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Life cycle analysis has some problems.

About six months ago, a climate scientist from Arizona State University, Stephanie Arcusa, emailed me a provocative new paper she had published that warned against our growing reliance on life cycle analysis. This practice of measuring all of the emissions related to a given product or service throughout every phase of its life — from the time raw materials are extracted to eventual disposal — was going to hinder our ability to achieve net-zero emissions, she wrote. It was a busy time, and I let the message drift to the bottom of my inbox. But I couldn’t stop thinking about it.
Life cycle analysis permeates the climate economy. Businesses rely on it to understand their emissions so they can work toward reducing them. The Securities and Exchange Commission’s climate risk disclosure rule, which requires companies to report their emissions to investors, hinges on it. The clean hydrogen tax credit requires hydrogen producers to do a version of life cycle analysis to prove their eligibility. It is central to carbon markets, and carbon removal companies are now developing standards based on life cycle analysis to “certify” their services as carbon offset developers did before them.
At the same time, many of the fiercest debates in climate change are really debates about life cycle analysis. Should companies be held responsible for the emissions that are indirectly related to their businesses, and if so then which ones? Are carbon offsets a sham? Does using corn ethanol as a gasoline substitute reduce emissions or increase them? Scientists have repeatedly reached opposite conclusions on that one depending on how they accounted for the land required to grow corn and what it might have been used for had ethanol not been an option. Though the debate plays out in calculations, it’s really a philosophical brawl.
Everybody, for the most part, knows that life cycle analysis is difficult and thorny and imprecise. But over and over, experts and critics alike assert that it can be improved. Arcusa disagrees. Life cycle analysis, she says, is fundamentally broken. “It’s a problematic and uncomfortable conclusion to arrive at,” Arcusa wrote in her email. “On the one hand, it has been the only tool we have had to make any progress on climate. On the other, carbon accounting is captured by academia and vested interests and will jeopardize global climate goals.”
When I recently revisited the paper, I learned that Arcusa and her co-authors didn’t just critique life cycle analysis, they proposed a bold alternative. Their idea is not economically or politically easy, but it also doesn’t suffer from the problems of trying to track carbon throughout the supply chain. I recently called her up to talk through it. Our conversation has been edited for clarity.
Can you walk me through what the biggest issues with life cycle analysis are?
So, life cycle analysis is a qualitative tool —
It seems kind of counterintuitive or even controversial to call it a qualitative tool because it’s specifically trying to quantify something.
I think the best analogy for LCA is that it’s a back-of-the-envelope tool. If you really could measure everything, then sure, LCA is this wonderful idea. The problem is in the practicality of being able to collect all of that data. We can’t, and that leads us to use emissions factors and average numbers, and we model this and we model that, and we get so far away from reality that we actually can’t tell if something is positive or negative in the end.
The other problem is that it’s almost entirely subjective, which makes one LCA incomparable to another LCA depending on the context, depending on the technology. And yes, there are some standardization efforts that have been going on for decades. But if you have a ruler, no matter how much you try, it’s not going to become a screwdriver. We’re trying to use this tool to quantify things and make them the same for comparison, and we can’t because of that subjectivity.
In this space where there is a lot of money to be made, it’s very easy to manipulate things one way or another to make it look a little bit better because the method is not robust. That’s really the gist of the problems here.
One of the things you talk about in the paper is the way life cycle analysis is subject to different worldviews. Can you explain that?
It’s mostly seen in what to include or exclude in the LCA — it can have enormous impacts on the results. I think corn ethanol is the perfect example of how tedious this can be because we still don’t have an answer, precisely for that reason. The uncertainty range of the results has shrunk and gotten bigger and shrunk and gotten bigger, and it’s like, well, we still don’t know. And now, this exact same worldview debate is playing into what should be included and not included in certification for things [like carbon removal] that are going to be sold under the guise of climate action, and that just can’t be. We’ll be forever debating whether something is true.
Is this one of those things that scientists have been debating for ever, or is this argument that we should stop using life cycle analysis more of a fringe idea?
I guess I would call it a fringe idea today. There’s been plenty of criticism throughout the years, even from the very beginning when it was first created. What I have seen is that there is criticism, and then there is, “But here’s how we can solve it and continue using LCA!” I’ve only come across one other publication that specifically said, “This is not working. This is not the right tool,” and that’s from Michael Gillenwater. He’s at the Greenhouse Gas Management Institute. He was like, “What are we doing?” There might be other folks, I just haven’t come across them.
Okay, so what is the alternative to LCA that you’ve proposed in this paper?
LCA targets the middle of the supply chain, and tries to attribute responsibility there. But if you think about where on the supply chain the carbon is the most well-known, it is actually at the source, at the point of origin, before it becomes an emission. At the point where it is created out of the ground is where we know how much carbon there is. If we focus on that source through a policy that requires mandatory sequestration — for every ton of carbon that is now produced, there is a ton of carbon that’s been put away through carbon removal, and the accounting happens there, before it is sold to anybody — anybody who’s now downstream of that supply chain is already carbon neutral. There is no need to track carbon all the way down to the consumer.
