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Buntel, a spring allergy sufferer, said his Somerville home’s air exchange has made a noticeable difference in the amount of pollen in the house. And customers have remarked on how quiet their homes are, due to their insulation.

“I’m from New England, so I’ve always lived in drafty, uncomfortable, older houses,†Buntel said. “This is really amazing to me, how consistent it is throughout the year.â€
Some panelized home customers are choosing to build not just to reduce their carbon footprint, but because of the looming threat of a warming planet, and the stronger storms it brings.

Burton DeWilde, a Unity homeowner based in Vermont, wanted to build a home that could withstand increasing climate impacts like severe flooding.

“I think of myself as a preemptive climate refugee, which is maybe a loaded term, but I wasn’t willing to wait around for disaster to strike,†he told CNN.

Sustainability is one of Unity’s founding principles, and the company builds houses with the goal of being all-electric.

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Goodson may drill oil by day, but the only fossil fuel he uses at home is diesel to power the house battery if the sun doesn’t shine for days. Goodson estimated he burned just 30 gallons of diesel last winter – hundreds of gallons less than Maine homeowners who burn oil to stay warm.

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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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