Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies.     

An Integrated Approach for Modeling Sustainability Risks in Freight Transportation Systems

Integrating sustainability into freight transportation systems (FTSs) is a complex and challenging task due to the sheer number of inherent sustainability risks. Sustainability risks disrupt the economic, social and environmental objectives of freight operations and act as impediments in the development of sustainable freight transportation systems. The area of sustainability risk management is still unexplored and immature in the operational research domain. This study addresses these research gaps and contributes in a threefold manner. First, a total of 36 potential sustainability risks related to FTSs are identified and uniquely classified into seven categories using a rigourous approach. Second, the research proposes two prominent perspectives, namely, ontological and epistemological perspectives to understand risks and develops a novel framework for managing sustainability risks in FTSs. Third, a novel approach by integrating fuzzy evidential reasoning algorithm (FERA) with expected utility theory is developed to quantitatively model and profile sustainability risk for different risk preferences, namely, risk-averse, risk-neutral, and risk-taking scenarios. The proposed FERA is a flexible and robust approach, which transforms the experts’ inputs into belief structures and aggregates them using the evidence combination rule proposed in Dempster–Shafer theory to overcome the problem of imprecise results caused by average scoring in existing models. A sensitivity analysis is conducted to demonstrate the robustness of the proposed model. Unlike conventional perception, our study suggests that most of the high priority sustainability risks are behaviorally and socially induced rather than financially driven. The results provide significant managerial implications including a focus on skills development, and on social and behavioral dimensions while managing risks in FTSs.     

A Multicriteria Decision Analysis Model and Risk Assessment Framework for Carbon Capture and Storage

Multicriteria decision analysis (MCDA) has been applied to various energy problems to incorporate a variety of qualitative and quantitative criteria, usually spanning environmental, social, engineering, and economic fields. MCDA and associated methods such as life‐cycle assessments and cost‐benefit analysis can also include risk analysis to address uncertainties in criteria estimates. One technology now being assessed to help mitigate climate change is carbon capture and storage (CCS). CCS is a new process that captures CO₂ emissions from fossil‐fueled power plants and injects them into geological reservoirs for storage. It presents a unique challenge to decisionmakers (DMs) due to its technical complexity, range of environmental, social, and economic impacts, variety of stakeholders, and long time spans. The authors have developed a risk assessment model using a MCDA approach for CCS decisions such as selecting between CO₂ storage locations and choosing among different mitigation actions for reducing risks. The model includes uncertainty measures for several factors, utility curve representations of all variables, Monte Carlo simulation, and sensitivity analysis. This article uses a CCS scenario example to demonstrate the development and application of the model based on data derived from published articles and publicly available sources. The model allows high‐level DMs to better understand project risks and the tradeoffs inherent in modern, complex energy decisions.     

Validation of a Novel Air Toxic Risk Model with Air Monitoring

Three modeling systems were used to estimate human health risks from air pollution: two versions of MNRiskS (for Minnesota Risk Screening), and the USEPA National Air Toxics Assessment (NATA). MNRiskS is a unique cumulative risk modeling system used to assess risks from multiple air toxics, sources, and pathways on a local to a state‐wide scale. In addition, ambient outdoor air monitoring data were available for estimation of risks and comparison with the modeled estimates of air concentrations. Highest air concentrations and estimated risks were generally found in the Minneapolis‐St. Paul metropolitan area and lowest risks in undeveloped rural areas. Emissions from mobile and area (nonpoint) sources created greater estimated risks than emissions from point sources. Highest cancer risks were via ingestion pathway exposures to dioxins and related compounds. Diesel particles, acrolein, and formaldehyde created the highest estimated inhalation health impacts. Model‐estimated air concentrations were generally highest for NATA and lowest for the AERMOD version of MNRiskS. This validation study showed reasonable agreement between available measurements and model predictions, although results varied among pollutants, and predictions were often lower than measurements. The results increased confidence in identifying pollutants, pathways, geographic areas, sources, and receptors of potential concern, and thus provide a basis for informing pollution reduction strategies and focusing efforts on specific pollutants (diesel particles, acrolein, and formaldehyde), geographic areas (urban centers), and source categories (nonpoint sources). The results heighten concerns about risks from food chain exposures to dioxins and PAHs. Risk estimates were sensitive to variations in methodologies for treating emissions, dispersion, deposition, exposure, and toxicity.     

