Sustainability and Environmental Performance in Selective Collection of Residual Materials: Impact of Modulating Citizen Participation Through Policy and Incentive Implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it