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This summer was hot. It was wet. It was deadly. For most of us, it was a preview of the rest of our lives.

So here we are: Another summer in the books.
After Labor Day, whites go back in the closet; kids go back to school. Astronomically speaking, there are still technically 20 days left of summer, and climatologically speaking, we may have even longer to go than that — summers are getting longer as autumns contract. But culturally, anyway, we’re now headed into fall, an incongruous transition epitomized by the bastardized existence of the iced pumpkin spice latte. You know you’re living in the age of climate change when ...
It’s a good time, though, for taking stock. An astonishing 96% of Americans have faced at least one extreme weather alert from the National Weather Service since May 1, the Union of Concerned Scientists’ Danger Season tracker reports. Further, only seven counties out of 3,224 in the whole country and its territories had no heat, flood, fire, or storm warnings between May 1 and August 29 of this year, according to additional numbers supplied to Heatmap by the UCS — including, surprisingly, San Fransisco County in California, home of what has been called the single-most economically vulnerable major city to climate change in the U.S.
These were the others that dodged extreme weather alerts: Aleutians East Borough (Alaska); Aleutians West Census Area (Alaska); Ketchikan Gateway Borough (Alaska); Kodiak Island Borough (Alaska); and Norton City (Virginia). Together, they have a population of around 39,500 — just a fraction of San Francisco County’s 815,200.
But while San Francisco, some islands and bays in remote regions of Alaska, and a sliver of Virginia got lucky (this time and so far), it was a bad summer to be in Arizona, where there were more NWS extreme weather alerts issued than in any other state. Coconino County, home of the capital of Flagstaff, saw 146 alerts this summer due to a parade of heat, flood, and fire threats, followed closely by Mohave County, in the state’s northwesternmost corner, with a total of 145. New Mexico was right on Arizona’s tail with five counties in the top 10:
When it came to heat alerts specifically, Texas and Puerto Rico dominated the top of the list. In fact, Louisiana’s Sabine Parish was the highest-ranked non-Texan or Puerto Rican county for heat alerts, clocking in way down at #96.
The most flood alerts were experienced by California’s Inyo County, the home of Death Valley, which might be surprising until you remember how little rain it takes to trigger a disaster in the desert. Washington’s Yakima, Kittitas, and Skamania counties lead the list for fire weather alerts; and South Carolina’s Georgetown, Colleton, and Charleston counties lead for storm alerts. (The data was collected just before the brunt of Hurricane Idalia swept through northern Florida, Georgia, and the Carolinas). California’s Los Angeles County, the most populous in the country, faced a total of 80 extreme weather alerts this summer; the average for all counties was around 44.
The UCS Danger Season data (which will continue to be collected here through October) did not account for air quality warnings, which were the main story of the early summer in the U.S. — at times, more than a third of Americans faced degraded AQI due to smoke billowing south from the Canadian wildfires (which are themselves record-breaking). June 7 was the worst day for wildfire smoke exposure in American history “by far,” my colleague Robinson Meyer reported, and it happened not on the West Coast, where fires are routine, but in New York City, Philadelphia, Washington, D.C., and Toronto.
The next month, July, was the hottest month on Earth in probably 120,000 years (so you have that bragging right on about 4,000 or so generations of your ancestors). Some 40,000 different locations around the world recorded their hottest days ever in 2023, with nearly 20,000 of those in the United States, according to NOAA’s records. Though we didn’t break the global heat record this year (Death Valley only reached 128 degrees Fahrenheit, short of the 130 it needed to beat), the planet recorded its warmest day ever a few different times. Meanwhile, Vermont saw catastrophic summer flooding.
Then, in August, a grass fire fueled by hurricane winds ripped through Maui. Even three weeks later, we still don’t know how many people were killed. Undoubtedly, though, it is the deadliest wildfire in modern U.S. history — and all the more shocking for the fact that it burned through a former wetland, a grim testament to the effects of colonialism. America might not be through reckoning with massive fires, either; the peak of fire season is known as “Snaptember” among hot shot crews for a reason.
And summer wasn’t through with us yet. Hilary became the first tropical storm to hit Southern California in 84 years, and while the damage wasn’t too bad, the Los Angeles Times credits the urgency of the early warnings for saving lives. Subsequently, Hurricane Idalia became the first hurricane to make U.S. landfall in what is predicted to be an “above normal” season, strengthening from a Category 1 to a Category 4 storm in 24 hours thanks to record-warm waters in the Gulf of Mexico. Reports of the damage are still trickling in, but it can’t be good news for insurers in Florida and the Southeast.
It is difficult to tie any one weather-related disaster to climate change, but as Michael Wehner, a senior staff scientist at the Lawrence Berkeley National Laboratory, once succinctly put it to Mother Jones: “It’s not: Climate change flooded my house. It’s: Climate change changed the chances of flooding my house.” So, let’s look at the chances.
