ESG Risk in Global Supply Chains: Evidence from Network-Based AI Models

Environmental, social, and governance (ESG) risk is increasingly shaped by the relationships firms maintain within global supply chains. However, most ESG assessment approaches still treat firms as independent entities, overlooking how sustainability risks can spread across interconnected supplier–buyer networks. This study approaches ESG risk as a network-driven phenomenon rather than a purely firm-level outcome. Drawing on a large international dataset that combines supply-chain linkages, ESG incident data, and ESG-related news sentiment, the study examines whether incorporating network structure improves ESG risk assessment. The analysis integrates network-based econometric models with machine learning and graph-based approaches, and compares their performance with traditional firm-level models. The results show that ESG risk tends to cluster among connected firms, and that companies occupying central or intermediary positions within supply chains are more exposed to ESG incidents. In addition, negative ESG-related media sentiment provides an early signal of future ESG controversies. Models that explicitly account for network structure consistently outperform conventional approaches in both predictive accuracy and probability calibration. Thus far, the findings highlight the importance of considering supply-chain interdependencies when assessing ESG risk and demonstrate how network-based AI models can enhance the monitoring and prediction of sustainability risks in global production systems.