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Flood risk reduction is an existent discourse and agenda in policy and insurance. Existing approaches such as linking hydrological models to economic loss models may be highly inequitable between areas of different socioeconomic vulnerability. To our knowledge, no one has tried to adapt the more advanced known heat risk theory by first informing flood risk with the socioeconomic vulnerability, and then investigating the sensitivity of risk reduction policies to that flood risk. In this article, we demonstrate two methods to combine water hazard data with a derived water vulnerability index to characterize water risk. We then compare the costs of two potential government policies: buyout of the home versus funding for foundation elevation. We use the case study area of Pittsburgh, PA, which faces severe precipitation and riverine flooding hazards. We find that while small differences in characterizing flood risk can result in large differences between flood risk maps, the cost of the flood risk reduction policy is not sensitive to the method of representing the socioeconomic vulnerability. This suggests that while validation of flood risk incorporating socioeconomic data is needed, for some policies, policymakers can prioritize environmental justice with little to no additional cost.
Extreme heat events can lead to increased risk of heat-related deaths. Furthermore, urban areas are often hotter than their rural surroundings, exacerbating heat waves. Unfortunately, validation is difficult; to our knowledge, most validations, even if they control for temperatures, really only validate a social vulnerability index instead of a heat vulnerability index. Here we investigate how to construct and validate a heat vulnerability index given uncertainty ranges in data for the city of Rio de Janeiro. First, we compare excess deaths of certain types of circulatory diseases during heat waves. Second, we use demographic and environmental data and factor analysis to construct a set of unobserved factors and respective weightings related to heat vulnerability, including a Monte Carlo analysis to represent the uncertainty ranges assigned to the input data. Finally, we use distance to hospital and clinics and their health record data as an instrumental variable to validate our factors. We find that we can validate the Rio de Janeiro heat vulnerability index against excess deaths during heat waves; specifically, we use three types of regressions coupled with difference in difference calculations to show this is indeed a heat vulnerability index as opposed to a social vulnerability index. The factor analysis identifies two factors that contribute to >70% of the variability in the data; one socio-economic factor and one urban form factor. This suggests it is necessary to add a step to existing methods for validation of heat vulnerability indices, that of the difference-in-difference calculation.
The effects of climate-related natural hazards pose a significant threat to sustainable development in Latin America and the Caribbean (LAC) region and in particular its transportation sector. Risk Management provides an appropriate framework for assessing and mitigating the impacts of climate change and other climate-related natural hazards on transportation systems and choosing actions to enhance their resilience. However, analysts and policymakers involved in transportation planning, policy, and investment face significant challenges in managing the risks triggered by the effects of climate change. Climate change impacts the lifespan of roads, airports, and railroads as they have time horizons that surpass 40 years, thus making it harder (if not impossible) to forecast with confidence all relevant future events that will affect such infrastructure. In addition, the climate has already changed, so the return frequency of storms, for example, and other extreme events may now be different than suggested by the historical record in ways that are not always currently well understood. Implementing Risk Management under conditions of such uncertainty can prove difficult. Decision Making Under Deep Uncertainty (DMDU) enables Risk Management under conditions of Deep Uncertainty, that is when risks cannot confidently be quantified. This guidebook is aligned with the Disaster and Climate Change Risk Assessment Methodology for IDB projects (IDB 2018) and introduces and provides guidance on applying methods for Decision Making Under Deep Uncertainty (DMDU) to transportation planning. It presents the methodological steps that are necessary for the implementation of DMDU methodologies and reviews several such methods, including scenario planning, Adaptive Pathways, and robust decision making (RDM). This review is geared towards supporting the incorporation of DMDU methods into IDBs transportation sector funding and planning processes.
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