub

2183.70

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ub

Sawnwood production

3,611

3,145

-13%

1.1.C

Coniferous

1000 m3ub

1571.20

1.1.C

Coniferous

1000 m3ub

veneer production

missing data

1.1.NC

Non-Coniferous

1000 m3ub

612.50

1.1.NC

Non-Coniferous

1000 m3ub

plywood production

missing data

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ub

8715.58

7603.96

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ub

particle board production (incl OSB)

2,688

2,610

-3%

1.58

1.2.C

Coniferous

1000 m3ub

8607.81

7486.34

1.2.C

Coniferous

1000 m3ub

fibreboard production

798

856

1.8

1.2.NC

Non-Coniferous

1000 m3ub

107.77

117.62

1.2.NC

Non-Coniferous

1000 m3ub

mechanical/semi-chemical pulp production

missing data

2.5

1.2.NC.T

of which: Tropical

1000 m3ub

0.00

1.2.NC.T

of which: Tropical

1000 m3ub

chemical pulp production

missing data

4.9

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ub

6354.27

5509.03

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ub

dissolving pulp production

missing data

5.7

1.2.1.C

Coniferous

1000 m3ub

6297.78

5452.60

1.2.1.C

Coniferous

1000 m3ub

Availability

Solid Wood Demand

missing data

1.2.1.NC

Non-Coniferous

1000 m3ub

56.49

56.43

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

1898.39

1646.45

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

1895.24

1633.39

1.2.2.C

Coniferous

1000 m3ub

Positive number means more roundwood available than demanded

1.2.2.NC

Non-Coniferous

1000 m3ub

3.15

13.06

1.2.2.NC

Non-Coniferous

1000 m3ub

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ub

462.91

448.48

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ub

1.2.3.C

Coniferous

1000 m3ub

414.78

400.36

1.2.3.C

Coniferous

1000 m3ub

% of particle board that is from recovered wood

35%

1.2.3.NC

Non-Coniferous

1000 m3ub

48.13

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

5.00

WOOD CHARCOAL

1000 t

WOOD CHIPS, PARTICLES AND RESIDUES

1000 m3

3121.78

2646.24

WOOD CHIPS, PARTICLES AND RESIDUES

1000 m3

3.1

WOOD CHIPS AND PARTICLES

1000 m3

2341.33

1984.68

3.1

WOOD CHIPS AND PARTICLES

1000 m3

3.2

WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)

1000 m3

780.44

661.56

3.2

WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)

1000 m3

3.2.1

of which: Sawdust

1000 m3

+++

3.2.1

of which: Sawdust

1000 m3

RECOVERED POST-CONSUMER WOOD

1000 t

4500.00

RECOVERED POST-CONSUMER WOOD

1000 t

WOOD PELLETS AND OTHER AGGLOMERATES

1000 t

304.41

326.56

WOOD PELLETS AND OTHER AGGLOMERATES

1000 t

5.1

WOOD PELLETS

1000 t

304.41

326.56

5.1

WOOD PELLETS

1000 t

5.2

OTHER AGGLOMERATES

1000 t

0.00

5.2

OTHER AGGLOMERATES

1000 t

SAWNWOOD (INCLUDING SLEEPERS)

1000 m3

3610.84

3144.97

SAWNWOOD (INCLUDING SLEEPERS)

1000 m3

6.C

Coniferous

1000 m3

3573.54

3108.36

6.C

Coniferous

1000 m3

6.NC

Non-Coniferous

1000 m3

37.30

36.60

6.NC

Non-Coniferous

1000 m3

6.NC.T

of which: Tropical

1000 m3

0.00

6.NC.T

of which: Tropical

1000 m3

VENEER SHEETS

1000 m3

0.00

VENEER SHEETS

1000 m3

7.C

Coniferous

1000 m3

0.00

7.C

Coniferous

1000 m3

7.NC

Non-Coniferous

1000 m3

0.00

7.NC

Non-Coniferous

1000 m3

7.NC.T

of which: Tropical

1000 m3

0.00

7.NC.T

of which: Tropical

1000 m3

WOOD-BASED PANELS

1000 m3

3486.00

3466.00

WOOD-BASED PANELS

1000 m3

8.1

PLYWOOD

1000 m3

0.00

8.1

PLYWOOD

1000 m3

8.1.C

Coniferous

1000 m3

0.00

8.1.C

Coniferous

1000 m3

8.1.NC

Non-Coniferous

1000 m3

0.00

8.1.NC

Non-Coniferous

1000 m3

8.1.NC.T

of which: Tropical

1000 m3

0.00

8.1.NC.T

of which: Tropical

1000 m3

8.1.1

of which: Laminated Veneer Lumber (LVL)

1000 m3

0.00

8.1.1

of which: Laminated Veneer Lumber (LVL)

1000 m3

8.1.1.C

Coniferous

1000 m3

0.00

8.1.1.C

Coniferous

1000 m3

8.1.1.NC

Non-Coniferous

1000 m3

0.00

8.1.1.NC

Non-Coniferous

1000 m3

8.1.1.NC.T

of which: Tropical

1000 m3

0.00

8.1.1.NC.T

of which: Tropical

1000 m3

8.2

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

1000 m3

2688.00

2610.00

8.2

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

1000 m3

8.2.1

of which: ORIENTED STRAND BOARD (OSB)

1000 m3

+++

8.2.1

of which: ORIENTED STRAND BOARD (OSB)

1000 m3

8.3

FIBREBOARD

1000 m3

798.00

856.00

8.3

FIBREBOARD

1000 m3

8.3.1

HARDBOARD

1000 m3

0.00

8.3.1

HARDBOARD

1000 m3

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

1000 m3

798.00

856.00

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

1000 m3

8.3.3

OTHER FIBREBOARD

1000 m3

0.00

8.3.3

OTHER FIBREBOARD

1000 m3

WOOD PULP

1000 t

+++

WOOD PULP

1000 t

ERROR:#VALUE!

9.1

MECHANICAL AND SEMI-CHEMICAL WOOD PULP

1000 t

+++

9.1

MECHANICAL AND SEMI-CHEMICAL WOOD PULP

1000 t

9.2

CHEMICAL WOOD PULP

1000 t

0.00

9.2

CHEMICAL WOOD PULP

1000 t

9.2.1

SULPHATE PULP

1000 t

0.00

9.2.1

SULPHATE PULP

1000 t

9.2.1.1

of which: BLEACHED

1000 t

0.00

9.2.1.1

of which: BLEACHED

1000 t

9.2.2

SULPHITE PULP

1000 t

0.00

9.2.2

SULPHITE PULP

1000 t

9.3

DISSOLVING GRADES

1000 t

0.00

9.3

DISSOLVING GRADES

1000 t

OTHER PULP

1000 t

2544.00

2398.00

OTHER PULP

1000 t

10.1

PULP FROM FIBRES OTHER THAN WOOD

1000 t

7.00

10.1

PULP FROM FIBRES OTHER THAN WOOD

1000 t

10.2

RECOVERED FIBRE PULP

1000 t

2537.00

2391.00

10.2

RECOVERED FIBRE PULP

1000 t

RECOVERED PAPER

1000 t

7103.00

6689.00

RECOVERED PAPER

1000 t

PAPER AND PAPERBOARD

1000 t

3642.00

3462.00

PAPER AND PAPERBOARD

1000 t

ERROR:#VALUE!

12.1

GRAPHIC PAPERS

1000 t

+++

12.1

GRAPHIC PAPERS

1000 t

ERROR:#VALUE!

12.1.1

NEWSPRINT

1000 t

+++

12.1.1

NEWSPRINT

1000 t

12.1.2

UNCOATED MECHANICAL

1000 t

+++

12.1.2

UNCOATED MECHANICAL

1000 t

12.1.3

UNCOATED WOODFREE

1000 t

+++

12.1.3

UNCOATED WOODFREE

1000 t

12.1.4

COATED PAPERS

1000 t

+++

12.1.4

COATED PAPERS

1000 t

12.2

HOUSEHOLD AND SANITARY PAPERS

1000 t

690.00

737.00

12.2

HOUSEHOLD AND SANITARY PAPERS

1000 t

12.3

PACKAGING MATERIALS

1000 t

1898.00

1842.00

12.3

PACKAGING MATERIALS

1000 t

ERROR:#VALUE!

12.3.1

CASE MATERIALS

1000 t

+++

12.3.1

CASE MATERIALS

1000 t

12.3.2

CARTONBOARD

1000 t

+++

12.3.2

CARTONBOARD

1000 t

12.3.3

WRAPPING PAPERS

1000 t

+++

12.3.3

WRAPPING PAPERS

1000 t

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

1000 t

+++

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

1000 t

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

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

1000 m3

+++

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

1000 m3

ERROR:#VALUE!

15.1

GLULAM

1000 m3

+++

15.1

GLULAM

1000 m3

15.2

CROSS-LAMINATED TIMBER (CLT or X-LAM)

1000 m3

+++

15.2

CROSS-LAMINATED TIMBER (CLT or X-LAM)

1000 m3

I BEAMS (I-JOISTS)1

1000 t

+++

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

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

Please complete each cell if possible with

m3 = cubic metres solid volume

data (numerical value)

t = metric tonnes

or "…" for not available

or "0" for zero data

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

JQ2 Trade

			61	62	61	62	91	92	91	92
		FOREST SECTOR QUESTIONNAIRE JQ2				Country:	United Kingdom		Date: 23-04-11
						Name of Official responsible for reply:																																											INTRA-EU	The difference might be caused by Intra-EU trade
		PRIMARY PRODUCTS				Official Address (in full):		Northern Research Station, Roslin, Midlothian, EH25 9SY																														This table highlights discrepancies between production and trade. For any negative number, indicating greater net exports than production, please verify your data!											CHECK
		Trade				Telephone:			Fax:																			This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!																		ZERO CHECK 2 - if no value in Zero Check 1
						E-mail:																																			Country:	United Kingdom				verifies whether the JQ2 figures refers only to intra-EU trade
	Specify Currency and Unit of Value (e.g.:1000 USD):			£1000 (Sterling)							Flag	Flag	Flag	Flag	Flag	Flag	Flag	Flag	Note	Note	Note	Note	Note	Note	Note	Note		Trade		Discrepancies
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
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
			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	763.75826087	110223.334	1071.5853240333	186143.834	184.65937334	32511.186	156.3207330333	30403.594																	1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	0	0	0	0	0	0	0	0	1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	11,478	10,703			1	ROUNDWOOD (WOOD IN THE ROUGH)	NAC/m3
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	124.38222403	22483.489	278.4085903448	83936.762	15.42902039	2686.436	14.5094868966	2686.655																	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	0	0	0	0	0	0	0	0	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	2,293	2,448			1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	NAC/m3
1.1.C	Coniferous	1000 m3ub	81.62164153	14590.429	46.969782069	13168.559	15.15982039	2488.283	4.9691765517	1451.233																	1.1.C	Coniferous	1000 m3ub									1.1.C	Coniferous	1000 m3ub	1,638	1,613			1.1.C	Coniferous	NAC/m3
1.1.NC	Non-Coniferous	1000 m3ub	42.7605825	7893.06	231.4388082759	70768.203	0.2692	198.153	9.5403103448	1235.422																	1.1.NC	Non-Coniferous	1000 m3ub									1.1.NC	Non-Coniferous	1000 m3ub	655	834			1.1.NC	Non-Coniferous	NAC/m3
1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	639.37603684	87739.845	793.1767336885	102207.072	169.23035295	29824.75	141.8112461368	27716.939																	1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	0	0	0	0	0	0	0	0	1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	9,186	8,255			1.2	INDUSTRIAL ROUNDWOOD	NAC/m3
1.2.C	Coniferous	1000 m3ub	572.00720434	75842.406	748.31799328	93407.33	164.8320192	27700.437	129.72767956	25939.548																	1.2.C	Coniferous	1000 m3ub									1.2.C	Coniferous	1000 m3ub	9,015	8,105			1.2.C	Coniferous	NAC/m3
1.2.NC	Non-Coniferous	1000 m3ub	67.3688325	11897.439	44.8587404085	8799.742	4.39833375	2124.313	12.0835665768	1777.391																	1.2.NC	Non-Coniferous	1000 m3ub									1.2.NC	Non-Coniferous	1000 m3ub	171	150			1.2.NC	Non-Coniferous	NAC/mt
1.2.NC.T	of which: Tropical1	1000 m3ub	1.29608125	482.973	2.26178375	1067.226	0.03245875	49.591	3.13384625	495.361																	1.2.NC.T	of which: Tropical1	1000 m3ub									1.2.NC.T	of which: Tropical1	1000 m3ub	1	-1			1.2.NC.T	of which: Tropical	1000 m3
2	WOOD CHARCOAL	1000 t	95.50561	46436.414	86.91064	52236.943	3.119786	2185.829	2.298526	1873.717																	2	WOOD CHARCOAL	1000 t									2	WOOD CHARCOAL	1000 t	97	90			2	WOOD CHARCOAL	1000 m3
3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	119.35124188	2583.716	95.82361824	11665.465	77.13640416	7724.14	69.3125736239	10770.898																	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	0	0	0	0	0	0	0	0	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	3,164	2,673			3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3
3.1	WOOD CHIPS AND PARTICLES	1000 m3	117.63380548	2234.873	59.75939116	3452.924	75.51996	7425.635	61.1832	9743.63																	3.1	WOOD CHIPS AND PARTICLES	1000 m3									3.1	WOOD CHIPS AND PARTICLES	1000 m3	2,383	1,983			3.1	WOOD CHIPS AND PARTICLES	1000 mt
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	1.7174364	348.843	36.06422708	8212.541	1.61644416	298.505	8.1293736239	1027.268																	3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3									3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	781	689			3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 mt
3.2.1	of which: Sawdust	1000 m3	+++	+++	6.71977424	1449.422	+++	+++	6.25198176	824.745																	3.2.1	of which: Sawdust	1000 m3									3.2.1	of which: Sawdust	1000 m3	ERROR:#VALUE!	ERROR:#VALUE!
4	RECOVERED POST-CONSUMER WOOD	1000 t	97.5270716	6961.972	18.72793628	4091.568	5.00619768	198.909	18.78293012	202.523																	4	RECOVERED POST-CONSUMER WOOD	1000 t									4	RECOVERED POST-CONSUMER WOOD	1000 t	4,593	4,500			4	RECOVERED POST-CONSUMER WOOD	1000 mt
5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	9160.535258	1301181.25	7585.407576	1351594.008	12.789769	2057.037	43.618492	13276.55																	5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	0	0	0	0	0	0	0	0	5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	9,452	7,868			5	WOOD PELLETS AND OTHER AGGLOMERATES	NAC/m3
5.1	WOOD PELLETS	1000 t	9128.015405	1294586.758	7515.693959	1322763.932	1.627909	420.361	22.816327	12008.768																	5.1	WOOD PELLETS	1000 t									5.1	WOOD PELLETS	1000 t	9,431	7,819			5.1	WOOD PELLETS	NAC/m3
5.2	OTHER AGGLOMERATES	1000 t	32.519853	6594.492	69.713617	28830.076	11.16186	1636.676	20.802165	1267.782																	5.2	OTHER AGGLOMERATES	1000 t									5.2	OTHER AGGLOMERATES	1000 t	21	49			5.2	OTHER AGGLOMERATES	NAC/m3
6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	8158.9758895	2668424.36	6506.3613032381	2345565.27057	276.57659198	96144.765	181.5429147177	84523.828																	6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	0	0	0	0	0	0	0	0	6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	11,493	9,470			6	SAWNWOOD (INCLUDING SLEEPERS)	NAC/m3
6.C	Coniferous	1000 m3	7623.372691	2384009.739	5719.4541802782	1902542.56557	237.3755191	75196.213	164.987493448	61825.424																	6.C	Coniferous	1000 m3									6.C	Coniferous	1000 m3	10,960	8,663			6.C	Coniferous	NAC/m3
6.NC	Non-Coniferous	1000 m3	535.6031985	284414.621	786.9071229599	443022.705	39.20107288	20948.552	16.5554212697	22698.404																	6.NC	Non-Coniferous	1000 m3									6.NC	Non-Coniferous	1000 m3	534	807			6.NC	Non-Coniferous	NAC/m3
6.NC.T	of which: Tropical1	1000 m3	79.30549418	60195.744	93.70630841	88757.177	3.153793651	3381.486	2.7554738406	2916.32																	6.NC.T	of which: Tropical1	1000 m3									6.NC.T	of which: Tropical1	1000 m3	76	91			6.NC.T	of which: Tropical	NAC/m3
7	VENEER SHEETS	1000 m3	14.205500085	12186.934	6.6362273936	33672.093	0.378327532	3329.648	0.6651	5003.892																	7	VENEER SHEETS	1000 m3	0	0	0	0	0	0	0	0	7	VENEER SHEETS	1000 m3	14	6			7	VENEER SHEETS	NAC/m3
7.C	Coniferous	1000 m3	4.534032	1398.573	0.5009175532	2015.438	0.047902	744.259	0.2393	740.101																	7.C	Coniferous	1000 m3									7.C	Coniferous	1000 m3	4	0			7.C	Coniferous	NAC/m3
7.NC	Non-Coniferous	1000 m3	9.671468085	10788.361	6.1353098404	31656.655	0.330425532	2585.389	0.4258	4263.791																	7.NC	Non-Coniferous	1000 m3									7.NC	Non-Coniferous	1000 m3	9	6			7.NC	Non-Coniferous	NAC/m3
7.NC.T	of which: Tropical	1000 m3	0.333484043	763.519	0.50946581	2523.851	0.035555851	305.041	0.01362984	96.919																	7.NC.T	of which: Tropical	1000 m3									7.NC.T	of which: Tropical	1000 m3	0	0			7.NC.T	of which: Tropical	NAC/m3
8	WOOD-BASED PANELS	1000 m3	3780.29090716	1301096.2991	3228.8308338938	1515323.39936107	321.036110253	154232.106	369.9936792488	166447.987192461																	8	WOOD-BASED PANELS	1000 m3	0	0	0	0	0	0	0	0	8	WOOD-BASED PANELS	1000 m3	6,945	6,325			8	WOOD-BASED PANELS	NAC/m3
8.1	PLYWOOD	1000 m3	1540.9895219	585552.318	1319.9539358769	669257.357	54.55172436	37301.91	65.6011743156	39599.617																	8.1	PLYWOOD	1000 m3	0	0	0	0	0	0	0	0	8.1	PLYWOOD	1000 m3	1,486	1,254			8.1	PLYWOOD	NAC/m3
8.1.C	Coniferous	1000 m3	456.7605499	146394.446	349.7803664745	143894.339	9.843804	4808.05	10.6509989899	5470.452																	8.1.C	Coniferous	1000 m3									8.1.C	Coniferous	1000 m3	447	339			8.1.C	Coniferous	NAC/m3
8.1.NC	Non-Coniferous	1000 m3	1084.228972	439157.872	970.1735694024	525363.018	44.70792036	32493.86	54.9501753258	34129.165																	8.1.NC	Non-Coniferous	1000 m3									8.1.NC	Non-Coniferous	1000 m3	1,040	915			8.1.NC	Non-Coniferous	NAC/m3
8.1.NC.T	of which: Tropical	1000 m3	223.0408005	109393.468	167.2355109968	117044.005	31.27745	17703.559	36.68536318	24042.35																	8.1.NC.T	of which: Tropical	1000 m3									8.1.NC.T	of which: Tropical	1000 m3	192	131			8.1.NC.T	of which: Tropical	NAC/m3
8.1.1	of which: Laminated Veneer Lumber (LVL)	1000 m3	+++	+++	54.532	33241.3312	+++	+++	0.5258894444	794.01																	8.1.1	of which: Laminated Veneer Lumber (LVL)	1000 m3	ERROR:#VALUE!	ERROR:#VALUE!	0	0	ERROR:#VALUE!	ERROR:#VALUE!	0	0	8.1.1	of which: Laminated Veneer Lumber (LVL)	1000 m3	ERROR:#VALUE!	54
8.1.1.C	Coniferous	1000 m3	+++	+++	49.718	32317.8842	+++	+++	0.0229445752	30.984																	8.1.1.C	Coniferous	1000 m3									8.1.1.C	Coniferous	1000 m3	ERROR:#VALUE!	50
8.1.1.NC	Non-Coniferous	1000 m3	+++	+++	4.814	923.447	+++	+++	0.5029448692	763.026																	8.1.1.NC	Non-Coniferous	1000 m3									8.1.1.NC	Non-Coniferous	1000 m3	ERROR:#VALUE!	4
8.1.1.NC.T	of which: Tropical	1000 m3	+++	+++	0.837	503.742	+++	+++	0.4564948692	726.43																	8.1.1.NC.T	of which: Tropical	1000 m3									8.1.1.NC.T	of which: Tropical	1000 m3	ERROR:#VALUE!	0
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3	1158.999377	302691.6431	1013.5404428176	345057.187361066	194.9852788	83106.714	244.6268437996	87381.4501924607																	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	3,652	3,379			8.2	PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD	NAC/m3
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	460.824008	121054.3991	365.4082229152	117376.516361066	132.9797109	58690.203	189.6719542152	61620.0651924607																	8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3									8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	ERROR:#VALUE!	ERROR:#VALUE!			8.2.1	of which: ORIENTED STRANDBOARD (OSB)	NAC/m3
8.3	FIBREBOARD	1000 m3	1080.30200826	412852.338	895.3364551994	501008.855	71.499107093	33823.482	59.7656611337	39466.92																	8.3	FIBREBOARD	1000 m3	0	0	0	0	0	0	0	0	8.3	FIBREBOARD	1000 m3	1,807	1,692			8.3	FIBREBOARD	NAC/m3
8.3.1	HARDBOARD	1000 m3	111.1596702	66686.982	109.7815340426	91953.474	10.51749149	5299.072	8.6520319149	6548.612																	8.3.1	HARDBOARD	1000 m3									8.3.1	HARDBOARD	1000 m3	101	101			8.3.1	HARDBOARD	NAC/mt
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	878.3691365	321010.992	738.9639442818	393218.186	54.41145654	22979.432	42.0405589063	24171.061																	8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3									8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	1,622	1,553			8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	NAC/mt
8.3.3	OTHER FIBREBOARD	1000 m3	90.77320156	25154.364	46.590976875	15837.195	6.570159063	5544.978	9.0730703125	8747.247																	8.3.3	OTHER FIBREBOARD	1000 m3									8.3.3	OTHER FIBREBOARD	1000 m3	84	38			8.3.3	OTHER FIBREBOARD	NAC/mt
9	WOOD PULP	1000 t	754.08485313	345532.107	838.384293	537160.548	2.303856	1243.531	1.463933	899.914																	9	WOOD PULP	1000 t	0	0	0	0	0	0	0	0	9	WOOD PULP	1000 t	ERROR:#VALUE!	ERROR:#VALUE!			9	WOOD PULP	NAC/mt
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	17	6877.616	17	8464.42	0.171	255.35	0.410593	136.033																	9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t									9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	ERROR:#VALUE!	ERROR:#VALUE!			9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	NAC/mt
9.2	CHEMICAL WOOD PULP	1000 t	686	303923.333	774	488480.629	2.132	975.4	1.031022	731.434																	9.2	CHEMICAL WOOD PULP	1000 t	0	0	9	7,064	0	0	0	0	9.2	CHEMICAL WOOD PULP	1000 t	684	773			9.2	CHEMICAL WOOD PULP	NAC/mt
9.2.1	SULPHATE PULP	1000 t	683	300739.103	772	484660.239	2.121	932.043	1	667.484																	9.2.1	SULPHATE PULP	1000 t									9.2.1	SULPHATE PULP	1000 t	681	762			9.2.1	SULPHATE PULP	NAC/mt
9.2.1.1	of which: BLEACHED	1000 t	673.4675567247	296541.77	762.580405	477596.532	2.108212	930.621	1	667.484																	9.2.1.1	of which: BLEACHED	1000 t									9.2.1.1	of which: BLEACHED	1000 t	671	ERROR:#REF!			9.2.1.1	of which: BLEACHED	NAC/mt
9.2.2	SULPHITE PULP	1000 t	3	3184.23	2	3820.39	0.011	43.357	0.031022	63.95																	9.2.2	SULPHITE PULP	1000 t									9.2.2	SULPHITE PULP	1000 t	3	2			9.2.2	SULPHITE PULP	NAC/mt
9.3	DISSOLVING GRADES	1000 t	51.08485313	34731.158	47.384293	40215.499	0.000856	12.781	0.022318	32.447																	9.3	DISSOLVING GRADES	1000 t									9.3	DISSOLVING GRADES	1000 t	51	47			9.3	DISSOLVING GRADES	NAC/mt
10	OTHER PULP	1000 t	23.991613	50866.098	20.418218	58526.151	2.852871	2058.711	5.187455	2778.061																	10	OTHER PULP	1000 t	0	0	0	0	0	0	0	0	10	OTHER PULP	1000 t	2,565	2,413			10	OTHER PULP	NAC/mt
10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	15.290399	45558.685	15.310149	54289.597	0.334595	960.083	0.430984	1301.866																	10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t									10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	22	22			10.1	PULP FROM FIBRES OTHER THAN WOOD	NAC/mt
10.2	RECOVERED FIBRE PULP	1000 t	8.701214	5307.413	5.108069	4236.554	2.518276	1098.628	4.756471	1476.195																	10.2	RECOVERED FIBRE PULP	1000 t									10.2	RECOVERED FIBRE PULP	1000 t	2,543	2,391			10.2	RECOVERED FIBRE PULP	NAC/mt
11	RECOVERED PAPER	1000 t	130	26338.382	168	39095.628	4298.624	731138.719	4082	748883.486																	11	RECOVERED PAPER	1000 t									11	RECOVERED PAPER	1000 t	2,934	2,775			11	RECOVERED PAPER	NAC/mt
12	PAPER AND PAPERBOARD	1000 t	4206	2665677.044	5015	4573783.666	1047.785224	939585.866	1055	1170902.718																	12	PAPER AND PAPERBOARD	1000 t	0	0	0	0	0	0	0	0	12	PAPER AND PAPERBOARD	1000 t	6,800	7,422			12	PAPER AND PAPERBOARD	NAC/mt
12.1	GRAPHIC PAPERS	1000 t	1744	1014693.363	2028	1843716.542	450.36358	394287.459	404	495975.536																	12.1	GRAPHIC PAPERS	1000 t	0	0	0	0	0	0	0	0	12.1	GRAPHIC PAPERS	1000 t	ERROR:#VALUE!	ERROR:#VALUE!			12.1	GRAPHIC PAPERS	NAC/mt
12.1.1	NEWSPRINT	1000 t	285	107278.179	382	231966.651	304.965143	121290.122	269	197005.666																	12.1.1	NEWSPRINT	1000 t									12.1.1	NEWSPRINT	1000 t	ERROR:#VALUE!	ERROR:#VALUE!			12.1.1	NEWSPRINT	NAC/mt
12.1.2	UNCOATED MECHANICAL	1000 t	182	92761.558	238	162153.772	8.900628	43686.061	10	53339.757																	12.1.2	UNCOATED MECHANICAL	1000 t									12.1.2	UNCOATED MECHANICAL	1000 t	ERROR:#VALUE!	ERROR:#VALUE!			12.1.2	UNCOATED MECHANICAL	NAC/mt
12.1.3	UNCOATED WOODFREE	1000 t	561	375178.5	693	738311.445	58.497809	170509.138	45	164446.109																	12.1.3	UNCOATED WOODFREE	1000 t									12.1.3	UNCOATED WOODFREE	1000 t	ERROR:#VALUE!	ERROR:#VALUE!			12.1.3	UNCOATED WOODFREE	NAC/mt
12.1.4	COATED PAPERS	1000 t	716	439475.126	715	711284.674	78	58802.138	80	81184.004																	12.1.4	COATED PAPERS	1000 t									12.1.4	COATED PAPERS	1000 t	ERROR:#VALUE!	ERROR:#VALUE!			12.1.4	COATED PAPERS	NAC/mt
12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	486	301824.715	543	516003.106	101	39091.723	115	51587.397																	12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t									12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	1,075	1,165			12.2	HOUSEHOLD AND SANITARY PAPERS	NAC/mt
12.3	PACKAGING MATERIALS	1000 t	1929	1302922.243	2388	2145389.725	456.421644	416805.178	498	533157.906																	12.3	PACKAGING MATERIALS	1000 t	0	0	0	0	0	0	0	0	12.3	PACKAGING MATERIALS	1000 t	3,371	3,732			12.3	PACKAGING MATERIALS	NAC/mt
12.3.1	CASE MATERIALS	1000 t	1047	536743.39	1241	842213.296	129.16777	64991.451	180	112464.498																	12.3.1	CASE MATERIALS	1000 t									12.3.1	CASE MATERIALS	1000 t	ERROR:#VALUE!	ERROR:#VALUE!			12.3.1	CASE MATERIALS	NAC/mt
12.3.2	CARTONBOARD	1000 t	599	532805.619	752	880655.333	233.674724	214204.191	231	247999.036																	12.3.2	CARTONBOARD	1000 t									12.3.2	CARTONBOARD	1000 t	ERROR:#VALUE!	ERROR:#VALUE!			12.3.2	CARTONBOARD	NAC/mt
12.3.3	WRAPPING PAPERS	1000 t	154	187056.966	225	356256.247	46.706652	115341.41	47	146582.062																	12.3.3	WRAPPING PAPERS	1000 t									12.3.3	WRAPPING PAPERS	1000 t	ERROR:#VALUE!	ERROR:#VALUE!			12.3.3	WRAPPING PAPERS	NAC/mt
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	129	46316.268	170	66264.849	46.872498	22268.126	40	26112.31																	12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t									12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	ERROR:#VALUE!	ERROR:#VALUE!			12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	NAC/mt
12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t	47	46236.723	56	68674.293	40	89401.506	38	90181.879																	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	ERROR:#VALUE!	ERROR:#VALUE!			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	+++	+++	35	57,867	+++	+++	3	4,198																	15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1	1000 m3	ERROR:#VALUE!	ERROR:#VALUE!	0	0	ERROR:#VALUE!	ERROR:#VALUE!	0	0	15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1	1000 m3	ERROR:#VALUE!	ERROR:#VALUE!
15.1	GLULAM	1000 m3	+++	+++	19	34,287	+++	+++	3	4,115																	15.1	GLULAM	1000 m3									15.1	GLULAM	1000 m3	ERROR:#VALUE!	ERROR:#VALUE!
15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	1000 m3	+++	+++	15	23,580	+++	+++	0	83																	15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	1000 m3									15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	1000 m3	ERROR:#VALUE!	ERROR:#VALUE!
16	I BEAMS (I-JOISTS)2	1000 t	+++	+++	31.199634	33030.883	+++	+++	0.764114	1281.181																	16	I BEAMS (I-JOISTS)1	1000 t									16	I BEAMS (I-JOISTS)1	1000 t	ERROR:#VALUE!	ERROR:#VALUE!
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
	m3 = cubic metres solid volume		Please complete each cell if possible with
	m3ub = cubic metres solid volume underbark (i.e. excluding bark)		data (numerical value)
	t = metric tonnes		or "…" for not available
			or "0" for zero data

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

JQ3 Secondary PP Trade

			62	91	91
			Country:	United Kingdom	Date:
			Name of Official responsible for reply:
			Daniel Braby
			Official Address (in full):
	FOREST SECTOR QUESTIONNAIRE JQ3		Northern Research Station
			Roslin, Midlothian, EH25 9SY
	SECONDARY PROCESSED PRODUCTS		Telephone/Fax:
	Trade		E-mail:
															This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!
		Specify Currency and Unit of Value (e.g.:1000 US $):	£1000 (Sterling)													Discrepancies
						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													13	SECONDARY WOOD PRODUCTS
13.1	FURTHER PROCESSED SAWNWOOD	144751.256	215688.661	29698.154	28575.493									13.1	FURTHER PROCESSED SAWNWOOD	0	0	0	0
13.1.C	Coniferous	50799.148	81938.395	20725.529	17690									13.1.C	Coniferous
13.1.NC	Non-coniferous	93952.108	133750.266	8972.625	10885.493									13.1.NC	Non-coniferous
13.1.NC.T	of which: Tropical	9242.094	14858.404	965.187	1481.261									13.1.NC.T	of which: Tropical
13.2	WOODEN WRAPPING AND PACKAGING MATERIAL	177996.105	281072.145	150347.702	189266.068									13.2	WOODEN WRAPPING AND PACKAGING MATERIAL
13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE	175370.518	190076.229	24111.817	25588.372									13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD1	812048.086	996889.721	77112.638	64768.436									13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD1
13.5	WOODEN FURNITURE	3728284.887	4696632.967	404648.392	542795.159									13.5	WOODEN FURNITURE
13.6	PREFABRICATED BUILDINGS OF WOOD	60833.12	137827.652	19445.675	30175.53									13.6	PREFABRICATED BUILDINGS OF WOOD
13.7	OTHER MANUFACTURED WOOD PRODUCTS	318949.26	322858.672	42164.102	35441.804									13.7	OTHER MANUFACTURED WOOD PRODUCTS
14	SECONDARY PAPER PRODUCTS													14	SECONDARY PAPER PRODUCTS
14.1	COMPOSITE PAPER AND PAPERBOARD	36193.583	68048.718	8920.389	11363.743									14.1	COMPOSITE PAPER AND PAPERBOARD
14.2	SPECIAL COATED PAPER AND PULP PRODUCTS	308206.929	468288.961	191915.985	260495.95									14.2	SPECIAL COATED PAPER AND PULP PRODUCTS
14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE	43548.869	401288.245	12430.071	229294.81									14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE
14.4	PACKAGING CARTONS, BOXES ETC.	756134.492	1126355.57	342301.253	407831.611									14.4	PACKAGING CARTONS, BOXES ETC.
14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	667084.588	927706.359	439363.702	485729.139									14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	599413.845	819612.783	338180.867	363911.367
14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE	21154.668	16772.07	2846.385	5390.368									14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE
14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP	28975.286	65368.879	19152.014	18681.35									14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP
14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE	17540.789	25952.627	79184.436	97746.054									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.
		Please complete each cell with
		data (numerical value)
		or "…" for not available
		or "0" for zero data

ECE-EU Species


							Country:	United Kingdom		Date:	4/23/11
							Name of Official responsible for reply:																						DISCREPANCIES

			FOREST SECTOR QUESTIONNAIRE ECE/EU Species Trade				Official Address (in full):		Northern Research Station,																				Checks
							Roslin, Midlothian, EH25 9SY																						- Checks that values reported on JQ2 match values reported on this sheet
			Trade in Roundwood and Sawnwood by species				Telephone:			Fax:																			- Checks that subitems are < or = to aggregate
							E-mail:

			Specify Currency and Unit of Value (e.g.:1000 national currency):	£1000 (Sterling)
													Flag	Flag	Flag	Flag	Flag	Flag	Flag	Flag	Note	Note	Note	Note	Note	Note	Note	Note
					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	Classification	Classification		Unit of	2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		0		0		0		0
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
1.2.C	4403.11/21/22/23/24/25/26		Industrial Roundwood, Coniferous	1000 m3ub	572.00720434	75842.406	748.31799328	93407.33	164.8320192	27700.437	129.72767956	25939.548																	OK	JQ2 Discrepancy	OK	OK	OK	OK	OK	OK
	4403.21/22		of which: Pine (Pinus spp.)	1000 m3ub	30.70472834	8738.64	32.56864897	8383.569	4.7813120704	1290.82	6.2471413212	1634.196																	OK	OK	OK	OK	OK	OK	OK	OK
		4403 21 10	sawlogs and veneer logs	1000 m3ub	0.01440725	43.092	0.1097811	125.056	0.0000241076	0.894	0.0145226067	13.113
		4403 21 90 4403 22 00	pulpwood and other industrial roundwood	1000 m3ub	30.69032109	8695.548	32.45886787	8258.513	4.7812879628	1289.926	6.2326187145	1621.083
	4403.23/24		of which: Fir/Spruce (Abies spp., Picea spp.)	1000 m3ub	463.16982569	53606.797	528.07094483	25171.173	152.711236235	20817.695	123.4805382388	19430.3																	OK	OK	OK	OK	OK	OK	OK	OK
		4403 23 10	sawlogs and veneer logs	1000 m3ub	359.4465303	19305.717	456.74293093	21344.957	142.26178506	20383.207	114.5097166061	18145.449
		4403 23 90 4403 24 00	pulpwood and other industrial roundwood	1000 m3ub	103.72329539	34301.08	71.3280139	3826.216	10.449451175	434.488	8.9708216327	1284.851
1.2.NC	4403.12/41/42/49/91/93/94 4403.95/96/97/98/99		Industrial Roundwood, Non-Coniferous	1000 m3ub	67.3688325	11897.439	44.8587404085	8799.742	4.39833375	2124.313	12.0835665768	1777.391																	OK	OK	OK	OK	OK	OK	OK	OK
	ex4403.12 4403.91		of which: Oak (Quercus spp.)	1000 m3ub	4.6542225	1951.079	19.525	5276.623	0.02474875	87.548	0.037	78.763
	ex4403.12 4403.93/94		of which: Beech (Fagus spp.)	1000 m3ub	0	0	0.415	99.675	1.4741975	537.219	0	1.04
	ex4403.12 4403.95/96		of which: Birch (Betula spp.)	1000 m3ub	45.85974875	3300.19	15.976	874.038	0.00489375	10.54	3.246	123.145																	OK	OK	OK	OK	OK	OK	OK	OK
		4403 95 10	sawlogs and veneer logs	1000 m3ub	0	0	0	0	0	0	0	0
		ex4403 12 00 4403 95 90 4403 96 00	pulpwood and other industrial roundwood	1000 m3ub	45.85974875	3300.19	15.976	874.038	0.00489375	10.54	3.246	123.145
	ex4403.12 4403.97		of which: Poplar/Aspen (Populus spp.)	1000 m3ub	0.30759625	49.93	0.016	8.297	0	0	0	0
	ex4403.12 4403.98		of which: Eucalyptus (Eucalyptus spp.)	1000 m3ub	0.30948	39.399	0.14	168.376	0	0	0	1.914
6.C	4406.11/91 4407.11/12/13/14/19		Sawnwood, Coniferous	1000 m3	7623.372691	2384009.739	5719.4541802782	1902542.56557	237.3755191	75196.213	164.987493448	61825.424																	OK	OK	OK	OK	OK	OK	OK	OK
	4407.11 ex4407.13 ex4406.11/91		of which: Pine (Pinus spp.)	1000 m3	2416.307056	744346.336	1470.557	515269.41	63.91547843	21945.047	17.6232708159	14131.0315041103
	4407.12 ex4407.13/14 ex4406.11/91		of which: Fir/Spruce (Abies spp., Picea spp.)	1000 m3	4221.945	1322089.356	3469.732	1246638.95	170.8961289	51577.024	147.3642226321	47694.3924958897
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	535.6031985	284414.621	786.9071229599	443022.705	39.20107288	20948.552	16.5554212697	22698.404																	OK	OK	OK	OK	OK	OK	OK	OK
	ex4406.12/92 4407.91		of which: Oak (Quercus spp.)	1000 m3	162.3259986	148483.306	203.95	194185.092	3.694046667	3630.562	2.644	3662.044
	ex4406.12/92 4407.92		of which: Beech (Fagus spp.)	1000 m3	20.57198098	8326.959	22.264	13526.993	0.100833333	100.53	0.632	467.151
	ex4406.12/92 4407.93		of which: Maple (Acer spp.)	1000 m3	4.701429198	4625.098	4.434	5416.624	0.173335385	148.872	0.178	204.757
	ex4406.12/92 4407.94		of which: Cherry (Prunus spp.)	1000 m3	0.832685249	709.455	0.454	530.95	0.020685	16.683	0.008	11.309
	ex4406.12/92 4407.95		of which: Ash (Fraxinus spp.)	1000 m3	11.67797902	8515.245	15.566	12502.824	1.425970149	1055.17	0.625	541.14
	ex4406.12/92 4407.96		of which: Birch (Betula spp.)	1000 m3	32.72466098	9756.686	42.819	19460.783	0.002198507	2.659	0.008	23.068
	ex4406.12/92 4407.97		of which: Poplar/Aspen (Populus spp.)	1000 m3	27.54462397	14893.397	20.576	15960.775	1.639631111	1163.953	0.665	694.843
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
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

ITTO1-Estimates

				Country:	0	Date:
				Name of Official responsible for reply:		0

				Official Address (in full):		0
	ITTO1
				Telephone:	0	Fax:	0
	FOREST SECTOR QUESTIONNAIRE			E-mail:		0
	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:	0	Date:
		ITTO2				Name of Official responsible for reply:		0

						Official Address (in full):		0
		FOREST SECTOR QUESTIONNAIRE
		Trade in Tropical Species				Telephone:	0	Fax:	0
						E-mail:		0
	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

ERROR:#REF!

