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An intercontinental machine learning analysis of factors explaining consumer awareness about food risk

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Abstract

This paper investigates to what extent food safety is perceived as a concern at the household level in different countries. It aims to identify the factors that best explain food safety concern, among the various foodrelated questions asked through a survey. To do so, a machine learning approach is used. The results show that the most significant explanatory variables of safety concern are the estimates of carbon footprints and calories associated with food products and primarily with beef and chicken meat. These results tend to indicate that people who are most concerned about food safety are also those who are best aware of environmental and nutritional impacts of food.
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Dates and versions

hal-03770730 , version 1 (06-09-2022)

Identifiers

  • HAL Id : hal-03770730 , version 1

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Alberto Tonda, Christian Reynolds, Nisrine Mouhrim, Rallou Thomopoulos. An intercontinental machine learning analysis of factors explaining consumer awareness about food risk. FoodSim 2022, Apr 2022, Ghent, Belgium. ⟨hal-03770730⟩
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