Computer processing demands for artificial intelligence, or AI, are spurring increasing levels of deadly air pollution from power plants and backup diesel generators that continuously supply electricity to the fast-growing number of computer processing centers.
This air pollution is expected to result in as many as 1,300 premature deaths a year by 2030 in the United States. Total public health costs from cancers, asthma, other diseases, and missed work and school days are approaching an estimated $20 billion a year.
Such are findings of a study by UC Riverside and Caltech scientists published online this week on the arXiv preprint server. Yet, these human and financial costs appear overlooked by the tech industry.
“If you look at those sustainability reports by tech companies, they only focus on carbon emissions, and some of them include water as well, but there’s absolutely no mention of unhealthful air pollutants and these pollutants are already creating a public health burden,” said Shaolei Ren, a UCR associate professor of electrical and computer engineering and a corresponding author of the study.
The authors, including Caltech professor and computer scientist Adam Wierman, recommend that standards and methods be adopted that require tech companies to report the air pollution caused by their power consumption and backup generators.
They further recommend that communities hit hardest by air pollution from data processing center electricity production be properly compensated by the tech companies for the health burden.
The authors also found that air pollution stemming from AI disproportionally affects certain low-income communities, partly because of their proximity to power plants or backup generators at the data processing centers. Additionally, the pollution drifts across county and state lines, creating health impacts on communities far and wide, Ren said.
“The data centers pay local property taxes to the county where they operate,” Ren said. “But this health impact is not just limited to a small community. Actually, it travels across the whole country, so those other places are not compensated at all.”
For example, pollution from backup generators at data centers in Northern Virginia drifts into Maryland, West Virginia, Pennsylvania, New York, New Jersey, Delaware, and the District of Columbia, creating regional public health costs of some $190 million to $260 million a year. If these backup generators emit at their maximum permitted level, the annual cost will become 10-fold and reach $1.9 billion to $2.6 billion.
In some areas, the public health cost associated with AI processing centers exceeds what the tech companies pay for electricity, the study shows.
As tech companies race to provide AI services that are reshaping how we work and play, the resulting air pollution in the form of lung-penetrating fine particles—those smaller than 2.5 micrometers—and other federally regulated pollutants, such as nitrogen oxides, is expected to steeply increase. The public health burden by 2030 is expected to be double that of the U.S. steel-making industry and rival that of all the cars, buses and trucks in California, the study projects.
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“The growth of AI is driving an enormous increase in demand for data centers and energy, making it the fastest-growing sector for energy consumption across all industries,” Ren said.
As an example, Ren and his colleagues calculated the emissions from training a large language model, or LLM, at the scale of Meta’s Llama-3.1, an advanced open-weight LLM released by the owner of Facebook in July to compete with leading proprietary models like OpenAI’s GPT-4. The study found that producing the electricity to train this model produced an air pollution equivalent of more than 10,000 round trips by car between Los Angeles and New York City.
The authors estimate the health costs, including premature deaths, with statistical methods developed by U.S. Environmental Protection Agency, which accounts for known epidemiological risks associated with air pollution from power plants and backup diesel generators. The 1,300 expected annual deaths by 2030 is the midpoint of a range between 940 and 1,590.
“If you have family members with asthma or other health conditions, the air pollution from these data centers could be affecting them right now. It’s a public health issue we need to address urgently,” Ren said.
In addition to Ren and Wierman, the paper authors are also Yuelin Han, Zhifeng Wu, and Pengfei Li all three with UCR’s Bourn’s College of Engineering. The paper follows Ren’s team’s previous research that revealed AI’s water consumption footprint.
More information:
Yuelin Han et al, The Unpaid Toll: Quantifying the Public Health Impact of AI, arXiv (2024). DOI: 10.48550/arxiv.2412.06288
Provided by
University of California – Riverside
Citation:
AI’s power demands driving toxic air pollution, study finds (2024, December 10)
