The oil and gas industry plays a pivotal role in the global energy and financial markets. With increasing concerns surrounding Environmental, Social, and Governance scores, their impact on this sector has become a growing area of focus. This study aims to forecast Environment, Social, and Governance scores in the oil and gas sector using extensive datasets comprising publicly available financial and Environment, Social, and Governance indicators of firms. The research analysed data from 497 companies within the industry over 12 years. A total of 11 machine learning algorithms were utilised to predict Environment, Social, and Governance scores, including Decision Tree, three ensemble methods (Boosting, Bagging, and Voting), XGBoost, LightGBM, Random Forest, Extreme Random Trees, linear regression, robust linear regression, and Elastic Net. The analysis incorporated a one-year lag in Environment, Social, and Governance scores and employed panel data regression techniques in machine learning. The findings demonstrated a high predictive performance, with the best R² value reaching 0.922. These results provide a practical framework for investors and decision-makers to evaluate a firm’s Environment, Social, and Governance performance, facilitating more informed and sustainable investment decisions within the oil and gas sector. © 2026, International Centre for Sustainable Development of Energy, Water and Environment Systems SDEWES. All rights reserved.

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