Unbalanced Growth: Data Center Expansion Outpacing Renewable Deployment
| dc.contributor.advisor | Shen, Suwan | |
| dc.contributor.author | Kovacevic, Elena | |
| dc.contributor.department | Oceanography | |
| dc.contributor.department | Global Environmental Science | |
| dc.date.accessioned | 2026-01-28T01:17:50Z | |
| dc.date.available | 2026-01-28T01:17:50Z | |
| dc.date.issued | 2025 | |
| dc.description.course | OCN 499 - Undergraduate Thesis | |
| dc.identifier.uri | https://hdl.handle.net/10125/112877 | |
| dc.publisher.place | Honolulu | |
| dc.subject | Artificial Intelligence | |
| dc.subject | AI | |
| dc.subject | Datacenters | |
| dc.subject | Hyperscale | |
| dc.subject | Renewable Energy | |
| dc.title | Unbalanced Growth: Data Center Expansion Outpacing Renewable Deployment | |
| dc.type | Thesis | |
| dcterms.abstract | Artificial Intelligence (AI) has captivated businesses and the public with readily available language learning models and integrations into critical sectors such as healthcare, customer service, and commerce. The servers that power these applications are housed in hyperscale data centers, alongside cloud computing and data storage. Hyperscale data centers have tremendous energy needs for sustaining such vast amounts of computing power. This study examines the geographic distribution of the most prominent hyperscale data center markets in Virginia, Texas, and Georgia in relation to renewable energy infrastructure. First, I quantified the maximum operational and planned megawatt capacities of both renewable energy assets and data centers to compare overall demand trajectories against available and upcoming clean energy supply. Through spatial analysis, I assessed the proximity between major data center clusters and renewable energy projects across multiple states. I also mapped data centers in relation to regional water stress, given the substantial cooling-related water demands of data centers. To contextualize these findings, I cross-referenced with state-specific policies on renewable energy goals and environmental governance. The findings reveal that while all three states promote data center investment, renewable energy growth is not keeping pace with the sector's rapidly increasing energy and water demands. Texas shows the highest projected expansion but limited environmental governance; Virginia has strong renewable mandates yet growing local resistance to further expansion; and Georgia’s incentive-heavy model prioritizes economic development over sustainability integration. Water use associated with data centers remains underreported, particularly in drought-prone regions. Overall, this research demonstrates that state policies are not regulating, but rather incentivizing, data center growth. This underscores the need for governance that links renewable development, water resilience, and local reinvestment to ensure that digital growth aligns with long term climate and resource sustainability. Additionally, this analysis underscores a broader need for transparent, standardized reporting on data center electricity and water consumption, as current gaps in public data limit accurate regional planning and policy evaluation. | |
| dcterms.extent | 52 pages | |
| dcterms.language | English | |
| dcterms.publisher | University of Hawaiʻi at Mānoa | |
| dcterms.rights | All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner. | |
| dcterms.type | Text |
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