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Community forest management (CFM) has gained prominence globally and shown great value as a community-based conservation approach to protecting and sustainably managing forest ecosystems while, at the same time, devolving tenure rights to local populations and stimulating local livelihoods and economies. Given CFM’s relative successes and socioeconomic goals, it is often assumed to be an inherently just approach. Here, we challenge that assumption. We present a framework rooted in environmental justice to uncover how some initiatives can perpetuate or exacerbate unfairness and thus undermine the core purpose and spirit of CFM. We put forward three questions on the fairness of CFM programs. First, we call to question the imposition of new CFM-related restrictions and rules, considering Indigenous and local communities’ legal autonomy and/or long-standing de facto rights. Second, we interrogate the burden of CFM-related economic costs and opportunity costs, in light of communities’ poverty conditions and vulnerable livelihoods. Third, we examine the fairness of focusing on the role of local communities in tackling deforestation and forest degradation, given these groups might not be the ones primarily responsible for those problems. Our discussion exposes several contradictions, trade-offs and justice implications of CFM that have remained largely unrecognized. We conclude by providing recommendations for a more just approach that centers a rights-needs-merit rationale. Our analysis is relevant for community-based conservation efforts around the globe. 

Nepal’s forest cover nearly doubled over the last three decades. While Community Forest (CF) management and agricultural abandonment are primary drivers of forest cover expansion, the contribution of afforestation on privately managed land is not well documented. We mapped forest cover change from 1988 through 2016 in 40 privately managed sites that transitioned from agriculture to forest and assessed how agricultural abandonment influenced private land management and afforestation. We used a mixed method analysis to integrate our 29- year Landsat satellite image-based record of annual forest cover with interview data on historical land cover and land use dynamics from 65 land managers in Bagmati Province. We find that privately managed land accounted for 37% of local forest cover gain, with mean forest area within private forests growing from 9% to 59%. Land managers identified two factors driving these gains on private land: implementation of CF man- agement in adjacent government forests and out-migration. These previously undocumented linkages between forest cover gain on private land and CF management merits further research in community forests and calls for greater policy and technical support for small-scale timber growers and rural households who rely on private forests for income generation. 

Abstract Scholars are increasingly assessing the impact of conservation interventions at national and regional scales with robust causal inference methods designed to emulate randomized control trials (quasi‐experimental methods). Although spatial and temporal data to measure habitat loss and gain with remote sensing tools are increasingly available, data to measure spatially explicit poverty and human well‐being at a high resolution are far less available. Bridging this data gap is essential to assess the social outcomes of conservation actions at scale and improve understanding of socioenvironmental synergies and trade‐offs. We reviewed the kinds of socioeconomic data that are publicly available to measure the effects of conservation interventions on poverty and well‐being, including national census data, representative household surveys funded by international organizations, surveys collected for individual research programs, and high‐resolution gridded poverty and well‐being data sets. We considered 4 challenges in the use of these data sets: consistency and availability of indicators and metrics across regions and countries, availability of data at appropriate temporal and spatial resolutions, and technical considerations associated with data available in different formats. Potential workarounds to these challenges include analytical methods to help resolve data mismatches and the use of emerging data products.