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Quarterly PM2.5 prediction using a novel seasonal grey model and its further application in health effects and economic loss assessment: evidences from Shanghai and Tianjin, China

Author

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  • Jianzhou Wang

    (Dongbei University of Finance and Economics)

  • Pei Du

    (Dongbei University of Finance and Economics
    Xi′an Jiaotong University)

Abstract

Previous research only focused on PM2.5 prediction without considering its further application or just evaluated the past years' health effects and economic losses caused by PM2.5 without studying the future scenarios. Thus, a novel hybrid system using a seasonal grey model with the fractional order accumulation, called SFGM (1, 1), and health economic loss assessment model was developed in this study, which can not only perform quarterly PM2.5 prediction, but also estimate its health effects and economic losses. The results indicated that (1) the designed SFGM (1, 1) can not only reflect the seasonal fluctuation, but also predict the seasonal PM2.5 concentrations with higher prediction accuracy in both out-and-in-samples than comparison models. (2) The total economic losses in 2020 of Shanghai and Tianjin will be 6867.25 million yuan (95% CI: 3072.34–10704.47) and 4869.20 million yuan (95% CI: 2194.50–7532.00), respectively, showing that Shanghai will suffer bigger economic losses than Tianjin. (3) The economic loss caused by the premature deaths attributable to PM2.5 is the largest, accounting for more than 70% of the total economic loss. Finally, the findings can help policymakers to formulate more policies and take effective measures to improve public awareness of environmental protection.

Suggested Citation

  • Jianzhou Wang & Pei Du, 2021. "Quarterly PM2.5 prediction using a novel seasonal grey model and its further application in health effects and economic loss assessment: evidences from Shanghai and Tianjin, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(1), pages 889-909, May.
    • Handle: RePEc:spr:nathaz:v:107:y:2021:i:1:d:10.1007_s11069-021-04614-y
    DOI: 10.1007/s11069-021-04614-y
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    References listed on IDEAS

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    1. Xuewei Hou & Dongdong Fei & Hanqing Kang & Yinglong Zhang & Jinhui Gao, 2018. "Seasonal statistical analysis of the impact of meteorological factors on fine particle pollution in China in 2013–2017," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 93(2), pages 677-698, September.
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    Cited by:

    1. Xiong, Xin & Hu, Xi & Tian, Tian & Guo, Huan & Liao, Han, 2022. "A novel Optimized initial condition and Seasonal division based Grey Seasonal Variation Index model for hydropower generation," Applied Energy, Elsevier, vol. 328(C).
    2. Xiaojun Guo & Rui Zhang & Houxue Shen & Yingjie Yang, 2022. "An Optimized Damping Grey Population Prediction Model and Its Application on China’s Population Structure Analysis," IJERPH, MDPI, vol. 19(20), pages 1-25, October.
    3. Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).

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