A benchmarking approach to the progress of green materials and systems’ use in the UAE construction industry
Bibliographic record
Abstract
Purpose Over the past decade, the United Arab Emirates (UAE) introduced several green regulatory guidelines, federal decrees, and a considerable number of environmentally friendly initiatives. Hence, the purpose of this paper is to investigate the top green materials and systems used currently in the UAE construction industry as per the new laws dictate as well as see if professionals are switching over to incorporate more green materials, systems, and/or designs. Design/methodology/approach The work involved reviewing internationally popular green materials and systems for construction, developing a questionnaire based on the literature review, surveying professionals in the seven UAE emirates, and ranking the findings based on the relative importance index. Findings Findings found the top used green materials and system in the UAE’s construction industry. As well as identified that there is a communication gap between the design and implementation phases that is possibly hindering the use of more green materials and systems. Originality/value This study sets a baseline to measure the UAE’s progress over the coming years in terms of integrating more green construction materials, systems, methodologies, and trends.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".