Disentangling and hedging global warming risk: A machine learning approach

Ding, Shusheng; Cui, Tianxiang; Du, Anna Min; Goodell, John W.; Du, Nanjiang

Abstract

As global warming provokes increasing attention from investors, this study disentangles global warming risk (GWR) for investors by leveraging energy futures volatilities. This study derives GWR from energy futures using an extreme gradient boosting (XGB)-genetic programming (GP) framework. Our XGB-GP framework develops volatility forecasting models for GWR from selected energy futures markets identified by XGB as key contributors to global warming, surpassing traditional models in forecasting accuracy. The originality of the study rests on the pioneering integration of the XGB-GP framework in predicting climate risk, linking energy futures markets with climate risk management and enabling feasible climate-featured portfolio hedging. Our study also sheds new insights for policymakers to design carbon trading systems and carbon pricing mechanisms, as they can use relevant energy futures prices as a basis for carbon trading calibration.

Citation

Ding, S., Cui, T., Du, A. M., Goodell, J. W., & Du, N. (2025). Disentangling and hedging global warming risk: A machine learning approach. Environmental Impact Assessment Review, 115, Article 107987. https://doi.org/10.1016/j.eiar.2025.107987

• Journal Article Type: Article
• Acceptance Date: May 11, 2025
• Online Publication Date: May 14, 2025
• Publication Date: 2025-08
• Deposit Date: May 15, 2025
• Publicly Available Date: May 15, 2025
• Journal: Environmental Impact Assessment Review
• Print ISSN: 0195-9255
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 115
• Article Number: 107987
• DOI: https://doi.org/10.1016/j.eiar.2025.107987
• Public URL: http://researchrepository.napier.ac.uk/Output/4291637