This dissertation provides an integrated examination of urban heat mitigation strategies and the effectiveness of electric vehicle (EV) purchase incentives, employing innovative methodologies across its four chapters to address pressing environmental and policy challenges. It leverages a unique dataset obtained through extensive mobile sampling, state-of-the-art machine learning techniques, revealed preference data collected through robust survey design, and detailed sociodemographic analyses to offer insights into the dynamics of urban heat islands, sustainable transportation, and policy efficiency.
The first section details the quantifying of micro-environment heat differences pertaining to the influence of the built environment across 10 cities over 20 days, using mobile sampling conducted via e-bike. Building on the empirical findings of the first chapter, I utilize the developed models to conduct a statewide analysis of the potential for heat mitigation through the strategic replacement of pavement with tree cover. Lastly, we advance the discussion by employing the mobile data collected to evaluate the comparative predictive and mapping capabilities of two distinct machine learning algorithms.
The final section shifts focus to examining the awareness and influence of EV purchase incentives through a survey of recent vehicle purchasers. This chapter quantifies the effectiveness and efficiency of these incentives in driving sustainable transportation choices, providing critical insights into consumer behavior and the policy measures most likely to accelerate the adoption of electric vehicles.
The following work offers a comprehensive and multi-faceted exploration of urban heat mitigation and sustainable transportation policies. By combining field-based mobile sampling, advanced analytical models, and sociodemographic insights, this work contributes significantly to our understanding of effective strategies for creating cooler, more sustainable urban environments and promoting the adoption of electric vehicles.