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

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  • 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
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    References listed on IDEAS

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    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).
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