Risk Analysis最新文献

The way political identity serves as a foundation for political polarization in the United States permits elites to extend conflict rapidly to new issue areas. Further, the types of cognitive mechanisms and shortcuts used in the politically polarized information environment are similar to some of those used in risk perception. Consequently, political elites may easily create partisan risk positions, largely through politically focused social amplification of risk. The COVID-19 pandemic provided a natural experiment for testing predictions about such risk politics. We asked questions about pandemic-related views, behaviors, and policies at the outset of the pandemic in April 2020 and again in September 2020 via public opinion surveys. Our data and analyses focus primarily on a single state, with some analysis extended to four states. We begin by demonstrating strong linkages between political partisan identification on the one hand and support for co-partisan elites, use of partisan information sources, and support for co-partisan policies on the other hand. We then find evidence that pandemic risk positions correspond with partisan information sources and find support for a mechanism involving partisan-tinted evaluation of elite cues. Partisan risk positions quickly became part of the larger polarized structure of political support and views. Finally, our evidence shows on the balance that partisan risk positions related to the pandemic coalesced and strengthened over time. Overall, while self-identified Democrats consistently viewed the coronavirus as the primary threat, self-identified Republicans quickly pivoted toward threats to their freedoms and to the economy.

The success of tunneling projects is crucial for infrastructure development. However, the potential leakage risk is particularly challenging due to the inherent uncertainties and fuzziness involved. To address this demanding challenge, a hybrid approach integrating the copula theory, cloud model, and risk matrix, is proposed. The dependence of multiple risk-related influential factors is explored by the construct of the copula-cloud model, and the diverse information is fused by applying the risk matrix to gain a crisp risk result. A case study is performed to test the applicability of the proposed approach, in which a risk index system consisting of nine critical factors is developed and Sobol-enabled global sensitivity analysis (GSA) is incorporated to investigate the contributions of different factors to the risk magnitude. Key findings are as follows: (1) Risk statuses of the studied three tunnel sections are perceived as under grade I (safe), II (low-risk), and III (medium-risk), respectively, and the waterproof material aspect is found prone to deteriorating the tunnel sections. Furthermore, the proposed approach allows for a better understanding of the trends in the risk statuses of the tunnel sections. (2) Strong interactions between influential factors exist and exert impacts on the final risk results, proving the necessity of studying the factor dependence. (3) The developed neutral risk matrix presents a strong robustness and displays a higher recognition capacity in risk assessment. The novelty of this research lies in the consideration of the dependence and uncertainty in multisource information fusion with a hybrid copula-cloud model, enabling to perform a robust risk assessment under different risk matrices with varying degrees of risk tolerance.

Listeria monocytogenes is a foodborne pathogen of concern for cancer patients, who face higher morbidity and mortality rates than the general population. The neutropenic diet (ND), which excludes fresh produce, is often utilized to mitigate this risk; however, an analysis weighing the theoretical listeriosis risk reduction of produce exclusion aspects of the ND and possible negative tradeoffs has never been conducted. Consequently, this work constructed decision analytic models using disability-adjusted life years (DALYs) to compare the impacts of the ND, such as increased neutropenic enterocolitis (NEC) likelihood, with three alternative dietary practices (safe food handling [SFH], surface blanching, and refrigeration only) across five age groups, for cancer patients who consume ready-to-eat salad. Less disruptive diets had fewer negative health impacts in all scenarios, with median alternative diet DALYs per person per chemotherapy cycle having lower values in terms of negative health outcomes (0.088-0.443) than the ND (0.619-3.102). DALYs were dominated by outcomes associated with NEC, which is more common in patients following the ND than in other diets. Switchover point analysis confirmed that, because of this discrepancy, there were no feasible values of other parameters that could justify the ND. Correspondingly, the sensitivity analysis indicated that NEC mortality rate and remaining life expectancy strongly affected DALYs, further illustrating the model's strong dependence on NEC outcomes. Given these findings, and the SFH's ease of implementation and high compliance rates, the SFH diet is recommended in place of the ND.

