Introduction Computation concept Simulations and impact Conclusion
Introduction
CFC for government sector → affects GNI
Depreciation from administrative sources → not aligned with
SNA/ESA → modelling techniques, but with . . .
. . . constraints → impacts to be simulated on Croatian data:
Different depreciation functions: geometric vs. linear
Available levels of aggregation → first GFCF might differ
Imputed vs some preliminary GFCF backward series
Introduction Computation concept Simulations and impact Conclusion
Conclusion
Depreciation data from the sources do not follow ESA/SNA
Modelling techniques with some constraints, therefore . . .
. . . sensitivity of the CFC to GNI:
Altering depreciation functions and preliminary GFCF back to
1953 → impact
Only to sector and subsectors → no impact
Economic Commission for Europe
Conference of European Statisticians
Group of Experts on National Accounts
Twenty-third session
Geneva, 23-25 April 2024
Item 3 of the provisional agenda
Improvement of measures of consumption of fixed capital
Different scenarios of estimating the consumption of fixed
capital for the government sector with possible impact on
gross national income
Prepared by Croatian Bureau of Statistics1
Summary
Since consumption of fixed capital (CFC) and capital stocks are generally not available
from administrative sources, statisticians use modelling techniques. However, they might
face certain challenges, such as insufficiently long series of gross fixed capital formation
(GFCF), selection of appropriate survival and depreciation functions, and determination of
average service life of fixed assets. In addition to the given challenges, and for transmitting
data as of ESA 2010 transmission program, capital stocks and CFC are required at the level
of institutional sectors and industries which sometimes might not be available. Accordingly,
the aim of this document is to simulate estimations on Croatian data using other depreciation
functions than those currently applied, to estimate CFC at different levels of classification,
and instead of imputation data, using preliminary GFCF data since 1953 for infrastructure
and non-residential buildings in the government sector. Computation methodology is also
described using principles of matrix algebra. Differences obtained with the new approaches
are put into relation to gross national income (GNI) as its percentage. Results indicate that
selection of different depreciation functions and introduction of a longer GFCF time series
have an impact on GNI for period 2013-2021.
1 Prepared by Nikola Motik.
United Nations ECE/CES/GE.20/2024/3
Economic and Social Council Distr.: General
12 February 2024
Original: English
ECE/CES/GE.20/2024/3
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I. Introduction and motivation
1. Consumption of fixed capital (CFC) and capital stocks are important variables in
national accounts and macroeconomic analysis for several reasons. Capital stocks are
important for calculating various capital productivity indicators such as net fixed assets to
gross value added, net fixed assets per employed person and net fixed assets per hour worked
(Eurostat, 2021). Gross capital stock has been widely used as indicator of the productive
capacity of a country or has sometimes been used as a measure of capital input in studies of
multifactor productivity (OECD, 2009). Gross capital stock contains all assets surviving from
past investments including CFC. In some literature, CFC is called depreciation, meaning a
decline during the course of accounting period, in the current value of the stock of fixed assets
owned and used by a producer as a result of physical deterioration, normal obsolescence or
normal accidental damage (SNA, 2008). Net capital stock is all assets survived adjusted for
CFC. While gross and net capital stock are important for various indicators and econometric
analyses, CFC is one of the inputs in the compilation of national accounts. CFC is a part of
the final consumption expenditure by government and non-profit institutions serving
households. Since the final consumption expenditure is the main aggregate on the use side of
the supply-use tables, CFC directly affects the GNI. Also, CFC transforms gross measures
into net ones, for example, gross income into net terms. Capital stocks and CFC are generally
compiled by statistical agencies, which might face certain limitations. Depreciation from
administrative sources is generally not aligned with the System of National Accounts
methodological guidelines which require a convex depreciation profile. In addition, average
service lives of fixed assets in national accounts might significantly differ from average
service lives for corporate taxation. Due to these limitations, it is necessary to apply a
mathematical model. Statisticians often use a model based on the concept of perpetual
inventory, defined with a basic formula
where 𝜅𝜅(𝑡𝑡) denotes net dwelling stock at the end of the year 𝑡𝑡, 𝜄𝜄(𝑡𝑡) the gross fixed capital
formation (GFCF) during year 𝑡𝑡, and 𝜚𝜚(𝑡𝑡) is the CFC during year 𝑡𝑡. Formula (1) can be
reformulated and adjusted to (OECD, 2009)
meaning that net capital stock of year 𝑡𝑡 equals net capital stock of previous year added GFCF
of year 𝑡𝑡 adjusted for CFC. The advantage of this method is that it is suitable for shorter
GFCF time series and that the depreciation factor is constant over time due to constant growth
rate of the geometric function. If the GFCF time series is not long enough, it is necessary to
somehow determine the initial capital stock. One way for initial capital stock to be estimated
is following the principle (Kohli, 1982):
meaning that the initial capital stock at some benchmark year 𝑡𝑡0 can be written as the
cumulative, depreciated GFCF of previous years 𝜄𝜄�𝑡𝑡0−𝑞𝑞�, 𝑞𝑞 ∈ ℤ+. Since GFCF is an
expenditure aggregate, an assumption could be made about the medium or long-term
investment or GDP growth rate 𝜆𝜆. By setting
Now, since 𝜄𝜄(𝑡𝑡) = (1 + 𝜆𝜆)𝜄𝜄(𝑡𝑡 − 1), the initial capital stock might be approximated
2. The final solution actually implies that, in order to estimate the initial net capital stock,
the first available GFCF 𝜄𝜄(𝑡𝑡0), depreciation rate 𝛿𝛿 and the long-run growth rate 𝜆𝜆 are required.
Expression (5) can be a critical point of estimation because initial stock relies only on the
first available GFCF value in the time series, assuming that growth- and depreciation rates
are considered reliable. Although the model can produce decent results, statistical agencies
often face additional limitations such as the length of the GFCF time series, reliable
determination of average service life, and the choice of appropriate depreciation and survival
functions. Potential users of capital stock data are also aware of the limitations. For example,
Burda and Severgnini (2008) pointed out in their work that little is known on precision of
standard measurement of total factor productivity growth especially when the capital stock
is poorly measured. In case the GFCF time series is not of sufficient length, then different
imputations are often used. Database on GFCF for the needs of Croatia’s national accounts
currently contains observations from 1995 onwards while imputation functions are used for
backward estimation with constant investment growth rate. However, this might be an issue,
as shown by Pionnier, Zinni and Baret (2023) in their study. They found that estimating initial
capital stock using constant growth rate might lead to unreliable results. Furthermore,
preliminary studies on total GFCF level (thus for all sectors) were carried out as part of an
EU project (Motik, 2023) where it was shown that the choice of depreciation function affects
the level of capital stock and CFC to a certain extent. All of the above lead to concerns
regarding GNI, thus the following three scenarios will be discussed. First scenario is related
to CFC which arises from geometric- and linear depreciation functions, where linear
depreciation is combined with survival function. The second scenario is dedicated to
estimation of CFC at different levels of aggregation because initial net capital stock 𝜅𝜅(𝑡𝑡0)
actually depends on the first available GFCF value as shown in (5). Statistical agencies often
do not have data at sufficiently detailed level of aggregation, e.g. the GFCF is missing at
government subsectors level or industry level. As mentioned earlier, common problem in
estimating CFC and capital stock is insufficiently long GFCF time series, therefore different
imputations are used. However, some historical GFCF series can subsequently be
reconstructed where significant differences may appear. This is to be the third scenario where
GFCF of infrastructure and non-residential buildings are estimated based on historical data.
Any of the scenarios can affect the GNI, which is the basis for payments to the EU budget.
In this regard, the impact is presented according to the formula
where 𝜚𝜚𝑗𝑗(𝑡𝑡) is CFC resulting from the scenarios described above 𝜚𝜚𝑣𝑣(𝑡𝑡) is CFC included in
currently valid GNI 𝐺𝐺(𝑡𝑡). The equation (6) refers to the period 2013-2021. Before analyzing
the scenarios, data sources and methodology, on which the algorithm for estimating CFC and
capital stocks is based, will be described. Mathematical procedures are generalization of
stock and flow calculus taking into account limited GFCF time series in terms of their length.
II. Data sources
3. The compilation of GFCF in current prices for government sector, consolidated into
database, is carried out from several sources. The main source is the report on income and
expenditure (PR-RAS), from which it is possible to extract data on expenditure incurred due
to investment in various fixed assets. In addition, there are also items of income resulting
from disposal of fixed assets, which is in accordance with the definition of GFCF. Other
sources are annual financial statements of entrepreneurs and non-profit organizations, which
are classified in government sector. Annual financial statements (GFI) contain data from
balance sheets and profit and loss accounts of reporting units. Fixed assets are identified from
a separate part of the GFI with the level of asset type being less detailed compared to PR-
RAS. Adjustments are applied for re-sectorization, research and development and software.
Assets are associated with internal code ensuring unique identification. Concatenating the
ECE/CES/GE.20/2024/3
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internal code and the year of investment, it is possible to create a primary key for joining
deflators for previous year’s prices (and chain link volumes accordingly) along with deflators
for assets’ revaluations for CFC and capital stocks computations. This enables the automatic
calculation of GFCF in different price concepts being eventually grouped into AN
classification as required by ESA 2010. For estimating CFC and capital stocks, revalued
GFCF is used, obtained as a product of GFCF in historical prices with a revaluation deflator.
Revaluation value 𝐼𝐼(𝑘𝑘, 𝜏𝜏) up to year 𝜏𝜏 introducing revaluation deflators 𝑝𝑝�(𝜏𝜏) equals to
(adjusted to Van den Bergen et. al., 2009)
where 𝑣𝑣1, 𝑣𝑣2 ∈ [0,1] denote a share of imported assets (estimated from the SUT tables),
otherwise domestic. Also, 𝑝𝑝𝑥𝑥 are indices where 𝑥𝑥 is odd for imported goods and even for
domestic goods. In terms of type of assets within a group of assets, 𝛼𝛼 is the weight of an asset
A, while (1 − 𝛼𝛼) is the weight of an asset B. It is the GFCF in terms of (7) that will be used
in the estimation of CFC and capital stock, which will be explained in the following section.
III. Computation methodology
4. As seen in the basic accumulation formula (2), CFC and net capital stock are
calculated dependently. However, the calculation becomes more complicated if depreciation
other than geometric is used which will be explained in subsection III.B. Before explaining
the mathematical concept that will serve as a basis for programming, a brief explanation of
the functions used in the estimation of CFC and stocks will first be given in subsection III.A.
A. Functions in the model
5. In the national accounts of Croatia, gross and net capital stock as well as CFC are
estimated. As a rule, CFC is derived directly from gross capital stock. Data on gross- and net
capital stock are required by the transmission program (Eurostat, 2014). To estimate gross
capital stock, survival function of log-normal probability distribution is used (as shown on
the second graphics from left in Figure 1). In absence of any solid empirical evidence on the
shape, this function seems convenient because only average service life is needed as a
parameter.
Figure 1
Survival and depreciation functions with altered average service life
6. Survival function is defined as 𝑆𝑆𝑇𝑇(𝑡𝑡) = 1 − 𝐹𝐹𝑇𝑇(𝑡𝑡), but due to lim𝑡𝑡→∞𝑆𝑆𝑇𝑇(𝑡𝑡) → 0 we
introduce a truncated function (e.g., Johnson and Johnson, 1980), that is
where 𝜇𝜇 = ln(𝑚𝑚) − 0.5𝜎𝜎2, 𝑚𝑚 is average service life and σ = �ln[1 + (𝑒𝑒/𝑚𝑚)2] with 𝑒𝑒 =
𝑚𝑚/3. As seen in Figure 1, the log-normal distribution is right-side asymmetric to all years of
service life, but differs in shape of peak and tail. Distributions with shorter average service
life have more rounded peak after which values decline more significantly, while
ECE/CES/GE.20/2024/3
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distributions with longer service life have a less rounded peak after which values decline
more slowly. The survival function is a cumulative log-normal distribution function whose
values are subtracted from the highest possible probability measure, that is 1. To provide
some practical explanation, we take a look at the 40 years service life survival function (green
function on the second graphics from the left). It tells that after 40 years of service, around
40% of the fixed assets are still being used. Thus, with survival function we estimate
percentage of assets not being discarded from the moment when the assets were pulled into
service. As the moment of ownership transfer is important in national accounts, and while
the timepoint of assets usage is unknown, the first year is actually considered zero year,
meaning that all assets survived. To estimate the CFC and eventually net capital stock, it is
necessary to include a depreciation function which is often narrowed to geometric or linear
function. Since CFC and capital stock are estimated for a group of homogeneous assets that
nevertheless differ in certain properties and may retire in different intervals, it is necessary
to achieve a convex or similar pattern (for more details, see the 2009 OECD manual for
capital measurement). Since geometric function itself holds a property of convexity, while a
linear function does not, the latter is generally combined with survival function. The linear
depreciation function 𝜑𝜑𝐷𝐷 = 1 − (𝑡𝑡/𝑚𝑚) is shown on third graphics from the left with the rates
(fourth graphics from the left)
where the rates at 𝑡𝑡 = 0 and 𝑡𝑡 = 𝑚𝑚 are adjusted to half amount because, unlike in commercial
accounting, the exact month of an asset being pulled into service remains unknown. For this
reason, the adjustment was applied halfway through the year, which will still result in the full
amount of depreciation due to symmetry.
7. The geometric depreciation function 𝜑𝜑𝑔𝑔 = (1 − |�̇�𝜑𝐷𝐷(𝑡𝑡)|)𝑡𝑡 is shown on fifth graphics
from the left with the rates (sixth graphics from the left)
8. Rates are nothing but derivatives of linear and geometric functions and are applied as
such when estimating CFC and capital stocks. The following subsection will explain how
these functions are integrated into the mathematical model.
B. Mathematics behind the methodology
9. The functions from subsection III.A should be combined in a meaningful way to
obtain capital stock and CFC. When deriving the model itself, it is necessary to pay attention
to basic concepts and algebra of calculations. Although data in the national accounts are
discrete, in the following procedures, all vectors will be viewed as continuous functions. The
aim is to mathematically present the procedure sketched in Figure 2.
Figure 2
The concept of deriving CFC from gross capital stock
10. In this context, the main problem to analyze is given
11. where 𝒯𝒯 [𝑘𝑘](𝑡𝑡), 𝑘𝑘 = {1,2,3}, denotes a sequence of operators acting on the revalued
GFCF vector 𝐼𝐼(𝑡𝑡) resulting with a set of three new vectors: gross capital stock 𝜅𝜅1(𝑡𝑡), net
capital stock 𝜅𝜅2(𝑡𝑡) and CFC 𝜚𝜚(𝑡𝑡). With the markings of having two capital stock, it is obvious
that 𝜅𝜅(𝑡𝑡) in (1) equals 𝜅𝜅2(𝑡𝑡). Since the GFCF series has been available since 1995, it is
ECE/CES/GE.20/2024/3
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necessary to apply the imputation function in order to achieve a sufficient level of stock and
CFC since 1995 to satisfy the condition
where 𝑑𝑑:ℳ × ℳ → ℝ+ and consequently 𝜚𝜚(𝑡𝑡) > 0. Following sample of cumulative
historic GFCF data at total levels, capital stock declines backwards at the fixed rate 𝜆𝜆
following differential equation
�̇�𝜌(𝑡𝑡) = −𝜆𝜆𝜌𝜌(𝑡𝑡) with the initial condition 𝑢𝑢(𝑡𝑡0) = 𝜌𝜌0 (14)
12. The solution is 𝑢𝑢(𝑡𝑡) = 𝜌𝜌0𝑒𝑒−𝜆𝜆𝑡𝑡 where 𝜌𝜌0 is capital stock at 𝑡𝑡0. The initial stock is
adjusted to the solution for initial net stock in (5), meaning for 𝛿𝛿 = 0
14. The parameter 𝜆𝜆 = 0.03 is set to avoid shocks at 𝑡𝑡0 due to inflation in prices in 1990s.
The time series taken for the estimation of capital stock and CFC will then consist of the
imputed non-revalued part and the revalued GFCF available from the database. We can write
this as
15. As can be seen from Figure 2, the first step is calculation of the gross capital stock,
which consists of the accumulated GFCF belonging to different vintages as indicated by
squares of different colors and the survival function as indicated by gradually fading gray
color. The formula for gross capital stock is given in the literature as (Biorn, 1989)
16. However, this expression could be challenging for programming, therefore, the
solution to the problem will be transformed to concepts of matrix algebra. For the purposes
of statistical computing, various operations between matrices are nicely summarized in
Gentle (2017) and may be of assistance when deriving procedures. Each GFCF vintage,
starting from 𝑡𝑡0 − 𝑞𝑞 is to be multiplied with the survival function and arranged in columns
that will eventually be transformed into the lower triangular matrix to have accumulation of
the flows. To achieve this, we introduce
where ⊗ is tensor product, and 𝐿𝐿𝑗𝑗=𝑡𝑡−1[⋅] is a column-wise lag operator. The period 𝑡𝑡0 + ℎ is
referred to the last GFGF realization in our database. Starting with
we get the square matrix and where ℎ, 𝑧𝑧 ∈ ℤ+. Applying the lag operator, we get survived
vintages 𝐰𝐰�(𝐭𝐭) : = 𝐿𝐿𝑗𝑗=𝑡𝑡−1[𝑆𝑆𝑇𝑇(𝑡𝑡) ⊗𝑊𝑊(𝑡𝑡)⊤], that is
17. The lag operator transforms the matrix (20) into a lower triangular matrix, ensuring
that each GFCF vintage is placed in the accompanying row, otherwise the GFCF from year
𝑡𝑡 would be recorded in the year 𝑡𝑡 − 1, which is impossible situation. Finally, adding up by
rows 𝑒𝑒, the gross capital stock 𝜅𝜅1(𝑡𝑡) ∈ ℝ𝑛𝑛 is obtained
18. The next step is to estimate the CFC by combining the survival function with the rates
of depreciation function and multiplying such vector with GFCF. Mathematically, one way
to achieve this is through
19. Since 𝑆𝑆𝑇𝑇(𝑡𝑡),𝜑𝜑(𝑡𝑡) ∈ ℝ𝑛𝑛, and with Hadamard product involved, the procedure is the
same as for the gross capital stock. The lower triangular matrix for CFC is then
21. This procedure is given on the right side of Figure 2. The elements of matrix (24) are
flows since the rates of depreciation functions are used. If 𝜑𝜑(𝑡𝑡) is linear depreciation then
𝑆𝑆𝑇𝑇(𝑡𝑡) in (9) is applied and if 𝜑𝜑(𝑡𝑡) is geometric depreciation, then 𝑆𝑆𝑇𝑇(𝑡𝑡) = (1,1, … ,1) is
applied. In other words, only linear depreciation is combined with a log-normal survival
function to achieve an effect similar to geometric depreciation. For net capital stock, an
accumulated 𝜚𝜚(𝑡𝑡) will be needed to satisfy the equation
23. Now, since 𝐰𝐰(𝐭𝐭) : = 𝐿𝐿𝑗𝑗=𝑡𝑡−1[𝟏𝟏(𝑡𝑡) ⊗𝑊𝑊(𝑡𝑡)⊤], we have
26. These procedures can serve for programming solution in a high-level programming
language, such as Python or R. For example, Schmalwasser and Schidlowski (2006) used
Visual Basic for Applications within Excel spreadsheets in their work. In our case, a language
and interface for statistical computing R (R Core Team, 2023) has been used in regular
compilation and is used for the accompanying simulations presented in the following section.
IV. Simulations with possible impact on the gross national
income
27. This part of the paper is, in a way, a continuation of the case study within the EG20-
CFC project where CBS was a beneficiary. In this section, several scenarios will be presented
with respect to different assumptions in the model and different levels of availability in the
database. Assumptions in the model are related to the selection of depreciation function,
while different levels of availability in the database are primarily related to industries and
institutional subsectors. GFCF refers only to the government sector, as any change in the
GFCF or assumptions may have a certain impact on GNI. In the case study of the EG20-CFC
project, the impacts were presented only visually along with qualitative interpretations,
without calculating the impact to any of the aggregate.
Table 1
The type of assets available in Croatian national accounts
ESA ASSET TYPE AVG SERVICE LIFE
AN.111 Dwellings 80 (70)
AN.112 Non-residential buildings (business and
industrial)
50
AN.112 Other structures (infrastructure) 55
AN.112 Land improvements 55
AN.11O Metal products 20
AN.11O General machinery and equipment 20
AN.11O Other special purpose machinery 20
AN.11O Machinery for agriculture 20
AN.1132 Computers and peripherals 6
AN.11O Electrical equipment 15
AN.1132 Communication equipment 6
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ESA ASSET TYPE AVG SERVICE LIFE
AN.11O Medical equipment 10
AN.11O Furniture and interior fittings 15
AN.1131 Passenger cars 7
AN.1131 Trucks and commercial vehicles 12
AN.1131 Other means of transport 25
AN.115 Perennial plantations 15
AN.115 Herd (-)
AN.117 Research and development 10
AN.117 Mineral exploration 10
AN.117 Software and database 5
AN.117 Literary and artistic originals 7
AN.11O Other tangible assets 10
AN.117 Other non-tangible assets 7
AN.11O Weapons system 25
28. First and foremost, the difference between the base scenario and some new approach
must be obtained. The base scenario is the one currently in use, referring to the application
of geometric depreciation for dwellings and linear depreciation in combination with the
survival function for all other assets. In addition, the base scenario includes the estimation of
CFC and capital stock at the level of institutional sectors, types of assets and industries. Type
of assets are kept fixed in all cases while different aggregations will be carried out on
industries and sub-sectors, which are discussed in more detail below. The level of detail for
fixed assets is shown in Table 1 and is used as such in all scenarios. Along with the impacts,
a graphical comparison of the results will be shown at the level in the column named ESA.
The level of detail for fixed assets is shown in the column ASSET TYPE and will be used as
such in all scenarios. It should be noted that for the herd, CFC is equal to zero, which means
that the gross stock is equal to the net stock. Average service life for dwellings in the
household sector is estimated at 80 years, and 70 years for dwellings in other sectors. This is
because average service life for household dwellings was estimated on the basis of census,
while for other sectors the recommendations of the Eurostat Task Force for Fixed Capital
were followed.
A. Altering depreciation functions
29. As mentioned earlier, for estimations of capital stock and CFC, it is recommended to
use a convex shape function instead of solely linear function which is normally used in
commercial accounting. This is because the estimation procedures are not undertaken by
individual assets but for cohorts of assets of similar ages and characteristics. Individual assets
within a cohort will retire at different timepoints but the depreciation profile for a cohort as
a whole is typically convex to the origin (SNA, 2008). In this regard, if a linear depreciation
function is used, then it needs to be adjusted for survival pattern derived from, at least in our
case, log-normal probability distribution. A retirement function combined with a linear
depreciation function may produce convex pattern similar to geometric function.
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Figure 3
The CFC vectors given from base scenario and new depreciation function
30. Unlike the basic scenario, the alternative one includes the application of linear
depreciation for dwellings and geometric depreciation for other assets. As expected, similar
vector shapes are achieved, but with pronounced differences at certain intervals, depending
on the type of fixed assets. When the results are put in relation to the GNI according to
equation (6), the results are obtained as in Table 2.
Table 2
The impact of changes in depreciation functions on GNI (106 EUR)
31. A certain impact exists for all years, ranging from 0.25% to 0.38%. If the threshold of
0.1% (document GNIC/283) is taken, application of geometric depreciation for all assets
except dwellings, would lead to revision of the time series from 2013 onwards.
B. Different levels of aggregation
32. At some point, statisticians often do not have data at desired classification level. For
example, having a satisfactory level of GFCF by type of assets (which we keep fixed
anyway), but, for some reason, lacking on the level of institutional subsectors or industries.
After some time, data at more detailed levels may become available, and the question is to
what extent the resulting change will affect GNI. For statistical agencies that have well-
organized registers and sufficiently long GFCF time series, this issue is probably not
applicable. Nevertheless, since Croatia lacks sufficiently long time series, this simulation will
serve to compare the results in relation to the currently valid estimation levels of CFC and
capital stock.
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Figure 4
GFCF matrices resulting from different levels of aggregation
33. The main issue is shown in Figure 4. Squares in various blue shades indicate GFCFs
different from zero. It is to be expected that at the sector level, the values will differ from
zero since institutions in any subsector and industry have invested in some assets. If data is
filtered for a sub-sector, zeros will appear in some places because, within that sub-sector,
some industries did not invest in certain assets in some years.
Figure 5
The CFC vectors from the sub-sectors aggregation only
34. When some chosen industry is additionally filtered from the subsector, the zeros are
more visible, and such a matrix becomes sparse. Sparse matrices on industry-level filtering
are not uncommon in the case of Croatia, especially if the zeros are at the beginning of
observable time series, i.e. 1995 or 1996. What adjustments are additionally applied due to
this issue will be described below as part of the interpretation of impacts. Figure 5 graphically
presented results when CFC for all types of assets is estimated only by subsectors, avoiding
the level of industries. CFC vectors overlap indicating that almost identical results are
obtained or the difference is negligible. The extent to which the difference is negligible is
presented as an impact on GNI, as in the previous case.
Table 3
The impact on GNI of estimating CFC at the subsectors level (106 EUR)
35. In this simulation, the differences resulting from bypassing the industry level and
calculating CFC only by subsectors of the general government are negligible. We perform
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the same procedure but this time on the government sector, meaning without sub-aggregation
of GFCF to sub-sectors and industries. Visually, the results are presented in Figure 6.
Figure 6
The CFC vectors from the sector aggregation only
36. The results are almost identical as in the case of the CFC estimation at the subsector
level, the impact is negligible, as can be seen from the graphic where the vectors overlap.
Numerical results are shown in Table 4.
Table 4
The impact on GNI of estimating CFC at government sector level (106 EUR)
37. Differences are also negligible in terms of percentage of GNI. In both cases, therefore,
the estimation of CFC at the sector and sub-sector level has almost no impact on the GNI.
Such outcomes are probably expected given the adjustments made in the regular CFC
estimation. Namely, according to (15), if the first available GFCF, i.e. for 1995, is equal to
zero, then the imputed values will also be zero. When the time series, obtained by filtering
sectors, sub-sectors and industries from the GFCF database as shown in Figure 4, contains
zeros in the first or first two places, then the initial capital stock is calculated for the first non-
zero value. This will result in the CFC also appearing in, for example, the first two years
where the GFCF is missing, which may seem unusual at first. However, due to (21) and (24),
and assuming that a certain industry within the (sub)sector invested in the assets before 1995,
these assets will survive in gross terms and accumulate into the future. The criterion for the
GFCF offset by a year or two forwards was the analysis of the time series noticing values
other than zero. If the time series contains a majority of zeros, then no offset is applied, and
imputation before 1995 is zero. It seems that this approach has an effect in achieving some
meaningful level of capital stock and CFC in 1995, at least according to (13).
C. Estimated GFCF time series back to 1953
38. Imputation functions for estimating historical GFCF time series can lead to
questionable results, which is especially true for fixed assets with longer average service life.
Accordingly, the backward GFCF reconstruction is based on the data from a series of
statistical yearbooks (Federal Statistical Bureau of Croatia, SFR Yugoslavia, 1954-1990).
However, different concepts of aggregate calculation in a mixed economy system (Croatia
was part of SFR Yugoslavia) as well as the denomination of the domestic currency create
difficulties in reliable GFCF estimation before 1995. From 1945 to 1990 in Yugoslavia, two
ownership systems were constituted: a privileged dominant system of public assets, later
ECE/CES/GE.20/2024/3
13
social ownership in which cooperative ownership was gradually included, and a legally and
economically limited system of private ownership mainly for arable agricultural land per
household (Simonetti, 2010). For these reasons, it has been quite difficult to allocate assets
within institutional sectors as requested by ESA 2010 transmission program. Along with the
debatable issue of ownership, the problem with currency denomination requires special
attention and careful analysis. For this reason, two type of fixed assets that are considered to
have been part of government sector in Yugoslavia were taken. It is about infrastructure such
as roads, tunnels, streets, squares, bridges, railways, and buildings such as administrative
buildings, cultural and educational institutions, health facilities, sports halls and similar
facilities. These two sets of data are added up to a single time series and joined with the GFCF
from 1995 onwards as shown in the first graph on the left in Figure 7.
Figure 7
The CFC vectors from historic data on public infrastructure (106 EUR)
39. Significant improvements are included in the GFCF. Since statistical yearbooks of
Yugoslavia 1954–1995 contain data with different denominations and different currencies, it
was difficult to generate meaningful time series. For period 1953–1990, the exchange rate of
the German mark (DEM) was used at intervals of denominations and new currencies, where
jumps in the exchange rate of the Yugoslav and Croatian dinar versus DEM were observed
with the denominations. Since the GFCF data 1991–1994 were in Croatian kuna, they were
converted directly to EUR. Data from 1953–1990 were first converted to DEM and
eventually EUR using fixed conversion rate of the two. The result of the conversions is shown
in Figure 7 with a black graph. It should be taken into account that no revaluation was applied
due to significant fluctuations in the price index of materials and wages in construction
industry in period 1953-1994. Although the estimate of GFCF 1953–1994 in EUR might not
be the most reliable, it could perhaps certainly reflect a more realistic situation compared to
the imputation function under (16) which is actually a rescaled exponential function.
40. The black vector in Figure 7 serves as an input to the CFC estimation. Although data
for infrastructure and non-residential buildings in the government sector are now preliminary
available since 1953, the estimation was approached in two ways; without initial capital stock
and with initial capital stock as shown by the cyan and green vectors. As is clearly visible,
there is a difference between the two since 1995, but it is gradually disappearing. The
inclusion of the imputation function based on GFCF in 1953 has almost no effect, therefore
the impact in Table 5 will be given only for the green vector, meaning with no initial stock
included.
Table 5
The impact of estimating CFC (based on GFCF back to 1953) on GNI
41. The fourth graph from the left shows a comparison of the green vector based on
historical GFCF data with the basic scenario. A significant difference between the two
vectors is observed, most pronounced since 1995 and decreasing towards 2020. According
ECE/CES/GE.20/2024/3
14
to Table 5, impacts greater than 0.1% for 2013–2017 are observed, while after 2018 the
impacts decrease.
V. Conclusion
42. Based on the limited GFCF data that serve as inputs for the CFC estimation and capital
stock, a mathematical methodology was presented serving as the basis for programming
solution in the statistical computing language R. That is, apart from regular estimation of
CFC and capital stock being the base scenario, R also served as a tool for simulating different
assumptions in the estimation procedure. The aim was to simulate estimations using different
depreciation functions, to estimate the CFC at different levels of classification, and using
preliminary real data on GFCF since 1953 for infrastructure and non-residential buildings in
the government sector. Results indicate that selection of other depreciation functions and the
introduction of longer time series of GFCF impact the GNI for period 2013-2021. Changes
in depreciation functions from linear to geometric (and vice versa for dwellings) would cause
a significant impact for all years from 2013 to 2021, ranging from 0.25% to 0.38%.
Introduction of a longer GFCF time series has an impact on GNI, the highest in 2013, with
gradual decreasing to be less than 0.1% for years 2019-2021. Regarding the preliminary
estimated GFCF for period 1953–1994, a caveat should be taken into account due to historical
currency denominations along with the GFCF recording in several currencies through all
years. In addition, assets revaluation was not carried out due to galloping price indices in
certain periods. Finally, further challenging analyses on the GFCF estimation before 1995,
with focus on assets with longer average service life, are certainly needed while this paper is
a preliminary step in considering any updates in regular methodological procedures.
ECE/CES/GE.20/2024/3
15
References
Biorn, E. (1989): Taxation, Technology, and the User Cost of Capital, North-Holland,
Amsterdam
Burda, M.C and Severgnini, B. (2008): Solow Residuals without Capital Stock, Discussion
Paper, Economic Risk, Berlin
Eurostat (2014): European System of Accounts, Transmission programme of data, European
Commission, Luxembourg
Eurostat (2021): Capital Productivity Indicators; Methodological note and quality aspects,
European Commission, Luxembourg
Gentle, J.E. (2017): Matrix Algebra: Theory, Computations and Applications in Statistics,
Springer Verlag, Cham
ISWGNA (2017): System of National Accounts 2008, New York
Johnson, R.C.E., and Johnson N.L. (1980): Survival Models and Data Analysis, Wiley-
Interscience, New York
Kohli, U. (1982): Production theory, technological change, and the demand for imports;
Switzerland 1948-1976, European Economic Review 18, North-Holland, Amsterdam
Motik, N. (2023): Sensitivity Analyses and Methodological Advances in Estimation of
Capital Stock and Consumption of Fixed Capital in Croatia, Case Study Report, Zagreb
OECD (2009): Measuring Capital; measurement of capital stocks, consumption of fixed
capital and capital services: OECD Publications Service, Paris
Pionnier, P.-A., Zinni, B. and Baret, K. (2023): Sensitivity of capital and MFP measurement
to asset depreciation patterns and initial capital stock estimates, SDD, Working Paper,
OECD, Paris
R Core Team (2023): R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, Vienna
Schmalwasser, O. and Schidlowski, M. (2006): Measuring Capital Stock in Germany,
Wirtschaft und Statistik, Statistisches Bundesamt, Wiesbaden
Simonetti, P. (2010): Ownership and its transformations, guarantee and protection in the
constitutional order of the Republic of Croatia, Zbornik Pravnog fakulteta Sveuèilišta u
Rijeci, Rijeka
Van den Bergen, D., de Haan, M., de Heij, R. and Horsten, M. (2009): Measuring capital in
the Netherlands, Statistics Netherlands, The Hague
Statistical yearbooks of Federal Statistical Bureau of Croatia, SFR Yugoslavia 1954-1990
относится к периоду 2013–2021 годов. Перед анализом сценариев будут описаны
источники данных и методология, на основе которой выведен алгоритм оценки ПОК
и запасов капитала. Математические действия представляют собой обобщенный
расчет запасов и потоков с учетом ограниченности временных рядов ВНОК по их
продолжительности.
II. Источники данных
3. Сбор данных по ВНОК в текущих ценах для государственного сектора,
сведенных в базу данных, осуществляется из нескольких источников. Основным
источником является отчет о доходах и расходах (PR-RAS), из которого можно извлечь
данные о расходах, понесенных в связи с инвестициями в различные фиксированные
активы. Кроме того, существуют также статьи доходов от выбытия основных фондов,
что соответствует определению ВНОК. Другие источники представлены годовой
финансовой отчетностью предпринимателей и некоммерческих организаций, которые
относятся к государственному сектору. В годовых финансовых ведомостях (ГФВ)
содержатся данные из балансов активов и пассивов и счетов прибылей и убытков
отчетных единиц. Фиксированные активы определяются в отдельной части ГФВ, при
этом уровень вида активов описан менее подробно по сравнению с PR-RAS.
Корректировки применяются в отношении ресекторизации, исследований и
разработок, а также программного обеспечения. Активы ассоциируются с внутренним
кодом, обеспечивающим их уникальную идентификацию. Связав внутренний код и
год инвестиций, можно создать первичный ключ для объединения индексов цен за
предыдущий год (и, соответственно, объемов цепочек) с индексами переоценки
активов для расчета ПОК и запасов капитала. Это позволяет автоматически
рассчитывать ВНОК в различных ценовых концепциях, которые в конечном итоге
будут сгруппированы в классификацию AN (кодов для нефинансовых активов), как
это требуется согласно ЕСС 2010. Для оценки ПОК и запасов капитала используется
ВНОК после переоценки, полученный как произведение ВНОК в исторических ценах
и индекса переоценки. Значение 𝐼(𝑘, 𝜏) в результате переоценки до года 𝜏
с использованием индексов переоценки �̃�(𝜏) равно (с поправкой на Van den Bergen
20. Эта процедура представлена в правой части рис. 2. Элементами матрицы (24)
являются потоки, так как используются функции норм амортизации. Если 𝜑(𝑡)
представляет собой линейную функцию амортизации, то в формуле (9) применяется
𝑆𝑇(𝑡), а если 𝜑(𝑡) является геометрической функцией амортизации, то применяется
𝑆𝑇(𝑡) = (1,1,… ,1). Иными словами, только линейная функция амортизации сочетается
с логнормальной функцией сохранения для достижения эффекта, схожего с
геометрической функцией амортизации. Для чистых запасов капитала необходимо
накопление 𝜚(𝑡), чтобы соответствовать уравнению
2. La solution à laquelle on parvient finalement implique en fait que, pour estimer le
stock initial net de capital, il est nécessaire de connaître la première valeur disponible 𝜄(𝑡0)
de la FBCF, le taux d’amortissement 𝛿 et le taux de croissance à long terme 𝜆. L’équation (5)
peut constituer un élément d’estimation très important, car le stock initial repose uniquement
sur la première valeur disponible de la FBCF dans la série chronologique, à supposer que les
taux de croissance et d’amortissement soient considérés comme fiables. Bien que le modèle
puisse produire des résultats satisfaisants, les organismes de statistique doivent souvent faire
face à d’autres limitations telles que la longueur de la série chronologique de données
relatives à la FBCF, la fiabilité de détermination de la durée de vie moyenne et le choix de
fonctions d’amortissement et de survie appropriées. Les utilisateurs potentiels de données sur
le stock de capital sont également conscients de ces limitations. Par exemple, Burda et
Severgnini (2008) ont souligné dans leurs travaux que la précision de la méthode habituelle
de mesure de la croissance de la productivité totale des facteurs était mal connue, en
particulier lorsque le stock de capital était imparfaitement mesuré. Dans le cas où la série
chronologique de données relatives à la FBCF n’est pas suffisamment longue, différentes
imputations sont souvent utilisées. La base de données sur la FBCF établie aux fins de la
comptabilité nationale croate regroupe actuellement les observations réalisées à partir de
1995, tandis que des fonctions d’imputation sont utilisées pour établir une estimation
rétrospective sur la base d’un taux de croissance constant de l’investissement. Toutefois, cela
peut poser problème, comme l’ont montré Pionnier, Zinni et Baret (2023) dans leur étude,
dans laquelle ils ont constaté que l’estimation du stock initial de capital sur la base d’un taux
de croissance constant pouvait conduire à des résultats peu fiables. En outre, des études
préliminaires sur le niveau global de la FBCF (incluant donc tous les secteurs), réalisées dans
le cadre d’un projet de l’UE (Motik, 2023), ont montré que le choix de la fonction
d’amortissement influait dans une certaine mesure sur le niveau du stock de capital et de la
CCF. Tous ces éléments conduisent à s’interroger sur le RNB, raison pour laquelle les trois
scénarios qui suivent seront examinés. Le premier scénario est lié à la CCF résultant de
fonctions d’amortissement géométriques et linéaires, où l’amortissement linéaire est combiné
à une fonction de survie. Le deuxième scénario porte sur l’estimation de la CCF à différents
niveaux d’agrégation, sachant que le stock initial net de capital 𝜅(𝑡0) dépend en fait de la
première valeur disponible de la FBCF, comme le montre la figure 5. Il arrive souvent que
les organismes de statistique ne disposent pas de données à un niveau d’agrégation
suffisamment détaillé, par exemple lorsque les données relatives à la FBCF ne sont pas
disponibles au niveau des sous-secteurs de l’administration publique ou à celui des branches
d’activité. Comme indiqué précédemment, la longueur insuffisante des séries chronologiques
de données relatives à la FBCF constitue un problème courant dans l’estimation de la CCF
et du stock de capital, d’où l’utilisation de différentes imputations. Toutefois, certaines séries
historiques de données relatives à la FBCF peuvent être reconstituées par la suite, là où
d’importantes différences peuvent apparaître. Il s’agit du troisième scénario, dans lequel la
FBCF liée aux infrastructures et aux bâtiments non résidentiels est estimée sur la base des
données historiques. Chacun des scénarios peut avoir une incidence sur le RNB, qui est la
base des contributions au budget de l’UE. Cette incidence est représentée au moyen de la
Dans cette formule, 𝒯[𝑘](𝑡), 𝑘 = {1,2,3}, désigne une séquence d’opérateurs ayant un effet
sur le vecteur de la FBCF réévaluée 𝐼(𝑡), ce qui donne un ensemble de trois nouveaux
vecteurs : le stock brut de capital 𝜅1(𝑡), le stock net de capital 𝜅2(𝑡) et la CCF 𝜚(𝑡).
Étant donné qu’il existe deux stocks de capital, il est évident que dans (1), 𝜅(𝑡) est égal
à 𝜅2(𝑡). La série de données relatives à la FBCF étant disponible à partir de 1995, il est
nécessaire d’appliquer une fonction d’imputation afin d’obtenir à partir de cette date un
niveau de stock et de CCF suffisant pour satisfaire à la condition suivante :
nous obtenons une matrice carrée et déterminons ℎ, 𝑧 ∈ ℤ+. En appliquant l’opérateur retard,
nous obtenons les montants annuels survivants �̃�(𝐭) : = 𝐿𝑗=𝑡−1[𝑆𝑇(𝑡) ⊗𝑊(𝑡)⊤], à savoir :
20. Ce processus est décrit dans la partie droite de la figure 2. Les éléments de la
matrice (24) sont des flux puisque les fonctions d’amortissement sont utilisées. Si 𝜑(𝑡) est
un amortissement linéaire, 𝑆𝑇(𝑡) défini dans (9) est appliqué, et s’il s’agit d’un amortissement
géométrique, c’est alors 𝑆𝑇(𝑡) = (1,1, … ,1) qui est appliqué. Autrement dit, seul
l’amortissement linéaire est combiné à une fonction de survie log-normale pour obtenir un
effet similaire à l’amortissement géométrique. S’agissant du stock net de capital, un 𝜚(𝑡)
cumulé sera nécessaire pour résoudre l’équation suivante :
Economic Commission for Europe
Conference of European Statisticians
Group of Experts on National Accounts
Twenty-third session
Geneva, 23-25 April 2024
Item 3 of the provisional agenda
Improvement of measures of consumption of fixed capital
Different scenarios of estimating the consumption of fixed
capital for the government sector with possible impact on
gross national income
Prepared by Croatian Bureau of Statistics1
Summary
Since consumption of fixed capital (CFC) and capital stocks are generally not available
from administrative sources, statisticians use modelling techniques. However, they might
face certain challenges, such as insufficiently long series of gross fixed capital formation
(GFCF), selection of appropriate survival and depreciation functions, and determination of
average service life of fixed assets. In addition to the given challenges, and for transmitting
data as of ESA 2010 transmission program, capital stocks and CFC are required at the level
of institutional sectors and industries which sometimes might not be available. Accordingly,
the aim of this document is to simulate estimations on Croatian data using other depreciation
functions than those currently applied, to estimate CFC at different levels of classification,
and instead of imputation data, using preliminary GFCF data since 1953 for infrastructure
and non-residential buildings in the government sector. Computation methodology is also
described using principles of matrix algebra. Differences obtained with the new approaches
are put into relation to gross national income (GNI) as its percentage. Results indicate that
selection of different depreciation functions and introduction of a longer GFCF time series
have an impact on GNI for period 2013-2021.
1 Prepared by Nikola Motik.
United Nations ECE/CES/GE.20/2024/3
Economic and Social Council Distr.: General
12 February 2024
Original: English
ECE/CES/GE.20/2024/3
2
I. Introduction and motivation
1. Consumption of fixed capital (CFC) and capital stocks are important variables in
national accounts and macroeconomic analysis for several reasons. Capital stocks are
important for calculating various capital productivity indicators such as net fixed assets to
gross value added, net fixed assets per employed person and net fixed assets per hour worked
(Eurostat, 2021). Gross capital stock has been widely used as indicator of the productive
capacity of a country or has sometimes been used as a measure of capital input in studies of
multifactor productivity (OECD, 2009). Gross capital stock contains all assets surviving from
past investments including CFC. In some literature, CFC is called depreciation, meaning a
decline during the course of accounting period, in the current value of the stock of fixed assets
owned and used by a producer as a result of physical deterioration, normal obsolescence or
normal accidental damage (SNA, 2008). Net capital stock is all assets survived adjusted for
CFC. While gross and net capital stock are important for various indicators and econometric
analyses, CFC is one of the inputs in the compilation of national accounts. CFC is a part of
the final consumption expenditure by government and non-profit institutions serving
households. Since the final consumption expenditure is the main aggregate on the use side of
the supply-use tables, CFC directly affects the GNI. Also, CFC transforms gross measures
into net ones, for example, gross income into net terms. Capital stocks and CFC are generally
compiled by statistical agencies, which might face certain limitations. Depreciation from
administrative sources is generally not aligned with the System of National Accounts
methodological guidelines which require a convex depreciation profile. In addition, average
service lives of fixed assets in national accounts might significantly differ from average
service lives for corporate taxation. Due to these limitations, it is necessary to apply a
mathematical model. Statisticians often use a model based on the concept of perpetual
inventory, defined with a basic formula
where 𝜅𝜅(𝑡𝑡) denotes net dwelling stock at the end of the year 𝑡𝑡, 𝜄𝜄(𝑡𝑡) the gross fixed capital
formation (GFCF) during year 𝑡𝑡, and 𝜚𝜚(𝑡𝑡) is the CFC during year 𝑡𝑡. Formula (1) can be
reformulated and adjusted to (OECD, 2009)
meaning that net capital stock of year 𝑡𝑡 equals net capital stock of previous year added GFCF
of year 𝑡𝑡 adjusted for CFC. The advantage of this method is that it is suitable for shorter
GFCF time series and that the depreciation factor is constant over time due to constant growth
rate of the geometric function. If the GFCF time series is not long enough, it is necessary to
somehow determine the initial capital stock. One way for initial capital stock to be estimated
is following the principle (Kohli, 1982):
meaning that the initial capital stock at some benchmark year 𝑡𝑡0 can be written as the
cumulative, depreciated GFCF of previous years 𝜄𝜄�𝑡𝑡0−𝑞𝑞�, 𝑞𝑞 ∈ ℤ+. Since GFCF is an
expenditure aggregate, an assumption could be made about the medium or long-term
investment or GDP growth rate 𝜆𝜆. By setting
Now, since 𝜄𝜄(𝑡𝑡) = (1 + 𝜆𝜆)𝜄𝜄(𝑡𝑡 − 1), the initial capital stock might be approximated
2. The final solution actually implies that, in order to estimate the initial net capital stock,
the first available GFCF 𝜄𝜄(𝑡𝑡0), depreciation rate 𝛿𝛿 and the long-run growth rate 𝜆𝜆 are required.
Expression (5) can be a critical point of estimation because initial stock relies only on the
first available GFCF value in the time series, assuming that growth- and depreciation rates
are considered reliable. Although the model can produce decent results, statistical agencies
often face additional limitations such as the length of the GFCF time series, reliable
determination of average service life, and the choice of appropriate depreciation and survival
functions. Potential users of capital stock data are also aware of the limitations. For example,
Burda and Severgnini (2008) pointed out in their work that little is known on precision of
standard measurement of total factor productivity growth especially when the capital stock
is poorly measured. In case the GFCF time series is not of sufficient length, then different
imputations are often used. Database on GFCF for the needs of Croatia’s national accounts
currently contains observations from 1995 onwards while imputation functions are used for
backward estimation with constant investment growth rate. However, this might be an issue,
as shown by Pionnier, Zinni and Baret (2023) in their study. They found that estimating initial
capital stock using constant growth rate might lead to unreliable results. Furthermore,
preliminary studies on total GFCF level (thus for all sectors) were carried out as part of an
EU project (Motik, 2023) where it was shown that the choice of depreciation function affects
the level of capital stock and CFC to a certain extent. All of the above lead to concerns
regarding GNI, thus the following three scenarios will be discussed. First scenario is related
to CFC which arises from geometric- and linear depreciation functions, where linear
depreciation is combined with survival function. The second scenario is dedicated to
estimation of CFC at different levels of aggregation because initial net capital stock 𝜅𝜅(𝑡𝑡0)
actually depends on the first available GFCF value as shown in (5). Statistical agencies often
do not have data at sufficiently detailed level of aggregation, e.g. the GFCF is missing at
government subsectors level or industry level. As mentioned earlier, common problem in
estimating CFC and capital stock is insufficiently long GFCF time series, therefore different
imputations are used. However, some historical GFCF series can subsequently be
reconstructed where significant differences may appear. This is to be the third scenario where
GFCF of infrastructure and non-residential buildings are estimated based on historical data.
Any of the scenarios can affect the GNI, which is the basis for payments to the EU budget.
In this regard, the impact is presented according to the formula
where 𝜚𝜚𝑗𝑗(𝑡𝑡) is CFC resulting from the scenarios described above 𝜚𝜚𝑣𝑣(𝑡𝑡) is CFC included in
currently valid GNI 𝐺𝐺(𝑡𝑡). The equation (6) refers to the period 2013-2021. Before analyzing
the scenarios, data sources and methodology, on which the algorithm for estimating CFC and
capital stocks is based, will be described. Mathematical procedures are generalization of
stock and flow calculus taking into account limited GFCF time series in terms of their length.
II. Data sources
3. The compilation of GFCF in current prices for government sector, consolidated into
database, is carried out from several sources. The main source is the report on income and
expenditure (PR-RAS), from which it is possible to extract data on expenditure incurred due
to investment in various fixed assets. In addition, there are also items of income resulting
from disposal of fixed assets, which is in accordance with the definition of GFCF. Other
sources are annual financial statements of entrepreneurs and non-profit organizations, which
are classified in government sector. Annual financial statements (GFI) contain data from
balance sheets and profit and loss accounts of reporting units. Fixed assets are identified from
a separate part of the GFI with the level of asset type being less detailed compared to PR-
RAS. Adjustments are applied for re-sectorization, research and development and software.
Assets are associated with internal code ensuring unique identification. Concatenating the
ECE/CES/GE.20/2024/3
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internal code and the year of investment, it is possible to create a primary key for joining
deflators for previous year’s prices (and chain link volumes accordingly) along with deflators
for assets’ revaluations for CFC and capital stocks computations. This enables the automatic
calculation of GFCF in different price concepts being eventually grouped into AN
classification as required by ESA 2010. For estimating CFC and capital stocks, revalued
GFCF is used, obtained as a product of GFCF in historical prices with a revaluation deflator.
Revaluation value 𝐼𝐼(𝑘𝑘, 𝜏𝜏) up to year 𝜏𝜏 introducing revaluation deflators 𝑝𝑝�(𝜏𝜏) equals to
(adjusted to Van den Bergen et. al., 2009)
where 𝑣𝑣1, 𝑣𝑣2 ∈ [0,1] denote a share of imported assets (estimated from the SUT tables),
otherwise domestic. Also, 𝑝𝑝𝑥𝑥 are indices where 𝑥𝑥 is odd for imported goods and even for
domestic goods. In terms of type of assets within a group of assets, 𝛼𝛼 is the weight of an asset
A, while (1 − 𝛼𝛼) is the weight of an asset B. It is the GFCF in terms of (7) that will be used
in the estimation of CFC and capital stock, which will be explained in the following section.
III. Computation methodology
4. As seen in the basic accumulation formula (2), CFC and net capital stock are
calculated dependently. However, the calculation becomes more complicated if depreciation
other than geometric is used which will be explained in subsection III.B. Before explaining
the mathematical concept that will serve as a basis for programming, a brief explanation of
the functions used in the estimation of CFC and stocks will first be given in subsection III.A.
A. Functions in the model
5. In the national accounts of Croatia, gross and net capital stock as well as CFC are
estimated. As a rule, CFC is derived directly from gross capital stock. Data on gross- and net
capital stock are required by the transmission program (Eurostat, 2014). To estimate gross
capital stock, survival function of log-normal probability distribution is used (as shown on
the second graphics from left in Figure 1). In absence of any solid empirical evidence on the
shape, this function seems convenient because only average service life is needed as a
parameter.
Figure 1
Survival and depreciation functions with altered average service life
6. Survival function is defined as 𝑆𝑆𝑇𝑇(𝑡𝑡) = 1 − 𝐹𝐹𝑇𝑇(𝑡𝑡), but due to lim𝑡𝑡→∞𝑆𝑆𝑇𝑇(𝑡𝑡) → 0 we
introduce a truncated function (e.g., Johnson and Johnson, 1980), that is
where 𝜇𝜇 = ln(𝑚𝑚) − 0.5𝜎𝜎2, 𝑚𝑚 is average service life and σ = �ln[1 + (𝑒𝑒/𝑚𝑚)2] with 𝑒𝑒 =
𝑚𝑚/3. As seen in Figure 1, the log-normal distribution is right-side asymmetric to all years of
service life, but differs in shape of peak and tail. Distributions with shorter average service
life have more rounded peak after which values decline more significantly, while
ECE/CES/GE.20/2024/3
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distributions with longer service life have a less rounded peak after which values decline
more slowly. The survival function is a cumulative log-normal distribution function whose
values are subtracted from the highest possible probability measure, that is 1. To provide
some practical explanation, we take a look at the 40 years service life survival function (green
function on the second graphics from the left). It tells that after 40 years of service, around
40% of the fixed assets are still being used. Thus, with survival function we estimate
percentage of assets not being discarded from the moment when the assets were pulled into
service. As the moment of ownership transfer is important in national accounts, and while
the timepoint of assets usage is unknown, the first year is actually considered zero year,
meaning that all assets survived. To estimate the CFC and eventually net capital stock, it is
necessary to include a depreciation function which is often narrowed to geometric or linear
function. Since CFC and capital stock are estimated for a group of homogeneous assets that
nevertheless differ in certain properties and may retire in different intervals, it is necessary
to achieve a convex or similar pattern (for more details, see the 2009 OECD manual for
capital measurement). Since geometric function itself holds a property of convexity, while a
linear function does not, the latter is generally combined with survival function. The linear
depreciation function 𝜑𝜑𝐷𝐷 = 1 − (𝑡𝑡/𝑚𝑚) is shown on third graphics from the left with the rates
(fourth graphics from the left)
where the rates at 𝑡𝑡 = 0 and 𝑡𝑡 = 𝑚𝑚 are adjusted to half amount because, unlike in commercial
accounting, the exact month of an asset being pulled into service remains unknown. For this
reason, the adjustment was applied halfway through the year, which will still result in the full
amount of depreciation due to symmetry.
7. The geometric depreciation function 𝜑𝜑𝑔𝑔 = (1 − |�̇�𝜑𝐷𝐷(𝑡𝑡)|)𝑡𝑡 is shown on fifth graphics
from the left with the rates (sixth graphics from the left)
8. Rates are nothing but derivatives of linear and geometric functions and are applied as
such when estimating CFC and capital stocks. The following subsection will explain how
these functions are integrated into the mathematical model.
B. Mathematics behind the methodology
9. The functions from subsection III.A should be combined in a meaningful way to
obtain capital stock and CFC. When deriving the model itself, it is necessary to pay attention
to basic concepts and algebra of calculations. Although data in the national accounts are
discrete, in the following procedures, all vectors will be viewed as continuous functions. The
aim is to mathematically present the procedure sketched in Figure 2.
Figure 2
The concept of deriving CFC from gross capital stock
10. In this context, the main problem to analyze is given
11. where 𝒯𝒯 [𝑘𝑘](𝑡𝑡), 𝑘𝑘 = {1,2,3}, denotes a sequence of operators acting on the revalued
GFCF vector 𝐼𝐼(𝑡𝑡) resulting with a set of three new vectors: gross capital stock 𝜅𝜅1(𝑡𝑡), net
capital stock 𝜅𝜅2(𝑡𝑡) and CFC 𝜚𝜚(𝑡𝑡). With the markings of having two capital stock, it is obvious
that 𝜅𝜅(𝑡𝑡) in (1) equals 𝜅𝜅2(𝑡𝑡). Since the GFCF series has been available since 1995, it is
ECE/CES/GE.20/2024/3
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necessary to apply the imputation function in order to achieve a sufficient level of stock and
CFC since 1995 to satisfy the condition
where 𝑑𝑑:ℳ × ℳ → ℝ+ and consequently 𝜚𝜚(𝑡𝑡) > 0. Following sample of cumulative
historic GFCF data at total levels, capital stock declines backwards at the fixed rate 𝜆𝜆
following differential equation
�̇�𝜌(𝑡𝑡) = −𝜆𝜆𝜌𝜌(𝑡𝑡) with the initial condition 𝑢𝑢(𝑡𝑡0) = 𝜌𝜌0 (14)
12. The solution is 𝑢𝑢(𝑡𝑡) = 𝜌𝜌0𝑒𝑒−𝜆𝜆𝑡𝑡 where 𝜌𝜌0 is capital stock at 𝑡𝑡0. The initial stock is
adjusted to the solution for initial net stock in (5), meaning for 𝛿𝛿 = 0
14. The parameter 𝜆𝜆 = 0.03 is set to avoid shocks at 𝑡𝑡0 due to inflation in prices in 1990s.
The time series taken for the estimation of capital stock and CFC will then consist of the
imputed non-revalued part and the revalued GFCF available from the database. We can write
this as
15. As can be seen from Figure 2, the first step is calculation of the gross capital stock,
which consists of the accumulated GFCF belonging to different vintages as indicated by
squares of different colors and the survival function as indicated by gradually fading gray
color. The formula for gross capital stock is given in the literature as (Biorn, 1989)
16. However, this expression could be challenging for programming, therefore, the
solution to the problem will be transformed to concepts of matrix algebra. For the purposes
of statistical computing, various operations between matrices are nicely summarized in
Gentle (2017) and may be of assistance when deriving procedures. Each GFCF vintage,
starting from 𝑡𝑡0 − 𝑞𝑞 is to be multiplied with the survival function and arranged in columns
that will eventually be transformed into the lower triangular matrix to have accumulation of
the flows. To achieve this, we introduce
where ⊗ is tensor product, and 𝐿𝐿𝑗𝑗=𝑡𝑡−1[⋅] is a column-wise lag operator. The period 𝑡𝑡0 + ℎ is
referred to the last GFGF realization in our database. Starting with
we get the square matrix and where ℎ, 𝑧𝑧 ∈ ℤ+. Applying the lag operator, we get survived
vintages 𝐰𝐰�(𝐭𝐭) : = 𝐿𝐿𝑗𝑗=𝑡𝑡−1[𝑆𝑆𝑇𝑇(𝑡𝑡) ⊗𝑊𝑊(𝑡𝑡)⊤], that is
17. The lag operator transforms the matrix (20) into a lower triangular matrix, ensuring
that each GFCF vintage is placed in the accompanying row, otherwise the GFCF from year
𝑡𝑡 would be recorded in the year 𝑡𝑡 − 1, which is impossible situation. Finally, adding up by
rows 𝑒𝑒, the gross capital stock 𝜅𝜅1(𝑡𝑡) ∈ ℝ𝑛𝑛 is obtained
18. The next step is to estimate the CFC by combining the survival function with the rates
of depreciation function and multiplying such vector with GFCF. Mathematically, one way
to achieve this is through
19. Since 𝑆𝑆𝑇𝑇(𝑡𝑡),𝜑𝜑(𝑡𝑡) ∈ ℝ𝑛𝑛, and with Hadamard product involved, the procedure is the
same as for the gross capital stock. The lower triangular matrix for CFC is then
21. This procedure is given on the right side of Figure 2. The elements of matrix (24) are
flows since the rates of depreciation functions are used. If 𝜑𝜑(𝑡𝑡) is linear depreciation then
𝑆𝑆𝑇𝑇(𝑡𝑡) in (9) is applied and if 𝜑𝜑(𝑡𝑡) is geometric depreciation, then 𝑆𝑆𝑇𝑇(𝑡𝑡) = (1,1, … ,1) is
applied. In other words, only linear depreciation is combined with a log-normal survival
function to achieve an effect similar to geometric depreciation. For net capital stock, an
accumulated 𝜚𝜚(𝑡𝑡) will be needed to satisfy the equation
23. Now, since 𝐰𝐰(𝐭𝐭) : = 𝐿𝐿𝑗𝑗=𝑡𝑡−1[𝟏𝟏(𝑡𝑡) ⊗𝑊𝑊(𝑡𝑡)⊤], we have
26. These procedures can serve for programming solution in a high-level programming
language, such as Python or R. For example, Schmalwasser and Schidlowski (2006) used
Visual Basic for Applications within Excel spreadsheets in their work. In our case, a language
and interface for statistical computing R (R Core Team, 2023) has been used in regular
compilation and is used for the accompanying simulations presented in the following section.
IV. Simulations with possible impact on the gross national
income
27. This part of the paper is, in a way, a continuation of the case study within the EG20-
CFC project where CBS was a beneficiary. In this section, several scenarios will be presented
with respect to different assumptions in the model and different levels of availability in the
database. Assumptions in the model are related to the selection of depreciation function,
while different levels of availability in the database are primarily related to industries and
institutional subsectors. GFCF refers only to the government sector, as any change in the
GFCF or assumptions may have a certain impact on GNI. In the case study of the EG20-CFC
project, the impacts were presented only visually along with qualitative interpretations,
without calculating the impact to any of the aggregate.
Table 1
The type of assets available in Croatian national accounts
ESA ASSET TYPE AVG SERVICE LIFE
AN.111 Dwellings 80 (70)
AN.112 Non-residential buildings (business and
industrial)
50
AN.112 Other structures (infrastructure) 55
AN.112 Land improvements 55
AN.11O Metal products 20
AN.11O General machinery and equipment 20
AN.11O Other special purpose machinery 20
AN.11O Machinery for agriculture 20
AN.1132 Computers and peripherals 6
AN.11O Electrical equipment 15
AN.1132 Communication equipment 6
ECE/CES/GE.20/2024/3
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ESA ASSET TYPE AVG SERVICE LIFE
AN.11O Medical equipment 10
AN.11O Furniture and interior fittings 15
AN.1131 Passenger cars 7
AN.1131 Trucks and commercial vehicles 12
AN.1131 Other means of transport 25
AN.115 Perennial plantations 15
AN.115 Herd (-)
AN.117 Research and development 10
AN.117 Mineral exploration 10
AN.117 Software and database 5
AN.117 Literary and artistic originals 7
AN.11O Other tangible assets 10
AN.117 Other non-tangible assets 7
AN.11O Weapons system 25
28. First and foremost, the difference between the base scenario and some new approach
must be obtained. The base scenario is the one currently in use, referring to the application
of geometric depreciation for dwellings and linear depreciation in combination with the
survival function for all other assets. In addition, the base scenario includes the estimation of
CFC and capital stock at the level of institutional sectors, types of assets and industries. Type
of assets are kept fixed in all cases while different aggregations will be carried out on
industries and sub-sectors, which are discussed in more detail below. The level of detail for
fixed assets is shown in Table 1 and is used as such in all scenarios. Along with the impacts,
a graphical comparison of the results will be shown at the level in the column named ESA.
The level of detail for fixed assets is shown in the column ASSET TYPE and will be used as
such in all scenarios. It should be noted that for the herd, CFC is equal to zero, which means
that the gross stock is equal to the net stock. Average service life for dwellings in the
household sector is estimated at 80 years, and 70 years for dwellings in other sectors. This is
because average service life for household dwellings was estimated on the basis of census,
while for other sectors the recommendations of the Eurostat Task Force for Fixed Capital
were followed.
A. Altering depreciation functions
29. As mentioned earlier, for estimations of capital stock and CFC, it is recommended to
use a convex shape function instead of solely linear function which is normally used in
commercial accounting. This is because the estimation procedures are not undertaken by
individual assets but for cohorts of assets of similar ages and characteristics. Individual assets
within a cohort will retire at different timepoints but the depreciation profile for a cohort as
a whole is typically convex to the origin (SNA, 2008). In this regard, if a linear depreciation
function is used, then it needs to be adjusted for survival pattern derived from, at least in our
case, log-normal probability distribution. A retirement function combined with a linear
depreciation function may produce convex pattern similar to geometric function.
ECE/CES/GE.20/2024/3
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Figure 3
The CFC vectors given from base scenario and new depreciation function
30. Unlike the basic scenario, the alternative one includes the application of linear
depreciation for dwellings and geometric depreciation for other assets. As expected, similar
vector shapes are achieved, but with pronounced differences at certain intervals, depending
on the type of fixed assets. When the results are put in relation to the GNI according to
equation (6), the results are obtained as in Table 2.
Table 2
The impact of changes in depreciation functions on GNI (106 EUR)
31. A certain impact exists for all years, ranging from 0.25% to 0.38%. If the threshold of
0.1% (document GNIC/283) is taken, application of geometric depreciation for all assets
except dwellings, would lead to revision of the time series from 2013 onwards.
B. Different levels of aggregation
32. At some point, statisticians often do not have data at desired classification level. For
example, having a satisfactory level of GFCF by type of assets (which we keep fixed
anyway), but, for some reason, lacking on the level of institutional subsectors or industries.
After some time, data at more detailed levels may become available, and the question is to
what extent the resulting change will affect GNI. For statistical agencies that have well-
organized registers and sufficiently long GFCF time series, this issue is probably not
applicable. Nevertheless, since Croatia lacks sufficiently long time series, this simulation will
serve to compare the results in relation to the currently valid estimation levels of CFC and
capital stock.
ECE/CES/GE.20/2024/3
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Figure 4
GFCF matrices resulting from different levels of aggregation
33. The main issue is shown in Figure 4. Squares in various blue shades indicate GFCFs
different from zero. It is to be expected that at the sector level, the values will differ from
zero since institutions in any subsector and industry have invested in some assets. If data is
filtered for a sub-sector, zeros will appear in some places because, within that sub-sector,
some industries did not invest in certain assets in some years.
Figure 5
The CFC vectors from the sub-sectors aggregation only
34. When some chosen industry is additionally filtered from the subsector, the zeros are
more visible, and such a matrix becomes sparse. Sparse matrices on industry-level filtering
are not uncommon in the case of Croatia, especially if the zeros are at the beginning of
observable time series, i.e. 1995 or 1996. What adjustments are additionally applied due to
this issue will be described below as part of the interpretation of impacts. Figure 5 graphically
presented results when CFC for all types of assets is estimated only by subsectors, avoiding
the level of industries. CFC vectors overlap indicating that almost identical results are
obtained or the difference is negligible. The extent to which the difference is negligible is
presented as an impact on GNI, as in the previous case.
Table 3
The impact on GNI of estimating CFC at the subsectors level (106 EUR)
35. In this simulation, the differences resulting from bypassing the industry level and
calculating CFC only by subsectors of the general government are negligible. We perform
ECE/CES/GE.20/2024/3
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the same procedure but this time on the government sector, meaning without sub-aggregation
of GFCF to sub-sectors and industries. Visually, the results are presented in Figure 6.
Figure 6
The CFC vectors from the sector aggregation only
36. The results are almost identical as in the case of the CFC estimation at the subsector
level, the impact is negligible, as can be seen from the graphic where the vectors overlap.
Numerical results are shown in Table 4.
Table 4
The impact on GNI of estimating CFC at government sector level (106 EUR)
37. Differences are also negligible in terms of percentage of GNI. In both cases, therefore,
the estimation of CFC at the sector and sub-sector level has almost no impact on the GNI.
Such outcomes are probably expected given the adjustments made in the regular CFC
estimation. Namely, according to (15), if the first available GFCF, i.e. for 1995, is equal to
zero, then the imputed values will also be zero. When the time series, obtained by filtering
sectors, sub-sectors and industries from the GFCF database as shown in Figure 4, contains
zeros in the first or first two places, then the initial capital stock is calculated for the first non-
zero value. This will result in the CFC also appearing in, for example, the first two years
where the GFCF is missing, which may seem unusual at first. However, due to (21) and (24),
and assuming that a certain industry within the (sub)sector invested in the assets before 1995,
these assets will survive in gross terms and accumulate into the future. The criterion for the
GFCF offset by a year or two forwards was the analysis of the time series noticing values
other than zero. If the time series contains a majority of zeros, then no offset is applied, and
imputation before 1995 is zero. It seems that this approach has an effect in achieving some
meaningful level of capital stock and CFC in 1995, at least according to (13).
C. Estimated GFCF time series back to 1953
38. Imputation functions for estimating historical GFCF time series can lead to
questionable results, which is especially true for fixed assets with longer average service life.
Accordingly, the backward GFCF reconstruction is based on the data from a series of
statistical yearbooks (Federal Statistical Bureau of Croatia, SFR Yugoslavia, 1954-1990).
However, different concepts of aggregate calculation in a mixed economy system (Croatia
was part of SFR Yugoslavia) as well as the denomination of the domestic currency create
difficulties in reliable GFCF estimation before 1995. From 1945 to 1990 in Yugoslavia, two
ownership systems were constituted: a privileged dominant system of public assets, later
ECE/CES/GE.20/2024/3
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social ownership in which cooperative ownership was gradually included, and a legally and
economically limited system of private ownership mainly for arable agricultural land per
household (Simonetti, 2010). For these reasons, it has been quite difficult to allocate assets
within institutional sectors as requested by ESA 2010 transmission program. Along with the
debatable issue of ownership, the problem with currency denomination requires special
attention and careful analysis. For this reason, two type of fixed assets that are considered to
have been part of government sector in Yugoslavia were taken. It is about infrastructure such
as roads, tunnels, streets, squares, bridges, railways, and buildings such as administrative
buildings, cultural and educational institutions, health facilities, sports halls and similar
facilities. These two sets of data are added up to a single time series and joined with the GFCF
from 1995 onwards as shown in the first graph on the left in Figure 7.
Figure 7
The CFC vectors from historic data on public infrastructure (106 EUR)
39. Significant improvements are included in the GFCF. Since statistical yearbooks of
Yugoslavia 1954–1995 contain data with different denominations and different currencies, it
was difficult to generate meaningful time series. For period 1953–1990, the exchange rate of
the German mark (DEM) was used at intervals of denominations and new currencies, where
jumps in the exchange rate of the Yugoslav and Croatian dinar versus DEM were observed
with the denominations. Since the GFCF data 1991–1994 were in Croatian kuna, they were
converted directly to EUR. Data from 1953–1990 were first converted to DEM and
eventually EUR using fixed conversion rate of the two. The result of the conversions is shown
in Figure 7 with a black graph. It should be taken into account that no revaluation was applied
due to significant fluctuations in the price index of materials and wages in construction
industry in period 1953-1994. Although the estimate of GFCF 1953–1994 in EUR might not
be the most reliable, it could perhaps certainly reflect a more realistic situation compared to
the imputation function under (16) which is actually a rescaled exponential function.
40. The black vector in Figure 7 serves as an input to the CFC estimation. Although data
for infrastructure and non-residential buildings in the government sector are now preliminary
available since 1953, the estimation was approached in two ways; without initial capital stock
and with initial capital stock as shown by the cyan and green vectors. As is clearly visible,
there is a difference between the two since 1995, but it is gradually disappearing. The
inclusion of the imputation function based on GFCF in 1953 has almost no effect, therefore
the impact in Table 5 will be given only for the green vector, meaning with no initial stock
included.
Table 5
The impact of estimating CFC (based on GFCF back to 1953) on GNI
41. The fourth graph from the left shows a comparison of the green vector based on
historical GFCF data with the basic scenario. A significant difference between the two
vectors is observed, most pronounced since 1995 and decreasing towards 2020. According
ECE/CES/GE.20/2024/3
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to Table 5, impacts greater than 0.1% for 2013–2017 are observed, while after 2018 the
impacts decrease.
V. Conclusion
42. Based on the limited GFCF data that serve as inputs for the CFC estimation and capital
stock, a mathematical methodology was presented serving as the basis for programming
solution in the statistical computing language R. That is, apart from regular estimation of
CFC and capital stock being the base scenario, R also served as a tool for simulating different
assumptions in the estimation procedure. The aim was to simulate estimations using different
depreciation functions, to estimate the CFC at different levels of classification, and using
preliminary real data on GFCF since 1953 for infrastructure and non-residential buildings in
the government sector. Results indicate that selection of other depreciation functions and the
introduction of longer time series of GFCF impact the GNI for period 2013-2021. Changes
in depreciation functions from linear to geometric (and vice versa for dwellings) would cause
a significant impact for all years from 2013 to 2021, ranging from 0.25% to 0.38%.
Introduction of a longer GFCF time series has an impact on GNI, the highest in 2013, with
gradual decreasing to be less than 0.1% for years 2019-2021. Regarding the preliminary
estimated GFCF for period 1953–1994, a caveat should be taken into account due to historical
currency denominations along with the GFCF recording in several currencies through all
years. In addition, assets revaluation was not carried out due to galloping price indices in
certain periods. Finally, further challenging analyses on the GFCF estimation before 1995,
with focus on assets with longer average service life, are certainly needed while this paper is
a preliminary step in considering any updates in regular methodological procedures.
ECE/CES/GE.20/2024/3
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Statistical yearbooks of Federal Statistical Bureau of Croatia, SFR Yugoslavia 1954-1990
относится к периоду 2013–2021 годов. Перед анализом сценариев будут описаны
источники данных и методология, на основе которой выведен алгоритм оценки ПОК
и запасов капитала. Математические действия представляют собой обобщенный
расчет запасов и потоков с учетом ограниченности временных рядов ВНОК по их
продолжительности.
II. Источники данных
3. Сбор данных по ВНОК в текущих ценах для государственного сектора,
сведенных в базу данных, осуществляется из нескольких источников. Основным
источником является отчет о доходах и расходах (PR-RAS), из которого можно извлечь
данные о расходах, понесенных в связи с инвестициями в различные фиксированные
активы. Кроме того, существуют также статьи доходов от выбытия основных фондов,
что соответствует определению ВНОК. Другие источники представлены годовой
финансовой отчетностью предпринимателей и некоммерческих организаций, которые
относятся к государственному сектору. В годовых финансовых ведомостях (ГФВ)
содержатся данные из балансов активов и пассивов и счетов прибылей и убытков
отчетных единиц. Фиксированные активы определяются в отдельной части ГФВ, при
этом уровень вида активов описан менее подробно по сравнению с PR-RAS.
