A framework to estimate a long-term power shortage risk following large-scale earthquake and tsunami disasters.

While power shortages during and after a natural disaster cause severe impacts on response and recovery activities, related modeling and data collection efforts have been limited. In particular, no methodology exists to analyze long-term power shortages such as those that occurred during the Great East Japan Earthquake. To visualize a risk of supply shortage during a disaster and assist the coherent recovery of supply and demand systems, this study proposes an integrated damage and recovery estimation framework including the power generator, trunk distribution systems (over 154 kV), and power demand system. This framework is unique because it thoroughly investigates the vulnerability and resilience characteristics of power systems as well as businesses as primary power consumers observed in past disasters in Japan. These characteristics are essentially modeled by statistical functions, and a simple power supply-demand matching algorism is implemented using these functions. As a result, the proposed framework reproduces the original power supply and demand status from the 2011 Great East Japan Earthquake in a relatively consistent manner. Using stochastic components of the statistical functions, the average supply margin is estimated to be 4.1%, but the worst-case scenario is a 5.6% shortfall relative to peak demand. Thus, by applying the framework, the study improves knowledge on potential risk by examining a particular past disaster; the findings are expected to enhance risk perception and supply and demand preparedness after a future large-scale earthquake and tsunami disaster.

Modeling economic impacts of mobility restriction policy during the COVID-19 pandemic.

The 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.

Business recovery from disasters: Lessons from natural hazards and the COVID-19 pandemic.

This paper compares economic recovery in the COVID-19 pandemic with other types of disasters, at the scale of businesses. As countries around the world struggle to emerge from the pandemic, studies of business impact and recovery have proliferated; however, pandemic research is often undertaken without the benefit of insights from long-standing research on past large-scale disruptive events, such as floods, storms, and earthquakes. This paper builds synergies between established knowledge on business recovery in disasters and emerging insights from the COVID-19 pandemic. It first proposes a disaster event taxonomy that allows the pandemic to be compared with natural hazard events from the perspective of economic disruption. The paper then identifies five key lessons on business recovery from disasters and compares them to empirical findings from the COVID-19 pandemic. For synthesis, a conceptual framework on business recovery is developed to support policy-makers to anticipate business recovery needs in economically disruptive events, including disasters. Findings from the pandemic largely resonate with those from disasters. Recovery tends to be more difficult for small businesses, those vulnerable to supply chain problems, those facing disrupted markets, and locally-oriented businesses in heavily impacted neighborhoods. Disaster assistance that is fast and less restrictive provides more effective support for business recovery. Some differences emerge, however: substantial business disruption in the pandemic derived from changes in demand due to regulatory measures as well as consumer behaviour; businesses in high-income neighborhoods and central business districts were especially affected; and traditional forms of financial assistance may need to be reconsidered.

Explaining the effective reproduction number of COVID-19 through mobility and enterprise statistics: Evidence from the first wave in Japan.

This study uses mobility statistics combined with business census data for the eight Japanese prefectures with the highest coronavirus disease-2019 (COVID-19) infection rates to study the effect of mobility reductions on the effective reproduction number (i.e., the average number of secondary cases caused by one infected person). Mobility statistics are a relatively new data source created by compiling smartphone location data; they can be effectively used for understanding pandemics if integrated with epidemiological findings and other economic data sets. Based on data for the first wave of infections in Japan, we found that reductions targeting the hospitality industry were slightly more effective than restrictions on general business activities. Specifically, we found that to hold back the pandemic (that is, to reduce the effective reproduction number to one or less for all days), a 20%-35% reduction in weekly mobility is required, depending on the region. A lesser goal, 80% of days with one or less observed transmission, can be achieved with a 6%-30% reduction in weekly mobility. These are the results if other potential causes of spread are ignored; for a fuller picture, more careful observations, expanded data sets, and advanced statistical modeling are needed.

Collaborative modelling-based shelter planning analysis: a case study of the Nagata Elementary School Community in Kobe City, Japan.

This study, based on a questionnaire survey and workshops, and with a focus on the impact of an earthquake on the Nagata Elementary School Community in Kobe City, Japan, develops a collaborative model to assess the allocation of residents to shelters. The current official allocation plan is compared with three alternative allocations developed within the framework of this model. The collaborative model identifies accessibility, amenity, capacity, connectivity, continuity, security, and stability as the basic, necessary criteria for shelter planning. The three alternative allocations are very similar to the local residents' own choice of shelters, but they are quite different from the current official allocation plan, which is supposed to be followed but has achieved relatively low satisfaction among households. The proposed collaborative approach provides an effective tool to assess the officially determined allocation plan by taking into account the viewpoints of local residents, and the results are useful for enhancing community evacuation planning.