Data Centers and Their Energy Use: Trends in State Capitals

Key Takeaways

• Agentic inference has changed the economics of AI. Tokens are becoming units of work and the economic driver is now the work produced, not token generation. Per-token costs are falling and the willingness to pay for work produced is rising; these two trends compound. This tailwind enhances AI economics and has spillover impacts on all layers of the AI stack.
• The AI infrastructure question has shifted from whether demand will show up to whether the physical stack can scale quickly enough. That stack includes power generation, grid capacity, interconnection, compute, memory, networking, cooling, siting, and community acceptance.
• Carbon Direct Capital and Relae (formerly Carbon Direct Inc.) have a differentiated view because the two entities work across both sides of the constraint: Relae advises hyperscalers and energy buyers on power and grid bottlenecks, while Carbon Direct Capital invests in the technologies that relieve those bottlenecks.
• Carbon Direct Capital sees better risk-adjusted returns investing in the physical foundations of AI, including clean firm power, energy system efficiency, data center efficiency, and inference-optimized compute, rather than chasing late-stage AI application valuations.

A Better Question Than "Is AI a Bubble?"

The most important development in AI economics is agentic AI turning tokens into work, a shift that reframes the bubble debate which dominated investor conversations, sell-side notes, and Chief Information Officer surveys through early 2026. Hyperscalers spent approximately US$380 billion on capital expenditure (capex) in 2025 and have guided to approximately US$720 billion of capex in 2026.¹ Carbon Direct Capital and Relae have worked together to build project-level models for both training and inference facilities to demystify the numbers and understand financial and technical sensitivities. The core finding was that the assets could be bankable using standard assumptions and that the binding constraints were physical, not financial. That conclusion has been reinforced in recent months by new developments.

Concretely, AI is moving from single prompts and answers to multi-step workflows that plan, reason, call tools, verify outputs, and keep state. This shift to inference is the structural successor to training in the initial AI capex cycle; it changes power requirements, time to power, and compute architectures all at once. Goldman Sachs estimates that agentic AI could drive a 24-fold increase, relative to a 2026 baseline, to roughly 120 quadrillion tokens per month globally by 2030 as per-token costs continue to fall. SemiAnalysis makes the same point from another angle: the value of frontier tokens has risen as agentic workflows become useful, while hardware and software improvements have reduced the cost of producing each token.

This does not mean every AI company is attractive, every data center project works, or every valuation is justified. It means the easy bubble framing is missing the more investable question. If token demand is compounding and the unit value of work produced is rising, the scarce resource is not abstract enthusiasm. It is the physical infrastructure required to turn that demand into work produced.

The Data Center Model Still Matters, But the Box is not a Black Box

Our internal modeling for an illustrative 167-megawatt inference data center using Nvidia Blackwell graphics processing unit (GPU) servers suggests the potential for high-teens percent equity returns under a defined set of assumptions.² We built a bottom-up underwriting, beginning with the number of users served per inference data center, assuming approximately how many tokens they will demand daily, and translating that token demand into compute needed based on industry-standard quantization and utilization rates. We then inferred the number of GPUs and servers needed to achieve the desired compute, which ultimately drove the total invested capital and power demand based on assumed thermal power designs and power usage effectiveness (PUE). On the revenue side, we used GPU-as-a-service rental rates as one proxy for the market value of compute capacity. A hyperscaler would not rent scarce compute externally if it had higher-value internal demand for that same capacity. As we will detail below, GPU rental prices have been steadily increasing on the back of inflecting inference demand.

This model is not the entire argument; it is the starting point. An important lesson is that power cost alone does not break data center economics. Electricity is slightly over 10% of total costs in our model: a 50% increase in power price reduces equity-level returns by less than 2%. Access to power, speed of interconnection, and equipment availability matter more. In other words, the economics of the model facility are workable, but only if the facility can be built and powered on the timeline customers need.

That is where most AI commentary remains too superficial. It treats the data center as a black box: capex goes in, tokens come out. That misses the bottlenecks inside and around the box. AI racks are moving far beyond traditional cloud power density. Cooling is shifting from air to liquid and two-phase systems. Networking and high-bandwidth memory become binding constraints in inference architectures. Grid interconnection queue wait times stretch to years. Communities can and do block projects. The technical, physical, and political constraints are increasingly the drivers of potential returns.

Inference Makes the Constraint Structural

While training is episodic, inference is recurring. A training run can be delayed, accelerated, or redesigned. Inference happens every time a user asks a question, a developer runs an agent, a business automates a workflow, or an application calls a model in the background. Agentic inference multiplies that load because one user action can become many model calls, validation loops, and memory reads; industry benchmarks show that agentic systems consume 5–30 times more tokens than a standard chat interaction.

Inference demand is also resilient in both directions. If efficiency gains lower the cost per token, more workflows become economic and total token consumption rises - the classic Jevons Paradox. However, token prices do not necessarily need to fall for inference spend to grow. As the economic unit shifts from tokens generated to work produced, customers may pay more per token when an agent delivers work produced that is worth more than the inference cost. Regardless of token price, tokens must all route through the same physical bottlenecks and we are seeing an increase in inference demand.

The architecture of inference is also changing. Some workloads will prioritize low-latency answers. Others, especially agentic work without a human waiting on every token, will prioritize memory, state, context, and cost per completed task. That means the AI infrastructure stack will become more heterogeneous, not less: XPUs (specialized AI accelerator chips), custom silicon, photonics, memory hierarchies, and edge or regional deployment models will all matter. The pricing data shows demand for more AI infrastructure overall: on-demand GPU rental capacity is effectively sold out across all chip generations in early 2026, with one-year Hopper H100 contract pricing rising 15–20% month-on-month through March 2026 and Blackwell B200 rental rates up 23% in March alone. When rental rates rise into a wave of new chip supply, supply is not catching up to demand.

Power Is Not One Constraint, It Is Several

Saying "AI is power constrained" is true, but not specific enough. The real problem has several layers. First, data centers need more electricity than many local grids can deliver on hyperscalers' timelines. Crucially, some grids can supply sufficient power but not continuously for 8,760 hours per year, conflicting with traditional assumptions about service reliability and leading to novel strategies around flexibility and intermittent self-supply. Second, the grid must be able to absorb large, fast-moving computational loads without creating reliability risks. Third, customers need energy procurement strategies that satisfy cost, reliability, climate, and public-acceptance requirements. Fourth, projects must get built in real communities, through real interconnection processes and real permitting fights. Power is not simply a commodity to purchase. It is an infrastructure development problem.

This is where Relae is directly relevant. Relae has assembled a team of scientific, engineering, and market experts to support a paying power and energy advisory practice serving hyperscalers, energy buyers, and power producers. Its work answers the questions customers are asking before the market prices them: how to get more capacity out of existing physical grid infrastructure; how to assess the costs and value of load flexibility through advanced modeling capabilities; how to make clean firm generation bankable; how to reduce data center energy intensity; how to validate "bring your own power" and "bring your own compute" structures; and how to build projects that communities will accept. 

In the last twelve months alone, Relae has supported hyperscalers on bankability assessments for next-generation geothermal, scoped load-flexibility programs for multi-hundred-megawatt, single-customer sites, and modeled the carbon and reliability profile of "bring your own power" configurations against grid-tied baselines. Carbon Direct Capital leverages our network of technical experts at Relae, including power engineers, geologists, and electrochemists, to conduct credible technical diligence and to gain insights into early stage market trends and emerging preferences.

What Carbon Direct Capital Is Investing Behind

Our investment focus follows the bottlenecks. On the power side, we are investing in technologies that can deliver reliable power on AI timelines. Sage Geosystems is a next-generation geothermal platform with hyperscaler buy-in; Carbon Direct Capital co-led its US$97 million Series B with Ormat Technologies. We could not have made this investment without the deep expertise of the Relae research team which analyzed Sage's technical results to date to help underwrite future project feasibility. ION Clean Energy is a company that retrofits carbon capture technology onto natural gas combined cycle plants to create "blue electrons"; Relae is in active dialogue with multiple large power users on this topic. Carbon Direct Capital is also actively evaluating the enabling picks and shovels around geothermal, nuclear, fuel cells, and more.

On the data center efficiency side, we are investing in technologies that reduce the amount of power required for a unit of AI work. While it is encouraging to see incremental annual gains in chip efficiency, these are scaling far more slowly than compute demand, driving the need for more innovative technological solutions. As one example, a team at Relae helped us understand the fundamental energy consumption requirements of a standard complementary metal-oxide-semiconductor (CMOS) chip, and the potential of all-optical computing as an alternative. This led to Carbon Direct Capital investing in Neurophos, a photonic compute company targeting step-function gains in energy efficiency per chip that are beyond those achievable by existing GPUs. Carbon Direct Capital joined the company's US$110 million Series A alongside Gates Frontier, Microsoft's M12, Aramco Ventures, Bosch Ventures, and others. More broadly, we are studying other layers of the data center technology stack including networking, memory, cooling, and inference-optimized architectures because the next phase of AI infrastructure will not be solved by simply buying more of yesterday's hardware.

