Risk Analysis最新文献

The harmful effects of climate change have brought global warming into focus, prompting a growing body of research on its economic impact and the development of targeted climate policies aimed at mitigating these effects and promoting sustainability. Within this context, the main objective of this paper is to investigate whether the presence of transition risk drivers, in particular, the implementation of environmental policies in the United States, initiates risks or fosters green innovation and financial performance. This performance is related to the adjustment process toward a low-carbon economy, widely known as the Porter hypothesis. Using a panel threshold regression model over the period 1990-2020, our results show that market-based climate policies have a heterogeneous effect on the firm's green innovation and financial performance. Specifically, we find an inverted-U-shaped relationship between carbon price and firm outcomes including green innovation and financial performance. These findings have significant implications for practice, as they reveal the mechanism through which climate policies can optimally affect a firm's green innovation activity and financial performance.

The objective of this study is to replicate the original study by Fischhoff et al. (1978) and its replication by Fox-Glassman and Weber (2016) and to examine whether risk perceptions for the previously studied activities and technologies have changed over the past 40 years, especially when activities/technologies related to contemporary concerns are included. To achieve this goal, the list of activities/technologies has been modified. To facilitate the analysis of individual data, all participants were asked to rate the benefits and risks of 24 activities. The within-participant approach was also used to achieve the second objective of our study: to analyze the relationship between perceived benefits and risks. In summary, the design of this study differed from previous studies in the following ways: (1) Nine activities/technologies were added related to contemporary concerns such as global warming and fake news on the Internet; (2) all participants rated both benefits and risks; (3) data were collected online (as in the 2016 study); (4) the study was conducted by Prolific with a sample size large enough to detect medium-size effects (n = 382). The two-factor structure proposed by Fischhoff et al.-unknown risk and dread risk-was confirmed on aggregated data for the new set of hazards, which included novel hazards. At the level of individual data, modest support for this structure was observed, and a very strong inverse relationship between perceived benefits and risks was observed.

The perception that the convergence of biological engineering and artificial intelligence (AI) could enable increased biorisk has recently drawn attention to the governance of biotechnology and AI. The 2023 Executive Order, Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, requires an assessment of how AI can increase biorisk. Within this perspective, quantitative and qualitative frameworks for evaluating biorisk are presented. Both frameworks are exercised using notional scenarios and their benefits and limitations are then discussed. Finally, the perspective concludes by noting that assessment and evaluation methodologies must keep pace with advances of AI in the life sciences.

A raft of authors argue that society is drowning in a sea of misinformation, disinformation, and fake news. Some claim we are living in a new world disorder, misinformation age, or post-truth era, fueled in part by social media, influential podcasters, and emerging AI systems. We argue that the now dominant interpretation of the risk of misinformation has been undergirded by an oversimplified understanding of broader communication processes. Thinking of misinformation as a standalone risk object has distorted conceptions of messages, messengers, and audiences, as well as how the underlying problems associated with false and misleading information could and should be addressed. Our article unpacks and then constructively critiques the dominant interpretation of misinformation by examining the tendency to (i) define misinformation in isolation from communication, (ii) neglect messengers' intentions, (iii) perceive audiences as susceptible misinformation recipients, and (iv) reduce communication to a one-way process of misinforming. We conclude by arguing that a communication-based approach, grounded in the agency of messengers and audiences, offers a more nuanced and holistic foundation for interpreting and addressing the complex challenges associated with false and misleading messages. This perspective encourages policymakers and researchers to approach communication in its full complexity, engage in multiway processes, draw on the existing interdisciplinary communication literature, and remain attentive to both the challenges and opportunities of today's evolving communication ecosystem.

Enhancing intraregional disaster preparedness and response capabilities is crucial for effectively managing noncatastrophic disasters in localized areas. This paper proposes a primary governmental strategy focused on signing disaster insurance contracts with capacity reservation, alongside two supplementary strategies: building predisaster stockpiles and spot market procurement. Among these, our focus is on developing a comprehensive disaster insurance model with capacity reservation functionality, which integrates both financial and operational elements to facilitate public-private collaboration. Using game-theoretical modeling, we analyze government-insurer interactions, with solutions derived through backward induction. The model is validated through a case study in China, focusing on the response of S Government and W Company to Typhoon Rumbia. The results offer a series of important insights. Zero-deductible contracts, though unconventional, emerge as an optimal mechanism in localized disasters by minimizing entry barriers and sustaining insurer profitability. For insurers, long-term cooperation is more attractive in low-volatility, short-duration events, as it enhances capacity amortization and operational efficiency. Meanwhile, policyholders exhibit highly context-sensitive behavior, with stockpiling decisions shaped by lead time, spot market prices, and disaster characteristics. The model uncovers distinct preparedness thresholds that support flexible, scenario-specific strategies, advancing the theory and practice of disaster readiness for regional governments.

