Risk Analysis最新文献

This paper addresses the question of identifying and distinguishing risk amplification incidents and patterns in the news media. To meet this objective, our study incorporates a novel "floodlight" approach utilizing the Society for Risk Analysis Glossary in conjunction with topic modeling and time-series analysis, to investigate risk-focused stories within a corpus of 271,854 US news articles over the past two decades. We find that risk amplification in the US news media is concentrated around seven core risk news categories-business, domestic affairs, entertainment, environment, geopolitics, health, and technology-which also vary in the risk-related terms that they predominantly employ. We also identify 14 signal events that can be distinguished relative to general risk news within their categories. Across these events, the "War on Terror" and COVID-19 are seen to display uniquely dynamic media reporting patterns, including a systemic influence between risk news categories and the attenuation of other risk news. We discuss possible explanations for these findings along with their wider research and policy implications.

The renewable energy market in the United States of America (USA) has experienced several crests and troughs owing to the changes in the climate policies. These changes in the climate policies have impacted the climate risk management scenario in the USA. This impact has changed the behavioral pattern of the renewable energy drivers, and a supply-side analysis of this aspect is largely ignored in the literature. In this pursuit, the present study aims at analyzing the moderating role of climate policy uncertainty in shaping the behavior of renewable energy drivers in the USA. Given the risk analysis perspective, a novel multivariate quantile-on-quantile causality test is introduced in the present study to address five aspects of risk analysis, i.e., tail dependence, co-movement, predictability, multivariate, and asymmetric impact. Moreover, this test also addresses the omitted variable bias and absence of ortho-partiality distribution, which were inherent to Granger causality test. Along with the analysis at the national level, a firm-level analysis is also done by taking the top-5 renewable energy generation firms of the USA. The results show that the climate policy uncertainty has a dampening effect on the renewable energy drivers, and this effect differs at the firm level. These impacts show a significant policy dimension for addressing the climatic risk management concerns in the USA, while achieving the Sustainable Development Goal (SDG) 7 objectives.

Government agencies, international institutions, and independent experts have published approaches for the assessment and management of health and environmental risks. This includes evidence-based strategies and publications supporting risk decision-making frameworks reflecting contemporary practices, the overarching context, and governance structures for addressing known and emerging risk issues. This scoping review surveys the literature, over the last five decades, to identify key attributes of health and environmental risk decision-making and how these inherent characteristics are related to the overarching regulatory decision-making context. The findings provide insights on how these publications accounted for the circumstances and triggers at that time. This includes incorporating factors reflecting advances in science and technology, a better understanding of underlying values (e.g., societal), and an expansion in the scope and complexity required for conducting different evaluations relevant to health and environmental risks. Consequently, the evolution from linear to more expanded and holistic decision-making frameworks incorporates foundational elements, such as the well-established steps for assessing risks, while adding aspects reflecting transformative changes and paradigm shifts (e.g., the use of non-animal testing strategies for evaluating human safety). Our analysis also resulted in the generation of a consolidated listing of ten attributes: trigger/issue, regulatory context, regulatory factors, core values, risk decision-making principles, cross-cutting attributes, design (scope and steps), structure, decision-making pathway, and evidence-knowledge requirements for risk decision-making. A better understanding of this evolution in risk decision-making and the listing of key attributes will be used in future work aimed at developing considerations for next generation decision-making approaches for health and environmental risks.

Current knowledge about public climate change perception mainly covers belief, concern, and attitudes. However, how this discourse is interpreted using individuals' own frame of reference remains largely unknown, particularly in many large emitters from non-Annex I countries such as China. This study, for the first time, performs a nationwide open-ended survey covering 4,037 respondents and collected 12,100 textual answers. Using a semiautomated coding method, we find seven mental images that exclusively represent the Chinese interpretation of the climate change issue, including global warming, distant icons, natural disasters, environmental degradation, cause, solution, and weather. Analysis of influencing factors shows that females, those with lower education levels, lower income, and older individuals tend to connect climate change with natural weather phenomena. Younger and well-educated residents in developed cities are more aware of various consequences and anthropogenic causes of climate change. People with stronger climate change beliefs, policy support, and personal experience of extreme weather are more likely to mention disastrous impacts, carbon emission as causes, and potential solutions. Employing the multilevel regression and post-stratification technique, we map the prevalence of mental images in China at the prefecture-city level. The results reveal significant geographical heterogeneity, with estimated national means ranging from a high of 55% (weather) to a low of 11% (solution). Our findings reveal diverse perspectives and a widespread misconception of climate change in China, suggesting the need for tailored clarification strategies to gain public consent.

A useful theoretical lens that has emerged for understanding urban resilience is the four basic types of interdependencies in critical infrastructures: the physical, geographic, cyber, and logical types. This paper is motivated by a conceptual and methodological limitation-although logical interdependencies (where two infrastructures affect the state of each other via human decisions) are regarded as one of the basic types of interdependencies, the question of how to apply the notion and how to quantify logical relations remains under-explored. To overcome this limitation, this study focuses on institutions (rules), for example, rules and planned tasks guiding human interactions with one another and infrastructure. Such rule-mediated interactions, when linguistically expressed, have a syntactic form that can be translated into a network form. We provide a foundation to delineate these two forms to detect logical interdependence. Specifically, we propose an approach to quantify logical interdependence based on the idea that (1) there are certain network motifs indicating logical relations, (2) such network motifs can be discerned from the network form of rules, and that (3) the higher the frequency of these motifs between two infrastructures, the greater the extent of logical interdependency. We develop a set of such motifs and illustrate their usage using an example. We conclude by suggesting a revision to the original definition of logical interdependence. This rule-focused approach is relevant to understanding human error in risk analysis of socio-technical systems, as human error can be seen as deviations from constraints that lead to accidents.

