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Revenue allocation for interfirm collaboration on carbon emission reduction: complete information in a big data context

Author

Listed:
  • Bin Zhang

    (Beijing Institute of Technology
    Center for Sustainable Development and Smart Decision
    Beijing Institute of Technology)

  • Qingyao Xin

    (Beijing Institute of Technology
    Center for Sustainable Development and Smart Decision)

  • Min Tang

    (Beijing Institute of Technology
    Center for Sustainable Development and Smart Decision)

  • Niu Niu

    (Beijing Institute of Technology)

  • Heran Du

    (Beijing National Day School)

  • Xiqiang Chang

    (State Grid Xinjiang Electric Power Co.Ltd)

  • Zhaohua Wang

    (Beijing Institute of Technology
    Center for Sustainable Development and Smart Decision
    Beijing Institute of Technology)

Abstract

Though interfirm collaboration on carbon emission reduction, the cross-enterprise flow of emission reduction resources and improved efficiency in greenhouse gas reduction can be realized. Especially in the context of big data, enterprises can find suitable partners for emission reduction faster and more accurately through interfirm collaboration. However, similar to other cooperative modes, revenue allocation is the key to ensuring the stability of the collaborative emission reduction system. Based on the premise of carbon trading, this paper discusses revenue allocation among enterprises participating in the collaborative emission reduction process under complete information in a big data context. Specifically, we constructed a Shapley value analysis model of revenue allocation for interfirm collaboration on carbon emission reduction, and amended this model with investment cost and risk-bearing. Consequently, this research provides not only a theoretical basis for solving the problem of revenue distribution in the process of collaborative emission reductions among enterprises but also a theoretical guide for enterprises countermeasures following the completion of China's future carbon trading mechanism.

Suggested Citation

  • Bin Zhang & Qingyao Xin & Min Tang & Niu Niu & Heran Du & Xiqiang Chang & Zhaohua Wang, 2022. "Revenue allocation for interfirm collaboration on carbon emission reduction: complete information in a big data context," Annals of Operations Research, Springer, vol. 316(1), pages 93-116, September.
    • Handle: RePEc:spr:annopr:v:316:y:2022:i:1:d:10.1007_s10479-021-04017-z
    DOI: 10.1007/s10479-021-04017-z
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    1. Li, Sijie & Zhu, Zhanbei & Huang, Lihua, 2009. "Supply chain coordination and decision making under consignment contract with revenue sharing," International Journal of Production Economics, Elsevier, vol. 120(1), pages 88-99, July.
    2. Jürgen Scheffran & Stefan Pickl, 2000. "Control and game-theoretic assessment of climate change: Options for Joint Implementation," Annals of Operations Research, Springer, vol. 97(1), pages 203-212, December.
    3. Liangjie Xia & Tingting Guo & Juanjuan Qin & Xiaohang Yue & Ning Zhu, 2018. "Carbon emission reduction and pricing policies of a supply chain considering reciprocal preferences in cap-and-trade system," Annals of Operations Research, Springer, vol. 268(1), pages 149-175, September.
    4. Ding, Huiping & Guo, Baochun & Liu, Zhishuo, 2011. "Information sharing and profit allotment based on supply chain cooperation," International Journal of Production Economics, Elsevier, vol. 133(1), pages 70-79, September.
    5. Gao, Evelyn & Sowlati, Taraneh & Akhtari, Shaghaygh, 2019. "Profit allocation in collaborative bioenergy and biofuel supply chains," Energy, Elsevier, vol. 188(C).
    6. Zhang, Bin & Wang, Zhaohua, 2014. "Inter-firm collaborations on carbon emission reduction within industrial chains in China: Practices, drivers and effects on firms' performances," Energy Economics, Elsevier, vol. 42(C), pages 115-131.
    7. Chauhan, Satyaveer S. & Proth, Jean-Marie, 2005. "Analysis of a supply chain partnership with revenue sharing," International Journal of Production Economics, Elsevier, vol. 97(1), pages 44-51, July.
    8. Yang, Lei & Zhang, Qin & Ji, Jingna, 2017. "Pricing and carbon emission reduction decisions in supply chains with vertical and horizontal cooperation," International Journal of Production Economics, Elsevier, vol. 191(C), pages 286-297.
    9. Shi, Qian & Lai, Xiaodong, 2013. "Identifying the underpin of green and low carbon technology innovation research: A literature review from 1994 to 2010," Technological Forecasting and Social Change, Elsevier, vol. 80(5), pages 839-864.
    10. Xavier Molina-Morales, F. & Belso-Martínez, José A. & Más-Verdú, Francisco & Martínez-Cháfer, Luis, 2015. "Formation and dissolution of inter-firm linkages in lengthy and stable networks in clusters," Journal of Business Research, Elsevier, vol. 68(7), pages 1557-1562.
    11. Min Yang & Qingxian An & Tao Ding & Pengzhen Yin & Liang Liang, 2019. "Carbon emission allocation in China based on gradually efficiency improvement and emission reduction planning principle," Annals of Operations Research, Springer, vol. 278(1), pages 123-139, July.
    12. Kimms, Alf & Çetiner, Demet, 2012. "Approximate nucleolus-based revenue sharing in airline alliances," European Journal of Operational Research, Elsevier, vol. 220(2), pages 510-521.
    13. An, Qingxian & Wen, Yao & Ding, Tao & Li, Yongli, 2019. "Resource sharing and payoff allocation in a three-stage system: Integrating network DEA with the Shapley value method," Omega, Elsevier, vol. 85(C), pages 16-25.
    14. Jingjing He & Yongfu Huang & Finn Tarp, 2014. "Is the Clean Development Mechanism effective for emission reductions?," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 4(6), pages 750-760, December.
    15. Ning Jiang & Linda Zhang & Yugang Yu, 2015. "Optimizing Cooperative Advertizing, Profit Sharing, and Inventory Policies in a VMI Supply Chain: A Nash Bargaining Model and Hybrid Algorithm," Post-Print hal-01563010, HAL.
    16. Linlin Zhao & Yong Zha & Kangning Wei & Liang Liang, 2017. "A target-based method for energy saving and carbon emissions reduction in China based on environmental data envelopment analysis," Annals of Operations Research, Springer, vol. 255(1), pages 277-300, August.
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