Shifting Playbook for Corporate Power Procurement

Key Takeaways

• The Greenhouse Gas (GHG) Protocol’s Actions and Market Instruments (AMI) proposal would formally recognize market-based instruments across emissions scopes for the first time, within Statement 2.
• The proposal substantially expands opportunities for companies to report lower emissions from purchasing market-based instruments for low-carbon fuels (LCF), such as book-and-claim environmental attribute certificates (EACs) for sustainable aviation fuel (SAF) and renewable natural gas (RNG).
• Companies purchasing or already holding market-based instruments for SAF, RNG, and other low-carbon fuels should begin mapping those instruments to the AMI’s proposed reporting structure now, before the standard is finalized.

The Current Reporting Problem With Low-Carbon Fuels

A low-carbon fuel is defined as a fuel whose lifecycle greenhouse gas (GHG) emissions are lower than those of a relevant, use-case-specific fossil fuel baseline. Most LCFs used today are created from biogenic sources like agricultural residues, used cooking oil, or landfill gas, and their use often results in reduced GHG emissions due to the fact that the CO₂ they release during use is biogenic and not fossil.

Consumers of LCFs face a reportability problem. Many buyers track LCF purchases through market-based instruments such as book-and-claim and mass balance EACs. These instruments unbundle environmental claims from physical molecules to facilitate investment in LCFs when direct provision of the molecule is infeasible. However, the current GHG Protocol only permits the use of market-based tools for electricity-based emissions. 

Currently, companies cannot recognize the emissions benefit of an LCF purchase unless they physically receive and combust the fuel. Given that sustainable aviation fuel, renewable natural gas, and other LCFs typically flow into shared pipeline and distribution infrastructure rather than being delivered to buyers as distinct physical molecules, direct delivery is often not achievable in practice. This limitation constrains demand growth in the voluntary market.

The value of LCFs to voluntary buyers is the sustainability claim attached to the molecule, not the molecule itself. Without a credible reporting framework for market-based instruments, buyers struggle to justify LCF procurement. Producers, in turn, may face a market where buyer reluctance limits commercial opportunities for scale-up. Non-reportability inhibits one of the few available levers to incentivize voluntary investment in climate solutions.

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How Does the AMI Proposal Address Non-Reportability?

The Actions and Market Instruments (AMI) proposal introduces a four-statement reporting framework, with all four statements relevant to elements of LCFs. Together, these statements give organizations across the LCF value chain a standardized way to account for LCF procurements in their sustainability reports, whether those procurements are physical or through market-based instruments.

• Statement 1 covers the physical GHG inventory and is the traditional GHG Protocol inventory as it exists today. Companies that physically receive and combust LCFs recognize the lower GHG emissions here.
• Statement 2 proposes a market-based GHG inventory for all emissions scopes. For the first time, this will allow companies to report the carbon intensities of book-and-claim and mass balance EACs for LCFs within a GHG Protocol-aligned inventory. This is a shift from the current framework, which relegates market-based instruments for LCFs and other products, along with carbon credits, to a supplemental section outside of the core emissions inventory. By bringing these purchases into the inventory itself, Statement 2 puts market-based LCF claims on a more equal footing with the emissions companies already report.
• Statement 3 covers beyond value chain interventions as well as GHG emissions reductions/avoidance reported relative to a baseline counterfactual (e.g., the domain of carbon credits). Avoided methane emissions upstream from RNG use is one LCF-relevant example that might live here. 
• Statement 4 captures non-GHG indicators and could include things such as the quantity of an LCF procured or produced. This rounds out the story an organization tells about how LCFs fit within their operations and value chain.

How the AMI Proposal Applies to Specific Fuel Types

Two important LCFs are impacted by the current GHG Protocol guidance and offer an example of the significance of the AMI proposal. 

1. SAF is a biobased or synthetic drop-in substitute for conventional jet fuel. 
2. RNG, generally produced from organic wastes, is a drop-in substitute for conventional natural gas. 

Both are typically delivered through existing infrastructure. SAF is typically blended into a common fuel supply. RNG is often injected into the broader gas network. The buyer typically does not have a direct physical link to the molecule’s consumption, but rather an indirect claim on molecules injected into a common system.

