The product carbon footprint (PCF) is crucial for decarbonizing the supply chain, as it measures the direct and indirect greenhouse gas emissions caused by all activities during the product's life cycle. However, PCF accounting often requires expert knowledge and significant time to construct life cycle models. In this study, we test and compare the emergent ability of five large language models (LLMs) in modeling the 'cradle-to-gate' life cycles of products and generating the inventory data of inputs and outputs, revealing their limitations as a generalized PCF knowledge database. By utilizing LLMs, we propose an automatic AI-driven PCF accounting framework, called AutoPCF, which also applies deep learning algorithms to automatically match calculation parameters, and ultimately calculate the PCF. The results of estimating the carbon footprint for three case products using the AutoPCF framework demonstrate its potential in achieving automatic modeling and estimation of PCF with a large reduction in modeling time from days to minutes.

翻译：产品碳足迹（PCF）用于衡量产品生命周期内所有活动产生的直接与间接温室气体排放，对供应链脱碳至关重要。然而，PCF核算通常需要专家知识，且构建生命周期模型耗时显著。本研究测试并比较了五种大语言模型（LLMs）在产品"从摇篮到大门"生命周期建模及输入输出清单数据生成中的涌现能力，揭示了其作为通用PCF知识数据库的局限性。通过利用LLMs，我们提出了一种名为AutoPCF的自动化AI驱动PCF核算框架，该框架同时应用深度学习算法自动匹配计算参数，并最终完成PCF计算。使用AutoPCF框架对三个案例产品进行碳足迹估算的结果表明，该框架在实现PCF自动化建模与估算方面具有潜力，可将建模时间从天级大幅缩短至分钟级。