» Prices are imputed by inserting bookings of both periods into equation (1)
» Price index is calculated via geometric mean
destatis.deAmelie Blasius – Transaction Data for the Price Index of Package Holidays in Germany
07.06.2023Federal Statistical Office (Destatis) 7
Methodology: hedonic double imputation
» Reliable indices in months with few bookings
» Takes into account many price-determining
characteristics
» Method controls for omitted variable bias
» For each destination, month and over time,
importance of variables differs
» (forward) stepwise variable selection
Possible variables
• log no of travellers
• log duration
• booking time
Binary variables
• school holidays
• online/offline booking
• travel service included
• number of children
• hotel stars (2-5)
• meal types (5 categories)
• clusters of room category
• clusters of room features
destatis.deAmelie Blasius – Transaction Data for the Price Index of Package Holidays in Germany
Deletion of erroneous and implausible bookings
07.06.2023Federal Statistical Office (Destatis) 8
Plausibility checks
» Cancellations, duplicates, bookings with missing values
» Duration, Price, Number of travellers, booking time ≤ 0
» Cruises
» Bookings including a rental car
» Booking time > 366
destatis.deAmelie Blasius – Transaction Data for the Price Index of Package Holidays in Germany
Clearing of outliers by destination country
07.06.2023Federal Statistical Office (Destatis) 9
Plausibility checks
0.5% of bookings are cut from the upper (and lower) end of the spectrum
» price per day and person
» duration
» number of:
» travellers
» adults
» children
Dataset Bookings
2019
Bookings
2020
Bookings
2021
Bookings
2022
Without erroneous bookings 1,656,985 1,029,711 740,073 1,349,654
Without 0.5%- and 99.5%-quantils 1,630,078 1,006,402 720,424 1,312,000
Difference 1.62 % 2.26 % 2.66 % 2.89 %
Number of booking after outlier clearing
destatis.deAmelie Blasius – Transaction Data for the Price Index of Package Holidays in Germany
Henn, K., Islam, C.-G., Schwind, P., and Wieland, E. (2019). Measuring Price Dynamics
of Package Holidays with Transaction Data. EURONA, 2/2019:95–132.
References
destatis.deAmelie Blasius – Transaction Data for the Price Index of Package Holidays in Germany
Measuring the distribution work of
couples with household survey data
New approaches and findings from the German Microcensus
Thomas Körner, Federal Statistical Office Germany
Group of Experts on Gender Statistics,
Geneva, 10-12 May 2023
destatis.de
» Distribution of (paid and unpaid) work in couples – a key indicator for gender
equality
» Conceptual considerations
» Employment
» Full/part-time job
» Family and household
» Indicators and findings
» Average difference of hours usually worked in couples
» Employment constellations in couples
» Cross-classifying the hours usually worked in (employed) couples
Measuring the distribution work of couples
25.01.2021Federal Statistical Office (Destatis) 2
Overview
destatis.deMeasuring the distribution work of couples
25.01.2021Federal Statistical Office (Destatis) 3
A key indicator for gender equality
Impacts of (paid and unpaid) working time distribution
in couples, e.g.
» Access to managerial positions
» Pay gap
» Pension gap
Issues particularly striking in couples with children
(40-50% earnings „child penalty“ for women; Bönke et
al. 2020)
Distribution of paid work as a proxy for the
distribution of unpaid work
Distribution of (paid and unpaid) work in couples
“The unequal sharing of unpaid
work, with women bearing the
brunt of housework and
childcare, is one of the main
drivers of gender inequality”
(Gimenez-Nadal/Molina 2022)
destatis.de
Labour Force Concept of the International Labour Organization (ILO 2013)
» Extensive defnition of employment, one hour criterion
» Persons absent from their job are considered employed provided they have a job
attachment
» Persons on annual leave, sick leave, partental leave, maternity leave often continue to be
counted as employed
Risk of over-estimating the employment of mothers (in particular of young children)
Concept of “realised employment” developed by the FSO Germany (Hochgürtel 2018)
» Persons employed according to the Labour Force Concept of the ILO
» Provided that they were not absent form their job due to maternity leave or parental leave
Measuring the distribution work of couples
25.01.2021Federal Statistical Office (Destatis) 4
Conceptual issues – employment (1)
destatis.de
Employment vs. realised employment of mothers and fathers (Germany, 2021)
Measuring the distribution work of couples
25.01.2021Federal Statistical Office (Destatis) 5
Conceptual issues – employment (2)
63%
58%
65%
74%
80%
82% 83%
14%
47%
62%
73%
79%
82% 83%
91% 91% 91% 91% 91% 91% 91%
86%
90% 91% 91% 91% 91% 91%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
below 1 year 1 to below 2 years 2 to below 3 years 3 to below 6 years 6 to below 10 years 10 to below 15
years
15 to below 18
years
Age of the youngest child
Employment - mothers
Realised employment -
mothers
Employment - fathers
Realised emplyoment - fathers
destatis.de
Definition of the ILO (2008)
» Part-time job: Hours worked less than those of
comparable full-time job
» Depending on national labour markets part-time jobs
are a highly heterogeneous group
» Part-time jobs held by women may sytematically differ
from those held by men
» Further differences in operationalisation
Relying solely on the full-time/part-time distinction risks
to over-estimate female employment participation
Hours usually worked should be analysed in addition
Measuring the distribution work of couples
25.01.2021Federal Statistical Office (Destatis) 6
Conceptual issues – part-time jobs
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
below 10 hours 11 to 20 hours 21 to 30 hours 31 to 40 hours 41 to 50 hours
Fathers Mothers
Hours usually worked in realised part-time
employment (Germany, 2022)
destatis.de
Housekeeping vs. household dwelling concept both limit household members to the dwelling
» In most surveys, also families are restricted to the persons living in one dwelling
» Not in line with family constellations beyond the traditional core family
» „Single“ parents
» Blended families including biological as well as foster children
» Frequently incomplete data regarding the actual split of care responsibilities
Age boundary for children
» Children living in a household with their parents might be over 18 years old
» Breakdown by the age of the youngest child is highly useful for analysis
Measuring the distribution work of couples
25.01.2021Federal Statistical Office (Destatis) 7
Conceptual issues – households and families
destatis.de
Indicator definition
» Difference weekly hours usually worked
» Population: couples, whose youngest child
is less than three years old
» Partners not in realised employment count
for „0“ hours
Pros & cons
» Simplicty, reduction of complexity
» Covers all couples, wheter employed or
not
» Suitable as a performance indicator, but
not for differentiated analysis
Measuring the distribution work of couples
25.01.2021Federal Statistical Office (Destatis) 8
Proposed indicators – Working time difference
Difference of hours usually worked in
couples (Germany)
0
5
10
15
20
25
30
35
2008 2010 2012 2014 2016 2018 2020 2022
Ho
ur
s
Year
destatis.de
Indicator definition
» Cross-tabulation of (realised) employment
status of both partners of a couple
» Population: couples, whose youngest child is
less than three years old
» Possible extension to full-time/part-time work
Pros & cons
» Covers all couples, wheter employed or not
» Included in UNECE Gender Statistics
questionnaiore
» Differentiation of employment situation of the
FT/PT-Employment constellations of
couples (Germany, 2021)
Both full time
25%
Man full time /
women part
time
66%
Woman full time
/ man part time
3%
Both part time
6%
destatis.de
Indicator definition
» Cross-tabulation of hours usually worked of
couples in (realised) employment
» Population: employed couples, whose youngest
child is less than three years old
Pros & cons
» Focus on employed couples (37% of the
population)
» Needs to be combined with employment
constellations
» Very differentiated picture
» Definiton of hour bands may affect the results
Measuring the distribution work of couples
Federal Statistical Office (Destatis) 11
Proposed indicators – Hours worked (1)
Working time constellations of couples
0
1
2
3
4
5
6
0 1 2 3 4 5 6below 10 11 to 20 21 to 30 31 to 40 41 to 50 51 or more
hours usually worked of the father
Ho
ur
s
us
ua
lly
w
or
ke
d
of
th
e
m
ot
he
r
be
lo
w
1
0
11
to
2
0
2
1
to
3
0
3
1
to
4
0
41
to
5
0
5
1
or
m
or
e
destatis.deMeasuring the distribution work of couples
Federal Statistical Office (Destatis) 12
Proposed indicators – Hours worked (2)
Couples with children below 3 years
0
1
2
3
4
5
6
0 1 2 3 4 5 6below 10 11 to 20 21 to 30 31 to 40 41 to 50 51 or more
Hours usually worked of the father
Ho
ur
s
us
ua
lly
w
or
ke
d
of
th
e
m
ot
he
r
be
lo
w
1
0
11
to
2
0
2
1
to
3
0
3
1
to
4
0
41
to
5
0
5
1
or
m
or
e
Couples without children
0
1
2
3
4
5
6
0 1 2 3 4 5 6below 10 11 to 20 21 to 30 31 to 40 41 to 50 51 or more
Hours usually worked of the father
Ho
ur
s
us
ua
lly
w
or
ke
d
of
th
e
m
ot
he
r
be
lo
w
1
0
11
to
2
0
2
1
to
3
0
3
1
to
4
0
41
to
5
0
5
1
or
m
or
e
destatis.de
Indicators on paid work of couples can be used as a proxy for the distribution of unpaid work
» No perfect correlation of paid / unpaid work: time use survey data still need
» Labour Force Surveys based on household samples provide a rich and frequently available
data source
Conceptual decisions need to be taken with care
» Employment vs. realised employment
» Pros & cons of working with breakdowns by full-time or part-time work
» Issues of analyses of „single“ parents and blended families
Different indicators complement one another
» No indicator alone shows the full picture
» Selection of indicator(s) depends on the specific reasearch context
Identifying economic ownership of
Intellectual Property Products:
The German experience using the
GMPG Decision Tree
Meeting of the Group of Experts on National
Accounts
Geneva, Switzerland, 25-27 April 2023
destatis.deIdentifying economic ownership of Intellectual Property Products
Definition
„the result of research and development, investigation or innovation leading to
knowledge, use of which is restricted by law or other means of protection” (ESA
3.132)
➢ challenging issue: identification of economic ownership of IPPs
➢ Guide on Measuring Global Production (GMGP) offers a decision tree
Intellectual Property Products (IPPs)
25.04.2023Meeting of the Group of Experts on National Accounts, Geneva, Switzerland, 25-27 April 2023 2
destatis.deIdentifying economic ownership of Intellectual Property Products
Share of GFCF in IPPs of total GFCF in Germany for the years 2000-2020 ,
percentage value
IPPs in German National Accounts
25.04.2023Meeting of the Group of Experts on National Accounts, Geneva, Switzerland, 25-27 April 2023 3
10%
12%
14%
16%
18%
20%
1
9
9
2
1
9
9
3
1
9
9
4
1
9
9
5
1
9
9
6
1
9
9
7
1
9
9
8
1
9
9
9
2
0
0
0
2
0
0
1
2
0
0
2
2
0
0
3
2
0
0
4
2
0
0
5
2
0
0
6
2
0
0
7
2
0
0
8
2
0
0
9
2
0
1
0
2
0
1
1
2
0
1
2
2
0
1
3
2
0
1
4
2
0
1
5
2
0
1
6
2
0
1
7
2
0
1
8
2
0
1
9
2
0
2
0
Share of GFCF in IPPs of total GFCF
destatis.deIdentifying economic ownership of Intellectual Property Products
GMGP decision tree
25.04.2023 4
Control/
ownership
of unit
The unit is
part
of a MNE
The unit
produced the
IPP
Production
of the IPP
Type of producer
The unit is a main
producer of other goods
and services and is
expected to use the IPP in
its production
process
The unit is a main
IPP producer.
Decision
about the
economic
ownership
The unit may, or may not, receive funding from the
parent as compensation for IPP development costs
but this aspect is not decisive.
The unit does not receive income from royalties or
licenses to use, but either receives compensation for
IPP development from the parent or sells the IPP
originals to the parent.
The unit receives income from royalties or licenses to
use, or does not receive any compensation for IPP
development from the parent, so it can be assumed
that it is expected to obtain income from royalties
and licences to use in the near future.
Income and expenditure related to IPP
destatis.deIdentifying economic ownership of Intellectual Property Products
GMGP decision tree
25.04.2023 5
The unit did
not produce
the IPP
Production
of the IPP
Type of producer
The unit is a main
producer of other (non
IPP) goods and services
and may use the IPP in
production
The unit is not a
producer of other (non
IPP) goods and services.
Its main output is IPP
related.
Decision
about the
economic
ownership
The unit pays royalties or licenses to use
No IPP related payments are being observed. IPP use
may be indirectly observed based on the nature of
the production process (with usually high IPP
requirements) and above average returns to capital.
Purchase of the IPP from the parent and income
from royalties and licenses to use may, or may not,
be observed.
Income and expenditure related to IPP
The unit purchased the IPP original for use in
production
destatis.deIdentifying economic ownership of Intellectual Property Products
Using the GMGP decision tree
• Data basis: Multinational enterprises (MNEs) of the GNI reservation on
globalisation
• Key advantages of the test sample:
• Data exchange between different institutions in Germany
• Further shared data of EU member states
• Additional information from direct MNE contacts
German Approach
25.04.2023Meeting of the Group of Experts on National Accounts, Geneva, Switzerland, 25-27 April 2023 6
destatis.deIdentifying economic ownership of Intellectual Property Products
Relevant data sources
Relevant data sources
25.04.2023Meeting of the Group of Experts on National Accounts, Geneva, Switzerland, 25-27 April 2023 7
Node Control/ownership of the unit Is the unit part of an MNE? Data sources
1 Production of the IPP Is the unit an IPPs producer? SBS, public sources, other
2 Type of producer What is the main kind of activity of the unit? NACE REV. 2 - ISIC REV. 4
3 Use of IPPs Is the unit expected to use the IPPs in the production
process? Public sources
4.1 Income from royalties Does the unit receive income from royalties or
licenses to use IPPs? Balance of payments
4.2 Expenses for royalties Does the unit pay royalties or for licenses to use
IPPs? Balance of payments
5 Compensation for R&D Does the unit receive compensation for IPPs
development? Balance of payments
6.1 Income from selling IPPs Does the unit receive income from selling IPPs? Balance of payments
6.2 Expenses for buying IPPs Has the unit expenses for buying IPPs? Balance of payments
destatis.deIdentifying economic ownership of Intellectual Property Products
Depending on the MNE case:
• GMGP decision tree is useful
• for small units with a distinct business activity
• GMGP decision tree is not feasible
• for large and complex units e.g. parent companies
• IPP flows are not available on the level of the individual transaction
General problem of data availability
Results
25.04.2023Meeting of the Group of Experts on National Accounts, Geneva, Switzerland, 25-27 April 2023 8
destatis.deIdentifying economic ownership of Intellectual Property Products
• Data availability
• Insufficient data basis in the German/European statistical system
• Limited information of financial reporting of MNEs
• Direct MNE contact can be useful
• Utilization of the derived results?
• Adequate coordination involving all relevant NSIs
• Possibility to share microdata
Open issues when applying the GMPG decision tree
25.04.2023Meeting of the Group of Experts on National Accounts, Geneva, Switzerland, 25-27 April 2023 9
destatis.deIdentifying economic ownership of Intellectual Property Products
➢ German experience led to differing results
➢ Useful instrument for a limited range of units
➢ Sufficiently large data basis is required to apply the GMPG decision tree
➢ Current data basis in ESS needed to be extended
➢ Remaining obstacles regarding the utilisation of the results are not solved,
e.g. microdata exchange
Conclusions
25.04.2023Meeting of the Group of Experts on National Accounts, Geneva, Switzerland, 25-27 April 2023 10
Accounting for CO2 emission trade certificates
in the Capital account
Meeting of the Group of Experts on National Accounts, Geneva
Susanne Goldhammer, Deutsche Bundesbank
Accounting for CO2 emission trade certificates
in the Capital account
Outline:
▪ European Emission Trading System/ Cap and trade principle
▪ Economic asset classification
▪ Data sources used in Germany
▪ Insights: trade volume, seasonal trend, trading partners, information about respondents
▪ Emissions trade certificates in the Capital account
▪ Lessons learned
▪ Outlook and discussion
Page 2
17 April 2023
Susanne Goldhammer, Deutsche Bundesbank
Cap and Trade principle
Source: Umweltbundesamt / Deutsche Emissionshandelsstelle (2022) https://www.umweltbundesamt.de/publikationen/der-europaeische-emissionshandel
Page 3
17 April 2023
Susanne Goldhammer, Deutsche Bundesbank
Emission Trading System
Sale of
certificates
Purchase of
certificates
Higher CO2
emissions
Lower CO2
emissions
- - - Cap
Installation A Installation B
CO2
emissions
Installation A CO2
emissions
Installation B
Emission trade certificates – key issues
− EU Emissions trading allowances entitle an installation or an aircraft operator to emit 1 tonne
of carbon dioxide equivalent (1 metric tonne of CO2 or an amount of another greenhouse gas
with an equivalent global-warming potential) during a specified period.
− At a certain moment, installations or aircraft operators must present allowances to cover each
unit of emissions.
− Relevant for National Accounts: issuance of emissions trading allowances by government and
surrender of allowances
− Relevant for Balance of Payments: trade in emissions trading allowances between residents
and non-residents (changes in economic ownership)
Page 4
17 April 2023
Susanne Goldhammer, Deutsche Bundesbank
Classification of the Emissions trading allowances
▪ Allowance entitles the holder to do a certain activity: to "emit one tonne of carbon dioxide
equivalent during a specified period“
▪ Allowance does not entitle property rights
▪ Allowance is registered and transferable
▪ Allowance is tradeable
▪ Allowance does not have a corresponding liability
Non-produced non-financial asset
License
Page 5
17 April 2023
Susanne Goldhammer, Deutsche Bundesbank
Source: BPM6, Table 5.1
Emission trade certificates in Germany: data source
− Direct reporting system
− We collect emission trade certificates with the same „form“ as for securities and financial
derivatives: we want to be informed about acquisition (outgoing payments) and disposal
(incoming payments), and we differentiate between foreign emission trade certificates and
domestic emission trade certificates
− We give explanatory information in our explanatory notes as well as in a separate leaflet on
Emission trade certificates in Germany: development of trade volume
Page 7
17 April 2023
Susanne Goldhammer, Deutsche Bundesbank
Emission trade certificates in Germany: insight in seasonal trend of
monthly source data
Page 8
17 April 2023
Susanne Goldhammer, Deutsche Bundesbank
Emission trade certificates in Germany: main trading partners 2022
Page 9
17 April 2023
Susanne Goldhammer, Deutsche Bundesbank
Emission trade certificates in Germany: main respondents 2022
Page 10
17 April 2023
Susanne Goldhammer, Deutsche Bundesbank
NACE Rev.2 Division Number
of reports
Reported volume
Financial and insurance activities 64.19 404 53,25 bn EUR
64.90 66 5,84 bn EUR
66 878 57,75 bn EUR
Electricity, gas, steam and air-conditioning 35 758 49,13 bn EUR
Manufacture of chemicals and chemical products 20 85 2,96 bn EUR
Manufacture of coke, and refined petroleum products 19 55 2,36 bn EUR
Manufacture of glass and glass products 23 115 0,87 bn EUR
Manufacture of basic metals 24 53 0,71 bn EUR
Emission trade certificates (net) in the German Capital Account
Page 11
17 April 2023
Susanne Goldhammer, Deutsche Bundesbank
Emission trade certificates in Germany – lessons learned
− In our direct reporting system, we are not able to separate between primary market and
secondary market, but we are able to separate derivatives
− Separation between foreign and domestic certificates is difficult
− If respondents are not able to identify foreign and domestic certificates separately, they are
allowed to report the total under domestic certificates
− Banks report on behalf of their clients (in this case, we do not know the branch of the client)
− Clearing by stock exchange causes geographic bias: Clearing house becomes central
counterparty, change in economic ownership happens. At the moment, we „look through“ the
clearing house.
− Domestic transactions (e.g. domestic airline buys emission trade certificates from domestic
stock exchange) are not reported.
Page 12
17 April 2023
Susanne Goldhammer, Deutsche Bundesbank
Outlook and discussion
− Proposed next generation of EU own resources: European Commission plans to use 25 %
from revenue of EU emissions trading flows as own resources
− BPM6 Update Process:
Guidance Note B.6 Sustainable finance
(„Making purchases and trade in CO2 emission permits visible”)
Guidance Note WS.7 Treatment of Emission Trading Schemes
Page 13
17 April 2023
Susanne Goldhammer, Deutsche Bundesbank
Outlook and discussion – Guidance Note WS.7
− Proposed option 4 (emission permits as financial assets) is problematic from a conceptual
perspective for both, the BPM and the SNA:
− Emission permits do not comply with the definition of financial claims in the SNA:
SNA 2008 para. 3.35: "A financial claim is the payment or series of
payments due to the creditor by the debtor under the terms of a liability.".
Yet emission permits do not include a payment from the debtor (the government) to
the creditor (the holder of the permit) in any case.
The fact that emission permits are traded on markets does not make them financial
assets as pointed out in BPM6 para. 5.8.
− GN WS.7 recommends to record every transaction of EPs at market prices and all positions at the
auction price. This would be extremely data demanding since the auction price of every single EP
worldwide has to be known at every point in time. The recording of positions at auction price
breaks with the principle that positions are to be recorded at market prices.
− Data from a centralized body that coordinates the sales and purchases of emission permits (e.g.
Union Registry) is not in accordance with change in economic ownership principle
− With emission permits as financial assets, they would of course no longer appear in the Capital
account
Page 14
17 April 2023
Susanne Goldhammer, Deutsche Bundesbank
Accounting for CO2 emission trade certificates
in the Capital account
Abstract - While indicators like the employment participation rate or the hours usually worked of men and women are widely used as standard indicators in the field of gender statistics, the division of labour of couples less frequently taken into account. This is surprising as – especially when children are born – the division of labour of mothers and fathers are renegotiated at family level, often with the effect that women reduce their engagement in paid work to focus on unpaid care work.
Languages and translations
English
*Prepared by Thomas Körner. I am grateful to Matthias Keller, who provided the tabulations of the results from
the Microcensus. The views expressed in this paper are those of the author and do not necessarily coincide with the
views of the Federal Statistical Office.
NOTE: The designations employed in this document do not imply the expression of any opinion whatsoever on the part
of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its
authorities, or concerning the delimitation of its frontiers or boundaries.
Economic Commission for Europe
Conference of European Statisticians
Group of Experts on Gender Statistics
Geneva, Switzerland, 10–12 May 2023
Item H of the provisional agenda
New approaches to measuring unpaid work and work-life balance
Measuring the distribution work of couples using
household survey data. New approaches and
findings from the German Microcensus
Note by the Federal Statistical Office Germany*
Abstract
While indicators like the employment participation rate or the hours usually worked
of men and women are widely used as standard indicators in the field of gender
statistics, the division of labour of couples less frequently taken into account. This is
surprising as – especially when children are born – the division of labour of mothers
and fathers are renegotiated at family level, often with the effect that women reduce
their engagement in paid work to focus on unpaid care work. Against this
background, informing policies to promote gender equality often particularly requires
indicators on the division of labour of couples. Based on household surveys, such
data are easily accessible. Still, the analysis requires a careful application of the
internationally agreed concepts such as the employment status and the measurement
of working time in order to avoid misinterpretations. Based on recent findings from
Germany, the contribution presents approaches to develop suitable indicators and
discusses the conceptual pitfalls.
Working paper 27
Distr.: General
25 April 2023
English
Working paper 27
2
I. Introduction
1. Distribution of paid and unpaid work in couples is a key indicator regarding gender equality.
The inequalities of the time spent on paid and unpaid work by men and women are one of
the main reasons, why women reach lower earnings (Allmendinger 2022) as well as for
gender gaps in various social fields. As pointed out by Schrenker and Zucco (2020), unequal
distribution of paid and unpaid work between men and women significantly contributes to
the Gender Pay Gap.
2. Earnings of men and women, as shown by the regular publications on the Gender Pay Gap,
still differ in almost all countries and sectors. It has been frequently noted that the Gender
Pay Gap becomes particularly visible when couples have their first child: At this crucial step
in life, the distribution of paid and unpaid between the two partners of a couple gets
renegotiated. The result is often that the female partner interrupts paid employment to take
care of the child, while the man continues paid work (and even increases it). When care
obligations decrease as the children grow older, women usually return to the labour market,
yet typically in part-time jobs. As a result, when estimating the average earnings over the
lifetime, the gap between men and women is around 40 to 45 percent in Germany, with even
larger gaps for women with children, which are due to a “child penalty” (Bönke et al. 2020;
Kleven et al. 2019).
3. While the distribution of earnings as well as the hours spent on paid employment are readily
available from a range of data sources, especially Labour Force Surveys, estimates regarding
the distribution of unpaid work are provided less frequently and with more limited precision:
Given the more complex data collection process such data are collected by time use surveys
in larger intervals and with smaller sample sizes (compared, e.g., to Labour Force Surveys),
which consequently have only limited possibilities to provide detailed breakdowns.
4. We argue that indicators on the distribution of paid work in couples can be used as a proxy
for the distribution of both paid and unpaid work in couples with and without children. It can
be assumed that the partner who is spending less work in paid employment is spending more
time on unpaid care and household work. It should however be kept in mind that this
relationship is not perfect: As shown by time use surveys, also in couples in which both
partners work full-time, the woman on average indicates to spend more time on unpaid
household work. In the case of Germany, comparing mothers and fathers in full-time
employment, the mothers still provided twice as much time of care work (28 versus 15
minutes per day; Hobler/Pfahl 2017). At the same time, a reduction of the inequality in the
hours worked in paid employment is often presented as a key element to close the gender
care gap, i.e. the average difference in the number of hours spent on unpaid care work by
women and men (Schäper et al. 2023).
5. Figures regarding the distribution of paid work in couples is equally available from many
Labour Force Surveys, provided that these are conducted on the basis of a household survey.
In Germany, in the Microcensus, one percent of the population is interviewed each year,
including detailed information regarding the person’s household situation and detailed
information on working time and paid employment.
6. Although indicators on the distribution of paid employment in couples are sometimes used,
including in international data bases, their operationalisation is not straightforward.
Important conceptual elements of possible indicators require further attention. This concerns
especially the definition of employment according to the labour force concept of the
International Labour Organization (ILO), the definition of full-time and part-time
employment, but also the household and family concepts used.
Working paper 27
3
7. This paper, in section II discusses some of the methodological and conceptual challenges. In
section III, a range of different indicators, based on results from the German microcensus for
the year 2021 are presented. In the concluding section IV, we summarise the findings and
provide suggestions for further developments.
II. Conceptual and methodological challenges
A. Defining employment of couples (with children)
8. The labour force concept of the International Labour Organization (ILO 2013) is the
international standard guiding the conceptualisation of work and employment in official
statistics. The labour force exhaustively subdivides the entire population in three mutually
exclusive groups, i.e. employed persons, unemployed persons and persons outside the labour
force. In this concept, employment is defined in an extensive way, i.e. also persons engaged
in small jobs of one hour or more are considered as employed (Körner 2012).
9. In the labour force concept, persons on temporary absence during the reference week are
considered as employed, provided that they maintain a job attachment during their absence.
For this reason, persons with a job but not at work are usually included as employed if they
were, e.g., on annual leave, sick leave, parental leave, educational leave, leave due care for
others, or absent due to strikes or lockouts.
10. The labour force concept therefore may lead to biased conclusions, when comparing the
employment situation of mothers and fathers (Hochgürtel 2018; Kahle/Keller 2018):
Mothers not at work due to parental leave (which may be longer than three months “where
the return to employment in the same economic unit is guaranteed”; ILO 2013: 6) may be
counted as employed, thereby systematically over-estimating the employment of mothers.
As men and women are typically affected differently by the different reasons for being
absent from the job, analyses on the employment situation of mothers and fathers, as well as
of couples may be biased if the Labour Force Concept is applied in a naïve way.
11. Against this background, for the area of analyses regarding the employment of men and
women, the Federal Statistical Office (FSO) Germany developed the concept of “realised
employment”, building on the available criteria of the labour force concept (Hochgürtel
2018). The group of peopled in realised employment comprises all persons that are
employment according to the labour force concept provided that they were not absent from
their job due maternity leave or parental leave. As shown in figure 1, referring to the persons
in realised employment instead of all employed leads to significant differences in the results,
in particular regarding mothers with young children. In 2021 the realised employment rate
for mothers with children below the age of one year was 14% while overall employment rate
for mothers with children below the age of one year amounted to 63%.
Working paper 27
4
Figure 1
Employment and realised employment for mothers and fathers, by the age of the
youngest child (Germany, 2021)
B. Defining full-time vs. part-time employment
12. A frequent indicator to capture a person’s volume of work is distinguishing whether the
main job is a full-time or a part-time job. This is sometimes less straightforward as it may
seem at first sight, since the hours usually worked within the group part-time jobs (as well as
full-time jobs) show considerable variation, which is different for women and for men
(figure 2). In the context of work arrangements in couples this can lead to biased results, if
the working time of men working part-time is different from the working time of women in
part-time employment.
13. According to the ILO convention 175, a part-time worker is “an employed person whose
normal hours of work are less than those of comparable full-time workers” (ILO 1994). The
ILO resolution on the measurement of working time reaffirms this definition, but refers
“contractual hours or hours usually worked” (ILO 2008) (and not normal hours), which is of
limited importance in the present context. Assuming that most full-time workers would work
around 40 hours per week results in a situation in which full-time workers are a relatively
homogeneous group (regarding their working time), while part-time workers can usually
work between 1 and 39 hours per week (Kahle/Keller 2018). This is a very heterogeneous
group, meaning that working part-time can have diverse implications not only on the related
earnings, but also regarding the distribution of work in couples.
14. Looking at the question how part-time employment is usually made operational in surveys
further complicates the matter. While the in the Labour Force Surveys of EU member states
it is recommended to base the measurement of full-time and part-time jobs on the self-
assessment given by the respondent, it is common practice in many other surveys to define a
fixed threshold (e.g. 30 hours) distinguishing full-time and part-time jobs. Further rules may
apply in data editing to correct the data for implausible replies. The German Microcensus,
e.g., until 2019 considered all persons as working full-time if their hours usually worked are
36 or above (regardless of the self-assessment provided in the survey). Persons usually
working less than 25 hours are considered part-time workers, while only for those whose
hours usually worked in the range from 25 to 36 were categorised according to their self-
assessment in the survey. Conclusions regarding to the distribution of work in couples can
diverge at least to some extent depend on the definition applied in a given survey.
Figure 2
Distribution of hours usually worked by mothers and fathers in in realised part-time
employment (Germany, 2022)
15. A further aspect to be noted is that survey estimates on full- and part-time work usually refer
the main job. Secondary jobs are usually not considered, even though their working time
may sum up to a full-time job.
C. Defining the household situation of couples
16. According to the UNECE recommendations on the 2020 census round household is defined
as “persons who combine to occupy the whole or part of a housing unit and to provide
themselves with food and possibly other essentials for living” (UNECE 2015: 162). The two
main household concepts in use – the housekeeping concept as well as the household
dwelling concept (often used in censuses) – have in common that the basic criterion
delimiting the household is that the household members share the same dwelling.
17. Linking the concept of “household” or in particular “family” to the criterion of occupying
the same dwelling can be useful for many analytical purposes and may be a straightforward
decision to make both concepts operational in surveys. However, it may deviate from the
everyday life’s perception of households or families. This may apply, e.g., if not all
members of a family share the same dwelling, as persons living in another household by
definition cannot be members in further households.
18. Since several decades this delimitation is no longer in line with some household and family
constellations, in particular as regards families: Families are no longer limited to the
traditional core families living together in a stable way in the same dwelling. A considerable
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
below 10 hours 11 to 20 hours 21 to 30 hours 31 to 40 hours 41 to 50 hours
Fathers Mothers
Working paper 27
6
share of families are recomposed as patchwork families after a separation of the parents. The
children living together in a households are no longer necessarily the biological descendants
of the adults they live with. In complex or blended families both members of the couple have
at least one pre-existing child. Furthermore, children may live in several households at a
time, shifting between the households of different biological and step parents, which is often
not reflected by survey concepts.
19. Surveys often have to make a choice what they can cover, and analyses of the division of
labour of couples is typically limited by such decisions. In household surveys of official
statistics (such as the German Microcensus), the focus is often on constellations of living
together within the boundaries of one household. Social ties or family relationships to
persons living in other households cannot currently be displayed. This means that only those
persons are considered as mother or father who live together with their children in the same
household. This might be problematic in some family types, such as “single parents” who
share the care responsibilities after a separation.
20. To date, there is no solution in the Microcensus regarding families that extend beyond the
boundaries of the household. Therefore, the following analyses refer to couples that live in
the same household, knowing that this does not fully reflect all current forms of family life.
21. Also the definition of a child deserves some attention: In surveys like the Microcensus, there
is principally no age boundary for children, e.g. in many standard tabulations, also full age
children living in the household might be included. For analyses of the division of labour of
parents, it is advisable to focus on underage children only. Apart from that, it is advisable to
apply breakdowns by the age of the youngest child in the household in order to be able to
make meaningful comparisons. Persons living together with their full-age children are
usually included as households without children.
III. Indicators and findings
22. As shown in the preceding section, analysing the division of paid and unpaid work in
couples requires some basic conceptual decisions. For the reasons already mentioned, we
have chosen to focus on persons in realised employment, i.e. we do not include employed
persons who were temporarily absent from their job due to maternity leave or parental leave.
23. As already shown in figure 1, the realised employment rate of mothers and fathers shows
large differences: While 90% of the fathers were in realised employment in 2021, this
applied to only 69% of the mothers. The gap gets much bigger, the younger the children
living in the household are. Note that the gap entirely disappears if we compare men and
women without children. If we focus on an age range comparable to the group of parents,
e.g. 21 to 55 years, the realised employment rate is 83% for men and 82% for women.
24. If we focus on couples living together, the results become much more focussed on the
specific distribution of work negotiated by the mother and father living together with their
child or children.
25. A simple, but very rough indicator boiling down the division of paid work of couples, is the
average difference of the weekly hours usually worked in parental couples. In Germany, the
indicator refers to mothers and fathers from 15 to 64 years whose youngest child is less than
three years old. In includes couples with at least one partner in realised employment
(partners not in realised employment counting for 0 hours). Such an indicators presents in
Working paper 27
7
one figure the inequality in the distribution of paid employment and unpaid care work
between women and men. According to data from the German Microcensus, the average
difference of hours usually worked in couples was 20.9 in 2021 (BMFSFJ 2023). Compared
to the year 2008, the difference decreased by 30% from 29.9 hours. Despite this reduction
the indicator shows a persistent inequality in the division of paid and unpaid work between
men and women, in particular for couples with young children.
26. While it is helpful for a quick assessment to concentrate a complex phenomenon like the
division of labour in couples to one single number, this simplification at the same time
prevents from a differentiated analysis. The average mixes couples in which one partner
works full-time and the other is not in paid employment, with full-time/part-time
constellations and couples in which both partners work part-time. A considerable variation
in reality is thus strongly reduced in complexity.
27. Another approach is to focus less on the number of hours usually worked, but on the general
types of division of labour. Looking at couples with children who are less than three years
old, broad categories are mother and father are both in (realised) paid employment, only one
of the partners is in paid employment, and none of the partners. As shown based on the
Microcensus for the year 2021, in 9% of the couples with children under the age of three
years neither the man nor the woman was in (realised) paid employment and 37% of the
couples were both employed. In 51% of the couples in this group, the father was employed,
while the mother was either not employed or in parental or maternity leave. Only in 3% of
the couples, the mothers was in paid employment, while the father was not employed or in
parental leave. This picture shows that in more than half of the couples a traditional division
of labour prevails, in which the mother fully interrupts her employment, while the man
continues to be employment (and even increases the working hours, as further analyses
show). Cases in which the father interrupts his employment are still very rare.
Figure 3
Employment constellations of couples, whose youngest child is less than three years old
(Germany, 2021)
Both employed
37%
Man employed / woman maternity
or parental leave 21%
Woman employed / man
parental leave 1%
Man employed /
woman not
employed
30%
Woman employed /
man not employed
2%
None employed 9%
Working paper 27
8
28. Following this analytical approach, we can distinguish couples in which the woman entirely
stopped working with a highly traditional division of labour. It is analytically fruitful to
distinguish this group before taking a closer look into the couples in which both the mother
and the father are in paid employment. It should however be noted that such an analysis only
covers 37 % of the couples whose youngest child is less than three years old. Out of these
37 % of couples, in which both partners are employed, in two thirds the father is in full-time
paid employment, while the mother is employed part time (see figure 4). In 25% of the
couples, both partners are working full time, and in 6% both are employed part time. In only
3 percent of the couples, the mother holds a full-time job, while the father is working part
time. Note that a very similar distribution can also be observed for employed couples with
children aged less than 18 years old (both full-time 27%; both part-time 5%; man full-
time/woman part-time 3%; woman part-time/man full-time 66%), the main difference being
that in this group the share of couples, in which both partners are in paid employment
amounts to 66% (instead of 37% for the couples with a child aged less than 3 years).
Figure 4
Employment constellations of couples in realised employment, whose youngest child is less
than three years old (Germany, 2021)
29. The figures presented in figure 4 still not provides a fully accurate picture. As already noted,
the range of hours usually worked by part-time (but also full-time) workers is subject to
considerable variation. This is particularly relevant, since mothers in part-time employment
tend to work less hours than fathers in part-time employment (see figure 2). Similarly,
fathers in full-time employment on average work more hours than mothers in full-time
employment.
30. A possible solution is to analyse the hours usually worked by couples, broken down by the
hours worked of the father and the mother. Figure 5 shows the hours worked by the father on
the x-axis and the hours worked by the mother on the y-axis. The figure shows, e.g. that the
most frequent employment constellation of couples with children below the age of three is
that both the mother usually works 21 to 30 hours and the father 31 to 40 hours (which
Both full time
25%
Man full time /
women part time
66%
Woman full time /
man part time
3%
Both part time
6%
Working paper 27
9
applies to 20 % of the couples).1 The bubbles on the diagonal represent cases, in which the
mother’s and the father’s usual working hours fall into the same category. The (few) bubbles
above the diagonal represent couples in which the mother usually works more hours than the
father and the bubbles below the diagonal represent cases in which the father usually works
longer. Overall, the hours usually worked are equal in 24.1 % of the couples, whose
youngest child is less than three years old. In 73.1 % of the couples, the father falls into a
higher category of hours usually worked and only in 2.8 % the mother is working longer
hours. Compared to the results shown in figure 4, one can conclude that the comparison
based on the full-time/part-time distinction underestimates the differences in working time
of mothers and fathers.
Figure 5
Employment constellations of couples in realised employment, whose youngest child is less
than three years old (Germany, 2021)
1 Note that in figure 5 data cannot be presented for several data points due to insufficient cell size.
0
1
2
3
4
5
6
0 1 2 3 4 5 6below 10 11 to 20 21 to 30 31 to 40 41 to 50 51 or more
hours usually worked of the father
ho
ur
s u
su
al
ly
w
or
ke
d
of
th
e
m
ot
he
r
be
lo
w
1
0
11
to
2
0
2
1
to
3
0
31
to
4
0
41
to
5
0
5
1
or
m
or
e
Working paper 27
10
IV. Conclusions
31. Information on the distribution of paid work in couples can provide important hints also
regarding the distribution of unpaid work in couples. Without measuring unpaid work
directly, information on paid work can be used as a proxy for arrangements regarding unpaid
work, and at least describes the potential of an equal distribution of unpaid work.
32. The indicators presented in this paper can be a highly useful complement to results on the
division of unpaid work from Time Use Surveys, typically collected in large intervals and in
relatively small samples. The indicators presented in this paper can be produced on the basis
of Labour Force Surveys based on a household sample. As Labour Force Surveys are usually
run more frequently and based on larger sample sizes, more differentiated analyses are
possible, e.g. focussing on the situation of couples with young children by their socio-
economic status.
33. As we have shown, several conceptual decisions need to be taken that have implications on
the interpretation of the indicators. This includes the definition of employment (for which we
suggest the concept of realised employment), which is facing different requirements when
applied to the division of labour of couples, compared, e.g. to economic analysis. We have
also shown that the common distinction between full-time and part-time employment is
going along with some uncertainty, since the group if part-time employed tends to be very
heterogeneous and often a harmonised operationalisation is lacking. Using the information
regarding the hours usually worked is analytically richer, however more difficult to
communicate.
