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Those 21-inch rims — and America’s opulent car culture — are doing more harm than good.
The biggest complaint drivers have about electric vehicles is their range. They might be far cleaner, much cheaper to operate and maintain, and not subsidize murderous dictators, but they can typically go only 200-350 miles on a charge (though some expensive models can top 500 miles). And because the U.S. car charging network is still being built out, that can mean having to carefully plan one’s road trip, having to wait in line at a charger, and so on.
So it’s strange that so many EVs are outfitted with snazzy features that badly sap their range. In particular, the fancy low-profile rims that are very common on American EVs knock their range down by as much as 15 percent. It’s just the most obvious example of how America’s addiction to big, fast cars is an unnecessary obstacle to the EV transition.
Jason Fensky explains the physics of the rim problem at Engineering Explained. All else equal, larger diameter wheels are heavier, which means more rotating mass, which means more energy needed to spin them. A larger diameter means more air resistance (particularly when they come with fancy angular decorations), and more resistance still because they typically come with wider tires. Wider tires in turn worsen rolling resistance, eating up still more energy. According to Tesla, moving from 20-inch rims to 18-inch ones on a Model 3 improves range by nearly 15 percent, under typical conditions.
This matters especially for EVs because batteries are considerably less energy-dense than diesel or gasoline. (Their range is as good as it is because electric motors are vastly more efficient than internal combustion engines.) Where you can compensate easily enough for inefficient wheels in a gas-powered car by adding a couple gallons to the fuel tank, additional battery capacity means a huge weight penalty, which itself saps range.
What’s more, low-profile tires have a considerably worse ride quality because there is less rubber to absorb shocks, and with no protruding sidewall, it’s very easy to damage those fancy rims when parking or driving too close to a curb.
The problem is compounded by the EV manufacturer habit of producing absurdly fast models. Zero-to-60 times for today’s crop of electric automobiles are routinely under five seconds and occasionally at three seconds or less. Now, I can’t blame people for enjoying the thrill of explosive electric acceleration — it’s surely one of the reasons EVs have gained market share — but that is preposterous speed. Sixty miles per hour in three seconds is faster than a 2020 Ferrari Portofino, equipped with a twin-turbocharged V8 making 591 horsepower.
We can see all these problems coming together with the Rivian R1T. This pickup truck starts with a dual-motor setup making “only” 600 horsepower and a 0-60 times of 4.5 seconds, with a range of 270 miles on the base battery. You can increase the range to 350 miles with the medium battery, and 400 miles with the biggest one. But if you option the quad-motor drivetrain making 835 horsepower with the medium battery (the only option available at time of writing) range is cut from 350 to 328 miles. And sure enough, if you pick the 21-inch wheels instead of the 20-inch, range is cut again to 303 miles.
Those battery upgrades are also extremely expensive, because they’re so large. The base battery is 105 kilowatt-hours, while the medium is 135 and the large 180 kilowatt-hours, and so the different options will set you back $6,000 and $16,000 respectively. That huge battery is also why the R1T has a base curb weight of over 7,000 pounds.
The R1T has gotten rave reviews because of its ridiculous speed and high build quality. But it is Caligula-esque levels of pointless excess to be driving a large truck around that is faster than a Ferrari sports car. Let’s be real: In ordinary road conditions nobody ever has a legitimate need to hit 60 miles per hour in three seconds. People who even use that capability outside of a race track are in the best case scenario impressing their friends on a highway on-ramp, or else they are breaking the law somehow.
It should also be noted that the heavier a car is, the more dangerous it is to other cars or pedestrians in an accident, because momentum is proportional to mass.
This isn’t the only way to go, of course. Consider the recently discontinued Chevy Bolt, with a 200 horsepower motor and a 63 kilowatt-hour battery. But that smaller drivetrain and battery means its weight comes in under 3,600 pounds, which together with relatively sensible 17-inch wheels (though I’d go even smaller) enables a perfectly respectable range of 259 miles. (That’s just 30 miles short of the Hummer EV, whose battery is 3.4 times larger.) Smaller and cheaper parts also mean the Bolt’s starting price is also $27,500, compared to the R1T’s $74,000 — and because the Bolt requires far less energy and fewer raw materials to produce, it is far better for the climate.
American drivers are simply spoiled by technology. Two hundred horsepower and 266 pound-feet of torque is plenty for 95 percent of the tasks American drivers actually perform with their cars — indeed, more than is strictly necessary. I remember when my family bought a Honda Accord in 2003, with its 160 horsepower four-cylinder engine, and it felt downright zippy.
