Scope 2 Emissions Explained: Tracking, Reporting, and Reducing Impact

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. 

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

‍

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. 

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

• Data centers are driving surging demand for new, firm electricity supply, accelerating natural gas-fired power generation.
• Carbon capture and storage (CCS) offers a practical way to balance long-term climate commitments with the need for new electricity generation in the near term.
• New natural gas-fired power plants must be capture-committed, not just capture-ready, potentially delivering power in 18 months and decarbonized power 18-24 months later.
• Capture-committed plants integrate planning and finance for the CO₂ capture, transport, and storage value chain from the start.
• Relae believes early investment in engineering, infrastructure, and community engagement is essential to meet capture commitments.

A New Era of Electricity Demand and Climate Pressure

The US and much of the developed world are experiencing profound growth in electricity demand. Two main forces are driving this trend: (1) the push to electrify existing uses, such as vehicles and heating, to improve energy security, enhance system efficiency, and reduce air pollution; and (2) the growth of energy-intensive sectors like manufacturing, telecommunications, and AI data centers.

Among these drivers, AI is creating unique demands that catalyze specific investments in electric power generation. Astonishing AI data center buildout, led by a handful of large technology firms (sometimes called “hyperscalers”) and their utility and construction partners, is accelerating energy consumption. These firms prioritize speed. When asked for their top five criteria for bringing new AI infrastructure online, one executive responded: “Speed, speed, speed, cost, and carbon emissions.”

Data centers require reliable, always-on power (referred to as “firm power”). This differs from other use cases, such as residential or commercial, which do not need the same amount of power across all hours. While hyperscalers and their partners are investing in renewables, nuclear, and geothermal energy at a remarkable pace, renewable resources alone do not yet meet the exploding demand for firm power. 

Natural Gas Provides Firm Power but Drives Emissions Higher

The mismatch between data center power needs and variable renewable generation is fueling a boom in natural gas-fired power generation. The pipeline of new natural gas-fired power plants is enormous. Plants under construction in 2025 would, by themselves, add roughly 25 million tonnes of greenhouse gases each year to the air and oceans. The full suite of plants in planning is at least 10 times larger. Existing gas plants are also being used more and staying online longer.

This rapid buildout is creating tension with corporate climate goals. Hyperscalers remain seriously committed to reducing emissions, but their ability to hit those targets is undermined by the need to procure new, large-scale electricity generation quickly.

Carbon Capture Aligns with Data Center Energy Demands

Carbon capture and storage is one way to bridge the gap. Data centers operate continuously and may have the ability to shift or curtail load. This demand profile suits the duty cycles of natural gas turbines and CCS facilities well. The potential to reduce direct emissions is profound: today’s CCS technology can capture 95% or more of CO₂ emissions at competitive costs in many markets.

This has led to a resurging interest in the concept of capture-ready gas power generation. New natural gas power plants can be built and brought online in 18 months. In a capture-ready plant, the developers integrate the necessary interfaces and reserve additional land, water, and energy to enable a carbon capture project to be built at a future date. In favorable locations, carbon capture can be added to a capture-ready plant in 18-24 months.  

However, past experience shows that capture-ready plants rarely deliver. The ambition and commitment of the developers were contingent on policy and market signals that were either too small or never materialized. While the base plant may have made economic sense in terms of energy value for investment, it does not appear anyone was willing to pay the climate premium for CCS.  

As David Hawkins of the Natural Resource Defense Council famously said, “If your plant is capture ready, my driveway is Ferrari ready.” To bring David’s humorous analogy back to the specifics here: don’t build a new driveway without at least a downpayment on the car.

How to Build Capture-Committed Power Plants for CCS

A better approach is building capture-committed plants, namely facilities that integrate CCS from the start. To be capture-committed, project developers must:

• Identify geologic storage for the many millions of metric tons of CO₂ that these plants will produce each year over the next 20-30 years.
• Plan reliable CO₂ transportation from power generation to geologic storage by pipeline, rail, barge, or truck.
• Engage credible vendors of carbon capture technology that serve their needs and fit their goals.
• Fund front-end engineering design (FEED) studies.
• Arrange, or help to arrange, financing for the construction, commissioning, and operation of all necessary components in the CO₂ capture, transportation, and storage value chain.
• Ensure natural gas supply has near-zero fugitive methane emissions.
• Partner with local and frontline stakeholders to incorporate community impact into project planning, design, and financing.

Capture-committed plants send strong market signals. They help build the permitting pathways and develop the workforce, infrastructure, and community acceptance needed to avoid extra expense and delays. Done well, early commitments and investments will likely create repeatable models that reduce build times and costs.  

