Risk Analysis最新文献

The unprecedented exposure of radiofrequency electromagnetic field (RF-EMF) to humans from mobile communications raises serious public concern about the possibility of unexpected adverse health effects and has stimulated authorities to adopt precautionary exposure limits. These limits are distinctly different across countries, and the causes of these differences are unclear from the literature. This article is the first empirical analysis on the determinants of RF-EMF exposure legislation, using a novel cross-sectional database of 164 countries worldwide. The analysis shows that decentralization and mobile competition in countries with low mobile network deployment tend to promote more stringent RF-EMF exposure limits across the dataset with 164 countries. In more decentralized countries, the regions had a greater influence on national legislation and could accommodate local demands with the advent of mobile technology in the 2000s. In contrast, decentralization and mobile competition in countries with high levels of mobile network deployment tend to relax RF-EMF exposure limits in the sample of 61 countries with fifth-generation (5G) technology. Indeed, restrictive RF-EMF exposure limits are constraining 5G deployment in a context of the widespread adoption of mobile-broadband technologies. These results should be useful for policymakers and mobile operators alike to anticipate the outcome of legislation in countries that have yet to introduce 5G technology. The results should also be useful when reviewing policies and strategies for the implementation of the upcoming 6G technology in frequency bands that will be increasingly higher (above 6 GHz up to THz for very local usage), and hence where the health effects on humans are less well studied.

Catastrophe bonds (cat bond in short) are an alternative risk-transfer instrument used to transfer peril-specific financial risk from governments, financial institutions, or (re)insurers, to the capital market. Current approaches for cat bond pricing are calibrated on seismic mainshocks, and thus do not account for potential effects induced by earthquake sequences. This simplifying assumption implies that damage arises from mainshocks only, while aftershocks yield no damage. Postearthquake field surveys reveal that this assumption is inaccurate. For example, in the 2011 Christchurch Earthquake sequence and 2016-2017 Central Italy Earthquake sequence, aftershocks were responsible for higher economic losses when compared to those caused by mainshocks. This article proposes a time-dependent aggregate loss model that takes into account seismicity clustering and damage accumulation effects in the computation of damage. The model is calibrated on the seismic events recorded during the recent 2016-2017 Central Italy Earthquake sequence. Furthermore, the effects of earthquake sequence on cat bond pricing is explored by implementing the proposed model on five Italian municipalities. The investigation showed that neglecting time-dependency may lead to higher difference (up to 45%) in the cat bond price when compared to standard approaches.

To understand citizens' reactions to the 5G rollout, their affective reaction and perception of radiofrequency electromagnetic fields (RF-EMF) exposure are of interest. Although precursor studies on 2G-4G have investigated exposure perception mostly quantitatively, the present study applied a qualitative exploratory approach. A number of 35 individual interviews and 6 focus groups with the same participants were conducted in December 2022. Participants were recruited from several locations in Germany, where 5G rollout was at different stages. Interactive tasks, particularly an affective evaluation task and a ranking task, encouraged participants to consider their affect regarding mobile communications and their exposure perception. This approach allowed the participants to first engage with the topic of mobile communications/5G in an intuitive way, before talking about their specific beliefs on RF-EMF exposure. Several pictures showing a person (1) interacting with a mobile phone, (2) surrounded by other peoples' mobile phones, or (3) in the vicinity of mobile phone base stations (antennas) were used as stimulus materials. Data were analyzed using an exploratory content analysis. In the affective evaluation task participants revealed more negative associations with base stations than with mobile phones. The analysis showed that the reasons for their evaluation were very diverse, whereby exposure to RF-EMF only played a subordinate role. Further, the ranking task indicated that most participants (n = 20) felt more exposed from base stations than from mobile devices. Results are mostly in-line with the literature on 2G-4G and do not indicate a substantially different exposure perception for 5G.

This study examines the inconsistent theories surrounding the roles of perceived threat and efficacy in risk communication theories, focusing on behavioral changes during a public health crisis. Utilizing a two-wave panel survey, the research found a nuanced interaction between efficacy beliefs and risk perceptions in dictating individuals' engagement in protective or defensive behaviors. Notably, those with high risk perceptions but lower efficacy beliefs were more likely to engage in future protective behaviors, contradicting previous assumptions about the avoidance group's propensity for defensive reactions. These findings initiate a complex discussion on the dynamics of risk and efficacy perceptions influencing behavior.

