Optimizing emissions targets for residential recycling programmes: Why ‘more’ is not necessarily better with respect to diversion
Bibliographic record
Abstract
This study highlights the economic and environmental challenges of recycling in Ontario, specifically examining the effect of attempting to increase the emissions target for the province's household recycling programme. The findings from the cost model analysis found that Ontario's Blue Box programme reduces overall carbon emissions by approximately 1.8 million tonnes every year. This study also found that targeting specific materials for recovery could result in a scenario where the province could improve both overall diversion and emissions offsets while reducing material management costs. Under our modelled scenario, as the tonnes of greenhouse gases (GHGs) avoided increases, the system cost per tonne of GHG avoided initial declines. However, after avoiding 2.05 million tonnes of GHGs, the system cost/tonne GHG avoided increases. To achieve an emissions target in excess of 2.05 million tonnes, the province will have to start recycling higher cost non-core materials (composite materials, other plastics, etc.).
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How this classification was reachedexpand
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 | 0.007 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".