类型化视域下自然保护地立法的制度建构

自然保护地在维持生态平衡,实现生态系统服务功能中可以发挥重要作用,但不同类型自然保护地的主导生态系统服务功能供给存在一定差异.《自然保护地法》立法工作须依赖理论上对自然保护地的类型化,并在此基础上建构对应型的规划建设、运营管理等方面的具体制度.因此,基于生态系统服务功能供给的空间差异,可以将当前相互重叠、缺乏体系的自然保护地划分为国家公园、自然保护区、自然公园、自然保护小区等四种类型,搭建"1部基础法律+4部类型化条例(或法律)"的自然保护地法律体系,并可以通过体系化公法、私法制度工具强化自然保护地的有效治理.     

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.     

电子商务平台吸附能力的影响机制研究———平台柔性和控制机制的交互效应

平台型电子商务已经成为未来经济发展的主要推动力,如何构建并应用这种平台商务模式已经成为企业管理者普遍关心的问题.然而现有文献对电子商务平台的研究还比较少,主要基于平台主体的案例研究,缺乏从平台商家角度对如何形成电子商务平台的吸附力并激发网络效应的实证检验.基于模块系统理论和控制机制的相关文献,提出了平台柔性、平台控制、以及二者交互作用对平台吸附能力的影响模型.通过对185家平台参与企业的实证研究,研究结果显示平台柔性、正式控制、和关系控制均显著影响电子商务平台吸附能力;另外,平台柔性和正式控制对电子商务平台吸附能力的形成存在替代效应,平台柔性和关系控制对电子商务平台吸附能力的形成存在互补效应,正式控制和关系控制对电子商务平台吸附能力也存在互补效应.研究发现,对于模块系统理论和控制机制的应用情景进行了扩展和延伸,也为电子商务平台的管理者提供了有益的建议.     

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.     

A new financial stress index model based on support vector regression and control chart

Financial stress index (FSI) is considered to be an important risk management tool to quantify financial vulnerabilities. This paper proposes a new framework based on a hybrid classifier model that integrates rough set theory (RST), FSI, support vector regression (SVR) and a control chart to identify stressed periods. First, the RST method is applied to select variables. The outputs are used as input data for FSI–SVR computation. Empirical analysis is conducted based on monthly FSI of the Federal Reserve Bank of Saint Louis from January 1992 to June 2011. A comparison study is performed between FSI based on the principal component analysis and FSI–SVR. A control chart based on FSI–SVR and extreme value theory is proposed to identify the extremely stressed periods. Our approach identified different stressed periods including internet bubble, subprime crisis and actual financial stress episodes, along with the calmest periods, agreeing with those given by Federal Reserve System reports.     

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。     

疲劳裂纹扩展试验裂纹图像采集系统

针对目前金属疲劳裂纹扩展检测方法存在效率低、操作繁琐、精度不高，不能实现实时性等缺点，文章提出了基 于机器视觉的裂纹检测方法。根据金属疲劳裂纹扩展试验过程中裂纹图像的采集要求，设计了摄像头位置可自动调整 的疲劳裂纹图像采集系统，包括摄像头安装运动装置，裂纹图像采集装置以及摄像头运动控制系统。图像采集装置由高 分辨率黑白面阵CCD摄像头、光学镜头，光源照明装置和图像采集卡组成，前3者安装在摄像头支架上，与摄像头运动 装置相连。摄像头运动装置中的X，Y，，Z轴运动机构由步进电机带动滚珠导轨机构。在基于ARM技术的运动控制系统 的控制下摄像头可沿X，Y，，Z轴作高精度直线运动，实现和试件之间精确位置的调整。实验结果表明，所设计的系统可 采集到疲劳裂纹扩展试验过程中高清晰度、高分辨率的试件裂纹图像，满足下一步裂绞尺寸计算的要求；摄像头运动定 位精度可达到0. 02 mm，满足精确的系统聚焦、位置调整、跟踪采集的要求。     

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.     

Cross‐Sectional Psychological and Demographic Associations of Zika Knowledge and Conspiracy Beliefs Before and After Local Zika Transmission

Perceptions of infectious diseases are important predictors of whether people engage in disease‐specific preventive behaviors. Having accurate beliefs about a given infectious disease has been found to be a necessary condition for engaging in appropriate preventive behaviors during an infectious disease outbreak, while endorsing conspiracy beliefs can inhibit preventive behaviors. Despite their seemingly opposing natures, knowledge and conspiracy beliefs may share some of the same psychological motivations, including a relationship with perceived risk and self‐efficacy (i.e., control). The 2015–2016 Zika epidemic provided an opportunity to explore this. The current research provides some exploratory tests of this topic derived from two studies with similar measures, but different primary outcomes: one study that included knowledge of Zika as a key outcome and one that included conspiracy beliefs about Zika as a key outcome. Both studies involved cross‐sectional data collections that occurred during the same two periods of the Zika outbreak: one data collection prior to the first cases of local Zika transmission in the United States (March–May 2016) and one just after the first cases of local transmission (July–August). Using ordinal logistic and linear regression analyses of data from two time points in both studies, the authors show an increase in relationship strength between greater perceived risk and self‐efficacy with both increased knowledge and increased conspiracy beliefs after local Zika transmission in the United States. Although these results highlight that similar psychological motivations may lead to Zika knowledge and conspiracy beliefs, there was a divergence in demographic association.