34. All the indicators presented in section III have their right, and are appropriate measures in
different contexts. We have shown that, nevertheless, relying on some of the indicators
proposed only can lead to biased results, as, e.g., the inequality of the division of labour of
couples might be under-estimated.
V. References
Allmendinger, Jutta, 2022: Es geht nur gemeinsam! Wie wir Geschlechtergerechtigkeit
erreichen. Berlin.
BMFSFJ (Federal Ministry for Family Affairs, Senior Citizens, Women and Youth), 2023:
Equality Atlas. At https://www.bmfsfj.de/bmfsfj/meta/en/equality/equalityatlas?view=
Bönke, Timm et al., 2020: Wer gewinnt? Wer verliert? Die Entwicklung und Prognose von
Lebenserwerbseinkommen in Deutschland. Gütersloh: Bertelsmann Stiftung. At:
https://www.bertelsmann-stiftung.de/de/publikationen/publikation/did/wer-gewinnt-wer-verliert-
2020
Eurostat, 2021: EU Labour Force Survey Explanatory Notes (to be applied from 2021Q1
onwards). European Commission: Luxembourg. At
https://ec.europa.eu/eurostat/documents/1978984/6037342/EU-
LFS+Explanatory+notes+from+Q1+2021+onwards.pdf
Hobler, Dietmar and Svenja Pfahl, 2017: Zeitaufwand für Hausarbeit 2012/2013. In: WSI
GenderDatenPortal. At
https://www.boeckler.de/data/wsi_gdp_zeitverwendung_20170601_01.pdf
Hochgürtel, Tim, 2018: Realisierte Erwerbstätigkeit zur Messung des
Vereinbarkeitsarrangements von Familie und Beruf. In: Wirtschaft und Statistik 1/2018.
ILO, 1994: C175 - Part-Time Work Convention, 1994. At
https://www.ilo.org/dyn/normlex/en/f?p=NORMLEXPUB:12100:0::NO::P12100_ILO_CODE:
C175
ILO, 2008: Resolution concerning the measurement of working time. Adopted by the Eighteenth
International Conference of Labour Statisticians (November/December 2008) At
https://www.ilo.org/wcmsp5/groups/public/---dgreports/---
stat/documents/normativeinstrument/wcms_112455.pdf
ILO, 2013: Resolution concerning statistics of work, employment and labour underutilization.
Adopted by the Nineteenth International Conference of Labour Statisticians (October 2013) At
https://www.ilo.org/global/statistics-and-databases/standards-and-guidelines/resolutions-
adopted-by-international-conferences-of-labour-statisticians/WCMS_230304/lang--en/index.htm
Kahle, Irene und Matthias Keller, 2018: Realisierte Erwerbstätigkeit von Müttern und Vätern zur
Vereinbarkeit von Familie und Beruf. In: Wirtschaft und Statistik 3/2018.
Kleven, Henrik et al. 2019a: Child Penalties Across Countries: Evidence and Explanations. AEA
Papers and Proceedings, 109, 122–26. At
https://www.henrikkleven.com/uploads/3/7/3/1/37310663/klevenetal_aea-pp_2019.pdf
Körner, Thomas, 2012: Measuring the Labour Status in Official Statistics: The Labour Force
Concept of the International Labour Organisation and its Implementation in the Labour Force
Survey. In: Hoffmeyer-Zlotnik, Jürgen/Warner, Uwe (editors). Demographic Standards for
Surveys and Polls in Germany and Poland: National and European Dimension. GESIS: Cologne
2012, pp. 123–138.
Schäper, Clara et al., 2023: Gender Care Gap and Gender Pay Gap Increase Substantially until
Middle Age. DIW weekly report 9/2023, pp. 84-88. At
https://www.diw.de/documents/publikationen/73/diw_01.c.867440.de/dwr-23-9-1.pdf
Schrenker, Annekatrin and Aline Zucco, 2020: The gender pay gap begins to increase sharply at
age of 30. DIW weekly report 10/2020, pp. 75-82. At
https://www.econstor.eu/bitstream/10419/220007/1/1692667602.pdf
UNECE, 2015: Conference of European Statisticians Recommendations for the 2020 Censuses
on Population and Housing. United Nations: New York and Geneva.
Economic Commission for Europe
Conference of European Statisticians
Group of Experts on National Accounts
Twenty-second session
Geneva, 25-27 April 2023
Item 7 of the provisional agenda
Real-time indicators and nowcasting
GDP Flash Estimate and GDP Nowcast: An R-Shiny App for
GDP Estimation
Prepared by the German Federal Statistics Office1
Summary
In recent years DESTATIS has been publishing a GDP flash estimate 30 days after the
end of the quarter and a purely model-based GDP nowcast 10 days after the end of the quarter
for internal use. Since spring 2020, the GDP flash estimate and nowcast have been facing new
challenges related to the ongoing corona pandemic, supply and material bottlenecks, price
increases, and the war in Ukraine. GDP Flash estimate and GDP nowcast: An R-Shiny app for
GDP estimation by DESTATIS presents a new tool developed to cope with these additional
uncertainties. The application allows to carry out the model estimations more flexible, faster and
less error-prone manner than before. It permits loading a comprehensive set of indicators that can
be expanded if new indicators become available. Furthermore, it is possible to test various
estimation scenarios based on previously defined models of the GDP sub-aggregates. The
application also includes a variety of graphical evaluation options to analyse the estimated GDP,
production- and use-side components and the underlying monthly economic indicators
1 Prepared by Arne Ackermann and Claudia Fries, Destatis.
United Nations ECE/CES/GE.20/2023/9
Economic and Social Council Distr.: General
22 March 2023
English only
ECE/CES/GE.20/2023/9
2
I. Scope of the R-Shiny App
1. Since the second quarter of 2020, the Federal Statistical Office has been publishing
gross domestic product (GDP) as a GDP flash estimate 30 days after the end of the quarter.
For this publication, expert estimates for production and expenditure side are supplemented
by econometric estimates2. In addition, a purely model-based GDP nowcast is estimated 10
days after the end of the quarter for internal use (see Dickopf, Janz and Mucha, 2019). After
an initial feasibility study in 2019, the econometric model as well as the estimation tool for
the nowcast has been continuously further developed.
2. Since spring 2020, the GDP estimates (flash estimate and nowcast) have been facing
new challenges. The corona pandemic, supply and material bottlenecks, price increases and
the Russian war of aggression in Ukraine have led to increasing estimation uncertainties for
the time series econometric models of GDP flash estimate and GDP nowcast (see
Ackermann, Dickopf and Mucha, 2021). To cope with these additional uncertainties, we
started to develop an R-Shiny app as a graphical user interface for the estimation process.
With this new tool, it is possible to carry out the model estimations more flexibly, faster and
less error-prone than before. The app allows loading a comprehensive set of indicators that
can be expanded if new indicators become available. Furthermore, it is possible to test various
estimation scenarios based on previously defined models of the GDP sub-aggregates. The
app also includes a variety of graphical evaluation options to analyse the estimated results of
GDP, its production and use side-aggregates and the underlying monthly economic
indicators. Other features as e.g. an integrated revision analysis could be added in the future.
3. This contribution will first explain the underlying econometric approach. Then, we
present the main functionalities and some application examples of the R-Shiny app that is
used internally by the Federal Statistical Office for the econometric part of the GDP flash
estimate and for the GDP nowcast.
II. Econometric Approach
4. We estimate the quarterly gross domestic product following the bottom-up structure
of the detailed GDP calculation. Therefore, estimated values for aggregates of GDP are
calculated first and then aggregated to form one overall GDP result for the production side,
and one for the expenditure side. Thus, the results of the production and expenditure approach
are determined independently. The basis of the calculations on the production side is the
gross value added in currently 15 aggregated economic sectors, plus taxes on goods and less
subsidies on goods (see Table 1, first column). In order to increase the estimation precision
in trade (section G) and to better assess the impulses of each of the three subsections, trade
has been divided into its three subsections G45, G46 and G47. Thus, trade (section G) is
estimated indirectly as the sum of the estimates of the three subsections. This procedure is
also planned for other aggregates on both the production and the expenditure side (e.g. private
consumption expenditure). On the expenditure side, we estimate 9 aggregates (see Table 1,
second column) and manually add an assumed value for changes in inventories and
acquisitions.
2 Following the Delphi method (see appendix Figure 7)
ECE/CES/GE.20/2023/9
3
Table 1
Estimated components of GDP
Production side Expenditure side
NACE
Section
Economic Area
A Agriculture, forestry and fishing Private consumption expenditure
B Mining and quarrying General government expenditure
C Manufacturing Gross fixed capital formation, Machinery and
equipment
D Energy supply Gross fixed capital formation, Buildings
E Water supply, sewerage, waste
management and remediation activities
Gross fixed capital formation, Other products
F Construction Export of Goods
G
G45
G46
G47
Wholesale and retail trade, sale and
repair of motor vehicles and motorcycles
Wholesale and retail trade and repair of
motor vehicles and motorcycles
Wholesale trade, except of motor
vehicles and motorcycles
Retail trade, except of motor vehicles
and motorcycles
Export of Services
H Transportation and storage Import of Goods
I Accommodation and food services Import of Services
J Information and communication
K Financial and insurance activities
L Real estate activities
M, N Business services
O, P, Q Public services, education, health
R, S, T Other services
Taxes on products
Subsidies on products
5. The individual aggregates and subsections are estimated using bridge equations. If
suitable indicators are available for an aggregate, these are included in the corresponding
model as external regressors. Currently, up to six indicators are included for each aggregate.
Most indicators are available on a monthly basis, while GDP aggregates are available on a
quarterly basis. Therefore, the monthly indicators are aggregated to quarterly frequency for
estimation. At the time of the estimation, normally only one to two monthly values of the
underlying indicators for the current quarter are available. The missing monthly values have
to be estimated before aggregating to quarterly frequency (Eurostat, 2016). The two steps of
the procedure are summarized for the example of private consumption in Figure 1. The
estimation of missing monthly values might include up to three predictors as external
regressors, if available.
6. Methodologically, ARIMA models with external regressors are used for the individual
estimates in both steps. Seasonality in the time series is considered within the modelling, and
indicators and predictors are adjusted for seasonal effects.
ECE/CES/GE.20/2023/9
4
Figure 1
Estimation steps, example private consumption
III. Features: Application
A. Menu and Data Input
7. The menu bar on the left allows to switch between different parts of the estimation
and to choose several options which will be explained in the following sections.
8. On the first page “Dateneingang”, four data files need to be selected for upload as
input for the estimations:
• Steuerungsdatei: In this file the estimation models with respect to external
regressors for the aggregates and all indicators are described.
• Datensatz Indikatoren: This file contains the time series of all possible indicators
and predictors in monthly frequency.
• Datensatz Entstehung: This file contains the time series of all production side
aggregates.
• Datensatz Verwendung: This file contains the time series of all expenditure side
aggregates.
9. The menu bar allows to set the start date of the model estimation (“Start
Modellkalibrierung”), to set the quarter to be estimated (“Aktuelles Quartal”), to manually
set the value for inventories and acquisitions and to choose the time interval to be displayed
in the graphical outputs.
ECE/CES/GE.20/2023/9
5
Figure 2
Menu bar and data upload
B. Selection of approach and aggregate: example private consumption
expenditure
10. The list in the menu under “BIP-Seite” allows to switch between production or
expenditure approach. In the example in Figure 3, the expenditure approach (“Verwendung”)
is chosen.
11. If the second menu point “Aggregate” is selected, output appears on the right panel.
In the header of this panel, a drop-down menu allows to choose between the aggregates of
the selected GDP approach. The example in Figure 3 shows the output for private
consumption expenditure.
12. The graph displays the chain index for unadjusted (blue line) and seasonally adjusted
(red line) data. The nowcasts for both series are displayed as dots at the current edge of the
time line. On the left hand of the graph the estimated model is printed.
13. Below, drop-down menus show the indicators for private consumption and their
predictors as given by the input file “Steuerungsdatei” and described in Figure 1. The drop-
down menus allow to change the indicators and their predictors in a flexible way by choosing
from the time series set given in the file “Datensatz Indikatoren”. To facilitate the analysis
and choice of indicators and predictors, the time series are displayed graphically below the
menus as shown in Figure 4. Additionally, the estimated models can be displayed (see Figure
5).
ECE/CES/GE.20/2023/9
6
Figure 3
Estimation of private consumption expenditure
ECE/CES/GE.20/2023/9
7
Figure 4
Estimation of indicators for private consumption expenditure
ECE/CES/GE.20/2023/9
8
Figure 5
Estimation of indicators for private consumption expenditure, including display of estimated model
ECE/CES/GE.20/2023/9
9
C. Calculation of GDP
14. If the estimation models of all aggregates of one GDP side, here the expenditure
approach, are chosen, the calculation of aggregated GDP for the expenditure side can be
displayed be selecting the third point “BIP” in the menu bar and clicking the button “BIP
rechnen” at the top of the right panel. Then, the right panel is showing a graph with unadjusted
(blue) and seasonally adjusted (red) GDP (see Figure 6). In addition, a table under the graph
summarizes the estimates for GDP and all aggregates, giving level estimates as well as
growth rates and confidence intervals for the aggregates. Final estimation models for each
aggregate can be displayed below.
15. The same procedure is to be applied to obtain estimates for the production approach
of GDP.
16. The last button in the menu bar allows to print the estimates for each approach to an
Excel-file.
IV. Advantages and further development
17. For our regular quarterly nowcast and flash estimation, the R shiny app has the
following advantages:
• It provides a compact graphical representation of the estimated GDP aggregates and
the indicators used for them.
• The inclusion of new (digital) data in the existing data set and testing of this data
indicators and predictors is easy and quick. It allows to adjust estimation models in
a flexible way, which is especially helpful in times of crises or other unusual
economic developments
• The user interface does not only allow fast and easy adoption of models, but also
helps to understand and interpret economic interdependencies thanks to the
visualization of the time series employed in the estimation.
18. Our plans for further development of the app in the future comprise two areas. First,
we envisage to include a (pseudo) out-of-sample analysis within the application. The
estimation errors of a given estimation model could be calculated and displayed to provide
the user with an additional criterion to select the best possible model inside the application.
Second, we plan to increase the depth of calculation for more than one aggregate.
ECE/CES/GE.20/2023/9
10
Figure 6
Estimation of GDP for expenditure side
ECE/CES/GE.20/2023/9
11
Literature
Ackermann, Dickopf and Mucha (2021). Flash und Nowcast: Schnellschätzungen des
Bruttoinlandsprodukts in der Corona-Pandemie. In WISTA – Wirtschaft und Statistik,
Ausgabe 4/2021.
Dickopf, Janz and Mucha (2019. Vom BIP-Flash zum BIP-Nowcast: Erste Ergebnisse einer
Machbarkeitsstudie zur weiteren Beschleunigung der BIP-Schnellschätzung. In: WISTA
Wirtschaft und Statistik. Ausgabe 6/2019.
Eurostat (2016). Overview of GDP flash estimation methods. In: Eurostat statistical working
papers. p. 15 ff.
ECE/CES/GE.20/2023/9
12
Appendix
Figure 7
Delphi-method-style three-pillar-model to obtain a reconciled quarterly GDP at t+30
Group of Experts on National Accounts
Twenty-second session
GDP Flash Estimate and GDP Nowcast: An R-Shiny App for GDP Estimation
Prepared by the German Federal Statistics Office0F
I. Scope of the R-Shiny App
II. Econometric Approach
III. Features: Application
A. Menu and Data Input
B. Selection of approach and aggregate: example private consumption expenditure
These tables show forest products production and trade forecasts for 2022 and 2023. These cover roundwood (logs, pulpwood and fuel wood), sawnwood (coniferous and non-coniferous), wood-based panels (plywood, particle board, OSB and fibreboard), pulp, paper and wood pellets. The forecast data are provided by national correspondents and approved at the meeting of the Committee on Forests and the Forest Industry.
Languages and translations
English
List of tables
List of Tables and Notes
Table 1 - Sawn Softwood
Table 2 - Sawn Hardwood (total)
Table 2a - Sawn Hardwood (temperate)
Table 2b - Sawn Hardwood (tropical)
Table 3 - Veneer Sheets
Table 4 - Plywood
Table 5 - Particle Board (excluding OSB)
Table 5a - Oriented Strand Board
Table 6 - Fibreboard
Table 6a - Hardboard
Table 6b - MDF/HDF
Table 6c - Other Fibreboard
Table 7 - Wood Pulp
Table 8 - Paper and Paperboard
Table 9 - Removals of wood in the rough
Table 9a - Removals of wood in the rough (softwood)
Table 9b - Removals of wood in the rough (hardwood)
Table 10 - Softwood sawlogs
Table 11 - Hardwood sawlogs
Table 11a - Hardwood logs (temperate)
Table 11b - Hardwood logs (tropical)
Table 12 - Pulpwood
Table 12a - Pulpwood (softwood)
Table 12b - Pulpwood (hardwood)
Table 12c - Wood Residues, Chips and Particles
Table 13 - Wood Pellets
Table 14 - Europe: Summary table of market forecasts for 2022 and 2023
Table 15 - North America: Summary table of market forecasts for 2022 and 2023
Source: UNECE Committee on Forests and the Forest Industry , November 2022, http://www.unece.org/forests/fpm/timbercommittee.html
Notes: Data in italics are estimated by the secretariat. EECCA is Eastern Europe, Caucasus and Central Asia.
Data for the two latest years are forecasts.
In contrast to previous years, data are shown only for countries providing forecasts. Sub-regional totals are only for reporting countries.
Data are shown only for countries providing forecasts. Sub-regional totals thus reflect only the reporting countries of the subregion. No sub-regional forecast is provided for "Eastern Europe, Caucasus and Central Asia" due to lack of information provided by countries in this sub-region.
Germany – Pellets consumption is an estimated consumption as reported by Pellet Federation. There is a difference between reported consumption and apparent consumption of 242,000 metric tonnes and 214,000 metric tonnes, respectively. For 2022 and 2023, this difference is additionally stored at newly installed plants, i.e. sold but not yet consumed.
Slovenia trade figures are lower than actual as they do not include estimates for non-recorded trade with other EU countries.
Polish trade data exclude non-reporters (estimated at 1-3% of total). Residues exclude recovered wood. Polish sawnwood data exclude shop lumber. Wood pulp production is in metric tonnes, not air-dried, and excludes recovered fibre pulp. Wood pellets production data includes briquettes and non-wood based material.
United Kingdom production figures for OSB is secretariat estimate.
Softwood = coniferous, hardwood = non-coniferous
For tables 1-13, data in italics are secretariat estimates or repeated data. All other data are from national sources and are of course estimates for the current and future year.
Countries with nil, missing or confidential data for all years on a table are not shown.
Table 1
5.C
TABLE 1
SAWN SOFTWOOD
SCIAGES CONIFERES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption
Imports
Exports
Country
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
6,547
6,469
6,400
10,582
10,370
10,300
1,911
2,045
2,000
5,947
5,947
5,900
Autriche
Bulgaria
Bulgaria
528
...
...
638
...
...
25
...
...
135
...
...
Bulgarie
Cyprus
Cyprus
32
33
32
2
1
1
31
32
31
0
0
0
Chypre
Czech Republic
Czech Republic
3,250
3,249
3,272
5,015
5,144
5,279
526
555
560
2,291
2,450
2,567
République tchèque
Estonia
Estonia
2,296
1,910
1,910
1,600
1,600
1,600
1,699
1,360
1,360
1,003
1,050
1,050
Estonie
Finland
Finland
3,731
3,650
3,570
11,900
11,750
12,200
547
350
70
8,716
8,450
8,700
Finlande
Germany
Germany
20,104
19,800
19,500
25,313
25,300
25,000
5,700
5,000
4,500
10,909
10,500
10,000
Allemagne
Latvia
Latvia
1,968
1,500
1,300
3,641
3,500
3,300
1,463
900
700
3,136
2,900
2,700
Lettonie
Luxembourg
Luxembourg
56
43
43
39
39
39
28
5
5
12
1
1
Luxembourg
Malta
Malta
7
8
9
0
0
0
7
8
9
0
0
0
Malte
Netherlands
Netherlands
3,036
2,905
2,850
110
100
100
3,408
3,276
3,226
481
470
475
Pays-Bas
Poland
Poland
4,857
4,750
4,900
4,583
4,500
4,650
1,239
1,250
1,300
965
1,000
1,050
Pologne
Portugal
Portugal
632
730
665
817
850
840
121
130
125
306
250
300
Portugal
Serbia
Serbia
379
400
422
99
110
120
295
300
310
15
10
8
Serbie
Slovakia
Slovakia
563
650
675
1,302
1,300
1,325
324
350
350
1,063
1,000
1,000
Slovaquie
Slovenia
Slovenia
627
600
550
904
1,000
970
563
500
500
840
900
920
Slovénie
Sweden
Sweden
6,954
6,450
5,300
19,000
18,500
17,500
514
450
300
12,560
12,500
12,500
Suède
Switzerland
Switzerland
1,245
1,275
1,315
1,150
1,180
1,220
280
275
270
185
180
175
Suisse
UK
United Kingdom
10,960
8,920
9,410
3,574
3,010
3,400
7,623
6,150
6,250
237
240
240
Royaume-Uni
Total Europe
67,771
63,342
62,124
90,268
88,255
87,844
26,303
22,936
21,866
48,800
47,848
47,586
Total Europe
Canada
Canada a
19,841
18,893
24,156
55,842
52,183
50,290
1,030
752
745
37,031
34,041
26,878
Canada a
United States
United States a
88,263
88,484
89,272
63,417
64,178
64,820
26,931
26,270
26,533
2,085
1,963
2,081
Etats-Unis a
Total North America
108,104
107,378
113,428
119,259
116,361
115,109
27,961
27,021
27,277
39,116
36,005
28,959
Total Amérique du Nord
a converted from nominal to actual size using factor of 0.72
a convertis du dimension nominale au véritable avec une facteur du 0.72
Table 2
5.NC
TABLE 2
SAWN HARDWOOD (total)
SCIAGES NON-CONIFERES (total)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption
Imports
Exports
Country
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
186
215
220
182
178
170
177
210
220
173
173
170
Autriche
Bulgaria
Bulgaria
71
...
...
79
...
...
22
...
...
30
...
...
Bulgarie
Cyprus
Cyprus
6
7
6
0
0
0
6
7
6
0
0
0
Chypre
Czech Republic
Czech Republic
416
427
430
145
147
151
338
340
344
67
60
65
République tchèque
Estonia
Estonia
230
150
150
150
100
100
177
140
140
97
90
90
Estonie
Finland
Finland
64
55
55
54
50
50
30
25
25
20
20
20
Finlande
Germany
Germany
786
760
700
1,061
1,060
1,000
459
400
400
735
700
700
Allemagne
Latvia
Latvia
234
160
150
797
850
750
75
60
50
638
750
650
Lettonie
Luxembourg
Luxembourg
38
51
51
39
39
39
18
12
12
19
0
0
Luxembourg
Malta
Malta
7
7
8
0
0
0
7
7
8
0
0
0
Malte
Netherlands
Netherlands
309
301
283
38
40
40
343
331
308
72
70
65
Pays-Bas
Poland
Poland
512
500
540
486
460
510
312
350
380
286
310
350
Pologne
Portugal
Portugal
224
220
225
148
160
150
106
90
100
31
30
25
Portugal
Serbia
Serbia
185
197
200
353
382
390
103
95
100
271
280
290
Serbie
Slovakia
Slovakia
225
325
350
350
375
400
52
100
100
177
150
150
Slovaquie
Slovenia
Slovenia
121
55
80
140
125
130
99
100
100
118
170
150
Slovénie
Sweden
Sweden
111
110
110
100
100
100
50
45
45
39
35
35
Suède
Switzerland
Switzerland
80
85
90
55
60
65
40
40
40
15
15
15
Suisse
UK
United Kingdom
534
540
540
37
40
40
536
540
540
39
40
40
Royaume-Uni
Total Europe
4,338
4,166
4,188
4,215
4,166
4,085
2,950
2,892
2,918
2,826
2,893
2,815
Total Europe
Canada
Canada
1,208
1,229
1,116
880
813
714
798
894
779
470
478
377
Canada
United States
United States
14,348
15,065
14,707
17,326
17,607
17,467
717
1,040
878
3,695
3,581
3,638
Etats-Unis
Total North America
15,556
16,295
15,823
18,206
18,420
18,181
1,514
1,934
1,658
4,165
4,059
4,015
Total Amérique du Nord
Table 2a
TABLE 2a
SAWN HARDWOOD (temperate)
SCIAGES NON-CONIFERES (zone tempérée)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption
Imports
Exports
Country
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
182
211
216
182
178
170
172
205
215
172
172
169
Autriche
Bulgaria
Bulgaria
70
...
...
79
...
...
21
...
...
30
...
...
Bulgarie
Cyprus
Cyprus
4
3
2
0
0
0
4
3
2
0
0
0
Chypre
Czech Republic
Czech Republic
409
421
424
145
147
151
329
331
335
65
57
62
République tchèque
Estonia
Estonia
227
149
149
150
100
100
172
136
136
94
87
87
Estonie
Finland
Finland
63
54
54
54
50
50
25
20
20
16
16
16
Finlande
Germany
Germany
747
718
658
1,059
1,058
998
385
325
325
698
665
665
Allemagne
Luxembourg
Luxembourg
27
49
49
39
39
39
6
10
10
19
0
0
Luxembourg
Malta
Malta
6
6
7
0
0
0
6
6
7
0
0
0
Malte
Netherlands
Netherlands
166
160
142
31
32
32
184
177
154
49
49
45
Pays-Bas
Poland
Poland
498
485
524
486
459
509
295
333
362
283
307
347
Pologne
Portugal
Portugal
222
190
197
136
150
137
74
50
70
-12
10
10
Portugal
Serbia
Serbia
184
196
199
352
381
389
103
95
100
271
280
290
Serbie
Slovenia
Slovenia
119
52
77
140
125
130
96
97
97
118
170
150
Slovénie
Sweden
Sweden
111
109
109
100
100
100
49
44
44
37
35
35
Suède
Switzerland
Switzerland
71
76
81
52
57
62
34
34
34
15
15
15
Suisse
UK
United Kingdom
458
460
460
37
40
40
456
460
460
36
40
40
Royaume-Uni
Total Europe
3,563
3,340
3,349
3,042
2,917
2,908
2,411
2,326
2,372
1,890
1,903
1,931
Total Europe
Canada
Canada
1,202
1,214
1,107
880
813
714
781
865
753
459
464
360
Canada
United States
United States
14,162
14,835
14,498
17,326
17,607
17,467
491
773
632
3,656
3,545
3,600
Etats-Unis
Total North America
15,364
16,049
15,605
18,206
18,420
18,181
1,272
1,638
1,385
4,115
4,009
3,960
Total Amérique du Nord
Table 2b
5.NC.T
TABLE 2b
SAWN HARDWOOD (tropical)
SCIAGES NON-CONIFERES (tropicale)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption
Imports
Exports
Country
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
4
4
4
0
0
0
5
5
5
1
1
1
Autriche
Bulgaria
Bulgaria
1
...
...
0
...
...
1
...
...
0
...
...
Bulgarie
Cyprus
Cyprus
2
4
4
0
0
0
2
4
4
0
0
0
Chypre
Czech Republic
Czech Republic
7
6
6
0
0
0
9
9
9
2
3
3
République tchèque
Estonia
Estonia
3
1
1
0
0
0
5
4
4
2
3
3
Estonie
Finland
Finland
1
1
1
0
0
0
5
5
5
4
4
4
Finlande
Germany
Germany
39
42
42
2
2
2
74
75
75
37
35
35
Allemagne
Luxembourg
Luxembourg
12
2
2
0
0
0
12
2
2
0
0
0
Luxembourg
Malta
Malta
1
1
1
0
0
0
1
1
1
0
0
0
Malte
Netherlands
Netherlands
143
141
141
8
8
8
159
154
153
23
21
20
Pays-Bas
Poland
Poland
14
15
16
0
1
1
17
17
18
3
3
3
Pologne
Portugal
Portugal
2
30
28
12
10
13
32
40
30
43
20
15
Portugal
Serbia
Serbia
1
1
1
1
1
1
0
0
0
0
0
0
Serbie
Slovenia
Slovenia
2
3
3
0
0
0
3
3
3
0
0
0
Slovénie
Sweden
Sweden
-0
1
1
0
0
0
1
1
1
1
0
0
Suède
Switzerland
Switzerland
9
9
9
3
3
3
6
6
6
0
0
0
Suisse
UK
United Kingdom
76
80
80
0
0
0
79
80
80
3
0
0
Royaume-Uni
Total Europe
316
341
339
26
25
28
412
406
396
122
90
84
Total Europe
Canada
Canada
6
16
9
0
0
0
16
29
26
11
14
17
Canada
United States
United States
186
230
208
0
0
0
226
267
246
39
36
38
Etats-Unis
Total North America
192
246
217
0
0
0
242
296
273
50
50
55
Total Amérique du Nord
Table 3
6.1x
TABLE 3
VENEER SHEETS
FEUILLES DE PLACAGE
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption
Imports
Exports
Country
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
59
56
56
8
7
7
70
65
65
18
16
16
Autriche
Bulgaria
Bulgaria
31
...
...
18
...
...
24
...
...
10
...
...
Bulgarie
Cyprus
Cyprus
1
1
1
0
0
0
1
1
1
0
0
0
Chypre
Czech Republic
Czech Republic
11
20
21
30
32
33
41
50
54
60
62
66
République tchèque
Estonia
Estonia
103
140
140
110
140
140
85
86
90
92
86
90
Estonie
Finland
Finland
8
8
7
170
184
178
9
9
9
171
185
180
Finlande
Germany
Germany
167
165
160
116
115
110
111
110
110
59
60
60
Allemagne
Latvia
Latvia
155
85
55
42
45
45
154
110
50
41
70
40
Lettonie
Luxembourg
Luxembourg
1
0
0
0
0
0
1
0
0
0
0
0
Luxembourg
Malta
Malta
1
1
2
0
0
0
1
1
2
0
0
0
Malte
Netherlands
Netherlands
34
34
34
0
0
0
41
41
41
7
7
7
Pays-Bas
Poland
Poland
146
145
150
46
42
44
121
125
130
21
22
24
Pologne
Portugal
Portugal
-71
12
3
21
22
23
37
30
30
130
40
50
Portugal
Serbia
Serbia
19
18
23
27
22
25
13
14
15
21
18
17
Serbie
Slovakia
Slovakia
16
25
25
29
30
30
21
20
20
34
25
25
Slovaquie
Slovenia
Slovenia
8
4
5
23
24
21
14
14
14
29
34
30
Slovénie
Sweden
Sweden
30
25
20
60
55
50
17
20
15
47
50
45
Suède
Switzerland
Switzerland
3
3
3
0
0
0
4
4
4
1
1
1
Suisse
UK
United Kingdom
14
10
10
0
0
0
14
10
10
0
0
0
Royaume-Uni
Total Europe
737
752
715
700
718
706
779
710
660
742
676
651
Total Europe
Canada
Canada
144
181
173
581
565
565
183
222
230
620
607
622
Canada
United States
United States
2,675
2,784
2,730
2,284
2,284
2,284
671
759
715
281
258
269
Etats-Unis
Total North America
2,819
2,965
2,903
2,866
2,849
2,849
854
981
945
901
865
891
Total Amérique du Nord
Note: Definition of veneers now includes all production (including converted directly to plywood). However most replies here continue to exclude the part going to plywood.
La définition des placages comprend maintenant toute la production (y compris la conversion directe en contreplaqué).
Cependant, la plupart des réponses continuent d'exclure la partie destinée au contreplaqué.
Table 4
6.2x
TABLE 4
PLYWOOD
CONTREPLAQUES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption
Imports
Exports
Country
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
94
85
80
184
180
170
267
205
200
357
300
290
Autriche
Bulgaria
Bulgaria
65
...
...
37
...
...
66
...
...
38
...
...
Bulgarie
Cyprus
Cyprus
13
15
14
0
0
0
13
15
14
0
0
0
Chypre
Czech Republic
Czech Republic
201
199
201
260
262
263
186
188
187
245
251
249
République tchèque
Estonia
Estonia
102
100
100
190
180
180
118
110
110
206
190
190
Estonie
Finland
Finland
296
280
285
1,130
1,120
1,120
121
110
100
955
950
935
Finlande
Germany
Germany
1,185
1,170
1,170
103
100
100
1,464
1,450
1,450
382
380
380
Allemagne
Latvia
Latvia
68
30
30
310
300
250
98
60
30
340
330
250
Lettonie
Luxembourg
Luxembourg
11
2
2
0
0
0
12
2
2
1
0
0
Luxembourg
Malta
Malta
10
11
11
0
0
0
10
11
11
0
0
0
Malte
Netherlands
Netherlands
600
580
565
0
0
0
695
670
650
95
90
85
Pays-Bas
Poland
Poland
773
770
790
543
540
550
604
620
650
374
390
410
Pologne
Portugal
Portugal
215
215
200
126
110
100
116
130
120
27
25
20
Portugal
Serbia
Serbia
41
43
48
15
14
17
30
32
34
4
3
3
Serbie
Slovakia
Slovakia
232
320
345
307
375
400
65
70
70
140
125
125
Slovaquie
Slovenia
Slovenia
79
66
68
102
96
98
57
50
50
80
80
80
Slovénie
Sweden
Sweden
260
245
245
101
90
90
206
200
200
47
45
45
Suède
Switzerland
Switzerland
209
214
220
7
7
8
205
210
215
3
3
3
Suisse
UK
United Kingdom
1,486
1,490
1,490
0
0
0
1,541
1,540
1,540
55
50
50
Royaume-Uni
Total Europe
5,940
5,835
5,864
3,415
3,374
3,346
5,874
5,673
5,633
3,349
3,212
3,115
Total Europe
Canada
Canada
2,485
2,288
2,490
1,698
1,644
1,639
1,421
1,144
1,406
634
500
555
Canada
United States
United States
17,031
17,295
17,163
9,705
9,895
9,800
8,086
8,163
8,124
759
762
761
Etats-Unis
Total North America
19,516
19,583
19,653
11,403
11,539
11,439
9,507
9,306
9,530
1,393
1,263
1,316
Total Amérique du Nord
Table 5
6.3xPB
TABLE 5
PARTICLE BOARD (excluding OSB)
PANNEAUX DE PARTICULES (ne comprennent pas l'OSB)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption
Imports
Exports
Country
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
868
776
766
2,550
2,350
2,300
361
370
360
2,043
1,944
1,894
Autriche
Bulgaria
Bulgaria
487
...
...
773
...
...
118
...
...
403
...
...
Bulgarie
Cyprus
Cyprus
47
42
41
0
0
0
47
42
41
0
0
0
Chypre
Czech Republic
Czech Republic
739
688
671
965
945
930
578
598
577
804
855
836
République tchèque
Estonia
Estonia
182
155
155
210
130
130
76
85
85
103
60
60
Estonie
Finland
Finland
107
119
119
54
50
50
83
93
93
30
24
24
Finlande
Germany
Germany
6,015
5,970
5,870
6,036
6,020
5,920
2,142
2,100
2,050
2,162
2,150
2,100
Allemagne
Latvia
Latvia
139
120
180
350
300
300
53
70
80
264
250
200
Lettonie
Luxembourg
Luxembourg
15
3
3
0
0
0
16
4
4
2
1
1
Luxembourg
Malta
Malta
10
10
11
0
0
0
10
10
11
0
0
0
Malte
Netherlands
Netherlands
446
430
430
0
0
0
520
500
500
74
70
70
Pays-Bas
Poland
Poland
7,601
7,700
7,740
6,333
6,370
6,370
2,093
2,150
2,220
824
820
850
Pologne
Portugal
Portugal
451
527
427
743
730
720
313
295
304
605
498
597
Portugal
Serbia
Serbia
420
417
422
272
230
235
208
235
240
60
48
53
Serbie
Slovakia
Slovakia
182
220
215
608
625
625
143
140
135
568
545
545
Slovaquie
Slovenia
Slovenia
155
155
147
0
0
0
162
163
154
6
8
7
Slovénie
Sweden
Sweden
971
985
975
561
550
550
506
520
510
97
85
85
Suède
Switzerland
Switzerland
280
300
320
380
390
400
125
130
135
225
220
215
Suisse
UK
United Kingdom
2,664
2,242
2,242
2,090
1,722
1,722
638
600
600
65
80
80
Royaume-Uni
Total Europe
21,780
20,859
20,734
21,926
20,412
20,252
8,189
8,105
8,099
8,336
7,658
7,617
Total Europe
Canada
Canada
1,487
1,594
1,591
1,647
1,724
1,686
593
594
586
754
724
681
Canada
United States
United States
5,111
7,189
5,725
4,136
4,220
3,874
1,462
3,144
2,159
488
175
309
Etats-Unis
Total North America
6,597
8,783
7,316
5,783
5,944
5,560
2,056
3,738
2,745
1,241
899
989
Total Amérique du Nord
Data are calculated by subtracting OSB from the particleboard/OSB total - les données sont calculées en soustrayant les OSB du total des panneaux de particules et OSB.
Table 5a
6.3.1
TABLE 5a
ORIENTED STRAND BOARD (OSB)
PANNEAUX STRUCTURAUX ORIENTES (OSB)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption
Imports
Exports
Country
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
186
224
214
0
0
0
192
230
220
6
6
6
Autriche
Bulgaria
Bulgaria
175
...
...
252
...
...
8
...
...
85
...
...
Bulgarie
Cyprus
Cyprus
15
18
17
0
0
0
15
18
17
0
0
0
Chypre
Czech Republic
Czech Republic
355
360
366
745
770
795
127
132
135
517
542
564
République tchèque
Estonia
Estonia
44
45
45
0
0
0
44
45
45
1
0
0
Estonie
Finland
Finland
47
47
47
0
0
0
47
47
47
0
0
0
Finlande
Germany
Germany
1,473
1,480
1,480
1,282
1,280
1,280
746
750
750
555
550
550
Allemagne
Latvia
Latvia
211
160
100
700
600
600
73
60
50
562
500
550
Lettonie
Luxembourg
Luxembourg
117
265
265
338
338
338
7
1
1
229
74
74
Luxembourg
Netherlands
Netherlands
192
185
185
0
0
0
208
200
200
16
15
15
Pays-Bas
Poland
Poland
802
800
860
827
830
880
316
350
380
341
380
400
Pologne
Portugal
Portugal
31
33
33
0
0
0
34
35
36
3
2
3
Portugal
Serbia
Serbia
44
53
58
0
0
0
46
55
60
2
2
2
Serbie
Slovakia
Slovakia
91
90
95
0
0
0
94
95
100
3
5
5
Slovaquie
Slovenia
Slovenia
33
35
34
0
0
0
36
37
36
2
2
2
Slovénie
Sweden
Sweden
116
95
95
0
0
0
121
100
100
5
5
5
Suède
Switzerland
Switzerland
90
90
90
0
0
0
90
90
90
0
0
0
Suisse
UK
United Kingdom
925
868
868
598
598
598
461
440
440
133
170
170
Royaume-Uni
Total Europe
4,948
4,848
4,852
4,741
4,416
4,491
2,665
2,685
2,707
2,459
2,253
2,346
Total Europe
Canada
Canada
1,618
1,589
1,570
7,240
7,581
7,646
124
72
72
5,746
6,064
6,147
Canada
United States
United States
19,804
20,091
20,381
13,839
14,040
14,243
6,147
6,236
6,326
182
185
188
Etats-Unis
Total North America
21,422
21,680
21,951
21,079
21,621
21,889
6,271
6,308
6,398
5,928
6,249
6,335
Total Amérique du Nord
Table 6
TABLE 6
FIBREBOARD
PANNEAUX DE FIBRES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption
Imports
Exports
Country
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
504
469
445
690
570
545
370
340
330
556
441
430
Autriche
Bulgaria
Bulgaria
106
...
...
75
...
...
118
...
...
87
...
...