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

ERROR:#REF!

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

ERROR:#REF!

1000 m3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_1

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_1

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_2

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_2

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_2_C

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_2_C

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_2_NC

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_2_NC

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

1000 m3

1_2_NC_T

ERROR:#REF!

€

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!

€

ERROR:#REF!

1000 NAC

1_2_NC_T

ERROR:#REF!

1000 m3

1_2_NC_T

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 m3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

3_1

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

3_1

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

3_2

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

3_2

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

4_1

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

4_1

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

4_2

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

4_2

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

1000 m3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

5_C

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

5_C

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

5_NC

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

5_NC

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

5_NC_T

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

5_NC_T

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

1000 m3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_1

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_1

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_1_C

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_1_C

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

€

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!

€

ERROR:#REF!

1000 NAC

6_1_NC_T

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_2

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_2

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_2_C

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_2_C

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

€

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!

€

ERROR:#REF!

1000 NAC

6_2_NC_T

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_3

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_3

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_3_1

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_3_1

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_4

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_4

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_4_1

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_4_1

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_4_2

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_4_2

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_4_3

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_4_3

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_1

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_1

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_2

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_2

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3_1

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3_1

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3_2

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3_2

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3_3

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3_3

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3_4

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3_4

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

8_1

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

8_1

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

8_2

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

8_2

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1_2

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1_2

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1_3

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1_3

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1_4

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1_4

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_2

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_2

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3_1

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3_1

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3_2

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3_2

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3_3

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3_3

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3_4

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3_4

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

€

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

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!

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

ERROR:#REF!

1000 m3

ST_1_2_C

ERROR:#REF!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

ERROR:#REF!

1000 NAC

ST_5_C

ERROR:#REF!

1000 m3

ST_5_C

ERROR:#REF!

1000 m3

ST_5_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_C

ERROR:#REF!

1000 m3

ST_5_C

ERROR:#REF!

1000 m3

ST_5_C_1

ERROR:#REF!

€

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!

€

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!

€

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!

€

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!

€

ERROR:#REF!

1000 NAC

ST_5_NC

ERROR:#REF!

1000 m3

ST_5_NC

ERROR:#REF!

1000 m3

ST_5_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC

ERROR:#REF!

1000 m3

ST_5_NC

ERROR:#REF!

1000 m3

ST_5_NC_1

ERROR:#REF!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

EX_M

1000 m3

1_1

ERROR:#REF!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

EX_M

1000 m3

3_1

ERROR:#REF!

€

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!

€

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!

€

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!

€

ERROR:#REF!

EX_X

1000 NAC

3_2

ERROR:#REF!

EX_X

1000 m3

3_2

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

4_1

ERROR:#REF!

€

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!

€

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!

€

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!

€

ERROR:#REF!

EX_X

1000 NAC

4_2

ERROR:#REF!

EX_X

1000 mt

4_2

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

EX_M

1000 m3

5_C

ERROR:#REF!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

EX_M

1000 m3

6_1

ERROR:#REF!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

7_1

ERROR:#REF!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

ERROR:#REF!

EX_X

1000 NAC

7_4

ERROR:#REF!

EX_X

1000 mt

7_4

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

8_1

ERROR:#REF!

€

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!

€

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!

€

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!

€

ERROR:#REF!

EX_X

1000 NAC

8_2

ERROR:#REF!

EX_X

1000 mt

8_2

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

10_1

ERROR:#REF!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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!

€

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

ERROR:#REF!

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.

