首页 | 本学科首页   官方微博 | 高级检索  
     

中国能源碳排放因素分解与情景预测
引用本文:王利兵,张赟.中国能源碳排放因素分解与情景预测[J].电力建设,2021,42(9):1-9.
作者姓名:王利兵  张赟
作者单位:全球能源互联网发展合作组织,北京市100031;全球能源互联网集团有限公司,北京市100031
基金项目:全球能源互联网集团有限公司科技项目“全球化石能源退出路径、成本及政策机制研究”
摘    要:我国能源活动碳排放占总碳排放85%以上,研究能源活动碳排放的变化规律对于实现碳达峰碳中和目标具有重要意义。首先,采用对数平均迪氏分解法(logarithmic mean Divisia index,LMDI)对1995—2017年我国能源消费碳排放变化的影响因素进行分解,从经济规模、产业结构、能源强度、能源结构、能源价格、人均可支配收入、人口规模这7个方面,模型给出了相关因素对一、二、三产业和居民部门碳排放变化的贡献。结果表明,对于3个产业部门,经济增长是碳排放增长的首要驱动力,而技术进步带来的能源强度下降、产业结构优化和能源消费结构改善呈现负效应,且产业结构优化和能源结构清洁化的作用越来越显著。对于居民部门,人均可支配收入是居民部门碳排放增长的推动力,而能源价格呈现明显的负效应。其次,设计了3种情景,运用可拓展的随机性的环境影响评估模型(stochastic impacts by regression population,affluence and technology,STIRPAT)对2030年我国能源碳排放进行预测,在以实现碳达峰为目标的低碳情景中,我国能源碳排放有望于2025—2029年实现达峰,峰值水平为101亿~110亿t。最后,为实现碳达峰碳中和目标,建议以构建中国能源互联网为基础平台,实施“清洁替代”和“电能替代”,推进能源转型。

关 键 词:对数平均迪氏分解法(LMDI)  能源消费强度  可拓展的随机性的环境影响评估模型(STIRPAT)  碳中和
收稿时间:2021-04-25

Factors Decomposition and Scenario Prediction of Energy-Related CO2 Emissions in China
WANG Libing,ZHANG Yun.Factors Decomposition and Scenario Prediction of Energy-Related CO2 Emissions in China[J].Electric Power Construction,2021,42(9):1-9.
Authors:WANG Libing  ZHANG Yun
Affiliation:1. Global Energy Interconnection Development and Cooperation Organization, Beijing 100031, China2. Global Energy Internet Group Co., Ltd., Beijing 100031, China
Abstract:Energy-related carbon emissions account for more than 85% of total carbon emissions in China, and the research on changes of energy-related carbon emissions is of great significance for achieving carbon peak and neutrality goals. Firstly, this paper uses the Logarithmic Mean Divisia Index (LMDI) to decompose the impacting factors of China's energy-related CO2 emissions changes from 1995 to 2017. From the aspects of economic scale, industry structure, energy intensity, energy structure, energy prices, per capita disposable income and population size, the model gives the contribution of related factors to energy-related CO2 changes in primary, secondary, tertiary and residential sectors. The results show that for the three industry sectors, economic growth is the primary drive of CO2 emission growth, while declining energy intensity, improved industrial structure and energy consumption structure show negative effects. For the residential sector, per capita disposable income and population size are the driving forces behind the CO2 emission growth, and energy prices show a significant negative effect. Secondly, in order to predict China's energy-related CO2 in 2030, three scenarios are designed and the Stochastic Impacts by Regression Population, Affluence and Technology (STIRPAT) model is implemented. In the low carbon scenario with the goal of achieving carbon peaks, China's energy-related CO2 emissions are expected to peak around 2025-2029 and the level is about 10.1 billion to 11.0 billion tons. Finally, in order to achieve CO2 emission peak before 2030 and carbon neutrality before 2060, it is recommended to build the China Energy Internet as the basic platform, to implement the clean replacement and electricity replacement, and accelerate the energy transition.
Keywords:logarithmic mean Divisia index (LMDI)  energy consumption intensity  stochastic impacts by regression population  affluence and technology (STIRPAT) model  carbon neutrality  
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《电力建设》浏览原始摘要信息
点击此处可从《电力建设》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号