Sustainability and Environmental Performance in Selective Collection of Residual Materials: Impact of Modulating Citizen Participation Through Policy and Incentive Implementation
Bibliographic record
Abstract
The effective management of urban waste represents a growing challenge in the face of demographic evolution and increased consumption. This study explores the impacts of municipal strategic decisions on household waste management behaviours and sustainability performance outcomes through agent-based modelling. Using data from Gatineau and Beaconsfield in Quebec, Canada, the model is calibrated and validated to represent diverse urban contexts. Our analysis demonstrates that reducing collection frequency leads to notable increases in participation rates, reaching 78.2 ± 5.1% for collections every two weeks and 96.5 ± 8.3% for collections every five weeks. While this reduction improves bin filling levels, it concurrently decreases the recovery of recyclable materials by 2.8% and 19.5%, significantly undermining the environmental benefits of the recycling program. These findings highlight a complex interplay between collection frequency, citizen participation behaviour, waste stream characteristics, and overall environmental performance. While reducing collection frequency initially appears beneficial, it leads to operational challenges and increased CO2 emissions due to reduced material recovery. The research emphasises the need for tailored holistic waste management strategies that optimise performance outcomes while minimising environmental impacts. By understanding these dynamics, municipalities can develop more effective waste management policies that promote sustainability.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".