More than 230 environmental and public-interest groups asked Congress to halt approvals for and construction of new data centers. They want a temporary national moratorium until federal rules address energy use, water needs, local impacts, and emissions. The request came from Food & Water Watch and was signed by national and local groups across the country.
They said that the fast growth of artificial intelligence (AI) and cloud services is putting big new demands on local grids and water systems. They also said current federal rules do not cover the environmental or social impacts linked to data center growth.
Why the Groups Want a Moratorium
Data centers are using more electricity each year. U.S. data centers consumed an estimated 183 terawatt-hours (TWh) of electricity in 2024. That was about 4% of all U.S. power use. Some national studies project that number could rise to 426 TWh by 2030, which would be about 6.7% to 12% of U.S. electricity, depending on growth rates.
Global data centers used around 415 TWh of electricity in 2024. Analysts expect double-digit annual growth as AI loads increase.
AI-ready data center capacity is projected to grow by about 33% per year from 2023 to 2030 in mid-range market scenarios. Industry groups say global data center capacity could reach over 220 gigawatts (GW) by 2030.
Some groups warn that data center CO₂ emissions might hit 1% of global emissions by 2030. That’s about the same as a mid-size industrial country’s yearly emissions. They say the growth rate is rising faster than the reductions in many other sectors.
An excerpt from their letter reads:
“The rapid expansion of data centers across the United States, driven by the generative artificial intelligence (AI) and crypto boom, presents one of the biggest environmental and social threats of our generation. This expansion is rapidly increasing demand for energy, driving more fossil fuel pollution, straining water resources, and raising electricity prices across the country. All this compounds the significant and concerning impacts AI is having on society, including lost jobs, social instability, and economic concentration.”
When AI Growth Collides With the U.S. Power Grid
Several utilities have linked new power plant plans to data center growth. In Virginia, the largest power company and grid planners see data centers as a key reason for new infrastructure.
In Louisiana, Entergy moved forward with a new gas-plant plan expected to support a large hyperscale data center campus. These cases show how utilities now size new plants with AI-related load in mind.
Some utilities believe these expansions might increase local electricity rates by a few percentage points. This depends on how costs are shared. Regulators in various areas say that extra load can increase distribution and transmission costs. This might lead to higher bills for households.
Several grid operators also report congestion or long waiting lines for new power connections. Northern Virginia, Texas, and parts of the Pacific Northwest now have interconnection queues. In these areas, data center projects make up a large part of the pending requests.
Water Use and Siting Concerns
Water demand is another point of conflict. Many large data centers rely on water-cooled systems. A typical water-cooled data center may use around 1.9 liters of water per kWh. More advanced or dry-cooled facilities may use as little as 0.2 liters per kWh, but these designs are not yet common.
One medium-sized data center can use about 110 million gallons of water per year. Large hyperscale sites can use several hundred million gallons annually, and, in some cases, even more. Global estimates suggest data centers could use over 1 trillion liters of water per year by 2030 if growth continues.
These demands have triggered local resistance. In parts of Arizona, California, and Georgia, community groups have raised concerns about water use during drought periods. In some cases, local governments paused or limited data center approvals. A single campus can use more water each year than some small towns.
Trump Plans Executive Order on AI Regulation
While groups push for limits on new data centers, the White House is also preparing an executive order that would reshape AI policy nationwide, as reported by CNN. President Donald Trump has said he plans to issue an order that would block states from creating their own AI rules.
The administration aims to create one national standard for AI. This way, companies won’t have to deal with different state regulations.
Drafts of the plan say the order may tell federal agencies to challenge state AI laws. This could happen through lawsuits or funding limits if the laws clash with federal policy. Supporters say a unified national rule could help U.S. companies compete globally and reduce compliance costs.
State leaders and consumer protection groups argue the opposite. They say states have a legal right to pass their own rules on privacy, safety, and data use. Some governors argue that an executive order cannot override state laws without action by Congress. Minnesota lawmakers, for example, continue to write their own AI bills focused on deepfakes and child-safety concerns.
