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Spatiotemporal Assessment of PM 2.5 -Related Economic Losses from Health Impacts during 2014–2016 in China

Author

Listed:
  • Yang Yang

    (School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
    These authors contributed equally to this paper.)

  • Liwen Luo

    (School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
    These authors contributed equally to this paper.)

  • Chao Song

    (School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
    State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
    Department of Geography, Dartmouth College, Hanover, NH 03755, USA)

  • Hao Yin

    (State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
    Department of Planning, Danish Centre for Environmental Assessment, Aalborg University, Rendsburggade 14, 9000 Aalborg, Denmark)

  • Jintao Yang

    (School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China)

Abstract

Background : Particulate air pollution, especially PM 2.5 , is highly correlated with various adverse health impacts and, ultimately, economic losses for society, however, few studies have undertaken a spatiotemporal assessment of PM 2.5 -related economic losses from health impacts covering all of the main cities in China. Methods : PM 2.5 concentration data were retrieved for 190 Chinese cities for the period 2014–2016. We used a log-linear exposure–response model and monetary valuation methods, such as value of a statistical life (VSL), amended human capital (AHC), and cost of illness to evaluate PM 2.5 -related economic losses from health impacts at the city level. In addition, Monte Carlo simulation was used to analyze uncertainty. Results : The average economic loss was 0.3% (AHC) to 1% (VSL) of the total gross domestic product (GDP) of 190 Chinese cities from 2014 to 2016. Overall, China experienced a downward trend in total economic losses over the three-year period, but the Beijing–Tianjin–Hebei, Shandong Peninsula, Yangtze River Delta, and Chengdu-Chongqing regions experienced greater annual economic losses. Conclusions : Exploration of spatiotemporal variations in PM 2.5 -related economic losses from long-term health impacts could provide new information for policymakers regarding priority areas for PM 2.5 pollution prevention and control in China.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:6:p:1278-:d:152952
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    References listed on IDEAS

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    2. Ming Zeng & Jiang Du & Weike Zhang, 2019. "Spatial-Temporal Effects of PM 2.5 on Health Burden: Evidence from China," IJERPH, MDPI, vol. 16(23), pages 1-23, November.
    3. 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.
    4. Siu-Kei Woo & Chih-Jui Pai & Yi-Te Chiang & Wei-Ta Fang, 2023. "Predicting the Protective Behavioral Intentions for Parents with Young Children Living in Taipei City and New Taipei City Using the Theory of Planned Behavior for Air Polluted with PM2.5," IJERPH, MDPI, vol. 20(3), pages 1-20, January.
    5. Xiang-Fei Ma & Ru Zhang & Yi-Fan Ruan, 2023. "How to Evaluate the Level of Green Development Based on Entropy Weight TOPSIS: Evidence from China," IJERPH, MDPI, vol. 20(3), pages 1-15, January.
    6. Chao Song & Yaqian He & Yanchen Bo & Jinfeng Wang & Zhoupeng Ren & Huibin Yang, 2018. "Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models," IJERPH, MDPI, vol. 15(7), pages 1-16, July.
    7. Qin Liao & Wangqiang Jin & Yan Tao & Jiansheng Qu & Yong Li & Yibo Niu, 2020. "Health and Economic Loss Assessment of PM 2.5 Pollution during 2015–2017 in Gansu Province, China," IJERPH, MDPI, vol. 17(9), pages 1-18, May.
    8. Deying Zhang & Kaixu Bai & Yunyun Zhou & Runhe Shi & Hongyan Ren, 2019. "Estimating Ground-Level Concentrations of Multiple Air Pollutants and Their Health Impacts in the Huaihe River Basin in China," IJERPH, MDPI, vol. 16(4), pages 1-18, February.
    9. Wenhao Xue & Xinyao Li & Zhe Yang & Jing Wei, 2022. "Are House Prices Affected by PM 2.5 Pollution? Evidence from Beijing, China," IJERPH, MDPI, vol. 19(14), pages 1-17, July.
    10. Daoru Liu & Qinli Deng & Zeng Zhou & Yaolin Lin & Junwei Tao, 2018. "Variation Trends of Fine Particulate Matter Concentration in Wuhan City from 2013 to 2017," IJERPH, MDPI, vol. 15(7), pages 1-18, July.
    11. Shumei Liu & Yi-Te Chiang & Chie-Chien Tseng & Eric Ng & Gwo-Liang Yeh & Wei-Ta Fang, 2018. "The Theory of Planned Behavior to Predict Protective Behavioral Intentions against PM2.5 in Parents of Young Children from Urban and Rural Beijing, China," IJERPH, MDPI, vol. 15(10), pages 1-17, October.

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