This paper presents a new approach to urban sustainability assessment through the use of Large Language Models (LLMs) to streamline the use of the ISO 37101 framework to automate and standardise the assessment of urban initiatives against the six "sustainability purposes" and twelve "issues" outlined in the standard. The methodology includes the development of a custom prompt based on the standard definitions and its application to two different datasets: 527 projects from the Paris Participatory Budget and 398 activities from the PROBONO Horizon 2020 project. The results show the effectiveness of LLMs in quickly and consistently categorising different urban initiatives according to sustainability criteria. The approach is particularly promising when it comes to breaking down silos in urban planning by providing a holistic view of the impact of projects. The paper discusses the advantages of this method over traditional human-led assessments, including significant time savings and improved consistency. However, it also points out the importance of human expertise in interpreting results and ethical considerations. This study hopefully can contribute to the growing body of work on AI applications in urban planning and provides a novel method for operationalising standardised sustainability frameworks in different urban contexts.

翻译：本文提出了一种新的城市可持续性评估方法，通过使用大型语言模型（LLMs）来简化ISO 37101框架的应用，从而自动化和标准化评估城市倡议是否符合该标准中概述的六项“可持续性目标”和十二个“议题”。该方法包括基于标准定义开发定制提示，并将其应用于两个不同的数据集：巴黎参与式预算的527个项目以及PROBONO Horizon 2020项目的398项活动。结果表明，大型语言模型能够根据可持续性标准快速且一致地对不同的城市倡议进行分类。该方法在打破城市规划中的信息孤岛方面尤其具有前景，因为它提供了项目影响的整体视图。本文讨论了该方法相较于传统人工主导评估的优势，包括显著节省时间和提高一致性。然而，文章也指出了人类专业知识在解释结果和伦理考量方面的重要性。本研究有望为人工智能在城市规划中的应用不断增长的研究体系做出贡献，并为在不同城市背景下实施标准化可持续性框架提供了一种新颖的方法。