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Blockchain-Powered Incentive System for JIT Arrival Operations and Decarbonization in Maritime Shipping

Author

Listed:
  • Son Nguyen

    (Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore
    These authors contributed equally to this work.)

  • Aengus Leman

    (Faculty of Science, National University of Singapore, 6 Science Drive 2, Singapore 117546, Singapore
    These authors contributed equally to this work.)

  • Zhe Xiao

    (Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore)

  • Xiuju Fu

    (Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore)

  • Xiaocai Zhang

    (Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore)

  • Xiaoyang Wei

    (Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore)

  • Wanbing Zhang

    (Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore)

  • Ning Li

    (Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore)

  • Wei Zhang

    (Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore)

  • Zheng Qin

    (Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore)

Abstract

Efficiency and sustainability are undisputedly the most critical objectives for modern ports. Current exercises for port services still lack performance profiling for arriving vessels regarding their arrival punctuality and compliance with port resource schedule for Just-in-time (JIT) service, as well as their efforts contributing towards less emission through reduced turnaround time within port. As a result, a performance-based incentive is missing. Bringing in the incentive component may facilitate the objectives of achieving both port efficiency and sustainability. Blockchain technology, owning to its intrinsic features like immutability, traceability, governance and provenance, and in-built tokens (for most public chain platforms), allow for the establishment of system solutions to record key performance indicators (KPIs) and distribute incentives to good performers. This paper is the first to propose a blockchain-based system to incentivize JIT and green operations in ports. The platform system design and operating mechanisms are elaborated in detail, and a prototype system has been implemented based on the Solana blockchain to demonstrate the core features. The current system’s potential is substantial, considering the industry’s increasing awareness about its environmental footprint. Continuous developments can be facilitated by connecting to market-based measures such as carbon pricing and emission trading in the maritime sector.

Suggested Citation

  • Son Nguyen & Aengus Leman & Zhe Xiao & Xiuju Fu & Xiaocai Zhang & Xiaoyang Wei & Wanbing Zhang & Ning Li & Wei Zhang & Zheng Qin, 2023. "Blockchain-Powered Incentive System for JIT Arrival Operations and Decarbonization in Maritime Shipping," Sustainability, MDPI, vol. 15(22), pages 1-22, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:22:p:15686-:d:1275474
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    References listed on IDEAS

    as
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    4. Simon Wong & John Kun-Woon Yeung & Yui-Yip Lau & Tomoya Kawasaki, 2023. "A Case Study of How Maersk Adopts Cloud-Based Blockchain Integrated with Machine Learning for Sustainable Practices," Sustainability, MDPI, vol. 15(9), pages 1-17, April.
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