JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION
Vol. 50, No. 3
AMERICAN WATER RESOURCES ASSOCIATION
June 2014
A GENERAL EQUILIBRIUM MODEL OF ECOSYSTEM SERVICES IN A RIVER BASIN1
Travis Warziniack2
ABSTRACT: This study builds a general equilibrium model of ecosystem services, with sectors of the economy
competing for use of the environment. The model recognizes that production processes in the real world require
a combination of natural and human inputs, and understanding the value of these inputs and their competing
uses is necessary when considering policies of resource conservation. We demonstrate the model with a numerical example of the Mississippi-Atchafalaya river basin, in which grain production in the upper basin causes
hypoxia that causes damages to the downstream fishing industry. We show that the size of damages is dependent on both environmental and economic shocks. While the potential damages to fishing are large, most of the
damage occurs from economic forces rather than a more intensive use of nitrogen fertilizers. We show that these
damages are exacerbated by increases in rainfall, which will likely get worse with climate change. We discuss
welfare effects from a tax on nitrogen fertilizers and investments in riparian buffers. A 3% nitrogen tax would
reduce the size of the hypoxic zone by 11% at a cost of 2% of Iowa’s corn output. In comparison, riparian buffers
are likely to be less costly and more popular politically.
(KEY TERMS: ecosystem services; general equilibrium; riparian; agriculture; nonpoint source.)
Warziniack, Travis, 2014. A General Equilibrium Model of Ecosystem Services in a River Basin. Journal of the
American Water Resources Association (JAWRA) 50(3): 683-695. DOI: 10.1111/jawr.12211
valuation and impact studies have largely focused on
a single ecosystem service (Hein et al., 2006).
We expand the literature by developing a bioeconomic model that considers the multiple factors and
industries that make up a resource-dependent economy, and the competing use of the ecosystem between
provision of inputs to productive activities and use
for disposal of waste. The health of ecosystems affects
the productivity of factors and mitigates damages
from negative environmental and economic shocks.
The model allows us to analyze the vulnerability
of resource-dependent economies to fluctuations in
market and environmental forces.
We frame our model around a story of nonpoint
source pollutants in the Mississippi-Atchafalaya river
INTRODUCTION
The ecosystem provides multiple services to multiple stakeholders, with benefits accruing at various
spatial scales (Hein et al., 2006), temporal scales
(Howarth and Norgaard, 1993), and with competing
economic interests (Jones, 1965). Management of a
given ecosystem requires acknowledgment of links
between resources within the biological system
(Adger et al., 2005), the impact of economic activity
on the natural resources, and feedbacks between biological and economic systems (Settle et al., 2002).
While gains have been made in understanding the
complexity of the biological system, economic
1
Paper No. JAWRA-13-0079-P of the Journal of the American Water Resources Association (JAWRA). Received March 26, 2013; accepted
January 30, 2014. © 2014 American Water Resources Association. This article is a U.S. Government work and is in the public domain in the
USA. Discussions are open until six months from print publication.
2
Research Economist, USFS Rocky Mountain Research Station, 240 W. Prospect Rd., Fort Collins, Colorado 80526 (E-Mail/Warziniack:
twwarziniack@fs.fed.us).
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1993-2004, to 17,300 km2 in 2005-2010 (Rabalias and
Turner, 2000-2010) (see Figure 1).
Hypoxia is defined as dissolved oxygen concentrations ≤2 mg/l. Because this threshold is generally too
low to support aquatic life, hypoxic zones are commonly referred to as dead zones. Perhaps the most
well-understood effect of hypoxia in the Gulf, and the
one we focus on in this study, is its effect on catch of
brown shrimp (Farfantepenaeus aztecus). Subadult
brown shrimp avoid the hypoxic zone, clustering on
its edge, and dispersing to inshore and offshore
waters (Zimmerman and Nance, 2001; Craig and
Crowder, 2005; Craig et al., 2005). It is estimated
that about 25% of brown shrimp habitat has been lost
to the hypoxic zone, and displacement to suboptimal
water temperatures reduces their size enough to
affect dockside value (Zimmerman and Nance, 2001;
Craig and Crowder, 2005; O’Connor and Whitall,
2007). Loss of shrimp habitat is of concern to shrimpers in the GOM, who account for 85% of landings
(51,000 metric tons) and 87% of dockside value
($138 million) of all shrimp harvested in the U.S.
(NMFS, 2001-2010).
Figure 2 shows major sources of nitrogen delivered
to the GOM. Fifty-two percent of delivered nitrogen
originates from lands cultivated in corn and soybeans
(Alexander et al., 2008), with Iowa as the largest contributor of agricultural-based nitrogen (Goolsby et al.,
1999). In 2010, there were over 55,000 km2 of corn
planted in Iowa with a productive value of over
$11.7 billion. Over the last half century virtually all
of Iowa’s rangeland and forests have been replaced
FIGURE 1. Size of the Hypoxic Zone, 2013 (data from NOAA Gulf
of Mexico Hypoxia Watch: http://www.ncddc.noaa.gov/hypoxia/).
basin (MARB), their effect on the size of the Gulf of
Mexico (GOM) hypoxic zone, and tradeoffs in the
regional economy between using fertilizer on agricultural land and loss of shrimp habitat in the Gulf. The
GOM hypoxic zone is the largest in the coastal United States (U.S.). It has increased in size from an
average 6,900 km2 in 1985-1992, to 13,600 km2 in
FIGURE 2. Total Nitrogen Delivered Incremental Yields (data from Robertson et al., 2009).
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A GENERAL EQUILIBRIUM MODEL
OF
ECOSYSTEM SERVICES
TDj ¼
Dsj
ð1Þ
TDj is a compound Poisson random variable with
expected value
E½TDj ¼ E½SE½Dsj ¼ lðZ; DÞxj ðZ; DÞ
ð2Þ
We assume that ecosystem services are provided by
nature and priced at expected marginal costs. Let cE
j be
nature’s constant marginal cost of providing ecosystem
services without risk of storms. With risk of storms,
the unit costs of providing ecosystem services to firm j
are cE
j þ E½TDj , which gives an equilibrium price of
The model follows the general equilibrium model of
a production economy developed by Copeland and
Taylor (2003), which we have adapted to include
payments to ecosystem services and damages to the
ecosystem. We begin by discussing the provision of
ecosystem services by nature then discuss the utilization of ecosystem services by firms.
Degradation occurs when firms deposit pollutants
directly into the environment or adversely change the
AMERICAN WATER RESOURCES ASSOCIATION
S
X
s¼1
MODEL
OF THE
RIVER BASIN
structure of the environment. In this model, pollutants do not cause direct damages if they remain
where deposited. However, natural and man-made
events, such as storms and runoff, transport the pollutants through the ecosystem, at which time damage
occurs. Damages are defined simply as activities that
raise the unit cost of using ecosystem services. In our
case study, the level of degradation equals the accumulated amount of nitrogen fertilizer on the agricultural fields, and damages are the effects of
diminished water quality following runoff.
