基于大语言模型与可解释机器学习的中国大陆电力政策量化框架与效力研究OACHSSCD

Electricity policies serve as pivotal instruments for advancing national energy strategies by shaping institutional frameworks and incentive mechanisms that guide the system̍s optimization and upgrading.Given the predominant textual form of the policy texts,quantitative analysis and efficacy assessment are of great significance for policy refinement and optimization.This study develops a poli-cy quantification framework based on prompt engineering and fine-tuned deep learning models and combines topic modelling with ex-plainable machine learning to examine the efficacy of China̍s electricity policies.Key findings reveal that current national electricity policies can be broadly classified into five thematic categories,among which renewable energy development and market-oriented re-forms have attracted relatively high attention in recent years.Substantial disparities in policy intensity have been observed across prov-inces,with Guangdong,Zhejiang,and Fujian exhibiting the highest average policy intensity.With an exception of the first policy theme,the remaining four categories exert heterogeneous effects on electricity system operational efficiency,manifesting U-shaped,in-verted U-shaped,and negative relationships respectively.This study suggests prioritizing a supporting policy system for renewable en-ergy while simultaneously advancing gradual market-oriented reforms on the premise of fully considering the policy portfolio effects and policy coordination.