Designing a Circular Economy Network for PPE Masks Supply Chain: A Case Study of British Columbia, Canada
Bibliographic record
Abstract
In recent years, there has been growing interest in building closed-loop supply chains (SCs). However, many of the current methods struggle when it comes to fully embracing circular economy principles. Enhancing the design and management of these networks holds significant potential to promote stronger collaboration among supply chain partners, ultimately fostering more sustainable and efficient operational practices. For this purpose, this study addresses a new circular economy model based on a real case study of a mask SC in the healthcare sector in British Columbia, Canada. The objective is to show that implementing circular practices can lead to considerable financial and environmental benefits, as well as the creation of new job opportunities. To achieve this, a multi-objective mixed-integer linear programming model is developed to identify the most efficient trade-off among sustainable objectives, while adhering to imposed constraints. The proposed closed-loop SC model outperforms the existing linear model in all three aspects of sustainability, namely economic, environmental and social. The improvement leads to significant economic and environmental benefits by preventing the disposal of used masks, giving them a second chance for disinfection and reprocessing, and reintroducing them into the SC as new ones. While the results of the circular economy model demonstrate profit gains and environmental recovery, the linear SC model showed negative profit with higher carbon emissions. Regarding the social aspects, compared to the current system, our approach not only nearly doubles the number of jobs created but also significantly reduces shortages, highlighting sustainable development aspects related to equity and social welfare. The findings offer valuable insights for researchers and practitioners seeking to implement sustainability within a circular economy framework in SCs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".