1
The value of coastal wetlands for hurricane protection
Robert Costanza1, Octavio Pérez-Maqueo2, M. Luisa Martinez2, Paul Sutton3,5, Sharolyn J. Anderson3, Kenneth Mulder4
1.Gund Institute of Ecological Economics, Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT 05405-1708, USA 2. Instituto de Ecología A.C., km 2.5 antigua carretera a Coatepec no. 351, Congregación El Haya, Xalapa, Ver. 91070, México. 3. Department of Geography, University of Denver, Denver, CO, 80208, USA 4. W. K. Kellogg Biological Station, Michigan State University, Hickory Corners, MI 49060, USA 5. Currently Visiting Research Fellow at Department of Geography, Population, and Environmental Management, Flinders University, Adelaide, South Australia
Coastal wetlands reduce wave height and storm surge and absorb wind energy thus moderating the damaging effects of hurricanes on coastal communities. We assembled data on storm damage, GDP in the storm track, wind speed, and coastal wetland area for 34 major hurricanes that have struck the US since 1980 in order to estimate the effects of coastal wetlands on moderating storm damage. A regression model with the natural log of damage per unit GDP in the hurricane swath as the dependent variable and the natural logs of wind speed and wetland area in the swath as the independent variables was highly significant and explained 60% of the variation in relative damages. A loss of 1 ha of coastal wetland in the model corresponded to an average $33,000 (median = $5,000) increase in storm damage from specific storms. Taking into account the annual probability of being struck by hurricanes of varying intensities, we estimated the annual value of coastal wetlands by state for storm protection. This value ranged from $600 to $237,000/ha/yr, with a mean of $26,000/ha/yr (median = $8,000/ha/yr) much larger than previous estimates. Overall, coastal wetlands in the US were estimated to currently provide $48 Billion/yr in storm protection services, a value that may change significantly in the future if wetlands continue to be lost or if storm frequency and intensity increase due to climate change. 2
Globally, since 1900, 2652 windstorms (including tropical storms, cyclones, hurricanes, tornadoes, typhoons and winter-storms) have been considered disasters. Altogether, they have caused 1.2 million human deaths and have cost $381 billion in property damage1. Of these, hurricanes, cyclones and tropical storms have resulted in $179 billion (47%) in property damage and the loss of 874 thousand (73%) human lives. The impact of cyclones and hurricanes over the last decades has increased, owing to an increase in built infrastructure along the coasts, an increased frequency of category 4 and 5 hurricanes2, and an upward trend in tropical cyclone destructive potential3. Coastal wetlands reduce the damaging effects of hurricanes on coastal communities by absorbing storm energy in ways that neither solid land nor open water can4. There is also evidence that property damage and loss of human lives from the 2004 tsunami that hit Southeast Asia was highly ameliorated by coastal ecosystems5
We estimated the value of coastal wetlands for hurricane protection using data on the major hurricanes (those considered “disasters”) that have hit the Atlantic and Gulf coasts of the US and for which data on total damages were available (34 of the total of 267 storms). We combined data on the tracks of these hurricanes and their wind speeds with data on storm damages and GIS data on Gross Domestic Product (GDP) and coastal wetland area in each storm’s swath (see Figure 1 and Methods for more details).
Using ordinary least squares (OLS) we fit the following multiple regression model6, which had an adjusted R2 of 0.604 and was highly significant: ln (TDi /GDP i)= + 1 ln(gi) + 2ln(wi) + ui (1)
Where:
TDi = total damages from storm i (in constant 2004 $US);
GDPi = Gross Domestic Product in the swath of storm i (in constant 2004 $US). The swath was
considered to be 100 km wide by 100 km inland. 3
gi = maximum wind speed of storm i (in m/sec)
wi = area of herbaceous wetlands in the storm swath (in ha).
ui = error
The best fit coefficients for the model are shown in Table 1. The data used in the model
are included in Table 2.
As expected, increasing wind speed increased relative damages (TD/GDP), while increasing wetland area decreased them. Figure 2 shows the observed vs. predicted relative damages from the model, with each of the hurricanes identified.
