Journal of Environmental Management 79 (2006) 305–315
www.elsevier.com/locate/jenvman
The conservation benefits of cost-effective land acquisition:
A case study in Maryland
Kent Donald Messer *
Department of Applied Economics and Management, Cornell University, 454 Warren Hall, Ithaca, NY 14850, USA
Received 13 December 2004; revised 28 May 2005; accepted 26 July 2005
Available online 25 October 2005
Abstract
Economic theory asserts that to achieve maximum conservation benefits land acquisition needs to be cost effective. Yet the most common
planning technique used by land conservation organizations is ‘benefit-targeting’ that focuses only on acquiring parcels with the highest benefits
and ignores costs. Unlike most of the literature which focuses on covering problems, this research applies optimization techniques to achieve
maximum aggregate conservation benefits for an ongoing land acquisition effort in the Catoctin Mountain Region in central Maryland. For this
case study, optimization yields additional conservation benefits worth an estimated $3.1–$3.9 million or achieves the same level of conservation
benefits but at a cost savings ranging from $0.9 to $3.5 million, depending on the initial budget size. Finally, the highest efficiencies are achieved
in low budget scenarios, like those most prevalent in conservation efforts.
q 2005 Elsevier Ltd. All rights reserved.
Keywords: Reserve selection; Binary linear programming; Benefit-targeting
1. Introduction
In its essence, the acquisition of land parcels to achieve
conservation objectives given a limited budget is a basic
economic program. Over the past 5 years, voters in the
United States have approved $20 billion in local and state
funding to acquire properties and/or development rights in
areas that they consider to offer significant ecological
benefits and Land Trusts have protected an additional
500,000 acres per year for the past 10 years (Land Trust
Alliance, 2004). These types of conservation organizations
typically invest significant resources in measuring and
mapping the various ecological benefits of areas targeted
for potential acquisition. However, seldom is equivalent
effort given to estimating parcel acquisition costs nor are
these cost estimates used to maximize aggregate conservation benefits. Instead, conservation organizations frequently
acquire the parcels with the highest ranked conservation
benefits until their budget is exhausted. This type of
planning can be referred to as ‘benefit-targeting’ (BT).
* Tel.: C1 607 255 4223; fax: C1 607 255 9984.
E-mail address: [email protected].
0301-4797/$ - see front matter q 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jenvman.2005.07.008
A number of economic studies have cautioned against
directing financial resources towards land acquisition in areas
without regard to cost, especially when the parcels with the
highest ecological values also tend to be the most expensive
(Ando et al., 1998; Babcock et al., 1996; 1997; Church et al.,
1996; Polasky et al., 2001; Wu et al., 2001) and when the
parcels’ costs are relatively more heterogeneous than the
benefits (Ferraro, 2003a). Yet, a significant gap exists between
the theoretical understanding of the problem and the actual
practices of conservation organizations (Prendergast et al.,
1999). One reason for this gap is that little research has been
done to measure the additional conservation benefits or cost
savings that may result from applying optimization techniques
to specific ongoing conservation efforts. Without compelling
case studies, adoption of more efficient methods may remain an
elusive goal.
Another challenge facing adoption is that most of the
existing literature on reserve site selection has focused on
‘covering’ problems, which are more abstract than the
problems facing most conservation organizations. Initially,
the literature on covering problems used information
regarding the distribution of important conservation objects,
such as endangered species, among potential preserve areas
and sought to determine the minimum number of preserves
needed to protect as many species as possible. Later studies
investigated how a maximum number of species could
be conserved within a given number of protected areas
306
K.D. Messer / Journal of Environmental Management 79 (2006) 305–315
(for a review, see Cabeza and Moilanen, 2001; ReVelle
et al., 2002; Rodrigues and Gaston, 2002). It is well known
that BT does not yield optimal results for covering problems
(Underhill, 1994; Rodrigues et al., 2000; Rodrigues and
Gaston, 2002).
This research uses similar analytical techniques as the
literature on covering, but applies these techniques to the
problem of how a conservation organization can maximize
aggregate benefits within a specific conservation target. This
case study analyzes the State of Maryland’s efforts to conserve
additional areas in the Catoctin Mountains Region, the location
of the US presidential retreat of Camp David. Binary linear
programming (BLP) guarantees optimality by favoring the
acquisition of ‘Best Buy’ parcels and selecting a cost-effective
portfolio (or set) of parcels given a particular budget level. In
this study, the Conservation Value of each of the potential
acquisitions is evaluated independently from one another and
the conservation organization seeks to obtain the most benefits
given a specific budget constraint. This research documents the
efficiency gains of using the BLP instead of BT and shows that
the largest efficiency gains are achieved in the low budget level
scenarios, the scenarios most prevalent in conservation
settings.
This paper is organized as follows. The second section
will outline the theory underlying the two models. The third
section describes the key biophysical characteristics and cost
information involved in the Catoctin Mountain case study.
