*Prepared by Christian Soerlien Molstad
NOTE: The designations employed in this document do not imply the expression of any opinion whatsoever on the part
of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its
authorities, or concerning the delimitation of its frontiers or boundaries.
Economic Commission for Europe
Conference of European Statisticians
Group of Experts on Migration Statistics
Geneva, Switzerland, 26−28 October 2022
Item A of the provisional agenda
Improvements in use of administrative data for migration statistics
Finding family relations: About quality issues
regarding family immigration statistics and their
potential solution using administrative data
Note by Statistics Norway*
Abstract
The implementation of Directive 2004/38/EC into the EEA-agreement and the establishment of a
registration scheme in the Norwegian immigration administration meant that citizens of the EEA-area
are no longer required to obtain residence permits to stay in Norway. This has led to data loss in
administrative records concerning reason for immigration and statistics derived from this. This paper
will outline the background of a project, financed by Ministry of Labour and Social Inclusion (AID),
initiated to address the issue of missing data regarding the persons of reference for family immigrants.
The project is still in its initial, explorative stage, and the paper will therefore mainly limit itself to a
description of the data sources used in the production of Statistics Norway’s (SN) statistics about family
immigration and the consequences of the introduction of the registration scheme for citizens of EEA-
countries for the quality of this data. The paper will also describe already implemented measures and
provide some tentative considerations of how administrative data can be applied to link family
immigrants with their actual person of reference.
Working paper 8/Rev
Distr.: General
19 October 2022
English
Working paper 8/Rev
2
I. Introduction
1. Data regarding reason for immigration constitute the foundation for several statistics
published by Statistics Norway (SN). Among these are numbers on family immigration, as
presented in the monitor for family migration which is published annually as alternately
article and report (Gulbrandsen & Molstad, 2020; Molstad et al., 2022; Molstad &
Steinkellner, 2020).
2. The primary source of data for reason for immigration is information derived from decisions
regarding residence permits, taken by the Norwegian Directorate of Immigration (UDI).
Until the late 2000s such permits were required for all non-Nordic immigrants. However, the
passing of Directive 2004/38/EC and its implementation in the EEA-agreement meant that
citizens of the EEA-area have no longer been required to obtain residence permits to stay in
Norway. Instead such citizens must register with the police within three months of arrival.
3. A consequence of the simplified procedure associated with this registration scheme is
gradual loss of data. Among the data increasingly lost is information regarding person of
reference in the case of family immigration. Data regarding the person of reference, the
person already living in Norway and to which the family migrant is immigrating, is of great
value, as it is used to categorize family immigrants according to the background of the
family member.
4. To address this information loss, a project, financed by Ministry of Labour and Social
Inclusion (AID), has been initiated. The outcome of the project, a new method for
identifying persons of reference, along with an internal memo documenting the process, is to
be published in March 2023.
5. The project is still in its early, explorative stage. This paper will outline the background of
the project, including a description of the data sources, their use in production of official
statistics about family immigration and the consequences of the introduction of the
registration scheme for citizens of EEA-countries for data quality. The paper will
furthermore describe some of the measures already in place to counter the challenges
associated with the loss of data regarding persons of reference and tentatively suggest an
approach through which administrative data can be used to further increase the quality of the
mentioned variable.
II. Family immigration statistics and its data sources
6. The registration system for immigration and emigration in Norway is based on three main
areas of administration: the adoption authorities, the immigration administration and the
national population register (Vassenden, 2015, pp. 129-130), for which only the two latter
are relevant for the topic at hand. Statistics regarding reason for immigration is mainly based
on data from the Aliens Register (UDB), administered by the Norwegian Directorate of
Immigration (UDI), and the Central Population Register (DSF), administered by the
Directorate of Taxes (Skatteetaten).
7. While data from both sources are used in producing the data file from which the annual
statistics are generated, assigning each immigrant (with non-Nordic citizenship) a reason for
immigration is first and foremost reliant on the values in the variable reason for decision in
UDB. If information is missing in this variable, supplementary sources are used. Among the
supplementary sources are data from administrative records regarding participation in labor
market and education.
Working paper 8/Rev
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8. On the finished file reasons for immigration are grouped in five main categories in variable
inngrunn1:
• Labour
• Family
• Refuge
• Education
• Other
Alongside these categories there is also a category for “unknown”, in which immigrants that
have no data for reason for immigration is assigned.
The latest file of which published statistics has been made covers immigration in the period
1990-2020.1 Figure 1 shows immigration by reason among immigrants with non-Nordic
citizenship in this period. Out of total 932 000 persons about 334 000 (36 percent)
immigrated due to family reasons. This makes family the single most common reason for
immigrating to Norway. As we can see the volume of immigration due to family increased
markedly from the mid-1990s until the early 2010s. There are several reasons for this. One
of the reasons is the increased influx of refugees from the former Yugoslavia, Syria, Eritrea,
Somalia, Afghanistan, Iraq and other countries with armed conflicts and/or systematic
persecution. Another is the enlargement of the EU in 2004 and 2007, after which labour
migration from new member states (primarily Poland, Lithuania and Romania) increased
dramatically. Most family migrants have immigrated to reunite with either refugees or
labour migrants.
Figure 1. Immigrations to Norway, by reason, 1990-2020
1 Statistics updated with data for immigrations in 2021 have not been published yet. Publication of updated statistics
is set for the 25th of October this year.
9. Family immigration has in other words to be considered in light of other types immigration,
as it to a considerable extent constitutes a consequence and extension of these. Data
regarding the so-called person of reference, the person to which family migrant is
immigrating, is therefore of central importance as it provides much needed context regarding
the migration. A key distinction can be made between immigration to immigrants and
Norwegian-born children of immigrants and immigration to persons without immigrant
background (most often males). Another important distinction, within the first category, can
be drawn between those immigrating to labor migrants and those immigrating to refugees.
An analysis of outcome with regards labor market, income and housing in a ten year-period
from arrival reveals significant differences among family immigrants according to the
background of the person of reference (Molstad et al., 2022).
10. With the aid of the social security number registered by UDI in the Aliens Register (UDB),
background information regarding the person of reference can be added from data derived
from the Central Population Register (DSF). The final data file therefore includes a range of
variables regarding the individual, including his/her country of birth (rpfodeland), gender
(rpkjoenn), first citizenship (rpforststatsborg), immigrant background (immigrant,
Norwegian-born to immigrant parents, Norwegian-born to Norwegian-born parents etc.)
(rpinvkat), country background (rplandbak3gen), registry status (resident, emigrated, dead
etc.) (rpregstatus) and municipality of residence (rpkommnr). For persons of reference that
are themselves immigrants, additional variables concerning their age at time of immigration
(rpinnalder) and reason for immigration (rpinngrunn and rpinngrunn1).
11. These variables have been frequently utilized in Statistics Norway’s publications regarding
family migration. Cross tables displaying family immigration by year according to
immigrant background and gender and reason for immigration formed, until recently, a part
of the package of tables published annually in the monitor for family immigration as a part
of Statistics Norway’s framework agreement with the Ministry of Labour and Social
Inclusion (AID).
12. Figure 2 shows a simplified graphic representation of the first cross table, excluding the
gender dimension. The overall tendency as glanced from the figure confirms the statement
made above about family migration to a large extent being an extension of labour migration
and the immigration of refugees. A third important category of family immigrants can also
be discerned: family immigrants to other family immigrants.
Working paper 8/Rev
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Figure 2. Family immigrations to Norway, by reason for immigration of person of
reference, 1990-2020
III. 2009: Introduction the registration scheme and loss of data
13. Publications made since 2021 have not included these cross tables or statistics derived from
them. This is due to the loss of data regarding the identity of the person of reference and the
gradual increase of this loss since the introduction of the so-called registration scheme in
2009.
14. The registration scheme was introduced as a result of the adoption of Directive 2004/38/EC
into the EEA agreement in 2007 (Regjeringen.no, 2022). The directive grants citizens of the
European Union “the right to move and reside freely within the territory of the Member
States”. This right is “also granted to their family members, irrespective of nationality”
(EUR-Lex, 2022, pp. 78-79). One of the main purposes of the directive was to simplify the
process of changing country residence within the EU/EEA-area for citizens and their family
members. However, while EU/EEA-citizens do not need permits to reside in member states,
the directive states that “for periods of residence of longer than three months, Member States
should have the possibility to require Union citizens to register with the competent
authorities in the place of residence, attested by a registration certificate issued to that effect”
(EUR-Lex, 2022, p. 81). In Norwegian immigration administration a registration scheme
was established by UDI, through which EU/EEA-citizens and family members of these have
been required to register within three months of arrival. The establishment of a registration
scheme was not stipulated in the directive and therefore not a given. Its creation instead
stemmed from domestic administrative concerns.
15. A consequence of these procedural changes has been the loss of data with regards to
immigrants from EU/EEA-countries. The codes used to classify immigrants in the
registration scheme are not as extensive as those applied for residence permits. While the
main categories for reasons of migration (labour, family, education etc.) may still be
discerned from the data, the quality of finer subdivisions has become more questionable
(Dzamarija, 2013, pp. 9-10). A further deterioration has taken place concerning data on the
relation between the family migrant and his/her person of reference (Dzamarija, 2013, p.
21), as well as a more general loss of data about the person of reference. The latter
developments have led to an increase in family immigrants with “unknown” person of
reference, starting from 2009. This situation is evident in figure 2, where the number of
family migrants with person of reference with “unknown” reason for immigration increases
from this year.2
16. A major division has in other words formed between family immigration from areas inside
and outside the EEA-area in terms of quality of data. From a total 334 000 family
immigrants arriving Norway in the period 1990-2020, 57 500 (17 percent) did not have a
person of reference registered. A majority of these, 47 700 (83 percent), were citizens from
EU/EEA-countries. However, the loss of data is not exclusive to family immigration from
the EU/EEA-area. As figure 3 shows, there has also been an increase in family immigrants
from countries outside the EU/EEA-area for which the social security number (rpfnr) of the
person of reference is not available or valid.
Figure 3. Family immigrations to Norway, percentage missing person of reference,
1990-2020
2 However, while the marked increase after 2009 of family immigrants coming to immigrants with “unknown” reason
for immigration clearly coincides with the introduction of the registration scheme, the scheme does not account for the
sudden burst of missing values from 2016 and on. At first glance the graphs in figure 2 and 3 may seem to present
clashing narratives. While in figure 3 the main increase in missing values occur in the aftermath of the introduction of
the registration scheme in 2009, in figure 2 the main increase is in 2016, well after the procedural changes. An
explanation for the conflicting patterns is to be found in differences of population. Figure 3 includes all family
migrations in the period from 1990 until 2020, while figure 2 only includes family migrations in which enough
information was available about the person of reference to indicate that he/she was an immigrant. The increase of
missing values regarding reason for immigration from 2016 henceforth is therefore a sign of additional loss of data, of
which cause we still have not been able to ascertain.
17. A second division in terms of data quality may be made between the two main types of
family immigration: a) family reunions and b) family establishments. Family reunions
includes family immigration where the family relation existed before the person of reference
was resident in Norway. Family establishments refers to those cases where the family
migrant immigrated in order to establish a new relationship (in most cases a marriage) with a
person resident in Norway (Molstad et al., 2022, p. 9).
18. In the case of family establishments, absence of social security numbers for persons of
reference is low on the finished data file. In less than one percent of the family
establishments in the period 1990-2020 there was no valid social security number available
for the person of reference. This was the case regardless of whether the family immigrant
was an EU/EEA-citizen or not (figure 4). The reason behind this difference in the data
quality between family reunions and establishments is to be found in already existing
measures implemented to cover the gaps in the data. These measures, along with some
tentative suggestions for an approach for further steps, will be outlined briefly below.
Figure 4. Percentage family immigrants with missing person of reference, by type of
family immigration
IV. Some preliminary considerations on solutions
19. Missing data is not a new challenge for statistics based on administrative data. So is not the
task of establishing linkage between related units in different data sources. An example of
the latter can be found in register-based census data, in which data from the Central
Population Register (DSF) and the Ground Parcel, Address and Building Register
(Matrikkelen) have been integrated. In order to account for incomplete linkage between
households in DSF and dwellings in Matrikkelen, a double nearest-neighbour imputation
method (DNNI) has been applied to impute dwellings for households that do not
0
10
20
30
40
50
60
Establishment Reunion Establishment Reunion
EU/EEA Non-EU/EEA
Working paper 8/Rev
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successfully link with a dwelling, using other, statistically similar households as “donors”
for picking a non-linked dwelling in the same geographical area (Zhang & Hendriks, 2012).
An imputation method also applying a near-neighbour methodology has furthermore been
used to impute missing data among immigrants in the Register of the Population’s Level of
Education (BU) (Jentoft, 2014).
20. As for the missing data regarding persons of reference for family immigrants, we are
currently hoping imputation will not be a necessary step. A central goal of the project is to
establish linkages that a) do not rest on the assumption of statistical similarity between
family immigrants with missing values and those without and b) reflect actual relations
between family immigrants and persons of reference, not relations based on statistical
prediction of similarity. The preconditions for succeeding with this goal is in principle
present. A central premise for family immigration is the residence of a person of reference
with who the family migrant may either reunite or start a new family with. We can therefore
assume that all persons of reference are present in administrative records mirroring the
resident population. The challenge is to find a method (or, as will be suggested below,
methods) to establish a link between the family migrant and what, with reasonable
probability, can be assumed to be the actual person of reference.
21. As a family relation constitutes the precondition for family immigration, data regarding this
relation should form the basis for a method for which to link the migrant to his/her person of
reference. The first task in such a method is to discern the family relation between the family
immigrant and his/her person of reference. The second is to find a variable or combination of
variables which can lead this relation to be associated with an identifiable person.
22. As mentioned above such a method already exists for family establishment. The family
relation in this case is the marriage between these individuals. Family migration due to
spouse is possible to identify through the variable inngrunn derived from data from UDB,
which provides a detailed classification of reason for migration. In this variable family
migration to spouse constitutes a foundation for a subgroup of variations (FAM-MAKE-,
FAMMAKE-) within a larger group of family migration-related codes (FAM-) (Dzamarija,
2013, p. 51). Marital data concerning the identity of married individuals’ spouses is present
in data Statistics Norway receives from the Central Population Register (DSF) through the
variable ekt_fnr. In the production of the annual file data containing the social security
number of spouses (in the variable ekt_fnr), is used to identify the partner in cases of family
migrants that have immigrated to such a family member. Other data regarding the
successfully identified reference persons are subsequently added.
23. The result of this can be seen in figure 4. As pointed out very few persons of reference are
missing in the case of family establishments on the finished data file. As for family reunions,
the number of migrations with missing information concerning person of reference remains
high.
24. A similar method, using the relation between the family migrant and the person of reference
as indicated in the variable inngrunn, may not be utilized for the remaining, overwhelmingly
family reunion-related, cases with missing information. In 79 percent of these migrations
reason for migration is registered as “EEA-citizen who is family member of an EEA-citizen
who is employed etc. or who is spouse/partner or dependent child under 21 year of age of an
EEA-citizen accepted at an approved institution of education” (EØSFAMBORG). A further
3 percent includes non-EEA-citizens with the same alternatives of relations to an EEA-
citizens (EØSFAMIKKEBORG). It is not possible to deduce from these codes whether the
family migrant is a) married or in another form of partnership with the person of reference,
b) is the child of the person of reference or c) has another relation to the him/her. The
Working paper 8/Rev
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process of identifying the actual relation between family migrant and person of reference
must hence rely on using additional, indirect indices.
25. One such indicator may be age at time of immigration of the family migrant, as already
available in the data file in the variable innalder. Breaking down the family immigrants with
missing persons of reference according to age reveals that 69 percent were below 18 years of
age. About half were furthermore below ten years of age and nearly two thirds less than 15
years old (table 1). It is natural to assume that many (but not all) of these family immigrants
of young age have immigrated to a parent and that the person of reference is therefore either
their father or mother.
Table 1. Family immigrants with missing person of reference, by five year-interval
age at time of immigration
26. For family immigrants above age of 18 at the time of immigration, a larger array of probable
alternatives is available. It is probable that in many cases the immigration will be of spouses
reuniting. However, it is also likely that at least some of the immigrations involve parents
reuniting with a son or daughter, older children immigrating to reunite with a father or
mother or siblings accompanying parents moving to reunite with a son or daughter. The
mentioned alternatives are among the ones mentioned in chapter 6 of the act relating to the
admission of foreign national into the realm (the immigration act) as legal options for family
immigration (Lovdata, 2022) and do not constitute an exhaustive list of possible alternatives.
27. Age at time of immigration does in other words not provide sufficient information, at least
not alone, to assign a family relation between family immigrants and the person of reference.
Finding a method for establishing linkages between family immigrants and a person of
Working paper 8/Rev
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reference will most probably have to rely on the use of a range of auxiliary variables in
combination. In order to identify these auxiliary variables, we will have to perform the
following preliminary steps:
i. Identify the possible constellations of family relations that qualify for family
immigration
ii. Identify data available, either in UDB or DSF or in other administrative data
sources, that may be utilized to distinguish family migrants according to their
probable relation to their person of reference as identified in step i
iii. Identify data that can act to utilize this relation to link the family migrant to a person
registered in DSF
While the first step is taxonomic, the second and third constitute the start of developing
methods that by default will have to be different according to each “class” of family relation.
V. Conclusion
28. The project described in this paper is only in its preliminary, explorative phase. In the
coming months a systematic mapping of possible data sources and of potential methods to
utilize these to identify linkages between family immigrants and their persons of reference
will be conducted. Statistics Norway has available a range administrative data that can
provide auxiliary variables for these methods.
29. An important challenge is the plurality of possible family relations that may legally qualify
for family immigration. A central task will therefore be to find devising a way of mutually
excluding family immigration according to the type of family relation between the
immigrants and his/her person of reference.
VI. References
Dzamarija, M. T. (2013). Innvandringsgrunn 1990-2011, hva vet vi og hvordan kan
statistikken utnyttes (34/2013). Statistisk Sentralbyrå.
https://www.ssb.no/befolkning/artikler-og-
publikasjoner/_attachment/135482?_ts=140bf5cf4f8
EUR-Lex (2022). Directive 2004/38/EC of the European Parliament and of the
Council of 29 April 2004 on the right of citizens of the Union and their family
members to move and reside freely within the territory of the Member States, 47 77
(2022). https://eur-lex.europa.eu/legal-
content/EN/TXT/PDF/?uri=CELEX:32004L0038&from=EN
Gulbrandsen, F. B., & Molstad, C. S. (2020). Færre familieinnvandringer i 2019.
