Abstract Background The European Union (EU) has committed to achieve climate neutrality by 2050. This requires a rapid reduction of greenhouse gas (GHG) emissions and ensuring that any remaining emissions are balanced through CO2 removals. Forests play a crucial role in this plan: they are currently the main option for removing CO2 from the atmosphere and additionally, wood use can store carbon durably and help reduce fossil emissions. To stop and reverse the decline of the forest carbon sink, the EU has recently revised the regulation on land use, land-use change and forestry (LULUCF), and set a target of − 310 Mt CO2e net removals for the LULUCF sector in 2030. Results In this study, we clarify the role of common concepts in forest management – net annual increment, harvest and mortality – in determining the forest sink. We then evaluate to what extent the forest sink is on track to meet the climate goals of the EU. For this assessment we use data from the latest national GHG inventories and a forest model (Carbon Budget Model). Our findings indicate that on the EU level, the recent decrease in increment and the increase in harvest and mortality are causing a rapid drop in the forest sink. Furthermore, continuing the past forest management practices is projected to further decrease the sink. Finally, we discuss options for enhancing the sinks through forest management while taking into account adaptation and resilience. Conclusions Our findings show that the EU forest sink is quickly developing away from the EU climate targets. Stopping and reversing this trend requires rapid implementation of climate-smart forest management, with improved and more timely monitoring of GHG fluxes. This enhancement is crucial for tracking progress towards the EU’s climate targets, where the role of forests has become – and is expected to remain – more prominent than ever before.

This online repository includes all the relevant data used in the paper "Harmonizing the land-use flux estimates of global models and national inventories for 2000-2020" (Grassi et al. 2023), plus some additional methodological information, organised in the following files: 1) "Global models land CO2 data 2000-2020" (MS Excel Format), including for each country data for: a. Land-use CO2 fluxes from each of three Bookkeeping Models (BMs) used, and for different categories (net LULUCF, deforestation, forest, other transitions, organic soils). b. The ensemble mean of the ‘natural terrestrial sink’ estimated by 16 Dynamic Global Vegetation Models (DGVMs), filtered with maps of intact/non-intact forest. The global model data included here are consistent with those included in the Global Carbon Budget 2022 (Friedlingstein et al., 2022). 2) “National inventories LULUCF data 2000-2020” (version Dec 2022, MS Excel Format), including a comprehensive collection of LULUCF CO2 data based on countries' submissions to the United Nations Framework Convention on Climate Change (UNFCCC). The data here represent a slight update of the dataset included in Grassi et al. (2022). 3) “Processing steps for DGVM results”, describing the protocol used to filter the results of DGVMs with maps of intact/non-intact forest and further details on the maps (PDF Format). 4) “Intact and non-intact forest maps”, available in two files with different resolutions (0.5 and 0.05 degrees) in NetCDF format. Grassi et al. (2023) used the 0.5 degree resolution. 5) "IntactAndNonIntactForest_0.5deg_script.js", the Google Earth Engine Java script to produce the forest maps (.js/text format) For further details, please refer to: Grassi et al. (2023) Harmonising the land-use flux estimates of global models and national inventories for 2000-2020. Earth Syst. Sci. Data. Other references: Friedlingstein et al. (2022) Global Carbon Budget 2022, Earth Syst. Sci. Data, 14, 4811–4900. Grassi et al (2022) Carbon fluxes from land 2000–2020: bringing clarity to countries' reporting. Earth Syst. Sci. Data, 14, 4643-4666.

