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Harmonizing data across federal agencies will go a long, long way toward simplifying environmental reviews.

Comprehensive permitting reform remains elusive.
In spite of numerous promising attempts — the Fiscal Responsibility Act of 2023, for instance, which delivered only limited improvements, and the failed Manchin-Barrasso bill of last year — the U.S. has repeatedly failed to overhaul its clogged federal infrastructure approval process. Even now there are draft bills and agreements in principle, but the Trump administration’s animus towards renewable energy has undermined Democratic faith in any deal. Less obvious but no less important, key Republicans are quietly disengaged, hesitant to embrace the federal transmission reform that negotiators see as essential to the package.
Despite this grim prognosis, Congress could still improve implementation of a key permitting barrier, the National Environmental Policy Act, by fixing the federal government’s broken systems for managing and sharing NEPA documentation and data. These opaque and incompatible systems frustrate essential interagency coordination, contributing immeasurably to NEPA’s delays and frustrations. But it’s a problem with clear, available, workable solutions — and at low political cost.
Both of us saw these problems firsthand. Marc helped manage NEPA implementation at the Environmental Protection Agency, observing the federal government’s slow and often flailing attempts to use technology to improve internal agency processes. Elizabeth, meanwhile, spent two years overcoming NEPA’s atomized data ecosystem to create a comprehensive picture of NEPA litigation.
Even so, it’s difficult to illustrate the scope of the problem without experiencing it. Some agencies have bespoke systems to house crucial and unique geographic information on project areas. Other agencies lack ready access to that information, even as they examine project impacts another agency may have already studied. Similarly, there is no central database of scientific studies undertaken in support of environmental reviews. Some agencies maintain repositories for their environmental assessments — arduous but less intense environmental reviews than the environmental impact statements NEPA requires when a federal agency action substantially impacts the environment. But there’s still no unified, cross-agency EA database. This leaves agencies unable to efficiently find and leverage work that could inform their own reviews. Indeed, agencies may be duplicating or re-duplicating tedious, time-consuming efforts.
NEPA implementation also relies on interagency cooperation. There, too, agencies’ divergent ways of classifying and communicating about project data throws up impediments. Agencies rely on arcane data formats and often incompatible platforms. (For the tech-savvy, an agency might have a PDF-only repository while another has XML-based data formats.) With few exceptions, it’s difficult for cooperating agencies to even know the status of a given review. And it produces a comedy of errors for agencies trying to recruit and develop younger, tech-savvy staff. Your workplace might use something like Asana or Trello to guide your workflow, a common language all teams use to communicate. The federal government has a bureaucratic Tower of Babel.
Yet another problem, symptomatic of inadequate transparency, is that we have only limited data on the thousands of NEPA court cases. To close the gap, we sought to understand — using data — just how sprawling and unwieldy post-review NEPA litigation had become. We read every available district and appellate opinion that mentioned NEPA from 2013 to 2022 (over 2,000 cases), screened out those without substantive NEPA claims, and catalogued their key characteristics — plaintiffs, court timelines and outcomes, agencies, project types, and so on. Before we did this work, no national NEPA litigation database provided policymakers with actionable, data-driven insights into court outcomes for America’s most-litigated environmental statute. But even our painstaking efforts couldn’t unearth a full dataset that included, for example, decisions taken by administrative judges within agencies.
We can’t manage what we can’t measure. And every study in this space, including ours, struggles with this type of sample bias. Litigated opinions are neither random nor representative; they skew toward high-stakes disputes with uncertain outcomes and underrepresent cases that settle on clear agency error or are dismissed early for weak claims. Our database illuminates litigation patterns and timelines. But like the rest of the literature, it cannot offer firm conclusions about NEPA’s effectiveness. We need a more reliable universe of all NEPA reviews to have any chance — even a flawed one — at assessing the law’s outcomes.
In the meantime, NEPA policy debates often revolve unproductively around assumptions and anecdotes. For example, Democrats can point to instances when early and robust public engagement appeared essential for bringing projects to completion. But in the absence of hard data to support this view, GOP reformers often prefer to limit public participation in the name of speeding the review process. The rebuttal to that approach is persuasive: Failing to engage potential project opponents on their legitimate concerns merely drives them to interfere with the project outside the NEPA process. Yet this rebuttal relies on assumptions, not evidence. Only transparent data can resolve the dispute.
