Improving efficiency and sustainability of the waste-economy-environment nexus in isolated island communities: A stochastic inexact mixed-integer fractional optimization model
Bibliographic record
Abstract
This study develops a stochastic inexact mixed-integer fractional optimization (SIMFO) model to enhance solid waste management (SWM) systems in isolated island communities. The model integrates inexact optimization, chance-constrained optimization, linear fractional programming, and mixed integer linear programming. It aims to maximize waste flow diversion from landfills, minimize system costs, and adhere to environmental emission caps. According to the analysis of a case study in British Columbia, Canada, by optimizing waste flows, implementing a reasonable facility expansion plan, and fully involving transfer stations, the island SWM system is expected to achieve a waste diversion rate of more than 75%. In the future scenario, the daily amount of waste transported outside the island is reduced from 62 tonnes to zero, thereby reducing costs and environmental burdens. Compared with the present scenario (62.02 billion CO 2 e) and optimization programming focusing on cost reduction (69.29 billion CO 2 e), the upper limit result of the five-year cycle under the future scenario is 54.92 billion CO 2 e, representing reductions of 12% and 21%, respectively. The proposed SIMFO framework addresses uncertainties, optimizes facility capacity, and supports dynamic decision-making processes. This research offers a robust tool for policymakers, promoting sustainable SWM practices and long-term environmental stewardship in isolated island regions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | medium |
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".