Analysis of Factors Affecting the Effectiveness of Oil Spill Clean-Up: A Bayesian Network Approach

Liangxia Zhong, Jiaxin Wu, Yiqing Wen, Bingjie Yang, Manel Grifoll, Yunping Hu () and Pengjun Zheng ()
Additional contact information
Liangxia Zhong: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China
Jiaxin Wu: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China
Yiqing Wen: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China
Bingjie Yang: Ningbo Development Planning Institute, Ningbo 315832, China
Manel Grifoll: Barcelona School of Nautical Studies, Universitat Politècnica de Catalunya (UPC—BarcelonaTech), 08003 Barcelona, Spain
Yunping Hu: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China
Pengjun Zheng: Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China

Sustainability, 2023, vol. 15, issue 6, 1-19

Abstract: Ship-related marine oil spills pose a significant threat to the environment, and while it may not be possible to prevent such incidents entirely, effective clean-up efforts can minimize their impact on the environment. The success of these clean-up efforts is influenced by various factors, including accident-related factors such as the type of accident, location, and environmental weather conditions, as well as emergency response-related factors such as available resources and response actions. To improve targeted and effective responses to oil spills resulting from ship accidents and enhance oil spill emergency response methods, it is essential to understand the factors that affect their effectiveness. In this study, a data-driven Bayesian network (TAN) analysis approach was used with data from the U.S. Coast Guard (USCG) to identify the key accident-related factors that impact oil spill clean-up performance. The analysis found that the amount of discharge, severity, and the location of the accident are the most critical factors affecting the clean-up ratio. These findings are significant for emergency management and planning oil spill clean-up efforts.

Keywords: oil spill clean-up ratio; analysis of factors; Bayesian network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/6/4965/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/6/4965/ (text/html)

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:gam:jsusta:v:15:y:2023:i:6:p:4965-:d:1093774

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().