AutoPCF: A Novel Automatic Product Carbon Footprint Estimation Framework Based on Large Language Models

Abstract

Estimating the product carbon footprint (PCF) is crucial for sustainable consumption and supply chain decar-bonlization. The current life cycle assessment (LCA) methods frequently employed to evaluate PCFs often en-counter challenges, such as difficulties in determining the emission inventory and emission factors (EFs), as well as significant labor and time costs. To address these limitations, this paper presents AutoPCF, a novel auto-matic PCF estimation framework to conduct cradle-to-gate LCA for products. It utilizes deep learning models and large language models (LLMs) to automate and en-hance the estimation process. The framework comprises five stages: Emission Inventory Determination (EID), Activity Data Collection (ADC), Emission Factor Matching (EFM), Carbon Emission Estimation (CEE), and Estimation Verification and Evaluation (EVE). EID generates production processes and activity inventory, while ADC collects comprehensive activity data and EFM identifies accurate EFs. Emissions are then estimat-ed using the collected activity data and corresponding EFs. Experimental evaluations on steel, textile, and bat-tery products demonstrate the effectiveness of AutoPCF in improving the efficiency of PCF estimation. By auto-mating data collection and analysis, AutoPCF reduces re-liance on subjective decision-making and enhances the consistency and efficiency of carbon footprint assess-ments, advancing sustainable practices and supporting climate change mitigation efforts.