It will take more than an article to cure America’s addiction to big cars. But right now, EV shoppers can take a simple and easy step to ease their range anxiety: skip the fancy wide rims.
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Why the new “reasoning” models might gobble up more electricity — at least in the short term
What happens when artificial intelligence takes some time to think?
The newest set of models from OpenAI, o1-mini and o1-preview, exhibit more “reasoning” than existing large language models and associated interfaces, which spit out answers to prompts almost instantaneously.
Instead, the new model will sometimes “think” for as long as a minute or two. “Through training, they learn to refine their thinking process, try different strategies, and recognize their mistakes,” OpenAI announced in a blog post last week. The company said these models perform better than their existing ones on some tasks, especially related to math and science. “This is a significant advancement and represents a new level of AI capability,” the company said.
But is it also a significant advancement in energy usage?
In the short run at least, almost certainly, as spending more time “thinking” and generating more text will require more computing power. As Erik Johannes Husom, a researcher at SINTEF Digital, a Norwegian research organization, told me, “It looks like we’re going to get another acceleration of generative AI’s carbon footprint.”
Discussion of energy use and large language models has been dominated by the gargantuan requirements for “training,” essentially running a massive set of equations through a corpus of text from the internet. This requires hardware on the scale of tens of thousands of graphical processing units and an estimated 50 gigawatt-hours of electricity to run.
Training GPT-4 cost “more than” $100 million OpenAI chief executive Sam Altman has said; the next generation models will likely cost around $1 billion, according to Anthropic chief executive Dario Amodei, a figure that might balloon to $100 billion for further generation models, according to Oracle founder Larry Ellison.
While a huge portion of these costs are hardware, the energy consumption is considerable as well. (Meta reported that when training its Llama 3 models, power would sometimes fluctuate by “tens of megawatts,” enough to power thousands of homes). It’s no wonder that OpenAI’s chief executive Sam Altman has put hundreds of millions of dollars into a fusion company.
But the models are not simply trained, they're used out in the world, generating outputs (think of what ChatGPT spits back at you). This process tends to be comparable to other common activities like streaming Netflix or using a lightbulb. This can be done with different hardware and the process is more distributed and less energy intensive.
As large language models are being developed, most computational power — and therefore most electricity — is used on training, Charlie Snell, a PhD student at University of California at Berkeley who studies artificial intelligence, told me. “For a long time training was the dominant term in computing because people weren’t using models much.” But as these models become more popular, that balance could shift.
“There will be a tipping point depending on the user load, when the total energy consumed by the inference requests is larger than the training,” said Jovan Stojkovic, a graduate student at the University of Illinois who has written about optimizing inference in large language models.
And these new reasoning models could bring that tipping point forward because of how computationally intensive they are.
“The more output a model produces, the more computations it has performed. So, long chain-of-thoughts leads to more energy consumption,” Husom of SINTEF Digital told me.
OpenAI staffers have been downright enthusiastic about the possibilities of having more time to think, seeing it as another breakthrough in artificial intelligence that could lead to subsequent breakthroughs on a range of scientific and mathematical problems. “o1 thinks for seconds, but we aim for future versions to think for hours, days, even weeks. Inference costs will be higher, but what cost would you pay for a new cancer drug? For breakthrough batteries? For a proof of the Riemann Hypothesis? AI can be more than chatbots,” OpenAI researcher Noam Brown tweeted.
But those “hours, days, even weeks” will mean more computation and “there is no doubt that the increased performance requires a lot of computation,” Husom said, along with more carbon emissions.
But Snell told me that might not be the end of the story. It’s possible that over the long term, the overall computing demands for constructing and operating large language models will remain fixed or possibly even decline.
While “the default is that as capabilities increase, demand will increase and there will be more inference,” Snell told me, “maybe we can squeeze reasoning capability into a small model ... Maybe we spend more on inference but it’s a much smaller model.”
OpenAI hints at this possibility, describing their o1-mini as “a smaller model optimized for STEM reasoning,” in contrast to other, larger models that “are pre-trained on vast datasets” and “have broad world knowledge,” which can make them “expensive and slow for real-world applications.” OpenAI is suggesting that a model can know less but think more and deliver comparable or better results to larger models — which might mean more efficient and less energy hungry large language models.
In short, thinking might use less brain power than remembering, even if you think for a very long time.
