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New research published today in Nature shocked even the study’s own authors.

Hurricane Helene is, by conventional measures, the deadliest hurricane to strike the continental United States since Katrina. At least 182 people have been confirmed killed by the storm, with hundreds of people still unaccounted for. Although all hurricanes are deadly, only a handful of storms have killed more than 100 people since 1950. Or at least that is what we have long thought. New research suggests that these conventional tallies may be a vast undercount.
Several years ago, two economists and public policy researchers — Rachel Young and Solomon Hsiang, now of Princeton and Stanford — began to study a seemingly simple question: How many Americans do hurricanes kill each year? According to the federal government, the average hurricane kills 24 people after making landfall. That seemed likely to be a modest underestimate. Economists know that natural disasters can have a long tail of suffering; Hsiang expected the real number to be a “single digit multiple” of that figure — perhaps 50 or 100 people per storm.
Yet when they ran the numbers and looked at mortality in places affected by storms, they were initially perplexed by the results, Hsiang told me earlier this week. The numbers they came up with didn’t even make sense at first.
“It was months of us trying to understand what we were looking at,” Hsiang said. “And then once we realized what we were seeing, it was years of us checking our work to find what we missed.” Only when it was clear that their work resembled other American public health statistics — specifically, that the white-Black mortality mirrored what has been found in other studies — that the horrifying truth sunk in.
The finding: Hurricanes are hundreds of times deadlier than anyone has realized.
Their study, which was published on Wednesday in Nature, finds that the average hurricane kills 7,000 to 11,000 people after making landfall in the United States. These previously uncounted deaths happened not during a storm or in its immediate aftermath, but as a long, slow trickle of mortality that plagues a region long after the clouds have cleared and floods have abated.
In any one year, the number of storm-related deaths is not very high. And yet a wave of excess deaths is visible in population data for at least 15 years after a storm hits an area, they found.
“It lasts for so many years, and because there’s so many storms hitting so many states, once you add up, it becomes this enormous number,” Hsiang told me. When added together, hurricanes’ long-term death toll exceeds American combat deaths in all wars, combined. The number so dwarfs previous estimates that it suggests tropical cyclones alone are a major determinant of public health across the United States.
Kerry Emanuel, an MIT meteorology professor who studies climate change and hurricanes, told me that the results were “truly astounding” and “persuasive,” although he noted that he is not an expert in the statistical approach used in the paper.
“Summed over all hurricanes, this amounts to three to five percent of all deaths near the Atlantic coast,” he said. “I expect this result will prove controversial and will be followed up by many other studies of long-term mortality from natural disasters.”
The paper fits into a growing body of research on what others have called the hidden or invisible public health threat of environmental threats. For years, researchers have known that air pollution and heat waves, seemingly silent hazards, can in fact kill tens of thousands of people. Lately they have begun to apply the same techniques to other hazards, with outsized results.
Officially, Hurricane Maria killed 64 people when it struck Puerto Rico in 2017. But when researchers surveyed households across the island months after the storm, they found the death toll was closer to 4,600. (The territory’s government later revised the official figure to 2,975.) These deaths were caused not by the cyclone’s high winds or torrential floods, but rather by secondary effects of the storm’s destruction. Maria took out the island’s power grid and road networks, for instance, and preventing people with heart attacks and strokes from reaching the hospital in time.
That paper was written six months after Maria struck the island; this new hurricane paper considers a wider time horizon, finding that more than 80,000 Americans die each year as a result of a hurricane, whenever it occurred. Black people were disproportionately killed by the aftermath of hurricanes, at least partly because a larger share of the country’s Black population lives in storm-afflicted areas. About 37,000 white deaths each year are due to a prior tropical cyclone.
How could such storms cause such a long tail of deaths, affecting areas 10 or 15 years after they come ashore? The paper cannot answer those questions today. But Hsiang and Young hypothesize that hurricanes cause extreme economic distress, which can resonate for years or decades afterward. “If someone suffers a loss and can’t invest in their business, then it will have ramifications for their income long into the future,” Hsiang told me. “If someone is on a fixed income and their garage is destroyed, and they pull from their retirement funds to fix the garage, then eight years later when they face a big medical decision, they might choose” a cheaper or less effective form of treatment.
“When you talk to people, you hear stories like this,” Hsiang added. The time and money invested in dealing with the storm is often a “pure loss,” even if some of the damage ultimately gets reimbursed. “Even if you have insurance, that just means you already paid for it in some way,” he said.
Storms cause disruption in other ways. They can break up communities and social networks. (If children move away, for instance, their parent can face higher medical bills.) Hurricanes can also impose high costs on states, towns, and cities, which may then have to reduce or restrict other services as a result.
“When you think about how communities rebuild — local municipalities and states — they also play a lot of games with their budget” in the aftermath of a storm, Hsiang said. “If they spend a lot of money to rebuild a bridge or boardwalk somewhere, does that come out of some social program 10 years later? Or building a new NICU hospital?” That could explain why an infant — even one born 15 years after a storm struck a given area — could face a higher chance of death.
Young and Hsiang think that these economic drivers are most likely to be the big reason for the excess deaths — the effect is just too big and drawn out to make any other cause likely — but other possibilities exist, they recognize. Hurricanes could be deadly simply because they are highly stressful events. “We see an effect on cancer rates and also cardiovascular illness. Stress matters a lot to those,” Hsiang said. It’s also possible that hurricanes unleash contamination into the environment that then makes people sick. A flooded basement can become a breeding ground for mold. “There’s gas stations in every town. What chemicals come out when there’s flooding?” Hsiang wondered.
The paper may also help resolve a riddle in American public health. On average, Americans die earlier in the eastern half of the continental United States than in the western half. This effect is worst in the Gulf Coast and Southeast but persists to some degree in the Mid-Atlantic and Northeast.
The paper suggests that hurricanes may have something to do with this geographic phenomenon. For infants, people below the age of 44, and Black people of all ages, hurricanes may explain a large share but not all of the mortality gap.
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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.