A Study on the Impact of Artificial Intelligence on Urban Green Total Factor Efficiency from the Perspective of Spatial Spillover and Threshold Effects

In recent years, the rapid advancement of artificial intelligence (AI) technology has exerted profound implications for urban green total factor efficiency (GTFE). Drawing on panel data of 279 Chinese cities from 2012 to 2021, this study empirically examines the impact of AI on urban GTFE from multi-dimensional perspectives including green finance and new-quality productive forces. The key findings are as follows: ➀ AI significantly enhances urban GTFE with a nonlinear threshold effect, and this conclusion remains robust after multiple robustness tests incorporating machine learning models and econometric approaches. ➁ Heterogeneity analysis reveals that AI exerts significantly heterogeneous effects across different regional locations, city sizes, urban hierarchies, and between transportation hubs/non-hubs and old industrial bases/non-bases. While an overall positive correlation is observed, the positive effect of AI is not statistically significant in western China, mega-cities, large cities, and central cities; conversely, an insignificant negative effect is detected in central-eastern China and old industrial bases. ➂ Mechanism tests demonstrate that AI facilitates GTFE improvement through channels such as upgrading green finance development and advancing new-quality productive forces. ➃ Spatial spillover effect analysis indicates that AI generates a positive spatial spillover effect on the GTFE of local cities. Based on these findings, targeted policy recommendations are proposed to promote urban GTFE enhancement and achieve sustainable development.