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How does smart artificial intelligence influence energy system resilience? Evidence from energy vulnerability assessments in G20 countries

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  • Zhang, Yingnan
  • Hu, Wei
  • Tao, Yirui
  • Zhang, Bin

Abstract

Energy system resilience has become an escalating concern amid global economic developments, worsening climate change impacts, and localized regional conflicts. Artificial Intelligence (AI) has emerged as a pivotal tool for enhancing energy system resilience. This study delves into the association between AI and energy system resilience, investigating underlying mechanisms and heterogeneity using a fixed effects model applied to data from the Group of 20 (G20) economies. Energy system resilience can be characterized by a country's energy vulnerability. Our findings suggest that AI effectively mitigates energy vulnerability. Specifically, its impact is more pronounced in high carbon-emitting countries, while it plays a more limited role in highly developed economies. Conversely, in less economically developed countries, AI adoption may exacerbate energy vulnerability. Through mediation analysis, we validate the role of AI in fostering industrial structural transformation and upgrading. However, the direct impact of industrial structure on energy vulnerability was statistically insignificant, warranting further investigation. These results underscore AI's efficacy in reducing energy vulnerability and provide strategic insights for addressing global energy challenges and promoting sustainable development on a global scale.

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  • Zhang, Yingnan & Hu, Wei & Tao, Yirui & Zhang, Bin, 2025. "How does smart artificial intelligence influence energy system resilience? Evidence from energy vulnerability assessments in G20 countries," Energy, Elsevier, vol. 314(C).
    • Handle: RePEc:eee:energy:v:314:y:2025:i:c:s0360544224040684
    DOI: 10.1016/j.energy.2024.134290
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    3. Lu Lv & Bingnan Guo, 2025. "Do Pilot Zones for Green Finance Reform and Innovation Policy Enhance China’s Energy Resilience?," Sustainability, MDPI, vol. 17(13), pages 1-22, June.
    4. Feng, Yanchao & Yan, Tong & Guo, Jia, 2026. "Assessing the global impact of artificial intelligence on energy resilience: The role of financial inclusion," Socio-Economic Planning Sciences, Elsevier, vol. 103(C).
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    7. Jie Yan & Hailing Wang, 2025. "Does the Development of Digital Finance Enhance Urban Energy Resilience? Evidence from Machine Learning," Sustainability, MDPI, vol. 17(14), pages 1-25, July.
    8. Zhou, Kunlu & Liu, Yuanhong & Liao, Qidong & Xu, Jiuping, 2026. "The impact of artificial intelligence on energy resilience: Empirical evidence from China," Energy Economics, Elsevier, vol. 153(C).

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