Blockchain-enabled Stackelberg game and bidding strategy for carbon trading among shipping enterprises

Abstract To address the inadequate effectiveness of carbon emission regulation in the shipping industry and insufficient initiative of enterprises in reducing carbon emissions, this study aims to design a blockchain-enabled game-theoretic mechanism for carbon trading. This mechanism facilitates strategic interactions between regulatory authorities and shipping enterprises, thereby achieving a dynamic equilibrium between operational efficiency and carbon emission control. A trustworthy carbon quota trading platform is constructed based on blockchain technology, and a bidding decision model based on an inner-outer layer master-slave game is designed. In the government-enterprise game, the regulatory authority optimizes overall carbon emission control through dynamic baseline pricing, with the objective of social welfare maximization. In the enterprise-enterprise game, shipping enterprises develop bidding strategies under incomplete information and utilize no-regret learning algorithms to reach Nash equilibrium solutions. The simulation results show that this model can substantially reduce carbon emissions and improve the efficiency of carbon trading and the overall revenue of shipping enterprises. The integration of blockchain and Stackelberg games significantly improves market transparency, while the no-regret learning algorithm provides an effective computational tool for multi-agent dynamic games. This research offers theoretical and methodological innovations for optimizing pathways toward carbon peaking in the shipping industry.