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A fight broke out in New York this week over a small calculation with big implications for the state’s climate policy.
The trouble started when a group of lawmakers, including Democratic Governor Kathy Hochul, proposed a change to the way the state accounts for the greenhouse gas methane. Her administration argued the adjustment would result in lower costs for consumers as the state transitions from fossil fuels, but a broad coalition of environmental groups were incensed, claiming it would “sabotage” New York’s climate law for the benefit of natural gas companies. Under pressure, Hochul backed down, announcing Wednesday that she wouldn’t be holding the state’s budget hostage before sorting it out.
The debate — which isn’t yet over — is an enlightening example of the high stakes of climate math, particularly the formulas used to calculate greenhouse gas emissions. And before you yawn and close this tab over having to read a story about math, give me a chance to show you how these calculations can translate into real differences in how we approach the energy transition, and our odds for avoiding the worst climate outcomes.
It may seem strange that there's more than one way to calculate greenhouse gas emissions. The dispute stems from the fact that methane is a very different beast than carbon dioxide, but when it comes to creating climate policy, we often elide those differences for the sake of simplicity.
When carbon dioxide emissions hit the atmosphere, they’re essentially permanent. The more carbon accumulates, the more it heats up the planet. Methane, on the other hand, is short-lived — it breaks down in a little over a decade. But while it’s around, it traps a lot more heat than carbon dioxide, like wrapping the quilt of CO2 in a second, goose-down duvet. As the methane in the atmosphere decays, it’s like that duvet is constantly losing feathers. If emissions decline, the blanket will thin out and eventually go away.
So while the warming power of carbon dioxide depends entirely on the total amount released, the effects of methane depend on the rate at which it is emitted.
Despite these key differences, the two pollutants often get lumped together into a common metric, like the Environmental Protection Agency’s estimate that the U.S. emitted almost 6 billion metric tons of “CO2 equivalent” in 2020, or the Biden administration’s goal of a 50-52% reduction in greenhouse gas emissions by 2030. To arrive at those numbers, scientists convert methane into carbon dioxide using a formula called global warming potential, or GWP, which basically asks how many tons of carbon dioxide it would take to warm the planet as much as one ton of methane.
The problem is, there’s not one answer. Because of methane’s short but powerful life span, there’s another variable at work in the calculation: time. Over 20 years, one metric ton of methane has a similar effect to about 80 metric tons of carbon, but over 100 years, it’s more akin to 25 metric tons of carbon. The federal government, as well as most U.S. states and much of the rest of the world, take the long view, accounting for methane emissions over 100 years. But scientists say it’s an arbitrary choice.
“There's no single timescale that's clearly most appropriate,” said Drew Shindell, an earth science professor at Duke University and an expert on methane.
Scientists have been arguing about the problems with using global warming potential formulas for years, so it’s somewhat surprising that so many governments have taken this consequential choice for granted.
New York’s climate law, which passed in 2019, is unique in that it explicitly requires a 20-year accounting of methane. When the law was being crafted, the state’s environmental community argued this would more accurately capture the consequences of emissions in the near term. Since methane can cook the planet quickly, it could push the climate past 1.5 degrees of warming, risking irreversible impacts. But encouraging steeper cuts to methane over the next few decades could actually cool the planet, buying slightly more time to reduce carbon emissions.
The Hochul administration has proposed aligning New York with the status quo and switching the state to 100-year accounting. This would have tangible repercussions for New York’s climate planning. For one, it would make the state look like it’s further along in achieving its climate goals, when in reality nothing has changed. The nonprofit outlet New York Focusestimates that under the current methane accounting method, the state must cut emissions by about 134 million metric tons this decade, but the 100-year method would change that number to 86 million.
It could also shift the state’s priorities. Under the current system, the largest source of emissions in New York is its buildings, most of which rely on natural gas and other fossil fuels for heating and cooking. Methane is the primary component of natural gas, and it leaks out of wells and pipelines, all the way to homes, where it can also leak out of boilers and stoves. That fact has dominated New York’s climate discussion over the past two years. It led the Climate Action Council, an appointed group of government officials, scientists, advocates, and industry leaders tasked with drawing up a roadmap to achieve the state’s emission targets, to conclude that one to two million homes should be electrified by 2030, followed by the large majority of buildings statewide by 2050.
But under a 100-year accounting system, buildings wouldn’t look like such an urgent problem. Methane emissions related to the residential use of natural gas equaled about 200,000 metric tons of carbon dioxide equivalent in 2020 under the 20-year scheme, according to state data. Using the 100-year formula, that number would drop to 60,000. Suddenly, transportation would look like New York’s number one climate culprit, which could lead regulators to turn more of their attention to boosting electric vehicles, improving public transit, and decarbonizing trucking.
Is that really the worst outcome? If it truly did result in steeper cuts to transportation emissions, that could mean lower temperatures in the long term, because less carbon would get lodged in the atmosphere. “Any reductions of methane we make today only really affects people who are alive today,” said Zeke Hausfather, a climate scientist at Berkeley Earth. “Whereas any emissions of CO2 we have today affect, you know, dozens of generations to come. And so trading off between those two is sort of a thorny, ethical question.”