Current GHG Protocol guidance disallows using and reporting market-based LCF emissions factors within corporate scope inventories. If finalized as proposed, the AMI proposal’s Statement 2 would open the aperture for companies to report the lower emissions from purchasing SAF or RNG market-based instruments. Here’s how that might work.

SAF: Reporting Across the Value Chain

When an airline physically receives and burns SAF in its aircraft, the lower combustion emissions are reflected in its Statement 1, scope 1 emissions inventory. This is reported the same way as conventional jet fuel.

The picture changes when airlines use market-based instruments. Many airlines purchase scope 1 SAF certificates that are decoupled from the physical fuel and tracked via book-and-claim or mass balance EACs. Under the AMI proposal, the lower emissions factors of these instrument purchases would be reflected in an airline’s Statement 2, scope 1 emissions inventory.

Corporate buyers face a parallel situation. Companies that purchase scope 3 EACs from SAF suppliers or through airline programs to address employee travel would reflect these lower emissions factors in their Statement 2, scope 3, category 6 emissions. Legitimate Statement 2 claims provide additional incentive for corporate climate action to address travel emissions.

RNG: Reporting Across the Value Chain

As with SAF, an organization that physically receives and combusts RNG can reflect the lower emissions in its Statement 1, scope 1 emissions inventory. 

More often, RNG is injected into the shared gas network and claimed by users through book-and-claim or mass balance certificates rather than physical delivery. Statement 2 introduces a reporting opportunity for RNG EAC purchasers to report lower scope 1 emissions. Lower emissions from RNG consumption could also be passed through as reduced scope 3 emissions for consumers of products associated with RNG consumption.

RNG carries an additional nuance. Some RNG projects may also claim to have avoided methane emissions (e.g., dairy manure methane emissions avoided by way of diversion to RNG). This type of methane avoidance attribute, typically tracked in the realm of carbon credits, would have a home within Statement 3, subject to additional disclosure requirements. Companies evaluating RNG certificates should understand which claims belong in which statement.

Application to Other LCFs and Market-Based Instruments

While SAF and RNG are two of the more prominent examples, the AMI proposal is relevant to all LCFs including renewable diesel and marine fuels. The emissions associated with market-based instrument purchases for these fuels would belong in Statement 2.

The proposed framework may also cover market-based instruments aimed at lower emissions conventional fuels, e.g., natural gas production that has taken measures to reduce fugitive methane emissions far below industry averages. Companies purchasing certificates of this type would theoretically report the reduced upstream natural gas emissions rates in Statement 2, scope 3, category 3.

Three Steps Companies Can Take Now to Prepare 

The AMI proposal is still in development. A public request for information on the initial white paper recently closed. A draft standard is in development, with a formal public consultation planned in Q3 2027. Companies that act early will be better positioned when the standard takes effect. Steps will vary depending on where in the fuels value chain an organization sits, but could include:

1. Map exposure. Identify where the organization sits in the LCFs value chain and what fuels it is currently procuring and consuming. This will help to better understand where the largest fuels-related emissions hot spots are in the current GHG inventory.
2. Record current instruments and assess quality. Catalog the instruments the company is currently producing or procuring. Understand where these instruments may sit within the AMI’s proposed four-statement framework and assess their quality against emerging eligibility criteria.
3. Engage with the process. The GHG Protocol is still defining eligibility and quality criteria for LCF instruments. Organizations can monitor interim developments from the AMI Technical Working Group. Companies with active programs have a real stake in how those criteria are written.

Frequently Asked Questions

How would the AMI proposal change the way low-carbon fuels get reported?

The core shift is the introduction of Statement 2, a new market-based GHG inventory. For the first time, companies would be able to report the lower emissions from purchasing market-based LCF instruments, including book-and-claim and mass balance EACs, within a GHG Protocol-aligned inventory. This applies to all LCFs, including SAF and RNG, and can be reflected in scope 1 or scope 3 inventories depending on where the organization sits in the LCF value chain.

Is the AMI proposal finalized, and what should companies do now?

No, the proposal is still in development. A public request for information on the initial white paper recently closed. A draft standard is in development, with a formal public consultation planned in Q3 2027. In the meantime, companies holding SAF, RNG, or other LCF instruments should map their exposure across the value chain, catalog existing instruments against the proposed four-statement framework, assess quality against emerging eligibility criteria, and monitor GHG Protocol AMI updates.

‍What should a company look for when evaluating a SAF or RNG certificate?

