Impact of artificial intelligence-driven urban renewal strategies green economic efficiency and resident health in China
Jing Peng, Xin Fu, Yufan Peng, Yang Ding

TL;DR
This study shows that AI-driven smart city policies in China improve green economic efficiency and public health, especially when aligned with industrial and innovation strategies.
Contribution
The study introduces a novel integration of DID and SDM to analyze AI-driven urban policies and their spillover effects on sustainability and health.
Findings
AI-driven smart city strategies significantly enhance green economic efficiency (coefficient = 0.098, p < 0.01).
Public health outcomes improve with AI-driven policies (coefficient = 0.085, p < 0.01).
Positive effects are stronger in regions with rationalized industrial structures and lower innovation capacity.
Abstract
This 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. To 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. Empirical findings demonstrate that AI-driven smart city strategies significantly enhance…
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Taxonomy
TopicsEnergy, Environment, Economic Growth · Smart Cities and Technologies · COVID-19 epidemiological studies
