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Spatial Economics of the Louisiana Wetland Mitigation Banking Industry CNREP Conference May 28, 2010 Ryan Bourriaque Rex Caffey.

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Presentation on theme: "Spatial Economics of the Louisiana Wetland Mitigation Banking Industry CNREP Conference May 28, 2010 Ryan Bourriaque Rex Caffey."— Presentation transcript:

1 Spatial Economics of the Louisiana Wetland Mitigation Banking Industry CNREP Conference May 28, 2010 Ryan Bourriaque Rex Caffey

2 Evolution of Wetland Policy 1972: Federal Water Pollution Control Act Sec. 404: Dredging and fill in navigable waterways Sequencing: 1) avoid, 2) minimize, 3) mitigate on-site, 4) mitigate off-site 1988: “no net loss” wetland policy 1995: Federal Guidance on Mitigation Banking

3 Mitigation Banking Definition What:.wetland restoration, creation, enhancement, and in exceptional circumstances, preservation

4 Mitigation Banking Definition What: “….wetland restoration, creation, enhancement, and in exceptional circumstances, preservation Why: compensating for unavoidable wetland losses in advance of development actions, when mitigation cannot be achieved on site or would not be as environmentally beneficial

5 Mitigation Banking Definition What: “….wetland restoration, creation, enhancement, and in exceptional circumstances, preservation Why: compensating for unavoidable wetland losses in advance of development actions, when mitigation cannot be achieved on site or would not be as environmentally beneficial Where: …involves consolidation of small, fragmented wetland mitigation projects into one large contiguous site...

6 Mitigation Banking Definition What: “….wetland restoration, creation, enhancement, and in exceptional circumstances, preservation Why: compensating for unavoidable wetland losses in advance of development actions, when mitigation cannot be achieved on site or would not be as environmentally beneficial Where: …involves consolidation of small, fragmented wetland mitigation projects into one large contiguous site...

7 Mitigation Banking Definition How:...Units of restored, created, enhanced, or preserved wetlands are expressed as “credits” which may be subsequently be withdrawn to offset “debits” incurred at a project development site. Wetland Acres Damaged (Developers) Army Corps of Engineers Wetland Mitigation Credits (Bankers) $$$$ Credits PermitApproval Acre to Credit “Trading Ratio”

8 Mitigation Banking Definition How:...Units of restored, created, enhanced, or preserved wetlands are expressed as “credits” which may be subsequently be withdrawn to offset “debits” incurred at a project development site. Who: banks are businesses, created by private entrepreneurs who sell credits to developers who impact wetlands.

9 Louisiana Situation 2005: ~ 405 banks in US, 96 in Louisiana 23% of the total banks in the US located in LA > 20 banks 6-20 banks < 5 banks None/sold-out

10 Louisiana Situation 2005: ~ 405 banks in US, 96 in Louisiana 23% of the total banks in the US located in LA 42 LA banks are currently active, 25 are pending approval, and 29 sold out of credits

11 Louisiana Situation 2005: ~ 405 banks in US, 96 in Louisiana 23% of the total banks in the US located in LA 42 LA banks are currently active, 25 are pending approval, and 29 sold out of credits LA ranks at the bottom of credit prices nationwide

12 Louisiana Mitigation Banks Issues Difficulties arise when trying to set a credit price: –Value of land –Cost to restore the land –Monitoring/maintenance costs for perpetuity Availability of credits Pricing info for prospective investors on the market Service areas (market limits) may not be fully enforced

13 Research Objectives The overall goal of this study is to characterize the market for mitigation banking credits in Louisiana. Specific objectives include: 1. Collect credit transaction data from state and federal institutions 2. Examine the functional relationship between credit prices and spatial and economic variables 3. Summarize these factors for use by prospective investors and policy-makers.

14 Data and Methods Transaction data from LaDNR and Corps sampled for temporal and spatial spread: Economic, descriptive, supply and demand data 189 permit files were reviewed at LaDNR with 85 having actual transaction data (45%) 427 permit files were reviewed at the Corps with 80 having actual transaction data (19%) 165 transactions collected, 145 for statistical analyses Data were organized in Microsoft Excel and geo-coded for spatial location in ArcView

15 Louisiana Mitigation Transactions ( , n=145)

16 Louisiana Mitigation Banks ( , n=80)

17 Data and Methods Dependent variable = Cost ($/acre, $/credit) ln(cost) for each transaction 13 independent variables collected from permit files 8 additional spatial variables created in ArcView 2 variables created from census and land value data Descriptive and statistical analysis in SpaceStat and SAS

