dataesr/scientific-paragraphs-categorization huggingface dataset:
By contrast, the NGHGI approach separates fluxes based on areas, with all those occurring on managed land being declared anthropogenic. Given that observational data of carbon stocks or fluxes cannot distinguish between the co-occurring effects of environmental changes and land-use activities, an area-based approach that does not require this distinction can more consistently be implemented across countries. These conceptual differences between global models' and NGHGI approaches have been acknowledged  (Canadell et al. , 2021a; Petrescu et al. , 2020a) , and approaches have been developed to map the two definitions to each other  (Grassi et al. , 2018 (Grassi et al. , , 2021)). For non-CO 2 GHGs, drivers and areas coincide, such that FAOSTAT data for CH 4 and N 2 O are complementary to bookkeeping CO 2 -LULUCF emissions.
Following the approach taken by the Global Carbon Budget  (Friedlingstein et al. , 2020) , we take the average of estimates from three bookkeeping models: BLUE  (Hansis et al. , 2015) , H&N  (Houghton and Nassikas, 2017) , and OSCAR  (Gasser et al. , 2020). Key differences across these estimates, including land-use forcing, are summarized in Table  4. Since bookkeeping models do not include emissions from organic soils, emissions from peat fires and peat drainage are added from external datasets: peat burning is based on the Global Fire Emission Database (GFED4s;  van der Werf et al. , 2017)  and introduces large interannual variability to the CO 2 -LULUCF emissions due to synergies of land-use and climate variability, particularly in Southeast Asia, strongly noticeable during El Niño events such as in 1997.
CO 2 emissions from fossil fuels and industrial processes Several studies have compared estimates of annual CO 2 -FFI emissions from different global inventories  (Andres et al. , 2012; Andrew, 2020a; Gütschow et al. , 2016; Janssens-Maenhout et al. , 2019; Macknick, 2011; Petrescu et al. , 2020b). However, estimates are not fully independent as they all ultimately rely on many of the same data sources. For example, all global inventories use one of four global energy datasets to estimate CO 2 emissions from energy use, and these energy datasets themselves all rely on the same national energy statistics, with few exceptions  (Andrew, 2020a). Some divergence between these estimates (see Fig. 1 ) are related to differences in the estimation methodology, conversion factors, emission coefficients, assumptions about combustion efficiency, and calculation errors  (Andrew, 2020a; Marland et al.
, 2009). Key differences for nine global datasets are highlighted in Table  3  (see also Table  1  for further information on the inventories). Another important source of divergence between datasets is differences in their respective system boundaries  (Andres et al. , 2012; Andrew, 2020a; Macknick, 2011). Hence, differences across CO 2 -FFI emissions estimates do not reflect full uncertainty due to source data dependencies. At the same time, the observed range across estimates from different databases exaggerates uncertainty, to the extent that they largely originate in system boundary differences  (Andrew, 2020a; Macknick, 2011). Across global inventories, mean global annual CO 2 -FFI emissions track at 34 ± 2 GtCO 2 in 2014, reflecting a variability of about ±5. 4 % (Fig. 1 ). However, this variability is almost halved when system boundaries are harmonized (Andrew, 2020a).
EDGAR CO 2 -FFI emissions as used in there track at the top of the range as shown in Fig. 1. This is partly due to the comprehensive system boundaries of EDGAR but also due to the assumption of 100 % oxidation of combusted fuels as per IPCC default assumptions. Once system boundaries are harmonized, EDGAR continues to track at the up-per end of the range but no longer at the top. EDGAR CO 2 -FFI estimates are further well-aligned with emissions inventories submitted by Annex I countries to the UNFCCCeven though some variation can occur for individual countries such as Kazakhstan, Ukraine, or Estonia, in general, or for certain years (see Fig. S4 ). Differences in FFI-CO 2 emissions across different versions of the EDGAR dataset are shown in the Supplement (see Fig. S1 ). Uncertainties in CO 2 -FFI emissions arise from the combination of uncertainty in activity data and uncertainties in emissions factors, including assumptions for combustion completeness and non-combustion uses.
