Assessing the Environmental Protection Agency's guidelines for cumulative risk assessment: Are they suitable for state agencies and EPA regional offices?

In the field of health risk analysis, cumulative risk assessment (CRA) is a necessary, although undeniably more complex approach to understanding the mixture of stressors, whether chemical or psychosocial, that exist in our environment, in all the pathways through which the chemicals may evolve—air, soil, or water, as well as the accumulation of these exposures over time. Related, or attached to the developing awareness of scientists understanding this mix of combined health effects is the burgeoning of the environmental justice movement, in which educated community advocates and even affected community members have called attention to evidence of a higher pollution burden in minority and/or lower SES communities. The intention of this paper is to 1) examine the development and understanding of CRA, primarily by the U.S. Environmental Protection Agency; 2) to assess several states agencies and some EPA regional offices' interpretation of CRA, again based primarily on EPA guidance, and 3) to analyze how CRA might be refined in its implementation—giving some cues as to how the EPA may more effectively interact with communities interested in CRA.^

A Methodological model to assist the optimization and risk management of mining investment decisions

Identifying, quantifying, and minimizing technical risks associated with investment decisions is a key challenge for mineral industry decision makers and investors. However, risk analysis in most bankable mine feasibility studies are based on the stochastic modelling of project “Net Present Value” (NPV)which, in most cases, fails to provide decision makers with a truly comprehensive analysis of risks associated with technical and management uncertainty and, as a result, are of little use for risk management and project optimization. This paper presents a value-chain risk management approach where project risk is evaluated for each step of the project lifecycle, from exploration to mine closure, and risk management is performed as a part of a stepwise value-added optimization process.

A Fuzzy approach based on dynamic programming and metaheuristics for selecting safeguards for risk management for information systems

In this paper we focus on the selection of safeguards in a fuzzy risk analysis and management methodology for information systems (IS). Assets are connected by dependency relationships, and a failure of one asset may affect other assets. After computing impact and risk indicators associated with previously identified threats, we identify and apply safeguards to reduce risks in the IS by minimizing the transmission probabilities of failures throughout the asset network. However, as safeguards have associated costs, the aim is to select the safeguards that minimize costs while keeping the risk within acceptable levels. To do this, we propose a dynamic programming-based method that incorporates simulated annealing to tackle optimizations problems.

In-House Risk

Over the last thirty years or so, as the number of in-house counsel rose and their role increased in scope and prominence, increased attention has been given the various challenges these lawyers face under the ABA Model Rules of Professional Conduct, from figuring out who is the client the in-house lawyer represents, to navigating conflicts of interest, maintaining independence, and engaging in a multijurisdictional practice of law. Less attention, to date, has been given to business risk assessment, perhaps in part because that function appears to be part of in-house counsel’s role as a business person rather than as a lawyer. Overlooking the role of in-house counsel in assessing risk, however, is a risky proposition, because risk assessment constitutes for some in-house counsel a significant aspect of their role, a role that in turn informs and shapes how in-house counsel perform other more overtly legal tasks. For example, wearing her hat as General Counsel, a lawyer for the entity-client may opine and explain issues of compliance with the law. Wearing her hat as the Chief Legal Officer, however, the same lawyer may now be called upon as a member of business management to participate in the decision whether to comply with the law. After outlining some of the traditional challenges faced by in-house counsel under the Rules, this short essay explores risk assessment by in-house counsel and its impact on their role and function under the Rules. It argues that the key to in-house lawyers’ successful navigation of multiple roles, and, in particular, to their effective assessment of business risk is keen awareness of the various hats they are called upon to wear. Navigating these various roles may not be easy for lawyers, whose training and habits of mind often teach them to zoom in on legal risks to the exclusion of business risks. Indeed, law schools continue to teach law students “to think like a lawyer” and law firms, the historical breeding grounds for in-house counsel positions, in a world of increased specialization master the narrower contemplation of legal questions. Yet the present and future of in-house counsel practice demand of its practitioners the careful and gradual coming to terms, buildup and mastery of business risk analysis skills, alongside the cultivation of traditional legal risk analysis tools.

A multiple account framework for cost-benefit analysis

The paper presents a spreadsheet-based multiple account framework for cost-benefit analysis which incorporates all the usual concerns of cost-benefit analysts such as shadow-pricing to account for market failure. distribution of net benefits. sensitivity and risk analysis, cost of public funds, and environmental effects. The approach is generalizable to a wide range of projects and situations and offers a number of advantages to both analysts and decision-makers, including transparency, a check on internal consistency, and a detailed summary of project net benefits disaggregated by stakeholder group. Of particular importance is the ease with which this framework allows for a project to be evaluated from alternative decision-making perspectives and under alternative policy scenarios where the trade-offs among the project's stakeholders can readily be identified and quantified. (C) 2004 Elsevier Ltd. All rights reserved.

Science-policy in environmental and health risk assessment: if we cannot do without, can we do better?

