Economic Assessment of Rapid Land-Building Technologies for ...

Economic Assessment of Rapid Land-Building Technologies for ...

Preferences for Timing of Wetland Loss Prevention in Louisiana Ross Moore, Daniel Petrolia, and Tae-goun Kim Dept. of Agricultural Economics, Mississippi State University May 28, 2010 Motivation Approximately 40 percent of the coastal wetlands of the lower 48 states is located in Louisiana Coastal Louisiana lost 1,900 square miles from 1932 to 2000 Has lost an average of 34 square mile per year for the last fifty years Hurricanes Katrina and Rita destroyed more than 217 square miles of marsh in a single season. By the year 2050 an additional 700 square miles is projected to be lost Restoration Projects Congress passed the Coastal Wetlands Planning, Protection and Restoration Act (CWPPRA) in 1990

Designates approximately $60 million annually for work in Louisiana. April of 2007 the Louisiana Governor signed Louisianas Comprehensive Master Plan for a Sustainable Coast A sustainable landscape is a prerequisite for both storm protection and ecological restoration. CPRA Master Plan Objectives Estimate the value that the public of Louisiana places on preventing the future loss of wetlands within their state Identify motivating factors that have an effect on willingness to pay/accept: demographics, proximity to the coast, risk preferences and risk perceptions, time preference of money, climate change, confidence in government, believe responses will influence decisions, previous knowledge of coastal protection efforts. Survey Mail survey sent to 3,000 taxpaying households in Louisiana. Stratified by county population Survey design Willingness to Pay/Willingness to Accept Long Run/Short Run Proposal Order of the two proposals Response Rate: 681 surveys (22.7%) Survey: Preference

We would now like to ask about your relative preferences between the two proposals. Please review the descriptions of each below and check the box above the one you would prefer if given the choice. Proposal #1 Proposal #2 Future losses prevented: Future losses prevented: Starting in 2015 Ending in 2050 Starting in 2035 Ending in 2185 Tax Cost: $X per year for 10 years Tax Cost: $X per year for 10 years No Action Future losses NOT prevented. Tax Cost: $0 Preference Preference Short run proposal Long run proposal No action Total

Frequency 370 34 107 511 Percentage 72.41% 6.65% 20.94% Potential Benefits Potential benefits Frequency Percentage Storm protection 282 55.19% Protection of recreational opportunities 16 3.13% Protection against sea-level rise due to climate change 25 4.89% Protection of the environment/ecosystem 102 19.96% Protection of commercial fisheries 8 1.57% Other 11 2.15% No potential benefits in mind 67 13.11% Total 511

Category 3 or Greater Hurricane Expectation Expected Hurricane Frequency Once a year or more Once every 2-5 years Once every 10 years Once every 20 years Once every 30 years Once every 50 years Once every 100 years or more I dont know Total Frequency Percentage 69 13.50% 204 39.92% 105 20.55% 29 5.68% 9 1.76% 7 1.37% 24 4.70% 64 12.52% 511 Actual Hurricane Frequency Multinomial Logit Model Variables and Descriptions Variables Income-Bid

Type Ordered Categorical Gender Race Age Household Binary Binary Continuous Ordered Categorical Ordered Categorical Description (y j t j ) ln tj 1 if male; 0 if female 1 if white; 0 otherwise Continuous between 19 - 84 Household size 1 if # is 1; 2 if # is 2; 3 if #; 4 if # is 4; 5 if # is 5 or greater Education Highest level of education 1 if some school or high school; 2 if associates or bachelors; 3 if masters, professional, or doctoral Latitude Continuous Latitude based upon zip code of respondent StormBenefit

Binary 1 if storm protection was most important benefit, 0 if otherwise EnivronmentBenefit Binary 1 if environment protection was most important benefit, 0 if otherwise CCBenefit Binary 1 if protection against sea-level rise due to climate change was most important benefit, 0 if otherwise Mean 5.15 0.56 0.76 54.31 2.46 1.64 30.69 0.54 0.18 0.06 Multinomial Logit Model Variables and Descriptions Variables CCperception Type Binary PreKnowledge Binary

Government Binary Influence Binary RiskPref Binary HurrFreqHI Binary LongRunFirst Binary WTP Binary Description Mean 1 if respondents do not at all believe in 0.13 climate change; 0 otherwise 1 if respondent had prior knowledge of 0.76 actions to protect wetlands; 0 otherwise 1 if no confidence that government agency 0.43 can accomplish such actions; 0 otherwise 1 if respondents believe responses will 0.20 influence policy; 0 otherwise

1 if respondents does not take a gamble; 0 0.69 otherwise 1 if respondent believes a Category 3 0.73 hurricane will affect them between 1 and 10 years; 0 otherwise 1 if long run proposal was presented first; 0 if 0.51 short sun was presented first 1 if the payment mechanism was willingness 0.47 to pay; 0 if willingness to accept Estimated Coefficients, Standard Errors, Average Marginal Effects, and Significance Levels for the Multinomial Logit Model. Short Run Long Run SE ME (%)b Coef SE ME (%)b 0.1 0.0009c 0.33 0.1 0.0001c

