Unveiling the ‘Puzzle’ of Climate Policies and Urban Energy Resilience in the Era of Artificial Intelligence: Evidence from China’s Carbon Emissions Trading Pilot Program

Wei Chen (), Qin Dai and Wenxing Zhu
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Wei Chen: School of Business, Jiangxi University of Science and Technology, Nanchang 330013, China
Qin Dai: School of Business, Jiangxi University of Science and Technology, Nanchang 330013, China
Wenxing Zhu: School of Business, Jiangxi University of Science and Technology, Nanchang 330013, China

Sustainability, 2025, vol. 17, issue 21, 1-25

Abstract: In recent years, the frequency of extreme weather has begun to increase, and the impact of climate on economic and social development has started to attract people’s attention. As an important foundation for the development of human society, urban energy systems are also confronted with the challenges brought about by climate change. To explore the impact of climate policies on the stability of urban energy systems, this paper uses advanced empirical models such as the M-PSM-DID model, DML model, and SDM model to analyze the specific relationship between the carbon emissions trading policy (CETP), one of the urban climate policies, and the resilience of urban energy systems (ESR). Research has found that the CETP can not only effectively reduce urban carbon emissions but also significantly promote the improvement of urban ESR, and this promoting effect is more pronounced in environmentally friendly cities. Meanwhile, the application of artificial intelligence is an important transmission path for the CETP to enhance the ESR of cities, and the effective market and agile government play a moderating role in the process of the CETP improving ESR. Finally, the CETP has a significant spatial effect on ESR. With the change in geographical distance, this effect shows an “inverted U” shaped distribution characteristic. The above results have significant reference value for environmental policymakers in different cities.

Keywords: carbon emissions trading policies; energy system resilience; artificial intelligence; DML models (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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