Emission of greenhouse gases (GHGs) and removals from land, including both
anthropogenic and natural fluxes, require reliable quantification, including
estimates of uncertainties, to support credible mitigation action under the
Paris Agreement. This study provides a state-of-the-art scientific overview
of bottom-up anthropogenic emissions data from agriculture, forestry and
other land use (AFOLU) in the European Union (EU28 We refer to
EU28 as communicated by EUROSTAT, including the UK:
The atmospheric concentrations of the main greenhouse gases (GHGs) have
increased significantly since preindustrial times (pre-1750), by 46 %
for carbon dioxide (
National greenhouse gas inventories (NGHGIs) are prepared and reported by countries based on IPCC Guidelines (GLs) using national data and different calculation methods (tiers) for well-defined sectors. The IPCC tiers represent the level of sophistication used to estimate emissions, with Tier 1 based on default assumptions, Tier 2 similar to Tier 1 but based on country-specific parameters, and Tier 3 based on the most detailed process-level estimates (i.e., models).
After 2020, European countries will report their GHG emission reductions
following the newly approved UNFCCC transparency framework (UNFCCC, 2018),
including the reporting principles of transparency, accuracy, consistency, completeness and comparability (TACCC), as well as using the IPCC methodological
guidance (IPCC Guidelines, 2006). Furthermore, the IPCC 2019 Refinement (IPCC, 2019a) (that may
be used to complement the 2006 IPCC GLs) has updated guidance on the
possible and voluntary use of atmospheric data for independent verification
of GHG inventories. So far, only few countries (e.g., Switzerland, UK and
Australia) are already using atmospheric GHG measurements, on a voluntary
basis, as an additional consistency check of their national inventories.
Annex I Annex I Parties include the industrialized countries that
were members of the OECD (Organisation for Economic Co-operation and
Development) in 1992 plus countries with economies in transition (the EIT
Parties), including the Russian Federation, the Baltic States, and several
central and eastern European states (UNFCCC, For most Annex I Parties, the historical base year is 1990.
However, parties included in Annex I with an economy in transition during
the early 1990s (EIT Parties) were allowed to choose one year up to a few
years before 1990 as reference because of a nonrepresentative collapse
during the breakup of the Soviet Union (e.g., Bulgaria, 1988, Hungary, 1985–1987, Poland, 1988, Romania, 1989, and Slovenia, 1986).
According to UNFCCC (2018) NGHGI estimates, the European Union (EU28) in 2016 emitted
3.9 Gt of In this study we refer to LULUCF (land use, land use
change and forestry) which is the same as FOLU (forestry and other land
use). The FOLU naming is mostly used in combination with agriculture (AFOLU)
since mitigation of GHG potential and efforts are focused on both sectors
and represent a new sector in IPCC AR5, while countries in NGHGI report
GWP100 refers to
the global warming potential for the 100-year time horizon. Under UNFCCC
reporting and SBSTA 34 (2011), GWPs are a well-defined metric based on
radiative forcing that continues to be useful in a multigas approach. UNFCCC NGHGI (2018) submissions use the IPCC AR4 as scientific base for GWP
conversion factors (
Total reported EU28 GHG emissions according to UNFCCC NGHGI (2018) data. Remaining land refers to
According to NGHGI 2018 data, total anthropogenic emission of GHGs in the
EU28 (Fig. 1) decreased by 24 % from 1990 to 2016 (UNFCCC, 2018).
Emissions from LULUCF represented in 2016 a sink of about 300 Mt
For
For
Zooming in on trends, non-
The main objective of the present study is to present a synthesis of AFOLU GHG emission estimates from bottom-up approaches that can serve as a benchmark for future assessments, which is important during the reconciliation process with top-down GHG emission estimates. We use existing officially reported data from NGHGI submitted under the UNFCCC as well as other emission estimates based on research data, from global emissions datasets to detailed biogeochemical models. The bottom-up approaches considered, although based on independent efforts from those in the NGHGI, have some level of redundancy among them and the inventories, since they often use similar activity data (AD) and largely apply the current IPCC (2006) methodology, albeit using different tiers.
