Study Finds Carbon Capture and Storage Could Help Curb Emissions From AI-Driven Data Center Boom

As artificial intelligence accelerates demand for computing power across the U.S., a new study co-authored by Hon Chung Lau, adjunct professor in the Department of Chemical and Biomolecular Engineering at Rice University and founder of Low Carbon Energies LLC, has found that carbon capture and storage could play a major role in limiting the climate impact of data centers.

Published in Energy & Fuels, the study estimates that U.S. data center power capacity could grow from 40 gigawatts in 2025 to 169 gigawatts by 2030 — a more than fourfold increase in just five years. Without new strategies to manage emissions, the carbon dioxide produced by fossil fuel power plants supplying electricity to data centers could rise from about 90 million metric tons per year in 2025 to more than 404 million metric tons per year by 2030.

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The AI Energy Challenge

“Data centers are becoming one of the defining energy challenges of the AI era,” Lau said. “The question is not only whether we can build enough computing infrastructure, but whether we can power it in a way that is reliable, affordable and compatible with decarbonization goals.”

Hon Chung Lau, adjunct professor in the Department of Chemical and Biomolecular Engineering at Rice University and founder of Low Carbon Energies LLC.

Assessing Data Center Emissions and Storage

The study, conducted by Lau and Steve C. Tsai, an energy transition consultant at Low Carbon Energies LLC, analyzed publicly available data on announced U.S. data centers, including projected power capacity, energy sources and locations. They then estimated data center-related carbon emissions based on each state’s electricity mix and examined whether those emissions could be captured and stored underground in saline aquifers. Their findings point to rapid growth in states including Texas, Virginia, Pennsylvania, Ohio, Arizona, Colorado, Utah and Illinois. They estimated that Texas alone would need to add approximately 25 gigawatts of power capacity by 2030 to meet projected data center demand.

Practical Pathways for Low-Carbon Power

Because data centers require highly reliable, around-the-clock electricity, Lau and Tsai found that natural gas combined cycle power plants equipped with carbon capture and storage may offer one of the most practical near-term pathways for providing low-carbon power. Natural gas is abundant in the U.S., gas-fired plants emit less carbon dioxide than coal-fired plants and many major data center growth areas are located near underground saline aquifers that could be used for long-term carbon storage.

Carbon Mitigation Potential

The study found that 34 states have enough saline aquifer storage capacity to store more than 100 years of projected data center-related carbon dioxide emissions beyond 2030. In 2025, those aquifers could store an estimated 59 million metric tons of data center-related carbon dioxide, or about 66% of the sector’s emissions. By 2030, that number could grow to 299 million metric tons, or about 74% of projected data center-related emissions.

Steve C. Tsai, an energy transition consultant at Low Carbon Energies LLC.

When out-of-state storage options are included, the researchers found that more than 90% of data center-related carbon dioxide emissions could potentially be mitigated through carbon capture and storage.

“This does not mean carbon capture is the only solution,” Lau said. “But it does show that the geology exists to make a meaningful impact, especially in states where data center growth is strongest.”

Methodology and Limitations

The authors noted that their estimates are conservative because they included only data centers with publicly announced power requirements. In addition, they also assumed that when companies did not specify a power source, electricity would come from the state grid and that each state’s energy mix would remain constant through 2030.

Even with those limitations, Lau said the results offer a useful state-by-state framework for balancing digital infrastructure growth with climate goals.

“The AI economy will require enormous amounts of energy,” Lau said. “Our study helps identify where that demand is growing, where emissions are likely to rise and where carbon storage could help.”