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Open AccessOpen Access JUSTC Management 30 June 2023

Optimal environment design and revenue allocation: Under cap-and-trade policy in the cooperation supply chain

  • School of Management, University of Science and Technology of China, Hefei 230026, China

Cite this:
https://doi.org/10.52396/JUSTC-2022-0093
More Information
  • Author Bio:

    Yuting Wei is a Ph.D. candidate at the University of Science and Technology of China. Her research mainly focuses on low-carbon operations

    Yu Dong is an Associate Professor at the University of Science and Technology of China (USTC) and a Vice President of Anhui University of Science & Technology. He received his Ph.D. degree in Management from USTC. His research mainly focuses on decision science and operations management

  • Corresponding author: E-mail: ydong@ustc.edu.cn
  • Received Date: 22 June 2022
  • Accepted Date: 23 November 2022
    • Available Online: 30 June 2023
    Abstract

  • Abstract

    Since the supply chains of the world’s 2500 largest companies alone emit more than 20% of global greenhouse gases, how to achieve optimal cooperative supply chain emission reduction effects in supply chain optimal emission reduction efforts and effectively distribute revenue in cooperative supply chains is a difficult complex problem. In this paper, a green supply chain model of joint production is constructed based on the framework of the Stackelberg model and with carbon trading under three quota methods being taken into account. First, from the perspective of a supply chain leader, we obtain the optimal efforts to reduce emissions, the optimal price, and the yield of the products. Then, from the perspective of carbon market regulators, we obtain the environment that is most conducive to reducing emissions in the supply chain. Finally, we offer a profit distribution method based on the modified Shapley value, which maximizes fairness and stability. The data calculation example analysis further verifies the results of the theoretical analysis.

    Graphical abstract

    Analyzing the influence of some environmental factors on carbon emission reduction and optimizing revenue allocation in cooperative supply chain.

    Abstract

    Since the supply chains of the world’s 2500 largest companies alone emit more than 20% of global greenhouse gases, how to achieve optimal cooperative supply chain emission reduction effects in supply chain optimal emission reduction efforts and effectively distribute revenue in cooperative supply chains is a difficult complex problem. In this paper, a green supply chain model of joint production is constructed based on the framework of the Stackelberg model and with carbon trading under three quota methods being taken into account. First, from the perspective of a supply chain leader, we obtain the optimal efforts to reduce emissions, the optimal price, and the yield of the products. Then, from the perspective of carbon market regulators, we obtain the environment that is most conducive to reducing emissions in the supply chain. Finally, we offer a profit distribution method based on the modified Shapley value, which maximizes fairness and stability. The data calculation example analysis further verifies the results of the theoretical analysis.

    • Public Summary

    • From the perspective of a supply chain manager, we analyze how to guide the carbon reduction decision of the whole supply chain is a question worth studying.
    • We analyze the external environment, such as how carbon trading price, unit carbon emission reduction cost, the impact of efforts on emissions per unit of product, and the sensitivity of demand to unit emissions of the product affect the cooperative supply chain emission reduction in a carbon trading environment.
    • Combined with Shapley value, we introduce the concept of a “core” to propose a fair and stable revenue allocation mechanism to stabilize the cooperation supply chain stable.

