The Unpaid Toll: Quantifying and Addressing the Public Health Impact of Data Centers

• URL: http://arxiv.org/abs/2412.06288v2
• Date: Thu, 23 Oct 2025 22:23:59 GMT
• Title: The Unpaid Toll: Quantifying and Addressing the Public Health Impact of Data Centers
• Authors: Yuelin Han, Zhifeng Wu, Pengfei Li, Adam Wierman, Shaolei Ren,
• Abstract summary: surging demand for AI has led to a rapid expansion of energy-intensive data centers.<n>While significant attention has been paid to data centers' growing environmental footprint, the public health burden has been largely overlooked.<n>This paper introduces a principled methodology to model lifecycle pollutant emissions for data centers and computing tasks.
• Score: 32.67029206382299
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The surging demand for AI has led to a rapid expansion of energy-intensive data centers, impacting the environment through escalating carbon emissions and water consumption. While significant attention has been paid to data centers' growing environmental footprint, the public health burden, a hidden toll of data centers, has been largely overlooked. Specifically, data centers' lifecycle, from chip manufacturing to operation, can significantly degrade air quality through emissions of criteria air pollutants such as fine particulate matter, substantially impacting public health. This paper introduces a principled methodology to model lifecycle pollutant emissions for data centers and computing tasks, quantifying the public health impacts. Our findings reveal that training a large AI model comparable to the Llama-3.1 scale can produce air pollutants equivalent to more than 10,000 round trips by car between Los Angeles and New York City. The growing demand for AI is projected to push the total annual public health burden of U.S. data centers up to more than $20 billion in 2028, rivaling that of on-road emissions of California. Further, the public health costs are more felt in disadvantaged communities, where the per-household health burden could be 200x more than that in less-impacted communities. Finally, we propose a health-informed computing framework that explicitly incorporates public health risk as a key metric for scheduling data center workloads across space and time, which can effectively mitigate adverse health impacts while advancing environmental sustainability. More broadly, we also recommend adopting a standard reporting protocol for the public health impacts of data centers and paying attention to all impacted communities.

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