Human error constitutes a significant cause of accidents across diverse industries, leading to adverse consequences and heightened disruptions in maintenance operations. Organizations can enhance their decision-making process by quantifying human errors and identifying the underlying influencing factors, thereby mitigating their repercussions. Consequently, it becomes crucial to examine the value of human error probability (HEP) during these activities. The objective of this paper is to determine and simulate HEP in maintenance tasks at a cement factory, utilizing performance shaping factors (PSFs). The research employs the cross-impact matrix multiplication applied to classification (MICMAC) analysis method to evaluate the dependencies, impacts, and relationships among the factors influencing human error. This approach classifies and assesses the dependencies and impacts of different factors on HEP, occupational accidents, and related costs. The study also underscores that PSFs can dynamically change under the influence of other variables, emphasizing the necessity to forecast the behavior of human error over time. Therefore, this paper utilizes the MICMAC method to analyze the interdependencies, relationships, and impact levels among different variables. These relationships are then utilized to optimize the implementation of the system dynamics (SD) method. An SD model is employed to forecast the system's behavior, and multiple scenarios are presented. By considering the HEP value, managers can adjust organizational conditions and personnel to ensure acceptability. The paper also presents various scenarios related to HEP to assist managers in making informed decisions.

Public knowledge and awareness about radiation (both natural and manmade) tend to be low, while perceived risk of radiation tends to be high. High perceptions of risk associated with radiation have been linked to the affect heuristic and general feelings of dread, which are often not proportionate to actual risk. For example, studies routinely show that members of the public rate the risk of radiation from nuclear power plants as significantly higher (and riskier) than radiation from medical X-rays. This disconnect can have implications for adoption of protective actions during a potential radiation emergency and the perceived efficacy of these actions. This study explores how risk communication efforts influence public risk perceptions, intended protective action, and perceived efficacy of those actions. Using unique data from a survey of New York City adults, we analyze how information provision using different formats-no information, an infographic, an informational video-impact perceptions and response to a hypothetical radiation emergency. We hypothesize that respondents who receive some information, either through the infographic or the video, will have higher perceived efficacy and are more likely to take protective action. Findings suggest that providing information about what to do during a radiation emergency has a statistically significant impact on both perceived efficacy and adoption of protective action. Respondents who saw the informational video were most likely to say that they would take the correct protective actions and had the highest perceived efficacy, followed by those who saw the infographic.

In this study, we conduct the first comprehensive, nationwide assessment of social equity performance of multiple federal post- and pre-disaster assistance programs that differ in targeted recipients, project types, forms of aid, and funding requirements. We draw on the social equity and distributive justice theory to develop and test a set of hypotheses on the influence of program design and specificity on their aid distributional patterns and equity performance. The analysis uses panel data of about 3000 US counties to examine the relationship between a county's receipt of federal assistance and its recent disaster damage, socioeconomic, demographic, political, local government, and geographic characteristics in a two-stage random effects Tobit model. Expectedly, we find that post-disaster grants are largely driven by recent disaster damage, while damage is simultaneously influenced by local socioeconomic conditions. For all disaster programs, disproportionately more federal aid is allocated to populous counties. For programs geared toward state and local governments and targeting community recovery and mitigation, more aid is received by counties with better socioeconomic conditions. Conversely, for programs targeting individual relief and recovery, more aid is given to counties with lower incomes and greater social vulnerability. Results also indicate that counties located in high-risk regions receive greater outlays. These findings shed light on the varying degrees of social equity of federal disaster assistance programs tied to their cost-share requirement, funding caps, and inherent complexity of application procedures.

