IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v11y2022i10p1783-d941055.html
   My bibliography  Save this article

Coupling Efficiency Assessment of Food–Energy–Water (FEW) Nexus Based on Urban Resource Consumption towards Economic Development: The Case of Shenzhen Megacity, China

Author

Listed:
  • Chaofan Xian

    (State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China)

  • Shuo Yang

    (Hengshui Ruifeng Composite Materials Corporation, Hengshui 053100, China)

  • Yupeng Fan

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Haotong Wu

    (China Qiyuan Engineering Corporation, Xi’an 710018, China)

  • Cheng Gong

    (State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China)

Abstract

The population aggregation and economic development caused by urbanization significantly influence the efficiency of urban resource consumption. However, the coupling interactions between crucial resource consumptions such as food, energy and water (FEW) and urbanization processes within highly urbanized areas has not been well-studied. In this study, we constructed an assessment framework for the coupling efficiency measurement of FEW resource consumptions in 10 administrative districts across Shenzhen megacity during 2012–2020, based on the data envelopment analysis (DEA). This study demonstrated that, from the perspective of the FEW nexus, increasing efficiencies in the energy consumption of most districts improved the municipal FEW efficiency, while more than half of the districts did not achieve water resource efficiencies throughout the period. Concerning regional economic development, 80% of the districts improved coupling FEW efficiencies by 2020, the average values of which were higher for Yantian, Nanshan, Luohu and Dapeng, and lower for Baoan, Longgang and Guangming, with a downtrend only being observed in Guangming. Overall, the value of the coupling FEW efficiency of Shenzhen megacity rose by 35% from 2012 to 2020. Correlation analysis showed that synergistic effects of efficient resource consumption occurred in most districts, and economic urbanization was the main driving factor of regional FEW efficiencies within Shenzhen megacity. This study provides instructive insights into the status of urban resource consumption and suggests that the coordination of FEW management should be further improved by fiscal intervention to maintain economic development with the limited resources available, which would have valuable implications for synergistic FEW governance in megacities in China and elsewhere.