Statistisk Sentralbyrå. https://www.ssb.no/befolkning/artikler-og-
publikasjoner/faerre-familieinnvandringer-i-2019
Jentoft, S. (2014). Imputation of missing data among immigrants in the Register of
the Population's Level of Education (BU) (Notater/Documents 2014/27). Statistisk
Sentralbyrå. https://www.ssb.no/utdanning/artikler-og-
publikasjoner/_attachment/183364?_ts=146a98197c8
Working paper 8/Rev
11
Lovdata (2022). Act relating to the admission of foreign nationals into the realm
and their stay here (Immigration Act), chapter 16, (2022).
https://lovdata.no/dokument/NL/lov/2008-05-15-35/KAPITTEL_6#%C2%A742
Molstad, C. S., Gulbrandsen, F. B., & Steinkellner, A. (2022). Familieinnvandring
og ekteskapsmønster 1990-2020. (2022/03). Statistisk Sentralbyrå.
https://www.ssb.no/befolkning/innvandrere/artikler/familieinnvandring-og-
ekteskapsmonster-1990-2020
Molstad, C. S., & Steinkellner, A. (2020). Familieinnvandring og
ekteskapsmønster 1990-2018 (2020/05). Statistisk Sentralbyrå.
https://www.ssb.no/befolkning/artiklerogpublikasjoner/_attachment/411225?_ts=1
6ffbcb2098
Regjeringen.no. (2022). Direktivet om fri personbevegelighet. EØS-notatbasen.
https://www.regjeringen.no/no/sub/eos-notatbasen/notatene/2006/mai/direktivet-
om-fri-personbevegelighet/id2431482/
Vassenden, K. (2015). Om kvaliteten på den norske inn- og utvandringsstatistikken
(Notater/Documents 2015/17). Statistisk Sentralbyrå.
https://www.ssb.no/befolkning/artikler-og-
publikasjoner/_attachment/225080?_ts=14ce62dab98
Zhang, L.-C., & Hendriks, C. (2012). Micro integration of register-based census
data for dwelling and household. United Nations Economic Commission for
Europe (UNECE): Conference of European Statisticians. Work Session on
Statistical Data Editing
https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.44/2012/16_Norway.
pdf
I. Introduction
II. Family immigration statistics and its data sources
III. 2009: Introduction the registration scheme and loss of data
IV. Some preliminary considerations on solutions
V. Conclusion
VI. References
Russian
*Подготовил Кристиан Соэрльен Молстад
ПРИМЕЧАНИЕ: Обозначения, используемые в настоящем документе, не подразумевают выражения какого бы
то ни было мнения со стороны Секретариата Организации Объединенных Наций относительно правового
статуса какой-либо страны, территории, города или района или их властей, или относительно делимитации их
границ.
Европейская экономическая комиссия
Конференция европейских статистиков
Группа экспертов по статистике миграции
Женева, Швейцария, 26-28 октября 2022 года
Пункт А предварительной повестки дня
Усовершенствования в области использования административных данных по статистике
миграции
Установление семейных отношений: О
проблемах качества, касающихся статистики
семейной иммиграции, и их потенциальном
решении с использованием административных
данных
Примечание от Статистического управления Норвегии*
Аннотация
Внедрение Директивы 2004/38/EC в соглашение о ЕЭЗ и создание системы регистрации в
норвежской иммиграционной администрации означало, что гражданам зоны ЕЭЗ больше не
требуется получать вид на жительство для пребывания в Норвегии. Это привело к исчезновению
из административных документов данных, касающихся причины иммиграции, и статистических
данных, полученных на этой основе. В этом документе будет изложен обзор проекта,
финансируемого Министерством труда и социальной интеграции (AID), инициированного для
решения проблемы отсутствия данных о референтных лицах семейных иммигрантов. Проект все
еще находится на начальной, ознакомительной стадии, поэтому документ в основном
ограничится описанием источников данных, используемых при подготовке статистики
Статистического управления Норвегии (SN) о семейной иммиграции и последствиях введения
схемы регистрации граждан стран ЕЭЗ для качества этих данных. В документе также будут
описаны уже реализованные меры и представлены некоторые предварительные соображения о
Рабочий документ 8/Рев
Распр.: Общее
07 February 2023
Английский
Рабочий документ 8/Рев
2
том, как можно применять административные данные для установления связи между семейными
иммигрантами и их фактическим референтным лицом.
I. Введение
1. Данные о причинах иммиграции составляют основу для нескольких видов
статистических данных, публикуемых Статистическим управлением Норвегии (SN).
Среди них цифры по семейной иммиграции, представленные в мониторинге семейной
миграции, который ежегодно публикуется в виде попеременно статьи и отчета
(Gulbrandsen & Molstad, 2020; Molstad et al., 2022; Molstad & Steinkellner, 2020).
2. Основным источником данных об основаниях иммиграции является информация,
полученная из решений по видам на жительство, принятых Норвежским управлением
иммиграции (UDI). До конца 2000-х годов такие виды на жительство требовались для
всех иммигрантов, не являющихся выходцами из Северной Европы. Однако принятие
Директивы 2004/38/EC и ее реализация в соглашении о ЕЭЗ означали, что гражданам
зоны ЕЭЗ больше не требуется получать вид на жительство для пребывания в
Норвегии. Вместо этого такие граждане должны зарегистрироваться в полиции в
течение трех месяцев с момента прибытия.
3. Следствием упрощенной процедуры, связанной с этой схемой регистрации, является
постепенная потеря данных. Среди данных, которые теряются в растущих объемах, и
информация о референтном лице в случае семейной иммиграции. Данные,
касающиеся референтного лица, то есть лица, уже проживающего в Норвегии, к
которому иммигрирует семейный мигрант, имеют большую ценность, поскольку они
используются для классификации семейных иммигрантов в соответствии с
происхождением члена семьи.
4. Для устранения потери этой информации был инициирован проект, финансируемый
Министерством труда и социальной интеграции (AID). Результат проекта - новый
метод идентификации референтных лиц - наряду с внутренним меморандумом,
документирующим процесс, должен быть опубликован в марте 2023 года.
5. Проект все еще находится на ранней, исследовательской стадии. В этом документе
будет изложена предыстория проекта, включая описание источников данных, их
использования при составлении официальной статистики семейной иммиграции и
последствий введения схемы регистрации граждан стран ЕЭЗ для качества данных.
Кроме того, в документе будут описаны некоторые из уже принятых мер по решению
проблем, связанных с потерей данных о референтных лицах, и предложено
предварительное описание подхода, с помощью которого административные данные
могут быть использованы для дальнейшего повышения качества упомянутой
переменной.
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II. Статистика семейной иммиграции и источники данных о ней
6. Система регистрации иммиграции и эмиграции в Норвегии основана на трех
основных областях управления: органах по усыновлению, иммиграционной
администрации и национальном регистре населения (Vassenden, 2015, стр. 129–130),
из них только две последние имеют отношение к рассматриваемой теме.
Статистические данные о причинах иммиграции в первую очередь основаны на
данных Регистра иностранцев (UDB), находящегося в ведении Норвежского
управления иммиграции (UDI), и Центрального регистра населения (DSF),
находящегося в ведении Налогового управления (Skatteetaten).
7. Хотя при создании файла данных, из которого генерируется годовая статистика,
используются данные из обоих источников, присвоение каждому иммигранту (с
гражданством, не относящимся к Северной Европе) причины иммиграции в первую
очередь зависит от значений переменной причины принятия решения в UDB. Если в
этой переменной отсутствует информация, используются дополнительные источники.
Среди дополнительных источников - данные из административных отчетов,
касающиеся участия в рынке труда и образовании.
8. В готовом файле причины иммиграции сгруппированы по пяти основным категориям
в переменной inngrunn1:
• Труд
• Семья
• Убежище
• Образование
• Другое
Наряду с этими категориями существует также категория «неизвестно», в которую
попадают иммигранты, у которых нет данных о причине иммиграции.
Последний файл, из которого была составлена опубликованная статистика,
охватывает иммиграцию в период 1990–2020 годов.1 На рисунке 1 показана
иммиграция в разбивке по причинам среди иммигрантов с гражданством стран, не
относящихся к Северной Европе, за этот период. Из общего числа 932 000 человек
около 334 000 (36 процентов) иммигрировали по семейным обстоятельствам. Это
делает семью единственной наиболее распространенной причиной иммиграции в
Норвегию. Как мы видим, объем иммиграции по семейным обстоятельствам заметно
увеличился с середины 1990-х до начала 2010-х годов. Этому есть несколько причин.
Одной из причин является возросший приток беженцев из бывшей Югославии, Сирии,
Эритреи, Сомали, Афганистана, Ирака и других стран, где идут вооруженные
конфликты и/или систематические преследования. Другая причина - расширение ЕС в
2004 и 2007 годах, после которого резко возросла трудовая миграция из новых
государств-членов (в первую очередь Польши, Литвы и Румынии). Большинство
семейных мигрантов иммигрировали, чтобы воссоединиться либо с беженцами, либо с
трудовыми мигрантами.
1 Статистика, актуализированная данными по иммиграции в 2021 году, еще не опубликована. Публикация
обновленной статистики назначена на 25 октября этого года.
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Рисунок 1. Иммиграция в Норвегию в разбивке по причинам, 1990–2020
9. Другими словами, семейную иммиграцию следует рассматривать в свете иммиграции
других видов, поскольку она в значительной степени является следствием и
продолжением этих видов. Таким образом, данные, касающиеся так называемого
референтного лица, лица, к которому иммигрирует семья мигранта, имеют
первостепенное значение, поскольку они обеспечивают столь необходимый контекст
миграции. Ключевое различие можно провести между иммиграцией иммигрантов и
детей иммигрантов, родившихся в Норвегии, и иммиграцией лиц без иммигрантского
происхождения (чаще всего мужчин). Еще одно важное различие в рамках первой
категории можно провести между теми, кто иммигрирует к трудовым мигрантам, и
теми, кто иммигрирует к беженцам. Анализ результатов в отношении рынка труда,
доходов и жилья за десятилетний период с момента прибытия позволяет выявить
значительные различия между семейными иммигрантами в зависимости от
происхождения референтного лица (Molstad et al., 2022).
10. С помощью номера социального страхования, зарегистрированного UDI в Реестре
иностранцев (UDB), можно добавить справочную информацию о референтном лице из
данных, полученных из Центрального регистра населения (DSF). Таким образом,
окончательный файл данных включает ряд переменных, касающихся человека,
включая его/ее страну рождения (rpfodeland), пол (rpkjoenn), первое гражданство
(rpforststatsborg), происхождение иммигранта (иммигрант, норвежец, родившийся у
родителей-иммигрантов, норвежец, родившийся у родителей, родившихся в Норвегии
и т.д.). (rpinvkat), сведения о стране (rplandbak3gen), статус регистрации (резидент,
эмигрировал, умер и т.д.) (rpregstatus) и муниципалитет проживания (rpkommnr). Для
референтных лиц, которые сами являются иммигрантами, - дополнительные
переменные, касающиеся их возраста на момент иммиграции (rpinnalder) и причины
иммиграции (rpinngrunn и rpinngrunn1).
11. Эти переменные часто использовались в публикациях Статистического управления
Норвегии на тему семейной миграции. Перекрестные таблицы, отображающие
семейную иммиграцию по годам в соответствии с происхождением иммигрантов,
полом и причиной иммиграции, до недавнего времени составляли часть пакета
таблиц, ежегодно публикуемых в мониторе семейной иммиграции по рамочному
соглашению между Статистическим управлением Норвегии и Министерством труда и
социальной интеграции (AID).
12. На рисунке 2 показано упрощенное графическое представление первой перекрестной
таблицы за исключением гендерного аспекта. Общая тенденция, как видно из рисунка,
подтверждает приведенное выше утверждение о том, что семейная миграция в
значительной степени является продолжением трудовой миграции и иммиграции
беженцев. Также можно выделить третью важную категорию семейных иммигрантов:
семейные иммигранты к другим семейным иммигрантам.
Рисунок 2. Семейная иммиграция в Норвегию по причине иммиграции
референтного лица, 1990–2020
III. 2009: Введение схемы регистрации и потеря данных
13. Публикации, издаваемые с 2021 года, не включали эти перекрестные таблицы или
статистические данные, полученные на их основе. Это связано с потерей данных
личности референтного лица и постепенным нарастанием этого пробела с момента
введения так называемой схемы регистрации в 2009 году.
14. Схема регистрации была введена в результате принятия Директивы 2004/38/EC в
соглашение о ЕЭЗ в 2007 году (Regjeringen.no, 2022). Директива предоставляет
гражданам Европейского союза «право свободно передвигаться и проживать на
территории государств-членов». Это право «также предоставляется членам их семей,
независимо от гражданства» (EUR-Lex, 2022, стр. 78–79). Одной из основных целей
директивы было упростить процесс смены места жительства в пределах ЕС/ЕЭЗ для
граждан и членов их семей. Однако, в то время как гражданам ЕС/ЕЭЗ не нужны
разрешения на проживание в государствах-членах, директива гласит, что «в случае
периодов проживания более трех месяцев государства-члены должны иметь
возможность требовать от граждан Союза регистрации в компетентных органах по
месту жительства, подтвержденной регистрационным свидетельством выданным с
этой целью» (EUR-Lex, 2022, стр. 81). В норвежской иммиграционной администрации
UDI разработана схема регистрации, в соответствии с которой граждане ЕС/ЕЭЗ и
члены их семей должны зарегистрироваться в течение трех месяцев после прибытия.
Создание схемы регистрации не было предусмотрено директивой и, следовательно,
схема не была предоставлена. Напротив, ее создание было обусловлено внутренними
административными проблемами.
15. Следствием этих процедурных изменений стала потеря данных об иммигрантах из
стран ЕС/ЕЭЗ. Коды, используемые для классификации иммигрантов в системе
регистрации, не столь обширны, как те, которые применяются для получения вида на
жительство. В то время как данные еще позволяют выделить основные категории по
причинам миграции (труд, семья, образование и т.д.), качество более подробных
подкатегорий стало более сомнительным (Dzamarija, 2013, стр. 9-10). Дальнейшее
ухудшение наблюдалось в ситуации с данными об отношениях между семейным
мигрантом и его/ее референтом (Dzamarija, 2013, стр. 21), и в целом произошла потеря
данных о референтном лице. Последние события привели к увеличению числа
семейных иммигрантов с «неизвестным» референтным лицом начиная с 2009 года.
Эта ситуация видна на рисунке 2, где с данного года увеличивается число семейных
мигрантов с референтным лицом с «неизвестной» причиной иммиграции.2
16. Другими словами, сформировалось серьезное разделение между семейной
иммиграцией с территорий внутри и за пределами зоны ЕЭЗ с точки зрения качества
данных. Из общего числа 334 000 семейных иммигрантов, прибывших в Норвегию в
период с 1990 по 2020 год, 57 500 (17 процентов) не имели зарегистрированного
референтного лица. Большинство из них, 47 700 (83 процента), были гражданами
стран ЕС/ЕЭЗ. Однако потеря данных не относится исключительно к семейной
иммиграции из зоны ЕС/ЕЭЗ. Как показано на рисунке 3, также увеличилось число
семейных иммигрантов из стран за пределами ЕС/ЕЭЗ, по которым недоступен или
недействителен номер социального страхования (rpfnr) референтного лица.
Рисунок 3. Семейная иммиграция в Норвегию, процент отсутствия данных о
референтных лицах, 1990–2020
2 Однако, хотя заметное увеличение после 2009 года числа семейных иммигрантов, приезжающих к
иммигрантам с «неизвестной» причиной иммиграции, явно совпадает с введением схемы регистрации, эта
схема не учитывает внезапный рост числа недостающих значений с 2016 года и далее. На первый взгляд может
показаться, что графики на рис. 2 и 3 представляют противоречивые картины. В то время как на рисунке 3
основной рост числа недостающих значений произошел после введения схемы регистрации в 2009 году, на
рисунке 2 основной пик приходится на 2016 год, значительно позже процедурных изменений. Объяснение
противоречивым моделям следует искать в различиях населения. Рисунок 3 включает все семейные миграции в
период с 1990 по 2020 год, в то время как рисунок 2 включает только те семейные миграции, по которым было
достаточно информации о референтном лице, указывающей, что он/она - иммигрант. Таким образом, рост
числа недостающих значений относительно причины иммиграции с 2016 года и далее является признаком
дополнительной потери данных, причину которой мы до сих пор не смогли установить.
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17. Второе разделение с точки зрения качества данных может быть проведено между
двумя основными типами семейной иммиграции: а) воссоединение семей и б)
создание семей. Воссоединение семьи подразумевает семейную иммиграцию, когда
семейные отношения существовали до того, как референтное лицо начало проживать
в Норвегии. Создание семей — это те случаи, когда семья мигранта иммигрировала с
целью установления новых отношений (в большинстве случаев брака) с лицом,
проживающим в Норвегии (Molstad et al., 2022, стр. 9).
18. В случае создания семей количество отсутствующих номеров социального
страхования референтных лиц в готовом файле данных является незначительным.
Менее чем в одном проценте случаев создания семей в период с 1990 по 2020 год
отсутствовал действительный номер социального страхования референтного лица.
Это наблюдалось независимо от того, был ли семейный иммигрант гражданином
ЕС/ЕЭЗ или нет (рисунок 4). Причину такой разницы в качестве данных между
воссоединениями и созданиями семей следует искать в уже существующих мерах,
принятых для устранения пробелов в данных. Эти меры, наряду с некоторыми
предварительными предложениями относительно подхода к дальнейшим шагам,
будут кратко изложены ниже.
Рисунок 4. Процент семейных иммигрантов, у которых отсутствует
референтное лицо, в разбивке по типу семейной иммиграции
IV. Некоторые предварительные соображения относительно
решений
19. Отсутствие данных - не новая проблема для статистики, основанной на
административных данных. Не нова и задача установления связи между
соответствующими единицами в разных источниках данных. Пример последнего
можно найти в данных переписи на основе регистров, в которые были интегрированы
данные из Центрального регистра населения (DSF) и Реестра земельных участков,
адресов и зданий (Matrikkelen). Для учета неполной связи между домохозяйствами в
DSF и жилищами в Matrikkelen был применен метод двойной подстановки ближайших
соседей (DNNI) для подстановки жилья для домохозяйств, по которым нет
результативной привязки к жилью, используя другие, статистически схожие
домохозяйства в качестве «доноров» для выбора жилья без привязки в том же
географическом районе (Zhang & Hendriks, 2012). Метод подстановки с
одновременным применением методологии близких соседей далее использовался для
подстановки недостающих данных по иммигрантам в Регистре уровня образования
населения (BU) (Jentoft, 2014).
20. Что касается недостающих данных о референтных лицах для семейных иммигрантов,
то сейчас мы надеемся, что не придется применять подстановку. Центральной целью
проекта является установление связей, которые а) не основываются на допущении
статистического сходства между семейными иммигрантами с отсутствующими
значениями и теми, по которым нет нехватки значений, и б) отражают фактические
отношения между семейными иммигрантами и референтными лицами, а не
отношения, основанные на статистическом прогнозировании аналогии. Предпосылки
для достижения этой цели в принципе имеются. Центральной предпосылкой для
0
10
20
30
40
50
60
Создание Воссоединение Создание Воссоединение
ЕС/ЕЭЗ Не ЕС/ЕЭЗ
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9
семейной иммиграции является проживание референтного лица, с которым семейный
мигрант может либо воссоединиться, либо создать новую семью. Таким образом, мы
можем предположить, что все референтные лица присутствуют в административных
записях, отражающих численность постоянного населения. Сложность состоит в том,
чтобы найти метод (или, как будет предложено ниже, методы) для установления связи
между семейным мигрантом и тем, кого с обоснованной вероятностью можно считать
фактическим референтным лицом.
