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ORIGINAL RESEARCH article

Front. Sustain.
Sec. Modeling and Optimization for Decision Support
Volume 5 - 2024 | doi: 10.3389/frsus.2024.1507030

Forecasting US Data Center CO2 Emissions using AI Models: Emissions Reduction Strategies and Policy Recommendations

Provisionally accepted
Rohan Jha Rohan Jha 1*Rishabh Jha Rishabh Jha 2Mazhar Islam Mazhar Islam 3
  • 1 Cinco Ranch High School, Katy, United States
  • 2 Rodger and Ellen Beck Junior High School, Katy, Texas, United States
  • 3 Loyola University New Orleans, New Orleans, Louisiana, United States

The final, formatted version of the article will be published soon.

    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 CO2 emissions. To estimate CO2 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 CO2 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 CO2 emissions from the US power sector by 2030. Using the state-level power mix forecasts for 2030 among increasing CO2 emissions 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 CO2 emissions due to higher natural gas in their power mix in 2030. The highest variability in data center CO2 emissions stems from AI-driven demand and improvements in data center efficiency and is followed by the power mix. To reduce CO2 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 CO2 emissions. Finally, we highlight a set of federal policies supplemented by states to facilitate CO2 emission reductions across energy, emissions, waste, R&D, and grid infrastructure.

    Keywords: data center, AI, CO2 emissions, Renewable power, Solar, wind 1

    Received: 11 Oct 2024; Accepted: 18 Dec 2024.

    Copyright: © 2024 Jha, Jha and Islam. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Rohan Jha, Cinco Ranch High School, Katy, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.