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Calculation and Allocation of Atmospheric Environment Governance Cost in the Yangtze River Economic Belt of China

Author

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  • Jiekun Song

    (School of Economics and Management, China University of Petroleum, Qingdao 266580, China)

  • Zhicheng Liu

    (School of Economics and Management, China University of Petroleum, Qingdao 266580, China)

  • Rui Chen

    (School of Economics and Management, China University of Petroleum, Qingdao 266580, China)

  • Xueli Leng

    (School of Economics and Management, China University of Petroleum, Qingdao 266580, China)

Abstract

Atmospheric environment governance requires necessary cost input. Only by accurately calculating regional atmospheric environment governance cost and scientifically allocating it within a region can the operability and realization of the coordinated governance of the regional environment be ensured. Firstly, based on the consideration of avoiding the technological regression of decision-making units, this paper constructs a sequential SBM-DEA efficiency measurement model and solves the shadow prices of various atmospheric environmental factors, that is, their unit governance costs. Secondly, combined with the emission reduction potential, the total regional atmospheric environment governance cost can be calculated. Thirdly, the Shapley value method is modified to calculate the contribution rate of each province to the whole region, and the equitable allocation scheme of the atmospheric environment governance cost is obtained. Finally, with the goal that the allocation scheme based on the fixed cost allocation DEA (FCA-DEA) model converges with the fair allocation scheme based on the modified Shapley value, a modified FCA-DEA model is constructed to achieve the efficiency and fairness of the allocation of atmospheric environment governance cost. The calculation and allocation of the atmospheric environmental governance cost in the Yangtze River Economic Belt in 2025 verify the feasibility and advantages of the models proposed in this paper.

Suggested Citation

  • Jiekun Song & Zhicheng Liu & Rui Chen & Xueli Leng, 2023. "Calculation and Allocation of Atmospheric Environment Governance Cost in the Yangtze River Economic Belt of China," IJERPH, MDPI, vol. 20(5), pages 1-21, February.
  • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:4281-:d:1082888
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    as
    1. Matsushita, Kyohei & Yamane, Fumihiro, 2012. "Pollution from the electric power sector in Japan and efficient pollution reduction," Energy Economics, Elsevier, vol. 34(4), pages 1124-1130.
    2. Choi, Yongrok & Zhang, Ning & Zhou, P., 2012. "Efficiency and abatement costs of energy-related CO2 emissions in China: A slacks-based efficiency measure," Applied Energy, Elsevier, vol. 98(C), pages 198-208.
    3. He, Weijun & Wang, Bo & Danish, & Wang, Zhaohua, 2018. "Will regional economic integration influence carbon dioxide marginal abatement costs? Evidence from Chinese panel data," Energy Economics, Elsevier, vol. 74(C), pages 263-274.
    4. Feng Li & Qingyuan Zhu & Liang Liang, 2019. "A new data envelopment analysis based approach for fixed cost allocation," Annals of Operations Research, Springer, vol. 274(1), pages 347-372, March.
    5. 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.
    6. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "Slacks-based efficiency measures for modeling environmental performance," Ecological Economics, Elsevier, vol. 60(1), pages 111-118, November.
    7. Wei, Chu & Löschel, Andreas & Liu, Bing, 2013. "An empirical analysis of the CO2 shadow price in Chinese thermal power enterprises," Energy Economics, Elsevier, vol. 40(C), pages 22-31.
    8. Joe Zhu, 2014. "DEA Cross Efficiency," International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, edition 3, chapter 4, pages 61-92, Springer.
    9. Boussemart, Jean-Philippe & Leleu, Hervé & Shen, Zhiyang, 2017. "Worldwide carbon shadow prices during 1990–2011," Energy Policy, Elsevier, vol. 109(C), pages 288-296.
    10. Wei, Xiao & Zhang, Ning, 2020. "The shadow prices of CO2 and SO2 for Chinese Coal-fired Power Plants: A partial frontier approach," Energy Economics, Elsevier, vol. 85(C).
    11. Zeng, Shihong & Jiang, Xue & Su, Bin & Nan, Xin, 2018. "China's SO2 shadow prices and environmental technical efficiency at the province level," International Review of Economics & Finance, Elsevier, vol. 57(C), pages 86-102.
    12. Du, Juan & Cook, Wade D. & Liang, Liang & Zhu, Joe, 2014. "Fixed cost and resource allocation based on DEA cross-efficiency," European Journal of Operational Research, Elsevier, vol. 235(1), pages 206-214.
    13. Cook, Wade D. & Kress, Moshe, 1999. "Characterizing an equitable allocation of shared costs: A DEA approach," European Journal of Operational Research, Elsevier, vol. 119(3), pages 652-661, December.
    14. Beasley, J. E., 2003. "Allocating fixed costs and resources via data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 147(1), pages 198-216, May.
    15. Petrosjan, Leon & Zaccour, Georges, 2003. "Time-consistent Shapley value allocation of pollution cost reduction," Journal of Economic Dynamics and Control, Elsevier, vol. 27(3), pages 381-398, January.
    16. Wu, Jie & Zhu, Qingyuan & Liang, Liang, 2016. "CO2 emissions and energy intensity reduction allocation over provincial industrial sectors in China," Applied Energy, Elsevier, vol. 166(C), pages 282-291.
    17. Molinos-Senante, María & Hanley, Nick & Sala-Garrido, Ramón, 2015. "Measuring the CO2 shadow price for wastewater treatment: A directional distance function approach," Applied Energy, Elsevier, vol. 144(C), pages 241-249.
    18. Fare, Rolf & Grosskopf, Shawna & Noh, Dong-Woon & Weber, William, 2005. "Characteristics of a polluting technology: theory and practice," Journal of Econometrics, Elsevier, vol. 126(2), pages 469-492, June.
    19. Limin Du & Aoife Hanley & Chu Wei, 2015. "Marginal Abatement Costs of Carbon Dioxide Emissions in China: A Parametric Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 61(2), pages 191-216, June.
    20. Wei, Chu & Ni, Jinlan & Du, Limin, 2012. "Regional allocation of carbon dioxide abatement in China," China Economic Review, Elsevier, vol. 23(3), pages 552-565.
    21. Nakaishi, Tomoaki, 2021. "Developing effective CO2 and SO2 mitigation strategy based on marginal abatement costs of coal-fired power plants in China," Applied Energy, Elsevier, vol. 294(C).
    22. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    Full references (including those not matched with items on IDEAS)

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