Let J denote the set of all firms in the economy
and Qj be the equilibrium output of firm j 2 J. The
amount of pollution produced by firm j is given by
Zj = wjQj, where wj is pollution per unit of output.
Aggregate pollution is Z = ∑ j2JZj.
We refer to any event large enough to cause damages as a “storm.” The probability of a storm occurring
and the mean damage from storms depend on
aggregate pollution level Z and a vector of climate
variables D. In many coastal areas, for example,
degraded ecosystems allow smaller more frequent
storms to travel inland, causing more damage than
they would otherwise. Similarly, smaller rain showers
carry more sediment to ditches and streams when
watersheds are dissected with unmaintained roads
(Ketcheson and Megahan, 1996). The number of
storms that occur in a given period is a random variable S with Poisson distribution and mean l(Z,D);
ol/oZ > 0. Damage to firm j from storm s is a random
variable Djs with normal distribution and mean
xj/ o Z > 0. Storms are i.i.d. Total damage to firm j if S
storms occur is
by row crops, with about 60% of the state’s land
covered by row crops in 1992 (Iowa Geological
Survey, 1992). For corn planted after a corn harvest,
Iowa State University Extension (1997) recommends
16,800-22,400 kg/km2 (150-200 lb/acre) N when
applied before crop emergence; it recommends
11,000-16,800 kg/km2 (100-150 lb/acre) N for corn
after soybeans when applied before crop emergence.
Goolsby et al. (1999) estimate that about 13.4% of
nitrogen fertilizer applied to agricultural fields in the
basin eventually ends up in the GOM. If, for example,
all planted corn in the basin receives 11,000 kg/
km2 N, over 600 million kg N is added to Iowa soils,
of which about 80 million kg reaches the GOM.
Climate change is expected to increase the amount
nitrogen reaching the GOM, due to a wetter Midwest
and more runoff (USGCRP, 2009). We incorporate the
effects of climate change following Donner and Scavia
(2007), who show that the amount of precipitation in
the Corn Belt can explain up to 70% of the nitrogen
flux in the GOM between 1980 and 2000. We also
provide discussion and present cost-benefit analysis,
for example, policies to reduce nitrogen runoff. We
examine two policies, one that increases the cost of
nitrogen use, such as a tax on fertilizer, and one that
reduces runoff by promoting riparian health, such
as through the use of riparian buffers and wetland
restoration.
This study proceeds as follows: the next two
sections develop the economic theory. The theoretical
model is incorporated into a numerical application
for the MARB in a separate section, in which we
study the vulnerability of the regional economy to
market and environmental changes, including climate change. The fifth section provides discussion
and presents cost-benefit analysis, for example, policies. Readers more interested in application than
theory could probably focus on the fourth and fifth
sections, referring back to earlier sections for clarification on terminology and notation. The final section
concludes.
JOURNAL
IN A
kj ¼ c E
j þ lðZ; DÞxj ðZ; DÞ
ð3Þ
The affect of risk is similar to that of a risk premium
paid on ecosystem services. If ecosystem services are
freely provided by the environment, kj is a shadow
price or nonmarket value of the ecosystem service.
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Lj = Lj(pK, pL, pi2J,Hj); Vij = Vij(pK, pL, pi2J,Hj);
H
Hj = Hj ðcH
j ; kj ; Qj Þ; Ej ¼ ðcj ; kj ; Qj Þ.
Firms face a unit price sj for pollution that could
be a direct cost due to associated costs of an input,
taxes, or a shadow price arising from regulations or
good will to reduce environmental pollutants. Firms
can reduce their pollution levels by investing in an
environmental control hj. Investment in control is
at the expense of output and normalized so one
unit of control requires one unit of output. Define
Yj as net output in the presence of control:
Yj
Zj
Qj
Hj
K j Lj
Ej
V1j ..... Vij
Yj ¼ ð1 hj ÞQj
FIGURE 3. Nested Structure of Firm Production.
We assume wj ðhj Þ ¼ ð1 hj Þ1=aj , and recalling Zj = wjQj
allows us to write a production function for Yj with
inputs Zj and Qj,
Production of output Qj occurs through a nested
production function, shown in Figure 3. In the
upper nest, a firm-specific composite of humanproduced inputs is combined with nature-provided
ecosystem inputs in amounts Hj and Ej using constant returns to scale technology Fj(Hj,Ej). In the
lower nest, the human-produced composite is produced combining capital, labor, and intermediate
inputs from industries i,j2J in amounts Kj, Lj, and
Vij. Firms vary in their ability to utilize ecosystem
services and their optimal mix of human-produced
inputs; they, therefore, face unique unit costs for
using ecosystem services kj and unique unit costs
for the human-produced composite cH
j .
Because production functions in each stage are
homogenous of degree 1, optimal technology mixes can
be found by minimizing unit cost functions. pK and pL
are the prices of capital and labor and pi is the price of
intermediate input and produced good i2J. The unit
cost functions for Hj and Qj are
n
pk Kj þ pL Lj
Kj ;Lj ;Vij
o
X
þ
p
V
:
H
ðK
;
L
;
V
Þ
¼
1
i
ij
j
j
j
ij
i2J
ð4Þ
n
o
H
H
cQ
ðc
;
k
Þ
¼
min
c
H
þ
k
E
:
F
ðH
;
E
Þ
¼
1
j
j
j j
j
j
j
j
j
j
ð5Þ
cH
j ¼ ðpk ; pL ; pi2J Þ ¼ min
1aj
Yj ¼ Zaj
j Qj
n
o
aj 1aj
Q
cYj ðcQ
;
s
Þ
¼
min
s
Z
þ
c
Q
:
Z
Q
¼
1
j
j
j
j
j
j
j
Zj ;Qj
Zj ¼
aj p j Y j
ð1 aj Þpj Yj
; Qj ¼
sj
cQ
ð10Þ
j
Copeland and Taylor show, given the functional
form for protection, that the first unit of protection
has a bounded marginal product. For small enough
sj, the optimal decision of the firm may be not to
protect. For this analysis, we assume that sj is
large enough to ensure some control.
Equilibrium levels of output are determined by the
full employment of factors and relative returns in the
economy from production of goods. The relative
returns of goods are found by looking at the profit maximization problem for firms. Profits in industry j are
pj ¼ pj Yj cH
j Hj kj Ej sj Zj
H
ð11Þ
Note that shares of expenses on Zj equal sjZj = ajpjYj.
