A Data‐Driven Approach to Assessing Supply Inadequacy Risks Due to Climate‐Induced Shifts in Electricity Demand

The U.S. electric power system is increasingly vulnerable to the adverse impacts of extreme climate events. Supply inadequacy risk can result from climate‐induced shifts in electricity demand and/or damaged physical assets due to hydro‐meteorological hazards and climate change. In this article, we focus on the risks associated with the unanticipated climate‐induced demand shifts and propose a data‐driven approach to identify risk factors that render the electricity sector vulnerable in the face of future climate variability and change. More specifically, we have leveraged advanced supervised learning theory to identify the key predictors of climate‐sensitive demand in the residential, commercial, and industrial sectors. Our analysis indicates that variations in mean dew point temperature is the common major risk factor across all the three sectors. We have also conducted a statistical sensitivity analysis to assess the variability in the projected demand as a function of the key climate risk factor. We then propose the use of scenario‐based heat maps as a tool to communicate the inadequacy risks to stakeholders and decisionmakers. While we use the state of Ohio as a case study, our proposed approach is equally applicable to all other states.     

Risk analysis and risk management in an uncertain world.

The tragic attacks of September 11 and the bioterrorist threats with respect to anthrax that followed have raised a set of issues regarding how we deal with events where there is considerable ambiguity and uncertainty about the likelihood of their occurrence and their potential consequences. This paper discusses how one can link the tools of risk assessment and our knowledge of risk perception to develop risk management options for dealing with extreme events. In particular, it suggests ways that the members of the Society for Risk Analysis can apply their expertise and talent to the risks associated with terrorism and discusses the changing roles of the public and private sectors in dealing with extreme events.     

Modeling the demand reduction input-output (I-O) inoperability due to terrorism of interconnected infrastructures.

Interdependency analysis in the context of this article is a process of assessing and managing risks inherent in a system of interconnected entities (e.g., infrastructures or industry sectors). Invoking the principles of input-output (I-O) and decomposition analysis, the article offers a framework for describing how terrorism-induced perturbations can propagate due to interconnectedness. Data published by the Bureau of Economic Analysis Division of the U.S. Department of Commerce is utilized to present applications to serve as test beds for the proposed framework. Specifically, a case study estimating the economic impact of airline demand perturbations to national-level U.S. sectors is made possible using I-O matrices. A ranking of the affected sectors according to their vulnerability to perturbations originating from a primary sector (e.g., air transportation) can serve as important input to risk management. For example, limited resources can be prioritized for the "top-n" sectors that are perceived to suffer the greatest economic losses due to terrorism. In addition, regional decomposition via location quotients enables the analysis of local-level terrorism events. The Regional I-O Multiplier System II (RIMS II) Division of the U.S. Department of Commerce is the agency responsible for releasing the regional multipliers for various geographical resolutions (economic areas, states, and counties). A regional-level case study demonstrates a process of estimating the economic impact of transportation-related scenarios on industry sectors within Economic Area 010 (the New York metropolitan region and vicinities).     

Strategic Preparedness for Recovery from Catastrophic Risks to Communities and Infrastructure Systems of Systems

Natural and human‐induced disasters affect organizations in myriad ways because of the inherent interconnectedness and interdependencies among human, cyber, and physical infrastructures, but more importantly, because organizations depend on the effectiveness of people and on the leadership they provide to the organizations they serve and represent. These human–organizational–cyber–physical infrastructure entities are termed systems of systems. Given the multiple perspectives that characterize them, they cannot be modeled effectively with a single model. The focus of this article is: (i) the centrality of the states of a system in modeling; (ii) the efficacious role of shared states in modeling systems of systems, in identification, and in the meta‐modeling of systems of systems; and (iii) the contributions of the above to strategic preparedness, response to, and recovery from catastrophic risk to such systems. Strategic preparedness connotes a decision‐making process and its associated actions. These must be: implemented in advance of a natural or human‐induced disaster, aimed at reducing consequences (e.g., recovery time, community suffering, and cost), and/or controlling their likelihood to a level considered acceptable (through the decisionmakers’ implicit and explicit acceptance of various risks and tradeoffs). The inoperability input‐output model (IIM), which is grounded on Leontief's input/output model, has enabled the modeling of interdependent subsystems. Two separate modeling structures are introduced. These are: phantom system models (PSM), where shared states constitute the essence of modeling coupled systems; and the IIM, where interdependencies among sectors of the economy are manifested by the Leontief matrix of technological coefficients. This article demonstrates the potential contributions of these two models to each other, and thus to more informative modeling of systems of systems schema. The contributions of shared states to this modeling and to systems identification are presented with case studies.     

