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To measure an AI workload’s carbon footprint, calculate its energy use, multiply that energy by the electricity grid’s carbon intensity for the workload’s location and time, then add an allocated share of hardware manufacturing and end-of-life emissions. Report both the total in kilograms of carbon-dioxide equivalent (kgCO2e) and an intensity such as kgCO2e per training run, inference request, output token, batch, user or transaction.
The Green Software Foundation’s SCI for AI specification expresses this as SCI = (E × I + M) / R: E is energy, I is carbon intensity, M is allocated embodied emissions, and R is the functional unit. The result is workload-specific, not a universal number for a model. Hardware, utilization, region, time window, software version and accounting boundary can all change it.
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Start with the SCI for AI equation
The most useful starting point is the SCI for AI formula:
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- E (energy): electricity consumed by the servers, accelerators, storage and other equipment included in your boundary.
- I (carbon intensity): kilograms of CO2e emitted per unit of electricity for the relevant location and time period.
- M (embodied emissions): the allocated manufacturing, transport and end-of-life emissions of the hardware used by the workload.
- R (functional unit): the denominator that makes the result useful, such as one training run, one request, one output token, one batch, one user transaction or the complete model lifecycle.
SCI for AI extends the Software Carbon Intensity method and ISO/IEC 21031:2024 for machine-learning systems. The Green Software Foundation describes its purpose as helping AI practitioners understand and reduce the carbon footprint of AI systems. Use the equation as an accounting structure, not as permission to hide missing data: if a term is estimated or excluded, label it beside the result.
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Define the boundary and functional unit before collecting data
Two teams can measure the same model and obtain different, defensible answers because they counted different activities. Write the boundary in plain language before opening a dashboard or exporting telemetry.
Choose what the boundary includes
A narrow operational boundary might include only accelerator electricity during inference. A lifecycle boundary could include data preparation, training, fine-tuning, evaluation, inference, storage, networking, cooling overhead and hardware manufacturing. Neither is automatically correct; the boundary must match the question and be applied consistently.
- Training: include every training run, failed run, hyperparameter experiment and evaluation run that belongs to the project if you are reporting the project total.
- Fine-tuning: measure it separately from pretraining when the hardware, duration or purpose differs.
- Inference: decide whether the boundary covers only the model server or also request routing, retrieval, vector databases, preprocessing, post-processing and storage.
- Data work: include data collection, cleaning, tokenization and embedding generation when they are material to the lifecycle you are reporting.
- Infrastructure overhead: document whether power usage effectiveness (PUE), networking, cooling and idle capacity are included.
- Embodied impacts: state whether manufacturing, transport, replacement and end-of-life are included or reported separately.
Pick a denominator that supports a decision
Use an absolute total for inventory and budgeting, then publish an intensity for comparison. A training team may need kgCO2e per completed run and per model version. A product team may need grams per successful request or per million output tokens. A business may need emissions per user transaction.
Do not divide a lifecycle total by a token count that excludes training or failed requests. The numerator and denominator must describe the same boundary and period. If a service has several model routes, report each route or publish a weighted average with the traffic mix and weighting period.
Collect energy from telemetry whenever possible
Energy is the largest operational input to the calculation, and measured telemetry is preferable to a generic per-token assumption. Capture the energy used by the actual hardware, software version and workload window.
Training and fine-tuning measurements
- Record the start and end timestamps for each run, including restarts and checkpoint recovery.
- Identify every host, accelerator type, quantity and region used during the run.
- Capture accelerator and host energy from provider or hardware telemetry. If only power is available, integrate power over the run duration and preserve the sampling interval.
- Record utilization, batch size, sequence length, precision, checkpoint frequency and data-loader behavior. These affect useful work and idle energy.
- Include failed and exploratory runs when the reporting question is the project or model lifecycle rather than the single successful run.
- Keep the model, framework, driver and firmware versions with the measurement so a later rerun can be compared.
