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- All Subjects: engineering
- Genre: Masters Thesis
Description
Vehicle type choice is a significant determinant of fuel consumption and energy sustainability; larger, heavier vehicles consume more fuel, and expel twice as many pollutants, than their smaller, lighter counterparts. Over the course of the past few decades, vehicle type choice has seen a vast shift, due to many households making more trips in larger vehicles with lower fuel economy. During the 1990s, SUVs were the fastest growing segment of the automotive industry, comprising 7% of the total light vehicle market in 1990, and 25% in 2005. More recently, due to rising oil prices, greater awareness to environmental sensitivity, the desire to reduce dependence on foreign oil, and the availability of new vehicle technologies, many households are considering the use of newer vehicles with better fuel economy, such as hybrids and electric vehicles, over the use of the SUV or low fuel economy vehicles they may already own. The goal of this research is to examine how vehicle miles traveled, fuel consumption and emissions may be reduced through shifts in vehicle type choice behavior. Using the 2009 National Household Travel Survey data it is possible to develop a model to estimate household travel demand and total fuel consumption. If given a vehicle choice shift scenario, using the model it would be possible to calculate the potential fuel consumption savings that would result from such a shift. In this way, it is possible to estimate fuel consumption reductions that would take place under a wide variety of scenarios.
ContributorsChristian, Keith (Author) / Pendyala, Ram M. (Thesis advisor) / Chester, Mikhail (Committee member) / Kaloush, Kamil (Committee member) / Ahn, Soyoung (Committee member) / Arizona State University (Publisher)
Created2013
Description
Life Cycle Assessment (LCA) quantifies environmental impacts of products in raw material extraction, processing, manufacturing, distribution, use and final disposal. The findings of an LCA can be used to improve industry practices, to aid in product development, and guide public policy. Unfortunately, existing approaches to LCA are unreliable in the cases of emerging technologies, where data is unavailable and rapid technological advances outstrip environmental knowledge. Previous studies have demonstrated several shortcomings to existing practices, including the masking of environmental impacts, the difficulty of selecting appropriate weight sets for multi-stakeholder problems, and difficulties in exploration of variability and uncertainty. In particular, there is an acute need for decision-driven interpretation methods that can guide decision makers towards making balanced, environmentally sound decisions in instances of high uncertainty. We propose the first major methodological innovation in LCA since early establishment of LCA as the analytical perspective of choice in problems of environmental management. We propose to couple stochastic multi-criteria decision analytic tools with existing approaches to inventory building and characterization to create a robust approach to comparative technology assessment in the context of high uncertainty, rapid technological change, and evolving stakeholder values. Namely, this study introduces a novel method known as Stochastic Multi-attribute Analysis for Life Cycle Impact Assessment (SMAA-LCIA) that uses internal normalization by means of outranking and exploration of feasible weight spaces.
ContributorsPrado, Valentina (Author) / Seager, Thomas P (Thesis advisor) / Landis, Amy E. (Committee member) / Chester, Mikhail (Committee member) / White, Philip (Committee member) / Arizona State University (Publisher)
Created2013