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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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The data center boom is everywhere you look in U.S. economic and emissions data.
This is an edition of Heatmap Daily, an evening review of the day’s news written by our executive editor. Sign up for it here.
It isn’t exactly a new thought, but I’ve been struck recently by how many trends in America’s economic and environmental data are fundamentally about the data center boom and the return of electricity demand:
First, the Energy Information Administration reported this week that U.S. emissions grew by more than 2% last year, driven by surging electricity demand and an increase in coal-fired generation. What caused that higher power demand? New factories and data centers — as well as record summertime cooling demand.
Second, many of the new factories driving that higher power demand are themselves producing goods that are … let’s say … data center-adjacent. There are the enormous new semiconductor fabs, of course. But Ford and General Motors have also set up new production lines (or repurposed old ones) to manufacture grid-scale batteries to meet power demand.
Third, take a look at the recent U.S. spending on private non-residential construction — in other words, everything American companies are building that is not houses, condos, or apartments.
The construction industry’s spent almost $60 billion on data centers over the past year, which is more than it spent on all other office buildings combined (and more than it spent building warehouses, too). Just a handful of categories — data centers, power plants, electricity infrastructure, and certain kinds of electronics manufacturing — now make up a third of all U.S. private non-residential construction investment. They’ve never made up such a large share of construction spending since data collection began in 2014.
As The New York Times recently noted, the American economy is unusually dependent on the American stock market right now — and the stock market is unusually dependent on artificial intelligence. This week, investors started to balk at the enormous spending hyperscalers are planning to keep building out the AI boom; Alphabet’s shares dropped 8% this week after it boosted its planned 2026 capital expenditure and signaled 2027 will be even bigger. If the data center boom started to slow down in earnest, then more than just that budget will change.
Speaking of which, my colleague Emily Pontecorvo wrote earlier this week about how many businesses are struggling to even estimate their carbon emissions from artificial intelligence. The carbon accounting startup Watershed recently unveiled a new formula to help companies get a sense of their AI-related emissions.
But even that formula is still limited by the amount of data hyperscalers publish — and they don’t publish that much. Google, for instance, is the only AI company that has (laudably) provided estimates of its emissions on a per-prompt basis. Yet no company has published its per-token emissions, or how emissions sync up with particular models or regions.
So Emily asked Google: Why aren’t you — or any other model provider — disclosing this kind of data yet?
The tech company didn’t get back to us until after we’d published Emily’s story. But its response was interesting enough that I wanted to quote some of it here.
The problem is “industry consensus,” Cooper Elsworth, a Google spokesperson, told us. “There is currently very little consensus on how to comprehensively and fairly measure the serving environmental impact of generative AI (such as text generation),” he wrote. “Without standardized, ‘apples-to-apples’ frameworks, it is difficult to compare different providers accurately.”
That’s partly because energy use — and emissions data — can vary from site to site and depend on “custom-built hardware, software compilers, and advanced inference techniques.” And he claimed Google doesn’t always have the measurement hardware in place to provide such specific estimates: “Providing precise, repeatable data requires highly advanced measurement infrastructure,” he said. “For example, software-based energy monitoring tools often suffer from sampling biases. For our study, we had to step away from top-down averages and directly measure actual energy at the physical power supply unit (PSU) level across our deployed fleet. Not all providers have the telemetry or data sets required to benchmark their operations at this level of granularity.”
Read Emily’s story to understand the other reasons why estimating — or even “guesstimating” — AI-related carbon emissions is so challenging.
A conversation with Emma Uridge of the Kansas Health Institute.
This week’s conversation is with Emma Uridge, analyst with the Kansas Health Institute. Uridge spent copious hours analyzing state and local laws on data center development to best understand how policymakers are responding to the potential environmental public health impacts of large AI infrastructure, including power and water. The report, which came out this week, also goes in depth into those health impacts. I reached out to her to discuss what she sees as must-watch territory for our readers on this emerging policy arena.
Our conversation was lightly edited for clarity.
What is actually being done on policy when it comes to data centers — beyond moratoria of course?
