Not in My Backyard: CCS Sites and Public Perception of CCS

Carbon capture and storage (CCS) is a technology that counteracts climate change by capturing atmospheric emissions of CO₂ from human activities, storing them in geological formations underground. However, CCS also involves major risks and side effects, and faces strong public opposition. The whereabouts of 408 potential CCS sites in Germany were released in 2011. Using detailed survey data on the public perception of CCS, this study quantifies how living close to a potential storage site affects the acceptance of CCS. It also analyzes the influence of other regional characteristics on the acceptance of CCS. The study finds that respondents who live close to a potential CCS site have significantly lower acceptance rates than those who do not. Living in a coal‐mining region also markedly decreases acceptance.     

An extension of the Electre I method for group decision-making under a fuzzy environment

Many real-world decision problems involve conflicting systems of criteria, uncertainty and imprecise information. Some also involve a group of decision makers (DMs) where a reduction of different individual preferences on a given set to a single collective preference is required. Multi-criteria decision analysis (MCDA) is a widely used decision methodology that can improve the quality of group multiple criteria decisions by making the process more explicit, rational and efficient. One family of MCDA models uses what is known as “outranking relations” to rank a set of actions. The Electre method and its derivatives are prominent outranking methods in MCDA. In this study, we propose an alternative fuzzy outranking method by extending the Electre I method to take into account the uncertain, imprecise and linguistic assessments provided by a group of DMs. The contribution of this paper is fivefold: (1) we address the gap in the Electre literature for problems involving conflicting systems of criteria, uncertainty and imprecise information; (2) we extend the Electre I method to take into account the uncertain, imprecise and linguistic assessments; (3) we define outranking relations by pairwise comparisons and use decision graphs to determine which action is preferable, incomparable or indifferent in the fuzzy environment; (4) we show that contrary to the TOPSIS rankings, the Electre approach reveals more useful information including the incomparability among the actions; and (5) we provide a numerical example to elucidate the details of the proposed method.     

A Multifactorial Risk Prioritization Framework for Foodborne Pathogens

We develop a prioritization framework for foodborne risks that considers public health impact as well as three other factors (market impact, consumer risk acceptance and perception, and social sensitivity). Canadian case studies are presented for six pathogen‐food combinations: Campylobacter spp. in chicken; Salmonella spp. in chicken and spinach; Escherichia coli O157 in spinach and beef; and Listeria monocytogenes in ready‐to‐eat meats. Public health impact is measured by disability‐adjusted life years and the cost of illness. Market impact is quantified by the economic importance of the domestic market. Likert‐type scales are used to capture consumer perception and acceptance of risk and social sensitivity to impacts on vulnerable consumer groups and industries. Risk ranking is facilitated through the development of a knowledge database presented in the format of info cards and the use of multicriteria decision analysis (MCDA) to aggregate the four factors. Three scenarios representing different stakeholders illustrate the use of MCDA to arrive at rankings of pathogen‐food combinations that reflect different criteria weights. The framework provides a flexible instrument to support policymakers in complex risk prioritization decision making when different stakeholder groups are involved and when multiple pathogen‐food combinations are compared.     

Can Carbon Nanomaterials Improve CZTS Photovoltaic Devices? Evaluation of Performance and Impacts Using Integrated Life‐Cycle Assessment and Decision Analysis

In emergent photovoltaics, nanoscale materials hold promise for optimizing device characteristics; however, the related impacts remain uncertain, resulting in challenges to decisions on strategic investment in technology innovation. We integrate multi‐criteria decision analysis (MCDA) and life‐cycle assessment (LCA) results (LCA‐MCDA) as a method of incorporating values of a hypothetical federal acquisition manager into the assessment of risks and benefits of emerging photovoltaic materials. Specifically, we compare adoption of copper zinc tin sulfide (CZTS) devices with molybdenum back contacts to alternative devices employing graphite or graphene instead of molybdenum. LCA impact results are interpreted alongside benefits of substitution including cost reductions and performance improvements through application of multi‐attribute utility theory. To assess the role of uncertainty we apply Monte Carlo simulation and sensitivity analysis. We find that graphene or graphite back contacts outperform molybdenum under most scenarios and assumptions. The use of decision analysis clarifies potential advantages of adopting graphite as a back contact while emphasizing the importance of mitigating conventional impacts of graphene production processes if graphene is used in emerging CZTS devices. Our research further demonstrates that a combination of LCA and MCDA increases the usability of LCA in assessing product sustainability. In particular, this approach identifies the most influential assumptions and data gaps in the analysis and the areas in which either engineering controls or further data collection may be necessary.     

