Green Finance Policies, Urban Green Energy Efficiency and Regional Relative Disparities—Causality Tests Based on Dual Machine Learning

China’s sustained economic growth and industrialisation have led to increasingly serious problems of resource consumption and environmental pressure, making green development an inevitable choice for the country’s transformation and development. Green finance policies are becoming an increasingly important tool for increasing the use of green energy in cities. Using a dual machine learning (DML) model, this paper assesses the specific impact of green finance policies on green energy efficiency in Chinese cities, the mechanism of action, and regional disparities. The analysis is based on objective and scientific measurement of the level of green finance policies and green energy efficiency in 282 Chinese cities at prefecture level and above from 2006 to 2022. Benchmark regression results show that green finance policies significantly promote green energy efficiency in Chinese cities, passing a rigorous robustness test. Green bond policies are found to have the greatest promotional effect, whereas green support policies are found to have no significant effect. The results of the heterogeneity analysis suggest that green finance policies are more effective in promoting green energy efficiency in resource-based cities, cities with established industrial bases, and more developed cities. The results of the impact mechanism suggest that green finance policies can promote green energy efficiency by allocating the three internal urban factors of labour, capital and technology. The results of the analysis of regional disparities demonstrate that green finance policies effectively reduce disparities in urban green energy efficiency at the national level, between the north and south, and between coastal and inland regions. However, they also widen the disparities between central and peripheral cities within each province, hindering balanced regional development. This paper makes relevant policy recommendations based on this.