Envisioning Ontario’s Food and Organic Waste Disposal Ban: A Comparative Case Analysis
Bibliographic record
Abstract
Roughly one-third, or 1.3 billion tonnes of edible food produced for human consumption is wasted around the world each year (Gustavsson et al., 2011). The production of food that is ultimately thrown away creates and exacerbates a host of economic, environmental, and social issues, including those related to greenhouse gas emissions, climate change, the depletion of finite resources, and food insecurity. Government, industry, and researchers around the world continue to assess the scope and cause of food waste, and investigate solutions through technology, regulation, and public outreach campaigns. In 2015, almost 3.7 million tonnes of food waste (including foods that could have been eaten and unavoidable waste such as vegetable peels) was generated in Ontario alone, and about 60 per cent of this waste was sent to landfill (Ministry of the Environment and Climate Change, 2018a). In 2017, Ontario put forward the Food and Organic Waste Framework, which contains an action plan and policy statement identifying how the province will address food waste within its borders. Within the Framework, Ontario states that a food and organic waste disposal ban regulation will be developed and implemented under the Environmental Protection Act, which will prohibit organic waste from ending up in disposal sites. This paper seeks to look at other jurisdictions in Canada that have implemented organic waste disposal bans in order to identify what these experiences can offer Ontario before implementing its own strategy. The key recommendation articulated throughout this paper is that food waste should be prevented above all other options, as it will have the greatest environmental, economic, and social benefits. This aligns with the frameworks guiding this research, including agroecology, the circular economy, and waste management hierarchy, and also fits within Ontario’s Food Hierarchy (which will be discussed later in this paper). Food waste prevention can be best achieved by facilitating coordination across the value chain (Gooch, Felfel, & Marenick, 2010). Ontario can support value chain coordination by funding research, reviewing existing regulations and programmes, and engaging with value chain stakeholders. Food waste prevention efforts would also benefit from developing programmes that shift behaviours at the household level, though this is secondary to value chain coordination. If food waste cannot be prevented, this paper offers recommendations for how recovered resources should be optimized in order of importance from (1) feeding people, (2) feeding livestock, and then (3) promoting soil health. These recommendations include additions and adjustments to existing regulations, legislations, and government-funded programmes, the use of tipping fees, reducing plastic contamination in the organic waste stream, and working with the agricultural community to ensure that compost meets their needs and is actively utilized by the industry to foster a viable end market.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".