The Bear Case Deserves to Be Taken Seriously

There are real risks to the AI boom: Hyperscaler free cash flow can compress if capex grows faster than revenue. Model efficiency gains can reduce the amount of compute required for a given task. Training demand may be more episodic than the market assumes. Local opposition can slow or cancel data center and power projects. Some new data center capacity could become expensive cloud infrastructure competing on price if AI revenue disappoints.

Those risks are why Carbon Direct Capital frames this as an investment in constraints, not in AI enthusiasm. If efficiency improves, inference use cases expand and the bottleneck shifts to deployment, memory, power, and cost per unit of work produced. If training demand slows, inference and enterprise agents still require recurring capacity. If local grids cannot absorb load, technologies that unlock power, reduce energy intensity, or improve flexibility become more valuable. If some AI applications or model developers fail, the upstream physical bottlenecks remain for the rest.

The Investment Conclusion

The AI infrastructure opportunity sits at the intersection of frontier technology risk, project-finance economics, and energy-system engineering. Underwriting this opportunity well requires addressing all three at once; Carbon Direct Capital is built to do just that. The technical team at Relae has a pulse on emerging stakeholder preferences and scientific breakthroughs, understands novel technologies deeply, and is highly experienced in conducting detailed technical diligence to ensure that projects are viable and scalable. Carbon Direct Capital combines these market and technical insights with our commercial underwriting to facilitate new investments. We are not picking AI winners. We are not picking pure energy assets. We are investing in the companies and technologies that have to exist for AI to sustainably scale.

Frequently Asked Questions

Is the AI capex boom a bubble? While valuations vary, token demand and physical infrastructure needs are real and compounding. The correct question to ask is not whether AI is a bubble, but what the binding constraints are. Our modeling shows that constraints are physical, not financial. 

What are the real constraints on AI infrastructure growth right now? AI infrastructure growth is constrained by power availability, grid capacity, and interconnection speed, not capital availability.

Why does inference matter more than training for long-term AI power demand? Inference is recurring and grows with AI usage, it is not episodic like training runs.

What is Carbon Direct Capital investing in, and why? We are investing in clean firm power, energy system efficiency, data center efficiency, and inference-optimized compute—the physical bottlenecks rather than application-layer valuations.

Disclaimer

Carbon Direct Capital Management LLC is an investment adviser registered with the US Securities and Exchange Commission (SEC). Registration as an investment adviser does not imply any particular level of skill or training. Additional information about Carbon Direct Capital Management LLC, including our Form ADV Part 2A Brochure, is available on the SEC's website at adviserinfo.sec.gov.

This content is provided for informational purposes only and should not be construed as or relied upon as investment, legal, tax, or other advice. You should consult your own advisers regarding legal, business, tax, and other matters related to any investment. Any projections, estimates, forecasts, targets, prospects, or opinions expressed are subject to change without notice and may differ from opinions expressed by other employees of Carbon Direct Capital Management LLC, its affiliates, investors, portfolio companies and other individuals, groups or entities. Certain information contained herein may have been obtained from third-party sources believed to be reliable; however, Carbon Direct Capital Management LLC makes no representations about the accuracy or completeness of any such information or its appropriateness for any given situation. Any investments or portfolio companies mentioned are not representative of all investments made by funds managed by Carbon Direct Capital Management LLC, and there can be no assurance that any investment will be profitable or that future investments will have similar characteristics or results. Past performance is not indicative of future results. The content speaks only as of the date indicated. This content does not constitute an offer to sell or a solicitation of an offer to buy any security. Any such offering will be made only pursuant to formal offering documents.

Key Takeaways

• The opportunity: Clean, firm power is a strategic priority for large electricity buyers. Natural gas-fired generation equipped with carbon capture and storage (CCS) is emerging as a key tool in meeting this demand. The existing gas-fired power fleet in the US should be assessed to identify plants well-positioned for carbon capture retrofits that would benefit grid decarbonization. 
• The challenge: The climate benefits of CCS-equipped natural gas plants depend entirely on how often they actually run. Adding carbon capture technology increases the cost to operate the equipment. These higher running costs can make the plant less competitive in auctions where the grid operator picks the cheapest power first. Without mechanisms to keep these plants running continuously, they may be outbid by cheaper, higher-polluting plants, causing grid-wide emissions to stay the same or even increase. 
• The solution: Hyperscalers and other large energy buyers are creating a robust market for clean, firm power. By paying a "clean, firm premium" through long-term offtake agreements, these buyers can offset the higher operational costs of CCS, ensuring these plants are continuously utilized. This corporate leadership not only maximizes the grid-wide climate impact of each retrofit but also provides an important hedge against policy volatility, securing the investment case for clean innovation even when the future of subsidies like the 45Q tax credit is uncertain.

We Need Clean, Firm Power Now

The market signals for clean, firm power are clear. Meta’s nuclear energy projects and Microsoft’s Crane Clean Energy Center demonstrate growing interest in reliable, low-carbon electricity to support the rapid expansion of AI. Similar commitments by Google and Meta to advanced geothermal power also illustrate this trend. 

One of the near-term options to meet this demand is natural gas with carbon capture and storage (CCS). As explored by Relae (formerly Carbon Direct), retrofitting existing gas facilities offers a path to reliable baseload power with low direct emissions, leveraging existing infrastructure to bypass the years-long delays typical of new grid interconnections. 

Recent initiatives from Google and Calpine are already working to prove this concept at scale. This type of corporate leadership is driving the market; over the last decade, voluntary corporate procurement led to more than 40% of new clean energy capacity in the US. Further, recent procurement decisions illustrate that these players are willing to pay a “clean, firm premium” to secure round-the-clock, low-emissions sources of power.

Why Systems-Level Analysis Matters for CCS

While news of corporate procurements often makes headlines, recent analysis finds the number of supply contracts for natural gas power with CCS may outpace the number of secured offtake agreements. Without a power purchase agreement (PPA) to ensure competitive operation, or strong policy support, a generator may need to operate as a “merchant plant” in power markets, competing solely on cost.

A power plant’s ultimate climate impact is determined primarily by how it is positioned in the market, not just its facility-level technology. 

How Power Markets Determine Which Plants Run

Understanding the potential of CCS to deliver clean, firm power and grid-wide decarbonization requires looking beyond the technology performance at a single facility. A retrofitted plant does not operate in isolation; its impact depends on how it interacts with the broader power market’s merit order.

The merit order is the ranking system in competitive power markets where the grid operator dispatches the cheapest offers first. Since carbon capture units are energy-intensive, the retrofitted natural gas plant incurs higher operating costs. This cost increase can inadvertently price the lower-emitting plant out of the market. Without mechanisms to ensure continuous utilization, the CCS plant is potentially outbid by cheaper, more carbon-intensive resources. This creates a risk of increased overall grid emissions.

To illustrate this dynamic, we’re sharing the results of our detailed grid modeling analyses of the Electric Reliability Council of Texas (ERCOT), which serves most of Texas, and the Southwest Power Pool (SPP), which covers parts of 14 states across the central US. Our analysis highlights the value of corporate “clean, firm premiums” in achieving maximum climate benefit and mitigating policy risk present in government subsidy support. 

This type of systems-level grid modeling is necessary in understanding how facility-level reductions translate into real climate benefits. Support to incentivize continuous operation, such as corporate offtake agreements or the 45Q tax credit, is key to ensuring that retrofitting a gas power plant with CCS reduces overall grid emissions. 

Offtake Agreements and Policy Support as Solutions

Power offtake from CCS retrofitted gas plants can meaningfully reduce system-level emissions. By directly matching electricity demand with the supply of power, large energy buyers – the offtakers – ensure the power plant is effectively utilized. This type of arrangement helps ensure any changes to reduce emissions intensity at the facility level translate into broader emissions reductions on the grid.

For these offtakers, the decision to pay a premium for clean power is driven by the goal of additionality – ensuring their procurement has a measurable, additional emissions reduction impact. Beyond physical energy, buyers secure Energy Attribute Certificates (EACs) for CCS, which serve as the verified proof of low-carbon generation required to satisfy corporate zero-emissions targets. As seen in the recent Google and Calpine agreement, these certificates allow buyers to claim the specific climate benefit of the CCS retrofit, justifying a premium over standard wholesale market rates to secure firm, clean delivery.