0.32 0.55 0.05 0.32 0.06 0.34 8.61 0.76 0.34 0.96 0.02 0.01 0.38 -0.03 0.02 -0.23 0.1 0.13 1.07

0.1 0.13 0.12 Educationa -0.06 0.2 -0.58 -0.06 0.2 -0.07 Latitudea 0.09 0.17 0.94 0.09 0.17 0.1 StormBenefita 2.33

0.32 29.13 2.33 * 0.32 2.91 EnvironmentBenefita 2.8 * 0.53 CCBenefit 1.26 0.67 CCperceptiona -0.06 0.37 PreKnowledgea 0.69 * 0.33 * Significant at p = 0.05 level or greater 22.94 2.3 2.8 2.46 * * 0.53

0.79 2.4 11.39 -0.65 -0.06 0.37 -0.07 7.81 0.69 0.33 0.87 Variable Income-Bida Coef Gendera 0.05 Racea 0.76 Age Householda 0.33

* * * * * * a Coefficient constrained to be equal across Short Run and Long Run Equations b Marginal effects shown for binary variables are for a discrete change from the base c Marginal effect for a $1,000 change in income Estimated Coefficients, Standard Errors, Average Marginal Effects, and Significance Levels for the Multinomial Logit Model. Short Run Long Run SE ME (%)b Coef 0.32 -11.52

-1.06 0.11 0.41 1.17 0.23 0.34 HurrFreqHIa 0 LongRunFirst 0 Variable Governmenta Coef -1.06 Influencea RiskPrefa WTP -1.56 Constant -5.87

* * SE ME (%)b 0.32 -1.3 0.11 0.41 0.13 2.39 0.23 0.34 0.27 0.36 -0.04 0 0.36 0 0.29

8.79 -2.68 0.72 -11.35 0.31 -20.49 -0.84 0.5 2.24 -5.62 5.68 5.65 * * Observations = 511 Log Pseudoliklihood = -282.80 Wald chi2(22) = 138.51 Prob > chi2 = 0.00 Pseudo R2 = 0.28 * Significant at p = 0.05 level or greater a Coefficient constrained to be equal across Short Run and Long Run Equations

b Marginal effects shown for binary variables are for a discrete change from the base c Marginal effect for a $1,000 change in income Parametric and Turnbull Nominal (Annual) Willingness to Pay and Willingness to Accept Estimates 95% Confidence Interval Estimates Lower Bound Upper Bound Multinomial Logit $3,943 $1,493 $33,067 $53,855 $13,651 $60,430 Short Run: WTP WTA Long Run: WTP $0.78 $0.00 WTA $10 $0.11 Turnbull Distribution Free Estimate $746 $742 Short Run

$24 $169 $749 Net Present Value of Willingness to Pay for the Short Run Proposal $350,000 $300,000 Median Lower Upper Turnbull NPV of WTP $250,000 $200,000 $150,000 $100,000 $50,000 $0 0 0.1 0.2 0.3 Discount Rate 0.4 0.5 0.6

Net Present Value of Willingness to Accept for the Short Run Proposal $600,000 $500,000 Median Lower Upper Turnbull NPV of WTA $400,000 $300,000 $200,000 $100,000 $0 0 0.1 0.2 0.3 Discount Rate 0.4 0.5 0.6 Present Value Estimates of Aggregate Welfare (millions of

dollars) by discount rate Discount Rate 2% 6% 18% 26% WTP $76,465 $62,000 $37,542 $28,963 WTA $1,044,282 $846,723 $512,706 $395,544 Turnbull $14,459 $11,724 $7,099 $5,477 Assuming $0 WTP/WTA for Non-Respondents 2% 6% 18% 26% WTP $17,358 $14,074 $8,522 $6,575 WTA $237,052 $192,206 $116,384 $89,788 Turnbull $3,282 $2,661 $1,611 $1,243 50% $16,634 $227,168

$3,145 50% $3,776 $51,567 $713 Summary of Results Probability of choosing short run over no action: Increases: Income White Storm protection primary benefit Environmental benefits primary concern Had prior knowledge of protection efforts Decreases: No confidence in government Received WTP payment mechanism Summary of Results The probability of choosing long run over no action Increases: Income White Storm protection primary benefit Environment protection primary benefit Climate change primary benefits Prior knowledge of protection efforts

Decreases: No confidence in government Presented with long run first Conclusions Found respondents are highly willing to fund prevention of wetland loss Overwhelming support for short run proposals over long run proposals Protection from hurricane and storm damage is primary benefit driving support Other factors: Government, Payment Mechanism, and Environmental Benefits Problems Low response rate High welfare estimates Thank you

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