Корректировки применяются в отношении ресекторизации, исследований и
разработок, а также программного обеспечения. Активы ассоциируются с внутренним
кодом, обеспечивающим их уникальную идентификацию. Связав внутренний код и
год инвестиций, можно создать первичный ключ для объединения индексов цен за
предыдущий год (и, соответственно, объемов цепочек) с индексами переоценки
активов для расчета ПОК и запасов капитала. Это позволяет автоматически
рассчитывать ВНОК в различных ценовых концепциях, которые в конечном итоге
будут сгруппированы в классификацию AN (кодов для нефинансовых активов), как
это требуется согласно ЕСС 2010. Для оценки ПОК и запасов капитала используется
ВНОК после переоценки, полученный как произведение ВНОК в исторических ценах
и индекса переоценки. Значение 𝐼(𝑘, 𝜏) в результате переоценки до года 𝜏
с использованием индексов переоценки �̃�(𝜏) равно (с поправкой на Van den Bergen
20. Эта процедура представлена в правой части рис. 2. Элементами матрицы (24)
являются потоки, так как используются функции норм амортизации. Если 𝜑(𝑡)
представляет собой линейную функцию амортизации, то в формуле (9) применяется
𝑆𝑇(𝑡), а если 𝜑(𝑡) является геометрической функцией амортизации, то применяется
𝑆𝑇(𝑡) = (1,1,… ,1). Иными словами, только линейная функция амортизации сочетается
с логнормальной функцией сохранения для достижения эффекта, схожего с
геометрической функцией амортизации. Для чистых запасов капитала необходимо
накопление 𝜚(𝑡), чтобы соответствовать уравнению
2. La solution à laquelle on parvient finalement implique en fait que, pour estimer le
stock initial net de capital, il est nécessaire de connaître la première valeur disponible 𝜄(𝑡0)
de la FBCF, le taux d’amortissement 𝛿 et le taux de croissance à long terme 𝜆. L’équation (5)
peut constituer un élément d’estimation très important, car le stock initial repose uniquement
sur la première valeur disponible de la FBCF dans la série chronologique, à supposer que les
taux de croissance et d’amortissement soient considérés comme fiables. Bien que le modèle
puisse produire des résultats satisfaisants, les organismes de statistique doivent souvent faire
face à d’autres limitations telles que la longueur de la série chronologique de données
relatives à la FBCF, la fiabilité de détermination de la durée de vie moyenne et le choix de
fonctions d’amortissement et de survie appropriées. Les utilisateurs potentiels de données sur
le stock de capital sont également conscients de ces limitations. Par exemple, Burda et
Severgnini (2008) ont souligné dans leurs travaux que la précision de la méthode habituelle
de mesure de la croissance de la productivité totale des facteurs était mal connue, en
particulier lorsque le stock de capital était imparfaitement mesuré. Dans le cas où la série
chronologique de données relatives à la FBCF n’est pas suffisamment longue, différentes
imputations sont souvent utilisées. La base de données sur la FBCF établie aux fins de la
comptabilité nationale croate regroupe actuellement les observations réalisées à partir de
1995, tandis que des fonctions d’imputation sont utilisées pour établir une estimation
rétrospective sur la base d’un taux de croissance constant de l’investissement. Toutefois, cela
peut poser problème, comme l’ont montré Pionnier, Zinni et Baret (2023) dans leur étude,
dans laquelle ils ont constaté que l’estimation du stock initial de capital sur la base d’un taux
de croissance constant pouvait conduire à des résultats peu fiables. En outre, des études
préliminaires sur le niveau global de la FBCF (incluant donc tous les secteurs), réalisées dans
le cadre d’un projet de l’UE (Motik, 2023), ont montré que le choix de la fonction
d’amortissement influait dans une certaine mesure sur le niveau du stock de capital et de la
CCF. Tous ces éléments conduisent à s’interroger sur le RNB, raison pour laquelle les trois
scénarios qui suivent seront examinés. Le premier scénario est lié à la CCF résultant de
fonctions d’amortissement géométriques et linéaires, où l’amortissement linéaire est combiné
à une fonction de survie. Le deuxième scénario porte sur l’estimation de la CCF à différents
niveaux d’agrégation, sachant que le stock initial net de capital 𝜅(𝑡0) dépend en fait de la
première valeur disponible de la FBCF, comme le montre la figure 5. Il arrive souvent que
les organismes de statistique ne disposent pas de données à un niveau d’agrégation
suffisamment détaillé, par exemple lorsque les données relatives à la FBCF ne sont pas
disponibles au niveau des sous-secteurs de l’administration publique ou à celui des branches
d’activité. Comme indiqué précédemment, la longueur insuffisante des séries chronologiques
de données relatives à la FBCF constitue un problème courant dans l’estimation de la CCF
et du stock de capital, d’où l’utilisation de différentes imputations. Toutefois, certaines séries
historiques de données relatives à la FBCF peuvent être reconstituées par la suite, là où
d’importantes différences peuvent apparaître. Il s’agit du troisième scénario, dans lequel la
FBCF liée aux infrastructures et aux bâtiments non résidentiels est estimée sur la base des
données historiques. Chacun des scénarios peut avoir une incidence sur le RNB, qui est la
base des contributions au budget de l’UE. Cette incidence est représentée au moyen de la
Dans cette formule, 𝒯[𝑘](𝑡), 𝑘 = {1,2,3}, désigne une séquence d’opérateurs ayant un effet
sur le vecteur de la FBCF réévaluée 𝐼(𝑡), ce qui donne un ensemble de trois nouveaux
vecteurs : le stock brut de capital 𝜅1(𝑡), le stock net de capital 𝜅2(𝑡) et la CCF 𝜚(𝑡).
Étant donné qu’il existe deux stocks de capital, il est évident que dans (1), 𝜅(𝑡) est égal
à 𝜅2(𝑡). La série de données relatives à la FBCF étant disponible à partir de 1995, il est
nécessaire d’appliquer une fonction d’imputation afin d’obtenir à partir de cette date un
niveau de stock et de CCF suffisant pour satisfaire à la condition suivante :
nous obtenons une matrice carrée et déterminons ℎ, 𝑧 ∈ ℤ+. En appliquant l’opérateur retard,
nous obtenons les montants annuels survivants �̃�(𝐭) : = 𝐿𝑗=𝑡−1[𝑆𝑇(𝑡) ⊗𝑊(𝑡)⊤], à savoir :
20. Ce processus est décrit dans la partie droite de la figure 2. Les éléments de la
matrice (24) sont des flux puisque les fonctions d’amortissement sont utilisées. Si 𝜑(𝑡) est
un amortissement linéaire, 𝑆𝑇(𝑡) défini dans (9) est appliqué, et s’il s’agit d’un amortissement
géométrique, c’est alors 𝑆𝑇(𝑡) = (1,1, … ,1) qui est appliqué. Autrement dit, seul
l’amortissement linéaire est combiné à une fonction de survie log-normale pour obtenir un
effet similaire à l’amortissement géométrique. S’agissant du stock net de capital, un 𝜚(𝑡)
cumulé sera nécessaire pour résoudre l’équation suivante :
Dear Correspondent,
Thank you for contributing to the Joint Forest Sector Questionnaire (JFSQ). Before filling in the worksheets, please read these guidelines.
Please use only this questionnaire to report your data.
Use this questionnaire also to revise any historical data - fill in the correct year and your name on the cover page.
The total number of sheets to be filled in is seven core sheets (green tabs - to be validated by Eurostat) plus three for ITTO (brown tabs - not validated by Eurostat). Four sheets containing cross-references are included at the end. The flat file is for Eurostat for validation purposes, please do not change any cells here. Also, please do not add / delete rows in any of the sheets, because this will affect the functioning of the flatfile.
Put all your data into one Excel file. If you send some data in later, give your file a new version number and date (see A.1. below) and notify us of the changes with respect to the previous version. Only send us completely filled-in sheets, highlighting the changes in yellow. Do not delete worksheets.
Each sheet has a working area for your input. Most sheets have checking cells and tables.
Each working area has white cells and shaded green cells. Eurostat has highlighted the variables it considers most important for its publications - please fill those in as a priority.
When you submit a revision, please highlight changes in yellow and explain them in the appropriate 'Note' column, but please fill in all the cells that were filled in previously.
Please use flags and notes (see A.6 below). This information is important for Eurostat.
A. General recommendations
A.1 Please use eDAMIS to send your questionnaire to Eurostat. Choose the correct domain ("FOREST_A_A") and the correct reference year (for this data collection: 2022).
A.2 Fill in the JFSQ quality report each year.
A.3 The cover page is for your contact details, which are automatically copied to the other worksheets
• Check your country code
• If necessary change the reference year as appropriate - the previous year will appear automatically
If you distribute worksheets to various experts, they can each put their contact details into the sheets. It will then be your job to put all the information together again and to verify the checking tables, since some of them will not work as designed in isolation.
A.4 Look at the unit of measurement to be used for each item and report in this unit if possible, using the conversion factors on the last page of the JFSQ definitions. Please report the monetary values in the same unit for both reporting years.
Only report data or modify cells in the working areas. Please do not delete checking areas or checking sheets.
• Look at the checking areas and make the necessary corrections to your data to remove all warnings (see the specific recommendations) before sending in your data.
Fill in real zeros '0' in the worksheets if there is no production or trade. Empty cells will be interpreted as 'Data not available'.
• There are counters at the bottom of the tables to indicate the number of cells left to be filled in.
Report all data with at least three decimals. Do not use a separator for thousands; for the decimal point, please use the one set up by default.
A.5 Report numbers only. If data are confidential, please provide them if possible, appropriately flagged (see A.6).
• Eurostat has a right to all confidential data necessary for its work. It has an obligation to use such data only in aggregates and to respect all the legal obligations.
• If you cannot provide confidential data, a good option is to send in your own estimate flagged as a national estimate '9'.
• As a last resort, leave the cell empty, flag it and write a note indicating data sources and links.
Checking tables contain formulae to sum up the totals for sub-items.
A.6 Flag cells and write notes as appropriate. Flags should be entered in the 'Flag' columns and notes in the 'Note' columns for the appropriate year and item. The flags to use are:
• 3 for break in time series, see metadata (please explain in the notes and in the quality report the reasons)
• 4 for definition differs, see metadata (please explain in the notes and in the quality report the reasons)
• 5 for repeating the data of a previous year
• 6 for confidential data
• 7 for provisional data
• 9 for national estimate
B Specific recommendations
B.1 Sheet 'Removals over bark' is for volumes of wood products measured over bark. General over bark/under bark conversion factors are calculated automatically.
• Should you use different conversion factor(s) please delete the ones provided and insert your own
• If you only have under bark data, please leave this worksheet empty, but revise the table with the conversion factors.
• Unchanged conversion factors will be considered revised.
A checking table verifies that sums of sub-items agree with the totals.
B.2 Checking tables on worksheets improve data quality, verifying that:
• The sum of the sub-items equals the total.
• The sum of 'of which' items is not larger than the total.
All cells in a checking table should be zero or empty. If this is not the case, please check your numbers for the sub-items and totals. The checking table indicates the difference, so if you see a negative value, you will have to decide which number should be increased by that amount. The only exception is when no data are entered due to confidentiality.
B.3 Worksheets 'JQ2' contains a checking table for apparent consumption.
Apparent consumption = Production + Imports – Exports. It should be positive or nil. If this is not the case, the cell will change colour and indicate the difference.
• Please correct the data in the sheets until checking results are positive or nil. One solution is to increase production.
• If the data are correct but apparent consumption is still negative, please explain why in the 'Note' column provided in the apparent consumption checking table.
B.4 Sheets 'JQ2', 'ECE-EU Species' and 'EU1' on trade have checking tables to verify data consistency. Both quantity and value must be present.
When something is missing, messages or coloured cells appear in the checking tables. Please correct your data until all warnings disappear.
The meaning of the messages is:
• 0: both value and quantity are zero – all is well, there is no trade
• ZERO Q: value is reported, quantity is zero - please correct
• ZERO V: quantity is reported, value is zero - please correct
• REPORT: both quantity and value are blank - please fill in
• NO Q: blank cell for quantity – please fill in
• NO V: blank cell for value – please fill in
Please enter even very small numbers to resolve problems, using as many decimal places as necessary.
If there is no way to correct the problem, please write an explanation in the 'Note' column.
If there is no trade for a product, please enter 0 for both quantity and value.
Thank you for collecting data for the JFSQ,
Eurostat's Forestry Team
JFSQ quality report
Joint Forest Sector Questionnaire
Quality Report
Quality information
Country reply
1
Contact
Country name
Country name
Contact organisation
Contact organisation
Contact name
Contact name
Contact email address
Contact email address
2
Changes to previous year
Necessity of update
Are there any changes to the quality report of the last data collection?
Please select YES or NO
If yes, please provide details below.
3
Statistical processing
Overview of the source data
Please provide an overview of the sources used to produce JFSQ data.
Do you use a dedicated survey (of the industry, of households, of forest owners, etc.)?
Please select YES or NO
If yes, please provide details (e.g., who are the respondents, what is its frequency?).
Do you use forestry statistics?
Please select YES or NO
If yes, please provide details.
Do you use national forest inventory?
Please select YES or NO
If yes, please provide details.
Do you use national PRODCOM data compiled according to the CPA classification?
Please select YES or NO
If yes, please provide details (which products, units, etc.).
Do you use any other national production statistics?
Please select YES or NO
If yes, please provide details.
Do you use data collected by associations of industry?
Please select YES or NO
If yes, please provide details.
Do you collect data from direct contacts with manufacturing companies?
Please select YES or NO
If yes, please provide details.
Do you use estimates of roundwood use (in manufacturing)?
Please select YES or NO
If yes, please provide details.
Do you use national trade data?
Please select YES or NO
If yes, please provide details.
Do you use felling reports?
Please select YES or NO
If yes, please provide details.
Do you use forestry companies' accounting network?
Please select YES or NO
If yes, please provide details.
Do you use administrative data (e.g. tax records, business registers)?
Please select YES or NO
If yes, please provide details.
Do you use data from national accounts?
Please select YES or NO
If yes, please provide details (e.g. for which data, from which account tables?).
Do you use SBS (Structural business statistics)?
Please select YES or NO
If yes, please provide details (e.g. for which data?).
Do you use other environmental accounts?
Please select YES or NO
If yes, please provide details.
Do you use other statistics (e.g. agriculture statistics)?
Please select YES or NO
If yes, please specify them.
Do you use any other sources?
Please select YES or NO
If yes, please specify them.
Methodological issues
Are there any pending classification or measurement issues?
Please select YES or NO
If yes, please specify them.
Data validation
Do you check the quality of the data collected to compile JFSQ?
Please select YES or NO
If yes, please explain the quality assurance procedure.
Do you compare JFSQ data with different data sources or do you perform other cross-checks?
Please select YES or NO
If yes, please explain your approach.
Do you have validation rules and other plausibility checks for the outputs of your JFSQ data compilation process?
Please select YES or NO
If yes, please briefly describe them.
4
Relevance
User needs
Please provide references to the relevance of JFSQ at national level e.g. main users, national indicator sets, quantitative policy targets etc.
5
Coherence and comparability
Coherence - cross domain
Do you compare the JFSQ results with business, energy and agricultural and foreign trade statistics?
Please select YES or NO
It not, please explain.
Do you cross-check the JFSQ data with the results of European Forest Accounts?
Please select YES or NO
If yes, please indicate for which reporting items, and comments on the discrepancies observed, if any. It not, please explain.
Coherence - internal
Are there any other consistency issues related to your JFSQ data?
Please select YES or NO
If yes, please explain them.
6
Accessibility and clarity
Publications
Do you disseminate JFSQ data nationally (e.g. in news releases or other documents)?
Please select YES or NO
If yes, please provide URLs and/or the reference to the relevant publications.
Online database
Do you publish your JFSQ accounts in an online data base?
Please select YES or NO
If yes, please provide URLs.
Documentation on methodology
Did you prepare a description of your national JFSQ methodology or metadata?
Please select YES or NO
If yes, please provide URLs.
Quality documentation
Do you have national quality documentation?
Please select YES or NO
If yes, please provide URLs.
7
Other comments
Other comments
Please provide any further feedback you might have on the quality of the reported data, sources and methods used and/or Eurostat's validation and quality report templates.
Cover
Joint Forest Sector Questionnaire
2022
DATA INPUT FILE
Correspondent country:
HR
Reference year:
2022
Fill in the year
Name of person responsible for reply:
Official address (in full):
Branimirova 19, 10000 Zagreb, Croatia
Telephone:
Fax:
E-mail:
Removals over bark
Country:
HR
Date:
Name of Official responsible for reply:
0
Check Table
Official Address (in full):
Branimirova 19, 10000 Zagreb, Croatia
FOREST SECTOR QUESTIONNAIRE
EU JQ1 OB
Telephone:
0
0
Discrepancies
Removals
E-mail:
0
Please verify, if there's an error!
Year 1
Year 2
Flag
Flag
Note
Note
Product
Product
Unit
2021
2022
2021
2022
2021
2022
Product
Product
Unit
2021
2022
Code
Quantity
Quantity
Code
Quantity
Quantity
ROUNDWOOD REMOVALS OVERBARK
ROUNDWOOD REMOVALS OVERBARK
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ob
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ob
OK
OK
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ob
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ob
OK
OK
1.1.C
Coniferous
1000 m3ob
1.1.C
Coniferous
1000 m3ob
1.1.NC
Non-Coniferous
1000 m3ob
1.1.NC
Non-Coniferous
1000 m3ob
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ob
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ob
OK
OK
1.2.C
Coniferous
1000 m3ob
1.2.C
Coniferous
1000 m3ob
OK
OK
1.2.NC
Non-Coniferous
1000 m3ob
1.2.NC
Non-Coniferous
1000 m3ob
OK
OK
1.2.NC.T
of which: Tropical
1000 m3ob
1.2.NC.T
of which: Tropical
1000 m3ob
OK
OK
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3ob
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3ob
OK
OK
1.2.1.C
Coniferous
1000 m3ob
1.2.1.C
Coniferous
1000 m3ob
1.2.1.NC
Non-Coniferous
1000 m3ob
1.2.1.NC
Non-Coniferous
1000 m3ob
1.2.2
PULPWOOD, ROUND AND SPLIT
1000 m3ob
1.2.2
PULPWOOD, ROUND AND SPLIT
1000 m3ob
OK
OK
1.2.2.C
Coniferous
1000 m3ob
1.2.2.C
Coniferous
1000 m3ob
1.2.2.NC
Non-Coniferous
1000 m3ob
1.2.2.NC
Non-Coniferous
1000 m3ob
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3ob
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3ob
OK
OK
1.2.3.C
Coniferous
1000 m3ob
1.2.3.C
Coniferous
1000 m3ob
1.2.3.NC
Non-Coniferous
1000 m3ob
1.2.3.NC
Non-Coniferous
1000 m3ob
To fill:
17
17
Product
Product
Unit
2021
2022
Code
CF
CF
OVERBARK/UNDERBARK CONVERSION FACTORS
1
ROUNDWOOD (WOOD IN THE ROUGH)
m3/m3
0.000
0.000
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
m3/m3
0.000
0.000
1.1.C
Coniferous
m3/m3
0.000
0.000
1.1.NC
Non-Coniferous
m3/m3
0.000
0.000
1.2
INDUSTRIAL ROUNDWOOD
m3/m3
1.100
1.100
1.2.C
Coniferous
m3/m3
1.200
1.200
1.2.NC
Non-Coniferous
m3/m3
0.000
0.000
1.2.NC.T
of which: Tropical
m3/m3
ERROR:#DIV/0!
ERROR:#DIV/0!
1.2.1
SAWLOGS AND VENEER LOGS
m3/m3
0.000
0.000
1.2.1.C
Coniferous
m3/m3
0.000
0.000
1.2.1.NC
Non-Coniferous
m3/m3
1.100
1.100
1.2.2
PULPWOOD, ROUND AND SPLIT
m3/m3
1.200
1.200
1.2.2.C
Coniferous
m3/m3
0.000
0.000
1.2.2.NC
Non-Coniferous
m3/m3
1.100
1.100
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
m3/m3
1.200
1.200
1.2.3.C
Coniferous
m3/m3
0.000
0.000
1.2.3.NC
Non-Coniferous
m3/m3
1.100
1.100
JQ1 Production
Country:
HR
Date:
Name of Official responsible for reply:
0
Official Address (in full):
FOREST SECTOR QUESTIONNAIRE JQ1
Branimirova 19, 10000 Zagreb, Croatia
Industrial Roundwood Balance
PRIMARY PRODUCTS
Telephone:
0
0
This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!
Discrepancies
Removals and Production
E-mail:
0
test for good numbers, missing number, bad number, negative number
Year 1
Year 2
Flag
Flag
Note
Note
Product
Product
Unit
2021
2022
2021
2022
2021
2022
Product
Product
Unit
2021
2022
2021
2022
% change
Conversion factors
Code
Quantity
Quantity
Code
Quantity
Quantity
Roundwood
Industrial roundwood availability
2,667
-34,400
-1390%
m3 of wood in m3 or t of product
ALL REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
ALL REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
Recovered wood used in particle board
80
70
-12%
Solid wood equivalent
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
4986
5343
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
OK
OK
Solid Wood Demand
agglomerate production
352
388
10%
2.4
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
2130
2604
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
OK
OK
Sawnwood production
1,298
1,163
-10%
1
1.1.C
Coniferous
1000 m3ub
50
43
1.1.C
Coniferous
1000 m3ub
veneer production
35
37
6%
1
1.1.NC
Non-Coniferous
1000 m3ub
2080
2561
1.1.NC
Non-Coniferous
1000 m3ub
plywood production
2
1
-41%
1
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
2856
2739
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
OK
OK
particle board production (incl OSB)
229
201
-12%
1.58
1.2.C
Coniferous
1000 m3ub
741
614
1.2.C
Coniferous
1000 m3ub
OK
OK
fibreboard production
2
2
4%
1.8
1.2.NC
Non-Coniferous
1000 m3ub
2115
2125
1.2.NC
Non-Coniferous
1000 m3ub
OK
OK
mechanical/semi-chemical pulp production
48
44
-8%
2.5
1.2.NC.T
of which: Tropical
1000 m3ub
0
0
1.2.NC.T
of which: Tropical
1000 m3ub
OK
OK
chemical pulp production
0
0
missing data
4.9
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3ub
2394
2426
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3ub
OK
OK
dissolving pulp production
0
0
missing data
5.7
1.2.1.C
Coniferous
1000 m3ub
517
461
1.2.1.C
Coniferous
1000 m3ub
Availability
Solid Wood Demand
2,664
2,563
-4%
1.2.1.NC
Non-Coniferous
1000 m3ub
1877
1965
1.2.1.NC
Non-Coniferous
1000 m3ub
Difference (roundwood-demand)
43
-36,377
-84306%
positive = surplus
1.2.2
PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)
1000 m3ub
458
310
1.2.2
PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)
1000 m3ub
OK
OK
gap (demand/availability)
0%
107%
Negative number means not enough roundwood available
1.2.2.C
Coniferous
1000 m3ub
222
151
1.2.2.C
Coniferous
1000 m3ub
Positive number means more roundwood available than demanded
1.2.2.NC
Non-Coniferous
1000 m3ub
236
159
1.2.2.NC
Non-Coniferous
1000 m3ub
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3ub
4
3
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3ub
OK
OK
1.2.3.C
Coniferous
1000 m3ub
3
2
1.2.3.C
Coniferous
1000 m3ub
% of particle board that is from recovered wood
35%
1.2.3.NC
Non-Coniferous
1000 m3ub
1
1
1.2.3.NC
Non-Coniferous
1000 m3ub
share of agglomerates produced from industrial roundwood residues
100%
PRODUCTION
PRODUCTION
usable industrial roundwood - amount of roundwood that is used, remainder leaves industry
98.5%
2
WOOD CHARCOAL
1000 t
13
10.2
2
WOOD CHARCOAL
1000 t
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
449
358.2
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
OK
OK
3.1
WOOD CHIPS AND PARTICLES
1000 m3
449
358.2
3.1
WOOD CHIPS AND PARTICLES
1000 m3
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
0
0.0
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
3.2.1
of which: Sawdust
1000 m3
0
0.0
3.2.1
of which: Sawdust
1000 m3
OK
OK
4
RECOVERED POST-CONSUMER WOOD
1000 t
0
0.0
4
RECOVERED POST-CONSUMER WOOD
1000 t
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 t
352
388.1
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 t
OK
OK
5.1
WOOD PELLETS
1000 t
306
330.2
5.1
WOOD PELLETS
1000 t
5.2
OTHER AGGLOMERATES
1000 t
46
57.9
5.2
OTHER AGGLOMERATES
1000 t
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
1298
1,163.1
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
OK
OK
6.C
Coniferous
1000 m3
257
139.5
6.C
Coniferous
1000 m3
6.NC
Non-Coniferous
1000 m3
1,042
1,023.6
6.NC
Non-Coniferous
1000 m3
6.NC.T
of which: Tropical
1000 m3
0
0.0
6.NC.T
of which: Tropical
1000 m3
OK
OK
7
VENEER SHEETS
1000 m3
35
37.5
7
VENEER SHEETS
1000 m3
OK
OK
7.C
Coniferous
1000 m3
0
0.0
7.C
Coniferous
1000 m3
7.NC
Non-Coniferous
1000 m3
35
37.5
7.NC
Non-Coniferous
1000 m3
7.NC.T
of which: Tropical
1000 m3
0
0.0
7.NC.T
of which: Tropical
1000 m3
OK
OK
8
WOOD-BASED PANELS
1000 m3
233
204.0
8
WOOD-BASED PANELS
1000 m3
OK
OK
8.1
PLYWOOD
1000 m3
2
1.3
8.1
PLYWOOD
1000 m3
OK
OK
8.1.C
Coniferous
1000 m3
1
0.2
8.1.C
Coniferous
1000 m3
8.1.NC
Non-Coniferous
1000 m3
1
1.0
8.1.NC
Non-Coniferous
1000 m3
8.1.NC.T
of which: Tropical
1000 m3
0
0.0
8.1.NC.T
of which: Tropical
1000 m3
OK
OK
8.1.1
of which: Laminated Veneer Lumber (LVL)
1000 m3
0
0.0
8.1.1
of which: Laminated Veneer Lumber (LVL)
1000 m3
OK
OK
8.1.1.C
Coniferous
1000 m3
0
0.0
8.1.1.C
Coniferous
1000 m3
8.1.1.NC
Non-Coniferous
1000 m3
0
0.0
8.1.1.NC
Non-Coniferous
1000 m3
8.1.1.NC.T
of which: Tropical
1000 m3
0
0.0
8.1.1.NC.T
of which: Tropical
1000 m3
OK
OK
8.2
PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD
1000 m3
229
200.6
8.2
PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD
1000 m3
8.2.1
of which: ORIENTED STRAND BOARD (OSB)
1000 m3
0
0.0
8.2.1
of which: ORIENTED STRAND BOARD (OSB)
1000 m3
OK
OK
8.3
FIBREBOARD
1000 m3
2
2.1
8.3
FIBREBOARD
1000 m3
OK
OK
8.3.1
HARDBOARD
1000 m3
0
0.0
8.3.1
HARDBOARD
1000 m3
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
0
0.0
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
8.3.3
OTHER FIBREBOARD
1000 m3
2
2.1
8.3.3
OTHER FIBREBOARD
1000 m3
9
WOOD PULP
1000 t
48
43.8
9
WOOD PULP
1000 t
OK
OK
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 t
48
43.8
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 t
9.2
CHEMICAL WOOD PULP
1000 t
0
0.0
9.2
CHEMICAL WOOD PULP
1000 t
OK
OK
9.2.1
SULPHATE PULP
1000 t
0
0.0
9.2.1
SULPHATE PULP
1000 t
9.2.1.1
of which: BLEACHED
1000 t
0
0.0
9.2.1.1
of which: BLEACHED
1000 t
OK
OK
9.2.2
SULPHITE PULP
1000 t
0
0.0
9.2.2
SULPHITE PULP
1000 t
9.3
DISSOLVING GRADES
1000 t
0
0.0
9.3
DISSOLVING GRADES
1000 t
10
OTHER PULP
1000 t
0
0.0
10
OTHER PULP
1000 t
OK
OK
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 t
0
0.0
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 t
10.2
RECOVERED FIBRE PULP
1000 t
0
0.0
10.2
RECOVERED FIBRE PULP
1000 t
11
RECOVERED PAPER
1000 t
0
0.0
11
RECOVERED PAPER
1000 t
12
PAPER AND PAPERBOARD
1000 t
374
353.2
12
PAPER AND PAPERBOARD
1000 t
OK
OK
12.1
GRAPHIC PAPERS
1000 t
0
0.0
12.1
GRAPHIC PAPERS
1000 t
OK
OK
12.1.1
NEWSPRINT
1000 t
0
0.0
12.1.1
NEWSPRINT
1000 t
12.1.2
UNCOATED MECHANICAL
1000 t
0
0.0
12.1.2
UNCOATED MECHANICAL
1000 t
12.1.3
UNCOATED WOODFREE
1000 t
0
0.0
12.1.3
UNCOATED WOODFREE
1000 t
12.1.4
COATED PAPERS
1000 t
0
0.0
12.1.4
COATED PAPERS
1000 t
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 t
0
0.0
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 t
12.3
PACKAGING MATERIALS
1000 t
374
353.2
12.3
PACKAGING MATERIALS
1000 t
OK
OK
12.3.1
CASE MATERIALS
1000 t
229
217.0
12.3.1
CASE MATERIALS
1000 t
12.3.2
CARTONBOARD
1000 t
1
0.1
12.3.2
CARTONBOARD
1000 t
12.3.3
WRAPPING PAPERS
1000 t
145
136.2
12.3.3
WRAPPING PAPERS
1000 t
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 t
0
0.0
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 t
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
1000 t
0
0.0
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
1000 t
15
GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1
1000 m3
0
0.0
15
GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1
1000 m3
OK
OK
15.1
GLULAM
1000 m3
0
0.0
15.1
GLULAM
1000 m3
15.2
CROSS-LAMINATED TIMBER (CLT or X-LAM)
1000 m3
0
0.0
15.2
CROSS-LAMINATED TIMBER (CLT or X-LAM)
1000 m3
16
I BEAMS (I-JOISTS)1
1000 t
0
0.0
16
I BEAMS (I-JOISTS)1
1000 t
1 Glulam, CLT and I Beams are classified as secondary wood products but for ease of reporting are included here
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
E-mail:
0
Country:
HR
NO V
no value reported
Treshold:
2
verifies whether the JQ2 figures refers only to intra-EU trade
Value must always be in 1000 NAC (national currency)
Flag
Flag
Flag
Flag
Flag
Flag
Flag
Flag
Note
Note
Note
Note
Note
Note
Note
Note
Trade
Discrepancies
REPORT
no figures reported
Product
Unit of
I M P O R T
E X P O R T
Import
Export
Import
Export
Product
I M P O R T
E X P O R T
Product
Apparent Consumption
Related Notes
Product
Value per
I M P O R T
E X P O R T
Column1
Column2
Product
Value per
I M P O R T
E X P O R T
code
Product
quantity
2021
2022
2021
2022
2021
2022
2021
2022
2021
2022
2021
2022
code
2021
2022
2021
2022
code
2021
2022
2021
2022
code
Product
unit
2021
2022
2021
2022
IMPORT
EXPORT
code
Product
unit
2021
2022
2021
2022
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
261.92
20993.85
378.07
42536.25
1014.83
103003.76
853.43
143432.53
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
OK
OK
OK
OK
OK
OK
OK
OK
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
4,233
4,867
1
ROUNDWOOD (WOOD IN THE ROUGH)
NAC/m3
80
113
101
168
ACCEPT
ACCEPT
1
ROUNDWOOD (WOOD IN THE ROUGH)
NAC/m3
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
108.17
8568.77
96.09
10996.32
676.21
53443.21
547.38
71869.42
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
1,562
2,153
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
NAC/m3
79
114
79
131
ACCEPT
ACCEPT
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
NAC/m3
1.1.C
Coniferous
1000 m3ub
1.59
186.24
23.73
1711.25
49.60
4390.80
45.01
5611.57
1.1.C
Coniferous
1000 m3ub
1.1.C
Coniferous
1000 m3ub
2
22
1.1.C
Coniferous
NAC/m3
117
72
89
125
ACCEPT
ACCEPT
1.1.C
Coniferous
NAC/m3
1.1.NC
Non-Coniferous
1000 m3ub
106.59
8382.52
72.37
9285.07
626.61
49052.40
502.36
66257.86
1.1.NC
Non-Coniferous
1000 m3ub
1.1.NC
Non-Coniferous
1000 m3ub
1,560
2,131
1.1.NC
Non-Coniferous
NAC/m3
79
128
78
132
ACCEPT
ACCEPT
1.1.NC
Non-Coniferous
NAC/m3
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
153.75
12425.08
281.98
31539.93
338.62
49560.55
306.05
71563.11
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
OK
OK
OK
OK
OK
OK
OK
OK
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
2,671
2,715
1.2
INDUSTRIAL ROUNDWOOD
NAC/m3
81
112
146
234
ACCEPT
ACCEPT
1.2
INDUSTRIAL ROUNDWOOD
NAC/m3
1.2.C
Coniferous
1000 m3ub
64.03
4282.41
153.32
14289.48
47.26
2604.99
70.41
7808.67
1.2.C
Coniferous
1000 m3ub
1.2.C
Coniferous
1000 m3ub
758
697
1.2.C
Coniferous
NAC/m3
67
93
55
111
ACCEPT
CHECK
1.2.C
Coniferous
NAC/m3
1.2.NC
Non-Coniferous
1000 m3ub
89.72
8142.67
128.66
17250.45
291.36
46955.56
235.64
63754.44
1.2.NC
Non-Coniferous
1000 m3ub
1.2.NC
Non-Coniferous
1000 m3ub
1,913
2,018
1.2.NC
Non-Coniferous
NAC/m3
91
134
161
271
ACCEPT
ACCEPT
1.2.NC
Non-Coniferous
NAC/mt
1.2.NC.T
of which: Tropical1
1000 m3ub
0.03
9.14
0.04
17.83
1.30
212.01
1.15
210.20
1.2.NC.T
of which: Tropical1
1000 m3ub
OK
OK
OK
OK
OK
OK
OK
OK
1.2.NC.T
of which: Tropical1
1000 m3ub
-1
-1
1.2.NC.T
of which: Tropical
NAC/m3
285
491
163
182
ACCEPT
ACCEPT
1.2.NC.T
of which: Tropical
1000 m3
2
WOOD CHARCOAL
1000 t
14.60
4727.81
9.23
4380.56
17.31
8454.01
10.72
6389.08
2
WOOD CHARCOAL
1000 t
2
WOOD CHARCOAL
1000 t
10
9
2
WOOD CHARCOAL
NAC / t
324
475
488
596
ACCEPT
ACCEPT
2
WOOD CHARCOAL
1000 m3
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
224.85
5243.80
109.23
6537.28
432.89
14568.91
258.20
16987.40
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
241
209
3
WOOD CHIPS, PARTICLES AND RESIDUES
NAC/m3
23
60
34
66
CHECK
ACCEPT
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
3.1
WOOD CHIPS AND PARTICLES
1000 m3
63.36
2212.67
23.85
2447.94
200.06
7357.56
145.94
9914.32
3.1
WOOD CHIPS AND PARTICLES
1000 m3
3.1
WOOD CHIPS AND PARTICLES
1000 m3
312
236
3.1
WOOD CHIPS AND PARTICLES
NAC/m3
35
103
37
68
CHECK
ACCEPT
3.1
WOOD CHIPS AND PARTICLES
1000 mt
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
161.50
3031.13
85.37
4089.34
232.83
7211.35
112.26
7073.07
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
-71
-27
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
NAC/m3
19
48
31
63
CHECK
CHECK
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 mt
3.2.1
of which: Sawdust
1000 m3
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
3.2.1
of which: Sawdust
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
3.2.1
of which: Sawdust
1000 m3
0
0
3.2.1
of which: Sawdust
NAC/m3
0
0
0
0
ACCEPT
ACCEPT
4
RECOVERED POST-CONSUMER WOOD
1000 t
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
4
RECOVERED POST-CONSUMER WOOD
1000 t
4
RECOVERED POST-CONSUMER WOOD
1000 t
0
0
4
RECOVERED POST-CONSUMER WOOD
NAC / t
0
0
0
0
ACCEPT
ACCEPT
4
RECOVERED POST-CONSUMER WOOD
1000 mt
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 t
35.75
6577.90
22.35
7003.07
433.99
68057.21
433.13
126460.61
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 t
-47
-23
5
WOOD PELLETS AND OTHER AGGLOMERATES
NAC / t
184
313
157
292
ACCEPT
ACCEPT
5
WOOD PELLETS AND OTHER AGGLOMERATES
NAC/m3
5.1
WOOD PELLETS
1000 t
31.43
6091.92
20.12
6418.08
360.02
60842.70
355.12
109623.60
5.1
WOOD PELLETS
1000 t
5.1
WOOD PELLETS
1000 t
-23
-5
5.1
WOOD PELLETS
NAC / t
194
319
169
309
ACCEPT
ACCEPT
5.1
WOOD PELLETS
NAC/m3
5.2
OTHER AGGLOMERATES
1000 t
4.32
485.98
2.23
584.99
73.97
7214.51
78.01
16837.01
5.2
OTHER AGGLOMERATES
1000 t
5.2
OTHER AGGLOMERATES
1000 t
-24
-18
5.2
OTHER AGGLOMERATES
NAC / t
112
262
98
216
CHECK
CHECK
5.2
OTHER AGGLOMERATES
NAC/m3
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
483.84
113178.70
447.16
151322.57
1775.89
424339.31
1572.79
546699.78
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
6
37
6
SAWNWOOD (INCLUDING SLEEPERS)
NAC/m3
234
338
239
348
ACCEPT
ACCEPT
6
SAWNWOOD (INCLUDING SLEEPERS)
NAC/m3
6.C
Coniferous
1000 m3
352.74
70601.95
308.49
94082.74
265.68
64595.33
281.13
83190.04
6.C
Coniferous
1000 m3
6.C
Coniferous
1000 m3
344
167
6.C
Coniferous
NAC/m3
200
305
243
296
ACCEPT
ACCEPT
6.C
Coniferous
NAC/m3
6.NC
Non-Coniferous
1000 m3
131.10
42576.75
138.67
57239.82
1510.21
359743.97
1291.66
463509.74
6.NC
Non-Coniferous
1000 m3
6.NC
Non-Coniferous
1000 m3
-337
-129
6.NC
Non-Coniferous
NAC/m3
325
413
238
359
ACCEPT
ACCEPT
6.NC
Non-Coniferous
NAC/m3
6.NC.T
of which: Tropical1
1000 m3
1.20
1406.59
1.17
1046.78
0.37
539.38
0.30
378.85
6.NC.T
of which: Tropical1
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
6.NC.T
of which: Tropical1
1000 m3
1
1
6.NC.T
of which: Tropical
NAC/m3
1170
895
1470
1246
ACCEPT
ACCEPT
6.NC.T
of which: Tropical
NAC/m3
7
VENEER SHEETS
1000 m3
20.00
24510.62
21.79
33226.55
45.04
75186.14
181.54
104741.94
7
VENEER SHEETS
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
7
VENEER SHEETS
1000 m3
10
-122
7
VENEER SHEETS
NAC/m3
1225
1525
1669
577
ACCEPT
CHECK
7
VENEER SHEETS
NAC/m3
7.C
Coniferous
1000 m3
6.94
3729.90
8.96
5328.44
9.20
70.28
0.43
347.46
7.C
Coniferous
1000 m3
7.C
Coniferous
1000 m3
-2
9
7.C
Coniferous
NAC/m3
537
595
8
814
ACCEPT
CHECK
7.C
Coniferous
NAC/m3
1.01
7.64
108.04
814.00
ACCEPT
ACCEPT
7.NC
Non-Coniferous
1000 m3
13.06
20780.72
12.83
27898.11
35.84
75115.87
181.11
104394.48
7.NC
Non-Coniferous
1000 m3
7.NC
Non-Coniferous
1000 m3
12
-131
7.NC
Non-Coniferous
NAC/m3
1591
2174
2096
576
ACCEPT
CHECK
7.NC
Non-Coniferous
NAC/m3
7.NC.T
of which: Tropical
1000 m3
0.04
222.86
0.03
58.31
0.02
164.71
0.07
44.21
7.NC.T
of which: Tropical
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
7.NC.T
of which: Tropical
1000 m3
0
-0
7.NC.T
of which: Tropical
NAC/m3
6338
2072
7370
643
CHECK
CHECK
7.NC.T
of which: Tropical
NAC/m3
8
WOOD-BASED PANELS
1000 m3
389.53
141076.71
352.24
158483.09
255.57
77803.51
220.19
83862.01
8
WOOD-BASED PANELS
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8
WOOD-BASED PANELS
1000 m3
367
336
8
WOOD-BASED PANELS
NAC/m3
362
450
304
381
ACCEPT
ACCEPT
8
WOOD-BASED PANELS
NAC/m3
8.1
PLYWOOD
1000 m3
37.18
24345.08
81.83
36731.95
10.34
6757.17
3.92
9306.17
8.1
PLYWOOD
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8.1
PLYWOOD
1000 m3
29
79
8.1
PLYWOOD
NAC/m3
655
449
653
2375
ACCEPT
CHECK
8.1
PLYWOOD
NAC/m3
8.1.C
Coniferous
1000 m3
12.22
7279.23
25.50
11689.29
2.55
2410.00
0.57
846.82
8.1.C
Coniferous
1000 m3
8.1.C
Coniferous
1000 m3
11
25
8.1.C
Coniferous
NAC/m3
596
458
945
1487
ACCEPT
ACCEPT
8.1.C
Coniferous
NAC/m3
8.1.NC
Non-Coniferous
1000 m3
24.96
17065.85
56.33
25042.66
7.62
4231.17
3.35
8459.36
8.1.NC
Non-Coniferous
1000 m3
8.1.NC
Non-Coniferous
1000 m3
18
54
8.1.NC
Non-Coniferous
NAC/m3
684
445
555
2525
ACCEPT
CHECK
8.1.NC
Non-Coniferous
NAC/m3
8.1.NC.T
of which: Tropical
1000 m3
1.03
831.48
16.82
3380.46
0.17
116.00
0.28
512.66
8.1.NC.T
of which: Tropical
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8.1.NC.T
of which: Tropical
1000 m3
1
17
8.1.NC.T
of which: Tropical
NAC/m3
811
201
684
1854
CHECK
CHECK
8.1.NC.T
of which: Tropical
NAC/m3
8.1.1
of which: Laminated Veneer Lumber (LVL)
1000 m3
0.00
0.00
0.50
223.75
0.00
0.00
0.00
2.62
8.1.1
of which: Laminated Veneer Lumber (LVL)
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8.1.1
of which: Laminated Veneer Lumber (LVL)
1000 m3
0
1
8.1.1
of which: Laminated Veneer Lumber (LVL)
NAC/m3
0
445
0
6143
CHECK
CHECK
8.1.1.C
Coniferous
1000 m3
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
8.1.1.C
Coniferous
1000 m3
8.1.1.C
Coniferous
1000 m3
0
0
8.1.1.C
Coniferous
NAC/m3
0
0
0
0
ACCEPT
ACCEPT
8.1.1.NC
Non-Coniferous
1000 m3
0.00
0.00
0.50
223.75
0.00
0.00
0.00
2.62
8.1.1.NC
Non-Coniferous
1000 m3
8.1.1.NC
Non-Coniferous
1000 m3
0
1
8.1.1.NC
Non-Coniferous
NAC/m3
0
445
0
6143
CHECK
CHECK
8.1.1.NC.T
of which: Tropical
1000 m3
0.00
0.00
0.37
91.91
0.00
0.00
0.00
1.64
8.1.1.NC.T
of which: Tropical
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8.1.1.NC.T
of which: Tropical
1000 m3
0
0
8.1.1.NC.T
of which: Tropical
NAC/m3
0
249
0
7122
CHECK
CHECK
8.2
PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD
1000 m3
174.89
55425.52
182.09
64670.20
215.50
50205.51
165.67
52428.52
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
188
217
8.2
PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD
NAC/m3
317
355
233
316
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
40.74
13112.46
44.19
13832.55
12.84
1507.79
3.78
1509.83
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
28
40
8.2.1
of which: ORIENTED STRANDBOARD (OSB)
NAC/m3
322
313
117
399
ACCEPT
CHECK
8.2.1
of which: ORIENTED STRANDBOARD (OSB)
NAC/m3
8.3
FIBREBOARD
1000 m3
94.83
48317.87
88.32
57080.94
29.73
20840.83
50.61
22127.32
8.3
FIBREBOARD
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8.3
FIBREBOARD
1000 m3
67
40
8.3
FIBREBOARD
NAC/m3
510
646
701
437
ACCEPT
ACCEPT
8.3
FIBREBOARD
NAC/m3
8.3.1
HARDBOARD
1000 m3
14.75
9067.02
16.98
11098.84
4.44
2754.46
6.75
5017.04
8.3.1
HARDBOARD
1000 m3
8.3.1
HARDBOARD
1000 m3
10
10
8.3.1
HARDBOARD
NAC/m3
615
654
621
744
ACCEPT
ACCEPT
8.3.1
HARDBOARD
NAC/mt
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
79.08
38999.13
69.02
45245.65
25.22
18031.65
43.65
17080.92
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
54
25
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
NAC/m3
493
656
715
391
ACCEPT
ACCEPT
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
NAC/mt
8.3.3
OTHER FIBREBOARD
1000 m3
1.00
251.72
2.32
736.45
0.08
54.73
0.21
29.36
8.3.3
OTHER FIBREBOARD
1000 m3
8.3.3
OTHER FIBREBOARD
1000 m3
3
4
8.3.3
OTHER FIBREBOARD
NAC/m3
251
318
725
140
ACCEPT
CHECK
8.3.3
OTHER FIBREBOARD
NAC/mt
9
WOOD PULP
1000 t
0.42
396.41
0.41
548.27
49.96
16623.88
43.27
19764.58
9
WOOD PULP
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
9
WOOD PULP
1000 t
-2
1
9
WOOD PULP
NAC/t
936
1325
333
457
ACCEPT
ACCEPT
9
WOOD PULP
NAC/mt
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 t
0.11
51.61
0.06
39.83
49.96
16622.27
43.27
19756.67
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 t
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 t
-2
1
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
NAC/t
487
641
333
457
ACCEPT
ACCEPT
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
NAC/mt
9.2
CHEMICAL WOOD PULP
1000 t
0.32
344.80
0.35
508.44
0.00
1.61
0.00
7.91
9.2
CHEMICAL WOOD PULP
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
9.2
CHEMICAL WOOD PULP
1000 t
0
0
9.2
CHEMICAL WOOD PULP
NAC/t
1087
1446
2338
2433
ACCEPT
ACCEPT
9.2
CHEMICAL WOOD PULP
NAC/mt
9.2.1
SULPHATE PULP
1000 t
0.20
123.79
0.26
294.57
0.00
0.00
0.00
0.00
9.2.1
SULPHATE PULP
1000 t
9.2.1
SULPHATE PULP
1000 t
0
0
9.2.1
SULPHATE PULP
NAC/t
607
1114
0
0
ACCEPT
ACCEPT
9.2.1
SULPHATE PULP
NAC/mt
9.2.1.1
of which: BLEACHED
1000 t
0.20
123.79
0.26
294.57
0.00
0.00
0.00
0.00
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
0
0
9.2.1.1
of which: BLEACHED
NAC/t
607
1114
0
0
ACCEPT
ACCEPT
9.2.1.1
of which: BLEACHED
NAC/mt
9.2.2
SULPHITE PULP
1000 t
0.11
221.01
0.09
213.87
0.00
1.61
0.00
7.91
9.2.2
SULPHITE PULP
1000 t
9.2.2
SULPHITE PULP
1000 t
0
0
9.2.2
SULPHITE PULP
NAC/t
1947
2453
2338
2433
ACCEPT
ACCEPT
9.2.2
SULPHITE PULP
NAC/mt
9.3
DISSOLVING GRADES
1000 t
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
9.3
DISSOLVING GRADES
1000 t
9.3
DISSOLVING GRADES
1000 t
0
0
9.3
DISSOLVING GRADES
NAC/t
0
0
0
0
ACCEPT
ACCEPT
9.3
DISSOLVING GRADES
NAC/mt
10
OTHER PULP
1000 t
0.44
276.22
0.72
658.54
0.03
28.74
0.04
72.34
10
OTHER PULP
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
10
OTHER PULP
1000 t
0
1
10
OTHER PULP
NAC/t
623
918
910
2028
ACCEPT
CHECK
10
OTHER PULP
NAC/mt
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 t
0.03
55.88
0.07
235.11
0.01
11.83
0.02
62.09
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 t
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 t
0
0
10.1
PULP FROM FIBRES OTHER THAN WOOD
NAC/t
1825
3397
1986
2517
ACCEPT
ACCEPT
10.1
PULP FROM FIBRES OTHER THAN WOOD
NAC/mt
10.2
RECOVERED FIBRE PULP
1000 t
0.41
220.34
0.65
423.43
0.03
16.90
0.01
10.25
10.2
RECOVERED FIBRE PULP
1000 t
10.2
RECOVERED FIBRE PULP
1000 t
0
1
10.2
RECOVERED FIBRE PULP
NAC/t
534
653
660
932
ACCEPT
ACCEPT
10.2
RECOVERED FIBRE PULP
NAC/mt
11
RECOVERED PAPER
1000 t
218.60
39841.38
190.98
40424.30
179.44
31678.94
171.34
33383.09
11
RECOVERED PAPER
1000 t
11
RECOVERED PAPER
1000 t
39
20
11
RECOVERED PAPER
NAC/t
182
212
177
195
ACCEPT
ACCEPT
11
RECOVERED PAPER
NAC/mt
12
PAPER AND PAPERBOARD
1000 t
400.10
304345.81
408.30
467643.31
226.16
124112.76
205.34
158852.73
12
PAPER AND PAPERBOARD
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
12
PAPER AND PAPERBOARD
1000 t
548
556
12
PAPER AND PAPERBOARD
NAC/t
761
1145
549
774
ACCEPT
ACCEPT
12
PAPER AND PAPERBOARD
NAC/mt
12.1
GRAPHIC PAPERS
1000 t
123.93
88825.09
118.63
135352.07
8.36
8434.90
9.48
13306.98
12.1
GRAPHIC PAPERS
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
12.1
GRAPHIC PAPERS
1000 t
116
109
12.1
GRAPHIC PAPERS
NAC/t
717
1141
1009
1403
ACCEPT
ACCEPT
12.1
GRAPHIC PAPERS
NAC/mt
12.1.1
NEWSPRINT
1000 t
18.27
8530.05
17.89
14466.77
0.03
13.46
0.42
398.91
12.1.1
NEWSPRINT
1000 t
12.1.1
NEWSPRINT
1000 t
18
17
12.1.1
NEWSPRINT
NAC/t
467
809
498
955
ACCEPT
ACCEPT
12.1.1
NEWSPRINT
NAC/mt
12.1.2
UNCOATED MECHANICAL
1000 t
15.39
9000.85
12.48
11965.94
0.47
362.41
0.46
644.34
12.1.2
UNCOATED MECHANICAL
1000 t
12.1.2
UNCOATED MECHANICAL
1000 t
15
12
12.1.2
UNCOATED MECHANICAL
NAC/t
585
959
765
1396
ACCEPT
ACCEPT
12.1.2
UNCOATED MECHANICAL
NAC/mt
12.1.3
UNCOATED WOODFREE
1000 t
43.76
33326.79
44.97
54477.52
5.04
4077.35
6.15
7288.32
12.1.3
UNCOATED WOODFREE
1000 t
12.1.3
UNCOATED WOODFREE
1000 t
39
39
12.1.3
UNCOATED WOODFREE
NAC/t
762
1211
809
1185
ACCEPT
ACCEPT
12.1.3
UNCOATED WOODFREE
NAC/mt
12.1.4
COATED PAPERS
1000 t
46.51
37967.40
43.29
54441.85
2.82
3981.69
2.45
4975.42
12.1.4
COATED PAPERS
1000 t
12.1.4
COATED PAPERS
1000 t
44
41
12.1.4
COATED PAPERS
NAC/t
816
1258
1414
2027
ACCEPT
ACCEPT
12.1.4
COATED PAPERS
NAC/mt
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 t
34.10
30704.35
38.98
60872.81
3.07
3009.00
0.75
1121.08
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 t
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 t
31
38
12.2
HOUSEHOLD AND SANITARY PAPERS
NAC/t
901
1562
981
1490
ACCEPT
ACCEPT
12.2
HOUSEHOLD AND SANITARY PAPERS
NAC/mt
12.3
PACKAGING MATERIALS
1000 t
239.86
172848.13
225.04
218820.96
214.60
111026.75
194.98
142802.49
12.3
PACKAGING MATERIALS
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
12.3
PACKAGING MATERIALS
1000 t
399
383
12.3
PACKAGING MATERIALS
NAC/t
721
972
517
732
ACCEPT
ACCEPT
12.3
PACKAGING MATERIALS
NAC/mt
12.3.1
CASE MATERIALS
1000 t
141.65
73843.24
132.59
94451.98
187.65
86345.83
169.24
112321.98
12.3.1
CASE MATERIALS
1000 t
12.3.1
CASE MATERIALS
1000 t
183
180
12.3.1
CASE MATERIALS
NAC/t
521
712
460
664
ACCEPT
ACCEPT
12.3.1
CASE MATERIALS
NAC/mt
12.3.2
CARTONBOARD
1000 t
46.02
54050.03
23.42
43328.03
1.31
5011.51
1.20
4386.57
12.3.2
CARTONBOARD
1000 t
12.3.2
CARTONBOARD
1000 t
45
22
12.3.2
CARTONBOARD
NAC/t
1175
1850
3817
3653
ACCEPT
ACCEPT
12.3.2
CARTONBOARD
NAC/mt
12.3.3
WRAPPING PAPERS
1000 t
43.50
41010.37
59.57
74380.11
25.64
19659.57
24.53
26088.39
12.3.3
WRAPPING PAPERS
1000 t
12.3.3
WRAPPING PAPERS
1000 t
163
171
12.3.3
WRAPPING PAPERS
NAC/t
943
1249
767
1063
ACCEPT
ACCEPT
12.3.3
WRAPPING PAPERS
NAC/mt
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 t
8.69
3944.50
9.46
6660.84
0.00
9.83
0.00
5.55
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 t
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 t
9
9
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
NAC/t
454
704
2020
1606
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
2.22
11968.24
25.65
52597.47
0.13
1642.11
0.13
1622.18
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
2
26
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
NAC/t
5400
2050
12243
12830
CHECK
ACCEPT
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
NAC/mt
15
GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)2
1000 m3
0.00
0.00
3.44
5886.00
0.00
0.00
1.29
3505.60
15
GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
15
GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1
1000 m3
0
2
15
GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1
NAC/m3
0
1711
0
2708
CHECK
CHECK
15.1
GLULAM
1000 m3
0.00
0.00
3.22
5516.41
0.00
0.00
1.29
3505.60
15.1
GLULAM
1000 m3
15.1
GLULAM
1000 m3
0
2
15.1
GLULAM
NAC/m3
0
1711
0
2708
CHECK
CHECK
15.2
CROSS-LAMINATED TIMBER (CLT or X-LAM)
1000 m3
0.00
0.00
0.22
369.58
0.00
0.00
0.00
0.00
15.2
CROSS-LAMINATED TIMBER (CLT or X-LAM)
1000 m3
15.2
CROSS-LAMINATED TIMBER (CLT or X-LAM)
1000 m3
0
0
15.2
CROSS-LAMINATED TIMBER (CLT or X-LAM)
NAC/m3
0
1710
0
0
CHECK
ACCEPT
16
I BEAMS (I-JOISTS)2
1000 t
0.00
0.00
0.04
45.11
0.00
0.00
0.00
0.92
16
I BEAMS (I-JOISTS)1
1000 t
16
I BEAMS (I-JOISTS)1
1000 t
0
0
16
I BEAMS (I-JOISTS)1
NAC/t
0
1084
0
187
CHECK
CHECK
1 Please include the non-coniferous non-tropical species exported by tropical countries or imported from tropical countries.
2 Glulam, CLT and I Beams are classified as secondary wood products but for ease of reporting are included here
This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!
Value must always be in 1000 NAC (national currency)
Discrepancies
Eurozone countries may use the old national currency, but only in both years
Flag
Flag
Flag
Flag
Note
Note
Note
Note
Product
Product
I M P O R T V A L U E
E X P O R T V A L U E
Import
Export
Import
Export
Product
Product
I M P O R T V A L U E
E X P O R T V A L U E
code
2021
2022
2021
2022
2021
2022
2021
2022
2021
2022
2021
2022
Code
2021
2022
2021
2022
13
SECONDARY WOOD PRODUCTS
322515.51
415856.20
370404.15
465929.73
13
SECONDARY WOOD PRODUCTS
OK
OK
OK
OK
13.1
FURTHER PROCESSED SAWNWOOD
15550.26
18609.73
78901.39
99069.03
13.1
FURTHER PROCESSED SAWNWOOD
OK
OK
OK
OK
13.1.C
Coniferous
9196.69
11083.64
1083.82
1006.56
13.1.C
Coniferous
13.1.NC
Non-coniferous
6353.57
7526.09
77817.57
98062.46
13.1.NC
Non-coniferous
13.1.NC.T
of which: Tropical
234.95
571.98
3.26
0.00
13.1.NC.T
of which: Tropical
OK
OK
OK
OK
13.2
WOODEN WRAPPING AND PACKAGING MATERIAL
19508.66
31435.65
19679.94
29791.47
13.2
WOODEN WRAPPING AND PACKAGING MATERIAL
13.3
WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
7050.86
8429.09
1390.01
1822.92
13.3
WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
13.4
BUILDER’S JOINERY AND CARPENTRY OF WOOD1
55050.25
63968.88
108559.63
126714.04
13.4
BUILDER’S JOINERY AND CARPENTRY OF WOOD1
13.5
WOODEN FURNITURE
202243.53
266697.70
108640.86
140802.89
13.5
WOODEN FURNITURE
13.6
PREFABRICATED BUILDINGS OF WOOD
5719.54
5973.00
2417.74
2332.87
13.6
PREFABRICATED BUILDINGS OF WOOD
13.7
OTHER MANUFACTURED WOOD PRODUCTS
17392.41
20742.15
50814.57
65396.51
13.7
OTHER MANUFACTURED WOOD PRODUCTS
14
SECONDARY PAPER PRODUCTS
200881.02
277722.76
210969.35
290505.43
14
SECONDARY PAPER PRODUCTS
OK
OK
OK
OK
14.1
COMPOSITE PAPER AND PAPERBOARD
3702.90
5375.14
306.74
308.37
14.1
COMPOSITE PAPER AND PAPERBOARD
14.2
SPECIAL COATED PAPER AND PULP PRODUCTS
25954.28
39502.51
15456.06
20291.59
14.2
SPECIAL COATED PAPER AND PULP PRODUCTS
14.3
HOUSEHOLD AND SANITARY PAPER, READY FOR USE
74874.71
100676.20
20925.02
35286.51
14.3
HOUSEHOLD AND SANITARY PAPER, READY FOR USE
14.4
PACKAGING CARTONS, BOXES ETC.
55511.18
74477.36
123024.64
161830.15
14.4
PACKAGING CARTONS, BOXES ETC.
14.5
OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE
40837.95
57691.54
51256.89
72788.81
14.5
OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE
OK
OK
OK
OK
14.5.1
of which: PRINTING AND WRITING PAPER, READY FOR USE
492.43
960.96
24.23
25.48
14.5.1
of which: PRINTING AND WRITING PAPER, READY FOR USE
14.5.2
of which: ARTICLES, MOULDED OR PRESSED FROM PULP
3738.22
4780.98
24455.13
34795.99
14.5.2
of which: ARTICLES, MOULDED OR PRESSED FROM PULP
14.5.3
of which: FILTER PAPER AND PAPERBOARD, READY FOR USE
764.64
979.07
48.89
64.88
14.5.3
of which: FILTER PAPER AND PAPERBOARD, READY FOR USE
1 In February 2023 this definition was updated to exclude Glulam, Cross-Laminated Timber and I-Beams which are now distinct items in the JFSQ (15.1, 15.2 and 16). This change was made to reflect the update of HS2022.
To fill:
0
0
0
0
ECE-EU Species
Country:
HR
Date:
Name of Official responsible for reply:
0
FOREST SECTOR QUESTIONNAIRE ECE/EU Species Trade
Official Address (in full):
Check Table
Branimirova 19, 10000 Zagreb, Croatia
0
both VALUE and quantity reported ZERO
Trade in Roundwood and Sawnwood by species
Telephone:
0
Fax:
0
DISCREPANCIES
ZERO Q
quantity ZERO when VALUE is reported
E-mail:
0
ZERO V
Value ZERO when quantity is reported
Checks whether the sum of subitems is bigger than the total
Zero check - if no value please CHECK
NO Q
no quantity reported
Value must always be in 1000 NAC ( national currency)
NO V
no value reported
Treshold:
2
Eurozone countries may use the old national currency, but only in both years
1000NAC
Flag
Flag
Flag
Flag
Flag
Flag
Flag
Flag
Note
Note
Note
Note
Note
Note
Note
Note
REPORT
no figures reported
I M P O R T
E X P O R T
Import
Export
Import
Export
I M P O R T
E X P O R T
Value per
I M P O R T
E X P O R T
Unit price check
Product
Classification
Classification
Unit of
2021
2022
2021
2022
2021
2022
2021
2022
2021
2022
2021
2022
2021
2022
2021
2022
Classification
Classification
unit
2021
2022
2021
2022
IMPORT
EXPORT
Code
HS2022
CN2022
Product
Quantity
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
HS2022
CN2022
Product
1.2.C
4403.11/21/22/23/24/25/26
Industrial Roundwood, Coniferous
1000 m3ub
64.03
4282.41
153.32
14289.48
47.26
2604.99
70.41
7808.67
OK
OK
OK
OK
OK
OK
OK
OK
4403.11/21/22/23/24/25/26
Industrial Roundwood, Coniferous
NAC/m3
67
93
55
111
ACCEPT
CHECK
PRODUCTION
I M P O R T
E X P O R T
4403.21/22
of which: Pine (Pinus spp.)
1000 m3ub
9.85
1162.54
12.17
1912.44
23.13
1067.55
12.16
988.29
OK
OK
OK
OK
OK
OK
OK
OK
4403.21/22
of which: Pine (Pinus spp.)
NAC/m3
118
157
46
81
ACCEPT
ACCEPT
Product
Classification
Classification
Unit of
2021
2022
2021
2022
2021
2022
4403 21 10
sawlogs and veneer logs
1000 m3ub
9.30
1139.92
11.94
1887.27
11.70
731.54
3.38
607.37
4403 21 10
sawlogs and veneer logs
NAC/m3
123
158
63
179
ACCEPT
CHECK
Code
HS2022
CN2022
Product
Quantity
Quantity
Quantity
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
4403 21 90
4403 22 00
pulpwood and other industrial roundwood
1000 m3ub
0.54
22.62
0.20
19.02
11.42
336.02
8.78
380.93
4403 21 90
4403 22 00
pulpwood and other industrial roundwood
NAC/m3
42
95
29
43
CHECK
ACCEPT
1
4401.11/12 44.03
Roundwood production
1000 m3
JQ1
4,986
5,343
4403.23/24
of which: Fir/Spruce (Abies spp., Picea spp.)
1000 m3ub
51.85
2788.94
138.06
11911.64
21.04
1190.23
53.43
6426.75
OK
OK
OK
OK
OK
OK
OK
OK
4403.23/24
of which: Fir/Spruce (Abies spp., Picea spp.)
NAC/m3
54
86
57
120
ACCEPT
CHECK
EU2
0
0
4403 23 10
sawlogs and veneer logs
1000 m3ub
16.02
1323.97
39.32
4381.26
4.92
380.27
4.07
530.35
4403 23 10
sawlogs and veneer logs
NAC/m3
83
111
77
130
ACCEPT
ACCEPT
dif
4,986
5,343
4403 23 90
4403 24 00
pulpwood and other industrial roundwood
1000 m3ub
35.84
1464.97
98.74
7530.39
16.11
809.95
49.36
5896.41
4403 23 90
4403 24 00
pulpwood and other industrial roundwood
NAC/m3
41
76
50
119
ACCEPT
CHECK
1.2.C
4403.11/21/22/23/24/25/26
Industrial Roundwood (wood in the rough), Coniferous
1000 m3
JQ2
64
4,282
153
14,289
47
2,605
70
7,809
1.2.NC
4403.12/41/42/49/91/93/94
4403.95/96/97/98/99
Industrial Roundwood, Non-Coniferous
1000 m3ub
89.72
8142.67
128.66
17250.45
291.36
46955.56
235.64
63754.44
OK
OK
OK
OK
OK
OK
OK
OK
4403.12/41/42/49/91/93/94
4403.95/96/97/98/99
Industrial Roundwood, Non-Coniferous
NAC/m3
91
134
161
271
ACCEPT
ACCEPT
ECE/EU
64
4,282
153
14,289
47
2,605
70
7,809
ex4403.12 4403.91
of which: Oak (Quercus spp.)
1000 m3ub
8.87
2262.27
24.82
7107.16
84.04
29687.47
81.33
46214.42
ex4403.12 4403.91
of which: Oak (Quercus spp.)
NAC/m3
255
286
353
568
ACCEPT
ACCEPT
dif
0
0
0
0
0
0
0
0
ex4403.12 4403.93/94
of which: Beech (Fagus spp.)
1000 m3ub
70.92
4730.37
86.69
8125.79
79.35
5713.98
80.20
7746.90
ex4403.12 4403.93/94
of which: Beech (Fagus spp.)
NAC/m3
67
94
72
97
ACCEPT
ACCEPT
1.2.NC
4403.12/41/42/49/91/93/94/95/96/97/98/99
Industrial Roundwood (wood in the rough), Non-Coniferous
Light blue cells are requested only for EU members using the Combined Nomenclature to fill in - other countries are welcome to do so if their trade classification nomenclature permits
Please note that information on tropical species trade is requested in questionnaire ITTO2 for ITTO member countries
"ex" codes indicate that only part of that trade classication code is used
Specify Currency and Unit of Value (e.g.:1000 US $):
____________
I M P O R T
E X P O R T
Product
Classifications
2021
2022
2021
2022
HS2022/HS2017/HS2012/HS2007
Scientific Name
Local/Trade Name
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
(1000 m3)
(1000 m3)
(1000 m3)
(1000 m3)
1.2.NC.T
HS2022:
Industrial Roundwood, Tropical
ex4403.12 4403.41/42/49
HS2017:
ex4403.12 4403.41/49
HS2012/2007:
ex4403.10 4403.41/49 ex4403.99
6.NC.T
HS2022:
Sawnwood, Tropical
ex4406.12/92 4407.21/22/23/25/26/27/28/29
HS2017:
ex4406.12/92 4407.21/22/25/26/27/28/29
HS2012/2007:
ex4406.10/90 4407.21/22/25/26/27/28/30
7.NC.T
HS2022:
Veneer Sheets, Tropical
4408.31/39
HS2017:
4408.31/39
HS2012/2007:
4408.31/39 ex4408.90
8.1.NC.T
HS2022:
Plywood, Tropical
4412.31/41/51/91
HS2017:
4412.31 ex4412.94/99
HS2012/2007:
4412.31 ex4412.32/94/99
Note: List the major species traded in each category. Use additional sheet if more species are to be explicitly reported. For tropical plywood, identify by face veneer if composed of more than one species.
ITTO3-Miscellaneous
Country:
Date:
Name of Official responsible for reply:
Official Address (in full):
FOREST SECTOR QUESTIONNAIRE ITTO3
Miscellaneous Items
Telephone:
Fax:
(use additional paper if necessary)
E-mail:
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
2017
2018
2019
2020
2021
2021
2022
17/18
18/19
19/20
20/21
21/21
21/22
2017
2018
2019
2020
HR
P.OB
1000 m3
1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_1_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_1_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2_1_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2_1_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2_3_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2_3_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
TS-JQ1
%
Min:
80%
Max:
120%
Notes
JQ1
Country
Flow
Unit
Product
2017
2018
2019
2020
2021
2021
2022
17/18
18/19
19/20
20/21
21/21
21/22
2017
2018
2019
2020
HR
P
1000 m3
1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_1_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_1_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_2_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_2_1_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_2_1_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_2_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_2_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_2_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_2_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_2_3_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_2_3_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
3_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
3_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
4_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
4_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
5
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
5_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
5_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
5_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_1_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_1_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_1_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_2_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_3_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_4_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_4_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_4_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
7
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
7_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
7_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
7_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
7_3_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
7_3_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
7_3_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
7_3_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
7_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
8
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
8_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
8_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
9
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_1_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_1_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_3_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_3_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_3_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_3_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
TS-JQ2
%
Min:
80%
Max:
120%
Notes
JQ2
Country
Flow
Unit
Product
2017
2018
2019
2020
2021
2021
2022
17/18
18/19
19/20
20/21
21/21
21/22
2017
2018
2019
2020
Q
HR
M
1000 m3
1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
1_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
1_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
1_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
1_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
1_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
1_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
1_2_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
1_2_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
1_2_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
1_2_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
1_2_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
1_2_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 mt
2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 mt
2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 mt
2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 mt
2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
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8
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8
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1000 mt
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1000 mt
8_2
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1000 mt
8_2
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1000 mt
8_2
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1000 mt
9
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9
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1000 mt
9
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9
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9
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1000 mt
9
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10
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10
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1000 mt
10
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10
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1000 mt
10
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1000 mt
10_1
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UV
HR
M
1000 mt
10_1
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1000 mt
10_1
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HR
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1000 mt
10_1
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10_1_1
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HR
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1000 mt
10_1_1
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HR
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1000 mt
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1000 mt
10_1_2
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UV
HR
M
1000 mt
10_1_2
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HR
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1000 mt
10_1_2
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UV
HR
X
1000 mt
10_1_2
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!!
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Q
HR
M
1000 mt
10_1_3
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10_1_3
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UV
HR
M
1000 mt
10_1_3
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Q
HR
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1000 mt
10_1_3
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UV
HR
X
1000 mt
10_1_3
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Q
HR
M
1000 mt
10_1_4
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€
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M
1000 NAC
10_1_4
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UV
HR
M
1000 mt
10_1_4
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Q
HR
X
1000 mt
10_1_4
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UV
HR
X
1000 mt
10_1_4
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Q
HR
M
1000 mt
10_2
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M
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10_2
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!!
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UV
HR
M
1000 mt
10_2
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Q
HR
X
1000 mt
10_2
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€
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X
1000 NAC
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UV
HR
X
1000 mt
10_2
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Q
HR
M
1000 mt
10_3
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€
HR
M
1000 NAC
10_3
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UV
HR
M
1000 mt
10_3
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HR
X
1000 mt
10_3
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X
1000 NAC
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UV
HR
X
1000 mt
10_3
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Q
HR
M
1000 mt
10_3_1
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M
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UV
HR
M
1000 mt
10_3_1
ERROR:#REF!
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ERROR:#REF!
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!!
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Q
HR
X
1000 mt
10_3_1
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€
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X
1000 NAC
10_3_1
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!!
!!
!!
UV
HR
X
1000 mt
10_3_1
ERROR:#REF!
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ERROR:#REF!
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!!
!!
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Q
HR
M
1000 mt
10_3_2
ERROR:#REF!
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M
1000 NAC
10_3_2
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UV
HR
M
1000 mt
10_3_2
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Q
HR
X
1000 mt
10_3_2
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X
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10_3_2
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UV
HR
X
1000 mt
10_3_2
ERROR:#REF!
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Q
HR
M
1000 mt
10_3_3
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M
1000 NAC
10_3_3
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UV
HR
M
1000 mt
10_3_3
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HR
X
1000 mt
10_3_3
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UV
HR
X
1000 mt
10_3_3
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ERROR:#REF!
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!!
!!
!!
Q
HR
M
1000 mt
10_3_4
ERROR:#REF!
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€
HR
M
1000 NAC
10_3_4
ERROR:#REF!
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ERROR:#REF!
ERROR:#REF!
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!!
!!
!!
!!
UV
HR
M
1000 mt
10_3_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
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ERROR:#REF!
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!!
!!
!!
Q
HR
X
1000 mt
10_3_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
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€
HR
X
1000 NAC
10_3_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
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!!
!!
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!!
UV
HR
X
1000 mt
10_3_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
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!!
!!
!!
!!
Q
HR
M
1000 mt
10_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
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!!
!!
!!
!!
€
HR
M
1000 NAC
10_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
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!!
!!
!!
!!
UV
HR
M
1000 mt
10_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
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!!
!!
!!
!!
Q
HR
X
1000 mt
10_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
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!!
!!
!!
!!
€
HR
X
1000 NAC
10_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 mt
10_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
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!!
!!
!!
!!
TS-JQ3
%
Min:
80%
Max:
120%
Notes
JQ3
Country
Flow
Unit
Product
2017
2018
2019
2020
2021
2021
2022
17/18
18/19
19/20
20/21
21/21
21/22
2017
2018
2019
2020
HR
M
1000 NAC
11_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
11_1_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
11_1_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
11_1_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
11_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
11_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
11_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
11_5
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
11_6
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
11_7
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
11_7_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_5
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_6
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_6_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_6_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_6_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_7
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_7_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_7_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_7_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_1_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_1_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_1_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_5
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_6
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_7
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_7_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
12_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
12_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
12_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
12_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
12_5
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
12_6
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
12_6_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
12_6_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
12_6_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
TS-ECEEU
%
Min:
80%
Max:
120%
Notes
ECEEU
Country
Flow
Unit
Product
2017
2018
2019
2020
2021
2021
2022
17/18
18/19
19/20
20/21
21/21
21/22
2017
2018
2019
2020
Q
HR
M
1000 m3
ST_1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
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!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_1_2_C_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
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€
HR
M
1000 NAC
ST_1_2_C_1
ERROR:#REF!
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ERROR:#REF!
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ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_1_2_C_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_1_2_C_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
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€
HR
X
1000 NAC
ST_1_2_C_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_1_2_C_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_1_2_C_1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
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€
HR
M
1000 NAC
ST_1_2_C_1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_1_2_C_1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_1_2_C_1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_1_2_C_1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_1_2_C_1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_1_2_C_1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_1_2_C_1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_1_2_C_1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_1_2_C_1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_1_2_C_1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_1_2_C_1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_1_2_C_1_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_1_2_C_1_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_1_2_C_1_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_1_2_C_1_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_1_2_C_1_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_1_2_C_1_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_1_2_C_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_1_2_C_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_1_2_C_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_1_2_C_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_1_2_C_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_1_2_C_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_1_2_C_2_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_1_2_C_2_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_1_2_C_2_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_1_2_C_2_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_1_2_C_2_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_1_2_C_2_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_1_2_C_2_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_1_2_C_2_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_1_2_C_2_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_1_2_C_2_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_1_2_C_2_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_1_2_C_2_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_1_2_C_2_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_1_2_C_2_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_1_2_C_2_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_1_2_C_2_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
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!!
€
HR
X
1000 NAC
ST_1_2_C_2_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_1_2_C_2_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_1_2_C_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_1_2_C_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_1_2_C_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_1_2_C_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_1_2_C_3
ERROR:#REF!
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1000 m3
ST_5_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_5_NC_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_5_NC_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_5_NC_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_5_NC_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_5_NC_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_5_NC_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_5_NC_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_5_NC_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_5_NC_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_5_NC_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_5_NC_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_5_NC_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_5_NC_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_5_NC_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_5_NC_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_5_NC_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_5_NC_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_5_NC_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_5_NC_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_5_NC_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_5_NC_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_5_NC_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_5_NC_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_5_NC_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_5_NC_5
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_5_NC_5
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_5_NC_5
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_5_NC_5
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_5_NC_5
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_5_NC_5
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_5_NC_6
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_5_NC_6
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_5_NC_6
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_5_NC_6
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_5_NC_6
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_5_NC_6
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_5_NC_7
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_5_NC_7
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_5_NC_7
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_5_NC_7
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_5_NC_7
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_5_NC_7
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
TS-EU1
%
Min:
80%
Max:
120%
Notes
EU1
Country
Flow
Unit
Product
2017
2018
2019
2020
2021
2021
2022
17/18
18/19
19/20
20/21
21/21
21/22
2017
2018
2019
2020
Q
HR
EX_M
1000 m3
1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 m3
1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 m3
1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 m3
1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 m3
1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 m3
1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 m3
1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 m3
1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 m3
1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 m3
1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 m3
1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 m3
1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 m3
1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 m3
1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 m3
1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 m3
1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 m3
1_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
1_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 m3
1_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 m3
1_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
1_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 m3
1_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 m3
1_2_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
1_2_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 m3
1_2_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 m3
1_2_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
1_2_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 m3
1_2_NC_T
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 m3
3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 m3
3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 m3
3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 m3
3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 m3
3_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
3_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 m3
3_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 m3
3_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
3_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 m3
3_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 m3
3_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
3_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
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Q
HR
EX_M
1000 mt
8_2
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€
HR
EX_M
1000 NAC
8_2
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!!
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UV
HR
EX_M
1000 mt
8_2
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!!
!!
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!!
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Q
HR
EX_X
1000 mt
8_2
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€
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EX_X
1000 NAC
8_2
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!!
!!
!!
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UV
HR
EX_X
1000 mt
8_2
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!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
9
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€
HR
EX_M
1000 NAC
9
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!!
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UV
HR
EX_M
1000 mt
9
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!!
!!
!!
!!
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Q
HR
EX_X
1000 mt
9
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EX_X
1000 NAC
9
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!!
!!
!!
!!
UV
HR
EX_X
1000 mt
9
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!!
!!
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Q
HR
EX_M
1000 mt
10
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€
HR
EX_M
1000 NAC
10
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!!
!!
!!
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UV
HR
EX_M
1000 mt
10
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HR
EX_X
1000 mt
10
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€
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EX_X
1000 NAC
10
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!!
!!
!!
