Understanding the Carbon Footprint of AI and How to Reduce It

Key Takeaways

• Enhanced geothermal systems (EGS) overcome traditional geothermal energy limitations by engineering subsurface conditions rather than searching for them, enabling widespread deployment of clean firm renewable power.
• Induced seismicity from high-pressure injection has caused major EGS project cancellations, but advanced approaches like Sage Geosystems’ gravity-assisted fracturing mitigate this risk by avoiding overpressures and directing fractures downward away from fault zones.
• Sage’s $97 million Series B financing, co-led by Ormat Technologies and Carbon Direct Capital, will fund the first commercial EGS facility at an existing Ormat plant—accelerating the transition from innovation to grid-scale deployment.
• For hyperscalers racing to power AI infrastructure, EGS offers a credible path to firm, 24/7 low-carbon power at scale.

Geothermal Energy: The Heat (And Pressure) Is On

For decades, geothermal energy has occupied a compelling yet narrow place in the clean energy landscape. It offers what the grid increasingly needs— firm, renewable, low-carbon power—yet has remained constrained by limited siting flexibility, high upfront resource risk, and persistent concerns around induced seismicity. 

Enhanced geothermal systems (EGS) change that equation. Instead of searching for ideal subsurface conditions, EGS engineers them directly. In doing so, EGS rewrites the rules of where geothermal energy can be deployed and how far it can scale, with the potential to transform this historically niche resource into a widely deployable form of clean firm power.

One such solution, Sage Geosystems, uses a pressure-managed EGS approach to extract geothermal energy from engineered subsurface reservoirs, while explicitly addressing the seismicity risks that have constrained earlier projects. 

How EGS Scales Geothermal Energy

Conventional geothermal power relies on a narrow set of subsurface conditions: sufficiently high temperatures, naturally occurring fluid, and enough permeability to circulate fluid through hot rock. In practice, those conditions coexist in only a few places—nearly all US commercial geothermal power generation is concentrated in California, Nevada, and a handful of sites across Utah and Hawaii.

EGS reduces this constraint by engineering permeability and fluid access rather than relying on their natural presence. While fluid access and permeability are harder to find, heat is not: the Earth’s natural geothermal gradient ensures that hot rock exists almost everywhere at sufficient depth. 

By reducing the number of variables that must be discovered rather than designed, EGS expands siting flexibility and lowers the resource risk that has historically constrained geothermal development. The Department of Energy (DOE) estimates this approach could unlock more than 5,500 GW annually of US resource potential, which, when converted to electric power, is roughly comparable to the total installed power capacity of the US today.

One remaining challenge has been induced seismicity. When you inject pressurized water into rock and create fractures, you are adding lubrication to geological systems that have been static for millions of years. If those fractures propagate into existing fault zones, the faults can slip, producing earthquakes. Projects in Basel, Switzerland (2006) and Pohang, South Korea (2017) triggered magnitude 3.4 and 5.4 events, respectively, both leading to project cancellations and regulatory backlash that set the industry back years.

Sage's approach to EGS is designed to address this risk directly. Rather than relying on high-pressure hydraulic stimulation, Sage uses a gravity-assisted fracturing approach that helps avoid the high overpressures that can drive fault slip. Further, its approach biases fracture growth downward and away from shallow, critically stressed fault systems. By understanding causes and conditions, Sage aims to work with the subsurface, not against it. 

This is not a minor technical detail. It is the difference between a technology that can scale with community acceptance and one that faces opposition at every site. For a hyperscaler evaluating geothermal offtake agreements, seismicity risk translates directly into permitting risk, timeline risk, and reputational risk. 

The Clean Firm Power Gap Driving EGS Adoption

To understand why this matters, start with the problem hyperscalers are trying to solve. Solar and wind have scaled dramatically, but they face a structural limitation: they do not generate power when the sun is not shining or the wind is not blowing. Batteries help bridge short gaps, but current technology cannot economically cover multi-day periods of low renewable output. Nuclear provides firm generation, but faces permitting timelines that extend well past 2030.

This creates what might be called the 'clean firm power gap'—the difference between what hyperscalers need (24/7, low-carbon, scalable to gigawatts) and what current markets can supply. A single large AI training cluster can consume more than 100 MW continuously. Meta, Google, and Microsoft are planning data center campuses that will require gigawatts of capacity. The gap between demand and available clean firm power supply is widening, not narrowing.

Geothermal energy aligns closely with this need. Unlike solar or wind, geothermal power plants run continuously, with capacity factors that routinely exceed 90%. And unlike nuclear, geothermal projects can, in principle, be permitted and built on shorter timelines. The challenge has never been performance, rather availability: with the emergence of EGS, geothermal power is expanding where clean firm power can realistically be built, arriving at a moment when the grid’s need for dependable, low-carbon supply has never been greater.

Sage Raises $97 Million to Deploy Geothermal at Ormat Site

Sage Geosystems announced $97 million in Series B financing co-led by Ormat Technologies, the world's largest geothermal operator, and Carbon Direct Capital, a leading energy investing firm. Ormat will host Sage's first commercial facility at an existing Ormat plant.

The investment signals that EGS has become investable to the industry built to scale it. For Ormat, the logic is clear: conventional geothermal is constrained by resource availability. EGS expands the addressable market, but requires the subsurface capabilities that conventional operators don't typically possess by Sage does.

Why the Partnership Structure Works

EGS proposes that the fastest way to scalable power is to eliminate the resource risks that beset conventional geothermal projects. These risks do not simply disappear: they are transferred into subsurface and remain unproven at scale. Conventional operators locate naturally permeable reservoirs. EGS requires creating permeability in crystalline rock and managing induced seismicity risks that don't exist in hydrothermal systems. Sage is actively addressing the seismicity problem that ended projects in Basel and Pohang. Ormat brings everything else: turbines, plant operations, grid expertise, and six decades of operational knowledge.

Building at an existing Ormat site provides another advantage: established subsurface characterization, proven geological stability, and grid infrastructure already in place. For a first commercial deployment, this de-risks demonstration in ways greenfield sites cannot.

Both companies move faster together because the technical capabilities required to make EGS work don't naturally exist within a single organization.

What Hyperscaler Demand Means for the Power Sector

Meta's 150 MW power purchase agreement with Sage—announced in August 2024, with delivery planned for sites east of the Rocky Mountains—adds another dimension to this story. Hyperscalers have concluded that waiting for clean firm power technologies to mature before signing contracts means those technologies may not be available when needed. So they are becoming anchor customers, providing the revenue certainty that enables projects to secure financing.

For geothermal power specifically, this demand signal is transformative. Contracted offtake from creditworthy counterparties changes project economics fundamentally. It lowers the cost of capital, enables debt financing, and de-risks the investment case for additional capacity. The hyperscaler model has already accelerated deployment in solar, wind, and battery storage. Its application to geothermal power may prove similarly catalytic.

The Final Constraint

EGS is not a silver bullet, but it is beginning to look like a credible answer to a growing-problem: how to deliver clean firm power at scale, in more places, and on timelines that match accelerating demand. Advances in subsurface engineering are reducing the resource and seismicity risks that once confined geothermal to a narrow footprint, while partnerships with incumbent operators are showing how those advances can be integrated into existing energy infrastructure. 

At the same time, hyperscalers are reshaping the market by signaling demand early, underwriting first deployments, and pulling technologies forward rather than waiting for them to mature on their own. That combination of technical progress, industrial adoption, and committed buyers is what turns promising concepts into deployable systems. 

Whether EGS ultimately fulfills its potential will depend on repeatable and continued performance under real-world conditions. But the recent alignment of science, incumbents, and demand suggests EGS is moving beyond possibility and into a phase where the final constraint is no longer what the Earth can provide, but what the energy system is prepared to build. 

Frequently Asked Questions

What is an enhanced geothermal system?

An enhanced geothermal system, or EGS, produces geothermal energy by engineering underground conditions needed to circulate fluid through hot rock. Unlike conventional geothermal projects, which depend on naturally occurring heat, fluids, and permeability occurring together, EGS can create or enhance permeability and fluid circulation, greatly expanding the locations where geothermal power may be developed. 

