The Financial Materiality of ESG in the Transport and Logistics Sector: A Multi-Method Analysis of Risk, Return, and Dynamic Linkages

This study investigates the financial materiality of Environmental, Social, and Governance (ESG) performance in the global transport and logistics sector. Recognizing the limitations of single-method approaches and the sector's unique characteristics, an innovative, integrated methodology is employed. Panel regression reveals that aggregate ESG scores significantly correlate with higher excess returns, while individual ESG pillars do not. Vector Autoregression (VAR) analysis demonstrates dynamic interdependencies and spillover effects between ESG portfolios and market factors, supported by bidirectional Granger causality. Volatility modeling using GARCH-family techniques indicates that ESG-leading firms exhibit enhanced resilience, characterized by the absence of a significant leverage effect in volatility. Critically, explainable machine learning (XGBoost with SHAP values) uncovers differentiated return drivers: the Medium-ESG portfolio is primarily influenced by market factors, while the High-ESG portfolio's returns are driven by profitability and momentum, suggesting superior dynamic capabilities. These findings provide crucial, practical insights for corporate strategy and investment decisions, advancing theoretical understanding by linking ESG to financial performance, resilience, and adaptive capacity in this vital industry.