AI Data Centers' Air Pollution Could Cost US $20 Billion in Public Health by 2030

US: Public health cost of AI data centers could exceed $20 billion

This issue, which is already affecting public health, is expected to worsen. By 2030, data centers in the US could contribute to 600,000 asthma cases and 1,300 premature deaths annually. This would account for more than a third of all asthma-related deaths in the country. According to Shaolei Ren from the University of California, Riverside, the effects of airborne pollution are widespread. "Public health impacts are direct and tangible impacts on people, and these impacts are substantial and not limited to a small radius of where data centers operate," Ren explains. Pollution from these facilities can travel long distances, meaning that the health impacts of increasing emissions will not be confined to the immediate area of the data centers but will affect people across the country. Ren and his colleague Adam Wierman at the California Institute of Technology calculated these alarming projections based on the energy demands of data centers. Many data centers in the US still rely on fossil fuels, which release harmful air pollutants. One example is the fine particulate matter, a known health hazard. According to their research, the electricity required to train a single large AI model could produce enough pollutants to be equivalent to driving a passenger car for over 10,000 roundtrips between Los Angeles and New York City.

Energy-hungry AI is already harming health - and it's getting worse

The electricity required to support Silicon Valley's AI ambitions could contribute to approximately 600,000 asthma cases and create a $20 billion public health burden by 2030 As data centres consume even more energy to serve the intensive computing needs of artificial intelligence, they could contribute to an estimated 600,000 asthma cases and 1300 premature deaths per year by 2030 - accounting for more than one third of asthma deaths annually in the US. "Public health impacts are direct and tangible impacts on people, and these impacts are substantial and not limited to a small radius of where data centres operate," says Shaolei Ren at the University of California, Riverside. "They affect people across the country." Ren and his colleagues, including Adam Wierman at the California Institute of Technology, developed those estimates based on data centres' projected electricity demand, which produces additional emissions and contributes to air pollution. For instance, the electricity usage required for training large AI models could produce air pollutants equivalent to driving a passenger car for more than 10,000 roundtrips between Los Angeles and New York City, according to the researchers. To model these air pollution and emissions impacts, the researchers used a tool provided by the US Environmental Protection Agency. They calculated that nationally, data centres will have an overall public health cost potentially exceeding $20 billion by 2030. That's approximately double the public health burden of the US steelmaking industry and possibly rivals the health impact of emissions from tens of millions of vehicles in the largest US states, such as California. Energy-hungry computing centres are already impacting public health. The researchers estimated that the gas-powered generators used as backup power for facilities in Virginia's Data Center Alley could already be causing 14,000 asthma symptom cases and imposing public health costs of $220 million to $300 million per year - if generator emissions are only at 10 per cent of the level permitted by state authorities. At the maximum permitted level, the total public health cost could multiply 10-fold to an estimated $2 billion or $3 billion per year. Such problems affect not only local residents, but also people in distant states such as Florida. "Technology companies [that operate] data centres cannot not be depended on to self-regulate and decide what's appropriate to report, as they have largely failed to include criteria air pollutants in their sustainability reports, despite their clear impact on public health," says Julie Bolthouse at the Piedmont Environmental Council, a nonprofit organisation in Virginia. Some of the tech companies racing to build data centres are also supporting low-emission energy sources, financing construction of renewable energy projects and investing in both conventional nuclear power plants and new nuclear reactor technologies. But for now, many data centres still heavily rely on fossil fuel power such as natural gas - with previous research suggesting that data centres could boost US demand for gas approximately equivalent to another New York State or California by 2030. "The question around the health impacts of artificial intelligence and data centre computing is an important one," says Benjamin Lee at the University of Pennsylvania. He described the paper as "the first to estimate these costs and quantify them in dollar terms" but also cautioned that the underlying approximations and assumptions behind the specific numbers need to be validated by additional research.

AI's power demands driving toxic air pollution, study finds

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. "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.