城市蔓延与生产率:促进还是抑制? ——基于夜间灯光数据的分析

城市蔓延有利于提高城市生产率吗?文章首先从城市规模、城市人口密度两个方面阐释城市蔓延影响生产率的内在机理,基于长江经济带104个城市2002年~2013年夜间灯光数据,实证研究了城市蔓延的生产率效应,主要结论如下:1)城市蔓延对长江经济带生产率具有显著负向影响,意味着城市无序、低密度扩张阻碍了长江经济带生产率的提高.2)分区域研究发现,城市蔓延对东、中、西部地区城市生产率的影响为负,但对东部地区生产率影响最小,对中西部地区生产率产生较大影响.3)分产业研究发现,城市蔓延降低第二产业生产率的同时显著提升了第三产业生产率.4)考虑空间溢出效应的空间杜宾模型研究发现,城市蔓延影响生产率的直接效应与间接效应均显著为负,且间接效应远大于直接效应,意味着城市蔓延对长江经济带城市生产率的影响具有明显的空间溢出效应.     

The Effects of Urban Form on Ambient Air Pollution and Public Health Risk: A Case Study in Raleigh,North Carolina

Since motor vehicles are a major air pollution source, urban designs that decrease private automobile use could improve air quality and decrease air pollution health risks. Yet, the relationships among urban form, air quality, and health are complex and not fully understood. To explore these relationships, we model the effects of three alternative development scenarios on annual average fine particulate matter (PM_2.5) concentrations in ambient air and associated health risks from PM_2.5 exposure in North Carolina's Raleigh‐Durham‐Chapel Hill area. We integrate transportation demand, land‐use regression, and health risk assessment models to predict air quality and health impacts for three development scenarios: current conditions, compact development, and sprawling development. Compact development slightly decreases (?0.2%) point estimates of regional annual average PM_2.5 concentrations, while sprawling development slightly increases (+1%) concentrations. However, point estimates of health impacts are in opposite directions: compact development increases (+39%) and sprawling development decreases (?33%) PM_2.5‐attributable mortality. Furthermore, compactness increases local variation in PM_2.5 concentrations and increases the severity of local air pollution hotspots. Hence, this research suggests that while compact development may improve air quality from a regional perspective, it may also increase the concentration of PM_2.5 in local hotspots and increase population exposure to PM_2.5. Health effects may be magnified if compact neighborhoods and PM_2.5 hotspots are spatially co‐located. We conclude that compactness alone is an insufficient means of reducing the public health impacts of transportation emissions in automobile‐dependent regions. Rather, additional measures are needed to decrease automobile dependence and the health risks of transportation emissions.     

A Robust Approach for Mitigating Risks in Cyber Supply Chains

In recent years, there have been growing concerns regarding risks in federal information technology (IT) supply chains in the United States that protect cyber infrastructure. A critical need faced by decisionmakers is to prioritize investment in security mitigations to maximally reduce risks in IT supply chains. We extend existing stochastic expected budgeted maximum multiple coverage models that identify “good” solutions on average that may be unacceptable in certain circumstances. We propose three alternative models that consider different robustness methods that hedge against worst‐case risks, including models that maximize the worst‐case coverage, minimize the worst‐case regret, and maximize the average coverage in the $(1?\alpha )$ worst cases (conditional value at risk). We illustrate the solutions to the robust methods with a case study and discuss the insights their solutions provide into mitigation selection compared to an expected‐value maximizer. Our study provides valuable tools and insights for decisionmakers with different risk attitudes to manage cybersecurity risks under uncertainty.     

考虑风险总关联的项目风险应对策略选择方法

本文针对考虑风险总关联的项目风险应对策略选择问题,首先结合MACBETH方法以及DEMATEL方法分析项目风险总关联;然后,在考虑项目管理者风险态度的基础上,以最大化项目管理者期望效用为目标构建考虑风险总关联的项目风险应对策略选择优化模型;最后,通过实际案例分析验证所提方法和模型的可行性与有效性,并比较分析了不同关联作用对风险应对决策的影响。结果表明：1）存在使项目管理者期望效用达到最大的最优项目风险应对预算;2）项目管理者的风险态度和对风险关联的关注程度对风险应对策略的选择和项目管理者的期望效用均有影响,在实际项目风险应对决策中均应予以考虑;3）在项目风险应对策略选择过程中,项目管理者不仅要考虑风险之间的直接关联还要重视风险之间的间接关联,而风险之间的积极关联则可以忽略。     

