Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO<sub>2</sub> Emissions in Chinese Cities: Fresh Evidence from MGWR
In this study, based on the multi-source nature and humanities data of 270 Chinese cities from 2007 to2018, the spatio-temporal evolution characteristics of SO<sub>2</sub> emissions are revealed by using <i>Moran’s I</i>, a hot spot analysis, kernel density, and standard devi...
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oai:doaj.org-article:c1ef22e215b647d5893045ae30d417b42021-11-11T19:42:40ZSpatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO<sub>2</sub> Emissions in Chinese Cities: Fresh Evidence from MGWR10.3390/su1321120592071-1050https://doaj.org/article/c1ef22e215b647d5893045ae30d417b42021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/12059https://doaj.org/toc/2071-1050In this study, based on the multi-source nature and humanities data of 270 Chinese cities from 2007 to2018, the spatio-temporal evolution characteristics of SO<sub>2</sub> emissions are revealed by using <i>Moran’s I</i>, a hot spot analysis, kernel density, and standard deviation ellipse models. The spatial scale heterogeneity of influencing factors is explored by using the multiscale geographically weighted regression model to make the regression results more accurate and reliable. The results show that (1) SO<sub>2</sub> emissions showed spatial clustering characteristics during the study period, decreased by 85.12% through pollution governance, and exhibited spatial heterogeneity of differentiation. (2) The spatial distribution direction of SO<sub>2</sub> emissions’ standard deviation ellipse in cities was “northeast–southwest”. The gravity center of the SO<sub>2</sub> emissions shifted to the northeast, from Zhumadian City to Zhoukou City in Henan Province. The results of hot spots showed a polarization trend of “clustering hot spots in the north and dispersing cold spots in the south”. (3) The MGWR model is more accurate than the OLS and classical GWR regressions. The different spatial bandwidths have a different effect on the identification of influencing factors. There were several main influencing factors on urban SO<sub>2</sub> emissions: the regional innovation and entrepreneurship level, government intervention, and urban precipitation; important factors: population intensity, financial development, and foreign direct investment; secondary factors: industrial structure upgrading and road construction. Based on the above conclusions, this paper explores the spatial heterogeneity of urban SO<sub>2</sub> emissions and their influencing factors, and provides empirical evidence and reference for the precise management of SO<sub>2</sub> emission reduction in “one city, one policy”.Weipeng YuanHui SunYu ChenXuechao XiaMDPI AGarticleSO<sub>2</sub> emissionMGWR modelinfluencing factorsspatial heterogeneityEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12059, p 12059 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
SO<sub>2</sub> emission MGWR model influencing factors spatial heterogeneity Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 |
| spellingShingle |
SO<sub>2</sub> emission MGWR model influencing factors spatial heterogeneity Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Weipeng Yuan Hui Sun Yu Chen Xuechao Xia Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO<sub>2</sub> Emissions in Chinese Cities: Fresh Evidence from MGWR |
| description |
In this study, based on the multi-source nature and humanities data of 270 Chinese cities from 2007 to2018, the spatio-temporal evolution characteristics of SO<sub>2</sub> emissions are revealed by using <i>Moran’s I</i>, a hot spot analysis, kernel density, and standard deviation ellipse models. The spatial scale heterogeneity of influencing factors is explored by using the multiscale geographically weighted regression model to make the regression results more accurate and reliable. The results show that (1) SO<sub>2</sub> emissions showed spatial clustering characteristics during the study period, decreased by 85.12% through pollution governance, and exhibited spatial heterogeneity of differentiation. (2) The spatial distribution direction of SO<sub>2</sub> emissions’ standard deviation ellipse in cities was “northeast–southwest”. The gravity center of the SO<sub>2</sub> emissions shifted to the northeast, from Zhumadian City to Zhoukou City in Henan Province. The results of hot spots showed a polarization trend of “clustering hot spots in the north and dispersing cold spots in the south”. (3) The MGWR model is more accurate than the OLS and classical GWR regressions. The different spatial bandwidths have a different effect on the identification of influencing factors. There were several main influencing factors on urban SO<sub>2</sub> emissions: the regional innovation and entrepreneurship level, government intervention, and urban precipitation; important factors: population intensity, financial development, and foreign direct investment; secondary factors: industrial structure upgrading and road construction. Based on the above conclusions, this paper explores the spatial heterogeneity of urban SO<sub>2</sub> emissions and their influencing factors, and provides empirical evidence and reference for the precise management of SO<sub>2</sub> emission reduction in “one city, one policy”. |
| format |
article |
| author |
Weipeng Yuan Hui Sun Yu Chen Xuechao Xia |
| author_facet |
Weipeng Yuan Hui Sun Yu Chen Xuechao Xia |
| author_sort |
Weipeng Yuan |
| title |
Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO<sub>2</sub> Emissions in Chinese Cities: Fresh Evidence from MGWR |
| title_short |
Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO<sub>2</sub> Emissions in Chinese Cities: Fresh Evidence from MGWR |
| title_full |
Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO<sub>2</sub> Emissions in Chinese Cities: Fresh Evidence from MGWR |
| title_fullStr |
Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO<sub>2</sub> Emissions in Chinese Cities: Fresh Evidence from MGWR |
| title_full_unstemmed |
Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO<sub>2</sub> Emissions in Chinese Cities: Fresh Evidence from MGWR |
| title_sort |
spatio-temporal evolution and spatial heterogeneity of influencing factors of so<sub>2</sub> emissions in chinese cities: fresh evidence from mgwr |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/c1ef22e215b647d5893045ae30d417b4 |
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