Buyers should confirm which claim a certificate actually conveys and where it would sit in the AMI’s proposed framework. For example, a lower-emissions carbon intensity claim belongs in Statement 2 whereas an avoided-methane claim falls under Statement 3. Buyers should also assess the instrument against the AMI's emerging eligibility and quality criteria to reduce the risk that a purchase won't qualify under the final standard. Relae (formerly Carbon Direct) has published a comprehensive set of criteria for high-quality low-carbon fuels that buyers can use as a starting point, until AMI’s eligibility and quality criteria are finalized.

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

• Scope 2 emissions (indirect emissions from energy use) are increasingly critical to address. With surging electricity demand, especially from data centers, scope 2 is a growing share of corporate emissions and a priority for decarbonization.
• Approaches to scope 2 accounting are evolving—and formal changes are now on the table. Both location-based and market-based methods remain accepted under the Greenhouse Gas Protocol. Still, the Protocol's recently closed public consultation proposes more granular approaches, including 24/7 power and carbon matching, that would better reflect the realities of modern power markets.
• Proven decarbonization levers, such as reducing energy use, entering power purchase agreements, procuring green tariffs, and buying high-quality renewable energy certificates, are already available and impactful. Decarbonization, not just measurement, must be the goal. Companies don’t need to wait to decarbonize. 

Accounting for Indirect Emissions From Energy Use

As businesses and organizations strive to reduce their environmental impact, carbon accounting has become an essential tool for tracking and managing greenhouse gas (GHG) emissions. Carbon accounting helps organizations measure, report, and mitigate their emissions across various activities. A key framework for categorizing these emissions is the Greenhouse Gas Protocol (GHG Protocol), which classifies emissions into three scopes:

Each scope presents unique challenges and opportunities for reduction. Among them, scope 2 emissions are particularly significant because they stem from purchased energy, which is often generated using fossil fuels. However, numerous reduction mechanisms exist today to help organizations eliminate these emissions, such as improving energy efficiency in order to use less energy, and transitioning to renewable energy sources through market-based mechanisms. Understanding scope 2 emissions is crucial for businesses looking to contribute meaningfully to the global energy transition and achieve sustainability goals.

What Are Scope 2 Emissions?

Scope 2 emissions refer to indirect GHG emissions associated with the consumption of purchased energy. Unlike scope 1 emissions, which result from direct fuel combustion, scope 2 emissions arise from the generation of electricity, steam, heat, or cooling that a company procures from external sources.

The primary sources of scope 2 emissions include:

Purchased electricity: When businesses buy electricity from a utility provider, the emissions from power plants that generate this electricity are classified under scope 2.

Purchased heat, steam, and cooling: Some companies purchase heat, steam, or cooling services instead of generating them on-site. These services often come from centralized facilities that may rely on fossil fuels, thereby contributing to scope 2 emissions.

What sets scope 2 emissions apart from other scopes is the presence of market-based mechanisms that offer multiple pathways for organizations to reduce their carbon footprint. Unlike scope 1, where emissions reductions often require technological shifts or operational changes, scope 2 reductions can be achieved through strategic procurement decisions. The transition to renewable energy sources is an essential component of sustainability strategies, setting the stage for a broader energy transition across industries and economies.

How Are Scope 2 Emissions Measured Today?

The GHG Protocol currently outlines two primary approaches for calculating scope 2 emissions: the location-based method and the market-based method.

Location-Based Method

The location-based method calculates emissions for electricity consumption based on the average emissions intensity of the grid where the energy consumption occurs. This approach is mandatory under various reporting frameworks and does not take into account a company’s procurement choices.

• Relies on grid averages: Emissions are calculated based on regional grid emissions factors rather than specific energy purchases.
• Time-delayed data: Since grid emissions factors are typically updated annually, this method may not reflect real-time energy sourcing changes.
• Limited control: Companies using this method have less direct influence over their reported emissions, as they depend on the overall energy mix of their region.

Market-Based Method

The market-based method, on the other hand, reflects an organization’s actual procurement decisions and energy-sourcing strategies. It accounts for specific contracts, such as power purchase agreements (PPAs), renewable energy credits (RECs), and green tariffs, which allow businesses to claim lower emissions from their purchased electricity.

