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Choose a cloud region by first ruling out locations that fail your data-residency, compliance, service, latency, cost or resilience needs. Then compare environmental evidence among the regions that remain. Keep provider-reported carbon-free energy (CFE) separate from local-grid carbon intensity: they describe different things, and neither alone gives the full environmental footprint of your workload.
Start with regions your workload can actually use
A region with favorable carbon data is not a practical choice if it cannot legally store your data, lacks a required service, misses user-latency targets or undermines your recovery plan. Make a shortlist using operational requirements first, then compare environmental impact within it.
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For each candidate region, check:
- Compliance and residency: Is the region permitted for this data and workload?
- Latency: Does it meet measured latency needs for users and dependent services?
- Services and capacity: Are the required products, features and capacity available?
- Cost: What will the workload cost in each viable region?
- Resilience: Can the design meet its availability, failure and disaster-recovery requirements?
AWS region-selection guidance highlights compliance, service and feature availability, cost and network latency. Google Cloud also identifies residency, latency, cost, redundancy and availability as region-selection considerations.
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Carbon-free energy is not the same as grid carbon intensity
Google Cloud’s CFE percentage is calculated hourly using the electricity-generation mix on the grid when energy is used together with clean energy Google attributes to that grid. Its annual regional CFE figure is the average percentage of time an application would run on carbon-free energy under that method.
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Grid carbon intensity, by contrast, is average operational gross emissions per unit of local-grid electricity, expressed in gCO₂eq/kWh. Google says its hourly grid-mix and carbon-intensity inputs come from Electricity Maps. Use CFE and grid intensity as related but distinct measures; neither is an independent, whole-lifecycle footprint for a particular workload.
Provider accounting methods are not automatically comparable
AWS’s 2022 architecture article distinguishes market-based accounting, which reflects purchased electricity attributes, from location-based accounting, which uses average emissions intensity for the grid where electricity is consumed. These accounting approaches describe different boundaries. Do not combine a provider’s procurement-based figure with a local-grid figure as if they were the same measure or a universal provider ranking.
Google’s “low carbon” indicator is Google’s own classification: it applies to regions with at least 75% CFE, or, when CFE information is unavailable, grid carbon intensity of no more than 200 gCO₂eq/kWh. That threshold is not a universal industry standard.
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Google Cloud publishes 2025 annual regional averages. Three examples from its listed regions are:
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| Google Cloud region | 2025 annual CFE average | 2025 grid carbon intensity |
|---|---|---|
| europe-north2 (Stockholm) | 100% | 19 gCO₂eq/kWh |
| europe-north1 (Finland) | 98% | 30 gCO₂eq/kWh |
| europe-central2 (Warsaw) | 81% | 499 gCO₂eq/kWh |
These are Google-published regional averages for 2025, not real-time readings or a cross-provider comparison. Google’s regional carbon page was last updated 2026-09-15 UTC. AWS’s London–Stockholm example is an illustration published in 2022, not a current, complete AWS regional ranking. Check the provider’s current figures and methodology before making an operational decision.
Compare candidates with a consistent worksheet
Record the operational constraints and the evidence boundary together. This makes it harder to mistake a convenient carbon number for a complete environmental comparison.
| Decision area | What to record for each viable region |
|---|---|
| Compliance and residency | Whether the region is allowed for the data and workload |
| Latency | Measured performance against your user and service requirements |
| Services and availability | Required services, features and capacity |
| Cost | Expected workload cost in that region |
| Resilience | How the region fits the required failure and recovery design |
| Carbon evidence | Whether the figure is provider CFE, market-based accounting or location-based grid intensity; include its year and boundary |
| Time flexibility | Whether non-urgent work can be scheduled at lower-carbon times |
| Water impact | Whether water information exists and is comparable for the decision |
Compare like with like where possible: use the same reporting year, understand whether figures are annual or hourly, and keep each provider’s accounting boundary visible. The cited information does not establish a provider-neutral dataset that ranks all clouds consistently across energy, carbon and water.
Use workload flexibility as another lever
Grid carbon intensity varies as the electricity mix changes through the day. If a batch job is not time-critical, scheduling it for hours when the grid has a higher share of CFE may reduce its operational emissions. Google recommends this approach for flexible workloads.
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A 2022 ACM FAccT study examined region choice, time of day and pausing cloud instances for AI workloads. In the workloads studied, choosing a region had the largest operational-emissions reduction impact among those approaches, and time of day also mattered. Those findings do not promise a particular reduction for a different workload. Use them as a reason to assess flexibility, not as a forecast of your own results.
Track impact and improve the workload after deployment
AWS says its Sustainability console can filter estimated environmental impact by region, service and account, and show Scope 1, 2 and 3 data, location-based and market-based carbon data, and estimated water withdrawals. AWS describes historical carbon data back to 2022 and water-withdrawal data back to 2023. These dates describe the tool’s reported data availability, not reductions in impact.
Google’s sustainability framework emphasizes customer-side workload efficiency. Relevant actions include right-sizing resources, scaling serverless services to zero when appropriate and managing data lifecycles. Use reporting to identify where to investigate and whether changes coincide with shifts in estimated impact; a dashboard estimate alone does not prove that a particular change caused a reduction.
Treat water as a separate decision criterion
Carbon performance does not establish water performance. AWS describes estimated water-withdrawal reporting, and Google’s sustainability framework identifies water as part of environmental impact, but the available information does not support a consistent cross-provider regional water ranking. If water is material to your decision, check what each provider reports, which boundary it covers and whether the figures are comparable before using them to choose a region.
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