SentData

Country	Flow	Year	Unit	Product	Conc	Data value
ERROR:#REF!	P	2021	1000 m3	1	ERROR:#REF!	10899.2780907871	JQ1
ERROR:#REF!	P	2021	1000 m3	1_C	ERROR:#REF!	2183.7
ERROR:#REF!	P	2021	1000 m3	1_NC	ERROR:#REF!	1571.2
ERROR:#REF!	P	2021	1000 m3	1_1	ERROR:#REF!	612.5
ERROR:#REF!	P	2021	1000 m3	1_1_C	ERROR:#REF!	8715.5780907872
ERROR:#REF!	P	2021	1000 m3	1_1_NC	ERROR:#REF!	8607.8082503998
ERROR:#REF!	P	2021	1000 m3	1_2	ERROR:#REF!	107.7698403873
ERROR:#REF!	P	2021	1000 m3	1_2_C	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	1_2_NC	ERROR:#REF!	6354.2744783305
ERROR:#REF!	P	2021	1000 m3	1_2_1	ERROR:#REF!	6297.7804111566
ERROR:#REF!	P	2021	1000 m3	1_2_1_C	ERROR:#REF!	56.4940671739
ERROR:#REF!	P	2021	1000 m3	1_2_1_NC	ERROR:#REF!	1898.3940792134
ERROR:#REF!	P	2021	1000 m3	1_2_2	ERROR:#REF!	1895.243306
ERROR:#REF!	P	2021	1000 m3	1_2_2_C	ERROR:#REF!	3.1507732134
ERROR:#REF!	P	2021	1000 m3	1_2_2_NC	ERROR:#REF!	462.9095332432
ERROR:#REF!	P	2021	1000 m3	1_2_3	ERROR:#REF!	414.7845332432
ERROR:#REF!	P	2021	1000 m3	1_2_3_C	ERROR:#REF!	48.125
ERROR:#REF!	P	2021	1000 m3	1_2_3_NC	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 mt	2	ERROR:#REF!	3121.7780570476
ERROR:#REF!	P	2021	1000 m3	3	ERROR:#REF!	2341.3335427857
ERROR:#REF!	P	2021	1000 m3	3_1	ERROR:#REF!	780.4445142619
ERROR:#REF!	P	2021	1000 m3	3_2	ERROR:#REF!	4500
ERROR:#REF!	P	2021	1000 mt	4	ERROR:#REF!	304.41140491
ERROR:#REF!	P	2021	1000 mt	4_1	ERROR:#REF!	304.41140491
ERROR:#REF!	P	2021	1000 mt	4_2	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	5	ERROR:#REF!	3610.836966144
ERROR:#REF!	P	2021	1000 m3	5_C	ERROR:#REF!	3573.5390557
ERROR:#REF!	P	2021	1000 m3	5_NC	ERROR:#REF!	37.297910444
ERROR:#REF!	P	2021	1000 m3	5_NC_T	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_1	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_1_C	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_1_NC	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_1_NC_T	ERROR:#REF!	3486
ERROR:#REF!	P	2021	1000 m3	6_2	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_2_C	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_2_NC	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_2_NC_T	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_3	ERROR:#REF!	2688
ERROR:#REF!	P	2021	1000 m3	6_3_1	ERROR:#REF!	+++
ERROR:#REF!	P	2021	1000 m3	6_4	ERROR:#REF!	798
ERROR:#REF!	P	2021	1000 m3	6_4_1	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_4_2	ERROR:#REF!	798
ERROR:#REF!	P	2021	1000 m3	6_4_3	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 mt	7	ERROR:#REF!	+++
ERROR:#REF!	P	2021	1000 mt	7_1	ERROR:#REF!	+++
ERROR:#REF!	P	2021	1000 mt	7_2	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 mt	7_3	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 mt	7_3_1	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 mt	7_3_2	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 mt	7_3_3	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 mt	7_3_4	ERROR:#REF!	2544
ERROR:#REF!	P	2021	1000 mt	7_4	ERROR:#REF!	7
ERROR:#REF!	P	2021	1000 mt	8	ERROR:#REF!	2537
ERROR:#REF!	P	2021	1000 mt	8_1	ERROR:#REF!	7103
ERROR:#REF!	P	2021	1000 mt	8_2	ERROR:#REF!	3642
ERROR:#REF!	P	2021	1000 mt	9	ERROR:#REF!	+++
ERROR:#REF!	P	2021	1000 mt	10	ERROR:#REF!	+++
ERROR:#REF!	P	2021	1000 mt	10_1	ERROR:#REF!	+++
ERROR:#REF!	P	2021	1000 mt	10_1_1	ERROR:#REF!	+++
ERROR:#REF!	P	2021	1000 mt	10_1_2	ERROR:#REF!	+++
ERROR:#REF!	P	2021	1000 mt	10_1_3	ERROR:#REF!	690
ERROR:#REF!	P	2021	1000 mt	10_1_4	ERROR:#REF!	1898
ERROR:#REF!	P	2021	1000 mt	10_2	ERROR:#REF!	+++
ERROR:#REF!	P	2021	1000 mt	10_3	ERROR:#REF!	+++
ERROR:#REF!	P	2021	1000 mt	10_3_1	ERROR:#REF!	+++
ERROR:#REF!	P	2021	1000 mt	10_3_2	ERROR:#REF!	+++
ERROR:#REF!	P	2021	1000 mt	10_3_3	ERROR:#REF!	+++
ERROR:#REF!	P	2021	1000 mt	10_3_4	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 mt	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	2022	1000 m3	1	ERROR:#REF!	9787.6603370088
ERROR:#REF!	P	2022	1000 m3	1_C	ERROR:#REF!	2183.7
ERROR:#REF!	P	2022	1000 m3	1_NC	ERROR:#REF!	1571.2
ERROR:#REF!	P	2022	1000 m3	1_1	ERROR:#REF!	612.5
ERROR:#REF!	P	2022	1000 m3	1_1_C	ERROR:#REF!	7603.9603370088
ERROR:#REF!	P	2022	1000 m3	1_1_NC	ERROR:#REF!	7486.3405955948
ERROR:#REF!	P	2022	1000 m3	1_2	ERROR:#REF!	117.619741414
ERROR:#REF!	P	2022	1000 m3	1_2_C	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	1_2_NC	ERROR:#REF!	5509.0272413044
ERROR:#REF!	P	2022	1000 m3	1_2_1	ERROR:#REF!	5452.5955857654
ERROR:#REF!	P	2022	1000 m3	1_2_1_C	ERROR:#REF!	56.431655539
ERROR:#REF!	P	2022	1000 m3	1_2_1_NC	ERROR:#REF!	1646.450180235
ERROR:#REF!	P	2022	1000 m3	1_2_2	ERROR:#REF!	1633.38709436
ERROR:#REF!	P	2022	1000 m3	1_2_2_C	ERROR:#REF!	13.063085875
ERROR:#REF!	P	2022	1000 m3	1_2_2_NC	ERROR:#REF!	448.4829154694
ERROR:#REF!	P	2022	1000 m3	1_2_3	ERROR:#REF!	400.3579154694
ERROR:#REF!	P	2022	1000 m3	1_2_3_C	ERROR:#REF!	48.125
ERROR:#REF!	P	2022	1000 m3	1_2_3_NC	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	2	ERROR:#REF!	2646.2432838809
ERROR:#REF!	P	2022	1000 m3	3	ERROR:#REF!	1984.6824629106
ERROR:#REF!	P	2022	1000 m3	3_1	ERROR:#REF!	661.5608209702
ERROR:#REF!	P	2022	1000 m3	3_2	ERROR:#REF!	4500
ERROR:#REF!	P	2022	1000 mt	4	ERROR:#REF!	326.55859162
ERROR:#REF!	P	2022	1000 mt	4_1	ERROR:#REF!	326.55859162
ERROR:#REF!	P	2022	1000 mt	4_2	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	5	ERROR:#REF!	3144.968192007
ERROR:#REF!	P	2022	1000 m3	5_C	ERROR:#REF!	3108.3637351
ERROR:#REF!	P	2022	1000 m3	5_NC	ERROR:#REF!	36.604456907
ERROR:#REF!	P	2022	1000 m3	5_NC_T	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_1	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_1_C	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_1_NC	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_1_NC_T	ERROR:#REF!	3466
ERROR:#REF!	P	2022	1000 m3	6_2	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_2_C	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_2_NC	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_2_NC_T	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_3	ERROR:#REF!	2610
ERROR:#REF!	P	2022	1000 m3	6_3_1	ERROR:#REF!	+++
ERROR:#REF!	P	2022	1000 m3	6_4	ERROR:#REF!	856
ERROR:#REF!	P	2022	1000 m3	6_4_1	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_4_2	ERROR:#REF!	856
ERROR:#REF!	P	2022	1000 m3	6_4_3	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	7	ERROR:#REF!	+++
ERROR:#REF!	P	2022	1000 mt	7_1	ERROR:#REF!	+++
ERROR:#REF!	P	2022	1000 mt	7_2	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	7_3	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	7_3_1	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	7_3_2	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	7_3_3	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	7_3_4	ERROR:#REF!	2398
ERROR:#REF!	P	2022	1000 mt	7_4	ERROR:#REF!	7
ERROR:#REF!	P	2022	1000 mt	8	ERROR:#REF!	2391
ERROR:#REF!	P	2022	1000 mt	8_1	ERROR:#REF!	6689
ERROR:#REF!	P	2022	1000 mt	8_2	ERROR:#REF!	3462
ERROR:#REF!	P	2022	1000 mt	9	ERROR:#REF!	+++
ERROR:#REF!	P	2022	1000 mt	10	ERROR:#REF!	+++
ERROR:#REF!	P	2022	1000 mt	10_1	ERROR:#REF!	+++
ERROR:#REF!	P	2022	1000 mt	10_1_1	ERROR:#REF!	+++
ERROR:#REF!	P	2022	1000 mt	10_1_2	ERROR:#REF!	+++
ERROR:#REF!	P	2022	1000 mt	10_1_3	ERROR:#REF!	737
ERROR:#REF!	P	2022	1000 mt	10_1_4	ERROR:#REF!	1842
ERROR:#REF!	P	2022	1000 mt	10_2	ERROR:#REF!	+++
ERROR:#REF!	P	2022	1000 mt	10_3	ERROR:#REF!	+++
ERROR:#REF!	P	2022	1000 mt	10_3_1	ERROR:#REF!	+++
ERROR:#REF!	P	2022	1000 mt	10_3_2	ERROR:#REF!	+++
ERROR:#REF!	P	2022	1000 mt	10_3_3	ERROR:#REF!	+++
ERROR:#REF!	P	2022	1000 mt	10_3_4	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	1	ERROR:#REF!	763.75826087	JQ2
ERROR:#REF!	M	2021	1000 m3	1_1	ERROR:#REF!	124.38222403
ERROR:#REF!	M	2021	1000 m3	1_2	ERROR:#REF!	81.62164153
ERROR:#REF!	M	2021	1000 m3	1_2_C	ERROR:#REF!	42.7605825
ERROR:#REF!	M	2021	1000 m3	1_2_NC	ERROR:#REF!	639.37603684
ERROR:#REF!	M	2021	1000 m3	1_2_NC_T	ERROR:#REF!	572.00720434
ERROR:#REF!	M	2021	1000 mt	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	3	ERROR:#REF!	1.29608125
ERROR:#REF!	M	2021	1000 m3	3_1	ERROR:#REF!	95.50561
ERROR:#REF!	M	2021	1000 m3	3_2	ERROR:#REF!	119.35124188
ERROR:#REF!	M	2021	1000 mt	4	ERROR:#REF!	117.63380548
ERROR:#REF!	M	2021	1000 mt	4_1	ERROR:#REF!	1.7174364
ERROR:#REF!	M	2021	1000 mt	4_2	ERROR:#REF!	97.5270716
ERROR:#REF!	M	2021	1000 m3	5	ERROR:#REF!	9160.535258
ERROR:#REF!	M	2021	1000 m3	5_C	ERROR:#REF!	9128.015405
ERROR:#REF!	M	2021	1000 m3	5_NC	ERROR:#REF!	32.519853
ERROR:#REF!	M	2021	1000 m3	5_NC_T	ERROR:#REF!	8158.9758895
ERROR:#REF!	M	2021	1000 m3	6	ERROR:#REF!	7623.372691
ERROR:#REF!	M	2021	1000 m3	6_1	ERROR:#REF!	535.6031985
ERROR:#REF!	M	2021	1000 m3	6_1_C	ERROR:#REF!	79.30549418
ERROR:#REF!	M	2021	1000 m3	6_1_NC	ERROR:#REF!	14.205500085
ERROR:#REF!	M	2021	1000 m3	6_1_NC_T	ERROR:#REF!	4.534032
ERROR:#REF!	M	2021	1000 m3	6_2	ERROR:#REF!	9.671468085
ERROR:#REF!	M	2021	1000 m3	6_2_C	ERROR:#REF!	0.333484043
ERROR:#REF!	M	2021	1000 m3	6_2_NC	ERROR:#REF!	3780.29090716
ERROR:#REF!	M	2021	1000 m3	6_2_NC_T	ERROR:#REF!	1540.9895219
ERROR:#REF!	M	2021	1000 m3	6_3	ERROR:#REF!	456.7605499
ERROR:#REF!	M	2021	1000 m3	6_3_1	ERROR:#REF!	1084.228972
ERROR:#REF!	M	2021	1000 m3	6_4	ERROR:#REF!	223.0408005
ERROR:#REF!	M	2021	1000 m3	6_4_1	ERROR:#REF!	1158.999377
ERROR:#REF!	M	2021	1000 m3	6_4_2	ERROR:#REF!	460.824008
ERROR:#REF!	M	2021	1000 m3	6_4_3	ERROR:#REF!	1080.30200826
ERROR:#REF!	M	2021	1000 mt	7	ERROR:#REF!	111.1596702
ERROR:#REF!	M	2021	1000 mt	7_1	ERROR:#REF!	878.3691365
ERROR:#REF!	M	2021	1000 mt	7_2	ERROR:#REF!	90.77320156
ERROR:#REF!	M	2021	1000 mt	7_3	ERROR:#REF!	754.08485313
ERROR:#REF!	M	2021	1000 mt	7_3_1	ERROR:#REF!	17
ERROR:#REF!	M	2021	1000 mt	7_3_2	ERROR:#REF!	686
ERROR:#REF!	M	2021	1000 mt	7_3_3	ERROR:#REF!	683
ERROR:#REF!	M	2021	1000 mt	7_3_4	ERROR:#REF!	673.4675567247
ERROR:#REF!	M	2021	1000 mt	7_4	ERROR:#REF!	3
ERROR:#REF!	M	2021	1000 mt	8	ERROR:#REF!	51.08485313
ERROR:#REF!	M	2021	1000 mt	8_1	ERROR:#REF!	23.991613
ERROR:#REF!	M	2021	1000 mt	8_2	ERROR:#REF!	15.290399
ERROR:#REF!	M	2021	1000 mt	9	ERROR:#REF!	8.701214
ERROR:#REF!	M	2021	1000 mt	10	ERROR:#REF!	130
ERROR:#REF!	M	2021	1000 mt	10_1	ERROR:#REF!	4206
ERROR:#REF!	M	2021	1000 mt	10_1_1	ERROR:#REF!	1744
ERROR:#REF!	M	2021	1000 mt	10_1_2	ERROR:#REF!	285
ERROR:#REF!	M	2021	1000 mt	10_1_3	ERROR:#REF!	182
ERROR:#REF!	M	2021	1000 mt	10_1_4	ERROR:#REF!	561
ERROR:#REF!	M	2021	1000 mt	10_2	ERROR:#REF!	716
ERROR:#REF!	M	2021	1000 mt	10_3	ERROR:#REF!	486
ERROR:#REF!	M	2021	1000 mt	10_3_1	ERROR:#REF!	1929
ERROR:#REF!	M	2021	1000 mt	10_3_2	ERROR:#REF!	1047
ERROR:#REF!	M	2021	1000 mt	10_3_3	ERROR:#REF!	599
ERROR:#REF!	M	2021	1000 mt	10_3_4	ERROR:#REF!	154
ERROR:#REF!	M	2021	1000 mt	10_4	ERROR:#REF!	129
ERROR:#REF!	M	2021	1000 NAC	1	ERROR:#REF!	110223.334
ERROR:#REF!	M	2021	1000 NAC	1_1	ERROR:#REF!	22483.489
ERROR:#REF!	M	2021	1000 NAC	1_2	ERROR:#REF!	14590.429
ERROR:#REF!	M	2021	1000 NAC	1_2_C	ERROR:#REF!	7893.06
ERROR:#REF!	M	2021	1000 NAC	1_2_NC	ERROR:#REF!	87739.845
ERROR:#REF!	M	2021	1000 NAC	1_2_NC_T	ERROR:#REF!	67.3688325
ERROR:#REF!	M	2021	1000 NAC	2	ERROR:#REF!	11897.439
ERROR:#REF!	M	2021	1000 NAC	3	ERROR:#REF!	482.973
ERROR:#REF!	M	2021	1000 NAC	3_1	ERROR:#REF!	46436.414
ERROR:#REF!	M	2021	1000 NAC	3_2	ERROR:#REF!	2583.716
ERROR:#REF!	M	2021	1000 NAC	4	ERROR:#REF!	2234.873
ERROR:#REF!	M	2021	1000 NAC	4_1	ERROR:#REF!	348.843
ERROR:#REF!	M	2021	1000 NAC	4_2	ERROR:#REF!	6961.972
ERROR:#REF!	M	2021	1000 NAC	5	ERROR:#REF!	1301181.25
ERROR:#REF!	M	2021	1000 NAC	5_C	ERROR:#REF!	1294586.758
ERROR:#REF!	M	2021	1000 NAC	5_NC	ERROR:#REF!	6594.492
ERROR:#REF!	M	2021	1000 NAC	5_NC_T	ERROR:#REF!	2668424.36
ERROR:#REF!	M	2021	1000 NAC	6	ERROR:#REF!	2384009.739
ERROR:#REF!	M	2021	1000 NAC	6_1	ERROR:#REF!	284414.621
ERROR:#REF!	M	2021	1000 NAC	6_1_C	ERROR:#REF!	60195.744
ERROR:#REF!	M	2021	1000 NAC	6_1_NC	ERROR:#REF!	12186.934
ERROR:#REF!	M	2021	1000 NAC	6_1_NC_T	ERROR:#REF!	1398.573
ERROR:#REF!	M	2021	1000 NAC	6_2	ERROR:#REF!	10788.361
ERROR:#REF!	M	2021	1000 NAC	6_2_C	ERROR:#REF!	763.519
ERROR:#REF!	M	2021	1000 NAC	6_2_NC	ERROR:#REF!	1301096.2991
ERROR:#REF!	M	2021	1000 NAC	6_2_NC_T	ERROR:#REF!	585552.318
ERROR:#REF!	M	2021	1000 NAC	6_3	ERROR:#REF!	146394.446
ERROR:#REF!	M	2021	1000 NAC	6_3_1	ERROR:#REF!	439157.872
ERROR:#REF!	M	2021	1000 NAC	6_4	ERROR:#REF!	109393.468
ERROR:#REF!	M	2021	1000 NAC	6_4_1	ERROR:#REF!	302691.6431
ERROR:#REF!	M	2021	1000 NAC	6_4_2	ERROR:#REF!	121054.3991
ERROR:#REF!	M	2021	1000 NAC	6_4_3	ERROR:#REF!	412852.338
ERROR:#REF!	M	2021	1000 NAC	7	ERROR:#REF!	66686.982
ERROR:#REF!	M	2021	1000 NAC	7_1	ERROR:#REF!	321010.992
ERROR:#REF!	M	2021	1000 NAC	7_2	ERROR:#REF!	25154.364
ERROR:#REF!	M	2021	1000 NAC	7_3	ERROR:#REF!	345532.107
ERROR:#REF!	M	2021	1000 NAC	7_3_1	ERROR:#REF!	6877.616
ERROR:#REF!	M	2021	1000 NAC	7_3_2	ERROR:#REF!	303923.333
ERROR:#REF!	M	2021	1000 NAC	7_3_3	ERROR:#REF!	300739.103
ERROR:#REF!	M	2021	1000 NAC	7_3_4	ERROR:#REF!	296541.77
ERROR:#REF!	M	2021	1000 NAC	7_4	ERROR:#REF!	3184.23
ERROR:#REF!	M	2021	1000 NAC	8	ERROR:#REF!	34731.158
ERROR:#REF!	M	2021	1000 NAC	8_1	ERROR:#REF!	50866.098
ERROR:#REF!	M	2021	1000 NAC	8_2	ERROR:#REF!	45558.685
ERROR:#REF!	M	2021	1000 NAC	9	ERROR:#REF!	5307.413
ERROR:#REF!	M	2021	1000 NAC	10	ERROR:#REF!	26338.382
ERROR:#REF!	M	2021	1000 NAC	10_1	ERROR:#REF!	2665677.044
ERROR:#REF!	M	2021	1000 NAC	10_1_1	ERROR:#REF!	1014693.363
ERROR:#REF!	M	2021	1000 NAC	10_1_2	ERROR:#REF!	107278.179
ERROR:#REF!	M	2021	1000 NAC	10_1_3	ERROR:#REF!	92761.558
ERROR:#REF!	M	2021	1000 NAC	10_1_4	ERROR:#REF!	375178.5
ERROR:#REF!	M	2021	1000 NAC	10_2	ERROR:#REF!	439475.126
ERROR:#REF!	M	2021	1000 NAC	10_3	ERROR:#REF!	301824.715
ERROR:#REF!	M	2021	1000 NAC	10_3_1	ERROR:#REF!	1302922.243
ERROR:#REF!	M	2021	1000 NAC	10_3_2	ERROR:#REF!	536743.39
ERROR:#REF!	M	2021	1000 NAC	10_3_3	ERROR:#REF!	532805.619
ERROR:#REF!	M	2021	1000 NAC	10_3_4	ERROR:#REF!	187056.966
ERROR:#REF!	M	2021	1000 NAC	10_4	ERROR:#REF!	46316.268
ERROR:#REF!	M	2022	1000 m3	1	ERROR:#REF!	1071.5853240333
ERROR:#REF!	M	2022	1000 m3	1_1	ERROR:#REF!	278.4085903448
ERROR:#REF!	M	2022	1000 m3	1_2	ERROR:#REF!	46.969782069
ERROR:#REF!	M	2022	1000 m3	1_2_C	ERROR:#REF!	231.4388082759
ERROR:#REF!	M	2022	1000 m3	1_2_NC	ERROR:#REF!	793.1767336885
ERROR:#REF!	M	2022	1000 m3	1_2_NC_T	ERROR:#REF!	748.31799328
ERROR:#REF!	M	2022	1000 mt	2	ERROR:#REF!	44.8587404085
ERROR:#REF!	M	2022	1000 m3	3	ERROR:#REF!	2.26178375
ERROR:#REF!	M	2022	1000 m3	3_1	ERROR:#REF!	86.91064
ERROR:#REF!	M	2022	1000 m3	3_2	ERROR:#REF!	95.82361824
ERROR:#REF!	M	2022	1000 mt	4	ERROR:#REF!	59.75939116
ERROR:#REF!	M	2022	1000 mt	4_1	ERROR:#REF!	36.06422708
ERROR:#REF!	M	2022	1000 mt	4_2	ERROR:#REF!	18.72793628
ERROR:#REF!	M	2022	1000 m3	5	ERROR:#REF!	7585.407576
ERROR:#REF!	M	2022	1000 m3	5_C	ERROR:#REF!	7515.693959
ERROR:#REF!	M	2022	1000 m3	5_NC	ERROR:#REF!	69.713617
ERROR:#REF!	M	2022	1000 m3	5_NC_T	ERROR:#REF!	6506.3613032381
ERROR:#REF!	M	2022	1000 m3	6	ERROR:#REF!	5719.4541802782
ERROR:#REF!	M	2022	1000 m3	6_1	ERROR:#REF!	786.9071229599
ERROR:#REF!	M	2022	1000 m3	6_1_C	ERROR:#REF!	93.70630841
ERROR:#REF!	M	2022	1000 m3	6_1_NC	ERROR:#REF!	6.6362273936
ERROR:#REF!	M	2022	1000 m3	6_1_NC_T	ERROR:#REF!	0.5009175532
ERROR:#REF!	M	2022	1000 m3	6_2	ERROR:#REF!	6.1353098404
ERROR:#REF!	M	2022	1000 m3	6_2_C	ERROR:#REF!	0.50946581
ERROR:#REF!	M	2022	1000 m3	6_2_NC	ERROR:#REF!	3228.8308338938
ERROR:#REF!	M	2022	1000 m3	6_2_NC_T	ERROR:#REF!	1319.9539358769
ERROR:#REF!	M	2022	1000 m3	6_3	ERROR:#REF!	349.7803664745
ERROR:#REF!	M	2022	1000 m3	6_3_1	ERROR:#REF!	970.1735694024
ERROR:#REF!	M	2022	1000 m3	6_4	ERROR:#REF!	167.2355109968
ERROR:#REF!	M	2022	1000 m3	6_4_1	ERROR:#REF!	1013.5404428176
ERROR:#REF!	M	2022	1000 m3	6_4_2	ERROR:#REF!	365.4082229152
ERROR:#REF!	M	2022	1000 m3	6_4_3	ERROR:#REF!	895.3364551994
ERROR:#REF!	M	2022	1000 mt	7	ERROR:#REF!	109.7815340426
ERROR:#REF!	M	2022	1000 mt	7_1	ERROR:#REF!	738.9639442818
ERROR:#REF!	M	2022	1000 mt	7_2	ERROR:#REF!	46.590976875
ERROR:#REF!	M	2022	1000 mt	7_3	ERROR:#REF!	838.384293
ERROR:#REF!	M	2022	1000 mt	7_3_1	ERROR:#REF!	17
ERROR:#REF!	M	2022	1000 mt	7_3_2	ERROR:#REF!	774
ERROR:#REF!	M	2022	1000 mt	7_3_3	ERROR:#REF!	762.580405
ERROR:#REF!	M	2022	1000 mt	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	7_4	ERROR:#REF!	2
ERROR:#REF!	M	2022	1000 mt	8	ERROR:#REF!	47.384293
ERROR:#REF!	M	2022	1000 mt	8_1	ERROR:#REF!	20.418218
ERROR:#REF!	M	2022	1000 mt	8_2	ERROR:#REF!	15.310149
ERROR:#REF!	M	2022	1000 mt	9	ERROR:#REF!	5.108069
ERROR:#REF!	M	2022	1000 mt	10	ERROR:#REF!	168
ERROR:#REF!	M	2022	1000 mt	10_1	ERROR:#REF!	5015
ERROR:#REF!	M	2022	1000 mt	10_1_1	ERROR:#REF!	2028
ERROR:#REF!	M	2022	1000 mt	10_1_2	ERROR:#REF!	382
ERROR:#REF!	M	2022	1000 mt	10_1_3	ERROR:#REF!	238
ERROR:#REF!	M	2022	1000 mt	10_1_4	ERROR:#REF!	693
ERROR:#REF!	M	2022	1000 mt	10_2	ERROR:#REF!	715
ERROR:#REF!	M	2022	1000 mt	10_3	ERROR:#REF!	543
ERROR:#REF!	M	2022	1000 mt	10_3_1	ERROR:#REF!	2388
ERROR:#REF!	M	2022	1000 mt	10_3_2	ERROR:#REF!	1241
ERROR:#REF!	M	2022	1000 mt	10_3_3	ERROR:#REF!	752
ERROR:#REF!	M	2022	1000 mt	10_3_4	ERROR:#REF!	225
ERROR:#REF!	M	2022	1000 mt	10_4	ERROR:#REF!	170
ERROR:#REF!	M	2022	1000 NAC	1	ERROR:#REF!	186143.834
ERROR:#REF!	M	2022	1000 NAC	1_1	ERROR:#REF!	83936.762
ERROR:#REF!	M	2022	1000 NAC	1_2	ERROR:#REF!	13168.559
ERROR:#REF!	M	2022	1000 NAC	1_2_C	ERROR:#REF!	70768.203
ERROR:#REF!	M	2022	1000 NAC	1_2_NC	ERROR:#REF!	102207.072
ERROR:#REF!	M	2022	1000 NAC	1_2_NC_T	ERROR:#REF!	93407.33
ERROR:#REF!	M	2022	1000 NAC	2	ERROR:#REF!	8799.742
ERROR:#REF!	M	2022	1000 NAC	3	ERROR:#REF!	1067.226
ERROR:#REF!	M	2022	1000 NAC	3_1	ERROR:#REF!	52236.943
ERROR:#REF!	M	2022	1000 NAC	3_2	ERROR:#REF!	11665.465
ERROR:#REF!	M	2022	1000 NAC	4	ERROR:#REF!	3452.924
ERROR:#REF!	M	2022	1000 NAC	4_1	ERROR:#REF!	8212.541
ERROR:#REF!	M	2022	1000 NAC	4_2	ERROR:#REF!	4091.568
ERROR:#REF!	M	2022	1000 NAC	5	ERROR:#REF!	1351594.008
ERROR:#REF!	M	2022	1000 NAC	5_C	ERROR:#REF!	1322763.932
ERROR:#REF!	M	2022	1000 NAC	5_NC	ERROR:#REF!	28830.076
ERROR:#REF!	M	2022	1000 NAC	5_NC_T	ERROR:#REF!	2345565.27057
ERROR:#REF!	M	2022	1000 NAC	6	ERROR:#REF!	1902542.56557
ERROR:#REF!	M	2022	1000 NAC	6_1	ERROR:#REF!	443022.705
ERROR:#REF!	M	2022	1000 NAC	6_1_C	ERROR:#REF!	88757.177
ERROR:#REF!	M	2022	1000 NAC	6_1_NC	ERROR:#REF!	33672.093
ERROR:#REF!	M	2022	1000 NAC	6_1_NC_T	ERROR:#REF!	2015.438
ERROR:#REF!	M	2022	1000 NAC	6_2	ERROR:#REF!	31656.655
ERROR:#REF!	M	2022	1000 NAC	6_2_C	ERROR:#REF!	2523.851
ERROR:#REF!	M	2022	1000 NAC	6_2_NC	ERROR:#REF!	1515323.39936107
ERROR:#REF!	M	2022	1000 NAC	6_2_NC_T	ERROR:#REF!	669257.357
ERROR:#REF!	M	2022	1000 NAC	6_3	ERROR:#REF!	143894.339
ERROR:#REF!	M	2022	1000 NAC	6_3_1	ERROR:#REF!	525363.018
ERROR:#REF!	M	2022	1000 NAC	6_4	ERROR:#REF!	117044.005
ERROR:#REF!	M	2022	1000 NAC	6_4_1	ERROR:#REF!	345057.187361066
ERROR:#REF!	M	2022	1000 NAC	6_4_2	ERROR:#REF!	117376.516361066
ERROR:#REF!	M	2022	1000 NAC	6_4_3	ERROR:#REF!	501008.855
ERROR:#REF!	M	2022	1000 NAC	7	ERROR:#REF!	91953.474
ERROR:#REF!	M	2022	1000 NAC	7_1	ERROR:#REF!	393218.186
ERROR:#REF!	M	2022	1000 NAC	7_2	ERROR:#REF!	15837.195
ERROR:#REF!	M	2022	1000 NAC	7_3	ERROR:#REF!	537160.548
ERROR:#REF!	M	2022	1000 NAC	7_3_1	ERROR:#REF!	8464.42
ERROR:#REF!	M	2022	1000 NAC	7_3_2	ERROR:#REF!	488480.629
ERROR:#REF!	M	2022	1000 NAC	7_3_3	ERROR:#REF!	477596.532
ERROR:#REF!	M	2022	1000 NAC	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	7_4	ERROR:#REF!	3820.39
ERROR:#REF!	M	2022	1000 NAC	8	ERROR:#REF!	40215.499
ERROR:#REF!	M	2022	1000 NAC	8_1	ERROR:#REF!	58526.151
ERROR:#REF!	M	2022	1000 NAC	8_2	ERROR:#REF!	54289.597
ERROR:#REF!	M	2022	1000 NAC	9	ERROR:#REF!	4236.554
ERROR:#REF!	M	2022	1000 NAC	10	ERROR:#REF!	39095.628
ERROR:#REF!	M	2022	1000 NAC	10_1	ERROR:#REF!	4573783.666
ERROR:#REF!	M	2022	1000 NAC	10_1_1	ERROR:#REF!	1843716.542
ERROR:#REF!	M	2022	1000 NAC	10_1_2	ERROR:#REF!	231966.651
ERROR:#REF!	M	2022	1000 NAC	10_1_3	ERROR:#REF!	162153.772
ERROR:#REF!	M	2022	1000 NAC	10_1_4	ERROR:#REF!	738311.445
ERROR:#REF!	M	2022	1000 NAC	10_2	ERROR:#REF!	711284.674
ERROR:#REF!	M	2022	1000 NAC	10_3	ERROR:#REF!	516003.106
ERROR:#REF!	M	2022	1000 NAC	10_3_1	ERROR:#REF!	2145389.725
ERROR:#REF!	M	2022	1000 NAC	10_3_2	ERROR:#REF!	842213.296
ERROR:#REF!	M	2022	1000 NAC	10_3_3	ERROR:#REF!	880655.333
ERROR:#REF!	M	2022	1000 NAC	10_3_4	ERROR:#REF!	356256.247
ERROR:#REF!	M	2022	1000 NAC	10_4	ERROR:#REF!	66264.849
ERROR:#REF!	X	2021	1000 m3	1	ERROR:#REF!	184.65937334
ERROR:#REF!	X	2021	1000 m3	1_1	ERROR:#REF!	15.42902039
ERROR:#REF!	X	2021	1000 m3	1_2	ERROR:#REF!	15.15982039
ERROR:#REF!	X	2021	1000 m3	1_2_C	ERROR:#REF!	0.2692
ERROR:#REF!	X	2021	1000 m3	1_2_NC	ERROR:#REF!	169.23035295
ERROR:#REF!	X	2021	1000 m3	1_2_NC_T	ERROR:#REF!	164.8320192
ERROR:#REF!	X	2021	1000 mt	2	ERROR:#REF!	4.39833375
ERROR:#REF!	X	2021	1000 m3	3	ERROR:#REF!	0.03245875
ERROR:#REF!	X	2021	1000 m3	3_1	ERROR:#REF!	3.119786
ERROR:#REF!	X	2021	1000 m3	3_2	ERROR:#REF!	77.13640416
ERROR:#REF!	X	2021	1000 mt	4	ERROR:#REF!	75.51996
ERROR:#REF!	X	2021	1000 mt	4_1	ERROR:#REF!	1.61644416
ERROR:#REF!	X	2021	1000 mt	4_2	ERROR:#REF!	5.00619768
ERROR:#REF!	X	2021	1000 m3	5	ERROR:#REF!	12.789769
ERROR:#REF!	X	2021	1000 m3	5_C	ERROR:#REF!	1.627909
ERROR:#REF!	X	2021	1000 m3	5_NC	ERROR:#REF!	11.16186
ERROR:#REF!	X	2021	1000 m3	5_NC_T	ERROR:#REF!	276.57659198
ERROR:#REF!	X	2021	1000 m3	6	ERROR:#REF!	237.3755191
ERROR:#REF!	X	2021	1000 m3	6_1	ERROR:#REF!	39.20107288
ERROR:#REF!	X	2021	1000 m3	6_1_C	ERROR:#REF!	3.153793651
ERROR:#REF!	X	2021	1000 m3	6_1_NC	ERROR:#REF!	0.378327532
ERROR:#REF!	X	2021	1000 m3	6_1_NC_T	ERROR:#REF!	0.047902
ERROR:#REF!	X	2021	1000 m3	6_2	ERROR:#REF!	0.330425532
ERROR:#REF!	X	2021	1000 m3	6_2_C	ERROR:#REF!	0.035555851
ERROR:#REF!	X	2021	1000 m3	6_2_NC	ERROR:#REF!	321.036110253
ERROR:#REF!	X	2021	1000 m3	6_2_NC_T	ERROR:#REF!	54.55172436
ERROR:#REF!	X	2021	1000 m3	6_3	ERROR:#REF!	9.843804
ERROR:#REF!	X	2021	1000 m3	6_3_1	ERROR:#REF!	44.70792036
ERROR:#REF!	X	2021	1000 m3	6_4	ERROR:#REF!	31.27745
ERROR:#REF!	X	2021	1000 m3	6_4_1	ERROR:#REF!	194.9852788
ERROR:#REF!	X	2021	1000 m3	6_4_2	ERROR:#REF!	132.9797109
ERROR:#REF!	X	2021	1000 m3	6_4_3	ERROR:#REF!	71.499107093
ERROR:#REF!	X	2021	1000 mt	7	ERROR:#REF!	10.51749149
ERROR:#REF!	X	2021	1000 mt	7_1	ERROR:#REF!	54.41145654
ERROR:#REF!	X	2021	1000 mt	7_2	ERROR:#REF!	6.570159063
ERROR:#REF!	X	2021	1000 mt	7_3	ERROR:#REF!	2.303856
ERROR:#REF!	X	2021	1000 mt	7_3_1	ERROR:#REF!	0.171
ERROR:#REF!	X	2021	1000 mt	7_3_2	ERROR:#REF!	2.132
ERROR:#REF!	X	2021	1000 mt	7_3_3	ERROR:#REF!	2.121
ERROR:#REF!	X	2021	1000 mt	7_3_4	ERROR:#REF!	2.108212
ERROR:#REF!	X	2021	1000 mt	7_4	ERROR:#REF!	0.011
ERROR:#REF!	X	2021	1000 mt	8	ERROR:#REF!	0.000856
ERROR:#REF!	X	2021	1000 mt	8_1	ERROR:#REF!	2.852871
ERROR:#REF!	X	2021	1000 mt	8_2	ERROR:#REF!	0.334595
ERROR:#REF!	X	2021	1000 mt	9	ERROR:#REF!	2.518276
ERROR:#REF!	X	2021	1000 mt	10	ERROR:#REF!	4298.624
ERROR:#REF!	X	2021	1000 mt	10_1	ERROR:#REF!	1047.785224
ERROR:#REF!	X	2021	1000 mt	10_1_1	ERROR:#REF!	450.36358
ERROR:#REF!	X	2021	1000 mt	10_1_2	ERROR:#REF!	304.965143
ERROR:#REF!	X	2021	1000 mt	10_1_3	ERROR:#REF!	8.900628
ERROR:#REF!	X	2021	1000 mt	10_1_4	ERROR:#REF!	58.497809
ERROR:#REF!	X	2021	1000 mt	10_2	ERROR:#REF!	78
ERROR:#REF!	X	2021	1000 mt	10_3	ERROR:#REF!	101
ERROR:#REF!	X	2021	1000 mt	10_3_1	ERROR:#REF!	456.421644
ERROR:#REF!	X	2021	1000 mt	10_3_2	ERROR:#REF!	129.16777
ERROR:#REF!	X	2021	1000 mt	10_3_3	ERROR:#REF!	233.674724
ERROR:#REF!	X	2021	1000 mt	10_3_4	ERROR:#REF!	46.706652
ERROR:#REF!	X	2021	1000 mt	10_4	ERROR:#REF!	46.872498
ERROR:#REF!	X	2021	1000 NAC	1	ERROR:#REF!	32511.186
ERROR:#REF!	X	2021	1000 NAC	1_1	ERROR:#REF!	2686.436
ERROR:#REF!	X	2021	1000 NAC	1_2	ERROR:#REF!	2488.283
ERROR:#REF!	X	2021	1000 NAC	1_2_C	ERROR:#REF!	198.153
ERROR:#REF!	X	2021	1000 NAC	1_2_NC	ERROR:#REF!	29824.75
ERROR:#REF!	X	2021	1000 NAC	1_2_NC_T	ERROR:#REF!	27700.437
ERROR:#REF!	X	2021	1000 NAC	2	ERROR:#REF!	2124.313
ERROR:#REF!	X	2021	1000 NAC	3	ERROR:#REF!	49.591
ERROR:#REF!	X	2021	1000 NAC	3_1	ERROR:#REF!	2185.829
ERROR:#REF!	X	2021	1000 NAC	3_2	ERROR:#REF!	7724.14
ERROR:#REF!	X	2021	1000 NAC	4	ERROR:#REF!	7425.635
ERROR:#REF!	X	2021	1000 NAC	4_1	ERROR:#REF!	298.505
ERROR:#REF!	X	2021	1000 NAC	4_2	ERROR:#REF!	198.909
ERROR:#REF!	X	2021	1000 NAC	5	ERROR:#REF!	2057.037
ERROR:#REF!	X	2021	1000 NAC	5_C	ERROR:#REF!	420.361
ERROR:#REF!	X	2021	1000 NAC	5_NC	ERROR:#REF!	1636.676
ERROR:#REF!	X	2021	1000 NAC	5_NC_T	ERROR:#REF!	96144.765
ERROR:#REF!	X	2021	1000 NAC	6	ERROR:#REF!	75196.213
ERROR:#REF!	X	2021	1000 NAC	6_1	ERROR:#REF!	20948.552
ERROR:#REF!	X	2021	1000 NAC	6_1_C	ERROR:#REF!	3381.486
ERROR:#REF!	X	2021	1000 NAC	6_1_NC	ERROR:#REF!	3329.648
ERROR:#REF!	X	2021	1000 NAC	6_1_NC_T	ERROR:#REF!	744.259
ERROR:#REF!	X	2021	1000 NAC	6_2	ERROR:#REF!	2585.389
ERROR:#REF!	X	2021	1000 NAC	6_2_C	ERROR:#REF!	305.041
ERROR:#REF!	X	2021	1000 NAC	6_2_NC	ERROR:#REF!	154232.106
ERROR:#REF!	X	2021	1000 NAC	6_2_NC_T	ERROR:#REF!	37301.91
ERROR:#REF!	X	2021	1000 NAC	6_3	ERROR:#REF!	4808.05
ERROR:#REF!	X	2021	1000 NAC	6_3_1	ERROR:#REF!	32493.86
ERROR:#REF!	X	2021	1000 NAC	6_4	ERROR:#REF!	17703.559
ERROR:#REF!	X	2021	1000 NAC	6_4_1	ERROR:#REF!	83106.714
ERROR:#REF!	X	2021	1000 NAC	6_4_2	ERROR:#REF!	58690.203
ERROR:#REF!	X	2021	1000 NAC	6_4_3	ERROR:#REF!	33823.482
ERROR:#REF!	X	2021	1000 NAC	7	ERROR:#REF!	5299.072
ERROR:#REF!	X	2021	1000 NAC	7_1	ERROR:#REF!	22979.432
ERROR:#REF!	X	2021	1000 NAC	7_2	ERROR:#REF!	5544.978
ERROR:#REF!	X	2021	1000 NAC	7_3	ERROR:#REF!	1243.531
ERROR:#REF!	X	2021	1000 NAC	7_3_1	ERROR:#REF!	255.35
ERROR:#REF!	X	2021	1000 NAC	7_3_2	ERROR:#REF!	975.4
ERROR:#REF!	X	2021	1000 NAC	7_3_3	ERROR:#REF!	932.043
ERROR:#REF!	X	2021	1000 NAC	7_3_4	ERROR:#REF!	930.621
ERROR:#REF!	X	2021	1000 NAC	7_4	ERROR:#REF!	43.357
ERROR:#REF!	X	2021	1000 NAC	8	ERROR:#REF!	12.781
ERROR:#REF!	X	2021	1000 NAC	8_1	ERROR:#REF!	2058.711
ERROR:#REF!	X	2021	1000 NAC	8_2	ERROR:#REF!	960.083
ERROR:#REF!	X	2021	1000 NAC	9	ERROR:#REF!	1098.628
ERROR:#REF!	X	2021	1000 NAC	10	ERROR:#REF!	731138.719
ERROR:#REF!	X	2021	1000 NAC	10_1	ERROR:#REF!	939585.866
ERROR:#REF!	X	2021	1000 NAC	10_1_1	ERROR:#REF!	394287.459
ERROR:#REF!	X	2021	1000 NAC	10_1_2	ERROR:#REF!	121290.122
ERROR:#REF!	X	2021	1000 NAC	10_1_3	ERROR:#REF!	43686.061
ERROR:#REF!	X	2021	1000 NAC	10_1_4	ERROR:#REF!	170509.138
ERROR:#REF!	X	2021	1000 NAC	10_2	ERROR:#REF!	58802.138
ERROR:#REF!	X	2021	1000 NAC	10_3	ERROR:#REF!	39091.723
ERROR:#REF!	X	2021	1000 NAC	10_3_1	ERROR:#REF!	416805.178
ERROR:#REF!	X	2021	1000 NAC	10_3_2	ERROR:#REF!	64991.451
ERROR:#REF!	X	2021	1000 NAC	10_3_3	ERROR:#REF!	214204.191
ERROR:#REF!	X	2021	1000 NAC	10_3_4	ERROR:#REF!	115341.41
ERROR:#REF!	X	2021	1000 NAC	10_4	ERROR:#REF!	22268.126
ERROR:#REF!	X	2022	1000 m3	1	ERROR:#REF!	156.3207330333
ERROR:#REF!	X	2022	1000 m3	1_1	ERROR:#REF!	14.5094868966
ERROR:#REF!	X	2022	1000 m3	1_2	ERROR:#REF!	4.9691765517
ERROR:#REF!	X	2022	1000 m3	1_2_C	ERROR:#REF!	9.5403103448
ERROR:#REF!	X	2022	1000 m3	1_2_NC	ERROR:#REF!	141.8112461368
ERROR:#REF!	X	2022	1000 m3	1_2_NC_T	ERROR:#REF!	129.72767956
ERROR:#REF!	X	2022	1000 mt	2	ERROR:#REF!	12.0835665768
ERROR:#REF!	X	2022	1000 m3	3	ERROR:#REF!	3.13384625
ERROR:#REF!	X	2022	1000 m3	3_1	ERROR:#REF!	2.298526
ERROR:#REF!	X	2022	1000 m3	3_2	ERROR:#REF!	69.3125736239
ERROR:#REF!	X	2022	1000 mt	4	ERROR:#REF!	61.1832
ERROR:#REF!	X	2022	1000 mt	4_1	ERROR:#REF!	8.1293736239
ERROR:#REF!	X	2022	1000 mt	4_2	ERROR:#REF!	18.78293012
ERROR:#REF!	X	2022	1000 m3	5	ERROR:#REF!	43.618492
ERROR:#REF!	X	2022	1000 m3	5_C	ERROR:#REF!	22.816327
ERROR:#REF!	X	2022	1000 m3	5_NC	ERROR:#REF!	20.802165
ERROR:#REF!	X	2022	1000 m3	5_NC_T	ERROR:#REF!	181.5429147177
ERROR:#REF!	X	2022	1000 m3	6	ERROR:#REF!	164.987493448
ERROR:#REF!	X	2022	1000 m3	6_1	ERROR:#REF!	16.5554212697
ERROR:#REF!	X	2022	1000 m3	6_1_C	ERROR:#REF!	2.7554738406
ERROR:#REF!	X	2022	1000 m3	6_1_NC	ERROR:#REF!	0.6651
ERROR:#REF!	X	2022	1000 m3	6_1_NC_T	ERROR:#REF!	0.2393
ERROR:#REF!	X	2022	1000 m3	6_2	ERROR:#REF!	0.4258
ERROR:#REF!	X	2022	1000 m3	6_2_C	ERROR:#REF!	0.01362984
ERROR:#REF!	X	2022	1000 m3	6_2_NC	ERROR:#REF!	369.9936792488
ERROR:#REF!	X	2022	1000 m3	6_2_NC_T	ERROR:#REF!	65.6011743156
ERROR:#REF!	X	2022	1000 m3	6_3	ERROR:#REF!	10.6509989899
ERROR:#REF!	X	2022	1000 m3	6_3_1	ERROR:#REF!	54.9501753258
ERROR:#REF!	X	2022	1000 m3	6_4	ERROR:#REF!	36.68536318
ERROR:#REF!	X	2022	1000 m3	6_4_1	ERROR:#REF!	244.6268437996
ERROR:#REF!	X	2022	1000 m3	6_4_2	ERROR:#REF!	189.6719542152
ERROR:#REF!	X	2022	1000 m3	6_4_3	ERROR:#REF!	59.7656611337
ERROR:#REF!	X	2022	1000 mt	7	ERROR:#REF!	8.6520319149
ERROR:#REF!	X	2022	1000 mt	7_1	ERROR:#REF!	42.0405589063
ERROR:#REF!	X	2022	1000 mt	7_2	ERROR:#REF!	9.0730703125
ERROR:#REF!	X	2022	1000 mt	7_3	ERROR:#REF!	1.463933
ERROR:#REF!	X	2022	1000 mt	7_3_1	ERROR:#REF!	0.410593
ERROR:#REF!	X	2022	1000 mt	7_3_2	ERROR:#REF!	1.031022
ERROR:#REF!	X	2022	1000 mt	7_3_3	ERROR:#REF!	1
ERROR:#REF!	X	2022	1000 mt	7_3_4	ERROR:#REF!	1
ERROR:#REF!	X	2022	1000 mt	7_4	ERROR:#REF!	0.031022
ERROR:#REF!	X	2022	1000 mt	8	ERROR:#REF!	0.022318
ERROR:#REF!	X	2022	1000 mt	8_1	ERROR:#REF!	5.187455
ERROR:#REF!	X	2022	1000 mt	8_2	ERROR:#REF!	0.430984
ERROR:#REF!	X	2022	1000 mt	9	ERROR:#REF!	4.756471
ERROR:#REF!	X	2022	1000 mt	10	ERROR:#REF!	4082
ERROR:#REF!	X	2022	1000 mt	10_1	ERROR:#REF!	1055
ERROR:#REF!	X	2022	1000 mt	10_1_1	ERROR:#REF!	404
ERROR:#REF!	X	2022	1000 mt	10_1_2	ERROR:#REF!	269
ERROR:#REF!	X	2022	1000 mt	10_1_3	ERROR:#REF!	10
ERROR:#REF!	X	2022	1000 mt	10_1_4	ERROR:#REF!	45
ERROR:#REF!	X	2022	1000 mt	10_2	ERROR:#REF!	80
ERROR:#REF!	X	2022	1000 mt	10_3	ERROR:#REF!	115
ERROR:#REF!	X	2022	1000 mt	10_3_1	ERROR:#REF!	498
ERROR:#REF!	X	2022	1000 mt	10_3_2	ERROR:#REF!	180
ERROR:#REF!	X	2022	1000 mt	10_3_3	ERROR:#REF!	231
ERROR:#REF!	X	2022	1000 mt	10_3_4	ERROR:#REF!	47
ERROR:#REF!	X	2022	1000 mt	10_4	ERROR:#REF!	40
ERROR:#REF!	X	2022	1000 NAC	1	ERROR:#REF!	30403.594
ERROR:#REF!	X	2022	1000 NAC	1_1	ERROR:#REF!	2686.655
ERROR:#REF!	X	2022	1000 NAC	1_2	ERROR:#REF!	1451.233
ERROR:#REF!	X	2022	1000 NAC	1_2_C	ERROR:#REF!	1235.422
ERROR:#REF!	X	2022	1000 NAC	1_2_NC	ERROR:#REF!	27716.939
ERROR:#REF!	X	2022	1000 NAC	1_2_NC_T	ERROR:#REF!	25939.548
ERROR:#REF!	X	2022	1000 NAC	2	ERROR:#REF!	1777.391
ERROR:#REF!	X	2022	1000 NAC	3	ERROR:#REF!	495.361
ERROR:#REF!	X	2022	1000 NAC	3_1	ERROR:#REF!	1873.717
ERROR:#REF!	X	2022	1000 NAC	3_2	ERROR:#REF!	10770.898
ERROR:#REF!	X	2022	1000 NAC	4	ERROR:#REF!	9743.63
ERROR:#REF!	X	2022	1000 NAC	4_1	ERROR:#REF!	1027.268
ERROR:#REF!	X	2022	1000 NAC	4_2	ERROR:#REF!	202.523
ERROR:#REF!	X	2022	1000 NAC	5	ERROR:#REF!	13276.55
ERROR:#REF!	X	2022	1000 NAC	5_C	ERROR:#REF!	12008.768
ERROR:#REF!	X	2022	1000 NAC	5_NC	ERROR:#REF!	1267.782
ERROR:#REF!	X	2022	1000 NAC	5_NC_T	ERROR:#REF!	84523.828
ERROR:#REF!	X	2022	1000 NAC	6	ERROR:#REF!	61825.424
ERROR:#REF!	X	2022	1000 NAC	6_1	ERROR:#REF!	22698.404
ERROR:#REF!	X	2022	1000 NAC	6_1_C	ERROR:#REF!	2916.32
ERROR:#REF!	X	2022	1000 NAC	6_1_NC	ERROR:#REF!	5003.892
ERROR:#REF!	X	2022	1000 NAC	6_1_NC_T	ERROR:#REF!	740.101
ERROR:#REF!	X	2022	1000 NAC	6_2	ERROR:#REF!	4263.791
ERROR:#REF!	X	2022	1000 NAC	6_2_C	ERROR:#REF!	96.919
ERROR:#REF!	X	2022	1000 NAC	6_2_NC	ERROR:#REF!	166447.987192461
ERROR:#REF!	X	2022	1000 NAC	6_2_NC_T	ERROR:#REF!	39599.617
ERROR:#REF!	X	2022	1000 NAC	6_3	ERROR:#REF!	5470.452
ERROR:#REF!	X	2022	1000 NAC	6_3_1	ERROR:#REF!	34129.165
ERROR:#REF!	X	2022	1000 NAC	6_4	ERROR:#REF!	24042.35
ERROR:#REF!	X	2022	1000 NAC	6_4_1	ERROR:#REF!	87381.4501924607
ERROR:#REF!	X	2022	1000 NAC	6_4_2	ERROR:#REF!	61620.0651924607
ERROR:#REF!	X	2022	1000 NAC	6_4_3	ERROR:#REF!	39466.92
ERROR:#REF!	X	2022	1000 NAC	7	ERROR:#REF!	6548.612
ERROR:#REF!	X	2022	1000 NAC	7_1	ERROR:#REF!	24171.061
ERROR:#REF!	X	2022	1000 NAC	7_2	ERROR:#REF!	8747.247
ERROR:#REF!	X	2022	1000 NAC	7_3	ERROR:#REF!	899.914
ERROR:#REF!	X	2022	1000 NAC	7_3_1	ERROR:#REF!	136.033
ERROR:#REF!	X	2022	1000 NAC	7_3_2	ERROR:#REF!	731.434
ERROR:#REF!	X	2022	1000 NAC	7_3_3	ERROR:#REF!	667.484
ERROR:#REF!	X	2022	1000 NAC	7_3_4	ERROR:#REF!	667.484
ERROR:#REF!	X	2022	1000 NAC	7_4	ERROR:#REF!	63.95
ERROR:#REF!	X	2022	1000 NAC	8	ERROR:#REF!	32.447
ERROR:#REF!	X	2022	1000 NAC	8_1	ERROR:#REF!	2778.061
ERROR:#REF!	X	2022	1000 NAC	8_2	ERROR:#REF!	1301.866
ERROR:#REF!	X	2022	1000 NAC	9	ERROR:#REF!	1476.195
ERROR:#REF!	X	2022	1000 NAC	10	ERROR:#REF!	748883.486
ERROR:#REF!	X	2022	1000 NAC	10_1	ERROR:#REF!	1170902.718
ERROR:#REF!	X	2022	1000 NAC	10_1_1	ERROR:#REF!	495975.536
ERROR:#REF!	X	2022	1000 NAC	10_1_2	ERROR:#REF!	197005.666
ERROR:#REF!	X	2022	1000 NAC	10_1_3	ERROR:#REF!	53339.757
ERROR:#REF!	X	2022	1000 NAC	10_1_4	ERROR:#REF!	164446.109
ERROR:#REF!	X	2022	1000 NAC	10_2	ERROR:#REF!	81184.004
ERROR:#REF!	X	2022	1000 NAC	10_3	ERROR:#REF!	51587.397
ERROR:#REF!	X	2022	1000 NAC	10_3_1	ERROR:#REF!	533157.906
ERROR:#REF!	X	2022	1000 NAC	10_3_2	ERROR:#REF!	112464.498
ERROR:#REF!	X	2022	1000 NAC	10_3_3	ERROR:#REF!	247999.036
ERROR:#REF!	X	2022	1000 NAC	10_3_4	ERROR:#REF!	146582.062
ERROR:#REF!	X	2022	1000 NAC	10_4	ERROR:#REF!	26112.31
ERROR:#REF!	M	0	1000 NAC	11_1	ERROR:#REF!	50799.148	JQ3
ERROR:#REF!	M	0	1000 NAC	11_1_C	ERROR:#REF!	93952.108
ERROR:#REF!	M	0	1000 NAC	11_1_NC	ERROR:#REF!	9242.094
ERROR:#REF!	M	0	1000 NAC	11_1_NC_T	ERROR:#REF!	177996.105
ERROR:#REF!	M	0	1000 NAC	11_2	ERROR:#REF!	175370.518
ERROR:#REF!	M	0	1000 NAC	11_3	ERROR:#REF!	812048.086
ERROR:#REF!	M	0	1000 NAC	11_4	ERROR:#REF!	3728284.887
ERROR:#REF!	M	0	1000 NAC	11_5	ERROR:#REF!	60833.12
ERROR:#REF!	M	0	1000 NAC	11_6	ERROR:#REF!	318949.26
ERROR:#REF!	M	0	1000 NAC	11_7	ERROR:#REF!	0
ERROR:#REF!	M	0	1000 NAC	11_7_1	ERROR:#REF!	36193.583
ERROR:#REF!	M	0	1000 NAC	12_1	ERROR:#REF!	43548.869
ERROR:#REF!	M	0	1000 NAC	12_2	ERROR:#REF!	756134.492
ERROR:#REF!	M	0	1000 NAC	12_3	ERROR:#REF!	667084.588
ERROR:#REF!	M	0	1000 NAC	12_4	ERROR:#REF!	21154.668
ERROR:#REF!	M	0	1000 NAC	12_5	ERROR:#REF!	28975.286
ERROR:#REF!	M	0	1000 NAC	12_6	ERROR:#REF!	17540.789
ERROR:#REF!	M	0	1000 NAC	12_6_1	ERROR:#REF!	0
ERROR:#REF!	M	0	1000 NAC	12_6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	0	1000 NAC	12_6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	0	1000 NAC	11_1	ERROR:#REF!	81938.395
ERROR:#REF!	M	0	1000 NAC	11_1_C	ERROR:#REF!	133750.266
ERROR:#REF!	M	0	1000 NAC	11_1_NC	ERROR:#REF!	14858.404
ERROR:#REF!	M	0	1000 NAC	11_1_NC_T	ERROR:#REF!	281072.145
ERROR:#REF!	M	0	1000 NAC	11_2	ERROR:#REF!	190076.229
ERROR:#REF!	M	0	1000 NAC	11_3	ERROR:#REF!	996889.721
ERROR:#REF!	M	0	1000 NAC	11_4	ERROR:#REF!	4696632.967
ERROR:#REF!	M	0	1000 NAC	11_5	ERROR:#REF!	322858.672
ERROR:#REF!	M	0	1000 NAC	11_6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	0	1000 NAC	11_7	ERROR:#REF!	0
ERROR:#REF!	M	0	1000 NAC	11_7_1	ERROR:#REF!	68048.718
ERROR:#REF!	M	0	1000 NAC	12_1	ERROR:#REF!	401288.245
ERROR:#REF!	M	0	1000 NAC	12_2	ERROR:#REF!	1126355.57
ERROR:#REF!	M	0	1000 NAC	12_3	ERROR:#REF!	927706.359
ERROR:#REF!	M	0	1000 NAC	12_4	ERROR:#REF!	16772.07
ERROR:#REF!	M	0	1000 NAC	12_5	ERROR:#REF!	65368.879
ERROR:#REF!	M	0	1000 NAC	12_6	ERROR:#REF!	25952.627
ERROR:#REF!	M	0	1000 NAC	12_6_1	ERROR:#REF!	0
ERROR:#REF!	M	0	1000 NAC	12_6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	0	1000 NAC	12_6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	0	1000 NAC	11_1	ERROR:#REF!	20725.529
ERROR:#REF!	X	0	1000 NAC	11_1_C	ERROR:#REF!	8972.625
ERROR:#REF!	X	0	1000 NAC	11_1_NC	ERROR:#REF!	965.187
ERROR:#REF!	X	0	1000 NAC	11_1_NC_T	ERROR:#REF!	150347.702
ERROR:#REF!	X	0	1000 NAC	11_2	ERROR:#REF!	24111.817
ERROR:#REF!	X	0	1000 NAC	11_3	ERROR:#REF!	77112.638
ERROR:#REF!	X	0	1000 NAC	11_4	ERROR:#REF!	404648.392
ERROR:#REF!	X	0	1000 NAC	11_5	ERROR:#REF!	19445.675
ERROR:#REF!	X	0	1000 NAC	11_6	ERROR:#REF!	42164.102
ERROR:#REF!	X	0	1000 NAC	11_7	ERROR:#REF!	0
ERROR:#REF!	X	0	1000 NAC	11_7_1	ERROR:#REF!	8920.389
ERROR:#REF!	X	0	1000 NAC	12_1	ERROR:#REF!	12430.071
ERROR:#REF!	X	0	1000 NAC	12_2	ERROR:#REF!	342301.253
ERROR:#REF!	X	0	1000 NAC	12_3	ERROR:#REF!	439363.702
ERROR:#REF!	X	0	1000 NAC	12_4	ERROR:#REF!	2846.385
ERROR:#REF!	X	0	1000 NAC	12_5	ERROR:#REF!	19152.014
ERROR:#REF!	X	0	1000 NAC	12_6	ERROR:#REF!	79184.436
ERROR:#REF!	X	0	1000 NAC	12_6_1	ERROR:#REF!	0
ERROR:#REF!	X	0	1000 NAC	12_6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	0	1000 NAC	12_6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	0	1000 NAC	11_1	ERROR:#REF!	17690
ERROR:#REF!	X	0	1000 NAC	11_1_C	ERROR:#REF!	10885.493
ERROR:#REF!	X	0	1000 NAC	11_1_NC	ERROR:#REF!	1481.261
ERROR:#REF!	X	0	1000 NAC	11_1_NC_T	ERROR:#REF!	189266.068
ERROR:#REF!	X	0	1000 NAC	11_2	ERROR:#REF!	25588.372
ERROR:#REF!	X	0	1000 NAC	11_3	ERROR:#REF!	64768.436
ERROR:#REF!	X	0	1000 NAC	11_4	ERROR:#REF!	542795.159
ERROR:#REF!	X	0	1000 NAC	11_5	ERROR:#REF!	30175.53
ERROR:#REF!	X	0	1000 NAC	11_6	ERROR:#REF!	35441.804
ERROR:#REF!	X	0	1000 NAC	11_7	ERROR:#REF!	0
ERROR:#REF!	X	0	1000 NAC	11_7_1	ERROR:#REF!	11363.743
ERROR:#REF!	X	0	1000 NAC	12_1	ERROR:#REF!	229294.81
ERROR:#REF!	X	0	1000 NAC	12_2	ERROR:#REF!	407831.611
ERROR:#REF!	X	0	1000 NAC	12_3	ERROR:#REF!	485729.139
ERROR:#REF!	X	0	1000 NAC	12_4	ERROR:#REF!	5390.368
ERROR:#REF!	X	0	1000 NAC	12_5	ERROR:#REF!	18681.35
ERROR:#REF!	X	0	1000 NAC	12_6	ERROR:#REF!	97746.054
ERROR:#REF!	X	0	1000 NAC	12_6_1	ERROR:#REF!	0
ERROR:#REF!	X	0	1000 NAC	12_6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	0	1000 NAC	12_6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C	ERROR:#REF!	572.00720434	ECEEU
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_1	ERROR:#REF!	30.70472834
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_1_1	ERROR:#REF!	0.01440725
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_2_1	ERROR:#REF!	30.69032109
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_2	ERROR:#REF!	463.16982569
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_1_2	ERROR:#REF!	359.4465303
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_2_2	ERROR:#REF!	103.72329539
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC	ERROR:#REF!	67.3688325
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_1	ERROR:#REF!	4.6542225
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!	45.85974875
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!	45.85974875
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!	0.30759625
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_3	ERROR:#REF!	0.30948
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!	7623.372691
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!	2416.307056
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_4	ERROR:#REF!	4221.945
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_5	ERROR:#REF!	535.6031985
ERROR:#REF!	M	2021	1000 m3	ST_5_C	ERROR:#REF!	162.3259986
ERROR:#REF!	M	2021	1000 m3	ST_5_C_1	ERROR:#REF!	20.57198098
ERROR:#REF!	M	2021	1000 m3	ST_5_C_2	ERROR:#REF!	4.701429198
ERROR:#REF!	M	2021	1000 m3	ST_5_NC	ERROR:#REF!	0.832685249
ERROR:#REF!	M	2021	1000 m3	ST_5_NC_1	ERROR:#REF!	11.67797902
ERROR:#REF!	M	2021	1000 m3	ST_5_NC_2	ERROR:#REF!	32.72466098
ERROR:#REF!	M	2021	1000 m3	ST_5_NC_3	ERROR:#REF!	27.54462397
ERROR:#REF!	M	2021	1000 m3	ST_5_NC_4	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_5_NC_5	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_5_NC_6	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_5_NC_7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C	ERROR:#REF!	75842.406
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_1	ERROR:#REF!	8738.64
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!	43.092
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!	8695.548
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_2	ERROR:#REF!	53606.797
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!	19305.717
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!	34301.08
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC	ERROR:#REF!	11897.439
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_1	ERROR:#REF!	1951.079
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!	3300.19
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!	3300.19
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!	49.93
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_3	ERROR:#REF!	39.399
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!	2384009.739
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!	744346.336
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_4	ERROR:#REF!	1322089.356
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_5	ERROR:#REF!	284414.621
ERROR:#REF!	M	2021	1000 NAC	ST_5_C	ERROR:#REF!	148483.306
ERROR:#REF!	M	2021	1000 NAC	ST_5_C_1	ERROR:#REF!	8326.959
ERROR:#REF!	M	2021	1000 NAC	ST_5_C_2	ERROR:#REF!	4625.098
ERROR:#REF!	M	2021	1000 NAC	ST_5_NC	ERROR:#REF!	709.455
ERROR:#REF!	M	2021	1000 NAC	ST_5_NC_1	ERROR:#REF!	8515.245
ERROR:#REF!	M	2021	1000 NAC	ST_5_NC_2	ERROR:#REF!	9756.686
ERROR:#REF!	M	2021	1000 NAC	ST_5_NC_3	ERROR:#REF!	14893.397
ERROR:#REF!	M	2021	1000 NAC	ST_5_NC_4	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_5_NC_5	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_5_NC_6	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_5_NC_7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C	ERROR:#REF!	748.31799328
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_1	ERROR:#REF!	32.56864897
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_1_1	ERROR:#REF!	0.1097811
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_2_1	ERROR:#REF!	32.45886787
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_2	ERROR:#REF!	528.07094483
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_1_2	ERROR:#REF!	456.74293093
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_2_2	ERROR:#REF!	71.3280139
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC	ERROR:#REF!	44.8587404085
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_1	ERROR:#REF!	19.525
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!	0.415
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!	15.976
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!	15.976
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!	0.016
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_3	ERROR:#REF!	0.14
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!	5719.4541802782
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!	1470.557
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_4	ERROR:#REF!	3469.732
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_5	ERROR:#REF!	786.9071229599
ERROR:#REF!	M	2022	1000 m3	ST_5_C	ERROR:#REF!	203.95
ERROR:#REF!	M	2022	1000 m3	ST_5_C_1	ERROR:#REF!	22.264
ERROR:#REF!	M	2022	1000 m3	ST_5_C_2	ERROR:#REF!	4.434
ERROR:#REF!	M	2022	1000 m3	ST_5_NC	ERROR:#REF!	0.454
ERROR:#REF!	M	2022	1000 m3	ST_5_NC_1	ERROR:#REF!	15.566
ERROR:#REF!	M	2022	1000 m3	ST_5_NC_2	ERROR:#REF!	42.819
ERROR:#REF!	M	2022	1000 m3	ST_5_NC_3	ERROR:#REF!	20.576
ERROR:#REF!	M	2022	1000 m3	ST_5_NC_4	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_5_NC_5	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_5_NC_6	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_5_NC_7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C	ERROR:#REF!	93407.33
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_1	ERROR:#REF!	8383.569
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!	125.056
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!	8258.513
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_2	ERROR:#REF!	25171.173
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!	21344.957
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!	3826.216
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC	ERROR:#REF!	8799.742
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_1	ERROR:#REF!	5276.623
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!	99.675
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!	874.038
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!	874.038
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!	8.297
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_3	ERROR:#REF!	168.376
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!	1902542.56557
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!	515269.41
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_4	ERROR:#REF!	1246638.95
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_5	ERROR:#REF!	443022.705
ERROR:#REF!	M	2022	1000 NAC	ST_5_C	ERROR:#REF!	194185.092
ERROR:#REF!	M	2022	1000 NAC	ST_5_C_1	ERROR:#REF!	13526.993
ERROR:#REF!	M	2022	1000 NAC	ST_5_C_2	ERROR:#REF!	5416.624
ERROR:#REF!	M	2022	1000 NAC	ST_5_NC	ERROR:#REF!	530.95
ERROR:#REF!	M	2022	1000 NAC	ST_5_NC_1	ERROR:#REF!	12502.824
ERROR:#REF!	M	2022	1000 NAC	ST_5_NC_2	ERROR:#REF!	19460.783
ERROR:#REF!	M	2022	1000 NAC	ST_5_NC_3	ERROR:#REF!	15960.775
ERROR:#REF!	M	2022	1000 NAC	ST_5_NC_4	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_5_NC_5	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_5_NC_6	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_5_NC_7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C	ERROR:#REF!	164.8320192
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_1	ERROR:#REF!	4.7813120704
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_1_1	ERROR:#REF!	0.0000241076
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_2_1	ERROR:#REF!	4.7812879628
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_2	ERROR:#REF!	152.711236235
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_1_2	ERROR:#REF!	142.26178506
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_2_2	ERROR:#REF!	10.449451175
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC	ERROR:#REF!	4.39833375
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_1	ERROR:#REF!	0.02474875
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!	1.4741975
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!	0.00489375
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!	0.00489375
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_3	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!	237.3755191
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!	63.91547843
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_4	ERROR:#REF!	170.8961289
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_5	ERROR:#REF!	39.20107288
ERROR:#REF!	X	2021	1000 m3	ST_5_C	ERROR:#REF!	3.694046667
ERROR:#REF!	X	2021	1000 m3	ST_5_C_1	ERROR:#REF!	0.100833333
ERROR:#REF!	X	2021	1000 m3	ST_5_C_2	ERROR:#REF!	0.173335385
ERROR:#REF!	X	2021	1000 m3	ST_5_NC	ERROR:#REF!	0.020685
ERROR:#REF!	X	2021	1000 m3	ST_5_NC_1	ERROR:#REF!	1.425970149
ERROR:#REF!	X	2021	1000 m3	ST_5_NC_2	ERROR:#REF!	0.002198507
ERROR:#REF!	X	2021	1000 m3	ST_5_NC_3	ERROR:#REF!	1.639631111
ERROR:#REF!	X	2021	1000 m3	ST_5_NC_4	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_5_NC_5	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_5_NC_6	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_5_NC_7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C	ERROR:#REF!	27700.437
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_1	ERROR:#REF!	1290.82
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!	0.894
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!	1289.926
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_2	ERROR:#REF!	20817.695
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!	20383.207
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!	434.488
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC	ERROR:#REF!	2124.313
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_1	ERROR:#REF!	87.548
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!	537.219
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!	10.54
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!	10.54
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_3	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!	75196.213
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!	21945.047
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_4	ERROR:#REF!	51577.024
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_5	ERROR:#REF!	20948.552
ERROR:#REF!	X	2021	1000 NAC	ST_5_C	ERROR:#REF!	3630.562
ERROR:#REF!	X	2021	1000 NAC	ST_5_C_1	ERROR:#REF!	100.53
ERROR:#REF!	X	2021	1000 NAC	ST_5_C_2	ERROR:#REF!	148.872
ERROR:#REF!	X	2021	1000 NAC	ST_5_NC	ERROR:#REF!	16.683
ERROR:#REF!	X	2021	1000 NAC	ST_5_NC_1	ERROR:#REF!	1055.17
ERROR:#REF!	X	2021	1000 NAC	ST_5_NC_2	ERROR:#REF!	2.659
ERROR:#REF!	X	2021	1000 NAC	ST_5_NC_3	ERROR:#REF!	1163.953
ERROR:#REF!	X	2021	1000 NAC	ST_5_NC_4	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_5_NC_5	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_5_NC_6	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_5_NC_7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C	ERROR:#REF!	129.72767956
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_1	ERROR:#REF!	6.2471413212
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_1_1	ERROR:#REF!	0.0145226067
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_2_1	ERROR:#REF!	6.2326187145
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_2	ERROR:#REF!	123.4805382388
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_1_2	ERROR:#REF!	114.5097166061
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_2_2	ERROR:#REF!	8.9708216327
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC	ERROR:#REF!	12.0835665768
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_1	ERROR:#REF!	0.037
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!	3.246
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!	3.246
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_3	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!	164.987493448
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!	17.6232708159
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_4	ERROR:#REF!	147.3642226321
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_5	ERROR:#REF!	16.5554212697
ERROR:#REF!	X	2022	1000 m3	ST_5_C	ERROR:#REF!	2.644
ERROR:#REF!	X	2022	1000 m3	ST_5_C_1	ERROR:#REF!	0.632
ERROR:#REF!	X	2022	1000 m3	ST_5_C_2	ERROR:#REF!	0.178
ERROR:#REF!	X	2022	1000 m3	ST_5_NC	ERROR:#REF!	0.008
ERROR:#REF!	X	2022	1000 m3	ST_5_NC_1	ERROR:#REF!	0.625
ERROR:#REF!	X	2022	1000 m3	ST_5_NC_2	ERROR:#REF!	0.008
ERROR:#REF!	X	2022	1000 m3	ST_5_NC_3	ERROR:#REF!	0.665
ERROR:#REF!	X	2022	1000 m3	ST_5_NC_4	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_5_NC_5	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_5_NC_6	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_5_NC_7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C	ERROR:#REF!	25939.548
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_1	ERROR:#REF!	1634.196
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!	13.113
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!	1621.083
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_2	ERROR:#REF!	19430.3
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!	18145.449
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!	1284.851
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC	ERROR:#REF!	1777.391
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_1	ERROR:#REF!	78.763
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!	1.04
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!	123.145
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!	123.145
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_3	ERROR:#REF!	1.914
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!	61825.424
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!	14131.0315041103
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_4	ERROR:#REF!	47694.3924958897
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_5	ERROR:#REF!	22698.404
ERROR:#REF!	X	2022	1000 NAC	ST_5_C	ERROR:#REF!	3662.044
ERROR:#REF!	X	2022	1000 NAC	ST_5_C_1	ERROR:#REF!	467.151
ERROR:#REF!	X	2022	1000 NAC	ST_5_C_2	ERROR:#REF!	204.757
ERROR:#REF!	X	2022	1000 NAC	ST_5_NC	ERROR:#REF!	11.309
ERROR:#REF!	X	2022	1000 NAC	ST_5_NC_1	ERROR:#REF!	541.14
ERROR:#REF!	X	2022	1000 NAC	ST_5_NC_2	ERROR:#REF!	23.068
ERROR:#REF!	X	2022	1000 NAC	ST_5_NC_3	ERROR:#REF!	694.843
ERROR:#REF!	X	2022	1000 NAC	ST_5_NC_4	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_5_NC_5	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_5_NC_6	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_5_NC_7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	1	ERROR:#REF!	ERROR:#REF!	EU1
ERROR:#REF!	EX_M	2021	1000 m3	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1	ERROR:#REF!	ERROR:#REF!	EU2
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_3_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_3_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_3_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_3_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1	ERROR:#REF!	ERROR:#REF!	OB
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_3_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_3_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_3_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_3_NC	ERROR:#REF!	ERROR:#REF!

