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 hypothesi...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Sustainability |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frsus.2024.1507030/full |
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| author | Rohan Jha Rishabh Jha Mazhar Islam |
| author_facet | Rohan Jha Rishabh Jha Mazhar Islam |
| author_sort | Rohan Jha |
| collection | DOAJ |
| description | 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 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 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. |
| format | Article |
| id | doaj-art-f96aa96a46d247d8af37922639587bba |
| institution | Kabale University |
| issn | 2673-4524 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Sustainability |
| spelling | doaj-art-f96aa96a46d247d8af37922639587bba2025-01-22T13:46:31ZengFrontiers Media S.A.Frontiers in Sustainability2673-45242025-01-01510.3389/frsus.2024.15070301507030Forecasting US data center CO2 emissions using AI models: emissions reduction strategies and policy recommendationsRohan Jha0Rishabh Jha1Mazhar Islam2Cinco Ranch High School, Katy, TX, United StatesRodger and Ellen Beck Junior High School, Katy, TX, United StatesCollege of Business, Loyola University New Orleans, New Orleans, LA, United StatesData 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 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 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.https://www.frontiersin.org/articles/10.3389/frsus.2024.1507030/fulldata centerAICO2 emissionsrenewable powersolarwind |
| spellingShingle | Rohan Jha Rishabh Jha Mazhar Islam Forecasting US data center CO2 emissions using AI models: emissions reduction strategies and policy recommendations Frontiers in Sustainability data center AI CO2 emissions renewable power solar wind |
| title | Forecasting US data center CO2 emissions using AI models: emissions reduction strategies and policy recommendations |
| title_full | Forecasting US data center CO2 emissions using AI models: emissions reduction strategies and policy recommendations |
| title_fullStr | Forecasting US data center CO2 emissions using AI models: emissions reduction strategies and policy recommendations |
| title_full_unstemmed | Forecasting US data center CO2 emissions using AI models: emissions reduction strategies and policy recommendations |
| title_short | Forecasting US data center CO2 emissions using AI models: emissions reduction strategies and policy recommendations |
| title_sort | forecasting us data center co2 emissions using ai models emissions reduction strategies and policy recommendations |
| topic | data center AI CO2 emissions renewable power solar wind |
| url | https://www.frontiersin.org/articles/10.3389/frsus.2024.1507030/full |
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