internet-based valuation and group valuation...

Internet-based Valuation and Group ValuationMethodologiesSENSOR Project Deliverable Report 3.3.1

SENSOR REPORT SERIES 2008/06

SENSOR Sustainable Impact Assessment: Tools for Environmental, Social and Economic Effects of Multifunctional Land Use in European Regions www.ip-sensor.eu

Title Internet-based valuation and group valuation methodologies

Authors Romano D (University of Florence), de Groot D (WUR), Grafakos S, Hein L,Nocella G, Tassone V

Date August 2008

Category Project Deliverable Report

Deliverable title D 3.3.1 Methodologies for Internet-based Valuation and Group Valuation

Submission date August 2008

SENSOR Project The Integrated EU project SENSOR aims to develop ex-ante SustainabilityAssessment Tools (SIAT) to support policy making regarding multifunctionalland use in European regions. Land use represents a key human activitywhich drives socio-economic development in rural regions and manipulatesstructures and processes in the environment. At the European level, policiesrelated to land use intend to support the efficient use of natural resources andto improve socio-economic developments. The project is financed by the EU6th Framework Programme. Project duration is four years, starting inDecember 2004. The project is carried out by a consortium of research insti-tutes, led by the Leibniz-Centre for Agricultural Landscape Research (ZALF).

This document reviews different valuation techniques for the assessment ofstakeholders targets and preferences, with specific reference to (a) internet-based valuation and (b) group valuation methodologies.

Keywords Sustainability Impact Assessment Tools, internet valuation, group valuation,valuation methodologies, Land Use Functions, internet surveys

Correct Reference Romano D et al. (2008) Internet -based valuation and group valuation methodologies. In: Helming K, Wiggering H (eds.): SENSOR Report Series 2008/6, www.sensor-ip.eu, ZALF, Germany

Prepared under contract from the European CommissionContract no 003874 (GOCE)

EU FP6 Integrated Project Priority Area 1.1.6.3 "Global Change and Ecosystems"December 2004 - December 2008

This publication has been funded under the EU 6th Framework Programme for Research, TechnologicalDevelopment and Demonstration, Priority 1.1.6.3. Global Change and Ecosystems (European Commission, DGResearch, contract 003874 (GOCE)). Its content does not represent the official position of the European Commission and is entirely under the responsibility of the authors.The information in this document is provided as is and no guarantee or warranty is given that the information isfit for any particular purpose. The user thereof uses the information at its sole risk and liability."

SENSOR Report Series 2008/06

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1 Introduction 62 Economic Valuation vs. Participatory Approaches:

A Critical Assessment 81.1 LUF Valuation within the SENSOR Project 82.2 Economic Valuation Approaches 9

2.2.1 Market Methods 112.2.2 Revealed-Preference Techniques 122.2.3 Stated-Preference Methods 172.2.4 Benefit Transfer 222.2.5 Some Concluding Remarks on Economic Valuation Methods 23

2.3 Participation (or Deliberative) Approaches 242.3.2 Focus Groups and In-depth Groups 26

2.4 Which Technique for the LUF Valuation in the SENSOR Project? 28

3 How to Implement Stakeholder Analysis: Group Valuation 303.1 Who is a Stakeholder? 303.2 What Group Valuation Is About? 31

3.2.1 Multi-attribute Utility Analysis as Basis of Group Valuation 323.2.2 Operationalising Multi-attribute Utility Analysis 33

3.3 The Implementation of Group Valuation: The Malta Case Study 363.3.1 Preparation Phase 373.3.2 Implementation Phase 373.3.3 Evaluation Phase 39

4 How to Implement a Policy Impact Valuation: Internet-based Choice Experiment 404.1 Why an Internet-based Survey? 404.2 The implementation of an Internet-based Policy Assessment:

bio-energy in the EU 424.2.1 Survey Design Issues 424.2.2 Questionnaire Design Issues 444.2.3 Data Elaboration Issues 46

5 Conclusions 476 References 49Annex 1 Land Use Functions Definition 54Annex 2 Total Economic Value 55Annex 3 The Swing Method 57Annex 4 Internet-based Questionnaire 58

FiguresFig 1 Stated-preference Valuation Methods 10Fig 2 Example of results for a hypothetical Scenario 18Fig 3 IT Necessary for Developing the Sensor Internet Survey 21

TablesTable 1 Main Economic Valuation Techniques 9Table 2 Characteristics of Stated Preference Approaches 16Table 3 Main Participatory (or Deliberative) Valuation Techniques 17Table 4 Adoption of Internet in World Region Areas, 2005 19Table 5 Sample Size for the Sensor Internet-based Survey 22Table 6 List of Possible Variables and Levels for the Bio-energy Policy Case 24Table A.1 Key Land Use Functions 31Table A.2 Total Economic Value (TEV) Components 33

Table of Contents


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Internet-based valuation and group valuation methods

Donato Romano / UNIFI ([email protected])Dolf de Groot / WUR ([email protected])

Executive SummaryThe general objective of this report is the critical review of different valuation techniques for the assessment of stakeholder's targets and preferences. In pursuance of this objective, thisdeliverable has addressed the following three research questions: on the basis of the evidence reported in the literature, is there any technique which is

suited for the analysis of stakeholder targets and preferences with specific reference toland use functions?

if so, how such a technique can be implemented in the specific context of the SENSORproject?

considering the importance given within the SENSOR project to the ICT, what are thepotential for using the internet as a medium for carrying out the stakeholder analysisand/or any other valuation which is relevant to validate SIAT result?

Section 2 reports a critical assessment of existing valuation methodologies, summarising thepros and cons of each technique belonging either to the so-called 'economic valuationapproaches' (i.e. the ones aiming at a monetary valuation of policy induced impacts – non-mar-ket goods and services – as developed in the environ-mental economics literature) or to the so-called 'participatory (or deliberative) approaches' (i.e. the ones that tend to explore how opini-ons are formed or how individuals' preferences are expressed in units other than money).

