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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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Wildfires in France and Spain — and a dire El Niño forecast — point to another era of public attention on disaster.
Wildfires and the Return of Climate Politics
Enormous wildfires are still burning in France and Spain. “We're facing a completely unprecedented fire,” French President Emmanuel Macron said on Monday, comparing the situation to World War II. The main fire in Gironde, a southwestern department along the Atlantic coast, have consumed more than 100,000 acres and forced roughly 200,000 people to evacuate.
There’s little sign the fires are under control in either country. In France, the blazes created a pyrocumulonimbus cloud — a towering, thundering “fire storm” that sometimes forms in the western United States but is not often observed in western Europe. Some fires have come within several miles of Bordeaux, the country’s sixth-largest metropolitan area and a longtime center of the wine industry. In Spain, enormous wildfires near Madrid, Ávila, and Toledo have killed one and displaced roughly another 79,000 people.
Firefighters are working fast, in part because a heatwave is forecast for the continent later this week. But of course it is hot — it is high summer in the Northern Hemisphere, and we are having a particularly hot year. 2026 now looks likely to be the world’s second-warmest year ever, and it has a better than one-third chance of being the warmest.
In the near term, expect more climate-addled disasters. The Pacific Ocean has slipped into its El Niño phase, which will likely spin off more extreme storms, droughts, heat waves, and wildfires. Recent modeling suggests it could be the most intense El Niño ever measured. Writing for his newsletter “The Climate Brink,” the climate researcher (and Heatmap contributor) Zeke Hausfather recently warned: “It looks like this year’s El Niño is not only very likely to be the strongest event since reliable records began — it may end up the strongest by a truly mind-blowing margin.”
How do we know this next El Niño will be bad? The most intense El Niño on record occurred from late 2015 to 2016, when sea surface temperatures in a benchmark region of the Pacific Ocean were 2.75 degrees Celsius warmer than normal. (That’s nearly 5 degrees Fahrenheit.) Those searing sea temperatures released huge amounts of heat into the atmosphere and eventually made 2016 the warmest year ever recorded. Today, a decade later, 2016 remains the fourth warmest year on record, coming in only under 2024, 2023, and 2025, per NOAA data.
But as Zeke writes, the middle 80% of modeled outcomes for this year’s El Niño are already projected to match or exceed that 2016 anomaly. The median forecast for this year’s event, in other words, would shatter the previous record. “The models are forecasting something outside the envelope of anything we have ever observed,” he writes. The National Weather Service agrees that there is an 81% chance of an event forming “that would rank among the largest El Niño events in the historical record going back to 1950,” and it says odds are better than 97% that the anomaly will stick around through spring 2027.
Scientists and activists once hoped that when global warming’s effects became unignorable, the public would take action. But disasters haven’t produced durable climate concern, and public attention has dissipated with every news cycle — and become ever more pessimistic. There are moments, however, when successive extremes can keep climate change more prominently in the public conversation. The years that followed the last mega-El Niño in 2016 made up one such period. If we are headed for another now, then experts should be ready with ideas not only for slowing and reversing the growth of heat-trapping emissions, but also for adapting our societies and infrastructure for our warming world. It’s clear we are going to need them.
This will be a big week for understanding the U.S. energy economy’s most important trend. A handful of tech companies driving the artificial intelligence boom — namely, Microsoft, Meta and Amazon — will report their quarterly earnings on Wednesday and Thursday. These companies are behind some of the country’s largest AI data center projects and therefore some of its most sizable planned power plants — clean and otherwise.
Last week, when Alphabet boosted its capital expenditure for this year by another $15 billion, the market rebelled and sent its shares tumbling. If investors’ interest in financing mega-scale data center projects is waning, then it could affect the electricity economy for years to come. In any case, we’ll know more soon. Rivian will also report its earnings this week.
Can AI help emergency managers make faster decisions when every second counts?
