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Global climate change, especially the phenomena of global warming, is expected to increase the intensity of land-falling hurricanes. Societal adaptation is needed to reduce vulnerability from increasingly intense hurricanes. This study quantifies the adaptation effects of potentially policy driven caps on housing densities and agricultural cover in coastal (and adjacent inland) areas vulnerable to hurricane damages in the Atlantic and Gulf Coastal regions of the U.S. Time series regressions, especially Prais-Winston and Autoregressive Moving Average (ARMA) models, are estimated to forecast the economic impacts of hurricanes of varying intensity, given that various patterns of land use emerge in the Atlantic and Gulf coastal states of the U.S. The Prais-Winston and ARMA models use observed time series data from 1900 to 2005 for inflation adjusted hurricane damages and socio-economic and land-use data in the coastal or inland regions where hurricanes caused those damages. The results from this study provide evidence that increases in housing density and agricultural cover cause significant rise in the de-trended inflation-adjusted damages. Further, higher intensity and frequency of land-falling hurricanes also significantly increase the economic damages. The evidence from this study implies that a medium to long term land use adaptation in the form of capping housing density and agricultural cover in the coastal (and adjacent inland) states can significantly reduce economic damages from intense hurricanes. Future studies must compare the benefits of such land use adaptation policies against the costs of development controls implied in housing density caps and agricultural land cover reductions.

The Intergovernmental Panel for Climate Change (IPCC) Fourth Assessment Report [

Equation (1) makes two major assumptions in “normalizing” the damages: First, damages from hurricanes are monotonically increasing functions of population and wealth. Second, population and wealth changes are given equal weights in normalizing the damages. It is hypothesized in this paper that both of these assumptions are not tenable and may lead to an incorrect “normalization” of damages. It is argued that normalization of damages should only be adjusted for inflation, as suggested in Equation (2):

Following the normalization proposed in Equation (2), it is possible to construct statistical and structural models for testing the two assumptions made by Pielke and Landsea [

While global coastal communities will very likely be exposed to a range of socio-economic impacts from unmitigated global climate change, such as sea level rise, flash floods, and heat waves [

This study uses hurricane damages data for one hundred and five years (1900–2005) [

Socio-economic data about the housing and population densities of the affected areas as well as % agricultural cover was derived from U.S. census data. Since the census data is measured on a decadal time-scale, linear interpolation method was used to derive annualized estimates for socio-economic variables of interest. SST Nino, which refers to observed Sea Surface Temperature variability at intra-decadal time-scale, is derived from a National Climate Center database. It is included in the models because Katz [

This study develops a time series forecasting model that can predict the inflation-adjusted damages at different intensities of hurricanes, while controlling for housing densities of the areas impacted by the hurricanes. Such forecasting model could potentially be used to predict the damages in the next one hundred years (2006–2106), if policy/planning interventions are employed to carry out various land-use adaptations (

Descriptive Statistics.

Variable | N | Minimum | Maximum | Mean | Standard Deviation |
---|---|---|---|---|---|

Annual Damages (2005 U.S. Million $) | 106 | 0 | 99,150 | 2,937.73 | 11,340.11 |

Ln (Damages) | 80 | 14.34 | 25.32 | 19.74 | 2.35 |

Annual Damages as % of Fixed Reproducible Tangible Wealth (%) | 106 | 0 | 0.99 | 0.05 | 0.13 |

Ln (Annual Damages as % of Fixed Reproducible Tangible Wealth) | 80 | −10.54 | −0.01 | −4.30 | 2.12 |

Annual Hurricane Frequency | 106 | 0 | 6 | 1.62 | 1.43 |

Average Annual Hurricane Intensity (Safire-Simpson Scale) | 106 | 0 | 4 | 1.71 | 1.20 |

Annual Affected Area (Square Miles) | 106 | 0 | 238,409 | 13,079.13 | 34,995.14 |

Annual Affected Population | 106 | 0 | 41,664,959 | 2,465,596 | 7,255,926 |

Annual Affected Population Density (People/Sq Mile) | 80 | 2.51 | 4,604.81 | 181.80 | 522.43 |

Annual Affected Houses | 106 | 0 | 21,742,632 | 1,011,516 | 3,299,449 |

Annual Affected Housing Density (Houses/ Sq Mile) | 80 | 0.51 | 1,512.35 | 66.01 | 174.73 |

% of Agricultural Land in the Affected Area (%) | 80 | 0.16 | 93.65 | 31.07 | 23.17 |

Deviations from Sea Surface Temperature (SST) | 106 | −1.9 | 2.52 | 0.06 | 0.98 |

SST Nino | 106 | −1 | 1 | 0.01 | 0.82 |

Year | 106 | 1,900 | 2,005 | 1,952.5 | 30.74 |

Direct damages from hurricanes in the Atlantic and Gulf coasts.

Log of direct damages from hurricanes in the Atlantic and Gulf coasts.

Inter-decadal variability of hurricane activity.

Variation in hurricane intensity.

