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AI data centers increase electricity demand and can increase both water consumption and greenhouse-gas emissions. Their effect on electricity prices is conditional, not automatic: prices depend on whether the local system has spare capacity, what new generation and grid investment is required, and how those costs are assigned. Water and emissions also depend heavily on cooling technology, climate, server efficiency, utilization, and the electricity mix. National and global estimates provide scale, but they cannot by themselves show what a particular facility will do to a household bill, watershed, or local emissions inventory.
Why AI data centers matter to the power system
A data center’s electricity load includes servers, storage and networking equipment, cooling, power conditioning, lighting, pumps, and other building systems. AI workloads are accelerating demand because training and serving large models generally require more high-performance computing than many conventional applications. Yet most published totals are for all data centers, not AI alone. That distinction matters whenever a statistic is presented as an “AI” number.
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Data centers are also geographically concentrated. A national electricity total can look manageable while one utility territory faces a very large, rapidly developing load. The local outcome depends on the facility’s size, operating schedule, connection point, and the capacity of nearby generation and transmission.
How much electricity data centers use
Global scale
The International Energy Agency (IEA) estimates that all data centers used about 415 terawatt-hours (TWh) of electricity in 2024, approximately 1.5% of global electricity consumption. In its 2030 Base Case, global data-center use reaches about 945 TWh. These are scenarios for the entire data-center sector; AI is a major growth driver, but it is not the whole category.
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In the IEA analysis, accelerated servers—equipment primarily associated with AI adoption—grow faster than conventional servers and account for almost half of net global data-center electricity growth through 2030. The result is a rapidly rising sectoral load even though data centers remain a minority of worldwide electricity use.
United States outlook
The latest cited U.S. projection is Lawrence Berkeley National Laboratory’s (LBNL) 2025 update, published in June 2026. Its bottom-up reference case estimates 649 TWh of data-center electricity use in 2030, equal to 11.8% of projected U.S. electricity consumption. Across the modeled scenarios, the share ranges from 9.5% to 15.3%; compounded uncertainty bounds span 521 to 843 TWh.
| Estimate | Value | What it covers |
|---|---|---|
| Global data-center electricity, 2024 | 415 TWh (about 1.5% of global electricity) | Observed estimate for all data-center workloads |
| Global data-center electricity, 2030 | 945 TWh | IEA Base Case projection for all workloads |
| U.S. data-center electricity, 2030 reference case | 649 TWh; 11.8% of U.S. electricity | LBNL 2025 update, a modeled outcome |
| U.S. 2030 scenario range | 9.5%–15.3% of U.S. electricity | LBNL estimate range, not a probability distribution |
| U.S. compounded uncertainty bounds | 521–843 TWh | LBNL model bounds around the 2030 estimate |
LBNL’s model combines equipment-shipment data, device electricity use, cooling-performance simulations, facility types, and locations. Its range reflects uncertainty about AI adoption, equipment shipments, server efficiency and utilization, cooling performance, supply-chain constraints, and how quickly power infrastructure can be built. A scenario is not an observed result, and the endpoints are not statements that every outcome is equally likely.
Do AI data centers raise electricity prices?
There is no guaranteed direction or amount of price change. As the IEA puts it, “The impact of electricity demand growth on electricity prices depends on a combination of fundamental factors and policy choices.” The same data-center load can have different effects in two power systems.
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When prices can rise
If a utility or wholesale market is already close to its supply limit, a large new customer can require additional generation, transmission, substations, or distribution upgrades. Construction, financing, fuel, and balancing costs then have to be recovered through some combination of the data center’s service agreement, special tariffs, wholesale prices, or broader regulated rates. If costs are spread across customers rather than assigned to the new load, other bills can be affected; the size and timing depend on regulatory decisions.
Large projects can also create a planning risk. A facility may initially request more interconnection capacity than it uses while servers are brought online in stages. If a utility builds assets for the requested capacity and the load arrives late, remains smaller than expected, or is canceled, those investments may be underused.
When prices can stay flat or fall
Where a system has surplus generation or transmission capacity, a predictable data-center load can improve utilization of capital-intensive assets. Higher utilization can spread fixed costs over more kilowatt-hours and may reduce average costs, although the outcome still depends on market rules and the investments required to connect the facility. A steady load can be easier to plan for than highly variable demand, while a flexible load that can adjust timing may reduce stress during constrained hours.
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Who pays is a tariff and regulatory question
Utility tariffs, interconnection agreements, minimum-demand commitments, exit fees, and regulator-approved cost-allocation rules determine how risk is shared. LBNL’s 2026 rate-design work examines ways to protect existing customers from insufficient supply, stranded infrastructure, or underused investments. Those mechanisms can include requirements for financial security, staged construction, or charges tied to reserved capacity, but the applicable rules differ by jurisdiction.
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Available evidence explains these mechanisms; it does not establish a nationwide bill increase attributable solely to AI data centers or a causal increase for a particular household. A locality-specific claim requires that territory’s utility filings, rate cases, regulator orders, or a credible empirical study. National forecasts cannot substitute for that evidence.
How data-center water use is counted
“Water use” has two separate pathways. Direct consumption occurs at the facility, commonly when cooling systems evaporate water or when water is used for other building operations. Indirect consumption occurs at power plants that generate the electricity the facility buys. Consumption means water removed from the immediate water cycle—for example, evaporation—not simply water withdrawn and later returned. The two pathways should not be added together or compared as if they were the same measurement.