• Reflects company choices: Emissions calculations take into account contractual agreements for renewable energy purchases.
• Mechanism for electricity transition: Encourages organizations to invest in low-carbon electricity options and actively support the transition to renewables.
• Multiple reduction options: Companies can reduce their scope 2 emissions through a portfolio of mechanisms like PPAs, RECs, and green tariffs, making this method a flexible and strategic tool for decarbonization.

While market-based mechanisms provide flexibility in reducing scope 2 emissions, they also highlight the need for more precise and updated carbon accounting methodologies. For example, some decarbonization strategies, such as time-shifting energy consumption to better match renewable generation, are not accounted for under these methods. This and other limitations mean that the traditional methods outlined in the GHG Protocol are increasingly seen as outdated in an era of rapid changes in energy generation and grid dynamics. As a result, the market is shifting toward more advanced power emission accounting methodologies that provide a more accurate reflection of emissions associated with electricity use.

Proposed Changes to the GHG Protocol Scope 2 Guidance

The current GHG Protocol Scope 2 Guidance provides a market-based instrument methodology, originally designed in the early 2000s, that allows US-based companies to procure renewable energy at any point within a year from anywhere in North America and apply it to any of its annual electricity consumption within that same year. This methodology, as written, allows for a potentially significant mismatch of “emissions caused” (by consuming electricity) versus “emissions avoided” (by generating renewable electricity) in that it does not account for any of the realities of electric grids and generators, which vary significantly over different regions, seasons, and time of day. 

In response to this, the GHG Protocol Scope 2 Guidance is currently undergoing a revision process, which will include how emissions associated with electricity consumption are calculated. A focus of the revision process is on how to better account for the real emissions associated with a corporate’s electricity consumption, and more impactful ways of mitigating them through market-based instruments and other approaches. Advanced power emission accounting methodologies, such as 24/7 power matching and carbon matching, are being explored as ways to better represent the GHG emissions associated with electricity consumption. 

• 24/7 power matching emphasizes matching electricity consumption with an equivalent amount of renewable energy production on an hourly basis.
• Carbon matching emphasizes measuring the emissions impact of incremental electricity consumption or production at a specific time.

These emerging methodologies propose a shift toward more granular temporal and region-specific matching, which could require companies to rethink their emissions reporting approach and explore more advanced tracking tools. They may also introduce new strategies beyond market-based instruments for reducing scope 2 emissions, such as time-shifting energy consumption.

As power grids continue to decarbonize and new digital tools emerge, businesses will need to adapt to these evolving methodologies to remain compliant, enhance sustainability strategies, and achieve meaningful reductions in emissions. Companies that proactively integrate advanced power emission tracking into their carbon accounting strategies will be better positioned to lead in the transition to a low-carbon economy.

How to Reduce Scope 2 Emissions

The GHG Protocol provides multiple mechanisms for reducing scope 2 emissions, allowing organizations to shift their energy consumption toward lower-carbon alternatives. These include:

• Reducing energy consumption: Improving energy efficiency in operations can significantly lower electricity use. In some cases, this involves capital investments in more energy-efficient equipment, but in other cases, it can be based on operational changes such as reducing unnecessary lighting, HVAC, and other services during non-working hours. (Electrification efforts, such as shifting from fossil fuel-powered systems to electric alternatives, may actually increase scope 2 emissions, but this can ultimately reduce overall emissions by correspondingly decreasing scope 1 emissions and allowing for renewable energy procurement.) 
• RECs: Companies can purchase unbundled RECs (emissions “attributes” separated from the actual electricity product) to offset emissions associated with purchased electricity. While there has been criticism of RECs due to their significant range in quality, high-quality RECs are available, which may include ensuring regional matching, financial additionality, on-line date additionality, or tighter temporal generation to consumption matching. The use of high-quality unbundled RECs is the most accessible and realistic option for most smaller-scale companies to address scope 2 emissions. 
• On-site generation and co-location: Installing on-site renewable energy generation, such as solar panels, allows companies to directly offset their electricity consumption from the grid. In some commercial settings, such as companies using leased real estate or co-located data centers, partnering with facilities that prioritize renewable energy procurement can help reduce scope 2 emissions for the facility owner while the facility occupant reduces scope 3 emissions. 
• PPAs: Entering into long-term contracts with renewable energy providers ensures companies receive electricity from clean energy sources while supporting the expansion of renewable generation capacity. PPAs are available with standardized contract terms, and some service providers will aggregate demand from multiple smaller companies to reach the minimum required amount for typical PPA contracts. Hedging products are also available to reduce market risks.
• Green tariffs: Many utilities offer green tariffs that enable businesses to purchase renewable energy directly through their electricity provider, often at a premium but with lower emissions impact. For many smaller companies, this is a more viable approach than a PPA with a single renewable generator.

