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Impact of altruistic preference on the dynamic decision and coordination of joint emission reduction in low-carbon supply chain |
| Jinchai Lin(林金钗)1, Chunyan Zheng(郑春艳)1, Ruguo Fan(范如国)2, Yuanyuan Wang(王圆缘)2, Yingqing Zhang(张应青)3, and Han Song(宋寒)1,† |
1 Management School, Chongqing University of Technology, Chongqing 400054, China; 2 Economics and Management School, Wuhan University, Wuhan 430072, China; 3 School of Business Administration, Guizhou University of Finance and Economics, Guiyang 550025, China |
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Abstract Under the carbon trading mechanism, this study establishes a decision-making model for a low-carbon supply chain comprising a single manufacturer and a retailer. It analyzes the impact of the retailer's altruistic preferences on decision variables, profits, supply chain coordination, and the complexity of the dynamic decision-making model. The study indicates that the continuous increase in the adjustment parameters of decision variables can destabilize the supply chain system, with the adjustment parameter for wholesale price having a greater impact than the adjustment parameter for carbon emission reduction level. An increase in altruistic preferences reduces the stability region of the supply chain system, yet its influence on supply chain profits varies depending on the system's state. When the system is stable, altruistic preferences have a positive impact on overall profits; when the system is unstable, profits first increase and then decrease. Compared to decentralized decision-making, centralized decision-making yields higher profits. Based on this, we innovatively design a side-payment self-enforcing contract mechanism to effectively coordinate the supply chain and promote more efficient cooperation. After implementing this contract, the increase in retailers' altruistic preferences can expand the stability region of the supply chain system, and the impact of the carbon emission reduction level adjustment parameter on system stability is greater than that of the wholesale price adjustment parameter, which is different from the situation before the contract was implemented.
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Received: 03 March 2025
Revised: 26 August 2025
Accepted manuscript online: 28 August 2025
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PACS:
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05.45.-a
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(Nonlinear dynamics and chaos)
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05.45.Pq
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(Numerical simulations of chaotic systems)
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| Fund: Project supported by the Science and Technology Research Project of Chongqing Municipal Education Committee (Grant No. KJQN202201140), Humanities and Social Sciences Research Project of Chongqing Municipal Education Commission (Grant No. 24SKGH231), General Project for Research on Philosophy and Social Sciences from Ministry of Education of China (Grant No. 23YJCZH124), and the National Natural Science Foundation of China (Grant No. 72361006). |
Corresponding Authors:
Han Song
E-mail: songhan@cqut.edu.cn
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Cite this article:
Jinchai Lin(林金钗), Chunyan Zheng(郑春艳), Ruguo Fan(范如国), Yuanyuan Wang(王圆缘), Yingqing Zhang(张应青), and Han Song(宋寒) Impact of altruistic preference on the dynamic decision and coordination of joint emission reduction in low-carbon supply chain 2026 Chin. Phys. B 35 040501
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[1] Liu B, Ding C J, Ahmed A D, Huang Y J and Su Y Q 2024 J. Clean. Prod. 463 142246 [2] Wang D, Sun M L, Meng B, An Y B, Cheng W Y and Ye B 2024 Energy 309 133157 [3] Yang M and Lin B 2024 Energy 306 132491 [4] Sun H and Yang J 2021 Comput. Ind. Eng. 156 107244 [5] Peng, Y F, Tao X Y, Hong J K, Sun L L and Yuan X 2024 J. Retail. Consum. Serv. 81 103950 [6] Xia C Y, Wang J, Perc M and Wang Z 2023 Phys. Life Rev. 46 8 [7] Zhu Y Y, Zhang Z P, Xia C Y and Chen Z Q 2023 Automatica 147 110707 [8] KuitiMR, Ghosh D, Basu P, Basu P and Bisi A 2020 Int. J. Prod. Econ. 223 107537 [9] Wu W Q, Li M, Zhang M, Wang Y Q, Wang L K and You Y 2024 Process Saf. Environ. Prot. 192 1467 [10] Cai J H and Jiang F Y 2023 Int. J. Prod. Econ. 264 108964 [11] Ji J N, Zhang Z Y and Yang L 2017 J. Clean. Prod. 141 852 [12] Wang K, Wu P Y and Zhang W H 2024 J. Franklin Inst. 361 106719 [13] Jiang K, Wang D, Xu L and Wang F 2024 Socioecon. Plann. Sci. 95 102033 [14] Xia X Q, Chen J, Zhu Q H and Li B 2023 Syst. Eng. Theory Pract. 43 2632 [15] Li Z M, Pan Y C, YangW, Ma J H and Zhou M 2021 Energy Econ. 101 105426 [16] Taleizadeh A A, Alizadeh-Basban N and Sarker B R 2018 Comput. Ind. Eng. 124 249 [17] Zou H, Qin J and Dai B 2021 Int. J. Environ. Res. Public Health 18 556 [18] Yu C H, Zhang Y X, Liu L and Archibald T W 2024 Oper. Res. 24 64 [19] Liu J J, Ke H, Zhang R R and Duan K F 2023 J. Clean. Prod. 386 135645 [20] Wang Y Y, Yu Z Q, Jin M Z and Mao J F 2021 Eur. J. Oper. Res. 293 910 [21] Zhou X and Wu X 2023 Mathematics 11 911 [22] Wang Y Y, Fan R J, Shen L and Miller W 2020 J. Clean. Prod. 259 120883 [23] Zhang Z Y and Yu L Y 2022 J. Clean. Prod. 366 132863 [24] Sun L H, Sun Y, Wu A B and Wang X P 2024 Syst. Sci. Math. Sci. 44 3718 [25] Hwarng H B and Xie N 2008 Eur. J. Oper. Res. 184 1163 [26] Macdonald J R, Frommer I D and Karaesmen I Z 2013 Oper. Manag. Res. 6 119 [27] Zhang Y H and Zhang T 2022 Chaos, Solitons and Fractals 32 2250090 [28] Ma J H, Tian Y, Xu T T, Koivumäki T and Xu Y Q 2022 Chaos, Solitons and Fractals 160 112131 [29] Wu R, Li M, Liu F, Zeng H J and Cong X P 2024 Int. Rev. Econ. Fin. 95 103482 [30] Chen J X, Hou R, Xiao L, Zhang T H and Zhou Y W 2023 Chaos, Solitons and Fractals 168 113158 [31] Lin J C, Fan RG, Wang Y Y and Du K 2023 Chin. Phys. B 32 100502 [32] Ma J H, Bao B S, Liu L X and Wang X Y 2024 Manag. Decis. Econ. 45 2566 [33] Loch C H and Wu Y 2008 Manag. Sci. 54 1835 [34] Ge Z H, Zhang Z K, Lü L Y, Zhou T and Xi N 2012 Physica A 391 647 [35] Borisov A B and Zverev V V 2017 Nonlinear Dynamics: Non- Integrable Systems and Chaotic Dynamics (Berlin: De Gruyter) pp. 145-173 [36] Wang C X, Peng Q Y and Xu L 2021 Kybernetes 50 2318 [37] Zhang X and Zhang Q 2017 J. Syst. Eng. 32 461 |
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