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Decoupling analysis and peak projection of manufacturing CO(2) emissions from the perspective of investment

Reducing carbon emissions has become an urgent task in China. As the category with the largest economic and emissions contribution to the industry, the carbon emissions research of the manufacturing industry is particularly important. This paper uses the LMDI method to decompose manufacturing carbon...

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Detalles Bibliográficos
Autores principales: Ge, Yihan, Yuan, Rong, Liao, Haoyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9968644/
https://www.ncbi.nlm.nih.gov/pubmed/37362982
http://dx.doi.org/10.1007/s10668-023-03047-w
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author Ge, Yihan
Yuan, Rong
Liao, Haoyun
author_facet Ge, Yihan
Yuan, Rong
Liao, Haoyun
author_sort Ge, Yihan
collection PubMed
description Reducing carbon emissions has become an urgent task in China. As the category with the largest economic and emissions contribution to the industry, the carbon emissions research of the manufacturing industry is particularly important. This paper uses the LMDI method to decompose manufacturing carbon emissions into seven influencing factors (i.e., population, urbanization, economic development, investment share, energy intensity, energy structure and emission intensity), in order to explore the factors driving manufacturing carbon emissions during 2003–2018. Then, the paper analyzes the decoupling relationship between manufacturing investment and carbon emissions in 30 provinces. Finally, three scenarios are developed to project future manufacturing emissions at the provincial level up to 2035, and whether manufacturing emissions in 30 provinces can realize peak is discussed. The paper results in three main findings. First, we find that energy intensity played the most important role in decreasing the manufacturing emissions during the whole study period, while the economic development and investment share were the main effect promoting manufacturing carbon emissions. Second, China experienced a process from weak decoupling to strong decoupling between manufacturing invest and emissions. Third, China's manufacturing carbon emissions can only achieve the carbon peaking target in 2030 under the High scenario, and 7 provinces cannot reach the peak before 2035 under the three scenarios. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10668-023-03047-w.
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spelling pubmed-99686442023-02-28 Decoupling analysis and peak projection of manufacturing CO(2) emissions from the perspective of investment Ge, Yihan Yuan, Rong Liao, Haoyun Environ Dev Sustain Article Reducing carbon emissions has become an urgent task in China. As the category with the largest economic and emissions contribution to the industry, the carbon emissions research of the manufacturing industry is particularly important. This paper uses the LMDI method to decompose manufacturing carbon emissions into seven influencing factors (i.e., population, urbanization, economic development, investment share, energy intensity, energy structure and emission intensity), in order to explore the factors driving manufacturing carbon emissions during 2003–2018. Then, the paper analyzes the decoupling relationship between manufacturing investment and carbon emissions in 30 provinces. Finally, three scenarios are developed to project future manufacturing emissions at the provincial level up to 2035, and whether manufacturing emissions in 30 provinces can realize peak is discussed. The paper results in three main findings. First, we find that energy intensity played the most important role in decreasing the manufacturing emissions during the whole study period, while the economic development and investment share were the main effect promoting manufacturing carbon emissions. Second, China experienced a process from weak decoupling to strong decoupling between manufacturing invest and emissions. Third, China's manufacturing carbon emissions can only achieve the carbon peaking target in 2030 under the High scenario, and 7 provinces cannot reach the peak before 2035 under the three scenarios. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10668-023-03047-w. Springer Netherlands 2023-02-27 /pmc/articles/PMC9968644/ /pubmed/37362982 http://dx.doi.org/10.1007/s10668-023-03047-w Text en © The Author(s), under exclusive licence to Springer Nature B.V. 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 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 Article
Ge, Yihan
Yuan, Rong
Liao, Haoyun
Decoupling analysis and peak projection of manufacturing CO(2) emissions from the perspective of investment
title Decoupling analysis and peak projection of manufacturing CO(2) emissions from the perspective of investment
title_full Decoupling analysis and peak projection of manufacturing CO(2) emissions from the perspective of investment
title_fullStr Decoupling analysis and peak projection of manufacturing CO(2) emissions from the perspective of investment
title_full_unstemmed Decoupling analysis and peak projection of manufacturing CO(2) emissions from the perspective of investment
title_short Decoupling analysis and peak projection of manufacturing CO(2) emissions from the perspective of investment
title_sort decoupling analysis and peak projection of manufacturing co(2) emissions from the perspective of investment
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9968644/
https://www.ncbi.nlm.nih.gov/pubmed/37362982
http://dx.doi.org/10.1007/s10668-023-03047-w
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