The fourth section compares the results of the two models
given the priorities established by the State of Maryland’s
Department of Natural Resources (Maryland DNR) and
describes different extensions of this type of analysis. The
fifth section discusses the implications of this research and
offers final comments.
a conservation organization could incorporate BLP in its
planning process.1
2.1. Binary linear programming (BLP)
This paper uses a variation of integer linear programming,
referred to as ‘binary’ linear programming, where the integers
are limited to zero or one. In this case, the binary choice is
whether to ‘protect’ or ‘not protect’ a particular parcel. BLP
takes into account both the benefits and costs of each parcel
and evaluates all of the possible purchase combinations that
lie within the specified budget constraint and selects the
portfolio which yields the highest possible aggregate
Conservation Value. Let iZ1,2,.,I denote an index for
various parcels of land. Let jZ1,2,.,J denote the index for
scores of the various biophysical attributes. The conservation
benefit of the ith parcel for the jth attribute is denoted by Ai,
jR0. Each of the J attributes is assigned a subjective weight,
denoted Wj. This weight reflects the relative importance a
conservation organization gives a certain attribute. Consequently, the Conservation Value (Vi) of the ith parcel is
given by:
Vi Z
For nearly three decades (Church and ReVelle, 1974),
academics have discussed how ‘greedy style heuristics’ often
provide suboptimal solutions and have advocated the use of
integer linear programming (Church et al., 1996). Integer linear
programming is a calculation-intensive process that finds the
optimal solution to a problem with multiple attributes and
constraints using a branch-and-bound algorithm. However, the
potential conservation benefits of applying integer linear
programming to conservation efforts did not gather significant
attention until Underhill (1994) pointed out that only integer
linear programming guaranteed an optimal solution. A further
limitation of integer linear programming has been the
complexity of its calculations, which took considerable time
even with the most sophisticated computers. However, with
advances in computer technology, planning tools based on
integer linear programming can be used on problems that were
previously nearly impossible to solve (Onal, 2004; Azzaino
et al., 2002; Rodrigues and Gaston, 2002; Camm et al., 1996;
Pressey et al., 1996; Csuti et al., 1997). This section outlines
the theoretical basis for both BLP and BT and describes how
Wj Ai;j
(1)
jZ1
BLP seeks to maximize the aggregate Conservation Value of
the portfolio given by V(X):
Max VðXÞ Z
I X
J
X
Xi Wj Ai;j
(2)
iZ1 jZ1
Subject to a budget constraint (B)
I
X
2. Theory
J
X
C i Xi % B
(3)
iZ1
and XiZ{0,1}, where XiZ0 indicates that the ith parcel is not
recommended for acquisition and XiZ1 indicates that the ith
parcel is recommended for acquisition.2 The vector XZ
[X1,X2,.,XI] represents the portfolio of the conservation
organization, where initially X is a vector of zeros.3 If the
conservation organization uses its financial resources to
acquire parcel iZ7, X7 changes from X7Z0 to X7Z1.
1
An alternative technique, referred to as ‘Benefit–Cost Ratio Targeting,’ is
used by the United State Department of Agriculture’s Conservation Reserve
Program and Environmental Quality Incentives Program. While this research
does not focus on the comparative advantages of BLP, Appendix A provides a
simple numerical example demonstrating that Benefit–Cost Ratio Targeting
does not guarantee optimality and can be quite inefficient when dealing with
situations with multiple constraints.
2
Alternatively, the acquisition of parcels could be considered as continuous
instead of discrete. This may be reasonable given that (i) land can be split,
(ii) conservation easements can be used instead of just acquisition, and
(iii) additional revenue can always be raised for a single parcel for which the
current budget is not sufficient enough to acquire.
3
In this research, the tolerance within Solver was set to zero and there were
no problems with non-convergence.
K.D. Messer / Journal of Environmental Management 79 (2006) 305–315
Given the increasing use of Geographic Information System
(GIS) software, organizations frequently have (or can readily
obtain) information on conservation benefits and acquisition
costs at a parcel-specific level. Tables of this parcel-specific
data can then be exported from GIS to Microsoft Excel, where
a binary optimization problem can be solved using Frontline
System’s Premium Solver V3.5. The results can then be
quantitatively analyzed within Excel and exported back to GIS
for special analysis.
307
iterative process, parcels are selected in this manner until the
financial resources are exhausted.
Note that if all the land costs were identical, then BT and BLP
would yield the same conservation portfolio. However, in cases
where land costs are heterogeneous, the efficiency of BLP will
become evident. In general, the efficiency of BLP is high when
parcels’ benefits and costs are positively correlated (see Babcock
et al., 1997). As pointed out by Ferraro (2003a), the efficiency
difference can be largest in situations, like this case study, where the
costs are relatively more heterogeneous than the benefits.
2.2. Benefit-targeting (BT)
3. Case study—Catoctin Mountains, Maryland
BT is used commonly in national and international
conservation efforts. The primary advantage of this technique
is that a conservation agency can identify the lands they want to
acquire without having to collect cost information until they
enter purchase negotiations. However, this advantage comes
with the disadvantage that the aggregate lands purchased do not
necessarily maximize aggregate conservation benefits. BT uses
the same linear equation to derive each parcel’s Conservation
Value (Eq. (1)). BT’s portfolio is determined by ranking all of
the parcels from highest to lowest based on the Conservation
Values and selecting as many of the highest-ranked parcels as
possible given the available budget.
Formally, BT’s selection algorithm can be written as follows.
