标题： 通过多模态AI代理实现自主可持续性评估 显示英文标题

标题： Towards Autonomous Sustainability Assessment via Multimodal AI Agents

摘要： 近年来，对可持续性信息的兴趣大幅增加。 然而，用于生命周期评估（LCA）的数据，这些数据将产品制造到处置的材料和工艺映射到环境影响（EI），通常无法获得。 在这里，我们通过引入多模式AI代理重新构想传统的LCA，这些代理模拟LCA专家与利益相关者如产品经理和工程师之间的互动，以计算电子设备的从摇篮到大门（生产）的碳排放。 AI代理迭代生成详细的生命周期清单，利用自定义的数据抽象和软件工具，从维修社区和政府认证的在线文本和图像中提取信息。 这种方法将专家所需的时间从几周或几个月减少到不到一分钟，并在不使用专有数据的情况下，将碳足迹估算结果与专家LCA的结果相差19%以内。 此外，我们开发了一种直接估计EI的方法，通过将输入与具有相似描述和已知碳足迹的产品群集进行比较。 这种方法在笔记本电脑上运行时间为3毫秒，电子产品的MAPE为12.28%。 此外，我们开发了一种数据驱动的方法来生成排放因子。 我们使用未知材料的属性将其表示为类似材料的排放因子的加权和。 与人类专家选择最接近的LCA数据库条目相比，这将MAPE提高了120.26%。 我们分析了数据并计算了这种方法的扩展性，并讨论了其对未来LCA工作流程的影响。 显示英文摘要

摘要： 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.