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Here’s a grim fact: The most destructive fires in recent American history swept over a state with the country’s strictest wildfire-specific building code, including in some of the neighborhoods that are now largely smoldering rubble.
California’s wildfire building code, Chapter 7A, went into effect in 2008, and it mandates fire-resistant siding, tempered glass, vegetation management, and vents for attics and crawlspaces designed to resist embers and flames. The code is the “most robust” in the nation, Lisa Dale, a lecturer at the Columbia Climate School and a former environmental policy advisor for the State of Colorado, told me. It applies to nearly any newly built structure in one of the zones mapped out by state and local officials as especially prone to fire hazard.
The adoption of 7A followed years of code development and mapping of hazardous areas, largely in response to devastating urban wildfires such as the Tunnel Fire, which claimed more than 3,000 structures and 25 lives in Oakland and Berkeley in 1991, and kicked off renewed efforts to harden Californian homes.
The Federal Emergency Management Agency’s report on the 1991 fire makes for familiar reading as the Palisades and Eaton fires still smolder. The wildland-urban interface, it says, was put at extreme risk by a combination of dry air, little rainfall, hot winds blowing east to west, built-up vegetation that was too close to homes, steep hills, and limited access to municipal water. The report also castigates the “unregulated use of wood shingles as roof and siding material.”
This was not the first time a destructive fire on the wildland-urban interface had been partially attributed to ignitable building materials. The 1961 Bel-Air fire, for instance, which claimed almost 200 homes, including that of Burt Lancaster, and the 1959 Laurel Canyon fire were both, FEMA said, evidence of “the wood roof and separation from natural fuels problems,” as were fires in 1970 and 1980 near where the Tunnel Fire eventually struck in 1970 and 1980.
But it was the sheer scale of the Tunnel Fire that prompted action by California lawmakers.
Throughout the 1990s, fire-resilient roofing requirements were ramped up, designating which materials were allowed in fire hazard areas and throughout the state. By all accounts, the building code works — but only when and where it’s in force. Dale told me that compliant homes were five times as likely to survive a wildfire. Research by economists Judson Boomhower and Patrick Baylis found that the code “reduced average structure loss risk during a wildfire by 16 percentage points, or about a 40% reduction.”
“The challenge from the perspective of wildfire vulnerability is that those codes are relatively recent, and the housing stock turns over really slowly, so we have this enormous stock of already built homes in dangerous places that are going to be out there for decades,” Boomhower told me.
The 7A building code applies only to new buildings, however. In long-settled areas of California like Pacific Palisades, which has little new housing construction or even existing home turnover due to high costs and permitting complications, especially in areas under the jurisdiction of the California Coastal Commission, many houses are not just failing to comply with Chapter 7A, but also with any housing code at all.
Looking at which homes had survived past fires, Steve Quarles, who helped advise the California State Fire Marshal on developing 7A, told me, “What really mattered was if it was built under any building code.” Many homes destroyed by the fires in Los Angeles likely were not. In Pacific Palisades, fire management is a frequent topic of concern and discussion. But as late as 2018, local media in Pacific Palisades noted that the area still had some homes with wood shingle roofs.
While a complete inventory of homes lost in the Palisades and Eaton fires has yet to be taken, the neighborhoods were full of older homes. According to CalFire incident reports, of the almost 47,000 structures in the zone of the Palisades Fire, more than 8,000 were built before 1939, and 44,560 were built before 2009. For the Eaton Fire area, of the around 41,000 structures, almost 14,000 were built before 1939, and only around 1,000 were built since 2010.
A Pacific Palisades home designed by architect Greg Chasen and built in 2024, however, survived the fire and went viral on X after he posted a photo of it still standing after the flames had moved through. The home embodied some of the best practices for fire-safe building, according to Bloomberg, including keeping vegetation away from the building, a metal roof, tempered glass, and fire-resistant siding.
When Michael Wara, the director of Stanford University’s Climate and Energy Policy Program, spoke with firefighters and insurance industry officials in the process of drafting a 2021 report for the Stanford Woods Institute for the Environment on strategies for mitigating wildfire risk, they told him that, from their perspective, wildfires are often a matter of “home ignition,” meaning that while building near forested areas puts any home at risk, the risk of a home itself igniting varies based on how it’s built and the vegetation clearance around it. “Existing homes in high fire threat areas” built before the implementation of California’s wildfire building codes, Wara wrote, “are a massive problem.” At the time he published the paper, there were somewhere between 700,000 and 1.3 million pre-building code homes still standing in “high or very high threat areas.”
The flipside of focusing on “home ignition” and the building code is that the building code works better over time, as more and more homes comply with it thanks to normal turnover, people extensively renovating, or even tearing down old homes — or rebuilding after fires. Homes that are close to homes that don’t ignite in a fire are more likely to survive.
One study that looked at the 2018 Camp Fire, which destroyed more than 18,000 structures and claimed more than 80 lives in the Northern California town of Paradise, sampled homes built before 1997, between 1997 and 2018, and from 2018 onwards, and found that only 11.5% of pre-1997 homes survived, compared to 38.5% from 1997 and after. The researchers also found that building survivability had a kind of magnifying effect, with distance from the nearest destroyed structure and the number structures destroyed in the immediate area among “the strongest predictors of survival.”
“The more homes that comply, the less chance you get those structural ignitions and the less chance you get those huge disasters like this,” Doug Green, who manages Headwaters Economics’ Community Assistance for Wildfire Program, told me. “It takes people doing the right thing to their own home — dealing with vegetation, making sure roofs are clean, having right roofing. It’s really a community-wide strategy to stop fires that happen like this.”
But just as any home hardening — or just building to code — is more effective the more the homes around you do it as well, it’s just as true in reverse. “If your next door neighbors don’t do that work, the effectiveness of your efforts will be less,” Dale said. “Building codes ultimately work best when we get an entire landscape or neighborhood to adopt them.”
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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.