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Enhancing the resilience of urban energy systems: The role of artificial intelligence

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  • Jiang, Mingdong
  • Yu, Xinxin

Abstract

Global climate change threaten the balance between energy supply and demand considerably, resulting in increased focus on urban energy resilience. Meanwhile, the rapid development of artificial intelligence (AI) has had an inevitable impact on energy production and consumption. This study takes China's New Generation AI Innovative Development Pilot Zones as a quasi-natural experiment to explore the impact of AI on the resilience of urban energy systems (RUES). The results demonstrate that AI has a positive effect on RUES, which can be enhanced under lower economic growth targets, higher talent levels and tighter carbon emission constraints. AI significantly promotes the technological innovation in new energy and intelligent grid industries on the production side, which further enhance RUES. From the consumption side, AI affects RUES by influencing technological innovation in energy-efficient and high-end equipment manufacturing industries, and the effect on the latter is stronger. Heterogeneity tests reveal that the effects of AI are more pronounced for cities in China's central and western regions. The study enriches the research on AI and offers solutions for enhancing RUES.

Suggested Citation

  • Jiang, Mingdong & Yu, Xinxin, 2025. "Enhancing the resilience of urban energy systems: The role of artificial intelligence," Energy Economics, Elsevier, vol. 144(C).
  • Handle: RePEc:eee:eneeco:v:144:y:2025:i:c:s0140988325001367
    DOI: 10.1016/j.eneco.2025.108313
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    1. Galaz, Victor & Centeno, Miguel A. & Callahan, Peter W. & Causevic, Amar & Patterson, Thayer & Brass, Irina & Baum, Seth & Farber, Darryl & Fischer, Joern & Garcia, David & McPhearson, Timon & Jimenez, 2021. "Artificial intelligence, systemic risks, and sustainability," Technology in Society, Elsevier, vol. 67(C).
    2. Hosseini, Seyedmohsen & Barker, Kash & Ramirez-Marquez, Jose E., 2016. "A review of definitions and measures of system resilience," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 47-61.
    3. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    4. Guanghui Zhou & Chao Zhang & Zhi Li & Kai Ding & Chuang Wang, 2020. "Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(4), pages 1034-1051, February.
    5. Jiang, Zhangsheng & Xu, Chenghao, 2023. "Policy incentives, government subsidies, and technological innovation in new energy vehicle enterprises: Evidence from China," Energy Policy, Elsevier, vol. 177(C).
    6. Xiekui Zhang & Peiyao Liu & Hongfei Zhu, 2022. "The Impact of Industrial Intelligence on Energy Intensity: Evidence from China," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    7. Sharifi, Ayyoob & Yamagata, Yoshiki, 2016. "Principles and criteria for assessing urban energy resilience: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1654-1677.
    8. Li, Munan & Wang, Wenshu & Zhou, Keyu, 2021. "Exploring the technology emergence related to artificial intelligence: A perspective of coupling analyses," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    9. Dong, Kangyin & Dong, Xiucheng & Jiang, Qingzhe & Zhao, Jun, 2021. "Assessing energy resilience and its greenhouse effect: A global perspective," Energy Economics, Elsevier, vol. 104(C).
    10. Yang, Chih-Hai, 2022. "How Artificial Intelligence Technology Affects Productivity and Employment: Firm-level Evidence from Taiwan," Research Policy, Elsevier, vol. 51(6).
    11. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    12. Charani Shandiz, Saeid & Foliente, Greg & Rismanchi, Behzad & Wachtel, Amanda & Jeffers, Robert F., 2020. "Resilience framework and metrics for energy master planning of communities," Energy, Elsevier, vol. 203(C).
    13. Francis, Royce & Bekera, Behailu, 2014. "A metric and frameworks for resilience analysis of engineered and infrastructure systems," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 90-103.
    14. Gangopadhyay, Partha & Jain, Siddharth & Bakry, Walid, 2022. "In search of a rational foundation for the massive IT boom in the Australian banking industry: Can the IT boom really drive relationship banking?," International Review of Financial Analysis, Elsevier, vol. 82(C).
    15. Nepal, Rabindra & Zhao, Xiaomeng & Liu, Yang & Dong, Kangyin, 2024. "Can green finance strengthen energy resilience? The case of China," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    16. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
    17. Tian, Lihui & Li, Xin & Lee, Cheng-Wen & Spulbăr, Cristi, 2024. "Investigating the asymmetric impact of artificial intelligence on renewable energy under climate policy uncertainty," Energy Economics, Elsevier, vol. 137(C).
    18. Huang, Yujie & Liu, Shucheng & Gan, Jiawu & Liu, Baoliu & Wu, Yuxi, 2024. "How does the construction of new generation of national AI innovative development pilot zones drive enterprise ESG development? Empirical evidence from China," Energy Economics, Elsevier, vol. 140(C).
    19. Ahmadi, Somayeh & Saboohi, Yadollah & Vakili, Ali, 2021. "Frameworks, quantitative indicators, characters, and modeling approaches to analysis of energy system resilience: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    20. He, Peijun & Ng, Tsan Sheng & Su, Bin, 2019. "Energy-economic resilience with multi-region input–output linear programming models," Energy Economics, Elsevier, vol. 84(C).
    21. Dileep, G., 2020. "A survey on smart grid technologies and applications," Renewable Energy, Elsevier, vol. 146(C), pages 2589-2625.
    22. Chen, B. & Li, J.S. & Wu, X.F. & Han, M.Y. & Zeng, L. & Li, Z. & Chen, G.Q., 2018. "Global energy flows embodied in international trade: A combination of environmentally extended input–output analysis and complex network analysis," Applied Energy, Elsevier, vol. 210(C), pages 98-107.
    23. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
    24. Gatto, Andrea & Drago, Carlo, 2020. "Measuring and modeling energy resilience," Ecological Economics, Elsevier, vol. 172(C).
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