1. No category

Ecological Economics 47 (2003) 121 – 133
www.elsevier.com/locate/ecolecon
METHODS
Evolutionary policies for sustainable development: adaptive
flexibility and risk minimising
Christian Rammel a,*, Jeroen C.J.M. van den Bergh b,c
a
Department of Ecology and Conservation Biology, University of Vienna, Althanstraße 9, 1190 Vienna, Austria
Department of Spatial Economics, Faculty of Economics and Business Administration, Free University, De Boelelaan 1105,
1081 HV Amsterdam, Netherlands
c
Faculty of Earth and Life Sciences, Institute for Environmental Studies, Free University, De Boelelaan 1105, 1081 HV Amsterdam, Netherlands
b
Received 15 October 2002; received in revised form 23 June 2003; accepted 25 June 2003
Abstract
An evolutionary perspective on policies to foster sustainable development is presented. It is argued that policies suggested
by the traditional economic theory of environmental policy can stimulate unsustainable socio-economic structures and patterns.
In addition, they are unable to remove undesired locked-in systems and technologies. Drawing on evolutionary thinking,
characterised by diversity, selection, innovation, path-dependence and bounded rationality, an alternative, partly complementary
theory of environmental policy is suggested. Specific attention is given to the role of strategies that are aimed at increasing
diversity and adaptive flexibility, and at reducing risk.
D 2003 Elsevier B.V. All rights reserved.
Keywords: Diversity; Evolution; Economic structure; Innovation; Lock-in; Selection; Stability; Uncertainty
1. Introduction
Corresponding with an increasing awareness of
global environmental problems and the need for a
sustainable development is the realisation that traditional economic approaches may fall short in offering
a complete perspective on the relevant issues, problem
causes, and solutions. Nevertheless, the current debate
about sustainable development is still dominated by
neoclassical economic theory. Concepts like sustainable growth (Solow, 1992) and weak sustainability
* Corresponding author. Tel.: +43-1-4277-54201; fax: +43-14277-9542.
E-mail addresses: [email protected]
(C. Rammel), [email protected] (J.C.J.M. van den Bergh).
0921-8009/$ - see front matter D 2003 Elsevier B.V. All rights reserved.
doi:10.1016/S0921-8009(03)00193-9
(Pearce and Atkinson, 1993) emphasise the fundamental role of markets in dealing with environmental
problems, and reflect the deterministic and aggregate
focus of neoclassical growth theory (Toman et al.,
1995). In addition, the standard normative economic
theory of environmental policy (Baumol and Oates,
1988) cannot do without the debatable assumptions of
individual rationality and the Kaldor –Hicks potential
Pareto improvement principle to arrive at conclusions
about social optimality of policy instruments. Recently, a significant body of literature has discussed the
shortcomings of traditional theories of environmental
and resource economics, shedding light on the limitations of various concepts and methodologies. These
include monetary valuation, substitution of natural by
economic capital, traditional cost-benefit analysis and
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C. Rammel, J.C.J.M. van den Bergh / Ecological Economics 47 (2003) 121–133
normative policy theory (e.g. Vatn and Bromley,
1994; Victor, 1991; Gute´s, 1996; Munda, 1996,
1997; van den Bergh et al., 2000; Gowdy, 2003).
Within the mainstream the gap between normative and
positive environmental policy theory has been identified (Dietz and Vollebergh, 1999; Oates and Portney,
2003). A possible reason for this gap is that assumptions about behaviour underlying normative policy
theory are inconsistent with actual behaviour, which
seems to characterised by bounded rationality—often
expressed through habits and imitation—and the formation of interest groups. Nevertheless, traditional
theory insufficiently recognises these fundamental
problems, which may contribute to persistence of
the gap.
Despite these shortcomings, policy making strongly depends on economic advice derived within the
neoclassical paradigm (Hall, 1993). This has initiated
a tendency to ‘‘economise’’ politics by focusing the
attention of decision-makers on efficiency and cost
minimising. As the theory and models supporting the
policy advice are founded on artificial planning-market systems that resemble closed mechanic systems
that do not change over time, they are especially
inappropriate to deal with the dynamics of structural
and adaptive changes in economic systems. Such
changes are either required for, or the result of,
sustainable development (Georgescu-Roegen, 1971;
Hodgson, 1993). In other words, the ‘‘mechanical
corset’’ of neoclassical economics prevents a clear
view on changes and adaptations stimulated by environmental policies. This calls for alternative
approaches that might be better in coping with complex systems (Funtovicz and Ravetz, 1994).
In the following we argue that a fruitful approach
to study policies for sustainable development is offered by evolutionary thinking. Not only does it
address the adaptive and structural elements of many
economic changes, but also it is consistent with the
view that economic behaviour of both firms and
consumers is more in accordance with bounded than
infinite rationality. Many authors have stressed the
universal character of evolutionary phenomena, which
has as a corollary that evolution applies equally to
economic and biological phenomena (Dennett, 1995).
Or better said, the fact that evolution was—somewhat
coincidentally—taken more seriously early on in
biology does not mean its relevance is restricted to
this field. This is in line with a growing body of
literature showing the potential of evolutionary thinking in economics in general (Dosi et al., 1988;
Hodgson, 1993; Nelson, 1995). Moreover, a small
literature has developed around the idea that evolutionary theories and models can be usefully employed
to study sustainable development and environmental
policies that foster it (Norgaard, 1994; Clark et al.
1995; Ring, 1997; Allen, 1997; Norton et al. 1998;
van den Bergh and Gowdy, 2000; Mulder and van den
Bergh, 2001; van den Bergh, 2003a,b; Rammel,
2003). Given the context of sustainable development,
the evolutionary perspective outlined in this article
will be based on an integration of elements from
evolutionary biology, evolutionary economics and
technology studies. The analysis of sustainable development as an (set of) evolutionary process(es) allows
us to address elementary issues like diversity, risk
minimising, path-dependency and lock-in, which are
ignored by conventional policy and growth theories in
the context of environmental economics.
The structure of this paper is as follows: Section 2
presents a short overview of evolutionary notions in
biology, economics and technology studies, highlighting their common background and their potential
relevance for sustainable development policies. Section 3 deals with an evolutionary foundation of
sustainable development policies, emphasising adaptive flexibility, evolutionary potential, variability, risk
minimising, and path-dependence and lock-in. Section
4 presents conclusions.
2. Evolutionary perspectives
2.1. Evolution in biology
Systematic thought about evolution has its starting
point in the last century, when Charles Darwin and
Alfred Russel Wallace initiated a major transition in
the discipline of biology. After an initially hesitant
development of theory and empirical methods, biology became the dominant domain of evolutionary
thinking after the 1930s. Not surprisingly, it has
served as a rich source for possible evolutionary
analogies in other fields, notably economics (see
Hodgson, 1993; Eldredge, 1997). It is important to
realise that economic development and technological
C. Rammel, J.C.J.M. van den Bergh / Ecological Economics 47 (2003) 121–133
change are driven by evolutionary dynamics that not
only share many similarities with biological evolution
but also differ in many respects (Hodgson, 1993; van
den Bergh, 2003a). In an appendix in the sixth edition
of his ‘‘Origin of Species’’, in which he discusses the
people that influenced his thinking, Charles Darwin
acknowledged that he received a critical influence
through the work of Thomas Malthus on (human)
population growth. This only illustrates that the fact
that biology was the first science to elaborate the idea
of evolution can be regarded as a ‘‘historical accident’’, to use the jargon of evolutionary thinking. This
is supported by the idea that Malthus and Darwin
were both directly affected by the rapid changes in
England during the Industrial Revolution, Darwin
especially through his family’s connections with various industrialists. The Industrial Revolution possessed many evolutionary features, starting with
changes in diversity caused by the interplay of innovation and selection. This underlines the multidisciplinary nature of evolutionary thinking.
