What AI Can and Cannot Do for the Climate, According to the IEA

The conventional framing on artificial intelligence and climate pits data centre energy consumption against everything else, casting AI as a net liability for the planet. That framing misses the larger number.

According to the International Energy Agency (IEA), broadly deploying AI tools that already exist across the energy sector could cut 1,400 million tonnes of CO2 per year by 2035, roughly three times the carbon footprint of even the most aggressive data centre growth scenarios. The IEA’s April 2025 “Energy and AI” report presents this not as a speculative vision but as a projection grounded in four specific, operational mechanisms running in parts of the energy sector today. The question is whether they scale.

For anyone weighing where AI fits into an energy, infrastructure, or clean technology strategy, understanding where those mechanisms operate and what they need to reach critical mass is now a material question, not a theoretical one. After reading this, you will know whether the climate upside of AI is real, how large it is relative to both the problem and the cost, and what conditions determine whether the benefit actually lands.

The four mechanisms that turn AI into a carbon-reduction tool

The 1.4 gigatonne headline can sound like a slogan until you break it into the operations that produce it. The IEA attributes the figure to four distinct delivery channels, each already deployed somewhere in the sector rather than waiting to be invented. Understanding them individually matters, because each carries a different deployment profile, cost structure, and policy dependency, and the one that applies to your context may not be the one generating the most attention.

Grid optimisation and transmission capacity

AI systems analyse real-time grid data, including line temperature, load, and weather conditions, to work out how much more electricity existing transmission lines can safely carry. That sounds incremental. It is not.

• What AI does: dynamically adjusts how existing lines are loaded, pushing more power through the same physical wires without breaching safety limits.
• Quantified outcome: up to 175 GW of additional transmission capacity unlocked on existing lines in the Widespread Adoption Case.
• Where the impact lands: the power sector, by allowing more renewable generation onto the grid without new fossil-fired plants.

That 175 GW figure is the one that reframes the investment thesis. It means AI can expand the effective capacity of infrastructure that already exists, which changes the economics of renewable integration for anyone weighing the cost of grid upgrades against the return.

The 175 GW figure sits within a broader context of grid infrastructure transformation that is already reshaping how transmission networks are planned and financed, with dynamic line rating only one of several AI-enabled techniques being integrated into system operations.

Power-plant operations and maintenance

Applied to power plants, AI improves predictive maintenance, performance tuning, and dispatch decisions, reducing unplanned outages and squeezing more output from every unit of fuel.

• What AI does: predicts equipment failures before they happen and optimises how plants run.
• Quantified outcome: up to USD 110 billion per year in electricity-sector cost savings by 2035, largely through lower fuel consumption.
• Where the impact lands: fossil-fired generation, where burning less fuel for the same output cuts emissions directly.

USD 110 billion per year The IEA estimates that applying existing AI systems to power-plant operations and maintenance could deliver up to this figure in annual cost savings by 2035, most of it from more efficient plant operation and reduced fuel use.

Demand forecasting and system planning

Better forecasts let system operators avoid over-building capacity and schedule cleaner generation more precisely, trimming waste across entire sectors.

• What AI does: improves demand-side and sectoral energy modelling.
• Quantified outcome: roughly 8 exajoules (EJ) of energy demand reduction in industry and 1.5 EJ in passenger road transport by 2035.
• Where the impact lands: industry and transport, the two sectors where the largest AI-enabled savings appear.

Curtailment reduction and renewable integration

Curtailment happens when wind or solar generation is switched off because the grid cannot absorb it. Every curtailed megawatt-hour is clean power wasted and often backfilled by fossil fuel.

• What AI does: detects curtailment events in near real time and coordinates distributed resources to absorb the surplus.
• Quantified outcome: a higher share of renewable generation actually delivered to consumers.
• Where the impact lands: the renewable fleet, by reducing the need for fossil-fuel backup generation.

Put the four together and the aggregate stops feeling asserted. Each lever pulls on a different part of the system, and the 1.4 gigatonne total is what they add up to when deployed broadly.

What 1,400 million tonnes actually means in context

At face value, 1.4 gigatonnes a year is a staggering number, comfortably larger than the annual emissions of many mid-sized economies. But a figure that large only becomes useful once you place it against the reference points that give it meaning.

Start with the framing itself. The 1.4 Gt estimate comes from the IEA’s Widespread Adoption Case, an exploratory scenario in which today’s AI tools are deployed broadly across end-use sectors. It is not a forecast, and the IEA does not present it as one. It is what becomes possible under favourable conditions.

Against the data centre debate, AI comes out ahead. The IEA calculates that AI-enabled savings under widespread adoption are three times larger than total data-centre emissions in its Lift-Off Case and four times larger in its Base Case. On the net-footprint question, the direction is clear: broadly deployed AI removes more carbon than the computing behind it emits.

The conventional framing relies on data centre energy demand as the primary variable in the AI-climate equation, but the IEA’s April 2025 report shifts that calculus by quantifying the efficiency gains on the other side of the ledger, gains that outpace the consumption growth by a factor of three or more under widespread adoption conditions.

Then comes the constraint that reframes everything. That same 1.4 Gt amounts to only about 4% of total energy-sector emissions in 2035 under current policy settings.

The IEA states plainly that AI-driven reductions, while real, are “far smaller than what is needed to address climate change.”

