Managing the Risk of Global Climate Catastrophe: An Uncertainty Analysis

Despite much scientific progress over many decades, the nature of global climate change remains highly uncertain, and the possibility of global climate catastrophe is one of the main concerns in public debates about global climate change. In this paper, we present a model which incorporates the risk of climate catastrophe in an analysis of greenhouse gas abatement strategy. In this model, the timing and severity of climate catastrophe are treated probabilistically. The impacts of key uncertainties on optimal policy are analyzed, and the expected values of additional information that reduces the uncertainty associated with the world economy, carbon cycle, climate change, and climate damage are estimated.     

A Framework for the Analysis of Error in Conjunctive,Multi-Criteria,Satisficing Decision Processes*

This paper examines the impact of certain types of error on ad hoc, unstructured decision making that incorporates a satisficing strategy in a multi-criteria context using the conjunctive rule. The impacts of error in evaluation and of error in minimal or satisficing levels are systematically modelled for each of the decision criteria. Error functions, analytical expressions, and procedures are developed to obtain information such as the likelihood of correct decision making in the presence of evaluation and minimal criteria level error. These are applied to a specific research design, which is then analyzed. This highlights certain features of decision making in a satisficing context, such as the importance of keeping the number of decision criteria to a minimum. The results yield various insights, some of which confirm information obtained from previous analyses. A major advantage of the framework presented is that it can provide quantitative measurements which support ideas previously couched in qualitative terms only. Although the framework is applied in the context of decision making that uses the conjunctive rule in the case of multiple criteria, other satisficing strategies can be accommodated as well.     

Robust Climate Policies Under Uncertainty: A Comparison of Robust Decision Making and Info‐Gap Methods

This study compares two widely used approaches for robustness analysis of decision problems: the info‐gap method originally developed by Ben‐Haim and the robust decision making (RDM) approach originally developed by Lempert, Popper, and Bankes. The study uses each approach to evaluate alternative paths for climate‐altering greenhouse gas emissions given the potential for nonlinear threshold responses in the climate system, significant uncertainty about such a threshold response and a variety of other key parameters, as well as the ability to learn about any threshold responses over time. Info‐gap and RDM share many similarities. Both represent uncertainty as sets of multiple plausible futures, and both seek to identify robust strategies whose performance is insensitive to uncertainties. Yet they also exhibit important differences, as they arrange their analyses in different orders, treat losses and gains in different ways, and take different approaches to imprecise probabilistic information. The study finds that the two approaches reach similar but not identical policy recommendations and that their differing attributes raise important questions about their appropriate roles in decision support applications. The comparison not only improves understanding of these specific methods, it also suggests some broader insights into robustness approaches and a framework for comparing them.     

Adaptive Flood Risk Management Under Climate Change Uncertainty Using Real Options and Optimization

It is well recognized that adaptive and flexible flood risk strategies are required to account for future uncertainties. Development of such strategies is, however, a challenge. Climate change alone is a significant complication, but, in addition, complexities exist trying to identify the most appropriate set of mitigation measures, or interventions. There are a range of economic and environmental performance measures that require consideration, and the spatial and temporal aspects of evaluating the performance of these is complex. All these elements pose severe difficulties to decisionmakers. This article describes a decision support methodology that has the capability to assess the most appropriate set of interventions to make in a flood system and the opportune time to make these interventions, given the future uncertainties. The flood risk strategies have been explicitly designed to allow for flexible adaptive measures by capturing the concepts of real options and multiobjective optimization to evaluate potential flood risk management opportunities. A state‐of‐the‐art flood risk analysis tool is employed to evaluate the risk associated to each strategy over future points in time and a multiobjective genetic algorithm is utilized to search for the optimal adaptive strategies. The modeling system has been applied to a reach on the Thames Estuary (London, England), and initial results show the inclusion of flexibility is advantageous, while the outputs provide decisionmakers with supplementary knowledge that previously has not been considered.     

