Leveraging Machine Learning to Evaluate the ESG Performance of Listed and OTC Firms in a Small Open Economy

This study investigates the predictability of Environmental, Social, and Governance (ESG) performance using financial fundamentals within the context of Taiwan, a prominent small open economy integrated into global value chains. As global markets transition toward mandatory sustainability reporting, identifying the financial ante-cedents of ESG outcomes is critical for risk management and regulatory oversight. Uti-lizing a decade of firm-level data (2014–2023) from the Taiwan Economic Journal (TEJ), we employ supervised machine learning (ML) architectures-including Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost)-to classify firms into ESG performance tiers based on indicators such as profitability, valuation, and scale. Our empirical results provide robust support for the Slack Resources Hypothesis, identifying Return on Assets (ROA) and Firm Size (SIZE) as the most consistent predictors of ESG excellence across the semiconductor, cement, and steel sectors. Conversely, mar-ket-based indicators (Tobin’s Q) dominate predictive models for the financial industry. Methodologically, XGBoost delivers superior predictive calibration for the financial sector, while Decision Trees offer highly interpretable threshold-based logic for risk screening. Our study contributes a transparent “early-warning” framework, enabling investors and regulators to identify sustainability risks through auditable financial benchmarks. The findings suggest that while financial latitude is a structural prerequisite for ESG engagement, it is not its sole determinant, pointing toward a “virtuous circle” of financial health and managerial quality.