IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i17p9690-d624604.html
   My bibliography  Save this article

Health Impact Attributable to Improvement of PM 2.5 Pollution from 2014–2018 and Its Potential Benefits by 2030 in China

Author

Listed:
  • Yu Ma

    (Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, China
    School of Geographic Sciences, Hunan Normal University, Changsha 410081, China)

  • Deping Li

    (Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, China
    School of Geographic Sciences, Hunan Normal University, Changsha 410081, China)

  • Liang Zhou

    (Hunan Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, China
    School of Geographic Sciences, Hunan Normal University, Changsha 410081, China)

Abstract

With the advancement of urbanization and industrialization, air pollution has become one of the biggest challenges for sustainable development. In recent years, ambient PM 2.5 concentrations in China have declined substantially due to the combined effect of PM 2.5 control and meteorological conditions. To this end, it is critical to assess the health impact attributable to PM 2.5 pollution improvement and to explore the potential benefits which may be obtained through the achievement of future PM 2.5 control targets. Based on PM 2.5 and population data with a 1 km resolution, premature mortality caused by exposure to PM 2.5 in China from 2014 to 2018 was estimated using the Global Exposure Mortality Model (GEMM). Then, the potential benefits of achieving PM 2.5 control targets were estimated for 2030. The results show that premature mortality caused by PM 2.5 pollution decreased by 22.41%, from 2,361,880 in 2014 to 1,832,470 in 2018. Moreover, the reduction of premature mortality in six major regions of China accounted for 52.82% of the national total reduction. If the PM 2.5 control target can be achieved by 2030, PM 2.5 -related premature deaths will further decrease by 403,050, accounting for 21.99% of those in 2018. Among them, 87.02% of cities exhibited decreases in premature deaths. According to the potential benefits in 2030, all cities were divided into three types, of which type III cities should set stricter PM 2.5 control targets and further strengthen the associated monitoring and governance. The results of this study provide a reference for the formulation of air pollution control policies based on regional differences.

Suggested Citation

  • Yu Ma & Deping Li & Liang Zhou, 2021. "Health Impact Attributable to Improvement of PM 2.5 Pollution from 2014–2018 and Its Potential Benefits by 2030 in China," Sustainability, MDPI, vol. 13(17), pages 1-18, August.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9690-:d:624604
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/17/9690/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/17/9690/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huanbi Yue & Chunyang He & Qingxu Huang & Dan Yin & Brett A. Bryan, 2020. "Stronger policy required to substantially reduce deaths from PM2.5 pollution in China," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    2. Liddle, Brantley & Lung, Sidney, 2010. "Age-Structure, Urbanization, and Climate Change in Developed Countries: Revisiting STIRPAT for Disaggregated Population and Consumption-Related Environmental Impacts," MPRA Paper 59579, University Library of Munich, Germany.
    3. Philippe Aghion & Steven Durlauf (ed.), 2005. "Handbook of Economic Growth," Handbook of Economic Growth, Elsevier, edition 1, volume 1, number 1.
    4. J. Lelieveld & J. S. Evans & M. Fnais & D. Giannadaki & A. Pozzer, 2015. "The contribution of outdoor air pollution sources to premature mortality on a global scale," Nature, Nature, vol. 525(7569), pages 367-371, September.
    Full references (including those not matched with items on IDEAS)

