Date of Award

1-2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School

College of Science and Mathematics

Department/Program

Earth and Environmental Studies

Thesis Sponsor/Dissertation Chair/Project Chair

Pankaj Lal

Committee Member

Yang Deng

Committee Member

Neeraj Vedwan

Committee Member

Leah Yasenchak

Abstract

The US Environmental Protection Agency (EPA) recognizes that abandoned, underutilized, and potentially contaminated sites known as “brownfields” have good redevelopment potential. The Federal Brownfields Program offers grants and technical assistance to communities who then report their progress in online system called ACRES (Assessment Cleanup, and Redevelopment Exchange System). The research conducted in this dissertation is to enhance understanding of the significance of brownfield redevelopment when appropriate factors are integrated into the planning process. The methods used in the analyses used machine learning approaches to investigate the relationship between environmental factors affecting human health and the financial allocation for remediation projects, random forest classification modeling technique to assess how factors such as proximity to transit stations, hospitals, food deserts, and opportunity zone designation can affect remediation outcomes, and a GIS-based Multi-Criteria Decision Analysis (MCDA) framework to evaluate brownfield parcels for their suitability to host AI/HPC data centers. The study results showed that random forest and gradient boosting provided better predictive performance compared to ridge regression, though all models demonstrated limited explanatory power. This suggests that environmental and regional factors alone do not sufficiently predict financial allocation for brownfield remediation. Results also revealed that the most critical determinants of brownfield redevelopment success in terms of locational factors were proximity to transit stations and hospitals, supporting principles of transit-oriented development and highlighting the role of healthcare access in redevelopment viability. In contrast, variables, such as opportunity zone status and food desert indicators, contributed minimally. Drawing from siting, sustainability, energy, and brownfield-redevelopment literature, the study integrated seven spatial criteria—land readiness, power access, connectivity, renewable/storage potential, cooling and water resilience, environmental risk, and supply-chain access to help identify brownfield parcels with high potential for resilient, low-carbon digital-infrastructure development. The dissertation research concluded that brownfield sites and their redevelopment locations are impacted by and can affect environmental, social, and financial factors. By integrating locational analysis into planning and funding strategies, brownfield redevelopment can more effectively contribute to environmental restoration, economic revitalization, and equitable urban development. Furthermore, sustainable data-center siting on brownfield sites can help align federal redevelopment and community-revitalization goals.

File Format

PDF

Available for download on Wednesday, March 03, 2027

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