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UV
HR
EX_X
1000 mt
10
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!!
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Q
HR
EX_M
1000 mt
10_1
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€
HR
EX_M
1000 NAC
10_1
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!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
10_1
ERROR:#REF!
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!!
!!
!!
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Q
HR
EX_X
1000 mt
10_1
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€
HR
EX_X
1000 NAC
10_1
ERROR:#REF!
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ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
10_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
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ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
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Q
HR
EX_M
1000 mt
10_1_1
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€
HR
EX_M
1000 NAC
10_1_1
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ERROR:#REF!
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!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
10_1_1
ERROR:#REF!
ERROR:#REF!
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ERROR:#REF!
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ERROR:#REF!
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!!
!!
!!
!!
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Q
HR
EX_X
1000 mt
10_1_1
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€
HR
EX_X
1000 NAC
10_1_1
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!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
10_1_1
ERROR:#REF!
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ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
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!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
10_1_2
ERROR:#REF!
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€
HR
EX_M
1000 NAC
10_1_2
ERROR:#REF!
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ERROR:#REF!
ERROR:#REF!
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!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
10_1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
10_1_2
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€
HR
EX_X
1000 NAC
10_1_2
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ERROR:#REF!
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ERROR:#REF!
ERROR:#REF!
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!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
10_1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
10_1_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
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!!
!!
!!
!!
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€
HR
EX_M
1000 NAC
10_1_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
10_1_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
10_1_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
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€
HR
EX_X
1000 NAC
10_1_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
10_1_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
10_1_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
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€
HR
EX_M
1000 NAC
10_1_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
10_1_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
10_1_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
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!!
!!
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€
HR
EX_X
1000 NAC
10_1_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
10_1_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
10_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
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€
HR
EX_M
1000 NAC
10_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
10_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
10_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
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€
HR
EX_X
1000 NAC
10_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
10_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
10_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
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€
HR
EX_M
1000 NAC
10_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
10_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
10_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
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!!
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€
HR
EX_X
1000 NAC
10_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
10_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
10_3_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
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€
HR
EX_M
1000 NAC
10_3_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
10_3_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
10_3_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
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€
HR
EX_X
1000 NAC
10_3_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
10_3_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
10_3_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
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€
HR
EX_M
1000 NAC
10_3_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
10_3_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
10_3_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
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!!
!!
!!
!!
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€
HR
EX_X
1000 NAC
10_3_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
10_3_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
10_3_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
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€
HR
EX_M
1000 NAC
10_3_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
10_3_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
10_3_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
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EX_X
1000 NAC
10_3_3
ERROR:#REF!
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ERROR:#REF!
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!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
10_3_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
10_3_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
10_3_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
10_3_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
10_3_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
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€
HR
EX_X
1000 NAC
10_3_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
10_3_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
10_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
10_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
10_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
10_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
10_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
10_4
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
TS-EU2
%
Min:
80%
Max:
120%
Notes
EU2
Country
Flow
Unit
Product
2017
2018
2019
2020
2021
2021
2022
17/18
18/19
19/20
20/21
21/21
21/22
2017
2018
2019
2020
HR
P
1000 m3
EU2_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_1
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_1_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_1_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_2
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_2_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_2_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_3
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_3_C
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_3_NC
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
ERROR:#REF!
!!
!!
!!
!!
!!
!!
Annex1 | JQ1-Corres.
Last updated in 2016
FOREST SECTOR QUESTIONNAIRE JQ1 (Supp. 1)
PRIMARY PRODUCTS
Removals and Production
CORRESPONDENCES to CPC Ver.2.1
Central Product Classification Version 2.1
(CPC Ver. 2.1)
Product
Product
Code
REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
1
ROUNDWOOD (WOOD IN THE ROUGH)
031
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
0313
1.1.C
Coniferous
03131
1.1.NC
Non-Coniferous
03132
1.2
INDUSTRIAL ROUNDWOOD
0311 0312
1.2.C
Coniferous
0311
1.2.NC
Non-Coniferous
0312
1.2.NC.T
of which: Tropical
ex0312
1.2.1
SAWLOGS AND VENEER LOGS
ex03110 ex03120
1.2.1.C
Coniferous
ex03110
1.2.1.NC
Non-Coniferous
ex03120
1.2.2
PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)
ex03110 ex03120
1.2.2.C
Coniferous
ex03110
1.2.2.NC
Non-Coniferous
ex03120
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
ex03110 ex03120
1.2.3.C
Coniferous
ex03110
1.2.3.NC
Non-Coniferous
ex03120
PRODUCTION
2
WOOD CHARCOAL
ex34510
3
WOOD CHIPS, PARTICLES AND RESIDUES
ex31230 ex39283
3.1
WOOD CHIPS AND PARTICLES
ex31230
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
ex39283
4
RECOVERED POST-CONSUMER WOOD
ex39283
5
WOOD PELLETS AND OTHER AGGLOMERATES
39281 39282
5.1
WOOD PELLETS
39281
5.2
OTHER AGGLOMERATES
39282
6
SAWNWOOD (INCLUDING SLEEPERS)
311 3132
6.C
Coniferous
31101 ex31109 ex3132
6.NC
Non-Coniferous
31102 ex31109 ex3132
6.NC.T
of which: Tropical
ex31102 ex31109 ex3132
7
VENEER SHEETS
3151
7.C
Coniferous
31511
7.NC
Non-Coniferous
31512
7.NC.T
of which: Tropical
ex31512
8
WOOD-BASED PANELS
3141 3142 3143 3144
8.1
PLYWOOD
3141 3142
8.1.C
Coniferous
31411 31421
8.1.NC
Non-Coniferous
31412 31422
8.1.NC.T
of which: Tropical
ex31412 ex31422
8.2
PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) and SIMILAR BOARD
1Please include the non-coniferous non-tropical species exported by tropical countries or imported from tropical countries.
2 Glulam, CLT and I Beams are classified as secondary wood products but for ease of reporting are included in JQ1 and JQ2
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the HS2012/HS2017/HS2022 or SITC Rev.4 code is applicable.
For instance "ex4401.49" under product 3.2 means that only a part of HS2022 code 4401.49 refers to wood residues coming from wood processing (the other part coded under 4401.49 is recovered post-consumer wood).
++ Please use your judgement or, as a default, assign half of 4401.49 to item 3.2 and half to item 4 (note different quantity units)
In SITC Rev.4, if only 4 digits are shown, then all sub-headings at lower degrees of aggregation are included (for example, 634.1 includes 634.11 and 634.12).
* Please assign the trade data for HS code 4411.14 to product 8.3.2 (MDF/HDF) and 8.3.3 (other fibreboard) if it is possible to do this in national statistics. If not, please assign all the trade data to item 8.3.2 as in most cases MDF/HDF will represent the large majority of trade.
Annex3 | JQ3-Corres.
FOREST SECTOR QUESTIONNAIRE JQ3 (Supp. 1)
SECONDARY PROCESSED PRODUCTS
Trade
CORRESPONDENCES to HS 2022, HS2017, HS2012 and SITC Rev.4
OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE
48.14/16/17/20/21/22/23
48.14/16/17/20/21/22/23
48.14/16/17/20/21/22/23
641.94 642.2 642.3 642.42/45/91/93/99 892.81
14.5.1
of which: PRINTING AND WRITING PAPER, READY FOR USE
ex4823.90
ex4823.90
ex4823.90
ex642.99
14.5.2
of which: ARTICLES, MOULDED OR PRESSED FROM PULP
4823.70
4823.70
4823.70
ex642.99
14.5.3
of which: FILTER PAPER AND PAPERBOARD, READY FOR USE
4823.20
4823.20
4823.20
642.45
1 In February 2023 this definition was updated to exclude Glulam, Cross-Laminated Timber and I-Beams which are now distinct items in the JFSQ (15.1, 15.2 and 16).
This change was made to reflect the update of HS2022.
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the HS2012/HS2017/2022 or SITC Rev.4 code is applicable.
For instance "ex811.00" under "Prefabricated buildings of wood" means that only a part of SITC code 811.00 refers to buildings prefabricated from wood, as that code does not distinguish between the materials buildings were prefabricated from.
In SITC Rev.4, if only 4 digits are shown, then all subheadings at lower degrees of aggregation are included (for example, 892.2 includes 892.21 and 892.29).
Annex4 |JQ2-JQ3-Corres.
JQ Product code
Nomenclature
HS Code
Remarks on HS codes
1
HS2002
440110
Annex 4 does not include HS2022 codes
1
HS2002
4403
1
HS2007
440110
1
HS2007
4403
1
HS2012
440110
1
HS2012
4403
1
HS2017
440111
1
HS2017
440112
1
HS2017
4403
1.1
HS2002
440110
1.1
HS2007
440110
1.1
HS2012
440110
1.1
HS2017
440111
1.1
HS2017
440112
1.1C
HS2002
440110
Only some part of it
1.1C
HS2007
440110
Only some part of it
1.1C
HS2012
440110
Only some part of it
1.1C
HS2017
440111
1.1NC
HS2002
440110
Only some part of it
1.1NC
HS2007
440110
Only some part of it
1.1NC
HS2012
440110
Only some part of it
1.1NC
HS2017
440112
1.2
HS2002
4403
1.2
HS2007
4403
1.2
HS2012
4403
1.2
HS2017
4403
1.2.C
HS2002
440310
Only some part of it
1.2.C
HS2002
440320
1.2.C
HS2007
440310
Only some part of it
1.2.C
HS2007
440320
1.2.C
HS2012
440310
Only some part of it
1.2.C
HS2012
440320
1.2.C
HS2017
440311
1.2.C
HS2017
440321
1.2.C
HS2017
440322
1.2.C
HS2017
440323
1.2.C
HS2017
440324
1.2.C
HS2017
440325
1.2.C
HS2017
440326
1.2.NC
HS2002
440310
Only some part of it
1.2.NC
HS2002
440341
1.2.NC
HS2002
440349
1.2.NC
HS2002
440391
1.2.NC
HS2002
440392
1.2.NC
HS2002
440399
1.2.NC
HS2007
440310
Only some part of it
1.2.NC
HS2007
440341
1.2.NC
HS2007
440349
1.2.NC
HS2007
440391
1.2.NC
HS2007
440392
1.2.NC
HS2007
440399
1.2.NC
HS2012
440310
Only some part of it
1.2.NC
HS2012
440341
1.2.NC
HS2012
440349
1.2.NC
HS2012
440391
1.2.NC
HS2012
440392
1.2.NC
HS2012
440399
1.2.NC
HS2017
440312
1.2.NC
HS2017
440341
1.2.NC
HS2017
440349
1.2.NC
HS2017
440391
1.2.NC
HS2017
440393
1.2.NC
HS2017
440394
1.2.NC
HS2017
440395
1.2.NC
HS2017
440396
1.2.NC
HS2017
440397
1.2.NC
HS2017
440398
1.2.NC
HS2017
440399
1.2.NC.T
HS2002
440310
Only some part of it
1.2.NC.T
HS2002
440341
1.2.NC.T
HS2002
440349
1.2.NC.T
HS2002
440399
Only some part of it
1.2.NC.T
HS2007
440310
Only some part of it
1.2.NC.T
HS2007
440341
1.2.NC.T
HS2007
440349
1.2.NC.T
HS2007
440399
Only some part of it
1.2.NC.T
HS2012
440310
Only some part of it
1.2.NC.T
HS2012
440341
1.2.NC.T
HS2012
440349
1.2.NC.T
HS2012
440399
Only some part of it
1.2.NC.T
HS2017
440312
Only some part of it
1.2.NC.T
HS2017
440341
1.2.NC.T
HS2017
440349
2
HS2002
440200
Only some part of it
2
HS2007
440290
2
HS2012
440290
2
HS2017
440290
3
HS2002
440121
3
HS2002
440122
3
HS2002
440130
Only some part of it
3
HS2007
440121
3
HS2007
440122
3
HS2007
440130
Only some part of it
3
HS2012
440121
3
HS2012
440122
3
HS2012
440139
Only some part of it
3
HS2017
440121
3
HS2017
440122
3
HS2017
440140
3.1
HS2002
440121
3.1
HS2002
440122
3.1
HS2007
440121
3.1
HS2007
440122
3.1
HS2012
440121
3.1
HS2012
440122
3.1
HS2017
440121
3.1
HS2017
440122
3.2
HS2002
440130
Only some part of it
3.2
HS2012
440130
Only some part of it
3.2
HS2012
440139
Only some part of it
3.2
HS2017
440140
Only some part of it
4
HS2002
440130
Only some part of it
4
HS2007
440130
Only some part of it
4
HS2012
440139
Only some part of it
4
HS2017
440140
Only some part of it
5
HS2002
440130
Only some part of it
5
HS2007
440130
Only some part of it
5
HS2012
440131
5
HS2012
440139
Only some part of it
5
HS2017
440131
5
HS2017
440139
5.1
HS2002
440130
Only some part of it
5.1
HS2007
440130
Only some part of it
5.1
HS2012
440131
5.1
HS2017
440131
5.2
HS2002
440130
Only some part of it
5.2
HS2007
440130
Only some part of it
5.2
HS2012
440139
Only some part of it
5.2
HS2017
440139
6
HS2002
4406
6
HS2002
4407
6
HS2007
4406
6
HS2007
4407
6
HS2012
4406
6
HS2012
4407
6
HS2017
4406
6
HS2017
4407
6.C
HS2002
440610
Only some part of it
6.C
HS2002
440690
Only some part of it
6.C
HS2002
440710
6.C
HS2007
440610
Only some part of it
6.C
HS2007
440690
Only some part of it
6.C
HS2007
440710
6.C
HS2012
440610
Only some part of it
6.C
HS2012
440690
Only some part of it
6.C
HS2012
440710
6.C
HS2017
440611
6.C
HS2017
440691
6.C
HS2017
440711
6.C
HS2017
440712
6.C
HS2017
440719
6.NC
HS2002
440610
Only some part of it
6.NC
HS2002
440690
Only some part of it
6.NC
HS2002
440724
6.NC
HS2002
440725
6.NC
HS2002
440726
6.NC
HS2002
440729
6.NC
HS2002
440791
6.NC
HS2002
440792
6.NC
HS2002
440799
6.NC
HS2007
440610
Only some part of it
6.NC
HS2007
440690
Only some part of it
6.NC
HS2007
440721
6.NC
HS2007
440722
6.NC
HS2007
440725
6.NC
HS2007
440726
6.NC
HS2007
440727
6.NC
HS2007
440728
6.NC
HS2007
440729
6.NC
HS2007
440791
6.NC
HS2007
440792
6.NC
HS2007
440793
6.NC
HS2007
440794
6.NC
HS2007
440795
6.NC
HS2007
440799
6.NC
HS2012
440610
Only some part of it
6.NC
HS2012
440690
Only some part of it
6.NC
HS2012
440721
6.NC
HS2012
440722
6.NC
HS2012
440725
6.NC
HS2012
440726
6.NC
HS2012
440727
6.NC
HS2012
440728
6.NC
HS2012
440729
6.NC
HS2012
440791
6.NC
HS2012
440792
6.NC
HS2012
440793
6.NC
HS2012
440794
6.NC
HS2012
440795
6.NC
HS2012
440799
6.NC
HS2017
4406.12
6.NC
HS2017
4406.92
6.NC
HS2017
4407.21
6.NC
HS2017
4407.22
6.NC
HS2017
4407.25
6.NC
HS2017
4407.26
6.NC
HS2017
4407.27
6.NC
HS2017
4407.28
6.NC
HS2017
4407.29
6.NC
HS2017
4407.91
6.NC
HS2017
4407.92
6.NC
HS2017
4407.93
6.NC
HS2017
4407.94
6.NC
HS2017
4407.95
6.NC
HS2017
4407.96
6.NC
HS2017
4407.97
6.NC
HS2017
4407.99
6.NC.T
HS2002
440610
Only some part of it
6.NC.T
HS2002
440690
Only some part of it
6.NC.T
HS2002
440724
6.NC.T
HS2002
440725
6.NC.T
HS2002
440726
6.NC.T
HS2002
440729
6.NC.T
HS2002
440799
Only some part of it
6.NC.T
HS2007
440610
Only some part of it
6.NC.T
HS2007
440690
Only some part of it
6.NC.T
HS2007
440721
6.NC.T
HS2007
440722
6.NC.T
HS2007
440725
6.NC.T
HS2007
440726
6.NC.T
HS2007
440727
6.NC.T
HS2007
440728
6.NC.T
HS2007
440729
6.NC.T
HS2007
440799
Only some part of it
6.NC.T
HS2012
440610
Only some part of it
6.NC.T
HS2012
440690
Only some part of it
6.NC.T
HS2012
440721
6.NC.T
HS2012
440722
6.NC.T
HS2012
440725
6.NC.T
HS2012
440726
6.NC.T
HS2012
440727
6.NC.T
HS2012
440728
6.NC.T
HS2012
440729
6.NC.T
HS2012
440799
Only some part of it
6.NC.T
HS2017
440612
Only some part of it
6.NC.T
HS2017
440692
Only some part of it
6.NC.T
HS2017
440721
6.NC.T
HS2017
440722
6.NC.T
HS2017
440725
6.NC.T
HS2017
440726
6.NC.T
HS2017
440727
6.NC.T
HS2017
440728
6.NC.T
HS2017
440729
7
HS2002
4408
7
HS2007
4408
7
HS2012
4408
7
HS2017
4408
7.C
HS2002
440810
7.C
HS2007
440810
7.C
HS2012
440810
7.C
HS2017
440810
7.NC
HS2002
440831
7.NC
HS2002
440839
7.NC
HS2002
440890
7.NC
HS2007
440831
7.NC
HS2007
440839
7.NC
HS2007
440890
7.NC
HS2012
440831
7.NC
HS2012
440839
7.NC
HS2012
440890
7.NC
HS2017
440831
7.NC
HS2017
440839
7.NC
HS2017
440890
7.NC.T
HS2002
440831
7.NC.T
HS2002
440839
7.NC.T
HS2002
440890
Only some part of it
7.NC.T
HS2007
440831
7.NC.T
HS2007
440839
7.NC.T
HS2007
440890
Only some part of it
7.NC.T
HS2012
440831
7.NC.T
HS2012
440839
7.NC.T
HS2012
440890
Only some part of it
7.NC.T
HS2017
440831
7.NC.T
HS2017
440839
8
HS2002
4410
8
HS2002
4411
8
HS2002
441213
8
HS2002
441214
8
HS2002
441219
8
HS2002
441299
Only some part of it
8
HS2007
4410
8
HS2007
4411
8
HS2007
441231
8
HS2007
441232
8
HS2007
441239
8
HS2007
441294
8
HS2007
441299
8
HS2012
4410
8
HS2012
4411
8
HS2012
441231
8
HS2012
441232
8
HS2012
441239
8
HS2012
441294
8
HS2012
441299
8
HS2017
4410
8
HS2017
4411
8
HS2017
441231
8
HS2017
441233
8
HS2017
441234
8
HS2017
441239
8
HS2017
441294
8
HS2017
441299
8.1
HS2002
441213
8.1
HS2002
441214
8.1
HS2002
441219
8.1
HS2002
441299
Only some part of it
8.1
HS2007
441231
8.1
HS2007
441232
8.1
HS2007
441239
8.1
HS2007
441294
8.1
HS2007
441299
8.1
HS2012
441231
8.1
HS2012
441232
8.1
HS2012
441239
8.1
HS2012
441294
8.1
HS2012
441299
8.1
HS2017
441231
8.1
HS2017
441233
8.1
HS2017
441234
8.1
HS2017
441239
8.1
HS2017
441294
8.1
HS2017
441299
8.1.C
HS2002
441219
8.1.C
HS2002
441299
Only some part of it
8.1.C
HS2007
441239
8.1.C
HS2007
441294
Only some part of it
8.1.C
HS2007
441299
Only some part of it
8.1.C
HS2012
441239
8.1.C
HS2012
441294
Only some part of it
8.1.C
HS2012
441299
Only some part of it
8.1.C
HS2017
441239
8.1.C
HS2017
441294
Only some part of it
8.1.C
HS2017
441299
Only some part of it
8.1.NC
HS2002
441213
8.1.NC
HS2002
441214
8.1.NC
HS2002
441299
Only some part of it
8.1.NC
HS2007
441231
8.1.NC
HS2007
441232
8.1.NC
HS2007
441294
Only some part of it
8.1.NC
HS2007
441299
Only some part of it
8.1.NC
HS2012
441231
8.1.NC
HS2012
441232
8.1.NC
HS2012
441294
Only some part of it
8.1.NC
HS2012
441299
Only some part of it
8.1.NC
HS2017
441231
8.1.NC
HS2017
441233
8.1.NC
HS2017
441234
8.1.NC
HS2017
441294
Only some part of it
8.1.NC
HS2017
441299
Only some part of it
8.1.NC.T
HS2002
441213
8.1.NC.T
HS2002
441214
Only some part of it
8.1.NC.T
HS2002
441299
Only some part of it
8.1.NC.T
HS2007
441231
8.1.NC.T
HS2007
441232
Only some part of it
8.1.NC.T
HS2007
441294
Only some part of it
8.1.NC.T
HS2007
441299
Only some part of it
8.1.NC.T
HS2012
441231
8.1.NC.T
HS2012
441232
Only some part of it
8.1.NC.T
HS2012
441294
Only some part of it
8.1.NC.T
HS2012
441299
Only some part of it
8.1.NC.T
HS2017
441231
8.1.NC.T
HS2017
441294
Only some part of it
8.1.NC.T
HS2017
441299
Only some part of it
8.2
HS2002
4410
8.2
HS2007
4410
8.2
HS2012
4410
8.2
HS2017
4410
8.2.1
HS2002
441021
Only some part of it
8.2.1
HS2002
441029
Only some part of it
8.2.1
HS2007
441012
8.2.1
HS2012
441012
8.2.1
HS2017
441012
8.3
HS2002
4411
8.3
HS2007
4411
8.3
HS2012
4411
8.3
HS2017
4411
8.3.1
HS2002
441111
Only some part of it
8.3.1
HS2002
441119
Only some part of it
8.3.1
HS2007
441192
8.3.1
HS2012
441192
8.3.1
HS2017
441192
8.3.2
HS2002
441111
Only some part of it
8.3.2
HS2002
441119
Only some part of it
8.3.2
HS2002
441121
Only some part of it
8.3.2
HS2002
441129
Only some part of it
8.3.2
HS2007
441112
8.3.2
HS2007
441113
8.3.2
HS2007
441114
Only some part of it
8.3.2
HS2012
441112
8.3.2
HS2012
441113
8.3.2
HS2012
441114
Only some part of it
8.3.2
HS2017
441112
8.3.2
HS2017
441113
8.3.2
HS2017
441114
Only some part of it
8.3.3
HS2002
441131
8.3.3
HS2002
441139
8.3.3
HS2002
441191
8.3.3
HS2002
441199
8.3.3
HS2007
441114
Only some part of it
8.3.3
HS2007
441193
8.3.3
HS2007
441194
8.3.3
HS2012
441114
Only some part of it
8.3.3
HS2012
441193
8.3.3
HS2012
441194
8.3.3
HS2017
441114
Only some part of it
8.3.3
HS2017
441193
8.3.3
HS2017
441194
9
HS2002
4701
9
HS2002
4702
9
HS2002
4703
9
HS2002
4704
9
HS2002
4705
9
HS2007
4701
9
HS2007
4702
9
HS2007
4703
9
HS2007
4704
9
HS2007
4705
9
HS2012
4701
9
HS2012
4702
9
HS2012
4703
9
HS2012
4704
9
HS2012
4705
9
HS2017
4701
9
HS2017
4702
9
HS2017
4703
9
HS2017
4704
9
HS2017
4705
9.1
HS2002
4701
9.1
HS2002
4705
9.1
HS2007
4701
9.1
HS2007
4705
9.1
HS2012
4701
9.1
HS2012
4705
9.1
HS2017
4701
9.1
HS2017
4705
9.2
HS2002
4703
9.2
HS2002
4704
9.2
HS2007
4703
9.2
HS2007
4704
9.2
HS2012
4703
9.2
HS2012
4704
9.2
HS2017
4703
9.2
HS2017
4704
9.2.1
HS2002
4703
9.2.1
HS2007
4703
9.2.1
HS2012
4703
9.2.1
HS2017
4703
9.2.1.1
HS2002
470321
9.2.1.1
HS2002
470329
9.2.1.1
HS2007
470321
9.2.1.1
HS2007
470329
9.2.1.1
HS2012
470321
9.2.1.1
HS2012
470329
9.2.1.1
HS2017
470321
9.2.1.1
HS2017
470329
9.2.2
HS2002
4704
9.2.2
HS2007
4704
9.2.2
HS2012
4704
9.2.2
HS2017
4704
9.3
HS2002
4702
9.3
HS2007
4702
9.3
HS2012
4702
9.3
HS2017
4702
10
HS2002
4706
10
HS2007
4706
10
HS2012
4706
10
HS2017
4706
10.1
HS2002
470610
10.1
HS2002
470691
10.1
HS2002
470692
10.1
HS2002
470693
10.1
HS2007
470610
10.1
HS2007
470630
10.1
HS2007
470691
10.1
HS2007
470692
10.1
HS2007
470693
10.1
HS2012
470610
10.1
HS2012
470630
10.1
HS2012
470691
10.1
HS2012
470692
10.1
HS2012
470693
10.1
HS2017
470610
10.1
HS2017
470630
10.1
HS2017
470691
10.1
HS2017
470692
10.1
HS2017
470693
10.2
HS2002
470620
10.2
HS2007
470620
10.2
HS2012
470620
10.2
HS2017
470620
11
HS2002
4707
11
HS2007
4707
11
HS2012
4707
11
HS2017
4707
12
HS2002
4801
12
HS2002
4802
12
HS2002
4803
12
HS2002
4804
12
HS2002
4805
12
HS2002
4806
12
HS2002
4808
12
HS2002
4809
12
HS2002
4810
12
HS2002
481151
12
HS2002
481159
12
HS2002
4812
12
HS2002
4813
12
HS2007
4801
12
HS2007
4802
12
HS2007
4803
12
HS2007
4804
12
HS2007
4805
12
HS2007
4806
12
HS2007
4808
12
HS2007
4809
12
HS2007
4810
12
HS2007
481151
12
HS2007
481159
12
HS2007
4812
12
HS2007
4813
12
HS2012
4801
12
HS2012
4802
12
HS2012
4803
12
HS2012
4804
12
HS2012
4805
12
HS2012
4806
12
HS2012
4808
12
HS2012
4809
12
HS2012
4810
12
HS2012
481151
12
HS2012
481159
12
HS2012
4812
12
HS2012
4813
12
HS2017
4801
12
HS2017
4802
12
HS2017
4803
12
HS2017
4804
12
HS2017
4805
12
HS2017
4806
12
HS2017
4808
12
HS2017
4809
12
HS2017
4810
12
HS2017
481151
12
HS2017
481159
12
HS2017
4812
12
HS2017
4813
12.1
HS2002
4801
12.1
HS2002
480210
12.1
HS2002
480220
12.1
HS2002
480254
12.1
HS2002
480255
12.1
HS2002
480256
12.1
HS2002
480257
12.1
HS2002
480258
12.1
HS2002
480261
12.1
HS2002
480262
12.1
HS2002
480269
12.1
HS2002
4809
12.1
HS2002
481013
12.1
HS2002
481014
12.1
HS2002
481019
12.1
HS2002
481022
12.1
HS2002
481029
12.1
HS2007
4801
12.1
HS2007
480210
12.1
HS2007
480220
12.1
HS2007
480254
12.1
HS2007
480255
12.1
HS2007
480256
12.1
HS2007
480257
12.1
HS2007
480258
12.1
HS2007
480261
12.1
HS2007
480262
12.1
HS2007
480269
12.1
HS2007
4809
12.1
HS2007
481013
12.1
HS2007
481014
12.1
HS2007
481019
12.1
HS2007
481022
12.1
HS2007
481029
12.1
HS2012
4801
12.1
HS2012
480210
12.1
HS2012
480220
12.1
HS2012
480254
12.1
HS2012
480255
12.1
HS2012
480256
12.1
HS2012
480257
12.1
HS2012
480258
12.1
HS2012
480261
12.1
HS2012
480262
12.1
HS2012
480269
12.1
HS2012
4809
12.1
HS2012
481013
12.1
HS2012
481014
12.1
HS2012
481019
12.1
HS2012
481022
12.1
HS2012
481029
12.1
HS2017
4801
12.1
HS2017
480210
12.1
HS2017
480220
12.1
HS2017
480254
12.1
HS2017
480255
12.1
HS2017
480256
12.1
HS2017
480257
12.1
HS2017
480258
12.1
HS2017
480261
12.1
HS2017
480262
12.1
HS2017
480269
12.1
HS2017
4809
12.1
HS2017
481013
12.1
HS2017
481014
12.1
HS2017
481019
12.1
HS2017
481022
12.1
HS2017
481029
12.1.1
HS2002
4801
12.1.1
HS2007
4801
12.1.1
HS2012
4801
12.1.1
HS2017
4801
12.1.2
HS2002
480261
12.1.2
HS2002
480262
12.1.2
HS2002
480269
12.1.2
HS2007
480261
12.1.2
HS2007
480262
12.1.2
HS2007
480269
12.1.2
HS2012
480261
12.1.2
HS2012
480262
12.1.2
HS2012
480269
12.1.2
HS2017
480261
12.1.2
HS2017
480262
12.1.2
HS2017
480269
12.1.3
HS2002
480210
12.1.3
HS2002
480220
12.1.3
HS2002
480254
12.1.3
HS2002
480255
12.1.3
HS2002
480256
12.1.3
HS2002
480257
12.1.3
HS2002
480258
12.1.3
HS2007
480210
12.1.3
HS2007
480220
12.1.3
HS2007
480254
12.1.3
HS2007
480255
12.1.3
HS2007
480256
12.1.3
HS2007
480257
12.1.3
HS2007
480258
12.1.3
HS2012
480210
12.1.3
HS2012
480220
12.1.3
HS2012
480254
12.1.3
HS2012
480255
12.1.3
HS2012
480256
12.1.3
HS2012
480257
12.1.3
HS2012
480258
12.1.3
HS2017
480210
12.1.3
HS2017
480220
12.1.3
HS2017
480254
12.1.3
HS2017
480255
12.1.3
HS2017
480256
12.1.3
HS2017
480257
12.1.3
HS2017
480258
12.1.4
HS2002
4809
12.1.4
HS2002
481013
12.1.4
HS2002
481014
12.1.4
HS2002
481019
12.1.4
HS2002
481022
12.1.4
HS2002
481029
12.1.4
HS2007
4809
12.1.4
HS2007
481013
12.1.4
HS2007
481014
12.1.4
HS2007
481019
12.1.4
HS2007
481022
12.1.4
HS2007
481029
12.1.4
HS2012
4809
12.1.4
HS2012
481013
12.1.4
HS2012
481014
12.1.4
HS2012
481019
12.1.4
HS2012
481022
12.1.4
HS2012
481029
12.1.4
HS2017
4809
12.1.4
HS2017
481013
12.1.4
HS2017
481014
12.1.4
HS2017
481019
12.1.4
HS2017
481022
12.1.4
HS2017
481029
12.2
HS2002
4803
12.2
HS2007
4803
12.2
HS2012
4803
12.2
HS2017
4803
12.3
HS2002
480411
12.3
HS2002
480419
12.3
HS2002
480421
12.3
HS2002
480429
12.3
HS2002
480431
12.3
HS2002
480439
12.3
HS2002
480442
12.3
HS2002
480449
12.3
HS2002
480451
12.3
HS2002
480452
12.3
HS2002
480459
12.3
HS2002
480511
12.3
HS2002
480512
12.3
HS2002
480519
12.3
HS2002
480524
12.3
HS2002
480525
12.3
HS2002
480530
12.3
HS2002
480591
12.3
HS2002
480592
12.3
HS2002
480593
12.3
HS2002
480610
12.3
HS2002
480620
12.3
HS2002
480640
12.3
HS2002
4808
12.3
HS2002
481031
12.3
HS2002
481032
12.3
HS2002
481039
12.3
HS2002
481092
12.3
HS2002
481099
12.3
HS2002
481151
12.3
HS2002
481159
12.3
HS2007
480411
12.3
HS2007
480419
12.3
HS2007
480421
12.3
HS2007
480429
12.3
HS2007
480431
12.3
HS2007
480439
12.3
HS2007
480442
12.3
HS2007
480449
12.3
HS2007
480451
12.3
HS2007
480452
12.3
HS2007
480459
12.3
HS2007
480511
12.3
HS2007
480512
12.3
HS2007
480519
12.3
HS2007
480524
12.3
HS2007
480525
12.3
HS2007
480530
12.3
HS2007
480591
12.3
HS2007
480592
12.3
HS2007
480593
12.3
HS2007
480610
12.3
HS2007
480620
12.3
HS2007
480640
12.3
HS2007
4808
12.3
HS2007
481031
12.3
HS2007
481032
12.3
HS2007
481039
12.3
HS2007
481092
12.3
HS2007
481099
12.3
HS2007
481151
12.3
HS2007
481159
12.3
HS2012
480411
12.3
HS2012
480419
12.3
HS2012
480421
12.3
HS2012
480429
12.3
HS2012
480431
12.3
HS2012
480439
12.3
HS2012
480442
12.3
HS2012
480449
12.3
HS2012
480451
12.3
HS2012
480452
12.3
HS2012
480459
12.3
HS2012
480511
12.3
HS2012
480512
12.3
HS2012
480519
12.3
HS2012
480524
12.3
HS2012
480525
12.3
HS2012
480530
12.3
HS2012
480591
12.3
HS2012
480592
12.3
HS2012
480593
12.3
HS2012
480610
12.3
HS2012
480620
12.3
HS2012
480640
12.3
HS2012
4808
12.3
HS2012
481031
12.3
HS2012
481032
12.3
HS2012
481039
12.3
HS2012
481092
12.3
HS2012
481099
12.3
HS2012
481151
12.3
HS2012
481159
12.3
HS2017
480411
12.3
HS2017
480419
12.3
HS2017
480421
12.3
HS2017
480429
12.3
HS2017
480431
12.3
HS2017
480439
12.3
HS2017
480442
12.3
HS2017
480449
12.3
HS2017
480451
12.3
HS2017
480452
12.3
HS2017
480459
12.3
HS2017
480511
12.3
HS2017
480512
12.3
HS2017
480519
12.3
HS2017
480524
12.3
HS2017
480525
12.3
HS2017
480530
12.3
HS2017
480591
12.3
HS2017
480592
12.3
HS2017
480593
12.3
HS2017
480610
12.3
HS2017
480620
12.3
HS2017
480640
12.3
HS2017
4808
12.3
HS2017
481031
12.3
HS2017
481032
12.3
HS2017
481039
12.3
HS2017
481092
12.3
HS2017
481099
12.3
HS2017
481151
12.3
HS2017
481159
12.3.1
HS2002
480411
12.3.1
HS2002
480419
12.3.1
HS2002
480511
12.3.1
HS2002
480512
12.3.1
HS2002
480519
12.3.1
HS2002
480524
12.3.1
HS2002
480525
12.3.1
HS2002
480591
12.3.1
HS2007
480411
12.3.1
HS2007
480419
12.3.1
HS2007
480511
12.3.1
HS2007
480512
12.3.1
HS2007
480519
12.3.1
HS2007
480524
12.3.1
HS2007
480525
12.3.1
HS2007
480591
12.3.1
HS2012
480411
12.3.1
HS2012
480419
12.3.1
HS2012
480511
12.3.1
HS2012
480512
12.3.1
HS2012
480519
12.3.1
HS2012
480524
12.3.1
HS2012
480525
12.3.1
HS2012
480591
12.3.2
HS2002
480442
12.3.2
HS2002
480449
12.3.2
HS2002
480451
12.3.2
HS2002
480452
12.3.2
HS2002
480459
12.3.2
HS2002
480592
12.3.2
HS2002
481032
12.3.2
HS2002
481039
12.3.2
HS2002
481092
12.3.2
HS2002
481151
12.3.2
HS2002
481159
12.3.2
HS2007
480442
12.3.2
HS2007
480449
12.3.2
HS2007
480451
12.3.2
HS2007
480452
12.3.2
HS2007
480459
12.3.2
HS2007
480592
12.3.2
HS2007
481032
12.3.2
HS2007
481039
12.3.2
HS2007
481092
12.3.2
HS2007
481151
12.3.2
HS2007
481159
12.3.2
HS2012
480442
12.3.2
HS2012
480449
12.3.2
HS2012
480451
12.3.2
HS2012
480452
12.3.2
HS2012
480459
12.3.2
HS2012
480592
12.3.2
HS2012
481032
12.3.2
HS2012
481039
12.3.2
HS2012
481092
12.3.2
HS2012
481151
12.3.2
HS2012
481159
12.3.2
HS2017
480442
12.3.2
HS2017
480449
12.3.2
HS2017
480451
12.3.2
HS2017
480452
12.3.2
HS2017
480459
12.3.2
HS2017
480592
12.3.2
HS2017
481032
12.3.2
HS2017
481039
12.3.2
HS2017
481092
12.3.2
HS2017
481151
12.3.2
HS2017
481159
12.3.3
HS2002
480421
12.3.3
HS2002
480429
12.3.3
HS2002
480431
12.3.3
HS2002
480439
12.3.3
HS2002
480530
12.3.3
HS2002
480610
12.3.3
HS2002
480620
12.3.3
HS2002
480640
12.3.3
HS2002
4808
12.3.3
HS2002
481031
12.3.3
HS2002
481099
12.3.3
HS2007
480421
12.3.3
HS2007
480429
12.3.3
HS2007
480431
12.3.3
HS2007
480439
12.3.3
HS2007
480530
12.3.3
HS2007
480610
12.3.3
HS2007
480620
12.3.3
HS2007
480640
12.3.3
HS2007
4808
12.3.3
HS2007
481031
12.3.3
HS2007
481099
12.3.3
HS2012
480421
12.3.3
HS2012
480429
12.3.3
HS2012
480431
12.3.3
HS2012
480439
12.3.3
HS2012
480530
12.3.3
HS2012
480610
12.3.3
HS2012
480620
12.3.3
HS2012
480640
12.3.3
HS2012
4808
12.3.3
HS2012
481031
12.3.3
HS2012
481099
12.3.3
HS2017
480421
12.3.3
HS2017
480429
12.3.3
HS2017
480431
12.3.3
HS2017
480439
12.3.3
HS2017
480530
12.3.3
HS2017
480610
12.3.3
HS2017
480620
12.3.3
HS2017
480640
12.3.3
HS2017
4808
12.3.3
HS2017
481031
12.3.3
HS2017
481099
12.3.4
HS2002
480593
12.3.4
HS2007
480593
12.3.4
HS2012
480593
12.3.4
HS2017
480593
12.4
HS2002
480240
12.4
HS2002
480441
12.4
HS2002
480540
12.4
HS2002
480550
12.4
HS2002
480630
12.4
HS2002
4812
12.4
HS2002
4813
12.4
HS2007
480240
12.4
HS2007
480441
12.4
HS2007
480540
12.4
HS2007
480550
12.4
HS2007
480630
12.4
HS2007
4812
12.4
HS2007
4813
12.4
HS2012
480240
12.4
HS2012
480441
12.4
HS2012
480540
12.4
HS2012
480550
12.4
HS2012
480630
12.4
HS2012
4812
12.4
HS2012
4813
12.4
HS2017
480240
12.4
HS2017
480441
12.4
HS2017
480540
12.4
HS2017
480550
12.4
HS2017
480630
12.4
HS2017
4812
12.4
HS2017
4813
13.1
HS2002
440910
13.1
HS2002
440920
Only some part of it
13.1
HS2007
440910
13.1
HS2007
440929
13.1
HS2012
440910
13.1
HS2012
440929
13.1
HS2017
440910
13.1
HS2017
440922
13.1
HS2017
440929
13.1.C
HS2002
440910
13.1.C
HS2007
440910
13.1.C
HS2012
440910
13.1.C
HS2017
440910
13.1.NC
HS2002
440920
Only some part of it
13.1.NC
HS2007
440929
13.1.NC
HS2012
440929
13.1.NC
HS2017
440922
13.1.NC
HS2017
440929
13.1.NC.T
HS2002
440920
Only some part of it
13.1.NC.T
HS2007
440929
Only some part of it
13.1.NC.T
HS2012
440929
Only some part of it
13.1.NC.T
HS2017
440922
13.2
HS2002
4415
13.2
HS2002
4416
13.2
HS2007
4415
13.2
HS2007
4416
13.2
HS2012
4415
13.2
HS2012
4416
13.2
HS2017
4415
13.2
HS2017
4416
13.3
HS2002
4414
13.3
HS2002
4419
Only some part of it
13.3
HS2002
4420
13.3
HS2007
4414
13.3
HS2007
4419
Only some part of it
13.3
HS2007
4420
13.3
HS2012
4414
13.3
HS2012
4419
Only some part of it
13.3
HS2012
4420
13.3
HS2017
4414
13.3
HS2017
441990
13.3
HS2017
4420
13.4
HS2002
441810
13.4
HS2002
441820
13.4
HS2002
441830
13.4
HS2002
441840
13.4
HS2002
441850
13.4
HS2002
441890
Only some part of it
13.4
HS2007
441810
13.4
HS2007
481820
13.4
HS2007
441840
13.4
HS2007
441850
13.4
HS2007
441860
13.4
HS2007
441871
Only some part of it
13.4
HS2007
441872
Only some part of it
13.4
HS2007
441879
Only some part of it
13.4
HS2007
441890
Only some part of it
13.4
HS2012
441810
13.4
HS2012
441820
13.4
HS2012
441840
13.4
HS2012
441850
13.4
HS2012
441860
13.4
HS2012
441871
Only some part of it
13.4
HS2012
441872
Only some part of it
13.4
HS2012
441879
Only some part of it
13.4
HS2012
441890
Only some part of it
13.4
HS2017
441810
13.4
HS2017
441820
13.4
HS2017
441840
13.4
HS2017
441850
13.4
HS2017
441860
13.4
HS2017
441874
13.4
HS2017
441875
13.4
HS2017
441879
13.4
HS2017
441899
13.5
HS2002
940161
13.5
HS2002
940169
13.5
HS2002
940190
Only some part of it
13.5
HS2002
940330
13.5
HS2002
940340
13.5
HS2002
940350
13.5
HS2002
940360
13.5
HS2002
940390
Only some part of it
13.5
HS2007
940161
13.5
HS2007
940169
13.5
HS2007
940190
Only some part of it
13.5
HS2007
940330
13.5
HS2007
940340
13.5
HS2007
940350
13.5
HS2007
940360
13.5
HS2007
940390
Only some part of it
13.5
HS2012
940161
13.5
HS2012
940169
13.5
HS2012
940190
Only some part of it
13.5
HS2012
940330
13.5
HS2012
940340
13.5
HS2012
940350
13.5
HS2012
940360
13.5
HS2012
940390
Only some part of it
13.5
HS2017
940161
13.5
HS2017
940169
13.5
HS2017
940190
Only some part of it
13.5
HS2017
940330
13.5
HS2017
940340
13.5
HS2017
940350
13.5
HS2017
940360
13.5
HS2017
940390
Only some part of it
13.6
HS2002
9406
Only some part of it
13.6
HS2007
9406
Only some part of it
13.6
HS2012
9406
Only some part of it
13.6
HS2017
940610
13.7
HS2002
4404
13.7
HS2002
4405
13.7
HS2002
4413
13.7
HS2002
4417
13.7
HS2002
442110
13.7
HS2002
442190
Only some part of it
13.7
HS2007
4404
13.7
HS2007
4405
13.7
HS2007
4413
13.7
HS2007
4417
13.7
HS2007
442110
13.7
HS2007
442190
Only some part of it
13.7
HS2012
4404
13.7
HS2012
4405
13.7
HS2012
4413
13.7
HS2012
4417
13.7
HS2012
442110
13.7
HS2012
442190
Only some part of it
13.7
HS2017
4404
13.7
HS2017
4405
13.7
HS2017
4413
13.7
HS2017
4417
13.7
HS2017
442110
13.7
HS2017
442199
14.1
HS2002
4807
14.1
HS2007
4807
14.1
HS2012
4807
14.1
HS2017
4807
14.2
HS2002
481110
14.2
HS2002
481141
14.2
HS2002
481149
14.2
HS2002
481160
14.2
HS2002
481190
14.2
HS2007
481110
14.2
HS2007
481141
14.2
HS2007
481149
14.2
HS2007
481160
14.2
HS2007
481190
14.2
HS2012
481110
14.2
HS2012
481141
14.2
HS2012
481149
14.2
HS2012
481160
14.2
HS2012
481190
14.2
HS2017
481110
14.2
HS2017
481141
14.2
HS2017
481149
14.2
HS2017
481160
14.2
HS2017
481190
14.3
HS2002
4818
14.3
HS2007
4818
14.3
HS2012
4818
14.3
HS2017
4818
14.4
HS2002
4819
14.4
HS2007
4819
14.4
HS2012
4819
14.4
HS2017
4819
14.5
HS2002
4814
14.5
HS2002
4816
14.5
HS2002
4817
14.5
HS2002
4820
14.5
HS2002
4821
14.5
HS2002
4822
14.5
HS2002
4823
14.5
HS2007
4814
14.5
HS2007
4816
14.5
HS2007
4817
14.5
HS2007
4820
14.5
HS2007
4821
14.5
HS2007
4822
14.5
HS2007
4823
14.5
HS2012
4814
14.5
HS2012
4816
14.5
HS2012
4817
14.5
HS2012
4820
14.5
HS2012
4821
14.5
HS2012
4822
14.5
HS2012
4823
14.5
HS2017
4814
14.5
HS2017
4816
14.5
HS2017
4817
14.5
HS2017
4820
14.5
HS2017
4821
14.5
HS2017
4822
14.5
HS2017
4823
14.5.1
HS2002
482390
Only some part of it
14.5.1
HS2007
482390
Only some part of it
14.5.1
HS2012
482390
Only some part of it
14.5.1
HS2017
482390
Only some part of it
14.5.2
HS2002
482370
14.5.2
HS2007
482370
14.5.2
HS2012
482370
14.5.2
HS2017
482370
14.5.3
HS2002
482320
14.5.3
HS2007
482320
14.5.3
HS2012
482320
14.5.3
HS2017
482320
12.6
HS2002
482110
Only some part of it
12.6
HS2002
482190
Only some part of it
12.6
HS2002
482210
Only some part of it
12.6
HS2002
482290
Only some part of it
12.6
HS2002
482312
Only some part of it
12.6
HS2002
482319
Only some part of it
12.6
HS2002
482320
Only some part of it
12.6
HS2002
482340
Only some part of it
12.6
HS2002
482360
Only some part of it
12.6
HS2002
482370
Only some part of it
12.6
HS2002
482390
Only some part of it
12.6
HS2002
480210
Only some part of it
12.6
HS2002
480220
Only some part of it
12.6
HS2002
480230
Only some part of it
12.6
HS2002
480240
Only some part of it
12.6
HS2002
480254
Only some part of it
12.6
HS2002
480255
Only some part of it
12.6
HS2002
480256
Only some part of it
12.6
HS2002
480257
Only some part of it
12.6
HS2002
480258
Only some part of it
12.6
HS2002
480261
Only some part of it
12.6
HS2002
480262
Only some part of it
12.6
HS2002
480269
Only some part of it
12.6
HS2002
481013
Only some part of it
12.6
HS2002
481014
Only some part of it
12.6
HS2002
481019
Only some part of it
12.6
HS2002
481022
Only some part of it
12.6
HS2002
481029
Only some part of it
12.6
HS2002
481031
Only some part of it
12.6
HS2002
481032
Only some part of it
12.6
HS2002
481039
Only some part of it
12.6
HS2002
481092
Only some part of it
12.6
HS2002
481099
Only some part of it
12.6
HS2007
481410
12.6
HS2007
481420
12.6
HS2007
481490
12.6
HS2007
481710
12.6
HS2007
481720
12.6
HS2007
481730
12.6
HS2007
482010
12.6
HS2007
482020
12.6
HS2007
482030
12.6
HS2007
482040
12.6
HS2007
482050
12.6
HS2007
482090
12.6
HS2007
482110
12.6
HS2007
482190
12.6
HS2007
482210
12.6
HS2007
482290
12.6
HS2007
482320
12.6
HS2007
482340
12.6
HS2007
482361
12.6
HS2007
482369
12.6
HS2007
482370
12.6
HS2007
482390
12.6
HS2012
481420
12.6
HS2012
481490
12.6
HS2012
481710
12.6
HS2012
481720
12.6
HS2012
481730
12.6
HS2012
482020
12.6
HS2012
482030
12.6
HS2012
482040
12.6
HS2012
482050
12.6
HS2012
482090
12.6
HS2012
482110
12.6
HS2012
482190
12.6
HS2012
482210
12.6
HS2012
482290
12.6
HS2012
482320
12.6
HS2012
482340
12.6
HS2012
482361
12.6
HS2012
482369
12.6
HS2012
482370
12.6
HS2012
482390
12.6.1
HS2002
480210
Only some part of it
12.6.1
HS2002
480220
Only some part of it
12.6.1
HS2002
480230
Only some part of it
12.6.1
HS2002
480240
Only some part of it
12.6.1
HS2002
480254
Only some part of it
12.6.1
HS2002
480255
Only some part of it
12.6.1
HS2002
480256
Only some part of it
12.6.1
HS2002
480257
Only some part of it
12.6.1
HS2002
480258
Only some part of it
12.6.1
HS2002
480261
Only some part of it
12.6.1
HS2002
480262
Only some part of it
12.6.1
HS2002
480269
Only some part of it
12.6.1
HS2002
481013
Only some part of it
12.6.1
HS2002
481014
Only some part of it
12.6.1
HS2002
481019
Only some part of it
12.6.1
HS2002
481022
Only some part of it
12.6.1
HS2002
481029
Only some part of it
12.6.1
HS2002
481031
Only some part of it
12.6.1
HS2002
481032
Only some part of it
12.6.1
HS2002
481039
Only some part of it
12.6.1
HS2002
481092
Only some part of it
12.6.1
HS2002
481099
Only some part of it
12.6.1
HS2002
482390
Only some part of it
12.6.1
HS2007
482390
Only some part of it
12.6.1
HS2012
482390
Only some part of it
12.6.2
HS2002
482370
12.6.2
HS2007
482370
12.6.2
HS2012
482370
12.6.3
HS2002
482320
12.6.3
HS2007
482320
12.6.3
HS2012
482320
Conversion factors
JFSQ
JOINT FOREST SECTOR QUESTIONNAIRE
Conversion Factors
NOTE THESE ARE ONLY GENERAL FACTORS. IT WOULD BE PREFERABLE TO USE SPECIES- OR COUNTRY-SPECIFIC FACTORS
Multiply the quantity expressed in units on the right side of "per" with the factor to get the value expressed in units on left side of "per".
Items in BOLD RED text were added to the JFSQ in February 2023
Product Code
Product
JFSQ Quantity Unit
Results from UNECE/FAO/ITTO 2020 publication "Forest Product Conversion Factors"
Source: Fonseca "Measurement of Roundwood" 2005, ITTO Annual Review 2007, table 3-2-a
Species weight averaged using m3/tonne from Fonseca 2005 and volume exported by species from each region as shown in ITTO 2007 (assumes that bark is removed)
1.37
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3 ub
1.04
0.96
1.12
Averaged C & NC
1.05
Based on 950 kg/m3 green. Bark is included in weight but not in volume.
1.2.1.C
Coniferous
1000 m3 ub
1.10
1.00
1.19
1.07
Based on 935 kg/m3 green. Bark is included in weight but not in volume.
1.43
1.2.1.NC
Non-Coniferous
1000 m3 ub
0.97
0.92
1.04
0.91
Based on 1093 kg/m3 green. Bark is included in weight but not in volume.
1.25
1.2.NC.Beech
Beech
1000 m3 ub
0.92
Austrian Energy Agency, 2009
1.2.NC.Birch
Birch
1000 m3 ub
0.88
Austrian Energy Agency, 2009
1.2.NC.Eucalyptus
Eucalyptus
1000 m3 ub
0.77
ATIBT, 1982
1.2.NC.Oak
Oak
1000 m3 ub
0.88
Austrian Energy Agency, 2009
1.2.NC.Poplar
Poplar
1000 m3 ub
1.06
Austrian Energy Agency, 2009
1.2.2
PULPWOOD (ROUND & SPLIT)
1000 m3 ub
1.05
1.14
1.30
Averaged C & NC
1.08
Based on 930 kg/m3 green. Bark is included in weight but not in volume.
1.48
1.2.2.C
Coniferous
1000 m3 ub
1.11
1.16
1.35
1.12
Based on 891 kg/m3 green. Bark is included in weight but not in volume.
1.54
1.2.2.NC
Non-Coniferous
1000 m3 ub
0.98
1.11
1.25
0.91
Based on 1095 kg/m3 green. Bark is included in weight but not in volume.
1.33
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3 ub
1.07
1.33
1.2.3.C
Coniferous
1000 m3 ub
1.11
1.16
1.35
used pulpwood data
1.12
same as 1.2.2.C
1.43
1.2.3.NC
Non-Coniferous
1000 m3 ub
0.98
1.11
1.25
0.91
same as 1.2.2.NC
1.25
2
WOOD CHARCOAL
1000 MT
6 m3rw/tonne
5.35
Does not include the use of any of the wood fiber to generate the heat to make (add about 30% if inputted wood fiber used to provide heat)
6.00
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
3.1
WOOD CHIPS AND PARTICLES
1000 m3
1.205
1.07
1.21
1.08
m3 /MT = green swe per odmt / avg delivered tonne/odmt, rwe= +1%
softwood=1.19
1.205
Based on swe/odmt of 2.41 and avg delivered mt / odmt of 2.0 in solid m3
1.60
1000 m3
hardwood = 1.05
1.123
Based on swe/odmt of 2.01 and avg delivered mt / odmt of 1.79 in solid m3
1000 m3
Woodchip, Green swe to oven-dry tonne m3/odmt
mix = 1.15
3.2
WOOD RESIDUES
1000 m3
1.205
1.07
1.21
1.08
Based on wood chips
Green=1.15
Based on wood chips
1.50
1000 m3
2.12
2.07
Seasoned = 2.12
2.07
Assumption for seasoned is based on average basic density of .42 from questionnaire and assumes 15% moisture content
3.2.1
of which: SAWDUST
1000 m3
1.205
1.07
1.21
1.08
Based on wood chips
4
RECOVERED POST-CONSUMER WOOD
1000 mt
Delivered MT (12-20% atmospheric moisture). Convert to dry weight for energy purposes (multiply by 0.88 - 0.80)
Dear Correspondent,
Thank you for contributing to the Joint Forest Sector Questionnaire (JFSQ). Before filling in the worksheets, please read these guidelines.
Please use only this questionnaire to report your data. The 2020 JFSQ contains no pre-filling.
Use this questionnaire also to revise any historical data - fill in the correct year and your name on the cover page.
The worksheet 'LAM & CHIPS' covers data on Glulam and X-lam. This sheet has a yellow drop-down cell where you can choose your unit of measurement.
The total number of sheets to be filled in is eight core sheets (green tabs - to be validated by Eurostat) plus three for ITTO (brown tabs - not validated by Eurostat). Four sheets containing cross-references are included at the end. The flat file is for Eurostat for validation purposes, please do not change any cells here.
Put all your data into one Excel file. If you send some data in later, give your file a new version number and date (see A.1. below) and notify us of the changes with respect to the previous version. Only send us completely filled-in sheets, highlighting the changes in yellow. Do not delete worksheets.
Each sheet has a working area for your input. Most sheets have checking cells and tables.
Each working area has white cells and shaded cells. Eurostat has highlighted the variables it considers most important for its publications - please fill those in as a priority.
When you submit a revision, please highlight changes in yellow and explain them in the appropriate 'Note' column.
Please use flags and notes (see A.6 below). This information is important for Eurostat.
A. General recommendations
A.1 Please use eDAMIS to send your questionnaire to Eurostat. Choose the correct domain ("FOREST_A_A") and the correct reference year (for this data collection: 2020).
A.2 Fill in the JFSQ quality report if you haven't done so yet. If you already sent the quality report to Eurostat before, please fill it in only if major changes had happened to its content.
A.3 The cover page is for your contact details, which are automatically copied to the other worksheets
• Check your country code
• If necessary change the reference year [red cell] as appropriate - the previous year will appear automatically
If you distribute worksheets to various experts, they can each put their contact details into the sheets. It will then be your job to put all the information together again and to verify the checking tables, since some of them will not work as designed in isolation.
A.4 Look at the unit of measurement to be used for each item and report in this unit if possible, using the conversion factors on the last page of the JFSQ definitions. Please report the monetary values in the same unit for both reporting years.
Only report data or modify cells in the working areas. Please do not delete checking areas or checking sheets.
• Look at the checking areas and make the necessary corrections to your data to remove all warnings (see the specific recommendations) before sending in your data.
Fill in real zeroes '0' in the worksheets if there is no production or trade. Empty cells will be interpreted as 'Data not available'.
• There are counters at the bottom of the tables to indicate the number of cells left to be filled in and the number of cells filled with text.
Report all data with at least three decimals. Do not use a separator for thousands; for the decimal point, please use the one set up by default.
A.5 Report numbers only. If data are confidential, please provide them if possible, appropriately flagged (see A.6).
• Eurostat has a right to all confidential data necessary for its work. It has an obligation to use such data only in aggregates and to respect all the legal obligations.
• If you cannot provide confidential data, a good option is to send in your own estimate flagged as a national estimate '9'.
• As a last resort, leave the cell empty, flag it and write a note indicating data sources and links.
Checking tables contain formulae to sum up the totals for sub-items.
A.6 Flag cells and write notes as appropriate. Flags should be entered in the 'Flag' columns and notes in the 'Note' columns for the appropriate year and item. The flags to use are:
• 5 for repeating the data of a previous year
• 6 for confidential data
• 7 for provisional data
• 9 for national estimate
B Specific recommendations
B.1 Sheet 'Removals over bark' is for volumes of wood products measured over bark. General over bark/under bark conversion factors are calculated automatically.
• Should you use different conversion factor(s) please delete the ones provided and insert your own
• If you only have under bark data, please leave this worksheet empty, but revise the table with the conversion factors.
• Unchanged conversion factors will be considered revised
A checking table verifies that sums of sub-items agree with the totals
B.2 Checking tables on worksheets improve data quality, verifying that:
• The sum of the sub-items equals the total
• The sum of 'of which' items is not larger than the total
All cells in a checking table should be zero or empty. If this is not the case, please check your numbers for the sub-items and totals. The checking table indicates the difference, so if you see a negative value, you will have to decide which number should be increased by that amount. The only exception is when no data are entered due to confidentiality.
B.3 Worksheets 'JQ2' and 'EU1' contain a checking table for apparent consumption and for unit values.
Apparent consumption = Production + Imports – Exports. It should be positive or nil. If this is not the case, the cell will change colour and indicate the difference.
• Please correct the data in the sheets until checking results are positive or nil. One solution is to increase production
• If the data are correct but apparent consumption is still negative, please explain why in the 'Note' column provided in the apparent consumption checking table.
B.4 Sheets 'JQ2', 'ECE-EU Species' and 'EU1' on trade have checking tables to verify data consistency. Both quantity and value must be present.
When something is missing, messages or coloured cells appear in the checking tables. Please correct your data until all warnings disappear.
The meaning of the messages is:
• 0: both value and quantity are zero – all is well, there is no trade
• ZERO Q: value is reported, quantity is zero - please correct
• ZERO V: quantity is reported, value is zero - please correct
• REPORT: both quantity and value are blank - please fill in
• NO Q: blank cell for quantity – please fill in
• NO V: blank cell for value – please fill in
Please enter even very small numbers to resolve problems, using as many decimal places as necessary.
If there is no way to correct the problem, please write an explanation in the 'Note' column.
If there is no trade for a product, please enter 0 for both quantity and value.
Thank you for collecting data for the JFSQ.
Eurostat's Forestry Team
JFSQ quality report
Joint Forest Sector Questionnaire
Quality Report
Quality information
Country reply
1
Contact
Country name
Country name
Contact organisation
Contact organisation
Contact name
Contact name
Contact email address
Contact email address
2
Changes to previous year
Necessity of update
Are there any changes to the quality report of the last data collection?
Please select YES or NO
If yes, please provide details below.
3
Statistical processing
Overview of the source data
Please provide an overview of the sources used to produce JFSQ data.
Do you use a dedicated survey (of the industry, of households, of forest owners, etc.)?
Please select YES or NO
If yes, please provide details (e.g., who are the respondents, what is its frequency?).
Do you use forestry statistics?
Please select YES or NO
If yes, please provide details.
Do you use national forest inventory?
Please select YES or NO
If yes, please provide details.
Do you use national PRODCOM data compiled according to the CPA classification?
Please select YES or NO
If yes, please provide details (which products, units, etc.).
Do you use any other national production statistics?
Please select YES or NO
If yes, please provide details.
Do you use data collected by associations of industry?
Please select YES or NO
If yes, please provide details.
Do you collect data from direct contacts with manufacturing companies?
Please select YES or NO
If yes, please provide details.
Do you use estimates of roundwood use (in manufacturing)?
Please select YES or NO
If yes, please provide details.
Do you use national trade data?
Please select YES or NO
If yes, please provide details.
Do you use felling reports?
Please select YES or NO
If yes, please provide details.
Do you use forestry companies' accounting network?
Please select YES or NO
If yes, please provide details.
Do you use administrative data (e.g. tax records, business registers)?
Please select YES or NO
If yes, please provide details.
Do you use data from national accounts?
Please select YES or NO
If yes, please provide details (e.g. for which data, from which account tables?).
Do you use SBS (Structural business statistics)?
Please select YES or NO
If yes, please provide details (e.g. for which data?).
Do you use other environmental accounts?
Please select YES or NO
If yes, please provide details.
Do you use other statistics (e.g. agriculture statistics)?
Please select YES or NO
If yes, please specify them.
Do you use any other sources?
Please select YES or NO
If yes, please specify them.
Methodological issues
Are there any pending classification or measurement issues?
Please select YES or NO
If yes, please specify them.
Data validation
Do you check the quality of the data collected to compile JFSQ?
Please select YES or NO
If yes, please explain the quality assurance procedure.
Do you compare JFSQ data with different data sources or do you perform other cross-checks?
Please select YES or NO
If yes, please explain your approach.
Do you have validation rules and other plausibility checks for the outputs of your JFSQ data compilation process?
Please select YES or NO
If yes, please briefly describe them.
4
Relevance
User needs
Please provide references to the relevance of JFSQ at national level e.g. main users, national indicator sets, quantitative policy targets etc.
5
Coherence and comparability
Coherence - cross domain
Do you compare the JFSQ results with business, energy and agricultural and foreign trade statistics?
Please select YES or NO
It not, please explain.
Do you compare the JFSQ results with business, energy and agricultural and foreign trade statistics?
Please select YES or NO
It not, please explain.
Do you cross-check the JFSQ data with the results of European Forest Accounts?
Please select YES or NO
If yes, please indicate for which reporting items, and comments on the discrepancies observed, if any. It not, please explain.
Coherence - internal
Are there any other consistency issues related to your JFSQ data?
Please select YES or NO
If yes, please explain them.
6
Accessibility and clarity
Publications
Do you disseminate JFSQ data nationally (e.g. in news releases or other documents)?
Please select YES or NO
If yes, please provide URLs and/or the reference to the relevant publications.
Online database
Do you publish your JFSQ accounts in an online data base?
Please select YES or NO
If yes, please provide URLs.
Documentation on methodology
Did you prepare a description of your national JFSQ methodology or metadata?
Please select YES or NO
If yes, please provide URLs.
Quality documentation
Do you have national quality documentation?
Please select YES or NO
If yes, please provide URLs.
7
Other comments
Other comments
Please provide any further feedback you might have on the quality of the reported data, sources and methods used and/or Eurostat's validation and quality report templates.
Cover
Joint Forest Sector Questionnaire
2020
DATA INPUT FILE
Correspondent country:
HR
Reference year:
2020
Fill in the year
Name of person responsible for reply:
Official address (in full):
Branimirova 19, 10000 Zagreb, Croatia
Telephone:
Fax:
E-mail:
Removals over bark
Country:
HR
Date:
Name of Official responsible for reply:
0
Check Table
Official Address (in full):
EU JQ1 OB
Branimirova 19, 10000 Zagreb, Croatia
FOREST SECTOR QUESTIONNAIRE
Telephone:
0
0
Discrepancies
Removals
E-mail:
0
Please verify, if there's an error!
Year 1
Year 2
Flag
Flag
Note
Note
Product
Product
Unit
2019
2020
2019
2020
2019
2020
Product
Product
Unit
2019
2020
Code
Quantity
Quantity
Code
Quantity
Quantity
ROUNDWOOD REMOVALS OVERBARK
ROUNDWOOD REMOVALS OVERBARK
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ob
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ob
OK
OK
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ob
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ob
OK
OK
1.1.C
Coniferous
1000 m3ob
1.1.C
Coniferous
1000 m3ob
1.1.NC
Non-Coniferous
1000 m3ob
1.1.NC
Non-Coniferous
1000 m3ob
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ob
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ob
OK
OK
1.2.C
Coniferous
1000 m3ob
1.2.C
Coniferous
1000 m3ob
OK
OK
1.2.NC
Non-Coniferous
1000 m3ob
1.2.NC
Non-Coniferous
1000 m3ob
OK
OK
1.2.NC.T
of which: Tropical
1000 m3ob
1.2.NC.T
of which: Tropical
1000 m3ob
OK
OK
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3ob
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3ob
OK
OK
1.2.1.C
Coniferous
1000 m3ob
1.2.1.C
Coniferous
1000 m3ob
1.2.1.NC
Non-Coniferous
1000 m3ob
1.2.1.NC
Non-Coniferous
1000 m3ob
1.2.2
PULPWOOD, ROUND AND SPLIT
1000 m3ob
1.2.2
PULPWOOD, ROUND AND SPLIT
1000 m3ob
OK
OK
1.2.2.C
Coniferous
1000 m3ob
1.2.2.C
Coniferous
1000 m3ob
1.2.2.NC
Non-Coniferous
1000 m3ob
1.2.2.NC
Non-Coniferous
1000 m3ob
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3ob
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3ob
OK
OK
1.2.3.C
Coniferous
1000 m3ob
1.2.3.C
Coniferous
1000 m3ob
1.2.3.NC
Non-Coniferous
1000 m3ob
1.2.3.NC
Non-Coniferous
1000 m3ob
To fill:
17
17
Text:
0
0
Product
Product
Unit
2019
2020
Code
CF
CF
OVERBARK/UNDERBARK CONVERSION FACTORS
1
ROUNDWOOD (WOOD IN THE ROUGH)
m3/m3
0.000
0.000
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
m3/m3
0.000
0.000
1.1.C
Coniferous
m3/m3
0.000
0.000
1.1.NC
Non-Coniferous
m3/m3
0.000
0.000
1.2
INDUSTRIAL ROUNDWOOD
m3/m3
1.100
1.100
1.2.C
Coniferous
m3/m3
1.200
1.200
1.2.NC
Non-Coniferous
m3/m3
0.000
0.000
1.2.NC.T
of which: Tropical
m3/m3
ERROR:#DIV/0!
ERROR:#DIV/0!
1.2.1
SAWLOGS AND VENEER LOGS
m3/m3
0.000
0.000
1.2.1.C
Coniferous
m3/m3
0.000
0.000
1.2.1.NC
Non-Coniferous
m3/m3
1.100
1.100
1.2.2
PULPWOOD, ROUND AND SPLIT
m3/m3
1.200
1.200
1.2.2.C
Coniferous
m3/m3
0.000
0.000
1.2.2.NC
Non-Coniferous
m3/m3
1.100
1.100
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
m3/m3
1.200
1.200
1.2.3.C
Coniferous
m3/m3
0.000
0.000
1.2.3.NC
Non-Coniferous
m3/m3
1.100
1.100
JQ1 Production
Country:
HR
Date:
Name of Official responsible for reply:
0
Official Address (in full):
FOREST SECTOR QUESTIONNAIRE JQ1
Branimirova 19, 10000 Zagreb, Croatia
Industrial Roundwood Balance
PRIMARY PRODUCTS
Telephone:
0
0
This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!
