Download the economics of biodiversity

Chapter 29
THE ECONOMICS OF BIODIVERSITY
STEPHEN POLASKY
University of Minnesota, USA
CHRISTOPHER COSTELLO
University of California at Santa Barbara, USA
ANDREW SOLOW
Woods Hole Oceanographic Institution, USA
Contents
Abstract
Keywords
1. Introduction
2. Measures of biodiversity
2.1. Measures based on relative abundance
2.2. Measures based on joint dissimilarity
3. Sources of value from biodiversity
3.1. Use value and existence values of individual species
3.2. Biological prospecting
3.3. Biodiversity and ecosystem services
4. Strategies to conserve biodiversity
4.1. Terrestrial habitat protection
4.2. Marine biodiversity and reserves
4.3. Introduced species
5. Incentives to conserve and conservation policy
6. Conclusions
References
Handbook of Environmental Economics, Volume 3. Edited by K.-G. Mäler and J.R. Vincent
© 2005 Elsevier B.V. All rights reserved
DOI: 10.1016/S1574-0099(05)03029-9
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Abstract
The conservation of biodiversity is a major environmental issue, one that promises to
remain at or near the top of the environmental agenda for the foreseeable future. The
loss of biodiversity affects human welfare as well as being lamentable for its own sake.
Humans depend on natural systems to produce a wide variety of ecosystem goods and
services, ranging from direct use of certain species for food or medicines to ecosystem functions that provide water purification, nutrient retention or climate regulation.
Threats to biodiversity include habitat loss and fragmentation, the introduction of nonindigenous species, over-harvesting, pollution, changes in geochemical cycles and climate change. Sustaining biodiversity in the face of increasing human populations and
increased human economic activity promises to be a major challenge. Economists have
an important role to play in helping to develop and evaluate conservation strategies.
Because biodiversity is at risk in large part because of human activity, finding ways
to conserve biodiversity will come from better understanding and management of human affairs, not from better biology alone. Economists can help set priorities to allocate
scarce conservation resources where they will do the most good. Economists can help
design incentive schemes to make conservation policy both effective and efficient. Economic methods can shed light on what are the most valuable components of biodiversity,
including analysis of species existence value, the value of bioprospecting and the value
of ecosystem services.
Keywords
biodiversity measures, valuation, ecosystem services, habitat conservation,
conservation policy
JEL classification: Q20, Q22, Q23, Q24, Q28
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1. Introduction
The second half of the 20th century was, in many respects, good to Homo sapiens.
Human population more than doubled between 1950 and 2000, growing from approximately 2.5 billion in 1950 to just over 6 billion in 2000 (U.S. Bureau of Census). Human
health, nutrition and average life expectancy improved dramatically [Johnson (2000)].
The value of economic activity increased by over 400% over the second half of the 20th
century [Delong (2003))]. The same period was not so good to many of the other species
on the planet. Some ecologists fear that we may now be witnessing the sixth great extinction wave on the planet. Though evidence is fragmentary, current rates of species
extinction are estimated to be several orders of magnitude above background or natural
extinction rates [NRC (1995), Lawton and May (1995), Pimm et al. (1995)]. The area
of natural habitat has declined as humans have converted lands to agriculture, managed
forests or urban development. Roughly half of useable terrestrial land (i.e., land that is
not tundra, ice, boreal, rock, desert) is devoted to grazing livestock or growing crops
[Tilman et al. (2001)]. There is roughly half the area of forest now than existed when
agriculture began 8000 years ago [WRI (1998)]. Increased movement of goods has led
to introductions of nonindigenous species with occasional substantial consequences for
native ecosystems. Over-harvesting of fish and game species, climate change, pollution, and changes in geochemical cycling has also threatened many species. Given this
experience, ecology may supplant economics as “the dismal science”.
Conserving biodiversity has become a major environmental issue. In the words of
ecologist Simon Levin: “The central environmental challenge of our time is embodied
in the staggering losses, both recent and projected of biological diversity at all levels,
from the smallest organisms to charismatic large animals and towering trees.” [Levin
(1999, p. 1)] The loss of biodiversity may be lamentable for its own sake but it also
has impacts on human welfare. Humans depend on natural systems to produce a wide
variety of ecosystem goods and services, ranging from direct use of certain species
for food or medicines to ecosystem functions that provide water purification, nutrient
retention or climate regulation. Cultural and spiritual values are also tied to elements of
biodiversity.
The great concern for conserving biodiversity, in contrast to concerns for specific
endangered species or particular ecosystems, is a relatively recent phenomenon. The
formation of the Society for Conservation Biology in 1985, the beginning of the journal Conservation Biology in 1987, and the publication of the edited volume BioDiversity [Wilson (1988)] serve as useful benchmarks signaling the beginning of broad
scientific and policy interests in the conservation of biodiversity. By now there are
thousands of journal articles and books devoted to various aspects of conservation (a
partial biodiversity bibliography containing approximately 5000 entries can be found at
http://www.apec.umn.edu/faculty/spolasky/Biobib.html).
While biological scientists have a central role in researching biodiversity, economists
have begun to play an important and expanding role [a collection of recent biodiversity
articles by economists is contained in Polasky (2002)]. Biodiversity is at risk largely
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because of human activity. Therefore, conservation solutions will come from better understanding and management of human affairs, not from better biology alone. Since
there are limited budgets for conservation that cannot support all worthy conservation
projects, economists can help set priorities to allocate scarce resources where they will
do the most good. Economists can help design incentive schemes to make conservation
policy both effective and efficient. Economic methods can shed light on what are the
most valuable components of biodiversity.
In this chapter we review the recent economics literature on biodiversity. We begin in
Section 2 by discussing various ways to define and measure biodiversity. In Section 3 we
discuss the sources of value generated by biodiversity and various empirical measures
of value. Section 4 covers strategies to conserve biodiversity in light of the main threats,
namely habitat loss and invasive species. Section 5 discusses incentives (and disincentives) for conservation as well as policies whose goal is to conserve biodiversity. We
offer brief concluding comments in Section 6.
2. Measures of biodiversity
The term biodiversity has been defined in a number of different ways. Most of the measures of diversity developed and used by economists are defined as measures of the
joint dissimilarity among a set of species. There is, however, another strand of ecological literature that defines diversity in terms of the relative abundances of species
within a community. We begin this section with a review of measures based on relative
abundance and then review measures based on joint dissimilarity.
2.1. Measures based on relative abundance
Perhaps the most common way in which the word diversity has been used in ecology
is to characterize the relative abundances of species within a community [e.g., May
(1972), Magurran (1988)]. The relative abundance of a species in a community is defined as the proportion of individual organisms in the community that belong to that
species. Consider a community containing s species
and let π = (π1 π2 . . . πs ) be the
vector of relative abundances with for all j and sj =1 πj = 1. In qualitative terms, the
community is said to be diverse if all of the elements of π are close to 1/s. There is
some evidence that communities that have recently been subjected to disturbance have
low diversity. A possible explanation is that, following disturbance, recolonization is led
by opportunistic species with high fecundity (so-called r-strategists) whose numbers
quickly dominate the community. Over time, less fecund, but longer-lived species (socalled K-strategists) are able to compete successfully, thereby making the community
more diverse. The extent to which diversity reflects dynamical properties like stability
and resilience remains an open question. The chapter on ecosystem dynamics by Simon
Levin and Stephen Pacala (2003) in Volume 1 of this handbook discusses some aspects
of this question and other matters related to the measurement of biodiversity.
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This qualitative notion of diversity is not amenable to analysis and, during the 1970s,
there was a burst of attention to devising quantitative measures that capture this notion.
An important contribution by Patil and Tailie (1977) was to define the diversity of a
community with relative abundance vector π by average rarity:
D(π) =
s
rj πj ,
j =1
where rj is a measure of the rarity of species j . Different choices of rarity measure yield
different diversity measures. Patil and Tailie (1977) focused on the one-parameter famβ
ily rj = (1 − πj )/β with β −1. This gives rise to the family of diversity measures:
Dβ (π) = 1 −
s
β+1
πj
β.
j =1
Special cases include:
D−1 (π) = s − 1
which is essentially species richness;
D1 (π) = 1 −
s
πj2
j =1
which is called the Simpson index and gives the probability that two individuals sampled
at random are of different species; and the limiting form
D0 (π) = −
s
πj log πj
j =1
which is called variously the Shannon–Weaver index, the Shannon–Wiener index, and
the entropy index.
It is fair to say that the notion that a high level of diversity of this type is preferable
to a low level is without a clear basis in either ecology or economics. Weitzman (2000)
attempts to provide such a basis through a model of the relationship between the abundance of a crop and the number of pests or pathogens specific to that crop. Briefly, under
this model:
Sj = kBjz ,
where Sj is the number of pests specific to crop j and Bj is the total biomass of the crop.
Weitzman (2000) imagines that this biomass is divided into Bj separate patches each
of unit biomass. If each pathogen has a probability ε of destroying a patch of biomass,
then (under two independence assumptions) the probability of complete extinction of
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the crop is:
z Bj
Pj (Bj ) = 1 − (1 − ε)kBj
.
Weitzman (2000) uses the approximation that, for small ε, Pj (Bj ) ∼
= (kBjz ε)Bj to show
that the set of relative abundances that minimizes the probability of a loss of all crops
maximizes the diversity measure D0 (π). The essential tension in this optimization problem is between the safety provided by a large number of patches and the corresponding
large number of potentially harmful pests.
2.2. Measures based on joint dissimilarity
The focus of the rest of this section is on quantitative measures of diversity that are
intended to reflect the joint dissimilarity of a collection of species. One practical motivation for work in this area has been the need to evaluate policies aimed at protecting
species from extinction. It is worth stressing that, as the goal of such policies is to preserve species from extinction, this kind of diversity measure should be sensitive only to
extinctions (or changes in extinction probabilities) and not to ecological changes (e.g.,
in population size or abundance distributions). This is not to say that maintaining populations is unimportant – indeed, it is a critical policy instrument in preventing extinction
– only that it is not the ultimate goal. In cases where species are harvested for food or
sport, increasing the population size will have utility in and of itself. There is a large
bioeconomics literature that analyze these issues [Clark (1990)] and we will not consider population size issues further in this section.
The standard measure by which policies aimed at preventing extinction are evaluated is the number of species that they protect. However, this measure of diversity –
species number – takes no account of the relative differences between species. A policy that protects a large number of species covering a small number of genera may
be, in some sense, inferior to a policy that protects a smaller number of species covering a larger number of genera. This point was made by Vane-Wright, Humphries and
Williams (1991), who went on to propose measures of the diversity of a collection of
species based on the phylogenetic tree connecting them. Under the simplest of these
measures, each species in the tree is assigned a numerical value inversely proportional
to the number of nodes in the tree associated with it. For a given species, this value
reflects the number of closely related species. The diversity of a collection of species is
then found by summing these values for the species in the collection. By measuring the
diversity of a collection of species by combining values assigned to the species in the
collection, this measure cannot distinguish between the case in which a pair of closely
related species are both in the collection and the case in which only one is. This creates a problem: it would not make sense to depreciate the value of preserving a species
with many close relatives when these close relatives are doomed to extinction. Other
proposed measures suffer from a similar problem [e.g., Haney and Eiswerth (1992)].
