Infrastructure in Developing and Transition Countries: Risk and Protection

This article examines two possible strategies for financing post-disaster infrastructure rehabilitation in developing and transition countries: relying on ex ante financing instruments (including insurance, catastrophe bonds, and other risk-transfer instruments) and ex post borrowing or credit. Insurance and other ex ante instruments will increase a country's stability, especially if the government authorities have a difficult time borrowing or otherwise raising funds after a major disaster; however, these instruments have an opportunity cost and can reduce the country's economic growth potential. The cost-benefit tradeoff is therefore one between economic growth through infrastructure investment and added solvency and stability for the economy. This article develops a model to illustrate this tradeoff. The model, which views the infrastructure of a developing or transition country as a nondiversifiable portfolio that generates returns, can provide a basis for evaluating alternative financing options depending on the country's objectives in terms of growth, solvency, and stability.     

Risk Perception in a Developing Country: The Case of Chile

In this work we characterize risk perception in Chile, based on the psychometric paradigm, exploring the difference between perceived social and personal risk. For this purpose, we conducted a survey including 54 hazards, 16 risk attributes, and 3 risk constructs. The survey, divided into four parts, was administered to 508 residents of Santiago, Chile. Using factor analysis, three main factors, which accounted for 80% of the sample's variance, were identified: factor 1, commonly called "Dread Risk" in the literature, explained 37% of variance; factor 2, "Unknown Risk," explained 28%; and factor 3, which we called "Personal Effect," explained 15% of the variance. On average, individuals perceived themselves as less exposed to risk and with more control and knowledge about them than the general population. OLS regression models were used to test the association of perceived risk with the three main factors. For social risk, factor 1 had the greatest explanatory power, while factor 2 had a negative sign. For personal risk, only factors 2 and 3 were significant, with factor 3 having the greatest explanatory power. Risk denial (defined as the difference between perceived personal and social risk) was associated with factors 1 and 2 only, with factor 2 having a negative sign. The difference between desired and actual regulation levels proved positive for all hazards, thus indicating that Chileans are dissatisfied with the current regulation level for all the hazards analyzed. The comparison of data at the aggregate and at the individual subject's level suggests that while the aggregate analysis overestimates the magnitude of the correlations it still reflects the tendency of the individual responses.     

Risk Analysis in Developing Countries1

In summary, risk analysis is not yet well practiced in developing countries, although there are numerous, diverse environmental and other risk-related issues and concerns that need attention. A few initiatives have been taken, but, so far, they have created only relatively small impact in a few areas. Many risk issues may not be addressed, partly as a result of lack of resources and inadequate knowledge by policy makers. Risk analysis is an essential tool for the planning and implementation of development projects. To enhance its use, however, risk analysis approaches and methods must be adapted to developing countries, and this requires research. Unfortunately, funds for research, in general, are hard to find. There are other problems in the implementation of risk analysis. Trained professionals on risk analysis are few. Existing institutions that are tasked with resolving environmental and other risk issues are overburdened. Risk-related data are sorely lacking. Resources are very limited for addressing numerous natural and technological hazards. In most developing countries, political and economic stability is still threatened by both internal and external forces, hence, that is what receives priority attention. Activities like risk analysis, which generally lead to long-term results and benefits, do not get enough interest. In addition, there is still little public awareness of risks even among those who have passed the stage of survival; thus, there is little public concern about them. We cannot afford this lack of concern for long. In the Philippines, the government was the first to recognize after our peaceful revolution in 1986 that “the environmental issues can add fuel to the insurgency; they can also hamper efforts towards national recovery”.⁽⁴⁾ To this point, I have added that risk management (including risk assessment and risk communication), like attainment of peace and freedom, is a social imperative because risk issues affect our health, safety, and both our physical and economic well-being.⁽⁷⁾ Many of such issues in developing countries have not only local, but also global origin and impacts. They thus deserve the interest of each of us.     

基础项目融资风险的分担比例研究

基础项目融资风险分配是否合理直接决定着基础项目融资能否成功,针对政府和项目主办方共同承担的风险,本文分别研究了影响项目成本和影响项目收益的风险分担比例模型.     

