GIS‐Based Integration of Social Vulnerability and Level 3 Probabilistic Risk Assessment to Advance Emergency Preparedness,Planning, and Response for Severe Nuclear Power Plant Accidents

In the nuclear power industry, Level 3 probabilistic risk assessment (PRA) is used to estimate damage to public health and the environment if a severe accident leads to large radiological release. Current Level 3 PRA does not have an explicit inclusion of social factors and, therefore, it is not possible to perform importance ranking of social factors for risk‐informing emergency preparedness, planning, and response (EPPR). This article offers a methodology for adapting the concept of social vulnerability, commonly used in natural hazard research, in the context of a severe nuclear power plant accident. The methodology has four steps: (1) calculating a hazard‐independent social vulnerability index for the local population; (2) developing a location‐specific representation of the maximum radiological hazard estimated from current Level 3 PRA, in a geographic information system (GIS) environment; (3) developing a GIS‐based socio‐technical risk map by combining the social vulnerability index and the location‐specific radiological hazard; and (4) conducting a risk importance measure analysis to rank the criticality of social factors based on their contribution to the socio‐technical risk. The methodology is applied using results from the 2012 Surry Power Station state‐of‐the‐art reactor consequence analysis. A radiological hazard model is generated from MELCOR accident consequence code system, translated into a GIS environment, and combined with the Center for Disease Control social vulnerability index (SVI). This research creates an opportunity to explicitly consider and rank the criticality of location‐specific SVI themes based on their influence on risk, providing input for EPPR.     

Information Specificity and Hazard Risk Potential as Moderators of Trust Asymmetry

Trust in risk managers appears to be an important antecedent of public acceptance for many hazards. However, such trust may be fragile since research suggests that negative performance information has a greater impact than positive performance information (Slovic, 1993). Closer examination of these findings suggests two potential moderators of this valence-related asymmetry-information specificity and hazard risk potential. First, we predicted that the asymmetry would be less evident for low versus high specificity information (risk management policies vs. concrete events). Second, we predicted that it would also be less evident for a low- versus high-risk hazard (pharmaceutical vs. nuclear industry). Study 1 reanalyzed Slovic's original trust asymmetry data for the nuclear industry. In line with Prediction 1, trust asymmetry was less evident for policy than event-related information. Using a new set of items with more clearly defined levels of specificity, Study 2 replicated and extended these findings for the high-risk hazard (nuclear power). In line with Prediction 2, trust asymmetry was even less evident for the low-risk hazard (pharmaceuticals). Positive policies in this industry actually had a greater impact on trust than negative ones, in contrast to previous findings. Results support an information diagnosticity account of earlier findings and suggest that trust in risk managers may be more robust than previously believed.     

The influence of mapped hazards on risk beliefs: a proximity-based modeling approach

Interview findings suggest perceived proximity to mapped hazards influences risk beliefs when people view environmental hazard maps. For dot maps, four attributes of mapped hazards influenced beliefs: hazard value, proximity, prevalence, and dot patterns. In order to quantify the collective influence of these attributes for viewers’ perceived or actual map locations, we present a model to estimate proximity‐based hazard or risk (PBH) and share study results that indicate how modeled PBH and map attributes influenced risk beliefs. The randomized survey study among 447 university students assessed risk beliefs for 24 dot maps that systematically varied by the four attributes. Maps depicted water test results for a fictitious hazardous substance in private residential wells and included a designated “you live here” location. Of the nine variables that assessed risk beliefs, the numerical susceptibility variable was most consistently and strongly related to map attributes and PBH. Hazard value, location in or out of a clustered dot pattern, and distance had the largest effects on susceptibility. Sometimes, hazard value interacted with other attributes, for example, distance had stronger effects on susceptibility for larger than smaller hazard values. For all combined maps, PBH explained about the same amount of variance in susceptibility as did attributes. Modeled PBH may have utility for studying the influence of proximity to mapped hazards on risk beliefs, protective behavior, and other dependent variables. Further work is needed to examine these influences for more realistic maps and representative study samples.     

