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New rules governing how companies report their scope 2 emissions have pit tech giant against tech giant and scholars against each other.

All summer, as the repeal of wind and solar tax credits and the surging power demands of data centers captured the spotlight, a more obscure but equally significant clean energy fight was unfolding in the background. Sustainability executives, academics, and carbon accounting experts have been sparring for months over how businesses should measure their electricity emissions.
The outcome could be just as consequential for shaping renewable energy markets and cleaning up the power grid as the aforementioned subsidies — perhaps even more so because those subsidies are going away. It will influence where and how — and potentially even whether — companies continue to voluntarily invest in clean energy. It has pitted tech heavyweights like Google and Microsoft against peers Meta and Amazon, all of which are racing each other to power their artificial intelligence operations without abandoning their sustainability commitments. And it could affect the pace of emissions reductions for decades to come.
In essence, the fight is over how to appraise the climate benefits of companies’ clean power purchases. The arena is the Greenhouse Gas Protocol, a nonprofit that creates voluntary emissions reporting standards. Companies use these standards to calculate emissions from their direct operations, from the electricity and gas that powers and heats their buildings, and from their supply chains. If you’ve ever seen a brand claim it “runs on 100% renewable energy,” that statement is likely backed by a Greenhouse Gas Protocol-sanctioned methodology.
For years, however, critics have poked holes in the group’s accounting rules and assumptions, charging it with enabling greenwashing. In response, the organization has decided to overhaul its standards, including for how companies should measure their electricity footprint, known as “scope 2” emissions.
The Greenhouse Gas Protocol first convened a technical working group to revise its Scope 2 Standard last September. By late June, the group had finalized a draft proposal with more rigorous criteria for clean energy claims, despite intense pushback on the underlying direction from companies and clean energy groups.
A flurry of op-eds, essays, and LinkedIn posts accused the working group of being on the “wrong track,” and called the proposal a “disaster” with “unintended consequences.” The Clean Energy Buyers Association, a trade group, penned a letter saying it was “inefficient and infeasible for most buyers and may curtail ambitious global climate action.” Similarly, the American Council on Renewable Energy warned that the plan “could unintentionally chill investment and growth in the clean energy sector.”
Next the draft will face a 60-day public consultation period that begins in early October. “There’ll be pushback from every direction,” Matthew Brander, a professor of carbon accounting at the University of Edinburgh and a member of the Scope 2 Working Group, told me. Ultimately, it will be up to the Working Group, the Protocol’s Independent Standards Board, and its Steering Committee, to decide whether the proposal will be adopted or significantly revised.
The challenge of creating a defensible standard begins with the fundamental physics of electricity. On the power grid, electrons from coal- and natural gas-fired power plants intermingle with those from wind and solar farms. There’s no way for companies hooking up to the grid to choose which electrons get delivered to their doors or opt out of certain resources. So if they want to reduce their carbon footprints, they can either decrease their energy consumption — by making their operations more efficient, say, or installing on-site solar panels — or they can turn to financial instruments such as renewable energy certificates, or RECs.
In general, a REC certifies that one megawatt-hour of clean power was generated, at some point, somewhere. The current Scope 2 Standard treats all RECs as interchangeable, but in reality, some RECs are far more effective than others at reducing emissions. The question now is how to improve the standard to account for these differences.
“There is no absolute truth,” Wilson Ricks, an engineering postdoctoral researcher at Princeton University and working group member, told me back in June. “I mean, there are more or less absolute truths about things like how much emissions are going into the atmosphere. But the system for how companies report a certain number, and what they’re able to claim about that number, is ultimately up to us.”
The current standard, finalized in 2015, instructs companies to report two numbers for their scope 2 emissions, based on two different methodologies. The formula for the first is straightforward: multiply the amount of electricity your facilities consume in a given year by the average emissions produced by the local power grids where you operate. This “location-based” number is a decent approximation of the carbon emitted as a result of the company’s actual energy use.
If the company buys RECs or similar market-based instruments, it can also calculate its “market-based” emissions. Under the 2015 standard, if a company consumed 100 megawatt-hours in a year and bought 100 megawatt-hours’ worth of certificates from a solar farm, it could report that its scope 2 emissions, under the market-based method, were zero. This is what enables companies to claim they “run on 100% renewable energy.”
