Investigating the spatially heterogeneous impacts of urbanization on city-level industrial SO2 emissions: Evidence from night-time light data in China

The spatial distribution and the identification of the influential factors of industrial sulfur dioxide (SO2) emissions have received extensive attention. However, evidence is still lacking on the spatial impacts of urbanization on industrial SO2 emissions at the city scale in China. This work build...

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Autores principales:	Ying Xu, Weishi Zhang, Jionghua Wang, Siping Ji, Can Wang, David G. Streets
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Spatial heterogeneity Geography Weighted Regression (GWR) model City-level SO2 emissions Urbanization STIRPAT framework Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/631771809476423ba3d12bb156d5301f
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id	oai:doaj.org-article:631771809476423ba3d12bb156d5301f
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spelling	oai:doaj.org-article:631771809476423ba3d12bb156d5301f2021-12-04T04:33:25ZInvestigating the spatially heterogeneous impacts of urbanization on city-level industrial SO2 emissions: Evidence from night-time light data in China1470-160X10.1016/j.ecolind.2021.108430https://doaj.org/article/631771809476423ba3d12bb156d5301f2021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21010955https://doaj.org/toc/1470-160XThe spatial distribution and the identification of the influential factors of industrial sulfur dioxide (SO2) emissions have received extensive attention. However, evidence is still lacking on the spatial impacts of urbanization on industrial SO2 emissions at the city scale in China. This work builds a Geography Weighted Regression (GWR) model on a Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) framework to investigate the spatially heterogeneous impacts of potential influencing factors on city-level industrial SO2 emissions from 288 prefecture-level cities in China. The results show that the GWR model significantly improved the goodness-of-fit of the model. The influence of night-time light intensity of the cities, as a proxy of the urbanization level, was calculated to be median (min, max): −0.505(-0.918, −0.413). The highest impacts of urbanization were observed in the Northeast and Southwest regions. Industrial influencing factors had generally promoted the growth of SO2 emissions, with higher positive impacts in western cities. We concluded that urbanization had a significant and negative effect on industrial SO2 emissions in most Chinese cities. It is necessary to formulate emission reduction and city development policies simultaneously based on the trade-offs between urbanization and air pollution control targets.Ying XuWeishi ZhangJionghua WangSiping JiCan WangDavid G. StreetsElsevierarticleSpatial heterogeneityGeography Weighted Regression (GWR) modelCity-level SO2 emissionsUrbanizationSTIRPAT frameworkEcologyQH540-549.5ENEcological Indicators, Vol 133, Iss , Pp 108430- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Spatial heterogeneity Geography Weighted Regression (GWR) model City-level SO2 emissions Urbanization STIRPAT framework Ecology QH540-549.5
spellingShingle	Spatial heterogeneity Geography Weighted Regression (GWR) model City-level SO2 emissions Urbanization STIRPAT framework Ecology QH540-549.5 Ying Xu Weishi Zhang Jionghua Wang Siping Ji Can Wang David G. Streets Investigating the spatially heterogeneous impacts of urbanization on city-level industrial SO2 emissions: Evidence from night-time light data in China
description	The spatial distribution and the identification of the influential factors of industrial sulfur dioxide (SO2) emissions have received extensive attention. However, evidence is still lacking on the spatial impacts of urbanization on industrial SO2 emissions at the city scale in China. This work builds a Geography Weighted Regression (GWR) model on a Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) framework to investigate the spatially heterogeneous impacts of potential influencing factors on city-level industrial SO2 emissions from 288 prefecture-level cities in China. The results show that the GWR model significantly improved the goodness-of-fit of the model. The influence of night-time light intensity of the cities, as a proxy of the urbanization level, was calculated to be median (min, max): −0.505(-0.918, −0.413). The highest impacts of urbanization were observed in the Northeast and Southwest regions. Industrial influencing factors had generally promoted the growth of SO2 emissions, with higher positive impacts in western cities. We concluded that urbanization had a significant and negative effect on industrial SO2 emissions in most Chinese cities. It is necessary to formulate emission reduction and city development policies simultaneously based on the trade-offs between urbanization and air pollution control targets.
format	article
author	Ying Xu Weishi Zhang Jionghua Wang Siping Ji Can Wang David G. Streets
author_facet	Ying Xu Weishi Zhang Jionghua Wang Siping Ji Can Wang David G. Streets
author_sort	Ying Xu
title	Investigating the spatially heterogeneous impacts of urbanization on city-level industrial SO2 emissions: Evidence from night-time light data in China
title_short	Investigating the spatially heterogeneous impacts of urbanization on city-level industrial SO2 emissions: Evidence from night-time light data in China
title_full	Investigating the spatially heterogeneous impacts of urbanization on city-level industrial SO2 emissions: Evidence from night-time light data in China
title_fullStr	Investigating the spatially heterogeneous impacts of urbanization on city-level industrial SO2 emissions: Evidence from night-time light data in China
title_full_unstemmed	Investigating the spatially heterogeneous impacts of urbanization on city-level industrial SO2 emissions: Evidence from night-time light data in China
title_sort	investigating the spatially heterogeneous impacts of urbanization on city-level industrial so2 emissions: evidence from night-time light data in china
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/631771809476423ba3d12bb156d5301f
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Investigating the spatially heterogeneous impacts of urbanization on city-level industrial SO2 emissions: Evidence from night-time light data in China

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