One Year into the Trump Administration: DOE’s Diminished Organizational Capacity

This piece is the first in a series analyzing the current state of play at DOE, one year into the second Trump administration.

As the heart of energy innovation and infrastructure policy in the federal government, the Department of Energy (DOE) and its national labs play a crucial role in ensuring that the energy sector can meet the needs of the American people and the economy. DOE serves as a key funder of R&D for not just energy technologies, but also basic science and emerging technologies like AI and quantum computing. DOE’s 17 national labs are key supporters of that mission, conducting R&D in house and hosting facilities used by tens of thousands of researchers and innovators from the private sector and academia. 

Over the course of 2025, the second Trump administration has overseen a major loss in staff at DOE; the cancellation and slow-walking of awards across the agency, primarily from Bipartisan Infrastructure Law (BIL) and the Inflation Reduction Act (IRA) programs but also others; the rescission of billions of dollars from IRA programs through the One Big Beautiful Bill Act (OBBBA). Most recently, Congress passed FY26 appropriations for DOE, reducing funding levels and reallocating BIL funding.

These changes will not deliver the energy and innovation impacts that this administration, or any administration, wants. The departure of seasoned career staff takes with them significant technical expertise and institutional knowledge; while the loss of new talent recruited from the private sector diminishes DOE’s industry and project finance expertise. Reducing DOE’s organizational capacity like this undermines DOE’s fundamental ability to carry out its mission and implement programs crucial to U.S. energy security, innovation and abundance. 

Staff Loss

DOE has experienced deep and systematic cuts to its career staff. Early in the administration, the President issued an executive order calling for “large-scale reductions in force” (RIFs) across all executive branch agencies.1 As a part of that effort, the administration launched the Deferred Resignation Program (DRP), which was first offered on January 28th, 2025 and then again at the end of March 2025. This “fork in the road” gave career staff the option to resign or, if eligible, retire voluntarily in return for retaining their pay and benefits through September or December 2025, respectively. Expectations of upcoming RIFs incentivized many career staff to opt in to the program, rather than risk being laid off without the DRP benefits. Congressional leaders have questioned the legality of this program.

Nevertheless, the DRP was fully implemented by the Trump administration over the course of 2025, driving the majority of staff departures at DOE during the first six months of the administration. Staff data obtained by FAS indicate that 21% of DOE staff departed the agency between January 16th, right before the Trump administration began, and June 6th of 2025.2 Nineteen percent (19%) of DOE staff participated in the DRP, far outnumbering those who left the agency through other paths (e.g. layoffs, other resignations or retirements, etc.).3,4

The largest number of departing staff came from the offices under the former Under Secretary for Infrastructure (S3), which lost 52% of its staff due to the DRP and 55% of staff overall. At the most extreme end, the Office of Clean Energy Demonstrations (OCED), established by BIL under the Biden administration, lost 80% of its staff due to the DRP and 84% of its staff overall.  Other new offices established under the Biden administration, such as the Grid Deployment Office (GDO) and the Office of State and Community Energy Programs (SCEP), also suffered heavy losses.

In addition to the DRP, the S3 offices lost a number of staff to the Trump administration’s decision to end remote work, despite a Government Accountability Office (GAO) report finding that remote work policies improve talent attraction and retention, while reducing costs and enhancing productivity. Under the Biden administration, remote work policies enabled DOE to hire early- and mid-career staff who were unable or unwilling to move, especially those from the private sector who had valuable experience with commercial project development and finance.5 The new S3 offices established under the Biden administration benefitted the most from this, since they needed to rapidly hire qualified staff to design and implement programs for the large amounts of funding they received from BIL and IRA.

By attracting many industry leaders from the private sector, the S3 offices were able to build trust with major energy companies, leading to much higher participation from top companies in BIL and IRA programs compared to the American Recovery and Reinvestment Act (ARRA). Many of the staff responsible for this heightened private sector trust have now left the agency. 

Offices under the former Under Secretary for Science and Innovation (S4) also suffered greater than average loss of staff: 28% due to the DRP and 29% overall. Even the Office of Fossil Energy (FE) and the Office of Nuclear Energy (NE) lost nearly a third of their staff. According to former DOE staff, some people moved from S3 to S4 in anticipation of the transition to the Trump administration.6 In particular, many of them moved to NE, which is why the number of staff in NE on January 16th actually exceeded the number of total positions the office was supposed to have.

During the October government shutdown, the Trump administration directed agencies to move forward with another round of RIFs. DOE leadership informed staff in OCED, the Office of Energy Efficiency and Renewable Energy (EERE), the Office of State and Community Energy Programs (SCEP), and the Office of Minority Economic Impact that they may be fired, transferred, or reassigned due to their involvement in implementing programs under the Biden administration. The Data Foundation estimated that 187 staff were impacted by the RIF. However, the Continuing Resolution, passed on November 12th to end the shutdown, rescinded the RIF notices and guaranteed backpay to impacted federal workers.

The Impacts of Staff Loss

Staff changes and resignations at DOE will inevitably slow down implementation and threaten DOE’s ability to fulfill its mandate. DOE has struggled over the past few years to obligate funding from its budget due to its lengthy application and award negotiation process. Crucial to that process are the institutional knowledge and cohesion between technical and legal contracting teams that career staff build up over time. Every staff member lost creates a gap in the implementation process; the loss of so many staff members threatens to break down DOE’s operations entirely. Even if new staff are hired, that institutional knowledge and working dynamic can’t be recovered.

Contracting in particular is a major bottleneck for implementation. Career staff with decades of contracting experience have now left the agency and national labs. In particular, this loss will make it more difficult to implement demonstration and deployment programs like those funded by BIL and IRA, which require novel and very detailed contracting work.

Furthermore, the deep cuts to S3 call into question DOE’s ability to implement the remaining BIL and IRA funding for demonstration and deployment programs, not to mention DOE’s ability to oversee the billions of dollars worth of demonstration and deployment awards it has already made. Many of the new S3 staff were intentionally hired from the private sector for their industry knowledge and connections. These federal workers were subsequently the first to leave after the presidential transition. They took a risk in working for the federal government, and then were made to feel expendable by the new administration’s heavy-handed attempts to push people out. That experience will color any future attempts by DOE to rehire private sector talent.

The damage to implementation from staff losses will have direct impacts on peoples’ lives. For example, a 63 percent cut to SCEP staff means that whichever new office in charge of its programs post-reorganization (see next section) will not have enough capacity to run key energy affordability programs, like rebates to low-income households for cost-saving appliances or weatherization programs that keep peoples’ homes warm and reduce utility bills. Gutting of OCED and GDO will mean that major projects have a smaller chance of getting built, denying communities the new jobs and energy infrastructure they were promised. 

In addition to implementation capacity, DOE is losing technical expertise that is crucial to informing its research and innovation agenda. DOE’s S4 offices have historically housed the top experts on technology areas from battery chemistry to solar panel design to advanced turbines. Many of these industry-leading experts have now left the agency, which will hamstring DOE’s ability to support private sector innovation in technologies that are critical to building an affordable and reliable energy sector and maintaining U.S. leadership globally.  

The loss of crucial staff can also be expensive. For example, DOE has traditionally relied on internal counsel for the majority of its programmatic work. Now, however, roughly 50% of the field lawyers at DOE who run contracting and oversee the national labs are gone. In September 2025, DOE issued a solicitation for up to $50 million worth of external counsel in support of the agency’s day-to-day needs.

Lastly, the management of national labs (NLs) from DOE headquarters is becoming significantly harder. As seasoned program managers leave, DOE is losing the deep institutional knowledge necessary to manage the Government-Managed Laboratory Complex and to execute core functions, especially the allocation and oversight of funds that Congress intends for the labs. The flow of funds requires experienced staff who understand authorizing statutes, lab agreements, and budget execution mechanics; losing them creates the risk of both bottlenecks and misalignment.

Reorganization

In November 2025, DOE leaders announced a sweeping reorganization that eliminated, consolidated, and rebranded major program offices while creating several new ones, formalizing a significant shift in the Department’s priorities (see Figures 1 and 2).7 Several of DOE’s most recognizable clean energy innovation and deployment offices — including EERE, OCED, SCEP, the Grid Deployment Office (GDO), and the Office of Manufacturing and Energy Supply Chains (MESC) — were dissolved as standalone entities. Their programs were redistributed across a new set of divisions organized around broad technology themes rather than the previous approach of differentiating between developmental stages (i.e. R&D vs. demonstration and deployment).

Figure 1. DOE organization chart prior to November 20th, 2025 (Source: DOE).

Figure 2. DOE’s new organizational structure after November 20th, 2025 (Source: DOE)

As part of this shift, DOE created or elevated new offices focused on emerging priorities. A new Office of Critical Minerals and Energy Innovation now centralizes critical minerals programs, which were previously spread across EERE, FE, and MESC, while also seeming to be a catch-all office for remaining EERE, OCED, MESC, and SCEP programs. A Hydrocarbons and Geothermal Office merges FE with the Geothermal Technologies Office. The reorganization also expanded the department’s work on emerging technologies by splintering off programs that used to be contained within the Office of Science: pairing AI and quantum programs into a new office and creating a dedicated fusion office with a more prominent role than before.

These changes significantly alter DOE’s internal map. Programs that once lived together are now split apart, while other functions have been consolidated under new leadership structures. The result is a department whose mission areas are organized very differently than they were even a few months ago, leaving open questions about how core clean energy, deployment, and innovation functions will be staffed and managed going forward.

Though previous administrations, including the Biden administration, have conducted reorganizations of DOE in the past, this reorganization was implemented with significantly less transparency. As of late December, the brief initial announcement and new organization chart are the only information the public has received on the reorganization. DOE’s website is currently inaccessible. Career staff have reported that they still lack clarity as to how their chains-of-command will be affected and whether or not the programs they work on will continue or change. 

These structural changes are unfolding at the same time DOE is experiencing substantial workforce losses, which heightens uncertainty about staff capacity. It remains unclear how remaining staff are being reassigned within the new organizational chart. With offices being renamed or re-scoped — and in many cases merged, split, or relocated — advocacy and stakeholder communities cannot easily determine whether DOE retains the necessary expertise or institutional knowledge to carry out ongoing work. 

Basic information like program areas and suboffices within each office, program leadership, and staffing data is now outdated, making it difficult to track where core functions have moved. Managing this transition is essential for retaining remaining staff and preventing further loss of expertise. DOE leaders must clearly communicate roles, reporting lines, and program continuity to restore internal morale and ensure the agency can continue driving energy innovation and promoting energy abundance amid an unprecedented U.S. energy affordability crisis.

This uncertainty underscores the need for greater transparency from DOE. Providing updated information on each new office’s missions and internal structure, staffing data, and explanations of how programs map onto the new structure would help rebuild trust and give stakeholders a clearer understanding of the agency’s operational capacity. Without this information, questions about DOE’s ability to execute its mission will persist at precisely the time when federal leadership on clean energy, innovation, and energy affordability is most needed.

Acknowledgements

The authors would like to thank Megan Husted and Arjun Krishnaswami for their pivotal roles in shaping the vision for this project, planning and executing the convenings that informed this report, and providing insightful feedback throughout the entire process. The authors would also like to thank Kelly Fleming for her leadership of the project team while she was at FAS. Additional gratitude goes to Colin Cunliff, Keith Boyea, Kyle Winslow, and all the other individuals and organizations who helped inform this report through participating in workshops and interviews and reviewing an earlier draft.

Appendix: DOE Staff Data

Barriers to Building: A Framework for the Next Era of Electricity Policy

The American power grid in 2025 faces a set of challenges unlike any in recent memory. The United States is deploying clean energy far too slowly to meet load growth, avoid spikes in electricity prices, and combat climate change. To get within striking distance of the Paris climate goals and plan for the lowest electricity costs, we must build 70 to 125 gigawatts of clean energy per year, much higher than the record 50 gigawatts built in 2024. 

Grid upgrades, too, are proceeding far too slowly. To meet growing electricity demand and integrate new clean power at lowest cost, transmission capacity must more than double within regions and increase more than four-fold between regions by 2035. But large transmission projects frequently take 7 to 15 years from initial planning to in-service operation and only 322 miles of new high-voltage transmission lines were completed in 2024—the third slowest year of new construction in the last 15 years.

Even before the One Big Beautiful Bill Act (OBBBA) gutted federal clean energy incentives, non-cost challenges like uncertain and lengthy interconnection and siting processes, local restrictions on development, and supply chain bottlenecks led to lower levels of clean energy deployment than projected and slowed down grid upgrades. Now, clean energy and transmission face additional cost and financing barriers from Congressional rollbacks and permitting restrictions from the Trump Administration.

Past federal and state clean energy policies, including the Inflation Reduction Act (IRA) and the Bipartisan Infrastructure Law (BIL) as well as state renewable portfolio standards, have leaned heavily on financial incentives to drive deployment and incentivize grid upgrades and expansion. These incentives successfully attracted massive investment in clean energy projects, but they largely did not grapple with non-cost challenges—like siting restrictions—to building projects.

Political challenges have made it difficult to pass, implement, and defend clean energy policies. A mismatch between public needs, government programs, and industry incentives has led to unsatisfactory outcomes and degraded public trust in the government. 

Now, policymakers, industry, and the advocacy community are paying more attention to non-financial issues that can impede deployment, like siting and permitting. The abundance movement, for example, has identified two causes of America’s building problem: ineffective government programs and burdensome permitting processes. This diagnosis is incomplete. Getting to a world where we can build things quickly and make government work will require us to identify the full suite of problems, not just these convenient two. 

To maximize clean energy deployment, we must address the project development barriers that slow down investment and construction. And to build more durable and effective energy policies, we must interrogate and address the political barriers that have held us back from smart policymaking and implementation that can withstand political change. Overcoming these challenges is necessary to address the climate crisis, rein in rising utility bills, and ensure that government can deliver on its energy promises to the public it serves. 

In early 2025, the Federation of American Scientists (FAS) set out to identify and categorize these barriers through research and interviews with experts and practitioners. Following this research, at the 2025 Climate Week NYC, FAS convened a group of researchers, advocates, industry leaders, and policymakers to solicit feedback on this framework. 

The outcome of that convening allowed us to ground-truth the following report—which we intend to use as a rubric for state-level electricity policies and efforts to rethink federal energy policy. We should ask: to what extent do new policies under consideration reduce the major barriers to building clean energy and transmission while addressing the shortcomings that have made past policy less durable? 

A future paper will detail the priority solutions that make progress on each of the project development barriers while improving our toolkit to overcome the political barriers that impede durable policy.

Contents

Jump to chapter
Interconnection
Siting & Permitting
Misaligned Incentives
Financing
Grid Planning
Supply Chains
Jump to chapter
Energy Affordability
Incumbent Interests
Delayed Impact
Jump to chapter

Project Development Barriers: Making it Harder to Build

Clean energy technologies are mature and cost competitive, if not least cost, across the country. Yet we are not building clean energy as fast as necessary, and in many places we are building new gas plants instead, raising costs for customers and intensifying the climate crisis. This trend is the result of several barriers that make it more difficult to build clean energy. 

The Barrier

The interconnection process is one of the most significant constraints on clean energy deployment in the United States. At the end of 2023, nearly 2,600 gigawatts (GW) of generation and storage were queued, which is more than double the U.S. installed capacity (~1,280 GW). Today’s grid was built around a small number of large, centralized fossil fuel plants; the grid must now accommodate thousands of diverse, geographically distributed projects. Processes that were designed for a handful of large plants per year are now evaluating orders of magnitude more proposals, each with more complex grid interactions. These processes are not able to adequately handle the current grid, nor have they kept pace with development in grid planning and analysis tools. The result is a massive backlog of projects waiting to interconnect to the grid and a review system that is fundamentally misaligned with the scale and pace of the energy transition. 

Developer experience confirms that interconnection challenges rank among the most decisive barriers to clean energy buildout. In the 2024 Lawrence Berkeley National Laboratory (LBNL) developer survey, respondents ranked interconnection delays and network upgrade costs higher than permitting, supply chain constraints, or workforce shortages as reasons for project cancellations or deferrals. Many projects face cost uncertainty on the order of tens to hundreds of millions of dollars as interconnection studies shift responsibility for broad system upgrades onto single developers. Interconnection costs are rising, and it is difficult for developers to predict what their interconnection bill will be at the end of the process. This unpredictability increases financing risk, reduces developer participation, and leads to large-scale attrition. 

Outdated processes for evaluating and approving new projects have led to enormous project delays, averaging 4-5 years from request to commercial operation. This delay has raised prices and led some grid operators to keep old, expensive coal plants online in lieu of new capacity. Both of these trends benefit incumbent transmission and generation companies, who have significant decision-making power over the entities that control interconnection, making it difficult to update the processes. Clean energy projects also face higher interconnection costs than gas projects because they are more likely to need transmission upgrades to connect to the grid, which increases the chances of project cancellation.

These barriers have direct system-wide consequences. Only about 15 to 20 percent of projects that enter the queue ultimately reach commercial operation, meaning most of the clean energy capacity counted as “planned” will not materialize unless interconnection processes are reformed.  Long queue timelines and uncertainty also make it more difficult to finance projects. The result is slower emissions reductions, delayed IRA-driven investment and job creation, and higher costs for consumers as operators extend the life of aging coal and gas resources to meet growing load. 

The Past Playbook

Federal interconnection policy has largely gone through the Federal Energy Regulatory Commission (FERC). In 2023, FERC issued Order 2023, which made significant changes intended to speed up interconnection and increase certainty for new projects. The rule (1) replaced outdated serial studies, in which operators study projects one by one as their applications come in, with cluster studies, in which operators study projects in batches, (2) required grid operators to speed up study timelines and imposed penalties for failing to meet deadlines, and (3) directed grid operators to update rules to reflect technological advancements, like grid-enhancing technologies and hybrid solar-plus-storage projects. Some grid operators have gone further than Order 2023 to improve interconnection processes, and some states have pushed grid operators for more ambitious reform. In addition to FERC rules, the federal government has also provided limited resources to grid operators to improve interconnection processes. 

To date, federal efforts have largely fallen short of what’s necessary to reform interconnection processes to enable adequate buildout of clean energy, and in most places states have limited tools. For one, FERC rules rely on effective implementation from grid operators, which has been a mixed bag. Order 2023 also strayed from making more fundamental changes to the interconnection process, like fixed entry fees that provide certainty to developers or proactive modeling and transparency of information to allow projects to connect quickly in places with transmission headroom. It fully does not address the fundamental problem that rising, variable interconnection costs are killing projects. The federal government has limited resources to support grid operators through, for example, funding for increased staffing or new technology to automate studies. 

Where Do We Go From Here?