It is shown that in principle, both economic valuation and participatory approaches can be usedfor LUF assessment within the SENSOR project. However, the choice is not a case of eithereconomic or non-economic valuation methods but of using a combination of both, as requiredby the context of the decision. In other words, the two groups of methods are not alternatives,rather complements. In fact, LUF valuation intervenes at different levels within the SENSORproject framework: at an earlier stage the 'stakeholder analysis', providing some insights to theSIAT about the sustainability space with specific reference to LUF change in a given regionalcontext; at a later stage the 'policy impact assessment', when alternative policy options havebeen set by the policy maker and the relevant impacts have been forecast through SIAT simu-lations.

If we keep in mind the differences between these two valuation exercises we are able to con-clude that participatory approaches are better suited to carry out what in the SENSOR projectjargon is properly called 'stakeholder analysis' aiming at identifying stakeholders' targets,understanding how do they form their own views and preferences, and assessing stakeholders'acceptability of the implied LUF change (i.e. exploring the sustainability space comparing theimplied changes and the sustainability limits as well as analysing the trade-offs between theimplied changes).

Vice versa, in the overall assessment of a given policy impacts – a 'policy case' assessment,according to the SENSOR project jargon – people’s preferences towards policy-induced econo-mic, social and environmental changes are assumed as pre-existing constructs and the focusis on selecting the most preferred policy option or on ranking the various policy options at handaccording to a given criterion. This is where economic valuation methods work better. In parti-cular, given the inherent complexity and multidimensionality of the implied policy changes thebest suited approaches are choice modeling techniques.

Section 3 shows that in order to stimulate communication and an exchange of views, opinionsand values among stakeholders it is necessary to bring them together and let them participatein policy choices. Participatory processes create a social and political space – 'forums' forexchange that are organised for the purposes of facilitating communication between stake-


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holders such as government, citizens, stakeholders/interest groups, and businesses regardinga specific decision problem.

A suitable participatory approach is the so-called 'group valuation', which is able to pursuethree specific objectives in the stakeholder analysis: assessing stakeholders' targets, views and preferences about LUF changes, validating previous work on regional sustainability problems done within the SENSOR

project, and attempting a monetary estimation of LUF changes.

This is why this approach will be used in the specific application of stakeholder analysis withinmodule 3.3.4 of the SENSOR project. Section 3 provides the background discussion on how todesign a group valuation and reports some summary information on how this approach is beingimplemented in a specific case study within the SENSOR project, i.e. Malta.

Section 4 shows that internet is a research tool that can properly fit the scope and the needs ofSENSOR project module 3.3, if coupled with group valuation exercises. This is because theadvantages in terms of reduced costs and larger sample size ensured by an internet-basedsurvey come at cost of a lack of flexibility in the interaction within the surveyed sample whicheventually prevents to explore the richness of different stakeholders' targets and preferencesas well as the way such preferences are formed and a consensus can be reached.

Therefore, internet-based surveys can be used only when clearly defined, simplequestions/issues are presented to the respondents. As such, only general questions like, forinstance, the relative importance of land use functions can be explored through this medium.On the other hand, it is acknowledged that, in a properly designed valuation exercise, internetrepresents a very powerful tool for a quick and vast assessment of competing policy alternati-ves, which is indeed the way it will be used within the module 3.3.3 of the SENSOR projectwith specific reference to the bioenergy policy case.

Therefore, the internet-based survey will address both issues. More specifically, the elicitationsection of the questionnaire (section IV) is built as a choice experiment exercise, consideringthat when faced with a choice situation, as in the case of bioenergy alternatives, consumersusually choose one of the available alternatives or rank them according to their own prefe-rences, rather than being able to evaluate each one of them. Moreover, this technique is lessdemanding in terms of computational burden as compared to, say, contingent ranking (i.e. theother suitable technique). The elicitation of information/data about the LUF (section V of thequestionnaire) is much easier. It is a simple translation of the LUF definitions (Annex 1) intoquestions about the relative importance of each LUF to the respondent, based on a five-pointLikert scale.

Further Reading:

Romano D (2006) Valuation Methods and Techniques. Contribution to Sensor Deliverable 3.3.1. Departmet ofAgricultural and Resource Economics, University of Florence. Florence, 12 September 2006;

de Groot D, Grafakos S, Hein L. and Tassone V (2006) Protocol for Group Valuation to determine Stakeholder prefe-rences for Landscape Functions. Contribution to SENSOR Deliverable 3.3.1. Environmental SystemsAnalysis (ESA) Group, Wageningen University. Wageningen, 12 September 2006.


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1 IntroductionThe main objective of the SENSOR project is the assessment of the sustainability impacts ofland use and land use changes in Europe by using a resource modeling approach to forecastpotential policy-induced environmental, economic and social impacts. In order to pursue thisobjective a huge modeling effort has been made, developing a macroeconomic model(Nemesis) to forecast the levels of activity in thirty economic sectors, plus six additionalmodels (agriculture, forestry, tourism, transport, energy and urban areas) targeting specific economic sectors and their land requirements given the level of economic activity foreseen by the macroeconomic model.

The core of such a modeling effort is a Sustainability Impact Assessment Tool (SIAT) whichforecasts the policy-induced changes in land use. The new land allocations (and also other out-puts from the economic and single sector models) provide a set of changes in social, environ-mental and economic indicators, thus relating the changes in land use induced by a policy tochanges in the indicators in a causal way. This impact pathway of a policy change is consistentwith the so called Driver-Pressure-State-Impact-Response framework (cf. SENSOR Deliverable2.3.1), where a given policy option (driver) determines a pressures on natural and/or man-madeecosystems which can be read as land use changes, which are recorded as changes to the social,environmental and economic indicators resulting from the land use models (state change), whilethe sustainability impacts are assessed by comparing the indicator changes against sustainabilitytargets. Finally, a certain response is selected by the decision maker using the SIAT.

