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OpenAI Hits Pause on $40B UK AI Project: Energy Costs Shake Data Center Economics

ChatGPT developer OpenAI has paused its flagship UK data center project, known as “Stargate UK,” citing high energy costs and regulatory uncertainty. The project was part of a broader £31 billion ($40+ billion) investment plan aimed at expanding artificial intelligence (AI) infrastructure in the country.

The initiative was designed to deploy up to 8,000 GPUs initially, with plans to scale to 31,000 GPUs over time. It was aimed to boost the UK’s “sovereign compute” capacity. This means building local infrastructure to support AI development and reduce reliance on foreign systems.

However, the company has now paused development. An OpenAI spokesperson stated that they:

“…support the government’s ambition to be an AI leader. AI compute is foundational to that goal – we continue to explore Stargate UK and will move forward when the right conditions such as regulation and the cost of energy enable long-term infrastructure investment.”

Energy Costs Are Now a Core Constraint

The main issue is energy. AI data centers require large amounts of electricity to run GPUs and cooling systems.

In the UK, industrial electricity prices are among the highest in developed markets. Recent estimates show costs at around £168 per megawatt-hour, compared to £69 in France and £38 in Texas. This gap creates a major disadvantage for large-scale data center investments.

AI workloads are especially power-intensive. A single large data center can consume as much electricity as tens of thousands of homes. As AI adoption grows, this demand is rising quickly.

Globally, the International Energy Agency estimates that data centers could consume over 1,000 terawatt-hours (TWh) of electricity by 2030, up sharply from about 415 TWh in 2024. This growth is largely driven by AI. 

data center electricity use 2035
Source: IEA

The result is clear. Energy is no longer just a cost. It is a key factor in where AI infrastructure gets built.

Regulation Adds Another Layer of Risk

Energy is only part of the challenge. Regulation is also slowing investment. In the UK, uncertainty around AI rules, especially copyright laws for training data, has created hesitation among companies.

Earlier proposals to allow AI firms to use copyrighted content were withdrawn after backlash. This left companies without clear guidance on compliance.

For large infrastructure projects, this uncertainty increases risk. Data centers require billions in upfront investment. Companies need stable rules before committing capital.

Planning delays and grid connection timelines also add friction. These factors increase both cost and project timelines.

Together, energy costs and regulatory uncertainty create a difficult environment for hyperscale AI infrastructure.

OpenAI’s Global Infrastructure Expands, But More Selectively

Despite the pause, ChatGPT-maker is still expanding globally. The company is investing heavily in AI infrastructure through partnerships with Microsoft, NVIDIA, and Oracle. It is also linked to a much larger $500 billion “Stargate” initiative in the United States, focused on building next-generation AI data centers.

At the same time, the company faces rising costs. Reports suggest OpenAI could lose billions of dollars annually as it scales infrastructure to meet demand.

This reflects a broader industry shift. AI is becoming more like energy or telecom infrastructure. It requires large capital investment, long timelines, and stable operating conditions.

The pause also highlights a deeper issue. AI growth is increasing pressure on energy systems and the environment.

The Hidden Carbon Cost Behind Every AI Query

ChatGPT and similar tools rely on large data centers. These facilities already account for about 1% to 1.5% of global electricity use. Projections for their energy use vary widely due to various factors. 

Each individual query may seem small. A typical ChatGPT request can use about 0.3 watt-hours of electricity, which is relatively low. However, usage at scale changes the picture.

ChatGPT now serves hundreds of millions of users. Even small energy use per query adds up quickly. Training models is even more energy-intensive. For example, training GPT-3 required about 1,287 megawatt-hours of electricity and produced roughly 550 metric tons of CO₂.

chatgpt environmental footprint

Newer models are even larger. Some estimates suggest training advanced models like GPT-4 could emit up to 15,000 metric tons of CO₂, depending on the energy source.

At the system level, the impact is growing fast. AI systems could generate between 32.6 and 79.7 million tons of CO₂ emissions in 2025 alone. By 2030, AI-driven data centers could add 24 to 44 million tons of CO₂ annually.

AI servers annual carbon emissions
Note: carbon emissions (g) of AI servers from 2024 to 2030 under different scenarios. The red dashed lines in e–g denote the forecast footprint of the US data centres, based on previous literature. Source: https://doi.org/10.1038/s41893-025-01681-y

Looking further ahead, global generative AI emissions could reach up to 245 million tons per year by 2035 if growth continues. These numbers show a clear pattern. Efficiency is improving, but total demand is rising faster.

Big Tech Scrambles to Balance AI Growth and Emissions

OpenAI has not published a detailed standalone net-zero target. However, its operations rely heavily on partners such as Microsoft, which has committed to becoming carbon negative by 2030.

The company has acknowledged that energy use is a real concern. Leadership has pointed to the need for more renewable energy, including nuclear and clean power, to support AI growth.

Across the industry, companies are responding in several ways:

  • Improving model efficiency to reduce energy per query
  • Investing in renewable energy and long-term power contracts
  • Exploring new cooling systems to reduce water and energy use

Efficiency gains are already visible. Some AI systems have reduced energy per query by more than 30 times within a year, showing how quickly technology can improve. Still, total emissions continue to rise because demand is scaling faster than efficiency gains.

The Global AI Infrastructure Race

The pause in the UK highlights a larger trend. AI infrastructure is becoming a global competition shaped by energy, policy, and cost.

Regions with lower energy prices and faster permitting processes have an advantage. The United States and parts of the Middle East are attracting large-scale AI investments due to cheaper power and supportive policies.

At the same time, governments are trying to attract these projects. The UK has pledged billions to support AI growth and improve compute capacity. But this case shows that policy ambition alone is not enough. Companies need reliable energy, clear rules, and predictable costs.

AI’s Next Phase Will Be Decided by Energy, Not Code

The decision by OpenAI does not signal a retreat from AI investment. Instead, it reflects a shift in priorities.

Companies are becoming more selective about where they build infrastructure. They are focusing on locations that offer the right mix of energy access, cost stability, and regulatory clarity.

The UK project may still move forward, but only if conditions improve. For now, the message is clear. The future of AI will not be shaped by technology alone. It will also depend on energy systems, policy frameworks, and long-term investment conditions.

The post OpenAI Hits Pause on $40B UK AI Project: Energy Costs Shake Data Center Economics appeared first on Carbon Credits.

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Where should an SME start with a carbon action plan?

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More and more small and medium-sized businesses are hearing the same question from their larger customers: What is your carbon footprint? That question now travels down entire supply chains, and it arrives next to tender requirements, certification criteria, and rising customer expectations.

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Insetting vs Offsetting: Which Actually Counts Toward Your Scope 3 Targets

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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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Carbon Footprint

Net zero needs nature: a carbon credit guide

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Net zero is often described as a balancing act: cut what you can, account for the rest, and reach zero on the ledger. That framing is useful, but it leaves something out. It treats every tonne of carbon as interchangeable and every route to zero as equally sound, while the science tells a more specific story.

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