How can empirical evidence of adverse effects from exposure to noxious agents, which is often incomplete and uncertain, be used most appropriately to protect human health? We examine several important questions on the best uses of empirical evidence in regulatory risk management decision-making raised by the US Environmental Protection Agency (EPA)'s science-policy concerning uncertainty and variability in human health risk assessment. In our view, the US EPA (and other agencies that have adopted similar views of risk management) can often improve decision-making by decreasing reliance on default values and assumptions, particularly when causation is uncertain. This can be achieved by more fully exploiting decision-theoretic methods and criteria that explicitly account for uncertain, possibly conflicting scientific beliefs and that can be fully studied by advocates and adversaries of a policy choice, in administrative decision-making involving risk assessment. The substitution of decision-theoretic frameworks for default assumption-driven policies also allows stakeholder attitudes toward risk to be incorporated into policy debates, so that the public and risk managers can more explicitly identify the roles of risk-aversion or other attitudes toward risk and uncertainty in policy recommendations. Decision theory provides a sound scientific way explicitly to account for new knowledge and its effects on eventual policy choices. Although these improvements can complicate regulatory analyses, simplifying default assumptions can create substantial costs to society and can prematurely cut off consideration of new scientific insights (e.g., possible beneficial health effects from exposure to sufficiently low 'hormetic' doses of some agents). In many cases, the administrative burden of applying decision-analytic methods is likely to be more than offset by improved effectiveness of regulations in achieving desired goals. Because many foreign jurisdictions adopt US EPA reasoning and methods of risk analysis, it may be especially valuable to incorporate decision-theoretic principles that transcend local differences among jurisdictions.

Project risk management:a combined analytic hierarchy process and decision tree approach

Time, cost and quality achievements on large-scale construction projects are uncertain because of technological constraints, involvement of many stakeholders, long durations, large capital requirements and improper scope definitions. Projects that are exposed to such an uncertain environment can effectively be managed with the application of risk management throughout the project life cycle. Risk is by nature subjective. However, managing risk subjectively poses the danger of non-achievement of project goals. Moreover, risk analysis of the overall project also poses the danger of developing inappropriate responses. This article demonstrates a quantitative approach to construction risk management through an analytic hierarchy process (AHP) and decision tree analysis. The entire project is classified to form a few work packages. With the involvement of project stakeholders, risky work packages are identified. As all the risk factors are identified, their effects are quantified by determining probability (using AHP) and severity (guess estimate). Various alternative responses are generated, listing the cost implications of mitigating the quantified risks. The expected monetary values are derived for each alternative in a decision tree framework and subsequent probability analysis helps to make the right decision in managing risks. In this article, the entire methodology is explained by using a case application of a cross-country petroleum pipeline project in India. The case study demonstrates the project management effectiveness of using AHP and DTA.

Managing risk in a large rural electrification programme in India

Rural electrification projects and programmes in many countries have suffered from design, planning, implementation and operational flaws as a result of ineffective project planning and lack of systematic project risk analysis. This paper presents a hierarchical risk-management framework for effectively managing large-scale development projects. The proposed framework first identifies, with the involvement of stakeholders, the risk factors for a rural electrification programme at three different levels (national, state and site). Subsequently it develops a qualitative risk prioritising scheme through probability and severity mapping and provides mitigating measures for most vulnerable risks. The study concludes that the hierarchical risk-management approach provides an effective framework for managing large-scale rural electrification programmes. © IAIA 2007.

Risk management in build-operate-transfer projects

Construction projects are risky. A build-operate-transfer (BOT) project is recognised as one of the most risky project schemes. This scheme has been employed rather frequently in the past few decades, in both developed and developing countries. However, because of its risky nature, there have been failures as well as successes. Risk analysis in an appropriate way is desirable in implementing BOT projects. There are various tools and techniques applicable to risk analysis. The application of these risk analysis tools and techniques (RATTs) to BOT projects depends on an understanding of the contents and contexts of BOT projects, together with a thorough understanding of RATTs. This paper studies key points in their applications through reviews of relevant literatures and discusses the application of RATTs to BOT projects. The application to BOT projects is considered from the viewpoints of the major project participants, i.e. government, lenders and project companies. Discussion is also made with regard to political risks, which are very important in BOT projects. A flow chart has been introduced to select an appropriate tool for risk management in BOT projects. This study contributes to the establishment of a framework for systematic risk management in BOT projects.

Construction risk management:a quantitative approach

Conventional project management techniques are not always sufficient for ensuring time, cost and quality achievement of large-scale construction projects due to complexity in planning and implementation processes. The main reasons for project non-achievement are changes in scope and design, changes in Government policies and regulations, unforeseen inflation) under-estimation and improper estimation. Projects that are exposed to such an uncertain environment can be effectively managed with the application of risk numagement throughout project life cycle. However, the effectiveness of risk management depends on the technique in which the effects of risk factors are analysed and! or quantified. This study proposes Analytic Hierarchy Process (AHP), a multiple attribute decision-making technique as a tool for risk analysis because it can handle subjective as well as objective factors in decision model that are conflicting in nature. This provides a decision support system (DSS) to project managenumt for making the right decision at the right time for ensuring project success in line with organisation policy, project objectives and competitive business environment. The whole methodology is explained through a case study of a cross-country petroleum pipeline project in India and its effectiveness in project1nana.gement is demonstrated.

