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As climate writers, my colleagues and I spend a lot of time telling readers that places are hot. The Arabian Peninsula? It’s hot. The Atlantic Ocean? It’s hot. The southern U.S. and northern Mexico? Hot and getting hotter.
But here’s a little secret: “Hot” doesn’t really mean … anything. The word is, of course, of critical importance when it comes to communicating that global temperatures are the highest they’ve been in 125,000 years because of greenhouse gases in the atmosphere, or for public health officials to anticipate and prevent deaths when the environment reaches the point where human bodies start malfunctioning. But when you hear it’s “100 degrees out,” what does that really tell you?
Beyond that you’re a fellow member of the Fahrenheit cult, the answer is: not a lot. Humans can “probably avoid overheating” in temperatures of 115 degrees — but only if they’re in a dry room with 10 percent relative humidity, wearing “minimal” clothing, and not moving, The New York Times reports. On the other hand, you have a high chance of life-threatening heat stroke when it’s a mere 90 degrees out … if the humidity is at 95%. Then there are all the variables in between: if there’s a breeze, if you’re pregnant, if you’re standing in the shade or the sun, if you’re a child, if you’re running a 10K or if you’re napping on your couch in front of a swamp cooler.
In order to better specify how hot “hot” is, a number of different equations and techniques have been developed around the world. In general, this math takes into account two main variables: temperature (the one we all use, also known as “dry bulb” or “ambient air temperature,” which is typically measured five feet above the ground in the shade) and relative humidity (the percentage of air saturated with water vapor, also known as the ugly cousin of the trendier dew point; notably Canada’s heat index equivalent, the Humidex, is calculated from the dew point rather than the relative humidity).
In events like the already deadly heat dome over the southern United States and northern Mexico this week, you typically hear oohing and ahhing about the “heat index,” which is sometimes also called the “apparent temperature,” “feels like temperature,” “humiture,” or, in AccuWeather-speak, the “RealFeel® temperature.”
But what does that mean and how is it calculated?
The heat index roughly approximates how hot it “actually feels.”
This is different than the given temperature on the thermometer because the amount of humidity in the air affects how efficiently sweat evaporates from our skin and in turn keeps us cool. The more humidity there is, the less efficiently our bodies can cool themselves, and the hotter we feel; in contrast, when the air is dry, it’s easier for our bodies to keep cool. Regrettably, this indeed means that insufferable Arizonans who say “it’s a dry heat!” have a point.
The heat index, then, tells you an estimate of the temperature it would have to be for your body to be similarly stressed in “normal” humidity conditions of around 20%. In New Orleans this week, for example, the temperature on the thermometer isn’t expected to be above 100°F, but because the humidity is so high, the heat toll on the body will be as if it were actually 115°F out in normal humidity.
Importantly, the heat index number is calculated as if you were standing in the shade. If you’re exposed to the sun at all, the “feels like” is, of course, actually higher — potentially as many as 15 degrees higher. Someone standing in the New Orleans sun this week might more realistically feel like they’re in 130-degree heat.

Here’s the catch, though: The heat index is “purely theoretical since the index can’t be measured and is highly subjective,” as meteorologist Chris Robbins explains. The calculations are all made under the assumption that you are a 5’7”, 147-pound healthy white man wearing short sleeves and pants, and walking in the shade at the speed of 3.1 mph while a 6-mph wind gently ruffles your hair.
Wait, what?
I’m glad you asked.
In 1979, a physicist named R. G. Steadman published a two-part paper delightfully titled “The Assessment of Sultriness.” In it, he observed that though many approaches to measuring “sultriness,” or the combined effects of temperature and humidity, can be taken, “it is best assessed in terms of its physiological effect on humans.” He then set out, with obsessive precision, to do so.
Steadman came up with a list of approximately 19 variables that contribute to the overall “feels like” temperature, including the surface area of an average human (who is assumed to be 1.7 meters tall and weigh 67 kilograms); their clothing cover (84%) and those clothes’ resistance to heat transfer (the shirt and pants are assumed to be 20% fiber and 80% air); the person’s core temperature (a healthy 98.6°F) and sweat rate (normal); the effective wind speed (5 knots); the person’s activity level (typical walking speed); and a whole lot more.
