Abstract
Although it is well known that climate change will alter future tropical cyclone characteristics, there have been relatively few studies that have measured global impacts. This paper utilizes new insights about the damage caused by tropical cyclones from Bakkensen and Mendelsohn (J Assoc Env Res Econ 3:555–587, 2016) to update the original methodology of Mendelsohn et al. (Nat Clim Change 2:205–209, 2012). We find that future cyclone losses are very sensitive to both adaptation and development. Future development (higher income) is predicted to sharply reduce future fatalities. However, damage may take two distinct paths. If countries follow the United States and adapt very little, damage is predicted to increase proportionally with income, rising 400% over the century. However, if development follows the remaining OECD countries, which have done a lot of adaptation, future cyclone damage will only increase slightly.
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Notes
- 1.
Hallegatte (2007) also utilizes simulation data to estimate future cyclone damages, thereby capturing sophisticated underlying distribution dynamics.
- 2.
In our dataset, the following is the fraction of OECD country landfalls by member state: Australia (11.7%), Canada (2.26%), Japan (26.8%), South Korea (10.6%), New Zealand (1.13%) and the United States (45.7%). France, Germany, Ireland, and the United Kingdom together receive 1.9% of OECD cyclone landfalls.
- 3.
If a tropical cyclone does not make landfall in a country and damages were observed in the historical evidence, characteristics were used from the storm when it was at its closest point to the given country.
- 4.
This emission scenario is similar to RCP 6.0 in IPCC (2013).
- 5.
The baseline simulated tracks reflect climate from 1980 to 2000. Using a more severe climate change assumption (IPCC AR5 RCP8.5), Emanuel (2013) finds only minor increases in cyclone power from 1995 to 2015, thus these tracks are still arguably a relevant baseline for the climate from 2000 to 2020. Climate signals can take up to a few decades to impact cyclones given the complex responses across ocean and atmosphere dynamics. In addition, by employing these simulation tracks, we can directly compare across the Mendelsohn et al. (2012) earlier results and the present results. All differences between these two papers are driven by assumptions of the damage caused by each simulation track since the tracks have not changed.
- 6.
This finding is empirically tested and discussed in Bakkensen and Mendelsohn (2016). In their analysis, they estimate damage and fatality functions using country-level data and, in a separate regression, county-level data for six large countries (plus Mexico at the state level). The results are qualitatively similar across the two geographic scales yet have important nuance to the interpretation. The country-level analysis examines the differences driven by more densely populated versus less densely populated countries. The county-level analysis explores the differences between urban and rural locations hit by storms.
- 7.
We leave empirical exploration of the efficiency versus demand hypothesis for future work.
- 8.
Bakkensen and Mendelsohn (2016) find evidence in heterogeneity of damages across urban versus rural locations. We leave exploration of the specific relationship for future work.
- 9.
The truncation assumption is an estimate of the maximum damage a single future cyclone could destroy. We calculate it as six time the losses of the most damaging cyclone to date (Hurricane Katrina at approximately $165 billion in losses, NCEI 2018).
- 10.
Note that we include Bangladesh and Myanmar, two high fatality outlier countries, in the current annual fatality statistic.
- 11.
We note that we use the same underlying income and population projections across both the damage and fatality estimates. Thus, the difference is driven by the estimated income and population density elasticities across the damage versus fatality functions across the outcomes. For the United States, damages increase sharply because they scale proportionately with GDP growth whereas fatalities decrease with development.
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Appendix
Appendix
9.1.1 Additional Historical Damage and Fatality Functions
In this section, we present additional historical damage and fatality functions. Namely, in Table 9.7, we present the all countries functions as well as damage functions partitioned based on OECD (including the United States) versus Non-OECD countries. While the estimated coefficients are qualitatively similar (in terms of coefficient sign) across the regressions, important differences are present, especially with respect to the magnitude of the cyclone elasticity coefficients. However, given Table 9.1, combining US and Non-US OECD countries hides the underlying heterogeneity in coefficient magnitudes. Thus, Table 9.1 is a preferred partitioning. Also see Bakkensen and Mendelsohn (2016) for a much more detailed analysis of damage and fatality functions.
Lastly, Table 9.8 presents additional historical fatality functions, namely the all countries model and partitioning between US, Non-US OECD, and Non-OECD countries. We find the estimated coefficient on US income and pressure to be imprecisely estimated, perhaps partly due to the small sample size. Thus, we prefer the partitioning in Table 9.2 in the main paper.
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Bakkensen, L.A., Mendelsohn, R.O. (2019). Global Tropical Cyclone Damages and Fatalities Under Climate Change: An Updated Assessment. In: Collins, J., Walsh, K. (eds) Hurricane Risk. Hurricane Risk, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-030-02402-4_9
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