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A new report from the Rhodium Group finds that the range of likely temperature outcomes has essentially not changed since 2023.

It’s that time of year when COP, the annual United Nations climate conference, draws near, and a flood of reports assess how much progress the world has made (or not made) to limit global warming. Given the sharp reversal in U.S. climate policy under President Trump, it may seem inevitable that the future will look bleaker than before. His administration has spent the past nine months dismantling nearly every bit of domestic climate policy implemented by its predecessor, and has even managed to thwart international efforts at climate cooperation.
The annual climate outlook from the Rhodium Group, a U.S. energy and climate research firm, offers a somewhat hopeful counterpoint to that narrative, however. It finds that the range of possible climate futures has essentially not changed in the past two years.
A full decade has passed since the landmark Paris Agreement on climate change, and in that time the world has avoided the most catastrophic scientific projections. In 2015, the UN’s Intergovernmental Panel on Climate Change, or IPCC, projected that global average temperatures could increase by as much as 7.8 degrees Celsius by the end of the century without significant shifts in policy and advancements in technology. Now, the Rhodium Group estimates that warming is highly unlikely to exceed 3.9 degrees by 2100, and could be limited to 2 degrees.
The numbers themselves are not hopeful. This is a vast range in terms of the potential impacts implied, and even 2 degrees of warming should not be considered “little.” The IPCC estimates that compared with a scenario that limits warming to 1.5 degrees, more than twice as many people would be exposed to severe heat at least once every five years in a world 2 degrees warmer; ice-free summers in the Arctic would occur 10 times more often; the number of plant, animal, and insect species that lose at least half their habitat would be two to three times larger; and crop yields and fisheries would suffer roughly twice the losses.
Putting those dire projections aside, what’s interesting is that this 2- to 3.9-degree range is about the same as what the Rhodium Group forecast when it published its first Climate Outlook report in 2023. Also relatively unchanged: a finding that global power sector emissions will peak within the next decade, and that total emissions will likely remain constant or subtly decline through 2060, but then go up again as Global South countries see more rapid economic development in the latter half of the century.
The reason the numbers haven’t changed much, despite some seemingly dramatic policy changes that have occurred in the interim, has to do with Rhodium’s unique approach to projecting the future.
Many of the reports that come out around this time, such as the UN’s annual Emissions Gap Report, try to assess where the world is headed based on currently enacted policies as well as pledges, such as the “nationally determined contributions” that countries submit to the UN. Those might include promises like, “We’ll build X quantity of renewable energy by 2030,” or “We’ll protect X amount of our forests.” The models assume that these policies and pledges are fixed. They do not contemplate future ramp-ups or potential reversals. They also use fixed assumptions about GDP and population growth, oil prices, technology costs, etc.
A recent report by Wood Mackenzie, for example, estimates that temperatures will climb to 2.6 degrees above the preindustrial average by 2100, and then models a few other potential discrete scenarios, including one that shows what it would take to limit warming to 2 degrees.
The limitation of this approach is that the trajectory for each variable these models use is deeply uncertain, Hannah Pitt, one of the authors of the Rhodium report, told me. “Even a small change in GDP growth can have really big implications for emissions — likewise for oil prices and renewable costs and all that,” she said. “We try to take into consideration the wide range of uncertainty we have in the future of those core drivers of emissions.” That requires modeling thousands of scenarios with different combinations of how those underlying drivers might evolve.
Then, rather than assuming that policies on the books today remain static, Rhodium uses data on how climate and energy policy has historically responded to economic inputs like oil prices and GDP growth, in different parts of the world to project how policy might change going forward, using a carbon price as a proxy for policy ambition.
This approach takes into account such a wide range of possibilities that the results aren’t likely to change much year to year. Both in 2023 and now, the modeling incorporated the prospect that a Trump administration or something like it could reverse some progress, and that energy demand could soar. “We are looking at the long-term evolution of policy, not the administration fluctuations,” Pitt explained. It would take a true step-change in policy or a major technological breakthrough to produce a noticeable change in the trajectory, she said.