Meteorologists had nothing polite to say about Tropical Storm Bertha. The “weak, disorganized, and lopsided” system made initial landfall in Louisiana last week as a “hot, sheared mess,” one that forecasters doubted would reach Texas with much oomph at all. Still, the Galveston County Consolidated Drainage District — the local flood mitigation and drainage management entity for the state’s most flood-prone county — had stood at the ready, posting updates on the storm’s progress to its Facebook feed in the lead-up.
There had been action behind the scenes, too. Since this spring, the county has relied on a new “AI-powered flood warning solution” pilot program to help local administrators identify the gaps in their understanding of the county’s flood risk and monitor rising water levels in real time. In a crisis, a chatbot could even advise them on when to issue an evacuation order.
“Imagine you’re an operator and you have to tell people to leave their homes because of floods coming in,” Todd Barr, the CEO of Axonis Decision Intelligence, which has partnered with the smart water-level sensor company Simplicity Integration in Texas’ Galveston County, told me. Axonis provides AI-assisted decision-making tools to clients in a number of time-sensitive industries, and in every case, “You want a paper trail of the data you used to make the decision — the reasoning and the model you used — and our platform does all of that,” Barr went on.
Issuing evacuation notices is a famously thorny business, and one that has resulted in high-profile and high-casualty failures, including in the Paradise, California, and Maui wildfires. Particularly noteworthy were the 2025 Kerr County floods that killed more than 100 people in Texas’ Hill Country after local officials took 90 minutes to send phone alerts once they became aware of the rising river.
In many cases, particularly in more rural counties, the teams making the evacuation decisions are small and lack sufficient training not only on when to make such a call, but even on how to word it. “The people who are put in the position of issuing the messages are doing 20 other things at the same time,” Jeannette Sutton, a researcher at the University at Albany’s Emergency and Risk Communication Message Testing Lab, told me when I reported on evacuation notices after the Los Angeles fires.
As for Galveston, “100%” of the buildings on the densely populated island are at flood risk, with modeling suggesting a worst-case-scenario hurricane could produce 26 feet of storm surge. Much of the city’s stormwater infrastructure additionally predates modern climate-change-intensified rainfall probabilities, with the district in the midst of a $54 million drainage project aimed at mitigating future flooding by building a pump station and enlarging sewer lines.
As part of the region’s ongoing resiliency work, the Galveston County Consolidated Drainage District installed seven of Simplicity’s water-level sensors —the county’s first — at locations on the mainland. (There are no sensors currently on Galveston Island proper.) Simplicity’s Axonis-powered system, SI-Ai, also pulls in data from NOAA, the U.S. Geological Survey, and Houston’s Harris County to present residents of the entire region with a live flood-risk dashboard, complete with intuitive green-yellow-red indicators to evaluate their neighborhood hazard level in real time. Operators also have their own proprietary dashboard where they can monitor sensors and are prompted to ask questions to interpret readings and open “investigations” if something appears amiss.

“If I’m the municipality, I can say, ‘Okay, here’s what the forecast is looking like and what is potentially going to happen,” Alison Reese, the COO and co-founder of Simplicity, explained to me. “Then I could ask a question like, ‘Hey, what other locations in this watershed are at high risk for flash flooding?’”
That’s where Axonis, the artificial intelligence company, comes in. “Today you would have to be like, ‘Alright! Get the weather report, quick! What’s happening? What are the sensors saying? Okay Bill, now what’s the upstream sensor saying?’” Barr said, acting out the frantic scenario of trying to source data from multiple streams at once. “All of that is what we’re automating.” (Galveston’s Office of Emergency Management is “not the POC for the flood sensor operations,” a representative told me; the drainage district oversees the Axonis-Simplicity partnership, and did not return a request for an interview. The mayor of League City, a city 35-minutes north of Galveston that is also managed by the district, has publicly criticized the SI-Ai program as a separate sensor network that duplicates the work of the Harris County Flood Control District.)