    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. Rong Tang & Jing Zhao & Yifan Liu & Xin Huang & Yanxu Zhang & Derong Zhou & Aijun Ding & Chris P. Nielsen & Haikun Wang, 2022. "Air quality and health co-benefits of China’s carbon dioxide emissions peaking before 2030," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    2. Xialing Sun & Rui Zhang & Geyi Wang, 2022. "Spatial-Temporal Evolution of Health Impact and Economic Loss upon Exposure to PM 2.5 in China," IJERPH, MDPI, vol. 19(4), pages 1-17, February.
    3. Huanbi Yue & Chunyang He & Qingxu Huang & Da Zhang & Peijun Shi & Enayat A. Moallemi & Fangjin Xu & Yang Yang & Xin Qi & Qun Ma & Brett A. Bryan, 2024. "Substantially reducing global PM2.5-related deaths under SDG3.9 requires better air pollution control and healthcare," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    4. Sicheng Wang & Pingjun Sun & Feng Sun & Shengnan Jiang & Zhaomin Zhang & Guoen Wei, 2021. "The Direct and Spillover Effect of Multi-Dimensional Urbanization on PM 2.5 Concentrations: A Case Study from the Chengdu-Chongqing Urban Agglomeration in China," IJERPH, MDPI, vol. 18(20), pages 1-19, October.
    5. Zhiqiao Xiong & Dandan Li & Hongwei Yu, 2023. "Does PM2.5 (Pollutant) Reduce Firms’ Innovation Output?," IJERPH, MDPI, vol. 20(2), pages 1-19, January.
    6. Gianfranco DI VAIO & Michele BATTISTI, 2010. "A Spatially-Filtered Mixture of Beta-Convergence Regression for EU Regions, 1980-2002," Regional and Urban Modeling 284100013, EcoMod.
    7. Claudia García-García & Catalina B. García-García & Román Salmerón, 2021. "Confronting collinearity in environmental regression models: evidence from world data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 895-926, September.
    8. Vieira, Flávio & MacDonald, Ronald & Damasceno, Aderbal, 2012. "The role of institutions in cross-section income and panel data growth models: A deeper investigation on the weakness and proliferation of instruments," Journal of Comparative Economics, Elsevier, vol. 40(1), pages 127-140.
    9. Lutz Arnold & Christian Bauer, 2009. "On the growth and welfare effects of monopolistic distortions," Journal of Economics, Springer, vol. 97(1), pages 19-40, May.
    10. Jing Xing, 2011. "Does tax structure affect economic growth? Empirical evidence from OECD countries," Working Papers 1120, Oxford University Centre for Business Taxation.
    11. Schreiner, Lena & Madlener, Reinhard, 2022. "Investing in power grid infrastructure as a flexibility option: A DSGE assessment for Germany," Energy Economics, Elsevier, vol. 107(C).
    12. Eduardo Fernández-Arias & Ricardo Hausmann & Ugo Panizza, 2020. "Smart Development Banks," Journal of Industry, Competition and Trade, Springer, vol. 20(2), pages 395-420, June.
    13. Peppel-Srebrny, Jemima, 2021. "Not all government budget deficits are created equal: Evidence from advanced economies' sovereign bond markets," Journal of International Money and Finance, Elsevier, vol. 118(C).
    14. Michele Peruzzi & Alessio Terzi, 2018. "Growth Accelerations Strategies," Growth Lab Working Papers 112, Harvard's Growth Lab.
    15. Milo Bianchi, 2012. "Financial Development, Entrepreneurship, and Job Satisfaction," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 273-286, February.
    16. François Fall & Akim Almouksit, 2016. "The impact of formal financing on small informal enterprises in Comoros," Working Papers hal-01566389, HAL.
    17. Roberto Martino & Phu Nguyen-Van, 2014. "Labour market regulation and fiscal parameters: A structural model for European regions," Working Papers of BETA 2014-19, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    18. Andrei A Levchenko & Jing Zhang, 2013. "The Global Labor Market Impact of Emerging Giants: A Quantitative Assessment," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 61(3), pages 479-519, August.
    19. Lei Gao & Taowu Pei & Jingran Zhang & Yu Tian, 2022. "The “Pollution Halo” Effect of FDI: Evidence from the Chinese Sichuan–Chongqing Urban Agglomeration," IJERPH, MDPI, vol. 19(19), pages 1-17, September.
    20. Jeni Klugman & Francisco Rodríguez & Hyung-Jin Choi, 2011. "The HDI 2010: new controversies, old critiques," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 9(2), pages 249-288, June.

    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:17:p:9690-:d:624604. 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.