In the absence of offtake agreements, policy frameworks like the 45Q tax credit (up to $85 per ton of CO₂ sequestered) serve a similar function by offsetting production costs.

However, access to this credit is not a guarantee and carries operational hurdles. To unlock the full credit value, facilities must meet stringent prevailing wage and apprenticeship requirements. Furthermore, the credit is limited to a 12-year window once the facility is placed in service, and requires construction to commence by 2033.

Beyond these eligibility requirements, the long-term outlook for 45Q involves inherent uncertainty. Recent regulatory shifts, including potential changes to the Greenhouse Gas Reporting Program (GHGRP), pose risks to the verification mechanisms required to substantiate captured tons. 

Corporate offtake agreements offer a crucial private-sector complement to this landscape; they provide a stable revenue model independent of policy cycles, ensuring the investment case remains robust over the full life of the asset.

Understanding the Merit Order in Power Markets

Most US power plants operate in competitive deregulated markets, where grid operators dispatch generators based on their marginal cost of production – the cost of generating one additional unit of electricity. The operator ranks these offers from lowest to highest price, creating the "merit order.”

In these auctions, the cheapest resources (typically renewables and base load) are dispatched first. Progressively more expensive units (gas and peaking plants) are called upon until demand is met. The price of the final, most expensive unit required sets the market-clearing price received by all generators in that period. 

The Figure below shows an example generation merit order in the ERCOT energy market.

Case Study: How Support Structures Influence Dispatch

The merit order figure illustrates a hypothetical scenario for a natural gas generator, showing how its market position changes based on technical and policy variables:

• Pre-Retrofit (Stage A): The plant operates with standard marginal costs, sitting competitively in the middle of the supply stack.
• Post-Retrofit (Stage B): Retrofitting with CCS introduces higher operating costs due to the energy-intensive nature of carbon capture. Without external support, the plant’s marginal cost increases (A to B), making it less competitive. The retrofitted plant may be utilized less while cheaper units are dispatched to meet demand.
• Post-Retrofit + policy or offtake support (Stage C): Financial support, whether through the 45Q tax credit (approx. $33/MWh¹) or a corporate offtake agreement, can effectively offset the plant’s higher operational costs (B to C). This effect restores the plant’s competitiveness, ensuring it dispatches consistently.

Testing This With Grid Modeling

At Relae, we apply state-of-the-art grid analysis tools to answer these and more complex analytical questions related to the future energy system. Our custom modeling framework has been used to simulate clean power strategies, assess data center demand response programs, and understand how procurement decisions today impact the future energy system.

While the theoretical impact of a CCS retrofit, a PPA agreement, and the 45Q tax credit on a plant’s dispatch is clear, it’s important to put the theory to the test by modeling their effects on system-wide emissions.

Our Modeling Approach

Because each grid region has distinct power plants and load requirements, they must be modeled separately. For this analysis, we chose to model the ERCOT and SPP power markets to determine the region-specific, grid-wide emissions impact of hypothetical CCS retrofits of natural gas power plants. 

As part of this modeling, we:

• Deployed detailed hourly simulation: We used our custom PyPSA-USA grid model to produce a set of hourly simulations of the ERCOT and SPP electricity markets.²
• Identified suitable retrofits: We identified suitable combined cycle gas power plants for a CCS retrofit in each of the markets, based on key commercial and operational criteria, including size, age, generation profile, and proximity to CO₂ transport/storage.
• Modeled plant and energy assumptions: To reflect the retrofit, we adjusted generator cost and energy use for the identified plants (up to 1.4 GW capacity), fitting all combustion turbines with capture and requiring each plant to consume 20% more fuel per unit of electricity produced to power CCS.³
• Carried out comparative scenario analysis: We simulated several scenarios, including (1) pre-retrofit, business-as-usual, (2) post-retrofit, with and without a PPA, and (3) post-retrofit, with and without the 45Q tax credit, to isolate the impact of different procurement agreements and policy landscapes on grid-wide emissions. 

What Our Analysis Reveals

Results of this analysis reveal how CCS deployment in the power grid interacts with market economics and the role mechanisms that drive high utilization of CCS retrofit plants can have in ensuring system-wide emissions reductions:

CCS With a Firm Offtake Agreement Can Significantly Reduce Grid-Wide Emissions

Pairing a retrofitted plant with a dedicated offtaker can drive meaningful emissions reductions in both ERCOT and SPP compared to business-as-usual (-0.8% to -1.7% CO₂ in ERCOT; -5.2% to -7.3% CO₂ in SPP). Under these arrangements, system-wide emissions fall because the PPA acts as an operational anchor, ensuring the retrofitted plant maintains high utilization rates despite its higher running costs. Ensuring the plant stays utilized prevents the grid from reverting to more carbon-intensive generation to fill the gap.

Our analysis finds the value of the operational “clean, firm premium” for natural gas with CCS power is up to $60 per MWh. This value varies by hour, region and scenario but results generally align with our previous estimate of a $30 per MWh value associated with this type of generation. Other estimates put this value between $19 and $72 per MWh.

CCS Without an Offtake Agreement Can Reduce Emissions, But Is More Reliant on Policy Support

Without a dedicated offtake agreement or policy support, retrofitting natural gas plants with CCS runs the risk of a small increase in grid emissions (+0.7% CO₂ in ERCOT; -0.0% CO₂ in SPP). System-wide emissions are higher because other power plants displace the plants with carbon capture. The higher operational costs of CCS mean the CCS plants have a less competitive place in the merit order and run for fewer hours in the year.

The story changes with the application of 45Q, and grid-wide emissions are lower for both ERCOT and SPP (-1.7% CO₂ in ERCOT; -3.4% CO₂ in SPP). Access to the 45Q tax credit improves each CCS plant’s position in the merit order, meaning that it runs for more hours and successfully displaces higher-emitting generation with clean, firm power.

The Path Forward for Clean, Firm Power

Our analysis illustrates that in competitive power markets, the overall carbon emissions impact of natural gas generation with CCS cannot be measured solely at the power plant level. While clean, firm power remains a strategic priority for large electricity buyers, and CCS is a key tool to meet this demand, the overall climate value of a successful retrofit is linked to the availability of offtake agreements and the plant’s position in the merit order. 

A systems-level perspective captures what facility-level analysis misses: how market dynamics determine the true climate impact of decarbonization investments. Support mechanisms for the continuous operation of low-carbon power plants, like PPAs and the 45Q tax credit, are important tools that ensure clean, firm power reaches the grid, effectively bridging the competitiveness gap.

Frequently Asked Questions 

How can companies ensure CCS retrofits actually reduce grid-wide emissions? 

By securing the plant’s dispatch through a long-term offtake agreement, or by utilizing a policy incentive like 45Q. Relae’s modeling found that offtake agreements have a substantial impact on the emissions reduction potential of CCS retrofits. 

Why would the dispatch decisions of one power plant affect others? 

Power plants dispatch according to marginal cost, and grid stability requires that total supply remain constant at any given moment. So, if one large plant suddenly dispatches less (say, because its operating costs have increased), other potentially dirtier plants may ramp up to fill the gap, increasing total system emissions.

Key Takeaways 

• AI data centers are driving the fastest electricity demand growth in decades: US data centers consume an estimated 4 to 5% of US electricity today, projected to reach as much as 9 to 17% by 2030 (EPRI).
• Without deliberate cost allocation, residential and small-business ratepayers subsidize private AI infrastructure. 
• Peer-reviewed modeling projects data center growth could raise US power costs 6 to 29% nationally by 2030, and up to 57% in the hardest-hit regions.
• Utility commissioners, state regulators, and policymakers now have working models to draw from, including large-load tariffs, dedicated rate classes, and direct assignment of transmission costs.

AI Data Center Energy Demand Is Testing the Limits of the Grid

AI is driving electricity demand at a pace the US grid has not seen in decades. US data centers already consume an estimated 4 to 5% of the nation's electricity, and EPRI projects that share could reach 9 to 17% by 2030. Behind nearly every AI model and digital product is the invisible infrastructure that powers it: data centers. These facilities are resource-intensive, requiring massive amounts of electricity to power servers, substantial water for cooling, and extensive new grid infrastructure.

In the race to decarbonize the grid, data centers are emerging as a critical pressure point. This infrastructure sits at the intersection of digital growth and climate action, forcing a difficult question: who pays to power AI?

Legacy Utility Models Weren’t Built for this Growth

Utilities must upgrade aging grid infrastructure to meet this new surge in electricity demand, while maintaining reliability. Under legacy utility frameworks, it's often ratepayers who foot the bill for those upgrades. And the costs are not distributed equitably.

