To what extent can spreadsheets shape sustainability? A machine learning approach to ESG score prediction

Abstract

This study examines whether corporate ESG ratings can be predicted using multi-year lagged financial indicators. The objective is to evaluate the extent to which historical financial information explains ESG performance among Slovak manufacturing firms. The analysis uses a sample of 974 Slovak manufacturing firms with ESG ratings for 2023 and financial data from 2018–2022. The primary model applies XGBoost with recursive feature elimination and SHAP analysis, while a PCA-based one-vs-rest XGBoost model is used as a robustness check. The findings show that historical financial indicators provide meaningful but incomplete information about ESG performance. Leverage, liquidity, debt-servicing capacity, firm age, and tax-related indicators emerge as important predictors. Older lagged variables also remain significant, suggesting that ESG outcomes reflect longer-term financial patterns. The alternative model improves prediction, particularly for the Environmental and Governance pillars, whereas the aggregate ESG score remains more difficult to classify. The models predict the middle ESG rating category most reliably, while performance for extreme categories is limited by class imbalance and the ordinal structure of ESG scores. The study concludes that financial data can support ESG prediction, especially at the pillar level, but cannot replace qualitative and ESG-specific information.