What are those breakthroughs? At this point, they aren’t a mystery. Cheaper clean firm power — like advanced nuclear, fusion, or geothermal — would be a huge help. Solutions for decarbonizing flying and shipping are also on the list. We also need to make it affordable to produce iron, steel, cement, and petrochemicals with far fewer emissions.
On the policy side, bending the curve might mean something like stricter electric vehicle requirements. As mentioned earlier, economic development in the Global South is expected to shift emissions back upward later this century — in part because if policy evolves the way it has historically, and if more and more people around the world are buying cars, the cars may not be 100% electric, and emissions from transport will go up.
None of this is to say that the Trump administration’s actions will have no effect on warming. Recall the report’s expansive range of future warming scenarios of 2 to 3.9 degrees — it’s very possible that policies enacted today will push the world closer to one or the other. A separate recent Rhodium study that dives into the specifics of U.S. policies found that emissions in 2035 could be 0.8 to 1.2 gigatonnes higher than what the group projected in the same report last year, largely due to Trump’s policies.
It should be comforting that one administration can’t veer the world too far off course — although by that same logic, we can’t expect a single administration to shift projections in a positive direction, either. A "breakthrough" in something like decarbonized cement will likely happen over years, through a feedback loop of sustained policy support and technological development.
There is no “too late” when it comes to addressing the technology and policy gaps the report highlights, Pitt said “Of course, the sooner the better,” she said. “But the difference between a 2.8-degree future and a 2.5-degree future saves lives. So the effort to drive these technology costs down is worthwhile, even if it doesn’t happen on the timeline that we would hope.”
Editor’s note: This story has been updated to correct the warming timeline outline in Wood Mackenzie’s report.
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The spinoff of Lawrence Livermore National Lab has a new 10-point plan to get onto the grid by the 2030s.
One of fusion energy’s newest startups, Inertia Enterprises, is betting that the fastest route to commercial fusion runs through one of the field’s oldest ideas. The company, which raised a $450 million Series A earlier this year, plans to build a power plant based on the laser-driven fusion system pioneered at Lawrence Livermore National Laboratory’s — the only tech yet to have produced more energy from a fusion reaction than it took to initiate it. Now, Inertia has shared its commercialization roadmap exclusively with Heatmap, detailing the 10 near-term capabilities it must demonstrate before this landmark experiment can become a grid-scale power plant by the mid-2030s.
The roadmap offers a route from the national lab’s impressive but commercially impractical fusion demonstrations to an economical power plant capable of producing electricity for the grid. At its core are a set of milestones — mostly aimed at developing cheap, mass-manufacturable components — that Inertia says it must clear before those individual systems can be integrated into a working plant. This road is not necessarily linear, however, as various teams will likely be working on many of these goals simultaneously.
At least the physics of Inertia’s approach are already proven, the startup’s CEO Jeff Lawson told me, pointing to the fusion experiments at Lawrence Livermore’s National Ignition Facility as a proof-of-concept. The lab’s demonstration of net energy gain caps more than six decades and $30 billion (in 2026 dollars) of U.S. fusion research. The remaining challenges, he argued, are all engineering-related, requiring “elbow grease, hard work, and smart people” rather than breakthroughs in fusion science.
"It seems to us like a startup or a commercial company of any variety should be focused on commercializing a proven scientific result, as opposed to actually trying to demonstrate the basic science to begin with," Lawson told me. Basic science, he argues, is better left to national labs and universities, where researchers can pursue "unbounded problems" that don’t align with the expectations and timelines of venture-backed startups.
Indeed, no fusion startup has yet achieved scientific breakeven, the milestone Lawrence Livermore first hit in 2022, and has since repeated numerous times. But leading players such as Commonwealth Fusion Systems and Helion Energy maintain that it’s only a matter of time before they validate the physics behind their own reactor designs, which they claim will be highly cost-competitive.
