【摘要】 提高我国电力行业碳排放效率是实现“双碳”目标的重要抓手。本文基于2005～2024年中国30个省(区、市)的相关数据，构建考虑非期望产出的全局Super-EBM模型对我国电力行业碳排放效率进行测算，利用双重机器学习模型实证检验碳交易政策对我国电力行业碳排放效率产生的影响效应与内在作用机制。结果表明:(1)碳交易政策可以显著提高我国电力行业碳排放效率;(2)机制分析发现，碳交易政策可通过技术进步效应、能源管理效应两条路径促进我国电力行业碳排放效率提升;(3)异质性分析发现，碳交易政策对电力行业碳排放效率的促进作用在东部与环保执法强度高的地区更显著。基于上述结论，本文提出了相关政策建议。更多还原

【Abstract】 Improving the carbon emission efficiency of China’s power industry is an important approach to achieving the “dual carbon” goals. Using data from 30 Chinese provinces(autonomous regions and municipalities) from 2005 to 2024,this paper constructs a global super-efficiency EBM model to measure the carbon emission efficiency of China’s power sector. A double machine learning model is used to examine the effects and mechanisms of the carbon trading policy on this efficiency. The findings indicate that:(1) The carbon trading policy can significantly improve the carbon emission efficiency of China’s power industry.(2)Mechanism analysis reveals that the policy enhances efficiency through two approaches: the technological progress effect and the energy management effect.(3) Heterogeneity analysis shows that the positive impact of the carbon trading policy is more pronounced in eastern regions and areas with stronger environmental law enforcement. Based on these findings,the paper proposes relevant policy recommendations.更多还原