IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i2p604-d1316577.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/2/604/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/2/604/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. Pastor, Jesus T. & Lovell, C.A. Knox, 2005. "A global Malmquist productivity index," Economics Letters, Elsevier, vol. 88(2), pages 266-271, August.
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mette Asmild & Tomas Baležentis & Jens Leth Hougaard, 2016. "Multi-directional productivity change: MEA-Malmquist," Journal of Productivity Analysis, Springer, vol. 46(2), pages 109-119, December.
    2. Magdalena Kapelko, 2018. "Measuring inefficiency for specific inputs using data envelopment analysis: evidence from construction industry in Spain and Portugal," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(1), pages 43-66, March.
    3. 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.
    4. Baležentis, Tomas & De Witte, Kristof, 2015. "One- and multi-directional conditional efficiency measurement – Efficiency in Lithuanian family farms," European Journal of Operational Research, Elsevier, vol. 245(2), pages 612-622.
    5. Manevska-Tasevska, Gordana & Hansson, Helena & Asmild, Mette & Surry, Yves, 2021. "Exploring the regional efficiency of the Swedish agricultural sector during the CAP reforms ‒ multi-directional efficiency analysis approach," Land Use Policy, Elsevier, vol. 100(C).
    6. Jiawei Yang, 2023. "Disentangling the sources of bank inefficiency: a two-stage network multi-directional efficiency analysis approach," Annals of Operations Research, Springer, vol. 326(1), pages 369-410, July.
    7. 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.
    8. Manevska-Tasevska, Gordana & Hansson, Helena & Asmild, Mette & Surry, Yves, 2018. "Assessing the regional efficiency of Swedish agriculture under the CAP ‒ a multidirectional efficiency approach," 162nd Seminar, April 26-27, 2018, Budapest, Hungary 271971, European Association of Agricultural Economists.
    9. Fusco, Elisa, 2023. "Potential improvements approach in composite indicators construction: The Multi-directional Benefit of the Doubt model," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    10. 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.
    11. 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).
    12. Kelly P. Murillo & Eugenio M. Rocha, 2018. "The Portuguese Manufacturing Sector during 2013-2016 after the Troika Austerity Measures," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 4(1), pages 21-38, June.
    13. Magdalena Kapelko & Alfons Oude Lansink, 2018. "Managerial and program inefficiency for European meat manufacturing firms: A dynamic multidirectional inefficiency analysis approach," Journal of Productivity Analysis, Springer, vol. 49(1), pages 25-36, February.
    14. Zhu, Ning & Wu, Yanrui & Wang, Bing & Yu, Zhiqian, 2019. "Risk preference and efficiency in Chinese banking," China Economic Review, Elsevier, vol. 53(C), pages 324-341.
    15. Huayong Niu & Zhishuo Zhang & Manting Luo, 2022. "Evaluation and Prediction of Low-Carbon Economic Efficiency in China, Japan and South Korea: Based on DEA and Machine Learning," IJERPH, MDPI, vol. 19(19), pages 1-28, October.
    16. Asmild, Mette & Kronborg, Dorte & Mahbub, Tasmina & Matthews, Kent, 2019. "The efficiency patterns of Islamic banks during the global financial crisis: The case of Bangladesh," The Quarterly Review of Economics and Finance, Elsevier, vol. 74(C), pages 67-74.
    17. Aparicio, Juan & Cordero, Jose M. & Gonzalez, Martin & Lopez-Espin, Jose J., 2018. "Using non-radial DEA to assess school efficiency in a cross-country perspective: An empirical analysis of OECD countries," Omega, Elsevier, vol. 79(C), pages 9-20.
    18. Qian Wang & Duo Li & Tzu-Han Chang, 2019. "Energy and Health Efficiencies in China with the Inclusion of Technological Innovation," IJERPH, MDPI, vol. 16(21), pages 1-20, October.
    19. Xiangyu Teng & Fan‐peng Liu & Yung‐ho Chiu, 2020. "The impact of coal and non‐coal consumption on China's energy performance improvement," Natural Resources Forum, Blackwell Publishing, vol. 44(4), pages 334-352, November.
    20. Zhou, Haibo & Yang, Yi & Chen, Yao & Zhu, Joe, 2018. "Data envelopment analysis application in sustainability: The origins, development and future directions," European Journal of Operational Research, Elsevier, vol. 264(1), pages 1-16.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:604-:d:1316577. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.