Heterogeneous Effects of Green Finance on Urban Decarbonization: Evidence from 285 Cities in China
Summary
This study uses econometric models and machine learning (SHAP analysis) to examine how green finance reduces carbon intensity across 285 Chinese cities, finding heterogeneous effects by city tier and financial instrument, with green bonds and investment being most impactful.
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# Heterogeneous Effects of Green Finance on Urban Decarbonization: Evidence from 285 Cities in China Source: [https://arxiv.org/abs/2606.06986](https://arxiv.org/abs/2606.06986) [View PDF](https://arxiv.org/pdf/2606.06986) > 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 ## Submission history From: Xueyang Li \[[view email](https://arxiv.org/show-email/7bffef89/2606.06986)\] **\[v1\]**Fri, 5 Jun 2026 07:24:43 UTC \(606 KB\)
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