Some of the necessary repair work is already underway at the Council on Environmental Quality, the White House entity that coordinates and guides agencies’ NEPA implementation. In May, CEQ published a “NEPA and Permitting Data and Technology Standard” so that agencies could voluntarily align on how to communicate NEPA information with each other. Then in June, after years using a lumbering Excel file containing agencies’ categorical exclusions — the types of projects that don’t need NEPA review, as determined by law or regulation — CEQ unveiled a searchable database called the Categorical Exclusion Explorer. The Pacific Northwest National Laboratory’s PermitAI has leveraged the EPA’s repository of environmental impact statements and, more recently, environmental review documents from other agencies to create an AI-powered queryable database. The FAST-41 Dashboard has brought transparency and accountability to a limited number of EISs.
But across all these efforts, huge gaps in data, resources, and enforcement authority remain. President Trump has issued directives to agencies to speed environmental reviews, evincing an interest in filling the gaps. But those directives don’t and can’t compel the full scope of necessary technological changes.
Some members of Congress are tuned in and trying to do something about this. Representatives Scott Peters, a Democrat from California, and Dusty Johnson, Republican of South Dakota, deserve credit for introducing the bipartisan ePermit Act to address all of these challenges. They’ve identified key levers to improve interagency communication, track litigation, and create a common and publicly accessible storehouse of NEPA data. Crucially, they recognize the make-or-break role of agency Chief Information Officers who are accountable for information security. Our own attempts to upgrade agency technology taught us that the best way to do so is by working with — not around — CIOs who have a statutory mandate.
The ePermit Act would also lay the groundwork for more extensive and innovative deployment of artificial intelligence in NEPA processes. Despite AI’s continuing challenges around information accuracy and traceability, large language models may eventually be able to draft the majority of an EIS on their own, with humans involved to oversee.
AI can also address hidden pain points in the NEPA process. It can hasten the laborious summarization and incorporation of public comment, reducing the legal and practical risk that agencies miss crucial public feedback. It can also help determine whether sponsor applications are complete, frequently a point of friction between sponsors and agencies. AI can also assess whether projects could be adapted to a categorical exclusion, entirely removing unnecessary reviews. And finally, AI tools are a concession to the rapid turnover of NEPA personnel and depleted institutional knowledge — an acute problem of late.
Comprehensive, multi-agency legislation like the ePermit Act will take time to implement — Congress may want or even need to reform NEPA before we get the full benefit of technology improvements. But that does not diminish the urgency or value of this effort. Even Representative Jared Huffman of California, a key Democrat on the House Natural Resources Committee with impeccable environmental credentials, offered words of support for the ePermit Act, while opposing other NEPA reforms.
Regardless of what NEPA looks like in coming years, this work must begin at some point. Under every flavor of NEPA reform, agencies will need to share data, coordinate across platforms, and process information. That remains true even as court-driven legal reforms and Trump administration regulatory changes wreak havoc with NEPA’s substance and implementation. Indeed, whether or not courts, Congress, or the administration reduce NEPA’s reach, even truncated reviews would still be handicapped by broken systems. Fixing the technology infrastructure now is a way to future-proof NEPA.
The solution won’t be as simple as getting agencies to use Microsoft products. It’s long past time to give agencies the tools they need — an interoperable, government-wide platform for NEPA data and project management, supported by large language models. This is no simple task. To reap the full benefits of these solutions will require an act of Congress that both provides funding for multi-agency software and requires all agencies to act in concert. This mandate is necessary to induce movement from actors within agencies who are slow to respond to non-binding CEQ directives that take time away from statutorily required work, or those who resist discretionary changes to agency software as cybersecurity risks, no matter how benign those changes may be. Without appropriated money or congressional edict, the government’s efforts in this area will lack the resources and enforcement levers to ensure reforms take hold.
Technology improvements won’t cure everything that ails NEPA. This bill won’t fix the deep uncertainty unleashed by the legal chaos of the last year. But addressing these issues is a no-regrets move with bipartisan and potentially even White House support. Let it be done.
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Citrine Informatics has been applying machine learning to materials discovery for years. Now more advanced models are giving the tech a big boost.
When ChatGPT launched three years ago, it became abundantly clear that the power of generative artificial intelligence had the capacity to extend far beyond clever chatbots. Companies raised huge amounts of funding based on the idea that this new, more powerful AI could solve fundamental problems in science and medicine — design new proteins, discover breakthrough drugs, or invent new battery chemistries.
Citrine Informatics, however, has largely kept its head down. The startup was founded long before the AI boom, back in 2013, with the intention of using simple old machine learning to speed up the development of more advanced, sustainable materials. These days Citrine is doing the same thing, but with neural networks and transformers, the architecture that undergirds the generative AI revolution.
