Dynamic Line Rating: The Fastest Gigawatt Is the One You Already Have
Key Takeaways
- Power demand is outrunning buildout. Meeting large load growth requires more than new generation; it requires faster interconnection and congestion relief on existing transmission lines.
- Dynamic line rating (DLR) is available today, deploys in months, and enables faster speed-to-power. On the right thermally congested lines, DLR can unlock more capacity at a fraction of new infrastructure cost. In one utility demonstration, 5% to 10% of additional capacity was enough to clear most of the congestion on the lines studied.
- DLR has been held back by weak incentives, but that is changing. Utilities earn a regulated return on capital they invest in new assets, which favors building new infrastructure over lower-cost solutions like DLR. Load growth and new Federal Energy Regulatory Commission (FERC) mandates are starting to shift the calculus.
The Grid Cannot Expand Fast Enough for AI Demand, But It Can Carry More
Power demand is booming as data centers scale across the US grid, and current grid infrastructure cannot supply it. This constraint is physical, not financial. Meeting this demand requires a significant amount of power generation and infrastructure upgrades. More than 2 terawatts of generation and storage sit in interconnection queues, roughly 1.5x the total installed generation capacity in the US.
Regional markets are working to accelerate generation buildouts, but connecting that generation to the transmission network remains expensive and slow to match speed-to-power needs. New high-voltage lines take years to permit, cost between $2 million and $6 million per mile to build, and major projects routinely take five to ten years from identification to energization. For example, PJM Interconnection LLC (PJM) identified the Doubs–Goose Creek 500 kilovolt (kV) corridor as a bottleneck feeding Data Center Alley in 2023 and set June 2027 as the date a fix was needed. Dominion Energy's published schedule for its portion of that rebuild anticipates a completion date of 2031.
A number of studies1,2 show there is headroom in the bulk transmission system. Grid-enhancing technologies, such as dynamic line rating (DLR), can convert part of that headroom into capacity today while new generation and transmission are being built. DLR lets suitable transmission lines increase their carrying capacity in real time, unlocking that headroom at a fraction of the cost of a buildout. Realizing that value is a targeting exercise with a key question: On which thermally limited lines can DLR actually relieve congestion?
What Is Dynamic Line Rating?
Dynamic line rating is a method for calculating a transmission line's real-time carrying capacity using live weather and conductor-temperature data. It lets grid operators safely carry more power whenever weather conditions allow.
Most transmission lines operate under a static rating: a fixed, conservative limit on current, set for worst-case weather and held all year. The limit is based on temperature, because pushing too much current can overheat the conductor wire. Metal conductors expand as they heat, which can make them sag and touch trees or other obstacles, causing short circuits or fires. Real conditions almost always cool a conductor better than the worst-case assumption a static rating is built on. That means the line can carry more current while staying at the same maximum conductor temperature, and therefore within the same sag and clearance envelope. That headroom is exactly what DLR captures: instead of leaving it on the table, DLR recalculates the line's rating in real time so operators can use the extra capacity safely.
Beyond a static rating is the ambient-adjusted rating (AAR), which many utilities have begun adopting. An AAR recalculates the rating from forecast ambient air temperature, typically hourly and out to several days. DLR goes further, adding wind speed and direction, solar heating, and in some deployments the conductor's measured temperature.
DLR technologies rest on a heat-balance algorithm: how fast a line heats up (from electric current and sunshine) versus how fast it cools off (from wind and cold air). The calculations are standardized in IEEE 738 in North America and CIGRE 601 internationally. The data feeding those calculations can come from line-mounted sensors, weather models, or both, depending on a tradeoff between per-span accuracy and the cost of installing sensors along every span.
Even so, DLR remains limited in the US, and AAR has been slow to arrive. FERC's Order 881 required the transmission providers it regulates to adopt AAR by July 2025, but FERC has granted numerous extensions. PJM became the first to fully implement AAR in March 2026, while Midcontinent Independent System Operator (MISO) and New York Independent System Operator (NYISO) are not expected until 2028.
The Near-Term Value of DLR: Reducing Grid Congestion
DLR's value is immediate. It can be installed in months, not years, so a currently congested line can start carrying more power the moment conditions allow, reducing congestion right away. When cheaper generation is available upstream of that line, DLR cuts costs directly, because grid operators no longer need to dispatch pricier generation downstream of the congestion to supply load. That means DLR can reduce congestion costs in the current delivery year, compared to a transmission line rebuild that sits in a decade-long queue.
Over a longer horizon, utility planners can build that headroom into long-term capacity models. This is important, because current capacity-expansion and integrated resource plan (IRP) models still run on static or seasonal ratings, and typically leave out the potential gains from grid-enhancing technologies like DLR.
NERC's large loads white paper and FERC's RM26-4 rulemaking both raise the issue of how utilities can absorb multi-hundred-megawatt data center requests without a decade-long transmission build. Solutions like DLR are one of the few tools that can compress that timeline.
The hardware itself is cheap: sensors and data management cost a small fraction of any physical upgrade. That means the economics comes down to identifying the lines that benefit most from DLR. This is particularly important because on most US grids, congestion concentrates on a small number of lines that repeatedly reach their limits. On those lines, DLR can cut congestion costs directly and defer costlier upgrades, while its potential on other lines may be far lower. As a result, identifying those high-potential, thermally congested lines is essential.
Proven DLR Examples in the Industry
Real deployments show DLR can reduce a meaningful share of transmission congestion costs, with extra carrying capacity above the static rating running roughly 5% to 30%, depending on how often that capacity is available. In Oncor's ERCOT demonstration, 5% of additional capacity would have relieved up to 60% of congestion on the target lines, and 10% would have practically eliminated it. PPL Electric in Pennsylvania/PJM reports annual customer savings of $23 million after deploying DLR across its initial three lines. The DLR installation cost about $250,000, against a rebuild alternative that would have cost about $50 million and taken far longer.
The contrast abroad is instructive. Austria's grid operator, APG, recorded about $13 million a year in congestion savings across roughly 15% of its network. While these savings are real, it's important to recognize that these results come from single, well-chosen, badly congested lines.
The UK's National Grid began with a two-year DLR trial on a single 275 kV circuit in 2022, expanded to more than 275 kilometers of its network by 2025, with estimated consumer savings of about $26 million a year. In April 2026, National Grid signed a five-year contract covering 585 kilometers more, with most installations due by 2028 and potential savings of up to $66 million. Each expansion followed measured results from the stage before it.
Where the Headroom Is: Screening PJM's Data Center Alley
To illustrate the congestion savings from DLR, Relae screened PJM's five-minute real-time market record for every binding transmission constraint in 2025. For each one, we captured the shadow price, the marginal value of relaxing that constraint.3
Our analysis focused on thermal constraints, and then identified lines that bind frequently, in conditions milder than the worst case their static rating was set for, which is when a conductor's true rating sits above its static assumption. For the lines that we identified, congestion costs were added over the binding hours to set a bound on the savings that could result from DLR. That full amount would not necessarily be realized in practice, because the shadow price values only the next megawatt freed, and relieving one line can shift the constraint to the next. However, it serves as a useful estimate for the scale of savings that could be achieved.
We ran the analysis on the Dominion (DOM) zone in PJM, home to Data Center Alley in Loudoun County, Virginia. Figure 2 shows a high-level section of the grid. The 500 kV bulk grid steps down through transformers to the 230 kV substations feeding the data centers, with the lines that experience recurring congestion highlighted. A handful of those 230 kV lines showed up as binding thermal constraints again and again.
The congestion in DOM isn't constant, and it concentrates in particular months and within the day in particular hours. Figure 3 shows three transmission lines within the DOM zone and the number of hours each was thermally congested in each hour-of-day slot over 2025. Binding concentrates in the warm months and, within the day, from late morning through early evening.
At first glance, this period looks like the wrong window for DLR. The local weather record says otherwise. These periods turn out to be some of the windiest hours of the day, not the stillest. Median wind speed at Dulles ran about 3.5 m/s, above the 0.6 m/s crossflow a static rating conventionally assumes, with fewer than 5% of observations falling below that threshold. Median ambient temperature in those hours was about 26°C, against the 35–40°C a static summer rating is typically built for. Across all three lines, the large majority of congested hours coincided with weather that would have supported a materially higher rating.
Valuing just one megawatt of DLR relief at each five-minute shadow price, the estimated savings are worth roughly $300,000 in this three-line example across about 263 line-hours.4
Because the value concentrates on a handful of thermally limited, heavily congested lines, and because the operational case has to be made line by line, capturing the opportunity is fundamentally an analytics problem: find the right lines, and prove the savings.