A Path Toward Power That’s Clean Firm and Future-Ready

Eventually more carbon-free power in the form of renewables, nuclear, and geothermal energy will be deployed to serve national and international electric load growth for all types of electrification. Over time, these resources will likely displace natural gas. Until then, hundreds of millions of tons of CO₂ will be emitted each year unless commitments are made to take tangible action now. 

Capture-committed natural gas-fired plants offer a pragmatic solution. With the right planning, financing, and community engagement, they can provide reliable power without locking in emissions, and they can deliver enormous benefits compared to uncontrolled operation. Federal and state governments can accelerate this transition by honoring and increasing CCS grants, supporting shared infrastructure, and streamlining permitting for CCS plants as they have for other clean energy supplies. These investments will enable the construction of cleaner, more resilient power infrastructure for the industries driving demand, from AI data centers to heavy industry.

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Frequently Asked Questions

What is the difference between a capture-ready and a capture-committed power plant?

A capture-ready plant creates an option to add carbon capture in the future, whereas a capture-committed plant treats capture as part of the project from day one. In a capture-ready plant, developers install the right interfaces and reserve extra land, water, and energy, but nothing obligates them to build the capture project, ever. A capture-committed developer secures options for CO₂ transportation and geologic storage, relationships with capture equipment vendors, funding for engineering studies, and financing across the full value chain before the base plant comes online.

Why have capture-ready plants historically failed to add carbon capture?

Nobody was willing to pay the climate premium. Capture-ready developers built plants that made economic sense on energy value alone, then waited for policy and market signals to justify carbon capture. Those signals were either too weak or never arrived, so the option went unexercised and no capture project was ever designed. The base plant runs uncontrolled for decades while the reserved land sits empty. David Hawkins of the Natural Resources Defense Council captured the problem well: "If your plant is capture ready, my driveway is Ferrari ready." Preserving an option costs very little. Exercising it costs a great deal, and capture-ready facilities rarely came with the funding to do so.

If renewables, nuclear, and geothermal will eventually displace gas, why invest in CCS for gas plants now?

Because greenhouse gas emissions happen in the meantime. Gas plants being built today will operate for 20 to 30 years, long before carbon-free resources scale enough to displace them. Left uncontrolled, they will emit hundreds of millions of tons of CO₂ over that span. Capture on those plants avoids most of it. Today's technology can capture 95% or more of CO₂ emissions at competitive costs in many markets.

What can federal and state governments do to accelerate capture-committed projects?

Three kinds of support matter most: funding, infrastructure, and permitting. Governments should honor and extend existing CCS incentives. Developers make capture commitments years before any revenue arrives, so uncertainty in government funding undermines the confidence these projects require. Governments should also support shared CO₂ transport and storage infrastructure. Common pipelines, rail terminals, and storage hubs make it easier for developers to secure physical CO₂ offtake. Finally, permitting for CCS should be streamlined the way it has been for other clean energy supplies. Permitting delay is a leading cause of cost overruns, and a capture-committed plant should be able to pursue capture and storage with the same intensity and speed as electricity generation.

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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This article was originally published in collaboration with the Center on Global Energy Policy at Columbia University as part of its Energy Explained series. 

Key Takeaways

• Attention to data centers is skyrocketing in state capitals across the United States.
• In data center bills passed by state legislatures in 2025, two topics dominated: locational incentives (such as reduced sales taxes) and ratepayer protection. Many bills addressing data centers' water use and environmental risks were proposed, but few were enacted.
• Almost all the enacted bills encouraging data centers to locate in a state were passed by Republican legislatures, and more bills addressing data centers' environmental risks were proposed in Democratic legislatures than Republican legislatures. Concern about the impacts of data centers on power prices was bipartisan.

Introduction

From east to west and north to south, in red states and blue states, attention to data centers is skyrocketing in state capitals across the United States. Our research identified more than 190 bills on data centers introduced in state legislatures in the first 11 months of 2025—roughly nine times the number of such bills introduced in 2024. The bills address a wide range of topics, including economic development, ratepayer protection, grid reliability, and disclosure of data centers' energy use and environmental impacts. More than two dozen of these bills were enacted into law.

This newfound interest in data centers in state capitals is unlikely to abate anytime soon. The data center industry is growing at a staggering pace. A recent McKinsey report projected roughly $2.8 trillion in spending on data center infrastructure in the US by 2030. In 2024, data centers used roughly 4–5% of the electricity produced in the United States—a percentage projected to grow sharply in the years ahead. A rapid buildout of data centers and electricity infrastructure to support them offers economic and strategic benefits but also creates risks for ratepayers, water resources and the environment.

State policymakers are on the front lines of these issues. State governments promote economic development, regulate electricity rates and have jurisdiction over many local resource and environmental issues. Different stakeholders have strongly conflicting views on data centers, setting up high-profile debates in state capitals as well as in Washington, DC.