Traditional utility planning assumes that increased demand justifies expanded investment in generation and transmission infrastructure. When a new type of large customer, like a tech company, moves into a utility’s service territory, utilities plan new infrastructure to meet that projected demand. 

Utilities typically recover the cost of new infrastructure through a process called rate base cost recovery. This allows utilities to charge all customers in the “rate base” for the expenses incurred, including thousands of individuals, families, and small businesses, even when those costs stem from the demands of just a few large users.   

This legacy model struggles to keep pace in the AI boom era, where massive new electricity demand can double within a few years, a scale of growth that used to take decades. Additionally, while data centers create short-term construction jobs, there are almost no lasting employment benefits for local communities.

It's clearly inequitable for all ratepayers to bear the costs of upgrading the grid to benefit just a small number of massive data centers. But that's not the only problem. If utilities decide to meet new power demand from large data centers with new fossil fuel generation, such as gas peaker plants, they risk creating stranded assets: infrastructure that becomes obsolete or uneconomical as climate targets, clean energy mandates, or the cost-effectiveness of renewables accelerates. Once built, ratepayers will have to continue paying for these long-lived investments for years, even if they are underutilized or retired early due to policy shifts. This risk is no longer hypothetical: to serve projected data center load, Georgia regulators approved a plan to extend the lives of two massive coal plants to as late as 2038, and Virginia regulators stripped roughly $350 million tied to speculative early-stage data center projects out of Dominion Energy's revenue forecast.

If utilities are locking in decades of new fossil fuel generation to meet short-term data center growth, ratepayers may be left holding the bag for infrastructure that contradicts their climate goals and state mandates, with little ratepayer or community input into the decision. Effectively, local communities may be subsidizing a technology that they did not directly ask for in the first place and has little to no direct community benefits. The result is a long-term misalignment between utility investment strategy and the public interest.

Ratepayers Bear the Cost of Private AI Expansion

The economic burden of data center expansion can fall disproportionately on households and small businesses. But data centers, as the largest and fastest-growing users, often negotiate bespoke contracts, subsidized rates, or fixed-price electricity agreements that shield them from long-term cost volatility.

This can result in other customers, especially residential and low-income ratepayers, bearing a disproportionate share of the infrastructure and maintenance costs. In many states, residential and low-income customers already experience energy cost burdens that exceed affordability thresholds. Adding the weight of infrastructure investments to serve energy-intensive data centers, without sharing those costs equitably, exacerbates an already regressive utility cost allocation system.

Georgia shows how these costs reach ratepayers even when regulators act. Georgia Power customers absorbed six rate increases totaling roughly $43 per month between 2023 and 2025, and while regulators approved a base-rate freeze through 2028, the freeze excluded fuel and storm costs. In 2026 fuel-cost proceedings, testimony showed that large industrial and data center customers raise other customers' monthly fuel costs by 5 to 11%, prompting the Georgia Public Service Commission to open an investigation into how fuel costs are allocated between large loads and residential customers. Ratepayers noticed: in November 2025, both Georgia PSC seats flipped in elections run explicitly on utility bills and data center cost-shifting. In Virginia, regulators approved a rate increase of roughly $16 per month for typical Dominion Energy residential customers amid surging data center demand.

These examples are not anomalies. A peer-reviewed study in Environmental Research Letters projects that data center growth could raise US power costs 6 to 29% nationally by 2030, and up to 57% in the hardest-hit regions, with Virginia among the steepest. This is a systemic shift in energy demand, one that places a growing burden on communities and lacks clear public benefits.

Environmental and Community Impacts Are Mounting

Beyond economic impacts, the geography of data center development reveals another layer of inequity: environmental justice. Data center siting often prioritizes affordable land, low resource costs (e.g., electricity, water), and climate considerations like heat variability. They also rely on proximity to pre-existing fossil fuel generation and transmission infrastructure. Research now confirms the pattern this creates: an analysis of 550 EPA-regulated data centers found that air pollution burdens near data centers rise with the share of people of color living nearby, and a 2026 Washington state study found more than half the state's data centers sit in census tracts with the highest concentrations of people of color.

These communities often absorb the negative externalities beyond their electricity bills, including increased air pollution from peaker plants and on-site diesel or gas backup generators, traffic and construction noise, water stress, and land use changes. Simultaneously, they do not receive direct net positive benefits. Frontline communities are paying attention to this trend, and opposition has become a defining force in where AI infrastructure gets built. Gallup finds 71% of Americans now oppose a data center in their own area, and Data Center Watch counted roughly $130 billion in projects blocked or delayed in the first quarter of 2026 alone. The stakes of community opposition are increasing and intensifying. 

The consequences of ignoring communities are now playing out in federal court. At xAI's Colossus facility in Memphis, developers operated dozens of on-site gas turbines without air permits in a majority-Black area already burdened by industrial pollution. After the Shelby County Health Department granted permits for a subset of turbines in July 2025, the fight moved to xAI's second campus across the state line: in April 2026, the NAACP filed a Clean Air Act lawsuit over roughly 27 unpermitted gas turbines at the Colossus 2 site in Southaven, Mississippi, seeking penalties of more than $100,000 per day. On-site power can help reduce demand on the grid, which can be a benefit. But when that generation runs without permits or oversight, nearby communities bear unmeasured health and environmental impacts from hazardous emissions, and the litigation now underway shows how quickly unpermitted power becomes a legal and reputational liability.

To date, data center developers do not appear to have maximized potential community benefits or engagement. Data centers have not typically employed many local residents beyond construction phases, resulting in limited economic benefits, particularly when facility ownership is distant from the local community or has few local ties. When these same communities already experience high pollution burden or economic precarity, the cumulative impact of a new data center can deepen existing vulnerabilities.

Water use is also a mounting environmental justice concern. Many data centers rely on evaporative cooling systems that draw millions of gallons of water per day, and peer-reviewed research finds significant gaps in how the industry discloses its water footprint. In drought-prone regions, this can stress already-depleted aquifers and heighten tensions over water access.

The result is a high-stakes tradeoff between digital infrastructure and local resource resilience, one that communities should be a part of deciding.

States and Regulators Are Writing the New Rules 

Virginia, the "Data Center Capital of the World," is home to 674 data centers that consume an estimated 25% of the state's electricity, a share EPRI projects could reach 39 to 57% by 2030, the highest of any state. After legislators considered but did not pass data center bills in the 2025 session, the 2026 General Assembly passed roughly 15 data center bills, including legislation, signed in May 2026, directing regulators to ensure data center costs are not subsidized by other customers, along with new requirements for site impact assessments and water-use reporting. Virginia's State Corporation Commission had already created a dedicated rate class for high energy use customers, with 14-year contract terms and minimum charges that apply whether or not the projected load materializes, and in August 2026 it went further, ordering Dominion to develop a tariff that directly assigns transmission costs to the data centers that trigger them.

Virginia is not alone. Ohio regulators approved a landmark tariff requiring large data centers to pay for 85% of the capacity they request, whether or not they use it. Oregon's POWER Act created the nation's first legislated rate class for data centers. Texas gave its grid operator authority to curtail large loads during emergencies. Minnesota, California, Alabama, Tennessee, South Dakota, Nebraska, and Florida have all enacted their own ratepayer-protection measures, and at the federal level, FERC ordered the nation's largest grid operator to write new rules for data centers that co-locate with power plants, citing the need for consumer protection and clear cost allocation. State energy officials are also proactively planning for data center expansion.

The direction is clear. The unresolved question is whether these reforms move faster than the costs already flowing to ratepayers.

What Is the Public Good of Data Centers?

AI infrastructure powers innovation, job creation, research, and the technologies we rely on every day. But it may also bring inequitable social and direct financial costs. Like highways, factories, and pipelines before them, the question remains: What is the public good of AI data centers? How should we hold data center developers accountable to the public interest, which values a clean energy future? We need clear-eyed assessments of how data centers impact energy affordability, climate progress, and environmental equity.

Yesterday's utility policy frameworks were not designed for hyperscale AI data centers. The reforms now underway are a start, but without sustained attention they may still force the public to subsidize private expansion, through economic and environmental costs, often without equitable community engagement, climate accountability, or local benefit.

AI Data Center Growth Needs Accountability, Equity, and Reform

To align data center growth with the public interest, the stakeholders involved now have proven models to build on:

• Utilities and regulators can require large customers to pay an equitable share of new infrastructure costs, as Ohio's minimum-take tariff and Virginia's dedicated rate class now do.
• Public Utility Commissions can mandate equity and community impact assessments during siting and permitting, following Virginia's new site assessment requirements.
• States can condition tax incentives and zoning approvals on local hiring, emissions reductions, and community benefits agreements.
• Data center developers can prioritize clean power and commit to transparent, equitable community engagement and benefits plans before opposition, litigation, and cancellations decide the outcome for them.

