政府动态奖惩机制下绿色建筑供给侧演化博弈研究

从绿色建筑供给侧角度出发，构建消费者效用函数、开发商利润函数和政府与开发商的演化博弈模型，分析消费者绿色偏好、建筑绿色度、成本等因素对建筑价格、需求和利润的影响以及政府动态奖惩政策下系统的演化稳定策略。结果表明：消费者绿色偏好支付系数和建筑绿色度的增大有利于绿色建筑价格、需求和利润的提高，但高成本却使需求和利润降低；政府采用静态补贴与静态税收政策时，博弈系统不存在演化稳定策略，采用动态补贴与静态税收、静态补贴与动态税收、动态补贴与动态税收三种政策组合时，系统存在演化稳定策略；动态补贴与静态税收的政策组合在推动绿色建筑发展方面优于其他政策；开发商开发绿色建筑的概率与补贴上限值呈负相关，与税收上限值呈正相关。     

货币政策反应函数渠道对金融周期影响时变机制的实证研究

在关于货币政策影响经济主体风险承担水平,进而影响金融周期波动机制的研究中,基于风险承担渠道的相关研究较为成熟.区别于以往相关研究多关注货币政策实际采取的立场,文章基于货币政策反应函数渠道探讨了数量型与价格型货币政策反应函数对金融周期波动影响的时变机制.滚动回归的实证结果显示:无论数量型货币政策规则还是价格型货币政策规则,货币政策对信贷波动反应的敏感性主要影响金融周期的波动,但在价格型货币政策规则下,基于信贷视角观察金融周期波动时,货币政策信贷敏感性与货币政策资产价格敏感性对金融周期影响差异较小;较之于价格型货币政策规则,货币政策对信贷波动反应的敏感性在数量型货币政策规则下,对金融周期波动的影响更显著,并在一定程度上表现出随时间扩大的趋势.文章的创新之处在于:强调了货币政策通过政策反应函数渠道而非以往研究中较多关注的狭义风险承担渠道影响金融周期波动的事实,并构建计量模型对货币政策反应函数渠道影响金融周期波动的时变机制进行了详细刻画.     

Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Cooperative innovation through a talent management pool: A qualitative study on coopetition in healthcare

In recent years, the Dutch healthcare sector has been confronted with increased competition. Not only are financial resources scarce, Dutch hospitals also need to compete with other hospitals in the same geographic area to attract and retain talented employees due to considerable labour shortages. However, four hospitals operating in the same region are cooperating to cope with these shortages by developing a joint Talent Management Pool. ‘Coopetiton’ is a concept used for simultaneous cooperation and competition. In this paper, a case study is performed in order to enhance our understanding of coopetition. Among other things, the findings suggest that perceptions of organizational actors on competition differ and might hinder cooperative innovation with competitors, while perceived shared problems and resource constraints stimulate coopetition. We reflect on the current coopetition literature in light of the research findings, which have implications for future research on this topic.     

Efficacy Foundations for Risk Communication: How People Think About Reducing the Risks of Climate Change

Believing action to reduce the risks of climate change is both possible (self‐efficacy) and effective (response efficacy) is essential to motivate and sustain risk mitigation efforts, according to current risk communication theory. Although the public recognizes the dangers of climate change, and is deluged with lists of possible mitigative actions, little is known about public efficacy beliefs in the context of climate change. Prior efficacy studies rely on conflicting constructs and measures of efficacy, and links between efficacy and risk management actions are muddled. As a result, much remains to learn about how laypersons think about the ease and effectiveness of potential mitigative actions. To bring clarity and inform risk communication and management efforts, we investigate how people think about efficacy in the context of climate change risk management by analyzing unprompted and prompted beliefs from two national surveys (N = 405, N = 1,820). In general, respondents distinguish little between effective and ineffective climate strategies. While many respondents appreciate that reducing fossil fuel use is an effective risk mitigation strategy, overall assessments reflect persistent misconceptions about climate change causes, and uncertainties about the effectiveness of risk mitigation strategies. Our findings suggest targeting climate change risk communication and management strategies to (1) address gaps in people's existing mental models of climate action, (2) leverage existing public understanding of both potentially effective mitigation strategies and the collective action dilemma at the heart of climate change action, and (3) take into account ideologically driven reactions to behavior change and government action framed as climate action.     

