文献类型：期刊文献

英文题名：Leverage effect of green finance on renewable energy investment based on machine learning

作者：Wang, Yue Li, Wei

第一作者：王瑜

通信作者：Li, W[1]

机构：[1]Guizhou Inst Technol, Coll Econ & Management, Guiyang 550003, Guizhou, Peoples R China;[2]Guizhou Univ Finance & Econ, Coll Big Data Applicat & Econ, Guiyang 550025, Guizhou, Peoples R China

第一机构：贵州理工学院

通信机构：corresponding author), Guizhou Univ Finance & Econ, Coll Big Data Applicat & Econ, Guiyang 550025, Guizhou, Peoples R China.

年份：2026

卷号：38

期号：1

起止页码：102.0-115.0

外文期刊名：INTERNATIONAL JOURNAL OF GLOBAL WARMING

收录：;Scopus(收录号：2-s2.0-105028430986);WOS:【SCI-EXPANDED(收录号:WOS:001663010900005)】；

语种：英文

外文关键词：green finance; renewable energy investment; REI; machine learning; leverage effect; gradient boosting decision tree

摘要：This study constructs a machine learning-econometrics hybrid framework to analyse the leverage effect of green finance on renewable energy investment (REI). Using random forests and gradient boosting decision trees, green finance is identified as the strongest driver, and a critical threshold (0.65) is determined. Panel threshold regression confirms the shift in leverage, with the marginal effect increasing from 0.28% to 0.68%. Climate risk significantly attenuates this effect, particularly in western China. Counterfactual simulations suggest that integrating green finance with carbon markets can boost REI by 22%, providing quantitative support for climate policy design.