21. Поскольку семейные отношения являются предпосылкой для семейной иммиграции,
данные касательно таких отношений должны служить основой для метода, с помощью
которого можно связать мигранта с его/ее референтным лицом. Первая задача в таком
методе состоит в том, чтобы определить семейные отношения между семейным
иммигрантом и его/ее референтом. Вторая заключается в том, чтобы найти
переменную или комбинацию переменных, которые позволять увязать такое
отношение с идентифицируемым лицом.
22. Как упоминалось выше, такой метод уже существует для случаев создания семьи.
Семейными отношениями в данном случае является брак между этими лицами.
Семейную миграцию к супругу можно определить с помощью переменной inngrunn,
полученной из данных UDB, где представлена подробная классификация причин
миграции. В этой переменной семейная миграция к супругу составляет основу для
подгруппы вариаций (FAM-MAKE-, FAMMAKE-) в рамках более крупной группы
кодов, связанных с семейной миграцией (FAM-) (Dzamarija, 2013, стр. 51). Семейные
данные, касающиеся личности супругов состоящих в браке лиц, присутствуют в
данных, которые Статистическое управление Норвегии получает из Центрального
регистра населения (DSF) через переменную ekt_fnr. При составлении ежегодного
файла данные, содержащие номер социального страхования супругов (в переменной
ekt_fnr), используются для идентификации партнера в случаях семейных мигрантов,
которые иммигрировали к такому члену семьи. Впоследствии добавляются другие
данные по успешно идентифицированным референтным лицам.
23. Результат этого можно увидеть на рисунке 4. Как указывалось, в случае создания
семей в готовом файле данных пробелы по референтным лицам очень невелики. Что
касается воссоединения семей, то число миграций с отсутствующей информацией о
референтном лице остается высоким.
24. Аналогичный метод, использующий связь между семейным мигрантом и
референтным лицом, как указано в переменной inngrunn, не применим для остальных
случаев с недостающей информацией, в подавляющем большинстве связанных с
воссоединением семьи. В 79 процентах этих миграций причина миграции
зарегистрирована как «гражданин ЕЭЗ, который является членом семьи гражданина
ЕЭЗ, работающего по найму и т.д., или который является супругом/партнером или
находящимся на иждивении ребенком в возрасте до 21 года гражданина ЕЭЗ,
принятого в аккредитованное учебное заведение» (EØSFAMBORG). Еще 3 процента
включают неграждан ЕЭЗ с теми же вариантами отношений, что и у граждан ЕЭЗ
(EØSFAMIKKEBORG). Из этих кодов невозможно сделать вывод о том, состоит ли
семейный мигрант а) в браке или в другой форме партнерства с референтным лицом,
б) является ребенком референтного лица или в) имеет другие отношения с ним/ней.
Следовательно, процесс определения фактической связи между семейным мигрантом
и референтным лицом должен основываться на использовании дополнительных
косвенных показателей.
Рабочий документ 8/Рев
10
25. Одним из таких показателей может быть возраст на момент иммиграции семейного
мигранта, который уже имеется в файле данных в переменной innalder. Разбивка
семей иммигрантов с отсутствующими референтными лицами по возрасту
показывает, что 69 процентов были моложе 18 лет. Кроме того, около половины из
них были моложе десяти лет и почти две трети - моложе 15 лет (таблица 1).
Естественно предположить, что многие (но не все) из этих семейных иммигрантов
молодого возраста иммигрировали к одному из родителей и что, следовательно,
референтным лицом является либо их отец, либо мать.
Таблица 1. Семейные иммигранты, у которых отсутствует референтное лицо, с
интервалом в пять лет на момент иммиграции
26. Для семейных иммигрантов старше 18 лет на момент иммиграции доступен более
широкий спектр вероятных вариантов. Вполне вероятно, что во многих случаях
иммиграция будет связана с воссоединением супругов. Однако также вероятно, что,
по крайней мере, некоторые иммиграции связаны с воссоединением родителей с
сыном или дочерью, детьми старшего возраста, иммигрирующими для воссоединения
с отцом или матерью, или братьями и сестрами, сопровождающими родителей,
переезжающих для воссоединения с сыном или дочерью. Упомянутые варианты в
числе прочих упоминаются в главе 6 закона о допуске иностранного гражданина в
королевство (закон об иммиграции) в качестве законных вариантов семейной
иммиграции (Lovdata, 2022) и не представляют собой исчерпывающий список
возможных альтернатив.
Рабочий документ 8/Рев
11
27. Другими словами, сам по себе возраст на момент иммиграции не всегда дает
достаточную информацию для установления родственных связей между членами
семьи иммигрантов и референтным лицом. Поиск метода установления связей между
семейными иммигрантами и референтным лицом, скорее всего, будет основываться на
использовании ряда вспомогательных переменных в комбинации. Чтобы
идентифицировать эти вспомогательные переменные, нам нужно будет выполнить
следующие предварительные шаги:
i. Определить возможные сочетания семейных отношений, которые дают право
на семейную иммиграцию
ii. Выявить имеющиеся данные либо в UDB, либо в DSF, либо в других
источниках административных данных, которые могут быть использованы для
различения семейных мигрантов в соответствии с их вероятным родством с
референтом, как определено на этапе i
iii. Определите данные, которые могут быть полезны для использования этой
привязки, чтобы связать семейного мигранта с лицом, зарегистрированным в
DSF
В то время как первый шаг является таксономическим, второй и третий представляют
собой начало разработки методов, которые по умолчанию должны различаться в
зависимости от каждого “класса” семейных отношений.
V. Вывод
28. Проект, описанный в этой статье, находится только на предварительной,
исследовательской стадии. В ближайшие месяцы будет проведено систематическое
сопоставление возможных источников данных и потенциальных методов их
использования для выявления связей между семейными иммигрантами и их
референтами. Статистическое управление Норвегии располагает рядом
административных данных, которые могут служить вспомогательными переменными
для этих методов.
29. Серьезную проблему составляет множество возможных семейных отношений,
которые могут служить юридическими основаниями для семейной иммиграции.
Таким образом, центральной задачей будет поиск способа взаимного исключения
семейной иммиграции в соответствии с типом семейных отношений между
иммигрантом и его/ее референтом.
VI. Литература
Dzamarija, M. T. (2013). Innvandringsgrunn 1990-2011, hva vet vi og hvordan kan
statistikken utnyttes (34/2013). Statistisk Sentralbyrå.
https://www.ssb.no/befolkning/artikler-og-
publikasjoner/_attachment/135482?_ts=140bf5cf4f8
EUR-Lex (2022). Директива 2004/38/EC Европейского парламента и Совета от
29 апреля 2004 года о праве граждан Союза и членов их семей свободно
передвигаться и проживать на территории государств-членов, 47 77 (2022).
Gulbrandsen, F. B., & Molstad, C. S. (2020). Færre familieinnvandringer i 2019.
Statistisk Sentralbyrå. https://www.ssb.no/befolkning/artikler-og-
publikasjoner/faerre-familieinnvandringer-i-2019
Джентофт, С. (2014). Подстановка недостающих данных среди иммигрантов
в Регистре уровня образования населения (BU) (Примечание/Документы
2014/27). Statistisk Sentralbyrå. https://www.ssb.no/utdanning/artikler-og-
publikasjoner/_attachment/183364?_ts=146a98197c8
Lovdata (2022). Закон, касающийся допуска иностранных граждан в
королевство и их пребывания здесь (Закон об иммиграции), глава 16, (2022).
https://lovdata.no/dokument/NL/lov/2008-05-15-35/KAPITTEL_6#%C2%A742
Molstad, C. S., Gulbrandsen, F. B., & Steinkellner, A. (2022). Familieinnvandring
og ekteskapsmønster 1990-2020. (2022/03). Statistisk Sentralbyrå.
https://www.ssb.no/befolkning/innvandrere/artikler/familieinnvandring-og-
ekteskapsmonster-1990-2020
Molstad, C. S., & Steinkellner, A. (2020). Familieinnvandring og
ekteskapsmønster 1990-2018 (2020/05). Statistisk Sentralbyrå.
https://www.ssb.no/befolkning/artiklerogpublikasjoner/_attachment/411225?_ts=1
6ffbcb2098
Regjeringen.no. (2022). Direktivet om fri personbevegelighet. EØS-notatbasen.
https://www.regjeringen.no/no/sub/eos-notatbasen/notatene/2006/mai/direktivet-
om-fri-personbevegelighet/id2431482/
Vassenden, K. (2015). Om kvaliteten på den norske inn- og utvandringsstatistikken
(Notater/Documents 2015/17). Statistisk Sentralbyrå.
https://www.ssb.no/befolkning/artikler-og-
publikasjoner/_attachment/225080?_ts=14ce62dab98
Чжан, Л.-К., Хендрикс, К. (2012). Микроинтеграция данных переписи на
основе регистров жилья и домохозяйств. Европейская экономическая
комиссия Организации Объединенных Наций (ЕЭК ООН): Конференция
европейских статистиков. Рабочее заседание по редактированию
статистических данных
https://unece.org/fileadmin/DAM/stats/documents/ece/ces/ge.44/2012/16_Norway.
pdf
I. Введение
II. Статистика семейной иммиграции и источники данных о ней
III. 2009: Введение схемы регистрации и потеря данных
IV. Некоторые предварительные соображения относительно решений
Public procurement represents 10 per cent of Georgia’s GDP. Therefore, innovation-enhancing procurement (IEP) can be a powerful policy lever to catalyze innovation and promote sustainability. Using public procurement to drive innovation is one of the recommendations arising from the Innovation for Sustainable Development Review (I4SDR) of Georgia, published by UNECE in 2021 with funding from the Swedish Government.
Dear Correspondent,
Thank you for contributing to the Joint Forest Sector Questionnaire (JFSQ). Before filling in the worksheets, please read these guidelines.
The 2019 JFSQ has been changed following the decisions of the Inter-Secretariat Working Group of January 2019. Please use only this questionnaire to report your data. The 2019 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' 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). The following auxiliary sheets (grey tabs) are meant to help you and to improve quality:
• 'Check JQ-EU' and 'Check EU1-JQ2' ensure that data are consistent
• Two cross-reference tables with the classifications; the CN and PRODCOM cross-references are provided in a separate file.
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.) 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.5). 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: 2019).
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.5).
• 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.5 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 Trade' 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.
B.5 'Check JQ-EU' cross-checks the data in four sheets. This concerns the three roundwood items in sheets 'JQ1' and 'EU2' and the four industrial roundwood and sawnwood items in sheets 'JQ2' and 'ECE/EU Species Trade'.
• When the four sheets are filled in correctly, this checking sheet should have no 'WRONG' messages (red)
• When there are such messages, please follow the row in which a cell in magenta indicates a mistake back to column F to find out which two tables are concerned. The difference between the numbers is in the 'dif'-row.
• Please correct working sheets until all warnings disappear
B.6 'Check EU1-JQ2' cross-checks data on total trade with extra-EU trade. The numbers in 'JQ2' should be greater than or equal to those in 'EU1'.
• If there are 'WRONG' messages (red), please go back to the data in EU1 and/or JQ2 and correct it
• Please correct working sheets until all warnings disappear
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
NOR
Contact organisation
Contact organisation
Statistics Norway
Contact name
Contact name
Contact email address
Contact email address
2
Changes to previous year
Necessity of update
Are there any changes to the quality report of the last data collection?
NO
If yes, please provide details below.
3
Statistical processing
Overview of the source data
Please provide an overview of the sources used to produce JFSQ data.
Roundwood removal statistics and national trade statistics from Statistics Norway; data on forestry industry production from the National timber industry associatoion, data on sawnwood from Norwegian Institute of Wood Technology, data on rondwood for firewood production, collected as a sample survey, Statistics Norway; Norwegian organisation of Bienergy, data on production of pellets. Data on wood based panels from The Construction Products Association
Do you use a dedicated survey (of the industry, of households, of forest owners, etc.)?
NO
If yes, please provide details (e.g., who are the respondents, what is its frequency?).
Do you use forestry statistics?
YES
If yes, please provide details.
Roundwood removals statistics
Do you use national forest inventory?
NO
If yes, please provide details.
Do you use national PRODCOM data compiled according to the CPA classification?
NO
If yes, please provide details (which products, units, etc.).
Do you use any other national production statistics?
NO
If yes, please provide details.
Do you use data collected by associations of industry?
YES
If yes, please provide details.
Data on forestry industry production from the National timber industry associatoion and data on wood based panels from The Construction Products Association
Do you collect data from direct contacts with manufacturing companies?
NO
If yes, please provide details.
Do you use estimates of roundwood use (in manufacturing)?
NO
If yes, please provide details.
Do you use national trade data?
YES
If yes, please provide details.
National trade statistics, registerbased statistics from Statistics Norway.
Do you use felling reports?
YES
If yes, please provide details.
Roundwood removals statistics are based on all national felling reports
Do you use forestry companies' accounting network?
NO
If yes, please provide details.
Do you use administrative data (e.g. tax records, business registers)?
NO
If yes, please provide details.
Do you use data from national accounts?
NO
If yes, please provide details (e.g. for which data, from which account tables?).
Do you use SBS (Structural business statistics)?
NO
If yes, please provide details (e.g. for which data?).
Do you use other environmental accounts?
YES
If yes, please provide details.
Data on recovered wood production
Do you use other statistics (e.g. agriculture statistics)?
NO
If yes, please specify them.
Do you use any other sources?
NO
If yes, please specify them.
Methodological issues
Are there any pending classification or measurement issues?
NO
If yes, please specify them.
Data validation
Do you check the quality of the data collected to compile JFSQ?
YES
If yes, please explain the quality assurance procedure.
Checing previous year data.
Do you compare JFSQ data with different data sources or do you perform other cross-checks?
YES
If yes, please explain your approach.
Compare with national statistics.
Do you have validation rules and other plausibility checks for the outputs of your JFSQ data compilation process?
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?
NO
It not, please explain.
Do you compare the JFSQ results with business, energy and agricultural and foreign trade statistics?
NO
It not, please explain.
There are difficult to find statistics to compare with.
Do you cross-check the JFSQ data with the results of European Forest Accounts?
YES
If yes, please indicate for which reporting items, and comments on the discrepancies observed, if any. It not, please explain.
Production of wood energy and roundwood.
Coherence - internal
Are there any other consistency issues related to your JFSQ data?
NO
If yes, please explain them.
6
Accessibility and clarity
Publications
Do you disseminate JFSQ data nationally (e.g. in news releases or other documents)?
NO
If yes, please provide URLs and/or the reference to the relevant publications.
Online database
Do you publish your JFSQ accounts in an online data base?
NO
If yes, please provide URLs.
Documentation on methodology
Did you prepare a description of your national JFSQ methodology or metadata?
NO
If yes, please provide URLs.
Quality documentation
Do you have national quality documentation?
NO
If yes, please provide URLs.
7
Other comments
Other comments
Please provide any further feedback you might have on the quality of the reported data, sources and methods used and/or Eurostat's validation and quality report templates.
Cover
Joint Forest Sector Questionnaire
2019
DATA INPUT FILE
Correspondent country:
NO
Reference year:
2019
Fill in the year
Name of person responsible for reply:
Official address (in full):
Telephone:
Fax:
E-mail:
Removals over bark
Country:
NO
Date:
Name of Official responsible for reply:
0
Check Table
Official Address (in full):
EU JQ1 OB
0
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
2018
2019
2018
2019
2018
2019
Product
Product
Unit
2018
2019
Code
Quantity
Quantity
Code
Quantity
Quantity
ROUNDWOOD REMOVALS OVERBARK
ROUNDWOOD REMOVALS OVERBARK
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
ERROR:#REF!
ERROR:#REF!
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
ERROR:#REF!
ERROR:#REF!
1.1.C
Coniferous
1000 m3ub
1.1.C
Coniferous
1000 m3ub
1.1.NC
Non-Coniferous
1000 m3ub
1.1.NC
Non-Coniferous
1000 m3ub
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
ERROR:#REF!
ERROR:#REF!
1.2.C
Coniferous
1000 m3ub
1.2.C
Coniferous
1000 m3ub
ERROR:#REF!
ERROR:#REF!
1.2.NC
Non-Coniferous
1000 m3ub
1.2.NC
Non-Coniferous
1000 m3ub
ERROR:#REF!
ERROR:#REF!
1.2.NC.T
of which: Tropical
1000 m3ub
1.2.NC.T
of which: Tropical
1000 m3ub
ERROR:#REF!
ERROR:#REF!
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3ub
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3ub
ERROR:#REF!
ERROR:#REF!
1.2.1.C
Coniferous
1000 m3ub
1.2.1.C
Coniferous
1000 m3ub
1.2.1.NC
Non-Coniferous
1000 m3ub
1.2.1.NC
Non-Coniferous
1000 m3ub
1.2.2
PULPWOOD, ROUND AND SPLIT
1000 m3ub
1.2.2
PULPWOOD, ROUND AND SPLIT
1000 m3ub
ERROR:#REF!
ERROR:#REF!
1.2.2.C
Coniferous
1000 m3ub
1.2.2.C
Coniferous
1000 m3ub
1.2.2.NC
Non-Coniferous
1000 m3ub
1.2.2.NC
Non-Coniferous
1000 m3ub
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3ub
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3ub
ERROR:#REF!
ERROR:#REF!
1.2.3.C
Coniferous
1000 m3ub
1.2.3.C
Coniferous
1000 m3ub
1.2.3.NC
Non-Coniferous
1000 m3ub
1.2.3.NC
Non-Coniferous
1000 m3ub
To fill:
ERROR:#REF!
ERROR:#REF!
Text:
ERROR:#REF!
ERROR:#REF!
Product
Product
Unit
2018
2019
Code
CF
CF
OVERBARK/UNDERBARK CONVERSION FACTORS
1
ROUNDWOOD (WOOD IN THE ROUGH)
m3/m3
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
m3/m3
1.1.C
Coniferous
m3/m3
1.100
1.100
1.1.NC
Non-Coniferous
m3/m3
1.150
1.150
1.2
INDUSTRIAL ROUNDWOOD
m3/m3
1.2.C
Coniferous
m3/m3
1.100
1.100
1.2.NC
Non-Coniferous
m3/m3
1.150
1.150
1.2.NC.T
of which: Tropical
m3/m3
ERROR:#DIV/0!
ERROR:#DIV/0!
1.2.1
SAWLOGS AND VENEER LOGS
m3/m3
1.2.1.C
Coniferous
m3/m3
1.100
1.100
1.2.1.NC
Non-Coniferous
m3/m3
1.150
1.150
1.2.2
PULPWOOD, ROUND AND SPLIT
m3/m3
1.2.2.C
Coniferous
m3/m3
1.100
1.100
1.2.2.NC
Non-Coniferous
m3/m3
1.150
1.150
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
m3/m3
1.2.3.C
Coniferous
m3/m3
1.100
1.100
1.2.3.NC
Non-Coniferous
m3/m3
1.150
1.150
JQ1 Production
Country:
NO
Date:
Name of Official responsible for reply:
0
Official Address (in full):
Should we make missing data into 0?
