Water, Energy, and Rooftops: Integrating Green Roof Systems into Building Policies in the Arab Region
Bibliographic record
Abstract
Recent research claim that adopting green roof systems in building sectors in the Arab region is becoming necessary because of the current environmental, social, and economical challenges. Some Arab countries have already developed green building rating systems and recognized the importance of green roofs; however, they still do not fully benefit from such systems owing to limited supporting policies and financial incentives. The purpose of this article is to contribute to a better understanding of the potential role of green roof systems in effective planning and moving towards sustainable urban development in the Arab region. We argue that integrating green roof systems within governmental policies and green building strategies would potentially help in saving energy, enhancing water management, and coping with climate change. This paper presents a conceptual framework to help governments in the Arab region to adopt green roofs in their environmental policies. To present this framework; first, we studied the current international policies that adopt green roof systems and practices, then proposed a conceptual framework for adopting green roof systems in the Arab region. Second, we have chosen Cairo, Egypt, and Amman, Jordan from the Arab region to demonstrate the applicability of this framework at city level while considering the national and local context. This demonstration provides a novel perspective for the benefits of green roof systems in energy savings and water management in the Arab region.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".