Forecasting and Explaining Ethanol Price Through Shapley Additive Explanations: Evidence From Two Decades of Panel Agroeconomic Data in Brazil

Abstract

Understanding the complex dynamics of the sugar–biofuel market is challenging due to the multifaceted effects of economic, agronomic, and policy-related factors, with sugar and ethanol fuel competing for the same sugarcane input. In this paper, we propose a comprehensive eXplainable Artificial Intelligence (XAI) framework to predict and interpret ethanol price formation in Brazil, leveraging a rich, multi-source agroeconomic panel database spanning nearly two decades of indicators across economic, fiscal, energy, and global commodity domains. Our approach integrates machine learning, exploratory data analysis, and XAI techniques to uncover the key drivers behind price fluctuations. Forecasting experiments demonstrate that deep learning networks show superior performance at capturing temporal dependencies and nonlinear trends from the data, while interpretability tools such as Shapley Additive Explanations and Permutation Feature Importance reveal gasoline prices, sugarcane quality, and VAT taxation as primary drivers, with inflation and exchange rates exerting moderating influences. We show that the proposed framework provides interpretable insights to support evidence-based decision-making for policymakers and industry actors in the sugar–biofuel sector.