Consumer Risk Perceptions Toward Agricultural Biotechnology, Self-Protection, and Food Demand: The Case of Milk in the United States

This study is an econometric systems approach to modeling the factors and linkages affecting risk perceptions toward agricultural biotechnology, self-protection actions, and food demand. This model is applied to milk in the United States, but it can be adapted to other products as well as other categories of risk perceptions. The contribution of this formulation is the ability to examine how explanatory factors influence risk perceptions and whether they translate into behavior and ultimately what impact this has on aggregate markets. Hadden's outrage factors on heightening risk perceptions are among the factors examined. In particular, the article examines the role of labeling as a means of permitting informed consent to mitigate outrage factors. The effects of attitudinal, economic, and demographic factors on risk perceptions are also explored, as well as the linkage between risk perceptions, consumer behavior, and food demand. Because risk perceptions and self-protection actions are categorical variables and demand is a continuous variable, the model is estimated as a two-stage mixed system with a covariance correction procedure suggested by Amemiya. The findings indicate that it is the availability of labeling, not the price difference, between that labeled milk and milk produced with recombinant bovine Somatotropin (rbST) that significantly affects consumer's selection of rbST-free milk. The results indicate that greater availability of labeled milk would not only significantly increase the proportion of consumers who purchased labeled milk, its availability would also reduce the perception of risk associated with rbST, whether consumers purchase it or not. In other words, availability of rbST-free milk translates into lower risk perceptions toward milk produced with rbST.     

Spatial Risk Analysis of Power Systems Resilience During Extreme Events

The increased frequency of extreme events in recent years highlights the emerging need for the development of methods that could contribute to the mitigation of the impact of such events on critical infrastructures, as well as boost their resilience against them. This article proposes an online spatial risk analysis capable of providing an indication of the evolving risk of power systems regions subject to extreme events. A Severity Risk Index (SRI) with the support of real‐time monitoring assesses the impact of the extreme events on the power system resilience, with application to the effect of windstorms on transmission networks. The index considers the spatial and temporal evolution of the extreme event, system operating conditions, and the degraded system performance during the event. SRI is based on probabilistic risk by condensing the probability and impact of possible failure scenarios while the event is spatially moving across a power system. Due to the large number of possible failures during an extreme event, a scenario generation and reduction algorithm is applied in order to reduce the computation time. SRI provides the operator with a probabilistic assessment that could lead to effective resilience‐based decisions for risk mitigation. The IEEE 24‐bus Reliability Test System has been used to demonstrate the effectiveness of the proposed online risk analysis, which was embedded in a sequential Monte Carlo simulation for capturing the spatiotemporal effects of extreme events and evaluating the effectiveness of the proposed method.     

Selection of Probability Distributions in Characterizing Risk of Extreme Events

Use of probability distributions by regulatory agencies often focuses on the extreme events and scenarios that correspond to the tail of probability distributions. This paper makes the case that assessment of the tail of the distribution can and often should be performed separately from assessment of the central values. Factors to consider when developing distributions that account for tail behavior include (a) the availability of data, (b) characteristics of the tail of the distribution, and (c) the value of additional information in assessment. The integration of these elements will improve the modeling of extreme events by the tail of distributions, thereby providing policy makers with critical information on the risk of extreme events. Two examples provide insight into the theme of the paper. The first demonstrates the need for a parallel analysis that separates the extreme events from the central values. The second shows a link between the selection of the tail distribution and a decision criterion. In addition, the phenomenon of breaking records in time-series data gives insight to the information that characterizes extreme values. One methodology for treating risk of extreme events explicitly adopts the conditional expected value as a measure of risk. Theoretical results concerning this measure are given to clarify some of the concepts of the risk of extreme events.     

Perceptions of Nuclear and Other Risks in Japan and the United States

As part of a study of nuclear power development in Japan and the United States, surveys of perceptions of risk toward 30 activities, substances, and technologies have been carried out in the Pacific Northwest and Tokyo, Japan. The results show that people in both countries have the highest level of dread toward nuclear waste disposal, nuclear accidents, and nuclear war, greater even than their dread of crime and AIDS. In addition to comparisons of dread, the paper also discusses similarities and differences between Japanese and American responses for other dimensions of risk perception.     

Risk Perceptions of Hong Kongese vs. Americans

Hong Kong students rated the risk of 30 hazards and quantified a subset on six risk characteristics; data are compared to a prior study of American students. Hong Kongese rated 10 hazards substantially higher and five hazards substantially lower than Americans, and factor structures indicated differences on five hazards. Possible explanations for these differences are discussed.     