The next era of energy policy must radically transform the way we connect projects to the grid to enable faster, greater deployment of clean energy, including through an expanded role for federal and state governments. Policy must shorten study timelines using automation and other new technology, enable smarter planning with proactive modeling and greater transparency for developers, increase upfront cost certainty, and reduce the amount that projects end up paying for interconnection. And in addition, the next playbook must address governance and decision-making structures that favor incumbents who benefit from a congested grid. 

The Barrier

Siting and permitting processes have become two of the most visible friction points in the clean energy buildout. While federal policy receives the most attention, most clean energy siting and permitting decisions are made at the state and local level, where zoning boards, planning commissions, county supervisors, and community members have significant influence over whether a project proceeds. In many states, local jurisdictions have adopted new ordinances that restrict or outright ban wind, solar, and transmission development. According to recent analyses, roughly one-fifth of U.S. counties now have formal restrictions on clean energy, and many more are considering them. Even in states with strong climate and clean energy targets, municipal-level land use rules can effectively halt projects that align with statewide goals.

These local barriers are often rooted in concerns about landscape change, perceived impacts on property values, agricultural land use, wildlife, or community identity. But they are also a reflection of who benefits and who bears the immediate impacts of clean energy development. Benefits like lower system-wide electricity prices, cleaner air, and national decarbonization progress tend to be distributed widely, while the visual and land-use impacts are concentrated locally. Developers may not readily have the resources to meet community needs to come to agreement on projects, and federal and state governments often do not have adequate resources to support community benefits. Misinformation and disinformation—spread by incumbent interests who stand to lose money with greater clean energy or transmission deployment—also seed opposition in communities.

Permitting requirements add an additional layer of delay and uncertainty. Most clean energy projects, particularly solar and storage projects—which make up the bulk of new planned capacity—rarely trigger major federal environmental statutes and primarily deal with state-level permitting. Developers must navigate state statutes governing clean water, conservation, and environmental impacts, which serve important purposes but are often still implemented through outdated processes (e.g., many states still require paper permits; in Arizona, digitization reduced timelines for one permit process by 91 percent) administered by understaffed agencies. Projects such as transmission lines, offshore wind facilities, pumped storage hydropower, nuclear plants, geothermal projects, and any project on federal land or receiving federal grants generally must also navigate federal permitting processes. When new projects trigger federal review, they must comply with the National Environmental Policy Act (NEPA) and sometimes other federal permitting statutes, like the Marine Mammal Protection Act, the National Historic Preservation Act, and the Endangered Species Act. These reviews can take multiple years, particularly when agencies have limited staffing or when studies must coordinate across several state and federal entities and jurisdictions. 

Delays from local siting and state and federal permitting translate directly into cost escalation and canceled projects. Developers report that siting challenges can add years to development schedules and millions of dollars in carrying costs before a shovel ever hits the ground. For technologies like wind and solar, where the business model depends on tight capital cost margins, extended pre-construction periods can be the difference between a viable project and one that never breaks ground. Transmission development is even more exposed: large lines can spend a decade or more navigating route identification, landowner negotiations, environmental review, and litigation. Without new transmission capacity, interconnection backlogs grow, power costs increase, and states are forced to rely on older fossil resources simply because they are already in place.

Yet, the challenge isn’t so simple. It is not simply “local opposition” or “slow permitting.” It is that the scale of clean energy land use today is fundamentally different from the past century of centralized fossil energy development. We are building more projects, in more places, at a pace that communities have not previously experienced.

The Past Playbook

Siting and permitting reforms have increasingly been part of the federal and state policy agenda. Reforms have largely focused on process changes and improving coordination across agencies, with some focus on building capacity for analysis and review in some federal agencies and states. In general, these reforms are insufficient and not widespread enough to match the urgency and scale of the U.S. energy transition. 

The federal government has pursued a range of reforms over the past few years to improve the permitting process for projects that involve federal land, funding, or regulatory triggers. Key cross-agency initiatives include the Coordinated Interagency Transmission Authorizations and Permits Program, which made the Department of Energy (DOE) the lead agency for coordinating environmental review and permitting for transmission lines, and FAST-41, which aims to align multiple agency reviews and reduce duplicative permitting processes. Agencies have taken additional steps to improve individual permitting processes. For example, the Bureau of Land Management (BLM) designated solar and wind energy zones on public lands to reduce conflicts and expedite approvals, and the Bureau of Ocean Energy Management modernized offshore wind leasing and programmatic NEPA reviews (although the Trump Administration overhauled these reforms by halting all offshore wind leasing). 

Several states have attempted to reduce delays and uncertainty by centralizing siting authority and standardizing permitting rules. For example, New York’s Office of Renewable Siting and Massachusetts’ Energy Facilities Siting Board can override local opposition for large projects, while other states provide model ordinances to guide counties on setbacks, noise, and environmental protection. DOE has also helped states: the agency provided a small amount of technical assistance to states to help local governments with planning, siting, and permitting decisions and a larger tranche of funding for transmission projects to provide benefits to local communities to help with siting and community buy-in. In some places, these reforms have improved consistency across counties and reduced the influence of NIMBY-driven delays.

This playbook, while directionally correct, has fallen short of what is necessary. Local restrictions on clean energy continue to proliferate, siting power plants and large transmission lines remains a major challenge, and many state and federal permitting processes still pose significant barriers. Existing efforts have several gaps: (1) many states have not addressed local restrictions on development, (2) process improvements, especially at the state level, have happened in a piecemeal fashion and have not extended to the full suite of state-level permitting requirements, (3) existing efforts often do not cover the full set of solutions (e.g., broken permitting for customer-owned solar is a huge impediment that keeps U.S. solar costs much higher than other countries), (4) governments and developers have insufficient tools to ensure that local communities get what they want out of projects, and (5) efforts to increase state and federal government capacity (i.e., hiring and training the right staff and increasing analytical capabilities) have fallen far short of what is needed to have a fast, effective, and responsible permitting and siting process. 

Where Do We Go From Here?

The next era of energy policy must wrestle with the fundamental siting and permitting challenges and introduce new frameworks for planning, permitting, and building projects. That means upfront planning to make major decisions about tradeoffs between clean energy, water, conservation, and other goals, expanding the tools and resources necessary to ensure that local communities benefit from projects, dramatically improving government capacity to do siting and permitting well, and taking a holistic approach across federal, state, and local governments to prevent new bottlenecks from emerging. 

The Barrier

Most clean energy and grid upgrade projects are financed by private capital and procured or built by companies, either utilities or independent power producers. The profit motives of those financiers and companies determines the solutions they invest in, within the bounds of policy requirements. Across states and regions, outdated utility regulations and market designs have created flawed incentives that have limited investment in some necessary solutions and resulted in overinvestment in others. Utilities have wielded significant political power, built by lobbying with ratepayer money, to maintain today’s incentive structure. 

For example, in vertically integrated states, utilities are incentivized to prioritize capital expenditures that earn them the highest returns, within the bounds of commission approval. This incentive structure deprioritizes solutions like increasing imports of clean energy through new transmission and leveraging distributed resources like customer-owned solar. 

Most commissions are often not well-equipped or willing to ensure that utilities pursue the full toolkit. In most states, utility planning is driven by the utilities, who conduct detailed analysis and provide proposals on planning and ratemaking to their commissions. Commissions have more limited capacity to conduct analysis and interrogate utility proposals. 

Organized markets also have flawed incentive structures. For example, incentive structures in organized markets were generally designed around an electricity grid made up of a small number of large power plants. As a result, market rules and incentive structures provide limited to no support for distributed energy resources, which makes it harder to finance these projects. Governance structures exacerbate this issue. In some organized markets, incumbent generators have significant decision-making power in important determinants of clean energy deployment, including interconnection and transmission planning. Some organized markets have maintained rules that make it difficult to connect new power plants.

Misaligned incentives reduce the effectiveness of other policy solutions. For example, tax credits to reduce the cost of clean energy projects are most effective if utility companies have a profit incentive to build those projects instead of other generation types. The effectiveness of bulk transmission grant programs is limited by the willingness of utility companies to collaborate on projects. 

The Past Playbook

Federal policy has largely ignored utility incentive structures and instead attempted to influence private-sector behavior by working within existing incentive structures (e.g., by making it easier for utility companies to use tax credits to build clean energy). Federal agencies have attempted to overcome misaligned incentives through regulations (e.g., pollution standards on power plants that require generation owners to make changes). Some efforts to change incentives structures (e.g., the Clean Electricity Payment Program included in the 2021 Build Back Better Bill) have gained momentum but failed to pass. 

Many states have also used tools that operate within existing incentive structures, like renewable portfolio standards that require utilities to procure an increasing share of their electricity from clean sources. States have attempted to change incentive structures to varying extents. More than 15 states have adopted some form of performance-based ratemaking to align utility incentives with desired outcomes. However, these efforts vary in how comprehensively they have changed the dominant incentives for companies. 

Where Do We Go From Here?

The next era of energy policy must reform incentives to realign private sector interests with public benefit, including affordable bills, reliability, and decarbonization. To achieve the scale, speed, and depth of transformation needed to address the challenges facing our grid, policy must address misaligned incentives for distribution utilities, generation owners, and integrated utilities in different regulatory contexts. That requires a greater focus on realigning incentive structures at the state and regional level (through organized market reform) as well as creative federal tools to directly change incentives or help states and organized markets to do so. Increasing regulator scrutiny of utilities and bolstering capacity at commissions must also play a larger role moving forward to ensure that utilities are focusing on the best solutions, not just what is most profitable. Greater use of publicly owned or publicly financed projects can also ensure investment in solutions that are underutilized by private companies. 

The Barrier

The federal government has created new financial barriers for clean energy projects.  OBBBA’s changes to tax incentives and increased regulatory and permitting uncertainty make clean energy projects more expensive and harder to finance. Macroeconomic changes like persistent inflation and other uncertainty, including on tariffs and interest rates, have also affected investment. 

While the clean energy industry has continued to move forward (2025 investment in solar, storage, and wind is similar to 2024 levels, and the industry is benefiting from demand growth, as many projects are able to find offtakers like tech companies willing to pay higher prices), the full effects of federal policy changes are likely delayed, as the tax credits have not fully expired. Moving forward, financing may become a larger barrier. In addition, rising utility bills have opened a conversation about the cost of private finance for grid projects and whether there are alternative approaches that come with lower costs for customers. 

Financing less mature clean energy technologies, like advanced nuclear, enhanced geothermal, and aggregated distributed generation (i.e., virtual power plants), remains a major issue. 

The Past Playbook

Financial support has played a dominant role in the federal energy policy playbook. Tax incentives, which were dramatically expanded by the IRA and pared down by OBBBA, have been central to energy policy for decades. Grant and loan programs, also dramatically expanded by the IRA, have also been a core driver of clean energy deployment, grid upgrades, and large-scale demonstrations and commercialization of advanced energy technologies. States have also used tax incentives, grant programs, and green banks to finance and incentivize clean energy and grid projects. This model has largely been successful at deploying mature technologies like wind, solar, and storage, but it has fallen short when it comes to commercializing some newer clean energy technologies. Gaps also remain in financial support for projects that struggle to get private capital.  

Where Do We Go From Here?

Financing and financial support should continue to be a major pillar of clean energy policy. The next era must incorporate a broader, more diverse set of financing tools in the capital stack, including state-led public financing for more types of projects and state efforts to create demand certainty for clean energy by leveraging procurement and working with corporate buyers. 

The Barrier

Today, the U.S. bulk transmission system faces significant constraints that limit where new clean energy projects can be built and threaten reliability. Congestion already causes curtailment of low-cost low-carbon power, higher consumer electricity prices, and dampened investment in clean energy. Many regions with abundant clean energy resources simply do not have enough high-voltage transmission capacity to deliver that power to population centers. As a result, developers are increasingly unable to move generation projects forward even when siting, permitting, financing, and interconnection queue positions are in place. 

These challenges stem in large part from fragmented and inadequate planning processes. Coordinated planning is essential to ensure that transmission is expanded in the right places and that new clean energy investments flow to areas with sufficient transmission capacity. Despite the need for coordination, the United States conducts virtually no interregional transmission planning, and regional planning has been lacking in many regions. The result is piecemeal grid planning, as transmission providers and developers focus on smaller lines which meet near-term needs and are profitable within their own footprint. Planning for these smaller lines is easier as fewer parties are involved. Where we have successfully built larger regional lines, they are the result of transmission providers conducting robust planning processes. And because no unified authority or planning framework exists to shepherd large, high-impact projects across regions, the U.S. has built essentially zero major interregional transmission lines in recent history.

Lack of coordination between transmission and generation planning also creates inefficiencies and prevents smart development. In deregulated markets (and some vertically integrated states), transmission and generation planning processes occur largely in isolation without systematic processes to align long-term clean energy expansion with major grid upgrades. 

Together, these gaps make expanding the transmission system an inefficient process at best, and an unworkable process at worst, at precisely the moment when the need for additional capacity is growing most rapidly. 

The Past Playbook

Policymakers have made progress in addressing transmission planning bottlenecks, but these reforms remain far short of what’s needed. FERC Order 1920 is the most significant recent step: it requires long-term, forward-looking, multi-value regional planning. It was designed to improve transparency in the planning stages and help regions identify beneficial projects earlier. Yet the rule stops at regional borders and thereby doesn’t meaningfully advance interregional planning. 

A patchwork of state and regional efforts has emerged alongside federal reforms. New Mexico created a new entity called the Renewable Energy Transmission Authority to map and finance new lines. Similarly, Colorado created the Colorado Electric Transmission Authority to plan and develop transmission lines to meet power needs, unlock clean energy, and lower costs. California conducts long-term transmission planning intended to incorporate transmission needs to accommodate clean energy deployment required to meet the state’s climate goals. Federal tools like National Interest Electric Transmission Corridors (NIETCs) were designed to accelerate siting of critically important lines, and part of DOE’s Grid Resilience and Innovation Partnerships (GRIP) funding has helped bring utilities, states, and developers together to plan large projects. On the interregional front, DOE has conducted analysis to demonstrate where new capacity would create the greatest benefits and inform planning.

These efforts certainly make progress and will likely result in expansion of local and regional transmission capacity. The magnitude of progress will depend in large part on how transmission providers implement Order 1920—for most regions, compliance filings will be submitted this month (December 2025) or by June 2026. 

However, this playbook had significant gaps and pitfalls. Lack of interregional planning is the most glaring gap, but other tools had limitations, too. GRIP had limited funding and power to solve cost allocation disputes. NIETCs did not translate into built infrastructure. In many places transmission planning will not take into account the long-term clean energy expansion required for deep decarbonization, leaving high-value opportunities—like pairing wind resources with long-distance transmission—unrealized. The result is a set of reforms that move in the right direction but still fall short. 

Where Do We Go From Here?

The next era of energy policy must tackle interregional planning, while following through on Order 1920 with effective implementation. We must require transmission providers to plan decisively for futures with significant load growth and levels of clean energy deployment necessary for deep decarbonization. Future federal policy must also expand the government’s tools to bring parties to the table for smart, effective planning. In parallel, states should continue to use creative policies, like Colorado and New Mexico’s transmission authorities, to strategically plan new transmission lines to maximize benefits. And the next era must also include national, forward-looking land-use planning for clean energy deployment, in sync with transmission planning. 

The Barrier

Grid components, such as electrical steel and transformers, are necessary to increase grid capacity to support additional generation and load. However, grid component supply chains are still suffering from disruptions caused by the COVID-19 pandemic and lack of domestic manufacturing capacity. The rising demand for grid components and battery technology have further stressed supply chains, drawing out lead times and increasing prices. For example, across transmission and distribution equipment, the lead time for components averaged 38 weeks in 2023, nearly double from the year prior, with costs escalating nearly 30 percent year-over-year. Bottlenecks in the supply chains from upstream suppliers to manufacturers among these components risk power system stability, the ability to deploy clean energy, and the ability to build new industrial production and technology facilities at scale. 

The Past Playbook

Federal policy has increasingly focused on building secure supply chains for clean energy technologies. The IRA included tax credits, grant programs, and loan authority to build out domestic supply chains for clean energy and storage technologies. The federal government has also used demand-side pressure to bolster supply chains (e.g., through a bonus tax incentive for clean energy projects that use domestic content and Build America Buy America requirements on federal grant programs). These policies led to major investment in domestic supply chains. 

This playbook was quite successful at building out domestic supply chains for some industries, but it had major gaps. For example, the IRA and BIL included no dedicated support for grid components, and the minimal support that was embedded in larger programs was insufficient. Federal demand-side programs were structured as incentives for downstream industries to use domestic content, but this design had too much uncertainty to sufficiently derisk upstream domestic supply chains.  

Today’s programs have also struggled to respond quickly when conditions change. For example, the federal government had limited tools with which to respond when the utility industry faced a debilitating shortage of large power transformers or when it became clear that incentives were not large enough to drive domestic investment for some clean energy components. 

Where Do We Go From Here?

The next era of energy policy must build on the same financial tools to support secure supply chains that enable clean energy deployment and grid upgrades. The playbook must include policies that more directly create demand for domestic components to provide certainty for manufacturers and derisk new investments. Future policy must also provide more flexible and dynamic tools to rapidly address supply chain shortages as they arise. 

Political Barriers: Making it Harder to Pass, Implement, and Defend Policy

Clean energy advocates have focused on economic competitiveness, climate, and public health benefits as the winning messages to support and defend policies. The BIL and IRA came out of this model, and the architects of those policies hoped that the industry that benefitted from these policies would step up to defend them. While this strategy has enabled passage of significant new policies, it has failed to withstand changing political dynamics. The swift rollback of major parts of BIL and IRA is the prime example. Our ability to successfully implement and defend clean energy policies—and make further progress—has been hampered by several key political barriers. The next era of clean energy policy must address these barriers to be successful. 

The Barrier

Rapidly rising utility bills have become an urgent cost-of-living issue. People pay 13 percent more for electricity in 2025 than they did in 2022, and nearly 40 percent of households sometimes have to choose between paying for food and medicine or keeping the lights on. 

Rising electricity prices are a political barrier to some clean energy policies. For example, states have struggled to follow through on procurement of advanced clean energy technologies like nuclear and offshore wind as prices have risen. New York recently cancelled a planned transmission line, using affordability as a justification. Clean energy opponents are using prices to oppose climate policies, even though deployment of wind and solar has generally reduced rates. Concerns about electricity affordability make it difficult to justify major grid infrastructure investments under current regulatory and ratemaking structures, as additional spending to update the grid will lead to near-term bill increases. High prices also make it difficult to replace direct fossil fuel use in vehicles, buildings, and factories with electricity. 

