Valuing the Multiple Impacts of Household Food Waste
Bibliographic record
Abstract
The Commission for Environmental Cooperation (CEC) has estimated that Canadian households waste 85 kg of food per person annually. Food waste has become an increasingly common focus for policy, regulation, interventions, and awareness-raising efforts in Canada. However, there is still a relative dearth of data to inform such decision-making processes or to provide narratives to contextualize behavior change efforts. In this paper, we describe the results of an uncommonly detailed observational study of household food waste. A total of 94 families with young children living in Guelph, Ontario chose to participate in this study. Over the course of multiple weeks, we collected data on their food purchases, food consumption, and waste generation. All three streams of waste (garbage, recycling, and organic waste) were audited and the food type, degree of avoidability, and weight of each individual component of the organic waste stream was recorded. Using this highly granular data set, we found that the average household in our study generated approximately 2.98 kg of avoidable food waste per week. This estimate was then contextualized in terms of economic losses (dollar value), nutritional losses (calories, vitamins and minerals) and environmental impacts (global warming potential, land, and water usage). In short, weekly avoidable food waste per household was calculated to be equivalent to $18.01, 3,366 calories and 23.3 kg of CO2. These multiple valuation frameworks, which are based in detailed observations of family food behaviors rather than estimations derived from system-wide data, will enable more informed and urgent conversations about policy, programming, and interventions in order to reduce the volume of wasted food at the consumer level.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".