Database

(France, UK) Proposal for a new supplement to UN Regulation No. 155

Read more about (France, UK) Proposal for a new supplement to UN Regulation No. 155

Languages and translations

English

Country	Flow	Year	Unit	Product	conc
ERROR:#REF!	P	2015	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	4	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	5	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	8	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	9	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	5	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	8	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	9	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	5	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	8	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	9	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_1_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_1_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_5	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_6	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_7	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_7_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_5	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_6	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_6_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_6_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_6_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_7	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_7_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_7_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_7_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_1_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_1_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_5	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_6	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_7	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_7_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_5	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_6	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_6_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_6_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_6_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_7	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_7_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_7_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_7_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	5	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	8	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	9	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	5	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	8	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	9	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_1_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_2_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_3_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_3_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	4	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	5	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	8	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	9	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	5	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	8	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	9	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	5	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	8	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	9	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_1_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_1_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_5	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_6	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_7	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_7_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_5	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_6	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_6_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_6_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_6_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_7	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_7_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_7_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_7_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_1_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_1_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_5	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_6	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_7	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_7_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_5	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_6	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_6_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_6_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_6_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_7	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_7_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_7_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_7_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	5	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	8	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	9	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	5	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	8	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	9	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_1_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_2_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_3_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_3_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	4	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	5	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	8	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	9	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	5	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	8	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	9	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	5	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	8	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	9	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_1_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_1_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_5	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_6	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_7	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_7_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_5	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_6	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_6_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_6_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_6_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_7	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_7_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_7_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_7_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_1_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_1_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_5	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_6	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_7	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_7_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_5	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_6	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_6_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_6_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_6_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_7	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_7_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_7_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_7_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	5	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	8	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	9	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	5	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	8	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	9	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_1_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_2_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_3_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_3_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	4	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	5	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	8	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	9	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	5	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	8	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	9	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	5	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	8	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	9	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_1_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_1_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_5	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_6	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_7	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_7_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_5	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_6	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_6_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_6_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_6_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_7	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_7_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_7_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_7_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_1_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_1_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_5	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_6	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_7	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_7_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_5	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_6	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_6_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_6_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_6_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_7	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_7_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_7_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_7_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	5	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	8	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	9	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	5	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	8	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	9	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_1_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_2_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_3_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_3_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	4	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	5	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	8	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	9	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	5	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	8	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	9	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	10	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	1	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	2	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	3	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	4	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	5	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	6	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2011	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	7	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	8	ERROR:#REF!
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ERROR:#REF!	X	2011	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	9	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	10	ERROR:#REF!
ERROR:#REF!	X	2011	1000 mt	10_1	ERROR:#REF!
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ERROR:#REF!	X	2011	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	X	2011	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	X	2011	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	X	2011	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	X	2011	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2011	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2011	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2011	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	X	2011	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	X	2011	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	X	2011	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	X	2011	1000 NAC	4	ERROR:#REF!
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Proposal for a new supplement to UN Regulation No. 155

The text below was prepared by the experts from France and the United Kingdom of Great Britain and Northern Ireland. The modifications to the existing text of the Regulation are marked in bold for new or ~~strikethrough~~ for deleted characters.

I. Proposal

Paragraph 1.1., amend to read:

“1.1. This Regulation applies to vehicles, with regard to cyber security, of ~~the~~ Categories L, M ~~and~~ , N, O, R, S and T, if fitted with at least one electronic control unit.

~~This Regulation also applies to vehicles of Category O if fitted with at least one electronic control unit~~.”

Paragraph 1.2., shall be deleted:

“1.2. This Regulation also applies to vehicles of the Categories L6 and L7 if equipped with automated driving functionalities from level 3 onwards, as defined in the reference document with definitions of Automated Driving under WP.29 and the General Principles for developing a UN Regulation on automated vehicles (ECE/TRANS/WP.29/1140).”

Paragraphs 1.3. (former) and 1.4., renumber as paragraphs 1.2. and 1.3.

Paragraph 7.3.1., amend to read:

“7.3.1. The manufacturer shall have a valid Certificate of Compliance for the Cyber

Security Management System relevant to the vehicle type being approved. However, for type approvals of vehicles of Categories M, N and O first issued before 1 July 2024, and for type approvals of vehicles of Categories L, R, S and T first issued before 1 July 2027, and for each extension thereof, if the vehicle manufacturer can demonstrate that the vehicle type could not be developed in compliance with the CSMS, then the vehicle manufacturer shall demonstrate that cyber security was adequately considered during the development phase of the vehicle type concerned.”

Paragraph 7.3.4., amend to read:

“7.3.4. The vehicle manufacturer shall protect the vehicle type against risks identified in the vehicle manufacturer’s risk assessment. Proportionate mitigations shall be implemented to protect the vehicle type. The mitigations implemented shall include all mitigations referred to in Annex 5, Part B and C which are relevant for the risks identified. However, if a mitigation referred to in Annex 5, Part B or C, is not relevant or not sufficient for the risk identified, the vehicle manufacturer shall ensure that another appropriate mitigation is implemented. In particular, for type approvals of vehicles of Categories M, N and O first issued before 1 July 2024, and for type approvals of vehicles of Categories L, R, S and T first issued before 1 July 2027, and for each extension thereof, the vehicle manufacturer shall ensure that another appropriate mitigation is implemented if a mitigation measure referred to in Annex 5, Part B or C is technically not feasible. The respective assessment of the technical feasibility shall be provided by the manufacturer to the approval authority.”

II. Justification

1. At the 16th session of GRVA in May 2023, the subsidiary Working Party accepted the Chair’s proposal to finalise the discussion of the inclusion of all categories of vehicles in UN Regulation No. 155 at its 17th session in September.

2. The purpose of UN Regulation No. 155 is to offer an international framework for the homologation of road vehicles with regard to cyber security. Therefore, GRVA should strive to offer the broadest scope possible to its Contracting Parties, and to allow manufacturers of vehicles of any relevant category to apply for a type approval.

3. During the previous sessions of GRVA and of its informal working group on cyber security and software updates, no technical argument was put forward to justify the exclusion of vehicles of Categories L, R, S and T from the scope of the Regulation. Not including these categories thus forces Contracting Parties and regional organisations to use national or regional laws on cyber security for these categories of vehicles. This could lead to unique requirements and a level of divergence that could be onerous on the industry.

4. The scope of UN Regulation No. 156 already includes all categories of vehicles: this current discrepancy between the two Regulations is an implicit statement that some vehicles, while able to receive over-the-air software updates, should not be type approved with regard to cyber security. Aligning the scope of UN Regulation No. 155 with that of UN Regulation No. 156 is a logical step towards a comprehensive regulatory environment for connected vehicles.

5. Similarly to what was granted to Categories M and N in the original version of the Regulation (paragraphs 7.3.1. and 7.3.4.), an adequate lead time is necessary for manufacturers of vehicles of the categories introduced in this proposal to demonstrate adequate cybersecurity measures for the approval of vehicle types whose development phase started prior to the implementation of the manufacturer’s Cyber Security Management System. Category L vehicles that were already in scope of the Regulation have been included in this lead time to simplify the drafting and remove reference to SAE levels of automation. As the provisions still require demonstration that cyber security was adequately addressed and any alternative mitigations are appropriate, there should be no issues in allowing additional time in this case.

Submitted by the experts from France and the United Kingdom of Great Britain and Northern Ireland

Informal document GRVA-17-13 17th GRVA, 25-29 September 2023 Provisional agenda item 5(a)

Proposal for a new supplement to UN Regulation No. 155

The text below was prepared by the experts from France and the United Kingdom of Great Britain and Northern Ireland. The modifications to the existing text of the Regulation are marked in bold for new or strikethrough for deleted characters.

I. Proposal

Paragraph 1.1., amend to read:

“1.1. This Regulation applies to vehicles, with regard to cyber security, of the Categories L, M and, N, O, R, S and T, if fitted with at least one electronic control unit.

This Regulation also applies to vehicles of Category O if fitted with at least one electronic control unit.”

Paragraph 1.2., shall be deleted:

Paragraphs 1.3. (former) and 1.4., renumber as paragraphs 1.2. and 1.3.

Paragraph 7.3.1., amend to read:

“7.3.1. The manufacturer shall have a valid Certificate of Compliance for the Cyber

Security Management System relevant to the vehicle type being approved. However, for type approvals of vehicles of Categories M, N and O first issued before 1 July 2024, and for type approvals of vehicles of Categories L, R, S and T first issued before 1 July 2027, and for each extension thereof, if the vehicle manufacturer can demonstrate that the vehicle type could not be developed in compliance with the CSMS, then the vehicle manufacturer shall demonstrate that cyber security was adequately considered during the development phase of the vehicle type concerned.”

Paragraph 7.3.4., amend to read:

“7.3.4. The vehicle manufacturer shall protect the vehicle type against risks identified in the vehicle manufacturer’s risk assessment. Proportionate mitigations shall be implemented to protect the vehicle type. The mitigations implemented shall include all mitigations referred to in Annex 5, Part B and C which are relevant for the risks identified. However, if a mitigation referred to in Annex 5, Part B or C, is not relevant or not sufficient for the risk identified, the vehicle manufacturer shall ensure that another appropriate mitigation is implemented. In particular, for type approvals of vehicles of Categories M, N and O first issued before 1 July 2024, and for type approvals of vehicles of Categories L, R, S and T first issued before 1 July 2027, and for each extension thereof, the vehicle manufacturer shall ensure that another appropriate mitigation is implemented if a mitigation measure referred to in Annex 5, Part B or C is technically not feasible. The respective assessment of the technical feasibility shall be provided by the manufacturer to the approval authority.”

II. Justification

ACCC/C/2022/194_United Kingdom

Read more about ACCC/C/2022/194_United Kingdom

Languages and translations

English

B E F O R E: THE AARHUS CONVENTION COMPLIANCE COMMITTEE

UNITED NATIONS, ECONOMIC COMMISSION FOR EUROPE RE: COMMUNICATION ACCC/C/2022/194 (THE FREE TRADE AGREEMENTS CASE)

COMMUNICANTS’ REPLY TO THE OBSERVATIONS OF

THE GOVERNMENT OF THE UNITED KINGDOM

1. The Communicants make the following observations in reply to those presented by the UK Government in its Response attached to a letter to the Secretariat dated 12 May and received on 15 May. The Communicants address the following issues:

a. The significant environmental effect of FTAs (paras 16-19 of the UK Response)

b. The feasibility of meeting the Requirements of Article 8 of the Convention in the context of FTA negotiations; (paras 9, 47 and 50 of the UK Response)

c. The incompatibility of the current arrangements with the requirements of Article 8 of the Convention

d. The Relevance to the Complaint of Article 3(7) of the Convention (para 8 of the UK Response)

e. Interpretation of Article 8 of the Convention in the light of the Vienna Convention on the Law of Treaties (paras 5-15 of the UK Response)

f. Breach of Article 3(1) of the Convention distinct from breach of Article 8 (para 51 of the UK Response);

2. The purpose of submitting this Reply is (1) to seek to narrow the issues which fall for determination by the Committee before the hearing; and (2) to correct factual inaccuracies. The Communicants maintain all the submissions presented in the Communication and will address the Committee further on the legal interpretation of the Convention, in particular as to the interpretation and application of Articles 8 and 3(1) of the Convention, at the hearing. The Communicants reserve the right to make further written submissions on these issues if the Committee should decide not to hold a hearing.

3. Documents referred to in this Reply are either contained in the original bundle of documents provided to the Compliance Committee with the Communication dated 10 August 2022 (hereafter referred to as “CB/Annex [X]/p [X]”) or they are contained in the bundle of documents provided with this Reply (hereafter referred to as “RB/Annex [X]/p [X]”).

4. In summary, the Communicants make the following observations in Reply: a. The evidence as to the significant environmental effect of FTAs is

overwhelming, both as regards those negotiated, or to be negotiated, by the

UK as well as those negotiated by the European Union and other states. It is not plausible for the UK to suggest that the FTAs referenced in the Communication do not have any significant environmental impact. The Communicants therefore invite the UK to accept that FTAs ‘may have a significant effect on the environment’ within the meaning of Article 8 of the Convention (without prejudice to their case that Article 8 does not apply to FTAs for other reasons). See further below (para’s 4-23);

b. The evidence as to the feasibility of meeting Article 8 requirements in the context of FTA negotiations is also compelling, having regard to the established practice of other states and the European Union. Again, the Communicants invite the UK to confirm that it accepts that it would be feasible to meet the full requirements of Article 8 in practice. See further paras 25-28 below;

c. The current arrangements set in place by the UK do not meet the requirements of Article 8 for the reasons set out in the Communication. The Communicants note, in particular, that the UK does not claim that the current arrangements allow for effective public participation ‘while options remain open’ or that the views of the public are sought or taken into account at any stage after the negotiating objectives have been published. Furthermore, there are a number of factual inaccuracies in the Response which relate to the current arrangements for consultation and which are addressed in this Reply;

d. The Communicants have not alleged a breach of Article 3(7) for the purposes of this Communication, but they do rely on Article 3(7) as relevant to the interpretation of Article 8. The Committee may wish to draw its own conclusions as to the UK’s compliance with Article 3(7) in this context, bearing in mind that the UK does not accept that Article 3(7) applies to FTA negotiations in any event (para 32 below);

e. The Parties agree that the Convention falls to be interpreted in line with the rules and principles laid down in the Vienna Convention on the Law of Treaties (VCLT). In this regard, the Communicants note that the primary general rule of interpretation is that laid down in Article 31 of the VCLT which requires that a treaty be interpreted ‘in good faith in accordance with the ordinary meaning to be given to the terms of the treaty in their context and in the light of its object and purpose.’ This taken with the broad scope of the wording of Article 8 as reaffirmed by the Implementation Guide, indicate that the Article is apt to cover FTAs. In relation to Article 18 VCLT, that provision obliges states to refrain from acts which would frustrate the object and purpose of a treaty when it has signed the treaty (until it makes clear its intention not to become a party). This is relevant to FTAs and their implications for domestic regulation and policies (paras 33-46 below);

f. The Communication refers to Article 3(1) as well as Article 8 on the basis that, even were the Committee to find that the UK’s current practice complies with Article 8, there would still be a breach of the Convention since Article 3(1) requires that the UK take the necessary legislative, regulatory and other measures, to establish and maintain a clear, transparent and consistent framework to implement Article 8 of the

Convention. Accordingly, the reference to a breach of Article 3(1) is not ‘parasitic’ or manifestly unreasonable as claimed by the UK (para 45 below).

5. (a) The significant environmental effect of FTAs (paras 16-19 of the UK Response: Article 8 of the Convention refers to other generally applicable legally binding rules ‘that may have a significant effect on the environment’. In its response, the UK states that the Committee cannot assume that FTAs have, or may have, a significant effect on the environment and that the environmental effect of each FTA has to be assessed individually. The UK then seeks to downplay, or even dismiss, the scale of the environmental effect of various individual FTAs.

6. Evidence of Significant Environmental Effect of FTAs: The UK does not specifically address most of the evidence referred to in the Communication as to the significant environmental effect of FTAs (Communication, paras 9- 17)The UK relies in the main on certain findings of the TAC Report on the Australia FTA and a general rebuttal as to the significant environment effect of FTAs (see Response, paras 16-19).

7. The Communicants submit that the evidence as to the environmental impact and implications of FTAs in general, as well as in the specific cases to which they have referred, is clear and on that basis the Communicants invite the UK to accept before the hearing that FTAs have, or may have, a significant effect on the environment. As set out in the Communication, the scope of existing FTAs, the recognised (including by the TAC) impacts of trade liberalisation in certain sectors and the practices associated with their negotiations (even in the UK), all confirm that FTAs in general have an environmental impact. The Communicants make a number of further submissions on this issue below.

8. The fact that FTAs generally have environmental impacts is evidenced by the UK’s own practice of commissioning a report into environmental impact (see Response, para 34) , as well as an initial scoping impact assessment which deals with environmental issues. If FTAs did not generally have significant environmental impacts, there would be no point in requiring an impact assessment into the environmental impacts of every agreement, which is what the UK Government has committed to doing (see CB/Annex 3/p 6).). The UK is of course not alone in acknowledging the environmental impacts of FTAs. The EU also recognises that FTAs have environmental impacts: it commits to publishing a Sustainability Impact Assessment for every trade negotiation (for example, see RB/Annex 7/p 19).

9. As recognised in the CPTPP’s initial impact assessment: “FTAs can impact the environment by changing patterns or techniques of production, the types of goods and services that are traded and the commitments made by countries in respect of environmental policies and outcomes” (see RB/Annex 5/p 12). Further, in practice, the decision of the UK to agree to lower tariff measures on Malaysian palm oil to 0% is a concrete example of environmental impacts

arising from FTAs, given widespread evidence of unsustainable palm oil production there which may be imported into the UK going forward without tariff (see further para 26 below).

10. Furthermore, the UK’s final Impact Assessment into the Australia FTA states that:

The economic improvements and increased trade arising from FTAs can also entail consequences for the environment. Other things equal, increased economic activity is typically associated with environmental implications for greenhouse gas emissions and other environmental outcomes such as air pollution, water quality and biodiversity. (see RB/Annex 6/p 14)

11. This is a comment from the UK Department for Trade on FTAs in general. The

Impact Assessment then includes a specific chapter on environmental impacts (Chapter 6) which states that: ‘The agreement could impact on the environment through a variety of channels’ (RB/Annex 6/p 15). Chapter 6 then addresses potential impacts on greenhouse gas emissions and climate change, trade- related transport emissions, carbon leakage, impacts on natural capital and nature loss, air and water quality, forests and the marine environment, as well as biodiversity and ecosystems and waste management. The Impact Assessment also points out that the FTA has a dedicated chapter on the environment.

12. The environmental and climate impact of trade and trade agreements is also evident from the 2022 Progress Report of the UK Climate Change Committee which considered the impact of trade in general as well as the three new FTAs in this regard (see RB/Annex 8). The Committee referred to the adoption of the three FTAs with Australia, New Zealand and Singapore and stated:

The most notable climate action within these trade agreements has been for the partners to reaffirm their commitment to all the aims of the Paris Agreement, although it is not clear whether these clauses will have any substantial effect on climate change action. A greater impact of the trade agreements may be the impact on trade flows and subsequently UK production and consumption emissions. (RB/Annex 8/p 29,, emphasis added)

13. The TAC Report: The UK claims that the TAC Report on the Australia FTA

supports its contention that the Australia FTA has no significant environmental impact in so far as it finds no “offshoring of environmental harm”, “race to the bottom” and “erosion of environmental regulations” (Response, para 18(ii)). However the TAC is not required to address these issues. The remit of the TAC’s Report is laid down in section 42 of the UK Agriculture Act which provides that the report:

…must explain whether, or to what extent, the measures referred to in subsection (1) are consistent with the maintenance of UK levels of statutory protection in relation to: (a)human, animal or plant life or health, (b)animal welfare, and (c) the environment. (see RB/Annex 1/p 1)

14. While a section 42 TAC Report can analyse if the FTA is consistent with existing

UK legislation on the environment, it cannot take into account the long-term effect on regulation of the competitive pressure that can result from the FTA. Further, the TAC does not have any remit under section 42 to analyse whether the FTA offshores environmental impacts, as confirmed in the UK government publication on the report (RB/Annex 9/p 31).1 A further limitation in the UK approach of relying on the TAC is that current regulations may not regulate all environmental impacts arising from an FTA such as land use change.

15. Nonetheless, the Communicants note that the TAC Report acknowledges its own limited remit and points towards environmental impacts of trade which are due to competitive pressure, rather than regulatory changes directly resulting from the trade agreement. For example, the TAC Report finds that the FTA has no effect on the UK’s existing WTO rights to regulate the import of products produced using pesticides that are harmful to UK animals, plants, or the environment, but also states that the FTA is likely to lead to increased imports of products that have been produced at lower cost by using pesticides in Australia that would not be permitted in the UK’ (RB/Annex 9/p 32) .

16. ISDS: One important feature of many FTAs which has clear and well documented environmental impacts relates to the inclusion of Investor State Dispute Settlement (ISDS) mechanisms (see Communication para 17). The UK government's position is that they will consider ISDS in FTAs on a case-by-case basis, and it has been included in the CPTPP trade agreement, for example. This reaffirms the need for considering the environmental impacts of ISDS and the need to consult on its inclusion in trade agreements, though the Communicants recognise ISDS does not form part of the Australia FTA which is the focus of the Communication.

17. The UK in its Response states that there has never been a successful ISDS claim against the UK, and denies that the threat of potential claims affected the Government’s legislative programme (Response, para 18(iii)). However, the independent evidence as to the significant impact that ISDS can have on environmental law and policy is clear as outlined below.

18. In September 2022, UNCTAD issued a report stating that: The risk of investor–State dispute settlement (ISDS) being used to challenge climate policies is a major concern. (RB/Annex 11/ p 38)

19. The UNCTAD report cites many examples of cases directly impacting states’ ability to combat climate change (see the 175 environment related cases listed

1 Report pursuant to Sec on 42 of the Agriculture Act 2020 S.42 Report criteria which refers to ‘the maintenance of UK levels of statutory protec ons’

in Annex 1 to the Report and the 192 fossil fuels cases listed in Annex 2 – RB/Annex 11/p 40-51). The Report states:

While not all claims brought by investors under IIAs are successful, ISDS is costly. In general, the disputing parties – including the respondent States – incur significant costs for the arbitrators’ work, the administration of proceedings and legal representation, all of which usually amount to several million dollars or more. In addition, claimants and respondent States face several years of uncertainty while ISDS proceedings concerning the challenged measures are ongoing. The amounts at stake in ISDS proceedings can be hundreds of millions and even billions of dollars. Moreover, ISDS proceedings may have reputational costs for the respondent States. (RB/Annex 11/p 39)

20. A recent example of the impact that ISDS (can have on environmental policy is Rockhopper v Italy (RB/Annex 4, p 9). In that case, the claimant companies sought arbitration of their claims for compensation arising from Italy's alleged violations of the ECT in respect of their investments in the putative Ombrina Mare oil and gas field located off the Italian coast in the Adriatic Sea. As the Arbitration Tribunal noted in the award:

…this came about because Italy decided to pass a law in late 2015 which banned offshore production within a certain distance of Italian shores. That was a sovereign decision made by Italy and the Tribunal indicates at the very outset that it should not be taken in any way to either criticize or deprecate that decision from either a political or environmental standpoint. Italy's sovereign choice to proscribe such offshore production, based on its own inherent authority and dignity, was its to make. However, that sovereign choice or act or decision (the label is not important) of Italy may carry with it a concomitant consequence to pay certain compensation pursuant to internationally-binding promises it made to foreign investors arising from its being a party to the ECT at the material time (emphasis added) (para 6)

21. The ICSID Tribunal held that there had been an unlawful expropriation and

awarded Rockhopper 184 million euros in damages, 6.7 million euro in decommissioning costs, plus interest. Rockhopper had applied for a production concession from the Italian Government prior to the introduction of the ban, and was claiming for both the funds spent and for anticipated profits (RB/Annex 4/p10.

22. In August 2022, in light of the concerns as to the adverse impact of ISDS

mechanisms, the Special Rapporteur on Human rights and the Environment recommended that states:

(j) Negotiate the removal of investor-State dispute settlement mechanisms from international trade and investment agreements or terminate the agreements (because such mechanisms constrain States from taking immediate and effective action to address the climate crisis, biodiversity loss and pollution)… (RB/Annex 13/p 62)

23. The Special Rapporteur had outlined the concern with ISDS, including in the context of climate change:

The fossil fuel industry is especially litigious, having brought to international arbitration tribunals more than 230 cases in which they have asserted that government actions have decreased the value of their investments. Fossil fuel corporations have been successful in nearly 75 per cent of cases, forcing Governments to pay billions of dollars in compensation… The average amount awarded in fossil fuel cases – over $600 million – is almost five times the amount awarded in non-fossil fuel cases. Governments acting to fulfil their commitments under the Paris Agreement may be liable for hundreds of billions of dollars in future investor-State dispute settlement cases, which discourages climate action…There is a deeply disturbing contradiction between human rights obligations (and the Sustainable Development Goals) and investment agreements that require Governments to compensate foreign corporations for stopping activities that exacerbate the climate crisis and result in human rights abuses. (RB/Annex 13/p 61))

24. Some states have acknowledged publicly that the possibility of being sued by investors under ISDS stopped them from implementing more ambitious environmental policies, for example as regards the phasing out of fossil fuels to meet climate change goals. By way of example, Denmark has set a 2050 deadline for ceasing exploration projects for fossil fuels, which is expected to affect only one fossil fuel licensing agreement. The Danish climate minister is reported to have acknowledged that an earlier target would have resulted in the Danish government needing to provide significant and costly compensation to investors under ISDS. New Zealand is reported to have been impacted by concern that it would face action from investors: it reportedly did not join the Beyond Oil and Gas Alliance which required that members should, at a minimum, be “implementing the guidance of the International Energy Agency to cease development of new oil and gas fields”, because it was concerned that this would lead to it cases brought by investors (see RB/Annex 12/p 54).

25. (b) The feasibility of meeting the Requirements of Article 8 of the Convention in the context of FTA negotiations (paras 9, 47 and 50 of the UK Observations) The UK Government has pointed to the complexity of negotiations as a basis for arguing that ‘public comment’ cannot be continually sought and taken into account at every stage. (Response paras 9, 47).

26. The Communicants recognise that there are complexities and sensitivities in conducting international negotiations, including for FTAs. However there is ample evidence of existing systems and practice in the negotiation of FTAs by states and the EU which provide for public consultation throughout the process, including at an early stage while options are still open. The Communication refers to a number of examples (see CB/Annex 25/p 108-110), including that of the EU, which consults the public on an impact assessment

conducted prior to starting negotiations, and again later during major negotiations as part of the Sustainability Impact Assessment. In the US, the Trade Promotion Authority requires the Government to engage in public consultation, and provide detailed information and regular consultation events during the negotiations, while publishing a series of impact assessments including on the environment (RB/Annex 15/p 65-68). US negotiators are also well known to invoke the need to gain domestic approval of trade deals as a leverage point in negotiations, which shows that the requirement to consult domestically can in fact be a useful tool in negotiations, rather than undercutting them as the UK asserts the Response.

27. In its Response, the UK states that a comparison between the UK’s approach to public participation in FTA negotiations and that of other countries is not ‘legitimate’ and not an issue for the Committee (Response, para 50). This is clearly not correct if the UK’s position is based on an argument that public participation required under Article 8 is not feasible. The argument as to what Article 8 requires is a matter of interpretation of the Convention but it is entirely legitimate to refer to broader international practice, particularly of other parties to the Convention, to counter an argument that public participation is simply not feasible. It would be helpful to clarify whether the UK maintains that public participation in the form which the Communicants submit is required under Article 8 is not feasible in the light of the international practice to which the Communicants have referred.

28. The UK currently limits public consultation to the period before the publication of the negotiating objectives, and there is no public consultation later in the negotiation process. In any event the broad phrasing of the negotiating objectives renders it difficult to know the extent to which consultation has influenced their framing or the conduct of negotiations (CB/Annex 15/p 69). In marked contrast to the EU, there is no public consultation on the initial scoping assessment (published alongside negotiating objectives) (see RB/Annex 5/p 12 and CB/Annex 25), nor on the full impact assessment published alongside the completed deal2. The importance of conducting public consultation beyond the stage of setting negotiating objectives is confirmed by the fact that circumstances can change significantly after this stage. Key issues can arise during the negotiation of the FTA and circumstances can change significantly which underlines the importance of public consultation during the different stages of the negotiation in order for it to be effective and meaningful. For example, when the UK was consulting on the CPTPP FTA, Malaysia had still not ratified the agreement, but it did ratify the CPTPP during the process of negotiations with the UK (on the 30.09.2022). In the final agreement, the UK agreed to lower tariff measures on Malaysian palm oil to 0%, which was a contentious environmental issue and underlines the environmental impacts

2 The absence of public consulta on on the ini al scoping assessment for the UK-Australia FTA is evidenced at CB/Annex 17 and CB/Annex 3, whilst the EU's prac ce on public par cipa on is set out in CB/Annex 25

which FTAs have (RB/Annex 10). With trade negotiations lasting several years, conducting public consultations only at the start of the process does not provide effective public consultation, contrary to the requirements of Article 8.

29. (c) The incompatibility of the current arrangements with the requirements of Article 8 of the Convention- The Response makes a number of assertions which are factually inaccurate or incomplete and these are addressed here.

30. The UK Response is factually inaccurate in two respects. First, some of the Thematic Working Groups referred to at paragraph 32(i) of the Response have been “under review” since September 2022. The Thematic Working Group concerned with sustainability has not met since October last year and the development group, of which one of the Communicants (Trade Justice Movement) is a member has not met since June last year (see RB/Annex 14/p 63). The Communicants are therefore not aware of the Sustainability TWG being "relaunched", as the Response claims (para 32(ii)). Government’s arguments around the willingness, or otherwise of groups or individuals to sign up to non-disclosure agreements (which they were required to sign by government order to join the meetings) are beside the point therefore, since these groups have not met for approximately a year.

31. Second, the contention in paragraph 38 of the Response, that it will accommodate a request for a Parliamentary debate in relation to a free trade deal overlooks the reality as to how this process played out in practice in relation to the Australia FTA. The debate took place after the formal process of Parliamentary scrutiny (under the Constitutional Reform and Governance Act 2010) had come to an end meaning that Parliament could not resolve against the trade deal under the CRAG rules (because no resolution or vote was permitted). Whilst government asserts (in paragraph 48 of the Response) that the House of Commons may resolve against a treaty under CRAG 2010, preventing the Executive from ratifying it, in reality, no such resolution was possible in the case of the Australia FTA, because the debate took place outside the CRAG process. The Communicants contend that this omission fundamentally impairs the effectiveness of the Parliamentary scrutiny in the case of the Australia deal.

32. (d) Relevance to the Communication of Article 3(7) of the Convention: The Presence of Article 3(7) in the Convention underscores the application of the Convention to international environmental fora (Communication, paras 4, 23, 30). The Communicants submit that the presence of Article 3(7) in the Convention forms part of the context for interpreting the distinct provisions of Article 8 which applies to other legally binding rules that may have a significant effect on the environment. The UK appears to suggest in its Response that Article 3(7) of the Convention, rather than Article 8, applies to the negotiation of FTAs but then immediately states that it does not apply. If the reason for that

position as to Article 3(7) is that FTAs have no significant environmental impact, this is strongly refuted (see above), and if it is because FTAs may be bilateral this is also refuted. The Aarhus Convention Implementation Guide states in relation to Article 3(7) that: ‘The definition of international forums implicitly includes both multilateral and bilateral decision-making processes’.3

33. (3) Interpretation of Article 8 under the principles laid down in the Vienna Convention on the Law of Treaties: The Communicants rely on the rules of interpretation as laid down under the VCLT (Communication, para 25) including that Article 8 should be interpreted in good faith in accordance with the ordinary meaning to be given to the terms of the Convention in their context and in the light of its object and purpose. The UK makes only cursory reference to the object and purpose of the Convention (Response, para 15) and does not examine its specific implications for the interpretation of Article 8. The object and purpose of the Convention is stated in Article 1 as being: to contribute to the protection of the right of every person of present and future generations to live in an environment adequate to his or her health and well-being. In the light of the significant effects on the environment that FTAs may have (see above) it is important to have regard to this objective together with the terms of Article 8, as the starting point in considering the interpretation of Article 8. The UK contests the effects of FTAs on the environment and thereby appears to sidestep considering Article 8 in the context of the object and purpose of the Convention. This is not an acceptable approach to the interpretation of the scope of Article 8.

34. By contrast the UK starts its analysis of Article 8 by reference to the principle of ejusdem generis, a subsidiary rule which is not relevant in this case (see further below).

35. In relation to the ordinary meaning of the terms of Article 8 in its context (Article 31(1) VCLT), the Communicants note the broad terms of Article 8 in relation to ‘other generally applicable legally binding rules that may have a significant effect on the environment.’ This phrase should be seen in the context of the Convention as a whole, and in particular the provisions for public participation and the way in which these provisions contribute to the objective of the Convention as set out in Article 1.

36. The considerations set out in the preamble confirm the direct relationship between the provisions on public participation, including those set out in Article 8, and securing the object and purpose of the Convention, particularly in a context where the public has clear concerns as is the case with the negotiation of FTAs.

3 The Aarhus Conven on: An Implementa on Guide 2014 at page 69.

37. The UK refers to the Implementation Guide and seeks to argue that this in some way narrows the scope of Article 8. In the first place that is not the purpose or effect of the Implementation Guide and second, the references made by the UK (in particular at para 6(ii) of the Response) do not indicate a narrowing of the scope of Article 8, quite the contrary. The reference to ‘preparation by public authorities’ is clearly broad enough to encompass the negotiation of FTAs, having regard to the generality of the phrase ‘other legally binding rules which may have a significant effect on the environment’ and the object and purpose of the Convention. The breadth of the language referred to in the Implementation Guide which includes ‘norms and rules’, rather emphasises the breadth of the scope of Article 8 than otherwise (Response, para 6(ii)).

38. The obligation is framed as one of ‘striving’ which reflects the breadth of the scope of Article 8 and the different types of measures it encompasses.

39. The ejusdem generis rule cannot override the primary rule laid down in Article 31 or the supplementary rules laid down in Article 32. In discussing the application of this rule, the UK again disregards the object and purpose of the Convention. This is fatal to its argument since unless the application of the rule is integrated with a consideration of the object and purpose of the Convention it does not elucidate the meaning of Article 8. In any event the intended breadth of the language is confirmed by the Implementation Guide.

40. A requirement that any measure covered by Article 8 must derive from a ‘unilateral’ act would open a major lacuna in the Convention since states could thereby avoid public participation in the development of norms derived from those processes, as the UK seeks to do here. In any event the decision to agree an FTA that it has negotiated is that of the UK and is in that sense ‘unilateral’.

41. The Communicants have addressed the implications of the UK’s dualist system in the Communication (paras 27 and 31) and would only add at this stage that the UK does not deny that an FTA has legal effects within the domestic legal system, simply describing these as ‘limited’ (Response, para 7(2)(e)). The extent of those implications, in line with established caselaw, is clearly fact specific and given the potential scope and environmental effect of FTAs is potentially significant, even within the limits of the Rayner exceptions.

42. The Communicants do not understand the point being made as to the specific rather than general nature of the rules laid down by FTAs (Response, para 7(iii)). As stated in the Communication the rules are clearly of general application (para 31).

43. In relation to the ‘sense check’ (Response, para 9), that concerns the issue of feasibility addressed above and, again, the UK avoids addressing the object and purpose of the Convention and the implications of this for the interpretation of Article 8.

44. Article 18 VCLT: The Communicants note that the effect of Article 18 VCLT has been considered by the EU Courts. In the case of T-115/94 Opel Austria GmbH v Council of the European Union, the Applicants had argued that:

Article 18 of the First Vienna Convention and Article 18 of the Second Vienna Convention constitute an expression of the general principle of protection of legitimate expectations in public international law, according to which a subject of international law may, under certain conditions, be bound by the expectations created by its acts in other subjects of international law. (see RB/Annex 3/p 6)

In finding for the Applicants, the CFI referred to Article 18 VCLT and then held:

In a situation where the Communities have deposited their instruments of approval of an international agreement and the date of entry into force of that agreement is known, traders may rely on the principle of protection of legitimate expectations in order to challenge the adoption by the institutions, during the period preceding the entry into force of that agreement, of any measure contrary to the provisions of that agreement which will have direct effect on them after it has entered into force. (see RB/Annex 3/p 7))

45. This reference to Article 18 VCLT was noted by Advocate General Francis

Jacobs in Case C-129/96 Inter-Environment Wallonie, Opinion 24 April 1997 (RB/Annex 2/p 4) in considering the legal effect of an EU directive which had been adopted but for which the deadline for transposition had not yet passed.

46. FTAs have a potentially far-reaching impact on UK law and policy and as indicated above they have clear and recognised environmental impacts. In that context the duty under Article 18 VCLT is also potentially far-reaching in terms of obligations placed on the UK.

47. Breach of Article 3(1) of the Convention distinct from breach of Article 8: If, contrary to the view taken by the Communicants, the Committee were to find that current UK practice meets the substantive requirements of Article 8 of the Convention, the Communicants maintain that there would still be a breach of Article 3(1) of the Convention on the basis that the UK has not provided a clear legal framework for its implementation of Article 8 in this context as required by Article 3 .

3 August 2023

Signatories

Toni Pearce, Interim Director of Advocacy, WWF-UK

Rebecca Newsom, Head of Politics, Greenpeace-UK

Sarah Williams, Head of Unit, Green Alliance

Kath Dalmeny, Chief Executive, Sustain

George Dunn, CEO, Tenant Farmers Association

Ruth Bergan, Director, Trade Justice Movement

Rob Percival, Head of Food Policy, Soil Association

Dr Nick Palmer, Head of Compassion in World Farming UK

Disclosure control issues in complex medical data, University of the West of England

Read more about Disclosure control issues in complex medical data, University of the West of England

COVID-19, routine access, medical records for research, microdata use,

Languages and translations

English

UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE

CONFERENCE OF EUROPEAN STATISTICIANS

Expert Meeting on Statistical Data Confidentiality

26-28 September 2023, Wiesbaden

Disclosure control issues in complex medical data

Elizabeth Green1, Felix Ritchie1, Jim Smith1, David Western1, Paul White1 1University of the West of England

[email protected]

Abstract

The covid19 pandemic assisted the acceleration of routine access to medical records for research. In the UK

platforms including OpenSafely and NHSDigital, alongside emerging hospital trust based Trusted Research

Environments (TREs), demonstrate the utility and need for medical researchers to access and use microdata

safely and securely. Whilst many employ traditional principles-based SDC standards to statistical outputs,

complexity arises when considering complex medical data which is required to remain highly detailed; for

example genome, medical imaging, or fMRI data where the output often includes reference to individual

observations. Current imaging libraries and databases have demonstrated awareness and need for metadata

standards, but consideration of both input and output protection is less clear. With the need to retain

observations with high level of detail this presentation discusses present considerations for potential SDC

solutions and also invites conversation from the wider community.