We can rearrange equation (1) to estimate the total damages from hurricane i as:
1 2 TDi e gi wi GDPi (2)
One can clearly see from this form of the relationship the relative influence of wind speed and
wetland area on total damages. The value of 1 from Table 1 indicates that total damages increase as the 3.878 power of wind speed, fairly consistent with the well-known relationship that the power in wind varies as the cube of speed.
The difference in TDi with a loss of an area a of wetlands (i.e. the “avoided cost” per unit area of wetland) is then:
1 2 2 TDi MVi e gi (wi a) wi GDPi (3)
If a is small relative to w, this represents the estimated “marginal value” per unit area of coastal
wetlands in preventing storm damage from a specific hurricane. Table 2 lists MVi for each of the 34 hurricanes in the database for unit areas of 1 ha. The values range from a minimum of $23/ha for hurricane Bill to a maximum of $465,730/ha for hurricane Opal, with an average value of just 4
over $33,000/ha. The median value was just under $5,000/ha, indicating a quite skewed distribution, mirroring the skewed distribution of damages. Equation 3 allows one to estimate the avoided damages from any area of wetlands (a) up to the total area of wetlands in the swath. For example, one might be interested in the “average” value of a larger area of wetlands, say half the wetlands in the swath. This could be estimated by using a = 1/2 of the total area of wetlands in the swath in equation 3 and then dividing the result by 1/2 the total area of wetlands in the swath. The average values per hectare calculated in this way (using 50% of the wetland area) are consistently 1.8 times higher than the marginal values.
Next we estimated the annual value of coastal wetlands for storm protection. This required information about the annual probability of being hit by hurricanes of various intensities. We used data on historical frequencies by state as proxies for these probabilities7. For each of the 19 states in the US that have been hit by a hurricane since 1980 (267 total hits), we calculated the average GDP and wetland area in their swaths using our GIS data base. We then calculated the annual expected marginal value (MV) for an average hurricane swath in each state using the following variation of equation (3):
5 1 2 2 M V sw p c ,s e g c (w sw 1) w sw G D Psw (4) c 1
where: s = state
sw = average swath in state s
gc = average wind speed of hurricane of category c
pc,s = the probability of a hurricane of category c striking state s in a given year
GDPsw = the GDP in state s in the average hurricane swath 5
wsw = the wetland area in state s in the average hurricane swath
We then estimated total annual value of wetlands for storm protection as the integral of the marginal values over all wetland areas (the “consumer surplus) from the first or “marginal” hectare in the swath down to a value k, as:
w k 5 2 2 1 T V s M V j (( k ) ( w sw ) ) p c ,s e g c G D Psw (5) j 1 c 0
Residual analysis of our regression results suggested that our model over-predicted the damage mitigation for smaller wetland areas, so we only integrated MV down to k = 1000 ha. This yields a conservative estimate of total damage avoided or total value.
We can then use this to estimate the average annual value per wetland hectare per state as:
AVs TVs /ws (6)
The estimated annual marginal value in an average swath (MVsw), the total value (TVs) for all the
state’s wetlands, and the average annual value per hectare (AVs) of coastal wetlands for each state estimated in this way are shown in Table 3.
The mean annual marginal value per hectare in a typical swath across states (MVsw) was almost $40,000/ha/yr, with a range from $126/ha/yr (for Louisiana) to $586,845/ha/yr (for New York) and a median value of $1,700, indicating a quite skewed distribution. MV varied inversely with wetland area (Figure 3A), indicating that the per hectare value of wetlands increases as they
become more scarce. The mean of the total annual values by state (TVs) for all the state’s wetlands, over the 19 states was $2.5 Billion/yr (± $5.5 Billion), with a median of $370 Million, again reflecting a skewed distribution across states. The total annual value summed over all states was almost $48 Billion/yr. TV generally increased with total area of wetlands in the state (Figure 3B). Differences by state in both Figures 3A and 3B reflect the relative amounts of coastal infrastructure vulnerable to damage and relative storm probabilities. For example, Louisiana has the most wetlands, but less vulnerable infrastructure than Florida and Texas, while
New York has fewer wetlands but more vulnerable infrastructure. The mean AVs by state was a 6 little over $26,000/ha/yr, with a range from $596/ha/yr (for Delaware) to $237,431/ha/yr (for New York) and a median value of $8,000/ha/yr.
