Workforce/Population, Economy, Infrastructure, Geography, Hierarchy, and Time (WEIGHT): Reflections on the Plural Dimensions of Disaster Resilience

Game-theoretic algorithm for interdependent infrastructure network restoration in a decentralized environment

Having reliable interdependent infrastructure networks is vital for well-being of a safe and productive society. Systems are vulnerable to failure or performance loss due to their interdependence among various networks, as each failure can propagate through the whole system. Although the conventional view has concentrated on optimizing the restoration of critical interdependent infrastructure networks using a centralized approach, having a lone actor as a decision-maker in the system is substantially different from the actual restoration decision environment, wherein infrastructure utilities make their own decisions about how to restore their network service. In a decentralized environment, the definition of whole system optimality does not apply as each decision-maker's interest may not converge with the others. Subsequently, this results in each decision-maker developing its own reward functions. Therefore, in this study, we address the concern of having multiple decision-makers with various payoff functions in interdependent networks by proposing a decentralized game theory algorithm for finding Nash equilibria solutions for network restoration in postdisaster situations.

How effective are community-based disaster reduction strategies? Evidence from the largest-scale program so far

Strategies of community-based disaster risk reduction have been advocated for more than 2 decades. However, we still lack in-depth quantitative assessments of the effectiveness of such strategies. Our research is based on a national experiment in this domain: the "Comprehensive Disaster Reduction Demonstration Community" project, a governmental program running in China since 2007. Information on more than 11,000 demonstration communities was collected. Combined with the local disaster information and socioeconomic conditions, the spatiotemporal characteristics of these communities over 12 years and their differences in performance by region and income group were analyzed. We performed an attribution analysis for disaster risk reduction effectiveness. This is the first time a series of quantitative evaluation methods have been applied to verify the effectiveness of a large-scale community-based disaster risk reduction project, both from the perspective of demonstrative effects and loss reduction benefits. Here, we find that the project is obviously effective from these two perspectives, and the disaster loss reduction effectiveness illustrates clear regional differences, where the regional economic level and hazard severity act as important drivers. Significant differences of urban-rural and income call for matching fortification measures, and the dynamic management of demonstration community size is required, since the loss reduction benefit converges when the penetration rate of the demonstration community reaches approximately 4% in a province. These and further results provide diverse implications for community-based disaster risk reduction policies and practices.

National resilience assessment and improvement based on multi-source data: Evidence from countries along the belt and road

National resilience is a consensus benchmark to characterize the ability of disaster resistance of a country. The occurrence of various disasters and the ravages of COVID-19 have created urgent needs in assessing and improving the national resilience of countries, especially for countries along the Belt and Road (i.e., B&R countries) with multiple disasters with high frequency and great losses. To accurately depict the national resilience profile, a three-dimensional assessment model based on multi-source data is proposed, where the diversity of losses, fusion utilization of disaster and macro-indicator data, and several refined elements are involved. Using the proposed assessment model, the national resilience of 64 B&R countries is clarified based on more than 13,000 records involving 17 types of disasters and 5 macro-indicators. However, their assessment results are not optimistic, the dimensional resilience are generally trend-synchronized and individual difference in a single dimension, and approximately one-half of countries do not obtain resilience growth over time. To further explore the applicable solutions for national resilience improvement, a coefficient-adjusted stepwise regression model with 20 macro-indicator regressors is developed based on more than 19,000 records. This study provides the quantified model support and a solution reference for national resilience assessment and improvement, which contributes to addressing the global national resilience deficit and promoting the high-quality development of B&R construction.

Analysis of Influencing Factors of Urban Community Function Loss in China under Flood Disaster Based on Social Network Analysis Model

The increasing frequency of floods is causing an increasing impact on urban communities. To identify the key influencing factors of functional loss in Chinese urban communities under floods, this paper explored the influencing factors and factor combinations through a social network analysis approach using the 265 cases of urban communities in China affected by floods collected from 2017-2021 as research data. The key influencing factors and factor combinations were identified comprehensively using multiple indicator analyses such as core-periphery structure, node centrality, and factor pairing. The analysis results showed that "road disruption", "housing inundation", and "power interruption" are the three most critical factors affecting the functional loss of urban communities in China under floods, followed by "residents trapped", "enterprises flooded", and "silt accumulation". In addition, "road disruption-housing inundation", "housing inundation-residents trapped", and "road disruption-residents trapped" are the most common combinations of influencing factors.

