Circular economy in winter road maintenance: Analysis of contract models for deploying a closed-loop supply chain
Bibliographic record
Abstract
In winter road maintenance, abrasives are spread on roads to ensure user safety. These abrasives must be swept in the spring and often end up in landfills. To reduce landfilling and the consumption of non-renewable resources, previous work has demonstrated the potential of reusing collected street sweepings for the production of abrasives. However, the current contractual approach of the linear supply chain requires revision to enable the sharing of financial gains between the road authority and the service provider to achieve a win-win situation, primarily considering the uncertainty in the quantity of abrasives spread, which directly impacts the service provider’s profit. This study proposes two closed-loop supply chain structures and analyzes three contract models. Results from a case study in a highway context in Quebec, Canada reveal that transitioning from a linear supply chain to a closed-loop supply chain generates an average systemic financial gain of 9%. Furthermore, a sensitivity analysis on the average quantity of abrasive spread demonstrates that when the road authority buys back reusable sweepings from the service provider at market value, it enables the sharing of a portion of the gain. Consequently, compared to a linear approach, adopting circular economy strategies in the winter road maintenance supply chain mitigates the potential profit loss of the service provider caused by uncertainty.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".