A Cross-Country Study of Governance and Environmental Sustainability Using Machine Learning

This study investigates the role of governance quality, human development, macroeconomic conditions, and energy structure in shaping CO 2 emissions and carbon intensity across countries. Despite extensive research, existing studies often analyze these factors in isolation and rely on linear models that fail to capture nonlinear relationships. To address this gap, this study applies a machine learning approach using a coarse decision tree model on an unbalanced panel dataset covering 195 countries from 1996 onward. The results reveal that governance quality is the most significant predictor of CO 2 emissions, followed by energy structure, human development, and macroeconomic factors. The findings highlight strong nonlinear and threshold effects, suggesting that improvements in institutional quality and energy systems significantly reduce emissions beyond critical levels. This study contributes by providing a unified, data-driven framework for cross-country environmental analysis and offers policy-relevant insights for achieving sustainable development.