Global Warming Risk Perceptions in India

Few studies have focused on global warming risk perceptions among people in poor and developing countries, who are disproportionately impacted by climate change. This analysis conducts a comprehensive assessment of global warming risk perceptions in India using a national sample survey. Consistent with cultural theory, egalitarianism was positively associated with global warming risk perceptions. In addition, perceived vulnerability and resilience to extreme weather events were also two of the strongest factors associated with global warming risk perceptions. While worry was positively associated with risk perceptions, it accounted for only a small proportion of the variance, unlike studies in developed countries. Finally, the study also collected global warming affective images. The most common responses were “don't know” or “can't say” (25%), followed by “pollution” (21%), “heat” (20%), and “nature” (16%). The study finds that the predictors of global warming risk perceptions among the Indian public are both similar and different than those in developed countries, which has important implications for climate change communication in India.     

Risk and Climate Change: Perceptions of Key Policy Actors in Canada

This article examines factors that predict perceptions of risk associated with global climate change. The research focuses on the perceptions of those associated with climate change policy making in the prairie region of Canada. The data are from an online survey ( n = 851) of those policy actors. The analysis integrates several dominant approaches to the study of risk perception: psychometric approaches that examine the effects of cognitive structure; demographic assessments that examine, for example, differences in perception based on gender or family status; and political approaches that suggest that one's position in the policy process may affect his or her perceived risk. Attitudes toward climate change are to a degree predicted by all of these factors, but only when indirect effects are observed. Sociodemographic characteristics have little direct effect on perceived risk, but do affect general beliefs that affect risk perceptions. Perceived risk is related more strongly to these general beliefs or world views than to more specific beliefs about the effects of climate change on weather patterns. Position within the policy process also contributes to our understanding of perceptions, with industry and governmental actors demonstrating similar attitudes, which contrast with environmental groups and university researchers.     

Hazard Experience,Geophysical Vulnerability,and Flood Risk Perceptions in a Postdisaster City,the Case of New Orleans

This article investigates the determinants of flood risk perceptions in New Orleans, Louisiana (United States), a deltaic coastal city highly vulnerable to seasonal nuisance flooding and hurricane‐induced deluges and storm surges. Few studies have investigated the influence of hazard experience, geophysical vulnerability (hazard proximity), and risk perceptions in cities undergoing postdisaster recovery and rebuilding. We use ordinal logistic regression techniques to analyze experiential, geophysical, and sociodemographic variables derived from a survey of 384 residents in seven neighborhoods. We find that residents living in neighborhoods that flooded during Hurricane Katrina exhibit higher levels of perceived risk than those residents living in neighborhoods that did not flood. In addition, findings suggest that flood risk perception is positively associated with female gender, lower income, and direct flood experiences. In conclusion, we discuss the implications of these findings for theoretical and empirical research on environmental risk, flood risk communication strategies, and flood hazards planning.     

Risk and Benefit Perceptions of Mobile Phone and Base Station Technology in Bangladesh

Research in developed countries showed that many citizens perceive that radio signals transmitted by mobile phones and base stations represent potential health risks. Less research has been conducted in developing countries focused on citizen perceptions of risks and benefits, despite the recent and rapid introduction of mobile communication technologies. This study aims to identify factors that are influential in determining the tradeoffs that Bangladeshi citizens make between risks and benefits in terms of mobile phone technology acceptance and health concerns associated with the technology. Bangladesh was selected as representative of many developing countries inasmuch as terrestrial telephone infrastructure is insubstantial, and mobile phone use has expanded rapidly over the last decade, even among the poor. Issues of importance were identified in a small‐scale qualitative study among Bangladeshi citizens (n = 13), followed by a survey within a sample of Bangladeshi citizens (n = 500). The results demonstrate that, in general, the perceived benefits of mobile phone technology outweigh the risks. The perceived benefits are primarily related to the social and personal advantages of mobile phone use, including the ability to receive emergency news about floods, cyclones, and other natural disasters. Base stations were seen as a symbol of societal advance. The results furthermore suggest that overall risk perceptions are relatively low, in particular health risks, and are primarily driven by perceptions that related to crime and social inconvenience. Perceived health risks are relatively small. These findings show that risk communication and management may be particularly effective when contextual factors of the society where the system is implemented are taken into consideration.     