FOREST SECTOR QUESTIONNAIRE JQ1
0
Not included: trade in chips
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
2018
2019
2018
2019
2018
2019
Product
Product
Unit
2018
2019
2018
2019
% 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
7,682
-1,615,356
-21127%
m3 of wood in m3 or mt of product
REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
Recovered wood used in particle board
103
97
-6%
Solid wood equivalent
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
12497.172
12568.430
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
OK
OK
Solid Wood Demand
agglomerate production
96
96
0%
2.4
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
1661.283
1529.731
9
9
Nat. estimates
Nat. estimates
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
OK
OK
Sawnwood production
Missing data
Missing data
missing data
1
1.1.C
Coniferous
1000 m3ub
586.373
544.816
9
9
Nat. estimates
Nat. estimates
1.1.C
Coniferous
1000 m3ub
veneer production
Missing data
Missing data
missing data
1
1.1.NC
Non-Coniferous
1000 m3ub
1074.910
984.915
9
9
Nat. estimates
Nat. estimates
1.1.NC
Non-Coniferous
1000 m3ub
plywood production
Missing data
Missing data
missing data
1
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
10835.889
11038.699
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
OK
OK
particle board production (incl OSB)
294
276
-6%
1.58
1.2.C
Coniferous
1000 m3ub
10577.624
10735.997
1.2.C
Coniferous
1000 m3ub
OK
ERROR:#REF!
fibreboard production
164
141
-14%
1.8
1.2.NC
Non-Coniferous
1000 m3ub
258.265
302.702
598.19091696
1.2.NC
Non-Coniferous
1000 m3ub
Error
OK
mechanical/semi-chemical pulp production
885
833
-6%
2.5
1.2.NC.T
of which: Tropical
1000 m3ub
0.000
0.000
1096.574776
1.2.NC.T
of which: Tropical
1000 m3ub
OK
OK
chemical pulp production
153
150
-2%
4.9
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3ub
6104.268
6012.323
1794.5745454545
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3ub
OK
ERROR:#REF!
dissolving pulp production
0
0
missing data
5.7
1.2.1.C
Coniferous
1000 m3ub
6101.667
6011.062
1.2.1.C
Coniferous
1000 m3ub
Availability
Solid Wood Demand
missing data
missing data
missing data
1.2.1.NC
Non-Coniferous
1000 m3ub
2.601
1.261
1.2.1.NC
Non-Coniferous
1000 m3ub
Difference (roundwood-demand)
missing data
missing data
missing data
positive = surplus
1.2.2
PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)
1000 m3ub
4667.201
4938.570
1.2.2
PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)
1000 m3ub
OK
OK
gap (demand/availability)
missing data
missing data
Negative number means not enough roundwood available
1.2.2.C
Coniferous
1000 m3ub
4411.536
4637.129
1.2.2.C
Coniferous
1000 m3ub
Positive number means more roundwood available than demanded
1.2.2.NC
Non-Coniferous
1000 m3ub
255.665
301.441
1.2.2.NC
Non-Coniferous
1000 m3ub
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3ub
64.422
87.807
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3ub
OK
OK
1.2.3.C
Coniferous
1000 m3ub
64.422
87.807
1.2.3.C
Coniferous
1000 m3ub
% of particle board that is from recovered wood
35%
1.2.3.NC
Non-Coniferous
1000 m3ub
0.000
0.000
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 mt
2
WOOD CHARCOAL
1000 mt
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
2441.707
2404.929
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
OK
OK
3.1
WOOD CHIPS AND PARTICLES
1000 m3
1587.110
1563.204
9
9
Nat. estimates
Nat. estimates
3.1
WOOD CHIPS AND PARTICLES
1000 m3
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
854.597
841.725
9
9
Nat. estimates
Nat. estimates
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
4
RECOVERED POST-CONSUMER WOOD
1000 mt
756.000
756.000
5
5
4
RECOVERED POST-CONSUMER WOOD
1000 mt
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 mt
95.768
95.768
5
5
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 mt
OK
OK
5.1
WOOD PELLETS
1000 mt
57.368
57.368
5
5
5.1
WOOD PELLETS
1000 mt
5.2
OTHER AGGLOMERATES
1000 mt
38.400
38.400
5
5
5.2
OTHER AGGLOMERATES
1000 mt
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
2675.122
2654.000
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
ERROR:#REF!
ERROR:#REF!
6.C
Coniferous
1000 m3
2675.122
2654.000
6.C
Coniferous
1000 m3
6.NC
Non-Coniferous
1000 m3
6.NC
Non-Coniferous
1000 m3
6.NC.T
of which: Tropical
1000 m3
0.000
0.000
6.NC.T
of which: Tropical
1000 m3
OK
OK
7
VENEER SHEETS
1000 m3
7
VENEER SHEETS
1000 m3
OK
OK
7.C
Coniferous
1000 m3
7.C
Coniferous
1000 m3
7.NC
Non-Coniferous
1000 m3
7.NC
Non-Coniferous
1000 m3
7.NC.T
of which: Tropical
1000 m3
7.NC.T
of which: Tropical
1000 m3
OK
OK
8
WOOD-BASED PANELS
1000 m3
458.000
417.000
8
WOOD-BASED PANELS
1000 m3
OK
OK
8.1
PLYWOOD
1000 m3
8.1
PLYWOOD
1000 m3
OK
OK
8.1.C
Coniferous
1000 m3
8.1.C
Coniferous
1000 m3
8.1.NC
Non-Coniferous
1000 m3
8.1.NC
Non-Coniferous
1000 m3
8.1.NC.T
of which: Tropical
1000 m3
8.1.NC.T
of which: Tropical
1000 m3
OK
OK
8.2
PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD
1000 m3
294.000
276.000
8.2
PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD
1000 m3
8.2.1
of which: ORIENTED STRAND BOARD (OSB)
1000 m3
8.2.1
of which: ORIENTED STRAND BOARD (OSB)
1000 m3
OK
OK
8.3
FIBREBOARD
1000 m3
164.000
141.000
8.3
FIBREBOARD
1000 m3
OK
OK
8.3.1
HARDBOARD
1000 m3
49.000
49.000
8.3.1
HARDBOARD
1000 m3
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
0.000
0.000
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
8.3.3
OTHER FIBREBOARD
1000 m3
115.000
92.000
8.3.3
OTHER FIBREBOARD
1000 m3
9
WOOD PULP
1000 mt
1038.000
983.000
9
WOOD PULP
1000 mt
OK
OK
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 mt
885.000
833.000
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 mt
9.2
CHEMICAL WOOD PULP
1000 mt
153.000
150.000
9.2
CHEMICAL WOOD PULP
1000 mt
OK
OK
9.2.1
SULPHATE PULP
1000 mt
0.000
0.000
9.2.1
SULPHATE PULP
1000 mt
9.2.1.1
of which: BLEACHED
1000 mt
0.000
0.000
9.2.1.1
of which: BLEACHED
1000 mt
OK
OK
9.2.2
SULPHITE PULP
1000 mt
153.000
150.000
9.2.2
SULPHITE PULP
1000 mt
9.3
DISSOLVING GRADES
1000 mt
0.000
0.000
9.3
DISSOLVING GRADES
1000 mt
10
OTHER PULP
1000 mt
0.000
0.000
10
OTHER PULP
1000 mt
OK
OK
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 mt
0.000
0.000
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 mt
10.2
RECOVERED FIBRE PULP
1000 mt
0.000
0.000
10.2
RECOVERED FIBRE PULP
1000 mt
11
RECOVERED PAPER
1000 mt
604.000
573.000
11
RECOVERED PAPER
1000 mt
12
PAPER AND PAPERBOARD
1000 mt
1134.000
1155.000
12
PAPER AND PAPERBOARD
1000 mt
OK
OK
12.1
GRAPHIC PAPERS
1000 mt
973.000
994.000
12.1
GRAPHIC PAPERS
1000 mt
OK
OK
12.1.1
NEWSPRINT
1000 mt
488.000
563.000
12.1.1
NEWSPRINT
1000 mt
12.1.2
UNCOATED MECHANICAL
1000 mt
485.000
431.000
12.1.2
UNCOATED MECHANICAL
1000 mt
12.1.3
UNCOATED WOODFREE
1000 mt
0.000
0.000
12.1.3
UNCOATED WOODFREE
1000 mt
12.1.4
COATED PAPERS
1000 mt
0.000
0.000
12.1.4
COATED PAPERS
1000 mt
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 mt
22.000
21.000
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 mt
12.3
PACKAGING MATERIALS
1000 mt
139.000
140.000
12.3
PACKAGING MATERIALS
1000 mt
OK
OK
12.3.1
CASE MATERIALS
1000 mt
33.000
24.000
12.3.1
CASE MATERIALS
1000 mt
12.3.2
CARTONBOARD
1000 mt
0.000
0.000
12.3.2
CARTONBOARD
1000 mt
12.3.3
WRAPPING PAPERS
1000 mt
38.000
37.000
12.3.3
WRAPPING PAPERS
1000 mt
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 mt
68.000
79.000
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 mt
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
1000 mt
0.000
0.000
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 mt
885.000
833.000
To fill:
11
11
Text:
0
0
JQ2 TTrade
61
62
61
62
91
92
91
92
FOREST SECTOR QUESTIONNAIRE JQ2
Country:
NO
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):
0
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:
NO
NO V
no value reported
Treshold:
2
verifies whether the JQ2 figures refers only to intra-EU trade
Eurozone countries may use the old national currency, but only in both years
1000NAC
Trade
Discrepancies
REPORT
no figures reported
Product
Unit of
I M P O R T
E X P O R T
Import
Export
Import
Export
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
2018
2019
2018
2019
2018
2019
2018
2019
2018
2019
2018
2019
code
2018
2019
2018
2019
code
2018
2019
2018
2019
code
Product
unit
2018
2019
2018
2019
IMPORT
EXPORT
code
Product
unit
2018
2019
2018
2019
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
594.756
483619.041
560.019
496138.000
3567.526
1942349.000
3664.753
2160268.000
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
OK
OK
OK
Error
OK
OK
OK
OK
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
9,524
-1,446,162
1
ROUNDWOOD (WOOD IN THE ROUGH)
NAC/m3
813
886
544
589
ACCEPT
ACCEPT
1
ROUNDWOOD (WOOD IN THE ROUGH)
NAC/m3
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
139.936
171840.674
157.633
207076.000
23.525
4264.000
16.250
11717.000
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
1,778
169,106
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
NAC/m3
1228
1314
181
721
ACCEPT
CHECK
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
NAC/m3
1.1.C
Coniferous
1000 m3ub
5.735
6626.281
4.944
8100.000
13.105
1960.000
8.685
5553.000
1.1.C
Coniferous
1000 m3ub
1.1.C
Coniferous
1000 m3ub
579
5,211
1.1.C
Coniferous
NAC/m3
1155
1638
150
639
ACCEPT
CHECK
1.1.C
Coniferous
NAC/m3
1.1.NC
Non-Coniferous
1000 m3ub
134.201
165214.393
152.689
198976.000
10.420
2304.000
7.565
6164.000
1.1.NC
Non-Coniferous
1000 m3ub
1.1.NC
Non-Coniferous
1000 m3ub
1,199
163,895
1.1.NC
Non-Coniferous
NAC/m3
1231
1303
221
815
ACCEPT
CHECK
1.1.NC
Non-Coniferous
NAC/m3
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
454.820
311778.367
402.386
289063.000
3544.001
1938085.000
3648.503
2148551.000
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
OK
OK
OK
OK
OK
OK
OK
OK
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
7,747
-1,615,268
1.2
INDUSTRIAL ROUNDWOOD
NAC/m3
685
718
547
589
ACCEPT
ACCEPT
1.2
INDUSTRIAL ROUNDWOOD
NAC/m3
1.2.C
Coniferous
1000 m3ub
450.532
305820.945
401.696
285621.000
3408.176
1878944.000
3492.326
2072136.000
1.2.C
Coniferous
1000 m3ub
1.2.C
Coniferous
1000 m3ub
7,620
-1,562,387
1.2.C
Coniferous
NAC/m3
679
711
551
593
ACCEPT
ACCEPT
1.2.C
Coniferous
NAC/m3
1.2.NC
Non-Coniferous
1000 m3ub
4.288
5957.422
0.690
3442.000
135.825
59140.000
156.177
76415.000
1.2.NC
Non-Coniferous
1000 m3ub
1.2.NC
Non-Coniferous
1000 m3ub
127
-52,880
1.2.NC
Non-Coniferous
NAC/mt
1389
4988
435
489
CHECK
ACCEPT
1.2.NC
Non-Coniferous
NAC/mt
1.2.NC.T
of which: Tropical
1000 m3ub
0.076
364.292
0.026
568.000
0.178
1818.000
0.003
30.000
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
-1,454
1.2.NC.T
of which: Tropical
1000 m3
4793
21846
10213
10000
CHECK
ACCEPT
1.2.NC.T
of which: Tropical
1000 m3
2
WOOD CHARCOAL
1000 mt
41.964
205340.000
40.050
237042.000
3.333
4599.000
2.324
3469.000
2
WOOD CHARCOAL
1000 mt
2
WOOD CHARCOAL
1000 mt
39
200,741
2
WOOD CHARCOAL
1000 m3
4893
5919
1380
1493
ACCEPT
ACCEPT
2
WOOD CHARCOAL
1000 m3
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
711.331
313413.000
780.679
387236.000
2135.814
286954.000
2033.097
301177.000
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
1,017
28,864
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
441
496
134
148
ACCEPT
ACCEPT
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
3.1
WOOD CHIPS AND PARTICLES
1000 m3
394.225
170000.000
292.073
167856.000
824.342
160083.000
603.686
166720.000
3.1
WOOD CHIPS AND PARTICLES
1000 m3
3.1
WOOD CHIPS AND PARTICLES
1000 m3
1,157
11,480
3.1
WOOD CHIPS AND PARTICLES
1000 mt
431
575
194
276
ACCEPT
ACCEPT
3.1
WOOD CHIPS AND PARTICLES
1000 mt
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
317.106
143413.000
488.606
219380.000
1,311
126,871
1,429
134,457
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
-140
17,384
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 mt
452
449
97
94
ACCEPT
ACCEPT
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 mt
4
RECOVERED POST-CONSUMER WOOD
1000 mt
4
RECOVERED POST-CONSUMER WOOD
1000 mt
4
RECOVERED POST-CONSUMER WOOD
1000 mt
756
756
4
RECOVERED POST-CONSUMER WOOD
1000 mt
REPORT
REPORT
REPORT
REPORT
CHECK
CHECK
4
RECOVERED POST-CONSUMER WOOD
1000 mt
CHECK
CHECK
CHECK
INTRA-EU
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 mt
106.110
103759.000
142.352
141236.000
213.691
50579.000
231.411
39209.000
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 mt
OK
OK
OK
OK
OK
Error
OK
OK
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 mt
-12
53,276
5
WOOD PELLETS AND OTHER AGGLOMERATES
NAC/m3
978
992
237
169
ACCEPT
ACCEPT
5
WOOD PELLETS AND OTHER AGGLOMERATES
NAC/m3
5.1
WOOD PELLETS
1000 mt
76.015
75577.000
101.716
107644.000
66.164
31792.000
17.707
17256.000
5.1
WOOD PELLETS
1000 mt
5.1
WOOD PELLETS
1000 mt
67
43,842
5.1
WOOD PELLETS
NAC/m3
994
1058
481
975
ACCEPT
CHECK
5.1
WOOD PELLETS
NAC/m3
5.2
OTHER AGGLOMERATES
1000 mt
30.095
28182.000
40.636
33592.000
147.527
18788.000
213.704
21953.000
5.2
OTHER AGGLOMERATES
1000 mt
5.2
OTHER AGGLOMERATES
1000 mt
-79
9,432
5.2
OTHER AGGLOMERATES
NAC/m3
936
827
127
103
ACCEPT
ACCEPT
5.2
OTHER AGGLOMERATES
NAC/m3
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
960.515
2826382.000
994.169
3022591.000
681.066
1387076.000
718.688
1338121.000
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
ERROR:#REF!
ERROR:#REF!