From a policy-impact assessment viewpoint, the term 'land use changes' subsumes a broader setof changes in the functions of natural and/or man-made ecosystems which are measured by theabove-mentioned environmental, economic and social indicators. These indicators illustrate thediverse ways in which ecosystems contribute to human well-being. Hein and de Groot (2006),following the Millennium Ecosystem Assessment (MEA, 2003 and 2005), have tried a mat-ching between ecosystem services and indicators. However, it became rapidly apparent withinthe SENSOR project that the ecosystem function approach presents fundamental discrepancieswith the SENSOR rationale (Petit et al., 2006). In fact, the ecosystem function approach is con-cerned with how environmental quality influences human well-being and assumes that theenvironment affects society and economics. On the other hand, the SENSOR project ac-knowledges that environment, society and economics all have a direct effect on sustainability.As a consequence, the links between the ecosystem functions and the indicators were too tenuous to be satisfactory, as shown by Kienast et al. (2006).

It was then suggested to adapt the ecosystem function approach by using fewer land use relatedfunctions and by ensuring (i) an optimal link between indicators and land use functions and (ii) representing each of the three pillars of sustainability (i.e. social, economic and environ-ment) with an equal weight in the regional assessment. The resulting subset of nine functions(three per each pillar) is what is meant within the SENSOR project as 'land use functions' (cf.SENSOR Deliverable 3.2.2.a) each of them having quite broad meaning (Annex 1).

In this context stakeholder analysis plays a central role as stakeholder opinions, values and pre-ferences need to be taken into account when making policy decisions. This is the specific taskof module 3.3 within SENSOR where a series of valuation efforts should be developed to (i)assess stakeholder targets, views and pref-erences about land use changes, and (ii) validate pre-vious work on regional sustain-ability problems done within SENSOR. More specifically,

1 Those Authors classified the services that ecosystem can provide into (i) provisioning services, such as foodand water; (ii) regulating services, such as flood and disease control; (iii) cultural services, such as spiritual,recreational, and cultural benefits; and (iv) supporting services, such as nutrient cycling, that maintain theconditions for life on Earth.


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module 3.3.1 (whom this deliverable is referred to) aims at 'critically reviewing different valuati-on techniques for the assessment of stakeholders targets and preferences'. This general objectiveis further articulated with specific reference to '(i) monetary valuation methods and techniquesand (ii) group valuation methods and techniques, conducting the analysis according to literatureevidence of actual applications and possible applicability of the methods'.

Therefore, the specific research questions addressed by this deliverable are the following: on the basis of the evidence reported in the literature, is there any technique which is


if so, how such a technique can be implemented in the specific context of the SENSORproject?

considering the importance given within the SENSOR project to the ICT, what are thepotential for using the internet as a medium for carrying out the stake-holder analysisand/or any other valuation which is relevant to validate SIAT result?

These three research questions are used as a framework to structuring the present deliverablewhich reports in section 2 a critical assessment of existing valuation methodologies, summari-sing the pros and cons of each technique belonging either to the so-called 'economic valuationapproaches' (i.e. the ones aiming at a monetary valuation of policy induced impacts – non-mar-ket goods and services – as developed in the environmental economics literature) or to the so-called 'participatory (or deliberative) approaches' (i.e. the ones that tend to explore how opini-ons are formed or how individuals' preferences are expressed in units other than money). Themain finding of this section is that techniques belonging to the latter group of approaches seemto be better suited for stakeholder analysis.

Therefore, in section 3 the focus is on one of these participatory approaches, namely the so-called 'group valuation', which will be used in the specific application of stakeholder analysiswithin module 3.3.4 of the SENSOR project. This section provides the background discussionon how to design a group valuation aiming at assessing stakeholder environmental interests andpreferences, the regional variations of targets, risks and land use functions. In the final part ofthis section are also reported some summary information on how this approach is being imple-mented in a specific case study within SENSOR, i.e. Malta.

Section 4 deals with the potentials offered by the internet to assess stakeholder environmentaltargets and preferences with specific reference to land use functions. The main conclusion isthat the advantages in terms of reduced costs and larger sample size ensured by an internet-based survey come at cost of a lack of flexibility in the interaction within the surveyed samplewhich eventually prevents to explore the richness of different stakeholders' targets and prefe-rences as well as the way such preferences are formed and a consensus can be reached. Indeed,internet-based surveys can be used only when clearly defined, simple questions/issues are pre-sented to the respondents. As such, only general questions like, for instance, the relative impor-tance of land use functions can be explored through this medium. On the other hand, it is ac-knowledged that, in a properly designed valuation exercise, internet represents a very powerfultool for a quick and vast assessment of competing policy alternatives, which is indeed the wayit will be used within the module 3.3.3 of the SENSOR project with specific reference to thebioenergy policy case.


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2 Economic Valuation vs. Participatory Approaches: A Critical Assessment

1.1 LUF Valuation within the SENSOR ProjectThe SENSOR project is, at the best of our knowledge, the first attempt to evaluate policy-inducedland use changes through the concept of land use functions (LUF). As such, there are no empiri-cal applications to which we can refer to as reference for the methodological assessment carriedout in this section. However, LUF are just a subset of nine ecosystem functions (cf. Annex 1) thathave been selected in the SENSOR project as a manageable (and suitable) subset of ecosystemfunctions that can be represented by available indicators in order to record policy-induced landuse changes (cf. SENSOR Deliverable 3.2.2.a). As a result, from the methodological viewpoint,the LUF evaluation effort is in principle not so much different from previous exercises carried outin the environmental economics literature and in the environmental impact assessment literature,which are themselves indicator-based.

There are, however, some specific features of the SENSOR valuation effort that qualifies it andmakes it quite difficult. First of all, in analysing policies that have impacts on land use, the techni-cal and economic features of the involved ecosystems frame the valuation study. For instance,there is a need for knowledge about the trade-off between the different environmental, social andeconomic characteristics which determine the specific and complex set of goods and services pro-vided by the natural and/or man-made ecosystem at hand. This inherent multidimensionality ofthe implied environmental, economic and social changes has important implications for the choi-ce of valuation method.

