Current trends and future research roadmap of multi-criteria decision-making in sustainable construction studies
Bibliographic record
Abstract
Purpose. Multi-criteria decision-making (MCDM) methods have become vital tools in sustainable construction (SC) research by tackling the intricacy of balancing social, environmental and economic factors of sustainable development. This paper aims to identify, analyse, and visualise the current trends in applying MCDM techniques to SC research. Results. The study synthesises 190 scholarly research outputs extracted from the Scopus database after a careful filtering and refinement process. A scientometric analysis and knowledge mapping were performed using VOSviewer to study the scholarly outputs that constituted the dataset. Due to its flexibility and decision-support capabilities, the results revealed the dominant use of key MCDM methods, such as the Analytic Hierarchy Process (AHP). Findings show that India, the USA, Canada, China, and Italy are among the top five published countries. The finding further revealed five clusters on the application of the MCDM method aiding “green decision dynamics”, “sustainable building design and development”, “smart and sustainable building assessment”, and “construction efficiency”. Scientific novelty. This research is motivated by the growing application of the MCDM method due to its efficiency as discovered in other disciplines. The scientific novelty is in the systematic and bibliometric analysis to highlight the future directions and potential research gaps in the application of MCDM methods in sustainable construction research studies. The study also showcased the gradual proliferation of MCDM techniques, such as AHP, in sustainable construction research. Practical value. This study provides valuable insights for policymakers, researchers, academics, sustainability proponents and relevant agencies seeking to leverage MCDM techniques, such as the AHP, to promote sustainable construction concepts while navigating the complexity of competing priorities.
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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 | 0.033 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".