“The technology transition we’re going through right now is pretty massive,” Greg Mulholland, Citrine’s founder and CEO, told me. “But the core underlying goal of the company is still the same: help scientists identify the experiments that will get them to their material outcome as fast as possible.”
Rather than developing its own novel materials, Citrine operates on a software-as-a-service model, selling its platform to companies including Rolls-Royce, EMD Electronics, and chemicals giant LyondellBassell. While a SaaS product may be less glamorous than independently discovering a breakthrough compound that enables something like a room-temperature superconductor or an ultra-high-density battery, Citrine’s approach has already surfaced commercially relevant materials across a variety of sectors, while the boldest promises of generative AI for science remain distant dreams.
“You can think of it as science versus engineering,” Mulholland told me. “A lot of science is being done. Citrine is definitely the best in kind of taking it to the engineering level and coming to a product outcome rather than a scientific discovery.” Citrine has helped to develop everything from bio-based lotion ingredients to replace petrochemical-derived ones, to plastic-free detergents, to more sustainable fire-resistant home insulation, to PFAS-free food packaging, to UV-resistant paints.
On Wednesday, the company unveiled two new platform capabilities that it says will take its approach to the next level. The first is essentially an advanced LLM-powered filing system that organizes and structures unwieldy materials and chemicals datasets from across a company. The second is an AI framework informed by an extensive repository of chemistry, physics, and materials knowledge. It can ingest a company’s existing data, and, even if the overall volume is small, use it to create a list of hundreds of potential new materials optimized for factors such as sustainability, durability, weight, manufacturability, or whatever other outcomes the company is targeting.
The platform is neither purely generative nor purely predictive. Instead, Mulholland explained, companies can choose to use Citrine’s tools “in a more generative mode” if they want to explore broadly and open up the field of possible materials discoveries, or in a more “optimized” mode that stays narrowly focused on the parameters they set. “What we find is you need a healthy blend of the two,” he told me.
The novel compounds the model spits out still need to be synthesized and tested by humans. “What I tell people is, any plane made of materials designed exclusively by Citrine and never tested is not a plane I’m getting on,” Mulholland told me. The goal isn’t to achieve perfection right out of the lab, but rather to optimize the experiments companies end up having to do. “We still need to prove materials in the real world, because the real world will complicate it.”
Indeed it will. For one thing, while AI is capable of churning out millions of hypothetical materials — as a tool developed by Google DeepMind did in 2023 — materials scientists have since shown that many are just variants of known compounds, while others are unstable, unable to be synthesized, or otherwise irrelevant under real world conditions.
Such failures likely stem, in part, from another common limitation of AI models trained solely on publicly available materials and chemicals data: Academic research tends to report only successful outcomes, omitting data on what didn’t work and which compounds weren’t viable. That can lead models to be overly optimistic about the magnitude and potential of possible materials solutions and generate unrealistic “discoveries” that may have already been tested and rejected.
Because Citrine’s platform is deployed within customer organizations, it can largely sidestep this problem by tuning its model on niche, proprietary datasets. These datasets are small when compared with the vast public repositories used to train Citrine’s base model, but the granular information they contain about prior experiments — both successes and failures — has proven critical to bringing new discoveries to market.
While the holy grail for materials science may be a model trained on all the world’s relevant data — public and private, positive and negative — at this point that’s just a fantasy, one of Citrine’s investors, Mark Cupta of Prelude Ventures, told me over email. “It’s hard to get buy-in from the entire material development world to make an open-source model that pulls in data from across the field.”
Citrine’s last raise, which Prelude co-led, came at the very beginning of 2023, as the AI wave was still gathering momentum. But Mulholland said there’s no rush to raise additional capital — in fact, he expects Citrine to turn a profit in the next year or so.
That milestone would strongly validate the company’s strategy, which banks on steady revenue from its subscription-based model to compensate for the fact that it doesn’t own the intellectual property for the materials it helps develop. While Mulholland told me that many players in this space are trying to “invent new materials and patent them and try to sell them like drugs,” Citriene is able to “invent things much more quickly, in a more realistic way than the pie in the sky, hoping for a Nobel Prize [approach].”
Citrines is also careful to assure that its model accounts for real world constraints such as regulations and production bottlenecks. Say a materials company is creating an aluminum alloy for an automaker, Mulholland explained — it might be critical to stay within certain elemental bounds. If the company were to add in novel elements, the automaker would likely want to put its new compound through a rigorous testing process, which would be annoying if it’s looking to get to market as quickly as possible. Better, perhaps, to tinker around the edges of what’s well understood.