The 4% figure is not a dismissal. It is a calibration. It tells you AI is a genuine, measurable tool in the decarbonisation toolkit, and it tells you anyone selling AI as the primary climate answer is overstating the case.

For scale, consider that global clean energy investment reached roughly USD 2.2 trillion in 2025 out of USD 3.3 trillion in total energy spending, according to the IEA’s “World Energy Investment 2025.” The structural reforms and capital flows running alongside AI are doing more of the heavy lifting than the AI itself. Used accurately, the 4% at 2035 scale is still a large absolute contribution. Used loosely, it becomes a greenwashing prop.

Why the benefit is not guaranteed, and what puts it at risk

Here is the assumption worth examining. Reading the two sections above, it is easy to treat “widespread adoption” as the natural default, the place the world drifts toward if left alone. It is not. It is a fragile condition dependent on choices that are still being made.

The IEA is explicit that the Widespread Adoption Case is exploratory, not a baseline. The report flags real uncertainty around deployment rates, behavioural responses, and policy decisions, any of which could pull the outcome well below the headline number.

Three risks stand out:

• Deployment pace uncertainty: the “widespread” in Widespread Adoption remains aspirational for much of the corporate sector.
• Generative AI consumption growth: rapid expansion of energy-hungry AI workloads could erode or outweigh efficiency gains, depending on whether AI is steered toward efficiency or simply piled on as new demand.
• Policy environment weakness: the largest near-term emissions lever is not AI at all.

On that last point, the IEA’s “World Energy Outlook 2024” frames doubling the global rate of energy-efficiency improvement as the single largest near-term emissions measure available, yet notes this target stays out of reach under current policies. That positions AI as a tool within a system, not a substitute for fixing the system.

The deployment gap is measurable. Research from the Infosys Knowledge Institute found that, as of 30 September 2026, 42% of enterprise-scale organisations had deployed AI at scale while 40% remained in exploratory or experimental phases.

That near-even split tells you the “widespread” scenario is still hypothetical for roughly half the corporate sector. In practical terms, the 1.4 Gt figure is a ceiling reached under good conditions, not an expected outcome under current ones.

The IEA warns that without stronger policies, investment shifts, and governance, the potential AI benefits “will not materialise at scale.”

For anyone treating AI as an emissions-reduction lever, the gap between exploratory and at-scale deployment is the gap between a strategic option and a realised return.

Governing AI deployment so the climate benefit actually lands

If widespread adoption is a choice rather than a default, the useful question becomes what “getting it right” looks like in practice. The research points to a set of deliberate design decisions, not vague policy aspiration.

The foundational principle is straightforward: energy and carbon performance must be built into AI deployment from the outset, not bolted on afterwards. The industry’s standard evaluation criteria for AI decisions tend to centre on latency and cost, with energy consumption per workload and the carbon implications of training and inference receiving far less weight.

Location matters more than it appears. Where an AI workload runs has a direct bearing on its carbon impact, since the proportion of clean power on the grid differs considerably from one region to another. That makes the choice of deployment region a decision with meaningful, lasting consequences for an organisation’s carbon position.

The IEA’s guidance translates into four priorities, in rough order of implementation:

1. Require energy and carbon performance to be factored in at the design stage, so that efficiency considerations shape deployment decisions before they are locked in rather than being addressed retrospectively.
2. Select deployment locations based on grid carbon intensity, hosting workloads where clean power is available.
3. Favour AI applications directed at efficiency in buildings, industry, and transport, the areas where the largest quantified savings appear.
4. Implement monitoring and disclosure frameworks for AI-related energy footprints, creating accountability.

That fourth point carries the sharpest edge for any organisation with stated carbon commitments. If you cannot measure your AI-related energy consumption, you cannot manage it, and as disclosure requirements tighten globally, that measurement gap stops being a technical oversight and becomes direct exposure to credibility risk.

AI governance frameworks that account for energy and carbon performance are still nascent in most jurisdictions, and the gap between disclosure requirements in leading markets and those in developing economies creates uneven pressure on where workloads get deployed.

All of this plays out against the USD 2.2 trillion clean energy investment backdrop of 2025, where clean technologies drew roughly twice the capital directed at fossil fuels. Governance decisions made now determine whether AI reinforces that shift or quietly adds to demand. Reading a deployment through these four principles is how you judge whether it is set up to realise the climate upside or undermine it.

A tool that works, within a system that still needs fixing

The tension at the heart of this is not a contradiction to resolve but a reality to hold. AI delivers real, quantified, mechanistically grounded emissions reductions. It is also, on its own, insufficient as a climate solution. Both things are true, which makes AI an accurate description of a meaningful contributor and a poor candidate for silver bullet.

Structural energy market shifts driven by renewable cost reductions are the larger system within which AI efficiency gains operate, and the pace of fossil fuel displacement in generation determines both how much curtailment exists to be managed and how much value AI-enabled grid optimisation can unlock.

Where the outcome lands between best case and worst case comes down to three variables: how broadly AI is deployed, how strong the surrounding policy is, and how well its energy footprint is governed.

So the better question is not whether AI is good or bad for the climate. It is whether the conditions that make its benefit real are being met in the contexts you actually care about, and whether you can tell the difference.

This article is for informational purposes only and should not be considered financial advice. Investors should conduct their own research and consult with financial professionals before making investment decisions. Financial projections and scenarios cited are exploratory and subject to change based on policy, deployment, and market developments.