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Estimate an AI data center’s footprint by defining what energy is included, determining electricity use in kilowatt-hours (kWh), allocating shared facility energy to AI where necessary, and multiplying by a location- and time-appropriate emissions factor. The result is only meaningful when its boundary and assumptions are stated: there is no single universal carbon footprint for an AI data center.
What the estimate measures
Start by deciding whether you are measuring the electricity used by IT equipment, such as servers and network hardware, or the electricity used by the entire data center facility. Whole-facility electricity also includes overhead such as cooling and other infrastructure. If you want an AI-specific result, you must also distinguish AI workloads from other work sharing the facility.
Set the reporting period and location, then specify whether the estimate covers grid electricity alone or also includes onsite generation, backup-generator fuel, and embodied emissions from equipment. These choices define what the result can—and cannot—be compared with.
How to calculate electricity use and emissions
- Collect facility electricity, if available. Use metered whole-facility kWh for the reporting period when you have it. If you already have a facility total, do not apply PUE again.
- Estimate facility electricity from IT energy when needed. Multiply IT electricity by the data center’s Power Usage Effectiveness (PUE) for a matching period and facility boundary:
facility kWh = IT kWh × PUE. PUE accounts for facility overhead; a PUE value from a different facility or period may not fit the estimate. - Attribute a share to AI. Use direct measurement of AI equipment or workloads where possible. If energy is shared and direct measurement is unavailable, document a defensible allocation basis—for example, the share of equipment capacity assigned to the AI service. Label the result as allocated, not directly measured.
- Choose an emissions factor. Multiply the applicable electricity in kWh by an emissions factor in kg CO2e/kWh that matches the location, period, and accounting method:
kg CO₂e = kWh × kg CO₂e/kWh. Divide kilograms by 1,000 to report tonnes. Keep the factor’s gases and lifecycle boundary consistent with the label on the result. - Record assumptions and omissions. Report the electricity boundary, period, location, PUE and its period, AI allocation method, emissions-factor source and year, and whether backup fuel or embodied equipment emissions are included.
For a company’s Scope 2 inventory, distinguish location-based and market-based emissions where relevant. The GHG Protocol Scope 2 Guidance describes these as different accounting approaches; they should not be treated as interchangeable totals. Read the GHG Protocol Scope 2 Guidance.
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How to separate AI electricity from a shared data center
A data center can host AI and non-AI workloads on the same equipment and infrastructure. If the facility does not directly meter AI energy, the AI share is an allocation rather than a direct measurement. State the basis used and its limits; capacity assigned to a service, for instance, is an allocation basis, not proof of the service’s exact energy use.
The GHG Protocol ICT Sector Guidance describes estimating data center service emissions using server and network equipment energy, PUE, an electricity emissions factor, and embodied emissions allocated to the service. It also discusses documenting a share of data center capacity or equipment when direct service energy is difficult to establish. See Chapter 4 of the GHG Protocol ICT Sector Guidance.
What global figures can—and cannot—tell you
The International Energy Agency (IEA) estimates that all data centers—not AI alone—used 415 TWh of electricity in 2024, about 1.5% of global electricity use. Its 2025 Base Case projects about 945 TWh of global data center electricity consumption in 2030. The IEA presents sensitivity cases that differ materially, reflecting uncertainty in AI uptake, efficiency, and energy-system constraints. These are global figures, not a way to calculate the footprint of a particular AI service or facility. IEA: Energy demand from AI.
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For emissions, the IEA estimates about 180 Mt of indirect CO2 emissions from data center electricity consumption, excluding backup power generation. Separately, it projects around 320 Mt CO2 from electricity generation for data centers by 2030 in its Base Case. The latter is a scenario projection, not the same measure or time point as the estimate of present-day indirect emissions. Both cover data centers broadly, not AI alone. IEA: AI and climate change and IEA: Energy supply for AI.
Why two estimates may not be comparable
Before comparing published or company estimates, check whether they use the same:
- IT-only or whole-facility electricity boundary;
- metered or modeled energy;
- location-based or market-based emissions method;
- region and reporting year;
- directly measured or allocated AI workload energy; and
- treatment of backup generation and embodied emissions.
A difference in any of these can change the total without either calculation necessarily being wrong. The IEA’s data-center electricity emissions estimate, for example, excludes backup power generation, while a broader footprint may include backup fuel and embodied emissions.
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What to say when reporting a result
Present the number alongside its boundary and method, rather than as a standalone claim. A useful report states the period and site, whether energy is IT-only or whole-facility, whether it was metered or estimated using PUE, how AI’s share was determined, and which emissions factor and accounting method were used. Identify significant exclusions, especially backup-generator fuel and embodied equipment emissions.
That context matters because the same kWh can correspond to different emissions depending on where and when it is consumed and which accounting method is applied. The IEA describes its projections as scenarios because future electricity demand remains uncertain: “The uncertainty surrounding future electricity demand requires a scenario-based approach to explore alternative pathways and provide perspectives on timelines relevant for energy sector decision-making.”
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