Journal of Environmental Management 68 (2003) 23–35

www.elsevier.com/locate/yjema

Economic growth, biodiversity loss and conservation effort

Simon Dietza, W. Neil Adgerb,*
a
Department of Geography and Environment, London School of Economics, Houghton Street, London WC2A 2AE, UK
b
Centre for Social and Economic Research on the Global Environment (CSERGE), School of Environmental Sciences,
University of East Anglia, Norwich NR4 7TJ, UK

Received 17 July 2002; revised 2 December 2002; accepted 5 December 2002

Abstract
This paper investigates the relationship between economic growth, biodiversity loss and efforts to conserve biodiversity using a
combination of panel and cross section data. If economic growth is a cause of biodiversity loss through habitat transformation and other
means, then we would expect an inverse relationship. But if higher levels of income are associated with increasing real demand for
biodiversity conservation, then investment to protect remaining diversity should grow and the rate of biodiversity loss should slow with
growth. Initially, economic growth and biodiversity loss are examined within the framework of the environmental Kuznets hypothesis.
Biodiversity is represented by predicted species richness, generated for tropical terrestrial biodiversity using a species-area relationship. The
environmental Kuznets hypothesis is investigated with reference to comparison of fixed and random effects models to allow the relationship
to vary for each country. It is concluded that an environmental Kuznets curve between income and rates of loss of habitat and species does not
exist in this case. The role of conservation effort in addressing environmental problems is examined through state protection of land and the
regulation of trade in endangered species, two important means of biodiversity conservation. This analysis shows that the extent of
government environmental policy increases with economic development. We argue that, although the data are problematic, the implications
of these models is that conservation effort can only ever result in a partial deceleration of biodiversity decline partly because protected areas
serve multiple functions and are not necessarily designated to protect biodiversity. Nevertheless institutional and policy response components
of the income biodiversity relationship are important but are not well captured through cross-country regression analysis.
q 2003 Elsevier Science Ltd. All rights reserved.
Keywords: Environmental Kuznets curve; Biodiversity; Species-area relationship; Conservation effort

1. Introduction contradictory conclusions for those that may (Ekins, 1997,

Stern et al., 1996; Stern, 1998).
The relationship between economic growth and various Global environmental parameters for which major
indicators of environmental quality has come under irreversibilities are thought to exist, such as carbon dioxide
increasing scrutiny because of widely observed manifes- and its role in climate change, have tended not to follow the
tations of the consequences of unsustainable resource use at environmental Kuznets curve (World Bank, 2000, Ravallion
local and global scales. For certain indicators and certain et al., 2000). There is reason to expect, as with other global
statistical designs, the reduced-form relationship between problems, that it is not possible to ‘grow out of’ the problem
income per capita and environmental quality is an inverted- of biodiversity decline. Indeed, current rates of species loss
U. Environmental degradation first increases with rising are many times the background rates of both extinction and
income, reaches a turning point, and then decreases. This speciation (Ehrlich and Wilson, 1991; Wilson, 1986; Reid,
has been termed the environmental Kuznets curve, though, 1992; Smith et al., 1993). Biodiversity loss is also the
as with Kuznets’ (1955) own hypothesis on the relationship product of a complex interaction of factors such as
between income and inequality, the evidence that an agricultural expansion and its protection, the product of
environmental Kuznets curve applies generically is equiv- equally complex effective demands for wilderness, recrea-
ocal. Some environmental indicators do not yield an
tion space and maintenance of genetic diversity for their use
inverted-U relationship and researchers have often drawn
in agriculture and pharmaceuticals. The services and
* Corresponding author.
functions provided by biodiversity are ultimately under-
E-mail address: n.adger@uea.ac.uk (W.N. Adger). valued by society within the context of the distribution of

0301-4797/03/$ - see front matter q 2003 Elsevier Science Ltd. All rights reserved.
doi:10.1016/S0301-4797(02)00231-1
24 S. Dietz, W.N. Adger / Journal of Environmental Management 68 (2003) 23–35

