Latest revision as of 13:44, 16 February 2021

Abstract

native vehicle technologies have a great potential to minimize the transportation-related environmental impacts, reduce the reliance of the U.S. on imported petroleum, and increase energy security. However, they introduce new uncertainties related to their environmental, economic, and social impacts and certain challenges for widespread adoption. In this study, a novel method, uncertainty-embedded dynamic life cycle sustainability assessment framework, is developed to address both methodological challenges and uncertainties in transportation sustainability research. The proposed approach provides a more comprehensive, system-based sustainability assessment framework by capturing the dynamic relations among the parameters within the U.S. transportation system as a whole with respect to its environmental, social, and economic impacts. Using multivariate uncertainty analysis, likelihood of the impact reduction potentials of different vehicle types, as well as the behavioral limits of the sustainability potentials of each vehicle type are analyzed. Seven sustainability impact categories are dynamically quantified for four different vehicle types (internal combustion, hybrid, plug-in hybrid, and battery electric vehicles) from 2015 to 2050. Although impacts of electric vehicles have the largest uncertainty, they are expected (90% confidence) to be the best alternative in long-term for reducing human health impacts and air pollution from transportation. While results based on deterministic (average) values indicate that electric vehicles have greater potential of reducing greenhouse gas emissions, plug-in hybrid vehicles have the largest potential according to the results with 90% confidence interval.

Original document

The different versions of the original document can be found in:

• http://hdl.handle.net/11498/38547

• https://api.elsevier.com/content/article/PII:S0360544216309033?httpAccept=text/xml,

https://api.elsevier.com/content/article/PII:S0360544216309033?httpAccept=text/plain,

http://dx.doi.org/10.1016/j.energy.2016.06.129 under the license https://www.elsevier.com/tdm/userlicense/1.0/

• https://www.sciencedirect.com/science/article/pii/S0360544216309033,

https://core.ac.uk/display/80959272,

https://ideas.repec.org/a/eee/energy/v112y2016icp715-728.html,

https://econpapers.repec.org/RePEc:eee:energy:v:112:y:2016:i:c:p:715-728,

https://academic.microsoft.com/#/detail/2479618603