Roger Pielke Jr: Why You Can’t Trust The Insurance Industry’s Secret Science On Climate Catastrophes

  • Date: 27/01/20
  • Roger Pielke Jr., Forbes

Whatever is going on here, it is clear that the Aon catastrophe dataset is not suitable for analysis of trends in losses, much less for academic research. 

The last decade was the most expensive for natural disasters,” proclaimed Fox News, “over $1 trillion increase.” The Weather Channel announced, “Planet Had 40 Billion-Dollar Weather Disasters in 2019, the Fourth-Most on Record.” However, a close look at the insurance industry report that produced these incredible claims reveals some serious data issues and offers some lessons for when academics and the media should rely on insurance industry data on catastrophes.

I have studied the economic costs of disasters and their relationship to changes in climate for more than 25 years. One dataset that I have relied on in my research in recent years has been released by the insurance broker Aon in the form of an annual global catastrophe report, the most recent which was released last week. (Disclosure: I have in the past given several talks at Aon conferences and I co-authored a paper with one of the main authors of the new Aon report).

The new Aon catastrophe report immediately raised concerns for me because it reported about $3 trillion in catastrophe losses for the most recent decade, 2010 to 2019, whereas Munich Re, which has been tracking global catastrophe losses for many decades, reports only about $2 trillion in total catastrophe losses over the same period.

Catastrophe loss estimation involves many uncertainties, and Aon explains in their report that they engage in a process of “reanalysis” to confirm past estimates. That makes good sense, but a $1 trillion difference between the estimates of Aon and Munich Re cannot be the result of small differences in loss estimation or a historical reanalysis.Today In: Business

Curious, I explored further, and what I found is extremely troubling, and offers some lessons for all of us who rely on industry data in our work.

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