When a provider reports machine energy rather than accelerator-only energy, use that scope consistently and document it. Do not add a second estimate for the same electricity; double counting host power and provider machine power is a common error.
Inference measurements
Measure a representative production window rather than extrapolating from a short benchmark. Record request count, input and output tokens, batching, cache-hit rate, concurrency, latency tier and the proportion of successful responses. A per-request figure that ignores retries, warm-up traffic or rejected requests can understate the service’s footprint.
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The SCI for AI reference implementation reads per-call energy from supported software-development-kit instrumentation when available. If instrumentation is unavailable, it falls back to model-specific energy-per-token estimates and labels the result as measured or estimated. Preserve that label in your dashboard and reports; an estimate is not equivalent to telemetry.
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Account for shared and idle capacity
Shared clusters require an allocation rule. Allocate energy by accelerator time, measured power, reservations, utilization or another documented driver. Report the rule and its limitations. If a server remains powered while your workload is idle, decide whether that idle electricity belongs to the workload, the platform overhead, or a shared pool; apply the same decision to every comparison.
Select the correct electricity carbon intensity
Operational emissions are calculated by multiplying energy by carbon intensity. The factor must match the workload’s physical location and time period as closely as the data allows.
Location-based accounting
Location-based intensity represents the average or marginal electricity mix of the grid where the workload physically runs. Hourly factors can reveal differences between daytime and nighttime operation, renewable generation and fossil-heavy periods. Google documents hourly, location-based factors and uses Electricity Maps data where available, with annual International Energy Agency factors as a fallback.
If your telemetry covers several regions or months, calculate each region and time slice separately before summing. Applying one annual global average to a multi-region workload can hide the effect of scheduling and placement.
Market-based accounting
Market-based figures reflect contractual instruments such as renewable-energy certificates or supplier agreements. They answer a procurement question, not necessarily the physical question of which electrons powered a server. Publish location-based and market-based results as separate columns when both are available. Never present a contractual clean-energy claim as proof that the physical grid mix changed for your workload.
Record factor provenance
- Grid region or facility location.
- Factor value and units.
- Timestamp, month or year represented.
- Whether the factor is hourly, monthly, annual, average or marginal.
- Data provider and dataset version.
- Fallback rule used when local or hourly data was unavailable.
Carbon intensity data can be revised. Keep the original factor and timestamp with the report so a later recalculation can distinguish a real workload change from a data-source change.
Add embodied emissions without hiding operational emissions
Manufacturing an accelerator, server, storage device and networking equipment creates emissions before the equipment consumes electricity. Allocate a defensible share of those emissions to the workload over the hardware’s useful life and expected utilization.
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- Identify the hardware model, quantity and expected service life.
- Obtain a manufacturing and end-of-life emissions estimate from the equipment manufacturer, a lifecycle assessment or a documented public dataset.
- Choose an allocation driver, such as accelerator-hours, server-hours, energy share or useful output.
- Allocate the hardware total across workloads using that driver and state the utilization assumption.
- Separate the resulting M term from operational electricity so readers can see which part is measured and which part is estimated.
Public data is often incomplete. Cloud Carbon Footprint documents limited compute embodied-emissions allocation, and provider reports may exclude some end-of-life, networking or grid-infrastructure impacts. If no credible value is available, report operational emissions separately and state that embodied emissions were not included. Do not silently set M to zero while calling the result a full lifecycle footprint.
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Calculate and report the result
For each reporting period, calculate operational emissions as energy multiplied by the selected location-based or market-based factor. Add the allocated embodied term, then divide by the functional unit.