Previous estimates of the value of coastal wetlands for storm protection8 used similar, but less spatially explicit methods and dealt only with hurricanes striking Louisiana. They estimated an annual average value of storm protection services of about $1,000/ha/yr (converted into $2004). Our current analysis included a much larger database of more recent hurricanes covering the entire US Atlantic and Gulf coasts and was able to utilize much more spatially explicit data on hurricane tracks, wetland area, and GDP. Our corresponding estimated value for Louisiana was about $3,500/ha/yr, significantly larger than the previous estimate. Our current study also allows one to assess not only the value of wetlands for storm protection, but how that value varies with location, area of remaining wetlands, proximity to built infrastructure, and storm probability, thus providing a richer and more useful analysis of this important ecosystem service. For example, Louisiana lost an estimated 480,000 hectares of coastal wetlands prior to Katrina and 20,000 hectares during hurricane Katrina itself. The value of the lost storm protection services from these wetlands can be estimated as the value per hectare ($3,500 ha/yr) times the area, yielding approximately $1.68 Billion/yr for services lost from wetlands lost prior to Katrina and an additional $70 Million/yr for wetlands lost during the storm. Converting this total of $1.75 Billion/yr to present value terms using a 3% discount rate implies a lost value for just the storm protection service of this natural capital asset of $58 Billion, and the lost value of the additional wetlands loss due to Katrina of $2.3 Billion.
If the frequency and intensity of hurricanes increase in the future, as some are predicting as a result of climate change, then the value of coastal wetlands for protection from these storms will also increase. Coastal wetlands provide “horizontal levees” that are maintained by nature and are far more cost-effective than constructed levees. The experience of hurricane Katrina provided a tragic example of the costs of allowing these natural capital assets to degrade. Coastal wetlands also provide a host of other valuable ecosystem services that constructed levees do not. They have been estimated to provide about $11,700/ha/yr (in $2004) in other ecosystem services (excluding storm protection)9, and experience (including the current study) has shown that as we learn more about the functioning of ecological systems and their connections to human welfare, 7 estimates of their value tend to increase. Investing in the maintenance and restoration of coastal wetlands is proving to be an extremely cost-effective strategy for society.
Methods
The following datasets were assembled for the analysis: 1) tracks of all hurricanes striking the US from 1980 to 2004, which included wind-speed10. Of these, only 33 hurricanes had sufficient information on damages (see below) to include in the regression analysis. All of the storms were used to estimate the strike probabilities, since this did not require damage information; 2)
Nighttime light imagery of the USA11. A one kilometer resolution GDP map was prepared by using a linear allocation of the national GDP to the light intensity values of the nighttime image composite12. GDP was adjusted to the year of the hurricane using national data on national GDP by year13; 4) The 30 meter resolution National Landcover dataset, which included mapping of both herbaceous and woody wetlands14. In this study we used the only the area of coastal herbaceous wetlands (marshes), since the area of woody wetlands was found not to correlate with relative damages (see below). Wetland area for Louisiana was adjusted from the year 2000 land use data to take into account the recent rate of coastal wetland loss of 65 km2/yr; 5) Total damage information for 34 hurricanes considered to be “disasters” from the Emergency Events Database1. The damage included both direct (e.g. damage to infrastructure, crops, housing) and indirect (e.g. loss of revenues, unemployment, market destabilization) consequences on the local economy. We adjusted these data for inflation to convert them to 2004 dollars based on U.S. Department of Commerce implicit Price Deflator for Construction6.
100km x 100km hurricane swaths were then overlaid on the spatially explicit GDP and wetland cover to obtain GDP and wetland area in each swath (Figure 1). The 100km x 100km swath was used as an average spatial extent in order to standardize the calculations. The GDP calculated within each hurricane swath and the reported total economic damage (TD) were used to generate a ratio of TD/GDP, which was used to represent the relative economic damage caused by each 8
hurricane. Pearson’s coefficients showed insignificant collinearity between the analyzed variables. Six alternative models were tried using the natural log of wind speed and various combinations of linear and log forms of the area of coastal herbaceous and woody vegetation. Akaike’s Information Criterion estimated the probability of the model we used being the “correct” model at 97.9%. All models with linear forms of wetland area had less than 0.01% chance of being the correct model.
