AI Queries And Their Surprising Climate Impact

The Hidden Energy Drain of AI Queries

Every time you ask an artificial intelligence a question there is a surprising cost carbon emissions.

How AI Models Process Your Questions

Before an AI like ChatGPT can respond it first breaks down your input into tokens small chunks of text such as words parts of words or punctuation. These tokens are turned into numbers the model can process using billions of internal settings called parameters which help it recognise patterns make connections and predict what comes next. These predictions are made one token at a time and then assembled into a final answer.

That entire process consumes energy.

Quantifying AIs Carbon Footprint New Research

And now researchers in Germany have calculated how much CO2 is released by different large language models LLMs when they answer a question.

LLMs are the software behind tools like ChatGPT Google Gemini and other AI assistants. They have been trained on massive volumes of text to learn how to read write and respond intelligently.

If users know the exact CO2 cost of their AI generated outputs such as casually turning themselves into an action figure they might be more selective and thoughtful about when and how they use these technologies.

The researchers tested 14 LLMs by asking them 1000 benchmark questions across diverse subjects. They then calculated the associated CO2 emissions revealing a big divide between concise models and those that generate lengthy reasoned responses.

The Great Divide Reasoning vs Concise AI Models

The environmental impact of questioning trained LLMs is strongly determined by their reasoning approach with explicit reasoning processes significantly driving up energy consumption and carbon emissions says first author Maximilian Dauner a researcher at Hochschule Munchen University of Applied Sciences. We found that reasoning enabled models produced up to 50 times more CO2 emissions than concise response models.

Reasoning models on average created 543.5 thinking tokens per question whereas concise models required just 37.7 tokens. More tokens mean higher CO2 emissions but it does not always correspond with accuracy.

The Accuracy Versus Sustainability Dilemma

The best performing model Cogito with 70 billion parameters scored 84.9 percent accuracy but emitted three times more CO2 than similar sized models that gave shorter answers.

Currently we see a clear accuracy sustainability trade off inherent in LLM technologies says Dauner. None of the models that kept emissions below 500 grams of CO2 equivalent achieved higher than 80 percent accuracy on answering the 1000 questions correctly.

Does the Type of Question Matter for Emissions

The subject mattered too. Philosophical or abstract mathematical questions caused up to six times more emissions than simpler topics like high school history due to longer reasoning chains.

Making Smarter Greener Choices with AI

The researchers hope these findings will encourage more thoughtful use of AI.

Users can significantly reduce emissions by prompting AI to generate concise answers or limiting the use of high capacity models to tasks that genuinely require that power says Dauner.

Not All AI Models Are Created Equal Environmentally

Even the choice of model makes a difference. For example DeepSeek R1 with 70 billion parameters answering 600000 questions generates emissions equivalent to a round trip flight from London to New York. By contrast another model Qwen 2.5 with 72 billion parameters can answer more than three times as many questions with similar accuracy while generating the same emissions.

The Bigger Picture Hardware and Energy Sources

The team notes that the emissions figures may vary depending on the hardware used and the energy source powering it for instance coal heavy grids versus renewables but the key message remains asking a chatbot is not free from climate consequences.

Towards Responsible AI Usage

If users know the exact CO2 cost of their AI generated outputs such as casually turning themselves into an action figure they might be more selective and thoughtful about when and how they use these technologies says Dauner.

These findings are published in Frontiers.