The University of Notre Dame maintains a “Global Adaptation Index”, a quantitative measure of how exposed different countries are to the predicted ravages of climate change. The index runs from 0 to 100, with zero representing maximum exposure and 100 representing no exposure (i.e. it’s backwards, but we’ll live with that). The methodology it uses to generate the numbers is described in this recent article and summarized thus:
The Notre Dame-Global Adaptation Index (ND-GAIN) is a free open-source index that shows which countries are most exposed to climate change impacts and their current vulnerability to the disruptions that will follow, such as floods, droughts, heat waves, cyclones, security risks and so forth, as well as their readiness to leverage private and public sector investment for adaptation actions. ND-GAIN brings together 45 indicators to measure the 178 UN countries from 1995 to the present.
The Huffington Post recently plotted the Notre Dame numbers on a map of the world and published it in an article entitled The Countries That’ll Survive Global Warming. Here’s the map:
Figure 1: Notre Dame “Global Adaptation Index” by country
I looked at it and thought; that distribution looks familiar …..
And indeed it was. Change a few greens to blues and pinks to browns and it’s very much like Wikipedia’s world map of per-capita GDP:
Figure 2: Nominal per-capita GDP by country, $US 2013
Which is interesting because Notre Dame didn’t use GDP in calculating the index values, meaning that we are looking at two independent variables:
Two kinds of indicators are explicitly not included in ND-GAIN. The first is GDP per capita or any of its closely related measures.
The possibility of a link between exposure to climate change and GDP seemed worth pursuing, so I downloaded the Notre Dame numbers and compared them with the UN’s nominal per-capita GDP data. My idea was to see whether I could obtain a numerical estimate of how much of a country’s exposure to climate change is governed by its per-capita GDP and how much by actual climatic changes.
I began by constructing an XY plot of exposure (I’m calling it that because “ND-GAIN” is ugly and uninformative) against per-capita GDP for all of the 174 countries for which I had data:
Figure 3: Exposure to climate change (Notre Dame ND-GAIN Index) versus per capita GDP, all data
There’s a clear relationship, although it isn’t linear and the plot gets a little ragged along the bottom edge. The raggedness, however, is caused dominantly by countries where per-capita GDP is swollen by oil and/or gas revenues that do not trickle down to the capitas in the street (the three worst offenders are labeled). Accordingly I discarded these countries as non-representative. I also discarded Luxembourg, which owes its high per-capita GDP partly to its banking industry and partly to the fact that many of the people who work there don’t live there (they commute in from France, Belgium and Germany). These discards cut the number of data points from 174 to 156 but cleaned up the plot considerably:
Figure 4: Exposure to climate change (Notre Dame ND-GAIN Index) versus per capita GDP, data for 18 countries discarded
Next I fitted the points using the equation exposure = 9 * log(GDP) – 21. The fit gives an R squared value of 0.89:
Figure 5: Figure 4 data with logarithmic fit
And an R squared value of 0.89 means in broad terms that about 90% of a country’s exposure to climate change is governed by its per-capita GDP and only 10% by actual changes in climate.
Which in its turn means that we are going about climate change mitigation entirely the wrong way. Instead of spending trillions of dollars building wind farms and rooftop solar installations in futile attempts to cut CO2 emissions we should be sending money to the poor countries to beef up their per-capita GDP. A mere $200 billion a year would double the per-capita GDPs of the fourteen most threatened countries and leave the 240 million people living there far better prepared to face the ravages of climate change, when and if they ever hit.