Moreover, many LUF are characterized by public good nature and pervasive externalities (cf.SENSOR deliverable 2.3.4). Both features call for appropriate valuation methodologies. Forinstance, with specific reference to the economic valuation techniques, this calls for 'non-marketvaluation', that is for the valuation of land use changes that are represented by indicators of goodsor services (i.e. changes in land use functions) that are not traded on a market and do not have amarket price which reveals consumer surplus2.

Last but not least, when we talk about LUF valuation within the SENSOR project framework, wehave to keep in mind we are talking of at least two different valuation exercises:a) a first one aims at examining the values underlying decisions, by asking stakeholders to

explain or discuss why they behave in a particular way, or hold a particular view. Herethe focus is on what people think society should do – not on their personal actions, moti-vations or values. This is the core of what in the SENSOR project jargon is properly cal-led 'stakeholder analysis' aiming at identifying stakeholders' targets, understanding howdo they form their own views and preferences, and assessing stakeholders' acceptabilityof the implied LUF change (i.e. exploring the sustainability space through the comparisonof implied changes and sustainability limits as well as analysing the trade-offs betweenthe implied changes);

b) a second valuation exercise refers to the overall assessment of a given policy option3

aiming at pursuing a certain policy objective. Here people preferences towards policy-induced economic, social and environmental changes are given for granted (i.e. we arenot interested in explaining how those preferences have been developed: they are meantas pre-existing constructs) and the focus is on selecting the most preferred policy optionor on ranking the various policy options at hand according to a given criterion.

2 Or that have a market price which does not fully reflect the utility society assigns to such good or service.


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The two valuation exercises intervene at different level within the SENSOR framework: at anearlier stage the stakeholder analysis, providing some insights to the SIAT about the sustainabi-lity space with specific reference to LUF change in a given regional context; at a later stage thepolicy impact assessment, when alternative policy options have been set by the policy makerand the relevant impacts have been forecast through SIAT simulations. Both of them are impor-tant in the SENSOR framework and we will make reference to both of them in the next secti-ons. Although it should be self-evident from the context to which one we will be referring to,we will make clear to which one we are referring whenever necessary as 'stakeholder analysis'and 'policy impact assessment', respectively.

Broadly speaking, there are two groups of methods to valuing LUF changes: the 'economicvaluation methods' and the 'participatory (or deliberative) methods' (EFTEC, 2006). In the nextsections we will present the most important techniques of both groups, providing a definition ofeach of them, emphasising the valuation context in which each technique is better suited, anddiscussing the main limitations of each of them. The overall assessment of their suitability forLUF valuation will be postponed to section 2.4, where some concluding remarks on both eco-nomic and participatory methods will be provided.

2.2 Economic Valuation ApproachesMany methods for measuring the economic value of ecosystem services are found in theresource and environmental economics literature. Table 1 summarises the main economicvaluation techniques and their ability to estimate all or part of the 'total economic value'4. Someare broadly applicable, some are applicable to specific issues, and some are tailored to particu-lar data sources. A common feature of all methods of economic valuation of ecosystem servicesis that they are founded in the theoretical axioms and principles of welfare economics (cf. Justet al., 1982; Freeman, 1993).

Economic theory assumes that individuals can maintain the same level of utility while tradingoff different 'bundles' of goods, services, and money. The willingness to trade off compensationfor goods or services can be measured either as 'willingness to pay' (WTP) or as 'willingness toaccept' compensation (WTA). Economists generally express WTP and WTA in monetary terms.In the case of policy analysis, willingness to pay is the maximum amount of money an indivi-dual would voluntarily exchange to obtain an improvement (or avoid a decrement) in theeffects (environmental, social or economic) induced by that policy. Conversely, willingness toaccept compensation is the least amount of money an individual would accept to forego theimprovement (or endure the decrement)5.

Economists have developed a number of methodologies to measure the benefits of environmen-tal improvements (Table 1), namely:a) market methods,b) revealed-preference methods,c) stated-preference methods, andd) benefit transfer.

3 A policy option is an action the decision maker (i.e. the Government) can undertake in pursuing its ownobjectives. It is defined by a given mix of policy instruments. Any policy option will result in different econo-mic, social and environmental impacts each of them measured by one or more indicator.

4 A thorough discussion of the Total Economic Value (TEV) concept is beyond the scope of this report.However, Annex 2 summarises some basics about TEV and its components which will prove useful in thediscussion on economic valuation methods below.

5 In the case of improvements, WTP is identified as the 'compensating variation' measure of welfare change,while WTA in this case is identified as the 'equivalent variation' measure. For decrements, these associationsare reversed. For a more detailed treatment of welfare measures that includes these issues see Just et al.(1982), Freeman (1993), and Hanley and Spash (1993).


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Method Suitable for Value type Consumersurplus

Use/non-usevalue

Data require-ments

Limitations

Market methods

Production function

Any impact thataffects producedgoods

Marketprice, eco-nomic rent

Included Use Change in ser-vice; impact onproduction; netvalue of producedgoods

Data on changein service andconsequentimpact on produc-tion often lacking

Revealed-preference methods

AvertingBehavior

Any loss of goodor services affec-ting consumerbehavior

Preventioncosts

Excluded Use Extent of loss ofgoods or ser-vices, cost ofreplacing them

Tends to overesti-mate actualvalue; should beused with extre-me caution

Travel Cost Recreation Travel cost Included Use Survey to collectmonetary andtime costs of tra-vel to destination,distance traveled

Limited to recrea-tional benefits;hard to use whentrips are to multi-ple destinations

HedonicPricing

Amenities/nuisan-ces related tocapital goods(e.g. air quality,scenic beauty,cultural benefits)

