Barriers to the Diffusion and Adoption of Green Buildings in Saudi Arabia
Bibliographic record
Abstract
Many countries around the globe have recently pursued sustainability. The public and governments are demanding sustainability due to worldwide environmental disasters caused by pollution and man-made activities that impact the ecological system. Green buildings represent a significant component of sustainability, as their construction is intended to reduce natural resources consumption through energy and water conservation. Saudi Arabia is one of the world’s richest countries, but its number of certified green buildings is notably low. In addition, all of these buildings are certified by the US Green Building Council LEED rating system and not by a national organization. It seems that numerous barriers exist, slowing the development and diffusion of green buildings in Saudi Arabia. Through a systematic qualitative research approach, this research aims to explore barriers to the diffusion and adoption of green buildings in Saudi Arabia, which will facilitate the development of green buildings in Saudi Arabia. This research concludes the identification of 14 green building barriers, with lack of skilled personal and unsupportive government policies and regulations being the most significant barriers.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".