How Neighbourhood Food Environments and a Pay-as-You-Throw (PAYT) Waste Program Impact Household Food Waste Disposal in the City of Toronto
Why this work is in the frame
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Bibliographic record
Abstract
Household food waste has negative, and largely unnecessary, environmental, social and economic impacts. A better understanding of current household food waste disposal is needed to help develop and implement effective interventions to reduce food wasting. A four-season waste characterization study was undertaken with 200 single-family households across eight neighbourhoods in Toronto, Ontario, Canada. The City of Toronto provides residents with a pay-as-you-throw (PAYT) waste program that includes a choice of four garbage cart sizes (Small [S], Medium [M], Large [L], Extra Large [XL]), with increasing annual user fees ($18.00–$411.00 CAD), as well as a green cart (organic waste) and blue cart (recycling). On average, each household disposed 4.22 kg/week of total food waste, 69.90% of which was disposed in the green cart, and disposal increased significantly (p = 0.03) by garbage cart size to L but not XL garbage carts. Of this total, 61.78% consisted of avoidable food waste, annually valued at $630.00–$847.00 CAD/household. Toronto’s PAYT waste program has been effective at diverting food waste into the green cart but not at reducing its generation. Higher median incomes were positively correlated, while higher neighbourhood dwelling and population density were negatively correlated, with total and avoidable food waste disposal. Regression analyses explained 40–67% of the variance in total avoidable food waste disposal. Higher supermarket density and distance to healthier food outlets were associated with more, while dwelling density was related to less, total and avoidable food waste disposal. Distance to fast food restaurants and less healthy food outlet density were both negatively associated with avoidable food waste disposal in the garbage and green cart, respectively. Avoidable food waste reduction interventions could include increasing garbage cart fees, weight-based PAYT, or messaging to households on the monetary value of avoidable food waste, and working with food retailers to improve how households shop for their food.
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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 | 0.001 | 0.001 |
| 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