IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2022i1p300-d1016893.html
   My bibliography  Save this article

Empirical Study on the Low-Carbon Economic Efficiency in Zhejiang Province Based on an Improved DEA Model and Projection

Author

Listed:
  • Hongyun Luo

    (College of Business, Quzhou University, Quzhou 324000, China
    School of Economics and Management, Northeast Petroleum University, Daqing 163318, China)

  • Xiangyi Lin

    (College of Business, Quzhou University, Quzhou 324000, China
    School of Economics and Management, Northeast Petroleum University, Daqing 163318, China)

Abstract

Low-carbon economic efficiency is an important indicator that can be used to measure the quality of regional economic development. In this study, an improved DEA model is introduced into the calculation of low-carbon economic efficiency in Zhejiang Province. Using the actual data of nine prefecture-level cities in Zhejiang Province from 2015 to 2020, the low-carbon economic efficiency of each prefecture-level city is calculated. The result is that the overall low-carbon economic efficiency of Zhejiang Province indicates a trend of first falling and then rising, and the low-carbon economic efficiencies of different prefecture-level cities largely differ. The causes of six inefficient DMUs (prefecture-level cities) are analyzed using projection. The improved DEA model is used to determine the “expansion coefficient” of the input and output of three DMUs (prefecture-level cities) with relatively low-carbon economic efficiency. The research results provide a strong basis and support for the development of a low-carbon economy for Zhejiang Province.