Why is EGS important for data centers and AI infrastructure?
AI and data centers require large amounts of electricity around the clock, creating demand for power sources that are both low-carbon and firmly available. EGS could provide high capacity factor (greater than 90%), 24/7 clean electricity in more locations than conventional geothermal, making it a potentially valuable complement to intermittent renewable resources. 

What is induced seismicity, and how are new EGS technologies addressing it?

Induced seismicity refers to earthquakes caused by changes in underground pressures or stresses caused by human activities. Earlier EGS projects demonstrated that high-pressure fluid injection can activate existing faults and in some cases triggered noticeable earthquakes and intense public backlash. New EGS approaches are being designed to better control reservoir pressure, fracture development, and proximity to faults, reducing seismicity risk while maintaining the fluid circulation needed to extract geothermal energy.

Can EGS be deployed anywhere?

EGS substantially expands geothermal’s geographic potential, but it does not make every location equally suitable. Projects still depend on factors including underground temperature, how deep they need to drill to access that temperature, water availability, seismic risk, and whether the rocks are of type suitable to hold and sustain engineered fracture networks.

Key Takeaways

• On May 4, 2026, the North American Electric Reliability Corporation (NERC) issued a rare Level 3 “Essential Actions” Alert in response to repeated events in which 1,000+ megawatts (MW) of computation load dropped off the bulk power system in seconds, leading to major grid stability issues.
• The pattern has since escalated: on July 22, 2026, a transmission fault in Ashburn, Virginia took more than 3 GW of data center load offline in seconds—roughly 3% of PJM demand at the time.
• NERC also published Reliability Guidelines that push the same concerns into long-term planning, explicitly recommending resource adequacy models that capture firm vs. flexible load, behind-the-meter resources, and AI training operating windows.
• For transmission operators and balancing authorities, the releases compel new scrutiny of how computational loads affect stability and resource adequacy. For hyperscalers and other large loads, those assessments now sit on the critical path: if operators cannot show through advanced modeling that they can integrate the new loads, interconnection and buildout plans stall.
• Meeting the bar takes advanced grid modeling at multiple time and spatial scales, from sub-second stability through long-horizon capacity and resource adequacy, to evaluate the role of large load portfolios considering demand response, storage, and co-located generation.

Why Grid Frequency Matters for Large Loads

When we turn on the lights or charge our phones, it’s easy to forget that electricity travels through the power grid as alternating current. Sixty times a second—far faster than our eyes can see—the flow of electricity alternates back and forth along the wires making up both the transmission and distribution parts of the North American grid. 

Power generation equipment and most large industrial loads are designed to work with this 60 Hertz (Hz) alternating flow and must be synchronized precisely to this rhythm to function. Grid synchronization is so important that it can even have geopolitical implications.

For some electrical equipment, getting out of sync with the grid’s frequency can lead to malfunctions or even physical damage and destruction. That’s why grid-connected equipment is protected by circuits that automatically disconnect from the grid (“trip offline”) if the grid frequency begins to deviate by even one percent. For minor equipment, this is easily managed. However, when large amounts of generation or load trip offline quickly, it can lead to rapidly cascading grid blackouts affecting tens of millions of people with costs in the billions.

Grid operators pay extremely careful attention to factors that could cause grid frequency to deviate. The grid’s frequency stays near 60 Hz only when total power generation and consumption (load) are closely balanced. If load suddenly drops below generation, physical rotating generators like gas turbines can begin to speed up, making grid frequency rise. 

This becomes particularly dangerous when large grid-connected loads all trip offline simultaneously because of minor frequency deviations or other factors. If these loads are large enough, they can trigger a cascading sequence of rising frequency and further equipment and generator trips, potentially causing a complete “grid collapse” blackout. The North American grid may be getting closer to this scenario. 

What Triggered NERC’s Highest-Urgency Alert

Data center load drops are now a documented grid stability threat. On May 4, 2026, NERC issued a rare Level 3 “Essential Actions” Alert—its highest-urgency notification—in response to a pattern of customer-initiated load reductions in which 1,000+ MW of computational load (data centers) dropped off the bulk power system (tripped offline) in seconds. These were “customer-initiated” because protection circuits at data centers detected problems with grid-supplied power and automatically disconnected to protect their sensitive computing equipment from electrical damage. 

Paired with a new Reliability Guideline on emerging large loads, the alert highlights the urgent need to better understand the potential for these events to cause grid instability or even blackouts. Together, these two documents reset the bar for the detailed grid modeling and planning needed for any utility, independent system operator (ISO), or hyperscaler with material data-center growth in its footprint.

How the Alert Reshapes Grid Interconnection

For utilities and ISOs, the alert and guideline raise the standard of evidence required to connect computational loads safely to the grid. Modeling assessments now sit on the critical path for large load interconnection decisions, and the same studies will increasingly inform reserve margin, transmission, and dispatch program designs.

For hyperscalers and other large loads, the consequence is direct. Plans that assume firm service without supporting analysis will face longer queues and tougher interconnection conditions. Buildout timelines now depend on whether utilities and ISOs can show, through stability and resource adequacy modeling, that the system can absorb the load and respond safely to its disturbances.

For storage developers, particularly long-duration and fast-responding assets, these events elevate the reliability value of rapid response and load-shifting resources. The same grid modeling improvements that capture flexible load behavior also surface storage's full reliability contribution.

For flexibility platforms, the same modeling work that satisfies NERC's expectations unlocks faster, cheaper interconnection. Demand response, large-load shifting, and co-located dispatch coordination are now both technical and commercial enablers.

A Higher Bar for Power Analysis

These pressures point to a higher bar for power analysis at multiple time and spatial scales, for utilities and the large loads they serve.

At sub-second to second timescales, electromagnetic transient (EMT) models capture fast electrical switching and the uninterruptible power supply behavior that determines whether a data center stays connected during a disturbance (“rides through”). The alert asks for these models to be more detailed, validated against actual equipment, and shared between large loads, transmission owners, and planners.

At seconds-to-minutes, dynamic stability simulation covers system frequency response, voltage recovery, and oscillation behavior after disturbances. NERC now expects annual stability studies and explicit load drop contingencies in planning files.

At hours-to-years, capacity expansion and production cost modeling determine whether the system has enough resources, in the right places, with the right flexibility, to keep up with computational load growth. NERC’s May 2026 Large Loads Reliability Guideline is most explicit at this scale, calling for resource adequacy studies that represent firm and flexible load components, behind-the-meter resources, AI training operating windows, and probabilistic scenarios across many weather, load, and outage combinations on a network-aware footprint. 

Rising to the Challenge

Since the alert was issued, its expectations have begun hardening into rules. Registered entities were required to report to NERC on their progress against the seven Essential Actions by August 3, 2026, and on July 16, 2026, FERC directed NERC to go further: to develop mandatory reliability standards for computational loads and revise its registration criteria, with the first standards and Rules of Procedure changes due December 31, 2026 and a second-phase work plan due March 1, 2027. NERC's Large Loads Action Plan anticipates new "Computational Load Owner" and "Computational Load Operator" registered entity types alongside the first three computational load standards. 

The practical consequence is that the modeling described above is no longer only good planning practice: utilities, ISOs, hyperscalers, and other large loads should expect the data-sharing, study, and commissioning expectations in the alert to return as auditable requirements, and should build the capability before the compliance deadline rather than after it. 

Frequently Asked Questions

What is a NERC Level 3 Alert, and what does it require?

A Level 3 “Essential Actions” Alert is the most urgent of NERC's three alert levels, reserved for risks that need immediate, documented industry response. The May 4, 2026 alert directed registered entities to take seven essential actions on computational load—covering modeling, system studies, commissioning, protection, fault recording, and direct operational communication with large load operators. Written responses were due to NERC by August 3, 2026.

Why do data centers disconnect from the grid during minor disturbances?

Data centers run voltage- and frequency-sensitive computing equipment protected by automatic transfer systems that switch to on-site UPS or backup generation the moment grid power looks abnormal. Those protection settings trip faster, and at smaller deviations, than conventional industrial loads, so a fault lasting milliseconds can move a gigawatt of demand off the system in seconds. Because the shift is customer-initiated, grid operators get no warning and no time to rebalance.

