Spatial heterogeneity and economic driving factors of SO2 emissions in China: Evidence from an eigenvector based spatial filtering approach

Sulfur dioxide (SO2) emissions have been a great challenge in China over the last few decades due to their serious impact on the environment and human health. In this paper, a random effect eigenvector spatial filtering (RE-ESF) approach without and with non-spatially varying coefficients (SNVC) is...

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Autores principales:	Wenming Shi, Yuquan Du, Chia-Hsun Chang, Son Nguyen, Jun Wu
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Environmental sustainability Spatial dependence Economic determinants Non-spatially varying coefficients Province-specific policies Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/42042c34b66e47edacad8d76290e95db
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spelling	oai:doaj.org-article:42042c34b66e47edacad8d76290e95db2021-12-01T04:57:43ZSpatial heterogeneity and economic driving factors of SO2 emissions in China: Evidence from an eigenvector based spatial filtering approach1470-160X10.1016/j.ecolind.2021.108001https://doaj.org/article/42042c34b66e47edacad8d76290e95db2021-10-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X2100666Xhttps://doaj.org/toc/1470-160XSulfur dioxide (SO2) emissions have been a great challenge in China over the last few decades due to their serious impact on the environment and human health. In this paper, a random effect eigenvector spatial filtering (RE-ESF) approach without and with non-spatially varying coefficients (SNVC) is identified to examine spatial heterogeneity and economic driving factors of SO2 emissions in China from 2011 to 2017. Using the Moran eigenvectors to extract information on spatial dependence, the main findings of the RE-ESF approach are as follows: First, after comparing different approaches for dealing with spatial dependence, it is found that the RE-ESF approach demonstrates the best fit to the dataset. Second, the global investigation shows that SO2 emissions are negatively determined by economic growth and government expenditure for environment protection, but are positively determined by road freight transport, coal consumption and oil consumption. Third, the local investigation indicates that the spatially varying coefficients of economic growth and coal consumption range from 0.1401 to 0.2732 with the median value of 0.2478 and from 0.2406 to 0.3611 with the median value of 0.3210, respectively, revealing significant spatial heterogeneity of SO2 emissions driven by economic growth and coal consumption. These findings provide meaningful insights into centralized and province-specific policies for reducing SO2 emissions.Wenming ShiYuquan DuChia-Hsun ChangSon NguyenJun WuElsevierarticleEnvironmental sustainabilitySpatial dependenceEconomic determinantsNon-spatially varying coefficientsProvince-specific policiesEcologyQH540-549.5ENEcological Indicators, Vol 129, Iss , Pp 108001- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Environmental sustainability Spatial dependence Economic determinants Non-spatially varying coefficients Province-specific policies Ecology QH540-549.5
spellingShingle	Environmental sustainability Spatial dependence Economic determinants Non-spatially varying coefficients Province-specific policies Ecology QH540-549.5 Wenming Shi Yuquan Du Chia-Hsun Chang Son Nguyen Jun Wu Spatial heterogeneity and economic driving factors of SO2 emissions in China: Evidence from an eigenvector based spatial filtering approach
description	Sulfur dioxide (SO2) emissions have been a great challenge in China over the last few decades due to their serious impact on the environment and human health. In this paper, a random effect eigenvector spatial filtering (RE-ESF) approach without and with non-spatially varying coefficients (SNVC) is identified to examine spatial heterogeneity and economic driving factors of SO2 emissions in China from 2011 to 2017. Using the Moran eigenvectors to extract information on spatial dependence, the main findings of the RE-ESF approach are as follows: First, after comparing different approaches for dealing with spatial dependence, it is found that the RE-ESF approach demonstrates the best fit to the dataset. Second, the global investigation shows that SO2 emissions are negatively determined by economic growth and government expenditure for environment protection, but are positively determined by road freight transport, coal consumption and oil consumption. Third, the local investigation indicates that the spatially varying coefficients of economic growth and coal consumption range from 0.1401 to 0.2732 with the median value of 0.2478 and from 0.2406 to 0.3611 with the median value of 0.3210, respectively, revealing significant spatial heterogeneity of SO2 emissions driven by economic growth and coal consumption. These findings provide meaningful insights into centralized and province-specific policies for reducing SO2 emissions.
format	article
author	Wenming Shi Yuquan Du Chia-Hsun Chang Son Nguyen Jun Wu
author_facet	Wenming Shi Yuquan Du Chia-Hsun Chang Son Nguyen Jun Wu
author_sort	Wenming Shi
title	Spatial heterogeneity and economic driving factors of SO2 emissions in China: Evidence from an eigenvector based spatial filtering approach
title_short	Spatial heterogeneity and economic driving factors of SO2 emissions in China: Evidence from an eigenvector based spatial filtering approach
title_full	Spatial heterogeneity and economic driving factors of SO2 emissions in China: Evidence from an eigenvector based spatial filtering approach
title_fullStr	Spatial heterogeneity and economic driving factors of SO2 emissions in China: Evidence from an eigenvector based spatial filtering approach
title_full_unstemmed	Spatial heterogeneity and economic driving factors of SO2 emissions in China: Evidence from an eigenvector based spatial filtering approach
title_sort	spatial heterogeneity and economic driving factors of so2 emissions in china: evidence from an eigenvector based spatial filtering approach
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/42042c34b66e47edacad8d76290e95db
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Spatial heterogeneity and economic driving factors of SO2 emissions in China: Evidence from an eigenvector based spatial filtering approach

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