Climate Risk Analysis: Uncertainty Modelling

Yui-yip Lau, Adolf K. Y. Ng, Zaili Yang, Tianni Wang and Mark Ching-Pong Poo
Additional contact information
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

A chapter in Maritime Transport and Supply Chain Resilience, 2025, pp 23-43 from Springer

Abstract: 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.

Keywords: Climate risk; Uncertainty modelling; Maritime transport (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-07566-6_3

Ordering information: This item can be ordered from
http://www.springer.com/9783032075666

DOI: 10.1007/978-3-032-07566-6_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().