Estimates of future damages from climate change in coastal areas are of growing interest for climate change research and policy-making. A newly-developed methodology is applied to estimate total insured coastal damages with climate change for the period 2025-2085 in New York State, USA. Total damages without adaptation include future insured damages from economic growth and additional damages from climate change due to increases in flood frequency, based on two sea level rise scenarios. Data and methodological issues are discussed, and a comparison with the results of a metro New York City flood damage study using different data and methods is presented.
Keywords: Climate change, Urban flood damage, Future damage scenarios, Coastal zones, Adaptation
JEL classification: O18, O21, O22, Q51, Q54
Suggested citation: Major, David C., Bader, Daniel, Leichenko, Robin, Johnson, Katie and Linkin, Megan, Projecting Future Insured Coastal Flooding Damages with Climate Change (April 11, 2014). Review of Environment, Energy and Economics (Re3), http://dx.doi.org/10.7711/feemre3.2014.04.001
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Estimates of future damages from climate change in coastal areas are a topic of growing interest within the climate change research and policy communities. In an earlier paper (Major et al., 2013), a scoping methodology was presented for projecting future insured coastal zone flood damage with climate change by linking existing damage data to sea level rise (SLR) scenarios for future benchmark years. In this paper, that methodology is applied and extended to produce estimates of total insured coastal damages with climate change for the period 2025-2085 in New York State (NYS) USA. Damages due to economic growth are projected, and estimated additional damages due to climate change are then added to yield total projected insured coastal damages without further adaptation measures. After presenting estimates of total insured damages for 2025-2085 under two sea level rise scenarios, data and methodological issues related to the damage projections are discussed and the results are compared to a recent case study of the New York City metropolitan area that uses different data and methods. Conclusions and perspectives on the work are presented.
2. Projection of insured and total damages for 2025-2085 under two sea level rise scenarios
2.1 Estimates for benchmark years. The inputs to the curve-fitting procedure used to generate estimates of damages from climate change for the period 2025-2085 are given in Table 1, which shows estimated future losses from insured coastal damages for 3 benchmark years and two sea level rise (SLR) scenarios. These estimates were generated using available historic insured loss data and climate scenarios for the New York coastal region, as well as assumptions as to the coverage of the damage data (Major et al., 2013). Estimates are also shown for damages with economic growth but without climate change, and for total costs with growth and climate change.
Table 1 - Estimated Future Insured Coastal Flood Losses in the Coastal Zone for Benchmark Years (Values in $US 2013)
Source: Major et al., 2013, columns 1-5; columns 6 and 7 added. Note: figures are rounded.
2.2 Curve-fitting procedure. Using the polyfit function in MATLAB version 2012b (http://www.mathworks.com), a 2nd degree polynomial equation is fitted to the data points for each of the two sea level rise scenarios given in columns 2 and 3 of Table 2. (Columns 4 and 5 of Table 1 are rounded from these values.).
Table 2 - Data points for the quadratic equations (data points in columns 2 and 3 in $US 2013 millions) Note: figures for curve-fitting are not rounded
The functions are fitted to the three points in the format (year, losses), producing the coefficients for the quadratic equations. With these equations, the values of the functions were calculated for each year from 2025 to 2085. These years were chosen to fit with the procedures used to develop the climate scenarios, which are centered on the middle of the relevant decades: the 2020s, 2050s, 2080s (Horton and Rosenzweig, 2010); the results will vary if additional (or different) data points are available. The two fitted equations are shown in graphical form in Figure 1; the equations from MATLAB are available from the corresponding author.
Figure 1 - Annual damages from SLR vs. time for two SLR scenarios
The estimated insured coastal damages due to climate change, summing the estimates for each year from 2025 to 2085 inclusive (a 61-year period), are given in Table 3: $10.8 B US$2013 for the low SLR scenario, and $16.8 B US$2013 for the high SLR scenario.