In biology, the essential role played by evolutionary
theory is no longer open for discussion. The most
famous statement that reflects this view is ‘‘. . . nothing
makes sense in biology without an evolutionary perspective . . .’’ (Dobzhansky, 1973: 1). In general, Darwinian biological evolution is defined as a gradual and
continuous adaptation through natural selection. The
key elements of biological evolution are genetic variation, heredity and individual selection, which are
facilitating a structure-building process with no predefined functionality (Mayr, 1988; Maynard Smith and
Szathma´ry, 1996). Variation and diversity are caused
by mutation and sexual recombination, and can be seen
as the fundamental base upon which natural selection
acts. Dependent on abiotic and biotic conditions—
which are inevitably changing—selection ‘‘chooses’’
between alternative variants—alleles at a genetic level.
Selective advantage is expressed by a greater fitness,
i.e. the number or quality of offspring of an individual.
Heredity is strictly bounded to a vertical transfer of
information through genes within a single species.
Going from one to the next generation, one can thus
identify a chain of gradual adaptations. Each generation
of a species thus can be seen to reflect the cumulative
genetic effect of past selective conditions.
A much debated cornerstone of biological evolution is the notion of adaptation, which can be seen as a
123
temporary feature providing a benefit over its alternatives under specific environmental conditions, or as
the process by which this feature arises. The Darwinian understanding of gradual adaptation is expressed
figuratively in Wright’s image of populations walking
up the side of an adaptive ‘‘fitness peak’’ (Wright,
1964). If a population wants to find the highest
position in an adaptive landscape—where foresight
and prediction are impossible—the best guess is just
to walk uphill step by step. Note that this routine is
very common in algorithms designed to solve nonlinear optimisation problems in mathematical form. Like
evolution, these will at best lead to local optima;
global optima are not guaranteed. In other words,
ultimately, a population may reach a very high point
in the landscape, but it is unlikely that this will be the
highest point. Furthermore, changes in the adaptive
landscape, due to ‘‘coevolution’’, can cause a current
state of the respective population to become less well
adapted or even maladapted.
Since the early 1970s, this understanding of gradual evolution and the concept of strong ‘‘adaptationism’’ were opposed—or perhaps it is better to say:
extended—by the idea that natural history consists of
punctuated equilibria (Eldredge and Gould, 1972).
This was mainly based on fossil records, which
suggested the existence of long periods of stasis
interrupted by sudden bursts of rapid change. The
interpretation by the inventors of this theory is that
‘‘higher level’’ selection processes, known as sorting,
emphasise that in biological evolution not every state
can be interpreted as adaptive or caused only by
natural selection (Gould and Lewontin, 1979; Gould,
1985). This idea of punctuated equilibrium has stimulated some debate in social science, and has, for
instance, been related to Schumpeter’s long waves
(Somit and Peterson, 1989; Gowdy, 1992).
Recently, approaches using insights from biological
evolution have started to influence the analysis of
policies and (ecosystem) management for sustainable
development. Much of the research on biodiversity
loss, ecosystem resilience and exotic species falls under
this heading, even if the link with evolution is not often
made explicit. The relation between biodiversity and
resilience has been stressed by ecologists and economists alike (Holling et al., 1994; Perrings, 1998;
Common and Perrings, 1992). Whereas stability
emerges at the level of biological populations, resil-
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ience is defined at the level of ecosystems and refers to
the ability to maintain structure and self-organisation of
a system in the face of external stress (Holling, 1992).
The linkages between biodiversity, stability and resilience are still under discussion, but it is likely that they
will be translated into implications for policies aiming
at sustainable development (van den Bergh and
Gowdy, 2000; Common and Perrings, 1992).
Natural resource management is an area where
environmental policies have to reckon with implications of evolutionary biology. Due to the selective
pressure caused by human activities, irreversible evolutionary patterns occur. As a result, management
systems could initiate negative changes in the quality
of the resource base, making future utilisation more
expensive or even impossible. In the context of
fisheries, this phenomena has been addressed by
authors like MacGlade and Allen (1987) and Heino
et al. (2000), who emphasise that the selection pressure created by small fishing net mazes tends to
increase the proportions of relative small fishes in
the fished population and affects the viability of the
population in the future. Munro (1997) has developed
an elegant model of pesticide use in agriculture. This
mixes a formulation of the traditional renewable
resource management problem with an evolutionary
model that reflects a positive correlation between
selective pressure on pests and the number of pesticides-resistant individuals in the treated population.
This allows to compare policies based on understanding the evolutionary impacts of resource use with
those that do not understand these, using both economic and biological indicators.
2.2. Evolution of technology
In the field of technology studies, there is a growing
awareness that a complete understanding of technological change requires that it is seen as an evolutionary
process (Hughes, 1983; Vicenti, 1990; Mokyr, 1990).
Focusing on the individual level, technology can be
understood as an ‘‘adaptive answer’’ to a priori defined
problems. According to Dosi (1988), the context for
this is given by ‘‘technological and scientific paradigms’’, which pre-define the patterns of solution and
point the whole innovative process in particular directions. Engineers and inventors always face different
possibilities and competing solutions. In other words,
there is a high level of uncertainty, which is only
resolved through ex post competition—i.e. selection—towards a better ability to solve the particular
problem (Nelson, 1995). The latter is determined by
technological criteria, costs, cultural values, life style,
etc. (Vicenti, 1990; Hughes, 1983).
Evolutionary thinking stresses another aspect of
technological development, namely path-dependence,
and the related notion of pre-adaptation. Technological systems are never created independently of their
selective context or evolving out of nothing. In human
history, most of the major technological innovations
were built on prior inventions and required further
innovations to become really effective (Kranzberg,
1997; Diamond, 1997). Due to this kind of required
historical sequence or path-dependence, any technology can be understood as an intermediate element in a
causal chain of undetermined evolutionary developments. The functionality and final purpose of the
particular element need not emerge immediately. As
authors like Diamond (1997), Hughes (1983, 1978)
and Sigmund (1995) emphasise, many technological
innovations were the result of pure curiosity and
creative ‘‘inventing around’’ (Hughes, 1983), rather
than driven by scarcity and real life problems. Without
the focus on problem solving, the final purpose and
need of these pre-adaptive innovations emerged only
later—sometimes much later. Sudden advantages of
‘‘useless’’ innovations resulted from altered selective
conditions. It is thus possible that technological innovations, that fail to develop successfully in one
cultural context expand and flourish in another (Reynolds and Cutcliffe, 1997).