Let R($) denote the rank operator over the Conservation Values
Vi. Let RiZR(V1,.,VI) be the rank of the ith parcel. The parcel
or parcels with the highest Conservation Value receive a rank of
one. Again, let XiZ{0,1}. After ranking all of the parcels, they
are arrayed in the following format:
Rank
Parcels
Cost
1
Xi ; Xk ; Xl
Ci ; Ck ; Cl
2
Xm ; Xn
Cm ; Cn
«
«
«
R
Xr
Cr
In situations, where the parcels have equal rank, the
conservation organization tries to purchase the lowest cost
parcel. For example, if parcels i, k, and l have the same rank and
Ci!Ck!Cl, then:
Xi Z 1
if
Ci % B
Xi Z 0
if
Ci O B
Xk Z 1
if
Ck % BKXi Ci
Xk Z 0
if
Ck O BKXi Ci
Xl Z 1
if
Cl % BKðXi Ci C Xk Ck Þ
Xl Z 0
if
Cl O BKðXi Ci C Xk Ck Þ
In May 2001, Maryland Governor Parris N. Glendening
signed legislation authorizing the establishment of the ‘GreenPrint’ program and the expenditure of $35 million to help
preserve two million acres of the state’s ecologically
significant areas. Since approximately three-quarters of this
identified area lacks formal protection and faces the threat of
development, the legislation sought to protect the ‘Green
Infrastructure’ (GI) of ecologically rich ‘hubs’ connected
together by habitat ‘corridors’ (Maryland DNR, 2001). The
GreenPrint legislation specifically called for all conservation
funds to be directed to areas within the targeted Green
Infrastructure. The GreenPrint program is administered by the
Maryland DNR, and the Catoctin Mountain Region was
selected as an area of special attention given its ecological,
historical, and political importance.
The Catoctin Mountains are situated in central Maryland and
are an extension of the Blue Ridge geological feature of the
Appalachian Mountains. This study focuses on the North Catoctin
area which lies primarily within Frederick County. The agricultural
lands to the east of the mountains tend to have lower ecological
values than the mountain areas, which extend from south to north.
The Catoctin Mountains already have significant areas protected
by a variety of local, state, and federal agencies and are also the
location for the Presidential retreat of Camp David.
This research involved an analysis of 186 parcels in the
Catoctin Mountain Region that were identified by the GreenPrint program as being within a special designation called the
Green Infrastructure and having the highest Ecological Score
(defined below). To determine the Conservation Value of each
parcel in the Catoctin Mountain area, Maryland DNR developed
scores for a variety of biophysical attributes based on data
sources including land cover, soil composition, proximity to
roadways, watershed boundaries, and endangered species
habitat (Maryland DNR, 2001).4
An expert panel assembled by the GreenPrint program
identified five biophysical attributes as being the most critical
in determining the Conservation Value for a particular parcel.
Table 1 summarizes the key statistics for these five attributes.
and so on:
BT can be viewed as a type of ‘greedy agent’ algorithm, where
once all parcels have been ranked the agency seeks to purchase
the parcel with the highest Conservation Value that it can afford
from the remaining set of unprotected parcels. Through an
4
The determination of the ‘correct’ biophysical attributes and corresponding
weights to measure conservation benefits is in itself an area of investigation.
This research takes as a given the attributes and weights determined by
Maryland DNR and focuses on demonstrating the efficiency of BLP within the
existing Maryland DNR planning framework.
308
K.D. Messer / Journal of Environmental Management 79 (2006) 305–315
Table 1
Descriptive statistics of the five biophysical attributes, conservation value, and parcel cost
Min
Max
Mean
Median
St. dev.
Coefficient
of variation
Acres of GI
Percent GI
Ecological Score
Protected land w/in 1 mile
Percent gain
Conservation value
Parcel cost
18.0
392.0
55.3
40.0
10.3
15.0%
100.0%
93.6%
100.0%
41.5%
53.7
94.6
86.0
86.9
18.2
0
2864
596
511
1275
0.1%
36.5%
1.5%
0.3%
18.0%
1.16
6.16
3.74
3.76
2.04
0.55
$3250
$766,530
$78,153
$66,930
$360,057
4.61
3.1. Acres of Green Infrastructure (acres of GI)
3.6. Conservation Value
This attribute is the number of acres within each parcel that
are part of the designated Green Infrastructure.
The Maryland DNR expert panel weighted the relative
importance of each of the biophysical attributes (Maryland
DNR, 2002). The weights (Wj) are as follows:
3.2. Percent of parcel’s acreage within the Green
Infrastructure (Percent GI)
Acres of Green Infrastructure ðW1 Þ :
2
Percent of Green Infrastructure ðW2 Þ : 1
This attribute is the amount of a particular parcel that lies
within the Green Infrastructure. For example, if a 50-acre
parcel has 35 acres within the GI, then it would be given a score
of 70%, while a 50-acre parcel that has all 50 acres within the
GI would be given a score of 100%.
3.3. Ecological Score
This attribute is calculated as the average score from a
variety of ecological conditions as identified by the GreenPrint
program. Factors included 16 different measures which include
both local features, such as the occurrence of rare flora and
fauna, and measurements from a landscape context, such as the
importance of the hub/corridor within the ‘physiographic
region’ (Maryland DNR, 2001). The parcel’s Ecological Score
is calculated based on the acreage of the parcel that lies within
the Green Infrastructure.
3.4. Protected land within 1 mile
This attribute is the number of previously protected acres
within 1 mile of the parcel.
3.5. Percent Gain to the Green Infrastructure
(Percent Gain)
This attribute is a measure of a parcel’s relative size
compared to its local hub or corridor of the Green Infrastructure, as identified by the GreenPrint program. In general,
the size of the hubs tends to be greater than the size of the
corridors. Therefore, this attribute has a wide range of values.
