The National Academies

NCHRP 25-25/Task 115 [Final]

Estimates of Emissions Reductions from Future Fleet Changes for Use in Air Quality Models
[ NCHRP 25-25 (Research for the AASHTO Committee on Environment and Sustainability) ]

  Project Data
Funds: $125,000
Research Agency: Louis Berger U.S. Inc. & Sonoma Technology Inc.
Principal Investigator: Dr. Garnet Erdakos
Effective Date: 12/1/2018
Completion Date: 11/30/2019

Status: Research is complete. Final report available HERE.

Modeling files developed for this project are available for download HERE. (Note this is a large zip file and will take time to download.)  



Early implementation of new vehicle technologies and publicly sponsored programs, which are anticipated to reduce vehicle emissions, is underway in the United States. These technologies include electric and fuel cell vehicles and improvements for these vehicle types such as enhanced battery storage capacity that extend the range of electric vehicles. Interest in these zero-emission vehicles (ZEVs) extends to freight and commercial operators (e.g. Tesla’s Semi truck, Toyota’s fuel cell semi truck, green vehicle taxi fleets). Further, most connected and automated vehicles are anticipated to be electric. Federal, state, and local programs, such as FHWA’s Alternative Fuels Corridor Program, encourage the adoption of ZEVs.


It is reasonable to assume that ZEVs will have a role in reducing mobile source emissions. Depending on the rate of adoption of these technologies and the timing of related program implementation, the reduction in vehicle emissions may be faster than assumed in state and regional plan documents; such an acceleration could benefit areas designated as nonattainment or maintenance areas for one or more pollutants.




The objective of this research is to develop projected changes in emissions of criteria pollutants, mobile source air toxic pollutants (MSATs), and greenhouse gases (GHGs) resulting from varying adoption rates of ZEVs based upon a set of plausible scenarios with a 20-year time horizon. 


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