6
SAWNWOOD (INCLUDING SLEEPERS)
NAC/m3
2943
3040
2037
1862
ACCEPT
ACCEPT
6
SAWNWOOD (INCLUDING SLEEPERS)
NAC/m3
6.C
Coniferous
1000 m3
933.642
2568287.000
968.933
2759018.000
675.162
1356328.000
708.242
1280475.000
6.C
Coniferous
1000 m3
6.C
Coniferous
1000 m3
2,934
1,214,613
6.C
Coniferous
NAC/m3
2751
2847
2009
1808
ACCEPT
ACCEPT
6.C
Coniferous
NAC/m3
6.NC
Non-Coniferous
1000 m3
26.873
258094.000
25.236
263573.000
5.904
30748.000
10.446
57645.000
6.NC
Non-Coniferous
1000 m3
6.NC
Non-Coniferous
1000 m3
2,696
230,000
6.NC
Non-Coniferous
NAC/m3
9604
10444
5208
5518
ACCEPT
ACCEPT
6.NC
Non-Coniferous
NAC/m3
6.NC.T
of which: Tropical
1000 m3
1.691
37006.000
1.854
37254.000
0.951
10721.000
0.455
27088.000
6.NC.T
of which: Tropical
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
6.NC.T
of which: Tropical
1000 m3
1
26,285
6.NC.T
of which: Tropical
NAC/m3
21884
20094
11273
59534
ACCEPT
CHECK
6.NC.T
of which: Tropical
NAC/m3
7
VENEER SHEETS
1000 m3
5.058
45309.000
4.709
42165.000
2.746
1433.000
0.537
1216.000
7
VENEER SHEETS
1000 m3
Error
OK
OK
Error
OK
OK
OK
OK
7
VENEER SHEETS
1000 m3
2
43,876
7
VENEER SHEETS
NAC/m3
8958
8954
522
2264
ACCEPT
CHECK
7
VENEER SHEETS
NAC/m3
7.C
Coniferous
1000 m3
0.164
849.000
0.174
1231.000
0.067
179.000
0.029
201.000
7.C
Coniferous
1000 m3
7.C
Coniferous
1000 m3
0
670
7.C
Coniferous
NAC/m3
5177
7075
2672
6931
ACCEPT
CHECK
7.C
Coniferous
NAC/m3
7.NC
Non-Coniferous
1000 m3
4.908
44460.000
4.535
40935.000
2.679
1254.000
0.508
1015.000
7.NC
Non-Coniferous
1000 m3
7.NC
Non-Coniferous
1000 m3
2
43,206
7.NC
Non-Coniferous
NAC/m3
9059
9026
468
1998
ACCEPT
CHECK
7.NC
Non-Coniferous
NAC/m3
7.NC.T
of which: Tropical
1000 m3
0.159
1248.000
0.017
376.000
2.141
516.000
0.374
529.000
7.NC.T
of which: Tropical
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
7.NC.T
of which: Tropical
1000 m3
-2
732
7.NC.T
of which: Tropical
NAC/m3
7849
22118
241
1414
CHECK
CHECK
7.NC.T
of which: Tropical
NAC/m3
8
WOOD-BASED PANELS
1000 m3
443.065
2312435.000
359.883
2327960.000
218.589
982644.000
153.284
1013952.000
8
WOOD-BASED PANELS
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
8
WOOD-BASED PANELS
1000 m3
682
1,330,208
8
WOOD-BASED PANELS
NAC/m3
5219
6469
4495
6615
ACCEPT
ACCEPT
8
WOOD-BASED PANELS
NAC/m3
8.1
PLYWOOD
1000 m3
149.416
856389.000
154.078
878881.000
22.809
168747.000
21.324
187024.000
8.1
PLYWOOD
1000 m3
OK
OK
OK
OK
Error
OK
Error
OK
8.1
PLYWOOD
1000 m3
127
687,642
8.1
PLYWOOD
NAC/m3
5732
5704
7398
8771
ACCEPT
ACCEPT
8.1
PLYWOOD
NAC/m3
8.1.C
Coniferous
1000 m3
68.342
358298.000
71.228
369482.000
15.457
152977.000
15.396
166146.000
8.1.C
Coniferous
1000 m3
8.1.C
Coniferous
1000 m3
53
205,321
8.1.C
Coniferous
NAC/m3
5243
5187
9897
10792
ACCEPT
ACCEPT
8.1.C
Coniferous
NAC/m3
8.1.NC
Non-Coniferous
1000 m3
81.074
498091.000
82.850
509399.000
7.362
15770.000
5.931
20878.000
8.1.NC
Non-Coniferous
1000 m3
8.1.NC
Non-Coniferous
1000 m3
74
482,321
8.1.NC
Non-Coniferous
NAC/m3
6144
6148
2142
3520
ACCEPT
ACCEPT
8.1.NC
Non-Coniferous
NAC/m3
8.1.NC.T
of which: Tropical
1000 m3
14.792
65313.000
15.886
63738.000
3.025
1659.000
4.236
4387.000
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
12
63,654
8.1.NC.T
of which: Tropical
NAC/m3
4415
4012
548
1036
ACCEPT
ACCEPT
8.1.NC.T
of which: Tropical
NAC/m3
8.2
PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD
1000 m3
156.023
462984.000
78.773
437321.000
157.919
369077.000
95.110
358066.000
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
292
94,183
8.2
PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD
NAC/m3
2967
5552
2337
3765
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
106.150
279776.000
54.709
254725.000
0.087
400.000
0.033
386.000
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
106
279,376
8.2.1
of which: ORIENTED STRANDBOARD (OSB)
NAC/m3
2636
4656
4598
11697
ACCEPT
CHECK
8.2.1
of which: ORIENTED STRANDBOARD (OSB)
NAC/m3
8.3
FIBREBOARD
1000 m3
137.626
993062.000
127.032
1011758.000
37.861
444820.000
36.850
468862.000
8.3
FIBREBOARD
1000 m3
OK
OK
Error
OK
OK
Error
OK
OK
8.3
FIBREBOARD
1000 m3
264
548,383
8.3
FIBREBOARD
NAC/m3
7216
7965
11749
12724
ACCEPT
ACCEPT
8.3
FIBREBOARD
NAC/m3
8.3.1
HARDBOARD
1000 m3
35.115
185356.000
14.403
110434.000
3.139
25312.000
3.624
30976.000
8.3.1
HARDBOARD
1000 m3
8.3.1
HARDBOARD
1000 m3
81
160,093
8.3.1
HARDBOARD
NAC/mt
5278
7667
8064
8547
ACCEPT
ACCEPT
8.3.1
HARDBOARD
NAC/mt
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
96.687
767355.000
107.992
868469.000
27.339
363331.000
27.466
392027.000
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
69
404,024
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
NAC/mt
7936
8042
13290
14273
ACCEPT
ACCEPT
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
NAC/mt
8.3.3
OTHER FIBREBOARD
1000 m3
5.823
40351.000
4.638
32855.000
7.383
56178.000
5.760
45859.000
8.3.3
OTHER FIBREBOARD
1000 m3
8.3.3
OTHER FIBREBOARD
1000 m3
113
-15,735
8.3.3
OTHER FIBREBOARD
NAC/mt
6930
7084
7609
7962
ACCEPT
ACCEPT
8.3.3
OTHER FIBREBOARD
NAC/mt
9
WOOD PULP
1000 mt
91.394
446302.000
63.966
380137.000
6
6
9
WOOD PULP
1000 mt
OK
OK
OK
OK
Error
Error
Error
Error
9
WOOD PULP
1000 mt
1,129
447,285
9
WOOD PULP
NAC/mt
4883
5943
REPORT
REPORT
ACCEPT
CHECK
9
WOOD PULP
NAC/mt
CHECK
CHECK
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 mt
0.976
4194.000
0.258
1590.000
235.130
851682.000
192.146
776518.000
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 mt
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 mt
651
-846,655
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
NAC/mt
4299
6163
3622
4041
ACCEPT
ACCEPT
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
NAC/mt
9.2
CHEMICAL WOOD PULP
1000 mt
90.414
442034.000
63.657
377929.000
6
6
9.2
CHEMICAL WOOD PULP
1000 mt
OK
OK
OK
OK
Error
Error
Error
Error
9.2
CHEMICAL WOOD PULP
1000 mt
243
442,184
9.2
CHEMICAL WOOD PULP
NAC/mt
4889
5937
REPORT
REPORT
ACCEPT
CHECK
9.2
CHEMICAL WOOD PULP
NAC/mt
CHECK
CHECK
9.2.1
SULPHATE PULP
1000 mt
68.628
364620.000
55.047
302300.000
6
6
9.2.1
SULPHATE PULP
1000 mt
9.2.1
SULPHATE PULP
1000 mt
69
364,620
9.2.1
SULPHATE PULP
NAC/mt
5313
5492
REPORT
REPORT
ACCEPT
CHECK
9.2.1
SULPHATE PULP
NAC/mt
CHECK
CHECK
9.2.1.1
of which: BLEACHED
1000 mt
68.628
364620.000
55.047
302297.000
6
6
9.2.1.1
of which: BLEACHED
1000 mt
OK
OK
OK
OK
OK
OK
OK
OK
9.2.1.1
of which: BLEACHED
1000 mt
69
364,620
9.2.1.1
of which: BLEACHED
NAC/mt
5313
5492
REPORT
REPORT
ACCEPT
CHECK
9.2.1.1
of which: BLEACHED
NAC/mt
CHECK
CHECK
9.2.2
SULPHITE PULP
1000 mt
21.785
77414.000
8.610
75629.000
16.564
5788.000
4.748
630.000
9.2.2
SULPHITE PULP
1000 mt
9.2.2
SULPHITE PULP
1000 mt
158
71,776
9.2.2
SULPHITE PULP
NAC/mt
3553
8784
349
133
CHECK
CHECK
9.2.2
SULPHITE PULP
NAC/mt
9.3
DISSOLVING GRADES
1000 mt
0.005
74.000
0.051
618.000
143.235
1484463.000
154.498
1630842.000
9.3
DISSOLVING GRADES
1000 mt
9.3
DISSOLVING GRADES
1000 mt
-143
-1,484,389
9.3
DISSOLVING GRADES
NAC/mt
14616
12118
10364
10556
ACCEPT
ACCEPT
9.3
DISSOLVING GRADES
NAC/mt
10
OTHER PULP
1000 mt
2.189
14483.000
2.106
17294.000
0.325
78.000
0.216
426.000
10
OTHER PULP
1000 mt
OK
Error
OK
Error
OK
Error
OK
OK
10
OTHER PULP
1000 mt
2
14,405
10
OTHER PULP
NAC/mt
6616
8212
240
1972
ACCEPT
CHECK
10
OTHER PULP
NAC/mt
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 mt
0.413
4844.000
0.260
5769.000
0.000
22.000
0.216
426.000
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 mt
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 mt
0
4,822
10.1
PULP FROM FIBRES OTHER THAN WOOD
NAC/mt
11738
22188
183333
1972
ACCEPT
CHECK
10.1
PULP FROM FIBRES OTHER THAN WOOD
NAC/mt
10.2
RECOVERED FIBRE PULP
1000 mt
1.776
9640.000
1.846
11526.000
0.325
57.000
0.000
0.000
10.2
RECOVERED FIBRE PULP
1000 mt
10.2
RECOVERED FIBRE PULP
1000 mt
1
9,583
10.2
RECOVERED FIBRE PULP
NAC/mt
5427
6244
175
0
ACCEPT
CHECK
10.2
RECOVERED FIBRE PULP
NAC/mt
11
RECOVERED PAPER
1000 mt
30.777
42508.000
24.905
29890.000
465.245
463208.000
427.386
381003.000
11
RECOVERED PAPER
1000 mt
11
RECOVERED PAPER
1000 mt
170
-420,127
11
RECOVERED PAPER
NAC/mt
1381
1200
996
891
ACCEPT
ACCEPT
11
RECOVERED PAPER
NAC/mt
12
PAPER AND PAPERBOARD
1000 mt
390.214
2560201.000
326.027
2488604.000
6
6
12
PAPER AND PAPERBOARD
1000 mt
OK
Error
OK
Error
Error
Error
Error
Error
12
PAPER AND PAPERBOARD
1000 mt
1,524
2,561,356
12
PAPER AND PAPERBOARD
NAC/mt
6561
7633
REPORT
REPORT
ACCEPT
CHECK
12
PAPER AND PAPERBOARD
NAC/mt
CHECK
CHECK
12.1
GRAPHIC PAPERS
1000 mt
234.008
1097725.000
165.201
980040.000
6
6
12.1
GRAPHIC PAPERS
1000 mt
OK
OK
OK
OK
Error
Error
Error
Error
12.1
GRAPHIC PAPERS
1000 mt
1,207
1,098,719
12.1
GRAPHIC PAPERS
NAC/mt
4691
5932
REPORT
REPORT
ACCEPT
CHECK
12.1
GRAPHIC PAPERS
NAC/mt
CHECK
CHECK
12.1.1
NEWSPRINT
1000 mt
35.121
161506.000
31.700
163109.000
6
6
12.1.1
NEWSPRINT
1000 mt
12.1.1
NEWSPRINT
1000 mt
523
162,069
12.1.1
NEWSPRINT
NAC/mt
4599
5145
REPORT
REPORT
ACCEPT
CHECK
12.1.1
NEWSPRINT
NAC/mt
CHECK
CHECK
12.1.2
UNCOATED MECHANICAL
1000 mt
5.493
40475.000
3.834
28373.000
6
6
12.1.2
UNCOATED MECHANICAL
1000 mt
12.1.2
UNCOATED MECHANICAL
1000 mt
490
40,906
12.1.2
UNCOATED MECHANICAL
NAC/mt
7368
7400
REPORT
REPORT
ACCEPT
CHECK
12.1.2
UNCOATED MECHANICAL
NAC/mt
CHECK
CHECK
12.1.3
UNCOATED WOODFREE
1000 mt
153.634
542140.000
92.718
436233.000
0.284
4655.000
0.191
3696.000
12.1.3
UNCOATED WOODFREE
1000 mt
12.1.3
UNCOATED WOODFREE
1000 mt
153
537,485
12.1.3
UNCOATED WOODFREE
NAC/mt
3529
4705
16369
19351
ACCEPT
ACCEPT
12.1.3
UNCOATED WOODFREE
NAC/mt
12.1.4
COATED PAPERS
1000 mt
39.760
353604.000
36.949
352325.000
0.328
5132.000
0.177
6167.000
12.1.4
COATED PAPERS
1000 mt
12.1.4
COATED PAPERS
1000 mt
39
348,472
12.1.4
COATED PAPERS
NAC/mt
8894
9535
15658
34842
ACCEPT
CHECK
12.1.4
COATED PAPERS
NAC/mt
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 mt
4.981
80267.000
8.685
123548.000
0.044
2861.000
0.042
1915.000
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 mt
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 mt
27
77,427
12.2
HOUSEHOLD AND SANITARY PAPERS
NAC/mt
16115
14225
64965
45595
ACCEPT
ACCEPT
12.2
HOUSEHOLD AND SANITARY PAPERS
NAC/mt
12.3
PACKAGING MATERIALS
1000 mt
148.787
1322433.000
150.063
1331054.000
118.507
1034487.000
120.998
1042840.000
12.3
PACKAGING MATERIALS
1000 mt
OK
OK
OK
OK
OK
Error
OK
OK
12.3
PACKAGING MATERIALS
1000 mt
169
288,086
12.3
PACKAGING MATERIALS
NAC/mt
8888
8870
8729
8619
ACCEPT
ACCEPT
12.3
PACKAGING MATERIALS
NAC/mt
12.3.1
CASE MATERIALS
1000 mt
97.033
610634.000
97.392
563414.000
46.480
232662.000
46.394
239230.000
12.3.1
CASE MATERIALS
1000 mt
12.3.1
CASE MATERIALS
1000 mt
84
377,996
12.3.1
CASE MATERIALS
NAC/mt
6293
5785
5006
5156
ACCEPT
ACCEPT
12.3.1
CASE MATERIALS
NAC/mt
12.3.2
CARTONBOARD
1000 mt
34.935
461949.000
34.941
492964.000
0.159
4478.000
0.210
5728.000
12.3.2
CARTONBOARD
1000 mt
12.3.2
CARTONBOARD
1000 mt
35
457,471
12.3.2
CARTONBOARD
NAC/mt
13223
14108
28147
27276
ACCEPT
ACCEPT
12.3.2
CARTONBOARD
NAC/mt
12.3.3
WRAPPING PAPERS
1000 mt
13.076
226563.000
14.377
254577.000
48.440
690724.000
47.276
676280.000
12.3.3
WRAPPING PAPERS
1000 mt
12.3.3
WRAPPING PAPERS
1000 mt
3
-464,124
12.3.3
WRAPPING PAPERS
NAC/mt
17327
17707
14259
14305
ACCEPT
ACCEPT
12.3.3
WRAPPING PAPERS
NAC/mt
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 mt
3.743
23287.000
3.353
20099.000
23.428
106624.000
27.118
121602.000
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 mt
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 mt
48
-83,258
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
NAC/mt
6222
5994
4551
4484
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 mt
2.439
59777.000
2.078
53963.000
0.011
841.000
0.029
740.000
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
1000 mt
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
1000 mt
2
58,936
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
NAC/mt
24513
25969
77669
25517
ACCEPT
CHECK
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 mt
1.388
9038.000
0.518
7359.000
235.130
851704.000
192.362
776944.000
To fill:
1
1
1
1
9
9
9
9
Text:
0
0
0
0
0
0
0
0
JQ3 SPW
62
91
91
Country:
NO
Date:
Name of Official responsible for reply:
0
Official Address (in full):
FOREST SECTOR QUESTIONNAIRE JQ3
0
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!
Vaue 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
1000NAC
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
2018
2019
2018
2019
2018
2019
2018
2019
2018
2019
2018
2019
Code
2018
2019
2018
2019
13
SECONDARY WOOD PRODUCTS
13
SECONDARY WOOD PRODUCTS
Error
Error
Error
Error
13.1
FURTHER PROCESSED SAWNWOOD
937280.000
1028617.000
119119.000
119577.000
13.1
FURTHER PROCESSED SAWNWOOD
Error
OK
OK
Error
13.1.C
Coniferous
739140.000
815908.000
105788.000
110405.000
13.1.C
Coniferous
13.1.NC
Non-coniferous
198141.000
212709.000
13331.000
9173.000
13.1.NC
Non-coniferous
13.1.NC.T
of which: Tropical
6121.000
5855.000
453.000
717.000
13.1.NC.T
of which: Tropical
OK
OK
OK
OK
13.2
WOODEN WRAPPING AND PACKAGING MATERIAL
493233.000
557984.000
232748.000
245708.000
13.2
WOODEN WRAPPING AND PACKAGING MATERIAL
13.3
WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
247724.000
250035.000
5735.000
5513.000
13.3
WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
13.4
BUILDER’S JOINERY AND CARPENTRY OF WOOD
7278666.000
7221526.000
699597.000
370574.000
13.4
BUILDER’S JOINERY AND CARPENTRY OF WOOD
13.5
WOODEN FURNITURE
7079667.000
7533227.000
1107216.000
1093624.000
13.5
WOODEN FURNITURE
13.6
PREFABRICATED BUILDINGS OF WOOD
2663636.000
2928883.000
154856.000
59016.000
13.6
PREFABRICATED BUILDINGS OF WOOD
13.7
OTHER MANUFACTURED WOOD PRODUCTS
234474.000
258723.000
15723.000
56971.000
13.7
OTHER MANUFACTURED WOOD PRODUCTS
14
SECONDARY PAPER PRODUCTS
14
SECONDARY PAPER PRODUCTS
Error
Error
Error
Error
14.1
COMPOSITE PAPER AND PAPERBOARD
71119.000
66407.000
435.000
251.000
14.1
COMPOSITE PAPER AND PAPERBOARD
14.2
SPECIAL COATED PAPER AND PULP PRODUCTS
337962.000
347574.000
5507.000
5704.000
14.2
SPECIAL COATED PAPER AND PULP PRODUCTS
14.3
HOUSEHOLD AND SANITARY PAPER, READY FOR USE
2068885.000
2147802.000
269776.000
296164.000
14.3
HOUSEHOLD AND SANITARY PAPER, READY FOR USE
14.4
PACKAGING CARTONS, BOXES ETC.
1446881.000
1511790.000
214185.000
245129.000
14.4
PACKAGING CARTONS, BOXES ETC.
14.5
OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE
766702.000
733579.000
62704.000
60957.000
14.5
OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE
OK
OK
OK
ERROR:#REF!
14.5.1
of which: PRINTING AND WRITING PAPER, READY FOR USE
125917.000
137652.000
2421.000
8260.000
14.5.1
of which: PRINTING AND WRITING PAPER, READY FOR USE
14.5.2
of which: ARTICLES, MOULDED OR PRESSED FROM PULP
112339.000
111327.000
350.000
1654.000
14.5.2
of which: ARTICLES, MOULDED OR PRESSED FROM PULP
14.5.3
of which: FILTER PAPER AND PAPERBOARD, READY FOR USE
33442.000
35787.000
362.000
235.000
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:
1000 m3
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
1000 m3
1000 m3
1000 NAC
1000 m3
1000 NAC
1000 m3
1000 NAC
1000 m3
1000 NAC
1000 m3
2018
Glulam
41.035
0.313
2018
40.722
0
ERROR:#DIV/0!
ERROR:#DIV/0!
ERROR:#DIV/0!
2019
41.198
0.408
2019
40.79
0
ERROR:#DIV/0!
ERROR:#DIV/0!
ERROR:#DIV/0!
2018
X-lam
2018
0
ERROR:#DIV/0!
ERROR:#DIV/0!
ERROR:#DIV/0!
ERROR:#DIV/0!
