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Evaluation of Atmospheric Environmental Efficiency and Spatiotemporal Differences in the Yangtze River Delta Region of China

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  • Chuanming Yang

    (School of Business, Suzhou University of Science and Technology, Suzhou 210059, China)

  • Jie Shen

    (School of Business, Suzhou University of Science and Technology, Suzhou 210059, China)

  • Zhonghua Jiang

    (School of Business, Suzhou University of Science and Technology, Suzhou 210059, China)

  • Junyu Chen

    (College of Management and Economics, Tianjin University, Tianjin 300072, China)

  • Yi Xie

    (School of Business, Suzhou University of Science and Technology, Suzhou 210059, China)

Abstract

The scientific measurement of regional atmospheric environmental efficiency is an important prerequisite for achieving energy conservation and haze reduction and regional green and high-quality development. Taking the cities in the Yangtze River Delta region from 2012 to 2021 as the research object, the atmospheric environmental efficiency is measured from both static and dynamic perspectives using the three-stage DEA model and the Malmquist index to analyze the characteristics of spatial and temporal differences. The study finds that the real atmospheric environmental efficiency of the Yangtze River Delta region is 0.915, and the elimination of environmental factors and random errors is crucial to the assessment of the efficiency. The atmospheric environmental efficiency of the Yangtze River Delta region is not 1, and there is still room for improvement, in which the pure technical efficiency is the main factor that leads to the overall low efficiency. Different environmental variables have different impacts on the atmospheric environmental efficiency, in which the positive impact of the industrial structure is the most significant. Urban agglomerations can be categorized into “high–high–high”, “high–low–high”, “low–low–high”, and “low–high–low”. The total factor productivity of the atmospheric environment showed a gradual growth trend during the study period, in which technological progress played the most important role. Based on this, countermeasures are proposed to better enhance the level of atmospheric environment management in the Yangtze River Delta region.

Suggested Citation

  • Chuanming Yang & Jie Shen & Zhonghua Jiang & Junyu Chen & Yi Xie, 2024. "Evaluation of Atmospheric Environmental Efficiency and Spatiotemporal Differences in the Yangtze River Delta Region of China," Sustainability, MDPI, vol. 16(6), pages 1-16, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:6:p:2445-:d:1357562
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    References listed on IDEAS

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    1. Wade D. Cook & Juan Du & Joe Zhu, 2017. "Units invariant DEA when weight restrictions are present: ecological performance of US electricity industry," Annals of Operations Research, Springer, vol. 255(1), pages 323-346, August.
    2. Xianhua Wu & Yufeng Chen & Ji Guo & Ge Gao, 2018. "Inputs optimization to reduce the undesirable outputs by environmental hazards: a DEA model with data of PM2.5 in 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. 90(1), pages 1-25, January.
    3. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    4. Qin, Quande & Li, Xin & Li, Li & Zhen, Wei & Wei, Yi-Ming, 2017. "Air emissions perspective on energy efficiency: An empirical analysis of China’s coastal areas," Applied Energy, Elsevier, vol. 185(P1), pages 604-614.
    5. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
    6. Jia Yu Xie & Dong Hee Suh & Sung-Kwan Joo, 2021. "A Dynamic Analysis of Air Pollution: Implications of Economic Growth and Renewable Energy Consumption," IJERPH, MDPI, vol. 18(18), pages 1-15, September.
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