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The effect of traffic-light labels and time pressure on estimating kilocalories and carbon footprint of food
Panzone, Luca Sniehotta, Falko Comber, Rob Lemke, Fred
Panzone, Luca
Sniehotta, Falko
Comber, Rob
Lemke, Fred
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Journal article with impact factor
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Publication Year
2020
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Publication Volume
155
Publication Issue
December
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Abstract
Food consumption decisions require consumers to evaluate the characteristics of products. However, the literature has given limited attention to how consumers determine the impact of food on health (e.g., kilocalories) and on the environment (e.g., carbon footprint). In this exercise, 1511 consumers categorised 43 food products as healthy/unhealthy and good/bad for the environment, and estimated their kilocalories and carbon footprint, which were known to the investigator. The task was performed either with no stimuli (a control group), under time pressure only, with traffic-light labels only, or both. Results show that traffic-light labels: 1) operate through improvements in knowledge, rather than facilitating information processing under pressure; 2) improve the ability to rank products by both kilocalories and carbon footprint, rather than the ability to use the metric; 3) reduce the threshold used to categorise products as unhealthy/bad for the environment, whilst raising the threshold used to classify products as good for the environment (but not healthy). Notably, traffic-light increase accuracy by reducing the response compression of the metric scale. The benefits of labels are particularly evident for carbon footprint. Overall, these results indicate that consumers struggle to estimate numerical information, and labels are crucial to ensure consumers make sustainable decisions, particularly for unfamiliar metrics like carbon footprint.
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Keywords
Numerical Assessments, Sustainable Diets, Carbon Footprint, Kilocalories, Threshold Analysis, Multi-level Modelling