Issue |
RAIRO-Oper. Res.
Volume 58, Number 5, September-October 2024
|
|
---|---|---|
Page(s) | 4499 - 4520 | |
DOI | https://doi.org/10.1051/ro/2024185 | |
Published online | 21 October 2024 |
Research on low-carbon supply chain emission reduction strategies based on blockchain technology
School of Economics and Management, Dalian University of Technology, Dalian 116024, P.R. China
* Corresponding author: zhanglrm@dlut.edu.cn
Received:
2
January
2024
Accepted:
14
September
2024
The difficulty of regulating carbon trading due to information asymmetry and low consumer trust in low-carbon products are key factors hindering companies from reducing emissions. This paper examines a manufacturer-led secondary low-carbon supply chain consisting of a single supplier and a retailer, focusing on the impact of blockchain technology on carbon transaction costs and consumers’ low-carbon preferences. Utilizing Stackelberg game theory, the paper constructs a supply chain decision model for emission reduction, determining the payment matrix and analyzing the stable strategy for blockchain adoption through evolutionary game theory. The findings indicate that retailers’ adoption of blockchain technology significantly promotes emission reduction within the supply chain, whereas manufacturers’ adoption has minimal impact. Additionally, the study reveals that variations in blockchain adoption costs and carbon quotas result in multiple evolutionary stable strategies. Specifically, when blockchain adoption costs and carbon quotas are below certain thresholds, the system reaches a unique equilibrium where both parties adopt blockchain technology.
Mathematics Subject Classification: 91A40 / 91B24
Key words: Blockchain technology / carbon emission reduction / strategy choice / evolutionary game
© The authors. Published by EDP Sciences, ROADEF, SMAI 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.