The debate adds another layer to the data center issue. AI systems require massive computing power. If AI keeps growing quickly, analysts expect even heavier pressure on local grids and water systems. Advocacy groups say that this makes federal regulation more urgent.
Scale of AI and Hyperscale Build-out
The U.S. is in the middle of a major build-out of hyperscale and AI-optimized data centers. Industry trackers report that hundreds of new hyperscale facilities are planned or already under construction through 2030. Many of these campuses are designed specifically for AI training and inference workloads.
Major cloud and social media companies have sharply increased capital spending to support this build-out. Amazon, Google, Microsoft, Meta, and other major platforms, combined spending on AI chips, data centers, and network upgrades reached hundreds of billions of dollars per year in the mid-2020s. These spending levels signal how fast demand is growing.
Some experts track how major technology firms have changed over time. For example, one big cloud provider said its data center electricity use has more than doubled in the last ten years. This increase happened as its global reach grew. This gives a sense of how long-term trends feed current infrastructure pressures.
AI also adds new layers of demand. Training one large AI model can use millions of kilowatt-hours of electricity. Operating a popular chatbot can require many megawatt-hours per day, especially at peak traffic.
Research shows that processing one billion AI queries uses as much electricity as powering tens of thousands of U.S. homes for a day. This varies with the model’s size and efficiency.
Cities and States Move Faster Than Washington
Local governments have acted faster than federal agencies to respond to public concerns. More than 100 counties and cities have passed temporary moratoria, zoning limits, or new environmental rules since 2023. Examples include parts of Georgia, Oregon, Arizona, and Virginia, where communities plan to evaluate energy and water impacts before approving new projects.
Advocacy groups also argue that federal standards have not kept up. The U.S. does not have national energy-efficiency rules for private data centers. It also does not require detailed, mandatory reporting on energy, water, or emissions for the sector. The groups pushing for a moratorium say Congress must update these policies before more sites break ground.
What the Debate Means for 2026 and Beyond
Congress will review the environmental groups’ request in the coming months. Lawmakers are expected to weigh economic benefits against rising tensions around energy, water, and local resources. At the same time, the White House may release its AI executive order, which could shape how states and companies set their own rules.
With rapid AI growth, rising electricity use, and expanding data center construction, both debates are likely to continue through 2026. Many experts say long-term solutions will require national standards, better reporting, and closer coordination between states, utilities, and federal agencies.
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The accounting differences that decide whether your nature investment shows up in inventory, in BVCM, or nowhere at all.
The question reaches a procurement team about three weeks before the next sustainability committee meeting. Someone has read about insetting. Someone else has just signed off on an offset purchase. The CSO wants to know if the two are interchangeable. The answer is no, and the GHG Protocol Land Sector and Removals Standard is the reason why.
This article walks through what each term means at audit-grade specificity, what the standards actually say about how each gets counted, and how to decide which tool fits which target. The insetting vs offsetting question is one of the most-searched in corporate climate strategy, and one of the most poorly answered. By the end of this piece, you should be able to brief a committee on the difference without notes.
The two definitions, in plain English
Offsetting means buying carbon credits generated outside your value chain and retiring them against your residual emissions. The reduction happens somewhere else, financed by you, and the credit is the receipt.
Insetting means investing in emission reductions or removals inside your own value chain, typically with suppliers, where the reduction is directly linked to the products and services you buy. The reduction happens inside the boundary of your Scope 3 inventory, and the accounting treatment is fundamentally different.
The shorthand from the University of Oxford’s Nature-based Insetting Initiative is useful: insetting is what you do with the supply chain you have; offsetting is what you do with the supply chain you do not have.