Assessing End‐Of‐Supply Risk of Spare Parts Using the Proportional Hazard Model

Operators of long field‐life systems like airplanes are faced with hazards in the supply of spare parts. If the original manufacturers or suppliers of parts end their supply, this may have large impacts on operating costs of firms needing these parts. Existing end‐of‐supply evaluation methods are focused mostly on the downstream supply chain, which is of interest mainly to spare part manufacturers. Firms that purchase spare parts have limited information on parts sales, and indicators of end‐of‐supply risk can also be found in the upstream supply chain. This article proposes a methodology for firms purchasing spare parts to manage end‐of‐supply risk by utilizing proportional hazard models in terms of supply chain conditions of the parts. The considered risk indicators fall into four main categories, of which two are related to supply (price and lead time) and two others are related to demand (cycle time and throughput). The methodology is demonstrated using data on about 2,000 spare parts collected from a maintenance repair organization in the aviation industry. Cross‐validation results and out‐of‐sample risk assessments show good performance of the method to identify spare parts with high end‐of‐supply risk. Further validation is provided by survey results obtained from the maintenance repair organization, which show strong agreement between the firm's and the model's identification of high‐risk spare parts.     

Location optimization of strategic alert sites for homeland defense

This research uses a location analysis approach for selecting aircraft alert sites for the defense of important national areas of interest identified by the US Department of Defense. Solutions are generated in a two step approach where the minimum number of sites is first identified using the location set covering problem and then the result is improved by finding the minimum aggregate network distance or p-median solution from the alternate optimal solutions to the LSCP. This approach also identifies the p-center solution to the problem ensuring equitable response to all areas of interest. Sensitivity analysis is performed to determine the impact of altering aircraft launch and flying times on the number of required alert sites and the amount of coverage provided by selecting fewer locations. Results indicate a significant increase in the number of alert locations needed in comparison to original military estimates. The research points out significant implications about future military base closure decisions and the trade-offs between cost and required response times of aircraft in a defense emergency.     

Murphy Scale: A locational equivalent intensity scale for hazard events

Empirical cross-hazard analysis and prediction of disaster vulnerability, resilience, and risk requires a common metric of hazard strengths across hazard types. In this paper, the authors propose an equivalent intensity scale for cross-hazard evaluation of hazard strengths of events for entire durations at locations. The proposed scale is called the Murphy Scale, after Professor Colleen Murphy. A systematic review and typology of hazard strength metrics is presented to facilitate the delineation of the defining dimensions of the proposed scale. An empirical methodology is introduced to derive equivalent intensities of hazard events on a Murphy Scale. Using historical data on impacts and hazard strength indicators of events from 2013 to 2017, the authors demonstrate the utility of the proposed methodology for computing the equivalent intensities for earthquakes and tropical cyclones. As part of a new area of research called hazard equivalency, the proposed Murphy Scale paves the way toward creating multi-hazard hazard maps. The proposed scale can also be leveraged to facilitate hazard communication regarding past and future local experiences of hazard events for enhancing multi-hazard preparedness, mitigation, and emergency response.     

The Elaboration Likelihood Model and Communication About Food Risks

Factors such as hazard type and source credibility have been identified as important in the establishment of effective strategies for risk communication. The elaboration likelihood model was adapted to investigate the potential impact of hazard type, information source, and persuasive content of information on individual engagement in elaborative, or thoughtful, cognitions about risk messages. One hundred sixty respondents were allocated to one of eight experimental groups, and the effects of source credibility, persuasive content of information and hazard type were systematically varied. The impact of the different factors on beliefs about the information and elaborative processing examined. Low credibility was particularly important in reducing risk perceptions, although persuasive content and hazard type were also influential in determining whether elaborative processing occurred.     

Property Loss Estimation for Wind and Earthquake Perils

This article describes the development of a generic loss assessment methodology, which is applicable to earthquake and windstorm perils worldwide. The latest information regarding hazard estimation is first integrated with the parameters that best describe the intensity of the action of both windstorms and earthquakes on building structures, for events with defined average return periods or recurrence intervals. The subsequent evaluation of building vulnerability (damageability) under the action of both earthquake and windstorm loadings utilizes information on damage and loss from past events, along with an assessment of the key building properties (including age and quality of design and construction), to assess information about the ability of buildings to withstand such loadings and hence to assign a building type to the particular risk or portfolio of risks. This predicted damage information is then translated into risk-specific mathematical vulnerability functions, which enable numerical evaluation of the probability of building damage arising at various defined levels. By assigning cost factors to the defined damage levels, the associated computation of total loss at a given level of hazard may be achieved. This developed methodology is universal in the sense that it may be applied successfully to buildings situated in a variety of earthquake and windstorm environments, ranging from very low to extreme levels of hazard. As a loss prediction tool, it enables accurate estimation of losses from potential scenario events linked to defined return periods and, hence, can greatly assist risk assessment and planning.     