On the societal level, evolutionary thinking is
opposing the traditional approach to deal with technological systems through isolated analyses focused
exclusively on technological criteria. In contrast, David Nye (1992) concept of ‘‘technological landscapes’’ describes technological evolution through
the relations between economic, political, technological and environmental processes. Technology is adapted to spatial and temporary conditions of cultural and
environmental systems. Hence, development, transfer
and diffusion of technology cannot be described only
in terms of technological and economic efficiency, but
are also strongly based on various other aspects of
technological systems (Hughes, 1983; Bruland, 1994;
Randall, 1994).
C. Rammel, J.C.J.M. van den Bergh / Ecological Economics 47 (2003) 121–133
Seeing technological development as evolving,
complex, multi-dimensional, culturally dependent
and out-of-equilibrium leads to an entirely different
perspective on sustainable development than one
based, for instance, on endogenous growth theory,
which focuses attention on the ‘‘optimal relationship’’
between technology and growth. Moreover, Nelson
(1995): 64) points out that ‘‘(. . .) recent scholarship
on the evolution of technology has proposed that there
may be a number of different evolutionary tracks that
go in quite different directions, and that movement
down one may block movement down another’’. If
one adds this to the discussion of ‘‘technological
landscapes’’ and path-dependence, then it is very clear
that there is no such notion as an ‘‘optimal technology’’. Perhaps optimality would makes sense if one
would focus on a single criteria, reducing the value of
alternative ones. However, policies for sustainable
development cannot rely on the notion of optimal
solutions based on a single measurement, the most
likely being of course economic efficiency. The reason is that this denies the immense uncertainty surrounding any decision about long term growth and
development. Uncertainty and keeping options is a
theme that fits surprisingly well in an evolutionary
setting. We will come back to this later.
2.3. Economic evolution
Already since the end of the 19th century, economists have adopted concepts from evolutionary biology. It has been well documented what Marshall,
Veblen and Schumpeter had to say on the subject
(Hodgson, 1993). In the 1950s, even neoclassical
economists showed an interest in evolution. However,
this was reduced to (mis)regarding selection as optimisation (Alchian, 1950; Friedman, 1953). This incorrectly transferred the old idea of Herbert Spencer’s,
‘‘survival of the fittest’’, to economics. From a modern evolutionary perspective, however, the correct
term would be ‘‘survival of the fitter or sufficiently
fit one’’ (van den Bergh, 2003b) or ‘‘survival of the
fitting’’ (Boulding, 1981).
In general, the neoclassical understanding of evolution is synonymous with a determined process
(Rostow, 1960) whereby progress is taken for granted
(Hirshleifer, 1977), and a unique, optimal equilibrium
is expected as the final result (Friedman, 1953;
125
Boulding, 1981). This is exemplified by evolutionary
game theory, which tries to identify equilibria, and is
sometimes more appropriately referred to as ‘‘equilibrium selection theory’’. The reason is that innovation
is missing; and of course, if selection works long
enough upon a given variety, in the absence of any
innovation ultimately the system will end up in a
situation with minimal variety, i.e. an equilibrium.
Evidently, this does not represent a complete view of
evolution, be it in economies, technological systems
or natural systems. Typical of evolution, and the
essence of its structure-forming character, is the interplay between selection and innovation forces.
A number of approaches are more in line with
evolution as it is generally regarded by natural scientists. Schumpeter (1934) work on innovative entrepreneurship and creative destruction was an initial
starting point for further approaches. Subsequent
theories, like the work of Nelson and Winter (1982)
complemented the idea of pure economic selection
with the concept of ‘‘routine’’, a concept similar to
‘‘gene’’. The evolutionary model of Silverberg et al.
(1988) extends this with the notions of diversity,
anticipation of firms and uncertainty. Opposing the
non-historic view of conventional economics, authors
like Arthur (1989) or David (1985) stress that ‘‘economic survival’’ is based on much more complex
reasons than competitive selection towards economic
efficiency. In their works they show that, due to pathdependence, non-predictable ‘‘historical small events’’
(Arthur, 1989) can cause inflexibility of current systems, followed by a lock-in of—possibly inferior or
undesirable—technologies (see further Section 3.4).
Gowdy (1992), starting from the biological theory
of ‘‘punctuationism’’, argues that economic survival
rates occur at several hierarchical levels. From this
point of view, in the economic world, there is much
more freedom in firm behaviour than pure maximisation, which gives room for differential success of
firms, innovations or technologies.
Opposed to equilibrium orientated concepts, evolutionary approaches in economics address the longterm effectiveness and stability of environmental
policies (Ring, 1997). In this sense, evolutionary
economics has the potential to integrate notions of
non-optimised change, uncertainty and long-term development into sustainable development policies. The
importance of this integration is underlined by find-
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C. Rammel, J.C.J.M. van den Bergh / Ecological Economics 47 (2003) 121–133
ings in the field of adaptive environmental management, which emphasise that handling complex systems requires a balanced between flexible policy
learning, monitoring and research (Walters, 1986).
3. An evolutionary foundation of sustainable
development policies
3.1. The lack of evolutionary elements in thinking
about sustainable development and environmental
policy
Environmental policy instruments are usually classified into ‘‘command-and-control’’, ‘‘market based’’
or ‘‘economic incentives’’, and ‘‘moral suasion’’.
Since environmental policy is moving from a curative
and effect-oriented focus towards amore source-oriented approach, the latter two approaches may become increasingly important. There is a large body of
literature on economic instruments of environmental
policies, well-covered in the large number of textbooks on environmental economics. Economists have
for long argued that market-based incentives are more
efficient than the traditional approach based on direct
policy regulations or command-and-control. This insight is, however, mainly dependent on static equilibrium analyses in which economic agents—consumers
and producers—are individually efficient, due to
rational behaviour (Baumol and Oates, 1988). It is
certainly not evident that the same insight will be
obtained when using theories and models that reflect a
more complex, evolutionary systems approach in
which agents act in accordance with bounded rationality. Indeed, a recent analysis of environmental
policy using alternative theories of individual behaviour generates results that deviate from the standard
ones (van den Bergh et al., 2000).
Based on the assumptions that there exist ‘correct’
or ‘optimal’ prices for all environmental services as
well as optimal equilibria, economic theory suggests
that polluters should pay the full marginal costs of
damages caused by their activity. Thus, an incentive to
reduce the damage up to the socially optimal pollution
or exploitation level is created. Various instruments
like Pigouvian taxes, tradable permits and depositrefund systems are practicable applications of this
approach. A number of disadvantages of such eco-
nomic instruments have been mentioned: negative
distributional effects, less certain environmental
effects than direct regulations, and difficulties in
measuring required tax levels due to marginal damage
and abatement costs being related to hypothetical
optimal equilibria (Cumberland, 1994; Dietz and
van der Straaten, 1992; van den Bergh et al., 2000).
