Risk Analysis最新文献

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.

This study examines the localized and regional impacts of natural gas leaks on air quality and safety, with a specific focus on PM_2.5 concentrations and incident dynamics across the United States. Using the Spatial Durbin Model, the analysis reveals significant direct and spillover effects of gas leaks, energy intensity, and environmental regulations on air pollution and safety outcomes. The results demonstrate that gas leaks substantially increase local PM_2.5 levels, confirming the role of methane emissions in exacerbating particulate pollution. Furthermore, positive spatial spillovers from gas leaks and energy intensity underscore the transboundary nature of air quality challenges, highlighting the necessity of coordinated regional interventions. Conversely, stringent environmental regulations exhibit significant positive spillovers, catalyzing pollution control efforts in neighboring regions. The study offers actionable policy recommendations, including strengthening monitoring systems, advancing interregional cooperation, and integrating sustainable energy practices to address the interconnected challenges of air quality management and climate risk mitigation.

Transitive disasters are closely associated with the dissemination of risk information and pose significant threats to public safety and well-being. Effective risk communication strategies are therefore essential for mitigating their adverse impacts. This study employs a system dynamics approach and adapts the classical SEIR model to construct the UCSR model (Uninformed-Contacts-Spreaders-Rationals). The model incorporates key factors such as media influence, information dissemination, and risk perception to simulate the dynamics of risk information flow during transitive disasters and its impact on evacuation strategies and disaster outcomes. By examining interregional and intergroup differences, the study explores how "Spreaders" and "Rationals" respond to risk information and identifies which population groups and variables most significantly influence evacuation effectiveness and disaster exposure. Key findings include: (1) in transitive disasters, timely and effective actions taken by different population groups can substantially reduce their vulnerability to risk information; (2) increasing disaster information push frequency, promoting risk awareness and preparedness, and encouraging Rational behavior can accelerate both information spread and evacuation processes, thereby increasing the number of Rational individuals; (3) the combined function of media dissemination and evacuation infrastructure yields the most effective risk mitigation outcomes. This research offers valuable insights for designing risk communication strategies and disaster preparedness plans tailored to different regions and population groups, ultimately contributing to reduced casualties and economic losses in the context of transitive disasters.

Understanding community disaster resilience is critical to mitigating the disproportionate impacts of climate change and natural disasters on socially vulnerable populations. However, despite extensive discussion on disaster resilience, a systematic analysis of the extent of social inequity across climate scenarios, geographic locations, spatial scales, and sociodemographic groups remains underexplored. Our study introduces a human-centric framework to investigate social inequities in community disaster resilience related to human well-being. We combined flood hazard maps under both historical and future SSP scenarios with a compound multilayer urban spatial network model consisting of roads, communities, and essential services to evaluate the residents' service resilience during flood events. Then, we utilized the Gini coefficient and Lorenz curve to quantify the degree of inequities in resilience among different sub-populations. With Central Chongqing as a case study, our analysis reveals a significant increase in both the number of affected communities and their vulnerability under future climate conditions. We further observed a striking spatial polarization in community resilience due to the islanding effect, whereby communities are increasingly divided into those with severely limited service availability and those with sufficient resources. In addition, we found that the extent of social inequity in resilience is highly spatial and scale-specific, with moderate levels of inequity at the city level, but the degree of inequity varies greatly across sociodemographic groups at a localized level. This widening socio-spatial differentiation may trigger widespread dissatisfaction in disadvantaged communities, hindering the collective disaster response actions and engagements to enhance community resilience. Our research highlights the importance of embedding future climate variabilities, human well-being, and social equity in inclusive disaster response policies, processes, and practices.

The rapid and nationwide expansion of fifth-generation (5G) wireless cellular technology infrastructure in China has prompted serious public concerns, predominantly due to the potential adverse health effects of electromagnetic field (EMF) exposure from 5G base stations. The literature offers mixed results regarding the effectiveness of risk communication on public concerns about EMF exposure from base stations. An online survey experiment with 815 adults in Shanghai examined how different strategies of risk communication could enhance public acceptance. We manipulated the framing of intervention materials (loss- vs. gain-framed) and their information source (government, industry, or civil society). Our analysis revealed that government and industry sources, compared to civil society, were more effective in increasing public support. Additionally, gain frames generated more acceptance than loss frames. Furthermore, participants held higher levels of competence-based trust in government and industry, but no significant difference in care-based trust was detected between government and the other two sources. Both dimensions of trust were critical for public acceptance. These results suggest that the Chinese government, along with professional private sectors, could leverage emerging media platforms to foster support. These results also highlight the need for the Chinese government to address the lack of public care-based trust, especially in the context of centralized 5G deployment.

The efficiency of emergency response is crucial, yet traditional top-down systems are often overwhelmed. Digital spontaneous volunteers (DSVs) offer a vital bottom-up force, but their effectiveness is frequently constrained by a dual dilemma of external integration and internal coordination. This study explores how to optimize DSV crowdsourcing by investigating the role of sustained trust from formal organizations and the logic of adaptive crowdsourcing based on complex adaptive systems theory. Using an agent-based model calibrated with data from the "life-saving document" case during China's 2021 Henan rainstorm, we conducted counterfactual experiments. The results demonstrate that sustained trust from formal organizations is fundamental; its erosion leads to a collapse in rescue efficiency, even with highly accessible information channels. Furthermore, the study reveals a counterintuitive finding: Adaptive crowdsourcing significantly improves efficiency not by maximizing volunteer numbers, but by restraining their generation based on real-time demand gaps. This research highlights that the effectiveness of DSV crowdsourcing hinges on dynamic trust-building and controlled, adaptive coordination, offering a conceptual shift from viewing self-organization as an uncontrollable force to a system that can be optimized through design.