除数指数
能量强度
温室气体
能源消耗
中国
发射强度
环境科学
驱动因素
碳纤维
自然资源经济学
节能
高效能源利用
城市化
分解
低碳经济
环境经济学
环境工程
经济
工程类
经济增长
计算机科学
地理
复合数
考古
电气工程
生物
激发
生态学
算法
作者
Xiaojun Ma,Cynthia Wang,Biying Dong,Guocui Gu,Ruimin Chen,Yifan Li,Hongfei Zou,Wenfeng Zhang,Qiunan Li
标识
DOI:10.1016/j.scitotenv.2018.08.183
摘要
To address climate change effectively, it is essential to quantify CO2 emissions and the driving factors in high-energy-consuming countries. China is the top CO2-emitting country; moreover, there is a lack of comprehensive analytical studies on quantifying the contributions of key drivers to high-energy-consuming countries' CO2 emissions. Therefore, based on data of China's energy consumption from 2005 to 2016, this paper combines the extended Kaya identity with the logarithmic mean Divisia index (LMDI) decomposition method to construct an optimized carbon emission decomposition model. Carbon emission and carbon emission intensity are measured and decomposed. Then, the results of the decomposition are discussed, and the effects of various drivers on carbon emissions from energy consumption in China are analysed. Furthermore, we demonstrate real applications of decomposition analysis in policy-making using examples from China and present some ideas to reduce CO2. The results show that from 2005 to 2016, China's total carbon emissions accounted for nearly one-third of the world's total carbon emissions, and the intensity of carbon emissions in China was generally higher than that of worldwide. The rapid development of economy and acceleration of urbanization are not conducive to reduction of carbon emissions. Reducing the intensity of energy consumption, adjusting the internal structure of the industry and perfecting the economic policy system should be important means used to promote the development of China's low-carbon economy in the future.
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