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Driving factors of carbon emissions in China’s municipalities: a LMDI approach
China, as the world’s largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities, must enhance efforts in carbon emission reduction to jointly realize China’s low-carbon t...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8586619/ https://www.ncbi.nlm.nih.gov/pubmed/34767167 http://dx.doi.org/10.1007/s11356-021-17277-w |
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author | Liu, Yuanxin Jiang, Yajing Liu, Hui Li, Bo Yuan, Jiahai |
author_facet | Liu, Yuanxin Jiang, Yajing Liu, Hui Li, Bo Yuan, Jiahai |
author_sort | Liu, Yuanxin |
collection | PubMed |
description | China, as the world’s largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities, must enhance efforts in carbon emission reduction to jointly realize China’s low-carbon transition. Taking four representative municipalities, namely, Beijing, Tianjin, Shanghai, and Chongqing as the case cities, this paper establishes a decomposition analysis for the driving factors of carbon emissions by applying the LMDI method covering data from 2007 to 2017. Kaya identity is used to decompose the effects into eight driving factors: GDP effect, industrial structure effect, energy intensity effect, overall energy structure effect, population effect, urbanization effect, per capita energy consumption effect, urban and rural energy structure effect. The results show that at the municipality level, the driving factors that contribute to carbon emissions are the GDP growth effect and the population effect, with the former still being the most important factor in the municipalities with faster economic growth; and industrial structure effect is the most important factor that inhibits carbon emissions, followed by energy structure effect. This paper considers the driving factors of both the production side and the residential consumption side from the city level. The research reveals the main driving factors that effect the carbon emissions of megacities in developing countries, and highlights the leading role of megacities in terms of carbon emission reduction in China and even the world. The paper thereby puts forward policy implications for China’s economic policies. |
format | Online Article Text |
id | pubmed-8586619 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-85866192021-11-12 Driving factors of carbon emissions in China’s municipalities: a LMDI approach Liu, Yuanxin Jiang, Yajing Liu, Hui Li, Bo Yuan, Jiahai Environ Sci Pollut Res Int Research Article China, as the world’s largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities, must enhance efforts in carbon emission reduction to jointly realize China’s low-carbon transition. Taking four representative municipalities, namely, Beijing, Tianjin, Shanghai, and Chongqing as the case cities, this paper establishes a decomposition analysis for the driving factors of carbon emissions by applying the LMDI method covering data from 2007 to 2017. Kaya identity is used to decompose the effects into eight driving factors: GDP effect, industrial structure effect, energy intensity effect, overall energy structure effect, population effect, urbanization effect, per capita energy consumption effect, urban and rural energy structure effect. The results show that at the municipality level, the driving factors that contribute to carbon emissions are the GDP growth effect and the population effect, with the former still being the most important factor in the municipalities with faster economic growth; and industrial structure effect is the most important factor that inhibits carbon emissions, followed by energy structure effect. This paper considers the driving factors of both the production side and the residential consumption side from the city level. The research reveals the main driving factors that effect the carbon emissions of megacities in developing countries, and highlights the leading role of megacities in terms of carbon emission reduction in China and even the world. The paper thereby puts forward policy implications for China’s economic policies. Springer Berlin Heidelberg 2021-11-12 2022 /pmc/articles/PMC8586619/ /pubmed/34767167 http://dx.doi.org/10.1007/s11356-021-17277-w Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Research Article Liu, Yuanxin Jiang, Yajing Liu, Hui Li, Bo Yuan, Jiahai Driving factors of carbon emissions in China’s municipalities: a LMDI approach |
title | Driving factors of carbon emissions in China’s municipalities: a LMDI approach |
title_full | Driving factors of carbon emissions in China’s municipalities: a LMDI approach |
title_fullStr | Driving factors of carbon emissions in China’s municipalities: a LMDI approach |
title_full_unstemmed | Driving factors of carbon emissions in China’s municipalities: a LMDI approach |
title_short | Driving factors of carbon emissions in China’s municipalities: a LMDI approach |
title_sort | driving factors of carbon emissions in china’s municipalities: a lmdi approach |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8586619/ https://www.ncbi.nlm.nih.gov/pubmed/34767167 http://dx.doi.org/10.1007/s11356-021-17277-w |
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