Most people have heard about the environmental impact of today’s AI boom, stemming from sprawling data centers packed with power-hungry servers. In the United States alone, the demand for AI is projected to push data center electricity consumption to 6.7 to 12.0 percent of the nation’s total by 2028. By that same date, water consumption for cooling these data center facilities is predicted to double, or even quadruple, compared to the 2023 level.
But many people haven’t made the connection between data centers and public health. The power plants and backup generators needed to keep data centers working generate harmful air pollutants, such as fine particulate matter and nitrogen oxides (NOx). These pollutants take an immediate toll on human health, triggering asthma symptoms, heart attacks, and even cognitive decline.
But AI’s contribution to air pollution and the public health burden is often missing from conversations about responsible AI design. Why?
Because ambient air pollution is a “silent killer.” While concerns about the public health impacts of data centers, including potential links to cancer rate increases, are beginning to surface, most AI model developers, practitioners, and users simply aren’t aware of the serious health risks tied to the energy and infrastructure powering modern AI systems.
The danger of ambient air pollution
Ambient air pollution is responsible for approximately 4 million premature deaths worldwide each year. The biggest culprit are tiny particles 2.5 micrometers or less in diameter (referred to as PM 2.5), which can travel deep into the respiratory tract and lungs. Along with high blood pressure, smoking, and high blood sugar, air pollution is a leading health risk factor. The World Bank estimates the global cost of air pollution at US $8.1 trillion, equivalent to 6.1 percent of global gross domestic product.
Contrary to common belief, air pollutants don’t stay near their emission sources: They can travel hundreds of miles. Moreover, PM 2.5 is considered a “non-threshold” pollutant, meaning that there’s no safe level of exposure.
With the danger of this pollution well established, the question becomes: How much is AI responsible for? In our research, we’ve set out to answer that question.
Quantifying the public health cost of AI
To ensure that AI services are available even during grid outages, data centers rely on large sets of backup generators that usually burn diesel fuel. While the total operation time of backup generators is limited and regulated by local environmental agencies, their emission rates are high. A typical diesel generator can release 200 to 600 times more NOx than a natural gas power plant producing the same amount of electricity.
A recent report by the state of Virginia revealed that backup generators at Virginia’s data centers emitted about 7 percent of what permits allowed in 2023. According to the U.S. Environmental Protection Agency’s COBRA modeling tool, which maps how air pollution affects human health at the local, state, and federal levels, the public health cost of those emissions in Virginia is estimated at $150 million, affecting communities as far away as Florida. Imagine the impact if data centers maxed out their permitted emissions.
Further compounding the public health risk, a large set of data center generators in a region may operate simultaneously during grid outages or grid shortages as part of demand-response programs, potentially triggering short-term spikes in PM2.5 and NOx emissions that are especially harmful to people with lung problems.
Next, let’s look beyond the backup generators to the supply of energy from the grid. The bulk of the electricity powering AI data centers comes from power plants that burn fossil fuels, which release harmful air pollutants, including PM 2.5 and NOx. Despite years of progress, power plants remain a leading source of air pollution in the United States.
We calculated that training a single large generative AI model in the United States, such as Meta’s Llama 3.1, can produce as much PM 2.5 as more than 10,000 round-trips by car between Los Angeles and New York City.
According to our research, in 2023, air pollution attributed to U.S. data centers was responsible for an estimated $6 billion in public health damages. If the current AI growth trend continues, this number is projected to reach $10 to $20 billion per year by 2030, rivaling the impact of emissions from California’s 30 million vehicles.
Why carbon and energy efficiency aren’t the whole story
To date, efforts to mitigate AI’s environmental footprint have focused mostly on carbon emissions and energy efficiency. These efforts are important, but they may not alleviate health impacts, which strongly depend on where the emissions occur.
Carbon anywhere is carbon everywhere. The climate impact of carbon dioxide is largely the same no matter where it’s emitted. But the health impact of air pollution depends heavily on regional factors such as local sources of energy, wind patterns, weather, and population density.
Even though carbon emissions and health-damaging air pollutants have some shared sources, an exclusive focus on cutting carbon does not necessarily reduce, and could even exacerbate, public health risks. For instance, our latest (and unpublished) research has shown that redistributing Meta’s energy loads in 2023 across its U.S. data centers to prioritize carbon reductions could potentially lower overall carbon emissions by 7.2 percent, but would increase public health costs by 2.8 percent.
Likewise, focusing solely on energy efficiency can reduce air pollutant emissions, but doesn’t guarantee a decrease in health impact. That’s because training the same AI model using the same amount of energy can yield vastly different health outcomes depending on the location. Across Meta’s U.S. data centers, we’ve found that the public health cost of training the same model can vary by more than a factor of 10.
We need health-informed AI
Supply-side solutions, such as using alternative fuels for backup generators and sourcing electricity from clean fuels, can reduce AI’s public health impact, but they come with significant challenges.
Clean backup generators that offer the same level of reliability as diesel are still limited. And despite advancements in renewable energy, fossil fuels remain deeply embedded in the energy fuel mix. The U.S. Energy Information Administration projects that coal-based electricity generation in 2050 will remain at approximately 30 percent of the 2024 level under the alternative electricity scenario, in which power plants continue operating under rules existing prior to April 2024. Globally, the share of coal and other fossil fuels in electricity generation has remained nearly flat over the past four decades, underscoring the difficulty of entirely changing the energy supply that powers data centers.
We believe that demand-side strategies that consider the spatial and temporal variations in health impacts can provide effective and actionable solutions immediately. These strategies are particularly well-suited for AI data centers with substantial operational flexibility. For example, AI training can often run at any available data centers and typically do not face hard deadlines, so those jobs can be routed to locations or deferred to times that have less impact on public health. Similarly, inference jobs can be routed among multiple data centers without affecting user experience.
By incorporating public health impact as a key performance metric, these flexibilities can be harnessed to reduce AI’s growing health burden. Crucially, this health-informed approach to AI requires minimal changes to existing systems. Companies simply need to consider public health costs when making decisions.
While the public health cost of AI is growing rapidly, AI also holds tremendous promise for advancing public health. For example, within the energy sector, AI can navigate the complex decision space of real-time power plant dispatch. By aligning grid stability with public health objectives, AI can help minimize health costs while maintaining a reliable power supply.
AI is rapidly becoming a public utility and will continue to reshape society profoundly. As such, we must examine AI through a public lens, with its public health impact as a critical consideration. If we continue to overlook it, the public health cost of AI will only grow. Health-informed AI offers a clear path forward for advancing AI while promoting cleaner air and healthier communities.
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