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A new study from the University of California, Berkeley, breaks down the issues, while also stirring up a controversy of its own.

A new study casts doubt on the integrity of yet another type of carbon offset.
Researchers from the University of California, Berkeley, investigated clean cookstove projects, in which companies distribute stoves that require less or cleaner types of fuel to people who cannot afford them and sell carbon credits based on the resulting emission reductions. These projects have generated, on average, nine times more carbon credits than they should have based on their climate benefits, the researchers found.
This kind of credit inflation obscures climate progress, as the individuals and businesses who buy these credits do so to justify their own emissions under the belief that they are funding climate action elsewhere.
It also threatens a key source of funding to remedy a major public health problem. Nearly a third of the global population — some 2.3 billion people — cook with wood and charcoal burned on open fires or in very basic stoves that expose people to dangerous levels of pollution, including particulate matter and carbon monoxide. The smoke contributes to respiratory and cardiovascular problems and leads to an estimated 4 million premature deaths every year. On top of that, this form of cooking releases roughly 2% of global greenhouse gas emissions.
Companies have jumped at the opportunity to finance solutions by selling carbon offsets, with great success. Between 2017 and 2022, the volume of finance secured for clean cookstoves through the carbon market increased 45-fold, according to a report by the Clean Cookstove Alliance published last fall. Now, cookstove projects make up some 10% of all credits on the carbon market. And they’re one of the fastest growing types of offset projects.
The new study, published in the peer-reviewed journal Nature Sustainability on Wednesday, finds that the methods developers are using to measure the amount of carbon these projects avoid are deeply flawed.
The first red flag the researchers identified was that academic studies of clean cookstoves report much lower adoption rates (whether the new stove was used) and usage rates (how often the new stove was used) than offset projects do. A representative sample of offset projects reported an 86% adoption rate and 98% usage rate, whereas the research literature reported a 58% adoption rate and 52% usage rate.
“The literature at large has found, honestly, devastatingly low rates of adoption and usage,” Annelise Gill-Wiehl, a PhD student at Berkeley and the lead author of the study told me. Some families totally abandon their new stoves, while others continue to use traditional cooking methods in addition to the clean stove. That’s because the new stoves might have smaller burners, not get as hot, change the taste of traditional foods, or else just create more work for cooks. “The first thing you have to ask yourself is, have these offset projects just solved it?” Gill-Wiehl said. “Or are there limitations in their methods?”
One big limitation, according to Gill-Wiehl and her coauthors, is the way offset data is collected. To measure adoption, many project managers use a simple one-time survey that asks households if they used the new stove in the last week or month. If they reply yes, the developer will generate credits as if the household used the stove 100% of the time. Not only is this not exactly robust methodologically, but it may also result in participants inflating their usage to please the survey collectors — a common effect known as “social desirability bias.”
Another major issue stems from the way these projects account for larger environmental impacts. One of the key ways clean cookstove initiatives cut emissions is by reducing the degradation of forests that results from the gathering of fuel to make fires. It would be impossible to measure these cuts directly, but the default estimates that project developers use vastly overstate the level of degradation that would otherwise occur compared to what the peer-reviewed literature has found.
But like anything offsets-related, this study, too, has attracted fierce scrutiny. After an earlier version of it was published a year ago, offset project developers responded with an open letter calling it “misguided.” For instance, the letter calls it inappropriate to compare carbon offset projects to non-commercial projects analyzed in the academic literature. It also accuses the Berkeley researchers of selectively choosing studies and carbon offset projects to include. Finally, the letter also points to the fact that the Better Cooking Company, a cookstove company that is trying to sell credits, provided funding for the study and asserts that the findings benefit that company.
Gill-Wiehl pushed back on all points. The Better Cookstove Company provided less than 5% of the funding, she said, and had no influence over the findings. She added that the results didn’t benefit the company — the study implied that it, too, was guilty of over-crediting, primarily due to inflated forest conservation estimates. (The Better Cookstove Company has since updated its forest conservation estimates to align with the findings in the study, decreasing its sellable credits.)
“We did not write this to burn cookstoves to the ground,” she told me. “This is an incredibly important project type, and it’s so incredibly important that it can't be based on a house of cards.”
Gill-Wiehl said she and her co-authors want the carbon market registries — the groups that design the methodologies project developers must follow to generate and sell credits — to adopt stronger rules that improve the integrity of the market. For example, to measure usage, they could require developers to collect metered data from the stoves or to use fuel sales data. They also want the registries to require that developers use more accurate estimates from the literature for forest degradation. Without significant change, buyers could lose confidence and funding could dry up.
Some of the issues with clean cookstove projects were already known, if not quantified to the extent in this new paper, and there are some ongoing efforts in the industry to improve them. An influential United Nations body recently supported research to establish more accurate estimates of forest degradation, and a consortium of government groups and NGOs is working to develop stronger rules for crediting cookstove projects.
The authors of the study hope this increased attention on cookstoves doesn’t just lead to more legitimate offset projects, but also to ones that better prioritize public health. The vast majority of the cookstoves handed out for offset projects are designed to run more efficiently, but still expose users to dangerous levels of pollution. As of November 2022, only 4% of projects provided the types of stoves that the World Health Organization deems “clean for health at point of use.”
“I feel like at this moment when there’s a shake up of the offset market in general — but also, right now around cookstoves — we have an opportunity to direct all of this finance to projects that have a transformative benefit to people’s lives and health,” Barbara Haya, director of the Berkeley Carbon Trading Project and one of the study’s authors, told me. “And we have an obligation to do that if we’re going to use those credits to make claims of reducing emissions.”
Editor’s note: This story has been updated to correct the proportion of funding the Better Cookstove Company provided for the study and to reflect changes the company has made to its offset methodology since the study’s completion. We regret the error.
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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.