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我国碳排放影响因素及电力碳排放核算方法研究综述

A Review of the Impact Factors of Carbon Emissions and the Accounting Methods for Electricity Carbon Emissions in China
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摘要 在全球气候变化的严峻挑战下,我国明确了在2030年前争取达到二氧化碳排放的峰值,并努力争取在2060年前实现碳中和的目标。实现“双碳”目标的基础是碳排放量统计核算,该文综述了IPAT系列模型、指数分解法、结构分解法3类主流影响因素分析方法的优势、局限性和应用范围。同时,从公式、优势、局限性、适用尺度和应用范围等方面对4种主流碳排放核算方法(排放因子法、物料平衡法、实测法、大数据计算法)进行总结。最后,聚焦于电力领域,从发电侧、电网侧和用户侧分析了电力碳排放核算方法。通过上述研究,指出我国现有碳排放核算研究的不足,并对我国碳排放核算方法研究提出建议。 In response to global climate change,China is aiming to reach peak carbon emissions before 2030 and carbon neutrality before 2060.The basis of realizing this goal is the statistical accounting of carbon emissions.This paper summarizes the advantages,limitations and application of three main impact factor analysis methods,including IPAT series model,index decomposition method and structural decomposition method.Meanwhile,four mainstream carbon emission accounting methods(carbon emission factor method,material balance method,measurement method and big data calculation method)are summarized from the aspects of formula,advantages,limitations,applicable scale and application scope.Finally,focusing on the electric power field,this paper analyzes the calculation methods of electric power carbon emissions from the generation side,the grid side and the user side.Through the above research,this paper indicates the shortcomings of Chinese carbon accounting methods,and proposes suggestions for the research direction of Chinese carbon emission calculation.
作者 吴杏平 康重庆 袁启恒 WU Xingping;KANG Chongqing;YUAN Qiheng(State Grid Information&Telecommunication Branch,Xicheng District,Beijing 100761,China;Department of Electrical Engineering,Tsinghua University,Haidian District,Beijing 100084,China;Big Data Center of State Grid Corporation of China,Xicheng District,Beijing 100052,China)
出处 《中国电机工程学报》 EI CSCD 北大核心 2024年第S01期1-18,共18页 PROCEEDINGS OF THE CHINESE SOCIETY FOR ELECTRICAL ENGINEERING
关键词 碳排放影响因素 碳排放核算 电力碳排放 电力大数据 influence factors of carbon emissions carbon emissions accounting electricity carbon emissions power big data
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