Hedonicprice

Included Use Prices and cha-racteristics ofgoods

Requires vastquantities of data;very sensitive tospecification

Stated-preference methods

ContingentValuation

Any service Willingnessto pay

Included Both Survey that pre-sents scenarioand elicits WTPfor specified ser-vice

Many potentialsources of bias inresponses; guide-lines exist for reli-able application

Multi-Attribute

Any service Willingnessto pay

Included Both Survey of respon-dents

Analysis of thedata generated iscomplex

Other methods

BenefitTransfer

Depending on thetype of sourcestudies

Dependingon the typeof sourcestudies

Dependingon thetype ofsourcestudies

Dependingon the typeof sourcestudies

Valuation exerci-ses at another,similar site

Inaccurate, asmany factors varyeven when con-texts seem 'simi-lar'; should beused with extre-me caution

Table 1 Main Economic Valuation Techniques

Market methods can be used when direct markets for environmental goods and services exist.The benefits of a change in quantity of a good are estimated using data on these market trans-actions. By knowing how the good was bought and sold, economists can infer directly howpeople appear to value that good. Unfortunately, direct markets for environmental goods andservices do not often exist. In the absence of these markets, environmental and natural resourceeconomists must rely upon alternative methodologies to measure the benefits of environmentalimprovements.

'Revealed-preference' methods (or indirect approaches) allow economists to infer the value placed on environmental goods using data on actual choices made by individuals in relatedmarkets, which are hypothesised to be related to the ecosystem service of interest6.

6 Technically, this is known as 'weak complementarity' condition (Mäler, 1974).


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For example, the availability or quality of the resource (say a forest or a lake) has an impact onindividuals' purchases of market goods (e.g. the number of trips to that forest or lake). Revealed-preference methods include averting behavior models, recreational demand models, and hedonicprice models.

'Stated-preference' methods (or direct approaches) allow economists to estimate the value placedon environmental goods using data on hypothetical rather than actual choices made by indivi-duals responding to a survey (Mitchell and Carson, 1989). They consist of asking questions des-cribing hypothetical (i.e. contingent choice) situations. From people's responses it is possible toinfer their preferences and therefore to reveal values. Stated-preference methods include contin-gent valuation and a class of multi-attribute valuation techniques, such as choice experiment andcontingent ranking.

The 'benefit transfer' approach, rather than collecting primary data, relies on information from exi-sting studies that have applied other methods (Boyle & Bergstrom, 1992; Desvousges et al., 1998). The choice of valuation technique in any given instance will be dictated by the characteristics ofthe case and by data availability7. In the next sections we will rather make a synthetic assess-ment of each technique, postponing a preliminary discussion of their suitability for LUF valuati-on to section 2.2.5.

2.2.1 Market Methods

DefinitionMarket methods are used to value environmental goods and services that are directly traded asmarket commodities (Just et al., 1982; Freeman, 1993)8. The method is meant to determine thevalue of changes in natural qualities on the economic production system. Market methods applywhen environmental goods are factor inputs: this is why this method is also known as the 'pro-duction factor' method (PF). Changes in the quality or stock of an environmental good can affectproduction costs, which can then alter the price and quantity of output and the returns to otherfactor inputs. In turn, these market responses affect the decisions and welfare of consumers andproducers. Changes in the prices of marketed goods consumers face and changes in the income ofthe owners of the factor inputs reveal information about the welfare of consumers and producers9.

Valuation contextFreeman (1993) signs out two cases where benefits assessment via PF methods is relativelystraightforward:a) when the environmental good or quality is a perfect substitute for another input. Here,

the benefits of an environmental improvement can be calculated by estimating thereduction in input costs caused by substituting away from the other input, as long as thechange in total costs does not affect marginal costs or output;

b) when observable market data (e.g., cost, demand, and market structure) imply that bene-fits from an environmental improvement will accrue to owners of fixed factors. Here,benefits can take the form of increased productivity and are realised as profit or quasi-rents9.

7 This deliverable does not provide detailed guidance on using of each technique. Many other sources do so.The interested reader is referred to, among others, Champ et al. (2003), Haab and McConnell (2003),Bateman et al. (2002).

8 Market methods are used, for example, to examine the effects of air quality improvements on agriculture andcommercial timber industries and the effects of water quality improvements on commercial fisheries (cf.Adams et al. (1986), Kopp and Krupnick (1987), Taylor (1993), and EPA (1997) for empirical examples).

9 For example, the benefits of an environmental improvement are often realised as increases in consumer andproducer surplus that arise from lower costs and prices and increases in the quantity of the marketed good.

10 One such case would be where the producer affected by the environmental improvement is small relative tothe market, and variable prices for factors and products are not affected by the environmental improvement.


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LimitationsPF allows one to determine the value of the production capacity of nature. The total economicvalue of nature does, however, comprise more attributes than fish production and water purifi-cation, and therefore the PF can only capture part of the total economic value of nature, that isuse value. Consequently, it is not directly suited to determine the economic value of naturalsites which are not cultivated.

Due to the use of market prices to value certain environmental qualities, the validity of the PFis doubtful. PF can only produce cost-based estimates of the value of the production capacity ofnature and it does not include consumers' surplus when market prices are used. The costs ofproduction losses may not cover the full social preference for nature. A bias of the PF is that effects on production may have been distorted by averting behaviour.Producers will try to avert the effects of reduced natural qualities by undertaking all sorts ofprevention activities, such as shifting to different crops or products, adapting cultivation or harvesting techniques. They may even have left the market to become involved in other typesof economic production. Because PF is based on dose-response relations which involve a con-siderable amount of ecological information and because it requires economic data on naturalproducts as well, it has a large data requirement. If one wishes to account for demand and sup-ply dynamics in the valuation of responses, data requirements will only become larger.