those benefits and the institutional context in which The disadvantage of the former has just been explained. The
individual and collective decisions are made (Adger et al., disadvantage of the latter is that it is an indicator of pressure
1995; Costanza et al., 1997; Turner et al., 1998; Pritchard on biodiversity, but not loss of biodiversity. More
et al., 2000). Developing a single indicator of biodiversity importantly, ecologists agree that only a fraction of all
loss to test the reduced-form relationship with income over species have been taxonomically classified. Wilson (1986)
time is difficult. suggests we do not even know the true number to the nearest
We regress an output measure of the relationship— order of magnitude. Thus, if the focus is on the loss of
predicted species richness at the national level—and two species from all taxa due to human impacts, the above
input measures of conservation effort: protected area indicators are inherently limited.
designation and implementation of international trade Attention must thus be focused on an indirect indicator of
regulations. If predicted species richness were to yield an species richness. Estimates of biodiversity change have
environmental Kuznets curve with per capita income, we been made using the species-area relationship.2 This
would expect to see a U-shape. However, we hypothesise relationship relates the number of species in a given area
speciation cannot match the current rate of extinction, such to the size of the area. An oft forgotten point is that, like the
that net biodiversity loss may slow at some level of income environmental Kuznets hypothesis, it is empirical and its
but will never be reversed. Conservation effort is hypoth- biological significance must be inferred.3 This study does
esised to increase with per capita income as governments not, however, demand a biological justification. A specific
respond to an increasing public demand for biodiversity, to a form of the equation has become established:
large extent the result of a positive and high income S ¼ cAz ð1Þ
elasticity of demand for its aesthetic benefits (see for
example the discussion in Kristrom and Reira, 1996). In the where S is the number of species, A is area, c is a constant
final section we discuss the implications of these findings in reflecting the density of species per unit and z is the slope of
terms of the scale at which conservation effort takes place, the relationship between S and A when S and A are
and the ultimate causal factors in human overexploitation of expressed as logarithms. S is particularly sensitive to the
the world’s biological resources. magnitude of z, which varies by region, taxa and between
island and (subsets of) continental flora and fauna. Research
has generated a range of values of Z between 0.15 and 0.35
2. Biodiversity measures and development processes (MacArthur and Wilson, 1967). A median value of 0.25 is
taken here, although it should be noted that Z may vary
2.1. Generating data for biodiversity within ecosystem types because, for example, of edge or
fragmentation effects that can determine different rates of
Quantifying species richness within the framework of the extinction with marginal change in habitat area. Little, on
environmental Kuznets hypothesis places particular require- the other hand, is known about c. Studies estimating the
ments on the data. The environmental Kuznets curve has change in biodiversity eliminate c through the ratio of
been tested using either cross section data or panel data. species in a given year (t) relative to a base year (0):
From an econometric point of view, panel data are favoured S0 ¼ cAz0 ð2Þ
because they allow a restrictive assumption manifest in
St ¼ cAzt
cross-sectional analysis to be relaxed; the effect on the
environment of income changes is the same for all countries. St =S0 ¼ At =Az0
In statistical terms, this means the regression coefficients are The simplicity of the relationship makes it attractive but
common to all groups in the cross-section; one curve fits all. introduces perhaps spurious certainty concerning the nature
In this study, local conditions are likely to generate of change in this area. In addition, results vary by region,
significant differences in the income environment relation- taxa and between continents and islands. Ultimately, the
ship between countries such as Brazil and India. In particular, predictions of any one species-area curve will be
national species richness (aggregated across all taxa) in any accompanied by wide confidence intervals. Empirical
given year will be in large part a function of natural habitat examinations of the species-area relationship using the
conditions, independent of anthropogenic impacts. This has increasing levels of location-mapped species data, demon-
the potential to confound the detection of human impacts on strate that it is a reasonable approximation at broad scales
species richness. Therefore, commonality should not be (though other simple power relations have also been
assumed and only panel data will be considered. suggested), while all rule of thumb relations break down
But no direct panel data exist for species richness. at finer resolution (Plotkin et al., 2000).
The only data sets explicitly dealing with species give
present day numbers of species or threatened species.1 2
Refer to Reid (1992) for a review of applications of the species-area
relationship up to 1992.
1 3
The World Conservation Monitoring Centre (2000) and the World For a detailed discussion of the species-area relationship see Connor
Resources Institute (1999) are two examples. and McCoy (1979).
 S. Dietz, W.N. Adger / Journal of Environmental Management 68 (2003) 23–35 25

The next step is defining habitat area (A). Since global (CITES) was first signed in 1973 by 21 states and presently has
estimates of ecosystem types would involve increased been adopted by 127 states. During this time, CITES has been
uncertainty due to variations in z value by region (notably viewed as the ‘flagship of the flora and fauna preservation
latitude), we restrict the study to one region and, treaties’ (Lanchberry, 1998, p. 69). Species covered by the
specifically, the most biologically diverse. Species diversity convention are listed in one of three Appendices A, B and C.
is recognised to be highest in tropical rainforests (Myers, Trade in Appendix A species is essentially banned. Trade in
1980; Wilson, 1986) and, in particular, in primary or Appendices B and C species is permitted but regulated
undisturbed tracts. We focus on tropical forests yet the through a system of permits. In principle, trade restrictions
availability of a suitable data set even in this restricted area help to drive prices up and quantities down, thus reducing the
entails limitations. threat of extinction. However, much depends on how
The need for a time series confines this study to one data effectively illegal trade is controlled. Furthermore, control-
source; the FAO’s Production Yearbook (FAO, various ling trade can cut off valuable sources of revenue for many
years). Unfortunately, this source adopts a broad definition societies. Moran and Pearce (1997) identify the same
of forests as ‘all woody vegetations’ (Koop and Tole, 1999) problems for those cut off from the resources they depend
and thus prevents us from disaggregating between forest on by state protected land. For them, these two strategies
types. To mitigate for this, only 34 tropical countries represent a ‘moral view’, which disinvests value in biodi-
identified as having ‘tracts of tropical moist forest that are versity, taking away its economic value.
appreciable in size or are significant for their ecological and Governments may be ineffective at protecting land if they
biotic values’ (Myers, 1980) are included4 (see Appendix lack the knowledge of how to use a resource properly or the
A). Nevertheless, significant uncertainty still exists, because funding to enforce policy (Bromley, 1997). Examples of
of inconsistencies in defining what constitutes a tropical conflicts between protected areas and local development
moist forest but also in what constitutes deforestation and in priorities abound, where bureaucratic land protection is not
how to measure it (Brown and Pearce, 1994). While matched by enforcement in the relevant areas, particularly,
confidence bands around the data are large, Allen and but not exclusively in developing countries (see reviews in
Barnes (1985) have demonstrated by rank correlation that Stoll-Kleemann, 2001; Smith et al., 1997). It is important to
the FAO data are sufficiently similar to other studies that note neither international trade regulations nor state land
only include tropical forests to permit their use in assessing protection necessarily represent the best means of protecting
tropical deforestation. biodiversity. Nevertheless, they are widely practised and thus
of great importance to biodiversity conservation globally.
2.2. Preventing biodiversity loss