The Past Playbook

Federal energy policy has largely dealt with affordability in two ways. 

First, the federal government has provided important but limited direct assistance to struggling households through the Low-Income Home Energy Assistance Program, which helps households pay for energy, and the Weatherization Assistance Program, which funds energy- and cost-saving home improvements. However, these programs are significantly underfunded and oversubscribed—many households that need support do not get it.

Second, federal financial support results in long-term savings. IRA incentives for low-cost clean energy were projected to reduce generation costs, which in the long term translates to lower prices. Tax incentives and grant programs for distributed energy resources and home energy improvements save energy and costs for customers that make upgrades. However, this approach falls short in two ways: (1) it does not address the root causes of rising electricity bills, which means bills will continue to rise, and (2) the benefits are long term and do not show up on peoples’ bills on politically relevant timelines. 

Where Do We Go From Here?

The next era of energy policy must provide sufficient and swift relief for customers that are on the edge of catastrophe due to rising costs, make it easier to deploy cheap, clean energy to reduce generation costs, and target the root causes of high and rising bills to unlock a sustainable utility ratemaking regime that allows for major new investments in the grid without harming regular people. The new playbook must also include more effective cross-sector tools to cut total system and household costs, including by transferring planned spending on gas infrastructure to home electrification and grid upgrades where possible. 

The Barrier

Many solutions, including adding new generation in organized markets, relying more on regional and interregional transmission, and deploying distributed and demand-side solutions, threaten the profits of incumbent interests under current market and regulatory structures. For example, utilities make money through a return on qualified capital investments in things like power plants and distribution infrastructure. Increasing bulk transmission capacity to connect the Southeast with other regions would lead to more imports of lower cost clean energy, which would reduce the utilities’ reliance on local generation. That makes it harder for the utilities to justify capital expenditures in new power plants, which is how the utilities make a profit, so new transmission poses a threat to the business model. As a result, Southeastern utilities are opposed to policies that would expand bulk transmission to better connect different regions, even though these policies would reduce costs and increase reliability. These dynamics make it politically difficult to pursue policies that expand transmission capacity.

The Past Playbook

Federal clean energy policy has largely avoided changing incumbent incentive structures or decision-making processes at the state and regional level. Instead, policymakers have used financial incentives to bring incumbents to the table and increase their investment in clean energy and grid upgrades. As a result, the misaligned incentives described above, combined with decision-making structures that reward incumbents over innovation, make it difficult to fully address the barriers to clean energy deployment and grid upgrades at the necessary scale. 

Where Do We Go From Here?

The next era of clean energy policy must address governance issues through reform of regional grid operators and public utility commissions. Strengthening the role of regulators is critical to reining in incumbent interests where they do not align with public benefit. It must also realign industry incentives (e.g., through performance-based ratemaking) where possible with affordability and decarbonization goals. 

The Barrier

Another major political barrier is the lengthy time it takes to get from enactment and implementation to tangible benefits for people. Transforming major sectors of the economy is a time-intensive, multi-stage project, and climate advocates have accordingly focused on long-term goals, such as 100 percent clean electricity by 2035 or net-zero emissions by 2050. The IRA and BIL were made up primarily of multi-year (even some decadal) programs to drive major changes in the economy. As a result, the largest benefits were projected to come in the late 2020s and early 2030s, far outside the window of political memory. That mismatch makes it difficult for the public to understand the point of policies and in turn makes those policies hard to defend. 

Where policies do have near-term benefits, those benefits have often been delayed by the implementation process. Successfully shifting the private sector requires precise policy and new programs, which take time to implement. Implementation of new programs can also run up against the government typically works, and that friction causes delays. Implementation delays make it difficult to connect the dots between policy and tangible improvements to peoples’ lives.

The Past Playbook

Policymakers have used three dominant approaches to overcoming this barrier. First, they tout near-term signs of economic change. For example, the Biden administration consistently cited private-sector investment in clean energy as a key metric to convince the public that the IRA and BIL were driving benefits for people. Second, they rely on the quickest economic changes to demonstrate impact. For example, the IRA and BIL drove a near-term increase in construction jobs. Real and announced job creation was the dominant message to support and defend these policies. Third, they cite projected benefits. For example, the Biden administration frequently cited the 1.5 million jobs and the $27 to $33 billion in energy cost savings that the IRA was projected to drive. 

Attention to long-term impact is important for addressing long-term problems like climate change and load growth. However, politics runs on instant gratification. As of late 2024, only 39 percent of Americans had heard of the IRA. And federal energy policy failed to make a near-term dent in the issue that was most visible for people: utility bills. 

Where Do We Go From Here?

The next era of clean energy policy must tangibly and visibly benefit people in the short term. The playbook must include a better balance of policies geared toward long-term transformation of the economy and policies focused on pressing issues for regular people. That means including programs that are designed for quick implementation and real-world change. 

Conclusion: What’s Next?

The power sector sits at an inflection point. The challenges facing the grid are immediate, interconnected, and solvable but only if we confront the real sources of delay and dysfunction. Accelerating clean energy deployment requires moving beyond our old playbook—dominated by financial incentives and regulations that see-saw based on the political winds—toward a new approach that addresses both project development barriers that slow investment and construction and political barriers that impede durable policymaking. Building durable, effective energy policy demands a clear-eyed assessment of the barriers that have undermined smart policymaking and implementation.

In a forthcoming publication, we will move from diagnosis to action, detailing policy solutions that can unlock faster, more reliable project development while expanding the policy toolkit needed to overcome the political barriers that have prevented durable reform. Together, these solutions aim to strengthen grid reliability, rein in rising utility bills, and put the United States back on a credible path to decarbonization. These stakes could not be higher, and the opportunity to build a more affordable, resilient, and clean energy system has never been more urgent. 

New DOE Re-Organization Raises Uncertainty for American Science, Energy Innovation, and Affordability

Last week, the U.S. Department of Energy (DOE) unveiled a sweeping reorganization aimed at “strengthening efficiency and unleashing American energy dominance.” While these ambitions sound bold — accelerating technology, expanding energy production, and cutting bureaucracy — the structural changes inspire more questions than confidence. The new alignment signals a clear shift in priorities: offices dedicated to clean energy and energy efficiency have been renamed, consolidated, or eliminated, while new divisions elevate hydrocarbons, fusion, and a combined Office of AI & Quantum. In total, nine offices with mission-critical responsibilities have been dismantled as part of the restructuring, implementing much of the restructuring proposal outlined in Project 2025 and fundamentally reshaping the agency’s role in advancing clean energy and innovation.

This realignment comes on the heels of nearly a year of capacity cuts that have already strained the agency. Several thousand members of the DOE workforce have departed amid this administration’s DOGE-driven efforts, leaving critical functions understaffed, institutional knowledge depleted, and staff morale undermined. The erosion of large-scale demonstration and deployment capacity, and the structures that supported it, raises serious concerns about DOE’s ability to drive energy innovation at scale and implement real-world infrastructure upgrades. At the same time, sectors that rely on DOE’s support are witnessing a general loss of capacity at the agency, eroding trust in government and its ability to deliver on its stated mission.

The financial and operational costs of restructuring a large federal agency can be staggering. Lost productivity alone often runs into the millions, as staff and leadership divert time and focus from mission-critical work toward the reorganization. In this context, DOE’s restructuring risks weakening — rather than strengthening — the American innovation engine responsible for advancing both basic science and demonstration and deployment at scale. Gutting DOE’s deployment, infrastructure, and clean energy programs also undermines the agency’s ability to carry out the critical functions needed to address today’s most pressing energy challenges, as electricity demand continues to outpace supply and utility bills rise rapidly. 

Looking ahead, this reorganization poses important long-term questions: how would one rebuild the structures necessary for large-scale demonstration and deployment? How can DOE rapidly respond to growing domestic energy challenges amid reduced capacity? And how can the agency restore staff morale, retain institutional knowledge, and rebuild trust with the sectors that rely on its leadership? Far from delivering efficiency or strength, this reorganization risks institutionalizing the losses and instability at DOE that have already been set in motion.

Clean Energy Innovation & Deployment Impacts

The reorganization dramatically reshapes offices responsible for clean energy innovation and deployment. At a time when electricity prices are rising, demand growth is outpacing new supply, and grid infrastructure is aging, stepping away from applied innovation and deployment programs is a costly mistake that threatens affordability and reliability.

Most notably, the reorganization creates a new Office of Critical Minerals & Energy Innovation, with significant breadth and responsibility and a direct line of report to the Secretary. The Office of Energy Efficiency and Renewable Energy (EERE), which among other things manages DOE’s work to make it easier to build new renewable energy projects and improve U.S. industry, will now mostly be consolidated under this office. The new office’s name signals a potential deprioritization of energy innovation behind critical minerals priorities. It also raises questions: how will renewable energy, manufacturing, industrial, and built environment programs — all essential to energy affordability — be integrated and staffed? How will existing programs under EERE and other offices be reorganized within this sprawling new structure? Will applied innovation and deployment functions, like supporting states with improved siting and permitting of new power plants, continue to exist?

For critical minerals, consolidating work previously scattered across FE, EERE, and MESC may improve program coordination and definitely elevates this important issue. The office reports directly to the Secretary and Deputy Secretary, reflecting a top-down, highly centralized approach to managing critical minerals programs. However, there is no dedicated funding line for critical minerals, raising questions about how funding will be pieced together for this office and long-term program sustainability. 

This new restructure also eliminates the offices of Clean Energy Demonstrations (OCED), Grid Deployment (GDO), Manufacturing and Energy Supply Chains, the Federal Energy Management Program (FEMP), Fossil Energy (FE), and State and Community Energy Programs (SCEP). While elements of these offices and their programs may wind up in the large new Office of Critical Minerals and Energy Innovation, shuttering these offices emphasizes a de-prioritization of their functions and missions by DOE leadership.

Additionally, to meet our urgent energy infrastructure challenges and ensure that innovations translate into real world impact, the US needs to be building large-scale demonstrations of the newest energy technologies domestically. The Office of Clean Energy Demonstrations (OCED) did just that. It supported first-of-a-kind or next-generation projects to de-risk technologies to enable quicker commercialization. This new organizational structure nearly fully guts any remaining demonstration and deployment apparatuses at DOE. 

On another technology front, consolidation of geothermal and fossil programs could be a good outcome for geothermal innovation. The reorg creates a new Hydrocarbons and Geothermal Office, which merges the Geothermal Technologies Office (GTO) with the Fossil Energy Office (FE). Previously, both GTO and FE were housed under the Office of the Under Secretary for Science and Innovation (S4); the new combined office will now report to the Office of the new Under Secretary for Energy (S3). One way to conceptualize this merger is as an “Office of Underground Resources,” which could be a logical alignment given the technical and operational overlap between geothermal and fossil energy programs. The restructuring may also provide an opportunity for geothermal to access additional funding streams that were previously less available. 

Impacts on Basic Science

The DOE reorganization introduces major shifts with significant implications for the agency’s basic science mission, particularly in fusion and quantum research. 

One notable change is the creation of a dedicated Fusion office under the Office of the Under Secretary for Science (S4), a move long advocated by industry. Pulling fusion out of the Office of Science (SC) may make sense at this stage: the field is transitioning into applied programs that require outcome-oriented management. Still, critical basic research needs remain — particularly around plasma-facing components like the blanket — which demand the scientific rigor and oversight traditionally provided by a science-focused office. Separating fusion from the broader SC could help reconcile the tension between applied and basic work, but execution will be key, particularly in light of DOE’s ongoing workforce reductions. Industry perspectives have been overwhelmingly positive, and former Fusion Energy Science staff reportedly also view the change favorably. Under the Office of Science, fusion increasingly had to compete with more fundamental research projects that push the boundaries of human knowledge, as opposed to energy technology.

By contrast, the creation of a combined Office of AI & Quantum raises deep concerns about both scientific coherence and organizational capacity. Quantum information science (QIS) is still largely in a foundational research phase, relying on robust support from the Advanced Scientific Computing Research (ASCR), Basic Energy Sciences (BES), High Energy Physics (HEP), and other programs in the Office of Science to build knowledge to eventually transfer technology into industry. If quantum information science and technology programs are pulled out of the Office of Science, it could risk destabilizing established programs that benefit from their close interaction with fundamental research activities while prematurely advancing commercial quantum solutions. 

Grouping quantum with AI — a technology already widely deployed and primarily application-driven — conflates fundamentally different stages of development, obscuring programmatic priorities and scientific needs. These structural choices are particularly risky given DOE’s (and particularly the Office of Science’s) massively depleted workforce, not to mention the enormous costs and operational challenges associated with large-scale federal reorganizations. Lost productivity and gaps in institutional knowledge could delay critical research, compromise collaboration across DOE-operated laboratories, and weaken the nation’s basic science enterprise at a moment when long-term investment in emerging technologies is most urgent.

Additional structural alignment concerns extend to offices like the Office of Technology Commercialization (OTC, formerly OTT), which spans both applied and basic domains but is more appropriately aligned with applied energy offices rather than SC. Conversely, the creation of OSTR is an interesting development: previously, functions such as liftoff reports and AI/data center analyses were handled in-house by specific program offices, and centralizing these functions could improve coordination if implemented effectively, but they could also limit the expertise and detail that made these reports valuable in the past.  Similar challenges exist as a result of the Department’s reorganization of its scientific advisory committees into a single entity earlier this year.

Exacerbating Staff Losses and Project Cancellations

To further complicate matters, the reorganization has been marked by limited communication with DOE staff, leaving many employees uncertain about how their roles and teams will be affected. Concerns about potential reductions in force have heightened anxiety among DOE staff, and a poor transition to the new organizational structure could drive additional staff to depart of their own accord. Either or both would compound the effects of previous layoffs, the Deferred Resignation Program, and other aggressive staffing actions already undertaken by the administration on DOE’s workforce capacity.

While some turnover in staff is to be expected at the start of a new administration, this administration has pursued an unprecedented and aggressive series of actions to reduce the federal workforce. Early on, DOE leadership laid off probationary employees en masse—sources range between 555 to up to 2,000—and placed 25 staffers from the Office of Energy Justice and Equity on leave. 

The biggest impact on career staff has been the Deferred Resignation Program (DRP), which incentivized employees to voluntarily resign with pay and benefits through the end of FY25, or risk losing their jobs in future reductions in force. DOE staff data obtained by FAS show that 3,051 federal employees took the DOGE-sponsored DRP offer. (FAS will soon release a report with more analysis of this data.) Given this significant attrition, it is imperative that DOE leaders determine how to best transition staff into their new roles, to maintain as much certainty as possible as the agency navigates ongoing capacity challenges within a new structure.   

Lost capacity will also impact DOE’s awards. Nearly 350 projects across multiple DOE offices have received cancellation notices, issued either in May or October. Awardees — many tied to offices now eliminated under the reorganization — have reported slow or nonexistent communication from DOE even before the restructuring took effect. As DOE’s political leadership moves forward with mass termination actions, recipients are left attempting to navigate informal dispute processes or pursue mutually acceptable resolutions with limited guidance or transparency. Reversing independently vetted, merit-based technical awards at this scale is highly unusual and has already brought about significant dysfunction, raising serious concerns as the reorganization proceeds. 

The broader instability created by this upheaval will likely make it harder for the hundreds of businesses and state and local governments that are seeking to contest their grant terminations, as well as the thousands of other organizations that administer DOE-funded projects in service of the American people.

Questions Remain

The DOE reorganization leaves several critical questions unanswered, raising concerns about the agency’s ability to deliver on its stated mission:

These remaining questions highlight the tension between ambitious structural change and the practical limits of an understaffed, under-resourced agency. This large-scale agency reorganization threatens DOE’s ability to sustain scientific leadership, advance clean energy deployment, and address the nation’s energy affordability crisis. 

Report: When Ambition Meets Reality — Lessons Learned in Federal Clean Energy Implementation, and a Path Forward

The Trump administration has scrapped over $8 billion (so far) in grants for dozens of massive clean energy projects in the United States. For those of us who worked on the frontlines of Bipartisan Infrastructure Law (BIL) and Inflation Reduction Act (IRA) implementation, the near-weekly announcements and headlines have been maddening, especially at a time when many of these projects would have helped address soaring electricity prices and surging demand growth.

While some of these cancellations were probably illegal, they nevertheless raise fundamental questions for clean energy advocates: why was so much money still unspent…and why was it so easy to cancel?

In a new report, we begin to address these fundamental implementation questions based on discussions with over 80 individuals – from senior political staff to individual project managers – involved in the execution of major clean energy programs through the Department of Energy (DOE). 

Their answer? There is significant opportunity – as our colleagues at FAS have written – for future Executive branch implementation to move much faster and produce much more durable results. But to do so, future implementation efforts must look drastically different from the past, with a ruthless focus on speed, outcomes, and the full use of Executive Branch authorities to more quickly get steel in the ground.

The risk of risk aversion

Take the grant cancellations example. The Trump administration has relied on one small clause in the Code of Federal Regulations (2CFR 200.340(a)(4)) as the legal basis for its widespread cancellations. This clause, traditionally included in most grants between the government and a private company, allows the government to cancel any grant that “no longer effectuates the program goals or agency priorities” and essentially functions as a “termination for convenience” clause.

But including this “termination for convenience” clause was optional. DOE could have leveraged a different, more flexible contracting authority for many awards. It also could have processed what’s known as a “deviation” in order to exclude the clause from standard contracts. Leaders of program offices were aware of these options, with some staffers strenuously objecting to the inclusion of termination for convenience.

But in the end, DOE offices generally opted to keep this clause because it was the way the agency had always executed (primarily R&D) grants in the past, and because sticking to established procedures was seen as the best way to avoid the risk of Congressional or Inspector General oversight. 

And yet, this risk-averse approach perversely increased the risk of project failure, by creating an easy kill switch for an administration looking for grounds on which to cancel particular projects.

This attitude toward risk – which saw defaulting to the status quo as the most prudent path – was a constant barrier to effective implementation. (In addition to opening up grants to cancellation, the embrace of 2CFR 200 regulations meaningfully slowed negotiations as companies bristled at the obscure accounting and other compliance measures the regulations would impose on them.)

Understanding this culture of risk aversion offers two takeaways for improving government: (1) rigorously question status quo decisions and avoid defaulting to agency precedent and (2) avoid excessive focus on eliminating every risk or avoiding external backlash or oversight (especially given that backlash and oversight are likely regardless of the approach.) 

Speed is paramount

Of course, excluding the termination for convenience clause would not have been a panacea. It’s likely the Trump administration would have devised some other pretext for cancelling the grants that may have been just as successful, though perhaps legally shakier.

