Human exposure to floods continues to increase, driven by changes in hydrology and land use. Adverse impacts amplify for socially vulnerable populations, who disproportionately inhabit flood-prone areas. This study explores the geography of flood exposure and social vulnerability in the conterminous United States based on spatial analysis of fluvial and pluvial flood extent, land cover, and social vulnerability. Using bivariate Local Indicators of Spatial Association, we map hotspots where high flood exposure and high social vulnerability converge and identify dominant indicators of social vulnerability within these places. The hotspots, home to approximately 19 million people, occur predominantly in rural areas and across the US South. Mobile homes and racial minorities are most overrepresented in hotspots compared to elsewhere. The results identify priority locations where interventions can mitigate both physical and social aspects of flood vulnerability. The variables that most distinguish the clusters are used to develop an indicator set of social vulnerability to flood exposure. Understanding who is most exposed to floods and where, can be used to tailor mitigation strategies to target those most in need.

Leading flood loss estimation models include Federal Emergency Management Agency’s (FEMA’s) Hazus, FEMA’s Flood Assessment Structure Tool (FAST), and (U.S.) Hydrologic Engineering Center’s Flood Impact Analysis (HEC-FIA), with each requiring different data input. No research to date has compared the resulting outcomes from such models at a neighborhood scale. This research examines the building and content loss estimates by Hazus Level 2, FAST, and HEC-FIA, over a levee-protected census block in Metairie, in Jefferson Parish, Louisiana. Building attribute data in National Structure Inventory (NSI) 2.0 are compared against “best available data” (BAD) collected at the individual building scale from Google Street View, Jefferson Parish building inventory, and 2019 National Building Cost Manual, to assess the sensitivity of input building inventory selection. Results suggest that use of BAD likely enhances flood loss estimation accuracy over existing reliance on default data in the software or from a national data set that generalizes over a broad scale. Although the three models give similar mean (median) building and content loss, Hazus Level 2 results diverge from those produced by FAST and HEC-FIA at the individual building level. A statistically significant difference in mean (median) building loss exists, but no significant difference is found in mean (median) content loss, between building inventory input (i.e., NSI 2.0 vs BAD), but both the building and content loss vary at the individual building scale due to difference in building-inventory-reported foundation height, foundation type, number of stories, replacement cost, and content cost. Moreover, building loss estimation also differs significantly by depth-damage function (DDF), for flood depths corresponding with the longest return periods, with content loss differing significantly by DDF at all return periods tested, from 10 to 500 years. Knowledge of the extent of estimated differences aids in understanding the degree of uncertainty in flood loss estimation. Much like the real estate industry uses comparable home values to appraise a home, flood loss planners should use multiple models to estimate flood-related losses. Moreover, results from this study can be used as a baseline for assessing losses from other hazards, thereby enhancing protection of human life and property.

Purpose This study aims to assess how new and emerging technologies can contribute to achieving the financial goals of the private commercial banking sector in Bangladesh. It considers the perception among the top management about the Fourth Industrial Revolution (4IR) and further measures the readiness of private commercial banks (PCBs) to become resilient. Design/methodology/approach This study attempts to measure the perception and readiness of the commercial banking sector because of the 4IR based on the stratified sampling method. The research is qualitative and selected PCBs listed in the Dhaka Stock Exchange. 4IR in the context of the banking sector in Bangladesh is a problem that has not been studied more clearly, intended to establish priorities, develop operational definitions and improve the final research design. Findings This research has identified a significant gap of study in the preparedness among the private commercial banking sector in Bangladesh to confront the 4IR while indicating the most significant risks and managerial insights. The findings show technologies will dramatically change the nature of work. Traditional system of banking from the branch will be shifting into banking from everywhere. Hence, digital products and services will foster value-driven business. The result of the study also states the readiness of the banking sector is in the preliminary stage and endorses some of the coping approaches. Research limitations/implications Different schools of thought regarding the role of the 4IR and its future consequences have been observed. The corporate sector in Bangladesh has an inclusive lack of understanding regarding the 4IR. Practical implications The insights may provide directions to banking financial institutions of Bangladesh to thrive during the 4IR. This study is intended to assist policymakers, decision-makers and employees of PCBs to increase awareness and preparedness for future challenges that may appear from the 4IR where the 41 competitive PCBs play vital role in turning the fast emerging Bangladesh economy. Originality/value The contribution of this paper associates with academics and bankers to increase understanding of coping in the context of the escalating use of emerging technology-driven banking services within the PCBs in Bangladesh by determining perception and testing different forms of readiness including a variety of important outcomes such as risks.

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