How does the alert change interconnection for hyperscalers and other large loads?

Modeling assessments now sit on the critical path for large load interconnection. A plan that assumes firm service without stability and resource adequacy analysis behind it will face longer queues and tougher interconnection conditions, because the utility or ISO has to be able to show the system can absorb the load and respond safely to its disturbances. In practice, buildout timelines are now tied to someone else's study queue.

What modeling do utilities and large loads need to meet NERC's expectations?

Electromagnetic transient (EMT) models validated against actual equipment for sub-second ride-through behavior; dynamic stability simulation with explicit load-drop contingencies for seconds-to-minutes frequency and voltage response; and capacity expansion and probabilistic resource adequacy modeling that separates firm from flexible load, represents behind-the-meter resources, and reflects AI training operating windows. The paired Reliability Guideline is most explicit about the last of these.

Key Takeaways

• Speed to operation is the value proposition. While grid interconnection can take 3-5 years in constrained regions, hydrogen fuel cell systems can be deployed in a fraction of that time—a large advantage for operators racing to meet data center capacity demands.
• Hydrogen is moving beyond backup. Fuel cells could supply 6-15% of incremental data center power, functioning as primary or near-primary power rather than emergency standby.
• Higher fuel costs can be rational. When the alternative is a multi-year wait for grid power and the development loss that comes with it, paying a premium for hydrogen is better than falling behind.

The Grid Can’t Keep Up With Data Centers

The data center infrastructure buildout is moving at a pace the US power grid was never designed to accommodate. Cleanview tracks about 63 GW of operating US data center capacity and another 392 GW in planning, roughly six times what exists today. Developers in high-demand corridors—Northern Virginia, Central Texas, Phoenix—are finding that the grid can't deliver new capacity fast enough.

Interconnection queues now stretch 3-5 years in many regions, and that’s before accounting for permitting, transmission upgrades, and substation buildouts. For operators that need power online faster than the grid can deliver it, this timeline gap is a fundamental constraint on the business.

This mismatch between AI deployment, generator bottlenecks, and grid timelines is why hydrogen is entering the conversation as a viable power option for a growing subset of data center deployments, alongside natural gas with carbon capture, geothermal, and long-duration energy storage.

Hydrogen Fuel Cells: From Standby to Primary Power

For most of its history in data centers, hydrogen has been discussed as a cleaner replacement for diesel backup generators. That framing undersells the opportunity. 

The shift happening now is more fundamental: hydrogen fuel cells are being evaluated as primary or near-primary power sources for behind-the-meter and facilities that can’t wait for grid connection. Notably, many modern fuel cell systems—including solid oxide fuel cells—can run on natural gas today and transition to hydrogen as clean supply becomes available. Bloom Energy's 2024 deal with American Electric Power to provide up to 1 GW of fuel cells for data center power illustrates this approach: the systems will initially run on natural gas, with the capability to switch to 100% hydrogen in the future.

Furthermore, fuel cell systems are modular, can be deployed incrementally as load grows, and provide continuous power output. This is a promising path, and one that will scale only as fast as the fuel cell supply chain can keep up. Unlike solar or wind, fuel cells aren’t weather-dependent. Running on hydrogen, they produce zero direct emissions at the point of use. Running on natural gas, they still emit CO₂, but their higher efficiency gives them a lower carbon intensity than a comparable gas turbine.

Goldman Sachs estimates that fuel cells could supply 6-15% of incremental data center power (the estimate doesn't specify fuel mix, though most near-term deployments would run primarily on natural gas). This is a meaningful share of new capacity, driven largely by operators who face years-long waits for both grid connections and new gas turbines. Bloom Energy, whose fuel cell products serve data center applications, has been a notable outlier in an otherwise struggling hydrogen sector, with its stock rising sharply on strong revenue performance and growing investor enthusiasm around data center power demand.

Speed to power depends on the fuel. Solid oxide fuel cells run on pipeline natural gas today, so operators do not wait for a hydrogen supply chain. Bloom states it can deliver onsite power for an entire data center in as little as 90 days, with the ability to add modules as load grows under certain circumstances. In all but a few select locations, hydrogen comes later when the same systems switch fuels as clean supply scales. Projects that need hydrogen from day one face additional challenges. Limited hydrogen availability and supporting infrastructure across much of the US can undermine the potential speed advantage of hydrogen-dependent projects. Nevertheless, locations with suitable hydrogen supply and supporting infrastructure may offer viable near-term opportunities.

The Hydrogen Premium: When Paying More Makes Sense

Hydrogen-generated power is more expensive than other forms of electricity generation in most markets. The logistics penalty (including compression, transport, and storage) can push hydrogen from $4/kg at the production site to $8-16/kg delivered, making the economics look unfavorable on a simple $/MWh comparison. In practical terms, hydrogen at $4-6/kg translates to roughly $120-180/MWh on an energy-content basis (LHV). At a fuel cell electrical efficiency of around 50%—typical for solid oxide systems—the effective cost of electricity rises to approximately $240-360/MWh for co-located production, compared to $40-60/MWh for natural gas fuel cells, $51-129/MWh for gas combined cycle, and $144-276/MWh for gas peakers. Operators can improve hydrogen economics by co-locating with production, avoiding the logistics penalty that can double or quadruple delivered costs.

While expensive, this cost comparison misses the central point. The relevant question is not whether hydrogen is cheaper than grid power, but whether it is better than having no access to power at all.

For an operator with signed contracts and a 100 MW facility sitting idle during a multi-year interconnection queue, the opportunity cost is substantial. At average US wholesale colocation rates (~$196/kW/month - a record high), a facility of that size represents roughly $235 million per year in potential revenue—meaning even a single year of delay can outweigh years of hydrogen fuel premiums. 

The Inflation Reduction Act’s 45V Production Tax Credit further compresses the gap, offering up to $3/kg for clean hydrogen production—but projects must begin construction by December 31, 2027, to qualify. That deadline adds urgency to what’s already a time-sensitive calculus.

Reframing the Cost Debate for Speed-to-Power

The debate about whether hydrogen is cost-competitive with grid power misses the strategic challenge facing data center operators today: the fastest path to reliable, clean power. Hydrogen can play a consequential role in that timeline in some markets.

Assessing that requires modeling the full picture: grid interconnection timelines, hydrogen supply chain costs at a specific location, the 45V credit’s impact on project economics, and how hydrogen compares to batteries and other bridging technologies for a data center’s particular load profile.

Relae’s advisory team helps data center operators and technology companies evaluate these tradeoffs with rigorous, independent analysis. If speed to operation is a factor in your energy planning, let’s talk.

Frequently Asked Questions

How much faster is hydrogen fuel cell deployment than waiting for grid interconnection?
Grid interconnection in constrained regions can take 3-5 years once permitting, transmission upgrades, and substation buildouts are factored in. Fuel cell systems, especially those that start on natural gas and then transition to hydrogen, can be deployed in a fraction of that time. They are modular and can be installed and scaled incrementally as a facility’s load grows, though the pace depends on equipment and fuel supply.

If hydrogen costs more than grid power, why would an operator choose it?
For an operator with a 100 MW facility sitting idle during a multi-year interconnection queue, the lost revenue—roughly $235 million a year at average US wholesale colocation rates—can outweigh years of hydrogen’s fuel premium. That makes the higher cost a rational tradeoff against a multi-year wait for grid power, not just an expensive alternative to it.

Do hydrogen fuel cell systems have to run on pure hydrogen from day one?
No. Many modern fuel cell systems, including solid oxide fuel cells, can run on natural gas today and transition to hydrogen as clean supply becomes available. Bloom Energy’s up to 1 GW agreement with American Electric Power follows this model: the systems start on natural gas, with the capability to switch to 100% hydrogen as the hydrogen supply chain matures.