To formalize the measurement problem, consider a collection S = (s1 , s2 , . . . , sn )
of n species or other types and let d(sj , sk ) be the distance or dissimilarity between
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species sj and sk (neither of which needs be in S). The problem is to define a nonnegative real-valued function D(S) that measures the diversity of S. It is natural to
require D to satisfy the following conditions [Weitzman (1992)]. First, diversity should
not be reduced by the addition of a species to s. That is, if S and S are two collections
of species with S ⊂ S , then D(S) < D(S ). Second, diversity should not be increased
by the addition of a species that is identical (in the sense of having 0 dissimilarity) to
a species already in S. That is, if s◦ is a species not in S, then D(S ∪ s◦ ) = D(S)
if and only if d(s◦ , si ) = 0 for some si ⊂ S. Third, diversity should not decrease
with an unambiguous increase in the dissimilarities between species. That is, for a oneto-one mapping of S onto S such that d(si , sj ) d(si , sj ) with at least one strict
inequality, D(S) D(S ). Other requirements are possible (e.g., involving continuity of
the measure with respect to increasing dissimilarity), but these three seem fundamental.
The first measure to satisfy these requirements was proposed by Weitzman (1992).
With the diversity of a single species defined as 0, this measure is given by the recursion
DW (S) = max DW (S − si ) + d(si , S − si ) ,
where S − si denotes the collection S with species si removed; the distance d(si , S − si )
between species si and the collection S − si is defined as the minimum of the distances
between si and the species in S − si ; and the maximum is taken over the species si in S.
In the important case where the distances between species are ultrametric (so that, for
any set of three species, the largest two distances between species are equal), the species
can be represented by a planar tree, and then Weitzman’s measure corresponds to the
total length of the tree. This is arguably the only sensible measure of pure diversity in
this case. Other diversity measures have been proposed [e.g., Crozier and Kusmierski
(1996), Faith (2002)], but these tend to be ad hoc.
It is worth noting at this point that Weitzman’s measure and other early measures of
diversity were not directly connected to any theory of economic (or ecological) value.
This is not to say that the qualitative connection had not been made. Ecologists have
long believed that diversity in nature supports the stability and resilience of ecosystems
[despite some surprising suggestions to the contrary, May (1972)]. Diversity, therefore,
has economic value arising not only from direct benefits but also from indirect benefits
from ecosystem functions that generate valuable ecosystem services, which we discuss
in more detail in Section 3.3. Economists have developed the argument that, to the
extent that similar species provide similar benefits and suffer similar susceptibilities, it is
sensible to maintain a diverse portfolio of species. The first attempt to base a measure of
diversity on a theory of value was by Polasky, Solow and Broadus (1993). Using a highly
stylized probability model of substitutability of species in providing a single benefit,
Polasky, Solow and Broadus (1993) derived a measure of the diversity of a collection of
species that reflected the probability that at least one species in the collection provided
the benefit. The measure satisfied the three requirements listed above.
The diversity measure of Polasky, Solow and Broadus (1993) was based on a complete (if stylized) model of substitutability. In later work, Solow and Polasky (1994)
took a slightly different approach. Suppose that interest in species conservation arises
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from the possibility of species providing a benefit, such as a cure for a disease, in the
future. Suppose further that having more than one species that provides this benefit is
no better than having a single species that provides it. Let Bi be the event that species si
provides this benefit. The event that the collection S also provides this benefit is given
by
B(S) =
n
Bi .
i=1
The expected benefit of S is p(S)V , where p(S) = prob(B(S)) and V is the fixed unit
value of the benefit. Because V is fixed, p(S) provides a basis for comparing different
collections of species.
In the absence of specific information, Solow and Polasky (1994) assumed that the
probability of the event Bi that species si would provide the benefit is an unknown
constant p that does not depend on i. They also assumed that the conditional probability
of Bi given the event Bj that species sj provides the benefit is:
prob(Bi | Bj ) = p + (1 − p)f d(si , sj ) ,
where f is a known function satisfying f (0) = 1; f (∞) = 0; and f 0. Although it
is not possible to obtain the n-variate probability p(S) from the univariate probability p
and the set of conditional probabilities prob(Bi | Bj ), Solow and Polasky (1994) used a
probability inequality due to Gallot (1966) to show that a lower bound on p(S) is given
by p 2 DSP (S), where
DSP (S) = et F −1 e
for the n-by-n matrix F = [f (d(si , sj ))]. In summary, a lower bound on the expected
value of a collection S is an increasing function of the quantity DSP (S). Solow and Polasky (1994) went on to show that, under reasonable assumptions about the function f ,
this quantity also meets the three main requirements of a diversity measure. Moreover,
DSP (S) is bounded below by 1 and above by n, so it has the interpretation as the effective number of species in S.
The main disadvantage of DSP (S) as a diversity measure is that it requires the specification of the function f that, in some sense, measures the “correlation” between species
as a function of the dissimilarity between them. On the other hand, from a practical perspective, it may be a disadvantage of DW (S) that it can increase without bound with
dissimilarity. A more serious objection to the practical use of these and other diversity
measures in actual conservation decision-making is that their information requirements
are utterly unrealistic. Except in extremely unusual situations, conservation decisions
involve large numbers of species from a wide variety of taxonomic groups whose identities – let alone genetic dissimilarities – are simply unknown. The numbers of species
themselves are also unknown and comparisons based on estimated species number are
fraught with problems arising from sampling. An attractive option in this situation is to
aim conservation efforts at conserving a diverse collection of habitats, on the assumption that dissimilar habitats tend to support dissimilar species. Of course, this merely
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transforms the problem to one of assessing the diversity of a collection of habitats. In
this case, however, the information needed to construct dissimilarities between habitats
(e.g., topography, climate, etc.) may be more readily available. Note that, in contrast
to the species case, there is no reason to assume that habitats are related through an
analogue to a phylogenetic tree. For this reason, diversity measures based on such a
structure have no particular appeal.
3. Sources of value from biodiversity
Some issues related to the value of biodiversity were touched upon in previous sections, for example the utilitarian motivation for the diversity measure of Solow and
Polasky (1994). For the most part, however, discussion of diversity measures and value
of biodiversity have been conducted in largely separate literatures. In this section we
review economics literature on the value of biodiversity. (For background on valuation
methods, see Volume 2 of this handbook, titled Valuing Environmental Changes.) Biodiversity is a broad term encompassing everything from genes to species to ecosystems.
Value from biodiversity can arise at any of these levels. We begin with value generated
at the species level.
3.1. Use value and existence values of individual species
Humans have recognized the direct use value of other species, at least implicitly, for
as long as our species has existed. For millennia, humans depended upon successfully
hunting animal species and gathering plants species. The switch to domesticated agriculture changed the form but not the substance of our reliance on other species. Many of
the direct use values of species which humanity relies upon for food and fiber are well
documented by agricultural economists, fishery economists and forestry economists.
Even for species that are not grown or harvested commercially, there is a long tradition
within economics of estimating the value of recreational hunting and fishing [see, for
example, Walsh, Johnson and McKean (1988), Markowski et al. (1997), Rosenberger
and Loomis (2001)].
The increased interest in the conservation of biodiversity, especially conserving endangered species, brought about a new type of species valuation effort focused on
species existence value rather than on direct use value. Beginning in the 1980s, economists began to use contingent valuation surveys to ask people what they were willingto-pay to conserve particular rare or endangered species. In reviewing studies covering
18 different species, Loomis and White (1996, p. 249) concluded “that the contingent
valuation method can provide meaningful estimates of the anthropocentric benefits of
preserving rare and endangered species.” Estimates of annual willingness-to-pay varied
from a low of $6 per household for the striped shiner to a high of $95 per household
for the spotted owl. The estimates of willingness-to-pay tended to be higher for more
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charismatic species and for situations with greater increases in population sizes, as one
would expect.
Not all economists agree that the contingent valuation method can “provide meaningful estimates” of value for conserving species. Brown and Shogren (1998) note that by
aggregating the estimates reported by Loomis and White (1996) over all households, the
implied willingness-to-pay to protect less than 2% of endangered species exceeds 1%
of GDP, which they remark seems “suspiciously high”. Responses to contingent valuation surveys on conserving species may exhibit “embedding effects” [Kahneman and
Knetsch (1992)]. A survey of willingness-to-pay for protecting a collection of species
might generate estimates similar to the willingness-to-pay for protecting an individual
species. Desvouges et al. (1993) found similar estimates for willingness-to-pay for preventing 2000, 20,000 and 200,000 bird deaths. Survey responses may reflect the value
of protecting an ecosystem (e.g., the value of old growth forests rather than the value of
spotted owls), the environmental more generally, or the “warm-glow” of contributing to
a worthy cause [Andreoni (1989, 1990)].
On a different tack, Stevens et al. (1991) found that many people object to survey
questions that try to elicit a monetary value for species existence. They found that the
vast majority of survey respondents thought conserving species was important but they
refused to state that they would pay a positive amount for conservation. Stevens et al.
(1991, p. 268) attribute the unwillingness to state a willingness-to-pay as arising from
moral or ethical concerns about asking “people to choose between ordinary goods (income) and a moral principle.”
3.2. Biological prospecting
With arguments about existence values unlikely to be viewed as conclusive, conservationists looked for other means to show that conserving biodiversity would make
financial, as well as moral, sense. One argument used extensively in the early 1990s
was that conserving species preserved option value: species might contain valuable
compounds that would yield valuable pharmaceuticals or other products at some future date [see, for example, Wilson (1992)]. If the species were to go extinct this option
value would be lost. Determining the magnitude of this option value was the central
focus of a literature on bioprospecting.
The early bioprospecting literature produced a wide range of estimates of the value of
conserving a species for pharmaceutical purposes, from $44 [Aylward (1993)] to $23.7
million [Principe (1989)] per untested species. The value of conserving an untested
species was derived by multiplying the probability of successfully identifying a commercially valuable product with the average revenue generated by a successful product.
Simpson, Sedjo and Reid (1996) criticized this method. This simple procedure generates
estimates of the average value rather than the marginal value of an untested species. Because multiple species may contain the same compound and in this sense be redundant,
marginal values are likely to be far less than average values.
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Simpson, Sedjo and Reid (1996) developed a model of sequential search that takes
species redundancy into account. In their model each of N species has an identical
probability p of containing a success. Testing each species costs c. In the event of a
success, revenue R is obtained and the search is terminated. Under this model, it is
shown that the value of a collection of N species is
V (N) = pR − c + (1 − p)(pR − c) + (1 − p)2 (pR − c) + · · ·
pR − c + (1 − p)N−1 (pR − c) =
1 − (1 − p)N .
p
The value of a “marginal species” can be found by comparing V (N + 1) with V (N ):
v(N) = V (N + 1) − V (N ) = (pR − c)(1 − p)N .