Prevention behaviors are important in mitigating the transmission of COVID-19. The protection motivation theory (PMT) links perceptions of risk and coping ability with the act of adopting prevention behaviors. The goal of this research is to test the application of the PMT in predicting adoption of prevention behaviors during the COVID-19 pandemic. Two research objectives are achieved to explore motivating factors for adopting prevention behaviors. (1) The first objective is to identify variables that are strong predictors of prevention behavior adoption. A data-driven approach is used to train Bayesian belief network (BBN) models using results of a survey of $N=7797$ participants reporting risk perceptions and prevention behaviors during the COVID-19 pandemic. A large set of models are generated and analyzed to identify significant variables. (2) The second objective is to develop models based on the PMT to predict prevention behaviors. BBN models that predict prevention behaviors were developed using two approaches. In the first approach, a data-driven methodology trains models using survey data alone. In the second approach, expert knowledge is used to develop the structure of the BBN using PMT constructs. Results demonstrate that trust and experience with COVID-19 were important predictors for prevention measure adoption. Models that were developed using the PMT confirm relationships between coping appraisal, threat appraisal, and protective behaviors. Data-driven and PMT-based models perform similarly well, confirming the use of PMT in this context. Predicting adoption of social distancing behaviors provides insight for developing policies during pandemics.

In light of the escalating global warming and the escalating frequency of extreme weather events, the agricultural sector, being a fundamental and pivotal industry worldwide, is encountering substantial challenges due to climate change. Using Chinese provincial panel data for 2000-2021, this paper utilizes a two-way fixed-effect model to investigate the impact of Climate Risk (CR) on green total factor productivity in agriculture (AGTFP), with China's climate policy uncertainty (CPU) being introduced as a moderating variable within the research framework to scrutinize its influence in this context. The findings reveal a noteworthy adverse effect of CR on AGTFP, further exacerbated by CPU. Heterogeneity analysis results show that there is a clear regional variation in the effect of CR on AGTFP across different Chinese regions, with CR significantly inhibiting AGTFP development in the northern regions and provinces in major grain producing regions. Consequently, there is a pressing necessity to bolster the establishment of climate change monitoring infrastructures, devise tailored climate adaptation strategies at a regional level, and enhance the clarity and predictability of climate policies to fortify the resilience and sustainability of agricultural production systems.

The Wells-Riley model has been widely used to estimate airborne infection risk, typically from a deterministic point of view (i.e., focusing on the average number of infections) or in terms of a per capita probability of infection. Some of its main limitations relate to considering well-mixed air, steady-state concentration of pathogen in the air, a particular amount of time for the indoor interaction, and that all individuals are homogeneous and behave equally. Here, we revisit the Wells-Riley model, providing a mathematical formalism for its stochastic version, where the number of infected individuals follows a Binomial distribution. Then, we extend the Wells-Riley methodology to consider transient behaviours, randomness, and population heterogeneity. In particular, we provide analytical solutions for the number of infections and the per capita probability of infection when: (i) susceptible individuals remain in the room after the infector leaves, (ii) the duration of the indoor interaction is random/unknown, and (iii) infectors have heterogeneous quanta production rates (or the quanta production rate of the infector is random/unknown). We illustrate the applicability of our new formulations through two case studies: infection risk due to an infectious healthcare worker (HCW) visiting a patient, and exposure during lunch for uncertain meal times in different dining settings. Our results highlight that infection risk to a susceptible who remains in the space after the infector leaves can be nonnegligible, and highlight the importance of incorporating uncertainty in the duration of the indoor interaction and the infectivity of the infector when estimating risk.

This article describes the design and analysis of web-based choice experiments that examine how the demand for earthquake protection in Quebec and British Columbia (BC), Canada, is influenced by the default option and the structure of the insurance plan. Homeowners in both provinces were given the opportunity to purchase protection against earthquake losses when presented with one of the following options: the current private insurance plan and proposed public-private Risk Pools with different levels of the public layer. The default frame was changed so the homeowner could either opt-in by purchasing this coverage or opt-out of being given this protection and receiving a premium discount. Assigning participants to the public-private Risk Pools rather than the current private insurance plan increases the likelihood of purchasing earthquake insurance protection by an odds ratio of 2.7 or greater in BC and Quebec. Furthermore, opt-out enrollment design substantially increases take-up of earthquake protection relative to opt-in enrollment. The policy implications of these findings are discussed.

Cultural theory and the psychometric paradigm are two frameworks proposed to explain risk perceptions, mostly used independently of each other. On the one hand, psychometric research identified key characteristics of hazards responsible for their level of perceived riskiness. On the other hand, cultural studies provided evidence that different worldviews lead to divergent perceptions of risk in a way supportive of individuals' cultural values. The purpose of this research was to combine both approaches into mediational models in which cultural values impact risk perceptions of controversial hazards through their influence on the characteristics associated with those hazards. Using data from an online survey completed by 629 French participants, findings indicated specific associations between cultural values and risk characteristics, both of them exhibiting effects on risk perceptions that depend largely on hazardous issues. More specifically, we found that people confer specific characteristics on hazards (common or dreadful, beneficial or costly, affecting few or many people), depending on whether they are hierarchists-individualists, egalitarians, or fatalists; in turn, such characteristics have an impact on the perceived riskiness of hazards such as cannabis, social movement, global warming, genetically modified organisms, nuclear power, public transportation, and coronavirus. Finally, this article discussed the interest of addressing the mechanisms that explain how cultural values shape individuals' perceptions of risk.