Biodiversity loss is the result of a complex set of 3. Data and models

proximate and underlying causes. The nature of land tenure
and property rights and overexploitation of particular 3.1. Predicted species richness and income
species for domestic and international trade have been the
focus of conservation efforts in many countries and are of The environmental Kuznets hypothesis predicts environ-
central importance in any discussion of global conservation. mental damage first increases and then decreases with rising
Although overexploitation of resources and biodiversity income. The relationship normally resembles an inverted-U
takes place under all types of property rights regime, clearly shape. However, referring to the nature of the environmental
the security of ownership for private resources and the indicator used in this study (Eq. (2)), the relationship will be
legitimacy of institutions for collective decision-making are reversed and should resemble a U shape (Fig. 1). Yet can
key determinants of sustainability (Bohn and Deacon 2000; species richness be expected to exhibit this behaviour? The
Adger, 2003; Agrawal, 2001). The state often takes tracts of environmental Kuznets curve can be divided into two
land under its own jurisdiction to protect biodiversity on halves, the ‘falling limb’ and the ‘rising limb’ in Fig. 1. The
behalf of society. State protected land can take different ‘falling limb’ indicates decreasing numbers of species. The
forms: nature reserves protected for scientific interest, accelerated anthropogenic impact on species is already well
natural monuments protecting natural features and national documented (Ehrlich and Wilson, 1991; Wilson, 1986;
parks that have some emphasis on recreation and access. Reid, 1992; Smith et al., 1993). However, species are not
The state also changes the property rights in goods and replenished at the same rate: background speciation would
services through regulation. The regulation of trade in not yield a ‘rising limb’ of the same magnitude.
endangered species is designed to prevent certain species, Instead, species richness might be expected to decrease
valued for their products, from being driven to extinction. The and then level off with increasing income. As discussed
Convention on International Trade in Endangered Species above, theory suggests economic forces fuel the drive for
environmental improvement, but species diversity cannot
4
Myers classifies 40 countries in this group, but missing data for certain replenish itself at an equivalent rate. This suggests a
independent variables reduces the data set to 34 countries. hyperbolic curve, which combines the ‘falling limb’ of
26 S. Dietz, W.N. Adger / Journal of Environmental Management 68 (2003) 23–35

Fig. 1. Possible forms of the income biodiversity relationship.

the environmental Kuznets curve and a slowing of To summarise, the variables and equations are as
biodiversity loss, may be a more realistic representation of follows:
the relationship, as shown in Fig. 1. Inspection of the data Sði; tÞ; the dependent variable, is the predicted species
for species richness also indicates a close clustering of richness in any year compared to the reference year 1970
observations around the base level, independent of income (Eq. (2)), multiplied by a factor of 1000 to prevent
level (Fig. 2). In view of this, a linear equation is also tested. clustering: 1000 ðAt =Az0 Þ:
Various techniques exist to estimate the curves. The three Gði; tÞ is income per capita in log form.7 Data comes from
examined in this study are; ordinary least squares (OLS), the Penn World Table.8
fixed effects and random effects. Two methods are of Cði; tÞ is population change, expressed as a percentage
particular interest to this study: fixed and random effects. of the previous year. Population is thought to be a
Both build on the regression equation for OLS, which is the determinant of the rate of tropical deforestation (e.g.
same for all countries. Fixed effect loosens up the assumption Rudel, 1994). Data comes from the Penn World Table.
of commonality across countries by estimating a separate Pði; tÞ is population density, expressed as people per
constant for each country (Koop and Tole, 1999). Random hectare. The variable is a combination of FAO
effects works in a similar way but assumes that international Production Yearbook data on land area and Penn
heterogeneity is randomly (and normally) distributed. World Table population statistics.
The difference between the two models, whether the TðtÞ is a linear time trend (T is simply the relevant
vertical displacement of the regression equation should be year). This captures the time dependency of both income
parametric or random—has been debated in the literature and biodiversity (current levels depend on previous
(Greene, 1997). A fixed effects model implies international levels).
differences are generated by country-specific factors not Fði; tÞ is forest area in hectares. The relative impact of
covered by the regressors. The random effects specification, deforestation in any country depends on absolute forest
on the other hand, implies national peculiarities are area and feeds back into future trends.
unimportant and differences should be assumed random. Dði; tÞ is democracy, the sum of political rights and civil
This is a responsible approach if the sample is part of a liberty indices taken from Freedom House data.9 Both
much larger population (Greene, 1997). Yet this data set indices are based on a ranking system of 1 – 7, with 1
represents an almost complete set of countries containing equalling most democracy. Here, the two indices are added
significant tropical rainforests (Myers, 1980). Furthermore, and the ranking scale reversed, so that 2 corresponds to least
intuition suggests environmental and economic factors democracy and 14 to most democracy (after Bhattarai and
should play a rather important role. We can discriminate Hammig, 2001). Several researchers argue that democracy,
between the three using LM statistics5 and the Hausman or a vector of related institutional variables, are an important
test,6 but, at this interjection, fixed effects are expected to be determinant of the rate of deforestation (Bhattarai and
favoured. Hammig, 2001; Mather and Needle, 1999; Torras and
Boyce, 1998; Barbier, 2001). The thinking behind this proxy
5
‘LM statistics’ is the standard Lagrange Multiplier test for fixed/random is that as democracy grows so institutional forms promoting
effects over the basic model. It analyses whether the variance of mðiÞ is
equal to zero—i.e. whether fixed/random effects are constant, in which case
7
simple OLS is valid. High values favour fixed/random effects. A semi-log form like this is recommended when y is increasing slower
6
The Hausman test is an empirical test used to differentiate between or faster than x.
8
random effects and fixed effects. It assesses whether individual effects are Summers and Heston (1991) explain the methodology and layout of
correlated with the regressors. If so, random effects are inconsistent. If not, mark 5 of the table. The most up-to-date version, 5.6, is available at
both random and fixed effects are consistent but random effects are more http://datacentre2.chass.utoronto.ca/pwt/
9
efficient. High values favour fixed effects. www.freedomhouse.org
 S. Dietz, W.N. Adger / Journal of Environmental Management 68 (2003) 23–35 27

