As the everyday use of AI has exploded in recent years, so have the energy demands of the computing infrastructure that supports it. But the environmental toll of these large data centers, which suck up gigawatts of power and require vast amounts of water for cooling, has been too diffuse and difficult to quantify.

Now, Cornell researchers have used advanced data analytics – and, naturally, some AI, too – to create a state-by-state look at that environmental impact. The team found that, by 2030, the current rate of AI growth would annually put 24 to 44 million metric tons of carbon dioxide into the atmosphere, the emissions equivalent of adding 5 to 10 million cars to U.S. roadways. It would also drain 731 to 1,125 million cubic meters of water per year – equal to the annual household water usage of 6 to 10 million Americans. The cumulative effect would put the AI industry’s net-zero emissions targets out of reach.

On the upside, the study also outlines an actionable roadmap that would use smart siting, faster grid decarbonization and operational efficiency to cut these impacts by approximately 73% (carbon dioxide) and 86% (water) compared with worst-case scenarios.

The findings were published Nov. 10 in Nature Sustainability. The first author is doctoral student Tianqi Xiao in the Process-Energy-Environmental Systems Engineering (PEESE) lab.

“Artificial intelligence is changing every sector of society, but its rapid growth comes with a real footprint in energy, water and carbon,” said Fengqi You, the Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering in Cornell Engineering, who led the project. “Our study is built to answer a simple question: Given the magnitude of the AI computing boom, what environmental trajectory will it take? And more importantly, what choices steer it toward sustainability?”

In order to quantify the environmental footprints of the nation’s AI computing infrastructure, the team began three years ago to compile “multiple dimensions” of financial, marketing and manufacturing data to understand how the industry is expanding, combined with location-specific data on power systems and resource consumption, and how they connect with changes in climate.

“There’s a lot of data, and that’s a huge effort. Sustainability information, like energy, water, climate, tend to be open and public. But industrial data is hard, because not every company is reporting everything,” You said. “And of course, eventually, we still need to be looking at multiple scenarios. There’s no way that one size fits all. Every region is different for regulations. We used AI to fill some of the data gap as well.”

But projecting the impacts wasn’t enough. The researchers also wanted to provide data-driven guidance for sustainable growth of AI infrastructure.

“There isn’t a silver bullet,” You said. “Siting, grid decarbonization and efficient operations work together – that’s how you get reductions on the order of roughly 73% for carbon and 86% for water.”

By far, one of the most important factors: location, location, location.