1 Introduction

The use of medical data for research purposes has clear public benefit and direct impact. Medical data by nature is highly detailed and specific to an individual: it is important to include a wide range of observations and background information to allow practitioners to make informed decisions and choices around treatment. Specific medical tests such as genome analysis or an MRI scan, generates large volumes of data which are specific to the individual and is evaluated and examined as a whole entity- not just a one particular fraction of the MRI scan is used, the whole scan is used and retained.

Historically, medical research has long been intertwined with delivery and provision of care to patients, as such research is conducted with direct informed consent and an expectation that the data will be used to further knowledge in the area. The medical data is of course highly detailed and often the number of observations used in a study can be low due to rarity of disease, or the collection of data is limited to particular hospitals/ sites. As such the research outputs can be highly detailed with descriptive tables and survival curves often including singular observations.

In contrast, microdata used in social science is often not directly collected by the researcher (for example census data) so informed consent specific to the research is not obtained. When it comes to accessing and publishing data outputs, social science has established data repositories and access arrangements for research with clear standards for statistical disclosure control (SDC) within both shared datasets and research outputs.

The aim of this paper is: first, to outline some present examples of sharing of medical data and also outputs of medical data; and second, to reflect on the disciplinary differences in disclosure control. In this paper we will illustrate this with some examples and consider whether this is due to lack of awareness or lack of concern. We will illustrate with three commonplace examples of shared data, to illustrate some of the issues and the expectations of the public health world. Finally, we reflection ways forward and where medical science may benefit from the experience of social scientists.

It should be noted that this paper is not intended to embarrass organisations or researchers- examples where potential disclosure and poor practice has been identified by the team are de-identified and described. The team has not directly referenced these examples, and we encourage the community to have an open conversation about how to integrate SDC standards when sharing data.

2 Medical examples

2.1 Genomic data

The devil is in the detail. A genome provides the complete set of all the genetic information in an organism. Genomic analysis (for example, microarray data) allows for the investigation of genes, and provides the necessary insights for developing cures, vaccines, and identification of new diseases and diagnostic tests. Whilst the sharing of individual genome data has facilitated remarkable breakthroughs in fields such as genetics and personalized medicine, it also raises significant privacy concerns.

The current practice of ‘anonymization’ of genomic data is performed by removing direct identifiers (for example, name, patient ID) and indirect identifiers (hospital, postcode) (Bonomi, Huang and Ohno-Machado, 2020). However other variables such as age of patient, gender, prognosis are not redacted. Below is an example of an ‘anonymised’ genome array data- available via website in the public domain which does not require sign in. The data is associated with a published research article, a condition of publication with the journal is that the raw data must be made available.

Data collection: The DRAGoN Hospital for Exhausted Researchers

Participant characteristics: Participant number Gender Age Prognosis

1 Male 48 Bad- chronic insomnia

2 Female 31 Good

Xlsx attachment with participant 1 microarray, participant 2 microarray etc.

The main issue here is not only the level of detail presented in the participant characteristics list, but also the level of detail within the array/ genome dataset. It is effectively the raw output of the individual’s entire genetic array. Whilst research has advanced an understanding of the specific roles of different structural points, mutations, and specific markers knowledge, we are still in the process of identifying and discovering the roles of specific which genetic markers. Therefore, when considering SDC we need to be aware that what is considered non-sensitive today may become sensitive in the near future (Ritchie and Smith, 2019; McKay et al. 2022).

For medical research it is difficult to define what information is disclosive and what is not. For example, it is possible to extract information about the individual such as eye colour, hair colour, hair texture (curly), baldness, physical traits etc from array data. Previous studies demonstrated the possibility of generating 3D face maps based on genomic data which could be used to reidentify individuals (Lippert et al. 2017, Crouch et al. 2018, Venkatesaramani and Vorobeychik, 2021). From a social science perspective we would be considering whether a form of input disclosure control could be employed; alternatively, could we safeguard who is accessing the data, and what might the consequences be if we did introduce such practices?

Input SDC on the sharing of genomic data is only one part of the puzzle. There are also disclosure issues in research outputs. As previously explained the data is uploaded to a shared platform- available for anyone to download, this sharing is often a mandatory requirement from both funders and journals. Below (figure 1) is an example of a published survival analysis which outlines the probability of survival for patients with a particular disease overtime. With small number of values it is easy to identify when individuals die at specific time points- accompanying the survival curve is a table detailing the change in numbers across time.

Figure 1 Example Kaplan-Meier curve with low numbers

Survival analysis is commonly used in medical research to demonstrate the relationship between diagnosis (or treatment) and death. Concerns around disclosure relates to number of observations between each step down in the curve, with detailed graphs often detailing a step down with less than 3 observations. O’Keefe et al. (2012) suggests smoothing and incorporating confidence intervals, while SDAP (2019) proposes checking to ensure thresholds are met within each step change.

Interestingly a tool which specifically generates Kaplan-Meier plots for genomic research is being used within the medical community- https://kmplot.com/ (Gyorffy, 2023). This open-access, free for use website allows researchers to perform survival analysis on different gene expressions from database of over 30k different samples. The user can select below the cancer subtype they wish to research and then the level of analysis (see below). By default the website is set to censor at the threshold for the plot, but the user is able to turn off this function.

Figure 2 Demonstration of a confidentialised output taken from O’Keefe et al. (2012) p134

Figure 2 Guidance for SDC in Kaplan Meier graphs by Welpton et al 2019

While this is an extremely useful resource for researchers, it is also of potential concern. It seems likely that very small subsets of the data could be selected and associated with personal characteristics – these would not produce meaningful graphs, but they could be used to challenge the anonymisation of the data.

2.2 Inappropriate use of medical dermal images

In dermatology, photographic capture of clinical findings is routine, with digital images providing support and awareness in both practice, research, training, and education. One publicly available tool is the DermAtlas (available http://www.dermatlas.net/reference/index.cfm) which stores a wide array of clinical images demonstrating the presentation of different dermological conditions. Anyone can access this tool and explore the wide range of photos it holds. In terms of impact this tool can help aid health professionals in identifying and evaluating their own patients, it can also be used by the general public to help them feel empowered or understand their own conditions/ potential diagnosis.

As the skin is the largest organ of our bodies, some dermatological conditions are localised to specific personal areas, this coupled with also an array of different clinical photos providing insights across the age range, the dermatology archives found it had become susceptible to misuse. Lehman, Cohen and Kim (2006) described the journey of discovery, ongoing detection, and management of misuse of DermAtlas content across a period of 4 years. A shocking 14.3% of all referrals originated from pornography / fetish sites (Lehman, Cohen and Kim, 2006).

This leads to concerns surrounding how to share safely medical information from what is undoubtedly a valuable medical resource. Any referral from a pornography/ fetish site resulted in the user being presented with a denial page (Lehman, Cohen and Kim, 2006). The DermAtlas implemented filters through user query patterns, with IP addresses of frequent queries for genital images being restricted. Restricted IP addresses were still able to use DermAtlas, but were presented with thumbnail sized images and unable to retrieve full images of genital sites. However, this approach was not straightforward: for example, the NHS in the UK and US military services were then inappropriately restricted.

DermAtlas presents an interesting example of the complexities when hosting data in a public domain which is aimed for a universal audience. The benefits of the tool for both public and health professionals are clear, but the tool is also being used for other purposes not intended by the designers. When considering potential solutions for de-identification or anonymisation of medical photos, current practice in social research where direct informed consent has not been obtained (such as photographing a busy city) is often to use object and

Figure 3 Website Kaplan- Meier plotter

face detection software to automatically mask individuals (Fitwi et al. 2021). When considering the clinical dermatological case photos, the current simplistic approach is to redaction is to mask the eyes and mouth, but for many case photos there is no form of redaction, and sometimes it is not possible to redact the eyes and mouth. We therefore assume, as is common practice within medical research, that the emphasis is on obtaining direct informed consent- and the patient consents to data being held within the public domain. However, can such consent be truly informed when unanticipated uses are made of the data? The DermAtlas and indeed other similar tools face a impossible triad: how can we retain detailed photographs and provide an open access tool and ensure no misuse?

2.3 fMRI scans

Functional magnetic resonance imaging or functional MRI (fMRI) provides a highly detailed image of the blood flow and structure of an item/ body part, these scans are being used to assist in treatment of the patient (diagnosis) but also medical research. Due to the large volume of high data produced by these scans sharing this information has proven to be invaluable for medical research. Current examples of sharing fMRI includes the Brain Imaging Data Structure (BIDS) website https://bids.neuroimaging.io/ . Here users can contribute, access, and download de-identified fMRI data.

When considering the input disclosure control BIDS requires contributors to remove all direct identifiers alongside ‘defacing’ the scan images (which can be achieved using a module https://raamana.github.io/visualqc/gallery_defacing.html ). Interestingly facial reconstruction based on detailed medical scans (such as CT, fMRI) has been achieved. Schwarz et al (2019) found that the software achieved an impressive re-identification rate of 83% (70 of the 84 participants) when comparing their MRI scan to photos.

BIDS ensures that the data entering the service is de-identified by providing excellent support to depositors- ensure that data uploaded to their service is stripped of direct identifiers and defaced. However uploading and publishing/ sharing data in tandem is common practice so the sticky issue of secondary disclosure is more apparent in this example. To highlight this a recent published journal article, cites that they have deposited the data used in publication in BIDS, but within the journal article the participants’ demographic characteristics are highly detailed with low numbers in particular cells and distinctive characteristics. If the identity of the depositor is known, then it increases the chance of knowing where the sample comes from (i.e. which hospital/patient group), dramatically increasing the chances of re-identification. Finally, with more researchers using data depositories such as BIDS to deposit datasets used in publications/ research, information already in the public domain about the dataset may be crucial for re-identification, but it is not necessarily considered by the individual depositor. Now the problem here is not within the data depository input side, but a lack of statistical disclosure control awareness from the authors- demonstrating the need for training and standards amongst the medical community.

3 Discussion

We are not stating that the above examples are necessarily disclosive or provide direct identification- a number of steps would be required to reidentify the individual and the value to an intruder would be questionable. For example, safe to assume that social media profile pictures in the public domain are not going to be viable for identification/ reconstruction of an fMRI scan. Venkatesaramani and Vorobeychik, (2021) found that the overall effectiveness of re-identification (when using social media photos) was substantially lower than previously suggested- as literature often uses high-quality data (both genomic and photographic) which is not consistent with real life scenarios. Conceptualisation of what is a reasonable threat is beyond the scope of this paper.

Nevertheless, the three examples have highlighted a number of issues and challenges within disclosure control from both an input and output side along with how to share. Many of these challenges are unique to the data, and traditional methods used to aid disclosure control in social research may be inappropriate. There are also some very unexpected factors; for example DermAtlas and the actual use. Going forward what mitigations and recommendations might social scientists offer the medical community?

On microdata access we must always accept a level of risk, risk needs to be conceptualised as to the realism of risk (i.e. what is the true likelihood of an intruder performing this for nefarious gain? And can we ever meaningfully and more importantly reliably measure this risk?). It is also essential that whilst discussing risk we must also discuss benefit, we are all to familiar of the invaluable findings and applications of health research and to potentially halt or delay findings is harm within itself. So, whilst we highlight areas of weakness and vulnerability we must objectively generate new paths going forward.

Our primary concern is the lack of standards, guidance and continuity- this is not being checked or reviewed or updated to current practices known within the SDC community (for example thresholds). Perhaps this demonstrates a lack in training and awareness around SDC, as in the examples there are demonstration of de- identification. This also could potentially be an area in which re-identification back to the individual is important for example if the research generates incidental findings on an individual and it’s necessary the receive intervention. Consent for data to be shared is often obtain directly with individuals being more inclined to trust the research and a presumption that they had “agreed to use this for research and we said we would anonymise it...”.

What about outputs? Sharing the data seems to happen in tandem with the outputs so output SDC not as relevant, however is this an output or input issue? Should we consider the attached journal participants characteristics tables as secondary disclosure or is this an example of input? What is clear however is a want to de-identify and a concern around ethics and consent in the medical community. Derrick et al 2022, highlights that training in OSDC is mostly limited to TRE users and lots of medical research on very sensitive data is not traditionally held in TREs (compare do social science), so moving forward training appears to be long-hanging fruit in supporting disclosure control in this area.

4 Future considerations

Identification of problem/risk – at first glance appears poor practice when compared to standards in social science but is it a genuine risk? How do we balance genuine risk vs perceived risk vs utility of data?

Training – what is done and to what level (again as social scientists not great but perhaps have experience and also conceptual understandings of thresholds, rounding etc).

Standards- what is done and to what level- can we support a harmonised approach?

Is open sharing good? In social science the move has been to open access not open data i.e. anyone with genuine reason has access to the data but not everyone gets access – need to review data sharing models and also pressures from funders and journals.

We especially welcome views from medical research community [email protected]

5 References

Bonomi, L., Huang, Y., & Ohno-Machado, L. (2020). Privacy challenges and research opportunities for genomic data sharing. Nature genetics, 52(7), 646-654.

Crouch, D. J., Winney, B., Koppen, W. P., Christmas, W. J., Hutnik, K., Day, T., ... & Bodmer, W. F. (2018). Genetics of the human face: Identification of large-effect single gene variants. Proceedings of the National Academy of Sciences, 115(4), E676-E685.

Derrick, B., Green, E., Ritchie, F., & White, P. (2022, September). The Risk of Disclosure When Reporting Commonly Used Univariate Statistics. In International Conference on Privacy in Statistical Databases (pp. 119- 129). Cham: Springer International Publishing.

Fitwi, A., Chen, Y., Zhu, S., Blasch, E., & Chen, G. (2021). Privacy-preserving surveillance as an edge service based on lightweight video protection schemes using face de-identification and window masking. Electronics, 10(3), 236.

Gyorffy B: Discovery and ranking of the most robust prognostic biomarkers in serous ovarian cancer, Geroscience, 2023, doi: 10.1007/s11357-023-00742-4.

Homer, N., Szelinger, S., Redman, M., Duggan, D., Tembe, W., Muehling, J., ... & Craig, D. W. (2008). Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS genetics, 4(8), e1000167.

Lehmann, C. U., Cohen, B. A., & Kim, G. R. (2006). Detection and management of pornography-seeking in an online clinical dermatology atlas. Journal of the American Academy of Dermatology, 54(4), 633-637.

Lippert, C., Sabatini, R., Maher, M. C., Kang, E. Y., Lee, S., Arikan, O., ... & Venter, J. C. (2017). Identification of individuals by trait prediction using whole-genome sequencing data. Proceedings of the National Academy of Sciences, 114(38), 10166-10171.

McKay, F., Williams, B. J., Prestwich, G., Bansal, D., Hallowell, N., & Treanor, D. (2022). The ethical challenges of artificial intelligence‐driven digital pathology. The Journal of Pathology: Clinical Research, 8(3), 209-216.

O'Keefe, C. M., Sparks, R. S., McAullay, D., & Loong, B. (2012). Confidentialising survival analysis output in a remote data access system. Journal of Privacy and Confidentiality, 4(1).

Schwarz CG, Kremers WK, Therneau TM, et al. (2019) Identification of anonymous MRI research participants with face-recognition software. N Engl J Med; 381:1684-6.

Venkatesaramani, R., Malin, B. A., & Vorobeychik, Y. (2021). Re-identification of individuals in genomic datasets using public face images. Science advances, 7(47), eabg3296.

Welpton, Richard (2019). SDC Handbook. figshare. Book. https://doi.org/10.6084/m9.figshare.9958520.v1

Disclosure control in

complex medical outputs

E L I Z A B E T H G R E E N , F E L I X R I T C H I E , J I M S M I T H ,

D A V I D W E S T E R N , P A U L W H I T E

U N I V E R S I T Y O F T H E W E S T O F E N G L A N D

Overview

➢Medical research and positioning

➢ Current practices

➢ Alignment with TRE standards

➢Examples

➢ Solutions?

➢ Future considerations

Medical research

Traditionally:

∙ Revolves around direct informed consent and primary data collection

∙ Some bad examples of mismanagement data and ethics... Henrietta Lacks for example

∙ Easy to see direct benefit/ public good

Sharing medical data and disclosure control

∙ Varied practices from depositing raw data in the public domain to secure access

∙ Tools are being developed- uptake poor

∙ Unforeseen consequences encountered

∙ Benefit to society

Genomic Data

∙ Tissue sample extracted, analysis is then conducted

∙ Microarray data of specimen often deposited/ shared in the public domain (remember knowledge advancing continually)

∙ Descriptive variables provided

Statistical Disclosure Control?

∙ Basic SDC principles- counts and thresholds

∙ Kaplan-Meier curves often result in low numbers, potential for low number of observations between each step.

∙ Not directly disclosive as alone, requires linking to contextual information (provided in the report).

∙ Recommendations exist- O'Keefe et al (2012) smoothing and adding CI. SDAP (2019) thresholds meet.

∙ Online tools- generating a Kaplan-Meier curve with SDC inbuilt

Dermatology photographs

• Online photo repository providing examples of different skin disorders.

• Used as an aid to help experts and public identify different ailments.

• Huge public benefit

• However unintended consequence was found...

Statistical Disclosure Control? Solutions?

FMRI scan • Huge amount of data • Brain anatomy and structure (remember knowledge advancing continually) • Variation in sharing online repositories open access. • Concern for disclosure is based on rebuilding face based on structure

Digital Facial Reconstruction Sorbonne University

FMRI solutions?

Scalp the face! Remove/ roughly the facial attributes - Potential loss of data - Disclosure elsewhere

Statistical Disclosure Control?

∙ SDC training and principles for medical data users!

∙ Unique attributes, highly detailed information, informed consent, understanding risk

∙ Use and implementation of tools and solutions

∙ Universal agreement on standards ∙ What is anonymized data

∙ Evaluation of data access

∙ Development of a network to support this work?

Thank you! Elizabeth Green [email protected]

This work is funded by UK research and Innovation [Grant Number MC_PC_23006] as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK) programme, delivered in partnership with Health Data Research UK (HDR UK) and Administrative Data Research UK (ADR UK).

mailto:[email protected]

Slide 1: Disclosure control in complex medical outputs
Slide 2: Overview
Slide 3: Medical research
Slide 4: Sharing medical data and disclosure control
Slide 5: Genomic Data
Slide 6: Statistical Disclosure Control?
Slide 7: Dermatology photographs
Slide 8: Statistical Disclosure Control? Solutions?
Slide 9: FMRI scan
Slide 10: FMRI solutions?
Slide 11: Statistical Disclosure Control?
Slide 12: Thank you! Elizabeth Green [email protected]

Experiments on Federated Data Synthesis, University of Manchester

Read more about Experiments on Federated Data Synthesis, University of Manchester

Federated Learning, decentralized approach to statistical model training, quality of synthetic microdata, synthetic datasets,

Languages and translations

English

UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE

CONFERENCE OF EUROPEAN STATISTICIANS

Expert meeting on Statistical Data Confidentiality 26–28 September 2023, Wiesbaden

Experiments on Federated Data Synthesis Claire Little, Mark Elliot, Richard Allmendinger (University of Manchester, UK)

[email protected]

Abstract Federated Learning (FL) is a decentralized approach to statistical model training in which training is performed across multiple clients to produce a global model. This approach can be used where multiple sites have data but do not have enough data to generate the required statistical power and cannot for legal, commercial or ethical reasons share their data. One paradigm case is randomized control trials for rare diseases. With FL, training data stays with each local client and is not shared or exchanged with other clients, so the use of FL can reduce privacy and security risks (compared to methods that pool multiple data sources) while addressing data access and heterogeneity problems. This study explores the feasibility of using FL to generate synthetic microdata, allowing multiple organizations to contribute to the construction of combined synthetic datasets (possibly for wider release) without the need to share or distribute their own data. The primary issue is whether it is possible in principle to produce good enough quality synthetic data and the study here focuses on this as a proof of concept before going on to discuss the issue of risk measurement. The results show that the approach is feasible and crucially in the main experiment the synthetic datasets better represented the full population than random samples of that population do. However the experiments are on toy datasets and the next step is to expand the dataset size.

1 Introduction

To enable the safe release of data, Statistical Disclosure Control (SDC) methods (Hundepool et al., 2012) can be applied to remove or alter disclosive information. Data synthesis (Rubin, 1993; Little, 1993) is an alternative to SDC which uses models of the original dataset to generate artificial data with the same structure and statistical properties as the original but (in the case of full synthesis) not containing any of the original data. In this study, we explore the feasibility of federated synthesis, allowing multiple organizations to contribute to the construction of combined synthetic datasets (possibly for wider release) without the need to share or distribute their own data. The primary issue is whether it is possible in principle to produce good enough quality synthetic data and the study here focuses on this as a proof of concept before going on to discuss the issue of risk measurement. The next section will present background information on Data Synthesis and Federated Learning, Section 3 outlines the methodology, Section 4 provides the results of our experiments, Section 5 discusses the results and their implications and final thoughts and ideas for future work an be found in Section 6.

2 Background

2.1 Data Synthesis

Data Synthesis (Rubin, 1993; Little, 1993) is an alternative to SDC and uses models built using the original dataset to generate artificial data with the same structure and statistical properties as the original but (in the case of full synthesis) not containing any of the original data. Synthetic data may be used where access to the original data is not possible or restricted due to privacy constraints. For example, the approval process to acquire access to safeguarded data can be lengthy, potentially delaying research; in these situations synthetic data can allow researchers to test code or plan analysis whilst awaiting access. Synthetic data may also be used to augment (add more records to) existing datasets. There is an increasing number of techniques to generate synthetic data, including statistical methods (such as Nowok et al. (2016); Zhang et al. (2017)), and deep learning (DL) methods based on neural networks (NN) such as Generative Adversarial Networks (GANs) (Goodfellow et al., 2014), variational autoencoders (VAE) (Kingma and Welling, 2014), large language models (Radford et al., 2019), diffusion models (Sohl-Dickstein et al., 2015; Ho et al., 2020), and genetic algorithms (GAs) (Chen et al., 2017, 2018).

2.2 Federated Learning

Federated Learning (FL) (McMahan et al., 2017) is a method that allows multiple clients (or devices) to collaboratively build a shared model without the clients transmitting or exchanging their raw data. In the context of synthetic data, this could allow multiple clients (organisations, users, etc.) to produce a shared synthetic dataset, without the need to share their own individual private data thereby minimising disclosure risk. It could allow the linkage of datasets that would otherwise be unlikely to be linked in the traditional sense, thereby producing opportunities to access unique synthetic data that is potentially more diverse, and richer, than each participant’s synthetic dataset alone. This paper explores the feasibility of using FL together with a GA, to produce a combined synthetic dataset, which as far as we are aware has not been attempted so far. The early focus of FL was its use on mobile and edge devices (e.g. Bonawitz et al. (2016); Konecny et al. (2016)), where an FL model could have many massively distributed clients, each with potentially different computational capabilities, limited communication and unbalanced data. An example of its usage is Google’s Gboard (keyboard) application which trains a model on each mobile device (when it is idle) using the local data and then sends only model updates (parameters) to the server; this allows it to predict the next word when typing, suggest emojis and discover new words (McMahan and Thakurta, 2022). As described by Kairouz

et al. (2021) interest has increased in the use of FL for other (non-mobile) applications, such as allowing cross- organisational collaboration to train models. For example, in healthcare, sensitive data is difficult to access and tightly regulated, making sharing/pooling data (between institutions) prohibitive – FL can allow the creation of more robust models, trained on a larger and more diverse pool of data (than a single institution could provide), without the need to exchange or centralise sensitive medical data (Rieke et al., 2020; Kumar and Singla, 2021). FL has generally been used to produce shared models (such as predictive models) collectively trained on each clients data. A central server controls the process but does not access any of the client data. NN based methods are typically used, where each client receives the current model weights from the central server, trains the model on their own data and then sends the model weights (or parameters) back to the server. All the clients’ weights are then aggregated (typically using the FedAvg, or Federated Averaging algorithm (McMahan et al., 2017)) by the server which updates the global shared model. The model is then sent back to the clients and the process continues until some stopping condition is met. There is a small body of research into the use of FL to generate synthetic data. We use microdata for this study and therefore focus on methods designed for tabular data (i.e. structured data comprising rows and columns containing mixed-type features, such as categorical and numerical). Duan et al. (2023); Fang et al. (2022); Zhao et al. (2021) use GAN-based methods to generate synthetic data, with a GAN training on each client and each sending the model weights to the server to aggregate, etc. (each client generates the final synthetic data individually using the shared model). Weldon et al. (2021) use a GAN on the clients and on the server, but differs in that the server GAN generates the final synthetic dataset. Lomurno et al. (2023) present a different method, using VAE, with each client training a data generator locally. The clients send their models (generators) to the server, but they are not aggregated or combined (as is typical in FL). In the final phase, each client can access the set of generators (from all clients) stored on the server and use some or all of these to generate their own synthetic data. Here we use a GA to generate synthetic data on the server, which is then sent to the clients who each calculate the fitness (utility) score, then send it back to the server where all client scores are combined and used to create the next generation of synthetic datasets. Qu et al. (2020) also generate synthetic data on the server, which is sent to the clients to evaluate, but this employs a GAN-based method, uses image data and focusses on the use-case where clients are temporary (i.e. they may not be available for the whole process).

2.3 Study Aims

In this study, our objective is to assess the feasibility of using a federated learning to generate a combined synthetic dataset. The research questions are as follows.

RQ1: Can a federated synthesis model reproduce the joint distribution of combined distributed datasets? RQ1.1: What information does the server need to be able to reproduce that joint distribution? RQ2: Is the utility of a synthesised combined dataset at least as good as that of the samples held by each client.

3 Methodology

The study is simulation of a server and two clients. The basic simulation scenario is that the sever generates synthetic data, which is then sent to the clients, who each calculate the similarity of the synthetic data to the sample that they hold, then send those similarity scores back to the server where all client scores are combined and used to create the next generation of synthetic datasets. Our machine learning model of choice is genetic algorithms. In Section 3.1, we describe GAs and motivate this choice, then in Section 3.2 we describe the data that we use and how it was set up for the simulation.

Table 1. Simple binary original dataset with ten rows, sampled from UK 1991 Census data, which was split into two five-row datasets, for clients A and B.

AGE MSTATUS SEX LTILL TENURE client

1 2 2 2 2 A 1 1 1 2 2 A 1 1 2 2 2 A 2 2 2 2 1 A 1 1 1 2 1 A 2 2 2 2 1 B 1 2 1 2 1 B 1 1 2 2 1 B 1 1 2 1 2 B 1 1 1 2 1 B

3.1 Genetic Algorithms

Genetic Algorithms (GAs) (Holland, 1992) perform iterative optimisation. There are three main (biologically inspired) operators: selection (parental and environmental), crossover, and mutation. Broadly speaking, an initial population of candidate solutions is specified (in this case, a candidate solution is a synthetic dataset), and the fitness (the utility) of the candidates is calculated. The parental selection operator is used to select candidates (parents) to reproduce for a new population, with fitter candidates more likely to be selected. A crossover operator combines some of the parents (there are a variety of methods for this) to produce new candidate solutions (children). A mutation operator then mutates some of the candidates (i.e. randomly changes some of the features). The children or a combination of children and parents form the population of the next generation (this step is called environmental selection). This process is repeated multiple times (generations), using the fitness to guide it, with ideally fitter solutions produced with each generation. Commonly, the process terminates when a specified number of generations has been produced or a particular fitness level has been reached. GAs are flexible in that there are many parameters that can be changed or set, and the fitness function can be designed for the specific purpose. Work by Chen et al. (2017, 2018) has shown the feasibility of using GAs to generate synthetic microdata, and demonstrated the viability of using risk and utility as conflicting objectives (Chen et al., 2019). More recently, Thogarchety and Das (2023) used a GA approach to produce synthetic data to augment class imbalanced datasets and Liu et al. (2023) presented a GA method that generates synthetic data capable of approximating a range of statistical queries.

3.2 Data

A (very) small binary dataset was used, which was randomly split into two datasets (of equal sizes) to represent two clients named client A and client B. The UK 1991 Census (University of Manchester and ONS, 2013) microdata was used, with 10 rows randomly sampled (from the same geographic area). Table 1 displays the data, with five variables (respectively: age, marital status, sex, long-term illness, and housing tenure) which were all converted to binary (using values of 1 and 2). This is called the original data set. It was randomly split into two five-row datasets, one representing client A and one client B, these are identified in Table 1.

Table 2. Parameters that were fixed in the experiments.

Parameter Type Value chosen Further details

No. of clients Simulation VARIES - Initial Metadata sent by clients Simulation Univariates - Combination of client scores Simulation VARIES - No. of objectives for GA Simulation 1 Similarity (utility) SDC applied to the output sent to server Simulation None - Output passed to client by server Simulation VARIES - Population size Model 50 - Parental selection Model Binary tournament k=2 Mutation rate Model 0.05 - Crossover Operator Model None - Environmental selection Model Elitism - No. of generations Experiment 150 - Choice of Dataset Experiment UK Census microdata 1991 No. of rows (per client) Experiment VARIES - No. of variables Experiment 5 - Type of variables Experiment Binary - No. of runs Experiment 5 -

Table 3. Parameters varied by experiment.

Parameter Value Experiment 1 Experiment 2 Experiment 3

No. of clients 1 2 2 Combination of client scores N/A None Mean Output passed to client by server Synthetic clients Synthetic clients Synthetic combined

dataset dataset dataset No. of rows (per client) 10 5 5

3.3 Method and Parameters

The potential range of variation in the simulation is huge. There are three types of parameters that could be varied in the study design: Model Parameters: These are changeable settings for the GA (e.g., mutation rate) Simulation Parameters: These are variations in the scenario being presented (e.g., number of clients) Experimental Parameters: These are elements of the study design that are not part of the simulation itself

(e.g., number of runs, data choices). A set of these is shown in Table 2. For proof-of-concept experiments we have chosen one value of most of these parameters; a much simpler set than might be used in practice. As well as using a very small sample (of real data), we kept the model complexity low. This simplicity assists us with the interpretation of the results. We have varied four of the parameters across three experiments. These are shown in Table 3. The first two experiments are used to establish a baseline. In experiment 1 we have just a single client. In effect, this tests whether a GA can reproduce the original data when unencumbered by the distributed data. In experiment 2 we split the data across two clients, but the server has a separate interaction with each client and then is deemed to combine the data at the end. This is in effect a minor variation on experiment 1. Experiment 3 is the main experiment, and we now describe what is simulated in more detail.

The experiment 3 simulation is represented graphically in Figure 1. The GA runs on the central server, and at the start of the process (labeled Initialisation, in the figure) each of the clients sends metadata about their individual data to the server. At the most basic, the server would need to know the variable names and the size (how many records) of the data. It is expected that the clients will agree in advance on the variables to be included. For this experiment, the clients send the univariate distributions (this information is used by the GA to mutate the data) and the number of records in each dataset.

Figure 1. An illustration of the federated synthesis simulation used for Experiment 3, with a server and two clients.

The server then combines the distributions of each client by taking the average to calculate a combined distribution. An initial population of synthetic datasets (candidates) is generated; these are drawn from the

uniform distributions of the five variables. The datasets in the initial population have the same number of records as both combined client data would have and the same variables. For this simple model, only one objective is assessed by the client, which is the similarity of the multivariate distribution of the clients data and each of the synthetic datasets passed by the server to the client. They then send those similarity scores back to the server. In detail the similarity measure calculates the proportion of every combination of values in the synthetic (candidates) and client data, then takes the mean of the absolute differences. This is then subtracted from 1 so that the similarity score takes a value between zero and one (where 0 indicates no similarity and 1 indicates an identical distribution). Once the server receives each of the client scores, it calculates the mean to produce an overall score for each synthetic dataset. In experiment 3 the server simply averages the clients scores, but they could be combined in other ways (e.g. using the lowest or the highest, or weighted by how similar each of the clients scores are to each other). This completes the initialisation phase. The main repeating process involves selection and mutation, but not crossover; this was excluded to reduce complexity. Firstly, parents are selected from the population using tournament selection (two synthetic datasets are randomly selected and the one with the highest similarity score wins). Two parents produce two children (i.e., two datasets), which are simply copies of themselves (where crossover is used, the children may be a combination of the parents), and the same amount of children are produced as the population size. Then, each child is mutated with a probability equal to the mutation rate (0.05), with the replacement value being drawn from the relevant univariate distribution. The children are then sent to the clients, who score the similarity and send the scores back to the server. These scores are then aggregated by the server. Finally elitism is used to select the next generation, that is the best (those with highest similarity) of the parents and the children are retained for the next generation (e.g. if a parent has higher similarity than the child, the parent is retained). This process is repeated for a set number of generations.

4 Results

Each experiment was repeated five times (using different random seeds). The plots in Figures 2-4 give the mean similarity score across the population for each of the generations for which the GA was run. Figure 2 shows the results for experiment 1. For all runs the GA converged (that is, the synthetic datasets reproduced the original). The goal would not generally be to reproduce the original dataset, but this demonstrates that the GA works (albeit on a very small dataset). In experiment 2, the GA was run separately on both clients five-row datasets, with the results plotted in Figure 3. Each of the five runs converged to one (that is, all runs reproduced the clients data), and so each of the clients resulting data could be combined to reproduce the original dataset. The results for experiment 3 (as described in Figure 1) are illustrated in Figure 4. Panels 1 and 2 illustrate the scores calculated by clients A and B, these individual scores are sent to the server which aggregates them, as displayed in panel 3. The aggregated score is what drives the GA (and clients A and B do not see each other’s scores, they only communicate with the server). The plot highlights that the synthetic datasets generated in run 3.3 scored highly with client A, but scored poorly with client B, however, when aggregated by the server, all five of the runs look remarkably similar. Run 3.3 is interesting as until about generation 20 the gradient is very similar on both clients to the other runs but around that generation a bifurcation happens. This appears to be the result of the process falling into a local optimum in which client A’s dataset was optimised at the expense of client B’s. This was the result of some mutation around generation 20 (subsequent test runs with the same starting seed failed to reproduce this result). Panel 4 shows the similarity scores of the synthetic datasets produced at each generation against the real combined data – by definition this would not be possible in a real-life scenario since the original data would not be available, but this is calculated post hoc to evaluate how the overall model is working (i.e., we are more interested in whether the server is reproducing the overall dataset than whether it replicates individual client

Figure 2. Experiment 1, the mean (n=50) similarity of five randomly initialised runs of the GA on the original ten-row dataset. Note the truncated y axis.

Figure 3. Experiment 2, the mean (n=50) similarity of five randomly initialised runs of the GA on the five-row datasets of client A and client B. Note the truncated y axis.

distributions). Panel 4 shows that for all but run 3.3 the model converges on the original data, that is, each run reproduces the original dataset. This is a particularly fascinating finding as it has done this despite the evaluations from the clients indicating sub-optimality. The baseline is included to indicate the combined client to server data similarity. Panel 3 (Figure 4) illustrates that (at least in this example) it may be difficult for the server to determine how well the overall model is performing. Other methods of combining the client scores as variations on experiment 3 were also considered (minimum, weighted, and alternating). The results are shown in the Appendices.

Figure 4. Experiment 3, the mean (n=50) similarity scores of five randomly initialised runs of the server GA, showing client A (1), client B (2), the server aggregated scores (3) and the comparison against the original data (4). Note the truncated y axes.

5 Discussion

The results of experiment 3 demonstrates our proof of concept. 4 out of 5 of the runs reproduced the original data. Fascinatingly they did this despite the mean evaluations scores from the client indicating that the operation had not achieved unity. This however was simple a reflection of the clients own sample not fully representing the combined datasets structure. Thus the synthetic datasets were a better representation of the ’real’ combined dataset than the ’samples’ held by each client. This emergent reproducibility shows how the approach could deliver the desired outcome to produce analytically useful datasets synthesised across distributed datasets. The experiments reported here focused on the single objective of utility, and in this case the goal was to reproduce the original data. In a real-life scenario, there would also be a consideration of risk – reproducing

the original data would not be desirable. A way to incorporate risk would be to use a multi-objective approach within the GA, and explore options such as Pareto optimality. The flexibility of GAs means that different utility and risk measures could be easily added. Another angle would be using deep learning methods (such as GANs and diffusion models) and adapting them to multi-objective optimisation (GANs are already used widely within FL). The fact that in our experiments it was not clear on the server that the original data had been reproduced might be thought of as useful, in terms of disclosure risk, but it would also mean that in this mechanism we could not rely on severe side restraint to manage risk. An obvious important expansion of these experiments is to test the method on larger and more complex datasets. Firstly, we need to establish if the emergent reproducibility effect scales. Also, for very large datasets, it simply may not be practical to send the entire population of datasets at each generation, and so alternatives may need to be explored. Another important expansion is to examine the effect of having more than two clients. The flexibility of the method also means that there are many parameters that can be experimented with.

6 Conclusion

The purpose of this study was as a proof of concept to determine whether using FL together with a GA to produce synthetic data was feasible. We have shown that it is feasible, albeit with a very small dataset, and with the focus being only synthetic data utility. The results are promising and there are many areas of future work including testing this on larger, more complex datasets, using a multiobjective approach that incorporates risk, and experimenting more generally with the various parameters.

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A Using the worst client scores to drive the GA, rather than averaging

Figure 5. Mean (n=50) similarity scores of five randomly initialised runs of the server GA, where only the worst (lowest) client score is used to drive the GA (rather than averaging both client scores). Showing client A (1), client B (2), the worst scores (3) and the comparison against the original data (4). Note the truncated y axes.

B Using weighted averaged score to drive the GA

Figure 6. Mean (n=50) similarity scores of five randomly initialised runs of the server GA, where a weighted averaged score is used to drive the GA. Where the client scores are close (the clients agree) the scores are weighted higher, where they are far apart (the clients disagree) the scores are weighted lower. Showing client A (1), client B (2), the server weighted averaged scores (3) and the comparison against the original data (4). Note the truncated y axes.

C Alternating the client scores to drive the GA, rather than averaging

Figure 7. Mean (n=50) similarity scores of five randomly initialised runs of the server GA, where the alternating client score is used to drive the GA (five generations using client A, five using client B, etc.). Showing client A (1), client B (2), the server mean alternating scores (3) and the comparison against the original data (4). Note the truncated y axes.

