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Developers can reduce AI’s environmental impact by first checking whether AI is needed, then choosing the smallest adequate model and data footprint, measuring impacts across the lifecycle, and asking suppliers for comparable environmental information. No single carbon estimate captures the whole picture: energy, water, hardware, and impacts beyond model training also matter.
Start by asking whether AI is the right tool
Define the outcome the feature must achieve before selecting a model. Compare AI with a simpler rules-based approach, conventional software, or no automated solution. The UK Government’s Data and AI Ethics Framework says to “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 deliver relative to its resource cost.
If AI is appropriate, specify the quality threshold that makes the feature useful. That gives the team a practical basis for comparing models and optimizations rather than assuming that a larger or more capable system is automatically better.
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Reduce the footprint at each development stage
AI’s environmental footprint spans data preparation, development, training, deployment, the infrastructure used to run it, and end of life. Direct impacts can include energy, water, minerals, emissions, and electronic waste; indirect and systemic effects also belong in a full assessment. The United Nations Environment Programme describes the need to assess the full AI lifecycle.
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Use only the data the task needs
Test whether a smaller, curated dataset can meet the quality threshold. Consider whether the task needs high-resolution or high-bit-rate inputs, and whether processing large volumes of images, video, or text is necessary. Data volume and type affect the resources required for storage and computation.
Choose and reuse an appropriately sized model
Compare task-specific or smaller models with multipurpose generative systems. Where an existing model can meet the task’s requirements, consider reusing it rather than training or building a similar system from scratch. Compare actual task quality alongside resource use; there is no universally most sustainable model without comparable evidence about workload and measurement boundaries.
Optimize training and keep compute purposeful
Evaluate quantization and pruning when they preserve the quality the feature requires. Use early stopping when performance plateaus, and remove idle or outdated compute resources. Treat each change as a quality-and-resource trade-off: an optimization is useful only if the resulting system still meets the task’s needs.
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Consider where and when workloads run
Workload location affects the electricity mix and can affect environmental impacts. Where feasible, schedule compute-heavy work for times when electricity is cleaner, while complying with data-residency requirements. A location or timing change is not a blanket solution: assess it against the workload, local conditions, and applicable constraints.
Measure more than carbon—and state what the numbers cover
Track indicators suited to the project and repeat them over time. Useful measures include energy in kilowatt-hours, carbon dioxide-equivalent emissions, water in litres, and compute efficiency such as FLOPs per watt. A carbon-only figure can omit water use and local impacts.
For each result, record the workload, time period, geography, measurement boundary, and whether the number is metered or estimated. Do not compare figures as if equivalent when one covers only model inference and another includes a broader set of lifecycle stages or infrastructure impacts.
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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 or training estimate is therefore not, by itself, a complete lifecycle assessment.
Use measurement tools as estimates with defined scopes
The UK framework names these tools as starting points, not interchangeable or comprehensive assessments:
- Data Carbon Ladder: estimates a data CO2 footprint.
- CodeCarbon: a Python package that estimates CO2 from cloud or personal computing resources.
- ML CO2 Impact: a machine-learning emissions calculator.
- Carburacy: measures carbon-aware NLP model accuracy.
- EcoLogits: tracks energy and environmental impacts of generative AI model API use.
Before adopting a tool, check its present availability, support, assumptions, and scope. Keep its method with the result so that later comparisons remain meaningful.
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Ask suppliers for scoped, comparable information
When buying AI services or infrastructure, request environmental information for both the model and the infrastructure supporting it. Ask what is measured, what is estimated, and what parts of the lifecycle are included. The UK framework says supplier reporting reliability may vary, so document both the information received and any gaps.
- Energy and carbon information for the model and infrastructure, including the workload and measurement boundary.
- Water information and any relevant geographic scope, rather than carbon figures alone.
- How the provider accounts for renewable energy practices.
- How hardware is handled at end of life.
Use these details to compare suppliers only where the boundaries and workloads are sufficiently alike. If they are not, treat the figures as separate disclosures, not a ranking.
Interpret provider efficiency claims in context
In 2025, Google reported a 33-fold reduction in median energy consumption and a 44-fold reduction in median carbon footprint per Gemini Apps text prompt over a 12-month period. These are company-reported figures for that product and prompt context, using Google’s methodology; they are not independent comparisons across providers or a benchmark for other AI workloads. Google also compared the energy for a median prompt with watching television for less than nine seconds, an analogy tied to the same company methodology and product context. See Google’s explanation of its approach.
Know what standards work is—and is not—final
IEEE lists P7100, “Standard for Measurement of Environmental Impacts of Artificial Intelligence Systems,” as an Active PAR project. Its stated aim is to harmonize reporting of environmental indicators for model training and inference, including energy, CO2 emissions, and water consumption, and to distinguish AI-specific compute from general-purpose compute. The project page lists PAR approval on 2024-06-06; P7100 is a project under development, not an approved final standard. Check the IEEE project page for its status.
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