Literature DB >> 32926275

Analyzing China's coal-related carbon emissions from economic growth perspective: Through decoupling and decomposition model.

Zhiwei You1, Tao Zhao1, Ce Song2, Juan Wang3.   

Abstract

To evaluate the relationship between coal-related carbon emissions (CCE) and economic growth, this paper analyzed the decoupling relationship between CCE and economic growth from national and provincial perspectives during period 1997-2016 through Tapio Decoupling Index. Then, to recognize its spatial characteristics during 1997-2016, gravity model was adopted to study the geographical changes of CCE. Finally, to identify the changes of (CCE) in China and reveal its internal driving forces, this paper employs the logarithmic mean Divisia index (LMDI) decomposition analysis to decompose decoupling indicator into six effects including emission factors, energy intensity, fossil energy structure, energy consumption structure, activity, and population at national and provincial levels. The results reflect that (1) CCE of China rose by 168.37% from 1997 to 2016, and reached the peak of 7948.43 Mton in 2013. The center of gravity has shifted from (114.64 E, 34.70 N) to (113.48 E, 35.06 N). (2) The decoupling curve showed an inverted "U" shape. The economic growth of 18 provinces has achieved a strong decoupling from CCE by 2016. Only Xinjiang, Shanxi, and Shaanxi's economic growth has increased the dependence on CCE. (3) Activity and energy intensity effects were the dominant factors driving and curbing the increase of decoupling indicator respectively.

Entities:  

Keywords:  Center of gravity; Coal-related emissions; Decoupling analysis; Economic growth; LMDI method

Year:  2020        PMID: 32926275     DOI: 10.1007/s11356-020-10734-y

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


  2 in total

1.  Analyzing driving forces of China's carbon emissions from 1997 to 2040 and the potential emission reduction path: through decomposition and scenario analysis.

Authors:  Ce Song; Tao Zhao; Juan Wang
Journal:  Clean Technol Environ Policy       Date:  2021-11-26       Impact factor: 4.700

2.  China's carbon dioxide emission forecast based on improved marine predator algorithm and multi-kernel support vector regression.

Authors:  Xiwen Qin; Siqi Zhang; Xiaogang Dong; Yichang Zhan; Rui Wang; Dingxin Xu
Journal:  Environ Sci Pollut Res Int       Date:  2022-08-18       Impact factor: 5.190

  2 in total

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