IHPO-XGBoost for ESG Performance Prediction: A High-Accuracy Machine Learning Framework

ESG Governance, ESG Environmental, and ESG Social and Energy Consumption were significant predictors of ESG performance. In this work, a machine learning model is introduced to forecast company Environmental, Social and Corporate Governance (ESG) performance as part of a sustainable business strategy. We used a simulated dataset created around <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1, 0 0 0}$</tex> companies, in nine industries and seven regions (period 2015-2025) for monitoring model development. Three models were applied: a Dummy Regressor baseline, the XGBoost with Improved Hunter-Prey Optimization (IHPO) and the Grey Wolf Optimization (GWO) tuned model Random Forest. The IHPO-XGBoost performed the best, with an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{\mathbf{2}}$</tex> of 0.9982 and RMSE of 0.6479. Significant predictors of ESG performance were ESG Governance, ESG Environmental AND ESG Social and Energy Consumption. This results demonstrate the benefits of bio-inspired optimization for improved predictive accuracy, and provide valuable implications for managers intending to infuse ESG considerations in business strategy.