Risk Analysis最新文献

This study investigates how local context-specifically urban versus rural environments and socioeconomic conditions-influences individual crisis preparedness and resilience in Sweden. Using multilevel survey data from 12,574 respondents, we analyze both proactive preparedness actions and perceived resilience. Results show that rural residents report higher levels of preparedness and resilience than their urban counterparts. However, these differences in preparedness attenuate when controlling for individual risk perception, suggesting a mediating role. Socioeconomic context, on the other hand, does not show an independent effect beyond individual characteristics, indicating compositional rather than contextual influences. The findings highlight the importance of tailoring crisis preparedness strategies to both individual and local characteristics and stress the need for authorities to consider spatial disparities in vulnerability when planning for future crises.

The COVID-19 pandemic has exposed critical gaps in our management of systemic risks within complex, interconnected systems. This review examines 10 key areas where artificial intelligence (AI) and data analytics can significantly enhance pandemic preparedness, response, and recovery. Inadequate early warning systems, insufficient real-time data on resource needs, and the limitations of traditional epidemiological models in capturing complex disease dynamics are among the challenges analyzed. To address these issues, we explore the potential of AI applications, including machine learning-based surveillance, deep learning for improved epidemiological modeling, and AI-driven optimization of non-pharmaceutical interventions. These technologies offer the promise of more timely, accurate, and granular analysis of pandemic risks, thereby supporting evidence-based decision-making in rapidly evolving crises. However, implementing AI in pandemic response raises significant ethical and governance challenges, particularly concerning privacy, fairness, and accountability. We parse the promise and challenges of AI in the evolving space of emergency response data analytics and highlight critical steps forward.

Warning information plays a vital role in encouraging disaster preparedness among residents. Using survey experiment data from 619 respondents in China, this study examines how warning messages from AI, experts, and a combination of the two influence public disaster preparedness behaviors and whether the degree of impact differs between these sources. The findings reveal that warnings from AI, experts, and a combination of those two sources significantly affect disaster preparedness behaviors. Notably, and contrary to conventional expectations, the combined warnings from AI and experts do not result in a mutually strengthening effect. Instead, a crowding-out effect is observed, whereby the combined impact is less than the sum of individual effects ("Experts + AI < 2"). This outcome can be attributed to information fatigue, suggesting that information overload does not always benefit the public but instead often becomes a burden. Additionally, the influence of AI-driven warnings on preparedness varies substantially with respondents' educational levels. The insights provided by this study hold practical implications for government agencies in promoting public disaster preparedness.

This study employed the XGBoost model to conduct an in-depth analysis of consumer complaints and identified the key risk factors predicting vehicle recalls in the US market, providing valuable proactive risk management support for automakers and regulatory agencies. We leveraged the extensive data resources from National Highway Traffic Safety Administration to construct high-precision recall risk prediction models to predict the risk of recall. The models exhibited exceptional performance across different time windows, particularly maintaining a high level of area under the curve values over a prediction timespan of up to 18 months, demonstrating their predictive accuracy and stability. Our study contributes to risk management theory by addressing the challenges of integrating consumer complaints into predictive models for vehicle recall risk. While prior research has primarily focused on text mining of complaint content, our work systematically incorporates structured complaint data and recall records to enhance predictive accuracy. Also, our research distinguishes the indicators for the initial recall after launch to the market and the indicators for subsequent recalls, and bridges a critical gap in recall risk prediction at different stages of a vehicle's life cycle.

The vulnerability of critical networks to disinformation creates significant risks of disruption with potentially severe societal consequences. Maintaining secure and resilient networks, including infrastructure and supply chain networks, is important for ensuring economic productivity along with securing the health and well-being of society. An over-the-horizon threat to critical networks deals with adversaries who attack such networks indirectly by altering the consumption behavior of unwitting users influenced by weaponized disinformation. The proliferation of disinformation through various online platforms could pose a significant and evolving challenge able to compromise the resilience of critical networks. In this perspectives article, we review the literature in this area and offer some future research directions aimed at protecting networks from weaponized disinformation, enhancing their robustness, resilience, and adaptability.

The risk analysis community has long struggled with effectively addressing surprising events, primarily because the concept of surprise remains inadequately defined in the literature. Four common misconceptions about surprise continue to obstruct progress: (1) Surprise is a result of ignorance or lack of knowledge, (2) better predictions can help avoid surprise, (3) surprises can be eliminated, and (4) surprises are inherently adverse events that must be prevented. These misconceptions frame surprise as a problem of missing information, leading to an overemphasis on closing knowledge gaps rather than preparing for inevitable, unexpected disruptions. In this work, we offer a critical examination of surprise in the context of infrastructure resilience. We discuss the misconceptions surrounding surprise and propose a corrective framework that introduces surprise as an event that violates expectations followed by a series of cognitive reactions that lead to one of two intrinsic responses-either an adaptive response that involves updating the belief system through learning or a maladaptive (shock) response that reinforces outdated mental models and leaves the system vulnerable to future disruptions. We argue that understanding these responses is essential for improving the resilience of infrastructure systems, and we propose training programs to strengthen the adaptive capacities of infrastructure managers to shift the focus from attempting to eliminate surprise to embracing it as an opportunity for learning and adaptation.