What the GHG Protocol Land Sector Standard actually says
The GHG Protocol Land Sector and Removals Standard, finalised in 2024 after a multi-year pilot, sets the rules for how land-based emission reductions and removals enter corporate inventories. The Standard distinguishes between inventory accounting (Scope 1, 2, and 3) and project or intervention accounting (a separate methodology for crediting).
For insetting, the practical implication is that supplier-level interventions, when properly measured and attributed, can reduce your Scope 3 category 1 (purchased goods and services) emissions in your inventory. The reduction is not a credit retired against the inventory; it is a lower inventory number, period.
For offsetting, the credit is retired separately. It can be reported as a contribution toward a net-zero claim under the SBTi Beyond Value Chain Mitigation framework or as part of a VCMI Carbon Integrity claim, but it does not lower the inventory number.
A practical consequence: if your Science Based Target requires a 50% absolute reduction in Scope 3 emissions by 2030, insetting moves you toward the target. Offsetting does not. This single point of difference reshapes the procurement decision.
When insetting counts toward Scope 3 (and when it does not)
Insetting counts toward Scope 3 only when several conditions are met:
- The intervention must occur with an entity in your value chain.
- The emissions reduction or removal must be measured against a defensible baseline.
- The reduction must be attributed to your share of that supplier’s output, not double-counted with other buyers.
- It must follow the inventory accounting rules in the GHG Protocol Land Sector Standard, not the project accounting rules used to generate credits.
The most common failure mode is double counting. If your supplier sells the same reduction as a credit on the voluntary market and also reports it to you as a Scope 3 reduction, the math breaks. The Standard requires you to address this risk, typically by purchasing and retiring the supplier-issued credit as part of your inventory or by contractual provisions that prevent the supplier from selling the reduction twice.
When insetting does not count toward Scope 3: when the intervention sits with a supplier you do not buy from, when the baseline is not defensible, when the attribution is unclear, or when the documentation does not survive audit. Those cases default to Beyond Value Chain Mitigation, which is still useful but operates on a different ledger.
The procurement and supplier engagement question
Insetting is harder than offsetting. That is the unfashionable truth most buyers eventually confront. Offsetting is a transaction; insetting is a relationship.
To run an insetting program, you need supplier mapping precise enough to know which farms or facilities sit at which Scope 3 boundary. You need an engagement model that gets suppliers to participate, which usually requires multi-year commitments and shared economics. You need an MRV architecture that measures the right things and produces audit-ready documentation. And you need a contractual structure that prevents double counting and protects both sides.
The trade-off you receive in return is significant. Reductions count against your inventory rather than your residual. Supplier relationships deepen, which protects sourcing continuity. Yield and quality improvements often follow regenerative interventions, which reduces your input cost over time. And the regulatory file, under CSRD, CSDDD, EUDR, and the SBTi FLAG Guidance, is materially stronger.
Choosing the right tool for the right target
A practical decision rule. If your target is a science-based Scope 3 reduction and you operate in a FLAG sector or source FLAG commodities, insetting is the structurally correct tool. If your target is a net-zero claim that includes neutralising hard-to-abate residual emissions outside your value chain, BVCM via high-integrity offsets is the structurally correct tool. Most companies with material Scope 3 exposure need both, in different proportions, sequenced over time.
The sequencing matters. Insetting takes longer to stand up but produces a permanent reduction in the inventory. Offsetting can be transacted faster but does not change the inventory and now sits under tighter claim restrictions. Treat them as complementary tools with different jobs, not as substitutes. The Accountability Framework Initiative and the IUCN Global Standard for Nature-based Solutions both provide useful guardrails for the insetting side, with biodiversity, human rights, and benefit-sharing requirements that go beyond carbon math.
If you are mapping a Scope 3 reduction roadmap and need to scope which interventions count toward your inventory versus which sit in Beyond Value Chain Mitigation, the carbon and sustainability experts at Carbon Credit Capital can help you structure a nature-based supply chain investment program that fits your FLAG exposure, your target architecture, and your audit horizon. Schedule a consultation.
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