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Counties that veered from Obama in 2008 to Trump in 2016 are more likely to oppose renewables development.

In Texas, the Oak Run Solar Project would have been a slam dunk.
Developers would install 800 megawatts of solar panels — enough to power 800,000 homes — across nine square miles of unused land. It would devote some of its acreage to new farming practices that incorporate solar panels. And it would sell its electricity cheaply — and profitably — because it was near the state capital and because it could take advantage of a pre-existing onsite connection to the regional power grid.
But Oak Run wasn’t proposed in Texas. It was proposed in Ohio, and that means it has faced enormous opposition. Ohio has some of the country’s strictest restrictions on solar development, and 10 counties have blocked solar development outright.
Although Madison County, where Oak Run was proposed, is not one of them, the blowback to the project cost a local Republican county commissioner his job. Oak Run was eventually approved by the state’s power siting board earlier this year, but its opponents are now appealing that decision in the state’s Supreme Court.
Madison County, Ohio, also illustrates the political transformation that has revolutionized the upper Midwest. The predominantly rural county near the state’s capital, Columbus, has favored Republicans since the 1960s. But in recent decades it has swung hard to the right. In 2008, Barack Obama won nearly 40% of the county’s vote. Eight years later, Hillary Clinton picked up just 27%.
These two facts may seem like they have little to do with each other. But they point to one of the biggest trends in clean energy development across the country: The counties that voted for Barack Obama in 2008 and then Donald Trump in 2016 are some of the worst places in the country to permit and build renewable projects.
The size of a county’s swing from 2008 to 2016 is one of the biggest predictors of whether a proposed wind or solar project will be contested or blocked, according to a new Heatmap Pro analysis of more than 8,500 projects and local policies around the country.
The magnitude of that swing is by far the most important political variable to emerge from Heatmap Pro’s analysis of more than 60 risk factors influencing community support or opposition to renewable projects. It is more strongly associated with a given project’s success than whether a county votes for Democratic or Republican candidates overall.
The only variables that are more closely correlated than the 2008-to-2016 swing are fundamental measures of a region’s population or local economy, such as its median income, racial demographics, or dominant industries. Towns and regions that heavily depend on farming, for instance, have become particularly reluctant to accept new solar projects in recent years.
Heatmap Pro’s analysis focused not only on whether a county’s residents support wind or solar projects in theory, but also on whether renewable projects proposed in the area are canceled, contested, or exposed to political turbulence. It surveyed more than 7,000 wind and solar projects proposed and built across the United States since the 1990s.
Many of the counties with the largest Obama-to-Trump swings have passed proposals meant to limit renewable development. Vermillion County in Indiana — where more than a quarter of voters swung from Obama to Trump — has an extensive set of restrictions on new solar projects. Solar projects in Elk County, Pennsylvania, which saw a similar swing, have also turned out against solar projects using up “prime farmland.”
There are a few reasons why the Obama-to-Trump swing might be associated with more opposition to renewables.
In 2008, solar and wind were still frontier technologies and were not price-competitive with fossil fuels. Although vaguely associated with Democrats, politicians on both sides of the aisles championed wind and solar so as to wean the country off foreign oil.
But in the following decade, the U.S. increased its solar capacity by roughly 100-fold, while it has more than doubled its installed wind capacity.. Today, solar and wind energy are major features of the electricity system, and many Republicans have openly embraced fossil fuels and cast doubt on the value of cleaner alternatives.
To be sure, the Obama-to-Trump swing was influenced by other social and economic factors, as well as a state’s specific political environment. Leah Stokes, a UC Santa Barbara political scientist who has studied the growing local opposition to wind farms, told me that the correlation with Obama-Trump voters may originate from Trump’s dominance of the upper Midwest in 2016. Because a small group of anti-renewable advocates can change an entire region’s policies, that could lead to more opposition to renewables in one part of the country or another.
“Is there a person, or a network of people, who are going place by place pushing these anti-solar and wind local laws? That would lead to a geographic concentration,” she said.
Even within individual counties, the electorate wasn’t the same in 2016 as it was in 2008. Throughout the 2010s, tens of millions of Americans moved around the country, with the largest net change moving from the Northeast to the South. Cities became younger on average, while rural areas and suburbs became older.
Even within counties, a different set of voters showed up to the polls in each election. One reason why the 2012 election might not be correlated with opposition to renewables is that many voters who voted for Obama in 2008 skipped the next cycle. Those same voters — many of whom were white and working class — showed back up in 2016 and backed Trump.
What is driving the opposition to renewables? Perhaps a county’s swing against renewable energy is happening precisely because voters there are persuadable. From 2008 to 2016, many voters in these counties changed their minds about which candidate or political party to support. As they shifted their stance to the right, they also adopted more seemingly Republican views about wind and solar development. Donald Trump has distinguished himself by his embrace of fossil fuels and climate change skepticism — perhaps as voters come to support him, they also adopt his positions.
What’s interesting, however, is that deep red counties that have not seen a political shift — places that backed, say, McCain and Romney by roughly the same margin as they backed Trump in 2016 — continue to build wind and solar at a good clip. Texas, for instance, is the No. 1 state for renewable deployment. A county’s partisanship, in other words, is not as good a predictor of its opposition to renewables as its swinginess.
Edgar Virguez, an energy systems engineer at the Carnegie Institution for Science at Stanford University, has studied what drives opposition to renewables in North Carolina. He told me that some of the same factors that predict a county’s Trump support — such as its population density and education level — also predict whether that county has enacted a local restriction on renewable energy.
When he and his colleagues studied local policies in North Carolina, they found that lower density and less educated counties “had significantly higher reductions in the land available for solar development” when compared with denser or more educated counties, he said. Once a county has fewer than 35 people per square mile, or when less than 20% of the population has a bachelor’s degree, the number of restrictions on local land use shot up. That’s a problem for decarbonization, he added, because less dense counties also usually have the best and most affordable land available for solar development.
That finding may not hold true in other states. Heatmap, for instance, has found that whiter and more educated counties are more likely to oppose renewables. And to some degree, less dense counties are exactly where you’d expect to see more solar and wind projects get built — and thus more local policies restricting them pop up. But it is nonetheless not great news for advocates, given that a couple of America’s political institutions — namely, the Senate and the Electoral College — favor rural voters or Midwestern states. If the trend takes root, then it could eventually curtail renewable development across the country. That question — and many others — will partly be decided in this week’s presidential election.
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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.