The majority of the parcels have scores less than 1%, but
some parcels have scores up to 36.5% and because the study
area includes several hubs and corridors, it is possible for a
portfolio to have an aggregate Percent Gain greater than
100%.
Ecological Score ðW3 Þ :
3
Protected Land within 1 mile ðW4 Þ :
1
Percent Gain ðW5 Þ :
1
The data for each of the biophysical attributes was normalized
to a scale from zero to one. The normalization establishes a
common metric for each of these attributes while preserving
the relative scores that a parcel has for each attribute. The
normalization equation is as follows:
NVi Z
Ai;j KAmin
j
min
Amax
KA
j
j
(4)
max
are the lowest and highest scores for
where Amin
j and Aj
each of the biophysical attributes, respectively. Consequently,
a parcel with the lowest value has a normalized score of zero
for that attribute and a parcel with the highest value has a
normalized score of one for that attribute. With these
normalized attributes, the parcel’s Conservation Value (Vi)
can be calculated by summing the product of each of the
normalized scores and each of the weights. Specifically, the
equation for a parcel’s Conservation Value is:
Vi Z 2 !ðAcres of GIÞ C 1 !ðPercent GIÞ C 3
!ðEcological ScoreÞ C 1
!ðProtected Land within 1 mileÞ C 1
!ðPercent GainÞ
(5)
In this study, the Conservation Values for the parcels are
roughly normally distributed (skewZ0.84) with a minimum of
1.36, a maximum of 6.99, a mean of 3.74, a median of 3.78, and
a standard deviation of 0.58. The aggregate Conservation Value
of 186 parcels included in this study is 695.38.
K.D. Messer / Journal of Environmental Management 79 (2006) 305–315
3.7. Cost (Ci)
The cost of each parcel was provided by the State of
Maryland’s MdProperty View. This research used the ‘land
value’ estimates for each parcel, which Maryland DNR
determined to be a better estimate of the market costs being
paid by the ongoing land acquisition effort. The land values
ranged from $3250 to $766,530. The general distribution was
skewed with a tail to the right (skewZ5.27) and had a mean
cost of $78,153, a median cost of $66,930, and a standard
deviation of $72,027. The costs per acre are more normally
distributed, though still skewed to the right (skewZ1.18) with
a mean cost of $1527 per acre, a median cost of $1323 per acre,
and a standard deviation of $2454. As discussed earlier, the
highest discrepancy between the efficiency of BLP and BT is
when benefits and costs are positively correlated, rZ0.37.
Additionally, the heterogeneity of costs, as measured by the
coefficient of variation (standard deviation divided by the
mean), is relatively much higher for costs, 4.61, than it is for
Conservation Values, 0.55 (Table 2).
3.8. Budget (B)
The Maryland DNR estimates that annual conservation
spending in the Northern Catoctin Mountain region would be
$1 million. However, Maryland DNR was also interested in a
broader, long-range analysis, so this study also explored the
results from a $2.5 million budget and a $5 million budget
(Wolf, personal communication).
4. Results
309
guarantee that its overall portfolio will have the highest
aggregate Conservation Value possible. In fact, BT may select
a portfolio that falls dramatically short of achieving the
maximum possible aggregate Conservation Value. In contrast,
BLP takes into account both the benefits and costs of each parcel
and selects the portfolio which yields the highest aggregate
Conservation Value.
When both of these techniques were run using the same
data, weights, and costs described above, the efficiency of
BLP’s portfolio in comparison to the BT’s portfolio becomes
readily apparent. As the Lorenz curves in Fig. 1 demonstrate,
BLP achieves a much higher percentage of the total possible
Conservation Value at all budget levels than does BT,
especially at the lower budget levels that are most relevant to
the ongoing acquisition efforts. With a budget of $1 million
(6.9% of the total cost of the study area), BLP achieves an
impressive 24.7% of the total possible Conservation Value of
the study area, while BT achieves only 5.0% of the total
possible Conservation Value. Similarly, for budgets of $2.5
million and $5 million (17.2 and 34.4% of the total cost,
respectively) BLP achieves 40.8 and 60.3% of the total
possible Conservation Value, while BT achieves only 11.5 and
30.6% of the total possible Conservation Value, respectively.