2.2.2 Revealed-Preference Techniques

In the absence of market data on the value of environmental improvements, WTP may be esti-mated by looking at related goods that are traded in markets. Methods that employ this generalapproach are referred to as 'revealed-preference' methods because people's behaviour in asso-ciated markets reveals the value they place on the environmental improvements. Three distinctrevealed preference methods have been widely used by economists: averting behavior models,recreation demand models, and hedonic pricing models11.

The Averting Behavior Method (AB)

DefinitionThe AB method infers values from observations of how people change defensive behaviour inresponse to changes in environmental quality12. By analysing the expenditures associated withthese defensive behaviours economists can attempt to estimate the value individuals place onsmall changes in risk (Desvousges et al., 1998). The economic theory underlying the ABmethod rests on a model of household production. In these models, households produce healthbenefits by combining an exogenous level of environmental quality with inputs such as defen-sive behaviours. If there is a continuous relationship between defensive actions and reductionsin health risks, then the individual will continue to avert until the cost just equals his or herWTP for these reductions.

Valuation contextFaced with a given level of environmental risk, the averting behavior method assumes thatindividuals engage in these defensive behaviours to achieve an optimal level of health. Thus,the benefit for a small reduction in health, or health risk, is estimated from two primary piecesof information (1) the cost of the averting behavior or good and (2) its effectiveness, as percei-ved by the individual, in offsetting the loss in environmental quality.

11 Sometimes a fourth techniques is also listed under the AB heading, i.e. the cost-of-illness studies. We will notdescribe this approach because it is more relevant for the health economics literature.

12 Defensive behaviours are usually defined as actions taken to reduce the risk of suffering environmentaldamages, as well as actions taken to mitigate the impact of environmental damages. The former categoryincludes behaviours such as the use of air filters or boiling water prior to drinking it, while the latter includesthe purchase of medical care or treatment.


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LimitationsSince AB is mainly oriented toward health risks, it can only capture the value of certain attribu-tes of nature (clean air in this case) but it cannot be used to determine the total economic valueof nature. It cannot be used to determine non-use values.

The validity of AB is questionable because of the assumption that people actually purchase certain goods to protect themselves against environmental deterioration. However, determiningthe value of a clean environment in terms of the expenses made to avert negative effects ofenvironmental deterioration, automatically introduces a bias. Only the willingness to pay ofpeople who display certain AB is measured, while people who behave differently may alsohave a willingness to pay for a clean environment. On the other hand, people may make thepurchase for the sake of the product and not to avert negative effects from their living environ-ment. Another concern with AB is that people may not react to small changes in environmentalquality. They may only react when a certain threshold has been passed.

AB methods can provide theoretically correct measures of WTP. To do so, however, they requi-re a great deal of data and particular assumptions about consumer preferences. In practice, ithas proven difficult to meet these requirements. AB results cannot always be interpreted as anunbiased WTP estimate, however, because conclusions may depend critically upon modelingconditions (cf. Bartik (1988a), Shogren and Crocker (1991), Quiggin (1992), Shogren andCrocker (1999)).

Recreation Demand Models

Recreation demand models focus on the choice of trips or visits to sites for recreational purpo-ses. The basic trade-off to be considered is between the satisfaction gained from participatingin an activity at a site and the value of money and time given up. The fundamental assumptionis that people may weigh the money and time costs of travel to a site in the same way as anadmission fee. Thus, by examining the patterns of travel to particular sites, one may infer howindividuals value the site or particular aspects of the site such as environmental quality.However, measurement of these values is complicated by the fact that access to recreation acti-vities is only partially regulated by observable market mechanisms.Recreation demand models include the travel cost model and the random utility model.

Travel Cost Method (TC) DefinitionThe simplest recreation demand model involves trips to a single site (Clawson and Knetsch,1966). User surveys provide data on visitors and trip origins and the data are organized bydistance to the site. Generally, an inverse relation between distance traveled and the number ofvisits emerges. The distance variable may be converted to cost by including factors for the costper kilometer of vehicle travel as well as the cost of travel times, and the relationship amongthe variables may be interpreted as a demand function, with the number of trips from a particu-lar area as a function of the travel costs of reaching the site. The single-site TC model may be extended to multiple sites, usually by estimating a system ofdemand equations, with the number of trips to a given site taken to be a function of the cost ofvisiting that site as well as the costs of visiting other available sites14.TC models allow for the estimation of consumers' surplus.

13 More detail on the difficulties inherent in applying the averting behavior model can be found in Cropper andFreeman (1991).

14 A number of extensions to the simple travel cost model are described in Freeman (1993).


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Valuation contextTC measures revealed preferences for natural sites in terms of willingness to pay for site visits.As such, it is typically limited to valuing environmental goods and services that have explicitrecreational uses, such as woodlands, wetlands, rivers and lakes, national parks and coastalareas.

LimitationsTC can only capture part of the total economic value of nature. The recreational value is onlypart of the total value of nature, since society does not only derive welfare from nature throughrecreation. A consequence of the fact that TC only reveals the welfare realised by the visitingpopulation is that it can only measure use values.A bias of TC concerns the representativeness of the sample. People who do not visit a site maystill value the site, but they are excluded from the sample. On the other hand, people who reallyappreciate the site may have very low travel cost, because they have moved to live closer to thesite. The practical requirements of the method depend on the readily available information. If asurvey is required to collect the visitation and cost data, the time and data requirements couldbe limiting.

Random Utility Models (RUM) DefinitionFor analyses focusing on the role of environmental quality variables, changes in social welfaremay best be estimated through 'random utility' models (RUM, also re-ferred to as discrete choi-ce, cf. Bockstael et al. (1986) and Bockstael et al. (1991)). RUM focus on the decision torecreate at a specific site as compared to alternative substitute sites. The model considers travelcost and environmental quality variables associated with all competing sites.

Valuation contextAs with the travel cost method, this method is suited to estimating the value of environmentalgoods and services associated with open-access recreation resources. The random utility modelcan be applied to estimate the change in recreational use value which arises from a change inthe characteristics of a recreational site. It can, therefore, be used for appraisal and site manage-ment planning.