The simplest time series forecasting model uses inflation-adjusted damages (D) for a year (t) as the dependent variable. The dependent variable could also be normalized through a measure of inflation-adjusted damages as a proportion of GDP or as a proportion of FRTW. FRTW is preferred here because it only contains fixed assets, while GDP contains both fixed and liquid assets. It is hypothesized that the following predictors affect the changes in inflation-adjusted hurricane damages: (1) Average intensity of land-falling hurricanes for a year (t), measured on a Saffire-Simpson scale, (X_{1}); (2) Annual frequency of land-falling hurricanes (X_{2}); (3) Fixed Reproducible Tangible Wealth (X_{3}); (4) Area-weighted housing density affected by the hurricane path and their squared and cubed values (X_{4} to X_{6}); (5) % of Agricultural land cover (X_{7}); (6) SST El Nino (X_{8}) and (7) Time variable measured in years (X_{9}). When the dependent variable is normalized by FRTW, the FRTW is dropped from the list of independent variables.

The simplest form of the model was initially specified as an OLS model, as shown in Equation (3):

Various statistical tests were employed to detect autocorrelation and heteroskedasticity for estimating the β_{r}.

Estimated Models Predicting Economic Damages from Land-falling Hurricanes in the Atlantic and Gulf Coasts of the U.S., 1900–2005.

Variable | OLS Model 1 Predicting |
Loglinear Model 2 Predicting |
Prais-Winston Model 3 Predicting Ln(Damages) | ARMA (5,1,5) Model 4 Predicting |
---|---|---|---|---|

Year | −246.2259 ** |
0.0013 |
0.0001 |
Dropped |

Average Annual Hurricane Intensity (Safire-Simpson Scale) | 2,885.8720 ** |
1.3166 *** |
1.3018 *** |
1.6768 *** |

Annual Affected Housing Density (Houses/ Square Mile) | 50.2466 |
0.0277 *** |
0.0268 *** |
0.0023 * |

Annual Affected Housing Density Squared | −0.1473 |
−0.00005** |
−0.00005 ** |
Dropped |

Annual Affected Housing Density Cubed | 0.00007 |
2.77e−08 ** |
2.76e−08 ** |
Dropped |

% of Agricultural Land in the Affected Area | 21.7046 |
0.0189 ** |
0.0151 ** |
0.0229 *** |

SST Nino | 771.0973 |
−0.1640 |
−0.1507 |
0.1572 |

Fixed Reproducible Tangible Wealth (2005 US $ Billions) | 4.5842 *** |
0.0002 * |
0.0003 |
−0.0013 |

Constant | 452,702.3000 ** |
10.3225 |
12.8121 |
0.1090 |

AR (1) | N/A | N/A | 0.3607 | −1.9835 *** |

AR (2) | N/A | N/A | N/A | −2.3611 *** |

AR (3) | N/A | N/A | N/A | −1.4897 ** |

AR (4) | N/A | N/A | N/A | −0.4514 |

AR (5) | N/A | N/A | N/A | 0.2234 |

MA (1) | N/A | N/A | N/A | 1.0750 |

MA (2) | N/A | N/A | N/A | 0.6469 |

MA (3) | N/A | N/A | N/A | −0.6469 ** |

MA (4) | N/A | N/A | N/A | −1.0750 |

MA (5) | N/A | N/A | N/A | −1.0000 *** |

Sigma | N/A | N/A | N/A | 1.0202 *** |

R^{2} |
0.4378 | 0.7318 | 0.8715 | N/A |

F-test or Wald-test score | 6.0600 *** |
21.22 *** |
52.76 *** |
7,229.2700 *** |

Durbin-Watson d-statistic | 1.0324 |
1.3290 |
1.6921 |
N/A |

Breusch-Pagan/Cook-Weisberg test for heteroskedasticity | Chi^{2} = 248.0900 |
Chi^{2} = 1.8100 |
N/A | N/A |

* shows significance at 0.01 level; ** shows significance at 0.05 level and *** shows significance at 0.001 level. Numbers in brackets show standard errors. F-test or Wald-test statistic shows joint significance for all variables. N/A stands for Not Applicable.

Estimated Models Predicting Economic Damages as a % Proportion of Fixed Reproducible Tangible Wealth (FRTW) from Land-falling Hurricanes.

Variable | OLS Model 5 Predicting |
Loglinear Model 6 Predicting |
Prais-Winston Model 7 Predicting Ln(Damages/FRTW) | ARMA (5,1,5) Model 8 Predicting |
---|---|---|---|---|

Year | 0.0006 |
0.0007 |
−0.00002 |
Dropped |

Average Annual Hurricane Intensity (Safire-Simpson Scale) | 0.0496 ** |
1.3142 *** |
1.3065 *** |
1.6710 *** |

Annual Affected Housing Density (Houses/ Square Mile) | 0.0015 ** |
0.0270 *** |
0.0261 *** |
0.0023 * |

Annual Affected Housing Density Squared | −4.08e−06 * |
−0.00005 ** |
−0.00005 ** |
Dropped |