Suggested Citation

  • Hongyun Luo & Xiangyi Lin, 2022. "Empirical Study on the Low-Carbon Economic Efficiency in Zhejiang Province Based on an Improved DEA Model and Projection," Energies, MDPI, vol. 16(1), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:16:y:2022:i:1:p:300-:d:1016893
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/1/300/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/1/300/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. IGNATIUS, Joshua & GHASEMI, M.-R. & ZHANG, Feng & EMROUZNEJAD, Ali & HATAMI-MARBINI, Adel, 2016. "Carbon Efficiency Evaluation: An Analytical Framework Using Fuzzy DEA," LIDAM Reprints CORE 2735, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Khalid Mehmood & Yaser Iftikhar & Shouming Chen & Shaheera Amin & Alia Manzoor & Jinlong Pan, 2020. "Analysis of Inter-Temporal Change in the Energy and CO 2 Emissions Efficiency of Economies: A Two Divisional Network DEA Approach," Energies, MDPI, vol. 13(13), pages 1-17, June.
    3. 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.
    4. Wang, Yajun & Huang, Junbing, 2022. "Pathway to develop a low-carbon economy through energy-substitution technology in China," Energy, Elsevier, vol. 261(PA).
    5. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    6. Ignatius, Joshua & Ghasemi, M.-R. & Zhang, Feng & Emrouznejad, Ali & Hatami-Marbini, Adel, 2016. "Carbon efficiency evaluation: An analytical framework using fuzzy DEA," European Journal of Operational Research, Elsevier, vol. 253(2), pages 428-440.
    7. Wang, Qiang & Zhang, Chen & Li, Rongrong, 2022. "Towards carbon neutrality by improving carbon efficiency - A system-GMM dynamic panel analysis for 131 countries’ carbon efficiency," Energy, Elsevier, vol. 258(C).
    8. Wang, Ailun & Hu, Shuo & Li, Jianglong, 2022. "Using machine learning to model technological heterogeneity in carbon emission efficiency evaluation: The case of China's cities," Energy Economics, Elsevier, vol. 114(C).
    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. Cayir Ervural, Beyzanur & Zaim, Selim & Delen, Dursun, 2018. "A two-stage analytical approach to assess sustainable energy efficiency," Energy, Elsevier, vol. 164(C), pages 822-836.
    2. Adel Hatami-Marbini & Per J. Agrell & Hirofumi Fukuyama & Kobra Gholami & Pegah Khoshnevis, 2017. "The role of multiplier bounds in fuzzy data envelopment analysis," Annals of Operations Research, Springer, vol. 250(1), pages 249-276, March.
    3. Khoshroo, Alireza & Izadikhah, Mohammad & Emrouznejad, Ali, 2022. "Total factor energy productivity considering undesirable pollutant outputs: A new double frontier based malmquist productivity index," Energy, Elsevier, vol. 258(C).
    4. Ghimire, Sarad & Amin, Saman Hassanzadeh & Wardley, Leslie J., 2021. "Developing new data envelopment analysis models to evaluate the efficiency in Ontario Universities," Journal of Informetrics, Elsevier, vol. 15(3).
    5. Sueyoshi, Toshiyuki & Li, Aijun & Liu, Xiaohong, 2019. "Exploring sources of China's CO2 emission: Decomposition analysis under different technology changes," European Journal of Operational Research, Elsevier, vol. 279(3), pages 984-995.
    6. Meng, Conghui & Du, Xiaoyun & Zhu, Mengcheng & Ren, Yitian & Fang, Kai, 2023. "The static and dynamic carbon emission efficiency of transport industry in China," Energy, Elsevier, vol. 274(C).
    7. Franz R. Hahn, 2007. "Determinants of Bank Efficiency in Europe. Assessing Bank Performance Across Markets," WIFO Studies, WIFO, number 31499, January.
    8. Azarnoosh Kafi & Behrouz Daneshian & Mohsen Rostamy-Malkhalifeh, 2021. "Forecasting the confidence interval of efficiency in fuzzy DEA," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 31(1), pages 41-59.
    9. Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & de Pinho Matos, Giordano Bruno Braz, 2015. "Statistical evaluation of Data Envelopment Analysis versus COLS Cobb–Douglas benchmarking models for the 2011 Brazilian tariff revision," Socio-Economic Planning Sciences, Elsevier, vol. 49(C), pages 47-60.
    10. Ahmad, Usman, 2011. "Financial Reforms and Banking Efficiency: Case of Pakistan," MPRA Paper 34220, University Library of Munich, Germany.
    11. Büschken, Joachim, 2009. "When does data envelopment analysis outperform a naïve efficiency measurement model?," European Journal of Operational Research, Elsevier, vol. 192(2), pages 647-657, January.
    12. António Afonso & Ana Patricia Montes & José M. Domínguez, 2024. "Measuring Tax Burden Efficiency in OECD Countries: An International Comparison," CESifo Working Paper Series 11333, CESifo.
    13. Jahangoshai Rezaee, Mustafa & Jozmaleki, Mehrdad & Valipour, Mahsa, 2018. "Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 78-93.
    14. Chai, Naijie & Zhou, Wenliang & Hu, Xinlei, 2022. "Safety evaluation of urban rail transit operation considering uncertainty and risk preference: A case study in China," Transport Policy, Elsevier, vol. 125(C), pages 267-288.
    15. Ravelojaona, Paola, 2019. "On constant elasticity of substitution – Constant elasticity of transformation Directional Distance Functions," European Journal of Operational Research, Elsevier, vol. 272(2), pages 780-791.
    16. Hu, Jin-Li & Wang, Shih-Chuan & Yeh, Fang-Yu, 2006. "Total-factor water efficiency of regions in China," Resources Policy, Elsevier, vol. 31(4), pages 217-230, December.
    17. Adler, Nicole & Friedman, Lea & Sinuany-Stern, Zilla, 2002. "Review of ranking methods in the data envelopment analysis context," European Journal of Operational Research, Elsevier, vol. 140(2), pages 249-265, July.
    18. Keh, Hean Tat & Chu, Singfat, 2003. "Retail productivity and scale economies at the firm level: a DEA approach," Omega, Elsevier, vol. 31(2), pages 75-82, April.
    19. Matthias Klumpp & Dominic Loske, 2021. "Sustainability and Resilience Revisited: Impact of Information Technology Disruptions on Empirical Retail Logistics Efficiency," Sustainability, MDPI, vol. 13(10), pages 1-20, May.
    20. da Silva, Aneirson Francisco & Miranda, Rafael de Carvalho & Marins, Fernando Augusto Silva & Dias, Erica Ximenes, 2024. "A new multiple criteria data envelopment analysis with variable return to scale: Applying bi-dimensional representation and super-efficiency analysis," European Journal of Operational Research, Elsevier, vol. 314(1), pages 308-322.

    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:jeners:v:16:y:2022:i:1:p:300-:d:1016893. 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.