Risk Analysis

doi: 10.1111/risa.13970pmid: N/A

Beck, Matthias; Sheppard, Gail

doi: 10.1111/risa.14103pmid: 36717212

The discrepancy between formal arrangements to ensure health security, as assessed in the Global Health Security Index, and COVID‐19 outcomes points to a broader problem where formal risk recognition is de‐coupled from potentially resource‐intensive follow‐up policy implementation. Germany is an extreme example of this. Pre‐COVID‐19, Germany's Federal Office of Civilian Protection conducted two pandemic preparation exercises based on scenarios which closely mirrored the current COVID‐19 pandemic: (a) a multi‐jurisdictional, multi‐agency crisis management exercise assuming a global influenza pandemic and (b) a joint federal and expert‐agency based risk‐analysis assuming the outbreak of a modified severe acute respiratory symptom virus. While informing legal and institutional reforms, key recommendations on storing personal protective equipment (PPE) and disinfectants for front‐line staff were subsequently ignored. PPE shortages initially put staff at risk, led to export restrictions on PPE, and later on hampered the country's ability to address a second wave of the pandemic. This short paper calls for a fuller exploration of factors which hinder ‘‘implementation post‐cognition.’’

Ottenburger, Sadeeb Simon; Ufer, Ulrich

doi: 10.1111/risa.14102pmid: 36717363

In this article, we analyze “digital massification” in smart cities, that is, an ever‐growing number of market participants, consumers, and Internet of Things devices with simultaneous accommodation of users to increasing disturbances and inconveniences due to data congestion—as a driver for systemic risk. We argue that digital massification phenomena largely escape societal awareness due to their protracted evolution and are therefore still in the blind spot of long‐term governance. Our analysis makes methodological use of historical and relational analogy, and we introduce and elaborate concepts and terms that allow us to discuss the evolutionary nature of massification, that is, the foreseeable increasing probability of the occurrence of trigger events. Using the analogy to the history of road traffic congestion, we deduce that digital massification will most likely lead to a future “risk transition” where tolerated disturbances and inconveniences of the present will turn into systemic impacts. This insight calls for heightened sensitivity in governance to massification phenomena to ensure the long‐term resilience of smart cities.

Huang, Hong; Liu, Tingting; Yang, Ruiju

doi: 10.1111/risa.14119pmid: 36868781

Upper echelons theory (UET) proposes that organizational outcomes are directly affected by the experiences, personalities, and values of individuals who occupy critical managerial roles within an organization. Using the lens of UET, this study investigates how governors’ characteristics affect the management level of major road accidents (MLMRA). The empirical work is based on fixed effects regression models that are applied to Chinese provincial panel data from 2008 to 2017. This study uncovers that the MLMRA is associated with governors’ tenure, central background, and Confucian values. We further document that the effect of Confucianism on the MLMRA is stronger when traffic regulation pressure is high. This study has the potential to advance our understanding of the impact of leaders’ characteristics on organizational outcomes in the public sector.

Liu, Yang; Ma, Xiaoxue; Qiao, Weiliang; Han, Bing

doi: 10.1111/risa.13987pmid: 35822648

The unique, ambiguous, and complex navigable environment determines the essential difference between Arctic shipping routes and conventional routes in regard to safety issues. To achieve a scientific understanding of the characteristics and variations of environmental risks involved in the Arctic shipping, it is essential to rationally address the uncertainty and incompleteness of environment‐related risk information. In this study, fuzzy evidential reasoning is introduced to carry out multisource heterogeneous data fusion and spatiotemporal dynamic assessment of navigable environmental risks for Arctic shipping routes. Based on big Earth data collected from the European Center for Medium‐Range Weather Forecasts, National Snow And Ice Data Center, National Center for Environmental Information, and University of Bremen from 2012 to 2019, a case study of the Northeast Passage is considered to demonstrate the feasibility of the proposed methodology. Finally, the results are described from three aspects: spatial distribution, temporal changes, and sensitivity analysis, with consideration of the entire passage and five marginal seas at the same time. Based on these findings, the prospect of application of big Earth data in risk assessment is further discussed from two aspects of knowledge acquisition by big data and risk analysis at different scales, to inspire sustainable development of Arctic shipping.

Vrieling, Leonie; Perlaviciute, Goda; Steg, Linda

doi: 10.1111/risa.14117pmid: 36788022

Energy projects can cause various risks over which people have little control, because they are usually developed, implemented, and managed by external parties, such as governments and industry. This study aims to examine how people cope with such externally controlled risks from energy projects, in particular earthquakes induced by gas extraction in their region. Specifically, we studied which factors influence people's intentions to engage in emotion‐focused coping aimed at reducing negative emotions, and problem‐focused coping aimed at reducing the risks and/or their negative consequences. Extending previous studies, we distinguish two types of problem‐focused coping that may be relevant when facing externally controlled risks, namely self‐focused coping, in which individual themselves take action to reduce the negative consequences of the risks, and others‐focused coping, in which case individuals urge responsible parties to take actions to reduce the risks. Our results show that the three types of coping can be distinguished empirically, and people are likely to engage in others‐focused coping. Further, people are most likely to engage in others‐focused coping when they experience strong morality‐based emotions toward the risks from energy projects, whereas they are most likely to engage in self‐focused coping and emotion‐focused coping when they experience strong negative consequence‐based emotions toward the risks from energy projects.

