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Haze Influencing Factors: A Data Envelopment Analysis Approach

Author

Listed:
  • Yi Zhou

    (School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Lianshui Li

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Ruiling Sun

    (School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Zaiwu Gong

    (School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Mingguo Bai

    (School of Business, Anhui University of Technology, Maanshan 243032, China)

  • Guo Wei

    (Department of Mathematics and Computer Science, University of North Carolina at Pembroke, Pembroke, NC 28372, USA)

Abstract

This paper investigates the meteorological factors and human activities that influence PM 2.5 pollution by employing the data envelopment analysis (DEA) approach to a chance constrained stochastic optimization problem. This approach has the two advantages of admitting random input and output, and allowing the evaluation unit to exceed the front edge under the given probability constraint. Furthermore, by utilizing the meteorological observation data incorporated with the economic and social data for Jiangsu Province, the chance constrained stochastic DEA model was solved to explore the relationship between the meteorological elements and human activities and PM 2.5 pollution. The results are summarized by the following: (1) Among all five primary indexes, social progress, energy use and transportation are the most significant for PM 2.5 pollution. (2) Among our selected 14 secondary indexes, coal consumption, population density and civil car ownership account for a major portion of PM 2.5 pollution. (3) Human activities are the main factor producing PM 2.5 pollution. While some meteorological elements generate PM 2.5 pollution, some act as influencing factors on the migration of PM 2.5 pollution. These findings can provide a reference for the government to formulate appropriate policies to reduce PM 2.5 emissions and for the communities to develop effective strategies to eliminate PM 2.5 pollution.

Suggested Citation

  • Yi Zhou & Lianshui Li & Ruiling Sun & Zaiwu Gong & Mingguo Bai & Guo Wei, 2019. "Haze Influencing Factors: A Data Envelopment Analysis Approach," IJERPH, MDPI, vol. 16(6), pages 1-16, March.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:6:p:914-:d:213767
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    References listed on IDEAS

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    Cited by:

    1. Wenhao Chen & Chang Zeng & Chuheng Ding & Yingfang Zhu & Yurong Sun, 2022. "Study on Spatio-Temporal Evolution Law and Driving Mechanism of PM 2.5 Concentration in Changsha–Zhuzhou–Xiangtan Urban Agglomeration," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    2. Huan Wang & Zhenyu Chen & Pan Zhang, 2022. "Spatial Autocorrelation and Temporal Convergence of PM 2.5 Concentrations in Chinese Cities," IJERPH, MDPI, vol. 19(21), pages 1-11, October.

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