The prediction of unmanned aerial vehicle (UAV) operators' unsafe acts is critical for preventing UAV incidents. However, there is a lack of research specifically focusing on UAV operators' unsafe acts, and existing approaches in related areas often lack precision and effectiveness. To address this, we propose a hybrid approach that combines the Human Factors Analysis and Classification System (HFACS) with random forest (RF) to predict and warn against UAV operators' unsafe acts. Initially, we introduce an improved HFACS framework to identify risk factors influencing the unsafe acts. Subsequently, we utilize the adaptive synthetic sampling algorithm (ADASYN) to rectify the imbalance in the dataset. The RF model is then used to construct a risk prediction and early warning model, as well as to identify critical risk factors associated with the unsafe acts. The results obtained through the improved HFACS framework reveal 33 risk factors, encompassing environmental influences, industry influences, unsafe supervision, and operators' states, contributing to the unsafe acts. The RF model demonstrates a significant improvement in prediction performance after applying ADASYN. The critical risk factors associated with the unsafe acts are identified as weak safety awareness, allowing unauthorized flight activities, lack of legal awareness, lack of supervision system, and obstacles. The findings of this study can assist policymakers in formulating effective measures to mitigate incidents resulting from UAV operators' unsafe acts.

The transition from one level of operations to a next larger, more complex level while maintaining coherence as a system has stymied organizational theorists for decades. Drawing on systems theory, network analysis, and collaborative governance, we explore how networks adapt during rapidly escalating crises. Specifically, we investigate the emergence of a synthesizing intelligence function among networks to support coordinated action. Using a case study of the 2020 Santa Clara Unit Lightning Complex Fire, we analyze field operations data from Incident Reports filed by the California Department of Forestry and Fire Protection to develop a system dynamics model. Our findings suggest that a synthesizing intelligence function, informed by various types of intelligence, influences the rate of change in operational systems during dynamic conditions. This system-wide intelligence function is crucial for decision-makers confronting extreme events, facilitating effective anticipation of complex transitions in large-scale operational systems.

In the residential sector, hurricane winds are a major contributor to storm-related losses, with substantial annual costs to the US economy. With the potential increase in hurricane intensity in changing climate conditions, hurricane impacts are expected to worsen. Current hurricane risk management practices are based on the hurricane risk assessment without considering climate impact, which would result in a higher level of risk for the built environment than expected. It is crucial to investigate the impact of climate change on hurricane risk to develop effective hurricane risk management strategies. However, investigation of future hurricane risk can be very time-consuming because of the high resolution of the models for climate-dependent hazard simulation and regional loss assessment. This study aims to investigate the climate change impact on hurricane wind risk on residential buildings across the southeastern US coastal states. To address the challenge of computational inefficiency, we develop surrogate models using machine learning techniques for evaluating wind and rain-ingress losses of simulated climate-dependent hurricane scenarios. We collect historical hurricane data and use selected climate variables to predict changing hurricane attributes under climate change. We build the surrogate loss model using data generated by the existing fragility-based loss model. The loss estimation of synthetic events using the surrogate model shows an accuracy with a 0.78 R-squared value compared to Hazard U.S. - Multi Hazard (HAZUS-MH) estimation. The results demonstrate the feasibility of utilizing surrogate models to predict risk changes and underline the increasing hurricane wind risk due to climate change.

This paper presents the design and implementation of an expert system for the domain of functional safety of machinery, featuring a novel multilingual chatbot interface developed using the Rasa framework. Unlike traditional expert systems, this approach aims to make the complex topic of functional safety more accessible to users with limited experience by assisting with tasks such as hazard identification, risk assessment, risk reduction, and safety function recommendation. The knowledge base of the system can be populated by functional safety experts through a graphical user interface, ensuring the system's utility and accuracy. This work demonstrates that the chatbot-based expert system retains many advantages of traditional expert systems while offering a more engaging user experience. An experimental evaluation of the presented expert system using hazard scenarios from real-life projects highlights the benefits of advanced machine learning techniques and pretrained embeddings, showing improvements in system performance. Continuous updates to the training dataset are essential for maintaining effectiveness in diverse environments. Compared to general-purpose chatbots like ChatGPT, this system provides reliable, standards-based insights. The system can be utilized by inexperienced machinery design personnel, such as mechanical and mechatronic engineers, before consulting with safety experts.