Fig. 2. Scatter plot of species richness versus per capita income.

positive environmental change such as secure property between categories. All categories are included and the sum
rights and environmental activism flourish. for each country is normalised as a percentage of national
The basic regression model for the hyperbolic equation is land territory (see Appendix B for countries and time
series10). This is the dependent variable in the regression
Sði; tÞ ¼ a þ b1 1= ln Gði; tÞ þ b2 Cði; tÞ þ b3 Pði; tÞ analysis. Development is measured through per capita GDP
only, again using the Penn World Table. This allows the
þ b4 TðtÞ þ b5 Fði; tÞ þ b6 Dði; tÞ þ 1ði; tÞ: ð3Þ relationship tested by Lightfoot for four decades to be
The linear equation is disaggregated into a single continuous time series. Popu-
lation density is included to account for the likelihood that
Sði; tÞ ¼ a þ b1 ln Gði; tÞ þ b2 Pði; tÞ þ b3 Dði; tÞ in densely populated countries, significant tracts of
ecosystem may have been lost prior to the advent of
þ b4 TðtÞ þ b5 Fði; tÞ þ b6 Dði; tÞ þ 1ði; tÞ: ð4Þ protection. In other words, there is little land protectable. A
linear time trend (as before) also accounts for the positive
time dependency of GDP and percentage protected area.
Population change is further present. The basic regression
3.2. Protected areas and income
model is
Government intervention to protect biodiversity is often
seen as the primary mechanism to constrain the tendency for Aði; tÞ ¼ a þ b1 ln Gði; tÞ þ b2 Pði; tÞ þ b3 Dði; tÞ
economic growth to cause habitat and species loss. We may
expect, therefore, a positive relationship between economic þ b4 TðtÞ þ 1ði; tÞ: ð5Þ
development and the level of government intervention, for
example in designating protected areas. Lightfoot (1994)
OLS, fixed effects and random effects are tested as before.
explores this hypothesis using 11 different indicators of
Again, theory in this case would suggest socioeconomic and
development and tests the correlation between development
natural factors varying by country and not captured by the
and area of designated conservation land in countries around
regressors in (Eq. 5) should exert a significant influence.
the world in four discrete time periods: 1950 –60, 1960 –70,
Therefore, fixed effects should be favoured. An inverted-U
1970 –80 and 1980 –90 using data from the 1990 United
shape should not be produced, as countries are expected to
Nations List of National Parks and Protected Areas.
supplement the area of protection as development proceeds.
This study uses the updated 1993 United Nations List of
Thus, only a linear model is tested. The direction of the
National Parks and Protected Areas (IUCN, 1994), which
relationship is expected to be positive, if the area of land
uses the general term ‘wildland management area’ to
protected by the state increases with national economic
embrace a range of protected area categories with different
development.
management objectives. Broadly, the UN list only includes
those areas ‘especially dedicated to the protection and 10
141 of the 144 countries with IUCN categorised ‘wildland management
maintenance of biological diversity’ (WCMC, 2000), yet areas’ are included. Canada, Mexico and the USA are omitted due to time
the extent to which species diversity is prioritised varies constraints.
28 S. Dietz, W.N. Adger / Journal of Environmental Management 68 (2003) 23–35

