Nudging consumers towards the most environmentally friendly warm dish: A field experiment applying traffic light label and single label interventions in a hospital cafeteria
Bibliographic record
Abstract
Switching to food with a lower environmental footprint has a substantial mitigation potential, but consumers still need significant assistance in making the switch. Ecolabels can help guide consumers towards more sustainable food options at the point of sale, without compromising their freedom of choice. However, the efficiency of ecolabels depends on many factors other than the label itself, including the choice settings (school canteen, hospital cafeteria, work launch room). The current paper investigates the role of two eco-label formats, in the context of a hospital cafeteria, in increasing the sales of low-impact (vs. high-impact) dishes (based on the carbon footprint), while also exploring the role of other factors such as price, dish name (hedonic vs. descriptive), foreign connotation of the dish (regional vs. local)., and dietarian type of the dish (vegan, vegetarian, fish or meat). The results show no statistically significant effect of the experiment. Nevertheless, a directional trend emerges: traffic light labels tended to increase the number of low-impact dishes purchased, while the single logo appeared to have the opposite effect. We further found that the price, dish's foreign connotation (regional vs non-regional) and dish type have significant direct effects on the participants' choice. The results are discussed in light of statistical test significance and its implications on our findings. Future research avenues are suggested.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".