The ND-GAIN Index is a metric developed by the Notre Dame Global Adaptability Initiative. It illustrates a country’s ability to adapt to global climate change by considering their vulnerabilities to climate disruptions and their readiness to leverage public and private investments for adaptation actions.

The ND-GAIN Index defines vulnerability as “the propensity or predisposition of human societies to be negatively impacted by climate hazards.” The index look at six sectors (food, water, health, ecosystem services, human habitat and infrastructure) and assesses each sector’s exposure to climate related hazards, sensitivity to the impacts of those hazards, and adaptive capacity to respond to those impacts.

Readiness is defined as a country’s capacity “to make effective use of investments for adaptation actions thanks to a safe and efficient business environment.” ND-GAIN measures a country’s ability to leverage public and private investments towards adaptation efforts by analyzing three components: economic readiness, governance readiness, and social readiness. Economic readiness measures a country’s investment climate and ability to mobilize capital from the private sector, governance readiness assesses the stability of a country’s social and political institutions and their impacts on investment risks, and social readiness looks at the social conditions that help society to make efficient and equitable use of investment and maximize the social benefits of those investments.

All in all, the ND-GAIN index brings together 74 variables to form 45 indicators which are utilized to compute readiness and vulnerability scores for 192 UN countries. These raw scores are then leveraged to create a final ND-GAIN index score. This score is scaled from 0 to 100 with a larger score indicating a country has a higher capacity to adapt to the impacts of climate change.

Below, the ND-GAIN Index of each country has been visualized utilizing open-source data from the ND-GAIN database along with QGIS software. This project was accomplished by spatially joining the tabular ND-GAIN data to a geospatial vector, classifying the ranges of the index, and visualizing those classes using graduated colors.