Working from the assumption that emergency managers have to parse reams of data in short periods of time — flash floods can rise as much as 10 feet in an hour — Axonis provides what is essentially a chatbot for authorities to query potential decisions ranging from road closures to evacuation notices, based on feedback from the sensors. It stops short, however, of having a dialogue box that pops up to tell operators, EVACUATE THIS NEIGHBORHOOD NOW.
When Barr demoed the program to me, he had the tool configured to create a credit risk review memo for a would-be banking client. (Axonis also has customers in the banking and defense sectors.) The dashboard essentially functioned the same as it would for Galveston County, though, and his investigation returned the kind of simplified, emoji-studded one-sheet that users of large language model-powered AI interfaces would immediately recognize. In this case, the tool identified a “🔴Risk Alert CANDIDATE” — Barr said that would be a particular sensor, in the case of Galveston — and followed it with a summary and bullet-pointed sections breaking down “⚠️Credit Risk Indicators” and “💧Liquidity Position.” (In a screenshot of an example flood report for Simplicity, shared with me, those sections were replaced by “📍Site Location” and “💧Water Level — Last 72 Hours.” I wondered what else was possible: “🌊Historic hydraulic risk”? “💀Vulnerable Populations”? )

The system then takes operators through a four-step decision-making model based on the OODA Loop, a common workflow in military contexts that involves justifying actions through evidence-based observations. “We always keep a human in the loop on these things, at least today in 2026 — though who knows in two or three years,” Barr said. He clarified in a later conversation with me, though, that “Axonis and [AI] tools should never tell you to evacuate now. It should tell you the information you need to make that decision.”
That was a point Barr stressed numerous times during our conversation: That Axonis’ chatbot is intended as a brainstorming tool or sounding board, and one that keeps a careful paper trail, “cryptographically sealing” any eventual decisions for review and attestation later. I likened it to a police body camera, and Barr didn’t dispute the similarities. “It’s an accountability tool,” he told me.
Of course, that means the burden of decision-making still falls on potentially fallible humans. I worried in particular that by sharing the responsibility with AI, human operators might get lazy or fail to properly question a decision the program might be leading them toward, particularly in an instance of hallucinated data. To the latter point, Barr told me that this is part of what Axonis is designed to address. “You can’t just take the sensor data and throw it into Claude and be like, ‘Alright, go make a decision for me.’ You need to set guard rails,” he said.
As to the former point, Barr told me the chat includes a disclaimer reminding its users that AI can make mistakes, and that the company trains its customers on how LLM technology works. “At the end of the day, it’s a tool, not a decider,” he said, although he allowed that it might be used to automatically trigger warning lights, sirens, or barriers, such as closing a flooded roadway.
I also posed the concern about complacency to Ali Mostafavi, a professor who supervises the UrbanResilience.AL Lab at Texas A&M, which researches, among other things, how artificial intelligence might be utilized in emergency contexts. Mostafavi agreed that there is always a risk in cognitive outsourcing, but that there is a “counter-argument that is also valid — that without these technologies, we have seen what can happen. We had the catastrophic floods last year in Kerr County, and if a similar technology had existed back then, an automated system could have identified the flash flood, and many young children would be alive today.”
Still, Barr told me he isn’t aware of Axonis advising in an actual evacuation order yet. While it is operational, the predictive model remains untested against its highest-stakes use case: the extremes of a climate-changed world, where formerly unthinkable outcomes may be one storm away.
“The more we can stress-test these technologies in real operational settings and use that feedback loop to improve the technologies, the better,” Mostafavi said. “But that’s easier said than done, because to have a technology implemented in an operational setting it should already be stress-tested, right?”
Bertha, though, was not that reckoning; the gusty squalls blew through Galveston last week without even disturbing the dinner reservations at the marina. But although it was already back to 90 and sunny by Monday morning on the Texas Gulf, the drainage in Galveston County, as in many places around the country, remains outdated and easily overwhelmed. One day, inevitably, the water will come. Hopefully when it does, someone or something will be watching.