A useful measure of the relative efficiency between the two
techniques is the Gini coefficient, which can be derived by
calculating the area between the Lorenz Curves and the 458 line
(see Babcock et al., 1996; 1997). The formula for the Gini
coefficient is
21
3
ð
1
G Z 24 FðBÞdBK 5
2
(6)
0
As previously discussed, BT represents the common
decision-rule (algorithm) used by conservation organizations
in land acquisition efforts. While BT guarantees the selection of
the parcels with the highest Conservation Values, it does not
where F(B) is the fraction of the total conservation benefits
achieved at a budget level, B. The Gini coefficient
was calculated by subtracting the area under the 458 line
Table 2
Results of BT and BLP portfolios
BT
$1 million budget
Parcels
Conservation Value
Cost
Acres protected
Ecological Score
$2.5 million budget
Parcels
Conservation Value
Cost
Acres protected
Ecological Score
$5 million budget
Parcels
Conservation Value
Cost
Acres protected
Ecological Score
BLP
Times greater
Percent increase
24.2%
24.7%
6.9%
18.8%
24.6%
6.4
4.9
1.0
1.6
6.2
542.9%
394.2%
K0.1%
64.0%
524.5%
74
283.5
$2,498,170
3333
6469
39.8%
40.8%
17.2%
32.4%
40.4%
4.4
3.6
1.0
1.6
4.2
335.3%
255.3%
0.0%
57.8%
324.5%
111
419
$4,995,740
5070
9615
59.7%
60.3%
34.4%
49.3%
60.1%
2.3
2.0
1.0
1.4
2.2
126.5%
96.9%
K0.1%
41.9%
116.6%
Values
% of total
Values
% of total
7
34.7
$999,700
1178
629
3.8%
5.0%
6.9%
11.4%
3.9%
45
171.5
$998,970
1932
3928
17
79.8
$2,497,320
2112
1524
9.1%
11.5%
17.2%
20.5%
9.5%
49
212.8
$4,999,000
3754
4440
26.3%
30.6%
34.4%
34.7%
27.8%
310
K.D. Messer / Journal of Environmental Management 79 (2006) 305–315
100%
90%
Percent of Total Benefits
80%
70%
60%
50%
40%
30%
20%
10%
0%
0%
BLP: Conservation Value
individual attributes, due to the general correlation between an
individual parcel’s Conservation Value and the weighted
biophysical attributes. Note that while BLP will frequently
achieve a higher score on the various biophysical attributes,
this is not guaranteed. To ensure a minimal score for a
particular attribute (i.e., protected acres of GI), an additional
constraint specifying this minimal score could be added. For
example, suppose the conservation organization has a goal of
purchasing a minimum number of acres of land (H), where
each parcel size is denoted by Hi. In this case, BLP would seek
to maximize aggregate Conservation Value (Eq. (1)) subject to
budget constraint (Eq. (2)) and a lower bound constraint on the
number of acres (Eq. (7)):
BLP: Acres Protected
45 degree line
BT: Conservation Value
BT: Acres Protected
I
X
Hi X i R H
(7)
iZ1
10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Percent of Total Cost
Fig. 1. Lorenz Curves for BLP and BT.
from the area of the trapezoids for 21 budget scenarios and
multiplying this number by two. Using this measure, BLP has
an efficiency of 74.4% while BT has an efficiency of K7.9%.
The greater the difference is between the two Gini Coefficients,
the greater the loss of efficiency in using BT instead of BLP.
Fig. 1 also demonstrates the relative efficiency for other
biophysical attributes, such as GI Acres Protected. BLP not
only achieves the highest possible aggregate Conservation
Value, but also generally yields higher scores for each of the
However, depending on the values for the budget constraint
and the minimum number of acres to protect, a portfolio may
not exist which can satisfy both constraints.
The relative efficiency of BLP can be shown through a
direct comparison of attribute statistics and maps at the three
different budget levels. For example, Fig. 2 highlights
the parcels recommended for purchase by BT and BLP at the
budget level of $2.5 million. The parcels considered by
the techniques either are shown in black (recommended for
purchase) or in light gray (not recommended). Currently
protected parcels are represented in a darker shade of gray and
the city of Thurmont is marked by slashes. The box on
Fig. 2. BT and BLP conservation portfolios with $2.5 million budget.
K.D. Messer / Journal of Environmental Management 79 (2006) 305–315
the bottom right-hand side of each map summarizes the key
statistics for the portfolios selected by each technique.
As anticipated, BLP achieves a significantly higher aggregate
Conservation Value than BT. For the budget scenarios of
$1 million, $2.5 million, and $5 million, BLP has an aggregate
Conservation Value, which is 4.9, 3.6, and 2.0 times larger than that
achieved by BT, respectively (Table 2). Likewise, BLP achieves
higher scores for each of the biophysical attributes than BT. For
example, BLP with a $2.5 million budget preserves 1.6 times as
many acres of Green Infrastructure and scores 4.2 times higher for
the aggregate Ecological Score.
The cost savings of BLP can be calculated in a variety of ways
(Table 3). One way is to calculate how much money would be
required for BT to achieve an equivalent aggregate Conservation
Value as BLP. For the three budget scenarios, the additional cost
would be $3.1, $3.7, and $3.9 million, respectively. As shown in
Fig. 1, the relative benefits, and thus cost savings, of BLP increase
at a decreasing rate. For example, with a $2.5 million budget, BLP
achieves an aggregate Conservation Value of 283.5, while BT can
achieve this aggregate Conservation Value at a cost of $6,176,960
(147.3% higher).
Another way of considering the cost effectiveness of BLP
is to calculate the minimum expenditure required for BLP to
achieve an aggregate Conservation Value equivalent to BT at a
specific budget level. This problem can be solved as a binary
optimization problem where the objective is to minimize total
cost such that the aggregate Conservation Value is greater than
or equal to the aggregate Conservation Value from BT. For
the three budget scenarios, BLP can achieve the aggregate
Conservation Value of BT for just $84,930, $290,030, and
$1,492,200 (a cost savings of $0.9 million, $2.2 million and
$3.5 million, respectively).