Lawson, on the other hand, readily acknowledged that Lawrence Livermore’s tech is uneconomical in its current form. His bet is simply that the more predictable path to a commercial reactor is to drive down the cost of the lab’s validated fusion approach, known as inertial confinement. This system relies on high-powered lasers firing at a millimeter-scale pellet of fusion fuel, compressing it to extreme temperatures and pressures until the atoms fuse. Today, the National Ignition Facility makes each individual fusion target by hand, a workable solution given that it only uses about a dozen per year.
That production model, however, isn’t remotely plausible for a grid-scale power plant. Because each fusion reaction lasts just a fraction of a billionth of a second, a commercial facility must fire its lasers at a fresh target about 10 times per second to generate continuous electricity — requiring the production of hundreds of millions of targets each year.
Scaling production to roughly a million pellets per day and making them inexpensive enough for commercial operation without compromising the strength or precision required for fusion ignition is central to Inertia’s roadmap. That includes goals five, seven, eight and nine — industrializing the manufacturing of the carbon shells that hold the fusion fuel, making the thin films that hold those carbon shells both durable and cheap, scaling up and automating fusion target assembly, and speeding up how fast targets are filled with the requisite deuterium-tritium fuel.
The other central focus of the roadmap is the laser system, which will ultimately consist of 1,000 individual units operating in concert to compress and heat the fusion fuel. Key priorities include reducing the system’s cost (goal two), dramatically increasing its firing cadence (goal three), and bolstering its durability to withstand high-intensity operations (goal four). Goal six also complements these efforts, calling for the development of a control system capable of tracking moving fusion targets to precisely align each laser shot.
Goals one and 10 bookend the journey with some broader milestones. The first focuses on increasing the fusion target’s energy gain — the ratio of fusion energy produced to laser energy delivered — to more than 25 times ignition. Today, the National Ignition Facility’s best-performing laser shot has yielded a gain of just over four times what it took to start the reaction. Goal 10 then zooms out to the ultimate objective: integrating all these technologies into a commercially viable power plant that can deliver either electricity or industrial heat to end customers.
To reach that point, Inertia has embarked on an industrial engineering hiring spree, recruiting folks with experience taking complex hardware systems from prototype to mass production, “not unlike the processes that are used in the semiconductor or consumer electronics world,” Lawson explained. The company has been making progress on its component development goals since the beginning of the year, he told me, and expects to announce the successful demonstration of a few of these milestones in the coming months. Lawson ultimately expects Inertia to complete the core components of its laser and target manufacturing systems by the middle of next year.
The team will spend the next two to three years integrating these individual pieces into two fully operational subsystems, a prototype laser system and a target manufacturing line. Around 2030, the company will begin combining those subsystems into a first-of-a-kind fusion power plant, which will also serve as the proving ground for the target chamber, tritium fuel breeding system, and power conversion system that turns fusion heat into electricity. By the middle of the next decade, Inertia aims to be generating power from this first plant, setting the stage for the company to build and connect additional grid-scale commercial power plants.
There are plenty of engineering trade-offs that the company will have to solve for. Take the decision around how to size the target chamber, for example. “If you make it bigger, your walls have an easier time and survive longer, but it’s more expensive. If you make it smaller, your walls have a tougher time because they’re closer to all the heat and energy that the fusion reaction is creating, but now your power plant costs less to build.”
But to Lawson, this represents exactly the type of problem Inertia was built to solve: complex engineering issues that come to the fore once scientists have demonstrated the fundamental physics are sound. He thinks other fusion companies may someday reach this stage, as well — though he’s unwilling to hazard a guess on exactly what approach or startup is best positioned to do so.
“There have been generations of scientists who’ve made their predictions about fusion energy and gotten it wrong,” he told me. “I’m not going to pretend to be smarter than them. All I’m here to say is, just knowing that one did work, we can commercialize it.”