Here’s an example of what just one of those many equations looked like:

Needless to say, Steadman’s equations and tables weren’t exactly legible for a normal person — and additionally they made a whole lot of assumptions about who a “normal person” was — but Steadman was clearly onto something. Describing how humidity and temperature affected the human body was, at the very least, interesting and useful. How, then, to make it easier?
In 1990, the National Weather Service’s Lans P. Rothfusz used multiple regression analysis to simplify Steadman’s equations into a single handy formula while at the same time acknowledging that to do so required relying on assumptions about the kind of body that was experiencing the heat and the conditions surrounding him. Rothfusz, for example, used Steadman’s now-outdated calculations for the build of an average American man, who as of 2023 is 5’9” and weighs 198 pounds. This is important because, as math educator Stan Brown notes in a blog post, if you’re heavier than the 147 pounds assumed in the traditional heat index equation, then your “personal heat index” will technically be slightly hotter.
Rothfusz’s new equation looked like this:
Heat index = -42.379 + 2.04901523T + 10.14333127R - 0.22475541TR - 6.83783x10-3T 2 - 5.481717x10-2R 2 + 1.22874x10-3T 2R + 8.5282x10-4TR2 - 1.99x10-6T 2R 2
So much easier, right?
If your eyes didn’t totally glaze over, it actually sort of is — in the equation, T stands for the dry bulb temperature (in degrees Fahrenheit) and R stands for the relative humidity, and all you have to do is plug those puppies into the formula to get your heat index number. Or not: There are lots of online calculators that make doing this math as straightforward as just typing in the two numbers.
Because Rothfusz used multiple regression analysis, the heat index that is regularly cited by the government and media has a margin of error of +/- 1.3°F relative to a slightly more accurate, albeit hypothetical, heat index. Also of note: There are a bunch of different methods of calculating the heat index, but Rothfusz’s is the one used by the NWS and the basis for its extreme heat alerts. The AccuWeather “RealFeel,” meanwhile, has its own variables that it takes into account and that give it slightly different numbers.
Midday Wednesday in New Orleans, for example, when the ambient air temperature was 98°F, the relative humidity was 47%, and the heat index hovered around 108.9°F, AccuWeather recorded a RealFeel of 111°F and a RealFeel Shade of 104°F.
You might also be wondering at this point, as I did, that if Steadman at one time factored out all these variables individually, wouldn’t it be possible to write a simple computer program that is capable of personalizing the “feel like” temperature so they are closer to your own physical specifications? The answer is yes, although as Randy Au writes in his excellent Substack post on the heat index equation, no one has seemingly actually done this yet. Math nerds, your moment is now.
Because we’re Americans, it is important that we use the weirdest possible measurements at all times. This is probably why the heat index is commonly cited by our government, media, and meteorologists when communicating how hot it is outside.
But it gets weirder. Unlike the heat index, though, the “wet-bulb globe temperature” (sometimes abbreviated “WBGT”) is specifically designed to understand “heat-related stress on the human body at work (or play) in direct sunlight,” NWS explains. In a sense, the wet-bulb globe temperature measures what we experience after we’ve been cooled by sweat.

The “bulb” we’re referring to here is the end of a mercury thermometer (not to be confused with a lightbulb or juvenile tulip). Natural wet-bulb temperature (which is slightly different from the WBGT, as I’ll explain in a moment) is measured by wrapping the bottom of a thermometer in a wet cloth and passing air over it. When the air is dry, it is by definition less saturated with water and therefore has more capacity for moisture. That means that under dry conditions, more water from the cloth around the bulb evaporates, which pulls more heat away from the bulb, dropping the temperature. This is the same reason why you feel cold when you get out of a shower or swimming pool. The drier the air, the colder the reading on the wet-bulb thermometer will be compared to the actual air temperature.
