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arXiv · 2412.06288

Health-Informed Computing: Estimating and Addressing the Public Health Impact of Data Centers

Yuelin Han·Zhifeng Wu·Pengfei Li·Adam Wierman·Shaolei Ren
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Abstract

The surging demand for artificial intelligence (AI) has led to a rapid expansion of energy-intensive data centers, contributing to criteria air pollutant emissions and raising public health concerns that have received comparatively limited attention in sustainability assessments. This paper introduces a principled methodology to model air pollutant emissions for data centers and estimate the public health impacts. Our findings reveal that the growing demand for AI and computing technologies is projected to push the total annual public health burden of U.S. data centers up to more than $20 billion in 2028. Although national-level impacts remain modest, data center health costs are unevenly distributed: in the most affected counties, the estimated per-household health burden can reach about seven times the national average. Next, we propose a health-informed computing framework that explicitly incorporates public health impacts into data center resource management across space and time, mitigating public health costs while supporting environmental sustainability. More broadly, we recommend extended energy reporting to include public health impact of data centers and paying attention to all impacted communities.

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Yuelin Han, Zhifeng Wu, Pengfei Li, Adam Wierman, Shaolei Ren. 2026-06-08. Health-Informed Computing: Estimating and Addressing the Public Health Impact of Data Centers. https://arxiv.org/abs/2412.06288

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