Discovering Transition Pathways Towards Coviability with Machine Learning
This addresses the problem of achieving sustainable human-nature coexistence for vulnerable populations, but it appears incremental as it applies existing methods to a new domain.
The paper tackles the challenge of transitioning to coviable socio-ecological states in degraded territories by combining machine learning with agroecology and social sciences to discover pathways for local implementation in North-East Brazil, with no concrete results reported as it is an ongoing project.
Coviability refers to the multiple socio-ecological arrangements and governance structures under which humans and nature can coexist in functional, fair, and persistent ways. Transitioning to a coviable state in environmentally degraded and socially vulnerable territories is challenging. This paper presents an ongoing French-Brazilian joint research project combining machine learning, agroecology, and social sciences to discover coviability pathways that can be adopted and implemented by local populations in the North-East region of Brazil.