U.S. quantities and accounting boundaries
| Pathway | Estimate | Year and qualification |
|---|---|---|
| Direct, on-site data-center consumption | About 66 billion liters | United States, 2023; LBNL estimate |
| Direct consumption projection | About 145–275 billion liters | United States, 2028 projection; LBNL range |
| Indirect consumption at electricity generators | Nearly 800 billion liters | United States, 2023; associated with data-center electricity |
The nearly 800-billion-liter figure is not a larger estimate of the same thing as the 66-billion-liter figure. It represents a different impact pathway and depends on the water intensity of the regional electricity supply. Combining them without labeling the pathways produces a misleading total.
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For the U.S. data-center electricity mix in 2023, LBNL estimated an average indirect water-consumption intensity of 4.52 liters per kilowatt-hour and an emissions intensity of 0.34 kilograms of CO2-equivalent per kilowatt-hour. Both vary among balancing authorities. LBNL’s method assigns impacts using regional generation and interchange; it does not incorporate an individual facility’s power-purchase agreement or behind-the-meter generation. A contract for renewable electricity therefore does not automatically prove that the physical grid’s water or emissions impacts disappear.
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Why one data center can use far more water than another
LBNL’s 2025 review found more than a 10,000-fold variation in workload-level water use across the evaluated conditions. That spread is a warning against quoting a universal “water per AI prompt” number. The result depends on a combination of factors:
- Cooling system: Evaporative, air-based, liquid, and hybrid designs have different on-site water requirements and efficiency profiles.
- Climate zone: Ambient temperature and humidity change the amount of cooling energy and water needed.
- Server efficiency and utilization: More efficient hardware and higher utilization can deliver more computation per kilowatt-hour, while idle equipment still has an environmental footprint.
- Inactive-server share and refresh cycle: Hardware that is powered but lightly used, and the frequency with which servers are replaced, affect total resource use.
- Infrastructure efficiency: Power and cooling overhead outside the servers changes both electricity demand and associated water use.
- Grid water intensity: The fuel, cooling technology, efficiency, and location of power plants determine indirect consumption.
There is no single cooling design that minimizes every impact in every place. A water-scarce region may prioritize low direct consumption even if that requires more electricity; a region with abundant water but carbon-intensive power may face the opposite trade-off. A meaningful comparison must state the site, climate, cooling system, workload, utilization, and whether indirect power-plant consumption is included.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How AI data centers affect emissions
Current estimates
LBNL estimates that U.S. data-center electricity use was associated with 61 billion kilograms of CO2-equivalent emissions in 2023. The IEA estimates around 180 million metric tons (Mt) of indirect CO2 emissions from global data-center electricity use in 2024, excluding backup generation. The IEA places that at about 0.5% of global fuel-combustion emissions at that time. The U.S. and global numbers cover different geographies and years and should not be combined.
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Electricity demand does not have one fixed emissions factor. A kilowatt-hour supplied by a region with substantial fossil generation has a different footprint from one supplied by a region with low-carbon generation, and the marginal generator during a constrained hour may differ from the annual average. Location, transmission congestion, time of use, and backup operation all matter.
In the IEA Base Case, renewables meet nearly half of additional global data-center electricity demand through 2030. Natural gas and coal also contribute, while nuclear generation grows later in the outlook. In the higher-demand Lift-Off Case, long grid-connection queues mean that more demand beyond 2030 is met by fossil fuels. These are scenario findings, not certainties.
The IEA’s Base Case has electricity-related data-center emissions peaking at around 320 Mt of CO2 by 2030 and then declining gradually to about 300 Mt by 2035. The projected peak reflects faster growth in lower-emissions generation, not a decline in data-center electricity use.
How to assess a proposed facility or expansion
When a company or local authority announces a large AI data center, use the following sequence rather than relying on a single headline number.
- Define the boundary and date. Ask whether the figure covers AI servers only or all facility workloads, and whether it is measured electricity, a forecast, direct water, indirect water, operational emissions, or a life-cycle estimate.
- Obtain the load profile. Request expected annual TWh, maximum megawatts, ramp-up schedule, operating hours, and any flexibility or curtailment commitments. Peak demand can drive infrastructure needs even when annual energy is moderate.
- Identify the supply and grid work. Determine which balancing authority and generation mix serve the site, and whether new substations, transmission, generation, or storage are required.
- Read the cost-allocation documents. Check the tariff, interconnection agreement, minimum-demand obligations, security payments, and rules for canceled or delayed capacity. These documents show who bears construction and under-utilization risk.
- Separate direct and indirect water. Obtain annual withdrawals and consumption for cooling, identify the source and watershed, and calculate electricity-related consumption using the supplying region’s generation mix. Do not add unlike accounting categories without labeling them.
- Match emissions to the same boundary. State whether the estimate includes backup generators, construction, fuel production, or only purchased electricity. Use the local or time-specific grid factor when available instead of applying a global average.
- Test alternatives. Compare cooling systems, server-efficiency assumptions, utilization, climate conditions, renewable and fossil supply scenarios, and delayed or flexible load cases. A range is more informative than one point estimate.
Common interpretation errors
- Calling a global all-data-center forecast an AI-only measurement.
- Presenting a modeled 2030 scenario as if it were an observed result.
- Adding on-site water consumption to power-plant consumption without identifying the two pathways.
- Confusing water withdrawal with water consumption.
- Assuming a renewable power contract alone establishes the physical grid impact at a specific location.
- Claiming that every customer’s bill will rise, or that rates must fall, without the local tariff and system-capacity evidence.
- Comparing one facility’s direct cooling water with another facility’s full electricity-related water footprint.
The defensible conclusion is therefore specific rather than absolute: AI data centers add substantial and concentrated electricity demand; they can add direct and indirect water consumption and emissions; and the size and distribution of those effects depend on engineering choices, geography, grid conditions, scenarios, and policy.
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