So first I’d like to just talk about the point of moratoria. It’s helpful to talk about how these policies emerge in the first place. One area where moratoria are helpful is when a data center is proposed but the county has no approach for how they’d like to potentially regulate them. That’s temporary, most of the time. It lets local governments conduct research on the various impacts and also negotiate community benefits, ones that can mitigate any potential negative impacts — like Lancaster Pennsylvania, which instituted a community benefit agreement that maximized the potential benefits of development while mitigating what large data centers can do. That agreement looked at capping municipal water use at 20,000 gallons per day and requiring 100% clean energy. It had financial penalties for non-compliance. The company also committed $20 million to their local economic development and clean energy fund. There are ways to negotiate with developers.
We also see amendments to existing zoning. Data center proposals are increasingly popping up in rural areas, many of which are unzoned, so there’s no way a county can negotiate unless there’s a moratorium in place.
Other policy solutions include different performance standards or requiring on-site renewable energy, like what Jefferson County, Missouri, looked at. Also setback requirements, mandatory noise buffers, ending by-right zoning.
Where are local governments getting ideas for regulating data centers?
A lot of the technical information comes from developers. That can in cases be seen as a biased source of information. I wouldn’t say there’s a dedicated group providing assistance to local governments when a project is proposed — which is a similar story to wind industry development, where we have only a handful of consultants who provide technical advice. It can be really helpful to get a multi-disciplinary approach to hearing information. It can be helpful to have the utility commission, public health folks, those in academia, as well as the developer.
As of right now, especially in rural areas, local governments have a hard task of balancing pushback while getting the most accurate, evidence-based, neutral information to make decisions. That balance can be contentious.
What is the federal government doing on data center policy? How is the Trump administration approaching it?
A few things there. In the early days, the drive was for AI expansion and to be competitive with foreign adversaries. Now due to the amount of public pushback in red and blue localities and a more cautious approach.
I’m not seeing a lot of actual policy movement at this time.
I know the EPA is looking at the chemicals used in cooling data centers because when that water is cycled through the system, some of it is discharged into the water system, so they’re looking at the Toxic Substances and Control Act for monitoring that.
How much of an impact does this minimal federal role have on industry behavior?
Y’know, this isn’t specific to data centers. This is true for all kinds of large-scale development: there’s a need to require some sort of federal monitoring and regulation.
That’s where I see an emerging role for public health. At the federal level, there could be policy movement towards requiring some sort of environmental monitoring at data centers to make sure they’re operating responsibility. Looking at specific water use relative to water availability and what happens when there’s a time of severe, persistent drought. With air quality too — we’ve seen areas where the grid isn’t as reliable so their diesel generators are kicking on more and affecting air quality for residents.
We’re just not seeing all of that right now. We need corporate disclosure.
What do you see as the most important public health impacts from data center development?
It varies by localities. The most discussed obviously is water usage. One thing I’d note about my conversations with folks enthusiastic around emerging tech is, there are still questions that need to be asked about the capacity of localities to support a data center. Like a small town in Kansas may only be using 40% of their water for their utility needs. If a data center came online, how much of that water goes to the data center?
One area underexplored within the public health discipline is energy poverty and energy security. The ability of a household to meet the needs of everything energy provides in our lives. It’s known we have an aging electric grid but we’re not talking enough about large-scale blackouts when the grid is not sufficient to support some of these new data centers.
Plus more of the week’s big development fights.
1. Laramie County, Wyoming — Meta is fighting the fine it received in the Cheyenne data center water pollution controversy, and the conflict between the tech giant and the city’s small board of public utilities is continuing to spill out into the public.
2. Niagara County, New York — This county just rejected a solar project’s highway work permits in a show of retaliation against the state’s Office of Renewable Energy Siting.
3. Barron County, Wisconsin — The anti-solar protest is the new campaign stop in deep red Wisconsin.
4. Chesapeake, Virginia — A large battery storage project on the Virginia coastline is on the rocks amidst rampant local opposition.
5. Lewis County, West Virginia — West Virginia is now a key battleground in the fight over transmission, as a line spanning all of West Virginia and Maryland — and cutting through Data Center Alley in Virginia — causes compounding consternation.