Wet bulb temperature - why & when is it used?www.youtube.com
If the air is humid, however, less water is able to evaporate from the wet cloth. When the relative humidity is at 100% — that is, the air is fully saturated with water — then the wet-bulb temperature and the normal dry-bulb temperature will be the same.
Because of this, the wet-bulb temperature is usually lower than the relative air temperature, which makes it a bit confusing when presented without context (a comfortable wet-bulb temperature at rest is around 70°F). Wet-bulb temperatures over just 80, though, can be very dangerous, especially for active people.
The WBGT is, like the heat index, an apparent temperature, or “feels like,” calculation; generally when you see wet-bulb temperatures being referred to, it is actually the WBGT that is being discussed. This is also the measurement that is preferred by the military, athletic organizations, road-race organizers, and the Occupational Safety and Health Administration because it helps you understand how, well, survivable the weather is, especially if you are moving.
Our bodies regulate temperature by sweating to shed heat, but sweat stops working “once the wet-bulb temperature passes 95°F,” explains Popular Science. “That’s because, in order to maintain a normal internal temperature, your skin has to stay at 95°F degrees or below.” Exposure to wet-bulb temperatures over 95°F can be fatal within just six hours. On Wednesday, when I was doing my readings of New Orleans, the wet-bulb temperature was around 88.5°F.
The WBGT is helpful because it takes the natural wet-bulb temperature reading a step further by factoring in considerations not only of temperature and humidity, but also wind speed, sun angle, and solar radiation (basically cloud cover). Calculating the WBGT involves taking a weighted average of the ambient, wet-bulb, and globe temperature readings, which together cover all these variables.
That formula looks like:
Wet-bulb globe temperature = 0.7Tw + 0.2Tg + 0.1Td
Tw is the natural wet-bulb temperature, Tg is the globe thermometer temperature (which measures solar radiation), and Td is the dry bulb temperature. By taking into account the sun angle, cloud cover, and wind, the WBGT gives a more nuanced read of how it feels to be a body outside — but without getting into the weeds with 19 different difficult-to-calculate variables like, ahem, someone we won’t further call out here.
Thankfully, there’s a calculator for the WBGT formula, although don’t bother entering all the info if you don’t have to — the NWS reports it nationally, too.
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Can AI help emergency managers make faster decisions when every second counts?
Meteorologists had nothing polite to say about Tropical Storm Bertha. The “weak, disorganized, and lopsided” system made initial landfall in Louisiana last week as a “hot, sheared mess,” one that forecasters doubted would reach Texas with much oomph at all. Still, the Galveston County Consolidated Drainage District — the local flood mitigation and drainage management entity for the state’s most flood-prone county — had stood at the ready, posting updates on the storm’s progress to its Facebook feed in the lead-up.
There had been action behind the scenes, too. Since this spring, the county has relied on a new “AI-powered flood warning solution” pilot program to help local administrators identify the gaps in their understanding of the county’s flood risk and monitor rising water levels in real time. In a crisis, a chatbot could even advise them on when to issue an evacuation order.
“Imagine you’re an operator and you have to tell people to leave their homes because of floods coming in,” Todd Barr, the CEO of Axonis Decision Intelligence, which has partnered with the smart water-level sensor company Simplicity Integration in Texas’ Galveston County, told me. Axonis provides AI-assisted decision-making tools to clients in a number of time-sensitive industries, and in every case, “You want a paper trail of the data you used to make the decision — the reasoning and the model you used — and our platform does all of that,” Barr went on.
Issuing evacuation notices is a famously thorny business, and one that has resulted in high-profile and high-casualty failures, including in the Paradise, California, and Maui wildfires. Particularly noteworthy were the 2025 Kerr County floods that killed more than 100 people in Texas’ Hill Country after local officials took 90 minutes to send phone alerts once they became aware of the rising river.
In many cases, particularly in more rural counties, the teams making the evacuation decisions are small and lack sufficient training not only on when to make such a call, but even on how to word it. “The people who are put in the position of issuing the messages are doing 20 other things at the same time,” Jeannette Sutton, a researcher at the University at Albany’s Emergency and Risk Communication Message Testing Lab, told me when I reported on evacuation notices after the Los Angeles fires.
