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Sustainable and Efficient Maritime Operations
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Sustainable and Efficient Maritime Operations

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 10 May 2025 | Viewed by 1607

Special Issue Editors

Prof. Dr. Jiaguo Liu

E-Mail Website
Guest Editor
School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China
Interests: supply chain operations management; ports management; shipping operations management
Special Issues, Collections and Topics in MDPI journals
Dr. Jun Ye

E-Mail Website
Guest Editor Assistant
College of Business Administration, Ningbo University of Finance & Economics, Ningbo 315175, China
Interests: ocean and maritime environmental governance; port and shipping management; green supply chain management

Special Issue Information

Dear Colleagues,

The maritime industry serves as a vital pillar of the global economy, facilitating the seamless movement of goods, energy resources, and raw materials across international borders. In recent years, as the maritime industry continues to expand, it has encountered increasingly complex challenges in balancing economic growth with environmental sustainability and operational efficiency. Thus, it has started to explore innovative strategies, technologies, and practices that support sustainable and efficient maritime operations. This Special Issue on “Sustainable and Efficient Maritime Operations” invites Authors to submit high-quality original empirical, quantitative, or conceptual research papers.

 Suggested topics of interest include but are not limited to the following:

  1. Data-driven optimization for operational management in shipping;
  2. Internet of Things (IoT) technologies and applications;
  3. Autonomous ships—technologies and applications;
  4. Risk management and resilience in maritime operations;
  5. Digitalization and smart shipping solutions for operational efficiency;
  6. Human–AI interface and AI ergonomics in shipping;
  7. Green shipping technologies and alternative fuels.

Prof. Dr. Jiaguo Liu
Guest Editor

Dr. Jun Ye
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sustainable maritime industry
  • digitalization in maritime operations
  • green shipping technologies
  • low-carbon shipping
  • smart ports
  • AI in maritime logistics
  • autonomous shipping
  • maritime network optimization

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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22 pages, 7406 KiB  
Article
Decarbonation Effects of Mainstream Dual-Fuel Power Schemes Focus on IMO Mandatory Regulation and LCA Method
by Zhanwei Wang, Shidong Fan and Zhiqiang Han
J. Mar. Sci. Eng. 2025, 13(5), 847; https://doi.org/10.3390/jmse13050847 - 24 Apr 2025
Abstract
Recently, the IMO has completed the guidelines on the life cycle greenhouse gas intensity of marine fuels to accelerate the application of alternative fuels. Low-carbon fuels may persist for decades and have become a key transitional phase in replacing marine fuels. A more [...] Read more.
Recently, the IMO has completed the guidelines on the life cycle greenhouse gas intensity of marine fuels to accelerate the application of alternative fuels. Low-carbon fuels may persist for decades and have become a key transitional phase in replacing marine fuels. A more comprehensive methodology for evaluating the carbon emission levels of marine fuels was explored, and the carbon emissions and environmental impacts of a 150,000-ton shuttle tanker under 19 dual-fuel power scenarios were evaluated using the Energy Efficiency Design Index (EEDI) and life cycle assessment (LCA) method. The results show that liquefied natural gas (LNG) has a higher carbon control potential level compared to liquefied petroleum gas (LPG) and methanol (MeOH), while LPG is superior to MeOH based on EEDI evaluation. LCA analysis results show that MeOH (biomass) has the best carbon control potential considering the carbon emissions of the well-to-tank phase of the fuel, followed by LNG, LPG, MeOH (natural gas, NG), and MeOH (coal). However, MeOH (NG) and MeOH (coal) had greater negative environmental impacts. This study provides method support and a direction toward improvement for revising related technical specifications and regulations for dual-fuel vessel performance evaluation, considering the limitations of various maritime regulations. Full article
(This article belongs to the Special Issue Sustainable and Efficient Maritime Operations)
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22 pages, 5859 KiB  
Article
Data-Driven Analysis of the Causal Chain of Waterborne Traffic Accidents: A Hybrid Framework Based on an Improved Human Factors Analysis and Classification System with a Bayesian Network
by Xiangyu Yin, Yan Yan, Jiahao Wang, Hongzhuan Zhao, Qingyan Wu and Qi Xu
J. Mar. Sci. Eng. 2025, 13(3), 393; https://doi.org/10.3390/jmse13030393 - 20 Feb 2025
Viewed by 401
Abstract
In the context of economic globalization, waterborne transportation plays an important role in international trade and logistics. However, waterborne traffic accidents pose a severe threat to life, property safety, and the environment. To gain a deeper understanding of the causal mechanisms behind waterborne [...] Read more.
In the context of economic globalization, waterborne transportation plays an important role in international trade and logistics. However, waterborne traffic accidents pose a severe threat to life, property safety, and the environment. To gain a deeper understanding of the causal mechanisms behind waterborne traffic accidents, we conducted a data-driven analysis of the causal chain of waterborne traffic accidents. By constructing a hybrid framework integrating an improved HFACS (Human Factors Analysis and Classification System) with a Bayesian network model, we conducted a multi-dimensional analysis of accident causes. The constructed model was quantitatively analyzed by applying genie software to the accident samples collected from the China MSA. The results indicate that there are 12, 3, 6, 2, 4, and 7 causal chains leading to collisions, contact, fires/explosions, windstorm accidents, sinking, and other types of accidents, respectively. These research results can serve as a reference for the enhancement of the safety of waterborne transportation. Full article
(This article belongs to the Special Issue Sustainable and Efficient Maritime Operations)
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Review