Bulgarie
Cyprus
Cyprus
15
16
15
0
0
0
15
16
15
0
0
0
Chypre
Czech Republic
Czech Republic
462
470
481
45
46
47
574
590
609
157
166
175
République tchèque
Estonia
Estonia
74
68
68
80
70
70
77
69
69
83
71
71
Estonie
Finland
Finland
162
165
165
46
46
46
164
161
161
48
41
41
Finlande
Germany
Germany
4,401
4,425
4,335
6,105
6,100
6,000
1,944
1,940
1,865
3,648
3,615
3,530
Allemagne
Latvia
Latvia
19
11
6
37
35
20
69
57
47
87
81
61
Lettonie
Luxembourg
Luxembourg
13
128
128
147
147
147
19
5
5
153
24
24
Luxembourg
Malta
Malta
...
...
...
...
...
...
...
...
...
...
...
...
Malte
Netherlands
Netherlands
454
436
436
29
29
29
572
550
550
147
143
143
Pays-Bas
Poland
Poland
4,398
4,650
4,770
5,750
5,850
6,050
912
970
990
2,264
2,170
2,270
Pologne
Portugal
Portugal
488
510
500
555
550
540
336
335
340
404
375
380
Portugal
Serbia
Serbia
123
151
163
21
18
20
143
168
181
41
35
38
Serbie
Slovakia
Slovakia
248
239
239
0
0
0
275
265
265
27
26
26
Slovaquie
Slovenia
Slovenia
26
30
30
136
135
135
57
55
55
167
160
160
Slovénie
Sweden
Sweden
308
317
293
0
0
0
391
395
365
84
78
72
Suède
Switzerland
Switzerland
292
302
312
205
210
215
266
266
266
179
174
169
Suisse
UK
United Kingdom
1,807
1,780
1,710
798
900
850
1,080
950
930
72
70
70
Royaume-Uni
Total Europe
13,901
14,168
14,097
14,719
14,706
14,714
7,385
7,132
7,043
8,203
7,670
7,660
Total Europe
Canada
Canada
1,492
1,348
1,355
1,349
1,395
1,395
1,009
889
885
866
936
924
Canada
United States
United States
9,727
10,134
9,985
7,560
7,691
7,663
3,008
3,190
3,123
841
747
801
Etats-Unis
Total North America
11,219
11,482
11,340
8,909
9,086
9,058
4,017
4,079
4,007
1,707
1,683
1,725
Total Amérique du Nord
Table 6a
6.4.1
TABLE 6a
HARDBOARD
PANNEAUX DURS
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption
Imports
Exports
Country
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
32
36
34
75
48
45
17
20
19
60
32
30
Autriche
Bulgaria
Bulgaria
45
...
...
51
...
...
40
...
...
46
...
...
Bulgarie
Cyprus
Cyprus
1
1
1
0
0
0
1
1
1
0
0
0
Chypre
Czech Republic
Czech Republic
146
146
147
0
0
0
167
168
170
21
22
23
République tchèque
Estonia
Estonia
27
24
24
0
0
0
34
30
30
7
6
6
Estonie
Finland
Finland
26
30
30
46
46
46
21
20
20
41
36
36
Finlande
Germany
Germany
213
210
210
0
0
0
242
240
240
29
30
30
Allemagne
Latvia
Latvia
11
10
5
0
0
0
23
20
15
12
10
10
Lettonie
Luxembourg
Luxembourg
-71
-9
-9
0
0
0
2
1
1
73
10
10
Luxembourg
Netherlands
Netherlands
44
35
35
0
0
0
66
55
55
22
20
20
Pays-Bas
Poland
Poland
-212
10
10
76
100
100
139
180
180
427
270
270
Pologne
Portugal
Portugal
44
20
30
12
0
0
42
30
40
10
10
10
Portugal
Serbia
Serbia
33
38
41
21
18
20
31
35
37
19
15
16
Serbie
Slovakia
Slovakia
17
20
20
0
0
0
22
25
25
5
5
5
Slovaquie
Slovenia
Slovenia
1
0
0
0
0
0
8
6
6
7
6
6
Slovénie
Sweden
Sweden
75
87
78
0
0
0
88
100
90
14
13
12
Suède
Switzerland
Switzerland
13
13
13
0
0
0
21
21
21
8
8
8
Suisse
UK
United Kingdom
101
100
100
0
0
0
111
110
110
11
10
10
Royaume-Uni
Total Europe
544
771
769
281
212
211
1,075
1,062
1,060
812
503
502
Total Europe
Canada
Canada
46
42
36
90
90
90
68
60
66
112
108
120
Canada
United States
United States
503
509
514
499
504
509
252
255
258
248
250
253
Etats-Unis
Total North America
549
551
550
589
594
599
320
315
324
360
358
373
Total Amérique du Nord
Table 6b
6.4.2
TABLE 6b
MDF/HDF
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption
Imports
Exports
Country
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
316
283
265
615
522
500
192
166
161
491
405
396
Autriche
Bulgaria
Bulgaria
58
...
...
24
...
...
75
...
...
41
...
...
Bulgarie
Cyprus
Cyprus
13
13
12
0
0
0
13
13
12
0
0
0
Chypre
Czech Republic
Czech Republic
248
249
251
45
46
47
243
250
258
40
47
54
République tchèque
Estonia
Estonia
21
20
20
0
0
0
38
35
35
17
15
15
Estonie
Finland
Finland
117
116
116
0
0
0
124
121
121
7
5
5
Finlande
Germany
Germany
2,385
2,425
2,400
4,693
4,700
4,650
625
625
600
2,932
2,900
2,850
Allemagne
Latvia
Latvia
1
0
0
37
35
20
28
25
20
64
60
40
Lettonie
Luxembourg
Luxembourg
80
136
136
147
147
147
13
3
3
80
14
14
Luxembourg
Malta
Malta
5
5
5
0
0
0
5
5
5
0
0
0
Malte
Netherlands
Netherlands
291
285
285
0
0
0
408
400
400
117
115
115
Pays-Bas
Poland
Poland
3,533
3,560
3,630
3,542
3,600
3,700
743
760
780
752
800
850
Pologne
Portugal
Portugal
456
485
465
535
550
540
280
285
280
359
350
355
Portugal
Serbia
Serbia
88
110
118
0
0
0
110
130
140
22
20
22
Serbie
Slovakia
Slovakia
162
150
150
0
0
0
183
170
170
22
20
20
Slovaquie
Slovenia
Slovenia
20
29
29
136
135
135
39
43
43
155
149
149
Slovénie
Sweden
Sweden
213
210
200
0
0
0
272
265
250
59
55
50
Suède
Switzerland
Switzerland
105
110
115
205
210
215
70
65
60
170
165
160
Suisse
UK
United Kingdom
1,622
1,600
1,530
798
900
850
878
750
730
54
50
50
Royaume-Uni
Total Europe
9,734
9,786
9,727
10,776
10,845
10,804
4,341
4,111
4,068
5,383
5,170
5,145
Total Europe
Canada
Canada
1,301
1,135
1,153
1,159
1,205
1,205
780
648
639
637
718
691
Canada
United States
United States
6,012
6,042
6,073
3,882
3,901
3,921
2,552
2,565
2,578
422
424
426
Etats-Unis
Total North America
7,313
7,177
7,226
5,041
5,106
5,126
3,332
3,213
3,217
1,059
1,142
1,117
Total Amérique du Nord
Table 6c
6.4.3
TABLE 6c
OTHER FIBREBOARD
AUTRES PANNEAUX DE FIBRES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption
Imports
Exports
Country
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
157
150
146
0
0
0
162
154
150
5
4
4
Autriche
Bulgaria
Bulgaria
3
...
...
0
...
...
3
...
...
0
...
...
Bulgarie
Cyprus
Cyprus
1
2
2
0
0
0
1
2
2
0
0
0
Chypre
Czech Republic
Czech Republic
68
75
83
0
0
0
164
172
181
96
97
98
République tchèque
Estonia
Estonia
26
24
24
80
70
70
5
4
4
59
50
50
Estonie
Finland
Finland
19
19
19
0
0
0
20
20
20
0
0
0
Finlande
Germany
Germany
1,803
1,790
1,725
1,412
1,400
1,350
1,078
1,075
1,025
686
685
650
Allemagne
Latvia
Latvia
7
1
1
0
0
0
18
12
12
11
11
11
Lettonie
Luxembourg
Luxembourg
4
1
1
0
0
0
4
1
1
0
0
0
Luxembourg
Malta
Malta
1
1
2
0
0
0
1
1
2
0
0
0
Malte
Netherlands
Netherlands
119
116
116
29
29
29
98
95
95
8
8
8
Pays-Bas
Poland
Poland
1,077
1,080
1,130
2,132
2,150
2,250
30
30
30
1,085
1,100
1,150
Pologne
Portugal
Portugal
-12
5
5
8
0
0
15
20
20
35
15
15
Portugal
Serbia
Serbia
2
3
4
0
0
0
2
3
4
0
0
0
Serbie
Slovakia
Slovakia
70
69
69
0
0
0
70
70
70
0
1
1
Slovaquie
Slovenia
Slovenia
5
1
1
0
0
0
10
6
6
5
5
5
Slovénie
Sweden
Sweden
20
20
15
0
0
0
31
30
25
11
10
10
Suède
Switzerland
Switzerland
174
179
184
0
0
0
175
180
185
1
1
1
Suisse
UK
United Kingdom
84
80
80
0
0
0
91
90
90
7
10
10
Royaume-Uni
Total Europe
3,628
3,616
3,607
3,661
3,649
3,699
1,976
1,965
1,922
2,009
1,997
2,013
Total Europe
Canada
Canada
144
171
166
100
100
100
162
181
180
117
110
114
Canada
United States
United States
3,212
3,583
3,398
3,179
3,286
3,233
204
370
287
171
73
122
Etats-Unis
Total North America
3,357
3,754
3,564
3,279
3,386
3,333
366
551
467
288
183
236
Total Amérique du Nord
Table 7
7.x
TABLE 7
WOOD PULP
PATE DE BOIS
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 mt
Apparent Consumption
Imports
Exports
Country
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
2,261
2,229
2,235
2,004
1,970
1,980
578
577
577
321
318
322
Autriche
Bulgaria
Bulgaria
125
...
...
210
...
...
7
...
...
92
...
...
Bulgarie
Czech Republic
Czech Republic
803
806
806
614
640
660
310
300
289
121
134
143
République tchèque
Estonia
Estonia
84
140
140
260
200
200
49
50
50
225
110
110
Estonie
Finland
Finland a
6,625
5,760
6,280
10,950
9,360
10,520
150
220
220
4,475
3,820
4,460
Finlande a
Germany
Germany
5,622
5,685
5,715
2,327
2,390
2,420
4,451
4,400
4,400
1,156
1,105
1,105
Allemagne
Latvia
Latvia
2
6
2
14
10
10
2
2
2
14
6
10
Lettonie
Netherlands
Netherlands
929
987
987
37
37
37
2,167
2,150
2,150
1,274
1,200
1,200
Pays-Bas
Poland
Poland
2,767
2,750
2,790
1,749
1,720
1,750
1,194
1,220
1,250
177
190
210
Pologne
Portugal
Portugal
1,660
1,640
1,645
2,809
2,750
2,800
141
140
145
1,290
1,250
1,300
Portugal
Serbia
Serbia
75
76
79
0
0
0
76
77
80
1
1
1
Serbie
Slovakia
Slovakia
680
685
695
769
775
800
160
160
170
248
250
275
Slovaquie
Slovenia
Slovenia
331
309
309
86
82
82
250
230
230
5
3
3
Slovénie
Sweden
Sweden
8,146
8,250
8,400
11,701
11,950
12,150
602
600
600
4,157
4,300
4,350
Suède
Switzerland
Switzerland
160
160
160
70
70
70
90
90
90
0
0
0
Suisse
UK
United Kingdom
984
990
...
220
220
...
766
780
790
2
10
10
Royaume-Uni
Total Europe
31,255
30,473
30,243
33,820
32,174
33,479
10,993
10,996
11,043
13,558
12,697
13,499
Total Europe
Canada
Canada
7,265
6,097
6,156
14,886
13,861
13,468
1,095
950
1,185
8,717
8,714
8,497
Canada
United States
United States
48,100
48,274
48,187
49,685
49,859
49,772
6,036
6,036
6,036
7,621
7,621
7,621
Etats-Unis
Total North America
55,365
54,372
54,344
64,571
63,720
63,240
7,131
6,986
7,221
16,337
16,335
16,118
Total Amérique du Nord
a imports exclude dissolving pulp
a les importations excluent pâte à dissoudre
Table 8
10.x
TABLE 8
PAPER AND PAPERBOARD
PAPIERS ET CARTONS
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 mt
Apparent Consumption
Imports
Exports
Country
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
2,334
2,340
2,315
5,065
5,100
5,080
1,296
1,310
1,300
4,028
4,070
4,065
Autriche
Bulgaria
Bulgaria
573
...
...
394
...
...
358
...
...
180
...
...
Bulgarie
Cyprus
Cyprus
44
48
46
0
0
0
44
48
46
0
0
0
Chypre
Czech Republic
Czech Republic
1,618
1,602
1,604
901
906
909
1,623
1,604
1,600
906
908
905
République tchèque
Estonia
Estonia
126
150
150
70
90
90
138
130
130
83
70
70
Estonie
Finland
Finland
636
590
620
8,660
7,450
8,150
361
350
350
8,385
7,210
7,880
Finlande
Germany
Germany
18,980
18,500
18,400
23,123
22,800
22,700
10,009
9,800
9,800
14,152
14,100
14,100
Allemagne
Latvia
Latvia
174
182
182
28
30
30
186
200
200
40
48
48
Lettonie
Luxembourg
Luxembourg
31
8
8
0
0
0
38
8
8
7
0
0
Luxembourg
Malta
Malta
26
27
27
0
0
0
26
27
27
0
0
0
Malte
Netherlands
Netherlands
2,869
2,890
2,890
2,942
2,950
2,950
2,268
2,260
2,260
2,341
2,320
2,320
Pays-Bas
Poland
Poland
8,002
8,100
8,150
5,324
5,450
5,550
5,233
5,300
5,400
2,556
2,650
2,800
Pologne
Portugal
Portugal
1,245
1,250
1,290
2,247
2,200
2,240
928
850
900
1,931
1,800
1,850
Portugal
Serbia
Serbia
760
780
785
535
520
525
462
470
480
237
210
220
Serbie
Slovakia
Slovakia
554
600
600
1,019
975
1,000
474
450
475
939
825
875
Slovaquie
Slovenia
Slovenia
491
435
440
635
605
590
435
420
420
579
590
570
Slovénie
Sweden
Sweden
704
950
950
8,924
8,700
8,850
897
750
750
9,117
8,500
8,650
Suède
Switzerland
Switzerland
1,050
1,055
1,060
1,170
1,175
1,180
610
600
590
730
720
710
Suisse
UK
United Kingdom
7,482
7,430
7,450
3,640
3,530
3,650
4,589
4,660
4,550
747
760
750
Royaume-Uni
Total Europe
47,697
46,937
46,967
64,677
62,481
63,494
29,977
29,237
29,286
46,957
44,781
45,813
Total Europe
Canada
Canada
4,940
4,796
4,930
8,787
8,436
8,436
2,424
2,567
2,538
6,272
6,207
6,045
Canada
United States
United States
65,622
68,268
66,945
67,476
70,196
68,836
8,223
8,555
8,389
10,077
10,483
10,280
Etats-Unis
Total North America
70,561
73,064
71,874
76,263
78,632
77,272
10,647
11,122
10,927
16,348
16,690
16,325
Total Amérique du Nord
Table 9
TABLE 9
REMOVALS OF WOOD IN THE ROUGH
QUANTITES ENLEVEES DE BOIS BRUT
TOTAL
TOTAL
1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
Country
Industrial wood - Bois industriels
Wood fuel c
Bois de chauffage c
Pays
Total
Logs
Pulpwood a
Other b
Total
Grumes
Bois de trituration a
Autre b
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
13,521
13,873
13,650
10,420
10,607
10,300
3,101
3,266
3,350
0
0
0
4,900
5,263
5,400
18,420
19,136
19,050
Autriche
Bulgaria
Bulgaria
3,172
...
...
1,524
...
...
1,606
...
...
42
...
...
2,357
...
...
5,529
...
...
Bulgarie
Cyprus
Cyprus
...
...
...
...
...
...
...
...
...
0
0
0
7
7
8
...
...
...
Chypre
Czech Republic
Czech Republic
25,146
21,122
20,576
17,739
15,858
14,572
7,294
5,149
5,887
113
115
117
5,110
4,933
4,536
30,256
26,055
25,112
République tchèque
Estonia
Estonia
6,520
6,317
6,317
4,145
4,060
4,060
2,323
2,200
2,200
52
57
57
4,148
4,100
4,100
10,667
10,417
10,417
Estonie
Finland
Finland
58,036
55,847
58,540
26,292
24,618
24,713
31,744
31,229
33,827
0
0
0
8,868
8,868
8,868
66,904
64,715
67,408
Finlande
Germany
Germany
59,187
57,179
54,270
47,403
44,256
42,085
11,624
12,765
12,027
161
158
158
23,224
23,900
24,100
82,411
81,079
78,370
Allemagne
Latvia
Latvia
13,003
12,650
12,550
7,827
7,400
7,300
3,986
4,100
4,100
1,190
1,150
1,150
2,940
3,150
3,200
15,943
15,800
15,750
Lettonie
Luxembourg
Luxembourg
217
332
197
39
86
144
84
160
38
94
86
15
46
73
45
262
405
242
Luxembourg
Netherlands
Netherlands
648
653
653
210
214
214
394
395
395
43
44
44
2,362
2,350
2,350
3,010
3,003
3,003
Pays-Bas
Poland
Poland
38,587
40,300
41,530
18,508
19,300
19,950
19,471
20,410
21,000
608
590
580
4,519
4,450
4,350
43,106
44,750
45,880
Pologne
Portugal
Portugal
12,136
12,240
12,155
2,147
2,190
2,220
9,659
9,700
9,600
331
350
335
1,762
1,830
1,780
13,899
14,070
13,935
Portugal
Serbia
Serbia
1,646
1,586
1,630
1,176
1,166
1,185
307
280
295
163
140
150
6,251
6,950
7,010
7,897
8,536
8,640
Serbie
Slovakia
Slovakia
7,170
7,475
7,590
4,243
4,335
4,400
2,893
3,100
3,150
34
40
40
495
550
610
7,665
8,025
8,200
Slovaquie
Slovenia
Slovenia
2,673
3,078
2,995
1,977
2,210
2,130
648
825
825
48
43
40
1,043
1,200
1,260
3,716
4,278
4,255
Slovénie
Sweden
Sweden
71,400
71,400
70,400
39,300
37,800
36,000
31,800
33,300
34,100
300
300
300
5,400
5,400
5,400
76,800
76,800
75,800
Suède
Switzerland
Switzerland
3,003
3,088
3,163
2,450
2,550
2,610
550
535
550
3
3
3
1,980
2,030
2,100
4,983
5,118
5,263
Suisse
UK
United Kingdom
8,716
7,660
8,410
6,354
5,360
6,060
1,898
1,900
1,900
463
400
450
2,184
2,180
2,180
10,899
9,840
10,590
Royaume-Uni
Total Europe
324,781
314,800
314,626
191,753
182,010
177,943
129,382
129,314
133,244
3,646
3,476
3,439
77,596
77,234
77,297
402,369
392,027
391,915
Total Europe
Canada
Canada
138,131
135,303
135,303
120,741
117,995
117,995
15,239
15,040
15,040
2,152
2,268
2,268
1,472
1,567
1,567
139,603
136,869
136,869
Canada
United States
United States
382,956
386,045
384,500
183,473
184,966
184,219
185,734
187,318
186,526
13,749
13,762
13,755
71,111
71,127
71,119
454,066
457,172
455,619
Etats-Unis
Total North America
521,087
521,348
519,803
304,213
302,961
302,214
200,973
202,358
201,566
15,901
16,030
16,023
72,582
72,693
72,685
593,669
594,041
592,488
Total Amérique du Nord
a
Pulpwood, round and split, as well as chips and particles produced directly
a
Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées
therefrom and used as pulpwood
directement à partir des rondins et quartiers et utilisées comme bois de trituration
b
Pitprops, poles, piling, posts etc.
b
Bois de mine, poteaux, pilotis, piquets etc.
c
Including chips and particles produced from wood in the rough and
c
Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées
used for energy purposes
à des fins energétiques
Table 9a
1.2.3.C
TABLE 9a
REMOVALS OF WOOD IN THE ROUGH
QUANTITES ENLEVEES DE BOIS BRUT
SOFTWOOD
CONIFERES
1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
Country
Industrial wood - Bois industriels
Wood fuel c
Bois de chauffage c
Pays
Total
Logs
Pulpwood a
Other b
Total
Grumes
Bois de trituration a
Autre b
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
12,671
12,947
12,700
10,139
10,315
10,000
2,531
2,632
2,700
0
0
0
2,993
3,158
3,200
15,663
16,105
15,900
Autriche
Bulgaria
Bulgaria
2,228
...
...
1,178
...
...
1,019
...
...
31
...
...
608
...
...
2,836
...
...
Bulgarie
Cyprus
Cyprus
...
...
...
2
2
2
...
...
...
0
0
0
6
6
7
...
...
...
Chypre
Czech Republic
Czech Republic
24,251
20,470
19,943
17,301
15,480
14,210
6,841
4,880
5,621
109
110
112
4,463
4,365
3,965
28,714
24,835
23,908
République tchèque
Estonia
Estonia
4,447
4,330
4,330
3,268
3,200
3,200
1,152
1,100
1,100
27
30
30
1,431
1,400
1,400
5,878
5,730
5,730
Estonie
Finland
Finland
48,840
46,602
48,616
25,247
23,457
23,590
23,593
23,145
25,026
0
0
0
4,279
4,279
4,279
53,119
50,881
52,895
Finlande
Germany
Germany
55,270
53,354
50,415
44,611
41,447
39,283
10,505
11,757
10,982
153
150
150
9,265
9,600
9,800
64,534
62,954
60,215
Allemagne
Latvia
Latvia
8,661
8,350
8,250
5,975
5,600
5,500
2,036
2,100
2,100
650
650
650
315
350
400
8,976
8,700
8,650
Lettonie
Luxembourg
Luxembourg
156
169
143
27
51
122
35
32
6
94
86
15
24
30
11
180
199
154
Luxembourg
Netherlands
Netherlands
452
449
449
154
154
154
263
260
260
35
35
35
451
450
450
903
899
899
Pays-Bas
Poland
Poland
31,131
32,500
33,350
15,698
16,370
16,900
14,861
15,570
15,900
572
560
550
2,189
2,150
2,100
33,320
34,650
35,450
Pologne
Portugal
Portugal
3,352
3,440
3,455
1,851
1,900
1,970
1,370
1,400
1,350
131
140
135
445
480
450
3,797
3,920
3,905
Portugal
Serbia
Serbia
319
315
335
202
210
220
76
70
75
41
35
40
129
150
160
448
465
495
Serbie
Slovakia
Slovakia
3,678
3,815
3,830
2,724
2,735
2,750
928
1,050
1,050
26
30
30
223
250
285
3,901
4,065
4,115
Slovaquie
Slovenia
Slovenia
1,790
1,978
1,888
1,510
1,680
1,600
262
285
275
18
13
13
106
150
160
1,896
2,128
2,048
Slovénie
Sweden
Sweden
64,850
64,650
63,450
39,100
37,600
35,800
25,600
26,900
27,500
150
150
150
2,700
2,700
2,700
67,550
67,350
66,150
Suède
Switzerland
Switzerland
2,602
2,652
2,712
2,224
2,300
2,350
376
350
360
2
2
2
834
880
900
3,436
3,532
3,612
Suisse
UK
United Kingdom
8,608
7,550
8,300
6,298
5,300
6,000
1,895
1,900
1,900
415
350
400
1,571
1,570
1,570
10,179
9,120
9,870
Royaume-Uni
Total Europe
273,305
263,571
262,166
177,509
167,801
163,651
93,345
93,431
96,205
2,454
2,341
2,312
32,032
31,968
31,837
305,330
295,533
293,995
Total Europe
Canada
Canada
113,236
110,975
110,975
108,690
106,633
106,633
4,232
3,975
3,975
314
367
367
659
724
724
113,895
111,700
111,700
Canada
United States
United States
306,264
307,884
307,074
150,702
151,554
151,128
143,462
144,219
143,840
12,100
12,111
12,106
33,760
33,770
33,765
340,023
341,654
340,839
Etats-Unis
Total North America
419,499
418,859
418,049
259,392
258,187
257,761
147,694
148,194
147,816
12,414
12,478
12,472
34,419
34,495
34,489
453,918
453,354
452,538
Total Amérique du Nord
a
Pulpwood, round and split, as well as chips and particles produced directly
a
Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées
therefrom and used as pulpwood
directement à partir des rondins et quartiers et utilisées comme bois de trituration
b
Pitprops, poles, piling, posts etc.
b
Bois de mine, poteaux, pilotis, piquets etc.
c
Including chips and particles produced from wood in the rough and
c
Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées
used for energy purposes
à des fins energétiques
Table 9b
1.2.3.NC
TABLE 9b
REMOVALS OF WOOD IN THE ROUGH
QUANTITES ENLEVEES DE BOIS BRUT
HARDWOOD
NON-CONIFERES
1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
Country
Industrial wood - Bois industriels
Wood fuel c
Bois de chauffage c
Pays
Total
Logs
Pulpwood a
Other b
Total
Grumes
Bois de trituration a
Autre b
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
850
926
950
280
292
300
570
634
650
0
0
0
1,907
2,105
2,200
2,757
3,031
3,150
Autriche
Bulgaria
Bulgaria
944
...
...
346
...
...
587
...
...
11
...
...
1,749
...
...
2,693
...
...
Bulgarie
Cyprus
Cyprus
...
...
...
...
...
...
...
...
...
0
0
0
1
1
1
...
...
...
Chypre
Czech Republic
Czech Republic
895
652
633
438
378
362
453
269
266
4
5
5
647
568
571
1,542
1,220
1,204
République tchèque
Estonia
Estonia
2,073
1,987
1,987
877
860
860
1,171
1,100
1,100
25
27
27
2,717
2,700
2,700
4,789
4,687
4,687
Estonie
Finland
Finland
9,196
9,245
9,924
1,045
1,161
1,123
8,151
8,084
8,801
0
0
0
4,589
4,589
4,589
13,785
13,834
14,513
Finlande
Germany
Germany
3,918
3,824
3,855
2,792
2,809
2,802
1,119
1,008
1,045
8
8
8
13,959
14,300
14,300
17,877
18,124
18,155
Allemagne
Latvia
Latvia
4,342
4,300
4,300
1,852
1,800
1,800
1,950
2,000
2,000
540
500
500
2,625
2,800
2,800
6,967
7,100
7,100
Lettonie
Luxembourg
Luxembourg
61
163
54
12
35
22
49
128
32
0
0
0
22
43
34
83
206
89
Luxembourg
Netherlands
Netherlands
196
204
204
57
60
60
131
135
135
9
9
9
1,912
1,900
1,900
2,108
2,104
2,104
Pays-Bas
Poland
Poland
7,456
7,800
8,180
2,810
2,930
3,050
4,610
4,840
5,100
36
30
30
2,330
2,300
2,250
9,787
10,100
10,430
Pologne
Portugal
Portugal
8,784
8,800
8,700
296
290
250
8,289
8,300
8,250
200
210
200
1,318
1,350
1,330
10,102
10,150
10,030
Portugal
Serbia
Serbia
1,327
1,271
1,295
974
956
965
231
210
220
122
105
110
6,122
6,800
6,850
7,449
8,071
8,145
Serbie
Slovakia
Slovakia
3,492
3,660
3,760
1,519
1,600
1,650
1,965
2,050
2,100
8
10
10
272
300
325
3,764
3,960
4,085
Slovaquie
Slovenia
Slovenia
883
1,100
1,107
467
530
530
386
540
550
30
30
27
937
1,050
1,100
1,820
2,150
2,207
Slovénie
Sweden
Sweden
6,550
6,750
6,950
200
200
200
6,200
6,400
6,600
150
150
150
2,700
2,700
2,700
9,250
9,450
9,650
Suède
Switzerland
Switzerland
401
436
451
226
250
260
174
185
190
1
1
1
1,146
1,150
1,200
1,547
1,586
1,651
Suisse
UK
United Kingdom
108
110
110
56
60
60
3
0
0
48
50
50
613
610
610
720
720
720
Royaume-Uni
Total Europe
51,476
51,228
52,460
14,247
14,211
14,294
36,038
35,883
37,039
1,191
1,135
1,127
45,564
45,266
45,460
97,039
96,493
97,919
Total Europe
Canada
Canada
24,896
24,328
24,328
12,051
11,361
11,361
11,007
11,065
11,065
1,838
1,901
1,901
812
842
842
25,708
25,170
25,170
Canada
United States
United States
76,692
78,161
77,427
32,771
33,412
33,091
42,272
43,099
42,685
1,649
1,651
1,650
37,351
37,356
37,354
114,043
115,517
114,780
Etats-Unis
Total North America
101,588
102,489
101,754
44,822
44,773
44,453
53,279
54,164
53,750
3,487
3,552
3,551
38,163
38,199
38,196
139,751
140,687
139,950
Total Amérique du Nord
a
Pulpwood, round and split, as well as chips and particles produced directly
a
Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées
therefrom and used as pulpwood
directement à partir des rondins et quartiers et utilisées comme bois de trituration
b
Pitprops, poles, piling, posts etc.
b
Bois de mine, poteaux, pilotis, piquets etc.
c
Including chips and particles produced from wood in the rough and
c
Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées
used for energy purposes
à des fins energétiques
Table 10
1.2.1.C
TABLE 10
SOFTWOOD SAWLOGS
GRUMES DE SCIAGES DES CONIFERES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption a
Imports
Exports
Country
Consommation Apparente a
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
17,589
16,095
16,500
10,139
10,315
10,000
8,044
6,660
7,000
594
880
500
Autriche
Bulgaria
Bulgaria
1,173
...
...
1,178
...
...
0
...
...
5
...
...
Bulgarie
Cyprus
Cyprus
2
2
2
2
2
2
0
0
0
0
0
0
Chypre
Czech Republic
Czech Republic
8,801
8,617
7,916
17,301
15,480
14,210
750
663
660
9,250
7,526
6,954
République tchèque
Estonia
Estonia
3,640
3,730
3,730
3,268
3,200
3,200
455
600
600
83
70
70
Estonie
Finland
Finland
25,080
23,224
23,365
25,247
23,457
23,590
165
78
86
332
311
311
Finlande
Germany
Germany
39,795
39,077
38,613
44,611
41,447
39,283
3,190
3,300
3,600
8,006
5,670
4,270
Allemagne
Latvia
Latvia
6,786
6,350
6,100
5,975
5,600
5,500
1,088
1,100
900
277
350
300
Lettonie
Luxembourg
Luxembourg
393
90
161
27
51
122
609
164
164
243
125
125
Luxembourg
Netherlands
Netherlands
176
169
169
154
154
154
87
80
80
65
65
65
Pays-Bas
Poland
Poland
14,868
15,470
16,000
15,698
16,370
16,900
1,090
1,150
1,200
1,920
2,050
2,100
Pologne
Portugal
Portugal
1,971
1,990
2,075
1,851
1,900
1,970
150
130
140
30
40
35
Portugal
Serbia
Serbia
226
220
233
202
210
220
28
12
15
4
2
2
Serbie
Slovakia
Slovakia
3,057
3,235
3,250
2,724
2,735
2,750
1,049
900
900
716
400
400
Slovaquie
Slovenia
Slovenia
1,511
1,740
1,620
1,510
1,680
1,600
287
320
300
286
260
280
Slovénie
Sweden
Sweden
39,240
37,680
35,880
39,100
37,600
35,800
880
1,010
1,010
740
930
930
Suède
Switzerland
Switzerland
1,935
2,045
2,105
2,224
2,300
2,350
52
55
55
341
310
300
Suisse
UK
United Kingdom
6,515
5,510
6,200
6,298
5,300
6,000
359
360
360
142
150
160
Royaume-Uni
Total Europe
172,758
165,244
163,919
177,509
167,801
163,651
18,283
16,582
17,070
23,033
19,139
16,802
Total Europe
Canada
Canada
104,025
102,730
102,894
108,690
106,633
106,633
2,221
1,489
1,245
6,887
5,392
4,984
Canada
United States
United States
142,644
143,443
143,043
150,702
151,554
151,128
278
280
279
8,336
8,391
8,364
Etats-Unis
Total North America
246,668
246,173
245,937
259,392
258,187
257,761
2,500
1,769
1,524
15,223
13,784
13,348
Total Amérique du Nord
a Countries which did not provide trade data are included in consumption data
a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce
Table 11
1.2.1.NC
TABLE 11
HARDWOOD SAWLOGS (total)
GRUMES DE SCIAGES DES NON-CONIFERES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption a
Imports
Exports
Country
Consommation Apparente a
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Country
Consumption
Production
Imports
Exports
Country
Austria
Austria
398
362
370
280
292
300
162
140
120
45
70
50
Autriche
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Bulgaria
Bulgaria
346
...
...
346
...
...
0
...
...
0
...
...
Bulgarie
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Czech Republic
Czech Republic
305
239
218
438
378
362
132
135
138
265
274
282
République tchèque
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Estonia
Estonia
901
885
885
877
860
860
48
45
45
23
20
20
Estonie
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Finland
Finland
1,218
1,193
1,123
1,045
1,161
1,123
173
32
0
0
0
0
Finlande
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Germany
Germany
2,174
2,346
2,348
2,792
2,809
2,802
110
111
120
727
574
574
Allemagne
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Latvia
Latvia
1,570
1,470
1,520
1,852
1,800
1,800
27
60
50
309
390
330
Lettonie
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Luxembourg
Luxembourg
199
139
126
12
35
22
209
111
111
22
7
7
Luxembourg
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Netherlands
Netherlands
62
70
70
57
60
60
65
70
70
59
60
60
Pays-Bas
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Poland
Poland
2,740
2,860
3,050
2,810
2,930
3,050
80
80
...
150
150
...
Pologne
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Portugal
Portugal
406
380
350
296
290
250
140
120
130
30
30
30
Portugal
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Serbia
Serbia
954
951
955
974
956
965
30
15
20
50
20
30
Serbie
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Slovakia
Slovakia
1,658
1,700
1,750
1,519
1,600
1,650
562
500
500
423
400
400
Slovaquie
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Slovenia
Slovenia
263
242
260
467
530
530
43
42
40
248
330
310
Slovénie
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Sweden
Sweden
226
226
226
200
200
200
26
26
26
0
0
0
Suède
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Switzerland
Switzerland
104
130
150
226
250
260
27
35
40
149
155
150
Suisse
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
UK
United Kingdom
67
80
80
56
60
60
15
20
20
4
0
0
Royaume-Uni
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Total Europe
13,592
13,273
13,481
14,247
14,211
14,294
1,849
1,542
1,430
2,504
2,480
2,243
Total Europe
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Canada
Canada
13,122
12,288
12,248
12,051
11,361
11,361
1,145
1,018
969
75
92
83
Canada
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
United States
United States
30,814
31,416
31,115
32,771
33,412
33,091
151
154
153
2,109
2,150
2,129
Etats-Unis
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
Total North America
43,935
43,704
43,363
44,822
44,773
44,453
1,297
1,173
1,122
2,183
2,241
2,212
Total Amérique du Nord
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
a Countries which did not provide trade data are included in consumption data
a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce
Table 11a
TABLE 11a
HARDWOOD LOGS (temperate)
GRUMES DE NON-CONIFERES (zone tempérée)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption a
Imports
Exports
Country
Consommation Apparente a
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
280
292
300
280
292
300
...
...
...
...
...
...
Autriche
Bulgaria
Bulgaria
346
...
...
346
...
...
...
...
...
...
...
...
Bulgarie
Czech Republic
Czech Republic
303
237
216
438
378
362
130
133
136
265
274
282
République tchèque
Estonia
Estonia
877
860
860
877
860
860
...
...
...
...
...
...
Estonie
Finland
Finland
1,045
1,161
1,123
1,045
1,161
1,123
...
...
...
...
...
...
Finlande
Germany
Germany
2,168
2,341
2,343
2,792
2,809
2,802
98
101
110
722
569
569
Allemagne
Latvia
Latvia
1,852
1,800
1,800
1,852
1,800
1,800
...
...
...
...
...
...
Lettonie
Luxembourg
Luxembourg
191
139
22
12
35
22
201
111
...
22
7
...
Luxembourg
Netherlands
Netherlands
53
62
62
57
60
60
50
55
55
53
53
53
Pays-Bas
Poland
Poland
2,739
2,858
3,050
2,810
2,930
3,050
78
78
...
150
150
...
Pologne
Portugal
Portugal
382
355
325
296
290
250
110
90
100
24
25
25
Portugal
Serbia
Serbia
953
950
954
974
956
965
29
14
19
50
20
30
Serbie
Slovakia
Slovakia
1,519
1,600
1,650
1,519
1,600
1,650
...
...
...
...
...
...
Slovaquie
Slovenia
Slovenia
262
241
259
467
530
530
42
41
39
248
330
310
Slovénie
Sweden
Sweden
200
200
200
200
200
200
...
...
...
...
...
...
Suède
Switzerland
Switzerland
226
250
260
226
250
260
...
...
...
...
...
...
Suisse
UK
United Kingdom
66
80
80
56
60
60
14
20
20
4
0
0
Royaume-Uni
Total Europe
13,462
13,426
13,504
14,247
14,211
14,294
753
643
479
1,538
1,428
1,269
Total Europe
Canada
Canada
12,051
11,361
11,361
12,051
11,361
11,361
...
...
...
...
...
...
Canada
United States
United States
30,813
31,415
31,114
32,771
33,412
33,091
150
152
151
2,108
2,149
2,128
Etats-Unis
Total North America
42,864
42,777
42,475
44,822
44,773
44,453
150
152
151
2,108
2,149
2,128
Total Amérique du Nord
a Countries which did not provide trade data are included in consumption data
a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce
Table 11b
1.2.1.NC.T
TABLE 11b
HARDWOOD LOGS (tropical)
GRUMES DE NON-CONIFERES (tropicale)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Net Trade
Imports
Exports
Country
Commerce Net
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Czech Republic
Czech Republic
-2
-2
-2
2
2
2
0
0
0
République tchèque
Germany
Germany
-7
-5
-5
11
10
10
5
5
5
Allemagne
Luxembourg
Luxembourg
-8
0
...
8
0
...
0
0
...
Luxembourg
Netherlands
Netherlands
-9
-8
-8
15
15
15
6
7
7
Pays-Bas
Poland
Poland
-1
-2
-2
2
2
2
0
0
0
Pologne
Portugal
Portugal
-24
-25
-25
30
30
30
6
5
5
Portugal
Serbia
Serbia
-1
-1
-1
1
1
1
0
0
0
Serbie
Slovenia
Slovenia
-1
-1
-1
1
1
1
0
0
0
Slovénie
UK
United Kingdom
-1
0
0
1
0
0
0
0
0
Royaume-Uni
Total Europe
-54
-44
-44
71
61
61
17
17
17
Total Europe
United States
United States
-1
-1
-1
2
2
2
1
1
1
Etats-Unis
Total North America
-1
-1
-1
2
2
2
1
1
1
Total Amérique du Nord
Table 12
TABLE 12
PULPWOOD (total)
BOIS DE TRITURATION (total)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption a
Imports
Exports
Country
Consommation Apparente a
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
14,154
13,876
13,900
10,568
10,416
10,450
4,953
4,650
4,500
1,367
1,190
1,050
Autriche
Bulgaria
Bulgaria
1,639
...
...
1,699
...
...
27
...
...
87
...
...
Bulgarie
Cyprus
Cyprus
...
...
...
...
...
...
...
...
...
...
...
...
