Impact of artificial intelligence-driven urban renewal strategies green economic efficiency and resident health in China - Peng, Jing; Fu, Xin; Peng, Yufan; Ding, Yang (Frontiers in Public Health, 2025)
IntroductionThis study investigates the impact of AI-driven smart city policies on green economic efficiency and public health. It further explores how industrial structure rationalization, upgrading, and technological innovation capacity moderate these effects, aiming to provide actionable insights for sustainable urban governance.MethodsTo account for potential policy spillover effects, the study adopts a Difference-in-Differences (DID) approach integrated with a Spatial Durbin Model (SDM). The analysis incorporates AI-enabled smart city renewal strategies into the empirical framework, focusing on their influence on green economic efficiency and public health across varying levels of industrial structure and innovation capacity. Data were sourced from the World Health Organization Global Health Observatory.ResultsEmpirical findings demonstrate that AI-driven smart city strategies significantly enhance green economic efficiency (coefficient = 0.098, p < 0.01) and public health outcomes (coefficient = 0.085, p < 0.01). The positive effects are amplified by rationalized and upgraded industrial structures. Notably, the gains in green economic efficiency are more substantial in regions with lower technological innovation capacity, while regions with higher innovation capacity benefit more in terms of improved public health.DiscussionThe results underscore the strategic importance of aligning AI applications with industrial and innovation policy to foster sustainable urban development. Policymakers are encouraged to leverage AI in optimizing industrial structures, promoting green growth, and integrating health policy with technological innovation to improve urban residents’ quality of life.