Risk Analysis最新文献

Nowadays, many multinational firms (MNFs) still stick to overseas manufacturing for the benefits of low production costs and tax planning opportunities. However, such a strategy comes along with production shocks caused by power outages, fires, strikes, and so on. In this article, we use a Resilience Triangle framework to measure the risk of production shocks during the shock and recovery time. We explore two risk management strategies for MNFs: enhancing overseas manufacturing resilience via advanced technologies and reshoring to local manufacturing. We outline the MNF's trade-offs among overseas resilience loss, production cost, tax planning opportunity, and local manufacturing subsidy. We quantify the production-and-delivery delays caused by overseas manufacturing shocks and highlight the value of advanced production technologies in mitigating shocks and accelerating recovery. We find that the MNF's production strategy may switch from overseas manufacturing to local manufacturing and then back to overseas manufacturing when the local manufacturing subsidy is not too high and the local manufacturing cost is moderate. We show that overseas manufacturing with advanced production technologies can achieve a win-win situation regarding the MNF's resilience performance and profitability, as they enable the MNF to better balance production risks and financial gains.

Floods remain one of the most devastating climate-related disasters worldwide, and their increasing frequency in South Asia has posed severe challenges for community resilience and disaster management. In Pakistan's Indus River plains, recurrent flooding continues to displace millions, underscoring the urgent need to understand psychosocial and digital dimensions of disaster preparedness. This study examines how flood-prone individuals utilize risk awareness, social support, and social media to enhance their coping appraisal and engage in collective action. Grounded in protection motivation theory (PMT), we have built a three-way interaction research model to examine how social media apps influence social support to affect the relationship between risk awareness and coping appraisal in times of flood. We collected data from perennial flood-prone inhabitants of the Indus River plains. AMOS 24 and SPSS 23 were used to analyze the collected data. Results revealed that risk awareness significantly enhances coping appraisal, which in turn strengthens collective action tendencies. This study found that social support moderates the relationship between risk awareness and coping appraisal, with stronger effects at higher social support levels. The three-way interaction analysis revealed that social media information sharing amplifies the impact of social support on the relationship between risk awareness and coping appraisal, demonstrating the fostering role of digital communication in disaster resilience. These findings underscore the synergistic impact of social support and digital platforms in fostering adaptive behaviors, offering crucial insights for disaster risk management practitioners, policymakers, and humanitarian agencies working in flood-prone regions. Ultimately, this study provides a framework for integrating social resources and digital tools into localized flood risk reduction strategies.

There is a lack of rigorous studies addressing the theory life cycle model in disaster management. Thus, this study aimed to review the theory life cycle to improve disaster management practices. The study employed a systematic literature review, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. A reductionist model was proposed, including (1) theory inception, (2) theory scrutiny, and (3) theory termination (X) or establishment (O). This model was applied to four theories: suicide rate (X1), risk perception (X2), redundancy (O1), and all hazards (O2). In pursuing the reductionist model, the field must consider disaster characteristics, the advantages and disadvantages of various theories, the changing environment, a hybridization perspective, emergency education and training, and continuous improvement. This study emphasizes the question of adaptive relevance more than previous research.

The participation frequency and duration of water-based activities are typically higher for training rowing athletes than for rowing tourists, resulting in great exposure risks of polluted water for training rowing athletes. Thus, evaluating the health risks of training rowing athletes is essential to ensure their safety. In this study, quantitative microbial risk assessment (QMRA) combined with disability-adjusted life years (DALYs) was used to probabilistically examine the health risks of training rowing athletes in the Dongshan River Watershed, Taiwan, and to inversely determine the critical levels of river fecal coliforms (FCs) for risk benchmarks of 10^-4, 10^-5, and 10^-6 per person per year (pppy). Monte Carlo simulation was employed to quantify the variability of QMRA and DALY parameters. The relationship between FC observations and critical FC levels was investigated to identify suitable risk benchmarks for river environmental management. The results indicated that the risk of disease burden (DB) for training rowing athletes ranged from 108.4 × 10^-6 to 267.2 × 10^-6 pppy. These risks posed potential health threats to training rowing athletes. Given the ratios of observations exceeding critical FC levels, preliminary environmental management for river water quality was suggested at a DB risk of 10^-5 pppy. The representative value of critical FC concentrations corresponding to this risk level was found to be 2603 colony-forming units/100 mL.

