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As organizations increase their dependence on supply chain networks, they become more susceptible to their suppliers’ disaster risk profiles, as well as other categories of risk associated with supply chains. Therefore, it is imperative that supply chain network participants are capable of assessing the disaster risks associated with their supplier base. The purpose of this paper is to assess the supplier disaster risks, which are a key element of external risk in supply chains.

The study participants are 15 automotive casting suppliers who display a significant degree of disaster risks to a major US automotive company. Bayesian networks are used as a methodology for examining the supplier disaster risk profiles for these participants.

The results of this study show that Bayesian networks can be effectively used to assist managers in making decisions regarding current and prospective suppliers vis-à-vis their potential revenue impact as illustrated through their corresponding disaster risk profiles.

A limitation to the use of Bayesian networks for modeling disaster risk profiles is the proper identification of risk events and risk categories that can impact a supply chain.

The methodology used in this study can be adopted by managers to assist them in making decisions regarding current or prospective suppliers vis-à-vis their corresponding disaster risk profiles.

As part of a comprehensive supplier risk management program, organizations along with their suppliers can develop specific strategies and tactics to minimize the effects of supply chain disaster risk events.

The Disaster Awareness Game (DAG) was designed to evaluate and promote disaster awareness among children in multicultural societies. This study seeks to discuss this.

The validation methodology was undertaken in four stages: Pre‐Test Stage – this stage is intended to evaluate the existing levels of disaster awareness among the target population using a questionnaire survey. DAG Exposure 1 – This represents the second stage of the pre‐test through exposure of the target population to the DAG. Provision of disaster information – In this stage, participants are provided with disaster management information on hazards that are pertinent to their environment. Post‐test stage – this stage was intended to evaluate the impact of the DAG and the provision of disaster information on the level of awareness among participants.

Preliminary results suggest that the tool is effective in educating children about hazards, and measuring levels of disaster awareness and is interesting enough to hold children's attention.

The present study provides a starting‐point for further research in the design and development of tools for measuring levels of disaster awareness and in educating children about disaster preparedness.

The DAG can be used as a benchmarking tool for gauging levels of diaster awareness within various groups in society (children, adults, gender, language groups etc.) or across regions in a country (rural versus urban) and in different countries in the Caribbean region (e.g. High income versus Low income) in order to determine and prioritize interventions for disaster education.

Over the last 30 years, despite immense and increasing expenditures by the federal government for disaster preparedness and relief, both catastrophic and chronic losses from natural hazards have continued to increase at an alarming pace. Although earthquakes, floods, tornadoes, and hurricanes account for the largest portion of these natural hazard losses, wildfire increasingly represents significant disaster losses of well over a billion dollars annually. There is considerable concern that losses from wildfires will only increase in the U.S. as some of the highest growth rates in the nation, both metropolitan and nonmetropolitan types of growth, are projected to continue in states with extensive wildland fire hazard areas. The land development patterns associated with that growth are problematic because so much of the development in the last 30 years (and that is still occurring) is not being steered away from the highest wildfire hazard settings, nor are adequate steps being taken to ensure that when development occurs in high wildfire hazard zones appropriate mitigation is used to reduce the vulnerability of people and property to loss. Fortunately, those anticipated future wildfire losses have a great potential to be reduced provided state and local governments take the initiative to create partnerships to ensure “safer” and “smarter” patterns of land development occur in and near wildland–urban interface areas. This chapter explores wildfire mitigation planning as an integral component of “safe smart growth” for wildland–urban interface communities.

The purpose of this paper was to develop an integrated framework for assessing the flood risk and climate adaptation capacity of an urban area and its critical infrastructures to help address flood risk management issues and identify climate adaptation strategies.

Using the January 2011 flood in the core suburbs of Brisbane City, Queensland, Australia, various spatial analytical tools (i.e. digital elevation modeling and urban morphological characterization with 3D analysis, spatial analysis with fuzzy logic, proximity analysis, line statistics, quadrat analysis, collect events analysis, spatial autocorrelation techniques with global Moran’s I and local Moran’s I, inverse distance weight method, and hot spot analysis) were implemented to transform and standardize hazard, vulnerability, and exposure indicating variables. The issue on the sufficiency of indicating variables was addressed using the topological cluster analysis of a two-dimension self-organizing neural network (SONN) structured with 100 neurons and trained by 200 epochs. Furthermore, the suitability of flood risk modeling was addressed by aggregating the indicating variables with weighted overlay and modified fuzzy gamma overlay operations using the Bayesian joint conditional probability weights. Variable weights were assigned to address the limitations of normative (equal weights) and deductive (expert judgment) approaches. Applying geographic information system (GIS) and appropriate equations, the flood risk and climate adaptation capacity indices of the study area were calculated and corresponding maps were generated.

The analyses showed that on the average, 36 (approximately 813 ha) and 14 per cent (approximately 316 ha) of the study area were exposed to very high flood risk and low adaptation capacity, respectively. In total, 93 per cent of the study area revealed negative adaptation capacity metrics (i.e. minimum of −23 to <0), which implies that the socio-economic resources in the area are not enough to increase climate resilience of the urban community (i.e. Brisbane City) and its critical infrastructures.

While the framework in this study was obtained through a robust approach, the following are the research limitations and recommended for further examination: analyzing and incorporating the impacts of economic growth; population growth; technological advancement; climate and environmental disturbances; and climate change; and applying the framework in assessing the risks to natural environments such as in agricultural areas, forest protection and production areas, biodiversity conservation areas, natural heritage sites, watersheds or river basins, parks and recreation areas, coastal regions, etc.

This study provides a tool for high level analyses and identifies adaptation strategies to enable urban communities and critical infrastructure industries to better prepare and mitigate future flood events. The disaster risk reduction measures and climate adaptation strategies to increase urban community and critical infrastructure resilience were identified in this study. These include mitigation on areas of low flood risk or very high climate adaptation capacity; mitigation to preparedness on areas of moderate flood risk and high climate adaptation capacity; mitigation to response on areas of high flood risk and moderate climate adaptation capacity; and mitigation to recovery on areas of very high flood risk and low climate adaptation capacity. The implications of integrating disaster risk reduction and climate adaptation strategies were further examined.

The newly developed spatially explicit analytical technique, identified in this study as the Flood Risk-Adaptation Capacity Index-Adaptation Strategies (FRACIAS) Linkage/Integrated Model, allows the integration of flood risk and climate adaptation assessments which had been treated separately in the past. By applying the FRACIAS linkage/integrated model in the context of flood risk and climate adaptation capacity assessments, the authors established a framework for enhancing measures and adaptation strategies to increase urban community and critical infrastructure resilience to flood risk and climate-related events.