A Data-Driven Framework for Fleet Electrification Planning: Integrating Total Cost of Ownership Analysis for University Facilities Management; Lost in Translation: An Actor-Network Theory Analysis of NEPA’s Permitting Network

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🔬研究者:Combines quantitative decision-support modeling with qualitative STS analysis, offering a methodological template for studying socio-technical transitions.

🏢実務担当者:Fleet electrification framework provides a replicable tool for institutional facilities managers using actual operational data to guide vehicle replacements.

🏛政策担当者:NEPA analysis identifies systemic translation failures in permitting, relevant to reforming environmental review processes to incorporate local knowledge and justice.

The transition to clean energy is often framed as a technical challenge: build more infrastructure, electrify more systems, and reduce emissions as quickly as possible. My capstone and STS research examine this transition from two scales, one institutional and one federal, to ask a shared question: how can clean energy systems be designed not only to work, but to work responsibly? Together, these projects show that sustainability depends on more than technological possibility. It depends on the decisions, data, policies, communities, and values that determine how technology moves from aspiration to implementation. My capstone project addressed the practical challenge of fleet electrification at the University of Virginia Facilities Management, which operates a large and varied vehicle fleet. Because institutional vehicles differ widely in usage, duty cycle, cost, and replacement feasibility, a uniform approach to electrification can create financial risk and miss meaningful emissions-reduction opportunities. To support more precise decision-making, my team developed a data-driven framework that combines vehicle-level telematics, machine learning, total cost of ownership analysis, and the social cost of carbon. The resulting dashboard allows fleet managers to compare current vehicles against potential replacements using actual operational data rather than generic assumptions. In doing so, the project translates sustainability goals into a practical planning tool that can guide vehicle-by-vehicle electrification decisions. Yet clean energy technologies do not enter the world in a vacuum. They move through institutions, permitting systems, financial constraints, public processes, and communities that experience their benefits and burdens unevenly. My STS research examined these human and social dimensions through an Actor-Network Theory analysis of the National Environmental Policy Act permitting process. Using environmental justice as a companion framework, I explored how NEPA’s procedural structure can disclose environmental harms while still failing to recognize the worldviews, treaty relationships, and lived experiences of affected communities. Through case studies of the Dakota Access Pipeline and Mountain Valley Pipeline, I argued that NEPA’s deepest weakness is not simply delay, but translation failure: the permitting network often cannot meaningfully incorporate forms of knowledge that do not fit its technical and legal categories. Considered in concert, these projects suggest that the clean energy transition must be both data-driven and justice-conscious. Analytical tools can make institutional decisions more transparent, cost-effective, and environmentally beneficial, but technical optimization alone cannot answer who bears risk, whose knowledge counts, or what forms of participation are meaningful. Responsible engineering requires both better models and better networks of accountability. Clean energy systems will succeed not merely when they are efficient, but when the processes that build them are capable of recognizing the people and places they transform.

• openalex https://doi.org/10.18130/ne6p-k146first seen 2026-05-31 05:10:46 · last seen 2026-06-03 04:44:38