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Evaluation, Driving Mechanism and Spatial Correlation Analysis of Atmospheric Environmental Efficiency in the “2+26” Cities Based on the Nonradial MEA Model

Author

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  • Yiru Jiang

    (Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China)

  • Xinjun Wang

    (Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China)

Abstract

The “2+26” cities are 26 cities in Beijing, Tianjin and the surrounding cities, constituting a region characterized by economic prosperity and diverse industries but plagued by severe atmospheric pollution. As a focal area for atmospheric pollution control, a scientific assessment of atmospheric environmental efficiency in the “2+26” cities that measures the degree of coordination between the economy and air pollution is very important for winning the battle of blue sky defense. Based on this, this study comprehensively used the nonradial multi-directional efficiency analysis (MEA) model, Global Reference Malmquist Model and spatial correlation analysis to evaluate the atmospheric environmental efficiency, calculate the driving factors and explore the spatiotemporal distribution characteristics of the “2+26” cities from 2009 to 2018. The research findings indicate the following: (1) Atmospheric environmental efficiency showed a trend of first decreasing and then increasing, with a significant improvement potential of 26.7% in the future. (2) There was a significant discrepancy between the best- and worst-performing cities, with the best being 0.910 and the worst being 0.573, demonstrating imbalanced development between cities. The relatively low-efficiency cities were mainly located in Hebei, Shanxi and Henan provinces. (3) A value of technological efficiency change (EC) less than 1 was the main restrictive factor for improving atmospheric environmental efficiency, whereas a value of technological change (TC) greater than 1 enhanced it. (4) The atmospheric environmental efficiency presented a distinct spatial distribution pattern of high–high and low–low aggregation, forming high-value areas centered in the Beijing–Tianjin region and along the Zibo–Zhengzhou line. The western and central regions were relatively low, whereas the northern and eastern regions were relatively high, with significant regional differences in spatial distribution. The conclusions from this article’s empirical analysis can help concerned developing countries determine key factors to improve their atmospheric environmental efficiency and then formulate policies for sustainable economic and environmental development.

Suggested Citation

  • Yiru Jiang & Xinjun Wang, 2024. "Evaluation, Driving Mechanism and Spatial Correlation Analysis of Atmospheric Environmental Efficiency in the “2+26” Cities Based on the Nonradial MEA Model," Sustainability, MDPI, vol. 16(2), pages 1-23, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:604-:d:1316577
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    1. Peter Bogetoft & Jens Hougaard, 1999. "Efficiency Evaluations Based on Potential (Non-Proportional) Improvements," Journal of Productivity Analysis, Springer, vol. 12(3), pages 233-247, November.
    2. Mette Asmild & Tomas Baležentis & Jens Hougaard, 2016. "Multi-directional program efficiency: the case of Lithuanian family farms," Journal of Productivity Analysis, Springer, vol. 45(1), pages 23-33, February.
    3. Tziogkidis, Panagiotis & Philippas, Dionisis & Tsionas, Mike G., 2020. "Multidirectional conditional convergence in European banking," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 88-106.
    4. Tothmihaly, Andras & Ingram, Verina & von Cramon-Taubadel, Stephan, 2019. "How Can the Environmental Efficiency of Indonesian Cocoa Farms Be Increased?," Ecological Economics, Elsevier, vol. 158(C), pages 134-145.
    5. Asmild, Mette & Matthews, Kent, 2012. "Multi-directional efficiency analysis of efficiency patterns in Chinese banks 1997–2008," European Journal of Operational Research, Elsevier, vol. 219(2), pages 434-441.
    6. Bian, Yiwen & Yang, Feng, 2010. "Resource and environment efficiency analysis of provinces in China: A DEA approach based on Shannon's entropy," Energy Policy, Elsevier, vol. 38(4), pages 1909-1917, April.
    7. Kapelko, Magdalena & Oude Lansink, Alfons, 2017. "Dynamic multi-directional inefficiency analysis of European dairy manufacturing firms," European Journal of Operational Research, Elsevier, vol. 257(1), pages 338-344.
    8. Wang, Jieyu & Wang, Shaojian & Li, Shijie & Feng, Kuishuang, 2019. "Coupling analysis of urbanization and energy-environment efficiency: Evidence from Guangdong province," Applied Energy, Elsevier, vol. 254(C).
    9. Zhu, Lin & Wang, Yong & Shang, Peipei & Qi, Lin & Yang, Guangchun & Wang, Ying, 2019. "Improvement path, the improvement potential and the dynamic evolution of regional energy efficiency in China: Based on an improved nonradial multidirectional efficiency analysis," Energy Policy, Elsevier, vol. 133(C).
    10. Mette Asmild & Jens Hougaard & Dorte Kronborg & Hans Kvist, 2003. "Measuring Inefficiency Via Potential Improvements," Journal of Productivity Analysis, Springer, vol. 19(1), pages 59-76, January.
    11. Meng, Fanyi & Su, Bin & Thomson, Elspeth & Zhou, Dequn & Zhou, P., 2016. "Measuring China’s regional energy and carbon emission efficiency with DEA models: A survey," Applied Energy, Elsevier, vol. 183(C), pages 1-21.
    12. Pastor, Jesus T. & Lovell, C.A. Knox, 2005. "A global Malmquist productivity index," Economics Letters, Elsevier, vol. 88(2), pages 266-271, August.
    13. Bai, Xiuguang & Salim, Ruhul & Bloch, Harry, 2019. "Environmental Efficiency of Apple Production in China: A Translog Stochastic Frontier Analysis," Agricultural and Resource Economics Review, Cambridge University Press, vol. 48(2), pages 199-220, August.
    14. Wang, Ke & Wei, Yi-Ming & Zhang, Xian, 2013. "Energy and emissions efficiency patterns of Chinese regions: A multi-directional efficiency analysis," Applied Energy, Elsevier, vol. 104(C), pages 105-116.
    15. Halkos, George E. & Tzeremes, Nickolaos G., 2013. "A conditional directional distance function approach for measuring regional environmental efficiency: Evidence from UK regions," European Journal of Operational Research, Elsevier, vol. 227(1), pages 182-189.
    16. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
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