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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 over the period 2000–2020, largely determined by a sink on forest land (-6.4 Gt CO2/yr), followed by source from deforestation (+4.4 Gt CO2/yr) and minor fluxes from organic soils (+0.9 Gt CO2/yr) and other land uses (-0.6 Gt CO2/yr). Furthermore, we compare our NGHGI database with two other sets of country-based data: those included in the UNFCCC GHG data interface, and those based on forest resources data reported by countries to FAO and used as inputs into estimates of GHG emissions in FAOSTAT. The first dataset, once gap-filled as in our study, results in a net global LULUCF sink of -5.4 Gt CO2/yr. The difference with the NGHGI database is in this case mostly explained by more updated and comprehensive data in our compilation for non-Annex I countries. The FAOSTAT GHG dataset instead estimates a net global LULUCF source of +1.1 Gt CO2/yr. In this case, most of the difference to our results is due to a much greater forest sink for non-Annex I countries in the NGHGI database than in FAOSTAT. The difference between these datasets can be mostly explained by a more complete coverage in the NGHGI database, including for non-biomass carbon pools and non-forest land uses, and by different underlying data on forest land. The latter reflects the different scopes of the country reporting to FAO, which focuses on area and biomass, and to UNFCCC, which explicitly focuses on carbon fluxes. Bearing in mind the respective strengths and weaknesses, both our NGHGI database and FAO offer a fundamental, yet incomplete, source of information on carbon-related variables for the scientific and policy communities, including under the Global Stocktake. Overall, while the quality and quantity of the LULUCF data submitted by countries to the UNFCCC significantly improved in recent years, important gaps still remain. Most developing countries still do not explicitly separate managed vs. unmanaged forest land, a few report implausibly high forest sinks, and several report incomplete estimates. With these limits in mind, the NGHGI database presented here represents the most up-to-date and complete compilation of LULUCF data based on country submissions to UNFCCC. Data from this study are openly available via the Zenodo portal (Grassi et al. 2022), at https://doi.org/10.5281/zenodo.6390739.

SUMMARY This study explores how deforestation relates to biophysical and socio-economic variables in a quantitative, spatially explicit analysis. Both patterns of historical (approximately 4000 B.C. — 2000 A.D.) and recent (1990–2005) deforestation were explored and compared. The study uses location analysis, combining spatially explicit global databases of deforestation with variables hypothesized to relate to deforestation (e.g. rural livestock density, cost-distance). The results show that historical and recent deforestation show similar patterns when plotted against the selected variables suggesting the relations to be empirical. The strongest relations were between deforestation and rural population density, cost-distance and crop suitability respectively. These findings support the hypothesis that agricultural expansion was the largest direct cause of deforestation historically and it continues to be the largest direct cause today. While crop suitability showed a strong correlation with deforestatio...

The need to provide transparent and reliable Greenhouse Gas (GHG) emission estimates is strongly emphasized in the context of international reporting under the United Nations Framework Convention on Climate Change (UN-FCCC) and the Paris Agreement. Yet it is difficult to find specific guidance about what information is really needed to evaluate the quality of the emission factors or activity data used for GHG emission estimates. The most commonly used indicator of the reliability of an estimation procedure (and one of the few indicators explicitly mentioned in the 2006 IPCC guidelines) is the so-called confidence interval, usually at a confidence level of 90% or 95%. This interval, however, is unlikely to be a meaningful indicator of the quality of the estimate, if not associated with additional information about the estimation and survey procedures (such as on the sampling design, measurement protocols or quality control routines, among others). We provide a review of the main sources of error that can have an impact on the precision and accuracy of the estimation of both emission factors and activity data and a list of the essential survey features that should be reported to properly evaluate the quality of a GHG emission estimate. Such list is also applicable to the reporting of national forest inventories and of area estimation of activity data, and includes the case in which confidence intervals are obtained using error propagation techniques.

This study explores how conservation and development are interlinked and quantifies their reciprocal trade-offs. It identifies interventions which hold a promise to improve both conservation and development outcomes. The study finds that development trajectories can either be at the cost of conservation or can benefit conservation, but in all cases sustained poverty negatively affects conservation in the long term. Most scenarios with better outcomes for conservation come at a cost for development and the financial benefits of payments for environmental services (PES) are not sufficient to compensate for lost opportunities to earn cash. However, implementation of strategies for reducing emissions from deforestation and forest degradation in locations with low population densities come close to overcoming opportunity costs. Environmental services and subsistence income enhance the attractiveness of conservation scenarios to local people and in situations where these benefits are obvious, PES may provide the extra cash incentive to tip the balance in favor of such a scenario. The paper stresses the importance of external factors (such as industrial investments and the development of the national economy) in determining landscape scale outcomes, and suggests a negotiating and visioning role for conservation agencies.

