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It’s useful for more than just decarbonization.

Now that President Donald Trump has been officially inaugurated and issued his barrage of executive orders celebrating fossil fuels and shelving climate technologies such as wind energy and electric vehicles, climate tech startups are in a pickle. Federal funding can play a critical role in helping companies scale up and build out first-of-a-kind projects and facilities. So how to work with a government hostile to one of these startups’ core value propositions: aiding in the energy transition?
Talk of clean tech and electrification may be out of vogue, but its utility is not. The potential of many of these companies goes beyond mitigating climate change and into the realm of energy security and resilience — something the Department of Defense is well aware of.
The White House’s climate webpage has gone dark; the Department of Defense’s climate resilience portal lasted a little longer, but that’s now down, too. Once upon a time, though, the site read, “The changing climate is one of many threat multipliers to National Security, which adds complexity to Department of Defense decisions.” That’s a major reason why this agency can’t stop, won’t stop funding climate technologies. Another reason is that many technologies that happen to be good for the planet might also simply be the best tool for the job, meaning the DOD need not utter the word “climate” at all when justifying its decision to deploy new solutions.
“The Defense Department, so far in our experience, has framed things largely in terms of alternative benefits that our technology can have, such as fuel supply chain redundancy and reliability,” Ted McKlveen, co-founder and CEO of the hydrogen storage company Verne, told me. Verne received a $250,000 Small Business Innovation Research grant from the Army last May to work on the development of hydrogen vehicles.
Cindy Taff, CEO of the next-generation geothermal startup Sage Geosystems, told me something similar. “What the military likes to talk about is energy resilience,” she said, though she has heard the DOD tout the climate benefits of her company’s tech, too. Sage currently has multiple DOD engagements, including feasibility studies with both the Army and Navy and a $1.9 million grant to build a demonstration project for the Air Force.
That’s not to say it’s clear what the Department of Defense’s funding priorities under Trump will be. When I contacted the DOD in mid-December to request an interview for this story, a spokesperson initially told me they would help connect me to the right person. But as Trump’s inauguration drew nearer, I got a message saying the agency would have to hold off until it got more guidance, as “it remains to be seen in the next few weeks what direction the new administration is going.”
Regardless of how the priorities shake out, practically every climate-focused company and venture capitalist I talk to emphasizes that their companies will only succeed if they can make or invest in products that can compete on economics and/or quality alone, sans government support. That was true even before a second Trump turn in the White House started to look like an inevitability, and this new administration will at least partially reveal which companies can do that. But while everybody aims to be independent of federal support, they might not actually need to say goodbye to that funding stream, so long as they can tout their economic and performance benefits to the right customers.
Take Pyka, for example. When Michael Norcia co-founded the autonomous electric aircraft company in 2017, the ultimate goal was to design a passenger plane. “We want that to be our legacy, but we were also very, very realistic about the challenges associated with actually doing that,” he told me. So when the DOD took an interest in the company’s commercial cargo planes and their potential ability to deliver supplies in contested environments, the startup jumped at the opportunity, delivering its first aircraft to AFWERX, the innovation arm of the Department of the Air Force, early last year. Interest from such a lucrative government customer helped the company to close its $40 million Series B round in September.
Of course, the decarbonization benefits of electrifying military cargo delivery would be huge. But unsurprisingly, Norcia told me that the DOD primarily frames the opportunity in terms of the capabilities of all-electric or hybrid-electric planes, which could take a variety of fuels, operate quietly, and give off minimal heat, making them more difficult to detect via thermal imaging. Plus, the more equipment is electrified the better, “in terms of having them be able to operate in a highly contested environment, where moving fuel around maybe is not feasible,” Norcia explained. Not to mention the fact that if a manned aircraft is shot down, people die, meaning that in a counterfactual sense, Pyka’s tech is saving lives.
Verne’s North Star is also decarbonization. And given that the military is the world’s largest oil consumer, McKlveen was excited to partner with the Army to put its hydrogen storage tech to use in medium and heavy-duty vehicles. The company stores hydrogen (ideally green hydrogen, produced via renewables-powered electrolysis) at high density as a cold, compressed gas, making it possible to build hydrogen vehicles with greater range and lower cost than has traditionally been done. Similar to Pyka, the Army is enthused that these vehicles would be difficult for adversaries to detect, as they’re quiet and give off little heat. Likewise, McKlveen told me that hydrogen power could replace the Army’s notoriously noisy generators.
