Literature DB >> 28399368

Emission Impacts of Electric Vehicles in the US Transportation Sector Following Optimistic Cost and Efficiency Projections.

Azadeh Keshavarzmohammadian1, Daven K Henze1, Jana B Milford1.   

Abstract

This study investigates emission impacts of introducing inexpensive and efficient electric vehicles into the US light duty vehicle (LDV) sector. Scenarios are explored using the ANSWER-MARKAL model with a modified version of the Environmental Protection Agency's (EPA) 9-region database. Modified cost and performance projections for LDV technologies are adapted from the National Research Council (2013) optimistic case. Under our optimistic scenario (OPT) we find 15% and 47% adoption of battery electric vehicles (BEVs) in 2030 and 2050, respectively. In contrast, gasoline vehicles (ICEVs) remain dominant through 2050 in the EPA reference case (BAU). Compared to BAU, OPT gives 16% and 36% reductions in LDV greenhouse gas (GHG) emissions for 2030 and 2050, respectively, corresponding to 5% and 9% reductions in economy-wide emissions. Total nitrogen oxides, volatile organic compounds, and SO2 emissions are similar in the two scenarios due to intersectoral shifts. Moderate, economy-wide GHG fees have little effect on GHG emissions from the LDV sector but are more effective in the electricity sector. In the OPT scenario, estimated well-to-wheels GHG emissions from full-size BEVs with 100-mile range are 62 gCO2-e mi-1 in 2050, while those from full-size ICEVs are 121 gCO2-e mi-1.

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Year:  2017        PMID: 28399368     DOI: 10.1021/acs.est.6b04801

Source DB:  PubMed          Journal:  Environ Sci Technol        ISSN: 0013-936X            Impact factor:   9.028


  2 in total

1.  Energy and emissions implications of automated vehicles in the U.S. energy system.

Authors:  Kristen E Brown; Rebecca Dodder
Journal:  Transp Res D Transp Environ       Date:  2019       Impact factor: 5.495

2.  Evaluating long-term emission impacts of large-scale electric vehicle deployment in the US using a human-Earth systems model.

Authors:  Yang Ou; Noah Kittner; Samaneh Babaee; Steven J Smith; Christopher G Nolte; Daniel H Loughlin
Journal:  Appl Energy       Date:  2021-10-15       Impact factor: 11.446

  2 in total

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