Abstract

According to the California Air Resources Board (CARB, 2020), light-duty vehicles are responsible for 13 percent of statewide NOx emissions and 28 percent of statewide greenhouse gas emissions. Scientists, policymakers, and car manufacturers have been striving to reduce the air pollution and greenhouse gas emissions from the transportation sector using various measures, ranging from cleaner engines to alternatives to driving to reduce VMT. In this dissertation, I focus on a subset of these measures: carsharing programs and Battery Electric Vehicles (BEVs). In the first part of this dissertation, I explore the profile of households engaging in carsharing by estimating zero-inflated negative binomial (ZINB) models on data from the 2017 National Household Travel Survey (NHTS). My results show that households who are more likely to carshare are those who participate in other forms of sharing, have more Silent generation members, are less educated (the highest educational achievement is a high school degree), and have fewer vehicles than drivers. Conversely, households with more young adults (18 – 20 years old), with 2 or more adults and no children, take part in carsharing program less often. Moreover, households who took more part in ridesharing and have fewer vehicles than drivers are less likely to never carshare. Furthermore, households whose annual income between $75,000 and $150,000 are more likely to never carshare.In the second part of this dissertation, I concentrate on the adoption of BEVs. More specifically, I focus on two questions: 1) What are the characteristics of households who own battery electric vehicles (BEVs)?; and 2) Does the travel behavior of these households differ from the travel of households who have motor vehicles but not BEVs? To answer those questions, I characterize three groups of households based on their vehicle holdings: BEV-only, BEV+ (i.e., households with both one or more BEV and at least one conventional vehicle), and non-BEV households. I analyze data from the 2017 NHTS using mixed methods. Results show that BEV households are more likely to be Asian, well-educated, with a higher income and to live in higher population and employment density areas. Furthermore, BEV-only households are more likely to be composed of one adult (not retired) with fewer Baby Boomers. Yet, BEV+ households are more likely to be larger households with 2 or more adults. Also, BEV+ households are more likely to have more Generation X (37-52 years old in 2017) and Z members (20 years old or younger in 2017). They are also more likely to own their home. My analysis on gender (at the individual level) concluded that BEV owners are more likely to be men. Furthermore, I find that BEV households travel as much as non-BEV households.Although carsharing and BEVs could substantially decrease the environmental footprint of transportation, they are currently far from mainstream. To promote carsharing programs, their reach could be extended, they could be made more affordable, while increasing the cost of owning and operating private vehicles. Similarly, state and federal governments could continue to provide financial incentives to lower the purchase price difference between conventional and BE vehicles, manufacturers could provide extended warranties on batteries, and the charging infrastructure needs to be developed in order to attract more customers. The Covid-19 crisis is giving governments around the world an opportunity to invest in clean technologies to jumpstart the economy. It is critical to take advantage of this crisis to reduce air pollution and greenhouse gas emissions from transportation for the good of current and future generations.

Abstract

Given our rapidly changing society, the complexity of residential energy often hinders the efficacy of energy conservation policies designed to address our current social and environmental problems. Therefore, understanding this complexity appears to be essential to successfully building and efficiently implementing energy policies. The present dissertation attempts to advance our understanding of the dynamics and complexity of residential energy consumption by investigating various determinants and contextual factors through the three interrelated pieces of applied research. Using American Housing Survey (AHS) data, the first study investigates the dynamics of residential energy consumption at the micro level. It is found that the electricity consumption of households who have moved into new homes is generally lower than average, and their consumption is found to increase as the period of residence increases. The second study examines the relationship between the choice of energy-efficient systems and inter-agent dynamics. By employing a logistic regression model with two national datasets, the Residential Energy Consumption Survey (RECS) and the American Community Survey Public Use Microdata Sample (ACS PUMS), the empirical analysis reveals statistically significant differences in the installation of solar energy systems among households with different degrees of two major inter-agent issues—split incentives and split decision-making problems. The last study focuses on the complexity of residential energy consumption relevant to the surrounding environments, and it pays special attention to seasonality. Based on city-wide data from Chicago and using a special econometric model, the empirical analysis reveals the seasonal dynamics between urban forms and residential energy consumption. Through these three empirical studies, this dissertation explores the dynamics of residential energy consumption in various dimensions and reveals the complicated mechanisms that determine residents’ choices with respect to energy consumption. The evidence from this study is especially important because it reinforces the conclusion that there is no panacea when addressing energy issues. This study suggests that policy-makers and planners should instead thoroughly understand a wide range of contextual factors and their influences in order to develop more effective, context-specific energy policies that best fit each distinct geographical and socio-economic situation. 

Abstract

Identifying socioeconomic characteristics and vehicle characteristics, including a market share of a specific vehicle, influencing on a choice of a vehicle is important for forecasting demands for alternative fuel vehicles (AFVs). Over the time, how changes in these characteristics will affect on the demands is also important. And by connecting with supply, how changes in demands for AFVs will make an effect on the supplies becomes important. This paper forecasts market shares of AFVs in demands and supplies.

First, in a demand part, a dataset of National Household Travel Survey in 2009 is used to identify factors which influence on a choice of AFVs by logit models. And then by using coefficients from the logit models, a dynamic normative model is proposed to forecast demands for Toyota Prius, a sort of hybrid vehicles, with respect to changes in characteristics such as a gas price and a vehicle price. Because a dynamic normative model is a simulation model with unknown values of parameters, these values are randomly defined to track the changes in market shares of Prius based on an annual vehicle market share data.

Next, in a supply part, proportions of hydrogen fuel cell vehicles (HFCVs) with respect to the density of hydrogen refueling stations are estimated by logit models. And then by using these results, a competition model is proposed to forecast supplies for HFCVs. Forecasting supplies for HFCVs is based on demands which is forecasted from a dynamic normative model.

Last, it is found that supplies of HFCVs from the competition model exceed affordable numbers of themselves for the market, because the demands for HFCVs from a dynamic normative model don’t consider affordable numbers of HFCVs for the market. Therefore, to connect results from two models, feedback methods are used.

The results indicate that the market share of AFVs will exceed that of ICEs when: 1) a gasoline price is increased, 2) a vehicle price of AFVs is decreased, 3) the initial market share of AFVs is large, and 4) the density of refueling stations is increased.