Decoupling economic growth from energy-related PM2.5 emissions in China: A GDIM-based indicator decomposition

Haze pollution has become a severe threat to China’s sustainable development. It is crucial to provide a complete explanation for the mechanism behind the changes in PM2.5 pollution and explore how to decouple economic growth from PM2.5 pollution. Compared with the traditional decomposition analysis...

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Autores principales:	Bolin Yu, Debin Fang
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Energy-related PM2.5 emissions Factor decomposition Generalized Divisia Index Method Technical and non-technical effects Decoupling indicator China Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/09ce1132f97e4e09ab9d9d6ad9f52265
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spelling	oai:doaj.org-article:09ce1132f97e4e09ab9d9d6ad9f522652021-12-01T04:53:44ZDecoupling economic growth from energy-related PM2.5 emissions in China: A GDIM-based indicator decomposition1470-160X10.1016/j.ecolind.2021.107795https://doaj.org/article/09ce1132f97e4e09ab9d9d6ad9f522652021-08-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X2100460Xhttps://doaj.org/toc/1470-160XHaze pollution has become a severe threat to China’s sustainable development. It is crucial to provide a complete explanation for the mechanism behind the changes in PM2.5 pollution and explore how to decouple economic growth from PM2.5 pollution. Compared with the traditional decomposition analysis methods, the Generalized Divisia Index Method (GDIM) integrates multiple influencing factors and provides a fuller understanding of the influence mechanism of PM2.5 pollution. In addition, the decomposition of the monitored PM2.5 concentration in previous studies leads to ambiguous economic meanings of some relative impact indicators. In this paper, GDIM decomposition is applied to study the driving factors of energy-related PM2.5 emissions changes in China. Then, based on the GDIM decomposition results, this paper constructs a novel decoupling indicator to study the decoupling relationship between PM2.5 emissions and economic growth, and the contributions of the technical and non-technical factors to the decoupling indicator are quantified. The main results are summarized as follows. (1) The output scale effect and energy use effect are the main reasons for the increase of PM2.5 emissions, while the emission intensity effect and emission coefficient effect make major contributions to decreasing PM2.5 emissions. The effects of population, per capita emissions, per capita output, and energy intensity are relatively minor. (2) China and its three economic regions move from strong decoupling in 1998–2000 to weak decoupling during 2000–2010, and then into strong decoupling over the period of 2010–2014. (3) Technical effect plays an important role in promoting the decoupling between PM2.5 emissions and economic growth, but its contribution shows a downward trend over time. In contrast, non-technical effect hinders the decoupling process, and its contribution is decreasing from 2000 to 2014. This paper is supportive for formulating emissions reduction policies and improving China’s air quality.Bolin YuDebin FangElsevierarticleEnergy-related PM2.5 emissionsFactor decompositionGeneralized Divisia Index MethodTechnical and non-technical effectsDecoupling indicatorChinaEcologyQH540-549.5ENEcological Indicators, Vol 127, Iss , Pp 107795- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Energy-related PM2.5 emissions Factor decomposition Generalized Divisia Index Method Technical and non-technical effects Decoupling indicator China Ecology QH540-549.5
spellingShingle	Energy-related PM2.5 emissions Factor decomposition Generalized Divisia Index Method Technical and non-technical effects Decoupling indicator China Ecology QH540-549.5 Bolin Yu Debin Fang Decoupling economic growth from energy-related PM2.5 emissions in China: A GDIM-based indicator decomposition
description	Haze pollution has become a severe threat to China’s sustainable development. It is crucial to provide a complete explanation for the mechanism behind the changes in PM2.5 pollution and explore how to decouple economic growth from PM2.5 pollution. Compared with the traditional decomposition analysis methods, the Generalized Divisia Index Method (GDIM) integrates multiple influencing factors and provides a fuller understanding of the influence mechanism of PM2.5 pollution. In addition, the decomposition of the monitored PM2.5 concentration in previous studies leads to ambiguous economic meanings of some relative impact indicators. In this paper, GDIM decomposition is applied to study the driving factors of energy-related PM2.5 emissions changes in China. Then, based on the GDIM decomposition results, this paper constructs a novel decoupling indicator to study the decoupling relationship between PM2.5 emissions and economic growth, and the contributions of the technical and non-technical factors to the decoupling indicator are quantified. The main results are summarized as follows. (1) The output scale effect and energy use effect are the main reasons for the increase of PM2.5 emissions, while the emission intensity effect and emission coefficient effect make major contributions to decreasing PM2.5 emissions. The effects of population, per capita emissions, per capita output, and energy intensity are relatively minor. (2) China and its three economic regions move from strong decoupling in 1998–2000 to weak decoupling during 2000–2010, and then into strong decoupling over the period of 2010–2014. (3) Technical effect plays an important role in promoting the decoupling between PM2.5 emissions and economic growth, but its contribution shows a downward trend over time. In contrast, non-technical effect hinders the decoupling process, and its contribution is decreasing from 2000 to 2014. This paper is supportive for formulating emissions reduction policies and improving China’s air quality.
format	article
author	Bolin Yu Debin Fang
author_facet	Bolin Yu Debin Fang
author_sort	Bolin Yu
title	Decoupling economic growth from energy-related PM2.5 emissions in China: A GDIM-based indicator decomposition
title_short	Decoupling economic growth from energy-related PM2.5 emissions in China: A GDIM-based indicator decomposition
title_full	Decoupling economic growth from energy-related PM2.5 emissions in China: A GDIM-based indicator decomposition
title_fullStr	Decoupling economic growth from energy-related PM2.5 emissions in China: A GDIM-based indicator decomposition
title_full_unstemmed	Decoupling economic growth from energy-related PM2.5 emissions in China: A GDIM-based indicator decomposition
title_sort	decoupling economic growth from energy-related pm2.5 emissions in china: a gdim-based indicator decomposition
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/09ce1132f97e4e09ab9d9d6ad9f52265
work_keys_str_mv	AT bolinyu decouplingeconomicgrowthfromenergyrelatedpm25emissionsinchinaagdimbasedindicatordecomposition AT debinfang decouplingeconomicgrowthfromenergyrelatedpm25emissionsinchinaagdimbasedindicatordecomposition
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Decoupling economic growth from energy-related PM2.5 emissions in China: A GDIM-based indicator decomposition

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