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The sustainable policies and practices such as agricultural, forestry and land management are crucial for ensuring the long-term provision of ecosystem services. According to the European Green Deal strategy, at least 3 billion more trees should be planted in the European Union by 2030. In support of this target, the Italian National Forest Strategy proposes actions aimed at reforestation, afforestation and agroforestry. Enhancing forest density and cover enhances the capacity to remove CO2 and store it as organic carbon in the global terrestrial biosphere. Spatial decision support tools can support policy makers in addressing these challenges and relevant assessment of land-based mitigation options to reduce emissions in the land sector. Despite the specific goal set in the European Green Deal strategy, there is still no clear understanding of which territory in Italy will be involved in the planned actions. Therefore, there is a need to build suitability maps to determine the future change of the land cover in connection with the European strategies and planned actions up to 2030. This study suggests not only possible future land cover maps for the Lazio region, compiled in accordance with the land use policy, but also an assessment of the planned carbon sequestration based on the proposed maps.

Quantification of land surface-atmosphere fluxes of carbon dioxide (CO2) fluxes and their trends and uncertainties is essential for monitoring progress of the EU27+UK bloc as it strives to meet ambitious targets determined by both international agreements and internal regulation. This study provides a consolidated synthesis of fossil sources (CO2 fossil) and natural sources and sinks over land (CO2 land) using bottom-up (BU) and top-down (TD) approaches for the European Union and United Kingdom (EU27+UK), updating earlier syntheses (Petrescu et al., 2020, 2021b). Given the wide scope of the work and the variety of approaches involved, this study aims to answer essential questions identified in the previous syntheses and understand the differences between datasets, particularly for poorly characterized fluxes from managed ecosystems. The work integrates updated emission inventory data, process-based model results, data-driven sectoral model results, and inverse modeling estimates, extending the previous period 1990–2018 to the year 2020 to the extent possible. BU and TD products are compared with European National Greenhouse Gas Inventories (NGHGIs) reported by Parties including the year 2019 under the United Nations Framework Convention on Climate Change (UNFCCC). The uncertainties of the EU27+UK NGHGI were evaluated using the standard deviation reported by the EU Member States following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), originate from within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, key sources of differences between estimates arise primarily in activities. System boundaries and emission categories create differences in CO2 fossil datasets, while different land use definitions for reporting emissions from Land Use, Land Use Change and Forestry (LULUCF) activities result in differences for CO2 land. The latter has important consequences for atmospheric inversions, leading to inversions reporting stronger sinks in vegetation and soils than are reported by the NGHGI. For CO2 fossil emissions, after harmonizing estimates based on common activities and selecting the most recent year available for all datasets, the UNFCCC NGHGI for the EU27+UK accounts for 3392 ± 49 Tg CO2 yr-1 (926 ± 13 Tg C yr-1), while eight other BU sources report a mean value of 3340 [3238,3401] [25th,75th percentile] Tg CO2 yr-1 (948 [937,961] Tg C yr-1). The sole top-down inversion of fossil emissions currently available accounts for 3800 Tg CO2 yr-1 (1038 Tg C yr-1), a value close to that of the NGHGI, but for which uncertainty estimates are not yet available. For the net CO2 land fluxes, during the most recent five-year period including the NGHGI estimates, the NGHGI accounted for -91 ± 32 Tg C yr-1 while six other BU approaches reported a mean sink of -62 [-117,-49] Tg C yr-1 and a 15-member ensemble of dynamic global vegetation models (DGVMs) reported -69 [-152,-5] Tg C yr-1. The five-year mean of three TD regional ensembles combined with one non-ensemble inversion of -73 Tg C yr-1 has a slightly smaller spread (0th–100th percentile of [-135,45] Tg C yr-1), and was calculated after removing land-atmosphere CO2 fluxes caused by lateral transport of carbon (crops, wood trade and inland waters) resulting in increased agreement with the the NGHGI and bottom-up approaches. Results at the sub-sector level (Forestland, Cropland, Grassland) show generally good agreement between the NGHGI and sub-sector-specific models, but results for a DGVM are mixed. Overall, for both CO2 fossil and net CO2 land fluxes, we find current independent approaches are consistent with the NGHGI at the scale of the EU27+UK. We conclude that CO2 emissions from fossil sources have decreased over the past 30 years in the EU27+UK, while large uncertainties on net uptake of CO2 by the land surface prevent trend identification. In addition, a gap on the order of 1000 Tg C yr-1 between CO2 fossil emissions and net CO2 uptake by the land exists regardless of the type of approach (NGHGI, TD, BU), falling well outside all available estimates of uncertainties. However, uncertainties in top-down approaches to estimate CO2 fossil emissions remain uncharacterized and are likely substantial. The data used to plot the figures are available at https://doi.org/10.5281/zenodo.7365863.

