Risk analysis : an official publication of the Society for Risk Analysis最新文献

In crisis situations, time is of the essence. Effective messaging to individuals at risk is critical to mitigating the most severe outcomes. Extant crisis communication literature has focused on differentiating crisis types based on perceived blame, particularly in cases of for-profit company malfeasance, but less work has been done to understand how the public makes these types of attributions. This quantitative systematic review investigates the relationship between severity of a large-scale crisis outcome and attributions of blame toward relevant entities. Moderators of interest include the attribution term used with participants (e.g., blame, responsibility), the type of crisis event, and the entity presented as at fault. Overall, a small but significant positive relationship is identified in the majority of studies between severity of a large-scale crisis outcome and attributions of blame. Results suggest that while crisis type and entity to blame are moderators, the attribution term(s) used with participants plays a less significant role. Implications and future directions are considered.

Climate change is a paradigmatic example of systemic risk. Recently, proposals for large-scale interventions-carbon dioxide removal (CDR) and solar radiation management (SRM)-have started to redefine climate governance strategies. We describe how evolving modeling practices are trending toward optimized and "best-case" projections-portraying deployment schemes that create both technically slanted and politically sanitized profiles of risk, as well as ideal objectives for CDR and SRM as mitigation-enhancing, time-buying mechanisms for carbon transitions or vulnerable populations. As promises, stylized and hopeful projections may selectively reinforce industry and political activities built around the inertia of the carbon economy. Some evidence suggests this is the emerging case for certain kinds of CDR, where the prospect of future carbon capture substitutes for present mitigation. Either of these implications are systemic: explorations of climatic futures may entrench certain carbon infrastructures. We point out efforts and recommendations to forestall this trend in the implementation of the Paris Agreement, by creating more stakeholder input and strengthening political realism in modeling and other assessments, as well as through policy guardrails.

Systemic risks, as opposed to conventional risks, bear the danger of destroying entire systems. Their understanding and governance remain a serious challenge. The phenomena of systemic risks show many analogies with those of dynamic structure generation in the systems of nature, technology, and society, including simple model systems of physics and chemistry. By analyzing these model systems, the elementary processes and the generic mechanisms by which they generate macroscopic dynamic structures become evident. Generalizing these insights makes it possible to formulate the basic framework of a theory of systemic risks with elements providing hints for adequate governance strategies. Although these insights cannot be applied to societal processes one by one, they reveal generic patterns and clusters.

This article deals with household-level flood risk mitigation. We present an agent-based modeling framework to simulate the mechanism of natural hazard and human interactions, to allow evaluation of community flood risk, and to predict various adaptation outcomes. The framework considers each household as an autonomous, yet socially connected, agent. A Beta-Bernoulli Bayesian learning model is first applied to measure changes of agents' risk perceptions in response to stochastic storm surges. Then the risk appraisal behaviors of agents, as a function of willingness-to-pay for flood insurance, are measured. Using Miami-Dade County, Florida as a case study, we simulated four scenarios to evaluate the outcomes of alternative adaptation strategies. Results show that community damage decreases significantly after a few years when agents become cognizant of flood risks. Compared to insurance policies with pre-Flood Insurance Rate Maps subsidies, risk-based insurance policies are more effective in promoting community resilience, but it will decrease motivations to purchase flood insurance, especially for households outside of high-risk areas. We evaluated vital model parameters using a local sensitivity analysis. Simulation results demonstrate the importance of an integrated adaptation strategy in community flood risk management.

Aerosol transmission has played a significant role in the transmission of COVID-19 disease worldwide. We developed a COVID-19 aerosol transmission risk estimation model to better understand how key parameters associated with indoor spaces and infector emissions affect inhaled deposited dose of aerosol particles that convey the SARS-CoV-2 virus. The model calculates the concentration of size-resolved, virus-laden aerosol particles in well-mixed indoor air challenged by emissions from an index case(s). The model uses a mechanistic approach, accounting for particle emission dynamics, particle deposition to indoor surfaces, ventilation rate, and single-zone filtration. The novelty of this model relates to the concept of "inhaled & deposited dose" in the respiratory system of receptors linked to a dose-response curve for human coronavirus HCoV-229E. We estimated the volume of inhaled & deposited dose of particles in the 0.5-4 μm range expressed in picoliters (pL) in a well-documented COVID-19 outbreak in restaurant X in Guangzhou China. We anchored the attack rate with the dose-response curve of HCoV-229E which provides a preliminary estimate of the average SARS-CoV-2 dose per person, expressed in plaque forming units (PFUs). For a reasonable emission scenario, we estimate approximately three PFU per pL deposited, yielding roughly 10 PFUs deposited in the respiratory system of those infected in restaurant X. To explore the model's utility, we tested it with four COVID-19 outbreaks. The risk estimates from the model fit reasonably well with the reported number of confirmed cases given available metadata from the outbreaks and uncertainties associated with model assumptions.

The uncertainty in the timing and severity of disaster events makes the long-term planning of mitigation and recovery actions both critical and extremely difficult. Planners often use expected values for hazard occurrences, leaving communities vulnerable to worse-than-usual and even so-called "black swan" events. This research models disasters in terms of their best-case, most-likely, and worst-case damage estimates. These values are then embedded in a fuzzy goal programming model to provide community planners and stakeholders with the ability to strategize for any range of events from best-case to worst-case by adjusting goal weights. Examples are given illustrating the modeling approach, and an analysis is provided to illustrate how planners might use the model as a planning tool.