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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchAI can help cut emissions and improve climate resilience, but it cannot solve the climate crisis on its own. Its climate value depends on whether useful applications are adopted at scale, powered by lower-carbon electricity, assessed across their full lifecycle and governed to limit rebound effects. The International Energy Agency (IEA) estimates substantial potential emissions reductions in one 2035 scenario, while also warning that data centers add emissions and that policy remains essential.
Where AI can help reduce emissions
AI can analyze data and adjust complex systems, which may help operators use energy and resources more efficiently. The IEA identifies several practical areas where those capabilities could contribute to mitigation:
| Area | Potential contribution | What to keep in view |
|---|---|---|
| Electricity systems | Optimize generation, transmission, storage and demand. | The emissions benefit depends on how the electricity is generated and whether optimization produces net reductions across the system. |
| Buildings | Improve heating, ventilation, air-conditioning and building-management controls. | Efficiency gains should be measured in actual energy use, not inferred from the presence of an AI control system. |
| Transport | Improve routing, operations and vehicle utilization, potentially reducing fuel use. The IEA notes efficiency gains of roughly 5–10% for some transport applications. | That range applies to some applications, not all transport or every deployment. Changes in travel behavior can offset operational savings. |
| Climate monitoring and innovation | Support monitoring and forecasting, and speed climate-relevant innovation such as materials discovery. | A useful forecast or discovery is not itself an emissions reduction; its climate impact depends on whether it leads to effective action or deployment. |
These uses can support mitigation in energy, industry, transport, buildings, agriculture, forestry, land use and waste—the sectors covered by the IPCC’s mitigation scope. AI can also support adaptation and resilience, for example through improved monitoring and forecasting. Those contributions should be assessed separately from emissions reductions: better information can help decisions, but does not by itself establish that emissions fell or that communities became more resilient.
How large could the emissions effect be?
The IEA’s 2025 widespread-adoption case estimates that AI could enable 1,400 Mt of CO2 reductions in 2035. This is a scenario estimate, not a measured outcome or a guaranteed forecast. It depends on applications being adopted and on barriers, electricity supply, regulation and rebound effects. The estimate also excludes possible breakthrough discoveries.
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AI’s own electricity-related emissions are part of the picture. The IEA’s 2025 assessment estimates data-center electricity emissions at 180 Mt today, rising by 2035 to 300 Mt in its base case or as much as 500 Mt in its lift-off case.
| IEA figure | What it represents | Qualification |
|---|---|---|
| 1,400 Mt CO2 | Potential emissions reductions in 2035 | IEA 2025 widespread-adoption scenario; not a guaranteed or measured reduction. |
| 180 Mt | Data-center electricity emissions today | IEA 2025 estimate. |
| 300 Mt | Data-center electricity emissions in 2035 | IEA 2025 base-case scenario. |
| Up to 500 Mt | Data-center electricity emissions in 2035 | IEA 2025 lift-off scenario. |
The figures describe different quantities: potential reductions enabled by AI and emissions from data-center electricity. They should not be treated as a guaranteed net balance for AI. A credible assessment needs to establish which emissions are actually avoided, what additional emissions the application causes, and what happens when people and organizations change their behavior.
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For broader context, the United Nations Environment Programme (UNEP) reported 2023 emissions of 15.1 GtCO2e from the power sector, 8.4 GtCO2e from transport, and 6.5 GtCO2e each from agriculture and industry. These sector totals show the scale of the systems in which climate action is needed; they do not measure the share AI can eliminate.
How AI can add to climate pressures
AI systems rely on computing infrastructure and hardware. UNEP’s September 2024 issue note identifies impacts across the lifecycle, including electricity use, water, mineral consumption, emissions and electronic waste. It also highlights indirect, systemic effects when AI changes economic activity or behavior. There is no single universally accepted carbon-footprint figure for AI: the result depends on what is counted and how impacts across the lifecycle are measured.
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Rebound effects can erase some gains
An efficiency improvement does not guarantee a net climate benefit if it encourages more use or shifts activity toward higher-emissions options. The IEA illustrates this risk with autonomous cars: if they reduce public-transport use, some efficiency gains may be offset. That is why assessments should examine the whole system and likely behavior changes, not only the performance of the AI tool.
Infrastructure can shape the outcome
Data-center electricity emissions vary across the IEA’s scenarios, and AI’s wider lifecycle also includes water, minerals and hardware disposal. Evaluating an application only by the energy it saves at the point of use leaves out these other impacts and the electricity used to run its computing infrastructure.
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How to judge whether an AI application is a climate solution
Assess a specific application against a non-AI alternative, or against the current practice it is intended to improve. A useful comparison asks:
- Net lifecycle emissions: Do measured avoided emissions exceed emissions from computing, hardware and implementation over the relevant lifecycle?
- Additionality: Does AI deliver a climate benefit beyond what a simpler tool, established practice or existing policy would achieve?
- Cost and speed to scale: How much does deployment cost, and how quickly can it deliver verifiable benefits?
- Infrastructure and data: What data, computing resources, electricity, water and hardware does it require?
- Reliability: Does it work dependably in the conditions where it will be used, and can its climate impact be measured?
- Equity and access: Who can use or benefit from it, and who bears its resource, infrastructure or waste impacts?
- Rebound and lock-in risk: Could the application increase demand, displace a lower-emissions option or make a high-emissions pathway harder to change?
These checks apply across mitigation and adaptation. For mitigation, consider the relevant sectors—energy, industry, transport, buildings, agriculture, forestry, land use and waste—and compare actual emissions outcomes. For resilience, examine whether better monitoring or forecasting improves decisions and outcomes, rather than treating the technology’s capability as proof of adaptation.
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Potential gains depend on choices beyond the technology itself: how quickly useful applications are adopted, what electricity supplies the computing infrastructure, how emissions and lifecycle impacts are measured, and whether rules limit harmful rebound or lock-in. The IEA cautions that barriers and rebound effects can erode modeled savings and that climate action is still needed across all major emitting sectors.
As the IEA puts it in Energy and AI (2025): “AI can be a tool in reducing emissions, but it is not a silver bullet and does not remove the need for proactive policy.” AI is best understood as a possible tool within climate action—not a substitute for clean energy, emissions cuts across sectors or effective policy.
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