From an evolutionary perspective, the major disadvantage of this approach is the assumed existence of
optimality and stable equilibria. As a result, it is not
focused at all on dealing with structural changes in
environmental and economic systems that involve
uncertainty, adaptations and path-dependence (Clark
et al., 1995). Based on the assumption of optimal
individual behaviour, the objective of social efficiency
is the guiding principle in the traditional approach.
Following this ‘‘ideology of efficiency’’ (Bromley,
1990), environmental policies aiming at market-based
incentives and stable equilibrium ignore evolutionary
characteristics like qualitative change that is irreversible and unpredictable and moving along non-equilibrium states. But the focus on efficiency—if realistic at
all—is short term and feeble. Worse, environmental
policies based on monetary cost-benefit analyses bear
the risk of stimulating unsustainable socio-economic
patterns and sacrificing long-term stability for shortterm ‘‘optimums’’ and gains of efficiency. This is
currently most relevant for climate policy, where
economic research is dominated by cost-benefit and
optimisation approaches (van den Bergh, 2003c).
3.2. Stability versus optimality
Evolutionary theory within the realm of biology,
especially ecology, has devoted much attention to the
issue of stability. The link between stability and
diversity1 has been addressed by many authors. (e.g.
Wagner and Altenberg, 1996; Holling, 1992; Perrings
1
In biology, diversity refers to the individual or species level,
stressing primarily a high variety among individuals within a
population or different species within an ecosystem (Hubbell,
1999). In contrast, variability is a term that describes the propensity
of a genotype to vary. Whether this gives rise to a variety of
phenotypic expressions depends highly on the hierarchical nature of
the genotype – phenotype mapping (Wagner and Altenberg, 1996).
Long-term stability depends both on variability and diversity. Given
the multidisciplinary audience of this journal, we will simplify
terminology by using the term diversity to cover also variability.
C. Rammel, J.C.J.M. van den Bergh / Ecological Economics 47 (2003) 121–133
et al., 1995). Although stability is, or should, be a
standard element of sustainability, it has received
surprisingly little attention. Economists often resist
too stringent environmental policies, feeling that these
may create economic instability. This runs largely
through expectations of economic agents, which are
very much influenced by the general economic climate, as reflected by GDP growth, a country’s competitive international position and (un)employment.
The link between expectations and these indicators is
reinforced by financial markets. Nevertheless, these
issues are seldomly approached from an explicit,
theoretically well-founded perspective on stability.
Traditional macroeconomics offers some perspective
on stability, but has seen little influence on modern
environmental economics and its policy theory. Evolutionary economics serves as another source of
thinking about economic stability and the factors that
contribute to it, but has seen equally little influence on
environmental economics.
In general, evolutionary systems do not relate to
stability in a static sense as they are faced with
moving equilibria and the dynamics of coevolutionary
interactions which can not be foreseen ex ante. Given
this permanent process of unpredictable change any
kind of optimising must be understood as local and
myopic (Nelson, 1995; Rammel and Staudinger,
2002; van den Bergh, 2003a). If optimality exists it
will be temporary, because through evolution, selection and innovation, and environmental change, including coevolution, it is easily transformed into
maladaptive traits (Vermeij, 1994; Norgaard, 1994).
Under such conditions, diversity is a key elements of
long term stability and even survival. This holds
equally for biological and economic systems (Utterback and Sua´rez, 1993). This will be substantiated in
the next section.
3.3. Diversity to enhance adaptive flexibility and
evolutionary potential
Since every successful adaptation is only a temporary ‘solution’ to changing selective conditions, maintained diversity represents a repertoire of alternative
options and increases the possibility that altered conditions can be successfully met through pre-adaptations and further evolution. This is shortly referred to
as ‘evolutionary potential’. It allows diversity to
127
contribute to long-term stability through adaptive
flexibility. In evolutionary biology, adaptive flexibility
is based on the ability to maintain genetic variability
and a large amount of pre-adaptive evolutionary traits.
The maintenance of diversity is mainly a passive
process that relies on the imperfection of diversityreducing forces (Rosenzweig, 1995; Hubbell and
Foster, 1986). In other words, adaptive flexibility is
fostered by an evolutionary setting that is characterised by lax selection pressure.
Evolutionary systems thus can be seen to express a
sort of implicit trade-off between realising short-term
local optima (like specific criteria of efficiency) and
maintaining evolutionary potential to achieve adaptability and stable long-term developments. This holds
equally for biological and social –economic systems
(Schu¨tz 1999; Matutinuvic, 2001). In both, selection
acts as a short-term adaptive force, which reduces
diversity and optimises biological features to the narrow and specific environmental—biotic and abiotic—
conditions of the moment. Hence, the selective process
is strictly myopic and is by no means aimed at maintaining diversity or achieving survival in the long run.
This trade-off between efficiency and diversity, or
adaptability, was extensively described by authors
such as Mayumi and Giampietro (2001), Giampietro
(1997) and Giampietro et al., (1997). Recalling a
general view on complex adaptive systems, Mayumi
and Giampietro (2001): 13) state that a sound balance
of this trade-off can be achieved when ‘‘increases in
efficiency are obtained by amplifying the most
performing activities, without eliminating completely
the obsolete ones [emphasis by the authors]’’ (ibid.:
13). Consequently, as complex adaptive systems tend
to move towards instability and new emerging sets of
boundary conditions (Giampietro, 1997), diversity, in
terms of a maintained repertoire of different meanings
of efficiency, increases adaptive flexibility, i.e. the
chances to cope with new socio-economical objectives and emerging properties.
In line with Mayumi and Giampietro’s ‘‘different
meanings of efficiency’’ it is useful to consider the
perspective coming from ecosystem theory. As Ulanowicz (1997) shows, in dynamic ecosystems, efficiency also stands for a extended and improved use of
resources by the diversity of species which are associated with different single-use efficiencies. From this
ecological point of view, less efficient (and competi-
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tive) species are redundant, but for the stability and
the development potential of ecosystems, functional
redundancy represents a reservoir of adaptive
responses and enhances the evolutionary potential
(see also Rammel and Staudinger, 2002).
Embedded in evolutionary system thinking, adaptive flexibility reflects a clear distinction between this
higher level of systemic efficiency (based on functional diversity) and the standard economic understanding of efficiency explicable in terms of cost
minimisation, factor endowments and the exclusion
of ‘‘weak’’ performers. Given that the partial perspective of standard economic theory tends to decrease
functional diversity in order to increase a specific and
narrow meaning of efficiency, Schu¨tz (1999): 25)
notes: ‘‘(. . .) the lower the diversity, the lower the
efficiency of the system, the lower its potential to
respond upon disturbances, and the lower its potential
for innovative combinations’’ (Schu¨tz, 1999: 25).
Using the notion of complex adaptive systems
(Holland, 1995) to bridge ecosystem theory with
socio-economic systems, Matutinuvic (2002) notes
that resource-intensive socio-economic systems, characterised by reduced diversity and over-efficiency,
may be too fragile to stand major perturbation. He
concludes that diversity and a sound ‘‘balance between thermodynamic and maximum power efficiency’’ (Matutinuvic, 2002: 438) are the essential
conditions for stable and sustainable economies and
ecosystems.
