除数指数
能量强度
发射强度
温室气体
人均
能源消耗
解耦(概率)
国内生产总值
碳纤维
高效能源利用
环境科学
自然资源经济学
环境经济学
经济
人口
工程类
经济增长
材料科学
控制工程
电气工程
社会学
人口学
复合材料
复合数
生物
激发
生态学
作者
Xiaopeng Guo,Rong Shi,Dongfang Ren
标识
DOI:10.1177/0958305x221140567
摘要
High-energy consumption and high-emission industries contribute a lot to economic development, but their carbon emissions are also huge. In order to achieve the dual-carbon target as early as possible, it is necessary to reduce the carbon emissions of high-energy consumption and high-emission industries. This paper selected five representative factors (population, per capita gross domestic product (GDP), energy intensity, energy structure and carbon emission coefficient) and adopted the logarithmic mean divisia index (LMDI) method to decompose the driving factors of carbon emissions. Therefore, this paper uses Tapio decoupling model to analyze the decoupling relationship between the two factors with the greatest impact on carbon emissions and carbon emissions. The results show that: (i) There is a good decoupling between high-energy consumption and high-emission industries and per capita GDP, and the impact of per capita GDP on carbon emissions will gradually decrease in the future; (ii) The decoupling relationship between carbon emissions and energy intensity is poor. For some industries, the reduction of energy intensity can help reduce carbon emissions. Finally, this paper puts forward some suggestions to promote carbon emission reduction. This paper provides theoretical support for studying how to reduce carbon emissions and formulate relevant emission reduction policies in the high-energy consumption and high-emission industries.
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