Equation (5) estimates the avoided damages for each hurricane category in an average swath and multiplies by the probability of a given storm striking a state in a year. Value is thus only ascribed to wetlands that are expected to be in the swath of a hurricane with average value being the expected damage mitigation divided amongst all wetlands in the state.
1) www.em-dat.net/index.htm downloaded, November 2005. This data set defined a hurricane
to be a disaster if it satisfied at least one of the following criteria: 1) 10 or more people killed;
2) 100 or more people affected, injured or homeless; 3) Declaration of a state of emergency
and/or an appeal for international assistance
2) Webster, P., Holland, J., Curry, G.J., and Chang, H. R., Changes in tropical cyclone number, duration, and intensity in a warming environment. Science, 309, 1844-1846 (2005)
3) Emanuel K. Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436, 686-688. (2005)
4) Simpson, R.H. and Riehl, H. The hurricane and its impact. Louisiana State University Press, Baton Rouge (1981)
5) Danielsen, F., et al. The Asian tsunami: a protective role for coastal vegetation. Science, 310, 643. (2005) 9
6) We fit several alternative models and this was the best according to the AIC criteria (see Methods)
7) Blake, E. S., Rappaport, E. N., Landsea, C. W., and Jerry G. J. Deadliest, Costliest, and Most Intense United States Tropical Cyclones from 1851 to 2004 (NOAA Technical Memorandum NWS TPC-4). Miami, Florida: National Weather Service, National Hurricane Center, Tropical Prediction Center. Updated August 2005. http://www.aoml.noaa.gov/hrd/Landsea/dcmifinal2.pdf
8) Costanza, R., Farber, S. C. and Maxwell. J. The valuation and management of wetland ecosystems. Ecological Economics 1, 335-361. (1989)
9) Costanza, R., d'Arge, R., de Groot, R., Farber, Grasso, S. M., Hannon, B., Naeem, S., Limburg, K. Paruelo, J. O'Neill, R.V., Raskin, R., Sutton, P., and van den Belt, M. The value of the world's ecosystem services and natural capital. Nature 387, 253-260 (1997).
10) www.grid.unep.ch/data/gnv199.php
11) Elvidge, C.D., Baugh, K.E., Dietz, J.B., Bland, T., Sutton, P.C. and Kroehl, H.W. Radiance Calibration of DMSP-OLS low-light imaging data of human settlements, Remote Sensing of Environment 68, 77-88 (1999)
12) Sutton, P. C. and Costanza, R. Global estimates of market and non-market values derived
from nighttime satellite imagery, land use, and ecosystem service valuation. Ecological Economics 41, 509-527 (2002)
13) US GDP data from: http://www.bea.gov/bea/dn/home/gdp.htm
14) Vogelmann, J. E. and Howard S. M. Completion of the 1990's National Land Cover Dataset for the conterminous United States from Landsat Thematic Mapper Data and Ancillary Data Sources. Photogrammetric Engineering and Remote Sensing 64, 45-57 (2001). 10
Acknowledgements. 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, and the students in the Advanced Course on Ecological Economics (University of Vermont) for their helpful comments on earlier versions of this text.
Correspondence and requests for materials should be addressed to RC (e-mail: [email protected]) 11
Table 1. Regression model coefficients.
Coefficient Std. Error t P -10.551 3.29 -3.195 0.003 1 3.878 0.706 5.491 <0.001 2 -0.77 0.16 -4.809 <0.001 12
Table 2. Maximum wind speed, herbaceous wetland area, GDP, Total Damage and calculated marginal value (per hectare of wetland) for storm protection for each hurricane.
