Green AI & Sustainability¶

AI systems have a substantial ecological footprint. The electricity demand for AI computing power is growing rapidly: it is expected to be 11 times higher in 2030 than in 2023. For project managers, sustainability is therefore not an afterthought but a strategic decision that must be made as early as the Business Understanding phase.

Sources: [so-47], [so-48]

1. The Ecological Footprint of AI¶

Energy¶

• A single AI query consumes an estimated 0.3–0.8 Wh of electricity — up to 10 times more than a standard search query. The exact value depends on model size and modality.
• Training a large language model can emit hundreds to thousands of tonnes of CO₂, depending on model size and infrastructure — equivalent to hundreds or thousands of transatlantic flights.
• Data centres are responsible for approximately 2% of global greenhouse gas emissions.

Water¶

• For every kilowatt-hour a data centre consumes, approximately 2 litres of water are required for cooling.
• By 2030, water consumption by data centres is expected to triple to 664 billion litres per year.

Hardware¶

• Rapid hardware refresh cycles lead to large quantities of e-waste containing specialised metals that are difficult to recycle.

Sources: [so-47], [so-48]

2. Reduction Potential¶

Research from Cornell University (2025) shows that the ecological impact of AI can be drastically reduced by combining two measures:

Source: [so-47]

3. Practical Measures per Project Phase¶

Phase 1 — Discovery & Strategy¶

Model selection as a sustainability consideration:

• Choose "lean" models or knowledge distillation (transferring knowledge from a large to a small model) when the task allows. According to compression research (Polino et al.), this can reduce operational emissions by up to 80%, though actual savings are task-dependent.
• Document the choice of a specific model including the motivation for the model size in the Technical Model Card.

Questions at model selection:

• Is a smaller specialised model sufficient for this task?
• Does the vendor provide transparency on energy consumption and data centre location?
• Are there alternatives with comparable performance on green infrastructure?

Phase 3 — Development¶

Temporal Workload Shifting:

• Schedule non-urgent training tasks at times when there is a surplus of solar or wind energy available on the grid. This leads to an average of 40% fewer emissions for the same computation.
• Consider carbon-aware schedulers (e.g. via the Carbon Aware SDK from the Green Software Foundation).

Green Coding Guidelines:

• Avoid unnecessary API calls: use caching for repeated queries (see also Cost Optimisation)
• Minimise prompt length without quality loss
• Limit model response length where possible (max_tokens)
• Use batch processing for non-real-time tasks

Phase 5 — Monitoring & Optimisation¶

Continuous monitoring of ecological KPIs:

4. Decision Framework: When is AI Sustainably Justified?¶

Ask yourself the following questions at every AI initiative:

1. Is the problem large enough? Does the value creation outweigh the energy cost?
2. Is there a leaner alternative? A simple rule-based system or a small specialised model may be better than a large foundation model.
3. Is the energy being decarbonised? Does your cloud provider choose renewable energy?
4. Is hardware being managed responsibly? Is there a plan for hardware lifecycle and e-waste?

5. Related Modules¶

• Cost Optimisation
• AI Architecture
• Goal Card (Doelkaart)
• Benefits Realisation
• Sources & Inspiration