Table 1 Table 2
Summary statistics for selected variables in the species richness analysis Comparing OLS, fixed and random effects for the linear and hyperbolic
equations linking species richness with income
Mean Standard deviation
Hyperbolic equation Linear equation
Proportion of predicted species lost 975.3 30.3
( £ 1000) LM statistics 2480.9** 2536.7**
GDP (PPP$) 2132.2 1525.0 Hausman test 34.2** 33.7**
Time trend (year) 1981.7 6.0
Forest area (000 ha) 40,952.7 89,269.5 ** shows statistical significance at the 1% level.
Percentage change in population 0.03 0.01
People per hectare of land 0.8 1.3
Table 2 displays the outcome of tests between OLS, fixed
Democracy 7.3 3.1 and random effects for Eqs. (3) and (4). Table 3 reports
results for Eqs. (3) and (4), using the model selected on the
3.3. CITES reporting and income basis of Table 2. Of particular note are the low variability in
species (even after being multiplied by 1000) and GDP, as
The Implementation of the Convention on International illustrated in Fig. 2. LM statistics clearly favour both fixed
Trade in Endangered Species (CITES) is reviewed through a and random effects over simple OLS. The results of the
reporting process. Importantly, almost all parties are Hausman test favour fixed effects over random effects in
required to report on trade in species listed in Appendices both cases. Thus, the hypothesis that national characteristics
A and B annually (see Appendix C) and there are strict play a significant role in determining change in biodiversity
deadlines for their submission. In the latest edition of World is borne out. The hyperbolic and linear equations both fit the
Resources (World Resources Institute, 2001), the percen- data significantly (see F-statistics) and in both equations,
tage of reports submitted relative to those expected is listed the income terms are significant at the 1% level. Therefore,
for all parties. Reporting does not necessarily reflect actual it is not possible to determine which relationship best
implementation but it does reflect the only systematic means represents the data.
the convention has of monitoring how strictly trade is Furthermore, the signs on the income terms are the
regulated (Lanchberry, 1998). Furthermore, failure to meet opposite of what we would expect. There is no theoretical
deadlines is identified as a particular problem among reason to explain this, because it is well known that species
developing countries (Ong, 1998), where resources and are currently being lost at rates significantly greater than
expertise may be lacking. they are being created. The relationship is therefore
On this premise, this study tests the relationship between assumed to be a product of low variation in both the species
development, as measured by per capita GNP for 1999 diversity and income data (Fig. 2). Time and forest area are
(World Bank, 2000),11 and the percentage of expected both significant at the 1% level. Democracy is significant at
reports actually submitted in 1999. GNP does not represent the 5% level. Population density is significant at the 5%
a direct causal factor, but it is correlated with the quality of level for the hyperbolic equation but insignificant for the
national bureaucracy (Rausch and Evans, 2000).12 The linear equation. Population change is statistically insignif-
number of reports required is also included, as the icant in both equations.
dependent variable is sensitive to changes in this factor. The strength of fit of the regression model is difficult to
For example, a country that fails to submit its one report measure where OLS, fixed and random effects are
meets 0% of its requirement, whereas a country failing to compared. One paper compares R2 values for OLS and
submit one of its 20 reports meets 95% of its requirement. fixed effects (Selden and Song, 1994), yet this ignores the
The regression model is a simple cross-section of the point that R2 is artificially higher for fixed effects. In
form addition, R2 cannot be calculated for random effects. It is of
RðiÞ ¼ a þ b1 GðiÞ þ b2 TðiÞ þ 1ðiÞ: ð6Þ interest, however, to compare R2 for fixed effects with other
studies. In both equations here, R2 ¼ 0:76: Cropper and
where R is the percentage of reports submitted for country i,
Griffiths (1994) returned values of 0.13– 0.64 for deforesta-
G is GNP per capita and T is the total number of reports
tion across Africa, Asia and Latin America. Shafik (1994)
expected. The model is estimated by OLS.
found no correlation ðR2 ¼ 0Þ for annual and total
deforestation yet values of between 0.96 and 1 for various
4. Results measures of water and air pollution. Selden and Song (1994)
also found values of above 0.95 for air pollution.13 Results
Tables 1 –3 report empirical results for the species thus far cannot identify the better equation. The linear
diversity analysis. Table 1 reports summary statistics.
13
This list is by no means comprehensive. Only results for fixed effects
11
In Purchasing Power Parity (PPP) dollars. are shown. Deforestation is strongly related to species diversity, as defined
12
High income countries tend to have high bureaucratic performance in this study. Water and air pollution are included to show that very strong
ratings (Rausch and Evans, 2000). correlations can result. The conclusions drawn should be put in this context.
 S. Dietz, W.N. Adger / Journal of Environmental Management 68 (2003) 23–35 29

Table 3 Eq. (6) on the relationship between conservation effort in

Estimates of the linear and hyperbolic equations linking species richness the area of trade in endangered species and economic
with income with fixed effects. Standard errors are in parenthesis
development, a cross-section, can only be estimated by
Linear Hyperbolic OLS. The results are shown in Table 5. Eq. (6) is significant
at the 1% level, as are the individual coefficients GNP per
ln GDP 16.3 (3.3)** – capita and number of reports expected. However, R2 is only
1/ln GDP – 22102176 (4295.1)** 0.3. Thus, GNP per capita, as a measure of development, is
Time trend 22.2 (0.1)** 22.1 (0.1)**
weakly related to the implementation of CITES. To put this
Forest area 1 £ 1024 (2 £ 1023)** 9 £ 1024 (2 £ 1023)**
Population change 2141.0 (139.5) 262.6 (141.0) in context, using the coefficient value for GNP per capita, an
Population density 24.3 (3.4) 25.8 (3.5)* increase in GNP from $500 per capita (corresponding
Democracy 20.7 (0.3)* 20.6 (0.3)* roughly to the world’s poorest nation) to $20,000 (for
F-statistics 53.7** 53.6** developed countries) would lead to an increase in reporting
Adjusted R2 0.76 0.76
requirement met of 20%. Other factors exert a greater
* shows statistical significance at the 5% level, **shows statistical influence on the percentage of expected reports submitted.
significance at the 1% level.