“Not in (or Under) My Backyard”: Geographic Proximity and Public Acceptance of Carbon Capture and Storage Facilities

Carbon capture and storage (CCS) is an innovative technical approach to mitigate the problem of climate change by capturing carbon dioxide emissions and injecting them underground for permanent geological storage. CCS has been perceived both positively, as an innovative approach to facilitate a more environmentally benign use of fossil fuels while also generating local economic benefits, and negatively, as a technology that prolongs the use of carbon‐intensive energy sources and burdens local communities with prohibitive costs and ecological and human health risks. This article extends existing research on the “not in my backyard” (NIMBY) phenomenon in a direction that explores the public acceptance of CCS. We utilize survey data collected from 1,001 residents of the coal‐intensive U.S. state of Indiana. Over 80% of respondents express support for the general use of CCS technology. However, 20% of these initial supporters exhibit a NIMBY‐like reaction and switch to opposition as a CCS facility is proposed close to their communities. Respondents’ worldviews, their beliefs about the local economic benefits that CCS will generate, and their concerns about its safety have the greatest impact on increasing or decreasing the acceptance of nearby facilities. These results lend valuable insights into the perceived risks associated with CCS technology and the possibilities for its public acceptance at both a national and local scale. They may be extended further to provide initial insights into likely public reactions to other technologies that share a similar underground dimension, such as hydraulic fracturing.     

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.     

Assessment of Ecological Risks at Former Landfill Site Using TRIAD Procedure and Multicriteria Analysis

Old industrial landfills are important sources of environmental contamination in Europe, including Finland. In this study, we demonstrated the combination of TRIAD procedure, multicriteria decision analysis (MCDA), and statistical Monte Carlo analysis for assessing the risks to terrestrial biota in a former landfill site contaminated by petroleum hydrocarbons (PHCs) and metals. First, we generated hazard quotients by dividing the concentrations of metals and PHCs in soil by the corresponding risk‐based ecological benchmarks. Then we conducted ecotoxicity tests using five plant species, earthworms, and potworms, and determined the abundance and diversity of soil invertebrates from additional samples. We aggregated the results in accordance to the methods used in the TRIAD procedure, conducted rating of the assessment methods based on their performance in terms of specific criteria, and weighted the criteria using two alternative weighting techniques to produce performance scores for each method. We faced problems in using the TRIAD procedure, for example, the results from the animal counts had to be excluded from the calculation of integrated risk estimates (IREs) because our reference soil sample showed the lowest biodiversity and abundance of soil animals. In addition, hormesis hampered the use of the results from the ecotoxicity tests. The final probabilistic IREs imply significant risks at all sampling locations. Although linking MCDA with TRIAD provided a useful means to study and consider the performance of the alternative methods in predicting ecological risks, some uncertainties involved still remained outside the quantitative analysis.     

An Effective Statistical Approach for Comparative Risk Assessment1

Comparative risk assessment is an evaluation process designed to rank environmental problems based on the severity of potential hazards. The purpose of this paper is to provide an effective statistical approach to analyze perceived environmental risks. Environmental problems, evaluative criteria, and other potential moderator variables need to be determined first, and then the risk perception data collected. Repeated measures analysis is used to first test for interactions between environmental problems and potential moderator variables. If there are no significant interactions, then the risk difference among environmental problems is tested unconditionally; otherwise the risk difference is tested conditionally. Cluster analysis for environmental problems is performed only when the risk difference is significant. The clustering results can be objectively determined by using the simultaneous T² confidence intervals. Risk-based priority setting is made according to the clusters obtained. To illustrate this approach, an empirical study of comparative socioeconomic risks in Taiwan was conducted. Socioeconomic impacts areas including social security, quality of life, production cost, investment willingness, and economic resources are used as evaluative criteria. Results indicate that selected impact areas do affect relative risk differences among 24 environmental problems, and the difference is significant for each area. Therefore, cluster analysis is conducted separately for each impact area. Risk-based priority settings for clusters of environmental problems are reported.     

