IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v7y2022i5p60-d810888.html
   My bibliography  Save this article

Formation of Dataset for Fuzzy Quantitative Risk Assessment of LNG Bunkering SIMOPs

Author

Listed:
  • Hongjun Fan

    (Australian Maritime College (AMC), College of Sciences and Engineering, University of Tasmania, Launceston, TAS 7248, Australia)

  • Hossein Enshaei

    (Australian Maritime College (AMC), College of Sciences and Engineering, University of Tasmania, Launceston, TAS 7248, Australia)

  • Shantha Gamini Jayasinghe

    (Australian Maritime College (AMC), College of Sciences and Engineering, University of Tasmania, Launceston, TAS 7248, Australia)

Abstract

New international regulations aimed at decarbonizing maritime transportation are positively contributing to attention being paid to the use of liquefied natural gas (LNG) as a ship fuel. Scaling up LNG-fueled ships is highly dependent on safe bunkering operations, particularly during simultaneous operations (SIMOPs); therefore, performing a quantitative risk assessment (QRA) is either mandated or highly recommended, and a dynamic quantitative risk assessment (DQRA) has been developed to make up for the deficiencies of the traditional QRA. The QRA and DQRA are both data-driven processes, and so far, the data of occurrence rates (ORs) of basic events (BEs) in LNG bunkering SIMOPs are unavailable. To fill this gap, this study identified a total of 41 BEs and employed the online questionnaire method, the fuzzy set theory, and the Onisawa function to the investigation of the fuzzy ORs for the identified BEs. Purposive sampling was applied when selecting experts in the process of online data collection. The closed-ended structured questionnaire garnered responses from 137 experts from the industry and academia. The questionnaire, the raw data and obtained ORs, and the process of data analysis are presented in this data descriptor. The obtained data can be used directly in QRAs and DQRAs. This dataset is first of its kind and could be expanded further for research in the field of risk assessment of LNG bunkering.

