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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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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.