As the focus of climate policy shifts from pledges to implementation, there is a growing need to track progress on climate change mitigation at the country level, particularly for the land-use sector. Despite new tools and models providing unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO2 fluxes between, on the one hand, the national greenhouse gas inventories (NGHGIs) used to assess compliance with national climate targets under the Paris Agreement and, on the other hand, the Global Carbon Budget and Intergovernmental Panel on Climate Change (IPCC) assessment reports, both based on global bookkeeping models (BMs). Recent studies have shown that these differences are mainly due to inconsistent definitions of anthropogenic CO(2 )fluxes in managed forests. Countries assume larger areas of forest to be managed than BMs do, due to a broader definition of managed land in NGHGIs. Additionally, the fraction of the land sink caused by indirect effects of human-induced environmental change (e.g. fertilisation effect on vegetation growth due to increased atmospheric CO2 concentration) on managed lands is treated as non-anthropogenic by BMs but as anthropogenic in most NGHGIs. We implement an approach that adds the CO2 sink caused by environmental change in countries' managed forests (estimated by 16 dynamic global vegetation models, DGVMs) to the land-use fluxes from three BMs. This sum is conceptually more comparable to NGHGIs and is thus expected to be quantitatively more similar. Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at a greater level of disaggregation in terms of regions, countries and land categories (i.e. forest land, deforestation, organic soils, other land uses). Our results confirm a large difference (6.7 GtCO2 yr(-1)) in global land-use CO2 fluxes between the ensemble mean of the BMs, which estimate a source of 4.8 GtCO(2) yr(-1) for the period 2000-2020, and NGHGIs, which estimate a sink of -1.9 GtCO(2) yr(-1) in the same period. Most of the gap is found on forest land (3.5 GtCO(2) yr(-1)), with differences also for deforestation (2.4 GtCO(2) yr(-1)), for fluxes from other land uses (1.0 GtCO(2) yr(-1)) and to a lesser extent for fluxes from organic soils (0.2 GtCO(2) yr(-1)). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (-6.4 GtCO(2) yr(-1)) to BM estimates, the gap between BMs and NGHGIs becomes substantially smaller both globally (residual gap: 0.3 GtCO(2) yr(-1)) and in most regions and countries. However, some discrepancies remain and deserve further investigation. For example, the BMs generally provide higher emissions from deforestation than NGHGIs and, when adjusted with the sink in managed forests estimated by DGVMs, yield a sink that is often greater than NGHGIs. In summary, this study provides a blueprint for harmonising the estimations of anthropogenic land-use fluxes, allowing for detailed comparisons between global models and national inventories at global, regional and country levels. This is crucial to increase confidence in land-use emissions estimates, support investments in land-based mitigation strategies and assess the countries' collective progress under the Global Stocktake of the Paris Agreement.

Abstract. With the focus of climate policy shifting from pledges to implementation, there is an increasing need to track progress on climate change mitigation at country level, especially for the land-use sector. Despite new tools and models offering unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO2 fluxes between the national greenhouse gas inventories (NGHGIs) used to assess compliance with the Paris Agreement, and the Global Carbon Budget and IPCC assessment reports, both based on global bookkeeping models (BMs). Recent evidence showed that these differences are mainly due to inconsistent definitions of anthropogenic forest CO2 fluxes. In particular, the part of the land sink that is caused by the indirect effects of human-induced environmental change (e.g., fertilization effect on vegetation growth due to increase atmospheric CO2 concentration, climate change) on managed lands is treated as non-anthropogenic by BMs, while in most cases is considered anthropogenic in NGHGIs. In addition, countries use a broader definition of managed land than BMs. Building on previous studies, we implement an approach that adds the CO2 sink due to environmental change from countries’ managed forest area (estimated by Dynamic Global Vegetation Models, DGVMs) to the original land-use flux from BMs. This sum is expected to be conceptually more comparable to NGHGIs. Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at a greater level of disaggregation in terms of land categories (i.e., forest land, deforestation, organic soils, other land uses) and countries. Our results confirm a large difference in land use CO2 fluxes between the ensemble mean of the BMs, estimating a source of 4.3 GtCO2 yr-1 globally for the period 2001–2020, and NGHGIs, which estimate a sink of -1.7 GtCO2 yr-1. Most of this 6.0 GtCO2 yr-1 gap is found on forest land (3.8 GtCO2 yr-1), with differences also for deforestation (1.1 GtCO2 yr-1), other land uses (1.0 GtCO2 yr-1), and to a lesser extent for organic soils (0.1 GtCO2 yr-1). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (-5.1 GtCO2 yr-1) to BMs estimates, the gap between BMs and NGHGIs becomes significantly smaller both globally (residual gap: 0.9 GtCO2 yr-1) and in most regions and countries. The remaining differences mostly reflect smaller net emissions from deforestation and agricultural land in the NGHGIs of developing countries than in the BMs. By reconciling most of the differences between NGHGIs and global models (BMs and DGVMs), offering a blueprint for operationalizing future comparisons, and identifying areas to be further investigated, this study represents an important step forward for increasing transparency and confidence in land-use CO2 flux estimates at the country level. This is crucial to support land-based mitigation investments and assess the countries’ collective progress under the Paris Agreement’s Global Stocktake.

