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Measure an AI workload’s footprint by defining a boundary and functional unit, collecting its energy use, multiplying energy by the electricity carbon intensity for the actual location and time, and adding an allocated share of hardware emissions. The Green Software Foundation’s SCI for AI expresses this as SCI = (E × I + M) / R, where E is energy, I is carbon intensity, M is embodied emissions, and R is the useful result being measured. Report both total kgCO2e and an intensity such as grams per request, output token, training run or transaction.
What an AI carbon-footprint measurement actually tells you
There is no universal “carbon cost of a model.” The result belongs to a specific workload: a particular model version, hardware type, utilization level, cloud region, time period and accounting boundary. Changing any of those can change the answer.
A useful inventory has two outputs:
- Absolute emissions: total kilograms of carbon-dioxide equivalent (kgCO2e) for a training run, month, deployment or other period.
- Functional-unit intensity: emissions per useful unit, such as one inference request, one output token, one batch, one user transaction or one complete model lifecycle.
Absolute totals support organizational inventories and disclosure. Intensity lets an engineering team compare models, hardware, regions and optimizations without confusing a small efficient system with a large system that simply serves fewer requests.
Start with the SCI for AI equation
The Green Software Foundation’s SCI for AI extends the Software Carbon Intensity specification and ISO/IEC 21031:2024 for AI systems. Its calculation is:
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SCI = (E × I + M) / R
- E — operational energy: electricity consumed by the workload and any explicitly included overhead.
- I — operational carbon intensity: emissions associated with a kilowatt-hour at the relevant place and time.
- M — embodied emissions: the allocated manufacturing, transport, maintenance and end-of-life impact of hardware used by the workload.
- R — functional unit: the useful output against which the impact is normalized.
The formula is not a magic estimator. It is a reporting framework that makes your boundary and assumptions visible. If you cannot obtain defensible embodied-emissions data, publish the operational result separately, mark M as omitted or incomplete, and explain the limitation rather than silently treating the operational figure as the full footprint.
Step 1: Set the boundary and functional unit
Write the boundary before collecting numbers. Otherwise, two apparently precise measurements may be answering different questions.
Choose what is inside the boundary
Possible components include model training, fine-tuning, evaluation, inference, data preparation, storage, networking, orchestration and the hardware lifecycle. A production service might measure only serving electricity for an operational KPI, while a product lifecycle inventory might include pre-training, repeated fine-tuning, data processing and retirement of equipment. State the choice explicitly.
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Select a functional unit that matches the decision
| Question | Useful functional unit | What it helps compare |
|---|---|---|
| How expensive was one training attempt? | One completed training run, or one successful run at a defined model quality | Model architectures, hardware and experiment efficiency |
| What does serving cost? | One inference request or one user transaction | Serving stacks and deployment configurations |
| How does output length affect impact? | One input/output token or one output token | Prompting, generation length and model choice |
| What does a batch job consume? | One batch, document, image or data record processed | Batch-size and pipeline changes |
| What reaches the customer? | One user task completed or one business transaction | End-to-end product efficiency |
| What is the full environmental burden? | One model lifecycle, from preparation through retirement | Training, upgrades, serving and hardware allocation together |
Define the denominator precisely. “Per token” can mean input tokens, output tokens or both; “per request” should specify whether failed, cached and batched requests are included.
Step 2: Collect workload energy
Measured telemetry is preferable to a generic energy-per-token assumption. Record the source, sampling interval, coverage and whether the value includes only accelerators or the complete host and facility overhead.
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Use direct or provider telemetry first
For a self-managed cluster, collect accelerator power and utilization, host power, job start and stop times, and the allocation of shared nodes to the workload. A per-job energy meter or board-level telemetry is more useful than multiplying a device’s maximum-rated wattage by elapsed time.
For cloud services, use the provider’s workload or instance measurements when available. Preserve the service, region, machine type, time window and report or methodology version. A provider report can be recalculated when its methodology changes, so retaining the original version is part of reproducibility.
Label measured and estimated values
The SCI for AI reference implementation reads per-call energy from supported SDK 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. Keep that label in your data export and dashboard; an estimate should never look like a meter reading.
When using an estimate, record the model version, tokenization method, sequence lengths, hardware assumption, utilization assumption and source date. A per-token coefficient derived for one accelerator and batch size cannot be transferred unchanged to another deployment.
Account for facility overhead consistently
Decide whether your energy value already includes power usage effectiveness (PUE), cooling and other facility overhead. If the telemetry is device-only, apply a documented PUE or an equivalent provider factor once. Do not apply a facility multiplier to a figure that already includes it.