Local Spatial and Temporal Factors Influencing Population and Societal Vulnerability to Natural Disasters

The identification of societal vulnerable counties and regions and the factors contributing to social vulnerability are crucial for effective disaster risk management. Significant advances have been made in the study of social vulnerability over the past two decades, but we still know little regarding China's societal vulnerability profiles, especially at the county level. This study investigates the county‐level spatial and temporal patterns in social vulnerability in China from 1980 to 2010. Based on China's four most recent population censuses of 2,361 counties and their corresponding socioeconomic data, a social vulnerability index for each county was created using factor analysis. Exploratory spatial data analysis, including global and local autocorrelations, was applied to reveal the spatial patterns of county‐level social vulnerability. The results demonstrate that the dynamic characteristics of China's county‐level social vulnerability are notably distinct, and the dominant contributors to societal vulnerability for all of the years studied were rural character, development (urbanization), and economic status. The spatial clustering patterns of social vulnerability to natural disasters in China exhibited a gathering–scattering–gathering pattern over time. Further investigations indicate that many counties in the eastern coastal area of China are experiencing a detectable increase in social vulnerability, whereas the societal vulnerability of many counties in the western and northern areas of China has significantly decreased over the past three decades. These findings will provide policymakers with a sound scientific basis for disaster prevention and mitigation decisions.     

Nuclear Safety in Ontario: A Critical Review of Quantitative Analyses

In this article quantitative analyses of CANDU nuclear generating stations are evaluated using an explicit set of criteria derived from a decision-analytic framework. A systematic search was made for relevant analyses, including both risk assessments and economic analyses. Only a small number of scientifically sound quantitative analyses that are being used to make decisions about specific safety measures or projects were located. The availability of scientifically sound quantitative data for making major energy policy decisions is even more limited, and what is available has major shortcomings. The province of Ontario is now heavily dependent on nuclear energy. Given the uncertainties surrounding the health, environmental, economic, and social consequences of nuclear energy, there is a need to assemble the information that is available within a comprehensive decision-making framework, and to decide future energy policies for the province in a public forum from a societal perspective.     

Characterizing Environmental Harm: Developments in an Approach to Strategic Risk Assessment and Risk Management

Environmental policymakers and regulators are often in the position of having to prioritize their actions across a diverse range of environmental pressures to secure environmental protection and improvements. Information on environmental issues to inform this type of strategic analysis can be disparate; it may be too voluminous or even absent. Data on a range of issues are rarely presented in a common format that allows easy analysis and comparison. Nevertheless, judgments are required on the significance of various environmental pressures and on the inherent uncertainties to inform strategic assessments such as “state of the environment” reports. How can decisionmakers go about this type of strategic and comparative risk analysis? In an attempt to provide practical tools for the analysis of environmental risks at a strategic level, the Environment Agency of England and Wales has conducted a program of developmental research on strategic risk assessment since 1996. The tools developed under this program use the concept of “environmental harm” as a common metric, viewed from technical, social, and economic perspectives, to analyze impacts from a range of environmental pressures. Critical to an informed debate on the relative importance of these perspectives is an understanding and analysis of the various characteristics of harm (spatial and temporal extent, reversibility, latency, etc.) and of the social response to actual or potential environmental harm from a range of hazards. Recent developments in our approach, described herein, allow a presentation of the analysis in a structured fashion so as to better inform risk‐management decisions.     

Technical and Democratic Values in Risk Analysis1

So, we can argue that the lay public are not fools in their attitudes about risk. That nonexperts should show more concern over hazardous waste facilities in their neighborhoods than radon levels in their homes is not a sign of irrationality (because aggregate and individual risks are greater from the radon), but simply a sign that nonexperts are working from a different set of criteria. These criteria are incorporated in what I call the democratic model. The democratic model evaluates risk based on its social and political consequences, such as possible disruption in the social fabric or a loss of communality. Lay criteria for assessing the impact of risk decisions are not explicit, like the those of the risk analyst, but are embedded in cultural values. Similarly, lay evaluations of risk incorporate substantive and procedural democratic values, such as the acceptability of processes for making decisions, the ethics of the distribution of risk, and the capacity to control a source of risk in the community's interests. Finally, the democratic model relates judgments about risks to the competence (Can we trust them?) and the legitimacy (Should we trust them?) of the social institutions that impose and control those risks. The public's judgments about risk are not inferior, but different, and arguably richer than those of the experts.     