Despite an increasing attention on the role of land in meeting countries' climate pledges under the Paris Agreement, the range of estimates of carbon fluxes from land use, land-use change, and forestry (LULUCF) in available databases is very large. A good understanding of the LULUCF data reported by countries under the United Nations Framework Convention on Climate Change (UNFCCC) – and of the differences with other datasets based on country-reported data – is crucial to increase confidence in land-based climate change mitigation efforts. Here we present a new data compilation of LULUCF fluxes of carbon dioxide (CO2) on managed land, aiming at providing a consolidated view on the subject. Our database builds on a detailed analysis of data from national greenhouse gas inventories (NGHGIs) communicated via a range of country reports to the UNFCCC, which report anthropogenic emissions and removals based on the IPCC (Intergovernmental Panel on Climate Change) methodology. Specifically, for Annex I countries, data are sourced from annual GHG inventories. For non-Annex I countries, we compiled the most recent and complete information from different sources, including national communications, biennial update reports, submissions to the REDD+ (reducing emissions from deforestation and forest degradation) framework, and nationally determined contributions. The data are disaggregated into fluxes from forest land, deforestation, organic soils, and other sources (including non-forest land uses). The CO2 flux database is complemented by information on managed and unmanaged forest area as available in NGHGIs. To ensure completeness of time series, we filled the gaps without altering the levels and trends of the country reported data. Expert judgement was applied in a few cases when data inconsistencies existed. Results indicate a mean net global sink of −1.6 Gt CO2 yr−1 over the period 2000–2020, largely determined by a sink on forest land (−6.4 Gt CO2 yr−1), followed by source from deforestation (+4.4 Gt CO2 yr−1), with smaller fluxes from organic soils (+0.9 Gt CO2 yr−1) and other land uses (−0.6 Gt CO2 yr−1). Furthermore, we compare our NGHGI database with two other sets of country-based data: those included in the UNFCCC GHG data interface, and those based on forest resources data reported by countries to the Food and Agriculture Organization of the United Nations (FAO) and used as inputs into estimates of GHG emissions in FAOSTAT. The first dataset, once gap filled as in our study, results in a net global LULUCF sink of −5.4 Gt CO2 yr−1. The difference with the NGHGI database is in this case mostly explained by more updated and comprehensive data in our compilation for non-Annex I countries. The FAOSTAT GHG dataset instead estimates a net global LULUCF source of +1.1 Gt CO2 yr−1. In this case, most of the difference to our results is due to a much greater forest sink for non-Annex I countries in the NGHGI database than in FAOSTAT. The difference between these datasets can be mostly explained by a more complete coverage in the NGHGI database, including for non-biomass carbon pools and non-forest land uses, and by different underlying data on forest land. The latter reflects the different scopes of the country reporting to FAO, which focuses on area and biomass, and to UNFCCC, which explicitly focuses on carbon fluxes. Bearing in mind the respective strengths and weaknesses, both our NGHGI database and FAO offer a fundamental, yet incomplete, source of information on carbon-related variables for the scientific and policy communities, including under the Global stocktake. Overall, while the quality and quantity of the LULUCF data submitted by countries to the UNFCCC significantly improved in recent years, important gaps still remain. Most developing countries still do not explicitly separate managed vs. unmanaged forest land, a few report implausibly high forest sinks, and several report incomplete estimates. With these limits in mind, the NGHGI database presented here represents the most up-to-date and complete compilation of LULUCF data based on country submissions to UNFCCC. Data from this study are openly available via the Zenodo portal (Grassi et al., 2022), at https://doi.org/10.5281/zenodo.7190601.

Mitigation pathways by Integrated Assessment Models (IAMs) describe future emissions that keep global warming below specific temperature limits and are compared with countries’ collective greenhouse gas (GHG) emission reduction pledges. This is needed to assess mitigation progress and inform emission targets under the Paris Agreement. Currently, however, a mismatch of ~5.5 GtCO2 yr−1 exists between the global land-use fluxes estimated with IAMs and from countries’ GHG inventories. Here we present a ‘Rosetta stone’ adjustment to translate IAMs’ land-use mitigation pathways to estimates more comparable with GHG inventories. This does not change the original decarbonization pathways, but reallocates part of the land sink to be consistent with GHG inventories. Adjusted cumulative emissions over the period until net zero for 1.5 or 2 °C limits are reduced by 120–192 GtCO2 relative to the original IAM pathways. These differences should be taken into account to ensure an accurate assessment of progress towards the Paris Agreement. There is a mismatch between emission estimates from global land use calculated from IAMs and countries’ greenhouse gas inventories. This study presents a method for reconciling these estimates by reallocating part of the land-use sink, facilitating progress assessment towards climate goals.