Networking, storage and orchestration may be outside a compute meter. Include them when material to your boundary, or list them as exclusions. Google’s Carbon Footprint documentation, for example, identifies exclusions that can include some networking, end-of-life and grid-infrastructure embodied emissions.
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Step 3: Choose the electricity carbon intensity
Operational emissions are energy multiplied by the carbon intensity of electricity:
Operational CO2e = E × I
Use a factor that matches both the physical location and the time period of consumption. Electricity can be substantially different between regions and between hours in the same region.
Location-based versus market-based reporting
| Method | Question answered | Typical input | Do not claim |
|---|---|---|---|
| Location-based | What emissions are associated with the grid physically supplying the workload? | Regional or hourly grid mix | That a contract or certificate changed the physical grid mix |
| Market-based | What emissions are assigned after contractual instruments or clean-energy purchases? | Supplier contracts, certificates or contractual factors | That the contractual figure is the same as physical grid emissions |
Publish both when your reporting purpose requires them, and label them rather than combining them into one number. Renewable-energy contracts and offsets affect market-based accounting; they do not automatically remove the physical emissions associated with electricity consumed at the site.
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Google describes hourly, location-based factors and uses Electricity Maps data where available, with annual International Energy Agency factors as a fallback. If you use an annual fallback, state that its temporal resolution is coarser than the workload’s actual operating pattern. Store the factor’s geographic scope, timestamp or vintage, source and version beside every result.
Step 4: Allocate embodied hardware emissions
Operational electricity is only part of the footprint. Manufacturing and end-of-life emissions for GPUs, CPUs, servers, networking equipment and storage must be allocated over useful life and across the workloads that share the equipment.
A defensible allocation needs four quantities
- The embodied-emissions estimate for the device or server, preferably from a manufacturer life-cycle assessment or a documented industry dataset.
- Expected useful lifetime or service hours.
- The workload’s share of those hours, capacity or delivered compute.
- A rule for shared components such as the host, memory, networking and cooling infrastructure.
Cloud Carbon Footprint documents limited compute embodied-emissions allocation because public data is incomplete. Treat such coefficients as estimates, preserve their version and date, and show the allocation method in your report. If you cannot establish a credible value, report E × I as operational emissions and state that embodied emissions were not quantified.
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Step 5: Calculate and normalize the result
Keep units consistent
Convert energy to kilowatt-hours and carbon intensity to kilograms of CO2e per kilowatt-hour before multiplying. Convert grams to kilograms only once, and keep the same CO2e convention for operational and embodied terms.
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Illustrative calculation
The following is a worked example, not a benchmark for any model or provider. Suppose a defined serving period uses 120 kWh of workload energy, the location-based factor for that period is 0.35 kgCO2e/kWh, and an allocation method assigns 8 kgCO2e of embodied hardware emissions. If the period contains 10,000 completed requests:
- Operational emissions:
120 × 0.35 = 42 kgCO2e. - Total emissions:
42 + 8 = 50 kgCO2e. - Intensity:
50 ÷ 10,000 = 0.005 kgCO2e/request, or 5 gCO2e per request.
The report should also show the boundary, request-count definition, energy source, carbon-factor timestamp, hardware allocation and exclusions. Without those fields, the arithmetic is reproducible but the comparison is not.
Separate totals for different lifecycle stages
Publish training, fine-tuning, evaluation and inference as separate rows when they have different denominators or uncertainty. A lifecycle total can then be calculated by adding compatible absolute values, while each stage retains its own intensity.
Step 6: Quantify uncertainty and exclusions
Uncertainty comes from telemetry gaps, estimated coefficients, changing utilization, carbon-factor resolution, shared hardware and incomplete lifecycle data. Do not hide it behind excessive decimal places.
- Mark every energy value as measured or estimated.
- Give a range when a coefficient or allocation is uncertain, and identify the variable driving the range.
- Record whether PUE, networking, storage, cooling, manufacturing, transport and end-of-life are included.
- Record model, software, hardware, region, utilization and time window so another team can repeat the measurement.
- Retain the provider report version and methodology date because historical values can change after recalculation.
Google notes that customer-specific Carbon Footprint data is not third-party assured and can change when methods or data sources change. AWS says its estimates use industry-standard methods, are independently verified and are recalculated when methodology versions change; its current coverage is AWS-operated infrastructure. These statements describe the reporting systems, not a guarantee that every workload has identical boundaries or precision.
