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 a product owner’s control is challenging, and practitioners rely on emission factors (EFs)─estimations of GHG emissions per unit of activity─to model and estimate indirect impacts. However, the current practice of manually selecting appropriate EFs from databases is time-consuming and error-prone and requires expertise. We present an AI-assisted method 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 operating in a fully automated manner, where the top recommendation is selected as final. Benchmarks across multiple real-world data sets 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.