That’s why implementers also told us that speed is critical. The best defense is a strong offense. And the best way to prevent money from being taken back is to have already spent it on promising projects. The federal government has moved faster in implementation of large policies before. During the New Deal, the Tennessee Valley Authority moved from passage of its founding law to beginning construction on a major dam in just four months. Operation Warp Speed delivered cutting-edge life-saving vaccines to millions of Americans in about a year. While the contexts and goals of these programs were different, we know from history that the federal government can move fast.

But at DOE, only 5% of the funds appropriated through the Bipartisan Infrastructure Law had actually been spent (not just obligated) by the time the Biden administration ended three years later. In addition to making clean energy projects more vulnerable to subsequent cancellations, the pace of the rollout meant that the basic political hypothesis animating clean energy legislation—that the economic development projects brought, especially to red states, would create a durable bipartisan coalition for clean energy—went untested.

Practically everyone we spoke with expressed frustration at the slow pace of implementation. Interviewees highlighted many challenges associated with a relatively slow pace of BIL and IRA implementation, such as:

The work begins now

One commonality between these and other issues identified in our report is encouraging: they are mostly within the Executive Branch’s power to solve. A sufficiently prepared future administration could address many of these challenges for future federal clean energy efforts without relying on the vagaries of the legislative process. But the work must begin now. 

On contracting, for instance, a future administration’s DOE could make better use of Other Transactions Authority for clean energy. But it should be prepared with drafts of the basic commercial terms of agreements between the government and companies it works with. Similarly, a proactive future administration will come in with a clear view on how to streamline compliance with environmental, prevailing wage, domestic sourcing, and other cross-cutting requirements. On decision-making, a future administration can set norms pushing decision-making to the lowest possible level, clarify processes to elevate and execute major issues, and establish small, clear, and empowered teams that own frontline negotiations. 

If pursued, this updated approach to federal clean energy implementation will look drastically different. But one way or another it will have to: the next time there is a federal government interested in accelerating clean energy, it is likely to be dealing with a private sector much more wary of working with the government, fiscal constraints that limit the likely scale of any clean energy funding, and a dramatically altered federal workforce and state apparatus.

Much can be done outside of the federal government — including at state and local levels — to prepare for those circumstances. It is possible for a future federal administration to achieve faster and more durable clean energy outcomes. But to make that possible, the work must begin now. 

It’s not enough to say we need to make full use of DOE’s authorities; we need the drafted Secretarial directives and advance legal legwork to do it, and leadership well-equipped with the details and government-insider knowledge to execute on it. 

It’s not enough to say we want more nuclear, transmission, or critical minerals projects; we need to have identified the priority projects and designed the strategies and programs needed to actually put them in motion on Day 1. 

It’s not enough to say we should take a “whole-of-government” approach to an issue like clean energy; we need a detailed plan for how to use the $5 billion/year in electricity purchases and the PMA’s 45,000 miles of transmission lines—all under the direct control of the federal government—to achieve explicit policy outcomes. 

And it’s not enough to say we need to rebuild the federal workforce; we need a roster of hundreds of people that can be brought on and trained rapidly to implement within weeks.

To live up to the spirit of the New Deal and Operation Warp Speed—the spirit that turned ambitious goals into massive real-world impact in a matter of months—the next administration must come armed not only with broad aspirations, but also with the detailed plans required to implement them.

Want abundant energy? Ask who benefits from scarcity.

This article originally was published July 30 on Utility Dive.

A new obsession with abundance is spreading through policy conversations and governors’ mansions across the country. Abundance advocates, boosted by a recent book from Ezra Klein and Derek Thompson, envision a future in which we defeat the climate crisis, reduce cost of living and improve quality of life by speeding up construction of housing and energy infrastructure.

Making clean energy abundant is certainly critical to addressing the climate crisis. We need plentiful, cheap, clean energy to replace polluting fossil fuels in buildings, vehicles and factories. As a senior policy advisor in the Biden White House, I worked on many policies aimed at clean energy abundance, directly or indirectly, and I also saw firsthand how those policies were insufficient. That’s why it is now clear to me that the abundance movement’s playbook — to streamline permitting, simplify government processes and make public investments more focused — falls short of what’s needed.

We won’t achieve energy abundance unless we contend with the powerful interests that benefit from scarcity. Doing so requires reforming electricity markets, refreshing regulation of electric companies and rethinking the way we pay for grid infrastructure.

Let’s start with the problem: we are not building nearly enough clean energy to curb climate change and keep electricity affordable. Analysis from three leading research projects found that for us to get within striking distance of the Paris climate goals and plan for the lowest electricity costs, we must build 70 GW to 125 GW of clean energy per year, much higher than the record 50 GW built in 2024. As a result of our failure to build new energy projects fast, families and businesses will pay more for power and the planet will warm faster.

This is no longer an economic issue. Clean electricity is now often cheaper to deploy than new coal and gas, and in many cases cheaper than existing fossil-fuel-fired power plants. So what is stopping us from building it fast enough?

To answer this question, abundance proponents, including Klein and Thompson, largely focus on two main obstacles: 1) opposition from people who live nearby specific projects and groups concerned with local environmental impacts and 2) “everything-bagel liberalism,” the tendency to add too many strings to government incentives. The solution to the first problem, they argue, is to limit the power of the opposition by streamlining federal permitting and constraining public input in state and local siting processes. And for the second, their remedy is to limit the number of goals of government programs and reduce the requirements for funding.

There is no doubt that some clean energy and transmission projects have been thwarted by local opposition and lengthy litigation. And it is a worthy goal to make government incentives as effective as possible. But by portraying the primary villains defending scarcity as local landowners, conservation groups and the diversity of the liberal coalition, Klein and Thompson ignore important characters and policies that, if left unchecked, will continue to hamstring the pursuit of abundance.

For example, consider the situation unfolding in the electricity market organized by the PJM Interconnection, which operates the electricity grid for 65 million people in 13 mid-Atlantic and midwestern states and Washington, D.C. An independent, non-governmental entity, PJM runs the process to connect new power plants to the grid, among other important processes to make the electricity system work. PJM has been notoriously bad at this job. It ranks as the worst grid operator in the country in terms of the speed and effectiveness of its interconnection process. The average project waits five years for PJM to give it permission to connect to the grid. In fact, PJM closed its doors to new project applications in 2022 and has yet to re-open it. As a result, electricity demand is outpacing supply, prices are rising rapidly, and new clean energy projects are dying as they wait for word from PJM.

PJM has the power to speed up the process to connect new projects, which would increase electricity supply and cut electricity prices. But PJM has largely resisted reforms and focused instead on extending the life of existing power plants. Here is where it is helpful to ask: who benefits from an electricity shortage and the resulting high prices? It is not conservation groups or liberal stakeholder groups with competing goals (who have no voting power in PJM’s governance structure). It is the incumbent utilities that own the fleet of aging coal-fired power plants, which are struggling to compete with new clean energy projects. If cheap clean energy is allowed to enter the market, these companies will make less money. The outdated processes for approving new projects help prevent cheaper energy resources from threatening their business model. The companies have significant decision-making power — together, power plant owners, transmission owners (many of whom also own generation) and other energy service suppliers make up 60% of the voting power in PJM decisions.

Energy will not be abundant in the mid-Atlantic unless we take on the interests that are benefitting from scarcity. That means reforming the electricity market to stop overpaying existing power plants at the expense of customers, changing the rules to make it easier to connect new power plants to the grid and updating governance structures to make sure that customers are properly represented.

Similar cases abound of powerful interests benefitting from scarcity and defending policies that prevent abundance. Monopoly utilities, for example, benefit from abundant energy to the extent that they can build it and can earn a return on their investments — but not if the energy comes from their competition. That’s why utilities in the southeast have gone to great lengths to block transmission lines that enable cheap clean energy to compete with their existing power plants. Changing how those utilities make money (for example, by paying for outcomes instead of investments) could flip the script and turn the utilities into energy abundance advocates.

If the abundance movement is to succeed, it must identify the defenders of scarcity and broaden the playbook to either overcome those interests or change their incentives to bring them on the team.

Beyond Binary Debates: How an “Abundance” Framing Can Restore Public Trust and Guide Climate Solutions

Public trust in U.S. government has ebbed and flowed over the decades, but it’s been stuck in the basement for a while. Not since 2005 have more than a third of Americans trusted the institution that underpins so much of American life.

We shouldn’t be surprised. Along with much progress, over the past two decades the U.S. became more unequal, saw stagnation or decline in many rural counties, stumbled into a housing crisis, and experienced worsening health outcomes. When the government can’t deliver (especially in core areas like health, housing, and economic vitality), trust in it wanes while the false promises of autocrats grow more appealing.

The strength of American democracy, in other words, hinges in large part on how well our government functions. This urgency helps explain why, at a moment when the United States is flirting with autocracy ever more vigorously, a book on precisely this topic became a #1 bestseller and prompted a debate around the “abundance agenda” that has turned quasi-existential for many in the policy world.

The abundance agenda, as described by Jonathan Chait, is “a collection of policy reforms designed to make it easier to build housing and infrastructure and for government bureaucracy to work”, such as by streamlining regulations that constrain infrastructure buildout while scaling up major government programs and investments that can deliver public goods. 

Unfortunately, popular discourse often flattens the conversation around abundance into a polarized binary around whether or not regulations are good. That frame is overly reductionist. Of course badly designed or out-dated regulatory approaches can block progress or (as in the case of the housing policies that the book Abundance centers on) dry up the supply of public goods. But a theory of the whole regulatory world can’t be neatly extrapolated from urban zoning errors. In an era of accelerating corporate capture, both private and public power structures act to block change and capture profits and power. We need a savvier understanding of what happens at the intersections between the government and the economy, and of how policy translates to communities at local scales.

We should therefore regard “abundance” less as a prescriptive policy agenda than as a frame from which to ask and answer questions at the heart of rebuilding public trust in government. Questions like: “Why is it so hard to build?” “Why are bureaucratic processes so badly matched to societal challenges?” “Why, for heaven’s sake, does nothing work?”

These questions can push us in a direction distinct from the usual big vs. small government debates, or squabbles about the welfare state versus the market. Instead, they may help us ask about interactions within and between government and the economy – the network of relationships, complex causation, and historical choices – that often seem to have left us with a government that feels ill-suited to its times.

At the Federation of American Scientists (FAS), we, along with colleagues in the broader government capacity movement, are exploring these questions, with a particular focus on agendas for renewal and advancing a new paradigm of regulatory ingenuity. One emerging insight is that at its core, abundance is largely about the dynamics of incumbency, that is, about the persistence of broken systems and legacy power structures even as society evolves. A second, related, insight is that the debate around abundance isn’t really about de-regulation or the regulatory state (every government has regulations), but rather about how multi-pronged and polycentric strategies can break through the inertia of incumbent systems, enabling government to better deliver the goods, services, and functions it is tasked with while also driving big and necessary societal changes. And a third is that the abundance discourse must center distributive justice in order to deliver shared prosperity and restore public trust.

Moving the Boulder: Inertia, Climate Change, and the Mission State

The above insights are particularly helpful in guiding new and more durable solutions to climate change – a challenge that touches every aspect of our society, that involves complex questions of market and government design, and that is rooted in the challenges of changing incumbent systems.

Consider the following. It’s now been almost 16 years since the U.S. Environmental Protection Agency (EPA) issued its 2009 finding that greenhouse gas (GHG) emissions are a public danger and began trying to regulate them. To simplify a complex history, what happened on the regulatory front was this: the Obama administration tried to push regulations forward, the Trump administration worked to undo them, and then the cycle repeated through Biden and Trump II, culminating in the EPA’s recent move to revoke the endangerment finding.

We can certainly see the power of incumbency and inertia within this history. Over a decade and a half, the EPA regulated greenhouse gases from new power plants (though never very stringently), new cars and trucks (quite effectively cutting pollution, though never with mandates to actually electrify the fleet), and…that’s about it. The agency never implemented standards for the existing power plants and existing vehicles that emit the lion’s share of U.S. GHGs. It never regulated GHGs from industry or buildings. And thanks to the efforts of entrenched fossil-fuel actors and their political allies, the climate regulations EPA managed to get over the finish line were largely rolled back. 

None of this should be read as a knock on the dedicated civil servants at EPA and partner federal agencies who worked to produce GHG regulations that were scientifically grounded, legally defensible, technically feasible, and cost effective, even while grappling with the monumental challenges of outdated statutes and internal systems. But it certainly speaks to the challenge of securing lasting change.

The work of economist Mariana Mazzucato offers clues to how we might tackle this challenge; she paints a portrait of a “mission state” that integrates all of government’s levers to define and execute a particular objective, such as an effective, equitable, and durable clean energy transition. This theory isn’t a case for simplistic deregulation, nor is it a claim that regulations somehow “don’t work”. Rather, it suggests that (especially in a post-Chevron world) another round of battles over EPA authority won’t ultimately get us where we need to go on climate, nor will it help us productively reshape our institutions in ways that engender public trust.

The shift from one energy system foundation to another is messy – and it is inherently about power. As giant investment firms hustle to buy public utilities, enormous truck companies side with the Trump administration to dismantle state clean freight programs, and subsidies for clean energy are decried as unfair and market-distorting even though subsidies for fossil energy have persisted for nearly a century, it’s clear that corporate incumbents can capture public investments or capture government power to throttle change. Delivering change means thinking through the many ways incumbency creates systems of dependencies throughout society, and what options – from regulations to monetary policy to the ability to shape the rule of law – we have to respond. To disrupt energy incumbents and achieve energy abundance, in other words, we must couple regulatory and non-regulatory tools.

After all, the past 16 years haven’t just been a story of regulatory back-and-forth. They are also a story of how U.S. emissions have fallen relatively steadily in part due to federal policies, in part to state and local leadership, and in part to ongoing technological progress. Emissions will likely keep falling (though not fast enough) despite Trump-era rollbacks. That’s evidence that there’s not a one-to-one connection between regulatory policy and results.

We also have evidence of how potent it can be when economic and regulatory efforts pull in tandem. The Inflation Reduction Act (IRA) was the first time the United States strongly invested in an economic pivot towards clean energy at scale and in a mission-oriented way. The results were immediate and transformative: U.S. clean energy and manufacturing investments took off in ways that far surpassed most expectations. And while the IRA has certainly come under attack during this Administration, it is nevertheless striking that today’s Republican trifecta retained large parts of the entirely Democratically-passed IRA, demonstrating the sticking power of a mission-oriented approach.

Conducting the Orchestra: The Need for an Expanded Playing Field

Thinking beyond regulatory levers (i.e., a multi-pronged approach) is necessary but not sufficient to chart the path forward for climate strategy. In a highly diverse and federalist nation like the United States, we must also think beyond federal government entirely.

That’s because, as Nobel-winning economist Dr. Elinor Ostrom put it, climate change is inherently a “polycentric” problem. The incumbent fossil systems at the root of the climate crisis are entrenched and cut across geographies as well as across public/private divisions. Therefore the federal government cannot effectively disrupt these systems alone. Many components of the fundamental economic and societal shifts that we need to realize the vision of clean energy abundance lie substantially outside sole federal control – and are best driven by the sustained investments and clear and consistent policies that our polarized politics aren’t delivering.  

For example, states, counties, and cities have long had primary oversight of their own economic development plans, their transportation plans, their building and zoning policies, and the make-up of their power mix. That means they have primary power both over most sources of climate pollution (two-thirds of the world’s climate emissions come from cities) and over how their economies and built environments change in response. These powers are fundamentally different from, and generally much broader than, powers held by federal regulatory agencies. Subnational governments also often have a greater ability to move funds, shape new complex policies across silos, and come up with creative responses that are inherently place-based. (The indispensable functions of subnational governments are also a reason why decades of cuts to subnational government budgets are a worryingly overlooked problem – austerity inhibits bottom-up climate progress.)

The private sector has similar ability to either constrain or drive forward new economic pathways. Indeed, with the private sector accounting for about half of funding for climate solutions, it is impossible to imagine a successful clean-energy transition that isn’t heavily predicated on private capabilities – particularly in the United States. While China’s clean-tech boom is largely the product of massive top-down subsidies and market interventions, a non-communist regime must rely on the private sector as a core partner rather than a mere executor of climate strategy. Fortunately, avenues for effectively engaging and leveraging the private sector in climate action are rapidly developing, including partnering public enterprise with private equity to sustain clean energy policies despite federal cutbacks.

An orchestra is an apt analogy. Just as many instruments and players come together in a symphony, so too can private and public actors across sectors and governance levels come together to achieve clean energy abundance. This analogy extends Mazzucato’s conception of a mission state into a “mission society”, envisioning a network that spans from cities to nation states, from private firms to civil actors, working in concert to overcome what Ben Rhodes calls a “crisis of short termism” and deliver a “coherent vision” of a better future.

Building Towards Shared Prosperity

For the vision to be coherent, it must resonate across socioeconomic and ideological boundaries, and it must recognize that the structures of racial, class, and gender disparity that have marked the American project from the beginning are emphatically still there. Such factors shape available pathways for progress and affect their justice and durability. For instance: electric vehicle adoption can only grow so quickly until we make it much easier for those living in rented or multifamily housing to charge. Cheaper renewables only mean so much when prevailing policies limit the financial benefits that are passed on to lower-income Americans.

To borrow, and complicate, a metaphor from Abundance: distributive justice questions are fundamentally not “everything bagel” seasonings to be disregarded as secondary to delivery goals. They are meaningful constraints on delivery as well as critical potentialities for better systems, and are hence central to policy and politics. No mission state or mission nation, addressing the polycentric landscape of networked change needed to shift big incumbent systems, can afford to dismiss or ignore them. Displacing those systems requires wrestling with inequality and striving to create shared prosperity through new approaches that are distributively fair.

That’s an approach rooted in orchestration, one that asks why some instruments drown out others, and how to alter relationships between players to produce better results. It understands that we can’t solve scarcity without centering distributive justice, because as long as deep structural disparities and structural power exist there is strong potential for the benefits of rapid energy or housing buildout to be channeled towards those who need them least. And it is capable of restabilizing the center of American society and restoring trust in U.S. government because it realistically grapples with the interests of incumbents while paying more than lip service to the interests of a dazzlingly diverse American public.

This re-fashioned abundance agenda can provide actual principles for administrative state reform because it knows what it is asking regulators, and the larger intersecting layers of government and civil society, to do: Systematically remove points of inertia to accelerate shared prosperity in a safe climate, while anticipating and solving for distributive risks of change.

Because again, the abundance debate isn’t really about whether or not regulations are good. It’s about unfreezing our politics by being clear and courageous about our goals for a society that works better and is capable of big things.

