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

2025-09-09

AI Summary

Assessing food carbon footprints remains challenging due to opaque global supply chains, fragmented data, and the methodological complexity of life cycle assessment (LCA). To address this, we propose a knowledge-enhanced AI framework that integrates LCA principles with retrieval-augmented generation (RAG) to build an interactive, explainable food carbon footprint analysis system. The system accepts arbitrary food items as input and supports multi-turn question-answering, dynamically translating technical emission data into intuitive analogies (e.g., “equivalent to driving X kilometers”). A publicly deployed prototype demonstrates feasibility in real-world environmental decision support. Empirical validation confirms improved interpretability and usability for non-expert stakeholders. However, limitations persist—including incomplete data coverage and ambiguities in system boundary definition. This work establishes a scalable technical pathway for promoting low-carbon food consumption, sustainable production practices, and evidence-informed climate policy.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Humans and AI: AI for Accessibility
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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. We introduce a chatbot interface that allows users to interactively explore the carbon impact of composite meals and relate the results to familiar activities. A live web demonstration showcases our proof-of-concept system with arbitrary food items and follow-up questions, highlighting both the potential and limitations - such as database uncertainties and AI misinterpretations - of delivering LCA insights in an accessible format.

Problem

• Estimating food carbon footprints with complex life cycle assessments
• Addressing fragmented data in global food supply chains
• Making carbon impact analysis accessible via AI interface

Innovation

• Knowledge-augmented AI with retrieval-augmented generation
• Chatbot interface for interactive carbon impact exploration
• Integration of LCA databases with AI techniques