How Much Uncertainty is Too Much and How Do We Know? A Case Example of the Assessment of Ozone Monitor Network Options

Limited time and resources usually characterize environmental decision making at policy organizations such as the U.S. Environmental Protection Agency. In these climates, addressing uncertainty, usually considered a flaw in scientific analyses, is often avoided. However, ignoring uncertainties can result in unpleasant policy surprises. Furthermore, it is important for decisionmakers to know how defensible a chosen policy option is over other options when the uncertainties of the data are considered. The purpose of this article is to suggest an approach that is unique from other approaches in that it considers uncertainty in two specific ways-the uncertainty of stakeholder values within a particular decision context and data uncertainty in the light of the decision-contextual data-values relationship. It is the premise of this article that the interaction between data and stakeholder values is critical to how the decision options are viewed and determines the effect of data uncertainty on the relative acceptability of the decision options, making the understanding of this interaction important to decisionmakers and other stakeholders. This approach utilizes the recently developed decision analysis framework and process, multi-criteria integrated resource assessment (MIRA). This article will specifically address how MIRA can be used to help decisionmakers better understand the importance of uncertainty on the specific (i.e., decision contextual) environmental policy options that they are deliberating.     

Confidence deficits and reducibility: Toward a coherent conceptualization of uncertainty level

Outside of the field of risk analysis, an important theoretical conversation on the slippery concept of uncertainty has unfolded over the last 40 years within the adjacent field of environmental risk. This literature has become increasingly standardized behind the tripartite distinction between uncertainty location, the nature of uncertainty, and uncertainty level, popularized by the “W&H framework.” This article introduces risk theorists and practitioners to the conceptual literature on uncertainty with the goal of catalyzing further development and clarification of the uncertainty concept within the field of risk analysis. It presents two critiques of the W&H framework's dimension of uncertainty level—the dimension that attempts to define the characteristics separating greater uncertainties from lesser uncertainties. First, I argue the framework's conceptualization of uncertainty level lacks a clear and consistent epistemological position and fails to acknowledge or reconcile the tensions between Bayesian and frequentist perspectives present within the framework. This article reinterprets the dimension of uncertainty level from a Bayesian perspective, which understands uncertainty as a mental phenomenon arising from “confidence deficits” as opposed to the ill-defined notion of “knowledge deficits” present in the framework. And second, I elaborate the undertheorized concept of uncertainty “reducibility.” These critiques inform a clarified conceptualization of uncertainty level that can be integrated with risk analysis concepts and usefully applied by modelers and decisionmakers engaged in model-based decision support.     

Uncertainty in competitive bidding – a framework for product–service systems

Owing to servitisation, manufacturing companies are increasingly required to compete through the provision of services around their products. The contracts for these services are often allocated through competitive bidding where the potential suppliers submit a price bid to the customer. The pricing decision is influenced by various uncertainties. This article proposes a conceptual framework depicting these influencing uncertainties on the bidding strategy. This framework is based on three empirical studies with industry investigating different viewpoints on the decision-making process. The intention is to support the pricing decision when competitively bidding for a service contract. The framework can be applied to specific competitive bidding situations to identify the influencing uncertainties, model them and depict their influences on the pricing decision.     

A new approach to risk evaluation and management: risk-based, precaution-based, and discourse-based strategies.

