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Using Large Language Models for a standard assessment mapping for sustainable communities
Computers and Society · Submitted 31 Oct 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The results show the effectiveness of LLMs in quickly and consistently categorising different urban initiatives according to sustainability criteria.
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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. | contribution 1.00 | 0.77 | 0.14 | asserted 0.99 |
| s1 | 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. | method 1.00 | 0.94 | 0.03 | asserted 0.98 |
| s2 | The results show the effectiveness of LLMs in quickly and consistently categorising different urban initiatives according to sustainability criteria. | result 0.99 | 0.94 | 0.68 | asserted 1.00 |
| s3 | 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. | interpretation 0.99 | 0.49 | 0.12 | hedged possibility 0.68 |
| s4 | The paper discusses the advantages of this method over traditional human-led assessments, including significant time savings and improved consistency. | interpretation 0.50 | 0.67 | 0.14 | asserted 0.99 |
| s5 | However, it also points out the importance of human expertise in interpreting results and ethical considerations. | limitation 1.00 | 0.58 | 0.07 | asserted 0.89 |
| s6 | 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. | future work 0.62 | 0.18 | 0.11 | hedged possibility 0.90 |
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The paper
Original abstract
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.
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