Entity Linking using LLMs for Automated Product Carbon Footprint Estimation
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 3 of its 7 references that are in the map. First posted on arXiv 11 Feb 2025.
Builds on
3 of 7 references.
References · 3
- 2024The Llama 3 Herd of Modelscited by 14,192
- 2023Towards General Text Embeddings with Multi-stage Contrastive Learningcited by 705
- 2021Improving Entity Linking through Semantic Reinforced Entity Embeddingscited by 8
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.