Discrepancies
Removals and Production
E-mail:
0
test for good numbers, missing number, bad number, negative number
Year -1
Year
Flag
Flag
Note
Note
Product
Product
Unit
2019
2020
2019
2020
2019
2020
Product
Product
Unit
2019
2020
2019
2020
% change
Conversion factors
Code
Quantity
Quantity
Code
Quantity
Quantity
Roundwood
Industrial roundwood availability
McCusker 14/6/07: McCusker 14/6/07:
minus 1.2.3 (other ind. RW) production
3,125
-60,402
-2033%
m3 of wood in m3 or t of product
REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
Recovered wood used in particle board
42
67
62%
Solid wood equivalent
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
5,400.11
5,233.86
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
OK
OK
Solid Wood Demand
agglomerate production
356
351
-1%
2.4
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
2,205.26
2,206.91
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
OK
OK
Sawnwood production
1,446
1,298
-10%
1
1.1.C
Coniferous
1000 m3ub
54.02
57.81
1.1.C
Coniferous
1000 m3ub
veneer production
34
32
-5%
1
1.1.NC
Non-Coniferous
1000 m3ub
2,151.24
2,149.10
1.1.NC
Non-Coniferous
1000 m3ub
plywood production
7
2
-70%
1
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
3,194.85
3,026.95
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
OK
OK
particle board production (incl OSB)
119
192
62%
1.58
1.2.C
Coniferous
1000 m3ub
910.99
818.54
1.2.C
Coniferous
1000 m3ub
OK
OK
fibreboard production
0
0
missing data
1.8
1.2.NC
Non-Coniferous
1000 m3ub
2,283.86
2,208.41
1.2.NC
Non-Coniferous
1000 m3ub
OK
OK
mechanical/semi-chemical pulp production
47
45
-4%
2.5
1.2.NC.T
of which: Tropical
1000 m3ub
0.00
0.00
1.2.NC.T
of which: Tropical
1000 m3ub
OK
OK
chemical pulp production
0
0
missing data
4.9
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3ub
2,627.91
2,468.90
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3ub
OK
OK
dissolving pulp production
0
0
missing data
5.7
1.2.1.C
Coniferous
1000 m3ub
623.74
534.35
1.2.1.C
Coniferous
1000 m3ub
Availability
Solid Wood Demand
2,647
2,592
-2%
1.2.1.NC
Non-Coniferous
1000 m3ub
2,004.17
1,934.55
1.2.1.NC
Non-Coniferous
1000 m3ub
Difference (roundwood-demand)
-2,605
-2,525
-3%
positive = surplus
1.2.2
PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)
1000 m3ub
562.31
553.81
1.2.2
PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)
1000 m3ub
OK
OK
gap (demand/availability)
15%
104%
Negative number means not enough roundwood available
1.2.2.C
Coniferous
1000 m3ub
283.50
280.84
1.2.2.C
Coniferous
1000 m3ub
Positive number means more roundwood available than demanded
1.2.2.NC
Non-Coniferous
1000 m3ub
278.81
272.97
1.2.2.NC
Non-Coniferous
1000 m3ub
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3ub
4.63
4.24
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3ub
OK
OK
1.2.3.C
Coniferous
1000 m3ub
3.75
3.35
1.2.3.C
Coniferous
1000 m3ub
% of particle board that is from recovered wood
35%
1.2.3.NC
Non-Coniferous
1000 m3ub
0.88
0.89
1.2.3.NC
Non-Coniferous
1000 m3ub
share of agglomerates produced from industrial roundwood residues
100%
PRODUCTION
PRODUCTION
usable industrial roundwood - amount of roundwood that is used, remainder leaves industry
98.5%
2
WOOD CHARCOAL
1000 t
13.63
12.58
2
WOOD CHARCOAL
1000 t
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
567.91
449.17
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
OK
OK
3.1
WOOD CHIPS AND PARTICLES
1000 m3
567.91
449.17
3.1
WOOD CHIPS AND PARTICLES
1000 m3
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
0.00
0.00
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
4
RECOVERED POST-CONSUMER WOOD
1000 t
0.00
0.00
4
RECOVERED POST-CONSUMER WOOD
1000 t
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 t
356.13
351.20
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 t
OK
OK
5.1
WOOD PELLETS
1000 t
307.69
311.24
5.1
WOOD PELLETS
1000 t
5.2
OTHER AGGLOMERATES
1000 t
48.44
39.96
5.2
OTHER AGGLOMERATES
1000 t
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
1445.95
1298.15
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
OK
OK
6.C
Coniferous
1000 m3
247.37
256.54
6.C
Coniferous
1000 m3
6.NC
Non-Coniferous
1000 m3
1198.58
1041.61
6.NC
Non-Coniferous
1000 m3
6.NC.T
of which: Tropical
1000 m3
0.00
0.04
6.NC.T
of which: Tropical
1000 m3
OK
OK
7
VENEER SHEETS
1000 m3
33.89
32.35
7
VENEER SHEETS
1000 m3
OK
OK
7.C
Coniferous
1000 m3
0.00
0.00
7.C
Coniferous
1000 m3
7.NC
Non-Coniferous
1000 m3
33.89
32.35
7.NC
Non-Coniferous
1000 m3
7.NC.T
of which: Tropical
1000 m3
0.00
0.00
7.NC.T
of which: Tropical
1000 m3
OK
OK
8
WOOD-BASED PANELS
1000 m3
126.17
194.57
8
WOOD-BASED PANELS
1000 m3
OK
OK
8.1
PLYWOOD
1000 m3
7.28
2.16
8.1
PLYWOOD
1000 m3
OK
OK
8.1.C
Coniferous
1000 m3
6.44
1.45
8.1.C
Coniferous
1000 m3
8.1.NC
Non-Coniferous
1000 m3
0.84
0.71
8.1.NC
Non-Coniferous
1000 m3
8.1.NC.T
of which: Tropical
1000 m3
0.00
0.00
8.1.NC.T
of which: Tropical
1000 m3
OK
OK
8.2
PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD
1000 m3
118.89
192.41
8.2
PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD
1000 m3
8.2.1
of which: ORIENTED STRAND BOARD (OSB)
1000 m3
0.00
0.00
8.2.1
of which: ORIENTED STRAND BOARD (OSB)
1000 m3
OK
OK
8.3
FIBREBOARD
1000 m3
0.00
0.00
8.3
FIBREBOARD
1000 m3
OK
OK
8.3.1
HARDBOARD
1000 m3
0.00
0.00
8.3.1
HARDBOARD
1000 m3
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
0.00
0.00
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
8.3.3
OTHER FIBREBOARD
1000 m3
0.00
0.00
8.3.3
OTHER FIBREBOARD
1000 m3
9
WOOD PULP
1000 t
46.85
45.11
9
WOOD PULP
1000 t
OK
OK
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 t
46.85
45.11
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 t
9.2
CHEMICAL WOOD PULP
1000 t
0.00
0.00
9.2
CHEMICAL WOOD PULP
1000 t
OK
OK
9.2.1
SULPHATE PULP
1000 t
0.00
0.00
9.2.1
SULPHATE PULP
1000 t
9.2.1.1
of which: BLEACHED
1000 t
0.00
0.00
9.2.1.1
of which: BLEACHED
1000 t
OK
OK
9.2.2
SULPHITE PULP
1000 t
0.00
0.00
9.2.2
SULPHITE PULP
1000 t
9.3
DISSOLVING GRADES
1000 t
0.00
0.00
9.3
DISSOLVING GRADES
1000 t
10
OTHER PULP
1000 t
0.00
0.00
10
OTHER PULP
1000 t
OK
OK
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 t
0.00
0.00
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 t
10.2
RECOVERED FIBRE PULP
1000 t
0.00
0.00
10.2
RECOVERED FIBRE PULP
1000 t
11
RECOVERED PAPER
1000 t
0.00
0.00
11
RECOVERED PAPER
1000 t
12
PAPER AND PAPERBOARD
1000 t
346.79
341.72
12
PAPER AND PAPERBOARD
1000 t
OK
OK
12.1
GRAPHIC PAPERS
1000 t
0.00
0.00
12.1
GRAPHIC PAPERS
1000 t
OK
OK
12.1.1
NEWSPRINT
1000 t
0.00
0.00
12.1.1
NEWSPRINT
1000 t
12.1.2
UNCOATED MECHANICAL
1000 t
0.00
0.00
12.1.2
UNCOATED MECHANICAL
1000 t
12.1.3
UNCOATED WOODFREE
1000 t
0.00
0.00
12.1.3
UNCOATED WOODFREE
1000 t
12.1.4
COATED PAPERS
1000 t
0.00
0.00
12.1.4
COATED PAPERS
1000 t
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 t
0.00
0.00
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 t
12.3
PACKAGING MATERIALS
1000 t
346.70
341.72
12.3
PACKAGING MATERIALS
1000 t
OK
OK
12.3.1
CASE MATERIALS
1000 t
213.11
208.96
12.3.1
CASE MATERIALS
1000 t
12.3.2
CARTONBOARD
1000 t
0.47
0.49
12.3.2
CARTONBOARD
1000 t
12.3.3
WRAPPING PAPERS
1000 t
133.12
132.27
12.3.3
WRAPPING PAPERS
1000 t
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 t
0.00
0.00
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 t
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
1000 t
0.09
0.00
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
Mechanical wood pulp, semi-chemical wood pulp and pulp from fibres other than wood 9.1+10.1
1000 t
46.850
45.110
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:
HR
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):
Branimirova 19, 10000 Zagreb, Croatia
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:
HR
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
minor changes from version 1
Trade
Discrepancies
REPORT
no figures reported
Product
Unit of
I M P O R T
E X P O R T
Import
Export
Import
Export
Product
I M P O R T
E X P O R T
Product
Apparent Consumption
Related Notes
Product
Value per
I M P O R T
E X P O R T
Column1
Column2
Product
Value per
I M P O R T
E X P O R T
code
Product
quantity
2019
2020
2019
2020
2019
2020
2019
2020
2019
2020
2019
2020
code
2019
2020
2019
2020
code
2019
2020
2019
2020
code
Product
unit
2019
2020
2019
2020
IMPORT
EXPORT
code
Product
unit
2019
2020
2019
2020
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
227.18
135,271.59
193.56
99,901.48
782.53
491,477.61
794.11
542,183.15
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
OK
OK
OK
OK
OK
OK
OK
OK
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
4,845
-350,972
1
ROUNDWOOD (WOOD IN THE ROUGH)
NAC/m3
595
516
628
683
ACCEPT
ACCEPT
1
ROUNDWOOD (WOOD IN THE ROUGH)
NAC/m3
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
59.01
35,313.56
72.64
40,728.58
548.69
328,094.67
538.23
303,771.43
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
1,716
-290,574
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
NAC/m3
598
561
598
564
ACCEPT
ACCEPT
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
NAC/m3
1.1.C
Coniferous
1000 m3ub
5.13
1,923.85
1.11
591.00
49.08
27,227.41
49.71
28,069.54
1.1.C
Coniferous
1000 m3ub
1.1.C
Coniferous
1000 m3ub
10
-25,246
1.1.C
Coniferous
NAC/m3
375
532
555
565
ACCEPT
ACCEPT
1.1.C
Coniferous
NAC/m3
1.1.NC
Non-Coniferous
1000 m3ub
53.88
33,389.71
71.53
40,137.58
499.61
300,867.26
488.52
275,701.89
1.1.NC
Non-Coniferous
1000 m3ub
1.1.NC
Non-Coniferous
1000 m3ub
1,706
-265,328
1.1.NC
Non-Coniferous
NAC/m3
620
561
602
564
ACCEPT
ACCEPT
1.1.NC
Non-Coniferous
NAC/m3
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
168.17
99,958.03
120.92
59,172.90
233.84
163,382.94
255.88
238,411.72
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
OK
OK
OK
OK
OK
OK
OK
OK
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
3,129
-60,398
1.2
INDUSTRIAL ROUNDWOOD
NAC/m3
594
489
699
932
ACCEPT
ACCEPT
1.2
INDUSTRIAL ROUNDWOOD
NAC/m3
1.2.C
Coniferous
1000 m3ub
104.23
39,767.82
70.13
22,366.67
115.19
59,451.27
73.53
31,115.76
1.2.C
Coniferous
1000 m3ub
1.2.C
Coniferous
1000 m3ub
900
-18,865
1.2.C
Coniferous
NAC/m3
382
319
516
423
ACCEPT
ACCEPT
1.2.C
Coniferous
NAC/m3
1.2.NC
Non-Coniferous
1000 m3ub
63.94
60,190.21
50.79
36,806.23
118.65
103,931.67
182.35
207,295.96
1.2.NC
Non-Coniferous
1000 m3ub
1.2.NC
Non-Coniferous
1000 m3ub
2,229
-41,533
1.2.NC
Non-Coniferous
NAC/mt
941
725
876
1137
ACCEPT
ACCEPT
1.2.NC
Non-Coniferous
NAC/mt
1.2.NC.T
of which: Tropical
1000 m3ub
0.06
80.25
0.14
60.45
0.00
0.00
0.00
0.00
1.2.NC.T
of which: Tropical
1000 m3ub
OK
OK
OK
OK
OK
OK
OK
OK
1.2.NC.T
of which: Tropical
1000 m3ub
0
80
1.2.NC.T
of which: Tropical
1000 m3
1338
432
0
0
CHECK
ACCEPT
1.2.NC.T
of which: Tropical
1000 m3
2
WOOD CHARCOAL
1000 t
12.83
32,962.59
13.58
33,915.95
11.60
42,883.81
13.20
49,238.75
2
WOOD CHARCOAL
1000 t
2
WOOD CHARCOAL
1000 t
15
-9,909
2
WOOD CHARCOAL
1000 m3
2569
2497
3697
3730
ACCEPT
ACCEPT
2
WOOD CHARCOAL
1000 m3
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
120.41
33,452.00
159.97
32,301.62
480.99
123,050.60
366.10
82,501.67
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
207
-89,149
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
278
202
256
225
ACCEPT
ACCEPT
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
3.1
WOOD CHIPS AND PARTICLES
1000 m3
84.26
24,650.20
80.74
21,745.39
258.73
75,437.55
160.64
43,121.78
3.1
WOOD CHIPS AND PARTICLES
1000 m3
3.1
WOOD CHIPS AND PARTICLES
1000 m3
393
-50,338
3.1
WOOD CHIPS AND PARTICLES
1000 mt
293
269
292
268
ACCEPT
ACCEPT
3.1
WOOD CHIPS AND PARTICLES
1000 mt
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
36.15
8,801.80
79.23
10,556.23
222.26
47,613.05
205.46
39,379.89
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
-186
-38,811
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 mt
243
133
214
192
ACCEPT
ACCEPT
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 mt
4
RECOVERED POST-CONSUMER WOOD
1000 t
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
4
RECOVERED POST-CONSUMER WOOD
1000 t
4
RECOVERED POST-CONSUMER WOOD
1000 t
0
0
4
RECOVERED POST-CONSUMER WOOD
1000 mt
0
0
0
0
ACCEPT
ACCEPT
4
RECOVERED POST-CONSUMER WOOD
1000 mt
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 t
36.68
45,066.71
46.67
56,805.65
367.88
422,562.02
407.68
458,834.94
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 t
25
-377,144
5
WOOD PELLETS AND OTHER AGGLOMERATES
NAC/m3
1229
1217
1149
1125
ACCEPT
ACCEPT
5
WOOD PELLETS AND OTHER AGGLOMERATES
NAC/m3
5.1
WOOD PELLETS
1000 t
33.19
42,027.59
42.50
53,499.69
292.22
363,658.02
334.09
415,856.31
5.1
WOOD PELLETS
1000 t
5.1
WOOD PELLETS
1000 t
49
-321,319
5.1
WOOD PELLETS
NAC/m3
1266
1259
1244
1245
ACCEPT
ACCEPT
5.1
WOOD PELLETS
NAC/m3
5.2
OTHER AGGLOMERATES
1000 t
3.49
3,039.12
4.17
3,305.96
75.66
58,904.00
73.59
42,978.63
5.2
OTHER AGGLOMERATES
1000 t
5.2
OTHER AGGLOMERATES
1000 t
-24
-55,825
5.2
OTHER AGGLOMERATES
NAC/m3
871
793
779
584
ACCEPT
ACCEPT
5.2
OTHER AGGLOMERATES
NAC/m3
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
506.13
811,990.50
454.68
692,263.19
1,269.28
2,858,959.66
1,201.20
2,588,916.79
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
683
-2,045,671
6
SAWNWOOD (INCLUDING SLEEPERS)
NAC/m3
1604
1523
2252
2155
ACCEPT
ACCEPT
6
SAWNWOOD (INCLUDING SLEEPERS)
NAC/m3
6.C
Coniferous
1000 m3
425.13
514,876.21
366.33
453,702.41
365.36
465,813.85
350.32
443,085.35
6.C
Coniferous
1000 m3
6.C
Coniferous
1000 m3
307
49,319
6.C
Coniferous
NAC/m3
1211
1239
1275
1265
ACCEPT
ACCEPT
6.C
Coniferous
NAC/m3
6.NC
Non-Coniferous
1000 m3
81.00
297,114.29
88.35
238,560.78
903.92
2,393,145.81
850.88
2,145,831.44
6.NC
Non-Coniferous
1000 m3
6.NC
Non-Coniferous
1000 m3
376
-2,094,990
6.NC
Non-Coniferous
NAC/m3
3668
2700
2648
2522
ACCEPT
ACCEPT
6.NC
Non-Coniferous
NAC/m3
6.NC.T
of which: Tropical
1000 m3
13.48
43,374.86
1.37
17,312.01
0.80
28,691.60
0.36
10,178.35
6.NC.T
of which: Tropical
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
6.NC.T
of which: Tropical
1000 m3
13
14,683
6.NC.T
of which: Tropical
NAC/m3
3218
12637
35865
28273
CHECK
ACCEPT
6.NC.T
of which: Tropical
NAC/m3
7
VENEER SHEETS
1000 m3
9.07
88,450.08
10.72
111,562.46
22.50
402,506.17
20.82
382,265.49
7
VENEER SHEETS
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
7
VENEER SHEETS
1000 m3
20
-314,024
7
VENEER SHEETS
NAC/m3
9752
10407
17889
18360
ACCEPT
ACCEPT
7
VENEER SHEETS
NAC/m3
7.C
Coniferous
1000 m3
3.25
20,043.40
3.91
21,005.56
0.06
1,023.15
0.03
360.88
7.C
Coniferous
1000 m3
7.C
Coniferous
1000 m3
3
19,020
7.C
Coniferous
NAC/m3
6167
5372
17053
12029
ACCEPT
ACCEPT
7.C
Coniferous
NAC/m3
7.NC
Non-Coniferous
1000 m3
5.82
68,406.68
6.81
90,556.90
22.44
401,483.02
20.79
381,904.61
7.NC
Non-Coniferous
1000 m3
7.NC
Non-Coniferous
1000 m3
17
-333,044
7.NC
Non-Coniferous
NAC/m3
11754
13298
17891
18370
ACCEPT
ACCEPT
7.NC
Non-Coniferous
NAC/m3
7.NC.T
of which: Tropical
1000 m3
0.12
4,845.52
0.05
2,835.18
0.11
7,423.09
0.07
3,172.13
7.NC.T
of which: Tropical
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
7.NC.T
of which: Tropical
1000 m3
0
-2,578
7.NC.T
of which: Tropical
NAC/m3
40379
56704
67483
45316
ACCEPT
ACCEPT
7.NC.T
of which: Tropical
NAC/m3
8
WOOD-BASED PANELS
1000 m3
366.87
844,115.31
369.95
820,374.23
159.20
300,534.77
190.06
336,199.10
8
WOOD-BASED PANELS
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8
WOOD-BASED PANELS
1000 m3
334
543,775
8
WOOD-BASED PANELS
NAC/m3
2301
2218
1888
1769
ACCEPT
ACCEPT
8
WOOD-BASED PANELS
NAC/m3
8.1
PLYWOOD
1000 m3
34.71
147,201.45
31.06
126,901.92
5.81
32,208.53
5.20
29,494.37
8.1
PLYWOOD
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8.1
PLYWOOD
1000 m3
36
114,995
8.1
PLYWOOD
NAC/m3
4241
4086
5544
5672
ACCEPT
ACCEPT
8.1
PLYWOOD
NAC/m3
8.1.C
Coniferous
1000 m3
11.55
56,200.05
9.31
45,139.15
1.69
9,310.91
1.16
6,692.67
8.1.C
Coniferous
1000 m3
8.1.C
Coniferous
1000 m3
16
46,891
8.1.C
Coniferous
NAC/m3
4866
4848
5509
5770
ACCEPT
ACCEPT
8.1.C
Coniferous
NAC/m3
8.1.NC
Non-Coniferous
1000 m3
23.16
91,001.40
21.75
81,762.77
4.12
22,897.62
4.04
22,801.70
8.1.NC
Non-Coniferous
1000 m3
8.1.NC
Non-Coniferous
1000 m3
20
68,104
8.1.NC
Non-Coniferous
NAC/m3
3929
3759
5558
5644
ACCEPT
ACCEPT
8.1.NC
Non-Coniferous
NAC/m3
8.1.NC.T
of which: Tropical
1000 m3
0.84
5,254.82
0.64
5,059.01
0.04
275.48
0.23
687.21
8.1.NC.T
of which: Tropical
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8.1.NC.T
of which: Tropical
1000 m3
1
4,979
8.1.NC.T
of which: Tropical
NAC/m3
6256
7905
6887
2988
ACCEPT
CHECK
8.1.NC.T
of which: Tropical
NAC/m3
8.2
PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD
1000 m3
227.72
440,982.22
225.64
418,075.88
123.75
179,758.89
154.08
200,138.49
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
223
261,416
8.2
PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD
NAC/m3
1937
1853
1453
1299
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
39.33
72,016.39
44.49
76,030.12
2.41
5,171.24
3.42
7,095.84
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
37
66,845
8.2.1
of which: ORIENTED STRANDBOARD (OSB)
NAC/m3
1831
1709
2146
2075
ACCEPT
ACCEPT
8.2.1
of which: ORIENTED STRANDBOARD (OSB)
NAC/m3
8.3
FIBREBOARD
1000 m3
104.44
255,931.64
113.25
275,396.43
29.64
88,567.35
30.78
106,566.24
8.3
FIBREBOARD
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8.3
FIBREBOARD
1000 m3
75
167,364
8.3
FIBREBOARD
NAC/m3
2451
2432
2988
3462
ACCEPT
ACCEPT
8.3
FIBREBOARD
NAC/m3
8.3.1
HARDBOARD
1000 m3
13.64
70,257.42
13.84
67,552.85
2.11
10,052.02
4.02
19,003.05
8.3.1
HARDBOARD
1000 m3
8.3.1
HARDBOARD
1000 m3
12
60,205
8.3.1
HARDBOARD
NAC/mt
5151
4881
4764
4727
ACCEPT
ACCEPT
8.3.1
HARDBOARD
NAC/mt
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
86.64
180,879.06
95.68
204,376.48
27.43
78,333.18
26.65
87,375.96
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
59
102,546
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
NAC/mt
2088
2136
2856
3279
ACCEPT
ACCEPT
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
NAC/mt
8.3.3
OTHER FIBREBOARD
1000 m3
4.16
4,795.16
3.73
3,467.10
0.10
182.15
0.11
187.23
8.3.3
OTHER FIBREBOARD
1000 m3
8.3.3
OTHER FIBREBOARD
1000 m3
4
4,613
8.3.3
OTHER FIBREBOARD
NAC/mt
1153
930
1822
1702
ACCEPT
ACCEPT
8.3.3
OTHER FIBREBOARD
NAC/mt
9
WOOD PULP
1000 t
0.20
2,085.27
0.22
2,112.78
47.36
117,749.79
44.50
112,461.90
9
WOOD PULP
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
9
WOOD PULP
1000 t
-0
-115,619
9
WOOD PULP
NAC/mt
10426
9604
2486
2527
ACCEPT
ACCEPT
9
WOOD PULP
NAC/mt
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 t
0.08
295.28
0.10
390.19
47.36
117,694.29
44.50
112,447.71
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 t
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 t
-0
-117,354
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
NAC/mt
3691
3902
2485
2527
ACCEPT
ACCEPT
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
NAC/mt
9.2
CHEMICAL WOOD PULP
1000 t
0.12
1,789.87
0.12
1,722.59
0.00
55.50
0.00
14.19
9.2
CHEMICAL WOOD PULP
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
9.2
CHEMICAL WOOD PULP
1000 t
0
1,734
9.2
CHEMICAL WOOD PULP
NAC/mt
14916
14355
ZERO Q
ZERO Q
ACCEPT
CHECK
9.2
CHEMICAL WOOD PULP
NAC/mt
CHECK
CHECK
9.2.1
SULPHATE PULP
1000 t
0.00
5.65
0.01
59.88
0.00
0.00
0.00
0.00
9.2.1
SULPHATE PULP
1000 t
9.2.1
SULPHATE PULP
1000 t
0
6
9.2.1
SULPHATE PULP
NAC/mt
ZERO Q
5988
0
0
CHECK
ACCEPT
9.2.1
SULPHATE PULP
NAC/mt
INTRA-EU
9.2.1.1
of which: BLEACHED
1000 t
0.00
5.65
0.01
59.88
0.00
0.00
0.00
0.00
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
0
6
9.2.1.1
of which: BLEACHED
NAC/mt
ZERO Q
5988
0
0
CHECK
ACCEPT
9.2.1.1
of which: BLEACHED
NAC/mt
INTRA-EU
9.2.2
SULPHITE PULP
1000 t
0.12
1,784.22
0.11
1,662.71
0.00
55.50
0.00
14.19
9.2.2
SULPHITE PULP
1000 t
9.2.2
SULPHITE PULP
1000 t
0
1,729
9.2.2
SULPHITE PULP
NAC/mt
14869
15116
ZERO Q
ZERO Q
ACCEPT
CHECK
9.2.2
SULPHITE PULP
NAC/mt
INTRA-EU
INTRA-EU
9.3
DISSOLVING GRADES
1000 t
0.00
0.12
0.00
0.00
0.00
0.00
0.00
0.00
9.3
DISSOLVING GRADES
1000 t
9.3
DISSOLVING GRADES
1000 t
0
0
9.3
DISSOLVING GRADES
NAC/mt
ZERO Q
0
0
0
CHECK
ACCEPT
9.3
DISSOLVING GRADES
NAC/mt
INTRA-EU
10
OTHER PULP
1000 t
0.39
2,340.84
0.39
2,008.39
0.03
215.74
0.03
194.11
10
OTHER PULP
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
10
OTHER PULP
1000 t
0
2,125
10
OTHER PULP
NAC/mt
6002
5150
7191
6470
ACCEPT
ACCEPT
10
OTHER PULP
NAC/mt
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 t
0.09
1,064.68
0.04
375.81
0.01
101.33
0.01
104.44
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 t
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 t
0
963
10.1
PULP FROM FIBRES OTHER THAN WOOD
NAC/mt
11830
9395
10133
10444
ACCEPT
ACCEPT
10.1
PULP FROM FIBRES OTHER THAN WOOD
NAC/mt
10.2
RECOVERED FIBRE PULP
1000 t
0.30
1,276.16
0.35
1,632.58
0.02
114.41
0.02
89.67
10.2
RECOVERED FIBRE PULP
1000 t
10.2
RECOVERED FIBRE PULP
1000 t
0
1,162
10.2
RECOVERED FIBRE PULP
NAC/mt
4254
4665
5721
4484
ACCEPT
ACCEPT
10.2
RECOVERED FIBRE PULP
NAC/mt
11
RECOVERED PAPER
1000 t
162.49
128,014.59
162.00
113,542.30
162.61
132,570.99
147.29
110,791.34
11
RECOVERED PAPER
1000 t
11
RECOVERED PAPER
1000 t
-0
-4,556
11
RECOVERED PAPER
NAC/mt
788
701
815
752
ACCEPT
ACCEPT
11
RECOVERED PAPER
NAC/mt
12
PAPER AND PAPERBOARD
1000 t
366.20
2,039,763.88
357.65
1,885,961.16
185.42
599,969.77
190.89
562,772.87
12
PAPER AND PAPERBOARD
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
12
PAPER AND PAPERBOARD
1000 t
528
1,440,136
12
PAPER AND PAPERBOARD
NAC/mt
5570
5273
3236
2948
ACCEPT
ACCEPT
12
PAPER AND PAPERBOARD
NAC/mt
12.1
GRAPHIC PAPERS
1000 t
125.57
693,225.35
109.95
574,394.32
4.70
52,731.59
4.94
46,115.04
12.1
GRAPHIC PAPERS
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
12.1
GRAPHIC PAPERS
1000 t
121
640,494
12.1
GRAPHIC PAPERS
NAC/mt
5521
5224
11219
9335
ACCEPT
ACCEPT
12.1
GRAPHIC PAPERS
NAC/mt
12.1.1
NEWSPRINT
1000 t
24.74
95,414.37
21.31
75,275.22
0.01
65.43
0.00
2.05
12.1.1
NEWSPRINT
1000 t
12.1.1
NEWSPRINT
1000 t
25
95,349
12.1.1
NEWSPRINT
NAC/mt
3857
3532
6543
ZERO Q
ACCEPT
CHECK
12.1.1
NEWSPRINT
NAC/mt
CHECK
12.1.2
UNCOATED MECHANICAL
1000 t
16.52
75,799.82
13.64
58,633.19
0.37
2,446.59
0.31
2,814.33
12.1.2
UNCOATED MECHANICAL
1000 t
12.1.2
UNCOATED MECHANICAL
1000 t
16
73,353
12.1.2
UNCOATED MECHANICAL
NAC/mt
4588
4299
6612
9078
ACCEPT
ACCEPT
12.1.2
UNCOATED MECHANICAL
NAC/mt
12.1.3
UNCOATED WOODFREE
1000 t
39.46
244,799.57
36.51
211,076.39
1.28
9,310.62
2.15
14,095.33
12.1.3
UNCOATED WOODFREE
1000 t
12.1.3
UNCOATED WOODFREE
1000 t
38
235,489
12.1.3
UNCOATED WOODFREE
NAC/mt
6204
5781
7274
6556
ACCEPT
ACCEPT
12.1.3
UNCOATED WOODFREE
NAC/mt
12.1.4
COATED PAPERS
1000 t
44.85
277,211.59
38.49
229,409.52
3.04
40,908.95
2.48
29,203.33
12.1.4
COATED PAPERS
1000 t
12.1.4
COATED PAPERS
1000 t
42
236,303
12.1.4
COATED PAPERS
NAC/mt
6181
5960
13457
11776
ACCEPT
ACCEPT
12.1.4
COATED PAPERS
NAC/mt
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 t
35.38
252,189.65
39.71
253,564.80
0.73
5,585.99
2.75
20,142.04
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 t
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 t
35
246,604
12.2
HOUSEHOLD AND SANITARY PAPERS
NAC/mt
7128
6385
7652
7324
ACCEPT
ACCEPT
12.2
HOUSEHOLD AND SANITARY PAPERS
NAC/mt
12.3
PACKAGING MATERIALS
1000 t
202.73
997,232.60
205.94
964,624.62
179.85
531,039.01
183.03
483,982.67
12.3
PACKAGING MATERIALS
1000 t
OK
OK
OK
OK
OK
OK
OK
OK
12.3
PACKAGING MATERIALS
1000 t
370
466,535
12.3
PACKAGING MATERIALS
NAC/mt
4919
4684
2953
2644
ACCEPT
ACCEPT
12.3
PACKAGING MATERIALS
NAC/mt
12.3.1
CASE MATERIALS
1000 t
118.38
368,502.84
116.66
341,304.87
154.25
390,409.88
161.00
360,953.61
12.3.1
CASE MATERIALS
1000 t
12.3.1
CASE MATERIALS
1000 t
177
-21,698
12.3.1
CASE MATERIALS
NAC/mt
3113
2926
2531
2242
ACCEPT
ACCEPT
12.3.1
CASE MATERIALS
NAC/mt
12.3.2
CARTONBOARD
1000 t
42.28
370,067.90
43.52
364,742.42
1.12
19,412.22
0.91
19,855.60
12.3.2
CARTONBOARD
1000 t
12.3.2
CARTONBOARD
1000 t
42
350,656
12.3.2
CARTONBOARD
NAC/mt
8753
8381
17332
21819
ACCEPT
ACCEPT
12.3.2
CARTONBOARD
NAC/mt
12.3.3
WRAPPING PAPERS
1000 t
32.77
229,655.91
36.71
235,947.27
24.48
121,151.88
21.12
103,169.33
12.3.3
WRAPPING PAPERS
1000 t
12.3.3
WRAPPING PAPERS
1000 t
141
108,636
12.3.3
WRAPPING PAPERS
NAC/mt
7008
6427
4949
4885
ACCEPT
ACCEPT
12.3.3
WRAPPING PAPERS
NAC/mt
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 t
9.30
29,005.95
9.05
22,630.06
0.00
65.03
0.00
4.13
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 t
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 t
9
28,941
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
NAC/mt
3119
2501
ZERO Q
ZERO Q
ACCEPT
CHECK
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
NAC/mt
CHECK
CHECK
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
1000 t
2.52
97,116.28
2.05
93,377.42
0.14
10,613.18
0.17
12,533.12
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
2
86,503
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
NAC/mt
38538
45550
75808
73724
ACCEPT
ACCEPT
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
Mechanical wood pulp, semi-chemical wood pulp and pulp from fibres other than wood 9.1+10.1
1000 t
0.170
1359.960
0.140
766.000
47.370
117795.620
44.510
112552.150
To fill:
0
0
0
0
0
0
0
0
Text:
0
0
0
0
0
0
0
0
JQ3 Secondary PP Trade
62
91
91
Country:
HR
Date:
Name of Official responsible for reply:
0
Specify Currency and Unit of Value (e.g.:1000 US $):
_____________________
Official Address (in full):
FOREST SECTOR QUESTIONNAIRE JQ3
Branimirova 19, 10000 Zagreb, Croatia
SECONDARY PROCESSED PRODUCTS
Telephone/Fax:
0
0
Trade
E-mail:
0
This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!
Value must always be in 1000 NAC (national currency)
Flag
Flag
Flag
Flag
Note
Note
Note
Note
Discrepancies
Eurozone countries may use the old national currency, but only in both years
Product
Product
I M P O R T V A L U E
E X P O R T V A L U E
Import
Export
Import
Export
Product
Product
I M P O R T V A L U E
E X P O R T V A L U E
code
2019
2020
2019
2020
2019
2020
2019
2020
2019
2020
2019
2020
Code
2019
2020
2019
2020
13
SECONDARY WOOD PRODUCTS
2,330,614.50
2,167,285.44
1,935,219.16
2,072,644.18
13
SECONDARY WOOD PRODUCTS
OK
OK
OK
OK
13.1
FURTHER PROCESSED SAWNWOOD
107,647.28
107,293.92
416,638.26
417,593.44
13.1
FURTHER PROCESSED SAWNWOOD
OK
OK
OK
OK
13.1.C
Coniferous
61,719.91
61,365.96
4,316.29
5,271.20
13.1.C
Coniferous
13.1.NC
Non-coniferous
45,927.37
45,927.96
412,321.97
412,322.24
13.1.NC
Non-coniferous
13.1.NC.T
of which: Tropical
1,794.63
2,378.26
0.00
269.70
13.1.NC.T
of which: Tropical
OK
OK
OK
OK
13.2
WOODEN WRAPPING AND PACKAGING MATERIAL
122,013.40
124,561.77
99,390.59
110,645.81
13.2
WOODEN WRAPPING AND PACKAGING MATERIAL
13.3
WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
52,102.39
50,351.40
14,148.17
11,389.13
13.3
WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
13.4
BUILDER’S JOINERY AND CARPENTRY OF WOOD
351,379.39
325,888.78
468,882.92
604,768.11
13.4
BUILDER’S JOINERY AND CARPENTRY OF WOOD
13.5
WOODEN FURNITURE
1,592,390.84
1,461,516.97
784,658.18
778,226.50
13.5
WOODEN FURNITURE
13.6
PREFABRICATED BUILDINGS OF WOOD
24,290.45
17,098.57
13,300.30
13,837.37
13.6
PREFABRICATED BUILDINGS OF WOOD
13.7
OTHER MANUFACTURED WOOD PRODUCTS
80,790.75
80,574.03
138,200.74
136,183.82
13.7
OTHER MANUFACTURED WOOD PRODUCTS
14
SECONDARY PAPER PRODUCTS
1,418,652.43
1,356,526.62
1,247,105.92
1,396,373.38
14
SECONDARY PAPER PRODUCTS
OK
OK
OK
OK
14.1
COMPOSITE PAPER AND PAPERBOARD
20,124.88
24,699.04
1,219.68
1,647.11
14.1
COMPOSITE PAPER AND PAPERBOARD
14.2
SPECIAL COATED PAPER AND PULP PRODUCTS
172,522.59
151,926.89
106,113.83
107,977.09
14.2
SPECIAL COATED PAPER AND PULP PRODUCTS
14.3
HOUSEHOLD AND SANITARY PAPER, READY FOR USE
563,229.62
548,226.25
193,492.34
206,616.95
14.3
HOUSEHOLD AND SANITARY PAPER, READY FOR USE
14.4
PACKAGING CARTONS, BOXES ETC.
390,242.82
372,894.89
633,321.65
724,380.16
14.4
PACKAGING CARTONS, BOXES ETC.
14.5
OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE
272,532.52
258,779.55
312,958.42
355,752.07
14.5
OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE
OK
OK
OK
OK
14.5.1
of which: PRINTING AND WRITING PAPER, READY FOR USE
5,915.50
3,337.12
552.79
219.97
14.5.1
of which: PRINTING AND WRITING PAPER, READY FOR USE
14.5.2
of which: ARTICLES, MOULDED OR PRESSED FROM PULP
26,677.98
34,145.90
144,477.16
187,817.88
14.5.2
of which: ARTICLES, MOULDED OR PRESSED FROM PULP
14.5.3
of which: FILTER PAPER AND PAPERBOARD, READY FOR USE
5,805.65
5,775.47
249.19
388.16
14.5.3
of which: FILTER PAPER AND PAPERBOARD, READY FOR USE
To fill:
0
0
0
0
Text:
0
0
0
0
LAM & CHIPS
Unit of quantity:
Please select
Year
Product
Flow
Year
App. Cons.
Unit price
Production
Total Export
Total Import
Extra-EU Export
Extra-EU Import
TOT EXP
TOT IMP
X-EU EXP
X-EU IMP
Please select
Please select
1000 NAC
Please select
1000 NAC
Please select
1000 NAC
Please select
1000 NAC
Please select
2019
Glulam
0
0
0
0
0
0
0
0
0
2019
0
ERROR:#DIV/0!
ERROR:#DIV/0!
ERROR:#DIV/0!
ERROR:#DIV/0!
2020
0
0
0
0
0
0
0
0
0
2020
0
ERROR:#DIV/0!
ERROR:#DIV/0!
ERROR:#DIV/0!
ERROR:#DIV/0!
2019
X-lam
0
0
0
0
0
0
0
0
0
2019
0
ERROR:#DIV/0!
ERROR:#DIV/0!
ERROR:#DIV/0!
ERROR:#DIV/0!
2020
0
0
0
0
0
0
0
0
0
2020
0
ERROR:#DIV/0!
ERROR:#DIV/0!
ERROR:#DIV/0!
ERROR:#DIV/0!
Definitions:
Glulam: Builders' carpentry also includes glue-laminated timber (glulam), which is a structural timber product obtained by gluing together a number of wood laminations having their grain essentially parallel. Laminations of curved members are arranged so that the plane of each lamination is at 90 degrees to the plane of the applied load; thus, laminations of a straight gluman beam are laid flat. [from HS 4418, Builders' joinery and carpentry of wood, including cellular wood panels, assembled flooring panels, shingles and shakes]
X-lam: Panels consisting of laths of roughly sawn wood, assembled with glue in order to facilitate transport or later working. [from HS4421, Other articles of wood]
ECE-EU Species
Country:
HR
Date:
Name of Official responsible for reply:
0
DISCREPANCIES
FOREST SECTOR QUESTIONNAIRE ECE/EU Species Trade
Official Address (in full):
Checks
Check Table
Branimirova 19, 10000 Zagreb, Croatia
0
both VALUE and quantity reported ZERO
Trade in Roundwood and Sawnwood by species
Telephone:
0
Fax:
0
- checks whether the sum of subitems is bigger than the total
ZERO Q
quantity ZERO when VALUE is reported
E-mail:
0
ZERO V
Value ZERO when quantity is reported
Zero check - if no value please CHECK
NO Q
no quantity reported
Value must always be in 1000 NAC ( national currency)
Flag
Flag
Flag
Flag
Flag
Flag
Flag
Flag
Note
Note
Note
Note
Note
Note
Note
Note
NO V
no value reported
Treshold:
2
Eurozone countries may use the old national currency, but only in both years
1000NAC
REPORT
no figures reported
I M P O R T
E X P O R T
Import
Export
Import
Export
I M P O R T
E X P O R T
Value per
I M P O R T
E X P O R T
Unit price check
Product
Classification
Classification
Unit of
2019
2020
2019
2020
2019
2020
2019
2020
2019
2020
2019
2020
2019
2020
2019
2020
Classification
Classification
unit
2019
2020
2019
2020
IMPORT
EXPORT
Code
HS2017
CN2017
Product
Quantity
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
HS2007
CN2007
Product
1.2.C
4403.11/21/22/23/24/25/26
Industrial Roundwood, Coniferous
1000 m3ub
104.23
39,767.82
70.13
22,366.67
115.19
59,451.27
73.53
31,115.76
OK
OK
OK
OK
OK
OK
OK
OK
4403.11/21/22/23/24/25/26
0
NAC/m3
382
319
516
423
ACCEPT
ACCEPT
PRODUCTION
I M P O R T
E X P O R T
4403.23/24
Fir/Spruce (Abies spp., Picea spp.)
1000 m3ub
96.62
34,551.48
64.47
18,743.83
92.94
41,550.07
59.86
24,285.85
OK
OK
OK
OK
OK
OK
OK
OK
4403.23/24
Fir/Spruce (Abies spp., Picea spp.)
NAC/m3
358
291
447
406
ACCEPT
ACCEPT
Product
Classification
Classification
Unit of
2019
2020
2019
2020
2019
2020
4403 23 10
sawlogs and veneer logs
1000 m3ub
27.14
10,086.99
15.30
4,283.03
54.59
27,290.26
32.68
14,831.59
4403 23 10
sawlogs and veneer logs (Abies alba, Picea abies)
NAC/m3
372
280
500
454
ACCEPT
ACCEPT
Code
HS2007
CN2007
Product
Quantity
Quantity
Quantity
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
4403 23 90
4403 24 00
pulpwood and other industrial roundwood
1000 m3ub
69.48
24,464.49
49.17
14,460.80
38.35
14,259.81
27.18
9,454.26
4403 23 90
4403 24 00
pulpwood and other industrial roundwood (Abies alba, Picea abies)
NAC/m3
352
294
372
348
ACCEPT
ACCEPT
1
4401.11/12 44.03
Roundwood production
1000 m3
JQ1
5,400
5,234
4403.21/22
Pine (Pinus spp.)
1000 m3ub
6.07
3,421.36
5.00
2,488.36
10.17
2,992.20
8.53
2,135.28
OK
OK
OK
OK
OK
OK
OK
OK
4403.21/22
Pine (Pinus spp.)
NAC/m3
564
498
294
250
ACCEPT
ACCEPT
EU2
0
0
4403 21 10
sawlogs and veneer logs
1000 m3ub
4.47
2,734.54
4.97
2,479.72
3.03
1,128.56
2.92
1,071.86
4403 21 10
sawlogs and veneer logs (Pinus sylvestris)
NAC/m3
612
499
372
367
ACCEPT
ACCEPT
dif
5,400
5,234
4403 21 90
4403 22 00
pulpwood and other industrial roundwood
1000 m3ub
1.60
686.82
0.03
8.64
7.14
1,863.64
5.61
1,063.42
4403 21 90
4403 22 00
pulpwood and other industrial roundwood (Pinus sylvestris)
NAC/m3
429
288
261
190
ACCEPT
ACCEPT
1.2.C
4403.11/21/22/23/24/25/26
Industrial Roundwood (wood in the rough), Coniferous
1000 m3
JQ2
104
39,768
70
22,367
115
59,451
74
31,116
ECE/EU
104
39,768
70
22,367
115
59,451
74
31,116
Please note the deleted rows, do not use this area.
dif
0
0
0
0
0
0
0
0
1.2.NC
4403.12/41/49/91/93/94/95/96/97/98/99
Industrial Roundwood (wood in the rough), Non-Coniferous
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
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
HS2012/2007:
ex4403.10 4403.41/49 ex4403.99
6.NC.T
HS2017:
Sawnwood, Tropical
ex4406.12/92 4407.21/22/25/26/27/28/29
HS2012/2007:
ex4406.10/90 4407.21/22/25/26/27/28/30
7.NC.T
HS2017:
Veneer Sheets, Tropical
4408.31/39
HS2012/2007:
4408.31/39 ex4408.90
8.1.NC.T
HS2017:
Plywood, Tropical
4412.31 ex4412.94/99
HS2012/2007:
4412.31 ex4412.32/94/99
Note: List the major species traded in each category. Use additional sheet if more species are to be explicitly reported. For tropical plywood, identify by face veneer if composed of more than one species.
ITTO3-Miscellaneous
Country:
Date:
Name of Official responsible for reply:
Official Address (in full):
FOREST SECTOR QUESTIONNAIRE ITTO3
Miscellaneous Items
Telephone:
Fax:
(use additional paper if necessary)
E-mail:
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
HR
P.OB
1000 m3
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
P.OB
1000 m3
1_2_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
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
HR
P
1000 m3
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5400.110
5233.860
!!
!!
!!
!!
!!
96.92%
HR
P
1000 m3
1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2205.260
2206.910
!!
!!
!!
!!
!!
100.07%
HR
P
1000 m3
1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
54.020
57.810
!!
!!
!!
!!
!!
107.02%
HR
P
1000 m3
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2151.240
2149.100
!!
!!
!!
!!
!!
99.90%
HR
P
1000 m3
1_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3194.850
3026.950
!!
!!
!!
!!
!!
94.74%
HR
P
1000 m3
1_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
910.990
818.540
!!
!!
!!
!!
!!
89.85%
HR
P
1000 m3
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2283.860
2208.410
!!
!!
!!
!!
!!
96.70%
HR
P
1000 m3
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2627.910
2468.900
!!
!!
!!
!!
!!
93.95%
HR
P
1000 m3
1_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
623.740
534.350
!!
!!
!!
!!
!!
85.67%
HR
P
1000 m3
1_2_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2004.170
1934.550
!!
!!
!!
!!
!!
96.53%
HR
P
1000 m3
1_2_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
562.310
553.810
!!
!!
!!
!!
!!
98.49%
HR
P
1000 m3
1_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
283.500
280.840
!!
!!
!!
!!
!!
99.06%
HR
P
1000 m3
1_2_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
278.810
272.970
!!
!!
!!
!!
!!
97.91%
HR
P
1000 m3
1_2_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4.630
4.240
!!
!!
!!
!!
!!
91.58%
HR
P
1000 m3
1_2_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3.750
3.350
!!
!!
!!
!!
!!
89.33%
HR
P
1000 m3
1_2_3_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.880
0.890
!!
!!
!!
!!
!!
101.14%
HR
P
1000 m3
1_2_3_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
567.910
449.170
!!
!!
!!
!!
!!
79.09%
HR
P
1000 m3
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
567.910
449.170
!!
!!
!!
!!
!!
79.09%
HR
P
1000 m3
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
356.130
351.200
!!
!!
!!
!!
!!
98.62%
HR
P
1000 mt
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
307.690
311.240
!!
!!
!!
!!
!!
101.15%
HR
P
1000 mt
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
48.440
39.960
!!
!!
!!
!!
!!
82.49%
HR
P
1000 m3
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1445.950
1298.150
!!
!!
!!
!!
!!
89.78%
HR
P
1000 m3
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
247.370
256.540
!!
!!
!!
!!
!!
103.71%
HR
P
1000 m3
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1198.580
1041.610
!!
!!
!!
!!
!!
86.90%
HR
P
1000 m3
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.040
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
33.890
32.350
!!
!!
!!
!!
!!
95.46%
HR
P
1000 m3
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
33.890
32.350
!!
!!
!!
!!
!!
95.46%
HR
P
1000 m3
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
126.170
194.570
!!
!!
!!
!!
!!
154.21%
HR
P
1000 m3
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7.280
2.160
!!
!!
!!
!!
!!
29.67%
HR
P
1000 m3
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6.440
1.450
!!
!!
!!
!!
!!
22.52%
HR
P
1000 m3
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.840
0.710
!!
!!
!!
!!
!!
84.52%
HR
P
1000 m3
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
118.890
192.410
!!
!!
!!
!!
!!
161.84%
HR
P
1000 m3
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
46.850
45.110
!!
!!
!!
!!
!!
96.29%
HR
P
1000 mt
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
46.850
45.110
!!
!!
!!
!!
!!
96.29%
HR
P
1000 mt
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
346.790
341.720
!!
!!
!!
!!
!!
98.54%
HR
P
1000 mt
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
346.700
341.720
!!
!!
!!
!!
!!
98.56%
HR
P
1000 mt
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
213.110
208.960
!!
!!
!!
!!
!!
98.05%
HR
P
1000 mt
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.470
0.490
!!
!!
!!
!!
!!
104.26%
HR
P
1000 mt
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
133.120
132.270
!!
!!
!!
!!
!!
99.36%
HR
P
1000 mt
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
HR
P
1000 mt
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.090
0.000
!!
!!
!!
!!
!!
0.00%
HR
P
1000 mt
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
changed from version 1
!!
!!
!!
!!
!!
!!
HR
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
HR
M
1000 m3
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
227.180
193.560
!!
!!
!!
!!
!!
85.20%
€
HR
M
1000 NAC
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
135271.590
99901.480
!!
!!
!!
!!
!!
73.85%
UV
HR
M
1000 m3
1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
595.438
516.127
!!
!!
!!
!!
!!
86.68%
Q
HR
X
1000 m3
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
782.530
794.110
!!
!!
!!
!!
!!
101.48%
€
HR
X
1000 NAC
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
491477.610
542183.150
!!
!!
!!
!!
!!
110.32%
UV
HR
X
1000 m3
1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
628.062
682.756
!!
!!
!!
!!
!!
108.71%
Q
HR
M
1000 m3
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
59.010
72.640
!!
!!
!!
!!
!!
123.10%
€
HR
M
1000 NAC
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
35313.560
40728.580
!!
!!
!!
!!
!!
115.33%
UV
HR
M
1000 m3
1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
598.433
560.691
!!
!!
!!
!!
!!
93.69%
Q
HR
X
1000 m3
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
548.690
538.230
!!
!!
!!
!!
!!
98.09%
€
HR
X
1000 NAC
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
328094.670
303771.430
!!
!!
!!
!!
!!
92.59%
UV
HR
X
1000 m3
1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
597.960
564.390
!!
!!
!!
!!
!!
94.39%
Q
HR
M
1000 m3
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5.130
1.110
!!
!!
!!
!!
!!
21.64%
€
HR
M
1000 NAC
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1923.850
591.000
!!
!!
!!
!!
!!
30.72%
UV
HR
M
1000 m3
1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
375.019
532.432
!!
!!
!!
!!
!!
141.97%
Q
HR
X
1000 m3
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
49.080
49.710
!!
!!
!!
!!
!!
101.28%
€
HR
X
1000 NAC
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
27227.410
28069.540
!!
!!
!!
!!
!!
103.09%
UV
HR
X
1000 m3
1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
554.756
564.666
!!
!!
!!
!!
!!
101.79%
Q
HR
M
1000 m3
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
53.880
71.530
!!
!!
!!
!!
!!
132.76%
€
HR
M
1000 NAC
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
33389.710
40137.580
!!
!!
!!
!!
!!
120.21%
UV
HR
M
1000 m3
1_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
619.705
561.129
!!
!!
!!
!!
!!
90.55%
Q
HR
X
1000 m3
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
499.610
488.520
!!
!!
!!
!!
!!
97.78%
€
HR
X
1000 NAC
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
300867.260
275701.890
!!
!!
!!
!!
!!
91.64%
UV
HR
X
1000 m3
1_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
602.204
564.362
!!
!!
!!
!!
!!
93.72%
Q
HR
M
1000 m3
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
168.170
120.920
!!
!!
!!
!!
!!
71.90%
€
HR
M
1000 NAC
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
99958.030
59172.900
!!
!!
!!
!!
!!
59.20%
UV
HR
M
1000 m3
1_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
594.387
489.356
!!
!!
!!
!!
!!
82.33%
Q
HR
X
1000 m3
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
233.840
255.880
!!
!!
!!
!!
!!
109.43%
€
HR
X
1000 NAC
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
163382.940
238411.720
!!
!!
!!
!!
!!
145.92%
UV
HR
X
1000 m3
1_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
698.695
931.733
!!
!!
!!
!!
!!
133.35%
Q
HR
M
1000 m3
1_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
104.230
70.130
!!
!!
!!
!!
!!
67.28%
€
HR
M
1000 NAC
1_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
39767.820
22366.670
!!
!!
!!
!!
!!
56.24%
UV
HR
M
1000 m3
1_2_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
381.539
318.932
!!
!!
!!
!!
!!
83.59%
Q
HR
X
1000 m3
1_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
115.190
73.530
!!
!!
!!
!!
!!
63.83%
€
HR
X
1000 NAC
1_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
59451.270
31115.760
!!
!!
!!
!!
!!
52.34%
UV
HR
X
1000 m3
1_2_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
516.115
423.171
!!
!!
!!
!!
!!
81.99%
Q
HR
M
1000 mt
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
63.940
50.790
!!
!!
!!
!!
!!
79.43%
€
HR
M
1000 NAC
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
60190.210
36806.230
!!
!!
!!
!!
!!
61.15%
UV
HR
M
1000 mt
2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
941.355
724.675
!!
!!
!!
!!
!!
76.98%
Q
HR
X
1000 mt
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
118.650
182.350
!!
!!
!!
!!
!!
153.69%
€
HR
X
1000 NAC
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
103931.670
207295.960
!!
!!
!!
!!
!!
199.45%
UV
HR
X
1000 mt
2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
875.952
1136.803
!!
!!
!!
!!
!!
129.78%
Q
HR
M
1000 m3
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.060
0.140
!!
!!
!!
!!
!!
233.33%
€
HR
M
1000 NAC
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
80.250
60.450
!!
!!
!!
!!
!!
75.33%
UV
HR
M
1000 m3
3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1337.500
431.786
!!
!!
!!
!!
!!
32.28%
Q
HR
X
1000 m3
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
12.830
13.580
!!
!!
!!
!!
!!
105.85%
€
HR
M
1000 NAC
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
32962.590
33915.950
!!
!!
!!
!!
!!
102.89%
UV
HR
M
1000 m3
3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2569.181
2497.493
!!
!!
!!
!!
!!
97.21%
Q
HR
X
1000 m3
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11.600
13.200
!!
!!
!!
!!
!!
113.79%
€
HR
X
1000 NAC
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
42883.810
49238.750
!!
!!
!!
!!
!!
114.82%
UV
HR
X
1000 m3
3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3696.880
3730.208
!!
!!
!!
!!
!!
100.90%
Q
HR
M
1000 m3
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
120.410
159.970
!!
!!
!!
!!
!!
132.85%
€
HR
M
1000 NAC
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
33452.000
32301.620
!!
!!
!!
!!
!!
96.56%
UV
HR
M
1000 m3
3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
277.817
201.923
!!
!!
!!
!!
!!
72.68%
Q
HR
X
1000 m3
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
480.990
366.100
!!
!!
!!
!!
!!
76.11%
€
HR
X
1000 NAC
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
123050.600
82501.670
!!
!!
!!
!!
!!
67.05%
UV
HR
X
1000 m3
3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
255.828
225.353
!!
!!
!!
!!
!!
88.09%
Q
HR
M
1000 mt
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
84.260
80.740
!!
!!
!!
!!
!!
95.82%
€
HR
M
1000 NAC
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
24650.200
21745.390
!!
!!
!!
!!
!!
88.22%
UV
HR
M
1000 mt
4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
292.549
269.326
!!
!!
!!
!!
!!
92.06%
Q
HR
X
1000 mt
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
258.730
160.640
!!
!!
!!
!!
!!
62.09%
€
HR
X
1000 NAC
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
75437.550
43121.780
!!
!!
!!
!!
!!
57.16%
UV
HR
X
1000 mt
4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
291.569
268.437
!!
!!
!!
!!
!!
92.07%
Q
HR
M
1000 mt
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
36.150
79.230
!!
!!
!!
!!
!!
219.17%
€
HR
M
1000 NAC
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8801.800
10556.230
!!
!!
!!
!!
!!
119.93%
UV
HR
M
1000 mt
4_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
243.480
133.235
!!
!!
!!
!!
!!
54.72%
Q
HR
X
1000 mt
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
222.260
205.460
!!
!!
!!
!!
!!
92.44%
€
HR
X
1000 NAC
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
47613.050
39379.890
!!
!!
!!
!!
!!
82.71%
UV
HR
X
1000 mt
4_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
214.222
191.667
!!
!!
!!
!!
!!
89.47%
Q
HR
M
1000 mt
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 mt
4_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 mt
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 mt
4_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
36.680
46.670
!!
!!
!!
!!
!!
127.24%
€
HR
M
1000 NAC
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
45066.710
56805.650
!!
!!
!!
!!
!!
126.05%
UV
HR
M
1000 m3
5
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1228.645
1217.177
!!
!!
!!
!!
!!
99.07%
Q
HR
X
1000 m3
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
367.880
407.680
!!
!!
!!
!!
!!
110.82%
€
HR
X
1000 NAC
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
422562.020
458834.940
!!
!!
!!
!!
!!
108.58%
UV
HR
X
1000 m3
5
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1148.641
1125.478
!!
!!
!!
!!
!!
97.98%
Q
HR
M
1000 m3
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
33.190
42.500
!!
!!
!!
!!
!!
128.05%
€
HR
M
1000 NAC
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
42027.590
53499.690
!!
!!
!!
!!
!!
127.30%
UV
HR
M
1000 m3
5_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1266.273
1258.816
!!
!!
!!
!!
!!
99.41%
Q
HR
X
1000 m3
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
292.220
334.090
!!
!!
!!
!!
!!
114.33%
€
HR
X
1000 NAC
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
363658.020
415856.310
!!
!!
!!
!!
!!
114.35%
UV
HR
X
1000 m3
5_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1244.467
1244.743
!!
!!
!!
!!
!!
100.02%
Q
HR
M
1000 m3
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3.490
4.170
!!
!!
!!
!!
!!
119.48%
€
HR
M
1000 NAC
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3039.120
3305.960
!!
!!
!!
!!
!!
108.78%
UV
HR
M
1000 m3
5_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
870.808
792.796
!!
!!
!!
!!
!!
91.04%
Q
HR
X
1000 m3
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
75.660
73.590
!!
!!
!!
!!
!!
97.26%
€
HR
X
1000 NAC
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
58904.000
42978.630
!!
!!
!!
!!
!!
72.96%
UV
HR
X
1000 m3
5_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
778.536
584.028
!!
!!
!!
!!
!!
75.02%
Q
HR
M
1000 m3
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
506.130
454.680
!!
!!
!!
!!
!!
89.83%
€
HR
M
1000 NAC
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
811990.500
692263.190
!!
!!
!!
!!
!!
85.26%
UV
HR
M
1000 m3
5_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1604.312
1522.528
!!
!!
!!
!!
!!
94.90%
Q
HR
X
1000 m3
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1269.280
1201.200
!!
!!
!!
!!
!!
94.64%
€
HR
X
1000 NAC
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2858959.660
2588916.790
!!
!!
!!
!!
!!
90.55%
UV
HR
X
1000 m3
5_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2252.426
2155.275
!!
!!
!!
!!
!!
95.69%
Q
HR
M
1000 m3
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
425.130
366.330
!!
!!
!!
!!
!!
86.17%
€
HR
M
1000 NAC
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
514876.210
453702.410
!!
!!
!!
!!
!!
88.12%
UV
HR
M
1000 m3
6
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1211.103
1238.507
!!
!!
!!
!!
!!
102.26%
Q
HR
X
1000 m3
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
365.360
350.320
!!
!!
!!
!!
!!
95.88%
€
HR
X
1000 NAC
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
465813.850
443085.350
!!
!!
!!
!!
!!
95.12%
UV
HR
X
1000 m3
6
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1274.945
1264.802
!!
!!
!!
!!
!!
99.20%
Q
HR
M
1000 m3
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
81.000
88.350
!!
!!
!!
!!
!!
109.07%
€
HR
M
1000 NAC
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
297114.290
238560.780
!!
!!
!!
!!
!!
80.29%
UV
HR
M
1000 m3
6_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3668.078
2700.179
!!
!!
!!
!!
!!
73.61%
Q
HR
X
1000 m3
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
903.920
850.880
!!
!!
!!
!!
!!
94.13%
€
HR
X
1000 NAC
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2393145.810
2145831.440
!!
!!
!!
!!
!!
89.67%
UV
HR
X
1000 m3
6_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2647.519
2521.897
!!
!!
!!
!!
!!
95.26%
Q
HR
M
1000 m3
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13.480
1.370
!!
!!
!!
!!
!!
10.16%
€
HR
M
1000 NAC
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
43374.860
17312.010
!!
!!
!!
!!
!!
39.91%
UV
HR
M
1000 m3
6_1_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3217.720
12636.504
!!
!!
!!
!!
!!
392.72%
Q
HR
X
1000 m3
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.800
0.360
!!
!!
!!
!!
!!
45.00%
€
HR
X
1000 NAC
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
28691.600
10178.350
!!
!!
!!
!!
!!
35.48%
UV
HR
X
1000 m3
6_1_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
35864.500
28273.194
!!
!!
!!
!!
!!
78.83%
Q
HR
M
1000 m3
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9.070
10.720
!!
!!
!!
!!
!!
118.19%
€
HR
M
1000 NAC
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
88450.080
111562.460
!!
!!
!!
!!
!!
126.13%
UV
HR
M
1000 m3
6_1_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9751.938
10406.946
!!
!!
!!
!!
!!
106.72%
Q
HR
X
1000 m3
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
22.500
20.820
!!
!!
!!
!!
!!
92.53%
€
HR
X
1000 NAC
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
402506.170
382265.490
!!
!!
!!
!!
!!
94.97%
UV
HR
X
1000 m3
6_1_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
17889.163
18360.494
!!
!!
!!
!!
!!
102.63%
Q
HR
M
1000 m3
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3.250
3.910
!!
!!
!!
!!
!!
120.31%
€
HR
M
1000 NAC
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
20043.400
21005.560
!!
!!
!!
!!
!!
104.80%
UV
HR
M
1000 m3
6_1_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6167.200
5372.266
!!
!!
!!
!!
!!
87.11%
Q
HR
X
1000 m3
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.060
0.030
!!
!!
!!
!!
!!
50.00%
€
HR
X
1000 NAC
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1023.150
360.880
!!
!!
!!
!!
!!
35.27%
UV
HR
X
1000 m3
6_1_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
17052.500
12029.333
!!
!!
!!
!!
!!
70.54%
Q
HR
M
1000 m3
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5.820
6.810
!!
!!
!!
!!
!!
117.01%
€
HR
M
1000 NAC
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
68406.680
90556.900
!!
!!
!!
!!
!!
132.38%
UV
HR
M
1000 m3
6_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11753.725
13297.636
!!
!!
!!
!!
!!
113.14%
Q
HR
X
1000 m3
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
22.440
20.790
!!
!!
!!
!!
!!
92.65%
€
HR
X
1000 NAC
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
401483.020
381904.610
!!
!!
!!
!!
!!
95.12%
UV
HR
X
1000 m3
6_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
17891.400
18369.630
!!
!!
!!
!!
!!
102.67%
Q
HR
M
1000 m3
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.120
0.050
!!
!!
!!
!!
!!
41.67%
€
HR
M
1000 NAC
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4845.520
2835.180
!!
!!
!!
!!
!!
58.51%
UV
HR
M
1000 m3
6_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
40379.333
56703.600
!!
!!
!!
!!
!!
140.43%
Q
HR
X
1000 m3
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.110
0.070
!!
!!
!!
!!
!!
63.64%
€
HR
X
1000 NAC
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7423.090
3172.130
!!
!!
!!
!!
!!
42.73%
UV
HR
X
1000 m3
6_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
67482.636
45316.143
!!
!!
!!
!!
!!
67.15%
Q
HR
M
1000 m3
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
366.870
369.950
!!
!!
!!
!!
!!
100.84%
€
HR
M
1000 NAC
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
844115.310
820374.230
!!
!!
!!
!!
!!
97.19%
UV
HR
M
1000 m3
6_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2300.857
2217.527
!!
!!
!!
!!
!!
96.38%
Q
HR
X
1000 m3
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
159.200
190.060
!!
!!
!!
!!
!!
119.38%
€
HR
X
1000 NAC
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
300534.770
336199.100
!!
!!
!!
!!
!!
111.87%
UV
HR
X
1000 m3
6_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1887.781
1768.910
!!
!!
!!
!!
!!
93.70%
Q
HR
M
1000 m3
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
34.710
31.060
!!
!!
!!
!!
!!
89.48%
€
HR
M
1000 NAC
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
147201.450
126901.920
!!
!!
!!
!!
!!
86.21%
UV
HR
M
1000 m3
6_2_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4240.895
4085.703
!!
!!
!!
!!
!!
96.34%
Q
HR
X
1000 m3
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5.810
5.200
!!
!!
!!
!!
!!
89.50%
€
HR
X
1000 NAC
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
32208.530
29494.370
!!
!!
!!
!!
!!
91.57%
UV
HR
X
1000 m3
6_2_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5543.637
5671.994
!!
!!
!!
!!
!!
102.32%
Q
HR
M
1000 m3
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11.550
9.310
!!
!!
!!
!!
!!
80.61%
€
HR
M
1000 NAC
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
56200.050
45139.150
!!
!!
!!
!!
!!
80.32%
UV
HR
M
1000 m3
6_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4865.805
4848.459
!!
!!
!!
!!
!!
99.64%
Q
HR
X
1000 m3
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.690
1.160
!!
!!
!!
!!
!!
68.64%
€
HR
X
1000 NAC
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9310.910
6692.670
!!
!!
!!
!!
!!
71.88%
UV
HR
X
1000 m3
6_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5509.414
5769.543
!!
!!
!!
!!
!!
104.72%
Q
HR
M
1000 m3
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
23.160
21.750
!!
!!
!!
!!
!!
93.91%
€
HR
M
1000 NAC
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
91001.400
81762.770
!!
!!
!!
!!
!!
89.85%
UV
HR
M
1000 m3
6_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3929.249
3759.208
!!
!!
!!
!!
!!
95.67%
Q
HR
X
1000 m3
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4.120
4.040
!!
!!
!!
!!
!!
98.06%
€
HR
X
1000 NAC
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
22897.620
22801.700
!!
!!
!!
!!
!!
99.58%
UV
HR
X
1000 m3
6_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5557.675
5643.985
!!
!!
!!
!!
!!
101.55%
Q
HR
M
1000 m3
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.840
0.640
!!
!!
!!
!!
!!
76.19%
€
HR
M
1000 NAC
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5254.820
5059.010
!!
!!
!!
!!
!!
96.27%
UV
HR
M
1000 m3
6_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6255.738
7904.703
!!
!!
!!
!!
!!
126.36%
Q
HR
X
1000 m3
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.040
0.230
!!
!!
!!
!!
!!
575.00%
€
HR
X
1000 NAC
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
275.480
687.210
!!
!!
!!
!!
!!
249.46%
UV
HR
X
1000 m3
6_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6887.000
2987.870
!!
!!
!!
!!
!!
43.38%
Q
HR
M
1000 m3
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
227.720
225.640
!!
!!
!!
!!
!!
99.09%
€
HR
M
1000 NAC
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
440982.220
418075.880
!!
!!
!!
!!
!!
94.81%
UV
HR
M
1000 m3
6_4_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1936.511
1852.845
!!
!!
!!
!!
!!
95.68%
Q
HR
X
1000 m3
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
123.750
154.080
!!
!!
!!
!!
!!
124.51%
€
HR
X
1000 NAC
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
179758.890
200138.490
!!
!!
!!
!!
!!
111.34%
UV
HR
X
1000 m3
6_4_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1452.597
1298.926
!!
!!
!!
!!
!!
89.42%
Q
HR
M
1000 m3
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
39.330
44.490
!!
!!
!!
!!
!!
113.12%
€
HR
M
1000 NAC
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
72016.390
76030.120
!!
!!
!!
!!
!!
105.57%
UV
HR
M
1000 m3
6_4_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1831.080
1708.926
!!
!!
!!
!!
!!
93.33%
Q
HR
X
1000 m3
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.410
3.420
!!
!!
!!
!!
!!
141.91%
€
HR
X
1000 NAC
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5171.240
7095.840
!!
!!
!!
!!
!!
137.22%
UV
HR
X
1000 m3
6_4_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2145.743
2074.807
!!
!!
!!
!!
!!
96.69%
Q
HR
M
1000 m3
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
104.440
113.250
!!
!!
!!
!!
!!
108.44%
€
HR
M
1000 NAC
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
255931.640
275396.430
!!
!!
!!
!!
!!
107.61%
UV
HR
M
1000 m3
6_4_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2450.514
2431.757
!!
!!
!!
!!
!!
99.23%
Q
HR
X
1000 m3
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
29.640
30.780
!!
!!
!!
!!
!!
103.85%
€
HR
X
1000 NAC
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
88567.350
106566.240
!!
!!
!!
!!
!!
120.32%
UV
HR
X
1000 m3
6_4_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2988.102
3462.191
!!
!!
!!
!!
!!
115.87%
Q
HR
M
1000 mt
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13.640
13.840
!!
!!
!!
!!
!!
101.47%
€
HR
M
1000 NAC
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
70257.420
67552.850
!!
!!
!!
!!
!!
96.15%
UV
HR
M
1000 mt
7
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5150.837
4880.986
!!
!!
!!
!!
!!
94.76%
Q
HR
X
1000 mt
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.110
4.020
!!
!!
!!
!!
!!
190.52%
€
HR
X
1000 NAC
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
10052.020
19003.050
!!
!!
!!
!!
!!
189.05%
UV
HR
X
1000 mt
7
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4763.991
4727.127
!!
!!
!!
!!
!!
99.23%
Q
HR
M
1000 mt
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
86.640
95.680
!!
!!
!!
!!
!!
110.43%
€
HR
M
1000 NAC
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
180879.060
204376.480
!!
!!
!!
!!
!!
112.99%
UV
HR
M
1000 mt
7_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2087.708
2136.042
!!
!!
!!
!!
!!
102.32%
Q
HR
X
1000 mt
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
27.430
26.650
!!
!!
!!
!!
!!
97.16%
€
HR
X
1000 NAC
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
78333.180
87375.960
!!
!!
!!
!!
!!
111.54%
UV
HR
X
1000 mt
7_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2855.748
3278.648
!!
!!
!!
!!
!!
114.81%
Q
HR
M
1000 mt
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4.160
3.730
!!
!!
!!
!!
!!
89.66%
€
HR
M
1000 NAC
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4795.160
3467.100
!!
!!
!!
!!
!!
72.30%
UV
HR
M
1000 mt
7_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1152.683
929.517
!!
!!
!!
!!
!!
80.64%
Q
HR
X
1000 mt
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.100
0.110
!!
!!
!!
!!
!!
110.00%
€
HR
X
1000 NAC
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
182.150
187.230
!!
!!
!!
!!
!!
102.79%
UV
HR
X
1000 mt
7_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1821.500
1702.091
!!
!!
!!
!!
!!
93.44%
Q
HR
M
1000 mt
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.200
0.220
!!
!!
!!
!!
!!
110.00%
€
HR
M
1000 NAC
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2085.270
2112.780
!!
!!
!!
!!
!!
101.32%
UV
HR
M
1000 mt
7_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
10426.350
9603.545
!!
!!
!!
!!
!!
92.11%
Q
HR
X
1000 mt
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
47.360
44.500
!!
!!
!!
!!
!!
93.96%
€
HR
X
1000 NAC
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
117749.790
112461.900
!!
!!
!!
!!
!!
95.51%
UV
HR
X
1000 mt
7_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2486.271
2527.234
!!
!!
!!
!!
!!
101.65%
Q
HR
M
1000 mt
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.080
0.100
!!
!!
!!
!!
!!
125.00%
€
HR
M
1000 NAC
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
295.280
390.190
!!
!!
!!
!!
!!
132.14%
UV
HR
M
1000 mt
7_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3691.000
3901.900
!!
!!
!!
!!
!!
105.71%
Q
HR
X
1000 mt
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
47.360
44.500
!!
!!
!!
!!
!!
93.96%
€
HR
X
1000 NAC
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
117694.290
112447.710
!!
!!
!!
!!
!!
95.54%
UV
HR
X
1000 mt
7_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2485.099
2526.915
!!
!!
!!
!!
!!
101.68%
Q
HR
M
1000 mt
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.120
0.120
!!
!!
!!
!!
!!
100.00%
€
HR
M
1000 NAC
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1789.870
1722.590
!!
!!
!!
!!
!!
96.24%
UV
HR
M
1000 mt
7_3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
14915.583
14354.917
!!
!!
!!
!!
!!
96.24%
Q
HR
X
1000 mt
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
55.500
14.190
!!
!!
!!
!!
!!
25.57%
UV
HR
X
1000 mt
7_3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 mt
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.010
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5.650
59.880
!!
!!
!!
!!
!!
1059.82%
UV
HR
M
1000 mt
7_3_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
5988.000
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 mt
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 mt
7_3_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 mt
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.010
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5.650
59.880
!!
!!
!!
!!
!!
1059.82%
UV
HR
M
1000 mt
7_3_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
5988.000
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 mt
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 mt
7_3_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 mt
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.120
0.110
!!
!!
!!
!!
!!
91.67%
€
HR
M
1000 NAC
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1784.220
1662.710
!!
!!
!!
!!
!!
93.19%
UV
HR
M
1000 mt
7_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
14868.500
15115.545
!!
!!
!!
!!
!!
101.66%
Q
HR
X
1000 mt
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
55.500
14.190
!!
!!
!!
!!
!!
25.57%
UV
HR
X
1000 mt
7_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 mt
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.120
0.000
!!
!!
!!
!!
!!
0.00%
UV
HR
M
1000 mt
8
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 mt
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 mt
8
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 mt
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.390
0.390
!!
!!
!!
!!
!!
100.00%
€
HR
M
1000 NAC
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2340.840
2008.390
!!
!!
!!
!!
!!
85.80%
UV
HR
M
1000 mt
8_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6002.154
5149.718
!!
!!
!!
!!
!!
85.80%
Q
HR
X
1000 mt
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.030
0.030
!!
!!
!!
!!
!!
100.00%
€
HR
X
1000 NAC
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
215.740
194.110
!!
!!
!!
!!
!!
89.97%
UV
HR
X
1000 mt
8_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7191.333
6470.333
!!
!!
!!
!!
!!
89.97%
Q
HR
M
1000 mt
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.090
0.040
!!
!!
!!
!!
!!
44.44%
€
HR
M
1000 NAC
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1064.680
375.810
!!
!!
!!
!!
!!
35.30%
UV
HR
M
1000 mt
8_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11829.778
9395.250
!!
!!
!!
!!
!!
79.42%
Q
HR
X
1000 mt
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.010
0.010
!!
!!
!!
!!
!!
100.00%
€
HR
X
1000 NAC
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
101.330
104.440
!!
!!
!!
!!
!!
103.07%
UV
HR
X
1000 mt
8_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
10133.000
10444.000
!!
!!
!!
!!
!!
103.07%
Q
HR
M
1000 mt
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.300
0.350
!!
!!
!!
!!
!!
116.67%
€
HR
M
1000 NAC
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1276.160
1632.580
!!
!!
!!
!!
!!
127.93%
UV
HR
M
1000 mt
9
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4253.867
4664.514
!!
!!
!!
!!
!!
109.65%
Q
HR
X
1000 mt
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.020
0.020
!!
!!
!!
!!
!!
100.00%
€
HR
X
1000 NAC
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
114.410
89.670
!!
!!
!!
!!
!!
78.38%
UV
HR
X
1000 mt
9
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5720.500
4483.500
!!
!!
!!
!!
!!
78.38%
Q
HR
M
1000 mt
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
162.490
162.000
!!
!!
!!
!!
!!
99.70%
€
HR
M
1000 NAC
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
128014.590
113542.300
!!
!!
!!
!!
!!
88.69%
UV
HR
M
1000 mt
10
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
787.831
700.878
!!
!!
!!
!!
!!
88.96%
Q
HR
X
1000 mt
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
162.610
147.290
!!
!!
!!
!!
!!
90.58%
€
HR
X
1000 NAC
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
132570.990
110791.340
!!
!!
!!
!!
!!
83.57%
UV
HR
X
1000 mt
10
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
815.270
752.199
!!
!!
!!
!!
!!
92.26%
Q
HR
M
1000 mt
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
366.200
357.650
!!
!!
!!
!!
!!
97.67%
€
HR
M
1000 NAC
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2039763.880
1885961.162
!!
!!
!!
!!
!!
92.46%
UV
HR
M
1000 mt
10_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5570.082
5273.203
!!
!!
!!
!!
!!
94.67%
Q
HR
X
1000 mt
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
185.420
190.890
!!
!!
!!
!!
!!
102.95%
€
HR
X
1000 NAC
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
599969.770
562772.870
!!
!!
!!
!!
!!
93.80%
UV
HR
X
1000 mt
10_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3235.734
2948.153
!!
!!
!!
!!
!!
91.11%
Q
HR
M
1000 mt
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
125.570
109.950
!!
!!
!!
!!
!!
87.56%
€
HR
M
1000 NAC
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
693225.350
574394.320
!!
!!
!!
!!
!!
82.86%
UV
HR
M
1000 mt
10_1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5520.629
5224.141
!!
!!
!!
!!
!!
94.63%
Q
HR
X
1000 mt
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4.700
4.940
!!
!!
!!
!!
!!
105.11%
€
HR
X
1000 NAC
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
52731.590
46115.040
!!
!!
!!
!!
!!
87.45%
UV
HR
X
1000 mt
10_1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11219.487
9335.028
!!
!!
!!
!!
!!
83.20%
Q
HR
M
1000 mt
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
24.740
21.310
!!
!!
!!
!!
!!
86.14%
€
HR
M
1000 NAC
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
95414.370
75275.220
!!
!!
!!
!!
!!
78.89%
UV
HR
M
1000 mt
10_1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3856.684
3532.389
!!
!!
!!
!!
!!
91.59%
Q
HR
X
1000 mt
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.010
0.000
!!
!!
!!
!!
!!
0.00%
€
HR
X
1000 NAC
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
65.430
2.050
!!
!!
!!
!!
!!
3.13%
UV
HR
X
1000 mt
10_1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6543.000
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 mt
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
16.520
13.640
!!
!!
!!
!!
!!
82.57%
€
HR
M
1000 NAC
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
75799.820
58633.190
!!
!!
!!
!!
!!
77.35%
UV
HR
M
1000 mt
10_1_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4588.367
4298.621
!!
!!
!!
!!
!!
93.69%
Q
HR
X
1000 mt
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.370
0.310
!!
!!
!!
!!
!!
83.78%
€
HR
X
1000 NAC
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2446.590
2814.330
!!
!!
!!
!!
!!
115.03%
UV
HR
X
1000 mt
10_1_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6612.405
9078.484
!!
!!
!!
!!
!!
137.29%
Q
HR
M
1000 mt
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
39.460
36.510
!!
!!
!!
!!
!!
92.52%
€
HR
M
1000 NAC
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
244799.570
211076.390
!!
!!
!!
!!
!!
86.22%
UV
HR
M
1000 mt
10_1_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6203.740
5781.331
!!
!!
!!
!!
!!
93.19%
Q
HR
X
1000 mt
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.280
2.150
!!
!!
!!
!!
!!
167.97%
€
HR
X
1000 NAC
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9310.620
14095.330
!!
!!
!!
!!
!!
151.39%
UV
HR
X
1000 mt
10_1_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7273.922
6555.967
!!
!!
!!
!!
!!
90.13%
Q
HR
M
1000 mt
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
44.850
38.490
!!
!!
!!
!!
!!
85.82%
€
HR
M
1000 NAC
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
277211.590
229409.520
!!
!!
!!
!!
!!
82.76%
UV
HR
M
1000 mt
10_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6180.860
5960.237
!!
!!
!!
!!
!!
96.43%
Q
HR
X
1000 mt
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3.040
2.480
!!
!!
!!
!!
!!
81.58%
€
HR
X
1000 NAC
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
40908.950
29203.330
!!
!!
!!
!!
!!
71.39%
UV
HR
X
1000 mt
10_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13456.891
11775.536
!!
!!
!!
!!
!!
87.51%
Q
HR
M
1000 mt
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
35.380
39.710
!!
!!
!!
!!
!!
112.24%
€
HR
M
1000 NAC
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
252189.650
253564.800
!!
!!
!!
!!
!!
100.55%
UV
HR
M
1000 mt
10_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7128.029
6385.414
!!
!!
!!
!!
!!
89.58%
Q
HR
X
1000 mt
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.730
2.750
!!
!!
!!
!!
!!
376.71%
€
HR
X
1000 NAC
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5585.990
20142.040
!!
!!
!!
!!
!!
360.58%
UV
HR
X
1000 mt
10_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7652.041
7324.378
!!
!!
!!
!!
!!
95.72%
Q
HR
M
1000 mt
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
202.730
205.940
!!
!!
!!
!!
!!
101.58%
€
HR
M
1000 NAC
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
997232.600
964624.622
!!
!!
!!
!!
!!
96.73%
UV
HR
M
1000 mt
10_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4919.018
4684.008
!!
!!
!!
!!
!!
95.22%
Q
HR
X
1000 mt
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
179.850
183.030
!!
!!
!!
!!
!!
101.77%
€
HR
X
1000 NAC
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
531039.010
483982.670
!!
!!
!!
!!
!!
91.14%
UV
HR
X
1000 mt
10_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2952.677
2644.281
!!
!!
!!
!!
!!
89.56%
Q
HR
M
1000 mt
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
118.380
116.660
!!
!!
!!
!!
!!
98.55%
€
HR
M
1000 NAC
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
368502.840
341304.870
!!
!!
!!
!!
!!
92.62%
UV
HR
M
1000 mt
10_3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3112.881
2925.637
!!
!!
!!
!!
!!
93.98%
Q
HR
X
1000 mt
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
154.250
161.000
!!
!!
!!
!!
!!
104.38%
€
HR
X
1000 NAC
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
390409.880
360953.610
!!
!!
!!
!!
!!
92.46%
UV
HR
X
1000 mt
10_3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2531.020
2241.948
!!
!!
!!
!!
!!
88.58%
Q
HR
M
1000 mt
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
42.280
43.520
!!
!!
!!
!!
!!
102.93%
€
HR
M
1000 NAC
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
370067.900
364742.422
!!
!!
!!
!!
!!
98.56%
UV
HR
M
1000 mt
10_3_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8752.789
8381.030
!!
!!
!!
!!
!!
95.75%
Q
HR
X
1000 mt
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.120
0.910
!!
!!
!!
!!
!!