This blog post—the first entry in a project that will explore state data center policies, power prices and related topics—presents these findings.

Methodology: Tracking Data Center Legislation

We (the authors of this article) queried StateNet's database of state legislation to identify bills proposed between January 1 and November 30, 2025 that used several terms including "data center" and "large load." After removing bills that used those terms but addressed different issues, we categorized the remaining bills into topic areas (including tax incentives, ratepayer protection, zoning and siting, disclosure requirements, environmental protections, labor, water resources, clean energy, and research studies) as well as status (enacted, pending, rejected, and passed but vetoed). We supplemented this research with queries to ChatGPT and Gemini to help identify possible gaps in the StateNet review, double-checking links provided by those large language models to ensure the information provided was accurate.

Almost all state legislatures have now adjourned for the year. (Only six state legislatures remain in session in December.) Trends with respect to state legislative activity on data centers in the first 11 months of 2025 included the following.  

Eight Key Data Center Trends From State Legislative Activity in 2025

1. One of the most common objectives of state bills related to data centers was to encourage those facilities to locate in a state.  

• Roughly 50 bills were introduced in state legislatures offering data centers tax incentives or other benefits.  
• Of the more than two dozen bills on data centers enacted by state legislatures, at least nine provided tax incentives or other inducements for siting decisions. Arkansas, Kansas, Kentucky and Minnesota, among other states, all extended or increased sales or use tax exemptions for data centers. Indiana and West Virginia, among others, established favorable zoning and fast-track permitting procedures to facilitate data center construction.

2. State legislatures are paying growing attention to the impact of data centers on power prices. Ratepayer protection and tariff rate issues were among the most popular topics for state legislation on data centers. More than 40 such bills were proposed and at least six such bills passed. Those included:

• Minnesota HF 16, which requires new large grid customers (including data centers), as a group, to cover all their grid costs;
• Texas SB 6, which requires the Texas Public Utility Commission "to support business development in this state while minimizing the potential for stranded infrastructure costs;" and  
• New Jersey A5466 and California SB 57, both of which require the state PUC to study within one year the effect of data centers on electricity costs.

3. Many bills related to the environmental impacts of data centers were introduced in state legislatures, including approximately 30 bills related to water consumption. Only a few of these bills were enacted. Minnesota HF 16, for example, requires close attention to water use in permitting new data centers. Kansas SB 98 makes tax credits for data centers contingent on practices that will "conserve, reuse and replace water."

4. Approximately 40 bills were introduced requiring data centers to disclose their energy use and/or environmental impacts to state authorities, with roughly a dozen bills requiring disclosure to the public. Details regarding metrics and anonymization of reports varied widely. At least three of these disclosure-related bills were enacted, including the following.

• Texas SB 6 requires interconnection applicants to disclose whether they are pursuing other interconnection applications in the state as well as information on onsite back-up generating facilities.
• Minnesota HF 16 requires data centers to disclose information on water consumption volumes.
• Iowa HB 976 requires data centers to submit an annual report to the Department of Revenue detailing the amount of backup power generation fuel and electricity purchased.

5. Several states passed bills limiting tax benefits for data centers.

Iowa limited sales tax exemptions for new data centers to 10 or 15 years (depending on their size), and Florida raised the minimum size for data centers receiving sales tax exemptions from 15 megawatts (MW) to 100 MW.  

6. Texas became the first state in the nation to pass a bill requiring data center operators to enable remote disconnections for use during grid emergencies (referred to as a "kill switch provision").

7. There is little consistency in the legislative text of state bills on data centers.

• Definitions of data centers, thresholds for incentives, and regulations related to disclosure, zoning, siting, environmental impact mitigation and ratepayer protection vary significantly.
• This may be the expected product of variance among state-level policy regimes, and suggests the absence of close coordination among state legislatures or stakeholder groups.

8. The pattern of proposed and enacted bills displayed some partisan patterns.

Almost all the enacted bills encouraging data centers to locate in a state were passed by Republican legislatures. More bills addressing environmental risks from data centers were proposed in Democratic legislatures than Republican legislatures. However bills concerning the impacts of data centers on other ratepayers were enacted in states with Democratic legislatures and governors (including California, New Jersey and Oregon), Republican legislatures and governors (including Texas and Utah) and in which the legislature is controlled by one party and the governor another (including Kansas).

Data centers will be a hot topic as many state legislatures reconvene in January. The Executive Order on state AI laws released by the White House December 11 2025 does not seek to preempt state laws related to data centers (see in particular Section 8b), however questions related to the optimal role of state governments and the federal government on AI and data centers will likely be prominent as well.

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.