You’re out of free articles.
Log in
To continue reading, log in to your account.
Create a Free Account
To unlock more free articles, please create a free account.
Sign In or Create an Account.
By continuing, you agree to the Terms of Service and acknowledge our Privacy Policy
Welcome to Heatmap
Thank you for registering with Heatmap. Climate change is one of the greatest challenges of our lives, a force reshaping our economy, our politics, and our culture. We hope to be your trusted, friendly, and insightful guide to that transformation. Please enjoy your free articles. You can check your profile here .
subscribe to get Unlimited access
Offer for a Heatmap News Unlimited Access subscription; please note that your subscription will renew automatically unless you cancel prior to renewal. Cancellation takes effect at the end of your current billing period. We will let you know in advance of any price changes. Taxes may apply. Offer terms are subject to change.
Subscribe to get unlimited Access
Hey, you are out of free articles but you are only a few clicks away from full access. Subscribe below and take advantage of our introductory offer.
subscribe to get Unlimited access
Offer for a Heatmap News Unlimited Access subscription; please note that your subscription will renew automatically unless you cancel prior to renewal. Cancellation takes effect at the end of your current billing period. We will let you know in advance of any price changes. Taxes may apply. Offer terms are subject to change.
Create Your Account
Please Enter Your Password
Forgot your password?
Please enter the email address you use for your account so we can send you a link to reset your password:

Here’s a grim fact: The most destructive fires in recent American history swept over a state with the country’s strictest wildfire-specific building code, including in some of the neighborhoods that are now largely smoldering rubble.
California’s wildfire building code, Chapter 7A, went into effect in 2008, and it mandates fire-resistant siding, tempered glass, vegetation management, and vents for attics and crawlspaces designed to resist embers and flames. The code is the “most robust” in the nation, Lisa Dale, a lecturer at the Columbia Climate School and a former environmental policy advisor for the State of Colorado, told me. It applies to nearly any newly built structure in one of the zones mapped out by state and local officials as especially prone to fire hazard.
The adoption of 7A followed years of code development and mapping of hazardous areas, largely in response to devastating urban wildfires such as the Tunnel Fire, which claimed more than 3,000 structures and 25 lives in Oakland and Berkeley in 1991, and kicked off renewed efforts to harden Californian homes.
The Federal Emergency Management Agency’s report on the 1991 fire makes for familiar reading as the Palisades and Eaton fires still smolder. The wildland-urban interface, it says, was put at extreme risk by a combination of dry air, little rainfall, hot winds blowing east to west, built-up vegetation that was too close to homes, steep hills, and limited access to municipal water. The report also castigates the “unregulated use of wood shingles as roof and siding material.”
This was not the first time a destructive fire on the wildland-urban interface had been partially attributed to ignitable building materials. The 1961 Bel-Air fire, for instance, which claimed almost 200 homes, including that of Burt Lancaster, and the 1959 Laurel Canyon fire were both, FEMA said, evidence of “the wood roof and separation from natural fuels problems,” as were fires in 1970 and 1980 near where the Tunnel Fire eventually struck in 1970 and 1980.
But it was the sheer scale of the Tunnel Fire that prompted action by California lawmakers.
Throughout the 1990s, fire-resilient roofing requirements were ramped up, designating which materials were allowed in fire hazard areas and throughout the state. By all accounts, the building code works — but only when and where it’s in force. Dale told me that compliant homes were five times as likely to survive a wildfire. Research by economists Judson Boomhower and Patrick Baylis found that the code “reduced average structure loss risk during a wildfire by 16 percentage points, or about a 40% reduction.”
“The challenge from the perspective of wildfire vulnerability is that those codes are relatively recent, and the housing stock turns over really slowly, so we have this enormous stock of already built homes in dangerous places that are going to be out there for decades,” Boomhower told me.
The 7A building code applies only to new buildings, however. In long-settled areas of California like Pacific Palisades, which has little new housing construction or even existing home turnover due to high costs and permitting complications, especially in areas under the jurisdiction of the California Coastal Commission, many houses are not just failing to comply with Chapter 7A, but also with any housing code at all.
Looking at which homes had survived past fires, Steve Quarles, who helped advise the California State Fire Marshal on developing 7A, told me, “What really mattered was if it was built under any building code.” Many homes destroyed by the fires in Los Angeles likely were not. In Pacific Palisades, fire management is a frequent topic of concern and discussion. But as late as 2018, local media in Pacific Palisades noted that the area still had some homes with wood shingle roofs.
While a complete inventory of homes lost in the Palisades and Eaton fires has yet to be taken, the neighborhoods were full of older homes. According to CalFire incident reports, of the almost 47,000 structures in the zone of the Palisades Fire, more than 8,000 were built before 1939, and 44,560 were built before 2009. For the Eaton Fire area, of the around 41,000 structures, almost 14,000 were built before 1939, and only around 1,000 were built since 2010.
A Pacific Palisades home designed by architect Greg Chasen and built in 2024, however, survived the fire and went viral on X after he posted a photo of it still standing after the flames had moved through. The home embodied some of the best practices for fire-safe building, according to Bloomberg, including keeping vegetation away from the building, a metal roof, tempered glass, and fire-resistant siding.
When Michael Wara, the director of Stanford University’s Climate and Energy Policy Program, spoke with firefighters and insurance industry officials in the process of drafting a 2021 report for the Stanford Woods Institute for the Environment on strategies for mitigating wildfire risk, they told him that, from their perspective, wildfires are often a matter of “home ignition,” meaning that while building near forested areas puts any home at risk, the risk of a home itself igniting varies based on how it’s built and the vegetation clearance around it. “Existing homes in high fire threat areas” built before the implementation of California’s wildfire building codes, Wara wrote, “are a massive problem.” At the time he published the paper, there were somewhere between 700,000 and 1.3 million pre-building code homes still standing in “high or very high threat areas.”
The flipside of focusing on “home ignition” and the building code is that the building code works better over time, as more and more homes comply with it thanks to normal turnover, people extensively renovating, or even tearing down old homes — or rebuilding after fires. Homes that are close to homes that don’t ignite in a fire are more likely to survive.
One study that looked at the 2018 Camp Fire, which destroyed more than 18,000 structures and claimed more than 80 lives in the Northern California town of Paradise, sampled homes built before 1997, between 1997 and 2018, and from 2018 onwards, and found that only 11.5% of pre-1997 homes survived, compared to 38.5% from 1997 and after. The researchers also found that building survivability had a kind of magnifying effect, with distance from the nearest destroyed structure and the number structures destroyed in the immediate area among “the strongest predictors of survival.”
“The more homes that comply, the less chance you get those structural ignitions and the less chance you get those huge disasters like this,” Doug Green, who manages Headwaters Economics’ Community Assistance for Wildfire Program, told me. “It takes people doing the right thing to their own home — dealing with vegetation, making sure roofs are clean, having right roofing. It’s really a community-wide strategy to stop fires that happen like this.”
But just as any home hardening — or just building to code — is more effective the more the homes around you do it as well, it’s just as true in reverse. “If your next door neighbors don’t do that work, the effectiveness of your efforts will be less,” Dale said. “Building codes ultimately work best when we get an entire landscape or neighborhood to adopt them.”
Log in
To continue reading, log in to your account.
Create a Free Account
To unlock more free articles, please create a free account.
Oil flows through the Strait of Hormuz, a critical waterway, seem to be returning to normal.
This is Heatmap Daily, a weekday news digest written by our executive editor. Sign up to receive it.
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.”
Sign up to receive Heatmap AM in your inbox every morning:
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.