While Verne has also partnered with the Department of Energy and its R&D arm, ARPA-E, McKlveen said that working with the DOD has been unique in a few ways. “The key difference is the DOD is a customer and a grant provider. So they can say both what their needs are as a potential customer and represent a potential customer,” he explained. This, along with the agency’s clear, phased approach that it puts companies through, helps bring a level of transparency to the whole process, from pilot to full-fledged military implementation, that McKlveen appreciates.
And lest we forget, “they also have a very large budget,” he told me. For fiscal year 2025, the DOD has requested $849.8 billion, while the DOE, by comparison, has requested a mere $51.4 billion.
“I find military people to be get-it-done type of people,” Taff of Sage Geosystems told me. “So I think that helps to create a sense of urgency and also push things along a lot faster than you would see with maybe other organizations.” Sage uses drilling technologies adopted from the oil and gas industry to access heat for clean electricity production across a wide variety of geographies. This is an especially attractive option for the DOD as the majority of geothermal infrastructure is underground, and thus well protected from attack. And unlike other renewables, this tech can provide 24/7 energy no matter the weather conditions. So it’s no surprise that the military is pouring money into this sector, pursuing partnerships with other big names in the geothermal space such as Fervo Energy and Eavor.
Electric planes, hydrogen, and geothermal all felt intuitively justifiable to me from a defense standpoint, but I was more surprised to learn that the DOD has gotten into the alternative proteins, a.k.a. “fake meat”, industry. Though meat substitutes won’t power tankers or keep the lights on, the Defense Department’s $1.4 million grant to The Better Meat Co. is intended to strengthen the American supply chain. China’s Ministry of Agriculture and Rural Affairs views lab-grown meat as critical to its five-year agricultural plan. “So we don’t want to have the United States be importing clean protein in the way that we’re currently dependent on Asia for our semiconductors and photovoltaics,” Paul Shapiro, the company’s CEO, told me.
The Better Meat Co. produces a protein called Rhiza that’s derived from microscopic fungi, which it then sells as an ingredient to other companies to make either 100% animal-free meat or a meat blend. “This isn’t an alternative protein program. It’s a domestic biomanufacturing program,” Shapiro told me when I asked if military funding for meat substitutes could be at risk under Trump. Looking at some of the other companies that got grants through the same program, he said, “it’s literally like bio manufacturing things for military planes and jet lubricants and chemical catalysts for bullets.” That is, probably not Republican targets for defunding. “It’s clearly solely about wanting the U.S. to be a leader in biomanufacturing for the products that the world is going to depend on in the future.”
The DOD also sees promise in numerous other clean energy technologies, including nuclear microreactors for their portability and ability to provide off-grid energy in remote locations and alternate battery chemistries that could help the U.S. move away from a dependence on Chinese-produced lithium-ion batteries.
But despite the deep well of funding and pragmatic approach to deployment that the Department of Defense offers, agreeing to work with the DOD isn’t always an obvious choice. Many fear their company’s tech could be used in ways and in wars that they oppose. In 2018, for example, thousands of Google employees signed a letter opposing the company’s participation in Project Maven, a partnership with the Pentagon that uses artificial intelligence to improve the accuracy of drone strikes. Supporters of the project said it would lead to fewer civilian deaths, while protestors argued that Google “should not be in the business of war.” Google did not renew the contract. More recently, employees at Microsoft, Google, and Amazon have signed petitions opposing their company’s provision of cloud computing and AI services to the Israeli government.
Norcia noted that most, but not all of his employees were neutral to positive when it came to working with the Air Force, while “for a small minority of the company, it unfortunately was not something that they really wanted to devote their life to.” While he understands that perspective, Norcia does believe that Pyka’s work with the DOD is a net positive for the world. “If you assume wars are going to keep happening — which, unfortunately, I think is the reality — I’d rather have it be the case that they’re more of a robot war than a human war,” he told me. And at the end of the day, passenger planes are still the goal.
As for his team at Verne, McKlveen told me everybody was on board. “The Defense Department has led to some of the biggest innovations of the last century, whether that’s the internet or GPS. And our team knows that.” Plus, even if the DOD doesn’t talk much about the climate benefits of sustainability-focused tech, that doesn’t negate them. A 2019 study revealed that the Pentagon purchases an average of 100 million barrels of oil per year, so from that perspective, “it’s hard to find a bigger customer that we can address,” McKlveen told me.
Norcia agreed. “I think the gains of your impact get turned way up if you’re doing work with the DOD,” he said, “as opposed to, you know, building an app that makes something incrementally more efficient or more addictive.”
Editor’s note: This story has been updated to reflect that DOD’s climate resilience portal has been taken down.
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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.