Dual-fairness optimization for supply chain profit and emissions reduction distribution under carbon cap-sharing mechanism

As global climate change intensifies and governments increasingly commit to net-zero emissions goals, supply chain management faces growing pressure to balance economic efficiency with environmental benefits. The carbon cap-and-trade policy, widely adopted to achieve net-zero emissions via carbon markets, often leads to uneven allocation of emissions reduction responsibilities among supply chain members. To address this challenge, the carbon cap-sharing mechanism has been proposed, enabling collaboration through the internal reallocation of surplus carbon quotas and aiming to promote both efficiency and fairness. Despite its potential, research on this mechanism in ensuring fair distribution remains limited. Accordingly, this study develops a mixed-integer linear programming model incorporating dual-fairness, combining internal carbon sharing with external carbon trading. Using a Nash bargaining approach with a fairness preference parameter, the model balances environmental and economic objectives, guiding fair decisions on production, capacity planning, and carbon trading among supply chain members. Numerical analyses based on realistic scenarios demonstrate the framework’s effectiveness in enhancing low-carbon operations, achieving equitable distribution of both profits and carbon emissions reduction (CER), and providing actionable insights for managers and policymakers seeking to support net-zero supply chains and mitigate climate change.