In the context of social–economic development,
diversity related to a wide range of activities, including agricultural techniques, industrial production
methods, means of communication, languages, institutions, legislation and informal rules (culture). Boyd
and Richerson (1985) have argued that cultural diversity is the essential condition for the evolution and
development of social systems. Schu¨tz (1999) extends
this by arguing that functional diversity in both socioeconomic and biological spheres is a key element for
any future sustainable economy. This interpretation of
diversity is shared by Matutinuvic (2001), who understands diversity as functional and structural analogues
of both socio-economic and biological systems. Notably, biological as well as cultural diversity relate to
systemic coherence and integrity of the world socioeconomic system in terms of adaptations to changing
environments, avoidance of head-to-head competi-
tion, efficient use of resources and energy and the
possible range of responses to new selective pressures
(Matutinuvic, 2001). Emphasising the inevitable contradiction of valuing biodiversity or socio-diversity
through a market-centred perspective, O’Hara (1995)
defines diversity in socio-economic systems as different social and economic arrangements by which
people organise their societies. This can be seen as
institutional diversity, which coevolves with behavioural routines at the individual and group levels (van
den Bergh and Stagl, 2003). Additionally various
authors have emphasised the relevance of diversity
in different socio-economic contexts, notably in resource based activities (Becker and Ostrom, 1995;
Norgaard, 1994; Perrings, 1998; Rammel and Staudinger, 2002).
As opposed to biological systems, socio-economic
systems do not maintain diversity solely passively.
They also devise active conservation policies—currently focusing on biodiversity, languages and cultural
heritage (UNESCO, UNEP)—and diversity-generating activities (R&D) and institutions—think of universities and patenting legislation. These can be
regarded as essential elements of policies aimed at
promoting sustainable development. They are foster a
sound balance between short-term efficiency goals
and long-term stability based on a maintained evolutionary potential.
Evolutionary potential and adaptive flexibility cannot be sufficiently realised through policies aimed at
optimal equilibrium solutions, i.e. the traditional economic approach to environmental policy. The conventional cost-benefit approach emphasises the
maintenance cost of variety or diversity, which subsequently is interpreted as a loss of efficiency or a
lower profitability. As a result, cost-benefit criteria
exclude options that try to maintain variety for the
purpose of evolutionary potential, unless a descriptive
underlying model explicitly accounts for evolutionary
change and its impacts. Notions of option value and
the Krutilla –Fisher algorithm are adaptations to the
traditional cost-benefit paradigm that have tried to
address the issue of irreversible changes.
Gowdy (1992) notes the standard environmental
economic theory of policy is indirectly influenced by
the notion of ‘‘survival of the fittest’’. Evolutionary
theory, however, instead supports a fundamentally
different notion of ‘survival of the fitter or sufficiently
C. Rammel, J.C.J.M. van den Bergh / Ecological Economics 47 (2003) 121–133
fit’ (see the discussion in Section 2.3). Survival of the
fittest incorrectly suggests that there is no space for
‘excess baggage’ or diversity supporting evolutionary
potential.
Related is that present economic developments and
policies that rely strongly on market selection tend to
stimulate a decrease in diversity at national and global
scales of human activity. This is reflected, among
others, in a reduced genetic crop variability in agriculture, a focus on fossil fuel energy supply, a
dominance of fuel-based transport, and dramatic losses of biodiversity and cultural diversity (including
languages). Conventional policy instruments insufficiently recognise these trends, and even contribute to
speed them up. They are not aimed at maintaining
diversity. At best, they realise it as an unintended sideeffect (Shiva, 1995; Gowdy, 1997; O’Hara, 1995).
3.4. The problem of path-dependence and lock-in
Evolutionary theory implies that future processes
are influenced by their antecedents, in a very subtle
way. Diversity in an evolving system changes in a
certain ways that are unique and irreversible, just on
probabilistic grounds, due to the large number of
heterogeneous or diverse units in a system. A full
understanding of such history or path-dependence can
only be realised by studying mathematical illustrations of it. Policies for sustainable development need
to tackle situations which are characterised by pathdependence and lock-in. In evolutionary economics,
there is a growing body of literature that addresses
these notions (Arthur, 1989; David, 1985; Katz and
Shapiro, 1994; Silverberg et al., 1988). Mechanisms
that contribute to lock-in create increasing returns to
scale, which include economies of scale and scope,
cumulative technological change, learning, network
externalities, and complementary production factors.
The general outcome of these studies shows that
socio-economic systems can become locked-in
through unpredictable historic events—historical accidents—in combination with the previous mechanisms.
Chandler (1990) and Williamson (1985) point out that
market selection tends to focus socio-economic developments in particular directions like economic efficiency and profitability, which means optimisation
and innovation within a present development trajectory at the costs of alternative opportunities, which
129
might better meet other objectives. But path-dependence can also easily lead to outcomes that are not
even efficient from a narrow private profit perspective. The well-known examples of lock-in given in the
literature illustrate this: VHS video recorder, Qwerty
keyboard and Windows operating system.
Unsustainable—larger—systems like fossil-fuel
cars, ‘‘industrialised agriculture’’ and fossil-based
energy supply have become locked-in as well. Strategies aimed at creating a diversity of alternative
options increase the possibility for future sustainable
changes in these areas. In addition to some of the
conventional regulatory instruments, notably ‘correct
prices’, the lock-in and path-dependence perspective
suggests a broad spectrum of interrelated strategies.
These involve the creation of protected niches outside
current market selection, stimulation of key-innovations and pathway technologies, and portfolio investment in different alternative innovations. These can
prevent potential undesirable lock-ins, or unlock current locked-in systems and technologies.
3.5. The concept of risk minimising
Maintaining diversity to foster adaptive flexibility
and evolutionary potential can be seen as a riskminimising strategy, much in line with the precautionary principle often mentioned in the context of
environmental policy. The concept of risk minimising
in an evolutionary setting originates from anthropology. Here it is argued that small-scale groups of
nomadic hunters and gatherers often utilise and exploit their natural environment at a significantly lower
level then would be possible. They invest a relatively
small part of their time in actual working hours,
devoting most of their time to social interactions with
the purpose to strengthen reciprocal social relations
(Groh, 1992; Sahlins, 1974; Smith and Wishnie,
2000). Such a strategy is capable of maintaining a
safety buffer against inevitable fluctuations and
changes in ecosystems, thus fostering long-term survival. The idea is that any short-term strategy of
optimising the human – environment relation in revenue terms would raise the risk of dangerous breakdowns within small groups human populations (Groh,
1992). Hence, this strategy of conservation and
‘‘avoiding unused natural resources’’ supports socialecological stability and can be interpreted as an
130
C. Rammel, J.C.J.M. van den Bergh / Ecological Economics 47 (2003) 121–133
investment in long-term survival. As emphasised by
Scott (1976) and Lipton (1968), such a societal
strategy has also been essential for recent agricultural
societies in Asia, where the traditional focus on risk
minimising through the use of a large variety of less
productive but rather invulnerable seeds is restricting
the diffusion of high productive but vulnerable seeds.
It resists the development of monocultures and contributes to combined biological –cultural diversity and
adaptive flexibility.