By adopting a combination of these strategies, businesses can significantly lower their scope 2 emissions while aligning with broader sustainability goals and regulatory requirements. The path to decarbonization requires proactive investment in cleaner energy sources, efficient consumption practices, and leveraging market-based instruments to drive the transition toward a low-carbon future.

Why Does Reducing Scope 2 Emissions Matter?

Reducing scope 2 emissions is the underpinning of decarbonizing the power sector and enabling the global energy transition. In 2025, S&P reported that corporate buyers added 15.2 GW of renewable capacity in the US, up from 9.1 GW in 2024, illustrating the growing impact of the corporate sector on the electricity grid. Cleaner grids translate to lower emissions for all energy users. Organizations that actively reduce their scope 2 emissions can contribute to decreasing demand for fossil fuel-based electricity and accelerate the deployment of renewable energy infrastructure.

For companies that own and operate data centers, this transition is especially important. AI data centers consume large amounts of electricity, and their reliance on purchased power makes them a significant source of scope 2 emissions. Since many businesses rely on third-party data center services, reducing emissions from these facilities also helps lower scope 3 emissions across industries. Corporates can influence data centers by requiring that they have a clear and explicit low-emission power strategy in place before procurement.

Beyond direct corporate benefits, reducing scope 2 emissions has a tangible long-term impact on power grids. Increased investment in renewable energy procurement sends a strong market signal, encouraging utilities and developers to expand clean energy projects. As more companies commit to sourcing renewable energy, the overall mix of grid power shifts, making low-carbon electricity more accessible and reducing reliance on fossil fuel-based generation. Ultimately, widespread corporate action in scope 2 emissions reduction supports the broader decarbonization of power markets and strengthens global climate commitments.

Frequently Asked Questions

Will RECs (renewable energy certificates) still count toward scope 2 reductions under the GHG Protocol's proposed changes?

Under the current Scope 2 Guidance, yes—RECs remain a valid market-based instrument. The proposals from the GHG Protocol's recent consultation range from retaining market-based accounting with stricter quality criteria to restructuring how instrument-based claims are reported altogether, and nothing is final until the revised standard is published. What's clear is that scrutiny is rising, particularly for unbundled RECs with weak temporal or geographic connection to a company's actual consumption, so prioritizing high-quality RECs now is the best way to future-proof a procurement strategy.

How would the proposed hourly and regional matching requirements affect companies that rely on unbundled RECs today?

Hourly (24/7) and regional matching would require renewable generation claims to line up much more closely with when and where a company actually consumes electricity. Companies relying on annually matched, unbundled RECs sourced from distant grids would likely see their reported market-based emissions rise under such requirements. The practical preparation is to start collecting more granular (ideally hourly) consumption data and shift toward RECs and contracts with tighter regional and temporal matching.

What's the practical difference between location-based and market-based scope 2 accounting, and will that distinction survive the GHG Protocol's revision?

The location-based method calculates emissions using the average emissions intensity of the local grid, regardless of procurement choices, while the market-based method reflects a company's actual contracts, such as PPAs, RECs, and green tariffs. The consultation explored options from strengthening the criteria for market-based claims to reporting emissions and market instruments in separate, complementary statements. Both concepts will exist in some form, but companies should expect the requirements behind market-based claims to tighten.

When is the new Scope 2 Guidance expected to take effect, and what should companies do now to prepare?

Per the GHG Protocol's July 2026 development plan, a draft of the revised consolidated Corporate Standard is expected for public consultation in 2027, with a final published standard currently estimated for late 2028, and adoption timelines will follow publication. Companies should take action now. Energy efficiency, PPAs, green tariffs, and high-quality RECs reduce real emissions under any accounting regime. Building hourly consumption tracking and auditing the quality of existing REC portfolios now will make any future transition smoother.

Key Takeaways

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

A Better Question Than "Is AI a Bubble?"