Suggested Citation

  • Hongjun Fan & Hossein Enshaei & Shantha Gamini Jayasinghe, 2022. "Formation of Dataset for Fuzzy Quantitative Risk Assessment of LNG Bunkering SIMOPs," Data, MDPI, vol. 7(5), pages 1-13, May.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:5:p:60-:d:810888
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/7/5/60/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2306-5729/7/5/60/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Agnieszka A. Tubis & Emilia T. Skupień & Stefan Jankowski & Jacek Ryczyński, 2022. "Risk Assessment of Human Factors of Logistic Handling of Deliveries at an LNG Terminal," Energies, MDPI, vol. 15(8), pages 1-24, April.
    2. Wang, Shuaian & Qi, Jingwen & Laporte, Gilbert, 2022. "Governmental subsidy plan modeling and optimization for liquefied natural gas as fuel for maritime transportation," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 304-321.
    3. Balcombe, Paul & Staffell, Iain & Kerdan, Ivan Garcia & Speirs, Jamie F. & Brandon, Nigel P. & Hawkes, Adam D., 2021. "How can LNG-fuelled ships meet decarbonisation targets? An environmental and economic analysis," Energy, Elsevier, vol. 227(C).
    4. Robert T. Clemen & Robert L. Winkler, 1999. "Combining Probability Distributions From Experts in Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 19(2), pages 187-203, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kenneth Gillingham & William D. Nordhaus & David Anthoff & Geoffrey Blanford & Valentina Bosetti & Peter Christensen & Haewon McJeon & John Reilly & Paul Sztorc, 2015. "Modeling Uncertainty in Climate Change: A Multi-Model Comparison," NBER Working Papers 21637, National Bureau of Economic Research, Inc.
    2. Avner Engel & Shalom Shachar, 2006. "Measuring and optimizing systems' quality costs and project duration," Systems Engineering, John Wiley & Sons, vol. 9(3), pages 259-280, September.
    3. Atanasov, Pavel & Witkowski, Jens & Ungar, Lyle & Mellers, Barbara & Tetlock, Philip, 2020. "Small steps to accuracy: Incremental belief updaters are better forecasters," Organizational Behavior and Human Decision Processes, Elsevier, vol. 160(C), pages 19-35.
    4. Franz Dietrich & Christian List, 2017. "Probabilistic opinion pooling generalized. Part one: general agendas," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 48(4), pages 747-786, April.
    5. repec:cup:judgdm:v:13:y:2018:i:6:p:607-621 is not listed on IDEAS
    6. Patrick Afflerbach & Christopher Dun & Henner Gimpel & Dominik Parak & Johannes Seyfried, 2021. "A Simulation-Based Approach to Understanding the Wisdom of Crowds Phenomenon in Aggregating Expert Judgment," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(4), pages 329-348, August.
    7. Chiara Franzoni & Paula Stephan & Reinhilde Veugelers, 2022. "Funding Risky Research," Entrepreneurship and Innovation Policy and the Economy, University of Chicago Press, vol. 1(1), pages 103-133.
    8. Robert F. Bordley, 2009. "Combining the Opinions of Experts Who Partition Events Differently," Decision Analysis, INFORMS, vol. 6(1), pages 38-46, March.
    9. Alessia Amato & Konstantina Tsigkou & Alessandro Becci & Francesca Beolchini & Nicolò M. Ippolito & Francesco Ferella, 2023. "Life Cycle Assessment of Biomethane vs. Fossil Methane Production and Supply," Energies, MDPI, vol. 16(12), pages 1-18, June.
    10. Jason R. W. Merrick & J. Rene van Dorp & Jack Harrald & Thomas Mazzuchi & John E. Spahn & Martha Grabowski, 2000. "A systems approach to managing oil transportation risk in Prince William Sound," Systems Engineering, John Wiley & Sons, vol. 3(3), pages 128-142.
    11. Donnacha Bolger & Brett Houlding, 2016. "Reliability updating in linear opinion pooling for multiple decision makers," Journal of Risk and Reliability, , vol. 230(3), pages 309-322, June.
    12. Robert L. Winkler & Robert T. Clemen, 2004. "Multiple Experts vs. Multiple Methods: Combining Correlation Assessments," Decision Analysis, INFORMS, vol. 1(3), pages 167-176, September.
    13. Ine H. J. Van Der Fels‐Klerx & Louis H. J. Goossens & Helmut W. Saatkamp & Suzan H. S. Horst, 2002. "Elicitation of Quantitative Data from a Heterogeneous Expert Panel: Formal Process and Application in Animal Health," Risk Analysis, John Wiley & Sons, vol. 22(1), pages 67-81, February.
    14. Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2020. "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1092-1110, July.
    15. Minh Ha-Duong, 2008. "Hierarchical fusion of expert opinion in the Transferable Belief Model, application on climate sensitivity," Post-Print halshs-00112129, HAL.
    16. Jahanbani, Zeinab & Ataee-pour, Majid & Mortazavi, Ali, 2024. "Application of Z-numbers theory to study the influencing criteria in underground mining method selection," Resources Policy, Elsevier, vol. 88(C).
    17. Pennings, Clint L.P. & van Dalen, Jan & Rook, Laurens, 2019. "Coordinating judgmental forecasting: Coping with intentional biases," Omega, Elsevier, vol. 87(C), pages 46-56.
    18. Franz Dietrich & Christian List, 2017. "Probabilistic opinion pooling generalized. Part two: the premise-based approach," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 48(4), pages 787-814, April.
    19. Vinicius Andrade dos Santos & Patrícia Pereira da Silva & Luís Manuel Ventura Serrano, 2022. "The Maritime Sector and Its Problematic Decarbonization: A Systematic Review of the Contribution of Alternative Fuels," Energies, MDPI, vol. 15(10), pages 1-30, May.
    20. Jeffrey M. Keisler, 2005. "Additivity of Information Value in Two‐Act Linear Loss Decisions with Normal Priors," Risk Analysis, John Wiley & Sons, vol. 25(2), pages 351-359, April.
    21. Chunchang Zhang & Hu Sun & Yuanyuan Zhang & Gen Li & Shibo Li & Junyu Chang & Gongqian Shi, 2023. "Fire Accident Risk Analysis of Lithium Battery Energy Storage Systems during Maritime Transportation," Sustainability, MDPI, vol. 15(19), pages 1-12, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:7:y:2022:i:5:p:60-:d:810888. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.