2019
2019
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:
NO
Date:
Name of Official responsible for reply:
0
DISCREPANCIES
FOREST SECTOR QUESTIONNAIRE ECE/EU Species Trade
Official Address (in full):
Checks
Check Table
0
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
2018
2019
2018
2019
2018
2019
2018
2019
2018
2019
2018
2019
2018
2019
2018
2019
Classification
Classification
unit
2018
2019
2018
2019
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
450.560
306078.000
401.712
285678.000
3409.055
1880399.000
3493.036
2072982.000
OK
OK
OK
OK
OK
OK
OK
OK
4403.11/21/22/23/24/25/26
0
NAC/m3
679
711
552
593
ACCEPT
ACCEPT
PRODUCTION
I M P O R T
E X P O R T
ex4403.11
Fir/Spruce (Abies spp., Picea spp.)
1000 m3ub
187.593
119386.000
305.485
179343.000
1544.136
976131.000
1741.117
1107617.000
OK
OK
OK
OK
OK
OK
OK
OK
ex4403.11
Fir/Spruce (Abies spp., Picea spp.)
NAC/m3
636
587
632
636
ACCEPT
ACCEPT
Product
Classification
Classification
Unit of
2018
2019
2018
2019
2018
2019
4403.23/24
4403 23 10
sawlogs and veneer logs
1000 m3ub
4403.23/24
4403 23 10
sawlogs and veneer logs (Abies alba, Picea abies)
NAC/m3
REPORT
REPORT
REPORT
REPORT
CHECK
CHECK
Code
HS2007
CN2007
Product
Quantity
Quantity
Quantity
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
ex4403 11 00 4403 23 90
4403 24 00
pulpwood and other industrial roundwood
1000 m3ub
ex4403 11 00 4403 23 90
4403 24 00
pulpwood and other industrial roundwood (Abies alba, Picea abies)
NAC/m3
REPORT
REPORT
REPORT
REPORT
CHECK
CHECK
1
4401.11/12 44.03
Roundwood production
1000 m3
JQ1
12,497
12,568
ex4403.11
Pine (Pinus spp.)
1000 m3ub
248.284
136382.000
81.411
57738.000
1860.669
893477.000
1744.937
947336.000
OK
OK
OK
OK
OK
OK
OK
OK
ex4403.11
Pine (Pinus spp.)
NAC/m3
549
709
480
543
ACCEPT
ACCEPT
EU2
12497.17202296
12568.4304747727
4403.21/22
4403 21 10
sawlogs and veneer logs
1000 m3ub
4403.21/22
4403 21 10
sawlogs and veneer logs (Pinus sylvestris)
NAC/m3
REPORT
REPORT
REPORT
REPORT
CHECK
CHECK
dif
0
0
ex4403 11 00
4403 21 90
4403 22 00
pulpwood and other industrial roundwood
1000 m3ub
ex4403 11 00
4403 21 90
4403 22 00
pulpwood and other industrial roundwood (Pinus sylvestris)
NAC/m3
REPORT
REPORT
REPORT
REPORT
CHECK
CHECK
1.2.C
4403.11/21/22/23/24/25/26
Industrial Roundwood (wood in the rough), Coniferous
1000 m3
JQ2
451
305,821
402
285,621
3,408
1,878,944
3,492
2,072,136
ex4403.11
Other / Non-specified
1000 m3ub
0.038
100.000
1.937
1366.000
0.211
349.000
0.001
20.000
OK
OK
OK
OK
OK
OK
OK
OK
ex4403.11
Other / Non-specified
NAC/m3
2632
705
1654
20000
CHECK
CHECK
ECE/EU
451
306,078
402
285,678
3,409
1,880,399
3,493
2,072,982
4403.25/26
4403 25 10
sawlogs and veneer logs
1000 m3ub
4403.25/26
4403 25 10
sawlogs and veneer logs
NAC/m3
REPORT
REPORT
REPORT
REPORT
CHECK
CHECK
dif
-0
-257
-0
-57
-1
-1,455
-1
-846
ex4403 11 00
4403 25 90
4403 26 00
pulpwood and other industrial roundwood
1000 m3ub
ex4403 11 00
4403 25 90
4403 26 00
pulpwood and other industrial roundwood
NAC/m3
REPORT
REPORT
REPORT
REPORT
CHECK
CHECK
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
"ex" codes indicate that only part of that trade classication code is used
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
2018
2019
2018
2019
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 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.
Norwegian public procurement policy requests the contractors not to use tropical timber in any form, whether in the actual buildings, or in the materials used during the construction period.
3
Please elaborate on any short or medium term plans for expanding capacity for (further) processing of tropical timber products in your country.
There are no plans for expending such capacity known to the Government.
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?
No significant changes is expected. Lesser-used tropical timber species have limited importance.
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.
The impact of domestic building activity and housing starts on the tropical timber consumption is unclear due to the low overall importance in domestic buildings and housing.
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.).
No area of forest are exclusively allocated to these industries.
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.
Forest plantations occupy approximately 300 000 ha. Annual establishment rate: approx. 100 ha. Proportion of industrial roundwood production from plantations: less than 2,5 percent.
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).
Dear Correspondent,
Thank you for contributing to the Joint Forest Sector Questionnaire (JFSQ). Before filling in the worksheets, please read these guidelines.
The 2019 JFSQ has been changed following the decisions of the Inter-Secretariat Working Group of January 2019. Please use only this questionnaire to report your data. The 2019 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' 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). The following auxiliary sheets (grey tabs) are meant to help you and to improve quality:
• 'Check JQ-EU' and 'Check EU1-JQ2' ensure that data are consistent
• Two cross-reference tables with the classifications; the CN and PRODCOM cross-references are provided in a separate file.
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.) 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.5). This information is important for Eurostat.
A. General recommendations
A.1 Please give your file a meaningful name by changing all the placeholders in the file sent to you.
Example:
2017_JFSQ_[Country]_V[Version]_[DD][MMM][YYYY].xls
• Country = your country's two-digit code from the cover sheet
• Version = the version of the file you are sending (1-99)
• DD = the day of sending
• MMM = the month of sending in text, e.g. APR for April, AUG for August
• YYYY = the year of sending
A.2 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.3 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.4 Report numbers only. If data are confidential, please provide them if possible, appropriately flagged (see A.5).
• 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.5 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 Trade' 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.
B.5 'Check JQ-EU' cross-checks the data in four sheets. This concerns the three roundwood items in sheets 'JQ1' and 'EU2' and the four industrial roundwood and sawnwood items in sheets 'JQ2' and 'ECE/EU Species Trade'.
• When the four sheets are filled in correctly, this checking sheet should have no 'WRONG' messages (red)
• When there are such messages, please follow the row in which a cell in magenta indicates a mistake back to column F to find out which two tables are concerned. The difference between the numbers is in the 'dif'-row.
• Please correct working sheets until all warnings disappear
B.6 'Check EU1-JQ2' cross-checks data on total trade with extra-EU trade. The numbers in 'JQ2' should be greater than or equal to those in 'EU1'.
• If there are 'WRONG' messages (red), please go back to the data in EU1 and/or JQ2 and correct it
• Please correct working sheets until all warnings disappear
Thank you for collecting data for the JFSQ.
Eurostat's Forestry Team
Cover
Joint Forest Sector Questionnaire
2018
DATA INPUT FILE
Correspondent country:
NO
Reference year:
2018
Fill in the year
Name of person responsible for reply:
Official address (in full):
Telephone:
Fax:
E-mail:
Removals over bark
Country:
NO
Date:
Country:
NO
Name of Official responsible for reply:
Check Table
Official Address (in full):
EU JQ1 OB
0
FOREST SECTOR QUESTIONNAIRE
Telephone:
0
0
Discrepancies
Removals
E-mail:
if not 0, please verify !!!
Year 1
Year 2
Flag
Flag
Note
Note
Product
Product
Unit
2017
2018
2017
2018
2017
2018
Product
Product
Unit
2017
2018
Code
Quantity
Quantity
Code
Quantity
Quantity
ROUNDWOOD REMOVALS OVERBARK
ROUNDWOOD REMOVALS OVERBARK
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
13575.000
14128.284
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
0
235
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
2012.000
1974.032
9
9
Nat. estimates
Nat. estimates
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
1
0
1.1.C
Coniferous
1000 m3ub
677.000
658.010
9
9
Nat. estimates
Nat. estimates
1.1.C
Coniferous
1000 m3ub
1.1.NC
Non-Coniferous
1000 m3ub
1334.000
1315.890
9
9
Nat. estimates
Nat. estimates
1.1.NC
Non-Coniferous
1000 m3ub
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
11563.000
11919.478
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
0
-1,082
1.2.C
Coniferous
1000 m3ub
11272.000
12693.149
1.2.C
Coniferous
1000 m3ub
41
1,102
1.2.NC
Non-Coniferous
1000 m3ub
291.000
308.610
1.2.NC
Non-Coniferous
1000 m3ub
23
25
1.2.NC.T
of which: Tropical
1000 m3ub
0.000
0.000
1.2.NC.T
of which: Tropical
1000 m3ub
OK
OK
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3ub
6374.962
6657.148
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3ub
3
3
1.2.1.C
Coniferous
1000 m3ub
6366.162
6651.161
1.2.1.C
Coniferous
1000 m3ub
1.2.1.NC
Non-Coniferous
1000 m3ub
5.834
2.861
1.2.1.NC
Non-Coniferous
1000 m3ub
1.2.2
PULPWOOD, ROUND AND SPLIT
1000 m3ub
5502.919
5600.641
1.2.2
PULPWOOD, ROUND AND SPLIT
1000 m3ub
442
450
1.2.2.C
Coniferous
1000 m3ub
4798.750
4869.386
1.2.2.C
Coniferous
1000 m3ub
1.2.2.NC
Non-Coniferous
1000 m3ub
262.046
281.232
1.2.2.NC
Non-Coniferous
1000 m3ub
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3ub
71.694
77.306
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3ub
6
6
1.2.3.C
Coniferous
1000 m3ub
65.720
70.864
1.2.3.C
Coniferous
1000 m3ub
1.2.3.NC
Non-Coniferous
1000 m3ub
0.000
0.000
1.2.3.NC
Non-Coniferous
1000 m3ub
To fill:
0
0
Text:
0
0
Product
Product
Unit
2017
2018
Code
CF
CF
OVERBARK/UNDERBARK CONVERSION FACTORS
1
ROUNDWOOD (WOOD IN THE ROUGH)
m3/m3
1.102
1.102
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
m3/m3
1.100
1.100
1.1.C
Coniferous
m3/m3
1.099
1.100
1.1.NC
Non-Coniferous
m3/m3
1.200
1.200
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
1.195
1.195
1.2.NC.T
of which: Tropical
m3/m3
ERROR:#DIV/0!
ERROR:#DIV/0!
1.2.1
SAWLOGS AND VENEER LOGS
m3/m3
1.091
1.091
1.2.1.C
Coniferous
m3/m3
1.090
1.090
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
1.104
1.104
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
1.100
1.100
1.2.3.NC
Non-Coniferous
m3/m3
1.100
1.100
JQ1 Production
Country:
NO
Date:
Country:
NO
Name of Official responsible for reply:
Official Address (in full):
Should we make missing data into 0?
FOREST SECTOR QUESTIONNAIRE JQ1
0
Not included: trade in chips
Industrial Roundwood Balance
PRIMARY PRODUCTS
Telephone:
0
0
This table highlights discrepancies between items and sub-items. Please verify your data for any non-zero figure!
Discrepancies
Removals and Production
E-mail:
test for good numbers, missing number, bad number, negative number
Year -1
Year
Flag
Flag
Note
Note
Product
Product
Unit
2017
2018
2017
2018
2017
2018
Product
Product
Unit
2017
2018
2017
2018
% 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
7,404
-1,385,074
-18808%
m3 of wood in m3 or mt of product
REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
Recovered wood used in particle board
96
103
7%
Solid wood equivalent
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
12320.000
12822.133
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
0
191
Solid Wood Demand
agglomerate production
96
96
0%
2.4
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
1829.000
1795.000
9
9
Nat. estimates
Nat. estimates
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
101
100
Sawnwood production
2,655
2,675
1%
1
1.1.C
Coniferous
1000 m3ub
616.000
598.191
9
9
Nat. estimates
Nat. estimates
1.1.C
Coniferous
1000 m3ub
veneer production
Missing data
Missing data
missing data
1
1.1.NC
Non-Coniferous
1000 m3ub
1112.000
1096.575
9
9
Nat. estimates
Nat. estimates
1.1.NC
Non-Coniferous
1000 m3ub
plywood production
Missing data
Missing data
missing data
1
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
10491.000
10835.889
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
-0
0
particle board production (incl OSB)
275
294
7%
1.58
1.2.C
Coniferous
1000 m3ub
10247.500
10577.624
1.2.C
Coniferous
1000 m3ub
0
0
fibreboard production
173
164
-5%
1.8
1.2.NC
Non-Coniferous
1000 m3ub
243.528
258.265
1.2.NC
Non-Coniferous
1000 m3ub
0
-0
mechanical/semi-chemical pulp production
902
885
-2%
2.5
1.2.NC.T
of which: Tropical
1000 m3ub
0.000
0.000
1.2.NC.T
of which: Tropical
1000 m3ub
OK
OK
chemical pulp production
153
153
0%
4.9
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3ub
5845.518
6104.268
1.2.1
SAWLOGS AND VENEER LOGS
1000 m3ub
0
0
dissolving pulp production
0
0
missing data
5.7
1.2.1.C
Coniferous
1000 m3ub
5840.214
6101.667
1.2.1.C
Coniferous
1000 m3ub
Availability
Solid Wood Demand
missing data
missing data
missing data
1.2.1.NC
Non-Coniferous
1000 m3ub
5.304
2.601
1.2.1.NC
Non-Coniferous
1000 m3ub
Difference (roundwood-demand)
missing data
missing data
missing data
positive = surplus
1.2.2
PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)
1000 m3ub
4585.766
4667.201
1.2.2
PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)
1000 m3ub
0
0
gap (demand/availability)
missing data
missing data
Negative number means not enough roundwood available
1.2.2.C
Coniferous
1000 m3ub
4347.542
4411.536
1.2.2.C
Coniferous
1000 m3ub
Positive number means more roundwood available than demanded
1.2.2.NC
Non-Coniferous
1000 m3ub
238.224
255.665
1.2.2.NC
Non-Coniferous
1000 m3ub
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3ub
59.745
64.422
1.2.3
OTHER INDUSTRIAL ROUNDWOOD
1000 m3ub
0
0
1.2.3.C
Coniferous
1000 m3ub
59.745
64.422
1.2.3.C
Coniferous
1000 m3ub
% of particle board that is from recovered wood
35%
1.2.3.NC
Non-Coniferous
1000 m3ub
0.000
0.000
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 mt
2
WOOD CHARCOAL
1000 mt
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
2012.200
2441.707
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
0
0
3.1
WOOD CHIPS AND PARTICLES
1000 m3
1308.000
1587.110
9
9
Nat. estimates
Nat. estimates
3.1
WOOD CHIPS AND PARTICLES
1000 m3
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
704.000
854.597
9
9
Nat. estimates
Nat. estimates
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
4
RECOVERED POST-CONSUMER WOOD
1000 mt
756.000
756.000
5
4
RECOVERED POST-CONSUMER WOOD
1000 mt
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 mt
95.768
95.768
5
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 mt
0
0
5.1
WOOD PELLETS
1000 mt
57.368
57.368
5
5.1
WOOD PELLETS
1000 mt
5.2
OTHER AGGLOMERATES
1000 mt
38.400
38.400
5
5.2
OTHER AGGLOMERATES
1000 mt
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
2655.000
2675.122
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
0
100
6.C
Coniferous
1000 m3
2655.000
2575.122
6.C
Coniferous
1000 m3
6.NC
Non-Coniferous
1000 m3
0.000
0.000
6.NC
Non-Coniferous
1000 m3
6.NC.T
of which: Tropical
1000 m3
0.000
0.000
6.NC.T
of which: Tropical
1000 m3
OK
OK
7
VENEER SHEETS
1000 m3
7
VENEER SHEETS
1000 m3
0
0
7.C
Coniferous
1000 m3
7.C
Coniferous
1000 m3
7.NC
Non-Coniferous
1000 m3
7.NC
Non-Coniferous
1000 m3
7.NC.T
of which: Tropical
1000 m3
7.NC.T
of which: Tropical
1000 m3
OK
OK
8
WOOD-BASED PANELS
1000 m3
448.000
458.000
8
WOOD-BASED PANELS
1000 m3
0
0
8.1
PLYWOOD
1000 m3
8.1
PLYWOOD
1000 m3
0
0
8.1.C
Coniferous
1000 m3
8.1.C
Coniferous
1000 m3
8.1.NC
Non-Coniferous
1000 m3
8.1.NC
Non-Coniferous
1000 m3
8.1.NC.T
of which: Tropical
1000 m3
8.1.NC.T
of which: Tropical
1000 m3
OK
OK
8.2
PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD
1000 m3
275.000
294.000
8.2
PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD
1000 m3
8.2.1
of which: ORIENTED STRAND BOARD (OSB)
1000 m3
8.2.1
of which: ORIENTED STRAND BOARD (OSB)
1000 m3
OK
OK
8.3
FIBREBOARD
1000 m3
173.000
164.000
8.3
FIBREBOARD
1000 m3
0
0
8.3.1
HARDBOARD
1000 m3
50.000
49.000
8.3.1
HARDBOARD
1000 m3
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
0.000
0.000
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
8.3.3
OTHER FIBREBOARD
1000 m3
123.000
115.000
8.3.3
OTHER FIBREBOARD
1000 m3
9
WOOD PULP
1000 mt
1055.000
1038.000
9
WOOD PULP
1000 mt
0
0
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 mt
902.000
885.000
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 mt
9.2
CHEMICAL WOOD PULP
1000 mt
153.000
153.000
9.2
CHEMICAL WOOD PULP
1000 mt
0
0
9.2.1
SULPHATE PULP
1000 mt
0.000
0.000
9.2.1
SULPHATE PULP
1000 mt
9.2.1.1
of which: BLEACHED
1000 mt
0.000
0.000
9.2.1.1
of which: BLEACHED
1000 mt
OK
OK
9.2.2
SULPHITE PULP
1000 mt
153.000
153.000
9.2.2
SULPHITE PULP
1000 mt
9.3
DISSOLVING GRADES
1000 mt
0.000
0.000
9.3
DISSOLVING GRADES
1000 mt
10
OTHER PULP
1000 mt
0.000
0.000
10
OTHER PULP
1000 mt
0
0
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 mt
0.000
0.000
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 mt
10.2
RECOVERED FIBRE PULP
1000 mt
0.000
0.000
10.2
RECOVERED FIBRE PULP
1000 mt
11
RECOVERED PAPER
1000 mt
602.000
604.000
11
RECOVERED PAPER
1000 mt
12
PAPER AND PAPERBOARD
1000 mt
1097.000
1134.000
12
PAPER AND PAPERBOARD
1000 mt
0
0
12.1
GRAPHIC PAPERS
1000 mt
968.000
973.000
12.1
GRAPHIC PAPERS
1000 mt
0
0
12.1.1
NEWSPRINT
1000 mt
483.000
488.000
12.1.1
NEWSPRINT
1000 mt
12.1.2
UNCOATED MECHANICAL
1000 mt
485.000
485.000
12.1.2
UNCOATED MECHANICAL
1000 mt
12.1.3
UNCOATED WOODFREE
1000 mt
0.000
0.000
12.1.3
UNCOATED WOODFREE
1000 mt
12.1.4
COATED PAPERS
1000 mt
0.000
0.000
12.1.4
COATED PAPERS
1000 mt
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 mt
22.000
22.000
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 mt
12.3
PACKAGING MATERIALS
1000 mt
107.000
139.000
12.3
PACKAGING MATERIALS
1000 mt
0
0
12.3.1
CASE MATERIALS
1000 mt
26.000
33.000
12.3.1
CASE MATERIALS
1000 mt
12.3.2
CARTONBOARD
1000 mt
0.000
0.000
12.3.2
CARTONBOARD
1000 mt
12.3.3
WRAPPING PAPERS
1000 mt
40.000
38.000
12.3.3
WRAPPING PAPERS
1000 mt
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 mt
41.000
68.000
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 mt
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
1000 mt
0.000
0.000
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 mt
902.000
885.000
To fill:
10
10
Text:
0
0
JQ2 TTrade
61
62
61
62
91
92
91
92
FOREST SECTOR QUESTIONNAIRE JQ2
Country:
NO
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):
0
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 for any non-zero figure!
