Risk Analysis最新文献

The Chinese public is increasingly experiencing the local impacts of climate change, whereas the government downplays its domestic effects and critical opinions on environmental governance. As climate change perceptions are crucial for individual risk management, adaptation, and collective climate actions, it is vital to explore how these perceptions are shaped. Given the increasing significance of social media in climate change discourse, this study employs survey data from the 2021 Environmental Risk Perceptions and Environmental Behaviors of Urban Residents Project to investigate how social media exposure influences risk perceptions of climate change among the Chinese public. Drawing on the social amplification of risk framework, this study examines the effect of exposure to environmental information, exposure to opinion diversity, individuals' social media network ties to environmental opinion leaders, and the interaction between social media exposure and cultural values. The results indicate that in the contexts where climate change is neither politically divisive nor openly debated, social media exposure to diverse opinions and social media network ties to environmental scholars positively predict risk perceptions. Additionally, egalitarianism and fatalism are found to moderate the effect of these connections with environmental scholars. This study extends previous research, which focuses largely on the association between the frequency of social media exposure and risk perceptions of climate change, by revealing a more comprehensive and nuanced process that links social media exposure to climate change perceptions.

According to recent Household Pulse Survey data, roughly 1.1% of households were displaced due to disasters in the United States in recent years. Although most households returned relatively quickly, 20% were displaced for longer than 1 month, and 14% had not returned by the time of the survey. Protracted displacement creates enormous hardships for affected households and communities, yet few disaster risk analyses account for the time component of displacement. Here, we propose predictive models for household displacement duration and return for practical integration within disaster risk analyses, ranging in complexity and predictive power. Two classification tree models are proposed to predict return outcomes with a minimum number of predictors: one that considers only physical factors (TreeP) and another that also considers socioeconomic factors (TreeP&S). A random forest model is also proposed (ForestP&S), improving the model's predictive power and highlighting the drivers of displacement duration and return outcomes. The results of the ForestP&S model highlight the importance of both physical factors (e.g., property damage and unsanitary conditions) and socioeconomic factors (e.g., tenure status and income per household member) on displacement outcomes. These models can be integrated within disaster risk analyses, as illustrated through a hurricane scenario analysis for Atlantic City, NJ. By integrating displacement duration models within risk analyses, we can capture the human impact of disasters more holistically and evaluate mitigation strategies aimed at reducing displacement risk.

This study investigates financial contagion during natural disasters and explores the potential advantage of environmental, social, and governance (ESG) investing in such contagion. Specifically, we propose a new edge-weighted undirected contagion network to explore disaster-driven contagion and transmission channels across sectors, asset classes, and ESG international indexes. Our empirical results demonstrate the existence of the disaster-driven contagion. Natural disasters may increase investors' risk aversion, which further magnify portfolio rebalancing behavior, leading to the spread of financial contagion. Moreover, we also find that ESG investing helps mitigate the spread of disaster-driven contagion, thereby contributing to the resilience of the financial system during natural disasters.

With the increasing global risk of floods, there is an urgent need for new adaptive emergency management (AEM) frameworks. This study aims to integrate machine learning, physical models (such as the Variable Infiltration Capacity model and InfoWorks-ICM), and social data to develop the IFloPhy (Integrated Machine Learning and River Physical Model) framework, which explores early flood risk warnings under AEM. The multidimensional integration design of IFloPhy overcomes the limitations of traditional single-warning systems, enhancing dynamic response capabilities and predictive accuracy. By integrating physical processes, IFloPhy can dynamically track the formation and development of floods, comprehensively considering natural and socio-economic factors, thereby achieving holistic and interactive flood risk assessments. The incorporation of real-time satellite data with multi-model forecast results establishes an immediate warning mechanism, significantly reducing prediction uncertainty. IFloPhy has been deployed and validated in the San Isabel Basin in South America, demonstrating exceptional performance in areas with scarce data and limited communication infrastructure. IFloPhy offers new technologies and insights for risk management and AEM, proposing novel methods for flood risk emergency management.

Disasters caused by tailings storage facilities (TSFs) have highlighted the complexity of safely managing mine tailings and the extension of consequences over time and throughout the tailings runoff. Investigations commissioned by mining companies following major failures in Mariana and Brumadinho, Brazil, primarily focused on immediate technical causes and hazards. However, for effective disaster risk reduction, the integration of technical, environmental, and social factors is needed to comprehensively address the complexity of risk management. Bow-tie models can be used for TSF's disaster analysis, as they consider causes, consequences, and preventive and mitigation controls. Here, an adapted bow-tie framework for TSF's disaster risk analysis is proposed to systematize the identification of threats and consequences and address the four disaster risk dimensions: hazard, exposure, vulnerability, and capacity. The framework was applied to the Pontal TSF, Brazil, using publicly available information, revealing gaps in the risk management, such as the lack of identification of social vulnerabilities. Our framework highlights the importance of reducing TSF's disaster risks through all dimensions and engaging multiple stakeholders. Although TSF stability control is primordial and irreplaceable, alone it is insufficient for effective disaster risk reduction.