The value of the final species is the expected profit of testing the species, pR − c,
multiplied by the probability that tests on all prior species tested have been unsuccessful,
(1 − p)N . If p is small, the expected profit of testing a species is likely to be small,
leading to low marginal value. On the other hand, if p is large, the species are likely to be
redundant for bioprospecting purposes, leading to low marginal value. Using evidence
to assign reasonable parameter values for revenues and costs, the number of species
and the expected number of potential products, Simpson, Sedjo and Reid (1996) solve
for the probability that generates the maximum marginal species value. For flowering
plants (with an N of 250,000), they find that when p = 0.000012, the marginal value
of a species may be as high as $9431.
Simpson, Sedjo and Reid (1996) then use this maximum estimate for value of marginal species along with species–area curves and estimates of endemism (species unique
to an area) to estimate the maximum marginal value of a hectare of land in each of
18 global biodiversity hotspots. These estimates range from a maximum of $20.63
per hectare in Western Ecuador to only $0.20 per hectare in the California Floristic
Province. On the basis of their theoretical and empirical results, Simpson, Sedjo and
Reid (1996) conclude that the incentive to conserve biodiversity for bioprospecting purposes is almost certainly too small to offset the opportunity cost of development.
Polasky and Solow (1995) used a similar model to value a collection of species. If the
probability of success on any given trial is p and the revenue upon success is R, then
the expected value of a collection of N species is V (N ) = R[1 − (1 − p)N ], which is
the same as by Simpson, Sedjo and Reid (1996) when c = 0. Polasky and Solow (1995)
considered two variants of the simple model to allow for imperfect substitutes among
species that generate success for the same product, and dependence in probabilities of
success across species that relate to genetic similarity. Both extensions are motivated
by the experience of bioprospecting. When taxol was found in the bark of the Pacific
yew tree, there was an intensified search of related species. It was found that the needles
of the European yew tree could be used to get taxotare, an imperfect substitute for
taxol. With imperfect substitutes, the marginal value of species need not fall as fast as
indicated by Simpson, Sedjo and Reid (1996). On the other hand, accounting for species
interrelationships tends to reduce the marginal value of species.
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Rausser and Small (2000) challenge the empirical conclusions of low value from
bioprospecting found in Simpson, Sedjo and Reid (1996). The existence of prior information makes it unlikely that all species will have the same probability of success
in yielding a valuable product. Under the assumption that the probability of success is
independent and differs across species, it is optimal to organize the search in order of
descending success probability of success. When this is done, the value of conserving a
species with a high probability of success may be large. Rausser and Small (2000) apply
their model to the empirical case examined by Simpson, Sedjo and Reid (1996), with
the assumption that probabilities are proportional to the density of endemic species in
each region, which range from one in ten thousand (Western Ecuador) to one in a million (California Floristic Province). Rausser and Small find optimal search yields an
incremental value of $9177 for the most promising hectare of land in Western Ecuador
compared with a marginal value of only $20.63 for the same hectare in Simpson, Sedjo
and Reid (1996). This result suggests that the benefits of protecting biodiversity hotspots
for future biological prospecting may indeed outweigh the costs.
Costello and Ward (2003) show, however, that the difference in results between
Rausser and Small (2000) and Simpson, Sedjo and Reid (1996) does not come from
whether search is optimally ordered or random, but rather from an assumption of different parameter values. In fact, the value of a hectare in Western Ecuador is $9177 when
conducting an optimal ordered search and only drops to $8840 with random search.
It appears as though the answer to the bioprospecting question may be contextdependent, and will likely hinge critically on the quality of information available to
the bioprospector, for example indigenous knowledge about what species are likely
prospects, and the opportunity cost of land. Neither of these two features has been carefully analyzed empirically in this literature. We will return to the issue of bioprospecting
in Section 5, where we consider its impacts on incentives for conservation.
3.3. Biodiversity and ecosystem services
In the previous sections, it was the elements of biodiversity, the genes or the species, that
were the focus of analysis as being the sources of value. An alternative line of reasoning
focuses on ecosystems as a whole, rather than the individual parts (genes or species),
as being the primary sources of value. Ecosystems provide a wide range of goods and
services of potential value to people. For example, a wetland may provide flood control,
absorbing high waters and gradually releasing water over time. It may also filter and
retain nutrients and pollutants thereby providing cleaner water downstream. Ecosystem
services include provision of clean air and water, climate regulation, mitigation of natural disturbances, waste decomposition, maintenance of soil fertility, pollination, and
pest control, among other things [Daily (1997)].
In principle, quantifying and valuing ecosystem services is no different from quantifying and valuing human produced goods or services. In practice, however, valuing
ecosystem services is problematic. To date there have been few reliable estimates of the
value for most ecosystem services like those mentioned in the previous paragraph. There
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are several difficulties in estimating the value of ecosystem services. First, the current
state of ecological knowledge may be insufficient to link ecosystem condition and functioning to the provision of ecosystem services. In other words, we may not understand
ecosystem “production functions” well enough to quantify how much service is produced, or how changes in ecosystem condition or function will translate into changes
in amounts of ecosystem services produced [Daily et al. (2000)]. Second, even if quantification of the services is possible, the current state of economic methods may not be
sufficient to yield reliable estimates of the value of services. For some ecosystem services, such as flood control benefits, establishing reasonable estimates of value may not
present great difficulties. For other services it may be exceedingly difficult to assign a
value, such as the value of habitat to lower the probability of species extinctions. Finally,
valuing ecosystem services requires integrating ecological knowledge with economics,
which requires cooperation between ecologists and economists. Such cooperation has
begun [e.g., Brown and Roughgarden (1995), Perrings and Walker (1995), Carpenter,
Ludwig and Brock (1999), Tilman et al. (2001)] but is still more the exception than the
rule.
Some of the most successful efforts to estimate the values of ecosystem services
have focused on the production of specific tangible outputs, such as the production of
fish and game species. Such outputs tend to be readily measurable and may even have
market prices (e.g., commercially harvested fish). The ecosystem (habitat) is a necessary
input into the production of the output (species). Focusing on the input side, such work
naturally fits into a classification of ecosystem services. One can just as easily focus on
the output side, however, in which case such studies are best considered as studies of
the values of individual species (as covered in Section 3.1). Some research in economics
has focused on how changes in ecosystem conditions translate into changes in the value
of output [e.g., Hammack and Brown (1974), Lynne and Prochaska (1981), Ellis and
Fisher (1987) and Swallow (1990)]. Barbier (2000) provides a review of a number of
papers that estimate the change in the value of fisheries production with changes in
coastal wetlands and mangrove ecosystems.
One widely cited example of valuing an ecosystem service is the provision of clean
drinking water for New York City. New York City gets a sizeable fraction of its water
from watersheds in the Catskills. Increased housing development with septic systems,
runoff from roads, and agriculture were causing water quality to decline. Continued
declines in water quality would have forced the U.S. Environmental Protection Agency
to require New York City to build a water filtration plant. The total present value cost
of building and operating the water filtration plant was estimated to be roughly $6 to
$8 billion [Chichilnisky and Heal (1998)]. Instead, New York City decided to invest
$1 to 1.5 billion to conserve the Catskills watersheds to avoid building the water filtration plant. Preserving the ecosystem was a far cheaper way to provide clean drinking
water. The value of the ecosystem service here is the savings provided by avoided cost.
Avoided cost is not the value of clean water. Rather it is the cost of replacing the ecosystem service with some human engineered alternative. Measures of avoided cost can be
used as measures of the value of ecosystem services, but only under certain conditions,
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S. Polasky et al.
namely that an alternative human engineered alternative exists and that the cost of such
an alternative does not exceed the value of the benefit provided by the service. Both
conditions hold for the New York City example.
At the other end of the spectrum are controversial efforts that attempt broad scale
or even global valuation of ecosystem services. Costanza et al. (1997) estimated that
the mean value of global ecosystem services was $33 trillion annually, which is greater
than the value of global GDP at the time ($18 trillion). The paper garnered a lot of
attention and it remains probably the most widely known work on valuing ecosystem
services, especially among non-economists, despite being roundly criticized by many
economists [Bockstael et al. (2000), Toman (1998)]. If what is being valued is the lifesupport system provided by the Earth’s ecosystems, then as Michael Toman noted, the
estimate of $33 trillion is “a serious underestimate of infinity”.
Other papers have since stressed the importance of more focused analysis that
matches the scale of analysis for ecosystem valuation to the scale of management questions [Daily et al. (2000), Balmford et al. (2002)]. For example, Balmford et al. (2002)
used studies of specific ecosystems to ask whether conservation or development options
generate greater value. Their study shows that conservation options are preferred, often
by wide margins. Earlier work by Peters, Gentry and Mendelsohn (1989) reached a similar conclusion on conserving tropical forests. However, much of the work to date on
ecosystem services requires making large leaps to overcome lack of data or understanding. Much greater understanding of ecosystem functioning, how these functions change
with management actions, and how such changes impact on human values is required
before firm conclusions can be reached.
One question that has received considerable attention in ecology over the past decade
is whether systems with greater diversity, in the sense of having more species, are
also more productive. Results from experiments in grassland plots [Tilman, Wedin and
Knops (1996)] and in controlled environments [Naeem et al. (1994, 1995)] found that
increasing the number of species in the system tended to increase system productivity.
Similar results were reported by Hector et al. (1999) in experiments across a number of
European countries.
Several reasons have been put forward to explain the diversity–productivity link
[Tilman, Lehman and Thomson (1997), Tilman (1999)]. When species differ in their
productivity, a collection with more species is more likely to include high productivity species in the mix (the sampling effect). In heterogeneous environments, having
more species will generally allow the collection of species to better utilize all ecological niches and so be more productive (the niche differentiation effect). Tilman, Lehman
and Thomson (1997) develop a model of niche differentiation that is formally similar
to the Polasky, Solow and Broadus (1993) model of the probability that a set of species
will contain a given trait. Both models show how coverage increases, either in niche
space or genetic space, when more species are added. The model can also be interpreted
as showing that diversity allows greater productivity on average when environmental
conditions vary over time.
Ch. 29:
The Economics of Biodiversity
1531
Greater diversity has also been linked to lower variance of ecosystem productivity
[Tilman and Downing (1994), McGrady-Steed, Harris and Morin (1997), Naeem and Li
(1997)]. Tilman, Lehman and Bristow (1998) explain this result using reasoning from
economics, which they call the portfolio effect. Increasing the number of species that
fluctuate independently will decrease system volatility, just as increasing the number of
independent assets in a financial portfolio will decrease the volatility of returns. Greater
diversity leads to diversification, which leads to lower variance.