The close-up examination of the study provides an intuitive
explanation for the results discussed above. For example,
Table 3
Estimated cost savings
Aggregate
Conservation
Value
Cost
Difference
Money BLP would need to achieve same aggregate Conservation Value as BT
$1 million budget
BT
35
$1,000,000
K$915,070
BLP
35
$84,930
$2.5 million budget
BT
80
$2,500,000
K$2,209,970
BLP
80
$290,030
$5 million budget
BT
213
$5,000,000
K$3,507,800
BLP
213
$1,492,200
Money BT would need to achieve the same aggregate Conservation Value
as BLP
BT
172
$4,125,120
$3,125,120
BLP
172
$1,000,000
BT
284
$6,176,960
$3,676,690
BLP
284
$2,500,000
BT
419
$8,938,980
$3,938,980
BLP
419
$5,000,000
311
Table 4
Biophysical attributes of the two “Cadillac” parcels
Parcel #1
Conservation Value
Cost
Acres of GI
Percent GI
Ecological Score
Protected within 1 mile
Percent gain
Parcel #2
Values
Rank
Values
Rank
5.47
$258,070
168
100.0%
93.3
2104
1.2%
3
5
7
1
11
2
18
6.99
$766,530
392
100.0%
93.4
2864
2.9%
1
1
1
1
8
1
12
consider two unprotected parcels in the middle of the Catoctin
Mountain Region (Parcel #1 and Parcel #2), which are
surrounded by large tracts of already protected lands. These
two tracts are among the largest parcels, have some of the highest
ecological scores, and have the highest number of acres protected
within 1 mile (Table 4). Consequently, they rank 3rd and 1st in
Conservation Value, respectively.
Since BT selects the parcels with the highest Conservation
Values, Parcel #2 is selected in all budget scenarios and Parcel
#1 is selected with the $2.5 and $5 million budgets. However,
BLP does not select either of these parcels under any budget
scenario. The reason for the differences between these two
techniques is that Parcel #1 and Parcel #2 are among the most
expensive of the study area, which is consistent with the positive
correlation between benefits and costs. Parcel #1 is the 5th most
expensive to purchase at a cost of $258,080 and Parcel #2 is the
most expensive to purchase at a cost of $766,530. Only six other
parcels cost more than $200,000 and just 37 parcels cost more
than $100,000. While Parcel #1 and Parcel #2 cost several times
more than other parcels, they do not provide a corresponding
increase in conservation benefits. In fact, 49 other parcels
(26.3%) have Conservation Values greater than 4.0 and 172
parcels (92.5%) have scores higher than 3.0.
This large difference in costs relative to the small difference
in conservation benefits explains why BLP consistently
excludes these high cost parcels from its portfolio. Since
BLP selects the cost efficient portfolio, these two highConservation Value, high-cost parcels are excluded in favor
of high Conservation Value, low-cost parcels. In other words,
BT selects the most expensive ‘Cadillac’ parcels of the study
area, such as parcels #1 and #2, while BLP selects the ‘Best
Buy’ parcels as low-cost substitutes and in the process achieves
a higher level of aggregate benefits.
In addition to determining the optimal portfolio, this
research explored a couple of real-world extensions.
4.1. Threat of development
The threat of development facing a particular parcel can be
included in a static analysis.5 In this case, the Conservation
Value of the parcel may need to include the probability that
5
Alternatively, the issue of development threat could be treated as a dynamic
problem (see Costello and Polasky, 2004).
312
K.D. Messer / Journal of Environmental Management 79 (2006) 305–315
some of its biophysical attributes will be diminished or
destroyed in the foreseeable future. To capture this factor, a
metric can be developed to represent the probability that a
parcel will be developed if not protected. For example, a parcel
with a high development risk would be given a higher score,
while a parcel that faces minimal development risk would be
given a lower score. By including this factor, financial
resources can be directed towards the parcels which are more
likely to lose their biophysical values if not protected. It would
be possible to weight each parcel i by Pi, the subjective
probability that the ith parcel will be developed at some future
date. The expected aggregate risk-weighted Conservation
Value of the portfolio XZ[X1,X2,.,XI] becomes:
EfVðXÞg Z
I
X
iZ1
Pi Xi
J
X
jZ1
Wj Ai;j Z
I X
J
X
Pi Xi Wj Ai;j
(8)
iZ1 jZ1
In this case, Maryland DNR has developed a ‘Risk’ score for
each of the parcels. This score takes into account a number of
factors, such as the parcel’s current level of protection from
development, the steepness of slopes, distance from roads such
as the Washington DC beltway, and proximity to commercial
and industrial land use (Maryland DNR, 2001). For the
Catoctin Mountain Region, most of the development pressure
occurs along Highway 15 east of the mountains near the City of
Thurmont (Fig. 3). In this study, the range for Risk scores was
1.3–68.1 with a mean of 29.2, a median of 27.6, and standard
deviation of 12.1. By normalizing these scores using Eq. (4)
provided earlier, an index was created from 0 to 1. This
index represents the probability of development. This index
was then multiplied by the parcel’s original Conservation
Value. Thus, if the parcel faced no development risk, the
risk-weighted Conservation Value would be zero. Since the
risk-weighted Conservation Values will be lower than
the Conservation Values presented earlier, comparisons of
the aggregate Conservation Values for portfolios that include
and do not include the threat of development are not
meaningful.
Including the measure of the risk of development for Parcels
#1 and #2 (the two ‘Cadillac’ parcels discussed earlier) has a
significant impact on their risk-weighted Conservation Values.
Parcel #1 faces a relatively low threat of development as its
Risk score is 23.8 (ranked 118th out of 186). Therefore,
including this measure reduces Parcel #1’s Conservation Value
Fig. 3. Development risk.
