Entity Linking using LLMs for Automated Product Carbon Footprint Estimation
This addresses the need for manufacturers to assess environmental impacts more efficiently, though it appears incremental as it applies existing LLM methods to a specific sustainability task.
The paper tackles the problem of automatically estimating product carbon footprints by using large language models (LLMs) to link components from manufacturer Bills of Materials (BOMs) to Life Cycle Assessment (LCA) database entries, reducing manual data processing for sustainability practices.
Growing concerns about climate change and sustainability are driving manufacturers to take significant steps toward reducing their carbon footprints. For these manufacturers, a first step towards this goal is to identify the environmental impact of the individual components of their products. We propose a system leveraging large language models (LLMs) to automatically map components from manufacturer Bills of Materials (BOMs) to Life Cycle Assessment (LCA) database entries by using LLMs to expand on available component information. Our approach reduces the need for manual data processing, paving the way for more accessible sustainability practices.