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Questioned by both Republicans and Democrats on Capitol Hill about the low staffing numbers, Interior Secretary Doug Burgum has brushed off concerns, testifying in May that slightly less than half of permanent NPS employees work on the ground in the parks, while other staff work at regional offices or at DC headquarters.
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“I want more people in the parks,” Burgum said. “I want less overhead. There’s an opportunity to have more people working in our parks … and have less people working for the National Park Service.”
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But internal NPS data tells a different story, Brengel said, showing that around 80% of National Park Service staff work in the parks. And regional offices play an important supporting staff role, with scientists on staff to help maintain fragile parks ecosystems, as well as specialists who monitor geohazard safety issues like landslides.

Republican Sen. Lisa Murkowski of Alaska recently pressed Burgum to provide a full list of staff positions that have been cut at the National Park Service, Bureau of Land Management and the US Forest Service since the Trump administration took over. The Interior Department has not provided the list, a Senate staffer said.
The regional offices within the park service are on edge, waiting to see how courts rule on a Trump administration reduction in force plan they fear could gut their ranks, a National Park Service employee in a Western state told CNN.

“If they greenlight the RIF plan, then it’s going to be a bloodbath,” the employee said.

In addition to probationary workers that were fired in February, early retirements are also culling the agency’s ranks, and the continued $1 spending limit on federal workers’ credit cards is making it extremely difficult to do field work in the parks, with a simple overnight trip needing to be requested 10 days in advance, the employee added.

The lack of superintendents and NPS supervisors creates more of a headache, they added.

“These times, when it’s all about fighting for scarce resources, you really need those upper-level people with clout working the system,” the employee said.

Hall, the retired NPS regional director, said losing rangers, maintenance professionals and park superintendents could profoundly alter American landmarks.

“What you’ve lost with all this attrition – you’ve lost all this knowledge that’s going to take years to build back up,” Hall said.

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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.

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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.