, 2020). Further differences between BLUE and H&N can be traced in particular to (1) differences in carbon densities between natural and managed vegetation or between primary and secondary vegetation, (2) a higher allocation of cleared and harvested material to fast turnover pools in BLUE compared to H&N, and (3) the inclusion of subgrid-scale transitions  (Bastos et al. , 2021). Uncertainties in CO 2 -LULUCF emissions can be more comprehensively assessed through comparisons across a suite of dynamic global vegetation models (DGVMs)  (Friedlingstein et al. , 2020). DGVMs are not included in the CO 2 -LULUCF mean estimate provided here because the typical DGVM setup includes the loss of additional sink ca-Table  3. System boundaries and other key features of global FFI-CO 2 emissions datasets as published.
Comparison of some important general characteristics of nine emissions datasets, with bold font indicating a characteristic that might be considered a strength. Columns four to six refer to CO 2 emissions estimates for industrial processes and product use. Since all datasets are under development, these details are subject to change. Further information on the individual inventories can be found in Table  1. Based on Andrew (2020a). pacity, i. e. the additional sink capacity forests could have provided in response to environmental changes, in particular the rise in CO 2 , due to their long-lived biomass, but that is lost because large areas of forest were historically cleared for agriculture. The loss of additional sink capacity makes up about 40 % of the DGVM estimate in recent years  (Obermeier et al.
, 2020) , CEDS (above), EDGAR (above), PRIMAP-hist (above); GAINS  (Winiwarter et al. , 2018) , EPA-2019 (above), and FAO (above). Differences in emissions across different versions of the EDGAR dataset are shown in the Supplement (Fig. S1 ). to a relative uncertainty of about ±50 % over 1970-2019, which is much higher than for most fossil-fuel-related emissions but reflects the large model spread and large differences between the current estimate of H&N and its previous model versions  (Houghton et al. , 2012). This corresponds to a relative uncertainty of about ±80 % for a 90 % confidence interval (5th-95th percentiles). However, here we opt for a slightly lower relative uncertainty estimate of about ±70 % for a 90 % confidence interval given that the mean of the CO 2 -LULUCF estimates has been increasing over the last few decades.
Other inventories -namely GAINS (Höglund-Isaksson, 2012), US-EPA  (EPA, 2011 (EPA, , 2021)) , CEDS  (Hoesly et al. , 2018; McDuffie et al. , 2020; O'Rourke et al. , 2021) , PRIMAP-hist  (Gütschow et al. , 2016 (Gütschow et al. , , 2021b)) , and FAOSTAT-CH 4  (Federici et al. , 2015; Tubiello, 2018 Tubiello, , 2019;; Tubiello et al. , 2013)  -can differ in terms of their country and sector coverage as well as detail. EDGAR, CEDS, US-EPA, and GAINS cover all major source sectors (fossil fuels, agriculture and waste, biofuel) -except large-scale biomass burning -but this can be added from different databases such as FINN  (Wiedinmyer et al. , 2011) , GFAS  (Kaiser et al. , 2012) , GFED (van der  Werf et al. , 2017) , or QFED (Darmenov and da Silva, 2013). Much like CO 2 -FFI, these inventories of anthropogenic emissions are not completely independent as they either follow the same IPCC methodology to derive emissions, rely on similar data sources (e.
g. FAOSTAT activity data for agriculture, reported fossil fuel production), or draw on reported country inventory data  (Petrescu et al. , 2020a, e. g. Fig. 4 ). However, the available estimates will also differ in many ways. For example, while the US-EPA inventory uses the reported emissions by the countries to the UN-FCCC, other inventories produce their own estimates using a consistent approach for all countries and country-specific activity data, emissions factors, and technological abatement when available. FAOSTAT and EDGAR mostly apply a Tier-1 approach to estimate CH 4 emissions, while GAINS uses a Tier-2 approach (see Box 1). CEDS is based on pre-existing emissions estimates from FAOSTAT and EDGAR, which are then scaled to match country-specific inventories, largely those reported to the UNFCCC.
Global anthropogenic CH 4 emissions estimates are compared in Fig. 1. EDGARv5 has revised total global CH 4 emissions by about 10 Mt CH 4 yr -1 compared to the previous version due to a higher waste sector estimate (see Fig. S1 ). Subsequent revisions of the estimation methodology in EDGARv6 in alignment with the IPCC guidelines re-  finement (IPCC, 2019) lead to very substantial differences in total CH 4 emissions that are up to 50 MtCH 4 yr -1 lower before the 1990s compared to previous versions, but differences are smaller, ranging from 1 to 13 MtCH 4 yr -1 since the 2000s (see Fig. S1 ). The cause of these differences is a new procedure to separately estimate the venting component for gas and oil in the venting and flaring sector (1B2a/b2). Differences across different versions of the EDGAR dataset are shown in the Supplement (Fig.
