An Analysis of Consumer Response to Plant-based Meat Alternative Labelling Policy
Bibliographic record
Abstract
Plant-based meat alternatives, defined as products made with plant-based protein that imitate the taste, texture, and appearance of real meat, have been subject to rapid market growth in recent years. These products tend to appeal to consumers who are actively reducing their meat consumption, typically due to concerns about animal welfare, environmental sustainability, or health issues. The simulant nature of these products introduces the need for regulation of labels to facilitate informed consumer decision-making when selecting meat and plant-based alternatives at the grocery store. In Canada, guidelines exist which regulate the use of meat-related terms (e.g., burger, ground, etc.) on the labels of plant-based meat alternatives, nutritional content, and other aspects of these products. While meat-related terms are permitted in Canada, provided certain disclaimers are also present, some jurisdictions abroad have banned such labels entirely. In Canada, some meat industry groups have called for the removal of such terms, and in 2020 the Canadian Food Inspection Agency (CFIA) conducted a consultation on its guidelines for plant-based meat alternative labelling. Despite a dynamic policy environment, research that investigates the consumer demand effects of plant-based meat alternative labelling policy remains elusive. \n\nA survey of 1203 Canadian consumers was conducted to assess the consumer demand effects of different regulatory approaches to the use of meat-related terms on plant-based meat alternative labels. The survey included a discrete choice experiment, where respondents were assigned to one of three labelling treatments – unregulated labels, current Canadian regulations, and a meat-related terms ban. Choice sets featured ground beef and plant-based alternatives with varying attributes and prices. The choice experiment facilitated the investigation of two secondary research objectives: consumer response to regulated protein label claims, and an assessment of preference heterogeneity for plant-based meat alternatives under different labelling policy scenarios. The data was analyzed using multinomial logit, random parameters logit, and latent class logit models, eliciting marginal utility and willingness-to-pay estimates for the attributes and policy effects. \n\nResults show that the labelling policy environment does impact consumer preferences for ground beef and plant-based alternatives. Ground beef is preferred by most consumers in the Canadian market under all three labelling treatments. Further, consumers prefer meat alternatives in an unregulated market relative to the current Canadian regulations and the meat-related terms ban treatments. On average, consumers exhibit similar reductions in willingness-to-pay under the two regulated treatments. However, these effects diverge when preference heterogeneity is accounted for. Five classes of consumers were identified in the latent class logit model, with varying preferences, characteristics, and responses to labelling policy. Preferences for protein claims are generally strong and positive, and there is a significant degree of heterogeneity in preferences for products, attributes, and labelling policy frameworks. The analysis reveals numerous insights into both market and policy issues of plant-based meat alternative labelling. It is in the firm’s best interest to utilize meat-related terms on product labels. However, the disparity in preferences among policy treatments indicates that the provision of information in the form of label disclaimers alongside meat-related terms likely provides valuable information to consumers who may be confused or inattentive otherwise.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".