Life cycle sustainability assessment; system dynamics; sustainable transportation; alternative vehicle technologies; spatial and temporal variations; uncertainty in life cycle assessment


This dissertation aims to investigate the sustainability impacts of alternative vehicle technologies and develop comprehensive sustainability assessment frameworks to analyze potential impacts of these vehicles in the U.S. In order to assess sustainability impact of vehicle alternatives, life-cycle based models has been extensively used in the literature. Although life cycle-based models are often used for environmental impacts of alternative vehicles, analysis of social and economic impacts of these vehicles has gained a tremendous interest. In this regard, there is a growing interest among the international platform and academia to use the Life Cycle Sustainability Assessment framework to have more informed sustainable products, material and technology choices by considering the environmental, as well as social and economic impacts. The Life Cycle Sustainability Assessment framework is still under development and there is an ongoing research to advance it for future applications. In this dissertation, current and future needs of sustainability assessment frameworks and the U.S. transportation are identified and addressed. The major research gaps are identified as follows: (1) there has been small emphasis on effects of spatial and temporal variations on the sustainability impacts of alternative vehicle technologies, (2) no national research efforts as of now have been directed specifically toward understanding the fundamental relationship between the adoption of electric vehicles and water demand, (3) there has been a lack of understanding the dynamic complexity of transportation sustainability, encompassing feedback mechanisms, and interdependencies, for the environmental, social, and economic impacts of alternative vehicles, and (4) there is no emphasis on addressing uncertainties inherent to the U.S. transportation and its complex relationships with the environment, society, and economy. The environmental, economic, and social impacts of alternative vehicles are highly critical for truly assessing and understanding the long-term sustainability of vehicles and propose economically viable, socially acceptable, and environmentally-friendly transportation solutions for U.S. passenger transportation. This dissertation provides a more comprehensive sustainability assessment framework by realizing following objectives: (1) inclusion of spatial and temporal variations when quantifying carbon, energy, and water footprints of alternative vehicle technologies, (2) quantifying environmental, social, and economic impacts of alternative vehicle technologies, (3) capturing the dynamic relations among the parameters of U.S. transportation system, environment, society, and the economy, (4) dealing with uncertainties inherent to the U.S. transportation sector considering the complexity of the system and dynamic relationships. The results of this dissertation reveal that the results with consideration of uncertainties, temporal and spatial variations, and dynamic complex relationships among the system variables can be significantly different than those of without consideration of those. Therefore, when developing policies the robustness of proposed scenarios should be valuated with consideration of uncertainties, temporal and spatial variations as well as the dynamic feedback mechanisms. The outcomes of this study can pave the way for advancement in the state-of-the-art and state-of-the-practice in the sustainability research by presenting novel approaches to deal with uncertainties and complex systems.


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Graduation Date





Tatari, Omer


Doctor of Philosophy (Ph.D.)


College of Engineering and Computer Science


Civil, Environmental and Construction Engineering

Degree Program

Civil Engineering








Release Date


Length of Campus-only Access

3 years

Access Status

Doctoral Dissertation (Campus-only Access)


Dissertations, Academic -- Engineering and Computer Science; Engineering and Computer Science -- Dissertations, Academic