Exploring the dynamics of urban energy efficiency in China: A double machine learning analysis of green finance influence

Analysing the impact of green finance policies on urban energy efficiency contributes to finding solutions to promote the development of China's low-carbon energy system and the global reduction of greenhouse gas emissions. Utilizing Chinese urban data from 2006 to 2021, the study employs an enhanced non-radial distance function (NDDF) model to evaluate the energy efficiency of 282 prefecture-level cities. It examines the 'Green Finance Reform and Innovation Pilot Zone' policy as a quasi-natural experiment. It uses a double machine learning model to analyze its effect on urban energy efficiency and its mechanisms. The research demonstrates that green finance policies significantly improve energy efficiency, mainly through strengthening environmental regulation, fostering green technology innovation, and optimizing industrial structure. Additionally, an in-depth analysis of regional heterogeneity reveals variations in the policy's effectiveness across eastern, central, and western regions and between resource-rich and resource-poor cities. The findings suggest that the efficacy of green finance policies, while generally positive, is influenced by regional economic development and resource endowment differences. Consequently, the paper proposes targeted policy recommendations to enhance the green finance system, tailor policies to regional needs, encourage corporate green innovation, and promote inter-departmental collaboration. This research offers new perspectives on sustainable development and practical policy advice for leveraging green finance to boost energy efficiency, advancing Chinese cities' economic and environmental sustainability.