Table 3 - Estimated total insured coastal damages due to climate change, 2025-2085 (US$ 2013 B)
Note: Estimates are rounded
2.3. Projection of insured damages for 2025-2085 with economic growth but without SLR. The estimated projected insured coastal damages absent climate change but with economic growth are $22.8 B US$2013. This result is produced by summing over the 61-year period 2025-2085 using the method described in Major et al. (2013), in which damages are projected to grow at approximately the long-term rate of growth in US GDP, 2.4%.
Table 4 gives the overall results of the estimates: insured damages in the New York coastal zone 2025-2085 from economic growth; the additional damages from two scenarios of SLR; and estimated total damages without adaptation.
Table 4 - Insured damages in the New York coastal zone 2025-2085 from economic growth; additional damages from two SLR scenarios; and total damages without adaptation
Note: Values in $US 2013 billions (rounded)
It is of interest that the additional damages from SLR represent from 47% of damages attributable to economic growth, to 74% of damages attributable to economic growth; in other words, the contribution to total damages of SLR is very substantial. Average annual damages under both scenarios are more than ½ Billion US$/year. These estimated values can contribute to an adaptation planning process such as that of the New York City Panel on Climate Change (NPCC) (Major and O’Grady, 2010).
4. Perspectives on the method and comparison with a recent case study
4.1 Methods. The estimating method used here depends, like all forecasting methods, on data and assumptions. In particular, the method depends on the availability of historic insured loss data (Property Claims Services, n.d) and climate scenarios, as well as on assumptions as to economic growth and the coverage of the damage data. The historic insured loss data are of significant value in considering the costs of climate change; by contrast, little time series information exists for uninsured public infrastructure. The climate scenarios (Horton and Rosenzweig, 2010, p. 177; revised estimates for the 2020s and 2050s are in NPCC, 2013) themselves are based both on data and on modeling results. Other data and assumptions include those for the percentage of insured losses in NYS in coastal counties (2012 figures; Air Worldwide, 2013, pp. 4-5); historic and projected growth rates in US GDP (US BEA, 2013; projections by the authors); and the proportion of storm damages from flooding, as opposed to wind. In applied planning, it is useful to do sensitivity analysis on these parameters. And, given the size of estimated damages, it would be appropriate to consider design of key infrastructure in terms of extreme events.
Just as the results given here will be useful to policy-makers in NYS, the methodology will be useful to planners interested in applying it in more detail to particular case studies: the method can be tailored to particular regions and time periods. As one example, it may be that in some areas exposure to damages will be controlled by legal and other constraints so that damages might not increase in the same percentage as GDP. On the other hand, there may well be uncontrolled development in other areas, which would result in more rapid growth in exposure. And these changes may vary over different time periods (the short, medium and long runs).
4.2 A case study comparison. A recent case study of total economic damages for a storm representing the current 100 year storm affecting the transportation infrastructure of the New York City (NYC) metro area is Jacob et al. (2011, pp. 322ff). This is a smaller geographic area than the NYS coastal area used in this work, but it represents the central and wealthiest portion of the NYS coastal zone. In the study damages for both physical transportation infrastructure and the loss of economic production consequent upon them are estimated for the current SL, and SLR of two and four feet (.61 and 1.22 meters). The results for the case study are given in Table 5. The larger portion of damages in each case is from the loss of economic activity.
Table 5 - Combined economic and physical-damage losses for the New York City Metropolitan region for a 100-year storm surge under current conditions and for two sea level rise scenarios, from Jacob et al., 2011.
Source: Adapted from Jacob et al., 2011, p. 348. Notes: TIELEM = time integrated economic loss for the metropolitan region (op. cit., p. 347). Values are in $ US 2010.
