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For the first time in my life I now own a car, and it’s electric.
It took me a few weeks to narrow down my choices to a Hyundai Kona or a Ford Mustang Mach E. After much agonizing comparison, I went with the Kona. While I liked the Mach E’s sporty performance, longer range, and sizable front trunk, ultimately the Kona’s cheaper price, lighter materials, heat pump, and numerous mechanical buttons clinched the deal. After trading in a clapped out 2011 Subaru Impreza, the out-the-door sticker price for the Kona was a bit over $31,000 (though we opted to lease).
Owning and driving an EV has been an instructive experience. I’ve long been a vocal proponent of going electric, but I was honestly surprised by the learning curve. As the automotive journalist Edward Neidermeyer continually points out, an EV simply is not a perfect drop-in replacement for an internal combustion car. But that doesn’t mean you can’t make it work, even for long trips, even in fairly bedraggled parts of the country like northeastern Pennsylvania, where I live, and even with a modest battery and range.
First, the buying experience. The nearest Kona for sale I could find was a 70-mile drive away from Wilkes-Barre to Easton, and the dealership let me take it home so my wife could check it out. This led to the first of several comical lessons. The car had only about a 60 percent charge when I left the dealership, and drained down to 33 percent when I got back home. So before going back to sign the lease papers, it would need a top-up.
I searched on Google Maps for chargers and blithely set out to fill up. It turns out Rust Belt cities like the Scranton-Wilkes-Barre area are not exactly bursting with EV charging infrastructure. The first one I found was a free employee charger at a charter school. Out of curiosity I plugged it in. It did in fact work — and if I had been willing to sit there stealing 6 kilowatts of power for 10 hours, I could have gotten up to 100 percent. This seemed less than ideal. I then tried another charger around the corner at a used dealership. This one had a credit card reader but it did not work.
Scrolling through Google some more, I discovered that if you poke around in the menus it actually tells you the supposed speed of each charger (rated as slow, fast, very fast, or ultra fast). A 10-minute drive across the river was a non-Tesla fast charger at a Chevy dealership, though irritatingly I had to download an app and connect my Apple pay to make it work instead of just tapping my credit card.
Then I learned that the temperature of the battery matters a great deal. When I first plugged in, the charger delivered a measly 28 kilowatts. But then as the battery warmed up, that nearly doubled to 49 kilowatts (as compared to the Kona’s claimed maximum rate of 100 kilowatts). That isn’t particularly fast — but it also demonstrated another lesson, which is that there are advantages to a smaller battery, at just 65 kilowatt-hours. That fairly pitiful charging speed, topping out at less than a seventh of the maximum at modern stations, was still enough to get me from 28 percent to 75 percent in about 35 minutes. If I had been driving a Hummer EV, it would have been more like two hours.
That lesson was underlined charging at home. My house was built in the 1940s and has no outdoor outlets whatsoever, but in the pinch, I could string an extension cord out the window to use the included level 1 charger … to deliver a pathetic 600 watts, or less than the power supply on my gaming PC. Yet this was still enough to add 10-12 percent of charge per day, or about 30 miles, which is more than we drive on average. If I’d gone with the Mach E, it would be more like 20 miles, thanks to its bigger battery.
I learned a more serious lesson the next day going down to sign the paperwork. My wife had to come with me to the dealership, since she owned the Subaru, and therefore my 2-month-old son had to come along as well. With a 75 percent charge, I figured we’d be fine to make it there and back. When we got to the dealership, the car still had 48 percent — surely more than enough to make it back given my prior trip, right?
But then we had to sit at the dealership for three hours thanks to some incomprehensible financing dispute going on in a back room. By the time we finished, moved the car around several times, and grabbed some food on the way out, it was only about 42 percent by the time we got going. As we headed up Route 33, the Kona’s computer informed us we’d arrive with about 35 miles of range to spare. Since it was already well past the boy’s bedtime and I really, really didn’t want to hunt around in the cold for a charger that might or might not work, I decided to risk it.
But by this point it was well past dark, and the temperature was dropping into the low 40s. Meanwhile, what with wife and baby in the back seat, I had to run the heater much more than I had the first time, when I had left the cabin heater low and just used the seat warmer.
It turns out heating and driving uphill sucks battery power. As the temperature fell further into the low 30s, and the Kona zipped up the long grades at Wind Gap and Tannersville, I watched with increasing alarm as the buffer mileage dropped to 30, then 25, then 20. I told myself I would stop to charge if it got below 10 miles of buffer, but it finally stabilized around 15 miles in the Poconos.
It was a genuine case of range anxiety, no question about it, and my wife was ready to strangle me. But there was one last surprise as we crested the ridge and headed down into the Wyoming Valley. On that long downslope, I alternated between coasting and turning up the regenerative braking around corners, which got back another 14 miles of range. We pulled up with 15 percent battery and 29 miles to spare — not so far off the original estimate after all!
This need for planning is the major difference between electric and gas, at least given the current state of America’s charging infrastructure. With a gas car you can assume that range will not change much depending on the weather, that you can run your tank nearly empty with the sole penalty being another few seconds of standing at the pump, and that even the tiniest settlement is virtually guaranteed to have a gas station.
But on an EV trip of any distance you want to charge early and often, and that means some careful route planning. A theoretical 270 mile range means you have more like 160-220 miles you can realistically use, depending significantly on the temperature, wind, number of passengers, and so on. But unless you are in an exceptionally cold and/or depopulated area, it’s not that big of a deal. Just find some charging stations on the route, ideally with good reviews, and stop every hour or two for 20-30 minutes of charging, or less if your car can take mega voltage like the Ioniq 5. (There are several chargers in East Stroudsburg I could have used, for instance.)
You can’t cannonball to cut the trip time down to the absolute minimum, but you also get a chance to stretch out regularly and cut your risk of deep vein thrombosis. Meanwhile, if you can charge at home, your cost of fuel goes down dramatically. I now spend maybe $3 on a week’s worth of driving electricity.
So yes, there are some tradeoffs that come with the EV lifestyle. But even for an EV with a modest battery, driving in the cold mountains of impoverished Appalachia, they are not remotely insurmountable — and everything will only get easier from here on out. More chargers are being built all the time, and soon Tesla’s network will open up to all. You don’t need a 500-mile range battery, or to carry a backup generator around. It just takes a change in mindset.
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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.