But that result is by no means guaranteed. When I ran the idea by Liz Moran, a New York policy advocate for Earthjustice, she argued that the transportation sector is harder to address so those emission reductions may not materialize. Moran added that giving more weight to methane has enabled the state to make strides in environmental justice, for example by leading to decisions to reject polluting power plants. “This would have some very tangible and immediate impacts to communities where we're already starting to make some progress,” she said.
While some New York environmental groups have claimed that using GWP 100 is “outdated science,” Shindell disagreed with that characterization. “I think we've learned more about the limitations of GWP 100,” he said. “I think you can make an argument that it's not very aligned with what most countries are talking about now: net-zero by 2050.”
The use of 100-year accounting doesn’t have to mean that policymakers ignore methane’s near-term impacts. While California uses this metric for its overall emissions goals, it also has a separate, specific target to reduce methane. Though in the case of New York, with the Hochul administration’s explicit intention to cut costs, that's not yet on the table and it would surely prompt another political fight.
Who said climate math had to be boring?
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On prepurchase agreements, Al Gore, and Norway’s EVs
Current conditions: Ecuador’s government-enforced blackouts will begin tomorrow night as drought threatens hydroelectric plants • Storm Boris is causing flooding in parts of Italy • Montana could see very heavy rainfall and flash flooding today.
Frontier, a coalition of carbon removal buyers, announced this morning a fourth round of prepurchase agreements, worth $4.5 million. The coalition facilitated agreements with nine suppliers to remove carbon from the atmosphere on behalf of five of Frontier’s buyers: Stripe, Shopify, Alphabet, H&M Group, and Match. The removal projects are located across six countries and utilize a range of techniques, including rock weathering, direct air capture, and ocean alkalinity enhancement. In a press release, Frontier said “a significant number of companies in this purchase cycle are integrating carbon removal into existing large-scale industries. This strategy can reduce costs and accelerate scale-up relative to standalone carbon removal projects.”
Frontier
Brazil’s worst drought on record, now in its second year, has caused water levels in the rivers that run through the Amazon to fall to historic lows, and some have even dried up entirely. One key tributary that supplies the mighty Amazon River, the Solimoes, has water levels that are 14 feet below average for the first half of September. The drought is fueling numerous large fires, many of which were started by humans but have plenty of dry vegetation to keep them going.
Plumes of wildfire smoke hang over South America.NASA
According to data from Brazil’s National Institute for Space Research, almost half of the Amazon fires are burning pristine forest. This is unusual, The New York Timesreported, and “means fighting deforestation in the Amazon is no longer enough to stop fires.” The Amazon rainforest is one of the world’s most important carbon sinks. If it collapses, it could release huge amounts of carbon into the atmosphere, exacerbating the climate crisis. Researchers with World Weather Attribution say climate change is the main driver of the Amazon’s ongoing drought. “Climate change is no longer something to worry about in the future, 10 or 20 years from now,” Greenpeace spokesperson Romulo Batista toldReuters. “It’s here and it’s here with much more force than we expected.”
A coalition of some of the world’s most prominent shipping and carrier companies is piloting the “first-ever U.S. over-the-road electrified corridor.” Participants include AIT Worldwide Logistics, DB Schenker, Maersk, Microsoft, and PepsiCo, who will drive their long-haul heavy-duty electric trucks along the I-10 corridor between L.A. and El Paso to identify pain points and share learnings in an effort to hasten the decarbonization of land freight. Terawatt Infrastructure will provide the charging infrastructure for the corridor with six of its own charging hubs. Terawatt’s website says it has 14 sites under development, four of which are expected to come online this year. Heavy-duty vehicles account for a quarter of transport-related greenhouse gas emissions in the U.S. The new coalition is supported by the global nonprofit Smart Freight Centre.
Former U.S. Vice President Al Gore’s green asset management business, Generation Investment Management, put out its eighth annual Sustainability Trends Report this week. The paper is packed full of interesting insights (both uplifting and depressing), but one stands out. It says upgrading the power grid is “the critical issue to get the energy transition moving faster in the big, developed economies.” It includes this graphic showing the cumulative backlog of renewable-energy projects wanting to connect to the grid in the U.S.:
Generation Investment Management
Gore has been doing the media rounds this week. He told the Financial Times that a Trump victory in November “would be very bad.” “Most climate activists that I know in the United States believe that the single most important near-term decision America can make with regard to climate is who is the next president. It’s a bit of a Manichaean choice.” But, he added that the energy transition was, at this point, “unstoppable.”
In case you missed it: Norway has become the first country in the world to have more electric vehicles on the road than gas-powered cars. Diesel still reigns supreme in terms of registered vehicles, but the share of fully electric cars registered is now larger than the share of cars that run on gasoline. The director of the Norwegian road federation said he expects EVs will overtake diesel cars, too, by 2026. EVs already make up the vast majority (94%!) of new vehicle sales in Norway, and could very well approach 100% sometime next year.