In fact, Mulholland told me it’s often these marginal improvements that initially bring customers into the fold, convincing them that this whole AI-for-materials thing is more than just hype. “The first project is almost always like, make the adhesive a little bit stickier — because that’s a good way to prove to these skeptical scientists that AI is real and here to stay,” he said. “And then they use that as justification to invest further and further back in their product development pipeline, such that their whole product portfolio can be optimized by AI.”
Overall, the company says that its new framework can speed up materials development by 80%. So while Mulholland and Citrine overall may not be going for the Nobel in Chemistry, don’t doubt for a second that they’re trying to lead a fundamental shift in the way consumer products are designed.
“I’m as bullish as I can possibly be on AI in science,” Mulholland told me. “It is the most exciting time to be a scientist since Newton. But I think that the gap between scientific discovery and realized business is much larger than a lot of AI folks think.”
Plus more insights from Heatmap’s latest event Washington, D.C.
At Heatmap’s event, “Supercharging the Grid,” two members of the House of Representatives — a California Democrat and a Colorado Republican — talked about their shared political fight to loosen implementation of the National Environmental Policy Act to accelerate energy deployment.
Representatives Gabe Evans and Scott Peters spoke with Heatmap’s Robinson Meyer at the Washington, D.C., gathering about how permitting reform is faring in Congress.
“The game in the 1970s was to stop things, but if you’re a climate activist now, the game is to build things,” said Peters, who worked as an environmental lawyer for many years. “My proposal is, get out of the way of everything and we win. Renewables win. And NEPA is a big delay.”
NEPA requires that the federal government review the environmental implications of its actions before finalizing them, permitting decisions included. The 50-year-old environmental law has already undergone several rounds of reform, including efforts under both Presidents Biden and Trump to remove redundancies and reduce the size and scope of environmental analyses conducted under the law. But bottlenecks remain — completing the highest level of review under the law still takes four-and-a-half years, on average. Just before Thanksgiving, the House Committee on Natural Resources advanced the SPEED Act, which aims to ease that congestion by creating shortcuts for environmental reviews, limiting judicial review of the final assessments, and preventing current and future presidents from arbitrarily rescinding permits, subject to certain exceptions.
Evans framed the problem in terms of keeping up with countries like China on building energy infrastructure. “I’ve seen how other parts of the world produce energy, produce other things,” said Evans. “We build things cleaner and more responsibly here than really anywhere else on the planet.”
Both representatives agreed that the SPEED Act on its own wouldn’t solve all the United States’ energy issues. Peters hinted at other permitting legislation in the works.
“We want to take that SPEED Act on the NEPA reform and marry it with specific energy reforms, including transmission,” said Peters.
Next, Neil Chatterjee, a former Commissioner of the Federal Energy Regulatory Commission, explained to Rob another regulatory change that could affect the pace of energy infrastructure buildout: a directive from the Department of Energy to FERC to come up with better ways of connecting large new sources of electricity demand — i.e. data centers — to the grid.
“This issue is all about data centers and AI, but it goes beyond data centers and AI,” said Chatterjee. “It deals with all of the pressures that we are seeing in terms of demand from the grid from cloud computing and quantum computing, streaming services, crypto and Bitcoin mining, reshoring of manufacturing, vehicle electrification, building electrification, semiconductor manufacturing.”
Chatterjee argued that navigating load growth to support AI data centers should be a bipartisan issue. He expressed hope that AI could help bridge the partisan divide.
“We have become mired in this politics of, if you’re for fossil fuels, you are of the political right. If you’re for clean energy and climate solutions, you’re the political left,” he said. “I think AI is going to be the thing that busts us out of it.”
Updating and upgrading the grid to accommodate data centers has grown more urgent in the face of drastically rising electricity demand projections.
Marsden Hanna, Google’s head of energy and dust policy, told Heatmap’s Jillian Goodman that the company is eyeing transmission technology to connect its own data centers to the grid faster.
“We looked at advanced transition technologies, high performance conductors,” said Hanna. “We see that really as just an incredibly rapid, no-brainer opportunity.”
Advanced transmission technologies, otherwise known as ATTs, could help expand the existing grid’s capacity, freeing up space for some of the load growth that economy-wide electrification and data centers would require. Building new transmission lines, however, requires permits — the central issue that panelists kept returning to throughout the event.
Devin Hartman, director of energy and environmental policy at the R Street Institute, told Jillian that investors are nervous that already-approved permits could be revoked — something the solar industry has struggled with under the Trump administration.
