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Allocation Model of Carbon Emission Permits for the Electric Power Industry with a Combination Subjective and Objective Weighting Approach

Author

Listed:
  • Xianxian Pan

    (Grid Planning & Research Center, Guangdong Power Grid Corporation, Guangzhou 510080, China)

  • Hong Liu

    (Tianjin University, Tianjin 300072, China)

  • Jiajia Huan

    (Grid Planning & Research Center, Guangdong Power Grid Corporation, Guangzhou 510080, China)

  • Yu Sui

    (Grid Planning & Research Center, Guangdong Power Grid Corporation, Guangzhou 510080, China)

  • Haifeng Hong

    (Grid Planning & Research Center, Guangdong Power Grid Corporation, Guangzhou 510080, China)

Abstract

The electric power industry plays a vital role in carbon emissions reduction efforts. The initial allocation of carbon emission permits to the electric power industry is the key to ensuring the effective operation of the carbon trading market. In this study, the multiple correlated factors that affect the carbon emission permit allocation system were extracted. Then, based on the experts’ knowledge and experience, the subjective weight of each index was determined using an improved analytic hierarchy process. Subsequently, the indices were mapped using an improved entropy weight method, and the objective weight of each index was adaptively determined. Finally, the comprehensive weight of each index was determined by optimizing the combination of its subjective and objective weights, and an allocation model of carbon emission permits for the electric power industry was established. A case study of a province by comparative simulation was performed. The simulation results showed that compared with conventional allocation schemes that consider single factors, the theoretical estimates obtained using the proposed model more objectively reflected the actual situation of carbon emissions reduction permits and responsibilities in the region.

Suggested Citation

  • Xianxian Pan & Hong Liu & Jiajia Huan & Yu Sui & Haifeng Hong, 2020. "Allocation Model of Carbon Emission Permits for the Electric Power Industry with a Combination Subjective and Objective Weighting Approach," Energies, MDPI, vol. 13(3), pages 1-12, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:706-:d:317264
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    References listed on IDEAS

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    1. R. H. Coase, 2013. "The Problem of Social Cost," Journal of Law and Economics, University of Chicago Press, vol. 56(4), pages 837-877.
    2. Fischer, Carolyn, 2005. "Project-based mechanisms for emissions reductions: balancing trade-offs with baselines," Energy Policy, Elsevier, vol. 33(14), pages 1807-1823, September.
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    1. Yudong Tan & Guosheng Xie & Yunhao Xiao & Yi Luo & Xintao Xie & Ming Wen, 2022. "Comprehensive Benefit Evaluation of Hybrid Pumped-Storage Power Stations Based on Improved Rank Correlation-Entropy Weight Method," Energies, MDPI, vol. 15(22), pages 1-17, November.
    2. Radosław Wolniak & Sebastian Saniuk & Sandra Grabowska & Bożena Gajdzik, 2020. "Identification of Energy Efficiency Trends in the Context of the Development of Industry 4.0 Using the Polish Steel Sector as an Example," Energies, MDPI, vol. 13(11), pages 1-16, June.
    3. Xixi Li & Baiyu Zhang & Wendy Huang & Cuirin Cantwell & Bing Chen, 2020. "Integration of Fuzzy Matter-Element Method and 3D-QSAR Model for Generation of Environmentally Friendly Quinolone Derivatives," IJERPH, MDPI, vol. 17(9), pages 1-25, May.
    4. Gao, Hongjun & Cai, Wenhui & He, Shuaijia & Liu, Chang & Liu, Junyong, 2023. "Stackelberg game based energy sharing for zero-carbon community considering reward and punishment of carbon emission," Energy, Elsevier, vol. 277(C).

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