Suggested Citation

  • Chaofan Xian & Shuo Yang & Yupeng Fan & Haotong Wu & Cheng Gong, 2022. "Coupling Efficiency Assessment of Food–Energy–Water (FEW) Nexus Based on Urban Resource Consumption towards Economic Development: The Case of Shenzhen Megacity, China," Land, MDPI, vol. 11(10), pages 1-25, October.
  • Handle: RePEc:gam:jlands:v:11:y:2022:i:10:p:1783-:d:941055
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/11/10/1783/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2073-445X/11/10/1783/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Wai-Ming To & Peter K. C. Lee & Antonio K. W. Lau, 2021. "Economic and Environmental Changes in Shenzhen—A Technology Hub in Southern China," Sustainability, MDPI, vol. 13(10), pages 1-17, May.
    3. Yaqing Wang & Chaofan Xian & Yaqiong Jiang & Xuelian Pan & Zhiyun Ouyang, 2020. "Anthropogenic reactive nitrogen releases and gray water footprints in urban water pollution evaluation: the case of Shenzhen City, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(7), pages 6343-6361, October.
    4. Xujing Yu & Liping Shan & Yuzhe Wu, 2021. "Land Use Optimization in a Resource-Exhausted City Based on Simulation of the F-E-W Nexus," Land, MDPI, vol. 10(10), pages 1-22, September.
    5. Robert Costanza & Ida Kubiszewski & Enrico Giovannini & Hunter Lovins & Jacqueline McGlade & Kate E. Pickett & Kristín Vala Ragnarsdóttir & Debra Roberts & Roberto De Vogli & Richard Wilkinson, 2014. "Development: Time to leave GDP behind," Nature, Nature, vol. 505(7483), pages 283-285, January.
    6. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    7. Jing Zhu & Shenghong Kang & Wenwu Zhao & Qiujie Li & Xinyuan Xie & Xiangping Hu, 2020. "A Bibliometric Analysis of Food–Energy–Water Nexus: Progress and Prospects," Land, MDPI, vol. 9(12), pages 1-22, December.
    8. Reza Maddahi & Gholam Reza Jahanshahloo & Farhad Hosseinzadeh Lotfi & Ali Ebrahimnejad, 2014. "Optimising proportional weights as a secondary goal in DEA cross-efficiency evaluation," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 19(2), pages 234-245.
    9. Ebrahimnejad, Ali & Tavana, Madjid & Santos-Arteaga, Francisco J., 2016. "An integrated data envelopment analysis and simulation method for group consensus ranking," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 119(C), pages 1-17.
    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. Georgios Tsaples & Jason Papathanasiou & Andreas C. Georgiou, 2022. "An Exploratory DEA and Machine Learning Framework for the Evaluation and Analysis of Sustainability Composite Indicators in the EU," Mathematics, MDPI, vol. 10(13), pages 1-27, June.
    2. K. Kounetas & G. Androulakis & M. Kaisari & G. Manousakis, 2023. "Educational reforms and secondary school's efficiency performance in Greece: a bootstrap DEA and multilevel approach," Operational Research, Springer, vol. 23(1), pages 1-29, March.
    3. Meng, Fanyong & Xiong, Beibei, 2021. "Logical efficiency decomposition for general two-stage systems in view of cross efficiency," European Journal of Operational Research, Elsevier, vol. 294(2), pages 622-632.
    4. Feng Li & Han Wu & Qingyuan Zhu & Liang Liang & Gang Kou, 2021. "Data envelopment analysis cross efficiency evaluation with reciprocal behaviors," Annals of Operations Research, Springer, vol. 302(1), pages 173-210, July.
    5. Dyckhoff, Harald & Souren, Rainer, 2022. "Integrating multiple criteria decision analysis and production theory for performance evaluation: Framework and review," European Journal of Operational Research, Elsevier, vol. 297(3), pages 795-816.
    6. Hamid Kiaei & Reza Farzipoor Saen & Reza Kazemi Matin, 2023. "Cross-efficiency evaluation and improvement in two-stage network data envelopment analysis," Annals of Operations Research, Springer, vol. 321(1), pages 281-309, February.
    7. Henriques, C.O. & Chavez, J.M. & Gouveia, M.C. & Marcenaro-Gutierrez, O.D., 2022. "Efficiency of secondary schools in Ecuador: A value based DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    8. Vincenzo Patrizii & Anna Pettini & Giuliano Resce, 2017. "The Cost of Well-Being," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 133(3), pages 985-1010, September.
    9. Kao, Chiang & Liu, Shiang-Tai, 2022. "Group decision making in data envelopment analysis: A robot selection application," European Journal of Operational Research, Elsevier, vol. 297(2), pages 592-599.
    10. Wu, Jie & Chu, Junfei & Sun, Jiasen & Zhu, Qingyuan, 2016. "DEA cross-efficiency evaluation based on Pareto improvement," European Journal of Operational Research, Elsevier, vol. 248(2), pages 571-579.
    11. Mohammad Izadikhah & Reza Farzipoor Saen, 2019. "Solving voting system by data envelopment analysis for assessing sustainability of suppliers," Group Decision and Negotiation, Springer, vol. 28(3), pages 641-669, June.
    12. Henriques, C.O. & Marcenaro-Gutierrez, O.D., 2021. "Efficiency of secondary schools in Portugal: A novel DEA hybrid approach," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    13. Dursun, Mehtap & Goker, Nazli & Tulek, Burcu Deniz, 2019. "Efficiency analysis of organized industrial zones in Eastern Black Sea Region of Turkey," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    14. Davtalab-Olyaie, Mostafa & Asgharian, Masoud, 2021. "On Pareto-optimality in the cross-efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 288(1), pages 247-257.
    15. Balk, Bert M. & (René) De Koster, M.B.M. & Kaps, Christian & Zofío, José L., 2021. "An evaluation of cross-efficiency methods: With an application to warehouse performance," Applied Mathematics and Computation, Elsevier, vol. 406(C).
    16. Khezrimotlagh, Dariush & Kaffash, Sepideh & Zhu, Joe, 2022. "U.S. airline mergers’ performance and productivity change," Journal of Air Transport Management, Elsevier, vol. 102(C).
    17. Christian Growitsch & Tooraj Jamasb & Christine Müller & Matthias Wissner, 2016. "Social Cost Efficient Service Quality: Integrating Customer Valuation in Incentive Regulation—Evidence from the Case of Norway," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, chapter 0, pages 71-91, Springer.
    18. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499, August.
    19. Alperovych, Yan & Hübner, Georges & Lobet, Fabrice, 2015. "How does governmental versus private venture capital backing affect a firm's efficiency? Evidence from Belgium," Journal of Business Venturing, Elsevier, vol. 30(4), pages 508-525.
    20. Wang, Zhao-Hua & Zeng, Hua-Lin & Wei, Yi-Ming & Zhang, Yi-Xiang, 2012. "Regional total factor energy efficiency: An empirical analysis of industrial sector in China," Applied Energy, Elsevier, vol. 97(C), pages 115-123.

    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:jlands:v:11:y:2022:i:10:p:1783-:d:941055. 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.