What Does It Take to Scale DLR?
DLR is cheap and effective, but two things stand between it and broader adoption: incentives and advanced grid analytics.
The utility cost-of-service model recovers investment in generation and transmission assets and earns its profit as a regulated return on the capital deployed. Because rates recover capital rather than power delivered, utilities have a stronger incentive to build or upgrade lines than to move more power across the ones they already own. That bias toward capital investment over optimization is why a mature technology has stayed niche in the US for years. Regulators have started to look more closely at this, but the main federal rule still mandates the milder AAR, not DLR, and leaves the return model untouched.
Contingency analysis compounds the problem. Current models are built around fixed line limits. A rating that changes hour to hour adds real modeling work, and more importantly, the system still has to hold under worst-case contingencies. So while operators already forecast weather daily for wind and solar, the harder step is trusting a forecast enough to commit a transmission limit against it. That takes significant predictive analytics built into system planning, not bolted on after.5
How Policy Is Starting to Shift the Calculus
Policy is starting to move the incentive problem. FERC's Order 881 made AAR the minimum for the transmission providers it regulates (effective July 2025, with several operators on extended timelines) and required markets to be capable of accepting dynamic ratings. PJM has started to implement this: PPL Electric has run sensor-based DLR on nine congested lines since 2022, feeding PJM's day-ahead markets.
Order 1920, FERC's first long-term transmission-planning overhaul in more than a decade, now requires planners to formally evaluate grid-enhancing technologies like DLR against conventional builds. It stops short of mandating deployment, but it forces a comparison utilities used to skip. That comparison is now written into filed tariff processes (PJM filed its plan in December 2025). Those first cycles only began in 2026, and the order allows up to three years to reach a selection, so the results are still pending.
A shared-savings incentive, letting a utility keep a slice of the congestion savings it creates, has been proposed to FERC and championed in the Advancing GETs Act, but it isn't yet a rule, so the core misalignment stands. DOE's GRIP program has funded grid-enhancing deployments, and by early 2026, 16 states had some form of advanced transmission technology requirement, with Colorado adding its Grid Optimization Act in April 2026.
The newest pressure is coming from the demand side. Through 2025–26, FERC began overhauling how large loads connect to the grid, and while none of it touches the utility's return on capital, it changes who sees the costs. FERC issued show-cause orders directing all six grid operators to justify or reform their large-load rules. This tees up consideration of alternative transmission technologies in study processes and greater transparency into costs.
And the rules are moving toward making the large load pay for the upgrades its connection requires. Pennsylvania's model large-load tariff, for example, recommends utilities charge data centers for the upgrades their interconnection makes necessary. It also instructs utilities to let those customers self-construct certain upgrades, including some affecting the wider grid. That combination is what matters. The party paying the bill now has a reason to ask whether a cheaper fix exists and, in at least one state, a route to build it. We have not yet seen a DLR deployment selected this way, because these frameworks are only months old, but the cost gap between a DLR fix and a rebuild is becoming visible to the party who pays the difference.
How Relae Helps Find the Value of DLR
Through our Power, Data, and Innovation practice, Relae combines transmission congestion data, line-level thermal constraints, and short-term weather forecasts into a single view of where dynamic ratings would actually pay. The output is a short list of candidate lines, each with a modeled capacity uplift and an estimated dollar value, turning a vague “DLR is promising” into a priced, line-by-line decision. It is the transmission-side complement to our work on the interconnection queue and demand-side flexibility. All three are ways of closing the gap between demand and delivered capacity faster than new construction allows.
Power & Energy
Relae provides independent advisory for large corporate buyers, power providers, and infrastructure investors making high-stakes decisions about clean firm power, grid constraints, data center energy optimization, and long-term investment strategy. Our insights help you evaluate solutions that can be deployed reliably, responsibly, and affordably, so you can navigate an evolving energy landscape with confidence.
Ready to Navigate What Comes Next?
Derya Eryilmaz, PhD
Derya Eryilmaz, PhD
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Dr. Derya Eryilmaz is a power markets expert with about 15 years of experience in the power sector, specializing in economic and statistical modeling, power market design, consumer demand modeling and renewable energy procurement. She recently worked at Amazon and Walmart integrating power market analysis and analytics to inform clean firm power investment decisions and corporate decarbonization. She also led teams of data scientists and analysts to deliver enterprise scale AI initiatives resulting in multimillion-dollar cost savings. In her previous work, Dr. Eryilmaz provided advisory and consulting services for the top 20 investor-owned electric and gas utilities clients in North America and she also worked in various oil pipeline, natural gas and power litigation and regulatory proceedings.
Dr. Eryilmaz is teaching renewable energy finance at the Carroll School of Management at Boston College. She also taught data analytics and energy economics in the Department of Economics at Northeastern University, and has published peer-reviewed research in The Energy Journal, Energy Economics, and The Electricity Journal. She holds a PhD in Applied Economics from the University of Minnesota, with a focus on consumer economics and environmental economics.
“Rising Star of the Profession” for Excellence in Energy, Consulting Magazine (2020)
Sagar Patel
Sagar Patel
Prior to Relae, Sagar worked within voluntary carbon markets, where he guided clients in the development of carbon assets and conducted due diligence on potential advanced market commitments. His efforts ensured the integrity and efficacy of decarbonization initiatives.
Before transitioning into the climate space, Sagar served as a project and systems integration engineer in the civil space industry. In this role, he led the implementation of hardware systems through various design phases, demonstrating a strong capability in project management and technical integration.
With a comprehensive understanding of both the carbon market landscape and the technical challenges of project implementation, Sagar brings a holistic approach to advancing carbon removal technologies and strategies.
- Prabha, R., Min, L., & Rajagopal, R. (2026). Widespread thermal headroom and localized bottlenecks coexist in the Western Interconnection bulk grid. Research Square (preprint), posted May 29, 2026. https://doi.org/10.21203/rs.3.rs-9316767/v1 Su, T., et al. (2025), Grid-enhancing technologies for clean energy systems, Nature Reviews Clean Technology, 1, 16–31
- United States Department of Energy (June 2019) - Dynamic Line Rating - Report to Congress https://www.energy.gov/sites/prod/files/2019/08/f66/Congressional_DLR_Report_June2019_final_508_0.pdf
- The congestion identified here focuses on transmission lines only and predates PJM's move to ambient-adjusted ratings in March 2026.
- The $300,000 savings figure is specific to this example and represents the first megawatt of relief. The value of savings can vary by line, time, weather conditions, and the shadow price realized in a specific location on the grid.
- A generation forecast error is a quantity error, and reserves exist to cover it. A rating forecast error moves the security limit itself, and there is no reserve product for that.
What to Read Next
Carbon Capture for Natural Gas-Fired Power Generation: An Opportunity for Hyperscalers
Key Takeaways
- AI-driven data center demand is outpacing grid capacity, and hyperscalers are bringing more natural gas, which already supplies about 40% of US electricity, online to meet their needs.
- Pairing carbon capture and sequestration (CCS) with natural gas lets data centers source firm power today while cutting plant-level emissions up to 95%—without waiting on multi-year renewable interconnection queues.
- The Google-Broadwing deal demonstrates real progress and commitment toward natural gas with CCS as the first major commercial deployment of this exact pathway
Meeting Electricity Demand and GHG Emission Reduction Targets
Rapid growth in electricity demand across the US, driven by AI data center expansion and increased industrial electrification, is placing significant pressure on power grids. After decades of stable electricity load, demand has increased significantly since 2022 and is expected to rapidly grow for the foreseeable future. Natural gas currently fuels around 40% of US electricity generation. Its share is expected to grow in the coming years. However, unabated natural gas generation is not compatible with stakeholder targets to reduce greenhouse gas (GHG) emissions. Combining CCS with natural gas-fired generation is one pathway to meet growing electricity demand and achieve GHG emission reduction targets.
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The Role of Natural Gas in Electricity Supply
Natural Gas Generation Versus Renewable Generation Deployment
Electricity generators can provide multiple products to regional grids, generally providing two services: energy (power production) and reliability (consistent availability). Natural gas-fired plants can provide both, whereas renewable energy sources like wind and solar generate energy but offer less reliability.