Key Takeaways

• Accurate load forecasting is needed to distinguish and prioritize real demand, align capital deployment, and reduce delays in bringing new power capacity online. Traditional load forecasting was built for predictable, gradual demand growth, not for the scale, uncertainty, and dynamic behavior of data centers. 
• The system-level fix to data-center load forecasting requires probabilistic, more frequent, category-specific methods paired with mandatory data standards and policy alignment. Together, these give planners visibility into the range of possible futures and the likelihood of each. 
• Without that fix, today's forecasts conflate real demand with speculative submissions, reducing accuracy. Inaccurate forecasting in either direction is expensive: underbuild adds friction to economic development; overbuild risks raising retail rates. Both can erode public trust in planning.
• Behind-the-meter generation (BTM) and load flexibility can help achieve speed-to-power in the near term. Just 1% data-center flexibility could unlock 100 GW—more than the entire US nuclear fleet.

Load Growth Is Increasing, Uncertain, and Concentrated

For two decades, US electricity demand was flat. Utilities, transmission planners, and corporate buyers built their planning models around that reality. Then AI workloads changed it.

AI load growth is large, uncertain, and concentrated in major power markets. While load forecasting projections vary across studies, the trajectory is clear: electricity demand is scaling faster than the bulk power grid was designed to handle. Accurate load forecasting is needed to distinguish and prioritize real demand, align capital deployment, and reduce delays in bringing new power capacity online.

On April 30, 2026, Relae (formerly Carbon Direct) hosted a Trellis Group panel on load forecasting in the AI era. Panelists included Derya Eryilmaz, PhD, Vice President of Power Commercialization at Relae; John Miller, Director of Transmission Policy at the Corporate Energy Buyers Association (CEBA); Daniel Padilla, Strategy and Business Development Lead at Emerald AI; and Sam Hodas, Head of US Government Affairs at National Grid. Jake Mitchell, Director of Climate Tech Innovation at Trellis Group, moderated.

The conversation explored where load forecasts fail, what they cost, how to fix them, and near-term solutions to overcome grid constraints. Here is what the panel found.

What Is Load Forecasting?

Load forecasting is the practice of predicting how much electricity will be consumed across a region, at what times, and under what conditions. It informs the major capital and procurement decisions on the grid: where to build transmission, how much generation to procure, what capacity to bid into wholesale markets, and how corporate buyers secure clean, firm power.

Long-term forecasts inform multi-year decisions about transmission and generation. Short-term operational forecasts inform real-time grid operations and trading. The two often sit in separate workflows, but short-term operational forecasts should feed into long-term system planning to improve accuracy as demand patterns shift.

The Bulk Power Grid Is Under Strain

Large power users face constraints on clean, firm power, transmission capacity, multi-year interconnection queues, and aging infrastructure. The strain is most acute in PJM Interconnection (PJM) and the Electric Reliability Council of Texas (ERCOT), the two US markets expected to see the most significant load growth. Each constraint raises the cost of getting load forecasts wrong.

Hodas from National Grid describes the operational reality on the utility side: aging infrastructure inherited from a different demand era. “We’ve got transmission lines that are 70 to 100 years old in New York and Massachusetts, some of the oldest in the country, still in operation.” Replacing or upgrading that infrastructure requires investment, and ratepayers are already pressed. 

Why Today’s Load Forecasts Fail

Traditional load forecasting was built for predictable, gradual demand growth, not for the scale, uncertainty, and dynamic behavior of data centers. 

Most utilities and Independent System Operators (ISOs) produce load forecasts on annual or biannual cycles. They aggregate submissions from individual customers, run that data through a deterministic single-peak load estimate against a single capacity scenario, and pass the consolidated forecast up to regional planners. Regional Transmission Organizations (RTOs) roll those bottom-up utility forecasts into a regional view. 

This worked when demand was flat and predictable. It no longer works with nonlinear growth driven by data centers. Eryilmaz from Relae identifies key structural limitations. 

Four Structural Limitations to Traditional Forecasting Methods

• Over-stating and double-counting. Data centers bid into multiple regions while shopping for power, inflating regional forecasts and blurring the line between real and hypothetical demand—the speculative-load problem.
• Deterministic models (vs probabilistic models). Most planning runs a single peak load estimate against a single capacity scenario, missing the geographic concentration and uncertainty inherent in integrating large loads into the system.
• Aggregated submissions. Utilities report large loads as a single block of gigawatts, with no resolution into workload type, ramp schedule, or operational shape. Planners reverse-engineer peak-demand assumptions rather than measure them.
• Infrequent cadence. Annual or biannual forecasts cannot catch an 80% queue reduction or a multi-gigawatt addition between cycles.

The Speculative-Load Problem

The core challenge in load forecasting is distinguishing real versus hypothetical load. While data center electricity demand is projected to grow by 13-27% annually through 2028, the majority of the projects in the data center queue may not materialize, inflating regional load forecasts.

American Electric Power's Ohio utility (AEP Ohio) introduced a tariff requiring data centers to put up firm financial commitments before getting in line for grid connection. Its interconnection queue dropped from 30 gigawatts to 5.6 gigawatts. More than 80% of the submitted load was speculative: projects that disappeared once commitment became required.

ERCOT shows the same overstatement problem on a larger scale. Roughly 225 gigawatts of data center demand sits in the ERCOT queue against a historic system peak of 85 gigawatts. Texas Senate Bill 6 introduced similar financial obligations for new loads, but those rules apply only to interconnections after 2025, and the cleanup of speculative demand has not yet materialized.

The speculative-load problem shows up in interconnection times. An average new project in PJM can wait 4 to 5 years to become operational. Some of that delay is a real backlog. The rest comes from the inability to distinguish real submissions from speculative ones.

As Eryilmaz puts it, “Load forecasting is actually the center of all of these problems. It is a tool to help planners make the right investment decisions.”

The Cost of Inaccurate Forecasting

As Miller from CEBA notes, “A single misforecasted project can swing a transmission plan by hundreds of megawatts.” Significant inaccuracies can erode public trust in the planning process in two main ways. Underbuilding adds friction to economic development and can limit corporate access to clean power markets. Conversely, overbuilding risks raising retail rates if capacity remains underutilized. 

The goal is to achieve right-sized infrastructure investment. When planning aligns with actual large load growth, it can be net beneficial to retail rates. By spreading fixed costs across more usage, significant new demand can put downward pressure on the rates via the “denominator effect.” 

On the other hand, forecasting variability can distort capacity procurement and interconnection queue prioritization. When load forecasts spike upward, grid operators like PJM have to scramble to buy additional electricity capacity on short notice. These emergency procurements lock in major dollar commitments on the basis of unstable forecast numbers. 

PJM, Midcontinent Independent System Operator (MISO), and Southwest Power Pool (SPP) have also reshaped their interconnection queues to make room for new large loads, but those queue priorities depend on the same forecasts that are unreliable in the first place. 

“There is no substitute for good backbone regional transmission planning,” Miller says. “Full stop. That is the enabler of all of the load growth that we’re talking about.”

BTM Generation and Load Flexibility: A Near-Term Bridge

Hyperscalers’ need for power is way faster than that of utilities and RTOs. Generation alone cannot scale fast enough to meet this new demand, and hyperscalers need speed-to-power.

As Eryilmaz frames it, behind-the-meter generation and load flexibility are interim solutions to the timing mismatch between data center urgency and the grid's slower build cycles. BTM generation and flexibility work differently:

• BTM is power generated on the data center's side of the utility meter, bypassing grid interconnection entirely. The structure gives operators large, reliable blocks of power without waiting years for grid approval.
• Load flexibility is the demand-side approach. A data center modifies its grid draw in response to grid signals. In practice, that can mean curtailing compute workloads during stress events, pre-cooling facilities ahead of a heat wave, drawing from on-site batteries or generators, or shifting workloads to data centers in less-constrained regions.

The Value of Load Flexibility

Relae’s power system modeling quantifies the dollar value of load flexibility in ERCOT. Load flexibility can eliminate forced load shedding risk, even at 40 gigawatts 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.

Padilla from Emerald AI reinforces the scale and value of load flexibility: “With just 1% flexibility, we can unlock 100 gigawatts of data centers across the US. That’s more than the entire US nuclear fleet.”

Silicon Valley Power, a municipal utility, is the first US utility to tie flexibility to interconnection speed: flexible data centers get connected faster. NVIDIA, EPRI, Digital Realty, and PJM are partnering on the Aurora AI Factory, the first purpose-built reference design for flexible AI data centers. 

