Some flaw in the over-eager Sb security, or more likely an intermediate layer, locked me out over the weekend, so my apologies for any delayed approvals and so on. One of which was to a reference to Estimating economic damage from climate change in the United States by Solomon Hsiang et al., Science 30 Jun 2017, Vol. 356, Issue 6345, pp. 1362-1369, DOI: 10.1126/science.aal4369. Which says:
Estimates of climate change damage are central to the design of climate policies. Here, we develop a flexible architecture for computing damages that integrates climate science, econometric analyses, and process models. We use this approach to construct spatially explicit, probabilistic, and empirically derived estimates of economic damage in the United States from climate change. The combined value of market and nonmarket damage across analyzed sectors—agriculture, crime, coastal storms, energy, human mortality, and labor—increases quadratically in global mean temperature, costing roughly 1.2% of gross domestic product per +1°C on average. Importantly, risk is distributed unequally across locations, generating a large transfer of value northward and westward that increases economic inequality. By the late 21st century, the poorest third of counties are projected to experience damages between 2 and 20% of county income (90% chance) under business-as-usual emissions (Representative Concentration Pathway 8.5).
I haven’t read it yet, having just got into work… ahem. Anyway, you’ll notice that the damages are broken down by area and (surprisingly, to me) a large portion is due to “mortality”. The associated discussion just says These results are not without caveats. Hsiang et al. appropriately focus much attention on quantifying uncertainty in the estimates. Yet key parameters are fixed, including the value associated with mortality consequences (which drives one-half to two-thirds of the estimated damages) (my bold). The paper says Rising mortality in hot locations more than offsets reductions in cool regions, so annual national mortality rates rise ∼5.4 (±0.5) deaths per 100,000 per °C (Fig. 3C). For lower GMST changes, this is driven by mortality between ages 1 and 44 and by infant mortality and ages ≥45 for larger GMST increases (fig. S13 and table S12). And, yes, there is politics involved, just as there was when there was all that fuss over the IPCC valuing Brown Lives Less some while ago: It is possible to use alternative approaches to valuing mortality in which the loss of lives for older and/or low-income individuals are assigned lower value than those of younger and/or high-income individuals (44), an adjustment that would alter damages differently for different levels of warming based on the age and income profile of affected individuals (e.g., fig. S13). Here, we focus on the approach legally adopted by the U.S. government for environmental cost-benefit analysis, in which the lives of all individuals are valued equally.
Actually, thinking about this while making a coffee, just using “the approach legally adopted by the U.S. government” and not using “the best”, or at least trying alternatives, is weird.