Current conditions: After forming into Tropical Storm Bertha late Monday, the system is barreling toward the Florida Panhandle as it makes landfall as far west as Texas • In the Pacific, Hurricane Fausto has strength as it heads toward Hawaii but remains a Category 1 storm • Temperatures in Ouargla, Algeria’s southern city in the Sahara desert, are soaring to nearly 120 degrees Fahrenheit this week.
Emissions from the United States’ electrical sector spiked 4% last year as demand for power drove up generation from coal. That’s according to the latest annual assessment published Tuesday morning by the U.S. Energy Information Administration. The report, which has tracked annual emissions data from all power sources since 2010, found that U.S. energy-related carbon dioxide emissions increased by 2%, or about 115 million metric tons, in 2025. But the power sector specifically saw a surge of 4%, or 58 million metric tons, due to a spike in fossil fuel use. Coal-fired generation rose by 13%, even as natural gas-fired power fell 4%. Renewables helped avoid more coal use. While wind generation increased 3%, solar skyrocketed by 34%. Generation from all other sources — including nuclear and the category of “other renewables” that includes hydropower and geothermal — were essentially flat last year.
The coal surge isn’t unique to the U.S., as my colleague Matthew Zeitlin wrote last year. Worldwide, rising demand for electricity and shrinking supply of natural gas coming through the Strait of Hormuz made for a good year for coal.
Watershed, the software platform focused on corporate sustainability, just published what it called its first comprehensive open framework for estimating the greenhouse gas emissions from companies’ use of AI programs. The framework has three elements: A comprehensive system that includes all phases of a data center’s use, from model training to inference to hardware production; a function unit of kilograms of carbon dioxide equivalent per million tokens; and a three-tier calculation approach “that aligns with companies’ data quality.”
In a statement to my colleague Emily Pontecorvo, Watershed’s science chief John Bistline said he had “heard from companies that they’re already being asked about AI emissions from investors, from auditors, from regulators, and right now most of them are guessing. We wanted to give them something that was more defensible.”
Oil prices spiked again Tuesday after President Donald Trump publicly weighed taking “a nice big fat shot” at Iran’s Pickaxe Mountain, where Israeli intelligence suggests the Islamic Republic moved its uranium-enriching centrifuges last fall. Brent crude, the main European benchmark for the price per barrel of oil, rose nearly 3% to over $91. West Texas Intermediate, the U.S. price signal, saw a 3% hike to just nearly $85. Murban crude — out of the United Arab Emirates, therefore the most sensitive to Persian Gulf disruptions — soared nearly 5% to just under $86 per barrel.
Shakeups among smaller producers, meanwhile, appeared to cancel out each other’s effects on the market. The shot: Kazakhstan, which falls just outside the top 10 oil-producing nations, is halting crude shipments to the Russia ports it relied on to get its hydrocarbons to market now that Ukraine is consistently attacking the Kremlin’s energy infrastructure, according to the Financial Times. The chaser: Norway’s oil output just beat forecasts, per Oil Price.
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Unlike the last man Trump put in charge of the Environmental Protection Agency during his first term in office, Lee Zeldin hadn’t formally worked for the coal industry before serving in government. But the EPA administrator sure made it sound like the industry’s executives are high-priority constituents. At a National Coal Council event in Washington, D.C.’s Willard Hotel that E&E News covered, Zeldin said “many of the items that were on your wish list are now done.” In the coming months, he added, the agency would get to “the remainder of those items,” but said he wouldn’t “prejudge” any rulemaking outcomes. “Between now and your next meeting, I’m excited to be able to share with great optimism, hope, and enthusiasm that you all, again, not prejudging the outcome of any rulemaking, we’ll have a lot to celebrate the next time you all get together again in January,” Zeldin said. One thing the EPA can’t do: Keep the coal plants the Trump administration wants open actually running. As Matthew wrote last year, the big problem with aging coal stations is that they keep breaking down.