Using this expression to eliminate Zj and subbing in
Yj = (1 hj)Fj(Hj, Ej) gives
ð6Þ
The equilibrium conditions in Equation (6) say
that the firm produces such that the ratio of input
prices equals the ratio of marginal products. This
condition implies that factor demands are functions
of input prices and output; Kj = Kj(pK, pL, pi2J,Hj);
JAWRA
ð9Þ
Taking the first-order conditions and assuming zero
profits give the following equilibrium conditions:
The firm’s first-order conditions for each stage imply
pK @Hj =@K pK
@Hj =@K cj
@Fj =@H
;
¼
¼
;
¼
@Hj =@L pi
@Hj =@Vij kj
@Fj =@E
pL
ð8Þ
Given optimal production technologies for Qj and
Hj, the amount of degradation is found by minimizing
the unit cost of producing Yj,
and
Hj ;Ej
ð7Þ
pj ¼ pj ð1 aj Þð1 hj ÞFj ðHj ; Ej Þ cH
j Hj kj Ej
ð12Þ
The first-order conditions for the profit maximization
problem are
686
JOURNAL
OF THE
AMERICAN WATER RESOURCES ASSOCIATION
A GENERAL EQUILIBRIUM MODEL
OF
ECOSYSTEM SERVICES
IN A
Q2
RIVER BASIN
MEASURING DAMAGES
Potential PPF
slope = p1(1-α1)(1-θ1) / p2 (1-α2)(1-θ2)
Net PPF
Our main intent is to show how economic development affects environmental degradation and the
economy’s vulnerability to environmental damages.
We do this by examining the impact of an exogenous
change in world prices. Such a change will affect the
value of production in the economy and change the
relative amount of each good produced. These
changes in production will, in turn, affect the level of
accumulated pollutants in the environment.
We begin by decomposing the effect of a change in
world prices on ecosystem services following
Copeland and Taylor (2003). First, we define scale of
the economy as the value of output at initial prices
X
s¼
p0 Y
ð16Þ
j j j
slope = p1/p2
Y1
Q1
Q1
FIGURE 4. Equilibrium Along Potential and Net PPFs.
dpj
dFj
¼ pj ð1 aj Þð1 hj Þ
cH
j ¼0
dHj
dHj
ð13Þ
dpi
dFj
¼ pj ð1 aj Þð1 hj Þ
kj ¼ 0
dEj
dEj
ð14Þ
and define an industry’s damage intensity as the per
unit of damages ei Zi/Yi. We can write
Zj ¼ ej cj S=p0j
where cj ¼ p0j Yj =S is the value share of net output of j
in total value of output. Let initial prices be p0j ¼ 1.
Taking logs of Equation (17) and differentiating with
respect to the price of good i yield
Equations (13) and (14) can be combined to get the
following relationship for production in any two
industries i and j:
Fif pj ð1 aj Þð1 hj Þ
; f ¼ fH; Eg
¼
Fif pi ð1 aj Þð1 hj Þ
ð15Þ
dZj
¼
dpi
Equation (15) describes the equilibrium along the
production possibilities frontier (PPF). It says that factors of production are employed such that the marginal
revenue products are equal between all firms and
equal to the ratio of marginal returns from production.
In our economy, there are two types of PPFs, a potential PPF expressed in total production Qj and a net
PPF expressed in net production Yj (Figure 4).
Because Yj = (1 hj)Qj, the two are interchangeable.
Equilibrium along the potential PPF occurs where the
ratio of world prices pj/pi equals the slope of the potential PPF, defined by the left side of Equation (15).
Because resources are devoted toward control and cost
of ecosystem services has a risk premium, firms do not
earn the full world price. Firm j earns pj(1 aj)
(1 hj). Equilibrium along the net PPF occurs where
p ð1a Þð1h Þ
the ratio of returns to firms, pij ð1aij Þð1hji Þ, equals the
marginal rate of transformation.
Degradation that leads to damages will increase
the price of ecosystem services, equivalent to a
decrease in production efficiency. Firms can switch to
methods that use relatively more human-produced
inputs, but damages represent a loss of real
resources; the PPF will shift in.
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ð17Þ
P
j dYj =dpi
S
þ
d
Yj
S
=dpi
cj
þ
dej =dpi
ej
ð18Þ
The first term in Equation (18) is the scale effect.
It measures changes in pollution levels that result
from a change in the overall size of the economy,
assuming production mix and methods stay constant.
The scale effect is positive if the price rises and negative if the price falls (with a price increase/decrease it
is necessary to decrease/increase the scale of the
economy to get back to the original value).
The second term in Equation (18) is the composition effect. Keeping scale and production methods
constant, the composition effect gives the change in
pollution that occurs due to a change in the relative
amounts of each good produced.
Proposition 1. All else equal, an increase in the
price of good j causes the economy to produce more
of good j.
Proof. Increasing pj increases the right term in
Equation (15). The left side must also increase to
maintain the equality, implying that more factors are
employed in j. Output of j must increase, and output of i
must decrease. Q.E.D.
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If i = j, the composition effect will be positive if
and only if pj increases because Yj will be a larger
share of the economy. Production levels of other
goods, i 6¼ j, will decrease, and the composition
effect will be negative.
The third term in Equation (18) is the technique
effect. The technique effect is the change in pollution
that occurs from changes in the damage intensity of
producing good j.
pollution level. The economy starts at point A, where
the slope of the PPF is tangent to the ratio of prices,
P0. The line S0 denotes all production mixes with
value equal to the value of the original production
mix at world prices, i.e., it denotes the initial scale of
the economy. Following an increase in the price of
good 1 to P1, production shifts to point D. Total
pollution changes from ZA to ZD via composition,
scale, and technique effects. The composition effect,
|ZA ZB|, is found by keeping the scale constant
and allowing the share of goods to change to reflect
the price change. The scale effect, |ZB ZC|, is
found by keeping the composition constant and
increasing the scale to the new production point. The
price increase causes industry 1 to increase its damage intensity, shown by a rotation of the Z-line. The
size of the technique effect is |ZC ZD|.
The increase in pollution causes additional effects,
measured by the change in the cost of using ecosystem services (Equation 7). There is an effect from a
change in the mean size of damages and an effect
from a change in the frequency of events. Increases
in the price of ecosystem services cause firms to
switch to production methods that use larger
amounts of human-produced inputs. These substitution possibilities will reduce the size of welfare losses
(Warziniack et al., 2011), but this must be less efficient than the previous technology else it would have
been used in the first place. This represents a real
loss in resources, causing the PPF to shift in.
This change is shown graphically in panel (b) of
Figure 5. Increased pollution increases the number of
storms and damages per storm, causing the PPF to
shift in. With relative prices still equal to P1, the economy moves to point F where the slope of the shifted
Proposition 2. A good’s damage intensity is positively correlated with its price.
z
Proof. From the demand functions, ej ¼ Yjj ¼
aj pj =sj . Taking the derivative with respect to pj gives
dej
dpj
¼
aj
sj
[ 0. Q.E.D.
The technique effect only matters if i = j, in which
case an increase in pj leads to a positive technique
effect. Because control comes at the expense of output, the real cost of controlling goes up when the
price of the good rises.
Thus, the effect on the aggregate pollution level of
a pure price increase is ambiguous depending on
which industry experienced the increase. If the price
of a polluting good increases, then aggregate pollution will increase. If the price of a less polluting good
increases, then the effect cannot be signed without
further information.