For example, global inventories, such as EDGAR, estimate uncertainties in national anthropogenic emissions of about ±32 % for the 24 member countries of OECD and up to ±57 % for other countries, whose activity data are more uncertain  (Janssens-Maenhout et al. , 2019). The 2020 UN emissions gap report (UNEP, 2020) gives an uncertainty range for global anthropogenic CH 4 emissions with 1 standard deviation of ±30 % (i. e. ±60 % for 2σ ). On the other hand, IPCC AR5 provides a comparatively low estimate at ± 20 % for a 90 % confidence interval. Overall, we apply a best value judgment of ±30 % for global anthropogenic CH 4 emissions for a 90 % confidence interval. This is justified by the larger uncertainties reported in studies on the EDGAR dataset  (Janssens-Maenhout et al. , 2019; Solazzo et al.
, 2013) , CEDS  (Hoesly et al. , 2018; Mc-Duffie et al. , 2020; O'Rourke et al. , 2021) , PRIMAP-hist  (Gütschow et al. , 2016 , 2021b ), and GFED (van der Werf et al. , 2017). While EDGAR and GAINS cover all sectors except biomass burning, FAOSTAT-N 2 O is focused on agri-culture and biomass burning and GFED on biomass burning only. As shown in Fig. 1 , EDGAR, GAINS, CEDS, and FAOSTAT emissions are consistent in magnitude and trend. Recent revisions in estimating indirect N 2 O emissions in EDGARv6 lead to an average increase of 1. 5 % yr -1 in total N 2 O emissions estimates between 1999 and 2018 compared to the two previous versions (differences before 1999 were negligible at less than 1 % yr -1 ). Differences across different versions of the EDGAR dataset are shown in the Supplement (Fig.
S1 ). The main discrepancies across different global inventories are in agriculture, where emissions estimates from the Global Nitrous Oxide Budget and FAOSTAT are on average 1. 5 Mt N 2 O yr -1 higher than those from GAINS and EDGAR during 1990-2016 due to higher estimates of direct emissions from fertilized soils and manure left on pasture. GCP-N 2 O provides the largest estimate (Fig. 1 ) -because it was synthesized from the other three inventories and further informed by additional bottom-up modelling estimatesand is as such more comprehensive in scope due to the new sector discussed above. EDGAR estimates of anthropogenic N 2 O emissions as used in this dataset should therefore be considered lower-bound estimates (see also Table  6 ). Differences in N 2 O emissions across different versions of EDGAR are shown in Fig.
, 2017; Rogelj et al. , 2018). The SSPs are grouped according to their radiative forcing ranging from 1. 9 to 8. 5 W m -2 , aimed at spanning the full range of potential outcomes. The Coupled Model Intercomparison Project Phase 6 (CMIP6)  (Eyring et al. , 2016)  took a subset of these quantified SSPs as the basis for future climate projections  (Gidden et al. , 2019; O'Neill et al. , 2016). In recent years, the use of the very high forcing scenarios -particularly SSP5-8. 5 -has been debated in the scientific community (e. g. Hausfather and Peters, 2020b, a; Pedersen et al. , 2020; Schwalm et al. , 2020). Historical GHG emissions from our database are consistent with the levels and trends in the scenario data, despite the scenarios being calibrated on older data sources  (Gidden et al. , 2019)  -mainly CEDS  (Hoesly et al.
, 2018). The observed differences are larger for the GHGs with the highest uncertainty, notably CO 2 -LULUCF, N 2 O, and F-gas emissions  (Sect. 3. 2, 3. 4, and 3. 5). Across the different GHGs, historical emissions track on aggregate with the higher forcing scenarios such as the SSP3-7. 0 and SSP5-8. 5 markers, in terms of both levels and growth rates. CO 2 -FFI emissions Figure  6. Levels of and changes in GHG emissions by country. Aggregate GHG emissions are reported in GtCO 2 eq. based on global warming potentials with a 100-year time horizon (GWP-100) from IPCC AR6  (Forster et al. , 2021). Panel (a) shows per capita GHG emissions levels (tCO 2 eq. yr -1 ) for the year 2018 using UN population data for normalization  (World Bank, 2021). Panel (b) shows average annual changes (in %) in GHG emissions by countries for 2009-2018.