Mergers and acquisitions within the global nuclear industry totaled more than $7 billion in value in the first half of 2026, doubling that same figure from a year earlier. That’s according to new data the law firm White & Case LLP shared Tuesday with World Nuclear News. The number of individual deals increased 10%, from 40 to 44. “At the current pace of dealmaking activity, 2026 is set to surpass all years aside from 2024 when a record $29 billion of M&A activity was registered,” the law firm said. More proof that the nuclear dealmaking boom, as Heatmap’s Katie Brigham wrote last year, “is real.”
It’s not just automobiles going hybrid-electric. The startup Electra, which has promised to build a nine-passenger hybrid-electric plane that can take off in as little as 150 feet, is now pumping $850 million into its first aircraft factory in Ohio. The plant, announced Tuesday, will build up to 800 aircraft per year at full capacity. But as Electrek put it, “that’s a big commitment for a plane that hasn’t flown yet.”
Frontier model developers still keep their energy use largely a secret, but Watershed is proposing a new formula that will at least get you close.
With companies now rapidly adding artificial intelligence into their products and using it across their workstreams, it stands to reason that all that extra energy use might show up in their climate accounting. But to any business that wants to get a sense of how big its AI-related emissions footprint is becoming — and, god forbid, maybe even try to reduce it — I say well, good luck. AI providers mostly keep the data required to make such calculations a secret.
Now Watershed, a startup that helps companies track and estimate their carbon emissions, is proposing a workaround. The firm published a white paper on Wednesday laying out a method for companies to produce rough estimates of their carbon impact from AI, while also encouraging them to demand better data from AI developers.
“We’ve heard from companies that they’re already being asked about AI emissions from investors, from auditors, from regulators, and right now most of them are guessing,” John Bistline, Watershed’s head of science, told me. “We wanted to give them something that was more defensible.”
For most frontier AI models, including those developed by OpenAI and Anthropic, there’s very little information to work with. Google is the only proprietary AI developer that has published a transparent estimate of its model’s operational energy use and related emissions. In a paper last August, researchers at the company found that “the median Gemini apps text prompt consumes 0.24 watts,” which is “less energy than watching nine seconds of television,” and released 0.03 grams of CO2-equivalent. These numbers may be out of date by now, however. In the paper, the authors note that this already represented a 33-fold reduction in energy consumption compared to the previous year.
That’s one challenge with estimating AI-related emissions — tech companies are both growing and innovating rapidly, expanding their energy footprints while also finding greater efficiencies, which may be one reason they don’t disclose this information yet.
Another obstacle is that the exercise involves making a number of carbon accounting decisions, and there’s no consensus yet on best practices. For instance, where do you draw the line on which emissions to include? You could just look at the energy required to operate the model, or you could include the energy used to train the model, or even the emissions related to fabricating and manufacturing the hardware it’s running on. Training a model tends to be more energy-intensive than running it to respond to queries, but it only happens once. If you’re going to include training emissions, the next question is, how should responsibility for those be allotted across the lifetime of the model and its use by hundreds of thousands of customers?
Another decision is how to account for differences in user behavior. A model’s energy intensity can vary widely depending on whether the user is asking a simple question, requesting complex research, generating images, or dispatching agents to conduct multiple tasks simultaneously. Models capable of “reasoning” use an estimated 30 times more electricity than those without that ability, according to research by HuggingFace, a company that creates tools for AI developers. A per-prompt emissions average would not capture these differences, and therefore would not give companies actionable information to help them reduce their emissions.
A “per token” average might be more useful in that sense. When AI models process queries, they break the sentence or code down into smaller components called tokens. One token might be just the first few letters of a word. When the model generates a response, it also processes it in terms of tokens. Estimating emissions per token is not a perfect system either, however, since a token’s value can vary across AI providers. Input tokens, i.e. user questions, also tend to be less energy-intensive than output tokens, or user responses, and a single per-token average will conceal that difference.