1. Introduction
2. Background

2.1. Data Synthesis
2.2. Federated Learning
2.3. Study Aims

3. Methodology

3.1. Genetic Algorithms
3.2. Data
3.3. Method and Parameters

4. Results
5. Discussion
6. Conclusion
References
A. Using the worst client scores to drive the GA, rather than averaging
B. Using weighted averaged score to drive the GA
C. Alternating the client scores to drive the GA, rather than averaging

Experiments on Federated Data Synthesis

C L A I R E L I T T L E , M A R K E L L I OT, R I C H A R D A L L M E N D I N G E R

U N I V E RS I T Y O F M A N C H E S T E R

Questions? https://tinyurl.com/QuestionsUoM

https://tinyurl.com/QuestionsUoM

Federated Learning (FL) FL (McMahan et al., 2017) is a decentralized approach to training statistical models • Multiple clients can produce one global model

• Clients do not share or exchange their own data

• Can reduce privacy and security risks (compared to methods that combine multiple data sources)

• Allows models to train on data that is more representative of the whole distribution

• Useful where clients do not possess enough data to generate the required statistical power

Federated Learning (FL) Central server controls the process (but does not access any client data) • Initialises model, sends to each client • Typically, neural network type models are used

Each client trains the model on their own data • Send updates (parameters or model weights) back to server

Server aggregates the client updates • Sends updated model back to clients

Iterative process • Training usually terminated when specific criterion is met: • E.g., maximum number of iterations

NVIDIA - A centralized-server approach to federated learning. https://blogs.nvidia.com/blog/2019/10/13/what-is-federated-learning/

Federated Synthesis Using FL to generate synthetic data • Emerging research field

• Small body of research focussing mostly on image data

• Less research on tabular data

• Methods predominantly use GANs (Generative Adversarial Networks, Goodfellow et al. 2014))

Is it possible to produce useful synthetic microdata in a federated way? • Proof of concept using Genetic Algorithm (GA)

Genetic Algorithms (GAs) GAs (Holland, 1992) perform iterative optimisation, training over multiple generations • Three main biologically inspired operators: • Selection, Crossover, Mutation

➢ Initial population of candidate solutions (candidate solution = synthetic dataset) ➢ Fitness (similarity to original data) of each candidate calculated ➢ Select fitter candidates (parents) to reproduce for new population ➢ Crossover – combines parents to produce new candidates (children) ➢ Mutation – randomly change some of the candidates features ➢ Next generation – children, or combination of best (fittest) parents and children

(elitism) ➢ Repeat process multiple times (generations) using fitness to guide

Study Design - Data A (very) simple binary dataset, randomly sampled from UK 1991 Census microdata (University of Manchester, 2023) • Small dataset to enable understanding

• 10 rows, 5 binary variables

• “Original” dataset

• Randomly split into two five-row datasets

• representing two clients (A and B)

AGE MSTATUS SEX LTILL TENURE client

1 2 2 2 2 A

1 1 1 2 2 A

2 2 2 2 1 A

1 1 1 2 1 A

2 2 2 2 1 B

1 2 2 2 1 B

1 1 1 2 1 B

1 1 1 1 2 B

1 1 1 2 1 B

Study Design - Parameters Huge potential range of variation in the simulation Three types of parameters: • Model: changeable settings for the GA (e.g., mutation rate)

• Simulation: variations in the scenario being presented (e.g., number of clients)

• Experimental: elements that are not part of the simulation itself (e.g., data choice, number of runs)

Model complexity is kept low to aid with interpreting the results

• Focus only on utility (not risk)

• Small dataset

• GA uses mutation but not crossover

• Two clients for FL

Study Design - Parameters

Results – Experiment 1 Running GA on original dataset (10 rows) • All five randomly initialised

runs converged • i.e., they reproduced the original

dataset

Results – Experiment 2 Running GA separately on client A and B datasets (5 rows each) • For each, all five randomly initialised runs converged and reproduced the original dataset

Results Experiment 3 FL with two clients (A and B) • All but one of the randomly

initialised runs converged and reproduced the original datasets

• Panel 4 would not be available in reality – used for evaluation

• Convergence achieved despite the evaluations from clients, and the server aggregated score indicating suboptimality

Discussion Experiment 3 demonstrates proof of concept • Analytically useful datasets were synthesised across distributed datasets

It was not clear on the server that the original data had been reproduced • Might be useful in terms of disclosure risk

• Means we cannot rely on server-side restraint to minimise risk

Caveats and future work Experiments conducted on small sample of binary Census microdata • May not scale to larger, more complex data

• Very large datasets may be computationally impractical

Would need to consider different parameters • More than 2 clients

Single-objective focus on utility • In a real-life scenario, the goal would not be to reproduce the original data

• Risk would need to be factored in

◦ A multi-objective approach within the GA could be used

◦ Deep learning methods also a possibility

Questions? https://tinyurl.com/QuestionsUoM

Email: [email protected]

https://tinyurl.com/QuestionsUoM

References McMahan, B., E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas (2017). Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, pp. 1273–1282. PMLR. http://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf

Goodfellow, I., J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio (2014). Generative Adversarial Nets. In Proceedings of the Advances in Neural Information Processing Systems, Volume 27. https://papers.nips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf

Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.

University of Manchester, Cathie Marsh Centre for Census and Survey Research, Office for National Statistics, Census Division. (2023). Census 1991: Individual Sample of Anonymised Records for Great Britain (SARs). [data collection]. UK Data Service. SN: 7210, DOI: http://doi.org/10.5255/UKDA-SN-7210-1

http://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf

https://papers.nips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf

http://doi.org/10.5255/UKDA-SN-7210-1

Slide 1: Experiments on Federated Data Synthesis
Slide 2: Questions?
Slide 3: Federated Learning (FL)
Slide 4: Federated Learning (FL)
Slide 5
Slide 6: Federated Synthesis
Slide 7: Genetic Algorithms (GAs)
Slide 8: Study Design - Data
Slide 9: Study Design - Parameters
Slide 10: Study Design - Parameters
Slide 11
Slide 12: Results – Experiment 1
Slide 13: Results – Experiment 2
Slide 14: Results Experiment 3
Slide 15: Discussion
Slide 16: Caveats and future work
Slide 17: Questions?
Slide 18: References

SACRO: semi-automated output checking, University of the West of England

Read more about SACRO: semi-automated output checking, University of the West of England

output checking, confidential data, automate checking, analytical languages, secure environments,

Languages and translations

English

UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE

CONFERENCE OF EUROPEAN STATISTICIANS

Expert meeting on Statistical Data Confidentiality 26–28 September 2023, Wiesbaden

SACRO: Semi-Automated Checking Of Research Outputs Jim Smith1, Richard Preen1, Maha Albashir1,Felix Ritchie1, Elizabeth Green1,Simon Davy2 Pete Stokes2, Sebastian Bacon2 1: University of the West of England, UK, 2: Bennett Institute, University of Oxford

[email protected]

Abstract Output checking can require significant resources, acting as a barrier to scaling up the research use of confidential data. We report on a project, SACRO, that is developing a general-purpose, semi-automatic output checking systems that works across the range of restricted research environments. SACRO is designed to

• Automate checking of most common statistics, using best-practice principles-based modelling. • Support researchers using the major analytical languages (R, Python and Stata), with minimal changes,

by exploiting the ‘wrapper’ approach successfully trialled already. • Support secure environments with different operating models and output checking workflows, through a

process of co-design to maximise useability. SACRO builds on previous work: (ACRO, funded by Eurostat and reported in in the 2021 Workshop) to establish the proof-of-concept; and Py-ACRO which showed how a software-independent tool might be developed. It differs from those earlier projects in terms of a wider range of statistics covered, and a requirement to achieve general applicability. To do this, the project draws on our extensive networks of practitioners. A series of workshops and ‘hands-on’ evaluations ensure the design frameworks support buy-in from a wide range of prospective users across health and social sciences, and from the public and private sectors.

1 Introduction

Statistical agencies and other custodians of secure facilities such as Trusted Research Environments (TREs) Hub- bard et al. (2020) provide researchers with access to confidential data under the ‘Five-Safes’ framework Ritchie (2017). This enforces five orthogonal layers of safety procedures, and the last requires explicit checking of research outputs for disclosure risk. This can be a time-consuming and costly task, requiring skilled staff. This paper discusses the development of an open source tool for automating the statistical disclosure control (SDC) of routine research outputs. The goal is to make the clearance process more efficient and timely, and to allow the skilled checkers to focus their attention on the less straightforward cases. The purpose of the tool, (SACRO, for Semi-Automated Checking of Research Outputs) is to assist researchers and output checkers by distinguishing between research output that is safe to publish, output that requires further analysis, and output that cannot be published because of substantial disclosure risk. This work builds upon a previous Eurostat-funded project Green et al. (2020, 2021) in which Green, Ritchie and Smith developed a proof-of-concept prototype for the proprietary Stata software.The primary new contributions reported in this paper are:

• The implementation of a Python toolkit. • An extensible multi-language platform with interfaces familiar to users of popular statistical tools. • ‘Skins’ in Stata and the language R, demonstrating cross-language support. • An open source repository with examples, help, documentation, etc.

2 Background

The Five Safes framework Ritchie (2017) is a set of principles that enable services to provide safe research access to their data and has been adopted by a range of TREs, including the Office for National Statistics (ONS), Health Data Research-UK (HDR-UK), and the National Institute for Health Research Design Service (NIHR), as well as many others worldwide. Ensuring the last of these, ‘safe outputs’ is a complex and often costly human labour-intensive process. Auto- mated output checking aims to improve the rigour and consistency of the output disclosure control process and reduce human workload by automatically identifying, reporting, and (optionally) suppressing disclosive outputs where possible and categorising outputs as ‘safe’ or ‘unsafe’. ‘Safe’ outputs requiring no or minimal further changes can be expedited through the clearing process whereas ‘unsafe’ outputs can be prioritised for human review Ritchie (2008). A small number of SDC tools have been produced to assist in the process of achieving ‘safe outputs’, such as tauArgus and sdcTable1, however these are primarily designed for users such as National Statistic Institutes as they require expert knowledge of SDC to use effectively. Moreover, they are designed for tabular outputs, and do not cover the range of statistics produced by researchers With the aim of improving the efficiency of the process, and (where applicable) reducing the amount of user training required, a recent Eurostat project Green et al. (2021) developed a proof-of-concept prototype in Stata where primary disclosure is regulated by a set of simple rules. For example, a minimum threshold rule applied to the number of observations used by a statistic ensures that there is sufficient uncertainty with respect to any individual respondent. Dominance rules protect large respondent values from being approximated where the contribution to a statistic is dominated by only a few individuals. For example, the p%-rule sorts the N observations by magnitude and checks whether the sum of the smallest N − 3 observations is at least p% of the largest observation. The NK rule checks that the largest N observations contribute less than K% of the total. Also, not all aggregation statistics are permitted: reporting minima or maxima values of a subgroup are prohibited, and regressions are protected by checking that the Residual degrees-of-freedom exceeds a minimum threshold.

1Respectively, https://github.com/sdcTools/tauargus and https://github.com/sdcTools/sdcTable 2

https://github.com/sdcTools/tauargus

https://github.com/sdcTools/sdcTable

Building on the experience of the initial proof-of concept, funding was secured from the UK Research Council’s DARE initiative2 for the project: Semi Automated Checking of Researcher Outputs (SACRO) which involves:

• Computer scientists with backgrounds ranging from AI research to commercial software development. • A range of TREs as co-designers of a toolset. • SDC theorists and statisticians to provide a conceptual framework for handling different types of output

and providing guidance to researchers and output checkers. • Public Involvement and Engagement specialists and groups to develop a consensus statement around the

use of (semi)-automation in disclosure control • Researchers from a previous DARE project examining the output checking of machine learning models

trained on sensitive data within a TRE Jefferson et al. (2022). In this paper we report on the principal tools developed within the SACRO project, specifically:

1. A toolkit for researchers to use within TREs that produces automated reports on disclosure risk with minimal changes to their practice - simply prefixing common commands with the word ‘acro’.

2. Explicit support for researchers to reduce the number of disclosive outputs they request. 3. Cross-language support: with exemplar interfaces provided for Stata and R. 4. Support for the output types that our TRE partners tell us form the majority of requested releases. 5. A stand-alone viewer for TRE output staff to facilitate rapid, informed, and audited, decision making. 6. A revised guide incorporating theoretical developments, directly linked to its implementation in SACRO.

3 The SACRO toolkit

SACRO is composed of three parts which may be deployed independently: the main ‘ACRO-engine’, a stand- alone viewer, and ‘AI-SDC’ - support for disclosure control of machine learning models (described elsewhere).

3.1 Design Philosophy

The operational design philosophy is extensively documented in Green et al. (2020), who studied the character- istics that an automated solution needs to have to be feasible, effective, and a positive choice for users. Essential criteria are that it should be:

• Acceptable to users, output checkers and TRE managers; • Able to implement an organisation’s business rules for primary and secondary disclosure, which may

vary across datasets or users; • Comprehensive, even if the automated tool’s response is “I don’t know so this needs manual checking"; • Consistent, providing the same results across different studies within a TRE, and across TREs; • Able to support exceptions under principles-based regimes; • Scalable over users and outputs.

Key operational requirements were for the tool to work in different technical environments, and to be easily updated through well understood mechanisms. This meant separating the software itself (distributed through a recognised channel3, from the specification of a given TRE’s risk appetite (held in a human and machine readable and editable file). Acceptability to users was identified as the most crucial element. If researchers and output checkers see the tool as something that makes their life better and easier, then they are more likely to use it effectively. Hence, designing the user interface was identified as a separate workstream in SACRO, and given the same resources as the design and implementation of the output-checking component. This is also one reason why SACRO set up a large network of potential users and tests (see Sec. 6 below).

2https://dareuk.org.uk/

3for example, PyPi (https://pypi.org or CRAN (https://cran.r-project.org) 3

https://dareuk.org.uk/

https://pypi.org

https://cran.r-project.org

Researcher

TRE Staff

Light-Weight Translation Functions

Python

Stata

. . .

Excel spreadsheet or JSON file with details and recommenda- tions for each requested output

TRE-specific file detailing risk appetite

Disclosure Control Checks

(Python) Tests:

- threshold - dominance

- degrees of freedom

Applies: - cell sup- pression - others

Standard Python

Libraries:

Pandas for tables

statsmodels for regression

Analysis commands prefixed by acro

SDC output

Approve/Discuss/Reject

Reads

Finalise

Figure 1. Schematic illustration of ACRO.

The ‘proof-of-concept’ version of ACRO did not address secondary disclosure (such as checking for differencing across tables), for two reasons. First, business rules for secondary checking are often not clear or comprehensive. Second, ACRO/SACRO works by intercepting commands and assessing disclosure risk at the time the output is being produced. Analysing results post-hoc is a considerably harder problem, requiring the researcher to produce a lot more information and also locate the other outputs to be compared. Although SACRO does not currently (as of July 2023) carry out secondary disclosure review, we are investigating how to at least flag potential differencing risks across the set of outputs from a research ‘session’, and in future, create a library of outputs which might allow secondary disclosure to be assessed, even if only partially.

3.2 Workflow

ACRO Preen et al. (2023) is an open source toolkit (MIT License) that provides a light-weight ‘skin’ that sits over well-known analysis tools, in a variety of languages researchers might use. The process is illustrated in Fig. 1. This adds functionality to identify potentially disclosive outputs against a range of commonly used disclosure tests and report to researchers and TREs reasons why outputs should not be released ‘as-is’. It creates simple summary documents TRE staff can use to streamline their workflow. ACRO has been designed with the following aims:

• Reducing barriers to adoption via a front-end application programming interface (API) that is similar to those already commonly used by researchers in their favoured language.

• Providing researchers with: immediate feedback on the results of disclosure checks (on-screen alongside their query results); facilities to add comments or exception requests, and control over what is submitted for review, e.g., removing disclosive outputs if they use feedback to design non-disclosive ones.

• Having a single back-end code base constituting a single source of truth for performing checks, with extensibility for different languages and ongoing support and consistency.

• Providing easy to understand help and documentation. In practice, researchers prepare their data and statistical queries in the usual way, in their preferred language, using common commands prefixed by ‘acro’. The lightweight ACRO translation functions then call the Python back-end, which executes the queries and performs the requisite output checks. The results of the checks, and the queries are immediately displayed to the researcher, and full details are stored in a list. When the user calls acro.finalise() to end their session, outputs and all SDC details are saved to file for review by a TRE output checker. A schematic illustration of the ACRO workflow is shown in Figure 1 and some notebooks demonstrating example code usage and output are available via the ACRO project wiki4.

4https://github.com/AI-SDC/ACRO/wiki

https://github.com/AI-SDC/ACRO/wiki

3.3 Checks Implemented

For tabular data (e.g., cross tabulation and pivot tables), we prohibit the reporting of the maximum or minimum value in any cell that represents a sub-group of one or more contributors. Moreover, we suppress, and report the reason, the value of the aggregation statistic (mean, median, variance, etc.) for any cell deemed to be sensitive. ACRO currently supports the three most common tests for sensitivity: ensuring the number of contributors is above a frequency threshold, and testing for dominance via p% and NK rules. ACRO builds a series of suppression masks, which indicate which cells are to be suppressed for each check. A summary outcome table indicating which suppression rule was applied to each cell is presented to the researcher (thre grey box in Fig. 2, alongside the query results. For regressions, e.g., linear, probit and logit regression, the tests verify the number of degrees of freedom exceeds a threshold. Immediate feedback on all these checks is designed to support researchers to improve their practice and so reduce the SDC bottleneck by making fewer disclosive requests The checking of graphical plots is not currently implemented, as this is a complex problem with many different methods for producing visualisations. However, we expect to have some support by Autumn 2023. As noted above, all of these tests and checks are configurable according to the TRE’s risk appetite. The data custodian, e.g., TRE staff member, specifies the parameter values used for the output checks in a YAML5

configuration file, which is loaded upon ACRO initialisation. The default ACRO parameters are shown in Table 1. Future releases will offer the option to over-ride these on a dataset, or even attribute level.

Table 1. ACRO Default Parameters for sensitivity tests

Description Parameter Value Min frequency threshold for tabular data safe_threshold 10.0 Min degrees-of-freedom for analytical stats safe_dof_threshold 10.0 N parameter in NK test safe_nk_n 2.0 K parameter in NK test safe_nk_k 0.9 Min ratio for p% test safe_pratio_p 0.1

3.4 The SACRO Python ‘Engine’

Python is a popular multi-platform language widely used for data analysis and machine learning. PyPI provides a simple package management system for distributing open source Python libraries. Pandas and Statsmodels6 are industry-standard, mature, popular, and well-supported python packages for data analysis, statistical testing, and statistical data exploration. Pandas is currently used by more than 55% of all Python users Python Software Foundation (2021) and there are many web-sites and user groups providing help with formulating queries. The use of Python as the primary implementation therefore enables the leveraging of existing expertise and community support with these packages so that the ACRO front-end can be as similar to the API researchers already know and trust, and further facilitates the rapid development of disclosure checking functionality on the back-end. As the PyPI distribution system is simple and allows the use of semantic versioning, it supports a rapid and iterative develop-and-deploy strategy to provide continuing functionality and improvements. For example, the current version of ACRO may be installed [or updated] as simply as: p i p i n s t a l l [−−upgrade ] a c r o The currently implemented methods are listed below, split into analysis commands, and sessions management commands. For more details see the ACRO project documentation7.

5https://yaml.org

6https://github.com/pandas-dev/pandas and https://www.statsmodels.org/stable/index.html respectively

7https://ai-sdc.github.io/ACRO/

https://yaml.org

https://github.com/pandas-dev/pandas

https://www.statsmodels.org/stable/index.html

https://ai-sdc.github.io/ACRO/

3.4.1 Analysis commands for Researchers. These are implemented via the use of multiple inheritance from Pandas and Statsmodels. For making tables, the relevant methods are:

: crosstab(index, columns[, values, rownames, . . . ]) Compute a simple cross tabulation of two (or more) factors, with options for hierarchies in rows/columns and multiple aggreagation functions. Same API as pandas.crosstab.

: pivot_table(data[, values, index, columns, . . . ]) Create a spreadsheet-style pivot table as a DataFrame. Same API as pandas.pivot_table.

and for regression analysis:

: logit(endog, exog[, missing, check_rank]) Fits Logit model. Same API as statsmodels.discrete.discrete_model.Logit.

: logitr(formula, data[, subset, drop_cols]) Fits Logit model from an R-style formula and DataFrame. Same API as statsmodels.formula.api.logit.

: ols(endog[, exog, missing, hasconst]) Fits Ordinary Least Squares Regression. Same API as statsmodels.regression.linear_model.OLS.

: olsr(formula, data[, subset, drop_cols]) Fits Ordinary Least Squares Regression from an R-style formula and DataFrame. Same API as statsmodels.formula.api.ols.

: probit(endog, exog[, missing, check_rank]) Fits Probit model. Same API as statsmodels.discrete.discrete_model.Probit.

: probitr(formula, data[, subset, drop_cols]) Fits Probit model from an R-style formula and DataFrame. Same API as statsmodels.formula.api.probit.

3.4.2 Session Management Commands.

: ACRO()(config,suppress) Creates an ACRO session object with optional parameters for a config (risk appetite) filename and whether disclosive tables should have suppression applied (default False).

: print_outputs() Prints the current results dictionary - i.e., the outputs that would be sent for checking.

: remove_output(key) Removes an output from the results dictionary.

: rename_output(key, newname=) Assigns a new (ideally more self-explanatory) name to an output from the results dictionary.

: add_comments(key,text) Allows researcher to add a description for an output

: add_exception(key,text) Allows a user to request and justify an exception to strict rules-based checking.

: custom_output(filename,description) Adds a file containing output from unsupported analysis to an ACRO session for inclusion in outputs shown in viewer.

: finalise(directory_name, format) Creates a results file for checking in the desired format(json or xlsx).

» safe_table = acro.crosstab( df.recommend, df.parents, values=df.children, aggfunc="mean")

» print(safe_table)

INFO:get_summary:fail; threshold: 4 cells may need suppressing

INFO:outcome_df: parents great_pret pretentious usual recommend not_recom ok ok ok priority ok ok ok recommend threshold threshold threshold spec_prior ok ok ok very_recommend threshold ok ok

INFO:acro:add(): output_1

grant_type great_pret pretentious usual recommend not_recom 1440 1440 1440 priority 858 1484 1924 recommend 0 0 0 spec_prior 2022 1264 758 very_recom 0 132 196

Figure 2. Example ACRO query for the ‘nursery’ data(top), with immediate disclosure control reporting (middle, grey background - pink onscreen) followed output (bottom). This ’researcher- view’ corresponds to the top image in the viewer screenshots

An example ACRO query run on the nursery admission dataset8 and its output is shown in Fig. 2. This is the ‘researchers-view’ of the output at run-time. The corresponding ‘TRE-view’ is shown in the top screenshot in Fig. 3. This example does not have an aggregation function so dominance rules are not applied, otherwise they would also show in the ‘INFO’ section of the report in any relevant cells. Note that if the user starts their session with acro= ACRO(suppress=True) then any disclosive cells would have their values set to NaN

3.5 The R interface to ACRO

The R front-end is an example of cross-language support. It provides a set of wrapper functions that execute Python back-end checking via the reticulate9 package, which provides automatic conversations for many types, e.g., R data frame to Pandas DataFrame. A session is created when the acro package is calledsource("../acro.R") and thereafter the acro methods work as callable functions with the prefix acro_ e.g., acro_rename_output(output5,"xy-plot") etc., and to end a session the user calls acro_finalise(results_dir,“json") For regressions, the common R lm() and glm() functions were shadowed with equivalent versions imple- mented as acro_lm() and acro_glm(), respectively. For tabular data, the dplyr10 package is commonly used within R, however no simple cross tabulation or pivot table functions are provided; instead various combinations of groupby() and summarize() etc. are used. Therefore, at this stage of development, the Python cross tabu- lation and pivot table functions were directly interfaced with acro_crosstab() and acro_pivot_table().

8https://www.openml.org/search?type=data&sort=runs&id=1568&status=active

9https://github.com/rstudio/reticulate

10https://github.com/tidyverse/dplyr

https://www.openml.org/search?type=data&sort=runs&id=1568&status=active

https://github.com/rstudio/reticulate

https://github.com/tidyverse/dplyr

3.6 Stata Interface

This makes extensive use of Stata’s SFIToolkit library to manage a python session, transfer data in memory from stata to a Pandas dataframe in the python session, and results back to the Stata window. A simple acro.ado file defines a new functionacrowhich takes as parameters either one of the ACRO session management methods (adding init() to start a session) or the name of a standard Stata function such as table, regress etc. Stata’s inbuilt parsing functions are used to separate out the parts of command and pass them as lists to a python function parse_and_run() which handles the rest of the translation between the two languages.

4 SACRO Viewer for Output Checking

We have also created an open-source platform-independent stand-alone viewer for output checkers to use to: view outputs and their risks; make decisions with reasons (all recorded for auditing purposes); and produce zipped packages of files for release Open-Safely (2023). Figure 3 illustrates two screenshots from the version currently (July 2023) being evaluated by TREs. The viewer supports and renders a range of different file types for results from unsupported queries. A separate script lets TRE staff create an ACRO session from a set of output files in a directory, and hence use the viewer for making and recording decisions, even if the researcher has not used ACRO during their analysis. Automated disclosure risk analysis is not provided in those cases.

5 Linking theory and implementation

As part of the project, the SACRO team committed to review and re-develop the theory and operational guidelines for output SDC. The aim was threefold; first, to bring together key points from the OSDC literature (and fill in some of the theoretical gaps) to provide an integrated guide to both theory and practice of output checking; second, to develop a new approach to OSDC based on classifications into groups (see Derrick et al. (2023), for details); third, to explicitly link theory to operational rules and their implementation in manual and automatic checking regimes. The third aim is essential to demonstrating that SACRO is not seen as a ‘black box’ implementing its own rules, but is fully integrated into core theory. It is also important for showing how manual and automatic output checking necessarily differs. For example, dominance checks are almost impossible for a human, but straightforward for computers; on the other hand, computers cannot easily identify whether zero cells in tables are structural or disclosive, but humans can. The purpose of the guide is to show precisely what checks have been made, where differences occur between humans and computers, and why they are necessary.

6 Engagement with TREs

One of the lessons learned from the original Stata version of ACRO Green et al. (2021) was the importance of user buy-in. Although that version met its design goals (and has subsequently been adopted by Eurostat in its TRE), reaction to it was a mixture of “this looks useful, I’ll give a go", “this looks useful, I’ll wait to see it installed before I commit myself", and “I’ve read the installation manual and have no idea what’s going on, so it’s a no". As a result,that version of ACRO has remained largely within the project remit: a demonstration of possibilities. The SACRO project was intended to involve co-design from the outset to take ACRO to the next stage, of general utility and application. This involved three tests:

1. Would a new tool be acceptable to users? 2. Would a new tool be acceptable to output checkers? 3. Could a new tool be installed in secure research environments?

Figure 3. Two screenshots of viewer. The left hand column shows list of files requested. In top image, colouring of file names suggests which files require special attention. In lower image background colour-coding and tick/cross symbols show decisions made by output checker. Top image shows checker viewing table that fails disclosure tests, with problematic cells highlighted in red. Bottom shows acceptable table. Also in this image the top right hand panel shows option to view TRE ’risk appetite’ expanded.

The SACRO project took two approaches. First, six TREs (OpenSafely at the University of Oxford, and the five Scottish Safe Havens) were funded as co-investigators on the project to provide detailed feedback on user and output checker perspectives (OpenSafely also took the lead in the design of the user interface). This group also directly tested the feasibility of installing and allowing the Python code to run on their systems as TREs differ in their perceptions of python’s ‘riskiness’. Second, the SACRO team contacted a large number of TREs in the UK and abroad, and set up a network of interested parties potentially willing to be testers. Several engagement events with this group identified how they worked and what they would expect from an automatic solution. At the time of writing (July 2023), the first ‘external’ TRE’s are starting to install and run the tool with genuine users. SACRO has a workpackage dedicated to helping TREs set up their systems, and then collecting evaluation feedback. This aims to make sure that the tool is tested in as wide a variety of environments as possible, given the time constrain. A secondary aim is to involve TREs in the development, to build a sense of ownership and lay the foundations for widespread adoption. This helps to address the concerns of ‘wait-and-see’ TREs.

7 Future Plans

By the current project end in October 2023 we aim to have added support for: more common types of analyses (including simple plots); different versions of Stata; and more ways of creating tables within R. Additional features and improved user experience will be facilitated by the involvement of end-users and output checkers. Beyond then, UWE has committed to web hosting various resources for the indefinite future, and partners have agreed to continue support and development of the toolkits. We are keen to engage with any interested parties to enrich and build an on-going community of support for SACRO.

References

Derrick, B., E. Green, F. Ritchie, J. Smith, and P. White (2023). Towards a comprehensive theory and practice of output SDC. In UNECE/Eurostat Workshop on Statistical Data Confidentiality.

Green, E., F. Ritchie, and J. Smith (2020). Understanding output checking. Technical report, European Commission (Eurostat - Methodology Directorate).

Green, E., F. Ritchie, and J. Smith (2021, October). Automatic checking of research outputs (ACRO): A tool for dynamic disclosure checks. ESS Statistical Working Papers 2021, 1–27. doi: 10.2785/75954.

Hubbard, T., G. Reilly, S. Varma, and D. Seymour (2020, July). Trusted research environments (TRE) green paper. ZENODO 2020, 1–31. doi: 10.5281/zenodo.4594704.

Jefferson, E., J. Liley, M. Malone, S. Reel, A. Crespi-Boixader, X. Kerasidou, F. Tava, A. McCarthy, R. Preen, A. Blanco-Justicia, E. Mansouri-Benssassi, J. Domingo-Ferrer, J. Beggs, A. Chuter, C. Cole, F. Ritchie, A. Daly, S. Rogers, and J. Smith (2022, September). GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs).

Open-Safely (2023). Sacro:a tool for fast, secure and effective output checking, which can work in any TRE. https://github.com/opensafely-core/sacro.

Preen, R. J., J. Smith, M. Albashir, and S. Davy (2023). ACRO. https://github.com/AI-SDC/ACRO. Python Software Foundation (2021). Python developers survey 2021 results. https://lp.jetbrains. com/python-developers-survey-2021/. Accessed: 24/07/2023.

Ritchie, F. (2008). Disclosure detection in research environments in practice. In Joint UNECE/Eurostat work session on statistical data confidentiality, Volume WP. 73. United Nations Statistical Commission and Economic Commission for Europe Conference of Europe Statisticians, European Commission Statistical Office of the European Communities (Eurostat).

Ritchie, F. (2017, September). The ‘five safes’: A framework for planning, designing and evaluating data access solutions. Zenodo 2017, 1–5. doi: 10.5281/zenodo.897821.

https://github.com/opensafely-core/sacro

https://github.com/AI-SDC/ACRO

https://lp.jetbrains.com/python-developers-survey-2021/

1. Introduction
2. Background
3. The SACRO toolkit

3.1. Design Philosophy
3.2. Workflow
3.3. Checks Implemented
3.4. The SACRO Python `Engine'
3.5. The R interface to ACRO
3.6. Stata Interface

4. SACRO Viewer for Output Checking
5. Linking theory and implementation
6. Engagement with TREs
7. Future Plans
References

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO: Semi-Automated Checking of Research Outputs

Professor Jim Smith,

University of the West of England

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Research results for

publication

Data Repository

Researcher Analytical

Environment

TRE

Disclosure Control

Checking Process

Subset of pseudonymised

data

Safe People

Safe Projects

Safe Setting

Safe Data

Safe Outputs

Graph or table of

summary results

Export from TRE

AI trained model

Software using AI model

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Research results for

publication

Data Repository

Researcher Analytical

Environment

TRE

Disclosure Control

Checking Process

Subset of pseudonymised

data

Safe People

Safe Projects

Safe Setting

Safe Data

Safe Outputs

Graph or table of

summary results

Export from TRE

AI trained model

Software using AI model

(Semi) Automating this bottleneck!

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Who are we? (alphabetically)

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Who are we? (alphabetically)

Universities

• Aberdeen

• Dundee

• Durham

• Edinburgh

• Oxford

• UWE

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Who are we? (alphabetically)

Universities

• Aberdeen

• Dundee

• Durham

• Edinburgh

• Oxford

• UWE

Public Data Bodies • Health Data Research UK • NHS Scotland • Public Health Scotland • Research Data Scotland

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Who are we? (alphabetically)

Universities

• Aberdeen

• Dundee

• Durham

• Edinburgh

• Oxford

• UWE

Public Data Bodies • Health Data Research UK • NHS Scotland • Public Health Scotland • Research Data Scotland

TREs • DASH (Aberdeen/Grampian) • DataLoch (Edinburgh) • HIC (Dundee) • eDRIS (Public Health Scot) • OpenSafely (Oxford)

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Who are we? (alphabetically)

Universities

• Aberdeen

• Dundee

• Durham

• Edinburgh

• Oxford

• UWE

Public Data Bodies • Health Data Research UK • NHS Scotland • Public Health Scotland • Research Data Scotland

TREs • DASH (Aberdeen/Grampian) • DataLoch (Edinburgh) • HIC (Dundee) • eDRIS (Public Health Scot) • OpenSafely (Oxford)

External steering group: UK: Office for National Statistics, ESRC, DARE Global: Eurostat, SDC-GESIS, ICPSR (US)

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

The current situation

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

The current situation

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

The current situation

Confidential data in

TRE

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

The current situation

Confidential data in

TRE

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

The current situation

Confidential data in

TRE Stata,R,Python

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

The current situation

Confidential data in

TRE Stata,R,Python

Analysis

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

The current situation

Confidential data in

TRE

File1 File1

File1 Filen

Stata,R,Python

Analysis

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

The current situation

Confidential data in

TRE

File1 File1

File1 Filen

Stata,R,Python

Analysis

request

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

The current situation

Confidential data in

TRE

File1 File1

File1 Filen

Stata,R,Python

TRE staff

Analysis

request

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

The current situation

Confidential data in

TRE

File1 File1

Filen

Stata,R,Python

TRE staff

Analysis

request

decision

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO in a nutshell

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO in a nutshell

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO in a nutshell

Confidential data in

TRE

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO in a nutshell

Confidential data in

TRE

TRE risk appetite

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO in a nutshell

Confidential data in

TRE

TRE risk appetite

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO in a nutshell

Confidential data in

TRE

Stata,R,Python

TRE risk appetite

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO in a nutshell

Confidential data in

TRE

Stata,R,Python

TRE risk appetite

ACRO engine

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO in a nutshell

Confidential data in

TRE

Stata,R,Python

TRE risk appetite

ACRO engine

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO in a nutshell

Confidential data in

TRE

Stata,R,Python

TRE risk appetite

ACRO engine

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO in a nutshell

Confidential data in

TRE

Stata,R,Python

Analysis

TRE risk appetite

ACRO engine

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO in a nutshell

Confidential data in

TRE

File1 File1

File1 Filen

Stata,R,Python

Analysis

request TRE risk appetite

ACRO engine

ACRO report

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO in a nutshell

Confidential data in

TRE

File1 File1

File1 Filen

Stata,R,Python

TRE staff

Analysis

request TRE risk appetite

ACRO engine

ACRO report

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO in a nutshell

Confidential data in

TRE

File1 File1

File1 Filen

Stata,R,Python

TRE staff

Analysis

request TRE risk appetite

ACRO engine

ACRO report

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO in a nutshell

Confidential data in

TRE

File1 File1

Filen

Stata,R,Python

TRE staff

Analysis

request

decision

TRE risk appetite

ACRO engine

ACRO report

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Similar for Machine Learning Models Except that we :

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Similar for Machine Learning Models Except that we :

•Run a range of “inference” attacks”

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Similar for Machine Learning Models Except that we :

•Run a range of “inference” attacks”

•Aim to support more ‘user journeys’

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Similar for Machine Learning Models Except that we :

•Run a range of “inference” attacks”

•Aim to support more ‘user journeys’

•Don’t have a set of ‘tried and trusted’ guidelines to work with

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: analytic commands

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: analytic commands

Using the same api as pandas, adding checks for cell count and dominance

• acro.crosstab() • acro.pivot_table() Using the same api as statsmodels, adding checks for DoF:

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: analytic commands

Using the same api as pandas, adding checks for cell count and dominance

• acro.crosstab() • acro.pivot_table() Using the same api as statsmodels, adding checks for DoF:

• acro.logit() , acro.logitr()

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: analytic commands

Using the same api as pandas, adding checks for cell count and dominance

• acro.crosstab() • acro.pivot_table() Using the same api as statsmodels, adding checks for DoF:

• acro.logit() , acro.logitr()

• acro.ols() , acro.olsr()

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: analytic commands

Using the same api as pandas, adding checks for cell count and dominance

• acro.crosstab() • acro.pivot_table() Using the same api as statsmodels, adding checks for DoF:

• acro.logit() , acro.logitr()

• acro.ols() , acro.olsr()

• acro.probit() , acro.probitr()

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: analytic commands

Using the same api as pandas, adding checks for cell count and dominance

• acro.crosstab() • acro.pivot_table() Using the same api as statsmodels, adding checks for DoF:

• acro.logit() , acro.logitr()

• acro.ols() , acro.olsr()

• acro.probit() , acro.probitr()

R versions are prefixed by “acro_”

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: analytic commands

Using the same api as pandas, adding checks for cell count and dominance

• acro.crosstab() • acro.pivot_table() Using the same api as statsmodels, adding checks for DoF:

• acro.logit() , acro.logitr()

• acro.ols() , acro.olsr()

• acro.probit() , acro.probitr()

R versions are prefixed by “acro_”

• Also support R’s built in ‘table’ command

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: analytic commands

Using the same api as pandas, adding checks for cell count and dominance

• acro.crosstab() • acro.pivot_table() Using the same api as statsmodels, adding checks for DoF:

• acro.logit() , acro.logitr()

• acro.ols() , acro.olsr()

• acro.probit() , acro.probitr()

R versions are prefixed by “acro_”

• Also support R’s built in ‘table’ command

• Stata versions prefixed by “acro ”

• Code currently captures and translates commands: table, regress, probit, logit 7

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: user commands for session management

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: user commands for session management

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: user commands for session management

• acro = ACRO()

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: user commands for session management

• acro = ACRO()

• acro.suppress= [True, False]

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: user commands for session management

• acro = ACRO()

• acro.suppress= [True, False]

• acro.print_outputs()

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: user commands for session management

• acro = ACRO()

• acro.suppress= [True, False]

• acro.print_outputs()

• acro.remove_output(key)

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: user commands for session management

• acro = ACRO()

• acro.suppress= [True, False]

• acro.print_outputs()

• acro.remove_output(key)

• acro.rename_output(key, newname)

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: user commands for session management

• acro = ACRO()

• acro.suppress= [True, False]

• acro.print_outputs()

• acro.remove_output(key)

• acro.rename_output(key, newname)

• acro.add_exception(key, text)

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: user commands for session management

• acro = ACRO()

• acro.suppress= [True, False]

• acro.print_outputs()

• acro.remove_output(key)

• acro.rename_output(key, newname)

• acro.add_exception(key, text)

• acro.add_comment(key, text)

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: user commands for session management

• acro = ACRO()

• acro.suppress= [True, False]

• acro.print_outputs()

• acro.remove_output(key)

• acro.rename_output(key, newname)

• acro.add_exception(key, text)

• acro.add_comment(key, text)

• acro.custom_output(filename, description)

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

ACRO: user commands for session management

• acro = ACRO()

• acro.suppress= [True, False]

• acro.print_outputs()

• acro.remove_output(key)

• acro.rename_output(key, newname)

• acro.add_exception(key, text)

• acro.add_comment(key, text)

• acro.custom_output(filename, description)

• acro.finalise(output_directory,format)

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Example: what happens if researcher requests a disclosive table

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO viewer for TRE output checkers

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO viewer for TRE output checkers

Outputs to review

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO viewer for TRE output checkers