Analysis of Urban Resilience in Water Network Cities Based on Scale-Density-Morphology-Function (SDMF) Framework: A Case Study of Nanchang City, China

In the face of increasing disturbance factors, resilience has become an important criterion for measuring the sustainable development of cities. Quantitatively describing the development process of urban resilience and identifying key areas and important dimensions of urban resilience are of scientific significance for understanding the evolutionary law of urban resilience, guiding regional risk prevention, and building an environment for urban resilience development. For this study, taking Nanchang City as a case study and dividing the natural water network groups, the resilience index system was constructed from scale, density, morphology, and function by drawing on the theory of landscape ecology on the basis of considering the internal relationship between urban development attributes and disturbance factors. On this basis, the study focuses on the evolution process and development differences of resilience in various dimensions from the water network groups and quantitatively describes the coordinated development status and adaptive phase characteristics of urban resilience. This study not only enriches the research scale and perspective of urban resilience but also provides specific spatial guidance for formulating resilient urban planning and promoting sustainable urban development.

Modelling vicious networks with P-graph causality maps

P-graph causality maps were recently proposed as a methodology for systematic analysis of intertwined causal chains forming network-like structures. This approach uses the bipartite representation of P-graph to distinguish system components ("objects" represented by O-type nodes) from the functions they perform ("mechanisms" represented by M-type nodes). The P-graph causality map methodology was originally applied for determining structurally feasible causal networks to enable a desirable outcome to be achieved. In this work, the P-graph causality map methodology is extended to the analysis of vicious networks (i.e., causal networks with adverse outcomes). The maximal structure generation algorithm is first used to assemble the problem elements into a complete causal network; the solution structure generation algorithm is then used to enumerate all structurally feasible causal networks. Such comprehensive analysis gives insights on how to deactivate vicious networks through the removal of keystone objects and mechanisms. The extended methodology is illustrated with an ex post analysis of the 1984 Bhopal industrial disaster. Prospects for other applications to sustainability issues are also discussed.

Using input-output analysis to model the impact of pandemic mitigation and suppression measures on the workforce

The "flatten the curve" graphic has recently become a common tool to visualize the extent to which pandemic suppression and mitigation measures could potentially reduce and delay the number of daily infections due to a pandemic. The COVID-19 pandemic has challenged the capacity of the many healthcare systems and created cascading economic impacts on interdependent sectors of the global society. This paper specifically explores the impact of pandemics on the workforce. The model proposed in this paper comprises of three major steps. First, sources for epidemic curves are identified to generate the attack rate, which is the daily number of infections normalized with respect to the population of the affected region. Second, the model assumes that the general attack rate can be specialized to reflect sector-specific workforce classifications, noting that each economic sector has varying dependence on the workforce. Third, using economic input-output (IO) data from the US Bureau of Economic Analysis, this paper analyzes the performance of several mitigation and suppression measures relative to a baseline pandemic scenario. Results from the IO simulations demonstrate the extent to which mitigation and suppression measures can flatten the curve. This paper concludes with reflections on other consequences of pandemics such as the mental health impacts associated with social isolation and the disproportionate effects on different socioeconomic groups.

Reflections on the impact of "flatten the curve" on interdependent workforce sectors

The expression "flatten the curve" has gained significant attention in the midst of the COVID-19 pandemic. The idea is to decrease and/or delay the peak of an epidemic wave so as not to strain or exceed the capacity of healthcare systems. There has been an increasing number of policy recommendations across the globe that favor the use of nonpharmaceutical interventions (NPIs) to flatten the curve. NPIs encompass containment, suppression, and mitigation measures such as quarantine, travel restrictions, and business closures. This paper provides perspectives on the impact of containment, suppression, and mitigation measures on interdependent workforce sectors. Reflections on the trade-offs between flattening the curve versus personal liberty and socioeconomic disparities are also presented in this paper.

Modelling the Economic Impact and Ripple Effects of Disease Outbreaks

The Coronavirus Disease 2019 (COVID-19) outbreak has had alarming effects on human lives and the economies of affected countries. With the world’s manufacturing hubs experiencing a period of extended factory closures, the economic impact transcends territorial borders via global supply chains. This paper provides a roadmap on how to evaluate the vulnerability that cascades through the supply chain due to a disease outbreak at the firm level, national level, and global scale. The final extent of losses is not yet known, but the development of economic models combined with epidemiological models and network analysis techniques can yield more realistic estimates to select appropriate strategies in a timely manner.