Knowledge of the spatial distribution of the fluxes of greenhouse gases (GHGs) and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its global stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27 + UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results and inverse modeling estimates, and it extends the previous period of 1990–2017 to 2019. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported by parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. Uncertainties in NGHGIs, as reported to the UNFCCC by the EU and its member states, are also included in the synthesis. Variations in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates, e.g., anthropogenic and natural fluxes, which in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015–2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 Tg CH4 yr−1 (EDGARv6.0, last year 2018) and 18.4 Tg CH4 yr−1 (GAINS, last year 2015), close to the NGHGI estimates of 17.5±2.1 Tg CH4 yr−1. TD inversion estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high-resolution regional TD inversions report a mean emission of 34 Tg CH4 yr−1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 and 24 Tg CH4 yr−1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soil emissions from the JSBACH–HIMMELI model, natural rivers, lake and reservoir emissions, geological sources, and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH4 yr−1. For N2O emissions, over the 2015–2019 period, both BU products (EDGARv6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N2O yr−1, close to the NGHGI data (0.8±55 % Tg N2O yr−1). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N2O yr−1 (excluding TOMCAT, which reported no data). The TD and BU comparison method defined in this study can be operationalized for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27 + UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, which is of great importance for CH4 and N2O, and may help identify sector contributions to divergence between prior and posterior estimates at the annual and/or inter-annual scale. Even if currently comparison between CH4 and N2O inversion estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modeling and observations, as well as modeling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emission inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.7553800 (Petrescu et al., 2023).

Knowledge of the spatial distribution of the fluxes of greenhouse gases and their temporal variability as well as flux attribution to natural and anthropogenic processes is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement and to inform its Global Stocktake. This study provides a consolidated synthesis of CH4 and N2O emissions using bottom-up (BU) and top-down (TD) approaches for the European Union and UK (EU27+UK) and updates earlier syntheses (Petrescu et al., 2020, 2021). The work integrates updated emission inventory data, process-based model results, data-driven sector model results, inverse modelling estimates, and extends the previous period 1990–2017 to 2020. BU and TD products are compared with European National GHG Inventories (NGHGI) reported by Parties under the United Nations Framework Convention on Climate Change (UNFCCC) in 2021. The uncertainties of NGHGIs were evaluated using the standard deviation obtained by varying parameters of inventory calculations, reported by the EU Member States following the guidelines of the Intergovernmental Panel on Climate Change (IPCC) and harmonized by gap-filling procedures. Variation in estimates produced with other methods, such as atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. By comparing NGHGIs with other approaches, the activities included are a key source of bias between estimates e.g. anthropogenic and natural fluxes, which, in atmospheric inversions are sensitive to the prior geospatial distribution of emissions. For CH4 emissions, over the updated 2015–2019 period, which covers a sufficiently robust number of overlapping estimates, and most importantly the NGHGIs, the anthropogenic BU approaches are directly comparable, accounting for mean emissions of 20.5 Tg CH4 yr−1 (EDGAR v5v6.0, last year 2018) and 18.4 Tg CH4 yr−1 (GAINS, 2015), close to the NGHGI estimates of 17.5 ± 2.1 Tg CH4 yr−1. TD inversions estimates give higher emission estimates, as they also detect natural emissions. Over the same period, high resolution regional TD inversions report a mean emission of 34 Tg CH4 yr−1. Coarser-resolution global-scale TD inversions result in emission estimates of 23 Tg CH4 yr−1 and 24 Tg CH4 yr−1 inferred from GOSAT and surface (SURF) network atmospheric measurements, respectively. The magnitude of natural peatland and mineral soils emissions from the JSBACH-HIMMELI model, natural rivers, lakes and reservoirs emissions, geological sources and biomass burning together could account for the gap between NGHGI and inversions and account for 8 Tg CH4 yr−1. For N2O emissions, over the 2015–2019 period, both BU products (EDGAR v5v6.0 and GAINS) report a mean value of anthropogenic emissions of 0.9 Tg N2O yr−1, close to the NGHGI data (0.8 ± 55 % Tg N2O yr−1). Over the same period, the mean of TD global and regional inversions was 1.4 Tg N2O yr−1 (excluding TOMCAT which reported no data). The TD and BU comparison method defined in this study can be "operationalized" for future annual updates for the calculation of CH4 and N2O budgets at the national and EU27+UK scales. Future comparability will be enhanced with further steps involving analysis at finer temporal resolutions and estimation of emissions over intra-annual timescales, of great importance for CH4 and N2O, which may help identify sector contributions to divergence between prior and posterior estimates at the annual/inter-annual scale. Even if currently comparison between CH4 and N2O inversions estimates and NGHGIs is highly uncertain because of the large spread in the inversion results, TD inversions inferred from atmospheric observations represent the most independent data against which inventory totals can be compared. With anticipated improvements in atmospheric modelling and observations, as well as modelling of natural fluxes, TD inversions may arguably emerge as the most powerful tool for verifying emissions inventories for CH4, N2O and other GHGs. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.6992472 (Petrescu et al., 2022).

This chapter is part of a forthcoming commentary on the Paris Agreement. It analyses Article 5 of the Paris Agreement, focusing on sinks, reservoirs of GHGs and forests.

