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University of Washington researchers develop AI agents to estimate carbon footprints of electronic devices
UW researchers built an AI system to estimate device carbon footprints in one minute. It achieves a 5% to 19% error rate, matching human expert accuracy.
University of Washington researchers built an AI system that estimates the carbon footprint of manufacturing electronic devices in about a minute - work that typically takes human experts days or months. The system, which uses two AI agents working together to comb through public data, achieves an average error rate of 5% to 19%, comparable to expert-conducted life cycle assessments. The findings were published June 12 in Nature Electronics.
"Recent studies have shown that people are willing to pay more for more sustainable devices," said senior author Vikram Iyer, a UW assistant professor in the Paul G. Allen School of Computer Science & Engineering. "So there's growing demand for this information. But a phone, for example, is made of hundreds of chips and other components, and producing each of those causes varying amounts of emissions. Since that data isn't public or sometimes not even measured, human experts can spend days, even months manually gathering information for LCA."
How the two-agent system works
The system assigns distinct roles to two AI agents that operate in a feedback loop. One agent acts as an analyst, defining what information needs to be gathered, how it fits together, and reviewing results for accuracy. The second agent functions more like an engineer, scraping publicly available sources - including FCC databases, iFixit posts, spreadsheets, and internal device images - for data on electronic components. The analyst reviews what the engineer finds and can send it back for additional searches, repeating until the picture is complete. The agents then reference LCA databases to convert the full parts list into carbon estimates.
"Some of our previous research made me curious about how LCA experts perform environmental assessments - and whether that process could be automated," said lead author Zhihan Zhang, a UW doctoral student in the Allen School. "So we interviewed LCA experts to understand the bottlenecks firsthand, and then built a system that emulates these interactions with two AI agents. Each of them mimics different roles in the LCA process."
This approach to AI Agents & Automation mirrors a broader shift in how AI for Science & Research is being applied - not by replacing experts, but by handling the tedious data-gathering that consumes their time.
Skipping detailed data with a nearest-neighbors approach
The team also developed a shortcut for common devices. For laptops and smartphones that already have public carbon footprint reports, they found that products with similar specifications - screen size, processor type - cluster around similar carbon values. Only a handful of companies manufacture the specialized parts inside these devices. An unknown device's footprint can therefore be estimated as a weighted average of similar products, bypassing the need for exhaustive component-by-component data collection.
This method proved especially useful for materials absent from LCA databases. When estimating missing emissions factors, the team's nearest-neighbors approach achieved an average error of 23%. Human experts, by comparison, had an average error of 143% when picking the single closest database entry.
"We tried this 'nearest-neighbors' approach and found that for materials, it's actually better than the standard approach of a human picking the single closest entry," Zhang said.
Managing the system's own energy footprint
The researchers took deliberate steps to limit the energy cost of running the system. They use small AI models rather than general-purpose large models, which reduces computational demands. The process also begins by checking whether a device's emissions have already been estimated - if so, it stops there. When the system does need to call its AI models repeatedly, the energy used is roughly equivalent to brewing a cup of tea.
"A lot of big companies have sustainability teams that perform these LCAs," Iyer said. "Our hope is that automating this will actually free up their time, so they can spend their time reducing the carbon footprint of the products themselves, instead of hunting down elusive stats."
Why this matters for science and research professionals
For researchers and analysts who conduct environmental assessments, the bottleneck has never been the analytical framework - it's the data collection. This system automates the most labor-intensive phase of LCA work, collapsing days of manual searching into roughly a minute. The accuracy parity with human experts, combined with the dramatically faster turnaround, points toward a practical tool rather than a proof-of-concept. The team plans to collaborate with companies to integrate the system into existing sustainability workflows, which could shift how corporate LCA teams allocate their time and expertise.