Disentangling and hedging global warming risk: A machine learning approach

Created by W.Langdon from gp-bibliography.bib Revision:1.8434

@Article{Ding:2025:eiar,

• author = "Shusheng Ding and Tianxiang Cui and Anna Min Du and John W. Goodell and Nanjiang Du",
• title = "Disentangling and hedging global warming risk: A machine learning approach",
• journal = "Environmental Impact Assessment Review",
• year = "2025",
• volume = "115",
• pages = "107987",
• keywords = "genetic algorithms, genetic programming, Climate finance, Extreme gradient boosting, Global warming risk, Greenhouse gas emission",
• ISSN = "0195-9255",
• URL = "https://www.sciencedirect.com/science/article/pii/S0195925525001842",
• DOI = "doi:10.1016/j.eiar.2025.107987",
• 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",

}

Genetic Programming entries for Shusheng Ding Tianxiang Cui Anna Min Du John W Goodell Nanjiang Du

Citations