A recent study finds that most people have a tendency to grossly underestimate the average carbon footprint of the richest individuals in society, while overestimating the carbon footprint of the poorest individuals.
Geothermal is getting closer to the big time. Last week, Fervo Energy — arguably the country’s leading enhanced geothermal company — announced that its Utah demonstration project had achieved record production capacity. The new approach termed “enhanced geothermal,” which borrows drilling techniques and expertise from the oil and gas industry, seems poised to become a big player on America’s clean, 24/7 power grid of the future.
Why is geothermal so hot? How soon could it appear on the grid — and why does it have advantages that other zero-carbon technologies don’t? On this week’s episode of Shift Key, Rob and Jesse speak with a practitioner and an expert in the world of enhanced geothermal. Sarah Jewett is the vice president of strategy at Fervo Energy, which she joined after several years in the oil and gas industry. Wilson Ricks is a doctoral student of mechanical and aerospace engineering at Princeton University, where he studies macro-energy systems modeling. Shift Key is hosted by Robinson Meyer, the founding executive editor of Heatmap, and Jesse Jenkins, a professor of energy systems engineering at Princeton University.
Subscribe to “Shift Key” and find this episode on Apple Podcasts, Spotify, Amazon, or wherever you get your podcasts.
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Here is an excerpt from our conversation:
Robinson Meyer: I just wanted to hit a different note here, which is, Sarah, you’ve alluded a few times to your past in the oil and gas industry. I think this is true across Fervo, is that of course, the technologies we’re discussing here are fracking derived. What has your background in the oil and gas industry and hydrocarbons taught you that you think about at Fervo now, and developing geothermal as a resource?
Sarah Jewett: There are so many things. I mean, I’m thinking about my time in the oil and gas industry daily. And you’re exactly right, I think today about 60% of Fervo’s employees come from the oil and gas industry. And because we are only just about to start construction on our first power facility, the percentage of contractors and field workers from the oil and gas industry is much higher than 60%.
Jesse Jenkins: Right, you can’t go and hire a bunch of people with geothermal experience when there is no large-scale geothermal industry to pull from.
Jewett: That’s right. That’s right. And so the oil and gas industry, I think, has taught us, so many different types of things. I mean, we can’t really exist without thinking about the history of the oil and gas industry — even, you know, Wilson and I are sort of comparing our learning rates to learning rates observed in various different oil and gas basins by different operators, so you can see a lot of prior technological pathways.
I mean, first off, we’re just using off the shelf technology that has been proven and tested in the oil and gas industry over the last 25 years, which has been, really, the reason why geothermal is able to have this big new unlock, because we’re using all of this off the shelf technology that now exists. It’s not like the early 2000s, where there was a single bit we could have tried. Now there are a ton of different bits that are available to us that we can try and say, how is this working? How is this working? How’s this working?
So I think, from a technological perspective, it’s helpful. And then from just an industry that has set a solid example it’s been really helpful, and that can be leveraged in a number of different ways. Learning rates, for example; how to set up supply chains in remote areas, for example; how to engage with and interact with communities. I think we’ve seen examples of oil and gas doing that well and doing it poorly. And I’ve gotten to observe firsthand the oil and gas industry doing it well and doing it poorly.
And so I’ve gotten to learn a lot about how we need to treat those around us, explain to them what it is that we’re doing, how open we need to be. And I think that has been immensely helpful as we’ve crafted the role that we’re going to play in these communities at large.
Wilson Ricks: I think it’s also interesting to talk about the connection to the oil and gas industry from the perspective of the political economy of the energy transition, specifically because you hear policymakers talk all the time about retraining workers from these legacy industries that, if we’re serious about decarbonizing, will unavoidably have to contract — and, you know, getting those people involved in clean energy, in these new industries.
And often that’s taking drillers and retraining some kind of very different job — or coal miners — into battery manufacturers. This is almost exactly one to one. Like Sarah said, there’s additional expertise and experience that you need to get really good at doing this in the geothermal context. But for the most part, you are taking the exact same skills and just reapplying them, and so it allows for both a potentially very smooth transition of workforces, and also it allows for scale-up of enhanced geothermal to proceed much more smoothly than it potentially would if you had to kind of train an entire workforce from scratch to just do this.
This episode of Shift Key is sponsored by …
Watershed’s climate data engine helps companies measure and reduce their emissions, turning the data they already have into an audit-ready carbon footprint backed by the latest climate science. Get the sustainability data you need in weeks, not months. Learn more at watershed.com.
As a global leader in PV and ESS solutions, Sungrow invests heavily in research and development, constantly pushing the boundaries of solar and battery inverter technology. Discover why Sungrow is the essential component of the clean energy transition by visiting sungrowpower.com.
Antenna Group helps you connect with customers, policymakers, investors, and strategic partners to influence markets and accelerate adoption. Visit antennagroup.com to learn more.
Music for Shift Key is by Adam Kromelow.
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.