As for Galveston, “100%” of the buildings on the densely populated island are at flood risk, with modeling suggesting a worst-case-scenario hurricane could produce 26 feet of storm surge. Much of the city’s stormwater infrastructure additionally predates modern climate-change-intensified rainfall probabilities, with the district in the midst of a $54 million drainage project aimed at mitigating future flooding by building a pump station and enlarging sewer lines.
As part of the region’s ongoing resiliency work, the Galveston County Consolidated Drainage District installed seven of Simplicity’s water-level sensors —the county’s first — at locations on the mainland. (There are no sensors currently on Galveston Island proper.) Simplicity’s Axonis-powered system, SI-Ai, also pulls in data from NOAA, the U.S. Geological Survey, and Houston’s Harris County to present residents of the entire region with a live flood-risk dashboard, complete with intuitive green-yellow-red indicators to evaluate their neighborhood hazard level in real time. Operators also have their own proprietary dashboard where they can monitor sensors and are prompted to ask questions to interpret readings and open “investigations” if something appears amiss.

“If I’m the municipality, I can say, ‘Okay, here’s what the forecast is looking like and what is potentially going to happen,” Alison Reese, the COO and co-founder of Simplicity, explained to me. “Then I could ask a question like, ‘Hey, what other locations in this watershed are at high risk for flash flooding?’”
That’s where Axonis, the artificial intelligence company, comes in. “Today you would have to be like, ‘Alright! Get the weather report, quick! What’s happening? What are the sensors saying? Okay Bill, now what’s the upstream sensor saying?’” Barr said, acting out the frantic scenario of trying to source data from multiple streams at once. “All of that is what we’re automating.” (Galveston’s Office of Emergency Management is “not the POC for the flood sensor operations,” a representative told me; the drainage district oversees the Axonis-Simplicity partnership, and did not return a request for an interview. The mayor of League City, a city 35-minutes north of Galveston that is also managed by the district, has publicly criticized the SI-Ai program as a separate sensor network that duplicates the work of the Harris County Flood Control District.)
Working from the assumption that emergency managers have to parse reams of data in short periods of time — flash floods can rise as much as 10 feet in an hour — Axonis provides what is essentially a chatbot for authorities to query potential decisions ranging from road closures to evacuation notices, based on feedback from the sensors. It stops short, however, of having a dialogue box that pops up to tell operators, EVACUATE THIS NEIGHBORHOOD NOW.
When Barr demoed the program to me, he had the tool configured to create a credit risk review memo for a would-be banking client. (Axonis also has customers in the banking and defense sectors.) The dashboard essentially functioned the same as it would for Galveston County, though, and his investigation returned the kind of simplified, emoji-studded one-sheet that users of large language model-powered AI interfaces would immediately recognize. In this case, the tool identified a “🔴Risk Alert CANDIDATE” — Barr said that would be a particular sensor, in the case of Galveston — and followed it with a summary and bullet-pointed sections breaking down “⚠️Credit Risk Indicators” and “💧Liquidity Position.” (In a screenshot of an example flood report for Simplicity, shared with me, those sections were replaced by “📍Site Location” and “💧Water Level — Last 72 Hours.” I wondered what else was possible: “🌊Historic hydraulic risk”? “💀Vulnerable Populations”? )

The system then takes operators through a four-step decision-making model based on the OODA Loop, a common workflow in military contexts that involves justifying actions through evidence-based observations. “We always keep a human in the loop on these things, at least today in 2026 — though who knows in two or three years,” Barr said. He clarified in a later conversation with me, though, that “Axonis and [AI] tools should never tell you to evacuate now. It should tell you the information you need to make that decision.”
That was a point Barr stressed numerous times during our conversation: That Axonis’ chatbot is intended as a brainstorming tool or sounding board, and one that keeps a careful paper trail, “cryptographically sealing” any eventual decisions for review and attestation later. I likened it to a police body camera, and Barr didn’t dispute the similarities. “It’s an accountability tool,” he told me.