As electricity demand rapidly grows, grids will need both energy and reliability to function effectively. However, the interconnection queue for renewable energy assets has a years-long backlog which is delaying their deployment. Grids will need additional reliability assets to support the large amounts of renewables (usually in the form of storage). Some jurisdictions are creating an alternate pathway for natural gas plants to bypass the lengthy interconnection queue which may allow for the rapid development of natural gas generators. Hyperscalers are also pursuing development of large behind-the-meter (BTM) generation of electricity from renewable and fossil sources, but these must also meet high standards for reliability.
The Case for Carbon Capture Deployment
Electric utilities and developers of data center infrastructure are planning to build substantial new natural gas generation assets in addition to maximal deployment of renewable electricity. CCS technology enables natural gas plants to deliver stable, continuous power while significantly reducing emissions by capturing up to 95% of emitted CO₂. Natural gas plants with CCS are viable options to deliver the lower-emission, reliable power needed to respond to rapidly emerging AI data center power demand growth. The 45Q tax credit, a key government incentive for CCS, was preserved and effectively strengthened under 2025's One Big Beautiful Bill Act. The Google-Broadwing deal, the first major commercial deployment of this exact pathway, was signed in October 2025 and serves as a useful proof point.
Benefits of Integrating CCS into Natural Gas Power Generation
Integrating CCS into natural gas-fired power plants provides several advantages for data center stakeholders:
- Reduced carbon emissions: Achieve emission intensities of approximately 80–120 kg of CO₂ equivalent per megawatt-hour (CO₂e/MWh), significantly below the current US grid average of approximately 340–420 kg CO2e/MWh.
- Reliable baseload power: Continuous, predictable electricity delivery.
- Compact infrastructure: Requires less land compared to renewable energy projects, simplifying data center siting near existing infrastructure.
- Cost: CCS integrated with new natural gas-fired generation can deliver low-cost decarbonization. Relae estimates $75-150/MWh, which is competitive in many markets with other firm baseload options such as new nuclear power or wind and solar with battery backup.
Seven Key Considerations for Implementing CCS
Stakeholders considering CCS technology must carefully evaluate seven critical factors:
1. Meeting Rapid Deployment Timelines
Traditional natural gas plants can be operational within roughly 18 months, provided they bypass interconnection queues for reliability purposes and have access to key equipment. Integrating CCS technology extends this by an additional 18–36 months. Designing plants to be "capture-ready" allows for quicker initial deployment and smoother CCS integration in the future. However, deploying a capture-ready plant without a commitment to build the carbon capture portion is inconsistent with serious climate action.
2. Sizing Plants Optimally
CCS is most economically and environmentally optimal at natural gas plants with capacities of 100 MW or greater. It offers significant opportunities for emissions reductions for the forecasted new data center load. CCS is not suitable for smaller or highly variable natural gas plants.
3. Selecting Effective Carbon Capture Technology
CCS technologies such as solvents, sorbents, membranes, and oxyfiring vary significantly in maturity, efficiency, and cost. Choosing the right approach requires thorough evaluations aligned with specific project requirements. These will vary by setting and configuration (e.g., turbine class, reciprocating engines, number of units, water availability, etc.).
4. Navigating CO₂ Transportation Logistics
The safe and efficient transport of captured CO₂ via pipelines, rail, or barges is critical. Aligning infrastructure planning with overall project timelines prevents delays.
5. Ensuring Safe and Effective Sequestration
If there is no CO₂ storage, there is no project. Permanent CO₂ storage in Class VI injection wells requires detailed geological studies and regulatory permitting. Early collaboration with experienced sequestration operators is essential to success.
6. Conducting a Comprehensive Life Cycle Analysis
Full life cycle emissions analyses, including upstream methane leakage, construction impacts, and CO₂ transportation, are critical for accurate environmental assessments and ensuring low-carbon electricity supply. Prioritizing low-leakage, third-party verified natural gas supply enhances positive climate impacts.
7. Performing Siting Feasibility Early
An early and quick feasibility assessment is critical to identifying promising opportunities and key barriers at candidate CCS sites. Important factors include available transmission capacity, the potential to expedite approval of interconnection for thermal resources, regulatory barriers, state and local incentives, the sufficiency of natural gas infrastructure, and water supply.
Frequently Asked Questions
How much longer does adding carbon capture take compared to building a natural gas plant alone? Traditional natural gas plants can be operational within roughly 18 months, provided they bypass interconnection queues for reliability purposes and have access to key equipment. Integrating CCS technology extends this by an additional 18–36 months.
Is a "capture-ready" natural gas plant a legitimate climate strategy if the capture portion isn't committed yet? Designing plants to be "capture-ready" allows for quicker initial deployment and smoother CCS integration in the future. However, deploying a capture-ready plant without a commitment to build the carbon capture portion is inconsistent with serious climate action. “Capture committed” is a better stance than “capture ready”.
How does the cost of natural gas-fired power with CCS compare to nuclear or renewables with battery storage? CCS integrated with new natural gas-fired generation can deliver low-cost decarbonization. Relae estimates $75-150/MWh, which is competitive in many markets with other firm baseload options such as new nuclear power or wind and solar with battery backup.
Has any hyperscaler actually deployed natural gas-fired power with CCS at scale yet? The Google-Broadwing deal, the first major commercial commitment of this exact pathway, was signed in October 2025 and serves as a useful proof point. Others are in development.
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How Relae Supports Data Center Decarbonization
Natural gas-fired generation combined with CCS is a proven solution for meeting the urgent electricity demands of data centers while significantly reducing emissions. Relae helps stakeholders navigate the complexities of CCS deployment through deep, science-backed expertise and strategic advisory services. Our experienced team provides comprehensive support throughout CCS project planning and execution, including technology selection, life cycle emissions analysis, infrastructure assessment, project viability, regulatory compliance, and risk management.
AI Scale and Climate Commitments: A 2026 Outlook
The AI and Climate Execution Challenge
Data center energy capacity in the US is projected to increase from 25 GW to 120 GW by 2030—a fivefold increase. Hyperscalers are projected to invest $7 trillion globally in data center infrastructure through 2030, with approximately $2.8 trillion invested in the US.
While 2025 was defined by a 'scale at all costs' scramble for compute, in 2026, the new mandate is responsible scale: reconciling voracious power demands with aggressive net-zero commitments and rising energy costs.
Grid constraints determine the geography and velocity of growth, forcing companies into complex trade-offs between speed-to-market and “clean, firm” power, which can take years to develop. Evolving carbon accounting rules are shifting procurement strategies and infrastructure choices at this trillion-dollar scale, creating a “carbon debt”—embodied emissions that will stay on the books for decades. In 2026, the competitive advantage likely belongs to those who integrate power, hardware, and climate strategy from day one.
Powering AI: Grid Reliability, Constraints, and Interconnection
Grid infrastructure faces reliability challenges from aging systems and capacity constraints. Interconnection queues stretch three to five years for renewables, while large electrical load interconnection lacks consistent standards.
Federal Regulatory Response
The federal government is moving to standardize these processes, with a critical decision point in 2026. For companies planning data center deployments in 2026, understanding these regulatory shifts is likely essential to realistic timeline and site selection planning.
On October 30, 2025, the US Department of Energy (DOE) leveraged Section 403(a) of the DOE Organization Act to direct the Federal Energy Regulatory Commission (FERC) to issue a rulemaking to “ensure efficient, timely, and non-discriminatory load interconnections” for large (>20 MW) electrical loads.
By April 30, 2026, FERC is expected to issue a final rule on large electrical load interconnections for grid operators, providing federal regulations for approval pathways, timelines, and rates.
Public comments on DOE’s advanced notice of proposed rulemaking were due on December 5, 2025, and grid operators, utilities, NGOs, and customers submitted over 150 comments reflecting a wide range of perspectives.
While federal standardization should reduce procedural uncertainty, it doesn't create new grid capacity. Even with clearer approval pathways, the underlying supply-demand mismatch remains a primary gating factor for growth.
Bridging the Supply-Demand Gap
Data center energy demand is surging, but new clean electricity generation takes years to build. This mismatch between accelerating demand and slow-building supply is forcing the industry to pursue solutions on two timelines: near-term load flexibility strategies that unlock existing capacity, and long-term generation investments that build new power supply.
Load Flexibility: Near-Term Grid Access
Load flexibility is emerging as a possible path to faster grid connection. Oracle, NVIDIA, Emerald AI, and Salt River Project's joint research demonstrated 25% power reduction during peak hours through workload tiering. The demonstration shows that if data centers reduce consumption during peak times (roughly 1% of the year), it unlocks 126 GW of currently constrained capacity that could be available now.