But standardized policy for load flexibility is lagging. Padilla highlights this challenge: “Today, if a data center wants to be flexible, they have nowhere to point. We need standardized tariffs, interconnection rules, and product definitions for large loads that reward them with upsizing interconnection in response to flexibility.” 

Flexibility Takes Many Forms, but it Isn't Universal

Flexibility means accepting brief, predictable downtime, and some workloads can't tolerate it. Hospital systems and mission-critical enterprise applications need 99.999% uptime, the "five nines" standard. As Padilla puts it: "99.9% uptime, with brief and predictable curtailments, is plenty" for most AI workloads. That distinction determines which data centers can participate in flexibility programs.

Miller points out that compute-level flexibility is not always feasible. BTM batteries and virtual power plants (VPPs) are among the alternatives that can offset what data centers withdraw when the grid is stressed, even at facilities whose compute workloads cannot pause directly.

Better Load Forecasting: The Longer-Term Fix

While BTM generation and load flexibility can help address near-term speed-to-power, the longer-term fix is improving load forecasting methods and the standardization of data provided by the data centers themselves.

Eryilmaz outlines three technical shifts for better load forecasting:

• Embed short-term operational forecasting into long-term planning. Short-term spikes, weather risk, and reserve considerations carry direct implications for multi-year capital decisions. The line between operations and planning breaks down when growth is nonlinear.
• Replace deterministic models with probabilistic methods. Risk metrics like loss of load hours (expected hours per year that demand exceeds supply) and expected unserved energy (total expected energy shortfall) measure both how much capacity the system has and the conditions under which it might fall short. The North American Electric Reliability Corporation (NERC) has suggested both metrics as part of its reliability framework.
• Forecast load by category. Treating all data center load as a single block hides the differences in load profiles, operational schedules, and ramp-up timing that drive system planning.

Policy Alignment

Technical forecasting improvements only scale with policy alignment, and Miller proposes a two-part fix:

On the top-down side, RTOs need authority to take an independent view of utility-submitted forecasts. They should require milestones, such as firm financial commitments and secured financing, before counting a submitted load against the regional forecast. 

On the bottom-up side, state regulators set the rules that govern how individual utilities prepare their forecasts. Large load tariffs play a big role in how utility-level forecasts come together. Federal and state authorities need to row in the same direction. Hodas frames the same alignment from the utility side: “Grid investment unlocks economic growth, but for us to make those investments, we need regulatory certainty.”

Standardizing Large-Load Data

The Federal Energy Regulatory Commission (FERC) has since moved: in June 2026 it issued show cause orders directing six RTOs and ISOs—CAISO, ISO-NE, MISO, NYISO, PJM, and SPP—to revise or justify their large-load interconnection rules, and in July 2026 it directed NERC to develop computational-load reliability standards and registration criteria by the end of the year. Both are useful first steps. But as Eryilmaz argues, voluntary disclosure has not closed the gap.

The industry cannot meaningfully compare ISO forecasts when each utility submits load data in different shapes (e.g., using different methods and data standards) on different schedules. Mandatory submission requirements and published methodologies, applied consistently across utilities, ISOs, and state regulators, are the only path to forecasts whose components are actually comparable.

Getting Load Forecasting Right Starts Now

The system-level fix to improving forecasting is through probabilistic, category-specific methods paired with data standardization and policy support. Together, these account for the scale, uncertainty, and dynamic behavior of data center loads, and give planners visibility into the range of possible futures and the likelihood of each.  

All forecasts will be wrong to some degree, but as Miller puts it, “It's ultimately not about having a perfect prediction. It's about baking in methods to account for uncertainty.” These system-level improvements won't eliminate errors entirely, but they will minimize them, leading to more confident investment decisions and a grid better prepared for what's ahead.

Frequently Asked Questions

What is load forecasting, and why is it harder now with AI data center loads?

Load forecasting predicts how much electricity a region will consume, when, and under what conditions—the basis for where to build transmission, how much generation to procure, and how corporate buyers secure clean, firm power. It was designed for two decades of flat, gradual demand growth. Data center load is none of those things: it is large, geographically concentrated, arrives in gigawatt blocks with no disclosed operating shape, and can be withdrawn as quickly as it appeared.

What is speculative load in an interconnection queue, and how do planners tell it apart from real demand?

Speculative load is capacity requested by projects that may never be built — often the same data center bidding into several regions at once while shopping for power, which counts the same gigawatts more than once. The tested filter is a financial commitment: when AEP Ohio required firm commitments before queue entry, the utility's reported data center pipeline fell from about 30 GW to roughly 5.7 GW. Milestone requirements, independent RTO review of utility submissions, and mandatory data standards are the tools planners have.

Is load flexibility proven and scalable today, or still emerging?

The modeling case for load flexibility is strong; the commercial case is still early. Duke's Nicholas Institute found the 22 largest US balancing authority areas could absorb roughly 76–126 GW of new load if it accepts modest curtailment, and Relae's ERCOT modeling shows demand response eliminating forced load shedding risk at 40 GW of data center buildout, avoiding $5.5 billion in annual consumer welfare losses. What is still missing is the market plumbing—standardized tariffs, interconnection rules, and product definitions—so a data center willing to be flexible has somewhere to sign up.

What should a company look for when evaluating a region's load forecast?

Ask whether the forecast is probabilistic or a single deterministic peak, how often it is refreshed, and whether large loads are broken out by category and operating shape rather than reported as one block of gigawatts. Then ask what milestone or financial commitment a project must clear before its megawatts count toward the forecast. A forecast that cannot answer those three questions cannot tell you how much of the queue ahead of you is real.

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Key Takeaways

• Power demand is outrunning buildout. Meeting large load growth requires more than new generation; it requires faster interconnection and congestion relief on existing transmission lines. 
• Dynamic line rating (DLR) is available today, deploys in months, and enables faster speed-to-power. On the right thermally congested lines, DLR can unlock more capacity at a fraction of new infrastructure cost. In one utility demonstration, 5% to 10% of additional capacity was enough to clear most of the congestion on the lines studied.
• DLR has been held back by weak incentives, but that is changing. Utilities earn a regulated return on capital they invest in new assets, which favors building new infrastructure over lower-cost solutions like DLR. Load growth and new Federal Energy Regulatory Commission (FERC) mandates are starting to shift the calculus.

The Grid Cannot Expand Fast Enough for AI Demand, But It Can Carry More

Power demand is booming as data centers scale across the US grid, and current grid infrastructure cannot supply it. This constraint is physical, not financial. Meeting this demand requires a significant amount of power generation and infrastructure upgrades. More than 2 terawatts of generation and storage sit in interconnection queues, roughly 1.5x the total installed generation capacity in the US. 

Regional markets are working to accelerate generation buildouts, but connecting that generation to the transmission network remains expensive and slow to match speed-to-power needs. New high-voltage lines take years to permit, cost between $2 million and $6 million per mile to build, and major projects routinely take five to ten years from identification to energization. For example, PJM Interconnection LLC (PJM) identified the Doubs–Goose Creek 500 kilovolt (kV) corridor as a bottleneck feeding Data Center Alley in 2023 and set June 2027 as the date a fix was needed. Dominion Energy's published schedule for its portion of that rebuild anticipates a completion date of 2031.

A number of studies^1,2 show there is headroom in the bulk transmission system. Grid-enhancing technologies, such as dynamic line rating (DLR), can convert part of that headroom into capacity today while new generation and transmission are being built. DLR lets suitable transmission lines increase their carrying capacity in real time, unlocking that headroom at a fraction of the cost of a buildout. Realizing that value is a targeting exercise with a key question: On which thermally limited lines can DLR actually relieve congestion? 

What Is Dynamic Line Rating?

Dynamic line rating is a method for calculating a transmission line's real-time carrying capacity using live weather and conductor-temperature data. It lets grid operators safely carry more power whenever weather conditions allow.

