The value of coastal wetlands for hurricane protection

Ambio

Volumn 37, Issue 4, 2008, Pages 241-248

  Costanza, Robert ^a   Pérez Maqueo, Octavio ^b   Martinez, M Luisa ^b   Sutton, Paul ^c   Anderson, Sharolyn J ^c   Mulder, Kenneth ^d  

^a UNIVERSITY OF VERMONT   (United States)

^b INSTITUTO DE ECOLOGÍA   (Mexico)

^c UNIVERSITY OF DENVER   (United States)

^d GREEN MOUNTAIN COLLEGE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANNUAL VARIATION; COASTAL PROTECTION; COASTAL WETLAND; COST-BENEFIT ANALYSIS; DAMAGE; ECOLOGICAL MODELING; ENVIRONMENTAL PROTECTION; ENVIRONMENTAL RESTORATION; ESTIMATION METHOD; HURRICANE; LEVEE; REGRESSION ANALYSIS; SWATH BATHYMETRY;

ARTICLE; DISASTER; PROBABILITY; REGRESSION ANALYSIS; UNITED STATES; WETLAND;

NATURAL DISASTERS; PROBABILITY; REGRESSION ANALYSIS; UNITED STATES; WETLANDS;

NORTH AMERICA; UNITED STATES;

EID: 48249115094     PISSN: 00447447     EISSN: None     Source Type: Journal    
DOI: 10.1579/0044-7447(2008)37[241:TVOCWF]2.0.CO;2     Document Type: Article

References (34)

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18. Pearson's coefficients showed insignificant collinearity between the analyzed variables. In addition no other land use areas covaried with either herbaceous or forested wetlands, which were also not correlated with each other.
19. c by 2.91 versus a model with just wind speed. The AIC evidence ratio implies that the model with wetlands included has an 81% chance of being the correct model versus a model with wetlands omitted. We also tested the model using a recently available alternative data set for hurricane damages from Pielke et al. (20). This data set normalized damages for wealth and population by county. Using this data set gave us very similar results, however, so we decided to report our results using the original damage data set.
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21. 2) was significant 93.04% of the time.
22. One potential problem with this formulation is endogeneity. If wetland area and GDP are negatively correlated (as one might expect if urban area and wetland area were "competing" for the same fixed landscape area), then this could cause bias in the estimate of the coefficient for wetlands or spurious correlation. For example, it might be the case that high wetland area correlates with lower GDP, which correlates with less TD. We tested for the possibility that the reduction in damage attributable to herbaceous wetlands was spurious and caused by an endogenous relationship with GDP. GDP and herbaceous wetland area (all variables assumed log-transformed) were actually positively (and weakly) correlated (r = 0.19). However, as hypothesized, the partial correlation coefficient of total damage upon herbaceous wetlands controlling for GDP and wind speed was negative (r = -0.34, p = 0.05) (23). Controlling for the relationship with GDP demonstrates that the perceived effect of herbaceous wetlands upon hurricane damage is not spurious but rather is partially suppressed (statistically) because of the positive correlation between GDP and wetlands. Further, if an endogenous relationship with GDP were causing a spurious effect of herbaceous wetlands upon total damage, a similar relationship should have been seen with other undeveloped land covers. However, the addition of other land cover types (e.g., forested wetlands and forest) did not improve any of the models we tested.
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24. A second potential problem with the formulation used is selection bias. It might be the case that we are using data that is selectively gathered from locations where recorded damage is greater, probably due to a higher intensity of GDP. In these places, since storm protection services are proportional to total damage (more potential damage to mitigate), they could be disproportionately high leading to overestimates of the average values. However, unlike the case of selection bias in typical valuation studies (see 25) in damage mitigation studies we find that selection bias tends to cause underestimates of value. We tested this by creating a Monte Carlo simulated data set using a log-log formulation and comparing regression results when the data was restricted to samples with damages above a given percentile. The higher the percentile, the lower the estimated damage mitigation of wetlands implying our estimates are conservative.
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27. While residual analysis suggests little correlation between wetland area and model error and we have several data points with low wetland area, the log-log formulation makes marginal value calculations at low wetland areas problematic because values go to infinity as area goes to 0. For this reason, we capped increases in marginal value in our calculations for wetland areas below a certain number, k. In other words, in the integration we used the marginal value at k for all wetland areas less than k rather than allowing it to climb to infinity. Given model fit across the range of areas, we believe this yields a conservative value estimate. If the model diverges from reality at some lower threshold of wetlands, then for hurricanes with wetland area below that threshold, observed damage should have been below the predicted. However, of the four hurricanes with the least wetland area, three have observed damages higher than the model prediction, and the hurricane with the lowest amount of herbaceous wetland, Emily, is an outlier for which total damage was strongly underestimated. We used k = 5000 (The minimum area of wetlands in any state in our sample was 3638 ha for Rhode Island. 5000 ha is thus just within the range of our data.) but also report values in Table 3 using k = 10 000 and k = 1000 to demonstrate sensitivity to this assumption. TV increases as k decreases, but in all cases, limiting the integration in this way leads to conservative estimates.
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33. MLM and OPM are grateful to Instituto de Ecología, A.C. for financial support during their research stay at the Gund Institute of Ecological Economics (University of Vermont). We are grateful to Joshua Farley, Brendan Fisher, the students in the Spring 2005 Advanced Course on Ecological Economics (University of Vermont), and to five anonymous reviewers for their helpful comments on earlier versions. Many of the underlying calculations for the paper can be found at (http://www.geography.du.edu/sutton/CheapFTPsite/ FiglCostanzaAmbioPaper.htm)
34. First submitted 1 November 2007. Accepted for publication 5 April 2008.