81.25%
€
HR
X
1000 NAC
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
19412.220
19855.600
!!
!!
!!
!!
!!
102.28%
UV
HR
X
1000 mt
10_3_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
17332.339
21819.341
!!
!!
!!
!!
!!
125.89%
Q
HR
M
1000 mt
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
32.770
36.710
!!
!!
!!
!!
!!
112.02%
€
HR
M
1000 NAC
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
229655.910
235947.270
!!
!!
!!
!!
!!
102.74%
UV
HR
M
1000 mt
10_3_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7008.114
6427.330
!!
!!
!!
!!
!!
91.71%
Q
HR
X
1000 mt
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
24.480
21.120
!!
!!
!!
!!
!!
86.27%
€
HR
X
1000 NAC
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
121151.880
103169.330
!!
!!
!!
!!
!!
85.16%
UV
HR
X
1000 mt
10_3_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4949.015
4884.911
!!
!!
!!
!!
!!
98.70%
Q
HR
M
1000 mt
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9.300
9.050
!!
!!
!!
!!
!!
97.31%
€
HR
M
1000 NAC
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
29005.950
22630.060
!!
!!
!!
!!
!!
78.02%
UV
HR
M
1000 mt
10_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3118.919
2500.559
!!
!!
!!
!!
!!
80.17%
Q
HR
X
1000 mt
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
65.030
4.130
!!
!!
!!
!!
!!
6.35%
UV
HR
X
1000 mt
10_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
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
HR
M
1000 NAC
11_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
11_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
11_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
11_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
11_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
11_6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
11_7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
M
1000 NAC
12_7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
11_7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
12_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
12_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
12_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
12_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
12_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
X
1000 NAC
12_6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
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
!!
!!
!!
!!
!!
!!
HR
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
HR
M
1000 m3
ST_1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
104.230
70.130
!!
!!
!!
!!
!!
67.28%
€
HR
M
1000 NAC
ST_1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
39767.820
22366.670
!!
!!
!!
!!
!!
56.24%
UV
HR
M
1000 m3
ST_1_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
381.539
318.932
!!
!!
!!
!!
!!
83.59%
Q
HR
X
1000 m3
ST_1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
115.190
73.530
!!
!!
!!
!!
!!
63.83%
€
HR
X
1000 NAC
ST_1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
59451.270
31115.760
!!
!!
!!
!!
!!
52.34%
UV
HR
X
1000 m3
ST_1_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
516.115
423.171
!!
!!
!!
!!
!!
81.99%
Q
HR
M
1000 m3
ST_1_2_C_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
96.620
64.470
!!
!!
!!
!!
!!
66.73%
€
HR
M
1000 NAC
ST_1_2_C_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
34551.480
18743.830
!!
!!
!!
!!
!!
54.25%
UV
HR
M
1000 m3
ST_1_2_C_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
357.602
290.737
!!
!!
!!
!!
!!
81.30%
Q
HR
X
1000 m3
ST_1_2_C_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
92.940
59.860
!!
!!
!!
!!
!!
64.41%
€
HR
X
1000 NAC
ST_1_2_C_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
41550.070
24285.850
!!
!!
!!
!!
!!
58.45%
UV
HR
X
1000 m3
ST_1_2_C_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
447.063
405.711
!!
!!
!!
!!
!!
90.75%
Q
HR
M
1000 m3
ST_1_2_C_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
27.140
15.300
!!
!!
!!
!!
!!
56.37%
€
HR
M
1000 NAC
ST_1_2_C_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
10086.990
4283.030
!!
!!
!!
!!
!!
42.46%
UV
HR
M
1000 m3
ST_1_2_C_1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
371.665
279.937
!!
!!
!!
!!
!!
75.32%
Q
HR
X
1000 m3
ST_1_2_C_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
54.590
32.680
!!
!!
!!
!!
!!
59.86%
€
HR
X
1000 NAC
ST_1_2_C_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
27290.260
14831.590
!!
!!
!!
!!
!!
54.35%
UV
HR
X
1000 m3
ST_1_2_C_1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
499.913
453.843
!!
!!
!!
!!
!!
90.78%
Q
HR
M
1000 m3
ST_1_2_C_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4.470
4.970
!!
!!
!!
!!
!!
111.19%
€
HR
M
1000 NAC
ST_1_2_C_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2734.540
2479.720
!!
!!
!!
!!
!!
90.68%
UV
HR
M
1000 m3
ST_1_2_C_1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
611.754
498.938
!!
!!
!!
!!
!!
81.56%
Q
HR
X
1000 m3
ST_1_2_C_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3.030
2.920
!!
!!
!!
!!
!!
96.37%
€
HR
X
1000 NAC
ST_1_2_C_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1128.560
1071.860
!!
!!
!!
!!
!!
94.98%
UV
HR
X
1000 m3
ST_1_2_C_1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
372.462
367.075
!!
!!
!!
!!
!!
98.55%
Q
HR
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
!!
!!
!!
!!
!!
!!
€
HR
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
HR
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
HR
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
!!
!!
!!
!!
!!
!!
€
HR
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
HR
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
HR
M
1000 m3
ST_1_2_C_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6.070
5.000
!!
!!
!!
!!
!!
82.37%
€
HR
M
1000 NAC
ST_1_2_C_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3421.360
2488.360
!!
!!
!!
!!
!!
72.73%
UV
HR
M
1000 m3
ST_1_2_C_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
563.651
497.672
!!
!!
!!
!!
!!
88.29%
Q
HR
X
1000 m3
ST_1_2_C_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
10.170
8.530
!!
!!
!!
!!
!!
83.87%
€
HR
X
1000 NAC
ST_1_2_C_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2992.200
2135.280
!!
!!
!!
!!
!!
71.36%
UV
HR
X
1000 m3
ST_1_2_C_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
294.218
250.326
!!
!!
!!
!!
!!
85.08%
Q
HR
M
1000 m3
ST_1_2_C_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
69.480
49.170
!!
!!
!!
!!
!!
70.77%
€
HR
M
1000 NAC
ST_1_2_C_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
24464.490
14460.800
!!
!!
!!
!!
!!
59.11%
UV
HR
M
1000 m3
ST_1_2_C_2_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
352.108
294.098
!!
!!
!!
!!
!!
83.52%
Q
HR
X
1000 m3
ST_1_2_C_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
38.350
27.180
!!
!!
!!
!!
!!
70.87%
€
HR
X
1000 NAC
ST_1_2_C_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
14259.810
9454.260
!!
!!
!!
!!
!!
66.30%
UV
HR
X
1000 m3
ST_1_2_C_2_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
371.833
347.839
!!
!!
!!
!!
!!
93.55%
Q
HR
M
1000 m3
ST_1_2_C_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.600
0.030
!!
!!
!!
!!
!!
1.88%
€
HR
M
1000 NAC
ST_1_2_C_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
686.820
8.640
!!
!!
!!
!!
!!
1.26%
UV
HR
M
1000 m3
ST_1_2_C_2_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
429.263
288.000
!!
!!
!!
!!
!!
67.09%
Q
HR
X
1000 m3
ST_1_2_C_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7.140
5.610
!!
!!
!!
!!
!!
78.57%
€
HR
X
1000 NAC
ST_1_2_C_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1863.640
1063.420
!!
!!
!!
!!
!!
57.06%
UV
HR
X
1000 m3
ST_1_2_C_2_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
261.014
189.558
!!
!!
!!
!!
!!
72.62%
Q
HR
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
!!
!!
!!
!!
!!
!!
€
HR
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
HR
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
HR
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
!!
!!
!!
!!
!!
!!
€
HR
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
HR
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
HR
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
!!
!!
!!
!!
!!
!!
€
HR
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
HR
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
HR
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
!!
!!
!!
!!
!!
!!
€
HR
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
HR
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
HR
M
1000 m3
ST_1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
63.940
50.790
!!
!!
!!
!!
!!
79.43%
€
HR
M
1000 NAC
ST_1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
60200.510
36806.230
!!
!!
!!
!!
!!
61.14%
UV
HR
M
1000 m3
ST_1_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
941.516
724.675
!!
!!
!!
!!
!!
76.97%
Q
HR
X
1000 m3
ST_1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
118.650
182.350
!!
!!
!!
!!
!!
153.69%
€
HR
X
1000 NAC
ST_1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
103931.670
207295.960
!!
!!
!!
!!
!!
199.45%
UV
HR
X
1000 m3
ST_1_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
875.952
1136.803
!!
!!
!!
!!
!!
129.78%
Q
HR
M
1000 m3
ST_1_2_NC_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13.730
4.580
!!
!!
!!
!!
!!
33.36%
€
HR
M
1000 NAC
ST_1_2_NC_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
23128.460
8328.000
!!
!!
!!
!!
!!
36.01%
UV
HR
M
1000 m3
ST_1_2_NC_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1684.520
1818.341
!!
!!
!!
!!
!!
107.94%
Q
HR
X
1000 m3
ST_1_2_NC_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
10.540
54.250
!!
!!
!!
!!
!!
514.71%
€
HR
X
1000 NAC
ST_1_2_NC_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
32425.280
125167.840
!!
!!
!!
!!
!!
386.02%
UV
HR
X
1000 m3
ST_1_2_NC_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3076.402
2307.241
!!
!!
!!
!!
!!
75.00%
Q
HR
M
1000 m3
ST_1_2_NC_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
40.870
38.720
!!
!!
!!
!!
!!
94.74%
€
HR
M
1000 NAC
ST_1_2_NC_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
26284.720
21744.210
!!
!!
!!
!!
!!
82.73%
UV
HR
M
1000 m3
ST_1_2_NC_1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
643.130
561.576
!!
!!
!!
!!
!!
87.32%
Q
HR
X
1000 m3
ST_1_2_NC_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
41.450
66.530
!!
!!
!!
!!
!!
160.51%
€
HR
X
1000 NAC
ST_1_2_NC_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
22300.560
30575.000
!!
!!
!!
!!
!!
137.10%
UV
HR
X
1000 m3
ST_1_2_NC_1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
538.011
459.567
!!
!!
!!
!!
!!
85.42%
Q
HR
M
1000 m3
ST_1_2_NC_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.120
0.000
!!
!!
!!
!!
!!
0.00%
€
HR
M
1000 NAC
ST_1_2_NC_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
75.540
0.000
!!
!!
!!
!!
!!
0.00%
UV
HR
M
1000 m3
ST_1_2_NC_1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
629.500
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_1_2_NC_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_1_2_NC_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_1_2_NC_1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_1_2_NC_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
425.130
366.330
!!
!!
!!
!!
!!
86.17%
€
HR
M
1000 NAC
ST_1_2_NC_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
514876.210
453702.410
!!
!!
!!
!!
!!
88.12%
UV
HR
M
1000 m3
ST_1_2_NC_1_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1211.103
1238.507
!!
!!
!!
!!
!!
102.26%
Q
HR
X
1000 m3
ST_1_2_NC_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
365.360
350.320
!!
!!
!!
!!
!!
95.88%
€
HR
X
1000 NAC
ST_1_2_NC_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
465813.850
443085.350
!!
!!
!!
!!
!!
95.12%
UV
HR
X
1000 m3
ST_1_2_NC_1_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1274.945
1264.802
!!
!!
!!
!!
!!
99.20%
Q
HR
M
1000 m3
ST_1_2_NC_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.050
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_1_2_NC_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
19.840
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_1_2_NC_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
396.800
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_1_2_NC_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.040
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_1_2_NC_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
26.670
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_1_2_NC_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
666.750
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_1_2_NC_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.120
0.050
!!
!!
!!
!!
!!
41.67%
€
HR
M
1000 NAC
ST_1_2_NC_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
75.540
19.840
!!
!!
!!
!!
!!
26.26%
UV
HR
M
1000 m3
ST_1_2_NC_2_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
629.500
396.800
!!
!!
!!
!!
!!
63.03%
Q
HR
X
1000 m3
ST_1_2_NC_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.040
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_1_2_NC_2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
26.670
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_1_2_NC_2_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
666.750
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_1_2_NC_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.320
0.060
!!
!!
!!
!!
!!
18.75%
€
HR
M
1000 NAC
ST_1_2_NC_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
221.820
71.170
!!
!!
!!
!!
!!
32.08%
UV
HR
M
1000 m3
ST_1_2_NC_2_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
693.188
1186.167
!!
!!
!!
!!
!!
171.12%
Q
HR
X
1000 m3
ST_1_2_NC_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.030
3.410
!!
!!
!!
!!
!!
167.98%
€
HR
X
1000 NAC
ST_1_2_NC_2_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
806.960
1457.530
!!
!!
!!
!!
!!
180.62%
UV
HR
X
1000 m3
ST_1_2_NC_2_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
397.517
427.428
!!
!!
!!
!!
!!
107.52%
Q
HR
M
1000 m3
ST_1_2_NC_2_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
414.510
354.940
!!
!!
!!
!!
!!
85.63%
€
HR
M
1000 NAC
ST_1_2_NC_2_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
489707.640
430459.290
!!
!!
!!
!!
!!
87.90%
UV
HR
M
1000 m3
ST_1_2_NC_2_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1181.413
1212.766
!!
!!
!!
!!
!!
102.65%
Q
HR
X
1000 m3
ST_1_2_NC_2_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
361.350
346.210
!!
!!
!!
!!
!!
95.81%
€
HR
X
1000 NAC
ST_1_2_NC_2_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
460293.490
437112.690
!!
!!
!!
!!
!!
94.96%
UV
HR
X
1000 m3
ST_1_2_NC_2_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1273.816
1262.565
!!
!!
!!
!!
!!
99.12%
Q
HR
M
1000 m3
ST_1_2_NC_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
M
1000 NAC
ST_1_2_NC_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
M
1000 m3
ST_1_2_NC_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
X
1000 m3
ST_1_2_NC_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
X
1000 NAC
ST_1_2_NC_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
X
1000 m3
ST_1_2_NC_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
M
1000 m3
ST_1_2_NC_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4.430
3.250
!!
!!
!!
!!
!!
73.36%
€
HR
M
1000 NAC
ST_1_2_NC_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8443.970
5813.820
!!
!!
!!
!!
!!
68.85%
UV
HR
M
1000 m3
ST_1_2_NC_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1906.088
1788.868
!!
!!
!!
!!
!!
93.85%
Q
HR
X
1000 m3
ST_1_2_NC_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3.520
2.480
!!
!!
!!
!!
!!
70.45%
€
HR
X
1000 NAC
ST_1_2_NC_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4190.580
2846.090
!!
!!
!!
!!
!!
67.92%
UV
HR
X
1000 m3
ST_1_2_NC_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1190.506
1147.617
!!
!!
!!
!!
!!
96.40%
Q
HR
M
1000 m3
ST_1_2_NC_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
81.000
88.350
!!
!!
!!
!!
!!
109.07%
€
HR
M
1000 NAC
ST_1_2_NC_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
297114.290
238560.780
!!
!!
!!
!!
!!
80.29%
UV
HR
M
1000 m3
ST_1_2_NC_5
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3668.078
2700.179
!!
!!
!!
!!
!!
73.61%
Q
HR
X
1000 m3
ST_1_2_NC_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
903.920
850.880
!!
!!
!!
!!
!!
94.13%
€
HR
X
1000 NAC
ST_1_2_NC_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2393145.810
2145831.440
!!
!!
!!
!!
!!
89.67%
UV
HR
X
1000 m3
ST_1_2_NC_5
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2647.519
2521.897
!!
!!
!!
!!
!!
95.26%
Q
HR
M
1000 m3
ST_5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
37.680
28.050
!!
!!
!!
!!
!!
74.44%
€
HR
M
1000 NAC
ST_5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
188673.650
130970.110
!!
!!
!!
!!
!!
69.42%
UV
HR
M
1000 m3
ST_5_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5007.262
4669.166
!!
!!
!!
!!
!!
93.25%
Q
HR
X
1000 m3
ST_5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
232.980
227.630
!!
!!
!!
!!
!!
97.70%
€
HR
X
1000 NAC
ST_5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1064889.820
1002720.160
!!
!!
!!
!!
!!
94.16%
UV
HR
X
1000 m3
ST_5_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4570.735
4405.044
!!
!!
!!
!!
!!
96.37%
Q
HR
M
1000 m3
ST_5_C_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
15.920
22.010
!!
!!
!!
!!
!!
138.25%
€
HR
M
1000 NAC
ST_5_C_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
20807.340
30535.820
!!
!!
!!
!!
!!
146.76%
UV
HR
M
1000 m3
ST_5_C_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1306.994
1387.361
!!
!!
!!
!!
!!
106.15%
Q
HR
X
1000 m3
ST_5_C_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
525.310
471.650
!!
!!
!!
!!
!!
89.79%
€
HR
X
1000 NAC
ST_5_C_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
931334.220
804244.500
!!
!!
!!
!!
!!
86.35%
UV
HR
X
1000 m3
ST_5_C_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1772.923
1705.172
!!
!!
!!
!!
!!
96.18%
Q
HR
M
1000 m3
ST_5_C_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.070
0.120
!!
!!
!!
!!
!!
171.43%
€
HR
M
1000 NAC
ST_5_C_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
371.830
255.790
!!
!!
!!
!!
!!
68.79%
UV
HR
M
1000 m3
ST_5_C_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5311.857
2131.583
!!
!!
!!
!!
!!
40.13%
Q
HR
X
1000 m3
ST_5_C_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.940
1.410
!!
!!
!!
!!
!!
150.00%
€
HR
X
1000 NAC
ST_5_C_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5976.950
6545.150
!!
!!
!!
!!
!!
109.51%
UV
HR
X
1000 m3
ST_5_C_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6358.457
4641.950
!!
!!
!!
!!
!!
73.00%
Q
HR
M
1000 m3
ST_5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.050
0.040
!!
!!
!!
!!
!!
80.00%
€
HR
M
1000 NAC
ST_5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
404.700
108.810
!!
!!
!!
!!
!!
26.89%
UV
HR
M
1000 m3
ST_5_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8094.000
2720.250
!!
!!
!!
!!
!!
33.61%
Q
HR
X
1000 m3
ST_5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.730
0.570
!!
!!
!!
!!
!!
78.08%
€
HR
X
1000 NAC
ST_5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2237.730
1633.950
!!
!!
!!
!!
!!
73.02%
UV
HR
X
1000 m3
ST_5_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3065.384
2866.579
!!
!!
!!
!!
!!
93.51%
Q
HR
M
1000 m3
ST_5_NC_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5.420
1.820
!!
!!
!!
!!
!!
33.58%
€
HR
M
1000 NAC
ST_5_NC_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9947.700
3073.810
!!
!!
!!
!!
!!
30.90%
UV
HR
M
1000 m3
ST_5_NC_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1835.369
1688.907
!!
!!
!!
!!
!!
92.02%
Q
HR
X
1000 m3
ST_5_NC_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
63.920
57.080
!!
!!
!!
!!
!!
89.30%
€
HR
X
1000 NAC
ST_5_NC_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
200511.840
183055.980
!!
!!
!!
!!
!!
91.29%
UV
HR
X
1000 m3
ST_5_NC_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3136.919
3207.007
!!
!!
!!
!!
!!
102.23%
Q
HR
M
1000 m3
ST_5_NC_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.570
0.270
!!
!!
!!
!!
!!
47.37%
€
HR
M
1000 NAC
ST_5_NC_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
350.850
709.790
!!
!!
!!
!!
!!
202.31%
UV
HR
M
1000 m3
ST_5_NC_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
615.526
2628.852
!!
!!
!!
!!
!!
427.09%
Q
HR
X
1000 m3
ST_5_NC_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
21.150
19.890
!!
!!
!!
!!
!!
94.04%
€
HR
X
1000 NAC
ST_5_NC_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
32992.950
29239.510
!!
!!
!!
!!
!!
88.62%
UV
HR
X
1000 m3
ST_5_NC_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1559.950
1470.061
!!
!!
!!
!!
!!
94.24%
Q
HR
M
1000 m3
ST_5_NC_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.120
0.080
!!
!!
!!
!!
!!
66.67%
€
HR
M
1000 NAC
ST_5_NC_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
249.610
172.720
!!
!!
!!
!!
!!
69.20%
UV
HR
M
1000 m3
ST_5_NC_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2080.083
2159.000
!!
!!
!!
!!
!!
103.79%
Q
HR
X
1000 m3
ST_5_NC_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.290
0.230
!!
!!
!!
!!
!!
79.31%
€
HR
X
1000 NAC
ST_5_NC_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
555.140
487.660
!!
!!
!!
!!
!!
87.84%
UV
HR
X
1000 m3
ST_5_NC_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1914.276
2120.261
!!
!!
!!
!!
!!
110.76%
Q
HR
M
1000 m3
ST_5_NC_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
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
HR
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
HR
X
1000 m3
ST_5_NC_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
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
HR
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
HR
M
1000 m3
ST_5_NC_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
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
HR
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
HR
X
1000 m3
ST_5_NC_5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
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
HR
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
HR
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%
€
HR
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
HR
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
HR
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%
€
HR
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
HR
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
HR
M
1000 m3
ST_5_NC_7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
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
HR
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
HR
X
1000 m3
ST_5_NC_7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
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
HR
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
HR
EX_M
1000 m3
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
43.430
48.550
!!
!!
!!
!!
!!
111.79%
€
HR
EX_M
1000 NAC
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
33529.770
32667.960
!!
!!
!!
!!
!!
97.43%
UV
HR
EX_M
1000 m3
1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
772.042
672.873
!!
!!
!!
!!
!!
87.15%
Q
HR
EX_X
1000 m3
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
28.850
42.420
!!
!!
!!
!!
!!
147.04%
€
HR
EX_X
1000 NAC
1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
21213.020
45438.650
!!
!!
!!
!!
!!
214.20%
UV
HR
EX_X
1000 m3
1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
735.287
1071.161
!!
!!
!!
!!
!!
145.68%
Q
HR
EX_M
1000 m3
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
36.720
45.760
!!
!!
!!
!!
!!
124.62%
€
HR
EX_M
1000 NAC
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
24907.030
28330.420
!!
!!
!!
!!
!!
113.74%
UV
HR
EX_M
1000 m3
1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
678.296
619.109
!!
!!
!!
!!
!!
91.27%
Q
HR
EX_X
1000 m3
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.140
0.310
!!
!!
!!
!!
!!
221.43%
€
HR
EX_X
1000 NAC
1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
185.680
360.790
!!
!!
!!
!!
!!
194.31%
UV
HR
EX_X
1000 m3
1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1326.286
1163.839
!!
!!
!!
!!
!!
87.75%
Q
HR
EX_M
1000 m3
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.040
0.190
!!
!!
!!
!!
!!
475.00%
€
HR
EX_M
1000 NAC
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
43.750
343.420
!!
!!
!!
!!
!!
784.96%
UV
HR
EX_M
1000 m3
1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1093.750
1807.474
!!
!!
!!
!!
!!
165.25%
Q
HR
EX_X
1000 m3
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 m3
1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 m3
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
36.680
45.570
!!
!!
!!
!!
!!
124.24%
€
HR
EX_M
1000 NAC
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
24863.280
27987.000
!!
!!
!!
!!
!!
112.56%
UV
HR
EX_M
1000 m3
1_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
677.843
614.154
!!
!!
!!
!!
!!
90.60%
Q
HR
EX_X
1000 m3
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.140
0.310
!!
!!
!!
!!
!!
221.43%
€
HR
EX_X
1000 NAC
1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
185.680
360.790
!!
!!
!!
!!
!!
194.31%
UV
HR
EX_X
1000 m3
1_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1326.286
1163.839
!!
!!
!!
!!
!!
87.75%
Q
HR
EX_M
1000 m3
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6.710
2.790
!!
!!
!!
!!
!!
41.58%
€
HR
EX_M
1000 NAC
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8622.740
4337.540
!!
!!
!!
!!
!!
50.30%
UV
HR
EX_M
1000 m3
1_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1285.058
1554.674
!!
!!
!!
!!
!!
120.98%
Q
HR
EX_X
1000 m3
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
28.710
42.110
!!
!!
!!
!!
!!
146.67%
€
HR
EX_X
1000 NAC
1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
21027.340
45077.860
!!
!!
!!
!!
!!
214.38%
UV
HR
EX_X
1000 m3
1_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
732.405
1070.479
!!
!!
!!
!!
!!
146.16%
Q
HR
EX_M
1000 m3
1_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.570
1.570
!!
!!
!!
!!
!!
61.09%
€
HR
EX_M
1000 NAC
1_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
906.760
542.510
!!
!!
!!
!!
!!
59.83%
UV
HR
EX_M
1000 m3
1_2_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
352.825
345.548
!!
!!
!!
!!
!!
97.94%
Q
HR
EX_X
1000 m3
1_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
15.940
15.450
!!
!!
!!
!!
!!
96.93%
€
HR
EX_X
1000 NAC
1_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
10751.000
7765.070
!!
!!
!!
!!
!!
72.23%
UV
HR
EX_X
1000 m3
1_2_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
674.467
502.594
!!
!!
!!
!!
!!
74.52%
Q
HR
EX_M
1000 mt
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4.140
1.220
!!
!!
!!
!!
!!
29.47%
€
HR
EX_M
1000 NAC
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7715.980
3795.030
!!
!!
!!
!!
!!
49.18%
UV
HR
EX_M
1000 mt
2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1863.763
3110.680
!!
!!
!!
!!
!!
166.90%
Q
HR
EX_X
1000 mt
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
12.770
26.660
!!
!!
!!
!!
!!
208.77%
€
HR
EX_X
1000 NAC
2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
10276.340
37312.790
!!
!!
!!
!!
!!
363.09%
UV
HR
EX_X
1000 mt
2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
804.725
1399.580
!!
!!
!!
!!
!!
173.92%
Q
HR
EX_M
1000 m3
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
46.770
0.000
!!
!!
!!
!!
!!
0.00%
UV
HR
EX_M
1000 m3
3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 m3
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 m3
3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 m3
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11.250
10.150
!!
!!
!!
!!
!!
90.22%
€
HR
EX_M
1000 NAC
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
27394.060
25342.280
!!
!!
!!
!!
!!
92.51%
UV
HR
EX_M
1000 m3
3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2435.028
2496.776
!!
!!
!!
!!
!!
102.54%
Q
HR
EX_X
1000 m3
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.450
0.376
!!
!!
!!
!!
!!
83.56%
€
HR
EX_X
1000 NAC
3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
841.060
1079.890
!!
!!
!!
!!
!!
128.40%
UV
HR
EX_X
1000 m3
3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1869.022
2872.048
!!
!!
!!
!!
!!
153.67%
Q
HR
EX_M
1000 m3
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.320
4.500
!!
!!
!!
!!
!!
193.97%
€
HR
EX_M
1000 NAC
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
709.690
1831.100
!!
!!
!!
!!
!!
258.01%
UV
HR
EX_M
1000 m3
3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
305.901
406.911
!!
!!
!!
!!
!!
133.02%
Q
HR
EX_X
1000 m3
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
15.700
15.710
!!
!!
!!
!!
!!
100.06%
€
HR
EX_X
1000 NAC
3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3451.150
3244.450
!!
!!
!!
!!
!!
94.01%
UV
HR
EX_X
1000 m3
3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
219.818
206.521
!!
!!
!!
!!
!!
93.95%
Q
HR
EX_M
1000 mt
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.740
4.050
!!
!!
!!
!!
!!
232.76%
€
HR
EX_M
1000 NAC
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
588.660
1575.680
!!
!!
!!
!!
!!
267.67%
UV
HR
EX_M
1000 mt
4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
338.310
389.057
!!
!!
!!
!!
!!
115.00%
Q
HR
EX_X
1000 mt
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4.420
2.960
!!
!!
!!
!!
!!
66.97%
€
HR
EX_X
1000 NAC
4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1481.690
1098.340
!!
!!
!!
!!
!!
74.13%
UV
HR
EX_X
1000 mt
4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
335.224
371.061
!!
!!
!!
!!
!!
110.69%
Q
HR
EX_M
1000 mt
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.580
0.450
!!
!!
!!
!!
!!
77.59%
€
HR
EX_M
1000 NAC
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
121.030
255.420
!!
!!
!!
!!
!!
211.04%
UV
HR
EX_M
1000 mt
4_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
208.672
567.600
!!
!!
!!
!!
!!
272.01%
Q
HR
EX_X
1000 mt
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11.280
12.750
!!
!!
!!
!!
!!
113.03%
€
HR
EX_X
1000 NAC
4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1969.460
2146.110
!!
!!
!!
!!
!!
108.97%
UV
HR
EX_X
1000 mt
4_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
174.598
168.322
!!
!!
!!
!!
!!
96.41%
Q
HR
EX_M
1000 mt
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
4_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
4_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 m3
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
18.060
17.290
!!
!!
!!
!!
!!
95.74%
€
HR
EX_M
1000 NAC
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
21910.180
19599.520
!!
!!
!!
!!
!!
89.45%
UV
HR
EX_M
1000 m3
5
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1213.188
1133.575
!!
!!
!!
!!
!!
93.44%
Q
HR
EX_X
1000 m3
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11.520
17.840
!!
!!
!!
!!
!!
154.86%
€
HR
EX_X
1000 NAC
5
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13115.830
20039.730
!!
!!
!!
!!
!!
152.79%
UV
HR
EX_X
1000 m3
5
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1138.527
1123.303
!!
!!
!!
!!
!!
98.66%
Q
HR
EX_M
1000 m3
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
15.960
15.450
!!
!!
!!
!!
!!
96.80%
€
HR
EX_M
1000 NAC
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
19618.770
17885.710
!!
!!
!!
!!
!!
91.17%
UV
HR
EX_M
1000 m3
5_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1229.246
1157.651
!!
!!
!!
!!
!!
94.18%
Q
HR
EX_X
1000 m3
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
10.950
17.170
!!
!!
!!
!!
!!
156.80%
€
HR
EX_X
1000 NAC
5_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
12415.910
19370.630
!!
!!
!!
!!
!!
156.01%
UV
HR
EX_X
1000 m3
5_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1133.873
1128.167
!!
!!
!!
!!
!!
99.50%
Q
HR
EX_M
1000 m3
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.100
1.840
!!
!!
!!
!!
!!
87.62%
€
HR
EX_M
1000 NAC
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2291.410
1713.810
!!
!!
!!
!!
!!
74.79%
UV
HR
EX_M
1000 m3
5_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1091.148
931.418
!!
!!
!!
!!
!!
85.36%
Q
HR
EX_X
1000 m3
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.570
0.670
!!
!!
!!
!!
!!
117.54%
€
HR
EX_X
1000 NAC
5_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
699.920
669.100
!!
!!
!!
!!
!!
95.60%
UV
HR
EX_X
1000 m3
5_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1227.930
998.657
!!
!!
!!
!!
!!
81.33%
Q
HR
EX_M
1000 m3
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
125.670
148.330
!!
!!
!!
!!
!!
118.03%
€
HR
EX_M
1000 NAC
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
244170.360
212839.450
!!
!!
!!
!!
!!
87.17%
UV
HR
EX_M
1000 m3
5_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1942.949
1434.905
!!
!!
!!
!!
!!
73.85%
Q
HR
EX_X
1000 m3
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
885.010
853.820
!!
!!
!!
!!
!!
96.48%
€
HR
EX_X
1000 NAC
5_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1501348.710
1325161.000
!!
!!
!!
!!
!!
88.26%
UV
HR
EX_X
1000 m3
5_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1696.420
1552.038
!!
!!
!!
!!
!!
91.49%
Q
HR
EX_M
1000 m3
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
90.090
122.310
!!
!!
!!
!!
!!
135.76%
€
HR
EX_M
1000 NAC
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
110496.080
104909.200
!!
!!
!!
!!
!!
94.94%
UV
HR
EX_M
1000 m3
6
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1226.508
857.732
!!
!!
!!
!!
!!
69.93%
Q
HR
EX_X
1000 m3
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
302.000
301.710
!!
!!
!!
!!
!!
99.90%
€
HR
EX_X
1000 NAC
6
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
381536.710
377360.660
!!
!!
!!
!!
!!
98.91%
UV
HR
EX_X
1000 m3
6
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1263.367
1250.740
!!
!!
!!
!!
!!
99.00%
Q
HR
EX_M
1000 m3
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
35.580
26.020
!!
!!
!!
!!
!!
73.13%
€
HR
EX_M
1000 NAC
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
133674.280
107930.250
!!
!!
!!
!!
!!
80.74%
UV
HR
EX_M
1000 m3
6_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3757.006
4147.973
!!
!!
!!
!!
!!
110.41%
Q
HR
EX_X
1000 m3
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
583.010
552.110
!!
!!
!!
!!
!!
94.70%
€
HR
EX_X
1000 NAC
6_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1119812.000
947800.340
!!
!!
!!
!!
!!
84.64%
UV
HR
EX_X
1000 m3
6_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1920.742
1716.688
!!
!!
!!
!!
!!
89.38%
Q
HR
EX_M
1000 m3
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11.880
0.760
!!
!!
!!
!!
!!
6.40%
€
HR
EX_M
1000 NAC
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
32399.410
12783.170
!!
!!
!!
!!
!!
39.45%
UV
HR
EX_M
1000 m3
6_1_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2727.223
16819.961
!!
!!
!!
!!
!!
616.74%
Q
HR
EX_X
1000 m3
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.070
0.060
!!
!!
!!
!!
!!
85.71%
€
HR
EX_X
1000 NAC
6_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
612.100
643.310
!!
!!
!!
!!
!!
105.10%
UV
HR
EX_X
1000 m3
6_1_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8744.286
10721.833
!!
!!
!!
!!
!!
122.62%
Q
HR
EX_M
1000 m3
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.110
1.880
!!
!!
!!
!!
!!
89.10%
€
HR
EX_M
1000 NAC
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
16992.860
16112.390
!!
!!
!!
!!
!!
94.82%
UV
HR
EX_M
1000 m3
6_1_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8053.488
8570.420
!!
!!
!!
!!
!!
106.42%
Q
HR
EX_X
1000 m3
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.760
1.800
!!
!!
!!
!!
!!
65.22%
€
HR
EX_X
1000 NAC
6_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
32371.140
24694.400
!!
!!
!!
!!
!!
76.29%
UV
HR
EX_X
1000 m3
6_1_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11728.674
13719.111
!!
!!
!!
!!
!!
116.97%
Q
HR
EX_M
1000 m3
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.040
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
15.210
191.030
!!
!!
!!
!!
!!
1255.95%
UV
HR
EX_M
1000 m3
6_1_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
4775.750
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 m3
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
6_1_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.070
17.590
!!
!!
!!
!!
!!
1643.93%
UV
HR
EX_X
1000 m3
6_1_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 m3
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.110
1.840
!!
!!
!!
!!
!!
87.20%
€
HR
EX_M
1000 NAC
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
16977.650
15921.360
!!
!!
!!
!!
!!
93.78%
UV
HR
EX_M
1000 m3
6_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8046.280
8652.913
!!
!!
!!
!!
!!
107.54%
Q
HR
EX_X
1000 m3
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.760
1.800
!!
!!
!!
!!
!!
65.22%
€
HR
EX_X
1000 NAC
6_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
32370.070
24676.810
!!
!!
!!
!!
!!
76.23%
UV
HR
EX_X
1000 m3
6_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11728.286
13709.339
!!
!!
!!
!!
!!
116.89%
Q
HR
EX_M
1000 m3
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.040
0.030
!!
!!
!!
!!
!!
75.00%
€
HR
EX_M
1000 NAC
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3239.370
2406.480
!!
!!
!!
!!
!!
74.29%
UV
HR
EX_M
1000 m3
6_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
80984.250
80216.000
!!
!!
!!
!!
!!
99.05%
Q
HR
EX_X
1000 m3
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
6_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.520
4.790
!!
!!
!!
!!
!!
190.08%
UV
HR
EX_X
1000 m3
6_2_C
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 m3
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
28.570
28.700
!!
!!
!!
!!
!!
100.46%
€
HR
EX_M
1000 NAC
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
68088.960
69829.370
!!
!!
!!
!!
!!
102.56%
UV
HR
EX_M
1000 m3
6_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2383.233
2433.079
!!
!!
!!
!!
!!
102.09%
Q
HR
EX_X
1000 m3
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
84.920
85.690
!!
!!
!!
!!
!!
100.91%
€
HR
EX_X
1000 NAC
6_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
148161.070
158937.650
!!
!!
!!
!!
!!
107.27%
UV
HR
EX_X
1000 m3
6_2_NC
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1744.713
1854.798
!!
!!
!!
!!
!!
106.31%
Q
HR
EX_M
1000 m3
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8.800
8.460
!!
!!
!!
!!
!!
96.14%
€
HR
EX_M
1000 NAC
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
29898.960
27337.670
!!
!!
!!
!!
!!
91.43%
UV
HR
EX_M
1000 m3
6_2_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3397.609
3231.403
!!
!!
!!
!!
!!
95.11%
Q
HR
EX_X
1000 m3
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.390
1.540
!!
!!
!!
!!
!!
110.79%
€
HR
EX_X
1000 NAC
6_2_NC_T
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6959.020
8618.970
!!
!!
!!
!!
!!
123.85%
UV
HR
EX_X
1000 m3
6_2_NC_T
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5006.489
5596.734
!!
!!
!!
!!
!!
111.79%
Q
HR
EX_M
1000 m3
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.610
0.690
!!
!!
!!
!!
!!
113.11%
€
HR
EX_M
1000 NAC
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3655.640
4243.040
!!
!!
!!
!!
!!
116.07%
UV
HR
EX_M
1000 m3
6_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5992.852
6149.333
!!
!!
!!
!!
!!
102.61%
Q
HR
EX_X
1000 m3
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.510
0.430
!!
!!
!!
!!
!!
84.31%
€
HR
EX_X
1000 NAC
6_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2920.670
2364.010
!!
!!
!!
!!
!!
80.94%
UV
HR
EX_X
1000 m3
6_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5726.804
5497.698
!!
!!
!!
!!
!!
96.00%
Q
HR
EX_M
1000 m3
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8.190
7.770
!!
!!
!!
!!
!!
94.87%
€
HR
EX_M
1000 NAC
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
26243.320
23094.630
!!
!!
!!
!!
!!
88.00%
UV
HR
EX_M
1000 m3
6_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3204.313
2972.282
!!
!!
!!
!!
!!
92.76%
Q
HR
EX_X
1000 m3
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.880
1.110
!!
!!
!!
!!
!!
126.14%
€
HR
EX_X
1000 NAC
6_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4038.350
6254.960
!!
!!
!!
!!
!!
154.89%
UV
HR
EX_X
1000 m3
6_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4589.034
5635.099
!!
!!
!!
!!
!!
122.79%
Q
HR
EX_M
1000 m3
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.040
0.060
!!
!!
!!
!!
!!
150.00%
€
HR
EX_M
1000 NAC
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
188.510
271.500
!!
!!
!!
!!
!!
144.02%
UV
HR
EX_M
1000 m3
6_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4712.750
4525.000
!!
!!
!!
!!
!!
96.02%
Q
HR
EX_X
1000 m3
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.020
0.120
!!
!!
!!
!!
!!
600.00%
€
HR
EX_X
1000 NAC
6_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
94.800
491.920
!!
!!
!!
!!
!!
518.90%
UV
HR
EX_X
1000 m3
6_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4740.000
4099.333
!!
!!
!!
!!
!!
86.48%
Q
HR
EX_M
1000 m3
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
10.930
11.090
!!
!!
!!
!!
!!
101.46%
€
HR
EX_M
1000 NAC
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13994.610
14782.620
!!
!!
!!
!!
!!
105.63%
UV
HR
EX_M
1000 m3
6_4_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1280.385
1332.968
!!
!!
!!
!!
!!
104.11%
Q
HR
EX_X
1000 m3
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
69.740
66.750
!!
!!
!!
!!
!!
95.71%
€
HR
EX_X
1000 NAC
6_4_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
106695.960
101203.170
!!
!!
!!
!!
!!
94.85%
UV
HR
EX_X
1000 m3
6_4_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1529.911
1516.152
!!
!!
!!
!!
!!
99.10%
Q
HR
EX_M
1000 m3
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.030
0.010
!!
!!
!!
!!
!!
33.33%
€
HR
EX_M
1000 NAC
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
104.960
9.120
!!
!!
!!
!!
!!
8.69%
UV
HR
EX_M
1000 m3
6_4_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3498.667
912.000
!!
!!
!!
!!
!!
26.07%
Q
HR
EX_X
1000 m3
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.060
2.660
!!
!!
!!
!!
!!
129.13%
€
HR
EX_X
1000 NAC
6_4_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4010.880
5149.840
!!
!!
!!
!!
!!
128.40%
UV
HR
EX_X
1000 m3
6_4_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1947.029
1936.030
!!
!!
!!
!!
!!
99.44%
Q
HR
EX_M
1000 m3
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8.840
9.150
!!
!!
!!
!!
!!
103.51%
€
HR
EX_M
1000 NAC
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
24195.390
27709.080
!!
!!
!!
!!
!!
114.52%
UV
HR
EX_M
1000 m3
6_4_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2737.035
3028.315
!!
!!
!!
!!
!!
110.64%
Q
HR
EX_X
1000 m3
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13.790
17.400
!!
!!
!!
!!
!!
126.18%
€
HR
EX_X
1000 NAC
6_4_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
34506.090
49115.510
!!
!!
!!
!!
!!
142.34%
UV
HR
EX_X
1000 m3
6_4_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2502.255
2822.730
!!
!!
!!
!!
!!
112.81%
Q
HR
EX_M
1000 mt
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.170
0.380
!!
!!
!!
!!
!!
223.53%
€
HR
EX_M
1000 NAC
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
878.070
1742.450
!!
!!
!!
!!
!!
198.44%
UV
HR
EX_M
1000 mt
7
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5165.118
4585.395
!!
!!
!!
!!
!!
88.78%
Q
HR
EX_X
1000 mt
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.000
3.990
!!
!!
!!
!!
!!
199.50%
€
HR
EX_X
1000 NAC
7
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9332.280
18805.330
!!
!!
!!
!!
!!
201.51%
UV
HR
EX_X
1000 mt
7
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4666.140
4713.115
!!
!!
!!
!!
!!
101.01%
Q
HR
EX_M
1000 mt
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8.580
8.770
!!
!!
!!
!!
!!
102.21%
€
HR
EX_M
1000 NAC
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
22906.560
25962.890
!!
!!
!!
!!
!!
113.34%
UV
HR
EX_M
1000 mt
7_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2669.762
2960.421
!!
!!
!!
!!
!!
110.89%
Q
HR
EX_X
1000 mt
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11.760
13.360
!!
!!
!!
!!
!!
113.61%
€
HR
EX_X
1000 NAC
7_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
25119.740
30236.870
!!
!!
!!
!!
!!
120.37%
UV
HR
EX_X
1000 mt
7_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2136.032
2263.239
!!
!!
!!
!!
!!
105.96%
Q
HR
EX_M
1000 mt
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.090
0.000
!!
!!
!!
!!
!!
0.00%
€
HR
EX_M
1000 NAC
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
410.760
3.740
!!
!!
!!
!!
!!
0.91%
UV
HR
EX_M
1000 mt
7_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4564.000
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.030
0.050
!!
!!
!!
!!
!!
166.67%
€
HR
EX_X
1000 NAC
7_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
54.070
73.310
!!
!!
!!
!!
!!
135.58%
UV
HR
EX_X
1000 mt
7_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1802.333
1466.200
!!
!!
!!
!!
!!
81.35%
Q
HR
EX_M
1000 mt
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
7_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9.330
6.000
!!
!!
!!
!!
!!
64.31%
€
HR
EX_X
1000 NAC
7_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
22874.790
14229.920
!!
!!
!!
!!
!!
62.21%
UV
HR
EX_X
1000 mt
7_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2451.746
2371.653
!!
!!
!!
!!
!!
96.73%
Q
HR
EX_M
1000 mt
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
7_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9.330
6.000
!!
!!
!!
!!
!!
64.31%
€
HR
EX_X
1000 NAC
7_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
22819.290
14216.320
!!
!!
!!
!!
!!
62.30%
UV
HR
EX_X
1000 mt
7_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2445.797
2369.387
!!
!!
!!
!!
!!
96.88%
Q
HR
EX_M
1000 mt
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
7_3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
7_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
55.500
13.600
!!
!!
!!
!!
!!
24.50%
UV
HR
EX_X
1000 mt
7_3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
7_3_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
7_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
7_3_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
7_3_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
7_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
7_3_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
7_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
7_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
55.500
13.600
!!
!!
!!
!!
!!
24.50%
UV
HR
EX_X
1000 mt
7_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
8
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
8
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_X
1000 mt
8
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
8_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.030
0.020
!!
!!
!!
!!
!!
66.67%
€
HR
EX_X
1000 NAC
8_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
202.250
187.720
!!
!!
!!
!!
!!
92.82%
UV
HR
EX_X
1000 mt
8_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6741.667
9386.000
!!
!!
!!
!!
!!
139.22%
Q
HR
EX_M
1000 mt
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
8_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.010
0.010
!!
!!
!!
!!
!!
100.00%
€
HR
EX_X
1000 NAC
8_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
88.280
98.050
!!
!!
!!
!!
!!
111.07%
UV
HR
EX_X
1000 mt
8_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
8828.000
9805.000
!!
!!
!!
!!
!!
111.07%
Q
HR
EX_M
1000 mt
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_M
1000 NAC
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
UV
HR
EX_M
1000 mt
9
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_X
1000 mt
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.020
0.020
!!
!!
!!
!!
!!
100.00%
€
HR
EX_X
1000 NAC
9
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
113.960
89.670
!!
!!
!!
!!
!!
78.69%
UV
HR
EX_X
1000 mt
9
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5698.000
4483.500
!!
!!
!!
!!
!!
78.69%
Q
HR
EX_M
1000 mt
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
41.590
34.900
!!
!!
!!
!!
!!
83.91%
€
HR
EX_M
1000 NAC
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
37335.580
27016.870
!!
!!
!!
!!
!!
72.36%
UV
HR
EX_M
1000 mt
10
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
897.706
774.122
!!
!!
!!
!!
!!
86.23%
Q
HR
EX_X
1000 mt
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
29.610
34.300
!!
!!
!!
!!
!!
115.84%
€
HR
EX_X
1000 NAC
10
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
27935.030
26577.380
!!
!!
!!
!!
!!
95.14%
UV
HR
EX_X
1000 mt
10
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
943.432
774.851
!!
!!
!!
!!
!!
82.13%
Q
HR
EX_M
1000 mt
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
40.260
44.000
!!
!!
!!
!!
!!
109.29%
€
HR
EX_M
1000 NAC
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
275434.860
279020.660
!!
!!
!!
!!
!!
101.30%
UV
HR
EX_M
1000 mt
10_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6841.402
6341.379
!!
!!
!!
!!
!!
92.69%
Q
HR
EX_X
1000 mt
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
54.840
64.790
!!
!!
!!
!!
!!
118.14%
€
HR
EX_X
1000 NAC
10_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
205206.750
199577.410
!!
!!
!!
!!
!!
97.26%
UV
HR
EX_X
1000 mt
10_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3741.917
3080.374
!!
!!
!!
!!
!!
82.32%
Q
HR
EX_M
1000 mt
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.940
2.880
!!
!!
!!
!!
!!
97.96%
€
HR
EX_M
1000 NAC
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
18895.590
17324.900
!!
!!
!!
!!
!!
91.69%
UV
HR
EX_M
1000 mt
10_1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6427.071
6015.590
!!
!!
!!
!!
!!
93.60%
Q
HR
EX_X
1000 mt
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.060
1.420
!!
!!
!!
!!
!!
68.93%
€
HR
EX_X
1000 NAC
10_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
24179.270
16323.550
!!
!!
!!
!!
!!
67.51%
UV
HR
EX_X
1000 mt
10_1_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11737.510
11495.458
!!
!!
!!
!!
!!
97.94%
Q
HR
EX_M
1000 mt
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.080
0.070
!!
!!
!!
!!
!!
87.50%
€
HR
EX_M
1000 NAC
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
446.460
353.230
!!
!!
!!
!!
!!
79.12%
UV
HR
EX_M
1000 mt
10_1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
5580.750
5046.143
!!
!!
!!
!!
!!
90.42%
Q
HR
EX_X
1000 mt
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.010
0.000
!!
!!
!!
!!
!!
0.00%
€
HR
EX_X
1000 NAC
10_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
65.430
2.050
!!
!!
!!
!!
!!
3.13%
UV
HR
EX_X
1000 mt
10_1_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6543.000
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
Q
HR
EX_M
1000 mt
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.060
0.020
!!
!!
!!
!!
!!
33.33%
€
HR
EX_M
1000 NAC
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
472.760
491.940
!!
!!
!!
!!
!!
104.06%
UV
HR
EX_M
1000 mt
10_1_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7879.333
24597.000
!!
!!
!!
!!
!!
312.17%
Q
HR
EX_X
1000 mt
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.070
0.030
!!
!!
!!
!!
!!
42.86%
€
HR
EX_X
1000 NAC
10_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
439.660
316.280
!!
!!
!!
!!
!!
71.94%
UV
HR
EX_X
1000 mt
10_1_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6280.857
10542.667
!!
!!
!!
!!
!!
167.85%
Q
HR
EX_M
1000 mt
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2.140
2.430
!!
!!
!!
!!
!!
113.55%
€
HR
EX_M
1000 NAC
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13357.390
13828.830
!!
!!
!!
!!
!!
103.53%
UV
HR
EX_M
1000 mt
10_1_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6241.771
5690.877
!!
!!
!!
!!
!!
91.17%
Q
HR
EX_X
1000 mt
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.510
0.530
!!
!!
!!
!!
!!
103.92%
€
HR
EX_X
1000 NAC
10_1_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3730.300
3821.450
!!
!!
!!
!!
!!
102.44%
UV
HR
EX_X
1000 mt
10_1_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7314.314
7210.283
!!
!!
!!
!!
!!
98.58%
Q
HR
EX_M
1000 mt
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.660
0.360
!!
!!
!!
!!
!!
54.55%
€
HR
EX_M
1000 NAC
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4618.980
2650.900
!!
!!
!!
!!
!!
57.39%
UV
HR
EX_M
1000 mt
10_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6998.455
7363.611
!!
!!
!!
!!
!!
105.22%
Q
HR
EX_X
1000 mt
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1.470
0.860
!!
!!
!!
!!
!!
58.50%
€
HR
EX_X
1000 NAC
10_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
19943.880
12183.770
!!
!!
!!
!!
!!
61.09%
UV
HR
EX_X
1000 mt
10_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13567.265
14167.174
!!
!!
!!
!!
!!
104.42%
Q
HR
EX_M
1000 mt
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
11.880
15.670
!!
!!
!!
!!
!!
131.90%
€
HR
EX_M
1000 NAC
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
82572.970
98331.470
!!
!!
!!
!!
!!
119.08%
UV
HR
EX_M
1000 mt
10_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6950.587
6275.142
!!
!!
!!
!!
!!
90.28%
Q
HR
EX_X
1000 mt
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.560
0.770
!!
!!
!!
!!
!!
137.50%
€
HR
EX_X
1000 NAC
10_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4183.350
5358.410
!!
!!
!!
!!
!!
128.09%
UV
HR
EX_X
1000 mt
10_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
7470.268
6958.974
!!
!!
!!
!!
!!
93.16%
Q
HR
EX_M
1000 mt
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
25.340
25.330
!!
!!
!!
!!
!!
99.96%
€
HR
EX_M
1000 NAC
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
170736.790
159261.390
!!
!!
!!
!!
!!
93.28%
UV
HR
EX_M
1000 mt
10_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6737.837
6287.461
!!
!!
!!
!!
!!
93.32%
Q
HR
EX_X
1000 mt
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
52.120
62.540
!!
!!
!!
!!
!!
119.99%
€
HR
EX_X
1000 NAC
10_3_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
167854.090
169468.520
!!
!!
!!
!!
!!
100.96%
UV
HR
EX_X
1000 mt
10_3_1
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3220.531
2709.762
!!
!!
!!
!!
!!
84.14%
Q
HR
EX_M
1000 mt
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4.870
4.280
!!
!!
!!
!!
!!
87.89%
€
HR
EX_M
1000 NAC
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13280.420
11901.220
!!
!!
!!
!!
!!
89.61%
UV
HR
EX_M
1000 mt
10_3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2726.986
2780.659
!!
!!
!!
!!
!!
101.97%
Q
HR
EX_X
1000 mt
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
38.470
50.750
!!
!!
!!
!!
!!
131.92%
€
HR
EX_X
1000 NAC
10_3_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
97723.060
105915.300
!!
!!
!!
!!
!!
108.38%
UV
HR
EX_X
1000 mt
10_3_2
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2540.241
2087.001
!!
!!
!!
!!
!!
82.16%
Q
HR
EX_M
1000 mt
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
10.450
10.690
!!
!!
!!
!!
!!
102.30%
€
HR
EX_M
1000 NAC
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
94921.680
84119.350
!!
!!
!!
!!
!!
88.62%
UV
HR
EX_M
1000 mt
10_3_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9083.414
7868.976
!!
!!
!!
!!
!!
86.63%
Q
HR
EX_X
1000 mt
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.240
0.220
!!
!!
!!
!!
!!
91.67%
€
HR
EX_X
1000 NAC
10_3_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4701.490
6897.400
!!
!!
!!
!!
!!
146.71%
UV
HR
EX_X
1000 mt
10_3_3
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
19589.542
31351.818
!!
!!
!!
!!
!!
160.04%
Q
HR
EX_M
1000 mt
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
9.950
10.270
!!
!!
!!
!!
!!
103.22%
€
HR
EX_M
1000 NAC
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
62304.450
62913.430
!!
!!
!!
!!
!!
100.98%
UV
HR
EX_M
1000 mt
10_3_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
6261.754
6125.943
!!
!!
!!
!!
!!
97.83%
Q
HR
EX_X
1000 mt
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
13.410
11.570
!!
!!
!!
!!
!!
86.28%
€
HR
EX_X
1000 NAC
10_3_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
65413.420
56652.480
!!
!!
!!
!!
!!
86.61%
UV
HR
EX_X
1000 mt
10_3_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
4877.958
4896.498
!!
!!
!!
!!
!!
100.38%
Q
HR
EX_M
1000 mt
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.070
0.090
!!
!!
!!
!!
!!
128.57%
€
HR
EX_M
1000 NAC
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
230.240
327.390
!!
!!
!!
!!
!!
142.20%
UV
HR
EX_M
1000 mt
10_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
3289.143
3637.667
!!
!!
!!
!!
!!
110.60%
Q
HR
EX_X
1000 mt
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.000
0.000
!!
!!
!!
!!
!!
!!
€
HR
EX_X
1000 NAC
10_4
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
16.120
3.340
!!
!!
!!
!!
!!
20.72%
UV
HR
EX_X
1000 mt
10_4
ERROR:#DIV/0!
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#DIV/0!
ERROR:#DIV/0!
!!
!!
!!
!!
!!
!!
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
HR
P
1000 m3
EU2_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0
0
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0
0
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0
0
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_1
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0
0
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_1_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0
0
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_1_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0
0
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_2
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0
0
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_2_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0
0
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_2_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0
0
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_3
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0
0
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_3_C
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0
0
!!
!!
!!
!!
!!
!!
HR
P
1000 m3
EU2_1_3_NC
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0
0
!!
!!
!!
!!
!!
!!
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).
Group of Experts on Population and Housing Censuses
Geneva, Switzerland, 21-23 September 2022
(Let's create an image of Croatia together)
3/20
Initially planned for April 2021
Carried out from 13 September to 14 November 2021
Reference day - August 31, 2021
Covid 19 pandemic and two earthquakes played a major role in determining the reference day
CAWI and CAPI methods were used
Costs: about 5 € per person
Group of Experts on Population
and Housing Censuses
4/20
CAWI
From 13 September to 27
September 2021
CAPI
From 28 September to 18
October 2021
CAPI
Extended
to 29 October 2021
CAPI
Extended for the second time
to 14 November 2021
Group of Experts on Population
and Housing Censuses
Implementation of the Census in the field:
The longest Implementation in the field since 1857
5/20
Pilot Census
The aim was
to:
• Postponed to March 2021
• test new methods of data
collection
• reveal possible shortcomings
of the census application
Group of Experts on Population
and Housing Censuses
6/20Group of Experts on Population
and Housing Censuses
Human resources
• Recruitment and selection of enumerators and supervisors
• Relatively small number of applied candidates
• A large flow of enumerators
Census application
• How to determine the limit of complexity of the Census application
• Intensity of validation rules
• Possibility to add the addresses (house number) if necessary
• Online self-enumeration outside the borders, yes or no?
7/20
Participants Census 2021 Census 2011 Census 2001
Enumerators 7 900 ≈14 000 ≈20 000
Supervisors 1 038 1 950 ≈ 2 900
Extra hired processing workers 25 400-500 400-500
Group of Experts on Population
and Housing Censuses
Source: Croatian Bureau of Statistics
Number of participants according to Censuses 2021, 2011 and 2001.
8/20Group of Experts on Population
and Housing Censuses
Source: Croatian Bureau of Statistics
Number of applied candidates, planned enumerators, enumerators participated in the Census and appointed
enumerators who quit before the Census started, by counties
Zagreb county 1072 607 588 221 36,4
Primorje Gorski-Kotar 840 556 513 222 39,9
Osijek-Baranja 1369 541 538 237 43,8
Split-Dalmatia 1452 877 810 384 43,8
City of Zagreb 2391 1541 1402 514 33,4
Total (21) counties 14820 7941 7652 2868 36,1
County Applied candidates Planned enumer. Enumer. participated in Census Enumer. quit before the Census starts %
9/20Group of Experts on Population
and Housing Censuses
10/20
The more
complex the
application is
the more time it
takes to create it
the longer it
takes for users
to fully master it
the more
expensive it is
it takes more
time to test it
Group of Experts on Population
and Housing Censuses
• CAWI module
• CAPI module
• Census Management System module
Application
included:
• Messaging system
• interactive maps
• reports generation
• finance reports
• log management system (monitoring changes of all data)
• rights management system-according to roles in Census
• many other functionalities
CMS module
11/20
Types of validation rules:
• Mandatory or optional questions
• Interdependence of questions or so-called skips - some questions were answered
depending on previous answers
• interdependence of offered answers – questions offered answers depending on previous
answers
• structural checks – e.g. number entry, letter entry, correct entry date of birth, entry of
the correct OIB according to mod 11, 10 rules, etc.
• content checks – e.g. the person gave contradictory answers to two related questions
Group of Experts on Population
and Housing Censuses
All validation rules were activated immediately after entering the answer
12/20
“soft error”
- warning
e.g. the respondent states
that has an apartment of
80m2 and has 10 rooms
not critical
did not block further use of
the system
encouraged the user to
check their entry
“hard error”
- errors
e.g. the user did not answer
the required question, the
user gave two answers in the
questionnaire which are in
direct contradiction, etc
critical
the system did not allow
continuation use until the
errors are corrected
Group of Experts on Population
and Housing Censuses
“soft” and “hard” errors were integrated in the answers on each question
Warnings and errors were displayed after entering and saving the answer
Group of Experts on Population
and Housing Censuses 13/20
option of manual entry the house number: Yes or No?
• newly added addresses were not recognized by the program
• in a large number of cases, newly assigned addresses by e-respondents were wrong
• in processing phase, were assigned to the right address
online self-enumeration outside the borders: Yes or No?
• country in which the person was self-enumerated is located according to the IP address
• special attention were dedicated to these persons in order to determine their real place of usual
residence.
Group of Experts on Population
and Housing Censuses
14/20
CAPI advantages
• enumerators can clarify
questions that may be
unclear to respondents
• can reach all enumeration
units
CAWI advantages
• do not require trained
enumerators
• safer environment in the
circumstances of COVID-19
• reduce costs
• Self-enumeration at the time
of their choice
CAPI disadvantages
• requires well-trained
interviewers
• costly
• coverage error is possible
• takes time to collect data
CAWI disadvantages
• coverage limitation
• rely on the respondent to
fill in a survey accurately
without the guidance of
an interviewer
Group of Experts on Population
and Housing Censuses
15/20
Source: Croatian Bureau of Statistics
CAWI CAPI
Population Coverage Low-medium High
Initial investment Low High
Cost per interview Low Medium-high
Reliability of collected data Low-medium Medium-high
Rate of dropout Low-medium Very low
Good for long questionnaire Low-medium High
Good for complex questionnaire Very low High
16/20Group of Experts on Population
and Housing Censuses
planned to:
• improve and modernize the data collection process,
• improve the accuracy and quality of collected data,
• improve the efficiency of fieldwork management and
• improve data processing
17/20Group of Experts on Population
and Housing Censuses
•changes in the legal framework
•changes in education
•overall reorganization of the Census (additional costs of transport, testing, protective equipment, loss of enumerators due to quitting…)
The pandemic had a significant impact on the
preparation and implementation of census
•get information about people who had left their homes because of the earthquake
Earthquakes coused an introduction of new
questions and modalities
•had certain benefits, it also showed to have certain drawbacks
An online education was prepared and
implemented
•A line has to be drawnCBS created a quite complex census application
•The understanding of the methodology and definitions by ordinary people was sometimes questionable
The questionnaire has been designed to be user
friendly and simple
18/20Group of Experts on Population
and Housing Censuses
19/20Group of Experts on Population
and Housing Censuses
Census 2031
• Traditional?
• Register based?
• Combined?
No answer at least until 2025
Russian
Опыт проведения переписей раунда
2020 года
Группа экспертов по переписям населения и жилого фонда
г. Женева (Швейцария), 21-23 сентября 2022 года
(Давайте вместе сформируем образ
Хорватии)
Содержание
Общие характеристики переписи года
Применяемые методы сроки проведения пилотная перепись
Проблемы переписи
Кадровые ресурсы программное приложение для проведения переписи некоторые методологические вопросы
Преимущества и недостатки методов и
Выводы
Извлеченные уроки и перспективы следующей переписи
Общие характеристики переписи года
3/20
Первоначально планировалась на апрель 2021 г.
Проводилась с 13 сентября по 14 ноября 2021 г.
Учетный день: 31 августа 2021 г.
Пандемия COVID-19 и два землетрясения сыграли важную роль в определении даты учетного дня.
Применялись методы CAWI и CAPI
Расходы: примерно 5 € на человека
Группа экспертов по переписям
населения и жилого фонда
Общие характеристики переписи года
4/20
CAWI
(компьютеризированный
онлайн-опрос)
С 13 сентября по 27
сентября 2021 г.
CAPI
(компьютеризированный
персональный опрос)
С 28 сентября по 18 октября
2021 г.
CAPI
(компьютеризированный
персональный опрос)
Продление сроков
до 29 октября 2021 г.
CAPI
(компьютеризированный
персональный опрос)
Продление сроков во второй
раз
до 14 ноября 2021 г.
Группа экспертов по переписям
населения и жилого фонда
Проведение переписи на местах:
Наибольшая продолжительность проведения с 1857 г.
Общие характеристики переписи года
5/20
Пилотная
перепись
Преследуемая
цель:
• Перенос сроков на март 2021 г.
• протестировать новые методы
сбора данных
• выявить возможные недостатки
программного приложения по
проведению переписи
Группа экспертов по переписям
населения и жилого фонда
Проблемы переписи
6/20Группа экспертов по переписям
населения и жилого фонда
Кадровые ресурсы
• Набор и отбор переписчиков и супервайзеров
• Относительно небольшое количество заявленных кандидатов
• Большой отток переписчиков
Программное приложение для проведения переписи
• Как определиться с пределами сложности программного приложения для
проведения переписи
• Активность применения правил валидации данных
• Возможность при необходимости добавлять адреса (номер дома)
• Самостоятельная онлайн-перепись для тех, кто находится за рубежом: да
или нет?
Проблемы переписи
Кадровые ресурсы Набор и отбор переписчиков и супервайзеров
7/20
Участники Перепись 2021 Перепись 2011 Перепись 2001
Переписчики 7 900 ≈14 000 ≈20 000
Супервайзеры 1 038 1 950 ≈ 2 900
Дополнительные сотрудники по обработке данных 25 400-500 400-500
Группа экспертов по переписям
населения и жилого фонда
Источник: Хорватское статистическое бюро (ХСБ)
Количество участников переписей 2021, 2011 и 2001 гг.
Проблемы переписи
Кадровые ресурсы Небольшое число заявленных кандидатов
8/20Группа экспертов по переписям
населения и жилого фонда
Источник: Хорватское статистическое бюро (ХСБ)
Количество заявленных кандидатов, запланированное число переписчиков, переписчики, участвовавшие в
переписи, и назначенные переписчики, уволившиеся до начала переписи, в разбивке по округам
Загребский округ 1072 607 588 221 36,4
Приморье-Горски-Котар 840 556 513 222 39,9
Осиек-Баранья 1369 541 538 237 43,8
Сплитско-Далматинский округ 1452 877 810 384 43,8
г. Загреб 2391 1541 1402 514 33,4
Всего (21) округ 14820 7941 7652 2868 36,1
Округ Заявившиеся кандидаты Запланированное число переписчиков Переписчики, участвовавшие в переписи Переписчики, уволившиеся до начала
переписи %
Проблемы переписи
Кадровые ресурсы Большой отток переписчиков
9/20Группа экспертов по переписям
населения и жилого фонда
Проблемы переписи
Программное приложение для проведения переписи
сложность ПО для проведения переписи
10/20
Чем
сложнее
приложение
тем больше
требуется времени
на его создание
тем больше
времени требуется
пользователям для
освоения
тем дороже
тем больше
времени требуется
для проверки
работоспособности
Группа экспертов по переписям
населения и жилого фонда
• Модуль CAWI
• Модуль CAPI
• Модуль системы управления переписью
Программное
приложение включает
модули:
• Система обмена сообщениями
• интерактивные карты
• функция формирования отчетов
• финансовые отчеты
• система управления журналами (отслеживание изменений всех
данных)
• система управления правами доступа — согласно присвоенным в
рамках переписи ролям
• другие многочисленные функции
CMS-модуль
Проблемы переписи
Программное приложение для проведения переписи активность
применения правил валидации проверки данных
11/20
Типы правил валидации (проверки):
• обязательные или факультативные вопросы
• взаимозависимость вопросов или так называемые опущения: на некоторые вопросы
ответы давались в зависимости от предыдущих ответов
• взаимозависимость предлагаемых ответов: вопросы предлагали дать определенные
ответы в зависимости от предыдущих ответов
• структурные проверки: например, ввод номера, ввод буквы, правильный ввод даты
рождения, ввод правильного числа OIB согласно правилам модулей 11, 10 и т.д.
• проверки контента: например, человек дал противоречивые ответы на два
взаимосвязанных вопроса
Группа экспертов по переписям
населения и жилого фонда
Все правила валидации (проверки данных) активировались сразу после ввода ответа
Проблемы переписи
Программное приложение для проведения переписи активность применения
правил валидации проверки данных
12/20
«некритическая
ошибка» —
предупреждение
например, респондент
утверждает, что общая
площадь его квартиры
— 80 м2, и в ней 10
комнат
некритическая
дальнейшее
использование системы
не блокируется
система подсказывает
пользователю
проверить введенные
данные
«критическая
ошибка» —
ошибки
например, респондент
не ответил на вопрос,
привел в анкете два
взаимоисключающих
ответа и т.п.
критическая
система запрещает ее
дальнейшее
использование, пока не
будут исправлены
ошибки
Группа экспертов по переписям
населения и жилого фонда
в ответах на каждый вопрос были предусмотрены некритические и критические ошибки
После ввода данных и сохранения ответа на экран выводятся предупреждения и ошибки
Проблемы переписи
Программное приложение для проведения переписи
Группа экспертов по переписям
населения и жилого фонда 13/20
возможность ввода номера дома вручную: Да или Нет?
• новые добавленные адреса не распознаются программой
• в большом числе случаев новые адреса, присвоенные электронными респондентами, были неправильными
• на этапе обработки данных присваивался правильный адрес
самостоятельная онлайн-перепись для тех, кто находится за рубежом: Да или Нет?
• страна, в которой человек самостоятельно проходил онлайн-перепись, определяется по IP-адресу
• таким респондентам уделялось особое внимание в целях установления их реального обычного места
проживания
Преимущества и недостатки методов и
Группа экспертов по переписям
населения и жилого фонда
14/20
Преимущества CAPI
•переписчики могут
пояснять вопросы
респондентам
•то, чем могут
воспользоваться все группы
переписчиков
Преимущества CAWI
• нет необходимости в специально
обученных переписчиках
• более безопасные условия труда
во время пандемии COVID-19
• сокращение расходов
• самостоятельная перепись в
любое удобное для респондента
время
Недостатки CAPI
• требуются хорошо
подготовленные переписчики
• дорого
• возможна погрешность
охвата
• на сбор данных уходит время
Недостатки CAWI
•ограниченный охват
•приходится полагаться на
то, что респондент сможет
точно заполнить анкету без
подсказок переписчика
Преимущества и недостатки методов и
Группа экспертов по переписям
населения и жилого фонда
15/20
Источник: Хорватское статистическое бюро (ХСБ)
CAWI CAPI
Охват населения Мало-средний Большой
Первоначальные инвестиции Малые Большие
Стоимость переписного интервью Малая Средне-высокая
Достоверность собранных данных Низко-средняя Средне-высокая
Кол-во отказов Мало-среднее Очень малое
Подходит для длинной анкеты Мало-средне Сильно
Подходит для сложной анкеты Очень слабо Сильно
Выводы извлеченные уроки и
перспективы следующей переписи
16/20Группа экспертов по переписям
населения и жилого фонда
планируется:
• усовершенствовать и модернизировать процесс сбора данных,
• повысить точность и качество собираемых данных,
• повысить эффективность управления полевыми работами и
• улучшить обработку данных
Выводы извлеченные уроки и
перспективы следующей переписи
17/20Группа экспертов по переписям
населения и жилого фонда
•изменения в нормативно-правовой базе
•изменения в подходах к обучению
•общая реорганизация переписи (дополнительные расходы на транспорт, тестирование, средства
индивидуальной защиты, отток переписчиков…)
Пандемия существенно сказалась на
подготовке и проведении переписи
населения
•необходимость получить информацию о людях, покинувших свои дома из-за землетрясения
Землетрясения подняли новые вопросы и
необходимости новых режимов работы
•были как определенные преимущества, так и недостатки Разработано и проведено онлайн-обучение
•необходимо суметь подвести черту и остановиться
Хорватское статистическое бюро (ХСБ)
разработало довольно сложное программное
приложение для проведения переписи
•Иногда неподготовленные кадры имели сложности с пониманием методологии и определений
Анкета разработана так, чтобы быть удобной
и простой
Выводы извлеченные уроки и
перспективы следующей переписи
18/20Группа экспертов по переписям
населения и жилого фонда
This study identifies key drivers of forest degradation and the potential for forest landscape restoration in 17 countries of Eastern and South-East Europe. It builds on assessments from national experts and the best available data to support countries in preparing restoration pledges in the run up to the Ministerial Roundtable on Forest Landscape Restoration in Eastern and South-East Europe, scheduled to take place in 2021.
* Year selection only affects the "Wood Energy Sources and Uses"
1
Total Population
810,138,548
2
Rural Population (% of total)
22.3%
Woody Biomass Supply
2017
2015
2013
2011
2009
2007
2005
3
Roundwood removals from forest and outside forest (1000m3)
1,038,334
1,024,064
843,444
910,248
838,837
1,210,203
995,703
4
Roundwood supply from forest and outside forest including net trade (1000m3)
1,027,981
1,019,567
838,040
909,320
821,446
1,168,661
1,011,844
5
Total calculated domestic supply of woody biomass (1000m3)
1,624,172
1,601,393
1,390,603
1,494,276
1,297,416
1,679,045
Energy Resources
2017
2015
2013
2011
2009
2007
2005
6
Total primary energy supply, TPES (ktoe)
3,852,890
3,865,968
3,627,303
4,019,732
4,082,578
4,390,095
3,734,501
7
Share of renewables (RES) in TPES
10.7%
10.3%
9.3%
8.6%
6.5%
6.0%
6.4%
8
Total wood energy supply, volume basis (1000 m3)
696,593
726,604
581,422
595,185
588,297
691,526
232,522
9
Total wood energy supply, energy basis (ktoe)
146,306
151,076
127,823
132,738
131,796
216,173
Wood Energy Intensity
2017
2015
2013
2011
2009
2007
2005
10
Average wood energy consumption (m3/capita)
0.86
0.91
0.85
0.73
0.74
0.91
0.39
11
Fuelwood consumption per rural inhabitant (m3/inhabitant)
1.13
0.63
0.89
0.75
0.86
1.00
0.00
12
Pellets consumption per inhabitant (kg/capita)
25.99
24.18
24.85
16.77
10.48
8.09
Role of Wood Energy in Forest Sector
2017
2015
2013
2011
2009
2007
2005
13
Share of net annual increment directly used for energy (%)
7.7%
10.3%
9.8%
8.2%
6.4%
4.5%
5.4%
14
Share of Roundwood supply directly used for energy purposes (%)
22.9%
27.1%
21.7%
21.0%
24.6%
15.1%
8.2%
15
Share of calculated domestic consumption of woody biomass used for energy purposes (%)
42.9%
45.4%
41.8%
39.8%
45.3%
41.2%
Role of Wood Energy in Energy Sector
2017
2015
2013
2011
2009
2007
2005
16
Share of woody biomass in TPES (%)
3.8%
3.9%
3.5%
3.3%
2.9%
4.9%
1.3%
17
Share of woody biomass in RES (%)
35.4%
37.9%
37.9%
38.6%
44.5%
81.5%
19.7%
18
Share of wood energy generated from black liquor, energy basis (%)
26.8%
21.3%
29.5%
29.4%
24.9%
73.4%
19
Imported wood fuel as share of wood energy, volume basis (%)
5.2%
5.0%
6.7%
4.5%
1.9%
1.0%
'Analysis 2007'!A1:CF70
Wood Energy Sources and Uses in 2017 [1000 m3]
U1 Power and Heat
U2 Industrial
U3 Residential
U4 Other
Sum
%
S1 Direct
26,801.4
4,374.1
196,408.2
8,111.6
235,695.3
33.8%
S2 Indirect
58,286.7
238,710.2
49,059.8
8,496.4
354,553.1
50.9%
S3 Recovered
23,209.8
9,377.6
3,165.6
1,058.1
36,811.2
5.3%
S4 Unspecified
33,606.5
21,159.5
2,479.0
12,288.4
69,533.5
10.0%
Sum
141,904.5
273,621.4
251,112.7
29,954.6
%
20.4%
39.3%
36.0%
4.3%
696,593.1
Notes/Comments:
1. The category "JWEE total 2017" refers to the set of responding countries providing sufficiently detailed data for 2017. This includes Armenia, Austria, Azerbaijan, Belarus, Bosnia and Herzegovina, Canada, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Georgia, Germany, Iceland, Ireland, Italy, Liechtenstein, Lithuania, Luxembourg, Netherlands, North Macedonia, Norway, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland, Ukraine, United Kingdom and the United States.
Disclaimer: Data comparisons between reference years are only indicative, due to changes in national and international statistical methodologies.