Most transmission lines operate under a static rating: a fixed, conservative limit on current, set for worst-case weather and held all year. The limit is based on temperature, because pushing too much current can overheat the conductor wire. Metal conductors expand as they heat, which can make them sag and touch trees or other obstacles, causing short circuits or fires. Real conditions almost always cool a conductor better than the worst-case assumption a static rating is built on. That means the line can carry more current while staying at the same maximum conductor temperature, and therefore within the same sag and clearance envelope. That headroom is exactly what DLR captures: instead of leaving it on the table, DLR recalculates the line's rating in real time so operators can use the extra capacity safely.

Beyond a static rating is the ambient-adjusted rating (AAR), which many utilities have begun adopting. An AAR recalculates the rating from forecast ambient air temperature, typically hourly and out to several days. DLR goes further, adding wind speed and direction, solar heating, and in some deployments the conductor's measured temperature.

DLR technologies rest on a heat-balance algorithm: how fast a line heats up (from electric current and sunshine) versus how fast it cools off (from wind and cold air). The calculations are standardized in IEEE 738 in North America and CIGRE 601 internationally. The data feeding those calculations can come from line-mounted sensors, weather models, or both, depending on a tradeoff between per-span accuracy and the cost of installing sensors along every span.

Even so, DLR remains limited in the US, and AAR has been slow to arrive. FERC's Order 881 required the transmission providers it regulates to adopt AAR by July 2025, but FERC has granted numerous extensions. PJM became the first to fully implement AAR in March 2026, while Midcontinent Independent System Operator (MISO) and New York Independent System Operator (NYISO) are not expected until 2028.

The Near-Term Value of DLR: Reducing Grid Congestion

DLR's value is immediate. It can be installed in months, not years, so a currently congested line can start carrying more power the moment conditions allow, reducing congestion right away. When cheaper generation is available upstream of that line, DLR cuts costs directly, because grid operators no longer need to dispatch pricier generation downstream of the congestion to supply load. That means DLR can reduce congestion costs in the current delivery year, compared to a transmission line rebuild that sits in a decade-long queue. 

Over a longer horizon, utility planners can build that headroom into long-term capacity models. This is important, because current capacity-expansion and integrated resource plan (IRP) models still run on static or seasonal ratings, and typically leave out the potential gains from grid-enhancing technologies like DLR. 

NERC's large loads white paper and FERC's RM26-4 rulemaking both raise the issue of how utilities can absorb multi-hundred-megawatt data center requests without a decade-long transmission build. Solutions like DLR are one of the few tools that can compress that timeline. 

The hardware itself is cheap: sensors and data management cost a small fraction of any physical upgrade. That means the economics comes down to identifying the lines that benefit most from DLR. This is particularly important because on most US grids, congestion concentrates on a small number of lines that repeatedly reach their limits. On those lines, DLR can cut congestion costs directly and defer costlier upgrades, while its potential on other lines may be far lower. As a result, identifying those high-potential, thermally congested lines is essential.

Proven DLR Examples in the Industry 

Real deployments show DLR can reduce a meaningful share of transmission congestion costs, with extra carrying capacity above the static rating running roughly 5% to 30%, depending on how often that capacity is available. In Oncor's ERCOT demonstration, 5% of additional capacity would have relieved up to 60% of congestion on the target lines, and 10% would have practically eliminated it. PPL Electric in Pennsylvania/PJM reports annual customer savings of $23 million after deploying DLR across its initial three lines. The DLR installation cost about $250,000, against a rebuild alternative that would have cost about $50 million and taken far longer. 

The contrast abroad is instructive. Austria's grid operator, APG, recorded about $13 million a year in congestion savings across roughly 15% of its network. While these savings are real, it's important to recognize that these results come from single, well-chosen, badly congested lines. 

The UK's National Grid began with a two-year DLR trial on a single 275 kV circuit in 2022, expanded to more than 275 kilometers of its network by 2025, with estimated consumer savings of about $26 million a year. In April 2026, National Grid signed a five-year contract covering 585 kilometers more, with most installations due by 2028 and potential savings of up to $66 million. Each expansion followed measured results from the stage before it.

Where the Headroom Is: Screening PJM's Data Center Alley

To illustrate the congestion savings from DLR, Relae screened PJM's five-minute real-time market record for every binding transmission constraint in 2025. For each one, we captured the shadow price, the marginal value of relaxing that constraint.³

Our analysis focused on thermal constraints, and then identified lines that bind frequently, in conditions milder than the worst case their static rating was set for, which is when a conductor's true rating sits above its static assumption. For the lines that we identified, congestion costs were added over the binding hours to set a bound on the savings that could result from DLR. That full amount would not necessarily be realized in practice, because the shadow price values only the next megawatt freed, and relieving one line can shift the constraint to the next. However, it serves as a useful estimate for the scale of savings that could be achieved.

We ran the analysis on the Dominion (DOM) zone in PJM, home to Data Center Alley in Loudoun County, Virginia. Figure 2 shows a high-level section of the grid. The 500 kV bulk grid steps down through transformers to the 230 kV substations feeding the data centers, with the lines that experience recurring congestion highlighted. A handful of those 230 kV lines showed up as binding thermal constraints again and again. 

The congestion in DOM isn't constant, and it concentrates in particular months and within the day in particular hours. Figure 3 shows three transmission lines within the DOM zone and the number of hours each was thermally congested in each hour-of-day slot over 2025. Binding concentrates in the warm months and, within the day, from late morning through early evening. 

At first glance, this period looks like the wrong window for DLR. The local weather record says otherwise. These periods turn out to be some of the windiest hours of the day, not the stillest. Median wind speed at Dulles ran about 3.5 m/s, above the 0.6 m/s crossflow a static rating conventionally assumes, with fewer than 5% of observations falling below that threshold. Median ambient temperature in those hours was about 26°C, against the 35–40°C a static summer rating is typically built for. Across all three lines, the large majority of congested hours coincided with weather that would have supported a materially higher rating. 

Valuing just one megawatt of DLR relief at each five-minute shadow price, the estimated savings are worth roughly $300,000 in this three-line example across about 263 line-hours.⁴

Because the value concentrates on a handful of thermally limited, heavily congested lines, and because the operational case has to be made line by line, capturing the opportunity is fundamentally an analytics problem: find the right lines, and prove the savings.

What Does It Take to Scale DLR?

DLR is cheap and effective, but two things stand between it and broader adoption: incentives and advanced grid analytics.

The utility cost-of-service model recovers investment in generation and transmission assets and earns its profit as a regulated return on the capital deployed. Because rates recover capital rather than power delivered, utilities have a stronger incentive to build or upgrade lines than to move more power across the ones they already own. That bias toward capital investment over optimization is why a mature technology has stayed niche in the US for years. Regulators have started to look more closely at this, but the main federal rule still mandates the milder AAR, not DLR, and leaves the return model untouched.

Contingency analysis compounds the problem. Current models are built around fixed line limits. A rating that changes hour to hour adds real modeling work, and more importantly, the system still has to hold under worst-case contingencies. So while operators already forecast weather daily for wind and solar, the harder step is trusting a forecast enough to commit a transmission limit against it. That takes significant predictive analytics built into system planning, not bolted on after.⁵

How Policy Is Starting to Shift the Calculus

Policy is starting to move the incentive problem. FERC's Order 881 made AAR the minimum for the transmission providers it regulates (effective July 2025, with several operators on extended timelines) and required markets to be capable of accepting dynamic ratings. PJM has started to implement this: PPL Electric has run sensor-based DLR on nine congested lines since 2022, feeding PJM's day-ahead markets. 

Order 1920, FERC's first long-term transmission-planning overhaul in more than a decade, now requires planners to formally evaluate grid-enhancing technologies like DLR against conventional builds. It stops short of mandating deployment, but it forces a comparison utilities used to skip. That comparison is now written into filed tariff processes (PJM filed its plan in December 2025). Those first cycles only began in 2026, and the order allows up to three years to reach a selection, so the results are still pending. 

A shared-savings incentive, letting a utility keep a slice of the congestion savings it creates, has been proposed to FERC and championed in the Advancing GETs Act, but it isn't yet a rule, so the core misalignment stands. DOE's GRIP program has funded grid-enhancing deployments, and by early 2026, 16 states had some form of advanced transmission technology requirement, with Colorado adding its Grid Optimization Act in April 2026.

