Abstract

The advent of Electric Vehicles (EV) in the private transportation sector is viewed as a means of reducing emissions and making significant efforts towards reducing climate change impacts. However, when it comes to adopting and/or promoting a new technology through subsidies, the consumers’ needs are seldom given significant attention. Moreover, most analyses informing policy making assess the potential of new and cleaner technologies like EVs based on an average consumer’s needs and behavior. Given heterogeneity, these analyses miss subpopulations that benefit (or lose) more than an average consumer. In fact, private transportation greatly depends upon how the diversity of consumers choose to commute and what kind of vehicles they choose to possess. Especially in the United States of America (U.S.), each consumer faces different needs for their daily commute, which dictates their preferences for vehicles. This behavioral heterogeneity in addition to the geographic locations of consumers makes the U.S. private transportation sector an intricate system. The locations of the U.S. define fuel prices as well as emissions from electricity production. Therefore, these behavioral and geographic heterogeneities are highly crucial while calculating the benefits and potentials of EVs. The analyses conducted for this dissertation consider these heterogeneities to accommodate the nuances in consumers. This consideration of heterogeneities is the most critical aspect of this work.

Chapter 2 of this dissertation builds a Marginal Abatement Cost Curve (MACC) for Electric Technology Vehicles (ETVs) which incorporates these heterogeneities, behavioral and geographical. With current gasoline and battery cell prices, result indicate that without federal tax credits, about 1.9% of the population would receive direct financial benefits from purchasing an ETV. This subpopulation drives over 4 times (over 48,000 miles annually) more than the average consumer (11,700 miles). The consideration of the heterogeneities has made it possible to recognize this subpopulation. The scenario analyses are conducted for different fuel and battery cell prices. These analyses shed light on how different subpopulations benefit financially and environmentally from ETVs. In this chapter, the impacts of federal tax credits with and without considering heterogeneities are estimated, suggesting why policy analyses need to incorporate consumer heterogeneities while assessing benefits of government subsidies.

Given these results on economic and carbon benefits of ETVs, Chapter 3 builds an integrated model of adoption that includes endogenous technological progress—through learning rates—where due to initial adopters the technology is made cheaper for the future ones. The feedback loop developed in this chapter takes into consideration the cumulative production of the technology and estimates price reductions using learning rates. Reduced capital costs then propel more consumers to adopt ETVs making the technology cheaper, again increasing the consumer base that benefits from them. The economic benefits of buying an ETV versus a conventional one costs depend on battery costs, non-battery EV costs, and the future of conventional vehicles. Results are that the future market penetration (share of consumers economically benefitting) is sensitive to two poorly understood quantities: non-battery EV costs and cost increases in conventional vehicles driven by future emission standards. Federal tax credits are also studied in how they stimulate adoption and in turn technological progress of ETVs.

Governments are not only investing in subsidies for consumer purchase of ETVs but also in installing public EV charging stations. These charging stations are expected to motivate consumers to choose ETVs over conventional vehicles and help reduce range-anxiety. In Chapter 4 an assessment is conducted to understand how these public resources are being used. Results reveal the behavior of consumers at the public EV charging stations using empirical data collected in the City of Rochester. A data distillation is first conducted for the raw data to construct the daily charging profiles of the EV users. A pattern analysis is then performed to identify 5 distinct and homogenous clusters of daily charging profiles of the consumers. This work defines the operational inefficiency of the public charging station as the time spent in parking without charging out of the total time a PEV user accessed the public charging station. This analysis uncovers a significant inefficient operation of these public EV charging stations, i.e. EVs remained parked at stations long after charging is finished. An estimation of the opportunity cost of reducing this observed inefficiency in terms of Greenhouse Gas emissions savings is also conducted in this chapter.

The main policy takeaways of this dissertation are that identifying key subpopulations who benefit from the ETVs is highly significant and possible only by incorporating behavioral and geographical heterogeneities. This allows a more precise estimation of impacts of policies such as the federal tax credits. Secondly, the initial adopters make the technology cheaper for the latter adopters. However, the future market parity of ETVs with conventional vehicles depends on poorly understood factors such as current costs and learning rates of non-battery EV technologies and future cost increases in conventional vehicles driven by stricter emissions requirements. Lastly, the use of public resources, such as public charging stations needs to be studied. They are expensive to create, and inefficient use may deter possible EV adopters. Furthermore, the possible opportunity cost of reducing emissions by using the charging station more efficiently allows better use of a public resource.

Library of Congress Subject Headings

Electric vehicles--United States--Costs; Battery charging stations (Electric vehicles); Carbon dioxide mitigation--United States

Publication Date

7-18-2019

Document Type

Dissertation

Student Type

Graduate

Degree Name

Sustainability (Ph.D.)

Department, Program, or Center

Sustainability (GIS)

Advisor

Thomas Trabold

Advisor/Committee Member

Rob Stevens

Advisor/Committee Member

Roger Chen

Campus

RIT – Main Campus

Plan Codes

SUST-PHD

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