Project ISF-DUIRI: Private Electric Vehicle Charger Adoption and Its Effects on Local Mobility and Fuel Use Program DUIRI - Discovery Undergraduate Interdisciplinary Research Internship Term Spring 2026 Status Accepted Research Area Environmental policy & economics, Environmental engineering, Urban planning Description As electric vehicles (EVs) and charging infrastructure expand, understanding how these technologies influence travel behavior and decision-making has become increasingly important. This project integrates knowledge and perspectives from environmental engineering, urban planning, and environmental and energy economics to investigate the behavioral implications of private EV charger adoption. Using EV charger permit data from Shovels, visits to points-of-interest (POI) data from the Dewey company, and U.S. Census demographic data, we will construct a census block group–level dataset to examine how home EV charger adoption affects visits to public charging stations and gas stations. We will also test whether neighborhoods with high home charger penetration exhibit distinct travel patterns compared to those with few or no chargers. The selected undergraduate researcher will work closely with Dr. Shan Zhou and Dr. Qingchun Li on data cleaning, merging, and analysis. The project will offer hands-on experience with large spatial datasets, data analysis, and interdisciplinary research on sustainable transportation and energy transitions. Supervisor Shan Zhou Mentor Qingchun Li Type of work required The selected undergraduate researcher will work closely with Dr. Shan Zhou and Dr. Qingchun Li on data cleaning, merging, and analysis. Websites Qualifications The ideal candidate will have: - Experience with large datasets and data management (e.g., merging, cleaning, or analyzing big data). - Proficiency in R or Python for data analysis, visualization, and statistical modeling. - Background in statistics or econometrics. - Strong interest in clean energy transitions, climate change mitigation, and sustainable transportation. - Attention to detail, curiosity, and willingness to learn new analytical tools and methods. Credit Hours 0 Weekly Hours 10 (estimated)