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Climate Risk Analysis: Uncertainty Modelling

In: Maritime Transport and Supply Chain Resilience

Author

Listed:
  • Yui-yip Lau

    (The Hong Kong Polytechnic University, Division of Business and Hospitality Management, College of Professional and Continuing Education)

  • Adolf K. Y. Ng

    (Beijing Normal-Hong Kong Baptist University, International Centre for Resilient Supply Chains, Faculty of Business and Management)

  • Zaili Yang

    (Liverpool John Moores University, School of Engineering)

  • Tianni Wang

    (Shanghai Maritime University, College of Transport & Communications)

  • Mark Ching-Pong Poo

    (Liverpool Hope University, Liverpool Hope Business School)

Abstract

Maritime transportation makes significant contribution to economic prosperity and suffers much from various risks, including classical (e.g. ship collisions) and emerging ones such as those relating to climate change risks (e.g. flooding, heatwaves, storms). Data uncertainty represents an important challenge for safety science in general climate risk analysis in specific given the insufficiency of historical failure data, compared to classical risks. Broadly data uncertainty is categorised into three groups: fuzziness, incompleteness and randomness, hence triggers risk studies using the uncertainty theories such as fuzzy logic, Dempster-Shafer (D-S) theory of evidence and Bayesian probabilistic inference. This chapter aim is to introduce the newest study on the development of climate risk modelling using the three uncertainty theories, individually and collectively within the maritime transport context. It will help understand the state of the art of the relevant research and inspire new ideas.

Suggested Citation

  • Yui-yip Lau & Adolf K. Y. Ng & Zaili Yang & Tianni Wang & Mark Ching-Pong Poo, 2025. "Climate Risk Analysis: Uncertainty Modelling," Springer Books, in: Maritime Transport and Supply Chain Resilience, pages 23-43, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-07566-6_3
    DOI: 10.1007/978-3-032-07566-6_3
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