International Trade Inoperability Input-Output Model (IT-IIM): Theory and Application

The inoperability input-output model (IIM) has been used for analyzing disruptions due to man-made or natural disasters that can adversely affect the operation of economic systems or critical infrastructures. Taking economic perturbation for each sector as inputs, the IIM provides the degree of economic production impacts on all industry sectors as the outputs for the model. The current version of the IIM does not provide a separate analysis for the international trade component of the inoperability. If an important port of entry (e.g., Port of Los Angeles) is disrupted, then international trade inoperability becomes a highly relevant subject for analysis. To complement the current IIM, this article develops the International Trade-IIM (IT-IIM). The IT-IIM investigates the resulting international trade inoperability for all industry sectors resulting from disruptions to a major port of entry. Similar to traditional IIM analysis, the inoperability metrics that the IT-IIM provides can be used to prioritize economic sectors based on the losses they could potentially incur. The IT-IIM is used to analyze two types of direct perturbations: (1) the reduced capacity of ports of entry, including harbors and airports (e.g., a shutdown of any port of entry); and (2) restrictions on commercial goods that foreign countries trade with the base nation (e.g., embargo).     

Use of Quality-Adjusted Life Year Weights with Dose-Response Models for Public Health Decisions: A Case Study of the Risks and Benefits of Fish Consumption

Risks associated with toxicants in food are often controlled by exposure reduction. When exposure recommendations are developed for foods with both harmful and beneficial qualities, however, they must balance the associated risks and benefits to maximize public health. Although quantitative methods are commonly used to evaluate health risks, such methods have not been generally applied to evaluating the health benefits associated with environmental exposures. A quantitative method for risk-benefit analysis is presented that allows for consideration of diverse health endpoints that differ in their impact (i.e., duration and severity) using dose-response modeling weighted by quality-adjusted life years saved. To demonstrate the usefulness of this method, the risks and benefits of fish consumption are evaluated using a single health risk and health benefit endpoint. Benefits are defined as the decrease in myocardial infarction mortality resulting from fish consumption, and risks are defined as the increase in neurodevelopmental delay (i.e., talking) resulting from prenatal methylmercury exposure. Fish consumption rates are based on information from Washington State. Using the proposed framework, the net health impact of eating fish is estimated in either a whole population or a population consisting of women of childbearing age and their children. It is demonstrated that across a range of fish methylmercury concentrations (0-1 ppm) and intake levels (0-25 g/day), individuals would have to weight the neurodevelopmental effects 6 times more (in the whole population) or 250 times less (among women of child-bearing age and their children) than the myocardial infarction benefits in order to be ambivalent about whether or not to consume fish. These methods can be generalized to evaluate the merits of other public health and risk management programs that involve trade-offs between risks and benefits.     

A Framework for Linking Cybersecurity Metrics to the Modeling of Macroeconomic Interdependencies

Hierarchical decision making is a multidimensional process involving management of multiple objectives (with associated metrics and tradeoffs in terms of costs, benefits, and risks), which span various levels of a large-scale system. The nation is a hierarchical system as it consists multiple classes of decisionmakers and stakeholders ranging from national policymakers to operators of specific critical infrastructure subsystems. Critical infrastructures (e.g., transportation, telecommunications, power, banking, etc.) are highly complex and interconnected. These interconnections take the form of flows of information, shared security, and physical flows of commodities, among others. In recent years, economic and infrastructure sectors have become increasingly dependent on networked information systems for efficient operations and timely delivery of products and services. In order to ensure the stability, sustainability, and operability of our critical economic and infrastructure sectors, it is imperative to understand their inherent physical and economic linkages, in addition to their cyber interdependencies. An interdependency model based on a transformation of the Leontief input-output (I-O) model can be used for modeling: (1) the steady-state economic effects triggered by a consumption shift in a given sector (or set of sectors); and (2) the resulting ripple effects to other sectors. The inoperability metric is calculated for each sector; this is achieved by converting the economic impact (typically in monetary units) into a percentage value relative to the size of the sector. Disruptive events such as terrorist attacks, natural disasters, and large-scale accidents have historically shown cascading effects on both consumption and production. Hence, a dynamic model extension is necessary to demonstrate the interplay between combined demand and supply effects. The result is a foundational framework for modeling cybersecurity scenarios for the oil and gas sector. A hypothetical case study examines a cyber attack that causes a 5-week shortfall in the crude oil supply in the Gulf Coast area.     