Chypre
Czech Republic
Czech Republic
6,155
4,306
4,709
9,027
6,877
7,621
475
469
475
3,347
3,040
3,387
République tchèque
Estonia
Estonia
3,575
3,600
3,600
6,323
6,100
6,100
268
240
240
3,015
2,740
2,740
Estonie
Finland
Finland
57,262
49,139
51,233
47,052
46,586
49,737
11,200
3,364
2,307
990
811
811
Finlande
Germany
Germany
27,289
28,098
28,031
28,327
29,465
28,527
3,819
3,509
3,680
4,858
4,876
4,176
Allemagne
Latvia
Latvia
4,879
5,025
5,180
8,296
8,600
8,800
1,417
875
780
4,834
4,450
4,400
Lettonie
Luxembourg
Luxembourg
605
681
559
605
681
559
...
...
...
...
...
...
Luxembourg
Malta
Malta
…
…
…
…
…
…
...
...
...
...
...
...
Malte
Netherlands
Netherlands
1,566
1,565
1,565
1,365
1,365
1,365
644
680
680
443
480
480
Pays-Bas
Poland
Poland
31,755
32,925
31,800
29,682
30,910
31,800
3,947
3,810
...
1,874
1,795
...
Pologne
Portugal
Portugal
13,642
12,870
12,780
11,483
11,620
11,500
2,833
1,815
1,850
674
565
570
Portugal
Serbia
Serbia
812
840
890
800
830
875
15
12
17
3
2
2
Serbie
Slovakia
Slovakia
3,849
4,000
4,100
4,043
4,250
4,350
1,043
1,000
1,000
1,237
1,250
1,250
Slovaquie
Slovenia
Slovenia
1,129
1,019
1,345
2,008
2,275
2,325
653
674
680
1,532
1,930
1,660
Slovénie
Sweden
Sweden
61,355
61,618
61,918
55,300
55,800
56,100
6,858
6,774
6,774
803
956
956
Suède
Switzerland
Switzerland
1,773
1,773
1,793
1,340
1,345
1,370
623
613
613
190
185
190
Suisse
UK
United Kingdom
5,304
5,040
5,280
5,020
4,770
5,010
384
380
380
101
110
110
Royaume-Uni
Total Europe
236,743
226,375
228,683
222,939
221,890
226,489
39,158
28,865
23,976
25,354
24,380
21,782
Total Europe
Canada
Canada
40,927
37,948
37,855
38,095
36,525
36,525
3,722
2,250
2,204
890
827
873
Canada
United States
United States
240,634
243,316
241,975
246,219
249,015
247,617
264
268
266
5,849
5,966
5,908
Etats-Unis
Total North America
281,560
281,264
279,830
284,314
285,540
284,142
3,986
2,517
2,469
6,740
6,794
6,781
Total Amérique du Nord
Includes wood residues, chips and particles for all purposes
Comprend les dechets de bois, plaquettes et particules pour toute utilisation
a Countries which did not provide trade data are included in consumption data
a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce
Table 12a
1.2.2.C
TABLE 12a
PULPWOOD LOGS (ROUND AND SPLIT)
BOIS DE TRITURATION (RONDINS ET QUARTIERS)
Softwood
Conifères
1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
Apparent Consumption a
Imports
Exports
Country
Consommation Apparente a
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
4,145
4,212
4,300
2,531
2,632
2,700
1,973
1,850
1,800
360
270
200
Autriche
Bulgaria
Bulgaria
1,000
...
...
1,019
...
...
15
...
...
34
...
...
Bulgarie
Czech Republic
Czech Republic
4,111
2,444
2,838
6,841
4,880
5,621
270
263
265
3,000
2,699
3,048
République tchèque
Estonia
Estonia
533
560
560
1,152
1,100
1,100
32
40
40
650
580
580
Estonie
Finland
Finland
24,151
23,246
25,181
23,593
23,145
25,026
1,294
754
808
736
653
653
Finlande
Germany
Germany
10,697
11,527
11,552
10,505
11,757
10,982
2,523
2,200
2,400
2,331
2,430
1,830
Allemagne
Latvia
Latvia
1,714
1,850
1,750
2,036
2,100
2,100
473
400
350
795
650
700
Lettonie
Luxembourg
Luxembourg
35
32
6
35
32
6
...
...
...
...
...
...
Luxembourg
Netherlands
Netherlands
195
180
180
263
260
260
113
110
110
182
190
190
Pays-Bas
Poland
Poland
14,706
15,470
15,900
14,861
15,570
15,900
1,174
1,200
1,250
1,329
1,300
1,250
Pologne
Portugal
Portugal
1,402
1,420
1,380
1,370
1,400
1,350
75
65
70
43
45
40
Portugal
Serbia
Serbia
76
70
75
76
70
75
0
0
0
0
0
0
Serbie
Slovakia
Slovakia
843
900
900
928
1,050
1,050
645
600
600
730
750
750
Slovaquie
Slovenia
Slovenia
288
325
315
262
285
275
264
270
270
239
230
230
Slovénie
Sweden
Sweden
28,302
29,632
30,232
25,600
26,900
27,500
3,110
3,255
3,255
408
523
523
Suède
Switzerland
Switzerland
306
280
290
376
350
360
20
20
20
90
90
90
Suisse
UK
United Kingdom
2,085
2,080
2,080
1,895
1,900
1,900
213
210
210
23
30
30
Royaume-Uni
Total Europe
94,588
94,228
97,539
93,345
93,431
96,205
12,194
11,237
11,448
10,950
10,440
10,114
Total Europe
Canada
Canada
5,139
4,236
4,204
4,232
3,975
3,975
961
297
273
54
36
45
Canada
United States
United States
143,467
144,224
143,845
143,462
144,219
143,840
5
5
5
0
0
0
Etats-Unis
Total North America
148,606
148,460
148,049
147,694
148,194
147,816
966
302
278
54
36
45
Total Amérique du Nord
a Countries which did not provide trade data are included in consumption data
a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce
Table 12b
1.2.2.NC
TABLE 12b
PULPWOOD LOGS (ROUND AND SPLIT)
BOIS DE TRITURATION (RONDINS ET QUARTIERS)
Hardwood
Non-conifères
1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
Apparent Consumption a
Imports
Exports
Country
Consommation Apparente a
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
1,182
1,214
1,200
570
634
650
688
700
650
75
120
100
Autriche
Bulgaria
Bulgaria
583
...
...
587
...
...
5
...
...
9
...
...
Bulgarie
Czech Republic
Czech Republic
365
187
190
453
269
266
2
9
8
90
91
84
République tchèque
Estonia
Estonia
336
370
370
1,171
1,100
1,100
139
130
130
974
860
860
Estonie
Finland
Finland
12,721
9,117
9,428
8,151
8,084
8,801
4,661
1,085
679
91
52
52
Finlande
Germany
Germany
1,111
1,021
1,079
1,119
1,008
1,045
261
259
280
269
246
246
Allemagne
Latvia
Latvia
432
375
480
1,950
2,000
2,000
166
175
180
1,684
1,800
1,700
Lettonie
Luxembourg
Luxembourg
49
128
32
49
128
32
...
...
...
...
...
...
Luxembourg
Netherlands
Netherlands
62
65
65
131
135
135
19
20
20
89
90
90
Pays-Bas
Poland
Poland
5,095
5,325
5,100
4,610
4,840
5,100
560
560
...
75
75
...
Pologne
Portugal
Portugal
8,939
8,930
8,890
8,289
8,300
8,250
1,000
950
970
350
320
330
Portugal
Serbia
Serbia
230
210
220
231
210
220
0
0
0
1
0
0
Serbie
Slovakia
Slovakia
1,909
2,000
2,050
1,965
2,050
2,100
91
100
100
147
150
150
Slovaquie
Slovenia
Slovenia
131
154
160
386
540
550
117
114
110
372
500
500
Slovénie
Sweden
Sweden
8,485
8,486
8,686
6,200
6,400
6,600
2,313
2,119
2,119
28
33
33
Suède
Switzerland
Switzerland
137
148
153
174
185
190
3
3
3
40
40
40
Suisse
UK
United Kingdom
54
50
50
3
0
0
52
50
50
1
0
0
Royaume-Uni
Total Europe
41,820
37,780
38,153
36,038
35,883
37,039
10,077
6,274
5,299
4,294
4,377
4,185
Total Europe
Canada
Canada
10,804
10,827
10,819
11,007
11,065
11,065
46
47
39
249
284
285
Canada
United States
United States
42,259
43,085
42,672
42,272
43,099
42,685
42
42
42
55
56
56
Etats-Unis
Total North America
53,063
53,912
53,491
53,279
54,164
53,750
88
89
81
304
340
340
Total Amérique du Nord
a Countries which did not provide trade data are included in consumption data
a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce
Table 12c
3
TABLE 12c
WOOD RESIDUES, CHIPS AND PARTICLES
DECHETS DE BOIS, PLAQUETTES ET PARTICULES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 m3
Apparent Consumption
Imports
Exports
Country
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
8,827
8,450
8,400
7,467
7,150
7,100
2,292
2,100
2,050
931
800
750
Autriche
Bulgaria
Bulgaria
56
...
...
93
...
...
7
...
...
44
...
...
Bulgarie
Cyprus
Cyprus
8
10
10
8
9
9
1
1
1
0
0
0
Chypre
Czech Republic
Czech Republic
1,679
1,675
1,681
1,733
1,728
1,734
203
197
202
257
250
255
République tchèque
Estonia
Estonia
2,706
2,670
2,670
4,000
3,900
3,900
96
70
70
1,390
1,300
1,300
Estonie
Finland
Finland
20,390
16,776
16,624
15,308
15,357
15,910
5,245
1,525
820
163
106
106
Finlande
Germany
Germany
15,481
15,550
15,400
16,703
16,700
16,500
1,036
1,050
1,000
2,258
2,200
2,100
Allemagne
Latvia
Latvia
2,733
2,800
2,950
4,310
4,500
4,700
778
300
250
2,355
2,000
2,000
Lettonie
Luxembourg
Luxembourg
680
517
517
521
521
521
283
17
17
124
21
21
Luxembourg
Malta
Malta
2
2
3
0
0
0
2
2
3
0
0
0
Malte
Netherlands
Netherlands
1,310
1,320
1,320
971
970
970
512
550
550
173
200
200
Pays-Bas
Poland
Poland
11,954
12,130
12,400
10,211
10,500
10,800
2,213
2,050
2,000
469
420
400
Pologne
Portugal
Portugal
3,301
2,520
2,510
1,824
1,920
1,900
1,758
800
810
281
200
200
Portugal
Serbia
Serbia
506
560
595
493
550
580
15
12
17
2
2
2
Serbie
Slovakia
Slovakia
1,097
1,100
1,150
1,150
1,150
1,200
307
300
300
360
350
350
Slovaquie
Slovenia
Slovenia
710
540
870
1,360
1,450
1,500
272
290
300
922
1,200
930
Slovénie
Sweden
Sweden
24,568
23,500
23,000
23,500
22,500
22,000
1,435
1,400
1,400
367
400
400
Suède
Switzerland
Switzerland
1,330
1,345
1,350
790
810
820
600
590
590
60
55
60
Suisse
UK
United Kingdom
3,164
2,910
3,150
3,122
2,870
3,110
119
120
120
77
80
80
Royaume-Uni
Total Europe
100,505
94,375
94,600
93,564
92,585
93,254
17,174
11,374
10,500
10,234
9,584
9,154
Total Europe
Canada
Canada
24,984
22,884
22,832
22,856
21,485
21,485
2,716
1,906
1,891
587
507
544
Canada
United States
United States
54,907
56,007
55,457
60,485
61,697
61,091
216
221
219
5,794
5,910
5,852
Etats-Unis
Total North America
79,892
78,891
78,290
83,341
83,182
82,576
2,932
2,127
2,110
6,382
6,417
6,396
Total Amérique du Nord
Table 13
4.1x
TABLE 13
WOOD PELLETS
GRANULES DE BOIS
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
1000 mt
Apparent Consumption
Imports
Exports
Country
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
Pays
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
Austria
Austria
1,144
1,270
1,410
1,607
1,720
2,060
413
328
400
875
778
1,050
Autriche
Bulgaria
Bulgaria
202
...
...
216
...
...
132
...
...
146
...
...
Bulgarie
Cyprus
Cyprus
4
3
3
0
0
0
4
3
3
0
0
0
Chypre
Czech Republic
Czech Republic
126
199
201
490
503
517
39
35
37
403
339
353
République tchèque
Estonia
Estonia
67
70
70
1,600
1,550
1,550
26
20
20
1,559
1,500
1,500
Estonie
Finland
Finland
552
479
456
365
375
380
196
110
80
9
6
4
Finlande
Germany
Germany
2,932
3,200
3,400
3,353
3,600
3,800
392
450
500
813
850
900
Allemagne
Latvia
Latvia
221
280
100
2,138
2,200
2,000
592
380
400
2,509
2,300
2,300
Lettonie
Luxembourg
Luxembourg
49
63
63
63
63
63
13
4
4
28
4
4
Luxembourg
Malta
Malta
1
1
1
0
0
0
1
1
1
0
0
0
Malte
Netherlands
Netherlands
2,449
2,457
2,457
307
315
315
2,297
2,297
2,297
155
155
155
Pays-Bas
Poland
Poland
1,169
1,220
1,330
1,594
1,620
1,680
269
280
300
694
680
650
Pologne
Portugal
Portugal
224
270
265
731
860
800
3
10
15
510
600
550
Portugal
Serbia
Serbia
497
430
485
468
420
460
84
60
80
55
50
55
Serbie
Slovakia
Slovakia
19
145
195
310
325
350
46
45
45
337
225
200
Slovaquie
Slovenia
Slovenia
111
112
150
149
162
170
166
120
150
204
170
170
Slovénie
Sweden
Sweden
1,771
1,985
1,985
1,900
2,100
2,100
154
235
235
282
350
350
Suède
Switzerland
Switzerland
350
355
360
275
285
295
75
70
65
0
0
0
Suisse
UK
United Kingdom
9,430
9,450
9,450
304
320
320
9,128
9,130
9,130
2
0
0
Royaume-Uni
Total Europe
21,318
21,989
22,381
15,870
16,418
16,860
14,030
13,578
13,762
8,582
8,007
8,241
Total Europe
Canada
Canada
706
761
548
3,830
4,131
4,131
29
33
35
3,153
3,402
3,618
Canada
United States
United States
1,122
1,136
1,129
8,449
8,557
8,503
196
198
197
7,523
7,619
7,571
Etats-Unis
Total North America
1,828
1,898
1,677
12,279
12,688
12,634
225
231
232
10,676
11,021
11,189
Total Amérique du Nord
Table 14
3+4
TABLE 14
Europe: Summary table of market forecasts for 2022 and 2023
Europe: Tableau récapitulatif des prévisions du marché pour 2022 et 2023
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
million m3 (pulp, paper and pellets million m.t. - pâte de bois, papiers et cartons, et granulés en millions de tonnes métriques)
Apparent Consumption
Consommation Apparente
Production
Imports - Importations
Exports - Exportations
2021
2022
2023
2021
2022
2023
2021
2022
2023
2021
2022
2023
actual
forecasts
actual
forecasts
actual
forecasts
actual
forecasts
réels
prévisions
réels
prévisions
réels
prévisions
réels
prévisions
Sawn softwood
67.77
63.34
62.12
90.27
88.25
87.84
26.30
22.94
21.87
48.80
47.85
47.59
Sciages conifères
Softwood logs a
172.76
165.24
163.92
177.51
167.80
163.65
18.28
16.58
17.07
23.03
19.14
16.80
Grumes de conifères a
Sawn hardwood
4.34
4.17
4.19
4.22
4.17
4.09
2.95
2.89
2.92
2.83
2.89
2.82
Sciages non-conifères
– temperate zone b
3.56
3.34
3.35
3.04
2.92
2.91
2.41
2.33
2.37
1.89
1.90
1.93
– zone tempérée b
– tropical zone b
0.32
0.34
0.34
0.03
0.02
0.03
0.41
0.41
0.40
0.12
0.09
0.08
– zone tropicale b
Hardwood logs a
13.59
13.27
13.48
14.25
14.21
14.29
1.85
1.54
1.43
2.50
2.48
2.24
Grumes de non-conifères a
– temperate zone b
13.46
13.43
13.50
14.25
14.21
14.29
0.75
0.64
0.48
1.54
1.43
1.27
– zone tempérée b
– tropical zone b
0.05
0.04
0.04
0.07
0.06
0.06
0.02
0.02
0.02
– zone tropicale b
Veneer sheets
0.74
0.75
0.72
0.70
0.72
0.71
0.78
0.71
0.66
0.74
0.68
0.65
Feuilles de placage
Plywood
5.94
5.84
5.86
3.41
3.37
3.35
5.87
5.67
5.63
3.35
3.21
3.12
Contreplaqués
Particle board (excluding OSB)
21.78
20.86
20.73
21.93
20.41
20.25
8.19
8.10
8.10
8.34
7.66
7.62
Pann. de particules (sauf OSB)
OSB
4.95
4.85
4.85
4.74
4.42
4.49
2.67
2.69
2.71
2.46
2.25
2.35
OSB
Fibreboard
13.90
14.17
14.10
14.72
14.71
14.71
7.39
7.13
7.04
8.20
7.67
7.66
Panneaux de fibres
– Hardboard
0.54
0.77
0.77
0.28
0.21
0.21
1.07
1.06
1.06
0.81
0.50
0.50
– Durs
– MDF
9.73
9.79
9.73
10.78
10.85
10.80
4.34
4.11
4.07
5.38
5.17
5.15
– MDF
– Other board
3.63
3.62
3.61
3.66
3.65
3.70
1.98
1.96
1.92
2.01
2.00
2.01
– Autres panneaux
Pulpwood a
236.74
226.37
228.68
222.94
221.89
226.49
39.16
28.87
23.98
25.35
24.38
21.78
Bois de trituration a
– Pulp logs
136.41
132.01
135.69
129.38
129.31
133.24
22.27
17.51
16.75
15.24
14.82
14.30
– Bois ronds de trituration
– softwood
94.59
94.23
97.54
93.34
93.43
96.20
12.19
11.24
11.45
10.95
10.44
10.11
– conifères
– hardwood
41.82
37.78
38.15
36.04
35.88
37.04
10.08
6.27
5.30
4.29
4.38
4.19
– non-conifères
– Residues, chips and particles
100.50
94.37
94.60
93.56
92.59
93.25
17.17
11.37
10.50
10.23
9.58
9.15
– Déchets, plaquettes et part.
Wood pulp
31.26
30.47
31.02
33.82
32.17
33.48
10.99
11.00
11.04
13.56
12.70
13.50
Pâte de bois
Paper and paperboard
47.70
46.94
46.97
64.68
62.48
63.49
29.98
29.24
29.29
46.96
44.78
45.81
Papiers et cartons
Wood Pellets
21.32
21.99
22.38
15.87
16.42
16.86
14.03
13.58
13.76
8.58
8.01
8.24
Granulés de bois
a Countries which did not provide trade data are included in consumption data
a La consommation comprend les pays qui n'ont pas fourni des données sur le commerce
b Trade figures by zone do not equal the total as some countries cannot provide data for both zones
b Les chiffres du commerce par zone ne correspondent pas aux totaux
en raison du fait que certains pays ne peuvent les différencier.
Table 15
3+4
TABLE 15
North America: Summary table of market forecasts for 2022 and 2023
Amérique du Nord: Tableau récapitulatif des prévisions du marché pour 2022 et 2023
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
million m3 (pulp, paper and pellets million m.t. - pâte de bois, papiers et cartons, et granulés en millions de tonnes métriques)
Dear Correspondent,
Thank you for contributing to the Joint Forest Sector Questionnaire (JFSQ). Before filling in the worksheets, please read these guidelines.
Please use only this questionnaire to report your data. The 2020 JFSQ contains no pre-filling.
Use this questionnaire also to revise any historical data - fill in the correct year and your name on the cover page.
The worksheet 'LAM & CHIPS' covers data on Glulam and X-lam. This sheet has a yellow drop-down cell where you can choose your unit of measurement.
The total number of sheets to be filled in is eight core sheets (green tabs - to be validated by Eurostat) plus three for ITTO (brown tabs - not validated by Eurostat). Four sheets containing cross-references are included at the end. The flat file is for Eurostat for validation purposes, please do not change any cells here.
Put all your data into one Excel file. If you send some data in later, give your file a new version number and date (see A.1. below) and notify us of the changes with respect to the previous version. Only send us completely filled-in sheets, highlighting the changes in yellow. Do not delete worksheets.
Each sheet has a working area for your input. Most sheets have checking cells and tables.
Each working area has white cells and shaded cells. Eurostat has highlighted the variables it considers most important for its publications - please fill those in as a priority.
When you submit a revision, please highlight changes in yellow and explain them in the appropriate 'Note' column.
Please use flags and notes (see A.6 below). This information is important for Eurostat.
A. General recommendations
A.1 Please use eDAMIS to send your questionnaire to Eurostat. Choose the correct domain ("FOREST_A_A") and the correct reference year (for this data collection: 2020).
A.2 Fill in the JFSQ quality report if you haven't done so yet. If you already sent the quality report to Eurostat before, please fill it in only if major changes had happened to its content.
A.3 The cover page is for your contact details, which are automatically copied to the other worksheets
• Check your country code
• If necessary change the reference year [red cell] as appropriate - the previous year will appear automatically
If you distribute worksheets to various experts, they can each put their contact details into the sheets. It will then be your job to put all the information together again and to verify the checking tables, since some of them will not work as designed in isolation.
A.4 Look at the unit of measurement to be used for each item and report in this unit if possible, using the conversion factors on the last page of the JFSQ definitions. Please report the monetary values in the same unit for both reporting years.
Only report data or modify cells in the working areas. Please do not delete checking areas or checking sheets.
• Look at the checking areas and make the necessary corrections to your data to remove all warnings (see the specific recommendations) before sending in your data.
Fill in real zeroes '0' in the worksheets if there is no production or trade. Empty cells will be interpreted as 'Data not available'.
• There are counters at the bottom of the tables to indicate the number of cells left to be filled in and the number of cells filled with text.
Report all data with at least three decimals. Do not use a separator for thousands; for the decimal point, please use the one set up by default.
A.5 Report numbers only. If data are confidential, please provide them if possible, appropriately flagged (see A.6).
• Eurostat has a right to all confidential data necessary for its work. It has an obligation to use such data only in aggregates and to respect all the legal obligations.
• If you cannot provide confidential data, a good option is to send in your own estimate flagged as a national estimate '9'.
• As a last resort, leave the cell empty, flag it and write a note indicating data sources and links.
Checking tables contain formulae to sum up the totals for sub-items.
A.6 Flag cells and write notes as appropriate. Flags should be entered in the 'Flag' columns and notes in the 'Note' columns for the appropriate year and item. The flags to use are:
• 5 for repeating the data of a previous year
• 6 for confidential data
• 7 for provisional data
• 9 for national estimate
B Specific recommendations
B.1 Sheet 'Removals over bark' is for volumes of wood products measured over bark. General over bark/under bark conversion factors are calculated automatically.
• Should you use different conversion factor(s) please delete the ones provided and insert your own
• If you only have under bark data, please leave this worksheet empty, but revise the table with the conversion factors.
• Unchanged conversion factors will be considered revised
A checking table verifies that sums of sub-items agree with the totals
B.2 Checking tables on worksheets improve data quality, verifying that:
• The sum of the sub-items equals the total
• The sum of 'of which' items is not larger than the total
All cells in a checking table should be zero or empty. If this is not the case, please check your numbers for the sub-items and totals. The checking table indicates the difference, so if you see a negative value, you will have to decide which number should be increased by that amount. The only exception is when no data are entered due to confidentiality.
B.3 Worksheets 'JQ2' and 'EU1' contain a checking table for apparent consumption and for unit values.
Apparent consumption = Production + Imports – Exports. It should be positive or nil. If this is not the case, the cell will change colour and indicate the difference.
• Please correct the data in the sheets until checking results are positive or nil. One solution is to increase production
• If the data are correct but apparent consumption is still negative, please explain why in the 'Note' column provided in the apparent consumption checking table.
B.4 Sheets 'JQ2', 'ECE-EU Species' and 'EU1' on trade have checking tables to verify data consistency. Both quantity and value must be present.
When something is missing, messages or coloured cells appear in the checking tables. Please correct your data until all warnings disappear.
The meaning of the messages is:
• 0: both value and quantity are zero – all is well, there is no trade
• ZERO Q: value is reported, quantity is zero - please correct
• ZERO V: quantity is reported, value is zero - please correct
• REPORT: both quantity and value are blank - please fill in
• NO Q: blank cell for quantity – please fill in
• NO V: blank cell for value – please fill in
Please enter even very small numbers to resolve problems, using as many decimal places as necessary.
If there is no way to correct the problem, please write an explanation in the 'Note' column.
If there is no trade for a product, please enter 0 for both quantity and value.
Thank you for collecting data for the JFSQ.
Eurostat's Forestry Team
Are there any changes to the quality report of the last data collection?
NO
If yes, please provide details below.
3
Statistical processing
Overview of the source data
Please provide an overview of the sources used to produce JFSQ data.
Do you use a dedicated survey (of the industry, of households, of forest owners, etc.)?
Please select YES or NO
If yes, please provide details (e.g., who are the respondents, what is its frequency?).
Do you use forestry statistics?
Please select YES or NO
If yes, please provide details.
Do you use national forest inventory?
Please select YES or NO
If yes, please provide details.
Do you use national PRODCOM data compiled according to the CPA classification?
Please select YES or NO
If yes, please provide details (which products, units, etc.).
Do you use any other national production statistics?
Please select YES or NO
If yes, please provide details.
Do you use data collected by associations of industry?
Please select YES or NO
If yes, please provide details.
Do you collect data from direct contacts with manufacturing companies?
Please select YES or NO
If yes, please provide details.
Do you use estimates of roundwood use (in manufacturing)?
Please select YES or NO
If yes, please provide details.
Do you use national trade data?
Please select YES or NO
If yes, please provide details.
Do you use felling reports?
Please select YES or NO
If yes, please provide details.
Do you use forestry companies' accounting network?
Please select YES or NO
If yes, please provide details.
Do you use administrative data (e.g. tax records, business registers)?
Please select YES or NO
If yes, please provide details.
Do you use data from national accounts?
Please select YES or NO
If yes, please provide details (e.g. for which data, from which account tables?).
Do you use SBS (Structural business statistics)?
Please select YES or NO
If yes, please provide details (e.g. for which data?).
Do you use other environmental accounts?
Please select YES or NO
If yes, please provide details.
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Please select YES or NO
If yes, please specify them.
Do you use any other sources?
Please select YES or NO
If yes, please specify them.
Methodological issues
Are there any pending classification or measurement issues?
Please select YES or NO
If yes, please specify them.
Data validation
Do you check the quality of the data collected to compile JFSQ?
Please select YES or NO
If yes, please explain the quality assurance procedure.
Do you compare JFSQ data with different data sources or do you perform other cross-checks?
Please select YES or NO
If yes, please explain your approach.
Do you have validation rules and other plausibility checks for the outputs of your JFSQ data compilation process?
Please select YES or NO
If yes, please briefly describe them.
4
Relevance
User needs
Please provide references to the relevance of JFSQ at national level e.g. main users, national indicator sets, quantitative policy targets etc.
5
Coherence and comparability
Coherence - cross domain
Do you compare the JFSQ results with business, energy and agricultural and foreign trade statistics?
Please select YES or NO
It not, please explain.
Do you compare the JFSQ results with business, energy and agricultural and foreign trade statistics?
Please select YES or NO
It not, please explain.
Do you cross-check the JFSQ data with the results of European Forest Accounts?
Please select YES or NO
If yes, please indicate for which reporting items, and comments on the discrepancies observed, if any. It not, please explain.
Coherence - internal
Are there any other consistency issues related to your JFSQ data?
Please select YES or NO
If yes, please explain them.
6
Accessibility and clarity
Publications
Do you disseminate JFSQ data nationally (e.g. in news releases or other documents)?
Please select YES or NO
If yes, please provide URLs and/or the reference to the relevant publications.
Online database
Do you publish your JFSQ accounts in an online data base?
Please select YES or NO
If yes, please provide URLs.
Documentation on methodology
Did you prepare a description of your national JFSQ methodology or metadata?
Please select YES or NO
If yes, please provide URLs.
Quality documentation
Do you have national quality documentation?
Please select YES or NO
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7
Other comments
Other comments
Please provide any further feedback you might have on the quality of the reported data, sources and methods used and/or Eurostat's validation and quality report templates.
Mechanical wood pulp, semi-chemical wood pulp and pulp from fibres other than wood 9.1+10.1
1000 t
794.489
749.329
To fill:
0
0
Text:
0
0
m3 = cubic metres solid volume
t = metric tonnes
JQ2 Trade
61
62
61
62
91
92
91
92
FOREST SECTOR QUESTIONNAIRE JQ2
Country:
DE
Date:
0
both VALUE and quantity reported ZERO
Name of Official responsible for reply:
0
ZERO Q
quantity ZERO when VALUE is reported
INTRA-EU
The difference might be caused by Intra-EU trade
PRIMARY PRODUCTS
Official Address (in full):
Thünen Institute, Leuschnerstr. 91, 21031 Hamburg
This table highlights discrepancies between production and trade. For any negative number, indicating greater net exports than production, please verify your data!
ZERO V
Value ZERO when quantity is reported
CHECK
Trade
Telephone:
0
Fax:
0
This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!
