| Scenario | vCPUs | RAM (GB) | CPU Util (%) | Hours/Day | Days | PUE | Grid EF (kg/kWh) | Energy (kWh) | Emissions (kgCO2e) |
|---|---|---|---|---|---|---|---|---|---|
| Dev VM (small) | 2 | 4 | 15 | 10 | 22 | 1.4 | 0.475 | ~1.1 | ~0.5 |
| App VM (steady) | 4 | 16 | 35 | 24 | 30 | 1.3 | 0.230 | ~10.0 | ~2.3 |
| Batch VM (bursty) | 8 | 32 | 70 | 6 | 26 | 1.5 | 0.708 | ~15.5 | ~11.0 |
This calculator estimates average power, converts it into energy, then multiplies by an emissions factor.
- PUE accounts for cooling and facility overhead.
- Loss % is a simple add-on for power delivery losses.
- Clean energy coverage linearly reduces the grid factor here.
- Average power override replaces the resource-based estimate.
- Enter your VM size: vCPUs, memory, and optional storage.
- Set average CPU utilization for the time window.
- Choose runtime: hours per day and number of days.
- Select a region preset or enter a custom grid factor.
- Adjust PUE and optional losses for the facility overhead.
- If you know measured power, set the average power override.
- Press Calculate to view results above the form.
- Use Download CSV or PDF to export your saved runs.
Why virtual machine emissions matter
Virtual machines look “invisible,” but they draw real electricity through the servers that host them. For many teams, VM energy is part of Scope 2 emissions because it is tied to purchased electricity, even when the infrastructure is outsourced. Estimating impact helps you prioritize right-sizing, scheduling, and region selection. It also supports internal chargeback models where sustainability is measured alongside cost, latency, and reliability.
Key drivers in the model
The calculator combines VM size and workload behavior into an average power estimate. vCPUs and utilization influence CPU watts, while memory contributes a steady background load. Storage adds a small conservative component because disk power is typically shared across many tenants. Runtime converts watts to kilowatt-hours, so the same VM can have very different footprints depending on whether it runs 24/7 or only during business hours.
Facility overhead and grid intensity
IT power is only part of the story. Data centers consume additional energy for cooling, power conversion, lighting, and networking. Power Usage Effectiveness (PUE) approximates that overhead and scales the estimate to facility power. Transmission and distribution losses can be added to reflect upstream delivery inefficiencies. Finally, the grid emissions factor converts energy to kgCO2e, and the clean energy coverage input linearly reduces that factor for simplified scenario planning.
Interpreting results for optimization
Use the energy value to understand how much electricity your workload drives over the selected period. Emissions and intensity (kgCO2e per hour) are most helpful for comparisons: test different VM sizes, utilization targets, and runtimes to see which changes move the needle. Typical improvements include downsizing over-provisioned memory, turning off idle environments, shifting batch jobs to lower-carbon regions, and smoothing spikes with autoscaling or scheduling.
Reporting and governance tips
For stakeholder reporting, document every assumption: utilization window, PUE, grid factor source, and any clean-energy claims. When possible, replace presets with provider-published regional factors and measured power or utilization from monitoring. Export CSV results to maintain an audit trail across experiments, and keep the modeled period consistent when benchmarking. Treat this tool as a decision aid and a starting point for more rigorous accounting workflows. Review assumptions quarterly as infrastructure and grids evolve.
1) What is PUE, and why does it change results?
PUE represents facility overhead beyond IT power. A higher PUE means more energy spent on cooling and power delivery, so total kWh and emissions rise even if the VM workload stays the same.
2) When should I use the average power override?
Use it when you have measured or provider-reported average watts for your VM or host allocation. It replaces the estimated power model and typically improves accuracy for stable workloads or dedicated hosts.
3) How do I choose a grid emissions factor?
Prefer a recent, region-specific factor published by your cloud provider or a reputable energy dataset. If you are unsure, start with a preset for comparisons, then switch to a custom value for reporting.
4) Does storage size significantly affect emissions?
Usually it is minor compared with CPU, memory, and runtime. Storage power is shared across infrastructure, so changes in GB often have a smaller marginal impact than reducing always-on hours.
5) How can I compare two VM configurations fairly?
Keep the same time period, region factor, PUE, and clean-energy coverage, then vary only one dimension at a time (size, utilization, or schedule). Compare intensity and total emissions to see tradeoffs.
6) Are these numbers suitable for formal ESG reporting?
They are best for planning and directional benchmarking. For formal reporting, use provider methodologies, verified factors, and documented evidence of renewable claims. Treat this as a transparent calculator, not a certified inventory.