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- W2904149197 abstract "This research presents an integrated sustainability assessment framework applied to electric vehicle technologies in the United States of America. Two methods; principal component analysis and life cycle assessment are jointly used to present a novel integrated framework for eco-efficiency analysis of battery electric vehicles for each state in the USA. Three electricity production scenarios; 1) marginal electricity mix; 2) average electricity mix; and 3) 100% solar energy are investigated. Three environmental (water withdrawal, energy consumption and carbon emission) and one economic indicator as life cycle costing are merged to obtain the eco-efficiency scores of each state. The scenarios are compared by applying ANOVA and Tukey/HSD test regarding their environmental and economic values. Then, a comparison is done based on the eco-efficiency values of states in each scenario, separately. The results showed that the maximum eco-efficiency scores are obtained in three states such as Indiana, Texas and New Mexico based on marginal electricity scenario, average electricity mix scenario and solar energy scenario, respectively. The findings also revealed that 100% solar charging scenario is the most environmentally friendly option because of the environmental impacts in terms of water, energy and carbon footprints. The researchers concluded that the proposed integrated framework for eco-efficiency of electric vehicle technologies has a strong application potential for policy making in sustainability performance assessment where multiple sustainability indicators' are aimed to be integrated into the decision making process, especially to deal with the multi-collinearity associated with environmental life cycle impact data." @default.
- W2904149197 created "2018-12-22" @default.
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- W2904149197 date "2019-03-01" @default.
- W2904149197 modified "2023-10-18" @default.
- W2904149197 title "Eco-efficiency of electric vehicles in the United States: A life cycle assessment based principal component analysis" @default.
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- W2904149197 doi "https://doi.org/10.1016/j.jclepro.2018.12.058" @default.
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