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

海運企業間の炭素取引におけるブロックチェーン対応スタッケルベルグゲームと入札戦略 (AI 翻訳)

Yang Wang, Wenhao Chen, Mengyi Di, Chengpeng Wan, Bing Wu, Song Gao

Transportation Safety and Environment📚 査読済 / ジャーナル2026-07-31#AI×ESGOrigin: CN経営インパクト: コスト削減対象セクター: shipping
DOI: 10.1093/tse/tdag044
原典: https://doi.org/10.1093/tse/tdag044

🤖 gxceed AI 要約

日本語

海運業界の炭素排出規制の不十分さに対し、ブロックチェーンとゲーム理論を組み合わせた炭素取引メカニズムを提案。政府と企業の階層ゲームで動的基準価格を設定し、企業間の入札戦略をno-regret学習で解く。シミュレーションで排出削減と取引効率向上を実証。

English

Proposes a blockchain-enabled game-theoretic carbon trading mechanism for shipping enterprises, combining a Stackelberg game between regulator and firms with a bidding game solved via no-regret learning. Simulations show reduced emissions and improved trading efficiency, offering a novel approach to carbon peaking in shipping.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本の海運業界は国際海運の排出規制対応が急務であり、ブロックチェーンを活用した炭素取引の設計は、今後の国内制度設計や企業の戦略立案に示唆を与える。

In the global GX context

Contributes to global discourse on carbon pricing and blockchain applications in emissions trading, relevant for shipping decarbonization and the design of market-based measures under IMO regulations.

👥 読者別の含意

🔬研究者:Game-theoretic and blockchain-based carbon trading mechanisms for shipping; useful for extending to other sectors.

🏢実務担当者:Insights into how blockchain and bidding strategies can enhance carbon trading efficiency and transparency for shipping firms.

🏛政策担当者:Consideration of dynamic baseline pricing and blockchain platforms for effective carbon regulation in shipping.

📄 Abstract（原文）

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.

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