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Afforestation efforts are proliferating as states promote tree-planting to accomplish sustainable rural development and combat forest loss and climate change. China's Returning Farmland to Forest Program (RFFP), one of the world's most ambitious afforestation programs, has often been presented as a great success. However, research on the RFFP shows substantial unexplained heterogeneity. Case studies reveal divergent outcomes, but the processes behind local variation are poorly understood. This study examines mechanisms differentiating tree cover change across 12 communities. We join narrative histories with formal case comparison using fuzzy-set qualitative comparative analysis (fsQCA), which applies Boolean analysis to identify combinatorial patterns across cases, to explain variation in remotely sensed land cover change. These analyses identify distinct pathways to vegetation gain and loss linked to livelihood patterns and environmental conditions. The RFFP's contribution to forest gain depends on how local governance and environmental conditions enable different land use patterns. In particular, whether community officials act in responsive, self-serving, or perfunctory ways shapes options available to other households. Responsive governance does not have a consistent relationship with forest gain; outcomes depend on the particular activities officials enable. Impacts on land cover change of labor outmigration, livestock husbandry, and cash crop expansion are likewise contingent. To make afforestation interventions effective and sustainable, policymakers must be mindful of crosscutting factors that may impede or facilitate forest establishment. (C) 2019 Elsevier Ltd. All rights reserved.