There is growing public awareness of global risks that are related to land degradation, poverty, food security, migration flows, natural disasters and levels of violence and conflict. In the past decades, a wealth of performance databases has become available, and these are used to quantify those risks and to influence governance globally. We name the monitoring of the 17 Sustainable Development Goals (SDGs), the establishing of priorities in humanitarian aid programs and the design of early warning forecasting systems. This article addresses a question that underlies the social and political application of risk indicators, namely: how reliable are such data that can be accessed or downloaded ‘in a few mouse clicks’? Reliability is an important issue for users of these data since poor data will lead to poor inferences. In addition, flawed data are usually related to poor and fragile countries, countries that need humanitarian aid and financial investments the most. In order to get a grip on this reliability issue, we explore the possible uncertainties attached to global risk-related indicators. In this article we (i) provide an overview of available data sources, (ii) briefly describe the way institutes aggregate risk indicators from an underlying set of basic indicators to form composites, and (iii) identify various sources of uncertainty related to global risk indicators and their composites. Furthermore, we give solutions for coping with uncertainties in the partial or complete absence of such information. We acknowledge that these solutions are insufficient to quantify all (cascading) uncertainties concerning global indicators, especially those related to ‘Campbell's law’. Therefore, we applied a ‘ringtest’ across data from leading institutes as for five open access risk indicators: governance, impacts of natural disasters, conflicts, vulnerability/coping capacity, and all security risks combined. We find that the coherence between indicators from different organisations but with identical definitions varies enormously. We find that indicators denoted as ‘impacts of natural disasters’ are almost uncorrelated across four organisations. However, indicators denoting ‘governance’ or ‘all security risks combined’ show remarkable high correlations.

公众日益意识到与土地退化，贫困，粮食安全，移民流动，自然灾害以及暴力和冲突程度有关的全球风险。在过去的几十年中，已经有了大量的绩效数据库，这些数据库可用于量化这些风险并影响全球的治理。我们命名为监测17个可持续发展目标（SDG），确定人道主义援助计划的优先事项以及设计预警预报系统。本文提出了一个风险指标在社会和政治上应用的基础问题，即：“只需单击几下”即可访问或下载的此类数据的可靠性如何？对于这些数据的用户来说，可靠性是一个重要的问题，因为不良的数据将导致不良的推断。此外，有缺陷的数据通常与贫穷和脆弱的国家有关，这些国家最需要人道主义援助和金融投资。为了掌握此可靠性问题，我们探索了与全球风险相关的指标可能存在的不确定性。在本文中，我们（i）提供了可用数据源的概述，（ii）简要描述了从一组基础指标中汇总风险指标以形成综合指标的方式，以及（iii）识别与全球风险相关的各种不确定性来源指标及其组合。此外，我们提供了解决方案，以解决部分或完全缺少此类信息的不确定性。我们承认，这些解决方案不足以量化有关全球指标的所有（连锁）不确定性，尤其是与“坎贝尔”相关的不确定性 的法律”。因此，我们对领先机构的数据进行了“环评”，以评估五个开放获取风险指标：治理，自然灾害的影响，冲突，脆弱性/应对能力以及所有安全风险的总和。我们发现，来自不同组织但具有相同定义的指标之间的一致性差异很大。我们发现，在四个组织中，被称为“自然灾害的影响”的指标几乎没有关联。但是，表示“治理”或“所有安全风险加总”的指标显示出显着的高度相关性。我们发现，来自不同组织但具有相同定义的指标之间的一致性差异很大。我们发现，在四个组织中，被称为“自然灾害的影响”的指标几乎没有关联。但是，表示“管理”或“所有安全风险加总”的指标显示出显着的高度相关性。我们发现，来自不同组织但具有相同定义的指标之间的一致性差异很大。我们发现，在四个组织中，被称为“自然灾害的影响”的指标几乎没有关联。但是，表示“管理”或“所有安全风险加总”的指标显示出显着的高度相关性。