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The same technology that powers your cell phone also helps expand the reach of renewable energy.

Batteries are the silent workhorses of our technological lives, powering our phones, computers, tablets, and remotes. But their impact goes far beyond our daily screentime — they’re also transforming the electricity grid itself. Grid-scale batteries store excess renewable energy and release it as needed, compensating for the fact that solar and wind resources aren’t always available on demand.
The price of the most ubiquitous battery technology — lithium-ion — has fallen remarkably in the past 15 years. That’s allowed for an enormous buildout of battery storage systems in the U.S. and beyond, which has in turn helped to integrate more renewables onto the grid than ever before. With the assistance of batteries, California ran entirely on clean energy for the equivalent of 51 days last year, while South Australia managed the same for 99 days.
Even as deployment accelerates, startups and other innovators are working to improve on standard lithium-ion tech — or in some cases, supplant it. We’ll get into all that soon, but first, let’s start with a little Battery 101.
All electrochemical batteries — that’s everything from your standard AA to grid-scale lithium-ion systems — work by turning chemical energy into electrical energy through what’s known as an electrochemical reaction. These batteries have three primary components:
Grid batteries charge when there’s excess renewable energy on the grid or when demand for energy is low. When a lithium-ion battery is charging, lithium ions move from the cathode to the anode, where they’re stored. When the battery discharges electricity back to the grid, lithium ions move from the anode to the cathode. This movement triggers the release of electrons at the anode, which move through an external wire that carries power to the grid.
There’s variation within the realm of lithium-ion batteries. For example, some use different cathode chemistries, a solid electrolyte, or a pure lithium metal anode. Within the broader world of electrochemical batteries, there are also a variety of alternate chemistries including sodium-ion, lithium-sulfur, and iron-air (more on those below).
But if one broadens the definition of a battery to include any system that stores energy, that’s when the possibilities really open up. In this sense, a battery could be a pumped hydropower storage system, in which energy is stored by moving water uphill into a reservoir and later releasing it to generate electricity through kinetic energy. A battery could also be energy stored as heat or compressed air. Many of these mechanisms rely on converting stored energy into electricity by turning a turbine or generator.
Batteries help to stabilize the electric grid and help communities and grid operators to take full advantage of their renewable energy resources by providing a reliable power supply when, as the saying goes, the sun isn’t shining and the wind isn’t blowing. New solar or wind plants combined with battery storage can also be highly cost-effective, achieving power prices that are competitive with or lower than those of new natural gas facilities in many cases.
Homes and businesses can also install their own personal battery storage systems to bank energy from rooftop solar panels or directly from the grid. This allows individuals and companies to lower their electricity bills by charging their batteries when grid prices are low and using stored energy when prices are high.
By the end of last year, the installed capacity of utility-scale batteries in the U.S. reached about 26 gigawatts, surpassing the cumulative capacity of pumped hydro for the first time. So while pumped hydro can still store a larger amount of total energy, batteries can now deliver more instantaneous power to the grid than any other energy storage resource. And though that 26 gigawatts represents a mere 2% of the U.S.’s total 1,230 gigawatts of generation capacity, the battery sector is growing rapidly. The International Energy Agency reported in February that planned capacity additions for this year totaled 18.2 gigawatts for the U.S. alone.
Lithium-ion batteries weren’t originally designed for grid-scale energy storage. Rather, they were commercialized in the early 1990s for use in portable consumer electronics such as camcorders, cell phones, and laptops. These batteries proved to be more energy dense, lighter, and longer lasting than their predecessors, and were thus eventually adopted for a whole host of applications, including the growing electric vehicle market in the 2010s.
As electric vehicle production ramped up throughout the decade, manufacturers scaled up their production of lithium-ion batteries, quickly driving down prices — from 2010 to 2020 the cost of battery packs declined nearly 90%. Production became primarily concentrated in East Asia, where companies such as CATL, LG Energy Solution, and Panasonic emerged as dominant players.
As the cheapest and most mature battery tech on the market, lithium-ion thus became the default for grid developers looking to manage the variability of intermittent solar and wind resources. As renewables deployment surged, adding battery storage to these facilities started to become more cost-effective than building new fossil-fuel facilities in some markets and provided a reliable way to regulate the grid’s frequency. Lithium-ion batteries can begin absorbing or delivering power at a moment’s notice, which is integral to keeping the grid balanced.
While lithium-ion batteries have never been a very practical or economical option when it comes to long-duration storage — that is, the ability to dispatch energy for more than about four to eight hours at a time — they are well suited to applications such as storing excess solar produced during the day for use in the evening, or smoothing out the fluctuations in renewable resources throughout the day.
