Risk Analysis最新文献

The term "real risk" and variations of this term are commonly used in everyday speech and writing, and in the scientific literature. There are mainly two types of use: i) in statements about what the real risk related to an activity is and ii) in statements about the risk related to an activity being real. The former type of use has been extensively discussed in the literature, whereas the latter type has received less attention. In the present study, we review both types of use and analyze and discuss potential meanings of type ii) statements. We conclude that it is reasonable to interpret a statement about the risk being real as reflecting a judgement that there is some risk and that the knowledge supporting this statement is relatively strong. However, such a statement does not convey whether the risk is small or large and needs to be supplemented by a characterization of the risk.

Social norms are often considered as behavioral guidelines to mitigate health and environmental risks. However, our understanding of the magnitude of their impact on risk-mitigating behaviors and how perceptions of risks affect the magnitude remains limited. Given the increasing importance of understanding factors influencing behavioral responses to health and environmental risks, this research examines whether the relationship between social norms and behavioral intention to mitigate health and environmental risks is a function of (1) risk perceptions and (2) uncertainty about risk perceptions. A cross-sectional survey involving a national sample (N = 803) across three health and environmental risks (i.e., infectious diseases, climate change, and water shortage) is conducted. The results reveal a three-way interaction between descriptive norms, uncertainty about susceptibility, and uncertainty about severity on behavioral intention to mitigate the risk. Individuals exhibit the strongest intention to engage in risk-mitigating behaviors when they perceive prevailing social norms and are uncertain about their susceptibility to the risk and the severity of the risk. Moreover, injunctive norms interact with uncertainty about susceptibility to influence behavioral intention, such that the more uncertain individuals feel about their vulnerability to a risk, the stronger the impact of injunctive norms is on behavioral intention. Neither descriptive nor injunctive norms interact with perceived risks to influence behavioral intention. This study contributes valuable insights into the interplay between social norms, uncertainty about perceived risk, and behavioral intention, and offers valuable theoretical and practical implications.

Global pandemics restrict long-haul mobility and international trade. To restore air traffic, a policy named "travel bubble" was implemented during the recent COVID-19 pandemic, which seeks to re-establish air connections among specific countries by permitting unrestricted passenger travel without mandatory quarantine upon arrival. However, travel bubbles are prone to bursting for safety reasons, and how to develop an effective restoration plan through travel bubbles is under-explored. Thus, it is vital to learn from COVID-19 and develop a formal framework for implementing travel bubble therapy for future public health emergencies. This article conducts an analytical investigation of the air travel bubble problem from a network design standpoint. First, a link-based network design problem is established with the goal of minimizing the total infection risk during air travel. Then, based on the relationship between origin-destination pairs and international candidate links, the model is reformulated into a path-based one. A Lagrangian relaxation-based solution framework is proposed to determine the optimal restored international air routes and assign the traffic flow. Finally, computational experiments on both hypothetical data and real-world cases are conducted to examine the algorithm's performance. The results demonstrate the effectiveness and efficiency of the proposed model and algorithm. In addition, compared to a benchmark strategy, it is found that the infection risk under the proposed travel bubble strategy can be reduced by up to 45.2%. More importantly, this work provides practical insights into developing pandemic-induced air transport recovery schemes for both policymakers and aviation operations regulators.

This article presents a planning framework to improve the weather-related resilience of natural gas-dependent electricity distribution systems. The problem is formulated as a two-stage stochastic mixed integer linear programing model. In the first stage, the measures for distribution line hardening, gas-fired distributed generation (DG) placement, electrical energy storage resource allocation, and tie-switch placement are determined. The second stage minimizes the electricity distribution system load shedding in realized hurricanes to achieve a compromise between operational benefits and investment costs when the dependence of electricity distribution system on the natural gas exists. The proposed stochastic model considers random failures of electricity distribution system lines and random errors in forecasting the load demand. The Monte Carlo simulation is employed to generate multiple scenarios for defining the uncertainties of electricity distribution system. For the sake of simplicity, weather-related outages of natural gas pipelines are considered deterministic. The nonlinear natural gas constraints are linearized and incorporated into the stochastic optimization model. The proposed framework was successfully implemented on an interconnected energy system composed of a 33-bus electricity distribution system and a 14-node natural gas distribution network. Numerical results of the defined case studies and a devised comparative study validate the effectiveness of the proposed framework as well.

Increasing awareness of climate change and its potential consequences on financial markets has led to interest in the impact of climate risk on stock returns and portfolio composition, but few studies have focused on perceived climate risk pricing. This study is the first to introduce perceived climate risk as an additional factor in asset pricing models. The perceived climate risk is measured based on the climate change sentiment of the Twitter dataset with 16 million unique tweets in the years 2010-2019. One of the main advantages of our proxy is that it allows us to capture both physical and transition climate risks. Our results show that perceived climate risk is priced into Standard and Poor's 500 (S&P 500) Index stock returns and is robust when different asset-pricing models are used. Our findings have implications for market participants, as understanding the relationship between perceived climate risk and asset prices is crucial for investors seeking to navigate the financial implications of climate change and for policymakers aiming to promote sustainable financing and mitigate the potential damaging effects of climate risk on financial markets, and a pricing model that accurately incorporates perceived climate risk can facilitate this understanding.