Our concept of nine risk evaluation criteria, six risk classes, a decision tree, and three management categories was developed to improve the effectiveness, efficiency, and political feasibility of risk management procedures. The main task of risk evaluation and management is to develop adequate tools for dealing with the problems of complexity, uncertainty. and ambiguity. Based on the characteristics of different risk types and these three major problems, we distinguished three types of management--risk-based, precaution-based, and discourse-based strategies. The risk-based strategy--is the common solution to risk problems. Once the probabilities and their corresponding damage potentials are calculated, risk managers are required to set priorities according to the severity of the risk, which may be operationalized as a linear combination of damage and probability or as a weighted combination thereof. Within our new risk classification, the two central components have been augmented with other physical and social criteria that still demand risk-based strategies as long as uncertainty is low and ambiguity absent. Risk-based strategies are best solutions to problems of complexity and some components of uncertainty, for example, variation among individuals. If the two most important risk criteria, probability of occurrence and extent of damage, are relatively well known and little uncertainty is left, the traditional risk-based approach seems reasonable. If uncertainty plays a large role, in particular, indeterminacy or lack of knowledge, the risk-based approach becomes counterproductive. Judging the relative severity of risks on the basis of uncertain parameters does not make much sense. Under these circumstances, management strategies belonging to the precautionary management style are required. The precautionary approach has been the basis for much of the European environmental and health protection legislation and regulation. Our own approach to risk management has been guided by the proposition that any conceptualization of the precautionary principle should be (1) in line with established methods of scientific risk assessments, (2) consistent and discriminatory (avoiding arbitrary results) when it comes to prioritization, and (3) at the same time, specific with respect to precautionary measures, such as ALARA or BACT, or the strategy of containing risks in time and space. This suggestion does, however, entail a major problem: looking only to the uncertainties does not provide risk managers with a clue about where to set priorities for risk reduction. Risks vary in their degree of remaining uncertainties. How can one judge the severity of a situation when the potential damage and its probability are unknown or contested? In this dilemma, we advise risk managers to use additional criteria of hazardousness, such as "ubiquity versibility," and "pervasiveness over time," as proxies for judging severity. Our approach also distinguishes clearly between uncertainty and ambiguity. Uncertainty refers to a situation of being unclear about factual statements; ambiguity to a situation of contested views about the desirability or severity of a given hazard. Uncertainty can be resolved in principle by more cognitive advances (with the exception of indeterminacy). ambiguity only by discourse. Discursive procedures include legal deliberations as well as novel participatory approaches. In addition, discursive methods of planning and conflict resolution can be used. If ambiguities are associated with a risk problem, it is not enough to demonstrate that risk regulators are open to public concerns and address the issues that many people wish them to take care ot The process of risk evaluation itself needs to be open to public input and new forms of deliberation. We have recommended a tested set of deliberative processes that are, at least in principle, capable of resolving ambiguities in risk debates (for a review, see Renn, Webler, & Wiedemaun. 1995). Deliberative processes are needed, however, for ail three types of management. Risk-based management relies on epistemiological, uncertainty-based management on reflective, and discourse-based management on participatory discourse forms. These three types of discourse could be labeled as an analytic-deliberative procedure for risk evaluation and management. We see the advantage of a deliberative style of regulation and management in a dynamic balance between procedure and outcome. Procedure should not have priority over the outcome; outcome should not have priority over the procedure. An intelligent combination of both can elaborate the required prerequisites of democratic deliberation and its substantial outcomes to enhance the legitimacy of political decisions (Guttman & Thompson, 1996; Bohman, 1997. 1998).     

不同权力结构下碳税对供应链减排水平的影响

随着我国生态文明建设的不断推进,促进供应链协同减排成为生产和消费领域落实绿色发展理念的重要举措。在绿色供应链的管理实践中,不同类型的供应链核心企业在进行减排决策时会有不同的侧重点,因此供应链权力的结构成为影响供应链减排水平的关键因素。本文在一个由两个制造商和一个零售商组成的两级供应链中研究了不同权力结构下的供应链最优减排和定价决策问题,分别给出了零售商主导和制造商主导的Stackelberg-Nash博弈均衡解,并分析了产品的单位生产成本、单位批发价格、碳税税率等参数对供应链最优决策的影响。本文的研究结果表明：（1）在两种供应链权力结构下,当产品的单位批发价格固定时,降低产品的生产成本都可以促使制造商提高减排水平;但是当产品的单位生产成本固定时,提高产品的批发价格却不一定总是能够激励制造商提高减排水平;（2）在两种供应链权力结构下,对于低排放行业的制造商来说,提高碳税税率可以增加产品的减排水平,但是对于高排放行业的制造商来说,提高碳税税率反而会降低产品的减排水平;（3）一般来说,零售商占主导地位的供应链中产品减排水平和供应链总利润相对较高,供应链碳排放总量相对较低。最后,与供应链集中决策模式相比,随着产品批发价格的增加,两种分散决策模式下的供应链双重边际效应逐渐增强。数值实验结果显示：在碳税税率的一定取值范围内,这种双重边际效应会使得两种分散决策模式下的产品最优减排水平和供应链碳排放总量均优于集中决策模式。本文的研究可为碳税政策下供应链的协同减排提供有益的管理启示。     