International audience; In support of the global stocktake of the Paris Agreement on climate change, this study presents a comprehensive framework to process the results of an ensemble of atmospheric inversions in order to make their net ecosystem exchange (NEE) carbon dioxide (CO2) flux suitable for evaluating national greenhouse gas inventories (NGHGIs) submitted by countries to the United Nations Framework Convention on Climate Change (UNFCCC). From inversions we also deduced anthropogenic methane (CH4) emissions regrouped into fossil and agriculture and waste emissions, as well as anthropogenic nitrous oxide (N2O) emissions. To compare inversion results with national reports, we compiled a new global harmonized database of emissions and removals from periodical UNFCCC inventories by Annex I countries, and from sporadic and less detailed emissions reports by non-Annex I countries, given by national communications and biennial update reports. No gap filling was applied. The method to reconcile inversions with inventories is applied to selected large countries covering ∼90 % of the global land carbon uptake for CO2 and top emitters of CH4 and N2O. Our method uses results from an ensemble of global inversions produced by the Global Carbon Project for the three greenhouse gases, with ancillary data. We examine the role of CO2 fluxes caused by lateral transfer processes from rivers and from trade in crop and wood products and the role of carbon uptake in unmanaged lands, both not accounted for by NGHGIs. Here we show that, despite a large spread across the inversions, the median of available inversion models points to a larger terrestrial carbon sink than inventories over temperate countries or groups of countries of the Northern Hemisphere like Russia, Canada and the European Union. For CH4, we find good consistency between the inversions assimilating only data from the global in situ network and those using satellite CH4 retrievals and a tendency for inversions to diagnose higher CH4 emission estimates than reported by NGHGIs. In particular, oil- and gas-extracting countries in central Asia and the Persian Gulf region tend to systematically report lower emissions compared to those estimated by inversions. For N2O, inversions tend to produce higher anthropogenic emissions than inventories for tropical countries, even when attempting to consider only managed land emissions. In the inventories of many non-Annex I countries, this can be tentatively attributed to a lack of reporting indirect N2O emissions from atmospheric deposition and from leaching to rivers, to the existence of natural sources intertwined with managed lands, or to an underestimation of N2O emission factors for direct agricultural soil emissions. Inversions provide insights into seasonal and interannual greenhouse gas fluxes anomalies, e.g., during extreme events such as drought or abnormal fire episodes, whereas inventory methods are established to estimate trends and multi-annual changes. As a much denser sampling of atmospheric CO2 and CH4 concentrations by different satellites coordinated into a global constellation is expected in the coming years, the methodology proposed here to compare inversion results with inventory reports (e.g., NGHGIs) could be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objective of their pledges. The dataset constructed by this study is publicly available at https://doi.org/10.5281/zenodo.5089799 (Deng et al., 2021).

Under the recently adopted 2030 EU climate change policy framework, land use, land use change and forestry (lulucf) will for the first time contribute to the EU’s economy wide emission reduction target. This article looks at the history of the lulucf Regulation, analysing its contents in light of the history of international and regional efforts to regulate emissions and removals in this specific sector. It highlights the challenges associated with regulating this specific sector and reviews the regulation, assessing how well it has addressed these challenges.

Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO 2 ), including that of their trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Kyoto Protocol and the Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO 2 for the European Union and UK (EU27 + UK), derived from a combination of state-of-the-art bottom-up (BU) and top-down (TD) data sources and models. Given the wide scope of the work and the variety of datasets involved, this study focuses on identifying essential questions which need to be answered to properly understand the differences between various datasets, in particular with regards to the less-well-characterized fluxes from managed ecosystems. The work integrates recent emission inventory data, process-based ecosystem model results, data-driven sector model results and inverse modeling estimates over the period 1990–2018. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported under the UNFCCC in 2019, aiming to assess and understand the differences between approaches. For the uncertainties in NGHGIs, we used the standard deviation obtained by varying parameters of inventory calculations, reported by the member states following the IPCC Guidelines. Variation in estimates produced with other methods, like atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arises from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. In comparing NGHGIs with other approaches, a key source of uncertainty is that related to different system boundaries and emission categories (CO 2 fossil) and the use of different land use definitions for reporting emissions from land use, land use change and forestry (LULUCF) activities (CO 2 land). At the EU27 + UK level, the NGHGI (2019) fossil CO 2 emissions (including cement production) account for 2624 Tg CO 2 in 2014 while all the other seven bottom-up sources are consistent with the NGHGIs and report a mean of 2588 ( ± 463 Tg CO 2 ). The inversion reports 2700 Tg CO 2 ( ± 480 Tg CO 2 ), which is well in line with the national inventories. Over 2011–2015, the CO 2 land sources and sinks from NGHGI estimates report −90 Tg C yr −1 ± 30 Tg C yr −1 while all other BU approaches report a mean sink of −98 Tg C yr −1 ( ± 362 Tg of C from dynamic global vegetation models only). For the TD model ensemble results, we observe a much larger spread for regional inversions (i.e., mean of 253 Tg C yr −1 ± 400 Tg C yr −1 ). This concludes that (a) current independent approaches are consistent with NGHGIs and (b) their uncertainty is too large to allow a verification because of model differences and probably also because of the definition of “CO 2 flux” obtained from different approaches. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4626578 (Petrescu et al., 2020a).

Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27 + UK). We integrate recent emission inventory data, ecosystem process-based model results and inverse modeling estimates over the period 1990-2017. BU and TD products are compared with European national greenhouse gas inventories (NGHGIs) reported to the UN climate convention UNFCCC secretariat in 2019. For uncertainties, we used for NGHGIs the standard deviation obtained by varying parameters of inventory calculations, reported by the member states (MSs) following the recommendations of the IPCC Guidelines. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model-specific uncertainties when reported. In comparing NGHGIs with other approaches, a key source of bias is the activities included, e.g. anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011-2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr-1 (EDGAR v5.0) and 19.0 Tg CH4 yr-1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr-1. The estimates of TD total inversions give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher-resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr-1. Coarser-resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4 yr-1) and surface network (24.4 Tg CH4 yr-1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions, and geological sources together account for the gap between NGHGIs and inversions and account for 5.2 Tg CH4 yr-1. For N2O emissions, over the 2011-2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr-1, respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr-1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr-1, respectively. The TD and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at the EU+UK scale and at the national scale. The referenced datasets related to figures are visualized at. (Petrescu et al. 2020b)., SCOPUS: re.j, info:eu-repo/semantics/published

Reliable quantification of the sources and sinks of atmospheric carbon dioxide (CO2), including that of their trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Kyoto Protocol and the Paris Agreement. This study provides a consolidated synthesis of estimates for all anthropogenic and natural sources and sinks of CO2 for the European Union and UK (EU27 + UK), derived from a combination of state-of-the-art bottom-up (BU) and top-down (TD) data sources and models. Given the wide scope of the work and the variety of datasets involved, this study focuses on identifying essential questions which need to be answered to properly understand the differences between various datasets, in particular with regards to the less-well characterized fluxes from managed ecosystems. The work integrates recent emission inventory data, process-based ecosystem model results, data-driven sector model results, and inverse modelling estimates, over the period 1990–2018. BU and TD products are compared with European national GHG inventories (NGHGI) reported under the UNFCCC in 2019, aiming to assess and understand the differences between approaches. For the uncertainties in NGHGI, we used the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines. Variation in estimates produced with other methods, like atmospheric inversion models (TD) or spatially disaggregated inventory datasets (BU), arise from diverse sources including within-model uncertainty related to parameterization as well as structural differences between models. In comparing NGHGI with other approaches, a key source of uncertainty is that related to different system boundaries and emission categories (CO2 fossil) and the use of different land use definitions for reporting emissions from Land Use, Land Use Change and Forestry (LULUCF) activities (CO2 land). At the EU27 + UK level, the NGHGI (2019) fossil CO2 emissions (including cement production) account for 2624 Tg CO2 in 2014 while all the other seven bottom-up sources are consistent with the NGHGI and report a mean of 2588 (± 463 Tg CO2). The inversion reports 2700 Tg CO2 (± 480 Tg CO2), well in line with the national inventories. Over 2011–2015, the CO2 land sources/sinks from NGHGI estimates report −90 Tg C yr−1 ± 30 Tg C while all other BU approaches report a mean sink of −98 Tg yr−1 (± 362 Tg C from DGVMs only). For the TD model ensemble results, we observe a much larger spread for regional inversions (i.e., mean of 253 Tg C yr−1 ± 400 T g C yr−1). This concludes that a) current independent approaches are consistent with NGHGI b) their uncertainty is too large to allow a verification because of model differences and probably also because of the definition of CO2 flux obtained from different approaches. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288883 (Petrescu et al., 2020).

Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27+UK). We integrate recent emission inventory data, ecosystem process-based model results, and inverse modelling estimates over the period 1990–2018. BU and TD products are compared with European National GHG Inventories (NGHGI) reported to the UN climate convention secretariat UNFCCC in 2019. For uncertainties, we used for NGHGI the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines recommendations. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model specific uncertainties when reported. In comparing NGHGI with other approaches, a key source of bias is the activities included, e.g. anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. TD total inversions estimates give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions and geological sources together account for the gap between NGHGI and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1 respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1 respectively, compared to 0.9 Tg N2O yr−1 from the BU data. The TU and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at EU+UK scale and at national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288969 (Petrescu et al., 2020).

