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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 policies ne...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6313346/ https://www.ncbi.nlm.nih.gov/pubmed/30513766 http://dx.doi.org/10.3390/ijerph15122712 |
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author | Dong, Feng Gao, Xinqi Li, Jingyun Zhang, Yuanqing Liu, Yajie |
author_facet | Dong, Feng Gao, Xinqi Li, Jingyun Zhang, Yuanqing Liu, Yajie |
author_sort | Dong, Feng |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-6313346 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63133462019-06-17 Drivers of China’s Industrial Carbon Emissions: Evidence from Joint PDA and LMDI Approaches Dong, Feng Gao, Xinqi Li, Jingyun Zhang, Yuanqing Liu, Yajie Int J Environ Res Public Health Article 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. MDPI 2018-12-01 2018-12 /pmc/articles/PMC6313346/ /pubmed/30513766 http://dx.doi.org/10.3390/ijerph15122712 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dong, Feng Gao, Xinqi Li, Jingyun Zhang, Yuanqing Liu, Yajie Drivers of China’s Industrial Carbon Emissions: Evidence from Joint PDA and LMDI Approaches |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6313346/ https://www.ncbi.nlm.nih.gov/pubmed/30513766 http://dx.doi.org/10.3390/ijerph15122712 |
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