Extreme Risk Analysis of Interdependent Economic and Infrastructure Sectors

Willful attacks or natural disasters pose extreme risks to sectors of the economy. An extreme-event analysis extension is proposed for the Inoperability Input-Output Model (IIM) and the Dynamic IIM (DIIM), which are analytical methodologies for assessing the propagated consequences of initial disruptions to a set of sectors. The article discusses two major risk categories that the economy typically experiences following extreme events: (i) significant changes in consumption patterns due to lingering public fear and (ii) adjustments to the production outputs of the interdependent economic sectors that are necessary to match prevailing consumption levels during the recovery period. Probability distributions associated with changes in the consumption of directly affected sectors are generated based on trends, forecasts, and expert evidence to assess the expected losses of the economy. Analytical formulations are derived to quantify the extreme risks associated with a set of initially affected sectors. In addition, Monte Carlo simulation is used to handle the more complex calculations required for a larger set of sectors and general types of probability distributions. A two-sector example is provided at the end of the article to illustrate the proposed extreme risk model formulations.     

自然异动下关键基础设施网络风险研究

关键基础设施保护是突发事件应急管理的重要对象构成,确定风险和保护排序是其保护机制的基本依据。在自然异动约束下,本文将信息扩散理论的超越概率引入风险分析模型,研究跨区域网络级联失效造成的风险,提出了确定关键基础设施网络构件的风险等级及保护排序的方法。以覆冰灾害下电网为例,研究了电网中不同支路在覆冰灾害下面临风险程度,与基本脆弱点进行对比,验证以异动风险作为保护参考的合理性。     

Addressing Climate Change as an Emerging Risk to Infrastructure Systems

The consequences that climate change could have on infrastructure systems are potentially severe but highly uncertain. This should make risk analysis a natural framework for climate adaptation in infrastructure systems. However, many aspects of climate change, such as weak background knowledge and societal controversy, make it an emerging risk where traditional approaches for risk assessment and management cannot be confidently employed. A number of research developments aimed at addressing these issues have emerged in recent years, such as the development of probabilistic climate projections, climate services, and robust decision frameworks. However, additional research is needed to improve the suitability of these methods for infrastructure planning. In this perspective, we outline some of the challenges in addressing climate change risks to infrastructure and summarize new developments aimed at meeting these challenges. We end by highlighting needs for future research, many of which could be well‐served by expertise within the risk analysis community.     

Cyber Risk Management for Critical Infrastructure: A Risk Analysis Model and Three Case Studies

Managing cyber security in an organization involves allocating the protection budget across a spectrum of possible options. This requires assessing the benefits and the costs of these options. The risk analyses presented here are statistical when relevant data are available, and system‐based for high‐consequence events that have not happened yet. This article presents, first, a general probabilistic risk analysis framework for cyber security in an organization to be specified. It then describes three examples of forward‐looking analyses motivated by recent cyber attacks. The first one is the statistical analysis of an actual database, extended at the upper end of the loss distribution by a Bayesian analysis of possible, high‐consequence attack scenarios that may happen in the future. The second is a systems analysis of cyber risks for a smart, connected electric grid, showing that there is an optimal level of connectivity. The third is an analysis of sequential decisions to upgrade the software of an existing cyber security system or to adopt a new one to stay ahead of adversaries trying to find their way in. The results are distributions of losses to cyber attacks, with and without some considered countermeasures in support of risk management decisions based both on past data and anticipated incidents.     

Catastrophe Risk Models for Evaluating Disaster Risk Reduction Investments in Developing Countries

Major natural disasters in recent years have had high human and economic costs, and triggered record high postdisaster relief from governments and international donors. Given the current economic situation worldwide, selecting the most effective disaster risk reduction (DRR) measures is critical. This is especially the case for low‐ and middle‐income countries, which have suffered disproportionally more economic and human losses from disasters. This article discusses a methodology that makes use of advanced probabilistic catastrophe models to estimate benefits of DRR measures. We apply such newly developed models to generate estimates for hurricane risk on residential structures on the island of St. Lucia, and earthquake risk on residential structures in Istanbul, Turkey, as two illustrative case studies. The costs and economic benefits for selected risk reduction measures are estimated taking account of hazard, exposure, and vulnerability. We conclude by emphasizing the advantages and challenges of catastrophe model‐based cost‐benefit analyses for DRR in developing countries.     

Deep Learning Models for Health and Safety Risk Prediction in Power Infrastructure Projects

Inappropriate management of health and safety (H&S) risk in power infrastructure projects can result in occupational accidents and equipment damage. Accidents at work have detrimental effects on workers, company, and the general public. Despite the availability of H&S incident data, utilizing them to mitigate accident occurrence effectively is challenging due to inherent limitations of existing data logging methods. In this study, we used a text-mining approach for retrieving meaningful terms from data and develop six deep learning (DL) models for H&S risks management in power infrastructure. The DL models include DNN_classify (risk or no risk), DNN_reg1 (loss time), DNN_reg2 (body injury), DNN_reg3 (plant and fleet), DNN_reg4 (equipment), and DNN_reg5 (environment). An H&S risk database obtained from a leading UK power infrastructure construction company was used in developing the models using the H2O framework of the R language. Performances of DL models were assessed and benchmarked with existing models using test data and appropriate performance metrics. The overall accuracy of the classification model was 0.93. The average R² value for the five regression models was 0.92, with mean absolute error between 0.91 and 0.94. The presented results, in addition to the developed user-interface module, will help practitioners obtain a better understanding of H&S challenges, minimize project costs (such as third-party insurance and equipment repairs), and offer effective strategies to mitigate H&S risk.     

