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A new Nature paper outlines the relationship between rising temperatures and the literal rotation of the Earth.

Thinking too hard about time is a little like thinking too hard about blinking; it seems natural and intuitive until suddenly you’re sweating and it makes no sense at all. At least, that’s how I felt when I came across an incredible new study published in Nature this afternoon by Duncan Agnew, a geophysicist at the Scripps Institution of Oceanography, suggesting that climate change might be affecting global timekeeping.
Our internationally agreed-upon clock, Coordinated Universal Time (UTC), consists of two components: the one you’re familiar with, which is the complete rotation of the Earth around its axis, as well as the average taken from 400 atomic clocks around the world. Since the 1970s, UTC has added 27 leap seconds at irregular intervals to keep pace with atomic clocks as the Earth’s rotation has gradually slowed. Then that rotation started to speed up in 2016; June 29, 2022, set a record for the planet’s shortest day, with the Earth completing a full rotation 1.59 milliseconds short of 24 hours. Timekeepers anticipated at that point that we’d need our first-ever negative leap second around 2026 to account for the acceleration.
But such a model doesn’t properly account for the transformative changes the planet is undergoing due to climate change — specifically, the billions of tons of ice melting from Greenland and Antarctica every year.
Using mathematical modeling, Agnew found that the melt-off, as measured by gravity-tracking satellites, has again decreased the Earth’s angular velocity to the extent that a negative leap second will actually be required three years later than estimates, in 2029.
While a second here or there might not seem like much on a cosmic scale, as Agnew explained to me, these kinds of discrepancies throw into question the entire idea of basing our time system on the physical position of the Earth. Even more mind-bogglingly, Agnew’s modeling makes the astonishing case that so long as it is, climate change will be “inextricably linked” to global timekeeping.
Confused? So was I, until Agnew talked me through his research. Our conversation has been edited and condensed for clarity.
How did you get involved in researching this? I’d never have expected there to be a relationship between climate change and timekeeping.
Pure accident. I’m a geophysicist and I have an avocational interest in timekeeping, so I know all about leap seconds and the history of atomic clocks. I thought about writing a paper figuring out statistically what the next century would bring in terms of leap seconds.
When I started working on the paper, I realized there was a signal that I needed to allow for, which was the change induced by melting ice — which has been studied, there are plenty of papers on this satellite gravity signal. But nobody has, as far as I can tell, related it to rotation. Mostly because, from a geophysical standpoint, that’s not very interesting.
Interesting. Or, well, I guess not interesting.
I mean, there is geophysical literature on this, but it’s largely, Okay, we see this signal, and gravity doesn’t mesh with what we think we know about ice melt. Does it measure what we think we know about sea level change? How does the geophysics all fit together? And the fact that it changes Earth’s rotation is kind of a side issue.
I did not know about this when I got started on this project; it appeared as I was working on it. I thought, “Wait, I need to allow for this.” And when I did, it produced the — I don’t want to use the words “more important” because of the climate change part, but it produced a secondary result, which was that this potential for a negative leap second became clear.
Walk me through how the ice melting at the poles changes the Earth’s rotation.
This is the part that’s easy to explain. Ice melts. A lot of water that used to be at the poles is now distributed all over the ocean. Some of it is close to the equator. The standard picture for what’s called change of angular velocity because of moment of inertia — ignore all the verbiage — but the standard picture is of an ice skater who is spinning. She has her arms over her head. When she puts her arms out, she will slow down — like the water going from the poles to the equator. And that’s it. This is the simple part of the problem.
So what’s the hard part?
The hard part is explaining the part about the Earth’s core. If you have two things that are connected to each other and rotating and one of them slows down, the other one has to speed up. I have not been able to think of an ice skater-like-metaphor to go with that, but the simple one is if you were to put a bowl of water on a lazy Susan and you spin the bowl, then the water will start to spin. It won’t spin initially, but then it will start.
If you started stirring the water in the other direction, that would slow the Lazy Susan down. And that’s the interaction between the core and the solid part of the Earth.
And is that causing the negative leap second to move back three years?
