The estimated economic damage costs that we use in our calculations originate from several sources. The National Oceanic and Atmospheric Administration (NOAA) produces estimates of the costs associated with US weather events. For Hurricane Harvey, their current estimate of US$125 billion is from an updated list of hurricane costs in 2017 (Information, 2018). Generally, this should be construed as a preliminary estimate and is conservative as the methodology used typically underestimates losses by 10–15% (Smith and Katz, 2013).
The world’s two largest reinsurance companies globally, Munich Re and Swiss Re, produce their own estimates of the total value of damages associated with natural disasters. These databases–NatCat and Sigma, respectively–are then used internally for their business. They also publish estimates for the most extreme events each year, always in January of the following year. For Hurricane Harvey, both sources cite a figure of US$85 billion, out of which they estimate that US$30 billion was insured. While these should be independent estimates, they do not appear to be, so we treat them as a single estimate.
Last, an international NGO, the Centre for Research on the Epidemiology of Disasters (CRED), produces its own publicly available database of disaster damages: EMDAT (www.emdat.be). It collects data from “various sources, including UN agencies, non-governmental organisations, insurance companies, research institutes and press agencies.” EMDAT includes a somewhat higher figure for the damages from Harvey of US$95 billion. The most authoritative source for disaster data might be the UN-supported DesInventar (www.desinventar.net) that is now the official data collection mechanism to support the Sendai Framework for Disaster Risk Reduction (an international agreement signed by almost all countries and endorsed by the United Nations’ General Assembly in 2015). Unfortunately for our purposes, the USA has not yet begun to report its data to DesInventar.
Given the discussion of the available data above, the average estimate for the direct economic cost associated with damages to physical assets from Hurricane Harvey is US$90 billion (average of EMDAT and the Munich Re/Swiss Re estimates).
In principle, the analysis tying FARs to damages should relate the amount of damage incurred in each asset class (horizontal infrastructure by type, commercial and industrial properties, each type of residential buildings, etc.) to its damage function, and by using the two points in the damage function (with and without anthropogenic climate change), calculate how much of the measured damages, for each asset class, is attributable to anthropogenic influence. Unfortunately, we do not have this information separated by asset class. Fortunately, however, the damage function for the most asset classes (especially the most significant ones) are about linear in flood depths of up to 2–3 m, and flood depths in Houston were generally significantly lower than that (Huizinga et al., 2017). Nordhaus (Nordhaus, 2010), using a more aggregated data, describes a very convex non-linear damage function in his investigation of hurricane damage costs and wind speed; however, the asset damage for Hurricane Harvey, and for many other tropical cyclones, is mostly associated with water, and not wind (Yonson et al., 2018), justifying the use of a linear damage function. Based on the methods described above, we assess the direct economic costs of Hurricane Harvey that are attributable to anthropogenic influences on the climate to likely be in the range of US$30bn to US$72bn, with a best estimate of US$67bn.
This estimate quantifies only the direct damage that can be easily monetized. It does not include mortality, morbidity, and temporary and permanent dislocations that are typically associated with hurricanes such as Harvey. In order to include these in the estimates, one could potentially calculate the attributable mortality using a similar method and measure it with the monetary value of life (VSL, the “value of a statistical life” (Viscusi and Aldy, 2003)). VSL measures, however, are difficult for any cross-country comparisons, and UNISDR (2015) (UNISDR, 2015) and Noy(Noy, 2016) suggest a different aggregate measure, of life-years lost, that overcomes some of these difficulties. It aims to account not only for the total direct damage estimates described in Table 2 but also for mortality and other affected population impacts. All are aggregated into a single measure, but without using VSL.
Table 2 Estimates of the direct and insured damages, number of people affected by and number of deaths arising from Hurricane Harvey This life-years index consists of the following: Lifeyearsi = Li(M, Adeath, Aexp) + Ii(N) + DAMi(Y, INC). L(∙) is the number of life-years lost due to mortality, calculated as the difference between the age at death (Adeath) and life expectancy (Aexp).Footnote 1 Using the information about the deaths from Harvey, we use an average age at death of 49 and life expectancy of 92 (following the WHO conventional practice when calculating disability-adjusted life years). I(N) is the cost function associated with the people who were injured or otherwise affected by the disaster (this information is only available from EMDAT; see Table 2). Since we do not have information about how each individual was affected, we assume that affected people were experiencing what the World Health Organization calls “generic uncomplicated disease: anxiety about diagnosis.” This generic diagnosis implies that the coefficient used to convert the number of people affected (N) to life-years lost is 0.054 (i.e., I(N) = 0.054 N).
The last component of the life-years index, DAM(Y, INC), attempts to account for the number of human life-years lost as a result of the damage to capital assets and infrastructure—including residential and commercial buildings, public buildings, and other types of infrastructure such as roads, water, sewage, electricity, and communication systems, all measured in monetary units (Y). This measure (DAM) aims to measure the opportunity cost of spending resources (especially human effort) on the reconstruction of these destroyed assets. We use income per capita (INC) as an indicator of the cost of human effort in calculating this loss and assume that a quarter of a life-year is spent on generating income. This implies that \( DAM\left(Y, INC\right)=0.25\left(\frac{Y}{INC}\right) \). Overall, we find that about 476,000 life-years were lost as a direct damage of Hurricane Harvey with almost 80% of the loss associated with the monetized damages to physical assets. For life-years, we therefore estimate that it is likely that at least 148,000 life-years, with a best estimate of 357,000 life-years, lost were directly attributable to anthropogenic climate change.This figure incorporates impacts (mortality and disruption to life) that are not accounted for by the direct damage figures (the US$90 billion that was previously discussed). As such, it shows that, at least by this metric, the $90 billion dollar is underestimating the impact of Harvey’s rainfall. A fuller accounting of direct damages that includes also mortality and morbidity will increase this figure by 25% due to the mortality and morbidity associated with the event and potentially more because of damage to non-monetized assets (such as environment amenities).