Risk Analysis最新文献

The study examines the reflections of various experts in risk management when asked about uncertainty generated by a health threat and the response to such a threat: what criteria should guide action when potential harm is anticipated, but not known with certainty? The objective of the research is to obtain a holistic perspective of ethical conflicts in risk management, based on experts' accounts within the Spanish territory. A qualitative study was conducted through semi-structured interviews with 27 experts from various fields related to health risk management and its ethical implications, following the grounded theory method. The method includes theory generation through an inductive approach, based on the identified categories. The 27 narratives obtained revealed a variety of fundamental issues grouped into 8 subcategories and subsequently grouped into three main categories. The first category focuses on human vulnerability in health matters. The second category explores the agents and instruments for decision-making that arise from uncertain or traumatic social events. The third category refers to the need for common ethical paradigms for all humanity that implement justice over universal values. A main theory was suggested on the concept of responsibility in a global common good. There is an urgent need to assume this integrative responsibility as an inherent strategy in decision-making. To achieve this, the involved actors must acquire specific humanistic training, conceptualizing fundamental ethical principles, and emphasizing skills more related to humanistic virtues than technical knowledge.

Disruptions to the pharmaceutical supply chain (PSC) have negative implications for patients, motivating their prediction to improve risk mitigation. Although data analytics and machine learning methods have been proposed to support the characterization of probabilities to inform decisions and risk mitigation strategies, their application in the PSC has not been previously described. Further, it is unclear how well these models perform in the presence of emergent events representing deep uncertainty such as the COVID-19 pandemic. This article examines the use of data-driven models to predict PSC disruptions before and during the COVID-19 pandemic. Using data on generic drugs from the pharmacy supply chain division of a Fortune 500 pharmacy benefit management firm, we have developed predictive models based on the naïve Bayes algorithm, where the models predict whether a specific supplier or whether a specific product will experience a supply disruption in the next time period. We find statistically significant changes in the relationships of nearly all variables associated with product supply disruptions during the pandemic, despite pre-pandemic stability. We present results showing how the sensitivity, specificity, and false positive rate of predictive models changed with the onset of the COVID-19 pandemic and show the beneficial effects of regular model updating. The results show that maintaining model sensitivity is more challenging than maintaining specificity and false positive rates. The results provide unique insight into the pandemic's effect on risk prediction within the PSC and provide insight for risk analysts to better understand how surprise events and deep uncertainty affect predictive models.

Anhydrous ammonia has seen a rapid increase in demand due to recent developments in clean energy technologies. As it is a potential carrier of hydrogen, the transportation industry is currently facing significant logistic challenges as well as safety risks. Based on the causes and effects, this study categorizes incident patterns based on the historical incidents from 1971 to 2021 in the United States during transportation. Analysis of temporal patterns revealed that government regulations and improvement of safety infrastructures have made the biggest impact on lowering incident rates. Spatial analysis methods are also applied to understand the relationships between these incidents and spatial factors, such as land area, number of ammonia production facilities, total average production capacity, and total length of freight railway and highway in each US state. The spatial and temporal patterns and interpretations provide safety references to manage the growing hazardous transport in clean energy.

Early investments in regional hydrogen systems carry two distinct types of risk: (1) economic risk that projects will not be financially viable, resulting in stranded capital, and (2) environmental risk that projects will not deliver deep reductions in greenhouse gas emissions and through leaks, perhaps even contribute to climate change. This article systematically reviews the literature and performs analysis to describe both types of risk in the context of recent efforts in the United States and worldwide to support the development of "hydrogen hubs" or regional systems of hydrogen production and use. We review estimates of hydrogen production costs and projections of how future costs are likely to change over time for different production routes, environmental impacts related to hydrogen and methane leaks, and the availability and effectiveness of carbon capture and sequestration. Finally, we consider system-wide risks associated with evolving regional industrial structures, including job displacement and underinvestment in shared components, such as refueling. We conclude by suggesting a set of design principles that should be applied in developing early hydrogen hubs if they are to be a successful step toward creating a decarbonized energy system.

The past decade has seen efforts to develop new forms of autonomous systems with varying applications in different domains, from underwater search and rescue to clinical diagnosis. All of these applications require risk analyses, but such analyses often focus on technical sources of risk without acknowledging its wider systemic and organizational dimensions. In this article, we illustrate this deficit and a way of redressing it by offering a more systematic analysis of the sociotechnical sources of risk in an autonomous system. To this end, the article explores the development, deployment, and operation of an autonomous robot swarm for use in a public cloakroom in light of Macrae's structural, organizational, technological, epistemic, and cultural framework of sociotechnical risk. We argue that this framework provides a useful tool for capturing the complex "nontechnical" dimensions of risk in this domain that might otherwise be overlooked in the more conventional risk analyses that inform regulation and policymaking.