equation is likely an artefact of the data distribution, which 5. Discussion

is strongly concentrated around the dependent variable’s
reference value. The results enable three broad conclusions to be drawn.
Eq. (5) was estimated by OLS, fixed and random effects. First, we conclude that an environmental Kuznets curve
An LM statistics value of 16,871.5 and a Hausman test value does not exist for species richness, as measured in this study.
of 85.3 indicate fixed effects are most suitable. This In other words it is unlikely that countries have ever been
confirms the posited relationship between government able to increase the species richness within their borders
conservation effort and economic development outlined in even if they have invested in conservation effort as incomes
the previous section. Table 4 outlines the coefficients and F- rise. Second, in such a model where no turning point is
statistic. The model is significant at the 1% level and, of the apparent, the fixed effects description is favoured over a
regressors, all but population change are significant at the random effects model. Third, economic development is
1% level. Thus there appears to be a relationship between related to the area of state protected land but it is not the
developments as measured by GDP per capita, and the overriding determinant of the rate of designation.
percentage of land protected by governments to preserve An environmental Kuznets curve does not exist for our
biological diversity. The relationship is not, however, strong measure of species richness. We find no reason to expect the
(R2 for fixed effects ¼ 0.36). So, although there may be a presence of an environmental Kuznets curve, since species
tendency towards increased conservation efforts (in terms of cannot be replenished at the rate at which they are being
designated protected areas) with increasing income, other lost. Therefore, we have tested for the ‘falling limb’ of an
geographical and cultural determinants may be important in environmental Kuznets curve, followed by stabilisation (but
this relationship and not captured in such analysis. Some non-recovery) of species richness. However, we cannot
countries, such as New Zealand and Japan and others, for discriminate between this hyperbolic curve and a linear
example, put much effort into marine conservation often decline in species richness, and the income coefficients are
without designating protected areas (see Brown et al., in any case inverted, certainly due to poor data.
2002). Historical legacies of land tenure arrangements are How does this conclusion compare with previous
also crucial and mean that countries such as Scotland are research? A number of studies have investigated environ-
only now implementing their first national parks. mental Kuznets curves for deforestation, the basis for
estimates of forest cover here. Most researchers, and
particularly those using less restrictive assumptions of
Table 4
The linear relationship between percentage protected area and income,
based on panel data for 141 countries (1950–1991) (see Appendix B) Table 5
The relationship between percentage reports for CITES expected in 1999,
Result for fixed effects actually submitted and income

ln GDP 12.3 (1.9)** Results for OLS

Time trend 0.2 (2 £ 1022)**
Population density 1.8 (0.2)** GNP 1 £ 1023 (3 £ 1024)**
Population change 2.7 (1.2)* Reports expected 1.3 (0.3)**
F-statistic 18.8** Constant 43.3 (5.6)**
Adjusted R 2 0.36 F-statistics 20.3**
Adjusted R 2 0.3
* shows statistical significance at the 5% level, ** shows statistical
significance at the 1% level. ** shows statistical significance at the 1% level.
30 S. Dietz, W.N. Adger / Journal of Environmental Management 68 (2003) 23–35

cross-country commonality, reject the environmental Kuz- of time series data on species loss, numbers must be derived
nets curve hypothesis for deforestation (Cropper and indirectly. The species-area relationship has been frequently
Griffiths, 1994; Koop and Tole, 1999; Shafik 1994). For used to predict rates of species loss over coming years, but
those models where the curve appears to exist with using a single value for the z constant belies wide confidence
institutional and other variables taken into account, turning bands. Finally, there is substantial uncertainty surrounding
points may be well above current levels of income in the the forest cover data. Species diversity is higher in primary
regions (e.g. Barbier, 2001, on the EKC for agricultural rainforest but the only data set providing a sufficiently long
expansion). Bhattarai and Hammig (2001) find an environ- time series, the FAO Production Yearbook (FAO, various
mental Kuznets curve for their sample of 66 countries, years), includes ‘all woody vegetations’ (see discussion in
separated into three continents: Latin America, Africa and Koop and Tole, 1999; Barbier, 2001). Additionally, the data
Asia. However, their large group of countries includes those itself has been compiled using different definitions and
Myers (1980) determines not to have significant tracts of methods of measurement. Therefore, one of the important
tropical rainforest. The danger inherent in this larger list is points to come out of this study is the need for a global
that reforestation through plantations is mistaken for the biodiversity time series or, at least, accurate forest data on
recovery of primary rainforest, with its accompanying which biodiversity calculations can be based.
species. Therefore, caution must be exercised in transferring The relationships between per capita income and state
those results to biodiversity. Naidoo and Adamowicz (2001) protected land and with the implementation of the CITES
estimate environmental Kuznets curves for species classi- convention reveal a number of insights. Economic devel-
fied as threatened by the IUCN. They use seven taxonomic opment is related to the area of state protected land,
groups across 137 countries, and generate an environmental although it is not a strong determinant on its own. Eq. (5) is
Kuznets curve for birds but not for other taxa such as significant at the 1% level, as is income per capita. Fixed
reptiles and invertebrates. Birds are a charismatic taxo- effects are once more favoured over random effects, again
nomic group, whose preservation is in demand relative to because the data represent an almost complete sample and
other groups. Thus their protection may be prioritised. because international differences are important, resonating
Naidoo and Adamowicz’s results provide a challenge to our with the results of Lightfoot (1994). Eleven indicators of
aggregate approach, by showing conservation may well socio-economic development were correlated with pro-
vary by taxonomic group. However, they are generating an tected land (also using UN/IUCN data), but the highest
environmental Kuznets curve for pressure on species, which coefficient was 0.53. Lightfoot (1994) concludes that socio-
is not the same as the measure of loss estimated on this economic development probably does affect government
paper. land protection, but that it cannot be confirmed empirically
Fixed effects are empirically favoured over random at the global scale. The reason is that ‘a more complex set of
effects. For all equations estimated, results of the Hausman interrelated social, economic, cultural and natural phenom-
test favour fixed effects over random effects confirming our ena all work together simultaneously and cannot be isolated
observations in the sections above. If the sample of groups and examined independent from the whole system of
(in this case countries) is comprehensive and not a small variables14, (p. 121). The result for fixed effects corrobo-
subset of a larger population, then fixed effects are favoured. rates this previous analysis.
From another viewpoint, international socioeconomic and Economic development is related to the percentage of
environmental differences not captured by the regressors reports required by CITES actually submitted, although it is
play a role in the relationship between species diversity and not a strong determinant. Eq. (6) is significant at the 1%
per capita income. This result should be compared with level. Income, GNP per capita, is also significant at the 1%
previous research, which has often overlooked the signifi- level. Thus, reporting for the CITES convention is only
cance of the estimation technique. Selden and Song (1994); weakly dependent on income. Alternative explanations have
Koop and Tole (1999) compare fixed and random effects as
been proposed. Ong (1998) states that ‘many developing
part of their studies. Selden and Song find fixed effects are
country parties in particular have failed to (submit their
favoured for measures of urban air pollution, whereas Koop
reports on time)’ (p. 294), yet Lanchberry (1998) argues that
and Tole find random effects are favoured for deforestation.
‘reporting and implementation problems occur more often
The most appropriate method must be confirmed by
with developed countries than with developing countries’
empirical test for the environmental issue in question
(p. 70). Neither study offers empirical evidence. The
given the discrepancy in models between studies.
empirical results presented here suggest that low levels of
The possibility of generating accurate data to test the
income in a country may be correlated with restrictions on
environmental Kuznets hypothesis is limited at present. The
government enforcement of CITES and other environmental
nature of the study tends to exclude any cross-sectional
legislation.
datasheets that specifically list species numbers. However, a
single ‘snapshot’ of species numbers would not be revealing 14
Lightfoot also pointed to data omissions: the UN/IUCN list does not
since there are numerous environmental and geographical include protected land under 1000 hectares, nor some public and private
determinants of underlying species richness. In the absence reserves.
 S. Dietz, W.N. Adger / Journal of Environmental Management 68 (2003) 23–35 31