Addressing the conflict between biodiversity conservation and enhancing human well-being is a complex exercise that must draw upon social, economic, cultural and biophysical perspectives. Project interventions seeking to achieve a ‘balance’ between conservation and development whereby biodiversity is conserved and people's livelihoods improve need to address these broad contextual issues. However, most attempts fail because they are not based upon understanding of system complexity, they assume “win-win” situations, they fail to plan for the impacts of society-wide changes happening beyond the project landscapes, and they use top-down – planning – approaches. In this paper we explore the use of system dynamics modelling for investigating trade-offs and synergies in landscape-scale approaches to conservation and development. We advocate the use of ‘scoping models’ because of their ability to incorporate complexity and promote social-learning in a participatory environment, while increasing the capacity of local actors to manage social-ecological complexity. We discuss their role in facilitating change in policies and practice in three landscapes in the tropics. Models do not provide a magic bullet solution and should not be used in isolation. They must be deployed along side other participatory and technical tools appropriate to local conditions.

Strategies for reducing emissions from deforestation and forest degradation (REDD) could become an important part of a new agreement for climate change mitigation under the United Nations Framework Convention on Climate Change. We constructed a system dynamics model for a cocoa agroforest landscape in southwestern Ghana to explore whether REDD payments are likely to promote forest conservation and what socio-economic implications would be. Scenarios were constructed for business as usual (cocoa production at the expense of forest), for payments for avoided deforestation of old-growth forest only and for payments for avoided deforestation of all forests, including degraded forest. The results indicate that in the short term, REDD is likely to be preferred by farmers when the policy focuses on payments that halt the destruction of old-growth forests only. However, there is the risk that REDD contracts may be abandoned in the short term. The likeliness of farmers to opt for REDD is much lower when also avoiding deforestation of degraded forest since this land is needed for the expansion of cocoa production. Given that it is mainly the wealthier households that control the remaining forest outside the reserves, REDD payments may increase community differentiation, with negative consequences for REDD policies.

Integrated conservation and development projects (ICDPs) have had limited success in addressing the often conflicting objectives of conservation and development. We developed a model with local participants to explore the trade-offs between conservation and development in southeastern Cameroon, where illegal hunting is regarded as the greatest challenge to conservation. We simulated the effects of different ICDP strategies by varying the degree of focus on antipoaching activities, anticorruption measures and direct development investments, and by varying the overall budget for such activities. Our outcome variables were numbers of selected wildlife species and household incomes. The model outcomes from the different scenarios were used to stimulate debate among stakeholders. Contributing to poverty alleviation while maintaining current animal population sizes will be extremely difficult and will require long-term external financial support. Devoting greater attention to improving local environmental governance emerged as the highest priority for this investment. We used the model outputs to inform some of the major policy makers in the region. Participatory modeling is a valuable means of capturing the complexities of achieving conservation at landscape scales and of stimulating innovative solutions to entrenched problems.

"Conservation organizations are increasingly turning to landscape approaches to achieve a balance between conservation and development goals. We use six case studies in Africa and Asia to explore the role of participatory modeling with stakeholders as one of the steps towards implementing a landscape approach. The modeling was enthusiastically embraced by some stakeholders and led to impact in some cases. Different stakeholders valued the modeling exercise differently. Noteworthy was the difference between those stakeholders connected to the policy process and scientists; the presence of the former in the modeling activities is key to achieving policy impacts, and the latter were most critical of participatory modeling. Valued aspects of the modeling included stimulating cross-sector strategic thinking, and helping participants to confront the real drivers of change and to recognize trade-offs. The modeling was generally considered to be successful in building shared understanding of issues. This understanding was gained mainly in the discussions held in the process of building the model rather than in the model outputs. The model itself reflects but a few of the main elements of the usually rich discussions that preceded its finalization. Problems emerged when models became too complex. Key lessons for participatory modeling are the need for good facilitation in order to maintain a balance between 'models as stories' and technical modeling, and the importance of inviting the appropriate stakeholders to achieve impact."