That’s why the leap second might happen at all. On a very long timescale, what’s happening is that the tides are slowing the Earth down. The Earth being slower than an atomic clock means that you need a positive leap second every so often. That was the case in 1972, when they started using leap seconds. The assumption was that the Earth would just keep slowing down and so there would be more positive leap seconds over time.
Instead, the Earth has sped up, entirely because of the core, and that’s not something that people necessarily anticipated. When you take the effect of melting ice out, it becomes clear there’s this steady deceleration of the core; the core is rotating more and more slowly. If you extrapolate that — which is a somewhat risky thing to do, you can’t really predict what the core is going to do — then you discover that there is a leap second, in 2029. The ice melting is going in the other direction; if the ice melting hadn’t occurred, then the leap second would come even earlier. Is this all making sense?
I think I’m grasping it.
Just so you know, one of the two reviewers of this paper was someone in geophysics who said, “I know all this stuff. I wasn’t familiar with the rotation part. This paper has an awful lot of moving parts.”
So, it’s just a difference of a second. Why does this even matter?
We are all familiar with the problem of not being synchronized — we just went through it. If you forget that we did Daylight Savings Time, then you’re an hour off from everybody else and it’s bewildering and a nuisance.
Same problem with leap seconds, except for us, a second is not a big deal. For a computer network, though, a second is a big deal. And why is that? Well, for example, in the United States, the rules for stock markets say that everything that is done has to be accurately timed to a 20th of a second. In Europe, it’s actually to the nearest 1,000th of a second. If we were all just farmers or something, it wouldn’t be a problem, but there’s this whole infrastructure that’s invisible to us that tells our phones what time it is, and allows GPS to work, and everything else.
The easiest thing to do would be to not have a negative leap second. Indeed, there are plans not to have leap seconds anymore because for computer networks, they’re an enormous problem. They arrive at irregular intervals; some human being has to put the information into the computer; the computer has to have a program that tells it when the leap seconds are; and most computer programs can’t tell whether it’s a plus or minus second because there’s never been a minus before. From the computer network standpoint, it would be simplest to just not do this.
So, you ask, why are we doing this? In 1972, when leap seconds were instituted, there were two communities that cared about precise time. One was the people who cared about the frequency of your radio station and other kinds of telecommunications. They wanted to use atomic clocks with frequencies that didn’t change, but that didn’t mesh with what the Earth was doing.
Who cares about time telling you how the Earth is rotating? Well, the answer then was that there were people who used the stars for celestial navigation. Back then, celestial navigation was used not just for ships, but for airplanes — if you flew across the ocean, there was a guy in the cockpit, an actual navigator, who would use a periscope to look at the stars and locate the plane, if only as sort of a backup system. That community is now gone. Almost nobody uses celestial navigation as a primary, or even a secondary, way of finding out where they are anymore because of GPS.
My own personal view — and I can warn you, there’s a huge amount of dispute about this — is that we’d be fine if we just stopped having leap seconds at all.
Is there a … governing body of time? That forces us to do leap seconds?
There’s a giant tangle of international organizations that deal with this, but the rules were set by the people in charge of keeping radio stations aligned because radio broadcasts were how time signals were distributed back in 1972. So the rule was created. Who makes that decision is something called the International Earth Rotation and Reference Systems Service, which uses astronomy to monitor what the Earth is doing. They can predict a little bit in advance where things are going to be, and if within six months things are going to be more than half a second out, they will announce there will be a leap second.
Back to climate change: It seems pretty amazing that something like melting ice can throw things off so much.
All the stuff about negative seconds is important, but it’s only important because of this infrastructure, because we have all these rules. Strip all of that away and the most important result becomes the fact that climate change has caused an amount of ice melt that is enough to change the rotation rate of the entire Earth in a way that’s visible.
How do you talk to people about the gigatons of ice that Greenland loses every year? Do you talk about “water that could cover the entire United States to the depth of X” to get it into people’s minds? Yes, these are small changes in the rotation rate, but just the fact that we can say, “Look, this is slowing down the entire Earth” seems like another way of saying that climate change is unprecedented and important.
How do we proceed, then, if climate change is messing with our system?