“Half the battle now is not just getting the permits on time and getting projects to break ground,” said Hartman. “It’s also permitting permanence.”
This event was made possible by the American Council on Renewable Energy’s Macro Grid Initiative.
On gas turbine backorders, Europe’s not-so-green deal, and Iranian cloud seeding
Current conditions: Up to 10 inches of rain in the Cascades threatens mudslides, particularly in areas where wildfires denuded the landscape of the trees whose roots once held soil in place • South Africa has issued extreme fire warnings for Northern Cape, Western Cape, and Eastern Cape • Still roiling from last week’s failed attempt at a military coup, Benin’s capital of Cotonou is in the midst of a streak of days with temperatures over 90 degrees Fahrenheit and no end in sight.

Exxon Mobil Corp. plans to cut planned spending on low-carbon projects by a third, joining much of the rest of its industry in refocusing on fossil fuels. The nation’s largest oil producer said it would increase its earnings and cash flow by $5 billion by 2030. The company projected earnings to grow by 13% each year without any increase in capital spending. But the upstream division, which includes exploration and production, is expected to bring in $14 billion in earnings growth compared to 2024. The key projects The Wall Street Journal listed in the Permian Basin, Guyana and at liquified natural gas sites would total $4 billion in earnings growth alone over the next five years. The announcement came a day before the Department of the Interior auctioned off $279 million of leases across 80 million acres of federal waters in the Gulf of Mexico.
Speaking of oil and water, early Wednesday U.S. armed forces seized an oil tanker off the coast of Venezuela in what The New York Times called “a dramatic escalation in President Trump’s pressure campaign against Nicolás Maduro.” When asked what would become of the vessel's oil, Trump said at the White House, “Well, we keep it, I guess.”
The Federal Reserve slashed its key benchmark interest rate for the third time this year. The 0.25 percentage point cut was meant to calibrate the borrowing costs to stay within a range between 3.5% and 3.75%. The 9-3 vote by the central bank’s board of governors amounted to what Wall Street calls a hawkish cut, a move to prop up a cooling labor market while signaling strong concerns about future downward adjustments that’s considered so rare CNBC previously questioned whether it could be real. But it’s good news for clean energy. As Heatmap’s Matthew Zeitlin wrote after the September rate cut, lower borrowing costs “may provide some relief to renewables developers and investors, who are especially sensitive to financing costs.” But it likely isn’t enough to wipe out the effects of Trump’s tariffs and tax credit phaseouts.
GE Vernova plans to increase its capacity to manufacture gas turbines by 20 gigawatts once assembly line expansions are completed in the middle of next year. But in a presentation to investors this week, the company said it’s already sold out of new gas turbines all the way through 2028, and has less than 10 gigawatts of equipment left to sell for 2029. It’s no wonder supersonic jet startups, as I wrote about in yesterday’s newsletter, are now eyeing a near-term windfall by getting into the gas turbine business.
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The European Union will free more than 80% of the companies from environmental reporting rules under a deal struck this week. The agreement between EU institutions marks what Politico Europe called a “major legislative victory” for European Commission President Ursula von der Leyen, who has sought to make the bloc more economically self-sufficient by cutting red tape for business in her second term in office. The rollback is also a win for Trump, whose administration heavily criticized the EU’s green rules. It’s also a victory for the U.S. president’s far-right allies in Europe. The deal fractured the coalition that got the German politician reelected to the EU’s top job, forcing her center-right faction to team up with the far right to win enough votes for secure victory.
Ravaged by drought, Iran is carrying out cloud-seeding operations in a bid to increase rainfall amid what the Financial Times clocked as “the worst water crisis in six decades.” On Tuesday, Abbas Aliabadi, the energy minister, said the country had begun a fresh round of injecting crystals into clouds using planes, drones, and ground-based launchers. The country has even started developing drones specifically tailored to cloud seeding.
The effort comes just weeks after the Islamic Republic announced that it “no longer has a choice” but to move its capital city as ongoing strain on water supplies and land causes Tehran to sink by nearly one foot per year. As I wrote in this newsletter, Iranian President Masoud Pezeshkian called the situation a “catastrophe” and “a dark future.”
The end of suburban kids whiffing diesel exhaust in the back of stuffy, rumbling old yellow school buses is nigh. The battery-powered bus startup Highland Electric Fleets just raised $150 million in an equity round from Aiga Capital Partners to deploy its fleets of buses and trucks across the U.S., Axios reported. In a press release, the company said its vehicles would hit the streets by next year.