Opportunity for scaling Product Carbon Footprinting using Large Language Models

Product carbon footprinting (PCF) is a method used to quantify the greenhouse gas (GHG) emissions associated with the life cycle of a product. This includes emissions from raw material extraction, production, distribution, use, and disposal. PCFs increasingly important for industries aiming to reduce their environmental impact and comply with regulatory requirements. It serves as a critical business indicator, influenced by life cycle assessment (LCA) methodologies, and is essential for making informed decisions about sustainability practices. PCFs are crucial for meeting regulatory requirements, such as the EU’s Battery Passport Initiative, which mandates transparency in carbon footprint documentation for electric vehicle batteries starting in 2026 (Gutwald et al., 2024). Companies use PCFs to identify emission hotspots and implement reduction strategies, which can lead to significant reductions in emissions and provide a competitive edge in markets where sustainability is a purchasing criterion (Rüdele & Wolf, 2023)(Rüdele & Wolf, 2023). Organizations like BASF have developed ISO-conformant methodologies to calculate PCFs, aiming to provide maximum transparency to consumers and stakeholders (Paliwal, 2022). This goal of this paper is to present key issues with scaling of PCFs in the industry and identify opportunities where Large Language Models (LLMs) can help in order to scale PCFs to millions of products.