21 Dec AI and the Environment: An IB ESS Evaluation Guide
AI and the environment provide a useful context for IB ESS systems thinking. Artificial intelligence relies on physical infrastructure and can support environmental work, but its net effect depends on how it is used. Avoid assuming that AI is automatically sustainable or that every AI service has the same footprint.
Electricity sources matter
Training and running AI models require electricity. Data centres are not always powered exclusively by fossil fuels: their supply can include renewables, nuclear power, coal and natural gas, depending on the location and electricity system. The IEA analysis of energy supply for AI describes these regional differences.
Assess electricity demand as well as the emissions associated with supplying it. Do not confuse a company’s renewable-energy purchasing claim with evidence that every hour of operation uses only renewable electricity. Distinguish measured data from projections and state the year and geographical scope.
Include water, materials and waste
The footprint extends beyond electricity used while a model runs. Producing computing equipment requires materials and manufacturing, and replacing equipment can generate electronic waste. Some data centres use water-based cooling; impacts depend on the cooling design and local water conditions.
UNEP’s data-centre guidance discusses energy and water concerns. A volume of water use should be interpreted in context, including local water stress and whether the measurement describes withdrawal or consumption.
Useful applications do not guarantee net benefits
AI can support activities such as analysing environmental observations or improving energy-system operation. UNEP describes using AI to analyse satellite data for methane-emissions monitoring. A useful output still needs verification and a practical response to produce an environmental benefit.
Compare the application with a credible alternative. Include its operating and infrastructure impacts, and ask whether it changes total demand. The IEA discussion of AI and climate change explains that potential savings can be affected by rebound effects and deployment choices.
Weather forecasts remain uncertain
Weather prediction and long-term climate projections answer different questions. AI does not make the precise time and location of every flood or hurricane certain. ECMWF’s Artificial Intelligence Forecasting System operates alongside its physics-based forecasting system; ensemble forecasts help represent possible outcomes and uncertainty.
See ECMWF’s operational AIFS announcement and its explanation of AI forecasting and uncertainty.
Original ESS evaluation task
This is an original exercise, not an official IB question or a graded response. A city proposes an AI system to reduce electricity demand in public buildings. What evidence would show that total energy use has fallen? Consider the comparison period, weather, occupancy, computing demand, costs and access to the data.
For SL or HL, connect evidence to systems and sustainability. At HL, apply relevant law, economics or ethics concepts where the question calls for them. Practise interpreting data and explaining environmental perspectives.
Practise with individual ESS support
In Marija’s online IB ESS SL and HL lessons, you can work on concepts, data interpretation and clear explanations. Request a free 20-minute trial lesson to discuss your priorities. For IA work, the investigation and submitted writing remain your own.
Course reference: Official IB ESS guide, first assessment 2026 and IB course updates. Reviewed 7 October 2026.
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