This is not the first time Americans have envisioned a better future in the midst of national crisis, or the first time we have collectively disrupted failed incumbent systems. From our messy foundation, to the beginnings of Reconstruction during the Civil War, to the architects of the New Deal envisioning an active and effective government in the midst of the Dust Bowl and Depression, the history of our nation is full of evidence that a compelling vision of truly democratic government can pull Americans back together despite deep and real problems. Each time, these debates have scrambled existing binaries, and driven realignment. We are on the verge of realignment again as the systems built up over the fossil era break down and our neoliberal order fragments. This is the right time to engage, together, in orchestrating what comes next.

Trump’s Cuts Could Exacerbate The Energy Emergency

Originally published at Forbes.

Demand for power is climbing to unprecedented levels. U.S. Energy Information Administration data reveals that July set a new record for electricity peak demand, driven by nationwide heat waves and increasing reliance on power-intensive artificial intelligence tools. And, given the state of the electrical grid in several jurisdictions, the U.S. The Department of Energy declared an emergency order on July 28 to secure the mid-Atlantic power grid. The situation could worsen as summer continues, although it has already been a long time in coming.

On day one of his second administration, President Donald Trump declared a national energy emergency. The declaration formalized the idea that economic prosperity, national security, and foreign policy are under threat due to an insufficient energy supply. While this claim may be intended to encourage leniency towards the fossil fuel industry, there are many issues with our energy infrastructure that do need urgent attention. The American Society of Civil Engineers gave the nation’s aging electrical infrastructure a D+, a failing grade, in the organization’s latest annual Infrastructure Report Card. The aging grid is in dire need of infrastructure upgrades so that it is able to serve the increasing power demand as we see more frequent extreme temperatures and weather events due to climate change, alongside the expansion of data center power use and the electrification of buildings and transportation.

Currently, energy generation takes years to connect to the grid due to slow permitting processes and a patchwork of utility regulations and forecasters predict electricity use will increase 50% by 2050 in the U.S.

However, instead of creating a pathway to speed up the connection of new energy sources to the grid, Trump followed this emergency declaration with a budget request for fiscal year 2026 that kneecaps energy innovation. Followed closely after that was the passage of the One Big Beautiful Bill Act, which, in combination with severe budget and personnel cuts in government-funded science and technology research and development, will likely result not in energy stability or even dominance, but a true national emergency: a long-lasting rise in energy prices. It may also lead to a decline in the U.S.’s leadership in technology innovation and talent.

Investing In Innovation Could Help Address The Energy Crisis

Today’s ubiquitous GPS, computer chips, solar photovoltaic cells, and lithium-ion batteries didn’t arrive from the ether fully-formed and ready for consumers to use. Many of these innovations are thanks to the equivalent of $7.4 billion (in today’s dollars) taxpayers invested shortly after WWII ended to create the Office of Scientific Research and Development. Funds from OSRD eventually led to discoveries in the 1940s and 50s that resulted in the establishment of the National Science Foundation National Aeronautics and Space Administration, Atomic Energy Commission (later becoming the Department of Energy), and other science agencies.

Federal funding for research and investment in these agencies has resulted in trillions of dollars of economic benefit, resulting in the U.S. being a leading developer of technologies that have transformed our world.

Funding cuts to the DOE pose a threat to our energy system and leadership in energy systems like next-generation grid technology, AI, and clean energy development and manufacturing.

DOE is the largest funder of basic physical science research in the government. The DOE’s Office of Science’s annual budget is $8.2 billion for projects related to critical minerals, quantum computing, enhanced geothermal energy, and artificial intelligence. All are topics that the Trump administration has identified as crucial to U.S. economic competitiveness and “dominance.” Applied technology offices within the DOE – the ones that transform scientific research into commercial applications, like the Manufacturing and Energy Supply Chain Office, Office of Clean Energy Demonstrations, and the Energy Efficiency and Renewable Energy office— have funded projects across industries and sectors. These include the private sector, national labs, and universities, all of whose work has resulted in historically low costs for battery storage, solar photovoltaics, grid components, and critical infrastructure.

These investments have fed into a resurgence of our domestic manufacturing industry across the Rust Belt (Midwest) and the Sunbelt (southern states like Georgia, South Carolina, and Texas). The DOE historically has a positive return on taxpayer investment across its research and development offices. From its start in 1976 through 2015, DOE invested $12 billion in research, development, and deployment in EERE and found that its return yielded $388 billion in economic benefits, or an ROI of over 27% by conservative estimates.

The Building Technologies Office within DOE, which is known for developing energy-saving technologies that save consumers money on their utility bills, has estimated that for every $1 invested in its R&D, it has yielded between $20 and $261 in economic benefits. The benefits of these investments are in the form of job creation, consumer cost savings, and new technologies like horizontal drilling – yes, the technology used for fracking developed through the DOE’s R&D program on natural fracturing.

Given that context, it may be counterintuitive that the same officials touting U.S. leadership in innovation and discovery are proposing the largest financial cuts to DOE in history. The president’s budget request is proposing a 26% cut to non-defense DOE programs. The table below summarizes the cuts, with a heavy focus on programs that have resulted in the development and economic success of new energy technologies, especially clean energy technology.

Changes between Trump’s budget request and the current funding level of DOE offices that fund research and development
DOE OfficePercent ChangeNotes
EERE-74%Zeros out solar, wind, and hydrogen programs
ARPA-E-57%Shifts focus to “firm, reliable power”
Office of Science-14%Largest federal sponsor of basic physical science research
Fossil Energy-31%Formerly fossil energy and carbon management, focus toward developing fossil fuel
Cybersecurity, Energy Security, and Emergency Response-25%This office was created under the first Trump administration, responsible for security for cyberphysical threats
Office of Electricity-31%
Grid Deployment Office-75%
Office of Clean Energy Deployment-100%Closes this office

Slashing research and development programs across the DOE, all while Congress rolls back clean energy tax incentives and programs, is not going to solve the nation’s energy emergency. It makes our current challenges even worse.

The problems that the aging U.S. power grid faces are already compounded by historically high (and growing) consumer demand – a result of increasingly extreme temperatures and weather events, the explosion of data centers’ energy demand, and the electrification of even more transportation and buildings. Budget cuts and the insistence on using energy derived from fossil fuels may only exacerbate the problems the U.S. is already facing. In fact, defunding scientific research will most likely allow international competitors to slingshot ahead of the U.S. in new technology development and adoption.

If Americans become increasingly reliant on foreign intellectual property and technology, while the domestic grid continues to decay, the national energy emergency is likely to grow even more dire in the years to come.

Position on the Environmental Protection Agency’s Proposal to Revoke the Endangerment Finding

Yesterday, the U.S. Environmental Protection Agency (EPA) proposed revoking its 2009 “endangerment finding” that greenhouse gases pose a substantial threat to the public. The Federation of American Scientists (FAS) stands in strong opposition.

The science couldn’t be clearer: unchecked emissions of greenhouse gases are increasing the frequency and toll of disasters like flash flooding in Texas, catastrophic wildfires in Los Angeles, and stifling heat domes that repeatedly blanket huge swathes of the country. Revoking the endangerment finding would shove science aside in favor of special interests – and at the expense of American health and wellbeing.

“The Environmental Protection Agency claims that the endangerment finding led to ‘costly burdens’ on American families and businesses, when in reality it is the cost of failing to regulate climate pollution that will hit Americans the hardest,” said Dr. Hannah Safford, Associate Director of Climate and Environment at the Federation of American Scientists. “Climate change is expected to cost each American child born today half a million dollars over their lifetimes. Is that the legacy we want to leave our kids?”

The EPA’s proposal is the latest move by the Trump Administration to gut federal climate policy. This campaign runs counter to public opinion: 4 in 5 of all Americans, across party lines, want to see the government take stronger climate action. At the same time, potential revocation of the endangerment finding underscores the need for a durable new approach to climate policy that integrates innovative regulatory design, complementary policy packages, and attention to real-world implementation capacity. FAS and its partners are leading on this priority alongside state and local leaders.

“Despite the Trump Administration’s short-sighted and ideologically motivated actions, the clean energy transition has unstoppable momentum, and there is tremendous opportunity for innovation on how we design and deliver climate policies that are equitable, efficient, and effective,” added Dr. Safford. “The Trump Administration may be stepping back, but many others are stepping forward to create a world free from climate danger.”

Unpacking the Department of Defense and MP Materials Critical Minerals Partnership

Rare earth elements have been the primary target of critical minerals trade tensions between the United States and the People’s Republic of China (PRC), which processes 91% of all rare earth elements globally. In response to significant new tariffs from the Trump administration, in April 2025, the PRC instated new export controls on seven rare earth elements, which resulted in a significant decrease in exports as companies had to apply for new licenses and wait for approval.1 Luckily, the list of materials did not include neodymium and praseodymium (collectively known as NdPr), which are key to the manufacture of permanent magnets used in all kinds of motors, from those in electric vehicles and wind turbines to aircrafts and submarines. 

The Department of Defense (DoD) has expressed significant interest in developing domestic NdPr supply chains for magnets, due to the risk of relying upon “overseas, single-points-of-failure” for such critical materials and components. A new multibillion dollar partnership between DoD and MP Materials, the only active domestic producer of NdPr, announced on July 10th, seems to be the agency’s big bet on a solution.

The announcement has received both excitement and questions regarding the structure of the partnership and its potential impact. The partnership is notable not only for the sheer amount of funding that DoD is committing, but for its use of both supply- and demand-side policy tools to strategically support the expansion of MP Materials’ NdPr processing and magnet manufacturing capacity. These policy tools have never been used in tandem like this before, marking a new development in U.S. industrial policy, so let’s unpack exactly what the partnership entails.

Supply-Side Policy Tools

Equity Investment. DoD will invest an initial amount of $400 million in MP Materials in return for Series A Preferred Stock. This investment, combined with a separate $1 billion loan from JPMorgan and Goldman Sachs, will provide MP Materials with the capital needed to construct a “10X” magnet manufacturing facility expansion that would increase MP Materials’ manufacturing capacity from 3,000 to 10,000 metric tons. MP Materials is obligated to raise another $350 million, either from other sources, another investment from DoD, or an addition to the JPMorgan and Goldman Sachs loan. 

Warrant. A 10 year warrant provides DoD with the option to increase its shares of MP Materials up to 15%, in which case DoD would become the largest shareholder of MP Materials. 

Loan. MP Materials will also receive a $150 million loan from DoD to expand their rare earth processing facility.

Demand-Side Policy Tools

Price Floor Commitment. To ensure a stable market for MP Materials’ NdPr processing facility and its expansion, starting in Q4 2025, DoD will guarantee a price floor of $110/kg for NdPr, nearly double the current market price, over 10 years. The structure of the price floor agreement is essentially a modified contract-for-difference.

When the market price is below the floor, DoD will pay MP Materials the difference between the two for every kg of material produced.2 Note that this includes not just materials sold to commercial buyers, but also materials that are internally consumed by MP Materials for magnet manufacturing and excess materials that are stockpiled, with priority given to magnet manufacturing. At the current market price of $60/kg, this would cost DoD roughly $300 million/year.3 When the market price is above the floor, DoD will in turn receive 30% of the amount above the floor, though given how high the price floor is, the expected amount of upside is low unless there is another supply crunch in the future.

Unlike a traditional price floor, this structure provides the federal government with the added benefit of sharing in the upside from windfall profits. This modified contract-for-difference also innovates upon the original contracts-for-difference model designed for less risky electricity markets by uncapping the amount of upside that companies can earn. This is key to ensuring that the project remains appealing to private-sector investment.

Source: MP Materials

Offtake Commitment. To derisk MP Materials’ magnet manufacturing facility expansion, DoD is also providing a 100% offtake commitment for the 7,000 MT/year of expanded magnet manufacturing capacity over the first 10 years of production. For the magnets, DoD will pay MP Materials a price equal to the cost of production. In addition, DoD will pay MP Materials $140 million/year to guarantee a minimum level of earnings before interest, taxes, depreciation, and amortization (EBITDA) (i.e. raw profits).

MP Materials also has the option to sell some or all of the 10X expansion magnets to commercial buyers with DoD’s consent. For additional profits (i.e. EBITDA) generated from those sales, the first $30 million above the $140 million threshold will go to DoD, after which profits will be shared 50/50 between DoD and MP Materials.  

Source: MP Materials

Funding and Authorities: the Defense Production Act

According to the Securities and Exchange Commission filing by MP Materials, DoD is using, in part, Defense Production Act (DPA) Title III authorities and funding to enter into this partnership.4 This is enabled by Executive Order (E.O.) 14241, which issued a Presidential Determination that critical minerals and their derivative products (e.g., rare earth magnets) meet the requirements for the use of DPA Title III and delegated Section 303 authorities to the Secretary of Defense. 

Section 303 authorities include subsidies and purchase commitments, which DoD likely used for the demand-side components of the partnership.5 Section 303 provides exemptions from the regulations (e.g. the Federal Acquisition Regulation) that govern most government transactions, providing DoD with significantly greater flexibility to customize contract terms. This flexibility is key for effectively designing and implementing demand-side tools for critical minerals, tailored to the unique market conditions for each material and the private-sector partner.

Funding could come from the DPA Fund for Title III actions, which is managed by the DoD and primarily funded through annual defense appropriations. Or, from the $1 billion in new appropriations for DPA passed as a part of the One Big Beautiful Bill Act (OBBBA). Given that DoD’s FY26 budget request for the DPA Fund is only $266 million—less than the estimated annual price floor payments DoD must make at the current market price for NdPr—DoD will most likely have to draw on the new OBBBA appropriations in addition to or instead of the DPA Fund in the immediate term.

For future funding, especially after the OBBBA appropriations run out, DoD will be reliant upon Congress to make sufficient annual appropriations to the DPA Fund to cover the cost of the price floor payments and, once the 10X Facility comes online, the offtake commitment. Any potential future revenue from this partnership could also be used to replenish the revolving DPA Fund. 

One key feature of the DPA Fund to note is that it’s capped at $750 million. The estimated annual cost of the MP Materials partnership once the 10X facility is in operation will take up at least 60% of the cap.6 Unless Congress decides to raise or remove the cap during the next DPA reauthorization, this partnership will limit the amount of funds available for other industries crucial to national security.7

A Model for the Future?

While the DoD and MP Materials partnership definitely stands out for its ambitious scale and its innovative use of policy tools, whether this can and/or should be used as a model for the future requires considering some additional questions.

First, will this partnership simply entrench a domestic monopoly on NdPr production? There was no known competitive bidding process for this funding opportunity, and it is unclear if DoD plans to offer similar support to other rare earth producers in the future. One argument for MP Materials is that they are the only currently operating producer of NdPr—all the other projects are still in development—so this was the least risky investment option for DoD. The current significant supply chain concentration and risk of future trade disruption to national security may also justify such an intervention in the industry. 

Given MP Materials’ domestic monopoly and the significant amount of taxpayer money going to support that, should the government have negotiated for significantly more upside to compensate for the lack of competition? Market competition is key to driving innovation and efficiency improvements and bringing down prices over time. How can the federal government continue to foster growth and competition in the NdPr market after this partnership? 

Lastly, does this model make sense for other critical minerals markets? There are a number of other critical minerals with similar levels of supply chain concentration in the processing step, including battery-grade graphite, nickel, cobalt, and lithium, which are crucial to the defense industrial base and multiple commercial sectors. Some of these materials also currently have only one domestic producer or no domestic producers. On the other hand, some of these materials have larger and more mature markets than NdPr, making them slightly less vulnerable (though definitely not free from) to market manipulation. The federal government could potentially make a similar impact on these material supply chains while using a smaller subset of policy tools, so long as it addresses both supply- and demand-side needs.

Supporting Data Center Development by Reducing Energy System Impact

In the last decade, American data center energy use has tripled. By 2028, the Department of Energy predicts it will either double or triple again. To meet growing tech industry energy demands without imposing a staggering toll on individual energy consumers, and to best position the United States to benefit from the advancements of artificial intelligence (AI), Congress should invest in innovative approaches to powering data centers. Namely, Congress should create a pathway for data centers to be viably integrated into Thermal Energy Networks (TENs) in order to curb costs, increase efficiency, and support grid resilience and reliability for all customers. 

Congress should invest in American energy security and maximize benefits from data center use by: 

  1. Authorizing a program for a new TEN pilot program that ties grants to performance metrics such as reducing the cost of installing underground infrastructure, 
  2. Including requirements for data centers related to Power Usage Effectiveness (PUE) in the National Defense Authorization Act for Fiscal Year 2026, and 
  3. Updating the 2018 Commercial Buildings Energy Consumption Survey (CBECS) Data Center Pilot to increase data center participation. 

These actions will position the federal government to deploy innovative approaches to energy infrastructure while unlocking technological advancement and economic growth from AI.

Challenge and Opportunity

By 2028, American data center energy demands are expected to account for up to 12% of the country’s electricity consumption from 4.4% in 2023. The development of artificial intelligence (AI) technologies is  driving this increase because they consume more compute resources than other technologies. As a result of their significant energy demand, data centers face two hurdles to development: (1) interconnection delays due to infrastructure development requirements and (2) the resulting costs borne by consumers in those markets, which heighten resident resistance to siting centers nearby.

Interconnection rates across the country are lengthy. In 2023, the interconnection request to commercial operations period was five years for typical power plant projects. In states like Virginia, widely-known as the “Data Center Capital of the World,” waits can stretch to seven years for data centers specifically. These interconnection timelines have grown over time, and are expected to continue growing based on queue lengths.

Interconnection is also costly. The primary cost drivers are various upgrade requirements to the broader transmission system. Unlike upgrades for energy generators, which are typically paid for by the energy generators, the cost of interconnection for new energy consumers such as data centers affects everyone around them as well. Experts believe that by socializing the costs of new data center infrastructure, utilities are passing these costs to ratepayers.

Efforts are underway to minimize data center energy costs while improving operational efficiency. One way to do that is to reclaim the energy that data centers consume by repurposing waste heat through thermal energy networks (TENs). TENs are shared networks of pipes that move heat between locations; they may incorporate any number of heat sources, including data centers. Data centers can not only generate heat for these systems, but also benefit from cooling—a major source of current data center energy consumption—provided by integrated systems.

Like other energy infrastructure projects, TENs require significant upfront financial investment to reap long-term rewards. However, they can potentially offset some of those upfront costs by shortening interconnection timelines based on demonstrated lower energy demand and reduced grid load. Avoiding larger traditional grid infrastructure upgrades would also avert the skyrocketing consumer costs described above.

