Abstract
China has overtaken the United States as the world’s largest producer of carbon dioxide, with industrial carbon emissions (ICE) accounting for approximately 65% of the country’s total emissions. Understanding the ICE decoupling patterns and factors influencing the decoupling status is a prerequisite for balancing economic growth and carbon emissions. This paper provides an overview of ICE based on decoupling elasticity and the Tapio decoupling model. Furthermore, the study identifies the factors contributing to ICE changes in China, using the Kaya identity and Log Mean Divisia Index (LMDI) techniques. Based on the effects and contributions of ICE, we close with a number of recommendations. The results revealed a significant upward trend of ICE during the study period 1994 to 2013, with a total amount of 11,147 million tons. Analyzing the decoupling relationship indicates that “weak decoupling” and “expansive decoupling” were the main states during the study period. The decomposition analysis showed that per capita wealth associated with industrial outputs and energy intensity are the main driving force of ICE, while energy intensity of industrial output and energy structure are major determinants for ICE reduction. The largest contributing cumulative effect to ICE is per capita wealth, at 1.23 in 2013. This factor is followed by energy intensity, with a contributing cumulative effect of −0.32. The cumulative effects of energy structure and population are relatively small, at 0.01 and 0.08, respectively.
Highlights
A continuous growth in energy consumption has increased atmospheric carbon greenhouse gas emissions [1,2]
We developed a Tapio model based on extended Kaya identity to analysis decoupling status, and developed decoupling index based on Logarithmic Mean Divisia Index (LMDI) techniques to study the contribution of different factors influencing industrial carbon emission in China from 1994–2013
According to the LMDI method, the change of carbon consumption between a base year 0 and a target year t, denoted by ∆C, is 0, because the carbon emission coefficients are basically unchanged and there is no systematic monitoring of industrial carbon emissions (ICE) in China
Summary
A continuous growth in energy consumption has increased atmospheric carbon greenhouse gas emissions [1,2]. Past studies used decomposition methodologies to quantitatively identify factors on changes carbon emission, at country-, regional-, and global-level. These factors can in turn be applied in energy policymaking. Chipper [50] qualified the three key influencing factors on changes in energy use and carbon emissions from freight transport in 10 industrialized countries. This paper is aimed to clarify the relationship between carbon emission and economic output in China’s industrial sector, and to examine these influencing factors of decoupling status. We developed a Tapio model based on extended Kaya identity to analysis decoupling status, and developed decoupling index based on LMDI techniques to study the contribution of different factors influencing industrial carbon emission in China from 1994–2013
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