ZERO CHECK 1 - if no value please CHECK
NO Q
no quantity reported
ZERO CHECK 2 - if no value in Zero Check 1
Value must always be in 1000 NAC (national currency)
E-mail:
0
Flag
Flag
Flag
Flag
Flag
Flag
Flag
Flag
Note
Note
Note
Note
Note
Note
Note
Note
Country:
DE
NO V
no value reported
Treshold:
2
verifies whether the JQ2 figures refers only to intra-EU trade
Specify Currency and Unit of Value (e.g.:1000 US $):
1000 NAC
Trade
Discrepancies
REPORT
no figures reported
Product
Unit of
I M P O R T
E X P O R T
Import
Export
Import
Export
Product
I M P O R T
E X P O R T
Product
Apparent Consumption
Related Notes
Product
Value per
I M P O R T
E X P O R T
Column1
Column2
Product
Value per
I M P O R T
E X P O R T
code
Product
quantity
2019
2020
2019
2020
2019
2020
2019
2020
2019
2020
2019
2020
code
2019
2020
2019
2020
code
2019
2020
2019
2020
code
Product
unit
2019
2020
2019
2020
IMPORT
EXPORT
code
Product
unit
2019
2020
2019
2020
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
7,642.251
455,841
6,169.747
353,412
9,056.743
695,046
13,087.034
860,136
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
OK
OK
OK
OK
OK
OK
OK
OK
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
76,407
-155,154
1
ROUNDWOOD (WOOD IN THE ROUGH)
NAC/m3
60
57
77
66
ACCEPT
ACCEPT
1
ROUNDWOOD (WOOD IN THE ROUGH)
NAC/m3
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
323.881
29,827
246.397
26,219
141.214
7,112
256.690
11,231
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
OK
OK
OK
OK
OK
OK
OK
OK
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
23,880
44,976
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
NAC/m3
92
106
50
44
ACCEPT
ACCEPT
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
NAC/m3
1.1.C
Coniferous
1000 m3ub
104.027
9,901
80.504
8,528
99.347
2,916
179.000
6,956
1.1.C
Coniferous
1000 m3ub
1.1.C
Coniferous
1000 m3ub
9,612
15,990
1.1.C
Coniferous
NAC/m3
95
106
29
39
ACCEPT
ACCEPT
1.1.C
Coniferous
NAC/m3
1.1.NC
Non-Coniferous
1000 m3ub
219.854
19,926
165.893
17,691
41.867
4,196
77.690
4,275
1.1.NC
Non-Coniferous
1000 m3ub
1.1.NC
Non-Coniferous
1000 m3ub
14,268
28,987
1.1.NC
Non-Coniferous
NAC/m3
91
107
100
55
ACCEPT
ACCEPT
1.1.NC
Non-Coniferous
NAC/m3
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
7,318.370
426,014
5,923.350
327,193
8,915.529
687,934
12,830.344
848,905
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
OK
OK
OK
OK
OK
OK
OK
OK
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
52,526
-200,130
1.2
INDUSTRIAL ROUNDWOOD
NAC/m3
58
55
77
66
ACCEPT
ACCEPT
1.2
INDUSTRIAL ROUNDWOOD
NAC/m3
1.2.C
Coniferous
1000 m3ub
6,866.440
375,644
5,559.520
285,233
7,506.234
515,811
11,816.202
728,553
1.2.C
Coniferous
1000 m3ub
1.2.C
Coniferous
1000 m3ub
47,091
-83,805
1.2.C
Coniferous
NAC/m3
55
51
69
62
ACCEPT
ACCEPT
1.2.C
Coniferous
NAC/m3
1.2.NC
Non-Coniferous
1000 m3ub
451.930
50,370
363.830
41,960
1,409.295
172,123
1,014.142
120,352
1.2.NC
Non-Coniferous
1000 m3ub
1.2.NC
Non-Coniferous
1000 m3ub
5,436
-116,325
1.2.NC
Non-Coniferous
NAC/mt
111
115
122
119
ACCEPT
ACCEPT
1.2.NC
Non-Coniferous
NAC/mt
1.2.NC.T
of which: Tropical
1000 m3ub
9.476
4,636
9.873
4,608
4.362
2,611
5.002
2,687
1.2.NC.T
of which: Tropical
1000 m3ub
OK
OK
OK
OK
OK
OK
OK
OK
1.2.NC.T
of which: Tropical
1000 m3ub
5
2,025
1.2.NC.T
of which: Tropical
1000 m3
489
467
599
537
ACCEPT
ACCEPT
1.2.NC.T
of which: Tropical
1000 m3
2
WOOD CHARCOAL
1000 t
214.280
104,312
164.236
89,189
22.385
21,369
31.704
28,408
2
WOOD CHARCOAL
1000 t
2
WOOD CHARCOAL
1000 t
222
82,973
2
WOOD CHARCOAL
1000 m3
487
543
955
896
ACCEPT
ACCEPT
2
WOOD CHARCOAL
1000 m3
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
1,499.692
49,781
1,114.652
32,480
2,761.950
130,926
2,790.501
123,202
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
13,628
-65,030
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
33
29
47
44
ACCEPT
ACCEPT
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
3.1
WOOD CHIPS AND PARTICLES
1000 m3
789.724
23,950
530.374
15,267
1,951.584
83,778
1,929.180
77,773
3.1
WOOD CHIPS AND PARTICLES
1000 m3
3.1
WOOD CHIPS AND PARTICLES
1000 m3
9,811
-48,121
3.1
WOOD CHIPS AND PARTICLES
1000 mt
30
29
43
40
ACCEPT
ACCEPT
3.1
WOOD CHIPS AND PARTICLES
1000 mt
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
709.968
25,831
584.278
17,213
810.366
47,148
861.321
45,429
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
3,817
-16,909
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 mt
36
29
58
53
ACCEPT
ACCEPT
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 mt
4
RECOVERED POST-CONSUMER WOOD
1000 t
875.813
33,157
904.489
38,540
657.738
38,758
558.245
40,400
4
RECOVERED POST-CONSUMER WOOD
1000 t
4
RECOVERED POST-CONSUMER WOOD
1000 t
6,819
1,000
4
RECOVERED POST-CONSUMER WOOD
1000 mt
38
43
59
72
ACCEPT
ACCEPT
4
RECOVERED POST-CONSUMER WOOD
1000 mt
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 t
619.382
99,520
550.222
79,256
815.856
184,296
851.375
180,362
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 t
3,465
-80,870
5
WOOD PELLETS AND OTHER AGGLOMERATES
NAC/m3
161
144
226
212
ACCEPT
ACCEPT
5
WOOD PELLETS AND OTHER AGGLOMERATES
NAC/m3
5.1
WOOD PELLETS
1000 t
316.740
58,364
291.268
48,974
771.279
171,398
801.083
167,129
5.1
WOOD PELLETS
1000 t
5.1
WOOD PELLETS
1000 t
2,366
-109,934
5.1
WOOD PELLETS
NAC/m3
184
168
222
209
ACCEPT
ACCEPT
5.1
WOOD PELLETS
NAC/m3
5.2
OTHER AGGLOMERATES
1000 t
302.642
41,156
258.954
30,282
44.577
12,898
50.292
13,233
5.2
OTHER AGGLOMERATES
1000 t
5.2
OTHER AGGLOMERATES
1000 t
1,099
29,064
5.2
OTHER AGGLOMERATES
NAC/m3
136
117
289
263
ACCEPT
ACCEPT
5.2
OTHER AGGLOMERATES
NAC/m3
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
5,280.868
1,224,150
5,410.720
1,231,226
9,656.877
2,082,909
10,353.824
2,252,310
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
20,197
-832,540
6
SAWNWOOD (INCLUDING SLEEPERS)
NAC/m3
232
228
216
218
ACCEPT
ACCEPT
6
SAWNWOOD (INCLUDING SLEEPERS)
NAC/m3
6.C
Coniferous
1000 m3
4,868.020
1,004,053
5,010.128
1,017,315
8,889.313
1,697,553
9,661.856
1,899,543
6.C
Coniferous
1000 m3
6.C
Coniferous
1000 m3
19,286
-668,283
6.C
Coniferous
NAC/m3
206
203
191
197
ACCEPT
ACCEPT
6.C
Coniferous
NAC/m3
6.NC
Non-Coniferous
1000 m3
412.848
220,097
400.592
213,911
767.564
385,356
691.968
352,767
6.NC
Non-Coniferous
1000 m3
6.NC
Non-Coniferous
1000 m3
912
-164,257
6.NC
Non-Coniferous
NAC/m3
533
534
502
510
ACCEPT
ACCEPT
6.NC
Non-Coniferous
NAC/m3
6.NC.T
of which: Tropical
1000 m3
73.779
59,149
66.453
54,194
33.498
38,315
30.893
38,976
6.NC.T
of which: Tropical
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
6.NC.T
of which: Tropical
1000 m3
42
20,835
6.NC.T
of which: Tropical
NAC/m3
802
816
1144
1262
ACCEPT
ACCEPT
6.NC.T
of which: Tropical
NAC/m3
7
VENEER SHEETS
1000 m3
106.340
150,740
105.626
153,653
58.135
140,291
55.892
124,914
7
VENEER SHEETS
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
7
VENEER SHEETS
1000 m3
146
10,549
7
VENEER SHEETS
NAC/m3
1418
1455
2413
2235
ACCEPT
ACCEPT
7
VENEER SHEETS
NAC/m3
7.C
Coniferous
1000 m3
27.836
18,481
26.417
17,562
0.532
2,453
0.469
2,252
7.C
Coniferous
1000 m3
7.C
Coniferous
1000 m3
41
16,040
7.C
Coniferous
NAC/m3
664
665
4611
4802
ACCEPT
ACCEPT
7.C
Coniferous
NAC/m3
7.NC
Non-Coniferous
1000 m3
78.504
132,259
79.209
136,091
57.603
137,838
55.423
122,662
7.NC
Non-Coniferous
1000 m3
7.NC
Non-Coniferous
1000 m3
105
-5,491
7.NC
Non-Coniferous
NAC/m3
1685
1718
2393
2213
ACCEPT
ACCEPT
7.NC
Non-Coniferous
NAC/m3
7.NC.T
of which: Tropical
1000 m3
8.410
12,574
8.139
10,238
2.535
10,821
1.743
8,431
7.NC.T
of which: Tropical
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
7.NC.T
of which: Tropical
1000 m3
7
1,754
7.NC.T
of which: Tropical
NAC/m3
1495
1258
4269
4837
ACCEPT
ACCEPT
7.NC.T
of which: Tropical
NAC/m3
8
WOOD-BASED PANELS
1000 m3
5,781.129
1,907,646
6,005.716
1,839,547
6,019.846
2,484,957
6,044.175
2,429,242
8
WOOD-BASED PANELS
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8
WOOD-BASED PANELS
1000 m3
12,277
-564,620
8
WOOD-BASED PANELS
NAC/m3
330
306
413
402
ACCEPT
ACCEPT
8
WOOD-BASED PANELS
NAC/m3
8.1
PLYWOOD
1000 m3
1,486.319
788,383
1,432.894
712,583
375.911
265,081
367.685
250,702
8.1
PLYWOOD
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8.1
PLYWOOD
1000 m3
1,222
523,402
8.1
PLYWOOD
NAC/m3
530
497
705
682
ACCEPT
ACCEPT
8.1
PLYWOOD
NAC/m3
8.1.C
Coniferous
1000 m3
521.635
223,420
529.001
208,849
133.416
72,489
151.551
76,326
8.1.C
Coniferous
1000 m3
8.1.C
Coniferous
1000 m3
432
150,978
8.1.C
Coniferous
NAC/m3
428
395
543
504
ACCEPT
ACCEPT
8.1.C
Coniferous
NAC/m3
8.1.NC
Non-Coniferous
1000 m3
964.684
564,963
903.893
503,734
242.495
192,592
216.134
174,377
8.1.NC
Non-Coniferous
1000 m3
8.1.NC
Non-Coniferous
1000 m3
790
372,424
8.1.NC
Non-Coniferous
NAC/m3
586
557
794
807
ACCEPT
ACCEPT
8.1.NC
Non-Coniferous
NAC/m3
8.1.NC.T
of which: Tropical
1000 m3
154.222
102,033
131.707
84,278
40.604
48,256
37.095
42,161
8.1.NC.T
of which: Tropical
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8.1.NC.T
of which: Tropical
1000 m3
114
53,778
8.1.NC.T
of which: Tropical
NAC/m3
662
640
1188
1137
ACCEPT
ACCEPT
8.1.NC.T
of which: Tropical
NAC/m3
8.2
PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD
1000 m3
2,787.203
703,127
2,775.957
667,280
2,349.216
607,576
2,188.631
545,906
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
7,316
102,341
8.2
PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD
NAC/m3
252
240
259
249
ACCEPT
ACCEPT
8.2
PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD
NAC/m3
8.2.1
of which: ORIENTED STRAND BOARD (OSB)
1000 m3
791.907
204,223
852.213
205,059
525.391
132,646
510.998
119,105
8.2.1
of which: ORIENTED STRAND BOARD (OSB)
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8.2.1
of which: ORIENTED STRAND BOARD (OSB)
1000 m3
1,430
72,811
8.2.1
of which: ORIENTED STRANDBOARD (OSB)
NAC/m3
258
241
252
233
ACCEPT
ACCEPT
8.2.1
of which: ORIENTED STRANDBOARD (OSB)
NAC/m3
8.3
FIBREBOARD
1000 m3
1,507.607
416,136
1,796.865
459,684
3,294.719
1,612,300
3,487.859
1,632,634
8.3
FIBREBOARD
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8.3
FIBREBOARD
1000 m3
3,740
-1,190,363
8.3
FIBREBOARD
NAC/m3
276
256
489
468
ACCEPT
ACCEPT
8.3
FIBREBOARD
NAC/m3
8.3.1
HARDBOARD
1000 m3
223.390
90,695
230.904
82,618
26.283
16,504
27.696
17,512
9
9
9
9
8.3.1
HARDBOARD
1000 m3
8.3.1
HARDBOARD
1000 m3
197
74,191
8.3.1
HARDBOARD
NAC/mt
406
358
628
632
ACCEPT
ACCEPT
8.3.1
HARDBOARD
NAC/mt
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
495.428
233,975
600.437
267,767
2,877.842
1,553,626
2,878.786
1,553,312
9
9
9
9
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
2,123
-1,315,051
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
NAC/mt
472
446
540
540
ACCEPT
ACCEPT
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
NAC/mt
8.3.3
OTHER FIBREBOARD
1000 m3
788.789
91,466
965.524
109,299
390.594
42,170
581.377
61,810
8.3.3
OTHER FIBREBOARD
1000 m3
8.3.3
OTHER FIBREBOARD
1000 m3
1,420
50,497
8.3.3
OTHER FIBREBOARD
NAC/mt
116
113
108
106
ACCEPT
ACCEPT
8.3.3
OTHER FIBREBOARD
NAC/mt
9
WOOD PULP
1000 t
4,755.000
2,758,929
4,034.000
2,059,887
1,254.000
739,434
1,146.000
591,738
9
9
9
9
9
9
9
9
9
WOOD PULP
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
9
WOOD PULP
1000 t
5,827
2,021,750
9
WOOD PULP
NAC/mt
580
511
590
516
ACCEPT
ACCEPT
9
WOOD PULP
NAC/mt
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 t
205.000
114,500
154.000
86,868
93.000
43,397
89.000
37,732
9
9
9
9
9
9
9
9
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 t
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 t
840
71,787
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
NAC/mt
559
564
467
424
ACCEPT
ACCEPT
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
NAC/mt
9.2
CHEMICAL WOOD PULP
1000 t
4,174.000
2,321,574
3,517.000
1,668,713
1,149.000
682,435
1,051.000
548,836
9
9
9
9
9
9
9
9
9.2
CHEMICAL WOOD PULP
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
9.2
CHEMICAL WOOD PULP
1000 t
4,623
1,640,710
9.2
CHEMICAL WOOD PULP
NAC/mt
556
474
594
522
ACCEPT
ACCEPT
9.2
CHEMICAL WOOD PULP
NAC/mt
9.2.1
SULPHATE PULP
1000 t
4,093.000
2,237,892
3,437.000
1,585,683
1,067.000
576,025
952.000
439,317
9
9
9
9
9
9
9
9
9.2.1
SULPHATE PULP
1000 t
9.2.1
SULPHATE PULP
1000 t
4,147
1,662,957
9.2.1
SULPHATE PULP
NAC/mt
547
461
540
461
ACCEPT
ACCEPT
9.2.1
SULPHATE PULP
NAC/mt
9.2.1.1
of which: BLEACHED
1000 t
3,981.000
2,178,450
3,341.000
1,541,061
1,054.000
568,446
944.000
435,200
9
9
9
9
9
9
9
9
9.2.1.1
of which: BLEACHED
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
9.2.1.1
of which: BLEACHED
1000 t
4,048
1,611,094
9.2.1.1
of which: BLEACHED
NAC/mt
547
461
539
461
ACCEPT
ACCEPT
9.2.1.1
of which: BLEACHED
NAC/mt
9.2.2
SULPHITE PULP
1000 t
81.000
83,682
80.000
83,030
82.000
106,410
99.000
109,519
9
9
9
9
9
9
9
9
9.2.2
SULPHITE PULP
1000 t
9.2.2
SULPHITE PULP
1000 t
475
-22,248
9.2.2
SULPHITE PULP
NAC/mt
1033
1038
1298
1106
ACCEPT
ACCEPT
9.2.2
SULPHITE PULP
NAC/mt
9.3
DISSOLVING GRADES
1000 t
376.000
322,855
363.000
304,306
12.000
13,602
6.000
5,170
9
9
9
9
9
9
9
9
9.3
DISSOLVING GRADES
1000 t
9.3
DISSOLVING GRADES
1000 t
364
309,253
9.3
DISSOLVING GRADES
NAC/mt
859
838
1134
862
ACCEPT
ACCEPT
9.3
DISSOLVING GRADES
NAC/mt
10
OTHER PULP
1000 t
139.000
34,162
92.000
36,526
119.000
57,448
111.000
52,193
9
9
9
9
9
9
9
9
10
OTHER PULP
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
10
OTHER PULP
1000 t
14,426
-9,088
10
OTHER PULP
NAC/mt
246
397
483
470
ACCEPT
ACCEPT
10
OTHER PULP
NAC/mt
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 t
14.000
20,354
16.000
17,734
1.000
1,265
1.000
1,084
9
9
9
9
9
9
9
9
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 t
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 t
79
19,154
10.1
PULP FROM FIBRES OTHER THAN WOOD
NAC/mt
1454
1108
1265
1084
ACCEPT
ACCEPT
10.1
PULP FROM FIBRES OTHER THAN WOOD
NAC/mt
10.2
RECOVERED FIBRE PULP
1000 t
125.000
13,808
76.000
18,792
118.000
56,183
110.000
51,109
9
9
9
9
9
9
9
9
10.2
RECOVERED FIBRE PULP
1000 t
10.2
RECOVERED FIBRE PULP
1000 t
14,347
-28,242
10.2
RECOVERED FIBRE PULP
NAC/mt
110
247
476
465
CHECK
ACCEPT
10.2
RECOVERED FIBRE PULP
NAC/mt
11
RECOVERED PAPER
1000 t
4,934.000
632,355
4,580.000
545,972
2,500.000
262,684
2,160.000
199,980
9
9
9
9
9
9
9
9
11
RECOVERED PAPER
1000 t
11
RECOVERED PAPER
1000 t
19,588
386,577
11
RECOVERED PAPER
NAC/mt
128
119
105
93
ACCEPT
ACCEPT
11
RECOVERED PAPER
NAC/mt
12
PAPER AND PAPERBOARD
1000 t
10,914.000
8,447,334
10,419.678
7,785,329
14,244.125
11,767,361
13,632.411
10,443,862
9
9
9
9
9
9
9
9
12
PAPER AND PAPERBOARD
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
12
PAPER AND PAPERBOARD
1000 t
18,750
-3,298,688
12
PAPER AND PAPERBOARD
NAC/mt
774
747
826
766
ACCEPT
ACCEPT
12
PAPER AND PAPERBOARD
NAC/mt
12.1
GRAPHIC PAPERS
1000 t
4,677.000
3,294,178
4,091.565
2,785,718
5,286.591
4,235,510
4,572.879
3,468,387
9
9
9
9
9
9
9
9
12.1
GRAPHIC PAPERS
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
12.1
GRAPHIC PAPERS
1000 t
6,683
-935,093
12.1
GRAPHIC PAPERS
NAC/mt
704
681
801
758
ACCEPT
ACCEPT
12.1
GRAPHIC PAPERS
NAC/mt
12.1.1
NEWSPRINT
1000 t
715.000
378,451
537.031
245,440
443.171
231,409
402.282
177,904
9
9
9
9
9
9
9
9
12.1.1
NEWSPRINT
1000 t
12.1.1
NEWSPRINT
1000 t
1,363
147,952
12.1.1
NEWSPRINT
NAC/mt
529
457
522
442
ACCEPT
ACCEPT
12.1.1
NEWSPRINT
NAC/mt
12.1.2
UNCOATED MECHANICAL
1000 t
527.000
369,763
583.215
360,379
927.657
536,967
872.046
453,721
9
9
9
9
9
9
9
9
12.1.2
UNCOATED MECHANICAL
1000 t
12.1.2
UNCOATED MECHANICAL
1000 t
1,506
-165,462
12.1.2
UNCOATED MECHANICAL
NAC/mt
702
618
579
520
ACCEPT
ACCEPT
12.1.2
UNCOATED MECHANICAL
NAC/mt
12.1.3
UNCOATED WOODFREE
1000 t
1,161.000
1,038,774
1,139.438
926,162
959.461
1,126,380
849.862
961,219
9
9
9
9
9
9
9
9
12.1.3
UNCOATED WOODFREE
1000 t
12.1.3
UNCOATED WOODFREE
1000 t
1,754
-86,229
12.1.3
UNCOATED WOODFREE
NAC/mt
895
813
1174
1131
ACCEPT
ACCEPT
12.1.3
UNCOATED WOODFREE
NAC/mt
12.1.4
COATED PAPERS
1000 t
2,274.000
1,507,190
1,831.881
1,253,738
2,956.302
2,340,754
2,448.689
1,875,541
9
9
9
9
9
9
9
9
12.1.4
COATED PAPERS
1000 t
12.1.4
COATED PAPERS
1000 t
2,060
-831,353
12.1.4
COATED PAPERS
NAC/mt
663
684
792
766
ACCEPT
ACCEPT
12.1.4
COATED PAPERS
NAC/mt
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 t
675.000
996,599
627.595
883,025
723.988
1,350,613
655.775
1,149,496
9
9
9
9
9
9
9
9
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 t
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 t
1,447
-352,500
12.2
HOUSEHOLD AND SANITARY PAPERS
NAC/mt
1476
1407
1866
1753
ACCEPT
ACCEPT
12.2
HOUSEHOLD AND SANITARY PAPERS
NAC/mt
12.3
PACKAGING MATERIALS
1000 t
5,348.000
3,687,889
5,512.698
3,665,899
7,914.692
5,369,800
8,087.247
5,058,243
9
9
9
9
9
9
9
9
12.3
PACKAGING MATERIALS
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
12.3
PACKAGING MATERIALS
1000 t
9,383
-1,669,659
12.3
PACKAGING MATERIALS
NAC/mt
690
665
678
625
ACCEPT
ACCEPT
12.3
PACKAGING MATERIALS
NAC/mt
12.3.1
CASE MATERIALS
1000 t
2,736.000
1,090,521
2,767.612
1,093,729
4,723.260
1,890,682
4,820.270
1,693,120
9
9
9
9
9
9
9
9
12.3.1
CASE MATERIALS
1000 t
12.3.1
CASE MATERIALS
1000 t
6,743
-791,113
12.3.1
CASE MATERIALS
NAC/mt
399
395
400
351
ACCEPT
ACCEPT
12.3.1
CASE MATERIALS
NAC/mt
12.3.2
CARTONBOARD
1000 t
1,388.000
1,433,671
1,464.751
1,463,286
2,099.554
2,342,189
2,089.815
2,237,314
9
9
9
9
9
9
9
9
12.3.2
CARTONBOARD
1000 t
12.3.2
CARTONBOARD
1000 t
1,074
-906,739
12.3.2
CARTONBOARD
NAC/mt
1033
999
1116
1071
ACCEPT
ACCEPT
12.3.2
CARTONBOARD
NAC/mt
12.3.3
WRAPPING PAPERS
1000 t
950.000
950,136
1,006.387
890,381
793.193
916,995
884.882
912,136
9
9
9
9
9
9
9
9
12.3.3
WRAPPING PAPERS
1000 t
12.3.3
WRAPPING PAPERS
1000 t
563
33,551
12.3.3
WRAPPING PAPERS
NAC/mt
1000
885
1156
1031
ACCEPT
ACCEPT
12.3.3
WRAPPING PAPERS
NAC/mt
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 t
274.000
213,561
273.948
218,502
298.685
219,934
292.280
215,673
9
9
9
9
9
9
9
9
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 t
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 t
1,003
-5,359
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
NAC/mt
779
798
736
738
ACCEPT
ACCEPT
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
NAC/mt
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
1000 t
214.000
468,668
187.820
450,687
318.854
811,438
316.510
767,736
9
9
9
9
9
9
9
9
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
1000 t
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
1000 t
1,238
-341,436
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
NAC/mt
2190
2400
2545
2426
ACCEPT
ACCEPT
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
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
We are not able to allocate the CN trade data to the EWPs
2019
4412 9940
./.
19.350
26,799
29.263
42,021
6.283
8,847
5.391
6,112
2019
./.
1,384.94
1435.9771804779
1407.9732841706
1133.8045235275
LVL or CLT?
2019
4412 9950
./.
0.791
3,124
9.480
11,784
0.224
1,073
4.448
2,925
2019
./.
3,947.43
1243.0772968529
4785.9055036928
657.5989023536
LVL or CLT?
2019
4412 9985
./.
5.988
8,860
25.031
33,594
2.565
3,460
1.417
1,838
2019
./.
1,479.55
1342.1118614612
1349.1908677629
1297.2897033491
LVL or CLT?
2019
4418 6000
./.
5.219
4,912
16.990
11,383
3.288
3,570
1.084
863
2019
./.
941.12
669.9981211386
1085.8324879233
795.8318030516
I-BEAMS / I-JOISTS?
2019
4418 9910
./.
195.708
214,759
192.667
208,893
73.451
77,131
18.322
26,806
2019
./.
1,097.35
1084.2189931128
1050.0957090811
1463.0339234569
GLULAM?
2019
4418 9990
./.
98.240
163,893
100.686
163,215
27.253
59,904
9.894
25,142
2019
./.
1,668.30
1621.0232864589
2198.0698743274
2541.1617171479
GLULAM or CLT?
2019
4421 9999
./.
411.053
359,391
518.339
578,941
48.349
79,421
150.271
229,163
2019
./.
874.32
1116.9148954448
1642.664080898
1525.0012047018
GLULAM or CLT?
2020
4412 9940
./.
16.736
25,348
23.951
33,413
5.931
9,093
3.973
3,617
2020
./.
1,514.57
1395.0682356227
1533.0793032436
910.4868568071
LVL or CLT?
2020
4412 9950
./.
1.281
3,602
10.213
12,190
0.790
1,889
5.252
3,347
2020
./.
2,811.87
1193.5884959372
2391.1391668093
637.2567523218
LVL or CLT?
2020
4412 9985
./.
10.741
11,812
23.906
29,860
4.429
4,808
1.362
1,450
2020
./.
1,099.72
1249.0640360571
1085.474343416
1064.5327174521
LVL or CLT?
2020
4418 6000
./.
8.049
7,454
21.750
14,249
5.203
4,952
1.213
742
2020
./.
926.05
655.1143774877
951.7586007936
611.5048758169
I-BEAMS / I-JOISTS?
2020
4418 9910
./.
200.012
215,594
181.321
192,708
75.299
77,564
13.199
17,244
2020
./.
1,077.91
1062.7984726418
1030.081492692
1306.4428156317
GLULAM?
2020
4418 9990
./.
82.296
149,199
107.694
160,348
28.995
60,303
13.504
28,872
2020
./.
1,812.96
1488.9264591644
2079.786777385
2138.0648176687
GLULAM or CLT?
2020
4421 9999
./.
432.224
358,028
546.072
624,079
53.306
83,718
160.045
253,543
2020
./.
828.34
1142.8517651252
1570.5292013539
1584.2001721445
GLULAM or CLT?
Definitions:
Glulam: Builders' carpentry also includes glue-laminated timber (glulam), which is a structural timber product obtained by gluing together a number of wood laminations having their grain essentially parallel. Laminations of curved members are arranged so that the plane of each lamination is at 90 degrees to the plane of the applied load; thus, laminations of a straight gluman beam are laid flat. [from HS 4418, Builders' joinery and carpentry of wood, including cellular wood panels, assembled flooring panels, shingles and shakes]
X-lam: Panels consisting of laths of roughly sawn wood, assembled with glue in order to facilitate transport or later working. [from HS4421, Other articles of wood]
EU1 ExtraEU Trade
EU1
Country:
DE
Date:
JQ2/EU1 comparison
0
both VALUE and quantity reported ZERO
Name of Official responsible for reply:
0
ZERO Q
quantity ZERO when VALUE is reported
FOREST SECTOR QUESTIONNAIRE
Official Address (in full):
Thünen Institute, Leuschnerstr. 91, 21031 Hamburg
JQ2>=EU1
ZERO V
Value ZERO when quantity is reported
Trade with countries outside EU
Telephone:
0
Fax:
0
Please verify if there's an error!
Zero check - if no value please CHECK
NO Q
no quantity reported
Value must always be in 1000 NAC (national currency)
E-mail:
0
Flag
Flag
Flag
Flag
Flag
Flag
Flag
Flag
Note
Note
Note
Note
Note
Note
Note
Note
NO V
no value reported
Treshold:
2
Eurozone countries may use the old national currency, but only in both years
1000NAC
Trade
Discrepancies
REPORT
no figures reported
Product
Unit of
I M P O R T
E X P O R T
Import
Export
Import
Export
I M P O R T
E X P O R T
Product
I M P O R T
E X P O R T
Product
Value per
I M P O R T
E X P O R T
Column1
Column2
code
Product
quantity
2019
2020
2019
2020
2019
2020
2019
2020
2019
2020
2019
2020
2019
2020
2019
2020
code
2019
2020
2019
2020
code
Product
unit
2019
2020
2019
2020
IMPORT
EXPORT
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
724.309
76,360
610.048
60,192
3,961.026
408,605
6,928.364
576,696
OK
OK
OK
OK
OK
OK
OK
OK
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
OK
OK
OK
OK
OK
OK
OK
OK
1
ROUNDWOOD (WOOD IN THE ROUGH)
NAC/m3
105
99
103
83
ACCEPT
CHECK
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
124.637
13,970
107.820
12,697
2.473
784
3.084
887
OK
OK
OK
OK
OK
OK
OK
OK
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
OK
OK
OK
OK
OK
OK
OK
OK
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
NAC/m3
112
118
317
288
CHECK
CHECK
1.1.C
Coniferous
1000 m3ub
11.158
1,091
8.002
1,136
0.021
20
0.209
60
OK
OK
OK
OK
OK
OK
OK
OK
1.1.C
Coniferous
1000 m3ub
1.1.C
Coniferous
NAC/m3
98
142
952
287
CHECK
CHECK
1.1.NC
Non-Coniferous
1000 m3ub
113.479
12,879
99.818
11,561
2.452
764
2.875
827
OK
OK
OK
OK
OK
OK
OK
OK
1.1.NC
Non-Coniferous
1000 m3ub
1.1.NC
Non-Coniferous
NAC/m3
113
116
312
288
CHECK
CHECK
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
599.672
62,390
502.228
47,495
3,958.553
407,821
6,925.280
575,809
OK
OK
OK
OK
OK
OK
OK
OK
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
OK
OK
OK
OK
OK
OK
OK
OK
1.2
INDUSTRIAL ROUNDWOOD
NAC/m3
104
95
103
83
ACCEPT
CHECK
1.2.C
Coniferous
1000 m3ub
548.270
43,304
454.733
31,694
3,307.599
294,969
6,446.901
500,887
OK
OK
OK
OK
OK
OK
OK
OK
1.2.C
Coniferous
1000 m3ub
1.2.C
Coniferous
NAC/m3
79
70
89
78
ACCEPT
CHECK
1.2.NC
Non-Coniferous
1000 m3ub
51.402
19,086
47.495
15,801
650.954
112,852
478.379
74,922
OK
OK
OK
OK
OK
OK
OK
OK
1.2.NC
Non-Coniferous
1000 m3ub
1.2.NC
Non-Coniferous
NAC/m3
371
333
173
157
ACCEPT
CHECK
1.2.NC.T
of which: Tropical
1000 m3ub
9.476
4,636
9.873
4,608
0.344
865
0.089
282
OK
OK
OK
OK
OK
OK
OK
OK
1.2.NC.T
of which: Tropical
1000 m3ub
OK
OK
OK
OK
OK
OK
OK
OK
1.2.NC.T
of which: Tropical
NAC/m3
489
467
2515
3169
CHECK
CHECK
2
WOOD CHARCOAL
1000 t
101.759
47,243
73.905
36,829
3.662
3,258
5.070
4,643
OK
OK
OK
OK
OK
OK
OK
OK
2
WOOD CHARCOAL
1000 t
2
WOOD CHARCOAL
NAC/t
464
498
890
916
ACCEPT
CHECK
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
45.452
1,648
37.878
1,044
264.053
21,440
247.634
19,032
OK
OK
OK
OK
OK
OK
OK
OK
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
3
WOOD CHIPS, PARTICLES AND RESIDUES
NAC/m3
36
28
81
77
CHECK
CHECK
3.1
WOOD CHIPS AND PARTICLES
1000 m3
15.534
808
10.366
305
162.442
12,040
147.815
10,956
OK
OK
OK
OK
OK
OK
OK
OK
3.1
WOOD CHIPS AND PARTICLES
1000 m3
3.1
WOOD CHIPS AND PARTICLES
NAC/m3
52
29
74
74
CHECK
CHECK
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
29.918
840
27.512
739
101.611
9,400
99.819
8,076
OK
OK
OK
OK
OK
OK
OK
OK
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
NAC/m3
28
27
93
81
CHECK
CHECK
4
RECOVERED POST-CONSUMER WOOD
1000 t
109.148
2,302
112.701
3,275
21.423
4,381
30.824
6,009
OK
OK
OK
OK
OK
OK
OK
OK
4
RECOVERED POST-CONSUMER WOOD
1000 t
4
RECOVERED POST-CONSUMER WOOD
NAC/t
21
29
204
195
CHECK
CHECK
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 t
191.849
23,548
197.772
21,789
55.166
23,410
85.130
29,208
OK
OK
OK
OK
OK
OK
OK
OK
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
5
WOOD PELLETS AND OTHER AGGLOMERATES
NAC/t
123
110
424
343
CHECK
CHECK
5.1
WOOD PELLETS
1000 t
41.976
5,918
59.020
8,139
42.729
17,864
64.057
22,729
OK
OK
OK
OK
OK
OK
OK
OK
5.1
WOOD PELLETS
1000 t
5.1
WOOD PELLETS
NAC/t
141
138
418
355
CHECK
CHECK
5.2
OTHER AGGLOMERATES
1000 t
149.873
17,630
138.752
13,650
12.437
5,546
21.073
6,479
OK
OK
OK
OK
OK
OK
OK
OK
5.2
OTHER AGGLOMERATES
1000 t
5.2
OTHER AGGLOMERATES
NAC/t
118
98
446
307
CHECK
CHECK
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
1,836.939
412,032
1,734.996
398,786
4,097.105
901,714
4,683.902
1,095,578
OK
OK
OK
OK
OK
OK
OK
OK
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
6
SAWNWOOD (INCLUDING SLEEPERS)
NAC/m3
224
230
220
234
ACCEPT
CHECK
6.C
Coniferous
1000 m3
1,668.443
307,595
1,562.935
301,509
3,628.667
690,634
4,270.661
906,884
OK
OK
OK
OK
OK
OK
OK
OK
6.C
Coniferous
1000 m3
6.C
Coniferous
NAC/m3
184
193
190
212
ACCEPT
CHECK
6.NC
Non-Coniferous
1000 m3
168.496
104,437
172.061
97,277
468.438
211,080
413.241
188,694
OK
OK
OK
OK
OK
OK
OK
OK
6.NC
Non-Coniferous
1000 m3
6.NC
Non-Coniferous
NAC/m3
620
565
451
457
ACCEPT
CHECK
6.NC.T
of which: Tropical
1000 m3
66.202
48,947
55.334
39,917
8.819
11,671
7.338
10,748
OK
OK
OK
OK
OK
OK
OK
OK
6.NC.T
of which: Tropical
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
6.NC.T
of which: Tropical
NAC/m3
739
721
1323
1465
ACCEPT
CHECK
7
VENEER SHEETS
1000 m3
31.564
45,908
31.656
46,277
15.112
51,942
12.257
45,982
OK
OK
OK
OK
OK
OK
OK
OK
7
VENEER SHEETS
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
7
VENEER SHEETS
NAC/m3
1454
1462
3437
3751
CHECK
CHECK
7.C
Coniferous
1000 m3
4.145
6,507
3.119
5,293
0.256
1,147
0.238
1,216
OK
OK
OK
OK
OK
OK
OK
OK
7.C
Coniferous
1000 m3
7.C
Coniferous
NAC/m3
1570
1697
4480
5109
CHECK
CHECK
7.NC
Non-Coniferous
1000 m3
27.419
39,401
28.537
40,984
14.856
50,795
12.019
44,766
OK
OK
OK
OK
OK
OK
OK
OK
7.NC
Non-Coniferous
1000 m3
7.NC
Non-Coniferous
NAC/m3
1437
1436
3419
3725
CHECK
CHECK
7.NC.T
of which: Tropical
1000 m3
6.415
5,465
6.103
4,465
1.169
3,923
0.889
2,723
OK
OK
OK
OK
OK
OK
OK
OK
7.NC.T
of which: Tropical
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
7.NC.T
of which: Tropical
NAC/m3
852
732
3356
3063
CHECK
CHECK
8
WOOD-BASED PANELS
1000 m3
1,067.284
409,434
1,049.915
371,625
1,528.232
723,440
1,615.792
734,695
OK
OK
OK
OK
OK
OK
OK
OK
8
WOOD-BASED PANELS
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8
WOOD-BASED PANELS
NAC/m3
384
354
473
455
ACCEPT
CHECK
8.1
PLYWOOD
1000 m3
692.276
262,514
663.420
227,771
95.884
74,876
99.873
76,261
OK
OK
OK
OK
OK
OK
OK
OK
8.1
PLYWOOD
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8.1
PLYWOOD
NAC/m3
379
343
781
764
CHECK
CHECK
8.1.C
Coniferous
1000 m3
259.852
70,584
235.367
52,739
69.578
43,189
72.681
44,017
OK
OK
OK
OK
OK
OK
OK
OK
8.1.C
Coniferous
1000 m3
8.1.C
Coniferous
NAC/m3
272
224
621
606
CHECK
CHECK
8.1.NC
Non-Coniferous
1000 m3
432.424
191,931
428.053
175,033
26.306
31,688
27.192
32,244
OK
OK
OK
OK
OK
OK
OK
OK
8.1.NC
Non-Coniferous
1000 m3
8.1.NC
Non-Coniferous
NAC/m3
444
409
1205
1186
CHECK
CHECK
8.1.NC.T
of which: Tropical
1000 m3
46.635
31,454
31.053
18,681
1.684
3,150
2.332
3,406
OK
OK
OK
OK
OK
OK
OK
OK
8.1.NC.T
of which: Tropical
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8.1.NC.T
of which: Tropical
NAC/m3
674
602
1871
1461
CHECK
CHECK
8.2
PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD
1000 m3
204.861
55,945
199.753
49,721
450.552
123,519
432.009
116,495
OK
OK
OK
OK
OK
OK
OK
OK
8.2
PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD
1000 m3
8.2
PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD
NAC/m3
273
249
274
270
ACCEPT
CHECK
8.2.1
of which: ORIENTED STRANDBOARD (OSB)
1000 m3
2.283
496
0.738
190
180.519
46,353
162.313
40,668
OK
OK
OK
OK
OK
OK
OK
OK
8.2.1
of which: ORIENTED STRANDBOARD (OSB)
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8.2.1
of which: ORIENTED STRANDBOARD (OSB)
NAC/m3
217
257
257
251
ACCEPT
CHECK
8.3
FIBREBOARD
1000 m3
170.147
90,975
186.742
94,133
749.408
403,915
832.739
402,561
OK
OK
OK
OK
OK
OK
OK
OK
8.3
FIBREBOARD
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8.3
FIBREBOARD
NAC/m3
535
504
539
483
ACCEPT
CHECK
8.3.1
HARDBOARD
1000 m3
17.394
15,487
37.777
15,739
10.678
6,844
11.926
7,642
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
8.3.1
HARDBOARD
1000 m3
8.3.1
HARDBOARD
NAC/m3
890
417
641
641
ACCEPT
CHECK
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
108.725
69,036
140.265
76,307
578.477
379,551
582.193
366,504
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
NAC/m3
635
544
656
630
ACCEPT
CHECK
8.3.3
OTHER FIBREBOARD
1000 m3
44.028
6,452
8.700
2,087
160.253
17,520
238.620
28,415
OK
OK
OK
OK
OK
OK
OK
OK
8.3.3
OTHER FIBREBOARD
1000 m3
8.3.3
OTHER FIBREBOARD
NAC/m3
147
240
109
119
CHECK
CHECK
9
WOOD PULP
1000 t
1,962.560
1,193,136
1,641.934
833,166
384.044
225,502
382.902
207,180
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
9
WOOD PULP
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
9
WOOD PULP
NAC/t
608
507
587
541
ACCEPT
CHECK
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 t
37.533
20,892
22.251
11,726
16.737
7,785
22.177
10,925
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 t
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
NAC/t
557
527
465
493
ACCEPT
CHECK
9.2
CHEMICAL WOOD PULP
1000 t
1,832.022
1,064,120
1,524.410
720,887
364.965
216,176
360.724
196,251
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
9.2
CHEMICAL WOOD PULP
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
9.2
CHEMICAL WOOD PULP
NAC/t
581
473
592
544
ACCEPT
CHECK
9.2.1
SULPHATE PULP
1000 t
1,804.345
1,033,874
1,495.340
690,335
336.349
171,162
330.009
150,056
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
9.2.1
SULPHATE PULP
1000 t
9.2.1
SULPHATE PULP
NAC/t
573
462
509
455
ACCEPT
CHECK
9.2.1.1
of which: BLEACHED
1000 t
1,790.091
1,024,545
1,472.613
679,461
333.175
169,338
327.927
149,042
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
9.2.1.1
of which: BLEACHED
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
9.2.1.1
of which: BLEACHED
NAC/t
572
461
508
454
ACCEPT
CHECK
9.2.2
SULPHITE PULP
1000 t
27.677
30,246
29.069
30,552
28.616
45,014
30.715
46,195
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
9.2.2
SULPHITE PULP
1000 t
9.2.2
SULPHITE PULP
NAC/t
1093
1051
1573
1504
ACCEPT
CHECK
9.3
DISSOLVING GRADES
1000 t
93.006
108,124
95.274
100,552
2.342
1,541
0.002
4
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
9.3
DISSOLVING GRADES
1000 t
9.3
DISSOLVING GRADES
NAC/t
1163
1055
658
1955
ACCEPT
CHECK
10
OTHER PULP
1000 t
111.507
16,760
51.731
14,269
48.295
25,631
44.818
21,999
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
10
OTHER PULP
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
10
OTHER PULP
NAC/t
150
276
531
491
ACCEPT
CHECK
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 t
6.479
13,412
9.157
12,361
0.522
620
0.527
553
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 t
10.1
PULP FROM FIBRES OTHER THAN WOOD
NAC/t
2070
1350
1187
1049
ACCEPT
CHECK
10.2
RECOVERED FIBRE PULP
1000 t
105.028
3,348
42.574
1,908
47.773
25,011
44.291
21,447
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
10.2
RECOVERED FIBRE PULP
1000 t
10.2
RECOVERED FIBRE PULP
NAC/t
32
45
524
484
CHECK
CHECK
11
RECOVERED PAPER
1000 t
413.701
50,872
419.793
51,194
534.223
53,917
401.001
34,420
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
11
RECOVERED PAPER
1000 t
11
RECOVERED PAPER
NAC/t
123
122
101
86
ACCEPT
CHECK
12
PAPER AND PAPERBOARD
1000 t
1,032.744
835,824
1,094.982
852,565
2,818.736
2,431,217
2,785.115
2,221,087
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
12
PAPER AND PAPERBOARD
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
12
PAPER AND PAPERBOARD
NAC/t
809
779
863
797
ACCEPT
CHECK
12.1
GRAPHIC PAPERS
1000 t
471.435
373,982
459.688
352,107
1,102.063
1,017,053
981.039
854,826
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
12.1
GRAPHIC PAPERS
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
12.1
GRAPHIC PAPERS
NAC/t
793
766
923
871
ACCEPT
CHECK
12.1.1
NEWSPRINT
1000 t
76.016
42,742
64.070
32,688
92.354
54,333
84.223
42,642
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
12.1.1
NEWSPRINT
1000 t
12.1.1
NEWSPRINT
NAC/t
562
510
588
506
ACCEPT
CHECK
12.1.2
UNCOATED MECHANICAL
1000 t
56.092
41,761
63.985
44,491
193.319
126,076
182.208
108,429
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
12.1.2
UNCOATED MECHANICAL
1000 t
12.1.2
UNCOATED MECHANICAL
NAC/t
745
695
652
595
ACCEPT
CHECK
12.1.3
UNCOATED WOODFREE
1000 t
123.445
117,318
128.854
118,146
199.950
264,466
179.138
228,830
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
12.1.3
UNCOATED WOODFREE
1000 t
12.1.3
UNCOATED WOODFREE
NAC/t
950
917
1323
1277
ACCEPT
CHECK
12.1.4
COATED PAPERS
1000 t
215.883
172,161
202.778
156,782
616.439
572,178
535.470
474,926
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
12.1.4
COATED PAPERS
1000 t
12.1.4
COATED PAPERS
NAC/t
797
773
928
887
ACCEPT
CHECK
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 t
19.971
29,020
23.175
30,584
25.314
48,550
24.967
44,333
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 t
12.2
HOUSEHOLD AND SANITARY PAPERS
NAC/t
1453
1320
1918
1776
ACCEPT
CHECK
12.3
PACKAGING MATERIALS
1000 t
532.490
408,562
603.477
443,294
1,672.197
1,294,908
1,760.501
1,250,296
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
12.3
PACKAGING MATERIALS
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
12.3
PACKAGING MATERIALS
NAC/t
767
735
774
710
ACCEPT
CHECK
12.3.1
CASE MATERIALS
1000 t
268.434
130,974
312.368
142,301
993.397
474,159
1,053.903
440,912
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
12.3.1
CASE MATERIALS
1000 t
12.3.1
CASE MATERIALS
NAC/t
488
456
477
418
ACCEPT
CHECK
12.3.2
CARTONBOARD
1000 t
146.620
161,089
161.790
180,802
417.658
535,334
419.926
517,343
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
12.3.2
CARTONBOARD
1000 t
12.3.2
CARTONBOARD
NAC/t
1099
1118
1282
1232
ACCEPT
CHECK
12.3.3
WRAPPING PAPERS
1000 t
98.812
107,021
108.723
110,056
207.001
252,131
233.232
261,002
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
12.3.3
WRAPPING PAPERS
1000 t
12.3.3
WRAPPING PAPERS
NAC/t
1083
1012
1218
1119
ACCEPT
CHECK
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 t
18.625
9,478
20.595
10,136
54.141
33,285
53.440
31,039
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 t
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
NAC/t
509
492
615
581
ACCEPT
CHECK
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
1000 t
8.848
24,259
8.642
26,579
19.162
70,706
18.609
71,631
9
9
9
9
9
9
9
9
OK
OK
OK
OK
OK
OK
OK
OK
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
1000 t
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
NAC/t
2742
3075
3690
3849
ACCEPT
CHECK
Mechanical wood pulp, semi-chemical wood pulp and pulp from fibres other than wood 9.1+10.1
1000 t
44.012
34303.771
31.407
24086.751
17.259
8404.302
22.704
11478.072
To fill:
0
0
0
0
0
0
0
0
Text:
0
0
0
0
0
0
0
0
EU2 Removals
Country:
DE
Date:
Name of Official responsible for reply:
0
Official Address (in full):
Check Table
Thünen Institute, Leuschnerstr. 91, 21031 Hamburg
Phone/Fax:
0
0
E-mail:
0
EU2
Please verify if there's an error!
FOREST SECTOR QUESTIONNAIRE
Removals by type of ownership
Discrepancies
Flag
Flag
Note
Note
Product code
Ownership
Product code
Ownership
Unit
2019
2020
2019
2020
2019
2020
Unit
2019
2020
Quantity
Quantity
Quantity
Quantity
ROUNDWOOD REMOVALS (under bark)
ROUNDWOOD REMOVALS
1
ROUNDWOOD
1000 m3
77,820.994
84,050.98
9
9
see Notes in JQ1
see Notes in JQ1
1
ROUNDWOOD
1000 m3
OK
OK
1.C
Coniferous
1000 m3
57,337.683
65,366.31
9
9
see Notes in JQ1
see Notes in JQ1
1.C
Coniferous
1000 m3
OK
OK
1.NC
Non-coniferous
1000 m3
20,483.311
18,684.68
9
9
see Notes in JQ1
see Notes in JQ1
1.NC
Non-coniferous
1000 m3
OK
OK
State forests
1000 m3
24,685.405
9
see Notes in JQ1
State forests
1000 m3
OK
OK
Coniferous
1000 m3
18,206.892
9
see Notes in JQ1
Coniferous
1000 m3
Non-coniferous
1000 m3
6,478.513
9
see Notes in JQ1
Non-coniferous
1000 m3
Other publicly owned forests
1000 m3
17,662.572
9
see Notes in JQ1
Other publicly owned forests
1000 m3
OK
OK
Coniferous
1000 m3
13,639.662
9
see Notes in JQ1
Coniferous
1000 m3
Non-coniferous
1000 m3
4,022.911
9
see Notes in JQ1
Non-coniferous
1000 m3
Private forest
1000 m3
35,473.016
9
see Notes in JQ1
Private forest
1000 m3
OK
OK
Coniferous
1000 m3
25,491.129
9
see Notes in JQ1
Coniferous
1000 m3
Non-coniferous
1000 m3
9,981.887
9
see Notes in JQ1
Non-coniferous
1000 m3
To fill:
0
9
Text:
0
0
Note:
–
Ownership categories correspond to those of the TBFRA.
State forests: Forests owned by national, state and regional governments, or government-owned corporations; Crown forests.
Other publicly owned forests: Forests belonging to cities, municipalities, villages and communes.
Private forests: Forests owned by individuals, co-operatives, enterprises and industries and other private institutions.
–
The unit should be solid cubic metres, under bark.
