Parakeet: Emission factor recommendation for carbon footprinting with generative AI

Accurately quantifying greenhouse gas (GHG) emissions from products and business activities is crucial for organizations to measure their environmental impact and undertake mitigation actions. Life cycle assessment (LCA) is the scientific discipline for estimating the environmental impact of products throughout their entire life cycle, from raw material extraction to end-of-life. Measuring the emissions outside of a product owner’s control is challenging, and practitioners rely on emission factors (EFs) – estimates of GHG emissions per unit of activity – to model and estimate indirect impacts. These EFs come from prior LCA studies and are collated into databases. The current practice of manually finding the appropriate EF to use from databases is time-consuming, error-prone, and requires domain expertise, hindering scalability and accuracy in emissions quantification. We present an AI-assisted method that leverages large language models to recommend EFs. Our method parses business activity descriptions and recommends the appropriate EF with a human-interpretable justification. We benchmark our solution across multiple domains and find it achieves state-of-the-art performance in EF recommendation, with an average Precision@1 of 86.9%. By streamlining and automating the EF selection process, our AI-assisted method enables scalable and accurate quantification of GHG emissions, supporting organizations’ sustainability initiatives and driving progress toward net-zero emissions targets across industries.