中国石油大学学报（社会科学版）2026，Vol.42Issue(1)：12-25,14.DOI:10.13216/j.cnki.upcjess.2026.01.0002
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
A Quantitative Framework and Efficacy Assessment of Electricity Policies in China̍s Mainland Based on Large Language Models and Explainable Machine Learning
摘要
Abstract
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.关键词
电力政策/提示工程/微调深度学习模型/量化框架/政策效力Key words
electricity policies/prompt engineering/fine-tuned deep learning models/quantitative framework/policy efficacy分类
信息技术与安全科学引用本文复制引用
马铁驹,蔡昀航,刘风..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[J].中国石油大学学报（社会科学版）,2026,42(1):12-25,14.基金项目
国家自然科学基金资助项目(72004218 （）
72140006 （）
72074212) （）
中国博士后科学基金面上项目(2022M710049) （2022M710049）
