Make AI more sustainable by first checking whether AI is needed, then using the smallest adequate data, model and compute footprint—and measuring impacts across the system’s lifecycle. Track energy, carbon and water where possible, and record what each measurement includes. Current estimates are incomplete, so treat reported figures as scoped evidence rather than universal benchmarks.
Start by asking whether AI is the right tool
Define the outcome the feature must deliver before choosing a model. Compare AI with a simpler rule-based method, conventional software or no automated solution. The UK Government’s Data and AI Ethics Framework advises that “you should always explore different options, including not using AI at all, before choosing a technical approach.” Record why AI is justified and what benefit it is expected to provide.
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This first decision matters because the environmental cost is not limited to a model’s response. It can include data preparation, development, training, deployment, infrastructure production and end of life. The UN Environment Programme’s lifecycle assessment calls for impacts to be considered across that full chain, including energy, water, minerals, emissions and electronic waste, as well as indirect and systemic effects. UNEP’s AI lifecycle report
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Use only the data the task needs
Test whether a smaller, curated dataset can meet the required quality. Consider whether the feature needs the original data resolution or bit rate, and whether high-volume image, video or text processing is necessary at all. Data volume and type affect resource use, as do storage and processing choices. The UK framework
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Choose the smallest adequate model
Benchmark task-specific or smaller models against a multipurpose generative system using the quality threshold the product actually requires. Reuse an existing model where it is suitable rather than training or building a similar system from scratch. There is no evidence here for a universally most sustainable model: comparisons need matched tasks and comparable measurement boundaries.
Optimize training and keep compute in use
- Evaluate quantization and pruning, and retain them only when the resulting model still meets required quality.
- Use early stopping when performance plateaus rather than continuing training without a useful gain.
- Remove idle or outdated compute resources instead of paying their energy and infrastructure costs without corresponding work.
Make deployment location and timing deliberate
Where feasible, consider the electricity mix at the workload’s location and schedule compute-heavy work for periods when electricity is cleaner. Balance those choices against latency, availability, data residency and other operational constraints; a location choice cannot be evaluated on carbon alone.
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Measure more than a single carbon number
Use indicators that fit the project and can be followed over time. Useful measures include electricity in kilowatt-hours (kWh), greenhouse-gas emissions in carbon dioxide equivalent (CO2e), water in litres, and compute efficiency such as FLOPs per watt. Carbon-only reporting can miss water use and local impacts.
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For each result, document the workload and time period, geography, system boundary and method. State whether the figure is metered or estimated, and what it includes—for example, inference alone or a broader lifecycle. Do not compare two figures as if they were equivalent when one covers a model’s direct compute and the other includes infrastructure or additional lifecycle stages.
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The International Telecommunication Union’s 2025 report, Measuring What Matters: How to Assess AI’s Environmental Impact, notes that training-energy assessments commonly rely on indirect estimates rather than real-time empirical measurement, while other lifecycle stages remain underexplored. A prompt-level estimate or training estimate is therefore not a complete lifecycle assessment.
Tools can help, but check their scope
The UK framework names several tools as starting points: Data Carbon Ladder for estimated data CO2 footprint; CodeCarbon, a Python package estimating CO2 from cloud or personal computing resources; ML CO2 Impact, a machine-learning emissions calculator; Carburacy, a carbon-aware NLP model accuracy measure; and EcoLogits, for tracking energy and environmental impacts of generative AI model API use. Their availability, support, assumptions and measurement boundaries may change, so check those details before choosing a tool or comparing its output with another source. UK Government framework
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Ask suppliers for comparable environmental information
When evaluating a model, cloud service or AI infrastructure supplier, ask for information that lets your team understand both the figure and its scope. The UK framework notes that supplier reporting reliability can differ, so retain the answers and identify gaps rather than treating every claim as directly comparable.
- Energy and carbon data for the model and supporting infrastructure, including the measurement boundary, workload and period.
- How the provider accounts for electricity sources and renewable energy practices.
- Water information and relevant geographic context, where available.
- Hardware production and end-of-life handling information, including reuse or disposal practices.
- Whether reported figures are measured or estimated, and what parts of the AI lifecycle they cover.
Use published figures with their original scope
In 2025, Google reported a 33-fold reduction in median energy consumption per Gemini Apps text prompt and a 44-fold reduction in median carbon footprint per such prompt over a 12-month period. These are company-reported figures tied to Google’s methodology and product context, not independent comparisons across providers. Google also equated the energy per median prompt to watching television for less than nine seconds; that is the company’s analogy for its own measured context, not a general estimate for AI prompts. Google’s methodology and explanation
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Track standards without overstating their status
IEEE lists P7100, “Standard for Measurement of Environmental Impacts of Artificial Intelligence Systems,” as an Active PAR project. Its stated purpose is to harmonize reporting of environmental indicators for model training and inference—including energy, CO2 emissions and water consumption—and distinguish AI-specific compute from general-purpose compute. The project page lists PAR approval as 2024-06-06; it is a standard under development, not an approved final standard. IEEE P7100 project page
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