At a community or utility level, TENs also offer other benefits. They improve grid resiliency and reliability: The network loops that compose a TEN increase redundancy, reducing the likelihood that a single point of failure will yield systemic failure, especially in light of increasing energy demands brought about by weather events such as extreme heat. Further, TENs allow utilities to decrease and transfer electrical demand, offering a way to balance peak loads. TENs offer building tradespeople such as pipefitters ”plentiful and high-paying jobs” as they become more prevalent, especially in rural areas. They also provide employment paths for employees of utilities and natural gas companies with expertise in underground infrastructure. By creating jobs, reducing water stress and grid strain, and decreasing the risk of quickly rising utility costs, investing in TENs to bolster data center development would reduce the current trend of community resistance to development. Many of these benefits extend to non-data center TEN participants, like nearby homes and businesses, as well. 

Federal coordination is essential to accelerating the creation of TENs in data-center heavy areas. Some states, like New York and Colorado, have passed legislation to promote TEN development. However, the states with the densest data center markets, many of which also rank poorly on grid reliability, are not all putting forth efforts to develop TENs. Because the U.S. grid is divided into multiple regions and managed by the Federal Energy Regulatory Commission, the federal government is uniquely well positioned to invest in improvements in grid resiliency through TENs and to make the U.S. a world leader in this technology.

Plan of Action

The Trump Administration and Congress can promote data center development while improving grid resiliency and reliability and reducing consumers’ financial burden through a three-part strategy:

Recommendation 1. Create a new competitive grant program to help states launch TEN pilots.

Congress should create a new TEN pilot competitive grant program administered by the Department of Energy. The federal TEN program should allow states to apply for funding to run their own TEN programs administered by states’ energy offices and organizations. This program could build on two strong precedents:

  1. The Department of Energy’s 2022 funding opportunity for Community Geothermal Heating and Cooling Design and Deployment. This opportunity supported geothermal heating and cooling networks, which are a type of TEN that relies on the earth’s constant temperature and heat pumps to heat or cool buildings. Though this program generated significant interest, an opportunity remains for the federal government to invest in non-geothermal TEN projects. These would be projects that rely on exchanging heat with other sources, such as bodies of water, waste systems, or even energy-intensive buildings like data centers. The economic advantages are promising: one funded project reported expecting “savings of as much as 70% on utility bills” for beneficiaries of the proposed design.
  1. The New York State’s Large-Scale Thermal program, run by its Energy Research and Development Authority (NYSERDA), has offered multiple funding opportunities that specifically include the development of TENs. In 2021, it launched a Community Heat Pump Systems (PON 4614) program that has since awarded multiple projects that include data centers. One project reported its design would save $2.4 million or roughly 77% annually in operations costs. 

Congress should authorize a new pilot program with $30 million to be distributed to state TEN programs, which states could disperse via grants and performance contracts. Such a program would support the Trump administration’s goal of fast-tracking AI data center development.

To ensure that the funding benefits both grant recipients and their host communities, requirements should be attached to these grants that incentivize consumer benefits such as reduced electricity or heating bills, improved air quality and decreased pollution. The grant awards should be prioritized according to performance metrics such as projected cost reductions related to drilling or to installing underground infrastructure and greater operational efficiency. 

Recommendation 2. Include power usage effectiveness in the amendments to the National Defense Authorization Act for Fiscal Year 2026 (2026 NDAA).

In the National Defense Authorization Act of 2024, Sec. 5302 (“Federal Data Center Consolidation Initiative amendments”) amended Section 834 of the Carl Levin and Howard P. “Buck” McKeon National Defense Authorization Act for Fiscal Year 2015 by specifying minimum requirements for new data centers.  Sec. 5302(b)(2)(b)(2)(A)(ii) currently reads:

 […The minimum requirements established under paragraph (1) shall include requirements relating to—…] “the use of new data centers, including costs related to the facility, energy consumption, and related infrastructure;.” 

To couple data center development with improved grid resilience and stability, the 2026 NDAA should amend Sec. 5302(b)(2)(b)(2)(A)(ii) as follows:

 […The minimum requirements established under paragraph (1) shall include requirements relating to—…] “the use of new data centers, including power usage effectiveness, costs related to the facility, energy consumption, and related infrastructure.” 

Power usage effectiveness (PUE) is a common metric to measure the efficiency of data center power use. It is the ratio of total power used by the facility over the amount of that power dedicated to IT services. The PUE metric has limitations, such as its inability to provide an apples-to-apples comparison of data center energy efficiency based on variability in underlying technology and its lack of precision, especially given the growth of AI data centers. However, introducing the PUE metric as part of the regulatory framework for data centers would provide a specific target for new builds to use, making it easier for both developers and policymakers to identify progress. Requirements related to PUE would also encourage developers to invest in technologies that increase energy efficiency without unduly hurting their bottom lines. In the future, legislators should continue to amend this section of the NDAA as new, more accurate, and useful efficiency metrics develop.

Recommendation 3. The U.S. Energy Information Administration (EIA) should update the 2018 Commercial Buildings Energy Consumption Survey (CBECS) Data Center Pilot. 

To facilitate community acceptance and realize benefits like better financing terms based on lower default risk, data center developers should seek to benchmark their facilities’ energy consumptions. Energy consumption benchmarking, the process of analyzing consumption data and comparing to both past performance and the performance of similar facilities, results in operational cost savings. These savings amplify the economic benefits of vehicles like TENs for cost-sensitive developers and lower the potential increase of community utility costs.

Data center developers should create industry-standard benchmarking tools, much as other industries do. However, it’s challenging for them to embark on this work without accurate and current information that facilitates the development of useful models and targets, especially in such a fast-changing field. Yet data sources such as those used to create benchmarks for other industries are unavailable. One popular source is the CBECS, which does not include data centers as a separate building type. This issue is longstanding; in 2018, the EIA released a report detailing the results of their data center pilot, which they undertook to address this gap. The pilot cited three main hurdles to accurately account for data centers’ energy consumption: the lack of a comprehensive frame or list of data centers, low cooperation rates, and a high rate of nonresponse to important survey questions. 

With the proliferation of data centers since the pilot, it has become only more pressing to differentiate this building type and enable data centers to seek accurate representation and develop industry benchmarks. To address the framing issue, CBECS should use a commercial data source like Data Center Map. At the time the EIA considered this source “unvalidated,” but it has been used as a data source by the U.S. Department of Commerce and the International Energy Agency. Additionally, the EIA should also perform the “cognitive research and pretests” recommended in the pilot to find ways to encourage complete responses in order to recreate its pilot and seek an improved outcome.

Conclusion

Data center energy demand has exploded in recent years and continues to climb, due in part to the advent of widespread AI development. Data centers need access to reliable energy without creating grid instability or dramatically increasing utility costs for individual consumers. This creates a unique opportunity for the federal government to develop and implement innovative technology such as TENs in areas working to support changing energy demands. The government should also seize this moment to define and update standards for site developers to ensure they are building cost-effective and operationally efficient facilities. By progressing systems and tools that benefit other area energy consumers down to the individual ratepayer, the federal government can transform data centers from infrastructural burdens to good neighbors.

Frequently Asked Questions
How was the $30 million budget to help states launch TEN pilots calculated?

This budget was calculated by using the allocation for the NYSERDA Large-Scale Thermal pilot program ($10 million) and multiplying by three (for a three year pilot). Because NYSERDA’s program funded projects at over 50 sites, this initial pilot would plan to fund roughly 150 projects across the states.

What are performance contracts?

Performance-based contracts differ from other types of contracts in that they focus on what work is to be performed rather than how specifically it is accomplished. Solicitations include either a Performance Work Statement or Statement of Objectives and resulting contracts include measurable performance standards and potentially performance incentives.

Fueling the Bioeconomy: Clean Energy Policies Driving Biotechnology Innovation

The transition to a clean energy future and diversified sources of energy requires a fundamental shift in how we produce and consume energy across all sectors of the U.S. economy. The transportation sector, a sector that heavily relies on fossil-based energy, stands out not only because it is the sector that releases the most carbon into the atmosphere, but also for its progress in adopting next-generation technologies when it comes to new technologies and fuel alternatives. 

Over the past several years, the federal government has made concerted efforts to support clean energy innovation in transportation, both for on-road and off-road. Particularly, in hard-to-electrify transportation sub-sectors, there has been added focus such as through the Sustainable Aviation Fuel (SAF) Grand Challenge. These efforts have enabled a wave of biotechnology-driven solutions to move from research labs to commercial markets, such as LanzaJets alcohol-to-jet technology in producing SAF. From renewable fuels to bio-based feedstocks, biotechnologies are enabling the replacement of fossil-derived energy sources and contributing to a more sustainable, secure, and diversified energy system. 

SAF in particular has gained traction, enabled in part by public investment and interagency coordination, like the SAF Grand Challenge Roadmap. This increased federal attention demonstrated how strategic federal action, paired with demand signals from government, targeted incentives, and industry buy-in, can create the conditions needed to accelerate biotechnology adoption.

To better understand the factors driving this progress, FAS conducted a landscape analysis at the federal and regional level of biotechnology innovation within the clean energy sector, complemented by interviews with key stakeholders. Several policy mechanisms, public-private partnerships, and investment strategies were identified that were enablers of advanced SAF adoption and production and similar technologies. By identifying the enabling conditions that supported biotechnology’s uptake and commercialization, we aim to inform future efforts on how to accelerate other sectors that utilize biotechnologies and overall, strengthen the U.S. bioeconomy.

Key Findings & Recommendations

An analysis of the federal clean energy landscape reveals several critical insights that are vital for advancing the development and deployment of biotechnologies. Federal and regional strategies are central to driving innovation and facilitating the transition of biotechnologies from research to commercialization. The following key findings and actionable recommendations address the challenges and opportunities in accelerating this transition.

Federal Level Key Findings & Recommendations

The federal government plays a pivotal role in guiding market signals and investment toward national priorities. In the clean energy sector, decarbonizing aviation has emerged as a strategic objective, with SAF serving as a critical lever. Federal initiatives such as the SAF Grand Challenge, the SAF Roadmap, and the SAF Metrics Dashboard have helped to elevate SAF within national climate priorities and enabled greater interagency coordination. These mechanisms not only track progress but also communicate federal commitment. Still, despite these efforts, current SAF production remains far below target levels, with capacity largely concentrated in HEFA, a pathway with constrained feedstock availability and limited scalability. 

This production gap reflects deeper structural challenges, many of which parallel broader issues across the clean-energy biotech interface. One of the main challenges is the fragmented, short-duration policy incentives currently in use. Tax credits like 40B and 45Z, while important, lack the longevity and clarity required to unlock large-scale, long-term private investment. The absence of binding fuel mandates further undermines market certainty. These policy gaps limit the ability of the clean energy sector to serve as a sustained demand signal for emerging biotechnologies and slow the transition from pilot to commercial scale. 

Importantly, these challenges point to a broader opportunity: SAF as a test case for how the clean energy sector can serve as a driver of biotechnology uptake. Promising biotechnologies, such as alcohol-to-jet and power-to-liquid, are currently stalled by high capital costs, uncertain regulatory pathways, and a lack of coordinated federal support. Addressing these bottlenecks through aligned incentives, technology-neutral mandates, and harmonized accounting frameworks could not only accelerate SAF deployment but also establish a broader policy blueprint for scaling biotechnology across other clean energy applications.

To alleviate some of the challenges identified, the federal government should:

Extend & Clarify Incentives

While tax incentives such as the 45Z Clean Fuel Production Credit offer a promising framework to accelerate low-carbon fuel deployment, current design and implementation challenges limit their impact, particularly for emerging bio-based and synthetic fuels. To fully unlock the climate and market potential of these incentives, Congress and relevant agencies should take the following steps:

Scale Biotech Commercialization Support

The clean energy transition depends in part on the successful commercialization of enabling biotechnologies, ranging from advanced biofuels to bio-based carbon capture, SAF and biomanufacturing platforms that reduce industrial emissions. Recent or proposed funding cuts to clean energy programs risk stalling this progress and undermining U.S. competitiveness in the bioeconomy. 

To accelerate biotechnology deployment and bridge the gap between lab-scale innovation and commercial-scale production, Congress should take the following actions:

Design and Promote Next-Gen Biofuel Policies

To accelerate the deployment of low-carbon fuels and enable innovation in next-generation bioenergy technologies, Congress and relevant agencies should take the following actions:

Regional Level Key Findings & Recommendations

Regional strengths continue to serve as foundational drivers of clean energy innovation, with localized assets shaping the pace and direction of technology development. Federal designations, such as the Economic Development Administration (EDA) Tech Hub program (Tech Hub), have proven catalytic. These initiatives enable regions to unlock state-level co-investment, attract private capital, and align workforce training programs with local industry needs. Early signs suggest that the Tech Hub framework is helping to seed innovation ecosystems where they are most needed, but long-term impact will depend on sustained funding support and continued regional coordination. 

Workforce readiness and enabling infrastructure remain critical differentiators. Regions with deep and committed involvement from major research universities, national labs, or advanced manufacturing clusters are better positioned to scale innovation from prototype to deployment. Real-world testbeds provide environments for stress-testing technologies and accelerating regulatory and market readiness, reinforcing the importance of place-based strategies in federal innovation planning. 

At the same time, private investment in clean energy and enabling biotechnologies remains crucial to developing and scaling innovative technologies. High capital costs, regulatory uncertainty, and limited early-stage demand signals continue to inhibit market entry, especially in geographies with less mature innovation ecosystems. Addressing these barriers through coordinated federal procurement, long-term incentives, and regional capacity-building will be essential to supporting growth in regions with strong assets to develop industry clusters that could yield clean energy benefits. 

To accomplish this, the federal government and regional governments should: 

Strengthen Regional Workforce Pipelines

A skilled and regionally distributed workforce is essential to realizing the full economic and technological potential of clean energy investments, particularly as they intersect with the bioeconomy. While federal funding is accelerating deployment through initiatives such as the IRA and DOE programs, workforce gaps, especially outside major innovation hubs, pose barriers to implementation. Addressing these gaps through targeted education, training, and talent retention efforts will be critical to ensuring that clean energy projects deliver durable, regionally inclusive economic growth. To this end:

Strengthen Regional Infrastructure and Foster Cross-Sector Collaboration

Robust regional infrastructure and cross-sector collaboration are essential to accelerating the deployment of clean energy technologies that leverage advancements in biotechnology and manufacturing. Strategic investments in shared facilities, modernized logistics, and coordinated innovation ecosystems will strengthen supply chain resilience and improve technology transfer across sectors. Facilitating access to R&D infrastructure, particularly for small and mid-sized enterprises, will ensure that innovation is not limited to large firms or major metropolitan areas. To support these outcomes: 

Attract and De-Risk Private Capital

Attracting and de-risking private capital is critical for scaling clean energy and biotechnology innovations. By offering targeted financial mechanisms and leveraging federal visibility, governments can reduce the financial uncertainties that often deter private investment. Effective strategies, such as state-backed loan guarantees and co-investment models, can help bridge funding gaps while strategic partnerships with philanthropic and venture capital entities can unlock additional resources for emerging technologies. To this end: 

Cross-Cutting Key Findings

The successful deployment of federal clean energy and biotechnology initiatives, such as the SAF Grand Challenge, relies heavily on the capacity of regional ecosystems and the private sector to absorb and implement national goals. Many regions, particularly those outside established innovation hubs, lack the infrastructure, resources, and technical expertise to effectively utilize federal funding. As a result, the impact of national policies is often limited, and the full potential of federal investments goes unrealized in certain areas.

Federal programs often take a one-size-fits-all approach, overlooking regional variability in feedstocks, industrial bases and cost structures. Programs like tax credits and life cycle analysis models can unintentionally disadvantage regions with different economic contexts, creating disparities in access to federal incentives. This lack of regional customization prevents certain areas from fully benefiting from national clean energy and biotech initiatives. 

The diffusion of innovation in clean energy and biotechnology remains concentrated in a few key regions, leaving others underutilized. Despite robust federal R&D investments, commercialization and scaling of innovations are primarily concentrated in regions with established infrastructure, hindering the broader geographic spread of these technologies. In addition, workforce development efforts across federal and regional programs are fragmented, creating misalignments in talent pipelines and further limiting the ability of local industries to leverage available resources effectively. The absence of a unified system for tracking key metrics, such as SAF production and emissions reductions, makes it difficult to coordinate efforts or assess progress consistently across regions. To address this, the federal and regional governments should:

Create a Federal–Regional Clean Energy Deployment Compact

A Federal-Regional Clean Energy Deployment Compact is critical for aligning federal clean energy initiatives with the unique capabilities and needs of regional ecosystems. By establishing formal mechanisms, such as intergovernmental councils and regional liaisons, federal programs can be more effectively tailored to local conditions. These mechanisms will ensure two-way communication between federal agencies and regional stakeholders, fostering a collaborative approach that adapts to evolving technological, economic, and environmental conditions. In addition, treating regional tech hubs and initiatives as testbeds for new policy tools, such as performance-based incentives or carbon standards, will allow for innovative solutions to be tested locally before scaling them nationally, ensuring that policies are effective and contextually relevant across diverse regions. To this end:

Build a National Innovation-to-Deployment Pipeline

Creating a seamless innovation-to-deployment pipeline is essential for scaling clean energy technologies and ensuring that regional ecosystems can fully participate in national clean energy transitions. By linking DOE national labs, Tech Hubs, and regional consortia into a coordinated network, the U.S. can support the full life cycle of innovation, from early-stage R&D to commercialization and deployment, across diverse geographies. Additionally, co-developing curricula and training programs between federal agencies, regional tech hubs, and industry partners will ensure that talent pipelines are closely aligned with the evolving needs of the clean energy sector, providing the skilled workforce necessary to implement and scale innovations effectively. To accomplish this the:

Develop a Shared Metrics and Monitoring Platform

A centralized dashboard for tracking key metrics related to clean energy and biotechnology initiatives is crucial for guiding investment and policy decisions. By integrating federal and regional data can provide a comprehensive, real-time view of progress across the country. This shared platform would enable better coordination among federal, state, and local agencies, ensuring that resources are allocated efficiently and that policy decisions are informed by accurate, up-to-date data. Moreover, a unified system would allow for more effective tracking of regional performance, enabling tailored solutions and based on localized needs and challenges. To this end:

The clean energy sector, and specifically SAF, highlights both the promise and the persistent challenges of scaling biotechnologies, reflecting broader issues, such as fragmented regulation, limited commercialization support, and misaligned incentives that hinder the deployment of advanced biotechnologies. Overcoming these systemic barriers requires coordinated, long-term policies including performance-based incentives, and procurement mechanisms that reduce investment risk and free up capital. SAF should be seen not as a standalone initiative but as a model for integrating biotechnology into industrial and energy strategy, supported by a robust innovation pipeline, expanded infrastructure, and shared metrics to guide progress. With sustained federal leadership and strategic alignment, the bioeconomy can become a key pillar of a low-carbon, resilient energy future.