9 % yr -1 in the sample (Fig. 8 ) -in line with some GHG emissions reduction scenarios that limit global warming to well below 2 • C  (Lamb et al. , 2021b). Further information on country contributions to GHG emissions changes since 1990s -an important reference for UN climate policy -is shown in Supplement Fig. S3. Official statistics submitted annually by Annex I countries of the Kyoto Protocol (see Fig. 9 ) to the UNFCCC (UNFCCC-CRFs) indicate 0. 9 % lower emissions over the period 1990-2018 (excluding CO 2 -LULUCF emissions) (UNFCCC, 2021, accessed through  Gütschow et al. , 2021a). The vast majority of the Annex I countries, which contributed 33 % of the global GHG emissions in 2018 (according to the dataset presented in this paper), report lower total GHG emissions in 2018 as compared with the data presented here.
The total emissions of the Annex I countries in 2018 stand at 17. 2 GtCO 2 eq. yr -1 according to the national inventories, 1. 2 % lower than the data presented here for the same countries. Both datasets, however, agree in terms of the average annual growth rates over the last decade  (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) , which stood at -0. 4 % for the total GHG emissions of the Annex I countries. For single countries there is still some divergence in growth rates observed between the national inventories and the dataset presented here (Fig. 8b  and c ). Additional analysis comparing our data with UNFCCC-CRF inventories for individual (groups of) gases and countries is provided in Supplement Figs. S3  and S4. Sectoral GHG emissions were either stable or increased between 2009 and 2018.
, 2021). The Shared Socioeconomic Pathways (SSPs) are from the SSP database version 2  (Riahi et al. , 2017; Rogelj et al. , 2018). See also https://tntcat. iiasa. ac. at/SspDb/ (last access: 3 November 2021). Highlighted scenarios are the markers used in CMIP6  (O'Neill et al. , 2016)  after harmonization  (Gidden et al. , 2019).
6 ± 4. 6 Gt yr -1. This was due to a surge of fire emissions from peat burning, deforestation, and degradation, occurring mainly in equatorial Asia and the Amazon and substantially exceeding average rates in the previous decade  (Friedlingstein 2020; GFED4. 1s; van der Werf et al. , 2017). Anthropogenic fire processes are not captured well by the underlying land-use datasets. Further, the 2019 estimate was extrapolated for all three bookkeeping estimates by applying additional information on emissions from equatorial Asia peat fires and tropical deforestation and degradation fires (GFED4. 1s; van der Werf et al. , 2017) in a similar way  (Friedlingstein et al. , 2020). This explains the consistent upward trend for all three bookkeeping estimates for 2019. Non-LULUCF CO 2 sources contributed relatively little to the 2019 increase in emissions.
Discussion In this article we provide a comprehensive, synthetic, and detailed dataset for global, regional, national, and sectoral GHG emissions from anthropogenic activities covering the last 5 decades. This is based on the EDGARv6 GHG emissions inventory but additionally includes a fast-track update to 2019 for non-CO 2 emissions and data on CO 2 -LULUCF emissions from three global bookkeeping models. We assess uncertainties in our estimates by combining statistical analysis of the underlying data and expert judgement based on an in-depth review of the literature by each gas. We report uncertainties at a 90 % confidence interval (5th-95th percentile range). We note that national emissions inventory submissions reported to the UNFCCC are requested to report uncertainty using a 95 % (2σ ) confidence interval.
, 2021 ) (see Sect. 3. 7). We therefore also use a simple climate model to assess the warming contribution by the individual groups of gases and find that for the historical period when emissions are growing, the GWP-100 gives a reasonable approximation to the warming contributions, but this is not expected to hold when emissions change trajectory under mitigation. In the absence of a comprehensive uncertainty analysis that covers CO 2 -LULUCF as well as F-gas emissions, we estimate the overall uncertainty of aggregated GHG emissions by simply adding the individual uncertainties judgements by (groups of) gases in quadrature under the assumption of their independence. Over time, uncertainties fluctuate between 10 % and 14 % depending on the composition of gases within the aggregate. Comprehensive uncertainty analysis of EDGAR data covering all GHGs should be performed in the future, building on  Solazzo et al.