Advances in artificial intelligence (AI) are reshaping mobility through autonomous vehicles (AVs), which may introduce risks such as technical malfunctions, cybersecurity threats, and ethical dilemmas in decision-making. Despite these complexities, the influence of consumers' risk preferences on AV acceptance remains poorly understood. This study explores how individuals' risk preferences affect their choices among private AVs (PAVs), shared AVs (SAVs), and private conventional vehicles (PCVs). Employing a lottery experiment and a self-reported survey, we first derive four parameters to capture individuals' risk preferences. Based on a stated preference experiment and the error component logit model, we analyze reference-dependent preferences for key attributes of PAVs and SAVs, using PCVs as the reference. Our analysis reveals that risk-tolerant consumers are more inclined toward PAVs or SAVs. Further, consumers exhibit a greater sensitivity to losses, such as higher purchasing prices and running costs, than to gains, such as reduced egress time. Specifically, for buying a PAV, consumers are willing to pay 3582 CNY more for 1000 CNY saving on annual running cost, 3470 CNY for a 1-min reduction in egress time, 28,880 CNY for removing driver liability for crashes, and 30,710 CNY for the improved privacy data security. For adopting SAVs, consumers are willing to pay 0.096 CNY extra per kilometer for a 1-min reduction in access time and 0.033 CNY extra per kilometer for a 1% increase in SAV availability. Therefore, this study enhances the understanding on risk preferences in AV acceptance and offers important implications for stakeholders in the AI-empowered mobility context.

Identifying critical risk factors is essential for controlling risk propagation and improving the safety management of carbon capture and storage (CCS) projects. Existing research has primarily focused on risk occurrence probability and potential consequences, with relatively less attention given to risk factor analysis, particularly their interactions within complex systems. To address this gap, 36 risk factors and 6 common accidents were identified through the literature review, analysis of accident reports, and expert interviews. We then established the CCS risk factor interaction network and identified critical structural nodes by topological analysis. To further examine the actual impact of these identified nodes and different parameters on risk propagation, we conducted a systematic simulation based on a susceptible-infected-recovered model. The results show that incomplete safety systems, inadequate safety supervision, and inadequate safety training serve as critical driving nodes, with a high potential to initiate widespread risk propagation, whereas equipment overload, adverse weather, and improper emergency handling act as critical bridge nodes whose intervention effectively suppresses risk propagation. Furthermore, the risk intervention step, propagation rate, and recovery rate affect the scale and duration of risk diffusion. This study aims to enhance system resilience by providing valuable insights for safety management in CCS projects.

Critical infrastructure is typically identified at the national level. However, disruption to certain infrastructure systems, facilities, and assets can have negative consequences for global societies. Such globally critical infrastructure entails a distinct risk profile for both countries dependent on the infrastructure, and countries that have such infrastructure in their territory. The goal of the article is to provide an initial framing and definition of "globally critical infrastructure" as a concept worthy of attention and explore the unique risk analysis and management challenges to support future, more rigorous examinations. For dependent countries, globally critical infrastructure exists outside of their border (or possibly outside any country's border), under sometimes drastically different economic, political, governance, and threat environments. Risk management entails unique challenges, because countries dependent on that infrastructure may have no legal or regulatory authority to shape risk management practices at facilities in other countries. Consequently, risk management may extend beyond the domains of the typical homeland or internal affairs agencies to include capabilities and responsibilities of ministries of foreign affairs, trade and commerce, and defense. However, those challenges also imply new risk management demands and options, such as new avenues for international cooperation on infrastructure protection and resilience, international funding, and enhanced monitoring. Having a globally critical infrastructure system in its borders changes the risk dynamics for a nation state, creating potential leverage over dependent nations and new avenues to garner international support, but also creates new risks to national sovereignty. Recognizing these common dependencies can better enable the global community to engage stakeholders to develop and implement systemic risk management approaches worldwide.

Hepatitis E virus (HEV) can lead to liver disease in humans. In the Netherlands, the consumption of pig meat is thought to be the main contributor to the total burden of disease caused by HEV. In this study, the number of cases and lost disability-adjusted-life-years (DALYs) due to HEV in pig meat were estimated by simulating HEV through the pig supply chain, including the farm, transport, lairage, slaughtering, processing, and consumption stages. The first four stages were modeled using a susceptible-exposed-infected-recovered (SEIR) model. For the last two stages, pig meat and liver products were divided into six product categories commonly consumed by Dutch consumers. Depending on the product category, different ways of heating and storing, leading to the reduction of infectious HEV genome copies, were assumed. Furthermore, the model was challenged by four selected control options at the pig farm: the cleaning of driving boards, the use of predatory flies, the use of rubber mats, and the vaccination of finishing pigs. Finally, the cost-effectiveness of these control measures was estimated by estimating the costs per avoided DALY. For the baseline situation, it was estimated that HEV in pig meat would lead to 70 cases and 21 DALYs per year. All control measures led to a decreased number of DALYs, with vaccination leading to the largest decrease: five DALYs per year. However, the costs per avoided DALY ranged from €0.5 to €7.5 million, making none of the control measures cost-effective unless the control measures are also effective against other pathogens.