Annual Affected Housing Density Cubed | 2.3e−09 * |
2.63e−08 * |
2.65e−08 ** |
Dropped |

Average Annual Hurricane Frequency | 0.0386 *** |
0.5785 *** |
0.5909 *** |
0.6979 *** |

% of Agricultural Land in the Affected Area | −0.0003 |
0.0167 ** |
0.0136 ** |
0.0224 ** |

SST Nino | 0.0209 |
−0.1040 |
−0.1212 |
0.1173 |

Constant | −1.4776 |
−11.5060 |
−9.7455 |
−0.0318 |

AR (1) | N/A | N/A | 0.3954 | −1.9573 *** |

AR (2) | N/A | N/A | N/A | −2.3032 *** |

AR (3) | N/A | N/A | N/A | −1.4156 ** |

AR (4) | N/A | N/A | N/A | −0.3951 |

AR (5) | N/A | N/A | N/A | 0.2503 |

MA (1) | N/A | N/A | N/A | 1.0865 |

MA (2) | N/A | N/A | N/A | 0.6467 |

MA (3) | N/A | N/A | N/A | −0.6467 ** |

MA (4) | N/A | N/A | N/A | −1.0865 |

MA (5) | N/A | N/A | N/A | −1.0000 ** |

Sigma | N/A | N/A | N/A | 1.0431 *** |

R^{2} |
0.4084 | 0.6550 | 0.6791 | N/A |

F-test or Wald-test score | 6.1300 *** |
16.85 *** |
18.78 *** |
8,172.2600 *** |

Durbin-Watson d-statistic | 1.1380 |
1.2462 |
1.6746 |
N/A |

Breusch-Pagan/Cook-Weisberg test for heteroskedasticity | Chi^{2} = 102.2400 |
Chi^{2} = 5.7400 |
N/A | N/A |

* shows significance at 0.01 level; ** shows significance at 0.05 level and *** shows significance at 0.001 level. Numbers in brackets show standard errors.

Both Prais-Winston and ARMA estimation is undertaken in the software STATA by maximum likelihood methods. Harvey [^{2} at 87.15% and 67.91% respectively for Models 3 and 7) while minimizing heteroskedasticity and first-order autocorrelation. The Prais-Winston regression models should therefore be used to interpret the study findings with respect to quantifying the effects of changing hurricane intensities, hurricane frequencies, housing densities and agricultural LULC on inflation adjusted damages (

^{2} of 87.15% appears most robust with no evidence for heteroskedaskticity and first-order auto-correlation.

Model 3 predicts that a one unit increase in the average intensity (on SS scale) of land-falling hurricanes is significantly (

Observed

Higher frequency of land-falling hurricanes significantly increases the log of annualized damages. Each additional land-falling hurricane increases damages by 57.62%. The effects of housing density and agricultural cover are also positive and significant in explaining the annualized variation in inflation-adjusted damages. Model 3 predicts that a 1% increase in the agricultural land cover of the affected lands increases annualized damages by 1.51%. Using the mean values from

As a proportion of FRTW, inflation-adjusted damages range from 0 to 0.99% during the study period. ^{2} of 67.91% appears most robust with no evidence for heteroskedaskticity and first-order auto-correlation.

Observed

One unit increase in average annual hurricane intensity (on SS scale) is predicted to lead to 130.65% increase in damages as a proportion of FRTW, which has a mean value of 0.05% during the 20th century. Each additional house per square mile leads to 2.61% increase in damager per FRTW with a decreasing second order effect of 0.005% and increasing third order effect of 0.000002%. Each additional hurricane causes 59.09% increase in damages as a proportion of FRTW. Further, each additional % of agricultural land in the affected area leads to 1.36% higher damage as a proportion of FRTW. Overall, the direction, magnitude and significance of predictors for damages as a proportion of FRTW (

In classical general equilibrium models, extreme weather events were treated as exogenous shocks/events [

This study has quantified the effects of changing housing densities and agricultural land cover on inflation-adjusted damages from land-falling hurricanes in the Atlantic and Gulf coastal (and adjacent inland) states of the U.S. From the larger climate policy design perspective, these estimates could be used to calculate marginal effects of climate change induced increases in the intensity of hurricanes. For example, 8 to 16% increase in the average intensity of hurricanes will very likely lead to increased annual damages from (2005) US$305.84 million to US$611.69 million, holding constant other effects. If global circulation models are revised in future that predict a revised estimate of changes in hurricane intensities and frequencies induced by anthropogenic global climate change, such dynamic time-series modeling approaches could be used to parse out revised damage estimates. Obviously, these estimates are based upon the assumption of “spontaneous” adaptation to climate change. In contrast, the findings from this and other related studies [

Reduced form models can be used to quantify land use adaptation benefits (

Work on this paper and research supporting it is funded by the National Science Foundation grants 0433165 and EPS-1101317. Thanks to Chris Landsea for providing hurricane damages data and Jennifer Boenhart for helping with GIS Census data.

The author declares no conflict of interest.