Secure and reliable power systems are vital for modern societies and economies. While there is a focus in the literature on predicting power outages caused by severe weather events, relatively little literature exists on identifying hot spots, locations where outages occur repeatedly and at a higher rate than expected. Reliably identifying hotspots can provide critical input for risk management efforts by power utilities, helping them to focus scarce resources on the most problematic portions of their system. In this article, we show how existing work on Moran's I spatial statistic can be adapted to identify power outage hotspots based on the types and quantities of data available to utilities in practice. The local Moran's I statistic was calculated on a grid cell level and a set of criteria were used to filter out which grid cells are considered hotspots. The hotspot identification approach utilized in this article is an easy method for utilities to use in practice, and it provides the type of information needed to directly support utility decisions about prioritizing areas of a power system to inspect and potentially reinforce.

We present an integrated framework that utilizes high-resolution seamless simulations of disasters and national economies for estimating the economic impacts of disasters. The framework consists of three components: a physics-based simulator to simulate the disaster and estimate the response of the infrastructure; a tool that estimates the losses suffered by the infrastructure based on its response; and an agent-based economic model (ABEM) that simulates the national economy considering the infrastructure damage and postdisaster decisions of the economic entities. The ABEM used in the framework has been implemented in a high-performance computing environment to simulate large economies at 1:1 scale. Furthermore, it has been calibrated to the Japanese economy using publicly available macroeconomic data and validated to the Japanese economy under the business-as-usual scenario. We demonstrate the integrated framework by simulating a potential Nankai-trough earthquake disaster and estimating its impacts on the Japanese economy. The seismic response of 1.8 million buildings of the Osaka-bay area has been estimated using a large-scale earthquake disaster simulator and corresponding repair costs are estimated using the Performance Assessment Calculation Tool. As per our estimates, repair costs amount to approximately 15 trillion Yen. Considering the investments made by impacted households and firms toward recovery, the postdisaster economy is simulated using the ABEM for 5 years under two recovery scenarios. Industrial production is expected to recover in three quarters whereas 10-13 quarters will be required to finish all the repair work.

Perceptions of efficacy play a central role in motivating people to engage in climate actions. However, there has been little investigation into how different climate efficacy beliefs are formed and how they may be associated with support for climate mitigation policies. This study, based on the motivated control framework, examines how risk perceptions may differentially be associated with four types of efficacy constructs (self-efficacy, personal outcome expectancy, collective efficacy, and collective outcome efficacy). It also places the motivated control framework in the context of the partisan information sphere and examines how exposure to partisan news may influence mitigation policy support through the mediators of risk perceptions and the efficacy constructs. Results suggest that liberal- and conservative-leaning news exposure, respectively, associate with higher and lower supports for policies. Overall, risk perception was a significant mediator, and the mediating function of efficacy varied depending on the specific construct being examined. For liberal news use, increased risk perceptions had a positive association with policy support through self-efficacy and collective outcome expectancy but also had an unexpected negative association with policy support through personal outcome expectancy and collective efficacy. For conservative news use, decreased risk perceptions resulted in further decreased beliefs of self-efficacy and collective outcome expectancy, resulting in lower levels of support for climate policies. We also find that political ideology is a significant moderator for the mediation model. Implications for climate change communication are discussed.

Extreme events like the credit crunch, the September 11th attacks, the coronavirus pandemic, and Hamas' attack on Israel each have in common that they should not have come as a surprise, yet still did. One reason surprises happen is that a risk assessment reflects the knowledge of the assessors, yet risk also includes uncertainties that extend beyond this knowledge. A risk assessment is thus susceptible to surprises as it focuses attention on what is known. Developing an expectation for surprises is key to their avoidance and requires that risk assessors specifically consider their "unknowledge"-that is, what they do not presently know about an event, outcome, or activity and its potential consequences and triggers. One way to emphasize the need for risk assessors to consider unknowledge is to explicitly include it as a separate component in risk-assessment frameworks. This article formalizes the inclusion of unknowledge in a contemporary risk-assessment framework.