There was a lot of resistance to even introducing atomic time. Time was thought of as being about Earth’s rotation and the astronomers didn’t want to give it up. In fact, in the 19th century, observatories would make money by selling time signals to the rest of the community. Then, in the 1950s, the physicists showed up, ran atomic clocks, never looked at the stars, and said, “We can do time better.” The physicists were right. But it took the astronomical community a while to come around to accepting that was how time was going to be defined.
If we get rid of leap seconds then we’d really have cut the connection between the way in which human beings have always thought of time as being, say, from noon to noon, or from sunrise to sunset, and we’d be replacing it with some bunch of guys in a laboratory somewhere running a machine. For some people, it’s very troubling to think of severing the keeping of time from the Earth’s rotation.
You lose a bit of the romance, I think. But clearly, tying our way of describing the linear passage of sequential events to the Earth’s rotation is going to be messy.
Exactly right. There’s a quote from, of all people, St. Augustine, saying, “I know what time is, but if somebody asked me, I can’t tell them.”
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A new dashboard from the Sustainable AI Group, founded by artificial intelligence alums, estimates the relative energy intensity of proprietary tools.
The rise of artificial intelligence is driving an historic surge in electricity demand that’s boosting fossil fuel use and threatening climate progress. All this electricity doesn’t power AI in some generalized, always-on way, though. Data centers’ energy consumption is a function of the millions of individual queries users submit to AI programs such as Claude and ChatGPT.
When it comes to how efficiently models process those queries and generate responses, AI models are not interchangeable. Some are more like gas guzzlers, others more like Priuses. When a user engages an AI chatbot or AI agent, however, there’s essentially no way for them to know which kind of vehicle they are stepping into. They may know which company built it, and even the precise model name and number, but no AI company has published information about how much energy one model uses compared to another.
In the absence of corporate disclosure from the big three proprietary AI developers — Anthropic, OpenAI, and Google — researchers with the Sustainable AI Group, a research and advisory company, developed a backdoor method to estimate and compare the amount of energy these developers’ models consume. They published their findings on Tuesday in an interactive dashboard that ranks AI programs by energy intensity.
“We think this is an important next step to get some science-based information out there to help folks start making better decisions,” Boris Gamazaychikov, the CEO of the Sustainable AI Group, told me. “We also hope that if the model providers think that this is really wrong, that they can come out and prove it with some actual data.”
In general, the researchers found that larger, higher-capability models, such as Anthropic’s Opus and OpenAI’s Sol, used nearly four times as much energy on average as smaller, nimbler models from those companies, Haiku and Terra. Newer iterations of each model also weren’t necessarily more efficient than their predecessors.
While the group has yet to evaluate the latest models that hit the market during the research period, so far the researchers found that for the same task, the least efficient models can consume more than 30 times the energy of the most efficient models. They also found a significant difference between “chat” sessions, where a user asks an AI chatbot a question, and “agentic” sessions,” where a user asks the AI to perform a series of tasks. A typical agentic session used 27 times more energy, on average, than a typical chat session conducted using the same AI model.
The Sustainable AI Group was founded by Sasha Luccioni, the former AI and climate lead at the open source AI platform Hugging Face, and Gamazaychikov, who previously led AI sustainability at Salesforce. In their earlier roles, the two collaborated on a project called AI Energy Score, which is similar in spirit to the Environmental Protection Agency’s EnergyStar program for home appliances. They developed a method to directly measure the energy efficiency of “open-weight” AI models, or those that fully disclose their inner workings, and published the results in a public leaderboard.
Luccioni and Gamazaychikov founded the Sustainable AI Group because they wanted to give AI users, particularly large corporate users, the tools to understand the relative emissions impacts of proprietary AI models. Gamazaychikov told me that Salesforce had tried to get energy-use data from its AI providers for years to no avail.
Their first hire was Nidhal Jegham, a graduate student at the University of Rhode Island who published a landmark paper last year called “How Hungry is AI?” Jegham and his co-authors developed a method to estimate the energy, water, and carbon effects of proprietary models at the level of a single prompt or query. The paper was accepted by the journal Communications of the Association for Computing Machinery, and the peer-reviewed version will come out in January.
