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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.

This article discusses the importance of quality deforestation area estimates for reliable and credible REDD+ monitoring and reporting. It discusses how countries can make use of global spatial tree cover change assessments, but how considerable additional efforts are required to translate these into national deforestation estimates. The article illustrates the relevance of countries’ continued efforts on improving data quality for REDD+ monitoring by looking at Mexico, Cambodia, and Ghana. The experience in these countries show differences between deforestation areas assessed directly from maps and improved sample-based deforestation area estimates, highlighting significant changes in both magnitude and trend of assessed deforestation from both methods. Forests play an important role in achieving the goals of the Paris Agreement, and therefore the ability of countries to accurately measure greenhouse gases from forests is critical. Continued efforts by countries are needed to produce credible and reliable data. Supporting countries to continually increase the quality of deforestation area estimates will also support more efficient allocation of finance that rewards REDD+ results-based payments.

Background The reliable monitoring, reporting and verification (MRV) of carbon emissions and removals from the forest sector is an important part of the efforts on reducing emissions from deforestation and forest degradation (REDD+). Forest-dependent local communities are engaged to contribute to MRV through community-based monitoring systems. The efficiency of such monitoring systems could be improved through the rational integration of the studies at permanent plots with the geospatial technologies. This article presents a case study of integrating community-based measurements at permanent plots at the foothills of central Nepal and biomass maps that were developed using GeoEye-1 and IKONS satellite images. Results The use of very-high-resolution satellite-based tree cover parameters, including crown projected area (CPA), crown density and crown size classes improves salience, reliability and legitimacy of the community-based survey of 0.04% intensity at the lower cost than increasing intensity of the community-based survey to 0.14% level (2.5 USD/ha vs. 7.5 USD/ha). Conclusion The proposed REDD+ MRV complementary system is the first of its kind and demonstrates the enhancement of information content, accuracy of reporting and reduction in cost. It also allows assessment of the efficacy of community-based forest management and extension to national scale. Electronic supplementary material The online version of this article (10.1186/s13021-017-0087-8) contains supplementary material, which is available to authorized users.

The Dzanga-Sangha landscape consists of a national park surrounded by production forest. It is subject to an integrated conservation and development project (ICDP). In collaboration with the ICDP personnel, a participatory model was constructed to explore wildlife conservation and industrial logging scenarios for the landscape. Three management options for the landscape's production forest were modelled: (I) 'predatory logging', exploitation by a logging company characterised by a lack of long-term plans for staying in the landscape, (II) sustainable exploitation by a certified logging company, and (III) conservation concession with no commercial timber harvesting. The simulation outcomes indicate the extreme difficulties to achieve progress on either conservation or development scenarios. Both logging scenarios give best outcomes for development of the local population. However, the depletion of bushmeat under the predatory logging scenario negatively impacts the population, especially the BaAka pygmy minority who most strongly depend on hunting for their income. The model suggests that conservation and development outcomes are largely determined by the level of economic activity, both inside and outside the landscape. Large investments in the formal sector in the landscape without any measures for protecting wildlife (Scenario I) leads to some species going nearly extinct, while investments in the formal sector including conservation measures (Scenario II) gives best outcomes for maintaining wildlife populations. The conservation concession at simulated investment levels does not reduce poverty, defined here in terms of monetary income. Neither does it seem capable of maintaining wildlife populations since the landscape is already filled with settlers lacking economic opportunities as alternatives to poaching, We would like to thank the Livelihoods and Landscapes initiative of the International Union for Conservation of Nature (IUCN) and the Center for International Forestry Research (CIFOR) for financing part of this research

At the heart of our disagreement with Dudley et al. (2008) is what constitutes participatory modeling. For us, participatory modeling can be defined by a number of elements, including types of stakeholders engaged, and degree of engagement with those stakeholders. Another element of disagreement probably centers on the continuum from models as predictive tools to model as tools to explore scenarios. Part of the disagreement is tied up in different approaches to “soft” variables.

The severe tensions between conservation and development are illustrated by events in Malinau Dstrict (Kalimantan, Indonesia). Conservationists decry proposed plans for logging and conversion of pristine tropical forest to oil palm (Elaeis guineensis). Although the local government is willing to declare the district a "conservation district," at the same time, it shows interest in oil palm conversion. This article explores the impact of the potential conversion of 500 000 ha of forest to oil palm on forest cover, in-migration, and the local economy in Malinau. The simulation model was developed using STELLA® software, and relies on a combination of empirical data, data from the literature, and stakeholder perceptions. If a company were to clear the forest for timber without planting oil palm (as commonly happens), poverty levels are likely to rise rather than decline over the long term. If large-scale oil palm plantations were to be established, they could yield significant benefits to local authorities. However, such development would induce massive employment-driven migration, with wide-ranging consequences for the current inhabitants of the region. By visualizing and quantifying these trade-offs between conservation and development, the model stimulates debate and information exchange among conservationists, development actors, and district authorities so that well-informed choices can be made.

There is strong interest in gaining an informed view of changes likely to occur in forest area and the impacts of these changes on production forestry and forest conservation. Despite the complexity of underlying causes, it is largely accepted that deforestation is mainly focused in the tropics and driven by conversion to agriculture. Similarly, energy demand and GDP are largely determining wood consumption and production. Based on these assumptions, we built a model predicting natural forests and planted forests’ evolution in the next 15 years, and compared the results of the modelling with survey results from country expertise. The results suggest that on a global level, forest resources loss is likely to slow down. The forests that are most at risk of conversion were clearly identified within the tropical domain, while the forest under protected areas showed very little risk of being converted to other land uses in the near future.