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.
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Acknowledgements
This study is supported by National Natural Science Fund of China (Reference No. 71774014, 91746208, 71573016, 71521002), National Science Fund for Distinguished Young Scholars (Reference No. 71625003), National Key Research and Development Program of China (Reference No. 2016YFA0602504, 2016YFA0602502), National Social Science Fund of China, (Reference No. 17ZDA065), Beijing Social Science Foundation Project (Reference No. 20JCC108) and Joint Development Program of Beijing Municipal Commission of Education, and Science and Technology Project of the Ministry of Housing and Urban-Rural Development of the People's Republic of China (Reference No. 2021-K-106).
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Zhang, B., Xin, Q., Tang, M. et al. Revenue allocation for interfirm collaboration on carbon emission reduction: complete information in a big data context. Ann Oper Res 316, 93–116 (2022). https://doi.org/10.1007/s10479-021-04017-z
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DOI: https://doi.org/10.1007/s10479-021-04017-z