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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 CO2 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 GtCO2 yr−1 for the period 2000–2020, and NGHGIs, which estimate a sink of −1.9 GtCO2 yr−1 in the same period. Most of the gap is found on forest land (3.5 GtCO2 yr−1), with differences also for deforestation (2.4 GtCO2 yr−1), for fluxes from other land uses (1.0 GtCO2 yr−1) and to a lesser extent for fluxes from organic soils (0.2 GtCO2 yr−1). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (−6.4 GtCO2 yr−1) to BM estimates, the gap between BMs and NGHGIs becomes substantially smaller both globally (residual gap: 0.3 GtCO2 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. Data from this study are openly available online via the Zenodo portal (Grassi et al., 2023) at https://doi.org/10.5281/zenodo.7650360.

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 over the period 2000–2020, largely determined by a sink on forest land (-6.4 Gt CO2/yr), followed by source from deforestation (+4.4 Gt CO2/yr) and minor fluxes from organic soils (+0.9 Gt CO2/yr) and other land uses (-0.6 Gt CO2/yr). 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 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. 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. 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.6390739.

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.

The European Union (EU) aims at reaching carbon neutrality by 2050. Within the land use, land-use change, and forestry (LULUCF) sector, forestry will contribute to this target with CO2 sink, harvested wood products (HWP), and use of wood for material or energy substitution. Despite the fact that the forest sink currently offsets about 9% of the total EU GHG emissions, evaluating its future mitigation potential is challenging because of the complex interactions between human and natural impacts on forest growth and carbon accumulation. The Regulation (EU) 2018/841 has improved robustness, accuracy, and credibility of the accounting of GHG emissions and removals in the LULUCF sector. For the forest sector, the accounting is based on the Forest Reference Level (FRL), i.e., a projected country-specific value of GHG emissions and removals against which the actual GHG emissions and removals will be compared. The resulting difference will count toward the EU GHG target for the period 2021–2030. Here, we provide an overview of the contribution of forests and HWP to the EU carbon sink for the period 2021–2025 (proposed FRLs) and focus on the contribution of mountain forests to the EU carbon sink, through exploring co-benefits and adverse side effects between climate regulation and other ecosystem services.

In this study, we investigate the effects of climate change mitigation and socioeconomic development on global forest resources use. The analysis is based on the Global Biosphere Management Model (GLOBIOM), which is a recursive dynamic land-use model. Climate change mitigation and socioeconomic development are included in the model as exogenous parameters taken from the SSP-RCP scenarios, which separate between the shared socioeconomic pathways(“SSPs”) and the representative concentration pathways (“RCPs”). The effect of SSP-RCP scenarios is restricted to factors that are quantitatively documented in the SSP database (economic growth, population growth, bioenergy demand, and carbon prices). Our results indicate that both climate change mitigation and socio-economic development may increase harvest volumes and harvested area considerably in the future. This happens because there are no opportunity costs of using forest area for harvesting in the model. We show that such opportunity costs can be added in the model by considering carbon storage changes between forest types and carbon payments on them. These payments increases woody biomass prices and make woody biomass harvesting for modern bioenergy less profitable mitigation option relative to carbon sequestration in the standing forests. However, the payments do not have much impact on the profitability of woody biomass harvesting for material products and traditional bioenergy. The reason is that energy crops provide a substitute for woody biomass use for modern bioenergy while there are less substitutes available for woody biomass use for material products and traditional bioenergy. Provided that carbon payments can be used as a policy instrument to control impacts of climate change mitigation on harvest volumes and harvested area, an unfavorable future socioeconomic development may cause a greater threat to the world’s forests than climate change mitigation.

Background In 2018, the European Union (EU) adopted Regulation 2018/841, which sets the accounting rules for the land use, land use change and forestry (LULUCF) sector for the period 2021–2030. This regulation is part of the EU’s commitments to comply with the Paris Agreement. According to the new regulation, emissions and removals for managed forest land are to be accounted against a projected forest reference level (FRL) that is estimated by each EU Member State based on the continuation of forest management practices of the reference period 2000–2009. The aim of this study is to assess how different modelling assumptions possible under the regulation may influence the FRL estimates. Applying the interlinked G4M and WoodCarbonMonitor modelling frameworks, we estimate potential FRLs for each individual EU Member State following a set of conceptual scenarios, each reflecting different modelling assumptions that are consistent with the regulation and the technical guidance document published by the European Commission. Results The simulations of the conceptual scenarios show that differences in the underlying modelling assumptions may have a large impact on the projected FRL. Depending on the assumptions taken, the projected annual carbon sink on managed forest land in the EU varies from −319 MtCO2 to −397 MtCO2 during the first compliance period (2021–2025) and from −296 MtCO2 to −376 MtCO2 during the second compliance period (i.e. 2026–2030). These estimates can be compared with the 2017 national GHG inventories which estimated that the forest carbon sink for managed forest land was −373 MtCO2 in 2015. On an aggregated EU level, the assumptions related to climate change and the allocation of forest management practices have the largest impacts on the FRL estimates. On the other hand, assumptions concerning the starting year of the projection, stratification of managed forest land, and timing of individual management activities are found to have relatively small impacts on the FRL estimates. Conclusions We provide a first assessment of the level of uncertainty associated with the different assumptions discussed in the technical guidance document and the LULUCF regulation, and the impact of these assumptions on the country-specific FRL. The results highlight the importance of transparent documentation by the EU Member States on how their FRL has been calculated, and on the underlying assumptions. Background