U1 Power and Heat U2 Industrial U3 Residential U4 Other 0.20371213254458462 0.39279941466138452 0.36048686892398235 4.3001583870048404E-2
2017
2017
Population
Rural Population (share)
Roundwood and Fuelwood removals from forest and outside forest (1000m3)
Roundwood and Fuelwood supply from forest and outside forest including net trade (1000m3)
SWE + (S1 -S4) Total calculated domestic consumption of woody biomass (1000m3)
Autor: see comment in external data sheet
Total primary energy supply, TPES (ktoe)
Share of renewables (RES) in TPES from ktoe
Total wood energy generation (1000m3)
Total wood energy generation (ktoe) (calculation totals*heating value)
Average wood energy consumption (m3/capita)
Fuelwood consumption per rural inhabitant (m3/inhabitant)
Pellets consumption per inhabitant (kg/inhabitant)
S1 (from Forests)/Net Annual Increments
Share of Total Roundwood supply directly used for energy purposes (%)
Share of energy use in calculated domestic consumption of woody biomass (%)
Share of woody biomass in TPES (%) (from TJ)
Share of woody biomass in RES (%) (from TJ)
Share of wood energy generated from black liquor (%) (heating value)
Imported wood fuel as share of wood energy (%)
1-S1-U1
1-S1-U2
1-S1-U3
1-S1-U4
1-S2-U1
1-S2-U2
1-S2-U3
1-S2-U4
1-S3-U1
1-S3-U2
1-S3-U3
1-S3-U4
1-S4-U1
1-S4-U2
1-S4-U3
1-S4-U4
Total wood energy consumption in TJ
Black liquor in TJ
woodfuel imports in 1000 m3
country
fuelwood supply
pellet consumption
Albania
2,884,169
0.45
n/a
n/a
n/a
2,354
0
n/a
n/a
n/a
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
Armenia
2,944,789
0.38
668
669
2,294
3,188
0
542
110
0
1
0
2
1
0
0
0
0
0
0
0
480
0
0
0
62
0
0
0
0
0
0
0
0
0
4,613
0
1
665,900
2,062
480
Austria
8,819,902
0.33
18,119
26,322
47,467
33,486
0
24,569
5,021
3
2
108
0
0
1
0
1
0
0
0
20
6,639
431
6,505
7,906
1,417
1,233
417
0
0
0
0
0
0
0
210,202
41,125
1,878
5,341,990
952,750,000
5,366
Azerbaijan
9,845,316
0.45
151
151
2,154
14,337
0
151
35
0
0
0
0
1
0
0
0
0
0
1
6
53
85
0
0
2
4
0
0
0
0
0
0
0
0
1,457
0
0
151,000
0
0
Belarus
9,450,233
0.22
19,493
17,498
25,622
25,513
0
7,797
1,568
1
3
2
0
0
0
0
1
0
0
1,452
91
1,837
804
2,045
1,167
181
221
0
0
0
0
0
0
0
0
65,653
42
29
5,306,454
16,600,000
4,183
Belgium
11,419,752
0.02
n/a
n/a
n/a
55,252
0
n/a
n/a
n/a
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
Bosnia and Herzegovina
3,351,534
0.68
4,375
3,438
3,023
6,758
0
277
50
0
0
51
0
0
0
0
0
1
0
0
0
0
0
0
187
90
0
0
0
0
0
0
0
0
0
2,114
1,663
40
614,400
169,960,000
0
Bulgaria
7,102,452
0.25
n/a
n/a
n/a
18,745
0
n/a
n/a
n/a
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
Canada
36,732,091
0.18
177,335
175,679
133,182
289,062
0
73,251
15,067
2
3
20
0
0
1
0
0
0
0
0
0
22,496
0
0
45,645
5,109
0
0
0
0
0
0
0
0
0
630,844
199,049
158
22,575,573
739,000,000
22,496
Croatia
4,182,847
0.41
6,297
5,379
5,642
8,748
0
3,435
885
1
1
10
0
0
1
0
0
0
0
397
0
2,130
0
43
247
415
0
23
180
0
0
0
0
0
0
37,035
0
166
1,174,130
42,370,000
1,538
Cyprus
1,179,685
0.33
24
27
307
2,218
0
112
15
0
0
3
0
0
0
0
0
0
1
0
0
12
0
0
2
43
56
0
0
0
0
0
0
0
0
640
0
92
15,980
3,370,000
9
Czech Republic
10,641,032
0.28
21,977
17,089
22,280
43,329
0
13,544
2,753
1
1
9
0
0
1
0
1
0
0
0
0
8,243
0
2,206
2,151
546
101
0
0
296
0
0
0
0
0
115,265
8,164
285
4,273,000
94,000,000
6,267
Denmark
5,732,277
0.12
n/a
n/a
n/a
17,007
0
n/a
n/a
n/a
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
Estonia
1,319,389
0.31
11,299
8,711
12,861
5,707
0
5,420
1,120
4
8
148
0
0
0
0
1
0
0
995
9
1,323
33
1,020
923
712
70
302
0
32
0
0
0
0
0
46,897
1,568
83
3,407,439
194,977,319
1,721
Finland
5,511,372
0.15
66,094
69,958
81,945
33,277
0
41,492
8,717
8
13
68
0
0
1
0
1
0
0
4,917
546
5,401
807
5,726
22,210
630
209
729
51
232
34
0
0
0
0
364,947
154,761
240
10,713,823
373,450,000
11,671
France
64,842,513
0.20
51,269
48,189
96,117
247,086
0
42,729
10,283
1
1
22
0
1
0
0
0
0
0
2,330
1,554
19,940
1,132
2,030
5,750
7,427
1,061
797
470
0
238
0
0
0
0
430,525
32,447
1,829
19,067,000
1,424,320,000
19,002
Georgia
4,008,723
0.49
1,923
1,914
2,873
4,808
0
1,922
388
0
0
0
0
1
1
0
0
0
0
0
6
1,849
18
0
0
48
1
0
0
0
0
0
0
0
0
16,231
0
0
471,500
0
0
Germany
82,658,409
0.24
65,717
70,695
145,711
311,245
0
59,684
13,075
1
1
27
0
0
0
0
0
0
0
3,432
1,416
19,545
2,949
3,278
5,695
6,422
3,181
7,805
3,219
1,208
716
445
184
27
162
547,404
25,683
2,598
22,649,116
2,190,500,000
22,133
Hungary
9,729,822
0.28
n/a
n/a
n/a
26,649
0
n/a
n/a
n/a
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
Iceland
334,395
0.06
4
5
198
5,471
1
74
9
0
0
2
0
1
0
0
0
0
1
0
2
0
0
0
0
0
72
0
0
0
0
0
0
0
0
380
0
80
1,420
520,000
2
Ireland
4,753,281
0.37
3,342
3,630
2,945
13,639
0
1,356
289
0
0
20
0
0
0
0
0
0
0
19
0
322
0
623
0
58
0
334
0
0
0
0
0
0
0
12,102
0
153
321,170
96,800,000
322
Italy
60,673,694
0.31
12,928
16,193
85,058
153,445
0
44,811
9,055
1
1
36
0
2
1
0
0
0
0
0
0
28,049
0
8,773
973
5,465
492
0
1,059
0
0
0
0
0
0
379,115
544
5,623
11,903,900
2,171,020,000
0
Kazachstan
18,080,023
0.44
n/a
n/a
n/a
84,988
0
n/a
n/a
n/a
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
Latvia
1,951,097
0.34
n/a
n/a
n/a
4,400
0
n/a
n/a
n/a
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
Liechtenstein
37,805
0.86
16
16
23
0
0
19
4
1
0
0
0
0
1
n/a
n/a
0
0
0
0
5
0
0
0
15
0
0
0
0
0
0
0
0
0
164
0
0
16,000
0
0
Lithuania
2,845,419
0.35
6,888
5,434
12,018
7,545
0
6,491
1,304
2
2
11
0
0
1
0
1
0
0
0
0
0
0
0
0
19
0
0
0
0
0
3,214
627
2,401
230
54,582
0
533
1,858,340
31,760,000
0
Luxembourg
591,914
0.09
433
923
802
3,775
0
421
136
1
2
43
0
0
1
0
0
0
0
0
0
89
0
182
93
57
0
0
0
0
0
0
0
0
0
5,690
0
53
93,660
25,180,000
89
Montenegro
627,563
0.35
n/a
n/a
n/a
1,018
0
n/a
n/a
n/a
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
Netherlands
17,021,343
0.08
3,184
2,488
12,848
74,203
0
4,385
937
0
0
19
0
1
0
0
0
0
0
165
0
1,606
87
54
139
282
161
1,434
0
457
0
0
0
0
0
39,226
0
915
339,628
328,800,000
338
North Macedonia
2,081,996
0.43
764
807
1,655
2,723
0
677
136
0
1
37
0
1
0
0
0
0
0
0
0
524
131
0
0
19
3
0
0
0
0
0
0
0
0
5,698
0
200
674,960
77,040,000
655
Norway
5,296,324
0.19
12,187
9,230
12,901
29,985
0
3,990
787
1
2
1
0
0
0
0
0
0
0
0
0
1,757
0
0
1,073
248
0
0
0
0
0
912
0
0
0
32,943
0
447
1,808,840
7,880,000
1,757
Poland
37,953,176
0.40
n/a
n/a
n/a
103,845
0
n/a
n/a
n/a
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
Portugal
10,288,527
0.37
n/a
n/a
n/a
22,770
0
n/a
n/a
n/a
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
Republic of Moldova
4,059,687
0.46
n/a
n/a
n/a
3,850
0
n/a
n/a
n/a
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
Romania
19,653,966
0.49
n/a
n/a
n/a
33,331
0
n/a
n/a
n/a
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
Russian Federation
145,530,091
0.25
n/a
n/a
n/a
732,160
0
n/a
n/a
n/a
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
Serbia
8,829,623
0.47
7,789
7,756
15,510
15,609
0
7,406
1,494
1
1
37
1
1
0
0
1
0
0
12
201
5,370
271
8
647
642
211
0
0
43
0
0
0
0
0
62,556
0
190
5,490,000
330,000,000
5,039
Slovak Republic
5,447,903
0.47
10,054
8,965
10,077
17,400
0
4,478
1,282
1
0
9
0
0
0
0
1
0
0
1,170
0
817
28
516
1,629
225
9
0
0
45
39
0
0
0
0
53,693
5,778
153
705,000
50,000,000
1,425
Slovenia
2,076,395
0.51
4,779
2,583
5,932
6,907
0
2,696
660
1
1
54
0
1
0
0
1
0
0
0
0
1,601
0
223
526
232
63
0
0
0
0
0
0
51
0
27,625
0
709
724,719
112,685,000
1,338
Sweden
9,904,895
0.14
74,817
81,874
96,157
49,174
0
45,650
9,496
5
5
183
0
0
0
0
0
0
0
6,677
0
3,873
590
7,502
21,460
1,245
1,066
3,238
0
0
0
0
0
0
0
397,599
159,041
976
6,339,770
1,810,300,000
10,998
Switzerland
8,455,797
0.26
5,544
5,160
10,246
23,678
0
5,138
1,064
1
1
28
0
1
1
0
0
0
0
1,059
59
1,113
490
161
601
547
40
755
306
0
7
0
0
0
0
44,542
0
712
2,092,000
234,880,000
2,411
Turkey
81,116,451
0.25
n/a
n/a
n/a
146,797
0
n/a
n/a
n/a
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
Ukraine
44,487,708
0.30
18,337
17,011
29,771
89,463
0
15,258
3,059
0
1
14
0
1
1
0
1
0
0
4,023
154
7,578
256
2,277
101
829
40
0
0
0
0
0
0
0
0
128,056
0
8
8,848,200
622,000,000
0
United Kingdom
66,727,463
0.16
12,950
13,082
61,578
175,883
0
33,681
6,906
1
0
106
0
1
1
0
0
0
1
151
311
7,702
0
15,115
6,120
2,575
203
605
45
852
1
0
0
0
0
289,122
0
17,005
2,138,860
7,046,000,000
5,581
United States
325,084,758
0.18
419,578
407,106
684,973
2,155,230
0
245,138
50,602
1
1
6
0
0
0
0
0
0
0
0
0
46,054
0
0
113,465
13,498
0
6,770
4,048
0
22
29,035
20,349
0
11,896
2,118,624
1,010,057
1,356
64,227,020
1,907,640,000
0
JWEE total
810,138,548
0.22
1,038,334
1,027,981
1,624,172
3,852,890
0
696,593
146,306
1
1
26
0
0
0
0
0
0
0
26,801
4,374
196,408
8,112
58,287
238,710
49,060
8,496
23,210
9,378
3,166
1,058
33,607
21,159
2,479
12,288
6,125,545
1,639,921
36,511
204,010,792
21,053,804,381
124,822
JWEE 12
264,351,249
0.22
310,876
330,198
564,717
915,976
0
264,934
57,155
1
1
55
0
0
0
0
0
0
0
18,597
4,107
71,606
6,670
41,353
71,009
21,295
7,323
14,681
4,092
2,335
997
445
184
79
162
2,392,955
413,057
26,433
74,988,088
14,600,235,000
83,959
JWEE Europe (excluding the Russian Federation)
378,447,723
0.24
400,753
407,814
743,942
1,271,754
0
352,400
75,451
1
1
47
0
0
0
0
0
0
0
21,325
4,118
116,017
6,818
53,965
78,331
29,375
8,228
16,440
5,330
3,166
1,036
4,571
810
2,479
392
3,158,981
430,773
34,760
101,756,085
17,691,524,381
97,487
external data reference
Population
Rural Population (share)
SWE in 1000 m3
TPES in ktoe
RES in TPES (share)
Net Annaul Increment in 1000 m3
TPES in TJ
RES in TJ
*We could make this better when converting with TJ figures.
*most often: bark, charcoal
JWEE total
Armenia, Austria, Azerbaijan, Belarus, Bosnia and Herzegovina, Canada, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Georgia, Germany, Iceland, Ireland, Italy, Liechtenstein, Lithuania, Luxembourg, Netherlands, North Macedonia, Norway, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland, Ukraine, United Kingdom and the United States
JWEE 12
Austria, Cyprus, Finland, France, Germany, Ireland, Luxembourg, Serbia, Slovenia, Sweden, Switzerland and the United Kingdom
Europe excl. Russia
Armenia, Austria, Bosnia and Herzegovina, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Germany, Iceland, Ireland, Italy, Liechtenstein, Lithuania, Luxembourg, Netherlands, Norway, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland and the United Kingdom
2015
2015
Population
Rural Population (share)
Roundwood and Fuelwood removals from forest and outside forest (1000m3)
Roundwood and Fuelwood supply from forest and outside forest including net trade (1000m3)
SWE + (S1 -S4) Total calculated domestic consumption of woody biomass (1000m3)
Autor: see comment in external data sheet
Total primary energy supply, TPES (ktoe)
Share of renewables (RES) in TPES from ktoe
Total wood energy generation (1000m3)
Total wood energy generation (ktoe) (calculation totals*heating value)
Average wood energy consumption (m3/capita)
Fuelwood consumption per rural inhabitant (m3/inhabitant)
Pellets consumption per inhabitant (kg/inhabitant)
S1 (from Forests)/Net Annual Increments
Autor: see comment in external data sheet
Share of Total Roundwood supply directly used for energy purposes (%)
Share of energy use in calculated domestic consumption of woody biomass (%)
Share of woody biomass in TPES (%) (from TJ)
Share of woody biomass in RES (%) (from TJ)
Share of wood energy generated from black liquor (%) (heating value)
Imported wood fuel as share of wood energy (%)
1-S1-U1
1-S1-U2
1-S1-U3
1-S1-U4
1-S2-U1
1-S2-U2
1-S2-U3
1-S2-U4
1-S3-U1
1-S3-U2
1-S3-U3
1-S3-U4
1-S4-U1
1-S4-U2
1-S4-U3
1-S4-U4
Total wood energy consumption in TJ
Black liquor in TJ
woodfuel imports in 1000 m3
country
fuelwood supply
pellet consumption
Albania
2,923,352
0.47
n/a
n/a
n/a
2189.67
0.34
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
0
0
n/a
n/a
0
Armenia
2,916,950
0.38
0
0
2,165
3067.47
0.12
507
102
0.17
0.00
0.00
1.60
2577.21
0.23
0.03
0.27
0.00
0.00
0
5
502
0
0
0
0
0
0
0
0
0
0
0
0
0
4,263
0
0
3
290
507
Austria
8,678,657
0.34
18,210
25,725
45,189
32793.80
0.29
23,606
4,802
2.72
1.94
93.76
0.25
0.29
0.52
0.15
0.50
0.14
0.08
0
0
6,859
529
6,352
7,357
1,105
1,404
0
0
0
0
0
0
0
0
201,038
27,826
1,909
5,645,340
813,710,000
5,640
Azerbaijan
9,617,484
0.45
390
390
1,774
14355.81
0.02
382
74
0.04
0.09
0.00
0.00
0.89
0.22
0.01
0.28
0.00
0.00
0
0
235
110
0
0
24
12
0
0
0
0
0
0
0
0
3,090
0
0
390,000
0
0
Belarus
9,485,772
0.23
n/a
n/a
n/a
25266.74
0.05
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
1.93
35.34
0.33
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
0
0
n/a
n/a
0
Belgium
11,287,940
0.02
n/a
n/a
n/a
53269.08
0.07
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0.91
13.40
0.33
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
0
0
n/a
n/a
0
Bosnia and Herzegovina
3,535,961
0.65
4,511
3,816
5,535
8032.08
0.25
3,502
704
0.99
0.34
16.90
0.00
0.92
0.63
0.09
0.35
0.00
0.00
0
78
3,385
40
0
0
0
0
0
0
0
0
0
0
0
0
29,481
0
3
791,940
59,770,000
0
Bulgaria
7,177,396
0.26
n/a
n/a
n/a
18607.00
0.11
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
0
0
n/a
n/a
0
Canada
35,949,709
0.18
166,817
165,837
131,295
270191.79
0.18
66,584
13,715
1.85
2.37
27.11
0.04
0.09
0.51
0.05
0.28
0.39
0.00
0
0
15,387
0
0
48,048
3,150
0
0
0
0
0
0
0
0
0
574,232
221,240
229
15,427,157
974,450,000
15,387
Croatia
4,236,016
0.41
6,169
5,234
5,735
8397.49
0.23
3,533
924
0.83
0.64
4.49
0.21
0.54
0.62
0.11
0.47
0.00
0.02
121
49
2,668
0
67
72
353
0
23
180
0
0
0
0
0
0
38,701
0
88
1,113,000
19,000,000
1,849
Cyprus
1,160,985
0.33
20
23
229
2014.31
0.07
106
15
0.09
0.03
4.98
0.18
0.51
0.46
0.01
0.10
0.00
0.85
0
0
12
0
0
1
39
54
0
0
0
0
0
0
0
0
626
0
90
9,820
5,780,000
6
Czech Republic
10,603,762
0.27
20,093
17,750
22,214
42147.82
0.10
12,970
2,615
1.22
1.44
6.04
0.30
0.47
0.58
0.06
0.61
0.07
0.02
0
0
8,384
0
1,620
2,251
547
131
0
35
0
0
0
0
0
0
109,506
8,118
276
4,194,000
64,000,000
6,122
Denmark
5,688,695
0.12
n/a
n/a
n/a
16096.14
0.30
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
0
0
n/a
n/a
0
Estonia
1,315,321
0.32
10,061
7,671
10,871
5423.86
0.17
5,096
1,042
3.87
7.74
160.44
0.20
0.28
0.47
0.19
1.15
0.04
0.01
805
22
1,292
25
1,334
1,020
548
23
29
0
0
0
0
0
0
0
43,641
1,544
67
3,214,904
211,023,900
1,947
Finland
5,481,966
0.16
63,479
68,370
77,670
32486.31
0.32
37,821
7,950
6.90
13.85
55.03
0.13
0.16
0.49
0.24
0.76
0.43
0.00
4,762
617
4,668
689
4,912
20,027
1,012
234
621
39
169
72
0
0
0
0
332,868
142,056
178
11,931,100
301,660,000
10,735
France
64,457,201
0.21
51,005
47,502
93,166
246505.85
0.09
40,657
9,744
0.63
1.41
16.55
0.22
0.48
0.44
0.04
0.45
0.08
0.04
1,818
1,658
19,472
0
1,105
5,126
7,037
2,222
1,253
967
0
0
0
0
0
0
407,977
34,211
1,578
18,704,230
1,066,900,000
18,080
Georgia
3,951,524
0.51
2,021
2,017
3,009
4631.41
0.25
2,122
426
0.54
0.24
0.02
0.00
1.05
0.71
0.09
0.37
0.00
0.00
0
4
2,082
35
0
0
1
0
0
0
0
0
0
0
0
0
17,856
0
1
484,600
60,000
0
Germany
81,707,789
0.25
55,613
61,010
132,125
307794.74
0.12
58,143
13,009
0.71
0.54
21.49
0.25
0.45
0.44
0.04
0.34
0.05
0.06
3,661
2,715
20,111
1,112
3,598
7,296
6,045
1,433
6,039
4,479
1,410
41
106
79
0
19
544,661
27,447
3,600
10,968,060
1,755,694,000
23,342
Hungary
9,783,925
0.29
n/a
n/a
n/a
25209.30
0.12
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
0
0
n/a
n/a
0
Iceland
330,243
0.06
4
4
171
5582.98
0.88
26
5
0.08
0.05
1.82
0.17
0.58
0.15
0.00
0.00
0.00
0.12
2
0
0
0
2
22
0
0
0
0
0
0
0
0
0
0
222
0
3
1,020
600,000
2
Ireland
4,700,107
0.37
2,943
2,929
3,297
13263.42
0.08
1,686
351
0.36
0.18
12.91
0.06
0.10
0.51
0.03
0.33
0.00
0.04
96
0
209
0
248
810
38
35
0
251
0
0
0
0
0
0
14,685
0
74
313,430
60,700,000
300
Italy
59,504,212
0.32
5,052
8,565
83,077
152604.76
0.17
42,701
8,623
0.72
0.21
34.97
0.00
3.13
0.51
0.06
0.33
0.00
0.12
0
0
26,786
0
8,644
939
4,945
414
0
974
0
0
0
0
0
0
361,020
551
5,274
4,052,510
2,080,740,000
0
Kazachstan
17,749,648
0.44
n/a
n/a
n/a
78092.74
0.01
n/a
n/a
n/a
n/a
n/a
0.00
n/a
n/a
0.62
54.70
0.33
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
0
0
n/a
n/a
0
Latvia
1,992,663
0.33
13,594
11,981
6,782
4262.68
0.36
2,992
601
1.50
3.51
75.78
0.03
0.05
0.44
0.14
0.39
0.00
0.11
66
223
322
32
810
1,235
172
132
0
0
0
0
0
0
0
0
25,172
0
331
2,328,709
151,000,000
642
Liechtenstein
37,403
0.86
n/a
n/a
n/a
0.00
0.00
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
0
0
n/a
n/a
0
Lithuania
2,931,926
0.34
6,664
5,448
11,106
7221.94
0.20
6,182
1,241
2.11
1.92
24.56
0.00
0.00
0.56
0.17
0.88
0.00
0.05
0
0
0
0
0
0
0
21
0
0
0
0
2,871
547
2,525
217
51,975
0
286
1,928,000
72,000,000
0
Luxembourg
566,741
0.09
479
581
732
3725.06
0.05
321
106
0.57
2.10
1.76
0.20
0.19
0.44
0.03
0.52
0.00
0.10
0
0
110
1
97
103
9
0
0
0
0
0
0
0
0
0
4,446
0
32
112,000
1,000,000
112
Montenegro
628,178
0.36
1,160
699
1,593
1010.37
0.31
784
158
1.25
1.77
10.19
0.00
1.00
0.49
0.16
0.51
0.00
0.01
0
42
637
19
0
2
74
9
0
0
0
0
0
0
0
0
6,605
0
7
396,630
6,400,000
0
Netherlands
16,938,499
0.09
3,053
2,420
12,181
73831.16
0.05
4,615
981
0.27
0.22
12.91
0.21
0.89
0.38
0.01
0.27
0.00
0.13
197
0
1,677
278
22
158
287
210
1,397
0
344
44
0
0
0
0
41,052
0
595
345,000
218,600,000
414
Norway
5,199,836
0.19
11,984
8,435
12,367
29616.39
0.45
4,240
836
0.82
1.88
20.19
0.09
0.22
0.34
0.03
0.06
0.00
0.13
0
0
1,827
0
0
1,240
364
0
0
0
0
0
808
0
0
0
34,996
0
544
1,885,140
104,990,000
1,827
Poland
38,265,226
0.39
41,680
41,643
84,605
94928.75
0.09
51,825
10,419
1.35
0.34
15.88
0.87
1.24
0.61
0.11
1.16
0.00
0.02
9,316
9,464
26,646
6,400
0
0
0
0
0
0
0
0
0
0
0
0
436,226
0
880
5,136,460
607,580,000
51,825
Portugal
10,418,473
0.37
11,533
13,181
26,646
21965.75
0.23
17,645
3,662
1.69
0.15
38.59
0.40
0.51
0.66
0.17
0.74
0.31
0.02
0
0
6,678
0
2,655
7,946
259
106
0
0
0
0
0
0
0
0
153,311
48,240
388
596,543
402,002,542
6,678
Republic of Moldova
4,065,980
0.46
1,497
1,527
5,309
3381.63
0.10
3,623
730
0.89
0.78
0.00
0.15
2.29
0.68
0.22
2.10
0.00
0.00
0
10
3,413
75
0
3
113
8
0
0
0
0
0
0
0
0
30,559
0
5
1,472,838
0
3,498
Romania
19,876,621
0.49
n/a
n/a
n/a
31906.06
0.19
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
0
0
n/a
n/a
0
Russian Federation
143,888,004
0.26
n/a
n/a
n/a
709731.51
0.02
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
0
0
n/a
n/a
0
Serbia
8,851,280
0.47
7,655
7,766
14,744
14755.59
0.13
6,947
1,396
0.78
1.29
19.66
1.11
0.77
0.47
0.09
0.72
0.00
0.01
10
182
5,466
293
7
458
349
144
0
0
38
0
0
0
0
0
58,443
0
44
5,408,000
174,000,000
5,116
Slovak Republic
5,439,318
0.47
10,580
8,098
9,498
16392.42
0.10
4,345
1,199
0.80
0.48
5.06
0.12
0.23
0.46
0.07
0.76
0.12
0.02
993
0
859
26
1,020
1,311
50
52
23
6
6
0
0
0
0
0
50,207
6,056
70
1,219,397
27,500,000
1,448
Slovenia
2,074,788
0.50
5,324
3,090
6,278
6571.37
0.16
2,877
691
1.39
0.97
66.57
0.16
0.49
0.46
0.11
0.65
0.00
0.20
0
0
1,528
0
220
484
346
23
0
0
10
0
0
0
264
0
28,918
0
589
1,014,352
138,120,000
1,257
Sweden
9,763,565
0.14
74,169
80,749
91,095
45450.61
0.42
44,233
9,172
4.53
4.44
154.68
0.13
0.14
0.49
0.20
0.48
0.42
0.03
6,712
0
3,982
507
7,351
21,921
1,084
663
2,012
0
0
0
0
0
0
0
384,003
159,402
1,253
6,108,910
1,510,240,000
11,033
Switzerland
8,319,769
0.26
5,493
5,051
10,294
24533.32
0.22
4,811
996
0.58
0.99
25.24
0.39
0.50
0.47
0.04
0.18
0.00
0.11
890
73
1,168
412
148
621
471
45
682
294
0
7
0
0
0
0
41,682
0
510
2,121,000
210,000,000
2,158
North Macedonia
2,079,308
0.44
n/a
n/a
n/a
2678.35
0.16
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
0
0
n/a
n/a
0
Turkey
78,271,472
0.26
n/a
n/a
n/a
128810.24
0.12
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
0
0
n/a
n/a
0
Ukraine
44,657,704
0.30
n/a
n/a
n/a
90089.64
0.03
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
0
0
0
n/a
n/a
0
United Kingdom
65,397,080
0.17
12,446
12,221
60,213
180749.26
0.08
34,129
6,101
0.52
0.13
104.03
0.29
0.64
0.57
0.03
0.41
0.00
0.47
520
77
7,199
0
16,009
6,775
1,379
497
863
15
796
0
0
0
0
0
255,432
0
16,156
1,494,180
6,803,030,000
5,325
United States
319,929,162
0.19
410,364
399,835
630,425
2188278.06
0.07
237,594
48,685
0.74
0.15
4.63
0.00
0.12
0.38
0.02
0.33
0.33
0.01
0
0
46,741
0
0
75,461
13,315
0
7,583
5,243
0
1,269
30,875
47,876
0
9,230
2,038,360
671,748
1,236
8,717,561
1,481,327,024
0
JWEE total
Autor: Autor:
still includes Ireland
Autor: see comment in external data sheet
Autor: see comment in external data sheet
798,930,371
0.23
1,024,064
1,019,567
1,601,393
3865968.26
0.10
726,604
151,076
0.91
0.63
24.18
0.10
0.27
0.45
0.04
0.38
0.21
0.05
29,968
15,219
220,306
10,580
56,221
210,687
43,117
7,875
20,524
12,482
2,774
1,433
34,661
48,503
2,789
9,465
6,325,255
1,348,439
36,294
117,525,834
19,321,877,756
175,250
JWEE 12
261,159,928
0.23
296,837
315,016
535,032
910643.64
0.14
255,336
54,332
0.98
1.07
49.17
0.20
0.31
0.48
0.06
0.44
0.17
0.10
18,468
5,323
70,785
3,542
40,047
70,978
18,915
6,754
11,470
6,044
2,424
120
106
79
264
19
2,274,780
390,941
26,012
63,830,422
12,840,834,000
83,105
JWEE Europe (excluding the Russian Federation)
429,482,492
0.27
444,472
451,487
834,889
1388511.18
0.14
419,922
88,175
0.98
0.81
39.27
0.25
0.47
0.50
0.06
0.44
0.12
0.08
29,968
15,215
155,860
10,434
56,221
87,178
26,627
7,863
12,941
7,239
2,774
164
3,786
626
2,789
236
3,691,717
455,451
34,828
92,506,516
16,866,040,732
159,864
external data reference
Population
Rural Population (share)
SWE in 1000 m3
TPES in ktoe
RES in TPES (share)
Net Annaul Increment in 1000 m3
TPES in TJ
RES in TJ
*We could make this better when converting with TJ figures.
JWEE total
Armenia, Austria, Azerbaijan, Bosnia and Herzegovina, Canada, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Georgia, Germany, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Netherlands, Norway, Poland, Portugal, Republic of Moldova, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland, the United Kingdoma and the United States
JWEE 12
Austria, Cyprus, Finland, France, Germany, Ireland, Luxembourg, Serbia, Slovenia, Sweden, Switzerland and the United Kingdom.
Europe excl. Russia
Armenia, Austria, Bosnia and Herzegovina, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Germany, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Netherlands, Norway, Poland, Portugal, Republic of Moldova, Serbia, Slovak Republic, Slovenia, Sweden, Switzland and the United Kingdom
2013
2013
Population
Rural Population
Roundwood and Fuelwood removals from forest and outside forest (1000m3)
Roundwood and Fuelwood supply from forest and outside forest including net trade (1000m3)
SWE + (S1 -S4) Total calculated domestic consumption of woody biomass (1000m3)
Total primary energy supply, TPES (ktoe)
Share of renewables (RES) in TPES from ktoe
Total wood energy generation (1000m3)
Total wood energy generation (ktoe) (calculation totals*heating value)
Average wood energy consumption (m3/capita)
Fuelwood consumption per rural inhabitant (m3/inhabitant)
Pellets consumption per inhabitant (kg/inhabitant)
S1/Net Annual Increments
Share of Total Roundwood supply directly used for energy purposes (%)
Share of energy use in calculated domestic consumption of woody biomass (%)
Share of woody biomass in TPES (%) (from TJ)
Share of woody biomass in RES (%) (from TJ)
Share of wood energy generated from black liquor (%) (heating value)
Imported wood fuel as share of wood energy (%)
1-S1-U1
1-S1-U2
1-S1-U3
1-S1-U4
1-S2-U1
1-S2-U2
1-S2-U3
1-S2-U4
1-S3-U1
1-S3-U2
1-S3-U3
1-S3-U4
1-S4-U1
1-S4-U2
1-S4-U3
1-S4-U4
Total domestic woody biomass supply (including cascaded use) (1000 m3)
Total wood energy generation (TJ) (calculation totals*heating value)
Share of woody biomass in TPES (%) (from ktoe)
Share of woody biomass in RES (%) (from ktoe)
Wood fuel imports (1000 m3)
Total Fuelwood (1000 m3)
Total Pellets (mt)
Total S1 (1000 m3)
share of rural population
Renewable energy supply (ktoe)
Total woody biomass from forest (TJ)
Total woody biomass from outside forest (TJ)
Unspecified (TJ)
Chips and Particles (TJ)
Wood Residues (TJ)
Bark (TJ)
Unspecified solid co-products (TJ)
Black Liquor (TJ)
Crude Tall Oil (TJ)
Unspecified liquid co-products (TJ)
Wood Charcoal (TJ)
Wood Pellets (TJ)
Wood Briquettes (TJ)
Pyrolysis Oils (TJ)
Cellulose based Ethanol (TJ)
Wood based Biodiesel (TJ)
Non-hazardous Wood Waste (TJ)
Hazardous Wood Waste (TJ)
Unspecified Wood Waste (TJ)
Wood from Unknown Sources (TJ)
Total wood energy generation (ktoe)
Total primary energy supply, TPES (TJ)
Renewable energy supply, RES (TJ)
Net Annual Increments
SWE
2013-2-FBI-SCP-CAP-DP-1000 m3
2013-2-FBI-SCP-WR-DP-1000 m3
2013-2-FBI-SCP-B-DP-1000 m3
2013-2-FBI-LCP-BL-DP-1000 mt
2013-2-WW-PCRW-NHWW-DP-1000 mt
2013-2-WW-PCRW-HWW-DP-1000 mt
2013-2-WFUS-DP-1000 m3
2013-3-WC-DP-1000 mt
2013-3-WP-DP-1000 mt
2013-3-WB-DP-1000 mt
2013-3-PO-DP-million litre
2013-3-CBE-DP-million litre
2013-3-WBB-DP-million litre
2013-2-FBI-SCP-CAP-I-1000 m3
2013-2-FBI-SCP-WR-I-1000 m3
2013-2-FBI-SCP-B-I-1000 m3
2013-2-FBI-LCP-TO-I-1000 mt
2013-2-WW-PCRW-NHWW-I-1000 mt
2013-2-WW-PCRW-HWW-I-1000 mt
2013-2-WFUS-I-1000 m3
2013-3-WC-I-1000 mt
2013-3-WP-I-1000 mt
2013-3-WB-I-1000 mt
2013-3-PO-I-million litre
2013-3-CBE-I-million litre
2013-3-WBB-I-million litre
2013-2-FBI-SCP-CAP-E-1000 m3
2013-2-FBI-SCP-WR-E-1000 m3
2013-2-FBI-SCP-B-E-1000 m3
2013-2-FBI-LCP-TO-E-1000 mt
2013-2-WW-PCRW-NHWW-E-1000 mt
2013-2-WW-PCRW-HWW-E-1000 mt
2013-2-WFUS-E-1000 m3
2013-3-WC-E-1000 mt
2013-3-WP-E-1000 mt
2013-3-WB-E-1000 mt
2013-3-PO-E-million litre
2013-3-CBE-E-million litre
2013-3-WBB-E-million litre
SUM biomass domestic production
SUM biomass Import
SUM biomass Export
for the derived sheet
Country
S1
S2
S3
S4
U1
U2
U3
U4
% S1
% S2
% S3
% S4
% U1
% U2
% U3
% U4
Tot S-U
why change from BL DP to TO I
Armenia
2992192
0.3683189448
1537.774
2592.224
1723.9407053194
2900.3608961498
0.067482287
1545.7157053194
311.0662984027
0.516583062
2.3482611532
n/a
4.8788323886
59.6%
0.896617674
10.7%
158.9%
0.0%
68.2%
0
0
1545.6141007194
0
0
0
0.1016046
0
0
0
0
0
0
0
0
0
165.2386845586
13023.7237815262
0.1072748044
1.5896735147
1053.5998669
2587974
0
1545.6141007194
1102081
195.7229865291
13023.2157585262
0
0
0
0
0
0
0
0
0
0.508023
0
0
0
0
0
0
0
0
0
1545.6141007194
0.1016046
0
0
0
0
1545.7157053194
0
0.999934267
0.0%
0.0%
0.0%
0.0%
0.0%
100.0%
0.0%
1545.7157053194
311
121432.31
8194.53
360
178.225
0
0
87.0246628571
0
0
0
0
0
0
0
0
0
0
63.4284892086
14.6762589928
0
0
0
0
0
0.1861344
0
0.0055071
0
0
0
0
0
0
0
0
0
0
0.082368
0
0
0
0
0
87.0246628571
78.2963897014
0.082368
Austria
8486962
0.3414881556
18271.4737705391
26535.1137705391
43881.975818449
33220.8980605713
0.3007626412
23656.130318449
4806.0936327428
2.7873496215
2.0118128616
101.8809793186
0.3232221808
29.9%
0.5390853506
14.5%
48.1%
8.2%
9.4%
0
0
7374.1572160822
569.8107918113
6247.3121591351
6574.2487757795
1838.1219495077
1052.4794261332
0
0
0
0
0
0
0
0
13768.2114493624
201221.528215675
0.1433350784
0.4765720831
2215.25545224
5830630
864660
7943.9680078935
2898197
9991.6050444253
60241.6918736218
6693.5213192913
0
49708.8061852649
39664.6485893032
9192.6982864903
6352.8302129206
16425.1261809672
0
0
377.8987823
11636.8961308711
927.4106546443
0
0
0
0
0
0
0
7943.9680078935
15712.1623105555
0
0
6247.3121591351
6574.2487757795
9212.2791655899
1622.2902179446
0.335810122
66.4%
0.0%
0.0%
26.4%
27.8%
38.9%
6.9%
23656.130318449
4,762
1390892.56
418328.52
25136
20225.8455
3545
2588
890.8216765824
2545.97625
0
0
0
7.9488
2453.1
0
0
0
0
1522.78
691.88
0
0
0
0
0
73.152
982.923
279.05099416
0
0
0
249.87
277.77
0
0
0
0
0
10.3104
1231.14
43.33087138
0
0
0
12030.8467265824
3549.78599416
1812.42127138
Azerbaijan
9497496
0.454936438
422
422
1950.5786446043
13880.3969618802
0.0183578279
416.1942446043
81.7659724563
0.0438214709
0.0976680707
0.010529091
n/a
91.5%
0.2133696305
0.6%
32.1%
0.0%
0.2%
0
3
285
98.1942446043
0
0
24
6
0
0
0
0
0
0
0
0
29.7182
3423.3777348
0.006035515
0.3287706511
0.66
422000
100
386.1942446043
4320757
254.8139390465
3273.3777348
0
0
0
0
0
0
0
0
0
150
0
0
0
0
0
0
0
0
0
386.1942446043
30
0
0
0
3
309
104.1942446043
0.9279182728
7.2%
0.0%
0.0%
0.0%
0.7%
74.2%
25.0%
416.1942446043
84
581144.46
10668.55
…
1534.3844
0.1
0.1
0
0
0
0
0
28.8
0.255
0
0
0
0
0.06
0
0
0
0
0
0
0.6336
0
0
0
0
0
0
0
0
0
0
0
0
0.2304
0
0
0
0
0
29.255
0.6936
0.2304
Bosnia and Herzegovina
3823533
0.6060549235
4675
3884
2168.0214784003
6454.0040126111
0.1241962363
1454.5704784003
293.0124124076
0.3804257681
0.3378974665
3.9230732414
n/a
26.3%
0.6709206956
4.5%
36.6%
15.2%
0.1%
0
0
1021
0
0
333.7688466937
56.074828984
43.7268027226
0
0
0
0
0
0
0
0
831.7500952381
12267.8436826819
0.0453654989
0.3652727345
1
783000
15000
1021
2317271
801.5630075475
6597.4928244894
2005.3681893084
0
0
0
0
1422.1146288841
1859.871
0
0
141
241.99704
0
0
0
0
0
0
0
0
1021
433.5704784003
0
0
0
333.7688466937
1077.074828984
43.7268027226
0.7019254241
29.8%
0.0%
0.0%
0.0%
22.9%
74.0%
3.0%
1454.5704784003
293
270216.24
33559.84
…
713.451
40
80
130.3914285714
171
390.5333333333
1.5833333333
0
144
469.2
205.092
0
0
0
0
4
0
0
0
0
0
0
0
0
0
0
0
22
46
0
0
0
0
0
115.2
430.95
189.9
0
0
0
1631.8000952381
4
804.05
Canada
35230612
0.1850274699
151977.913
149823.128916
185202.37852
253197.895051113
0.1886562053
85191.08772
15790.8346116891
2.4180984344
0.5906258538
23.7118764783
0.0084768056
2.2%
0.4599891665
6.2%
33.1%
33.2%
9.2%
0
0
3297.3621103117
0
0
57459.7601918465
8031.08772
0
0
0
0
0
0
0
16402.8776978417
0
116242.305627248
661130.6635222
0.0677258127
0.3589906446
7823.913090288
3850072
835383.92
3297.3621103117
6518631
47767.3540651572
27783.298675
0
0
0
247734
0
0
219643.2
0
0
36495.6786
6354.4862472
0
0
0
0
0
0
0
123120
3297.3621103117
65490.8479118465
0
16402.8776978417
0
57459.7601918465
27731.3275281535
0
0.038705482
76.9%
0.0%
19.3%
0.0%
67.4%
32.6%
0.0%
85191.08772
17,148
10600889.47
1999923.58
442030
100011.2908
25774.4
37459
5029.7764707514
19611
73.4903125
0
16402.8776978417
0
6255.15
0
0
36.9097861552
0
3231.192
201.8270775
0
0
0
0
0
7460.02368
57.6685764
0
0
0
0
943.2336
219.5995935
0
0
0
0
0
5.5872
4182.5895804
0
0
0
0
110642.604267248
10950.7113339
5351.0099739
Croatia
4271497
0.418182197
5437
4380
4369.5211757868
7719.5270851247
0.1642248128
2112.5316757868
519.0756588241
0.4945647102
0.4848107559
5.8527490479
0.1903471553
38.2%
0.4834698336
6.7%
40.9%
0.0%
1.6%
55.7849139128
48.3469253911
1556.3990981682
13.016479913
70.6608909563
76.2393823475
87.7773725261
1.8191125717
22.9625
179.525
0
0
0
0
0
0
395.8028571429
21732.6596836465
0.0550849155
0.3354238286
34.272
866000
25000
1673.5474173851
1786264
1267.737890513
17421.3571141898
0
0
0
0
0
1529.2080133567
0
0
0
150
468.4396913
203.669431
0
0
0
1959.9854338
0
0
0
1673.5474173851
236.4967584016
202.4875
0
149.4083048691
304.1113077387
1644.1764706943
14.8355924847
0.7921999166
11.2%
9.6%
0.0%
7.1%
14.4%
77.8%
0.7%
2112.5316757868
425
323201.16
53077.65
9991
2256.9895
400
530
147.0428571429
0
0
0
0
34.56
464.1
63.3
0
0
0
2
7
0
0
0
0
0
17.28
5.1
4.22
0
0
0
375
431
0
0
0
0
0
23.04
405.45
44.31
0
0
0
1639.0028571429
35.6
1278.8
Cyprus
1141652
0.3285344396
22.43
24.1
326.8231707866
1930.6510939142
0.0695351498
88.2515707866
11.8783704858
0.077301639
0.019276299
0.56935038
0.0771631499
48.7%
0.2700285007
0.6%
8.8%
0.0%
81.5%
0
0
11.7482014388
0
0
2.2326139089
34.1282534388
40.142502
0
0
0
0
0
0
0
0
82.7705714286
497.3236154987
0.0092010799
0.1323227161
71.9049
7230
650
11.7482014388
375072
134.2481131174
21.7416940904
77.2476733342
0
0
25.1161020022
0
0
0
0
0
360
13.2181460719
0
0
0
0
0
0
0
0
11.7482014388
76.5033693477
0
0
0
2.2326139089
45.8764548777
40.142502
0.1331217262
86.7%
0.0%
0.0%
0.0%
2.5%
52.0%
45.5%
88.2515707866
18
80832.5
5620.7
38
238.5716
1.82
4.25
0.8090714286
0
0
0
0
6.912
0
0
0
0
0
0.03
1.34
0
0
0
0
0
66.0672
1.6575
0
0
0
0
0
0
0
0
0
0
0
0.1152
0
0
0
0
0
13.7910714286
69.0947
0.1152
Czech Republic
10545314
0.2734111094
19049
17083
18883.9251080889
41951.139056081
0.0851551149
10414.5211080889
2112.1989162364
0.9875970605
1.3408684638
6.4483618032
0.1902819612
36.3%
0.5515019281
5.0%
59.1%
16.7%
3.1%
0
446.0431654676
5755.3956834532
0
1292.5659472422
2304.4329575133
441.502
131.8944844125
0
42.68687
0
0
0
0
0
0
3686.6407142857
88433.5442249875
0.0499707307
0.5868200731
321.186
3866000
68000
6201.4388489209
2883206
3572.3540651572
32572.1290648
16164.82832
3515.8501596
7961.1779476
8405.7107264
1190.693739
0
14778.96
0
0
600
1160.9157567
1670.0893342
0
0
0
0
0
413.1891766875
0
6201.4388489209
4170.395389168
42.68687
0
1292.5659472422
2793.162992981
6196.8976834532
131.8944844125
0.5954607787
40.0%
0.4%
0.0%
12.4%
26.8%
59.5%
1.3%
10414.5211080889
2,096
1756410.29
149567.32
23086
8469.404
733
668
573.5007142857
1330
0
0
0
34.56
415.65
253.2
0
0
0
293
124
0
0
0
0
0
92.16
91.8
78.07
0
0
0
356
180
0
0
0
0
0
5.76
334.05
124.49
0
0
0
4007.9107142857
679.03
1000.3
Denmark
5624293
0.1266470292
4240
4469
17054.5663044086
17446.400114646
0.2508548477
10684.7042044086
2184.7098503523
1.8997417461
3.8761757686
408.5846878888
0.7485071608
100.0%
0.6265010798
12.5%
49.9%
0.0%
61.7%
3410.071942446
1057.5539568345
0
0
3213.5373804353
0
2754.3679136691
36.2480110237
212.925
0
0
0
0
0
0
0
7191.4619304814
91469.43201455
0.1232755158
0.4914217003
6597.19140503
2761000
2298000
4467.6258992806
712300
4376.5140441387
32168.0083568
5475.8355934
0
5031.3028146
969.8896992
932.71009555
0
0
0
0
390
42526.1771928
1914.4926514
0
0
0
2061.0156108
0
0
0
4467.6258992806
6004.153305128
212.925
0
6836.5343228814
1057.5539568345
2754.3679136691
36.2480110237
0.4181328574
56.2%
2.0%
0.0%
64.0%
9.9%
25.8%
0.3%
10684.7042044086
2,151
730445.88
183235.89
5796
6369.8621
400
100
111.9047619048
0
257.385
45.8566666667
0
0
234.6
34.2664
0
0
0
205
29
0
0
0
0
0
86.4
5895.6
183.57866577
0
0
0
8
15
0
0
43.9383333333
45.8566666667
0
5.76
270.3
3.27456386
0
0
0
1184.0128285714
6399.57866577
392.12956386
Estonia
1320050
0.31476838
8967.6
6185.93
12152.4894381298
6093.468042419
0.1396628475
4972.4484381298
1009.2534106724
3.7668637083
8.0315515872
32.5945229347
0.2733725073
44.8%
0.4091711796
16.6%
118.6%
4.1%
3.4%
1164.6
31
1540
34
768.81664
785.6486941007
598.8783040291
17.1068
32.398
0
0
0
0
0
0
0
3655.3530024286
42255.4217980334
0.1642577629
1.1761020619
169.106759931
3337190
43026.4000000001
2769.6
415510
851.0310977357
22704.4753063463
631.943757135
0
3892.7735439516
9976.2854459712
2173.2037443725
0
1733.472
0
0
16.356
618.340392516
194.9739383327
0
0
0
313.597669408
0
0
0
2769.6
2170.4504381298
32.398
0
1965.81464
816.6486941007
2138.8783040291
51.1068
0.5569891844
43.6%
0.7%
0.0%
39.5%
16.4%
43.0%
1.0%
4972.4484381298
1,001
255121.32
35630.97
11201
7180.041
1856.7224827586
1517.7772413793
306.2988
155.674125
80.6852433333
4.972435
0
17.28
1556.53632
41.653299
0
0
0
94.32
63.41
0
0
0.0531666667
0
0
30.5856
115.668
11.178577229
0
0
0
529.2
23.95
0
0
0
4.3142133333
0
44.5248
1562.487
32.986274605
0
0
0
5537.5999464713
315.2153438957
2197.4622879383
Finland
5453061
0.1596904931
60938.4
66824.39
69506.7019135925
33040.3940957294
0.2998949091
38661.7770135925
8077.1131336672
7.0899219748
13.2928266127
46.1942384287
0.1745242015
20.9%
0.5562309238
24.4%
81.5%
41.6%
0.6%
5895.4084167586
920.8324455774
6067.5659867466
1097.9272952162
4483.0110880273
19497.2134115003
14.8332
125.18256
533.0948222416
26.64084218
0
0.0669453445
0
0
0
0
37799.4954464609
338172.572680378
0.235535702
0.7853941326
234.91844
11575420
251900
13981.7341442989
870802
9908.645982612
117808.92808548
0
0
7481.6870364
16531.6244616
42214.848084
0
140700.557
4349.41932
0
74.166
3592.7121060983
0
0
0
0
5197.2701868
221.3604
0
0
13981.7341442989
24120.2402595276
559.802609766
0
10911.5143270275
20444.6866992577
6082.3991867466
1223.1768005607
0.3616423047
62.4%
1.4%
0.0%
28.2%
52.9%
15.7%
3.2%
38661.7770135925
7,782
1383335.22
414855.19
91038
30844.9249
7923
5307
7392.4285714286
12662.0770338373
568.4066666667
106.9193745284
0
0
688.5
0
0
0
0
3145.06
412.49
0
0
0
0
0
15.552
153.357
0
0
0
0
171.64
203.74
0
0
0
0
0
0.4032
199.512
0
0
0
0
34648.3316464609
3726.459
575.2952
France
63844529
0.2109123399
51671.17
47563.23
108806.164655687
253323.417884781
0.0920604897
46626.9626556867
11339.1506209153
0.7303204109
1.4528295399
13.0003308506
0.2587215099
57.6%
0.4285323612
4.5%
48.6%
7.3%
1.9%
585.7843137255
3110.1960784314
23694.4444444444
0
764.3652611476
5497.5884693697
7584.7822683926
1616.2280701754
1366.26875
946.055
1461.25
0
0
0
0
0
43994.8195392857
474747.55819648
0.0370494649
0.4024469672
872.602
19563220
830000
27390.4248366013
13465599
23321.0779115315
221404.60199019
59587.5983946
4578.68762164
0
34519.99087736
48758.90861205
14891.8480898
34822.23
0
0
3270
15552.19775116
835.0446671
0
0
0
36526.45019258
0
0
0
27390.4248366013
15462.9640690854
3773.57375
0
2716.4183248731
9553.8395478011
32740.4767128371
1616.2280701754
0.587437467
33.2%
8.1%
0.0%
5.8%
20.5%
70.2%
3.5%
46626.9626556867
9,385
10606144.86
976406.89
94367
62179.202
6047.14
8313
10395.9523809524
4662.122625
12000
1500
0
259.2
2269.5
94.95
0
0
0
259.68
688.62
0
0
0
0
0
520.6464
234.6
0
0
0
0
360.64
721.81
0
0
1666.6666666667
0
0
113.8752
387.6
0
0
0
0
45541.8650059524
1703.5464
3250.5918666667
Germany
80565861
0.2578364054
57947.1529965977
63312.0205528489
153773.237622473
317658.472102799
0.1049801909
65910.817822473
14849.4432185593
0.8180985967
0.5671015824
23.9284789078
0.2842118324
49.8%
0.4286234643
4.7%
44.5%
4.1%
5.3%
2373.1143395391
6351.4777929077
22776.5186023483
0
2290.5815485065
10441.0978220598
6683.5947892832
0
3151.5204990355
8434.8284994158
1868.8425519875
0
68.6965383683
183.8615740615
1286.6832649597
0
42797.979152203
621716.488674643
0.0417653775
0.3978405554
3490.8644290754
11780294.5562512
1927818.50562406
31501.1107347951
20772812
33347.8470430878
263310.376626748
47459.6596047539
0
16607.6359817656
43950.8621706822
32470.8017754822
7738.3795812233
25455.8814660475
0
0
5862.7433621751
25200.2988030025
12651.8973802005
0
0
0
0
0
125681.85814495
15326.0937776118
31501.1107347951
19415.2741598495
13455.1915504389
1539.2413773895
7883.9129254494
25411.2656884449
32615.6392085788
0
0.4779353644
29.5%
20.4%
2.3%
12.0%
38.6%
49.5%
0.0%
65910.817822473
13,267
13299724.91
1396207.66
107000
87862.4198
10184.0064
3299.216
2125.4749757028
4677.313
5954.9866004023
5954.9866004023
0
11.52
5629.1709
2327.9376137098
0
0
0
992.4313437226
2178.0358625232
0
0
721.6666666667
721.6666666667
0
1256.4840999583
987.6004751782
499.0540245368
0
0
0
1277.2728086778
1350.5281631465
0
0
143.3333333333
143.3333333333
0
70.5075834289
1700.8341858368
37.7626695098
0
0
0
40164.6120902172
7356.9391392524
4723.5720772665
Hungary
9924507
0.2978461298
6027.202
5013.957
5721.35098
22560.037021114
0.0827323138
2617.05068
526.3632573787
0.2636957866
0.8840315348
-0.2471659298
0.267548264
52.1%
0.4574183072
2.3%
28.2%
0.0%
4.6%
0
0
0
2613.176
0
0
3.87468
0
0
0
0
0
0
0
0
0
580.8588659524
22037.7768599335
0.0233503007
0.2822391837
120.535674
2613176
-2453
2613.176
2955976
1866.444062291
22018.4034599335
0
0
0
0
0
0
0
0
0
19.3734
0
0
0
0
0
0
0
0
0
2613.176
3.87468
0
0
0
0
3.87468
2613.176
0.9985194479
0.1%
0.0%
0.0%
0.0%
0.0%
0.1%
99.9%
2617.05068
527
944543.63
78144.28
11099
3104.3003
375.797
129.931
168.3256992857
0
52.5083333333
2.0033333333
0
0
11.26845
3.65874
0
0
0
117.11
122.723
0
0
0
0
0
4.14144
21.6852
17.09311
0
0
0
216.09
139.027
0
0
0
0
0
0.18432
39.2088
50.87632
0
0
0
743.4925559524
282.75275
445.38644
Iceland
325392
0.0613475439
3.64
4.96
318.8241446043
5885.1399636954
0.8959532066
112.9244446043
22.1808650138
0.3470412444
0.0826570484
n/a
0.6932076716
191.9%
0.354190379
0.4%
0.4%
0.0%
5.6%
4.7961630695
0
2.3600959233
2.3600959233
0
0
5.2452
85.1366906475
0
0
0
12.525
0
0
0.5011990408
0
97.816647619
928.6684563971
0.0038623473
0.0043108806
6.35
1650
0
9.5163549161
19962
5272.8100219738
80.1840143985
0
0
626.3870974
40.4120708
0
0
0
0
0
56.226
0
0
0
0
0
121.2362124
0
0
4.2230613986
9.5163549161
90.3818906475
12.525
0.5011990408
4.7961630695
0
8.106494964
100.0217865707
0.0842718771
80.0%
11.1%
0.4%
4.2%
0.0%
7.2%
88.6%
112.9244446043
23
246399.04
220762.01
15.6
205.8997
0
0
0.1665142857
0
13.3333333333
0
0
0
0
0
0
0
0
74.56
4.4
0
0
0
0
0
5.3568
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
13.499847619
84.3168
0
Ireland
4671263
0.3698098352
2756
2651.26
4194.6986725851
13060.0599503201
0.0651468761
1687.9100725851
351.1607827267
0.3613391223
0.1213328787
15.6274652059
0.063599063
11.5%
0.4023912572
2.7%
41.3%
0.0%
6.5%
95.9232613909
0
208.6330935252
0
247.4734628297
809.0002548393
41.19
35.19
0
250.5
0
0
0
0
0
0
1595.3873714286
14702.3996512
0.0260150274
0.399328854
109.356
209600
73000
304.5563549161
1727479
850.8221075762
2525.754425
40.4120708
0
4748.418319
2020.60354
1547.9018607
0
0
0
0
30
1364.5851877
0
0
0
0
2424.724248
0
0
0
304.5563549161
1132.853717669
250.5
0
343.3967242206
1059.5002548393
249.8230935252
35.19
0.1804339934
67.1%
14.8%
0.0%
20.3%
62.8%
14.8%
2.1%
1687.9100725851
340
546798.59
35622.22
5356
2506.7886
552.4
178.95
185.7619047619
0
416.6666666667
0
0
0
81.6
0
0
0
0
23.25
85.32
0
0
0
0
0
6.048
104.55
0
0
0
0
13.11
25.07
0
0
0
0
0
0.9792
0
0
0
0
0
1415.3785714286
219.168
39.1592
Luxembourg
544885
0.1015425273
399
796
1832.5415243166
3972.1300277061
0.0393791196
398.5114243165
79.7658530768
0.7313679479
2.2411393663
75.2452352331
0.3568320169
25.6%
0.2174637895
2.0%
51.0%
0.0%
10.1%
0
0
201.8824940048
2.2254196643
33.5923261391
151.5443645084
9.26682
0
0
0
0
0
0
0
0
0
759.1452571429
3339.636736618
0.0201947031
0.5128276936
40.358
124000
41000
204.1079136691
55329
156.4189834719
1719.7962910002
0
0
1559.9463449508
0
0
0
0
0
0
27.918
31.976100667
0
0
0
0
0
0
0
0
204.1079136691
194.4035106475
0
0
33.5923261391
151.5443645084
211.1493140048
2.2254196643
0.5121758153
48.8%
0.0%
0.0%
8.4%
38.0%
53.0%
0.6%
398.5114243165
80
166305.14
6548.95
650
1434.0301
422.46
98.5
26.7228571429
0
0
0
0
0
114.75
0
0
0
0
193.98
29.15
0
0
0
0
0
7.0272
7.65
0
0
0
0
33.99
87.93
0
0
0
0
0
1.3248
17.85
0
0
0
0
662.4328571429
237.8072
141.0948
Netherlands
16809157
0.106972051
1847
1549
14926.0630133392
77391.4751122576
0.0427808239
4134.7830133392
893.6837112938
0.2459839606
0.1735155246
44.6185373841
0.1574497977
62.1%
0.2770176576
1.2%
27.0%
0.0%
51.1%
136.9565217391
0
770.4462516943
54.347826087
1541.322
212.4183217472
222.33
110.4797391304
872.3294117647
0
214.1529411765
0
0
0
0
0
4555.6560685714
37416.74962445
0.0107542407
0.2513799336
2111.42
312000
750000
961.7505995204
1798110
3310.871070985
2626.784602
6041.6045846
0
0
2202.4578586
297.67343475
0
0
0
0
960
14053.190739
61.1008293
0
0
0
11173.9375762
0
0
0
961.7505995204
2086.5500608777
1086.4823529412
0
2550.6079335038
212.4183217472
1206.9291928708
164.8275652174
0.2326000171
50.5%
26.3%
0.0%
61.7%
5.1%
29.2%
4.0%
4134.7830133392
832
3240226.28
138619.55
2250
10791.28
74
791
141
0
2203.3333333333
101.6666666667
0
0
573.75
45.154
0
0
0
131
442
0
0
16.6666666667
0
0
247.68
1963.5
0
0
0
0
105
514
44.7619047619
0
706.6666666667
101.6666666667
0
40.32
624.75
37.92936
0
0
0
3929.904
2800.8466666667
2175.0945980952
Norway
5083450
0.1990022524
11598.29
9733.85
12991.4488158273
32706.4490780548
0.3848327241
5580.5725158273
1116.7868070431
1.0977923489
2.6587605203
19.3175894324
0.1397547288
27.6%
0.4295573646
3.4%
8.9%
9.1%
8.6%
0
0
2690.6474820144
0
0
1621.2339040767
486.5908
0
0
0
110.6375
0
671.4628297362
0
0
0
4223.7006166667
46757.63003728
0.0343451576
0.0892469778
482.4968
2689650
98200
2690.6474820144
1011618
12586.5118945257
22671.1717188
0
0
0
0
0
9450.36275658
4264.23
0
0
1056.654
2016.3273669
570.2744068
0
0
0
1070.9198762
0
0
5657.689912
2690.6474820144
2107.8247040767
110.6375
671.4628297362
671.4628297362
1621.2339040767
3287.8757820144
0
0.4821454205
37.8%
2.0%
12.0%
12.0%
29.1%
58.9%
0.0%
5580.5725158273
1,123
1369353.61
526972.08
21878
7410.8763
330
1740
326.77925
848.5875
1787.1383333333
55.5966666667
0
0
124.695
63.3
0
0
0
315.04
182.11
0
0
0
0
0
221.4144
129.54
10.55
0
0
0
142.67
982.94
0
0
738.7533333333
26.725
0
5.5872
3.825
10.55
0
0
0
5276.09675
858.6544
1911.0505333333
Republic of Moldova
4073714
0.4718512394
1829
1865.19
2285.1124
3069.8258813414
0.0958989545
1877.83
377.4509673802
0.4609626498
0.6799546558
n/a
0.0775420389
100.0%
0.8217670168
12.3%
128.2%
0.0%
0.1%
9
561.19
1268
27
0
0
6
6.64
0
0
0
0
0
0
0
0
6.5496
15803.1171022743
0.1231296607
1.2839520653
2.76
1307000
0
1865.19
1922187
294.3930925767
15715.9356849417
0
0
0
58.9814173326
0
0
0
0
0
28.2
0
0
0
0
0
0
0
0
0
1865.19
12.64
0
0
9
561.19
1274
33.64
0.9932688263
0.7%
0.0%
0.0%
0.5%
29.9%
67.8%
1.8%
1877.83
378
128527.47
12325.65
27334
407.2824
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2.6496
0
0
0
0
0
0
0.1
0
0
0
0
0
0
0
0
0
0
0
4
2.6496
0.1
Serbia
8937605
0.474877442
7690
7758
10345.652426373
14890.5780070698
0.1288145369
7295.213426373
1379.2498924799
0.8162380667
1.2885617234
7.7201890216
1.2071655645
83.6%
0.7051477399
9.3%
71.9%
0.0%
0.3%
9
284.5445563549
5857
333.6043165468
4.692
495.0036457975
161.2500815348
110.4563261391
0
0
39.6625
0
0
0
0
0
1061.0669761905
57746.43449835
0.098615822
0.7655643869
19.3815
5469000
69000
6484.1488729017
4244267
1918.1229100984
44392.6597738
6546.7554696
0
313.1935487
2000.3975046
2163.09362585
0
0
0
0
480
1283.1174153
183.3024879
0
0
0
363.7086372
20.2060354
0
0
6484.1488729017
771.4020534714
39.6625
0
13.692
779.5482021524
6057.9125815348
444.0606426859
0.888822368
10.6%
0.5%
0.0%
0.2%
10.7%
83.0%
6.1%
7295.213426373
1,468
623438.72
80307.97
5232
3050.439
164
298
260.4471428571
0
36.6666666667
1.6666666667
0
155.52
425.85
44.31
0
0
0
15
3
0
0
0
0
0
0
7.65
0.3165
0
0
0
4
9
0
0
0
0
0
57.6
257.55
23.21
0
0
0
1386.4604761905
25.9665
351.36
Slovenia
2064819
0.5041027809
3685.176
2709.328
4901.565487214
6845.7358842075
0.1646803069
2890.036587214
711.5602618673
1.399656138
0.9820190781
67.4955044486
0.1963284942
68.4%
0.5896150107
10.4%
63.1%
0.0%
22.6%
0
0
1853.4285714286
0
196.19
424.4366666667
353.2226517867
0
0
0
0
13.6459875
0
0
49.1127098321
0
843.9776104762
29791.6050438597
0.0849773834
0.5160142398
651.749662
1022165
139366
1853.4285714286
1040881
1127.3578866915
17917.095709888
3055.15255248
0
183.87492214
0
0
5266.9698466773
0
0
0
80.0598
2611.3826407086
131.163113564
0
0
0
132.0868534098
0
0
413.819604992
1853.4285714286
973.8493184533
13.6459875
49.1127098321
196.19
424.4366666667
2255.7639330474
13.6459875
0.6413166462
33.7%
0.5%
1.7%
6.8%
14.7%
78.1%
0.5%
2890.036587214
582
286617.27
47200.22
9165
2011.5289
170
340
30.3186704762
0
87.7883333333
0.1283333333
0
5.76
229.5
10.55
0
0
0
258.397
164.013
0
0
0.12
0
0
15.54048
303.31485
101.28
0
0
0
335.75
256.092
0
0
1.4516666667
0
0
4.94784
177.43155
97.06
0
0
0
874.0453371429
842.66533
872.7330566667
Sweden
9624247
0.1440576078
69079.5
76185.08
88913.7111321343
49264.7009649374
0.3467390974
50514.3837321343
10487.060081862
5.2486582828
4.6005397974
201.2250932463
0.0522785326
19.7%
0.5681281671
21.3%
61.4%
44.2%
4.7%
10091.1270983213
0
4376.4988009592
503.5971223022
2629.645822542
28984.3669880096
1312.9884
777.072
1839.0875
0
0
0
0
0
0
0
46720.7879047619
439072.2315074
0.2063950898
0.5952460837
2358.7869
6378400
1936640
14971.2230215827
1386446
17081.9979459253
37401.3715254
0
88744.9074768
0
0
0
56677.929297
193946.07
3542.4
0
875.892
40082.1440208
0
0
0
0
17801.5171874
0
0
0
14971.2230215827
33704.0732105516
1839.0875
0
14559.8604208633
28984.3669880096
5689.4872009592
1280.6691223022
0.2963754463
66.7%
3.6%
0.0%
28.8%
57.4%
11.3%
2.5%
50514.3837321343
10,168
2062614.5
715189.09
96486
38399.3274
14845.61
4154.39
2557.6419714286
16522.875
1833.3333333333
0
0
4.032
3535.4985
0
0
0
0
1096.64
786.37
0
0
0
0
0
182.016
1817.2575
0
0
0
0
157
36.41
0
0
0
0
0
7.1424
414.324
0
0
0
0
43453.3808047619
3882.2835
614.8764
Switzerland
8118719
0.2608140521
6004.1854007861
5515.2454007861
11777.3010861311
26727.4648896532
0.2027418589
5043.224386131
1279.1542917869
0.6211847443
1.1642235382
25.0336252039
0.448955452
50.3%
0.4282156285
4.8%
23.6%
0.0%
6.3%
915.7631828456
78.6574079416
1332.9143320645
448.8086086723
146.8816647832
637.9419902785
439.1303006282
78.3712220015
678.9561957267
278.6068275579
0.2117995578
6.9808540733
0
0
0
0
3587.0092574433
53555.6318885321
0.0379813745
0.1873385924
319.53286
2465215.4007861
203240.968581722
2776.143531524
2117476
5418.7759147798
27372.537678692
3808.847371797
0
0
7728.5978040094
607.1378642844
0
0
0
0
340.374
3806.1605783613
191.1233940504
0
74.242155309
0
8889.5667509154
737.0442911131
0
0
2776.143531524
1302.3251776914
964.7556769156
0
1741.6010433556
995.206225778
1772.2564322504
534.1606847471
0.5504699611
25.8%
19.1%
0.0%
34.5%
19.7%
35.1%
10.6%
5043.224386131
1,015
1119025.5
226873.31
6232
6734.0767
0
1101.63
475.5952380952
0
1699.2910294067
144.7991731814
0
0.6336
309.9804698834
19.412
0
0
0
517.79
97.35
16.7857142857
0
8.5390946502
0
0
69.2352
215.1435
4.22
0
29.5573567531
0
108.25
34.52
110.7857142857
0
791.2757201646
68.8065843621
0
0.3456
6.8595
2.11
0
0
0
3751.3415105667
958.620865689
1122.9531188125
North Macedonia
2072543
0.4373742788
691
723.05
961.6776505995
2796.8450845514
0.1048981834
564.5363505995
113.3634834975
0.2723882451
0.6288631703
-0.4487241037
0.3928926546
77.7%
0.5870328277
4.1%
38.6%
0.0%
0.9%
0
21.019
513.9064748201
26.5652757794
0
0
3.0456
0
0
0
0
0
0
0
0
0
27.9227214286
4746.3023270722
0.0406297035
0.387325139
5.22768
570050
-930
561.4907505995
906477
293.3839686634
4731.0743270722
0
0
0
0
0
0
0
0
0
15.228
0
0
0
0
0
0
0
0
0
561.4907505995
3.0456
0
0
0
21.019
516.9520748201
26.5652757794
0.9946051304
0.5%
0.0%
0.0%
0.0%
3.7%
91.6%
4.7%
564.5363505995
114
117098.31
12283.4
1624
397.1413
0
0
24.2738214286
0
0
0
0
0
0
0
0
0
0
0.07
3.52
0
0
0
0
0
3.1104
0.204
0
0
0
0
0
0.68
0
0
0
0
0
0
2.5755
0
0
0
0
24.2738214286
6.9044
3.2555
United Kingdom
63955654
0.1767883571
12658.1586330993
12120.2054957294
52221.4882605564
190952.489968472
0.0529768063
13364.4479605564
2671.1876059999
0.208964292
0.1070663199
56.8701521526
0.148809153
31.5%
0.2559185578
1.4%
26.4%
0.0%
65.2%
408.8126618705
163.8061147141
3240.5759441074
0
6605.3595155151
1806.579132122
630
235.2300172272
274.084575
0
0
0
0
0
0
0
17985.4737351786
111837.282688005
0.0140878867
0.2659255573
8709.9793917731
1210557.65863855
3637167.774
3813.194720692
11306615
10116.0530715582
22840.1960224949
9289.4652905915
0
21843.3679858059
13394.6827616553
0
1307.2870784579
0
1886.282613
0
3150
35472.9889000503
0
0
0
0
2653.0120359492
0
0
0
3813.194720692
9277.1686648643
274.084575
0
7288.2567523856
1970.3852468361
3870.5759441074
235.2300172272
0.2853237733
69.4%
2.1%
0.0%
54.5%
14.7%
29.0%
1.8%
13364.4479605564
2,690
7994798.85
423538.91
20700
38857.0403
2314.64101819
771.5470060633
1173.3722044423
0
3823.77
19.49
0
28.8
903.6435
0
0
0
0
134.5085118343
0
0
19.297331
0
0
0
630.8504823911
8640.8347677
0
0
0
0
185.85248
0
0
1.164471
0
0
0
18.5636914424
269.700444
0
0
0
0
9035.2637286956
9425.4910929254
475.2810864424
United States
317135919
0.1889480043
334019
318317.1
559411.549116612
2188362.73502436
0.0644869667
193605.127116612
46428.591996256
0.6104799725
0.6706880033
9.2811940359
0.060012394
12.6%
0.3460871114
2.1%
32.9%
47.6%
0.5%
0
0
40189.1
0
12920.2509212476
119835.886246355
11502.119088
5544.136881998
3613.6339790115
0
0
0
0
0
0
0
228878.74074673
1943872.28969924
0.0178081045
0.2761504447
983.5304
40189100
2943400
40189.1
59922199
141120.874892519
397910.564874129
0
0
0
247638.540913368
258103.093714601
0
924342.967587258
0
0
25746.6
55152.2154948968
0
0
0
0
34978.3071149933
0
0
0
40189.1
149802.393137601
3613.6339790115
0
16533.884900259
119835.886246355
51691.219088
5544.136881998
0.207582829
77.4%
1.9%
0.0%
8.5%
61.9%
26.7%
2.9%
193605.127116612
38,971
91622370.99
5908448.79
761000
365806.422
14500
81975.0903811625
38317.5568829635
83184.2123458655
3606.421136738
0
0
4907.52
14535
0
0
0
0
85.5
137.5
0
0
0
0
0
489.6
388.62
0
0
0
0
5623.6
68.49
0
0
0
0
0
138.24
7417.95
0
0
0
0
241025.80074673
1101.22
13248.28
JWEE total
686138926
0.2
843444.1
838040.4
1,390,603.31
3627302.7
0.1
581422.3
127823.2
0.8
0.9
24.8
0.097690442
0.2
0.42
3.5%
37.9%
29.5%
6.7%
25156.1
13077.7
137430.6
5824.6
43456.3
257950.6
43325.5
10053.5
12597.3
10158.8
3694.8
33.2
740.2
183.9
17739.2
0.0
581565.6
5351699.9
3.2%
34.7%
38807.9
131791594.6
17047170.6
181489.0
148853524.0
337205.4
1438254.2
166878.2
96839.4
119958.6
676862.8
399652.8
104636.9
1577972.6
9778.1
0.0
80754.9
263239.8
19534.5
0.0
74.2
0.0
125667.3
978.6
126095.0
144521.8
181489.0
354785.9
26484.1
18663.2
81949.8
281371.0
202190.0
15911.4
31.2%
61.0%
4.6%
3.2%
14.1%
48.4%
34.8%
2.7%
581422.3
117,034
151867909.1
14118113.7
1779064.6
809181.0
90654.1
151449.4
70879.4
146370.8
34885.7
7939.7
16402.9
5647.0
41281.3
3206.8
0.0
36.9
0.0
12771.8
6473.7
16.8
19.3
747.0
721.7
0.0
11503.8
22125.9
1188.6
0.0
29.6
0.0
11218.2
5623.7
155.5
1.2
4092.1
390.7
0.0
670.6
19936.9
697.8
0.0
0.0
0.0
568754.027920115
55598.2976719606
42786.6849423617
JWEE 12
257409257
0.2
291122.646801022
311993.973219903
550,481.86
944886.992930162
0.1199857483
256137.666970298
56042.8177461698
1.0
1.1
38.7
0.2133754283
0.4
0.47
5.9%
49.4%
17.5%
7.5%
20374.9332744516
10909.5143959271
76995.3676871501
2955.9735542131
23649.1048486257
75321.2541348401
19102.508714572
4070.3521236765
7843.0123420038
9936.6311691537
3369.9668515453
20.6937869177
68.6965383683
183.8615740615
1335.7959747918
0
210996.124271363
2346400.69339664
5.5%
45.5%
19094.6895350885
65635732.6156759
9974443.24820578
111235.788911742
60260975
113372.972914875
816956.751696405
136558.659747248
93323.59509844
102446.930324027
159836.523811212
136955.390108857
92235.2441060791
411349.864647015
9778.101933
0
14929.0519444751
140647.677780791
14919.9416974593
0
74.242155309
0
73988.3360922544
978.6107265131
125681.85814495
15739.9133826038
111235.8
122143.2
21170.3
1588.4
51935.7
96351.3
100803.6
7047.0
43.4%
47.7%
8.3%
0.6%
20.3%
37.6%
39.4%
2.8%
256137.7
51,558
39560528.62
4746699.63
461400
294344.1948
46170.07741819
26454.4830060633
25515.3466652989
41070.3639088373
26420.9092964757
7727.9901481121
0
480.3264
16641.0933698834
2497.1596137098
0
0
0
8159.5468555569
5137.5688625232
16.7857142857
19.297331
730.3257613169
721.6666666667
0
2842.6190623493
13456.5385928782
883.9215186968
0
29.5573567531
0
2897.3752886778
3002.8701631465
110.7857142857
1.164471
2602.7273868313
212.1399176955
0
286.1151148713
4662.8016798368
203.4735408898
0
0
0
192977.74982657
31997.8277220268
13979.4532772347
JWEE Europe (excluding the Russian Federation)
324274899
0.2
357025.152801022
369478.134219903
644,038.80
1171861.66427821
0.1263479424
302209.855584803
65521.9633846726
0.9
1.1
40.9
0.2151647066
0.4
0.47
5.6%
44.3%
15.8%
4.2331410038
25156.1428156192
13074.6674436204
93659.136873943
5726.4392319157
30536.0077072595
80654.9962413193
23768.2970183803
4503.4037641848
8983.6272537685
10158.8430391537
3694.7572927218
33.2187869177
740.1593681045
183.8615740615
1336.2971738326
0
236414.876075736
2743273.56298947
5.2%
41.1%
29999.8357209495
87330422.6156759
13268286.6482058
137616.386365098
78091937
148062.310117512
1009286.9839287
166878.240191691
96839.44525804
119958.571727579
181490.261029516
141549.671122529
104636.9295049
433986.397647015
9778.101933
0
18362.5973674751
201,733.07
19534.542288492
0
74.242155309
0
90689.0284710624
978.6107265131
126095.047321638
21401.8263560024
137616.4
139462.7
22870.4
2260.3
65415.9
104072.4
122458.5
10263.1
45.5%
46.1%
7.6%
0.7%
21.6%
34.4%
40.5%
3.4%
302209.9
60,832
49063504.16
6199072.8
576034.6
341828.9474
50379.5969009486
32015.1912474426
27532.0551750608
43575.6255338373
31205.8262064757
7939.6692497787
0
710.7264
20490.8931398834
3206.7840527098
0
0
0
9455.0753447656
6134.408121516
16.7857142857
19.297331
747.0455946502
721.6666666667
0
3553.5834367493
21679.6357928782
1188.6173787958
0
29.5573567531
0
4651.3352886778
5335.5671631465
155.5476190476
1.164471
4092.0857201646
390.7024643621
0
526.5738028713
8336.3979798368
697.7900593548
0
0
0
217056.367906137
43545.6727380606
24187.1645684617
2011
2011
Population
Rural Population
Roundwood and Fuelwood removals from forest and outside forest (1000m3)
Roundwood and Fuelwood supply from forest and outside forest including net trade (1000m3)
SWE + (S1 -S4) Total calculated domestic consumption of woody biomass (1000m3)
Total primary energy supply, TPES (ktoe)
Share of renewables (RES) in TPES from ktoe
Total wood energy generation (1000m3)
Total wood energy generation (ktoe) (calculation totals*heating value)
Average wood energy consumption (m3/capita)
Fuelwood consumption per rural inhabitant (m3/inhabitant)
Pellets consumption per inhabitant (kg/inhabitant)
S1/Net Annual Increments
Share of Total Roundwood supply directly used for energy purposes (%)
Share of energy use in calculated domestic consumption of woody biomass (%)
Share of woody biomass in TPES (%) (from TJ)
Share of woody biomass in RES (%) (from TJ)
Share of wood energy generated from black liquor (%) (heating value)
Imported wood fuel as share of wood energy (%)
1-S1-U1
1-S1-U2
1-S1-U3
1-S1-U4
1-S2-U1
1-S2-U2
1-S2-U3
1-S2-U4
1-S3-U1
1-S3-U2
1-S3-U3
1-S3-U4
1-S4-U1
1-S4-U2
1-S4-U3
1-S4-U4
share of rural population
Total domestic woody biomass supply (including cascaded use) (1000 m3)
Renewable energy supply (ktoe)
Total wood energy generation (TJ) (calculation totals*heating value)
Total woody biomass from forest (TJ)
Total woody biomass from outside forest (TJ)
Unspecified (TJ)
Chips and Particles (TJ)
Wood Residues (TJ)
Bark (TJ)
Unspecified solid co-products (TJ)
Black Liquor (TJ)
Crude Tall Oil (TJ)
Unspecified liquid co-products (TJ)
Wood Charcoal (TJ)
Wood Pellets (TJ)
Wood Briquettes (TJ)
Pyrolysis Oils (TJ)
Cellulose based Ethanol (TJ)
Wood based Biodiesel (TJ)
Non-hazardous Wood Waste (TJ)
Hazardous Wood Waste (TJ)
Unspecified Wood Waste (TJ)
Wood from Unknown Sources (TJ)
Share of woody biomass in TPES (%) (from ktoe)
Share of woody biomass in RES (%) (from ktoe)
Wood fuel imports (1000 m3)
Total Fuelwood (1000 m3)
Total Pellets (mt)
Total S1 (1000 m3)
Total wood energy generation (ktoe)
Total primary energy supply, TPES (TJ)
Renewable energy supply, RES (TJ)
Net Annual Increments
SWE
Country
S1
S2
S3
S4
U1
U2
U3
U4
% S1
% S2
% S3
% S4
% U1
% U2
% U3
% U4
Tot S-U
Albania
2831741
0.466
430
373.82
1374.0022307735
2173
0.2655315232
1160.0022307735
233.4499436324
0.4096427713
0.222568911
n/a
2.5802757551
310.3%
0.8442506168
10.7%
40.5%
0.0%
0.0%
0
111.5386760359
892.3094082873
156.1541464503
0
0
0
0
0
0
0
0
0
0
0
0
1319591.306
41.8736714286
577
9774.08224
8992.1556608
0
781.9265792
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1074531747
0.4046720079
0
293700
0
1160.0022307735
1160.0022307735
0
0
0
0
111.5386760359
892.3094082873
156.1541464503
1
0.0%
0.0%
0.0%
0.0%
9.6%
76.9%
13.5%
1160.0022307735
233
90979.164
24157.836
470
214
Armenia
3024127
0.367
2075.656
2075.646
2338.356
2716
0.0821060383
2074.356
417.4632413858
0.6859354782
1.8690340007
n/a
n/a
99.9%
0.8871001678
15.4%
187.2%
0.0%
0.0%
0
0
2074.356
0
0
0
0
0
0
0
0
0
0
0
0
0
1109854.609
70.0176014286
223
17478.3509903404
0
0
17478.3509903404
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.1537353282
1.8723997816
0.12
2074356
0
2074.356
2074.356
0
0
0
0
0
2074.356
0
1
0.0%
0.0%
0.0%
0.0%
0.0%
100.0%
0.0%
2074.356
418
113713.488
9336.564
360
264
Austria
8423136
0.323
19418.4060139913
26631.6060139913
45334.2672151528
33019
0.2656652231
23688.2672151528
4837.3648559324
2.8122859722
2.139261188
119.8814788221
0.2937269498
29.5%
0.522524542
14.7%
55.1%
14.1%
15.9%
3.9728278659
