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Quantitative evaluation of China’s energy storage policies: A ChatGPT-based PMC index modelling approach

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  • Liang, Jing
  • Wang, Yuqi
  • Li, Wei
  • Wang, Weihan

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.

Suggested Citation

  • Liang, Jing & Wang, Yuqi & Li, Wei & Wang, Weihan, 2025. "Quantitative evaluation of China’s energy storage policies: A ChatGPT-based PMC index modelling approach," Energy Policy, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:enepol:v:206:y:2025:i:c:s0301421525002769
    DOI: 10.1016/j.enpol.2025.114769
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