Information is crucial for risk management; however, no quantified measure to evaluate risk information exists to date. The standard measure of value of factual information is information entropy-that is, the negative logarithm of probability. Despite its applications in various fields, this measure is insufficient for the evaluation of risk information; there are three reasons. First, it requires precise probabilities, which are generally absent in the context of risks. Second, it does not consider the effect of the consequences, which is essential for risks. Third, it does not account for human preferences and subjectivity. This study proposes a quantified measure for the evaluation of factual risk information-that is, observations of occurrence, particularly for binary, unambiguous, and rare phenomena. To develop such a measure, precise probabilities are replaced with updated probabilities, based on the Prospective Reference Theory. Additionally, utility is included as a proxy for the size of consequences. The third challenge-human preferences and subjectivity-is partly addressed by the application of updated perceived probabilities and utility as a measure of human preferences. Such a conventional, quantified measure facilitates the comparison of the potential impact of different messages for a new observation of occurrence for a risk, as well as of messages for different risks. Moreover, it clarifies the factors that systematically affect this impact. More particularly, it indicates the major effects of the perceived number of past occurrences.

Multifarious applications of unmanned aerial vehicles (UAVs) are thriving in extensive fields and facilitating our lives. However, the potential third-party risks (TPRs) on the ground are neglected by developers and companies, which limits large-scale commercialization. Risk assessment is an efficacious method for mitigating TPRs before undertaking flight tasks. This article incorporates the probability of UAV crashing into the TPR assessment model and employs an A* path-planning algorithm to optimize the trade-off between operational TPR cost and economic cost, thereby maximizing overall benefits. Experiments demonstrate the algorithm outperforms both the best-first-search algorithm and Dijkstra's algorithm. In comparison with the path with the least distance, initially, the trade-off results in a $1.88\%$ increase in distance while achieving an $89.47\%$ reduction in TPR. As the trade-off progresses, this relationship shifts, leading to a $20.62\%$ reduction in the distance with only a negligible increase in TPR by 0.0001, matching the TPR-cost-based algorithm. Furthermore, we conduct simulations on the configuration of UAV path networks in five major cities in China based on real-world travel data and building data. Results reveal that the networks consist of one-way paths that are staggered in height. Moreover, in coastal cities particularly, the networks tend to extend over the sea, where the TPR cost is trivial.

Advances in artificial intelligence (AI) are reshaping mobility through autonomous vehicles (AVs), which may introduce risks such as technical malfunctions, cybersecurity threats, and ethical dilemmas in decision-making. Despite these complexities, the influence of consumers' risk preferences on AV acceptance remains poorly understood. This study explores how individuals' risk preferences affect their choices among private AVs (PAVs), shared AVs (SAVs), and private conventional vehicles (PCVs). Employing a lottery experiment and a self-reported survey, we first derive four parameters to capture individuals' risk preferences. Based on a stated preference experiment and the error component logit model, we analyze reference-dependent preferences for key attributes of PAVs and SAVs, using PCVs as the reference. Our analysis reveals that risk-tolerant consumers are more inclined toward PAVs or SAVs. Further, consumers exhibit a greater sensitivity to losses, such as higher purchasing prices and running costs, than to gains, such as reduced egress time. Specifically, for buying a PAV, consumers are willing to pay 3582 CNY more for 1000 CNY saving on annual running cost, 3470 CNY for a 1-min reduction in egress time, 28,880 CNY for removing driver liability for crashes, and 30,710 CNY for the improved privacy data security. For adopting SAVs, consumers are willing to pay 0.096 CNY extra per kilometer for a 1-min reduction in access time and 0.033 CNY extra per kilometer for a 1% increase in SAV availability. Therefore, this study enhances the understanding on risk preferences in AV acceptance and offers important implications for stakeholders in the AI-empowered mobility context.

Recent developments in risk and crisis communication (RCC) research combine social science theory and data science tools to construct effective risk messages efficiently. However, current systematic literature reviews (SLRs) on RCC primarily focus on computationally assessing message efficacy as opposed to message efficiency. We conduct an SLR to highlight any current computational methods that improve message construction efficacy and efficiency. We found that most RCC research focuses on using theoretical frameworks and computational methods to analyze or classify message elements that improve efficacy. For improving message efficiency, computational and manual methods are only used in message classification. Specifying the computational methods used in message construction is sparse. We recommend that future RCC research apply computational methods toward improving efficacy and efficiency in message construction. By improving message construction efficacy and efficiency, RCC messaging would quickly warn and better inform affected communities impacted by current hazards. Such messaging has the potential to save as many lives as possible.

In recent years, "black swan" events have increasingly occurred across climate, epidemics, geopolitics, and economics, leading to a gradual coupling of different types of risk. Different from isolated shocks as a single type of risk affecting a specific industry, a nexus of risks allows one risk area to quickly relate to others, resulting in more catastrophic impacts. Utilizing an integrated framework, we investigate the contagion effects among climate policy uncertainty, the infectious disease equity market volatility tracker, geopolitical risk, and economic policy uncertainty using volatility, skewness, and kurtosis as risk measures. The results indicate that: (1) The contagion effect of different types of risk increases with higher order risk measures, suggesting that more extreme events are more likely to be contagious across domains. (2) Approximately two-thirds of risk contagion occurs contemporaneously, while about one-third occurs with a lag, indicating that risk contagion combines both immediacy and continuity. (3) Risk contagion exhibits significant time-varying and heterogeneous characteristics. Our study elucidates the inherent contagion characteristics between different types of risk, transforming the understanding of risk from a one-dimensional to a multidimensional perspective. This underscores that risk management should not be confined to a single domain; it is crucial to consider the potential impacts of risks from other industries on one's own.