Background The contribution of EU forests to climate change mitigation in 2021–2025 is assessed through the Forest Reference Levels (FRLs). The FRL is a projected country-level benchmark of net greenhouse gas emissions against which the future net emissions will be compared. The FRL models the hypothetical development of EU forest carbon sink if the historical management practices were continued, taking into account age dynamics. The Member States’ FRLs have been recently adopted by the European Commission with the delegated Regulation (EU) 2021/268 amending the Regulation (EU) 2018/841. Considering the complexity of interactions between forest growth, management and carbon fluxes, there is a need to understand uncertainties linked to the FRL determination. Results We assessed the methodologies behind the modelled FRLs and evaluated the foreseen impact of continuation of management practices and age dynamics on the near-future EU27 + UK forest carbon sink. Most of the countries implemented robust modelling approaches for simulating management practices and age dynamics within the FRL framework, but faced several challenges in ensuring consistency with historical estimates. We discuss that the projected 16% increase in harvest in 2021–2025 compared to 2000–2009, mostly attributed to age dynamics, is associated to a decline of 18% of forest sink (26% for living biomass only). Conclusions We conclude that the FRL exercise was challenging but improved the modelling capacity and data availability at country scale. The present study contributes to increase the transparency of the implementation of forest-related EU policies and provides evidence-based support to future policy development.

Land use and land cover change (LULCC) affects the climate through both biogeochemical (BGC) and biophysical (BPH) mechanisms. While BGC effects are assessed at global scale and are at the heart of climate treaties such as the Paris Agreement, BPH effects are totally absent despite their increasingly recognized impact, especially at local scale. This stems from the complexity in characterizing their climate impacts both at local and global scale, which makes it impractical to offer clear advices for the development of climate policies. To overcome this barrier, we built a prototype for an assessment tool to evaluate the local BPH impact of a series of land cover transitions. It relies on a dedicated methodology, based on satellite remote sensing data, that can estimate the local change in near surface air temperature associated with BPH effects of potential LULCC. This tool follows a tiered methodological approach, using transparent methods and mirroring what is currently provided by the IPCC to estimate the BGC effects, i.e. through different levels of increasing methodological complexity, from Tier 1 (i.e. default method and factors) to Tier 2 (i.e. similar to Tier 1 but with higher level of details and complexity) and Tier 3 (i.e. tailored solution to address national circumstances). The prototype tool enables the evaluation of the local impacts of land-related BPH effects, therefore facilitating a scientifically informed and comprehensive assessment of land-based climate policies.

Achieving the long-term temperature goal of the Paris Agreement requires forest-based mitigation. Collective progress towards this goal will be assessed by the Paris Agreement’s Global stocktake. At present, there is a discrepancy of about 4 GtCO2 yr−1 in global anthropogenic net land-use emissions between global models (reflected in IPCC assessment reports) and aggregated national GHG inventories (under the UNFCCC). We show that a substantial part of this discrepancy (about 3.2 GtCO2 yr−1) can be explained by conceptual differences in anthropogenic forest sink estimation, related to the representation of environmental change impacts and the areas considered as managed. For a more credible tracking of collective progress under the Global stocktake, these conceptual differences between models and inventories need to be reconciled. We implement a new method of disaggregation of global land model results that allows greater comparability with GHG inventories. This provides a deeper understanding of model–inventory differences, allowing more transparent analysis of forest-based mitigation and facilitating a more accurate Global stocktake. The model–inventory discrepancy in net land-use carbon emissions mainly results from conceptual differences in estimating anthropogenic forest sinks. A revised disaggregation of global land model results allows greater comparability with inventories.

