Sustainable Territorial Planning Based on Agroecology and Energy Matrix-Conditioned Transition Modeling

Abstract

Abstract

The transition from conventional agriculture to regenerative systems represents one of the greatest contemporary challenges in the face of climate, ecological, and food crises. This study proposes a first-order Markov chain model, conditioned by energy indicators, to represent and simulate agroecological transitions in Brazil between 2010 and 2023. The transition matrix was parameterized based on three structural variables: renewable energy consumption, fossil fuel use, and energy depletion relative to gross national product. The results indicate a progressive decline in conventional agriculture and a significant increase in consolidated regenerative agriculture, particularly in contexts with higher shares of renewable sources. The modeling revealed that transitions occur sequentially, moving through intermediate stages and being strongly influenced by the energy structure. The model was statistically validated and demonstrated high sensitivity to decarbonization incentive policies. The proposed approach contributes to sustainable territorial planning by integrating energy and agroecological variables, offering a robust tool to support public policies and ecological transition strategies in rural territories.