A Discourse on the Incorporation of Organizational Factors into Probabilistic Risk Assessment: Key Questions and Categorical Review

This article presents a discourse on the incorporation of organizational factors into probabilistic risk assessment (PRA)/probabilistic safety assessment (PSA), a topic of debate since the 1980s that has spurred discussions among industry, regulatory agencies, and the research community. The main contributions of this article include (1) identifying the four key open questions associated with this topic; (2) framing ongoing debates by considering differing perspectives around each question; (3) offering a categorical review of existing studies on this topic to justify the selection of each question and to analyze the challenges related to each perspective; and (4) highlighting the directions of research required to reach a final resolution for each question. The four key questions are: (I) How significant is the contribution of organizational factors to accidents and incidents? (II) How critical, with respect to improving risk assessment, is the explicit incorporation of organizational factors into PRA? (III) What theoretical bases are needed for explicit incorporation of organizational factors into PRA? (IV) What methodological bases are needed for the explicit incorporation of organizational factors into PRA? Questions I and II mainly analyze PRA literature from the nuclear domain. For Questions III and IV, a broader review and categorization is conducted of those existing cross-disciplinary studies that have evaluated the effects of organizational factors on safety (not solely PRA-based) to shed more light on future research needs.     

Risk Analysis: Celebrating the Accomplishments and Embracing Ongoing Challenges

As part of the celebration of the 40th anniversary of the Society for Risk Analysis and Risk Analysis: An International Journal, this essay reviews the 10 most important accomplishments of risk analysis from 1980 to 2010, outlines major accomplishments in three major categories from 2011 to 2019, discusses how editors circulate authors’ accomplishments, and proposes 10 major risk-related challenges for 2020–2030. Authors conclude that the next decade will severely test the field of risk analysis.     

Supply Constraint from Earthquakes in Japan in Input–Output Analysis

Disasters often cause exogenous flow damage (i.e., the [hypothetical] difference in economic scale with and without a disaster in a certain period) to production (“supply constraint”). However, input-output (IO) analysis (IOA) cannot usually consider it, because the Leontief quantity model (LQM) assumes that production is endogenous; the Ghosh quantity model (GQM) is considered implausible; and the Leontief price model (LPM) and the Ghosh price model (GPM) assume that quantity is fixed. This study proposes to consider a supply constraint in the LPM, introducing the price elasticity of demand. This study uses the loss of social surplus (SS) as a damage estimation because production (sales) is less informative as a damage index than profit (margin); that is, production can be any amount if without considering profit, and it does not tell exactly how much profit is lost for each supplier (upstream sector) and buyer (downstream sector). As a model application, this study examines Japan's largest five earthquakes from 1995 to 2017 and the Great East Japan Earthquake (GEJE) in March 2011. The worst earthquake at the peak tends to increase price by 10–20% and decrease SS by 20–30%, when compared with the initial month's prices/production. The worst damage tends to last eight months at most, accumulating 0.5-month-production damage (i.e., the sum of [hypothetical] differences in SS with and without an earthquake [for eight months] is 50% of the initial month production). Meanwhile, the GEJE in the five prefectures had cumulatively, a 25-month-production damage until the temporal recovery at the 37th month.     

Health Risk,Inequality Indexes,and Environmental Justice

Inequality indexes have long been used to analyze distributions of income. Studies have recently begun to use these tools to evaluate the equity of distributions of environmental harm. In response, issues have been raised regarding the appropriateness of using income-based measures in the context of undesirable outcomes. We begin from first principles, identifying a theoretical preference structure under which income-based tools can be appropriate for ranking distributions of “bads.” While some critiques of existing applications are valid, they are not a justification for rejecting the approach altogether. Instead, we show how standard income-based measures can be adjusted to accommodate bad outcomes. Rather than inequality indexes, we argue that equally distributed equivalents (EDEs) are well-suited for this purpose since they account for levels and dispersion of outcome distributions. The Kolm–Pollak EDE is particularly useful, having the advantage of consistently evaluating both bads and their complementary goods (e.g., mortality risk and survival probability). As an illustration, we show how these tools can inform an environmental justice analysis of a proposed Environmental Protection Agency (EPA) rule addressing indoor air pollution.     

