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Analysis of the Effectiveness of Air Pollution Control Policies Based on Historical Evaluation and Deep Learning Forecast: A Case Study of Chengdu-Chongqing Region in China

Author

Listed:
  • Hao Gao

    (Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
    All the authors contributed equally to this work.)

  • Weixin Yang

    (Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
    All the authors contributed equally to this work.)

  • Jiawei Wang

    (Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
    All the authors contributed equally to this work.)

  • Xiaoyun Zheng

    (Department of Reference Services, Tongji University Libraries, Tongji University, Shanghai 200092, China
    All the authors contributed equally to this work.)

Abstract

Air pollution is a common problem for many countries around the world in the process of industrialization as well as a challenge to sustainable development. This paper has selected Chengdu-Chongqing region of China as the research object, which suffers from severe air pollution and has been actively involved in air pollution control in recent years to achieve sustainable development. Based on the historical data of 16 cities in this region from January 2015 to November 2019 on six major air pollutants, this paper has first conducted evaluation on the monthly air quality of these cities within the research period by using Principal Component Analysis and the Technique for Order Preference by Similarity to an Ideal Solution. Based on that, this paper has adopted the Long Short-Term Memory neural network model in deep learning to forecast the monthly air quality of various cities from December 2019 to November 2020. The aims of this paper are to enrich existing literature on air pollution control, and provide a novel scientific tool for design and formulation of air pollution control policies by innovatively integrating commonly used evaluation models and deep learning forecast methods. According to the research results, in terms of historical evaluation, the air quality of cities in the Chengdu-Chongqing region was generally moving in the same trend in the research period, with distinct characteristics of cyclicity and convergence. Year- on-year speaking, the effectiveness of air pollution control in various cities has shown a visible improvement trend. For example, Ya’an’s lowest air quality evaluation score has improved from 0.3494 in 2015 to 0.4504 in 2019; Zigong’s lowest air quality score has also risen from 0.4160 in 2015 to 0.6429 in 2019. Based on the above historical evaluation and deep learning forecast results, this paper has proposed relevant policy recommendations for air pollution control in the Chengdu-Chongqing region.

Suggested Citation

  • Hao Gao & Weixin Yang & Jiawei Wang & Xiaoyun Zheng, 2020. "Analysis of the Effectiveness of Air Pollution Control Policies Based on Historical Evaluation and Deep Learning Forecast: A Case Study of Chengdu-Chongqing Region in China," Sustainability, MDPI, vol. 13(1), pages 1-28, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2020:i:1:p:206-:d:469415
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    2. Zhang, Xu & Huang, Tao & Wang, Chunping & Zeng, Chunhua, 2023. "The temporal correlation of fluctuation–variation in the non-stationary complex climate system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).

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