Algebra, philosophy and…: These AI chatbot queries cause most harm to environment, study claims

Time of India

20-06-2025

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Queries demanding complex reasoning from AI chatbots, such as those related to abstract algebra or philosophy, generate significantly more
carbon emissions
than simpler questions, a new study reveals. These high-level computational tasks can produce up to six times more emissions than straightforward inquiries like basic history questions. A study conducted by researchers at Germany's
Hochschule München University
of Applied Sciences, published in the journal Frontiers (seen by The Independent), found that the energy consumption and subsequent carbon dioxide emissions of
large language models
(LLMs) like OpenAI's ChatGPT vary based on the chatbot, user, and subject matter. An analysis of 14 different AI models consistently showed that questions requiring extensive logical thought and reasoning led to higher emissions.
To mitigate their environmental impact, the researchers have advised frequent users of AI chatbots to consider adjusting the complexity of their queries.
Why do these queries cause more carbon emissions by AI chatbots
In the study, author Maximilian Dauner wrote:
'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. We found that reasoning-enabled models produced up to 50 times more carbon dioxide emissions than concise response models.'
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The study evaluated 14 large language models (LLMs) using 1,000 standardised questions to compare their carbon emissions. It explains that AI chatbots generate emissions through processes like converting user queries into numerical data. On average, reasoning models produce 543.5 tokens per question, significantly more than concise models, which use only 40 tokens.
'A higher token footprint always means higher CO2 emissions,'
the study adds.
The study highlights that Cogito, one of the most accurate models with around 85% accuracy, generates three times more carbon emissions than other similarly sized models that offer concise responses.
'Currently, we see a clear accuracy-sustainability trade-off inherent in LLM technologies. None of the models that kept emissions below 500 grams of carbon dioxide equivalent achieved higher than 80 per cent accuracy on answering the 1,000 questions correctly,'
Dauner explained.
Researchers used carbon dioxide equivalent to measure the climate impact of AI models and hope that their findings encourage more informed usage.
For example, answering 600,000 questions with DeepSeek R1 can emit as much carbon as a round-trip flight from London to New York. In comparison, Alibaba Cloud's Qwen 2.5 can answer over three times more questions with similar accuracy while producing the same emissions.
'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,'
Dauner noted.
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