On Azerbaijan’s plans, offshore wind auctions, and solar jobs
Current conditions: Thousands of firefighters are battling raging blazes in Portugal • Shanghai could be hit by another typhoon this week • More than 18 inches of rain fell in less than 24 hours in Carolina Beach, which forecasters say is a one-in-a-thousand-year event.
Azerbaijan, the host of this year’s COP29, today put forward a list of “non-negotiated” initiatives for the November climate summit that will “supplement” the official mandated program. The action plan includes the creation of a new “Climate Finance Action Fun” that will take (voluntary) contributions from fossil fuel producing countries, a call for increasing battery storage capacity, an appeal for a global “truce” during the event, and a declaration aimed at curbing methane emissions from waste (which the Financial Times noted is “only the third most common man-made source of methane, after the energy and agricultural sectors”). The plan makes no mention of furthering efforts to phase out fossil fuels in the energy system.
The Interior Department set a date for an offshore wind energy lease sale in the Gulf of Maine, an area which the government sees as suitable for developing floating offshore wind technology. The auction will take place on October 29 and cover eight areas on the Outer Continental Shelf off Massachusetts, New Hampshire, and Maine. The area could provide 13 gigawatts of offshore wind energy, if fully developed. The Biden administration has a goal of installing 30 GW of offshore wind by 2030, and has approved about half that amount so far. The DOI’s terms and conditions for the October lease sale include “stipulations designed to promote the development of a robust domestic U.S. supply chain for floating wind.” Floating offshore wind turbines can be deployed in much deeper waters than traditional offshore projects, and could therefore unlock large areas for clean power generation. Last month the government gave the green light for researchers to study floating turbines in the Gulf of Maine.
In other wind news, BP is selling its U.S. onshore wind business, bp Wind Energy. The firm’s 10 wind farm projects have a total generating capacity of 1.3 gigawatts and analysts think they could be worth $2 billion. When it comes to renewables, the fossil fuel giant said it is focusing on investing in solar growth, and onshore wind is “not aligned” with those plans.
The number of jobs in the U.S. solar industry last year grew to 279,447, up 6% from 2022, according to a new report from the nonprofit Interstate Renewable Energy Council. Utility-scale solar added 1,888 jobs in 2023, a 6.8% increase and a nice rebound from 2022, when the utility-scale solar market recorded a loss in jobs. The report warns that we might not see the same kind of growth for solar jobs in 2024, though. Residential installations have dropped, and large utility-scale projects are struggling with grid connection. The report’s authors also note that as the industry grows, it faces a shortage of skilled workers.
Interstate Renewable Energy Council
Most employers reported that hiring qualified solar workers was difficult, especially in installation and project development. “It’s difficult because our projects are built in very rural areas where there just aren't a lot of people,” one interviewee who works at a utility-scale solar firm said. “We strive to hire as many local people as possible because we want local communities to feel the economic impact or benefit from our projects. So in some communities where we go, it is difficult to find local people that are skilled and can perform the work.”
The torrential rain that has battered central Europe is tapering off a bit, but the danger of rising water remains. “The massive amounts of rain that fell is now working its way through the river systems and we are starting to see flooding in areas that avoided the worst of the rain,” BBC meteorologist Matt Taylor explained. The Polish city of Nysa told its 44,000 residents to leave yesterday as water rose. In the Czech Republic, 70% of the town of Litovel was submerged in 3 feet of flooding. The death toll from the disaster has risen to 18. Now the forecast is calling for heavy rain in Italy. “The catastrophic rainfall hitting central Europe is exactly what scientists expect with climate change,” Joyce Kimutai, a climate scientist with Imperial College London’s Grantham Institute, toldThe Guardian.
A recent study examining the effects of London’s ultra-low emissions zone on how students get to school found that a year after the rules came into effect, many students had switched to walking, biking, or taking public transport instead of being driven in private vehicles.
Welcome to Decarbonize Your Life, Heatmap’s special report that aims to help you make decisions in your own life that are better for the climate, better for you, and better for the world we all live in. This is our attempt, in other words, to assist you in living something like a normal life while also making progress in the fight against climate change.
That means making smarter and more informed decisions about how climate change affects your life — and about how your life affects climate change. The point is not what you shouldn’t do (although there is some of that). It’s about what you should do to exert the most leverage on the global economic system and, hopefully, nudge things toward decarbonization just a little bit faster.
We certainly think we’ve hit upon a better way to think about climate action, but you don’t have to take our word for it. Keep reading here for more on how (and why) we think about decarbonizing your life — or just skip ahead to our recommendations, below.