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On food, bias and seasons: A recipe for sustainability

Abstract : Food is a common thread, linking all seventeen Sustainable Development Goals set by the United Nations (2016) for 2030. In this paper, we consider local-seasonal food as a proxy for social and environmental impact. We present a static and a dynamic generative model to re-sample ingredients from a dataset of 10k vegan recipes, in various context (location, season). We compare the static and dynamic behaviors in terms of greenhouse gas emissions and our results suggest that eating local-seasonal could save 0.25 to 1.5 kg CO2 per kg of product compared to randomly picked recipes, in Paris. We introduce a label, local-seasonal, to inform Human and Machine decisions for food and to protect/celebrate (bio)diversity. We propose an application to gather and share knowledge on local-seasonal food, worldwide, with professional and amateur cooks, farmers or markets, accessible at We encourage initiatives to grow and support local communities as part of our recipe for sustainability.
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Contributor : Michel Deudon Connect in order to contact the contributor
Submitted on : Saturday, April 4, 2020 - 11:29:07 PM
Last modification on : Tuesday, April 7, 2020 - 1:56:33 AM


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  • HAL Id : hal-02532348, version 1



Michel Deudon. On food, bias and seasons: A recipe for sustainability. 2020. ⟨hal-02532348⟩



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