Discovering Transition Pathways Towards Coviability with Machine Learning

TL;DR

This paper introduces a joint research project that uses machine learning, agroecology, and social sciences to identify pathways for achieving sustainable coexistence of humans and nature in vulnerable regions.

Contribution

It presents a novel interdisciplinary approach combining machine learning with social and ecological sciences to discover coviability pathways in degraded territories.

Findings

Development of a framework for coviability pathways

Identification of key socio-ecological factors

Potential for local implementation of pathways

Abstract

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.