The newest pressure is coming from the demand side. Through 2025–26, FERC began overhauling how large loads connect to the grid, and while none of it touches the utility's return on capital, it changes who sees the costs. FERC issued show-cause orders directing all six grid operators to justify or reform their large-load rules. This tees up consideration of alternative transmission technologies in study processes and greater transparency into costs. 

And the rules are moving toward making the large load pay for the upgrades its connection requires. Pennsylvania's model large-load tariff, for example, recommends utilities charge data centers for the upgrades their interconnection makes necessary. It also instructs utilities to let those customers self-construct certain upgrades, including some affecting the wider grid. That combination is what matters. The party paying the bill now has a reason to ask whether a cheaper fix exists and, in at least one state, a route to build it. We have not yet seen a DLR deployment selected this way, because these frameworks are only months old, but the cost gap between a DLR fix and a rebuild is becoming visible to the party who pays the difference.

How Relae Helps Find the Value of DLR  

Through our Power, Data, and Innovation practice, Relae combines transmission congestion data, line-level thermal constraints, and short-term weather forecasts into a single view of where dynamic ratings would actually pay. The output is a short list of candidate lines, each with a modeled capacity uplift and an estimated dollar value, turning a vague “DLR is promising” into a priced, line-by-line decision. It is the transmission-side complement to our work on the interconnection queue and demand-side flexibility. All three are ways of closing the gap between demand and delivered capacity faster than new construction allows.

Key Takeaways

• Carbon accounting is a regulatory and strategic necessity, with policies like the European Union’s Corporate Sustainability Reporting Directive (CSRD) and California’s SB 253 requiring emissions tracking. 
• Many companies track emissions inconsistently, underscoring the need for structured, repeatable carbon accounting to ensure accuracy and impact.
• Accurate carbon data drives efficiency and risk management, helping organizations reduce costs, streamline supply chains, and comply with climate regulations.
• Scope 2 emissions are increasingly challenging to quantify, especially for data centers and power-intensive operations. Grid-average emissions factors mask location-specific and time-specific variations that drive real procurement and siting decisions. Precise scope 2 accounting requires understanding which generators actually serve the load, when, and under what grid conditions.
• Scope 3 emissions have historically been a complex challenge, requiring better supplier engagement, standardized reporting, and expert guidance to support accuracy.

Carbon Accounting: More Than Compliance, a Strategic Advantage

Companies increasingly recognize the business value of reporting carbon emissions: it reduces regulatory risks, attracts sustainability-focused investors, enhances market competitiveness, and drives cost savings through efficiency. Transparent emissions reporting strengthens brand trust and aligns companies with global sustainability standards, ultimately turning climate accountability into a strategic advantage.

Yet, many companies struggle with incomplete and inconsistent tracking. According to the State of Corporate Climate Commitment, 80% of corporate professionals surveyed have tracked emissions at least once, but only 52% do so annually. Without a structured process and reliable data, businesses face compliance risks, financial penalties, and missed cost-saving opportunities. 

This guide provides a clear roadmap to effective carbon accounting, equipping businesses with the knowledge to navigate regulations, enhance data accuracy, and implement effective strategies for compliance and operational success.

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What Are the Benefits of Effective Carbon Accounting?

By embedding annual carbon accounting into operations, organizations can enhance sustainability efforts while achieving financial and strategic benefits. Key benefits include:

Enhancing Transparency and Accountability

By providing accurate and verifiable emissions data, companies can showcase corporate responsibility and build a foundation of transparency. Aligning with recognized standards like the GHG Protocol strengthens confidence among investors and regulators. As climate disclosure laws tighten globally, ensuring credible emissions reporting reduces regulatory risks and enhances stakeholder trust. This commitment to authenticity minimizes the risk of greenwashing and strengthens brand reputation.

Guiding Regulatory Compliance and Risk Mitigation

Businesses navigating evolving environmental regulations must proactively align with policies to avoid financial and legal risks. Mandated emissions reporting, under policies like the European Union’s Corporate Sustainability Reporting Directive (CSRD) and Carbon Border Adjustment Mechanism (CBAM) as well as California’s SB 253, ensures compliance and enhances corporate accountability. Staying ahead of these evolving requirements prepares businesses for future policy shifts and safeguards their long-term resilience.

Improving Operational Efficiency and Reducing Costs

By analyzing energy consumption patterns, organizations can identify operational inefficiencies, optimize supply chains, and implement cost-saving measures while reducing carbon emissions. For example, evo, an outdoor experiences company, collaborated with Relae (formerly Carbon Direct) to assess its carbon footprint. This analysis revealed opportunities to reduce emissions across facilities, products, and shipping. By promoting sustainable practices throughout their supply chain, evo enhanced both environmental performance and operational efficiency.

Building a Competitive Advantage in a Low-Carbon Economy

Building a competitive advantage in a low-carbon economy requires prioritizing emissions transparency and sustainability. Companies that integrate emissions transparency into their operations build stronger relationships with supply chain partners and meet consumer demand for responsible brands. Aligning corporate values with sustainability fosters long-term customer loyalty and enhances market positioning.

Driving Strategic Planning and Net-Zero Alignment

Setting and tracking net-zero commitments requires structured, data-driven carbon reduction roadmaps. Businesses that measure emissions annually are more likely to set public sustainability goals and take action. Carbon accounting supports clean energy transitions, fosters supply chain collaboration, and integrates carbon removal strategies to address residual emissions. By embedding emissions measurement into long-term planning, organizations facilitate resilience and profitability in an evolving business landscape.

Carbon emissions measurement correlates to climate action: 61% of companies that calculate their footprint annually have both set a public goal and begun working toward it.

What Is Carbon Accounting? The Basics You Need to Know

Carbon accounting is the systematic measurement, analysis, and reporting of an organization's greenhouse gas (GHG) emissions. Using standardized metrics like carbon dioxide equivalent (CO₂e), companies can assess emissions across operations and supply chains, identify high-impact areas, and set and track progress toward emissions reduction targets. When conducted annually, carbon accounting supports regulatory compliance, risk management, and decarbonization strategies essential for long-term sustainability.

The GHG Protocol is the most widely used framework for carbon accounting, setting the baseline for how organizations measure and report their emissions. It classifies emissions into three scopes: 

Keep in mind that scope 2 emissions are increasingly difficult to quantify using traditional methods, which fail to account for real-time grid fluctuations and locational energy variations. Cutting-edge, advanced carbon accounting methodologies now provide more precise tracking, particularly benefiting large power consumers like enterprise data centers and hyperscalers. 

Scope 3 emissions also continue to pose a great challenge, requiring extensive data collection and supplier coordination, complexities that are difficult to navigate without expert guidance.

The Corporate Carbon Accounting Process: A Step-by-Step Guide

The carbon accounting process involves systematically measuring, analyzing, and managing an organization’s greenhouse gas emissions across its operations, supply chain, or product life cycle. 

1. Collect emissions data across all three scopes (scope 1, scope 2, and scope 3).
2. Categorize and quantify emissions from each source to estimate the total impact. 
3. Verify data and report findings to promote compliance, accuracy, and transparency.
4. Develop and implement reduction strategies based on insights from the data.

Activity Data Versus Spend Data

Carbon measurement primarily relies on two data types: activity data and spend data.

• Activity data includes direct measurements reflecting the physical amount of an emitting source, such as fuel consumption (in liters or gallons) or travel distance (in kilometers or miles). It accurately represents emissions from a particular operational emission source and allows for measurable decarbonization strategies.
• Spend data serves as an alternative when activity data is unavailable. It estimates emissions based on financial expenditures related to goods, services, or travel. While less precise, it is useful for approximating scope 3 emissions, where direct measurement is often challenging.

Tips for Accurate Data Collection

Effective carbon accounting relies on seamless collaboration across departments, suppliers, and external data sources. Key strategies include:

• Stakeholder engagement: Finance, operations, procurement, and sustainability teams must coordinate to track and validate emissions data. Engaging suppliers is essential for capturing and reducing scope 3 emissions.
• Addressing data gaps: When data is unavailable, proxy data can be used to estimate emissions, but it should be a temporary solution while organizations work toward obtaining accurate, real-world data.
• Standardization and verification: Implementing consistent methodologies and third-party audits enhances the credibility of carbon reporting, building stakeholder trust.

