Constrained LLM Reporting for Geospatial Climate Risk: A One-Shot In-Context Framework for Critical Infrastructure

Abstract

Climate risk assessments for critical infrastructure are essential to identifying and predicting vulnerabilities early in the asset life cycle, enabling proactive mitigation through the implementation of technical and nature-based solutions (NbS) before impacts occur. However, such assessments often rely on dense quantitative indices that are difficult for non-technical stakeholders to interpret. To address this challenge, this paper presents an open-source decision support platform that combines OpenStreetMap site characterization, qualitative pre-screening, a quantitative IPCC AR6-aligned risk chain, and a downstream NbS recommendation layer. The approach deploys Large Language Models (LLMs) to translate analytical outputs into accessible narrative explanations. End-to-end site-characterization processing across three European demonstration sites took between 29 and 70 s. An exploratory ablation study investigated the faithfulness of the AI-generated explanations using three complementary metrics, demonstrating that the generated hazard assessments remained factually grounded and free from fabricated numerical values. Introducing example reports (exemplars) into the prompt context further stabilized the reliability of the output for complex risk indicators. Finally, a small blind expert evaluation with six researchers from adjacent technical domains provided convergent evidence: five of six raters independently rated with-exemplar Hazard Reports higher on completeness; among the five raters who expressed a directional preference, all five favored the with-exemplar condition (sign test, p = 0.031). Furthermore, seven of eight aggregate dimension-level comparisons confirmed that with-exemplar reports scored at least as high as their ablated counterparts.