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Bridge the gap: nexus between artificial intelligence and urban energy resilience, evidence from low-carbon city in China

Author

Listed:
  • Wang, Chang-song
  • Chen, Wei
  • Zheng, Yang
  • Dai, Qin

Abstract

The development of low-carbon cities is essential for achieving sustainable, high-quality growth and plays a crucial role in ensuring the reliability and stability of energy production and distribution networks. This study employs a PSM-DID model, a dual machine learning model, and a spatial Durbin model to empirically analyze the impact, underlying mechanisms, and spatial effects of low-carbon city pilot on energy resilience. The benchmark regression results show that the low-carbon city pilot has significantly enhanced urban energy resilience, but it shows heterogeneous results in different types of cities. The low-carbon city pilot in large cities (population greater than 1 million) and net inflow cities can greatly enhance urban energy resilience, while the construction of low-carbon cities in small cities (population fewer than 1 million) and net outflow cities has a negative impact on urban energy resilience. The mechanistic analysis demonstrates that the application of artificial intelligence is the primary channel through which low-carbon city pilot drives improvements in urban energy resilience, but the effect shows a non-linear growth. When the application level of artificial intelligence exceeds 4.2909, the effect of low-carbon city pilot on urban energy resilience will be enhanced. In addition, spatial analysis shows that low-carbon city pilot strengthens energy resilience in pilot cities and has a spillover effect on surrounding cities. The series of methods proposed in this paper not only tests the causal relationship between low-carbon city pilot and urban energy resilience but also provides new experience and evidence for improving the resilience of urban energy systems.

Suggested Citation

  • Wang, Chang-song & Chen, Wei & Zheng, Yang & Dai, Qin, 2025. "Bridge the gap: nexus between artificial intelligence and urban energy resilience, evidence from low-carbon city in China," Energy Economics, Elsevier, vol. 152(C).
    • Handle: RePEc:eee:eneeco:v:152:y:2025:i:c:s0140988325008357
    DOI: 10.1016/j.eneco.2025.109005
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