Smart Cities with Green Resilience: A Quasi-Natural Experiment Based on Artificial Intelligence

Amidst climate change and the energy crisis worldwide, the synergy between smart city and environmental policies has become a key path to improving the green resilience of cities. This study examines the spatial effects of carbon emission trading (CET) policy on urban energy performance under the context of artificial intelligence (AI)-empowered smart cities. Using the spatial Durbin model (SDM) and analyzing data from 262 Chinese cities covering the period 2013–2021, the results reveal that: (1) smart cities significantly benefit from the institutional support of the local CET policy, resulting in an 8.55% reduction in energy consumption in the pilot city; (2) AI advancement contributes directly to reducing energy consumption in surrounding areas by 21.84% through spatial effects, and compensates for the imbalance of regional renewable energy caused by the “siphon effect” of CET policy. This study provides empirical evidence for developing countries to build green and resilient cities. This paper proposes the need to build a national CET market, strengthen government supervision, and make reasonable use of AI technology, transforming the green and resilient model of smart cities from Chinese experience to global practice.