Background and Objectives: Over the last few decades, the European mountain environment has been characterized by the progressive abandonment of agro-pastoral activities and consequent forest expansion due to secondary succession. While woody encroachment is commonly considered as a climate change mitigation measure, studies suggest a still uncertain role of the soil organic carbon (SOC) pool in contributing to climate change mitigation during this process. Therefore, the objective of the study is to investigate the possible SOC variations occurring as a consequence of the secondary succession process at the provincial level in an Alpine area in Italy. Materials and Methods: A chronosequence approach was applied to identify, in five different study areas of the Belluno province, the land use/land cover change over four different stages of natural succession, from managed grazing land to secondary forest developed on abandoned grazing land. In each chronosequence stage, soil samples were collected down to the bedrock (0&ndash, 60 cm depth) to determine the changes in the SOC stock due to the woody encroachment process. Results: In all areas, small or no significant (p &lt, 0.05) SOC stock changes were observed during the secondary succession in the upper 30 cm of mineral soil, while significant changes were evident in the 30&ndash, 60 cm compartment, with the SOC stock significantly decreasing from 30% to 60% in the final stage of the succession. This fact indicates the great importance of considering also the subsoil when dealing with land use/land cover change dynamics. Conclusions: The recorded trend in SOC has been proved to be the opposite in other Italian regions, so our results indicate the importance of local observation and data collection to correctly evaluate the soil contribution to climate change mitigation during woody encroachment.

The time for action on the Paris Agreement is upon us, requiring all signatory countries to have a robust reporting and accounting system that is transparent, accurate, complete, consistent and comparable (through the Enhanced Transparency Framework), with a periodical review of the collective achievement of the 2°C temperature goal (the Global Stocktake). The research community is therefore called to reinforce databases and methodologies to improve national greenhouse gas inventory estimates, especially for developing countries that are subject to new reporting obligations, but also to define a comparable scientific “benchmark" to assess the achievement of the Paris Agreement goal.Despite the key role of science in the process, often research communities working on emission statistics have approached the problem of climate change through different angles and by using terminologies, metrics, rules and approaches (e.g. spatial and temporal scales) that do not always match with those used by the inventory communities. Within the VERIFY project (Horizon 2020, grant agreement No 776810) a networking between the two communities (research and inventory) has been established. The discussion between them highlighted the importance of a continue exchange to increase the mutual understanding of needs, terms, rules, procedures and guidelines in use, especially those adopted under the UNFCCC and Paris Agreement process.The presentation will therefore guide the researchers through the monitoring, reporting and verification frameworks under the UNFCCC and Paris Agreement, identifying how and where science production can assist the inventory communities in improving greenhouse gasses estimations and verification systems. Land Use, Land-Use Change and Forestry is the most complicate sector to deal with because of intricacy of flux attribution (that can be both anthropogenic and non-anthropogenic) and methodological complexity, affected also by common misunderstandings in the use of terminologies and different definitions. On the basis of the available literature and the outcomes of the work undertaken under VERIFY project, we provide an analysis on the possible critical issues and main misunderstanding that could arise, identifying options on how to solve them.

Edith Wharton's novel The House of Mirth revolves around the life of Lily Bart, a woman of high society, as she begins to near the end of marriageable age. Bart encounters many obstacles throughout the novel from not being able to find a suitable husband to lagging behind her high society friends. While The House of Mirth seemingly promotes the lavish lifestyle of New York high society, it is truly a massive critique on the social structures which cripple even its most promising members — including Lily Bart.

The 2030 European Union (EU) climate and energy policy framework includes for the first time a dedicated instrument concerning greenhouse gases (GHGs) emissions and removals from land use, land-use change and forestry (LULUCF). Regulation 2018/841 (LULUCF Regulation) marks a significant expansion of EU climate and energy acquis, with ramifications in other sensitive areas, such as agriculture, renewable energy from wood biomass and forestry. The Regulation has had a particularly troubled negotiation history and has already been at the centre of litigation before the Court of Justice of the EU. This article analyses the implications of the LULUCF Regulation for the EU’s climate ambition in the next decade, trying to make sense of its troubled history and of its projected impacts, with a view to ascertaining whether the scepticism with which it has been welcomed is justified.

This chapter is part of a forthcoming commentary on the Paris Agreement. It analyses Article 5 of the Paris Agreement, focusing on sinks, reservoirs of GHGs and forests.