652.278177458
6681.3703026871
514.2814463066
6722.4217831921
6798.4260452837
1445.6396939669
869.8769383924
0
0
0
0
0
0
0
0
2720672.928
16179.9581520548
8772
202530.791788178
54744.4125367189
5885.5501472027
5529.5163457072
37229.5049837434
39720.317989593
9050.5865301947
11370.7768187822
28599.51
0
0
343.9915
9511.2267087408
545.3982274946
0
0
0
0
0
0
0
0.1444074271
0.5435691786
3777.925048
5820230
1009778
7851.9027543177
7851.9027543177
15836.3644608352
0
0
6726.394611058
7450.7042227418
8127.009996654
1384.158384699
0.3314680083
66.9%
0.0%
0.0%
28.4%
31.5%
34.3%
5.8%
23688.2672151528
4,768
1382439.492
367266.096
25136
21646
Belarus
9473172
0.25
10364.2
8177.69
n/a
29501
0.0527100776
n/a
n/a
n/a
0.9658771106
n/a
n/a
0.0%
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2368293
2893.5383857143
1555
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
3.86
2287480
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
1235147.868
65104.74
29975
6761
Belgium
10996266
0.025
5128
8530.89
n/a
59094
0.0484990016
n/a
n/a
n/a
3.5779781973
n/a
n/a
0.0%
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
274906.65
3905.5013642857
2866
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
657.02
983610
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
2474147.592
119993.688
5289
14617
Bosnia and Herzegovina
3843183
0.517
3969
3261.52
2122.4924275185
7095
0.0789288231
1166.4924275185
236.7746232321
0.303522478
0.3611207164
2.1024239543
n/a
27.5%
0.5495861433
3.3%
42.3%
10.8%
0.3%
81.5347721823
0
813.9088729017
0
0
196.6846522782
35.9948256
38.3693045564
0
0
0
0
0
0
0
0
1986925.611
665.0301666667
560
9913.2799254813
6049.68699876
808.241416
687.0052036
0
0
0
969.8896992
1068.8355
0
0
92.778
151.3997082282
85.4433996931
0
0
0
0
0
0
0
0.0330940316
0.4192895609
3.73008
717520
8080
895.4436450839
895.4436450839
271.0487824345
0
0
81.5347721823
196.6846522782
849.9036985017
38.3693045564
0.7676377694
23.2%
0.0%
0.0%
7.0%
16.9%
72.9%
3.3%
1166.4924275185
235
297053.46
23446.08
…
956
Bulgaria
7348328
0.269
6205
5361.31
ERROR:#N/A
19216
0.0743130724
ERROR:#N/A
ERROR:#N/A
n/a
1.2353162915
n/a
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
1976700.232
485.8257857143
1428
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
n/a
n/a
7.79
2441850
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
n/a
804535.488
59787.504
14677
2484
Canada
34482773
0.193
142013
140645
220613.122302158
251845
0.1800472513
71093.1223021583
14469.5155730052
2.061699687
0.4824816641
n/a
0.0262016521
7.2%
0.3222524642
5.7%
31.9%
35.2%
2.9%
0
0
10192.0863309353
0
0
59035.035971223
1866
0
0
0
0
0
0
0
0
0
6655175.189
106975.213571429
45344
605809.67801058
85877.67105354
0
0
0
0
0
297586.40695704
213015.6
0
0
9330
0
0
0
0
0
0
0
0
0
0.0568217242
0.3155934003
2092.578232812
3211000
0
10192.0863309353
10192.0863309353
60901.035971223
0
0
0
59035.035971223
12058.0863309353
0
0.1433624801
85.7%
0.0%
0.0%
0.0%
83.0%
17.0%
0.0%
71093.1223021583
14,310
10544246.46
1898462.592
442030
149520
Cyprus
850881
0.295
21.247
21.857
448.4412470024
2368
0.0405405405
79.4412470024
10.5869596928
0.093363522
0.0144615813
n/a
0.0286851858
46.8%
0.1771497326
0.4%
11.0%
0.0%
83.4%
0
0
10.2398081535
0
0
1.2110311751
31.9904076739
36
0
0
0
0
0
0
0
0
251009.895
76.8761707143
96
443.254828417
8.08241416
78.197356998
0
0
26.975057259
0
0
0
0
0
330
0
0
0
0
0
0
0
0
0
0.0067528096
0.1665693046
66.26
3630
0
10.2398081535
10.2398081535
69.2014388489
0
0
0
1.2110311751
42.2302158273
36
0.1288978779
87.1%
0.0%
0.0%
0.0%
1.5%
53.2%
45.3%
79.4412470024
16
99143.424
4019.328
38
369
Czech Republic
10494847
0.266
18831
16533
16232.739139481
43429
0.0688710309
9055.739139481
1871.7894431045
0.8628748127
1.2709423839
5.3359520153
0.1084796044
34.1%
0.5578688268
4.3%
62.6%
15.6%
2.7%
0
357.3141486811
5275.7793764988
0
1107.9136690647
2034.1486023107
182.262
98.3213429257
0
0
0
0
0
0
0
0
2791629.302
3825.8516285714
2991
78368.0804039
18569.3465326
14750.405842
14144.22478
8001.5900184
6486.1373634
1389.1426955
0
12223.2
0
0
360
957.2463257
1486.7868463
0
0
0
0
0
0
0
0.041972441
0.6094353528
242.654
3548000
56000
5633.0935251799
5633.0935251799
3422.6456143011
0
0
1107.9136690647
2391.4627509918
5458.0413764988
98.3213429257
0.6220467969
37.8%
0.0%
0.0%
12.2%
26.4%
60.3%
1.1%
9055.739139481
1,823
1818285.372
125227.188
23086
7177
Denmark
5566856
0.131
2566
2565
18110.991146283
17997
0.2219258765
7390.991146283
1528.0926857624
1.3276778035
2.4778605967
368.6102173291
0.5604400349
111.4%
0.4080942388
8.5%
38.3%
0.0%
70.8%
2858.5131894484
0
0
0
2582.946
0
1239.1046522782
150.9773045564
559.45
0
0
0
0
0
0
0
729258.136
6598.6483142857
3994
63978.1845675
24085.5941968
0
0
222.2663894
262.6784602
0
0
0
0
0
0
33992.4280339
0
0
0
0
5415.2174872
0
0
0
0.0826651995
0.3724901342
5234.274
1807000
2052000
2858.5131894484
2858.5131894484
3973.0279568345
559.45
0
6000.9091894484
0
1239.1046522782
150.9773045564
0.3867564083
53.8%
7.6%
0.0%
81.2%
0.0%
16.8%
2.0%
7390.991146283
1,488
753498.396
167220.792
5796
10720
Estonia
1339929
0.305
8054
5601.6
11225.1285224273
5603
0.1483134035
4565.1285224273
944.744332037
3.4069928499
5.6831981151
10.1135955711
0.2539343078
46.0%
0.4066883077
16.9%
113.7%
4.2%
1.7%
876
41
1646
15
841.9840340899
733.0110733374
324.7916
13.42344
71.309
2.609375
0
0
0
0
0
0
408678.345
3353.3799410806
831
39554.5556937272
21090.0696547854
631.943757135
0
7682.3464594047
5729.2636113783
970.5940013458
0
1666.8
0
0
0
243.588639476
824.453856688
0
0
0
535.2326202018
180.2630933122
0
0
0.1640032821
1.1057886755
76.1801294
2322600
13551.5
2578
2578
1913.2101474273
73.918375
0
1789.2930340899
776.6204483374
1970.7916
28.42344
0.564715755
41.9%
1.6%
0.0%
39.2%
17.0%
43.2%
0.6%
4565.1285224273
919
234586.404
34792.308
11201
6660
Finland
5388272
0.163
56556.8839676411
61649.4984936411
66155.1651332089
34749
0.2606118162
36357.1651332089
7601.0606031187
6.7474628477
12.5981757528
34.6128736634
0.1610610523
20.9%
0.5495740969
21.9%
83.9%
42.5%
0.4%
5159.5029038375
670.1190813561
7045.8801906462
27.6527773282
4341.1925443449
18682.0264689456
0.921493221
7.1556932303
401.3200871409
17.7780180718
0
3.6158750865
0
0
0
0
878288.336
36680.8510850214
9056
318241.205331374
108720.9096
0
0
21471.6051213738
5520.001
41982.9262
0
135108.98841
0
0
0
1345.099
0
0
0
0
3884.673
207.003
0
0
0.2106046009
0.8081160863
136.450231376
11064830.8185977
186503.578
12903.1549531679
12903.1549531679
23031.2961997418
422.7139802992
0
9902.0155353232
19369.9235683734
7046.8016838672
38.424345645
0.3548999188
63.3%
1.2%
0.0%
27.2%
53.3%
19.4%
0.1%
36357.1651332089
7,318
1454871.132
379156.608
91038
29798
France
63294268
0.142
55040.5467803582
49324.8421489191
90007.6345665486
252827
0.0723142702
37445.6345665486
9221.2023288667
0.5916117802
2.1166228848
7.8206133927
0.1727582141
56.2%
0.4160273153
3.6%
50.4%
6.3%
1.6%
99.3333333333
7854.5062745098
19185.401262963
563.137254902
81.2360349651
3228.1957981154
4809.5292922601
84.272
124.415
132.12205
1283.4862655
0
0
0
0
0
8987786.056
37481.0410193837
18283
386073.29910499
147840.292264273
52800.1043800977
87768.1113547003
0
0
39426.6162715377
5707.1138745884
24194.8694918079
0
0
3404.868
9275.10588774
749.50350608
0
0
0
14906.7140741645
0
0
0
0.0298124657
0.412262553
614.14905
19023753.65
495000
27702.3781257081
27702.3781257081
8203.2331253406
1540.0233155
0
304.9843682984
11214.8241226252
25278.416820723
647.409254902
0.7398026084
21.9%
4.1%
0.0%
0.8%
29.9%
67.5%
1.7%
37445.6345665486
7,537
10585360.836
765472.644
94367
52562
Germany
81797706
0.261
60586.57
64591.38
144010.721616121
311770
0.1001250922
58189.7216161207
13537.2871275466
0.71138574
0.5208639828
17.7217537128
0.2629521119
45.1%
0.4040652041
4.3%
43.4%
2.3%
3.8%
1851.5290325646
6425.8948777244
20878.3911374079
0
1790.3351768664
8459.3912672474
4890.0264367093
0
2409.3018324645
8361.6945950237
1713.0987599924
0
51.5823841853
179.0212157018
1179.4549002329
0
21349201.266
23707.47204
31216
566779.137456121
243715.504429762
44030.2179126985
0
15643.846360477
41859.5705579498
29940.0714993471
7093.4859926246
13263.0905753506
0
0
5250
23734.3932750682
11597.5258004065
0
0
0
15992.0995949038
0
100619.105415822
14040.2260417098
0.037569214
0.3752227659
2212.8488186
11120030
1449598.8
29155.815047697
29155.815047697
15139.7528808231
12484.0951874807
1410.0585001199
6102.7484260808
23426.0019556973
28660.9712343425
0
0.5010475087
26.0%
21.5%
2.4%
10.5%
40.3%
49.3%
0.0%
58189.7216161207
11,713
13053186.36
1306951.488
107000
85821
Iceland
318006
0.063
0.5
2.1
189.1325093525
5731
0.8382481242
19.1325093525
4.0712132128
0.0601639886
0.0314459476
n/a
0.135555891
88.6%
0.1011592847
0.1%
0.1%
0.0%
9.5%
0
0
0.9304556355
0.9304556355
0
0
1.5792
3.5011990408
0
0
0
11.69
0
0
0.5011990408
0
20034.378
19.5699666667
4804
170.453554793
15.6798834704
0
0
29.500811684
0
0
0
0
0
0
7.896
0
0
0
0
0
113.15379824
0
0
4.2230613986
0.0006719882
0.0008016579
1.81
630
0
1.860911271
1.860911271
5.0803990408
11.69
0.5011990408
0
0
3.0108546763
16.1216546763
0.0972643597
26.6%
61.1%
2.6%
0.0%
0.0%
15.7%
84.3%
19.1325093525
4
239945.508
201133.872
15.6
170
Ireland
4484323
0.378
2717
2519.38
3000.0815319472
13216
0.0618946731
710.0815319472
146.4758013351
0.1583475436
0.1670605438
13.1569469907
0.0484047131
9.4%
0.2366874115
1.1%
17.9%
0.0%
13.9%
4.7961630695
39.491466
193.4496630695
0
137.890853717
243.7273860911
55.536
35.19
0
0
0
0
0
0
0
0
1695074.094
1484.5508380952
818
6132.6488502967
1922.3304931397
80.8241416
0
626.3870974
424.3267434
1888.965447357
0
0
0
0
90
1099.8149274
0
0
0
0
0
0
0
0
0.0108150452
0.1747330537
98.418
283180
59000
237.7372921391
237.7372921391
472.3442398082
0
0
142.6870167866
283.2188520911
248.9856630695
35.19
0.3348028099
66.5%
0.0%
0.0%
20.1%
39.9%
35.1%
5.0%
710.0815319472
143
553327.488
34248.024
5356
2290
Italy
60738612
0.316
21305.72
25575.89
51572.5013932854
167416
0.1191045061
11926.5013932854
2432.6004899444
0.1963578192
0.2964805395
26.3423866189
n/a
32.1%
0.2312569891
1.5%
12.2%
0.0%
12.2%
5035.9712230216
1007.1942446043
1812.9496402878
350.5035971223
0
1127.0983213429
1510.7913669065
0
1081.993
0
0
0
0
0
0
0
19193401.392
12479.2119642857
19940
101848.117312992
0
0
69148.286104464
0
0
0
22226.63894
0
0
0
0
0
0
0
0
0
0
0
10473.192268528
0
0.0143395742
0.1203948922
1453.54
5690470
1600000
8206.618705036
8206.618705036
2637.8896882494
1081.993
0
6117.9642230216
2134.2925659472
3323.7410071942
350.5035971223
0.6880994211
22.1%
9.1%
0.0%
51.3%
17.9%
27.9%
2.9%
11926.5013932854
2,401
7009373.088
834847.92
32543
39646
Latvia
2065544
0.323
13017.16
8158.06
n/a
4371
0.4735758408
n/a
n/a
n/a
0.3669975249
n/a
n/a
0.0%
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
667170.712
2049.8001285714
2070
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
20.74
244850
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
183005.028
86666.76
25280
3937
Liechtenstein
36312
0.856
25
19
n/a
116.4668099742
0.0734893604
n/a
n/a
n/a
0.4825777838
n/a
n/a
0.0%
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
31083.072
16.6378571429
8.5590713672
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
0
15000
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
4876.2324
358.3512
…
26
Lithuania
3222186
0.329
8052.55
6157.06
n/a
7287
0.1594620557
n/a
n/a
n/a
1.5930490369
n/a
n/a
0.0%
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1060099.194
2277.9437
1162
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
24.01
1688790
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
305092.116
48650.616
10750
4868
Luxembourg
511840
0.146
332.0479486707
1288.0728645699
2032.0534627158
4171
0.0297290818
342.0534627158
68.8548218898
0.6682820075
1.061478766
2.4774871077
n/a
16.2%
0.1683289682
1.7%
55.5%
0.0%
6.0%
0
0
207.4964028777
1.7482014388
14.2326139089
115.8393285372
2.7369159532
0
0
0
0
0
0
0
0
0
74728.64
912.6804425208
124
2882.8136828825
0
0
1763.077618827
1095.975360096
0
0
0
0
0
0
0
23.7607039595
0
0
0
0
0
0
0
0
0.0165072206
0.5552549758
20.3996618363
79322.8645699279
1268.0770012
209.2446043165
209.2446043165
132.8088583993
0
0
14.2326139089
115.8393285372
210.2333188309
1.7482014388
0.6117307004
38.8%
0.0%
0.0%
4.2%
33.9%
61.5%
0.5%
342.0534627158
69
174631.428
5191.632
650
1690
Netherlands
16693074
0.168
1637
1385
17607.6054076304
77419
0.0427801961
4869.6054076304
1047.2682953389
0.2917141209
0.0317354314
72.6648668783
0.1622944069
66.8%
0.2765626157
1.4%
31.6%
0.0%
60.9%
216.439891565
0
653.8160775727
54.347826087
2805.1863597122
227.6357130516
65.3913043478
0
709.2588235294
0
137.5294117647
0
0
0
0
0
2804436.432
5085.4804761905
3312
43847.02898925
2707.6087436
5607.1748235
0
1576.0707612
1980.1914692
297.67343475
0
0
0
0
240
22729.5084996
0
0
0
0
8688.595222
20.2060354
0
0
0.0126609521
0.2959535787
2963.554
89000
1213000
924.6037952247
924.6037952247
3098.2133771116
846.7882352941
0
3730.8850748067
227.6357130516
856.7367936853
54.347826087
0.1898724266
63.6%
17.4%
0.0%
76.6%
4.7%
17.6%
1.1%
4869.6054076304
980
3241378.692
138666.816
2250
12738
Norway
4953088
0.206
10678.96
11356.73
13164.7817314149
28137
0.427764154
5686.7817314149
1180.8381119972
1.1481285476
2.334867829
12.3155494108
0.1249322576
21.2%
0.4319693138
4.2%
9.8%
22.1%
10.7%
0
0
2405.2757793765
0
0
2553.0935251799
119.646
0
0
0
66.8
0
541.9664268585
0
0
0
1020336.128
7693.6613166667
12036
49439.3300731
20266.6535062
0
0
0
0
0
11194.1436116
10911.984
0
0
0
1038.7140981
814.677724
0
0
0
646.5931328
0
0
4566.5640004
0.0406826207
0.0951052592
605.784
2382350
61000
2405.2757793765
2405.2757793765
2672.7395251799
66.8
541.9664268585
541.9664268585
2553.0935251799
2591.7217793765
0
0.4229590466
47.0%
1.2%
9.5%
9.5%
44.9%
45.6%
0.0%
5686.7817314149
1,145
1178039.916
503923.248
21878
7478
Poland
38204328
0.391
36878
38628.38
44774.5508393285
101313
0.0784795633
9996.5508393285
2011.8015017981
0.2616601669
0.3295699231
n/a
n/a
0.0%
0.2232641233
2.0%
25.3%
0.0%
4.8%
0
0
0
0
0
0
0
0
0
0
0
0
7130.6522781775
2865.8985611511
0
0
14937892.248
11515.0299
7951
84230.1052772842
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
84230.1052772842
0.019861185
0.2530746113
479.57
4923080
0
0
0
0
0
9996.5508393285
7130.6522781775
2865.8985611511
0
0
0
0.0%
0.0%
100.0%
71.3%
28.7%
0.0%
0.0%
9996.5508393285
2,012
4241772.684
332892.468
67595
34778
Romania
21384832
0.472
14358.6290248
14213.22961826
24507.0095923261
35830
0.1403014234
17988.0095923261
3620.0790946642
0.8411573957
0.3924571624
1.6324277514
n/a
27.9%
0.7339944731
10.1%
72.0%
0.0%
0.6%
0
0
3961.6306954436
0
0
0
0
0
0
0
0
0
134.0527577938
1508.3932853717
11800.9592326139
582.9736211031
10093640.704
2254.5840055987
5027
151565.4715354
0
0
33380.3704808
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
118185.1010546
0.1010546856
0.7202684276
101.710157334
3961321.58898
34909.1932156863
3961.6306954436
3961.6306954436
0
0
14026.3788968825
134.0527577938
1508.3932853717
15762.5899280576
582.9736211031
0.2202373017
0.0%
0.0%
78.0%
0.7%
8.4%
87.6%
3.2%
17988.0095923261
3,621
1500130.44
210470.436
…
6519
Russian Federation
142865433
0.262
197000
176301.15
n/a
730970
0.0242636497
n/a
n/a
n/a
1.1633835182
n/a
n/a
0.0%
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
37430743.446
28821.5440642857
17736
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
18.24
43546310
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
30604251.96
742570.848
852927
60266
Serbia
7276195
0.436
7758
7788
10914.6560695864
16185
0.1120173
7528.6560695865
1420.8943212931
1.0346968532
1.730539536
2.4738204515
1.2203876744
83.8%
0.6897749248
8.8%
78.4%
0.0%
0.2%
0
4.8266666667
6282.7001704672
239.8081534772
0
207.3093367691
208.0464844125
554.6527577938
0
0
31.3125
0
0
0
0
0
3172421.02
1093.7357142857
1813
59490.0034439
44715.9563402
6405.3132218
0
2889.4630622
3374.4079118
277.8285391
0
0
0
0
750
346.2380327
427.7058051
0
0
0
282.8844956
20.2060354
0
0
0.0936321309
0.8358720562
15.636
5490000
18000
6527.3349906111
6527.3349906111
970.0085789753
31.3125
0
0
212.1360034358
6522.0591548797
794.460911271
0.8669986954
12.9%
0.4%
0.0%
0.0%
2.8%
86.6%
10.6%
7528.6560695865
1,515
677633.58
75906.684
5232
3386
Slovak Republic
5398384
0.453
10010.91
8343
12035.7014257663
17349
0.0744711511
5502.7014257663
1133.4552613217
1.0193238246
0.2579301845
13.1520840311
0.0909176671
17.4%
0.4571982331
6.5%
87.7%
34.6%
2.7%
743.309352518
0
687.9136690647
22.1582733813
106.7625899281
3553.7907026728
160.5966205755
163.1654676259
0
49.995625
0
15.009125
0
0
0
0
2445467.952
4478.3991202381
1292
47455.504881017
8893.888541664
3352.18127286
0
10065.030353448
2656.285413684
839.439085995
4273.5764871
16417.98
0
0
0
301.227088449
26.680695461
0
0
0
629.215942356
0
0
0
0.0638442302
0.857301509
151.31
630760
71000
1453.381294964
1453.381294964
3984.3153808023
65.00475
0
850.071942446
3603.7863276728
848.5102896403
200.3328660072
0.2641214165
72.4%
1.2%
0.0%
15.4%
65.5%
15.4%
3.6%
5502.7014257663
1,108
726367.932
54093.456
13193
6533
Slovenia
2052843
0.501
3657.867
2963.64
4079.3865175851
7249
0.1306387088
2250.3865175851
567.8062197717
1.096229238
1.1712280508
30.2020173973
0.1685725622
55.0%
0.5516482706
7.8%
60.0%
0.0%
15.0%
0
0
1629.5714285714
0
149.0931020408
385.7831530612
25.1348262857
0
0
0
0
17.63854
0
0
43.1654676259
0
1028474.343
707.7017714286
947
23772.9108094026
15384.067112144
3055.15255248
0
138.209282136
0
0
4539.2459889887
0
0
0
0
76.8241093732
44.9702103648
0
0
0
170.7329167158
0
0
363.7086372
0.0624883663
0.478329638
336.638
1204578
62000
1629.5714285714
1629.5714285714
560.0110813878
17.63854
43.1654676259
149.0931020408
385.7831530612
1697.871722483
17.63854
0.7241295732
24.9%
0.8%
1.9%
6.6%
17.1%
75.4%
0.8%
2250.3865175851
453
303501.132
39648.996
9165
1829
Sweden
9449213
0.148
72103.33
78730.12
90731.1418189448
49045
0.3211132633
48781.1418189448
10070.8277082115
5.1624555208
4.754299844
212.504469949
n/a
21.0%
0.5376449678
20.5%
63.9%
36.4%
5.1%
11342.9256594724
0
4695.4436450839
503.5971223022
2968.335971223
26065.3904652278
1392.528
888.1584556355
920.5875
4.175
0
0
0
0
0
0
1398483.524
48912.9589573339
15749
421645.4144874
0
848.6534868
138532.5787024
0
0
0
68963.1988202
153442.83
1660.5
3472.5
600
45173.8797958
0
0
0
0
8951.2736822
0
0
0
0.2002061323
0.6234751259
2489.96
6648810
2008000
16541.9664268585
16541.9664268585
31314.4128920863
924.7625
0
15231.8491306954
26069.5654652278
6087.9716450839
1391.7555779377
0.3391057653
64.2%
1.9%
0.0%
31.2%
53.4%
12.5%
2.9%
48781.1418189448
9,819
2053416.06
659379.132
96486
41950
Switzerland
7911345
0.263
5845.1643909805
5162.4243909805
9976.9867343594
25375
0.1806108374
4148.9867343594
1056.5885722263
0.5244350656
0.934766951
18.2601220383
0.3546494615
43.8%
0.4158556932
4.2%
23.1%
0.0%
5.2%
709.0855375795
70.6291751846
1142.2297513436
337.0099268727
125.7177818992
595.0820418827
319.1778533675
65.4919066762
550.3957302225
227.7303439315
0.146321167
6.2903642324
0
0
0
0
2080683.735
2854.2914853961
4583
44237.2503419706
21622.7594650007
3808.847371797
0
0
7008.1433525614
611.0913326726
0
0
0
0
339.81
2706.8717330893
236.0936044152
0
75.0394611727
0
7133.5064035018
695.08761776
0
0
0.032912142
0.1822268391
217.52637
1944954.3909805
144462.125187196
2258.9543909805
2258.9543909805
1105.4695838256
784.5627595533
0
1385.1990497011
893.4415609988
1461.5539258781
408.7921977813
0.5444592947
26.6%
18.9%
0.0%
33.4%
21.5%
35.2%
9.9%
4148.9867343594
835
1062400.5
191881.044
6232
5828
Turkey
74223629
0.285
21039
22447.59
54458.2156692229
112459
0.0997874781
21538.2156692229
4320.0127524768
0.2901800405
0.2296516511
n/a
n/a
94.8%
0.3954998416
3.8%
38.5%
0.0%
2.0%
0
0
21290.175059952
0
0
68.0406092708
180
0
0
0
0
0
0
0
0
0
21153734.265
6910.3526095238
11222
180870.293920699
179389.242899306
0
0
376.3172032896
122.9133133382
81.820504765
0
0
0
0
900
0
0
0
0
0
0
0
0
0
0.0385510008
0.3863310461
434
4857990
0
21290.175059952
21290.175059952
248.0406092708
0
0
0
68.0406092708
21470.175059952
0
0.9884836974
1.2%
0.0%
0.0%
0.0%
0.3%
99.7%
0.0%
21538.2156692229
4,335
4708433.412
469842.696
…
32920
Ukraine
45778534
0.311
17510.292
13381.172
13408
126438
0.0205080751
6182
1224.4864191893
0.1350414585
0.544925433
n/a
n/a
0.0%
0.4610680191
1.0%
47.2%
0.0%
0.0%
0
0
0
0
0
240
0
0
0
0
0
428
386
5019
109
14237124.074
1675.1185266667
2593
51266.7973986156
0
0
0
0
0
0
0
0
0
0
1200
0
0
0
0
0
0
0
0
50066.7973986156
0.009841732
0.4798954551
2.75
7758171
0
0
0
240
0
5942
428
386
5259
109
0
3.9%
0.0%
96.1%
6.9%
6.2%
85.1%
1.8%
6182
1,244
5293706.184
108563.724
20935
7226
United Kingdom
62435709
0.204
11459.3862909672
11348.1362909672
51636.0701980198
188074
0.0414677202
10333.0701980198
2139.0435638053
0.1654993651
0.0862675632
19.5609399262
0.1781183749
33.4%
0.2001134122
1.1%
27.4%
0.0%
27.5%
1215.8273381295
2577.9376498801
0
3944.547292744
1794.4984172662
372
192.372
235.8875
0
0
0
0
0
0
0
12736884.636
14397.3005910524
7799
89557.4759294
27338.7658962
7253.9667086
0
19034.0853468
545.5629558
873.1754086
1070.9198762
0
3431.7
0
1860
25866.017737
0
0
0
0
2283.2820002
0
0
0
0.0110591251
0.2666923833
2837.65210429
1098780
1221301.153
3793.7649880096
3793.7649880096
6303.4177100102
235.8875
0
5396.2621308735
1794.4984172662
2949.9376498801
192.372
0.3671478965
61.0%
2.3%
0.0%
52.2%
17.4%
28.5%
1.9%
10333.0701980198
2,080
7874282.232
326528.532
20700
41303
United States
311591917
0.176
324432.68
313360.35
526672.802028767
2191193
0.0608604536
206652.802028767
49703.2253034664
0.6632161836
0.7289507784
6.4070026436
0.0596938687
12.8%
0.3923741671
2.3%
37.3%
49.2%
0.2%
0
39975.79
0
15498.216396343
131056.816993231
9720.5550076225
6642.0927412899
3759.3308902805
0
0
0
0
0
0
0
54840177.392
250347.502138357
133357
2080974.63700553
395798.591662653
0
0
0
269866.534623526
289913.37792246
0
1024541.63862333
0
0
27058.746
37407.1622816697
0
0
0
0
36388.5858918904
0
0
0
0.0189836981
0.3119217324
495.22
39975790
1996370.23593466
39975.79
39975.79
162917.681138487
3759.3308902805
0
19257.5472866235
131056.816993231
49696.3450076225
6642.0927412899
0.1934442195
78.8%
1.8%
0.0%
9.3%
63.4%
24.0%
3.2%
206652.802028767
41,597
91740868.524
5583390.876
761000
320020
n/a
JWEE total
820519962
0.2
910247.8
909320.4
1,494,275.52
4019732.0
0.1
595184.5
132737.6
0.7
0.7
16.8
0.0818276727
0.2
0.40
3.3%
38.6%
29.4%
4.5%
30198.7
17234.8
140922.9
2786.3
43218.0
267094.2
29020.0
9843.0
10823.2
8796.1
3232.4
54.2
8286.3
4939.3
18043.1
692.0
190967331.7
601568.0
344088.0
5557459.9
1258360.0
149396.8
369213.4
125705.9
385460.4
417461.5
434995.4
1634455.3
5092.2
3472.5
51258.1
215980.5
16839.2
0.0
75.0
0.0
106021.8
1122.8
111092.3
271456.7
3.0%
34.8%
26728.6
143167848.3
13760822.7
191142.7
191142.7
349175.2
22906.0
31960.6
92526.3
298064.4
191218.3
13375.6
32.1%
58.7%
3.8%
5.4%
15.5%
50.1%
32.1%
2.2%
595184.5
119,804
168298139.4
14406276.4
1863752.6
899091.0
JWEE 13
259274115
0.2
305507.359392609
320361.957203069
530,362.31
955397
0.1
235357.307536958
51811.4481450117
0.9
1.1
25.9
0.1722980136
0.3
0.44
5.4%
51.5%
17.1%
5.5%
21130.2821483703
15717.7457188996
71218.0250822159
2209.393156009
20381.7657448295
70130.6714422753
13713.8640244257
2896.335219354
4641.9076498278
8793.495632027
3028.0438466594
42.5539043189
51.5823841853
179.0212157018
1222.6203678588
0
58819176.425
188967.817387525
100548
2169241.71093535
674906.969093263
127599.008552934
233593.284021634
108194.106967674
101135.590982047
124890.700314804
103018.317858484
371027.268477159
5092.2
3472.5
12968.6695
119460.45899932
13627.8778493222
0
75.0394611727
0
54234.3821096419
922.29665316
100619.105415822
14403.9346789098
5.0%
47.1%
12975.1732841023
64412859.7241481
6725911.7331884
110275.446105495
110275.4
107122.6
16506.0
1453.2
46205.5
94820.9
89182.6
5148.3
46.9%
45.5%
7.0%
0.6%
19.6%
40.3%
37.9%
2.2%
235357.3
47,375
40000561.596
4209743.664
474593
295005
JWEE Europe (excluding the Russian Federation)
474445272
0.3
443802.116417409
455315.044821329
746,989.60
1576694
0.1
317438.598476779
68564.9075403108
0.7
0.8
24.8
0.1445350701
0.3
0.42
4.3%
41.5%
13.8%
7.6%
30198.7412245876
17234.7927882208
90754.9813882199
2786.3291813041
27719.7958076963
77002.343329776
17433.4249735581
3200.9278104332
7063.9184733572
8796.105007027
3232.3732584241
54.2439043189
8286.253847015
4939.3130622246
18043.0807995135
691.9736211031
129471979.09
244245.274867061
165387
2870675.54889773
776683.764270279
149396.774391569
369213.448160039
125705.881407763
115593.861886225
127548.1104464
137408.990109284
396898.087977159
5092.2
3472.5
14869.3435
178573.344304324
16839.2396760033
0
75.0394611727
0
69633.1743700836
1122.7657818723
111092.29768435
271456.725471208
4.1%
38.6%
24140.8496508363
99981058.3131281
11764452.4264041
140974.844582332
140974.8
125356.5
19146.6
31960.6
73268.7
107972.6
129463.9
6733.5
44.4%
39.5%
6.0%
10.1%
23.1%
34.0%
40.8%
2.1%
317438.6
63,897
66013024.392
6924422.916
660722.6
429551
JWEE 12
253875731
0.2
295496.449392609
312018.957203069
518,326.61
938048
0.1
229854.606111192
50677.99288369
0.9
1.1
26.2
0.1746249553
0.3
0.44
5.4%
51.1%
16.7%
5.6%
20386.9727958523
15717.7457188996
70530.1114131512
2187.2348826277
20275.0031549014
66576.8807396025
13553.2674038501
2733.1697517281
4641.9076498278
8743.500007027
3028.0438466594
27.5447793189
51.5823841853
179.0212157018
1222.6203678588
0
56373708.473
184489.418267287
99256
2121786.20605433
666013.080551599
124246.827280074
233593.284021634
98129.0766142263
98479.3055683632
124051.261228809
98744.741371384
354609.288477159
5092.2
3472.5
12968.6695
119159.231910871
13601.1971538612
0
75.0394611727
0
53605.1661672859
922.29665316
100619.105415822
14403.9346789098
4.9%
46.6%
12823.8632841023
63782099.7241481
6654911.7331884
108822.064810531
108822.1
103138.3
16441.0
1453.2
45355.5
91217.1
88334.0
4947.9
47.3%
44.9%
7.2%
0.6%
19.7%
39.7%
38.4%
2.2%
229854.6
46,267
39274193.664
4155650.208
461400
288472
2009
2009
Population
Rural Population
Roundwood and Fuelwood removals from forest and outside forest (1000m3)
Roundwood and Fuelwood supply from forest and outside forest including net trade (1000m3)
SWE + (S1 -S4) Total calculated domestic consumption of woody biomass (1000m3)
Total primary energy supply, TPES (ktoe)
Share of renewables (RES) in TPES from ktoe
Total wood energy generation (1000m3)
Total wood energy generation (ktoe) (calculation totals*heating value)
Average wood energy consumption (m3/capita)
Fuelwood consumption per rural inhabitant (m3/inhabitant)
Pellets consumption per inhabitant (kg/inhabitant)
S1/Net Annual Increments
Share of Total Roundwood supply directly used for energy purposes (%)
Share of energy use in calculated domestic consumption of woody biomass (%)
Share of woody biomass in TPES (%) (from ktoe)
Share of woody biomass in RES (%) (from ktoe)
Share of wood energy generated from black liquor (%) (heating value)
Imported wood fuel as share of wood energy (%)
1-S1-U1
1-S1-U2
1-S1-U3
1-S1-U4
1-S2-U1
1-S2-U2
1-S2-U3
1-S2-U4
1-S3-U1
1-S3-U2
1-S3-U3
1-S3-U4
1-S4-U1
1-S4-U2
1-S4-U3
1-S4-U4
share of rural population
Total domestic woody biomass supply (including cascaded use) (1000 m3)
Renewable energy supply (ktoe)
Total wood energy generation (1000 m3) (calculation totals*heating value)
Total woody biomass from forest (1000 m3)
Total woody biomass from outside forest (1000 m3)
Unspecified (1000 m3)
Chips and Particles (1000 m3)
Wood Residues (1000 m3)
Bark (1000 m3)
Unspecified solid co-products (1000 m3)
Black Liquor (1000 m3)
Crude Tall Oil (1000 m3)
Unspecified liquid co-products (1000 m3)
Wood Charcoal (1000 m3)
Wood Pellets (1000 m3)
Wood Briquettes (1000 m3)
Pyrolysis Oils (1000 m3)
Cellulose based Ethanol (1000 m3)
Wood based Biodiesel (1000 m3)
Non-hazardous Wood Waste (1000 m3)
Hazardous Wood Waste (1000 m3)
Unspecified Wood Waste (1000 m3)
Wood from Unknown Sources (1000 m3)
Wood fuel imports (1000 m3)
Total Fuelwood (1000 m3)
Total Pellets (mt)
Total S1 (1000 m3)
Total wood energy generation (ktoe)
Total primary energy supply, TPES (1000 m3)
Renewable energy supply, RES (1000 m3)
Share of woody biomass in TPES (%) (from 1000 m3)
Share of woody biomass in RES (%) (from 1000 m3)
Net Annual Increments
SWE
Country
S1
S2
S3
S4
U1
U2
U3
U4
% S1
% S2
% S3
% S4
% U1
% U2
% U3
% U4
Tot S-U
different numbers than if collected
Albania
3194417
0.48089
430
373.82
n/a
2085.42
0.3185487815
n/a
n/a
n/a
0.1911906246
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1536163.19113
330.572
664.308
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
293700
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
87312.36456
27813.247344
n/a
n/a
470
214
Armenia
3243729
0.35809
1502
1504.78
n/a
2601.206
0.0672691821
n/a
n/a
n/a
1.2913813271
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1161546.91761
0.9176
174.981
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.06
1500000
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
108907.292808
7326.104508
n/a
n/a
360
217
Austria
8365275
0.32447
18455.9263406115
26182.0963406115
39538.2723303327
31761.634
0.2789512341
20750.2723303327
4237.0111624938
2.480524828
1.8681645019
64.2350669882
0.3081375978
0.3053232847
0.5248148467
0.1315046522
0.471425239
0.1298068597
0.1074358296
4.8214440822
0
7001.69917647
987.4830334939
5172.2370130928
3629.1523302868
1188.5975650899
2766.2817678172
0
0
0
0
0
0
0
0
2714280.77925
16316.4223134436
8859.947
177395.183351291
57430.2486809439
6170.167597453
3756.3931041199
34946.9854371013
25397.6589831964
11209.8419006669
7687.5050920723
23027.1116742486
0
0
427.5307154
6957.4241679807
384.315998108
0
0
0
0
0
0
0
2229.322722
5070723
537344
7994.003654046
7994.003654046
12756.2686762867
0
0
5177.0584571749
3629.1523302868
8190.2967415599
3753.764801311
0.385248132
61.5%
0.0%
0.0%
24.9%
17.5%
39.5%
18.1%
20750.2723303327
4176.8026316744
1329796.092312
370948.260996
0.1334002892
0.4782208249
25136
18788
Belarus
9576045
0.25305
13218
11107.024
n/a
26763.98
0.0501580856
n/a
n/a
n/a
2.0555408614
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2423218.18725
434.87315
1342.43
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4.01484
4981024
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
1120554.31464
56204.85924
n/a
n/a
22809
6568
Belgium
10796492
0.02589
8670
10899.5
18344.5616623484
57101.707
0.0383058076
2522.5616623484
518.5447241126
0.2336464161
6.5415436994
20.8401025074
n/a
0
0.1375100539
0.0088922743
0.2321390647
0.2374464729
0.197517471
0
0
0
0
684.26729784
1204.373501199
0
10.8681055156
0
0
0
0
0
0
623.0527577938
0
279521.17788
6577.4230952381
2187.327
21710.4305091445
0
0
0
91.5737524328
0
0
5273.4923549044
5155.06515
0
0
0
5940.5085764271
0
0
0
0
0
0
0
5249.7906753802
498.25
1828500
225000
0
0
1899.5089045546
0
623.0527577938
684.26729784
1204.373501199
623.0527577938
10.8681055156
0
75.3%
0.0%
24.7%
27.1%
47.7%
24.7%
0.4%
2522.5616623484
507.7640438702
2390734.268676
91579.006836
0.0090810722
0.2370677654
5289
15822
Bosnia and Herzegovina
3942701
0.51379
3708.53
3176.62
n/a
6034.075
0.1192787627
n/a
n/a
n/a
0.4273837706
5.072664653
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2025720.34679
473.7530666667
719.737
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4.41
865760
20000
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
252634.6521
30133.948716
n/a
n/a
…
627
Bulgaria
7585130
0.28542
5754
6031.57
ERROR:#N/A
17492.305
0.0621737387
ERROR:#N/A
ERROR:#N/A
n/a
1.063240414
n/a
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
n/a
n/a
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2164947.8046
3.2656
1087.562
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
0.6
2301860
0
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
n/a
732367.82574
45534.045816
ERROR:#N/A
ERROR:#N/A
14120
2002
Canada
33739859
0.19418
116306
118242.1
192915.059952038
250745.622
0.1755924456
47875.0599520384
9634.8349604008
1.4189466516
0.4614288592
n/a
0.0266966976
0.087825337
0.2481665245
0.0384322778
0.2188720457
0
0.0038051127
0
0
10384.6522781775
0
0
37490.4076738609
0
0
0
0
0
0
0
0
0
0
6551605.82062
93099.64
44029.037
403391.27012206
87500.21569616
0
0
0
0
0
315891.0544259
0
0
0
0
0
0
0
0
0
0
0
0
0
182.17
3023100
0
10384.6522781775
10384.6522781775
37490.4076738609
0
0
0
37490.4076738609
10384.6522781775
0
0.2169115253
78.3%
0.0%
0.0%
0.0%
78.3%
21.7%
0.0%
47875.0599520384
9636.7253988726
10498217.701896
1843407.721116
0.0384247385
0.2188291095
442030
145040
Cyprus
800011
0.2968
13.879
14.823
435.3198832422
2537.126
0.0385538598
67.3198832422
8.5842346154
0.084148697
0.0181811853
0.0449993813
0.0748719206
0.3242093635
0.1546446322
0.0053409864
0.1385331176
0
0.8835941647
0
0
4.8057553957
0
0
1.6163069544
27.8053772553
33.0924436368
0
0
0
0
0
0
0
0
237443.2648
69.73112
97.816
359.4047348768
22.2064329046
18.286462037
0
0
14.8312299836
0
0
0
0
0
303.3474
0.7332099516
0
0
0
0
0
0
0
0
59.483456
4317
36
4.8057553957
4.8057553957
62.5141278465
0
0
0
1.6163069544
32.611132651
33.0924436368
0.0713868647
92.9%
0.0%
0.0%
0.0%
2.4%
48.4%
49.2%
67.3198832422
13.5507554317
106224.391368
4095.360288
0.0033834483
0.0877590028
40
368
Czech Republic
10487178
0.26459
17166
14463
15339.3731544426
42072.125
0.0576855816
8356.3731544426
1725.2591499833
0.796818091
0.6245489708
2.669927029
0.104224065
0.3748919553
0.5447662737
0.0399800681
0.6930686496
0.1617192381
0.0179221293
0
400.479616307
5021.582733813
0
865.7074340528
1829.9930920924
53.958
184.6522781775
0
0
0
0
0
0
0
0
2774802.42702
3563.6638095238
2426.955
72233.1500915
17518.6326918
14022.9885676
14144.22478
8143.0322662
4182.6493278
992.2447825
0
11681.49
0
0
0
468.4396913
1079.4479843
0
0
0
0
0
0
0
149.764
1733000
28000
5422.0623501199
5422.0623501199
2934.3108043227
0
0
865.7074340528
2230.4727083994
5075.5407338129
184.6522781775
0.6488535457
35.1%
0.0%
0.0%
10.4%
26.7%
60.7%
2.2%
8356.3731544426
1682.0464246008
1761475.7295
101611.75194
0.0410071787
0.7108739758
22669
6983
Denmark
5523094
0.13134
2786
2188.89
n/a
18353.144
0.1801648263
n/a
n/a
n/a
1.4512067901
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
725403.16596
1386.3436
3306.591
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
60.54
1052710
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
768409.432992
138440.351988
n/a
n/a
5176
7453
Estonia
1340271
0.30513
5150
4129.6
8827.9687992861
4749.296
0.1510470183
4322.9687992861
874.354644479
3.225443809
2.8019090212
33.6872169882
0.1735966293
0.4202740094
0.4896900859
0.183220058
1.2130001637
0.0129006421
0.0331899689
8.5611510791
48.345323741
1653.4772182254
25.1798561151
1355.3959165468
540.3946632909
668.3466290647
7.194291223
16.07375
0
0
0
0
0
0
0
408956.89023
4362.6343809524
717.367
36607.4802550485
14623.714000042
0
0
10527.546503754
8894.494722726
1221.8502251705
0
472.26
0
0
0
712.028330776
0
0
0
0
155.58647258
0
0
0
143.4792
1145860
45150
1735.5635491607
1735.5635491607
2571.3315001254
16.07375
0
1380.0308176259
588.7399870319
2321.8238472902
32.3741473381
0.4014749191
59.5%
0.4%
0.0%
31.9%
13.6%
53.7%
0.7%
4322.9687992861
870.1662884255
198843.524928
30034.721556
0.1841019478
1.218838676
11361
4505
Finland
5338870
0.1491
45508.7523144861
49619.5093144861
54406.6617013705
33245.245
0.2397147021
29805.6617013705
6227.1559788481
5.5827659601
12.1735661829
39.8554652951
0.1289258164
0.2048401526
0.5478311069
0.1804635029
0.7528261777
0.4224133381
0.0342302038
3949.8372629176
675.8908449961
5538.3397524078
0
3338.8404055866
15165.6738937449
746.9782752414
0
352.7027870099
37.3984794662
0
0
0
0
0
0
796025.517
32568.9774554939
7969.374
260718.566522413
85641.5897523561
0
0
18717
8772.9767700568
32239
0
110131
0
0
0
1441
0
0
0
0
3776
0
0
0
1020.253875022
9690469.31448612
212783.148
10164.0678603216
10164.0678603216
19251.4925745729
390.1012664761
0
7641.3804555141
15878.9632182072
6285.3180276492
0
0.341011314
64.6%
1.3%
0.0%
25.6%
53.3%
21.1%
0.0%
29805.6617013705
5999.5533673598
1391911.91766
333661.750632
0.1873096733
0.7813858377
89587
24601
France
64540518
0.14749
54501.561
51542.929
90175.4533168775
253489.154
0.0754638757
41103.4533168775
9860.3215943402
0.6368627738
1.9410623778
4.4158306879
0.1619906311
0.4584998816
0.4558164312
0.0326391682
0.432513807
0.0812384365
0.0088033966
534.1176470588
1162.7450980392
21753.2111563181
182.3529411765
401.4134231579
6996.2018059855
7960.1415377289
4.2136
497.555625
0
1611.5004824125
0
0
0
0
0
9519080.99982
37295.6748495238
19129.274
412831.944511834
150508.577678673
77992.9001071714
19365.46432736
0
0
0
102752.205403325
33537.8217
0
0
2581.983
5314.957471376
363.346264904
0
0
0
20414.6885590243
0
0
0
361.85
18477130
285000
23632.4268425926
23632.4268425926
15361.9703668724
2109.0561074125
0
1433.0866952167
8158.9469040248
31324.8531764595
186.5665411765
0.5749499114
37.4%
5.1%
0.0%
3.5%
19.8%
76.2%
0.5%
41103.4533168775
8273.6751234766
10613083.899672
800904.443832
0.0388983964
0.5154571781
102456
49072
Germany
81902306
0.26151
61604.61
64816.05
129587.914527538
317104.31
0.0870316711
48653.9145275378
10349.9605388047
0.5940481643
0.4343977881
19.1081310946
n/a
0.3957661721
0.3754510188
0.0308841602
0.3548611647
0
0.0260250385
5700.4
0
19951.6
0
6803.3993617021
3220.3046808511
3154.2944680851
0
7584.8209258343
0
1463.0420779221
0
508.4011016744
0
267.6519114688
0
21418272.04206
14321.2171850883
27598.118
433332.147838675
0
0
216141.616773694
0
0
0
125149.299071339
0
0
0
0
0
0
0
0
0
0
0
84512.6753669734
7528.5566266683
1266.22
9304050
1565000
25652
25652
13177.9985106383
9047.8630037564
776.0530131432
20597.0213892108
3220.3046808511
24836.588457476
0
0.5272340417
27.1%
18.6%
1.6%
42.3%
6.6%
51.0%
0.0%
48653.9145275378
9793.5002974788
13276523.25108
1155478.004424
0.0308841602
0.3548611647
122000
80934
Iceland
318499
0.06584
0
1.6
n/a
5384.195
0.8175491415
n/a
n/a
n/a
0.00619934
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
20969.97416
7.3328
4401.844
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1.81
130
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
225425.47626
184296.404592
n/a
n/a
14
190
Ireland
4458942
0.38059
2405
2313.5
3067.1503847293
14402.127
0.0457733083
701.1503847293
148.3138029581
0.1572459083
0.0826267682
11.2134223769
0.0370636905
0.0632301593
0.228599937
0.0097995175
0.2140880322
0
0.0858703087
0
0
88.7290167866
57.5539568345
0
328.5864111082
113.556
0
0
112.725
0
0
0
0
0
0
1697028.73578
1523.4253714286
659.233
6209.60230225
1232.5681594
0
0
666.7991682
646.5931328
1607.43654765
0
0
0
0
28.2
936.8793826
0
0
0
0
1091.1259116
0
0
0
60.208
140220
50000
146.2829736211
146.2829736211
442.1424111082
112.725
0
0
441.3114111082
202.2850167866
57.5539568345
0.208632808
63.1%
16.1%
0.0%
0.0%
62.9%
28.9%
8.2%
701.1503847293
141.1338957637
602988.253236
27600.767244
0.0102980485
0.2249793365
4485
2366
Italy
60192698
0.3164
26948
29624
45220.4497002398
164858.378
0.0971074336
11925.4497002398
2432.3688357696
0.1981212023
0.3333688695
20.2848524916
n/a
0.2770130641
0.2637180696
0.014560755
0.1499448032
0
0.1305872767
5035.9712230216
1007.1942446043
1812.9496402878
350.1199040767
0
1127.0983213429
1510.7913669065
0
1081.325
0
0
0
0
0
0
0
19044969.6472
8962.8204761905
16008.974
101838.418416
0
0
69145.0531388
0
0
0
22226.63894
0
0
0
0
0
0
0
0
0
0
0
10466.7263372
0
1557.312
6349000
1221000
8206.2350119904
8206.2350119904
2637.8896882494
1081.325
0
6117.2962230216
2134.2925659472
3323.7410071942
350.1199040767
0.6881279296
22.1%
9.1%
0.0%
51.3%
17.9%
27.9%
2.9%
11925.4497002398
2400.462456536
6902290.570104
670263.723432
0.0147542931
0.1519378341
31352
33295
Latvia
2254834
0.3228
10442.31
7020.05
n/a
4221.013
0.3710009896
n/a
n/a
n/a
0.9566944231
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
727860.4152
1299.53
1566
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
12.78
696340
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
176725.372284
65565.288
n/a
n/a
25280
2794
Liechtenstein
35911
0.85693
25
17
52.3381294964
116.4668099742
0.0734893604
32.3381294964
6.5080344743
0.9005076299
0.454941117
n/a
n/a
0.8823529412
0.6178694158
0.0558898403
0.7605160803
0
0
0
0
15
0
0
2.3381294964
0
15
0
0
0
0
0
0
0
0
30773.21323
17.32
8.5590713672
272.478387369
126.388751427
0
0
126.388751427
19.700884515
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
14000
0
15
15
17.3381294964
0
0
0
2.3381294964
15
15
0.4638487208
53.6%
0.0%
0.0%
0.0%
7.2%
46.4%
46.4%
32.3381294964
6.5093114073
4876.2324
358.3512
0.0558788764
0.7603668897
…
20
Lithuania
3339456
0.33016
5612.132
5041.54034044
7185.3642406622
8634.709
0.1218057262
3392.3642406622
693.3922267818
1.0158433711
1.5618137805
2.6651047356
0.1747368518
0.3752992544
0.4721214022
0.0790815046
0.6492429142
0
0.0856968153
187.0503597122
0
1661.8705035971
43.1654676259
995.0036457975
0
361.112583841
144.1616800884
0
0
0
0
0
0
0
0
1102554.79296
2833.0027238095
1051.757
29030.9457509
14083.6066738
1293.1862656
565.7689912
3940.176903
4425.1217526
4723.0851647
0
0
0
0
0
0
0
0
0
0
0
0
0
0
290.7148119
1721985.26944
8900
1892.0863309353
1892.0863309353
1500.2779097269
0
0
1182.0540055097
0
2022.9830874381
187.3271477143
0.5577485779
44.2%
0.0%
0.0%
34.8%
0.0%
59.6%
5.5%
3392.3642406622
682.8457796808
361517.996412
44034.962076
0.0803029062
0.6592703702
10870
3793
Luxembourg
497783
0.14814
349
761
1946.4835760336
3951.878
0.0310872451
387.4835760336
77.9949819667
0.7784186604
0.2305348146
n/a
n/a
0.3436724996
0.1990685053
0.0197364902
0.6348742108
0
0.0154845273
0
0
259.6666666667
1.8681055156
13.2685851319
110.3741007194
2.306118
0
0
0
0
0
0
0
0
0
73741.57362
83.76
122.853
3265.4939049799
0
0
2203.670220724
1041.8029791886
0
0
0
0
0
0
0
20.0207050673
0
0
0
0
0
0
0
0
6
17000
0
261.5347721823
261.5347721823
125.9488038513
0
0
13.2685851319
110.3741007194
261.9727846667
1.8681055156
0.674957052
32.5%
0.0%
0.0%
3.4%
28.5%
67.6%
0.5%
387.4835760336
77.9962014157
165457.228104
5143.609404
0.0197361816
0.6348642847
650
1559
Netherlands
16530388
0.17139
1016.13
836.93
n/a
78174.849
0.0402639601
n/a
n/a
n/a
0.0951240305
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2833143.19932
2503.0664
3147.629
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
280.04
269500
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
3273024.577932
131784.930972
n/a
n/a
2239
13782
Norway
4828726
0.20589
9235
9433
11584.3380623501
28157.112
0.4316263685
4680.3380623501
967.414630429
0.969269754
2.6986891094
9.5263222639
0.1320596586
0.2865088402
0.4040229176
0.0334587158
0.0775177752
0.102193587
0.0758321291
0
0
2702.6378896883
0
0
1507.6738609113
107.916
0
0
0
0
0
362.1103117506
0
0
0
994186.39614
6102.4066666667
12153.352
40503.7157468
22772.2018958
0
0
0
0
0
8789.625399
4139.22
0
0
0
936.8793826
814.677724
0
0
0
0
0
0
3051.1113454
354.92
2683000
46000
2702.6378896883
2702.6378896883
1615.5898609113
0
362.1103117506
362.1103117506
1507.6738609113
2810.5538896882
0
0.5774450165
34.5%
0.0%
7.7%
7.7%
32.2%
60.1%
0.0%
4680.3380623501
942.1008083529
1178881.965216
508836.541536
0.0343577363
0.0796006427
23256
6904
Poland
38151602
0.39042
34629.18
35428.56
n/a
93986.673
0.0666604615
n/a
n/a
n/a
0.2719469371
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
14895148.45284
7363.2804
6265.195
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
371.34
4050690
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
3935034.025164
262311.18426
n/a
n/a
67595
30355
Romania
21480401
0.42533
12556.5
12729.63
n/a
34789.327
0.1514074705
n/a
n/a
n/a
0.4297371625
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9136258.95733
2338.1104
5267.364
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
38.41
3926190
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
1456559.542836
220533.995952
n/a
n/a
27334
7567
Russian Federation
141909244
0.26833
151400
129170.6
124191.532
646914.985
0.0282570607
74758.532
15002.1915323732
0.5268052305
0.9955904931
0.3216985639
0.0506018329
0.394046947
0.6019615895
0.0232612852
0.8232025783
0.0579163018
0.0000802584
13100.7194244604
0
37798.5611510791
0
18568.345323741
4082.3741007194
98.532
1110
0
0
0
0
0
0
0
0
38078507.44252
29749.2826
18279.916
628111.7550774
318487.5299748
0
110385.5713902
156455.3321022
0
0
0
36377.91
0
0
5550
855.4116102
0
0
0
0
0
0
0
0
6
37910600
45652
50899.2805755396
50899.2805755396
23859.2514244604
0
0
31669.0647482014
4082.3741007194
37897.0931510791
1110
0.680849118
31.9%
0.0%
0.0%
42.4%
5.5%
50.7%
1.5%
74758.532
15048.0739831671
27085036.59198
765343.523088
0.0231903602
0.8206925859
848841
49433
Serbia
7320807
0.43939
7051
7120
11022.8864230525
15243.664
0.1297398709
7952.8864230525
1520.5344000506
1.0863401293
1.5537714083
0.4097908878
1.5895797563
1.0367453913
0.7214885573
0.1050160226
0.8094352326
0
0.0023269539
0
9.6533333333
7266.4739936007
105.4998589364
0
80.7372354056
36.9396122782
424.3573894982
0
0
29.225
0
0
0
0
0
3216689.38773
902.3
1977.711
63661.73426132
58152.9698812
505.150885
0
909.271593
2121.633717
357.2081217
0
0
0
0
676.8
56.212762956
599.602804864
0
0
0
262.6784602
0
20.2060354
0
18.506
4998000
3000
7381.6271858704
7381.6271858704
542.0342371821
29.225
0
0
90.390568739
7332.6386058789
529.8572484346
0.9281695718
6.8%
0.4%
0.0%
0.0%
1.1%
92.2%
6.7%
7952.8864230525
1600.8289632255
638221.724352
82802.804148
0.0997486169
0.7688354871
5232
3070
Slovak Republic
5418590
0.45033
10143
7933.72
8824.2075990916
16722.558
0.0725388424
3208.2075990916
670.0891070458
0.5920742479
0.2039707635
13.8412391415
0.0504256073
0.0752638488
0.3635689169
0.0386171221
0.5323647413
0.487627768
0.0268355452
110.3117505995
0
462.829736211
23.9808153477
158.273381295
2111.2129541995
134.4535583693
127.8204030695
4.175
2.0875
62.625
10.4375
0
0
0
0
2440153.6347
4247.1784952381
1213.035
28055.290733792
4829.2424606
202.060354
0
3616.8803366
969.8896992
2024.1793563
0
13680.5388
258.3
0
253.8
1330.368723292
122.2016586
0
0
0
767.8293452
0
0
0
86.094
497720
75000
597.1223021583
597.1223021583
2531.7602969333
79.325
0
272.7601318945
2113.3004541995
659.9082945803
162.2387184173
0.1861233364
78.9%
2.5%
0.0%
8.5%
65.9%
20.6%
5.1%
3208.2075990916
645.7770640077
700140.058344
50787.34938
0.0400709692
0.5524070674
12916
5616
Slovenia
2039669
0.50495
3200.22
2694.597
3908.0276190476
7090.512
0.1418302374
1852.0276190476
460.884933475
0.9080040041
0.7965495847
24.96826691
0.1354264292
0.4648561547
0.473903411
0.0525762995
0.3706988049
0
0.1056281343
0
0
1252.6
0
127.8380952381
415.0714285714
35.0895238095
21.4285714286
0
0
0
0
0
0
0
0
1029930.86155
945.0826840476
1005.649
19296.3303947292
11118.4922150624
3055.15255248
0
0
0
0
4991.3352765788
0
0
0
56.4
0
74.950350608
0
0
0
0
0
0
0
195.626222
820391
50927
1252.6
1252.6
599.4276190476
0
0
127.8380952381
415.0714285714
1287.6895238095
21.4285714286
0.6763398057
32.4%
0.0%
0.0%
6.9%
22.4%
69.5%
1.2%
1852.0276190476
372.7928824271
296865.556416
42104.512332
0.0650002332
0.4582960193
8245
2056
Sweden
9298514
0.15318
63500
67504.73
90148.3869727578
45406.513
0.3483816958
48593.3869727578
10039.6432234812
5.2259303984
4.4973119703
206.2695179036
n/a
0.2338587347
0.5390377865
0.2154166025
0.6183350191
0.3750903354
0.0364794494
10330.9352517986
0
4968.824940048
486.8105515588
4328.9311223022
25867.6981870504
1292.83368
311.17824
1006.175
0
0
0
0
0
0
0
1424346.37452
51646.7602476191
15818.798
420339.782480712
0
0
133016.3310382
0
0
0
68902.580714
157665.39
221.4
0
620.4
50174.371665712
0
0
0
0
4869.6545314
0
4869.6545314
0
1772.66
6405730
1918000
15786.5707434053
15786.5707434053
31800.6412293525
1006.175
0
15666.0413741007
25867.6981870504
6261.658620048
797.9887915588
0.3248707638
65.4%
2.1%
0.0%
32.2%
53.2%
12.9%
1.6%
48593.3869727578
9781.3167633994
1901079.886284
662301.434664
0.2211057965
0.6346653661
92564
41555
Switzerland
7743831
0.26377
6242.3540197412
5649.3540197412
10038.8877644567
26973.503
0.178253377
4207.8877644567
1073.8223293417
0.543385795
0.9925407052
16.3465465363
0.3316494731
0.4142862498
0.4191587617
0.0314012509
0.1761607631
0
0.0415370347
522.8370745966
73.114171109
1350.4908429338
394.007602042
150.3920352298
630.7822845741
316.8803347979
48.7074912281
574.8812199561
138.6478630173
0.1496552861
6.9971896859
0
0
0
0
2042590.30287
2611.1003921184
4808.118
44958.7932848779
22530.6329001725
3806.81706936
0
0
7565.6978165203
806.6929760614
0
0
0
0
479.4
2371.8955431992
179.8808414592
0
26.6633914422
0
6884.2217074374
306.8910392257
0
0
174.78318
2027354.01974118
126584.893810813
2340.4496906813
2340.4496906813
1146.7621458299
720.6759279454
0
1248.1103297825
842.5443187003
1667.5208330178
449.7122829561
0.5562053509
27.3%
17.1%
0.0%
29.7%
20.0%
39.6%
10.7%
4207.8877644567
847.0017361016
1129326.623604
201306.284424
0.039810266
0.2233352695
6944
5831
Turkey
72039206
0.30351
20930
21897.87
n/a
97660.685
0.1015279997
n/a
n/a
n/a
0.2330706016
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
21864619.41306
6477.1439047619
9915.294
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
179
5096000
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
4088857.55958
415133.529192
n/a
n/a
…
26973
Ukraine
45872976
0.31213
15404.2
12141.22
n/a
112338.459
0.0219241213
n/a
n/a
n/a
0.5046411831
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
14318331.99888
1521.244
2462.922
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.66
7225620
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
4703386.601412
103117.618296
n/a
n/a
21325
7072
United Kingdom
61801570
0.20362
9128
9036
44337.1731296216
197073.414
0.0316672598
5004.1731296216
1022.7003715176
0.0809716182
0.0745388859
2.4433036248
0.0650336121
0.2231946183
0.1128663101
0.0051112184
0.161403874
0
0.088683982
992.8057553957
74.3405275779
949.6402877698
0
1174.3160516839
325.426235012
349.9567721823
0
467.6
296.425
373.6625
0
0
0
0
0
12584035.6834
11910.1328571429
6240.775
42818.4191547
9981.7814876
7011.4942838
0
7213.5546378
2727.814779
79.3795826
0
0
701.1
0
1260
2831.0050909
0
0
0
0
11012.289293
0
0
0
443.79
938000
151000
2016.7865707434
2016.7865707434
1849.6990588782
1137.6875
0
2634.7218070796
696.1917625899
1673.259559952
0
0.4030209424
37.0%
22.7%
0.0%
52.7%
13.9%
33.4%
0.0%
5004.1731296216
1007.285261823
8251069.697352
261288.7677
0.0051894385
0.1638739374
20700
39333
United States
307007000
0.17714
332527.68
323479.15
579233.470901145
2164971.703
0.0543789528
266018.470901145
63878.8776009111
0.8664899201
0.7412279011
5.8072198447
0.0503851974
0.1043095327
0.4592594943
0.0247331665
0.4548297688
0.3667513923
0.0016398109
0
0
33741.9589756038
0
16384.2840954866
185691.224641888
19346.0970468769
7588.3429687058
3266.5631725845
0
0
0
0
0
0
0
54383219.98
380909.554923307
117728.894
2674480.84739495
334077.696587935
0
0
12921.4537997482
721896.204115945
532491.472421449
0
980869.5744
0
0
27199.182
33406.4419852799
0
0
0
0
31618.8220845881
0
0
0
436.22
40310360
1782857.14285714
33741.9589756038
33741.9589756038
229009.948752957
3266.5631725845
0
19650.847268071
185691.224641888
53088.0560224807
7588.3429687058
0.1268406621
86.1%
1.2%
0.0%
7.4%
69.8%
20.0%
2.9%
266018.470901145
53546.6056370585
90643035.261204
4929073.333992
0.0295056409
0.5425930324
761000
313215
JWEE total
799463662
0.2
838837
821446
1,297,416.22
4082578
0.1
588297
131796
0.7
0.9
10.5
0.0636626282
0.2
0.45
2.9%
44.5%
0.2494979012
0.0
40478
3452
155257
2658
60522
254868
37507
12797
14852
587
3540
17
871
0
891
0
176291111
617520
266064
5518032
1123138
114078
568724
259318
787635
587752
345773
1376737
1181
0
39437
113755
3618
0
27
0
80853
307
99869
15829
11131
152087410
8377234
201845
201845.1
365694.1
18996.8
1761.2
116722.7
258907.4
197194.4
15472.8
34.3%
62.2%
3.2%
0.3%
19.8%
44.0%
33.5%
2.6%
588297.2
118417.8
170929381
11139559
3.2%
49.5%
2205593
709119
JWEE 13
259526686
0.2
282103.302674839
295188.308674839
487,436.83
965001.638
0.1
212287.825228151
45697.0166589388
0.8
1.0
19.2
0.1014253861
0.3
0.44
4.4%
44.7%
0.1766853015
0.0
22146.066186449
1995.7439750556
70848.9113246081
2239.5568649053
21668.9094744203
58882.8378544634
15359.8328228378
3737.0799066785
10487.9105578002
587.2838424835
3540.2047156207
17.4346896859
508.4011016744
0
267.6519114688
0
59193619.1571
174441.762971144
95500.701
1913242.69347645
401448.309648912
98762.0293113014
374483.475464098
67112.2941518899
48217.0961277571
48323.7384849783
309482.925557315
338041.862174249
1180.8
0
6687.8611154
71434.8687230349
1724.2979185432
0
26.6633914422
0
49078.4878078617
306.8910392257
89402.5359337734
7528.5566266683
7694.797455022
58391104.3342273
4974675.04181081
97230.2783510179
97230.3
99648.7
14632.8
776.1
54811.3
61465.9
90016.6
5994.1
45.8%
46.9%
6.9%
0.4%
25.8%
29.0%
42.4%
2.8%
212287.8
42731.2
40402688.579784
3998423.349468
4.7%
47.8%
490955
275149
JWEE Europe (excluding the Russian Federation)
350049635
0.2
354560.434674839
368034.949015279
592,044.74
1266739.55380997
0.1
247132.735400943
52836.8639230016
0.7
0.9
18.7
0.0993406198
0.3
0.42
3.9%
38.3%
0.1625052671
0.0
27377.6489202619
3451.7631597079
83456.7626435529
2656.1539872074
25556.0151835254
64984.3353220769
18059.65128465
4098.9562616829
11585.3093078002
587.2838424835
3540.2047156207
17.4346896859
870.511413425
0
890.7046692626
0
83755642.12814
206777.274123525
129932.139071367
2212173.81872823
470572.853661781
114078.204144501
456134.852153374
88899.2093495151
65739.0628153981
55260.9186573488
345772.68225122
359489.897324249
1180.8
0
6687.8611154
79472.7039990707
3618.4236268432
0
26.6633914422
0
49234.0742804417
306.8910392257
99869.2622709734
15829.4586474485
10683.237466922
73849449.6036673
6548725.04181081
116942.32871073
116942.3
112699.0
15730.2
1761.2
65389.5
69023.4
105947.3
6772.5
47.3%
45.6%
6.4%
0.7%
26.5%
27.9%
42.9%
2.7%
247132.7
49745.1
53035851.638916
5439998.79864
4.2%
40.7%
595102
344912
JWEE 12
254108096
0.2
271960.302674839
287254.588674839
478,612.62
948279.08
0.1
209079.61762906
45026.927551893
0.8
1.0
19.3
0.1028033347
0.3
0.44
4.4%
44.6%
17.2%
3.6%
22035.7544358495
1995.7439750556
70386.081588397
2215.5760495576
21510.6360931254
56771.6249002639
15225.3792644685
3609.2595036089
10483.7355578002
585.1963424835
3477.5797156207
6.9971896859
508.4011016744
0
267.6519114688
0
56753465.5224
170194.584475906
94287.666
1885187.40274266
396619.067188312
98559.9689573014
374483.475464098
63495.4138152899
47247.2064285571
46299.5591286783
309482.925557315
324361.323374249
922.5
0
6434.0611154
70104.4999997429
1602.0962599432
0
26.6633914422
0
48310.6584626617
306.8910392257
89402.5359337734
7528.5566266683
7608.703455022
57893384.3342273
4899675.04181081
96633.1560488596
96633.2
97116.9
14553.5
776.1
54538.5
59352.6
89356.7
5831.8
46.2%
46.4%
7.0%
0.4%
26.1%
28.4%
42.7%
2.8%
209079.6
42,085
39702548.52144
3947636.000088
4.7%
47.8%
478039
269533
2007
2007
Population
Rural Population
Roundwood and Fuelwood removals from forest and outside forest (1000m3)
Roundwood and Fuelwood supply from forest and outside forest including net trade (1000m3)
SWE + (S1 -S4) Total calculated domestic consumption of woody biomass (1000m3)
Total primary energy supply, TPES (ktoe)
Share of renewables (RES) in TPES from ktoe
Total wood energy generation (1000m3)
Total wood energy generation (from TJ converted to ktoe) (calculation totals*heating value)
Average wood energy consumption (m3/capita)
Fuelwood consumption per rural inhabitant (m3/inhabitant)
Pellets consumption per inhabitant (kg/inhabitant)
S1/Net Annual Increments
Share of Total Roundwood supply directly used for energy purposes (%)
Share of energy use in calculated domestic consumption of woody biomass (%)
Share of woody biomass in TPES (%) (from TJ)
Share of woody biomass in RES (%) (from TJ)
Share of wood energy generated from black liquor (%) (heating value)
Imported wood fuel as share of wood energy (%)
1-S1-U1
1-S1-U2
1-S1-U3
1-S1-U4
1-S2-U1
1-S2-U2
1-S2-U3
1-S2-U4
1-S3-U1
1-S3-U2
1-S3-U3
1-S3-U4
1-S4-U1
1-S4-U2
1-S4-U3
1-S4-U4
share of rural population
Total domestic woody biomass supply (including cascaded use) (1000 m3)
Renewable energy supply (ktoe)
Total wood energy generation (TJ) (calculation totals*heating value)
Total woody biomass from forest (TJ)
Total woody biomass from outside forest (TJ)
Unspecified (TJ)
Chips and Particles (TJ)
Wood Residues (TJ)
Bark (TJ)
Unspecified solid co-products (TJ)
Black Liquor (TJ)
Crude Tall Oil (TJ)
Unspecified liquid co-products (TJ)
Wood Charcoal (TJ)
Wood Pellets (TJ)
Wood Briquettes (TJ)
Pyrolysis Oils (TJ)
Cellulose based Ethanol (TJ)
Wood based Biodiesel (TJ)
Non-hazardous Wood Waste (TJ)
Hazardous Wood Waste (TJ)
Unspecified Wood Waste (TJ)
Wood from Unknown Sources (TJ)
Share of woody biomass in TPES (%) (from m3)
Share of woody biomass in RES (%) (from m3)
Wood fuel imports (1000 m3)
Total Fuelwood (1000 m3)
Total Pellets (mt)
Total S1 (1000 m3)
Total wood energy generation (ktoe)
Total primary energy supply, TPES (TJ)
Renewable energy supply, RES (TJ)
Net Annual Increments
SWE
Country
S1
S2
S3
S4
U1
U2
U3
U4
% S1
% S2
% S3
% S4
% U1
% U2
% U3
% U4
Tot S-U
Albania
3161337
0.48089
430
373.82
n/a
2050.135
0.2248827516
n/a
n/a
n/a
0.1931912294
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1520255.34993
337.1
461.04
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
0
293700
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
85835.05218
19302.82272
470
214
Armenia
3226520
0.35809
46
44.6
n/a
2845.324
0.0564501617
n/a
n/a
n/a
0.034620508
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1155384.5468
0.12
160.619
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
0
40000
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
119128.025232
6724.796292
360
260
Austria
8300788
0.32447
22783
30703
39339.8153094105
33389.497
0.2408957523
17603.8153094105
3622.3939131128
2.120740261
1.8597611051
48.4291370892
0.2631066301
0.2065956675
0.4474808834
0.1084890232
0.4503567294
0.1652193419
0.0245157082
4.1189393858
403.4254902325
5477.0217948189
458.5405547901
3697.2599343696
5441.5940506282
1032.5343067142
1011.9073605593
0
0
58.8602346866
18.5526432252
0
0
0
0
2693356.68236
16311.8014285714
8043.388
151662.388354209
49037.4407607099
975.1135315698
3433.9354406998
15456.7431408777
11542.9791079731
6850.2991880819
26928.3905415998
25057.56
0
0
600.563260762
9013.7364124163
2016.3060825715
0
0
0
749.3208869473
0
0
0
0.1061248477
0.4405426274
431.57
5009000
402000
6343.1067792274
6343.1067792274
11183.2956522713
77.4128779118
0
3701.3788737554
5845.0195408608
6568.4163362197
1489.0005585746
0.3603256833
63.5%
0.4%
0.0%
21.0%
33.2%
37.3%
8.5%
17603.8153094105
3543.4552829638
1397951.460396
336760.568784
25136
21736
Belarus
9702116
0.25305
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
28057.753
0.0456910787
ERROR:#N/A
ERROR:#N/A
n/a
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2455120.4538
ERROR:#N/A
1281.989
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
n/a
n/a
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
n/a
1174722.002604
53674.315452
22809
4893
Belgium
10625700
0.02589
4945
7413.65
n/a
57025.137
0.026717095
n/a
n/a
n/a
2.5687081446
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
275099.373
n/a
1523.546
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
399.87
706650
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
2387528.435916
63787.823928
5289
15085
Bosnia and Herzegovina
3934816
0.51379
3753
3361.15
n/a
5304.575
0.0994980748
n/a
n/a
n/a
0.5190513094
