Heterogeneous Effects of Green Finance on Urban Decarbonization: Evidence from 285 Cities in China

2026-06-05

AI Summary

This study addresses the ambiguous empirical effectiveness and underlying mechanisms of green finance in facilitating urban low-carbon transitions, particularly the lack of systematic analysis on heterogeneity and regional disparities. Leveraging a dataset covering 285 Chinese cities, the authors integrate econometric and machine learning approaches to evaluate the impact of green finance on carbon intensity and its transmission channels through energy structure optimization and industrial upgrading. The findings reveal that the carbon-mitigation effects of green finance are more pronounced in cities characterized by weaker technological capabilities, higher industrial dependence, and greater coal consumption shares. Green bonds and green investments exhibit the strongest efficacy and generate significant spatial spillovers, with tier-four and tier-five cities deriving the greatest benefits. These results provide empirical support for designing a multi-tiered and differentiated green finance system.

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

Problem

• Green Finance
• Carbon Intensity
• Decarbonization Effect
• Heterogeneous Effects
• City

Innovation

• Green Finance
• Machine Learning
• Carbon Intensity
• Decarbonization Effect
• SHAP Analysis