Risk‐Based Decision Making for Reoccupation of Contaminated Areas Following a Wide‐Area Anthrax Release

This article presents an analysis of postattack response strategies to mitigate the risks of reoccupying contaminated areas following a release of Bacillus anthracis spores (the bacterium responsible for causing anthrax) in an urban setting. The analysis is based on a hypothetical attack scenario in which individuals are exposed to B. anthracis spores during an initial aerosol release and then placed on prophylactic antibiotics that successfully protect them against the initial aerosol exposure. The risk from reoccupying buildings contaminated with spores due to their reaerosolization and inhalation is then evaluated. The response options considered include: decontamination of the buildings, vaccination of individuals reoccupying the buildings, extended evacuation of individuals from the contaminated buildings, and combinations of these options. The study uses a decision tree to estimate the costs and benefits of alternative response strategies across a range of exposure risks. Results for best estimates of model inputs suggest that the most cost‐effective response for high‐risk scenarios (individual chance of infection exceeding 11%) consists of evacuation and building decontamination. For infection risks between 4% and 11%, the preferred option is to evacuate for a short period, vaccinate, and then reoccupy once the vaccine has taken effect. For risks between 0.003% and 4%, the preferred option is to vaccinate only. For risks below 0.003%, none of the mitigation actions have positive expected monetary benefits. A sensitivity analysis indicates that for high‐infection‐likelihood scenarios, vaccination is recommended in the case where decontamination efficacy is less than 99.99%.     

Evaluating Efficiency-Equality Tradeoffs for Mobile Source Control Strategies in an Urban Area

In environmental risk management, there are often interests in maximizing public health benefits (efficiency) and addressing inequality in the distribution of health outcomes. However, both dimensions are not generally considered within a single analytical framework. In this study, we estimate both total population health benefits and changes in quantitative indicators of health inequality for a number of alternative spatial distributions of diesel particulate filter retrofits across half of an urban bus fleet in Boston, Massachusetts. We focus on the impact of emissions controls on primary fine particulate matter (PM_2.5) emissions, modeling the effect on PM_2.5 concentrations and premature mortality. Given spatial heterogeneity in baseline mortality rates, we apply the Atkinson index and other inequality indicators to quantify changes in the distribution of mortality risk. Across the different spatial distributions of control strategies, the public health benefits varied by more than a factor of two, related to factors such as mileage driven per day, population density near roadways, and baseline mortality rates in exposed populations. Changes in health inequality indicators varied across control strategies, with the subset of optimal strategies considering both efficiency and equality generally robust across different parametric assumptions and inequality indicators. Our analysis demonstrates the viability of formal analytical approaches to jointly address both efficiency and equality in risk assessment, providing a tool for decisionmakers who wish to consider both issues.     

Improving Risk Communication

This paper explores reasons for difficulties in communicating risks among analysts, the laypublic, media, and regulators. Formulating risk communication problems as decisions involving objectives and alternatives helps to identify strategies for overcoming these difficulties. Several strategies are suggested to achieve risk communication objectives like improving public knowledge about risks and risk management, encouraging risk reduction behavior, understanding public values and concerns, and increasing trust and credibility.     

Aggregate Human Health Risk Assessment from Dust of Daily Life in the Urban Environment of Beijing

Because of the high emissions of polycyclic aromatic hydrocarbons (PAHs) into the environment by the increasing number of vehicles in Beijing and the absorption of these PAHs onto particulates, the performance of a preliminary health risk assessment of the aggregate exposure to PAHs of urban citizens in daily life is very important. Urban dust can be used to examine the aggregation of atmospheric particulates from local pollution sources over a long time period and the direct exposure of the urban human population. The environment's correlative with clothing, dining, residing, and traveling in urban daily life was assessed using exposure‐receptor‐oriented analysis. The multipathway exposure model was used to simulate the lifetime exposure of a female citizen to PAHs in dust. All of the PAH concentrations in dust for each behavior and its correlative environment in Beijing were acceptable because all of the carcinogenic risks of PAHs in the dust were approximately 1.0 × 10^–6. The dominant induced carcinogenic risks in the dust were Benzo(a)pyrene and Dibenzo(a,h)anthracene. The main carcinogenic risk routes for humans were dermal contact and oral intake, which contributed on average 99.78% of the risk. Indoor risk is especially important, as the decoration and height within the building were important impact factors for carcinogenic risk induced by indoor PAHs. For people living in an urban area, a healthy lifestyle includes less decoration per room, living on a low floor, wearing a respirator, and reducing exposed skin area when traveling.     

