Towards Indirect Top-Down Road Transport Emissions Estimation

Mukherjee, Ryan; Rollend, Derek; Christie, Gordon; Hadžić, Armin; Matson, Sally; Saksena, Anshu; Hughes, Marisa

doi:10.1109/cvprw53098.2021.00120

Cited by 4 publications

(1 citation statement)

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“…Carbon Monitor (Liu et al, 2020) is a global emissions inventory that utilizes a variety of activity data to estimate daily GHG emissions, however, the reliance on proprietary traffic data in the ground transportation sector limits the ability to extend to locations where this data is not available. Other methods have used machine learning (ML) to directly predict emissions, but their ability to generalize globally is unclear (Mukherjee et al, 2021; Scheibenreif et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Machine learning for activity-based road transportation emissions estimation

Rollend,

Foster,

Kott

et al. 2023

Environ. Data Science

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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.

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Section: Introductionmentioning

confidence: 99%