A recent study found that the prime wildfire conditions in Canada this year, which caused the choking smoke on the East Coast, were “at least twice as likely to occur there as they would be in a world that humans hadn’t warmed by burning fossil fuels,” The New York Times reports. The July heat dome that baked the South was “at least five times more likely due to human-caused climate change,” an analysis by Climate Central and The Guardian found. The odds of Vermont’s supposedly once-a-century flooding happening within 12 years of another 100-year storm in the state, Hurricane Irene, was just 0.6 percent. The fires in Maui were caused by compounding climate problems, The Washington Post reports, such as higher average temperatures and more intense hurricanes — both of which also have links to emissions-fueled warming. And Hurricane Idalia’s rapid intensification is what we’d expect to see from human-fueled ocean warming, too. Then there’s El Niño, which plays a part in all this chaos as well; it’s why scientists expect next year to be an even bumpier ride for earthly life than this summer has been.
That might not be very heartening to hear but consider this: If you’re a resident of anywhere other than San Francisco and a few odd places like Alaska’s Bristol Bay Borough (population: 838), then this summer was your dry run. You’ve learned more than you ever expected you’d need to know about indoor air purification; you spent 90 minutes prepping for wildfire season; you’ve checked in on elderly neighbors; you’ve even brushed up on your gin rummy skills so you can stay off your phone when the power goes out during the next (or same?!) hurricane. Look at you go. You’re adaptable. Heck, even iced pumpkin spice lattes are starting to grow on you now.
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Oil flows through the Strait of Hormuz, a critical waterway, seem to be returning to normal.
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For the first decade of my professional career, the U.S. economy was defined largely by its deficiencies. Employment lagged for years after the Great Recession, especially for non-college-educated men in their prime earning years. Money was free or cheap to borrow. Even after the housing market had recovered, economic activity remained moribund.
Americans hated it, and politicians proposed various schemes to backfill the gap. One of these ideas was the Green New Deal — a once-in-a-generation investment in clean energy and low-carbon infrastructure that would put Americans back to work and deploy the economy’s spare capacity for the public good.
How things have changed: The main story of the American economy today is one of constraints. On Tuesday, yields on 30-year Treasury bonds reached their highest level since 2002, meaning the federal government’s long-term borrowing costs are higher than they’ve been at any point since I was in elementary school. Electricity demand is surging, and global stockpiles of gasoline, diesel fuel, and crude oil are in short supply. No wonder, then, that many of the inputs to the energy system — such as transformers or natural gas turbines — remain expensive or in short supply. Or that the inflation rate remains stuck higher than 3%.
What’s funny is that Americans still hate it. Consumer confidence fell last month to its lowest level since 2014, according to new Conference Board data released on Tuesday. You can chock that up to oil prices, which have climbed since Iran closed the Strait of Hormuz in March. But there’s a deeper mystery going on, too — the economy is picking up, hiring is increasing, and the U.S. is experiencing a physical investment boom of a scale not seen in decades.
It’s not what I would have predicted back in the 2010s. Talking to a fellow policy nerd at a Climate Week event in New York on Thursday, we joked that we all owe an apology to Boomer politicians, who had once seemed inordinately obsessed with the 1970s. It turns out that Americans really do despise inflation above all else, even when it accompanies robust economic growth.
On that front, there are two interesting developments. The first is that America is seemingly succeeding in its battle to wrench the Strait of Hormuz back open in its ongoing war with Iran.
In recent days, more than 10 million barrels of oil have exited the Strait of Hormuz on tankers accompanied by U.S. Navy ships, according to the ship tracking data provider Kpler. Another 6 million barrels have left through other routes. Last year, about 20 million barrels of crude oil were shipped through the strait each day, according to the International Energy Agency.
That could eventually help lower some fossil fuel costs — at enormous cost, of course, to the American public — but it will not happen quickly. Global refinery capacity remains constrained, and future diesel prices are much higher now than they were when the war began. This means diesel prices could likely stay stubbornly high for some time, helping China’s effort to electrify its heavy-duty truck fleet and even providing some tailwind for Tesla’s new Semi truck, if it ever gets released.
The second development is that the Senate seems to be moving ahead with permitting reform. The bipartisan team that has been negotiating the deal announced a deal late on Monday (as my colleague Alexander Kaufman detailed in Heatmap AM), and the lawmakers will hold a press conference on Wednesday where they’ll release bill text. Senate leadership seems to be eyeing a vote on the legislation after the midterm election. That bill — if the deal is a good one — could theoretically help grow the power grid, loosen some of the constraints on the clean energy economy, and make it easier to build new kinds of public infrastructure.
But we’ll know more when we see it — and we’ll see it soon. We’ll be covering the deal on Wednesday and, I’m sure, for many weeks to come.
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.