UC Riverside researchers design an AI routing system that cuts emissions up to 45% while extending server life by 1.6 years.
Artificial intelligence may be powering the future, but it’s burning through the planet to get there.Every chatbot response, video recommendation, and AI-generated image is backed by massive data centers consuming enormous amounts of electricity and water.
Most of that power still comes from fossil fuels, driving up emissions as AI adoption accelerates.With millions of computers running nonstop, the environmental cost is rising so sharply that data centers now consume more energy than entire nations.And as AI systems heat up and age, their power demands and their carbon footprint grow even faster.A new study from the University of California, Riverside, proposes a solution that tackles both the pollution and the hardware strain behind AI’s runaway energy use.Carbon-smart AI routingThe researchers developed a system called Federated Carbon Intelligence , designed to reduce emissions from large data centers while extending the life of the servers that run AI models.Unlike current approaches that merely time-shift workloads to cleaner energy periods, FCI goes deeper. It combines environmental data with real-time knowledge of each server’s physical condition to route tasks intelligently.“Our results show that sustainability in AI cannot be achieved by focusing on clean energy alone,” said Mihri Ozkan. “AI systems age, they heat up, and their efficiency changes over time—and these shifts have a measurable carbon cost.”Using simulations, the team showed that FCI could cut carbon dioxide emissions by up to 45% over five years. It could also add 1.6 years to the operational life of a server fleet.Extending machine lifespansThe system works by continuously monitoring the age, temperature, and wear of servers. By avoiding machines that are already stressed or deteriorating, FCI prevents breakdowns and reduces the need for energy- and water-intensive cooling.“By integrating real-time hardware health with carbon-intensity data, our framework learns how to route AI workloads in a way that cuts emissions while protecting the long-term reliability of the machines themselves,” Ozkan said.This dual focus—cleaner energy use and smarter hardware management—addresses a major gap in today’s sustainability strategies. While most efforts emphasize renewable power, the Ozkans point out that replacing worn-out servers carries its own carbon cost.Server manufacturing generates significant embodied emissions. By extending hardware lifespan, FCI reduces the hidden environmental load of producing new machines.“We reduce operational emissions in real time, but we also slow down hardware degradation,” said Cengiz Ozkan. “By preventing unnecessary wear, we reduce not only the energy used today but also the environmental footprint of tomorrow’s hardware production.”The researchers say the system dynamically decides where and when each AI task should be processed, drawing on constantly updated data about workload demands, electricity carbon intensity, and server health.Adopting such adaptive frameworks wouldn’t require new equipment. “Establishing the adaptive framework would not require new equipment, just smarter coordination across the systems already in place,” Mihri Ozkan said.The team now hopes to partner with cloud providers to test FCI in operating data centers, a step they say is urgent as demand surges. The study is published in the journal MRS Energy and Sustainability.
Carbon Intelligence Data Centers Energy Efficiency Server Lifespan Sustainable Computing UC Riverside Workload Routing
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