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Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO 2 Emissions in Chinese Cities: Fresh Evidence from MGWR

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  • Weipeng Yuan

    (School of Economics and Management, Xinjiang University, Urumqi 830046, China
    Center for Innovation Management Research of Xinjiang, Xinjiang University, Urumqi 830046, China)

  • Hui Sun

    (School of Economics and Management, Xinjiang University, Urumqi 830046, China
    Center for Innovation Management Research of Xinjiang, Xinjiang University, Urumqi 830046, China)

  • Yu Chen

    (Xinjiang Academy of Social Sciences, Urumqi 830012, China)

  • Xuechao Xia

    (School of Economics and Management, Xinjiang University, Urumqi 830046, China
    Center for Innovation Management Research of Xinjiang, Xinjiang University, Urumqi 830046, China)

Abstract

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 2 emissions are revealed by using Moran’s 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 2 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 2 emissions’ standard deviation ellipse in cities was “northeast–southwest”. The gravity center of the SO 2 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 2 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 2 emissions and their influencing factors, and provides empirical evidence and reference for the precise management of SO 2 emission reduction in “one city, one policy”.

Suggested Citation

  • Weipeng Yuan & Hui Sun & Yu Chen & Xuechao Xia, 2021. "Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO 2 Emissions in Chinese Cities: Fresh Evidence from MGWR," Sustainability, MDPI, vol. 13(21), pages 1-26, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:12059-:d:669842
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    2. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
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    4. Wang, Bin & Yu, Minxiu & Zhu, Yucheng & Bao, Pinjuan, 2021. "Unveiling the driving factors of carbon emissions from industrial resource allocation in China: A spatial econometric perspective," Energy Policy, Elsevier, vol. 158(C).
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