Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 7, Issue 1


Published
on

October 14, 2022


Pages

1-18


DOI

Article

Game theoretic model for low carbon supply chain under carbon emissions reduction sensitive random demand

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Authors

Chao Ma Affiliation:
Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang, 441053, China
, Fuyou Huang Affiliation:
Institute of Transportation Development Strategy & Planning of Sichuan Province, Chengdu 610041, China
and Yongwen Hu Affiliation:
Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Hubei University of Arts and Science, Xiangyang, 441053, China


Abstract

This paper considers a low carbon supply chain consisting of a single manufacturer and a single retailer under the condition that the demand is uncertain. We first establish three games, including manufacturer Stackelberg (MS), retailer Stackelberg (RS) and Nash according to the different power structure of the firms. We then determine that the equilibrium stocking factors, emission reduction levels, wholesale prices and retail prices for the three models, respectively. After that, we demonstrate the effects of power structure. Results show that when the power shifts from the retailer to the manufacturer, the stocking factor decreases, whereas the wholesale price increases. Finally, we discuss the impacts of the random demand. We find that the expected profits of the firms, the emission reduction levels and the retail prices are increasing with respect to the market potential and low-carbon sensitivity coefficient, respectively. Meanwhile, they decrease with respect to the price sensitivity coefficient.


Keywords

low carbon, demand uncertainty, power structure, game theory, 90B50


Citation

Ma, C., Huang, F., & Hu, Y. (2022). Game theoretic model for low carbon supply chain under carbon emissions reduction sensitive random demand. Applied Mathematics and Nonlinear Sciences, 7(1), 1–18. https://doi.org/10.2478/amns.2022.1.00101

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