Disentangling and hedging global warming risk: A machine learning approach
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gp-bibliography.bib Revision:1.8444
- @Article{Ding:2025:eiar,
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author = "Shusheng Ding and Tianxiang Cui and Anna Min Du and
John W. Goodell and Nanjiang Du",
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title = "Disentangling and hedging global warming risk: A
machine learning approach",
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journal = "Environmental Impact Assessment Review",
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year = "2025",
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volume = "115",
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pages = "107987",
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keywords = "genetic algorithms, genetic programming, Climate
finance, Extreme gradient boosting, Global warming
risk, Greenhouse gas emission",
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ISSN = "0195-9255",
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URL = "
https://www.sciencedirect.com/science/article/pii/S0195925525001842",
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DOI = "
doi:10.1016/j.eiar.2025.107987",
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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