The approach the Sustainable AI Group developed builds on both Jegham’s paper and the AI Energy Score project. The work began with testing open-weight models to see how they perform in realistic deployment configurations and directly measuring their energy consumption. From there the researchers identified mathematical relationships between various open models’ energy use and other measurable statistics, such as their size.
The next step was to take those statistical relationships from the open-weight models and apply them to similarly-sized proprietary models. The problem is, no one knows how “big” proprietary models are. The size of an AI model usually refers to the number of parameters it contains, i.e. the quantity of numerical representations of what the model has learned that it uses to produce a response.
“When we have a closed model, we don't have the model size. We don't have the deployment conditions. We don't have anything, so we need to find things we can observe from this closed model that can reflect its size,” Jegham explained to me. One key discovery, he said, was that “knowledge retention,” or how well the model can remember factual information, is a strong predictor of model size. A company called Artificial Analysis tests models for knowledge retention, so the researchers compared those results to model size for open models and applied the same statistical relationship to estimate the size of closed models.
This is a simplified explanation — there were many other variables and data points that went into the Sustainable AI Group’s estimates. The researchers also had to develop a separate methodology to evaluate Google’s models, since those mostly run on the company’s proprietary “tensor processing units,” rather than the Nvidia chips the researchers’ initial measurements were based on.
The group’s main findings are based on a per-token estimate of each model’s energy use, i.e. the energy required to process the smallest units of data that an AI deals with. Every time you type a question into a chatbot, the model breaks down the words into smaller bits — i.e. tokens — each just a few characters long, usually. The model also first formulates its response in tokens before translating it to text, an image, or whatever you’re requesting; input tokens are less energy-intensive than the tokens the models spit out. The Sustainable AI Group reports each of its per-token estimates as a range to reflect uncertainty.
For now, the firm is keeping its per-token estimates behind a paywall, but it has already started to use them to advise corporate clients in estimating their AI-related emissions, Gamazaychikov said. For example, he mentioned working with Etsy to help the online retailer develop a “model router,” essentially some software that routes a given query to the most appropriate model for the task, taking into account carbon and cost. It’s also partnering with the corporate emissions accounting platform Watershed to explore how to integrate its model-specific energy numbers into Watershed’s system.
Instead of displaying per-token energy use, the Sustainable AI Group’s public dashboard ranks models’ energy intensity per “typical” session, whether chat or agentic. It defines a typical chat session as “a short back-and-forth” with “a question, an answer, and a follow-up or two to refine or clarify it,” whereas a typical agentic session is “an hour or two of the assistant reading files, making changes and checking its own work across a project.” There are also results for a “heavier” or “lighter” session — generally tasks that take more or less time or require greater or fewer back-and-forths with the AI.
The least efficient AI model for both a typical chat and agentic session, per the dashboard, is Anthropic’s Claude Fable 5. A typical agentic session uses 76 watt-hours, according to the Sustainable AI Group’s estimate, or about the amount of electricity it would take to charge four smartphones, per Department of Energy estimates. The most efficient model for a typical chat session was Claude Haiku 4.5, while the most efficient model for a typical agentic session was Open AI’s GPT-5 nano.
Jegham said the point of the dashboard is not to villainize particular companies or models or to argue that more efficient models are superior. He acknowledged that a more complex task may require a larger model, and a larger model is likely going to be more energy intensive than a smaller one.
The ranking is also flawed in that it assumes every model delivers responses with the same amount of verbosity. In reality, some models may use more words, and therefore more tokens, to answer the same question. Jegham gave the example of Anthropic’s Sonnet and Opus models: Sonnet is less energy intensive per token, but it typically requires more tokens for the same task, so sometimes it’s more energy intensive than Opus. The dashboard doesn’t reflect these differences.
While energy intensity is the core of the dashboard’s function, it also includes estimates of each model’s carbon emissions per session. That calculation opens up many more cans of worms, since actual emissions depend on where in the country the hardware that’s processing the AI session is located and what’s powering it. There’s no easy way to know which data center is processing a given AI request. Instead, the dashboard offers users the option to toggle between different emissions intensities to reflect different scenarios — a data center powered by behind-the-meter natural gas plants, for example, versus one located on a relatively clean grid.