18 Model Variables and Definitions Dependent VariableDefinition AVGCO LNAVGCO Total cost of acres sold divided by the total number of acres sold. Natural log of the average cost variable. Independent VariablesDefinition Expected Sign IMP xy Projected spatial coordinate for the impact N/A BANK xy Centroid point for mitigation bank N/A DATE1 Date of transaction labeled by month of transaction + TOTAC Total number of acres/credits sold - TOTCO Total cost of transaction - PARISH Parish of impact N/A COMPT Number of banks in a particular hydrologic unit. -

19 Model Variables and Definitions (continued) Independent VariablesDefinition Expected Sign HUCNO USGS Hydrologic Unit Code (HUC) number N/A PAPOP Parish population estimate for year of transaction. + LANVA Rural land value estimates for the parish impacted. + COMMERCIAL Dummy, clientele type: commercial + GOVERNMENT Dummy, clientele type: governmental + RESIDENT Dummy, clientele type: private/residential - RESTORAT Dummy, restoration-based wetland mitigation bank + ENHANCEM Dummy, enhancement-based wetland mitigation bank - PRESERVA Dummy, preservation-based wetland mitigation bank -

20 Model Variables and Definitions (continued) Independent VariablesDefinition Expected Sign BLHDummy, bank selling bottomland hardwood credits - PF_SDummy, bank selling pine forested savannah credits. + SW Dummy, bank selling swamp credits. + COASTAL Dummy, bank located in the Louisiana Coastal Zone. + D_IMP_URBA Distance from the impact to nearest urban area (measured in miles). Urban area centroid points were identified through US Census Data. - D_BANK_URB Distance from the mitigation bank to nearest urban area (measured in miles). Urban area centroid points were identified through US Census Data. - D_IMP_BANK Distance from the impact to nearest mitigation bank (measured in miles). -

21 Results: Descriptive Statistics Restoration banks accounted for 83% of the observations

22 Results: Descriptive Statistics Restoration banks accounted for 83% of the observations Commercial clientele made up 54% of the transactions

23 Results: Descriptive Statistics Restoration banks accounted for 83% of the observations Commercial clientele made up 54% of the transactions Bottomland Hardwood Forests made up 67% of the habitat impacted

24 Results: Descriptive Statistics Restoration banks accounted for 83% of the observations Commercial clientele made up 54% of the transactions Bottomland Hardwood Forests made up 67% of the habitat impacted Average credit price over ten- year time span was $6,382

25 Results: Descriptive Statistics Restoration banks accounted for 83% of the observations Commercial clientele made up 54% of the transactions Bottomland Hardwood Forests made up 67% of the habitat impacted Average credit price over ten- year time span was $6,382

26 Results: Descriptive Statistics Restoration banks accounted for 83% of the observations Commercial clientele made up 54% of the transactions Bottomland Hardwood Forests made up 67% of the habitat impacted Average credit price over ten- year time span was $6,382 Two markets or spike in prices from 2003 onward?

27 Regression Procedure Results for Overall SAS Model Approx VariableEstimateStd. Errt Value Pr > |t| PAPOP E PF_S RESIDENT RESTORATION LANVA TOTAC BLH COMPT DATE D_BANK_URB E D_IMP_BANK E N=145, Adjusted R² = ,  =0.10

28 Results: Statistical Analyses (SAS: Sub-Models) Evidence of market segregation Sub-models developed for coastal (n=94) transactions and non-coastal (n=51) Same independent variables as overall model

29 Regression Procedure Results for Coastal Model Approx VariableEstimateStd. Errt ValuePr > |t| LNAVGCOC PAPOP1.822E E PF_S RESIDENT RESTORAT LANVA TOTAC BLH COMPT DATE D_BANK_URB E D_IMP_BANK-5.14E n=91, Adjusted R² = ,  =0.10

30 Regression Procedure Results for Non-Coastal Model Approx VariableEstimateStd. Errt Value Pr > |t| PAPOP2.414E E PF_S RESIDENT RESTORAT LANVA TOTAC BLH COMPT DATE D_BANK_URB E D_IMP_BANK-7.86E n=54, Adjusted R² = ,  =0.10

31 Summary and Conclusions Rapidly expanding industry Lack of information, proprietary nature of business Coastal mitigation credit prices increased by 18% annually with non-coastal 11% Coastal mitigation banks accounted for only 10% of total number of banks

32 Summary and Conclusions What drives the price of credits overall? Very limited, lucrative market for coastal banks (+) Land prices - strive for rural land near urban areas (+) Scale of transaction (+) Population (+) Presence of other mitigation banks in watershed (+) Transaction distance (-) Out-of-watershed transactions? In Coastal Zone? Economic factors: competition, population, time In non-coastal banks? Scale, habitat

33 Additional Research Survey of bank operators Investigation into credit pricing strategies In-depth inventory of credits in different watersheds Aggregating hydrologic units by like habitats Hindrances and opportunities for coastal banks

34 Thank You!!


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