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

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

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

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

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

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

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

Inference Makes the Constraint Structural

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

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

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

Power Is Not One Constraint, It Is Several

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

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

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

What Carbon Direct Capital Is Investing Behind

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

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

The Bear Case Deserves to Be Taken Seriously

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

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

The Investment Conclusion

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

Frequently Asked Questions

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

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

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

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

Disclaimer

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

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

Key Takeaways

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

Load Growth Is Increasing, Uncertain, and Concentrated

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

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

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

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

What Is Load Forecasting?

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

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

The Bulk Power Grid Is Under Strain

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

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

Why Today’s Load Forecasts Fail

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

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

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

Four Structural Limitations to Traditional Forecasting Methods

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

The Speculative-Load Problem

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

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

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

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

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

The Cost of Inaccurate Forecasting

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

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

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

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

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

BTM Generation and Load Flexibility: A Near-Term Bridge

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

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

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

The Value of Load Flexibility

Relae’s power system modeling quantifies the dollar value of load flexibility in ERCOT. Load flexibility can eliminate forced load shedding risk, even at 40 gigawatts of data center buildout, preventing $5.5 billion in annual consumer welfare losses by curtailing an average of 5% of demand for under 1% of operating hours.

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

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

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

Flexibility Takes Many Forms, but it Isn't Universal

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

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

Better Load Forecasting: The Longer-Term Fix

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

Eryilmaz outlines three technical shifts for better load forecasting:

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

Policy Alignment

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

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

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

Standardizing Large-Load Data

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

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

Getting Load Forecasting Right Starts Now

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

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

Frequently Asked Questions

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

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

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

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

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

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

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

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

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Key Takeaways

• Sustainable aviation fuel (SAF) will not reach price parity with fossil jet fuel under any realistic near-term scenario. The cost premium is structural—rooted in the chemistry of feedstocks—not a temporary artifact of early-stage markets.
• Neither airlines nor corporate buyers are purchasing SAF for its energy content. Both are buying sustainability claims: airlines for regulatory compliance and scope 1 credentials, corporates for scope 3 emissions reporting and social license to operate. The fuel is incidental to both transactions.
• Corporate offtakes can play a genuine role in building the SAF industry, but only if they create capacity that would not otherwise exist. Additionality is not a technicality; it is the entire value proposition.
• High-integrity SAF procurement requires evaluating not just carbon reduction, but feedstock sourcing, leakage, and social and environmental harms. The newly released Criteria for High-Quality Low Carbon Fuels from Relae (formerly Carbon Direct) provides a framework for doing this rigorously.

A Major Deal Illustrates How the SAF Market Really Works 

On June 5, 2026, Google and American Airlines announced a three-year agreement under which Google will purchase sustainable aviation fuel (SAF) certificates (SAFc) associated with 35 million gallons of SAF. American will take physical delivery of the fuel at Chicago O'Hare. Google receives the emissions attributes. The arrangement relies on book-and-claim accounting, in which the physical fuel and the environmental attribute are legally separated and transferred to different parties.

The deal is a window into how the SAF market works, and what every company in the value chain needs to understand before entering it.

The SAF Cost Premium Is Structural, Not a Market Inefficiency

There is a persistent hope in the aviation industry that SAF will eventually reach price parity with fossil jet fuel. This will not happen, at least not through any mechanism that currently exists or is credibly in development.

The economics are the product of thermodynamics. Petroleum is pre-deoxygenated; over millions of years, heat and pressure stripped oxygen from biological material, concentrating energy into the hydrocarbons we pump out of the ground today. Bio-based SAF feedstocks, e.g., vegetable oils, agricultural residues, and other biomass, are oxygen-rich (carbohydrates, not hydrocarbons). Power-to-liquid e-fuels start from captured CO₂, which is fully oxidized. 

Either way, every SAF production pathway must pay an energy debt to remove or chemically reduce that oxygen, in the form of hydrogen deoxygenation, energy inputs, and processing costs. This is not a manufacturing inefficiency that scale will solve. It is a constraint baked into the feedstocks themselves.

The numbers reflect this. According to the European Union Aviation Safety Agency (EASA), the average market price of SAF in 2025 was approximately €1,925 per tonne, roughly three times the €640 per tonne average for conventional jet fuel. 

