Environmental Performance Assessment of a Decentralized Network of Recyclable Waste Sorting Facilities: Case Study in Montreal
Bibliographic record
Abstract
The generation of waste grows yearly. In a centralized approach, more trucks are dispatched to collect the growing demand, with a higher pressure on the road network and greenhouse gas emissions. In contrast, a decentralized approach creates a network of distributed facilities. This study analyzes the impact of a decentralized approach for recyclable waste sorting facilities. It models waste generation, collection, and location of recyclable waste sorting facilities. This approach is applied to a case study in Montreal for polyethylene terephthalate. The case study computes two performance indicators: costs and CO2 emissions. Six scenarios were developed and compared to a baseline scenario. The results show that decentralization reduces greenhouse gas emissions by 20.3% and operation costs by 8.04%. However, investment costs for the new facilities remain an obstacle. These costs can represent up to 89.7% of the expenses in a decentralized context. Nonetheless, decentralization increases the flexibility of waste collection under growing demand, since the distance to collect one ton has reduced by 35.3% and the average truck load per trip has reduced by 12.8%. To apply the model to the real world, further improvements are required. They span technical, economic, and social acceptability constraints.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".