Given the objective of sustainable development,
risk minimising is a necessary consequence of our
ability to recognise complex systems driven by evolutionary dynamics. By addressing unpredictable
changes and uncertain fluctuations typical of such
systems, risk minimising helps to clarify and reduce
potential problems of unstable and undesirable developments. Note that the concept of risk minimising has
nothing to do with transforming uncertainties into
calculated or insured risks, which is per definition
strictly impossible for many natural phenomena, for
pervasive and extreme uncertainty, and at a global
scale (Dosi and Egidi, 1987). Authors such as Dosi
(1988), Dosi and Egidi (1987) and Spash (2002) also
distinguish between ‘‘weak’’ and ‘‘strong’’ uncertainty. While ‘‘weak’’ uncertainty is more familiar to
imperfect information about the occurrence of known
events, ‘‘strong’’ uncertainty refers to extreme uncertainty, i.e. unknown events and unknown consequences of particular actions.
Risk minimising has to deal with three interrelated
aspects: (1) adaptive flexibility and evolutionary potential; (2) pervasive and extreme uncertainty; and (3)
path-dependence and lock-in. These were discussed in
the previous sections.
Risk minimising is based on the awareness that
evolving systems are complex and can neither be
understood nor controlled completely. Therefore,
decentralised policies are attractive, notably ones based
on self-organisation or self-regulation (Ostrom, 1990)
and co-evolutionary mechanisms (Norgaard, 1984).
Risk minimising strongly suggests to take the
precautionary principle more seriously than is currently done. An original interpretation of this principle is
in the statement of Ayres (1995): 99): ‘‘If it is
impossible to know how far it is safe to perturb the
system we live in without triggering a catastrophic
collapse, then the only reasonable policy is not to
perturb it more than it has been perturbed by natural
phenomena in the past’’. A concrete elaboration is the
safe minimum standard by Ciriacy-Wantrup (1968).
From the perspective of path-dependence and lockin, risk minimising emphasises an adaptive design of
environmental policies, which on one hand reduces
the selective dominance of risky technologies and
systems, and on the other hand supports alternative
and sustainable trajectories by stimulating and fostering diversity. Specific instruments were already mentioned in the previous section.
Finally, risk minimising can be linked to the notion
of adaptive management (Walters, 1986). This says
that any successful strategy for sustainability is characterised by a dynamic process of continuous adaptive
learning.
4. Conclusion
Sustainable development policies have to deal with
uncertainty, unpredictable changes, and evolving
properties of complex systems. An evolutionary perspective has been proposed here as the basis for
developing a theoretical approach for studying such
policies. It emphasises that economies are faced with a
continuous process of adapting technologies, strategies, networks and structures to altered environmental
conditions. The current economic focus on conventional policies is entirely based on optimum-andequilibrium oriented approaches that do not recognise
the pivotal role of diversity. We have tried to shown
that long-term sustainability calls for adaptive flexibility and evolutionary potential, which enables a
continuous process of adaptive learning and a diversity of co-existing alternatives, at any level and in
every subsystem of the economy. Implementing adaptive flexibility means shifting ones focus from a shortterm to a long-term horizon, where evolutionary
potential and long-term stability can be linked to
diversity.
As a general policy approach, risk minimising was
proposed. It is founded upon three pillars, namely
evolutionary potential, uncertainty, and path-dependence. One can regard our plea as an evolutionary
perspective on the much-adhered-to precautionary
principle, which so far has seen, however, very little
theoretical elaboration. It can be operationalised
C. Rammel, J.C.J.M. van den Bergh / Ecological Economics 47 (2003) 121–133
through notions like self-regulation, adaptive management, policies avoiding and ‘undoing’ lock-in, and
coevolutionary mechanisms. Maintaining and increasing diversity are the key to all of these. Ultimately, the
objective is to arrive at a coherent and complete set of
instruments that guarantee as much as is possible a
sustainable development in the future.
References
Alchian, A., 1950. Uncertainty, evolution and economic theory.
Journal of Political Economy 58, 211 – 222.
Allen, P.M., 1997. Evolutionary complex systems and sustainable
development. In: van den Bergh, J.C.J.M., Hofkes, M.W. (Eds.),
Theory and Implementation of Economic Models for Sustainable Development. Kluwer Academic Publishers, Dordrecht.
Arthur, B., 1989. Competing technologies, increasing returns, and
lock-in by historical events. Economic Journal 99, 971 – 983.
Ayres, R., 1995. Industrial metabolism: theory and policy. In:
Richards, D., Allenby, B., Frosch, R. (Eds.), The Greening
of Industrial Ecosystems National Academy of Science,
Washington.
Baumol, W., Oates, W., 1988. The theory of Environmental Policy
Cambridge University Press, Cambridge.
Becker, D., Ostrom, E., 1995. Human ecology and resource sustainability: the importance of institutional diversity. Annual Review
Ecological System 26, 113 – 133.
Boulding, K., 1981. Evolutionary Economics. Sage Publications,
Beverly Hills.
Boyd, R., Richerson, P., 1985. Culture and the Evolutionary Process University of Chicago Press, Chicago.
Bromley, D., 1990. The ideology of efficiency: searching for a
theory of policy analyses. Journal of Environmental Economics
and Management 19, 86 – 107.
Bruland, K., 1994. Patterns of resistance to new technologies in
Scandinavia: a historical perspective. In: Bauer, M. (Ed.),
Resistance to New Technology. Cambridge University Press,
Cambridge.
Chandler, A., 1990. Scale and Scope: The Dynamics Industrial
Capitalism. Harvard University Press, Cambridge.
Ciriacy-Wantrup, S., 1968. Resource conservation: Economics and
Policies. University of California Press, Berkeley.
Clark, N., Perez-Trejo, F., Allen, P., 1995. Evolutionary Dynamics
and Sustainable Development: A Systems Approach. Edward
Elgar, Aldershot and Vermont.
Common, M., Perrings, C., 1992. Towards and ecological economics of sustainability. Ecological Economics 6, 7 – 34.
Cumberland, J., 1994. Ecology, economic incentives and public
policy in the design of a trans disciplinary pollution control.
In: Van den Bergh, J., Van der Straaten, J. (Eds.), Towards
Sustainable Development. Concepts, Methods and Policy Island
Press, Washington DC.
David, P., 1985. Clio and the economics of QWERTY. American
Economic Review Proceedings 75, 332 – 337.
131
Dennett, D., 1995. Darwins Dangerous Idea: Evolution and the
Meanings of Life. Simon and Schuster, New York.
Diamond, J., 1997. Guns, Germs, and Steel. The fates of human
societies. W. Norton and Company, New York.
Dietz, F., van der Straaten, J., 1992. Rethinking environmental
economics: missing links between economic theory and economic environmental policy. Journal of Economic Issues 26,
27 – 54.
Dietz, F.J., Vollebergh, H.R.J., 1999. Explaining instrument choice
in environmental policies. In: van den Bergh, J.C.J.M. (Ed.),
Handbook of Environmental and Resource Economics. Edward
Elgar, Cheltenham.
Dobzhansky, T., 1973. Nothing in biology makes sense except in
the light of evolution. American Biology Teacher 35, 311 – 319.
