LEAF : Predicting the Environmental Impact of Food Products based on their Name (2024)

Abstract: Although food consumption represents a sub- stantial global source of greenhouse gas emis- sions, assessing the environmental impact of off-the-shelf products remains challenging. Currently, this information is often unavailable, hindering informed consumer decisions when grocery shopping. The present work introduces a new set of models called LEAF, which stands for Linguistic Environmental Analysis of Food Products. LEAF models predict the life-cycle environmental impact of food products based on their name. It is shown that LEAF models can accurately predict the environmental im- pact based on just the product name in a multi- lingual setting, greatly outperforming zero-shot classification methods. Models of varying sizes and capabilities are released, along with the code and dataset to fully reproduce the study.

## introduction reducing global greenhouse gas emissions is a key objective for mitigating rapid climate change. recent estimates based on life-cycle assessment (lca) data say the global food system accounts for up to 30% of global greenhouse gas emissions @xcite . although one can not completely eliminate the emissions from food consumption, the environmental impact can be greatly reduced by avoiding foods with a high climate impact. for most food products however, this information is not readily available, which makes it more difficult for consumers to make informed decisions 1 . in this work, a new set of models is introduced which can predict the environmental impact of a product based on the product name. these models learn relationships from products with existing lca data, which can subsequently be applied to any text. ## results the accuracy and mae values of different models are summarised in ablation studies are performed on the bestperforming leaf c model configuration to gain deeper insights into which configurations contribute to it's performance. the results are summarized in ## conclusion modern nlp methods can accurately predict the environmental impact of food products using only their names across various languages. empirical evidence shows that classification is preferred over regression for estimating ef scores. among different model configurations, leaf c models substantially outperform others in both accuracy and error, and they also surpass gpt-3.5 in a zero-shot classification setting. all in all, predictions based on the product name are a simple yet powerful approach, and leaf models can be considered for tasks like eco-score prediction.