A Macro-Level Sustainability Assessment Framework for Optimal Distribution of Alternative Passenger Vehicles

Although there are many studies focusing on the environmental impacts of alternative vehicle options, social and economic dimensions and trade-off relationships among all of these impacts were not investigated sufficiently. Moreover, most economic analyses are limited to life cycle cost analyses and do not consider macro-level economic impacts. Therefore, this thesis aims to advance the Life Cycle Sustainability Assessment literature and electric vehicle sustainability research by presenting a novel combined application of Multi Criteria Decision Making techniques with Life Cycle Sustainability Assessment for decision analysis. With this motivation in mind, this research will construct a compromise-programming model (multi-objective optimization method) in order to calculate the optimum vehicle distribution in the U.S. passenger car fleet while considering the trade-offs between environmental, economic, and social dimensions of the sustainability. The findings of this research provide important insights for policy makers when developing strategies to estimate optimum vehicle distribution strategies based on various environmental and socio-economic priorities. For instance, compromise programming results can present practical policy conclusions for different states which might have different priorities for environmental impact mitigation and socio-economic development. Therefore, the conceptual framework presented in this work can be applicable for different regions in U.S. and decision makers can generate balanced policy conclusions and recommendations based on their environmental, economic and social constraints. The compromise programming results provide vital guidance for policy makers when optimizing the use of alternative vehicle technologies based on different environmental and socio-economic priorities. This research also effort aims to increase awareness of the inherent benefits of Input-Output based a Life Cycle Sustainability Assessment and multi-criteria optimization