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.
翻译:环境可持续性,特别是与气候变化相关的议题,已成为消费者、生产者和政策制定者关注的核心问题。基于温室气体排放的碳足迹是量化活动对气候变化贡献的标准指标,通常通过生命周期评估(LCA)进行评估。然而,由于供应链的不透明性、全球化特性以及数据碎片化,执行LCA十分复杂。本文提出一种方法,将LCA进展与公开数据库相结合,并利用知识增强型人工智能技术(包括检索增强生成技术),以估算食品从生产到销售环节的碳足迹。我们引入了一种聊天机器人界面,使用户能够交互式地探索复合餐食的碳影响,并将结果与常见活动相关联。通过一个实时网络演示,我们展示了概念验证系统如何处理任意食品项目及后续问题,同时揭示了以可访问形式提供LCA见解的潜力与局限——例如数据库的不确定性和人工智能的误判。