IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i21p12059-d669842.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/21/12059/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/21/12059/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Oshan, Taylor M. & Smith, Jordan & Fotheringham, Alexander Stewart, 2020. "Targeting the spatial context of obesity determinants via multiscale geographically weighted regression," OSF Preprints u7j29, Center for Open Science.
    2. Sharon Ron & Noelle Dimitri & Shir Lerman Ginzburg & Ellin Reisner & Pilar Botana Martinez & Wig Zamore & Ben Echevarria & Doug Brugge & Linda S. Sprague Martinez, 2021. "Health Lens Analysis: A Strategy to Engage Community in Environmental Health Research in Action," Sustainability, MDPI, vol. 13(4), pages 1-13, February.
    3. 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).
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Guangzhi Qi & Zhibao Wang & Zhixiu Wang & Lijie Wei, 2022. "Has Industrial Upgrading Improved Air Pollution?—Evidence from China’s Digital Economy," Sustainability, MDPI, vol. 14(14), pages 1-18, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiang Li & Qipeng Yan & Yafeng Ma & Chen Luo, 2023. "Spatially Varying Impacts of Built Environment on Transfer Ridership of Metro and Bus Systems," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    2. Abolfazl Mollalo & Moosa Tatar, 2021. "Spatial Modeling of COVID-19 Vaccine Hesitancy in the United States," IJERPH, MDPI, vol. 18(18), pages 1-14, September.
    3. Sisman, S. & Aydinoglu, A.C., 2022. "A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul," Land Use Policy, Elsevier, vol. 119(C).
    4. Liang, Fachao & Zhu, Runmiao & Lin, Sheng-Hau, 2023. "Exploring spatial relationship between landscape configuration and ecosystem services: A case study of Xiamen–Zhangzhou–Quanzhou in China," Ecological Modelling, Elsevier, vol. 486(C).
    5. Cynthia Sin Tian Ho & Mats Wilhelmsson, 2022. "Geographical accessibility to bank branches and its relationship to new firm formation in Sweden via multiscale geographically weighted regression," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 42(2), pages 191-218, August.
    6. Huxiao Zhu & Xiangjun Ou & Zhen Yang & Yiwen Yang & Hongxin Ren & Le Tang, 2022. "Spatiotemporal Dynamics and Driving Forces of Land Urbanization in the Yangtze River Delta Urban Agglomeration," Land, MDPI, vol. 11(8), pages 1-21, August.
    7. Rémy Le Boennec & Julie Bulteau & Thierry Feuillet, 2022. "The role of commuter rail accessibility in the formation of residential land values: exploring spatial heterogeneity in peri-urban and remote areas," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 69(1), pages 163-186, August.
    8. Yanxia Hu & Changqing Wang & Xingxiu Yu & Shengzhou Yin, 2021. "Evaluating Trends of Land Productivity Change and Their Causes in the Han River Basin, China: In Support of SDG Indicator 15.3.1," Sustainability, MDPI, vol. 13(24), pages 1-20, December.
    9. Yanzhao Wang & Jianfei Cao, 2023. "Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China’s Cities Based on Spatial Autocorrelation Analysis and MGWR Model," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    10. Abudureheman, Maliyamu & Jiang, Qingzhe & Dong, Xiucheng & Dong, Cong, 2022. "Spatial effects of dynamic comprehensive energy efficiency on CO2 reduction in China," Energy Policy, Elsevier, vol. 166(C).
    11. Taylor M. Oshan & Levi J. Wolf & Mehak Sachdeva & Sarah Bardin & A. Stewart Fotheringham, 2022. "A scoping review on the multiplicity of scale in spatial analysis," Journal of Geographical Systems, Springer, vol. 24(3), pages 293-324, July.
    12. Oshan, Taylor M., 2022. "Navigating the methodological landscape in spatial analysis: a comment on ‘A Route Map for Successful Applications of Geographically-Weighted Regression’," OSF Preprints rckzj, Center for Open Science.
    13. Yigong Hu & Binbin Lu & Yong Ge & Guanpeng Dong, 2022. "Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression," Environment and Planning B, , vol. 49(6), pages 1715-1740, July.
    14. Tao Wang & Kai Zhang & Keliang Liu & Keke Ding & Wenwen Qin, 2023. "Spatial Heterogeneity and Scale Effects of Transportation Carbon Emission-Influencing Factors—An Empirical Analysis Based on 286 Cities in China," IJERPH, MDPI, vol. 20(3), pages 1-17, January.
    15. Junfeng Wang & Shaoyao Zhang & Wei Deng & Qianli Zhou, 2024. "Metropolitan Expansion and Migrant Population: Correlation Patterns and Influencing Factors in Chengdu, China," Land, MDPI, vol. 13(1), pages 1-20, January.
    16. Xin Lao & Hengyu Gu, 2020. "Unveiling various spatial patterns of determinants of hukou transfer intentions in China: A multi‐scale geographically weighted regression approach," Growth and Change, Wiley Blackwell, vol. 51(4), pages 1860-1876, December.
    17. Zhenbao Wang & Jiarui Song & Yuchen Zhang & Shihao Li & Jianlin Jia & Chengcheng Song, 2022. "Spatial Heterogeneity Analysis for Influencing Factors of Outbound Ridership of Subway Stations Considering the Optimal Scale Range of “7D” Built Environments," Sustainability, MDPI, vol. 14(23), pages 1-21, December.
    18. Jiansheng Qu & Lina Liu & Jingjing Zeng & Tek Narayan Maraseni & Zhiqiang Zhang, 2022. "City-Level Determinants of Household CO 2 Emissions per Person: An Empirical Study Based on a Large Survey in China," Land, MDPI, vol. 11(6), pages 1-14, June.
    19. Li Yue & Hongbo Zhao & Xiaoman Xu & Tianshun Gu & Zeting Jia, 2022. "Quantifying the Spatial Fragmentation Pattern and Its Influencing Factors of Urban Land Use: A Case Study of Pingdingshan City, China," Land, MDPI, vol. 11(5), pages 1-15, May.
    20. Zhang, Wei & Li, Yuqing & Zheng, Caigui, 2023. "The distribution characteristics and driving mechanism of vacant land in Chengdu, China: A perspective of urban shrinkage and expansion," Land Use Policy, Elsevier, vol. 132(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:12059-:d:669842. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.