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Climeworks, which launched in 2009, is among around 140 direct air capture companies globally, but is one of the most high-profile and best funded.
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In 2021, it opened its Orca plant in Iceland, followed in 2024 by a second called Mammoth. These facilities suck in air and extract carbon using chemicals in a process powered by clean, geothermal energy.
The carbon can then be reused or injected deep underground where it will be naturally transformed into stone, locking it up permanently. Climeworks makes its money by selling credits to companies to offset their own climate pollution.
The appeal of direct air capture is clear; to keep global warming from rising to even more catastrophic levels means drastically cutting back on planet-heating fossil fuels. But many scientists say the world will also need to remove some of the carbon pollution already in the atmosphere. This can be done naturally, for example through tree planting, or with technology like direct air capture.
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The advantage of direct air capture is that carbon is removed from the air immediately and “can be measured directly and accurately,” said Howard Herzog, senior research engineer at the MIT Energy Initiative.
But there are big challenges, he told CNN. The concentration of carbon dioxide in the atmosphere has been shooting upward, but still only makes up about 0.04%. Herzog compares removing carbon directly from the air to needing to find 10 red marbles in a jar of 25,000 marbles of which 24,990 are blue.
This makes the process energy-intensive and expensive. The technology also takes time to scale.
Climeworks hasn’t come anywhere close to the full capacity of its plants. Orca can remove a maximum of 4,000 tons of carbon a year, but it has never captured more than 1,700 tons in a year since it opened in 2021. The company says single months have seen a capture rate much closer to the maximum.
The company’s Mammoth plant has a maximum capacity of 36,000 tons a year but since it opened last year it has removed a total of 805 tons, a figure which goes down to 121 tons when taking into account the carbon produced building and running the plants.
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Rainfall in Grand Island began Wednesday afternoon but the intensity picked up quickly after dark, falling at more than an inch per hour at times.
A total of 6.41 inches of rain fell by midnight, which made it the rainiest June day and the second rainiest day of any month in the city’s 130-year history of weather records.
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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.