Water supply and sanitation are essential household services frequently shared in resource-poor settings. Shared sanitation can increase the risk of enteric pathogen transmission due to suboptimal cleanliness of facilities used by large numbers of individuals. It also can potentially increase the risk of respiratory disease transmission. As sanitation is an essential need, shared sanitation facilities may act as important respiratory pathogen transmission venues even with strict control measures such as stay-at-home recommendations in place. This analysis explores how behavioral and infrastructural conditions surrounding shared sanitation may individually and interactively influence respiratory pathogen transmission. We developed an individual-based community transmission model using COVID-19 as a motivating example parameterized from empirical literature to explore how transmission in shared latrines interacts with transmission at the community level. We explored mitigation strategies, including infrastructural and behavioral interventions. Our review of empirical literature confirms that shared sanitation venues in resource-poor settings are relatively small with poor ventilation and high use patterns. In these contexts, shared sanitation facilities may act as strong drivers of respiratory disease transmission, especially in areas reliant on shared facilities. Decreasing dependence on shared latrines was most effective at attenuating sanitation-associated transmission. Improvements to latrine ventilation and handwashing behavior were also able to decrease transmission. The type and order of interventions are important in successfully attenuating disease risk, with infrastructural and engineering controls being most effective when administered first, followed by behavioral controls after successful attenuation of sufficient alternate transmission routes. Beyond COVID-19, our modeling framework can be extended to address water, sanitation, and hygiene measures targeted at a range of environmentally mediated infectious diseases.

Community recovery from a disaster is a complex process, in which the importance of different types of infrastructure functionality can change over time. Most of the myriad of metrics available for measuring disaster resilience do not capture the dynamic importance of functionality explicitly, however. This means that very different recovery trajectories of a given infrastructure can correspond to the same resilience value, regardless of variations in its utility over time. While some efforts have been made to integrate features of time dependency into individual facility resilience quantification, the resulting metrics either capture only a limited set of temporal instances throughout the post-disaster phase or do not offer a way to prioritize time steps in line with variations in the importance of facility functionality. This study proposes a novel, straightforward metric for component-level post-disaster resilience quantification that overcomes the aforementioned limitations. The metric involves a dynamic weighting component that enables stakeholders to place varying emphasis on different temporal points throughout the recovery process. The end-user-centered approach to resilience quantification facilitated by the metric allows for flexible, context-specific interpretations of infrastructure functionality importance that may vary across different communities. The metric is demonstrated through a hypothetical case study of infrastructure facilities with varying degrees of importance across the post-disaster recovery period, which showcases its versatility relative to a previously well-established measurement of component-level resilience. The proposed metric has significant potential for use in stakeholder-driven approaches to decision making on critical infrastructure (as well as other types of built environment) recovery and resilience.

Realizing positive social and environmental outcomes from assisted ecosystem adaptation requires the management of complex, uncertain, and ambiguous risks. Using assisted coral reef adaptation as a case study, this article presents a conceptual framework that defines social impacts as the physical and cognitive consequences for people of planned intervention and social risks as potential impacts transformed into objects of management through assessment and governance. Reflecting on its multiple uses in the literature, we consider "social risk" in relation to risks to individuals and communities, risks to First Peoples, risks to businesses or project implementation, possibilities for amplified social vulnerability, and risk perceptions. Although much of this article is devoted to bringing clarity to the different ways in which social risk manifests and to the multiple characters of risk and uncertainty, it is apparent that risk governance itself must be an inherently integrative and social process.

The global sustainability movement is reshaping the operational requirements and managerial approaches of maritime firms, resulting in the emergence of unprecedented and complex risks in the sector. This has driven maritime firms to leverage digital tools, such as artificial intelligence (AI) capabilities, to enhance their sustainability risk management (SRM) endeavors. Drawing on the organizational information-processing theory (OIPT), this study proposes four AI capabilities: customer value proposition, key process optimization, key resource optimization, and societal good. It examines their influence on sustainability-related knowledge management capabilities (SKMC), stakeholder engagement, and SRM. A survey questionnaire was used to gather responses from 157 maritime professionals across various sectors of the industry, providing empirical data for analysis. Employing structural equation modeling, the findings reveal that AI capabilities can improve SKMC. These findings enhance existing literature by using OIPT concepts to investigate the interplay among the constructs that lead to better SRM in maritime firms. Furthermore, the study offers managerial guidance by providing insights into AI capabilities that maritime firms should incorporate into their operations, fostering best practices to effectively manage sustainability risks and ensure the firm's long-term survival.

As urbanization continues to accelerate worldwide, urban flooding is becoming increasingly destructive, making it important to improve emergency scheduling capabilities. Compared to other scheduling problems, the urban flood emergency rescue scheduling problem is more complicated. Considering the impact of a disaster on the road network passability, a single type of vehicle cannot complete all rescue tasks. A reasonable combination of multiple vehicle types for cooperative rescue can improve the efficiency of rescue tasks. This study focuses on the urban flood emergency rescue scheduling problem considering the actual road network inundation situation. First, the progress and shortcomings of related research are analyzed. Then, a four-level emergency transportation network based on the collaborative water-ground multimodal transport transshipment mode is established. It is shown that the transshipment points have random locations and quantities according to the actual inundation situation. Subsequently, an interactive model based on hierarchical optimization is constructed considering the travel length, travel time, and waiting time as hierarchical optimization objectives. Next, an improved A* algorithm based on the quantity of specific extension nodes is proposed, and a scheduling scheme decision-making algorithm is proposed based on the improved A* and greedy algorithms. Finally, the proposed decision-making algorithm is applied in a practical example for solving and comparative analysis, and the results show that the improved A* algorithm is faster and more accurate. The results also verify the effectiveness of the scheduling model and decision-making algorithm. Finally, a scheduling scheme with the shortest travel time for the proposed emergency scheduling problem is obtained.