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This book brings together a collection of innovative papers on strategies for analyzing the spatial and economic impacts of disasters. Natural and human-induced disasters pose several challenges for conventional modeling. For example, disasters entail complex linkages between the natural, built, and socio-economic environments. They often create chaos and economic disequilibrium, and can also cause unexpected long-term, structural changes. Dynamic interactions among agents and behavioral adjustments in a disaster become complicated. The papers in this volume make notable progress in tackling these challenges through refinements of conventional methods, as well as new modeling frameworks and multidisciplinary, integrative strategies. The papers also provide case study applications that afford new insights on disaster processes and loss reduction strategies

Economic recovery refers to the process by which businesses and local economies return to conditions of stability following a disaster. Its importance and complexity are being increasingly recognized in disaster risk reduction research and practice. This paper provides an overview of current research on economic recovery and suggests a research agenda to address key gaps in knowledge. Empirical studies have provided a number of robust findings on the disaster recovery of businesses and local economies, with particular insights into short- and long-term recovery patterns, influential factors in recovery, and disparities in recovery across types of businesses and economies. Modeling studies have undertaken formal analyses of economic impacts of disasters in which recovery is usually addressed through the incorporation of resilience actions and investments in repair and reconstruction. Core variables for assessing and understanding economic recovery are identified from the literature, and approaches for measuring or estimating them are discussed. The paper concludes with important gaps in the development of a robust theory of economic recovery. Systematic data collection is needed to establish patterns and variations on how well and how quickly local economies recover from disasters. Research is urgently needed on the effectiveness of resilience approaches, decisions, and policies for recovery at both the business and local economy levels. Detailed, testable theoretical frameworks will be important for advancing understanding and developing sound recovery plans and policies. It will be especially important to consider the relationship between economic recovery and recovery of the built environment and sociopolitical fabric of communities in developing a comprehensive theory of disaster recovery.

AbstractThe economic impacts of pandemics can be enormous. However, lockdown and human mobility restrictions are effective policies for containing the spread of the disease. This study proposes a framework for assessing the economic impact of varying degrees of movement restrictions and examines the effectiveness of this framework in a case study examining COVID‐19 control measures in Japan. First, mobile network operators data and total employment statistics on a 500‐meter grid scale are used to determine the status of mobility restrictions and impacts on consumption in 30 industrial sectors. Next, the economic impacts are assessed using a spatial computable general equilibrium (CGE) model, proven to yield valuable insights into the total economic impacts of natural disasters. In sectors that implement telework and e‐commerce—wholesale/retail, finance/insurance, and communication sectors—estimates of production and GDP are obtained that are close to the actual figures. The current case study is limited to Japan, but similar analysis can be conducted by using the CGE model for each country and open mobility data. Thus, the framework has potential to serve as an effective tool for assessing trade‐offs between infection risks and economic impacts to inform policy‐making by combining with findings from epidemiology.

Resilient infrastructure systems are essential for cities to withstand and rapidly recover from natural and human‐induced disasters, yet electric power, transportation, and other infrastructures are highly vulnerable and interdependent. New approaches for characterizing the resilience of sets of infrastructure systems are urgently needed, at community and regional scales. This article develops a practical approach for analysts to characterize a community's infrastructure vulnerability and resilience in disasters. It addresses key challenges of incomplete incentives, partial information, and few opportunities for learning. The approach is demonstrated for Metro Vancouver, Canada, in the context of earthquake and flood risk. The methodological approach is practical and focuses on potential disruptions to infrastructure services. In spirit, it resembles probability elicitation with multiple experts; however, it elicits disruption and recovery over time, rather than uncertainties regarding system function at a given point in time. It develops information on regional infrastructure risk and engages infrastructure organizations in the process. Information sharing, iteration, and learning among the participants provide the basis for more informed estimates of infrastructure system robustness and recovery that incorporate the potential for interdependent failures after an extreme event. Results demonstrate the vital importance of cross‐sectoral communication to develop shared understanding of regional infrastructure disruption in disasters. For Vancouver, specific results indicate that in a hypothetical M7.3 earthquake, virtually all infrastructures would suffer severe disruption of service in the immediate aftermath, with many experiencing moderate disruption two weeks afterward. Electric power, land transportation, and telecommunications are identified as core infrastructure sectors.