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Provider and tool options
| Option | What it provides | Important limitations to record | Best fit |
|---|---|---|---|
| AWS Sustainability carbon estimates | Estimated carbon impacts for covered AWS services, with methodology updates and historical recalculation | Coverage is AWS-operated infrastructure; version changes can alter historical results | Workloads that remain within covered AWS services and need provider reporting |
| Google Cloud Carbon Footprint | Allocates machine energy and emissions to products and customers; supports location-based and market-based views | Customer-specific data is not third-party assured; documented exclusions include some networking, end-of-life and grid-infrastructure embodied emissions | Google Cloud inventories requiring both accounting views |
| Cloud Carbon Footprint | Energy coefficients, PUE assumptions, optional Electricity Maps data and limited compute embodied-emissions allocation | Coefficients and embodied estimates are estimates constrained by public data; preserve version and date | Multi-cloud or self-managed reporting where a common estimation layer is needed |
| SCI for AI reference implementation | AI-specific functional units, per-call instrumentation where supported and estimated energy-per-token fallback | Measured coverage depends on SDK support; fallback values must remain labeled as estimates | Application-level intensity per request, token or other useful output |
Compare tools on measurement granularity (machine, hour, request or token), measured versus estimated energy, location- versus market-based factors, embodied-emissions coverage, regional and time coverage, audit or assurance status, exclusions, versioning and export/API support. A tool that reports a more detailed number is not necessarily more accurate if its underlying coefficient is generic.
Measure each part of the AI lifecycle
Training and fine-tuning
Capture job start and stop times, retries, failed experiments, checkpointing, evaluation runs and the hardware allocation. Decide whether the denominator is one attempted run or one successful model meeting a quality target. Include repeated experiments when reporting the project or model lifecycle; otherwise a single-run number can understate development emissions.
Inference and serving
Instrument requests or batches at the application boundary. Record prompt and output tokens, cache hits, batching, queue time, model routing and failed requests. Report both per-request and per-token values when output length varies substantially. A request counter without token or batch context can conceal changes in workload mix.
Data preparation, storage and networking
Include these stages when they are inside your stated boundary and material to the use case. Data preparation may run on different hardware and in a different region from training. Storage can persist for the model’s entire lifecycle, while networking may be excluded by a provider’s compute-centric report. List each exclusion rather than implying that a compute number is end to end.
Turn the measurement into lower emissions
Optimize useful output, not just utilization
A highly utilized accelerator can still produce a poor result if it generates unnecessary tokens or retries. Track emissions per useful output, user or transaction alongside throughput and quality. AWS recommends comparing productive output with total impact and using units such as users or transactions.
Right-size and consolidate
- Remove idle replicas and consolidate low-utilization jobs.
- Match accelerator size and precision to the quality target rather than defaulting to the largest instance.
- Use batching, caching and shorter outputs where they preserve service quality.
- Schedule experiments to avoid repeated failed runs and unnecessary checkpoint or evaluation work.
Choose lower-carbon times or regions carefully
Latency-flexible training and batch inference can be shifted toward lower-carbon hours or regions. Check data-residency requirements, user latency, availability, network energy, service reliability and regulatory constraints before moving a workload. A lower grid factor is not a complete win if the relocation causes retries, extra data transfer or overprovisioned standby capacity.
Use model and hardware efficiency together
Compare a smaller model, quantization, distillation, efficient kernels and hardware generations using the same functional unit and quality requirement. Keep training and serving measurements separate: a change that lowers inference energy may require additional fine-tuning energy, and the net lifecycle effect depends on request volume and service lifetime.
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Attach this information to every published number, dashboard export or internal decision:
- Boundary: training, fine-tuning, inference, data, storage, networking, facility overhead and hardware lifecycle components included.
- Functional unit: exact definition of run, request, token, batch, user transaction or lifecycle.
- Energy source: meter, provider telemetry, SDK instrumentation or estimated coefficient; sampling interval and measured/estimated status.
- Workload identity: model and tokenizer versions, software stack, hardware type, batch size, sequence lengths and utilization.
- Location and time: physical region, operating window, carbon-factor timestamp or annual vintage, and location- or market-based method.
- Facility assumptions: PUE or provider overhead treatment, with confirmation that it was not applied twice.
- Embodied allocation: hardware estimate, useful life, workload share, shared-component rule and omitted lifecycle stages.
- Exclusions: networking, storage, end-of-life, grid infrastructure, failed jobs, retries and other items outside the boundary.
- Uncertainty: ranges, dominant assumptions and any known telemetry gaps.
- Versioning: provider report, coefficient, software and methodology versions, plus the date of calculation.
With these fields, a carbon figure becomes an auditable engineering measurement rather than a portable but misleading “grams per token” claim. The most useful practice is to maintain the absolute total for inventory purposes and a clearly defined per-unit intensity for every optimization decision.
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