We illustrate this decomposition in panel (a) of Figure 5 for a two-good economy. We assume that only
good 1 causes damages to the ecosystem. The upper
half of the graph is the usual two-good potential PPF,
and the lower half of the graph shows the aggregate
FIGURE 5. Decomposition of Changes in Degradation Following a Change in World Prices.
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AMERICAN WATER RESOURCES ASSOCIATION
A GENERAL EQUILIBRIUM MODEL
OF
ECOSYSTEM SERVICES
IN A
RIVER BASIN
GEMES to measure damages from economic and
ecological changes in the MARB.
Our intent is to highlight impacts of upstream economic development on downstream communities, and
so we maintain focus by using Iowa as a representative upstream agricultural economy and Louisiana as
the downstream economy affected. We assume that
upstream runoff affects shrimp production in Louisiana, but otherwise the two economies are modeled
separately. For each scenario, the Iowa GEMES
model is run, which determines nitrogen runoff from
Iowa. This amount is passed to the Louisiana
GEMES model where it is combined with runoff from
Louisiana agriculture to determine the size of the
hypoxic zone.
Model inputs are shown in Table 1. In most years,
there is a clear relationship between corn prices and
corn output (the economic link), corn output and size
of the hypoxic zone (ecosystem degradation), and size
of the hypoxic zone and shrimp catch (economic damages, accounting also for changes in the price of
shrimp). Note that in 2003 and 2006 corn was up in
production but shrimp catch was barely affected. One
explanation, explored in this study, is that reduced
rainfall in those years kept the hypoxic zone smaller
and the productivity of the Gulf higher than they
would have otherwise been.
The MARB model includes six producing sectors
(J = {corn and other grains, other agriculture,
commercial fishing, power, oil and gas production,
miscellaneous}). Firm output, capital, labor, and
intermediate inputs are calibrated to 2010 IMPLAN
data (Minnesota IMPLAN Group). We assume that H
(K, L, V) is constant elasticity of substitution and F
(H, E) is Cobb-Douglas. Calibration techniques are
described in Annabi et al. (2008) and Rutherford
(2002).
We use arable land as our ecosystem services for
grain production, measured by the cash rent equivalent of land. As defined by the Millennium Ecosystem
Assessment, this is a provisioning service (food production). Agricultural land is privately costed, with a
PPF equals the ratio of prices. Total change in pollution is |ZD ZF|. The figure has been drawn such
that industry 2 feels the greatest direct impact from
the increase, shown by a larger inward shift of the
PPF along the good 2 axis. There are composition and
scale effects. There is no technique effect because relative prices do not change. We calculate the composition effect by looking at the amount of pollution that
would occur with scale S1 and production mix equal to
that at point F. At this point, pollution equals ZE, and
the size of the composition effect is |ZD ZE|. The
size of the scale effect is found by comparing ZE with
that from the final level of output, |ZE ZF|. In this
example, production shifts toward relatively more of
good 2, causing a positive composition effect. The scale
effect is negative because pollution causes a loss in
real resources. In net, overall pollution increases. If
good 1 was heavily affected by the increase in k, the
composition effect may have been negative and overall
pollution in the economy could have declined.
NUMERICAL APPLICATION
We use our theoretical results to develop a computational model of ecosystem services, dubbed the
General Equilibrium Model of Ecosystem Services
(GEMES). GEMES is part of an ongoing effort to
apply general equilibrium models to ecosystem service valuation, to measure the contribution of ecosystem services to regional economies, and to isolate the
effects of economic shocks from environmental and
climatic shocks. This section is meant to demonstrate
how GEMES works and to demonstrate the importance of modeling multiple ecosystem services within
a river basin. This section highlights how interactions
and general equilibrium adjustments affect impact
measures. Complete computer code and a model
library with other GEMES examples are maintained
on the author’s website. In this section, we use
TABLE 1. Benchmark Data. Corn prices are annual average dollar per bushel received by U.S. corn producers (USDA Season-Average Price
Forecasts). Iowa corn production is in billions of bushels, taken from USDA National Agricultural Statistics Service. Shrimp price is in
dollars per ton. Shrimp catch is in thousand metric tons. Size of hypoxia is in square kilometers. Rainfall is amount of rainfall during the
growing season defined as May 1-Oct 31 (Source http://mesonet.agron.iastate.edu/request/coop/fe.phtml). Prices are adjusted for inflation.
Year
Corn price
Corn output
Shrimp price
Shrimp catch
Hypoxic zone
Rainfall
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OF THE
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
1.85
1.728
2,913
70.74
4,400
22.11
1.97
1.664
2,350
64.95
20,720
22.96
2.32
1.932
1,874
54.70
22,000
23.66
2.42
1.868
1,572
61.33
8,560
18.06
2.06
2.244
1,481
53.92
15,040
24.72
2.00
2.163
1,778
42.84
11,840
25.48
3.04
2.050
1,362
64.26
17,280
17.64
4.20
2.377
1,599
51.92
20,500
29.75
4.06
2.189
1,835
35.86
20,720
25.86
3.55
2.421
1,217
53.26
8,000
25.31
5.18
2.153
1,896
33.244
20,000
28.71
AMERICAN WATER RESOURCES ASSOCIATION
689
JAWRA
WARZINIACK
well-defined market value. Because it occurs on the
balance sheets, it is often studied (Iowa State University Extension, 1997; Sandhu et al., 2008) and
appears clearly in our benchmark data. It is estimated that rents from land make up about one-third
of production costs in grain farming. Agricultural
subsidies are included in the model as lump-sum payments from the government, not tied to the value of
land. Subsidies affect total sector output and may
cause market distortions. The computable general
equilibrium model includes these distortions, but they
are not directly addressed in this study. For discussion on tax distortions in general equilibrium, see
Goulder (1995).
The level of ecosystem services used in fishing
depends on available fish habitat and the quality of
that habitat, both of which manifest themselves in
the catchability of fish in the GOM. These are also
provisioning services as defined by the Millennium
Ecosystem Assessment. Habitat as a productive input
is not directly priced in the fishing industry, and so
accrues as nonmarketed rents to owners of the fishing fleet and to labor employed on the ships. We
assume one-fifth of the value of the fishing sector is
from nonmarketed ecosystem services.
Key features of general equilibrium models are the
links between sectors of the economy. These links are
particularly important when policies affect products
used as inputs in other sectors of the economy. Corn,
in our application, is a good example. Corn is used to
produce fuel, to feed livestock, and as an additive to
processed foods. Impacts to the corn sector should
have far-reaching impacts that will be picked up in
our general equilibrium model that would otherwise
be missed in a single-sector analysis.
We normalize damages in all industries such that
the risk free cost of providing ecosystem services
cE
j ¼ 1. We assume number of storms l(D) = rain/
rain0, where rain is the amount of precipitation in
Iowa between May 1 and October 31. Benchmark precipitation level rain0 is the year 2000 level. We do not
model the effect of precipitation in Louisiana.