Independent bottom-up estimates are valuable to compare with estimates
officially reported to the UNFCCC and may identify differences that need
closer investigation. The uncertainties presented in this paper are taken
from the UNFCCC NGHGI (2018) submissions. For the global emissions dataset
EDGAR uncertainties are only calculated for the year 2012 as described in
the Appendix B. We evaluate the reason for differences in emissions by
carefully comparing the estimates, quantifying uncertainties and detecting
discrepancies. We compare the inconsistencies (defined by differences
between estimates) to the uncertainties (error associated with each estimate)
and identify those sectors that would yield the most benefit from improvements.
Uncertainties from the other datasets and models were not yet available. We
do include natural
We collected available data of AFOLU emissions and removals (Table 1) between 1990 and 2016 (or last available year) that have been documented in peer-reviewed literature. The collection of data represents the latest data available and most recent state of the art of available estimates of GHGs representing the AFOLU sector in Europe as derived from our knowledge of the scientific literature and the scientific networks in Europe. UNFCCC NGHGI and other data sources for AFOLU emissions or component fluxes as well as methodologies are described in Appendix B. For all three GHGs, total emissions from agriculture and LULUCF for the EU28 are presented in Appendix Table A2.
Whenever necessary we provide details on individual countries separating
Summary of AFOLU data sources for the three main GHGs available and their references. The last reported year for each underlying database used in this study is highlighted in bold.
As an overview of potential uncertainty sources, Tables A1a and b
present the use of emission factor data (EF), activity data (AD), and,
whenever available, uncertainty estimation methods used for all agriculture
and forestry data sources used in this study. The referenced data used for
the figures' replicability purposes are available for download at
As part of the AFOLU sectors, agricultural activities play a significant
role in non-
Regarding the forestry subsector of AFOLU, LULUCF, the major GHG gas is
Total EU28 agriculture
At the EU28 level, GHG emission reporting is mandatory for all countries and is
done under the consistent framework of UNFCCC. Every year in May all EU
parties report to the convention their National Inventory Report (NIR) and
provide data using the standardized common reporting format (CRF) tables.
The NIRs contain detailed descriptive and numerical information on all
emission sources and the CRF tables contain all GHG emissions and removals,
implied EFs, and AD for the whole time series from 1990 to 2 years before the
submission year (
Further in this section, we present estimates of
Change in EU28 total agricultural
According to UNFCCC NGHGI (2018) data, in 2016 agricultural activities accounted
for 53 % of the total
Agricultural
As a consequence of the similar trends and distribution of emissions to sectors presented in Table 2, we notice a small but consistent variability of total emissions between the five data sources (Fig. 2).
One possible cause for the similarity lies in the fact that almost all sources use EFs from the same IPCC GLs (2006). In EU28, AD are produced by four main sources and further disseminated to the end users (see Fig. 4), and this can be subject to a certain amount of commonalities. Therefore, excluding AD and EFs, we might conclude that differences shown in Fig. 2 are mainly due to the choice of the tier method for calculating emissions (e.g., in CAPRI as shown in Appendix A, Table A1a).
To better understand the differences between emissions in the EU28 we plotted in
Fig. 3 the
Example of flow of AD, EFs and emission estimates in the EU based on IPCC regulations.
We therefore conclude that all inventory-based data sources are consistent
with each other for capturing recent
From the detailed analysis of
To exemplify the shares of
The highest share is attributed to enteric fermentation, which for almost all
countries counts as
According to UNFCCC NGHGI (2018) data, in 2016 agricultural activities accounted
for 78 % of the total
Agricultural
Similar to
Total EU28 agriculture
In Fig. 7 we present the
Change in EU28 total agricultural
Nevertheless, despite the inconsistent sign of
The two most important sources for
We notice for the eastern European former communist centralized economy
block (all country data and figures are provided in the excel spreadsheet
“Figures5,8_AppendixD_
EDGAR is using data from FAOSTAT; thus, for the majority of countries
(figures found as described in Appendix D), we observe similar
estimates between these two sources (e.g., France, Italy, Poland). A reason
for discrepancies may be attributed to the different way the data sources
allocate their emissions to subactivities (Table 3). For example, CAPRI
N2OSYN – synthetic fertilizer application – does not have a
correspondent in GAINS activities. The leaching, ammonia and atmospheric
deposition
For
In recent assessments of the global
In the EU28, natural emissions of
Wetlands are sinks for
Distribution of
Under the new EU LULUCF Regulation article 7 (footnote 7), the accounting of natural wetland emissions will become mandatory from 2026 onwards; i.e., the reported numbers will be compared to numbers already reported under category 4(II) wetlands between 2005 and 2009, and the net difference will count towards reaching the EU climate targets.
