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AI has a real and growing environmental footprint, but there is no single reliable figure for “AI’s carbon footprint.” The headline numbers often cited measure all data centers, not AI alone; estimates also change with the task, location, electricity supply, and what impacts are counted. The clearest conclusion is that AI’s footprint is significant, increasingly shaped by everyday use as well as training, and broader than carbon emissions alone.
Why there isn’t one number for AI’s carbon footprint
A carbon-footprint figure depends on what is being counted. A prompt answered by a model, a model’s initial training, and the electricity used by an entire data center are different units of analysis. A complete lifecycle assessment may also include the manufacture and transport of computing hardware, cooling, water use, land use, and equipment disposal.
Place and time matter too. The same computing task can have different emissions depending on the facility’s electricity supply and cooling system. And a short text response is not equivalent to generating a long answer, an image, or a video. Estimates that use different boundaries or tasks cannot be compared as if they measured the same thing.
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- Workload: training, fine-tuning, inference (running a trained model), or all data-center activity.
- Metric: electricity, greenhouse-gas emissions, water, and land are related but distinct impacts.
- Boundary: operational impacts alone, or impacts across hardware’s lifecycle as well.
- Conditions: location, year, grid mix, cooling, model, task, and output size.
- Evidence type: a measured result, an estimate, or a projection based on a scenario.
Without those details, a precise-sounding number can obscure more than it explains.
What the global data-center figures actually measure
The best-known global electricity figures are for data centers as a whole. They include AI workloads, but also non-AI computing, storage, and other services. They show the scale of the infrastructure supporting digital services—not AI’s share of it.
| Source and publication year | Estimate | What it covers |
|---|---|---|
| International Energy Agency (IEA), 2025 | 415 TWh in 2024, about 1.5% of global electricity consumption that year | Estimated electricity use by all data centers worldwide, not AI alone. |
| IEA, 2025 | About 945 TWh by 2030 | Global data-center electricity use in the agency’s Base Case projection. |
| IEA, 2026 | 485 TWh in 2025; 950 TWh in 2030 | Updated estimates and central projection for all data centers. The IEA says the 2030 trajectory remains close to its 2025 report. |
| United Nations University Institute for Water, Environment and Health (UNU-INWEH), 2026 | About 448 TWh in 2025 | Its estimate for global data-center electricity use. It differs from the IEA’s 2025 estimate; the figures come from different sources and should not be merged into a false-precision average. |
These figures point to substantial growth in data-center electricity demand, with AI among its major drivers. They do not establish how much of the total belongs to AI, or provide a universal footprint for a particular AI service.
Electricity use is not the same as carbon emissions
Electricity is an input; emissions depend in part on how that electricity is generated. A workload powered by a lower-emissions supply can have a different operational carbon footprint from the same workload on a more emissions-intensive grid. Backup generators and other parts of the system can also affect the accounting boundary.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsThe IEA estimated around 180 million tonnes (Mt) of indirect CO2 emissions from data-center electricity consumption in 2024. That estimate covers all data-center workloads, not AI alone, and excludes emissions from backup power. The IEA’s scenarios show indirect data-center emissions increasing through 2030; the scenario projections are not measurements of AI-only emissions.
Why inference matters after training
Training a model can attract attention because it is a large, identifiable computing job. But a deployed model keeps using energy when it responds to prompts or performs other tasks. That ongoing use is called inference, and its cumulative effect depends on how often the model is used and what it is asked to do.
UNU-INWEH attributes 80–90% of total AI energy use to inference in its 2026 report. This is the report’s estimate, not a measured share that applies to every model, service, or deployment. It nevertheless highlights why counting training alone can miss a large part of AI’s energy demand once a system is in use.
AI’s environmental footprint includes water and land
Electricity and carbon do not capture every environmental impact. Data centers and the electricity supply serving them can be associated with water use and land use; hardware production and disposal add lifecycle considerations. OECD’s lifecycle framing distinguishes hardware production and transport from operations, and identifies energy, greenhouse-gas emissions, and water consumption among operational impacts. It also notes that evidence is uneven, particularly beyond energy use.
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UNU-INWEH projects that electricity associated with global data centers in 2030 would have an estimated footprint of 399 million tonnes of carbon, 9.3 trillion litres of water, and more than 14,500 km2 of land. These are report projections associated with data-center electricity—not observed impacts or AI-only totals.
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Environmental choices can involve trade-offs. A power source that looks favorable on carbon may have a different water or land footprint. As UNU-INWEH researcher Miriam Aczel put it, “What surprised us most is how often the choices that look greenest from a carbon perspective end up worse for water or for land.”
What AI-specific estimates can—and can’t—show
A 2025 Nature Sustainability study’s abstract gives a scenario range of 24–44 Mt of CO2-equivalent per year in 2024–2030 and 731–1,125 million cubic metres of water per year for US AI-server deployments. These are US deployment scenario ranges, dependent on expansion assumptions—not observed global totals. The abstract-level figures do not support treating them as a definitive footprint for all AI or as a directly comparable measure of a specific model or prompt.
More broadly, available estimates do not establish a robust, directly comparable operational footprint for every commercial model. A figure for one service or task should not be generalized to AI as a whole unless its system boundary and assumptions are clear.
Is using AI worse for the climate than a web search?
There is no fair universal comparison in the available evidence. A search and an AI request can involve different systems, workloads, answer lengths, and energy sources; estimates may also count different stages of each service. Comparing one prompt-level estimate with annual or national infrastructure totals would mix incompatible units and boundaries.
A meaningful comparison would specify the exact task and output, the systems being counted, the location and time period, and whether it includes only operational electricity or additional lifecycle impacts. Without that alignment, a claim that every AI request is worse—or better—than every search is not established.
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For a given task, using a smaller or more efficient model, choosing a fit-for-purpose system, keeping outputs concise, or using a lower-compute modality can reduce resource use. Model defaults, output format, and where computing is sited can also shape impacts. These are ways to reduce demand per task, not guarantees that overall environmental impact will fall.
If efficiency makes AI cheaper or easier to use, people and organizations may use it more. Growth in total use can offset per-task savings. IEA also describes ways AI applications could help reduce emissions in energy, industry, transport, and buildings, but potential benefits in those sectors are not an automatic offset for the emissions of AI infrastructure.
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Organizations assessing their own AI use should report the workload and boundary, rather than presenting an unexplained carbon number. A useful accounting approach keeps energy, emissions, water, and land visible as separate metrics and records whether a result is measured or projected.
- Separate AI-specific computing from general-purpose data-center activity where the data allows it.
- Distinguish training, fine-tuning, and inference, and state the tasks and outputs being assessed.
- Record the facility or region, reporting period, electricity assumptions, and cooling context.
- State whether the estimate includes hardware manufacture, transport, and end of life, as well as operational impacts.
- Report water and land alongside carbon where evidence is available, noting uncertainty rather than implying precision.
- Track total demand as well as per-task efficiency, so growth in usage is not hidden by lower impact per request.
IEEE’s P7100 project describes work toward a harmonized framework for measuring environmental indicators for AI training and inference, including energy, carbon, and water, and separating AI-specific compute from general-purpose compute. Its project page describes it as an active PAR, so it is a standards project—not a finalized, approved standard.
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