Forecasting US data center CO2 emissions using AI models: emissions reduction strategies and policy recommendations

Data centers are poised for unprecedented growth due to a revolution in Artificial Intelligence (AI), rise in cryptocurrency mining, and increasing cloud demand for data storage. A sizable portion of the data centers’ growth will occur in the US, requiring a tremendous amount of power. Our hypothesis is that the expansion of data centers will contribute to an increase in US CO 2 emissions. To estimate CO 2 emissions, we applied three forecasted power demands for data centers and applied 56 NREL (National Renewable Energy Laboratory) power mixes and policy scenario cases using 11 AI models. Among these, the linear regression model yielded the most accurate predictions with the highest R-square. We found that overall CO 2 emissions in the US could increase up to 0.4–1.9% due to expansion of data centers by 2030. This increase represents ~3–14% of CO 2 emissions from the US power sector by 2030. Using the state-level power mix forecasts for 2030 among increasing CO 2 emission scenarios, we predict that Virginia’s power mix will maintain emissions in line with the US average, while the Texas, Illinois, and Washington’s power mix are expected to reduce emissions due to greater renewables in their power mix in 2030. However, Illinois and Washington may face challenges due to their limited power resource availability. In contrast, New York and California’s power mix may increase CO 2 emissions due to higher natural gas in their power mix in 2030. The highest variability in data center CO 2 emissions stems from AI-driven demand and improvements in data center efficiency and is followed by the power mix. To reduce CO 2 emissions from data centers, we offer pathways such as reducing power consumption, improving power mix with renewable sources, and using hydrogen in power plants. We propose focusing on New Mexico and Colorado for data centers to minimize CO 2 emissions. Finally, we highlight a set of federal policies supplemented by states to facilitate CO 2 emission reductions across energy, emissions, waste, R&D, and grid infrastructure.

Authors

Publication Details

Journal
Frontiers in Sustainability
Published
2025-01-09
DOI
https://doi.org/10.3389/frsus.2024.1507030
Citations
16
Primary Topic
Vehicle emissions and performance
Type
article
Field-Weighted Citation Impact
10.93
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article

Forecasting US data center CO2 emissions using AI models: emissions reduction strategies and policy recommendations

Mazhar Islam, Rohan Jha, Rishabh Jha
16 citations
Frontiers in Sustainability
Vehicle emissions and performance
10.93
article

Forecasting US data center CO2 emissions using AI models: emissions reduction strategies and policy recommendations

Mazhar Islam, Rohan Jha, Rishabh Jha
article en
16 citations

Abstract

Data centers are poised for unprecedented growth due to a revolution in Artificial Intelligence (AI), rise in cryptocurrency mining, and increasing cloud demand for data storage. A sizable portion of the data centers’ growth will occur in the US, requiring a tremendous amount of power. Our hypothesis is that the expansion of data centers will contribute to an increase in US CO 2 emissions. To estimate CO 2 emissions, we applied three forecasted power demands for data centers and applied 56 NREL (National Renewable Energy Laboratory) power mixes and policy scenario cases using 11 AI models. Among these, the linear regression model yielded the most accurate predictions with the highest R-square. We found that overall CO 2 emissions in the US could increase up to 0.4–1.9% due to expansion of data centers by 2030. This increase represents ~3–14% of CO 2 emissions from the US power sector by 2030. Using the state-level power mix forecasts for 2030 among increasing CO 2 emission scenarios, we predict that Virginia’s power mix will maintain emissions in line with the US average, while the Texas, Illinois, and Washington’s power mix are expected to reduce emissions due to greater renewables in their power mix in 2030. However, Illinois and Washington may face challenges due to their limited power resource availability. In contrast, New York and California’s power mix may increase CO 2 emissions due to higher natural gas in their power mix in 2030. The highest variability in data center CO 2 emissions stems from AI-driven demand and improvements in data center efficiency and is followed by the power mix. To reduce CO 2 emissions from data centers, we offer pathways such as reducing power consumption, improving power mix with renewable sources, and using hydrogen in power plants. We propose focusing on New Mexico and Colorado for data centers to minimize CO 2 emissions. Finally, we highlight a set of federal policies supplemented by states to facilitate CO 2 emission reductions across energy, emissions, waste, R&D, and grid infrastructure.

Frontiers in SustainabilityVol. 5
Affordable and clean energy
Openalex Percentile: Top 1%
Vehicle emissions and performance
10.93
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Forecasting US data center CO2 emissions using AI models: emissions reduction strategies and policy recommendations — Mazhar Islam, Rohan Jha, et al. · Frontiers in Sustainability (2025) | TGRS Research Map | TGRS