.png){kind=icon}
.png){kind=icon}
Return
Assessing the accuracy of the Climate Trace global vehicular CO2 emissions
.png){kind=icon}
PRE
AI
K
B
P
DOI：10.1088/1748-9326/ae6355.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Accurate estimation of greenhouse gas (GHG) emissions at the infrastructure scale remains essential to climate science and policy applications. Vehicle emissions often dominate GHG emissions in urban areas and are rapidly increasing globally. Climate Trace (CT), co-founded by former U.S. Vice President Al Gore, is a new AI-based effort to estimate roadway-scale GHG emissions. However, limited independent peer-reviewed assessment has been made of this dataset. Here, we compare CT on road CO2 emissions in U.S. urban areas to atmospherically calibrated, multi-constraint estimates of on road CO2 emissions from the Vulcan Project. Across 260 urban areas in 2021, we find a mean relative difference (MRD) of 70.4%. These large differences are driven by biases in CT's machine learning model, fuel economy values, and fleet distribution values. We conclude that sub-national policy guidance or climate science applications using the on road CO2 emissions estimates made by CT should be done so with caution.
Keywords:
CO2 emissions
machine learning
on road
Journal
IF:
5.6
Papers:
9.5K
Citations:
5.0W
Organization
Cited Papers