Datensatz

Zusammenfassung

Forest restoration has become a key global environmental policy objective, underscoring the need for cost-effective approaches capable of operating at large scales. In this context, Artificial Intelligence (AI) is increasingly seen as a disruptive tool for forest restoration, offering new opportunities to enhance precision, efficiency, and scalability. However, its application entails substantial technical challenges, including uneven and incomplete data availability across regions, which often leads to simplified representations of ecosystem dynamics. Beyond these, governance-related risks, such as limited transparency of models, high entry costs, capacity barriers, and persistent power imbalances, may undermine both the effectiveness and legitimacy of outcomes. Here, we argue that the effectiveness of AI applications in restoration ultimately hinges on governance systems capable of addressing these challenges and delivering equitable benefit-sharing. To this end, we propose development of a governance framework built on three pillars: (1) participatory knowledge integration, embedding plural knowledge systems into AI design and use; (2) multi-scalar policy alignment, linking international commitments with national and local priorities; and (3) polycentric governance, promoting distributed stewardship and access to innovation across multiple centers of authority. This framework provides a basis for researchers, practitioners, and policymakers to critically assess and guide AI applications in forest restoration efforts.