There are two primary sources of uncertainty in the interpretability of toxicity values, like the reference dose (RfD): estimates of the point of departure (POD) and the absence of chemical-specific human variability data. We hypothesize two solutions-employing Bayesian benchmark dose (BBMD) modeling to refine POD determination and combining high-throughput toxicokinetic modeling with population-based toxicodynamic in vitro data to characterize chemical-specific variability. These hypotheses were tested by deriving refined probabilistic estimates for human doses corresponding to a specific effect size (M) in the Ith population percentile (HD_M ^I) across 19 Superfund priority chemicals. HD_M ^I values were further converted to biomonitoring equivalents in blood and urine for benchmarking against human data. Compared to deterministic default-based RfDs, HD_M ^I values were generally more protective, particularly influenced by chemical-specific data on interindividual variability. Incorporating chemical-specific in vitro data improved precision in probabilistic RfDs, with a median 1.4-fold reduction in uncertainty variance. Comparison with US Environmental Protection Agency's Exposure Forecasting exposure predictions and biomonitoring data from the National Health and Nutrition Examination Survey identified chemicals with margins of exposure nearing or below one. Overall, to mitigate uncertainty in regulatory toxicity values and guide chemical risk management, BBMD modeling and chemical-specific population-based human in vitro data are essential.

This paper presents a new approach for quantitatively modeling the resilience of a system that has been disrupted by a sudden-impact event. It introduces a new theoretical model that explicitly incorporates representations of the enabling and inhibiting forces that are inherent within postdisruption recovery behavior. Based on a new, more comprehensive measure of resilience that is able to capture both negative and positive deviations in performance, a generic mass-spring system is then used to illustrate the applicability of the theoretical model. The interplay between the enabling and inhibiting forces that is revealed by the new model provides a new theoretical basis for understanding the complexity of resilience and disaster recovery. With the addition of the new resilience measure, it lends support for defining and characterizing a new type of resilient behavior: unstable resilience.

Limited access to food stores is often linked to higher health risks and lower community resilience. Socially vulnerable populations experience persistent disparities in equitable food store access. However, little research has been done to examine how people's access to food stores is affected by natural disasters. Previous studies mainly focus on examining potential access using the travel distance to the nearest food store, which often falls short of capturing the actual access of people. Therefore, to fill this gap, this paper incorporates human mobility patterns into the measure of actual access, leveraging large-scale mobile phone data. Specifically, we propose a novel enhanced two-step floating catchment area method with travel preferences (E2SFCA-TP) to measure accessibility, which extends the traditional E2SFCA model by integrating actual human mobility behaviors. We then analyze people's actual access to grocery and convenience stores across both space and time under the devastating winter storm Uri in Harris County, Texas. Our results highlight the value of using human mobility patterns to better reflect people's actual access behaviors. The proposed E2SFCA-TP measure is more capable of capturing mobility variations in people's access, compared with the traditional E2SFCA measure. This paper provides insights into food store access across space and time, which could aid decision making in resource allocation to enhance accessibility and mitigate the risk of food insecurity in underserved areas.

This study aims to assess the frequency and associated factors of surgical "near-miss" incidents (NMIs) in neurosurgery using an event reporting system, to inform the development of appropriate interventions. This retrospective study collected reports of NMIs in our hospital's neurosurgery operating room (OR) from January 2019 to January 2022 through an adverse event reporting system and anonymous surveys. We conducted intergroup difference analysis using t-tests and investigated factors contributing to NMIs using Pearson correlation coefficients. We further constructed multinomial logistic regression models to explore the important factors affecting the types of lost objects and search times. A total of 195 NMIs were included in this study, with the primary items lost being 62 brain cotton pads and 133 needles. Statistical analysis revealed that smaller pads (48.4%) and size 3.0 needles (49.6%) were the most commonly missed items, with the longest retrieval times. The likelihood of NMIs occurring was higher for nurses with junior and/or non-neurosurgical backgrounds (needles: 82.7%, pads: 83.9%). Furthermore, factors such as extended working hours, nighttime surgeries, larger incisions, and more surgical instruments all increased the incidence of NMIs. The results of the multinomial logistic regression model showed that the type and search time for lost needles in the OR were jointly influenced by multiple factors (p < 0.05) compared to cotton pads. The occurrence of NMIs is associated with various factors. Reporting NMIs and their causes helps identify solutions before adverse events occur, thereby enhancing patient safety.

We constructed a rapid infection risk assessment model for contacts of COVID-19. The improved Wells-Riley model was used to estimate the probability of infection for contacts of COVID-19 in the same place and evaluate their risk grades. We used COVID-19 outbreaks that were documented to validate the accuracy of the model. We analyzed the relationship between controllable factors and infection probability and constructed common scenarios to analyze the infection risk of contacts in different scenarios. The model showed the robustness of the fitting (mean relative error = 5.89%, mean absolute error = 2.03%, root mean squared error = 2.03%, R² = 0.991). We found that improving ventilation from poorly ventilated to naturally ventilated and wearing masks can reduce the probability of infection by about two times. Contacts in places of light activity, loud talking or singing, and heavy exercise, oral breathing (e.g., gyms, KTV, choirs) were at higher risk of infection. The model constructed in this study can quickly and accurately assess the infection risk grades of COVID-19 contacts. Simply opening doors and windows for ventilation can significantly reduce the risk of infection in certain places. The places of light activity, loud talking or singing, and heavy exercise, oral breathing, should pay more attention to prevent and control transmission of the epidemic.