  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.