Aligning Chemical Assessment Tools Across the Hazard-Risk Continuum

With the growing number and diversity of hazard and risk assessment algorithms, models, databases, and frameworks for chemicals and their applications, risk assessors and managers are challenged to select the appropriate tool for a given need or decision. Some decisions require relatively simple tools to evaluate chemical hazards (e.g., toxicity), such as labeling for safe occupational handling and transport of chemicals. Others require assessment tools that provide relative comparisons among chemical properties, such as selecting the optimum chemical for a particular use among a group of candidates. Still other needs warrant full risk characterization, coupling both hazard and exposure considerations. Examples of these include new chemical evaluations for commercialization, evaluations of existing chemicals for novel uses, and assessments of the adequacy of risk management provisions. Even well-validated tools can be inappropriately applied, with consequences as severe as misguided chemical management, compromised credibility of the tool and its developers and users, and squandered resources. This article describes seven discrete categories of tools based on their information content, function, and the type of outputs produced. It proposes a systematic framework to assist users in selecting hazard and risk assessment tools for given applications. This analysis illustrates the importance of careful selection of assessment tools to achieve responsible chemical assessment communication and sound risk management.     

A Note on the Influence of Rail Defects on the Risk Associated with Shipping Hazardous Materials by Rail

Existing approaches to routing hazardous material shipments by rail recognize that track condition is an important influence, but have not included it in the risk assessment and routing models. This note explores the influence of track condition based on predictions of internal defects in the rail. The method developed predicts the expected frequency of accidents and subsequent consequences in terms of the expected number of fatalities accounting for one aspect of track condition-internal defects. It is intended to indicate the magnitude and impact of track condition. The formulation integrates models of consequences and the risk of a hazardous spill found in the literature with the frequency of accidents as a function of the number of defects. The number of defects may be based on observations or predicted as a function of the cumulative traffic. The models are used to calculate the expected number of fatalities per year for a particular route. Application of the methodology to a hypothetical route shows that the risk associated with the transportation of hazardous material shipments varies significantly with the expected number of defects in the track. Therefore, risk not only varies from route to route but over time for any section of track as the condition deteriorates.     

The Impact of Portfolio Location Uncertainty on Probabilistic Seismic Risk Analysis

Probabilistic seismic risk analysis is a well‐established method in the insurance industry for modeling portfolio losses from earthquake events. In this context, precise exposure locations are often unknown. However, so far, location uncertainty has not been in the focus of a large amount of research. In this article, we propose a novel framework for treatment of location uncertainty. As a case study, a large number of synthetic portfolios resembling typical real‐world cases were created. We investigate the effect of portfolio characteristics such as value distribution, portfolio size, or proportion of risk items with unknown coordinates on the variability of loss frequency estimations. The results indicate that due to loss aggregation effects and spatial hazard variability, location uncertainty in isolation and in conjunction with ground motion uncertainty can induce significant variability to probabilistic loss results, especially for portfolios with a small number of risks. After quantifying its effect, we conclude that location uncertainty should not be neglected when assessing probabilistic seismic risk, but should be treated stochastically and the resulting variability should be visualized and interpreted carefully.     

Risk-Adjusted Mission Value: Trading Off Mission Risk for Mission Value

This article develops a decision-theoretic methodology for the risk-adjusted mission value (RAMV) for selecting between alternative missions in the presence of uncertainty in the outcomes of the missions. This methodology permits trading off mission risk for mission value, something that probabilistic risk analysis cannot do unless it explicitly incorporates both mission value and risk aversion of the project management. The methodology, in its complete implementation, is consistent with the decision theory known as expected utility theory, although it differs from conventional decision theory in that the probabilities and all but one of the utilities are not those of the decision maker. The article also introduces a new interpretation of risk aversion. The methodology is consistent with the elementary management concept concerning division of labor. An example is presented for selecting between discrete alternatives-four landing sites on Mars. A second example is presented for selecting among a set of continuous alternatives-a comet flyby distance. The methodology is developed within the context of scientific missions, but the methodology is equally applicable to any situation requiring outcome value judgments, probability judgments, and risk aversion judgments by different constituencies.     

