Predicting ESG Scores Using Machine Learning for Data-Driven Sustainable Investment

Environmental, social and governance (ESG) metrics increasingly inform sustainable investment yet suffer from inter-rater heterogeneity and incomplete reporting, limiting their utility for forward-looking allocation. In this study, we developed and validated a two-level stacked-ensemble machine-learning framework to predict total ESG risk scores for S&P 500 firms using a comprehensive feature set comprising pillar sub-scores, controversy measures, firm financials, categorical descriptors and geospatial environmental indicators. Data pre-processing combined median/mean imputation, one-hot encoding, normalization and rigorous feature engineering; models were trained with an 80:20 train–test split and hyperparameters tuned by k-fold cross-validation. The stacked ensemble substantially outperformed single-model baselines (RMSE = 1.006, MAE = 0.664, MAPE = 3.13%, R2 = 0.979, CV_RMSE_Mean = 1.383, CV_R2_Mean = 0.957), with LightGBM and gradient boosting as competitive comparators. Permutation importance and correlation analysis identified environmental and social components as primary drivers (environmental importance = 0.41; social = 0.32), with potential multicollinearity between component and aggregate scores. This study concludes that ensemble-based predictive analytics can produce reliable, actionable ESG estimates to enhance screening and prioritization in sustainable investment, while recommending human review for extreme predictions and further work to harmonize cross-provider score divergence.