Using Machine Learning to Protect Land and Resources
Community-based forest management relies on the involvement of local communities in resource governance to improve social and ecological outcomes. National and international funding organizations spend millions of dollars every year to promote community participation in forest management. Such initiatives include formation of local community institutions and downward transfer of forest resource tenure to enhance and protect forest resources. Evidence of the success of such community-based interventions is mixed and highly heterogeneous. Understanding how forest management policies perform, and which social and ecological contexts are more conducive for the success of these policies over the long term is critical to enhancing their effectiveness. Recent machine learning–based approaches are especially promising for building such knowledge.