Will AI accelerate or delay the race to net-zero emissions?

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser . In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.Driverless taxis run by artificial intelligence use less fuel than conventional ones, but the wider climate impacts are unclear. Credit: Allen J. Schaben/Los Angeles Times/Getty.

AI should be integrated into these pathways, along with the global shocks and technological breakthroughs that might accompany it. This would require major work, including incorporating expertise from the AI community, rethinking each of the pathway narratives and exploring whether new ones need to be added. Could AI take the world to a more radically green future, or a more dystopian one? What factors define those outcomes? How plausible are they? Scenarios can help to narrow down answers.

Comparing and replicating scenarios will be key to improving them as AI systems are rolled out. Researchers should regularly run comparisons between different models for direct and indirect AI-related emissions, coordinated through platforms used by the climate community, such as the Energy Modeling Forum and the Integrated Assessment Modeling Consortium.

Because of the potential for AI to either reduce or increase energy demand, researchers must update models that represent societal demand for energy, as well as explore how this demand will change as AI technologies evolve. Scenarios with varying resolutions might be released on different time frames. For example, coarse-resolution scenarios might be updated every few months; more-detailed scenarios could be released every 2–3 years.

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