An Integrated Framework for Environmental Multi‐Impact Spatial Risk Analysis

Quantitative risk analysis is being extensively employed to support policymakers and provides a strong conceptual framework for evaluating decision alternatives under uncertainty. Many problems involving environmental risks are, however, of a spatial nature, i.e., containing spatial impacts, spatial vulnerabilities, and spatial risk‐mitigation alternatives. Recent developments in multicriteria spatial analysis have enabled the assessment and aggregation of multiple impacts, supporting policymakers in spatial evaluation problems. However, recent attempts to conduct spatial multicriteria risk analysis have generally been weakly conceptualized, without adequate roots in quantitative risk analysis. Moreover, assessments of spatial risk often neglect the multidimensional nature of spatial impacts (e.g., social, economic, human) that are typically occurring in such decision problems. The aim of this article is therefore to suggest a conceptual quantitative framework for environmental multicriteria spatial risk analysis based on expected multi‐attribute utility theory. The framework proposes: (i) the formal assessment of multiple spatial impacts; (ii) the aggregation of these multiple spatial impacts; (iii) the assessment of spatial vulnerabilities and probabilities of occurrence of adverse events; (iv) the computation of spatial risks; (v) the assessment of spatial risk mitigation alternatives; and (vi) the design and comparison of spatial risk mitigation alternatives (e.g., reductions of vulnerabilities and/or impacts). We illustrate the use of the framework in practice with a case study based on a flood‐prone area in northern Italy.     

Use of Multicriteria Decision Analysis to Support Weight of Evidence Evaluation

Weight of evidence (WOE) methods are key components of ecological and human health risk assessments. Most WOE applications rely on the qualitative integration of diverse lines of evidence (LOE) representing impact on ecological receptors and humans. Recent calls for transparency in assessments and justifiability of management decisions are pushing the community to consider quantitative methods for integrated risk assessment and management. This article compares and contrasts the type of information required for application of individual WOE techniques and the outcomes that they provide in ecological risk assessment and proposes a multicriteria decision analysis (MCDA) framework for integrating individual LOE in support of management decisions. The use of quantitative WOE techniques is illustrated for a hypothetical but realistic case study of selecting remedial alternatives at a contaminated aquatic site. Use of formal MCDA does not necessarily eliminate biases and judgment calls necessary for selecting remedial alternatives, but allows for transparent evaluation and fusion of individual LOE. It also provides justifiable methods for selecting remedial alternatives consistent with stakeholder and decision‐maker values.     

Integrating Household Risk Mitigation Behavior in Flood Risk Analysis: An Agent‐Based Model Approach

Recent studies showed that climate change and socioeconomic trends are expected to increase flood risks in many regions. However, in these studies, human behavior is commonly assumed to be constant, which neglects interaction and feedback loops between human and environmental systems. This neglect of human adaptation leads to a misrepresentation of flood risk. This article presents an agent‐based model that incorporates human decision making in flood risk analysis. In particular, household investments in loss‐reducing measures are examined under three economic decision models: (1) expected utility theory, which is the traditional economic model of rational agents; (2) prospect theory, which takes account of bounded rationality; and (3) a prospect theory model, which accounts for changing risk perceptions and social interactions through a process of Bayesian updating. We show that neglecting human behavior in flood risk assessment studies can result in a considerable misestimation of future flood risk, which is in our case study an overestimation of a factor two. Furthermore, we show how behavior models can support flood risk analysis under different behavioral assumptions, illustrating the need to include the dynamic adaptive human behavior of, for instance, households, insurers, and governments. The method presented here provides a solid basis for exploring human behavior and the resulting flood risk with respect to low‐probability/high‐impact risks.     

The Blind Spot in Risk Ethics: Managing Natural Hazards

Many risk scholars recognize the importance of including ethical considerations in risk management. Risk ethics can provide in‐depth ethical analysis so that ethical considerations can be part of risk‐related decisions, rather than an afterthought to those decisions. In this article, I present a brief sketch of the field of risk ethics. I argue that risk ethics has a bias toward technological hazards, thereby overlooking the risks that stem from natural and semi‐natural hazards. In order to make a contribution to the field of risk research, risks ethics should broaden its scope to include natural and semi‐natural hazards and develop normative distribution criteria that can support decision making on such hazards.     

