A Data-Driven Framework for Fleet Electrification Planning: Integrating Total Cost of Ownership Analysis for University Facilities Management; The Hidden Costs of Clean Technology: Social Costs of the Electric Vehicle Transition Across Global Networks

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Researchers studying EV adoption barriers, supply chain justice, or TCO modeling will find empirical methods and a critical framework linking micro-level decisions to global impacts.

🏢実務担当者:Fleet managers can directly use the TCO workbook for replacement planning, including maintenance cost modeling and carbon cost integration.

🏛政策担当者:Policymakers should note the empirical evidence on supply chain externalities such as child labor and water depletion, informing regulations on battery sourcing and end-of-life management.

This portfolio brings together two pieces of work that both examine the electric vehicle transition from different angles. The technical capstone project analyzes the economics of fleet electrification for a single institutional fleet, asking when and how the University of Virginia’s Facilities Management should replace internal combustion vehicles with hybrid and battery-electric alternatives. The STS research paper takes a wider view, tracing the global supply chain that makes electric vehicles possible and examining who bears the social and environmental costs of that transition. Read together, the two projects reflect a tension at the heart of electrification. The capstone treats the EV as a tool a fleet manager can evaluate on cost and emissions. The STS paper shows that the same EV is also the endpoint of a supply chain running through lithium mines in Chile, cobalt operations in the Democratic Republic of the Congo, and battery factories in China. Both projects were motivated by a sense that the clean energy narrative deserves a harder look than it typically receives in either engineering or policy conversations. The capstone project, completed for UVA Facilities Management under client Mike Duffy and advisor Brian Park, built a data-driven decision framework for replacing vehicles in UVA’s roughly 300-vehicle fleet. The team developed a total cost of ownership (TCO) model spanning ten years of ownership and covering purchase price, maintenance, insurance, fuel or electricity, carbon cost, and residual value. Maintenance costs, which historical records showed to be the largest source of variance in fleet expense, were modeled using an empirical peer-group sampling approach that reduced mean absolute error from roughly $1,339 to $919 per vehicle per year. The final deliverable is an Excel workbook with assumptions, a fleet master sheet, a TCO sheet, and a dashboard that lets a fleet manager look up any vehicle, enter replacement specifications, and receive a recommendation comparing the current vehicle against an internal combustion, hybrid, plug-in hybrid, or battery-electric replacement. Case studies of past replacement decisions validate the model against real outcomes and identify additional vehicles in the fleet that are strong candidates for electrification. The work was submitted to the 2026 Systems and Information Engineering Design Symposium and presented to Facilities Management leadership. The STS research paper asks a question that fleet-level analysis cannot answer: what is the full social cost of switching to electric vehicles, and how are those costs distributed across global networks? Drawing on Actor-Network Theory supplemented by environmental justice and political ecology, the paper traces the EV lifecycle through four case studies. Lithium extraction in Chile’s Atacama region consumes scarce water in one of the driest places on earth, often in tension with Indigenous land and water rights. Cobalt mining in the Democratic Republic of the Congo, which supplies more than two-thirds of global output, sustains itself through artisanal labor that includes an estimated 40,000 child workers. Battery manufacturing in China concentrates both air pollution and documented forced labor abuses tied to state labor transfer programs in Xinjiang. At the end of the product’s life, e-waste flows from wealthy markets into informal recycling sectors in South Asia and sub-Saharan Africa, where lead, mercury, and other contaminants damage workers and surrounding communities. The paper argues that electrification does not eliminate the harms of transportation but redistributes them, concentrating costs in regions that receive few of the benefits. Working on both projects simultaneously changed how I read each of them. The capstone, taken alone, treats an EV as a line item on a balance sheet. The STS paper, taken alone, risks reducing every EV to a symbol of exploitation. Together they force a more honest framing. A fleet manager making a replacement decision is, in a small way, also a participant in the global network the STS paper describes. The capstone’s recommendation to electrify more of UVA’s fleet is still the right one on emissions and cost grounds, but it carries upstream and downstream obligations that TCO models do not capture. I leave this thesis portfolio convinced that good engineering work in the clean energy transition requires both lenses at once: the discipline to model costs rigorously at the scale where decisions are actually made, and the humility to recognize that those decisions sit inside much larger systems of extraction, production, and disposal.

• openalex https://doi.org/10.18130/nf3r-1g61first seen 2026-05-25 04:38:11 · last seen 2026-05-27 04:31:59