A report should show at least these fields:
| Field | What to record |
|---|---|
| Boundary | Activities, infrastructure and lifecycle stages included. |
| Functional unit | Run, request, token, batch, user, transaction or lifecycle. |
| Energy | Measured kWh or an estimate, with source and measurement window. |
| Carbon intensity | Location-based and, if used, market-based factor, including region and timestamp. |
| Embodied emissions | Hardware allocation method, useful life and utilization assumptions. |
| Result | Absolute kgCO2e and kgCO2e per functional unit. |
| Uncertainty | Ranges, confidence notes or a list of estimated inputs. |
| Versioning | Model, hardware, software, telemetry, grid dataset and methodology versions. |
| Exclusions | Networking, storage, cooling, end-of-life or other omitted terms. |
Use a worked calculation template
Keep the arithmetic auditable by retaining the unrounded inputs:
- Sum measured energy for the defined workload window:
E = Σ kWh. - Apply the matching carbon factor:
Operational CO2e = E × I. - Add the allocated hardware term:
Total CO2e = (E × I) + M. - Divide by the matching functional unit:
Intensity = Total CO2e / R. - Publish both the total and intensity, with units and rounding only at the final display step.
For example, if a report covers several training runs and later inference, calculate training and inference separately first. A single combined average can conceal an inefficient training run or a high-volume inference path. Combine them only after each component has a documented numerator, denominator and boundary.
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Tools differ in granularity, methodology and exclusions. Select one that matches your boundary, then preserve its report version because providers can recalculate historical values after methodology updates.
| Tool or source | What it provides | Important qualifications |
|---|---|---|
| AWS Sustainability | Estimated carbon impacts for AWS services using industry-standard methods. | Coverage is AWS-operated infrastructure. Methods are independently verified, and historical values may be recalculated when methodology versions change. |
| Google Cloud Carbon Footprint | Allocates machine energy and emissions to products and customers; supports location-based and market-based reporting. | Customer-specific data is not third-party assured. Documented exclusions include some networking, end-of-life and grid-infrastructure embodied emissions, and data can change when methods or sources change. |
| Cloud Carbon Footprint | Energy-coefficient estimates, PUE assumptions, optional Electricity Maps data and limited compute embodied-emissions allocation for multi-cloud or self-managed environments. | Coefficients and embodied estimates are estimates constrained by public data. Preserve the coefficient and dataset version and date. |
| SCI for AI reference implementation | A standardized formula and instrumentation approach; per-call energy from supported software-development-kit instrumentation when available, otherwise model-specific energy-per-token estimates. | Each result should be labeled measured or estimated, with the functional unit, uncertainty and omitted terms visible. |
The most salient provider-level information is the CO2e generated by the cloud instances running the workload, as discussed in the Microsoft Research and FAccT paper Measuring the Carbon Intensity of AI in Cloud Instances. Instance-level information is more actionable than a broad corporate average because it can be tied to a model, region and operating period.
Make measurements comparable across models and regions
A lower per-token figure does not automatically mean a lower total footprint. Compare both intensity and volume: a more efficient model can emit more overall if it handles far more traffic.
Compare like with like
- Use the same functional unit and output quality target.
- Keep prompt length, output length, batch size and latency requirement comparable.
- Use the same boundary for storage, networking, cooling and embodied emissions.
- Separate measured values from estimates instead of averaging them without labels.
- Report absolute totals alongside per-request or per-token intensity.
Evaluate regional placement carefully
Moving a latency-flexible batch job to a lower-carbon region or time can reduce location-based emissions, but only when data-residency, security, availability, network transfer and service-level constraints permit it. A region with a cleaner grid may require more network traffic or duplicate capacity elsewhere. Include those effects if they are inside your boundary.
Quantify uncertainty instead of presenting false precision
Uncertainty usually comes from energy estimation, workload allocation, grid factors, PUE, hardware lifetime and embodied-emissions datasets. A result reported to several decimal places is not necessarily precise.
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Use a range when an input is a range. For example, calculate a low and high case using plausible energy, utilization and carbon-intensity values, then show which assumption drives the spread. Keep measured telemetry distinct from modeled estimates. If a provider changes a methodology, retain the old report and explain that a historical difference may reflect accounting rather than a change in electricity use.
