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This paper evaluates whether mangroves can mitigate the impact of hurricanes on economic activity. The paper assembles a regionwide panel dataset that measures local economic activity using nightlights, potential hurricane damages using a detailed wind field model, and mangrove protection by mapping the width of mangrove forests on the path to the coast. The results show that hurricanes have negative short-run effects on economic activity, with losses likely concentrated in coastal lowlands that are exposed to both wind and storm surge hazards. In these coastal lowlands, the estimates show that nightlights decrease by up to 24% in areas that are unprotected by mangroves. By comparison, the impact of the hurricanes observed in the sample is fully mitigated in areas protected by mangrove belts of 1 km or more.

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The Dominican Republic is highly exposed to adverse natural events that put the country at risk of losing hard-won economic, social, and environmental gains due to the impacts of disasters. This study used monthly nightlight composites in conjunction with a wind field model to econometrically estimate the impact of tropical cyclones on local economic activity in the Dominican Republic since 1992. It was found that the negative impact of storms lasts up to 15 months after a strike, with the largest effect observed after 9 months. Translating the reduction in nightlight intensity into monetary losses by relating it to quarterly gross domestic product (GDP) suggests that on average the storms reduced GDP by about USD 1.1 billion (4.5% of GDP 2000 and 1.5% of GDP 2016).

The main motivation of this paper is to shed new light on the problem of spatial identification of urban and rural areas globally, and to provide a compatible disaggregation framework for linking associated country-specific, non-spatial data compilations, such as building type inventories. Existing homogeneously setup global urban extent models commonly ignore local-level specifics. While global consistency and regional comparability of urban characteristics are much strived-for goals in the global development and remote sensing communities, non-conformity at the national level often renders such models inapplicable for effective decision-making. Furthermore, the focus on identifying 'urban' leads to an illdefined 'rural', which is simply defined by contrast as 'everything else'; a questionable definition when referring to strongly spatially localized residential patterns. In this paper we introduce the novel iURBAN geospatial modeling approach, identifying Urban-Rural patterns in Built-up-Adjusted and Nationally-adaptive manner. The model operates at global scale, but at the same time conforms to country specifics. In this model, high-resolution, satellitederived, built-up data is used to consistently detect global human settlements at unprecedented spatial detail. In combination with global gridded population data, and with reference to national level statistical information on urban population ratios globally compiled in the annually-released UN World Urbanization Prospects, iURBAN identifies matching urban extents. Additionally, a novel reallocation algorithm is introduced which addresses the poor representation of rural areas that is inherent in existing global population grids. Associating all of the population with inhabitable, built-up area and conforming to national urban-rural ratios, iURBAN sets a new standard by enabling careful consideration of both urban and rural as opposed to traditional urban-biased approaches.