Multiobjective Prioritization Methodology and Decision Support System for Evaluating Inventory Enhancement Strategies for Disrupted Interdependent Sectors

Disruptions in the production of commodities and services resulting from disasters influence the vital functions of infrastructure and economic sectors within a region. The interdependencies inherent among these sectors trigger the faster propagation of disaster consequences that are often associated with a wider range of inoperability and amplified losses. This article evaluates the impact of inventory‐enhanced policies for disrupted interdependent sectors to improve the disaster preparedness capability of dynamic inoperability input‐output models (DIIM). In this article, we develop the dynamic cross‐prioritization plot (DCPP)—a prioritization methodology capable of identifying and dynamically updating the critical sectors based on preference assignments to different objectives. The DCPP integrates the risk assessment metrics (e.g., economic loss and inoperability), which are independently analyzed in the DIIM. We develop a computer‐based DCPP tool to determine the priority for inventory enhancement with user preference and resource availability as new dimensions. A baseline inventory case for the state of Virginia revealed a high concentration of (i) manufacturing sectors under the inoperability objective and (ii) service sectors under the economic loss objective. Simulation of enhanced inventory policies for selected critical manufacturing sectors has reduced the recovery period by approximately four days and the expected total economic loss by $33 million. Although the article focuses on enhancing inventory levels in manufacturing sectors, complementary analysis is recommended to manage the resilience of the service sectors. The flexibility of the proposed DCPP as a decision support tool can also be extended to accommodate analysis in other regions and disaster scenarios.     

A Scale of Risk

This article proposes a conceptual framework for ranking the relative gravity of diverse risks. This framework identifies the moral considerations that should inform the evaluation and comparison of diverse risks. A common definition of risk includes two dimensions: the probability of occurrence and the associated consequences of a set of hazardous scenarios. This article first expands this definition to include a third dimension: the source of a risk. The source of a risk refers to the agents involved in the creation or maintenance of a risk and captures a central moral concern about risks. Then, a scale of risk is proposed to categorize risks along a multidimensional ranking, based on a comparative evaluation of the consequences, probability, and source of a given risk. A risk is ranked higher on the scale the larger the consequences, the greater the probability, and the more morally culpable the source. The information from the proposed comparative evaluation of risks can inform the selection of priorities for risk mitigation.     

PRICING RISK IN ECONOMIES WITH HETEROGENEOUS AGENTS AND INCOMPLETE MARKETS

Habit formation has been proposed as a possible solution to the equity premium puzzle. This paper extends the class of models that support the habits explanation in order to account for heterogeneity in earnings, wealth, habits, and consumption. I find that habit formation does indeed increase the equity premium. However, contrary to earlier results, the habit hypothesis does not imply a price for risk as largeas the one measured in the data. There are three reasons for this. First, households in a habits economy modify their consumption/savings decision. Second, they modify their portfolio choice. These two changes in behavior diminish the consumption fluctuations faced by households. Third, the composition of the set of agents pricing risk in the economy changes so that relatively better self‐insured households end up pricing risk. (JEL: D52, G12, E21, C68)     

Critical Radionuclide/Critical Pathway Analysis for the U.S. Department of Energy's Savannah River Site

Many different radionuclides have been released to the environment from the Savannah River Site (SRS) during the facility's operational history. However, as shown by this analysis, only a small number of the released radionuclides have been significant contributors to potential doses and risks to off-site people. This article documents the radiological critical contaminant/critical pathway analysis performed for SRS. If site missions and operations remain constant over the next 30 years, only tritium oxide releases are projected to exceed a maximally exposed individual (MEI) risk of 1.0E-06 for either the airborne or liquid pathways. The critical exposure pathways associated with site airborne releases are inhalation and vegetation consumption, whereas the critical exposure pathways associated with liquid releases are drinking water and fish consumption. For the SRS-specific, nontypical exposure pathways (i.e., recreational fishing and deer and hog hunting), cesium-137 is the critical radionuclide.     