Enhancing knowledge construction processes within multicriteria decision analysis: The Collaborative Value Modelling framework

This paper proposes a new Collaborative Value Modelling framework, that combines Delphi and multicriteria decision conferencing, to build widely informed evaluation models. Multicriteria Decision Analysis (MCDA) is commonly used to help decision-makers and other stakeholders in complex evaluation contexts. Further to the technical soundness and meaningfulness of the methods and tools used, it is critical to design adequate social processes to promote shared understanding around key evaluation issues while capturing multiple stakeholders’ values and perspectives. Multicriteria decision conferencing processes have been typically adopted for collaborative modelling using MCDA methods in decision conferences with relatively small groups. Such a socio-technical approach has proven to be effective, in a variety of contexts, in creating a collaborative environment that enables surfacing individual beliefs, identifying common concerns, managing eventual value conflicts and promoting agreement in group model building. But, extending this framework to broader participatory contexts requires a different design of the social process, in order to ensure that model building captures the full panoply of points of view. This challenge can be tackled by enhancing multicriteria decision conferencing with an all-embracing (Web-)Delphi participatory process. We depart from the existing collaborative knowledge acquisition methodology to design, with the Delphi method, a participatory knowledge construction process that elicits and analyses individual judgemental knowledge from a (very) large and diverse number of stakeholders. The knowledge acquired is then digested by a small group of key-players, in a subsequent decision conferencing, to collaboratively develop a widely informed multicriteria evaluation model. This new Web-Delphi-decision conferencing social setting has been tested already in real complex evaluation contexts using a specific multicriteria method, the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH), to develop a variety of value modelling activities. We call this socio-technical design the Collaborative Value Modelling framework. Here, we describe its real use to support the construction of value functions, focusing on how the judgemental knowledge collected flows between the participatory and collaborative stages of the framework. Results validate that enhancing MACBETH decision conferencing with an ex ante Web-Delphi process fosters higher participation and collaboration in multicriteria modelling.     

DESIGNING INSPECTION STRATEGIES FOR UNCERTAIN ENVIRONMENTS

This paper considers the problems of designing inspection strategies for production systems terms in the presence of environmental uncertainty. The framework for determining information priorities to support inspection system planning is presented in the contaxt of a generic production system that encompasses the characteristics of many real-world serial production systems. The impact on the design decision of five key variables is considered: quality of producton processes, quality of inspection procedures, penalty cost for undetected defective units, relative cost of improving process vs. inspection, and shape of the cost functions for process and inspection enhancement. The framework for analysis involves varying factors over two or three orders of magnitude to determine optimal inspection strategies across a wide range of environments These results are used to compare design decisions made in the presence of environmental uncertainty using expected-opportunity-cost and minimization-of-maximum-opportunity-cost approaches. Design strategies are identified for situations ranging from complete lack of knowledge about the environment through increasing levels of information. Information-gathering priorities are established, and the impact on the design decision of this additional information is assayed.     

Decision Making Under Uncertainty—An Example for Seismic Risk Management

Decision-making techniques are used to select the "best" alternatives under multiple and often conflicting criteria. Multicriteria decision making (MCDM) necessitates to incorporate uncertainties in the decision-making process. The major thrust of this article is to extend the framework proposed by Yager^{( 1 )} for multiple decisionmakers and fuzzy utilities (payoffs). In addition, the concept of expert credibility factor is introduced. The proposed approach is demonstrated for an example of seismic risk management using a heuristic hierarchical structure. A step-by-step formulation of the proposed approach is illustrated using a hypothetical example and a three-story reinforced concrete building.     