RECs are fundamentally different from carbon offsets, in that they do not certify that any specific amount of emissions has been prevented. They can cut carbon indirectly by creating an additional revenue stream for renewable energy projects. But when a company buys RECs from a solar project in California, where the grid is saturated with solar, it will do less to reduce emissions than if it bought RECs from a solar project in Wyoming, where the grid is still largely powered by coal, or from a battery storage project in California, which can produce clean power at night.
There are other ways RECs can vary — for instance, companies can buy them directly from power producers by means of a long-term contract, or as one-off purchases on the spot market. Spot market REC purchases are generally less effective at displacing fossil fuels because they’re more likely to come from pre-existing wind and solar farms — sometimes ones that have been operating for years and would continue with or without REC sales. Long-term contracts, by contrast, can help get new clean energy projects financed because the guaranteed revenue helps developers secure financing. (There are exceptions to these rules, but these are broadly the dynamics.)
All this is to say that the current standard allows for two companies that consumed the same amount of power and bought the same number of RECs to report that they have “zero emissions,” even if one helped reduce emissions by a lot and the other did little to nothing. Almost everyone agrees the situation can be improved. The question is how.
The proposal set for public comment next month introduces more granularity to the rules around RECs. Instead of tallying up annual aggregate energy use, companies would have to tally it up by hour and location. To lower companies' scope 2 footprints further, purchased RECs will have to be generated within the same grid region as the company’s operations, and match a distinct hour of consumption. (This “hourly matching” approach may sound familiar to anyone who followed the fight over the green hydrogen tax credit rules.)
Proponents see this as a way to make companies’ claims more credible — businesses would no longer be able to say they were using solar power at night, or wind power generated in Texas to supply a factory in Maine. While companies would still not be literally consuming the power from the RECs they buy, it would at least be theoretically possible that they could be. “It’s really, in my view, taking how we do electricity accounting back to some fundamentals of how the power system itself works,” Killian Daly, executive director of the nonprofit EnergyTag, which advocates for hourly matching, told me.
The granularity camp also argues that these rules create better incentives. Today, companies mostly buy solar RECs because they’re cheap and abundant. But solar alone can’t get us to zero emissions electricity, Ricks told me. Hourly matching will force companies to consider signing contracts with energy storage and geothermal projects, for example, or reducing their energy use during times when there’s less clean energy available. “It incentivizes the actions and investments in the technologies and business practices that will be needed to actually finish the job of decarbonizing grids,” he said.
While the standard is technically voluntary, companies that object to the revision will likely be stuck with it, as governments in California and Europe have started to integrate the Greenhouse Gas Protocol’s methodologies into their mandatory corporate disclosure rules.
The proposal’s critics, however, contend that time and location matching will be so costly and difficult to implement that it may lead companies to simply stop buying clean energy. One analysis by the electricity data science nonprofit WattTime found that the draft revision could increase emissions compared to the status quo if it causes a decline in corporate clean power procurement. “We’re looking at a potentially really catastrophic failure of the renewable energy market,” Gavin McCormick, the co-founder and executive director of WattTime, told me.
Another concern is that companies with operations in multiple regions could shift from signing long-term contracts for RECs, often called power purchase agreements, to relying on the spot market. These contracts must be large to be beneficial for developers because negotiating multiple offtake agreements for a single renewable energy project increases costs and risk. Such deals may still make sense for big energy users like data centers, but a company like Starbucks, with cafes throughout the country, will have to start sourcing fewer RECs in more places to cover all the parts of the world where they operate.
The granularity fans assert that their proposal will not be as challenging or expensive as critics claim — and regardless, they argue, real decarbonization is difficult. It should be hard for companies to make bold claims like saying they are 100% clean, Daly told me. “We need to get to a place where companies can be celebrated for being like, I’m not 100% matched, but I will be in five years,” he said.
The proposal does include carve-outs allowing smaller companies to continue to use annual matching and for legacy clean energy contracts, even if they don’t meet hourly or location requirements. But critics like McCormick argue that the whole point of revising the standard is to help catalyze greater emission reductions. Less participation in the market would hurt that goal — but more than that, these accounting rules aren’t designed to measure emissions, let alone maximize real-world emission reductions. You could still have one company that spends the time and money to invest in scarce resources at odd hours and achieves 60% clean power, while another achieves the same proportion by continuing to buy abundant solar RECs. Both would still get to claim the same sustainability laurels.
