Emission Reduction of Low-Carbon Supply Chain Based on Uncertain Differential Game

Xiangfeng Yang () and Peng Zhang
Additional contact information
Xiangfeng Yang: University of International Business and Economics
Peng Zhang: University of International Business and Economics

Journal of Optimization Theory and Applications, 2023, vol. 199, issue 2, No 11, 732-765

Abstract: Abstract This paper studies the low-carbon production problem based on an uncertain differential game. In a two-stage low-carbon product supply chain consisting of a single supplier and a single manufacturer, we construct an uncertain differential equation by taking the emission reduction efforts of the supplier and manufacturer as decision variables and product emission reduction as a state variable. Based on the uncertain differential game, we obtain the supply chain’s dynamic equilibrium strategy, emission reduction, and revenue trajectory under non-cooperative and cooperative situations. By comparison, cooperation leads to more effort on both sides of the supply chain, more carbon emission reduction, and higher profits than non-cooperative ones. To further illustrate the supply chain game in the cooperative situation, we give different income situations under the two distribution methods of Nash bargaining solution and Shapley value, respectively.

Keywords: Emission reduction; Low-carbon supply chain; Uncertain differential equation; Nash bargaining solution; Shapley value; 49J53; 49K99 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10957-023-02305-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joptap:v:199:y:2023:i:2:d:10.1007_s10957-023-02305-1

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10957/PS2

DOI: 10.1007/s10957-023-02305-1

Access Statistics for this article

Journal of Optimization Theory and Applications is currently edited by Franco Giannessi and David G. Hull

More articles in Journal of Optimization Theory and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().