Climate Change Beliefs and Perceptions of Weather‐Related Changes in the United Kingdom

Public perception research in different countries has suggested that real and perceived periods of high temperature strengthen people's climate change beliefs. Such findings raise questions about the climate change beliefs of people in regions with moderate climates. Relatively little is known about whether public concerns about climate change may also be associated with perceived changes in other weather‐related events, such as precipitation or flooding. We examine the relationship between perceived changes in weather‐related events and climate change beliefs among U.K. residents at a time of below‐average winter temperatures and recent flooding. National survey data (n = 1,848) revealed that heat waves and hot summers were perceived to have become less common during respondents’ lifetimes, while flooding, periods of heavy rainfall, coastal erosions, and mild winters were perceived to have increased in frequency and cold winters were perceived to be unchanged. Although perceived changes in hot‐weather‐related events were positively associated with climate change beliefs, perceived changes in wet‐weather‐related events were found to be an even stronger predictor. Self‐reported experience of “flooding in own area” and “heat‐wave discomfort” also significantly contributed to climate change beliefs. These findings highlight the importance of salient weather‐related events and experiences in the formation of beliefs about climate change. We link our findings to research in judgment and decision making, and propose that those wishing to engage with the public on the issue of climate change should not limit their focus to heat.     

Big Data and Predictive Analytics in Fire Risk Using Weather Data

The objective of this article is to study the impact of weather on the damage caused by fire incidents across the United States. The article uses two sets of big data—-fire incidents data from the National Fire Incident Reporting System (NFIRS) and weather data from the National Oceanic and Atmospheric Administration (NOAA)—to obtain a single comprehensive data set for prediction and analysis of fire risk. In the article, the loss is referred to as “Total Percent Loss,” a metric that is calculated based on the content and property loss incurred by an owner over the total value of content and property. Gradient boosting tree (GBT), a machine learning algorithm, is implemented on the processed data to predict the losses due to fire incidents. An R² value of 0.933 and mean squared error (MSE) of 124.641 out of 10,000 signify the extent of high predictive accuracy obtained by implementing the GBT model. In addition to this, an excellent predictive performance demonstrated by the GBT model is further validated by a strong fitting between the predicted loss and the actual loss for the test data set, with an R² value of 0.97. While analyzing the influence of each input variable on the output, it is observed that the state in which a fire incident takes place plays a major role in determining fire risk. This article provides useful insights to fire managers and researchers in the form of a detailed framework of big data and predictive analytics for effective management of fire risk.     

Feeling at Risk Matters: Water Managers and the Decision to Use Forecasts

Experts contend that weather and climate forecasts could have an important role in risk management strategies for community water systems. Yet, most water managers make minimal use of these forecasts. This research explores the determinants of the use of weather and climate forecasts by surveying managers of community water systems in two eastern American states (South Carolina and the Susquehanna River Basin of Pennsylvania). Assessments of the reliability of weather and climate forecasts are not driving their use as water managers who find forecasts reliable are no more likely to use them than are managers who find them unreliable. Although larger systems and those depending on surface water are more likely to use forecasts for some (but not all) purposes, the strongest determinant of forecast use is risk perceptions. Water managers who expect to face problems from weather events in the next decade are much more likely to use forecasts than are water managers who expect few problems. Their expectations of future problems are closely linked with past experience: water managers who have had problems with specific types of weather events (e.g., flood emergencies) in the last 5 years are likely to expect to experience problems in the next decade. Feeling at risk, regardless of the specific source of that weather-related risk, stimulates a decision to use weather and climate forecasts.     

Risk Perceptions of Offshore Workers on UK Oil and Gas Platforms

Knowledge of the workforce's risk perceptions and attitudes to safety is necessary for the development of a safety culture, where each person accepts responsibility for working safely. The ACSNI Human Factors report stresses the importance of assessing workforce perceptions of risk to achieve a proper safety culture. Risk perception research has been criticized for insufficient analysis of the causal relationships between risk factors and perceived risk. The present study reports some of the factors which predicted risk perception in a sample of 622 employees from six UKCS offshore oil installations who completed a 15-section questionnaire. This paper focuses on the accuracy of workers' risk perceptions and what underlying factors predict the perception of personal risk from both major and minor hazards.     

Understanding Public Perceptions of Benefits and Risks of Childhood Vaccinations in the United States

In the face of a growing public health concern accompanying the reemerging threat of preventable diseases, this research seeks mainly to explain variations in the perceived benefits and risks of vaccinations among the general public in the United States. As Mary Douglas and Aaron Wildavsky's grid‐group cultural theory of risk perception claims, the analytical results based upon original data from a nationwide Internet survey of 1,213 American adults conducted in 2010 suggest that individuals’ cultural predispositions contribute to the formation of their perceptions pertaining to vaccine benefits and risks at both societal and individual levels, in conjunction with other factors suggested by previous risk perception literature, such as perceived prevalence of diseases, trust, knowledge level, and demographic characteristics. Those with a strong hierarch orientation tend to envision greater benefits and lesser risks and conceive of a relatively high ratio of benefit to risk when compared to other cultural types. By contrast, those with a strong fatalist tendency are inclined to emphasize risks and downplay benefits while conceiving of a low vaccination benefit‐risk ratio. Situated between hierarchs and fatalists, strong egalitarians are prone to perceive greater benefits, smaller risks, and a more positive benefit‐risk ratio than strong individualists.     

