Estimating Consumers' Valuation of Sustainability Labeling Using Stated Choice Analysis: Evidence from McGill University's Residential Dining Halls
Bibliographic record
Abstract
As the threat of human-induced climate change grows, individuals across the globe are becoming increasingly concerned with finding ways to reduce their impact on environmental degradation.Household food consumption contributes largely to global greenhouse gas emissions through the high usage of land and water materials involved in food production to meet this demand.Sustainable food consumption practices, such as adopting a plant-based diet, are encouraged to minimize the effects of agricultural production.Although a sustainable diet can be effective in reducing greenhouse gas emissions relative to the consumption of conventional food products, it is challenging to bring this shift in dietary preferences.The labeling of sustainable foods can inform consumers about the environmental benefits of selecting certain food products and may incentivize producers to invest in and increase the use of sustainable agricultural practices.Higher education institutions such as universities can serve as a meaningful out-of-home setting to implement sustainable food labeling, as dining halls and food chains on university campuses are visited by a large and diverse population of consumers.However, little is known about how labels that inform diners of food attributes influence diners' willingness to pay for meals in these settings.Using a discrete choice experiment, this study examines consumers' preferences and willingness to pay (WTP) for labeled sustainable ingredients used in McGill University's residential dining hall meals in Montreal, Quebec.A sample of 408 consumers was presented with hypothetical meal choices that differed in their sustainability attributes, including locally sourced, vegetarian, and organic ingredients.Participants were randomly placed into a control group or one of two information treatment groups, providing them with basic or enhanced information about the sustainability attributes.Using a conditional logit model, our results show that participants were willing to pay price premiums for meals containing the sustainability attributes.However, those who received detailed information were not willing to pay an additional price premium.A latent class model was used to determine the influence of individual characteristics on consumption behaviors and WTP.The model resulted in three distinct classes: the "combined preferences" group of consumers; the "loyal meat-eaters" group; and the "sustainably-conscious" group.Despite these distinct groups, however, the enhanced information did not have a significant effect on participants' WTP for the sustainability attributes.The findings from this research iii suggest that McGill University's Food and Dining Services, as well as other out-of-home dining settings and food marketers, can effectively promote the sustainability attributes of their meals without providing detailed information to diners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".