Neither has a universally bigger environmental footprint. Cloud computing runs on data-center infrastructure, so the categories overlap; a fair comparison has to specify the same computing workload, the infrastructure counted, and the environmental measure. Cloud services can use energy more efficiently through consolidation and facility design, but that advantage depends on utilization, electricity supply, facility efficiency, and location.
Why “data centers vs. cloud computing” is not a simple comparison
A data center is a physical facility that houses computing equipment and supporting systems. Cloud computing is a way to access computing resources, usually over a network, that are hosted in data centers. A cloud workload therefore contributes to data-center impacts; adding a cloud total to data-center totals would count overlapping activity.
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The question becomes meaningful when it compares a particular workload hosted in a cloud service with the same workload run on an organization’s own equipment. It also matters whether the comparison includes only electricity used to operate servers and facilities, or other impacts across equipment and infrastructure lifecycles. The available sources do not establish a current, globally comparable cloud-only water footprint or a current like-for-like lifecycle estimate for cloud versus on-premises computing.
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What the global data-center numbers do—and do not—show
The International Energy Agency’s 2025 figures describe data centers broadly, not cloud computing alone. They establish the scale of the infrastructure, but cannot by themselves determine whether a specific cloud workload is cleaner than its on-premises equivalent.
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| Measure | Reported value | Scope and qualification |
|---|---|---|
| Electricity consumed | About 415 TWh, or 1.5% of global electricity, in 2024 | Global data centers; IEA, 2025. Not a cloud-only figure. |
| Growth in electricity consumption | About 12% per year since 2017 | Global data centers; IEA, 2025. |
| Electricity-related emissions | About 180 Mt today | Data centers broadly; IEA, 2025. The figure is not identified as a cloud-only inventory. |
| Emissions scenario for 2035 | 300 Mt in the Base Case; 500 Mt in the Lift-Off Case | IEA, 2025 scenario estimates for data centers; outcomes depend on the scenario, not a forecast specific to cloud. |
The IEA also estimates global electricity generation to supply data centers at 460 TWh in 2024, rising to more than 1,000 TWh in 2030 and 1,300 TWh in 2035 in its Base Case. These generation figures are not the same measure as the 415 TWh of data-center electricity consumption: generation to supply demand and electricity consumed at the facilities have different accounting boundaries. None of these totals should be relabeled as the footprint of cloud computing alone. See the IEA’s 2025 executive summary and its energy-supply analysis.
When cloud computing can use less energy
Cloud providers can consolidate workloads across shared infrastructure. If servers are more fully utilized than separate, lightly used on-premises machines, fewer servers may be needed to deliver the same computing service. Purpose-built facilities can also be more efficient than a small or older server room because facility design and operation affect the energy required beyond the computing equipment itself.
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A Lawrence Berkeley National Laboratory (LBNL) U.S. case study identifies server consolidation and facility efficiency as potential energy-saving mechanisms. It also cautions that net effects are difficult to analyze across the wider system. Savings for an individual workload are not guaranteed: they depend on how much capacity is used, how efficiently the facility operates, and whether moving or expanding workloads changes total demand. The study supports these mechanisms, not a current global ranking of cloud and on-premises computing. See the LBNL cloud-computing energy-efficiency case study.
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What determines the footprint of a particular workload?
- Utilization and consolidation: Compare the amount of computing delivered with the infrastructure required to deliver it. Low utilization can leave equipment consuming energy without doing much useful work.
- Facility efficiency: Account for the energy used to support computing, not just the servers. The NRDC/WSP analysis frames on-premises and cloud comparisons around both facility efficiency and the electricity supplying the facility; it is an older analytical source, useful for identifying variables rather than as a present-day numeric benchmark.
- Electricity supply: The same workload can have different electricity-related emissions depending on the electricity supplying its facility. The relevant comparison is tied to the workload’s location and power supply, not simply whether it is labelled “cloud” or “on-premises.”
- Geography and water: Facility location affects local water impacts and exposure to water stress. A comparison should distinguish water consumed directly at a facility from water associated with generating the electricity it uses.
- Boundary and year: Keep the workload, included infrastructure, metric, geography, and time period consistent. Data-center-wide totals, U.S.-specific estimates, and workload-level comparisons answer different questions.
The NRDC/WSP analysis is available at its cloud-computing report. Its framework remains useful for understanding the comparison, but its age means it should not be treated as a current performance benchmark.
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Why water and location complicate the answer
Energy and carbon are not the only relevant measures. Data centers can affect water resources through facility operations, while electricity generation can also carry indirect water impacts. A facility’s location matters because water use in a water-stressed area has different local significance from the same amount used where water is more available.
A 2021 U.S. study by Siddik, Shehabi, and Marston estimated that data centers accounted for around 1.8% of U.S. electricity use and approximately 0.5% of total U.S. greenhouse-gas emissions. It also examined water and carbon impacts, including exposure to water-stressed areas. These are U.S.-specific study estimates, not global figures and not a cloud-only water measure. See the LBNL environmental-footprint study.
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How to compare cloud with on-premises computing fairly
- Choose one workload. Define the service being delivered and the period being measured, rather than comparing an organization’s entire data center with a provider’s entire cloud estate.
- Set the boundary. State which computing equipment and facility systems are included, and whether the assessment concerns operational electricity or a broader lifecycle footprint.
- Compare useful work and utilization. Measure the computing delivered and how much capacity is actually used in each option; do not treat theoretical capacity as equivalent to work performed.
- Include facility efficiency and electricity supply. Account for supporting-facility energy and the carbon intensity or sourcing of the electricity at the relevant locations.
- Check water and geography. Separate direct facility water from indirect electricity-related water and consider local water stress.
- Report the date and uncertainty. Identify the year, region, assumptions, and whether results are measured or estimated. Do not use data-center-wide totals as a substitute for a workload-level comparison.
Is cloud computing greener than on-premises computing?
It can be, particularly where shared infrastructure improves utilization and facilities are more efficient than the equipment they replace. But “cloud” alone does not establish a lower footprint. A workload’s result depends on the efficiency of both setups, the electricity and location involved, water impacts, and the boundary used for the comparison. The strongest supported conclusion is conditional: assess the same workload on a consistent basis rather than assigning a universal winner to either category.
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