ITTO1-Estimates
Country:
DE
Date:
Name of Official responsible for reply:
0
Official Address (in full):
Thünen Institute, Leuschnerstr. 91, 21031 Hamburg
ITTO1
Telephone:
0
Fax:
0
FOREST SECTOR QUESTIONNAIRE
E-mail:
0
Production and Trade Estimates for
2020
Value must always be in 1000 NAC (national currency)
Value must always be in 1000 NAC (national currency)
1000 NAC
I M P O R T
E X P O R T
Product
Classifications
2019
2020
2019
2020
HS2007/HS2002/HS96
Scientific Name
Local/Trade Name
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
(1000 m3)
(1000 m3)
(1000 m3)
(1000 m3)
1.2.NC.T
HS2017:
Industrial Roundwood, Tropical
ex4403.12 4403.41/49
4403 49 10
2.239
1,183
3.264
1,706
0.517
301
0.257
152
HS2012/2007:
4403 49 35
1.209
690
0.785
414
0.018
12
0.093
72
ex4403.10 4403.41/49 ex4403.99
4403 49 85
6.028
2,763
5.824
2,488
3.827
2,298
4.652
2,463
6.NC.T
HS2017:
Sawnwood, Tropical
ex4406.12/92 4407.21/22/25/26/27/28/29
4407 21 10
0
63
0
74
0
110
0
73
4407 21 91
0
11
0
18
0
6
HS2012/2007:
4407 21 99
0
5
0
13
0
28
0
30
ex4406.10/90 4407.21/22/25/26/27/28/30
4407 22 10
2
1,560
3
2,597
2
1,598
3
2,601
4407 22 91
0
41
0
129
0
24
0
14
4407 22 99
0
295
0
285
0
52
0
70
4407 25 10
0
1
0
3
0
57
4407 25 30
0
123
0
191
0
126
0
147
4407 25 90
13
9,331
12
8,219
3
2,497
3
2,073
4407 26 90
0
217
0
157
0
172
0
92
4407 27 91
1
1,422
0
1,071
4407 27 99
9
6,407
5
3,943
5
4,783
4
3,784
4407 28 99
2
1,912
3
2,378
3
2,893
2
1,949
4407 29 15
1
715
2
1,313
0
50
0
98
4407 29 20
0
24
4407 29 83
2
1,784
2
1,916
1
2,116
2
2,856
4407 29 85
0
3
4407 29 95
37
29,347
30
26,637
15
18,063
15
18,822
4407 29 96
0
133
0
268
0
126
0
374
4407 29 97
0
37
0
5
4407 29 98
6
7,167
7
6,031
3
4,217
2
4,862
7.NC.T
HS2017:
4408 31 21
0
2
0
0
Veneer Sheets, Tropical
4408.31/39
4408 31 30
0
67
0
124
0
252
0
126
HS2012/2007:
4408 39 15
0
128
0
206
0
261
0
403
4408 39 21
0
18
4408.31/39 ex4408.90
4408 39 30
1
2,741
1
2,340
1
2,899
1
1,754
4408 39 55
0
100
0
90
0
97
0
147
4408 39 70
1
1,341
1
1,251
4408 39 85
1
4,224
1
3,020
1
5,570
1
4,418
4408 39 95
5
3,973
5
3,207
0
1,722
0
1,583
8.1.NC.T
HS2017:
Plywood, Tropical
4412.31 ex4412.94/99
4412 31 10
21
19,764
16
15,162
1
1,849
1
1,574
HS2012/2007:
4412 31 90
112
67,461
93
53,952
38
43,262
34
36,983
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:
1
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:
Non-Tropical:
Non-Tropical:
Non-Tropical:
Comments (if any):
2
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.
3
Please elaborate on any short or medium term plans for expanding capacity for (further) processing of tropical timber products in your country.
4
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?
5
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.
6
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.).
7
Please provide details of any relevant forest law enforcement activities (e.g. legislation, fines, arrests, etc.) in your country in the past year.
8
Please indicate the current extent of forest plantations in your country (ha), annual establishment rate (ha/yr) and proportion of industrial roundwood production from plantations.
TS-OB
%
Min:
80%
Max:
120%
Notes
JQ1
Country
Flow
Unit
Product
2015
2016
2017
2018
2019
2019
2020
15/16
16/17
17/18
18/19
19/19
19/20
2015
2016
2017
2018
DE
P.OB
1000 m3
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_2_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_2_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_2_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_2_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_2_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_2_3_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P.OB
1000 m3
1_2_3_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
TS-JQ1
%
Min:
80%
Max:
120%
Notes
JQ1
Country
Flow
Unit
Product
2015
2016
2017
2018
2019
2019
2020
15/16
16/17
17/18
18/19
19/19
19/20
2015
2016
2017
2018
DE
P
1000 m3
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
77820.994
84050.981
!!
!!
!!
!!
!!
108.01%
DE
P
1000 m3
1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
23697.485
22261.463
!!
!!
!!
!!
!!
93.94%
DE
P
1000 m3
1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9607.242
9004.662
!!
!!
!!
!!
!!
93.73%
DE
P
1000 m3
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
14090.243
13256.801
!!
!!
!!
!!
!!
94.08%
DE
P
1000 m3
1_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
54123.509
61789.518
!!
!!
!!
!!
!!
114.16%
DE
P
1000 m3
1_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
47730.441
56361.643
!!
!!
!!
!!
!!
118.08%
DE
P
1000 m3
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6393.068
5427.874
!!
!!
!!
!!
!!
84.90%
DE
P
1000 m3
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P
1000 m3
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
41333.776
48212.969
!!
!!
!!
!!
!!
116.64%
DE
P
1000 m3
1_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
38141.158
45700.100
!!
!!
!!
!!
!!
119.82%
DE
P
1000 m3
1_2_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3192.618
2512.869
!!
!!
!!
!!
!!
78.71%
DE
P
1000 m3
1_2_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
12717.937
13502.586
!!
!!
!!
!!
!!
106.17%
DE
P
1000 m3
1_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9517.940
10589.855
!!
!!
!!
!!
!!
111.26%
DE
P
1000 m3
1_2_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3199.997
2912.731
!!
!!
!!
!!
!!
91.02%
DE
P
1000 m3
1_2_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
71.796
73.962
!!
!!
!!
!!
!!
103.02%
DE
P
1000 m3
1_2_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
71.343
71.688
!!
!!
!!
!!
!!
100.48%
DE
P
1000 m3
1_2_3_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.453
2.274
!!
!!
!!
!!
!!
501.48%
DE
P
1000 m3
1_2_3_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P
1000 mt
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
14890.121
16114.994
!!
!!
!!
!!
!!
108.23%
DE
P
1000 m3
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
10972.406
11707.400
!!
!!
!!
!!
!!
106.70%
DE
P
1000 m3
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3917.715
4407.594
!!
!!
!!
!!
!!
112.50%
DE
P
1000 m3
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6601.000
6601.000
!!
!!
!!
!!
!!
100.00%
DE
P
1000 mt
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3661.508
3905.978
!!
!!
!!
!!
!!
106.68%
DE
P
1000 mt
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2821.000
3100.000
!!
!!
!!
!!
!!
109.89%
DE
P
1000 mt
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
840.508
805.978
!!
!!
!!
!!
!!
95.89%
DE
P
1000 m3
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
24573.352
26219.416
!!
!!
!!
!!
!!
106.70%
DE
P
1000 m3
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
23306.983
25217.069
!!
!!
!!
!!
!!
108.20%
DE
P
1000 m3
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1266.369
1002.347
!!
!!
!!
!!
!!
79.15%
DE
P
1000 m3
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.596
1.424
!!
!!
!!
!!
!!
89.26%
DE
P
1000 m3
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
97.740
99.925
!!
!!
!!
!!
!!
102.24%
DE
P
1000 m3
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13.966
11.707
!!
!!
!!
!!
!!
83.83%
DE
P
1000 m3
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
83.774
88.218
!!
!!
!!
!!
!!
105.30%
DE
P
1000 m3
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.406
1.255
!!
!!
!!
!!
!!
89.26%
DE
P
1000 m3
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
12515.558
12690.846
!!
!!
!!
!!
!!
101.40%
DE
P
1000 m3
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
111.162
99.827
!!
!!
!!
!!
!!
89.80%
DE
P
1000 m3
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
43.845
47.046
!!
!!
!!
!!
!!
107.30%
DE
P
1000 m3
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
67.317
52.781
!!
!!
!!
!!
!!
78.41%
DE
P
1000 m3
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.165
0.165
!!
!!
!!
!!
!!
100.00%
DE
P
1000 m3
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6877.729
6790.233
!!
!!
!!
!!
!!
98.73%
DE
P
1000 m3
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1163.010
1233.873
!!
!!
!!
!!
!!
106.09%
DE
P
1000 m3
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5526.667
5800.786
!!
!!
!!
!!
!!
104.96%
DE
P
1000 m3
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P
1000 m3
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4505.051
4599.693
!!
!!
!!
!!
!!
102.10%
DE
P
1000 m3
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1021.616
1201.093
!!
!!
!!
!!
!!
117.57%
DE
P
1000 mt
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2325.959
2254.696
!!
!!
!!
!!
!!
96.94%
DE
P
1000 mt
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
728.308
684.064
!!
!!
!!
!!
!!
93.93%
DE
P
1000 mt
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1597.651
1570.632
!!
!!
!!
!!
!!
98.31%
DE
P
1000 mt
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1121.457
1090.341
!!
!!
!!
!!
!!
97.23%
DE
P
1000 mt
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1121.457
1090.341
!!
!!
!!
!!
!!
97.23%
DE
P
1000 mt
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
476.194
480.291
!!
!!
!!
!!
!!
100.86%
DE
P
1000 mt
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P
1000 mt
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
14406.181
14198.265
!!
!!
!!
!!
!!
98.56%
DE
P
1000 mt
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
66.181
65.265
!!
!!
!!
!!
!!
98.62%
DE
P
1000 mt
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
14340.000
14133.000
!!
!!
!!
!!
!!
98.56%
DE
P
1000 mt
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
17153.576
16905.566
!!
!!
!!
!!
!!
98.55%
DE
P
1000 mt
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
22080.042
21339.418
!!
!!
!!
!!
!!
96.65%
DE
P
1000 mt
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7292.298
6239.278
!!
!!
!!
!!
!!
85.56%
DE
P
1000 mt
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1091.498
909.971
!!
!!
!!
!!
!!
83.37%
DE
P
1000 mt
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1906.322
1741.811
!!
!!
!!
!!
!!
91.37%
DE
P
1000 mt
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1552.180
1376.541
!!
!!
!!
!!
!!
88.68%
DE
P
1000 mt
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2742.298
2210.955
!!
!!
!!
!!
!!
80.62%
DE
P
1000 mt
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1495.531
1514.476
!!
!!
!!
!!
!!
101.27%
DE
P
1000 mt
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11949.644
12251.681
!!
!!
!!
!!
!!
102.53%
DE
P
1000 mt
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8730.078
9048.017
!!
!!
!!
!!
!!
103.64%
DE
P
1000 mt
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1785.611
1779.172
!!
!!
!!
!!
!!
99.64%
DE
P
1000 mt
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
406.021
410.356
!!
!!
!!
!!
!!
101.07%
DE
P
1000 mt
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1027.934
1014.136
!!
!!
!!
!!
!!
98.66%
DE
P
1000 mt
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1342.569
1333.983
!!
!!
!!
!!
!!
99.36%
DE
P
1000 mt
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
DE
P
1000 mt
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
TS-JQ2
%
Min:
80%
Max:
120%
Notes
JQ2
Country
Flow
Unit
Product
2015
2016
2017
2018
2019
2019
2020
15/16
16/17
17/18
18/19
19/19
19/20
2015
2016
2017
2018
Q
DE
M
1000 m3
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7642.251
6169.747
!!
!!
!!
!!
!!
80.73%
€
DE
M
1000 NAC
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
455841.000
353412.000
!!
!!
!!
!!
!!
77.53%
UV
DE
M
1000 m3
1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
59.647
57.281
!!
!!
!!
!!
!!
96.03%
Q
DE
X
1000 m3
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9056.743
13087.034
!!
!!
!!
!!
!!
144.50%
€
DE
X
1000 NAC
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
695046.000
860136.000
!!
!!
!!
!!
!!
123.75%
UV
DE
X
1000 m3
1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
76.743
65.724
!!
!!
!!
!!
!!
85.64%
Q
DE
M
1000 m3
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
323.881
246.397
!!
!!
!!
!!
!!
76.08%
€
DE
M
1000 NAC
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
29827.000
26219.000
!!
!!
!!
!!
!!
87.90%
UV
DE
M
1000 m3
1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
92.092
106.410
!!
!!
!!
!!
!!
115.55%
Q
DE
X
1000 m3
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
141.214
256.690
!!
!!
!!
!!
!!
181.77%
€
DE
X
1000 NAC
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7112.000
11231.000
!!
!!
!!
!!
!!
157.92%
UV
DE
X
1000 m3
1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
50.363
43.753
!!
!!
!!
!!
!!
86.88%
Q
DE
M
1000 m3
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
104.027
80.504
!!
!!
!!
!!
!!
77.39%
€
DE
M
1000 NAC
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9901.000
8528.000
!!
!!
!!
!!
!!
86.13%
UV
DE
M
1000 m3
1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
95.177
105.933
!!
!!
!!
!!
!!
111.30%
Q
DE
X
1000 m3
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
99.347
179.000
!!
!!
!!
!!
!!
180.18%
€
DE
X
1000 NAC
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2916.000
6956.000
!!
!!
!!
!!
!!
238.55%
UV
DE
X
1000 m3
1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
29.352
38.860
!!
!!
!!
!!
!!
132.40%
Q
DE
M
1000 m3
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
219.854
165.893
!!
!!
!!
!!
!!
75.46%
€
DE
M
1000 NAC
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
19926.000
17691.000
!!
!!
!!
!!
!!
88.78%
UV
DE
M
1000 m3
1_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
90.633
106.641
!!
!!
!!
!!
!!
117.66%
Q
DE
X
1000 m3
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
41.867
77.690
!!
!!
!!
!!
!!
185.56%
€
DE
X
1000 NAC
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4196.000
4275.000
!!
!!
!!
!!
!!
101.88%
UV
DE
X
1000 m3
1_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
100.222
55.026
!!
!!
!!
!!
!!
54.90%
Q
DE
M
1000 m3
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7318.370
5923.350
!!
!!
!!
!!
!!
80.94%
€
DE
M
1000 NAC
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
426014.000
327193.000
!!
!!
!!
!!
!!
76.80%
UV
DE
M
1000 m3
1_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
58.212
55.238
!!
!!
!!
!!
!!
94.89%
Q
DE
X
1000 m3
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8915.529
12830.344
!!
!!
!!
!!
!!
143.91%
€
DE
X
1000 NAC
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
687934.000
848905.000
!!
!!
!!
!!
!!
123.40%
UV
DE
X
1000 m3
1_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
77.161
66.164
!!
!!
!!
!!
!!
85.75%
Q
DE
M
1000 m3
1_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6866.440
5559.520
!!
!!
!!
!!
!!
80.97%
€
DE
M
1000 NAC
1_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
375644.000
285233.000
!!
!!
!!
!!
!!
75.93%
UV
DE
M
1000 m3
1_2_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
54.707
51.305
!!
!!
!!
!!
!!
93.78%
Q
DE
X
1000 m3
1_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7506.234
11816.202
!!
!!
!!
!!
!!
157.42%
€
DE
X
1000 NAC
1_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
515811.000
728553.000
!!
!!
!!
!!
!!
141.24%
UV
DE
X
1000 m3
1_2_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
68.718
61.657
!!
!!
!!
!!
!!
89.73%
Q
DE
M
1000 mt
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
451.930
363.830
!!
!!
!!
!!
!!
80.51%
€
DE
M
1000 NAC
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
50370.000
41960.000
!!
!!
!!
!!
!!
83.30%
UV
DE
M
1000 mt
2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
111.455
115.329
!!
!!
!!
!!
!!
103.48%
Q
DE
X
1000 mt
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1409.295
1014.142
!!
!!
!!
!!
!!
71.96%
€
DE
X
1000 NAC
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
172123.000
120352.000
!!
!!
!!
!!
!!
69.92%
UV
DE
X
1000 mt
2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
122.134
118.674
!!
!!
!!
!!
!!
97.17%
Q
DE
M
1000 m3
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9.476
9.873
!!
!!
!!
!!
!!
104.19%
€
DE
M
1000 NAC
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4636.000
4608.000
!!
!!
!!
!!
!!
99.40%
UV
DE
M
1000 m3
3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
489.236
466.727
!!
!!
!!
!!
!!
95.40%
Q
DE
X
1000 m3
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4.362
5.002
!!
!!
!!
!!
!!
114.67%
€
DE
X
1000 NAC
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2611.000
2687.000
!!
!!
!!
!!
!!
102.91%
UV
DE
X
1000 m3
3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
598.579
537.185
!!
!!
!!
!!
!!
89.74%
Q
DE
M
1000 m3
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
214.280
164.236
!!
!!
!!
!!
!!
76.65%
€
DE
M
1000 NAC
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
104312.000
89189.000
!!
!!
!!
!!
!!
85.50%
UV
DE
M
1000 m3
3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
486.802
543.054
!!
!!
!!
!!
!!
111.56%
Q
DE
X
1000 m3
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
22.385
31.704
!!
!!
!!
!!
!!
141.63%
€
DE
X
1000 NAC
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
21369.000
28408.000
!!
!!
!!
!!
!!
132.94%
UV
DE
X
1000 m3
3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
954.612
896.038
!!
!!
!!
!!
!!
93.86%
Q
DE
M
1000 m3
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1499.692
1114.652
!!
!!
!!
!!
!!
74.33%
€
DE
M
1000 NAC
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
49781.000
32480.000
!!
!!
!!
!!
!!
65.25%
UV
DE
M
1000 m3
3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
33.194
29.139
!!
!!
!!
!!
!!
87.78%
Q
DE
X
1000 m3
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2761.950
2790.501
!!
!!
!!
!!
!!
101.03%
€
DE
X
1000 NAC
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
130926.000
123202.000
!!
!!
!!
!!
!!
94.10%
UV
DE
X
1000 m3
3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
47.403
44.150
!!
!!
!!
!!
!!
93.14%
Q
DE
M
1000 mt
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
789.724
530.374
!!
!!
!!
!!
!!
67.16%
€
DE
M
1000 NAC
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
23950.000
15267.000
!!
!!
!!
!!
!!
63.75%
UV
DE
M
1000 mt
4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
30.327
28.785
!!
!!
!!
!!
!!
94.92%
Q
DE
X
1000 mt
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1951.584
1929.180
!!
!!
!!
!!
!!
98.85%
€
DE
X
1000 NAC
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
83778.000
77773.000
!!
!!
!!
!!
!!
92.83%
UV
DE
X
1000 mt
4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
42.928
40.314
!!
!!
!!
!!
!!
93.91%
Q
DE
M
1000 mt
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
709.968
584.278
!!
!!
!!
!!
!!
82.30%
€
DE
M
1000 NAC
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
25831.000
17213.000
!!
!!
!!
!!
!!
66.64%
UV
DE
M
1000 mt
4_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
36.383
29.460
!!
!!
!!
!!
!!
80.97%
Q
DE
X
1000 mt
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
810.366
861.321
!!
!!
!!
!!
!!
106.29%
€
DE
X
1000 NAC
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
47148.000
45429.000
!!
!!
!!
!!
!!
96.35%
UV
DE
X
1000 mt
4_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
58.181
52.743
!!
!!
!!
!!
!!
90.65%
Q
DE
M
1000 mt
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
875.813
904.489
!!
!!
!!
!!
!!
103.27%
€
DE
M
1000 NAC
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
33157.000
38540.000
!!
!!
!!
!!
!!
116.23%
UV
DE
M
1000 mt
4_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
37.859
42.610
!!
!!
!!
!!
!!
112.55%
Q
DE
X
1000 mt
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
657.738
558.245
!!
!!
!!
!!
!!
84.87%
€
DE
X
1000 NAC
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
38758.000
40400.000
!!
!!
!!
!!
!!
104.24%
UV
DE
X
1000 mt
4_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
58.926
72.370
!!
!!
!!
!!
!!
122.81%
Q
DE
M
1000 m3
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
619.382
550.222
!!
!!
!!
!!
!!
88.83%
€
DE
M
1000 NAC
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
99520.000
79256.000
!!
!!
!!
!!
!!
79.64%
UV
DE
M
1000 m3
5
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
160.676
144.044
!!
!!
!!
!!
!!
89.65%
Q
DE
X
1000 m3
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
815.856
851.375
!!
!!
!!
!!
!!
104.35%
€
DE
X
1000 NAC
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
184296.000
180362.000
!!
!!
!!
!!
!!
97.87%
UV
DE
X
1000 m3
5
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
225.893
211.848
!!
!!
!!
!!
!!
93.78%
Q
DE
M
1000 m3
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
316.740
291.268
!!
!!
!!
!!
!!
91.96%
€
DE
M
1000 NAC
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
58364.000
48974.000
!!
!!
!!
!!
!!
83.91%
UV
DE
M
1000 m3
5_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
184.265
168.141
!!
!!
!!
!!
!!
91.25%
Q
DE
X
1000 m3
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
771.279
801.083
!!
!!
!!
!!
!!
103.86%
€
DE
X
1000 NAC
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
171398.000
167129.000
!!
!!
!!
!!
!!
97.51%
UV
DE
X
1000 m3
5_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
222.226
208.629
!!
!!
!!
!!
!!
93.88%
Q
DE
M
1000 m3
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
302.642
258.954
!!
!!
!!
!!
!!
85.56%
€
DE
M
1000 NAC
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
41156.000
30282.000
!!
!!
!!
!!
!!
73.58%
UV
DE
M
1000 m3
5_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
135.989
116.940
!!
!!
!!
!!
!!
85.99%
Q
DE
X
1000 m3
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
44.577
50.292
!!
!!
!!
!!
!!
112.82%
€
DE
X
1000 NAC
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
12898.000
13233.000
!!
!!
!!
!!
!!
102.60%
UV
DE
X
1000 m3
5_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
289.342
263.123
!!
!!
!!
!!
!!
90.94%
Q
DE
M
1000 m3
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5280.868
5410.720
!!
!!
!!
!!
!!
102.46%
€
DE
M
1000 NAC
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1224150.000
1231226.000
!!
!!
!!
!!
!!
100.58%
UV
DE
M
1000 m3
5_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
231.808
227.553
!!
!!
!!
!!
!!
98.16%
Q
DE
X
1000 m3
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9656.877
10353.824
!!
!!
!!
!!
!!
107.22%
€
DE
X
1000 NAC
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2082909.000
2252310.000
!!
!!
!!
!!
!!
108.13%
UV
DE
X
1000 m3
5_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
215.692
217.534
!!
!!
!!
!!
!!
100.85%
Q
DE
M
1000 m3
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4868.020
5010.128
!!
!!
!!
!!
!!
102.92%
€
DE
M
1000 NAC
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1004053.000
1017315.000
!!
!!
!!
!!
!!
101.32%
UV
DE
M
1000 m3
6
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
206.255
203.052
!!
!!
!!
!!
!!
98.45%
Q
DE
X
1000 m3
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8889.313
9661.856
!!
!!
!!
!!
!!
108.69%
€
DE
X
1000 NAC
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1697553.000
1899543.000
!!
!!
!!
!!
!!
111.90%
UV
DE
X
1000 m3
6
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
190.966
196.602
!!
!!
!!
!!
!!
102.95%
Q
DE
M
1000 m3
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
412.848
400.592
!!
!!
!!
!!
!!
97.03%
€
DE
M
1000 NAC
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
220097.000
213911.000
!!
!!
!!
!!
!!
97.19%
UV
DE
M
1000 m3
6_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
533.119
533.987
!!
!!
!!
!!
!!
100.16%
Q
DE
X
1000 m3
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
767.564
691.968
!!
!!
!!
!!
!!
90.15%
€
DE
X
1000 NAC
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
385356.000
352767.000
!!
!!
!!
!!
!!
91.54%
UV
DE
X
1000 m3
6_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
502.051
509.802
!!
!!
!!
!!
!!
101.54%
Q
DE
M
1000 m3
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
73.779
66.453
!!
!!
!!
!!
!!
90.07%
€
DE
M
1000 NAC
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
59149.000
54194.000
!!
!!
!!
!!
!!
91.62%
UV
DE
M
1000 m3
6_1_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
801.705
815.524
!!
!!
!!
!!
!!
101.72%
Q
DE
X
1000 m3
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
33.498
30.893
!!
!!
!!
!!
!!
92.22%
€
DE
X
1000 NAC
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
38315.000
38976.000
!!
!!
!!
!!
!!
101.73%
UV
DE
X
1000 m3
6_1_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1143.800
1261.645
!!
!!
!!
!!
!!
110.30%
Q
DE
M
1000 m3
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
106.340
105.626
!!
!!
!!
!!
!!
99.33%
€
DE
M
1000 NAC
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
150740.000
153653.000
!!
!!
!!
!!
!!
101.93%
UV
DE
M
1000 m3
6_1_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1417.529
1454.689
!!
!!
!!
!!
!!
102.62%
Q
DE
X
1000 m3
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
58.135
55.892
!!
!!
!!
!!
!!
96.14%
€
DE
X
1000 NAC
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
140291.000
124914.000
!!
!!
!!
!!
!!
89.04%
UV
DE
X
1000 m3
6_1_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2413.193
2234.917
!!
!!
!!
!!
!!
92.61%
Q
DE
M
1000 m3
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
27.836
26.417
!!
!!
!!
!!
!!
94.90%
€
DE
M
1000 NAC
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
18481.000
17562.000
!!
!!
!!
!!
!!
95.03%
UV
DE
M
1000 m3
6_1_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
663.924
664.799
!!
!!
!!
!!
!!
100.13%
Q
DE
X
1000 m3
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.532
0.469
!!
!!
!!
!!
!!
88.16%
€
DE
X
1000 NAC
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2453.000
2252.000
!!
!!
!!
!!
!!
91.81%
UV
DE
X
1000 m3
6_1_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4610.902
4801.706
!!
!!
!!
!!
!!
104.14%
Q
DE
M
1000 m3
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
78.504
79.209
!!
!!
!!
!!
!!
100.90%
€
DE
M
1000 NAC
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
132259.000
136091.000
!!
!!
!!
!!
!!
102.90%
UV
DE
M
1000 m3
6_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1684.742
1718.125
!!
!!
!!
!!
!!
101.98%
Q
DE
X
1000 m3
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
57.603
55.423
!!
!!
!!
!!
!!
96.22%
€
DE
X
1000 NAC
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
137838.000
122662.000
!!
!!
!!
!!
!!
88.99%
UV
DE
X
1000 m3
6_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2392.896
2213.197
!!
!!
!!
!!
!!
92.49%
Q
DE
M
1000 m3
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8.410
8.139
!!
!!
!!
!!
!!
96.78%
€
DE
M
1000 NAC
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
12574.000
10238.000
!!
!!
!!
!!
!!
81.42%
UV
DE
M
1000 m3
6_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1495.125
1257.894
!!
!!
!!
!!
!!
84.13%
Q
DE
X
1000 m3
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.535
1.743
!!
!!
!!
!!
!!
68.76%
€
DE
X
1000 NAC
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
10821.000
8431.000
!!
!!
!!
!!
!!
77.91%
UV
DE
X
1000 m3
6_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4268.639
4837.063
!!
!!
!!
!!
!!
113.32%
Q
DE
M
1000 m3
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5781.129
6005.716
!!
!!
!!
!!
!!
103.88%
€
DE
M
1000 NAC
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1907646.000
1839547.000
!!
!!
!!
!!
!!
96.43%
UV
DE
M
1000 m3
6_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
329.978
306.299
!!
!!
!!
!!
!!
92.82%
Q
DE
X
1000 m3
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6019.846
6044.175
!!
!!
!!
!!
!!
100.40%
€
DE
X
1000 NAC
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2484957.000
2429242.000
!!
!!
!!
!!
!!
97.76%
UV
DE
X
1000 m3
6_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
412.794
401.915
!!
!!
!!
!!
!!
97.36%
Q
DE
M
1000 m3
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1486.319
1432.894
!!
!!
!!
!!
!!
96.41%
€
DE
M
1000 NAC
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
788383.000
712583.000
!!
!!
!!
!!
!!
90.39%
UV
DE
M
1000 m3
6_2_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
530.427
497.303
!!
!!
!!
!!
!!
93.76%
Q
DE
X
1000 m3
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
375.911
367.685
!!
!!
!!
!!
!!
97.81%
€
DE
X
1000 NAC
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
265081.000
250702.000
!!
!!
!!
!!
!!
94.58%
UV
DE
X
1000 m3
6_2_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
705.170
681.839
!!
!!
!!
!!
!!
96.69%
Q
DE
M
1000 m3
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
521.635
529.001
!!
!!
!!
!!
!!
101.41%
€
DE
M
1000 NAC
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
223420.000
208849.000
!!
!!
!!
!!
!!
93.48%
UV
DE
M
1000 m3
6_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
428.307
394.799
!!
!!
!!
!!
!!
92.18%
Q
DE
X
1000 m3
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
133.416
151.551
!!
!!
!!
!!
!!
113.59%
€
DE
X
1000 NAC
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
72489.000
76325.500
!!
!!
!!
!!
!!
105.29%
UV
DE
X
1000 m3
6_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
543.331
503.629
!!
!!
!!
!!
!!
92.69%
Q
DE
M
1000 m3
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
964.684
903.893
!!
!!
!!
!!
!!
93.70%
€
DE
M
1000 NAC
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
564963.000
503734.000
!!
!!
!!
!!
!!
89.16%
UV
DE
M
1000 m3
6_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
585.646
557.294
!!
!!
!!
!!
!!
95.16%
Q
DE
X
1000 m3
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
242.495
216.134
!!
!!
!!
!!
!!
89.13%
€
DE
X
1000 NAC
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
192592.000
174376.500
!!
!!
!!
!!
!!
90.54%
UV
DE
X
1000 m3
6_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
794.210
806.798
!!
!!
!!
!!
!!
101.58%
Q
DE
M
1000 m3
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
154.222
131.707
!!
!!
!!
!!
!!
85.40%
€
DE
M
1000 NAC
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
102033.000
84278.000
!!
!!
!!
!!
!!
82.60%
UV
DE
M
1000 m3
6_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
661.598
639.890
!!
!!
!!
!!
!!
96.72%
Q
DE
X
1000 m3
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
40.604
37.095
!!
!!
!!
!!
!!
91.36%
€
DE
X
1000 NAC
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
48255.500
42161.000
!!
!!
!!
!!
!!
87.37%
UV
DE
X
1000 m3
6_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1188.442
1136.568
!!
!!
!!
!!
!!
95.64%
Q
DE
M
1000 m3
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2787.203
2775.957
!!
!!
!!
!!
!!
99.60%
€
DE
M
1000 NAC
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
703127.000
667280.000
!!
!!
!!
!!
!!
94.90%
UV
DE
M
1000 m3
6_4_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
252.270
240.378
!!
!!
!!
!!
!!
95.29%
Q
DE
X
1000 m3
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2349.216
2188.631
!!
!!
!!
!!
!!
93.16%
€
DE
X
1000 NAC
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
607576.000
545906.000
!!
!!
!!
!!
!!
89.85%
UV
DE
X
1000 m3
6_4_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
258.629
249.428
!!
!!
!!
!!
!!
96.44%
Q
DE
M
1000 m3
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
791.907
852.213
!!
!!
!!
!!
!!
107.62%
€
DE
M
1000 NAC
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
204223.000
205059.000
!!
!!
!!
!!
!!
100.41%
UV
DE
M
1000 m3
6_4_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
257.888
240.619
!!
!!
!!
!!
!!
93.30%
Q
DE
X
1000 m3
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
525.391
510.998
!!
!!
!!
!!
!!
97.26%
€
DE
X
1000 NAC
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
132646.000
119105.000
!!
!!
!!
!!
!!
89.79%
UV
DE
X
1000 m3
6_4_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
252.471
233.083
!!
!!
!!
!!
!!
92.32%
Q
DE
M
1000 m3
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1507.607
1796.865
!!
!!
!!
!!
!!
119.19%
€
DE
M
1000 NAC
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
416136.000
459684.000
!!
!!
!!
!!
!!
110.46%
UV
DE
M
1000 m3
6_4_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
276.024
255.826
!!
!!
!!
!!
!!
92.68%
Q
DE
X
1000 m3
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3294.719
3487.859
!!
!!
!!
!!
!!
105.86%
€
DE
X
1000 NAC
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1612300.000
1632634.000
!!
!!
!!
!!
!!
101.26%
UV
DE
X
1000 m3
6_4_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
489.359
468.091
!!
!!
!!
!!
!!
95.65%
Q
DE
M
1000 mt
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
223.390
230.904
!!
!!
!!
!!
!!
103.36%
€
DE
M
1000 NAC
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
90695.000
82618.000
!!
!!
!!
!!
!!
91.09%
UV
DE
M
1000 mt
7
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
405.994
357.802
!!
!!
!!
!!
!!
88.13%
Q
DE
X
1000 mt
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
26.283
27.696
!!
!!
!!
!!
!!
105.37%
€
DE
X
1000 NAC
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
16504.015
17512.005
!!
!!
!!
!!
!!
106.11%
UV
DE
X
1000 mt
7
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
627.923
632.298
!!
!!
!!
!!
!!
100.70%
Q
DE
M
1000 mt
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
495.428
600.437
!!
!!
!!
!!
!!
121.20%
€
DE
M
1000 NAC
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
233975.000
267767.000
!!
!!
!!
!!
!!
114.44%
UV
DE
M
1000 mt
7_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
472.268
445.954
!!
!!
!!
!!
!!
94.43%
Q
DE
X
1000 mt
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2877.842
2878.786
!!
!!
!!
!!
!!
100.03%
€
DE
X
1000 NAC
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1553625.985
1553311.995
!!
!!
!!
!!
!!
99.98%
UV
DE
X
1000 mt
7_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
539.858
539.572
!!
!!
!!
!!
!!
99.95%
Q
DE
M
1000 mt
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
788.789
965.524
!!
!!
!!
!!
!!
122.41%
€
DE
M
1000 NAC
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
91466.000
109299.000
!!
!!
!!
!!
!!
119.50%
UV
DE
M
1000 mt
7_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
115.957
113.202
!!
!!
!!
!!
!!
97.62%
Q
DE
X
1000 mt
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
390.594
581.377
!!
!!
!!
!!
!!
148.84%
€
DE
X
1000 NAC
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
42170.000
61810.000
!!
!!
!!
!!
!!
146.57%
UV
DE
X
1000 mt
7_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
107.964
106.317
!!
!!
!!
!!
!!
98.47%
Q
DE
M
1000 mt
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4755.000
4034.000
!!
!!
!!
!!
!!
84.84%
€
DE
M
1000 NAC
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2758929.000
2059887.000
!!
!!
!!
!!
!!
74.66%
UV
DE
M
1000 mt
7_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
580.216
510.631
!!
!!
!!
!!
!!
88.01%
Q
DE
X
1000 mt
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1254.000
1146.000
!!
!!
!!
!!
!!
91.39%
€
DE
X
1000 NAC
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
739434.000
591738.000
!!
!!
!!
!!
!!
80.03%
UV
DE
X
1000 mt
7_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
589.660
516.351
!!
!!
!!
!!
!!
87.57%
Q
DE
M
1000 mt
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
205.000
154.000
!!
!!
!!
!!
!!
75.12%
€
DE
M
1000 NAC
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
114500.000
86868.000
!!
!!
!!
!!
!!
75.87%
UV
DE
M
1000 mt
7_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
558.537
564.078
!!
!!
!!
!!
!!
100.99%
Q
DE
X
1000 mt
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
93.000
89.000
!!
!!
!!
!!
!!
95.70%
€
DE
X
1000 NAC
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
43397.000
37732.000
!!
!!
!!
!!
!!
86.95%
UV
DE
X
1000 mt
7_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
466.634
423.955
!!
!!
!!
!!
!!
90.85%
Q
DE
M
1000 mt
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4174.000
3517.000
!!
!!
!!
!!
!!
84.26%
€
DE
M
1000 NAC
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2321574.000
1668713.000
!!
!!
!!
!!
!!
71.88%
UV
DE
M
1000 mt
7_3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
556.199
474.471
!!
!!
!!
!!
!!
85.31%
Q
DE
X
1000 mt
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1149.000
1051.000
!!
!!
!!
!!
!!
91.47%
€
DE
X
1000 NAC
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
682435.000
548836.000
!!
!!
!!
!!
!!
80.42%
UV
DE
X
1000 mt
7_3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
593.938
522.204
!!
!!
!!
!!
!!
87.92%
Q
DE
M
1000 mt
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4093.000
3437.000
!!
!!
!!
!!
!!
83.97%
€
DE
M
1000 NAC
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2237892.000
1585683.000
!!
!!
!!
!!
!!
70.86%
UV
DE
M
1000 mt
7_3_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
546.761
461.357
!!
!!
!!
!!
!!
84.38%
Q
DE
X
1000 mt
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1067.000
952.000
!!
!!
!!
!!
!!
89.22%
€
DE
X
1000 NAC
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
576025.000
439317.000
!!
!!
!!
!!
!!
76.27%
UV
DE
X
1000 mt
7_3_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
539.855
461.467
!!
!!
!!
!!
!!
85.48%
Q
DE
M
1000 mt
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3981.000
3341.000
!!
!!
!!
!!
!!
83.92%
€
DE
M
1000 NAC
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2178450.000
1541061.000
!!
!!
!!
!!
!!
70.74%
UV
DE
M
1000 mt
7_3_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
547.212
461.257
!!
!!
!!
!!
!!
84.29%
Q
DE
X
1000 mt
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1054.000
944.000
!!
!!
!!
!!
!!
89.56%
€
DE
X
1000 NAC
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
568446.000
435200.000
!!
!!
!!
!!
!!
76.56%
UV
DE
X
1000 mt
7_3_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
539.323
461.017
!!
!!
!!
!!
!!
85.48%
Q
DE
M
1000 mt
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
81.000
80.000
!!
!!
!!
!!
!!
98.77%
€
DE
M
1000 NAC
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
83682.000
83030.000
!!
!!
!!
!!
!!
99.22%
UV
DE
M
1000 mt
7_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1033.111
1037.875
!!
!!
!!
!!
!!
100.46%
Q
DE
X
1000 mt
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
82.000
99.000
!!
!!
!!
!!
!!
120.73%
€
DE
X
1000 NAC
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
106410.000
109519.000
!!
!!
!!
!!
!!
102.92%
UV
DE
X
1000 mt
7_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1297.683
1106.253
!!
!!
!!
!!
!!
85.25%
Q
DE
M
1000 mt
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
376.000
363.000
!!
!!
!!
!!
!!
96.54%
€
DE
M
1000 NAC
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
322855.000
304306.000
!!
!!
!!
!!
!!
94.25%
UV
DE
M
1000 mt
8
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
858.657
838.309
!!
!!
!!
!!
!!
97.63%
Q
DE
X
1000 mt
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
12.000
6.000
!!
!!
!!
!!
!!
50.00%
€
DE
X
1000 NAC
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13602.000
5170.000
!!
!!
!!
!!
!!
38.01%
UV
DE
X
1000 mt
8
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1133.500
861.667
!!
!!
!!
!!
!!
76.02%
Q
DE
M
1000 mt
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
139.000
92.000
!!
!!
!!
!!
!!
66.19%
€
DE
M
1000 NAC
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
34162.000
36526.000
!!
!!
!!
!!
!!
106.92%
UV
DE
M
1000 mt
8_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
245.770
397.022
!!
!!
!!
!!
!!
161.54%
Q
DE
X
1000 mt
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
119.000
111.000
!!
!!
!!
!!
!!
93.28%
€
DE
X
1000 NAC
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
57448.000
52193.000
!!
!!
!!
!!
!!
90.85%
UV
DE
X
1000 mt
8_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
482.756
470.207
!!
!!
!!
!!
!!
97.40%
Q
DE
M
1000 mt
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
14.000
16.000
!!
!!
!!
!!
!!
114.29%
€
DE
M
1000 NAC
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
20354.000
17734.000
!!