Joint supplier selection and scheduling of customer orders under disruption risks: Single vs. dual sourcing

This paper presents a stochastic mixed integer programming approach to integrated supplier selection and customer order scheduling in the presence of supply chain disruption risks, for a single or dual sourcing strategy. The suppliers are assumed to be located in two different geographical regions: in the producer's region (domestic suppliers) and outside the producer's region (foreign suppliers). The supplies are subject to independent random local disruptions that are uniquely associated with a particular supplier and to random semi-global (regional) disruptions that may result in disruption of all suppliers in the same geographical region simultaneously. The domestic suppliers are relatively reliable but more expensive, while the foreign suppliers offer competitive prices, however material flows from these suppliers are more exposed to unexpected disruptions. Given a set of customer orders for products, the decision maker needs to decide which single supplier or which two different suppliers, one from each region, to select for purchasing parts required to complete the customer orders and how to schedule the orders over the planning horizon, to mitigate the impact of disruption risks. The problem objective is either to minimize total cost or to maximize customer service level. The obtained combinatorial stochastic optimization problem will be formulated as a mixed integer program with conditional value-at-risk as a risk measure. The risk-neutral and risk-averse solutions that optimize, respectively average and worst-case performance of a supply chain are compared for a single and dual sourcing strategy and for the two different objective functions. Numerical examples and computational results are presented and some managerial insights on the choice between the two sourcing strategies are reported.     

What is a Healthy Sustainable Built Environment? Developing Evidence-Based Healthy Built Environment Indicators for Policy-Makers and Practitioners

Abstract

Healthy built environments place people and communities at the heart of urban planning, encouraging decision-making based on human health, well-being and environmental sustainability. This paper describes the development of a set of indicators to assist in this endeavour—by translating and linking the research evidence base with policy-makers and other practitioners. The paper describes how the indicators were chosen, contextualized and subsequently structured to maximize useability for urban planners and health professionals working together to improve community health and neighbourhood sustainability.     

Overcoming Learning Aversion in Evaluating and Managing Uncertain Risks

Decision biases can distort cost‐benefit evaluations of uncertain risks, leading to risk management policy decisions with predictably high retrospective regret. We argue that well‐documented decision biases encourage learning aversion, or predictably suboptimal learning and premature decision making in the face of high uncertainty about the costs, risks, and benefits of proposed changes. Biases such as narrow framing, overconfidence, confirmation bias, optimism bias, ambiguity aversion, and hyperbolic discounting of the immediate costs and delayed benefits of learning, contribute to deficient individual and group learning, avoidance of information seeking, underestimation of the value of further information, and hence needlessly inaccurate risk‐cost‐benefit estimates and suboptimal risk management decisions. In practice, such biases can create predictable regret in selection of potential risk‐reducing regulations. Low‐regret learning strategies based on computational reinforcement learning models can potentially overcome some of these suboptimal decision processes by replacing aversion to uncertain probabilities with actions calculated to balance exploration (deliberate experimentation and uncertainty reduction) and exploitation (taking actions to maximize the sum of expected immediate reward, expected discounted future reward, and value of information). We discuss the proposed framework for understanding and overcoming learning aversion and for implementing low‐regret learning strategies using regulation of air pollutants with uncertain health effects as an example.     

Improving Risk Management: From Lame Excuses to Principled Practice

The three classic pillars of risk analysis are risk assessment (how big is the risk and how sure can we be?), risk management (what shall we do about it?), and risk communication (what shall we say about it, to whom, when, and how?). We propose two complements as important parts of these three bases: risk attribution (who or what addressable conditions actually caused an accident or loss?) and learning from experience about risk reduction (what works, and how well?). Failures in complex systems usually evoke blame, often with insufficient attention to root causes of failure, including some aspects of the situation, design decisions, or social norms and culture. Focusing on blame, however, can inhibit effective learning, instead eliciting excuses to deflect attention and perceived culpability. Productive understanding of what went wrong, and how to do better, thus requires moving past recrimination and excuses. This article identifies common blame‐shifting “lame excuses” for poor risk management. These generally contribute little to effective improvements and may leave real risks and preventable causes unaddressed. We propose principles from risk and decision sciences and organizational design to improve results. These start with organizational leadership. More specifically, they include: deliberate testing and learning—especially from near‐misses and accident precursors; careful causal analysis of accidents; risk quantification; candid expression of uncertainties about costs and benefits of risk‐reduction options; optimization of tradeoffs between gathering additional information and immediate action; promotion of safety culture; and mindful allocation of people, responsibilities, and resources to reduce risks. We propose that these principles provide sound foundations for improving successful risk management.     