Since
According to Poulter et al. (2017), between 2005 and 2017, the total wetland
Given this current gap between modeled and NGHGI reported data on
The forestry and other land uses, referred to here as the LULUCF section,
include
Model descriptions and their references therein.
Achieving the well-below-2
We compared net
Total EU28 single-year values of
To better illustrate differences between estimates we exemplify how four of
the data sources interpret and calculate the NBP:
UNFCCC NBP definition depends on the method used by each country. CBM calculates NBP as the total ecosystem stock change calculated as the
difference between net ecosystem production (NEP) and the direct losses due
to harvest and natural disturbances (e.g., fires) (Pilli et al., 2017; Kurz
et al., 2009). Adding to the NBP the total changes in the harvested wood
product (HWP) carbon stock, CBM estimates the net sector exchange (NSE)
(Karjalainen et al., 2003; Pilli et al., 2017). EFISCEN's NBP is derived from total tree gross growth minus (density
related) mortality minus harvest, minus turnover of leaves, branches and
roots. From input of litter minus decomposition, the soil balance is
calculated with the Yasso soil model (Liski et al., 2005). Natural disturbances tend to occur
relatively rarely in Europe and, when happening, are included in regular
harvest; therefore EFISCEN does not consider them in addition for the NBP
calculation. DGVMs calculate NBP as the net flux between land and atmosphere defined as photosynthesis minus the sum of plant and soil heterotrophic respiration,
carbon fluxes from fires, harvest, grazing, land use change and any other C
flux in/out of the ecosystem (e.g., dissolved inorganic carbon, DIC;
dissolved organic carbon, DOC; and volatile organic compounds, VOCs). Land use change emissions are calculated as the imbalance between photosynthesis and
respiration over land areas that followed a transition. NBP should be equal
to changes in total carbon reservoirs. The net land use change flux is
derived by differencing the NBP of a simulation with and without land use
change.
Net
Figure 10 presents the total net The Global
Forest Resource Assessment (FRA) is the supplementary source of forest land
data disseminated in FAOSTAT (
Total EU28 net
The UNFCCC NGHGI (2018) uncertainty of
From Fig. 10 we see that while UNFCCC estimates are very stable, FAOSTAT
shows an increasing sink, while CBM and EFISCEN show a saturating sink. And
although all four are based on almost the same raw data, estimates differ by
up to 50 %. The sink of EFISCEN is somewhat lower because a higher
harvesting was implemented in these runs. In 2015, most of the differences
between FAOSTAT estimates and UNFCCC country data were generated by a few
countries. For Finland, FAOSTAT reports around zero sink and UNFCCC reports
a large sink of 38 Mt
Cropland and grassland (CL and GL) (in UNFCCC NGHGI, 2018, IPCC sector 4B and 4C, respectively) include net
The cropland definition in IPCC includes cropping systems, and agroforestry
systems where vegetation falls below the threshold used for the forest land
category, consistent with the selection of national definitions (IPCC
glossary). According to EUROSTAT, the term “crop” within cropland covers a
very broad range of cultivated plants. In 2015 more than one-fifth (22 %)
of the EU28's area was covered by cropland (EUROSTAT, available at
Grassland definition in IPCC includes rangelands and pasture land that is not considered cropland, as well as systems with vegetation that fall below the threshold used in the forest land category. This category also includes all grassland from wild lands to recreational areas as well as agricultural and silvopastoral systems, subdivided into managed and unmanaged, consistent with national definitions. Grasslands tend to be concentrated in regions with less favorable conditions for growing crops or where forests have been cut down. Some of these are found in northern Europe (e.g., Finland and Sweden), while others are in the far south, i.e., the south of Spain.
In 2015 just above one-fifth of the EU28's area (21 %) was covered
by grassland. There is a broad range across EU member states, with Ireland
having 56 % of its total land area as grassland and Finland and Sweden
less than 6 % of the land (EUROSTAT,
Figure 11 shows that in the EU28 croplands and grasslands are
Climate change and climate effects on soil temperature and moisture are key drivers in the 21st century increase in soil decomposition and decrease in the soil carbon stock (Smith et al., 2005). Avoiding soil carbon losses or restoring stocks requires practices that increase C input in excess of losses from erosion and decomposition, such as diminished grazing intensity for grasslands, higher return of residues or reduced tillage for croplands, and manure additions for both. Further change in land use and management will also affect the soil carbon stock of European cropland and grasslands (Smith et al., 2005).