Large power loads increasingly face incentives or mandates to demonstrate flexibility as part of interconnection agreements, making this an access requirement, not an optional efficiency measure. For example, Senate Bill 6 in Texas mandates that data centers and other large loads must reduce their consumption during certain grid peak times. Many other state legislatures are passing legislation that will impact data centers.
Storage has shifted from smoothing renewables to enabling multiple strategies: making intermittent renewables firmer, providing grid reliability services, and supporting 24/7 matching. Storage may emerge as a solution to allow data centers to reduce grid consumption during peak hours while maintaining operations.
Relae helps clients design load flexibility strategies under evolving regulatory frameworks: evaluating behind-the-meter generation options, sizing storage for peak reduction scenarios, and structuring interconnection configurations that preserve optionality across accounting methodologies.
Clean Firm Power: Long-Term Generation
Hyperscalers remain committed to clean, firm generation that’s reliable: power that's both low-carbon and dispatchable 24/7. Natural gas with carbon capture and storage (CCS) is emerging as a critical bridge technology. Google's 400 MW CCS power agreement with Broadwing, expected online in 2029, demonstrates commercial demand at scale.
In our analysis evaluating CCS pathways, commercial viability depends on rigorous assessment of permitting timelines, capital and operating costs, storage geology, vendor compatibility, and 45Q tax credit optimization. Execution has been most prevalent where technology intersects with regulatory approval and storage access.
Hyperscalers are also investing across geothermal, nuclear, including Small Modular Reactors (SMR), hydrogen, and fusion. Long-duration energy storage has also been an area of focus. Each has different risk profiles and opportunities across technical maturity, permitting, commercial viability, emission accounting methodology, dispatchability, and political support.
Evaluating these pathways requires multi-dimensional frameworks. Each technology faces distinct challenges: SMRs struggle with execution complexity, geothermal with extended development periods, and hydrogen with production-dependent carbon intensity. Tax credit eligibility (particularly 45Q for CCS and 45V for hydrogen) significantly impacts project economics.
Both power generation and data center Infrastructure site selection require integrating environmental and social vulnerability data to avoid community conflicts that delay or stop projects.
Power Accounting Rules Determine Clean Energy Procurement
The Greenhouse Gas (GHG) Protocol extended the public consultation period for proposed scope 2 guidance changes to January 31, 2026. The results will determine clean energy procurement strategies and the carbon value of load flexibility for the next decade.
The proposed shift in electricity emissions accounting could increase clean energy procurement costs for buyers. The accounting methodological debates matter for hyperscalers: 24×7 energy matching versus carbon matching. The issues of deliverability (being located in the same grid region) and additionality (being new, rather than repurposed, generation) are also hotly debated.
These different frameworks strongly influence whether natural gas with CCS, nuclear, geothermal, or battery-backed renewables are considered optimal for a site, and whether load flexibility has carbon value.
Companies need to model scenarios across advanced power emissions methodologies, evaluating portfolio costs and carbon performance before final standards are published in 2027. Companies are also signing forward renewable energy certificate contracts (RECs) and structuring power purchase agreements (PPAs) now to preserve optionality across scenarios.
AI Infrastructure Emissions at Scale
Data center construction creates substantial scope 3 emissions, and their relative importance depends on grid carbon intensity. For facilities powered by average-carbon grids, scope 2 operational emissions dominate. But for data centers powered by very low-carbon electricity (renewables or nuclear), scope 3 embodied emissions can represent 40% of total lifetime greenhouse gas emissions.
In AI data centers, IT equipment drives the majority of embodied emissions. Chips and memory account for 67%, followed by structural materials at 17%, with server power supplies, aluminum, and other components comprising the final 16%.
Direct procurement of low-carbon materials faces constraints: limited supply, geographic concentration, and contracting complexity. Environmental Attribute Credits (EACs) provide an interim pathway by decoupling environmental benefits from physical materials, but require rigorous quality standards and verification to ensure real emissions reductions.
Our high-quality EAC criteria, developed with Microsoft, establish standards that separate market-making from greenwashing. Levelized Cost of Carbon Abatement frameworks make materials decisions comparable to power decisions, treating infrastructure decarbonization as portfolio optimization, not separate workstreams.
Carbon Removal: Addressing Residual Emissions
Complete supply chain decarbonization by 2030 isn't feasible. Despite aggressive efforts to procure clean power and reduce construction emissions, residual emissions will remain significant. For hyperscalers with net-zero commitments, carbon dioxide removal (CDR) has shifted from an optional component to a structural necessity. Microsoft remains the world's largest CDR buyer, and Google increased purchases 14-fold from 2023 to 2024.
CDR credit quality varies widely. Companies must apply science-based principles to evaluate credits. Our Criteria for High-Quality CDR, developed in collaboration with Microsoft, establishes six science-based principles for evaluating credits—critical as emerging hyperscaler and other corporate demand high-integrity supply.
AI and Climate: Looking Ahead
The window for strategic maneuvering is narrow. The AI infrastructure buildout is happening now, and the decisions made in 2026 will impact a company’s cost structure and carbon profile for years.
Companies treating power, infrastructure, and decarbonization as separate workstreams will face compounding constraints. The winners of the AI era will be those who integrate power, infrastructure, and carbon strategy into a single, cohesive system.
Understanding the Carbon Footprint of AI and How to Reduce It
Key Takeaways
- AI's carbon footprint has two distinct parts: embodied emissions from building data centers and operational emissions from running them, both accelerating as global data center electricity use is set to double by 2030, and AI-focused use to triple.
- Managing that footprint will require deliberately steering technology architecture, power sourcing, and materials choices, instead of leaving them to react to demand after the fact.
- Eight concrete strategies, from smarter chip design to firm clean power and carbon removal, can cut AI's footprint today, without waiting on new regulation.
- US data centers used 4% of the USA's total electricity in 2024, and are projected to use as much as 15% by 2030.
Introduction
The rapid growth of artificial intelligence (AI), particularly large-language models (LLM) and generative AI, has taken many by surprise. This surge has led to escalating electricity demands at data centers and raised concerns about the strain on the power grid. It has also sparked the construction of new, larger data centers, resulting in growing embodied emissions tied to building and maintaining AI physical infrastructure.
Managing the risks of increased greenhouse gas (GHG) emissions from AI requires investment, expertise, and new approaches to building and operating many aspects of AI operation and supply chains. The immediate task is to understand these risks, gather the necessary information, and to avoid poor outcomes by proactively managing construction, operation, and emissions associated with the growth in AI. In parallel to that work, it's important to recognize that AI can itself be a real force to reduce emissions incrementally and dramatically across a wide range of sectors.
What Is the Carbon Footprint of AI?
The carbon footprint of AI consists of two main parts: "embodied" emissions that come from manufacturing IT equipment and constructing data centers, and "operational" emissions that come from electricity consumed by servers, memory and networking equipment as they perform AI-related calculations. Both of these aspects of emissions are growing as more data centers are built and existing data centers increase their share of power-hungry AI applications like generative LLM searches, AI agents, and AI image generation.
Understanding Electricity Demand for Data Centers
Today, the electricity demand from AI-specific applications is estimated to be less than 1% of global electricity use. To understand this number, it helps to start with the electricity consumed by the 12,000+ data centers worldwide, which was about 1.5% of global electricity consumption in 2024. (This excludes another 0.4% from cryptocurrency mining.) However, most of the computation at these data centers is not AI; instead, it's more conventional applications like e-commerce, video streaming, social media, and online gaming.
The amount of AI-based computation at data centers is hard to determine, but AI-dedicated accelerated servers consumed about one third of overall data center electricity in 2025, or roughly 0.5% of global electricity. Notably, this is projected to grow at 30% annually, much faster than conventional (non-AI) data center electricity use. However, that electricity use results in a relatively small share of greenhouse gas emissions: about 0.5% of global fuel combustion emissions, with AI data centers representing only a small portion of that value.
Still, the demand for AI applications is rapidly growing, and this is likely to drive up the electricity used by data centers and the associated greenhouse gas emissions. The most important implications of this trend are in the US, which hosts about half the world's data centers. Currently, data centers use about 4% of US electricity, but projections for the future range from a low of 9.5% to a high of 15.3% in 2030.
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How Electricity Sources Impact Data Center Emissions
A large increase in electricity use doesn't necessarily result in a similarly large increase in greenhouse gas emissions. Currently, a significant portion of the electricity powering data centers comes from zero-carbon sources such as wind and solar. This is partly because of large, corporate power-purchase agreements (PPAs) signed by leading data center operators, particularly Amazon, Meta and Google.