Total degradation in our model is the amount of
nitrogen fertilizer applied to farms in Iowa and Louisiana that does not get absorbed by plants, accumulates in fields, and has potential to become runoff.
Iowa Extension estimates that 14% of farming costs
are on nitrogen fertilizer, about half of which is never
taken up by the plants (Iowa Policy Project). Therefore, in the benchmark, Z0 = Q0 9 0.14 9 0.5 =
0.07Q0, or w0 = 0.07.
Disposal of excess nitrogen is an unpriced ecosystem service in our application. It does not affect the
profitability of farming, but there is a shadow price
associated with it due to damages in the fishing sector. For fishing, we assume cost of ecosystem services
JAWRA
changes proportionally to changes in the size of the
hypoxic zone, such that kfish ¼ cE
fish þ lðDÞxfish ðZ; DÞ ¼
1 þ ðsize=size0 1Þ. Nitrogen accumulated on the
land is proportional to the amount of nitrogen applied
to the crop; delivery of nitrogen from land to water
depends on soil permeability, drainage density, temperature, precipitation, and a host of variables specific to the drainage. Alexander et al. (2008) find
nearly a one-to-one land-to-water delivery factor for
precipitation for nitrogen in their SPARROW model
for the Mississippi River basin. Simplifying somewhat
for our purposes, we assume Size of Hypoxic
Zone = q 9 Zgrain 9 (rain/rain0); therefore, xfish = qZ/
size0 rain/rain0. With this specification, per storm
damages increase in Z and decrease in the number of
storms each year given benchmark hypoxic zone of
4,400 km2 and degradation of 6,269 million kg N
(based on 0.07 9 Qo), q = 0.7.
A report on the effects of climate change in Iowa
(Takle, 2011) shows an 8% increase in precipitation
statewide between 1873 and 2008. Cedar Rapids saw
a 32% increase in precipitation over the same period.
Iowa also saw an increase in extreme precipitation
events, leading to more annual flooding. Such
changes are expected to cause denitrification of soils
due to saturation, increased soil erosion due to surface runoff, and increased nitrogen-nitrate runoff due
to wider use of tile drainage. Thus, damages to corn
arise from too much and too little rain. We assume
that the costs of ecosystem services in corn production (i.e., land) are quadratic in percent deviation
2
0
from benchmark rain, i.e., kcorn ¼ 1 þ rainrain
,
rain0
which gives xcorn = (rain rain0)2/(rain 9 rain0). For
all other sectors kj = 1 in the experiments shown in
this study.
NUMERICAL EXPERIMENTS
We run the calibrated model for each year between
2000 and 2010. In each year, we vary global prices of
corn and shrimp to reflect 2000-2010 levels and allow
for damages to ecosystem services. This scenario
serves as our base case. It includes impacts to the
fishing sector from both economic and environmental
forces. Using Equation (18), we decompose changes in
degradation into scale, composition, and technique
effects. We run one counterfactual, “prices only,” that
varies world prices but sets damages equal to zero.
We compare results of this first counterfactual with
those of the base case to disentangle impacts from
price changes from those of the environmental externality. We run a second counterfactual, “rain,” that
690
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AMERICAN WATER RESOURCES ASSOCIATION
A GENERAL EQUILIBRIUM MODEL
OF
ECOSYSTEM SERVICES
IN A
RIVER BASIN
The increase in nitrogen use can be decomposed into
scale, composition, and technique effects. The scale
effect is the increase due to an increase in the size of
the economy, calculated as the share of current nitrogen that would have occurred if the output were scaled
up in equal proportion to GDP. Percentage changes in
excess nitrogen due to the scale effect are therefore
very close to percentage changes in regional GDP. The
technique effect represents the change in nitrogen runoff due to changes in per unit degradation. Proposition
2 shows the technique effect will be larger in years
with the largest price increases, which is confirmed
here. Because the price of nitrogen does not change in
these scenarios, the size of the technique effect is proportional to the change in price of corn. The remaining
changes in nitrogen use are due to composition effects,
which represent a larger share of changes in nitrogen
use in Louisiana because the fishing sector contracts
at the same time the corn sector expands. Years with
large increases in the price of corn and large decreases
in the price of shrimp (for example, year 2004) cause
the largest composition effects.
adds the effects of precipitation in Iowa to the base
case.
BASE CASE: CHANGE IN GLOBAL PRICES
WITH ECOSYSTEM DAMAGES
Between 2000 and 2010, price per bushel of corn
rose 280% while the price of shrimp fell 35% (realworld, not simulated prices). The increase in the
world price made MARB corn more competitive and
led to more domestic corn production. Table 2 shows
model results for the base case.
The model projects an increase in corn production
of 206% in Iowa and 167% in Louisiana. In earlier
years, when the price of fishing falls more than the
price of corn rises, net welfare in Louisiana falls.
Increased corn prices increase the marginal return
from a unit of nitrogen fertilizer, providing an incentive to increase the application rate. Per unit damages for grain increase, so nitrogen runoff, and thus
the size of the hypoxic zone, increase relatively more
than the increase in corn output. By 2010, the simulation shows an increase in the size of the hypoxic
zone by more than 211%. The increased size of the
hypoxic zone increases the cost of ecosystem services
to GOM fishing. The Louisiana fishing sector shrinks
by 56%.
PRICES-ONLY COUNTERFACTUAL
This counterfactual “turns off” damages from
hypoxia (kj = 1, ∀j 2 J) to isolate the effects of market
TABLE 2. Base Case Numerical Results. Results are percent deviations from 2000 levels, with the exception of results
for scale, technique, and composition effects, which are percent of degradation changes due to that effect.
2000
Pcorn
Pfish
Iowa
GDP
Ycorn
kcorn
Degradation (Z)
Damage intensity (w)
Scale effect
Technique effect
Composition effect
Louisiana
GDP
Yfish
Ycorn
kcorn
kfish
Degradation (Z)
Damage intensity (w)
Scale effect
Technique effect
Composition effect
Ecological effects
Hypoxic zone
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0
0
2001
6
19
2002
25
36
2003
31
46
2004
11
49
2005
8
39
2006
64
53
2007
2008
2009
2010
127
45
119
37
92
58
180
35
0
0
0
0
0
0
0
0
0.85
6.76
0
6.85
6.486
0.797
6.091
93.112
3.40
26.74
0
27.18
25.405
2.671
20.259
77.070
4.41
32.52
0
33.09
30.811
3.110
23.554
73.336
1.49
11.86
0
12.03
11.351
1.334
10.194
88.472
1.06
8.46
0
8.58
8.108
0.980
7.500
91.520
8.91
69.15
0
70.74
64.324
5.216
39.145
55.639
18.61
141.39
0
146.26
127.027
7.558
55.952
36.490
17.38
132.41
0
136.77
119.459
7.343
54.433
38.224
13.04
100.30
0
103.08
91.892
6.422
47.887
45.691
27.69
206.48
0
215.90
180.00
8.767
64.286
26.948
0
0
0
0
0
0
0
0
0
0
0.061
21.13
5.30
0
6.71
5.30
6.486
0.058
6.091
93.966
0.008
40.80
21.05
0
26.62
21.05
25.405
0.007
20.259
79.748
0.015
51.12
25.63
0
32.41
25.63
30.811
0.012
23.554
76.458
0.178
51.10
9.31
0
11.78
9.30
11.351
0.163
10.194
89.968
0.150
40.65
6.64
0
8.40
6.62
8.108
0.140
7.500
92.640
0.233
61.17
54.78
0
69.28
54.81
64.324
0.151
39.145
60.705
0.863
59.92
113.24
0
143.27
113.40
127.027
0.405
55.952
43.643
0.818
53.37
105.89
0
133.97
106.04
119.459
0.397
54.433
45.170
0.466
67.35
79.83
0
100.97
79.91
91.892
0.259
47.887
51.854
1.477
56.48
167.07
0
211.48
167.43
180.00
0.552
64.286
35.162
0
6.710
26.624
32.411
11.782
8.397
69.283
143.265
133.965
100.970
211.476
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691
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TABLE 3. Prices-Only Counterfactual Results. Reported values are [percent change in base case]
[percent change in price only counterfactual] = [percent change due to hypoxia].