LimitationsThe method is subject to the same practical limitations as the travel cost method. The compo-nent of TEV estimated is direct use value. Note that users – in this case, visitors to a site –could also hold non-use values but these cannot be estimated separately. Although well suited for analyzing welfare effects of changes in site quality per visit, thediscrete choice model is less useful for predicting the number of trips over a period and measu-ring seasonal welfare changes. The ability to collect sufficient data may be limiting.

The Hedonic Pricing Method (HP)

Hedonic pricing methods (HP) apply to heterogeneous goods and services, i.e. consisting of'bundles' of attributes, differentiated from each other by the quantity and quality of these attri-butes. Job opportunities, housing units, computers, and cars are common examples of heteroge-neous goods. Hedonic pricing methods explain variations in price using information on attribu-tes. Rosen (1974) is the seminal article on the economic theory of hedonic methods.

15 For example, determinants of wages are expected to include worker characteristics (e.g., level of education,tenure, age) and job characteristics (e.g., risk of fatal injury). Determinants of housing prices may includestructural attributes (e.g., number of bedrooms and age of house), neighborhood attributes (e.g., populationdemographics, crime, and school quality), and environmental attributes (e.g., air quality and proximity tohazardous waste sites).


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The economic theory underlying hedonic pricing methods extends from a model of marketequilibrium, where suppliers and demanders of heterogeneous goods interact under conditionsof perfect information and zero transactions costs. Consumers derive utility from the attributesof the heterogeneous goods and adjust purchases in response to differences in these attributes.Producers or sellers of goods and services incur costs that vary with the range of attributesoffered. An equilibrium price schedule develops from the market interactions of consumers andsuppliers. The equilibrium price schedule, termed the hedonic price function, is approximatedby regressing price on measures of attributes. This function forms the basis for benefits assess-ment using hedonic pricing methods: the estimated coefficients represent in fact the marginalWTP for the associated attribute16.

Empirical hedonic pricing research typically concentrates on the hedonic price function and thedecisions of consumers or workers. Applications of hedonic methods to labour wages and pro-perty values have been used to characterise the benefits of environmental improvements17.These are known as hedonic wage studies and hedonic property value studies, respectively.

Hedonic WageDefinitionHedonic wage studies (sometimes known as wage-risk or compensating wage studies) arebased on the premise that individuals make tradeoffs between higher wages and increasedoccupational risks of death or injury. Essentially, higher risk jobs are expected to pay higherwages, all else held constant. Hedonic wage studies use statistical regression and data fromlabour markets to isolate the increment in wages associated with higher job risks. The outcomeof these models is an estimated value of small changes in mortality risks. Some models alsoattempt to estimate the value of small changes in morbidity, or non-fatal risks. Hedonic wage models have also used wage differentials across geographic areas to estimatevalues for environmental quality differences. Theoretically, jobs in areas with poor environ-mental quality should pay less than identical jobs in areas with high environmental quality,again holding all else equal19.

Valuation contextThese studies have often been used to measure the (negative) values of noxious facilities and tovalue job risks. The scope of these studies is limited to environmental characteristics which areobservable by individuals and are likely to have an impact over the period of employment.The HP measures revealed preferences and it includes the consumers' surplus as it measures thetotal area underneath the derived demand curve.

LimitationsFrom the conceptual viewpoint, it should be emphasised that the method can estimate the envi-ronmental costs and benefits that workers are aware of and hence can reflect in the labour mar-ket. Within this scope, the value components that can be measured are limited to direct andindirect use values.

From the empirical viewpoint, hedonic wage studies share some limitations common to all HPmethods, but show also some specific limitations, both mainly referring to the econometricestimation of the hedonic price function (and the WTP estimates can be inferred from it).

16 See Palmquist (1988, 1991) and Bartik (1988b) for a detailed discussion of benefits assessment using hedo-nic methods, including guidance on developing lower and upper bound measures of benefits.

17 Palmquist (1991) and Freeman (1993) contain current discussions of the use of hedonic methods for charac-terising the demand for environmental quality and benefits assessment.

18 A thorough treatment of the hedonic wage model can be found in Viscusi (1992 and 1993).

19 There are a number of difficulties with employing hedonic wage models in this manner, including integratingwage and housing choices, and the need to assess intra-city variation in amenities.


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Among the former it should be emphasised that the hedonic price function does not typicallyprovide general information on individuals' WTP for the different attributes. Methods for identi-fying demand (or WTP) functions for the different attributes (e.g., using data from multiple mar-kets or imposing assumptions about the functional form of the hedonic and/or the utility functi-ons of individuals) exist, but they often rely on restrictive assumptions20. Furthermore, identify-ing WTP functions does not ensure the ability to measure the welfare gain from a discrete envi-ronmental improvement because markets intervene and prices change. As a result of these diffi-culties, approximations of welfare gains based on the hedonic price function are sometimesemployed to assess benefits.

Limitations specific to hedonic wage studies can be primarily traced back to the fact that the keyto an effective hedonic wage study lies in separating the portion of compensation associatedwith occupational health risks from other job characteristics, including supervisory responsibili-ty, job security, and similar factors. The wage rate is also affected by the industry in which theindividual is employed, characteristics of the location and the personal characteristics of theworkers (e.g., age, education, experience). All of these data are needed to disentangle the effectsof worker characteristics from those of job attributes in determining wages paid.

There are some assumptions that limit the use of hedonic wage studies, namely (i) workers' per-ceptions of risk levels across jobs are assumed to match actual risk levels, and (ii) the existenceof perfect labour markets in which the workers are freely mobile and there is perfect informationabout jobs and job risks.

Hedonic Property ValueDefinitionHedonic property value studies assert that individuals perceive housing units as bundles of attri-butes and derive different levels of utility from different combinations of these attributes21. Whentransaction decisions are made, individuals make tradeoffs between money and attributes thatcan reveal the marginal values of these attributes. Hedonic property value studies use statisticalregression methods and data from real estate markets to examine the increments in propertyvalues associated with different attributes.