US technology companies have been buying renewable energy for years. Global corporate clean energy procurement hit a record 62 GW in 2024, then fell to 55.9 GW in 2025, the first annual decline in nearly a decade, as elevated power prices and policy uncertainty made even large buyers more selective. Meta, Amazon, Google, and Microsoft still accounted for roughly 49% of global clean energy procurement in 2025, with Meta and Amazon alone securing 20.4 GW combined, including 4.7 GW of nuclear power.
The use of low-carbon power means that the net emissions from these data centers is smaller than the electricity consumption numbers might suggest. Of course, a crucial consideration is whether this low-carbon power is truly "additional," meaning that it is being added to the grid and not simply taken away from other uses. Data center operators are also expanding beyond their traditional wind and solar PPAs by exploring novel approaches to try to meet this standard, including geothermal projects in the US and Taiwan.
However, the projected electricity demand from AI applications at data centers will be difficult to meet entirely with low-carbon power, at least in the near term. Despite installing over 43.2 GW of wind, solar and battery projects in the US in 2025, these generators face a long wait for interconnection approval in many parts of the country. Geothermal and hydro power, which offer steady ("baseload") low-carbon electricity, remain constrained in the near term. And the interest in scaling up nuclear power, from restarting full-scale reactors to novel small modular reactors (SMRs), faces significant regulatory, cost, and supply chain hurdles.
One important source of low-carbon electricity that has not received enough attention is natural gas-fired power equipped with carbon capture and storage (CCS). This technology has the potential to significantly reduce emissions from existing power plants and enable new projects to achieve near-zero emissions.
The Role of Embodied Emissions in Data Center Construction
Embodied emissions include all emissions associated with the extraction, production, transportation, construction, and disposal of materials used in construction.
The embodied emissions from constructing data centers are substantial, and include concrete, steel, and IT hardware. Scope 3 GHG emissions for data centers—which include embodied emissions—range from approximately one-third to two-thirds of overall lifetime emissions. At Microsoft, Scope 3 emissions made up about 86% of the company's total FY2025 footprint and grew roughly 12% year over year, with capital goods driving most of that increase. In FY2024, capital goods alone accounted for about 41% of Microsoft's Scope 3 emissions, and purchased goods and services (including IT hardware) accounted for another 34%. In response, Microsoft has started using wood in some data center construction to reduce this impact. While using wood offers a partial solution, it cannot fully offset the emissions of even a single facility, and wood supply chains remain limited.
Major data center operators are working hard to address this challenge, including emphasizing the need for standardized emissions measurements and disclosures for key building materials. Ultimately, achieving deeper decarbonization will require further action to address both operational and embodied emissions.
Eight Strategies to Reduce the Carbon Footprint of AI
1. Adapt Technology Architecture
Efficiency is the foundational strategy in any clean energy approach. As such, chipmakers are developing ways to cut energy use from the outset, such as incorporating more memory directly onto computer chips or hard-wiring basic calculations. These innovations have already reduced energy consumption in new computer chips substantially, in some cases a 96% improvement. Examples of this include NVIDIA's Blackwell platform and the company's newer Rubin platform, launched in 2026, continues that trajectory. Likewise, servers are being designed with new architectures that minimize internal data transfers, delivering additional efficiencies. Even more efficiency gains may be possible with emerging technologies like photonic computing.
2. Optimize Training Geography
There are also significant opportunities to manage AI's energy use through time and space optimization. For example, a large portion of the energy consumption for LLMs occurs during the training phase, prior to a model's deployment for inference. Because these training tasks are not location-dependent, they can be carried out in regions with abundant, low-cost, low-carbon electricity, as part of broader efforts to dynamically move computing tasks to reduce emissions, known as carbon-aware computing. Additionally, server requests for generative AI tasks, like ChatGPT searches, can potentially be routed through systems powered by low-carbon electricity. Although this may add only a few milliseconds of latency, it could substantially reduce emissions from computing operations.
3. Select Appropriately-Sized Models
Not all generative AI tasks, like ChatGPT queries, are equal in terms of energy demand. Leading AI companies are increasingly focusing on using smaller, more efficient AI models to perform these tasks, achieving nearly equivalent quality for far less energy consumption. A notable recent test of that idea came in January 2025, when China's DeepSeek released a model with competitive performance that was trained using less powerful chips and far fewer computing hours than its established rivals. Similarly, many AI applications, such as digital twinning and satellite-based pattern recognition, consume far less electricity than generative LLMs, because of their specialized, relatively efficient models. This can even save energy compared to non-AI approaches: for example, some of the most advanced AI-driven weather prediction models require far less energy than traditional weather simulations, running on a laptop rather than a supercomputer.
4. Address Fugitive Methane Emissions
As data center operators increasingly plan on using natural gas for new electricity supply, reducing upstream emissions from gas production and transmission will be crucial. In the U.S., the Environmental Protection Agency (EPA) 2024 Methane Rule was designed to cut these non-carbon dioxide greenhouse gas emissions by approximately 80%. However, Congress repealed the rule's methane fee in 2025 and barred the EPA from collecting it until 2034. The EPA has since extended compliance deadlines and loosened flare and vent-gas requirements, with litigation over those changes still ongoing. Meanwhile, tools from companies like Kayrros and organizations like Carbon Mapper help detect methane leaks and attribute them to specific operators. The best actors in the industry emit minimal methane, less than 0.5% of what is produced. This standard is achievable for nearly all gas producers.
5. Use Carbon Capture on Power Plants
For both new and existing natural gas-fired power plants, carbon capture and storage technology offers the potential for generating firm, low-carbon power. While many plants currently in operation continue to emit unchecked, this doesn't have to be the case: their emissions can be captured and securely stored geologically. Hyperscalers and project developers should pursue new investments and business models for CCS to reduce existing emissions by 95% or more. For new generation projects, options like NetPower, Arbor, and CES will soon enable emissions abatement of 100%, or even more if combined with biopower to deliver carbon dioxide removal as well. Achieving this will require the development of carbon dioxide pipelines, barges, and storage facilities, which face their own challenges, such as permitting and community approval, that must be addressed directly.
6. Add More Zero-Carbon Power to the Grid
Roughly 8,200 solar, wind, and battery projects in the U.S. are seeking grid interconnection. By the end of 2025, the interconnection queue held roughly 2,060 GW of proposed generation and storage across thousands of projects, and its composition shifted meaningfully. Solar, wind, and storage volumes in the queue all declined year over year (although remained at high absolute levels) while natural gas capacity in the queue grew by 86%. Our blog post, The $5.5 Billion-Dollar Case for Enabling Data Center Load Flexibility, covers one way hyperscalers are working around the wait rather than simply enduring it. These delays need to be addressed, and permitting reform remains an unresolved, live debate. The Manchin-Barrasso bill, which once looked likely to pass, was tabled in December 2024 and never became law. As of 2026, no comprehensive federal permitting law has replaced it. One potential innovation is to use AI to accelerate the development of power flow models and streamline the paperwork required to complete the regulatory process.
7. Invest in Low-Carbon Building Materials
While wood is a promising low-carbon building material, we'll also need glass, concrete, steel, aluminum, and computer chips with minimal embodied carbon emissions. Hyperscalers currently face significant challenges accessing low-carbon versions of these materials, which will eventually be produced using low-carbon hydrogen, carbon capture and storage, and low-carbon electricity. However, these systems require significant investment, workforce development, and permitting to be built. Without these advancements, the embodied emissions from data centers will increase rapidly and significantly in the US, Europe, and globally.
8. Increase Carbon Dioxide Removals
It's already clear that AI applications at data centers will generate emissions from electricity use and embodied carbon that cannot be avoided in the near term. Estimates of current greenhouse gas emissions exceed 300 million tons per year and are likely to grow this decade. These emissions should be measured using full life-cycle analysis and then offset through high-quality carbon removal projects, preferably those with high durability.
To effectively reduce the environmental impact of AI, all eight strategies discussed must prioritize the communities most affected: frontline communities near new infrastructure, consumers facing price increases, and tribal authorities with limited legal protections. Our own research on community opposition to AI data centers found that transparency, not cost or environmental impact alone, is the dominant driver of pushback across 46 stalled or blocked projects. We explore this concern further in our blog, Who Pays for the AI? The Hidden Costs of Rising Data Center Demand, including how ratepayers, not just data center operators, often absorb the cost of new grid infrastructure. Planning should begin by understanding the needs of these communities, ensuring that efforts focus on minimizing harm while maximizing benefits. Equity and justice must be embedded in every stage of planning, production, and permitting across all strategies.
