Using Machine Learning to Protect Land and Resources

Machine learning promises to advance analysis of the social and ecological impacts of forest and other natural resource policies around the world. However, realizing this promise requires addressing a number of challenges characteristic of the forest sector. Forests are complex social-ecological systems (SESs) with myriad interactions and feedbacks potentially linked to policy impacts
People Impacted
$ 31B
Potential Funding
I have this challenge
the problem
Nature and Context

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.

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