Then there’s the question of how to get from a model’s energy intensity to an emissions estimate. Should you use the real-world average carbon intensity of the electric grid? What about any clean energy agreements the AI company may have signed? And how should you factor in companies that decide to bypass the grid entirely and build their own on-site generation, which tends to use natural gas?
The Watershed paper proposes some answers to these questions, and also offers guidance for how companies can develop emissions estimates based on the data available to them.
While most of the published research on AI emissions to date has calculated energy intensity on a per-query basis, Watershed advocates for a per-token approach. — i.e. “kilowatt-hours per thousand tokens.” The authors reason that electricity use scales more directly with the number of tokens used than the number of queries submitted. AI application customers are also often billed based on their token usage, so there’s a business case for tracking tokens and trying to use them more efficiently.
For those companies working with essentially zero data — not even the number of tokens they’re using per year — Watershed recommends they approximate their AI emissions using a “spend-based” method. This means simply multiplying the amount they spend per year on AI services by an emissions factor of 0.134 kilograms of carbon dioxide equivalent per U.S. dollar, which is based on U.S. Bureau of Economic Analysis numbers for the data processing sector of the economy.
The Watershed paper concedes that whatever number this method spits out will be wrong, noting that it “can misestimate true AI emissions by several times in either direction,” and advising companies to treat this as a “provisional placeholder.” But publishing these numbers, even though they are wrong, could help push AI companies toward more transparency if they want to correct the record.
For companies that do track their token volumes, Watershed has a more rigorous solution. The paper proposes a formula companies can use to calculate their AI emissions, accounting not just for inference energy use, but also training emissions, embodied emissions of the data processing equipment, and a figure known as “power usage effectiveness.” This captures the energy consumed by cooling systems, power conversion, and other data center infrastructure that’s not directly serving AI processing. Since model-specific values for the various inputs to the formula are mostly not available today, Watershed has provided default values gathered from previous studies, including papers by Microsoft and Google. Companies can substitute the actual numbers disclosed by AI providers into the formula as that information becomes available.
I reached out to Google, Microsoft, Anthropic, and OpenAI to ask why they didn’t share token carbon intensity, and whether they planned to in the future. Only Microsoft responded to my inquiry, pointing me to its blog post and peer-reviewed paper estimating general AI energy use across frontier models.
To get the most accurate estimate, companies would also need to know where, geographically, their AI queries are being serviced, since emissions from the electric grid varies by region. In some cases, companies may be able to actually choose where their queries are being processed, offering another lever by which they could potentially reduce their emissions.
The right data, disclosed in sufficient detail, will unlock companies’ ability to reduce their AI-related emissions, Watershed argues. Employees would have more reason to choose the most appropriate model for a given task, for example, like avoiding using energy-intensive reasoning models for basic questions.
“I think about a John von Neumann test here,” Bistline said, referring to the mathematician and proto-computer scientist. “You wouldn’t ask an advanced model like Fable anything that you would be embarrassed to ask John von Neumann, or Marie Curie, right? You wouldn’t want to ask ‘how many R’s are there in Strawberry?’ or ‘which restaurants would you recommend I go to in Miami?’”
Of course, companies can already implement this recommendation today, but there will be no way to account for and prove that they are reducing their emissions as a result until AI providers disclose distinct model-based energy estimates.
As Bistline mentioned, this information isn’t just nice-to know — companies are already being asked for it. Upcoming regulations in California and the European Union will require large companies to disclose their total direct emissions, and will eventually require them to disclose indirect emissions like AI energy use. The EU’s AI Act will also require AI companies to disclose a breakdown of the energy consumption of its general purpose AI models.
“There are customer-side disclosure rules and provider-side ones developing in parallel,” Bistline said, “and right now there’s no agreed methodology connecting the two, which is the gap we’re trying to address with our AI emissions framework.”