A typical agentic session with Claude Fable 5 powered by a behind-the-meter gas plant emits roughly 52 grams of CO2, it says, while a heavy session emits just over 200 grams — equivalent to driving about half a mile in a gasoline-powered vehicle.
I reached out to OpenAI and Anthropic to ask why they don’t publish energy intensity data, whether there are barriers to doing so, and whether they have plans to do so in the future. A spokesperson from OpenAI told me the company relies “on infrastructure partners to operate the data centers that run our models, so we don’t directly collect the underlying energy data. That’s an important consideration in how we assess and provide this information.” Anthropic declined to comment.
Google, on the other hand, has published an energy use estimate for “the median Gemini Apps text prompt in May 2025,” but has not provided an update for subsequent model versions. In response to my request for comment, the company reiterated statements from Cooper Elsworth, a senior technical manager for AI energy, which Google shared with me for a previous story on Watershed’s efforts to calculate AI-related emissions. He said there is no industry consensus for how to measure and disclose the environmental footprint of frontier AI models. He also echoed OpenAI’s comments, noting that gathering accurate energy use data requires “highly advanced measurement infrastructure,” which not all AI providers have access to.
“We believe there is immense value in aligning the industry on comparable metrics to fairly compare and incentivize action,” he said.
Current conditions: Last weekend’s nor’easter caused up to $13 billion in damages across the Mid-Atlantic and Northeast regions of the United States • Hurricane Nolo shut down a major highway on Hawaii’s Big Island • A heat dome forming over eastern Africa is driving temperatures in Juba, the impoverished capital of South Sudan, past 100 degrees Fahrenheit.
At last, right after hopes dimmed, we have a deal. Senate negotiators reached a bipartisan agreement on a package of federal permitting reforms, locking in what Politico described as “the contours of long-sought legislation to speed up approvals for new energy projects in the U.S.” Democratic negotiators Senators Martin Heinrich of New Mexico and Sheldon Whitehouse of Rhode Island told the news outlet they were withholding endorsements of a final deal as “the last five yards” of the agreement are hammered out. Whitehouse cautioned that he needed “more clarity from the Trump administration” on what their easing of the blockade on wind and solar approvals would mean. Neither Democrats nor Republicans released text of the bill, which both parties said should come out this week.
The Nuclear Regulatory Commission is set to issue only its second construction permit for a novel type of nuclear reactor in decades. At 11 a.m. EDT, the agency is scheduled to give the Tennessee Valley Authority the go ahead to begin building what could be the nation’s first commercial small modular reactor, a 300-megawatt unit at the federally-owned utility’s Clinch River site. The project is one of two the Department of Energy is financing to support deployment of third-generation SMRs, a technology based on existing large-scale reactors but shrunken down to force developers to buy more and help the industry bring down the cost of atomic power through repeatedly building the same design. (The second one is Holtec’s expansion of the Palisades nuclear plant in Michigan.) The permit comes six months after the NRC gave TerraPower, the Bill Gates-backed fourth-generation nuclear developer, the green light to start constructing its liquid sodium-cooled reactor at the site of an old coal plant in Kemmerer, Wyoming. The unit planned at Clinch River is a BWRX-300, a boiling water reactor from GE Vernova Hitachi Nuclear Energy that borrows from the technology behind roughly a third of the American nuclear fleet. Boiling water reactors, pioneered by General Electric in the mid-20th century, traditionally represented a competitor to the more dominant pressurized water reactor invented by Westinghouse. By the time Clinch River comes online, North America may already have its first BWRX-300 in operation in Canada, where Ontario Power Generation is building the first reactor at its Darlington plant. TVA has said it plans to bring its debut BWRX-300 online by the end of 2033 at the latest. Yet, despite the forthcoming permit, no start date for construction has been announced.