The large renewables developer changes tack “in response to federal energy objectives.”
Trump’s solar freeze is now so tough that at least one renewable energy developer has asked his administration to turn their permitting application into a data center and gas-fired power plant instead.
Renew Development HoldCo – an LLC created by Clearway Energy Group – wrote the Bureau of Land Management in April asking if they could amend their 2021 application to build the Amber solar project, a 500-megawatt solar project in the Nevada desert that would require building on federal land. Their requested change? “[T]o formally remove the proposed solar facility and replace it with the development of a proposed data center and natural gas facility,” according to a copy of the letter I obtained.
“This amendment is the result of a shift in our internal development priorities and an updated assessment of project timing, in order to better align with the goals of our Administration,” reads the letter, which is dated April 3 and signed by Clearway’s chief development officer John Woody. “The data center concept is in exploratory early stages and as such has a longer and more flexible development horizon, and we believe its schedule will better align with the Bureau’s current workload and staffing plans.”
Now, this swap is somewhat shocking but shouldn’t exactly be a surprise. Companies with federal energy leases are struggling to get their renewable projects permitted by a hostile Trump administration. We’ve already seen some offshore wind developers ditch their leases in favor of payouts and commitments to build more fossil infrastructure. Clearway Energy Group is owned by Global Infrastructure Partners and TotalEnergies, the latter of which struck such a deal in March.
But this does appear to represent an aberration for Clearway, one of the nation’s largest operators of renewable energy projects and whose marketing materials primarily focus on “clean energy.” Nearly all of the company’s portfolio is carbon-free power or energy storage generation sans a handful of “flexible generation” energy projects in California, according to an online map of their project pipeline. The company did not disclose in the documents I reviewed if the gas plant itself would power the data center, provide power to the wider grid, or both.
Candidly, I’ve been watching like a hawk to see if Trump’s chokehold on solar and wind permits would lead to more gas infrastructure and data centers on federal property instead. And companies are getting data center permits when they ask to swap out their solar farm for AI infrastructure. On Friday, I reported that a joint venture involving renewables developer Arevon and energy trader Bill Perkins got permission from BLM to switch an environmental permit tied to a solar farm for one allowing a new data center. Environmentalists plan to legally challenge BLM’s determination as they say it’s a test case for the future of federal land policy.
It’s unclear if Clearway would be the one to build and construct this hypothetical data center and power plant. I for one can’t find any evidence of Clearway developing data centers before. My best guess is that if they do move forward with this, it would look like the joint venture I covered on Friday, where Arevon distanced itself from the actual day-to-day operations of the development and a new firm specializing in data centers came in. But that’s just a hunch and there’s a saying about assumptions.
Nevertheless, Clearway is clearly handling the permitting side. Attached to the Clearway letter was an application also sent to BLM for constructing utility and telecommunications facilities on federal lands, a document technically known as an SF299. The application states Clearway considered using solar energy for the data center as well as using private land, but their alternative designs weren’t selected because they had “higher environmental and stakeholder conflicts.”
Also, in a section of the document requesting Clearway provide a “statement of need for the project,” the developer said it was submitting this proposal “in response to federal energy objectives” and specifically cited Trump’s Day 1 executive order which the company said “encourage[d] development of reliable energy projects on federal lands.”
I reached out to Clearway asking for more information on the letter and application. In response, the company claimed the solar project wasn’t being killed – it simply was moved to private land. They also declined to comment on the data center and gas project. Instead, I was provided a statement attributable to an unnamed spokesperson that “while we do not comment on any individual application while it moves through federal approval processes, we are pleased to be advancing more than 4 GW of solar and battery resources in Nevada on private and public lands and expect those projects to deliver tremendous economic benefits to the communities where they’re built.”
“Clearway values its strong working partnership with the BLM, its Southern Nevada office, and also with state and local interests in Nevada. Across all of these relationships, we continuously assess how best to develop and deliver infrastructure that meets needs and aligns with local and national policies and goals.”