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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteChoose a cloud region by first ruling out locations that cannot meet your data-residency, compliance, service, latency, cost, or resilience requirements. Then compare environmental evidence among the regions that remain, keeping provider-reported carbon-free energy (CFE) separate from local grid carbon intensity. For flexible workloads, timing can matter too. Treat water as a separate consideration: available reporting does not establish a consistent regional water ranking across providers.
Start with the regions your workload is allowed to use
Environmental comparisons are useful only after you know which regions can actually run the workload. For each candidate, check whether it meets your data-residency and compliance obligations, offers the services and features you need, satisfies measured latency requirements, fits your budget, and supports your availability and disaster-recovery design.
AWS’s region-selection guidance calls out compliance, services and features, cost, and network latency. Google Cloud also identifies residency, latency, cost, redundancy, and availability as region-selection considerations. Check actual service and capacity availability for your deployment rather than assuming every feature is offered in every region.
Understand what each environmental metric measures
CFE percentage and grid carbon intensity are different measures
Google Cloud’s regional CFE percentage is calculated hourly using the electricity generation on the local grid at the time of use together with clean energy Google attributes to that grid. Its annual figure represents 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, describes average operational gross emissions per unit of local grid electricity, measured in gCO₂eq/kWh. Google says to use this metric to compare the carbon intensity of electricity from the local grid. Neither figure is, by itself, an independent whole-lifecycle footprint for a particular workload. Do not treat CFE and grid intensity as interchangeable or combine them into a single score without a defensible common method.
Provider thresholds and accounting boundaries are not universal
Google labels a region “low carbon” when its CFE is at least 75%, or, if CFE information is unavailable, when its grid carbon intensity is no more than 200 gCO₂eq/kWh. That is Google’s criterion for its indicator, not an industry-wide standard.
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AWS’s 2022 architecture article distinguishes market-based accounting, which reflects purchased electricity attributes, from location-based accounting, which uses the average emissions intensity of the grid where electricity is consumed. Its London–Stockholm example favored Stockholm after considering operational requirements and reported grid intensity. It is an illustration from 2022, not a current, complete AWS region ranking.
Compare dated regional data rather than relying on a universal ranking
Google Cloud’s regional page lists the following 2025 annual averages. The figures use Google’s stated methodology; they are not real-time measurements or a comparison across cloud providers.
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| Google Cloud region | Location | 2025 average CFE | 2025 average grid carbon intensity |
|---|---|---|---|
| europe-north2 | Stockholm | 100% | 19 gCO₂eq/kWh |
| europe-north1 | Finland | 98% | 30 gCO₂eq/kWh |
| europe-central2 | Warsaw | 81% | 499 gCO₂eq/kWh |
Google’s page says its hourly grid-mix and carbon-intensity inputs come from Electricity Maps. The page was last updated September 15, 2026. Treat the table as provider-published historical regional data, not a live forecast or an apples-to-apples comparison with AWS or another provider. Recheck the provider’s current reporting when making an operational decision.
Consider when flexible work runs
Grid carbon intensity changes as the electricity mix changes through the day. Google recommends scheduling flexible, non-urgent batch work for hours with a higher share of CFE. This can complement region selection, but it is not a guaranteed reduction for every workload: the result depends on its schedule, region, and the applicable accounting method.
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A 2022 ACM FAccT study of AI workloads examined region choice, time of day, and pausing cloud instances. In the cases studied, region selection had the largest operational-emissions reduction impact among the approaches considered, and time of day also mattered. Those findings support evaluating both location and flexibility; they do not establish a percentage reduction for a different application.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate water separately from carbon
Water is part of cloud environmental impact, but the reporting described here does not support a consistent, cross-provider regional water ranking. AWS’s Sustainability console reports estimated water withdrawals, with historical data going back to 2023 according to its capability description. A withdrawal estimate is a distinct measure from carbon emissions and should not be used as a substitute for a comparable regional water-impact assessment.
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Review impact after deployment
Use provider reporting to inspect your own footprint
AWS says its Sustainability console can filter estimated environmental impact by region, service, account, and Scope 1, 2, and 3. It includes both location-based and market-based carbon data; the capability description says historical carbon data goes back to 2022 and water-withdrawal data to 2023. These are AWS tool capabilities and data-availability dates, not evidence of emissions reductions.
Reduce avoidable workload demand
Google’s sustainability framework puts responsibility for efficient workload design on customers. Its suggested approaches include right-sizing resources, scaling serverless services to zero where appropriate, and managing data lifecycles. Provider dashboards can help teams monitor reported estimates, but a change in a dashboard value alone does not establish that a particular design change caused the difference.
Use a consistent decision record
For each region that passes your operational requirements, record the evidence alongside its definition and date. This makes trade-offs visible without implying that unlike provider metrics share one scale.
- Operational fit: note residency and compliance status, required service availability, measured latency, workload cost, and resilience or recovery needs.
- Carbon evidence: identify whether the figure is provider CFE, market-based accounting, or location-based grid intensity; include the reporting year and boundary.
- Scheduling flexibility: record whether non-urgent work can move to lower-carbon hours and what operational constraints limit that option.
- Water evidence: identify whether a regional or provider estimate exists, what it measures, and whether it is comparable to the alternatives.
There is no provider-neutral regional dataset in the sources described here that supports a single ranking across energy use, carbon, and water for all cloud providers. Compare like with like, state where evidence is provider-specific, and select from the regions that meet the workload’s actual requirements.
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