Addressing the Externalities of Food Waste Generated Within the Retail Food Market
Bibliographic record
Abstract
An oversupply of food within the retail food market has led to high levels of food waste which generates various negative externalities. These externalities include the release of harmful greenhouse gasses, the foregone opportunity to divert food, and foregone cost savings to retailers. Thus, this paper explores the effectiveness of various public policy responses to the market-based incentives that are resulting in food waste within the retail food market. There is a current global trend towards government involvement in reducing food waste. However, Canada's existing regulations and guidelines simply aim to encourage retailers to donate excess food rather than directly regulate food waste. Conversely, local government efforts have focused mainly on reducing landfill use through compost and diversion programs. As a result, present food waste initiatives throughout Canada consist of a patchwork of policies and programs, while the appropriate role of government has remained relatively unexplored. In addition to disorganized initiatives, food waste policy in Canada has centered on influencing consumer behaviour rather than retailer behaviour. However, the retail food market is an important piece of the food supply chain that creates avoidable food waste. Although, current research indicates that numerous challenges exist for reducing food waste within the retail food market, it is essential that this sector is included in a comprehensive food waste reduction program. Thus, policy-makers are faced with the challenge of developing an appropriate policy response to the growing levels of food waste specifically within the retail food market. This paper uses a model of supply and demand to develop a conceptual model in order to explain why there is an oversupply of food within the retail food market and discuss the various policy responses governments can use to combat this waste. The policies explored include a tax on food waste, a subsidy to encourage the donation of food, and educational policy. The model demonstrates that retailers choose to maintain fully stocked shelves in order to receive a higher price from consumers, which results in food waste. Therefore, to reduce food waste in the retail food market, policy-makers must aim to increase the cost for retailers to maintain fully stocked shelves until the cost outweighs the benefit. Thus, while a subsidy and education can encourage the diversion and reduction of food waste, a tax is the most effective policy response to food waste because it forces retailers to internalize the cost of the negative externalities of food waste.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".