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Doctoral Defense Announcements

Yiyi Wang; Encouraging Eco-Driving Behavior: Driver Response to Different Types of In-Vehicle Eco-Driving Feedback

Date:  Thursday, June 24, 2021 at 1:00 PM (EST)

The success of the change towards frequently undertaking eco-driving behavior is highly dependent on the individual drivers and appropriate in-vehicle feedback systems that drivers respond to. This work uses the data coming from 822 individuals who participated an online survey over a two-month period using 14 graphics of different types of in-vehicle eco-driving feedback interfaces and finds that researcher-identified eco-drivers are those ICEV drivers with strong environmental beliefs, and self[1]identified eco-drivers are those who have higher level of education, lower income, and strong environmental beliefs. This variation in researcher-identified and self-identified eco-drivers by demographics, vehicle characteristics, and motivational factors suggests an intention-behavior gap that self-identified eco-drivers are not those who are actually engaging in frequent eco-driving behaviors. Beyond the identification of eco-drivers, the use of in-vehicle eco-driving feedback itself plays an important role in encouraging eco-driving behavior. This work finds that eco-drivers are more likely than non-eco-drivers to be influenced by the use of eight different types of feedback. Eco Mode is the feedback type which has strong impact on eco-driving intentions, is not perceived as a driver distraction, and leads to self-reported behavior change whether you are an eco-driver or not. In particular, it was found that feedback design attributes of 1) Eco Mode, 2) haptic mode, 3) feedback standards, 4) color & movement, 5) specific behavior, 6) biophilic design, and 7) comparisons between current to average/remaining estimates are relevant to drivers’ behavioral response. In the analyses of a causal chain leading from intentions to behavior, support was found for the ability of the variables of environmental beliefs, social norms, and the use of some types of feedback to predict eco[1]driving intentions. Eco-driving intentions account for up to 22.4% of the variance in models explaining self-reported eco-driving behavior. The use of Biophilic Rewards and Eco-Driving Coach feedback types were found to play the most important role in motivating drivers to eco-drive. Ultimately, this research posits that feedback design attributes are relevant in whether and how drivers perceive information and the impact on driver response towards frequent eco-driving behavior. It also provides direction for further research to expand on them.

 

Kyung Hoon (Daniel) Kim; The Influences of Psychosocial Variables on STEM Achievement and Developmental Differences between High School and College Students

DATE:  Wednesday, June 30, 2021 at 9:00 AM

Research has shown that psychosocial variables are general predictors of Science, Technology, Engineering, and Mathematics (STEM) achievement and are important for students' success in STEM. However, studies on psychosocial models have often focused on each level of adolescents or college students, and few studies have examined the developmental perspective. This study investigated how the psychosocial variables of identity, self-efficacy, interest, and sense of belonging (SOB) influence STEM achievement and identified differences between high school and college students and between underrepresented (UREP) and nonunderrepresented (UREP) racial groups in the United States. I designed psychosocial models using the data of the High School Longitudinal Study 2009-2013 for high school and the Louis Stokes Alliances for Minority Participation (LSAMP) 2018-2019 for college students. Structural equation modeling (SEM) analyses including latent psychosocial variables demonstrated that the psychosocial models predicting STEM achievement differed between high school and college students. Identity was more important and was the strongest predictor of math achievement among high school students. In contrast, self-efficacy was more important and was the strongest predictor of STEM achievement among college students. Self-efficacy was the only direct predictor and the key mediator of STEM achievement among college students, while the three variables of identity, self-efficacy, and sense of belonging (SOB) were direct predictors of STEM achievement among high school students. This suggests that there are developmental changes during the transition from high school to college. In addition, there were differences between underrepresented and non-underrepresented racial groups among college students. Identity had different associations with a sense of belonging (SOB) and, therefore, with STEM achievement in the UREP and non-UREP racial groups. Therefore, high school education professionals should consider the influence of the environment on identity and college education professionals should consider the influence of the environment on selfefficacy to improve students' STEM achievement.

 

Mohammed R. Osman; States and Carbon:  A Look Ahead

Date:  April 28, 2021 at 7:30 PM

To tackle global climate change, the United States must lower its large carbon footprint. 
An extensive data exploration was undertaken to understand the United States’ energy past and future under carbon constraint using a blended mix of historical and simulation data. Historical data was sourced from the World Bank, EIA, and EPA to study trends at the world stage and state level, along with historical energy regulation changes. Integrated assessment models GCAM and GCAM-USA were used to simulate atmospheric carbon dioxide stabilization scenarios at national and state levels. These simulations projected the world from 2015 to 2100 in five year increments, stopping global carbon dioxide levels at light constraint (650-700 PPM), medium constraint (525-600 PPM), and “Deep Blue” high constraint (450-500 PPM) scenarios, along with a ‘‘Business As Usual’ projection. Data exploration techniques Principal Component Analysis, Hierarchical Clustering on Principal Components and Multidimensional Analysis were applied over the combined datasets to create state energy signatures for analysis, find groupings, and understand  important drivers of technology transition.

 

Raphael Wentemi Apeaning;  Technological and Socio-economic Feasibility of Climate Mitigation: A Focus on Developing Economies

Date:  November 20, 2019

"The Paris Accord is hailed as a turning point in global climate policy governance (Hale, 2016). The bottom-up approach of this climate agreement allowed developing for the first time in the history of the Conference of Parties to frame their “national determined contributions” (NDC) to climate stabilization. The NDC have renewed and spurred the discourse “on how developing economies can contribute to climate mitigation without compromising their legitimate aspirations for development” (IPCC, 2014). This dissertation contributes to this policy dialogue by assessing the salient technology and economic pathways, critical for developing nations to contribute to cost-effective climate mitigation."