Louisiana
GDP
Yfish
Ycorn
kcorn
kfish
Degradation (Z)
Ecological effects
Hypoxic zone
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
0
0
0
0
0
0
0.0125
1.83
0.0019
0
6.7099
0.004
0.0332
5.1479
0.0044
0
26.6244
0.0108
0.0324
5.0966
0.0041
0
32.411
0.0105
0.0131
1.9627
0.0019
0
11.7822
0.0042
0.0116
1.7147
0.0018
0
8.3972
0.0037
0.047
7.9372
0.004
0
69.2834
0.0155
0.0796
14.7897
0.0002
0
143.2655
0.0272
0.0888
16.3327
0.0012
0
133.9651
0.0302
0.0516
9.132
0.0025
0
100.9705
0.0173
0.1091
21.4932
0.0078
0
211.4758
0.0385
0
0.0004
0.001
0.001
0.0004
0.0003
0.0014
0.0025
0.0028
0.0016
0.0035
decreased productivity of agricultural land. Farmers
respond by intensifying their use of nitrogen fertilizers. In all years, the amount of nitrogen applied to
the land increases. The downstream effects depend
on the amount of rainfall. For high rain years, Louisiana fishing declines by as much as 6%, as rain carries
a higher percentage of the nitrogen to the Gulf. In
2010, heavy rains joined a large increase in corn production to produce the largest hypoxic zone on record.
The increase was smaller in 2004; even though there
were heavy rains, corn prices kept nitrogen use low.
Reductions in rainfall in 2003 and 2006 kept the hypoxic zone 24 and 34% smaller than it would have
otherwise been. While drought harms the Iowa economy, it benefits the Louisiana economy. Heavy rains
harm both the Iowa and Louisiana economies.
fluctuations on the regional economy. Deviations from
the base case are shown in Table 3. Base case damages only occur in Louisiana, so the results for the
prices-only counterfactual for the Iowa economy are
the same as in the base case.
The prices-only scenario allows us to distinguish
between the effects of ecological damages and economic shocks. In 2010, for example, Louisiana fishing
declined by over 56% relative to the 2000 benchmark
(base case results). Twenty-one percent of that
decline is from hypoxia; the other 35% is from
changes in relative market prices. We also see that
total economic damages from growth of hypoxia are
0.1% of Louisiana’s GDP, or about $240 million annually. Because all impacts are relative to a baseline, it
is impossible to know what fishing output would be if
agriculture were to stop using nitrogen fertilizers
altogether.
In most years, corn output is (slightly) lower than
the base case, even though it is not directly affected
by hypoxia. This reduction is due to the drain of
hypoxia on the rest of the economy; it reduces the
efficiency of capital and labor that could otherwise be
productively employed. These feedbacks between the
economic and ecological systems mean hypoxia itself
causes a smaller hypoxic zone, though the effects are
modest.
POLICY CONSIDERATIONS
The GEMES model is useful for analyzing the costs
and benefits of policies to reduce nitrogen deliveries to
the GOM. Here, we consider two such policies: (1) a tax
on nitrogen fertilizers and (2) improvements in riparian zones and wetlands in agricultural areas. The policies modeled are meant to demonstrate how GEMES
can be used; while they are tied closely to recommendations in the literature, they are not calibrated to
actual policies.
A tax on fertilizers or, if possible, a tax on nitrogen
runoff (Table 5) are probably the most straightforward policies to implement in GEMES. The price for
agricultural pollutants is scorn, so a 3% tax on runoff
is modeled by setting scorn = 1.03. The effects of the
tax are largest in the years with the largest price
increases in corn. By raising the cost of farming, it
causes the corn sector to contract and other sectors to
expand. In 2010, the model shows about a 2% reduction in Iowa corn output. Reductions in pollution are
considerably larger than reduction in corn output,
RAIN COUNTERFACTUAL
This scenario “turns on” the effects of rain relative
to the base case scenario. In agriculture, increases in
precipitation cause denitrification of soils, increased
runoff, and increased use of tile drainage. Increases
in precipitation allow a greater percentage of nitrogen to reach the Gulf. Table 4 shows differences
between the rain and base case scenarios.
In all years, Iowa grain output falls in the rain scenario relative to the base case. This decline is due to
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AMERICAN WATER RESOURCES ASSOCIATION
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RIVER BASIN
TABLE 4. Rain Counterfactual Numerical Results. Reported values are [percent change in rain counterfactual]
[percent change in base case] = [percent change due to climate change].
Rain
Iowa
GDP
Ycorn
kcorn
Degradation (Z)
Louisiana
GDP
Yfish
Ycorn
kcorn
kfish
Degradation (Z)
Ecological effects
Hypoxic zone
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
0
3.844
3.049
23.669
36.877
3.074
30.769
68.651
13.076
2.127
13.433
0
0
0
0
0.0002
0.0104
0.1423
0.0003
0.0006
0.0393
0.4593
0.0012
0.0057
0.3598
4.1077
0.0118
0.0015
0.0946
1.2464
0.0026
0.0024
0.1481
2.0159
0.0039
0.0089
0.5573
5.123
0.0242
0.0207
1.2906
8.8738
0.0858
0.0059
0.3565
2.4595
0.0233
0.0039
0.2345
1.8299
0.0127
0.0203
1.2151
6.8622
0.113
0
0
0
0
0
0
0.0127
1.942
0.0019
0
7.8043
0.0041
0.0135
2.3564
0.0018
0
15.4288
0.0044
0.0324
5.0966
0.0041
0
32.411
0.0105
0.0195
3.2186
0.0029
0
23.7557
0.0063
0.0297
4.873
0.0045
0
29.751
0.0096
0.0368
6.4268
0.0031
0
59.5459
0.0122
0.0173
4.9448
0
0
109.844
0.0059
0.0124
3.3102
0.0002
0
54.2075
0.0042
0.0088
2.1064
0.0004
0
41.7428
0.0029
0.0140
4.6089
0.0010
0
116.0991
0.0049
0
4.1022
8.8777
24.2449
13.1974
16.5249
34.2058
84.1634
39.7063
29.0996
93.1106
TABLE 5. Tax on Nitrogen Runoff Results. Reported values are [percent change in tax policy]
[percent change in base case] = [percent change due to tax policy].