Outputs to review

Output with problematic cells

highlighted

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO viewer for TRE output checkers

Outputs to review

view TRE risk appetite

Output with problematic cells

highlighted

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO viewer for TRE output checkers

Outputs to review

view TRE risk appetite

Output with problematic cells

highlighted

Type of output Recommendation Comments from user Exception. request

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO viewer for TRE output checkers

Outputs to review

view TRE risk appetite

Output with problematic cells

highlighted

Type of output Recommendation Comments from user Exception. request

Comments needed to override

recommendation

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

SACRO viewer for TRE output checkers

Outputs to review

view TRE risk appetite

Output with problematic cells

highlighted

Type of output Recommendation Comments from user Exception. request

Comments needed to override

recommendation

Record overall comments, create release package

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Get Involved

Get involved Thanks to:

https://github.com/AI-SDC/ACRO

https://github.com/opensafely-core/sacro

mailto:[email protected]

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Get Involved

Get involved

ACRO ‘engine’:

• https://github.com/AI-SDC/ACRO

Thanks to:

https://github.com/AI-SDC/ACRO

https://github.com/opensafely-core/sacro

mailto:[email protected]

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Get Involved

Get involved

ACRO ‘engine’:

• https://github.com/AI-SDC/ACRO

Viewer:

• https://github.com/opensafely-core/sacro

Consensus statement: [email protected]

Thanks to:

https://github.com/AI-SDC/ACRO

https://github.com/opensafely-core/sacro

mailto:[email protected]

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Get Involved

Get involved

ACRO ‘engine’:

• https://github.com/AI-SDC/ACRO

Viewer:

• https://github.com/opensafely-core/sacro

Consensus statement: [email protected]

Anything else: [email protected]

Thanks to:

https://github.com/AI-SDC/ACRO

https://github.com/opensafely-core/sacro

mailto:[email protected]

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Get Involved

Get involved

ACRO ‘engine’:

• https://github.com/AI-SDC/ACRO

Viewer:

• https://github.com/opensafely-core/sacro

Consensus statement: [email protected]

Anything else: [email protected]

Thanks to:

• All the project partners and TRE staff for all their feedback on the tools as they developed

https://github.com/AI-SDC/ACRO

https://github.com/opensafely-core/sacro

mailto:[email protected]

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Get Involved

Get involved

ACRO ‘engine’:

• https://github.com/AI-SDC/ACRO

Viewer:

• https://github.com/opensafely-core/sacro

Consensus statement: [email protected]

Anything else: [email protected]

Thanks to:

• All the project partners and TRE staff for all their feedback on the tools as they developed

• Members of the public for input to consensus statement

https://github.com/AI-SDC/ACRO

https://github.com/opensafely-core/sacro

mailto:[email protected]

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Get Involved

Get involved

ACRO ‘engine’:

• https://github.com/AI-SDC/ACRO

Viewer:

• https://github.com/opensafely-core/sacro

Consensus statement: [email protected]

Anything else: [email protected]

Thanks to:

• All the project partners and TRE staff for all their feedback on the tools as they developed

• Members of the public for input to consensus statement

• External steering group

https://github.com/AI-SDC/ACRO

https://github.com/opensafely-core/sacro

mailto:[email protected]

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Get Involved

Get involved

ACRO ‘engine’:

• https://github.com/AI-SDC/ACRO

Viewer:

• https://github.com/opensafely-core/sacro

Consensus statement: [email protected]

Anything else: [email protected]

Thanks to:

• All the project partners and TRE staff for all their feedback on the tools as they developed

• Members of the public for input to consensus statement

• External steering group

• You for listening

https://github.com/AI-SDC/ACRO

https://github.com/opensafely-core/sacro

mailto:[email protected]

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Get Involved

Get involved

ACRO ‘engine’:

• https://github.com/AI-SDC/ACRO

Viewer:

• https://github.com/opensafely-core/sacro

Consensus statement: [email protected]

Anything else: [email protected]

Thanks to:

• All the project partners and TRE staff for all their feedback on the tools as they developed

• Members of the public for input to consensus statement

• External steering group

• You for listening

https://github.com/AI-SDC/ACRO

https://github.com/opensafely-core/sacro

mailto:[email protected]

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Get Involved

Get involved

ACRO ‘engine’:

• https://github.com/AI-SDC/ACRO

Viewer:

• https://github.com/opensafely-core/sacro

Consensus statement: [email protected]

Anything else: [email protected]

Thanks to:

• All the project partners and TRE staff for all their feedback on the tools as they developed

• Members of the public for input to consensus statement

• External steering group

• You for listening

https://github.com/AI-SDC/ACRO

https://github.com/opensafely-core/sacro

mailto:[email protected]

DARE UK

SACRO: Professor Jim Smith, UNECE Expert meeting on Statistical Data Confidentiality 2023

Get Involved

Get involved

ACRO ‘engine’:

• https://github.com/AI-SDC/ACRO

Viewer:

• https://github.com/opensafely-core/sacro

Consensus statement: [email protected]

Anything else: [email protected]

Thanks to:

• All the project partners and TRE staff for all their feedback on the tools as they developed

• Members of the public for input to consensus statement

• External steering group

• You for listening

This work is funded by UK research and Innovation, [Grant Number MC_PC_23006], as part of Phase 1 of the DARE UK (Data and Analytics Research Environments UK) programme, delivered in partnership with Health Data Research UK (HDR UK) and Administrative Data Research UK (ADR UK)

https://github.com/AI-SDC/ACRO

https://github.com/opensafely-core/sacro

mailto:[email protected]

Towards a comprehensive theory and practice of output SDC, University of the West of England

Read more about Towards a comprehensive theory and practice of output SDC, University of the West of England

statistical disclosure control of outputs, OSDC, secure research environments, OSCD associated risks,

Languages and translations

English

UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE

CONFERENCE OF EUROPEAN STATISTICIANS

Expert Meeting on Statistical Data Confidentiality

26-28 September 2023, Wiesbaden

Towards a comprehensive theory and practice of output SDC

Ben Derrick(University of the West of England, UK)

Elizabeth Green(University of the West of England, UK)

Felix Ritchie(University of the West of England, UK)

Paul White(University of the West of England, UK)

e-mail: [email protected]

Abstract

In 2000, the statistical disclosure control of outputs (OSDC) was largely limited to models of table protection developed

by and intended for national statistical institutes (NSIs), as a particular branch of general SDC theory. However, in this

century OSDC as a field of enquiry has expanded significantly, reflecting the important of secure research environments

run by NSIs and others. OSDC is still a relatively under-developed field compared to SDC for tables or microdata. There

are a small number of practitioner guides, and some theoretical articles, but this is a diffuse literature.

In the UK, a consortium of universities and data providers is collaborating to provide an integrated analysis of output

checking including

- Key theoretical and operational concepts (eg safe statistics, principles-based OSDC)

- A comprehensive listing of statistics, associated risks, and mitigation measures as well as various practical element to

support output checking.

A key element of this is a theory-driven classification which enables us to have that comprehensive listing whilst still

limiting the dimensionality of OSDC guidelines to a manageable number of rules. This paper explains this model and how

it has been co-developed with RDCs and others, and considers whether this provides a sustainable model for future

development of the OSDC field.

1 Introduction

Increasingly social scientists are making use of confidential data for research. This has accelerated in the 21st

century with the growth of secure environments, referred to as ‘safe havens’, ‘secure data centres’, ‘research

data centres’, ‘trusted research environments’ (TREs) and similar names. These TREs provide standardised

secure access to a range of sensitive datasets for research purposes. In OECD countries these are now common

as part of the portfolio of research data services offered by National Statistics Institutes (NSIs), and academic

groups are also adopting them.

TREs have introduced one substantial change to the way social scientists work. When working with

confidential data, researchers are generally unaware of the potential disclosure risk in statistical outputs, as this

is not covered in research methods courses (Derrick et al, 2022). However, TREs generally require researchers

to submit outputs for a confidentiality review before release (Green et al, 2021). The efficiency of this process

relies substantially on the researchers being aware of confidentiality risks and actively aiming to produce non-

disclosive outputs (Alves and Ritchie, 2019). Hence, most TREs (Green et al, 2021) provide researchers with

some training and/or guidelines in output statistical disclosure control (OSDC). Some organisations that allow

downloads have also provided OSDC guidelines eg Eurostat (2015).

The practice of output checking, and the training of researchers and checkers, lags considerably behind other

areas of confidential data protection, such as source data anonymisation. For many years, OSDC was limited to

models of table protection (frequencies and magnitudes) developed by and intended for national statistical

institutes (NSIs). In this century OSDC as a field of enquiry has expanded significantly, largely as a result of

the growth of TREs and the need to cover the much wider range of outputs generated by researchers.

Nevertheless, general OSDC is still a relatively under-developed field compared to SDC for tables or

microdata.

A part of the problem is that the conceptual framework for generalised OSDC is lacking. There are a small

number of practitioner guides, and a few theoretical articles, but this is a sparse literature. However, that

literature does contains the seeds for a new overarching framework; in particular, the realisation that statistics

could be grouped to minimise the need for rules covering every potential output.

In 2023 the UK academic funding council UKRI funded the project SACRO (Semi-automated checking of

research outputs; see Green et al, 2023a) to deliver a general-purpose toolkit for automating output checking

processes, based on the Eurostat funded pilot ACRO (Green, Ritchie and Smith 2020 and 2021). As part of the

project, the team undertook to provide a comprehensive review of SDC theory, integrated with practical

guidelines. A key part of the project was to formalise the use of classifications (‘statbarns’) and push the

concept to its limit to minimise the dimensionality problem.

This paper describes the statbarn concept, how it was operationalised, how it simplifies disclosure control

processes (both automatic and manual). As of July 2023, this is still a work in progress, so we review the

current status and highlight areas where research needs to be done.

2 Generalised OSDC development1

Statistical disclosure control (SDC, sometimes called statistical disclosure limitation) is the practice of using

statistical analysis to ensure that the use of confidential or sensitive data does not breach the privacy of the data

subjects. SDC can be split into ‘input SDC’ (removing identifying information from the data before analysis is

carried out) and ‘output SDC’ (checking that statistical aggregates do not reveal information).

1 This short review is based on our own understanding and experience in the last two decades. We would very much

appreciate comments from colleagues working in this area as to the accuracy of our representation.

Input SDC is a very well-established process. It has a large and stable literature, a large evidence base of the

efficacy of different measures in different circumstances, and software tools implementing these to de-identify

datasets. Research methods courses rarely teach formal de-identification, but researchers are usually given

some basic guidance on broad principles.

In contrast, OSDC is a largely unknown quantity. Until 2000, ‘output SDC’ (had the term been coined then)

would have been seen as the need to protect frequency and magnitude tables from inadvertent disclosure. This

field had seen some study, and there was a relatively well-established literature, but it remained a specialist

area, even for statisticians. We are not aware of research methods courses, then or now, that teach this as a

matter of course, with one exception.

The exception is courses in the production of official statistics, which do cover OSDC for tables. Until recently,

SDC was very heavily influenced by the needs of national statistics institutes (NSIs), who produce statistical

tables and, increasingly, microdata for secondary analysis. These organisations promoted research into relevant

SDC, which explains the overwhelming focus on tables for OSDC. The first OSDC papers not focusing on

tables appear to be Reznek (2004), Reznek and Riggs (2005) and Corscadden et al (2006), both tacking specific

problems.

In 2003 the TRE at the UK Office for National Statistics was set up, and it was run by social science

researchers rather than the teams producing official statistics.. The ONS team realised that (a) the literature on

tabular OSDC was of limited value in research environments, and (b) the vast majority of research outputs had

no guidance at all. As a result, the team began developing guidelines with a research focus. This included an

analysis of the principles behind output SDC for research (Ritchie, 2007), and the first statement of ‘safe

statistics’ (Ritchie, 2008).

The concept of ‘safe statistics’ is key for efficient processing of research outputs. It recognises that certain

types of output have no meaningful disclosure risk in any reasonable use. For example, the regression

coefficients cannot by themselves reveal an individual value, nor can they be differenced to reveal individual

values, nor are they affected by special cases such as single observations in a category (Ritchie, 2019). Of

course, it is possible to construct special cases such that the regression is informative about individuals, but

these have no meaningful research purpose. For all reasonable purposes, regressions coefficients are non-

informative about individuals in all cases2, and therefore they do not need to undergo output checking.

Ritchie (2016) proposed a method for classifying outputs as safe or unsafe:

- Does the statistic itself pose a risk in the case of low numbers, extreme values or something else which is a

legitimate value?

- If the statistic is compared to another with one more observation, does any differencing risk arise?

- Are there are any other reasonable risks to disclosure, specific to this statistic?

If the answer to all three of these is ‘no’ then the statistic is classified as ‘safe’. The innovation in Ritchie

(2008) was that the classification should be based upon the mathematical characteristics of the statistic, not the

statistical ones; in other words, a ‘safe’ statistic should be safe irrespective of the data it is calculated on.

The ONS guidelines formed the basis for Brandt et al (2010; subsequently re-released, with minor revisions, as

Bond et al, 2016). This Eurostat-sponsored project (complementing a second piece on ‘traditional’ SDC;

Hundepool et al, 2010) aimed to provide the first comprehensive guide for researchers and output checkers. The

guide covered broad theory, including a discussion of safe statistics; guidelines and ‘rules’ on specific statistics,

grouped into similar types; and suggestions for operationalising good practice, including training. Brandt et al

(2010) has been the basis for many of the practice manuals now being produced by NSIs and others for TRE

users.

Despite its influence, Brandt et al (2010) has some significant limitations. The most obvious is that the list of

statistics covered is not comprehensive but selective, neglecting the interests of the report committee. Thus, it

2 There are basic rules that can be checked to make sure that the regression is a genuine regression (sufficient degrees of

freedom to be clear this is not an equation, regression must not be saturated to ensure this is an estimate and not a table

masquerading as a regression) but in genuine situations we would not expect these conditions to occur.

is strong on the measures used by social scientists but has significant gaps relating to health research, for

example. The second limitation is that the recommendations are presented ‘as is’ with little in the way of

explanation as to why this came about. A third limitation is that the report is very laconic, offering rules but

very little in the way of practical interpretation for researchers or output checkers. Subsequent manuals based

on the guide have managed to address some of these; for example, the popular SDAP manual (Griffiths et al,

2019) has both a wider range of statistics, and a commentary for output checkers on how to usefully assess the

output.

However, the major limitation of Brandt et al (2010) is that there is no overall integrating conceptual

framework. The guide reduces the range of rules somewhat by grouping statistics, but these are as likely to be

on whether they are commonly put together, rather than on their disclosure characteristics. Moreover, the

structure of the guide implies that any additional statistics will need to have their own rules added, rather than

being seen as variations on existing ones. Other manuals follow this (implicit) approach as well, listing outputs

and associated rules as if they were separate entities. The implications of safe statistics and the grouping

approach used in Brandt et al (2010) have not been followed through. We consider this now.

3 Conceptual foundations of an integrated approach

Analysts use a great range of statistical techniques in their models. Devising statistical rules for all of these

separately is not feasible. However, it is possible to combine statistics into groups based not on statistical

relation but on common disclosure risks and solutions. For example:

- means and totals are identical in terms of the disclosure risk for all practical purposes

- means and frequencies generate the same risks of low numbers and potential for differencing

- means have the potential for dominance

- survival tables are frequencies but they also generate an implicit secondary table

So a grouping would put means, totals, frequency tables and survival tables into three different disclosure

groups:

Everything in the groups should have the same risks and solutions. For example, suppression, rounding or noise

addition are valid solutions to disclosure risks in both frequency and survival tables, but on the latter they need

to be implemented in a different way to allow for the monotonic relationship between cells.

The advantages of this approach are both statistical and operational:

- Fewer rules/cases for researchers and output checkers to learn

- More consistent treatment of outputs

- Clearer distinctions between outputs

- Easier to develop the theoretical basis for any guidance

- Easier to update guidance when it changes (which it does)

- Adding new statistics is now a case of ‘what category does it fall into?’ rather than ‘what rules are needed?’

- Output checker (and researcher) training can focus on the risky classes rather than trying to cover all cases

Because classification is used in this field in many different ways, we refer to the groupings as ‘statistical

barns’ or ‘statbarns’3.

The real value of this comes from finding that, in terms of disclosure characteristics, the minimum number of

statbarns is fairly small. To a researcher, estimation of a hazard model bears little analytical relation to a

quantile regression; but they pose the same disclosure risks: that is, no meaningful risk in any reasonable use,

and so the only test needed is to make sure that this a genuine research use. In the case of estimated models, the

tests are always

- Are there sufficient residual degrees of freedom (ie making sure this a model not an equation)?

- Is the model saturated (explanatory factors all categorical and all fully interacted ie making sure this is not a table

masquerading as an estimate)?

And just like that, a large and essential part of research output is consigned to the box ‘nothing to see here’.

4 The SACRO classification model

As it currently stands, the SACRO models contains fourteen statbarns:

Barn Example Class Status

1 Frequencies Frequency tables Unsafe Very well understood

2 Statistical hypothesis tests t-stats, p-stats, f-stats Safe Provisional

3 Correlation coefficients Regression coefficients Safe Confirmed

4 Position Median, quartiles, min, max Unsafe Provisional

5 Shape s.d., skewness, kurtosis Safe Provisional

6 Linear aggregations Means, totals Unsafe Very well understood

7 Mode n/a Safe Confirmed

8 Smooth distributions Kernel density functions Safe Provisional

9 Concentration ratios Herfindahl index Safe Provisional

10 Calculated ratios Odds & risk ratios Unsafe Provisional

11 Implicit tables Hazard/survival tables Unsafe Provisional

12 Linked/multi-level tables Nested categorical data ? No knowledge

13 Clusters Cluster analysis ? No knowledge

14 Gini/lorenz curves n/a ? No knowledge

It is clear that some of these statbarns cover a very large number of cases (‘correlation coefficients’ cover linear

and non-linear regression, ANOVA, ANCOVA, pairwise correlation etc). In contrast, the disclosure risks of the

mode are unlike any other statistic, and so it merits its own class. This shows the importance of identifying

exactly what are the disclosure characteristics of a particular statistic.

The act of creating the list is itself a useful exercise, forcing one to consider what are the meaningful

differences. For example, mean and median are often grouped together in OSDC guidelines, but they have quite

different characteristics. On the other hand, maxima and minima are often dealt with on their own but they can

be considered as a special case of percentiles. This means that we no longer need separately rules for

‘structural’ end points (such as 0% or 100% in a proportion variable) but can apply general percentile rules.

This list is likely to undergo change over time. Even in the development process, the list changed as more

statistics were deemed to be of the same type, and others demand a new type. The process of identifying risks

3 The term originally came from an analogy with a farmer trying to organise her livestock, but as a neologism it has the

advantage of being unambiguous

and defining OSDC guidelines for each class is crucial, as this is usually the point at which it becomes clear

whether a new type is needed or not. It may also be the case that trying to identify a minimal set is counter-

productive. As noted, formally maxima/minima can be treated as percentiles; but in terms of communication of

risk to researchers, it may be sensible to separate them again. Finally, we have created some categories as, at

the moment, we don’t have enough information to be comfortable that they fit an existing category. Category

12 “linked/multiple tables” is an example – it seems like these should be covered by frequency tables, but we

suspect there are nuances which need to be explored, and so creating it as a separate category show the need for

more understanding.

The coverage of OSDC theory is decidedly patchy. The ‘status’ column has four values:

Very well

understood

This disclosure issues, things to be checked and protection mechanisms have been

comprehensively studied and there is a consensus

Confirmed These have not been so well studied (conclusions rest on one or two papers) but we are

confident that the conclusions and guidance are robust, well-founded and comprehensive

Provisional We have confidence in our conclusions but this is based on extrapolation from other types,

and from our own understanding; there is substantial further work to be done (for example,

on the impact of extreme values) before the classification can be confirmed

knowledge

While we may have suspicion of how these should be seen, basic analysis has not been

carried out

At present, the focus is to get the ‘provisional’ status raised to ‘confirmed’.

The list above is provisional and was devised by the SACRO team based at the University of the West of

England, Bristol. SACRO’s network of output checkers was consulted as to whether this was a sensible

approach in general; the response was positive, but expected: earlier evidence-gathering sessions had already

indicated a desire for simplification of the current OSDC landscape. The initial categories seemed both sensible

and comprehensive, although these are likely to be modified as they develop in practice.

Of more concern to the output checkers was how they (and researchers) would easily check the guidelines for

statistics. This is achieved by a look-up table, linking statistics to the appropriate statbarn, from which the

corresponding checks, problems and solutions could be found:

This will be created as a searchable file, but the output tools being developed by the SACRO project (Green et

al, 2023) intend to incorporate this in the user front end. Researchers and output checkers should be able to

click on a link to see more information about the output, drawn from the statbarn classification. In the initial

project this will only include basic data such as that shown above, but in future it may be useful to expand the

information on each classification. This highlights the advantage of classification: the SACRO coders only

need to know the statbarn code and then can draw all this information from a finite set of outputs.

5 Graphical outputs

Graphs do not present new issues. In theory, every graph can be represented as a table in some way, and so the

above rules could be applied. To take an obvious example, a pie chart or a histogram are clearly just one-way

tabulations, whereas a waterfall graph is a two-way table. As a counter example, a kernel density estimate could

be represented as a mathematical form, but in practice is almost always show graphically. In practice, we need

separate rules because (a) the quantity of information differs, and (b) precision is likely to be lower in a graph.

Consider the Kaplan-Meier graph, which is simply a survival table re-presented, usually in proportional form

(we assume that counts and proportions are equally disclosive as the total from which the proportion is

calculated is likely to be published somewhere). Survival tables are classed as ‘unsafe but very low risk’

because, even in the case of a unit being identified, the personal information content in the survival table is

negligible. Griffiths et al (2019) suggest that the underlying survival table should be supplied along with the

graph, but this can cause more problems:

In the left-hand graph, the source table would have 15 steps and be checkable by a human. But that table would

have precise numbers easily readable, whereas getting the exact figures from the graph depends on the way that

the image was produced (and even then, some laborious analysis). In the right-hand diagram, a survival table

with 100 rows in it is much harder to assess accurately, whereas identifying individual data points from the

image has become harder.

The above graphs are presented as numbers. Formally Kaplan-Meier graphs should show the survival rate

rather than numbers (ie 0%-100%). In theory this makes graphs slightly more disclosive than the survival table:

tables are likely to limit the number of decimal points shown, whereas the full decimal value may be used in

creating the graph points.

Given the low information content in any data point, even if relating to one person, producing survival tables

alongside graphs seems to increase risk rather than reducing it. Hence, the current guidance from SACRO is

that Kaplan-Meier graphs should be released subject to the researcher confirming that each step and the end

point meets thresholds

The objective for the SACRO guide is that it will show the statbarns that each graph falls into (which in itself

might lead to additional statbarns being defined, as in the case of kernel densities), but will concentrate on the

practical assessment; in particular, how graphical representation adjusts the perspective on what is discoverable

from an output. Again, this is the value of the grouping – we can see what we should be looking for in the

output.

6 Conclusion

As the use of confidential microdata for research rises, so does the need for efficient and effective OSDC.

OSDC for research has made considerable advances in this century, but guidelines have tended to develop on

an ad hoc basis as new statistical queries are raised. The strategic approach being taken by SACRO and

described in this paper attempts to provide a longer-term solution to the problem.

The idea of grouping statistics was first raised in Ritchie (2008) partly as a response to proliferation of OSDC

rules emerging from research use of the ONS TRE. While the safe-unsafe classification is crude, it highlights

how applying a structure can significantly improve operational as well as statistical outcomes. Classification

also changes the way we think about outputs. When Brandt et al (2010) was written, the implication is that

additional statistics would require new rules. In the statbarn model, risk assessment for a new statistic should be

a matter of deciding whether it fits into an existing category. If it does, then no further work is needed. If not,

then a new category is added, but this should be a rare event.

The statbarn approach is part of the development of a wider set of operational guidelines aiming to bring

consistency between theory and practice to output checking.

7 References

Alves, K., & Ritchie, F. (2020). Runners, repeaters, strangers and aliens: Operationalising efficient output

disclosure control. Statistical Journal of the IAOS, 36(4), 1281-1293.

Brandt M., Franconi L., Guerke C., Hundepool A., Lucarelli M., Mol J., Ritchie F., Seri G. and Welpton R.

(2010), Guidelines for the checking of output based on microdata research, Final report of ESSnet sub-

group on output SDC

Bond S., Brandt M., de Wolf P-P (2015) Guidelines for Output Checking. Eurostat.

Corscadden, L., Enright J., Khoo J., Krsnich F., McDonald S., and Zeng I. (2006) Disclosure assessment of

analytical outputs, mimeo, Statistics New Zealand, Wellington

Derrick, B., Green, E., Ritchie, F., Smith J. & White, P. (2022, April). Disclosure protection: a systemic gap in

statistical training?. Paper presented at Scottish Economic Society Annual Conference 2022: Special

session 'Protecting confidentiality in social science research outputs', Glasgow

Eurostat (2015) Self-study material for Microdata users. Eurostat.

Green, E., Ritchie, F., Tava, F., Ashford, W., & Ferrer Breda, P. (2021, July). The present and future of

confidential microdata access: Post-workshop report.

Green, E., Ritchie, F., & Smith, J. (2020). Understanding output checking. Luxembourg: European

Commission (Eurostat - Methodology Directorate)

Green, E., Ritchie, F., & Smith, J. (2021). Automatic Checking of Research Outputs (ACRO): A tool for

dynamic disclosure checks. ESS Statistical Working Papers, 2021 Edition

Griffiths E., Greci C., Kotrotsios Y., Parker S., Scott J., Welpton R., Wolters A. and Woods C. (2019)

Handbook on Statistical Disclosure Control for Outputs. Safe Data Access Professionals Working

Group.

Hundepool, A., Domingo-Ferrer, J., Franconi, L., Giessing, S., Lenz, R., Longhurst, J., Schulte Nord-holt, E.,

Seri, G. and De Wolf, P. (2010). Handbook on Statistical Disclosure Control. ESSNet SDC.

Reznek, A. (2004) Disclosure risks in cross-section regression models, mimeo, Center for Economic Studies,

US Bureau of the Census, Washington

Reznek A. and Riggs T. (2005) "Disclosure Risks in Releasing Output Based on Regression Residuals" ASA

2004 Proceedings, Section on Government Statistics and Section on Social Statistics pp1397-1404

Ritchie F. (2007) Statistical disclosure control in a research environment, mimeo, Office for National Statistics;

available as WISERD Data Resources Paper No. 6

Ritchie F. (2008) “Disclosure detection in research environments in practice”, in Work session on statistical

data confidentiality 2007; Eurostat; pp399-406

Ritchie, F. (2014). Operationalising ‘safe statistics’: The case of linear regression. UWE Working Papers in

Economics no 14/10. Bristol

Ritchie, F. (2019). Analyzing the disclosure risk of regression coefficients. Transactions on data privacy, 12(2),

145-173

Smith J., Preen R., Ritchie F., Green E., Stokes P., & Bacon S. (2023) SACRO: Semi-Automated Checking Of

Research Outputs. Paper prepared for the 2023 UNECE/Eurostat Workshop on Statistical Data

Confidentiality,September.

Towards a comprehensive theory and practice of output checking Ben Derrick

Elizabeth Green

Felix Ritchie

Paul White

Data Research Access & Governance Network

UNECE/Eurostat

Expert Group on

Statsitical Data

Confidentiality

September 2023

A brief history of output SDC

late 20th Century 21st CenturyIn the beginning

TA BL ES

TABLES

everything else

S A C R O

2023

SACRO Feb-Oct 2023

• Review/revise theory

• Re-integrate theory and practice guidelines

• Tools (regular SDC and AI-SDC)

• Community engagement

now

User/checker guides

General, structured by use, maybe organisation-specific

Why a new guideline/manual?

Theory papers

Specific, rarely structural

New guide: Structured

Comprehensive Definitive

Theoretically sound Evidenced/sourced Practice-relevant

but not necessarily user-friendly

Practice papers

Operational, not integrated

Structured? okay

Comprehensive? No way!

• Building block: ‘safe statistic’

o unsafe: data-dependent; check before release

o safe: no disclosure risk [minimal check before] release

o based on mathematical (not statistical) characteristics

• Expand: define all statistics by

o common disclosure characteristics

o common mitigation responses

The statistical barn

• Place homologous statistical analysis into ‘statbarns’ eg

o histogram, count table, pie chart  'frequencies’

FREQUENCIES

Applying the group rules

• In the Frequencies barn we know all outputs are

FREQUENCIES

• With any statistic in the Frequencies barn we need to check:

• Low counts

• Differencing

• Class disclosure

• We would apply the following rules in this barn

• Minimum count

• Appropriate mitigation techniques for this barn are

• Cell suppression, noise addition, rounding

The statistical barn

• Place homologous statistical analysis into ‘statbarns’ eg

o histogram, count table, pie chart  'frequencies’

o median, interquartile range  ‘position’

o ANOVA, proportional hazards  ‘correlation’

FREQUENCIES

The barns so far 1.Frequencies

2.Statistical hypothesis tests

3.Correlation coefficients

4.Position

5.End points

6.Shape

7.Means and total

8.Mode

9.Non-linear concentration ratios

10.Calculated risk ratios

11.Hazard/survival tables

12.Clusters

13.Linked/multi-level tables

14.Gini coefficient

• Each barn has its own set of rules for output checking

Progress (as of today)

• Pretty confident on decisions…

• Using in output checker training – positive feedback

• Statbarn model is basis for SACRO (coming up)

• Some rethinking; some revelations

Rethinking example: survival tables

2010 O’Keefe et al JPC

• No detail in tables

• Blur lines in K-M graphs

2019 SDAP manual

• treat as frequency table

• Minimum thresholds

o tables & graphs

2023 DRAGonTome

• Risk

1. outliers

2. absolute dates

3. differencing via subsets

• Recommendation

o Approve unless the above

o Apply threshold to final count

Rethinking example: survival tables

2010 O’Keefe et al JPC

• No detail in tables

• Blur lines in K-M graphs

2019 SDAP manual

• treat as frequency table

• Minimum thresholds

o tables & graphs

2023 DRAGonTome

• Risk

1. outliers

2. absolute dates

3. differencing via subsets

• Recommendation

o Approve unless the above

o Apply threshold to final count

mode

regression on single binary variable

linear vs non-linear concentration ratios

Min/max versus medians and percentiles

Revelation example

• What is disclosure?

• Small numbers + finite values = rationale for higher thresholds

• Evidential vs structural zeros

Still to be done

• To be written:

o Basic concepts

o Operational issues

o Directory of other guides

o FAQs for researchers and output checkers

• Classifications

o Lookup table – is it comprehensive?

o Web pages

• Community buy-in

o More feedback!

Thank you; questions?

[email protected]

mailto:[email protected]

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Do samples of synthetic microdata population replicate the relationship between samples taken from an original population and that population? University of Manchester

Read more about Do samples of synthetic microdata population replicate the relationship between samples taken from an original population and that population? University of Manchester

disclosure risk in sample surveys, k-anonymity, synthetic population, original population, synthetic data,

Languages and translations

English

UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE

CONFERENCE OF EUROPEAN STATISTICIANS

Expert Meeting on Statistical Data Confidentiality

26-28 September 2023, Wiesbaden

Do samples taken from a synthetic microdata population replicate

the relationship between samples taken from an original

population? Mark Elliot, Claire Little and Richard Allmendinger (University of Manchester)

[email protected]

Abstract

Assessment of disclosure risk in sample surveys by data controllers who don’t have access to the population

data are constrained by verifiability challenges. A sample unique may not be population uniques. Statistics

generated at the sample level may not carry over to the population level. Privacy models such as k-anonymity

simply may not make sense when applied to sample data (or only make sense for some scenarios) This study

aims to understand whether samples generated from a synthetic population present the same relationship, in

terms of risk and utility, to the synthetic population, as samples generated from the original population. Note

that this is a very different question from the more general questions about the utility of synthetic data which

compares the synthetic and original data. Here we are comparing two relationships. This opens the possibility of

being able to test and set parameters for models of risk assessment to be applied to real data using synthetic data.

1 Introduction

This document explores whether the relationship between a population dataset and samples drawn

from it is maintained when the samples are drawn from (and compared to) a synthetic version of the

same population. This extends the work of Little et al. (2022), where samples were used to determine

the sample equivalence of synthetic data to the original dataset (for example, to be able to say “the

synthetic dataset has utility equivalent to a 10% original data sample and risk equivalent to a 5%

sample”). In real-life scenarios the population data may not be available, so if synthetic samples were

able to mimic this relationship, it would be useful.

As visualised below, two scenarios are explored: Experiment A (Figure 1), where we do not have

access to the original population data but have a synthetic dataset generated from it that is the same

size as the original population; and Experiment B (Figure 2), where we have a sample of the original

population dataset and from that create a larger synthetic population. An extension to Experiment B

(named B2) is to include the original sample within the synthetic population.

Figure 1: Diagram of data relationships for Experiment A

Experiments were performed using the UK 1991 Census dataset (although it may make sense to

repeat these experiments on other Census datasets in the future). The synthetic data was generated

using Synthpop (Nowok et al., 2016). This was selected because in previous experiments it produced

data with the highest utility compared to other methods (although it should be noted this came with

higher disclosure risk). It may make sense to also experiment with other methods in the future.

The next section introduces the dataset and data/sample generation approach adopted in this study.

Section 3 describes the risk and utility measures used, and Section 4 presents an analysis of

Experiment A and B. Finally, Section 5 concludes the paper and discusses areas for future research.

2 Data

2.1 UK 1991 Census

A subset of the UK 1991 Individual Sample of Anonymised Records for Great Britain (SARs) was

used to simulate a population. The SARs data was downloaded from the UK Data Service on

29/05/21.1 This consists of a 2% sample of the population of Great Britain (excluding Northern

Ireland), with 1,116,181 individual records and 67 attributes. The dataset includes children and adults

and contains information on topics such as age, gender, ethnicity, employment, and housing. To

reduce the computational load the data was subsetted on geographical region (the REGIONP

attribute); there are 12 regions, and the West Midlands was randomly selected for use in this study.

Details of each of the variables are contained in Appendix A. The subset consisted of 104,267 records

(9.34% of overall sample) and fifteen variables (thirteen categorical, two numeric). This subset will be

henceforth referred to as the original population.

Figure 2: Data relationships for Experiment B

2.2 Synthetic Data Generation

Synthpop, developed by Nowok et al. (2016), was used to generate the synthetic data. Synthetic data

the same size as the original population (104,267 records) was generated. Default parameters were

used, with the visit sequence ordered with numerical variables first, followed by categorical variables

with least number of categories to most (with ties decided alphabetically). That gave a visit sequence

of: AGE, HOURS, LTILL, SEX, QUALNUM, MSTATUS, TENURE, RELAT, FAMTYPE,

SOCLASS, ECONPRIM, ETHGROUP, TRANWORK, AREAP, COBIRTH.

2.3 Sample Generation

Random samples of sizes 99%, 98%, 97%, 96%, 95%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20%,

10%, 5%, 4%, 3%, 2%, 1%, 0.5%, 0.25%, 0.1% were drawn (without replacement) from both the

original and synthetic populations. For each sample size 100 samples were drawn. This follows the

framework developed in earlier experiments (as reported in Little et al., 2022).

3 Risk and Utility Measures

For calculating the associated risk and utility the sample datasets were measured against the

population dataset. That is, the synthetic samples were measured against the synthetic population they

1 Study Number 7210 (Office for National Statistics, Census Division, University of Manchester, Cathie Marsh

Centre for Census and Survey Research 2013).

were sampled from, and the original samples were measured against the original population that they

were sampled from. Risk-Utility (R-U) maps, as developed by Duncan et al. (2004), were used to

visualise the trade-off between risk and utility.

3.1 TCAP for disclosure Risk

Elliot (2014) and Taub et al. (2018) introduced a measure for the disclosure risk of synthetic data

called the Correct Attribution Probability (CAP) score. The disclosure risk is calculated using an

adaptation used in Taub et al. (2019) called the Targeted Correct Attribution Probability (TCAP).

TCAP is based on a scenario whereby an intruder has partial knowledge about a particular individual.

Specifically, they know (i) the values for some of the variables in the dataset (the keys) and (ii) that

the individual is in the original dataset. We assume that the intruder wishes to infer the value of a

sensitive variable (the target) for that individual. The TCAP metric is then the probability that those

matched records yield a correct value for the target variable (i.e., that the adversary makes a correct

attribution inference).

Three target variables, and corresponding key variables were identified from the UK Census data. For

each target, the TCAP score was calculated using sets of 3, 4, 5 and 6 keys. The overall mean of the

TCAP scores (for each of the target and key combinations) was calculated as the overall disclosure

risk score.

The TCAP statistic has a value between 0 and 1; a low value indicates that the synthetic dataset

carries little risk of disclosure whereas a score close to 1 indicates a higher risk. A baseline value can

be calculated (the usual one being the probability of the intruder being correct if they drew randomly

from the univariate distribution of the target variable) and then the TCAP score is rescaled so that the

baseline equals zero.2 We refer to the rescaled TCAP value as the marginal TCAP, i.e., it is the

increase in risk above the baseline. Rescaling is performed by subtracting the baseline from the TCAP

score and then dividing by 1 minus the baseline. For all experiments the targets were:

• LTILL : baseline = 0.774

• FAMTYPE : baseline = 0.223

• TENURE : baseline = 0.329

With a mean baseline of 0.442. The keys for each were:

• 6 keys: AREAP, AGE, SEX, MSTATUS, ETHGROUP, ECONPRIM

• 5 keys: AREAP, AGE, SEX, MSTATUS, ETHGROUP

• 4 keys: AREAP, AGE, SEX, MSTATUS

• 3 keys: AREAP, AGE, SEX

3.2 Utility

Following previous work (Little et al. 2022) the mean of the Ratio of Counts (ROC) and Confidence

Interval Overlap (CIO) was calculated as the overall utility score. This was to provide a more

complete view of the utility, rather than just using a single measure.

3.2.1 Ratio of Counts (ROC)

The Ratio of Counts (ROC) was calculated for univariate and bivariate cross tabulations of the data.

This is calculated by taking the ratio of the synthetic and original data estimates (where the smaller is

divided by the larger one). Thus, given two corresponding estimates (for example, the number of

records with SEX = female in the original dataset, compared to the number in the synthetic dataset),

where yorig is the estimate from the original data and ysynth is the corresponding estimate from the

synthetic data, the ROC is calculated as:

ROC = min(𝑦𝑜𝑟𝑖𝑔 , 𝑦𝑠𝑦𝑛𝑡ℎ)

max(𝑦𝑜𝑟𝑖𝑔 , 𝑦𝑠𝑦𝑛𝑡ℎ)

2 This does create the possibility of a synthetic dataset receiving a negative TCAP score (which can still be

plotted on the R-U map) but that simply indicates a risk level below that of the baseline and will only occur in

degenerate cases.

If yorig = ysynth then the ROC = 1. Where the original and synthetic (or sample) datasets are of different

sizes (as is the case when calculating the ROC for the various sample datasets) the proportion, rather

than the count can be used. The ROC was calculated over univariate and bivariate cross-tabulations of

the data and takes a value between 0 and 1. For each variable the ROC was averaged across categories

to give an overall score.