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2021669.11264
n/a
527.795
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
1.83
1049350
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
222091.9461
22097.72106
…
650
Bulgaria
7659764
0.28542
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
20115.636
0.0478590883
ERROR:#N/A
ERROR:#N/A
n/a
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
2186249.84088
ERROR:#N/A
962.716
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
n/a
n/a
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
ERROR:#N/A
n/a
842201.448048
40306.993488
14120
2849
Canada
32931956
0.19418
198236
199893.52
241867.016972706
271731.208
0.1617114476
77816.0169727057
37351.6252133957
2.3629333457
0.467028522
7.4699480347
n/a
0
0.3217305855
0.0576434056
0.3564584106
0.9941533987
0.0023455325
0
0
0
0
0
29992.45
611.9047619048
0
347.619047619
0
0
0
10047.1860609576
27433.5672034854
9383.2898987389
0
6394727.21608
408102.493333333
43942.047
1563837.84443445
0
0
0
0
0
0
0
1554694.708
0
0
330
5860.8730142284
0
0
0
0
2952.2634202232
0
0
0
0.0576434056
0.3564584106
182.52
2986520
246000
0
0
30604.3547619048
347.619047619
46864.0431631819
10394.8051085766
57426.0172034854
9995.1946606437
0
0
39.3%
0.4%
60.2%
13.4%
73.8%
12.8%
0.0%
77816.0169727057
15663.5122326996
11376842.216544
1839765.623796
442030
164051
Cyprus
783976
0.2968
24.832
25.53
573.5257841727
2437.236
0.0299371911
31.5257841727
4.2206586849
0.0402126904
0.0345661814
n/a
0.0572269457
0.1578056004
0.0549683816
0.0017317398
0.0578457689
0
1.7242013617
0
0
4.0287769784
0
0
0
27.4970071942
0
0
0
0
0
0
0
0
0
232684.0768
70.7111904762
72.964
176.71053782
16.973069736
16.973069736
0
0
12.984398348
0
0
0
0
0
129.78
0
0
0
0
0
0
0
0
0
0.0026036852
0.0869715913
54.3568
8043
0
4.0287769784
4.0287769784
27.4970071942
0
0
0
0
31.5257841727
0
0.1277930774
87.2%
0.0%
0.0%
0.0%
0.0%
100.0%
0.0%
31.5257841727
6.3457951877
102042.196848
3054.856752
40
542
Czech Republic
10334160
0.26459
18508
16472
n/a
45845.189
0.0466675576
n/a
n/a
n/a
0.608196115
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2734315.3944
2340.236
2139.483
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
50
1663000
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
1919446.373052
89575.874244
22669
10887
Denmark
5461438
0.13134
2358.4
2829.86
n/a
19762.281
0.1629229946
n/a
n/a
n/a
1.9941997724
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
717305.26692
1673.47
3219.73
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
447.32
1430450
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
827407.180908
134803.65564
5176
9191
Estonia
1341672
0.30513
4500
4500
n/a
5623.844
0.1069592258
n/a
n/a
n/a
2.3439218814
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
409384.37736
2123.2
601.522
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
0.3
959565
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
235459.100592
25184.523096
11361
4698
Finland
5288720
0.1491
56869.76
69311.47
71204.1575584171
36795.024
0.2349330442
35729.1575584171
7545.6126563698
6.7557287129
6.788868868
26.5920676459
0.031747636
0.1032426451
0.5017847101
0.2050715514
0.872893603
0.4847786789
0.0047370051
2141.8481653034
361.0262474952
4653.025086576
0
8871.5418099739
18158.8517890686
1070.73946
0
446.75
25.375
0
0
0
0
0
0
788548.152
43952.4121595679
8644.367
315919.710696893
21089.0114674795
0
39206.0020700569
21739.4797303562
4207.2840832877
62658.2191557329
9021.9615635347
153151.14
0
0
0
276.6607877746
0
0
0
0
4341.2727554012
228.6790832695
0
0
0.1954581747
0.8319739582
169.2492
5353350
140638
7155.8994993746
7155.8994993746
28101.1330590425
472.125
0
11460.1399752773
18545.2530365638
5723.764546576
0
0.2002817863
78.7%
1.3%
0.0%
32.1%
51.9%
16.0%
0.0%
35729.1575584171
7191.8882288284
1540534.064832
361922.357556
89587
35475
France
63824699
0.14749
63191
62021
86343.4554160619
263483.089
0.0632997816
38469.4554160619
9371.3673297677
0.6027361824
2.5803354195
2.3501873468
0.2235665269
0.4416537055
0.4455399107
0.0355672441
0.5618857305
0.0754397983
0.0079153707
205.5992704058
0
27095.0287277702
91.1764705882
479.3179848552
7759.8400412797
712.32312
27.1602
437.7722465116
0
1564.1686046512
97.06875
0
0
0
0
9413504.85551
27693.6919333333
16678.422
392360.407362712
207719.914145044
75702.14658
3058.2971754776
0
0
0
50711.3144787552
29599.59
0
0
2160
2810.6381478
281.06381478
0
0
0
20317.4430208552
0
0
0
0.0293889077
0.4642813442
304.5
24290000
150000
27391.8044687642
27391.8044687642
8978.6413461348
2099.0096011628
0
1122.6895017726
7759.8400412797
29371.5204524213
215.4054205882
0.7120403492
23.3%
5.5%
0.0%
2.9%
20.2%
76.4%
0.6%
38469.4554160619
7743.4801848844
11031509.970252
698292.172296
102456
47874
Germany
82266372
0.26151
81173.08
78879.85
124990.679100986
331169.028
0.0785358044
41349.6791009864
10959.7992721848
0.5026316111
0.4233587708
15.6078354835
0.1645336741
0.2719942408
0.3308221013
0.0330942762
0.4213909367
0.0407741603
0.0333925203
4190.1417868488
0
17264.7231299149
0
1236.3687902548
3573.2792106017
3288.7695383446
0
8651.375
0
3145.0216450216
0
0
0
0
0
21513478.94172
29742.6242857143
26008.626
458864.875927834
173063.884959584
37722.64748826
0
0
0
4385.72193865
0
18709.83
0
0
4410
34868.2065872
16497.223911
0
0
0
169207.36104314
0
0
0
0.0251328988
0.3200183538
1380.77
9107920
1284000
21454.8649167636
21454.8649167636
8098.4175392011
11796.3966450216
0
14077.8855771036
3573.2792106017
23698.5143132811
0
0.5188641214
19.6%
28.5%
0.0%
34.0%
8.6%
57.3%
0.0%
41349.6791009864
8323.2376779665
13865384.864304
1088929.153368
122000
83641
Iceland
311566
0.06584
0
1.42
n/a
4836.174
0.8165454758
n/a
n/a
n/a
0.0019499349
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
20513.50544
9.14
3948.956
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
1.72
40
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
202480.933032
165334.889808
14
188
Ireland
4356930
0.38059
2772.529
2828.529
4832.0418161879
15088.82
0.0315161822
434.0418161879
95.5136305532
0.0996210213
0.0337714783
5.6783101863
0.0151900371
0.021195483
0.0898257575
0.0063300928
0.2008521446
0
0.1818973589
0
0
38.3693045564
21.5827338129
0
198.8022778186
64.65
0
0
110.6375
0
0
0
0
0
0
1658203.9887
1924.1391295238
475.542
3998.964684
505.150885
0
0
424.3267434
424.3267434
912.8651999
0
0
0
0
30
509.1735775
122.2016586
0
0
0
1070.9198762
0
0
0
0.0057902384
0.1837227109
78.95106
56000
24740
59.9520383693
59.9520383693
263.4522778186
110.6375
0
0
309.4397778186
103.0193045564
21.5827338129
0.1381250288
60.7%
25.5%
0.0%
0.0%
71.3%
23.7%
5.0%
434.0418161879
87.3678654068
631738.71576
19909.992456
4485
4398
Italy
59375288
0.3164
8124
13071
n/a
179598.604
0.0670098527
n/a
n/a
n/a
0.3086817152
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
18786341.1232
3970
12034.876
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
1050
5799000
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
7519434.352272
503876.188368
31352
41810
Latvia
2276100
0.3228
14320.9
11879.08
11431.7770718819
4669.844
0.3013239843
6860.7770718819
1385.2925788632
3.0142687368
3.587872623
15.816528272
0.203331775
0.3855602016
0.6001496555
0.2966464359
0.9844766809
0
0.0022242722
1197.7
0
3080
302.4004796163
1191.0126526919
4.89141
323.1156383405
719.6240349832
42.03285625
0
0
0
0
0
0
0
734725.08
3014.164113551
1407.136
57999.4296918464
38113.7918803261
230.0275275971
247.725994004
13904.6903127472
4249.6505205829
477.468189339
0
0
0
0
12.51
237.8044276356
118.9022138178
0
0
0
265.4163779967
141.4422478
0
0
0.2957270753
0.9814256106
15.260236
2636100
36000
4580.1004796163
4580.1004796163
2238.6437360156
42.03285625
0
2430.7455089419
4.89141
3403.1156383405
1022.0245145995
0.6675775108
32.6%
0.6%
0.0%
35.4%
0.1%
49.6%
14.9%
6860.7770718819
1380.999308008
195517.028592
58913.970048
25280
4571
Liechtenstein
35341
0.85693
38
30
40.1007194245
112.4086844368
0.0303156168
14.1007194245
2.8377636417
0.3989903915
0.4292587644
n/a
n/a
0.4700239808
0.3516325798
0.025245057
0.832740996
0
0.0049642857
7.0503597122
0
0
7.0503597122
0
0
0
0
0
0
0
0
0
0
0
0
30284.76313
0.09
3.4077386071
118.811488152
118.811488152
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.0252500103
0.832904387
0.07
13000
0
14.1007194245
14.1007194245
0
0
0
7.0503597122
0
0
7.0503597122
1
0.0%
0.0%
0.0%
50.0%
0.0%
0.0%
50.0%
14.1007194245
2.8383204357
4706.3268
142.6752
…
26
Lithuania
3375618
0.33016
6195
4878.18
8831.4988009592
9452.75
0.1019324535
3376.4988009592
286.5110520656
1.0002609303
1.2118413853
34.3640779259
0.0170747129
0.1233899662
0.3823245496
0.0718999487
0.7053685676
0
0.0280533196
601.9184652278
0
0
0
1645.0839328537
0
1043.1654676259
0
0
0
86.3309352518
0
0
0
0
0
1114494.03888
3120.9119047619
963.542
11995.644727884
1381.8503489352
0
758.332508562
3707.403375192
3707.5246114044
2410.5279212214
0
0
0
0
0
0
0
0
0
0
30.005962569
0
0
0
0.0718999487
0.7053685676
94.722
1350590
116000
601.9184652278
601.9184652278
2688.2494004796
86.3309352518
0
2247.0023980815
0
1129.4964028777
0
0.1782670455
79.6%
2.6%
0.0%
66.5%
0.0%
33.5%
0.0%
3376.4988009592
679.6522403745
395767.737
40341.576456
10870
5455
Luxembourg
479993
0.14814
268
380
1479.0622241775
4208.341
0.0304818455
359.0622241775
72.2697252889
0.7480572095
0.2823946497
n/a
n/a
0.5650637385
0.2427634337
0.0171729727
0.56338363
0
0.0724108476
0
0
213.2398081535
1.48441247
8.654676259
134.273381295
1.409946
0
0
0
0
0
0
0
0
0
71106.16302
2074
128.278
3025.7888583945
0
0
1809.248409716
1204.2999158754
0
0
0
0
0
0
0
12.2405328031
0
0
0
0
0
0
0
0
0.0171742961
0.5634270449
26
20080
0
214.7242206235
214.7242206235
144.338003554
0
0
8.654676259
134.273381295
214.6497541535
1.48441247
0.5980139546
40.2%
0.0%
0.0%
2.4%
37.4%
59.8%
0.4%
359.0622241775
72.2752944638
176194.820988
5370.743304
650
1120
Netherlands
16381696
0.17139
1222.06
997.96
16834.8714869945
79349.595
0.0302317107
3236.8714869946
666.5670754036
0.1975907432
0.0931737121
40.8993061524
0.0973682056
0.5189922234
0.1922718263
0.0084003841
0.2778666472
0
0.5411398034
194.734647065
0
240.5901365864
82.6086956522
1789.9917672818
304.347826087
270
124.727826087
196.4705882353
0
0
33.4
0
0
0
0
2807658.87744
4564.0085714286
2398.874
27907.830313
1616.482832
3030.90531
0
1515.452655
4344.297611
0
0
0
0
0
1350
13645.851877
40.7338862
0
0
0
2364.1061418
0
0
0
0.0082110916
0.2716052576
1751.6
261600
670000
517.9334793035
517.9334793035
2489.0674194557
229.8705882353
0
2181.1970025821
304.347826087
510.5901365864
240.7365217391
0.1600105168
76.9%
7.1%
0.0%
67.4%
9.4%
15.8%
7.4%
3236.8714869946
651.5467908104
3322208.84346
100436.056632
2239
13598
Norway
4709152
0.20589
10514
12321
11358.652705036
27545.596
0.4650058398
2794.652705036
584.2421203807
0.5934513698
2.5145237331
6.1582212679
0.1199903198
0.1993048853
0.2460373407
0.0212100011
0.0456123327
0
0.1202403431
0
0
2455.6354916067
0
0
0
68.034
0
0
0
0
0
270.9832134293
0
0
0
969567.30528
6175.0333333333
12808.863
24461.0490961
20690.9802496
0
0
0
0
0
0
0
0
0
0
590.6413499
896.1454964
0
0
0
0
0
0
2283.2820002
0.0204218849
0.0439174806
336.03
2438000
29000
2455.6354916067
2455.6354916067
68.034
0
270.9832134293
270.9832134293
0
2523.6694916067
0
0.8786907537
2.4%
0.0%
9.7%
9.7%
0.0%
90.3%
0.0%
2794.652705036
562.5329917211
1153279.013328
536281.476084
23256
8564
Poland
38120560
0.39042
35934.56
37639.03
n/a
96823.686
0.0498177481
n/a
n/a
n/a
0.2301688717
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
14883029.0352
8012.14
4823.538
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
239.58
3425610
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
4053814.085448
201951.888984
67595
32047
Romania
21546873
0.42533
15341
15573.05
n/a
39697.939
0.1244406668
n/a
n/a
n/a
0.409840918
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9164531.49309
1961.6
4940.038
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
35
3756000
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
1662073.310052
206829.510984
27334
7175
Russian Federation
142114903
0.26833
207000
158024
103692.381294964
672590.682
0.028846711
38273.381294964
7763.1781711665
0.2693129326
1.1748141274
n/a
n/a
0.20905667
0.3691050472
0.011542203
0.4001219754
0.1435885315
0.0003326594
0
0
33035.9712230216
0
0
5237.4100719424
0
0
0
0
0
0
0
0
0
0
38133691.92199
32297.488
19402.029
325028.7436704
0
0
278358.3436704
0
0
0
0
46670.4
0
0
0
0
0
0
0
0
0
0
0
0
0.0114542363
0.3970725232
12.732
44800000
0
33035.9712230216
33035.9712230216
5237.4100719424
0
0
0
5237.4100719424
33035.9712230216
0
0.8631578947
13.7%
0.0%
0.0%
0.0%
13.7%
86.3%
0.0%
38273.381294964
7704.0126110633
28160026.673976
812324.150172
848841
65419
Serbia
7381579
0.43939
7125
7138
9662.8045564296
16642.753
0.1001612534
6228.8045564296
1202.0818261142
0.8438309143
1.7056217705
0.1368270935
1.2826953869
0.839136729
0.6446166349
0.0722285445
0.7211226108
0
0.0063115578
15
7.1942446043
5814.0865087365
153.4772182254
2.3980815348
16.7865707434
25.038
184.3864325853
0
0
10.4375
0
0
0
0
0
3243391.99681
1014.7080666667
1666.959
50328.76189575
46958.8262696
585.9750266
0
343.5026018
1353.8043718
19.84489565
0
0
0
0
90
61.1008293
814.677724
0
0
0
101.030177
0
0
0
0.0753355025
0.7521421702
39.31346
5532000
1010
5989.7579715662
5989.7579715662
228.6090848634
10.4375
0
17.3980815348
23.9808153477
5849.5620087364
337.8636508107
0.9616223976
3.7%
0.2%
0.0%
0.3%
0.4%
93.9%
5.4%
6228.8045564296
1253.7901599224
696798.782604
69792.239412
5232
3434
Slovak Republic
5397318
0.45033
8209
7089
10098.1938847655
17848.643
0.0544825172
3561.1938847655
742.1864789799
0.6598080537
0.1448217453
0.7411088248
0.07000509
0.1217138858
0.3526565171
0.0415822356
0.7632216303
0.4208857453
0.0063720204
230.7913669065
49.7841726619
562.3501199041
19.9040767386
69.4716834532
2122.7409363242
274.8776978417
12.0863309353
0
100.2
118.9875
0
0
0
0
0
2430574.21494
5744.0590476191
972.439
31073.86350193
6704.36254572
565.7689912
0
3538.885039956
3632.236923504
1250.22842595
0
13078.5462
0
0
60
122.2016586
0
0
0
0
2121.633717
0
0
0
0.0401615595
0.7371458144
22.692
352000
4000
862.829736211
862.829736211
2479.1766485545
219.1875
0
300.2630503597
2272.7251089861
956.2153177458
31.9904076739
0.24228665
69.6%
6.2%
0.0%
8.4%
63.8%
26.9%
0.9%
3561.1938847655
716.8293385744
747286.985124
40714.076052
12916
6537
Slovenia
2010323
0.50495
3159.01
2707.01
3147.5816520712
7319.578
0.1004721857
950.5816520712
238.5772295111
0.4728502097
0.6373677174
24.871625107
0.0002687579
0.2463880813
0.3020038103
0.0325943968
0.32441214
0
0.0746911113
1.95
0
665.025
0
16.6888784
224.2687736712
18.1
0
24.549
0
0
0
0
0
0
0
1015112.59885
439.8357142857
735.414
9988.7514451712
22.0649906568
0
7525.0104674556
0
0
58.343993211
2127.5742914076
0
0
0
0
18.1347261362
0
0
0
0
237.622976304
0
0
0
0.0261410807
0.2601822632
71
647000
50000
666.975
666.975
259.0576520712
24.549
0
43.1878784
224.2687736712
683.125
0
0.7016493518
27.3%
2.6%
0.0%
4.5%
23.6%
71.9%
0.0%
950.5816520712
191.3416789325
306456.091704
30790.313352
8245
2197
Sweden
9148092
0.15318
78003.54568
81586.74568
93132.379763789
50059.598
0.305490128
45945.379763789
9506.2741179946
5.0224002736
4.229472621
180.3654794901
n/a
0.1791799013
0.4933341109
0.1898991302
0.6216211681
0.4026965143
0.0178821027
9640.2877697842
0
4546.7625899281
431.654676259
2931.9020383693
26370.1145371703
905.556
206.8646522782
912.2375
0
0
0
0
0
0
0
1401304.73256
49490.9808
15292.713
398008.6847722
0
0
123175.9917984
0
0
0
71468.7472098
160276.71
0
0
0
34257.1982942
0
0
0
0
7577.263275
1252.7741948
0
0
0.1847458285
0.6047522048
821.6
5926780
1650000
14618.7050359712
14618.7050359712
30414.4372278178
912.2375
0
13484.4273081535
26370.1145371703
5452.3185899281
638.5193285372
0.3181757363
66.2%
2.0%
0.0%
29.3%
57.4%
11.9%
1.4%
45945.379763789
9248.3019044596
2095895.249064
640275.307884
92564
47187
Switzerland
7551116
0.26377
6491.251
5327.926
11092.8571428571
25765.341
0.1778563691
3714.8571428571
482.5042638416
0.4919613396
0.9106669188
16.9511367591
n/a
0.2774194115
0.3348873149
0.0290219357
0.163176252
0
0.0674646401
0.00
0.00
1,120.44
357.63
0.00
0.00
307.99
273.15
265.00
0.00
0.00
0.00
202.24
830.95
331.09
26.36
1991757.86732
2594.2118080139
4582.53
20201.4885185216
0
0
12540.3828437462
0
0
0
2050.710532746
0
0
0
982.35
2382.9323427
0
0
0
0
2245.1127993294
0
0
0
0.0290219357
0.163176252
250.6215
1813828
128000
1478.0700952381
1478.0700952381
581.1452380952
265
1390.6418095238
467.2380952381
830.9523809524
1759.5238095238
657.1428571429
0.3978807363
15.6%
7.1%
37.4%
12.6%
22.4%
47.4%
17.7%
3714.8571428571
747.760070016
1078743.296988
191861.36604
6944
7378
Turkey
70137756
0.30351
18319
20390
n/a
100005.025
0.0960137653
n/a
n/a
n/a
0.2291014743
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
21287510.32356
3796
9601.859
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
364
4877000
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
4187010.3867
402010.632612
…
27615
Ukraine
46329000
0.31213
16884.3
13660.45
n/a
137343.645
0.0125750849
n/a
n/a
n/a
0.6020273982
n/a
n/a
0
n/a
n/a
n/a
n/a
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
14460670.77
870.01
1727.108
n/a
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
n/a
n/a
15.96
8705720
0
0
0
0
0
0
0
0
0
0
…
…
…
…
…
…
…
…
0
n/a
5750303.72886
72310.557744
21325
8512
United Kingdom
60986648
0.20362
9118
8658.9074125
50550.4626196684
210967.834
0.0219264563
2274.4626196684
446.8615341658
0.0372944357
0.0246229269
4.2252855084
0.0194326526
0.0524298481
0.0449939032
0.0021181501
0.0966024809
0
0.2802094195
217.6772
0
236.308
0
1072.3917946684
0
530.46375
0
217.621875
0
0
0
0
0
0
0
12418101.26576
13796.5171687133
4625.777
18709.1987124556
2982.6498301091
842.59167618
0
2038.2126641503
808.0666073162
0
0
0
0
0
1428
8503.19874425
0
0
0
0
2106.47919045
0
0
0
0.0021701148
0.098972437
637.3258504
305770
257686
453.9852
453.9852
1602.8555446684
217.621875
0
1507.6908696684
0
766.77175
0
0.1996010821
70.5%
9.6%
0.0%
66.3%
0.0%
33.7%
0.0%
2274.4626196684
457.8244229397
8832801.273912
193672.031436
20700
48276
United States
301621157
0.17714
444003
436301.24
789899.841185512
2337013.725
0.0466771298
365295.841185512
124427.385299529
1.2111081491
0.9488449538
3.1728543499
0.0644230933
0.1161936731
0.462458431
0.0314632527
0.6740614258
0.8915051086
0.0014399288
0
0
50695.4436450839
0
28559
265617.52
13379.7944072135
4113.0831332141
2814
117
0
0
0
0
0
0
53429171.75098
492273.891904762
109085.093
5209525.76772068
427157.002778478
0
0
0
0
90480.023430784
0
4644318.83523
0
0
26610
20959.9062814134
0
0
0
0
0
0
0
0
0.0314632527
0.6740614258
526
50696000
957000
50695.4436450839
50695.4436450839
311669.397540428
2931
0
31373
265734.52
64075.2380522975
4113.0831332141
0.1387791426
85.3%
0.8%
0.0%
8.6%
72.7%
17.5%
1.1%
365295.841185512
73530.0533175643
97846090.6383
4567174.673724
761000
424604
JWEE total
756513325
0.2
1210203
1168661
1679045
4390095
0.1
691526
216173
0.9
1.0
8.1
0.044596221
0.2
0.41
4.9%
81.5%
0.7342562011
0.0
18649
821
154742
1928
51571
365157
23888
6673
14355
353
4984
149
10249
28265
9714
26
161515879
1138223
265130
9050734
976488
119672
470113
63873
34283
169504
162309
6645557
0
0
38193
133540
19891
0
0
0
215687
1623
0
0
3.2%
52.5%
6871
161165581
6117074
176140
176140.2
447289.2
19841.5
48254.7
94824.8
394596.3
193328.5
8775.9
25.5%
64.7%
2.9%
7.0%
13.7%
57.1%
28.0%
1.3%
691525.5
139196.5
183804497
11100444
2581215
987519
JWEE 13
257776554
0.2
339188.00768
356656.9680925
506,447.02
1015174.782
0.1
196652.016828995
44289.6626365695
0.8
1.0
15.9
0.1232833972
0.2
0.39
4.4%
50.4%
0.2156442596
0.0
16647.4144986345
821.4301549939
67690.4048473368
1535.4542381224
18385.9956721381
64000.5515686007
8259.9528260947
1715.5562144532
10955.3056215116
236.2125
4897.4754843594
115.6213932252
202.2380952381
830.9523809524
331.0938095238
26.3575238095
58871125.53635
194849.692732486
87927.419
1854319.59526789
508100.278923639
116411.216363546
190748.868205552
44745.4498364155
21981.682235629
76135.5227971758
162308.698617843
399873.3762
0
0
9890.693260762
92835.4226406802
19731.4731909515
0
0
0
210075.459717627
1481.4532780695
0
0
3.9%
45.0%
4287.9498704
58421771
4092074
86694.7037390876
86694.7
92362.1
16204.6
1390.6
46191.0
65889.1
81178.9
3393.0
44.1%
47.0%
8.2%
0.7%
23.5%
33.5%
41.3%
1.7%
196652.0
39583.9
42503337.772776
3681345.178692
490955
309795
JWEE Europe (excluding the Russian Federation)
279365316
0.2
360695.96768
374062.1880925
542106.202684077
1104551.03868444
0.1
209781.202684077
46558.6013812549
0.8
1.0
17.6
0.1249994527
0.2
0.39
4.2%
50.3%
0.2051352752
0.0
18648.8179706395
821.4301549939
70797.7551757698
1926.0293606331
23003.4293487065
64175.5174233927
9894.8239860612
2559.9080755234
11193.8090659969
236.2125
4983.8064196112
149.0213932252
202.2380952381
830.9523809524
331.0938095238
26.3575238095
63487182.13278
203474.867322227
92572.1007386071
1949315.52263038
549331.215473052
119672.149201143
189945.678298402
62668.6962634792
34283.1549786162
79023.5189077362
162308.698617843
399873.3762
0
0
11253.203260762
106706.838412513
19891.1092909693
0
0
0
212734.988199993
1622.8955258695
0
0
3.8%
45.6%
6123.6021064
62662981
4914074
92194.0326620362
92194.0
99633.7
16562.8
1390.6
53048.3
66064.1
86007.5
4661.3
43.9%
47.5%
7.9%
0.7%
25.3%
31.5%
41.0%
2.2%
209781.2
42226.7
46245342.88764
3875808.713724
528694
332325
JWEE 12
252379236
0.2
330979.00768
349567.9680925
496,348.82
997326.139
0.1
193090.822944229
43547.4761575896
0.8
1.0
16.2
0.1247229086
0.2
0.39
4.4%
50.1%
21.2%
2.2%
16416.623131728
771.645982332
67128.0547274328
1515.5501613838
18316.5239886849
61877.8106322765
7985.075128253
1703.469883518
10955.3056215116
136.0125
4778.4879843594
115.6213932252
202.2380952381
830.9523809524
331.0938095238
26.3575238095
56440551.32141
189105.633684867
86954.98
1823245.73176596
501395.916377919
115845.447372346
190748.868205552
41206.5647964595
18349.445312125
74885.2943712258
162308.698617843
386794.83
0
0
9830.693260762
92713.2209820802
19731.4731909515
0
0
0
207953.826000627
1481.4532780695
0
0
3.9%
44.7%
4265.2578704
58069771
4,088,074.00
85831.8740028766
85831.9
89882.9
15985.4
1390.6
45890.7
63616.4
80222.7
3361.0
44.5%
46.5%
8.3%
0.7%
23.8%
32.9%
41.5%
1.7%
193090.8
38,867
41756050.787652
3640631.10264
478039
303258
2005
2005
Population
Rural Population
Roundwood and Fuelwood removals from forest and outside forest (1000m3)
Roundwood and Fuelwood supply from forest and outside forest including net trade (1000m3)
Total primary energy supply, TPES (ktoe)
Share of renewables (RES) in TPES
Total wood energy generation (1000m3)
Average wood energy consumption (m3/capita)
Fuelwood consumption per rural inhabitant (m3/inhabitant)
S1/Net Annual Increments
Share of Total Roundwood supply directly used for energy purposes (%)
Share of woody biomass in TPES (%)
Share of woody biomass in RES (%)
Res (ktoe)
Total wood energy generation (ktoe)
Direct mobilisation of woody biomass for energy production from forests available for wood supply (m3/ha)
Total woody biomass used for energy per ha of Forest available for wood supply (m3/ha)
Total Fuelwood (1000 m3)
Total S1 (1000 m3)
FAWS (1000 ha)
OWL (1000 ha)
Net Annual Increments
Albania
3142065
0.48089
296.2
240.023
2206.3
31.5%
n/a
n/a
0.1
n/a
n/a
n/a
n/a
1510987.63785
694.3
n/a
n/a
n/a
164.7
0
611.3
215.9
470.0
Armenia
3217500
0.35809
42.7
42.2
2505.0
6.1%
n/a
n/a
0.0
n/a
n/a
n/a
n/a
1152154.575
153.5
n/a
n/a
n/a
38.7
0
283
…
360.0
Austria
8227828
0.32447
16471
24471
33761.3
21.0%
14443.0
1.8
1.5
31.3%
28%
9%
41%
2669683.35116
7085.2
2907.2
2.1
4.3
3892
6929.323
3343
0
25136.0
Belarus
9795287
0.25305
8696
7254.954
26873.1
4.2%
n/a
n/a
0.5
n/a
n/a
n/a
n/a
2478697.37535
1133.5
n/a
n/a
n/a
1265.954
0
6376.3
50
22809.0
Belgium
10478617
0.02589
4950
7044
58684.7
2.0%
n/a
n/a
2.3
n/a
n/a
n/a
n/a
271291.39413
1156.1
n/a
n/a
n/a
635
0
667
0
5289.0
Bosnia and Herzegovina
3915238
0.51379
3806
3414.124
5042.1
13.9%
n/a
n/a
0.5
n/a
n/a
n/a
n/a
2011610.13202
699.6
n/a
n/a
n/a
1072.346
0
1252
0
…
Bulgaria
7739900
0.28542
5861.67
5353.397
19945.6
5.5%
n/a
n/a
1.2
n/a
n/a
n/a
n/a
2209122.258
1098.1
n/a
n/a
n/a
2555.04
0
2561
0
14120.0
Canada
32245209
0.19418
203121
203534.156
272197.6
15.9%
47810.0
1.5
0.4
n/a
n/a
4%
22%
6261374.68362
43411.2
9623.6
n/a
0.2
2605.156
0
310134
0
442030.0
Cyprus
757794
0.2968
9.656
10.149
2220.1
2.4%
n/a
n/a
0.0
n/a
n/a
n/a
n/a
224913.2592
53.7
n/a
n/a
n/a
4.182
0
43.2
0
40.0
Czech Republic
10235828
0.26459
15510
13456
44940.9
4.0%
7939.0
0.8
0.4
25.2%
37%
4%
90%
2708297.73052
1785.2
1598.0
2.0
3.2
966
5036
2518
0
22669.0
Denmark
5419432
0.13134
2962.5
3433.959
18888.6
15.1%
n/a
n/a
2.2
n/a
n/a
n/a
n/a
711788.19888
2860.0
n/a
n/a
n/a
1548.95
0
385
0
5176.0
Estonia
1346097
0.30513
5500
5460.16
5164.3
11.4%
n/a
n/a
2.3
n/a
n/a
n/a
n/a
410734.57761
588.5
n/a
n/a
n/a
932.825
0
2074.1
92
11361.0
Finland
5246096
0.1491
52250.182
67708.447
34254.2
23.6%
32913.1
6.3
6.8
8.6%
10%
19%
82%
782192.9136
8081.5
6625.0
0.3
1.6
5309.608
6802.5293347627
20004
734
89587.0
France
62885822
0.14749
52498.739
50567.122
270627.9
5.8%
41265.1
0.7
2.6
38.6%
69%
3%
53%
9275029.88678
15565.9
8306.2
2.4
2.8
24141.112
34786.6197183099
14743
0
102456.0
Germany
82469422
0.26151
56946
53478
338258.0
4.9%
30270.6
0.37
0.30
12.1%
24%
0.018
0.364
21566578.54722
16727.2
6093.140
1.232
2.864
6387.000
13023.000
10568.000
0.000
122000.000
Iceland
296734
0.06584
0
0.604
3480.5
75.7%
n/a
n/a
0.0
n/a
n/a
n/a
n/a
19536.96656
2635.3
n/a
n/a
n/a
0.030
0.000
41.500
94.100
14.000
Ireland
4159096
0.38059
2648
2542.994
14436.9
2.5%
n/a
n/a
0.0
n/a
n/a
n/a
n/a
1582910.34664
363.5
n/a
n/a
n/a
19.298
0
656.3
0
4485.0
Italy
58607043
0.3164
8690.855
14295.779
183872.9
6.3%
n/a
n/a
0.4
n/a
n/a
n/a
n/a
18543268.4052
11625.2
n/a
n/a
n/a
6537.542
0
7741
616.7
31352.0
Latvia
2300512
0.3228
12842.6
9663.651
4416.0
33.4%
n/a
n/a
0.8
n/a
n/a
n/a
n/a
742605.2736
1476.0
n/a
n/a
n/a
607.757
0
3088.3
112
25280.0
Liechtenstein
34716
0.85693
22.167
22.167
0.0
n/a
n/a
n/a
0.1
n/a
n/a
n/a
n/a
29749.18188
0.0
n/a
n/a
n/a
4.167
0
4
0
…
Lithuania
3414304
0.33016
6045
5158.987
8842.1
10.0%
3208.6
0.9
1.0
15.5%
29%
7%
73%
1127266.60864
880.9
645.9
0.8
1.7
1116.307
1484.2
1835
0
10870.0
Luxembourg
465158
0.14814
248.95
311.463
4388.1
1.7%
n/a
n/a
0.3
n/a
n/a
n/a
n/a
68908.50612
72.7
n/a
n/a
n/a
20.08
0
86.1
0
650.0
Netherlands
16319868
0.17139
1110
938.795
78823.9
2.7%
1962.0
0.1
0.1
15.9%
33%
1%
18%
2797062.17652
2153.5
394.9
1.1
6.7
263.695
312.5
295
0
2239.0
Norway
4623291
0.20589
9667.179
12398.112
26759.3
48.5%
5772.8
1.2
1.4
15.3%
25%
4%
9%
951889.38399
12972.9
1162.0
0.5
0.9
1287.305
3128
6291
0
23256.0
Poland
38165445
0.39042
31944.5
33386.1
92376.7
4.9%
n/a
n/a
0.2
n/a
n/a
n/a
n/a
14900553.0369
4484.4
n/a
n/a
n/a
3404.2
0
8417
0
67595.0
Romania
21634370
0.42533
14501
14678.037
38652.9
13.0%
n/a
n/a
0.3
n/a
n/a
n/a
n/a
9201746.5921
5025.4
n/a
n/a
n/a
2891.04
0
0
0
27334.0
Russian Federation
143953092
0.26833
185000
137430
651711.6
2.9%
n/a
n/a
1.2
n/a
n/a
n/a
n/a
38626933.17636
18695.1
n/a
n/a
n/a
46720
0
690978.2
2882
848841.0
Serbia
7440768
0.43939
3170
2786
16051.0
11.4%
n/a
n/a
0.6
n/a
n/a
n/a
n/a
3269399.05152
1837.1
n/a
n/a
n/a
1856
0
1534
0
5232.0
Slovak Republic
5387001
0.45033
9302
7591.531
18830.5
4.3%
n/a
n/a
0.1
n/a
n/a
n/a
n/a
2425928.16033
808.5
n/a
n/a
n/a
178
0
1751.2
0
12916.0
Slovenia
2000474
0.50495
2732.822
2719.322
7292.6
10.6%
2213.6
1.1
0.8
21.9%
59%
6%
58%
1010139.3463
773.8
445.6
1.4
1.9
837.53
1591.162
1166
44
8245.0
Sweden
9029572
0.15318
98200
103923.081
51569.0
28.7%
39377.1
4.4
4.4
8.5%
7%
15%
53%
1383149.83896
14825.2
7926.2
0.3
1.9
6032
6923.1539657854
20632
0
92564.0
Switzerland
7437115
0.26377
5284.639
4042.528
25946.6
16.0%
3784.8
0.5
0.6
31.7%
48%
3%
18%
1961687.82355
4156.6
761.8
1.6
3.2
1218.646
1934.8
1178
0
6944.0
Turkey
72064992
0.30351
16185
18478.907
84379.1
12.0%
10800.0
0.1
0.2
n/a
n/a
3%
21%
21872445.72192
10129.9
2173.9
n/a
1.2
5318.991
0
8665
0
…
Ukraine
46924816
0.31213
14606.3
12106.371
142887.2
0.9%
n/a
n/a
0.5
n/a
n/a
n/a
n/a
14646642.81808
1328.0
n/a
n/a
n/a
7864.34
0
5307
0
21325.0
United Kingdom
60224306
0.20362
8519
8285.183
222366.3
1.8%
1562.1
0.0
0.0
3.2%
7%
0%
8%
12262873.18772
3959.9
314.4
0.2
0.7
126.025
580.1
2375
0
20700.0
United States
295895897
0.17714
467347.35
461163.501
2318861.1
4.5%
n/a
n/a
0.8
n/a
n/a
n/a
n/a
52414999.19458
105186.4
n/a
n/a
n/a
43953.429
0
ERROR:#N/A
ERROR:#N/A
761000.0
JWEE total
600255032
20%
995702.911
1011844.234
3734500.894
6.4%
232521.833182847
0.4
0.0
5.4%
8%
1%
20%
117172224.67316
237565.496
46804.1
ERROR:#N/A
ERROR:#N/A
98135.813
82531.3880188579
ERROR:#N/A
ERROR:#N/A
1729696
JWEE 16
n/a
n/a
n/a
n/a
n/a
ERROR:#VALUE!
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
JWEE 15
n/a
n/a
n/a
n/a
n/a
ERROR:#VALUE!
n/a
n/a
n/a
ERROR:#VALUE!
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
JWEE 12
250343451
0.223922239
298978.988
320845.289
1021171.964
0.0719784224
165829.393182847
0.6624075546
0.0008891497
0.1725103829
0.2261859236
0.0326875809
0.4541302774
56057466.05877
73502.347
33379.6412321095
0.9507666591
2.150651088
49843.481
72570.6880188579
76328.6
778
478039
JWEE Europe (excluding the Russian Federation)
272113926
21%
325234.561
347146.577
1143442.174
7.8%
184,711.83
0.7
0.0
17.8%
24%
3%
42%
58495850.79496
88967.924
37180.5
1.0
2.2
51577.228
82531.3880188579
84948
778
526666
External 2017 data
Population
Autor: UN Population Division 2020, World Population Prospects 2019n, File POP/1-1: Total population (both sexes combined) by region, subregion and country, annually for 1950-2100 (thousands)
Rural Population
Autor: UN Population Division 2020, World Urbanization Prospects: The 2014 Revision, File 20: Annual Rural Population at Mid-Year by Major Area, Region and Country, 1950-2050 (thousands)
Rural Population (share)
TPES in TJ
Autor: IEA 2019, Extended Energy Balances, Total primary energy supply
RES in TJ
Autor: IEA 2019, Extended Energy Balances, Memo: Renewables
conv: ktoe/TJ
TPES in ktoe
RES in ktoe
RES in TPES (share)
Net Annaul Increment in 1000 m3
SWE in 1000 m3
SWE
Albania
2,884,169
1,308,750
0.45
98,553
27,038
0.02
2,354
646
0.27
470
1471
Albania
1470.687
Armenia
2,944,789
1,123,366
0.38
133,468
13,740
0.02
3,188
328
0.10
360
1753
Armenia
1752.5575
Austria
8,819,902
2,921,577
0.33
1,401,992
406,894
0.02
33,486
9,718
0.29
25136
22898
Austria
22898.158
Azerbaijan
9,845,316
4,389,902
0.45
600,267
9,403
0.02
14,337
225
0.02
2003
Azerbaijan
2002.927
Belarus
9,450,233
2,070,335
0.22
1,068,190
63,680
0.02
25,513
1,521
0.06
29975
17825
Belarus
17825.277
Belgium
11,419,752
232,562
0.02
2,313,287
169,143
0.02
55,252
4,040
0.07
5289
8016
Belgium
8016.4445
Bosnia and Herzegovina
3,351,534
2,279,787
0.68
282,964
33,836
0.02
6,758
808
0.12
2746
Bosnia and Herzegovina
2745.705
Bulgaria
7,102,452
1,779,383
0.25
784,812
77,889
0.02
18,745
1,860
0.10
14677
5196
Bulgaria
5196.1545
Canada
36,732,091
6,516,445
0.18
12,102,468
2,095,825
0.02
289,062
50,058
0.17
442030
59932
Canada
59931.5255
Croatia
4,182,847
1,706,522
0.41
366,255
79,312
0.02
8,748
1,894
0.22
9991
2207
Commonwealth of Independent States
134189.009
Cyprus
1,179,685
394,500
0.33
92,870
6,968
0.02
2,218
166
0.08
38
195
Croatia
2207.3585
Czech Republic
10,641,032
2,929,652
0.28
1,814,081
184,019
0.02
43,329
4,395
0.10
23086
8736
Cyprus
195.2735
Denmark
5,732,277
683,907
0.12
712,055
240,430
0.02
17,007
5,743
0.34
5796
6209
Czech Republic
8736.3675
Estonia
1,319,389
414,727
0.31
238,944
44,475
0.02
5,707
1,062
0.19
11201
7441
Denmark
6209.141
Finland
5,511,372
851,924
0.15
1,393,233
469,994
0.02
33,277
11,226
0.34
91038
40453
EFTA
14145.744
France
64,842,513
13,143,055
0.20
10,344,992
1,017,284
0.02
247,086
24,297
0.10
94367
53388
Estonia
7441.008
Georgia
4,008,723
1,961,357
0.49
201,290
49,553
0.02
4,808
1,184
0.25
951
EU/EFTA
488684.821
Germany
82,658,409
19,988,713
0.24
13,031,211
1,743,285
0.02
311,245
41,638
0.13
107000
86027
Europe
542935.873
Hungary
9,729,822
2,752,277
0.28
1,115,745
123,084
0.02
26,649
2,940
0.11
11099
6579
European Union
479806.595
Iceland
334,395
19,534
0.06
229,046
201,321
0.02
5,471
4,808
0.88
15.6
124
Finland
40453.044
Ireland
4,753,281
1,744,814
0.37
571,018
55,213
0.02
13,639
1,319
0.10
5356
1589
France
53388.124
Italy
60,673,694
18,824,961
0.31
6,424,447
1,095,715
0.02
153,445
26,171
0.17
32543
40247
Georgia
951.158
Kazachstan
18,080,023
7,989,659
0.44
3,558,263
44,682
0.02
84,988
1,067
0.01
2430
Germany
86027.262
Latvia
1,951,097
655,898
0.34
184,215
80,979
0.02
4,400
1,934
0.44
25280
5319
Greece
2923.958
Liechtenstein
37,805
32,566
0.86
0.02
0
0
4
Hungary
6578.75
Lithuania
2,845,419
996,165
0.35
315,882
65,427
0.02
7,545
1,563
0.21
10750
5527
Iceland
123.656
Luxembourg
591,914
51,831
0.09
158,039
11,500
0.02
3,775
275
0.07
650
381
Ireland
1588.9895
Montenegro
627,563
220,970
0.35
42,625
11,610
0.02
1,018
277
0.27
1394
860
Israel
1887.365
Netherlands
17,021,343
1,434,489
0.08
3,106,716
171,312
0.02
74,203
4,092
0.06
2250
8463
Italy
40247.3005
North Macedonia
2,081,996
900,142
0.43
114,006
14,479
0.02
2,723
346
0.13
1624
978
Kazachstan
2429.8615
Norway
5,296,324
997,986
0.19
1,255,398
590,543
0.02
29,985
14,105
0.47
21878
8911
Kyrgyzstan
558.1
Poland
37,953,176
15,076,574
0.40
4,347,775
371,114
0.02
103,845
8,864
0.09
67595
35912
Latvia
5319.2335
Portugal
10,288,527
3,757,994
0.37
953,314
202,302
0.02
22,770
4,832
0.21
18870
10177
Liechtenstein
3.68
Republic of Moldova
4,059,687
1,857,633
0.46
161,196
32,956
0.02
3,850
787
0.20
27334
1718
Lithuania
5527.403
Romania
19,653,966
9,663,435
0.49
1,395,501
252,902
0.02
33,331
6,040
0.18
16274
Luxembourg
381.471
Russian Federation
145,530,091
36,463,707
0.25
30,654,064
785,607
0.02
732,160
18,764
0.03
852927
83806
Malta
71.3685
Serbia
8,829,623
4,122,584
0.47
653,525
78,877
0.02
15,609
1,884
0.12
5232
8104
Montenegro
859.7805
Slovak Republic
5,447,903
2,548,195
0.47
728,499
66,649
0.02
17,400
1,592
0.09
13193
5599
Netherlands
8462.975
Slovenia
2,076,395
1,050,654
0.51
289,186
45,178
0.02
6,907
1,079
0.16
9165
3236
North America
499766.71
Sweden
9,904,895
1,364,656
0.14
2,058,801
811,115
0.02
49,174
19,373
0.39
96486
50507
North Macedonia
977.912
Switzerland
8,455,797
2,177,862
0.26
991,343
209,306
0.02
23,678
4,999
0.21
6232
5107
Norway
8911.2675
Turkey
81,116,451
20,055,180
0.25
6,146,099
742,675
0.02
146,797
17,738
0.12
32939
Other C.I.S.
18044.3885
Ukraine
44,487,708
13,156,907
0.30
3,745,616
163,444
0.02
89,463
3,904
0.04
20935
14513
Other Europe
142313.659
United Kingdom
66,727,463
10,927,278
0.16
7,363,871
714,870
0.02
175,883
17,074
0.10
20700
27897
Other european countries
3798.6655
United States
325,084,758
59,579,343
0.18
90,235,182
6,801,457
0.02
2,155,230
162,450
0.08
761000
439835
Poland
35911.6175
Portugal
10176.8985
JWEE total
810,138,548
180,621,829
0.22
161,312,800
17,314,674
0.02
3,852,890
413,554
0.11
1,842,232
927,579
Republic of Moldova
1717.6935
JWEE 12
264,351,249
58,739,448
0.22
38,350,081
5,570,484
0.02
915,976
133,049
0.15
461,400
299,783
Romania
16273.9475
JWEE Europe (excluding the Russian Federation)
378,447,723
92,047,398
0.24
53,245,781
8,116,833
0.02
1,271,754
193,867
0.15
586,668
391,542
Russian Federation
83806.2765
*Using 2005 and 2010 data from external data of analysis 2013.
Serbia
8103.61
Slovak Republic
5598.955
JWEE total
Armenia, Austria, Azerbaijan, Belarus, Bosnia and Herzegovina, Canada, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Georgia, Germany, Iceland, Ireland, Italy, Liechtenstein, Lithuania, Luxembourg, Netherlands, Norway, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland, North Macedonia, Ukraine, United Kingdom and the United States
Slovenia
3236.0935
JWEE 12
Austria, Cyprus, Finland, France, Germany, Ireland, Luxembourg, Serbia, Slovenia, Sweden, Switzerland and the United Kingdom
Spain
18335.2905
Europe excl. Russia
Armenia, Austria, Bosnia and Herzegovina, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Georgia, Germany, Iceland, Ireland, Italy, Liechtenstein, Lithuania, Luxembourg, Netherlands, Norway, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland and the United Kingdom
Sweden
50507.062
Switzerland
5107.187
Tajikistan
4574.12
Turkey
32938.506
Turkmenistan
527.811
Ukraine
14513.118
United Kingdom
27897.0175
United States
439835.185
Uzbekistan
3529.5325
External 2015 data
Population
Rural Population
Rural Population (share)
TPES in TJ
RES in TJ
conv: ktoe/TJ
TPES in ktoe
RES in ktoe
RES in TPES (share)
Net Annaul Increment in 1000 m3
SWE in 1000 m3
SWE
Albania
2,923,352
1,361,679
0.47
91,677
31,548
0.02
2,190
754
0.34
470
1570
394.2246
Albania
1569.9975
Armenia
2,916,950
1,115,876
0.38
128,429
15,922
0.02
3,067
380
0.12
360
1659
178.225
Austria
21583.057
Austria
8,678,657
2,912,360
0.34
1,373,011
404,102
0.02
32,794
9,652
0.29
25136
21583
20225.8455
Belarus
13144.34
Azerbaijan
9,617,484
4,362,213
0.45
601,049
10,892
0.02
14,356
260
0.02
1393
1534.3844
Belgium
7994.301
Belarus
9,485,772
2,160,568
0.23
1,057,868
57,682
0.02
25,267
1,378
0.05
29975
13144
4266.88
Bosnia and Herzegovina
2032.668
Belgium
11,287,940
239,533
0.02
2,230,270
152,096
0.02
53,269
3,633
0.07
5289
7994
14020.2259
Bulgaria
4550.594
Bosnia and Herzegovina
3,535,961
2,300,713
0.65
336,287
83,788
0.02
8,032
2,001
0.25
2033
713.451
Croatia
2202.897
Bulgaria
7,177,396
1,853,000
0.26
779,038
83,284
0.02
18,607
1,989
0.11
14677
4551
2649.8954
Cyprus
122.9575
Canada
35,949,709
6,518,663
0.18
11,312,390
2,047,439
0.02
270,192
48,902
0.18
442030
64711
100011.2908
Czech Republic
9244.149
Croatia
4,236,016
1,746,216
0.41
351,586
82,069
0.02
8,397
1,960
0.23
9991
2203
2256.9895
Denmark
6122.1085
Cyprus
1,160,985
385,265
0.33
84,335
6,132
0.02
2,014
146
0.07
38
123
238.5716
Estonia
5775.1045
Czech Republic
10,603,762
2,910,655
0.27
1,764,645
179,147
0.02
42,148
4,279
0.10
23086
9244
8469.404
Finland
39849.352
Denmark
5,688,695
697,731
0.12
673,913
199,348
0.02
16,096
4,761
0.30
5796
6122
6369.8621
France
52508.29
Estonia
1,315,321
415,593
0.32
227,086
37,906
0.02
5,424
905
0.17
11201
5775
7180.041
Georgia
887.603
Finland
5,481,966
861,607
0.16
1,360,137
439,107
0.02
32,486
10,488
0.32
91038
39849
30844.9249
Germany
73981.972
France
64,457,201
13,308,464
0.21
10,320,707
913,258
0.02
246,506
21,813
0.09
94367
52508
62179.202
Greece
2923.958
Georgia
3,951,524
1,995,548
0.51
193,908
47,883
0.02
4,631
1,144
0.25
888
545.332
Hungary
6227.9345
Germany
81,707,789
20,391,913
0.25
12,886,750
1,605,702
0.02
307,795
38,352
0.12
107000
73982
87862.4198
Iceland
144.795
Hungary
9,783,925
2,851,842
0.29
1,055,463
126,190
0.02
25,209
3,014
0.12
11099
6228
3104.3003
Ireland
1611.612
Iceland
330,243
19,741
0.06
233,748
206,418
0.02
5,583
4,930
0.88
15.6
145
205.8997
Italy
40375.438
Ireland
4,700,107
1,737,531
0.37
555,313
44,810
0.02
13,263
1,070
0.08
5356
1612
2506.7886
Latvia
3790.731
Italy
59,504,212
18,976,152
0.32
6,389,256
1,099,713
0.02
152,605
26,266
0.17
32543
40375
31519.8912
Liechtenstein
3.7
Kazachstan
17,749,648
7,840,762
0.44
3,269,587
37,261
0.02
78,093
890
0.01
2430
Lithuania
4924.026
Latvia
1,992,663
662,579
0.33
178,470
64,344
0.02
4,263
1,537
0.36
25280
3791
4670.179
Luxembourg
410.9255
Liechtenstein
37,403
32,108
0.86
0.02
0
0
4
0
Malta
55.2725
Lithuania
2,931,926
1,004,416
0.34
302,368
59,335
0.02
7,222
1,417
0.20
10750
4924
5118.2938
Montenegro
809.451
Luxembourg
566,741
53,458
0.09
155,961
8,567
0.02
3,725
205
0.05
650
411
1434.0301
Netherlands
7565.6
Montenegro
628,178
223,599
0.36
42,302
12,933
0.02
1,010
309
0.31
1394
809
2.9158
Norway
8127.829
Netherlands
16,938,499
1,600,924
0.09
3,091,163
152,624
0.02
73,831
3,645
0.05
2250
7566
10791.28
Poland
32779.851
Norway
5,199,836
1,004,221
0.19
1,239,979
561,988
0.02
29,616
13,423
0.45
21878
8128
7410.8763
Portugal
9001.1935
Poland
38,265,226
15,082,630
0.39
3,974,477
376,252
0.02
94,929
8,987
0.09
67595
32780
36571.7124
Republic of Moldova
1686.9
Portugal
10,418,473
3,876,030
0.37
919,662
207,934
0.02
21,966
4,966
0.23
18870
9001
10796.6872
Romania
16260.8305
Republic of Moldova
4,065,980
1,890,424
0.46
141,582
14,562
0.02
3,382
348
0.10
27334
1687
407.2824
Russian Federation
80401.116
Romania
19,876,621
9,804,711
0.49
1,335,843
250,058
0.02
31,906
5,973
0.19
16261
9588.9832
Serbia
7797.44
Russian Federation
143,888,004
36,934,612
0.26
29,715,039
729,103
0.02
709,732
17,414
0.02
852927
80401
61424.5828
Slovak Republic
5152.514
Serbia
8,851,280
4,188,657
0.47
617,787
80,856
0.02
14,756
1,931
0.13
5232
7797
3050.439
Slovenia
3401.013
Slovak Republic
5,439,318
2,532,566
0.47
686,318
65,969
0.02
16,392
1,576
0.10
13193
5153
5077.8751
Spain
20289.343
Slovenia
2,074,788
1,046,826
0.50
275,130
44,242
0.02
6,571
1,057
0.16
9165
3401
2011.5289
Sweden
46861.5005
Sweden
9,763,565
1,375,071
0.14
1,902,926
803,548
0.02
45,451
19,192
0.42
96486
46862
38399.3274
Switzerland
5482.7215
Switzerland
8,319,769
2,149,288
0.26
1,027,161
228,656
0.02
24,533
5,461
0.22
6232
5483
6734.0767
North Macedonia
970.973
North Macedonia
2,079,308
904,795
0.44
112,137
17,647
0.02
2,678
421
0.16
1624
971
397.1413
Turkey
32834.535
Turkey
78,271,472
20,402,156
0.26
5,393,027
656,208
0.02
128,810
15,673
0.12
32835
36412.5499
Ukraine
12380.01
Ukraine
44,657,704
13,529,815
0.30
3,771,873
113,059
0.02
90,090
2,700
0.03
20935
12380
7610.094
United Kingdom
26084.3985
United Kingdom
65,397,080
11,113,712
0.17
7,567,610
617,117
0.02
180,749
14,740
0.08
20700
26084
38857.0403
Israel
1887.365
United States
319,929,162
59,766,644
0.19
91,618,826
6,156,601
0.02
2,188,278
147,048
0.07
761000
392831
365806.422
Europe
513312.195
Armenia
1658.8465
JWEE total
798,930,371
187,529,555
0.23
161,860,359
16,679,816
0.02
3,865,968
398,391
0.10
1,930,171
874,789
Azerbaijan
1392.658
JWEE 12
261,159,928
59,524,152
0.23
38,126,828
5,196,097
0.02
910,644
124,107
0.14
461,400
279,695
Kazachstan
2429.8615
JWEE Europe (excluding the Russian Federation)
429,482,492
114,886,487
0.27
58,134,186
8,417,001
0.02
1,388,511
201,037
0.14
727,141
414,967
Kyrgyzstan
508.6585
*Using 2005 and 2010 data from external data of analysis 2013.
Tajikistan
990.099
Turkmenistan
527.811
Uzbekistan
3638.3545
Canada
64710.7665
JWEE total
Armenia, Austria, Azerbaijan, Bosnia and Herzegovina, Canada, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Georgia, Germany, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Netherlands, Norway, Poland, Portugal, Republic of Moldova, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland, the United Kingdoma and the United States
United States
392830.7515
JWEE 12
Austria, Cyprus, Finland, France, Germany, Ireland, Luxembourg, Serbia, Slovenia, Sweden, Switzerland and the United Kingdom.
North America
457541.5285
Europe excl. Russia
Armenia, Austria, Bosnia and Herzegovina, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Germany, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Netherlands, Norway, Poland, Portugal, Republic of Moldova, Serbia, Slovak Republic, Slovenia, Sweden, Switzland and the United Kingdom
Commonwealth of Independent States
119646.9525
European Union
451650.7445
Other C.I.S.
13721.5015
Other european countries
3062.619
EFTA
13759.056
Other Europe
133783.332
EU/EFTA
460803.8835
CountryComments
Country
Abbreviated country profile for publication
Albania2011
TPES and RES figures are based on IEA 2010 data.
Armenia2011
TPES and RES figures are based on IEA 2010 data.
Austria2011
Bosnia and Herzegovina2011
TPES and RES figures are based on IEA 2010 data.
Belarus2011
Canada2011
Figures are Secretariat estimates.
Cyprus2011
TPES and RES figures are based on IEA 2010 data.
Czech Republic2011
Denmark2011
Estonia2011
Finland2011
France2011
Germany2011
Iceland2011
Ireland2011
Italy2011
Figures are Secretariat estimates.
Latvia2011
Luxembourg2011
Liechtenstein2011
Netherlands2011
Norway2011
Poland2011
Romania2011
1. Figures are Secretariat estimates.
2. TPES and RES figures are based on IEA 2010 data.
Serbia2011
Slovenia2011
Sweden2011
Switzerland2011
Turkey2011
Ukraine2011
TPES and RES figures are based on IEA 2010 data.
United Kingdom2011
United States2011
JWEE total 2011
1. The category "JWEE total 2011" refers to the set of responding countries providing sufficiently detailed data for 2011. This includes Albania, Armenia, Austria, Bosnia and Herzegovina, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Iceland, Ireland,Luxembourg, Netherlands, Norway, Poland, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland, Ukraine, the United Kingdom and the United States.
2. The data for Canada, Italy and Romania is based on IEA 2010 estimates.
JWEE total 2009
1. The category "JWEE total 2009" refers to the set of responding countries providing sufficiently detailed data for 2009. This includes Austria, Belgium, Cyprus, Czech Republic, Estonia, Finland, France, Germany, Ireland, Italy, Liechtenstein, Lithuania, Luxembourg, Norway, the Russian Federation, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland, the United Kingdom and the United States.
JWEE total 2007
1. The category "JWEE total 2007" refers to the set of responding countries providing sufficiently detailed data for 2007. This includes Austria, Canada, Cyprus, Finland, France, Germany, Ireland, Latvia, Liechtenstein, Lithuania, Luxembourg,Netherlands, the Russian Federation, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland, the United Kingdom and the United States.
JWEE total 2005
1. The category "JWEE total 2005" refers to the set of responding countries providing sufficiently detailed data for 2005. This includes Austria, Canada, Czech Republic, Finland, France, Germany, Lithuania, Netherlands, Norway, Slovenia, Sweden, Switzerland, the United Kingdom and the United States.
JWEE 13
1. The category "JWEE 13" refers to the set of responding countries providing sufficiently detailed data for 2007, 2009 and 2011. This includes Austria, Cyprus, Finland, France, Germany, Ireland, Luxembourg, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland and the United Kingdom.
JWEE Europe (excluding the Russian Federation) 2011
1. The category "JWEE Europe (excluding the Russian Federation) 2011" refers to the set of responding countries from the European Region providing sufficiently detailed data for 2011. This includes Albania, Armenia, Austria, Bosnia and Herzegovina, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Iceland, Ireland,Luxembourg, Netherlands, Norway, Poland, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland, Ukraine, and the United Kingdom.
2. The data for Italy and Romania is based on IEA 2010 estimates.
JWEE Europe (excluding the Russian Federation) 2009
1. The category "JWEE Europe (excluding the Russian Federation) 2009" refers to the set of responding countries from the European Region providing sufficiently detailed data for 2009. This includes Austria, Belgium, Cyprus, Czech Republic, Estonia, Finland, France, Germany, Ireland, Italy, Liechtenstein, Lithuania, Luxembourg, Norway, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland, and the United Kingdom.
JWEE Europe (excluding the Russian Federation) 2007
1. The category "JWEE total 2007" refers to the set of responding countries providing sufficiently detailed data for 2007. This includes Austria, Canada, Cyprus, Finland, France, Germany, Ireland, Latvia, Liechtenstein, Lithuania, Luxembourg,Netherlands, the Russian Federation, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland, the United Kingdom and the United States.
Armenia2013
Austria2013
Azerbaijan2013
Bosnia and Herzegovina2013
Canada2013
Croatia2013
Cyprus2013
Czech Republic2013
Denmark2013
Estonia2013
Finland2013
France2013
Germany2013
Hungary2013
Iceland2013
Ireland2013
Luxembourg2013
Netherlands2013
Norway2013
Republic of Moldova2013
Serbia2013
Slovenia2013
Sweden2013
Switzerland2013
North Macedonia2013
United Kingdom2013
United States2013
JWEE total 2013
1. The category "JWEE total 2013" refers to the set of responding countries providing sufficiently detailed data for 2013. This includes Armenia, Austria, Azerbaijan, Bosnia and Herzegovina, Canada, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Iceland, Ireland, Luxembourg, Netherlands, North Macedonia, Norway, Republic of Moldova, Serbia, Slovenia, Sweden, Switzerland, United Kingdom and the United States.
2. The data for Hungary is based on estimates made by the secretariat.
JWEE 122013
1. The category "JWEE 12" refers to the set of responding countries providing sufficiently detailed data for 2007, 2009, 2011 and 2013. This includes Austria, Cyprus, Finland, France, Germany, Ireland, Luxembourg, Serbia, Slovenia, Sweden, Switzerland and the United Kingdom.
JWEE Europe (excluding the Russian Federation) 2013
1. The category "JWEE Europe (excluding the Russian Federation) 2013" refers to the set of responding countries from the European Region providing sufficiently detailed data for 2013. This includes Armenia, Austria, Bosnia and Herzegovina, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Iceland, Ireland, Luxembourg, Netherlands, North Macedonia, Norway, Republic of Moldova, Serbia, Slovenia, Sweden, Switzerland and the United Kingdom.
2. The data for Hungary is based on estimates made by the secretariat.
Ireland 2015
1. Consumption data derives from 2013 data submission.
JWEE total 2015
1. The category "JWEE total 2015" refers to the set of responding countries providing sufficiently detailed data for 2015. This includes Armenia, Austria, Azerbaijan, Bosnia and Herzegovina, Canada, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Georgia, Germany, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Netherlands, Norway, Poland, Portugal, Republic of Moldova, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland, the United Kingdom and the United States.
2. Consumption data for Ireland is based on 2013 data.
JWEE 12 2015
1. The category "JWEE 12" refers to the set of responding countries providing sufficiently detailed data for 2007, 2009, 2011, 2013 and 2015. This includes Austria, Cyprus, Finland, France, Germany, Ireland, Luxembourg, Serbia, Slovenia, Sweden, Switzerland and the United Kingdom.
2. Consumption data for Ireland is based on 2013 data.
JWEE Europe (excluding the Russian Federation) 2015
1. The category "JWEE Europe (excluding the Russian Federation) 2015" refers to the set of responding countries from the European Region providing sufficiently detailed data for 2015. This includes Armenia, Austria, Bosnia and Herzegovina, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Germany, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Netherlands, Norway, Poland, Portugal, Republic of Moldova, Serbia, Slovak Republic, Slovenia, Sweden, Switzland and the United Kingdom.
2. Consumption data for Ireland is based on 2013 data.
JWEE total 2017
1. The category "JWEE total 2017" refers to the set of responding countries providing sufficiently detailed data for 2017. This includes Armenia, Austria, Azerbaijan, Belarus, Bosnia and Herzegovina, Canada, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Georgia, Germany, Iceland, Ireland, Italy, Liechtenstein, Lithuania, Luxembourg, Netherlands, North Macedonia, Norway, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland, Ukraine, United Kingdom and the United States.
JWEE 12 2017
1. The category "JWEE 12" refers to the set of responding countries providing sufficiently detailed data for 2007, 2009, 2011, 2015 and 2017. This includes Austria, Cyprus, Finland, France, Germany, Ireland, Luxembourg, Serbia, Slovenia, Sweden, Switzerland and the United Kingdom.
JWEE Europe (excluding the Russian Federation) 2017
1. The category "JWEE Europe (excluding the Russian Federation) 2017" refers to the set of responding countries from the European Region providing sufficiently detailed data for 2017. This includes Armenia, Austria, Bosnia and Herzegovina, Croatia, Cyprus, Czech Republic, Estonia, Finland, France, Germany, Iceland, Ireland, Italy, Liechtenstein, Lithuania, Luxembourg, Netherlands, Norway, Serbia, Slovak Republic, Slovenia, Sweden, Switzerland and the United Kingdom.
Long-term trend of more efficient use of wood resource
Detailed data on energy wood sources show indirect wood supply now accounts for largest share
The UNECE/FAO Joint Wood Energy Enquiry (JWEE) 2017 was sent out to all UNECE member states. Overall 31
countries replied to the enquiry representing 54% of the countries in the UNECE region1. Although overall country
replies slightly decreased compared with 2015, the number of countries providing good quality data remained stable.
Wood energy supply
Final results reveal that wood energy accounts for 3.8% of the total primary energy supply (TPES) and 35.4% of the
renewable energy supply (RES) in the UNECE region, remaining the leading source of renewable energy. Woody
biomass covers 26.2 to 19.6% of the primary energy demands of Finland and Estonia and 19.3 to 17.3% of the primary
energy demands of Sweden and Lithuania. Woody biomass accounts for over half of the renewable energy supply in
Austria, Belarus, Czech Republic, Estonia, Finland, Lithuania, Serbia, Slovak Republic, Slovenia and Ukraine. Around
42.9% of the total mobilised woody biomass supply is used for energy purposes.
These are the main conclusions of the UNECE/FAO Joint Wood Energy Enquiry (JWEE), a biennial questionnaire that
aims to shed light on the real role of wood energy within the region by promoting cross-sectoral communication and
cooperation between the energy and forestry sectors in the member States. Now in its seventh round since 2005, the
JWEE has become a reference source of information on wood energy, drawing responses from an increasing number of
countries.
Sources of wood energy
Indirect sources including co-products and residues2 from the forest-based industries and processed wood-based fuels
contribute 50.9% of the wood fibres for energy generation. The source of wood fibre for wood pellets3) is 75.6% from
indirect sources, 23.2% from direct sources and 1.1% from post-consumer wood.
33.8% of the wood fibres for energy generation derive directly from woody biomass from forests and wooded areas
outside forests. The proportion varies among countries with Armenia, Azerbaijan, Croatia, Czech Republic, Georgia,
Italy, North Macedonia, Serbia and Ukraine relying heavily (60% or more) on direct supplies of wood fibres whereas
countries such as Austria, Bosnia and Herzegovina, Canada, Cyprus, Finland, Iceland, Liechtenstein, Luxembourg,
Sweden and United Kingdom rely mainly (60% or more) on wood supply from indirect sources.
The United States (47.7%), Finland (42.4%), Sweden (40.0%) and Canada (31.6%) have large shares of energy
generated from black liquor reflecting the relative importance of the pulp and paper industries in the forest sector for the
generation of wood energy.
Overall, recovered post-consumer wood (mainly wood waste from construction, but also packaging and old furniture)
constitutes a minor category contributing 5.3% of wood energy. It is mainly consumed in power applications and waste
to energy plants. In general, however, data on recovered wood is difficult to obtain and often not discernible from
generic waste statistics. It is reported as a significant source of wood energy in Germany, Ireland, Netherlands and
Switzerland.
1 Armenia, Austria, Azerbaijan, Belarus Bosnia and Herzegovina, Canada, Croatia, Cyprus, Czech Republic, Estonia,
Finland, France, Georgia, Germany, Iceland, Ireland, Italy, Liechtenstein, Lithuania, Luxembourg, Netherlands, North
Macedonia, Norway, Serbia, Slovakia, Slovenia, Sweden, Switzerland, Ukraine, United Kingdom and United States.
These are not necessarily the same reporting countries as in past years.
2 These co-products can be solid (sawdust, chips, slabs, etc.) or liquid (e.g. black liquor or tall oil).
3 Weighted average of available information in 2017 for Belarus, Bosnia and Herzegovina, Canada, Croatia, Estonia,
France, Ireland, Netherlands, Serbia, Slovak Republic, Slovenia, Switzerland and the United Kingdom.
Uses of wood energy
Wood energy is consumed in roughly equal measure by industry (39.3%) and other final consumers (40.3%). The
residential sector accounted for 89.3% of wood energy consumed by other final consumers. Wood energy consumption
in the power and heat sector accounted for 20.4%. The highest shares of wood energy use in the power and heat sector
were reported from Belarus, Estonia, Ireland, Lithuania, Luxembourg and United Kingdom. The forest products
industry typically consumes energy generated from the solid and liquid co-products of its manufacturing processes.
Countries with important forest industries, such as Bosnia Herzegovina, Canada, Finland, Sweden and United States
therefore have a higher share of industrial consumption. Residential use, mainly dependent on primary solid biomass
sources, is most important in central Asian countries, Liechtenstein, North Macedonia and Serbia.
Main Trends4
Based on a subset of 12 countries that have responded to all rounds of the enquiry (JWEE12 - Austria, Cyprus, Finland,
France, Germany, Ireland, Luxembourg, Serbia, Slovenia, Sweden, Switzerland and the United Kingdom) it was
possible to assess the development of the use of wood energy across all reporting years. The results from these countries
confirm the continuing predominant role of wood in renewables. Total wood energy consumption increased from 193
million m³ in 2007 to 265 million m³ in 2017. The role of wood in TPES increased from 4.4% in 2007 to 5.9% in 2013.
Since 2013 the share of wood energy in TPES remained constant at 6%. The share of wood in renewables (RES) was
43.0% in 2017 - a decline of 7.1 percentage points recorded between 2007 and 2017. This perhaps reflects the faster
rates of growth of other sources of renewable energy such as wind and solar and the overall growth of renewables.
A larger share of the wood supply is being mobilised for energy purposes as confirmed by the substantial increase in the
reported energy use of wood (38.9% in 2007 to 46.9% in 2017). Data reveals the tendency that member states
increasingly source wood energy from indirect sources and less wood directly from the forests (from 46.5% in 2007 to
53.2% in 2017). Also wood energy use in the industry and residential sector decreased while power and heat sector
consumed more wood for energy.
Consumption of wood pellets dramatically increased in 2017. In 2007 16.2 kg wood pellets were consumed per capita
while this figure increased to 55.2 kg in 2017. The leading consumer of wood pellets was the United Kingdom.
One of the biggest movers, in relative terms, is the United Kingdom where wood now accounts for 3.9% of TPES (up
from 0.1% in 2005). After a sharp increase of the share of wood energy in the RES between 2005 and 2011 (from 7.9%
to 27.4%), a slight decrease (1%) was recorded between 2011 and 2013. However, in 2015 the share almost doubled to
41.4%. In 2017 the share of wood energy in RES dropped to 40.4%.
Further Info:
Trends can best be seen at the national level using the country profiles available at http://www.unece.org/forests/jwee
along with a complete database. For any additional comments or questions kindly contact:
