Economic Valuation of Biodiversity a Comparative Study

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EC O L O G IC A L E C O N O M IC S 6 7 ( 2 0 08 ) 21 7 –2 31

a v a i l a b l e a t w w w. s c i e n c e d i r e c t . c o m

w w w. e l s e v i e r. c o m / l o c a t e / e c o l e c o n

Economic valuation of biodiversity: A comparative study
Peter Nijkampa , Gabriella Vindignib , Paulo A.L.D. Nunesc,⁎
a

Free University, Department of Economics, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands University of Catania, DISEAE, Via Santa Sofia, 98, 95123 Catania, Italy c University of Venice, Department of Economics and Foundazione Eni Enrico Mattei, Palazzo Querini Stampalia, 30122 Venice, Italy
b

AR TIC LE I N FO
Article history: Received 23 June 2007 Received in revised form 27 February 2008 Accepted 5 March 2008 Available online 4 July 2008 Keywords: Meta-analysis Biodiversity values Policy formulation Non-market valuation methods Market valuation methods Benefit transfer

ABS TR ACT
In recent years, an intensive debate on the economic valuation of biodiversity has entered the environmental-economics literature. The present paper seeks to offer first a critical review of key concepts that are essential for a proper understanding of such evaluation issues. Particular attention is given here to various monetary valuation approaches and to comparative (i.e., meta-analytical) methods from the perspective of conservation and sustainable use of biodiversity. Several illustrative examples are presented in order to highlight the usefulness of the various approaches discussed. Next, an attempt is made to infer general findings and lessons from past applied research by means of meta-analysis. In this context, a multi-dimensional technique originating from the field of artificial intelligence is deployed. It allows us to identify the most important variables responsible for changes in economic estimates of biodiversity. © 2008 Elsevier B.V. All rights reserved.

1.

Introduction

Biodiversity requires our attention for two reasons. First, it provides a wide range of indirect benefits to humans. Second, human activities have been contributing to unprecedented rates of biodiversity loss, which threaten the stability of ecosystems in terms of their provision of goods and services to humans. Consequently, in recent years many studies of biodiversity and its loss have appeared. This article critically evaluates the application of economic valuation methods for the assessment of monetary values for biodiversity benefits. Particular attention is given to comparative (i.e. meta-analytical) methods as an alternative valuation approach to the well known, and often costly, non-market methods. Finally, an attempt is made to infer general findings and lessons from available valuation studies by means of meta-analysis, as far

as they address similar issues. In this context, a multidimensional technique originating from the field of artificial intelligence is deployed. Estimation results allow us to identify the most important variables responsible for changes in economic estimates of biodiversity. The organization of the article is as follows. Section 2 discusses the challenge that comparative research is able to put forward in the field of economic valuation of environmental quality, in general, and biodiversity in particular. Section 3 offers a classification of biodiversity value, characterizing the approach adopted in the evaluation here offered, i.e. economic approach. Section 4 critically evaluates the use of the economic approach to the valuation of biodiversity and its wide range of revealed and stated preference methods. Section 5 shows an empirical attempt to infer general findings and lessons from past applied

⁎ Corresponding author. E-mail addresses: [email protected] (P. Nijkamp), [email protected] (G. Vindigni), [email protected], [email protected] (P.A.L.D. Nunes). 0921-8009/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolecon.2008.03.003

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research by means of meta-analysis, discusses the range of empirical findings and evaluates their basis against the earlier presented framework. Section 6 concludes.

2. Biodiversity as a comparative research challenge
In recent years, the awareness has grown that biological diversity is of critical importance for the stability of the earth's ecosystem, as it forms the base for sustainable functions of natural systems. In addition, it also offers a great potential for human use (such as recreation or scientific research) (ten Kate and Laird, 2004). Biodiversity may reflect a great variety of appearances depending on specific geophysical and climatological conditions. For example, European ecosystems encompass more than 2500 habitat types and 215,000 species (Stanners and Bourdeau, 1995). Biodiversity has both quantitative and qualitative characteristics. It is generally accepted that biodiversity cannot exclusively be expressed in numbers, as it also depends on the ecological structure of a whole area. It is nowadays broadly recognized that human activities are adversely affecting the earth's biological diversity, as a result of prevailing production and consumption patterns and of land use changes (cf. van Kooten et al., 2000). Consequently, biodiversity tends to become a scarce economic good, for which however a proper pricing system does not exist. In the past years, research on the economic valuation of living natural resources and also of biodiversity has shown a significant progress, but there is certainly not yet an established framework for valuing biological variety. Apart from the lack of a solid economic valuation mechanism for biological diversity, there is also a serious lack of reliable and up-to-date information and monitoring systems with a sufficient geographical detail on biodiversity. Clearly, studies on biodiversity require a pluridisciplinary approach (see also Cattizone, 1999). Any economic approach to biodiversity is therefore, by definition, limited and partial in nature (see e.g. Pearce and Moran, 1994; Barbier et al., 1995). Although various approaches deploy contingent valuation methods, it ought to be recognized that this class of methods may certainly be helpful in assessing the use value of biodiversity, but has serious shortcomings in case of non-use values such as bequest and existence values (see also Desaigues and Ami, 2001). The economic valuation of natural resources, in general, and biodiversity, in particular, is among the most pressing and challenging issues confronting today's environmental economists. Economists value biodiversity because valuation allows for a direct comparison with economic values of alternative options, a corner stone for any cost-benefit analysis exercise. In addition, the monetary valuation of biodiversity allows economists to perform environmental accounting, natural resource damage assessment, and to carry out benefit assessment. Valuation is also essential in the research of individual consumer behaviour. It indicates the opinion of individual consumers about certain biodiversity management objectives and identifies individual consumer motivations with respect to biodiversity conservation. Despite some flaws in economic valuation approaches to biodiversity, there is a clear need to continue with developing