We know this is accurate because that is where governments already collect royalties and taxes — they want to know exactly how much is being sold. So we already do this. The big difference is that the policy would be required there instead of taxing everybody downstream.
You’re saying that fossil fuel producers should be required to remove a ton of carbon from the atmosphere for every ton of carbon in the fuels they sell?
Yeah, and maybe I should be more specific. They should pay for an equal amount of carbon to be removed from the atmosphere. In no way are we implying that a fossil carbon producer needs to also be doing the sequestration themselves.
What would be the biggest challenges of implementing something like this?
The ultimate challenge is convincing people that we need to be managing carbon and that this is a waste management type of system. Nobody really wants to pay for waste management, and so it needs to be regulated and demanded by some authority.
What about the fact that we don’t really have the ability to remove carbon or store carbon at scale today, and may not for some time?
Yes, we need to build capacity so that eventually we can match the carbon production to the carbon removal, which is why we also proposed that the liability needs to start today, not in the future. That liability is as good as a credit card debt — you actually have to pay it. It can be paid little by little every year, but the liability is here now, and not in the future.
The risk in the system that I’m describing, or even the system that is currently being deployed, is that you have counterproductive technologies that are being developed. And by counterproductive, I mean [carbon removal] technologies that are producing more emissions than they are storing, and so they’re net-positive. You can create a technology that has no intention of removing more carbon than its sequesters. The intention is just to earn money.
Do you mean, like, the things that are supposed to be removing carbon from the atmosphere and sequestering it, they are using fossil fuels to do that, and end up releasing more carbon in the process?
Yeah, so basically, what we show in the paper is that when we get to full carbon neutrality, the market forces alone will eliminate those kinds of technologies that are counterproductive. The problem is during the transition, these technologies can be economically viable because they are cheaper than they would be if 100% of the fossil fuel they used was carbon neutral through carbon removal. And so in order to prevent those technologies from gaming the system, we need a way to artificially make the price of fossil carbon as expensive as it would be if 100% of that fossil carbon was covered by carbon removal.
That’s where the idea of permits comes in. For every amount that I produce, I now have an instant liability, which is a permit. Each of those permits has to be matched by carbon removal. And since we don’t have enough carbon removal, we have futures and these futures represent the promise of actually doing carbon removal.
What if we burn through the remaining carbon budget and we still don’t have the capacity to sequester enough carbon?
Well, then we’re going into very unchartered territory. Right now we’re just mindlessly going through this thinking that if we just reduce emissions it will be good. It won’t be good.
In the paper, you also argue against mitigating greenhouse gases other than carbon, and that seems pretty controversial to me. Why is that?
We’re not arguing against mitigating, per se. We’re arguing against lumping everything under the same carbon accounting framework because lumping hides the difficulty in actually doing something about it. It’s not that we shouldn’t mitigate other greenhouse gases — we must. It’s just that if we separate the problem of carbon away from the problem of methane, away from the problem of nitrous oxide, or CFCs, we can tackle them more effectively. Because right now, we’re trying to do everything under the same umbrella, and that doesn’t work. We don’t tackle drinking and driving by sponsoring better tires. That’s just silly, right? We wouldn’t do that. We would tackle drinking and driving on its own, and then we would tackle better tires in a different policy.
So the argument is: Most of climate change is caused by carbon; let’s tackle that separately from the others and leave tackling methane and nitrous oxide to purposefully created programs to tackle those things. Let’s not lump the calculations altogether, hiding all the differences and hiding meaningful action.
Is there still a role for life cycle analysis?
You don’t want to be regulating carbon using life cycle analysis. So you can use the life cycle analysis for qualitative purposes, but we’re pretending that it is a tool that can deliver accurate results, and it just doesn’t.
What has the response been like to this paper? What kind of feedback have you gotten?
Stunned silence!
Nobody has said anything?
In private, they have. Not in public. In private, it’s been a little bit like, “I’ve always thought this, but it seemed like there was no other way.” But then in public, think about it. Everything is built on LCA. It’s now in every single climate bill out there. Every single standard. Every single consulting company is doing LCA and doing carbon footprinting for companies. It’s a huge industry, so I guess I shouldn’t have been surprised to hear nothing publicly.
Yeah, I was gonna ask — I’ve been writing about the SEC rules and this idea that companies should start reporting their emissions to their investors, and that would all be based on LCA. There’s a lot of buy-in for that idea across the climate movement.
Yeah, but there’s definitely a fine line with make-believe. I think in many instances, we kid ourselves thinking that we’re going to have numbers that we can hang our hats on. In many instances we will not, and they will be challenged. And so at that point, what’s the point?
One thing I hear when I talk to people about this is, well, having an estimate is better than not having anything, or, don’t let the perfect be the enemy of the good, or, we can just keep working to make them better and better. Why not?
I mean, I wouldn’t say don’t try. But when it comes to actually enforcing anything, it’s going to be extremely hard to prove a number. You could just be stuck in litigation for a long time and still not have an answer.
I don’t know, to me it just seems like an endless debate while time is ticking and we will just feel good because we’ll have thought we measured everything. But we’re still not doing anything.
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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.