Regional Well-to-Wheel Carbon, Energy, and Water Footprint Analysis of Electric Vehicles

Abstract

Adoption of alternative vehicle technologies such as electric vehicles (EVs), plug-in hybrid electric vehicles (PHEVs), and hybrid electric vehicles (HEVs) have the potential of reducing some of the environmental impacts and reducing oil-dependency of the U.S transportation sector. However, this potential depends on the regional driving patterns and the source of the electricity generation to power PHEVs and EVs. In this study, state-specific electricity generation mix scenarios and driving patterns in Alabama, Florida, and Hawaii are considered to calculate regional impacts associated with alternative vehicle technologies (HEVs, PHEVs, EVs) compared to internal combustion vehicles (ICVs). Three electricity generation mix scenario are evaluated, which are namely; average electricity generation mix, marginal electricity generation mix, and 100% solar electricity generation mix. Well-to-wheel carbon, energy, and water footprint of these vehicles are quantified for each state and potential environmental reductions are evaluated. According to comparative evaluation for the proposed scenarios, shifting to low carbon, energy, and water intensive electricity generation mix by utilization of solar energy is crucial to achieve environmental friendly transportation in the U.S.

How eco‐efficient are electric vehicles across Europe? A regionalized life cycle assessment‐based eco‐efficiency analysis

“…The PCA and FA are preferable for their ability to reduce the dimension of the sustainability matrix without loss of information (Gan et al, 2017). This feature explains the widespread applications of the PCA as a weighting approach (Jollands, Lermit, & Patterson, 2004;Reisi et al, 2014;Hengen, Pacheco, McGregor, Van Hooser, & Turrigiano, 2016;Onat et al, 2019b;Basset-Mens, Edewa, & Gentil, 2019).…”

“…The PCA is widely used in literature to compute eco-efficiency scores (EES) (Park, Egilmez, & Kucukvar, 2015;Onat et al, 2019b). The PCA is a universal approach to reducing high-dimensional data while preserving as much information as possible (Duong & Duong, 2008;Han, 2010;Lever, Krzywinski, & Altman, 2017;Pereira, Diego, Lizandro, & Luciane, 2018).…”

“…The PCA converts a dataset into unrelated linear combinations of the original indicators. These combinations are known as principal components (PCs) and usually retain the most significant variation in the original dataset (Park et al, 2015;Onat et al, 2019b). There are several approaches to deriving the PCs.…”

“…The PC loadings are utilized to represent the relative weights of the three environmental indicators involved in this study. Like Onat et al 2019b, we propose a PCA-based approach for computing the aggregated environmental impacts (AEI). Figure 2 outlines the proposed LCA-based PCA approach.…”

“…Determine the significant PCs and calculate the EES Define m // number of eigenvalues Set ω = {;} the index subset of the significant PCs Specify the significant PCs For i=1 to m IF λ j ≥ 1; then Add the jth index to ω Else End of the "For" loop • Find the aggregated environmental impact (AEI) and ecoefficiency score (EES) for each country in the EU (Onat et al, 2019b) For i=1 to C…”