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<p>This paper applies Machine Learning (ML) and explainable AI (XAI) to predict Environmental Social and Governance (ESG) controversies by using 9,609 firm-year observations from 3,194 leading companies from 27 countries in five regions, over the period of 2002 to 2023. We run Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Support Vector Regression (SVR), Lasso and Ridge regression on high dimensional dataset with firm level ESG scores, financial and governance indicators, country level governance and macroeconomic indicators, and global ESG uncertainty indicator. Our results indicate that XGBoost and LightGBM outperform other Machine Learning (ML) algorithms and linear regression in training and testing data respectively. The predictive power and accuracy remain consistent across regions, countries and sub-sample period. Application of XAI confirms that ESG combined score is the most important feature to predict ESG controversies. Moreover, ESG sub-pillar scores are also important features to predict ESG controversies in global, regional and country setting. Further analysis shows that firms with higher ESG combined scores tend to have higher ESG controversies score i.e., lower ESG controversies. However, increase in individual sub-pillar scores lead to increase ESG controversies. Importantly, higher country level governance score, firms’ financial indicators and gender diversity can also predict ESG controversies accurately and reduce it to a certain extent. We recommend a number of policy measures for investors, regulators and policy makers.</p>

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Predicting ESG Controversies: An Ensemble Machine Learning with XAI

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Md Akther Uddin,
  • Sakera Begum

摘要

This paper applies Machine Learning (ML) and explainable AI (XAI) to predict Environmental Social and Governance (ESG) controversies by using 9,609 firm-year observations from 3,194 leading companies from 27 countries in five regions, over the period of 2002 to 2023. We run Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Support Vector Regression (SVR), Lasso and Ridge regression on high dimensional dataset with firm level ESG scores, financial and governance indicators, country level governance and macroeconomic indicators, and global ESG uncertainty indicator. Our results indicate that XGBoost and LightGBM outperform other Machine Learning (ML) algorithms and linear regression in training and testing data respectively. The predictive power and accuracy remain consistent across regions, countries and sub-sample period. Application of XAI confirms that ESG combined score is the most important feature to predict ESG controversies. Moreover, ESG sub-pillar scores are also important features to predict ESG controversies in global, regional and country setting. Further analysis shows that firms with higher ESG combined scores tend to have higher ESG controversies score i.e., lower ESG controversies. However, increase in individual sub-pillar scores lead to increase ESG controversies. Importantly, higher country level governance score, firms’ financial indicators and gender diversity can also predict ESG controversies accurately and reduce it to a certain extent. We recommend a number of policy measures for investors, regulators and policy makers.