, 2018 (Grassi et al. , , 2021)) , the attribution of environmental and anthropogenic changes differs between methods. Further, it should also be mentioned that system boundaries partly differ across datasets, and FAOSTAT data  (Conchedda and Tubiello, 2020)  are currently limited to CO 2 fluxes related to forests and emissions from drainage of organic soils under cropland/grassland, excluding other managed land or agricultural conversions. In principle, bookkeeping and DGVMs include all fluxes but are often coarse in their description of management, which observation-based approaches capture  (Arneth et al. , 2017). Several authors  (Grassi et al. , 2018; Obermeier et al. , 2021; Pongratz et al. , 2014)  have shown the strong dependence of CO 2 -LULUCF emissions estimates on the time a certain land-use change event happened to occur if environmental changes are represented transiently over time, as is the case for typical simulations with dynamic global vegetation models.
This dependence is eliminated by using bookkeeping estimates, as done here. Comparisons with other global emissions inventories highlight the comprehensive nature of our dataset covering anthropogenic sources of GHG emissions. However, there are still some important data issues. In particular, F-gas emissions estimates for some individual species in EDGAR do not align well with atmospheric measurements, and the F-gas aggregate emissions over the last decade either overestimate top-down estimates by around 30 % (EDGAR v5) or underestimate them by around 10 % (EDGAR v6). Furthermore, EDGAR and official national emissions reports under the UNFCCC do not comprehensively cover all relevant F-gas species. In particular, CFCs and HCFCs, which are regulated under the Montreal Protocol, have historically contributed more to CO 2 eq.
However, average annual GHG emissions growth slowed considerably between 2009 and 2018 compared to between 2000 and 2009. While there is a growing number of countries today on a sustained GHG emissions reduction trajectory  (Lamb et al. , 2021b; Le Quéré et al. , 2019a) , GHG emissions are growing over time for all global sectors and sub-sectors in our dataset, mirroring global GHG emissions trends that are characterized by distinct patterns of development and industrialization. It is therefore important to study the drivers of these reductions as well as patterns of emissions growth in more detail at the regional level  (Lamb et al. , 2021a)  and systematically evaluate the impact of climate-relevant policies on regional drivers and trends. There is a growing availability of global datasets on anthropogenic emission sources over the last 10-20 years (see Table  1 ).
However, such global emissions inventories often heavily rely on relatively simple Tier-1 estimation methods, and few use more complex Tier-2 or Tier-3 methods (see Box 1). Comparison of our estimates with UNFCCC-CRFs by Annex I countries shows considerable discrepancies for some gases and countries (see  Figs. 8, S3, and S4). On aggregate, there is a clear trend towards smaller values for GHG emissions reductions and larger values for GHG emissions increases in our dataset. Further work needs to be done to fully appreciate underlying differences, as has been done, for example, for CO 2 emissions  (Andrew, 2020a)  and for Europe across all GHGs  (Petrescu et al. , 2020b (Petrescu et al. , , 2021b, a), a). Figure  9  further highlights the lack of recent official national emissions inventories for many non-Annex I countries.
The BURs are also associated with less stringent reporting requirements in terms of sector, gas, and time coverage  (Deng et al. , 2021; Gütschow et al. , 2016). This highlights the important role of global inventories such as EDGAR, CEDS, PRIMAP-hist, FAOSTAT, or those from IEA or BP among others that are equally as comprehensive in scope as those from Annex I countries. Despite the importance of highquality emissions statistics for climate change research and tracking progress in climate policy, our analysis here emphasizes considerable prevailing uncertainties and the need for improvement in emissions reporting. Additionally, there are significantly fewer independent estimates for full GHG accounting, in contrast to fossil CO 2 emissions. In sectors where production efficiencies are changing rapidly, as is often the case in developing countries, using emissions estimates based on Tier-1 methodologies (see Box 1) may mischaracterize trends as both activity data and emissions factors change over time  (Wilkes et al.
, 2017). Moving confidently towards net-zero emissions requires high-quality emissions statistics for tracking countries' progress based at least on Tier-2 if not on complex Tier-3 (see Box 1) estimation models using comprehensive, countryspecific activity data and emissions factors or atmospheric inversions  (IPCC, 2006 (IPCC, , 2019)). This would also support the formulation of more nuanced climate policy goals that reflect changes in emissions intensities as entry points for more comprehensive and ambitious targets to reduce absolute emissions. However, underpinning such approaches with robust evidence requires the collection of a range of countryspecific activity data and development of adequate statistical infrastructure for all countries of the world  (FAO and GRA, 2020). In parallel, it might be a pragmatic way forward to continue and intensify work on comprehensive, up-to-date global emissions inventories such as EDGAR or CEDS as well as synthetic datasets as presented here or in PRIMAPhist.