Of course, that means the burden of decision-making still falls on potentially fallible humans. I worried in particular that by sharing the responsibility with AI, human operators might get lazy or fail to properly question a decision the program might be leading them toward, particularly in an instance of hallucinated data. To the latter point, Barr told me that this is part of what Axonis is designed to address. “You can’t just take the sensor data and throw it into Claude and be like, ‘Alright, go make a decision for me.’ You need to set guard rails,” he said.
As to the former point, Barr told me the chat includes a disclaimer reminding its users that AI can make mistakes, and that the company trains its customers on how LLM technology works. “At the end of the day, it’s a tool, not a decider,” he said, although he allowed that it might be used to automatically trigger warning lights, sirens, or barriers, such as closing a flooded roadway.
I also posed the concern about complacency to Ali Mostafavi, a professor who supervises the UrbanResilience.AL Lab at Texas A&M, which researches, among other things, how artificial intelligence might be utilized in emergency contexts. Mostafavi agreed that there is always a risk in cognitive outsourcing, but that there is a “counter-argument that is also valid — that without these technologies, we have seen what can happen. We had the catastrophic floods last year in Kerr County, and if a similar technology had existed back then, an automated system could have identified the flash flood, and many young children would be alive today.”
Still, Barr told me he isn’t aware of Axonis advising in an actual evacuation order yet. While it is operational, the predictive model remains untested against its highest-stakes use case: the extremes of a climate-changed world, where formerly unthinkable outcomes may be one storm away.
“The more we can stress-test these technologies in real operational settings and use that feedback loop to improve the technologies, the better,” Mostafavi said. “But that’s easier said than done, because to have a technology implemented in an operational setting it should already be stress-tested, right?”
Bertha, though, was not that reckoning; the gusty squalls blew through Galveston last week without even disturbing the dinner reservations at the marina. But although it was already back to 90 and sunny by Monday morning on the Texas Gulf, the drainage in Galveston County, as in many places around the country, remains outdated and easily overwhelmed. One day, inevitably, the water will come. Hopefully when it does, someone or something will be watching.
The large renewables developer changes tack “in response to federal energy objectives.”
Trump’s solar freeze is now so tough that at least one renewable energy developer has asked his administration to turn their permitting application into a data center and gas-fired power plant instead.
Renew Development HoldCo – an LLC created by Clearway Energy Group – wrote the Bureau of Land Management in April asking if they could amend their 2021 application to build the Amber solar project, a 500-megawatt solar project in the Nevada desert that would require building on federal land. Their requested change? “[T]o formally remove the proposed solar facility and replace it with the development of a proposed data center and natural gas facility,” according to a copy of the letter I obtained.
“This amendment is the result of a shift in our internal development priorities and an updated assessment of project timing, in order to better align with the goals of our Administration,” reads the letter, which is dated April 3 and signed by Clearway’s chief development officer John Woody. “The data center concept is in exploratory early stages and as such has a longer and more flexible development horizon, and we believe its schedule will better align with the Bureau’s current workload and staffing plans.”
Now, this swap is somewhat shocking but shouldn’t exactly be a surprise. Companies with federal energy leases are struggling to get their renewable projects permitted by a hostile Trump administration. We’ve already seen some offshore wind developers ditch their leases in favor of payouts and commitments to build more fossil infrastructure. Clearway Energy Group is owned by Global Infrastructure Partners and TotalEnergies, the latter of which struck such a deal in March.
But this does appear to represent an aberration for Clearway, one of the nation’s largest operators of renewable energy projects and whose marketing materials primarily focus on “clean energy.” Nearly all of the company’s portfolio is carbon-free power or energy storage generation sans a handful of “flexible generation” energy projects in California, according to an online map of their project pipeline. The company did not disclose in the documents I reviewed if the gas plant itself would power the data center, provide power to the wider grid, or both.