Land-related carbon emissions can also be estimated by global models such as
DGVMs (here we used the TRENDY v6 ensemble) and two bookkeeping models (BLUE
and H&N). In this section we compare these global model results with data
from FAOSTAT and UNFCCC NGHGI (2018). There is significant uncertainty in the
underlying datasets of land use changes, the coverage of different land use
change practices and the calculation of carbon fluxes. In addition, marked
differences in definitions must also be considered to compare independent
estimates. Bookkeeping models give net emissions from land use change,
including immediate emissions during land conversion, legacy emissions from
slash and soil carbon after land use change, regrowth of secondary forest
after abandonment, and emissions from harvested wood products when they
decay. DGVMs estimate net land use emission as the difference between a run
with and a run without land use change, and their estimate includes the loss
of additional sink capacity, that is, the sink that favors the environmental
changes (e.g.,
The key difference between DGVMs and bookkeeping models, on the one hand, and FAO and UNFCCC methodologies, on the other, is that the latter are based on the managed land proxy (Grassi et al., 2018a) (Fig. 12).
Summary of the main conceptual differences in defining the anthropogenic land
Land fluxes can be differentiated into three processes (IPCC, 2010): (1) direct anthropogenic effects (land use and land use change, e.g., harvest,
other management, deforestation), (2) indirect anthropogenic effects (e.g.,
changes induced by human-induced climate change, including
Models and GHGIs capture these effects in a different way:
The difference between biogeochemical models and NGHGIs of around 4–5 Gt
Independent estimates of the land-related flux for the EU28 are presented in Fig. 13. The data behind the three main estimates, bookkeeping models, NGHGIs and FAOSTAT represent the total net land use emissions/removal from forests, including conversions to and from one category to another. Next to them, we plotted each of the net land use change fluxes (in grey; difference of simulation with and without land use change) from eight of the TRENDYv6 DGVMs used in the GCB 2017 (Le Quéré et al., 2018a) with their mean, as they mostly simulate the indirect and natural sink considered unmanaged. FAOSTAT includes emissions from peatland drainage and fires and from biomass fires (not considered herein). It does not include however other carbon stock changes in cropland and grassland. We additionally excluded from the UNFCCC estimate the categories wetlands remaining wetlands and settlements remaining settlements, as well as biomass burning and drainage and transitions between nonforest lands.
The UNFCCC NGHGI (2018) and H&N's estimates are similar because the managed
areas for the EU28 are similar in both estimates (Grassi et al., 2018a).
Differences between the two bookkeeping models, BLUE and H&N, relate to
the different input data applied by each of the models and differences in
biome types. The input used by H&N is based directly on FAOSTAT
agricultural and wood harvest data and FRA forest area changes, while BLUE
uses LUH2 (Hurtt et al., 2011, 2020). LUH2 is based on HYDE3.2 (Klein
Goldewijk et al., 2017a, b), which provides annual, 0.5
The EU28 has a very small area of unmanaged land and this denotes that most of the LULUCF emissions in the EU28 are from direct effects in the forestry sector (including agricultural expansion/abandonment). According to FAOSTAT and UNFCCC NGHGIs, the net forest conversion is relatively small in the EU, so the simulations include mostly managed net area.
A comparison of different estimates of the land use change flux in the EU28 from five available data sources: BLUE, H&N, UNFCCC NGHGI (2018), DGVMs (TRENDY v6) and FAOSTAT. The grey lines represent the individual model data for eight DGVMs. The UNFCCC estimate includes the following categories: forest land, cropland, grassland net and with conversions and wetlands, settlements, and other land-only conversions. The FAOSTAT estimate includes the following categories: forest land remaining forest land, afforestation and deforestation (conversion of forest land to other land types). The negative values represent a sink, while the positive values represent a source.
DGVMs differ strongly in their estimate of the net land use change flux due to different comprehensiveness of including land use practices such as wood harvesting, shifting cultivation, or fire management (Le Quéré et al., 2018a); different land use change datasets (HYDE3.2 or LUH2) and their implementation; and general model differences of how photosynthesis, respiration, and natural disturbances are simulated. Most striking in comparison to the other, more empirical, approaches is the large interannual variability, related to the climate dependency of vegetation processes. Though DGVMs are conceptually similar to NGHGIs in simulating all indirect and direct fluxes on a given area, differencing of the simulations with and without land use change leaves only the land-use-related effects to be attributed to the net land use change flux (see Fig. 12). DGVMs are thus closer to the bookkeeping definition of LULUCF emissions, apart from differing assumptions on environmental changes (constant in bookkeeping, historical in TRENDY) and the loss of additional sink capacity included in DGVMs.