The Impact of Dietary Changes Among the Inuit of Nunavik (Canada): A Socioeconomic Assessment of Possible Public Health Recommendations Dealing with Food Contamination

Inuit populations meet a large portion of their food needs by eating country food in which pollutants are concentrated. Despite the fact that they contain pollutants, the consumption of country food has many health, social, economic, and cultural benefits. A risk determination process was set up in order to help regional health authorities of Nunavik to deal with this particular issue. Based on Nunavik health authorities' objectives to encourage the region's inhabitants to change their dietary habits, and on both the risks and the benefits of eating country food, several management options were developed. The options aimed at reducing exposure to contaminants by either substituting certain foods with others that have a lower contaminant content or by store-bought foods. This article aims at assessing the potential economic impact of these risk management options before being implemented. Relevant economic data (aggregate income and monetary outlays for the purchase of food and equipment required for food production by households) were collected and identified to serve as a backdrop for the various replacement scenarios. Results show that household budgets, and the regional economy, are not significantly affected by the replacement of contaminated foods with the purchase of store-bought meat, and even less so if the solution involves replacing contaminated foods with other types of game hunted in the region. When financial support is provided by the state, the households can even gain some monetary benefits. Results show that public health authorities' recommended changes to dietary habits among the Inuit of Nunavik would not necessarily involve economic constraints for Inuit households.     

极端情况下对我国股市风险的实证研究

准确地度量风险是对风险进行有效管理的前提也是投资者做出合理的投资决策的基础,然而在极端事件频繁发生的情况下,传统的VaR计算方法难以准确地度量股市风险,极值理论却可以很好地解决这一问题。本文特别关注了由2007年美国"次贷" 危机所引发的全球金融危机爆发时我国股市的风险度量问题,考虑到全球股市间极端事件的联动效应,利用基于极值理论的POT模型对上证综指日收益率的尾部数据直接建模拟合分布,进而计算出风险值VaR和CVaR,通过比较危机前后的风险值,发现随着金融危机的到来,我国股市的风险有了一定程度的释放。     

Flood Risk Assessment in the Netherlands: A Case Study for Dike Ring South Holland

Large parts of the Netherlands are below sea level. Therefore, it is important to have insight into the possible consequences and risks of flooding. In this article, an analysis of the risks due to flooding of the dike ring area South Holland in the Netherlands is presented. For different flood scenarios the potential number of fatalities is estimated. Results indicate that a flood event in this area can expose large and densely populated areas and result in hundreds to thousands of fatalities. Evacuation of South Holland before a coastal flood will be difficult due to the large amount of time required for evacuation and the limited time available. By combination with available information regarding the probability of occurrence of different flood scenarios, the flood risks have been quantified. The probability of death for a person in South Holland due to flooding, the so‐called individual risk, is small. The probability of a flood disaster with many fatalities, the so‐called societal risk, is relatively large in comparison with the societal risks in other sectors in the Netherlands, such as the chemical sector and aviation. The societal risk of flooding appears to be unacceptable according to some of the existing risk limits that have been proposed in literature. These results indicate the necessity of a further societal discussion on the acceptable level of flood risk in the Netherlands and the need for additional risk reducing measures.     

Harnessing the Theoretical Foundations of the Exponential and Beta‐Poisson Dose‐Response Models to Quantify Parameter Uncertainty Using Markov Chain Monte Carlo

Dose‐response models are the essential link between exposure assessment and computed risk values in quantitative microbial risk assessment, yet the uncertainty that is inherent to computed risks because the dose‐response model parameters are estimated using limited epidemiological data is rarely quantified. Second‐order risk characterization approaches incorporating uncertainty in dose‐response model parameters can provide more complete information to decisionmakers by separating variability and uncertainty to quantify the uncertainty in computed risks. Therefore, the objective of this work is to develop procedures to sample from posterior distributions describing uncertainty in the parameters of exponential and beta‐Poisson dose‐response models using Bayes's theorem and Markov Chain Monte Carlo (in OpenBUGS). The theoretical origins of the beta‐Poisson dose‐response model are used to identify a decomposed version of the model that enables Bayesian analysis without the need to evaluate Kummer confluent hypergeometric functions. Herein, it is also established that the beta distribution in the beta‐Poisson dose‐response model cannot address variation among individual pathogens, criteria to validate use of the conventional approximation to the beta‐Poisson model are proposed, and simple algorithms to evaluate actual beta‐Poisson probabilities of infection are investigated. The developed MCMC procedures are applied to analysis of a case study data set, and it is demonstrated that an important region of the posterior distribution of the beta‐Poisson dose‐response model parameters is attributable to the absence of low‐dose data. This region includes beta‐Poisson models for which the conventional approximation is especially invalid and in which many beta distributions have an extreme shape with questionable plausibility.     