By leveraging precise data, understanding emission scopes, and adopting structured data collection methods, organizations can create a transparent, science-based approach to carbon accounting and lay the foundation for meaningful climate action. 

Climate Standards Businesses Need to Know

Global standards and regulations define carbon accounting methodologies by establishing guidelines for how organizations set boundaries, measure, and disclose emissions. Several key frameworks and policies guide both the international and regulatory levels.

Guidance frameworks

GHG Protocol: The Foundation of Carbon Accounting

• The GHG Protocol is the most widely adopted framework for measuring and managing emissions across organizations globally.
• Developed by the World Resources Institute (WRI) and the World Business Council for Sustainable Development (WBCSD), it provides common standards for businesses, governments, and supply chains.
• Sector-specific guidance has been developed for industries such as energy, finance, manufacturing, and agriculture, which face stricter reporting requirements than service-based sectors.
• The GHG Protocol is currently undergoing its first major revision since its launch. Partnering with the International Organization for Standardization (ISO) to co-develop a consolidated corporate standard that merges the Corporate Standard, Scope 2 Guidance, Scope 3 Standard, and Actions and Market Instruments standard with ISO 14064-1. As of mid-2026, the effort is in active technical development, with a draft opening for public consultation targeted for mid-2027 and a final published standard expected by the end of 2028. Current standards stay in effect until that replacement is finalized, so nothing changes for reporting yet.

ISO 14064: Standardized Emissions Quantification and Reporting

• The ISO 14064 series, created by the International Organization for Standardization (ISO), offers detailed methodologies for greenhouse gas accounting.
• It includes guidelines for organization-level emissions quantification, reporting, and reduction projects.
• ISO 14064 serves as the foundation for independent verification and compliance with voluntary carbon markets, enhancing the credibility of emissions reduction projects.

Policies

EU CSRD: Expanding Mandatory Climate Disclosure

• The Corporate Sustainability Reporting Directive (CSRD) enforces detailed sustainability reporting requirements for companies operating in the EU.
• Following the EU's 2025 to 2026 Omnibus simplification package, CSRD's scope narrowed sharply. It now applies to roughly 5,000 large companies, with new thresholds of more than 1,000 employees and more than 450 million euros in net turnover (up from the original 250 employees and 50 million euros)
• In scope companies must still report scope 1, 2, and 3 emissions data aligned with the EU Taxonomy and European Sustainability Reporting Standards (ESRS).

California SB 253: State-Level Mandatory Emissions Disclosures

• California’s SB 253 Climate Corporate Data Accountability Act sets strict emissions reporting mandates within the US.
• It applies to companies with over US$1 billion in revenue doing business in California, requiring scope 1 and 2 emissions reporting by November 2026..
• CARB has said scope 3 reporting and third-party assurance requirements will be addressed in a subsequent rulemaking covering 2027 and beyond.

EU CBAM: Preventing Carbon Leakage and Promoting Decarbonization 

• The Carbon Border Adjustment Mechanism Requirements (CBAM) requires importers of specific carbon-intensive goods to disclose embedded emissions to prevent carbon leakage and promote global decarbonization by ensuring that domestic and foreign producers face comparable carbon costs. 
• During the transitional phase (2023–2025), importers were required to submit quarterly reports on embedded emissions. 
• As of 2026, they must purchase CBAM certificates to compensate for the carbon footprint of imported goods.

Although carbon accounting requirements vary across regions and industries, they are all fundamentally rooted in the principles established by the GHG Protocol and ISO 14064. As global regulations like the EU CSRD and state-level legislation evolve, organizations must proactively align with these stricter standards to mitigate regulatory risks and support compliance with emerging sustainability expectations.

What Are the Industry-Specific Challenges of Carbon Accounting?

Carbon accounting presents unique challenges across industries due to varying operational structures, emissions sources, and reporting requirements. While the challenges outlined below focus on financial services, data centers, and philanthropies, similar complexities exist across manufacturing, transportation, healthcare, and other sectors. Tailored strategies are essential to effectively addressing these complexities. 

Data Centers: Measuring and Reducing Scope 2 and 3 Emissions

Data center operators consume vast amounts of electricity making scope 2 emissions a major concern. In addition, data center developers and owners face significant scope 3 emissions from embodied carbon from the building materials and the IT hardware required to develop these assets. Effective strategies to reduce data center emissions include:

• Optimizing computing needs and power usage: Use real-time metering and AI-powered analytics to optimize electricity usage across time and locations.
• Procuring low-carbon electricity: Secure long-term access to compliant low-carbon electricity through power purchase agreements (PPAs) or high-impact renewable energy credits (RECs).
• Tracking life cycle emissions: To provide a comprehensive emissions assessment, account for embodied carbon in server manufacturing and end-of-life disposal.

Financial Services: Assessing Emissions From Investments and Portfolios

Financial institutions face significant challenges in evaluating scope 3 financed emissions from investments, loans, and asset portfolios. Key strategies to address these challenges include:

• Adopting industry standards: Frameworks like the Partnership for Carbon Accounting Financials (PCAF) can be used to standardize emissions calculations.
• Understanding asset level data: To accurately report on financed emissions, investors need visibility of emissions data, ideally at the company level.
• Prioritizing green investment strategies: Shift toward sustainable finance by integrating sustainability criteria and emphasizing green bonds or low-carbon funds or investments.
• Ensuring regulatory compliance: To enhance transparency, align with global disclosure frameworks like the International Sustainability Standards Board's (ISSB) IFRS S2 climate-related disclosure standard.

Philanthropies: Managing Emissions From Private Financing

Philanthropic organizations face challenges in tracking emissions across diverse funding activities, operational footprints, and investment portfolios. Effective strategies include:

• Assessing grantmaking impact: Many philanthropies support climate initiatives but may not track the carbon impact of grantees or funded projects. Establishing emissions metrics for grants can enhance transparency and effectiveness.
• Measuring operational emissions: While some philanthropies have relatively low direct emissions, travel, events, and office space still contribute to their carbon footprint. Implementing sustainable operations policies can help reduce emissions.
• Decarbonizing investment portfolios: Endowments and investment funds often hold assets with varying GHG emissions. Aligning investments with sustainability goals and engaging with asset managers on emissions reduction can drive impact.

Organizations across these sectors can enhance emissions transparency, improve sustainability efforts, and align with global climate goals by implementing industry-specific carbon accounting methods. 

Navigating Evolving Standards and Scope 2 Complexity

Carbon accounting standards are actively evolving. In October 2025, the GHG Protocol released two proposals for scope 2 accounting revisions, with final standards expected by 2027. The proposals shift toward hourly and regional renewable energy matching, moving away from today's annual, region-agnostic approach. They also introduce consequential methodology that calculates actual emissions displaced by renewable projects, which varies significantly by region.

For organizations with 2030 climate targets, timing matters. Existing long-term contracts are expected to be grandfathered in under new rules. Meanwhile, new renewable projects face interconnection delays of 3 to 5 years, and the US power grid is experiencing sustained demand growth driven largely by data centers. These pressures converge: power demand is rising while new clean electricity supply is constrained.

Navigating these changes requires understanding emerging methodologies and their strategic implications. See our companion pieces on Navigating Scope 2 Accounting Changes and Scope 2 Emissions Explained for detailed context.

Frequently Asked Questions

How long does carbon accounting implementation take?

Initial measurement typically takes 3 to 6 months, depending on data availability and organizational coordination. Starting with data you already have (utility bills, fuel records) accelerates the process. Scope 2 and scope 3 require more extensive work than scope 1, so actual timelines vary based on which scopes are your focus.

What are the biggest obstacles to getting accurate emissions data?
Data siloes across departments (finance, operations, procurement track separately). For scope 2, grid-average factors mask location- and time-specific variations that actually drive emissions. For scope 3, extensive data collection and supplier coordination are required. Seamless collaboration across departments is essential.

Do we need to measure all three scopes to start?
Yes. Organizations should measure all three scopes. Start where your business is most materially affected, but eventually measure all three for compliance and complete visibility into your emissions sources.