Table A1 Table B1
Panel data for protected areas Panel data for protected areas

Africa: Africa:
Cameroon 1972–1992 Angola 1960–1989
Congo 1972–1992 Benin 1959–1991
Gabon 1972–1992 Botswana 1960–1989
Ghana 1972–1992 Burkina Faso 1959–1991
Ivory Coast 1972–1992 Burundi 1960–1991
Kenya 1972–1992 Cameroon 1960–1991
Liberia 1972–1986 Central African Republic 1960–1991
Madagascar 1972–1992 Chad 1960–1991
Nigeria 1972–1992 Comoros 1960–1991
Sierra Leone 1972–1992 Congo 1960–1991
Tanzania 1972–1988 Djibuoti 1970–1987
Uganda 1972–1992 Ethiopia 1950–1986
Zaire 1972–1989 Gabon 1960–1991
Guinea 1959–1991
Central America: Guinea-Bissau 1960–1991
Costa Rica 1972–1992
Kenya 1950–1991
El Salvador 1972–1992 Lesotho 1960–1991
Guatemala 1972–1992 Liberia 1960–1986
Honduras 1972–1992 Madagascar 1960–1991
Mexico 1972–1992
Malawi 1954–1991
Nicaragua 1972–1990 Mali 1960–1991
Panama 1972–1992 Mauritania 1960–1991
South America: Mauritius 1950–1991
Bolivia 1972–1992 Mozambique 1960–1991
Brazil 1972–1992 Namibia 1960–1991
Colombia 1972–1992 Niger 1950–1991
Ecuador 1972–1992 Reunion 1960–1989
Guyana 1972–1990 Rwanda 1960–1991
Peru 1972–1992 Senegal 1960–1991
Venezuela 1972–1992 Seychelles 1960–1990
Sierra Leone 1961–1991
Asia: Somalia 1960–1989
Bangladesh 1972–1992 South Africa 1950–1991
India 1972–1992 Sudan 1970–1991
Indonesia 1972–1992 Swaziland 1960–1989
Malaysia 1972–1992 Tanzania 1960–1988
Myanmar 1972–1989 Togo 1960–1991
Philippines 1972–1992 Uganda 1950–1991
Sri Lanka 1972–1992 Zaire 1950–1989
Thailand 1972–1992 Zambia 1955–1991
Zimbabwe 1954–1991
Central America:
6. Conclusion Belize 1980–1991
Costa Rica 1950–1991
El Salvador 1950–1991
These analyses of protected area designation and Guatemala 1950–1991
regulation do not lead to the conclusion that states are Honduras 1950–1991
powerless to preserve biodiversity within their borders. Nicaragua 1950–1990
Panama 1950–1991
Because of the interaction of habitat loss with agricultural
expansion, land use practices and urban sprawl, there are South America:
many options to promote and protect locally and globally Argentina 1950–1990
Bolivia 1950–1991
important biodiversity. The costs of such interactions may
Brazil 1950–1991
even be relatively modest and the benefits potentially huge. Chile 1950–1991
James et al. (2001) suggest that current global expenditure Colombia 1950–1991
on protected areas is approximately $6bn and that to expand Ecuador 1950–1991
this area to meet stated conservation goals may be achieved Guyana 1950–1991
Paraguay 1950–1991
at a cost of $12– 21bn even accounting for opportunity costs
Peru 1950–1991
of land use. Balmford et al. (2002) demonstrate that such Suriname 1960–1989
modest investment has a high rate of economic return when Uruguay 1950–1991
the benefits of ecosystem services are brought into (continued on next page)
32 S. Dietz, W.N. Adger / Journal of Environmental Management 68 (2003) 23–35

Table B1 (continued) Table B1 (continued)

