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What the heck is “surficial mineralization”?

According to one of the world’s leading carbon removal buyers, the sector’s future lies in piles of industrial waste.
When Frontier, the Stripe-led coalition of carbon removal supporters, announced its latest $915 million funding commitment, it took the opportunity to lay out the five technologies it views as most promising. I was familiar with four of them — ocean alkalinity enhancement, biomass carbon removal and storage, enhanced rock weathering, and direct air capture. Heatmap has covered them all. But the name on the very top of the list stumped me: surficial mineralization.
It sounds technical, and like all methods of carbon removal, it is — sort of. The idea is to take advantage of the tailings ponds and slag heaps left behind by the mining and steelmaking industries. These piles of calcium- or magnesium-rich debris naturally capture and store carbon from the air — not enough to change the trajectory of our warming planet without any human intervention, but managed well, they could one day capture carbon at a significant scale.
How significant, exactly? While there’s been very little action in the space to date, Frontier says surficial mineralization has the potential to remove over 10 gigatons of carbon from the atmosphere per year — as much or more than any other pathway — at an eventual cost of $80 to $120 per ton. That would put it among the cheapest approaches on Frontier’s list, in part because those heaps of industrial waste alone could absorb anywhere from a gigaton to 4 gigatons of carbon before there’s a need to mine rocks solely for carbon removal purposes.
“The beauty of surficial mineralization is twofold,” Hannah Bebbington Valori, who heads the Frontier coalition, told me. “One, we are working with an abundant source of highly reactive rock, and so there is a significant opportunity for carbon dioxide drawdown. And two, it is carbonating in place, and so sufficient mineralization technologies can be considered closed system approaches, and have generally more straightforward measurement reporting and verification infrastructure.”
At a chemical level, the process resembles other carbon removal pathways Frontier champions, such as enhanced rock weathering and ocean alkalinity enhancement. All three rely on alkaline minerals reacting with moisture and ambient carbon dioxide to form stable carbonate compounds that permanently lock away the gas. The difference is exactly where this reaction takes place: While surficial mineralization contains it to waste piles at industrial sites, the other approaches disperse the reaction across open, difficult-to-monitor systems such as farmland soils and the ocean.
That makes measurement, reporting, and verification — known as MRV — far more challenging and expensive for ocean- and soil-based systems, as scientists must track carbon uptake across ecologically complex environments where countless biological and chemical processes are unfolding simultaneously. These intersecting processes makes it difficult to demonstrate that human intervention was responsible for any given ton of carbon removed, as opposed to natural variability. MRV for these pathways thus relies heavily on modeling, which can never provide the same level of certainty as direct measurement.
Surficial mineralization, however, can be measured much more directly. On-site sensors continuously monitor CO2 concentrations above mine tailings or steel slag, providing a real-time signal of how quickly and to what degree the materials are drawing down carbon. Scientists can then validate these measurements in the lab by comparing physical samples of the material taken before and after the reaction, quantifying exactly how much solid carbonate formed as a result of various engineered interventions. The primary tool for this is X-ray diffraction — a well-established geological technique that identifies a sample’s mineral composition like a chemical fingerprint, making it possible to directly measure how much carbon the material locked away.
Don’t mistake the relative simplicity of the MRV framework for evidence that surficial mineralization is a proven carbon removal pathway — the reality is far from it. While mineralization may look simpler than, say, direct air capture, which typically uses giant fans and specialized sorbents to pull CO2 from the air, there are very few companies working in this space today. All are extremely early stage, and the time and capital required to secure feedstock partnerships, gain site access, and acquire necessary industrial equipment remain significant barriers to getting these projects off the ground.
Why is this heavy equipment needed in the first place? Because these waste piles won’t do much carbon capture work if they’re simply left untouched. That’s because the minerals at the pile’s surface will begin to slowly carbonate, eventually becoming fully saturated and acting as a seal that blocks carbon from reaching the reactive minerals below. As yet there’s no consensus on how to most quickly and cost-effectively break through this natural process to maximize carbon uptake — companies are testing a range of approaches, from crushing and spreading material to maximize air exposure (similar to enhanced rock weathering) to actively churning piles of waste to constantly reveal fresh reactive surfaces.
“Understanding exactly what is the best system to use to maximize your carbon removal efficiency and minimize your cost — this is what we need real-world deployment to do, and to understand,” Bebbington Valori told me.
One of the seed-stage startups Frontier has supported with a small pre-purchase agreement, Arca, spun out of the University of British Columbia to commercialize its approach to carbon removal from mine tailings. The company’s focus is ultramafic waste — magnesium- and iron-rich rock that locks away carbon dioxide as stable magnesium carbonate. “My pathway for interest on that was knowing that there was already about 2 billion tons of ultramafic mine waste sitting on the surface of the Earth in Canada alone,” Greg Dipple, Arca’s co-founder and head of science, told me.
