A Comparative Analysis of the Stackelberg Game Approach to Green Closed-Loop Supply Chains under Different Recycling Channels
- DOI
- 10.2991/978-94-6463-102-9_78How to use a DOI?
- Keywords
- Green closed-loop supply chain; Recycling channel; Stackelberg game
- Abstract
In the face of rapid economic development and increasingly severe environmental challenges, today's society is more critical to creating sustainable green supply chains. The article explores the application of game theory in the green closed-loop supply chain by discussing and analyzing the green closed-loop supply chain. On this basis, the game theory approach is used to construct the green closed-loop supply chain decision model according to the different modes of recycling, and the optimal pricing strategies under other methods are obtained based on the Stackelberg game solution. The impact of the green-related parameters of the products on the decision results is explored. The optimal pricing strategies under different models were obtained based on the Stackelberg game solution. The impact of the green-related parameters on the decision outcome was investigated, which provides a reference for further extension of game theory in green closed-loop supply chain projects.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Lifan Shen PY - 2022 DA - 2022/12/29 TI - A Comparative Analysis of the Stackelberg Game Approach to Green Closed-Loop Supply Chains under Different Recycling Channels BT - Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022) PB - Atlantis Press SP - 763 EP - 773 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-102-9_78 DO - 10.2991/978-94-6463-102-9_78 ID - Shen2022 ER -