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A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

Author

Listed:
  • Pingping Xiong

    (College of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Jia Shi

    (College of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Lingling Pei

    (School of Business Administration, Zhejiang University of Finance and Economics, Hangzhou 310018, China)

  • Song Ding

    (School of Economics, Zhejiang University of Finance and Economics, Hangzhou 310018, China)

Abstract

Haze is the greatest challenge facing China’s sustainable development, and it seriously affects China’s economy, society, ecology and human health. Based on the uncertainty and suddenness of haze, this paper proposes a novel linear time-varying grey model (GM)(1,N) based on interval grey number sequences. Because the original GM(1,N) model based on interval grey number sequences has constant parameters, it neglects the dynamic change characteristics of parameters over time. Therefore, this novel linear time-varying GM(1,N) model, based on interval grey number sequences, is established on the basis of the original GM(1,N) model by introducing a linear time polynomial. To verify the validity and practicability of this model, this paper selects the data of PM 10 , SO 2 and NO 2 concentrations in Beijing, China, from 2008 to 2018, to establish a linear time-varying GM(1,3) model based on interval grey number sequences, and the prediction results are compared with the original GM(1,3) model. The result indicates that the prediction effect of the novel model is better than that of the original model. Finally, this model is applied to forecast PM 10 concentration for 2019 to 2021 in Beijing, and the forecast is made to provide a reference for the government to carry out haze control.

Suggested Citation

  • Pingping Xiong & Jia Shi & Lingling Pei & Song Ding, 2019. "A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China," Sustainability, MDPI, vol. 11(14), pages 1-14, July.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:14:p:3832-:d:248133
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    References listed on IDEAS

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    1. Zeng, Bo & Li, Chuan, 2016. "Forecasting the natural gas demand in China using a self-adapting intelligent grey model," Energy, Elsevier, vol. 112(C), pages 810-825.
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    Cited by:

    1. Zhou, Weijie & Wu, Xiaoli & Ding, Song & Pan, Jiao, 2020. "Application of a novel discrete grey model for forecasting natural gas consumption: A case study of Jiangsu Province in China," Energy, Elsevier, vol. 200(C).
    2. Hsin-Yi Yang & Sheng-Kung Chen & Jiun-Shiuan Wang & Chih-Jen Lu & Hung-Yu Lai, 2020. "Farmland Trace Metal Contamination and Management Model—Model Development and a Case Study in Central Taiwan," Sustainability, MDPI, vol. 12(23), pages 1-19, December.
    3. Pruethsan Sutthichaimethee & Sthianrapab Naluang, 2019. "The Efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand: Adapting the SEM-VARIMAX Model," Energies, MDPI, vol. 12(16), pages 1-21, August.

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