Emission Factor Recommendation for Life Cycle Assessments with Generative AI

• DOI: 10.1021/acs.est.4c12667
• Publication Date: 2025-03-22T08:25:16Z
• Authors: Bharathan Balaji; Fahimeh Ebrahimi; Nina Gabrielle G ...; Venkata Sai Garge...; Abu-Zaher Faridee; Soma Ramalingam; Shikha Gupta; Anran Wang; Harsh Gupta; Domenic Belcastro; Kellen Axten; Jeremie Hakian; Jared Kramer; Aravind Srinivasan; Qingshi Tu
• Keywords: Factor (programming language)

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