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.

利用大型语言模型，本研究测试和比较五个大型语言模型在建模产品的全生命周期和生成输入输出清单数据方面的表现能力，揭示它们作为通用产品碳足迹知识数据库的局限性。通过利用语言模型，提出了一种名为 AutoPCF 的自动人工智能驱动产品碳足迹核算框架，该框架还应用深度学习算法自动匹配计算参数，并最终计算产品碳足迹。使用 AutoPCF 框架估算三个案例产品的碳足迹的结果表明，它在实现自动建模和碳足迹估算方面具有潜力，并能大大减少建模时间，从几天缩短到几分钟。