Digital Twin and sustainability: A data-driven scientometric exploration

Abstract

Digital Twin (DT) technology has emerged as a transformative innovation, enabling the creation of precise digital replicas of real-world systems, processes, and objects. By establishing a seamless connection between the physical and digital realms, DTs facilitate real-time data integration, advanced simulations, and optimization processes. This capability significantly enhances decision-making, predictive maintenance, and operational efficiency across diverse industries. The adoption of DT technology has accelerated rapidly, with sectors such as agriculture, healthcare, energy, and transportation leading its implementation. The present study employs scientometric analysis to evaluate the impact and efficacy of DT models in advancing the United Nations’ Sustainable Development Goals (SDGs). Utilizing cutting-edge tools such as CiteSpace and VOSviewer for network analysis, the research leverages data sourced from the Scopus database, encompassing publications from 2018 to 2024. The analysis examines key dimensions, including publication trends, citation dynamics, keyword co-occurrence networks, document co-citation patterns, country-level collaboration, and author co-citation networks. The study identifies influential publications, prominent researchers, and leading nations contributing to the evolution of DT technology, highlighting critical innovations and contributions. These insights not only provide a comprehensive understanding of the current state of DT technology in the context of sustainable development but also reveal emerging research directions and trends. Furthermore, the findings underscore the potential for interdisciplinary collaboration to advance the role of DT technology in achieving the SDGs, paving the way for future advancements in the domain.