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:
NO
Country:
NO
NO V
no value reported
Treshold:
2
verifies whether the JQ2 figures refers only to intra-EU trade
Eurozone countries may use the old national currency, but only in both years
1000NAC
Trade
Discrepancies
REPORT
no figures reported
Product
Unit of
I M P O R T
E X P O R T
Import
Export
Import
Export
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
2017
2018
2017
2018
2017
2018
2017
2018
2017
2018
2017
2018
code
2017
2018
2017
2018
code
2017
2018
2017
2018
code
Product
unit
2017
2018
2017
2018
IMPORT
EXPORT
code
Product
unit
2017
2018
2017
2018
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
625.877
443272.000
594.756
483619.041
3569.256
1723560.000
3567.526
1942349.000
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
0
0
0
0
0
0
0
0
1
ROUNDWOOD (WOOD IN THE ROUGH)
1000 m3ub
9,377
-1,267,466
1
ROUNDWOOD (WOOD IN THE ROUGH)
NAC/m3
708
813
483
544
ACCEPT
ACCEPT
1
ROUNDWOOD (WOOD IN THE ROUGH)
NAC/m3
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
112.680
121986.000
139.936
171840.674
28.340
6429.000
23.525
4264.000
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
0
0
0
0
0
1
0
0
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
1000 m3ub
1,913
117,352
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
NAC/m3
1083
1228
227
181
ACCEPT
ACCEPT
1.1
WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)
NAC/m3
1.1.C
Coniferous
1000 m3ub
4.278
5099.000
5.735
6626.281
19.246
3724.000
13.105
1960.000
1.1.C
Coniferous
1000 m3ub
1.1.C
Coniferous
1000 m3ub
601
1,973
1.1.C
Coniferous
NAC/m3
1192
1155
193
150
ACCEPT
ACCEPT
1.1.C
Coniferous
NAC/m3
1.1.NC
Non-Coniferous
1000 m3ub
108.402
116887.000
134.201
165214.393
9.094
2704.000
10.420
2304.000
1.1.NC
Non-Coniferous
1000 m3ub
1.1.NC
Non-Coniferous
1000 m3ub
1,211
115,280
1.1.NC
Non-Coniferous
NAC/m3
1078
1231
297
221
ACCEPT
ACCEPT
1.1.NC
Non-Coniferous
NAC/m3
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
513.197
321286.000
454.820
311778.367
3540.916
1717131.000
3544.001
1938085.000
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
0
0
0
0
0
0
0
1
1.2
INDUSTRIAL ROUNDWOOD
1000 m3ub
7,463
-1,385,009
1.2
INDUSTRIAL ROUNDWOOD
NAC/m3
626
685
485
547
ACCEPT
ACCEPT
1.2
INDUSTRIAL ROUNDWOOD
NAC/m3
1.2.C
Coniferous
1000 m3ub
510.723
316280.000
450.532
305820.945
3359.802
1645117.000
3408.176
1878944.000
1.2.C
Coniferous
1000 m3ub
1.2.C
Coniferous
1000 m3ub
7,398
-1,318,259
1.2.C
Coniferous
NAC/m3
619
679
490
551
ACCEPT
ACCEPT
1.2.C
Coniferous
NAC/m3
1.2.NC
Non-Coniferous
1000 m3ub
2.474
5006.000
4.288
5957.422
181.114
72014.000
135.825
59140.000
1.2.NC
Non-Coniferous
1000 m3ub
1.2.NC
Non-Coniferous
1000 m3ub
65
-66,750
1.2.NC
Non-Coniferous
NAC/mt
2023
1389
398
435
ACCEPT
ACCEPT
1.2.NC
Non-Coniferous
NAC/mt
1.2.NC.T
of which: Tropical
1000 m3ub
0.026
183.000
0.076
364.292
0.178
1818.000
0.178
1818.000
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
-1,635
1.2.NC.T
of which: Tropical
1000 m3
7038
4793
10213
10213
ACCEPT
ACCEPT
1.2.NC.T
of which: Tropical
1000 m3
2
WOOD CHARCOAL
1000 mt
39.040
191245.000
41.964
205340.000
3455.856
5795.000
3333.695
4599.000
2
WOOD CHARCOAL
1000 mt
2
WOOD CHARCOAL
1000 mt
-3,417
185,450
2
WOOD CHARCOAL
1000 m3
4899
4893
2
1
ACCEPT
ACCEPT
2
WOOD CHARCOAL
1000 m3
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
649.981
231742.000
719.467
313413.000
1678.598
206020.000
2135.814
286954.000
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
0
0
0
0
0
1
0
0
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
984
28,164
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
357
436
123
134
ACCEPT
ACCEPT
3
WOOD CHIPS, PARTICLES AND RESIDUES
1000 m3
3.1
WOOD CHIPS AND PARTICLES
1000 m3
360.093
122523.000
390.900
170000.000
644.920
120023.000
824.342
160083.000
3.1
WOOD CHIPS AND PARTICLES
1000 m3
3.1
WOOD CHIPS AND PARTICLES
1000 m3
1,023
4,087
3.1
WOOD CHIPS AND PARTICLES
1000 mt
340
435
186
194
ACCEPT
ACCEPT
3.1
WOOD CHIPS AND PARTICLES
1000 mt
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
289.888
109219.000
328.567
143413.000
1033.678
85996.000
1311.472
126871.000
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 m3
-40
24,078
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 mt
377
436
83
97
ACCEPT
ACCEPT
3.2
WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)
1000 mt
4
RECOVERED POST-CONSUMER WOOD
1000 mt
0.000
520.578
520.578
4
RECOVERED POST-CONSUMER WOOD
1000 mt
4
RECOVERED POST-CONSUMER WOOD
1000 mt
235
756
4
RECOVERED POST-CONSUMER WOOD
1000 mt
NO V
REPORT
NO V
NO V
CHECK
CHECK
4
RECOVERED POST-CONSUMER WOOD
1000 mt
CHECK
CHECK
CHECK
CHECK
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 mt
85.390
75335.000
106.110
103759.000
127.599
52138.000
68.596
50579.000
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 mt
0
0
0
0
0
ERROR:#REF!
0
0
5
WOOD PELLETS AND OTHER AGGLOMERATES
1000 mt
54
ERROR:#REF!
5
WOOD PELLETS AND OTHER AGGLOMERATES
NAC/m3
882
978
NO V
760
ACCEPT
CHECK
5
WOOD PELLETS AND OTHER AGGLOMERATES
NAC/m3
CHECK
5.1
WOOD PELLETS
1000 mt
46.581
45599.000
76.015
75577.000
35.337
24180.000
24.217
31792.000
5.1
WOOD PELLETS
1000 mt
5.1
WOOD PELLETS
1000 mt
69
ERROR:#REF!
5.1
WOOD PELLETS
NAC/m3
979
994
NO V
998
ACCEPT
CHECK
5.1
WOOD PELLETS
NAC/m3
CHECK
5.2
OTHER AGGLOMERATES
1000 mt
38.809
29736.000
30.095
28182.000
92.262
27958.000
44.379
18788.000
5.2
OTHER AGGLOMERATES
1000 mt
5.2
OTHER AGGLOMERATES
1000 mt
-15
ERROR:#REF!
5.2
OTHER AGGLOMERATES
NAC/m3
766
936
NO V
630
ACCEPT
CHECK
5.2
OTHER AGGLOMERATES
NAC/m3
CHECK
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
1035.032
2899314.000
960.737
2826382.000
653.562
1181767.000
681.066
1387076.000
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
0
-1
0
1
0
1
0
0
6
SAWNWOOD (INCLUDING SLEEPERS)
1000 m3
3,036
1,720,222
6
SAWNWOOD (INCLUDING SLEEPERS)
NAC/m3
2801
2942
1808
2037
ACCEPT
ACCEPT
6
SAWNWOOD (INCLUDING SLEEPERS)
NAC/m3
6.C
Coniferous
1000 m3
1006.828
2656050.000
933.723
2568287.000
646.159
1155761.000
675.162
1356328.000
6.C
Coniferous
1000 m3
6.C
Coniferous
1000 m3
3,016
1,502,864
6.C
Coniferous
NAC/m3
2638
2751
1789
2009
ACCEPT
ACCEPT
6.C
Coniferous
NAC/m3
6.NC
Non-Coniferous
1000 m3
28.204
243265.000
27.014
258094.000
7.403
26005.000
5.904
30748.000
6.NC
Non-Coniferous
1000 m3
6.NC
Non-Coniferous
1000 m3
21
217,260
6.NC
Non-Coniferous
NAC/m3
8625
9554
3513
5208
ACCEPT
ACCEPT
6.NC
Non-Coniferous
NAC/m3
6.NC.T
of which: Tropical
1000 m3
1.499
25719.000
1.787
37006.000
2.003
15921.000
0.951
10721.000
6.NC.T
of which: Tropical
1000 m3
OK
OK
OK
OK
OK
OK
OK
OK
6.NC.T
of which: Tropical
1000 m3
-1
9,798
6.NC.T
of which: Tropical
NAC/m3
17157
20708
7949
11273
ACCEPT
ACCEPT
6.NC.T
of which: Tropical
NAC/m3
7
VENEER SHEETS
1000 m3
4.888
38785.000
5.072
45309.000
0.674
774.000
2.746
1433.000
7
VENEER SHEETS
1000 m3
0
1
0
0
0
1
0
0
7
VENEER SHEETS
1000 m3
4
38,011
7
VENEER SHEETS
NAC/m3
7935
8933
1148
522
ACCEPT
CHECK
7
VENEER SHEETS
NAC/m3
7.C
Coniferous
1000 m3
0.177
1129.000
0.164
849.000
0.003
27.000
0.067
179.000
7.C
Coniferous
1000 m3
7.C
Coniferous
1000 m3
0
1,102
7.C
Coniferous
NAC/m3
6379
5177
9000
2672
ACCEPT
CHECK
7.C
Coniferous
NAC/m3
7.NC
Non-Coniferous
1000 m3
4.711
37655.000
4.908
44460.000
0.671
746.000
2.679
1254.000
7.NC
Non-Coniferous
1000 m3
7.NC
Non-Coniferous
1000 m3
4
36,909
7.NC
Non-Coniferous
NAC/m3
7993
9059
1112
468
ACCEPT
CHECK
7.NC
Non-Coniferous
NAC/m3
7.NC.T
of which: Tropical
1000 m3
0.068
651.000
0.159
1248.000
0.353
31.000
2.141
516.000
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
620
7.NC.T
of which: Tropical
NAC/m3
9574
7849
88
241
ACCEPT
CHECK
7.NC.T
of which: Tropical
NAC/m3
8
WOOD-BASED PANELS
1000 m3
279.630
2127383.000
296.219
2277213.000
163.365
933188.000
180.728
982643.000
8
WOOD-BASED PANELS
1000 m3
-147
-6
-137.62557
0
1.508
-1
0
1
8
WOOD-BASED PANELS
1000 m3
564
1,194,653
8
WOOD-BASED PANELS
NAC/m3
7608
7688
5712
5437
ACCEPT
ACCEPT
8
WOOD-BASED PANELS
NAC/m3
8.1
PLYWOOD
1000 m3
134.776
760697.000
140.196
821167.000
29.427
156407.000
22.809
168745.000
8.1
PLYWOOD
1000 m3
-7
-25235
-9.22
-35222
-0.38
-1809
-0.01
-2
8.1
PLYWOOD
1000 m3
105
604,290
8.1
PLYWOOD
NAC/m3
5644
5857
5315
7398
ACCEPT
ACCEPT
8.1
PLYWOOD
NAC/m3
8.1.C
Coniferous
1000 m3
66.002
325131.000
68.342
358298.000
15.308
144147.000
15.457
152977.000
8.1.C
Coniferous
1000 m3
8.1.C
Coniferous
1000 m3
51
180,984
8.1.C
Coniferous
NAC/m3
4926
5243
9416
9897
ACCEPT
ACCEPT
8.1.C
Coniferous
NAC/m3
8.1.NC
Non-Coniferous
1000 m3
76.154
460801.000
81.074
498091.000
14.499
14069.000
7.362
15770.000
8.1.NC
Non-Coniferous
1000 m3
8.1.NC
Non-Coniferous
1000 m3
62
446,732
8.1.NC
Non-Coniferous
NAC/m3
6051
6144
970
2142
ACCEPT
CHECK
8.1.NC
Non-Coniferous
NAC/m3
8.1.NC.T
of which: Tropical
1000 m3
13.641
61137.000
14.792
65313.000
9.096
5012.000
3.025
1659.000
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
5
56,125
8.1.NC.T
of which: Tropical
NAC/m3
4482
4415
551
548
ACCEPT
ACCEPT
8.1.NC.T
of which: Tropical
NAC/m3
8.2
PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD
1000 m3
144.854
390106.000
156.023
462984.000
92.131
315847.000
157.919
369077.000
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
328
74,553
8.2
PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD
NAC/m3
2693
2967
3428
2337
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
91.865
195535.000
106.150
279776.000
0.197
682.000
0.269
400.000
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
92
194,853
8.2.1
of which: ORIENTED STRANDBOARD (OSB)
NAC/m3
2129
2636
3462
1487
ACCEPT
CHECK
8.2.1
of which: ORIENTED STRANDBOARD (OSB)
NAC/m3
8.3
FIBREBOARD
1000 m3
146.732
976586.000
137.626
993062.000
40.299
460935.000
0.000
444820.000
8.3
FIBREBOARD
1000 m3
0
-1
0
0
0
1
0
-1
8.3
FIBREBOARD
1000 m3
279
515,815
8.3
FIBREBOARD
NAC/m3
6656
7216
11438
ZERO Q
ACCEPT
CHECK
8.3
FIBREBOARD
NAC/m3
CHECK
8.3.1
HARDBOARD
1000 m3
32.492
164087.000
35.115
185356.000
2.707
21182.000
0.000
25312.000
8.3.1
HARDBOARD
1000 m3
8.3.1
HARDBOARD
1000 m3
80
142,954
8.3.1
HARDBOARD
NAC/mt
5050
5278
7825
ZERO Q
ACCEPT
CHECK
8.3.1
HARDBOARD
NAC/mt
CHECK
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
107.986
768948.000
96.687
767355.000
28.314
370497.000
0.000
363331.000
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
1000 m3
80
398,451
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
NAC/mt
7121
7936
13085
ZERO Q
ACCEPT
CHECK
8.3.2
MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)
NAC/mt
CHECK
8.3.3
OTHER FIBREBOARD
1000 m3
6.254
43552.000
5.823
40351.000
9.277
69255.000
0.000
56178.000
8.3.3
OTHER FIBREBOARD
1000 m3
8.3.3
OTHER FIBREBOARD
1000 m3
120
-25,588
8.3.3
OTHER FIBREBOARD
NAC/mt
6964
6930
7465
ZERO Q
ACCEPT
CHECK
8.3.3
OTHER FIBREBOARD
NAC/mt
CHECK
9
WOOD PULP
1000 mt
88.756
443326.000
91.394
446302.000
+++
+++
+++
+++
9
WOOD PULP
1000 mt
0
0
0
0
ERROR:#VALUE!
ERROR:#VALUE!
ERROR:#VALUE!
ERROR:#VALUE!
9
WOOD PULP
1000 mt
ERROR:#VALUE!
ERROR:#VALUE!
9
WOOD PULP
NAC/mt
4995
4883
REPORT
REPORT
ACCEPT
CHECK
9
WOOD PULP
NAC/mt
CHECK
CHECK
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 mt
1.738
2720.000
0.976
4194.000
213.895
749906.000
235.130
851682.000
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 mt
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
1000 mt
690
-746,301
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
NAC/mt
1565
4299
3506
3622
CHECK
ACCEPT
9.1
MECHANICAL AND SEMI-CHEMICAL WOOD PULP
NAC/mt
9.2
CHEMICAL WOOD PULP
1000 mt
87.018
440606.000
90.414
442034.000
+++
+++
+++
+++
9.2
CHEMICAL WOOD PULP
1000 mt
0
0
0
0
ERROR:#VALUE!
ERROR:#VALUE!
ERROR:#VALUE!
ERROR:#VALUE!
9.2
CHEMICAL WOOD PULP
1000 mt
ERROR:#VALUE!
ERROR:#VALUE!
9.2
CHEMICAL WOOD PULP
NAC/mt
5063
4889
REPORT
REPORT
ACCEPT
CHECK
9.2
CHEMICAL WOOD PULP
NAC/mt
CHECK
CHECK
9.2.1
SULPHATE PULP
1000 mt
74.931
368505.000
68.628
364620.000
+++
+++
+++
+++
9.2.1
SULPHATE PULP
1000 mt
9.2.1
SULPHATE PULP
1000 mt
ERROR:#VALUE!
ERROR:#VALUE!
9.2.1
SULPHATE PULP
NAC/mt
4918
5313
REPORT
349
ACCEPT
CHECK
9.2.1
SULPHATE PULP
NAC/mt
CHECK
9.2.1.1
of which: BLEACHED
1000 mt
74.903
368344.000
68.628
364620.000
+++
+++
+++
+++
9.2.1.1
of which: BLEACHED
1000 mt
OK
OK
OK
OK
OK
OK
ERROR:#REF!
Error
9.2.1.1
of which: BLEACHED
1000 mt
ERROR:#VALUE!
ERROR:#VALUE!
