Towards Autonomous Sustainability Assessment via Multimodal AI Agents

• URL: http://arxiv.org/abs/2507.17012v1
• Date: Tue, 22 Jul 2025 20:49:25 GMT
• Title: Towards Autonomous Sustainability Assessment via Multimodal AI Agents
• Authors: Zhihan Zhang, Alexander Metzger, Yuxuan Mei, Felix Hähnlein, Zachary Englhardt, Tingyu Cheng, Gregory D. Abowd, Shwetak Patel, Adriana Schulz, Vikram Iyer,
• Abstract summary: We introduce multimodal AI agents to calculate cradle-to-gate carbon emissions of electronic devices.<n>The approach reduces weeks or months of expert time to under one minute and closes data availability gaps.<n>It yields carbon footprint estimates within 19% of expert LCAs with zero proprietary data.
• Score: 46.77807327332175
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Interest in sustainability information has surged in recent years. However, the data required for a life cycle assessment (LCA) that maps the materials and processes from product manufacturing to disposal into environmental impacts (EI) are often unavailable. Here we reimagine conventional LCA by introducing multimodal AI agents that emulate interactions between LCA experts and stakeholders like product managers and engineers to calculate the cradle-to-gate (production) carbon emissions of electronic devices. The AI agents iteratively generate a detailed life-cycle inventory leveraging a custom data abstraction and software tools that extract information from online text and images from repair communities and government certifications. This approach reduces weeks or months of expert time to under one minute and closes data availability gaps while yielding carbon footprint estimates within 19% of expert LCAs with zero proprietary data. Additionally, we develop a method to directly estimate EI by comparing an input to a cluster of products with similar descriptions and known carbon footprints. This runs in 3 ms on a laptop with a MAPE of 12.28% on electronic products. Further, we develop a data-driven method to generate emission factors. We use the properties of an unknown material to represent it as a weighted sum of emission factors for similar materials. Compared to human experts picking the closest LCA database entry, this improves MAPE by 120.26%. We analyze the data and compute scaling of this approach and discuss its implications for future LCA workflows.

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