Capturing the broader picture of value co-creation management

The interactional, interdependent, and dynamic nature of value co-creation has made value management in business relationships a particularly challenging issue for both academicians and practitioners. In addition, studies on this topic have mostly focused on managing value co-creation in isolation from the wider relational context without completely capturing the influence of other value processes on value co-creation. This paper examines the broader picture of value co-creation management by providing a model based on interlinked value processes, namely, value communication, value appropriation, value measurement, and value representation. We adopted a qualitative approach based on 86 interviews with managers of both customer and supplier companies, which operated in different industries. Results showed that the management of value co-creation implies considering complex patterns of interconnections with other value processes. In particular, the study will shed light on the centrality of value appropriation to co-create value and on the importance of value representation to coordinate ideas and enable future co-creation opportunities.     

中国员工企业伦理态度调查及分析

随着中国市场经济的发展,各种企业丑闻的频发引起业界广泛关注,公司治理越来越受到重视。已有的研究表明,员工企业伦理态度关乎员工承诺度和企业竞争力。本文依据个人层次研究理论,采用实证分析方法,选择国内6家企业的员工为调研对象,沿用国外企业伦理个体层次研究中广泛使用的量表"Attitude Towards Business Ethics Questionnaire"(ATBEQ)即企业伦理态度量表,考察当代中国企业员工对企业伦理态度的看法;另外,也分析了不同的工作年限,性别,学历,户口等因素下的企业员工伦理态度差异。调查结果显示:中国员工的企业伦理观存在困惑;大部分中国员工有企业利益相关者意识;教育对企业员工伦理态度存在显著影响。     

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.     

行业系统性风险的动态特征及其影响因素

现代经济主体间网络关联性越来越强,风险很容易在不同行业间扩散,因此有效识别并分析系统性风险是防范金融危机的关键步骤。基于条件风险价值(CoVaR)和边际期望损失(MES)两个指标,对巨潮行业指数系统性风险的静态和动态特征进行了研究。结果发现,各行业间系统性风险的相关性较强,动态特征显示2009年年初和2016年3月为系统性风险的两个峰值;从分行业来看,材料行业的系统性风险最高,而消费和医药行业的系统性风险最低。采用动态面板模型分析影响行业系统性风险的市场面因素发现,短期涨幅较高、长期涨幅较低及流动性较充分的行业,其系统性风险往往更低。因此,应加强对系统性风险较高行业的监管力度,建立好金融防火墙,防止外部金融风险的过度传染;同时应加强对各行业的实时监控,尤其是关注短期暴涨暴跌及流动性充分与否的监控。     

A Probabilistic Framework for Risk Analysis of Widespread Flood Events: A Proof‐of‐Concept Study

This article presents a flood risk analysis model that considers the spatially heterogeneous nature of flood events. The basic concept of this approach is to generate a large sample of flood events that can be regarded as temporal extrapolation of flood events. These are combined with cumulative flood impact indicators, such as building damages, to finally derive time series of damages for risk estimation. Therefore, a multivariate modeling procedure that is able to take into account the spatial characteristics of flooding, the regionalization method top‐kriging, and three different impact indicators are combined in a model chain. Eventually, the expected annual flood impact (e.g., expected annual damages) and the flood impact associated with a low probability of occurrence are determined for a study area. The risk model has the potential to augment the understanding of flood risk in a region and thereby contribute to enhanced risk management of, for example, risk analysts and policymakers or insurance companies. The modeling framework was successfully applied in a proof‐of‐concept exercise in Vorarlberg (Austria). The results of the case study show that risk analysis has to be based on spatially heterogeneous flood events in order to estimate flood risk adequately.