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Endogenous or Exogenous? Examining Trans-Boundary Air Pollution by Using the Air Quality Index (AQI): A Case Study of 30 Provinces and Autonomous Regions in China

Author

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  • Wenyang Huang

    (School of Economics and Management, Beihang University, Beijing 100191, China)

  • Huiwen Wang

    (School of Economics and Management, Beihang University, Beijing 100191, China
    Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations, Beihang University, Beijing 100191, China)

  • Yigang Wei

    (School of Economics and Management, Beihang University, Beijing 100191, China
    Beijing Key Laboratory of Emergency Support Simulation Technologies for City Operations, Beihang University, Beijing 100191, China)

Abstract

China is experiencing severe environmental degradation, particularly air pollution. To explore whether air pollutants are spatially correlated (i.e., trans-boundary effects) and to analyse the main contributing factors, this research investigates the annual concentration of the Air Quality Index (AQI) and 13 polluting sectors in 30 provinces and autonomous regions across China. Factor analysis, the linear regression model and the spatial auto-regression (SAR) model are employed to analyse the latest data in 2014. Several important findings are derived. Firstly, the global Moran’s I test reveals that the AQI of China shows a distinct positive spatial correlation. The local Moran’s I test shows that significant high–high AQI agglomeration regions are found around the Beijing–Tianjin–Hebei area and the regions of low–low AQI agglomeration all locate in south China, including Yunnan, Guangxi and Fujian. Secondly, the effectiveness of the SAR model is much better than that of the linear regression model, with a significantly improved R-squared value from 0.287 to 0.705. A given region’s AQI will rise by 0.793% if the AQI of its ambient region increases by 1%. Thirdly, car ownership, steel output, coke output, coal consumption, built-up area, diesel consumption and electric power output contribute most to air pollution according to AQI, whereas fuel oil consumption, caustic soda output and crude oil consumption are inconsiderably accountable in raising AQI. Fourthly, the air quality in Beijing and Tianjin is under great exogenous influence from nearby regions, such as Hebei’s air pollution, and cross-boundary and joint efforts must be committed by the Beijing–Tianjin–Hebei region in order to control air pollution.

Suggested Citation

  • Wenyang Huang & Huiwen Wang & Yigang Wei, 2018. "Endogenous or Exogenous? Examining Trans-Boundary Air Pollution by Using the Air Quality Index (AQI): A Case Study of 30 Provinces and Autonomous Regions in China," Sustainability, MDPI, vol. 10(11), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:4220-:d:183152
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    References listed on IDEAS

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    Cited by:

    1. Moon Joon Kim & Xiaolin Xie & Xiaochen Zhang, 2021. "How Does an Environmental Amenity Attract Voluntary Migrants? Evidence from Ambient Air Quality in China," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
    2. Yunpeng Yang & Weixin Yang, 2019. "Does Whistleblowing Work for Air Pollution Control in China? A Study Based on Three-party Evolutionary Game Model under Incomplete Information," Sustainability, MDPI, vol. 11(2), pages 1-20, January.
    3. Min-Xing Wang & Lufei Huang & Zhen-Ming Chen, 2023. "The Impact of Green Financial Policy on the Regional Economic Development Level and AQI—Evidence from Zhejiang Province, China," Sustainability, MDPI, vol. 15(5), pages 1-23, February.
    4. Xinghan Xu & Weijie Ren, 2019. "Application of a Hybrid Model Based on Echo State Network and Improved Particle Swarm Optimization in PM 2.5 Concentration Forecasting: A Case Study of Beijing, China," Sustainability, MDPI, vol. 11(11), pages 1-19, May.
    5. Qingchen Liu & Xinyi Li & Tao Liu & Xiaojun Zhao, 2020. "Spatio-Temporal Correlation Analysis of Air Quality in China: Evidence from Provincial Capitals Data," Sustainability, MDPI, vol. 12(6), pages 1-21, March.

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