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Enterprise data-centre customers can support AI growth while limiting emissions by using less avoidable compute, shifting flexible workloads to cleaner times or locations, and requiring providers to report customer emissions with clear boundaries and allocation methods. Clean-energy contracts can help, but annual matching does not prove that every hour of a customer’s use is supplied by carbon-free electricity on the local grid.
Why do data-centre customers need to act now?
Data centres consumed 415 terawatt-hours (TWh) of electricity in 2024, about 1.5% of global electricity use, according to the International Energy Agency’s (IEA) 2025 Energy and AI report. In its Base Case, the IEA projects consumption will reach around 945 TWh in 2030 and around 1,200 TWh in 2035. These are projections, not observed outcomes, and depend on assumptions about demand and energy supply.
| Year | Data-centre electricity use | How to read the figure |
|---|---|---|
| 2024 | 415 TWh | IEA estimate of actual consumption, around 1.5% of global electricity use. |
| 2030 | Around 945 TWh | IEA 2025 Base Case projection. |
| 2035 | Around 1,200 TWh | IEA 2025 Base Case projection. |
AI is a major driver of growth alongside other digital services, but the scale and emissions consequences depend on how quickly demand rises and how electricity is generated. The load is geographically concentrated: in 2024, the United States accounted for 45% of data-centre electricity consumption, China 25%, and Europe 15%, according to the IEA. A global average can therefore obscure pressure on particular grids and communities.
The IEA’s 2025 Base Case estimates that emissions from electricity generation serving data centres peak at around 320 million tonnes of CO2 in 2030 and ease to around 300 million tonnes in 2035. Those are modeled estimates, not guaranteed future results. The customer’s decisions matter because they influence how much computing is needed, where it runs, and when flexible work is scheduled; providers retain control over many facility and infrastructure choices.
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Which emissions figures should buyers compare?
Ask for both location-based and market-based reporting, and understand what each figure represents. Location-based reporting uses the electricity mix associated with the region where consumption occurs. Market-based reporting reflects eligible contractual instruments or clean-energy purchases under the provider’s methodology. They answer different questions, so a market-based result should not be treated as proof of the physical mix serving a facility at every moment.
Annual matching can show that a provider procured an amount of energy attributes over a year, but it can conceal when and where generation occurred. Hourly and regional detail can reveal whether clean generation aligns more closely with consumption. Google Cloud’s published methodology describes both location-based and market-based customer emissions reporting, including hourly location-based factors and annual factors for market-based results. It also states that customer-specific emissions estimates have not been third-party verified or assured. A review of an allocation methodology is not the same as assurance of the customer’s reported data.
- Boundaries: Ask which scopes and lifecycle stages are included or excluded. Clarify whether figures cover direct facility emissions, purchased electricity, and relevant upstream or supply-chain emissions.
- Allocation: Find out how shared infrastructure is apportioned to your account, project, service, or workload.
- Resolution: Request the geography and time interval of the energy and emissions factors, and whether the figures distinguish regions and hours.
- Assurance: Separate independent assurance of underlying customer-level data from a review of the method used to allocate emissions.
- Change control: Ask how often data is updated and how revised methods or factors affect previously reported periods.
How can customers reduce the computing they need?
Find and remove low-value work
Inventory workloads to identify idle resources, duplicated processing, oversized instances, unnecessary retention, and computation with little business value. Improving utilization and storage practices can reduce demand without requiring a change in provider. For AI, review whether a task needs the largest model or repeated inference, and whether applications can avoid redundant calls. The IEA estimates that servers account for around 60% of electricity demand in modern data centres, making compute efficiency an important lever. Actual savings vary by workload; there is no universal reduction percentage that applies to every customer.
Ask for workload-level evidence
Where available, request service- or workload-level energy and emissions information rather than relying only on a corporate-wide sustainability figure. Check whether estimates are measured or modeled, what utilization assumptions they use, and whether the same method is applied across the services and regions being compared.
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When should a workload move to another region or time?