The biggest corporate defender of time and location matching is Google. On the other side are tech giants Meta and Amazon, among others, arguing for an approach more explicitly focused on emissions. They want the Greenhouse Gas Protocol to endorse a different accounting scheme that measures the fossil fuel emissions displaced by a given clean energy purchase and allows companies to subtract that amount from their total scope 2 footprint — much more akin to the way carbon offsets work.
If done right, this method would recognize the difference between a solar REC in California and one in Wyoming. It would give companies more flexibility, potentially deploying capital to less developed parts of the world that need help to decarbonize. It could also, eventually, encourage investment in less mature and therefore more expensive resources, like energy storage and geothermal — although perhaps not until there’s solar panels on every corner of the globe.
This idea, too, is risky. Calculating the real-world emissions impact of a REC, which the scope 2 working group calls “consequential accounting” is an exercise in counterfactuals. It requires making assumptions about what the world would have looked like if the REC hadn’t been purchased, both in the near term and long term. Would the clean energy have been generated anyway?
McCormick, who is a proponent of this emissions-focused approach, argues that it’s possible to measure the counterfactual in the electricity market with greater certainty than with something like forestry carbon offsets. With electricity, he told me, “there's five minute-level data for almost every power plant in the world, as opposed to forests. If you're lucky, you measure some forests, once a year. It's like a factor of 10,000 times more data, so all the models are more accurate.”
Some granularity proponents, including Ricks, agree that consequential accounting is valuable and could have a place in corporate reporting, but worry that it’s ripe for abuse. “At the end of the day, you can't ever verify whether the system you're using to assign a given company a given number is right, because you can't observe that counterfactual world,” he said. “We need to be very cautious about how it’s designed, and also how companies actually report what they’re doing and what level of confidence is communicated.”
Both proposals are flawed, and both have potential to allow at least some companies to claim progress on paper while having little real-world impact. In some ways, the disagreement is more philosophical than scientific. What should this standard be trying to achieve? Should it be steering corporate dollars into clean energy, accuracy of claims be damned? Or should it be protecting companies from accusations of greenwashing? What impacts do we care about more, faster emissions reductions or strategic decarbonization?
“They’re actually not opposing views,” McCormick told me. “There’s these people making this point and there’s these people making this point. They’re running into each other, but they’re actually not saying opposite things.”
To Michael Gillenwater, executive director of the Greenhouse Gas Management Institute, a carbon accounting research and training nonprofit, people are attempting to hide policy questions within the logic and principles of accounting. “We’re asking the emissions inventories to do too much — to do more than they can — and therefore we end up with a mess,” he told me. Corporate disclosures serve many different purposes — helping investors assess risk, informing a company’s internal target setting and performance tracking, creating transparency for consumers. “A corporate inventory might be one little piece of that puzzle,” he said.
Gillenwater is among those that think the working group’s time- and location-matching proposal would stifle corporate investment in clean energy when the goal should be to foster it. But his preferred solution is to forget trying to come up with a single metric and to encourage companies to make multiple disclosures. Companies could publish their location-based greenhouse gas inventory and then use market-based accounting to make a separate “mitigation intervention statement.” To sum it up, Gillenwater said, “keep the emissions inventory clean.”
The risk there is that the public — or indeed anyone not deeply versed in these nuances — will not understand the difference. That’s why Brander, the Edinburgh professor, argues that regardless of how it all shakes out, the Greenhouse Gas Protocol itself needs to provide more explicit guidance on what these numbers mean and how companies are allowed to talk about them.
“At the moment, the current proposals don’t include any text on how to interpret the numbers,” he said. “It’s almost incredible, really, for an accounting standard to say, here’s a number, but we’re not going to tell you how to interpret it. It’s really problematic.”
All this pushback may prompt changes. After the upcoming comment period closes in late November or early December, the working group could decide to revise the proposal and send it out for public consultation again. The entire revision process isn’t estimated to be completed until the end of 2027 at the earliest.
With wind and solar tax credits scheduled to sunset around then, voluntary action by companies will take on even greater importance in shaping the clean energy transition. While in theory, the Greenhouse Gas Protocol solely develops accounting rules and does not force companies to take any particular action, it’s undeniable that its decisions will set the stage for the next chapter of decarbonization. That chapter could either be about solving for round-the-clock clean power, or just trying to keep corporate clean energy investment flowing and growing, hopefully with higher integrity.
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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.