# Application of Blockchain Technology in Emergency Management Systems: A Bibliometric Analysis
## 5 Discussion
### Comparison with Existing Literature  
As discussed in Section 2, previous literature reviews have predominantly offered qualitative syntheses or summarized challenges, opportunities, and use cases across diverse sectors such as healthcare [39; 40; 41; 42], humanitarian logistics [37; 43; 44; 45], and crisis communication [45; 46]. While these contributions offer valuable conceptual overviews and practical insights, they are inherently limited by their subjective nature and lack of quantitative rigor. As such, previous reviews often fail to capture the broader research dynamics and long-term trends shaping the field. In contrast, bibliometric reviews such as the present study address the above-mentioned limitations by employing quantitative methods to systematically analyze publication patterns, collaboration networks, citation dynamics, and emerging research themes. These techniques allow for a more comprehensive and objective understanding of the field's intellectual landscape. Compared to the bibliometric reviews discussed in Section 2, many prior studies demonstrate limitations in practical focus. For instance, the works in [51; 52; 54] do not concentrate specifically on blockchain, instead addressing emergency response more broadly. Other studies examine narrow applications such as emergency logistics [49] or health emergencies [50]. In contrast, our study is uniquely centered on blockchain technologies within the domain of emergency management systems, filling a notable gap in the literature. Furthermore, few reviews have conducted in-depth analysis of major research clusters, keyword bursts, or co-citation networks, all of which are critical methods for uncovering intellectual structures and evolving trends. For example, [PERSON] et al. [34] and [PERSON] [49] omitted cluster analysis and burst detection entirely; similarly, [PERSON] et al. [53] did not employ a full range of quantitative techniques such as publication and citation trend analysis, institutional collaboration mapping, or co-citation analysis, limiting the objectivity and depth of their findings. In contrast, our study introduces several methodological enhancements that distinguish it from earlier works. It spans the years 2017 to 2024, offering one of the most up-to-date perspectives available in the literature. This extended time frame is particularly relevant given the rapid pace of technological innovation and evolving regulatory contexts surrounding blockchain applications in emergency management. In terms of data sourcing, our review adheres to scientometric best practices by drawing from the WoSCC, a widely accepted and authoritative database. While some earlier reviews, such as that by [PERSON] et al. [49], also incorporate Scopus, WoSCC remains the dominant source across most comparable studies. Moreover, our analytical approach represents a significant advancement. Unlike previous reviews [49; 51; 52; 54] that predominantly relied on VOSviewer, a tool that is limited in its temporal and structural analytical capabilities, our study employs CiteSpace, enabling a deeper exploration of thematic evolution through techniques such as co-citation and citation burst detection, keyword burst analysis, and institutional collaboration mapping. These methods permit us to offer a more comprehensive understanding of the intellectual landscape and to provide insights that are often absent from earlier bibliometric analyses. In terms of depth of analysis, only [PERSON] et al. [51] and our study provide comprehensive examinations of major research clusters, which allow for nuanced insights into thematic evolution. This dimension is vital for understanding structural shifts in the field over time, but is underexplored in most prior works. Although several studies [34; 49; 51; 53] claim practical relevance, few have addressed theoretical implications or proposed a conceptual foundation. Our work contributes meaningfully in this regard by identifying theoretical gaps and mapping knowledge clusters, an approach previously seen only in [PERSON] et al. [51].