What to Do at Low Doses: A Bounding Approach for Economic Analysis

To quantify the health benefits of environmental policies, economists generally require estimates of the reduced probability of illness or death. For policies that reduce exposure to carcinogenic substances, these estimates traditionally have been obtained through the linear extrapolation of experimental dose-response data to low-exposure scenarios as described in the U.S. Environmental Protection Agency's Guidelines for Carcinogen Risk Assessment (1986). In response to evolving scientific knowledge, EPA proposed revisions to the guidelines in 1996. Under the proposed revisions, dose-response relationships would not be estimated for carcinogens thought to exhibit nonlinear modes of action. Such a change in cancer-risk assessment methods and outputs will likely have serious consequences for how benefit-cost analyses of policies aimed at reducing cancer risks are conducted. Any tendency for reduced quantification of effects in environmental risk assessments, such as those contemplated in the revisions to EPA's cancer-risk assessment guidelines, impedes the ability of economic analysts to respond to increasing calls for benefit-cost analysis. This article examines the implications for benefit-cost analysis of carcinogenic exposures of the proposed changes to the 1986 Guidelines and proposes an approach for bounding dose-response relationships when no biologically based models are available. In spite of the more limited quantitative information provided in a carcinogen risk assessment under the proposed revisions to the guidelines, we argue that reasonable bounds on dose-response relationships can be estimated for low-level exposures to nonlinear carcinogens. This approach yields estimates of reduced illness for use in a benefit-cost analysis while incorporating evidence of nonlinearities in the dose-response relationship. As an illustration, the bounding approach is applied to the case of chloroform exposure.     

企业效益评价因子分析模型及应用

本文将因子分析方法应用于社会科学领域的经济评价系统。从一些错综复杂的经济现象中找出少数几个主因子,每一个主因子代表经济变量之间相互依赖的一种经济作用,抓住这些主因子就可以帮助我们对复杂的经济问题进行分析和解释。在简要介绍因子模型以后,从实证分析的角度,选取深沪股市高科技板块 2 0家上市公司1998年中期的一系列经济评价指标,采用因子分析方法,并借助社会科学统计软件SPSS,进行企业的综合经济效益分析评价,得出因子分析是一种进行企业综合效益评价的有效方法。     

语篇分析与机器翻译

机译系统应该建立在具有语篇分析的大型语料库基础之上,语篇语料库内的原语和目的语文本不是单向、而应是双向或多向流动的网络系统。文本的译文也应该有历时和共时的区别,因为互文性已是翻译的某种本质特征。     

计算结构力学课程的改革与学科发展

文章介绍了计算结构力学教学改革的目的和意义,进一步确立了适应21世纪人才培养的学科教学内容与体系,在课程教学的改革中特别就结构分析的技术应用和创新能力培养等方面进行了探索。     

管理会计在我国的应用思考

随着对管理会计理论及实践方法的不断探讨和研究,管理会计对提高企业内部管理水平的作用日益突出。本文主要分析、论述了管理会计面临的挑战及存在的主要问题,并对管理会计今后的发展和应用前景进行了探索和思考。     

Valuation of childhood risk reduction: the importance of age, risk preferences, and perspective.

This article explores two problems analysts face in determining how to estimate values for children's health and safety risk reductions. The first addresses the question: Do willingness‐to‐pay estimates for health risk changes differ across children and adults and, if so, how? To answer this question, the article first examines the potential effects of age and risk preferences on willingness to pay. A summary of the literature reporting empirical evidence of differences between willingness to pay for adult health and safety risk reductions and willingness to pay for health and safety risk reductions in children is also provided. The second dimension of the problem is a more fundamental issue: Whose perspective is relevant when valuing children's health effects—society's, children's, adults‐as‐children, or parents'? Each perspective is considered, followed ultimately by the conclusion that adopting a parental perspective through an intrahousehold allocation model seems closest to meeting the needs of the estimation problem at hand. A policy example in which the choice of perspective affects the outcome of a regulatory benefit‐cost analysis rounds out the article and emphasizes the importance of perspective.     

基于聚集经济的影响外商直接投资的因素分析

文章选取广东省 2 1个行政区 1996至 2 0 0 2年的数据 ,采用主因子分析和多元统计回归分析对影响外资在广东聚集的因素进行分析 ,因子分析从 11个解释变量中提取出五个主因子 ,它们分别是经济发展规模和水平、产业聚集、劳动力聚集、经济开放度和政策因子。回归分析表明了每个解释变量对利用外资额的弹性。文章最后提出了产业聚集的引资策略。     

重庆市股份制商业银行的SWOT分析

根据一般企业的SWOT分析理论,对商业银行的SWOT分析要素进行设计。运用这种方法对重庆市股份制商业银行的内部优势、劣势以及外部机遇和威胁进行分析。根据分析结果,提出相应的经营措施。     

Uncertainty Analysis for Computer Model Projections of Hurricane Losses

Projecting losses associated with hurricanes is a complex and difficult undertaking that is wrought with uncertainties. Hurricane Charley, which struck southwest Florida on August 13, 2004, illustrates the uncertainty of forecasting damages from these storms. Due to shifts in the track and the rapid intensification of the storm, real-time estimates grew from 2 to 3 billion dollars in losses late on August 12 to a peak of 50 billion dollars for a brief time as the storm appeared to be headed for the Tampa Bay area. The storm hit the resort areas of Charlotte Harbor near Punta Gorda and then went on to Orlando in the central part of the state, with early poststorm estimates converging on a damage estimate in the 28 to 31 billion dollars range. Comparable damage to central Florida had not been seen since Hurricane Donna in 1960. The Florida Commission on Hurricane Loss Projection Methodology (FCHLPM) has recognized the role of computer models in projecting losses from hurricanes. The FCHLPM established a professional team to perform onsite (confidential) audits of computer models developed by several different companies in the United States that seek to have their models approved for use in insurance rate filings in Florida. The team's members represent the fields of actuarial science, computer science, meteorology, statistics, and wind and structural engineering. An important part of the auditing process requires uncertainty and sensitivity analyses to be performed with the applicant's proprietary model. To influence future such analyses, an uncertainty and sensitivity analysis has been completed for loss projections arising from use of a Holland B parameter hurricane wind field model. Uncertainty analysis quantifies the expected percentage reduction in the uncertainty of wind speed and loss that is attributable to each of the input variables.