rigorous valuation tools in order to cope with complicated trade-offs in environmental policy analysis in the context of sustainable development initiatives and emerging policies which take explicitly account of the variety in the earth's ecosystem. The current biodiversity conservation programmes in various countries require for their implementation considerable financial expenditures, which have to be traded-off against alternative uses. Although world-wide much progress has been made in identifying and prioritising such programmes, innovative valuation strategies are still needed to generate additional information in order to support the actions advocated in Agenda 21 of the 1992's Earth Summit United Nations Conference in Rio, Brazil. Biodiversity conservation programme funds have, in general, a rather poor underpinning and are not based on solid and explicit economic choice mechanisms. The reasons for this are manifold, but in general they are due to insufficient information on a given biodiversity issue as well as on undefined property rights, high transaction costs, divergence between private and social costs, inappropriate economic instruments and bureaucratic inertia of relevant political institutions. Public authority choices concerning biodiversity preservation programmes should ideally be based on sound economic principles and information, such as fair market prices, benefits of specific biodiversity policies and cost-opportunities of alternative decisions. There is a growing awareness that biodiversity conservation programmes may generate many social benefits but sometimes at high costs, in particular in terms of management and information gathering. Against this background, many efficiency problems and fair public funds allocation issues have arisen. Although general information about biodiversity programmes is available through traditional policy channels, it is challenging to allocate and manage biodiversity funds adequately from the perspective of the non-market value of environmental resources. In order to obtain a balanced tradeoff between programme costs and benefits, it is necessary to optimise an efficient use of the information available (van den Bergh et al., 1999). Fortunately, the number of studies concerning monetary biodiversity evaluation is quickly growing. Consequently, there is the need to deploy and develop adjusted methodologies and analysis instruments that can improve our understanding of economic biodiversity values and, concurrently, that would allow for a more accurate forecast of biodiversity values. Comparative analysis of many case studies is a key for enhancing an understanding. The ecological economics of biodiversity centres around the crossroads of natural and human values of ecosystems. In addition to an analysis of methodological complexities, there is also a need to draw policy lessons and general findings from past applied research. The large number of applied economic valuation studies currently available has induced the search for commonalities and contrasts in different empirical investigations and has also induced the current popularity of metaanalysis and value transfer. In particular, in recent years we have seen a rising number of publications on the economic aspects of biodiversity, both theoretical and empirical. This prompts the intriguing question of whether more general valuation conclusions might be inferred from a set of specific empirical investigations on closely related research themes or issues in the area of biodiversity. Meta-analysis has originally

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been developed in the context of the natural sciences (in particular, in medical sciences) as a statistical tool for developing comparative studies and for creating synthetic knowledge from controlled experimentation studies (see Florax et al., 2002 for a detailed review). More recently, meta-analysis has been diffused to the social sciences, including psychology, sociology and economics. This has generated a new stream of quantitative research seeking for a synthesis of scientific results based on statistical applied analysis. The application of conventional statistical methods in meta-analysis is particularly appropriate, when the study focuses on identifying common trends and relationships between the variables under investigation. The methods are also appropriate for the construction of synthetic indicators and the determination of parameters or other common elements that can be described in quantitative terms (more details can inter alia be found in Hedges and Olkin, 1985; Hunter and Schmidt, 1990; Wolf, 1986). In recent years, meta-analysis has proven to be a research instrument of great potential for research synthesis of previously undertaken case studies. The main objective of modern meta-analysis is to synthesize in quantitative terms the empirical results obtained from different studies on a predetermined field or topic of research. Although of great interest and potentiality, this analysis instrument is of considerable methodological complexity. In particular, it is characterized by a high degree of transversality, both horizontal and vertical. Transversality refers to both the heterogeneous nature of the studies and the heterogeneous nature of the different empirical processes and/or the diverse evaluation approaches of policies in the selected studies. Therefore, this analysis instrument requires the identification and the selection of comparable empirical case studies as well as the statistical application of comparative analyses by means of appropriate technical research methods (horizontal transversality). Moreover, it also needs the adoption of a ‘vertical’ perspective in terms of the description of the problem studied, the objective of the study, and the utilization of the results obtained (Matarazzo and Nijkamp, 1997). The intrinsic methodological complexity of meta-analysis becomes particularly obvious when transferring applications from the field of natural sciences to the field of social sciences, in particular in the area of environmental economics. In this field, the cause–effect relations are often characterized by uncertainty, since social phenomena are influenced by individual behaviour (limited rationality, subjective preferences, rigidity in behaviour etc.) and by the adoption of specific multi-faceted policies (Munda et al., 1995). Variations in comparable case studies can thus be attributed to the various dimensions of the studies, time and space factors, the presence of a number of determining factors (i.e., moderator variables), and finally by variation in time in regard to the policy effects to be expected. This is closely linked to the fact that in the social sciences it is only possible to speak of “quasi-experiments” and “quasi-scientifically methods” (Button and Jongma, 1995). For this reason, it is not always possible or methodologically correct to apply standard statistical methods in meta-analysis: the absence of general laws and the difficulty to generate random, independent and equal–dimensional samples render the use of descriptive statistics and classical Bayesian inference

methods difficult. Therefore, the use of specific procedures and of new instruments of analysis becomes increasingly necessary. The goal of this paper is to propose an operational methodological framework to support decision-makers in synthesizing information concerning the economic value of biodiversity conservation studies. This study reviews economic and other values ascribed to biodiversity as well as related evaluation methods. The focus is in particular on the issues associated with a multi-dimensional evaluation of biodiversity, i.e. connections between economic and non-economic valuation. However, a clear economic foundation in assessing the loss of biodiversity is still missing, but is certainly a potentially promising tool for coping with this loss. The case study approach developed here is constructed in order to illustrate some recent issues in metaanalysis. It deals in particular with rough set theory based approaches to decision rules induction for examining different price attributes to biodiversity conservation programmes. A discussion on different approaches of decision rules induction (i.e., conditional predictions) will be presented and extended to a small illustrative example of a set of studies characterized by the use of contingent valuation methods. The main aim is here to find rules that determine whether an object (a study) belongs to a particular subset called a decision class (i.e., a numerical range of monetary values of a given biodiversity conservation plan). These technicalities will be further discussed in Section 2.

3. Meta-analytical methods for comparative biodiversity valuation
3.1. Prefatory remarks

Meta-analysis refers to the use of quantitative methods – mainly statistical – that can be deployed for the comparison or synthesis of outcomes from a set of empirical investigations on a common, or largely similar, issue (Stanley and Jarrell, 1989; Cooper and Hedges, 1994). In contrast, value transfer aims to develop a quantitative framework for the transferability of value (or benefit) estimates for policy decisions. Recently, many efforts are undertaken to carry out comparative studies using a framework offered by meta-analysis and value transfer (for a survey, see van den Bergh et al., 1997). In this context, applied environmental-economic research has in recent years proposed to develop a test on value transfer by conducting two parallel case studies with the aim of deriving non-market environmental values and comparing them with the obtained results (see Bergland et al., 1995; Kirchhoff et al., 1997; Bateman et al., 1995). Besides the case study approach used to obtain the required values for value transfer purposes, statistical methods based on meta-analysis can be used to obtain quasi-estimations of non-market values (see van den Bergh and Button, 1999). Thus, comparative studies seem to cover new ground in economics, in general, and in environmental economics, in particular. Meta-analysis clearly has many advantages in applied quantitative research. It avoids the need to develop a costly and new methodological basis for new case studies, as far as they address similar issues related to past studies. It allows for the statistical identification of major driving factors in causal relationships and can also act as a robustness check on

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existing studies. In the context of value transfer, such research synthesis experiments also offer operational frameworks for making conditional forecasts. In the present section we will offer a slightly more extensive review of meta-analytical methods for research comparison.

3.2.

Statistical techniques

Meta-analytical methods have gained much popularity, not only in the context of research synthesis of previous case study surveys, but also as a tool for comparative case study research and benefit transfer in case of uncertain outcomes in different situations. In recent years, meta-analysis has seen many applications in environmental economics, particularly in studies that have performed monetary valuations of environmental goods — see Table 1 for some examples. A variety of statistical methods has been applied in order to compare outcomes from different methods on a given issue (van den Bergh et al., 1997; Florax et al., 2002). In general, different outcomes from various studies can be explained by differences in the formulation of the research, the size and type of data analyzed, the statistical methods applied, the publication bias, and temporal and geographical characteristics of the studies under consideration. The use of (quantitative and qualitative) multi-criteria techniques is of great importance, when the objective of the synthesis is the qualitative comparison of studies and when the result of studies can be interpreted in terms of compliance with desirable ‘criteria’. The application of these methods is especially important when the studies do not provide one simple value per indicator or when the analysis focuses on a number of indicators. In principle, meta-analytical methods are able to encapsulate these multi-dimensional components.

also may also concern numerical methods for dealing with categorical data. The format of study findings and the objective of synthesis in many areas of the social sciences, including economics, are not exclusively based on an experimental investigation of empirical phenomena (van den Bergh et al., 1997). As a result, over the past decades several new non-statistical methods that synthesize study findings from many areas of the social sciences have been developed. Complementary non-statistical or non-parametric statistical techniques used for synthesis can be classified into main categories, in particular: (1) rough set analysis; (2) fuzzy set analysis; and (3) content analysis. These tools appear to be promising for drawing quantitative inferences, even on the basis of a collection of qualitative study findings (Hogenraad, 1989). We will offer here a concise introduction and discussion of these complementary meta-analytical methods.

3.3.2.

Rough set analysis

3.3. 3.3.1.

Alternative techniques Introduction

Meta-analysis comprises not only the class of quantitativestatistical techniques for synthesizing research outcomes, but Table 1 – Applications of meta-analysis Subject area
Urban pollution valuation Recreation benefits Recreational fishing Water quality Valuation of life estimates Contingent valuation versus revealed preference Wetlands valuation Noise nuisance Travel congestion Visibility improvement Transport issues Multiplier effects of tourism Price elasticity of demand and travel cost Price elasticity of gasoline demand Valuing morbidity Forest ecosystem services Source: Nunes et al. (2004), adapted.

Rough set analysis – developed by Pawlak (1982) – is the most suitable technique for synthesis when the studies are to be grouped and classified according to numerical characteristics that are imprecisely measured. This analysis originates from artificial intelligence and aims to pinpoint data regularities that are not immediately evident; it searches for the possible existence of the principle of causality among data sets and attempts to eliminate irrelevant information. An important feature of this synthesis technique is that it does not necessarily require numerical information about the data being used, provided it is classified in distinct groups. In this way it is able to synthesize a mixture of classified qualitative and quantitative data as well as to combine study findings that are subject to inconsistencies and inaccuracy (Pawlak, 1982; Slowinski and Stefanowski, 1989). In fact, rough set theory also has the advantage that it is able to create a ranking of actions in multi-attribute decision support processes (van den Bergh et al., 1997). Given its features, it is clear that rough set analysis is a readily applicable synthesis technique for decision-making processes that deal with a large input of similar study findings. Several software packages are at present available.

Studies
Smith (1989), Smith and Huang (1993), Smith and Huang (1995), Schwartz (1994), van den Bergh et al. (1997) Smith and Kaoru (1990a), Walsh et al. (1989a,b) Sturtevant et al. (1995) Magnussen (1993) van den Bergh et al. (1997) Carson et al. (1996) Brouwer et al. (1999), Woodward and Wui (2001) Nelson (1980), Button (1995), Schipper (1996) Waters (1993) Smith and Osborne (1996) Button (1995), Button and Kerr (1996) Nijkamp and Baaijens (2001) Smith and Kaoru (1990b) Espey (1996) Johnson et al., (1996) Markandya et al. (2008)

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3.3.3.

Fuzzy set analysis

Fuzzy set analysis is applicable for the analysis of the same category of issues as rough set analysis. However, it is the most suitable technique for synthesis, whenever study findings contain a clear component of linguistic uncertainty in terms of imprecise measurement (see, for example, Kacprzyk, 1978; Munda et al., 1993). As Dubois and Prade (1992) have demonstrated the distinction between fuzzy set analysis and rough set analysis is more subtle than is commonly expected. For example, instead of the discrete classes used in rough set analysis, fuzzy set analysis employs a continuous classification scale. Fuzzy sets refer to linguistically defined variables that do not have an unambiguous measurement scale. Fuzzy variables can be categorized into classes for which the boundaries are weakly demarcated, so that variables can belong to these classes with some degree of membership. A good example of the use of fuzzy set theory in environmental quality valuation can be found in Munda (1995), who also developed the NAIADE software package.

3.3.4.

Content analysis

Content analysis is a method for making inferences by identifying characteristics of text messages in a systematic way in order to convert the text message into distinct classes that can be studied with the use of quantitative methods. Therefore, this synthesis technique is able to consider simultaneously the dynamic context of cultural, social, economic and political factors. Content analysis is able to synthesize all kinds of verbal messages and texts by means of quantitative methods, concluding a mapping of specific types of words in a text into fewer content categories. Besides coding, sophisticated computer software can be used, such as the LISREL econometric package (see Weber, 1983). It is clear that a collection of published study findings from previously undertaken case studies rather than a single text may serve as the basis for content analysis. Many interesting applications can be thought of such as newspapers, Internet sites, and scientific magazines. Even when we limit ourselves to the social sciences, a wide range of application possibilities can be observed. According to Weber (1983, p. 128), in general, “social scientists will be able to use computer-aided content analysis with greater confidence to address a wide variety of theoretical problems involving the relationships among cultural, social, economic and political change”. As Weber (1983) points out, content analysis is not only capable of considering mutual socio-economic relationships but also their dynamic aspects, which are highly useful in economic research. In particular, content analysis can be used to generate a flow of benefits over time and to compute net present values, which can serve as a value relating to a particular scenario of ecosystem change or management. Studies that consider these features of content analysis are, for example, Namenwirth (1969) and Rosengren (1981). These show the existence of a strong relationship over time between political statements and the state of the economy. It is clear that content analysis is also a powerful tool to extract quantitative or coded information from qualitative data input.

3.3.5.

Value transfer

In the context of ecosystem and biodiversity valuation, the general idea is to explore the use of previous and original

valuation studies (‘study site’) and to transfer their estimates' values to the site where the new value estimate is needed (‘policy site’) — see Brouwer et al. (1999); and Navrud and Bergland (2001). Value transfer brings up an important research question: which lessons can be drawn from a comparative analysis of monetary estimates derived from earlier empirical studies for an additional similar case not included in the meta-sample? The solution is to perform essentially a meta-analysis, and to use the estimation results for a prediction of the new or as yet unknown empirical case. In economics, value transfer can be applied across different sites – spatial value transfer – or, for one specific site or valuation object over time — temporal valuation transfer. A major advantage of value transfer for policy guidance is that it ensures more comparability and consistency across different evaluation studies. It may also be helpful in an initial screening of a large number of public projects that cannot be investigated in full detail. Furthermore, the outcome of a previous study may be used as a benchmark against which results of studies can be evaluated. But the most important advantage of benefit transfer is that it is a cost-effective way to make quantitative statements about phenomena that have not been subject to previous analysis (see Johnson and Button, 1997). In empirical research, two value transfer approaches are available: unit value transfer and function value transfer. Both can be based on the principles of meta-analysis. The former transfers mean monetary value estimates, for example, mean willingness-to-pay, directly from the study site to the policy site, with possible income adjustments. The latter, instead of transferring individual willingness-to-pay estimates, explores the use of more information, such as characteristics of the object of valuation and the subject who performs the valuation exercise, and it is able to generate a benefit function that allows ‘prediction’ for the policy site. Examples of these approaches can be found in income elasticity studies, savings rate studies, consumer's surplus studies, accessibility studies, value of time studies and evaluation studies on environmental decay. The implicit assumption is then, that the degree of variation in estimated parameters is sufficiently small to be able to deploy valuations from the original data in a given case study to assess corresponding parameters from other similar cases, usually in different contextual settings. Clearly, the more uniform the set of previous studies, the more likely the validity of the above implicit assumption (see Smith and Kaoru, 1990a; Smith, 1992). In conclusion, next to quantitative-statistical techniques such as standard meta-regression analysis, there is a variety of complementary methods which also offer a great potential in research synthesis in the economic valuation of biodiversity. The application of these research synthesis techniques is, however, anchored to a set of primary valuation studies which, in turn, are exposed to some discussion. The central problem refers to assessing the validity of value measures obtained from any primary economic valuation method since we observe an absence of an unambiguous clear criterion against which to compare those measures. In fact, many environmental goods and services, including biodiversity, are not directly observable and consequently different factors

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may cause differences in biodiversity estimation. In Section 3 we will address this discussion in detail.

perceive things. Something has a value if it contributes to the welfare of someone. Therefore, goods do not have a value per se, but their value is related to people's perceptions.

4. Caveats in the use of the economic approach for the valuation of biodiversity
4.1. Market and non-market approaches

4.2.

A classification of biodiversity benefits

Economic, monetary valuation of biodiversity can proceed in different ways: using market price information and elicit consumer's preferences using a wide range of non-market valuation methods. Monetary indicators of biodiversity values are based on market price valuation mechanisms such as the value of the financial revenues from tourism activities related to visits to natural areas and the value of contracts signed by firms and governmental agencies, also known in the literature as bioprospecting contracts. Such contracts are characterized by the search among the genetic codes contained in living organisms, for the development of chemical compounds of commercial value, i.e. market priced value (Simpson et al., 1996). This is dominated by pharmaceutical research since most prescribed drugs are derived, or patented after natural sources. In this context, the marginal value of such an input, often translated in terms of genetic information for medicinal purposes, is measured by its contribution to the improvement of health care. Recent registrations and applications of bioprospecting contracts and agreements between states and pharmaceutical industries represent important benchmarks of monetary indicators for these types of biodiversity values. The most noted of these agreements is the pioneering venture between Merck and Co., the world's largest pharmaceutical firm, and “Instituto National de Biodiversidad” (INBio) in Costa Rica. At the moment of the signature of the contract, in 1991, Merck paid Costa Rica about 1 million dollars and agreed to pay royalties whenever a new commercial product was explored. Since then, INBio has signed contracts on the supply of genetic resources with Bristol-Myers Squibb, other companies and non-profit organizations (ten Kate and Laird, 1999; INBio, 2001). Another illustration of the market value of genetic diversity refers to the commercial agreement signed in 1997 between Diversa, a San Diego based biotechnology, and the US National Park Service. Diversa paid $175,000 for the right to conduct research on heat-resistant microorganisms found in hot springs in Yellowstone National Park (Sonner, 1998; Macilwain, 1998). More recently, a Brazilian company, Extracta, signed a $3.2 million agreement with Glaxo Wellcome, the world's second largest pharmaceutical company, to screen 30,000 samples of compounds of plant, fungus and bacterial origin from several regions in the country (Bonalume and Dickson, 1999). Despite the fact that these agreements show a positive economic value of genetic diversity, concern remains with respect to the fairness of such deals. Indeed, some environmental groups have been very critical, claiming that these are unequivocally “biopiracy” actions (see RAFI, 2001). In the absence of market prices for biodiversity values – which is commonly the case – different methods have been developed to derive consumers' preferences — these will be discussed in detail in Section 4. However, value is a cultural and psychological concept, and is related to the question how human beings

Independently of the valuation approach engaged, value estimation is concerned with a change in people's welfare, which stems from a change in the provision or enjoyment of the good. This change may relate to the quantity of the good or its quality, or to whichever characteristic of it (or even of a related good). These changes may be marginal or discrete. Prices reflect marginal changes; many valuation methods are used to account for discrete changes. Values may thus be expressed in marginal or discrete terms. Changes may of course be positive or negative. When they refer to values, they could reflect an increase or a decrease in welfare. Both can be accounted for. Generally, it is expected that a loss of biological diversity results in a decrease in welfare, and therefore in a negative value expressed in monetary terms. Similar patterns can also be observed for landscapes, although it is not as straightforward, and it may vary more from person to person. Once the physical change is identified, the value should reflect the welfare change related to the individuals whose welfare has been affected by it, or the average welfare change of the individuals of the population (Riera, 2001). It should be noted that biological diversity – and in particular the preservation of threatened species – can affect the welfare of many people, even living far away from the site concerned. In other words, people may derive satisfaction out of knowing that there is an improvement in biodiversity for present and future generations, even if they would not benefit from it directly. Therefore, the relevant population is often not local, but global. These welfare gains are usually known as passive or non-use values. The consideration of both use and non-use values introduces the notion of total economic value. In the literature, there is a wide range of systematization of the total economic value of the environment.1 They differ both in terms of the services included and in the terminological definition. The total economic value (TEV) of a species or habitat is constituted by a combination of the use value and non-use value: - The use value is a value related to the present or future use of a particular habitat by individuals. It can be subdivided into direct use values and indirect use values. Direct use values are derived from the actual use of a resource either in a consumptive way or a non-consumptive way (e.g., timber in forest, recreation, fishing); indirect use values refer to the benefits derived from ecosystem functions (e.g., watershed protection or carbon sequestration by forests); - The non-use values are associated with the benefits derived simply from the knowledge that a natural resource – such
1 A frequent question concerns how TEV is related to the notion of intrinsic value. Intrinsic value is often regarded as a value that resides in the assets in question, especially in the environmental assets, but that is independent from human preferences. Since by definition TEV relates to preferences of individual human beings, it cannot encompass an intrinsic value (Pearce and Moran, 1994).

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as species or habitat – is maintained. By definition, such a value is not associated with the use of the resource or the tangible benefits deriving from its use. It can be subdivided into two parts that overlap in relation to its definition. First, there are existence values which are not connected to the real or potential use of the good, but reflect a value that is inherent in the fact that it will continue to exist independently from any possible present or future use of individuals. Secondly, bequest values are associated with the benefits of the individuals derived from the awareness that future generations may benefit from the use of the resource. These can be altruistic values, when e.g. the resource in question should in principle be available to other individuals in the current generation. A separate category is made up by option values, i.e. values attributed by individuals from the knowledge that a resource will be available for future use. Thus it can be considered like an assurance that a resource will be able to supply benefits in a possible future, but not for present use. The quasi-option value, which is sometimes classified as a non-use value, represents the value derived from the preservation of the future potential use of the resource, given some expectation of increase of knowledge. The quasi-option value is important when the decisions on consumption are characterized by a high reversibility. However, any economic valuation of biodiversity according to this definition is based on an instrumental perspective on the value of biodiversity. This means that the value of biodiversity is regarded as the result of the integration between humans and the object of valuation, which is the change in biodiversity. Economic valuation in biodiversity changes is based on a reductionist approach of the TEV; it is regarded as a result of the aggregation between various use and non-use values, reflecting a variety of human motivations, as well as the aggregation of local values to attain a global value, i.e. a bottom-up approach (Nunes and van den Bergh, 2001).

economic relevance of non-market ecological functions. Farnworth et al. (1981) identified a value category, inherent value, which was defined as “values that support other values” in ecological systems. It includes natural processes of selection and evolution and life support functions of ecosystems in an all encompassing perspective. Another category of value is the contributory value which focuses on the fact that species can only survive in interactive relationships and therefore each species contributes to the survival of other species (Norton, 1986). Wood (1977) illustrates the importance of the contributory value in the example of the productive use of the wild species for the preservation of the resistance of cultivated plants. Because of their limited genetic diversity, cultivated plants can only perform minor adaptations to changes in environmental conditions. Wild species on the other hand possess higher adaptability to environmental conditions because of their higher genetic diversity. Thus, the wild species forms the basis for preserving or improving the resistance of cultivated plants against disease or pest, by cross-breeding with wild species. Finally, besides ecological values, psychological values are gaining attention in the field of environmental psychology (Boerwinkel, 1992; Nunes 2002; Nunes and Onofri, 2004). In this field the concept of value is interpreted as a reason why people feel that certain things are important (de Boer and Hisschemöller, 1998). Examples of such values are justice, safety, and beauty. While ecological values are meant to determine the well functioning of a system, the psychological values are used to determine the perceived quality or the perception of nature. As such, the differences between ecological values and the psychological values pertain to what is being valued: the quality of the system versus how the system is perceived. However, empirical findings show that the psychological value has a strong positive relation with the total economic value, because they both measure social preferences.

5.
5.1.

Alternative economic valuation methods
Revealed preferences techniques

4.3.

A critical evaluation

This classification of the TEV is not always used in straightforward way and free of significant criticism. While its success is based on several legal cases (e.g., the Exxon Valdez Alaskan oil spill disaster), this model has its criticism among those who raise the question about its ability to capture actual values of natural resources. Nevertheless, an understanding of the wide range of values attributed to biodiversity forms a basis for keeping policy-makers informed on their choices on its conservation and sustainable use (Spash, 2001). According to Brown and Moran (1994), the economic valuation of biodiversity is required for the purpose of placing a “common concern” in the context of the management of natural resources. Thus, the above classification is often complemented with other categories of values which consider the ecological–functional importance of biodiversity in natural systems (see for a review Fromm, 2000). Early thoughts on these complementary relationships in the literature can be found in an analysis carried out by Farnworth et al. (1981) aimed at distinguishing the manifold values of the ecosystems and to point out the

Revealed preferences techniques seek to elicit preferences from actual, observed market-based information. Preferences for environmental goods are usually revealed indirectly, when an individual purchases a market good to which the environmental good is related in some way. They are all indirect, because they do not rely on people's direct answers to questions on how much they may be willing to pay (or accept) for an environmental quality change. Thus the emphases of these techniques are mainly on their contribution to valuing biological resources. The values obtained could be considered sufficient for cost-benefit purposes, but they will rarely reflect biological health. As such these techniques provide only a lower bound estimate of the value of a particular biological resource. The techniques included in this group are in particular the travel cost method, or hedonic price and wage techniques on adverting behaviour. In most cases they only capture use values, leaving non-use values out of consideration. This is not the case, though, with simulated market methods.

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Fig. 1 – Methodologies for economic valuation of biodiversity.

The common underlying feature is a relationship between a market good and the environmental commodity. For example, when using the travel cost method, researchers estimate the economic value of recreational sites by looking at the generalized travel costs of visiting these sites (Bockstael et al., 1991). Conversely, practitioners of the hedonic price method estimate the economic value of an environmental commodity, say, clean air, by studying the relation between house prices and air quality (Palmquist, 1991). The averting behaviour or production cost function methods is characterized by exploring the relationship of the environmental commodity through a generalized cost function (Cropper and Freeman, 1991). For instance, improvement of air quality can be assessed on the basis of expenditures made to avert or mitigate the adverse effects of air pollution. Avoided cost damage costs, preventive expenditures, repair costs (or restoration), compensation costs, replacement costs, and relocation costs are specific instances of this method. Finally, the production factor method estimates the economic value of an environmental commodity through the input–output relationship of such a commodity in a production function. For example, the economic value of a cleaner soil is related to the value of the increased agricultural output through a dose response method (Nunes et al., 2004).

5.2.

Stated preferences techniques

method is currently one of the most often used techniques for the valuation of environmental goods. This is partly due to CV features that constitute important advantages over revealed preference methods. First, the CV method is the only valuation technique that is capable of shedding light on the monetary valuation of the non-use values, which typically leave no ‘behavioural market trace’. Ignoring such values will lead to a systematic bias in the estimation – essentially an underestimation – of the total benefits of biodiversity. Second, CV allows environmental changes to be valued even if they have not yet occurred (i.e., ex ante valuation). Therefore, CV offers a greater potential scope and flexibility than revealed preference methods. It allows the specification of hypothetical policy scenarios or states of nature that lie outside the current or past institutional arrangements or levels of provision. Third, CV allows to enrich the information base by submitting the process of value formation to public discussion, and hence it is recognized as “an effective tool for policy decisionmaking” (Sen, 1995). Fig. 1 and Table 2 provide a summary of monetary valuation techniques and their suitability to measure different components of the TEV of biodiversity. Not all valuation methods are usually suitable to measure non-use values. For instance, if a market would exist, it may be that it fails to capture non-use values. Sometimes, the ability to capture passive use values could be a decisive criterion for choosing for a specific method selection process.

Stated preferences (SP) techniques are based on the simulation of the market, and thus on ‘prices observed’ for the good to be valued. Results are achieved through a questionnaire to be filled out by the population, or a sample of it. In simulated market conditions, the supply side is represented by the interviewer, who typically offers to provide a given amount of units of the good at a given price. The respondent, who either accepts or rejects the offer, represents the demand side. One of the most crucial issues in this kind of method is to be precise in the description of the market, and yet simple and clear enough for people to understand it. This is important, because biological and landscape diversity are among the goods for which it is difficult to simulate a clear, credible, precise and understandable market in a poll process. They are based on collecting data by means of questionnaires. The best known method is the contingent valuation methodology (Mitchell and Carson, 1989). Indeed, the contingent valuation (CV)

5.3.

Application of the valuation techniques to biodiversity

Extending these procedures to biological biodiversity is complex. Indeed, the evaluation for biodiversity is perhaps the most challenging issue in the context of economic valuation. It is worth noting that all the techniques briefly described above have been applied to biodiversity valuation. Each one of them has some advantages over the overs, but also some disadvantages. The use of one or another depends mainly on the purpose of the valuation exercise and the availability of data and resources. So far, the most popular valuation methodology has been the family of CVM and SP methods. The reasons for the momentum of SP methods are diverse. It has a format that respondents tend to find comfortable, thus reducing the proportion of no-answers and protest-answers. SP methods

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Table 2 – Review of economic methods for biodiversity conservation Method
Travel cost method Use of real market data

Pros

Cons

Can estimate use values only May have substantial data requirements Requires estimates of value of travel/leisure time Cannot predict the changes in use values due to environmental changes without prior information Random Estimates recreational use value of (i) changing Can estimate use values only utility model environmental quality of site attributes and (ii) site in May have substantial data requirements total Requires estimates of value of travel/leisure time Problems arise with multi-purpose trips Cannot predict the changes in use values due to environmental changes without prior information Can be hard to handle participation decisions (i.e. whether to make the visit or not) Hedonic Use of real market data Can estimate use values only pricing Requires extensive house market data method Cannot predict the changes in use values due to environmental changes without prior information Current evidence suggests it is not suitable for use in benefits transfer Avertive Modest data requirements Can estimate use values only problems arise when (i) individuals make expenditure multiple averting expenditures, (ii) there are secondary benefits of an method averting expenditure and (iii) averting behaviour is not a continuous decision but a discrete one (e.g., double glazing is either purchased or not) Use of real market data Cannot predict the changes in use values due to environmental changes without prior information Contingent Can estimate both use and non-use values Relatively expensive valuation Suitable for valuing environmental changes Complex and multi-dimensional scenarios may be too much of a irrespective of whether or not they have a precedence cognitive burden for respondents Completed surveys give full profile of target The concept of diversity may similarly be difficult to put across to the population respondents Choice Can estimate both use and non-use values Not yet as widely tested as CV modelling Suitable for valuing environmental changes Some techniques are not based on economic theory irrespective of whether or not they have a precedence Completed surveys give full profile of target The concept of ‘diversity’ may be difficult to put across to the population respondents

can cope with valuing different attributes of a forest, like biological and landscape diversity aspects, in an integrated manner, therefore being more informative. SP methods tend to cope better with the so-called embedding problem (valuation being rather insensitive to the scale of the physical change) in as far as the respondent gets a richer perspective of the scale of the changes proposed. Both CVM and SP ‘design’ exactly the market so as to value the good of interest, whereas with other methods it is often difficult to isolate the value of the good from other closely related goods. On the other hand, expressing biodiversity changes in simple, accurate and understandable terms in a questionnaire can prove to be a challenging task. Table 2 shows that certain valuation methods are more appropriate than others to address certain types of biodiversity value. For example, revealed preference methods can only be used for a limited number of biodiversity value categories, as they do not allow for a monetary assessment of non-use values. On the contrary, the contingent valuation method is in principle applicable to a multiplicity of biodiversity value categories. However, one needs to recognize that this method will fail for those biodiversity value categories that the general public is not informed about nor has experience with. In this respect a questionnaire should be designed that is compre-

hensively enough in order to convey detailed information on changes in ecosystem life support functions and processes as related to biodiversity changes so that the latter are not regarded by respondents as too cumbersome. Such information is crucial to obtain a practical, reliable and effective questionnaire. There tends to be an inverse relationship between familiarity with the good and the ability of the respondents to answer meaningfully. The biodiversity-related goods tend to be very unfamiliar for a market situation. Therefore, the use of CVM and SP requires state-of-the-art practice to overcome this and other potential related problems. In general, the more specific the change in biodiversity is, the more reliable are the values obtained by all methods. This is especially the case with CVM and SP methods. Other techniques tend to be more suitable for ex post valuation, since they rely on existing markets, whereas CVM and SP tend to be more adequate for valuing changes ex ante. They can also be used in ex post valuation, but there might be a lack of incentives to answer (see for details Nunes et al., 2004). In summary, even though estimating the economic values of changes in biological and landscape diversity of forests is not a straightforward task, the tools developed by environmental economics makes it possible and, overall, fairly

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Table 3 – List of the valuation case studies Biodiversity and habitat values Type of good Country
Biodiversity UK UK Norway Germany UK UK Wildlife UK UK UK UK UK UK UK UK UK National parks and nature reserves Hungary UK UK UK Watercourses Norway UK UK UK UK UK UK UK UK UK UK Landscape Austria Sweden UK Netherlands Netherlands Endangered Species Norway Norway Sweden Sweden UK Sweden Wetlands UK UK Austria UK UK UK Woodlands UK UK Netherlands Sweden Sweden Sweden Sweden

Range of the monetary value estimate

Method

Year of study

8.01–15.42 once-off payment/person 46.99–62.26/household/year NOK 194 annual payment/year DM 16.1/month 75/household 308/household 13.83–61.74/household/year 0.98–3.12/visitor/year 13–65 ha/year 25 ha/year/person 0.6–1.7 visit/person 1.02–2.3 visit/person (full travel cost) 1.18–2.53/adult 1.99–2.60/person 16.8 once-off payment/person 6/visitor 24/household 4.54/person 0.82/person $50–$100/person/year 0.75–0.95/adult visitor 0.36/visitor 13.90–16.20/household/year 13.59–5.56/person/year 546–582 once-off payment/household 12.08/person/year 3–5% increase in property sale price 9.2–11.2/person/visit 4.9% increase in property sale price 0.51–3/visit ATS 9.2/visitor/day SEK 750/person/year 49–55/household/year NLG 55/household/year NLG 80/household/year NOK 1700–NOK 2750/person/year $15/person/year $7/person/year SEK 85/person/year 2.94/year SEK 406/person 67/household/year 75/household/year ATS 329.25/Austrian/year 21.75/household/year 76.74/visitor/year 83.67/visitor/year 9.94/household/year 18.5–20.7/household/year NLG 22.83/household/month SEK 95/person/year SEK 1014/household/year $3–$4/person/year $5–$8/person/year

CV–OE CV–OE CV–DC CV CV–OE CV–DC CV–PC CV–OE TCM CV–OE TCM TCM CV ZTCM CV CV–PC CV CV CV CV CV CV CV–OE CV CV–OE CV HPM ITCM HPM TCM CV–OE CV–PC CV–DC CV–PC CV–OE CV CV CV CV CV CV–DC CV–OE CV–IB CV–OE CV–OE CV–OE CV–IB CV–OE CR – – CV–OE CV–OE CV

1993 1993 1991 1990⁎ 1994 1994 1994 1986 1986 1985 1985 1986 1988 1988 1990⁎ 1996 1990 1985 1986 1990 1991 1989 1988 1987 1987 1987 1989 1988 1990⁎ 1989⁎ 1991 1991 1994 1993 1994 1991 1990 1990 1991 1993⁎ 1993 1991 1991 1993 1991 1991 1991 1991 1995 1987 1991 1988 1988 1990

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Table 3 (continued) Biodiversity and habitat values Type of good Country
Woodlands Norway UK UK UK UK UK UK UK UK UK UK UK UK UK UK UK UK UK UK UK UK

Range of the monetary value estimate

Method

Year of study

$13–$18/person/year 1.21–7.09/person/year 9.73/visitor/year 0.53/visitor 0.33/visitor 1.3–3.3/visit 1.25/visit 15.13/visitor/year 1/visitor 14.6–24.5/visitor/year 7.1% increase in property sale price 43 increase in property sale price 0.06–0.96/visitor 0.43–0.72/person 1.95/visitor 0.53/visitor 12.55/year 3.51/year 1.82–2.78/visit 20.6–30.59/person 0.33–0.93/visitor/visit

CV CV–OE CV CV CV ZTCM CV–PC TCM CV TCM HPM HPM TCM CV TCM CV–OE CV CV CV–OE CV CV–OE

1990 1991 1990 1988 1987 1988⁎ 1987 1988⁎ 1986⁎ 1986⁎ 1990 1988 1988 1988 1988 1988 1994⁎ 1994⁎ 1990 1990⁎ 1991

Source: RIVM (2000). Note: CV = Contingent valuation; CR = contingent ranking; OE = open ended; PC = payment card; DC = dichotomous choice; TCM = travel cost method; ZTCM = zone travel cost method; – = valuation method unspecified, and ⁎ = base year unspecified and assumed to be the year before presentation of the paper.

reliable. Provided, of course, that the methods are applied according to the state-of-the-art knowledge.

6.
6.1.

Biodiversity values in plural
Valuation data set

• the country or region concerned; • the evaluation method deployed (e.g., contingent valuation, hedonic prize); • the monetary value of the asset (or a range), including measurement units and scales used. The monetary valuation figures have been presented as standardized willingness-to-pay figures in the study by ten Brink et al. (2000), and these will be used by us — see Table 4 for a summary. This database contains both numerical information (such as willingness-to-pay) and alpha-numerical, linguistic and categorical information (e.g., country, year of study, etc.). This makes the application of standard statistical tools rather problematic. Nevertheless, even a linguistic information base may incorporate a hidden structure in terms of associations between patterns, or the frequency of occurrence of a given phenomenon (or qualitative characteristic or parameter). In this context, we may resort to qualitative pattern recognition methods, elucidated inter alia in the artificial intelligence literature. There is a wide variety of such methods, such as computational neural networks, genetic algorithms, fuzzy and rough set methods, decision tree induction methods, etc. An interesting recently developed algorithm in this framework is the a priori algorithm, which is able to identify association rules among qualitative data (Agrawal et al., 1996). This metaanalytical method is used by us and will be concisely described in Subsection 5.2. Subsection 5.3 will then present the quantitative results of our comparative study.

Comparative analysis, such as meta-analysis, serves to provide a framework for quantitative research synthesis. In particular, it may also serve as a quantitative framework for the comparative study of previously undertaken studies that have generated different values. As an illustration of the potential of this approach, a meta-analysis study will be here presented based on a data set originating from the Dutch National Institute of Public Health and the Environment (RIVM) (ten Brink et al., 2000) – Table 3 presents a full list of the case studies. This data set contains a set of 75 distinct empirical case studies on the valuation of different aspects of biodiversity and different kinds of habitat, ranging from wildlife and endangered species preservation to the protection of national parks and nature areas (ten Brink et al., 2000) provide a full list of the case studies and details. The database includes the following items: • type of environmental asset (e.g., biodiversity, wildlife, landscape, wetlands, etc.); • study characteristics;

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Table 4 – Mean willingness-to-pay for biodiversity and habitat services and values Type of good study
Biodiversity preservation Wildlife preservation National parks and nature reserves Wetlands Watercourses Landscape Endangered species protection Woodlands

WTP/person/year (in €, 2006)
28.66 1.8 8.7 35.0 27.2 57.5 120.9 18.8 UK, UK UK, UK, UK, UK, UK, UK,

Countries of the study
Norway, Germany Hungary Austria Norway Netherlands, Austria, Sweden Sweden, Norway Netherlands, Sweden, Norway

6.2.

Assessment of values ‘by association’

Nowadays, various methods for data set characterization are rather well developed. In particular, association rules provide a competitive advantage by using repeated interactions among factors within the data, the a priori algorithm, first introduced by Agrawal et al. (1993), is one of the most popular methods. It identifies combinations of attribute values or items that occur with a higher frequency than might be expected, if the values or items where considered independent of one another. In such a context, a relation R contains ntuples over a set of Boolean attributes A1, A2,… An. Let I = {Ai1, Ai2,… Ain} and J = {Aj1, Aj2,… Ajn} be two sets of attributes. Then I ⇒ J is an association rule, if the following two conditions are satisfied: the support for the set I ∪ J appears in at least an sfraction of the tuples; and a confidence amongst the tuples shows up if at least a c-fraction also has J appearing in them. To some extent, the relative popularity of this method can be attributed to the simplicity of the problem statement and its wide applicability in identifying hidden patterns in a data set. However, its success has also much to do with the availability of an efficient algorithm (a priori). The analysis aims to identify all valid association rules within the database for the valuation of different biodiversity indicators and different kinds of habitats. The first step in the a priori analysis is to split the database into two distinct sets of

variables: viz. on the basis of valuation method and the good under valuation. The second set is characterized by the price variable. In addition, we use the information about the number of studies that support an association rule to be identified and its relative share in terms of frequency, which incorporates the weight of the association rule on the database (see Table 5). This table offers also the relevant information concerning the strength of the association rule within the database used in our comparative analysis.

6.3.

Empirical results

The use of the a priori algorithm leads to interesting empirical findings. Table 5 shows that that there are 18 possible association rules, which correspond to the most frequently appearing regularities in the database. One particularly interesting association rule that emerges from the database links (2) the good under valuation to (3) the price, which is interpreted in terms of WTP. For example, if the good under consideration is ‘woodland habitat’, the corresponding association rule (see first row) shows that the woodland monetary valuation falls between € 9.4 and € 20.1. This price range is supported by 30 cases, which corresponds to 38% of all case studies in the database. The empirical magnitude of this association rule appears to be very high: it corresponds to a confidence level of 100% (see the last column). In other words,

Table 5 – Empirical findings for alternative association rules Association rule Good, method (LHS) if
(1) Good = woodlands (2) Good = watercourses (3) Good = wildlife (4) Good = endangered species (5) Good = wetlands (6) Good = biodiversity (7) Good = landscape (8) Good = national parks and nature reserves (9) Method = CV (10) Method = CV–OE (11) Method = CV–OE (12) Method = TCM (13) Good = woodlands and method = CV (14) Good = woodlands and method = TCM (15) Good = woodlands and method = CV–OE (16) Good = wetlands and method = CV–OE (17) Good = watercourses and method = CV (18) Good = endangered and method = CV

Price in € (RHS) then
9.4 b price ≤ 20.1 20.1 b price ≤ 31.1 Price ≤ 9.4 Price N 38.0 31.1 b price ≤ 38.0 20.1 b price ≤ 31.1 Price N 38.0 Price ≤ 9.4 9.4 b price ≤ 20.1 31.1 b price ≤ 38.0 20.1 b price ≤ 31.1 9.4 b price ≤ 20.1 9.4 b price ≤ 20.1 9.4 b price ≤ 20.1 9.4 b price ≤ 20.1 31.1 b price ≤ 38.0 20.1 b price ≤ 31.1 Price N 38.0

Share of studies (%)
38.0 13.9 11.4 8.9 7.6 7.6 6.3 5.1 16.5 5.1 6.3 5.1 16.5 5.1 8.9 5.1 6.3 5.1

Number of studies
30 11 9 7 6 6 5 4 13 4 5 4 13 4 7 4 5 4

Confidence level (%)
100.0 91.7 100.0 100.0 100.0 100.0 100.0 100.0 48.1 20.0 25.0 57.1 100.0 100.0 100.0 100.0 83.3 100.0

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229

all the ‘woodland habitat’ valuation cases support this association rule. If the good under consideration is ‘biodiversity’, this price-good association rule (see row 6) shows us that the respective price ranges between € 20.1 and € 31.1, which is true for all biodiversity case studies presented in the database. However, the empirical significance of this rule is rather low, since it is only valid for 7.6% of the case studies in the database, i.e. only 7.6% of the sample of case studies was concerned with biodiversity. Another association rule that emerges from the database links (a) the method of valuation to (c) the price. This rule shows (see row 9) that if the valuation method under consideration is contingent valuation the respective monetary estimates range between € 9.4 and € 20.1. This is true for 13 of all the 27 case studies that used CV as the selected valuation method. Therefore, the empirical magnitude of this association rule corresponds to a confidence level with a strength of 48.1%. In other words, about half of the CV valuation cases support this price association rule, which corresponds to 16.5% of the database. A similar price association rule can be found for the TCM, i.e., the respective price estimates range between € 9.4 and € 20.1 (see row 12). However, despite the fact that this price rule appears to have stronger empirical support because it corresponds to a higher confidence level with a strength of 57%, this association rule is valid for only 5.1% of the case studies registered in the database for our analysis. Clearly, the present database is somewhat too small for the identification of robust compound rules. It seems that the relatively aggregate and standardized valuation figures are too imprecise to support firm conclusions and are thus rather poor to be used for policy guidance. Nevertheless, the technical method deployed there indicates clearly that there is no more reliable quantitative information to be extracted from the present database. In general, the above described multi-dimensional classification methods are in principle able to identify relevant causal linkages and structures in a qualitative database.

assessment of an environmental system, but rather system changes. The goal is then to assess the human welfare significance of biodiversity change under consideration, through the determination of the changes in provision of biodiversity-related goods and services and consequent impacts on the well-being of humans who enjoy both use or non-use benefits from such a provision. Different instruments are available to assess the economic value of biodiversity. The choice is not always evident. Survey valuation studies have often been used, because the use of revealed preference methods leaves out important biodiversity value types, notably non-use values. Alternatively, researchers can combine valuation techniques. Special attention however, should then be given to value aggregation across the resulting values so as to avoid double counting. Meta-analysis appears to be a fruitful instrument in this context. There is a clear need to obtain information about the cause, type, and persistence of stress on biodiversity and the estimation of the respective impacts on human welfare. The combination or integration of the ecological and economic characteristics to assess and value biodiversity leads to an integrated framework. Interdisciplinary work is thus required, involving both economists and ecologists transferring elements or even theories and models from one discipline to another and transforming them for their specific, mutually consistent purpose. In other words, the underlying objective is the development of a common way of thinking about modelling and valuing biodiversity.

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