K.D. Messer / Journal of Environmental Management 79 (2006) 305–315
from 5.5 (ranked 3rd) to 1.8 (ranked 63rd). Similarly, Parcel #2
has a Risk score of 26.2 (ranked 106th), which reduces its
Conservation Value from 7.0 (ranked 1st) to 2.6 (ranked 17th).
Interestingly, by including the threat of development, Parcel #1
is never selected by either BT or BLP and Parcel #2 is only
selected by BT at the $2.5 and $5 million budget levels.
By including development risk into the analysis, both BLP
and BT should be more efficient since resources would be
directed towards the areas with the highest probability of
losing their biophysical value. Examination of the key
statistics with the risk of development again shows that BLP
outperforms BT.
313
be passed over in favor of next highest ranked, lower cost
parcel. While this finding occurs only at the lowest budget
level, it further illustrates a weakness of BT, especially since
the low budget levels are the most likely to be relevant in
conservation efforts.
Similarly, when evaluated with Lorenz Curves and Gini
Coefficients, the BT with easements performed even worse in
comparison to the BLP with easements. In fact, the Gini
coefficient for the BT decreased from K7.9 to K15.2% while
the inclusion of easements increased BLP’s efficiency from
74.4 to 87.4%. These measures again demonstrate the
flexibility of BLP and its ability to account for multiple factors
when selecting the optimal portfolio.
4.2. Easements and fee-simple purchases
4.3. Spatial continuity
Conservation organizations have increasingly turned to
easements as a cost-savings approach. Unlike fee-simple
purchases that secure title to the property, conservation
easements seek to preserve the Conservation Value of the
parcel by preserving the current land use (i.e. forestry or
agriculture) through purchase of just the development rights.
Frequently, a combination of fee-simple purchases and
conservation easements are employed, because conservation
easements may not provide the level of protection desired
(i.e. for endangered species) or the current land use may
conflict with planned recreational uses (i.e. trail system).
In the study area, Maryland DNR recommended the use of
the three decision rules for fee-simple purchase: (i) if the parcel
provides habitat to sensitive or threatened species; (ii) if the
parcel contains stream footage for brook trout; and (iii) if the
parcel contains an existing/planned greenway. If a parcel
did not meet any of these three qualifications, a conservation
easement would be pursued, which was conservatively
estimated to secure the same Conservation Value at half
the cost.6
Including the easement option for all three of the budget
scenarios for BLP yields aggregate Conservation Values
between 25.6 and 33.5% higher than BLP portfolios that
considered only fee-simple purchases. Similarly, the BTs with
budgets of $2.5 million and $5 million yield higher aggregate
Conservation Values, than BTs with budget of $2.5 million
and $5 million that consider only fee-simple purchases.
Surprisingly, with a $1 million budget the BT yields an
aggregate Conservation Value of 27.2 which is 28.6% less
than the aggregate Conservation Value of 34.7 from BT that
considers only fee-simple purchases. An explanation for this
unexpected result is that when easements are available it can
make some of the highest ranking parcels cost less, which in
turn can free up money for a ‘Cadillac’ parcel that is high
ranking but also has a high price that exhausts the remaining
budget. Without the option of easements, this ‘Cadillac’
parcel might not have been affordable, and therefore, would
6
In practice, the cost of easements has a wider range than examined here. In
this case, Maryland DNR recommended using a conservative cost estimate,
which is consistent with estimates in other published research (see Ferraro,
2003a).
The scenarios presented in this paper assumed that the
biophysical attributes do not change as a consequence of the
portfolio choice, an assumption that does not fully account for
the importance of contiguous habitat areas. Recent work has
focused on developing new models to identify more contiguous
reserve systems (see Williams et al., 2004 for an overview),
while other research has focused on the importance of
ecological thresholds, where environmental benefits are only
achieved if a certain amount of protection is obtained (see
Ferraro, 2003b; Bulte and van Kooten, 2001; Wu et al., 2000).
Interaction between the ecological attributes and the selected
portfolio could introduce a non-linear element that could also
be modeled, albeit with additional difficulty.
In this case study, the attribute of ‘Number of protected
acres within 1 mile’ could be modified to include the values of
adjacent parcels if two or more parcels are selected. For
instance, if three other non-protected parcels (Parcel A, B, and
C) bordered a Parcel Z, the parcel’s value for this attribute
would be defined as the following where the Xi’s are binary
choice variables:
Parcel Zð# of protected acres within 1 mileÞ
Z Parcel Z 0 s Conservation Value
C XA !ðacres of Parcel AÞ C XB !ðacres of Parcel BÞ
C XC !ðacres of Parcel CÞ
(9)
An advantage of this type of non-linear modeling is that an
optimal portfolio might do a better job of preserving
contiguous areas. Unfortunately, this type of modeling would
be difficult, as each parcel would have a unique equation
because the number of unprotected parcels within one mile
would be different for each parcel.
