Cornell Study Reveals AI’s Hidden Carbon and Water Costs and How To Fix Them

Cornell researchers quantify AI’s environmental footprint

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.

Roadmap for sustainable AI infrastructure

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.

Study published in Nature Sustainability

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

Understanding AI’s growing environmental cost

"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?"

Comprehensive data analysis across the U.S. infrastructure

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."

Strategies for sustainable AI expansion

But projecting the impacts wasn't enough. The researchers also wanted to provide data-driven guidance for the 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."

Location plays a critical role in sustainability

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

Many current data clusters are being constructed in water-scarce regions, such as Nevada and Arizona. In some hubs, such as Northern Virginia, rapid clustering can strain local infrastructure and water resources. Locating facilities in regions with lower water stress and improving cooling efficiency could reduce water demands by approximately 52%. When combined with grid and operational best practices, total water reductions could reach 86%, the study found. The Midwest and "windbelt" states – notably Texas, Montana, Nebraska, and South Dakota – would deliver the best combined carbon and water profile.

Regional opportunities for low-carbon computing

"New York state remains a low-carbon, climate-friendly option thanks to its clean electricity mix of nuclear, hydropower and growing renewables," You said, "although prioritizing water-efficient cooling and additional clean power is key."

If decarbonization does not catch up with the computing demand, emissions could rise roughly 20%.

"Even if each kilowatt-hour gets cleaner, total emissions can rise if AI demand grows faster than the grid decarbonizes," You said. "The solution is to accelerate the clean-energy transition in the same places where AI computing is expanding."

Limits of decarbonization and efficiency improvements

However, decarbonizing the grid can only do so much. Even in the ambitious high-renewables scenario, by 2030, carbon dioxide would drop roughly 15% compared to the baseline, and approximately 11 million tons of residual emissions would remain, requiring approximately 28 gigawatts of wind or 43 gigawatts of solar capacity to reach net-zero.

The researchers determined that deploying an array of energy- and water-efficient technologies, such as advanced liquid cooling and improved server utilization, could potentially reduce carbon dioxide emissions by an additional 7% and lower water use by 29%, resulting in a total water reduction of 32% when combined.

Coordinated action needed for sustainable AI

As companies such as OpenAI and Google funnel more money into rapidly building AI data centers to keep up with demand, this is a pivotal moment for coordinated planning between industry, utilities, and regulators to avoid local water scarcity and higher grid emissions, according to You.

"This is the build-out moment," he said. "The AI infrastructure choices we make this decade will decide whether AI accelerates climate progress or becomes a new environmental burden."

Global collaboration and funding support

Co-authors include researchers from the KTH Royal Institute of Technology in Stockholm, Sweden; Concordia University in Montreal, Canada; and RFF-CMCC European Institute on Economics and the Environment in Milan, Italy.

The research was supported by the National Science Foundation and the Eric and Wendy Schmidt AI in Science program.