摘要
对区域碳排放影响因素进行研究,可以有效判断影响因素的驱动力并以此制定高效率低碳排的发展方向,对实现中国2060年碳中和的目标具有重要意义。文章运用LMDI分解法,对新疆各地区2008—2020年工业能源碳排放进行因素分解,研究结果表明:在影响能源碳排放的因素中,经济增长效应是影响能源碳排放量增长的最大正向效应,技术进步效应是抑制能源碳排放增长的最大负向效应,人口和能源结构效应对能源碳排放分别具有正向和负向作用,但作用效果较小。根据分析结果,各地区整体碳排放量呈上升趋势,碳排放整体2020年相对于2014年变化率低于2014年相对于2008年变化率;各地区间经济增长、技术进步效应差别较大,人口、能源结构效应差别较小。
The research on the influencing factors of regional carbon emissions can effectively determine the driving force of the influencing factors and formulate the development direction of high-efficiency and low-carbon emissions.It is of great significance to achieve China’s goal of carbon neutrality by 2060.This paper uses the LMDI decomposition method to decompose the industrial energy carbon emissions in various regions of Xinjiang from 2008to 2020.The research results show that:among the factors affecting energy carbon emissions,the economic growth effect is the largest positive effect affecting the growth of energy carbon emissions.The effect of technological progress is the largest negative effect of restraining the growth of energy carbon emissions.The effects of population and energy structure have positive and negative effects on energy carbon emissions,respectively,but the effect is relatively small.According to the analysis results,the overall carbon emissions in various regions are on the rise,and the overall change rate of carbon emissions in 2020 relative to 2014 is lower than that in 2014 relative to 2008;the effects of economic growth and technological progress vary greatly among regions,while the effects of population and energy structure vary less.
作者
周跃
徐妍
王小飞
ZHOU Yue;XU Yan;WANG Xiao-fei(School of Business,Xinjiang Normal University,Urumqi,Xinjiang,830017,China;Urumqi High-tech Industrial Development Zone(New Urban Area)Bureau of Industry and Information Technology,Urumqi,Xinjiang,830011,China)
出处
《新疆师范大学学报(自然科学版)》
2022年第2期37-47,共11页
Journal of Xinjiang Normal University(Natural Sciences Edition)
基金
新疆维吾尔自治区自然科学基金项目“全生命周期视角下新疆工业碳足迹演变及低碳化转型对策研究”(2019D01A68)。
关键词
碳排放
LMDI分解法
影响因素
Carbon emissions
LMDI decomposition method
Influencing factors