## 3 Scientometric Analysis  
Scientometric analysis is a quantitative approach used to evaluate the development and impact of scientific research within a specific field. It involves the systematic examination of research trends, publication outputs, and patterns of citation to identify key contributors, influential publications, and emerging themes. By analyzing factors such as the annual growth of publications, country-wise contributions, research collaborations, and the prevalence of specific keywords, scientometric analysis provides a comprehensive understanding of how a research area has evolved. In the context of emerging technologies such as AI, Digital Twins, and IoT in sustainable environments, this method helps map the trajectory of innovations, highlight influential works, and uncover gaps for future exploration. Ultimately, scientometric analysis offers valuable insights into the scientific landscape, enabling researchers and policymakers to track progress and set strategic directions for further research. Figure 4: PRISMA and final research sample identification. Figure 5 presents a comprehensive analysis of research activity and impact in the field of AI-driven Digital Twins and IoT for Sustainable Building Environments from 2018 to 2024. Two key trends are evident: a significant increase in the number of research papers published and a rapid growth in cumulative citations. The blue bars represent the annual count of published papers, while the yellow line illustrates the cumulative number of citations received by these papers. A clear upward trend is observed in both the number of papers and cumulative citations, indicating growing interest and research activity in this field. The trend line, visualized by the red dotted line, indicates a strong positive correlation between the number of papers and the cumulative citations, suggesting a significant impact on the published research. The high R\({}^{2}\) value of 0.999 confirms the strength of this relationship. From 2018 to 2024, the number of published papers has consistently increased, with a particularly sharp rise in 2023 and 2024. This trend suggests a surge in research interest and activity in AI-driven Digital Twins and IoT for SBE. Similarly, the cumulative citations have experienced a rapid increase, indicating that the published research is being widely cited and recognized by the scientific community. Therefore, the chart reveals a positive and accelerating trend in research activity and impact within the field of AI-driven Digital Twins and IoT for Sustainable Building Environments. The increasing number of published papers and cumulative citations highlight the growing importance and relevance of this research area. Figure 6 is a pie chart that offers a comprehensive analysis of the research output in the field of AI-driven Digital Twins and IoT for SBE. Based on PRISMA, the final sample of filtered paper was 125 papers consisting of 97 articles and 28 review papers. The pie chart illustrates the distribution of citations across two types of academic publications: articles and reviews. Articles received 6634 citations (97 articles), while reviews received 4071 citations (28 review papers). The dominant category is articles, accounting for 97 papers and a total of 6634 citations. This indicates that the majority of research in this field is focused on original research studies, contributing significantly to the knowledge base. In contrast, reviews constitute a smaller proportion of the research output, with 28 papers and a total of 4071 citations. While reviews play a crucial role in synthesizing existing knowledge and providing critical perspectives, they represent a lesser volume of research compared to articles. Figure 5: Annual research publications and citations. Table 2 presents the top-cited articles related to AI-driven digital twins (DT) and IoT for the sustainable building environment, reflecting their influence and contribution to the field.