At the European level the largest inconsistencies between estimates from AFOLU
emission sources/sinks were found to be mainly caused by the use of
different methodologies, including use of different AD and/or tier level.
When looking at final emission estimates, inconsistencies in methodology and tier application in calculating emissions give as much as 10 %–20 %
variation across estimates (e.g.,
Within the UNFCCC practice, for agriculture, each country uses its own
country-specific method which considers specific national
circumstances (as long as they are in accordance with the 2006 IPCC GLs) as
well as IPCC default values, which are usually more conservative. The EU GHG
inventory underlies the assumption that the individual use of national
country-specific methods leads to more accurate GHG estimates than the
implementation of a single EU-wide approach (UNFCCC, 2018). The tier
level a country applies depends on the national circumstances, which
explains the variability of uncertainties among the sector itself as well as
among EU countries. For example, inventory estimates of
Concerning the IPCC calculation of
There is as well the need to define a common methodology for overall
uncertainty calculation while checking for consistency in the way
uncertainties are calculated for different data sources and the way data are aggregated for different sectors. We noticed that for agricultural
For the LULUCF sector, methods for the estimation of GHGs and
In general the definition of NBP denotes the net gain or loss of carbon from a region. NBP is equal to the net ecosystem production (NEP) minus the carbon lost due to a disturbance (e.g., forest fire, harvest) taking into account as well the net C balance of harvested products (described by the 2006 IPCC GLs) and C emitted by inland waters. In the context of land use change, the GCB 2017 (Le Quéré et al., 2018a) highlighted harvest as one of the main uncertainties. As an example, according Nabuurs et al. (2018) the uncertainty affecting all studies is that EU harvesting levels are rather uncertain. According to the FRA report 2015 (FRA, 2015), most European countries have a solid forest inventory, but there is still large uncertainty over harvesting levels. For many countries forest statistics from FAO have shortcomings such as very large differences between reported periods, data corrected in later versions and unreported (harvest) removals (Nabuurs et al., 2018).
Checking collective progress towards meeting the goals of the PA will be
done by the PA's global stocktake. At present, there is a discrepancy of
about 4–5 Gt
It is important to distinguish between reporting and accounting in the GHG inventory context, as not all reported emissions account towards emission reduction efforts (Grassi et al., 2018b). Reporting refers to the inclusion of estimates of anthropogenic GHG fluxes in NIRs, following the methodological guidance provided by the IPCC. The NIR should, in principle, aim to reflect “what the atmosphere sees” (Peters et al., 2009) in managed lands, within the limits given by the method used and the data available. In the context of mitigation targets (e.g., the PA), accounting refers to the comparison of emissions and removals with the target and quantifies progress toward the target. For the LULUCF sector, specific accounting rules are used to filter reported flux estimates with the aim to better quantify the results of mitigation actions (Grassi et al., 2018b). The UNFCCC reporting principles allocate emissions to the territorial location (national boundaries) at the time that they occur (Peters et al., 2009).
The different definitions and concepts used by the global models and
inventory communities mean that the land fluxes cannot necessarily be
consistently compared. The framework developed by Grassi et al. (2018a) and
shown in Fig. 12 can be generalized to make a more direct comparison as
applied to EU28 (Fig. 14). Figure 14 disaggregates managed forest land
into components that are reported in the UNFCCC CRFs: converted land (e.g.,
land changing from cropland to forest land) and the remaining land (e.g.,
forest land remaining forest land) are split into land that is “production”
(remaining forestry, RF) or land that is used for ecological or social functions
(other
A conceptual extension of Fig. 12, applied to EU28, to disaggregate the managed land into the components reported in the UNFCCC inventories. The vertical axis represents density, the horizontal axis represents the area and the area of each box is the
Overall, our results suggest that most of the LULUCF emissions in the EU28 are from direct effects in the managed forest sector, including age-legacy effects (forest expansion and regrowth after WWII), with small net emissions from land conversion as they are largely compensated for by deforestation (from CRFs). With appropriate data and models, it is theoretically possible to expand and enumerate the estimates more accurately.