Reducing Drinking Water Supply Chemical Contamination: Risks from Underground Storage Tanks

Drinking water supplies are at risk of contamination from a variety of physical, chemical, and biological sources. Ranked among these threats are hazardous material releases from leaking or improperly managed underground storage tanks located at municipal, commercial, and industrial facilities. To reduce human health and environmental risks associated with the subsurface storage of hazardous materials, government agencies have taken a variety of legislative and regulatory actions—which date back more than 25 years and include the establishment of rigorous equipment/technology/operational requirements and facility‐by‐facility inspection and enforcement programs. Given a history of more than 470,000 underground storage tank releases nationwide, the U.S. Environmental Protection Agency continues to report that 7,300 new leaks were found in federal fiscal year 2008, while nearly 103,000 old leaks remain to be cleaned up. In this article, we report on an alternate evidence‐based intervention approach for reducing potential releases from the storage of petroleum products (gasoline, diesel, kerosene, heating/fuel oil, and waste oil) in underground tanks at commercial facilities located in Rhode Island. The objective of this study was to evaluate whether a new regulatory model can be used as a cost‐effective alternative to traditional facility‐by‐facility inspection and enforcement programs for underground storage tanks. We conclude that the alternative model, using an emphasis on technical assistance tools, can produce measurable improvements in compliance performance, is a cost‐effective adjunct to traditional facility‐by‐facility inspection and enforcement programs, and has the potential to allow regulatory agencies to decrease their frequency of inspections among low risk facilities without sacrificing compliance performance or increasing public health risks.     

Game Theory and Risk Analysis

Risk analysts often analyze adversarial risks from terrorists or other intelligent attackers without mentioning game theory. Why? One reason is that many adversarial situations—those that can be represented as attacker‐defender games, in which the defender first chooses an allocation of defensive resources to protect potential targets, and the attacker, knowing what the defender has done, then decides which targets to attack—can be modeled and analyzed successfully without using most of the concepts and terminology of game theory. However, risk analysis and game theory are also deeply complementary. Game‐theoretic analyses of conflicts require modeling the probable consequences of each choice of strategies by the players and assessing the expected utilities of these probable consequences. Decision and risk analysis methods are well suited to accomplish these tasks. Conversely, game‐theoretic formulations of attack‐defense conflicts (and other adversarial risks) can greatly improve upon some current risk analyses that attempt to model attacker decisions as random variables or uncertain attributes of targets (“threats”) and that seek to elicit their values from the defender's own experts. Game theory models that clarify the nature of the interacting decisions made by attackers and defenders and that distinguish clearly between strategic choices (decision nodes in a game tree) and random variables (chance nodes, not controlled by either attacker or defender) can produce more sensible and effective risk management recommendations for allocating defensive resources than current risk scoring models. Thus, risk analysis and game theory are (or should be) mutually reinforcing.     

Risk Analysis for U.S. Offshore Wind Farms: The Need for an Integrated Approach

Wind power is becoming an increasingly important part of the global energy portfolio, and there is growing interest in developing offshore wind farms in the United States to better utilize this resource. Wind farms have certain environmental benefits, notably near‐zero emissions of greenhouse gases, particulates, and other contaminants of concern. However, there are significant challenges ahead in achieving large‐scale integration of wind power in the United States, particularly offshore wind. Environmental impacts from wind farms are a concern, and these are subject to a number of on‐going studies focused on risks to the environment. However, once a wind farm is built, the farm itself will face a number of risks from a variety of hazards, and managing these risks is critical to the ultimate achievement of long‐term reductions in pollutant emissions from clean energy sources such as wind. No integrated framework currently exists for assessing risks to offshore wind farms in the United States, which poses a challenge for wind farm risk management. In this “Perspective”, we provide an overview of the risks faced by an offshore wind farm, argue that an integrated framework is needed, and give a preliminary starting point for such a framework to illustrate what it might look like. This is not a final framework; substantial work remains. Our intention here is to highlight the research need in this area in the hope of spurring additional research about the risks to wind farms to complement the substantial amount of on‐going research on the risks from wind farms.     

碳交易机制下供应链CCS投资时机研究

在碳交易机制下,碳价格不确定性对发电商的碳捕获与封存(Carbon Capture and Storage,CCS)投资时机具有重要影响。基于实物期权理论,考虑碳价格不确定性及CCS投资成本递减情况下CCS投资的期权价值,建立集碳捕获-运输-封存于一体的CCS投资时机决策基本模型。在此基础上,进一步构建考虑供应链内发电商与CCS运营商合作的CCS投资博弈模型,分析发电商与CCS运营商在收益转移比例与投资时机之间进行决策的均衡条件。基于上述模型,结合数值算例进一步分析了CCS投资时机的影响因素。结果表明:碳价格波动率、政府对CCS投资补贴系数、CCS技术学习效应以及供应链内部企业间的收益转移比例等都将对CCS投资门槛产生影响,供应链企业应综合考虑各因素的影响,科学选择投资时机与收益转移比例,以实现CO2减排的同时保证各方的经济利益,保障供应链的协调运行与CCS投资优化。