H e r b a c e o u s G D P i n s w a t h M a x w i n d W e t l a n d i n y e a r h i t O b s e r v e d t o t a l E s t i m a t e d s p e e d a r e a i n ( 2 0 0 4 $ U S ) d a m a g e ( 2 0 0 4 m a r g i n a l v a l u e H u r r i c a n e Y e a r S t a t e s h i t ( m / s e c ) s w a t h ( h a ) ( m i l l i o n s ) $ U S ) ( m i l l i o n s ) ( 2 0 0 4 $ U S / h a ) A l b e r t o 1 9 9 4 G A , M I , F L , A L 2 8 . 3 4 , 4 6 6 $ 5 , 0 4 0 $ 3 0 5 $ 1 5 , 6 0 7 A l i c i a 1 9 8 3 T X 5 1 . 4 9 3 , 5 9 0 $ 1 0 0 , 1 9 9 $ 2 , 8 2 3 $ 1 4 , 4 4 9 A l l e n 1 9 8 0 T X 8 4 . 9 2 6 , 0 6 2 $ 1 3 , 1 5 1 $ 1 , 6 7 4 $ 1 2 7 , 0 9 0 A l l i s o n 1 9 8 9 T X , L A , F L , N C , P A , V A 2 3 . 1 1 6 7 , 4 9 4 $ 1 4 9 , 4 3 3 $ 6 3 $ 3 4 8 A l l i s o n 2 0 0 1 T X , L A , F L , N C , P A , V A 2 5 . 7 1 0 0 , 2 9 8 $ 1 8 5 , 6 1 0 $ 6 , 9 9 5 $ 1 , 6 1 1 A n d r e w 1 9 9 2 F L , L A 6 9 . 4 9 0 1 , 8 1 9 $ 8 3 , 4 5 0 $ 3 4 , 9 5 5 $ 6 9 9 B i l l 2 0 0 3 L A , M S , A L , F L 2 5 . 7 6 4 2 , 5 4 4 $ 7 0 , 6 6 9 $ 1 7 $ 2 3 B o b 1 9 9 1 N C , M E , N Y , R I , C T , M A 5 1 . 4 6 8 , 4 6 5 $ 1 2 2 , 3 5 8 $ 8 2 9 $ 3 0 , 6 8 3 B o n n i e 1 9 9 8 N C , S C , V A 5 1 . 4 4 9 , 7 7 4 $ 1 5 , 8 4 0 $ 3 7 3 $ 6 , 9 8 4 B r e t 1 9 9 9 T X 6 1 . 7 2 9 , 6 9 5 $ 2 , 0 4 3 $ 3 5 $ 4 , 5 5 7 C h a n t a l 1 9 8 9 T X 3 6 . 0 1 0 4 , 9 6 8 $ 8 1 , 3 1 9 $ 1 1 1 $ 2 , 4 0 0 C h a r l e y 1 9 9 8 T X 2 5 . 7 5 5 , 1 2 6 $ 1 8 , 7 7 5 $ 3 3 $ 4 7 0 C h a r l e y 2 0 0 4 F L 6 4 . 3 3 5 8 , 7 7 8 $ 4 8 3 , 2 8 1 $ 6 , 8 0 0 $ 1 5 , 3 4 7 D a n n y 1 9 9 7 O H , P A , I L , N Y , N J 3 6 . 0 2 7 1 , 3 1 7 $ 6 6 , 7 1 1 $ 1 1 1 $ 3 6 7 D e n n i s 1 9 9 9 N C 4 6 . 3 2 2 , 7 5 2 $ 1 7 , 6 6 9 $ 4 5 $ 2 0 , 7 0 4 E l e n a 1 9 8 5 F L , A R , K Y , S D , I O , M I , I N , M O 5 6 . 6 5 0 , 5 6 8 $ 1 4 , 2 4 0 $ 1 , 7 7 4 $ 8 , 8 3 5 E m i l y 1 9 9 3 N C 5 1 . 4 6 1 5 $ 6 $ 3 8 $ 5 , 7 9 5 E r i n 1 9 9 5 F L , A L , M S 4 1 . 