All raw data files reported in this work which were used for calculations
and figures are available for public download at
There are many independent estimates of GHG emissions, but adequate
understanding of their differences (either qualitatively or quantitatively)
is lacking. For
At the EU28 level, there is room to improve NGHGIs' consistency between UNFCCC
tier use and models (e.g., 10 %–20 %
difference for
It is of great importance to better distinguish between direct and indirect effects on land use emissions especially for the purpose of reconciling land-related emissions from global datasets and NGHGIs. Currently our comparisons give significant uncertainty, mostly related to coverage of different land use practices and the differences in definitions (Fig. 12).
It is important to recognize that just because independent inventories agree
well for a sector does not necessarily mean that the estimate is closer to
the actual emissions. The reason for agreement across inventories may simply
be that the different inventories used the same methodology and data
sources. In recent years there has been increased attention to the
quantitative differences between land-based
The current atmospheric GHG network is coordinated by the Integrated Carbon
Observation System (ICOS) infrastructure at the European level. Within the
future UNFCCC reporting framework, we argue that countries should use,
whenever possible, global inversions to provide additional constraints for
the verification and reconciliation purposes. A synthesis of available
top-down non-
The main challenge for the inversion community remains the separation of
the natural and anthropogenic part of the total emission column. For the moment,
global inverse models are widely used to estimate emissions of
Continued.
Total EU28 agriculture and LULUCF estimates in kilotons of gas per year reported by the five data sources for the last available year (in bold).
The UNFCCC committed in articles 4 and 12 in particular developed country
parties listed in the Annex I of the UNFCCC to provide a national inventory
of anthropogenic emissions by sources and removals by sinks of all GHGs not
controlled by the Montreal Protocol using comparable methodologies. The
Conference of the Parties (COP) decided in 2013 (Decision 24/CP.19
The exclusion of developing countries is explained by the fact that in the 1990s, when the Convention's and the Kyoto Protocol's reporting system was developed and adopted, a clear division of the regional distribution of GHG emissions existed. In industrialized countries, most GHG emissions were released, while in developing and emerging countries, emissions were low (Berger et al.. 2016).
The UNFCCC reporting guidelines decided to commonly use the global warming
potentials (GWP) The common global warming potential (GWP) metric
enables the comparison of different GHGs by converting them into
Whereas before 2015 no The revised UNFCCC reporting guidelines
include within the IPCC AFOLU sector the agriculture and LULUCF sectors. This
represents a distinction between the UNFCCC Annex I reporting guidelines as
determined in negotiation between parties and the UNFCCC, as well as the IPCC reporting guidelines.
Chapter 3 of the error propagation; Monte Carlo simulations.
For both approaches chap. 3 uses two main statistical concepts – the probability density function (PDF) and confidence limits, where the probability density function describes the range and relative likelihood of possible values and the confidence limits give the range (confidence interval) within which the underlying value of an uncertain quantity is thought to lie for a specified probability.
Under Approach 1, there are two ways in which uncertainties can be
calculated.
Where uncertain quantities are to be combined by multiplication, the
standard deviation of the sum will be the square root of the sum of the
squares of the standard deviations of the quantities that are added. Where uncertain quantities are to be combined by addition or subtraction,
the standard deviation of the sum will be the square root of the sum of the
squares of the standard deviations of the quantities that are added. All MS analyzed in this study have performed their
uncertainty assessment using the approach 1, i.e., the methodology of
propagation of error.
For this study an analysis of the reported uncertainties under the NGHGI for
Since the EU MS all report on different subsectors, the uncertainties have
been aggregated to the subsectors per gas that all countries have in common; see the following Table B1 All sectors and subsectors are covered; however, the table explains which subsectors are aggregated for uncertainty
calculation purposes.
Aggregation of subsectors for the uncertainty analysis.
Generally, for almost all countries, the uncertainties for
The Emissions Database for Global Atmospheric Research (EDGAR) with versions
EDGARv4.3.2 and EDGAR FT2017 provide global, country-level and gridded
annual emissions of
EDGAR is developed and maintained by the Joint Research Centre of the
European Commission, with continued inputs by PBL. The version v4.3.2
released in 2017, Janssens-Maenhout et al. (2019), provides 0.1
The EDGAR v4.3.2FT2015 has been producing 2015 grid maps at
EDGAR uses emission factors (EFs) and activity data (AD) to estimate emissions. Both EFs and AD are uncertain to some degree, and when combined their uncertainties need to be combined too. To estimate EDGAR's uncertainties (stemming from lack of knowledge of the true value of the EF and AD), the methodology devised by IPCC (2006, chap. 3) is adopted, that is, the sum of squares of the uncertainty of the EF and AD (uncertainty of the product of two variables). When aggregating the emissions from subcategories, or different sources, or countries the covariance of the respective probability distribution enters into play.