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    • References(32)
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    Zhang T, Hao Y Q, Zhu X Y. Consignment inventory management in a closed-loop supply chain for deteriorating items under a carbon cap-and-trade regulation. Computers & Industrial Engineering, 2022, 171: 108410. doi: 10.1016/j.cie.2022.108410
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    Li G, Zheng H, Ji X, et al. Game theoretical analysis of firms’ operational low-carbon strategy under various cap-and-trade mechanisms. Journal of Cleaner Production, 2018, 197: 124–133. doi: 10.1016/j.jclepro.2018.06.177
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    Estevez-Fernandez A, Reijnierse H. On the core of cost-revenue games: Minimum cost spanning tree games with revenues. European Journal of Operational Research, 2014, 237 (2): 606–616. doi: 10.1016/j.ejor.2014.01.056
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    Lin X, Zhou J, Zhang L, et al. Revenue sharing for resource reallocation among project activity contractors. Annals of Operations Research, 2020, 301: 121–141. doi: 10.1007/s10479-020-03753-y
    [21]
    Tang J, Meng F, Zhang Q. Characterizations of a Shapley value for multichoice games. International Journal of General Systems, 2018, 48 (2): 186–209. doi: 10.1080/03081079.2018.1549550
    [22]
    Kaewpuang R, Niyato D, Wang P, et al. A framework for cooperative resource management in mobile cloud computing. IEEE Journal on Selected Areas in Communications, 2013, 31 (12): 2685–2700. doi: 10.1109/JSAC.2013.131209
    [23]
    Lopez-Navarrete F, Sanchez-Soriano J, Bonastre, O M. Allocating revenues in a Smart TV ecosystem. International Transactions in Operational Research, 2019, 26 (5): 1611–1632. doi: 10.1111/itor.12636
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    Sosic G. Transshipment of inventories among retailers: Myopic vs. farsighted stability. Management Science, 2006, 52 (10): 1493–1508. doi: 10.1287/mnsc.1060.0558
    [25]
    Nguyen T-D. The fairest core in cooperative games with transferable utilities. Operations Research Letters, 2015, 43 (1): 34–39. doi: 10.1016/j.orl.2014.11.001
    [26]
    Zhang B, Xin Q, Tang M, et al. Revenue allocation for interfirm collaboration on carbon emission reduction: Complete information in a big data context. Annals of Operations Research, 2022, 316: 93–116. doi: 10.1007/s10479-021-04017-z
    [27]
    Zhang Y J, Sun Y F, Huo B F. The optimal product pricing and carbon emissions reduction profit allocation of CET-covered enterprises in the cooperative supply chain. Annals of Operations Research, 2021: DOI: 10.1007/s10479-021-04162-5.
    [28]
    Golombek R, Kittelsen S A C, Rosendahl K E. Price and welfare effects of emission quota allocation. Energy Economics, 2013, 36: 568–580. doi: 10.1016/j.eneco.2012.11.006
    [29]
    Wang B, Ji F, Zheng J, et al. Carbon emission reduction of coal-fired power supply chain enterprises under the revenue sharing contract: Perspective of coordination game. Energy Economics, 2021, 102: 105467. doi: 10.1016/j.eneco.2021.105467
    [30]
    Murali K, Lim M K, Petruzzi N C. The effects of ecolabels and environmental regulation on green product development. Manufacturing & Service Operations Management, 2019, 21 (3): 519–535. doi: 10.1287/msom.2017.0703
    [31]
    Shapley L S, Shubik M. The assignment game Ⅰ: The core. International Journal of Game Theory, 1972, 1 (2): 111–130. doi: 10.1007/BF01753437
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    Shanghai Environment and Energy Exchange. National carbon market daily transaction data (20220725). 2022. https://www.cneeex.com/c/2022-07-25/492933.shtml.
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    [1]
    Caro F, Corbett C J, Tan T, et al. Double counting in supply chain carbon footprinting. Manufacturing & Service Operations Management, 2013, 15 (4): 545–558. doi: 10.1287/msom.2013.0443
    [2]
    Eltayeb T K, Zailani S, Ramayah T. Green supply chain initiatives among certified companies in Malaysia and environmental sustainability: Investigating the outcomes. Resources, Conservation and Recycling, 2011, 55 (5): 495–506. doi: 10.1016/j.resconrec.2010.09.003
    [3]
    Govindan K, Soleimani H, Kannan D. Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 2015, 240 (3): 603–626. doi: 10.1016/j.ejor.2014.07.012
    [4]
    Seuring S, Müller M. From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, 2008, 16 (15): 1699–1710. doi: 10.1016/j.jclepro.2008.04.020
    [5]
    Validi S, Bhattacharya A, Byrne P J. Integrated low-carbon distribution system for the demand side of a product distribution supply chain: A DoE-guided MOPSO optimiser-based solution approach. International Journal of Production Research, 2013, 52 (10): 3074–3096. doi: 10.1080/00207543.2013.864054
    [6]
    Yu B, Wang J, Lu X, Yang H. Collaboration in a low-carbon supply chain with reference emission and cost learning effects: Cost sharing versus revenue sharing strategies. Journal of Cleaner Production, 2020, 250: 119460. doi: 10.1016/j.jclepro.2019.119460
    [7]
    Cheng P Y, Ji G X, Zhang G T, et al. A closed-loop supply chain network considering consumer’s low carbon preference and carbon tax under the cap-and-trade regulation. Sustainable Production and Consumption, 2022, 29: 614–635. doi: 10.1016/j.spc.2021.11.006
    [8]