According to the post-2012 rules under the Kyoto protocol, developed countries that are signatories to the protocol have to estimate and report the greenhouse gas (GHG) emissions and removals from forest management (FM), with the option to exclude the emissions associated to natural disturbances, following the Intergovernmental Panel on Climate Change (IPCC) guidelines. To increase confidence in GHG estimates, the IPCC recommends performing verification activities, i.e. comparing country data with independent estimates. However, countries currently conduct relatively few verification efforts. The aim of this study is to implement a consistent methodological approach using the Carbon Budget Model (CBM) to estimate the net CO2 emissions from FM in 26 European Union (EU) countries for the period 2000–2012, including the impacts of natural disturbances. We validated our results against a totally independent case study and then we compared the CBM results with the data reported by countries in their 2014 Greenhouse Gas Inventories (GHGIs) submitted to the United Nations Framework Convention on Climate Change (UNFCCC).

Forests and the forest sector may play an important role in mitigating climate change. The Paris Agreement and the recent legislative proposal to include the land use sector in the EU 2030 climate targets reflect this expectation. However, greater confidence on estimates from national greenhouse gas inventories (GHGI) and more comprehensive analyses of mitigation options are needed to seize this mitigation potential. The aim of this paper is to provide a tool at EU level for verifying the EU GHGI and for simulating specific policy and forest management scenarios. Therefore, the Carbon Budget Model (CBM) was applied for an integrated assessment of the EU forest carbon (C) balance from 2000 to 2012, including: (i) estimates of the C stock and net CO2 emissions for forest management (FM), afforestation/reforestation (AR) and deforestation (D), covering carbon in both the forest and the harvest wood product (HWP) pools; (ii) an overall analysis of the C dynamics associated with harvest and natural disturbances (mainly storms and fires); (iii) a comparison of our estimates with the data reported in the EU GHGI.

Reporting national greenhouses gas inventories for the Land Use, Land Use Change and Forestry (LULUCF) under the United Nations Framework Convention for Climate Change (UNFCCC) has been driving significant development of national data. Lands classification and definitions for C pools are very different across the 28 EU member states, as are the methods used for data collection and processing. Even when definitions or sampling methods were in substance the same, specifications were-different. However, member states' inventories are assumed to be fully comparable and consistent with reporting principles. For forest, data collection rely on forest inventory repeated measurements (both statistical sampling and stand-wise), while for most important contributor, the biomass, there is no preference for "gain-loss" or "stock-change" method. For cropland and grasslands, operational records information is equally important to statistical sampling and aerial photography sources for area estimation, while default emission factors are used less and less as they are being replaced by country-specific data. The obvious trend is the move toward statistical sampling covering all land categories within national territory and slight increasing use of models. Such heterogeneity requires a better harmonization of data collection and processing as to increase the credibility of the EU GHG inventory.

Carbon (C) stock change and related Carbon Dioxide (CO2) emissions/removals from living biomass for Forest Land remaining Forest Land (IPCC sector 5-FL-1) are one of the most difficult to estimate because of the large natural fluxes of CO2 and the high accuracy requirements for deriving net changes of the large C stock. Following the Good Practice Guide (GPG) methodology of IPCC (2003) with IPCC default values and data from FAO (FRA and ForesSTAT 2010) for harvested woodfuel and industrial roundwood and with fires emissions from the Global Fires Emission Database GFED v.3, a Tier-1 level estimate of country-specific C stock for sector 5-FL-1 was carried out for the years 1990, 2000 and 2005, which mounted to about 6 Pg C or about half of the mean total CO2 emissions in that period. The resulting bottom up globally harmonized CO2 inventory for 5-FL-1 from 1990 to 2010 completes further the Land Use, Land-Use Change and Forestry sector in the Emission Database for Global Atmospheric Research (EDGAR). Even though the GPG methodology of IPCC (2003) is assumed adequate and detailed enough to account well for C stock changes, the estimates of sector 5-FL-1 in the national GHG inventories remain highly uncertain. In all three years ~35% of the national inventories for 5-FL-1 show similarities but the values differ in most cases by an average factor of -5 to +5. In all other cases, Tier 1 values are the opposite of what countries report regarding sink and source. Vis-a-vis forested area the average difference for Annex I countries between Tier 1 calculations and FRA2010 and UNFCCC mount to 9% and 16% respectively. It is concluded that differences are mainly due to the activity data explicitly forested area, the definition of forests used by each country and losses which are not always accounted by countries or perhaps double counted. The feasibility for establishing national inventories in a consistent way for all world countries was demonstrated but only with a limited accuracy of Tier 1. Better forestry data, such as annual activity data from satellite images are needed to derive estimates at higher Tier, but are too scarce at the moment for obtaining more accurate estimates of global level C stock budgets.