This tells us that understanding regression to the tail and how to mitigate its specific manifestations for climate will be key to mitigating the climate crisis.
Here it is worth remembering that regression to the tail for the climate crisis will be just as indifferent to human ignorance and folly as regression to the tail is for covid-19.
We either cut the tail and practice the precautionary principle for the climate crisis, or we die in large numbers and destroy our economic and social fabric, like we are presently doing for covid-19, just worse
We are unlikely to return to the same state of the world as when we entered the #pandemic, nor is this desirable. #Remoteworking has the potential to become more prevalent after the crisis.
2/4 Investments might shift from face-to-face working (and lower the cost of #officerealestate and time for #commuting).
3/4 #Stimulus spending should focus investments on the enablers of this new approach to work, e.g., #5G rollouts, #broadband infrastructure, #satellites for global reach, server farms for adequate capacity, etc, all with effective #privacy protection.
1/4 #Nuclear power plants are bespoke, slow to build, and fat tailed for financial and safety risks. #Wind farms and #energystorage are modular, fast, and thin tailed.
2/4 By choosing wind over nuclear, the risk of regression to the tail will be significantly reduced, and #climate goals will be achieved sooner. This is just one example. Many others exist.
3/4 Every investment alternative must be assessed in this manner to ensure that #stimulus spending becomes a boost instead of a drag on the economy. The latter is happening more often than we like to think.
1/4 Rebuilding the economy after covid-19 will be subject to the law of regression to the tail, if less dramatically than the pandemic. Loss of life will hopefully fade as a main risk. But financial fragility, wealth destruction, and debt will continue to be key risks for a while
2/4 The massive stimulus spending governments use to restart economies in recession comprise giant construction and investment projects with fat-tailed financial risks, like multi-billion-dollar megaprojects in IT, transport, energy, water, education, housing, health, and defense
3/4 Some projects are more fat-tailed than others, i.e., they are more susceptible to the law of regression to the tail. Data analytics should be used to separate fat-tailed projects from thin-tailed ones, and stick with the latter whenever possible. We know how to do this.