As we build the digital backbone of the next century, we must avoid repeating injustices of the past. A just energy transition requires more than megawatts: it demands equity, policy interventions, and real climate progress.

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Frequently Asked Questions

How do utilities typically recover the cost of infrastructure built to serve large data center customers, and why does this burden fall on other ratepayers?

Utilities recover infrastructure investments through rate base cost recovery: regulators approve new generation, transmission, and distribution spending, and the costs are spread across all customers in the rate base through their monthly bills. That model worked when demand growth was gradual and diffuse, but when a single data center campus drives hundreds of megawatts of new investment, standard cost allocation spreads those costs across households and small businesses unless regulators adopt a special tariff or rate class that assigns them to the customer who caused them.

What are stranded assets in the context of data center power demand, and how do they create long-term risk for utilities and ratepayers?

Stranded assets are long-lived infrastructure investments, like new gas plants built for projected data center load, that become underused or uneconomical before they are paid off, whether because demand never materializes or because policy and market shifts overtake them. Because utilities recover those costs through rates over decades, ratepayers keep paying even if the asset sits idle. The risk is acute today because data center demand forecasts are highly uncertain: Virginia regulators removed roughly $350 million tied to speculative data center projects from one utility's revenue forecast in 2025.

Why do data centers often locate in rural or low-income communities, and what are the environmental justice implications?

Data center siting favors cheap land, fast permitting, low-cost power and water, and proximity to existing generation and transmission, conditions most common in rural, low-income, and historically marginalized communities. Research confirms the consequences: analysis of 550 EPA-regulated data centers found air pollution burdens rise with the share of people of color living nearby. These communities absorb the air pollution, water stress, noise, and land use impacts while receiving few lasting jobs or direct benefits.

What regulatory or policy tools can states and Public Utility Commissions use to ensure data center growth doesn't unfairly shift costs to residential and small-business ratepayers?

The toolkit has expanded rapidly since 2025. Commissions can create dedicated large-load rate classes and tariffs with minimum take-or-pay provisions, long contract terms, collateral requirements, and exit fees, as Ohio and Virginia have done; directly assign infrastructure enhancement costs to the customers that trigger them; and require site impact assessments during permitting. Legislatures can codify ratepayer protections, require water and load-forecast transparency, and condition tax incentives on community benefits, models now in place in at least eight states.

This commentary reflects public policy analysis and opinion, not legal advice or regulatory determinations. 

Key Takeaways

• The electricity demand surge is real and accelerating. Just last year, data center load in the US was projected to increase from 25 GW to 120 GW by 2030. Today, Texas’ preliminary long-term load forecast projects over 187 GW of data center load by 2030, more than double today’s total peak demand of approximately 91 GW.
• Flexible loads that respond dynamically to policy signals are becoming a regulatory requirement. Texas Senate Bill 6 (SB6), signed into law in June 2025, is the clearest signal yet. It makes remote curtailment equipment a condition of interconnection for new loads of 75 MW or more, so utilities can disconnect them during declared firm load shed events, and separately creates a voluntary demand response program that those loads can elect to join. 
• Relae’s power system modeling puts a dollar value on what data center load flexibility is worth. Our ERCOT analysis shows that data center demand response can eliminate forced load shedding risk, even at 40 GW of data center buildout – preventing $5.5 billion in annual consumer welfare losses by curtailing an average of 5% of demand for under 1% of operating hours. 
• Flexible load curtailment and compute uptime do not need to be in conflict. In our modeling, demand response operates as a "ghost battery" at the data center's grid node, absorbing grid stress as a physical battery would. That construct has a direct real-world analog: on-site battery storage lets data centers draw from stored energy during grid stress events rather than curtailing workloads. Technologies such as those demonstrated by Emerald AI have shown 25% load curtailment at cluster scale while preserving compute service quality.

Data Center Electricity Demand is Testing Grid Limits

Electricity demand in the United States is growing at its fastest pace in decades. Leading the surge is a rapid buildout of data centers, driven by the expansion of artificial intelligence. ERCOT, the grid serving most of Texas, is projecting up to 187 GW of new data center load by 2030, against a total peak demand today of approximately 91 GW.

The scale of this shift extends across the country. According to NERC's 2025 Long-Term Reliability Assessment (LTRA), summer peak demand across the US bulk power system is forecast to grow by 224 GW over the next 10 years, 69% above the prior year’s 10-year projection of 132 GW, with data centers as the dominant driver. As previously explored by Relae, data center energy capacity in the US is projected to increase from 25 GW to 120 GW by 2030, characterizing it as the first wave of a longer demand surge that electrification of buildings and transport will reinforce.

Policymakers are responding in real time, and regulatory responses like Texas SB6 are already rewriting the rules for how data centers connect to the grid. The pace of change is fast, but the siting, infrastructure, and interconnection decisions made today will have lasting consequences: they will determine whether data centers are grid assets or grid liabilities, and the financial difference between the two is measured in billions.

New Solutions for Data Center Demand Response: Texas SB6 as a Test Case

The rapid nature of this new wave of load growth means traditional approaches to managing the grid may not be sufficient. In the past, lead times on new sources of electricity demand allowed utilities to procure supply-side resources in advance. The scale and immediacy of data center deployment is revealing limitations of this approach. States, utilities, independent system operators (ISOs), and regulators are actively pursuing novel approaches for grid management in response.

Texas has become a focal point of US data center expansion, with SB6 as a leading policy response. The bill, which took immediate effect upon Governor Greg Abbott's signature on June 20, 2025, is the most significant restructuring of large-load interconnection rules in ERCOT's history. It requires large loads over 75 MW to install remote curtailment equipment as a condition of interconnection, so grid operators can disconnect them during declared firm load shed events, and creates a separate voluntary demand response program, procured competitively with at least 24 hours’ notice, that those loads may elect to join. If data centers are adding substantial new load to the grid, this reasoning goes, they should also contribute to grid stability by demonstrating load flexibility—reducing power draw at times of peak demand.

While demand response programs currently exist, incentivizing voluntary curtailment from data centers is challenging. In an AI compute arms race, the value of uninterrupted compute time far exceeds any available curtailment payment, such as via PJM’s capacity market mechanisms. 

Approaches to bridge that gap are coming to fruition: EPRI’s DCFlex program is working with hyperscalers and utilities to develop the technical protocols, measurement standards, and contractual frameworks that would make large-load demand response a routine grid service. Innovators like Emerald AI have demonstrated a 25% power reduction across a 256-GPU cluster over three hours during an Arizona grid stress event, while preserving compute service quality, helping to bridge the valuation asymmetry between energy and compute. 

Hyperscalers are already putting a flexible load commercial strategy into action. In March 2026, Google announced  1 GW in demand response contracts with multiple US utilities, including Entergy Arkansas, Minnesota Power, and DTE Energy.

The conversation has shifted from whether data centers can be flexible to how that flexibility gets structured and deployed. 

Putting a Dollar Value on Data Center Load Flexibility

For developers, investors, and grid operators navigating data center growth, the challenge has been making high-stakes siting and interconnection decisions without a clear picture of what load flexibility is actually worth, what inflexibility costs the system, or how curtailment requirements will reshape the regulatory landscape. 

Prior research has established that flexible data center load can absorb substantial grid stress. For instance, research from Duke University’s Nicholas Institute found that 22 of the largest US balancing authority areas could absorb approximately 98 GW of new flexible load if 0.5% of that load’s annual energy is curtailed, or 76 GW at a stricter 0.25% curtailment level. 

Relae’s analysis goes further by quantifying the economic cost at each increment of flexible load growth, and the precise threshold at which that flexibility stops being optional. We zeroed in on ERCOT, a region with high data center load growth and immediate regulatory stakes, determining the value of implementing flexibility and, conversely, the system risk of failing to do so.

How We Modeled It 

Assessing the impacts of load growth and flexibility solutions requires a systems-level analysis, best achieved via power market modeling. At Relae, we deploy our in-house power system modeling framework to navigate this complexity. 

Our toolkit includes CD-PyPSA-USA, used for this analysis, which is built on the Python for Power System Analysis (PyPSA) platform. This grid model simulates how power networks operate and evolve over time by solving for the least-cost optimization of the entire power system. Critically, our model is customized to explicitly represent complex, real-world dynamics, including data center load flexibility, co-located generation, and various policy constraints.

For this analysis, we simulated ERCOT operations under a range of data center growth scenarios. We modeled loads from 5 GW up to 40 GW in 5 GW increments, pairing each with sufficient on-site gas generation to cover roughly 70% of data center energy needs–a conservative estimate on the approach developers are taking today. 