我国企业并购的风险与控制分析

随着我国企业并购数量的增多及规模的扩大,企业面临的并购风险也逐渐显现出来.本文针对企业并购各个阶段所面临的风险,进行了详尽的分析,并提出了一系列的风险防范和控制建议,提高了我国企业并购的成功率.     

入世后我国基因产业的发展战略

基因产业是我国二十一世纪最重要的产业之一,同时又面临发展机遇和严峻挑战,本文论述了国外发达国家基因产业发展的情况、我国基因产业发展的现状和存在的问题,并就其入世后的发展战略提出了建议.     

信贷风险管理系统的开发实践

信贷风险管理一直是金融界关注的焦点,而对处于新兴市场和转轨型经济环境下的我国商业银行而言,这个问题尤为重要。本文分析了如何利用计算机辅助进行信贷风险的管理,提出了具体的思路和方案。     

基于模态分解的国家风险多尺度特征分析

国家风险是国际市场中的主要风险要素之一,理解其本质特征能有效的减少对外经济活动中源于其它国家的不确定性损失。针对国家风险的复杂性与易变性特征,本文引入分解思想,试图将国家风险波动分解成不同时间尺度的模态并分别进行特征识别,以把握国家风险的内在规律。在实证研究中,本文以12个代表性国家为研究对象,从长期趋势、中期走势与短期波动三个时间尺度对国家风险进行了多尺度特征分析,得出一些有意义的结论。     

Developing a Broadly Applicable Measure of Risk Perception

Decades of research identify risk perception as a largely intuitive and affective construct, in contrast to the more deliberative assessments of probability and consequences that form the foundation of risk assessment. However, a review of the literature reveals that many of the risk perception measures employed in survey research with human subjects are either generic in nature, not capturing any particular affective, probabilistic, or consequential dimension of risk; or focused solely on judgments of probability. The goal of this research was to assess a multidimensional measure of risk perception across multiple hazards to identify a measure that will be broadly useful for assessing perceived risk moving forward. Our results support the idea of risk perception being multidimensional, but largely a function of individual affective reactions to the hazard. We also find that our measure of risk perception holds across multiple types of hazards, ranging from those that are behavioral in nature (e.g., health and safety behaviors), to those that are technological (e.g., pollution), or natural (e.g., extreme weather). We suggest that a general, unidimensional measure of risk may accurately capture one's perception of the severity of the consequences, and the discrete emotions that are felt in response to those potential consequences. However, such a measure is not likely to capture the perceived probability of experiencing the outcomes, nor will it be as useful at understanding one's motivation to take mitigation action.     

新世纪我国领导科学发展的十大新趋势

进入新世纪,随着人类领导实践活动的不断变革与创新,作为领导活动理论形态的领导科学,在我国呈现了许多新的发展趋势。把握这些新的发展趋势,对于推进领导科学理论创新,加强领导科学学科建设,构建中国特色领导科学理论体系,具有十分重要的意义。综观近年来领导科学研究的最新成果,进入新世纪,我国领导科学发展主要呈现了以下十大新趋势。一、指导思想上的一元化、服务化趋势。领导科学在我国作为哲学社会科学的重要学科,既具有科学性,又具有强烈的意识形态性。坚持以什么思想为指导、沿着怎样的方向发展,对领导科学健康发展至关重要。进入新…     

基于元网络分析的重大基础设施建设项目风险评估框架与实证

重大基础设施项目具有战略性、集成性、复杂性等特征，项目容易受到多种风险因素的综合影响，导致项目目标的偏离。现有风险评估与风险决策的方法缺乏对于风险因素、风险事件之间关联的分析。为了实现重大基础设施建设项目综合系统的风险评估，本文采用元网络分析方法，构建项目目标、风险事件和风险因素的交互模型，揭示重大基础设施风险事件发生机制的黑箱过程。风险评估过程中，通过多个网络叠加运算分析每个风险因素对于各种风险事件以及项目各目标的影响情况，改进了以往仅对风险因素单一影响程度的风险评估方法。同时，本研究选择我国某河流水电站过坝运输项目方案比选的风险评估过程验证方法的适用性。