Developing Detailed Shared Socioeconomic Pathway (SSP) Narratives for the Global Forest Sector

Sustainable forest management is driving the development of forest decision support systems (DSSs) to include models and methods concerned with climate change, biodiversity and various ecosystem services (ESs). The future development of forest landscapes is very much dependent on how forest owners act and what goes on in the wider world; thus, models are needed that incorporate these aspects. The objective of this study is to assess how nine European state-of-the-art forest DSSs cope with these issues. The assessment focuses on the ability of these DSSs to generate landscape-level scenarios to explore the output of current and alternative forest management models (FMMs) in terms of a range of ESs and the robustness of these FMMs in the face of increased risks and uncertainty. Results show that all DSSs assessed in this study can be used to quantify the impacts of both stand- and landscape-level FMMs on the provision of a range of ESs over a typical planning horizon. DSSs can be used to assess how timber price trends may impact that provision over time. The inclusion of forest owner behavior as reflected by the adoption of specific FMMs seems to be also in the reach of all DSSs. Nevertheless, some DSSs need more data and development of models to estimate the impacts of climate change on biomass production and other ESs. Spatial analysis functionality needs to be further developed for a more accurate assessment of the landscape-level output of ESs from both current and alternative FMMs. European Commission Horizon 2020 Portuguese Science Foundation

Additional file 1. Detailed input data and information sources; detailed assessment results for degree of fulfilment and model adequacy.

Additional file 2. Supplementary figure supporting the harvest-biomass sink assessment results.

This study investigates the need for change of current forest management approaches in a southern Swedish region within the context of future climate change mitigation through empirically derived projections, rather than forest management according to silvicultural guidelines. Scenarios indicate that climate change mitigation will increase global wood demand. This might call for adjustments of well-established management approaches. This study investigates to what extent increasing wood demands in three climate change mitigation scenarios can be satisfied with current forest management approaches of different intensities in a southern Swedish region. Forest management practices in Kronoberg County were mapped through interviews, statistics, and desk research and were translated into five different management strategies with different intensities regulating management at the property level. The consequences of current practices, as well as their intensification, were analyzed with the Heureka Planwise forest planning system in combination with a specially developed forest owner decision simulator. Projections were done over a 100-year period under three climate change mitigation scenarios developed with the Global Biosphere Management Model (GLOBIUM). Current management practices could meet scenario demands during the first 20 years. This was followed by a shortage of wood during two periods in all scenarios unless rotations were reduced. In a longer timeframe, the wood demands were projected to be easily satisfied in the less ambitious climate change mitigation scenarios. In contrast, the demand in the ambitious mitigation scenario could not be met with current management practices, not even if all owners managed their production forests at the intensive extreme of current management approaches. The climate change mitigation scenarios provide very different trajectories with respect to future drivers of forest management. Our results indicate that with less ambitious mitigation efforts, the relatively intensive practices in the study region can be softened while ambitious mitigation might push for further intensification.

Background and Objectives: Under the Paris Agreement, the European Union (EU) sets rules for accounting the greenhouse gas emissions and removals from forest land (FL). According to these rules, the average FL emissions of each member state in 2021&ndash, 2025 (compliance period 1, CP1) and in 2026&ndash, 2030 (compliance period 2, CP2) will be compared to a projected forest reference level (FRL). The FRL is estimated by modelling forest development under fixed forest management practices, based on those observed in 2000&ndash, 2009. In this context, the objective of this study was to estimate the effects of large-scale uptake of alternative forest management models (aFMMs), developed in the ALTERFOR project (Alternative models and robust decision-making for future forest management), on forest harvest and forest carbon sink, considering that the proposed aFMMs are expanded to most of the suitable areas in EU27+UK and Turkey. Methods: We applied the Global Forest Model (G4M) for projecting the harvest and sink with the aFMMs and compared our results to previous FRL projections. The simulations were performed under the condition that the countries should match the harvest levels estimated for their FRLs as closely as possible. A representation of such aFMMs as clearcut, selective logging, shelterwood logging and tree species change was included in G4M. The aFMMs were modeled under four scenarios of spatial allocation and two scenarios of uptake rate. Finally, we compared our results to the business as usual. Results: The introduction of the aFMMs enhanced the forest sink in CP1 and CP2 in all studied regions when compared to the business as usual. Conclusions: Our results suggest that if a balanced mixture of aFMMs is chosen, a similar level of wood harvest can be maintained as in the FRL projection, while at the same time enhancing the forest sink. In particular, a mixture of multifunctional aFMMs, like selective logging and shelterwood, could enhance the carbon sink by up to 21% over the ALTERFOR region while limiting harvest leakages.