The NRC, meanwhile, has sought to advance plans to restart the functional reactor at Constellation Energy’s Christopher Crane Clean Energy Center, the facility formerly known as Three Mile Island. Last week, the agency issued an environmental assessment finding no significant impact from plans to begin generating electricity at the plant again. While America’s attempt at restarting a permanently shuttered reactor for the first time are largely going according to plan, regulators are investigating what the Detroit Free-Press described as a “mishap” in the handling of fuel for Holtec’s Palisades nuclear plant in Michigan, which could come online in a matter of weeks. The company said nuclear fuel rods “tipped” during installation, halting the refueling process and forcing plant operators to return to the NRC for approval to retrieve the assembly from within the reactor vessel.
Arevia Power marketed itself as a renewable energy powerhouse led by solar industry veterans. Now, my colleague Jael Holzman reported yesterday, the company is making data centers and gas turbines central to its business. “Arevia is an energy company that delivers reliable and affordable electricity to the communities and utilities we serve,” Ricardo Graf, the company’s chief development officer, told her via email, acknowledging that “in some cases, that energy may be solar; in others, it may be gas.” He added that “yes, we also develop data center projects, but ones with accompanying power solutions to ensure ratepayers are not impacted by the data center’s energy needs.”
The shift in focus comes right as American solar offers a major new business opportunity. Solar panels are aging, and newer technologies are as much as 70% more efficient than those designed and built two decades ago. “All across the United States, solar panels are withering on the vine. Equipment installed 10 to 15 years ago is still capturing sunlight and pumping out electricity, but significantly less of it than when the cells were new,” my colleague Emily Pontecorvo wrote yesterday about a new report examining the potential to swap out the country’s existing panels for new ones. “This is not a story about decline, however, but about growth. America’s aging solar farms represent an opportunity to expand clean energy capacity without using more land — and potentially without having to wait years for new projects to get through the grid’s interconnection queue.”
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The TVA isn’t the only government-owned utility making progress on clean power plants. The New York Power Authority — the state electrical company that then-Governor Franklin Delano Roosevelt established in the 1930s and later used as a model for New Deal investments such as the TVA — said Monday that it will take a 51% stake in a 240-megawatt solar plant in the state’s rural northern reaches, according to the Watertown Daily Times. The Rich Road solar farm in Canton, near the Canadian border, will follow a model promoted by progressive legislators with a bill meant to encourage the state to finance and own renewable projects to speed up decarbonization of the grid. Governor Kathy Hochul, a Democrat, has used that authority to support her plans to build at least 1 gigawatt of new nuclear power through NYPA. (That effort, as I told you yesterday, has drawn some blowback from left-wing Democrats who oppose nuclear energy.) EDF Power Solutions North America, a subsidiary of the French electrical giant, will own the other 49% share of the project, which is set to begin construction next year. Once completed, the facility is expected to provide credits to low-income New Yorkers to lower bills.

When I used to think about the Rhine River, the first thing that came to mind was a song off my favorite album from high school. Written and performed by Beirut, the stage name of an American guy who galavanted around Europe making folksy songs that sounded straight out of an American teenager’s romantic notion of an Old World beer hall, the song was called “Rhineland.” Over mournful horns and a plunky mandolin, the song repeats a refrain: “Life, life was all right on the Rhine,” bringing to mind some kind of bucolic interwar existence in an ill-fated era of European history. Two decades later, I can’t tell which has changed more, me or the place I was imagining. The correct answer is probably “both,” but the clearest answer today is the latter. Levels at a key gauge of the mostly German waterway dropped to 1.2 inches below the threshold ship operators use to determine how much cargo their vessel can safely carry down the river without risking damage or running aground, Bloomberg reported. Despite a slight recovery on Monday, the cost of shipping diesel from Rotterdam to Karlsruhe hit a record €260 per ton (equal to just under $296), after more than doubling this month amid the aftershocks of the summer’s record heat waves and droughts.
The latest trouble comes as the Trump administration weighs the merits of a ban on diesel exports. At Heatmap’s Climate Week event last Wednesday, Secretary of Energy Chris Wright ruled out such a step. But Trump said he was “very seriously” considering the step, despite warnings from Goldman Sachs that doing so would raise prices in Europe.