It is instructive to compare the methods and results of the Jacob et al. (2011) case study with the work presented here. The transportation case study covers economic losses, which are not covered in our method (with the exception of some business losses [Air Worldwide, 2013]); the two foot rise in sea level is at the high end of our “low” scenario for the 2080s; and the 4 foot rise is within the “high” end scenarios (NPCC, 2010, p. 172). Most importantly, this is for a single storm, whereas the results presented here are for all storms over a 61-year period. In addition, the physical damages are estimated not by insured losses but by taking a percentage of the assumed total value of transportation infrastructure in the study region based on typical flood scenarios (Jacob et al., 2011, p. 360). (The future SLR damages shown in Table 5 are a first approximation taking into account considerations of economic growth, additional infrastructure, and modeling and other information; Jacob et al., 2011, pp. 360-1.) Note that most public transportation infrastructure is uninsured or self-insured, and that there are now proposals for more widespread use of insurance (Kunreuther and Michel-Kerjan, 2013). Since Hurricane Sandy (October, 2012), NYC transportation agencies have developed new approaches to insurance from storm surge (Keohane, 2014). The estimates are thus complementary to ours to the significant extent that public infrastructure damages are not included in the coastal insured damage figures used here. With these differences, the incremental physical damages due to the 100-year storm with SLR, $3 B for the 2 foot measure and $6 B for the 4-foot measure (US$2010), fall within the general range of our own results.
It appears that there is no source of historic data for uninsured transportation infrastructure losses comparable to the insured loss data that we used. It would be both useful and challenging (given the number of different agencies dealing with transportation in the NYC metropolitan area) to develop such a timeline, which would permit our method to be directly applied. (Some information on public facility insurance is available, for example Port Authority of New York and New Jersey, n.d.). Such differences in data suggest that climate change damages for different sectors can appropriately be analyzed and forecast separately. In addition, the relative importance of sectors will differ among cities. Smaller cities with no substantial transportation networks may have relatively more damages in the insured sector studied here.
While comparisons with other studies estimating future damages, even for the same region, are highly approximate because of the many different assumptions involved in each study, they nevertheless provide a generalized check on estimates and an opportunity for considering data and methodology issues. There are other studies dealing with forecast future coastal damages from climate change, for example Hallegatte et al. (2011), a case study of Copenhagen; Aerts et al., 2013, dealing with New York City; the application of Swiss Re’s catastrophe models to estimate the potential impacts of wind and storm surge on New York City (New York City, 2013, pp. 33-36); and Hallegate et al. (2013), for 136 coastal cities. A useful research effort would be the detailed comparison of these and other studies in terms of methods and results, to help develop the possibility of more standard methods.
It is now widely accepted that damages from climate change impacts such as SLR may be substantial and should be analyzed carefully in order to guide adaptation policy-making. The work shown here is a step forward in guiding policymakers in NYS and elsewhere, and aiding planners interested in applying the methodology in more detail to particular case studies. Estimated average coastal insured damages under both climate scenarios used here are more than ½ Billion US$/year. The results indicate that both economic growth and climate change may have significant effects on total damages in the insured sector: damages from SLR range from 47% to 74 % of damages attributable to economic growth given the data and assumptions used in this study. These estimates assume no further adaptation; adaptation measures could reduce the total damages, and the assessment of such measures and their impacts can be a next step in the work described here.
In addition to the results, several observations relating to further implementation of the method should be mentioned. With respect to detailed applications of this approach, sensitivity analysis on the main parameters will be helpful; as part of sensitivity analysis, and because estimated damages are so significant, design on the basis of extreme events (including the possibility of more intense storms) should be considered. It seems clear also that sector damages studies (as opposed to overall generalized estimates) can be important because of differences in data and climate impacts among sectors. Moreover, the relative importance of sectors will differ among cities. In terms of data development, for self-insured infrastructure such as transportation and other public infrastructure, the development of historic loss data comparable to the insured loss data employed here would permit our method to be more widely used. Finally, a research effort could usefully be undertaken to compare the methods and results of this and other studies; this would help to develop the possibility of more standard methods of future damage estimation.
This work builds on an earlier case study (Leichenko et al., 2011). The authors acknowledge the New York State Energy Research and Development Authority for financial support for the earlier work, and Columbia University, Rutgers University, and Ca’ Foscari University of Venice for support of the current work. We also wish to thank Cynthia Rosenzweig and William Solecki for their overall guidance of the original project. The authors are solely responsible for the contents of this article.
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Projecting Future Coastal Flooding Damages with Climate Change