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


Zincir B. A.

4. Bilsel Uluslararası Midas Bilimsel Araştırmalar Kongresi, Eskişehir, Türkiye, 27 - 28 Haziran 2026, ss.1-10, (Tam Metin Bildiri)

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Eskişehir
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.1-10
  • Galatasaray Üniversitesi Adresli: Evet

Özet

The maritime industry is undergoing a significant transformation driven by increasingly stringent decarbonization targets and the rapid adoption of digital technologies. Among these technologies, digital twins have emerged as a promising tool for enhancing operational efficiency, supporting predictive maintenance, and facilitating data-driven decision-making in maritime systems. Despite the growing interest in digital twin applications, the intellectual structure and research evolution of this field remain insufficiently explored. This study aims to examine the development of research on digital twin applications supporting maritime decarbonization through a bibliometric analysis. A dataset consisting of 102 English-language articles and review papers indexed in the Scopus database was compiled using a structured search strategy. Bibliometric mapping was conducted using VOSviewer to identify publication trends, influential research themes, and relationships among author keywords. The analysis revealed a substantial increase in publication activity in recent years, indicating growing academic and industrial interest in the topic. Keyword co-occurrence analysis identified several major thematic clusters, including operational optimization and energy efficiency, artificial intelligence-supported monitoring and predictive maintenance, maritime decarbonization and digitalization, smart maritime infrastructure, and sustainability-oriented simulation applications. The findings suggest that digital twin research in the maritime sector is increasingly integrated with machine learning, artificial intelligence, and energy efficiency studies, highlighting its role as an enabling technology for decarbonization strategies. Furthermore, the results indicate that research on digital twins is evolving from asset monitoring applications toward broader sustainability and emissions-reduction objectives. This study provides an overview of the current research landscape and identifies emerging directions for future investigations in digital twin-enabled maritime decarbonization.