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<p>This study investigates how artificial intelligence (AI) shapes urban energy sustainability across 223 Chinese cities between 2008 and 2021, filling a critical research gap in understanding AI’s role beyond traditional energy forecasting and emission control. Employing a multidimensional AI evaluation index, spatial econometric models, and mediation analyses, the research offers a novel perspective on how AI-driven innovation, human capital transformation, and shifts in energy consumption structure interact to improve energy outcomes. These results bridge existing gaps in urban-focused AI scholarship by integrating a spatial Durbin Model that uncovers both positive direct effects and negative spillovers in geographically proximate cities, underscoring the importance of regionally coordinated policies to prevent uneven gains. Methodologically, this study contributes an innovative framework combining difference-in-differences, advanced spatial analysis, and a comprehensive AI index. Overall, the findings support targeted policy interventions aimed at expanding AI’s benefits to smaller and resource-based cities, including tax incentives for green AI start-ups and cross-regional investments in digital infrastructure.</p>

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Artificial Intelligence and Urban Energy Sustainability: A Spatial Analysis from Chinese Cities

  • 期刊论文

  • 发表日期：

    2026年1月10日

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  • Jing Ge

摘要

This study investigates how artificial intelligence (AI) shapes urban energy sustainability across 223 Chinese cities between 2008 and 2021, filling a critical research gap in understanding AI’s role beyond traditional energy forecasting and emission control. Employing a multidimensional AI evaluation index, spatial econometric models, and mediation analyses, the research offers a novel perspective on how AI-driven innovation, human capital transformation, and shifts in energy consumption structure interact to improve energy outcomes. These results bridge existing gaps in urban-focused AI scholarship by integrating a spatial Durbin Model that uncovers both positive direct effects and negative spillovers in geographically proximate cities, underscoring the importance of regionally coordinated policies to prevent uneven gains. Methodologically, this study contributes an innovative framework combining difference-in-differences, advanced spatial analysis, and a comprehensive AI index. Overall, the findings support targeted policy interventions aimed at expanding AI’s benefits to smaller and resource-based cities, including tax incentives for green AI start-ups and cross-regional investments in digital infrastructure.