Measuring and Standardizing AI’s Energy and Environmental Footprint to Accurately Access Impacts

The rapid expansion of artificial intelligence (AI) is driving a surge in data center energy consumption, water use, carbon emissions, and electronic waste—yet these environmental impacts, and how they will change in the future, remain largely opaque. Without standardized metrics and reporting, policymakers and grid operators cannot accurately track or manage AI’s growing resource footprint. Currently, companies often use outdated or narrow measures (like Power Usage Effectiveness, PUE) and purchase renewable credits to obscure true emissions. Their true carbon footprint may be as much as 662% higher than the figures they report. A single hyperscale AI data center can guzzle hundreds of thousands of gallons of water per day​ and contribute to a “mountain” of e-waste​, yet only about a quarter of data center operators even track what happens to retired hardware​.

This policy memo proposes a set of congressional and federal executive actions to establish comprehensive, standardized metrics for AI energy and environmental impacts across model training, inference, and data center infrastructure. We recommend that Congress directs the Department of Energy (DOE) and the National Institute of Standards and Technology (NIST) to design, collect, monitor and disseminate uniform and timely data on AI’s energy footprint, while designating the White House Office of Science and Technology Policy (OSTP) to coordinate a multi-agency council that coordinates implementation. Our plan of action outlines steps for developing metrics (led by DOE, NIST, and the Environmental Protection Agency [EPA]), implementing data reporting (with the Energy Information Administration [EIA], National Telecommunications and Information Administration [NTIA], and industry), and integrating these metrics into energy and grid planning (performed by DOE’s grid offices and the Federal Energy Regulatory Commission [FERC]). By standardizing how we measure AI’s footprint, the U.S. can be better prepared for the growth in power consumption while maintaining its leadership in artificial intelligence.

Challenge and Opportunity

Inconsistent metrics and opaque reporting make future AI power‑demand estimates extremely uncertain, leaving grid planners in the dark and climate targets on the line.

AI’s Opaque Footprint

Generative AI and large-scale cloud computing are driving an unprecedented increase in energy demand. AI systems require tremendous amounts of computing power both during training (the AI development period) and inference (when AI is used in real world applications).  The rapid rise of this new technology is already straining energy and environmental systems at an unprecedented scale. Data centers consumed an estimated 415 Terawatt hours (TWh) of electricity in 2024 (roughly 1.5% of global power demand), and with AI adoption accelerating, the International Energy Agency (IEA) forecasts that data center energy use could more than double to 945 TWh by 2030. This is an added load comparable to powering an entire country the size of Sweden or even Germany. There are  a range of projections of AI’s energy consumption, with some estimates suggesting even more rapid growth than the IEA. Estimates suggest that much of this growth will be concentrated in the United States. 

The large divergence in estimates for AI-driven electricity demand stem from the different assumptions and methods used in each study. One study uses one of the parameters like the AI Query volume (the number of requests made by users for AI answers), another tries to estimate energy demand from the estimated supply of AI related hardware. Some estimate the Compound Annual Growth Rate (CAGR) of data center growth under different growth scenarios. Different authors make various assumptions about chip shipment growth, workload mix (training vs inference), efficiency gains, and per‑query energy.  Amidst this fog of measurement confusion, energy suppliers are caught by surges in demand from new compute infrastructure on top of existing demands from sources like electric vehicles and manufacturing. Electricity grid operators in the United States typically plan for gradual increases in power demand that can be met with incremental generation and transmission upgrades. But if the rapid build-out of AI data centers, on top of other growing power demands, pushes global demand up by an additional hundreds of terawatt hours annually this will shatter the steady-growth assumption embedded in today’s models. Planners need far more granular, forward-looking forecasting methods to avoid driving up costs for rate-payers, last-minute scrambles to find power, and potential electricity reliability crises. 

This surge in power demand also threatens to undermine climate progress. Many new AI data centers require 100–1000 megawatts (MW), equivalent to the demands of a medium-sized city, while grid operators are faced with connection lead times of over 2 years to connect to clean energy supplies.  In response to these power bottlenecks some regional utilities, unable to supply enough clean electricity, have even resorted to restarting retired coal plants to meet data center loads, undermining local climate goals​ and efficient operation. Google’s carbon emissions rose 48% over the past five years and Microsoft’s by 23.4% since 2020, largely due to cloud computing and AI.  

In spite of the risks to the climate, carbon emissions data is often obscured: firms often claim “carbon neutrality” via purchased clean power credits, while their actual local emissions go unreported. One analysis found Big Tech (Amazon, Meta) data centers may emit up to 662% more CO₂ than they publicly report​. For example, Meta’s 2022 data center operations reported only 273 metric tons CO₂ (using market-based accounting with credits), but over 3.8 million metric tons CO₂ when calculated by actual grid mix according to one analysis—a more than 19,000-fold increase​. Similarly, AI’s water impacts are largely hidden. Each interactive AI query (e.g. a short session with a language model) can indirectly consume half a liter of fresh water through data center cooling​, contributing to millions of gallons used by AI servers—but companies rarely disclose water usage per AI workload. This lack of transparency masks the true environmental cost of AI, hinders accountability, and impedes smart policymaking.

Outdated and Fragmented Metrics 

Legacy measures like Power Usage Effectiveness (PUE) miss what is important for AI compute efficiency, such as water consumption, hardware manufacturing, and e-waste.

The metrics currently used to gauge data center efficiency are insufficient for AI-era workloads. Power Usage Effectiveness (PUE), the two-decades-old standard, gives only a coarse snapshot of facility efficiency under ideal conditions​. PUE measures total power delivered to a datacenter  versus how much of that power actually makes it to the IT equipment inside. The more power used (e.g. for cooling), the worse the PUE ratio will be. However, PUE does not measure how efficiently the IT equipment actually uses the power delivered to it. Think about a car that reports how much fuel reaches the engine but not the miles per gallon of that engine. You can ensure that the fuel doesn’t leak out of the line on its way to the engine, but that engine might not be running efficiently. A good PUE is the equivalent of saying that fuel isn’t leaking out on its way to the engine; it might tell you that a data center isn’t losing too much energy to cooling, but won’t flag inefficient IT equipment. An AI training cluster with a “good” PUE (around 1.1) could still be wasteful if the hardware or software is poorly optimized.

In the absence of updated standards, companies “report whatever they choose, however they choose” regarding AI’s environmental impact. Few report water usage or lifecycle emissions. Only 28% of operators track hardware beyond its use, and just 25% measure e-waste​, resulting in tons of servers and AI chips quietly ending up in landfills. This data gap leads to misaligned incentives—for instance, firms might build ever-larger models and data centers, chasing AI capabilities, without optimizing for energy or material efficiency because there is no requirement or benchmark to do so.

Opportunities for Action

Standardizing metrics for AI’s energy and environmental footprint presents a win-win opportunity. By measuring and disclosing AI’s true impacts, we can manage them. With better data, policymakers can incentivize efficiency innovations (from chip design to cooling to software optimization) and target grid investments where AI load is rising. Industry will benefit too: transparency can highlight inefficiencies (e.g. low server utilization or high water-cooled heat that could be recycled) and spur cost-saving improvements. Importantly, several efforts are already pointing the way. In early 2024, bicameral lawmakers introduced the Artificial Intelligence Environmental Impacts Act, aiming to have the EPA study AI’s environmental footprint and develop measurement standards and a voluntary reporting system via NIST. Internationally, the European Union’s upcoming AI Act will require large AI systems to report energy use, resource consumption, and other life cycle impacts​, and the ISO is preparing “sustainable AI” standards for energy, water, and materials accounting​. The U.S. can build on this momentum. A recent U.S. Executive Order (Jan 2025) already directed DOE to draft reporting requirements for AI data centers covering their entire lifecycle—from material extraction and component manufacturing to operation and retirement—including metrics for embodied carbon (greenhouse-gas emissions that are “baked into” the physical hardware and facilities before a single watt is consumed to run a model), water usage, and waste heat​. It also launched a DOE–EPA “Grand Challenge” to push the PUE ratio below 1.1 and minimize water usage in AI facilities​. These signals show that there is willingness to address the problem. Now is the time to implement a comprehensive framework that standardizes how we measure AI’s environmental impact. If we seize this opportunity, we can ensure innovation in AI is driven by clean energy, a smarter grid, and less environmental and economic burden on communities.

Plan of Action

To address this challenge, Congress should authorize DOE and NIST to lead an interagency working group and a consortium of public, private and academic communities to enact a phased plan to develop, implement, and operationalize standardized metrics, in close partnership with industry.

Recommendation 1. Identify and Assign Agency Mandates

Creating and Implementing this measurement framework requires concerted action by multiple federal agencies, each leveraging its mandate. The Department of Energy (DOE) should serve as the co-lead federal agency driving this initiative. Within DOE, the Office of Critical and Emerging Technologies (CET) can coordinate AI-related efforts across DOE programs, given its focus on AI and advanced tech integration. The National Institute of Standards and Technology (NIST) will also act as a co-lead for this initiative leading the metrics development and standardization effort as described, convening experts and industry. The White House Office of Science and Technology Policy (OSTP) will act as the coordinating body for this multi-agency effort. OSTP, alongside the Council on Environmental Quality (CEQ), can ensure alignment with broader energy, environment, and technology policy. The Environmental Protection Agency (EPA) should take charge of environmental data collection and oversight. The Federal Energy Regulatory Commission (FERC) should play a supporting role by addressing grid and electricity market barriers. FERC should streamline interconnection processes for new data center loads, perhaps creating fast-track procedures for projects that commit to high efficiency and demand flexibility.

Congressional leadership and oversight will be key. The Senate Committee on Energy and Natural Resources and House Energy & Commerce Committee (which oversee energy infrastructure and data center energy issues) should champion legislation and hold hearings on AI’s energy demands. The House Science, Space, and Technology Committee and Senate Commerce, Science, & Transportation Committee (which oversee NIST, and OSTP) should support R&D funding and standards efforts. Environmental committees (like Senate Environment and Public Works, House Natural Resources) should address water use and emissions. Ongoing committee oversight can ensure agencies stay on schedule and that recommendations turn into action (for example, requiring an EPA/DOE/NIST joint report to Congress within a set timeframe(s).

Congress should mandate a formal interagency task force or working group, co-led by the Department of Energy (DOE) and the National Institute of Standards and Technology (NIST), with the White House Office of Science and Technology Policy (OSTP) serving as the coordinating body and involving all relevant federal agencies. This body will  meet regularly to track progress, resolve overlaps or gaps, and issue public updates. By clearly delineating responsibilities, The federal government can address the measurement problem holistically.

Recommendation 2. Develop a Comprehensive AI Energy Lifecycle Measurement Framework

A complete view of AI’s environmental footprint requires metrics that span the full lifecycle, including every layer from chip to datacenter, workload drivers, and knock‑on effects like water use and electricity prices.

Create new standardized metrics that capture AI’s energy and environmental footprint across its entire lifecycle—training, inference, data center operations (cooling/power), and hardware manufacturing/disposal. This framework should be developed through a multi-stakeholder process led by NIST in partnership with DOE and EPA, and in consultation with industry, academia as well as state and local governments. 

Key categories should include:

  1. Data Center Efficiency Metrics: how effectively do data centers use power?
  2. AI Hardware & Compute Metrics: e.g. Performance per Watt (PPW)—the throughput of AI computations per watt of power.
  3. Cooling and Water Metrics: How much energy and water are being used to cool these systems?
  4. Environmental Impact Metrics: What is the carbon intensity per AI task?
  5. Composite or Lifecycle Metrics: Beyond a single point in time, what are the lifetime characteristics of impact for these systems?

Designing standardized metrics

NIST, with its measurement science expertise, should coordinate the development of these metrics in an open process, building on efforts like NIST’s AI Standards Working Group—a standing body chartered under the Interagency Committee on Standards Policy which brings together technical stakeholders to map the current AI-standards landscape, spot gaps, and coordinate U.S. positions and research priorities. The goal is to publish a standardized metrics framework and guidelines that industry can begin adopting voluntarily within 12 months. Where possible, leverage existing standards (for example, those from the Green Grid consortium on PUE and Water Usage Effectiveness (WUE), or IEEE/ISO standards for energy management) and tailor them to AI’s unique demands. Crucially, these metrics must be uniformly defined to enable apples-to-apples comparisons and periodically updated as technology evolves.

Review, Governance, and improving metrics

We recommend establishing a Metrics Review Committee (led by NIST with DOE/EPA and external experts) to refine the metrics whenever needed, host stakeholder workshops, and public updates. This continuous improvement process will keep the framework current with new AI model types, cooling tech, and hardware advances, ensuring relevance into the future.  For example, when we move from the current model of chatbots responding to queries to agentic AI systems that plan, act, remember, and iterate autonomously, traditional “energy per query” metrics no longer capture the full picture.

Recommendation 3. Operationalize Data Collection, Reporting, Analysis and Integrate it into Policy

Start with a six‑month voluntary reporting program, and gradually move towards a mandatory reporting mechanism which feeds straight into EIA outlooks and FERC grid planning.

The task force should solicit inputs via a Request for Information (RFI) — similar to DOE’s recent RFI on AI infrastructure development​, asking data center operators, AI chip manufacturers, cloud providers, utilities, and environmental groups to weigh in on feasible reporting requirements and data sharing methods. Within 12 months of starting, this taskforce should complete (a) a draft AI energy lifecycle measurement framework (with standardized definitions for energy, water, carbon, and e-waste metrics across training and data center operations), and (b) an initial reporting template for technology companies, data centers and utilities to pilot. 

With standardized metrics in hand, we must shift the focus to implementation and data collection at scale. In the beginning, a voluntary AI energy reporting program can be launched by DOE and EPA (with NIST overseeing the standards). This program would provide guidance to AI developers (e.g. major model-training companies), cloud service providers, and data center operators to report their metrics on an annual or quarterly basis.

After a trial run of the voluntary program, Congress should enact legislation to create a mandatory reporting regime that borrows the best features of existing federal disclosure programs. One useful template is EPA’s Greenhouse Gas Reporting Program, which obliges any facility that emits more than 25,000 tons of CO₂ equivalent per year to file standardized, verifiable electronic reports. The same threshold logic could be adapted for data centers (e.g., those with more than 10 MW of IT load) and for AI developers that train models above a specified compute budget. A second model is DOE/EIA’s Form EIA-923 “Power Plant Operations Report,” whose structured monthly data flow straight into public statistics and planning models. An analogous “Form EIA-AI-01” could feed the Annual Energy Outlook and FERC reliability assessments without creating a new bureaucracy. EIA could also consider adding specific questions or categories in the Commercial Buildings Energy Consumption Survey and Form EIA-861 to identify energy use by data centers and large computing loads. This may involve coordinating with the Census Bureau to leverage industrial classification data (e.g., NAICS codes for data hosting facilities) so that baseline energy/water consumption of the “AI sector” is measured in national statistics. NTIA, which often convenes multi stakeholder processes on technology policy, can host industry roundtables to refine reporting processes and address any concerns (e.g. data confidentiality, trade secrets). NTIA can help ensure that reporting requirements are not overly burdensome to smaller AI startups by working out streamlined methods (perhaps aggregated reporting via cloud providers, for instance). DOE’s Grid Deployment Office (GDO) and Office of Electricity (OE), with better data, should start integrating AI load growth into grid planning models and funding decisions. For example, GDO could prioritize transmission projects that will deliver clean power to regions with clusters of AI data centers, based on EIA data showing rapid load increases. FERC, for its part, can use the reported data to update its reliability and resource adequacy guidelines and possibly issue guidance for regional grid operators (RTOs/ISOs) to explicitly account for projected large computing loads in their plans.

Table 1. Roles and Responsibilities to Measure AI’s Environmental Impact

Agency/EntityRoleKey Responsibilities
Department of Energy (DOE)Co-leadOffice of Critical and Emerging Technologies (CET): coordinate AI-related efforts across DOE programs

Office of Energy Efficiency and Renewable Energy (EERE) can lead on promoting energy-efficient data center technologies and practices (e.g. through R&D programs and partnerships)

Office of Electricity (OE) and Grid Deployment Office address grid integration challenges (ensuring AI data centers have access to reliable clean power).

DOE should also collaborate with utilities and FERC to plan for AI-driven electricity demand growth and to encourage demand-response or off-peak operation strategies for energy-hungry AI clusters.
National Institute of Standards and Technology (NIST)Co-lead for metrics and standardsLead metrics development and standardization efforts

Convene experts and industry stakeholders

Revive/expand AI Standards Coordination Working Group for sustainability metrics

Publish technical standards for measuring AI energy use, water use, and emissions

Host stakeholder consortium on AI environmental impacts (with EPA and DOE)
White House Office of Science and Technology Policy (OSTP)Coordinating bodyCoordinate multi-agency efforts

Work with Council on Environmental Quality (CEQ) to align with climate and tech policy

Integrate AI energy metrics into federal sustainability requirements via Federal Chief Sustainability Officer and OMB guidance

Update OMB memos on data center optimization to include AI-specific measures
Environmental Protection Agency (EPA)Environmental oversightLead environmental data collection and oversight

Conduct comprehensive study of AI’s environmental impacts (with DOE)

Examine AI systems’ lifecycle emissions, water use, and e-waste

Apply greenhouse gas (GHG) accounting expertise

Quantify metrics like carbon intensity using location-based grid emissions factors
Federal Energy Regulatory Commission (FERC)Grid and market supportAddress grid and electricity market barriers

Streamline interconnection processes for new data center loads

Create fast-track procedures for high-efficiency, demand-flexible projects

Ensure regional grid reliability assessments account for projected AI/data center load growth
Congressional CommitteesLegislative oversightEnergy Committees: Champion legislation and hold hearings on AI energy demands
Senate Committee on Energy and Natural Resources

House Energy & Commerce Committee


Science/Technology Committees:Support R&D funding and standards efforts
House Science, Space, and Technology Committee

Senate Commerce, Science, & Transportation Committee


Environmental Committees: Address water use and emissions
Senate Environment and Public Works

House Natural Resources


Oversight Functions:
Ensure agencies stay on schedule

Require EPA/DOE/NIST joint report to Congress

Address further legislative needs

This transparency will let policymakers, researchers, and consumers track improvements (e.g., is the energy per AI training decreasing over time?) and identify leaders/laggards. It will also inform mid-course adjustments that if certain metrics prove too hard to collect or not meaningful, NIST can update the standards. The Census Bureau can contribute by testing the inclusion of questions on technology infrastructure in its Economic Census 2027 and annual surveys, ensuring that the economic data of the tech sector includes environmental parameters (for example, collecting data center utility expenditures, which correlate with energy use). Overall, this would establish an operational reporting system and start feeding the data into both policy and market decisions.

