{"id":35755,"topic":"ai","source":"GovTech","title":"UW Researchers Build AI to Calculate Carbon Footprints of Devices - GovTech","url":"https://www.govtech.com/education/higher-ed/uw-researchers-build-ai-to-calculate-carbon-footprints-of-devices","url_hash":"da5ba94a10135743dab66e43a7d4bcdccf4d9d4b","author":"","summary":"UW Researchers Build AI to Calculate Carbon Footprints of Devices GovTech","content":"It's even designed to teach us about our carbon footprints.\nFor the past three years, researchers have been working on an AI tool that can quickly calculate the carbon footprints of digital devices, like your cellphone. They say consumers can use the tool to make more sustainable purchases, and companies can use it to develop more sustainable technology.\n“The grand vision of both this work and others that we've been doing in this space are, can we get to a point where your carbon footprint is as easy to find as a nutrition label?” said Zhihan Zhang, a Ph.D. student on the research team behind the tool.\nThe tool determines devices’ carbon footprints by conducting what are called life cycle assessments, according to researchers’ study published on June 12.\nThese assessments follow devices from the very beginning of their lives, when they are something like copper ore buried underground, to the end of their lives, when they are no longer used. This process considers the energy and resources required to harvest raw materials, transport those materials, manufacture devices and eventually use them.\n“If you want to model the carbon footprint of a mobile phone, you need to collect information for every single component in the system,” Zhang said. “But if you look at a phone, there are so many components here. Like, you have display, you have wire connected to a display, you have battery, you have so many silicon chips in it.”\nLife cycle assessments are difficult to conduct manually, since they require information from manufacturers, suppliers, engineers and other agencies involved in each step of the production process.\n“This is a really time-consuming data collection process,” Zhang said.\nResearchers developed this tool to simplify things. It's a multimodal multiagent AI system, which means it can sift through various types of data, including text and images. It also employs two AI agents, communicating with one another and building upon the other's findings.\nThe system examines publicly available information, like product descriptions, guides released by electronics companies and existing life cycle assessment databases. Working together, the AI agents can compile information about the carbon emissions associated with a digital device in under one minute, researchers said.\nThe AI tool has a 5 percent to 19 percent error rate, which is comparable to such assessments conducted by humans, according to a June 12 news release from UW.\nWhen it comes to calculating the carbon emissions of materials not included in existing life cycle assessment databases, humans are much more prone to error, researchers said. By comparing devices that include similar materials, this AI tool can produce a close-to-accurate figure much faster, they explained.\nThe AI tool’s average error rate was 23 percent, compared with the 143 percent error rate by its human counterparts.\nThe UW news release addressed skepticism surrounding the researchers' decision to use AI for sustainability.\n“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 release said.\nAdditionally, researchers said the carbon footprint of each device only needs to be calculated once.\n“When we do this process of coming up with a carbon footprint for a particular device, that's actually something that we only have to do once with our system,” said Vikram Iyer, assistant professor at the Paul G. 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They say consumers can use the tool to make more sustainable purchases, and companies can use it to develop more sustainable technology.\n“The grand vision of both this work and others that we've been doing in this space are, can we get to a point where your carbon footprint is as easy to find as a nutrition label?” said Zhihan Zhang, a Ph.D. student on the research team behind the tool.\nThe tool determines devices’ carbon footprints by conducting what are called life cycle assessments, according to researchers’ study published on June 12.\nThese assessments follow devices from the very beginning of their lives, when they are something like copper ore buried underground, to the end of their lives, when they are no longer used. This process considers the energy and resources required to harvest raw materials, transport those materials, manufacture devices and eventually use them.\n“If you want to model the carbon footprint of a mobile phone, you need to collect information for every single component in the system,” Zhang said. “But if you look at a phone, there are so many components here. Like, you have display, you have wire connected to a display, you have battery, you have so many silicon chips in it.”\nLife cycle assessments are difficult to conduct manually, since they require information from manufacturers, suppliers, engineers and other agencies involved in each step of the production process.\n“This is a really time-consuming data collection process,” Zhang said.\nResearchers developed this tool to simplify things. It's a multimodal multiagent AI system, which means it can sift through various types of data, including text and images. It also employs two AI agents, communicating with one another and building upon the other's findings.