!!
!!
!!
!!
87.13%
UV
DE
M
1000 mt
8_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1453.857
1108.375
!!
!!
!!
!!
!!
76.24%
Q
DE
X
1000 mt
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.000
1.000
!!
!!
!!
!!
!!
100.00%
€
DE
X
1000 NAC
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1265.000
1084.000
!!
!!
!!
!!
!!
85.69%
UV
DE
X
1000 mt
8_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1265.000
1084.000
!!
!!
!!
!!
!!
85.69%
Q
DE
M
1000 mt
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
125.000
76.000
!!
!!
!!
!!
!!
60.80%
€
DE
M
1000 NAC
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13808.000
18792.000
!!
!!
!!
!!
!!
136.10%
UV
DE
M
1000 mt
9
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
110.464
247.263
!!
!!
!!
!!
!!
223.84%
Q
DE
X
1000 mt
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
118.000
110.000
!!
!!
!!
!!
!!
93.22%
€
DE
X
1000 NAC
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
56183.000
51109.000
!!
!!
!!
!!
!!
90.97%
UV
DE
X
1000 mt
9
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
476.127
464.627
!!
!!
!!
!!
!!
97.58%
Q
DE
M
1000 mt
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4934.000
4580.000
!!
!!
!!
!!
!!
92.83%
€
DE
M
1000 NAC
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
632355.000
545971.981
!!
!!
!!
!!
!!
86.34%
UV
DE
M
1000 mt
10
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
128.163
119.208
!!
!!
!!
!!
!!
93.01%
Q
DE
X
1000 mt
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2500.000
2160.000
!!
!!
!!
!!
!!
86.40%
€
DE
X
1000 NAC
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
262684.000
199980.067
!!
!!
!!
!!
!!
76.13%
UV
DE
X
1000 mt
10
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
105.074
92.583
!!
!!
!!
!!
!!
88.11%
Q
DE
M
1000 mt
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
10914.000
10419.678
!!
!!
!!
!!
!!
95.47%
€
DE
M
1000 NAC
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8447334.000
7785328.781
!!
!!
!!
!!
!!
92.16%
UV
DE
M
1000 mt
10_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
773.991
747.176
!!
!!
!!
!!
!!
96.54%
Q
DE
X
1000 mt
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
14244.125
13632.411
!!
!!
!!
!!
!!
95.71%
€
DE
X
1000 NAC
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11767361.000
10443861.961
!!
!!
!!
!!
!!
88.75%
UV
DE
X
1000 mt
10_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
826.120
766.105
!!
!!
!!
!!
!!
92.74%
Q
DE
M
1000 mt
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4677.000
4091.565
!!
!!
!!
!!
!!
87.48%
€
DE
M
1000 NAC
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3294178.000
2785718.316
!!
!!
!!
!!
!!
84.56%
UV
DE
M
1000 mt
10_1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
704.336
680.844
!!
!!
!!
!!
!!
96.66%
Q
DE
X
1000 mt
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5286.591
4572.879
!!
!!
!!
!!
!!
86.50%
€
DE
X
1000 NAC
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4235510.000
3468386.560
!!
!!
!!
!!
!!
81.89%
UV
DE
X
1000 mt
10_1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
801.180
758.469
!!
!!
!!
!!
!!
94.67%
Q
DE
M
1000 mt
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
715.000
537.031
!!
!!
!!
!!
!!
75.11%
€
DE
M
1000 NAC
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
378451.000
245439.567
!!
!!
!!
!!
!!
64.85%
UV
DE
M
1000 mt
10_1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
529.302
457.030
!!
!!
!!
!!
!!
86.35%
Q
DE
X
1000 mt
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
443.171
402.282
!!
!!
!!
!!
!!
90.77%
€
DE
X
1000 NAC
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
231409.000
177904.254
!!
!!
!!
!!
!!
76.88%
UV
DE
X
1000 mt
10_1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
522.167
442.237
!!
!!
!!
!!
!!
84.69%
Q
DE
M
1000 mt
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
527.000
583.215
!!
!!
!!
!!
!!
110.67%
€
DE
M
1000 NAC
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
369763.000
360378.913
!!
!!
!!
!!
!!
97.46%
UV
DE
M
1000 mt
10_1_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
701.638
617.918
!!
!!
!!
!!
!!
88.07%
Q
DE
X
1000 mt
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
927.657
872.046
!!
!!
!!
!!
!!
94.01%
€
DE
X
1000 NAC
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
536967.000
453721.421
!!
!!
!!
!!
!!
84.50%
UV
DE
X
1000 mt
10_1_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
578.842
520.295
!!
!!
!!
!!
!!
89.89%
Q
DE
M
1000 mt
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1161.000
1139.438
!!
!!
!!
!!
!!
98.14%
€
DE
M
1000 NAC
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1038774.000
926161.732
!!
!!
!!
!!
!!
89.16%
UV
DE
M
1000 mt
10_1_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
894.724
812.823
!!
!!
!!
!!
!!
90.85%
Q
DE
X
1000 mt
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
959.461
849.862
!!
!!
!!
!!
!!
88.58%
€
DE
X
1000 NAC
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1126380.000
961219.387
!!
!!
!!
!!
!!
85.34%
UV
DE
X
1000 mt
10_1_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1173.972
1131.029
!!
!!
!!
!!
!!
96.34%
Q
DE
M
1000 mt
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2274.000
1831.881
!!
!!
!!
!!
!!
80.56%
€
DE
M
1000 NAC
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1507190.000
1253738.104
!!
!!
!!
!!
!!
83.18%
UV
DE
M
1000 mt
10_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
662.792
684.399
!!
!!
!!
!!
!!
103.26%
Q
DE
X
1000 mt
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2956.302
2448.689
!!
!!
!!
!!
!!
82.83%
€
DE
X
1000 NAC
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2340754.000
1875541.497
!!
!!
!!
!!
!!
80.13%
UV
DE
X
1000 mt
10_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
791.784
765.937
!!
!!
!!
!!
!!
96.74%
Q
DE
M
1000 mt
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
675.000
627.595
!!
!!
!!
!!
!!
92.98%
€
DE
M
1000 NAC
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
996599.000
883024.789
!!
!!
!!
!!
!!
88.60%
UV
DE
M
1000 mt
10_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1476.443
1406.998
!!
!!
!!
!!
!!
95.30%
Q
DE
X
1000 mt
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
723.988
655.775
!!
!!
!!
!!
!!
90.58%
€
DE
X
1000 NAC
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1350613.000
1149496.247
!!
!!
!!
!!
!!
85.11%
UV
DE
X
1000 mt
10_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1865.518
1752.883
!!
!!
!!
!!
!!
93.96%
Q
DE
M
1000 mt
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5348.000
5512.698
!!
!!
!!
!!
!!
103.08%
€
DE
M
1000 NAC
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3687889.000
3665898.670
!!
!!
!!
!!
!!
99.40%
UV
DE
M
1000 mt
10_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
689.583
664.992
!!
!!
!!
!!
!!
96.43%
Q
DE
X
1000 mt
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7914.692
8087.247
!!
!!
!!
!!
!!
102.18%
€
DE
X
1000 NAC
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5369800.000
5058243.282
!!
!!
!!
!!
!!
94.20%
UV
DE
X
1000 mt
10_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
678.460
625.459
!!
!!
!!
!!
!!
92.19%
Q
DE
M
1000 mt
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2736.000
2767.612
!!
!!
!!
!!
!!
101.16%
€
DE
M
1000 NAC
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1090521.000
1093729.464
!!
!!
!!
!!
!!
100.29%
UV
DE
M
1000 mt
10_3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
398.582
395.189
!!
!!
!!
!!
!!
99.15%
Q
DE
X
1000 mt
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4723.260
4820.270
!!
!!
!!
!!
!!
102.05%
€
DE
X
1000 NAC
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1890682.000
1693120.114
!!
!!
!!
!!
!!
89.55%
UV
DE
X
1000 mt
10_3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
400.292
351.250
!!
!!
!!
!!
!!
87.75%
Q
DE
M
1000 mt
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1388.000
1464.751
!!
!!
!!
!!
!!
105.53%
€
DE
M
1000 NAC
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1433671.000
1463286.348
!!
!!
!!
!!
!!
102.07%
UV
DE
M
1000 mt
10_3_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1032.904
999.000
!!
!!
!!
!!
!!
96.72%
Q
DE
X
1000 mt
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2099.554
2089.815
!!
!!
!!
!!
!!
99.54%
€
DE
X
1000 NAC
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2342189.000
2237313.686
!!
!!
!!
!!
!!
95.52%
UV
DE
X
1000 mt
10_3_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1115.565
1070.580
!!
!!
!!
!!
!!
95.97%
Q
DE
M
1000 mt
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
950.000
1006.387
!!
!!
!!
!!
!!
105.94%
€
DE
M
1000 NAC
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
950136.000
890381.073
!!
!!
!!
!!
!!
93.71%
UV
DE
M
1000 mt
10_3_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1000.143
884.731
!!
!!
!!
!!
!!
88.46%
Q
DE
X
1000 mt
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
793.193
884.882
!!
!!
!!
!!
!!
111.56%
€
DE
X
1000 NAC
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
916995.000
912136.426
!!
!!
!!
!!
!!
99.47%
UV
DE
X
1000 mt
10_3_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1156.081
1030.800
!!
!!
!!
!!
!!
89.16%
Q
DE
M
1000 mt
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
274.000
273.948
!!
!!
!!
!!
!!
99.98%
€
DE
M
1000 NAC
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
213561.000
218501.786
!!
!!
!!
!!
!!
102.31%
UV
DE
M
1000 mt
10_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
779.420
797.603
!!
!!
!!
!!
!!
102.33%
Q
DE
X
1000 mt
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
298.685
292.280
!!
!!
!!
!!
!!
97.86%
€
DE
X
1000 NAC
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
219934.000
215673.057
!!
!!
!!
!!
!!
98.06%
UV
DE
X
1000 mt
10_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
736.341
737.899
!!
!!
!!
!!
!!
100.21%
TS-JQ3
%
Min:
80%
Max:
120%
Notes
JQ3
Country
Flow
Unit
Product
2015
2016
2017
2018
2019
2019
2020
15/16
16/17
17/18
18/19
19/19
19/20
2015
2016
2017
2018
DE
M
1000 NAC
11_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
11_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
11_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
11_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
11_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
11_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
11_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
11_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
11_6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
11_7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
11_7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
12_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
12_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
12_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
12_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
12_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
12_6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
12_6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
12_6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
12_6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
12_7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
12_7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
12_7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
M
1000 NAC
12_7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
11_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
11_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
11_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
11_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
11_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
11_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
11_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
11_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
11_6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
11_7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
11_7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
12_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
12_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
12_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
12_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
12_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
12_6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
12_6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
12_6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
DE
X
1000 NAC
12_6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
TS-ECEEU
%
Min:
80%
Max:
120%
Notes
ECEEU
Country
Flow
Unit
Product
2015
2016
2017
2018
2019
2019
2020
15/16
16/17
17/18
18/19
19/19
19/20
2015
2016
2017
2018
Q
DE
M
1000 m3
ST_1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6866.440
5559.520
!!
!!
!!
!!
!!
80.97%
€
DE
M
1000 NAC
ST_1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
375644.000
285233.000
!!
!!
!!
!!
!!
75.93%
UV
DE
M
1000 m3
ST_1_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
54.707
51.305
!!
!!
!!
!!
!!
93.78%
Q
DE
X
1000 m3
ST_1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7506.234
11816.202
!!
!!
!!
!!
!!
157.42%
€
DE
X
1000 NAC
ST_1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
515811.000
728553.000
!!
!!
!!
!!
!!
141.24%
UV
DE
X
1000 m3
ST_1_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
68.718
61.657
!!
!!
!!
!!
!!
89.73%
Q
DE
M
1000 m3
ST_1_2_C_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4799.849
4273.464
!!
!!
!!
!!
!!
89.03%
€
DE
M
1000 NAC
ST_1_2_C_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
268003.000
216444.000
!!
!!
!!
!!
!!
80.76%
UV
DE
M
1000 m3
ST_1_2_C_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
55.836
50.648
!!
!!
!!
!!
!!
90.71%
Q
DE
X
1000 m3
ST_1_2_C_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6423.192
11064.894
!!
!!
!!
!!
!!
172.26%
€
DE
X
1000 NAC
ST_1_2_C_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
450837.000
689756.000
!!
!!
!!
!!
!!
152.99%
UV
DE
X
1000 m3
ST_1_2_C_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
70.189
62.337
!!
!!
!!
!!
!!
88.81%
Q
DE
M
1000 m3
ST_1_2_C_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3670.520
3309.016
!!
!!
!!
!!
!!
90.15%
€
DE
M
1000 NAC
ST_1_2_C_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
215186.000
173298.000
!!
!!
!!
!!
!!
80.53%
UV
DE
M
1000 m3
ST_1_2_C_1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
58.625
52.371
!!
!!
!!
!!
!!
89.33%
Q
DE
X
1000 m3
ST_1_2_C_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4905.552
9454.542
!!
!!
!!
!!
!!
192.73%
€
DE
X
1000 NAC
ST_1_2_C_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
376844.000
620829.000
!!
!!
!!
!!
!!
164.74%
UV
DE
X
1000 m3
ST_1_2_C_1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
76.820
65.665
!!
!!
!!
!!
!!
85.48%
Q
DE
M
1000 m3
ST_1_2_C_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
595.614
255.149
!!
!!
!!
!!
!!
42.84%
€
DE
M
1000 NAC
ST_1_2_C_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
44369.000
19151.000
!!
!!
!!
!!
!!
43.16%
UV
DE
M
1000 m3
ST_1_2_C_1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
74.493
75.058
!!
!!
!!
!!
!!
100.76%
Q
DE
X
1000 m3
ST_1_2_C_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
358.058
242.379
!!
!!
!!
!!
!!
67.69%
€
DE
X
1000 NAC
ST_1_2_C_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
20318.000
12816.000
!!
!!
!!
!!
!!
63.08%
UV
DE
X
1000 m3
ST_1_2_C_1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
56.745
52.876
!!
!!
!!
!!
!!
93.18%
Q
DE
M
1000 m3
ST_1_2_C_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
DE
M
1000 NAC
ST_1_2_C_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
DE
M
1000 m3
ST_1_2_C_1_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
DE
X
1000 m3
ST_1_2_C_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
DE
X
1000 NAC
ST_1_2_C_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
DE
X
1000 m3
ST_1_2_C_1_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
DE
M
1000 m3
ST_1_2_C_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1678.355
939.291
!!
!!
!!
!!
!!
55.96%
€
DE
M
1000 NAC
ST_1_2_C_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
85209.000
44533.000
!!
!!
!!
!!
!!
52.26%
UV
DE
M
1000 m3
ST_1_2_C_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
50.769
47.411
!!
!!
!!
!!
!!
93.39%
Q
DE
X
1000 m3
ST_1_2_C_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
700.462
459.474
!!
!!
!!
!!
!!
65.60%
€
DE
X
1000 NAC
ST_1_2_C_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
37458.000
21741.000
!!
!!
!!
!!
!!
58.04%
UV
DE
X
1000 m3
ST_1_2_C_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
53.476
47.317
!!
!!
!!
!!
!!
88.48%
Q
DE
M
1000 m3
ST_1_2_C_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1129.329
964.448
!!
!!
!!
!!
!!
85.40%
€
DE
M
1000 NAC
ST_1_2_C_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
52817.000
43146.000
!!
!!
!!
!!
!!
81.69%
UV
DE
M
1000 m3
ST_1_2_C_2_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
46.768
44.736
!!
!!
!!
!!
!!
95.66%
Q
DE
X
1000 m3
ST_1_2_C_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1517.640
1610.352
!!
!!
!!
!!
!!
106.11%
€
DE
X
1000 NAC
ST_1_2_C_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
73993.000
68927.000
!!
!!
!!
!!
!!
93.15%
UV
DE
X
1000 m3
ST_1_2_C_2_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
48.755
42.802
!!
!!
!!
!!
!!
87.79%
Q
DE
M
1000 m3
ST_1_2_C_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1082.741
684.142
!!
!!
!!
!!
!!
63.19%
€
DE
M
1000 NAC
ST_1_2_C_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
40840.000
25382.000
!!
!!
!!
!!
!!
62.15%
UV
DE
M
1000 m3
ST_1_2_C_2_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
37.719
37.100
!!
!!
!!
!!
!!
98.36%
Q
DE
X
1000 m3
ST_1_2_C_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
342.404
217.095
!!
!!
!!
!!
!!
63.40%
€
DE
X
1000 NAC
ST_1_2_C_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
17140.000
8925.000
!!
!!
!!
!!
!!
52.07%
UV
DE
X
1000 m3
ST_1_2_C_2_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
50.058
41.111
!!
!!
!!
!!
!!
82.13%
Q
DE
M
1000 m3
ST_1_2_C_2_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
DE
M
1000 NAC
ST_1_2_C_2_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
DE
M
1000 m3
ST_1_2_C_2_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
DE
X
1000 m3
ST_1_2_C_2_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
DE
X
1000 NAC
ST_1_2_C_2_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
DE
X
1000 m3
ST_1_2_C_2_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
DE
M
1000 m3
ST_1_2_C_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
DE
M
1000 NAC
ST_1_2_C_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
DE
M
1000 m3
ST_1_2_C_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
DE
X
1000 m3
ST_1_2_C_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
DE
X
1000 NAC
ST_1_2_C_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
DE
X
1000 m3
ST_1_2_C_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
DE
M
1000 m3
ST_1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
451.930
363.830
!!
!!
!!
!!
!!
80.51%
€
DE
M
1000 NAC
ST_1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
50370.000
41960.000
!!
!!
!!
!!
!!
83.30%
UV
DE
M
1000 m3
ST_1_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
111.455
115.329
!!
!!
!!
!!
!!
103.48%
Q
DE
X
1000 m3
ST_1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1409.295
1014.142
!!
!!
!!
!!
!!
71.96%
€
DE
X
1000 NAC
ST_1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
172123.000
120352.000
!!
!!
!!
!!
!!
69.92%
UV
DE
X
1000 m3
ST_1_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
122.134
118.674
!!
!!
!!
!!
!!
97.17%
Q
DE
M
1000 m3
ST_1_2_NC_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
46.200
27.157
!!
!!
!!
!!
!!
58.78%
€
DE
M
1000 NAC
ST_1_2_NC_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
12221.000
8680.000
!!
!!
!!
!!
!!
71.03%
UV
DE
M
1000 m3
ST_1_2_NC_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
264.524
319.623
!!
!!
!!
!!
!!
120.83%
Q
DE
X
1000 m3
ST_1_2_NC_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
205.941
134.846
!!
!!
!!
!!
!!
65.48%
€
DE
X
1000 NAC
ST_1_2_NC_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
36190.000
24059.000
!!
!!
!!
!!
!!
66.48%
UV
DE
X
1000 m3
ST_1_2_NC_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
175.730
178.418
!!
!!
!!
!!
!!
101.53%
Q
DE
M
1000 m3
ST_1_2_NC_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
89.332
88.328
!!
!!
!!
!!
!!
98.88%
€
DE
M
1000 NAC
ST_1_2_NC_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4539.000
4817.000
!!
!!
!!
!!
!!
106.12%
UV
DE
M
1000 m3
ST_1_2_NC_1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
50.810
54.535
!!
!!
!!
!!
!!
107.33%
Q
DE
X
1000 m3
ST_1_2_NC_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
763.762
574.312
!!
!!
!!
!!
!!
75.20%
€
DE
X
1000 NAC
ST_1_2_NC_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
83592.000
58383.000
!!
!!
!!
!!
!!
69.84%
UV
DE
X
1000 m3
ST_1_2_NC_1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
109.448
101.657
!!
!!
!!
!!
!!
92.88%
Q
DE
M
1000 m3
ST_1_2_NC_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
24.586
31.012
!!
!!
!!
!!
!!
126.14%
€
DE
M
1000 NAC
ST_1_2_NC_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1770.000
1753.000
!!
!!
!!
!!
!!
99.04%
UV
DE
M
1000 m3
ST_1_2_NC_1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
71.992
56.527
!!
!!
!!
!!
!!
78.52%
Q
DE
X
1000 m3
ST_1_2_NC_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
30.955
14.290
!!
!!
!!
!!
!!
46.16%
€
DE
X
1000 NAC
ST_1_2_NC_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1543.000
844.000
!!
!!
!!
!!
!!
54.70%
UV
DE
X
1000 m3
ST_1_2_NC_1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
49.847
59.062
!!
!!
!!
!!
!!
118.49%
Q
DE
M
1000 m3
ST_1_2_NC_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4868.020
5010.128
!!
!!
!!
!!
!!
102.92%
€
DE
M
1000 NAC
ST_1_2_NC_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1004053.000
1017315.000
!!
!!
!!
!!
!!
101.32%
UV
DE
M
1000 m3
ST_1_2_NC_1_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
206.255
203.052
!!
!!
!!
!!
!!
98.45%
Q
DE
X
1000 m3
ST_1_2_NC_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8889.313
9661.856
!!
!!
!!
!!
!!
108.69%
€
DE
X
1000 NAC
ST_1_2_NC_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1697553.000
1899543.000
!!
!!
!!
!!
!!
111.90%
UV
DE
X
1000 m3
ST_1_2_NC_1_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
190.966
196.602
!!
!!
!!
!!
!!
102.95%
Q
DE
M
1000 m3
ST_1_2_NC_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.013
0.315
!!
!!
!!
!!
!!
2423.08%
€
DE
M
1000 NAC
ST_1_2_NC_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11.000
208.000
!!
!!
!!
!!
!!
1890.91%
UV
DE
M
1000 m3
ST_1_2_NC_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
846.154
660.317
!!
!!
!!
!!
!!
78.04%
Q
DE
X
1000 m3
ST_1_2_NC_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.815
0.381
!!
!!
!!
!!
!!
46.75%
€
DE
X
1000 NAC
ST_1_2_NC_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
48.000
24.000
!!
!!
!!
!!
!!
50.00%
UV
DE
X
1000 m3
ST_1_2_NC_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
58.896
62.992
!!
!!
!!
!!
!!
106.96%
Q
DE
M
1000 m3
ST_1_2_NC_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
24.599
31.327
!!
!!
!!
!!
!!
127.35%
€
DE
M
1000 NAC
ST_1_2_NC_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1781.000
1961.000
!!
!!
!!
!!
!!
110.11%
UV
DE
M
1000 m3
ST_1_2_NC_2_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
72.401
62.598
!!
!!
!!
!!
!!
86.46%
Q
DE
X
1000 m3
ST_1_2_NC_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
31.770
14.671
!!
!!
!!
!!
!!
46.18%
€
DE
X
1000 NAC
ST_1_2_NC_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1591.000
868.000
!!
!!
!!
!!
!!
54.56%
UV
DE
X
1000 m3
ST_1_2_NC_2_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
50.079
59.164
!!
!!
!!
!!
!!
118.14%
Q
DE
M
1000 m3
ST_1_2_NC_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
31.053
18.922
!!
!!
!!
!!
!!
60.93%
€
DE
M
1000 NAC
ST_1_2_NC_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1111.000
708.000
!!
!!
!!
!!
!!
63.73%
UV
DE
M
1000 m3
ST_1_2_NC_2_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
35.778
37.417
!!
!!
!!
!!
!!
104.58%
Q
DE
X
1000 m3
ST_1_2_NC_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
58.494
13.058
!!
!!
!!
!!
!!
22.32%
€
DE
X
1000 NAC
ST_1_2_NC_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2977.000
855.000
!!
!!
!!
!!
!!
28.72%
UV
DE
X
1000 m3
ST_1_2_NC_2_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
50.894
65.477
!!
!!
!!
!!
!!
128.65%
Q
DE
M
1000 m3
ST_1_2_NC_2_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3420.314
3582.852
!!
!!
!!
!!
!!
104.75%
€
DE
M
1000 NAC
ST_1_2_NC_2_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
694850.000
695298.000
!!
!!
!!
!!
!!
100.06%
UV
DE
M
1000 m3
ST_1_2_NC_2_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
203.154
194.063
!!
!!
!!
!!
!!
95.52%
Q
DE
X
1000 m3
ST_1_2_NC_2_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6266.466
7290.366
!!
!!
!!
!!
!!
116.34%
€
DE
X
1000 NAC
ST_1_2_NC_2_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1203919.000
1447530.000
!!
!!
!!
!!
!!
120.23%
UV
DE
X
1000 m3
ST_1_2_NC_2_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
192.121
198.554
!!
!!
!!
!!
!!
103.35%
Q
DE
M
1000 m3
ST_1_2_NC_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.420
0.803
!!
!!
!!
!!
!!
56.55%
€
DE
M
1000 NAC
ST_1_2_NC_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1373.000
870.000
!!
!!
!!
!!
!!
63.36%
UV
DE
M
1000 m3
ST_1_2_NC_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
966.901
1083.437
!!
!!
!!
!!
!!
112.05%
Q
DE
X
1000 m3
ST_1_2_NC_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.041
0.000
!!
!!
!!
!!
!!
0.46%
€
DE
X
1000 NAC
ST_1_2_NC_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
51.000
1.000
!!
!!
!!
!!
!!
1.96%
UV
DE
X
1000 m3
ST_1_2_NC_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1243.902
5268.293
!!
!!
!!
!!
!!
423.53%
Q
DE
M
1000 m3
ST_1_2_NC_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
866.884
791.206
!!
!!
!!
!!
!!
91.27%
€
DE
M
1000 NAC
ST_1_2_NC_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
141107.000
131991.000
!!
!!
!!
!!
!!
93.54%
UV
DE
M
1000 m3
ST_1_2_NC_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
162.775
166.823
!!
!!
!!
!!
!!
102.49%
Q
DE
X
1000 m3
ST_1_2_NC_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1708.669
1368.741
!!
!!
!!
!!
!!
80.11%
€
DE
X
1000 NAC
ST_1_2_NC_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
319266.000
261303.000
!!
!!
!!
!!
!!
81.84%
UV
DE
X
1000 m3
ST_1_2_NC_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
186.851
190.908
!!
!!
!!
!!
!!
102.17%
Q
DE
M
1000 m3
ST_1_2_NC_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
412.848
400.592
!!
!!
!!
!!
!!
97.03%
€
DE
M
1000 NAC
ST_1_2_NC_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
220097.000
213911.000
!!
!!
!!
!!
!!
97.19%
UV
DE
M
1000 m3
ST_1_2_NC_5
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
533.119
533.987
!!
!!
!!
!!
!!
100.16%
Q
DE
X
1000 m3
ST_1_2_NC_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
767.564
691.968
!!
!!
!!
!!
!!
90.15%
€
DE
X
1000 NAC
ST_1_2_NC_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
385356.000
352767.000
!!
!!
!!
!!
!!
91.54%
UV
DE
X
1000 m3
ST_1_2_NC_5
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
502.051
509.802
!!
!!
!!
!!
!!
101.54%
Q
DE
M
1000 m3
ST_5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
112.832
100.372
!!
!!
!!
!!
!!
88.96%
€
DE
M
1000 NAC
ST_5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
82848.000
80460.000
!!
!!
!!
!!
!!
97.12%
UV
DE
M
1000 m3
ST_5_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
734.260
801.618
!!
!!
!!
!!
!!
109.17%
Q
DE
X
1000 m3
ST_5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
120.279
102.525
!!
!!
!!
!!
!!
85.24%
€
DE
X
1000 NAC
ST_5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
86696.000
77951.000
!!
!!
!!
!!
!!
89.91%
UV
DE
X
1000 m3
ST_5_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
720.791
760.312
!!
!!
!!
!!
!!
105.48%
Q
DE
M
1000 m3
ST_5_C_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
21.656
18.153
!!
!!
!!
!!
!!
83.82%
€
DE
M
1000 NAC
ST_5_C_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9148.000
8922.000
!!
!!
!!
!!
!!
97.53%
UV
DE
M
1000 m3
ST_5_C_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
422.423
491.489
!!
!!
!!
!!
!!
116.35%
Q
DE
X
1000 m3
ST_5_C_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
528.868
484.843
!!
!!
!!
!!
!!
91.68%
€
DE
X
1000 NAC
ST_5_C_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
211996.000
192549.000
!!
!!
!!
!!
!!
90.83%
UV
DE
X
1000 m3
ST_5_C_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
400.849
397.137
!!
!!
!!
!!
!!
99.07%
Q
DE
M
1000 m3
ST_5_C_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4.512
3.169
!!
!!
!!
!!
!!
70.23%
€
DE
M
1000 NAC
ST_5_C_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3014.000
2668.000
!!
!!
!!
!!
!!
88.52%
UV
DE
M
1000 m3
ST_5_C_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
667.996
841.906
!!
!!
!!
!!
!!
126.03%
Q
DE
X
1000 m3
ST_5_C_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3.924
2.527
!!
!!
!!
!!
!!
64.40%
€
DE
X
1000 NAC
ST_5_C_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3175.000
2055.000
!!
!!
!!
!!
!!
64.72%
UV
DE
X
1000 m3
ST_5_C_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
809.123
813.217
!!
!!
!!
!!
!!
100.51%
Q
DE
M
1000 m3
ST_5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.910
1.719
!!
!!
!!
!!
!!
188.90%
€
DE
M
1000 NAC
ST_5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
853.000
1397.000
!!
!!
!!
!!
!!
163.77%
UV
DE
M
1000 m3
ST_5_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
937.363
812.682
!!
!!
!!
!!
!!
86.70%
Q
DE
X
1000 m3
ST_5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.423
0.565
!!
!!
!!
!!
!!
133.57%
€
DE
X
1000 NAC
ST_5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
440.000
621.000
!!
!!
!!
!!
!!
141.14%
UV
DE
X
1000 m3
ST_5_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1040.189
1099.115
!!
!!
!!
!!
!!
105.66%
Q
DE
M
1000 m3
ST_5_NC_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11.464
12.259
!!
!!
!!
!!
!!
106.93%
€
DE
M
1000 NAC
ST_5_NC_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5513.000
5882.000
!!
!!
!!
!!
!!
106.69%
UV
DE
M
1000 m3
ST_5_NC_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
480.897
479.811
!!
!!
!!
!!
!!
99.77%
Q
DE
X
1000 m3
ST_5_NC_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
33.135
22.737
!!
!!
!!
!!
!!
68.62%
€
DE
X
1000 NAC
ST_5_NC_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11587.000
8532.000
!!
!!
!!
!!
!!
73.63%
UV
DE
X
1000 m3
ST_5_NC_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
349.691
375.247
!!
!!
!!
!!
!!
107.31%
Q
DE
M
1000 m3
ST_5_NC_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13.963
17.004
!!
!!
!!
!!
!!
121.78%
€
DE
M
1000 NAC
ST_5_NC_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2145.000
2514.000
!!
!!
!!
!!
!!
117.20%
UV
DE
M
1000 m3
ST_5_NC_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
153.620
147.848
!!
!!
!!
!!
!!
96.24%
Q
DE
X
1000 m3
ST_5_NC_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3.557
3.616
!!
!!
!!
!!
!!
101.66%
€
DE
X
1000 NAC
ST_5_NC_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
789.000
886.000
!!
!!
!!
!!
!!
112.29%
UV
DE
X
1000 m3
ST_5_NC_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
221.816
245.022
!!
!!
!!
!!
!!
110.46%
Q
DE
M
1000 m3
ST_5_NC_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13.707
20.013
!!
!!
!!
!!
!!
146.01%
€
DE
M
1000 NAC
ST_5_NC_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2178.000
3038.000
!!
!!
!!
!!
!!
139.49%
UV
DE
M
1000 m3
ST_5_NC_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
158.897
151.801
!!
!!
!!
!!
!!
95.53%
Q
DE
X
1000 m3
ST_5_NC_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.477
2.692
!!
!!
!!
!!
!!
182.26%
€
DE
X
1000 NAC
ST_5_NC_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
399.000
552.000
!!
!!
!!
!!
!!
138.35%
UV
DE
X
1000 m3
ST_5_NC_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
270.142
205.052
!!
!!
!!
!!
!!
75.91%
Q
DE
M
1000 m3
ST_5_NC_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
DE
M
1000 NAC
ST_5_NC_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
DE
M
1000 m3
ST_5_NC_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
DE
X
1000 m3
ST_5_NC_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
DE
X
1000 NAC
ST_5_NC_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
DE
X
1000 m3
ST_5_NC_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
DE
M
1000 m3
ST_5_NC_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
DE
M
1000 NAC
ST_5_NC_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
DE
M
1000 m3
ST_5_NC_5
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
DE
X
1000 m3
ST_5_NC_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
DE
X
1000 NAC
ST_5_NC_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
DE
X
1000 m3
ST_5_NC_5
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
DE
M
1000 m3
ST_5_NC_6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3.000
3.000
!!
!!
!!
!!
!!
100.00%
€
DE
M
1000 NAC
ST_5_NC_6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3.000
3.000
!!
!!
!!
!!
!!
100.00%
UV
DE
M
1000 m3
ST_5_NC_6
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.000
1.000
!!
!!
!!
!!
!!
100.00%
Q
DE
X
1000 m3
ST_5_NC_6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3.000
3.000
!!
!!
!!
!!
!!
100.00%
€
DE
X
1000 NAC
ST_5_NC_6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3.000
3.000
!!
!!
!!
!!
!!
100.00%
UV
DE
X
1000 m3
ST_5_NC_6
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.000
1.000
!!
!!
!!
!!
!!
100.00%
Q
DE
M
1000 m3
ST_5_NC_7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
DE
M
1000 NAC
ST_5_NC_7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
DE
M
1000 m3
ST_5_NC_7
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
DE
X
1000 m3
ST_5_NC_7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
DE
X
1000 NAC
ST_5_NC_7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
DE
X
1000 m3
ST_5_NC_7
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
TS-EU1
%
Min:
80%
Max:
120%
Notes
EU1
Country
Flow
Unit
Product
2015
2016
2017
2018
2019
2019
2020
15/16
16/17
17/18
18/19
19/19
19/20
2015
2016
2017
2018
Q
DE
EX_M
1000 m3
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
724.309
610.048
!!
!!
!!
!!
!!
84.22%
€
DE
EX_M
1000 NAC
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
76360.000
60192.000
!!
!!
!!
!!
!!
78.83%
UV
DE
EX_M
1000 m3
1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
105.425
98.668
!!
!!
!!
!!
!!
93.59%
Q
DE
EX_X
1000 m3
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3961.026
6928.364
!!
!!
!!
!!
!!
174.91%
€
DE
EX_X
1000 NAC
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
408605.000
576696.000
!!
!!
!!
!!
!!
141.14%
UV
DE
EX_X
1000 m3
1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
103.156
83.237
!!
!!
!!
!!
!!
80.69%
Q
DE
EX_M
1000 m3
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
124.637
107.820
!!
!!
!!
!!
!!
86.51%
€
DE
EX_M
1000 NAC
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13970.000
12697.000
!!
!!
!!
!!
!!
90.89%
UV
DE
EX_M
1000 m3
1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
112.085
117.761
!!
!!
!!
!!
!!
105.06%
Q
DE
EX_X
1000 m3
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.473
3.084
!!
!!
!!
!!
!!
124.71%
€
DE
EX_X
1000 NAC
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
784.000
887.000
!!
!!
!!
!!
!!
113.14%
UV
DE
EX_X
1000 m3
1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
317.024
287.613
!!
!!
!!
!!
!!
90.72%
Q
DE
EX_M
1000 m3
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11.158
8.002
!!
!!
!!
!!
!!
71.72%
€
DE
EX_M
1000 NAC
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1091.000
1136.000
!!
!!
!!
!!
!!
104.12%
UV
DE
EX_M
1000 m3
1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
97.777
141.965
!!
!!
!!
!!
!!
145.19%
Q
DE
EX_X
1000 m3
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.021
0.209
!!
!!
!!
!!
!!
995.24%
€
DE
EX_X
1000 NAC
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
20.000
60.000
!!
!!
!!
!!
!!
300.00%
UV
DE
EX_X
1000 m3
1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
952.381
287.081
!!
!!
!!
!!
!!
30.14%
Q
DE
EX_M
1000 m3
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
113.479
99.818
!!
!!
!!
!!
!!
87.96%
€
DE
EX_M
1000 NAC
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
12879.000
11561.000
!!
!!
!!
!!
!!
89.77%
UV
DE
EX_M
1000 m3
1_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
113.492
115.821
!!
!!
!!
!!
!!
102.05%
Q
DE
EX_X
1000 m3
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.452
2.875
!!
!!
!!
!!
!!
117.25%
€
DE
EX_X
1000 NAC
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
764.000
827.000
!!
!!
!!
!!
!!
108.25%
UV
DE
EX_X
1000 m3
1_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
311.582
287.652
!!
!!
!!
!!
!!
92.32%
Q
DE
EX_M
1000 m3
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
599.672
502.228
!!
!!
!!
!!
!!
83.75%
€
DE
EX_M
1000 NAC
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
62390.000
47495.000
!!
!!
!!
!!
!!
76.13%
UV
DE
EX_M
1000 m3
1_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
104.040
94.569
!!
!!
!!
!!
!!
90.90%
Q
DE
EX_X
1000 m3
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3958.553
6925.280
!!
!!
!!
!!
!!
174.94%
€
DE
EX_X
1000 NAC
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
407821.000
575809.000
!!
!!
!!
!!
!!
141.19%
UV
DE
EX_X
1000 m3
1_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
103.023
83.146
!!
!!
!!
!!
!!
80.71%
Q
DE
EX_M
1000 m3
1_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
548.270
454.733
!!
!!
!!
!!
!!
82.94%
€
DE
EX_M
1000 NAC
1_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
43304.000
31694.000
!!
!!
!!
!!
!!
73.19%
UV
DE
EX_M
1000 m3
1_2_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
78.983
69.698
!!
!!
!!
!!
!!
88.24%
Q
DE
EX_X
1000 m3
1_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3307.599
6446.901
!!
!!
!!
!!
!!
194.91%
€
DE
EX_X
1000 NAC
1_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
294969.000
500887.000
!!
!!
!!
!!
!!
169.81%
UV
DE
EX_X
1000 m3
1_2_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
89.179
77.694
!!
!!
!!
!!
!!
87.12%
Q
DE
EX_M
1000 mt
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
51.402
47.495
!!
!!
!!
!!
!!
92.40%
€
DE
EX_M
1000 NAC
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
19086.000
15801.000
!!
!!
!!
!!
!!
82.79%
UV
DE
EX_M
1000 mt
2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
371.309
332.688
!!
!!
!!
!!
!!
89.60%
Q
DE
EX_X
1000 mt
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
650.954
478.379
!!
!!
!!
!!
!!
73.49%
€
DE
EX_X
1000 NAC
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
112852.000
74922.000
!!
!!
!!
!!
!!
66.39%
UV
DE
EX_X
1000 mt
2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
173.364
156.616
!!
!!
!!
!!
!!
90.34%
Q
DE
EX_M
1000 m3
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9.476
9.873
!!
!!
!!
!!
!!
104.19%
€
DE
EX_M
1000 NAC
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4636.000
4608.000
!!
!!
!!
!!
!!
99.40%
UV
DE
EX_M
1000 m3
3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
489.236
466.727
!!
!!
!!
!!
!!
95.40%
Q
DE
EX_X
1000 m3
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.344
0.089
!!
!!
!!
!!
!!
25.87%
€
DE
EX_X
1000 NAC
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
865.000
282.000
!!
!!
!!
!!
!!
32.60%
UV
DE
EX_X
1000 m3
3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2514.535
3168.539
!!
!!
!!
!!
!!
126.01%
Q
DE
EX_M
1000 m3
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
101.759
73.905
!!
!!
!!
!!
!!
72.63%
€
DE
EX_M
1000 NAC
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
47243.000
36829.000
!!
!!
!!
!!
!!
77.96%
UV
DE
EX_M
1000 m3
3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
464.264
498.329
!!
!!
!!
!!
!!
107.34%
Q
DE
EX_X
1000 m3
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3.662
5.070
!!
!!
!!
!!
!!
138.45%
€
DE
EX_X
1000 NAC
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3258.000
4643.000
!!
!!
!!
!!
!!
142.51%
UV
DE
EX_X
1000 m3
3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
889.678
915.779
!!
!!
!!
!!
!!
