What users of global risk indicators should know
Bibliographic Data
| ID | 12262425 |
|---|---|
| Authors | Hubregt J Visser (0000-0001-8726-9579, Netherlands Environmental Assessment Agency, corresponding author), H Visser (0000-0002-0440-8447), Sophie de Bruin (0000-0003-3429-349X, Netherlands Environmental Assessment Agency), Astrid L Martens (0000-0002-2055-5527, Netherlands Environmental Assessment Agency), A Martens, J M Knoop (Netherlands Environmental Assessment Agency), J Knoop, Willem Ligtvoet (Netherlands Environmental Assessment Agency) |
| Year | 2020 |
| Volume | 62 |
| Pages | 102068-102068 |
| Publication date | 2020-03-25 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Global Environmental Change (JOURNAL) |
| Journal identifiers | ISSN: 0959-3780 • E-ISSN: 1872-9495 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.gloenvcha.2020.102068 |
| OpenAlex | W3013673846 |
| Language | EN |
| Citations received | 14 |
| References cited | 23 |
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
Business · Economic growth · Economics · Geography · Natural disaster · Order (exchange · Political science · Poverty · Risk analysis (engineering · Sustainable development · Warning system · Computer Science · Disaster Management and Resilience · International Development and Aid · Natural Resources and Economic Development · Finance
Measuring the Statistical Capacity of Nations
Understanding human vulnerability to climate change
Climate Change and Homicide
Temporal characteristics of disaster data
Prioritising humanitarian aid funding for multi-risk disasters in an era of climatic damage
The role of good governance to tackle gender inequalities within natural hazard-related disasters
Measuring the Statistical Capacity of Nations
Catastrophes, Confrontations, and Constraints
Regional clusters of vulnerability show the need for transboundary cooperation
Identifying Potential Clusters of Future Migration Associated With Water Stress in Africa
Identifying disaster risk factors and hotspots in Africa from spatiotemporal decadal analyses using Inform data for risk reduction and sustainable development
Assessing future vulnerability and risk of humanitarian crises using climate change and population projections within the Inform framework
Projecting long-term armed conflict risk
Exploring dependencies among global environmental, socioeconomic, and technological risks
A future for the world's children? A WHO–Unicef–Lancet Commission
Assessing the impact of planned social change
Measuring the Statistical Capacity of Nations
Uncertainty and Sensitivity Analysis Techniques as Tools for the Quality Assessment of Composite Indicators
Greed and grievance in civil war
Forecasting civil conflict along the shared socioeconomic pathways
Climate, conflict and forced migration
Normalizing economic loss from natural disasters
Governing the world at a distance
Poor numbers. How we are misled by African development statistics and what to do about it
Introduction
A Global Model for Forecasting Political Instability
Politics by Number
Test-retest
ViEWS
| Unique citing works | 14 |
|---|---|
| Citations per year | 2 |
| Citation span | 2019 - 2026 (8) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 14 |