在线用户点赞与评论行为的产生机理差异研究———以医疗健康类企业微信公众号为例

很多企业或组织利用移动社交媒体服务平台(如微信公众号)发布内容来吸引用户参与互动(点赞、评论等)以达到宣传和营销目的,而用户不同互动行为的影响机制及其差异尚无明确结论.基于精细加工可能性模型和用户互动参与的相关理论文献,本文以医疗健康类企业微信公众号为研究情景,从中心路径(信息质量)和边缘路径(来源可信度和情绪因素)出发,探究用户对健康信息点赞和评论互动行为意向的影响机制及其差异.为了验证所提模型和假设,本文采用大规模在线情景问卷调研方法收集数据并进行实证检验,结果显示:1)信息质量、来源可信度对用户点赞意向的影响显著强于评论意向;2)信息负情绪性对评论意向影响不显著而对点赞意向的影响负向显著;3)信息正情绪性与来源可信度对两种用户互动行为意向的影响均显著强于信息质量.研究结论丰富了在线信息互动行为的相关理论,为移动社交媒体运营商在发布信息方面提供实践指导.     

Observing Unobservables: Identifying Information Asymmetries With a Consumer Credit Field Experiment

Information asymmetries are important in theory but difficult to identify in practice. We estimate the presence and importance of hidden information and hidden action problems in a consumer credit market using a new field experiment methodology. We randomized 58,000 direct mail offers to former clients of a major South African lender along three dimensions: (i) an initial “offer interest rate” featured on a direct mail solicitation; (ii) a “contract interest rate” that was revealed only after a borrower agreed to the initial offer rate; and (ii) a dynamic repayment incentive that was also a surprise and extended preferential pricing on future loans to borrowers who remained in good standing. These three randomizations, combined with complete knowledge of the lender's information set, permit identification of specific types of private information problems. Our setup distinguishes hidden information effects from selection on the offer rate (via unobservable risk and anticipated effort), from hidden action effects (via moral hazard in effort) induced by actual contract terms. We find strong evidence of moral hazard and weaker evidence of hidden information problems. A rough estimate suggests that perhaps 13% to 21% of default is due to moral hazard. Asymmetric information thus may help explain the prevalence of credit constraints even in a market that specializes in financing high‐risk borrowers.     

Perception of Risk and Credibility at Toxic Sites

This study integrates previous research methodologies to compare the risk perceptions and responses to risk messages of agency personnel and neighbors of Superfund sites in Michigan. The integration attempted and the focus on risk messages are shaped by a critical review of the social amplification conceptual framework. The study involved all four agency groups and three groups of site neighbors actively involved in Superfund planning across the state. The first part of the study utilized the psychometric techniques of hazard rating and hazard profiles that had not previously been used in studies involving stakeholders. While agency personnel responded similarly to experts in previous studies, the responses of individuals in the neighbor groups reflected experience with toxic sites and were dissimilar to previous ratings by the general public. The second part of the study consisted of a hypothetical toxic site scenario that focused on specific risk messages at different times in the site history. Results indicate that the difference in perception of risk occurs after the first testing at a site, and that dramatic differences arise between agency and resident groups regarding the credibility of information sources and the need for independent testing. A general lack of trust in the Superfund program was demonstrated by all groups. The results indicate that problems of institutional credibility and program adequacy cannot be addressed by better risk communication.     

Implementation of Equity in Resource Allocation for Regional Earthquake Risk Mitigation Using Two‐Stage Stochastic Programming

This article presents a new methodology to implement the concept of equity in regional earthquake risk mitigation programs using an optimization framework. It presents a framework that could be used by decisionmakers (government and authorities) to structure budget allocation strategy toward different seismic risk mitigation measures, i.e., structural retrofitting for different building structural types in different locations and planning horizons. A two‐stage stochastic model is developed here to seek optimal mitigation measures based on minimizing mitigation expenditures, reconstruction expenditures, and especially large losses in highly seismically active countries. To consider fairness in the distribution of financial resources among different groups of people, the equity concept is incorporated using constraints in model formulation. These constraints limit inequity to the user‐defined level to achieve the equity‐efficiency tradeoff in the decision‐making process. To present practical application of the proposed model, it is applied to a pilot area in Tehran, the capital city of Iran. Building stocks, structural vulnerability functions, and regional seismic hazard characteristics are incorporated to compile a probabilistic seismic risk model for the pilot area. Results illustrate the variation of mitigation expenditures by location and structural type for buildings. These expenditures are sensitive to the amount of available budget and equity consideration for the constant risk aversion. Most significantly, equity is more easily achieved if the budget is unlimited. Conversely, increasing equity where the budget is limited decreases the efficiency. The risk‐return tradeoff, equity‐reconstruction expenditures tradeoff, and variation of per‐capita expected earthquake loss in different income classes are also presented.     

