Can Green Finance Policies Promote the Transformation of Urban Energy Consumption Structure? Causal Inference Based on a Double Machine Learning Model

This study constructs an urban energy consumption structure transformation (UECST) index and utilizes a double machine learning model to investigate the impact and underlying mechanisms of green finance policies on this transformation. Based on panel data from 281 prefecture-level cities in China from 2010 to 2022, we find that green finance policies significantly promote the UECST. This finding holds after a series of robustness checks and endogeneity tests. Furthermore, our analysis reveals that these policies facilitate the transition not only through direct financial support but also indirectly by driving green technological innovation, enhancing green economic efficiency, and promoting industrial upgrading. The positive impact is more substantial in central cities, transportation hubs, non-resource-based cities, non-old industrial bases, and key environmental protection cities. By providing empirical evidence and policy insights, this study contributes to optimizing green finance policy design and addressing specific bottlenecks in energy transition, thereby supporting the achievement of the “Beautiful China” development goal.