Analyzing Green Construction Development Barriers by a Hybrid Decision-Making Method Based on DEMATEL and the ANP
Bibliographic record
Abstract
There is a great deal of interest in analyzing construction development barriers to identify and rank them based on sustainability criteria and have less environmental pollution. Due to the importance of construction projects in developing countries such as Iran, this study implements a green construction development paradigm to identify and rank barriers for a case study in Tehran, Iran. The main novelty of this paper is the development of a new decision-making method using the DEMATEL and Delphi techniques and the ANP. In this regard, first of all, data collection is performed through a literature review and survey studies using questionnaires, interviews, and observations. The applied method for experts’ agreement was integrated through brainstorming and the classical Delphi method. By analyzing different economic, environmental, cultural, and social criteria using a hybrid decision-making framework, the results show that the main economic barrier with a weight of 0.2607 is ranked first, while the main feature of economic assessment is connected to the risk of investment. The cultural and social barriers, with a weight of 0.2258, ranked second, and the managerial barrier, with a weight of 0.2052, ranked third. In the social and managerial aspects, the main barriers were related to looking at green construction as luxurious and the uncertainty of green construction performance due to the climate and texture of the local area, respectively. According to the findings and results, the proposed barriers and sub-barriers in this study can be used to develop and create planning at the strategic level for the development of green construction for our case study in Tehran, Iran. With a concentration on the outcomes of the present research, the sustainable green building framework can be implemented by the application of a prioritized knowledge management concept.
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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 | 0.030 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".