Parakeet: Emission factor recommendation for life cycle assessments with generative AI

Accurately quantifying greenhouse gas (GHG) emissions is crucial for organizations to measure and mitigate their environmental impact. Life cycle assessment (LCA) estimates the environmental impacts throughout a product’s entire lifecycle, 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 GHGemissions per unit of activity– to model and estimate indirect impacts. However, the current practice of manually selecting appropriate EFs from databases is timeconsuming, error-prone, and requires expertise. We present an AI-assisted method 10 11 leveraging natural language processing and machine learning to automatically recommend EFs with human-interpretable justifications. Our algorithm can assist experts by providing a ranked list of EFs or operate in a fully-automated manner where the top recommendation is selected as final. Benchmarks across multiple real-world datasets show our method recommends the correct EF with an average precision of 86.9% in the fully-automated case, and shows the correct EF in the top 10 recommendations with an average precision of 93.1%. By streamlining EF selection, our approach enables scalable and accurate quantification of GHG emissions, supporting organizations’ sustainability initiatives and progress toward net-zero emissions targets across industries.