Dosi, G. (Ed.), 1988. Technical Change and Economic Theory.
Pinter Publisher, London.
Dosi, G., Egidi, M., 1987. Substantive and procedural uncertainty.
An exploration of economic behaviours in complex and changing environments. Presented at the Conference on Programmable Automation, Paris, April.
Dosi, G., Freeman, C., Nelson, R., Silverberg, G., Soete, L.
(Eds.), 1988. Technical Change and Economic Theory. Pinter
Publishers, London.
Eldredge, N., 1997. Evolution in the marketplace. Structural
Change and Economic Dynamics 8, 385 – 398.
Eldredge, N., Gould, S., 1972. Punctuated equilibria: an alternative
to phyletic gradualism. In: Schopt, T. (Ed.), Models in Paleobiology. W. Freeman, San Francisco.
Friedman, M., 1953. Essays in Positive Economics University of
Chicago Press, Chicago.
Funtovicz, S., Ravetz, J., 1994. The worth of a songbird: ecological
economics as a post-normal science. Ecological Economics 10,
197 – 207.
Georgescu-Roegen, N., 1971. The Entropy Law and The Economic
Process. Harvard University Press, Cambridge.
Giampietro, M., 1997. Linking technology, natural resources and
the socio-economic structure of human society: a theoretical
model. Advances in Human Ecology 6, 75 – 130.
Giampietro, M., Bukkens, S.G.F., Pimentel, D., 1997. Linking technology, natural resources and the socio-economic structure of
human society: examples and applications. Advances in Human
Ecology 6, 131 – 200.
Gould, S., 1985. The Flamingo’s Smile. Reflections in Natural
History Norton, New York.
Gould, S., Lewontin, R., 1979. The spandrels of San Marco and the
Panglossian Paradigm: a critique on the adaptationist programme. Proceedings of the Royal Society of London B 205,
581 – 598.
Gowdy, J., 1992. Higher selection processes in evolutionary economic change. Evolutionary Economics 2, 1 – 16.
Gowdy, J., 1997. The value of biodiversity. Markets, society and
ecosystems. Land Economics 73, 25 – 41.
Gowdy, J., 2003. Assumptions of the new welfare economics and
environmental valuation. Department of Economics, Rensselaer
Polytechnic University, Troy, NY, Unpublished paper.
Groh, D., 1992. Strategien, Zeit und Ressourcen. Risikominimierung, Unterproduktivita¨t und Mußepra¨ferenz—die zentra-
132
C. Rammel, J.C.J.M. van den Bergh / Ecological Economics 47 (2003) 121–133
len Kategorien von Subsistenzo¨konomien. Anthroplogische
Dimensionen der Geschichte, Frankfurt, pp. 54 – 113.
Gute´s, M., 1996. The concept of weak sustainability. Ecological
Economics 17, 147 – 156.
Hall, J., 1993. The iceberg and the titanic: human economic behaviour in ecological models. In: Pickett, T. (Ed.), Humans as
Components of Ecosystems—The Ecology of Subtle Human
Effects and Populated Areas Springer, New York. pp. 51 – 60.
Heino, M., Dieckmann, U., Godo, O., 2000. Shrinking Cod: Fishery-Induced Change in an Oceanic Stock IIASA. (Options
Spring 2000), Laxenburg.
Hirshleifer, J., 1977. Economics from a biological viewpoint. Journal of Law and Economics 20, 1 – 52.
Hodgson, G., 1993. Economics and Evolution: Bringing Life Back
into Economics. Polity Press, Cambridge.
Holland, J.H., 1995. Hidden Order: Self-Organization and Selection
in Evolution. Oxford University Press, New York.
Holling, C., 1992. Cross-scale morphology geometry and dynamics
of ecosystems. Ecological Monographs 62, 447 – 502.
Holling, C., Schindler, D., Walker, B., Roughgarden, J., 1994. Biodiversity in the functioning of ecosystems: an ecological primer
and synthesis. In: Perrings, C., Maler, K., Folke, C., Holling, C.,
Janson, B. (Eds.), Biodiversity Loss: Ecological and Economical Issues. Cambridge University Press, Cambridge.
Hubbell, S., 1999. An Unified Theory of Biogeography and Biodiversity. Princeton University Press, New York.
Hubbell, S., Foster, R., 1986. Biology, change, and history and
the structure of tropical rain forest tree communities. In: Diamond, J., Case, T. (Eds.), Community Ecology Harper and
Row, New York, pp. 314 – 329.
Hughes, T., 1978. Inventors: the problems they choose, the ideas
they have, and the inventions they make. In: Kelly, P., Kranzberg, M. (Eds.), Technological Innovations: A Critical Review
of Current Knowledge, San Francisco Press, San Francisco.
Hughes, T., 1983. Networks of Power: Electrification in Western
society, 1880 – 1930. J. Hopkins Press, Baltimore.
Katz, M., Shapiro, C., 1994. Systems competition and networks
effects. Journal of Economic Perspectives 8, 93 – 116.
Kranzberg, M., 1997. Technology and history: ‘‘Kranzberg’s laws’’.
In: Reynolds, T., Cutcliffe, S. (Eds.), Technology and The West:
A Historical Anthology from Technology and Culture. Chicago
University Press, Chicago.
Lipton, M., 1968. The theory of optimising peasants. Journal of
Development Studies 4 (3), 327 – 351.
MacGlade, J., Allen, P., 1987. Modelling complex human systems:
a fishery example. European Journal of Operational Research
31, 147 – 167.
Matutinuvic, I., 2001. The aspects and the role of diversity in socioeconomic systems: an evolutionary perspective. Ecological Economics 39, 239 – 256.
Matutinuvic, I., 2002. Organizational patterns of economies: an
ecological perspective. Ecological Economics 40, 421 – 440.
Maynard Smith, J., Szathma´ry, E., 1996. The Major Transitions in
Evolution. Oxford University Press, Oxford.
Mayr, E., 1988. Towards a New Philosophy of Biology. University
Press, Cambridge.
Mayumi, K., Giampietro, M., 2001. The epistemological challenge
of modelling sustainability: risk, uncertainty and ignorance. Presented at the Frontiers I Conference, New Hall, Cambridge, 4 – 7
July.
Mokyr, J., 1990. The Lever of the Riches. Oxford University Press,
New York.
Mulder, P., van den Bergh, J.C.J.M., 2001. Evolutionary economic
theories of sustainable development. Growth and Change 32 (4),
110 – 134.
Munda, G., 1996. Cost-benefit analyses in integrated environmental
assessment: some methodological issues. Ecological Economics
19, 157 – 168.
Munda, G., 1997. Environmental economics, ecological economics,
and the concept of sustainable development. Environmental
Values 6, 213 – 233.
Munro, A., 1997. Economics and biological evolution. Environmental and Resource Economics 9, 429 – 449.
Nelson, R., 1995. Recent evolutionary theorizing about economic
change. Journal of Economic Literature 33, 48 – 90.
Nelson, R., Winter, S., 1982. An Evolutionary Theory of Economic
Change. Harvard University Press, Cambridge.