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48 pages, 10120 KiB  
Review
Machine Learning in Maritime Safety for Autonomous Shipping: A Bibliometric Review and Future Trends
by Jie Xue, Peijie Yang, Qianbing Li, Yuanming Song, P. H. A. J. M. van Gelder, Eleonora Papadimitriou and Hao Hu
J. Mar. Sci. Eng. 2025, 13(4), 746; https://doi.org/10.3390/jmse13040746 - 8 Apr 2025
Viewed by 448
Abstract
Autonomous vessels are becoming paramount to ocean transportation, while they also face complex risks in dynamic marine environments. Machine learning plays a crucial role in enhancing maritime safety by leveraging its data analysis and predictive capabilities. However, there has been no review grounded [...] Read more.
Autonomous vessels are becoming paramount to ocean transportation, while they also face complex risks in dynamic marine environments. Machine learning plays a crucial role in enhancing maritime safety by leveraging its data analysis and predictive capabilities. However, there has been no review grounded in bibliometric analysis in this field. To explore the research evolution and knowledge frontier in the field of maritime safety for autonomous shipping, a bibliometric analysis was conducted using 719 publications from the Web of Science database, covering the period from 2000 up to May 2024. This study utilized VOSviewer, alongside traditional literature analysis methods, to construct a knowledge network map and perform cluster analysis, thereby identifying research hotspots, evolution trends, and emerging knowledge frontiers. The findings reveal a robust cooperative network among journals, researchers, research institutions, and countries or regions, underscoring the interdisciplinary nature of this research domain. Through the review, we found that maritime safety machine learning methods are evolving toward a systematic and comprehensive direction, and the integration with AI and human interaction may be the next bellwether. Future research will concentrate on three main areas: evolving safety objectives towards proactive management and autonomous coordination, developing advanced safety technologies, such as bio-inspired sensors, quantum machine learning, and self-healing systems, and enhancing decision-making with machine learning algorithms such as generative adversarial networks (GANs), hierarchical reinforcement learning (HRL), and federated learning. By visualizing collaborative networks, analyzing evolutionary trends, and identifying research hotspots, this study lays a groundwork for pioneering advancements and sets a visionary angle for the future of safety in autonomous shipping. Moreover, it also facilitates partnerships between industry and academia, making for concerted efforts in the domain of USVs. Full article
(This article belongs to the Special Issue Sustainable and Efficient Maritime Operations)
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