In a closely related line of reasoning, Perrings et al. (1995, p. 4) state that “The importance of biodiversity is argued to lie in its role in preserving ecosystem resilience,
by underwriting the provision of key ecosystem functions under a range of environmental conditions.” Conserving biodiversity maintains species that may look unimportant
for ecosystem function under current conditions, but who may play a crucial role in
a drought, pest infestation or other shock [Walker, Kinzig and Langridge (1999)]. As
above, conserving diversity can lead to a greater probability of having a productive mix
of species under a range of potential environmental conditions. Resilience has been defined in two main ways in the ecological literature. First, resilience can be defined in
terms of how quickly an ecosystem returns to equilibrium after a shock. Second, resilience can be defined in terms of the magnitude of shock that can be absorbed without
the ecosystem flipping into the basin of attraction of another equilibrium state. Under
either definition, if the equilibrium is desirable, then greater resilience will tend to increase welfare, which is what most of the literature on biodiversity and resilience has
implicitly assumed. However, increased resilience in an ecosystem in an undesirable
equilibrium will not in general be beneficial.
Some ecologists have been skeptical of the findings linking diversity to greater productivity, stability, or resilience [e.g., Grime (1997), Wardle et al. (1997)]. Huston
(1994) notes that there is an inverse relationship between plant diversity and ecosystem
productivity across ecosystems. Examples of low-diversity, high-productivity systems
include salt marshes, redwood, bamboo and Douglas fir forests. Critics of the “pots and
plots” experiments point out that the processes operating under artificial selection in diversity experiments are quite different from processes operating under natural selection
in real ecosystems. Therefore, experimental results may not be very informative about
the role of diversity in ecosystem functioning.
If the experimental results are correct, this suggests that the loss of biodiversity will
lead to the loss of ecosystem function, with the consequent loss of ecosystem services.
There remain questions, however, about each of these links. As noted above, there are
questions about the link between diversity and ecosystem function. Further, the link
between ecosystem function and ecosystem services is not well understood. In some
cases, productivity in an ecological sense is fairly directly linked to value of ecosystem
services in an economic sense. For example, the production of biomass ties fairly directly to the provision of forage and the level of carbon sequestration [Tilman, Polasky
and Lehman (2005)]. In other cases, the mapping between functions and services may
be quite complex.
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In sum, there remain a number of unanswered questions about the production and
value of ecosystem services. We currently lack understanding of the link between management actions and changes and ecosystem functioning. There remain questions about
the link between ecosystem functioning and provision of ecosystem services. Finally,
there remain questions about the link between the provision of ecosystem services and
value of these services to humans. Provision of valuable ecosystem services may ultimately prove to be the most important reason to conserve biodiversity. At present,
however, it is hard to know with any precision either the benefits of the provision of
ecosystem services or the opportunity costs involved in ensuring their continued provision.
4. Strategies to conserve biodiversity
A number of human actions cause threats to biodiversity. Though over-harvesting is a
culprit for some very high profile species (e.g., elephant, rhino, various fish stocks),
and climate change poses a threat on the horizon, habitat loss is widely thought to be
the primary cause of biodiversity decline followed by introduction of invasive species
[Wilcove et al. (1998), Wilson (1992)]. We begin this section looking at strategies aimed
at conserving habitat.
4.1. Terrestrial habitat protection
Because habitat loss and degradation is viewed as the leading threat to biodiversity,
a major strategy of conservation groups is targeted toward to the goal of preserving
habitat. A number of private conservation groups, such as the Nature Conservancy, and
government agencies set aside land for biological reserves. Additionally, governments
establish national parks and other areas where biodiversity is protected. Globally, it is
estimated that 6.4% of land (excluding Greenland and Antarctica) is in some form of
protected areas management [UNDP (2000)]. Much of the land is set aside because
it is of low value, or because it is of high aesthetic appeal, or for other reasons unrelated to biodiversity conservation. For example, high alpine ecosystems are very well
represented in national park systems and wilderness areas, but there are relatively few
ecosystems with high agricultural potential conserved.
Increasing human population and expansion of economic activity make it unlikely
that habitat loss and fragmentation will cease anytime in the near future, especially in
developing countries. Threats to habitat are generally less in the developed countries
than in tropical developing countries, where much of biodiversity resides. Threats also
vary by type of ecosystem. Forests in most developed countries, and some developing
countries are expanding even while tropical rainforests, coastal mangroves and wetlands
continue to disappear. Given limited budgets and facing large threats, conservation planning agencies need to set priorities to ensure that conservation efforts are directed where
Ch. 29:
The Economics of Biodiversity
1533
they will do the most good. Though conservation organizations typically have not relied on the advice of economists, the problem facing these organizations is a classic
economic problem: how can the greatest conservation return be achieved given limited
resources?
One common approach to addressing habitat conservation is to analyze the “reserve
site selection problem.” In the standard formulation of the reserve site selection problem, the goal is to choose a collection of reserve sites that among them contain as many
species as possible, subject to a constraint on the number of sites that may be included.
In other words, the selected sites represent a kind of Noah’s ark. Species that inhabit
at least one selected site (in the ark) survive but those that inhabit sites outside of the
reserve network (out of the ark) do not. This problem of maximizing the coverage of
species within a reserve network is what is called a maximal coverage problem in operations research [Church and ReVelle (1974), Church, Stoms and Davis (1996)]. The
problem can be written formally as follows. Define yi as an indicator variable for species
survival: yi = 1 if species i survives and yi = 0 if species i goes extinct, for all i ∈ I ,
where I is the set of all species. Define xj as an indicator variable for whether site j is
selected: xj = 1 if site j is selected in the reserve network and xj = 0 if site j is not
selected, for all j ∈ J , where J is the set of all potential reserve sites. The reserve site
selection problem is:
Max
m
i=1
yi
s.t.
m
j ∈Ni
xj y i ,
n
xj k,
j =1
where Ni is the set of sites in which species i occurs, and k is the number of sites that
may be included in the reserve network. The reserve site selection problem written in
this way is an integer-programming problem that can be solved using linear programming based branch and bound algorithms [Csuti et al. (1997)].
Much of the early reserve site selection literature did not use optimization methods
to solve for optimal solutions but rather used heuristic methods. Two popular heuristic
methods are choosing “hotspots” and the “greedy algorithm”. Hotspots are sites that
have the greatest numbers of species. The greedy algorithm begins with the site with
the greatest number of species. It then picks sites that add the greatest complement
of species to existing reserve sites. To see why neither approach guarantees choosing
an optimal reserve network, consider the following simple example from Polasky and
Solow (1999) with four potential sites, labeled A through D, of which only two can be
included in a reserve network. Species, which are labeled 1 through 6, that inhabit each
site are listed in the column for that site (see Table 1).
A hotspots approach would select sites A and B since they have four species each
while the other sites only have three. The greedy algorithm would start by selecting either site A or site B and then add either site C or site D. The optimal reserve network
involves choosing sites C and D. The hotspots approach fails because it does not consider the complementarity among sites [Pressey et al. (1993)]. The greedy algorithm,
though better than hotspots, fails because it does not allow discarding sites once they
included.
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S. Polasky et al.
Table 1
Site A
Site B
Site C
Site D
1
2
3
4
1
2
3
4
1
2
5
3
4
6
Applications of optimization methods (maximal coverage problem) to the reserve
site selection problem include Church, Stoms and Davis (1996), Csuti et al. (1997) and
Pressey, Possingham and Day (1997). These studies have generally found that optimal
reserve networks span a broad range of ecosystem types to include the broadest array
of species. Accumulation curves, which show the number of species included in reserve
networks of various sizes, have a rapid falloff in the accumulation rate after the first few
sites. For example, Csuti et al. (1997) found that over 90% of all terrestrial vertebrate
species in Oregon were included in a reserve network of five sites, and over 95% were
included in 10 sites, but that it took 23 sites to include all species.
The approach outlined in the previous paragraph implicitly assumes that all sites have
the same cost for inclusion in the reserve
network. It is a simple matter tochange from
cj xj B,
a constraint on the number of sites,
xj k, to a budget constraint,
where cj is the cost of selecting site j and B is the total conservation budget [Ando et
al. (1998), Polasky, Camm and Garber-Yonts (2001)]. Ando et al. (1998) used this approach to find the minimum cost way of covering endangered species within a reserve
network in the U.S. In an earlier study, Dobson et al. (1997) showed that endangered
species hotspots occurred largely along the coast of California and in Hawaii. Such biological hotspots happen to coincide with real estate hotspots. These places include some
of the most expensive real estate in the U.S. Ando et al. (1998) show that choosing sites
that are not necessarily the most biologically rich sites but have a high species per dollar ratio is a cost-effective conservation strategy. Doing so thereby shifted the focus of
conservation toward the inner-mountain west and away from expensive coastal areas.
The cost-effective approach resulted in the same number of endangered species in selected sites at one-third to one-half the cost of an approach that included the biologically
richest sites regardless of cost.
Balmford et al. (2003) recently estimated the costs of acquiring reserve sites for biodiversity protection worldwide. They develop a model relates annual management cost
[shown by James, Gaston and Balmford (2001) to be proportional to acquisition cost at
a ratio of about 50 : 1, i.e., a site costing $50 has annual management costs of about $1]
to variables such as GNP per unit area and purchasing power parity. Balmford et al.
(2003) conclude that for acquisition, the highest benefit to cost ratios appear to be almost exclusively in the developing world.
The reserve site selection problem can also be modified to incorporate different objectives besides just maximizing the number of species. Polasky et al. (2001) compared
site selection results when the objective was to maximize phylogenetic diversity versus
Ch. 29:
The Economics of Biodiversity
1535
maximizing number of species. They found that similar patterns emerged, in large part
because the two objectives are highly correlated. Sites with high richness will also tend
to have high phylogenetic diversity and vice versa. Adding more species to the mix adds
branch lengths to the phylogenetic tree. Rodrigues and Gaston (2002) showed how to incorporate phylogenetic diversity measure into an integer-programming problem so that
maximizing diversity is no more difficult than maximizing species richness. They also
found similar site selection results under richness and diversity objectives. In discussed
earlier, there are a number of reasons why biodiversity generates both direct and indirect
benefits. To date, there have been only limited attempts to tie the objective function of
site selection problem to the value of conserving various elements of biodiversity.
It is also worth noting that most of the literature on conserving biodiversity by
protecting habitat focuses on conserving specific taxonomic groups (e.g., terrestrial vertebrates) and does not include all of biodiversity. If conserving all of biodiversity is the
goal, the question arises whether conserving a particular taxonomic group is a good surrogate for conserving all of biodiversity. The conclusion from the biological literature
is that taxonomic groups are not generally reliable proxies for other taxonomic groups
or for all of biodiversity [Prendergast et al. (1993), Howard et al. (1998), Andelman and
Fagan (2000)]. It may be possible to use environmental surrogates or ecosystem types
rather than species as the units of analysis. As yet, however, there is not a solution for a
good biodiversity surrogate that commands widespread acceptance.
A key simplification of the reserve site selection approach is embedded in the assumptions that species within reserves will survive for sure while those outside of the
reserve network will go extinct for sure. A more reasonable approach is to model species
conservation in probabilistic terms. One reason for viewing the problem of conservation
in probabilistic terms is that there is generally uncertainty about the geographic range
of most species. Biologists may only have limited information about whether a given
species exists at a given site. Polasky et al. (2000) used heuristic methods to find solutions to the site selection problem when the goal was to maximize expected species
covered in a reserve network when there was uncertainty about the geographic range
of species. They used the same data on terrestrial vertebrates in Oregon as Csuti et al.
