As AI continues to scale rapidly, the environmental cost of powering massive data centres is becoming increasingly urgent. Machines require substantial amounts of electricity and water to stay cool, and a significant portion of this energy comes from fossil-fuel sources.
Scientists at UC Riverside’s Bourns College of Engineering, led by Professors Mihri and Cengiz Ozkan, have proposed a novel solution called Federated Carbon Intelligence (FCI). Their system doesn’t just prioritise low-carbon energy; it also monitors the health of servers in real-time to decide where and when AI tasks should be run.
Using simulations, the team found that FCI could reduce carbon dioxide emissions by up to 45 percent over five years and extend the operational life of hardware by about 1.6 years.
Their model takes into account server temperature, age and physical wear, and dynamically routes computing workloads to optimise both environmental and machine-health outcomes.
Unlike other approaches that only shift workloads to regions with cleaner energy, FCI also addresses the embodied emissions of manufacturing new servers. Keeping current hardware running longer and more efficiently helps reduce the carbon footprint associated with production.
If adopted by cloud providers, this adaptive system could mark a significant milestone in the sustainable development of AI infrastructure, one that aligns compute demand with both performance and ecological goals. The researchers are now calling for pilots in real data centres.
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