Customer-specific cloud footprints may not have third-party assurance. Treat provider dashboards as valuable operational data, but do not describe them as audited unless the provider explicitly states that assurance applies to the exact figure and boundary you report.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Turn the measurement into carbon-aware engineering
Measurement matters when it changes design or operations. Track emissions per useful output as a service-quality and sustainability KPI.
- Right-size models: use the smallest model that meets accuracy, safety and latency requirements.
- Improve utilization: consolidate idle capacity, increase safe batching and avoid leaving accelerators powered for sparse workloads.
- Reduce unnecessary work: cache repeated results, limit retries and stop abandoned experiments promptly.
- Choose efficient hardware: compare useful output per unit of energy, not just peak throughput.
- Schedule flexibly: move delay-tolerant training or batch inference to lower-carbon hours or regions when reliability and residency requirements allow.
- Monitor quality: pair emissions with accuracy, latency, error rate and user success so an apparent carbon improvement is not caused by degraded service.
- Use lifecycle decisions: include replacement and embodied impacts when extending hardware life or consolidating clusters.
AWS recommends comparing productive output with total impact and using units such as users or transactions. That framing prevents an optimization that lowers emissions per token while increasing total emissions through unnecessary token generation or traffic growth.
Common measurement failures and how to correct them
Using a universal model number
There is no single carbon footprint for a model independent of hardware, utilization, region and time. Replace a generic claim with a measured or estimated result tied to a specific workload and boundary.
Counting only accelerator power
Accelerator telemetry can omit host, memory, storage, networking and facility overhead. State the scope, apply a documented PUE or overhead factor where appropriate, and avoid adding overlapping estimates.
Mixing location-based and market-based values
Keep the two methods in separate columns and explain what each answers. Do not use a market-based certificate to claim that the physical grid intensity at the server changed.
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For a project or service total, include failed experiments, retries, warm-up periods and idle capacity according to the boundary you declared. If you report only successful runs, name that narrower scope.
Hiding exclusions
List omitted networking, storage, end-of-life, grid infrastructure or embodied terms beside the result. An operational-only number is useful when clearly labeled; it is misleading when presented as a complete lifecycle footprint.
Dropping version information
Record model, hardware, software, telemetry, grid-factor and provider-methodology versions. Without them, a later reader cannot tell whether a change came from engineering or accounting.
Reproducibility checklist
Before publishing or using a footprint in a decision, confirm that the record contains:
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- Boundary, geography and reporting dates.
- Functional unit and denominator definition.
- Training, fine-tuning, inference and data-processing components separated where relevant.
- Energy source: direct telemetry, provider estimate or model-specific coefficient.
- Measured-versus-estimated label for every major input.
- Hardware models, quantities, utilization and allocation rule.
- Facility overhead or PUE assumption.
- Location-based factor, and market-based factor separately if used.
- Grid-factor source, timestamp, region and dataset version.
- Embodied-emissions source, useful life and allocation method.
- Networking, storage, cooling, end-of-life and other exclusions.
- Absolute kgCO2e and intensity per request, token, run, user or transaction.
- Uncertainty range or explanation of the largest unknowns.
- Model, framework, driver, firmware, provider report and methodology versions.
Keeping this metadata with the number makes the result reproducible, comparable and useful for reducing emissions rather than merely producing a polished estimate.
Frequently Asked Questions
Should training and inference emissions be reported together?
Report them separately first because they have different workloads, denominators and optimization opportunities. Combine them only for a lifecycle total after each component’s boundary and functional unit are documented.
What should I do when my cloud provider does not expose energy telemetry?
Use the provider’s documented estimate or a model-specific energy-per-token coefficient, label it as estimated, preserve the coefficient and methodology version, and report uncertainty instead of presenting it as measured electricity.
Is a renewable-energy contract enough to claim zero operational emissions?
No. A contract supports a market-based accounting result. Publish the location-based physical grid result separately and explain the difference.
Which number should I put on a sustainability dashboard?
Show absolute kgCO2e for inventory control and an intensity such as kgCO2e per successful request, output token, user or transaction for operational decisions. Keep the boundary and exclusions visible beside both.
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