The assumptions introduced by Bond et al. (2004), Bergamaschi et al. (2015) and Olivier et al. (2002) hold:
uncertainties of different source categories are uncorrelated; subsectors for when dealing with aggregated emissions from same categories but different countries assume full correlation, unless the emission factor is country-specific or derived from higher tiers (i.e., not default EF defined by IPCC). When uncertainty is defined within a range (e.g., for the energy sector, IPCC
recommends that the methane emission factors are treated with an uncertainty
ranging from 50 % to 150 %), the upper bound of the range is assigned
to developing countries, while the lower bound is assigned to developed countries.
Uncertainty of country or process-specific EF is not propagated (no
correlation).
In addition, the following assumption is adopted:
Uncertainty assigned to activity data (AD) and emission factors (EF) for
I: industrialized (developed) countries; D: developing countries; CS: country-specific.
Although assuming full correlation when aggregating emissions is quite conservative (overestimating the uncertainty introduced by emission factors), this approach is intended to balance other sources of uncertainty that are not taken into account, such as covariance among activity data (deemed negligible); uncertainty of technology factors (no information available as to how these factors are uncertain, as for example on the different rice cultivar practices); and uncertainty due to the fast track, i.e., applying trends to estimate latest year's emissions.
The EFs and AD uncertainties are reported in Table B2.
A lognormal probability distribution function is assumed to avoid negative values, and uncertainties are reported as 95 % confidence intervals according to IPCC (2006, chap. 3, Eq. 3.7). For emission uncertainty in the range 50 % to 230 % a correction factor is adopted as suggested by Frey et al. (2003) and IPCC (2006, chap. 3, Eq. 3.4). The correction factor is used as an empirical adjustment, based on Monte Carlo simulations, to correct for the deviation introduced by using the standard uncertainty calculation method suggested by IPCC error propagation, which is only a first-order approximation; for large uncertainties (as they accumulate in the propagation chain) the method systematically underestimates the uncertainty half range.
CAPRI is an economic, partial equilibrium model for the agricultural sector, focused on the EU (as well as less detailed worldwide for market module)
(Britz and Witzke, 2014
Among other environmental indicators, CAPRI simulates
FAOSTAT (Statistics Division of the Food and Agriculture Organization of
the United Nations)
The Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS)
model (
The Carbon Budget Model developed by the Canadian Forest Service (CBM-CFS3) can simulate the historical and future stand- and landscape-level C dynamics under different scenarios of harvest and natural disturbances (fires, storms), according to the standards described by the IPCC (Kurz et al., 2009), under an annual time step. Since 2009, the CBM has been tested and validated by the Joint Research Centre of the European Commission (JRC) and adapted to the European forests. It is currently applied to 26 EU MS, both at the country and at the NUTS2 level (Pilli et al., 2016).
Based on the model framework, each stand is described by area, age, and land use classes, as well as up to 10 classifiers based on administrative and ecological information and on silvicultural parameters (such as forest composition and management strategy). A set of yield tables define the merchantable volume production for each species while species-specific allometric equations convert merchantable volume production into aboveground biomass at the stand level. At the end of each year the model provides data on the net primary production (NPP), carbon stocks and fluxes, as the annual C transfers between pools and to the forest product sector.
The model can support policy anticipation, formulation and evaluation under the LULUCF sector, and it is used to estimate the current and future forest C dynamics, both as a verification tool (i.e., to compare the results with the estimates provided by other models) and to support the EU legislation on the LULUCF sector (Grassi et al., 2018a). In the biomass sector, the CBM can be used in combination with other models to estimate the maximum wood potential and the forest C dynamic under different assumptions of harvest and land use change (Jonsson et al., 2018).