We ran each scenario under two conditions: no load flexibility (“flex00”, the baseline) and 25% emergency curtailment capability (“flex25”), consistent with solutions exhibited by Emerald AI. This approach allowed us to determine the system's response to step-changes in electricity demand.

What Our Analysis Reveals

Before doing the analysis, we expected to see load flexibility become more important as more data center load is added to the grid, preventing forced load shedding with high lost-load costs. But we didn’t know how large this effect would be, or what amount of new data center load would start to trigger it.

Our analysis found that without flexible load management, forced load shedding first appears at 30 GW, small in scale at first (4.4 GWh over 3 hours) but growing sharply as load increases. At 35 GW, we observe 50 GWh of shedding across 39 hours. At 40 GW, shedding reaches 158 GWh across 81 hours, equivalent to nearly three times ERCOT’s average hourly energy consumption, with an economic cost at VoLL of approximately $5.5 billion.¹

By enabling on-demand data center load flexibility, forced load shedding is eliminated in every scenario we tested. The same 40 GW case instead sees 165 GWh of controlled, short-duration curtailment spread across 86 hours, less than 1% of hours in a year. Further, the average demand response in these hours was less than 5% of the nameplate data center load, with the largest event reaching 14% of data center load. The grid stays balanced, consumers remain connected, and data centers deliver substantial value to the system via flexible loads.

Load Flexibility Is High Value and Presents an Opportunity for Storage

Our modeling puts a dollar figure on what flexible load is worth. At a VoLL of $35,000/MWh, each hour of demand response in the 40 GW scenario delivers approximately $64 million in avoided consumer welfare losses. Over a full year, that adds up to $5.5 billion, achieved through an average of just 5% demand response across the 86 hours of curtailment needed to eliminate all forced load shedding.

That value points directly to an opportunity for storage. In our model, demand response functions as a “ghost battery” at the data center’s grid node, absorbing grid stress exactly as a physical battery would, without any electrons needing to flow. That virtual battery can become a real one. On-site battery storage allows a data center to dispatch stored energy during grid stress events rather than curtailing workloads, maintaining compute continuity while relieving grid pressure.

The implications point in two directions. 

• For the hyperscaler or data center operator, physical storage converts a compliance obligation into an uptime guarantee: the curtailment event becomes a battery discharge, with negligible impact to the compute stack. 
• For the storage developer, co-location with large data center loads represents a high-value deployment opportunity with a clear commercial case. The avoided welfare costs per curtailment hour our model quantifies is the value a well-positioned battery asset, co-located at a data center node, can credibly claim to preserve.

The Data Centers of Tomorrow Need to be Grid Assets

The data center buildout underway is large enough to reshape grid reliability across entire regions, and the regulatory environment is beginning to reflect that scale. Texas SB6 is the most prescriptive example to date: it requires new large loads above 75 MW to install remote curtailment equipment operable during firm load shed events.

Our modeling quantifies what load flexibility is worth across this landscape: data centers with curtailment capability can provide significant value and avoid billions in consumer welfare losses annually. For developers and investors, designing that capability in from the start can convert a compliance requirement into a long-term grid asset.

The federal picture has moved in the same direction since this analysis was published. In May 2026, NERC issued a rare Level 3 Alert on computational loads; in June, FERC ordered six RTOs and ISOs to revise or justify their large-load interconnection rules, and in July, FERC directed NERC to develop mandatory computational-load reliability standards by the end of the year. We covered what that means for grid modeling and interconnection in Inside NERC’s Level 3 Alert on data center loads. Flexibility is no longer only a Texas statutory question; it is becoming part of the federal reliability framework.

Frequently Asked Questions

What is data center load flexibility, and how does it work?

Load flexibility is a data center’s ability to reduce the power it draws from the grid on short notice, during the small number of hours when the system is under stress. In practice, that means shifting or pausing deferrable compute, drawing on on-site batteries or generation, or pre-cooling the facility ahead of a peak. The point is not to consume less overall—it is to move a thin slice of demand out of the hours when the grid can least afford it.

What does Texas SB6 require of large data centers in ERCOT?

Texas Senate Bill 6, signed June 20, 2025, makes remote curtailment equipment a condition of interconnection for new loads of 75 MW or more in ERCOT, so utilities can disconnect them during declared firm load shed events. Separately, it creates a voluntary, competitively procured demand response program those same large loads can elect to join, with at least 24 hours’ notice. The mandatory piece is the disconnection capability; paid participation in demand response is a choice.

What does data center inflexibility cost? How much curtailment avoids it?

Relae’s ERCOT modeling found that without flexibility, forced load shedding first appears at 30 GW of data center load and reaches 158 GWh across 81 hours at 40 GW—roughly $5.5 billion a year in consumer welfare losses at ERCOT’s $35,000/MWh value of lost load. Enabling curtailment eliminated forced shedding in every scenario we tested, at an average of under 5% of data center demand across 86 hours, less than 1% of the year. Each hour of demand response in the 40 GW case is worth about $64 million in avoided losses.

Is data center load flexibility proven today, and how does it compare to on-site batteries?

Data center load flexibility is past proof of concept and into commercial deployment: Emerald AI cut power to a 256-GPU cluster by 25% for three hours during an Arizona grid stress event without degrading compute service quality; EPRI’s DCFlex initiative is building the protocols and contracts, and Google has signed 1 GW of data center demand response with US utilities. 

On-site batteries reach the same result from the other direction—instead of curtailing workloads, the facility discharges stored energy, which is why our modeling treats demand response as a “ghost battery” at the data center’s grid node. For operators who cannot pause compute, storage turns the same compliance obligation into an uptime guarantee.

Key Takeaways

• Voice your opinion: The Greenhouse Gas (GHG) Protocol is updating its scope 2 guidance with final standards expected in 2027, which may require hourly and regional matching of renewable energy certificates (RECs), potentially changing how companies claim their electricity-related emission reductions. 
• Act now to secure renewable energy contracts: Companies should move forward with their scope 2 climate commitments today. The GHG Protocol is expected to grandfather in contracts entered into under existing rules.
• Beyond the megawatt hour (MWh): High-impact forward REC contracts measure impact beyond the current annual MWh match requirement, maximizing near-term carbon abatement and social impact for every dollar invested.

Why 2027 Rule Changes Matter for 2030 Targets

Companies racing to meet 2030 climate targets face converging pressures: surging electricity demand, constrained renewable energy supply, and scope 2 accounting rules that could undergo significant changes by 2027.

In a recent webinar, power market experts from Relae (formerly Carbon Direct) and Ever.green explored these changes. Patti Smith, former Electricity Decarbonization Lead at Relae; Julia Millot, Senior Power Decarbonization Manager at Relae; and Liz Pearce, Chief Revenue Officer at Ever.green, unpacked what's changing and how companies can respond.

The stakes are high. Based on GHG Protocol Scope 2 Public Consultation materials, companies may need to match RECs to electricity consumption on an hourly and locational basis as early as 2028. However, we expect the GHG Protocol to grandfather forward REC contracts signed before new rules take effect, enabling companies to continue advancing toward 2030 targets amid rule uncertainty.

Big Changes to the Power Grid

The US power grid is entering sustained demand growth for the first time in decades. "Over the next five years, data centers alone are going to put [the equivalent] of four New York Cities onto the grid," Smith explains, citing forecasts that project around 200 terawatt hours of new data center load through 2030 (i.e. cumulative energy consumption). That demand growth also shows up in near-term grid planning. NERC's January 2026 Long-Term Reliability Assessment forecasts North American summer peak demand rising by 224 gigawatts—a 24% increase—over the next decade, with new data centers cited as the primary driver. These figures highlight that peak capacity and total energy consumption are directly impacted by the data center boom.

Meanwhile, new renewable projects face headwinds. Smith points to interconnection queue delays: "Solar and battery projects are taking three to five years from initial request to operation." At the same time, clean energy tax credits, which were driving wind and solar expansion, have been curtailed. New restrictions on foreign supply chain materials, which are critical to renewable project development, are further hampering the development of new clean electricity projects.

The result: Power demand is rising while new renewable electricity supply is getting throttled. 

The Messy Reality of Electricity Emissions Accounting

Quantifying the emissions from an individual power plant is straightforward. Allocating those emissions to the companies that consume power is far more complicated. 

Grid-supplied electricity comes from many generators that shift constantly, sometimes even second to second. Companies can’t directly measure emissions from a grid-connected load because the generators serving it continuously change. 

Without direct measurement, companies need rules to estimate the emissions they are responsible for. The GHG Protocol’s Scope 2 Guidance provides that framework, establishing how companies estimate electricity-related emissions and how to reduce them through renewable energy purchases.

