A Data-Driven Framework for Fleet Electrification Planning: Integrating Total Cost of Ownership Analysis for University Facilities Management, Interagency Conflict in U.S. Climate and Vehicle Emissions Policy Before Massachusetts v. EPA

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🔬研究者:Useful for researchers working on vehicle fleet optimization and the political economy of climate policy.

🏢実務担当者:Fleet managers can apply the TCO framework to evaluate electrification opportunities.

🏛政策担当者:Provides insights on institutional barriers that can hinder effective emissions regulation.

Tradeoffs are apparent in every aspect of our society. In the U.S., efforts to reduce emissions and promote sustainability continue to conflict with economic and efficiency goals. As of right now, organizations are under growing pressure to balance efficiency and productivity with environmental responsibility. Yet, while technologies have advanced rapidly, the broader regulatory and institutional systems have often struggled to align around shared environmental goals. I am aiming to explore and address this imbalance between efficiency and sustainability from two perspectives. My technical project involves the use of telematics data to analyze and improve the overall performance of UVA’s fleet of vehicles. By optimizing vehicle selection and operations, we seek to demonstrate how data-driven insights can reduce emissions while maintaining or even improving productivity. My STS research project, meanwhile, investigates how interagency coordination and conflict among federal departments influenced the development of U.S. climate and vehicle emissions policy leading up to Massachusetts v. EPA. My research will highlight the political and organizational actions that shape how environmental policy is made and explore what can be learned about aligning institutional goals with sustainable outcomes today. My technical project developed a data-driven decision-support framework to evaluate electrification strategies for UVA Facilities Management (FM), which oversees over 400 vehicles. The framework identified which vehicles were suitable candidates for replacement with plug-in hybrid vehicles or battery electric vehicles and quantified trade-offs between cost and emissions. The methodology combined vehicle-level telematics data with a supporting machine learning based modeling approach that learned patterns in vehicle usage and energy consumption from historical telematics data for future fuel and energy estimates. Sensitivity analysis was used to evaluate uncertainty in key parameters such as fuel prices, electricity rates, charging availability, and vehicle utilization. Results indicated that a subset of high-emitting vehicles, often characterized by repeated excessive idling, contributed disproportionately to overall fleet emissions but were not always suitable candidates for electrification based on their operational profiles. Case studies of real FM replacement decisions demonstrated that while electric and hybrid alternatives offer meaningful emissions reductions of 69–82%, their higher upfront acquisition costs mean the total cost of ownership premium of electrification varies substantially by vehicle utilization, and targeted replacement strategies are more cost-effective than uniform fleet-wide electrification. These findings demonstrate that the framework provides FM with a structured, data-grounded tool to evaluate when electrification is economically justified and when operational emissions reduction should take priority. My STS research paper examines how internal dynamics within the federal government shaped the development of U.S. climate and vehicle emissions policy leading up to Massachusetts v. EPA. While existing research highlights the slow and often ineffective progression of emissions policy, this paper focuses on the underlying cause of fragmentation and disagreement between regulatory agencies and executive leadership. Using a historical analysis of primary sources such as government memos and reports, along with secondary academic literature, the paper traces how these dynamics unfolded over time. The analysis shows that divided authority across agencies like the EPA and DOT created a fragmented policy structure, making coordination difficult and leading to inconsistent priorities. Executive branch oversight further intensified these challenges, as political leadership often influenced which scientific findings were prioritized and whether regulatory action was taken. Applying the Social Construction of Technology framework, the paper argues that different government actors functioned as competing social groups, each with their own understanding of the problem and preferred solutions. As a result, closure and stabilization around vehicle emissions policy were delayed. Over time, these patterns of fragmentation and political influence contributed to a lack of meaningful federal action, even as emissions remained a growing concern. This inaction ultimately prompted states to pursue their own policies and legal challenges, culminating in the Supreme Court’s decision. Working on these two projects together provided a perspective that neither would have offered on its own. The technical project showed that improving emissions outcomes is not simply a matter of adopting new technology, but requires careful evaluation of tradeoffs between cost and operational needs. At the same time, the STS research made it clear that even when technical solutions exist, their implementation depends heavily on how institutions define standards, prioritize goals, and coordinate action. This connection became especially clear when considering why more efficient solutions are not always adopted at scale. The technical work emphasized that data-driven decisions can identify practical, targeted ways to reduce emissions without sacrificing performance. However, the STS research highlighted that similar opportunities at the national level have historically been limited not by a lack of technology, but by fragmentation and disagreement within government. Together, these projects reinforced that technological capability and institutional alignment must work in parallel. The experience ultimately showed that addressing sustainability challenges requires both strong technical tools and coordinated decision-making structures.

• openalex https://doi.org/10.18130/azy2-cw48first seen 2026-05-28 04:49:05 · last seen 2026-06-03 04:43:44