IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i6p776-d823504.html
   My bibliography  Save this article

Do We Need More Urban Green Space to Alleviate PM 2.5 Pollution? A Case Study in Wuhan, China

Author

Listed:
  • Yuanyuan Chen

    (College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China
    Department of Sustainable Landscape Development, Institute for Geosciences and Geography, Martin Luther University Halle-Wittenberg, 06120 Halle (Saale), Germany)

  • Xinli Ke

    (College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China)

  • Min Min

    (College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China)

  • Yue Zhang

    (College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China)

  • Yaqiang Dai

    (College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China)

  • Lanping Tang

    (College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China
    Department of Environmental Geography, Institute for Environmental Studies, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands)

Abstract

Urban green space can help to reduce PM 2.5 concentration by absorption and deposition processes. However, few studies have focused on the historical influence of green space on PM 2.5 at a fine grid scale. Taking the central city of Wuhan as an example, this study has analyzed the spatiotemporal trend and the relationship between green space and PM 2.5 in the last two decades. The results have shown that: (1) PM 2.5 concentration reached a maximum value (139 μg/m 3 ) in 2010 and decreased thereafter. Moran’s I index values of PM 2.5 were in a downward trend, which indicates a sparser distribution; (2) from 2000 to 2019, the total area of green space decreased by 25.83%. The reduction in larger patches, increment in land cover diversity, and less connectivity led to fragmented spatial patterns of green space; and (3) the regression results showed that large patches of green space significantly correlated with PM 2.5 concentration. The land use/cover diversity negatively correlated with the PM 2.5 concentration in the ordinary linear regression. In conclusion, preserving large native natural habitats can be a supplemental measure to enlarge the air purification function of the green space. For cities in the process of PM 2.5 reduction, enhancing the landscape patterns of green space provides a win-win solution to handle air pollution and raise human well-being.

Suggested Citation

  • Yuanyuan Chen & Xinli Ke & Min Min & Yue Zhang & Yaqiang Dai & Lanping Tang, 2022. "Do We Need More Urban Green Space to Alleviate PM 2.5 Pollution? A Case Study in Wuhan, China," Land, MDPI, vol. 11(6), pages 1-16, May.
  • Handle: RePEc:gam:jlands:v:11:y:2022:i:6:p:776-:d:823504
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/6/776/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/11/6/776/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Philip Stessens & Frank Canters & Marijke Huysmans & Ahmed Z. Khan, 2020. "Urban green space qualities: An integrated approach towards GIS-based assessment reflecting user perception," ULB Institutional Repository 2013/298795, ULB -- Universite Libre de Bruxelles.
    2. Bo-Xun Huang & Shang-Chia Chiou & Wen-Ying Li, 2021. "Landscape Pattern and Ecological Network Structure in Urban Green Space Planning: A Case Study of Fuzhou City," Land, MDPI, vol. 10(8), pages 1-23, July.
    3. Stern, David I. & Common, Michael S. & Barbier, Edward B., 1996. "Economic growth and environmental degradation: The environmental Kuznets curve and sustainable development," World Development, Elsevier, vol. 24(7), pages 1151-1160, July.
    4. Chen, Yang & Shao, Shuai & Fan, Meiting & Tian, Zhihua & Yang, Lili, 2022. "One man's loss is another's gain: Does clean energy development reduce CO2 emissions in China? Evidence based on the spatial Durbin model," Energy Economics, Elsevier, vol. 107(C).
    5. Man Yuan & Mingrui Yan & Zhuoran Shan, 2021. "Is Compact Urban Form Good for Air Quality? A Case Study from China Based on Hourly Smartphone Data," Land, MDPI, vol. 10(5), pages 1-14, May.
    6. Lee, Lung-fei & Yu, Jihai, 2010. "Estimation of spatial autoregressive panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 154(2), pages 165-185, February.
    7. Sanduijav, Chimedregzen & Ferreira, Susana & Filipski, Mateusz & Hashida, Yukiko, 2021. "Air pollution and happiness: Evidence from the coldest capital in the world," Ecological Economics, Elsevier, vol. 187(C).
    8. Xinghua Zhao & Zheng Cheng & Chen Jiang, 2021. "Could Air Quality Get Better during Epidemic Prevention and Control in China? An Analysis Based on Regression Discontinuity Design," Land, MDPI, vol. 10(4), pages 1-16, April.
    9. Berglihn, Elisabeth Cornelia & Gómez-Baggethun, Erik, 2021. "Ecosystem services from urban forests: The case of Oslomarka, Norway," Ecosystem Services, Elsevier, vol. 51(C).
    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. Gamal El Afandi & Hossam Ismael, 2023. "Spatiotemporal Variation of Summertime Urban Heat Island (UHI) and Its Correlation with Particulate Matter (PM2.5) over Metropolitan Cities in Alabama," Geographies, MDPI, vol. 3(4), pages 1-32, October.
    2. Peng Zhou & Siwei Sun & Tao Chen & Yue Pan & Wanqing Xu & Hailu Zhang, 2022. "Impacts of Social Inequality, Air Pollution, Rural–Urban Divides, and Insufficient Green Space on Residents’ Health in China: Insight from Chinese General Social Survey Data Analysis," IJERPH, MDPI, vol. 19(21), pages 1-17, October.

