Can ESG disclosure predict carbon risk? Evidence from machine and deep learning models

■ The study assessed ESG disclosure's ability to predict carbon risk amid evolving regulatory standards. ■ We applied machine learning, deep learning, and ensemble models for prediction. ■ The study found environmental disclosure is the strongest predictor via SHAP interpretability. ■ We offered a data-driven framework to support ESG decision-making for investors and regulators. As ESG disclosure becomes increasingly shaped by shifting regulatory agendas, its role in predicting carbon risk warrants closer examination. This study employs machine learning, deep learning, and ensemble techniques to assess whether ESG and financial indicators can effectively predict carbon risk. Results demonstrate that advanced AI models significantly outperform traditional regressions by capturing complex, non-linear relationships often overlooked by conventional methods. SHAP analysis further identifies environmental disclosure as the most influential predictor. These findings offer a timely, data-driven framework for investors, firms, and policymakers navigating the uncertainties of evolving sustainability reporting standards.