Iowa
GDP
Ycorn
kcorn
Degradation (Z)
Louisiana
GDP
Yfish
Ycorn
kcorn
kfish
Degradation (Z)
Ecological effects
Hypoxic zone
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
0.0344
0.2004
0
3.1118
0.0393
0.2285
0
3.3399
0.0557
0.3225
0
4.028
0.061
0.3527
0
4.2312
0.0432
0.251
0
3.5136
0.0406
0.2359
0
3.3975
0.1001
0.575
0
5.5592
0.2069
1.1695
0
8.3991
0.1914
1.0843
0
8.0293
0.1413
0.806
0
6.7468
0.3378
1.8799
0
11.2251
0.0029
0.0002
0.2038
0
0
3.1108
0.0027
0.8897
0.2286
0
3.3352
3.2883
0.0004
0.677
0.3142
0
4.01
3.8306
0.0016
0.5613
0.3415
0
4.2093
3.9907
0
0.5537
0.2494
0
3.5055
3.4254
0.0011
0.6704
0.2355
0
3.3917
3.3339
0.0055
0.457
0.5416
0
5.5114
5.036
0.0135
0.4949
1.0789
0
8.2959
7.3673
0.0121
0.5723
1.0013
0
7.9333
6.9768
0.009
0.3924
0.7495
0
6.676
5.9699
0.0230
0.5606
1.7344
0
11.0662
9.4826
3.1117
3.3352
4.01
4.2093
3.5055
3.3917
5.5514
8.2959
7.9333
6.676
11.0662
The benefits of riparian buffers are simulated in GEMES by altering the relationship between nitrogen
deposited on the land and the size of the hypoxic zone.
The costs of riparian buffers are measured by increases
in the amount of land required per unit of agricultural
output. A complete policy description would, therefore,
require an estimate of the effectiveness of the buffer
and the percentage of agricultural land required for
the buffer. In this example, we have assumed buffers
cut the percentage of nitrogen delivered to the GOM in
half (q = 0.35), a conservative estimate given the number of studies that show riparian vegetation routinely
removes as much as 90% of nitrates in the subsurface
water (Hill, 1996). We then solve for the amount of
land that can be retired to make this policy welfare
neutral; that is, to make the increase in Louisiana’s
GDP due to a smaller hypoxic zone equal the decrease
in Iowa’s GDP due to requiring more land per unit of
corn produced. Using the year 2000 for model simulations, we find land efficiency in Iowa can fall by as
showing agriculture’s ability to use less damaging
production methods when appropriate incentives
exist. In 2010, runoff from Iowa grain fields falls by
over 11%, and the size of the hypoxic zone decreases
by 11%. We do not consider the redistribution of tax
revenues, so pollution decreases by a technique effect
(reduced damage intensity), a composition effect (corn
contracts, other sectors expand), and a scale effect
(the tax reduces economy-wide incomes).
Pollution taxes on agriculture are problematic to
implement. Runoff is a nonpoint source pollutant,
monitoring is difficult, and taxes on agriculture are
politically unpopular. We, therefore, turn to policies
that promote healthy riparian zones. The effectiveness of riparian vegetation in removing nitrogen
from subsurface water has been well documented
(see Dosskey et al., 2010, for a review), and programs to restore riparian zones are often promoted
in the context of payments for ecosystem services
schemes.
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to substitute factors of production in farming keep the
impact modest compared to the benefits from a smaller
hypoxic zone. We also showed that improvements in
riparian zones would lead to large benefits to the Louisiana economy and probably not cost the Iowa economy
much. Due to political difficulties in instituting a tax
on agriculture and the growing interest in restoring
riparian zones through payments for ecosystem services schemes, we feel the latter is the better policy. In
both cases, however, Iowa pays for the gains achieved
in Louisiana; objections are likely to be strong among
Iowa stakeholders.
This model is useful in light of calls for “polluterpays” policies found in most OECD countries, championed by the European Community, and drafted into
the Rio Declaration. Such policies assume that we
can measure the damages directly caused by polluters. In reality, adjustments in the economy depend
on a suite of environmental factors. Isolating the
effects of each factor is necessary for economic efficiency and sound policy. Because of this complication,
the polluter-pays principle is rarely put into practice.
We offer a way forward.
much as 14% for the policy to provide net benefits to
society. Such a policy would benefit Louisiana (and
cost Iowa) about $1.7 million annually. It is unlikely
that a 14% reduction in productivity of agricultural
land would be needed to achieve such reductions in
runoff (Mitsch et al., 2001). Based on our model, therefore, riparian buffers seem a worthwhile investment
provided transfers between upstream and downstream
communities are possible.
CONCLUSION
Environmental problems like hypoxia and climate
change affect nearly all sectors of the economy, as do
many of the policies aimed at correcting them. Economic impact analysis, however, usually focuses on a
single sector. Such analysis misses the mark.
Here, we have shown that the portfolio of production activities in an economy has a large bearing on
the amount of pollution produced and the size of
damages. If the price of a dirty good rises, more pollution increases because (1) as the dirty sector
expands, competition for factors of production cause
cleaner industries to contract, (2) production methods
in that industry become dirtier, and (3) increases in
the price of produced goods lead to economic expansion, which causes output in all sectors to increase.
The first and last reasons for increased pollution fall
near the realm of economic growth, and it is hard to
believe policies would try to limit either industry or
economic expansion. On the other hand, the fact that
production methods become dirtier is somewhat
troubling and should be the target of environmental
policies.
On the damages side, industries contract because of
both economic forces and environmental damages.
Untangling those differences is important. In our
numerical example of the Mississippi-Atchafalaya
river basin, grain production in the upper basin causes
hypoxia, which in turn causes damages to the downstream fishing industry. We showed the size of these
damages is dependent on both environmental and
economic shocks, and while the potential damages to
fishing are large, most of the reduction in fishing output occurs from economic forces rather than a more
intensive use of nitrogen fertilizers. We have shown
that these damages are exacerbated by increases in
rainfall, which will likely get worse with climate
change.
We showed that a nitrogen tax makes agriculture
cleaner, reducing the amount of nitrogen runoff per
unit of corn production. The agricultural sector is nimble, and although a tax may be unpopular, the ability
JAWRA
LITERATURE CITED
Adger, W.N., T.P. Hughes, C. Folke, S.R. Carpenter, and J. Rockstr€om, 2005. Social-Ecological Resilience to Coastal Disasters.
Science 309(5737):1036-1039.