This is the pre-print version of the chapter"Climate change sustainable agriculture and food systems_The world after the Paris agreement"published as final paper inAchieving the Sustainable Development Goals Through Sustainable Food Systems,10 October 2019, Pages 1-262https://doi.org/10.1007/978-3-030-23969-5 lntroduction 1.1 Climate Change and Agriculture According to the fifth assessment report of the IPCC (2014), the concentrations of greenhouse gases (GHG) in the atmosphere are at the highest they have been in the past 800 thousand years. Current levels of CO2 have increased by 30% from 280 ppm in pre-industrial times to 407 ppm today (2019), and they continue to rise. Present CH4 concentrations of 2000 ppb are nearly triple their pre-industrial value of 700 ppm. N2O levels reached 328 ppb in 2019 compared with the 280 ppb of pre-industrial time. Only in the past 50 years we have doubled human population (from 4 to 7 billion), increased GHG emissions by 2.5 times, doubled freshwater withdrawal, halved the agricultural land per capita (from 1. 4 to 0.7 ha) by agricul­ture intensification. This is an unprecedented velocity of transformation that our Planet and human society had never experienced. Today, the agro-food sector alone accounts for some 80% of the world freshwa­ter use, 30% of world energy demand, and more than 12% of man-made greenhouse gas emissions worldwide, including indirect emissions such as those of deforesta­tion (Foley et al. 2011). Moreover, according to the Food and Agriculture Organization (FAO) of the United Nations, croplands and pastures occupy about 38% of Earth's terrestrial surface, the largest use of land on the planet (Foley et al. 2011). With global food production expected to increase 70% by 2050, and consid­ering the meat dietary changes, the sector is facing unprecedented resource pres­sures and strong perturbations to the climate systems. By 2050 more than nine billion of people will be in search of food and most of them (68%) will be living in mega-cities (UN DESA 2018). Under these circumstances, a substantial redefinition of the actual food supply chain is essential. Meanwhile, many rural-communities, which strongly depends on domestic-subsistence agriculture, will be exposed to food scarcity and accessibility. lndeed, in some regions of the world (i.e. tropics and part of temperate regions) increasing of climate extremes will produce adverse effects on agriculture, forestry and fisheries sectors with yield reduction of 35% in African countries and 2% globally per decade, despite the increasing food demand (Barros et al. 2014). It is time to reflect on the global agro-food systems, its para­ doxes, inequalities and capacities to support future generations. On the other side global warming can also expand the land suitability for some crops, like wheat at high latitudes (Di Paola et al. 2018). Humanity needs to act urgently and fast, push­ ing the high-level governmental agenda (SDGs, Climate Paris agreement) as well as industry sector and citizens in the most difficult and challenging transformation of our society to feed the new two billion of people expected by 2050 and, at same time, stabilize climate below 2.0° (possibly 1.5°) and reducing the pressures on natural resources. 1.2The Paris Agreement: Implications for the Agriculture Sector The significant role that agriculture can play in climate change was relatively under­ represented in the previous discussions and decisions under the frame of the United Nations Framework Convention on Climate Change (UNFCCC), before the Paris Agreement (PA) was adopted by the 21st Conference of the Parties (COP21) of the UNFCCC on the 12th of December 2015. Food security and food production are explicitly mentioned in the PA: in its pre­ amble the Parties recognize "the fundamental priority of safeguarding food security and ending hunger, and the particular vulnerabilities of food production systems to the adverse impacts of climate change", and the Artide 2 highlights the importance of "increasing the ability to adapt to the adverse impacts of climate change and foster climate resilience and low greenhouse gas emissions development, in a man­ ner that does not threaten food production". Moreover, the role of agriculture in the PA is also linked to the capacity to be a possible sink and reservoir of GHG in soils and vegetation biomass: in the Artide 5 Parties are invited to "take action to con­ serve and enhance, as appropriate, sinks and reservoirs of greenhouse gases". This opened to agriculture as a key sector, not only impacted by climate change but also with a great potential for mitigation. In fact, more in generai, the PA has the ambition to keep "the increase in the global average temperature to well below 2 °C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5 °C above pre-industrial levels, recognizing that this would significantly reduce the risks and impacts of climate change". In order to achieve this long-term temperature goal, parties are therefore committed to achieve a balance between anthropogenic emissions by sources and removals by sinks in the second half of this century. Different options are feasible and the agriculture sector can play an active role, in particular by contributing to the net emissions reduction, while guaranteeing food security. Besides, under the UNFCCC processa specific programme on agriculture was launched (the "Koronivia Joint Work on Agriculture", see Box below) that provides space and opportunities for Parties to fully engage on climate related discussions in the agriculture sector in order to foster actions related to adaptation and mitigation taking into consideration the sectors vulnerabilities. In pathways limiting global warrning to 1.5 °C with limited or no overshoot, Agriculture, Forestry and Other Land-Use (AFOLU) related carbon dioxide removal measures are projected to remove 0-5, 1-11, and 1-5 GtCO2 year- 1 in 2030, 2050, and 2100 respectively (IPCC 2018). Due to this key potential role plaid by the agri­ culture, the 80% of the Nationally Determined Contributions (NDCs) submitted by countries committed to actions on agricultural mitigation, and 90% ofNDCs selected agriculture as a priority sector for action on adaptation (CCAFS 2016; FAO 2016). Despite the inclusion of agriculture and the land sector in general in most NDC, this is still not enough to achieve the 2 °C goal and additional reduction targets are needed (Fujimori et al. 2016). According to the Fujimori et al. (2016) modelling exercise, large-scale negative CO2 emissions and land-based CO2 emissions­ reduction measures are required and the bioenergy crops need to be an important component in addition to agriculture, with an area of cropland used for bioenergy to be 24-36% of the total cropland. Indeed, the deployment of such a large-scale land­ related measures, like afforestation and bioenergy supply, can compete with food production raising not only food security concerns (IPCC 2018) but also increasing the environmental footprint, due to the increased use of natural resource (e.g. water and nitrogen for bioenergy crops) and environmental impacts (e.g. loss of biodiver­sity and increasing pollution from fertilizers). Furthermore, climate-related risks on food security are projected to increase under global warming, with projected net reductions in maize, rice, wheat and, potentially, other cereal yields in many regions of the world (IPCC 2018). Studies based on multi-model inter-comparisons (Schleussner et al. 2016) fore­ see a general crop yield reduction, particularly in tropics, for maize and wheat under increasing temperature scenarios, with more significant reductions projected at 2 °C than 1.5 °C. On the other side, local rice and soy yields are projected to increase in the tropics, as the positive effect of CO2 fertilization counterbalances the detrimen­tal impacts of climate change in the model projections (Schleussner et al. 2016). However, additional gains for warming above 1.5 °C resulted not significant, and yield reductions are expected for all the four widespread global crops. In the light of these evidences, trade-offs between mitigation and adaptation, as well as economie and environmental benefits, need to be addressed when balancing the need of land for bioenergy crops, reforestation or afforestation, versus the land needed for agricultural adaptation under a changing climate, not undermining food security, livelihoods, ecosystem functions and services and other aspects of sustain­able development.

