Impact of Vermont's Food Waste Ban on Residents and Food Businesses
Bibliographic record
Abstract
In the United States, an estimated 30-40% of food produced each year is wasted, with most of this waste coming from households, food retailers, and food service businesses. To reduce the burden on Vermont’s only municipal landfill, the Vermont Legislature unanimously passed Act 148, a universal recycling and composting law, in 2012. Among other features, the law included a phased-in food waste ban that went into full effect on July 1, 2020. This ban requires everyone in Vermont – from residents to businesses and institutions – to keep their food waste out of the trash. To study the impact of the food waste ban, we conducted two statewide online surveys in 2021 and 2022: a general population survey and a food business survey. This policy report summarizes the findings of the surveys relevant to the food waste ban. Key findings include: 1. Following implementation of the ban, residents reported increasing the amount of food waste that they separate from their trash by 48% (from 48% to 71%). The leading disposal method for food waste is composting (46% of all food waste disposal). 2.Over a year after full implementation of the ban, about one quarter of respondents to the resident survey (26%)report feeling confused about its requirements. Oft hose who engaged in composting, one out of five (20%) find it to be hard or very hard. 3. Support for and knowledge of the food waste ban is high among Vermont’s food retail and food service professionals, and few continue to dump food waste in the trash. 4.However, over one-third of food retailers (37%), half of food service operators (53%) and two-fifths of those who run both types of businesses (40%) felt that compliance had been difficult. 5. The impacts of the ban differ for different types of food businesses, with food service businesses reporting more negative impacts on operating costs and revenue than food retailers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".