Risk Analysis最新文献

The development of hydrogen energy (HE) has complex impacts on the natural gas (NG) industry chain across production, transportation, storage, and application stages. This paper contributes to the risk identification and transmission characteristic analysis of industry coupling on the NG industry chain. We first identify 37 risk factors in the NG industry chain under multiple coupling modes with the HE industry, and then develop a two-layer complex network research framework to reveal the intricate interrelationships between these elements. An improved gravity model is integrated into the framework for more accurate risk influence evaluation. Based on this analysis, key risks and transmission paths are identified. The framework further facilitates exploring the collaborative potential of coupling modes. On this basis, scenario analysis is performed to reveal phased risk characteristics of the NG industry chain in the context of HE development. We conclude by suggesting establishing a risk-buffering community to promote collaborative governance throughout the entire industry chain.

This study reveals that the risk exposure of Chinese A-share listed companies with respect to public health, safety, and environmental (HS&E) concerns is associated with an increase in fraudulent behavior. Based on the reflection effect and the loss aversion effect posited by prospect theory, we demonstrate that firm-specific HS&E risk exposure increases the firm's risk-taking and propensity to disclose good news, thereby increasing the likelihood of the firm engaging in fraudulent activities. In addition, from the perspectives of motivation and governance, our research further demonstrates that the impact of HS&E risk exposure on corporate fraud is more pronounced in companies that are characterized by lower executive compensation, lower environmental, social, and governance (ESG) performance, lower independent director network centrality, and a lower proportion of members of the Communist Party of China among executives.

Flood hazards intensified by global warming pose a severe threat to global infrastructure, including the high-speed rail (HSR) system. However, future climate impacts on HSR remain underexplored. This study presents an integrated framework for comprehensively analyzing HSR flood risks under climate change. First, we developed a three-layer HSR model to evaluate HSR performance across the topological, functional, and service dimensions. Subsequently, we simulated future flood scenarios using the CaMa-Flood model to generate flood events with varying occurrence probabilities. By integrating HSR performance losses under these flood conditions with their occurrence probabilities, we assessed the HSR flood risks and identified key influencing factors through a multifactor correlation analysis. The results predicted a considerable rise in flood risk for Chinese HSR by the late 21st century, especially in the function and service dimensions, with 12%-35% and 12%-33% increase, respectively, compared with historical baselines. We also observed significant heterogeneity in flood risk among provinces; the situation is projected to deteriorate over time. However, areas with higher socioeconomic levels and operational capacity experience lower flood risk. Furthermore, a cost-benefit analysis of varied maintenance strategies revealed that a risk-based maintenance strategy (RMS), considering both track failure probability and criticality, exhibits better efficiency than other strategies, achieving the highest average risk mitigation effect (0.02) per 1000 km of maintenance track. These insights offer a multidimensional and multiscale understanding of the HSR flood risk under climate change and provide practical guidance for climate-resilient infrastructure development and maintenance planning.

Risk science is increasingly interwoven across various domains, aiming to be both generalizable and domain-specific to build consistency and applicability across risk applications. However, those risk discussions, such as in materials that aim to share risk-related information with stakeholders, may have varying levels of alignment with risk science. In this paper, we present a framework for comparing and broadly understanding how generalizable risk concepts and risk study quality criteria are addressed in various domain-specific risk discussions that are not intended to be formal risk studies, such as materials used to inform policymakers, investor reports, and reports for regulatory compliance. While the framework is supported by criteria developed for risk study quality in formal risk studies, we discuss how to apply the framework using text analysis methodologies and technologies. The results of the framework then identify areas in which risk discussions do not sufficiently align with risk science principles and identify areas in which the use of risk science for these discussions can be improved. We then develop key findings related to features in mapping risk concepts to domain-specific risk discussions. This leads to opportunities to build consistency in risk-related discourse across various domain areas.

Tailwater discharged from wastewater treatment plants (WWTPs) can contain pathogenic microorganisms, posing potential health risks during recreational water activities. While Quantitative Microbial Risk Assessment (QMRA) is commonly used to evaluate these risks, its complex outputs were not easily translated into operational standards. To address this gap, this study introduces the concept of a threshold limit value (TLV) defined as the maximum acceptable E. coli concentration in tailwater that ensures compliance with specific health risk benchmarks. TLVs were derived using reverse QMRA for four age groups (children, early teens, teens, and adults) under two risk criteria: the U.S. EPA annual infection risk (10^-4) and the World Health Organization disease burden benchmark (10^-6 DALYs per person per year). Results showed that TLVs decrease with age, as adult individuals inhale or ingest larger volumes, resulting in higher exposure doses under identical conditions. Consequently, lower TLVs indicate stricter health protection requirements. WWTPs with higher treatment capacity and larger receiving water flows exhibited lower TLVs, reflecting more stringent acceptable concentrations due to reduced exposure risk. Swimming TLVs (4.43E+01-7.72E+02 CFU/100 mL) were about three times lower than rowing TLVs (1.25E+02-1.09E+03 CFU/m³), based on WHO and U.S. EPA benchmarks, due to more direct exposure and higher contact frequency. TLVs based on the WHO benchmark were consistently lower than those based on the EPA benchmark, emphasizing the impact of risk criteria on regulatory limits. Sensitivity analysis identified annual exposure frequency as the dominant variable for both rowing and swimming, with exposure time also being a key determinant for swimming exposure. This study provides a practical, risk-based framework for defining site-specific microbial limits, supporting evidence-based water quality management.

