An algorithm-based calculation of the economic damage from climate change compares well with a study led by Prof. Solomon Hsiang of the University of California at Berkeley.
As shown in the figure, the Predict Ability Ltd (PAL) prediction (red line) is a close fit to the Hsiang et al. data correlating aggregate climate change damage against temperature anomaly for the US.
Behind our carbon pricing methodology are several models of loss and damage. These are described in the book Predicting the Price of Carbon. For example, there is the simple economics-based model that in turn leads to the PAL 1year (PAL-1Y) carbon price used in our enhanced carbon auditing. It uses a lightning claims algorithm as a proxy for event attribution. On a more granular level, there is the big-data analysis engine, PALgamma, that calculates the REACT (Reinsurance Event-Attributed Carbon Tax) loss and damage-based carbon price. Look out for our REACT global carbon price index which will be featured in a forthcoming blog.
These methods have in common a basic equation that shows that a realistic and fair carbon price can be determined from the cumulative total of (climate-change-attributable) global loss and damage and the cumulative total of CO2 emissions. In the equation, the cumulative carbon price is given by y = ∫(L.x) / ∫C where L = weather-related losses (US dollars), x = extent to which losses can be probabilistically attributed to climate change, and C = CO2 emissions (tonnes).
In their major study reported in Science magazine on 30 June 2017, Estimating economic damage from climate change in the United States , Hsiang et al. propose a detailed approach that includes the effects of climate change on agriculture, crime, coastal storms, energy, human mortality and labour. Their methodology yields spatially explicit and detailed maps of projected damages across the contiguous United States. At state and county level, there is a wide variation in absolute and relative economic impacts.
Here, the figure shows the aggregate losses that Hsiang et al. expect will accrue as the global temperature anomaly increases. Also shown in the figure, is a dotted line estimate that for each 1°C rise there will be a one percent impact on GDP. The 1%/°C approximation was published in Nature magazine on 13 September 2017 by my colleague, Richard H. Clarke, co-authored with Anthony J. Webster of Oxford University. The Nature Comment piece proposes that insurance companies should collect a carbon levy to help fund adaptation and mitigation efforts that are increasingly needed to counteract the damage caused by historical and on-going CO2 emissions. By ‘historical emissions’, Webster and Clarke mean the emissions that have accrued since 1992, the year the world accepted the underlying principles of climate science in the form of the United Nations Framework Convention on Climate Change. Tellingly, the 1%/°C rule-of-thumb indicates that were GDP to have been growing at 3 percent, say, that would, in reality, have dropped to 2 percent because of global warming. If a similar picture holds worldwide in 2016 there would have been about $1 trillion of damage to the global economy.
The figure shows PAL’s red line lying just above Hsiang et al.’s best fit green line. It is a validation of our fundamental approach that our algorithm-based predictions agree closely with those of a bottom-up, major study of economic loss caused by climate change in the United States.
Bruce Menzies, Chairman, Predict Ability Ltd (PAL)
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Author: Bruce Menzies
Bruce Menzies is Chairman and co-founder of PAL. He founded Global Digital Systems Ltd that won the Queen’s Award For Enterprise 2011. Bruce is co-author of six books on geotechnics and geology, one of which won the British Geotechnical Association Prize 2002. He holds doctorates from the Universities of London and Auckland, and is a Fellow of the Institution of Civil Engineers.