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  For this reason, Dauner suggests users be more straightforward when communicating with AI models. Specify the length of the answer you want and limit it to one or two sentences, or say you don’t need an explanation at all.
  
  Most important, Dauner’s study highlights that not all AI models are created equally, said Sasha Luccioni, the climate lead at AI company Hugging Face, in an email. Users looking to reduce their carbon footprint can be more intentional about which model they chose for which task.
  
  “Task-specific models are often much smaller and more efficient, and just as good at any context-specific task,” Luccioni explained.
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  If you are a software engineer who solves complex coding problems every day, an AI model suited for coding may be necessary. But for the average high school student who wants help with homework, relying on powerful AI tools is like using a nuclear-powered digital calculator.
  
  Even within the same AI company, different model offerings can vary in their reasoning power, so research what capabilities best suit your needs, Dauner said.
  
  When possible, Luccioni recommends going back to basic sources — online encyclopedias and phone calculators — to accomplish simple tasks.
  
  Why it’s hard to measure AI’s environmental impact
  Putting a number on the environmental impact of AI has proved challenging.
  
  The study noted that energy consumption can vary based on the user’s proximity to local energy grids and the hardware used to run AI models.
  That’s partly why the researchers chose to represent carbon emissions within a range, Dauner said.
  
  Furthermore, many AI companies don’t share information about their energy consumption — or details like server size or optimization techniques that could help researchers estimate energy consumption, said Shaolei Ren, an associate professor of electrical and computer engineering at the University of California, Riverside who studies AI’s water consumption.
  
  “You can’t really say AI consumes this much energy or water on average — that’s just not meaningful. We need to look at each individual model and then (examine what it uses) for each task,” Ren said.
  
  One way AI companies could be more transparent is by disclosing the amount of carbon emissions associated with each prompt, Dauner suggested.
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