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.

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.

Changes in soil organic carbon (SOC) as a consequence of selective logging activities are often neglected in tropical areas, even within activities that aim to promote the permanence of forest C stocks (e.g. REDD+). In this context, we assessed the magnitude of the impact of selective logging on the SOC levels in three chronosequences in Ghana, Cameroon and Gabon. In each chronosequence, from unlogged forest to forest that was selectively logged at different times in the past, adjacent plots were investigated by sampling the soil at various depths to 1 m. Both SOC concentrations and stocks drastically decrease after selective logging in all sites. The 0–5 cm depth represents the layer with the most evident SOC decreases, particularly in the first and second decades after selective logging. The SOC loss is later stabilised, but the C levels remain lower than those of the unlogged forest 45–50 years after selective logging. In all the investigated chronosequences, the SOC levels are strongly affected by selective logging and the soils continue losing C for many years. In conclusion, SOC measurements should be used to provide precise C emission-removal estimates also for forests managed using sustainable management practices.

International audience; Anthropogenic land cover changes (LCC) affect regional and global climate through biophysical variations of the surface energy budget mediated by albedo, evapotranspiration, and roughness. This change in surface energy budget may exacerbate or counteract biogeochemical greenhouse gas effects of LCC, with a large body of emerging assessments being produced, sometimes apparently contradictory. We reviewed the existing scientific literature with the objective to provide an overview of the state-of-the-knowledge of the biophysical LCC climate effects, in support of the assessment of mitigation/adaptation land policies. Out of the published studies that were analyzed, 28 papers fulfilled the eligibility criteria, providing surface air temperature and/or precipitation change with respect to LCC regionally and/or globally. We provide a synthesis of the signal, magnitude and uncertainty of temperature and precipitation changes in response to LCC biophysical effects by climate region (boreal/temperate/tropical) and by key land cover transitions. Model results indicate that a modification of biophysical processes at the land surface has a strong regional climate effect, and non-negligible global impact on temperature. Simulations experiments of large-scale (i.e. complete) regional deforestation lead to a mean reduction in precipitation in all regions, while air surface temperature increases in the tropics and decreases in boreal regions. The net global climate effects of regional deforestation are less certain. There is an overall consensus in the model experiments that the average global biophysical climate response to complete global deforestation is atmospheric cooling and drying. Observed estimates of temperature change following deforestation indicate a smaller effect than model-based regional estimates in boreal regions, comparable results in the tropics, and contrasting results in temperate regions. Regional/local biophysical effects following LCC are important for local climate, water cycle, ecosystems, their productivity and biodiversity, and thus important to consider in the formulation of adaptation policy. However before considering the inclusion of biophysical climate effects of LCC under the UNFCCC, science has to provide robust tools and methods for estimation of both country and global level effects

Forest ecosystems are characterized by high spatial heterogeneity, often related to complex composition and vertical structure which is a challenge in many process-based models. The need to expand process-based models (PBMs) to take in account such structural complexity led to development and testing of a new approach into Forest Ecosystem Models (FEMs), named 3D-CMCC-FEM, able to investigate carbon and water fluxes, including biomass pools and their partitioning, for complex multi-layer forests. 3D-CMCC FEM integrates several characteristics of the functional structural tree models and the robustness of the light use efficiency CLUE) approach to investigate forest growth patterns and yield processes. The modelling approach was tested by simulating the effects of competition for light and water, growth and yield of a two-layered deciduous forest dominated by Turkey Oak in central Italy for a period of eight years. The model outputs were validated against a series of independently measured data for the major biomass pools, the inter-annual stem increments and above-ground net primary productivity of the overstorey and understorey, respectively. The comparison of Leaf Area Index, Gross Primary Production, and evapotranspiration produced by the model against MODIS data showed agreement in results. In addition, the multi-layered model approach was evaluated against a series of simplified versions to determine whether the enhanced complexity of the model positively contributed to its predictive ability. The proposed model reduced the error in the estimates of forest productivity (e.g. NPP) and dynamics (e.g. growth, mortality) and indicates the importance of considering, as far as possible, the structural complexity in PBMs. (C) 2013 Elsevier B.V. All rights reserved.

