A Bibliometric Analysis of Multi-Criteria Decision-Making Techniques in Disaster Management and Transportation in Emergencies: Towards Sustainable Solutions
Bibliographic record
Abstract
Disaster management minimizes potential harm and protects populations across four phases: preparedness, mitigation, response, and recovery. Diverse scientific approaches could be applied at each phase, among which Multi-Criteria Decision-Making (MCDM) methods are widely recognized and utilized. Their integration provides a systematic framework for prioritizing disaster-related criteria, optimizing resource use, and minimizing environmental impact, ultimately enhancing community resilience. This study conducts a bibliometric analysis to identify pioneering researchers, leading institutions, contributing countries, and interaction levels working on MCDM methods in disaster management and emergency transportation, as well as to reveal key trends. 365 Web of Science and Scopus publications (2000–2024) were analyzed using the Bibliometrix tool in R. As a significant outcome, three important clusters emerged: Disaster Planning and Logistics, Risk and Resilience, and Crisis Response and Decision Support. The interplay between these clusters and the methodologies shaping them was highlighted, alongside insights from the most recent studies. This study could serve as a roadmap for future research, guiding efforts to address gaps such as real-time applications, multi-hazard integration, and scalability. It contributes to the limited body of research on MCDM in disaster management and emergency transportation, laying the groundwork for upcoming studies that could enhance resilience and promote sustainable development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.189 | 0.258 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".