Maximizing Health Benefits and Minimizing Inequality: Incorporating Local‐Scale Data in the Design and Evaluation of Air Quality Policies

The U.S. Environmental Protection Agency undertook a case study in the Detroit metropolitan area to test the viability of a new multipollutant risk‐based (MP/RB) approach to air quality management, informed by spatially resolved air quality, population, and baseline health data. The case study demonstrated that the MP/RB approach approximately doubled the human health benefits achieved by the traditional approach while increasing cost less than 20%—moving closer to the objective of Executive Order 12866 to maximize net benefits. Less well understood is how the distribution of health benefits from the MP/RB and traditional strategies affect the existing inequalities in air‐pollution‐related risks in Detroit. In this article, we identify Detroit populations that may be both most susceptible to air pollution health impacts (based on local‐scale baseline health data) and most vulnerable to air pollution (based on fine‐scale PM_2.5 air quality modeling and socioeconomic characteristics). Using these susceptible/vulnerable subpopulation profiles, we assess the relative impacts of each control strategy on risk inequality, applying the Atkinson Index (AI) to quantify health risk inequality at baseline and with either risk management approach. We find that the MP/RB approach delivers greater air quality improvements among these subpopulations while also generating substantial benefits among lower‐risk populations. Applying the AI, we confirm that the MP/RB strategy yields less PM_2.5 mortality and asthma hospitalization risk inequality than the traditional approach. We demonstrate the value of this approach to policymakers as they develop cost‐effective air quality management plans that maximize risk reduction while minimizing health inequality.     

Public Perceptions of Regulatory Costs,Their Uncertainty and Interindividual Distribution

Public perceptions of both risks and regulatory costs shape rational regulatory choices. Despite decades of risk perception studies, this article is the first on regulatory cost perceptions. A survey of 744 U.S. residents probed: (1) How knowledgeable are laypeople about regulatory costs incurred to reduce risks? (2) Do laypeople see official estimates of cost and benefit (lives saved) as accurate? (3) (How) do preferences for hypothetical regulations change when mean‐preserving spreads of uncertainty replace certain cost or benefit? and (4) (How) do preferences change when unequal interindividual distributions of hypothetical regulatory costs replace equal distributions? Respondents overestimated costs of regulatory compliance, while assuming agencies underestimate costs. Most assumed agency estimates of benefits are accurate; a third believed both cost and benefit estimates are accurate. Cost and benefit estimates presented without uncertainty were slightly preferred to those surrounded by “narrow uncertainty” (a range of costs or lives entirely within a personally‐calibrated zone without clear acceptance or rejection of tradeoffs). Certain estimates were more preferred than “wide uncertainty” (a range of agency estimates extending beyond these personal bounds, thus posing a gamble between favored and unacceptable tradeoffs), particularly for costs as opposed to benefits (but even for costs a quarter of respondents preferred wide uncertainty to certainty). Agency‐acknowledged uncertainty in general elicited mixed judgments of honesty and trustworthiness. People preferred egalitarian distributions of regulatory costs, despite skewed actual cost distributions, and preferred progressive cost distributions (the rich pay a greater than proportional share) to regressive ones. Efficient and socially responsive regulations require disclosure of much more information about regulatory costs and risks.     

Municipal Budget Management and the Generation of Urban Sprawl. A Case Study of the Lombardy Region (Italy)

ABSTRACT

The paper describes one of the Italian sprawl drivers: the circle triggered by the use of urban charges to solve budget problems. From 2005 to 2018, Italian municipalities could use urban charges to solve normal budget problems: they could plan new urban areas to provide new urban charges, new building and services, and they could manage old and new services at the expense of normal budget. The planning of unnecessary urban areas was effectively influenced by budget problems. The paper illustrates this issue during a period without open data sources in order to improve land management and environmental sustainability.     

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.