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Lookup NU author(s): Adam Booth, Professor Philip JamesORCiD, Dr Ellis SolaimanORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Urban sustainability management increasingly relies on large volumes of heterogeneous environmental data generated by smart city infrastructures. While these data streams offer significant potential for evidence-informed policymaking, environmental governance, and public engagement, their effective use is often constrained by technical barriers and persistent data-skills gaps among non-specialist stakeholders. Using urban air quality as a policy-relevant and data-rich sustainability domain, this paper presents a proof-of-concept dashboard that investigates how large language model (LLM)-enabled natural language interfaces can lower barriers to querying, analysing, and visualising urban environmental data. The system translates natural language questions into executable database queries and automatically generates visualisations over air-quality datasets. A controlled comparative benchmark of proprietary and open-source LLMs is conducted to assess their suitability for text-to-SQL generation in this application context. In this benchmark, proprietary GPT-based models achieved the highest observed query accuracy and robustness among the evaluated models, highlighting practical trade-offs between performance, transparency, reproducibility, and long-term governance. This paper makes a twofold contribution: First, it demonstrates the technical feasibility of an LLM-enabled natural language access layer for smart-city environmental data. Second, it uses the implemented system as a concrete case through which to analyse the trust, transparency, inclusivity, vendor-dependency, and data-quality challenges that arise when such systems are incorporated into sustainability-oriented decision-support workflows. The study provides a transferable design contribution for urban sustainability data access by showing how natural language interfaces, model benchmarking, automated visualisation, and governance-aware system design can be combined to support more inclusive interaction with complex environmental datasets.
Author(s): Booth A, James P, Solaiman E
Publication type: Article
Publication status: Published
Journal: Sustainability
Year: 2026
Volume: 18
Issue: 11
Print publication date: 01/06/2026
Online publication date: 01/06/2026
Acceptance date: 26/05/2026
Date deposited: 05/06/2026
ISSN (electronic): 2071-1050
Publisher: MDPI AG
URL: https://doi.org/10.3390/su18115506
DOI: 10.3390/su18115506
Data Access Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.
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