\nThe system examines publicly available information, like product descriptions, guides released by electronics companies and existing life cycle assessment databases. Working together, the AI agents can compile information about the carbon emissions associated with a digital device in under one minute, researchers said.\nThe AI tool has a 5 percent to 19 percent error rate, which is comparable to such assessments conducted by humans, according to a June 12 news release from UW.\nWhen it comes to calculating the carbon emissions of materials not included in existing life cycle assessment databases, humans are much more prone to error, researchers said. By comparing devices that include similar materials, this AI tool can produce a close-to-accurate figure much faster, they explained.\nThe AI tool’s average error rate was 23 percent, compared with the 143 percent error rate by its human counterparts.\nThe UW news release addressed skepticism surrounding the researchers' decision to use AI for sustainability.\n“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 release said.\nAdditionally, researchers said the carbon footprint of each device only needs to be calculated once.\n“When we do this process of coming up with a carbon footprint for a particular device, that's actually something that we only have to do once with our system,” said Vikram Iyer, assistant professor at the Paul G. 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By comparing devices that include similar materials, this AI tool can produce a close-to-accurate figure much faster, they explained.\nThe AI tool’s average error rate was 23 percent, compared with the 143 percent error rate by its human counterparts.\nThe UW news release addressed skepticism surrounding the researchers' decision to use AI for sustainability.\n“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 release said.\nAdditionally, researchers said the carbon footprint of each device only needs to be calculated once.\n“When we do this process of coming up with a carbon footprint for a particular device, that's actually something that we only have to do once with our system,” said Vikram Iyer, assistant professor at the Paul G. 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This process considers the energy and resources required to harvest raw materials, transport those materials, manufacture devices and eventually use them.\n“If you want to model the carbon footprint of a mobile phone, you need to collect information for every single component in the system,” Zhang said. “But if you look at a phone, there are so many components here. Like, you have display, you have wire connected to a display, you have battery, you have so many silicon chips in it.”\nLife cycle assessments are difficult to conduct manually, since they require information from manufacturers, suppliers, engineers and other agencies involved in each step of the production process.\n“This is a really time-consuming data collection process,” Zhang said.\nResearchers developed this tool to simplify things. It's a multimodal multiagent AI system, which means it can sift through various types of data, including text and images. It also employs two AI agents, communicating with one another and building upon the other's findings.\nThe system examines publicly available information, like product descriptions, guides released by electronics companies and existing life cycle assessment databases. Working together, the AI agents can compile information about the carbon emissions associated with a digital device in under one minute, researchers said.\nThe AI tool has a 5 percent to 19 percent error rate, which is comparable to such assessments conducted by humans, according to a June 12 news release from UW.\nWhen it comes to calculating the carbon emissions of materials not included in existing life cycle assessment databases, humans are much more prone to error, researchers said. 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They say consumers can use the tool to make more sustainable purchases, and companies can use it to develop more sustainable technology.\n“The grand vision of both this work and others that we've been doing in this space are, can we get to a point where your carbon footprint is as easy to find as a nutrition label?” said Zhihan Zhang, a Ph.D. student on the research team behind the tool.\nThe tool determines devices’ carbon footprints by conducting what are called life cycle assessments, according to researchers’ study published on June 12.\nThese assessments follow devices from the very beginning of their lives, when they are something like copper ore buried underground, to the end of their lives, when they are no longer used. This process considers the energy and resources required to harvest raw materials, transport those materials, manufacture devices and eventually use them.\n“If you want to model the carbon footprint of a mobile phone, you need to collect information for every single component in the system,” Zhang said. “But if you look at a phone, there are so many components here. Like, you have display, you have wire connected to a display, you have battery, you have so many silicon chips in it.”\nLife cycle assessments are difficult to conduct manually, since they require information from manufacturers, suppliers, engineers and other agencies involved in each step of the production process.\n“This is a really time-consuming data collection process,” Zhang said.\nResearchers developed this tool to simplify things. It's a multimodal multiagent AI system, which means it can sift through various types of data, including text and images. 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