For one, China essentially has a virtual monopoly on the lithium-ion battery industry. The country made EV production a national priority beginning in the 2000s, and by the 2010s it was heavily subsidizing battery and EV manufactures alike. Thus, China came to dominate the supply chain at nearly every level, from raw materials refining to cell manufacturing, anode and cathode production, and battery pack assembly. Ideally, the U.S. would lessen its technological reliance on a nation that it’s long seen as an adversary, but building a domestic lithium-ion battery industry from scratch is an extremely complex and expensive endeavor.
In terms of technical drawbacks, most lithium-ion batteries use a flammable liquid electrolyte. That’s prone to catching fire if a battery component or surrounding equipment fails, if a cell is punctured or simply overheats, as illustrated by the Moss Landing fire in California, which broke out in January at one the world’s largest battery storage facilities. While the energy density of lithium-ion is a main selling point, the flipside is that in a fire, more energy equals more heat. And since grid-scale systems pack battery cells close together, a fire in one cell can spread quickly across an entire facility.
Finally, in terms of cost, there’s only so far lithium-ion batteries can fall due to the expense of the raw materials. The price of lithium itself has been notoriously volatile. After hitting record highs in 2022, the commodity price subsequently collapsed after a wave of new mining projects oversupplied the market. This type of volatility wreaks havoc for battery storage developers and their balance sheets, thus spurring interest in chemistries that offer lower, more stable costs, as well as technologies with potentially superior cycle life, energy density, discharge times, and safety profiles.
The most widely commercialized spin on conventional lithium-ion batteries, which are traditionally made with an NMC cathode, is a variant known as lithium iron phosphate, or LFP. The iron-phosphate bond in a LFP cathode is very strong, making it more thermally stable than those in NMC batteries. LFP materials are also more structurally durable than nickel and cobalt, meaning these batteries can be charged and discharged more times before wearing out. Finally, LFPs are also cheaper and more sustainable, as the cathode materials are plentiful and less environmentally damaging to mine. LFP’s main drawback is its lower energy density, but its many advantages have enabled it to overtake NMC as the leading chemistry for new battery energy storage systems.
All the other competitors have much lower levels of commercial maturity. But on the plus side, this means there’s an opportunity to build out domestic supply chains for them. Sodium-ion batteries, for example, replace lithium with sodium, which is far more abundant. They’re also more thermally stable. Unfortunately for U.S. manufacturers, China is already surging ahead in the race to scale up this tech. Then there’s the more nascent lithium-sulfur batteries. They have a very high theoretical energy density, which could lead to lighter and more compact energy storage systems if companies can overcome core technical challenges such as short cycle life.
Flow batteries are also an option that’s been studied for decades. These store energy in liquid electrolytes held in external tanks rather than in solid electrodes. This presents a promising option for longer-duration energy storage since the design can be scaled easily — more energy simply means bigger tanks. Because the active materials are liquid, these batteries also have a very long cycle life, and their water-based designs are non-flammable. Flow batteries are also much bulkier, however, and haven’t yet scaled enough to become cost-competitive with lithium-ion under most circumstances.
Getting into the realm of long-duration storage also opens up possibilities such as iron-air batteries, which are being commercialized by the Massachusetts-based Form Energy. In theory, these can discharge for 100-plus hours by taking in oxygen from the air and reacting it with iron to form rust, releasing electrons in the process. When the battery is charging, an electrical current converts the rust back into iron. Because iron is cheap and plentiful, this tech could also be significantly less expensive than LFP batteries. And since it uses a water-based electrolyte, these batteries aren’t flammable. The first iron-air battery plant is set to come online at the end of the year.
Beyond the electrochemical domain, there’s a wider, weirder world of energy storage technologies, many of which are being explored for their long-duration storage potential. Pumped hydro can only be built only in very specific geographies, so it’s not a main competitor in many regions today. But gravity-based storage companies such as Energy Vault often take inspiration from this approach, storing energy by using excess electricity to raise heavy objects such as concrete blocks. When energy is needed, the blocks are lowered, causing the motors that lifted them to run in reverse and act as generators to produce electricity.
Canadian company Hydrostor is pursuing another method, which involves using surplus energy to compress air and pump it into a water-filled cavern, displacing the water to the surface. To discharge, water is released back into the cavern, pushing the air to the surface, where it mixes with stored heat to turn an electricity-generating turbine.
Then there’s thermal energy storage — essentially storing energy as heat in materials such as carbon blocks. This method has the potential to decarbonize industrial processes such as steel and cement production, which demand high temperatures that are difficult to achieve with electricity. Via resistance heating — the same technology as a toaster — electricity from renewable energy is converted into heat, which is then stored in thermally conductive rocks or bricks. When that heat is needed, it can be delivered directly as hot air or steam to the facility, or in some cases converted back into electricity for use at the facility or on the grid.