(1997) but used probabilities of occurrence. The broad geographic pattern of the optimal
solution was similar but there was in general more value to adding similar sites under
uncertainty in order to increase the probability that at least one site would contain certain species. Camm et al. (2002) showed how to use linear approximations that achieve
a solution arbitrarily close to the optimal solution using linear programming techniques
that enable rapidly finding the solution of a probabilistic reserve site selection problem.
Currently inhabiting a reserve site, however, does not necessarily guarantee the longterm survival of a viable population of the species. Survival probabilities can be modeled
as a function of the amount, spatial pattern and quality of habitat. Such an approach
requires much more biological information than is required for the reserve site selection problem, which requires only presence/absence data for species at each site. Most
studies that incorporate species survival probabilities into a decision analysis about
which lands to protect focus on a single species, largely because of the difficulty of
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S. Polasky et al.
constructing reasonable spatial population biology models for species. Several studies analyzed the optimal spatial pattern of timber harvests to maximize the survival
probability for a species of concern for a given value of timber harvests, or vice
versa [e.g., Hyde (1989), Hof and Raphael (1997), Hof and Bevers (1998), Marshall,
Homans and Haight (2000), Rohweder, McKetta and Riggs (2000), Calkin et al. (2002)].
Montgomery, Brown and Adams (1994) combined timber harvest and demand models
with a population biology model for the spotted owl to develop an estimated marginal
cost curve for increasing survival probabilities. They found that the marginal cost increased sharply for survival probabilities above 90%. Combining population biology
models with economic analysis allows for greater biological realism as well as allowing
for marginal analysis, showing how slight increases in protected area change survival
probability and cost.
One of the large challenges facing conservation researchers is how to bridge the gap
between single species models, which often include detailed biological and economic
models and data, with the larger breadth and scale of reserve site selection models. Conservation biologists have proposed doing conservation at both a coarse scale and a fine
scale [Noss (1987)]. At the coarse scale, the results of large-scale multi-species analysis can direct attention to particular high priority areas for conservation. Then a more
in-depth fine scale analysis of those particular sites can help develop on the ground
conservation plans for those sites. Such a two-step process has merit but it does not
necessarily result in optimal or near optimal conservation plans. Suppose the fine scale
analysis of a particular site shows that that conservation is actually more difficult or expensive to accomplish than assumed in the coarse scale analysis. If this site is discarded
it may require not just including one different site, but because of the overlap of species
among sites, it may change the entire pattern of sites considered to be high priority.
Several recent studies have tried to bridge the gap between fine scale and coarse scale
in a single unified analysis. Montgomery et al. (1999) analyzed the effects of land use
in Monroe County Pennsylvania on 147 native bird species. The study incorporated
information on species–habitat associations and territory size to estimate a viability
function for each species based on land use decisions. Lichtenstein and Montgomery
(2003) modeled the effect of forestry harvesting decisions on both economic returns
and viability for 166 terrestrial vertebrates in the Coast Range of Oregon. Polasky et
al. (2003) have expanded this approach to analyze economic returns and viability for
land uses including agriculture, development, forestry as well as conserving land in its
natural state. They find that a strategy that partially integrates conservation within the
working landscape of agricultural and forestry production in addition to having some
natural areas is more efficient than strategies that enforces complete separation of lands
into natural areas, devoted solely to conservation purposes, and production areas, devoted solely to maximizing economic returns. The complete separation of land is an
underlying assumption of the reserve site selection approach.
A general question in land management with multiple outputs is whether it is better
to have specialized management, with some land devoted to particular outputs, versus
uniform management, with integrated joint production [Boscolo and Vincent (2003)].
Ch. 29:
The Economics of Biodiversity
1537
Nonconvexities in production may give rise to specialization. One simple example of a
nonconvexity in the joint production of timber and biodiversity occurs with species that
require large tracts of undisturbed forests. The first units of timber production may result
in a fall in the species population to near zero. Further increases in timber production
would not therefore affect the species population significantly. In this case, it is better
to have some undisturbed tracts with intensive timber production elsewhere rather than
non-intensive timber production everywhere. Forestry models with joint production of
biodiversity and timber typically find that it is optimal to have at least some degree of
specialization [Bowes and Krutilla (1989), Swallow, Parks and Wear (1990), Swallow
and Wear (1993), Vincent and Binkley (1993), Swallow, Talukdar and Wear (1997),
Boscolo and Vincent (2003)]. Similar results on specialization have been found for allocating conservation funds across different areas [Wu and Boggess (1999), Wu, Adams
and Boggess (2000)].
Another challenge to conservation researchers is to include dynamics into the analysis. Development activity, climate change, biological invasions, changes in relative
prices, and chance events such as forest fires and drought may all play a role in changing the landscape that require changes in conservation priorities and an ability to adapt.
Work is beginning to appear that combines predictions of regional climate change and its
implications for conservation [Parmesan and Yohe (2003), Root et al. (2003)]. Costello
and Polasky (2003) analyze a dynamic reserve site selection problem in which each site
that is not protected has a probability of being developed during that period. A conservation agency facing a budget constraint each period must choose sites knowing that it will
get to choose again in the future, but also knowing that not all remaining high priority
conservation sites will remain undeveloped. Meir, Andelman and Possingham (2003)
analyze a similar problem except that in their paper the probability is whether a site will
become available during the period. When a site becomes available it must either be
purchased by the conservation agency that period or face a development risk. For these
types of problems, the optimal sites to choose are those that combine high biological
value added per unit cost and face a high development threat. This conclusion provides
some support for the strategy of giving high priority to conserving biodiversity hotspots,
defined as areas of high biodiversity or high endemism that face large threats of habitat
loss [e.g., Mittermeier et al. (1998), Myers (1988), Myers et al. (2000)]. Kareiva and
Marvier (2003) criticize this approach for ignoring other important issues in conservation, such as cost and other conserving objectives beyond species (e.g., ecosystem
functions and services). As noted above, hotspots will fail to be optimal if it does not
factor in complementarity among sites. Many of these criticisms can be addressed correctly specifying the objective function and the set of constraints. For any dynamic
conservation problem, biological value added, cost and threat will be essential elements
that will drive the analysis.
There is a fast-growing literature analyzing habitat conservation issues. Some work
in this vein has begun to incorporate both biological and economic objectives in an integrated fashion. A key challenge, though, is to be able to incorporate realistic economic
and biological details while maintaining the broad-scale focus necessary for setting
1538
S. Polasky et al.
biodiversity conservation priorities. At present, most models are too stylized to be of
use in helping decision-makers choose conservation strategies on the ground, though
the gap between models and reality is closing. Another challenge in habitat conservation models is to incorporate dynamics and stochastic events in the analysis.
4.2. Marine biodiversity and reserves
While similar in many respects to the terrestrial environment, marine systems present
a host of challenges for biodiversity conservation and reserve design. Ecologists warn
that marine biodiversity, and the coastal ecosystems on which it depends, is in peril.
A recent paper cites a precipitous decline in many marine consumer species from “fantastically large” historical levels [Jackson et al. (2001)]. Analyzing data beginning in
the 1950s, Myers and Worm (2003) find that the global ocean has lost more than 90%
of large predatory fish, and that industrialized fisheries have typically reduced community biomass by 80% since large scale exploitation began approximately 50 years
ago. Although this collapse has been attributed to several anthropogenic sources such
as pollution, degradation of habitat quality, and changes in climatic regimes, it is human overexploitation of marine resources that tops the list of threats [Jackson et al.
(2001)]. The traditional approach to managing marine resources – with single species
management and little account of uncertainty, learning, or dynamic ecological effects
[popularized for example by the early models of Gordon (1954), Smith (1969), among
others] – has largely failed. And an emerging body of literature makes it clear that new
approaches to the management of marine resources will be required if biological diversity is to be protected.
In response to this call, attention has recently shifted from single-species policy design to the implementation of marine reserves. Botsford, Hastings and Gaines (2001)
argue that perhaps 35% of marine ecosystems must be set aside in order to protect
biodiversity. Currently only about 0.5% is protected [IUCN (1997), Kelleher, Bleakley
and Wells (1995)]. In addition, some claim that by setting aside large tracts of productive marine ecosystems, we may earn a “double payoff” through enhanced biodiversity
within the reserve and increased fishery yield from spillovers outside the reserve, though
this claim is subject to some dispute [Sanchirico and Wilen (2001)].
Several key features distinguish the design of marine reserves from the design of terrestrial reserves. Important differences include the biology of target organisms, threats
to native biodiversity, and political economy considerations. Many marine organisms
are characterized by planktonic larval stages, where larvae may drift with ocean currents for several weeks, often traveling hundreds of miles before settling. And in their
adult stages, many marine species are highly mobile. This high level of mobility during several life-history stages has at least two important implications for marine reserve
design. First, even if marine reserves are sited in the most productive areas, small (contiguous) reserves may not protect biodiversity as effectively as a reserve around sessile
species [see, e.g., Botsford, Hastings and Gaines (2001)]. And second, there may exist a “spillover” effect where larvae produced within a reserve spill into adjacent areas
Ch. 29:
The Economics of Biodiversity
1539
thus enhancing fishery production. Terrestrial species are also mobile (e.g., migratory
species) and there may be some spillover effects from conservation, however, these issues tend to be of lesser significance in terrestrial than in marine systems.
A second important difference between terrestrial and marine reserves concerns the
threats to biodiversity. In terrestrial environments, reserves are primarily seen as a way
of preventing harmful land conversion. In the marine environment, a primary threat
to biodiversity is overexploitation [Jackson et al. (2001)]. While reserves are likely a
second best instrument for reducing fishing pressure, they may be justified on the basis
of spatial nonconvexities, though this has not been well explored.
Sanchirico and Wilen (2002) address the political economy of marine reserve design. They argue that the most productive sites (i.e. those that should be protected on
the basis of biological criteria) are also the most important sites to the fishing industry, and therefore that these will likely be the areas that attract the greatest political
pressure against reserve designation. Similar conflicts may exist in terrestrial environments, where hotspots of biodiversity may coincide with areas that are highly desirable
economically [Ando et al. (1998)].
An emerging body of literature focuses on the principles of design of marine reserve
networks, accounting for the biological (and in a few cases economic) idiosyncrasies
of the marine environment. Botsford, Hastings and Gaines (2001) develop a theoretical
model of an infinite linear coastline along which reserves are placed at periodic intervals in order achieve either biodiversity or fishery production objectives (accounting for
larval drift). Results suggest over 35% of the coastline would need to be protected in
reserves in order to conserve biodiversity. Boersma and Parrish (1999) review the academic and gray literature on marine reserve design and draw several general conclusions.
Factors increasing the efficacy of marine reserves on a local scale include:
(1) an intimate knowledge of the biology of protected organisms,
(2) the ability to control threats, and
(3) the scale of protection exceeds the scale of threat [Boersma and Parrish (1999)].
There exist only a few empirical analyses of efficient reserve design for marine systems.