102.93%
Q
DE
EX_M
1000 m3
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
45.452
37.878
!!
!!
!!
!!
!!
83.34%
€
DE
EX_M
1000 NAC
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1648.000
1044.000
!!
!!
!!
!!
!!
63.35%
UV
DE
EX_M
1000 m3
3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
36.258
27.562
!!
!!
!!
!!
!!
76.02%
Q
DE
EX_X
1000 m3
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
264.053
247.634
!!
!!
!!
!!
!!
93.78%
€
DE
EX_X
1000 NAC
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
21440.000
19032.000
!!
!!
!!
!!
!!
88.77%
UV
DE
EX_X
1000 m3
3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
81.196
76.855
!!
!!
!!
!!
!!
94.65%
Q
DE
EX_M
1000 mt
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
15.534
10.366
!!
!!
!!
!!
!!
66.73%
€
DE
EX_M
1000 NAC
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
808.000
305.000
!!
!!
!!
!!
!!
37.75%
UV
DE
EX_M
1000 mt
4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
52.015
29.423
!!
!!
!!
!!
!!
56.57%
Q
DE
EX_X
1000 mt
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
162.442
147.815
!!
!!
!!
!!
!!
91.00%
€
DE
EX_X
1000 NAC
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
12040.000
10956.000
!!
!!
!!
!!
!!
91.00%
UV
DE
EX_X
1000 mt
4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
74.119
74.120
!!
!!
!!
!!
!!
100.00%
Q
DE
EX_M
1000 mt
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
29.918
27.512
!!
!!
!!
!!
!!
91.96%
€
DE
EX_M
1000 NAC
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
840.000
739.000
!!
!!
!!
!!
!!
87.98%
UV
DE
EX_M
1000 mt
4_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
28.077
26.861
!!
!!
!!
!!
!!
95.67%
Q
DE
EX_X
1000 mt
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
101.611
99.819
!!
!!
!!
!!
!!
98.24%
€
DE
EX_X
1000 NAC
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9400.000
8076.000
!!
!!
!!
!!
!!
85.91%
UV
DE
EX_X
1000 mt
4_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
92.510
80.906
!!
!!
!!
!!
!!
87.46%
Q
DE
EX_M
1000 mt
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
109.148
112.701
!!
!!
!!
!!
!!
103.26%
€
DE
EX_M
1000 NAC
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2302.000
3275.000
!!
!!
!!
!!
!!
142.27%
UV
DE
EX_M
1000 mt
4_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
21.091
29.059
!!
!!
!!
!!
!!
137.78%
Q
DE
EX_X
1000 mt
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
21.423
30.824
!!
!!
!!
!!
!!
143.88%
€
DE
EX_X
1000 NAC
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4381.000
6009.000
!!
!!
!!
!!
!!
137.16%
UV
DE
EX_X
1000 mt
4_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
204.500
194.945
!!
!!
!!
!!
!!
95.33%
Q
DE
EX_M
1000 m3
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
191.849
197.772
!!
!!
!!
!!
!!
103.09%
€
DE
EX_M
1000 NAC
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
23548.000
21789.000
!!
!!
!!
!!
!!
92.53%
UV
DE
EX_M
1000 m3
5
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
122.742
110.172
!!
!!
!!
!!
!!
89.76%
Q
DE
EX_X
1000 m3
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
55.166
85.130
!!
!!
!!
!!
!!
154.32%
€
DE
EX_X
1000 NAC
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
23410.000
29208.000
!!
!!
!!
!!
!!
124.77%
UV
DE
EX_X
1000 m3
5
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
424.356
343.099
!!
!!
!!
!!
!!
80.85%
Q
DE
EX_M
1000 m3
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
41.976
59.020
!!
!!
!!
!!
!!
140.60%
€
DE
EX_M
1000 NAC
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5918.000
8139.000
!!
!!
!!
!!
!!
137.53%
UV
DE
EX_M
1000 m3
5_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
140.985
137.902
!!
!!
!!
!!
!!
97.81%
Q
DE
EX_X
1000 m3
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
42.729
64.057
!!
!!
!!
!!
!!
149.91%
€
DE
EX_X
1000 NAC
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
17864.000
22729.000
!!
!!
!!
!!
!!
127.23%
UV
DE
EX_X
1000 m3
5_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
418.077
354.825
!!
!!
!!
!!
!!
84.87%
Q
DE
EX_M
1000 m3
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
149.873
138.752
!!
!!
!!
!!
!!
92.58%
€
DE
EX_M
1000 NAC
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
17630.000
13650.000
!!
!!
!!
!!
!!
77.42%
UV
DE
EX_M
1000 m3
5_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
117.633
98.377
!!
!!
!!
!!
!!
83.63%
Q
DE
EX_X
1000 m3
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
12.437
21.073
!!
!!
!!
!!
!!
169.44%
€
DE
EX_X
1000 NAC
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5546.000
6479.000
!!
!!
!!
!!
!!
116.82%
UV
DE
EX_X
1000 m3
5_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
445.927
307.455
!!
!!
!!
!!
!!
68.95%
Q
DE
EX_M
1000 m3
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1836.939
1734.996
!!
!!
!!
!!
!!
94.45%
€
DE
EX_M
1000 NAC
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
412032.000
398786.000
!!
!!
!!
!!
!!
96.79%
UV
DE
EX_M
1000 m3
5_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
224.304
229.848
!!
!!
!!
!!
!!
102.47%
Q
DE
EX_X
1000 m3
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4097.105
4683.902
!!
!!
!!
!!
!!
114.32%
€
DE
EX_X
1000 NAC
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
901714.000
1095578.000
!!
!!
!!
!!
!!
121.50%
UV
DE
EX_X
1000 m3
5_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
220.086
233.903
!!
!!
!!
!!
!!
106.28%
Q
DE
EX_M
1000 m3
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1668.443
1562.935
!!
!!
!!
!!
!!
93.68%
€
DE
EX_M
1000 NAC
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
307595.000
301509.000
!!
!!
!!
!!
!!
98.02%
UV
DE
EX_M
1000 m3
6
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
184.361
192.912
!!
!!
!!
!!
!!
104.64%
Q
DE
EX_X
1000 m3
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3628.667
4270.661
!!
!!
!!
!!
!!
117.69%
€
DE
EX_X
1000 NAC
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
690634.000
906884.000
!!
!!
!!
!!
!!
131.31%
UV
DE
EX_X
1000 m3
6
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
190.327
212.352
!!
!!
!!
!!
!!
111.57%
Q
DE
EX_M
1000 m3
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
168.496
172.061
!!
!!
!!
!!
!!
102.12%
€
DE
EX_M
1000 NAC
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
104437.000
97277.000
!!
!!
!!
!!
!!
93.14%
UV
DE
EX_M
1000 m3
6_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
619.819
565.363
!!
!!
!!
!!
!!
91.21%
Q
DE
EX_X
1000 m3
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
468.438
413.241
!!
!!
!!
!!
!!
88.22%
€
DE
EX_X
1000 NAC
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
211080.000
188694.000
!!
!!
!!
!!
!!
89.39%
UV
DE
EX_X
1000 m3
6_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
450.604
456.620
!!
!!
!!
!!
!!
101.34%
Q
DE
EX_M
1000 m3
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
66.202
55.334
!!
!!
!!
!!
!!
83.58%
€
DE
EX_M
1000 NAC
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
48947.000
39917.000
!!
!!
!!
!!
!!
81.55%
UV
DE
EX_M
1000 m3
6_1_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
739.358
721.383
!!
!!
!!
!!
!!
97.57%
Q
DE
EX_X
1000 m3
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8.819
7.338
!!
!!
!!
!!
!!
83.21%
€
DE
EX_X
1000 NAC
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11671.000
10748.000
!!
!!
!!
!!
!!
92.09%
UV
DE
EX_X
1000 m3
6_1_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1323.393
1464.704
!!
!!
!!
!!
!!
110.68%
Q
DE
EX_M
1000 m3
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
31.564
31.656
!!
!!
!!
!!
!!
100.29%
€
DE
EX_M
1000 NAC
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
45908.000
46277.000
!!
!!
!!
!!
!!
100.80%
UV
DE
EX_M
1000 m3
6_1_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1454.442
1461.871
!!
!!
!!
!!
!!
100.51%
Q
DE
EX_X
1000 m3
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
15.112
12.257
!!
!!
!!
!!
!!
81.11%
€
DE
EX_X
1000 NAC
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
51942.000
45982.000
!!
!!
!!
!!
!!
88.53%
UV
DE
EX_X
1000 m3
6_1_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3437.136
3751.489
!!
!!
!!
!!
!!
109.15%
Q
DE
EX_M
1000 m3
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4.145
3.119
!!
!!
!!
!!
!!
75.25%
€
DE
EX_M
1000 NAC
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6507.000
5293.000
!!
!!
!!
!!
!!
81.34%
UV
DE
EX_M
1000 m3
6_1_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1569.843
1697.018
!!
!!
!!
!!
!!
108.10%
Q
DE
EX_X
1000 m3
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.256
0.238
!!
!!
!!
!!
!!
92.97%
€
DE
EX_X
1000 NAC
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1147.000
1216.000
!!
!!
!!
!!
!!
106.02%
UV
DE
EX_X
1000 m3
6_1_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4480.469
5109.244
!!
!!
!!
!!
!!
114.03%
Q
DE
EX_M
1000 m3
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
27.419
28.537
!!
!!
!!
!!
!!
104.08%
€
DE
EX_M
1000 NAC
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
39401.000
40984.000
!!
!!
!!
!!
!!
104.02%
UV
DE
EX_M
1000 m3
6_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1436.996
1436.171
!!
!!
!!
!!
!!
99.94%
Q
DE
EX_X
1000 m3
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
14.856
12.019
!!
!!
!!
!!
!!
80.90%
€
DE
EX_X
1000 NAC
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
50795.000
44766.000
!!
!!
!!
!!
!!
88.13%
UV
DE
EX_X
1000 m3
6_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3419.157
3724.603
!!
!!
!!
!!
!!
108.93%
Q
DE
EX_M
1000 m3
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6.415
6.103
!!
!!
!!
!!
!!
95.14%
€
DE
EX_M
1000 NAC
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5465.000
4465.000
!!
!!
!!
!!
!!
81.70%
UV
DE
EX_M
1000 m3
6_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
851.910
731.607
!!
!!
!!
!!
!!
85.88%
Q
DE
EX_X
1000 m3
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.169
0.889
!!
!!
!!
!!
!!
76.05%
€
DE
EX_X
1000 NAC
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3923.000
2723.000
!!
!!
!!
!!
!!
69.41%
UV
DE
EX_X
1000 m3
6_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3355.860
3062.992
!!
!!
!!
!!
!!
91.27%
Q
DE
EX_M
1000 m3
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1067.284
1049.915
!!
!!
!!
!!
!!
98.37%
€
DE
EX_M
1000 NAC
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
409434.000
371625.000
!!
!!
!!
!!
!!
90.77%
UV
DE
EX_M
1000 m3
6_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
383.622
353.957
!!
!!
!!
!!
!!
92.27%
Q
DE
EX_X
1000 m3
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1528.232
1615.792
!!
!!
!!
!!
!!
105.73%
€
DE
EX_X
1000 NAC
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
723440.000
734695.000
!!
!!
!!
!!
!!
101.56%
UV
DE
EX_X
1000 m3
6_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
473.384
454.697
!!
!!
!!
!!
!!
96.05%
Q
DE
EX_M
1000 m3
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
692.276
663.420
!!
!!
!!
!!
!!
95.83%
€
DE
EX_M
1000 NAC
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
262514.000
227771.000
!!
!!
!!
!!
!!
86.77%
UV
DE
EX_M
1000 m3
6_2_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
379.204
343.329
!!
!!
!!
!!
!!
90.54%
Q
DE
EX_X
1000 m3
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
95.884
99.873
!!
!!
!!
!!
!!
104.16%
€
DE
EX_X
1000 NAC
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
74876.000
76261.000
!!
!!
!!
!!
!!
101.85%
UV
DE
EX_X
1000 m3
6_2_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
780.902
763.580
!!
!!
!!
!!
!!
97.78%
Q
DE
EX_M
1000 m3
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
259.852
235.367
!!
!!
!!
!!
!!
90.58%
€
DE
EX_M
1000 NAC
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
70583.500
52738.500
!!
!!
!!
!!
!!
74.72%
UV
DE
EX_M
1000 m3
6_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
271.630
224.069
!!
!!
!!
!!
!!
82.49%
Q
DE
EX_X
1000 m3
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
69.578
72.681
!!
!!
!!
!!
!!
104.46%
€
DE
EX_X
1000 NAC
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
43188.500
44017.000
!!
!!
!!
!!
!!
101.92%
UV
DE
EX_X
1000 m3
6_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
620.721
605.619
!!
!!
!!
!!
!!
97.57%
Q
DE
EX_M
1000 m3
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
432.424
428.053
!!
!!
!!
!!
!!
98.99%
€
DE
EX_M
1000 NAC
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
191930.500
175032.500
!!
!!
!!
!!
!!
91.20%
UV
DE
EX_M
1000 m3
6_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
443.848
408.904
!!
!!
!!
!!
!!
92.13%
Q
DE
EX_X
1000 m3
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
26.306
27.192
!!
!!
!!
!!
!!
103.37%
€
DE
EX_X
1000 NAC
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
31687.500
32244.000
!!
!!
!!
!!
!!
101.76%
UV
DE
EX_X
1000 m3
6_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1204.573
1185.790
!!
!!
!!
!!
!!
98.44%
Q
DE
EX_M
1000 m3
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
46.635
31.053
!!
!!
!!
!!
!!
66.59%
€
DE
EX_M
1000 NAC
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
31454.000
18681.000
!!
!!
!!
!!
!!
59.39%
UV
DE
EX_M
1000 m3
6_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
674.472
601.584
!!
!!
!!
!!
!!
89.19%
Q
DE
EX_X
1000 m3
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.684
2.332
!!
!!
!!
!!
!!
138.48%
€
DE
EX_X
1000 NAC
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3150.000
3406.000
!!
!!
!!
!!
!!
108.13%
UV
DE
EX_X
1000 m3
6_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1870.546
1460.549
!!
!!
!!
!!
!!
78.08%
Q
DE
EX_M
1000 m3
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
204.861
199.753
!!
!!
!!
!!
!!
97.51%
€
DE
EX_M
1000 NAC
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
55945.000
49721.000
!!
!!
!!
!!
!!
88.87%
UV
DE
EX_M
1000 m3
6_4_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
273.088
248.912
!!
!!
!!
!!
!!
91.15%
Q
DE
EX_X
1000 m3
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
450.552
432.009
!!
!!
!!
!!
!!
95.88%
€
DE
EX_X
1000 NAC
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
123519.000
116495.000
!!
!!
!!
!!
!!
94.31%
UV
DE
EX_X
1000 m3
6_4_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
274.150
269.659
!!
!!
!!
!!
!!
98.36%
Q
DE
EX_M
1000 m3
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.283
0.738
!!
!!
!!
!!
!!
32.33%
€
DE
EX_M
1000 NAC
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
496.000
190.000
!!
!!
!!
!!
!!
38.31%
UV
DE
EX_M
1000 m3
6_4_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
217.258
257.453
!!
!!
!!
!!
!!
118.50%
Q
DE
EX_X
1000 m3
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
180.519
162.313
!!
!!
!!
!!
!!
89.91%
€
DE
EX_X
1000 NAC
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
46353.000
40668.000
!!
!!
!!
!!
!!
87.74%
UV
DE
EX_X
1000 m3
6_4_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
256.776
250.553
!!
!!
!!
!!
!!
97.58%
Q
DE
EX_M
1000 m3
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
170.147
186.742
!!
!!
!!
!!
!!
109.75%
€
DE
EX_M
1000 NAC
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
90975.000
94133.000
!!
!!
!!
!!
!!
103.47%
UV
DE
EX_M
1000 m3
6_4_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
534.685
504.080
!!
!!
!!
!!
!!
94.28%
Q
DE
EX_X
1000 m3
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
749.408
832.739
!!
!!
!!
!!
!!
111.12%
€
DE
EX_X
1000 NAC
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
403914.831
402560.513
!!
!!
!!
!!
!!
99.66%
UV
DE
EX_X
1000 m3
6_4_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
538.979
483.417
!!
!!
!!
!!
!!
89.69%
Q
DE
EX_M
1000 mt
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
17.394
37.777
!!
!!
!!
!!
!!
217.18%
€
DE
EX_M
1000 NAC
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
15487.000
15739.000
!!
!!
!!
!!
!!
101.63%
UV
DE
EX_M
1000 mt
7
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
890.364
416.629
!!
!!
!!
!!
!!
46.79%
Q
DE
EX_X
1000 mt
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
10.678
11.926
!!
!!
!!
!!
!!
111.68%
€
DE
EX_X
1000 NAC
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6844.016
7641.547
!!
!!
!!
!!
!!
111.65%
UV
DE
EX_X
1000 mt
7
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
640.931
640.766
!!
!!
!!
!!
!!
99.97%
Q
DE
EX_M
1000 mt
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
108.725
140.265
!!
!!
!!
!!
!!
129.01%
€
DE
EX_M
1000 NAC
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
69036.000
76307.000
!!
!!
!!
!!
!!
110.53%
UV
DE
EX_M
1000 mt
7_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
634.960
544.020
!!
!!
!!
!!
!!
85.68%
Q
DE
EX_X
1000 mt
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
578.477
582.193
!!
!!
!!
!!
!!
100.64%
€
DE
EX_X
1000 NAC
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
379550.815
366503.966
!!
!!
!!
!!
!!
96.56%
UV
DE
EX_X
1000 mt
7_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
656.121
629.523
!!
!!
!!
!!
!!
95.95%
Q
DE
EX_M
1000 mt
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
44.028
8.700
!!
!!
!!
!!
!!
19.76%
€
DE
EX_M
1000 NAC
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6452.000
2087.000
!!
!!
!!
!!
!!
32.35%
UV
DE
EX_M
1000 mt
7_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
146.543
239.885
!!
!!
!!
!!
!!
163.70%
Q
DE
EX_X
1000 mt
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
160.253
238.620
!!
!!
!!
!!
!!
148.90%
€
DE
EX_X
1000 NAC
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
17520.000
28415.000
!!
!!
!!
!!
!!
162.19%
UV
DE
EX_X
1000 mt
7_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
109.327
119.081
!!
!!
!!
!!
!!
108.92%
Q
DE
EX_M
1000 mt
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1962.560
1641.934
!!
!!
!!
!!
!!
83.66%
€
DE
EX_M
1000 NAC
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1193135.636
833165.963
!!
!!
!!
!!
!!
69.83%
UV
DE
EX_M
1000 mt
7_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
607.948
507.430
!!
!!
!!
!!
!!
83.47%
Q
DE
EX_X
1000 mt
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
384.044
382.902
!!
!!
!!
!!
!!
99.70%
€
DE
EX_X
1000 NAC
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
225501.600
207180.124
!!
!!
!!
!!
!!
91.88%
UV
DE
EX_X
1000 mt
7_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
587.177
541.078
!!
!!
!!
!!
!!
92.15%
Q
DE
EX_M
1000 mt
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
37.533
22.251
!!
!!
!!
!!
!!
59.28%
€
DE
EX_M
1000 NAC
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
20891.636
11726.163
!!
!!
!!
!!
!!
56.13%
UV
DE
EX_M
1000 mt
7_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
556.621
527.002
!!
!!
!!
!!
!!
94.68%
Q
DE
EX_X
1000 mt
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
16.737
22.177
!!
!!
!!
!!
!!
132.50%
€
DE
EX_X
1000 NAC
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7784.600
10925.313
!!
!!
!!
!!
!!
140.35%
UV
DE
EX_X
1000 mt
7_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
465.125
492.651
!!
!!
!!
!!
!!
105.92%
Q
DE
EX_M
1000 mt
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1832.022
1524.410
!!
!!
!!
!!
!!
83.21%
€
DE
EX_M
1000 NAC
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1064120.000
720887.301
!!
!!
!!
!!
!!
67.74%
UV
DE
EX_M
1000 mt
7_3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
580.845
472.896
!!
!!
!!
!!
!!
81.42%
Q
DE
EX_X
1000 mt
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
364.965
360.724
!!
!!
!!
!!
!!
98.84%
€
DE
EX_X
1000 NAC
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
216176.000
196250.815
!!
!!
!!
!!
!!
90.78%
UV
DE
EX_X
1000 mt
7_3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
592.320
544.047
!!
!!
!!
!!
!!
91.85%
Q
DE
EX_M
1000 mt
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1804.345
1495.340
!!
!!
!!
!!
!!
82.87%
€
DE
EX_M
1000 NAC
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1033874.000
690335.301
!!
!!
!!
!!
!!
66.77%
UV
DE
EX_M
1000 mt
7_3_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
572.991
461.658
!!
!!
!!
!!
!!
80.57%
Q
DE
EX_X
1000 mt
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
336.349
330.009
!!
!!
!!
!!
!!
98.12%
€
DE
EX_X
1000 NAC
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
171162.000
150055.649
!!
!!
!!
!!
!!
87.67%
UV
DE
EX_X
1000 mt
7_3_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
508.882
454.702
!!
!!
!!
!!
!!
89.35%
Q
DE
EX_M
1000 mt
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1790.091
1472.613
!!
!!
!!
!!
!!
82.26%
€
DE
EX_M
1000 NAC
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1024545.000
679461.449
!!
!!
!!
!!
!!
66.32%
UV
DE
EX_M
1000 mt
7_3_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
572.343
461.399
!!
!!
!!
!!
!!
80.62%
Q
DE
EX_X
1000 mt
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
333.175
327.927
!!
!!
!!
!!
!!
98.42%
€
DE
EX_X
1000 NAC
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
169338.000
149041.536
!!
!!
!!
!!
!!
88.01%
UV
DE
EX_X
1000 mt
7_3_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
508.256
454.497
!!
!!
!!
!!
!!
89.42%
Q
DE
EX_M
1000 mt
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
27.677
29.069
!!
!!
!!
!!
!!
105.03%
€
DE
EX_M
1000 NAC
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
30246.000
30552.000
!!
!!
!!
!!
!!
101.01%
UV
DE
EX_M
1000 mt
7_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1092.824
1051.009
!!
!!
!!
!!
!!
96.17%
Q
DE
EX_X
1000 mt
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
28.616
30.715
!!
!!
!!
!!
!!
107.34%
€
DE
EX_X
1000 NAC
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
45014.000
46195.166
!!
!!
!!
!!
!!
102.62%
UV
DE
EX_X
1000 mt
7_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1573.043
1503.997
!!
!!
!!
!!
!!
95.61%
Q
DE
EX_M
1000 mt
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
93.006
95.274
!!
!!
!!
!!
!!
102.44%
€
DE
EX_M
1000 NAC
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
108124.000
100552.499
!!
!!
!!
!!
!!
93.00%
UV
DE
EX_M
1000 mt
8
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1162.555
1055.409
!!
!!
!!
!!
!!
90.78%
Q
DE
EX_X
1000 mt
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.342
0.002
!!
!!
!!
!!
!!
0.09%
€
DE
EX_X
1000 NAC
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1541.000
3.997
!!
!!
!!
!!
!!
0.26%
UV
DE
EX_X
1000 mt
8
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
657.921
1955.487
!!
!!
!!
!!
!!
297.22%
Q
DE
EX_M
1000 mt
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
111.507
51.731
!!
!!
!!
!!
!!
46.39%
€
DE
EX_M
1000 NAC
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
16760.135
14269.017
!!
!!
!!
!!
!!
85.14%
UV
DE
EX_M
1000 mt
8_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
150.305
275.831
!!
!!
!!
!!
!!
183.51%
Q
DE
EX_X
1000 mt
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
48.295
44.818
!!
!!
!!
!!
!!
92.80%
€
DE
EX_X
1000 NAC
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
25630.702
21999.473
!!
!!
!!
!!
!!
85.83%
UV
DE
EX_X
1000 mt
8_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
530.711
490.857
!!
!!
!!
!!
!!
92.49%
Q
DE
EX_M
1000 mt
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6.479
9.157
!!
!!
!!
!!
!!
141.32%
€
DE
EX_M
1000 NAC
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13412.135
12360.588
!!
!!
!!
!!
!!
92.16%
UV
DE
EX_M
1000 mt
8_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2070.024
1349.920
!!
!!
!!
!!
!!
65.21%
Q
DE
EX_X
1000 mt
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.522
0.527
!!
!!
!!
!!
!!
100.90%
€
DE
EX_X
1000 NAC
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
619.702
552.759
!!
!!
!!
!!
!!
89.20%
UV
DE
EX_X
1000 mt
8_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1186.531
1048.881
!!
!!
!!
!!
!!
88.40%
Q
DE
EX_M
1000 mt
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
105.028
42.574
!!
!!
!!
!!
!!
40.54%
€
DE
EX_M
1000 NAC
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3348.000
1908.430
!!
!!
!!
!!
!!
57.00%
UV
DE
EX_M
1000 mt
9
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
31.877
44.826
!!
!!
!!
!!
!!
140.62%
Q
DE
EX_X
1000 mt
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
47.773
44.291
!!
!!
!!
!!
!!
92.71%
€
DE
EX_X
1000 NAC
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
25011.000
21446.714
!!
!!
!!
!!
!!
85.75%
UV
DE
EX_X
1000 mt
9
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
523.541
484.218
!!
!!
!!
!!
!!
92.49%
Q
DE
EX_M
1000 mt
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
413.701
419.793
!!
!!
!!
!!
!!
101.47%
€
DE
EX_M
1000 NAC
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
50872.000
51193.592
!!
!!
!!
!!
!!
100.63%
UV
DE
EX_M
1000 mt
10
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
122.968
121.949
!!
!!
!!
!!
!!
99.17%
Q
DE
EX_X
1000 mt
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
534.223
401.001
!!
!!
!!
!!
!!
75.06%
€
DE
EX_X
1000 NAC
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
53917.000
34419.603
!!
!!
!!
!!
!!
63.84%
UV
DE
EX_X
1000 mt
10
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
100.926
85.834
!!
!!
!!
!!
!!
85.05%
Q
DE
EX_M
1000 mt
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1032.744
1094.982
!!
!!
!!
!!
!!
106.03%
€
DE
EX_M
1000 NAC
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
835823.642
852564.744
!!
!!
!!
!!
!!
102.00%
UV
DE
EX_M
1000 mt
10_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
809.323
778.611
!!
!!
!!
!!
!!
96.21%
Q
DE
EX_X
1000 mt
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2818.736
2785.115
!!
!!
!!
!!
!!
98.81%
€
DE
EX_X
1000 NAC
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2431216.842
2221087.096
!!
!!
!!
!!
!!
91.36%
UV
DE
EX_X
1000 mt
10_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
862.520
797.485
!!
!!
!!
!!
!!
92.46%
Q
DE
EX_M
1000 mt
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
471.435
459.688
!!
!!
!!
!!
!!
97.51%
€
DE
EX_M
1000 NAC
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
373982.215
352107.383
!!
!!
!!
!!
!!
94.15%
UV
DE
EX_M
1000 mt
10_1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
793.284
765.970
!!
!!
!!
!!
!!
96.56%
Q
DE
EX_X
1000 mt
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1102.063
981.039
!!
!!
!!
!!
!!
89.02%
€
DE
EX_X
1000 NAC
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1017053.400
854826.253
!!
!!
!!
!!
!!
84.05%
UV
DE
EX_X
1000 mt
10_1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
922.863
871.348
!!
!!
!!
!!
!!
94.42%
Q
DE
EX_M
1000 mt
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
76.016
64.070
!!
!!
!!
!!
!!
84.29%
€
DE
EX_M
1000 NAC
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
42741.938
32688.452
!!
!!
!!
!!
!!
76.48%
UV
DE
EX_M
1000 mt
10_1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
562.279
510.195
!!
!!
!!
!!
!!
90.74%
Q
DE
EX_X
1000 mt
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
92.354
84.223
!!
!!
!!
!!
!!
91.20%
€
DE
EX_X
1000 NAC
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
54333.206
42641.963
!!
!!
!!
!!
!!
78.48%
UV
DE
EX_X
1000 mt
10_1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
588.313
506.300
!!
!!
!!
!!
!!
86.06%
Q
DE
EX_M
1000 mt
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
56.092
63.985
!!
!!
!!
!!
!!
114.07%
€
DE
EX_M
1000 NAC
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
41760.722
44490.997
!!
!!
!!
!!
!!
106.54%
UV
DE
EX_M
1000 mt
10_1_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
744.510
695.331
!!
!!
!!
!!
!!
93.39%
Q
DE
EX_X
1000 mt
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
193.319
182.208
!!
!!
!!
!!
!!
94.25%
€
DE
EX_X
1000 NAC
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
126076.075
108428.939
!!
!!
!!
!!
!!
86.00%
UV
DE
EX_X
1000 mt
10_1_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
652.165
595.084
!!
!!
!!
!!
!!
91.25%
Q
DE
EX_M
1000 mt
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
123.445
128.854
!!
!!
!!
!!
!!
104.38%
€
DE
EX_M
1000 NAC
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
117318.262
118145.702
!!
!!
!!
!!
!!
100.71%
UV
DE
EX_M
1000 mt
10_1_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
950.369
916.895
!!
!!
!!
!!
!!
96.48%
Q
DE
EX_X
1000 mt
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
199.950
179.138
!!
!!
!!
!!
!!
89.59%
€
DE
EX_X
1000 NAC
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
264466.102
228829.719
!!
!!
!!
!!
!!
86.53%
UV
DE
EX_X
1000 mt
10_1_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1322.659
1277.392
!!
!!
!!
!!
!!
96.58%
Q
DE
EX_M
1000 mt
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
215.883
202.778
!!
!!
!!
!!
!!
93.93%
€
DE
EX_M
1000 NAC
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
172161.292
156782.232
!!
!!
!!
!!
!!
91.07%
UV
DE
EX_M
1000 mt
10_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
797.474
773.171
!!
!!
!!
!!
!!
96.95%
Q
DE
EX_X
1000 mt
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
616.439
535.470
!!
!!
!!
!!
!!
86.86%
€
DE
EX_X
1000 NAC
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
572178.017
474925.632
!!
!!
!!
!!
!!
83.00%
UV
DE
EX_X
1000 mt
10_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
928.198
886.932
!!
!!
!!
!!
!!
95.55%
Q
DE
EX_M
1000 mt
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
19.971
23.175
!!
!!
!!
!!
!!
116.04%
€
DE
EX_M
1000 NAC
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
29020.395
30583.554
!!
!!
!!
!!
!!
105.39%
UV
DE
EX_M
1000 mt
10_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1453.109
1319.695
!!
!!
!!
!!
!!
90.82%
Q
DE
EX_X
1000 mt
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
25.314
24.967
!!
!!
!!
!!
!!
98.63%
€
DE
EX_X
1000 NAC
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
48549.551
44333.326
!!
!!
!!
!!
!!
91.32%
UV
DE
EX_X
1000 mt
10_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1917.873
1775.709
!!
!!
!!
!!
!!
92.59%
Q
DE
EX_M
1000 mt
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
532.490
603.477
!!
!!
!!
!!
!!
113.33%
€
DE
EX_M
1000 NAC
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
408562.492
443294.415
!!
!!
!!
!!
!!
108.50%
UV
DE
EX_M
1000 mt
10_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
767.268
734.568
!!
!!
!!
!!
!!
95.74%
Q
DE
EX_X
1000 mt
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1672.197
1760.501
!!
!!
!!
!!
!!
105.28%
€
DE
EX_X
1000 NAC
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1294908.102
1250296.133
!!
!!
!!
!!
!!
96.55%
UV
DE
EX_X
1000 mt
10_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
774.376
710.193
!!
!!
!!
!!
!!
91.71%
Q
DE
EX_M
1000 mt
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
268.434
312.368
!!
!!
!!
!!
!!
116.37%
€
DE
EX_M
1000 NAC
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
130974.414
142301.237
!!
!!
!!
!!
!!
108.65%
UV
DE
EX_M
1000 mt
10_3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
487.921
455.556
!!
!!
!!
!!
!!
93.37%
Q
DE
EX_X
1000 mt
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
993.397
1053.903
!!
!!
!!
!!
!!
106.09%
€
DE
EX_X
1000 NAC
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
474158.527
440911.799
!!
!!
!!
!!
!!
92.99%
UV
DE
EX_X
1000 mt
10_3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
477.310
418.361
!!
!!
!!
!!
!!
87.65%
Q
DE
EX_M
1000 mt
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
146.620
161.790
!!
!!
!!
!!
!!
110.35%
€
DE
EX_M
1000 NAC
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
161089.302
180801.592
!!
!!
!!
!!
!!
112.24%
UV
DE
EX_M
1000 mt
10_3_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1098.688
1117.505
!!
!!
!!
!!
!!
101.71%
Q
DE
EX_X
1000 mt
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
417.658
419.926
!!
!!
!!
!!
!!
100.54%
€
DE
EX_X
1000 NAC
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
535334.070
517342.727
!!
!!
!!
!!
!!
96.64%
UV
DE
EX_X
1000 mt
10_3_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1281.752
1231.986
!!
!!
!!
!!
!!
96.12%
Q
DE
EX_M
1000 mt
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
98.812
108.723
!!
!!
!!
!!
!!
110.03%
€
DE
EX_M
1000 NAC
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
107020.583
110055.516
!!
!!
!!
!!
!!
102.84%
UV
DE
EX_M
1000 mt
10_3_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1083.077
1012.253
!!
!!
!!
!!
!!
93.46%
Q
DE
EX_X
1000 mt
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
207.001
233.232
!!
!!
!!
!!
!!
112.67%
€
DE
EX_X
1000 NAC
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
252130.784
261002.439
!!
!!
!!
!!
!!
103.52%
UV
DE
EX_X
1000 mt
10_3_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1218.017
1119.066
!!
!!
!!
!!
!!
91.88%
Q
DE
EX_M
1000 mt
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
18.625
20.595
!!
!!
!!
!!
!!
110.57%
€
DE
EX_M
1000 NAC
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9478.193
10136.070
!!
!!
!!
!!
!!
106.94%
UV
DE
EX_M
1000 mt
10_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
508.896
492.173
!!
!!
!!
!!
!!
96.71%
Q
DE
EX_X
1000 mt
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
54.141
53.440
!!
!!
!!
!!
!!
98.71%
€
DE
EX_X
1000 NAC
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
33284.721
31039.168
!!
!!
!!
!!
!!
93.25%
UV
DE
EX_X
1000 mt
10_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
614.783
580.820
!!
!!
!!
!!
!!
94.48%
TS-EU2
%
Min:
80%
Max:
120%
Notes
EU2
Country
Flow
Unit
Product
2015
2016
2017
2018
2019
2019
2020
15/16
16/17
17/18
18/19
19/19
19/20
2015
2016
2017
2018
DE
P
1000 m3
EU2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
77820.9935152241
84050.9808367001
!!
!!
!!
!!
!!
108.01%
DE
P
1000 m3
EU2_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
57337.6829864333
65366.3052556628
!!
!!
!!
!!
!!
114.00%
DE
P
1000 m3
EU2_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
20483.3105287908
18684.6755810373
!!
!!
!!
!!
!!
91.22%
DE
P
1000 m3
EU2_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
24685.4049412654
0
!!
!!
!!
!!
!!
0.00%
DE
P
1000 m3
EU2_1_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
18206.8922398216
0
!!
!!
!!
!!
!!
0.00%
DE
P
1000 m3
EU2_1_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6478.5127014438
0
!!
!!
!!
!!
!!
0.00%
DE
P
1000 m3
EU2_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
17662.5724515379
0
!!
!!
!!
!!
!!
0.00%
DE
P
1000 m3
EU2_1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13639.6618897635
0
!!
!!
!!
!!
!!
0.00%
DE
P
1000 m3
EU2_1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4022.9105617744
0
!!
!!
!!
!!
!!
0.00%
DE
P
1000 m3
EU2_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
35473.0161224208
0
!!
!!
!!
!!
!!
0.00%
DE
P
1000 m3
EU2_1_3_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
25491.1288568482
0
!!
!!
!!
!!
!!
0.00%
DE
P
1000 m3
EU2_1_3_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9981.8872655726
0
!!
!!
!!
!!
!!
0.00%
Annex1 | JQ1-Corres.
FOREST SECTOR QUESTIONNAIRE JQ1 (Supp. 1)
PRIMARY PRODUCTS
Removals and Production
CORRESPONDENCES to CPC Ver.2.1
Central Product Classification Version 2.1
(CPC Ver. 2.1)
Product
Product
Code
REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
1
ROUNDWOOD (WOOD IN THE ROUGH)
031
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
0313
1.1.C
Coniferous
03131
1.1.NC
Non-Coniferous
03132
1.2
INDUSTRIAL ROUNDWOOD
0311 0312
1.2.C
Coniferous
0311
1.2.NC
Non-Coniferous
0312
1.2.NC.T
of which: Tropical
ex0312
1.2.1
SAWLOGS AND VENEER LOGS
ex03110 ex03120
1.2.1.C
Coniferous
ex03110
1.2.1.NC
Non-Coniferous
ex03120
1.2.2
PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)
ex03110 ex03120
1.2.2.C
Coniferous
ex03110
1.2.2.NC
Non-Coniferous
ex03120
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
ex03110 ex03120
1.2.3.C
Coniferous
ex03110
1.2.3.NC
Non-Coniferous
ex03120
PRODUCTION
2
WOOD CHARCOAL
ex34510
3
WOOD CHIPS, PARTICLES AND RESIDUES
ex31230 ex39283
3.1
WOOD CHIPS AND PARTICLES
ex31230
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
ex39283
4
RECOVERED POST-CONSUMER WOOD
ex39283
5
WOOD PELLETS AND OTHER AGGLOMERATES
39281 39282
5.1
WOOD PELLETS
39281
5.2
OTHER AGGLOMERATES
39282
6
SAWNWOOD (INCLUDING SLEEPERS)
311 3132
6.C
Coniferous
31101 ex31109 ex3132
6.NC
Non-Coniferous
31102 ex31109 ex3132
6.NC.T
of which: Tropical
ex31102 ex31109 ex3132
7
VENEER SHEETS
3151
7.C
Coniferous
31511
7.NC
Non-Coniferous
31512
7.NC.T
of which: Tropical
ex31512
8
WOOD-BASED PANELS
3141 3142 3143 3144
8.1
PLYWOOD
3141 3142
8.1.C
Coniferous
31411 31421
8.1.NC
Non-Coniferous
31412 31422
8.1.NC.T
of which: Tropical
ex31412 ex31422
8.2
PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) and SIMILAR BOARD
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the HS2012/HS2017 or SITC Rev.4 code is applicable.
For instance "ex4401.40" under product 3.2 means that only a part of HS2017 code 4401.40 refers to wood residues coming from wood processing (the other part coded under 4401.40 is recovered post-consumer wood).
In SITC Rev.4, if only 4 digits are shown, then all sub-headings at lower degrees of aggregation are included (for example, 634.1 includes 634.11 and 634.12).
* - Please assign the trade data for HS code 4411.14 to product 8.3.2 (MDF/HDF) and 8.3.3 (other fibreboard) if it is possible to do this in national statistics. If not, please assign all the trade data to item 8.3.2 as in most cases MDF/HDF will represent the large majority of trade.
OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE
48.14/16/17/20/21/22/23
48.14/16/17/20/21/22/23
641.94 642.2 642.3 642.42/45/91/93/99 892.81
14.5.1
of which: PRINTING AND WRITING PAPER, READY FOR USE
ex4823.90
ex4823.90
ex642.99
14.5.2
of which: ARTICLES, MOULDED OR PRESSED FROM PULP
4823.70
4823.70
ex642.99
14.5.3
of which: FILTER PAPER AND PAPERBOARD, READY FOR USE
4823.20
4823.20
642.45
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the HS2012/HS2017 or SITC Rev.4 code is applicable.
For instance "ex811.00" under "Prefabricated buildings of wood" means that only a part of SITC code 811.00 refers to buildings prefabricated from wood, as that code does not distinguish between the materials buildings were prefabricated from.
In SITC Rev.4, if only 4 digits are shown, then all subheadings at lower degrees of aggregation are included (for example, 892.2 includes 892.21 and 892.29).