How Companies Currently Claim Renewable Energy

For the past decade, companies have used renewable energy purchases to achieve their scope 2 emission reduction goals. The most widely used mechanism is the REC, each representing clean energy attributes for one MWh of renewable electricity generated and added to the grid. Currently, when a company buys RECs equal to its annual electricity consumption, it can claim 100% renewable electricity. Under current rules, companies can use purchased renewable energy from anywhere in North America and apply it to any load in North America at any time during the year. 

Importantly, emissions from different power grids vary widely across North America depending on time of day, time of year, and the power grid makeup.

This flexibility allows companies to match a REC from a clean grid against electricity consumption from a dirtier one, creating a potential mismatch between emissions claimed and actual emissions avoided. This gap has drawn scrutiny, contributing to the motivations for the scope 2 rules rewrite.

What's Changing in GHG Protocol Scope 2 Accounting?

On October 19, 2025, after years of consultation, the GHG Protocol released two separate proposals for public consultation: 

1. Scope 2 changes: Moving away from annual REC matching to an ‘hourly and regional’ REC matching requirement.

2. New consequential methodology: A new approach to estimating emissions caused by a company’s consumption and avoided by its renewable energy contracts. 

The hourly matching proposal (24/7): Companies would match RECs to consumption hour by hour within the same grid region, rather than annually across any North American grid. 

"A REC generated on a Texas wind farm would not be able to be used for electricity consumed in New York," Millot explains.

The consequential approach: This proposes a carbon matching methodology, which estimates emissions caused by a load and estimates the emissions a renewable project displaces. 

"Projects in the Carolinas are avoiding 0.6 or 0.7 tons of CO₂ per megawatt hour, whereas a California project is probably closer to 0.2 or 0.3," Smith explains. 

Projects in the Carolinas deliver more than double the climate impact per REC under the consequential rules. In this methodology, the load and generator do not need to be located in the same region.

While the proposed rules and new methodologies work through the public consultation process, it will be important for companies to start to anticipate the potential impacts on their climate goals and strategies. 

Timeline for Scope 2 Accounting Changes

Both the Scope 2 and Consequential Electricity-Sector Emissions consultations closed January 31, 2026, after GHG Protocol extended the original deadline. The GHG Protocol is analyzing feedback with a second consultation and final standards expected by 2027, though the exact timeline is still being finalized.

Companies are encouraged to participate in the public consultation. The GHG Protocol is asking for comments on critical questions, such as: 

• Should proposed rules apply to energy consumers of all sizes? 
• Which geographical boundaries should be used for locational matching? 
• Should existing contracts be grandfathered in? 

The public consultation period is an opportunity to shape the standards that will govern electricity-related emission accounting for years to come.

Why Act Now Instead of Waiting

With final rules still in development, companies with scope 2 emission reduction goals or science-based targets face a decision: Wait for clarity or act now.

Several factors favor early action:

• Inclusion of legacy contracts. "There are a lot of indications from the committees that existing long-term contracts will be grandfathered in," Pearce notes. The draft considers a legacy clause that would allow organizations to apply pre-existing contractual agreements, even if they don’t comply with new rules. 
• Throttled renewable project development. Interconnection delays for new renewable energy projects, elimination of clean energy tax credits by 2028, and limitations on foreign materials needed to develop renewable energy project components mean that new REC supply may be harder to access in future years.
• Renewable project development timelines. "There's generally a lag, sometimes six to 18 months" between contract signing and project operation, Pearce explains. That means even if you sign today, the RECs won’t be generated for up to 18 months from the signing date.
• High-impact opportunity. Through careful project selection, renewable energy investment can go beyond the annual energy match requirement and incorporate additional impactful metrics, such as higher avoided emissions and positive social impacts.

Renewable Energy Buying Options for Companies

Previously, companies have been able to buy renewable energy through the following three paths; however, they all come with their own tradeoffs.

Traditional REC Buying Options

1. REC spot markets make up most corporate renewable procurement. However, they mainly come from existing projects rather than financing new development, which is critical to expanding renewable energy supply to meet rising decarbonization needs.
2. Virtual power purchase agreements (VPPAs) are highly impactful but require large power loads and the ability to manage long-term financial risks. Unavailable to most companies.
3. Utility green tariffs have limited availability throughout the US (depending on the utility(s) that serve your load) and vary in quality. 

Alternative REC Procurement Approach

For companies that want to go beyond the REC spot market and are not large enough to pursue a VPPA, there’s an alternative procurement option available: a high-impact forward REC contract. These multi-year contracts commit to purchasing RECs from specific new projects before they're built, providing the upfront revenue certainty developers need to secure financing at a fraction of the scale and complexity of a VPPA.

Comparing Renewable Energy Procurement Options

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The Path Forward

Despite rapidly increasing grid demand, renewable project headwinds, and changing accounting rules, companies can still meet 2030 scope 2 goals. 

What companies should do now:

• Watch for the next round of GHG Protocol consultation on Scope 2 revisions
• Evaluate forward REC contracts to lock in terms before rule changes
• Prioritize high-impact RECs that deliver measurable climate and social benefits

Key Takeaways

• The Greenhouse Gas (GHG) Protocol’s proposed scope 2 revisions would shift many large power buyers from annual renewable energy certificate (REC) accounting to 24/7 hourly matching and reveal a larger emissions gap than most inventories currently report.
• Of all the US grid regions modeled, the emissions gap between annual and 24/7 hourly matching is widest in PJM Interconnection (PJM) and the Electric Reliability Council of Texas (ERCOT), the markets where data center load is growing fastest.
• Relae's modeling quantifies the shift from annual to 24/7 hourly matching: serving a 4-gigawatt (GW) data center load at 100% hourly carbon-free energy requires 9.6 GW of additional clean capacity in ERCOT and 10.5 GW in PJM, a roughly 800-megawatt premium in PJM that translates directly into cost and siting strategy.
• Closing that gap requires investments in clean, firm generation technologies, like natural gas with carbon capture and storage (CCS), battery storage, and geothermal. The optimal mix varies by market and load profile, which means modeling current and future emissions positions under 24/7 accounting to understand the best procurement options for a specific portfolio.

Annual REC Accounting No Longer Holds at Data Center Scale

For years, large corporate energy buyers have relied on a straightforward approach: purchase renewable energy certificates (RECs) or sign virtual power purchase agreements (VPPAs) to offset market-based scope 2 emissions. Under the current GHG Protocol guidance, these instruments allow companies to claim low or zero emissions regardless of when or where clean energy is actually generated. When corporate clean energy demand was modest, this fueled new renewable project development while aggregate grid emissions were trending down.

That approach worked, until now. Energy demand from data centers and hyperscalers is surging. The Federal Energy Regulatory Commission (FERC) reported more than 50 GW of data center capacity operating in the US at the end of 2025, much of it concentrated in regions where local clean generation cannot keep pace. When corporate clean energy demand was modest, the gap between contractual claims and physical generation was small enough that few questioned this argument. At hyperscaler levels, with load concentrated in a handful of grids, that gap is becoming too large to ignore.

From a climate perspective, well-designed renewable procurement has created real impact by channeling corporate capital into new clean generation, and reducing CO2 emissions anywhere to benefit the climate everywhere. From a grid perspective, power consumption and generation must balance in real time, and the flow of electricity is constrained by the physics of the transmission system. Some regulators, investors, and standard-setters argue that corporate clean energy claims should be grounded in this second, engineering perspective rather than the first. The GHG Protocol's proposed revisions reflect that view, and would force buyers to defend their claims against it.

Relae’s modeling of this 24/7 framework in PJM and ERCOT helps quantify its costs and emissions implications in the markets where the stakes are highest.  

What Does 24/7 Hourly Matching Mean for Scope 2 Accounting?

The biggest proposed change to the GHG Protocol’s current Scope 2 Guidance is the move from annual power reporting and matching to a 24/7 approach. Instead of calculating emissions with an annual emissions factor (EF) based on their independent system operator (ISO) or eGRID region for each megawatt-hour (MWh) consumed, companies would need to use hourly-specific EFs. 

Companies would still be able to retire RECs to reduce their market-based emissions. However, companies would need to show that these RECs came from clean energy that was generated on the same grid, in the same hour as their facilities consumed power. This makes annual, location-agnostic REC retirement, currently the dominant practice, insufficient for 24/7 market-based accounting. 

Both the time restriction (hourly matching) and the location restriction (generation on the same grid as consumption) will make it more difficult for companies to retire RECs. For example, because today's methodology is location-agnostic, a New York-based company can retire RECs from a Texas wind farm (purchased unbundled or via a VPPA) to reduce its reported market-based scope 2 value. This has allowed renewable development to follow the best resource sites rather than the load. Similarly, the time of day that the wind farm generates energy is irrelevant, as long as it is approximately in the same calendar year. 

