Using machine learning to uncover synergies between forest restoration and livelihood support in the Himalayas

Abstract

In recent years, governments and international organizations have initiated numerous large-scale tree planting projects with the dual goals of restoring landscapes and supporting rural livelihoods. However, there remains a need for greater knowledge of drivers and conditions that enable positive social and environmental outcomes over the long term. In this study, we used interpretable machine learning (IML) to explore win–win and win–lose outcomes between livelihood benefits and forest cover using four decades of tree plantation data from northern India. Our results indicated that, in areas with a larger population of socioeconomically marginalized groups, moderate levels of education, and existing histories of community collective action, there is a higher probability of achieving joint positive outcomes. We also found that joint positive outcomes are more common within a consolidated local institutional space, suggesting that decentralized governance structures with cross-sectoral duties and functions may be better equipped to mediate conflicts between intersecting forest and land use challenges. Finally, our findings showed that non-forestry and anti-poverty interventions such as universal labor generation programs and universal education are associated with improved forest cover alongside livelihood benefits from plantations. Whereas contemporary policy discussions have given substantial attention to tree plantation schemes, our work suggests that effective restoration requires much more than planting alone. A broad mixture of socioeconomic, institutional, and policy interventions is needed to create favorable conditions for long-term success. In particular, anti-poverty programs may serve as important indirect policy pathways for ensuring restoration gains.

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