Candidly, I’ve been watching like a hawk to see if Trump’s chokehold on solar and wind permits would lead to more gas infrastructure and data centers on federal property instead. And companies are getting data center permits when they ask to swap out their solar farm for AI infrastructure. On Friday, I reported that a joint venture involving renewables developer Arevon and energy trader Bill Perkins got permission from BLM to switch an environmental permit tied to a solar farm for one allowing a new data center. Environmentalists plan to legally challenge BLM’s determination as they say it’s a test case for the future of federal land policy.
It’s unclear if Clearway would be the one to build and construct this hypothetical data center and power plant. I for one can’t find any evidence of Clearway developing data centers before. My best guess is that if they do move forward with this, it would look like the joint venture I covered on Friday, where Arevon distanced itself from the actual day-to-day operations of the development and a new firm specializing in data centers came in. But that’s just a hunch and there’s a saying about assumptions.
Nevertheless, Clearway is clearly handling the permitting side. Attached to the Clearway letter was an application also sent to BLM for constructing utility and telecommunications facilities on federal lands, a document technically known as an SF299. The application states Clearway considered using solar energy for the data center as well as using private land, but their alternative designs weren’t selected because they had “higher environmental and stakeholder conflicts.”
Also, in a section of the document requesting Clearway provide a “statement of need for the project,” the developer said it was submitting this proposal “in response to federal energy objectives” and specifically cited Trump’s Day 1 executive order which the company said “encourage[d] development of reliable energy projects on federal lands.”
I reached out to Clearway asking for more information on the letter and application. In response, the company claimed the solar project wasn’t being killed – it simply was moved to private land. They also declined to comment on the data center and gas project. Instead, I was provided a statement attributable to an unnamed spokesperson that “while we do not comment on any individual application while it moves through federal approval processes, we are pleased to be advancing more than 4 GW of solar and battery resources in Nevada on private and public lands and expect those projects to deliver tremendous economic benefits to the communities where they’re built.”
“Clearway values its strong working partnership with the BLM, its Southern Nevada office, and also with state and local interests in Nevada. Across all of these relationships, we continuously assess how best to develop and deliver infrastructure that meets needs and aligns with local and national policies and goals.”
Current conditions: Hurricane Genevieve formed into the first major storm of the season, strengthening to Category 4 off Mexico’s Pacific Coast on Sunday but steering clear of any land for now • Hurricane Fausto, meanwhile, is weakening as it heads toward Hawaii • China evacuated hundreds of thousands of people as Typhoon Noul made landfall.

Wildfires in France and Spain forced roughly 300,000 people to evacuate their homes in what the French Interior Minister Laurent Nuñez called an “unprecedented” blaze. In Spain, the central western province of Avila suffered what the broadcaster France24 described as its “worst blaze in recent history” as Prime Minister Pedro Sanchez directly linked the disaster to climate change. By Sunday evening, in France, flames had come within nine miles of the southwestern city of Bordeaux in the heart of the nation's storied winelands as President Emmanuel Macron vowed to “rebuild.” Others saw the disaster as a sign of overdue lifestyle and infrastructure changes in the face of a warming planet. In Le Monde, the newspaper of record, the philosopher Cynthia Fleury and the Socialist mayor of the town of Saint-Médard-en-Jalles, Stéphane Delpeyrat-Vincen, argued: “What is burning is not just forests, but a way of inhabiting the land that is no longer possible.” The fires come weeks after a series of historic heat waves in Europe, including the hottest June on record, which made tinderboxes of parched woodlands.