Under the proposed revisions, retiring these RECs would no longer be acceptable for the New York company, since they would fail both location- and hourly-matching requirements. As a result, companies with large REC portfolios today may no longer be able to retire them in order to reduce their market-based scope 2 emissions, if the proposed revisions take effect. These companies may face significant unmatched consumption under 24/7 accounting, especially during evening peaks or grid stress events when fossil-based generation fills the gap. 

Annual Matching vs 24/7 Hourly Matching

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The figure below illustrates the gap between what a representative large buyer reports under the current annual location- and market-based methodologies, versus what an hourly 24/7 analysis reveals.

Understanding this emissions gap is the essential first step for buyers to make informed decisions about which instruments to retain, which contracts to renegotiate, and where new investment will matter most. If the proposed scope 2 revisions are enacted, companies procuring clean energy will be disincentivized from buying RECs sourced from variable renewables in distant locations, and instead will find it more favorable to invest in same-grid clean, firm generation, such as geothermal, nuclear, and renewables plus storage. RECs from these projects would qualify to be retired against market-based scope 2 emissions under the proposed revisions, where today's distant-wind or off-peak-solar RECs would not.

Where Pressure Is the Highest: ERCOT and PJM

Two markets stand out for projected hyperscaler load growth: PJM, which covers the extended mid-Atlantic region, and ERCOT in Texas. Both are on track to absorb massive increases in data center demand over the next decade, and both expose the limits of annual REC accounting in ways that will be hard to ignore under the new proposed framework.

PJM: 60% Fossil Generation Means High Marginal Emissions

PJM is one of the largest and most complex wholesale electricity markets in the world. Its generation mix still includes 60% coal and natural gas, which means hourly emissions intensity remains high, particularly during evening peaks and grid stress events when fossil generation dominates the dispatch stack. 

Buyers relying solely on annual REC retirement may show low market-based scope 2 emissions today, but a 24/7 analysis tells a different story. For PJM-based buyers, this means hourly matching gaps will be largest during evening and overnight hours, when nuclear and storage become disproportionately valuable relative to additional solar.

ERCOT: Solar and Wind Don’t Peak When Demand Does

Texas has abundant wind and solar, with solar generation growing nearly 7x since 2020, but those resources don’t always run when demand peaks. While fossil-based generation has declined since 2020, it still comprises more than half of ERCOT’s generation. Solar dominates midday, wind peaks in the evening, and natural gas fills the gaps, especially during high-demand evenings or extreme weather events. 

Buyers with large ERCOT footprints may find that VPPA portfolios, which generate most of their clean energy in off-peak hours, already satisfy the proposed location-based test but fail on hourly matching. Battery storage and demand flexibility could help bridge the gap.

Figure 3 below quantifies that gap in both markets by showcasing the carbon-free energy (CFE) score in ERCOT and PJM, as well as the additional capacity required for a 4 GW load to achieve a 100% CFE target. The CFE score is the share of grid-supplied electricity in a given hour that comes from carbon-free sources, and is the metric the proposed scope 2 revisions would use to evaluate hourly matching. A 100% CFE target means electricity consumption is matched to carbon-free generation in every hour of the year.

In the left panel, a representation¹ of each market's 2030 hours are sorted by grid (CFE) score, from the dirtiest hour on the left to the cleanest on the right. Neither grid approaches 100% carbon-free on its own, and the shaded areas represent the unmatched hours a buyer claiming 100% clean energy through annual instruments would actually carry under 24/7 accounting. The gap is the maximum unmatched hours a buyer might be exposed to, as some RECs procured through annual matching may qualify under the new rules, if satisfying the locational and hourly requirements.

The right panel translates that gap into action. The additional co-located clean generation and storage required to serve a representative 4 GW load (roughly 5% of the forecast 2030 C&I load in ERCOT and 4% in PJM) at a 100% hourly CFE target, on top of what the underlying grid already provides.

A few patterns are worth highlighting. First, the left panel confirms that PJM’s grid will still spend materially more hours below 100% carbon-free than ERCOT’s in 2030, a direct consequence of the coal- and gas-heavy generation mix described above. Notably, ERCOT's curve reaches 100% in a meaningful share of hours (windows when the grid is running entirely on carbon-free resources), while PJM's never does, meaning some fossil generation is dispatched in every hour.  

Second, the ISO a buyer operates in drives a meaningful difference in build-out: hitting 100% hourly CFE for a 4 GW load takes 9.6 GW of additional capacity in ERCOT and closer to 10.5 GW in PJM. This indicates the advantage of achieving hourly and locational matching in already clean grids, which may influence a buyer choosing where to site new workloads. 

Renewables have the largest share of the additional capacity in both markets (5-6 GW), paired with significant long-duration energy storage (~2 GW), while natural gas with CCS provides meaningful clean, firm capacity (~3 GW). ERCOT’s storage share of capacity is slightly larger, reflecting the midday-solar/evening-load mismatch, while PJM leans a bit more on natural gas with CCS, where clean, firm generation does more of the heavy lifting due to lower wind speeds and solar irradiance than Texas.

The right panel also illustrates why clean, firm technologies (natural gas with CCS, advanced nuclear, and enhanced geothermal) are likely to be included alongside renewables and batteries in any serious 24/7 portfolio. With only renewables and batteries, hitting the same target requires about double the total generation and storage capacity. In both markets, targets that look achievable today on an annual REC basis will require materially more capital and a different mix of resources, under 24/7 accounting. 

Top Questions Large Power Buyers Need to Model Before the Rules Change

The GHG Protocol revisions are not finalized, and the timing of any mandate remains uncertain, which is exactly why modeling cannot wait.

A useful self-test for any large power buyer is: can your team answer the following today with defensible numbers?

• What is your hourly CFE score across your largest load centers, and how far does it sit from your reported market-based emissions?
• Which of your existing VPPAs and REC contracts hold value under 24/7 accounting, and which become effectively stranded?
• What mix of resources delivers the incremental clean, firm capacity that closes your gap in PJM, ERCOT, or wherever your load is concentrated at the lowest cost?
• If your next gigawatt of load were sited in a different ISO, how would your emissions position change?

Clean firm projects do not appear off the shelf. Advanced nuclear, enhanced geothermal, and natural gas with CCS all carry multi-year development timelines, and corporate offtake agreements are often what get these projects financed in the first place. Buyers who engage now help shape the project pipeline that will be available in their target markets in 2030, and can lock in offtake terms before competition for the most valuable sites tightens. Buyers who wait until the methodology is final will be working with shorter lead times, fewer development partners, and less leverage to specify projects that fit their load profiles and hourly matching needs.

Frequently Asked Questions

What is 24/7 hourly matching, and how does it differ from today's REC accounting?

Today's scope 2 accounting lets companies retire renewable energy certificates (RECs) from any grid, at any time of year, to offset their emissions. The GHG Protocol's proposed 24/7 hourly matching would require RECs to come from clean generation on the same grid, in the same hour a facility consumes power, making most of today's location-agnostic RECs ineligible for market-based accounting.

Why are PJM and ERCOT under the most pressure from this shift?

Both markets are absorbing the fastest-growing data center load in the country, and both still lean on fossil generation to meet demand outside peak renewable hours. PJM's generation mix is 60% coal and gas, while ERCOT's solar and wind often don't peak when demand does, so buyers in these markets face the largest gaps between their annual REC claims and their actual hourly carbon-free energy score.

How much additional clean capacity does it take to close the gap?

Relae's modeling finds that serving a 4 GW data center load at 100% hourly carbon-free energy requires 9.6 GW of additional clean capacity in ERCOT and 10.5 GW in PJM. That capacity mix leans on renewables and long-duration storage in both markets, with natural gas with CCS playing a larger role in PJM, where wind and solar resources are weaker.

What should power buyers do before the GHG Protocol revisions are finalized?

Start modeling now. Buyers should know their hourly carbon-free energy score, understand which existing VPPAs and REC contracts hold value under 24/7 accounting, and identify the lowest-cost mix of clean, firm resources that closes their gap. Clean firm projects like advanced nuclear, enhanced geothermal, and natural gas with CCS take years to develop, so buyers who engage early have more influence over the project pipeline and better offtake terms.

Modeling the 24/7 Emissions Gap with Relae

For large power buyers assessing what the proposed GHG Protocol revisions mean for their power procurement portfolio, Relae's Advanced Power Emissions Analysis solution models the gap between current market-based reporting and what 24/7 accounting would reveal—by market, load profile, and technology stack.