气候变化领域集成服务门户(CCPortal): Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models

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DOI	10.1016/j.techfore.2021.121255
	Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models
	Yu, Baojun; Li, Changming; Mirza, Nawazish; Umar, Muhammad
发表日期	2022
ISSN	0040-1625
EISSN	1873-5509
卷号	174
英文摘要	Maintaining low carbon energy transitions is a phenomenon that is critical in curtailing greenhouse emissions. However, such shifts usually warrant incremental capital expenditures, which require an uninterrupted access to financing. Credit ratings are an essential consideration of the financing process. In this paper, we assess the ability of various machine learning models, in order to forecast the credit ratings of eco-friendly firms. For this purpose, we have employed a sample of 355 Eurozone firms that are ranked on the basis of the extent of their climate change score by SDP, between the years spanning from 2010 to 2019. The study uses various machine learning methods, and the findings suggest that classification and regression trees have the most precision for the credit rating predictions. Even when the forecasting was constrained to the investment grades, speculative grades, or default categories, the accuracy remained robust. The results also suggest that a random forest ensemble can be used alongside the regression trees in order to predict default or near default ratings. Given that such firms face dynamic risk exposure towards environmental, ecological, and social factors, these results have important implications that can be taken into consideration when assessing the credit risk of pro-ecological firms.
英文关键词	Carbon neutrality; Low carbon transitions; Machine learning; Credit ratings
语种	英语
WOS研究方向	Business ; Regional & Urban Planning
WOS类目	Social Science Citation Index (SSCI)
WOS记录号	WOS:000711381400023
来源期刊	TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281448
作者单位	Jilin University; University of Central Punjab
推荐引用方式 GB/T 7714	Yu, Baojun,Li, Changming,Mirza, Nawazish,et al. Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models[J],2022,174.
APA	Yu, Baojun,Li, Changming,Mirza, Nawazish,&Umar, Muhammad.(2022).Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models.TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE,174.
MLA	Yu, Baojun,et al."Forecasting credit ratings of decarbonized firms: Comparative assessment of machine learning models".TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 174(2022).