# Entity Linking using LLMs for Automated Product Carbon Footprint Estimation

arXiv: https://arxiv.org/abs/2502.07418 · HTML: https://mlsanity.com/paper/2502.07418/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 11 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://mlsanity.com/paper/2308.03281/) (2023)
- [Improving Entity Linking through Semantic Reinforced Entity Embeddings](https://mlsanity.com/paper/2106.08495/) (2021)

## Built on by (most cited first, 2 of 2)

- [Criteria for Credible AI-assisted Carbon Footprinting Systems: The Cases of Mapping and Lifecycle Modeling](https://mlsanity.com/paper/2509.00240/) (2025)
- [PCFBench: A Diagnostic Benchmark for Product Carbon Footprint Estimation](https://mlsanity.com/paper/2608.27716/) (2026)

## 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.