绿金融能降碳，尤其对三四线城市和高煤依赖城市效果更明显。
Heterogeneous Effects of Green Finance on Urban Decarbonization: Evidence from 285 Cities in China
- 用计量与机器学习分析285个中国城市的绿金融影响。
- 绿债、绿投显著降碳，且存在空间溢出效应，三四线城市效果最突出。
- 对技术弱、重工业、煤炭多的城市，绿金融边际效果更强。
绿色金融已成为低碳城市转型的关键工具，但其实际减碳效果与作用机制仍不明确。本研究基于285个中国城市的面板数据，采用计量经济模型与机器学习方法，分析绿色金融对城市碳强度的影响。结果表明，绿色金融显著降低碳强度，其中绿色债券与绿色投资影响最大，并存在明显的空间溢出效应。影响效果因城市发展水平而异，在四线和五线城市最为显著。中介分析显示，绿色金融主要通过优化能源结构实现减排，其次为产业升级、外商直接投资和技术进步。SHAP分析进一步揭示不同金融工具贡献差异，绿色债券、基金与信贷对减碳贡献最大。此外，对技术能力弱、产业依赖度高、以煤炭为主的地区，绿金融的边际减碳效应更强。研究为构建多层次、区域差异化绿色金融体系提供了理论支持与政策参考。
原文摘要 · Abstract (English)
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
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