Background and aims In the Jomoro district in Ghana, tree plantations were the first cause of deforestation in the past, drastically reducing the area occupied by primary forests. The aim of this study was to quantify soil organic carbon (SOC) losses due to a change in land use from primary forest to tree plantations (cocoa, coconut, rubber, oil palm) on the different substrates of the district. Secondary forests and mixed plantations were also included in the study.

Background: Peru has a large extension of deforested lands converted into crops of Erythroxylum coca. Methods: A local cost–benefit analysis investigates the conversion of 500 ha of coca crops into sustainable agroforestry eligible under the Kyoto Protocol (afforestation/reforestation CDM) in the Amazon. Results: From a social and environmental perspective, CDM agroforestry generates more benefits than coca monoculture, although the cost–benefit analysis results show that illegal plantations generate higher profits. In a way, CDM makes the alternative of agroforestry to illegal coca more economically attractive than the same reforestations without carbon credit, with the advantage of attracting international investors interested in environmentally sustainable projects. Conclusion: For the permanence and success of Kyoto projects, local government and the UN continue to play a major role in avoiding leakage effects and risks of narcotraffic return.

The aim of this work was to quantify the soil organic C (SOC) stock in the top 30 cm of mineral soil for the whole Italian territory, according to the different land use types of the Intergovernmental Panel on Climate Change (IPCC) cropland category (arable land, agroforestry, vineyards, olive groves, orchards and rice fields), as a basis for future land use scenarios and to address mitigation policy at country level. A database for SOC stock was created with the data from the national project denominated SIAS and partly from regional map reports. All data were referred to the year 2000 since they were derived from surveys conducted from 1995 to 2005. The data were stratified according to the Italian climatic regions, the landscape position and the IPCC cropland subcategories. Taking into account the uncertainty in the estimate, the mean SOC stock values of the different subcategories show significant differences (p

Land use inventories are sound measures to provide information on the area occupied by different land use or land cover types and their changes, although less widespread than traditional mapping; as such, they are distinctively wellestablished tools for generating statistics on the state and the dynamics of land use in the European Union. Italy has recently set up a land use inventory system (IUTI) as a key instrument for accounting removals and emissions of greenhouse gases (GHG) associated to land use, land use change and forestry (LULUCF) activities elected by Italy under the Kyoto Protocol. IUTI adopts a statistical sampling procedure to estimate the area covered by LULUCF land use categories in Italy, and associated uncertainty estimates. Estimates of land use have been so far processed for the period 1990-2008 and highlight three interlinked land use change patterns in Italy: (i) increase in forest land for a total uptake of 1.7% of the Italian territory; forest cover estimates, with a standard error of 0.1%, indicate an annual increase of forestland higher over the period 1990-2000 (32 901 ha year-1) than in 2000-2008 (22 857 ha year-1); surprisingly, also a significant deforestation rate is observed (-7000 ha year-1), due to forest land conversion mainly into artificial areas; (ii) consumption of arable land (-4.2% of the Italian territory) primarily due to land uptake by urban areas and to conversions to permanent crops (mainly orchards and vineyards); (iii) urban sprawl uptakes 1.6% of the Italian territory in this period, with a total coverage of settlements reaching 7.1% of total land surface in Italy in 2008. Overall, land use dynamic results in land uptake by forest land is of the same magnitude of land uptake by urban areas, but the effects of these processes on GHG removals (by forest sinks) and emissions (by urban areas) is expected to be significantly different. In a broader perspective, IUTI methodology, by providing reliable estimates and well-defined levels of statistical uncertainty for assessing stocks and flows of land use at national level, can be further implemented to frame other key questions for sustainable development policies, like the set up of environmental-economic accounting systems. L'articolo è disponibile sul sito dell'editore www.sisef.it

Wildfires are the most common disturbances in Mediterranean forest ecosystems that cause significant emissions of greenhouse gases as a result of biomass burning. Despite this, there is reasonably high uncertainty regarding the actual fraction of burnt biomass and the related CO2 and non-CO2 gas emissions released during forest fires. The aim of this paper is to compare existing methodologies adopted in the National Greenhouse Gas Inventory reports of five of the most fire-affected countries of southern Europe (Italy, Spain, Greece, Portugal, France) with those proposed in the literature, to operationally estimate forest fire emissions, and to discuss current perspectives on reducing uncertainties in reporting activities for the Land Use, Land Use Change and Forestry sector under the United Nations Framework Convention on Climate Change and the Kyoto Protocol. Five selected approaches have been experimentally applied for the estimation of burnt biomass in forest fire events that occurred in Italy in the period 2008–2010. Approaches based on nominal rates of biomass loss can lead to an overly conservative value or, conversely, to underestimation of the fraction of burnt biomass. Uncertainties can be greatly reduced by an operational method able to assess inter-annual and local variability of fire effects on fire-affected forest types. L'articolo è disponibile sul sito dell'editore http://www.publish.csiro.au