Risk based decision support system for effective implementation of projects

Conventional project management techniques are not always sufficient to ensure time, cost and quality achievement of large-scale construction projects due to complexity in planning, design and implementation processes. The main reasons for project non-achievement are changes in scope and design, changes in government policies and regulations, unforeseen inflation, underestimation and improper estimation. Projects that are exposed to such an uncertain environment can be effectively managed with the application of risk management throughout the project's life cycle. However, the effectiveness of risk management depends on the technique through which the effects of risk factors are analysed/quantified. This study proposes the Analytic Hierarchy Process (AHP), a multiple attribute decision making technique, as a tool for risk analysis because it can handle subjective as well as objective factors in a decision model that are conflicting in nature. This provides a decision support system (DSS) to project management for making the right decision at the right time for ensuring project success in line with organisation policy, project objectives and a competitive business environment. The whole methodology is explained through a case application of a cross-country petroleum pipeline project in India and its effectiveness in project management is demonstrated.

The board of directors, executives and risk knowledge management

Enterprise Risk Management (ERM) and Knowledge Management (KM) both encompass top-down and bottom-up approaches developing and embedding risk knowledge concepts and processes in strategy, policies, risk appetite definition, the decision-making process and business processes. The capacity to transfer risk knowledge affects all stakeholders and understanding of the risk knowledge about the enterprise's value is a key requirement in order to identify protection strategies for business sustainability. There are various factors that affect this capacity for transferring and understanding. Previous work has established that there is a difference between the influence of KM variables on Risk Control and on the perceived value of ERM. Communication among groups appears as a significant variable in improving Risk Control but only as a weak factor in improving the perceived value of ERM. However, the ERM mandate requires for its implementation a clear understanding, of risk management (RM) policies, actions and results, and the use of the integral view of RM as a governance and compliance program to support the value driven management of the organization. Furthermore, ERM implementation demands better capabilities for unification of the criteria of risk analysis, alignment of policies and protection guidelines across the organization. These capabilities can be affected by risk knowledge sharing between the RM group and the Board of Directors and other executives in the organization. This research presents an exploratory analysis of risk knowledge transfer variables used in risk management practice. A survey to risk management executives from 65 firms in various industries was undertaken and 108 answers were analyzed. Potential relationships among the variables are investigated using descriptive statistics and multivariate statistical models. The level of understanding of risk management policies and reports by the board is related to the quality of the flow of communication in the firm and perceived level of integration of the risk policy in the business processes.

On the Lp-Norm Regression Models for Estimating Value-at-Risk

Analysis of risk measures associated with price series data movements and its predictions are of strategic importance in the financial markets as well as to policy makers in particular for short- and longterm planning for setting up economic growth targets. For example, oilprice risk-management focuses primarily on when and how an organization can best prevent the costly exposure to price risk. Value-at-Risk (VaR) is the commonly practised instrument to measure risk and is evaluated by analysing the negative/positive tail of the probability distributions of the returns (profit or loss). In modelling applications, least-squares estimation (LSE)-based linear regression models are often employed for modeling and analyzing correlated data. These linear models are optimal and perform relatively well under conditions such as errors following normal or approximately normal distributions, being free of large size outliers and satisfying the Gauss-Markov assumptions. However, often in practical situations, the LSE-based linear regression models fail to provide optimal results, for instance, in non-Gaussian situations especially when the errors follow distributions with fat tails and error terms possess a finite variance. This is the situation in case of risk analysis which involves analyzing tail distributions. Thus, applications of the LSE-based regression models may be questioned for appropriateness and may have limited applicability. We have carried out the risk analysis of Iranian crude oil price data based on the Lp-norm regression models and have noted that the LSE-based models do not always perform the best. We discuss results from the L1, L2 and L∞-norm based linear regression models. ACM Computing Classification System (1998): B.1.2, F.1.3, F.2.3, G.3, J.2.

Does Lost Time Cost You Money and Create High Risk?

The aim of the case study is to express the delayed repair time impact on the revenues and profit in numbers with the example of the outage of power plant units. Main steps of risk assessment: • creating project plan suitable for risk assessment • identification of the risk factors for each project activities • scenario-analysis based evaluation of risk factors • selection of the critical risk factors based on the results of quantitative risk analysis • formulating risk response actions for the critical risks • running Monte-Carlo simulation [1] using the results of scenario-analysis • building up a macro which creates the connection among the results of the risk assessment, the production plan and the business plan.