META-DESIGN CONSIDERATIONS IN DEVELOPING MODEL MANAGEMENT SYSTEMS*

This paper examines cognitive considerations in developing model management systems (MMSs). First, two approaches to MMS design are reviewed briefly: one based on database theory and one based on knowledge-representation techniques. Then three major cognitive issues—human limitations, information storage and retrieval, and problem-solving strategies—and their implications for MMS design are discussed. Evidence indicates that automatic modeling, which generates more complicated models by integrating existing models automatically, is a critical function of model management systems. In order to discuss issues pertinent to automatic modeling, a graph-based framework for integrating models is introduced. The framework captures some aspects of the processes by which human beings develop models as route selections on a network of all possible alternatives. Based on this framework, three issues are investigated: (1) What are proper criteria for evaluating a model formulated by an MMS? (2) If more than one criterion is chosen for evaluation, how can evaluations on each of the criteria be combined to get an overall evaluation of the model? (3) When should a model be evaluated? Finally, examples are presented to illustrate various modeling strategies.     

Multicriteria Decision Analysis: A Comprehensive Decision Approach for Management of Contaminated Sediments

Contaminated sediments and other sites present a difficult challenge for environmental decisionmakers. They are typically slow to recover or attenuate naturally, may involve multiple regulatory agencies and stakeholder groups, and engender multiple toxicological and ecotoxicological risks. While environmental decision-making strategies over the last several decades have evolved into increasingly more sophisticated, information-intensive, and complex approaches, there remains considerable dissatisfaction among business, industry, and the public with existing management strategies. Consequently, contaminated sediments and materials are the subject of intense technology development, such as beneficial reuse or in situ treatment. However, current decision analysis approaches, such as comparative risk assessment, benefit-cost analysis, and life cycle assessment, do not offer a comprehensive approach for incorporating the varied types of information and multiple stakeholder and public views that must typically be brought to bear when new technologies are under consideration. Alternatively, multicriteria decision analysis (MCDA) offers a scientifically sound decision framework for management of contaminated materials or sites where stakeholder participation is of crucial concern and criteria such as economics, environmental impacts, safety, and risk cannot be easily condensed into simple monetary expressions. This article brings together a multidisciplinary review of existing decision-making approaches at regulatory agencies in the United States and Europe and synthesizes state-of-the-art research in MCDA methods applicable to the assessment of contaminated sediment management technologies. Additionally, it tests an MCDA approach for coupling expert judgment and stakeholder values in a hypothetical contaminated sediments management case study wherein MCDA is used as a tool for testing stakeholder responses to and improving expert assessment of innovative contaminated sediments technologies.     

Physical–cyber–human framework-based resilience evaluation toward urban power system: Case study from China

Because the increased frequency, intensity, and duration of extreme weather events have significantly challenged power systems, there has been an increased interest in resilient power systems. This article establishes a multicriteria resilience evaluation framework for urban power systems from a physical–cyber–human system perspective, in which the two principal elements responsible for power system function degradation are described, the three major domains comprising urban power systems are explained, four core capacities that positively contribute to power system resilience are proposed, and 15 (11 objective and four subjective) power system resilience evaluation indicators are identified. Fuzzy hesitant judgment and a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) aggregation method are employed to minimize the expert divergence and maximize the group consensus. A validation method is designed and a comparison with commonly applied performance-based and attributes-based evaluation methods is conducted. The applicability of the evaluation framework is verified using data from four Chinese municipalities: Shanghai, Beijing, Chongqing, and Tianjin. It was found that Shanghai's resilience was the best, and Chongqing's physical resistance disadvantages would result in the greatest difficulties in coping with extreme event disturbances. Physical, cyber, and human domain resilience enhancement strategies are given for different cities separately. This study provides a practical tool to evaluate, compare, and enhance power system resilience for governments and public utilities.     

