What is this Estimator for? 

This is designed to help you estimate the environmental footprint of AI use. These calculations and sources are very complex and data are highly variable, so there will probably be some errors. 

• What types of AI use the most energy and have the biggest carbon footprint? 

• How is this equivalent to other forms of energy use? 

• How could we use this information to help us choose when and how to use AI? 

• How could we use this information to help us reduce our impacts of AI use on the environment? 

Find out how this app was created (and a link to the technical report) on the Methods & About page. 

Find all the references and resources on the Research page. 

For Lesson ideas connected to this work, see the Lessons page. 

To see how this app and site align with UNESCO's Guidance on GenAI in Education & Research, UNESCO's AI Competencies for Students and Teachers and the OECD's AI Literacies for Students, see this page. 

Disclaimer: This page and the app are designed for educational illustration only. With so many variables and changes to AI models, data can change quickly. This is not for serious technical use, but could support classroom conversations around AI use. If you can make a better, more reliable version of this, please go ahead and share! 

Why Does AI Have Such a Big Environmental Footprint? 

AI needs massive amounts of computing power - imagine thousands of powerful computers all working at once. When we "train" AI (teach it to do things like chat or create images), these computers work hard, using lots of electricity. They also need to be cooled, which has a high water demand. 

Think of it like baking cookies: training AI is like figuring out the perfect recipe through hundreds of attempts (using lots of energy), while using AI after training is like following the recipe once you know it (using less energy, but still significant). Even after training, simple tasks like generating one AI image uses about as much energy as charging your smartphone (MIT Technology Review, 2023).

The environmental impact gets bigger because most of this electricity still comes from fossil fuels like coal and natural gas. At the same time, more and more AI tools are being created, AI is being included in more of the things we use every day and there is more demand for AI. This means ever-increasing demands for computing power, electricity and water - not to mention the precious metals and other materials needed to create the computers. 

It's not just energy. The estimator also tries to deal with carbon costs and water impacts. Datacenters take a lot of water to run, for coooling and processing. Although some of this is recycled, they still put a lot of pressure on their local water systems. On Jan 31 2026, the community of Qilicura, Chile, hosted Quili.AI, an event where they responded as humans to user prompts, to raise awareness of the impacts on their local community. Find out more here. 

So what can we do about it?

We can try to be aware of our impacts of AI use, and make mindful, informed choices about the best time to use AI tools: 

• Do we really need to use AI for this task? 

• Am I missing the point of learning if I use AI for this? 

• If I need to use AI, how can I learn to use more efficiently, for better results and a reduced impact? 

How about using these tools or your own research to estimate the carbon footprints of your class over year, and develop a community engagement project to offset those impacts? 

• Could you plant some trees? 

• Raise awareness about making sensible choices? 

• Purchase Green Energy Certificates (China)/Renewable Energy Certificates (USA)?