AI's Power Demand Is Indicative of Broader Electricity Demand
AI is just one part of a broader trend of rapidly growing electricity demands, including from electric vehicles, heat pumps, industrial electrification, green hydrogen, and various e-fuels. The challenges AI presents to hyperscalers, communities, regulators, and investors serve as a preview of the complex, far-reaching impacts emerging in other sectors. The same questions keep recurring. Who secures reliable, affordable power fast enough? Who ends up carrying the cost and emissions burden of getting there the wrong way?
Managing AI's power demand will require building the technology architecture, clean firm power supply, and materials strategy to meet that demand deliberately, rather than reactively. AI's carbon footprint underscores the critical need for expertise in clean electricity, grid management, decarbonization, and carbon removal—expertise that will become increasingly vital as more companies realize the complexity and cost of the journey ahead.
Fortunately, AI itself can be part of the solution. With applications in grid management, material science, and advanced manufacturing, AI has the potential to play a powerful role in the climate response.
Read the full 2025 report: ICEF Sustainable Data Centers.
Frequently Asked Questions
How much electricity do AI data centers actually use?
AI-specific computation likely accounts for around 0.04% of global electricity use today, but data centers overall (most of it non-AI computation) used about 1.5% of global electricity in 2024. In the US, which hosts roughly half the world's data centers, Lawrence Berkeley National Laboratory puts current usage at 4% of US electricity, projected to reach 9.5-15.3% by 2030 as AI-specific demand grows.
Will more efficient AI models like DeepSeek reduce data center energy demand?
Not necessarily. DeepSeek's 2025 debut showed that competitive models can be trained with less powerful chips and fewer computing hours, but whether that translates into lower total energy demand is contested. Historically, efficiency gains in computing have tended to get absorbed by increased usage rather than reducing total consumption, so the honest answer is that it depends on whether demand growth outpaces the efficiency gained.
What is being done about the embodied emissions from building AI data centers?
Embodied emissions, from concrete, steel, and IT hardware, can account for one-third to two-thirds of a data center's lifetime emissions. Strategies include using lower-carbon materials like wood where feasible, developing low-carbon concrete, steel, and chips, and standardizing emissions disclosures for building materials so operators can compare and choose lower-footprint options.
Who Pays for AI? The Hidden Cost of Rising Data Center Demand
Key Takeaways
- AI data centers are driving the fastest electricity demand growth in decades: US data centers consume an estimated 4 to 5% of US electricity today, projected to reach as much as 9 to 17% by 2030 (EPRI).
- Without deliberate cost allocation, residential and small-business ratepayers subsidize private AI infrastructure.
- Peer-reviewed modeling projects data center growth could raise US power costs 6 to 29% nationally by 2030, and up to 57% in the hardest-hit regions.
- Utility commissioners, state regulators, and policymakers now have working models to draw from, including large-load tariffs, dedicated rate classes, and direct assignment of transmission costs.
AI Data Center Energy Demand Is Testing the Limits of the Grid
AI is driving electricity demand at a pace the US grid has not seen in decades. US data centers already consume an estimated 4 to 5% of the nation's electricity, and EPRI projects that share could reach 9 to 17% by 2030. Behind nearly every AI model and digital product is the invisible infrastructure that powers it: data centers. These facilities are resource-intensive, requiring massive amounts of electricity to power servers, substantial water for cooling, and extensive new grid infrastructure.
In the race to decarbonize the grid, data centers are emerging as a critical pressure point. This infrastructure sits at the intersection of digital growth and climate action, forcing a difficult question: who pays to power AI?
Legacy Utility Models Weren’t Built for this Growth
Utilities must upgrade aging grid infrastructure to meet this new surge in electricity demand, while maintaining reliability. Under legacy utility frameworks, it's often ratepayers who foot the bill for those upgrades. And the costs are not distributed equitably.
Traditional utility planning assumes that increased demand justifies expanded investment in generation and transmission infrastructure. When a new type of large customer, like a tech company, moves into a utility’s service territory, utilities plan new infrastructure to meet that projected demand.
Utilities typically recover the cost of new infrastructure through a process called rate base cost recovery. This allows utilities to charge all customers in the “rate base” for the expenses incurred, including thousands of individuals, families, and small businesses, even when those costs stem from the demands of just a few large users.
This legacy model struggles to keep pace in the AI boom era, where massive new electricity demand can double within a few years, a scale of growth that used to take decades. Additionally, while data centers create short-term construction jobs, there are almost no lasting employment benefits for local communities.
It's clearly inequitable for all ratepayers to bear the costs of upgrading the grid to benefit just a small number of massive data centers. But that's not the only problem. If utilities decide to meet new power demand from large data centers with new fossil fuel generation, such as gas peaker plants, they risk creating stranded assets: infrastructure that becomes obsolete or uneconomical as climate targets, clean energy mandates, or the cost-effectiveness of renewables accelerates. Once built, ratepayers will have to continue paying for these long-lived investments for years, even if they are underutilized or retired early due to policy shifts. This risk is no longer hypothetical: to serve projected data center load, Georgia regulators approved a plan to extend the lives of two massive coal plants to as late as 2038, and Virginia regulators stripped roughly $350 million tied to speculative early-stage data center projects out of Dominion Energy's revenue forecast.
If utilities are locking in decades of new fossil fuel generation to meet short-term data center growth, ratepayers may be left holding the bag for infrastructure that contradicts their climate goals and state mandates, with little ratepayer or community input into the decision. Effectively, local communities may be subsidizing a technology that they did not directly ask for in the first place and has little to no direct community benefits. The result is a long-term misalignment between utility investment strategy and the public interest.
Ratepayers Bear the Cost of Private AI Expansion
The economic burden of data center expansion can fall disproportionately on households and small businesses. But data centers, as the largest and fastest-growing users, often negotiate bespoke contracts, subsidized rates, or fixed-price electricity agreements that shield them from long-term cost volatility.
This can result in other customers, especially residential and low-income ratepayers, bearing a disproportionate share of the infrastructure and maintenance costs. In many states, residential and low-income customers already experience energy cost burdens that exceed affordability thresholds. Adding the weight of infrastructure investments to serve energy-intensive data centers, without sharing those costs equitably, exacerbates an already regressive utility cost allocation system.
Georgia shows how these costs reach ratepayers even when regulators act. Georgia Power customers absorbed six rate increases totaling roughly $43 per month between 2023 and 2025, and while regulators approved a base-rate freeze through 2028, the freeze excluded fuel and storm costs. In 2026 fuel-cost proceedings, testimony showed that large industrial and data center customers raise other customers' monthly fuel costs by 5 to 11%, prompting the Georgia Public Service Commission to open an investigation into how fuel costs are allocated between large loads and residential customers. Ratepayers noticed: in November 2025, both Georgia PSC seats flipped in elections run explicitly on utility bills and data center cost-shifting. In Virginia, regulators approved a rate increase of roughly $16 per month for typical Dominion Energy residential customers amid surging data center demand.
These examples are not anomalies. A peer-reviewed study in Environmental Research Letters projects that data center growth could raise US power costs 6 to 29% nationally by 2030, and up to 57% in the hardest-hit regions, with Virginia among the steepest. This is a systemic shift in energy demand, one that places a growing burden on communities and lacks clear public benefits.
Environmental and Community Impacts Are Mounting
Beyond economic impacts, the geography of data center development reveals another layer of inequity: environmental justice. Data center siting often prioritizes affordable land, low resource costs (e.g., electricity, water), and climate considerations like heat variability. They also rely on proximity to pre-existing fossil fuel generation and transmission infrastructure. Research now confirms the pattern this creates: an analysis of 550 EPA-regulated data centers found that air pollution burdens near data centers rise with the share of people of color living nearby, and a 2026 Washington state study found more than half the state's data centers sit in census tracts with the highest concentrations of people of color.
These communities often absorb the negative externalities beyond their electricity bills, including increased air pollution from peaker plants and on-site diesel or gas backup generators, traffic and construction noise, water stress, and land use changes. Simultaneously, they do not receive direct net positive benefits. Frontline communities are paying attention to this trend, and opposition has become a defining force in where AI infrastructure gets built. Gallup finds 71% of Americans now oppose a data center in their own area, and Data Center Watch counted roughly $130 billion in projects blocked or delayed in the first quarter of 2026 alone. The stakes of community opposition are increasing and intensifying.
