If you shop on Google Flights, you get a quick comparison for different itineraries: One flight’s carbon emissions may be average, while another’s are 14% higher. But if you go shopping for a new laptop, you likely won’t find quick, comprehensible information on different models’ sustainability bonafides, despite the notable environmental impacts of producing and discarding electronics. In part, that’s because understanding a device’s emissions is difficult and time-consuming, even for experts.
University of Washington researchers developed an artificial intelligence system that automatically estimates the environmental impacts of making different electronic devices. The system uses AI agents — programs that perform tasks autonomously — to comb through publicly available data and conduct life cycle assessments, or LCAs. The system achieves an average error rate of 5%-19%, similar to the accuracy of LCAs conducted by experts.
The team published its findings 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. Instead we designed multiple AI agents that work together to automatically find this data and produce comparable estimates in about a minute.”
AI agents have recently grown increasingly capable of performing complex tasks. Today's agents can search the web and pull information about electronic parts from product descriptions, images and documents.
“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.”
One agent acts as a sort of analyst, defining what information needs to be gathered and how it will fit together. It also reviews results for accuracy. The second agent is more like an engineer. It scrapes publicly available data for information on an electronic device’s components. That might entail sifting through spreadsheets, or looking up images of the insides of devices and taking chip information from them — including from sources not typically used for LCAs, such as FCC databases and posts on iFixit .
The two agents work in a loop. The first sets the scope, the second gathers information. The first then looks that information over and might send the second agent searching again, and so on. The agents then reference LCA databases to convert the complete list of parts to carbon estimates.
The team also developed a new method to bypass this detailed data collection and directly estimate carbon footprints. For common devices like laptops and smartphones with publicly available carbon footprint reports, they found that products with similar specs like screen size and processors clustered around similar carbon values, because only a handful of companies make specialized parts for all these devices. So an unknown device's footprint can be represented as a weighted average of similar products.
They also use this to estimate the carbon for materials not in LCA databases. For example, a new type of sustainable plastic could be estimated based on plastics with similar properties and chemistry.
“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,” said Zhang. “When estimating missing emissions factors in a test, the average error for our method was 23%. Human experts had an average error of 143%.”
The authors note that while the aim of the system is to help reduce carbon emissions overall, running AI models requires energy, so they’ve taken several steps to mitigate its impact. They use small AI models that aren’t as energy-intensive as general-purpose models. They also start the process by running a search to see if the device’s estimated emissions have already been calculated. If so, it can stop there. If the system does need to call its AI models repeatedly, estimating a device’s carbon footprint is currently on par with the emissions generated by brewing a cup of tea.
The team plans to collaborate with companies in the future to help automate their workflows.
“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.”
Co-authors include Alexander Metzger , a UW student in the Allen School;, Felix Hähnlein , a UW postdoctoral researcher in the Allen School; Zachary Englhardt , a UW doctoral student in the Allen School; Shwetak Patel , a UW professor in the Allen School; Yuxuan Mei of Wesleyan University, who completed this research as a UW doctoral student in the Allen School; Tingyu Cheng of the University of Notre Dame; Gregory D. Abowd of Northeastern University; and Adriana Schulz of Brown University, who completed this research as a UW assistant professor in the Allen School.
This research was funded by Amazon Research Awards and the National Science Foundation. Zhang was supported by the Google PhD Fellowship .
For more information, contact Iyer at vsiyer@uw.edu and Zhang at zzhihan@cs.washington.edu .
Nature Electronics
Sustainability assessment using multimodal artificial intelligence agents'
12-Jun-2026