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    Well-to-wheel water footprints of conventional versus electric vehicles in the United States: A state-based comparative analysis

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    Date
    2018
    Author
    Onat N.C.
    Kucukvar M.
    Tatari O.
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    Abstract
    Today, increasing levels of water demand become a particularly serious challenge for many countries, especially since water is an essential element for production of transportation fuels. Unfortunately, no research efforts as of now have been directed specifically toward understanding the fundamental relationship between the adoption of electric vehicles (EVs) and water demand. This research aims to fill this knowledge gap by analyzing the water consumption and withdrawal impacts resulting from the increased usage of alternative vehicle technologies in the United States. 5 vehicle types - Internal Combustion Vehicles (ICVs), Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEV20, PHEV40) and Battery Electric Vehicles (BEVs) - are analyzed across 50 U.S. states with 3 different electricity generation mix profiles: the state-based average electricity generation mix, the state-based marginal electricity generation mix, and a hypothetical electricity generation mix consisting entirely of solar-powered charging stations. The well-to-wheel (WTW) life cycle analysis is used for the water footprint calculations. In worst case, BEVs may consume up to 70 times more water than ICVs. BEVs with solar charging have the lowest levels of water consumption and withdrawal and can reduce transportation water footprint by up to 97%. In most of the states, the marginal electricity generation mix has higher water consumption and withdrawal values than those of the average electricity generation mix. In particular, the authors suggest the use of BEVs with solar charging for states with the highest water-stressed areas (California (CA), Arizona (AZ), Nevada (NV), Florida (FL), etc.), and recommend the inclusion of incentives by federal and state governments for these states. ? 2018 Elsevier Ltd
    DOI/handle
    http://dx.doi.org/10.1016/j.jclepro.2018.09.010
    http://hdl.handle.net/10576/11930
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