Roberts et al. (2002) identify marine biodiversity hotspots in tropical reef ecosystems,
in which 0.012% of the world’s oceans contain about 50% of the world’s restrictedrange species. As discussed above, however, the hotspot approach is not necessarily an
efficient method to choose a set of conservation reserves [Polasky and Solow (1999),
Sanchirico and Wilen (2001)]. Sala et al. (2002) conduct what amounts to a reserve
site selection (optimization) exercise to design a network of marine reserves in the Gulf
of California. Using data on larval production, transport, spawning success, and several socioeconomic factors, they find, for example that most conservation goals can be
achieved with an optimally designed network covering 40% of rocky reef habitat.
The ecology literature on marine reserves focuses primarily on increasing conservation, rather than exploitation. There is, however, an emerging body of literature that
suggests that in some cases fishery yields may be unaffected, or even enhanced, by the
creation of marine reserves. For example, Roberts et al. (2001) show that within 5 years
of its creation, a small network of marine reserves in St. Lucia has increased harvest of
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adjacent fisheries by 46–90%. Theoretical models seem to support these (albeit scarce)
empirical findings. Hastings and Botsford (1999) create a simple analytical model in
which an optimal fraction of the coastline is placed in reserve, and fishers rely on larval spillovers for harvest. Under simplifying assumptions, they show an equivalence in
harvest between an optimally designed system of marine reserves, where all fish outside reserves are harvested and a non-reserve system in which a fixed fraction or fixed
number of the population is harvested.
Bioeconomic models give mixed predictions about the effect of reserves on yields
[Hannesson (2002), Pezzey, Roberts and Urdal (2000), Sanchirico and Wilen (2001,
2002)]. Pezzey, Roberts and Urdal (2000) develop a simple bioeconomic model of
a marine reserve that allows for basic economic behavior outside the reserve (open
access). They give conditions under which the reserve will decrease (when the open
access density is large) or increase (when the open access density is small) the harvest.
When parameterized with data from three Caribbean sites, the model predicts harvestmaximizing reserves of 20–40% of total area, with dividends of 10–80% increases in
yields. Sanchirico and Wilen (2001) more carefully account for both spatial ecological
and economic considerations. Spatial ecology is modeled using metapopulation theory,
where resource patches are distributed through space and connect with each other via
larval dispersal. Economic behavior is also modeled in space such that spatial arbitrage
is eliminated via migration of effort. This spatial bioeconomic model allows them to
derive several important conclusions about the conditions under which reserves are an
effective management tool. In many cases, these results contradict biological analyses
that focus only on hotspots of biological production.
In summary, overexploitation (among other sources) has caused precipitous declines
in the populations of marine organisms. There is a growing body of literature – spawned
from both ecology and economics – that is suggestive of the biodiversity, and in some
cases fishery, benefits of marine reserves. In the absence of spatial nonconvexities and/or
habitat-modifying fishing technology, marine reserves are most likely a second-best
instrument with which to manage a fishery for economic gain. Siting reserves in the
most biologically productive areas will likely generate the most opposition from fishers
[Sanchirico and Wilen (2002)]. But to achieve multiple objectives of biodiversity protection and fishery production, there is mounting evidence that marine reserves may be
an effective, even efficient, policy option.
4.3. Introduced species
A major threat to both marine and terrestrial systems that may be largely unchecked
by reserves alone is the introduction of nonindigenous species. Biological invasions
by nonindigenous species of plants, animals, and microbes cause significant ecological
and economic damage worldwide. And while the consequences of invasions have been
carefully studied, few workable policy solutions exist. Despite the fact that biological
invasions are driven by economic activity and that they cause significant economic damage, economists have been largely absent from the discussion about introduced species.
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In this section we describe the ecological and economic consequences of introduced
species and the policy implications from the economics literature. We begin with a
description of the problem from both economic and ecological perspectives. The magnitude of the problem hinges critically on the rates of introduction, population growth, and
damage. We discuss the various modes of entry, and then turn to economic approaches
to controlling damage by employing both ex ante and ex post control instruments.
Introductions of nonindigenous species (also referred to as exotic, alien, transplanted,
invasive, and introduced species) have important economic and ecological effects. The
U.S. Office of Technology Assessment (OTA) estimated that the annual monetary losses
associated with nonindigenous species introductions were approximately $5 billion
[OTA (1993)]. A more recent estimate placed this figure at over $100 billion annually
[Pimentel et al. (2000)] but this estimate is quite speculative. Losses from nonindigenous species introductions include damage to municipal infrastructure, losses in agricultural and forest production, and reduced abundance of harvested marine resources.
For example, Knowler and Barbier (2000) describe how the introduction of the comb
jelly to the Black Sea caused a decline in the commercial anchovy fishery. [See Perrings,
Williamson and Dalmazzone (2000) for a compilation of early papers on the economics
of biological invasions.]
Nonindigenous species can cause significant damage to native ecosystems, mostly
via competition with and predation on native species [see Cox (1999) for an excellent
review of impacts to North American ecosystems]. Nonindigenous species are widely
acknowledged as the second leading cause of global biodiversity decline, next to habitat
conversion [Wilson (1992)]. In the U.S., 42% (400 of 958) of species on the endangered
species list are at risk primarily because of adverse interactions with nonindigenous
species [The Nature Conservancy (1996), Wilcove et al. (1998)]. Globally, the picture
may be worse. Displaced, reduced, or otherwise degraded native communities are often
linked to invasion and spread by nonindigenous species.
Estimating the present and future magnitude of the problem is not an easy task.
Costello and McAusland (2003) develop a model of stochastic introductions of nonindigenous species where the rate of introduction is linked to the volume of trade. They
examine whether agricultural protection (to reduce imports of agricultural products for
a net importer of agricultural products) will reduce pecuniary damage to the agricultural
sector from nonindigenous species introductions. They find that while protectionism reduces the volume of trade and hence the rate of new introductions, the overall damage
may increase because domestic agricultural production both creates a larger platform
for new introductions and creates a larger agricultural base subject to damage.
Others have conducted empirical analyses of the rate of introductions over time. It is
widely believed that the rate of introductions has increased over time. And this belief
appears to be borne out empirically. For example, Cohen and Carlton (1998) show that
even after correcting for extraordinary taxonomic effort, the rate of introductions to
the San Francisco estuary has steadily increased over the past 150 years. However, as
Costello and Solow (2003) point out, the observed rate of introductions depends on
variables such as the true rate of introductions, collection effort, and the observability of
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newly introduced species. They show that the discovery process of introduced species
typically does not reflect the true rate of introductions, and that it would appear as
though species were arriving at an increasing rate, even if in fact the rate were constant
or even zero.
The design of efficient policy to limit damage from invasive species requires a solid
understanding of the pathways through which they enter a host country. Entry can occur
via intentional or unintentional means. Non-native species may be intentionally introduced to a region as ornamentals, for purposes of habitat modification (e.g. erosion
control), to control other pests (as biological control agents), or for other direct economic gain (e.g. introduced fish species for recreation). Many of these introductions
provide net benefits. In fact nearly all of the food produced in the United States is
of nonnative origin. However, some introductions cause damage that outweighs their
benefit. For example the OTA reports that approximately 50% of intentional mollusk
introductions cause harm. The percentage of introduced species that cause harm in
other taxonomic groups ranges from 0% (for plant pathogens, though data are limited) to 62% (for terrestrial vertebrates). Unintentional introductions occur primarily
via international trade and tourism when individuals “hitchhike” on traded products or
in shipping materials. Hitchhikers on agricultural and forest products are common, and
many pests to domestic agriculture and forestry can be linked to infected shipments
of agricultural products. The percentage of unintentional introductions that cause harm
ranges from 39% for fish to 98% for plant pathogens [OTA (1993)].
But not all introduced species find their new home to their liking. The conventional
wisdom is the “rule of tens”, which states that approximately 10% of imports escape to
become introduced, 10% of introduced species become established, and 10% of established species become harmful. There has been considerable interest among ecologists
in identifying the most important determinants of successful introductions. For example,
Brown (1989) links introduction success with both the level of disturbance of the host
environment and the similarity of the physical environment between original and host
locations. Consistent with the rule of tens, Case (1996) correlates success (of avian populations) with the number of failed attempts in the same region. In a statistical analysis
of the determinants of success, Dalmazzone (2000) uses a regression model to explain
the share of alien to native plant species in 26 countries as a function of variables such
as GDP, the volume of imports, percentage of land in agriculture and pasture, whether
the country is an island, and human population density. She finds that human population
density (positive effect), GDP per capita (positive effect), extent of permanent pasture
(positive effect), and extent of agriculture (negative effect) may all play a significant
role in determining the percentage of alien to native plant species in a country. More
recent work has focused on the role of genetics. For example, Sakai et al. (2001) show
that colonization success is enhanced by greater genetic variation, probably because this
variation allows better exploitation of novel habitats.
With perfect foresight, only species with positive net benefits would be intentionally
introduced (by a social planner) to a new location. Despite the rules of thumb cited
above, scientists do not have a precise understanding of the conditions under which
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species will escape, breed, and cause damage. Therefore whether to admit a species
with unknown consequences is a situation of decision-making under uncertainty. One
issue here that has not been well explored is the relative merit of a “black list” versus
a “white list” policy. A black list contains all species known to cause damage in a
particular country. Under a black list policy, all species are admitted that do not appear
on the list, and it therefore creates a burden on policymakers to identify those species.
Conversely, a white list contains all species thought to cause no damage. A white list
policy is conservative, and will likely reject economically valuable species that are in
fact benign. It imposes a burden on potential importers to show that their import is in
fact safe.
Thus far we have considered only the case where damage from species introductions
can be avoided by acquiring ex ante information about the likely economic or ecological
costs imposed by the species. Thomas and Randall (2000) contrast this approach with
one in which decisions are completely revocable (i.e. extermination of problem species
is costless). Given that perfect ex ante information is typically impossible to acquire,
they propose a management model that balances the two approaches. Their protocol is
unique in its explicit treatment of the revocability of outcomes.
While intentional introductions can cause serious damage, the OTA reports that approximately 81% of all harmful new exotics detected from 1980–1993 were unintentionally introduced, primarily via international trade. This has led some to suggest that
large-scale trade reduction may be required to effectively control the problem [see for
example Jenkins (1996)]. Employing a more formal economic framework, McAusland
and Costello (2003) examine the economic tradeoffs between tariffs and port inspections to reduce imports of species. They find that the optimal policy hinges critically on
two partner-specific characteristics: the expected damage per species and the proportion
of infected traded goods. Among other results they find that while tariffs should always
be positive, there are important cases (e.g. for sufficiently high infectiousness) where
inspections should be zero. They discuss how the optimal partner-specific instruments
a country would like to pursue would violate the World Trade Organization policy of
nondiscrimination.
Others have focused on decentralized strategies to mitigate damage. For example,
Shogren (2000) discusses the risk-reduction strategies an economy may engage in with
respect to harmful biological invasions. He points out that the private sector will engage
in self-protection, or averting behavior, to protect itself from risk. This private agent
adaptation effect must be accounted for in the policy design process in order to maintain
economic efficiency.
