IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i6p2445-d1357562.html

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/6/2445/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/6/2445/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    5. 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.
    6. Quande Qin & Xin Li & Li Li & Wei Zhen & Yi-Ming Wei, 2017. "Air emissions perspective on energy efficiency: An empirical analysis of China's coastal areas," CEEP-BIT Working Papers 98, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    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.
    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. Huaming Chen & Jia Liu & Ying Li & Yung-Ho Chiu & Tai-Yu Lin, 2019. "A Two-stage Dynamic Undesirable Data Envelopment Analysis Model Focused on Media Reports and the Impact on Energy and Health Efficiency," IJERPH, MDPI, vol. 16(9), pages 1-23, April.
    2. Fan, Di & Peng, Bo & Wu, Jianxin & Zhang, ZhongXiang, 2024. "The convergence of total-factor energy efficiency across Chinese cities: A distribution dynamics approach," Structural Change and Economic Dynamics, Elsevier, vol. 69(C), pages 406-416.
    3. Xingle Long & Yusen Luo & Huaping Sun & Gang Tian, 2018. "Fertilizer using intensity and environmental efficiency for China’s agriculture sector from 1997 to 2014," 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. 92(3), pages 1573-1591, July.
    4. Qi, Xiaoyan & Guo, Pibin & Guo, Yanshan & Liu, Xiuli & Zhou, Xijun, 2020. "Understanding energy efficiency and its drivers: An empirical analysis of China’s 14 coal intensive industries," Energy, Elsevier, vol. 190(C).
    5. Liu, Fangmei & Li, Li & Ye, Bin & Qin, Quande, 2023. "A novel stochastic semi-parametric frontier-based three-stage DEA window model to evaluate China's industrial green economic efficiency," Energy Economics, Elsevier, vol. 119(C).
    6. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    7. Zebin Zheng & Wenjun Xiao & Ziye Cheng, 2023. "China’s Green Total Factor Energy Efficiency Assessment Based on Coordinated Reduction in Pollution and Carbon Emission: From the 11th to the 13th Five-Year Plan," Sustainability, MDPI, vol. 15(9), pages 1-20, April.
    8. Zhang, Qi & Gu, Baihe & Zhang, Haiying & Ji, Qiang, 2023. "Emission reduction mode of China's provincial transportation sector: Based on “Energy+” carbon efficiency evaluation," Energy Policy, Elsevier, vol. 177(C).
    9. Hsiang-Hsi Liu & Tser-Yieth Chen & Jia-Wen Chen, 2013. "Incorporating the Credit Ranking Measure to Evaluate the Operating Efficiency of Financial Holding Companies in Taiwan," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 3(10), pages 1386-1404, October.
    10. Zhang, Ning & Zhao, Yu & Wang, Na, 2022. "Is China's energy policy effective for power plants? Evidence from the 12th Five-Year Plan energy saving targets," Energy Economics, Elsevier, vol. 112(C).
    11. Tavana, Madjid & Ebrahimnejad, Ali & Santos-Arteaga, Francisco J. & Mansourzadeh, Seyed Mehdi & Matin, Reza Kazemi, 2018. "A hybrid DEA-MOLP model for public school assessment and closure decision in the City of Philadelphia," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 70-89.
    12. Ze Tian & Fang-Rong Ren & Qin-Wen Xiao & Yung-Ho Chiu & Tai-Yu Lin, 2019. "Cross-Regional Comparative Study on Carbon Emission Efficiency of China’s Yangtze River Economic Belt Based on the Meta-Frontier," IJERPH, MDPI, vol. 16(4), pages 1-19, February.
    13. Liu, Xuyi & Zhang, Shun & Bae, Junghan, 2017. "The nexus of renewable energy-agriculture-environment in BRICS," Applied Energy, Elsevier, vol. 204(C), pages 489-496.
    14. Guangdong Li, 2019. "Spatiotemporal Dynamics of Ecological Total-Factor Energy Efficiency and Their Drivers in China at the Prefecture Level," IJERPH, MDPI, vol. 16(18), pages 1-23, September.
    15. Liu, Xiuli & Guo, Pibin & Yue, Xiaohang & Qi, Xiaoyan & Guo, Shufeng & Zhou, Xijun, 2021. "Measuring metabolic efficiency of the Beijing–Tianjin–Hebei urban agglomeration: A slacks-based measures method," Resources Policy, Elsevier, vol. 70(C).
    16. 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.
    17. 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.
    18. Kyuseok Lee & Kyuwan Choi, 2010. "Cross redundancy and sensitivity in DEA models," Journal of Productivity Analysis, Springer, vol. 34(2), pages 151-165, October.
    19. Ying Li & Yung-Ho Chiu & Liang Chun Lu, 2018. "Regional Energy, CO 2 , and Economic and Air Quality Index Performances in China: A Meta-Frontier Approach," Energies, MDPI, vol. 11(8), pages 1-20, August.
    20. Liang Chun Lu & Yung-ho Chiu & Shih-Yung Chiu & Tzu-Han Chang, 2022. "Do Forests help environmental development of Cities in China?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(5), pages 6602-6629, May.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:6:p:2445-:d:1357562. 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 The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (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.