The consequences of ignoring communities are now playing out in federal court. At xAI's Colossus facility in Memphis, developers operated dozens of on-site gas turbines without air permits in a majority-Black area already burdened by industrial pollution. After the Shelby County Health Department granted permits for a subset of turbines in July 2025, the fight moved to xAI's second campus across the state line: in April 2026, the NAACP filed a Clean Air Act lawsuit over roughly 27 unpermitted gas turbines at the Colossus 2 site in Southaven, Mississippi, seeking penalties of more than $100,000 per day. On-site power can help reduce demand on the grid, which can be a benefit. But when that generation runs without permits or oversight, nearby communities bear unmeasured health and environmental impacts from hazardous emissions, and the litigation now underway shows how quickly unpermitted power becomes a legal and reputational liability.
To date, data center developers do not appear to have maximized potential community benefits or engagement. Data centers have not typically employed many local residents beyond construction phases, resulting in limited economic benefits, particularly when facility ownership is distant from the local community or has few local ties. When these same communities already experience high pollution burden or economic precarity, the cumulative impact of a new data center can deepen existing vulnerabilities.
Water use is also a mounting environmental justice concern. Many data centers rely on evaporative cooling systems that draw millions of gallons of water per day, and peer-reviewed research finds significant gaps in how the industry discloses its water footprint. In drought-prone regions, this can stress already-depleted aquifers and heighten tensions over water access.
The result is a high-stakes tradeoff between digital infrastructure and local resource resilience, one that communities should be a part of deciding.
States and Regulators Are Writing the New Rules
Virginia, the "Data Center Capital of the World," is home to 674 data centers that consume an estimated 25% of the state's electricity, a share EPRI projects could reach 39 to 57% by 2030, the highest of any state. After legislators considered but did not pass data center bills in the 2025 session, the 2026 General Assembly passed roughly 15 data center bills, including legislation, signed in May 2026, directing regulators to ensure data center costs are not subsidized by other customers, along with new requirements for site impact assessments and water-use reporting. Virginia's State Corporation Commission had already created a dedicated rate class for high energy use customers, with 14-year contract terms and minimum charges that apply whether or not the projected load materializes, and in August 2026 it went further, ordering Dominion to develop a tariff that directly assigns transmission costs to the data centers that trigger them.
Virginia is not alone. Ohio regulators approved a landmark tariff requiring large data centers to pay for 85% of the capacity they request, whether or not they use it. Oregon's POWER Act created the nation's first legislated rate class for data centers. Texas gave its grid operator authority to curtail large loads during emergencies. Minnesota, California, Alabama, Tennessee, South Dakota, Nebraska, and Florida have all enacted their own ratepayer-protection measures, and at the federal level, FERC ordered the nation's largest grid operator to write new rules for data centers that co-locate with power plants, citing the need for consumer protection and clear cost allocation. State energy officials are also proactively planning for data center expansion.
The direction is clear. The unresolved question is whether these reforms move faster than the costs already flowing to ratepayers.
What Is the Public Good of Data Centers?
AI infrastructure powers innovation, job creation, research, and the technologies we rely on every day. But it may also bring inequitable social and direct financial costs. Like highways, factories, and pipelines before them, the question remains: What is the public good of AI data centers? How should we hold data center developers accountable to the public interest, which values a clean energy future? We need clear-eyed assessments of how data centers impact energy affordability, climate progress, and environmental equity.
Yesterday's utility policy frameworks were not designed for hyperscale AI data centers. The reforms now underway are a start, but without sustained attention they may still force the public to subsidize private expansion, through economic and environmental costs, often without equitable community engagement, climate accountability, or local benefit.
AI Data Center Growth Needs Accountability, Equity, and Reform
To align data center growth with the public interest, the stakeholders involved now have proven models to build on:
- Utilities and regulators can require large customers to pay an equitable share of new infrastructure costs, as Ohio's minimum-take tariff and Virginia's dedicated rate class now do.
- Public Utility Commissions can mandate equity and community impact assessments during siting and permitting, following Virginia's new site assessment requirements.
- States can condition tax incentives and zoning approvals on local hiring, emissions reductions, and community benefits agreements.
- Data center developers can prioritize clean power and commit to transparent, equitable community engagement and benefits plans before opposition, litigation, and cancellations decide the outcome for them.
As we build the digital backbone of the next century, we must avoid repeating injustices of the past. A just energy transition requires more than megawatts: it demands equity, policy interventions, and real climate progress.
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Frequently Asked Questions
How do utilities typically recover the cost of infrastructure built to serve large data center customers, and why does this burden fall on other ratepayers?
Utilities recover infrastructure investments through rate base cost recovery: regulators approve new generation, transmission, and distribution spending, and the costs are spread across all customers in the rate base through their monthly bills. That model worked when demand growth was gradual and diffuse, but when a single data center campus drives hundreds of megawatts of new investment, standard cost allocation spreads those costs across households and small businesses unless regulators adopt a special tariff or rate class that assigns them to the customer who caused them.
What are stranded assets in the context of data center power demand, and how do they create long-term risk for utilities and ratepayers?
Stranded assets are long-lived infrastructure investments, like new gas plants built for projected data center load, that become underused or uneconomical before they are paid off, whether because demand never materializes or because policy and market shifts overtake them. Because utilities recover those costs through rates over decades, ratepayers keep paying even if the asset sits idle. The risk is acute today because data center demand forecasts are highly uncertain: Virginia regulators removed roughly $350 million tied to speculative data center projects from one utility's revenue forecast in 2025.
Why do data centers often locate in rural or low-income communities, and what are the environmental justice implications?
Data center siting favors cheap land, fast permitting, low-cost power and water, and proximity to existing generation and transmission, conditions most common in rural, low-income, and historically marginalized communities. Research confirms the consequences: analysis of 550 EPA-regulated data centers found air pollution burdens rise with the share of people of color living nearby. These communities absorb the air pollution, water stress, noise, and land use impacts while receiving few lasting jobs or direct benefits.
What regulatory or policy tools can states and Public Utility Commissions use to ensure data center growth doesn't unfairly shift costs to residential and small-business ratepayers?
The toolkit has expanded rapidly since 2025. Commissions can create dedicated large-load rate classes and tariffs with minimum take-or-pay provisions, long contract terms, collateral requirements, and exit fees, as Ohio and Virginia have done; directly assign infrastructure enhancement costs to the customers that trigger them; and require site impact assessments during permitting. Legislatures can codify ratepayer protections, require water and load-forecast transparency, and condition tax incentives on community benefits, models now in place in at least eight states.
This commentary reflects public policy analysis and opinion, not legal advice or regulatory determinations.
Why AI Data Centers Are Being Blocked: A Project-Level Examination
Key Takeaways
- Community opposition has blocked, withdrawn, or stalled more than $170 billion in announced AI data center investment across 20 US states since January 2024. The pace is accelerating: 6 cancellations in 2024, 25 in 2025, and more than 20 additional cancellations by May 15, 2026.
- Data center opposition is bipartisan. It spans red and blue counties, every region, and multiple grid operators, with nearly two-thirds of the blocked investment sitting in counties that voted for Donald Trump in 2024.
- Process and transparency, more than resource concerns alone, drive the fastest and most durable opposition. How a developer runs the engagement process shapes both community sentiment and the project’s ultimate success.
How Many AI Data Center Projects Have Been Cancelled?
Between January 1, 2024, and May 15, 2026, community opposition blocked, withdrew, or stalled 46 announced AI data center projects across 20 US states, representing more than $170 billion in announced investment. These values are disclosed or derived for 35 of the 46 projects; the remaining 11 carry no public figure.
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The pace of successful opposition has accelerated sharply. Cancellations rose from 6 in 2024 to 25 in 2025. The first five months of 2026 added more than 20 additional cancellations, the fastest stretch on record.
Virginia leads the state count with 11 blocked projects, followed by Indiana with 7 and Texas with 5. Together, those three states account for roughly half of all cancellations in the dataset. The PJM grid region carries the largest single share of blocked investment at $70 billion across 13 projects, followed by MISO at $37 billion.
Is Opposition to Data Centers Bipartisan?
Yes. The opposition wave crosses party lines on every measure we examined. Republican-leaning counties hosted 28 of the 46 host counties (61%), Democratic-leaning counties hosted 16 (35%), and 2 fell within five points.
