Simulation of a residential composting program and behavioural spillover from composting to recycling using agent-based modeling
Bibliographic record
Abstract
Composting is a pro-environmental behaviour that mitigates climate change through the diversion of organic waste from landfills. Although the city of Winnipeg piloted a residential composting program, limited survey responses made it difficult to estimate the success of the city-wide program and how it may impact existing recycling behaviours due to spillover effects. Behavioural spillover occurs when an intervention is aimed at increasing a pro-environmental behaviour, which unintentionally increases or decreases the likelihood of engaging a non-targeted pro-environmental behaviour (Truelove et al., 2014). The present thesis used Agent-Based Modeling (ABM) to simulate a composting knowledge intervention and residential composting program. The net impact on waste diversion was quantified by modeling the effects of positive and negative spillover from composting to recycling. Furthermore, the ABM generated data on household environmental concern (high vs. low) and model type (proportions of high vs. low concern: 70/30, 50/50, 30/70) to simulate their effects on total waste diversion, composting, recycling, and frequency of spillover. The results indicate that the composting knowledge intervention may become more promising in reducing overall landfill waste as we mobilize more people to possess higher environmental concern in a population. Additionally, after the dissemination of the composting intervention, recycling behaviours decreased across the high and low concern households due to negative spillover effects. However, due to robust increases in composting, significantly more waste was diverted annually through this composting intervention. Therefore, these computational estimates suggest that the intervention was successful despite spillover effects. Implications on policy and research are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".