Congress finds the following:
(1)
Multiple estimates indicate that the amount of computational power being used for artificial intelligence applications has increased rapidly over the last decade.
(2)
According to the Department of Energy, data center energy demand has tripled in the last decade and is expected to double or triple again by 2028.
(3)
Accelerating use of artificial intelligence greatly increases energy consumption due to the power utilization of computer hardware required for training and operating artificial intelligence models, despite ongoing efficiency gains in both artificial intelligence models and hardware.
(4)
Rapid growth in data center infrastructure supporting artificial intelligence and other computing-intensive technologies, including cooling systems and backup power equipment, can contribute to air and water pollution, increased energy demand, increasing water scarcity, and land-use changes.
(5)
According to the Department of Energy, hyperscale facilities are projected to consume between 16,000,000,000 and 33,000,000,000 gallons annually by 2028.
(6)
Resource and energy-intensive manufacturing processes are required for the hardware that runs artificial intelligence and other computing-intensive technologies, leading to significant environmental impacts.
(7)
Electricity prices have already risen significantly as demand from data centers grows, and are projected to continue rising rapidly.
(8)
According to the Energy Information Agency, between January, 2025, and December, 2025, household electricity prices increased by as much as 13 percent nationwide.
(9)
Prices are projected to rise by another 25 percent in certain places over the next five years due to increased demand from data centers.
(10)
Yearly increases in electronic waste (known as “e-waste”) pose environmental and health risks and will likely be exacerbated by outdated and discarded hardware used for artificial intelligence and other computing-intensive technologies.
(11)
Certain applications of artificial intelligence may have direct and indirect positive environmental impacts, including optimizing systems for energy efficiency, developing renewable energy, advancing planetary systems research, enabling discovery of new materials, and automatically monitoring environmental changes. Applications of artificial intelligence also have direct and indirect negative environmental impacts, including rebound effects, behavioral impacts, and accelerating high-pollution activities.
(12)
Different communities and regions will experience disparate effects from data center infrastructure, with risks ranging from higher energy costs to more adverse environmental effects, and with certain communities at greater risk from cumulative negative impacts, such as low-income communities, Black and Brown communities, Indigenous communities, and rural communities.
(13)
Various options exist to reduce the negative environmental impacts of artificial intelligence, including using more energy-efficient and water-efficient models, hardware, and data centers, using renewable and co-located energy, and examining the impacts of artificial intelligence applications.
(14)
Promoting transparency on energy use and environmental impacts and developing and maintaining accurate environmental impact metrics may help mitigate negative environmental impacts of the rapid growth in artificial intelligence use, while promoting artificial intelligence uses with net positive environmental impacts.