Arca proposes to increase the surface area available for carbon capture in two ways. The first is by using customized robots to continuously till and churn tailings piles, constantly exposing fresh feedstock to the air to maximize carbon uptake before the next layer of tailings is deposited on top. That strategy, Dipple told me, “can give us a five- to 10-fold increase in the rate of CO2 capture” at active mine sites.
It successfully demonstrated this approach in an 18-month pilot project with Australian mining giant BHP at an active mine in the country's Northern Goldfields region where Arca says it increased the tailings’ mineralization rate by an order of magnitude. But the startup plans to push the efficacy of its tech further through what it calls “mineral activation.” This technique uses industrial-scale microwaves to heat the minerals rapidly enough to drive off the water that’s chemically bound within their crystal structure. This essentially blows apart the minerals from the inside out, exposing fresh magnesium-rich surfaces primed to react with carbon dioxide. The expected result is faster mineralization and more carbon captured per ton of mine tailings — but the startup has yet to test it in the field.
“Essentially we’re making microwave popcorn out of silicate minerals,” Dipple explained. “The microwaves cause the release of that water in the same way that when you make popcorn, you’re essentially boiling the water out of the center of the kernel, and that’s what blows the kernel up and creates this high surface area.” The idea is to eventually integrate this step into the mine’s tailings processing stream, with minerals moving through the giant microwave before they’re deposited at the storage facility.
Dipple told me that mineral activation will be a core part of Arca’s future projects, including those intended to fulfill the company’s 10-year carbon removal offtake agreement with Microsoft. Signed last October, the deal calls for Arca to deliver nearly 300,000 metric tons of carbon removal to the software giant.
While no other startup in the space has landed an offtake agreement of that scale, several have secured early backing from Frontier through pre-purchase agreements. One of them, Karbonetiq, is working to capture carbon from steel slag, the calcium-rich byproduct of steel production that accumulates in large piles at processing sites. Like the magnesium-rich minerals in mine tailings, calcium compounds in steel slag naturally react with moisture and carbon dioxide to form a stable calcium carbonate — a.k.a. limestone — permanently locking up the CO2.
Unlike mine tailings however, slag doesn’t begin as a fine powder. Instead, the molten byproducts poured off from high-temperature steel furnaces cool into chunks the size of large rocks, leaving only their outer surfaces exposed to the air and able to react with CO2. Karbonetiq’s strategy is essentially to crush and disperse those rocks to increase their reactive surface area. As the company’s commercial vice president, Luke Rondel, explained, “We crush [the slag] down so you get smaller particle sizes. We then spread that out in a field of material, and we till that material with a tractor and plow, which is just turning over new surfaces.”
Each pathway has its advantages — while Arca’s magnesium-rich mine tailings are the most abundant feedstock, Rondel told me that the calcium-based reactions in slag happen significantly faster. For its part, Frontier hopes to test and evaluate a range of approaches at its new Surficial Mineralization Hub in Quebec, which it announced at the end of April. Located at a former asbestos mine, the hub will give participating startups access to “10,000 tons of serpentinite tailings and space for pilot scale testing,” Bebbington Valori told me, as well as local labs with specialized equipment.
This should eliminate some of the hurdles facing the nascent sector, chief among them being access to the right kinds of reactive rocks. Small startups “really need to either partner with large academic labs or with large mining companies to get access to that feedstock,” Bebbington Valori told me — a difficult and expensive proposition for a company that’s just getting off the ground.
While Frontier has yet to announce the cohort of participating startups, both Arca and Karbonetiq told me they hope to test their technology there, with the latter planning what would be one of its first mine tailings pilots through the program. Ultimately the goal is to generate the proof points needed to give both the startups and Frontier a clearer roadmap for which approaches can realistically scale — and what kind of support they’ll need to get there.
It certainly won’t be a straightforward process — bringing new technology into old-school industries never is — and the economics will only start to pencil if their operations reach meaningful scale. In theory, mining companies could benefit from hosting surficial mineralization projects, whether through site access fees, outsourcing elements of waste management, or even critical minerals recovery. Miners could even develop and scale the technology themselves, if they so desire. But the sector has historically been reluctant to adopt new tech. “The classic quote is, in mining you always want to be No. 2, you don’t want to be the first one,” Dipple told me. “You don’t want to put up a $2 billion plant that doesn’t work.”
So like nearly everything in the carbon removal space, early execution is falling to the startups that aren’t afraid of a little risk. “They’re watching for sure,” Dipple said of the mining industry at large. “But they want to be No. 2. We’re going to have to be No. 1.”
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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.