Norgaard, R., 1984. Coevolutionary development potential. Land
Economics 60, 160 – 173.
Norgaard, R.B., 1994. Development Betrayed: The End of Progress
and a Coevolutionary Revisioning of the Future. Routledge,
London and New York.
Norton, B., Costanza, R., Bishop, R.C., 1998. The evolution of
preferences. Why ‘‘sovereign’’ preferences may not lead to sustainable policies and what to do about it. Ecological Economics
24, 193 – 211.
Nye, D., 1992. Electrifying America Social Meanings of a New
Technology, 1880 – 1940. MIT Press.
O’Hara, S., 1995. Valuing socio-diversity. International Journal of
Social Economics 22 (5), 31 – 49.
Oates, W.E., Portney, P.R., 2003. The political economy of environmental policy. In: Ma¨ler, K.-G., Vincent, J. (Eds.), Resources for
the Future. Working paper, Washington, DC, (written for The
Handbook of Environmental Economics, (in preparation)).
Ostrom, E., 1990. Governing the Commons: The Evolution of
Institutions for Collective Action New York. Cambridge University Press, Cambridge.
Pearce, D., Atkinson, G., 1993. Capital theory and the measurement
of sustainable development: an indicator of ‘‘weak’’ sustainability. Ecological Economics 8, 103 – 108.
Perrings, C., 1998. Resilience in the dynamics of economy-environment systems. Environmental and Resource Economics 11,
503 – 520.
Perrings, C., Maeler, K., Folke, C., Holling, C., Jansson, B., 1995.
Biological Diversity: Economic and Ecological Issues. Cambridge University Press, Cambridge.
Rammel, C., 2003. Sustainable development and Innovations:
lessons from the Red Queen. International Journal of Sustainable Development (in press).
Rammel, C., Staudinger, M., 2002. Evolution, variability and sustainable development. International Journal of Sustainable Development and Global Ecology 9, 301 – 313.
Randall, A., 1994. Reinterpreting ‘‘Luddism’’: resistance to new
technology in the British industrial revolution. In: Bauer, M.
C. Rammel, J.C.J.M. van den Bergh / Ecological Economics 47 (2003) 121–133
(Ed.), Resistance to New Technology. Cambridge University
Press, Cambridge.
Reynolds, T., Cutcliffe, S., 1997. Technology in the preindustrial
west. In: Reynolds, T., Cutcliffe, S. (Eds.), Technology and the
West: A historical Anthology from Technology and Culture.
Chicago University Press, Chicago.
Ring, I., 1997. Evolutionary strategies in environmental policy.
Ecological Economics 23, 237 – 249.
Rosenzweig, M., 1995. Species Diversity in Space and Time. Cambridge University Press, Cambridge.
Rostow, W., 1960. The Stages of Economic Growth, third ed. University of Cambridge Press, Cambridge.
Sahlins, M., 1974. Stone Age Economy. Tavistock, London.
Schumpeter, J., 1934. The Theory of Economic Development
Harvard University Press, Cambridge.
Schu¨tz, J., 1999. The value of systemic reasoning. Ecological Economics 31, 23 – 29.
Scott, J., 1976. The Moral Economy of Peasants. London, New
Haven.
Shiva, V., 1995. Biopolitics: A feminist and Ecological Reader on
Biotechnology. Zed Books, London.
Sigmund, K., 1995. Spielpla¨ne: Zufall, Chaos und die Strategien
der Evolution. Hoffmann und Campe, Hamburg.
Silverberg, G., Dosi, G., Orsengio, L., 1988. Innovation, diversity
and diffusion: a self-organization model. Economic Journal 88,
1032 – 1054.
Smith, E., Wishnie, M., 2000. Conservation and subsistence in
small-scale societies. Annual Review of Anthropology 29,
493 – 524.
Solow, R., 1992. An Almost Practical Step Towards Sustainability.
Resource for the future, Washington DC.
Somit, A., Peterson, S. (Eds.), 1989. The Dynamics of Evolution:
The Punctuated Equilibrium Debate in the Natural and Social
Sciences. Cornell University Press, Ithaca, NY.
Spash, C., 2002. Greenhouse Economics: Values and Ethics. Routledge, London.
Toman, M., Pezzey, J., Krautkraemer, J., 1995. Neoclasical econonomic growth theory and ‘‘sustainability’’. In: Bromley, D. (Ed.),
Handbook of Environmental Economics Blackwell, Oxford.
Ulanowicz, R.E., 1997. Ecology, the Ascendant Perspective. Columbia University Press, New York.
Utterback, J., Sua´rez, F., 1993. Innovation, competition, and the
industry structure. Research Policy 22, 1 – 21.
133
van den Bergh, J.C.J.M., 2003a. Evolutionary analysis of the relationship between economic growth, environmental quality and
resource scarcity. In: Ayres, R., Simpson, D., Toman M. (Eds.),
Scarcity and Growth in the New Millennium, Resources for the
Future, Washington DC, in press.
van den Bergh, J.C.J.M., 2003b. Evolutionary thinking in environmental economics: retrospect and prospect. In: Foster, J., Ho¨lzl,
W. (Eds.), Evolutionary Thinking in Environmental Economics:
Retrospect and Prospect. Applied Evolutionary Economics and
Complex Systems, Edward Elgar, Cheltenham, in press.
van den Bergh, J.C.J.M., 2003c. Optimal climate policy is a utopia:
from quantitative to qualitative cost-benefit analysis. Working
paper, Department of Spatial Economics, Free University,
Amsterdam.
van den Bergh, J.C.J.M., Gowdy, J., 2000. Evolutionary theories in
environmental and resource economics: approaches and applications. Environmental and Resource Economics 17, 37 – 52.
van den Bergh, J.C.J.M., Stagl, S., 2003. Coevolution of economic
behaviour and institutions: towards a theory of institutional
change. Journal of Evolutionary Economics, in press.
van den Bergh, J.C.J.M., Ferrer-i-Carbonell, A., Munda, G., 2000.
Alternative models of individual behaviour and implications for
environmental policy. Ecological Economics 32 (1), 43 – 61.
Vatn, A., Bromley, D., 1994. Choices without prices without apologies. Journal of Environmental Economics and Management
26, 129 – 148.
Vermeij, G., 1994. The evolutionary interaction among species:
selection, escalation, and coevolution. Annual Review of Ecology and Systematics 25, 219 – 236.
Vicenti, W., 1990. What Engineers Know and How They Know It.
J. Hopkins Press, Baltimore.
Victor, P., 1991. Indicators of sustainable development: some lessons from capital theory. Ecological Economics 4, 191 – 213.
Wagner, G., Altenberg, L., 1996. Complex adaptation and the evolution of evolvability. International Journal of Organic Evolution
50, 967 – 976.
Walters, C., 1986. Adaptive Management of Renewable Resources.
Macmillan, New York.
Williamson, O., 1985. The Economic Institutions of Capitalism.
Free Press, New York.
Wright, S., 1964. Stochastic processes in evolution. In: Garland, J.
(Ed.), Stochastic Models in Medicine and Biology. University of
Chicago Press, Chicago.