Stripe graphs have emerged as a popular format for the visual communication of environmental risks. The apparent appeal of the format has been attributed to its capacity to summarize complex data in an eye-catching way that can be understood quickly and intuitively by diverse audiences. Despite the growing use of stripe graphs among academics and organizations (e.g., Intergovernmental Panel on Climate Change [IPCC]) to communicate with both lay and expert audiences, there has been no reported empirical assessment of the format. Hence, it is not clear to what extent stripe graphs facilitate data comprehension and influence risk perceptions and the willingness to engage in mitigation actions. To address these knowledge gaps, we conducted two studies in which lay participants saw "climate warming" stripe graphs that varied in color and design. We found no evidence that traditional stripe graphs (i.e., unlabeled axes), irrespective of the stripe colors, improved the accuracy of estimates of past or predicted global temperature changes. Nor did the traditional stripe graph influence risk perceptions, affective reactions, or environmental decision-making. Contrary to expectations, we found that viewing (cf., not viewing) a traditional stripe graph led to a lower willingness to engage in mitigation behaviors. Notably, we found that a stripe graph with date and temperature labels (cf., without labels): (i) helped participants develop more accurate estimates of past and predicted temperature changes and (ii) was rated more likable and helpful. We discuss how these and other findings can be utilized to help improve the effectiveness of stripe graphs as a risk communication format.

Given the critical importance of lifeline infrastructures in maintaining society functioning, the main objective of infrastructure restorations following disasters is to satisfy community demand in a rapid and effective manner. In existing literature, community demand on infrastructure services is often assumed to remain constant before and after disasters, which might lead to a mismatch between restored infrastructure serviceability and actual community demand. To address this gap, this study proposes an integrated demand-oriented infrastructure restoration framework. The integrated framework is designed to (1) estimate community demand using a Bayesian-based method, allowing for the integration of multiple information sources and the rapid updating of demands as new data becomes available; (2) develop a demand-oriented optimization model that prioritizes resource allocation to the infrastructure components serving communities with higher levels of demand; and (3) create a reliable solution method using an iterative process to accommodate the dynamics of disaster situations, complemented by a hybrid simulation-optimization approach to manage demand uncertainty. For illustrative purposes, the restoration of interdependent power and water infrastructure networks in Shelby County, TN, is studied. The results demonstrate that the proposed framework significantly improves the satisfaction of community demand, and meanwhile decreases the penalty costs associated with unmet demands. Beyond post-disaster restoration, the framework is capable of assisting decision-makers in simulating various disaster scenarios, enabling more community-centered resilience planning.

This paper examines the rationality of elite bunker building as a response to anticipated societal collapse. Indeed, the phenomenon of "prepping" for "the Event" can be framed as self-insurance and relies on a transactional view of humanity, if one is to ensure the control of a compound and fight off potential assailants. We draw on economic decision modeling to analyze how the necessity of internal control by the leader, resentment, or the perception of potential loot by outsiders interact with fortification strategies. We introduce a "Machiavelli index" to represent hostility and show that excessive investment in defense can be counterproductive and provoke attack. Maximum bunkerization may not be optimal compared to a degree of cooperation, redistribution, and efforts to reduce perceived inequality. Survival in the end times may depend less on walls and more on legitimacy, reciprocity, and strategic restraint.

Construction vehicles and equipment are a vital resource for all construction projects, with its demand expected to increase alongside technological advancements. While the use of such equipment reduces manual labor, it also introduces new risks, potentially leading to accidents. This study quantitatively analyzes the likelihood of accidents by examining utilization rate, subcontractor types, and construction costs. A regression-based prediction model for accidents involving construction equipment is proposed, utilizing data augmentation techniques with multivariate normal and Poisson distributions to improve prediction accuracy. The study is structured around three main steps: (i) Data collection and classification, (ii) calculation of hourly operating costs (HOC) and construction costs, and (iii) data augmentation and regression analysis. Regression analysis showed high R² values exceeding 0.6 for seven types of equipment, with loaders, bulldozers, and air compressors as exceptions. Although dump trucks had the highest frequency of fatalities, the prediction model identified excavators as having the highest predicted fatality count in the case study. The proposed model emphasizes safety management by categorizing risk groups based on operating costs and construction costs. It also offers a practical process for field application, providing a valuable tool for developing regulations and making investment decisions related to safety management in construction equipment.

This study, based on Complex Adaptive Systems theory and the "4C" framework, explores the dynamics of information sharing and collaboration networks within China's emergency management system during disasters. It rigorously explores the nuances in the connections and differences between these networks. Employing Social Network Analysis (SNA) and Temporal Exponential Random Graph Models (TERGMs), the research scrutinizes the relationships of disaster information sharing and collaboration among local public departments in the aftermath of the 2016 Funing tornado in Jiangsu, China. This study is dedicated to understanding how these networks evolve within a hierarchical administrative framework. The findings underscore three pivotal trends in the evolution of information and collaboration networks: a reduction in network redundancy, localized strengthening in ties, and differential adaptations. These trends are instrumental in enhancing the broader understanding of emergency management. They spotlight the importance of efficient information dissemination and robust collaborative frameworks, particularly in the context of China's centralized and hierarchical emergency management structure.