2 2 6 4 , 2 2 6 $ 1 3 2 , 1 3 8 $ 8 2 1 $ 1 , 2 7 8 N C , F L , S C , V A , M D , P A , N J , F l o y d 1 9 9 9 N Y , D E , R I , C T , M A , V T 6 9 . 4 1 8 8 , 6 3 7 $ 4 2 0 , 9 4 0 $ 7 , 2 5 9 $ 5 6 , 2 1 4 N C , S C , V A , M D , V A , P A , O H , F r a n 1 9 9 6 W a s h i n g t o n D C 5 4 . 0 9 , 0 3 3 $ 1 0 , 4 7 1 $ 3 , 9 0 0 $ 1 1 4 , 3 8 9 F r a n c e s 2 0 0 4 F L , N C , S C , O H 6 4 . 3 3 4 0 , 0 5 1 $ 1 5 0 , 9 8 6 $ 4 , 4 0 0 $ 5 , 2 7 2 G a s t o n 2 0 0 4 V A , S C , N C 3 0 . 9 1 0 0 , 5 0 2 $ 8 2 , 0 6 3 $ 6 2 $ 1 , 4 3 9 G l o r i a 1 9 8 5 N C , N Y , C T , N H , M E 6 4 . 3 8 7 , 8 6 3 $ 1 8 8 , 5 3 1 $ 1 , 4 5 1 $ 7 2 , 2 2 9 H u g o 1 9 8 9 S C 7 2 . 0 3 2 , 9 0 6 $ 1 3 , 6 8 4 $ 1 , 3 9 1 $ 4 6 , 2 8 8 I r e n e 1 9 9 9 F L 4 6 . 3 6 9 2 , 2 1 9 $ 1 1 4 , 9 0 3 $ 1 0 4 $ 3 1 9 I s a b e l 2 0 0 3 N C , M D , V A , W a s h i n g t o n D C 7 2 . 0 3 7 , 9 4 2 $ 3 5 , 0 6 8 $ 5 , 4 0 6 $ 9 2 , 1 7 6 I s i d o r e 2 0 0 2 L A , M S , A L , T N 5 6 . 6 5 7 4 , 1 5 7 $ 6 4 , 9 9 0 $ 7 9 $ 5 4 7 A L , L A , M S , F L , P A , M D , N J , I v a n 2 0 0 4 O H , N C , V A , G A , T N 7 4 . 6 5 0 4 , 0 3 3 $ 2 2 6 , 1 5 0 $ 6 , 0 0 0 $ 6 , 9 9 6 J e a n n e 2 0 0 4 F L 5 6 . 6 4 0 4 , 7 6 9 $ 1 3 3 , 6 5 7 $ 7 , 0 0 0 $ 2 , 0 8 8 J e r r y 1 9 8 9 T X 3 8 . 6 9 8 , 5 4 0 $ 8 6 , 1 7 3 $ 4 9 $ 3 , 7 1 7 K a t r i n a 2 0 0 5 A L , L A , M S , G A , F L 7 8 . 2 7 0 8 , 5 1 9 $ 2 1 4 , 2 7 7 $ 2 2 , 3 2 1 $ 4 , 3 6 3 K e i t h 1 9 8 8 F L 3 3 . 4 2 2 2 , 3 2 4 $ 5 5 , 8 5 6 $ 4 4 $ 3 2 8 L i l i 2 0 0 2 L A 6 4 . 3 2 2 4 , 5 0 4 $ 2 4 , 4 3 9 $ 2 9 5 $ 1 , 7 7 9 O p a l 1 9 9 5 F L , G A , A L 6 6 . 9 7 , 2 6 1 $ 1 2 , 6 5 2 $ 3 , 5 2 1 $ 4 6 5 , 7 3 0
M e a n 5 2 2 1 8 , 9 9 5 $ 9 9 , 9 0 5 $ 3 , 5 6 1 $ 3 3 , 2 6 8 M e d i a n 5 3 1 0 0 , 4 0 0 $ 7 5 , 9 9 4 $ 8 2 5 $ 4 , 9 1 4 S . D . 1 7 2 4 3 , 1 1 1 $ 1 1 0 , 8 1 6 $ 7 , 0 0 1 $ 8 3 , 4 6 6 13
Table 3. Coastal wetland area in each state, wetland area and GDP in the average 100km swath, estimated annual
storm probabilities by category, and calculated MVsw, TVs and AVs .
