Parametric Insurance for Urban Green Zones: Linking NDVI Triggers with Climate Risk in Four Global Cities

Urban green zones, including mangroves, wetlands, parks, and grasslands, provide critical ecosystem services that help buffer cities against climate-related hazards such as floods and heatwaves. However, these natural infrastructures remain largely excluded from formal climate risk financing frameworks. This study introduces a novel parametric insurance prototype that uses satellite-derived vegetation indices to trigger financial payouts in response to ecological stress in urban environments. Using Sentinel-2 imagery from 2018 to 2023, we extract monthly Normalised Difference Vegetation Index (NDVI) values across delineated green zones in four highly exposed cities: Mumbai, Jakarta, Manila, and Nairobi. We construct dynamic seasonal NDVI baselines and define trigger thresholds as a 30% decline from rolling three-month norms. Simulated payouts are generated for each trigger event, and basis risk is assessed by comparing NDVI-based triggers with historical flood disaster records from the EM-DAT database. Results indicate that 15 to 38 percent of NDVI trigger events coincide with documented floods, with significant variation across cities and vegetation types. Notably, large NDVI anomalies in mangrove zones are often detected within one to two months of major flood events, suggesting temporal alignment between ecological stress and climate hazard exposure. Sensitivity analysis shows that mangroves and wetlands consistently exhibit higher trigger frequencies and payout volumes, reflecting a stronger link between vegetation health and climate stress. The findings demonstrate the feasibility of operationalising remote-sensing-based insurance mechanisms for urban nature and provide a methodological foundation for linking ecological performance with disaster recovery finance. This approach contributes to ongoing efforts in nature-based solutions, adaptation finance, and climate-resilient urban planning, particularly in regions with limited data and high exposure to climate risk.