Urban governance is hindered by the disconnect between qualitative knowledge embedded in textual sources and the predictive capabilities of quantitative models, making proactive policymaking challenging. This study proposes and validates a two-phase framework that automates the ‘dialogue’ between the evidence paradigms for urban air quality management. In the first phase, a Retrieval-Augmented Generation (RAG) pipeline synthesises extensive literature into a structured knowledge graph, generating evidence-based policy hypotheses. This approach navigates the challenge of evaluating overwhelming potential policy combinations. Following this, these qualitative hypotheses are automatically translated into simulation scenarios and subjected to rigorous, ex-ante quantitative testing, providing the rapid feedback necessary to overcome slow policy evaluation cycles. A case study in London, addressing post-2023 ULEZ expansion air quality challenges, demonstrates the framework’s real-world utility. The RAG identified a portfolio of next-generation interventions targeting heating systems, construction machinery, and solvent emissions. Subsequent simulation with the SHERPA model produced spatial impact maps of pollution-level reductions and, critically, uncovered complex system dynamics. The framework’s ability to efficiently screen a broad portfolio of options while revealing such complex consequences underscores its value for proactive governance. By integrating textual and simulation evidence into a scalable methodology, this research provides a robust tool to enable more effective, evidence-informed urban environmental policy. • A novel technical framework overcomes bottlenecks in environmental policymaking. • Grounds policy options in verifiable scientific evidence for screening and prioritisation. • Integrates qualitative evidence with quantitative simulation for robust ex-ante assessment. • Reveals complex urban system dynamics to prevent potentially ineffective policies.
Xie et al. (Wed,) studied this question.