Venezuela 1950–1991 UK 1950– 1991

Carribean: USSR 1960– 1989
Bahamas 1977–1987 Yugoslavia 1960– 1990
Barbados 1960–1989 Middle East:
Dominican Republic 1950–1991 Algeria 1960– 1991
Grenada 1984–1990 Bahrain 1975– 1988
Haiti 1960–1989 Cyprus 1950– 1991
Jamaica 1953–1991 Egypt 1950– 1991
Puerto Rico 1955–1989 Iran 1955– 1991
St Kitts and Nevis 1983–1991 Iraq 1953– 1987
Trinidad and Tobago 1950–1989 Israel 1953– 1991
Asia: Jordan 1954– 1990
Bangladesh 1959–1991 Kuwait 1980– 1989
China 1960–1991 Morocco 1950– 1991
Hong Kong 1960–1991 Oman 1967– 1987
India 1950–1991 Qatar 1980– 1989
Indonesia 1960–1991 Saudi Arabia 1960– 1989
Japan 1950–1991 Syria 1960– 1991
Laos 1984–1991 Tunisia 1960– 1991
Malaysia 1955–1991 Turkey 1960– 1991
Mongolia 1984–1990 UAE 1980– 1989
Myanmar 1950–1989 Yemen 1969– 1989
Nepal 1960–1986
Pakistan 1950–1991 the account. Clearly a change in societal demand for both
Philippines 1950–1991
biodiversity and protected areas would be required to realise
Singapore 1960–1991
South Korea 1953–1991 such actions. But it would also require protected areas to
Sri Lanka 1950–1991 actually fulfil their conservation aims—an area on which
Taiwan 1951–1990 there remains much controversy (e.g. see Bruner et al.,
Thailand 1950–1991 2001).
Oceania: The results presented in this paper paint a broad
Australia 1950–1990 picture of the relationship between level of economic
Fiji 1960–1990 activity, terrestrial biodiversity loss and conservation
New Zealand 1950–1991
Papua New Guinea 1960–1991
effort. The cross-country nature of the data and analysis
Solomon Islands 1980–1988 provides general lessons for conservation policy and
Vanuatu 1983–1990 practice that economic growth is ultimately as much part
Western Samoa 1979–1990 of the problem as the solution. But of course conserva-
Europe: tion of biological diversity is a multi-faceted issue that is
Austria 1950–1991 spatially and culturally differentiated. The evidence in
Belgium 1950–1991 Naidoo and Adamowicz (2001) shows that the relation-
Bulgaria 1980–1991 ship even with income is complex when patterns between
Czechoslovakia 1960–1990
Denmark 1950–1991
taxa are examined.
Finland 1950–1991 A more complete understanding is required of the
France 1950–1991 benefits of conservation, for example, of the role of
Germany, Federation Republic 1950–1991 diversity in underpinning economic activity, particularly
Greece 1950–1991 for resource-dependent economies. Ecosystem resilience
Hungary 1970–1991
Iceland 1950–1991
and keystone processes within ecological systems may, in
Ireland 1950–1991 fact, represent what Folke et al. (1996) term the natural
Italy 1950–1991 insurance capital for a society. Yet conservation policy
Luxembourg 1950–1991 focussed on protected areas may not deliver the resilience
Malta 1954–1989
required or may not avoid the potential for irreversible
Netherlands 1950–1991
Norway 1950–1991 habitat change. An understanding of the cultural and
Poland 1970–1991 institutional landscape of conservation in society is, there-
Portugal 1950–1990 fore, a further necessary element in implementing policy
Romania 1960–1989 such that economic growth can be realised without the
Spain 1950–1991
inevitability of biodiversity loss. This paper has shown that
Sweden 1950–1991
Switzerland 1950–1991 biodiversity conservation is, indeed, one of the class of
environmental problems where economic growth on its own
 S. Dietz, W.N. Adger / Journal of Environmental Management 68 (2003) 23–35 33

Table C1 Table C1 (continued)

Parties to the CITES Convention
Carribean:
Reports expected in 1999, GNP data available: Trinidad and Tobago
Africa:
Algeria Asia:
Benin Bangladesh
Botswana China
Burkina Faso India
Burundi Indonesia
Cameroon Japan
Central African Republic Malaysia
Chad Mongolia
Congo, Democratic Republic Nepal
Congo, Republic Pakistan
Egypt Philippines
Ethiopia Singapore
Gabon Sri Lanka
Gambia Thailand
Ghana Vietnam
Guinea
Oceania:
Guinea-Bissau
Australia
Ivory Coast
New Zealand
Kenya
Papua New Guinea
Madagascar
Malawi Europe:
Mali Austria
Mauritius Belarus
Morocco Belgium
Mozambique Bulgaria
Namibia Czech Republic
Niger Denmark
Nigeria Estonia
Senegal Finland
Sierra Leone France
South Africa Germany
Sudan Greece
Tanzania Hungary
Togo Italy
Tunisia Netherlands
Uganda Norway
Zambia Poland
Zimbabwe Portugal
North America: Romania
Canada Russia
USA Slovakia
Spain
Central America:
Sweden
Costa Rica
Switzerland
Dominican Republic
UK
El Salvador
Guatemala Middle East:
Honduras Georgia
Mexico Iran
Nicaragua Israel
Panama Jordan
South America: Saudi Arabia
Argentina Turkey
Bolivia UAE
Brazil
Chile Reports expected in 1999, no GNP data available:
Colombia Equatorial Guinea
Equador Liberia
Paraguay Rwanda
Peru Somalia
Uruguay (continued on next page)
Venezuela
34 S. Dietz, W.N. Adger / Journal of Environmental Management 68 (2003) 23–35

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