Research on the Impact of Artificial Intelligence on Urban Green Energy Efficiency: An Empirical Test Based on Neural Network Models

In recent years, the rapid progress of artificial intelligence (AI) technologies has significantly influenced urban green energy efficiency. Leveraging panel data from 271 cities in China spanning the period of 2010–2022, this paper conducts an empirical analysis of the impact of AI on urban green energy efficiency from multiple perspectives, including green finance, industrial chain resilience, and the intensity of environmental regulation. The key findings are as follows: ① AI has a substantial positive effect on urban green energy efficiency, a conclusion that is consistently confirmed through multiple robustness tests; ② Heterogeneity analysis shows that the influence of AI varies markedly across different regions, city sizes, and whether cities are central, coastal, or transportation hubs, yet it maintains an overall positive correlation. However, its impact is relatively weaker in the northeastern region and in megacities; ③ Mechanism tests reveal that AI enhances urban green energy efficiency by improving green finance, strengthening industrial chain resilience, and intensifying environmental regulation; ④ Spatial spillover analysis indicates that AI exerts a positive spatial spillover effect on local urban green energy efficiency. Based on these findings, this paper offers targeted policy recommendations to enhance urban green energy efficiency and advance sustainable development.