TotalEnergies may be taking up President Donald Trump on his legally sketchy offer of nearly $1 billion to abandon its offshore wind ambitions in the U.S. But the French energy giant — the second-largest European oil company after Shell — sees the energy shock brought on by the U.S. war against Iran as a boon to that very business. CEO Patrick Pouyanne said “high oil prices” are “accelerating electrification,” according to a snippet shared on X by Bloomberg columnist Javier Blas. “We have seen a huge surge in EV sales,” he added, noting that sales are booming well beyond China, in India, Latin America, and Europe. Increased profits from higher crude prices spurred the company to start buying back roughly $5 billion in shares over the next two quarters.
By neutering the Corporate Average Fuel Economy standards, the Trump administration cements the country’s dependence on oil and liquid fuels.
This is Heatmap Daily, a weekday news digest written by our executive editor.
President Trump’s big fuel efficiency rollback is here. This afternoon, the Department of Transportation significantly weakened the Corporate Average Fuel Economy standards, the federal government’s rules that encourage new cars and trucks to get gradually more fuel-efficient over time. Instead of mandating that new cars and trucks hit a target of more than 50 miles per gallon, as the old Biden-era rules had required, new vehicles sold in the U.S. will now need to average only 34.9 miles per gallon.
That target is below the level that most automakers have already achieved in their vehicle fleet. (For reasons too obscure to recount here, the regulatory standard of 34 miles per gallon aligns to real-world gas mileage in the mid-to-high 20s — something my 15-year-old hatchback manages to achieve without much straining.) The new rules also retroactively rewrite the standard back to 2022, meaning that automakers whose fleets once broke the law may now be in the clear.
These changes, in other words, render the fuel economy law, first enacted in 1975, is now moot. But Republicans in Congress had arguably already achieved this last year, when they zeroed out all of the law’s fines for automakers as part of the president’s tax and spending bill. These two changes, taken together, mean that the Trump administration has successfully neutered the U.S. fuel efficiency rules.
We are digging into the rule-making here at Heatmap, and I hope to have more on the documents in the days to come. But one of the lasting ironies of President Trump’s approach to fuel efficiency will be that his own presidency demonstrates its strategic inadequacy.
The Corporate Average Fuel Economy law, after all, did not originate as an environmental policy — climate change had scarcely emerged as a pressing issue in the mid-1970s — but as a national security and economic sovereignty measure. In the aftermath of the oil embargo, American politicians realized that the U.S. economy was far too dependent on oil for its long-term good. This set off a scramble to find new energy sources, prompting a dash back to coal in the electricity sector and a surge in federal R&D spending on alternative energy. (This funding boost eventually created the modern solar, wind, battery, and fracking industries.)
It also led to a successful push to regulate gas mileage. Crucially, this effort did not limit emissions from any one type of vehicle, as the Environmental Protection Administration’s toxic air pollution rules aim to do. Rather, it targeted the average fuel efficiency of cars and light-duty trucks sold in the United States in each model-year. The point was not to regulate any one type of vehicle out of existence, but to increase the country’s overall fuel efficiency over time.
That decades-long effort was never perfect. It created in American statute, for instance, a lasting distinction between cars and trucks, which has bedeviled regulators as SUVs have taken up a larger portion of the new vehicle fleet. But it has also inarguably succeeded: The United States ekes far more value out of every barrel of oil today than it did half a century ago.
Yet the time is ripe to keep making progress. President Trump’s administration has illustrated the persistence of our oil dependence — and the political and strategic problems that it can still engender. Even though the United States has since become the world’s largest producer of oil, the linked and globalized nature of fuel markets means that a supply shock anywhere leads to price hikes everywhere. When an oil crisis arrives — even a largely self-inflicted one, as in the case of the Iran war — then the price of moving things and people rises, the economy suffers, and the president’s popularity falls. Countries can protect themselves from these shocks on a short-term basis by stockpiling oil (as the United States, in fact, does), but they can avoid them only by switching to a far more efficient and electrified transportation system.
President Trump, in other words, may regret the current oil and refining crisis. But by gutting the fuel economy standards — and waging war on electric vehicle incentives more broadly — he is increasing the likelihood that America will face many more crises like it in future years. Consider it his particular gift to his successors.