The cheapest pathway, hydroprocessed esters and fatty acids (HEFA), produced from waste oils like used cooking oil or tallow, represents almost all current SAF supply and sits at the lower end of the SAF cost range. Costlier cellulosic and e-fuel pathways push it higher. EASA estimates 2025 production costs for power-to-liquid e-fuels at €7,520 per tonne, more than ten times the cost of conventional kerosene. While these costs can and will come down, none are expected to approach price parity.

The feedstock ceiling compounds this. HEFA from waste oils is the cheapest SAF pathway, but waste oil supply is finite and competes with renewable diesel, which typically offers better margins for producers. As mandates push SAF volumes beyond what HEFA can supply, the industry must move to cellulosic biomass or power-to-liquid pathways, at progressively higher cost. Scaling the SAF industry does not automatically bring prices down. In the near term, it pushes them up.

Policy Determines Who Absorbs the Cost

If price parity is not coming, the cost premium lands somewhere. Two policy philosophies have emerged to answer that question.

Europe has largely adopted the polluter-pays principle: SAF mandates place the cost burden on fuel suppliers and, by extension, on airlines and their passengers. The EU's ReFuelEU Aviation regulation and the UK's SAF mandate both carry steep penalties for non-compliance. As Relae has documented, in the UK, those penalties range from three to 13 times the cost of compliance, depending on the obligation type and year, signaling that regulators are serious about pushing aviation toward sustainable fuels.

The United States approached the problem differently, leaning on taxpayer subsidies.The Inflation Reduction Act (IRA) 45Z Clean Fuel Production Credit and its predecessor, the 40B Sustainable Aviation Fuel Credit sought to socialize much of the cost premium. The appeal of this approach was that it made SAF economics viable without raising ticket prices. Its vulnerability was political: when the IRA's incentive landscape was revised, the project pipelines that had formed around those credits evaporated quickly. US taxpayer-funded support proved politically less durable than the UK and EU’s mandated compliance obligations.

Neither model works in isolation. Mandates without bankable project finance generate demand signals but no new supply. Incentives without policy durability attract project interest but cannot get facilities to final investment decisions. The deals that have actually moved capital combine a stable policy floor, whether mandate or incentive, with long-term private commitments that provide the revenue certainty project finance requires.

The Google-American Airlines deal illustrates the incentives-plus-private-commitment structure. The deal explicitly credits the Illinois SAF tax credit as the enabling policy lever. HEFA SAF of this type is eligible to generate Renewable Fuel Standard credits (RINs), and fuel produced from 2025 onward qualifies for the IRA's 45Z Clean Fuel Production Credit. This stack provides additional floor economics. The corporate offtake completes the structure by delivering the revenue certainty that volatile policy credits alone cannot. Remove any one of those elements and the deal's economics likely do not hold.

Nobody in this Market Is Buying SAF for its Energy Content

This is the key to understanding how the SAF market works. Neither airlines nor corporate buyers purchase SAF for its energy content. Airports have kerosene. Airlines do not need SAF to keep planes in the air. Corporate buyers, like Google, have minimal operational use for aviation fuel at all. While recent global disruptions in crude oil supply have highlighted SAF in the context of energy security, the industry as it exists today does not represent a realistic hedge against conventional fuel volatility.

What all parties are buying is the sustainability attribute attached to the fuel. For airlines, the relevant claim is a scope 1 emissions reduction: the right to report lower lifecycle carbon intensity for their flight operations. For corporate buyers, the relevant claim is a scope 3 reduction, a documented abatement of the emissions associated with their employees' business travel. Book-and-claim accounting makes this architecture explicit: it legally severs the physical fuel from the environmental attribute, allowing each to be transferred to the party that values it. The fuel is the delivery mechanism for the claim. 

This distinction matters for assessing the market. Airlines operate on among the lowest margins of any major industry. They cannot absorb the cost premium voluntarily without fundamentally compromising their finances. They participate in SAF markets when required to by mandate, or when a corporate partner subsidizes the premium by purchasing certificates downstream. The cost premium does not disappear; it shifts. Understanding where it lands is the starting point for any serious procurement decision. 

Corporate climate programs have finite budgets. SAF competes with renewable electricity procurement, fleet electrification, CO₂ removal, and supply chain decarbonization for the same dollars. Buyers who want their sustainability claims to match the actual sources of their emissions (rather than offsetting aviation with unrelated activities elsewhere) have a genuine reason to prefer SAF. 

