Why Gains in Renewable Energy May Not Be Enough to Offset It
Like many people, I have thought about the climate impact of AI mainly in terms of data centres: their electricity use, cooling water, hardware and supporting infrastructure. These impacts matter, but a recent study by Alpine and colleagues (2026) helped me understand why they may not be the main thing to focus on.
The larger question is where AI is being used. AI can make an industry more efficient, lower its costs and expand what it can profitably produce. Its climate impact therefore differs greatly depending on whether it is applied to fossil fuels or renewable energy.
In renewables, AI can improve forecasting, maintenance, generation and grid integration, helping to avoid emissions. In fossil fuels, it can make exploration and extraction cheaper, bring previously uneconomic reserves into production and prolong the life of oil and gas fields. In other words, it can enable more fossil fuel to be extracted and burned.
The study models AI adoption in both sectors at comparable rates. Although its use in renewables helps avoid emissions, this benefit is outweighed by the additional emissions enabled when AI also makes fossil-fuel production more productive. Renewable productivity gains would need to be four to five times greater than fossil-fuel gains for the combined effect to break even (Alpine et al., 2026).
The most realistic response may not be to regulate AI itself. It may be to establish binding, progressively declining limits on fossil-fuel production. AI could still be used to detect methane leaks, prevent spills, improve safety and reduce operational emissions, but increased productivity could not simply lead to increased extraction.
What I take from the study is that AI’s climate impact depends not only on the resources needed to run it, but also on what we use it to make cheaper, faster and more profitable. Data centres remain part of the problem, but the more important question may be what we are asking AI to amplify.
Alpine, W., Geldner, N., Alpine, H., & Chepeliev, M. G. (2026). AI-driven productivity gains enable more CO₂ emissions than they avoid in a global energy–economy model. npj Climate Action, 5, Article 71.
https://doi.org/10.1038/s44168-026-00411-0
Last Updated on August 13, 2026 | Published: August 13, 2026