Enhancing Waste Management and Circular Economy Practices in Higher Education Institutions in UAE: A Study on Current Policies and Strategic Framework
Bibliographic record
Abstract
The study investigates the integration of Circular Economy (CE) principles into higher education institutions (HEIs) in the UAE to address the growing challenges of waste management and sustainability. The UAE's economic growth and population increase have led to significant waste generation, necessitating a transition from the traditional linear economy model to a sustainable CE framework. Despite HEIs’ critical role in promoting sustainable development goals, existing accreditation standards lack requirements for CE implementation. Through a qualitative survey of 43 participants from 45 HEIs in UAE, the presented work examines awareness levels among HEIs employees, existing waste management practices in HEIS, and challenges to CE adoption within HEIs. The preliminary findings reveal a gap in providing formal training on CE and sustainability to HEIs employees, limited awareness of CE concepts within the employees, and inconsistent implementation of waste management practices in HEIs. The study proposes the need for a new CE framework designed for HEIs, focusing on: Awareness and Knowledge, Institutional Policies and Governance, and Infrastructure and Operations, to align with the UAE's 2021-2031 Circular Economy Policy.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".