9.2.1.1
of which: BLEACHED
NAC/mt
4918
5313
REPORT
NO Q
ACCEPT
CHECK
9.2.1.1
of which: BLEACHED
NAC/mt
CHECK
CHECK
9.2.2
SULPHITE PULP
1000 mt
12.087
72101.000
21.785
77414.000
6.402
2375.000
16.564
5788.000
9.2.2
SULPHITE PULP
1000 mt
9.2.2
SULPHITE PULP
1000 mt
159
69,879
9.2.2
SULPHITE PULP
NAC/mt
5965
3553
371
REPORT
ACCEPT
CHECK
9.2.2
SULPHITE PULP
NAC/mt
CHECK
9.3
DISSOLVING GRADES
1000 mt
0.000
0.000
0.005
74.000
151.029
1571668.000
143.235
1484463.000
9.3
DISSOLVING GRADES
1000 mt
9.3
DISSOLVING GRADES
1000 mt
-151
-1,571,668
9.3
DISSOLVING GRADES
NAC/mt
0
14616
10406
NO V
CHECK
CHECK
9.3
DISSOLVING GRADES
NAC/mt
INTRA-EU
10
OTHER PULP
1000 mt
2.954
15613.000
2.189
14483.000
0.225
113.000
0.325
78.000
10
OTHER PULP
1000 mt
0
0
0
-1
0
0
0
-1
10
OTHER PULP
1000 mt
3
15,500
10
OTHER PULP
NAC/mt
5285
6616
502
240
ACCEPT
CHECK
10
OTHER PULP
NAC/mt
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 mt
0.161
3502.000
0.413
4844.000
0.000
71.000
0.000
22.000
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 mt
10.1
PULP FROM FIBRES OTHER THAN WOOD
1000 mt
0
3,431
10.1
PULP FROM FIBRES OTHER THAN WOOD
NAC/mt
21752
11738
ZERO Q
183333
ACCEPT
CHECK
10.1
PULP FROM FIBRES OTHER THAN WOOD
NAC/mt
INTRA-EU
10.2
RECOVERED FIBRE PULP
1000 mt
2.793
12111.000
1.776
9640.000
0.225
42.000
0.325
57.000
10.2
RECOVERED FIBRE PULP
1000 mt
10.2
RECOVERED FIBRE PULP
1000 mt
3
12,069
10.2
RECOVERED FIBRE PULP
NAC/mt
4336
5427
187
175
ACCEPT
ACCEPT
10.2
RECOVERED FIBRE PULP
NAC/mt
11
RECOVERED PAPER
1000 mt
29.757
64570.000
30.777
42508.000
458.207
574972.000
465.245
463208.000
11
RECOVERED PAPER
1000 mt
11
RECOVERED PAPER
1000 mt
174
-509,798
11
RECOVERED PAPER
NAC/mt
2170
1381
1255
996
ACCEPT
ACCEPT
11
RECOVERED PAPER
NAC/mt
12
PAPER AND PAPERBOARD
1000 mt
364.016
2426492.000
390.214
2560201.000
+++
+++
+++
+++
12
PAPER AND PAPERBOARD
1000 mt
86
264485
129.729843
339774
ERROR:#VALUE!
ERROR:#VALUE!
ERROR:#VALUE!
ERROR:#VALUE!
12
PAPER AND PAPERBOARD
1000 mt
ERROR:#VALUE!
ERROR:#VALUE!
12
PAPER AND PAPERBOARD
NAC/mt
6666
6561
REPORT
REPORT
ACCEPT
CHECK
12
PAPER AND PAPERBOARD
NAC/mt
CHECK
CHECK
12.1
GRAPHIC PAPERS
1000 mt
132.130
858432.000
104.278
757950.000
+++
+++
+++
+++
12.1
GRAPHIC PAPERS
1000 mt
-86
-264486
-129.729843
-339775
ERROR:#VALUE!
ERROR:#VALUE!
ERROR:#VALUE!
ERROR:#VALUE!
12.1
GRAPHIC PAPERS
1000 mt
ERROR:#VALUE!
ERROR:#VALUE!
12.1
GRAPHIC PAPERS
NAC/mt
6497
7269
REPORT
REPORT
ACCEPT
CHECK
12.1
GRAPHIC PAPERS
NAC/mt
CHECK
CHECK
12.1.1
NEWSPRINT
1000 mt
49.067
202468.000
35.121
161506.000
+++
+++
+++
+++
12.1.1
NEWSPRINT
1000 mt
12.1.1
NEWSPRINT
1000 mt
ERROR:#VALUE!
ERROR:#VALUE!
12.1.1
NEWSPRINT
NAC/mt
4126
4599
REPORT
REPORT
ACCEPT
CHECK
12.1.1
NEWSPRINT
NAC/mt
CHECK
CHECK
12.1.2
UNCOATED MECHANICAL
1000 mt
5.241
34857.000
5.493
40475.000
+++
+++
+++
+++
12.1.2
UNCOATED MECHANICAL
1000 mt
12.1.2
UNCOATED MECHANICAL
1000 mt
ERROR:#VALUE!
ERROR:#VALUE!
12.1.2
UNCOATED MECHANICAL
NAC/mt
6651
7368
REPORT
REPORT
ACCEPT
CHECK
12.1.2
UNCOATED MECHANICAL
NAC/mt
CHECK
CHECK
12.1.3
UNCOATED WOODFREE
1000 mt
115.363
501605.000
153.634
542140.000
0.278
3644.000
0.284
4655.000
12.1.3
UNCOATED WOODFREE
1000 mt
12.1.3
UNCOATED WOODFREE
1000 mt
115
497,961
12.1.3
UNCOATED WOODFREE
NAC/mt
4348
3529
13108
16369
ACCEPT
ACCEPT
12.1.3
UNCOATED WOODFREE
NAC/mt
12.1.4
COATED PAPERS
1000 mt
47.990
383988.000
39.760
353604.000
0.374
5071.000
0.328
5132.000
12.1.4
COATED PAPERS
1000 mt
12.1.4
COATED PAPERS
1000 mt
48
378,917
12.1.4
COATED PAPERS
NAC/mt
8001
8894
13559
15658
ACCEPT
ACCEPT
12.1.4
COATED PAPERS
NAC/mt
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 mt
6.478
92459.000
4.981
80267.000
0.050
2702.000
0.044
2861.000
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 mt
12.2
HOUSEHOLD AND SANITARY PAPERS
1000 mt
28
89,779
12.2
HOUSEHOLD AND SANITARY PAPERS
NAC/mt
14273
16115
54040
64965
ACCEPT
ACCEPT
12.2
HOUSEHOLD AND SANITARY PAPERS
NAC/mt
12.3
PACKAGING MATERIALS
1000 mt
137.165
1156120.000
148.787
1322433.000
116.753
971003.000
118.507
1034487.000
12.3
PACKAGING MATERIALS
1000 mt
0
0
0
0
-0.002
0
0
-1
12.3
PACKAGING MATERIALS
1000 mt
127
185,256
12.3
PACKAGING MATERIALS
NAC/mt
8429
8888
8317
8729
ACCEPT
ACCEPT
12.3
PACKAGING MATERIALS
NAC/mt
12.3.1
CASE MATERIALS
1000 mt
81.447
439319.000
97.033
610634.000
40.281
174793.000
46.480
232662.000
12.3.1
CASE MATERIALS
1000 mt
12.3.1
CASE MATERIALS
1000 mt
67
264,559
12.3.1
CASE MATERIALS
NAC/mt
5394
6293
4339
5006
ACCEPT
ACCEPT
12.3.1
CASE MATERIALS
NAC/mt
12.3.2
CARTONBOARD
1000 mt
36.040
448236.000
34.935
461949.000
0.278
4544.000
0.159
4478.000
12.3.2
CARTONBOARD
1000 mt
12.3.2
CARTONBOARD
1000 mt
36
443,692
12.3.2
CARTONBOARD
NAC/mt
12437
13223
16345
28147
ACCEPT
ACCEPT
12.3.2
CARTONBOARD
NAC/mt
12.3.3
WRAPPING PAPERS
1000 mt
15.152
243455.000
13.076
226563.000
52.847
696555.000
48.440
690724.000
12.3.3
WRAPPING PAPERS
1000 mt
12.3.3
WRAPPING PAPERS
1000 mt
2
-453,062
12.3.3
WRAPPING PAPERS
NAC/mt
16068
17327
13181
14259
ACCEPT
ACCEPT
12.3.3
WRAPPING PAPERS
NAC/mt
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 mt
4.526
25110.000
3.743
23287.000
23.349
95111.000
23.428
106624.000
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 mt
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
1000 mt
22
-69,933
12.3.4
OTHER PAPERS MAINLY FOR PACKAGING
NAC/mt
5548
6222
4073
4551
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 mt
2.712
54996.000
2.439
59777.000
0.031
706.000
0.011
841.000
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
1000 mt
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
1000 mt
3
54,290
12.4
OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)
NAC/mt
20279
24513
22774
77669
ACCEPT
CHECK
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 mt
1.899
6222.000
1.388
9038.000
213.895
749977.000
235.130
851704.000
To fill:
0
1
1
1
0
1
0
1
Text:
0
0
0
0
8
8
8
8
JQ3 SPW
62
91
91
Country:
NO
Date:
Country:
Name of Official responsible for reply:
0
Official Address (in full):
FOREST SECTOR QUESTIONNAIRE JQ3
0
SECONDARY PROCESSED PRODUCTS
Telephone/Fax:
0
0
Trade
E-mail:
0
This table highlights discrepancies between items and sub-items. Please verify your data for any non-zero figure!
Vaue 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
1000NAC
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
2017
2018
2017
2018
2017
2018
2017
2018
2017
2018
2017
2018
Code
2017
2018
2017
2018
13
SECONDARY WOOD PRODUCTS
13
SECONDARY WOOD PRODUCTS
-18706713
-18934680
-2347251
-2334994
13.1
FURTHER PROCESSED SAWNWOOD
876029.000
937280.000
99507.000
119119.000
13.1
FURTHER PROCESSED SAWNWOOD
0
-1
-1
0
13.1.C
Coniferous
688337.000
739140.000
85921.000
105788.000
13.1.C
Coniferous
13.1.NC
Non-coniferous
187692.000
198141.000
13587.000
13331.000
13.1.NC
Non-coniferous
13.1.NC.T
of which: Tropical
5205.000
6121.000
486.000
453.000
13.1.NC.T
of which: Tropical
OK
OK
OK
OK
13.2
WOODEN WRAPPING AND PACKAGING MATERIAL
456019.000
493233.000
213557.000
232748.000
13.2
WOODEN WRAPPING AND PACKAGING MATERIAL
13.3
WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
223638.000
247724.000
6970.000
5735.000
13.3
WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
13.4
BUILDER’S JOINERY AND CARPENTRY OF WOOD
7327670.000
7278666.000
680197.000
699597.000
13.4
BUILDER’S JOINERY AND CARPENTRY OF WOOD
13.5
WOODEN FURNITURE
7101940.000
7079667.000
1123413.000
1107216.000
13.5
WOODEN FURNITURE
13.6
PREFABRICATED BUILDINGS OF WOOD
2532539.000
2663636.000
201037.000
154856.000
13.6
PREFABRICATED BUILDINGS OF WOOD
13.7
OTHER MANUFACTURED WOOD PRODUCTS
188878.000
234474.000
22570.000
15723.000
13.7
OTHER MANUFACTURED WOOD PRODUCTS
14
SECONDARY PAPER PRODUCTS
14
SECONDARY PAPER PRODUCTS
-4538528
-4691549
-491641
-552607
14.1
COMPOSITE PAPER AND PAPERBOARD
69494.000
71119.000
448.000
435.000
14.1
COMPOSITE PAPER AND PAPERBOARD
14.2
SPECIAL COATED PAPER AND PULP PRODUCTS
313921.000
337962.000
4666.000
5507.000
14.2
SPECIAL COATED PAPER AND PULP PRODUCTS
14.3
HOUSEHOLD AND SANITARY PAPER, READY FOR USE
2038608.000
2068885.000
233305.000
269776.000
14.3
HOUSEHOLD AND SANITARY PAPER, READY FOR USE
14.4
PACKAGING CARTONS, BOXES ETC.
1370487.000
1446881.000
199733.000
214185.000
14.4
PACKAGING CARTONS, BOXES ETC.
14.5
OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE
746018.000
766702.000
53489.000
62704.000
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
119975.000
125917.000
4367.000
2421.000
14.5.1
of which: PRINTING AND WRITING PAPER, READY FOR USE
14.5.2
of which: ARTICLES, MOULDED OR PRESSED FROM PULP
109822.000
112339.000
460.000
350.000
14.5.2
of which: ARTICLES, MOULDED OR PRESSED FROM PULP
14.5.3
of which: FILTER PAPER AND PAPERBOARD, READY FOR USE
31296.000
33442.000
415.000
362.000
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:
1000 m3
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
1000 m3
1000 m3
1000 NAC
1000 m3
1000 NAC
1000 m3
1000 NAC
1000 m3
1000 NAC
1000 m3
2017
Glulam
42.917
0.531
8416
15.972
216142
0
0
0.207
2420
2017
58.358
15849.34086629
13532.5569747057
ERROR:#DIV/0!
11690.8212560386
2018
41.035
2018
41.035
ERROR:#DIV/0!
ERROR:#DIV/0!
ERROR:#DIV/0!
ERROR:#DIV/0!
2017
X-lam
2017
0
ERROR:#DIV/0!
ERROR:#DIV/0!
ERROR:#DIV/0!
ERROR:#DIV/0!
2018
2018
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:
NO
Date:
Name of Official responsible for reply:
0
DISCREPANCIES - please note cells with notes and review data
FOREST SECTOR QUESTIONNAIRE ECE/EU Species Trade
Official Address (in full):
Checks
Check Table
0
0
both VALUE and quantity reported ZERO
Trade in Roundwood and Sawnwood by species
Telephone:
0
Fax:
0
- checks whether subitems are 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
2017
2018
2017
2018
2017
2018
2017
2018
2017
2018
2017
2018
2017
2018
2017
2018
Classification
Classification
unit
2017
2018
2017
2018
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
500.625
317511.000
450.560
306078.000
3361.022
1645671.000
3409.055
1880399.000
OK
OK
OK
OK
OK
OK
OK
OK
4403.11/21/22/23/24/25/26
0
NAC/m3
634
679
490
552
ACCEPT
ACCEPT
PRODUCTION
I M P O R T
E X P O R T
ex4403.11
Fir/Spruce (Abies spp., Picea spp.)
1000 m3ub
224.908
126847.000
187.593
119386.000
1684.394
890430.000
1544.136
976131.000
OK
OK
OK
OK
OK
OK
OK
OK
ex4403.11
Fir/Spruce (Abies spp., Picea spp.)
NAC/m3
564
636
529
632
ACCEPT
ACCEPT
Product
Classification
Classification
Unit of
2017
2018
2017
2018
2017
2018
4403.23/24
4403 23 10
sawlogs and veneer logs
1000 m3ub
4403.23/24
4403 23 10
sawlogs and veneer logs (Abies alba, Picea abies)
NAC/m3
REPORT
REPORT
REPORT
REPORT
CHECK
CHECK
Code
HS2007
CN2007
Product
Quantity
Quantity
Quantity
Quantity
Value
Quantity
Value
Quantity
Value
Quantity
Value
ex4403 11 00 4403 23 90
4403 24 00
pulpwood and other industrial roundwood
1000 m3ub
ex4403 11 00 4403 23 90
4403 24 00
pulpwood and other industrial roundwood (Abies alba, Picea abies)
NAC/m3
REPORT
REPORT
REPORT
REPORT
CHECK
CHECK
1
4401.11/12 44.03
Roundwood production
1000 m3
JQ1
0
0
ex4403.11
Pine (Pinus spp.)
1000 m3ub
271.135
141536.000
248.284
136382.000
1644.893
720726.000
1860.669
893477.000
OK
OK
OK
OK
OK
OK
OK
OK
ex4403.11
Pine (Pinus spp.)
NAC/m3
522
549
438
480
ACCEPT
ACCEPT
EU2
0
0
4403.21/22
4403 21 10
sawlogs and veneer logs
1000 m3ub
4403.21/22
4403 21 10
sawlogs and veneer logs (Pinus sylvestris)
NAC/m3
REPORT
REPORT
REPORT
REPORT
CHECK
CHECK
dif
0
0
ex4403 11 00
4403 21 90
4403 22 00
pulpwood and other industrial roundwood
1000 m3ub
ex4403 11 00
4403 21 90
4403 22 00
pulpwood and other industrial roundwood (Pinus sylvestris)
NAC/m3
REPORT
REPORT
REPORT
REPORT
CHECK
CHECK
1.2.C
4403.11/21/22/23/24/25/26
Industrial Roundwood (wood in the rough), Coniferous
1000 m3
JQ2
511
316,280
451
305,821
3,360
1,645,117
3,408
1,878,944
ex4403.11
Other / Non-specified
1000 m3ub
0.014
88.000
0.038
100.000
24.401
13828.000
0.211
349.000
OK
OK
OK
OK
OK
OK
OK
OK
ex4403.11
Other / Non-specified
NAC/m3
6286
2632
567
1654
CHECK
CHECK
ECE/EU
501
317,511
451
306,078
3,361
1,645,671
3,409
1,880,399
4403.25/26
4403 25 10
sawlogs and veneer logs
1000 m3ub
4403.25/26
4403 25 10
sawlogs and veneer logs
NAC/m3
REPORT
REPORT
REPORT
REPORT
CHECK
CHECK
dif
10
-1,231
-0
-257
-1
-554
-1
-1,455
ex4403 11 00
4403 25 90
4403 26 00
pulpwood and other industrial roundwood
1000 m3ub
ex4403 11 00
4403 25 90
4403 26 00
pulpwood and other industrial roundwood
NAC/m3
REPORT
REPORT
REPORT
REPORT
CHECK
CHECK
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
"ex" codes indicate that only part of that trade classication code is used
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
2017
2018
2017
2018
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 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.
Norwegian public procurement policy requests the contractors not to use tropical timber in any form, whether in the actual buildings, or in the materials used during the construction period.
3
Please elaborate on any short or medium term plans for expanding capacity for (further) processing of tropical timber products in your country.
There are no plans for expending such capacity known to the Government.
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?
No significant changes is expected. Lesser-used tropical timber species have limited importance.
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.
The impact of domestic building activity and housing starts on the tropical timber consumption is unclear due to the low overall importance in domestic buildings and housing.
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.).
No area of forest are exclusively allocated to these industries.
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.
Forest plantations occupy approximately 300 000 ha. Annual establishment rate: approx. 100 ha. Proportion of industrial roundwood production from plantations: less than 2,5 percent.
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).
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).
* 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:
Forest products conversion factors provides ratios of raw material input to the output of wood-based forest products for 37 countries of the world. Analysts, policymakers, forest practitioners and forest-based manufacturers often have a need for this information for understanding the drivers of efficiency, feasibility and economics of the sector.
Forest products conversion factors provides ratios of raw material input to the output of wood-based forest products for 37 countries of the world. Analysts, policymakers, forest practitioners and forest-based manufacturers often have a need for this information for understanding the drivers of efficiency, feasibility and economics of the sector.
Forest products conversion factors provides ratios of raw material input to the output of wood-based forest products for 37 countries of the world. Analysts, policymakers, forest practitioners and forest-based manufacturers often have a need for this information for understanding the drivers of efficiency, feasibility and economics of the sector.