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The world is betting heavily on carbon capture — a term that refers to various techniques to stop carbon pollution from being released during industrial processes, or removing existing carbon from the atmosphere, to then lock it up permanently.

The practice is not free of controversy, with some arguing that carbon capture is expensive, unproven and can serve as a distraction from actually reducing carbon emissions. But it is a fast-growing reality: there are at least 628 carbon capture and storage projects in the pipeline around the world, with a 60% year-on-year increase, according to the latest report from the Global CCS (Carbon Capture and Storage) Institute. The market size was just over $3.5 billion in 2024, but is projected to grow to $14.5 billion by 2032, according to Fortune Business Insights.
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Perhaps the most ambitious — and the most expensive — type of carbon capture involves removing carbon dioxide (CO2) directly from the air, although there are just a few such facilities currently in operation worldwide. Some scientists believe that a better option would be to capture carbon from seawater rather than air, because the ocean is the planet’s largest carbon sink, absorbing 25% of all carbon dioxide emissions.

In the UK, where the government in 2023 announced up to ?20 billion ($26.7 billion) in funding to support carbon capture, one such project has taken shape near the English Channel. Called SeaCURE, it aims to find out if sea carbon capture actually works, and if it can be competitive with its air counterpart.

“The reason why sea water holds so much carbon is that when you put CO2 into the water, 99% of it becomes other forms of dissolved carbon that don’t exchange with the atmosphere,†says Paul Halloran, a professor of Ocean and Climate Science at the University of Exeter, who leads the SeaCURE team.

“But it also means it’s very straightforward to take that carbon out of the water.â€

Pilot plant
SeaCURE started building a pilot plant about a year ago, at the Weymouth Sea Life Centre on the southern coast of England. Operational for the past few months, it is designed to process 3,000 liters of seawater per minute and remove an estimated 100 tons of CO2 per year.

“We wanted to test the technology in the real environment with real sea water, to identify what problems you hit,†says Halloran, adding that working at a large public aquarium helps because it already has infrastructure to extract seawater and then discharge it back into the ocean.

The carbon that is naturally dissolved in the seawater can be easily converted to CO2 by slightly increasing the acidity of the water. To make it come out, the water is trickled over a large surface area with air blowing over it. “In that process, we can constrict over 90% of the carbon out of that water,†Halloran says.

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Whether it’s answering work emails or drafting wedding vows, generative artificial intelligence tools have become a trusty copilot in many people’s lives. But a growing body of research shows that for every problem AI solves, hidden environmental costs are racking up.

Each word in an AI prompt is broken down into clusters of numbers called “token IDs†and sent to massive data centers — some larger than football fields — powered by coal or natural gas plants. There, stacks of large computers generate responses through dozens of rapid calculations.

The whole process can take up to 10 times more energy to complete than a regular Google search, according to a frequently cited estimation by the Electric Power Research Institute.
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So, for each prompt you give AI, what’s the damage? To find out, researchers in Germany tested 14 large language model (LLM) AI systems by asking them both free-response and multiple-choice questions. Complex questions produced up to six times more carbon dioxide emissions than questions with concise answers.

In addition, “smarter†LLMs with more reasoning abilities produced up to 50 times more carbon emissions than simpler systems to answer the same question, the study reported.

“This shows us the tradeoff between energy consumption and the accuracy of model performance,†said Maximilian Dauner, a doctoral student at Hochschule Munchen University of Applied Sciences and first author of the Frontiers in Communication study published Wednesday.

Typically, these smarter, more energy intensive LLMs have tens of billions more parameters — the biases used for processing token IDs — than smaller, more concise models.

“You can think of it like a neural network in the brain. The more neuron connections, the more thinking you can do to answer a question,†Dauner said.
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Complex questions require more energy in part because of the lengthy explanations many AI models are trained to provide, Dauner said. If you ask an AI chatbot to solve an algebra question for you, it may take you through the steps it took to find the answer, he said.