    Kou X, Liu H, Gao H, et al. Cooperative emission reduction in the supply chain: the value of green marketing under different power structures. Environ Sci Pollut Res Int, 2022, 29 (45): 68396–68409. doi: 10.1007/s11356-022-20683-3
    [9]
    Wang Q, Chiu Y-H, Chiu C-R. Driving factors behind carbon dioxide emissions in China: A modified production-theoretical decomposition analysis. Energy Economics, 2015, 51: 252–260. doi: 10.1016/j.eneco.2015.07.009
    [10]
    Jiang M, An H, Gao X, et al. Structural decomposition analysis of global carbon emissions: The contributions of domestic and international input changes. J Environ Manage, 2021, 294: 112942. doi: 10.1016/j.jenvman.2021.112942
    [11]
    İslegen Ö, Reichelstein S. Carbon capture by fossil fuel power plants: An economic analysis. Management Science, 2011, 57 (1): 21–39. doi: 10.1287/mnsc.1100.1268
    [12]
    Toptal A, Özlü H, Konur D. Joint decisions on inventory replenishment and emission reduction investment under different emission regulations. International Journal of Production Research, 2013, 52 (1): 243–269. doi: 10.1080/00207543.2013.836615
    [13]
    Chen X, Yang H, Wang X, et al. Optimal carbon tax design for achieving low carbon supply chains. Annals of Operations Research, 2020: DOI: 10.1007/s10479-020-03621-9.
    [14]
    Hovelaque V, Bironneau L. The carbon-constrained EOQ model with carbon emission dependent demand. International Journal of Production Economics, 2015, 164: 285–291. doi: 10.1016/j.ijpe.2014.11.022
    [15]
    Wang Y, Yang H, Sun R. Effectiveness of China’s provincial industrial carbon emission reduction and optimization of carbon emission reduction paths in “lagging regions”: Efficiency-cost analysis. J Environ Manage, 2020, 275: 111221. doi: 10.1016/j.jenvman.2020.111221
    [16]
    Mirzaee H, Samarghandi H, Willoughby K. A three-player game theory model for carbon cap-and-trade mechanism with stochastic parameters. Computers & Industrial Engineering, 2022, 169: 108215. doi: 10.1016/j.cie.2022.108285
    [17]
    Zhang T, Hao Y Q, Zhu X Y. Consignment inventory management in a closed-loop supply chain for deteriorating items under a carbon cap-and-trade regulation. Computers & Industrial Engineering, 2022, 171: 108410. doi: 10.1016/j.cie.2022.108410
    [18]
    Li G, Zheng H, Ji X, et al. Game theoretical analysis of firms’ operational low-carbon strategy under various cap-and-trade mechanisms. Journal of Cleaner Production, 2018, 197: 124–133. doi: 10.1016/j.jclepro.2018.06.177
    [19]
    Estevez-Fernandez A, Reijnierse H. On the core of cost-revenue games: Minimum cost spanning tree games with revenues. European Journal of Operational Research, 2014, 237 (2): 606–616. doi: 10.1016/j.ejor.2014.01.056
    [20]
    Lin X, Zhou J, Zhang L, et al. Revenue sharing for resource reallocation among project activity contractors. Annals of Operations Research, 2020, 301: 121–141. doi: 10.1007/s10479-020-03753-y
    [21]
    Tang J, Meng F, Zhang Q. Characterizations of a Shapley value for multichoice games. International Journal of General Systems, 2018, 48 (2): 186–209. doi: 10.1080/03081079.2018.1549550
    [22]
    Kaewpuang R, Niyato D, Wang P, et al. A framework for cooperative resource management in mobile cloud computing. IEEE Journal on Selected Areas in Communications, 2013, 31 (12): 2685–2700. doi: 10.1109/JSAC.2013.131209
    [23]
    Lopez-Navarrete F, Sanchez-Soriano J, Bonastre, O M. Allocating revenues in a Smart TV ecosystem. International Transactions in Operational Research, 2019, 26 (5): 1611–1632. doi: 10.1111/itor.12636
    [24]
    Sosic G. Transshipment of inventories among retailers: Myopic vs. farsighted stability. Management Science, 2006, 52 (10): 1493–1508. doi: 10.1287/mnsc.1060.0558
    [25]
    Nguyen T-D. The fairest core in cooperative games with transferable utilities. Operations Research Letters, 2015, 43 (1): 34–39. doi: 10.1016/j.orl.2014.11.001
    [26]
    Zhang B, Xin Q, Tang M, et al. Revenue allocation for interfirm collaboration on carbon emission reduction: Complete information in a big data context. Annals of Operations Research, 2022, 316: 93–116. doi: 10.1007/s10479-021-04017-z
    [27]
    Zhang Y J, Sun Y F, Huo B F. The optimal product pricing and carbon emissions reduction profit allocation of CET-covered enterprises in the cooperative supply chain. Annals of Operations Research, 2021: DOI: 10.1007/s10479-021-04162-5.
    [28]
    Golombek R, Kittelsen S A C, Rosendahl K E. Price and welfare effects of emission quota allocation. Energy Economics, 2013, 36: 568–580. doi: 10.1016/j.eneco.2012.11.006
    [29]
    Wang B, Ji F, Zheng J, et al. Carbon emission reduction of coal-fired power supply chain enterprises under the revenue sharing contract: Perspective of coordination game. Energy Economics, 2021, 102: 105467. doi: 10.1016/j.eneco.2021.105467
    [30]
    Murali K, Lim M K, Petruzzi N C. The effects of ecolabels and environmental regulation on green product development. Manufacturing & Service Operations Management, 2019, 21 (3): 519–535. doi: 10.1287/msom.2017.0703
    [31]
    Shapley L S, Shubik M. The assignment game Ⅰ: The core. International Journal of Game Theory, 1972, 1 (2): 111–130. doi: 10.1007/BF01753437
    [32]
    Shanghai Environment and Energy Exchange. National carbon market daily transaction data (20220725). 2022. https://www.cneeex.com/c/2022-07-25/492933.shtml.
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