The Unpaid Toll: Estimating and Addressing the Public Health Impact of Data Centers
For policymakers and data center operators, this work quantifies the uneven public health costs of AI-driven data center expansion and offers a framework to reduce them.
This paper models air pollutant emissions from U.S. data centers and estimates their public health burden, projecting it to exceed $20 billion annually by 2028, with per-household costs in the most affected counties reaching about seven times the national average. It proposes a health-informed computing framework to mitigate these impacts.
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.