 Quantitative evaluation of China’s energy storage policies: A ChatGPT-based PMC index modelling approachJing Liang, Yuqi Wang, Wei Li and Weihan Wang Energy Policy, 2025, vol. 206, issue C Abstract: Efficient energy grid systems can improve operational efficiency and reduce carbon emissions by integrating diverse renewable energy generation sources. As a distinct asset class within the electric grid, energy storage necessitates well-defined regulatory and financial policies to support its development and large-scale deployment. This makes it essential to establish an effective and consistent policy evaluation framework to support the growth of the energy storage industry. In this study, we propose a ChatGPT-based Policy Model Consistency framework to evaluate 203 energy supply policies issued by China’s central and local governments during the “14th Five-Year Plan” period (2021–2024). The results demonstrate the effectiveness of AI-powered policy analysis in building quantitative and objective policy evaluation systems. In addition, the findings highlight the ability of the system to provide a comprehensive analysis and practical recommendations for the development of energy storage systems in China. Keywords: Energy storage system; Policy evaluation; Policy modelling consistency; ChatGPT-based (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:enepol:v:206:y:2025:i:c:s0301421525002769 DOI: 10.1016/j.enpol.2025.114769 Access Statistics for this article Energy Policy is currently edited by N. France More articles in Energy Policy from Elsevier |
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