In today's algorithm-driven era, individuals not only actively seek health information through search engines or health websites but also passively encounter health-related content while browsing social media feeds. These two distinct behaviors (i.e., intentional information seeking and incidental information scanning) may each contribute to individuals' perceptions of health risks. A substantial body of work has examined the relationship between online health information behaviors (e.g., seeking) and risk perceptions across various contexts. However, the findings on the directionality of these relationships remain equivocal. Drawing on the literature on health information acquisition, this study investigates the longitudinal associations among online health information seeking, scanning, and risk perceptions. Data from a three-wave panel survey with 654 participants indicate that health information scanning and seeking exhibit a stable, reciprocal relationship over time. Moreover, information seeking is positively associated with risk perceptions across waves, whereas information scanning does not exert a direct effect. These findings contribute to theoretical developments in digitally mediated risk communication by highlighting the temporal dynamics and interplay of online information behaviors. They also offer practical guidance for designing more targeted and psychologically informed digital health communication strategies.

An increasing number of countries have begun to utilize electric ambulances (EAs) in emergency medical transport (EMT) to meet net-zero emission targets. However, the extended battery-recovery time and limited battery capacity of EAs pose significant risks to time-sensitive and efficiency-critical EMT. On the basis of this, we aim to examine the effect of battery recovery on the performance of the EMT system with EAs and explore the carbon-reduction benefits in deploying EAs compared to fuel-powered ones. We develop a queuing model to characterize the EAs using the EMT system with two battery-recovery strategies (plug-in charging and battery swapping) and derive its key performance indicators for risk assessment. The results illustrate that when the ambulance fleet is small and most of them are EAs, the throughput time for EMT increases significantly. However, with a larger ambulance fleet, incorporating EAs can deliver a level of transportation service comparable to that of the fuel-powered ambulances, especially when the battery-swapping strategy is employed. While the use of EAs raises the input costs, achieving a critical scale of EAs enables the reduced energy cost and the social cost of carbon to quickly offset the initial investment. Finally, this study proposes policy recommendations on the construction of battery-recovery infrastructure and the deployment scale and timing of vehicles, providing optimized solutions to balance the risks of using EAs with the safety of EMT.

In the complex scenario of a major epidemic, apart from the superior government's efforts to promote policy implementation through political incentives and fiscal transfer payments, local governments must strive to balance the inherent tension between public health containment policies and economic stability objectives. In addition, they must carefully select the appropriate transition timing from the emergency response phase to the routine management phase. Considering the hierarchical governance structure and the strategic dynamic decision-making processes involved, this paper examines the equilibrium decision-makings of resource allocation and transition timing between superior and local governments within a differential game framework. Our analysis reveals that during the emergency stage, amplifying political incentives and subsidizing localized epidemic prevention costs robustly enhance policy implementation efficiency. Conversely, in the routine management phase, increased fiscal support for epidemic control exhibits diminishing returns, as effectiveness becomes contingent on the prioritization of economic recovery over sustained containment. Furthermore, the optimal transition timing between two phases depends critically on the marginal cost of containment policies and regional economic growth rates. Methodologically, this study develops a dynamic public crisis management framework that integrates fiscal mechanisms with political incentive structures, offering policymakers a quantitative instrument for designing multi-level and multi-stage governance strategies. The findings not only enhance the flexibility of the pandemic response systems but also provide a theoretical foundation for analyzing the dynamic implementation of policies in the hierarchical governance.

Understanding what factors influence an attacker's decision to attack a soft target is important for allocating resources effectively to defend valuable targets. In this study, we aim to validate a game-theoretic model that explores the relationship between the reward and probability of successfully attacking through multiple layers of defense. We created multiple scenarios corresponding to each of four game-theoretic cases, resulting in a 2 × 2 factorial design (defended vs. undefended targets X low vs. high expected values [EVs] for attackers). We recruited 454 US adults from Prolific.com to decide whether to attack for a series of 24 scenarios, which varied the probability of success, the magnitude of reward, and whether Layer 1 was signaled to be defended or not. Results were generally consistent with the game model predictions, including a greater tendency to attack undefended targets with a higher EV. Targets with a low probability of success and greater reward were less likely to be attacked than targets with a higher probability of success and smaller reward. Additionally, participants with a higher self-reported risk-taking were significantly more likely to attack for a given trial compared to participants with lower self-reported risk-taking. This validated game model can be used as a tool to help stakeholders identify where threats are the most likely to occur based on inherent defenses and appeal to attackers.

Calls to regulate artificial intelligence (AI) have sought to establish guardrails to protect the public against AI going awry. Although physical guardrails can lower risks on roadways by serving as fixed, immovable protective barriers, the regulatory equivalent in the digital age of AI is unrealistic and even unwise. AI is too heterogeneous and dynamic to circumscribe fixed paths along which it must operate-and, in any event, the benefits of the technology proceeding along novel pathways would be limited if rigid, prescriptive regulatory barriers were imposed. But this does not mean that AI should be left unregulated, as the harms from irresponsible and ill-managed development and use of AI can be serious. Instead of "guardrails," though, policymakers should impose "leashes." Regulatory leashes imposed on digital technologies are flexible and adaptable-just as physical leashes used when walking a dog through a neighborhood allow for a range of movement and exploration. But just as a physical leash only protects others when a human retains a firm grip on the handle, the kind of leashes that should be deployed for AI will also demand human oversight. In the regulatory context, a flexible regulatory strategy known in other contexts as management-based regulation will be an appropriate model for AI risk governance. In this article, we explain why regulating AI by management-based regulation-a leash approach-will work better than a prescriptive or guardrail regulatory approach. We discuss how some early regulatory efforts include management-based elements. We also elucidate some of the questions that lie ahead in implementing a management-based approach to AI risk regulation. Our aim is to facilitate future research and decision-making that can improve the efficacy of AI regulation by leashes, not guardrails.