Tao, Zhigang; Zhang, Haibo

doi: 10.1111/risa.14121pmid: 36855024

This study aims to identify the driving forces behind interorganizational networks in China following disasters. Using the theory of complex adaptive systems, we identified the self‐organization process of disaster response as the network formation process. We identified interorganizational networks that emerged in response to two natural hazards and two technical disasters by collecting data from multiple sources. The exponential random model analysis is performed to analyze the effects of structures and organizational attributes on network formation. In structuring networks for disaster response, findings demonstrate that bonding structures take precedence over bridging structures for organizations. Meanwhile, the sector and jurisdiction‐based homophily effects facilitate network formation in disaster response. On the basis of research findings, five propositions describing the network formation process in China's disaster response are proposed. Such a theoretical model is essential for advancing research and practice in complex disaster network management.

Sambo, Beatrice; Bonato, Marta; Sperotto, Anna; Torresan, Silvia; Furlan, Elisa; Lambert, James H.; Linkov, Igor; Critto, Andrea

doi: 10.1111/risa.14097pmid: 36690591

Climate change influences the frequency of extreme events that affect both human and natural systems. It requires systemic climate change adaptation to address the complexity of risks across multiple domains and tackle the uncertainties of future scenarios. This paper introduces a multirisk analysis of climate hazard, exposure, vulnerability, and risk severity, specifically designed to hotspot geographic locations and prioritize system receptors that are affected by climate‐related extremes. The analysis is demonstrated for the Metropolitan City of Venice. Representative scenarios (RCP4.5 and RCP8.5) of climate threats (i.e., storm surges, pluvial flood, heat waves, and drought) are selected and represented by projections of Regional Climate Models for a 30‐year period (2021–2050). A sample of results is as follows. First, an increase in the risk is largely due to drought, pluvial flood, and storm surge, depending on the areas of interest, with the overall situation worsening under the RCP8.5 scenario. Second, particular locations have colocated vulnerable receptors at higher risk, concentrated in the urban centers (e.g., housing, railways, roads) and along the coast (e.g., beaches, wetlands, primary sector). Third, risk communication of potential environmental and socio‐economic losses via the multirisk maps is useful to stakeholders and public administration. Fourth, the multirisk maps recommend priorities for future investigation and risk management, such as collection of sensor data, elaboration of mitigations, and adaptation plans at hotspot locations.

Cullen, Alison C.; Prichard, Susan J.; Abatzoglou, John T.; Dolk, Alexandra; Kessenich, Lee; Bloem, Sunniva; Bukovsky, Melissa S.; Humphrey, Reed; McGinnis, Seth; Skinner, Haley; Mearns, Linda O.

doi: 10.1111/risa.14113pmid: 36792115

We apply a convergence research approach to the urgent need for proactive management of long‐term risk associated with wildfire in the United States. In this work we define convergence research in accordance with the US National Science Foundation—as a means of addressing a specific and compelling societal problem for which solutions require deep integration across disciplines and engagement of stakeholders. Our research team brings expertise in climate science, fire science, landscape ecology, and decision science to address the risk from simultaneous and impactful fires that compete for management resources, and leverages climate projections for decision support. In order to make progress toward convergence our team bridges spatial and temporal scale divides arising from differences in disciplinary and practice‐based norms. We partner with stakeholders representing US governmental, tribal, and local decision contexts to coproduce a robust information base for support of decision making about wildfire preparedness and proactive land/fire management. Our approach ensures that coproduced information will be directly integrated into existing tools for application in operations and policy making. Coproduced visualizations and decision support information provide projections of the change in expected number of fires that compete for resources, the number of fire danger days per year relative to prior norms, and changes in the length and overlap of fire season in multiple US regions. Continuing phases of this work have been initiated both by stakeholder communities and by our research team, a demonstration of impact and value.

Du, Yan; Chatterjee, Samrat; Bhattacharya, Arnab; Dutta, Ashutosh; Halappanavar, Mahantesh

doi: 10.1111/risa.14104pmid: 36746175

Critical infrastructures such as cyber‐physical energy systems (CPS‐E) integrate information flow and physical operations that are vulnerable to natural and targeted failures. Safe, secure, and reliable operation and control of CPS‐E is critical to ensure societal well‐being and economic prosperity. Automated control is key for real‐time operations and may be mathematically cast as a sequential decision‐making problem under uncertainty. Emergence of data‐driven techniques for decision making under uncertainty, such as reinforcement learning (RL), have led to promising advances for addressing sequential decision‐making problems for risk‐based robust CPS‐E control. However, existing research challenges include understanding the applicability of RL methods across diverse CPS‐E applications, addressing the effect of risk preferences across multiple RL methods, and development of open‐source domain‐aware simulation environments for RL experimentation within a CPS‐E context. This article systematically analyzes the applicability of four types of RL methods (model‐free, model‐based, hybrid model‐free and model‐based, and hierarchical) for risk‐based robust CPS‐E control. Problem features and solution stability for the RL methods are also discussed. We demonstrate and compare the performance of multiple RL methods under different risk specifications (risk‐averse, risk‐neutral, and risk‐seeking) through the development and application of an open‐source simulation environment. Motivating numerical simulation examples include representative single‐zone and multizone building control use cases. Finally, six key insights for future research and broader adoption of RL methods are identified, with specific emphasis on problem features, algorithmic explainability, and solution stability.