Entity Linking using LLM s for Automated Product Carbon Footprint Estimation (2025)

Abstract: 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.

## introduction increasing awareness of climate change and sustainability has put pressure on manufacturers to reduce their carbon footprints. regulation such as the eu corporate sustainability reporting directive (csrd) and the european green deal has further emphasized the need for transparent and accurate environmental impact assessments. a fundamental step in this process is determining the environmental impact of a product, particularly the carbon emissions generated during production. lca databases, such as ecoinvent @xcite , provide detailed information for this purpose. however, linking the raw components of a product, represented in a bom, to relevant entries in an lca database remains a labor-intensive task requiring specialized knowledge of manufacturing materials and processes. recent advances in artificial intelligence, particularly large language models (llms), present an opportunity to automate this process. because they are trained on vast amounts of information and are able to efficiently synthesize their knowledge as textual data, they may be capable of providing additional context in order to link components to their production processes in a lca database. in this work, we investigate the use of llms to streamline the product carbon footprint estimation process by mapping components from boms directly to a lca database. we propose a multi-step approach using a pretrained llm to identify and summarize component information and mapping this summary to a database using semantic similarity. ## related work semantic similarity techniques are well-explored in entity linking, where embedding models match textual mentions to corresponding database entries. hou et al. introduced a method that enhances entity embeddings by incorporating finegrained semantic information, thereby improving the learning of contextual commonality and achieving state-of-the-art performance in entity linking @xcite . similarly, pereira and ferreira proposed e-bela, an approach that aligns vector representations of mentions and entities in a shared space using literal embeddings, facilitating effective linking through similarity metrics @xcite . these methodologies are particularly effective in domains with straightforward entity disambiguation requirements, where minimal additional context is necessary. existing methods for carbon footprint estimation rely heavily on manual mapping of product components to lca databases. this is a challenging task, requiring both lca expertise and specialist knowledge of components and materials. several studies have explored machine learning approaches to partially automate this process. flamingo @xcite uses semantic similarity to match end products to environmental impact factors such as carbon emissions in the ecoinvent database. it does not make use of the fine-grained component details present in a bom, and additionally it uses a private dataset and supervised approach to train an auxiliary classifier. this classifier is used in conjunction with zeroshot semantic similarity matching to find database matches. similarly, caml @xcite maps product text descriptions to industry sector codes, which can be used to make a coarse-grained estimate of carbon production. ## methodology our key contributions are: we break the process of mapping components to database entries into three connected steps, outlined below. an illustration of the complete pipeline is shown in figure 3 . ## conclusion and future work this paper presents a novel approach to estimate product carbon footprints using llms to map bom components to lca database entries. the proposed pipeline streamlines the traditionally manual process, achieving reasonable accuracy and scalability. future work includes an expanded evaluation with a larger evaluation dataset and integration of additional context from sources such as web search results.