In the climate crisis, vulnerable countries bear the least responsibility

David Wendler

Hey, I’m David Wendler, working on design at Datawrapper. This week’s chart is a scatterplot. I’m especially interested in the multidimensional realm that scatterplots have to offer, and wanted to create one that explored their visual richness.

Climate change is happening and affects all of us. It is really scary and we’re already starting to see its consequences everywhere. But not every country is truly facing an equal risk from climate change. Not every region will see the same changes in local weather, or be affected by rising sea levels to the same degree. And not every country has the same capability to prepare and protect itself from the dangers it will face.

A third very important difference to consider is climate responsibility. Not every country contributes the same amount to climate change: Sudan, for example, only emits 0.48 tons of CO2 per capita, while Germany emits 7.91.

This data is taken from the ND GAIN Country Index, developed at the University of Notre Dame. The chart here is mostly a copy of their ND-GAIN Matrix, which sorts countries into four quadrants:

• Red ⬤ for countries that have a higher-than-average vulnerability and a lower-than-average readiness. These countries face the worst risk from climate change.
• Blue ⬤ for high vulnerability and high readiness.
• Orange ⬤ for low vulnerability and low readiness.
• Green ⬤ for low vulnerability and high readiness. These countries face the least risk from climate change.

It’s important to notice that “readiness” here means “readiness to make effective use of investments for adaptation actions thanks to a safe and efficient business environment.” This score does not really reflect how well a country is prepared to fight the consequences of climate change. Instead it says how well a country can make use of investments because of its economic situation, social conditions, and stability of governance.

While this helps to direct effective investments for climate adaptation, it also seems to add to the unfairness of this situation. Many countries with high vulnerability are also less economically and politically stable, which leads them to be rated lower for investment.

CO2 emissions

Besides the two measures from the index, I also added another dimension of data: CO2 emissions per capita. The size of each country’s square is defined by the average amount of CO2 emitted by each person who lives there. You can see that the smallest squares on the chart are in the red quadrant, mostly countries in Africa that face extreme risks from heat, drought, and fire. That means that the people in these parts of the world, who are most vulnerable to the consequences of climate change and least prepared to cope with them, are also the least responsible for causing the crisis.

Scatterplots are a powerful tool

Scatterplots are very good at showing the relationships between multiple variables. Besides the two dimensions of the main axes, you can encode even more information using the size, color, and shape of the symbols. With symbol size as a variable, I was able to visualize climate vulnerability, readiness, and responsibility in one chart.

That’s it for this week. If you have any thoughts on this visualization, or on the multidimensional realm of scatterplots in general, please leave a comment!

References

• ND-GAIN Index: Read more about the index that was used here
• Wordbank: Source on CO2 emissions (in metric tons per capita)
• Customize Tooltips: Read more about how to create custom tooltips in Datawrapper. You can do crazy stuff!

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David Wendler

(he/him, @newreld) is Datawrapper’s head of design. He’s responsible for all things design at Datawrapper, especially the interface and user experience. David has worked as a designer in several other settings, most recently in a newsroom visualizing data with journalists. He lives in Berlin.

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