3.2.2 Confidence Interval Overlap (CIO)

To calculate the CIO (using 95% confidence intervals), the coefficients from regression models built

on the original and synthetic datasets are used. The CIO, proposed by Karr et al. (2006), is defined as:

𝐶𝐼𝑂 = 1

2 { min(𝑢𝑜, 𝑢𝑠) − max(𝑙𝑜, 𝑙𝑠)

𝑢𝑜 − 𝑙𝑜 +

min(𝑢𝑜, 𝑢𝑠) − max(𝑙𝑜, 𝑙𝑠)

𝑢𝑠 − 𝑙𝑠 }

where uo, lo and us, ls denote the respective upper and lower bounds of the confidence intervals for the

original and synthetic/sample data. This can be summarised by the average across all regression

coefficients, with a higher CIO indicating greater utility (maximum value is 1 and a negative value

indicating no overlap).

For each synthetic (or sample) dataset two logistic regressions were performed, and the CIO (between

the same regression on the original data) for each was calculated. The mean CIO over all coefficients

was used (where a negative overlap was equivalent to no overlap and therefore set to zero). The mean

of the two CIOs was then calculated as the overall score.

The target variables were marital status (MSTATUS) and housing tenure (TENURE), and they were

converted into a binary attribute: for marital status this was married (or living as married) and

anything else; and for tenure this was whether an individual owns their property (or lives in property

that is owned by a family member), and anything else. Eight variables were used as predictors, using

more would seem to overcomplicate the models. The predictors were: AGE, ECONPRIM,

ETHGROUP, LTILL, QUALNUM, SEX, SOCLASS, and TENURE or MSTATUS (whichever was

not the target).

4 Results

4.1 Experiment A

The scenario where we do not have access to the original/population data but have a synthetic dataset

the same size created from it. This explores using a synthetic dataset to model the relationship

between samples and population data. To be clear, throughout this section, the original dataset (the

UK 1991 sample, n=104,267) is referred to as the original population, and the synthetic dataset

created from this is referred to as the synthetic population. The samples are referred to as original

samples and synthetic samples.

The synthetic population was created (using Synthpop) from the original population. The synthetic

population had utility = 0.7596 and Marginal TCAP = 0.7228 (to 4dp) compared to the original.

Samples were drawn from the synthetic population to determine if the results follow the same patterns

as samples drawn from the original population. The same sample sizes were used as in previous

experiments (0.1%, 0.25%, …, 99%, see Little et al., 2022).

The utility and TCAP scores for each sample size were calculated by measuring against the 100%

synthetic population dataset, not the original population since this would not be available in this

scenario. The baseline TCAP scores (used for calculating Marginal TCAP) were calculated from the

100% synthetic population, and these vary slightly from the original population:

• Original TCAP baseline = 0.442

• Synthetic TCAP baseline = 0.441

For each sample size 100 datasets were drawn, and the results are the mean of the 100. The risk and

utility of the synthetic samples were contrasted with the equivalent results from the original samples.

Tables with the mean utility and TCAP scores for each sample size, and the standard deviation (all

values less than 0.04) are contained in Appendix B. Figure 3 displays the R-U map for the original

sample data at each sample size, together with the results for the synthetic sample data. The plot and

tables indicate that the relationship in terms of (risk and utility) between synthetic samples and the

synthetic population follows closely to the relationship between the original samples and original

population. However, the synthetic samples have moderately higher risk (particularly around the 50%

sample size) and moderately lower utility.

Figure 3: R-U map showing the original samples and the synthetic samples (mean of n=100) in experiment A.

Appendix B contains a table with the mean absolute error (MAE) and standard deviation (SD) of the

synthetic utility and TCAP values (when calculated against the original samples), for each sample

size. Figure 4 illustrates the values in the table, displaying the MAE of the utility and TCAP scores. It

highlights that the MAE in terms of utility is low and generally decreases as sample size increases,

whereas whilst the MAE for the TCAP is also low it displays an interesting curve around the 50%

point and then decreases beyond that as sample size increases.

4.2 Experiment B

This scenario where the original (UK 1991 Census sample, n=104,267) dataset represents the

population, then:

• take smaller samples from the original population (1%, 2%, 3%, 4%, 5%)

• generate synthetic populations (the same size as the original population) from the smaller

samples

• then draw multiple samples of different sizes from each synthetic population

• calculate the risk and utility of the samples and contrast with original population samples

This is perhaps the more likely scenario (compared to Experiment A) since we do not usually have

access to the population data – it is more likely a small sample will be provided, and we can then use

this to generate a synthetic population. From this synthetic population samples can be drawn and the

resulting utility and risk of these can be compared to the equivalent results from the original

population samples.

Figure 4: Mean Absolute Error of the utility and marginal TCAP for each synthetic sample size (calculated against the

original samples), with error bars shows +-1 standard deviation.

To calculate the utility and risk, the synthetic samples are measured against the synthetic population

they were drawn from. They are not measured against the original population as that data would not

be available.

4.2.1 Samples to generate the synthetic populations

Sample sizes of 1%, 2%, … 5% were drawn from the population data, Table 1 lists the number of

records in each sample. Note that only 1 sample was (randomly) drawn for each size, this is because

emanating from each of these individual samples were hundreds of datasets, therefore, to keep

complexity down only one of each size was drawn initially.

Table 1: Number of records for each sample size

Sample size 1% 2% 3% 4% 5%

Number of records 1042 2085 3128 4170 5213

Synthpop was used to generate a synthetic population from each sample, using default parameters

(and with the visit sequence as detailed in Section 2.2). One synthetic population the same size as the

original population (104,267) was generated for each sample; therefore 5 synthetic populations were

produced. Table 2 indicates the utility and risk values for each synthetic population measured against

the original population. It highlights that (even with these small sample sizes), the utility of a

population generated from a smaller sample is lower than the utility of a population generated from a

larger sample, as might be expected. The risk (TCAP) exhibits a different pattern, and it is notable

that the TCAP score for the synthetic population generated from a 1% sample is higher than that for

the 2% and 3% sample populations.

For each of these five synthetic populations, random samples the same size as used in previous

experiments (0.1%, 0.25%, …, 99%, see Little et al., 2022) were drawn (without replacement). For

each sample size 100 samples were drawn.

Table 2: Utility and risk scores for each synthetic population, to 3dp

Synthetic population

generated from a: Utility TCAP

Marginal

TCAP

1% sample 0.539 0.669 0.407

2% sample 0.585 0.638 0.351

3% sample 0.591 0.648 0.370

4% sample 0.616 0.670 0.409

5% sample 0.643 0.678 0.423

4.2.2 Utility and Risk

Appendix C contains tables with the results for utility and Appendix D for TCAP. To calculate the

utility and TCAP the synthetic samples are measured against the synthetic population they were

drawn from (they are not compared against the original population as that data would not be

available). Error! Reference source not found. plots (in the left panel) the utility for each of the

synthetic populations at different sample sizes, with the original population plotted for comparison.

The plot highlights that, regardless of the synthetic population origin (whether it was generated from a

1% sample of the original population or a 5% sample) the relationship between the utility and the

sample proportion is similar.

Figure 5:The utility (left) and marginal TCAP (right) for samples drawn from the synthetic populations, contrasted with

samples from the original population, in experiment B

The panel on the right in Figure 5 displays the marginal TCAP results for each synthetic population.

This illustrates that, whilst they all follow a similar curve, the synthetic samples all overestimate the

TCAP compared to the original samples - the samples taken from the synthetic population generated

from a 1% sample of the original population particularly so.

The R-U map (plotting the utility against the marginal TCAP) can be visualised for each synthetic

population. Figure 6 plots them all in one plot, alongside the original population results. Whilst they

all follow a similar pattern, the results from synthetic populations generated from smaller original

samples tend to have higher TCAP values than those generated from larger samples.

Figure 6: R-U map contrasting the results for samples generated from synthetic populations to the original population (with

sample sizes labelled) in experiment B.

Plots and tables of the MAE (and standard deviation) are in Appendix C (utility) and Appendix D

(marginal TCAP). The marginal TCAP plot indicates that the overall pattern of the MAE fluctuates at

lower sample sizes and then generally decreases as the sample size gets larger. The samples from the

synthetic population generated from a 1% sample of the original data have higher MAE than those

generated from larger samples. The samples generated from a 2%, 3% and 4% synthetic population

exhibit unusual behaviour in that they are not in the order one might expect, this is likely due to

variation in the samples for the TCAP key and target variables.

5 Final Thoughts

The results show that, at least in terms of the risk and utility of samples drawn from a synthetic

population, the relationship is similar to the results obtained by drawing samples from the original

population. For Experiment A, which used a synthetic population generated directly from the original

population, the relationship between the synthetic samples and the synthetic population follows

closely the relationship between the original samples and the original population; the lines on the R-U

map were very close together when compared.

For Experiment B, which is perhaps a more likely scenario (since we do not usually have access to the

population data), synthetic populations were generated from samples (of varying sizes) drawn from

the original population. For each synthetic population samples were drawn, and the risk and utility

calculated, with the results compared (in terms of risk and utility) to the results of samples drawn

from the original population. For each of the synthetic populations, the overall relationship, in terms

of the curve on the R-U map, is similar to the original population results. However, each of the

synthetic populations had higher risk (TCAP), pushing the curve upwards; and as the sample that the

synthetic population was generated from gets smaller the curve moves further away from the original

population curve.

Further work on this might involve using a different data synthesizer – Synthpop was selected because

it generally produces data of high utility (and therefore higher risk) – but it may make sense to

perform these experiments with synthetic data of lower utility/risk to determine whether the results

replicate. It is also possible that using different risk and utility metrics may produce different results.

Repeating the experiments with different datasets may also make sense. As in previous work, we have

used a sample to represent the population data, so a further extension would be to access population

data and repeat these experiments.

6 References

Duncan, G.T., Keller-McNulty, S.A. and Stokes, S.L. (2004). Database Security and Confidentiality:

Examining Disclosure Risk vs. Data Utility through the R-U Confidentiality Map.

Elliot, M. (2014). Final Report on the Disclosure Risk Associated with the Synthetic Data Produced

by the SYLLS Team. [online]. Available from:

https://hummedia.manchester.ac.uk/institutes/cmist/archive-publications/reports/2015-02 -Report on

disclosure risk analysis of synthpop synthetic versions of LCF_ final.pdf.

Karr, A.F. et al. (2006). A framework for evaluating the utility of data altered to protect

confidentiality. American Statistician, 60(3), pp.224–232.

Little, C., Elliot, M. and Allmendinger, R. (2022). Comparing the Utility and Disclosure Risk of

Synthetic Data with Samples of Microdata. In Privacy in Statistical Databases. PSD 2022. Springer

International Publishing, pp. 234–249. [online]. Available from: https://doi.org/10.1007/978-3-031-

13945-1_17.

Nowok, B., Raab, G.M. and Dibben, C. (2016). Synthpop: Bespoke creation of synthetic data in R.

Journal of Statistical Software, 74(11).

Office for National Statistics, Census Division, University of Manchester, Cathie Marsh Centre for

Census and Survey Research. (2013). Census 1991: Individual Sample of Anonymised Records for

Great Britain (SARs). UK Data Service. [online]. Available from: http://doi.org/10.5255/UKDA-SN-

7210-1 [Accessed May 29, 2021].

Taub, J. et al. (2019). Creating the Best Risk-Utility Profile: The Synthetic Data Challenge. In Joint

UNECE/Eurostat Work Session on Statistical Data Confidentiality.

Taub, J. et al. (2018). Differential Correct Attribution Probability for Synthetic Data: An Exploration.

In Privacy in Statistical Databases. pp. 122–137. [online]. Available from:

http://dx.doi.org/10.1007/978-3-319-99771-1_9.

7 Appendix A

The UK 1991 Census dataset sample, 104267 records and 15 variables:

Variable

Name

Description Number

of Values

Number

of missing

AREAP Individual SAR area,

e.g., Birmingham, Solihull

21 0

AGE Age

Range: 0 - 95

94 0

COBIRTH Country of birth 42 0

ECONPRIM Primary economic position,

e.g., Employee FT, Student, Retired

Note: omits individuals < 16

10 21467

(20.6%)

ETHGROUP Ethnic group

e.g., White, Black Caribbean

10 0

FAMTYPE Family type

e.g., Married no children, Cohabiting with children

Note: n/a for individuals in communal establishments or with

no family

9 0

HOURS Number of hours worked weekly

Range: 1-81

Note: excludes individuals aged <=16 and those who have not

worked in previous ten years

72 46979

(45.1%)

LTILL Limiting long-term illness.

Two categories: Yes or no

2 0

MSTATUS Marital status

e.g., Single, married, divorced

Note: individuals < 16 are categorised as ‘single’

5 0

QUALNUM Number of higher educational qualifications

Three categories: 0, 1 or 2+

Note: individuals < 18 have a “0”

3 0

RELAT Relationship to household head

e.g., Head, spouse, daughter

8 2113

(2.0%)

SEX Sex

Two categories: Male or female

2 0

SOCLASS Social class (based on occupation)

e.g., Professional, skilled

Note: omits individuals < 16, & those not in paid work in last

10 years

9 44537

(42.7%)

TENURE

Tenure of household space

e.g., Owner occupied outright, rented privately

Note: omits individuals not in a household

7 2113

(2.0%)

TRANWORK Mode of transport to work

e.g., Bus, on foot

Note: omits individuals not in employment in the week before

Census

11 59249

(56.8%)

8 Appendix B

Experiment A: the mean utility and TCAP scores for each synthetic sample size (to 3dp, n=100),

contrasted with the mean utility and TCAP of samples taken from the original population

Experiment A, the standard deviation to 4dp (n=100) of the utility and TCAP scores for the original

and synthetic data samples

Overall utility TCAP (3 targets) Marginal TCAP (3 targets)

Sample size Original Synthetic Original Synthetic Original Synthetic

0.1% 0.424 0.420 0.609 0.607 0.300 0.298

0.25% 0.503 0.497 0.613 0.612 0.306 0.306

0.5% 0.559 0.554 0.617 0.618 0.313 0.317

1% 0.610 0.605 0.627 0.627 0.331 0.333

2% 0.657 0.653 0.643 0.643 0.360 0.362

3% 0.682 0.680 0.655 0.655 0.382 0.384

4% 0.702 0.701 0.664 0.666 0.398 0.403

5% 0.715 0.712 0.674 0.675 0.416 0.419

10% 0.762 0.760 0.710 0.713 0.480 0.486

20% 0.810 0.808 0.762 0.768 0.574 0.585

30% 0.842 0.840 0.800 0.807 0.641 0.656

40% 0.865 0.864 0.831 0.840 0.696 0.713

50% 0.887 0.887 0.858 0.868 0.746 0.764

60% 0.905 0.904 0.885 0.895 0.794 0.812

70% 0.922 0.921 0.913 0.921 0.843 0.859

80% 0.940 0.939 0.941 0.947 0.895 0.905

90% 0.960 0.960 0.970 0.973 0.947 0.952

95% 0.974 0.974 0.985 0.986 0.974 0.976

96% 0.977 0.977 0.988 0.989 0.979 0.981

97% 0.980 0.980 0.991 0.992 0.984 0.985

98% 0.985 0.985 0.994 0.995 0.989 0.990

99% 0.990 0.990 0.997 0.997 0.995 0.995

Overall utility TCAP (3 targets) Marginal TCAP (3 targets)

Sample size Original Synthetic Original Synthetic Original Synthetic

0.1% 0.0106 0.0125 0.0192 0.0185 0.0344 0.0331

0.25% 0.0114 0.0122 0.0108 0.0107 0.0193 0.0192

0.5% 0.0101 0.0108 0.0077 0.0078 0.0138 0.0139

1% 0.0078 0.0089 0.0061 0.0062 0.0109 0.0110

2% 0.0064 0.0076 0.0044 0.0039 0.0080 0.0070

3% 0.0066 0.0066 0.0034 0.0030 0.0061 0.0053

4% 0.0060 0.0057 0.0029 0.0031 0.0052 0.0055

5% 0.0068 0.0066 0.0028 0.0031 0.0050 0.0056

10% 0.0054 0.0065 0.0024 0.0022 0.0042 0.0039

20% 0.0059 0.0061 0.0021 0.0018 0.0037 0.0032

30% 0.0049 0.0060 0.0019 0.0019 0.0035 0.0033

40% 0.0067 0.0050 0.0016 0.0018 0.0028 0.0033

50% 0.0048 0.0049 0.0022 0.0017 0.0039 0.0030

60% 0.0045 0.0041 0.0021 0.0017 0.0037 0.0030

70% 0.0041 0.0041 0.0018 0.0017 0.0032 0.0031

80% 0.0036 0.0038 0.0021 0.0014 0.0038 0.0025

90% 0.0027 0.0028 0.0017 0.0014 0.0030 0.0025

95% 0.0019 0.0018 0.0013 0.0010 0.0024 0.0017

96% 0.0019 0.0016 0.0012 0.0010 0.0021 0.0018

97% 0.0016 0.0015 0.0011 0.0008 0.0020 0.0014

98% 0.0011 0.0011 0.0009 0.0008 0.0016 0.0014

99% 0.0010 0.0009 0.0005 0.0005 0.0010 0.0009

Experiment A: Mean Absolute Error (n=100) and standard deviation to 4dp of the utility and TCAP

values of synthetic samples compared to the original samples

Overall utility TCAP (3 targets) Marginal TCAP (3 targets)

Sample size MAE SD MAE SD MAE SD

0.1% 0.0108 0.0074 0.0147 0.0113 0.0262 0.0201

0.25% 0.0111 0.0081 0.0080 0.0071 0.0143 0.0127

0.5% 0.0088 0.0075 0.0062 0.0049 0.0114 0.0090

1% 0.0079 0.0065 0.0048 0.0038 0.0088 0.0068

2% 0.0060 0.0062 0.0032 0.0023 0.0060 0.0043

3% 0.0052 0.0043 0.0024 0.0017 0.0045 0.0031

4% 0.0043 0.0039 0.0029 0.0022 0.0059 0.0044

5% 0.0053 0.0050 0.0026 0.0021 0.0051 0.0040

10% 0.0053 0.0047 0.0029 0.0018 0.0061 0.0035

20% 0.0050 0.0039 0.0054 0.0018 0.0106 0.0032

30% 0.0045 0.0044 0.0075 0.0019 0.0142 0.0033

40% 0.0040 0.0032 0.0091 0.0018 0.0169 0.0033

50% 0.0039 0.0030 0.0101 0.0017 0.0186 0.0030

60% 0.0034 0.0026 0.0097 0.0017 0.0177 0.0030

70% 0.0035 0.0025 0.0084 0.0017 0.0153 0.0031

80% 0.0030 0.0025 0.0055 0.0014 0.0100 0.0025

90% 0.0022 0.0018 0.0029 0.0014 0.0054 0.0024

95% 0.0014 0.0012 0.0013 0.0008 0.0024 0.0014

96% 0.0012 0.0011 0.0012 0.0007 0.0022 0.0013

97% 0.0012 0.0009 0.0009 0.0006 0.0016 0.0011

98% 0.0009 0.0007 0.0008 0.0005 0.0014 0.0009

99% 0.0007 0.0005 0.0005 0.0003 0.0009 0.0005

9 Appendix C

Experiment B: Mean utility of original samples and synthetic samples, by sample size to 3dp. This is

the mean utility (across 100 samples) of each sample size (the rows) for each of the synthetic

populations (columns).

Experiment B: the standard deviation to 4dp (n=100) of the utility for samples taken from the original

population, and the five synthetic populations

Sample size Original

Population

Synthetic population generated from:

1% sample 2% sample 3% sample 4% sample 5% sample

0.1% 0.424 0.429 0.425 0.428 0.425 0.425

0.25% 0.503 0.509 0.506 0.505 0.505 0.500

0.5% 0.559 0.569 0.564 0.564 0.566 0.558

1% 0.610 0.626 0.617 0.618 0.619 0.611

2% 0.657 0.673 0.666 0.667 0.666 0.660

3% 0.682 0.700 0.692 0.694 0.694 0.687

4% 0.702 0.718 0.712 0.714 0.711 0.706

5% 0.715 0.733 0.725 0.728 0.727 0.721

10% 0.762 0.776 0.771 0.773 0.772 0.766

20% 0.810 0.823 0.817 0.820 0.818 0.813

30% 0.842 0.851 0.848 0.849 0.848 0.844

40% 0.865 0.874 0.871 0.871 0.872 0.868

50% 0.887 0.894 0.891 0.892 0.893 0.890

60% 0.905 0.911 0.909 0.909 0.909 0.907

70% 0.922 0.927 0.926 0.925 0.925 0.924

80% 0.940 0.944 0.944 0.943 0.943 0.941

90% 0.960 0.963 0.962 0.962 0.962 0.961

95% 0.974 0.976 0.974 0.975 0.975 0.974

96% 0.977 0.978 0.978 0.978 0.978 0.977

97% 0.980 0.981 0.981 0.981 0.981 0.980

98% 0.985 0.985 0.985 0.985 0.985 0.984

99% 0.990 0.990 0.990 0.990 0.990 0.990

Sample

size

Original

Population

Synthetic population generated from:

1% sample 2% sample 3% sample 4% sample 5% sample

0.1% 0.0106 0.0140 0.0125 0.0145 0.0113 0.0116

0.25% 0.0114 0.0110 0.0120 0.0119 0.0102 0.0113

0.5% 0.0101 0.0095 0.0092 0.0102 0.0097 0.0084

1% 0.0078 0.0086 0.0087 0.0085 0.0075 0.0076

2% 0.0064 0.0073 0.0066 0.0061 0.0066 0.0070

3% 0.0066 0.0062 0.0069 0.0070 0.0066 0.0064

4% 0.0060 0.0064 0.0059 0.0057 0.0073 0.0071

5% 0.0068 0.0062 0.0067 0.0058 0.0064 0.0057

10% 0.0054 0.0071 0.0063 0.0062 0.0056 0.0060

20% 0.0059 0.0056 0.0057 0.0047 0.0060 0.0064

30% 0.0049 0.0058 0.0056 0.0064 0.0055 0.0060

40% 0.0067 0.0059 0.0051 0.0053 0.0053 0.0048

50% 0.0048 0.0050 0.0052 0.0052 0.0049 0.0047

60% 0.0045 0.0045 0.0046 0.0051 0.0046 0.0045

70% 0.0041 0.0046 0.0046 0.0044 0.0044 0.0043

80% 0.0036 0.0035 0.0035 0.0034 0.0037 0.0038

90% 0.0027 0.0025 0.0024 0.0025 0.0026 0.0028

95% 0.0019 0.0020 0.0020 0.0019 0.0019 0.0019

96% 0.0019 0.0016 0.0018 0.0017 0.0017 0.0018

97% 0.0016 0.0014 0.0014 0.0012 0.0014 0.0016

98% 0.0011 0.0012 0.0014 0.0012 0.0011 0.0015

99% 0.0010 0.0009 0.0009 0.0009 0.0009 0.0010

Experiment B: The MAE to 4dp (n=100) between the utility of the original population samples and

each of the synthetic population samples

Experiment B: the standard deviation for the MAE of the utility, to 4dp

Sample

size

Synthetic population generated from:

1% sample 2% sample 3% sample 4% sample 5% sample

0.1% 0.0121 0.0097 0.0109 0.0092 0.0093

0.25% 0.0103 0.0099 0.0095 0.0081 0.0092

0.5% 0.0118 0.0084 0.0097 0.0091 0.0069

1% 0.0166 0.0096 0.0097 0.0101 0.0065

2% 0.0159 0.0092 0.0102 0.0096 0.0060

3% 0.0181 0.0104 0.0128 0.0125 0.0066

4% 0.0158 0.0103 0.0119 0.0108 0.0067

5% 0.0179 0.0107 0.0128 0.0121 0.0068

10% 0.0138 0.0092 0.0110 0.0096 0.0057

20% 0.0125 0.0077 0.0098 0.0086 0.0061

30% 0.0097 0.0076 0.0085 0.0074 0.0056

40% 0.0092 0.0067 0.0069 0.0072 0.0047

50% 0.0079 0.0057 0.0065 0.0069 0.0044

60% 0.0065 0.0056 0.0054 0.0052 0.0042

70% 0.0060 0.0047 0.0045 0.0045 0.0038

80% 0.0048 0.0046 0.0039 0.0042 0.0032

90% 0.0037 0.0027 0.0027 0.0028 0.0025

95% 0.0022 0.0016 0.0017 0.0016 0.0016

96% 0.0018 0.0016 0.0016 0.0015 0.0015

97% 0.0014 0.0013 0.0011 0.0013 0.0013

98% 0.0011 0.0011 0.0010 0.0010 0.0011

99% 0.0008 0.0007 0.0008 0.0007 0.0008

Synthetic population generated from:

Sample size 1% sample 2% sample 3% sample 4% sample 5% sample

0.1% 0.0085 0.0079 0.0102 0.0067 0.0069

0.25% 0.0071 0.0072 0.0073 0.0063 0.0070

0.5% 0.0069 0.0063 0.0063 0.0074 0.0048

1% 0.0079 0.0055 0.0063 0.0063 0.0042

2% 0.0071 0.0056 0.0049 0.0057 0.0042

3% 0.0062 0.0059 0.0056 0.0058 0.0047

4% 0.0063 0.0048 0.0051 0.0052 0.0045

5% 0.0061 0.0055 0.0052 0.0058 0.0043

10% 0.0056 0.0047 0.0046 0.0050 0.0037

20% 0.0051 0.0045 0.0039 0.0046 0.0037

30% 0.0046 0.0039 0.0043 0.0041 0.0034

40% 0.0044 0.0041 0.0042 0.0041 0.0031

50% 0.0042 0.0037 0.0037 0.0037 0.0032

60% 0.0039 0.0032 0.0034 0.0034 0.0025

70% 0.0032 0.0032 0.0027 0.0028 0.0028

80% 0.0027 0.0025 0.0024 0.0026 0.0026

90% 0.0019 0.0017 0.0017 0.0019 0.0015

95% 0.0014 0.0012 0.0012 0.0012 0.0010

96% 0.0011 0.0011 0.0011 0.0011 0.0010

97% 0.0010 0.0009 0.0007 0.0009 0.0010

98% 0.0008 0.0008 0.0007 0.0007 0.0010

99% 0.0005 0.0005 0.0005 0.0005 0.0006

Experiment B: the MAE for the utility by sample proportion, for each synthetic population, with error

bars indicating +- 1 standard deviation

Appendix D

Experiment B: the mean (n=100) Marginal TCAP values from each of the synthetic populations, and the original

population, to 3dp.

Experiment B: The standard deviation (to 4dp) of the marginal TCAP scores for samples from each of the synthetic

populations. The original population results are included for comparison.

Sample size Original

Population

Synthetic

Population

(1%)

Synthetic

Population

(2%)

Synthetic

Population

(3%)

Synthetic

Population

(4%)

Synthetic

Population

(5%)

0.1% 0.300 0.432 0.410 0.373 0.385 0.378

0.25% 0.306 0.448 0.414 0.378 0.388 0.381

0.5% 0.313 0.458 0.425 0.390 0.398 0.393

1% 0.331 0.482 0.440 0.415 0.418 0.412

2% 0.360 0.516 0.468 0.448 0.450 0.439

3% 0.382 0.539 0.489 0.472 0.473 0.461

4% 0.398 0.558 0.504 0.491 0.491 0.478

5% 0.416 0.574 0.520 0.506 0.510 0.492

10% 0.480 0.635 0.584 0.569 0.574 0.556

20% 0.574 0.717 0.670 0.658 0.670 0.646

30% 0.641 0.776 0.731 0.723 0.738 0.711

40% 0.696 0.822 0.781 0.775 0.790 0.765

50% 0.746 0.861 0.823 0.820 0.833 0.811

60% 0.794 0.894 0.863 0.860 0.872 0.853

70% 0.843 0.924 0.899 0.898 0.907 0.892

80% 0.895 0.951 0.934 0.933 0.939 0.930

90% 0.947 0.976 0.968 0.967 0.970 0.965

95% 0.974 0.988 0.984 0.984 0.985 0.983

96% 0.979 0.991 0.987 0.987 0.988 0.986

97% 0.984 0.993 0.991 0.990 0.991 0.990

98% 0.989 0.995 0.994 0.993 0.994 0.993

99% 0.995 0.998 0.997 0.997 0.997 0.997

Sample

size

Original

Population

Synthetic

Population

(1%)

Synthetic

Population

(2%)

Synthetic

Population

(3%)

Synthetic

Population

(4%)

Synthetic

Population

(5%)

0.1% 0.0344 0.0374 0.0456 0.0429 0.0389 0.0374

0.25% 0.0193 0.0246 0.0250 0.0256 0.0270 0.0241

0.5% 0.0138 0.0176 0.0179 0.0162 0.0170 0.0177

1% 0.0109 0.0097 0.0128 0.0135 0.0118 0.0114

2% 0.0080 0.0082 0.0086 0.0086 0.0088 0.0092

3% 0.0061 0.0066 0.0066 0.0080 0.0062 0.0067

4% 0.0052 0.0052 0.0059 0.0067 0.0059 0.0058

5% 0.0050 0.0046 0.0057 0.0052 0.0048 0.0047

10% 0.0042 0.0031 0.0040 0.0045 0.0042 0.0040

20% 0.0037 0.0025 0.0039 0.0037 0.0032 0.0032

30% 0.0035 0.0025 0.0028 0.0032 0.0027 0.0031

40% 0.0028 0.0024 0.0028 0.0025 0.0024 0.0031

50% 0.0039 0.0021 0.0025 0.0025 0.0021 0.0027

60% 0.0037 0.0016 0.0019 0.0024 0.0019 0.0025

70% 0.0032 0.0016 0.0020 0.0020 0.0021 0.0023

80% 0.0038 0.0011 0.0016 0.0017 0.0013 0.0018

90% 0.0030 0.0008 0.0012 0.0013 0.0010 0.0015

95% 0.0024 0.0005 0.0009 0.0009 0.0008 0.0007

96% 0.0021 0.0005 0.0008 0.0008 0.0007 0.0009

97% 0.0020 0.0005 0.0008 0.0008 0.0006 0.0007

98% 0.0016 0.0004 0.0006 0.0007 0.0005 0.0007

99% 0.0010 0.0003 0.0004 0.0004 0.0003 0.0005

Experiment B: the MAE of the marginal TCAP for each synthetic population by sample proportion, with error

bars indicating +- 1 standard deviation

Do samples taken from a synthetic microdata population replicate the relationship between samples taken from an original population?

M A R K E L L I OT, C L A I R E L I T T L E , R I C H A R D A L L M E N D I N G E R

U N I V E RS I T Y O F M A N C H E S T E R

Introduction Is the relationship between:

• a population dataset and samples drawn from it

replicated by

• a synthetic version of the same population and samples drawn from it?

Population data usually unavailable - if synthetic samples can mimic this relationship, it would be useful

Extends previous work (Little et al., 2022) using samples to determine the sample equivalence of synthetic data to the original dataset • (to be able to say, for example, “the synthetic dataset has utility equivalent to a 10% original sample and

risk equivalent to a 5% original sample”)

Study Design - Data UK 1991 Census microdata (University of Manchester, 2023) is used to represent the population • subsetted on geographical region (West Midlands)

• 104267 records

• 15 variables (13 categorical, 2 numerical)

Area Age Country

of birth

Economic

group

Ethnic

group Family type

Hours

worked

Long term

illness

Marital

status

Num

qualifications Relationship Sex

Social

class

Transport

to work

Housing

tenure

Sandwell 7 England NA Bangladeshi Married dep.

Children NA No Single None Child M NA NA

Own

outright

Coventry 40 England Employee FT White NA 50 No Married None NA F Manag.

tech Car NA

Walsall 70 England Retired White Married no

children 39 Yes Married None

Household

head M

Part

skilled NA

Own

buying

Study Design synthpop (Nowok et al. 2016) used to generate synthetic data • Default parameters

• Visit sequence ordered by ascending number of categories, with numerical variables first

Data samples were drawn randomly without replacement

Various sample fractions • 0.1%, 0.25%, 0.5%, 1%, 2%, 3%, 4%, 5%, 10%, 20%, …, 80%, 90%, 95%, 96%, 97%, 98%, 99%

◦ 22 overall

• n = 100 samples randomly drawn for each sample fraction

• 2200 samples

Study Design – Metrics Disclosure Risk • For synthetic data reidentification risk not meaningful

• Attribution is possible

• Measured using the Targeted Correct Attribution Probability (TCAP) (Taub & Elliot, 2019) ◦ Probability that an intruder makes a correct attribution inference about a particular target variable, given partial

knowledge (key variables)

• We use marginal TCAP score ◦ Calculate baseline – probability of intruder being correct if they drew randomly from univariate distribution of

target variable

◦ Scale TCAP score between baseline and 1

◦ marginal TCAP indicates risk above the baseline

◦ Value between -x and 1, where a higher value indicates greater risk

Study Design – Metrics Utility • Confidence Interval Overlap (CIO) (Karr et al., 2006)

◦ Logistic regressions performed on synthetic and original data (using same target/predictors for each)

◦ Regression coefficients are compared

◦ Score between 0 (no overlap) and 1

• Ratio of Counts/Estimates (ROC) ◦ For univariate and bivariate cross-tabulations

◦ Compares proportion of synthetic and original data estimates by taking the ratio

◦ Score between 0 and 1

• Overall utility score ◦ Mean of CIO, ROC univariate and ROC bivariate

◦ Value between 0 and 1, where a higher value indicates greater utility

Study Design – Metrics Risk-Utility comparison • R-U confidentiality map (developed by Duncan et al. 2004)

• Plots utility against risk (TCAP) score

• Ideally disclosure risk is minimised, utility is maximised

Synthetic / Sample data • Utility and risk metrics calculated in the same way for samples of original data as for

samples of synthetic data ◦ By comparing against the dataset that the samples were drawn from

• Allows comparison on R-U map

Results - Experiment A A synthetic population was generated from the original population

Random samples taken from both populations

Risk and utility calculated for each sample compared to the population it was sampled from

Results compared

Experiment A: Risk-Utility map showing the original samples and synthetic samples

Experiment A: Individual plots showing the original samples and synthetic samples for:

Utility Risk (Marginal TCAP)

Mean Absolute Error of the utility and marginal TCAP for each synthetic sample size (calculated against the original samples, error bars show +- 1 standard deviation)

Results - Experiment B UK 1991 Census data represents the population

Take samples from the population (1%, 2%, 3%, 4%, 5%)

Generate synthetic populations from the samples

Random samples taken from original and synthetic populations

Risk and utility calculated for each sample compared to the population it was sampled from

Results compared

Experiment B Synthetic population generated from smaller samples

• A more likely scenario

Process:

• Take samples from the original population

• 1%, 2%, 3%, 4%, 5%

• From each sample, a synthetic dataset the same size as the population (n=104267) was generated

• Utility increases with sample size

• TCAP differs

Synthetic population generated from a:

Utility Marginal TCAP

1% sample 0.539 0.407

2% sample 0.585 0.351

3% sample 0.591 0.370

4% sample 0.616 0.409

5% sample 0.643 0.423

Risk-Utility map contrasting the results for samples drawn from synthetic populations to those drawn from original population

Individual plots contrasting the results for samples drawn from synthetic populations to samples drawn from the original population, for:

Utility Risk (Marginal TCAP)

Mean Absolute Error of the utility and marginal TCAP for each synthetic sample size (calculated against the original samples, error bars show +- 1 standard deviation)

Utility Risk (marginal TCAP)

Risk-Utility map contrasting the results for samples drawn from synthetic populations to those drawn from original population…

where the synthetic population also contains the original sample used to generate it • very little difference

whether or not the original sample is included

An aside:

Observations Experiment A → Synthetic population generated from original population • Relationship between synthetic samples and the synthetic population follows closely the

relationship between original samples and the original population

Experiment B → Synthetic populations generated from samples drawn from original population • Overall relationship similar to original populations results (similar curve on the RU map)

• But the smaller the original sample (used to generate the synthetic population) the more the risk is overestimated

• Utility similar no matter the original sample size

Caveats Experiments conducted on samples of Census microdata

◦ May not generalise to full population data

Only one data synthesis method used ◦ Synthpop – which tends to create high utility (but also higher risk) synthetic data

Only one dataset used ◦ It may be useful to repeat this on other datasets

Underestimation of the risk of samples, relative to synthetic data ◦ Whilst synthetic data should not contain re-identification risk, sample data does

Risk measure uses a response knowledge attribution disclosure ◦ OK for Census data, but presence detection may be a significant risk in other data

Different risk and utility metrics may produce different results

Future Work Run experiments on full population data

Use different data synthesis methods

Use different datasets

Assess other utility measures

Assess other disclosure control methods

References Nowok, B., Raab, G.M. and Dibben, C., 2016. synthpop: Bespoke creation of synthetic data in R. Journal of statistical software, 74(1), pp.1- 26.

Little, C., Elliot, M. & Allmendinger, R., 2022, Comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdata. In Privacy in Statistical Databases: International Conference, PSD 2022, Paris, France, September 21–23, 2022, Proceedings. Lecture Notes in Computer Science vol. 13463 LNCS, Springer Nature, Cham, Switzerland, pp. 234-249. https://doi.org/10.1007/978-3-031-13945-1_17

Taub, J., Elliot, M., Raab, G., Charest, A., Chen, C., O'Keefe, C. M., Nixon, M. P., Snoke, J., Slavkovic, A., 2019. The synthetic data challenge. Joint UNECE/Eurostat Work Session on Statistical Data Condentiality. https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2019/mtg1/SDC2019_S3_UK_Synthethic_Data_Challenge_Elliot_AD.p df

Duncan, G.T., Keller-McNulty, S.A. and Stokes, S.L., 2004. Database security and confidentiality: examining disclosure risk vs. data utility through the RU confidentiality map.

Karr, A.F., Kohnen, C.N., Oganian, A., Reiter, J.P., Sanil, A.P.: A framework for evaluating the utility of data altered to protect confidentiality. Am. Stat. 60(3), 224–232 (2006).

https://doi.org/10.1007/978-3-031-13945-1_17

http://doi.org/10.5255/UKDA-SN-7210-1

https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.46/2019/mtg1/SDC2019_S3_UK_Synthethic_Data_Challenge_Elliot_AD.pdf

Slide 1: Do samples taken from a synthetic microdata population replicate the relationship between samples taken from an original population?
Slide 2: Introduction
Slide 3: Study Design - Data
Slide 4: Study Design
Slide 5: Study Design – Metrics
Slide 6: Study Design – Metrics
Slide 7: Study Design – Metrics
Slide 8: Results - Experiment A
Slide 9
Slide 10
Slide 11
Slide 12: Results - Experiment B
Slide 13: Experiment B
Slide 14
Slide 15
Slide 16
Slide 17
Slide 18: Observations
Slide 19: Caveats
Slide 20: Future Work
Slide 21: References

United Kingdom of Great Britain and Northern Ireland

List of tables

Table1

Table2

Table 2a

Table 2b

Table 3

Table 4

Table 5

Table 5a

Table 6

Table 6a

Table 6b

Table 6c

Table 7

Table 8

Table 9

Table 9a

Table 9b

Table 10

Table 11

Table 11a

Table 11b

Table12

Table 12a

Table 12b

Table 12c

Table 13

Table 14

Table 15

Cover

Manual

conversion factors

JQ1 Production

JQ2 Trade

JQ3 Secondary PP Trade

ECE-EU Species

ITTO1-Estimates

ITTO2-Species

ITTO3-Miscellaneous

TS-OB

TS-JQ1

TS-JQ2

TS-JQ3

TS-ECEEU

TS-EU1

TS-EU2

Annex1 | JQ1-Corres.

Annex2 | JQ2-Corres.

SentData

Database

Follow UNECE