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Carbon efficiency evaluation: An analytical framework using fuzzy DEA

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  • Ignatius, Joshua
  • Ghasemi, M.-R.
  • Zhang, Feng
  • Emrouznejad, Ali
  • Hatami-Marbini, Adel

Abstract

Data Envelopment Analysis (DEA) is a powerful analytical technique for measuring the relative efficiency of alternatives based on their inputs and outputs. The alternatives can be in the form of countries who attempt to enhance their productivity and environmental efficiencies concurrently. However, when desirable outputs such as productivity increases, undesirable outputs increase as well (e.g. carbon emissions), thus making the performance evaluation questionable. In addition, traditional environmental efficiency has been typically measured by crisp input and output (desirable and undesirable). However, the input and output data, such as CO2 emissions, in real-world evaluation problems are often imprecise or ambiguous. This paper proposes a DEA-based framework where the input and output data are characterized by symmetrical and asymmetrical fuzzy numbers. The proposed method allows the environmental evaluation to be assessed at different levels of certainty. The validity of the proposed model has been tested and its usefulness is illustrated using two numerical examples. An application of energy efficiency among 23 European Union (EU) member countries is further presented to show the applicability and efficacy of the proposed approach under asymmetric fuzzy numbers.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:253:y:2016:i:2:p:428-440
    DOI: 10.1016/j.ejor.2016.02.014
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    Citations

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    Cited by:

    1. Baitong Li & Jian Li & Chen Liu & Xinyan Yao & Jingxuan Dong & Meijun Xia, 2023. "Provincial Inclusive Green Growth Efficiency in China: Spatial Correlation Network Investigation and Its Influence Factors," Land, MDPI, vol. 12(3), pages 1-24, March.
    2. 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.
    3. Wenhui Zhao & Ye Qiu & Wei Lu & Puyu Yuan, 2022. "Input–Output Efficiency of Chinese Power Generation Enterprises and Its Improvement Direction-Based on Three-Stage DEA Model," Sustainability, MDPI, vol. 14(12), pages 1-14, June.
    4. Zhang, Puwei & You, Jianxin & Jia, Guangshe & Chen, Jindao & Yu, Anyu, 2018. "Estimation of carbon efficiency decomposition in materials and potential material savings for China's construction industry," Resources Policy, Elsevier, vol. 59(C), pages 148-159.
    5. Lei, Ming & Yin, Zihan & Yu, Xiaowen & Deng, Shijie, 2017. "Carbon-weighted economic development performance and driving force analysis: Evidence from China," Energy Policy, Elsevier, vol. 111(C), pages 179-192.
    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. Zhigang Pei & Jiaming Chen & Jun Fang & Jiangpeng Fan & Zhilan Gong & Qingying Zheng, 2023. "The Impact of “Dual-Control” Regulations on the Green Total Factor Efficiency of Shaoxing’s Industrial Sector," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    8. 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.
    9. Ke Wang & Jieming Zhang & Yi-Ming Wei, 2017. "Operational and environmental performance in China¡¯s thermal power industry: Taking an effectiveness measure as complement to an efficiency measure," CEEP-BIT Working Papers 100, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    10. 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.
    11. 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.
    12. Ozden Tozanli & Gazi Murat Duman & Elif Kongar & Surendra M. Gupta, 2017. "Environmentally Concerned Logistics Operations in Fuzzy Environment: A Literature Survey," Logistics, MDPI, vol. 1(1), pages 1-42, June.
    13. 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).
    14. 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).

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