Implementing workforce development in health care: A conceptual framework to guide and evaluate health service reform

Abstract

The term ‘workforce development’ is increasingly popular in the health-care field. It appears to encompass a range of human and organizational development activity. However, there has been limited explication of the concept of workforce development in Australian health care at area health service levels. It is timely to develop a framework for workforce development and processes to guide any evaluation of the implementation of workforce development strategies. This paper presents a framework that has been developed through consultative processes in an area health service and an associated review of literature.     

Decision Making During Nuclear Power Plant Incidents—A New Approach to the Evaluation of Precursor Events

Renewed interest in precursor analysis has shown that the evaluation of near misses is an interdisciplinary effort, fundamental within the life of an organization for reducing operational risks and enabling accident prevention. The practice of precursor analysis has been a part of nuclear power plant regulation in the United States for over 25 years. During this time, the models used in the analysis have evolved from simple risk equations to quite complex probabilistic risk assessments. But, one item that has remained constant over this time is that the focus of the analysis has been on modeling the scenario using the risk model (regardless of the model sophistication) and then using the results of the model to determine the severity of the precursor incident. We believe that evaluating precursors in this fashion could be a shortcoming since decision making during the incident is not formally investigated. Consequently, we present the idea for an evaluation procedure that enables one to integrate current practice with the evaluation of decisions made during the precursor event. The methodology borrows from technologies both in the risk analysis and the decision analysis realms. We demonstrate this new methodology via an evaluation of a U.S. precursor incident. Specifically, the course of the incident is represented by the integration of a probabilistic risk assessment model (i.e., the risk analysis tool) with an influence diagram and the corresponding decision tree (i.e., the decision analysis tools). The results and insights from the application of this new methodology are discussed.     

碳排放权的非对称拍卖模型及其配置效率

作为我国碳交易体系的试金石,“七省市”试点运营状况如何?是学界和管理层特别关注的一个焦点问题.以碳排放权的初始配置准则为切入点,展开理论探讨与定性分析,发现:在现行的溯源免费配置准则下,试点地区的碳交易市场有明显的“聚类效应”特征,其原因是排放权的误配导致企业逆向选择所致.为了从机制设计本源上消除企业减排的逆向选择,把拍卖有偿配置准则引入碳交易市场中,构建企业减排投资回报率指标,定义排放权的社会最优配置,建立碳排放权的非对称拍卖模型,分析异质性企业在碳排放权拍卖中的投标策略及其拍卖前的减排投资决策行为,剖析企业所有权差异对其减排投资和排放权配置结果的影响,阐述政府补贴企业减排投资的政策效果,基于政府补贴政策效果提出完善我国碳交易市场的相关建议.     

Regulatory Models and the Environment: Practice, Pitfalls, and Prospects

Computational models support environmental regulatory activities by providing the regulator an ability to evaluate available knowledge, assess alternative regulations, and provide a framework to assess compliance. But all models face inherent uncertainties because human and natural systems are always more complex and heterogeneous than can be captured in a model. Here, we provide a summary discussion of the activities, findings, and recommendations of the National Research Council's Committee on Regulatory Environmental Models, a committee funded by the U.S. Environmental Protection Agency to provide guidance on the use of computational models in the regulatory process. Modeling is a difficult enterprise even outside the potentially adversarial regulatory environment. The demands grow when the regulatory requirements for accountability, transparency, public accessibility, and technical rigor are added to the challenges. Moreover, models cannot be validated (declared true) but instead should be evaluated with regard to their suitability as tools to address a specific question. The committee concluded that these characteristics make evaluation of a regulatory model more complex than simply comparing measurement data with model results. The evaluation also must balance the need for a model to be accurate with the need for a model to be reproducible, transparent, and useful for the regulatory decision at hand. Meeting these needs requires model evaluation to be applied over the "life cycle" of a regulatory model with an approach that includes different forms of peer review, uncertainty analysis, and extrapolation methods than those for nonregulatory models.