The European Forest Information SCENario Model (EFISCEN) is a large-scale
forest model that projects forest resource development on a regional to
European scale. The model uses National Forest Inventory data as a main
source of input to describe the current structure and composition of
European forest resources. The model runs for 5-year-interval emission
projections and projects the development of forest resources, based on
scenarios for policy, management strategies and climate change impacts. With
the help of biomass expansion factors, stem wood volume is converted into
whole-tree biomass and subsequently to whole-tree carbon stocks. Information
on litter fall rates, felling residues and natural mortality is used as
input into the soil model Yasso (Liski et al., 2005), which is dynamically
linked to EFISCEN and delivers information on forest soil carbon stocks. The
core of the EFISCEN model was developed by Ola Sallnäs at the
Swedish Agricultural University (Sallnäs, 1990). It has been applied to
European countries in many studies since then, dealing with a diversity of
forest resource and policy aspects. A detailed model description is given by
Verkerk et al. (2016), with online information on availability and
documentation of EFISCEN at
This study uses the ensemble of eight DGVMs that participated in TRENDY
version 6 (v6) for the GCB 2017 (Le Quéré et al., 2018a) including
the following models: ORCHIDEE (Krinner et al., 2005), OCN (Zaehle et al.,
2011), JULES (Clark et al., 2011), JSBACH (Reick et al., 2013), VEGAS (Zeng
2003, 2005), LPX-Bern (Lienert and Joos, 2018), LPJ (Sitch, 2003) and ISAM
(Jain et al., 2013). We make use of carbon trends in net land carbon
exchange over Europe, during the period 1990–2016. Data are available for
download at
The LULUCF chapter makes use of data from two bookkeeping models:
H&N (Houghton and Nassikas, 2017) and BLUE (Hansis et
al., 2015). As described by GCB 2017 (Le Quéré et al., 2018a), the
H&N model (Houghton et al., 1983) calculates land use change
The BLUE model provides a data-driven estimate of the net land use
change fluxes. BLUE stands for bookkeeping of land use emissions.
Bookkeeping models (Hansis et al., 2015; Houghton et al., 1983) calculate land use change
In BLUE, land use forcing is taken from the Land Use Harmonization dataset, LUH2,
for estimates within the annual global carbon budget. The model provides
data at annual time steps and 0.25
This model ensemble simulates natural
For a better understanding and overview of the single steps of the
uncertainty analysis, an example calculation for the uncertainty assessment is
included, where the combined uncertainty and contribution to variance is
calculated for 4.A
Aggregation of IPCC subsectors for the uncertainty analysis.
Calculation example of the uncertainty analysis; uncertainty assessment 2016.
Table C2 shows the subsectors 4.A and 4.B of one the EU28 MS uncertainty assessments for 2016. To calculate the contribution to variance for the sector 4.A Results can be found in Table C3.
Calculation example of the uncertainty analysis; section from one of the MS of the EU28 uncertainty assessment 2016.
To check for correctness, the total uncertainty for the aggregated sectors can be calculated. If the total uncertainty for the aggregated sectors matches the total uncertainty of the uncertainty assessment, the calculated uncertainties for the subsectors are correct. This was the case for all calculations performed for this analysis.
The results of the uncertainty analysis show a clear trend of the main uncertainties and gases across the analyzed 26 EU MS.
Detailed agriculture
AMRP and HD designed research and led the discussions; AMRP
analyzed the data and wrote the initial version of the paper; GPP provided
the figures and initial text for the LULUCF chapter; AMRP, PC, HD,
GPP, GG, GJM, FNT and WW made significant changes throughout all
versions of the paper; GJN, AL, GCG, LHJ, ES, RP, AK, AB,
JP, GC and RMA reviewed the initial versions of the paper and
provided comments, suggestions and advice during the preparation of this
paper. AK developed the methodology for the UNFCCC uncertainty
calculation for each member state, provided the information and contributed
to the writing of Appendix C; ES developed the methodology for the EDGAR
uncertainty calculation and provided the
The authors declare that they have no conflict of interest.
FAOSTAT statistics are produced and disseminated with the support of its member countries to the FAO regular budget. We thank Stephen Sitch and the TRENDY international modeling team for access to DGVMs
v6 output and Benjamin Poulter for providing us with the data for natural
The WUR authors also received funding from the Dutch Ministry of Agriculture, Nature and Food Quality through the knowledge base
program Circular and Climate Neutral (
This research has been supported by the European Commission, Horizon 2020 Framework Programme (VERIFY, grant no. 776810).
This paper was edited by David Carlson and reviewed by two anonymous referees.