The Impact of Green Finance on Urban Energy Efficiency: A Double Machine Learning Analysis

Urban areas globally face the critical challenge of meeting growing energy demands while maintaining environmental sustainability. However, existing research provides limited and often inconsistent evidence on how green finance affects urban energy efficiency, largely due to heterogeneous measurement systems, methodological constraints, and insufficient identification of underlying mechanisms. To address these research gaps, this study investigates two core questions: Does green finance significantly improve urban energy efficiency? If so, what are the specific transmission mechanisms driving this impact? Methodologically, this exploration employs a Double Machine Learning (DML) approach to analyze panel data from 210 Chinese cities between 2006 and 2022. The analysis demonstrates a significant and positive impact of green finance on urban energy efficiency, with an estimated coefficient of 0.1910. Further analysis identifies three constructive mechanisms, including environmental regulations, industrial structures, and green technological innovation, which enhance resource allocation and energy utilization efficiency. Moreover, green finance shows a stronger positive impact in non-resource-dependent cities, regions outside traditional industrial bases, and financially developed areas. These findings recommend establishing standardized green finance frameworks, increasing targeted financial support for key regions, and integrating green innovation with industrial restructuring. These measures help consolidate China’s green finance system and improve regional energy efficiency through market expansion, energy transition, and technological advancement.