    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. Nan, Shijing & Huo, Yuchen & You, Wanhai & Guo, Yawei, 2022. "Globalization spatial spillover effects and carbon emissions: What is the role of economic complexity?," Energy Economics, Elsevier, vol. 112(C).
    2. Chai, Jian & Tian, Lingyue & Jia, Ruining, 2023. "New energy demonstration city, spatial spillover and carbon emission efficiency: Evidence from China's quasi-natural experiment," Energy Policy, Elsevier, vol. 173(C).
    3. Shahbaz, Muhammad & Hoang, Thi Hong Van & Mahalik, Mantu Kumar & Roubaud, David, 2017. "Energy consumption, financial development and economic growth in India: New evidence from a nonlinear and asymmetric analysis," Energy Economics, Elsevier, vol. 63(C), pages 199-212.
    4. repec:rri:wpaper:201303 is not listed on IDEAS
    5. Zanin, Luca & Marra, Giampiero, 2012. "Assessing the functional relationship between CO2 emissions and economic development using an additive mixed model approach," Economic Modelling, Elsevier, vol. 29(4), pages 1328-1337.
    6. Cem Ertur & Antonio Musolesi, 2017. "Weak and Strong Cross‐Sectional Dependence: A Panel Data Analysis of International Technology Diffusion," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 477-503, April.
    7. Kaika, Dimitra & Zervas, Efthimios, 2013. "The environmental Kuznets curve (EKC) theory. Part B: Critical issues," Energy Policy, Elsevier, vol. 62(C), pages 1403-1411.
    8. Yue, Shen & Munir, Irfan Ullah & Hyder, Shabir & Nassani, Abdelmohsen A. & Qazi Abro, Muhammad Moinuddin & Zaman, Khalid, 2020. "Sustainable food production, forest biodiversity and mineral pricing: Interconnected global issues," Resources Policy, Elsevier, vol. 65(C).
    9. Yiridoe, Emmanuel K. & Nanang, David M., 2001. "An Econometric Analysis Of The Causes Of Tropical Deforestation: Ghana," 2001 Annual meeting, August 5-8, Chicago, IL 20750, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    10. Zhigang Li & Jie Yang & Jialong Zhong & Dong Zhang, 2022. "Assessment of Urban Agglomeration Ecological Sustainability and Identification of Influencing Factors: Based on the 3DEF Model and the Random Forest," IJERPH, MDPI, vol. 20(1), pages 1-15, December.
    11. Juan Antonio Duro & Jordi Teixidó-Figueras & Emilio Padilla, 2017. "The Causal Factors of International Inequality in $$\hbox {CO}_{2}$$ CO 2 Emissions Per Capita: A Regression-Based Inequality Decomposition Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 67(4), pages 683-700, August.
    12. Bradford David F. & Fender Rebecca A & Shore Stephen H. & Wagner Martin, 2005. "The Environmental Kuznets Curve: Exploring a Fresh Specification," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 4(1), pages 1-30, June.
    13. Ghimire, Narishwar & Woodward, Richard T., 2013. "Under- and over-use of pesticides: An international analysis," Ecological Economics, Elsevier, vol. 89(C), pages 73-81.
    14. Esposito, Piero & Patriarca, Fabrizio & Salvati, Luca, 2018. "Tertiarization and land use change: The case of Italy," Economic Modelling, Elsevier, vol. 71(C), pages 80-86.
    15. Jha, Raghbendra & Murthy, K. V. Bhanu, 2003. "An inverse global environmental Kuznets curve," Journal of Comparative Economics, Elsevier, vol. 31(2), pages 352-368, June.
    16. Askarov, Zohid & Doucouliagos, Hristos, 2015. "Spatial aid spillovers during transition," European Journal of Political Economy, Elsevier, vol. 40(PA), pages 79-95.
    17. Atems, Bebonchu, 2013. "The spatial dynamics of growth and inequality: Evidence using U.S. county-level data," Economics Letters, Elsevier, vol. 118(1), pages 19-22.
    18. Shuaibing Zhang & Kaixu Zhao & Shuoyang Ji & Yafang Guo & Fengqi Wu & Jingxian Liu & Fei Xie, 2022. "Evolution Characteristics, Eco-Environmental Response and Influencing Factors of Production-Living-Ecological Space in the Qinghai–Tibet Plateau," Land, MDPI, vol. 11(7), pages 1-26, July.
    19. G. Mythili & Shibashis Mukherjee, 2011. "Examining Environmental Kuznets Curve for river effluents in India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 13(3), pages 627-640, June.
    20. George Halkos & Iacovos Psarianos, 2016. "Exploring the effect of including the environment in the neoclassical growth model," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 18(3), pages 339-358, July.
    21. Tan, Xiujie & Yan, Yaxue & Dong, Yuyang, 2022. "Peer effect in green credit induced green innovation: An empirical study from China's Green Credit Guidelines," Resources Policy, Elsevier, vol. 76(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:jlands:v:11:y:2022:i:6:p:776-:d:823504. 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.