A n n u a l W e t l a n d s e x p e c t e d P r o b a b i l i t y o f s t a t e b e i n g h i t b y a s t o r m o f A v e r a g e a n n u a l w i t h i n 1 0 0 W e t l a n d a r e a G D P i n m a r g i n a l v a l u e T o t a l a n n u a l v a l u e o f k m o f c o a s t t h e g i v e n c a t e g o r y i n a y e a r . p e r a v e r a g e v a l u e p e r s t a t e i n a v e r a g e a v e r a g e s w a t h S t o r m C a t e g o r y w e t l a n d s p e r b y s t a t e W s s w a t h W s w ( $ s w a t h M V s w T V s ( $ h a p e r s t a t e S t a t e ( h a ) ( h a ) m i l l i o n s / y e a r ) 1 2 3 4 5 ( $ / h a / y r ) m i l l i o n s / y r ) A V s ( $ / h a / y r ) A l a b a m a 1 6 , 7 5 9 6 , 3 8 8 $ 9 , 4 9 9 7 . 1 4 % 3 . 2 5 % 3 . 9 0 % 0 . 0 0 % 0 . 0 0 % $ 1 4 , 1 5 5 $ 3 7 2 $ 2 2 , 2 0 9 C o n n e c t i c u t 2 1 , 5 9 1 1 2 , 6 0 1 $ 6 5 , 6 7 3 2 . 6 0 % 1 . 9 5 % 1 . 9 5 % 0 . 0 0 % 0 . 0 0 % $ 1 4 , 4 2 8 $ 1 , 4 2 5 $ 6 5 , 9 9 9 D e l a w a r e 3 3 , 9 6 4 1 2 , 0 8 9 $ 1 0 , 4 8 8 1 . 3 0 % 0 . 0 0 % 0 . 0 0 % 0 . 0 0 % 0 . 0 0 % $ 2 2 2 $ 2 0 $ 5 9 6 F l o r i d a 1 , 4 3 3 , 2 8 6 1 8 6 , 3 4 6 $ 7 0 , 4 9 1 2 7 . 9 2 % 2 0 . 7 8 % 1 7 . 5 3 % 3 . 9 0 % 1 . 3 0 % $ 1 , 6 8 4 $ 2 2 , 4 1 9 $ 1 5 , 6 4 1 G e o r g i a 1 4 0 , 5 5 6 2 9 , 1 2 0 $ 7 , 3 5 6 7 . 7 9 % 3 . 2 5 % 1 . 3 0 % 0 . 6 5 % 0 . 0 0 % $ 6 3 0 $ 2 9 6 $ 2 , 1 0 5 L o u i s i a n a 1 , 6 4 8 , 6 1 1 3 7 0 , 2 2 9 $ 3 6 , 2 5 0 1 1 . 0 4 % 9 . 0 9 % 8 . 4 4 % 2 . 6 0 % 0 . 6 5 % $ 1 2 6 $ 5 , 6 8 2 $ 3 , 4 4 6 M a i n e 6 0 , 3 8 8 1 5 , 5 0 0 $ 1 4 , 6 7 0 3 . 2 5 % 0 . 6 5 % 0 . 0 0 % 0 . 0 0 % 0 . 0 0 % $ 7 1 5 $ 1 0 4 $ 1 , 7 2 9 M a r y l a n d 6 0 , 5 1 1 1 6 , 0 1 1 $ 2 1 , 9 2 4 0 . 6 5 % 0 . 6 5 % 0 . 0 0 % 0 . 0 0 % 0 . 0 0 % $ 4 4 5 $ 6 9 $ 1 , 1 4 1 M a s s a c h u s e t t s 4 9 , 3 5 2 1 6 , 8 0 1 $ 6 7 , 2 6 6 3 . 2 5 % 1 . 3 0 % 1 . 9 5 % 0 . 0 0 % 0 . 