Goal Programming Model for Sustainability and Circular Economy Evaluation
Bibliographic record
Abstract
This study investigates into the sustainability landscape of the United Arab Emirates (UAE) through the Circular Economy (CE) principles, emphasizing four conflicting multi-objectives: economic, environmental, energy, and circularity development. Over recent years, CE has witnessed substantial growth, offering compelling opportunities for sustainable development. This expansion enables businesses and industry sectors to integrate CE into their overarching strategies, positioning it as an appealing alternative for manufacturing companies aiming to enhance performance through optimized resource efficiency. The study quantifies these objectives by maximizing GDP, minimizing GHG emissions, electricity consumption, and waste generation, respectively and optimizing number of employees. Two models are formulated based on these objectives, with the second model incorporating waste recycling. Utilizing a goal programming approach, the models are applied to assess eight economic sectors in the UAE. This research seeks to make a substantial contribution to both researchers and practitioners, enhancing sustainable theory and offering practical guidance for those aiming to promote their enterprise’s sustainable development. The findings emphasize the significance of waste minimization and recycling in attaining the country’s sustainability goals, highlighting their impact on energy conservation and the reduction of greenhouse gas emissions.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".