Unlocking Green Growth: How Artificial Intelligence Policies Enhance Green Economic Efficiency—Evidence from China

With growing environmental pressure and tightening resource constraints, artificial intelligence has become a key technical path for urban low-carbon transformation. This study aims to empirically examine whether and how AI-oriented pilot policies affect green economic efficiency (GEE) and identify its underlying mechanisms and boundary conditions. Taking China’s National New-Generation Artificial Intelligence Innovation Development Pilot Zone (NAIDPZ) as a quasi-natural experiment, we use a staggered difference-in-differences model to test the policy effect based on panel data of 267 Chinese prefecture-level cities from 2007 to 2023, with a series of robustness checks to ensure the reliability of the conclusion. We find that the NAIDPZ policy significantly improves urban GEE, with a stronger effect in inland, central, and non-resource-based cities. The composite NAIDPZ policy effect is associated with higher GEE, mainly through green technological innovation and industrial structure optimisation, while its impact is positively moderated by government attention and public environmental attention. These conclusions provide empirical reference for global governments to optimise artificial intelligence policies for low-carbon development.