While most species introductions cause either no damage or have positive benefits,
some cause large damage. For intentional introductions, this is an information problem. For unintentional introductions, economic tradeoffs exist between tariffs (to reduce
trade volumes), port inspections or standards of cleanliness, and ex post control. The important ecological and economic consequences of nonindigenous species introductions
make this a fruitful area of future research for environmental economists.
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5. Incentives to conserve and conservation policy
In prior sections we discussed both the value of conserving biodiversity and what optimal strategies for conservation might entail. As with many other environmental issues,
specifying an optimal outcome and achieving it are two different matters. Not all of the
benefits of conservation accrue to those who make decisions that impact biodiversity.
For example, a person who decides to cut down a portion of tropical forest to plant crops
gains the benefits of growing the crops. The benefits of maintaining the tropical forest,
however, accrue to a wider community. Nutrient retention, local climate effects, prevention of erosion, and water quality all may contribute local or regional benefits. Carbon
sequestration and species existence value may contribute global benefits. Conservation
often yields significant positive externalities and in some instances may provide global
public goods. Unless there is some way to provide decision-makers with incentives that
appropriately reward them for conservation, decision-makers will generally fail to make
appropriate conservation decisions. As with other externalities, a solution to this problem is to find some way to internalize externalities. A variety of different approaches for
doing so in the context of conservation have been proposed. In some cases, there may
be ways to set up markets that allow conservation to pay for itself (e.g., ecotourism,
genetic prospecting). In other cases, it may be necessary for government intervention
to overcome market failure and foster conservation. Such intervention may make use
of market mechanisms (e.g., payments for ecosystem services, tradable development
rights), or it may employ more traditional command and control regulatory approaches
(e.g., portions of the Endangered Species Act and the Convention on International Trade
in Endangered Species).
We begin by considering cases where at least partial solutions may lie within the
market system itself without explicit need for government intervention. Conserving biodiversity can yield valuable goods and services that can, under the right circumstances,
be sold in the market. Doing so may generate enough revenue to make conservation financially viable. This point is the core thesis behind several recent books [Heal (2000),
Daily and Ellison (2002), and Pagiola, Bishop and Landell-Mills (2002)]. One example in which conservation can be made to be financially attractive is ecotourism. The
World Tourism Organization estimates that tourism generated revenues of $463 billion
in 2001. One of the fastest growing segments of tourism may be nature-based or ecotourism. Some areas have had a long history of profiting from the richness of the local
biodiversity, including Yellowstone National Park in the U.S., Krueger National Park
in South Africa and a variety of National Parks in Kenya and Tanzania. Costa Rica has
also done well promoting ecotourism with approximately 1 million tourists spending $1
billion in 2000 [Daily and Ellison (2002, p. 178)]. Several economic studies have found
that ecotourism can generate significant revenues in a variety of developing country
settings [e.g., Aylward et al. (1996), Lindberg (2001), Maille and Mendelsohn (1993),
Wunder (2000)].
Private landowners can also capture benefits of ecotourism, and contribute significantly to conservation efforts. There are approximately 5000 game ranches and 4000
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mixed game ranch and livestock operations in South Africa. These operations make
up 13% of the land base of South Africa. Notably, the land base in game ranches is
more than double the land area in all the protected areas in the country [Absa Economic
Research Group (2002)].
Ecotourism works best where there is spectacular scenery or where there are
large concentrations of charismatic megafauna. Southern African game ranches attract
tourists and hunters to who want a shot at the “big five” (lions, leopards, elephants,
rhinos, and buffalo). Other areas without star attractions may find it difficult to make
ecotourism competitive with other land uses. There are also complaints that ecotourism
may harm the very biodiversity it seeks to promote through over-utilization. Further, if
revenues from ecotourism do not find their way to the local community or landowners,
then the locale community or landowners will not have an incentive to conserve.
A second way in which biodiversity conservation may generate market rewards is
from bioprospecting in which the search for useful genetic material from plant or animal
species may lead to the development of valuable pharmaceuticals or other products (see
Section 3.2). Dan Jantzen noted that coffee was “the world’s most popular rain-forest
drug. . . A one-cent tax on each cup worldwide would fund all of tropical conservation
forever. Now, no one is going to succeed in imposing that tax. . . but if the bioprospecting
contracts are written right, it’ll be there for the next cup of coffee to come along” [Daily
and Ellison (2002, p. 173)]. Despite some early excitement about the potential for bioprospecting to provide large incentives for conservation, this potential has failed to pan
out. Merck and Costa Rica’s Instituto Nacional de Biodiversidad (INBio) signed a $1
million deal in 1991. However, few bioprospecting contracts have followed since. One
reason, as discussed in the section on bioprospecting, is that returns from bioprospecting are likely to be small and insufficient by themselves to generate much incentive for
conservation [Simpson, Sedjo and Reid (1996)].
A second problem with bioprospecting has arisen over the distribution of rents. Developing countries have argued strenuously that rents should accrue to the country
providing the biological material and that failure to do so amounts to “biopiracy”. The
U.S. has argued that there should be strong intellectual property rights protecting the
investment and discovery process by pharmaceutical companies. Disputes over property rights were the major reason the U.S. failed to ratify the Convention on Biological
Diversity.
There are unanswered questions about the optimal allocation of rents from a bioprospecting agreement. Barrett and Lybbert (2000) conclude that “If the rents do not
accrue to local land users who ultimately make conservation or conversion decisions,
the debate surrounding the size of bioprospecting rents is irrelevant since the key questions ultimately surround the calculus of land and labor use in fragile ecosystems”
[Barrett and Lybbert (2000, p. 295)]. Consider, for example, the case of the rosy periwinkle, a plant native to Madagascar that contains vincristine, a powerful cancer-fighting
compound. No synthetic substitute for vincristine exists, and one ounce of vincristine
requires 15 tons of periwinkle leaves. This has resulted in depletion of nearly the entire native periwinkle habitat in Madagascar [Koo and Wright (1999)], though the plant
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has been extensively cultivated elsewhere. However, if drug companies do not keep a
significant fraction of rents from developing new drugs they may not have sufficient incentive to develop new drugs via bioprospecting. Mendelsohn and Balick (1995) found
a significant difference between likely social and private returns to development of new
drugs. Koo and Wright (1999) also argue that biodiversity will be underprovided by the
private sector via bioprospecting on the grounds that although the value of biodiversity
is very large, market and social values are grossly misaligned.
Both ecotourism and bioprospecting have been subject to criticism that revenues
generated by conservation activities have not necessarily resulted in benefits to local
communities. Local communities with no financial stake in conservation or that in
fact suffer financial losses from conservation activities (e.g., wildlife damage to crops)
might resent or actively oppose such activities, leading to a greater probability that
conservation will fail. Trying to give local communities a stake in conservation has
led to efforts to promote community-based conservation [Western and Wright (1994)]
and integrated conservation–development projects [Wells and Brandon (1992)]. The
goal of community-based conservation is to give local communities control over resources, thereby giving the community a stake in conservation. The most well-known
community-based conservation program is the Communal Areas Management Program
for Indigenous Resources (CAMPFIRE) in Zimbabwe [see Barbier (1992) for an early
review and economic assessment]. Integrated conservation-development projects try to
“link biodiversity conservation in protected areas with local socio-economic development” [Wells and Brandon (1992)]. Both approaches arose because of the failure of
traditional protected areas conservation strategies that ignored the needs of local communities.
The extent to which conservation and local control over resources, or local economic
development, are mutually consistent remains to be seen. Overall, community-based
conservation and integrated conservation-development projects have had mixed success
to date. There is no guarantee that once they are given the choice, local communities
will in fact choose to conserve. Cultural, social or political factors may block conservation even when economic factors favor conservation. However, there is no guarantee
that conservation and local economic development are in fact consistent goals. Certainly
in some communities with ecotourism potential or where ecosystems provide valuable
ecosystem services, conservation and development may go hand-in-hand. In other cases,
the conservation of biodiversity and economic development may not be consistent. Because of the pervasive nature of external benefits created by biodiversity conservation,
it may require more than just allowing local control and market forces to achieve an
efficient level of conservation.
Recognition that the conservation of biodiversity may generate benefits that reach
well beyond the local community provides a rationale for governments and nongovernmental organizations to provide resources for conservation, and for the institution
of national or international conservation policies. At present, though there are a number of policies to promote conservation, there are also a number of policies that have
the exact opposite effect. Agricultural subsidies, subsidies to clearing land, resource
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extraction and new development, all may contribute to driving a further wedge between
private and social returns from actions that conserve biodiversity. Perhaps the first rule
for policy should be to “do no harm”. Beyond doing no harm by eliminate perverse subsidies, however, positive external benefits from conservation require policies that create
positive incentives to conserve.
Both governments and non-governmental organizations, such as the Nature Conservancy and World Wildlife Fund, are actively engaged in acquiring land for conservation
and in other activities promoting conservation. Buying land is a direct and secure way
to promote conservation but it is often a costly instrument for protecting biodiversity. Boyd, Caballero and Simpson (1999) find that acquisition is often “conservation
overkill”. Conservation easements that rule out certain incompatible land uses, but not
all land uses, is often a far cheaper route to secure conservation objective than acquisition. Recently, interest has shifted away from land acquisition toward conservation
easements and other ways of working with landowners to promote both conservation
and landowner interests. For example the Nature Conservancy’s approach, once heavily
weighted toward acquisition, now incorporates mechanisms such as community development projects to reduce the demand for fuel wood and the purchase of conservation
easements to limit development (see www.nature.org for examples).
Acknowledging that donors from high-income nations invest billions of dollars toward ecosystem protection in low-income nations, a related literature debates the
relative merits of direct conservation payments versus indirect mechanisms (e.g. payments to promote ecotourism which generates ecosystem protection as a joint product). Although indirect approaches are the predominant form of intervention in lowincome countries, Ferraro and Simpson (2002) argue that direct payments can be far
more cost-effective, often requiring no additional institutional infrastructure or donor
sophistication.
Another approach to conservation is to institute a system of transferable development
rights (TDR). TDR are virtually identical to cap-and-trade schemes to limit pollution
emissions. In a TDR system, the conservation planner determines how much land can
be developed in a given area. Development rights are then allocated and trades for the
right to develop are allowed. Developers can increase density in a growth zone (“receiving area”) only by purchasing a development rights from the preservation area (“sending
area”). The approach was developed and implemented extensively in the 1970s to direct
development within urban areas [see Field and Conrad (1975) for what appears to be the
first economic model of the supply and demand for development rights; see Mills (1980)
for a model of TDR and a discussion of their appropriateness for use in protecting public
goods]. Not until recently have economists explicitly considered TDR as a mechanism
to conserve biodiversity. Panayotou (1994) develops the TDR approach for conservation. He argues that “biodiversity conservation is ultimately a development rather than
a conservation issue” [Panayotou (1994, p. 91)]. Given that most biodiversity exists in
the developing world, and that the public good nature of biodiversity requires a mechanism for paying developing countries to be stewards of this resource, Panayotou argues
that TDR may also be an effective way to protect global (as well as local) biodiversity.
