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Unity, a brand of custom home company Bensonwood, is one of several companies in New England building homes in a factory. It’s a modern spin on the 1900s Sears Roebuck catalogue of mail-order houses, now with energy efficiency front of mind.
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Companies like Unity and Maine-based BrightBuilt Home offer several basic designs that owners can customize. At Unity, much of the design is done ahead of time, before the house’s walls, windows and doors are assembled inside the company’s Keene, New Hampshire, factory, then wrapped tightly in reusable plastic and put on a truck bed. Once it’s on site, a panelized house can be built in a matter of days.

It is a very different model from traditional “stick-built” home construction, where the structure is built “stick by stick” on site and can take months to finish, all the while leaving wood and materials exposed to rain, snow and wind.
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“There’s a logic to building a structure in a climate-controlled environment. That really resonates with a lot of folks,” said Parlin Meyer, managing principal for BrightBuilt Home.
The number of factory-made homes still pales in comparison to traditional stick-built homes in the United States — just 3% of all single-family homes as of 2024, according to the National Association of Home Builders. The trend is much more popular in Europe; in Sweden, most new homes are built in factories.

“That’s been relatively flat for a number of years,” said Devin Perry, an assistant vice president at the National Association of Home Builders. “But anecdotally, there seems to be momentum behind that method of construction, and people looking for energy efficient options.”

Three New England companies specializing in factory-built and energy efficient homes told CNN they are seeing a rise in interest. Prefabricated homes used to be associated with cheap, poorly made housing, but that has changed.

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“AI expends a lot of energy being polite, especially if the user is polite, saying ‘please’ and ‘thank you,’”
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Dauner explained. “But this just makes their responses even longer, expending more energy to generate each word.”

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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