Emission Factor Recommendation for Life Cycle Assessments with Generative AIClick to copy article linkArticle link copied!
- Bharathan Balaji*Bharathan Balaji*Email: [email protected]Amazon, Seattle, Washington 98121, United StatesMore by Bharathan Balaji
- Fahimeh Ebrahimi
- Nina Gabrielle G DomingoNina Gabrielle G DomingoAmazon, New York, New York 10018, United StatesMore by Nina Gabrielle G Domingo
- Venkata Sai Gargeya VunnavaVenkata Sai Gargeya VunnavaAmazon, New York, New York 10018, United StatesMore by Venkata Sai Gargeya Vunnava
- Abu-Zaher Faridee
- Soma Ramalingam
- Shikha Gupta
- Anran Wang
- Harsh Gupta
- Domenic Belcastro
- Kellen Axten
- Jeremie Hakian
- Jared Kramer
- Aravind SrinivasanAravind SrinivasanUniversity of Maryland and Amazon, College Park, Maryland 20742, United StatesMore by Aravind Srinivasan
- Qingshi TuQingshi TuThe University of British Columbia, Vancouver, British Columbia V6T 1Z4, CanadaMore by Qingshi Tu
Abstract
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.
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This article is cited by 1 publications.
- Lili Jin, Hui Huang, Hongqiang Ren. AI-driven transformation of water treatment technology and industry: toward a new era of comprehensive innovation. Frontiers of Environmental Science & Engineering 2025, 19
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https://doi.org/10.1007/s11783-025-2034-3
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