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Merrifield (1996) proposes use of a similar concept where “habitat preservation credits” would be required for development. There is no guarantee that TDR schemes, like
cap-and-trade schemes, will result in efficient outcomes unless the planner chooses the
correct amount of rights/permits to allocate. An additional problem faced in TDR for
conservation is deciding what are appropriate trades. Land units, unlike air emissions,
have unique characteristics and may contribute to a number of conservation objectives.
What constitutes an equal trade is not obvious. Similar problems over establishing the
proper trading ratios exist in mitigation banking schemes for wetlands.
Another direct method to create positive incentives for conservation is to institute a
system of payments for the provision of ecosystem services. The country that has moved
furthest in this direction is Costa Rica. The 1996 Forestry Law instituted payments for
ecosystem services. The law recognizes four ecosystem services: mitigation of greenhouse gas emissions, watershed protection, biodiversity conservation, and scenic beauty.
The National Forestry Financial Fund enters into contracts with landowners that agree
to do forest preservation, reforestation or sustainable timber management. Funds to pay
landowners come from taxes on fuel use, sale of carbon credits, payments from industry and from the Global Environment Fund. Many developed countries have adopted
some form of “green payments” in which agricultural support payments are targeted to
farmers who adopt environmentally friendly management practices or land uses [OECD
(2001)].
While market oriented policies have been of increasing importance in recent years,
other important policies directed at the conservation of biodiversity, including the U.S.
Endangered Species Act and the Convention on International Trade in Endangered
Species, are at their core largely command and control regulatory regimes. The Endangered Species Act (ESA), enacted in 1973, changed conservation policy from a largely
voluntary and toothless regime that existed prior to 1973 into a powerful environmental
law capable of stopping large government projects and actions of private landowners
[Brown and Shogren (1998)]. Section 7 of the ESA prohibits federal agencies from actions that cause “jeopardy” (i.e., risk of extinction) to species listed as threatened or
endangered. Section 9 prohibits public and private parties from “taking” listed species.
“Taking” includes causing harm to species through adverse habitat modification from
otherwise legal land uses, such as timber harvesting or building, as well as more obvious
prohibitions against killing, injuring or capturing a listed species.
The way the law is written, the ESA appears to have very limited scope for economic
considerations. Sections 7 and 9 are absolute prohibitions. Biological criteria are the
basis for listing species. In TVA v. Hill, the U.S. Supreme Court wrote: “The plain intent of Congress in enacting this statue was to halt or reverse the trend toward species
extinction, whatever the cost” [437 U.S. 153, 184 (1978)]. When it looked like a small
unremarkable fish (the snail darter) that was previously all but unknown would halt construction of a large dam backed by politically powerful members of Congress, Congress
amended the ESA. They authorized the formation of the Endangered Species Committee (“The God Squad”) to allow an exemption to the ESA if the benefits of doing so
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would clearly outweigh the costs. There are high hurdles to be met for convening this
Committee and it has been used rarely.
Despite the fact that the law is written in a way that appears to marginalize economic
considerations, it has proved impossible to administer the Act while totally ignoring
economics. Several writers have noted that economic and political considerations influence agency actions at all stages of the ESA process including the listing stage, which
is supposed to be done strictly on biological grounds [e.g., Bean (1991, p. 41), Houck
(1993, pp. 285–286), Thomas and Verner (1992, p. 628)]. Endangered species whose
protection threatens to impose large costs run into political opposition that translates
into pressure on the Fish and Wildlife Service. This pressure appears to translate to
lower probability of listing [Ando (1999)]. The benefits side of the equation also seems
to affect listing and recovery spending even though the ESA does not base such decisions on the popularity of the species. Metrick and Weitzman (1996) found that more
charismatic species were more likely to be listed than uncharismatic species, and that
once listed “visceral characteristics play a highly significant role in explaining the observed spending patterns, while the more scientific characteristics appear to have little
influence” [Metrick and Weitzman (1996, p. 3)].
While much of the early regulatory activity under the ESA targeted government actions under Section 7, the 1990s saw an increase in the emphasis on conservation on
private lands under Section 9. More than half of endangered species have over 80% of
their habitat on private land [USFWS (1997)]. Conservation on private lands presents a
number of incentive issues [Innes, Polasky and Tschirhart (1998)]. A landowner whose
parcel contains endangered species habitat may face restrictions on what activities may
be undertaken. The landowner need not be compensated if restrictions are imposed and
losses to the landowner result [though the law on regulatory takings is quite unsettled, see Polasky and Doremus (1998)]. The potential losses the ESA may impose on
a landowner give rise to several perverse incentives. Innes (1997) shows that there can
be a race to develop in order to beat the imposition of an ESA ruling. Similarly, there
may be an incentive to “shoot, shovel and shut up” in order to lower the likelihood of
imposition of restrictions under the ESA [Stroup (1995)]. Further, because current law
stipulates that acquiring specific information about species is a prerequisite to imposing restrictions on a landowner, there is no incentive for the landowner to cooperate in
allowing biological information to be collected [Polasky and Doremus (1998)].
There are several possible ways to reform the ESA to cure the worst of the perverse
incentives. One method is to provide compensation. When eminent domain is used and
there is a physical taking of property, the government is required to provide compensation equal to market value of the property. The same approach could be taken when
the government mandates conservation on private land. There are two potential problems with this approach. First, Blume, Rubinfeld and Shapiro (1984) show that when
landowners are fully compensated in the event of a taking, there is an incentive to overinvest. It is socially optimal to take account of the probability of future takings that
render the investment worthless. The landowner, however, is fully reimbursed and so
ignores this factor. Second, use of government funds to pay for compensation may be
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costly. On the other hand, others point out that there is an advantage to forcing regulators to understand the costs of imposing regulations by paying compensation [e.g.,
Stroup (1995)]. Rather than tying compensation to market value, paying compensation
tied to the value of conservation, along the lines of green payments discussed above,
can generate efficient incentives to conserve [Hermalin (1995)].
A different approach to reform is to allow landowners to avoid sanctions if they can
prove that their proposed actions will not cause harm [Polasky and Doremus (1998)].
This type of approach is exemplified in the ESA by the provision to allow landowner
actions that cause some minor and unintended harm to a listed species for landowners
with approved Habitat Conservation Plans. The incentive for filing Habitat Conservation
Plans was further sweetened by promises of “no surprises” and “safe harbors” that put
the burden on the government for costs imposed by future regulatory actions.
The Convention on International Trade in Endangered Species (CITES) has arguably
been the international agreement that has had the greatest impact on conservation outcomes [WCMC (1992)]. CITES authorizes banning international trade in species listed
under Appendix I, and regulating trade in species listed under Appendix II. In 1989,
CITES initiated a ban on trade in ivory. In the 1970s and 1980s rampant poaching of
elephants caused a drop in elephant populations of roughly 50% [Barbier et al. (1990)].
Particularly threatened were elephant populations in east African countries. Elephant
populations in southern African countries were less threatened. Imposing the ivory trade
ban was controversial. Southern African countries with relatively healthy elephant populations (Botswana, Malawi, Namibia, South Africa, Zimbabwe) objected and did not
sign on to the ban. Opponents of a ban argued that the ban would likely result in high
ivory prices as supply was choked off, which would increase the rewards of successful
poaching [Barbier and Swanson (1990)]. Opponents also argued that by denying rights
to sell ivory legally there would be less financial reason to conserve elephant populations and less money available for enforcement efforts against poaching. Proponents of
the ban, including east African countries and many developed countries, argued that
without the ban elephant populations would continue to decline, as it was too easy to
sell illegally harvested ivory and because anti-poaching efforts of impoverished governments were no match for well-organized poaching gangs. Van Kooten and Bulte (2000)
summarize economic arguments about the ivory ban and present results from application of several dynamic models.
The ivory ban appears to have been largely successful in halting the decline in elephant populations. A main explanation for this apparent success is that by making the
purchase of ivory illegal the ban appears to have decreased demand for ivory. The
increased price for ivory on the black market never materialized and in fact prices
appeared to fall after the ban was put in place. Kremer and Morcom (2000) offer an
alternative explanation for the failure of prices to rise with the ban, namely that it created expectations of lower future prices, which were then self-fulfilling. They point out
that in dynamic renewable resource models with storage it is possible to have multiple
equilibria, one leading to sustainable populations and the other leading to extinction. If
expectations are that there will be sufficient future stocks there will be low prices and
Ch. 29:
The Economics of Biodiversity
1551
little incentive to poach, leading to a rise in population and fulfilling expectations. On
the other hand, if expectations are that there will not be sufficient future stocks, prices
will be high leading to high poaching pressure and falling stocks thereby fulfilling expectations.
How trade affects conservation has been applied to other contexts besides elephants
and ivory. Brown and Layton (2001) argue that a trade ban would fail to work as well for
rhino. Rhino horn is ground into a powder and used in traditional medicine in Asia. This
type of demand would be unlikely to shift with imposition of a trade ban. They argue
that creating expectations of plentiful future supply, as in the sustainable equilibrium of
Kremer and Morcom (2000), is really the only hope for rhino conservation. There have
also been analyses of the effect of trade on habitat conservation, particularly for tropical
forests [see, for example, Barbier and Rauscher (1994)].
6. Conclusions
The conservation of biodiversity is a major environmental issue, one that promises to
remain at or near the top of the environmental agenda for the foreseeable future. The
threats to biodiversity from habitat loss and fragmentation, introduction of nonindigenous species, over-harvesting, pollution, changes in geochemical cycles and climate
change are likely to intensify rather than abate. Sustaining biodiversity in the face of
increasing human populations and increased human economic activity promises to be a
major challenge. Successful conservation efforts will require management that simultaneously keeps in mind the needs of Homo sapiens and other species. Economists have
an important role to play in helping to develop and evaluate conservation policies.
The economics literature on biodiversity conservation has gone from virtually nonexistent to fairly substantial in a short amount of time. A quick glance through the
references shows that the large majority of articles have been written within the past few
years. Advances have been made in defining and measuring biodiversity, evaluating bioprospecting and ecosystem services, analyzing strategies for habitat conservation and
controlling introduction of nonindigenous species, and analysis of conservation policies. However, several key challenges remain. First, economists face the challenge of
trying to ascertain the values of conservation to society. Getting a clear picture of values
is problematic at present, particularly for existence values and the value of ecosystem
services. Second, there is the difficult challenge of understanding how various human
actions impact ecosystems and how changes in ecosystems translate into changes in
biodiversity or ecosystem services. This challenge will require advances in ecological
understanding as well as in economics. Third, there is the difficult challenge of incorporating dynamics and uncertainty into conservation planning. Conservation is a long-term
issue and strategies will need to adjust to changes in conditions and unforeseen events.
Finally, there is the challenge of designing and implementing conservation policies that
provide proper incentives to conserve. Doing so is particularly important in developing
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countries, which contain a large share of biodiversity, may have weak existing institutions and have the most urgent need for continued economic development to improve
the welfare of their citizens. These important challenges promise to keep economists,
as well as ecologists, busy for a long time to come.
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