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A new study found that majority Black neighborhoods faced higher solar costs.

Higher-income people are more likely to have solar panels on their roofs. This fact has underlined the nature of home solar adoption and is responsible for any number of state, local, and now federal programs to give lower-income people access to solar power, either through subsidizing their own solar panels or letting them “subscribe” to solar power generated elsewhere.
While this seems like an obviously sensible solution — the upfront cost of solar can be around $15,000 to $20,000, and you typically need to own a single family home to get it — it’s not quite as simple as those with more money are more likely to get solar. When the University of Texas economist Jackson Dorsey and Derek Wolfson looked at data provide by the solar marketplace EnergySage, they found that, yes, those with higher incomes are more likely to buy solar — but also that what solar installers offered them and what they paid for it varied depending on the demographics of the surrounding area.
“Econ 101, there’s usually two possible reasons why you might have lower quantities in a market. One would be demand is lower, and the other would be supply is lower,” Dorsey told me when I asked what had motivated his research. While the data about high-income demand for energy transition products like solar panels or electric vehicles is plentiful, there had been less attention paid to supply-side reasons for the disparities.
Dorsey and Wolfson looked at hundreds of thousands of bids for solar installation placed in EnergySage’s 15 largest markets, including much of urban California, New York City, Washington, D.C. and metro areas in Florida, where prospective solar buyers are able to pick among bids from installers. Unsurprisingly, lower-income buyers were less likely to purchase home solar, received fewer bids overall, and, because they were likely seeking smaller systems, paid more per watt than wealthier buyers. (The researchers were able to match data from EnergySage with census data to extract demographic information about potential customers along with their location.)
What did stand out, however, is that Black households in particular got fewer bids and paid notably higher prices, a disparity that could not be explained entirely by differences in income. Low-income households were more likely to be in an area with a lower cost of living, and therefore didn’t necessarily face higher overall project costs because prices for everything tended to be lower.
Black households, on the other hand, received fewer bids and then face higher prices. “If you look at Black vs. white households, Black households get about 8% higher prices,” Dorsey told me. “On a $20,000 system, that would be $1,600.”
The reason, he determined, is not so much that installers don’t want to serve people they know are Black. It’s that they don’t want to serve neighborhoods they know are majority Black.
Dorsey put the difference down to “some kind of perceived higher cost of doing business.” Part of it could be explained by installers setting up shop in areas where they think they’ll find higher demand for their services — high-income ones — and so Black neighborhoods, which are more likely to be low-income, may be literally farther away and more expensive to serve. According to the data Dorsey and Wolfson collected, there are three installers within 10 miles of white households on average, compared to two installers on average for Black households.
There could also, Dorsey said, “be some implicit preference that they don’t want to go to those neighborhoods.” In the paper, Dorsey and Wolfson write that “some sellers may prefer to serve certain households or neighborhoods either because of intolerant views, crime rates, or other variables correlated with household demographic characteristics.”
While the study didn’t get into remediation, fixing the income side of things should be fairly straightforward, Dorsey told me. “Just making prices lower or financing terms more comparable [to high income households] should be fairly effective,” he said.
The sociogeographic side of things will be trickier to address. “That might suggest a supply side policy might be effective,” Dorsey said, “like giving installers incentives to locate in or serve communities that are getting fewer bids and facing higher prices.”
Policymakers and solar advocates are very aware of the income and race disparities in solar adoptions and have come up with a slew of policies to try and narrow them. California, which has long been the epicenter of rooftop solar (with the most attendant controversy over how its incentives are designed), has a program that subsidizes low-income households that want to install solar and incentives for affordable multifamily buildings to install solar.
The Environmental Protection Agency’s $7 billion Solar For All program also supports states, tribes, and non-profits with programs to reach low-income households. “The program will help unlock new markets for residential solar in areas that have never seen this kind of investment before,” an EPA spokesperson told Heatmap in an emailed statement. “Much of the program will fund solar projects to benefit multi-family and affordable housing, as well as community solar projects, bringing the benefits of clean energy to households that may not have had access to it before.”
Another favored solution for getting solar access to those who wouldn’t otherwise have it is community solar, where households “subscribe” to small-scale solar installations and then get credits on their utility bill as if they had physically installed solar in their homes.
The share of community solar capacity that serves low-to-moderate income consumers has grown from 2% in 2022 to 12% this year, according to data from Wood Mackenzie and the Coalition for Community Solar Access, and they project it will continue to grow to 25% in 2025.
The Inflation Reduction Act also includes an “adder” for community solar projects that serve lower income consumers that boosts existing subsidies by 10 to 20 percentage points. These community solar projects are “already seeing impact and projects on the ground,” Molly Knoll, vice president of policy for CCSA, told me.
EnergySage’s chief executive, Charlie Hadlow, said in a statement that the company is “working diligently to ensure every eligible shopper gets three to seven quotes on our platform,” and that “we welcome more installers to sign up on our platform and are actively seeking them out, with a deliberate focus on underserved areas.” He said consumers typically save 20% using EnergySage compared to what they might get on their own, and that the company also has a marketplace for community solar.
All that said, Dorsey is skeptical that “installing panels at individual rooftop” is even the best way to decarbonize. "If you want to cost-effectively reduce emissions, it’s not clear to me rooftop solar is the way to do it as opposed to utility-scale or community solar,” he said.
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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.