Heterogeneous Effects of Green Finance on Urban Decarbonization: Evidence from 285 Cities in China
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Computer Science > Machine Learning
Title:Heterogeneous Effects of Green Finance on Urban Decarbonization: Evidence from 285 Cities in China
Abstract:While green finance has become a key instrument for low-carbon city transitions, its actual decarbonization effects and transmission mechanisms remain unclear. This study employs econometric models and machine learning-based analysis to examine whether and how green finance reduces city-level carbon intensity. Results show that green finance significantly lowers carbon intensity, with green bonds and green investment having the strongest impacts and evident spatial spillovers. The effects vary by development level, being most pronounced in Fourth- and Fifth-tier cities. Mediation analysis reveals that green finance operates mainly through energy structure optimization, followed by industrial upgrading, foreign direct investment, and technological innovation. SHAP analysis confirms substantial differences across financial instruments, with green bonds, funds, and credit contributing most to decarbonization. Moreover, the marginal impact is stronger in cities with low technological capacity, high industrial dependency, and coal-based energy mixes. These findings provide theoretical support and policy guidance for building a multi-level, regionally differentiated green finance system to promote inclusive low-carbon transitions. Keywords: Green Finance; Carbon Intensity; Decarbonization Effect; Machine Learning; City
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.06986 [cs.LG] |
| (or arXiv:2606.06986v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.06986
arXiv-issued DOI via DataCite (pending registration)
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