5. Conclusion
While much of the previous literature has focused on a
variety of covering problems, this research compared the
results of binary linear programming (BLP) to results from
benefit-targeting (BT), commonly used by conservation
314
K.D. Messer / Journal of Environmental Management 79 (2006) 305–315
organizations, for a case study of an ongoing conservation
effort in the Catoctin Mountains of Maryland. The results show
that the ‘greedy algorithm’ of the BT can lead to highly
inefficient results as the BLP was able to achieve far higher
aggregate conservation benefits for the same level of
expenditure. The use of optimization techniques can help
conservation organizations look beyond the ‘Cadillac’ parcels,
which deplete the conservation budget while not contributing
much to the aggregate conservation benefits. The resulting
portfolio will likely favor acquiring multiple ‘Best Buy’
parcels which individually may not appear as valuable, but
when combined provide significantly more aggregate conservation benefits for the same cost. For this case study,
optimization yields additional conservation benefits worth an
estimated $3.1–$3.9 million or achieves the same level of
conservation benefits but at a cost savings ranging from $0.9
to $3.5 million, depending on the initial budget size. Finally,
the highest efficiencies are achieved in low budget scenarios,
like those common in conservation efforts. BLP can be applied
to a number of settings, including watershed protection,
endangered species conservation, and historical preservation,
and in all of these instances this technique can help secure
substantial benefits that otherwise may be lost.
Acknowledgements
The author would like to thank Jon Conrad, who made
substantial contributions to this paper, and John Wolf and
William Jenkins from the State of Maryland Department of
Natural Resources for their cooperation. Helpful comments
were also received from Greg Poe and two anonymous
reviewers. All errors are the sole responsibility of the author.
Financial support for this research was provided by the Cornell
University’s Agricultural Experiment Station and the Cornell
University Department of Applied Economic & Management.
Appendix A
A simple example illustrates why BLP guarantees an
optimal solution while Benefit–Cost Ratio Targeting (B/CT),
currently used by the United State Department of Agriculture’s
Conservation Reserve Program and Environmental Quality
Incentives Program, does not. BLP takes into account the
benefits and costs of each parcel in evaluating all of the
possible portfolio combinations given a budget constraint and
selects the portfolio which yields the highest possible
aggregate Conservation Value. In contrast, B/CT ranks, from
highest to lowest, the benefit–cost ratios of each parcel and
then acquires the parcels with the highest ratios until reaching
the budget constraint. B/CT’s selection algorithm is identical to
BT’s selection algorithm described above except R($) denotes
a rank operative over the benefit–cost ratios (Vi/Ci), iZ1,.,I.
Consider a simple 10-parcel example below. The 10 parcels,
lettered from A to J, are listed from highest to lowest based on
their benefit–cost ratios. Note that with a budget of $2500,
B/CT would select a portfolio that yields aggregate benefits of
only 9.64, in contrast to the 10.48 yielded from BLP portfolio
(92% of the total possible). As can be seen, B/CT first selects
the four parcels (Parcel A, B, C, D) with the highest benefit–
cost ratios and uses the remaining funds to select Parcel J
(ranked 10th). In contrast, BLP finds a portfolio that maximizes
aggregate benefits (Parcels B, C, E, F), by forgoing Parcel A
(ranked 1st), Parcel D (ranked 4th), and Parcel J (ranked 10th)
in exchange for the Parcel E and Parcel F (ranked 5th and 6th,
Fig. A1. Efficiency difference benefit–cost targeting and binary linear programming.
K.D. Messer / Journal of Environmental Management 79 (2006) 305–315
respectively). Note that in this example not only does BLP
select the portfolio that maximizes aggregate benefits, but it
does so while costing less money ($2450 versus $2500) and
requiring the purchase of fewer parcels (4 versus 5) thereby
reducing potential transaction costs.
Table A1 Ten parcel example for BLP and B/CT
Parcel
Benefits
A
B
C
D
E
F
G
H
I
J
2.26
2.43
2.60
2.10
2.65
2.80
2.05
1.10
0.85
0.25
Budget
$2500
Cost
$450
$500
$600
$500
$650
$700
$700
$650
$900
$450
B/C
0.0050
0.0049
0.0043
0.0042
0.0041
0.0040
0.0029
0.0017
0.0009
0.0006
B–C ranking portfolio
BLP portfolio
Selected?
Cost
Benefits
Selected?
Cost
Benefits
Yes
Yes
Yes
Yes
$450
$500
$600
$500
2.26
2.43
2.60
2.10
Yes
Yes
$500
$600
2.43
2.60
Yes
Yes
$650
$700
2.65
2.80
$2450
10.48
Yes
$450
0.25
Total
$2500
9.64
Not only does BLP continue to guarantee optimality, but the
efficiency difference between BLP and B/CT becomes greater
when two or more constraints are included in the problem. For
example, the number of parcels selected for acquisition can
also be a constraint, especially since in the short-term labor is
often fixed in conservation organizations, such as state
agencies and Land Trusts. Formally, a constraint on the
number of parcels selected
for the optimal portfolio (S) can be
P
imposed such that IiZ1 Xi % S.
When BLP and B/CT are analyzed using the data from the
Catoctin Mountain Region, the imposition of this additional
constraint reduces the aggregate benefits achieved by both
B/CT and BLP. However, BLP continues to find the optimal
solution to the problem, while the level of inefficiency of
B/CT increases the smaller the size of the acquisition
constraint. As can be see in Fig. A1, BLP’s ability to adjust
to multiple constraints creates a wedge that separates the
efficiency of the portfolios selected by the two techniques. At
the $2.5 million budget level with a constraint on the number
of parcels of 60, B/CT is 5.9% less efficient than BLP.
Likewise, when a conservation organization is constrained to
only 20 parcels, B/CT is 17.1% less efficient.
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