{"title":"The influence of carbon sink trading on carbon emission reduction in agricultural supply chains","authors":"Tingting Meng, Yukun Cheng, Xujin Pu, Rui Li","doi":"10.1007/s10878-025-01316-0","DOIUrl":null,"url":null,"abstract":"<p>As global climate change intensifies, the agricultural sector, responsible for over 30% of global greenhouse gas emissions, faces an urgent imperative to mitigate emissions and align with international climate commitments. Carbon sink trading, a market-based mechanism that incentivizes emission reductions through sequestration credits, has emerged as an important tool for accelerating carbon peaking and neutrality goals. This study investigates the influence of carbon sink trading on the strategic interactions between farmers and retailers in agricultural supply chains. Employing differential game theory, we construct three cooperative models: decentralized, Stackelberg leader-follower, and centralized, and derive equilibrium strategies for each using the Hamilton-Jacobi-Bellman framework. Through numerical simulations, we evaluate the influence of carbon sink trading on the emission reduction efforts of farmers and retailers, the extent of emission reductions in the supply chain, and the overall profits. Comparative analysis against baseline scenarios without carbon trading reveals that the integration of carbon sink markets enhances profit margins across all models and improves the level of emission reduction in the agricultural supply chain. In addition, our results show that the centralized model outperforms other configurations, followed by the Stackelberg model, with the decentralized model exhibiting the least effectiveness. These findings provide actionable insights for policymakers and supply chain managers to design carbon trading frameworks that harmonize economic incentives with ecological sustainability.</p>","PeriodicalId":50231,"journal":{"name":"Journal of Combinatorial Optimization","volume":"40 1","pages":""},"PeriodicalIF":0.9000,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Combinatorial Optimization","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s10878-025-01316-0","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
As global climate change intensifies, the agricultural sector, responsible for over 30% of global greenhouse gas emissions, faces an urgent imperative to mitigate emissions and align with international climate commitments. Carbon sink trading, a market-based mechanism that incentivizes emission reductions through sequestration credits, has emerged as an important tool for accelerating carbon peaking and neutrality goals. This study investigates the influence of carbon sink trading on the strategic interactions between farmers and retailers in agricultural supply chains. Employing differential game theory, we construct three cooperative models: decentralized, Stackelberg leader-follower, and centralized, and derive equilibrium strategies for each using the Hamilton-Jacobi-Bellman framework. Through numerical simulations, we evaluate the influence of carbon sink trading on the emission reduction efforts of farmers and retailers, the extent of emission reductions in the supply chain, and the overall profits. Comparative analysis against baseline scenarios without carbon trading reveals that the integration of carbon sink markets enhances profit margins across all models and improves the level of emission reduction in the agricultural supply chain. In addition, our results show that the centralized model outperforms other configurations, followed by the Stackelberg model, with the decentralized model exhibiting the least effectiveness. These findings provide actionable insights for policymakers and supply chain managers to design carbon trading frameworks that harmonize economic incentives with ecological sustainability.
期刊介绍:
The objective of Journal of Combinatorial Optimization is to advance and promote the theory and applications of combinatorial optimization, which is an area of research at the intersection of applied mathematics, computer science, and operations research and which overlaps with many other areas such as computation complexity, computational biology, VLSI design, communication networks, and management science. It includes complexity analysis and algorithm design for combinatorial optimization problems, numerical experiments and problem discovery with applications in science and engineering.
The Journal of Combinatorial Optimization publishes refereed papers dealing with all theoretical, computational and applied aspects of combinatorial optimization. It also publishes reviews of appropriate books and special issues of journals.