For jobs that do not need an immediate response, ask whether they can be scheduled when electricity is lower-carbon or when clean supply is more available. For workloads that can run in more than one region, compare the local grid signal alongside the provider’s market-based reporting. Hourly location-based factors can help expose changes in grid mix that annual averages hide.
Carbon is only one part of a region decision. Evaluate latency, resilience, cost, data residency requirements, water needs, and local grid constraints as well. The available evidence does not establish a universally best region; the right choice depends on the workload and the locations being considered.
What should customers require from a provider?
Make comparable emissions reporting and operational transparency part of procurement and renewal requirements. Ask providers to answer the same questions in the same format so differences in scope or methodology do not masquerade as differences in performance.
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- Request location-based and market-based emissions for the relevant services and regions.
- Document the included scopes, lifecycle stages, exclusions, allocation rules, and whether customer figures are estimated or measured.
- Request time and geographic resolution, the factors used, update frequency, and treatment of historical revisions.
- Ask what has been independently assured: customer-level data, the methodology, or neither.
- Discuss workload flexibility, demand-response participation, and how the provider addresses local grid and community impacts.
- Compare the resulting figures alongside service reliability, latency, cost, water needs, and contractual terms.
Provider progress can coexist with rising demand. Google reported that its data-centre electricity demand increased 27% in 2024 while energy-related emissions fell 12%; it also reported that hourly carbon-free energy use rose from 64% to 66%. These are company-reported results for Google, not sector-wide outcomes, and they illustrate why buyers should examine demand, emissions, and hourly clean-energy measures separately.
How much can clean-energy procurement accomplish?
Where a customer has direct procurement influence, options include long-term power purchase agreements, utility green tariffs or supply arrangements, energy-attribute instruments, on-site generation, storage, and pooled demand with other buyers. Evaluate whether a procurement choice supports additional clean generation, whether that generation can serve the relevant market, how long the contract lasts, and whether supply is matched by hour rather than only by year. Also consider local grid constraints and how the contract interacts with existing generation.
Large buyers can sometimes aggregate demand for emerging clean technologies. Google, Microsoft, and Nucor have described an initiative to pool demand for advanced clean energy. This is an example of collective procurement, not evidence that the same structure or outcome is available to every enterprise.
Company disclosures also show why procurement totals should be read with care. Microsoft reported contracting 34 gigawatts of new renewable energy across 24 countries in 2025. That company-reported figure describes contracted capacity; it does not establish that every hour of Microsoft’s consumption, or a customer’s consumption, is matched at every location.
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Which facility choices remain with the operator?
Customers can use procurement requirements to press providers on operational efficiency and facility impacts, but they do not directly control cooling systems, construction materials, or grid interconnection. Facility type matters: the IEA estimates cooling uses about 7% of electricity in efficient hyperscale data centres, compared with more than 30% in less-efficient enterprise facilities. Those figures are ranges for different facility types, not a single benchmark for every site.
Google Data Centers reports a fleet-wide average power usage effectiveness (PUE) of 1.09 for 2025. This is Google’s own reported fleet figure, not an industry benchmark or a guarantee for a specific facility. Microsoft reported that its direct-to-chip cooling design saves more than 125 million litres of water per facility each year, and that hybrid timber-steel construction can reduce embodied carbon by up to 65% compared with traditional concrete models. These are company-reported design claims, not universal outcomes. They are useful topics for provider discussions, but customers should request site- and project-specific evidence before using them to compare offers.
How can a buyer make a defensible decision?
Use a consistent scorecard for each provider and region under consideration. Record the reported emissions basis, time and geographic resolution, boundary, workload allocation method, and assurance status; then assess operational flexibility and local impacts alongside business requirements. A low market-based number, strong annual renewable matching, or an efficient fleet-wide PUE figure may be relevant, but none alone establishes the full emissions impact of a particular customer workload.
The strongest customer approach combines demand reduction with careful workload placement and transparent measurement. Procurement can reinforce those actions by requiring providers to explain what the numbers cover, how they were produced, and where the underlying electricity and infrastructure impacts occur.
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