A SAF Claim Is Only as Strong as the Quality Behind it

SAF buyers and sellers trade sustainability claims. The quality of those claims is the entire value proposition, and the reputational liability travels with them. The companies with the greatest willingness to pay for SAF certificates tend to be those with the most brand exposure: high-profile technology companies, professional services firms, and financial institutions. These are also the companies most likely to face scrutiny from regulators, NGOs, and investors if a claim does not hold up. Buying a certificate does not protect a company from that scrutiny. It transfers the liability along with the attribute.

That means a rigorous buyer needs to answer at least three distinct questions before relying on a SAF claim.

1. Does this fuel actually reduce lifecycle emissions? 

SAF's climate case rests on a carbon cycle argument: the feedstock absorbs CO₂ from the atmosphere as it grows, so when that carbon is released during combustion, the net addition to the atmosphere is theoretically near zero. But that logic holds only if upstream production is clean, and it often is not. 

Indirect land use change (when demand for a feedstock crop displaces food agriculture elsewhere, triggering clearing of forests or grasslands) can generate substantial emissions elsewhere in the global land and food system, eroding or eliminating the lifecycle benefit. Even waste-based feedstocks are not automatically clean: used cooking oil and tallow have existing market uses, and diverting them without careful accounting can displace those uses, alter commodity markets, and create emissions leakage elsewhere. 

2. Is the purchase additional? 

Additionality asks whether the procurement caused SAF to exist that otherwise would not have. This is a harder question than it appears, especially in markets where multiple policy support mechanisms are already active. For the Google-American Airlines deal, one critical variable for financial additionality—the SAFc price—has not been disclosed. If Illinois credits and federal RINs already cover most of the HEFA cost premium, then the question of what Google's purchase actually caused to happen is genuinely open. However, American Airlines has stated publicly that the long-term nature of the agreement enabled them to secure a new SAF offtake with Valero Marketing and Supply Company. The supply arrangement that may not have been bankable on the basis of volatile RIN markets and changeable policy alone. That is a real additionality argument. But it requires transparency to evaluate. The undisclosed SAF credit price is a current market liability, not just in this deal, but across the voluntary SAF market broadly.

Long-term offtakes do something that policy credits cannot: they provide stable, bankable revenue certainty. RIN prices fluctuate. Tax credits change with administrations. Neither can reliably anchor a final investment decision at the project level. A multi-year, creditworthy offtake agreement can. This is the distinctive and genuinely valuable role that corporate buyers play in this market: not paying the cost premium per gallon, but reducing the financial risk premium that keeps capital on the sidelines.

3. Is the full supply chain sound? 

Carbon claims are not the only dimension of sustainability that matters to a buyer's reputation. Companies making claims about their SAF procurement are implicitly making claims about their supply chains. That means labor practices, community impacts, Indigenous rights, feedstock sourcing integrity, and market leakage from displaced uses all fall within the scope of what a rigorous buyer should evaluate. A SAF supply chain that displaces food crops, harms a proximate community, or causes deforestation through indirect land use change creates a reputational problem that no certificate can fix.

What High-Integrity SAF Procurement Looks Like

The voluntary SAF market is still early, and the transparency it requires does not yet exist consistently. Relae recently released the Criteria for High-Quality Low Carbon Fuels, a comprehensive, publicly available framework designed to help close that gap. 

The criteria address six principles across the full supply chain: carbon accounting, additionality, feedstock sourcing, leakage, environmental harms, and social harms and environmental justice. They are designed as a practical reference for what a credible SAF claim requires, and where existing certifications may leave gaps that require additional diligence.

At the transaction level, five questions should have clear answers before any buyer signs an offtake:

1. Is the certificate linked to a specific project or supply agreement? 
2. Is that project financially dependent on the offtake, after accounting for all policy support already in the stack? 
3. Is the lifecycle emissions profile documented across the full well-to-wheel boundary, including indirect effects? 
4. Are the feedstock sourcing and supply chain risks assessed and disclosed? 
5. Has the producer evaluated leakage and community impacts?

The Google-American Airlines deal demonstrates what serious voluntary SAF procurement can look like. The Criteria for High-Quality Low Carbon Fuels provides the framework buyers need to evaluate deals like this one rigorously.