Risk Analysis最新文献

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.

The transportation of liquid hydrogen (LH₂) via road tankers could prove to be the most cost-effective short-term option for long-distance delivery. However, there are significant risks, particularly in confined spaces like road tunnels. An accidental release of LH₂ in these structures is likely to create a flammable hydrogen cloud, the explosion of which generates overpressures whose magnitude depends on several mutually dependent variables, including geometry, traffic, and ventilation. Nevertheless, the combined effect of the above-mentioned variables on user safety in the event of an accidental leakage and explosion of LH₂ from a road tanker in a tunnel has yet to be investigated in detail. This study develops 3D CFD models of both the release and explosion of LH₂ to address this issue, along with a comprehensive parametric analysis that considers different tunnel lengths, negative and positive longitudinal slopes, traffic volumes, and ventilation types (i.e., natural or longitudinal mechanical). The CFD code used was preliminarily calibrated against experimental literature tests. Subsequently, a risk analysis was carried out using the CFD results in terms of overpressures, which, combined with a probit function, made it possible to estimate the number of potential fatalities. Consequently, a probability matrix of the risk of having a given number (N) of fatalities was built as a function of the tunnel length, ventilation type (i.e., natural or mechanical), longitudinal slope, and traffic volume. The results revealed the benefits of positive gradients as well as of implementing a longitudinal mechanical ventilation system. In contrast, longer tunnels increase the probability of having a given number of fatalities. This study might serve as a reference for tunnel operators in the choice of mitigation measures and/or traffic control strategies to limit the negative consequences of the release of liquid hydrogen in road tunnels.

Medical waste surged during the initial response to COVID-19, greatly increasing the risk of medical waste pollution. Medical waste management involves numerous stakeholders, including environmental, healthcare, agricultural, and laboratory regulatory authorities, organizations generating medical waste, and transportation and disposal companies. The traditional regulatory system suffers from inter-departmental obstacles and opportunistic behavior by stakeholders. All of these issues have drawn widespread attention from the government and the public in the wake of the pandemic. To address these issues, Shanghai has taken the lead in implementing digital transformation to enhance the effectiveness of medical waste management. This study constructs an innovative analytical framework encompassing function, structure, and institution to comprehensively examine digital transformation. This framework reveals how functional enhancements through digital technologies first improve operations via real-time information transmission, process reengineering, and increased efficiency, and then subsequently reshape inter-organizational relationships while requiring calibrated institutional adaptation for effective implementation. It also indicates a critical role of technological-institutional alignment in determining transformation outcomes. Based on this framework, a quadratic assignment procedure and social network analysis are used to compare the planned, traditional, and digital networks formed before and after the digital transformation of medical waste management in Shanghai. Through systematic analysis of these networks, this study investigates how digital transformation can enhance medical waste management effectiveness and provides a nuanced analysis that goes beyond mere issues of technological implementation to reveal the complex interplay between digital capabilities and institutional adaptation, and to uncover the challenges involved in achieving comprehensive collaboration.

As extreme weather events unfold on a timescale of minutes, hours, or days, individuals must respond accordingly. Dynamic risk communication-that is, risk communication that updates alongside evolving risks-can support individuals in making timely, well-informed behavioral decisions that safeguard their well-being. Existing literature on dynamic risk communication during extreme weather events uses multiple methods to examine how different populations encounter dynamic risk communication during different types of events. In this scoping review, we synthesize empirical evidence from 14 articles to advance an integrated understanding of the temporal dynamics of risk communication during extreme weather events. First, we describe how individuals interact with heterogeneous forms of dynamic risk communication over the course of extreme weather events. Second, we identify environmental, personal, and social factors that may lead individuals to encounter dynamic risk communication (i.e., antecedents). Third, we identify individuals' appraising and behavioral responses to dynamic risk communication (i.e., consequences). Taken together, our review suggests opportunities to better align the provision of risk communication with individuals' informational needs during extreme weather events. Additional longitudinal research is necessary to more precisely examine changes and relations among time-varying constructs, consider the role of individuals' multiple, varied contexts in their protective behavioral decision-making processes, and identify individual differences in people's needs for and uses of dynamic risk communication during extreme weather events.

Non‑communicable diseases (NCDs) in animals, including obesity, diabetes, metabolic syndrome, cardiovascular disorders, cancers, and degenerative joint disease, are rising across companion, livestock, wildlife, and aquaculture sectors. Recent surveys document that 50%-60% of domestic cats and dogs are overweight, driving a 0.8% per year increase in feline diabetes from 0.4% in 2005 to 1.6% in 2020. In dairy herds, subclinical ketosis affects 30%-40% of cows during the transition period, reducing 305‑day milk yield by 6%, while osteoarthritis impacts 20% of intensively reared pigs. Wildlife exposed to industrial pollutants show liver tumor rates up to 25% in fish and marine mammals. Therefore, there is a need for integrative research focused on predictive risk modeling and management frameworks. Using an integrated One Health/EcoHealth framework, this mini‑review (i) quantifies NCD prevalence across taxa; (ii) dissects mechanisms linking risk factors to NCD emergence; (iii) proposes an evidence-based risk assessment model to assist future surveillance and mitigation strategies; and (iv) outlines tiered mitigation strategies spanning individual, herd, ecosystem, and policy levels.

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.