Drivers of China’s Industrial Carbon Emissions: Evidence from Joint PDA and LMDI Approaches

As the world’s top carbon-emitting country, China has placed great emphasis on understanding the driving factors of carbon emissions and developing appropriate emissions reduction policies. Due to the obvious variations in carbon emissions among various industries in China, corresponding p...

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Main Authors: Feng Dong, Xinqi Gao, Jingyun Li, Yuanqing Zhang, Yajie Liu
Format: Article
Language:English
Published: MDPI AG 2018-12-01
Series:International Journal of Environmental Research and Public Health
Subjects:
PDA
Online Access:https://www.mdpi.com/1660-4601/15/12/2712
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spelling doaj-3635bd6dcc784b9f9fa46ea0b6fab2c72020-11-24T20:51:34ZengMDPI AGInternational Journal of Environmental Research and Public Health1660-46012018-12-011512271210.3390/ijerph15122712ijerph15122712Drivers of China’s Industrial Carbon Emissions: Evidence from Joint PDA and LMDI ApproachesFeng Dong0Xinqi Gao1Jingyun Li2Yuanqing Zhang3Yajie Liu4School of Management, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Management, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Management, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Management, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Management, China University of Mining and Technology, Xuzhou 221116, ChinaAs the world’s top carbon-emitting country, China has placed great emphasis on understanding the driving factors of carbon emissions and developing appropriate emissions reduction policies. Due to the obvious variations in carbon emissions among various industries in China, corresponding policies need to be formulated for different industries. Through data envelopment analysis, this study introduced the Shephard distance function into the logarithmic mean Divisia index (LMDI) for decomposition analysis, built a carbon emissions decomposition model of 23 industries in China during 2003⁻2015, and analyzed the impact of 10 factors driving carbon emissions. The main results are as follows. (1) Potential gross domestic production (GDP) is a crucial factor for increasing carbon emissions, whereas potential energy intensity and technological advances of carbon emissions have a significant inhibitory effect on carbon emissions; (2) the technological progress of energy usage and the technological advances of GDP output are manifested by inhibiting carbon emissions at the early stage of development and increasing emissions at the later stage; (3) the structure of coal-based energy consumption is difficult to change in the long term, resulting in a weak effect of energy mix on carbon emissions and an increase in carbon emissions due to the potential energy carbon intensity factor.https://www.mdpi.com/1660-4601/15/12/2712carbon emissionsfactor decompositionLMDIShephard distance functionPDAChinese industry
collection DOAJ
language English
format Article
sources DOAJ
author Feng Dong
Xinqi Gao
Jingyun Li
Yuanqing Zhang
Yajie Liu
spellingShingle Feng Dong
Xinqi Gao
Jingyun Li
Yuanqing Zhang
Yajie Liu
Drivers of China’s Industrial Carbon Emissions: Evidence from Joint PDA and LMDI Approaches
International Journal of Environmental Research and Public Health
carbon emissions
factor decomposition
LMDI
Shephard distance function
PDA
Chinese industry
author_facet Feng Dong
Xinqi Gao
Jingyun Li
Yuanqing Zhang
Yajie Liu
author_sort Feng Dong
title Drivers of China’s Industrial Carbon Emissions: Evidence from Joint PDA and LMDI Approaches
title_short Drivers of China’s Industrial Carbon Emissions: Evidence from Joint PDA and LMDI Approaches
title_full Drivers of China’s Industrial Carbon Emissions: Evidence from Joint PDA and LMDI Approaches
title_fullStr Drivers of China’s Industrial Carbon Emissions: Evidence from Joint PDA and LMDI Approaches
title_full_unstemmed Drivers of China’s Industrial Carbon Emissions: Evidence from Joint PDA and LMDI Approaches
title_sort drivers of china’s industrial carbon emissions: evidence from joint pda and lmdi approaches
publisher MDPI AG
series International Journal of Environmental Research and Public Health
issn 1660-4601
publishDate 2018-12-01
description As the world’s top carbon-emitting country, China has placed great emphasis on understanding the driving factors of carbon emissions and developing appropriate emissions reduction policies. Due to the obvious variations in carbon emissions among various industries in China, corresponding policies need to be formulated for different industries. Through data envelopment analysis, this study introduced the Shephard distance function into the logarithmic mean Divisia index (LMDI) for decomposition analysis, built a carbon emissions decomposition model of 23 industries in China during 2003⁻2015, and analyzed the impact of 10 factors driving carbon emissions. The main results are as follows. (1) Potential gross domestic production (GDP) is a crucial factor for increasing carbon emissions, whereas potential energy intensity and technological advances of carbon emissions have a significant inhibitory effect on carbon emissions; (2) the technological progress of energy usage and the technological advances of GDP output are manifested by inhibiting carbon emissions at the early stage of development and increasing emissions at the later stage; (3) the structure of coal-based energy consumption is difficult to change in the long term, resulting in a weak effect of energy mix on carbon emissions and an increase in carbon emissions due to the potential energy carbon intensity factor.
topic carbon emissions
factor decomposition
LMDI
Shephard distance function
PDA
Chinese industry
url https://www.mdpi.com/1660-4601/15/12/2712
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AT xinqigao driversofchinasindustrialcarbonemissionsevidencefromjointpdaandlmdiapproaches
AT jingyunli driversofchinasindustrialcarbonemissionsevidencefromjointpdaandlmdiapproaches
AT yuanqingzhang driversofchinasindustrialcarbonemissionsevidencefromjointpdaandlmdiapproaches
AT yajieliu driversofchinasindustrialcarbonemissionsevidencefromjointpdaandlmdiapproaches
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