Through these recommendations, responsible offices have clear roles: DOE spearheads efficiency measures in data center initiatives; OE (Office of Electricity and GDO (Grid Deployment Office) use the data to guide grid improvements; NIST creates and maintains the measurement standards; EPA oversees environmental data and impact mitigation; EIA institutionalizes energy data collection and dissemination; FERC adapts regulatory frameworks for reliability and resource adequacy; OSTP coordinates the interagency strategy and keeps the effort a priority; NTIA works with industry to smooth data exchange and involve them; and Census Bureau integrates these metrics into broader economic data. See the table below.Meanwhile, non-governmental actors like utilities, AI companies, and data center operators must not only be data providers but partners. Utilities could use this data to plan investments and can share insights on demand response or energy sourcing; AI developers and data center firms will implement new metering and reporting practices internally, enabling them to compete on efficiency (similar to car companies competing on miles per gallon ratings). Together, these actions create a comprehensive approach: measuring AI’s footprint, managing its growth, and mitigating its environmental impacts through informed policy.

Table 2. Example Metrics to Illustrate the Types of Shared Information

Metric CategoryMetric NameDefinitionPurpose/Benefit
Data Center Efficiency MetricsPower Usage Effectiveness (PUE)Refined for AI workloads – ratio of total facility energy to IT equipment energyMeasures overall data center energy efficiency for AI-specific operations
Data Center Infrastructure Efficiency (DCIE)IT power versus total facility power (inverse of PUE)Alternative perspective on facility efficiency, focusing on IT equipment proportion
Energy Reuse Factor (ERF)Quantifies how much waste heat is reused on-siteMeasures ability to capture and utilize waste heat, reducing overall energy needs
Carbon Usage Effectiveness (CUE)Links energy use with carbon emissions (kg CO₂ per kWh)Provides holistic view of facility carbon intensity beyond just power usage
Environmental MetricsEnergy IntensityAnalyzes energy consumed per unit of data volume processed (Kwh/Gb)Reveals energy cost per data unit. Useful for tuning AI models.
Annual water consumptionMeasures total liters of water used annually at a data center levelTracks overall water consumption, essential for annual planning and sustainability reporting.
AI Hardware & Compute MetricsPerformance per Watt (PPW)Throughput of AI computations (FLOPS or inferences) per watt of powerEncourages energy-efficient model training and inference hardware
Compute UtilizationAverage utilization rates of AI accelerators (GPUs/TPUs)Ensures expensive hardware is well-utilized rather than idling
Training Energy per ModelTotal kWh or emissions per training run (normalized by model size/training-hours)Quantifies energy cost of model development

Conclusion

AI’s extraordinary capabilities should not come at the expense of our energy security or environmental sustainability. This memo outlines how we can effectively operationalize measuring AI’s environmental footprint by establishing standardized metrics and leveraging the strengths of multiple agencies to implement them. By doing so, we can address a critical governance gap: what isn’t measured cannot be effectively managed. Standard metrics and transparent reporting will enable AI’s growth while ensuring that data center expansion is met with commensurate increases in clean energy, grid upgrades, and efficiency gains.

The benefits of these actions are far-reaching. Policymakers will gain tools to balance AI innovation with energy and environment goals. For example, by being able to require improvements if an AI service is energy-inefficient, or to fast-track permits for a new data center that meets top sustainability standards. Communities will be better protected: with data in hand, we can avoid scenarios where a cluster of AI facilities suddenly strains a region’s power or water resources without local officials knowing in advance. Instead, requirements for reporting and coordination can channel resources (like new transmission lines or water recycling systems) to those communities ahead of time. The AI industry itself will benefit by building trust and reducing the risk of backlash or heavy-handed regulation; a clear, federal metrics framework provides predictability and a level playing field (everyone measures the same way), and it showcases responsible stewardship of technology. Moreover, emphasizing energy efficiency and resource reuse can reduce operating costs for AI companies in the long run, a crucial advantage as energy prices and supply chain concerns grow.

This memo is part of our AI & Energy Policy Sprint, a policy project to shape U.S. policy at the critical intersection of AI and energy. Read more about the Policy Sprint and check out the other memos here.

Frequently Asked Questions
Why do we need AI-specific environmental metrics? Don’t data centers already have efficiency standards?

While there are existing metrics like PUE for data centers, they don’t capture the full picture of AI’s impacts. Traditional metrics focus mainly on facility efficiency (power and cooling) and not on the computational intensity of AI workloads or the lifecycle impacts. AI operations involve unique factors—for example, training a large AI model can consume significant energy in a short time, and using that AI model continuously can draw power 24/7 across distributed locations. Current standards are outdated and inconsistent​: one data center might report a low PUE but could be using water recklessly or running hardware inefficiently. AI-specific metrics are needed to measure things like energy per training run, water per cooling unit, or carbon per compute task, which no standard reporting currently requires. In short, general data center standards weren’t designed for the scale and intensity of modern AI. By developing AI-specific metrics, we ensure that the unique resource demands of AI are monitored and optimized, rather than lost in aggregate averages. This helps pinpoint where AI can be made more efficient (e.g., via better algorithms or chips)—an opportunity not visible under generic metrics.

How will multiple agencies work together to implement these recommendations?

AI’s environmental footprint is a cross-cutting issue, touching on energy infrastructure, environmental impact, technological standards, and economic data. No single agency has the full expertise or jurisdiction to cover all aspects. Each agency will have clearly defined roles (as outlined in the Plan of Action). For instance, NIST develops the methodology, DOE/EPA collect and use the data, EIA disseminates it, and FERC/Congress use it to adjust policies. This collaborative approach prevents blind spots. A single-agency approach would likely miss critical elements (for instance, a purely DOE-led effort might not address e-waste or standardized methods, which NIST and EPA can). The good news is that frameworks for interagency cooperation already exist​, and this initiative aligns with broader administration priorities (clean energy, reliable grid, responsible AI). Thus, while it involves multiple agencies, OSTP and the White House will ensure everyone stays synchronized. The result will be a comprehensive policy that each agency helps implement according to its strength, rather than a piecemeal solution. See below:


Roles and Responsibilities to Measure AI’s Environmental Impact



  • Department of Energy (DOE): DOE should serve as the co-lead federal agency driving this initiative. Within DOE, the Office of Critical and Emerging Technologies (CET) can coordinate AI-related efforts across DOE programs, given its focus on AI and advanced tech integration. DOE’s Office of Energy Efficiency and Renewable Energy (EERE) can lead on promoting energy-efficient data center technologies and practices (e.g. through R&D programs and partnerships), while the Office of Electricity (OE) and Grid Deployment Office address grid integration challenges (ensuring AI data centers have access to reliable clean power). DOE should also collaborate with utilities and FERC to plan for AI-driven electricity demand growth and to encourage demand-response or off-peak operation strategies for energy-hungry AI clusters.

  • National Institute of Standards and Technology (NIST): NIST will also act as a co-lead for this initiative leading the metrics development and standardization effort as described, convening experts and industry. NIST should revive or expand its AI Standards Coordination Working Group​ to focus on sustainability metrics, and ultimately publish technical standards or reference materials for measuring AI energy use, water use, and emissions. NIST is also suited to host stakeholder consortium on AI environmental impacts, working in tandem with EPA and DOE.

  • White House, including the Office of Science and Technology Policy (OSTP): OSTP will act as the coordinating body for this multi-agency effort. OSTP, alongside the Council on Environmental Quality (CEQ), can ensure alignment with broader climate and tech policy (such as the U.S. Climate Strategy and AI initiatives). The Administration can also use the Federal Chief Sustainability Officer and OMB guidance to integrate AI energy metrics into federal sustainability requirements (for instance, updating OMB’s memos on data center optimization to include AI-specific measures​).

  • Environmental Protection Agency (EPA): EPA should take charge of environmental data collection and oversight. In the near term, EPA (with DOE) would conduct the comprehensive study of AI’s environmental impacts, examining AI systems’ lifecycle emissions, water and e-waste. EPA’s expertise in greenhouse gas (GHG) accounting will ensure metrics like carbon intensity are rigorously quantified (e.g. using location-based grid emissions factors rather than unreliable REC-based accounting).

  • Federal Energy Regulatory Commission (FERC): FERC plays a supporting role by addressing grid and electricity market barriers. FERC should streamline interconnection processes for new data center loads, perhaps creating fast-track procedures for projects that commit to high efficiency and demand flexibility. FERC can ensure that regional grid reliability assessments start accounting for projected AI/data center load growth using data​.

  • Congressional Committees: Congressional leadership and oversight will be key. The Senate Committee on Energy and Natural Resources and House Energy & Commerce Committee (which oversee energy infrastructure and data center energy issues) should champion legislation and hold hearings on AI’s energy demands. The House Science, Space, and Technology Committee and Senate Commerce, Science, & Transportation Committee (which oversee NIST and OSTP) should support R&D funding and standards efforts. Environmental committees (like Senate Environment and Public Works, House Natural Resources) should address water use and emissions. Ongoing committee oversight can ensure agencies stay on schedule and that recommendations turn into action (for example, requiring the EPA/DOE/NIST joint report to Congress in four years as the Act envisions​, and then moving on any further legislative needs).

What exactly will companies and utilities have to report?

The plan requires high-level, standardized data that balances transparency with practicality. Companies running AI operations (like cloud providers or big AI model developers) would report metrics such as: total electricity consumed for AI computations (annually), average efficiency metrics (e.g. PUE, Carbon Usage Effectiveness (CUE), and WUE for their facilities), water usage for cooling, and e-waste generated (amount of hardware decommissioned and how it was handled). These data points are typically already collected internally for cost and sustainability tracking but the difference is they would be reported in a consistent format and possibly to a central repository. For utilities, if involved, they might report aggregated data center load in their service territory or significant new interconnections for AI projects (much of this is already in utility planning documents). See below for examples.


Metrics to Illustrate the Types of Shared Information



  • Data Center Efficiency Metrics: Power Usage Effectiveness (PUE) (refined for AI workloads), Data Center Infrastructure Efficiency (DCIE) which measures IT versus total facility power (the inverse of PUE), Energy Reuse Factor (ERF) to quantify how much waste heat is reused on-site, and Carbon Usage Effectiveness (CUE) to link energy use with carbon emissions (kg CO₂ per kWh). These give a holistic view of facility efficiency and carbon intensity, beyond just power usage​.

  • AI Hardware & Compute Metrics: Performance per Watt (PPW)—the throughput of AI computations (like FLOPS or inferences) per watt of power, which encourages energy-efficient model training and inference​. Compute Utilization—ensuring expensive AI accelerators (GPUs/TPUs) are well-utilized rather than idling (tracking average utilization rates). Training energy per model—total kWh or emissions per training run (possibly normalized by model size or training-hours). Inference efficiency—energy per 1000 queries or per inference for deployed models. Idle power draw—measure and minimize the energy hardware draws when not actively in use​.

  • Cooling and Water Metrics: Cooling Energy Efficiency Ratio (EER)—the output cooling power per watt of energy input, to gauge cooling system efficiency​. Water Usage Effectiveness (WUE)—liters of water used per kWh of IT compute, or simply total water used for cooling per year​. These help quantify and benchmark the significant water and electricity overhead for thermal management in AI data centers.

  • Environmental Impact Metrics: Carbon Intensity per AI Task—CO₂ emitted per training or per 1000 inferences, which could be aggregated to an organizational carbon footprint for AI operations​. Greenhouse Gas emissions per kWh—linking energy use to actual emissions based on grid mix or backup generation. Also, e-waste metrics—such as total hardware weight decommissioned annually, or a recycling ratio. For instance, tracking the tons of servers/chips retired and the fraction recycled versus landfilled can illuminate the life cycle impact​.

  • Composite or Lifecycle Metrics: Develop ways to combine these factors to rate overall sustainability of AI systems. For example, an “AI Sustainability Score” could incorporate energy efficiency, renewables use, cooling efficiency, and end-of-life recycling. Another idea is an “AI Energy Star” rating for AI hardware or cloud services that meet certain efficiency and transparency criteria, modeled after Energy Star appliance ratings.

Won’t this be a burden or risk revealing trade secrets?

No, the intention is not to force disaggregation down to proprietary details (e.g., exactly how a specific algorithm uses energy) but rather to get macro-level indicators. Regarding trade secrets or sensitive info, the data collected (energy, water, emissions) is not about revealing competitive algorithms or data, it’s about resource use. These are analogous to what many firms already publish in sustainability reports (power usage, carbon footprint), just more uniformly. There will be provisions to protect any sensitive facility-level data (e.g., EIA could aggregate or anonymize certain figures in public releases). The goal is transparency about environmental impact, not exposure of intellectual property.

How will these metrics and data actually be used by the government?

Once collected, the data will become a powerful tool for evidence-based policymaking and oversight. At the strategic level, DOE and the White House can track whether the AI sector is becoming more efficient or not—for instance, seeing trends in energy-per-AI-training decreasing (good) or total water use skyrocketing (a flag for action).

What are some examples?

Energy planning: EIA will incorporate the numbers into its models, which guide national energy policy and investment. If data shows that AI is driving, say, an extra 5% electricity demand growth in certain regions, DOE’s Grid Deployment Office and FERC can respond by facilitating grid expansions or reliability measures in those areas​.


Climate policy: EPA can use reported emissions data to update greenhouse gas inventories and identify if AI/data centers are becoming a significant source—if so, that could shape future climate regulations or programs (ensuring this sector contributes to emissions reduction goals).


Water resource management: If we see large water usage by AI in drought-prone areas, federal and state agencies can work on water recycling or alternative cooling initiatives.


Research and incentives: DOE’s R&D programs (through ARPA-E or National Labs) can target the pain points revealed—e.g., if e-waste volumes are high, fund research into longer-lasting hardware or recycling tech; if certain metrics like Energy Reuse Factor are low, push demonstration projects for waste heat reuse.


This could inform everything from ESG investment decisions to local permitting. For example, a company planning a new data center might be asked by local authorities, “What’s your expected PUE and water usage? The national average for AI data centers is X—will you do better?” In essence, the data ensures the government and public can hold the AI industry accountable for progress (or regress) on sustainability. By integrating these data into models and policies, the government can anticipate and avert problems (like grid strain or high emissions) before they grow, and steer the sector toward solutions.

The tech industry is global, so how will U.S. metrics align internationally?

AI services and data centers are worldwide, so consistency in how we measure impacts is important. The U.S. effort will be informed by and contribute to international standards. Notably, the ISO (International Organization for Standardization) is already developing criteria for sustainable AI, including energy, raw materials, and water metrics across the AI lifecycle NIST, which often represents the U.S. in global standards bodies, is involved and will ensure that our metrics framework aligns with ISO’s emerging standards. Similarly, the EU’s AI Act also has requirements for reporting AI energy and resource use​. By moving early on our own metrics, the U.S. can actually help shape what those international norms look like, rather than react to them. This initiative will encourage U.S. agencies to engage in forums like the Global Partnership on AI (GPAI) or bilateral tech dialogues to promote common sustainability reporting frameworks. In the end, aligning metrics internationally will create a more level playing field—ensuring that AI companies can’t simply shift operations to avoid transparency. If the U.S., EU, and others all require similar disclosures, it reinforces responsible practices everywhere.

What if these measures make AI development more expensive or slow down innovation?

Shining a light on energy and resource use can drive new innovation in efficiency. Initially, there may be modest costs—for example, installing better sub-meters in data centers or dedicating staff time to reporting. However, these costs are relatively small in context. Many leading companies already track these metrics internally for cost management and corporate sustainability goals. We are recommending formalizing and sharing that information. Over time, the data collected can reduce costs: companies will identify wasteful practices (maybe servers idling, or inefficient cooling during certain hours) and correct them, saving on electricity and water bills. There is also an economic opportunity in innovation: as efficiency becomes a competitive metric, we expect increased R&D into low-power AI algorithms, advanced cooling, and longer-life hardware. Those innovations can improve performance per dollar as well. Moreover, policy support can offset any burdens—for instance, the government can provide technical assistance or grants to smaller firms to help them improve energy monitoring. We should also note that unchecked resource usage carries its own risks to innovation: if AI’s growth starts causing blackouts or public backlash due to environmental damage, that would seriously hinder AI progress.

Table 3. Roles of Government and Non-Government Stakeholders

TypeAgency / OfficeMetric DevelopmentData Collection & ReportingAnalysis & Planning IntegrationPolicy & Oversight
GovernmentDOE – EERELead role in energy efficiency metricsSupports voluntary reporting systemsIntegrates energy data into planning toolsLeads clean energy transitions
DOE – OEUses data for grid forecastingCoordinates grid reliability planning
DOE – GDOIntegrates data into infrastructure planningPrioritizes transmission buildout
EPACo-leads lifecycle, emissions, water, and e-waste metricsLeads environmental impact data collectionTracks emissions, water use, and e-wasteOversees regulation and Congressional briefings
NISTLead on standardized metrics (PUE, WUE, etc.)Provides protocols for reportingEnsures data accuracyAligns with international standards
EIAAdvises on metric use in national statsCollects energy/water usage dataPublishes AI-specific trendsMaintains transparency and reporting
FERCCollects grid data from ISOs/RTOsIntegrates demand into reliability planningIssues grid and rate guidance
OSTPCoordinates interagency frameworkOversees implementation roadmapMonitors alignment with national AI goalsEnsures cross-agency cohesion
NTIASupports digital infrastructure metric designIndustry interface for data exchangeHighlights interconnection/data demandAligns broadband/data policy with AI metrics
Census BureauDevelops AI/data infrastructure codesAdds metrics to Economic CensusCross-validates with energy dataIncorporates AI sector into federal stats
Non-GovernmentAI DevelopersWork with NIST to refine compute/task efficiency metricsReport training/inference energy, water, and emissionsShare model-specific data for load estimationParticipate in voluntary federal programs, support transparency
Data Center OperatorsSupport infrastructure-level metric development (PUE, WUE)Report operational metrics (PUE, WUE, emissions)Share utilization/design data for planningEngage in certifications, ESG benchmarks
Utility Companies & Grid OperatorsProvide energy delivery data, load forecasts, interconnection dataInform regional reliability and grid expansion modelsAlign rates, plans with AI load growth
Infrastructure DevelopersReport energy/cooling projections and needsSupport planning/zoning/water coordinationComply with environmental regulations
Industry Consortia & AuditorsAssist in standard-setting and benchmarksAggregate anonymized member dataValidate and synthesize trends for government useProvide third-party verification, build trust