Risk Analysis最新文献

As global climate change intensifies, carbon emission trading systems have become vital tools to reduce greenhouse gas emissions through market-based mechanisms. Carbon sink trading incentivizes emission reduction and fosters global cooperation. Recently, artificial intelligence (AI) has been widely applied in these systems to enhance efficiency, transparency, and intelligence in data analysis, market forecasting, and trading optimization. However, AI integration also introduces security risks such as data tampering, algorithmic manipulation, and system intrusions, threatening market stability, and fairness. To address these challenges, this paper adopts the attack tree method to systematically assess security risks in AI-driven carbon sink trading. The attack tree provides a structured framework to visualize potential attack paths and identify threat sources. By combining attack tree modeling with risk assessment theory, this study identifies key risk scenarios-data theft, system manipulation, market manipulation, and service disruption-and quantitatively evaluates their likelihood, potential impact, and overall threat level. Based on the analysis, corresponding protection strategies are proposed for each attack path, offering practical security measures for regulators, AI developers, and trading platform operators. The proposed framework enhances risk identification and management for AI systems in carbon markets, providing a scientific basis for targeted mitigation. Ultimately, this contributes to improving the security and stability of carbon trading systems and supports the advancement of global climate governance.

As sociotechnical systems grow more interconnected, data-rich, and intricately distributed across organizational and disciplinary boundaries and over levels of organizational and technology hierarchies, there is an increasing need for risk assessment and risk management modeling tools that describe how local information, constraints, and decisions interact to shape global outcomes. "Systems-of-systems" (SoS) provide a starting point, but additional methods are needed to understand how risk assessments and management decisions based on partial, local knowledge can (or cannot) be integrated into globally coherent strategies. This paper explores applications of a powerful branch of mathematics, sheaf theory, as a mathematically principled framework for reasoning about local-to-global relationships, structural consistency, and context-dependent interpretation and integration of risk-related information and messages in complex systems. We seek to present an accessible, risk analyst-oriented introduction to core technical concepts and to illustrate the practical value of these mathematical techniques for risk analysis through examples drawn from risk psychology and decision-making under uncertainty; fusion of sensor data and other partial information; causal modeling and quantitative risk assessment of complex engineering and biological systems; risk communication and Social Amplification of Risk (SARF); collective and distributed planning and policy coordination; and ongoing monitoring and evaluation of threats and intervention outcomes. We show how sheaf-theoretic methods can detect irreconcilable policy conflicts, identify gaps in information fusion, simulate belief propagation under constraints, and support modular, multi-level modeling. Applications span risk communication, environmental monitoring, emergency planning, and regulatory governance. We conclude with a discussion of how sheaf theory might be integrated into mainstream risk science.

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.

In parallel with the evolution of epidemiological evidence over nearly two decades, various advisory and regulatory groups in Europe and the United States have conducted several hazard assessments on inhalation exposure to formaldehyde and risk of leukemia. All of the hazard assessments addressed all or the myeloid leukemias (MLs) combined, and none focused on the acute type, etiologically distinct and shown to have a chemical cause (e.g., benzene). However, conclusions regarding formaldehyde as a human leukemogen have been conflicting, although largely based on the same modest body of evidence. As there are no known animal models for formaldehyde and leukemia (any type), good evidence that formaldehyde cannot reach the bone marrow, and no demonstrated mechanism whereby formaldehyde induces leukemia, the hazard assessment ultimately rests on about ten epidemiological studies and their interpretation. Beginning in 2009 with the International Agency for Research on Cancer (IARC), followed by other groups in the United States and Europe, formaldehyde has been classified as causing human ML. On the other hand, the EU Scientific Committee on Occupational Exposure Limits (SCOEL) and the European Chemicals Agency (ECHA) concluded that formaldehyde unlikely causes ML. Multiple classifications of formaldehyde as leukemogenic were motivated by claims of "new studies," including Beane Freeman et al., Hauptmann et al., and Zhang et al. Closer evaluation of these reveals no increased occurrence of ML in Beane Freeman et al. and several important criticisms in the others that limit their value for classifying formaldehyde. Nevertheless, the hazard assessments summarized here varied with some observing consistent positive findings and some others-including recent meta-analyses-finding no association. That similar hazard assessment approaches applied to the same modest collection of epidemiological studies can lead to different-and even conflicting-causal conclusions is problematic. One might reasonably conclude that the research methods for evaluating epidemiological data are unreliable, leading to non-replicable and sometimes unexplained findings and conclusions. Assuming that frameworks for critical review and synthesis of epidemiological evidence are reasonably valid-and faithfully followed-it remains possible that they are methodologically inadequate, inadvertently increasing the potential for subjective elements to be incorporated and allowed to influence interpretations and conclusions. A serious international effort to develop robust and replicable hazard assessment methods based on epidemiological evidence and promote universal adoption is overdue.

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.

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.

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.