“网络社会”的伦理分析及对策

"网络社会"是一个全新的世界,网络发展作为一场意义深远的科技革命,在给道德进步提供机遇的同时,也带来了许多不容忽视的问题.这些问题反映了人自身发展的一种危机,走出这种危机,战胜这种危机,关键还要靠人自己.     

The structure of employee attitudes to safety: A European example

This paper concerns organizational safety culture and the structure or architecture of employee attitudes to safety as part of that culture. It begins by reviewing the somewhat scant literature relevant to this area, and then reports a study, conducted in a European company, which collected and factor analysed data on employee attitudes to safety. The framework provided for the study was that offered by Purdham (1984), and the results suggested that employees' attitudes to safety, within this company (across occupation/occupational level and country), could be mapped By five orthogonal factors: personal scepticism, individual responsibility, the safeness of the work environment, the effectiveness of arrangements for safety, and personal immunity. The theoretical and practical implications of these findings are discussed, and attention is drawn to their subsequent use in an intervention to enhance safety culture within the organization by attacking supervisors' attitudes to safety.     

Fire Risks in Oil Refineries: Economic Analysis of Camera Monitoring

A probabilistic method is presented to evaluate the economic value of fire monitoring by closed circuit TV camera in petroleum refineries. The proposed model is restricted to the analysis of risk reduction in an area where fires can be caused either by pump failure or by failure of valves and lines. The benefits come from reducing the time during which the fire grows undetected. Fire growth and expected values of losses are analyzed by a Markov model that includes five phases: (1) active undetected growth, (2) detection, (3) fire growth at the beginning of the firemen's intervention, (4) fire control, and (5) fire extinction. The results (e.g., the expected net present value of the investment) show that the proposed monitoring investment is attractive for an illustrative example.     

Mlonte Carlo Techniques for Quantitative Uncertainty Analysis in Public Health Risk Assessments

Most public health risk assessments assume and combine a series of average, conservative, and worst-case values to derive a conservative point estimate of risk. This procedure has major limitations. This paper demonstrates a new methodology for extended uncertainty analyses in public health risk assessments using Monte Carlo techniques. The extended method begins as do some conventional methods--with the preparation of a spreadsheet to estimate exposure and risk. This method, however, continues by modeling key inputs as random variables described by probability density functions (PDFs). Overall, the technique provides a quantitative way to estimate the probability distributions for exposure and health risks within the validity of the model used. As an example, this paper presents a simplified case study for children playing in soils contaminated with benzene and benzo(a)pyrene (BaP).     

Modeling Demand For Unionization With Nontraditional Data Analysis Methods

Upon reviewing the extant literature on determinants of unionism, it becomes clear that many areas that have had a plethora of research attention do not converge upon singularly directional findings. This study explores a potential cause of such an apparent anomaly: nonlinearity of data. An exploratory examination of correlation coefficients among typical union determinant variables seems to show different patterns of relationships at different levels of union demand. Thus, a break from traditional linear data analysis techniques is explored in the interest of explaining more variance with typical, theoretically derived variables by using neural network analysis. Results of analyses on industry level data reveal that using neural network analysis to model union demand explained over four times as much variance as multiple regression analysis.     

Retirement patterns in Hong Kong: a censored regression analysis

This paper provides an overview of retirement patterns in Hong Kong on the basis of limited data. A censored regression model is used to infer the retirement age from people‘s current retirement status and their current age. This model is equivalent to a restricted probit model, and the interpretation of parameters is straightforward. The results clearly show a negative income effect on the retirement decision. The retirement age seems to be positively related to lifetime earnings but negatively related to the rate of decline of earnings with age. JEL classification: C24, J14, J26 Received May 6, 1996 / Accepted February 5, 1997