Catastrophe Risk Models for Evaluating Disaster Risk Reduction Investments in Developing Countries

Major natural disasters in recent years have had high human and economic costs, and triggered record high postdisaster relief from governments and international donors. Given the current economic situation worldwide, selecting the most effective disaster risk reduction (DRR) measures is critical. This is especially the case for low‐ and middle‐income countries, which have suffered disproportionally more economic and human losses from disasters. This article discusses a methodology that makes use of advanced probabilistic catastrophe models to estimate benefits of DRR measures. We apply such newly developed models to generate estimates for hurricane risk on residential structures on the island of St. Lucia, and earthquake risk on residential structures in Istanbul, Turkey, as two illustrative case studies. The costs and economic benefits for selected risk reduction measures are estimated taking account of hazard, exposure, and vulnerability. We conclude by emphasizing the advantages and challenges of catastrophe model‐based cost‐benefit analyses for DRR in developing countries.     

An Empirical Bayes Approach to Estimating the Relation of Mortality to Exposure to Particulate Matter

As part of its assessment of the health risks associated with exposure to particulate matter (PM), the U.S. Environmental Protection Agency analyzed the risks associated with current levels, and the risk reductions that might be achieved by attainment of alternative PM standards, in two locations in the United States, Philadelphia, and Los Angeles. The concentration-response function describing the relation between a health endpoint and ambient PM concentrations is an important component, and a source of substantial uncertainty, in such risk analyses. In the absence of location-specific estimates, the concentration-response functions necessary for risk assessments in Philadelphia and Los Angeles must be inferred from the available information in other locations. Although the functional form of the concentration-response relations is assumed to be the same everywhere, the value of the PM coefficient in that function may vary from one location to another. Under this model, a distribution describes the probability that the PM coefficient in a randomly selected location will lie in any range of interest. An empirical Bayes estimation technique was used to improve the estimation of location-specific concentration-response functions relating mortality to short-term exposure to particles of aerodynamic diameter less than or equal to 2.5 microm (PM-2.5), for which functions have previously been estimated in several locations. The empirical Bayes-adjusted parameter values and their SEs were used to derive an estimate of the distribution of PM-2.5 coefficients for mortality associated with short-term exposures. From this distribution, distributions of relative risks corresponding to different specified changes in PM-2.5 concentrations could be derived.     

The Risk Management of Third Parties During Construction in Multifunctional Urban Locations

Buildings above roads, railways, and existing buildings themselves are examples of multifunctional urban locations. The construction stage of those buildings is in general extremely complicated. Safety is one of the critical issues during the construction stage. Because the traffic on the infrastructure must continue during the construction of the building above the infrastructure, falling objects due to construction activities form a major hazard for third parties, i.e., people present on the infrastructure or beneath it, such as car drivers and passengers. This article outlines a systematic approach to conduct quantitative risk assessment (QRA) and risk management of falling elements for third parties during the construction stage of the building above the infrastructure in multifunctional urban locations. In order to set up a QRA model, quantifiable aspects influencing the risk for third parties were determined. Subsequently, the conditional probabilities of these aspects were estimated by historical data or engineering judgment. This was followed by integrating those conditional probabilities, now used as input parameters for the QRA, into a Bayesian network representing the relation and the conditional dependence between the quantified aspects. The outcome of the Bayesian network—the calculation of both the human and financial risk in quantitative terms—is compared with the risk acceptance criteria as far as possible. Furthermore, the effect of some safety measures were analyzed and optimized in relation with decision making. Finally, the possibility of integration of safety measures in the functional and structural building design above the infrastructure are explored.     

Spatial Stochastic Simulation Offers Potential as a Quantitative Method for Pest Risk Analysis

Pest risk analysis represents an emerging field of risk analysis that evaluates the potential risks of the introduction and establishment of plant pests into a new geographic location and then assesses the management options to reduce those potential risks. Development of new and adapted methodology is required to answer questions concerning pest risk analysis of exotic plant pests. This research describes a new method for predicting the potential establishment and spread of a plant pest into new areas using a case study, Ralstonia solanacearum, a bacterial disease of potato. This method combines current quantitative methodologies, stochastic simulation, and geographic information systems with knowledge of pest biology and environmental data to derive new information about pest establishment potential in a geographical region where a pest had not been introduced. This proposed method extends an existing methodology for matching pest characteristics with environmental conditions by modeling and simulating dissemination behavior of a pest organism. Issues related to integrating spatial variables into risk analysis models are further discussed in this article.