2.2.2.4. Integrated Analysis of Vulnerability to Climate Risks: an example using ND-GAIN

2.2.2.4. Integrated Analysis of Vulnerability to Climate Risks: an example using ND-GAIN

The Notre Dame Global Adaptation Initiative’s (ND-GAIN) Country Index is a free, open-source index that shows a country’s current vulnerability to climate disruptions. It also assesses a country’s readiness to leverage private and public sector investment for adaptive actions. The ND-GAIN Country Index brings together more than 40 core indicators to measure vulnerability and readiness of 182 UN countries from 1995 to the present (University of Notre Dame, 2024).
ND-GAIN offers an interesting example of how an integrated multidisciplinary analysis of climatic vulnerability can be undertaken. The following text is entirely based on the Technical Report.

The ND-GAIN framework adheres to the IPCC framework measuring vulnerability through its components ‘exposure’, ‘sensitivity’ and ‘adaptive capacity’, and adds a measure of ‘readiness’ by means of economic, governance and social components.

Vulnerability
ND-GAIN assesses the vulnerability of a country by considering six life-supporting sectors: food, water, health, ecosystem services, human habitat and infrastructure. Each sector is in turn represented by six indicators for each of the three cross-cutting components, exposure, sensitivity, and adaptive capacity.

Readiness
ND-GAIN understands by ‘readiness’ the ability to make effective use of investments for adaptation actions thanks to a safe and efficient business environment. ND-GAIN measures overall readiness by considering three components: economic readiness, governance readiness and social readiness.
• Economic Readiness: The investment climate that facilitates mobilizing capitals from private sector.
• Governance Readiness: The stability of the society and institutional arrangements that contribute to the investment risks. A stable country with high governance capacity reassures investors that the invested capitals could grow under the help of responsive public services and without significant interruption.
• Social readiness: Social conditions that help society to make efficient and equitable use of investment and yield more benefit from the investment.

Figure 13. summarizes the framework of ND-GAIN indicators.

Figure 13. Summary of ND-GAIN Vulnerability and Readiness Indicators

Vulnerability is composed of 36 indicators. Each component has 12 indicators, crossed with 6 sectors. Readiness is composed of 9 indicators. The 45 ND-GAIN indicators come from 74 data sources that provide 74 underlying data. 20 of the 45 indicators come directly from the sources; the rest 25 are computed by compiling underlying data.
The procedure for data conversion to index is summarized in Figure 14.  and described in the Technical Report pp 9-11.

Figure 14. Steps to Creating ND-GAIN

ND-GAIN can be represented as a scatter plot of readiness against vulnerability, that is, the ND-GAIN Matrix (Fig. 15). The Matrix provides a visual tool for quickly comparing countries and tracking their progress through time. The plot is divided into four quadrants, delineated by the median score of vulnerability across all the countries and overall years, and the median score of readiness calculated the same way.

Figure 15. ND-GAIN matrix

The 45 indicators used in the ND-GAIN index are listed in Tables 20 and 21. The Technical Report provides for each one a description, rationale, calculation, data source, and time series. In addition, it explains the scaling mechanism (the ‘proximity-to-reference point’ approach) used for scoring the level of vulnerability and readiness for each indicator.

SectorExposure componentSensitivity componentAdaptive capacity component
FoodProjected change of cereal yieldsFood import dependency  Agriculture capacity (fertilizer, irrigation, pesticide, tractor use
Projected population changeRural populationChild malnutrition
WaterProjected change of annual runoffFresh water withdrawal rate  Access to reliable drinking water
Projected change of annual groundwater rechargeWater dependency ratio  Dam capacity
HealthProjected change of deaths from climate change induced diseasesSlum populationMedical staffs (physicians, nurses, and midwives)
Projected Change in Vector Borne DiseaseDependency on external resource for health servicesAccess to improved sanitation facilities
Ecosystem servicesProjected change of biome distributionDependency on natural capital  Protected biomes
Projected change of marine biodiversityEcological footprint  Engagement in international environment conventions
Human habitatProjected change of warm periodUrban concentrationQuality of trade and transport-related infrastructure
Projected change of flood hazardAge dependency ratioPaved roads
InfrastructureProjected change of hydropower generation capacityDependency on imported energy  Electricity access
Projected change of sea level rise impactsPopulation living under 5m above sea levelDisaster preparedness
Table 20. ND-GAIN Vulnerability Indicators
ComponentIndicators
Economic ReadinessDoing business
Governance ReadinessPolitical stability and non-violence  Control of corruption   Rule of law Regulatory quality
Social ReadinessSocial inequality ICT infrastructure Education Innovation
Table 21. ND-GAIN Readiness Indicators

The ND-GAIN Index is a valuable tool for understanding global variations in climate vulnerability and readiness. By integrating these two dimensions, it provides a comprehensive view of climate risks and enables meaningful comparisons between countries. Its sector-specific insights, combined with a timeline feature, allow users to track progress in climate action over time.
However, like all indices that rely on country-level statistics, the ND-GAIN Index faces challenges related to data gaps, particularly in poorer countries with high vulnerability and low readiness. The selection of indicators prioritizes cross-country comparability, which may limit their relevance for specific national contexts.

For assessments within individual countries, the ND-GAIN concepts and methodology remain highly useful. However, careful attention must be given to selecting the most relevant indicators, ensuring they are based on reliable data sources that support consistent comparisons across different geographical entities.