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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.

BackgroundThe 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.ResultsIn 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.ConclusionsOur 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.

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.

BackgroundThe 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.ResultsWe 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).ConclusionsWe 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.

BackgroundIn 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.ResultsThe 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.ConclusionsWe 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.

Land carbon fluxes are key to the Paris Agreement. However, data comparability issues persist between countries’ land greenhouse gas inventories and mitigation targets, and what land models (bookkeeping and integrated assessments) provide as Paris-aligned benchmarks for land. As a result, the Global Stocktake, aiming to track collective mitigation progress, did not explicitly consider country targets for land. This blind spot leaves countries uninformed of the 2030 gap between their ambitions for mitigation on land and models’ benchmarks. Here we track the contribution and evolution of land-related targets under countries’ 2020 Nationally Determined Contributions, splitting land pledges between reduced emissions and additional sinks. Land retains a quarter of the global mitigation pledges in 2030, mostly relying on external support (−1.5ǂ1.1 GtCO 2 e/yr), of which −0.55 GtCO 2 e/yr are additional sinks. It is crucial that future Global Stocktakes include appropriate comparisons between modelled and country-provided land use net emissions. We here offer some concrete suggestions. Discrepancies exist between data used in countries’ greenhouse gas inventories and modelled benchmarks for the Paris agreement, according to an analysis of countries’ land-based greenhouse gas targets.

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 2021–2025 (compliance period 1, CP1) and in 2026–2030 (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 2000–2009. 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.

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.

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.

Habitat fragmentation is a key biodiversity issue in forest planning at the landscape level. More consideration is needed in assessing the choice of the landscape index in spatial forest planning optimizations. The objective of this study was to evaluate the effects of four different landscape indices on the fragmentation when applied to forest planning optimization problems. The indices were included in a long-term planning problem from a case study area in Northern Sweden, with both timber production and biodiversity goals. The results show that different spatial indices affect the landscape structure differently and that the effect of the index is very dependent on the available area of habitat. For mean distance between patches and shape index, for example, very good index values were achieved, but at the cost of total area of habitat. Moreover, the optimization algorithm chose small and regular-shaped patches, which had a positive effect on the index value, but a negative impact on the overall fragmentation pattern. This is an important result for the practical application of landscape indices to forest planning situations: by only tracking the numerical changes in landscape configuration, the detrimental effects of the optimization setup on fragmentation as a whole may be overlooked.