Alexander, R.B., R.A. Smith, G.E. Schwarz, E.W. Boyer, J.V. Nolan, and J.W. Brakebill, 2008. Differences in Phosphorous and
Nitrogen Delivery to the Gulf of Mexico from the Mississippi
River Basin. American Chemical Society, Environmental Science & Technology 42(3):822-830, doi: 10.1021/es0716103.
Annabi, N., J. Cockburn, and B. Decaluwe, 2008. Functional Forms
and Parametrization of CGE Models, MPIA Working Paper,
Partnership for Economic Policy, Quebec.
Copeland, B.R. and M.S. Taylor, 2003. Trade and the Environment:
Theory and Evidence. Princeton University Press, Princeton,
New Jersey.
Craig, J.K. and L.B. Crowder, 2005. Hypoxia-Induced Habitat
Shifts and Energetic Consequences in Atlantic Croaker and
Brown Shrimp on the Gulf of Mexico Shelf. Marine Ecology Progress Series 294:79-94.
Craig, J.K., L.B. Crowder, and T.A. Henwood, 2005. Spatial Distribution of Brown Shrimp (Farfantepenaeus aztecus) on the
Northwestern Gulf of Mexico Shelf: Effects of Abundance and
Hypoxia. Canadian Journal of Fisheries and Aquatic Sciences
62(6):1295-1308.
Donner, S.D. and D. Scavia, 2007. How Climate Controls the Flux
of Nitrogen by the Mississippi River and the Development of
Hypoxia in the Gulf of Mexico. Limnology and Oceanography
52(2):856-861.
Dosskey, M.G., P. Vidon, N.P. Gurwick, C.J. Allan, T.P. Duval, and
R. Lawrance, 2010. The Role of Riparian Vegetation in
Protecting and Improving Chemical Water Quality in Streams.
Journal of the American Water Resources Association 46(2):
261-277.
Goolsby, D.A., W.A. Battaglin, G.B. Lawrence, R.S. Artz, B.T. Aulenbach, R.P. Hooper, D.R. Kenney, and G.J. Stensland, 1999.
694
JOURNAL
OF THE
AMERICAN WATER RESOURCES ASSOCIATION
A GENERAL EQUILIBRIUM MODEL
OF
ECOSYSTEM SERVICES
OF THE
AMERICAN WATER RESOURCES ASSOCIATION
RIVER BASIN
Warziniack, T.W., D. Finnoff, J.M. Bossenbroek, J.F. Shogren, and
D.M. Lodge, 2011. Stepping Stones for Biological Invasion: A
Bioeconomic Model of Transferable Risk. Environmental and
Resource Economics 50: 605-627.
Zimmerman, R.J. and J.M. Nance, 2001. Effects of Hypoxia on the
Shrimp Fishery of Louisiana and Texas. Coastal and Estuarine
Studies 57:293-310.
Flux and Sources of Nutrients in the Mississippi-Atchafalaya
River Basin (Report of Task Group 3 to White House Committee
on Environment and Natural Resources, Hypoxia Work Group).
US Department of Commerce, National Oceanic and Atmospheric Administration Coastal Ocean Program, Silver Spring,
Maryland.
Goulder, L.H., 1995. Effects of Carbon Taxes in an Economy with
Prior Tax Distortions: An Intertemporal General Equilibrium
Analysis. Journal of Environmental Economics and Management 29(3):271-297.
Hein, L., K. van Koppen, R.S. de Groot, and E.C. van Ierland,
2006. Spatial Scales, Stakeholders and the Valuation of Ecosystem Services. Ecological Economics 57(2):209-228.
Hill, A.R., 1996. Nitrate Removal in Stream Riparian Zones. Journal of Environmental Quality 25(25):743-755.
Howarth, R.B. and R.B. Norgaard, 1993. Intergenerational Transfers and the Social Discount Rate. Environmental and Resource
Economics 3(4):337-358.
Iowa Geological Survey, 1992. Landsat data. www.igsb.uiowa.edu/
landcvr/landcvr.htm, accessed October 2013.
Iowa State University Extension, 1997. Nitrogen Fertilizer Recommendations for Corn in Iowa, Tech. Report Pm-1714, Iowa State
University Extension, May.
Jones, R.W., 1965. The Structure of Simple General Equilibrium
Models. The Journal of Political Economy 73(6):557-572.
Ketcheson, G.L. and W.F. Megahan, 1996. Sediment Production
and Downslope Sediment Transport from Forest Roads in
Granitic Watersheds. Intermountain Research Station, Forest
Service, U.S. Department of Agriculture, Odgen, Utah.
Mitsch, W.J., J.W. Day, Jr., J.W. Gilliam, P.M. Groffman, D.L.
Hey, G.W. Randall, and N. Wang, 2001. Reducing Nitrogen
Loading to the Gulf of Mexico from the Mississippi River Basin:
Strategies to Counter a Persistent Ecological Problem: Ecotechnology-the Use of Natural Ecosystems to Solve Environmental
Problems-Should be a Part of Efforts to Shrink the Zone of
Hypoxia in the Gulf of Mexico. BioScience 51(5):373-388.
NMFS (National Marine Fisheries Service), 2001-2010. Commercial
Landings Data, NOAA Office of Science and Technology, www.st.
nmfs.noaa.gov/commercial-fisheries/commercial-landings/, accessed
October 2013.
O’Connor, T. and D. Whitall, 2007. Linking Hypoxia to Shrimp
Catch in the Northern Gulf of Mexico. Marine Pollution Bulletin
54(4):460-463.
Rabalias, N. and R. Turner, 2000-2010. Data from Mid-Summer
Bottom Water Cruises of the Gulf of Mexico, Louisiana Universities Marine Consortium and Louisiana State University. www.
gulfhypoxia.net, accessed October 2013.
Robertson, D.M., G.E. Schwarz, D.A. Saad, and R.B. Alexander,
2009. Incorporating Uncertainty into the Ranking of SPARROW
Model Nutrient Yields from Mississippi/Atchafalya River Basin
Watersheds. Journal of the American Water Resources Association 42(2):534-549.
Rutherford, T.F., 2002. Lecture Notes on Constant Elasticity Functions, University of Colorado, Boulder, Colorado.
Sandhu, H.S., S.D. Wratten, R. Cullen, and B. Case, 2008. The
Future of Farming: The Value of Ecosystem Services in Conventional and Organic Arable Land. An Experimental Approach.
Ecological Economics 64(4):835-848.
Settle, C., T.D. Crocker, and J.F. Shogren, 2002. On the Joint
Determination of Biological and Economic Systems. Ecological
Economics 42(1-2):301-311.
Takle, E.S., 2011. Climate Changes in Iowa, Report to the Governor and the Iowa General Assembly, 8 pp.
USGCRP, 2009. Global Climate Change Impacts in the United
States, T.R. Karl, J.M. Melillo, and T.C. Peterson (Editors).
United States Global Change Research Program. Cambridge
University Press, New York City, New York.
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IN A
695
JAWRA