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Combining Data from Multiple Sources to Evaluate Spatial Variations in the Economic Costs of PM 2.5 -Related Health Conditions in the Beijing–Tianjin–Hebei Region

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  • Xiya Zhang

    (Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China)

  • Haibo Hu

    (Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089, China)

Abstract

Fine particulate matter, known as PM 2.5 , is closely related to a range of adverse health outcomes and ultimately imposes a high economic cost on the society. While we know that the costs associated with PM 2.5 -related health outcomes are not uniform geographically, a few researchers have considered the geographical variations in these costs because of a lack of high-resolution data for PM 2.5 and population density. Satellite remote sensing provides highly precise, high-resolution data about how PM 2.5 and population density vary spatially, which can be used to support detailed health-related assessments. In this study, we used high-resolution PM 2.5 concentration and population density based on remote sensing data to assess the effects of PM 2.5 on human health and the related economic costs in the Beijing–Tianjin–Hebei (BTH) region in 2016 using exposure-response functions and the relationship between health and economic costs. The results showed that the PM 2.5 -related economic costs were unevenly distributed and as with the population density, the costs were mainly concentrated in urban areas. In 2016, the economic costs of PM 2.5 -related health endpoints amounted to 4.47% of the total gross domestic product in the BTH region. Of the health endpoints, the cost incurred by premature deaths accounted for more than 80% of the total economic costs associated with PM 2.5 . The results of this study provide new and detailed information that could be used to support the implementation of national and regional policies to reduce air pollution.

Suggested Citation

  • Xiya Zhang & Haibo Hu, 2019. "Combining Data from Multiple Sources to Evaluate Spatial Variations in the Economic Costs of PM 2.5 -Related Health Conditions in the Beijing–Tianjin–Hebei Region," IJERPH, MDPI, vol. 16(20), pages 1-17, October.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:20:p:3994-:d:278172
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    References listed on IDEAS

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    1. Yang Yang & Liwen Luo & Chao Song & Hao Yin & Jintao Yang, 2018. "Spatiotemporal Assessment of PM 2.5 -Related Economic Losses from Health Impacts during 2014–2016 in China," IJERPH, MDPI, vol. 15(6), pages 1-16, June.
    2. Guo, Xiaoqi & Haab, Timothy C. & Hammitt, James K., 2006. "Contingent Valuation and the Economic Value of Air-Pollution-Related Health Risks in China," 2006 Annual meeting, July 23-26, Long Beach, CA 21366, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    3. James Hammitt & Ying Zhou, 2006. "The Economic Value of Air-Pollution-Related Health Risks in China: A Contingent Valuation Study," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 33(3), pages 399-423, March.
    4. Guangqing Chi & Jun Zhu, 2008. "Spatial Regression Models for Demographic Analysis," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 27(1), pages 17-42, February.
    5. Gerardo Sanchez Martinez & Joseph V. Spadaro & Dimitris Chapizanis & Vladimir Kendrovski & Mihail Kochubovski & Pierpaolo Mudu, 2018. "Health Impacts and Economic Costs of Air Pollution in the Metropolitan Area of Skopje," IJERPH, MDPI, vol. 15(4), pages 1-11, March.
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    Cited by:

    1. Nathaniel R. Fold & Mary R. Allison & Berkley C. Wood & Pham T. B. Thao & Sebastien Bonnet & Savitri Garivait & Richard Kamens & Sitthipong Pengjan, 2020. "An Assessment of Annual Mortality Attributable to Ambient PM 2.5 in Bangkok, Thailand," IJERPH, MDPI, vol. 17(19), pages 1-13, October.

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