Digital twin applications for maritime decarbonization: A bibliometric analysis of research trends and emerging themes

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a structured overview of the field and identifies research gaps for future work.

🏢実務担当者:Offers insights into emerging digital twin applications for operational efficiency and emissions reduction in shipping.

<p class="MsoNormal" style="margin-top:6.0pt;margin-right:0cm;margin-bottom:6.0pt;margin-left:0cm;text-align:justify;line-height:normal"><span lang="EN-US" style="font-family:&quot;Times New Roman&quot;,serif">The maritime industry is undergoinga significant transformation driven by increasingly stringent decarbonizationtargets and the rapid adoption of digital technologies. Among thesetechnologies, digital twins have emerged as a promising tool for enhancingoperational efficiency, supporting predictive maintenance, and facilitatingdata-driven decision-making in maritime systems. Despite the growing interestin digital twin applications, the intellectual structure and research evolutionof this field remain insufficiently explored. This study aims to examine thedevelopment of research on digital twin applications supporting maritimedecarbonization through a bibliometric analysis. A dataset consisting of 102English-language articles and review papers indexed in the Scopus database wascompiled using a structured search strategy. Bibliometric mapping was conductedusing VOSviewer to identify publication trends, influential research themes,and relationships among author keywords. The analysis revealed a substantialincrease in publication activity in recent years, indicating growing academicand industrial interest in the topic. Keyword co-occurrence analysis identifiedseveral major thematic clusters, including operational optimization and energyefficiency, artificial intelligence-supported monitoring and predictivemaintenance, maritime decarbonization and digitalization, smart maritimeinfrastructure, and sustainability-oriented simulation applications. Thefindings suggest that digital twin research in the maritime sector isincreasingly integrated with machine learning, artificial intelligence, andenergy efficiency studies, highlighting its role as an enabling technology fordecarbonization strategies. Furthermore, the results indicate that research on digitaltwins is evolving from asset monitoring applications toward broadersustainability and emissions-reduction objectives. This study provides anoverview of the current research landscape and identifies emerging directionsfor future investigations in digital twin-enabled maritime decarbonization.<o:p></o:p></span>

• openalex https://avesis.gsu.edu.tr/publication/details/6db4fb2b-aff4-4c7d-9b4d-97eedf14e0c4/oaifirst seen 2026-07-24 05:54:08