President Donald Trump last week announced a landmark deal with Saudi Arabia to help build the kingdom’s first nuclear power station, besting the Russians and the Chinese in a race to tap into one of the world’s most coveted new export markets for atomic power technology. While the White House has yet to release all the details on the geopolitically meteoric agreement with Riyadh, sources with knowledge of the deal have confirmed to me what’s been reported elsewhere — that the deal will almost certainly include new large-scale Westinghouse AP1000s. Over the weekend, The New York Times identified another element to the partnership: Trump’s family and personal friends may benefit. The newspaper pointed to ties between a firm owned by Secretary of Commerce Howard Lutnick’s sons and Westinghouse; links between Eric Trump and Donald Trump Jr.’s investments into quantum computing and former Texas Governor Rick Perry’s Fermi America project to build AP1000s in Texas; and suggested that TAE Technologies, the fusion company merging with the corporate parent of Trump’s Truth Social platform, could see potential benefits from the Saudi deal. “There is no evidence at this point that Mr. Trump’s friends or family helped orchestrate the Saudi nuclear deal,” reporters Eric Lipton and Kate Kelly wrote. “Yet a number of the president’s allies and relatives, including members of his cabinet, stand to benefit if his big bet on nuclear power pays off. Certain investors with ties to these deals are positioned to profit, even if the delivery of large new loads of nuclear-powered electricity remains years away.”
The Trump administration is, in fact, making a real attempt at building new AP1000s at home. As my colleague Robinson Meyer wrote last month, a major Department of Energy deal would help utilities buy the parts needed to build more Westinghouse reactors.
Chip giant Nvidia is considering providing a $250 billion backstop to fund OpenAI’s data center project in southern Ohio, The Wall Street Journal reported on Sunday. The deal would guarantee up to half of the capital needed to lease SoftBank’s 10-gigawatt data center to supply computing power to the ChatGPT maker.
GE Vernova’s backlog of orders for gas turbines, meanwhile, now stretches to 2031 and accounts for a cumulative 116 gigawatts of power-producing capacity. In its latest earnings call, covered in Utility Dive at the end of last week, the company posted double-digit revenue and order growth in the division that supplies equipment for gas, hydro, nuclear, and grid facilities.
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Back in February, I told you that Japan was stepping up its efforts to extract rare earths from seabed minerals. On Friday, Tokyo confirmed it had discovered that medium and heavy rare earth elements accounted for about 54% of the rare earths mined from mud recovered from a remote Pacific island, Mining.com reported. The finds come after the government-backed vessel Chikyu sucked nearly 50 metric tons of mud from Minamitori Island, an uninhabited atoll located closer to Wake Island than Tokyo. Heavy rare earths, such as dysprosium, terbium, and yttrium — and medium rare earths such as samarium, europium, and gadolinium — are trickier to process. China controls the market for both categories by a wider margin than for light rare earths. That makes Japan’s discovery so exciting. Separating metals out of the mud could be an easier process than from other ores, potentially supplying the democratic world with a new source of non-Chinese minerals.
When the Biden administration tried putting rules in place for producing clean hydrogen, as my colleague Emily Pontecorvo explained nicely at the time, the regulations posed a problem for efforts to make fuel through nuclear-powered electrolysis. That’s because the incentives to ensure developers built new solar and wind rather than cannibalizing existing grid resources for hydrogen production made it impossible for nuclear reactors to qualify. Companies such as Constellation Energy, which had the nation’s leading experiment in nuclear-powered hydrogen production, protested. It all turned out to be for nought, since Trump ultimately wiped out the tax credits. As with so much nuclear technology that faces political tumult in America, South Korea is moving in to try its hand at hydrogen fuel production. Korea Hydro & Nuclear Power, the country’s state-owned nuclear giant, said it will launch a pilot program to produce hydrogen using heat and electricity from reactors, Hydrogen Insight reported last week.
India, meanwhile, is beefing up its plans for small modular reactors. Earlier this month, I reminded you about New Delhi’s plans to open its nuclear sector to foreign investments after years of icing out all but Russia’s state nuclear vendor. That isn’t to say India isn’t looking to continue building its own indigenously-designed units. On Friday, NucNet reported that the country plans to develop and operate at least five of its own SMR designs by 2033.
Last week, Heatmap editorial fellow Ameya Hadap broke news that Koloma, a startup seeking to spur natural production of hydrogen, had inked a deal to look for gas deposits across 817 square miles of the Philippines’ largest island, Luzon. It’s not the only subsurface search for clean energy. Last week, the country’s Economy and Development Council approved the Philippines’ first financing package to de-risk geothermal investments, Think Geo Energy reported.