Enterprise data-centre customers can support AI growth while limiting emissions by using less avoidable compute, shifting flexible work to cleaner times and places, and demanding emissions data they can compare and scrutinise. Clean-energy contracts can help, but an annual matching claim does not prove that a facility is powered by carbon-free electricity in every hour. Customers can influence workload demand, timing, location, provider choice and procurement; the operator controls many facility and infrastructure decisions.
Why data-centre choices matter as AI use grows
The International Energy Agency (IEA) estimates that data centres used 415 terawatt-hours (TWh) of electricity in 2024—around 1.5% of global electricity consumption. Its 2025 Base Case projects use of around 945 TWh in 2030 and 1,200 TWh in 2035. These are scenario-based projections, not guaranteed outcomes; AI is a major driver alongside other digital services.
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Demand and its effects are 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 hide pressure on a particular grid or community.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute| Measure | Figure | What it represents |
|---|---|---|
| Global data-centre electricity use, 2024 | 415 TWh; around 1.5% of global electricity | IEA estimate for 2024, reported in 2025 |
| Global use, 2030 | Around 945 TWh | IEA 2025 Base Case projection |
| Global use, 2035 | Around 1,200 TWh | IEA 2025 Base Case projection |
| Share of global use, 2024 | United States 45%; China 25%; Europe 15% | IEA estimates for 2024, reported in 2025 |
The emissions path is also conditional. In the IEA’s 2025 Base Case, emissions from electricity generation for data centres peak at around 320 million tonnes of CO2 in 2030, then ease to around 300 million tonnes in 2035. Those are modeled estimates, not observed future results; electricity supply and demand scenarios affect them.
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Find avoidable demand
Inventory AI and other data-centre workloads to identify idle resources, duplicated jobs, oversized infrastructure, low-value computation and data retained beyond its useful life. Then review model and application efficiency, utilization, storage-retention policies and job scheduling. The IEA estimates that servers account for around 60% of electricity demand in modern data centres, so efficient use of computing capacity matters as well as facility cooling. There is no universal customer-savings percentage established for these measures: results depend on the workload and implementation.
Ask for workload-relevant information
Where available, request energy and emissions information at the service, project or workload level rather than relying only on a company-wide sustainability figure. Ask how the provider attributes shared servers, networking, storage and cooling to your usage. Without an explained allocation method, a precise-looking customer total may still be difficult to compare with another provider’s estimate.
Move flexible work to a better time or place
For workloads that can move without harming service, compare candidate regions and operating times using both local grid conditions and provider-reported emissions data. Google Cloud’s methodology describes hourly location-based factors that reflect changes in grid mix and can inform timing or location choices. Its market-based reporting instead reflects clean-energy purchases and uses annual factors. Those views answer different questions: what the grid serving a location emits, and how procurement instruments are treated in accounting.
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There is no universally best region. A lower reported emissions figure must be considered alongside latency, resilience, cost, data-residency rules, water needs and local grid constraints. Treat demand response or shifting non-urgent computation as options to discuss with the provider, not capabilities every workload or facility necessarily offers. Google says it uses demand response and flexibility when possible.
Compare emissions reports on the same basis
Ask providers to explain the accounting boundary and data behind a customer-level figure before comparing totals. In particular, distinguish location-based from market-based reporting; neither alone answers every question about physical electricity supply or a customer’s complete footprint.
| What to compare | Question for the provider | Why it matters |
|---|---|---|
| Reporting basis | Do you provide both location-based and market-based emissions, and what factors produce each figure? | The approaches reflect different views of grid electricity and procurement claims. |
| Time and geography | Are data and emissions factors specific to a region and an hour, or are they annual and broader? | Annual or wide-area averages can conceal when and where electricity is consumed. |
| Boundaries | Which scopes and lifecycle stages are included or excluded? | Figures with different boundaries are not like-for-like, even if both are labelled customer emissions. |
| Allocation | How are shared infrastructure emissions assigned to my account, project, service or workload? | Allocation choices affect the customer’s reported share of a shared facility’s impact. |
| Assurance and review | Is the customer-level data independently assured, or has only the calculation method been reviewed? | Review of a method is not the same as independent verification of each customer’s data. |
| Updates | How often is the information updated, and how are methodology changes reflected in past periods? | Consistent comparisons need clear dates and treatment of revisions. |
For example, Google Cloud publishes both location-based and market-based customer reporting, but its methodology says customer-specific estimates have not been third-party verified or assured. The company describes a review of its allocation methodology; buyers should distinguish that from assurance of the reported customer data.
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Use clean-energy procurement carefully
Where your organisation has direct influence over energy procurement, options include long-term power-purchase agreements (PPAs), utility green tariffs or supply arrangements, energy-attribute instruments, on-site generation, storage and pooled demand with other buyers. Assess whether a purchase supports additional clean generation, whether that generation can serve the relevant market, how long the arrangement lasts, whether it matches consumption hourly or annually, and how it interacts with local grid constraints.
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Company examples illustrate the difference between procurement activity and an hour-by-hour outcome. Microsoft reported in 2025 that it had contracted 34 gigawatts (GW) of new renewable energy across 24 countries. That company-reported figure does not mean every hour or location is matched. Google describes using PPAs, utility and developer arrangements, supply contracts and targeted investments; its initiative with Microsoft and Nucor illustrates pooled demand for emerging clean technologies. These approaches are examples, not guaranteed results for every buyer.
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Include the facility, water and community in provider decisions
Ask providers about operational efficiency, cooling, flexible-load participation, plans for new local generation and transmission, and how they manage effects on nearby communities. These are largely operator decisions, but enterprise demand and procurement requirements can influence what operators build and disclose.
Facility type matters: the IEA estimates that cooling accounts for around 7% of electricity use in efficient hyperscale data centres, compared with over 30% in less-efficient enterprise centres. Those figures describe different facility conditions, not a universal cooling share. Google reports a fleet-wide average power usage effectiveness (PUE) of 1.09 for 2025. That is the company’s own figure, not an industry benchmark; PUE is a facility-efficiency measure, not a direct measure of the carbon intensity of its electricity.
Design choices also involve water and embodied carbon, not only operating electricity. Microsoft reported in 2025 that direct-to-chip cooling saves over 125 million litres of water per facility each year, and that hybrid timber-steel construction can reduce embodied carbon by up to 65% versus traditional concrete models. These are company-reported design claims, not universal results. Ask what applies to the specific facility and what baseline or boundary underlies a claim.
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Interpret provider progress without mistaking it for a guarantee
Google reported that its data-centre electricity demand rose 27% in 2024 while its energy-related emissions fell 12%; its hourly carbon-free energy share rose from 64% to 66%. These are company-reported results for Google, not a sector-wide measure or a promise about the electricity associated with any one customer’s workload. They show why buyers should examine demand, emissions and hourly clean-energy coverage together rather than treating any single metric as the whole story.
Make comparisons only after aligning region, workload, reporting period, accounting boundary and assurance status. Then weigh the carbon information against reliability, latency, cost, water and local impacts; a provider or region cannot be declared the better choice without that context.
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