Valuing Spatially Valuing Spatially Delineated Nutrient Delineated Nutrient Pollution Pollution Martin D. Smith Martin D. Smith Larry B. Crowder Larry B. Crowder Nicholas School of the Environment and Earth Nicholas School of the Environment and Earth Sciences Sciences Duke University Duke University Image source: Dr. James Bowen, UNC Charlot http://www.coe.uncc.edu/~jdbowen/neem/
Valuing Spatially Delineated Nutrient Pollution. Martin D. Smith Larry B. Crowder Nicholas School of the Environment and Earth Sciences Duke University. Image source: Dr. James Bowen, UNC Charlotte http://www.coe.uncc.edu/~jdbowen/neem/. - PowerPoint PPT Presentation
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Martin D. SmithMartin D. SmithLarry B. CrowderLarry B. Crowder
Nicholas School of the Environment and Earth SciencesNicholas School of the Environment and Earth SciencesDuke UniversityDuke University
Image source: Dr. James Bowen, UNC Charlotte http://www.coe.uncc.edu/~jdbowen/neem/
What can we learn from a lumped-parameter bioeconomic model about
valuing ecosystem services?
Preview
• Develop a method that provides an exact welfare measure of a portion of ecosystem service value
• A 30% reduction in nitrogen loading in the Neuse generates $2.04 million in fisheries benefits under open access
• The value of the environmental change is contingent on the institutional arrangement
Outline
• Background and literature• Analytical Model with Open Access• Parameterizing the model (briefly)• Qualitative and Quantitative Results• Discussion of the results• Linking Models of Economics and Ecosystems• Preliminary results from a “quasi-optimized”
model
The ProblemNitrogen in the estuary
algaeOxygen demand
hypoxia
Migration into oxygenatedareas (crowding)
Prey Mortality
TMDL and the Neuse
• Nutrient pollution in Neuse linked to hypoxia/anoxia, toxic algal blooms, fish kills, effects on the trophic system
• Clean Water Act requires Total Maximum Daily Load (TMDL) plan
• Neuse TMDL recommends 30% reduction in nitrogen loadings
• Schwabe (2001) estimates annualized cost of 30% reduction ranges from $5.4 million to $9.1 million (1999 dollars)
• 9 species that depend on estuarine soft-bottom habitat make up > 2/3 dockside value of NC commercial fisheries (Peterson et al., 2000)
Image Source: NCSU Center for Applied Aquatic Ecologywww.ncsu.edu/wq/ pics-dp/dpncmap.gif
• Largest commercial fishery in NC ($34.4 million ex vessel revenues in 2002)
• 80,000 – 100,000 trips per year
• 35% in Neuse River and Pamlico Sound
• Essentially open access
• ~ 25 % of East Coast production from NC
Image Source: Dept. of Fisheries Science, VIMS, William and Mary http://www.fisheries.vims.edu/femap/fish%20pages/blue%20crab.htm
Total Catch and Revenues
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
1950
1952
1954
1956
1958
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
Total Revenue (1000s 2002 dolllars) Total Catch (1000s Pounds)
4 strands of the bioeconomic literature
• Multispecies models with predator-prey interaction (Hannesson, 1983; Ragozin and Brown, 1985; Kaplan and Smith, 2001; Brock and Xepapadeas, 2004)
• Habitat dependence of a renewable resource (Swallow, 1990; Barbier and Strand, 1998)
• Spatial fisheries models (Sanchirico and Wilen, 1999; Smith and Wilen, 2003)
• Empirical bioeconomics of open access (Wilen, 1976; Bjorndal and Conrad, 1987)
Model StructureLumped-parameter system of 8 ordinary differential equations
1. Nutrient loadings accumulate in the estuary 2. Nutrient accumulation increases algal carrying capacity
Two species 3. blue crabs as harvested mobile predator4. clams as unharvested stationary prey
Two patches5. Patch 1 subject to hypoxia6. Patch 2 has no hypoxia
7. Dynamic open access 8. Discrete choice model of fishing locations
Nutrients (N) and Algae (A)
tNtLtN
tNtAtAtA 1
This parameter will matter a lot.
Loadings minus natural decay
Logistic growth a function of nutrients
Blue Crab (X) population dynamics
tXtYtXtYtXtA
thtXtYktXtXrtX xx
22111
111111
)()(
1
Logistic growth predation harvest
Hypoxia-inducedmigration
Migration from relative prey availability
Blue Crab (X) population dynamics
tXtYtXtYtXtA
thtXtYktXtXrtX xx
22111
111111
)()(
1
tXtYtXtYtXtA
thtXtYktXtXrtX xx
22111
222222
)()(
1
Prey (Y) population dynamics
tYtAtYtXtYtYrtY y 111111 1
tYtXtYtYrtY y 22222 1
Predation
Logistic growth Hypoxia-inducedmortality
Dynamic Open Access
• Rents are dissipated in the long run
• Transitional rents are the welfare metric
• Reducing hypoxia generates a short-run economic benefit by increasing prey stocks and reducing predator crowding
Dynamic Open Access
cptE tE
thth 21
tEcththptTotal 21
Profit/Rent Function
Vernon Smith Rent Dissipation
is speed of adjustment
costsrevenues
Marginal cost of effort + opp cost of capital (per unit effort)
Spatial Effort
tXtX
tXt
21
11 expexp
exp
tXtXtXtX
tXtXt
1221
211 exp2exp
tEtEtE 21
tEttE ii
.,121 ttt
Implied Dynamic Spatial Adjustment
Adding up
Define an effort share state variable
Based on empirical fisherieseconomics literature
Closing the Model
tXtqEth iii
ctXttXtpqtE 2111 1
Schaefer Production
q is “catchability”
Parameterization(Short Version)
• Nitrogen loadings, algal production, hypoxia, and prey mortality: Various pieces of the Neu-BERN model due to Borsuk, Stow, Reckhow, and others
• Blue Crab population dynamics: Eggleston et al. (2004) stock assessment and related work
• Blue crab migration: Eby and Crowder• Costs – Rhodes, Lipton, and Shabman survey of
Chesapeake blue crabbers• Prices and trips– NC DMF data + BLS CPI South Size D• Discount rate – 2.5%• Other parameters – used nonlinear solver to back them
out or used 1-period-ahead forecasting to choose them
Results Summary
Initial Loadings PV Rents Catch Effort SS Catch SS EffortConditons (million pounds) (1000s Trips) (million pounds) (1000s Trips)
Sensitivity to Impact of Nitrogen on Primary Production
Sensitivity to Per Trip Costsc + delta Loadings Total PV Rents Change in PV % Change Total Catch % Change Total Effort % Change Long-Run Catch Long-Run Effort