The Carbon Footprint Wizard: A Knowledge-Augmented AI Interface for Streamlining Food Carbon Footprint Analysis

Mustafa Kaan Aslan, Reinout Heijungs, Filip Ilievski

Keywords: life cycle assessment, carbon footprint, large language models, knowledge-augmented AI, food sustainability

TL;DR: We develop a knowledge-enhanced AI methodology and application based on life cycle assessment databases to help users understand the carbon footprint of meals.

Abstract: Environmental sustainability, particularly in relation to climate change, is a key concern for consumers, producers, and policymakers. The carbon footprint, based on greenhouse gas emissions, is a standard metric for quantifying the contribution to climate change of activities and is often assessed using life cycle assessment (LCA). However, conducting LCA is complex due to opaque and global supply chains, as well as fragmented data. This paper presents a methodology that combines advances in LCA and publicly available databases with knowledge-augmented AI techniques, including retrieval-augmented generation, to estimate cradle-to-gate carbon footprints of food products. Our methodology is implemented as a chatbot interface that allows users to interactively explore the carbon impact of composite meals and relate the results to familiar activities. A web demonstration showcases our proof-of-concept system with user recipes and follow-up questions, highlighting both the potential and limitations \textemdash such as database uncertainties and AI misinterpretations \textemdash of delivering LCA insights in an accessible format.

Submission Number: 9