Personal Efficacy, the Information Environment, and Attitudes Toward Global Warming and Climate Change in the United States

Despite the growing scientific consensus about the risks of global warming and climate change, the mass media frequently portray the subject as one of great scientific controversy and debate. And yet previous studies of the mass public's subjective assessments of the risks of global warming and climate change have not sufficiently examined public informedness, public confidence in climate scientists, and the role of personal efficacy in affecting global warming outcomes. By examining the results of a survey on an original and representative sample of Americans, we find that these three forces—informedness, confidence in scientists, and personal efficacy—are related in interesting and unexpected ways, and exert significant influence on risk assessments of global warming and climate change. In particular, more informed respondents both feel less personally responsible for global warming, and also show less concern for global warming. We also find that confidence in scientists has unexpected effects: respondents with high confidence in scientists feel less responsible for global warming, and also show less concern for global warming. These results have substantial implications for the interaction between scientists and the public in general, and for the public discussion of global warming and climate change in particular.     

Agreement Between Scales in the Measurement of Breast Cancer Risk Perceptions

The objective of this article is to compare the accuracy and numeric responses of breast cancer risk perception as measured by a frequency scale and percentage scale. A cross-sectional survey was conducted. Perceptions of five-year and lifetime breast cancer risk were measured using a frequency and a percentage scale. Estimation error was calculated as the absolute difference between actual breast cancer risk as determined by the Gail model and perceived risk. Agreement between scales was determined by calculating the mean and standard deviation of the difference between numeric responses. The study was conducted among women enrolled in two primary care clinics associated with an academic medical center. Two-hundred-fifty-four participants were recruited from one of the two participating internal medicine clinics. Inclusion criteria included female gender and age 40-84 years. Exclusion criteria included a history of breast cancer, dementia, or a life expectancy of less than two years. The frequency scale was more accurate than the percentage scale in estimating lifetime risk (p= 0.05), but less accurate in estimating five-year risk (p < 0.02). Only 79 participants (31%) were considered consistent scale users, providing identical responses when using the frequency and percentage scale for a given risk estimate. Although the mean difference (percentage-frequency scale) for estimates of breast cancer lifetime risk was only 2.4, the empirically determined 90% limits of agreement between the frequency and percentage scale for lifetime risk were wide, from -30 to 40. Higher numeracy was associated with consistent use of scales (OR 1.61, 95% CI; 1.09-2.37). We report disagreement in breast cancer risk perceptions when measured by a frequency and a percentage scale. The accuracy and direction of bias associated with each scale varies according to the time frame of risk being assessed.     

The Politicization of Risk: Party Cues,Polarization, and Public Perceptions of Climate Change Risk

Previous research shows that public perceptions of climate change risk are strongly related to the individual willingness to support climate mitigation and adaptation policy. In this article, I investigate how public perceptions of climate change risk are affected by communications from political parties and the degree of polarization among them. Specifically, using survey data from Sweden, Norway, Australia, and New Zealand, I study the relationship between party source cues, perceived polarization, and public perceptions of climate change risk. The results reveal a positive relationship between party cues and perceptions of climate change risk, indicating that individuals adjust their risk perceptions to align with their party preference. Furthermore, a negative relationship between perceived polarization and individual risk perceptions is also discovered, showing that individuals tend to be less concerned with climate change the more polarization they perceive. However, the effect of perceived polarization is found to be limited to more abstract perceptions of risk, while being unrelated to perceptions of concrete risks. Even with some contextual variance, the results generally hold up well across the four countries.     

Risk of Extreme Events Under Nonstationary Conditions

The concept of the return period is widely used in the analysis of the risk of extreme events and in engineering design. For example, a levee can be designed to protect against the 100-year flood, the flood which on average occurs once in 100 years. Use of the return period typically assumes that the probability of occurrence of an extreme event in the current or any future year is the same. However, there is evidence that potential climate change may affect the probabilities of some extreme events such as floods and droughts. In turn, this would affect the level of protection provided by the current infrastructure. For an engineering project, the risk of an extreme event in a future year could greatly exceed the average annual risk over the design life of the project. An equivalent definition of the return period under stationary conditions is the expected waiting time before failure. This paper examines how this definition can be adapted to nonstationary conditions. Designers of flood control projects should be aware that alternative definitions of the return period imply different risk under nonstationary conditions. The statistics of extremes and extreme value distributions are useful to examine extreme event risk. This paper uses a Gumbel Type I distribution to model the probability of failure under nonstationary conditions. The probability of an extreme event under nonstationary conditions depends on the rate of change of the parameters of the underlying distribution.