0 0 % $ 8 , 4 2 2 $ 1 , 4 3 0 $ 2 8 , 9 6 8 M i s s i s s i p p i 2 5 , 4 5 6 6 , 0 4 8 $ 3 , 8 9 0 1 . 3 0 % 3 . 2 5 % 4 . 5 5 % 0 . 0 0 % 0 . 6 5 % $ 7 , 1 5 4 $ 1 6 8 $ 6 , 6 1 6 N e w H a m p s h i r e 1 9 , 3 7 5 9 , 9 0 5 $ 2 3 , 0 5 1 0 . 6 5 % 0 . 6 5 % 0 . 0 0 % 0 . 0 0 % 0 . 0 0 % $ 1 , 0 9 5 $ 6 8 $ 3 , 5 2 2 N e w J e r s e y 6 9 , 0 0 1 2 1 , 8 6 4 $ 7 8 , 7 0 3 1 . 3 0 % 0 . 0 0 % 0 . 0 0 % 0 . 0 0 % 0 . 0 0 % $ 5 8 3 $ 1 6 2 $ 2 , 3 4 2 N e w Y o r k 5 , 3 0 6 2 , 1 1 7 $ 9 0 , 7 7 0 3 . 9 0 % 0 . 6 5 % 3 . 2 5 % 0 . 0 0 % 0 . 0 0 % $ 5 8 6 , 8 4 5 $ 1 , 2 6 0 $ 2 3 7 , 4 3 1 N o r t h C a r o l i n a 6 4 , 8 6 2 2 1 , 2 9 5 $ 1 3 , 0 2 3 1 3 . 6 4 % 8 . 4 4 % 7 . 1 4 % 0 . 6 5 % 0 . 0 0 % $ 5 , 0 7 2 $ 1 , 3 3 8 $ 2 0 , 6 2 6 P e n n s y l v a n i a 7 , 4 4 6 2 , 9 9 4 $ 9 3 , 1 1 7 0 . 6 5 % 0 . 0 0 % 0 . 0 0 % 0 . 0 0 % 0 . 0 0 % $ 1 1 , 6 5 1 $ 6 0 $ 8 , 0 7 1 R h o d e I s l a n d 3 , 6 3 8 1 , 7 5 9 $ 1 2 , 8 1 0 1 . 9 5 % 1 . 3 0 % 2 . 6 0 % 0 . 0 0 % 0 . 0 0 % $ 9 5 , 1 9 3 $ 1 1 8 $ 3 2 , 5 3 4 S o u t h C a r o l i n a 1 0 7 , 8 9 4 3 9 , 1 7 7 $ 1 5 , 3 6 7 1 2 . 3 4 % 3 . 9 0 % 2 . 6 0 % 1 . 3 0 % 0 . 0 0 % $ 1 , 2 8 1 $ 1 , 0 3 3 $ 9 , 5 7 8 T e x a s 4 4 8 , 6 2 1 7 9 , 1 1 0 $ 6 3 , 6 6 1 1 4 . 9 4 % 1 1 . 0 4 % 7 . 7 9 % 4 . 5 5 % 0 . 0 0 % $ 3 , 9 0 1 $ 1 1 , 2 0 2 $ 2 4 , 9 7 0 V i r g i n i a 7 1 , 5 0 9 2 3 , 5 8 8 $ 2 7 , 7 8 6 5 . 8 4 % 1 . 3 0 % 0 . 6 5 % 0 . 0 0 % 0 . 0 0 % $ 1 , 5 5 5 $ 4 9 6 $ 6 , 9 3 0
M e a n 2 2 5 , 6 9 1 4 5 , 9 4 4 $ 3 8 , 2 0 0 6 . 3 9 % 3 . 7 6 % 3 . 3 5 % 0 . 7 2 % 0 . 1 4 % $ 3 9 , 7 4 5 $ 2 , 5 1 2 $ 2 6 , 0 2 4 M e d i a n 6 0 , 3 8 8 1 6 , 0 1 1 $ 2 3 , 0 5 1 3 . 2 5 % 1 . 3 0 % 1 . 9 5 % 0 . 0 0 % 0 . 0 0 % $ 1 , 6 8 4 $ 3 7 2 $ 8 , 0 7 1 S . D . 4 7 5 , 1 5 7 8 9 , 1 6 0 $ 3 1 , 0 8 3 7 . 0 1 % 5 . 2 5 % 4 . 3 9 % 1 . 4 0 % 0 . 3 5 % $ 1 3 4 , 1 9 5 $ 5 , 5 2 6 $ 5 3 , 6 6 2