Stochastic comparative assessment of life-cycle greenhouse gas emissions from conventional and electric vehicles
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Electric vehicles (EVs) are promoted due to their potential for reducing fuel consumption and greenhouse gas (GHG) emissions. A comparative life-cycle assessment (LCA) between different technologies should account for variation in the scenarios under which vehicles are operated in order to facilitate decision-making regarding the adoption and promotion of EVs. In this study, we compare life-cycle GHG emissions, in terms of CO2eq, of EVs and conventional internal combustion engine vehicles (ICEV) over a wide range of use-phase scenarios in the USA, aiming to identify the vehicles with lower GHG emissions and the key uncertainties regarding this impact.
An LCA model is used to propagate the uncertainty in the use phase into the greenhouse gas emissions of different powertrains available today for compact and midsize vehicles in the US market. Monte Carlo simulation is used to explore the parameter space and gather statistics about GHG emissions of those powertrains. Spearman’s partial rank correlation coefficient is used to assess the level of contribution of each input parameter to the variance of GHG intensity.
Results and discussion
Within the scenario space under study, battery electric vehicles are more likely to have the lowest GHG emissions when compared with other powertrains. The main drivers of variation in the GHG impact are driver aggressiveness (for all vehicles), charging location (for EVs), and fuel economy (for ICEVs).
The probabilistic approach developed and applied in this study enables an understanding of the overall variation in GHG footprint for different technologies currently available in the US market and can be used for a comparative assessment. Results identify the main drivers of variation and shed light on scenarios under which the adoption of current EVs can be environmentally beneficial from a GHG emissions standpoint.
KeywordsElectric vehicles Greenhouse gas emissions Life-cycle assessment Uncertainty analysis
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