Abstract Efforts to reduce emissions from deforestation and forest degradation and enhancing forest carbon stocks (REDD+) have evolved over the past decade. Early REDD+ programs and local/subnational projects used various interventions (i.e. enabling measures, disincentives and incentives), implemented by government, the commercial and non-commercial private sector, but are currently understudied vis-a-vis their effectiveness to address site-specific drivers of deforestation and forest degradation (DD). We assess how well REDD+ interventions addressed DD at five project sites in Peru (1), Brazil (1), Vietnam (1) and Indonesia (2). Our study design includes an integrated assessment of remotely sensed, spatially modelled, and locally reported drivers. First, we observe follow-up land use from high resolution imagery as proxy for direct deforestation drivers. Second, spatial Random Forest modelling of DD drivers allows for influence quantification of topographic, climatic and proximity variables at each site. Third, we report direct and indirect DD drivers from pre-intervention surveys and semi-structured interviews with five REDD+ implementers, 40 villages and 1200 households. Data gathered included perceived changes in forest cover and quality, and their causes. We found general agreement between observed, modelled and reported local DD drivers, yet some were inadequately addressed by interventions. Intra-site differences in drivers underscores the importance of analysing micro-level DD drivers. Our interdisciplinary approach reveals the complexities of local direct and indirect DD drivers, and the complementarity of remotely sensed, spatially modelled and locally reported methods for driver identification. A better understanding of the alignment between DD drivers and REDD+ interventions is vital for practitioners and policy makers to enhance the effectiveness, efficiency, equity and co-benefits of REDD+ at the local level.

中文翻译：

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森林砍伐驱动因素的综合评估及其与地方气候变化减缓工作的一致性

摘要 在过去十年中，为减少森林砍伐和森林退化造成的排放以及增加森林碳储量 (REDD+) 所做的努力已经发生了变化。早期的 REDD+ 计划和地方/次国家级项目使用了由政府、商业和非商业私营部门实施的各种干预措施（即扶持措施、抑制措施和激励措施），但目前尚未对其解决特定地点的有效性进行研究森林砍伐和森林退化 (DD) 的驱动因素。我们评估了秘鲁 (1)、巴西 (1)、越南 (1) 和印度尼西亚 (2) 的五个项目地点的 REDD+ 干预措施如何解决 DD。我们的研究设计包括对遥感、空间建模和本地报告驱动程序的综合评估。第一的，我们从高分辨率图像中观察后续土地使用情况，作为直接森林砍伐驱动因素的代表。其次，DD 驱动因素的空间随机森林建模允许对每个站点的地形、气候和邻近变量的影响进行量化。第三，我们通过干预前调查和半结构化访谈报告了对 5 个 REDD+ 实施者、40 个村庄和 1200 户家庭的直接和间接 DD 驱动因素。收集的数据包括森林覆盖和质量的感知变化及其原因。我们发现观察到的、建模的和报告的本地 DD 驱动因素之间普遍一致，但干预措施未能充分解决一些问题。驱动程序的站点内差异强调了分析微观级别的 DD 驱动程序的重要性。我们的跨学科方法揭示了本地直接和间接 DD 驱动因素的复杂性，以及遥感、空间建模和本地报告的驾驶员识别方法的互补性。更好地理解 DD 驱动因素和 REDD+ 干预措施之间的一致性对于从业者和政策制定者在地方层面提高 REDD+ 的有效性、效率、公平性和共同利益至关重要。