Modeling Extreme Risks in Ecology

Extreme risks in ecology are typified by circumstances in which data are sporadic or unavailable, understanding is poor, and decisions are urgently needed. Expert judgments are pervasive and disagreements among experts are commonplace. We outline approaches to evaluating extreme risks in ecology that rely on stochastic simulation, with a particular focus on methods to evaluate the likelihood of extinction and quasi‐extinction of threatened species, and the likelihood of establishment and spread of invasive pests. We evaluate the importance of assumptions in these assessments and the potential of some new approaches to account for these uncertainties, including hierarchical estimation procedures and generalized extreme value distributions. We conclude by examining the treatment of consequences in extreme risk analysis in ecology and how expert judgment may better be harnessed to evaluate extreme risks.     

Setting Risk Priorities: A Formal Model

This article presents a model designed to capture the major aspects of setting priorities among risks, a common task in government and industry. The model has both design features, under the control of the rankers (e.g., how success is evaluated), and context features, properties of the situations that they are trying to understand (e.g., how quickly uncertainty can be reduced). The model is demonstrated in terms of two extreme ranking strategies. The first, sequential risk ranking , devotes all its resources, in a given period, to learning more about a single risk, and its place in the overall ranking. This strategy characterizes the process for a society (or organization or individual) that throws itself completely into dealing with one risk after another. The other extreme strategy, simultaneous risk ranking , spreads available resources equally across all risks. It characterizes the most methodical of ranking exercises. Given ample ranking resources, simultaneous risk ranking will eventually provide an accurate set of priorities, whereas sequential ranking might never get to some risks. Resource constraints, however, may prevent simultaneous rankers from examining any risk very thoroughly. The model is intended to clarify the nature of ranking tasks, predict the efficacy of alternative strategies, and improve their design.     

Estimated Distributions for Average Daily Consumption of Total and Self-Caught Fish for Adults in Michigan Angler Households

Fish consumption rates play a critical role in the assessment of human health risks posed by the consumption of fish from chemically contaminated water bodies. Based on data from the 1989 Michigan Sport Anglers Fish Consumption Survey, we examined total fish consumption, consumption of self-caught fish, and consumption of Great Lakes fish for all adults, men, women, and certain higher risk subgroups such as anglers. We present average daily consumption rates as compound probability distributions consisting of a Bernoulli trial (to distinguish those who ate fish from those who did not) combined with a distribution (both empirical and parametric) for those who ate fish. We found that the average daily consumption rates for adults who ate fish are reasonably well fit by lognormal distributions. The compound distributions may be used as input variables for Monte Carlo simulations in public health risk assessments.     

Systemic Valuation of Strategic Preparedness Through Application of the Inoperability Input-Output Model with Lessons Learned from Hurricane Katrina

The U.S. Department of Homeland Security (DHS) has mandated all regions to "carefully weigh the benefit of each homeland security endeavor and only allocate resources where the benefit of reducing risk is worth the amount of additional cost" (DHS, 2006, p. 64). This mandate illuminates the need to develop methods for systemic valuation of preparedness measures that support strategic decision making. This article proposes an analysis method that naturally emerges from the structure of the inoperability input-output model (IIM) through which various regional- and sector-specific impact analyses can be cost-effectively integrated for natural and man-made disasters. The IIM is described extensively in a companion paper (Lian et al., 2007). Its reliance on data classifications structured by the U.S. Census Bureau and its extensive accounting of economic interdependencies enables us to decompose a risk analysis activity, perform independent assessments, and properly integrate the assessment for a systemic valuation of risk and risk management activity. In this article, we account for and assess some of the major impacts of Hurricanes Katrina and Rita to demonstrate this use of the IIM and illustrate hypothetical, reduced impacts resulting from various strategic preparedness decisions. Our results indicate the capability of the IIM to guide the decision-making processes involved in developing a preparedness strategy.