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Sustainable forest transitions: A new analytical framework to understand social and ecological outcomes of reforestation

Urheber*innen

Oldekop,  Johan A.
External Organizations;

Devenish,  Katie
External Organizations;

Alencar,  Lucas
External Organizations;

Erbaugh,  James T.
External Organizations;

Hernández-Montilla,  Mariana
External Organizations;

/persons/resource/Sreeja.Jaiswal

Jaiswal,  Sreeja
Potsdam Institute for Climate Impact Research;

Khuu,  Duong T.
External Organizations;

Mansourian,  Stephanie
External Organizations;

Meyfroidt,  Patrick
External Organizations;

Nofyanza,  Sandy
External Organizations;

Pritchard,  Rose
External Organizations;

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Zitation

Oldekop, J. A., Devenish, K., Alencar, L., Erbaugh, J. T., Hernández-Montilla, M., Jaiswal, S., Khuu, D. T., Mansourian, S., Meyfroidt, P., Nofyanza, S., Pritchard, R. (2025): Sustainable forest transitions: A new analytical framework to understand social and ecological outcomes of reforestation. - One Earth, 8, 5, 101248.
https://doi.org/10.1016/j.oneear.2025.101248


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_32989
Zusammenfassung
Restoring forests is key to addressing the climate and biodiversity crises and can benefit forest-dependent communities. However, frequent social and ecological trade-offs between these goals pose significant challenges for forest restoration efforts. Our understanding of how to maximize positive social and ecological restoration outcomes is hindered by the absence of a social-ecological theory of forest restoration. We present a new analytical “sustainable forest transitions” framework to study the joint social and ecological outcomes of reforestation drivers. Our framework advances forest transition theory, the main existing framework for understanding reforestation drivers, by incorporating social outcomes and a wider set of ecological outcomes, paying particular attention to interactions between drivers and the sociopolitical contexts in which they operate. Advances in data availability, computing power, and causal inference methods allow our framework to be operationalized. Doing so could inform forest restoration actions that maximize benefits for climate, biodiversity, and forest-dependent communities.