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When I was an analyst at the U.S. Treasury, my team’s work centered around promising private investors that we would make it easier for them to invest in renewable energy projects across the Global South. I kept hearing that our job was ultimately to make these projects “bankable.” As the logic went, “there is a sizeable universe of good projects that fall just below many private investors’ desired rate of return,” and therefore lowering the risks of investing in these “good projects” would put them within reach of private investors’ return expectations. To make decarbonization possible, we had to make decarbonization profitable.
This claim cuts straight through Brett Christophers’ latest book, The Price is Wrong: Why Capitalism Won’t Save the Planet, which argues that the cost of developing and generating renewable energy is not what will determine the speed or scale of its uptake. It might finally be cheaper to build solar panels and wind farms than a coal or gas plant, that’s for sure. But given the structure of our energy markets today, it does not follow that assets that are cheap to build are necessarily profitable enough to provide adequate returns to investors.
My old colleagues might have already been aware of this fact, but as Christophers highlights, it’s certainly not intuitive, even to many analysts. Nor are its implications: Decarbonization won’t happen if it’s not profitable enough ― and it’s not profitable enough.
Christophers is a professor at Sweden’s Uppsala University in its “department of human geography,” whose research focuses on how capitalism and the modern financial system shape our lives; in this book, that also includes our energy systems. To make his case, he highlights the vicious feedback loop affecting renewables endemic to today’s energy markets. Government support to build renewable energy drives down its marginal cost, but because there’s now more renewable energy available at any given moment, the falling costs cut into developers’ expected returns, requiring more government support to keep investors and developers interested in the sector.
Combine this dynamic with technical features endemic to renewable energy generation, including its intermittency, and the result is a wholesale electricity market with perennially unstable prices. This volatility throttles the expected returns on any investment in renewable energy. No matter how cheap it is to build renewable energy, private investors and developers won’t decarbonize our globe at the speed or scale we deserve ― not under these financial conditions, at least.
Christophers leans on two theoretical guideposts here. First, Andreas Malm, whose assessment of how the profit motive, not relative costs, drove Britain’s first energy transition from water-wheels to coal and steam is an unmistakable conceptual parallel to today’s transition. Second, Karl Polanyi, whose theory of “fictitious commodities” — referring to land, labor, and money, each of which the state and society must painstakingly regulate into fungible market-friendly products ― Christophers aptly applies to electricity and the artificial markets created around it.
But rather than hew to theory to justify why the energy system needs to be socialized to achieve decarbonization ― which is definitely true, by the way; the profit motive is supremely unhelpful here ― Christophers embraces a holistic understanding of the economy as a set of financial relationships, supply chains, planned markets, and legal institutions connecting various public and private entities with different motives.
That means interviewing investors, who tell him things like: “Low returns and volatility don’t go. No bank in the world will take power price risk at low returns.” Christophers also produces a detailed and data-rich breakdown of the interlocking global energy crises in 2021 and 2022, jumping between Texas, China, India, Australia, and across Europe, to make a larger point about energy markets. These crises were “not taken to be evidence of the failings of markets, or even a reason to question their role as the pre-eminent mechanism of coordination to the state’s electricity sector,” he writes; “the market was regarded as the very means to manage the crisis.” But the markets aren’t working. Something has to give.
He ends the book with a call for socialized power, inspired by the Green New Deal and New York’s Build Public Renewables Act, championed by the state’s democratic socialists on the explicit grounds that, because delivering on the state’s emissions targets is not profitable enough for the private sector to do alone, the public sector must get the job done. With the force of the whole book’s arguments and evidence behind it, this policy prescription hardly appears radical.
Public developers can accept lower profitability thresholds, and public finance institutions can provide debt on more forgiving terms; under the public aegis, rates of return and costs of capital become policy choices. Christophers admits in his introduction that he is more focused on unearthing the fragile relationships among actors across the renewable energy industry than on describing the ways a New York-inspired socialized power sector could function. Given how much there is to unearth, it’s a reasonable choice, but it leaves readers without a working heuristic for the different ways states can intervene in the business of energy.
Here’s my attempt: Energy must be financed, generated, distributed, and consumed. Government intervention in favor of decarbonization looks distinct at each step.
Governments can provide consumption support by shielding ratepayers from the higher electricity bills that come from potential utility investments into renewable energy procurement and decarbonization-related grid management, backstopping utility investments through a demand guarantee. Consumption support is equitable, but it’s also indirect and incomplete — it might provide a utility with more financial breathing room to procure or develop renewables, but if renewables are not available to procure on the grid or are not easy to develop, this demand guarantee likely just pads the utility’s bottom line.
Governments can provide distribution support by encouraging utilities to purchase renewable energy. Distribution support most often takes the form of regulatory nudges: In the United States, mandates like Renewable Portfolio Standards force utilities to increase their clean energy procurement, guaranteeing purchase demand for clean electricity and Renewable Energy Certificates, which companies might buy to clean up their own energy portfolios.
These demand-guarantee interventions have helped speed up renewable energy development nationwide, but with limits. In particular, utility power purchase agreements don’t provide developers with adequate price stability because utilities fix the quantity of energy they purchase rather than the price; corporate PPAs, meanwhile, cannot be relied on at scale because there aren’t enough large creditworthy corporations like Google and Amazon willing to commit to buying energy from new projects at a fixed price. For these reasons and more, supporting utilities’ efforts to decarbonize will not call forth adequate renewable energy generation sources into existence.
Generation support is what most governments already do. Whether through feed-in tariffs, production tax credits, or contracts for difference, generation support entails propping up generators’ profitability, ensuring that the sale price of their energy is never too low. Christophers explains why this mechanism — that is, a revenue guarantee rather than a demand guarantee — is deeply necessary: Renewable energy sources and the energy markets they’re plugged into are both structurally volatile, so, no matter how much energy they generate, they never generate all that much profit. Withdrawing generation support would be, in no uncertain terms, a death knell for renewables development.
And, finally, financing support targets renewable energy sources as capital-intensive assets requiring huge amounts of upfront debt. Whether through the investment tax credit, viability gap funding, concessional financing, or other forms of cost-share plans, financing support is another form of direct price support for generation companies; by lowering a project’s cost of capital, it helps lower its developer’s threshold for project profitability, meaning that generators pay less debt service and keep more of their revenues. High interest rates have lately forced up the cost of debt for renewable energy projects to unsustainable levels, far above private developers’ prospective rates of return. Financing support is a must-have these days ― and it’s all the more necessary across the Global South, where the costs of capital are far higher.
None of this is to say that socializing generation and finance solves every problem ― as far as the United States is concerned, non-financial barriers abound, such as regulations and interconnection queues ― but within the existing structure of energy markets, public ownership does solve a lot.
What does direct government intervention into energy consumption and distribution look like? Public ownership of local distribution utilities is a start. Unlike private utility companies, they don’t need to promise ten percent returns to shareholders, and can use the financial breathing room that comes from lower profitability thresholds to tamp down rate hikes and, perhaps more importantly, rate volatility. Public utilities will not drive decarbonization, but they could potentially help advance transmission reform and better integrate distributed energy resources into the grid.
Christophers all but argues that the best thing governments can do for all four support categories is to redesign energy markets. Beyond simply incentivizing the deployment of clean firm and battery technologies to complement renewables, policymakers’ biggest task is to build an energy system where volatile wholesale energy prices ― which even publicly owned renewable energy developers will have to face for the foreseeable future ― are not the reason that a project fails to get built. That would be a policy failure, and we don’t have time for those.
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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.”