We are interested in your feedback about the index. We are also interested in your feedback about the website and/or any issue you have found navigating it:Contact us
About the Index
A technical document provides further information.
Vulnerability measures a country's exposure, sensitivity and capacity to adapt to the negative effects of climate change. ND-GAIN measures overall vulnerability by considering six life-supporting sectors: food, water, health, ecosystem service, human habitat, and infrastructure.
Readiness measures a country's ability to leverage investments and convert them to adaptation actions. ND-GAIN measures overall readiness by considering three components: economic readiness, governance readiness and social readiness.
A country's ND-GAIN score is composed of a vulnerability score and a readiness score. Vulnerability and readiness are based on compiled indicators. 36 indicators contribute to the measure of vulnerability. 9 indicators contribute to the measure of readiness. Each indicator comes from a public data source. Below is the overview of the methodology used to calculate the index score. Detailed explanation of the indicators with references to their sources also can be found below. A technical document provides further information.
The degree to which a system is exposed to significant climate change from a biophysical perspective. It is a component of vulnerability independent of socio economic context. Exposure indicators are projected impacts for the coming decades and are therefore invariant overtime in ND-GAIN.
The extent to which a country is dependent upon a sector negatively affected by climate hazard, or the proportion of the population particularly susceptible to a climate change hazard. A country's sensitivity can vary over time.
The availability of social resources for sector-specific adaptation. In some cases, these capacities reflect sustainable adaptation solutions. In other cases, they reflect capacities to put newer, more sustainable adaptations into place. Adaptive capacity also varies over time.
Economic readiness captures the ability of a country's business environment to accept investment that could be applied to adaptation that reduces vulnerability (reduces sensitivity and improves adaptive capacity).
Governance readiness captures the institutional factors that enhance application of investment for adaptation.
Social readiness captures the factors such as social inequality, ICT infrastructure, education and innovation, that enhance the mobility of investment and promote adaptation actions.
Selecting Indicators in ND-GAIN
To select indicators to measure climate vulnerability and adaptation readiness, ND-GAIN surveys literature and consults scholars, adaptation practitioners, and global development experts. The following criteria are used when making the indicator selection.
- Actionable through adaptation (except exposure indicators)
- Consistent with current knowledge and best practice
- Potentially down-scalable from national to regional or urban
- Directly representative of the vulnerability or readiness sector
- Not including broad socio-economic measures such as GDP/capita or Human Development Index
The indicators also need to be quantified at the country-level with data that are/have:
- Global coverage
- Transparent and conceptually clear
- Freely accessible
- Quality checked
Measuring Vulnerability in ND-GAIN
The vulnerability score measures the exposure, sensitivity and adaptive capacity in six life-supporting sectors: food, water, health, ecosystem services, human habitat, and infrastructure. For details (such as data sources and references) please refer to our technical document.
The vulnerability score takes the simple mean of the sector scores, which are the average scores of component indicators.
Indicators and data sources for vulnerability measures
Agricultural capacity ( Fertilizer, Irrigation, Pesticide, Tractor Use)
Medical Staff (physicians,nurses and midwives)
Projected change in deaths from climate change induced diseases
All measures are weighted equally.
Measuring Readiness in ND-GAIN
The readiness score measures economic, governance and social readiness by taking the average of the scores within and across three components. For details (data sources and references please request here
Indicators and data sources of readiness measures
Doing Business (one indicator considering the following 10 sub-indicators)
Starting a Business
Dealing with Construction Permits
Trading Across Borders
Scaling Indicators in ND-GAIN
Indicators can be compared and aggregated only when the raw data are scaled to unit-free scores. To scale indicators, ND-GAIN follows a “proximity-to-goalpost” approach with the score values from 0 to 1. For each indicator that measures vulnerability, the indicator score shows a country’s distance from a target of zero (the lowest possible score). Similarly, for each indicator that measures readiness, the indicator score shows how far a country is from a target readiness of one (the highest possible score)
The value of the goalposts and reference points and detailed information about how the goalposts are set for each indicator can be found in the technical document.
Adjustments for GDP
In exploring the relationship between ND-GAIN with other global indicators, a linear correlation is found between ND-GAIN scores and GDP per capita for all years. This allows the index to create a linear model that represents the time-wise relationship between the two variables. In this context, the "GDP adjusted ND-GAIN score" is defined as the distance of a country's measured ND-GAIN score to the expected value for its GDP per capita, as represented by the regression line. Positive values reflect better resilience than expected.
The strong correlation with GDP per capita is also present for the vulnerability and readiness scores. Thus, for a given year, the "GDP adjusted Vulnerability" is the distance of a country's measured readiness to the expected value for its GDP per capita (the same for readiness). Positive values reflect lower vulnerability (higher readiness) than expected, given certain level of GDP per capita. The year-by-year calculation of the regression and distance from the expected value allows the index to determine that changes in the relationship over time.