Machine learning for activity-based road transportation emissions estimation

Measuring and attributing greenhouse gas (GHG) emissions remains a challenging problem as the world strives toward meeting emissions reductions targets. As a significant portion of total global emissions, the road transportation sector represents an enormous challenge for estimating and tracking emi...

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Bibliographic Details
Published in:Environmental Data Science 2023, Vol.2, Article e38
Main Authors: Rollend, Derek, Foster, Kevin, Kott, Tomek M., Mocharla, Rohita, Muñoz, Rai, Fendley, Neil, Ashcraft, Chace, Willard, Frank, Reilly, Elizabeth P., Hughes, Marisa
Format: Article
Language:English
Subjects:
greenhouse gas emissions
machine learning
road networks
road transportation
satellite imagery
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Summary:Measuring and attributing greenhouse gas (GHG) emissions remains a challenging problem as the world strives toward meeting emissions reductions targets. As a significant portion of total global emissions, the road transportation sector represents an enormous challenge for estimating and tracking emissions at a global scale. To meet this challenge, we have developed a hybrid approach for estimating road transportation emissions that combines the strengths of machine learning and satellite imagery with localized emissions factors data to create an accurate, globally scalable, and easily configurable GHG monitoring framework.
ISSN:2634-4602
2634-4602
DOI:10.1017/eds.2023.32