Weighted by announced investment, about two-thirds of blocked dollars sat in Republican-voting counties. Strong Republican counties (those Trump won by more than 15 points) account for $99 billion across 23 projects. Strong Democratic counties account for $29 billion across 9 projects. The remaining $44 billion spans Lean Republican, Tossup, and Lean Democratic counties.
Why Are Communities Opposing Data Centers?
Communities raise a consistent set of concerns across the country: water demand, electricity rates, air quality where developers propose gas co-generation, rural character, and a lack of transparency in the development process.
Across the seven cases that we studied in depth, process, and transparency concerns were the most consistently cited factors associated with opposition. Non-disclosure agreements between developers and local officials, ownership structures in which the ultimate end-user was not publicly identified, and closed-door pre-application negotiations produce opposition faster and more durably than any other concern.
The pattern holds across very different communities: a diffuse civic mobilization in rural Georgia, an NGO water coalition in a Texas college town, a conservation coalition anchored by the Southern Environmental Law Center in Southside, Virginia, and an institutional civic organization with legal-expert and celebrity support in northern Virginia. Each produced the same outcome, and each flagged process and transparency as a dominant or top-three concern in the public record.
By the time a project reaches its first public hearing against organized community opposition, the political path of the project is largely set. Late-stage benefits packages consistently fail to reverse that trajectory. Communities read them as concessions, not commitments.
Assess Community Opposition Risk Before You Site
Community opposition is now a structural feature of the AI data center siting environment. The patterns are clear enough to act on now. Relae's Community Impacts team helps developers and capital partners implement responsible development standards through pre-siting community intelligence, calibration of benefits design to specific community contexts, and building the verification scaffolding that turns commitments into outcomes.
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Frequently Asked Questions
Which states are banning data centers in the US?
The first statewide moratoriums on data centers have arrived. In July 2026, Governor Hochul signed the country's first statewide moratorium, an executive order pausing state environmental permits for up to one year for new data centers of 50 MW or more. Texas followed weeks later, halting approvals of new data center grid connections until state regulators audit their power, water, tax, and ownership details. This is a snapshot from August 2026, and we will likely see additional changes in the months ahead.
The local picture is more developed. Individual municipalities and counties have adopted moratoria and zoning ordinance amendments that block or restrict data centers within their jurisdictions. The City of Peculiar, Missouri, removed data centers from its light-industrial zoning entirely. Monroe County, Georgia, and Jones County, Georgia, both adopted moratoria after project denials in 2025. Cassville Township, Wisconsin, and San Marcos, Texas adopted zoning and code amendments in 2026. State legislatures in Virginia, Indiana, Texas, and Missouri have taken up data center siting, ratepayer, and permitting legislation, though most bills remain in progress rather than enacted.
How much data center investment has been blocked in the US?
More than $170 billion in announced AI data center investment has been blocked, withdrawn, or stalled by community opposition across 46 projects and 20 US states between January 1, 2024 and May 15, 2026.
Relae arrived at this figure from data on 35 of the 46 projects; 11 have no publicly disclosed investment value. The pace has accelerated sharply: 6 cancellations in 2024, 25 in 2025, and more than 20 additional cancellations in the first five months of 2026 alone. Virginia leads the state count with 11 projects. The PJM grid region carries the largest single share of blocked capacity at $70 billion across 13 projects.
What causes a data center project to be cancelled by community opposition?
As of August 2026, communities cite a consistent set of concerns across cancelled projects: water demand, grid strain and residential rate impacts, air quality where developers propose gas-fired co-generation, rural character and farmland conversion, and lack of transparency in the development process.
In the seven cases we studied in depth, process and transparency were together the most consistent driver of opposition. Non-disclosure agreements between developers and local officials, shell LLC ownership structures that conceal the end-user, and closed-door pre-application negotiations produce faster and more durable opposition than any single resource concern. Late-stage benefits packages consistently fail once that transparency-driven frame has formed.
Why Behind-the-Meter Power Emissions Belong in Scope 2
Key Takeaways
- Larger power users are securing behind-the-meter (BTM) power to bypass grid constraints, pairing data centers with third-party-owned generation assets that deliver electricity through a private line rather than the grid.
- BTM power arrangements can create confusion about electricity emissions classification: the power users neither own the generating asset nor purchase electricity from the grid, leading some to misclassify those emissions as scope 3 in their corporate GHG inventories. But the GHG Protocol's Corporate Standard is clear: BTM electricity emissions belong in scope 2.
- Misclassifying BTM emissions can create reputational and regulatory risk. Relae can help organizations get this right before the contract closes.
Why Large Power Users Are Turning to Behind-the-Meter Power
Large power users are consuming more electricity due to data center growth and are looking to add capacity faster than the grid can support, which is having a direct impact on corporate emissions. For example, between 2020 and 2024, Microsoft’s location-based scope 2 emissions rose 130%, and Google’s rose 92%, driven almost entirely by soaring electricity demand from AI infrastructure.
To bypass grid congestion and long interconnection queues, many are turning to behind-the-meter (BTM) power. It’s a pragmatic solution to a real supply problem, but it’s opening an urgent carbon accounting question: when the BTM asset is owned and operated by a third party, where should we account for those emissions?
There has been some confusion that has resulted in companies pursuing an interpretation that would place those emissions in scope 3. The GHG Protocol’s Corporate Standard says otherwise, and the stakes of getting this wrong are high.
What Is Behind-the-Meter Power Generation?
Behind-the-meter refers to electricity generated on the power consumer’s side of the utility meter, bypassing the grid, and typically located on or near the site where the power is consumed.
In most BTM arrangements for a data center, a third-party developer builds and operates a generation asset, such as natural gas, geothermal, or renewable energy, and delivers electricity directly to the facility through a private transmission line. There is no utility meter, no grid connection, and no standard energy invoice.
This structure allows companies to access large, reliable blocks of power without waiting years for grid interconnection approvals. Since the company does not own or operate the generation asset and is not purchasing electricity through a conventional utility relationship, this arrangement has created some uncertainty around how to account for the associated emissions.
Can BTM Electricity Emissions Be Classified As Scope 3?
In this scenario, no. The GHG Protocol's Corporate Standard is unambiguous: BTM electricity emissions belong in scope 2, not scope 3. Yet, some companies have been confused about this classification.
There is broad agreement that since the power users do not own or operate the generating asset, those emissions do not belong in scope 1. Divergence starts when we consider that the company is purchasing BTM power, i.e., not from the grid. Since no electricity is acquired from the grid, some argue that rather than accounting for these emissions in scope 2, they are better placed in scope 3, category 8: emissions from leased assets.
The appeal is obvious for BTM power consumers. Scope 3 emissions face less scrutiny from investors, auditors, and regulators who focus most of their attention on scopes 1 and 2. Classifying BTM emissions as scope 3 would reduce near-term pressure to act. However, the GHG Protocol is unambiguous in its stance.
What the GHG Protocol Actually Says
The GHG Protocol’s Scope 2 Guidance states that “organizations must quantify emissions from the generation of acquired and consumed electricity, steam, heat, or cooling (collectively referred to as ‘electricity’).” The method of delivery, whether grid or BTM, does not change the classification.
If a company consumes electricity from a BTM source, the emissions from generating that electricity belong in scope 2. Section 5.4 of the Scope 2 Guidance addresses BTM power generation directly: “the company with operational or financial control of the energy generation facility reports those emissions in scope 1, following the operational control approach, while the consumer of the energy reports the emissions in scope 2.”
This resolves the question completely. The emissions sit in scope 1 if the company has operational or financial control of the asset, or in scope 2 if a third party controls it.
The GHG Protocol’s Corporate Value Chain (Scope 3) Accounting and Reporting Standard reinforces this conclusion. “Category 8 includes emissions from the operation of assets that are leased by the reporting company in the reporting year and not already included in the reporting company’s scope 1 or scope 2 inventories.”
Because BTM electricity emissions are captured by the Scope 2 Guidance, the scope 3 category 8 does not apply.
Get the Accounting Right Before the Contract Closes
The GHG Protocol is unambiguous: behind-the-meter electricity emissions belong in scope 2 for companies that consume, but do not control the generating asset. This means that BTM contract terms are crucial to determining how the emissions will be classified, since the GHG Protocol assigns scope based on who holds operational or financial control of the generating asset.
Companies that move fast on BTM capacity without understanding this distinction risk locking in a scope 1 or scope 2 obligation they didn't anticipate or building a reporting strategy around a scope 3 interpretation the GHG Protocol doesn't support. This can become a reputational or even a regulatory liability that is far harder to address after the contract is signed.
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