Experts say that none of the aforementioned technologies is likely to fully replace lithium-ion anytime soon. That’s in large part because lithium-ion is a fully mature technology with well-established supply chains, but also because it’s simply efficient and cost effective for what it can do.
Many of the technologies mentioned could, however, become effective complements to lithium-ion on the grid. For example, it’s possible that some combination of iron-air batteries, gravity energy storage, and compressed air energy storage could meet longer-duration needs — in some cases discharging continuously for days at a time. Thermal energy storage could also play a role here, as well as in decarbonizing high-heat heavy industries, which don’t make economic sense to electrify with lithium-ion batteries.
Sodium-ion batteries could eventually become cheaper than LFP, but because the tech has yet to scale and reach that price point, it’s still primarily viewed as a complementary solution. Having other viable battery chemistries such as sodium-ion would help reduce the overall demand for lithium, thus working to stabilize prices and risk in the battery supply chain as a whole. But because sodium-ion is less energy dense, it probably won’t make sense in space-constrained regions.
As for lithium-sulfur, the tech is just beginning to hit the market as companies such as Lyten focus on early applications in drones, satellites, and two- and three-wheelers. But it doesn’t yet have the cycle life to make sense for any grid-scale applications, and whether it will ever get there has yet to be discovered.
Yes, but battery recycling — especially for battery energy storage systems — is still a nascent industry. And it remains uncertain whether recycling and reusing battery materials is financially viable in an environment where lithium prices have plummeted and other key battery minerals such as nickel, cobalt, and graphite have become significantly cheaper. LFP’s cost efficiency improvements have further depressed interest in recycling their materials. But there’s still interest in this sector as it could help establish a domestic mineral supply chain, greatly reduce the need for environmentally disruptive mining projects, and ameliorate problems such as toxic chemical leaching and fire risk, which can occur when batteries are improperly disposed of.
Because grid-scale battery deployments didn’t begin to ramp in earnest until 2019, most systems have yet to reach the end of their useful life, which can last on the order of 10 to 20 years. As such, most leading battery recyclers — such as the well-funded startup Redwood Materials — are primarily focused on old EV batteries for now. Redwood says it can recover, on average, over 95% of battery materials such as lithium, nickel, cobalt, copper, aluminum, and graphite. Recently, the company has also been working to repurpose old EV batteries with some life left in them to make grid-scale battery storage systems, and it’s made forays into recycling grid batteries as well.
One of the industry’s former leaders, Li-Cycle, filed for bankruptcy in May, while another player, Ascend Elements, has paused construction on its recycling facility in Kentucky due to “changing market conditions.” As the U.S. seeks to develop a more localized battery supply chain, however, recycling will only become more critical.
It’s a mixed bag. On the one hand, President Trump’s steep tariffs on Chinese goods are set to substantially increase prices for domestic battery energy storage systems, given that the U.S. imports nearly all of its battery cells from China. This will threaten developers’ margins, potentially leading to project cancellations or delays.
Trump’s One Big Beautiful Bill maintained tax credits for battery energy storage projects through 2032, however stringent foreign sourcing rules now apply, withholding tax credits from projects that source a certain percentage of their components from Russia, Iran, North Korea, and most importantly, China. Given how China-centric the battery supply chain is, achieving the required sourcing levels could prove difficult, though exactly how difficult ultimately depends on forthcoming guidance from the Treasury department.
On the bright side, the administration is also bullish on bolstering the U.S. supply chain for critical minerals and rare earths. In a recent meeting, White House officials told a group of critical minerals firms that they would guarantee a price floor for their products. Such a policy could, of course, bolster the domestic battery supply chain, though at the risk of making this tech more expensive.
Assuming the U.S. navigates the current political headwinds and maintains a degree of momentum in its transition to clean energy, battery energy storage will play an increasingly critical role on the future grid, both domestically and globally. As electricity demand grows and renewables make up a progressively larger proportion of the mix, batteries will help ensure grid flexibility and resiliency. That will be increasingly important as extreme weather events become more common and severe.
In some markets, solar plus storage facilities have been more economical than so-called fossil fuel “peaker plants” for years. Peakers fire up during times of maximum electricity demand, and as batteries continue to fall in price, stored renewable power becomes an ever-cheaper way to supplement supply. As long-duration storage tech advances and comes down the cost curve, renewables will be able to provide firm baseload power over a period of days or even weeks, making fossil fuel infrastructure increasingly obsolete.
The International Energy Agency reports that in order to reach net zero emissions by 2050, global grid-scale battery storage needs to expand to nearly 970 gigawatts of capacity by 2030. That means annual grid-scale deployments must average about 120 gigawatts per year from 2023 to 2030. So while last year saw a record-setting 55 gigawatts of newly installed grid-scale capacity, that type of hockey-stick growth will need to accelerate even further if batteries are to pull their weight in the IEA’s net zero scenario.
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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.”