Literature DB >> 32297109

Driving forces of China's multisector CO2 emissions: a Log-Mean Divisia Index decomposition.

Wei Pan1,2, Haiting Tu3, Cheng Hu4, Wulin Pan3.   

Abstract

To figure out which factor contributes more on carbon emissions caused by energy consumption, this research took multisector analysis based on the Log-Mean Divisia Index Method (LMDI) and decoupling theory to assess the driving factors of carbon dioxide (CO2) emissions in China's six sectors from 2003 to 2016. Our empirical results reveal that China's economy can be divided as three decoupling stages and exhibited a distinct tendency toward strong decoupling with a turning point in 2008. Thus, we discuss the impact of 2008 economic crisis on carbon emissions based on decomposition results. The empirical results of our study show the following five conclusions. (1) Most sectors in China are in weak decoupling state due to the inhibition of energy intensity on carbon emissions. (2) Different factors contribute differently to reducing emissions in different sectors, economic output has the most prominent effect, followed by energy intensity and population scale. (3) China's current carbon emission reduction measures benefit more on energy efficiency. (4) The economic crisis has greatly reduced energy efficiency and has no significant impact on other factors. (5) If all industries adjust their energy mix, carbon emissions in China can be reduced by almost 17% every year.

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Keywords:  CO2 emissions; Decoupling theory; LMDI method; Multisector

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Year:  2020        PMID: 32297109     DOI: 10.1007/s11356-020-08490-0

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  1 in total

1.  Decomposition and Decoupling Analysis of CO2 Emissions Based on LMDI and Two-Dimensional Decoupling Model in Gansu Province, China.

Authors:  Lele Xin; Junsong Jia; Wenhui Hu; Huiqing Zeng; Chundi Chen; Bo Wu
Journal:  Int J Environ Res Public Health       Date:  2021-06-03       Impact factor: 3.390

  1 in total

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