Accurately calculating travel time-to map transport accessibility and assess transport resilience to inform sustainability-oriented decisions-remains a critical modeling and computational challenge if considering large-scale all-modal transport. Vector-based methods cannot capture movement across off-network areas and are still computationally expensive in some applications for large-scale networks, whereas conventional raster-based methods may bring considerable inaccuracies if computationally acceptable. Here, we propose an optimal local connectivity-based method to rasterize transportation networks and enable a smooth integration of all travel modes to support fastest travel time-based accessibility and resilience analysis. Experimental studies on road networks in cities worldwide, together with theoretical analyses of lattices and simulations of random planar graphs, show its capability for remarkably accurate and rapid estimation of travel time in various network conditions. Successful applications in accurately mapping national-scale accessibility to healthcare facilities and rapidly estimating the worst-case resilience against local disruption demonstrate its utility to support many research and policy needs.

Multi-hazard early warning systems (MHEWS) play a pivotal role in reducing disaster risks and significantly enhancing community resilience. However, despite their widespread implementation, the effectiveness of MHEWS is constrained by challenges in warning dissemination and communication (WDC). To address this limitation, this study developed and validated a broadly applicable instrument for assessing the effectiveness of WDC-the warning response index (WRI). Using data from two surveys (n = 1580), we examined the internal consistency, dimensional structure, and predictive validity of the WRI. The results show that (1) the WRI demonstrates a stable multidimensional structure and high reliability and validity across multiple hazard types; (2) the translation of intentions into concrete protective actions is relatively weak, particularly under extreme weather conditions; (3) conventional measures of warning response intention primarily capture individuals' cognitive evaluation and information processing, and therefore fail to adequately capture the intention-action gap; (4) compared with unidimensional measure, the WRI more effectively predicts the overall effectiveness of WDC. We argue that future research should pay greater attention to the intention-action gap and that targeted WDC strategies should be optimized based on the characteristics of both warning messages and recipients.

Climate change, along with its associated extreme and abnormal temperature events, poses risks to human health. We examine the impact of temperatures on health insurance decisions using a proprietary dataset that links critical illness insurance records with long-term, daily meteorological data. We show that both heat and cold increase health insurance purchases. The effect of heat is driven by both heightened physical health risks and the salience of unexpected, abnormal heatwaves in health insurance decisions. However, the heat impact decays with prior experience to abnormal heat events. In contrast, we find no evidence that cold temperatures either increase physical health risks or trigger a salience effect. Risk preference changes and business cycles do not explain our findings. Air conditioning mitigates heat-induced insurance demand and centralized heating system mitigates cold-induced insurance demand. Males, the elderly, and outdoor workers are more sensitive to heat compared to females, the young, and indoor workers. This research uniquely quantifies the effects of abnormal temperatures on insurance purchases, highlighting the salience effect in insurance decision-making.

Cross-organizational governance for extreme disaster risk represents a critical challenge for modern society. This study develops an integrated theoretical framework examining how emerging technologies transform collaborative governance for extreme disaster risks through complex adaptive mechanisms. Employing an innovative methodological triangulation approach combining qualitative comparative analysis (QCA), machine learning (XGBoost with SHAP), and agent-based modeling-systems dynamics (ABM-SD), we analyze disaster cases in China to identify and validate key technology-organization configurations that enhance system resilience. Initially, QCA analysis of 12 representative cases reveals that data analysis precision and inter-organizational links are necessary foundations for high-performance collaborative governance, with three distinct configuration pathways identified: non-pressure-responsive type, pressure-state type, and pressure-responsive type. Machine learning validation across an expanded sample of 120 cases confirms the robustness of these configurations while revealing their temporal evolution from network-dominated to data-driven patterns. The ABM-SD simulation demonstrates that proactive policies with cyclical technological upgrading significantly enhance system resilience, while loosely coupled networks with high heterogeneity better prevent "complexity traps" during extreme events. This research makes unique contributions by (1) establishing a systematic framework for analyzing technology-organization interactions in disaster contexts; (2) identifying equifinal pathways to effective collaborative governance; and (3) developing a theoretical model that illustrates how technological empowerment and organizational collaboration dynamically interact across threshold conversion areas to generate system emergence and reconstruction under varying pressure levels. Practical implications include configuration selection strategies for policy-makers based on regional development levels and disaster characteristics. Study limitations include the focus on Chinese cases, which may limit generalizability to different institutional contexts, and the need for longitudinal studies to further validate the proposed adaptation mechanisms.