Valuation contextThe method is generally used for localised and site-specific impacts, including both 'goods' suchas pleasant views (and related increases in property price) and 'bads' such as traffic noise, dis-amenity due to proximity of landfills, and so on (and related decreases in property price). Thescope of these studies is limited to environmental characteristics which are observable by indivi-duals and are likely to have an impact over the period of occupancy. This, by definition, exclu-des changes that are yet to occur.The HP measures revealed preferences and it includes the consumers' surplus as it measures thetotal area underneath the derived demand curve.

LimitationsAlthough HP can be used to value amenities such as natural beauty, this may not be enough tocapture the total economic value of a natural site. Beauty is only one attribute of a natural site.All other welfare-generating characteristics of the site, such as biomass production cannot becaptured by this method. Hedonic property value studies were not developed to determine thetotal value of nature, but to determine the value of amenities only: they cannot be used to deter-mine non-use values of nature.

20 See Palmquist (1991) for a detailed discussion on identification issues.

21 Bartik (1988b) and Palmquist (1988, 1991) provide excellent, detailed discussions of benefits assessmentusing hedonic methods.


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The empirical estimation also poses some problems. Structural attributes (e.g., number ofbedrooms and age of house), neighborhood attributes (e.g., population demographics, crime,and school quality), and environmental attributes (e.g., air quality and proximity to hazardouswaste sites) may influence property values. When assessing an environmental improvement, itis essential to separate the effect of the relevant environmental attribute on the price of a hou-sing unit from the effects of other attributes. While deriving measures of marginal WTP usinghedonic methods is straightforward, estimating measures of WTP for substantial or discrete(non-marginal) improvements in environmental quality is difficult. The use of hedonic propertyvalue studies for benefits assessment rests on careful interpretation of the hedonic price functi-on and its relevance to the policy scenario being considered.

When using hedonic property value studies for benefits assessment, the measurement of theenvironmental attribute is central to the analysis. If the measurement of the environmental attri-bute does not match individuals' perceptions, then the results of the analysis may be biased.The hedonic price function for housing units represents an equilibrium that results from theinteraction of suppliers and demanders of housing in a market with full information. When thisassumption is not met, the results of a hedonic analysis will not provide an exact representationof the tradeoffs individuals make across housing attributes and the marginal values associatedwith these different attributes.

The validity of the method may be also questioned because there are several amenities thatinfluence the price of a house in opposite directions. Since the number of explaining variablescan be numerous, one runs the risk of not including an important variable or encounteringmulti-collinearity and thus drawing spurious conclusions about the value of an amenity. Another possible source of bias is the representativeness of the sample for the population. Thevalue of, for instance, parks may be underestimated, because people who do not live near apark may also appreciate it. Their appreciation is, however, not reflected in the price of theirhouse and they are not included in the sample. Hedonic property value studies have a very large data requirement because both primary data(characteristics of the surroundings) and secondary data (market transactions) need to be col-lected. The value of a house depends on many factors: there are social factors, such as employ-ment opportunities, taxes and accessibility. Data need to be gathered for all these factors.

2.2.3 Stated-Preference Methods

In the last decades stated-preference methods have gained momentum with both academics andpractitioners and they have been used in different disciplines (environmental economics, mar-keting, transportation, health economics, etc.). Stated-preference approaches attempt to measureWTP values directly. Unlike the revealed-preference methods that infer values for environmen-tal goods and services from observed behavior, stated-preference methods rely on data fromsurveys that directly ask respondents about their preferences to measure the value of environ-mental goods and services22.

This class of methods comprises several related techniques, including contingent valuation,choice experiment, contingent ranking, etc. The common feature of these methods is directquestioning of members of a population about their likely choices in a hypothetical market. In the questionnaire, it is defined the good itself, the context in which it would be provided and the way it would be financed; finally the respondents' willingness to pay (or accept) isobtained through different elicitation format23.

22 In earlier application of stated-preference techniques, this feature lead some scholar to judge revealed-refe-rence techniques as more reliable as compared to stated-preference technicques. However, nowadays it iswidely maintained that, if properly implemented, stated-preference techniques yield values which are asmuch reliable as the ones obtained by revealed-preference techniques (NOAA, 1993; Carson, 1996).

23 The reader is referred to Bateman et al. (2002) for a wide description of elicitation formats.


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Elicited values are subsequently used to estimate mean/median individual willingness to pay(or accept) for the provision/quality change of the good, which is 'contingent' upon the scenariospecified in the questionnaire.

Figure 1 classifies stated-preference methods according to different criteria, namely the numberof attribute (or scenarios) used in the contingent choice, the elicitation format, and the measure-ment scale. The first, and most important difference, is between mono-attribute approaches(such as the contingent valuation methods) and multi-attributes approaches (such as choiceexperiment, contingent ranking, etc.).

Referring to this, when analysing policies that have complex environmental impacts24 there is a need for knowledge about the trade-offs between the different environmental characteristics.This inherent multidimensionality of environmental change has important implications in choosing the suitable stated-preference method (cf. Section 2.2.5).

Fig 1 Stated-preference Valuation Methods (From Merino-Castellò, 2003)

24 As in the bio-energy policy case, cf. section 4.

25 For a good overview of the CV method see Mitchell and Carson, 1989; Hanemann, 1991; Kopp et al., 1997;Carson, 2000).

26 This is why CV is classified under 'mono-asttribute' valuation techniques: despite the hypothetical commodityis featured by many characteristics, for the sake of valuation it is treated as if those characteristics are 'bund-led' in a single commodity, i.e. the comparison is between the status quo ante (itself considered as a bundledcommodity) and the implied change as described in the hypothetical scenario (again considered as a bund-led commodity, although it may imply the change of more than one characteristic).

DefinitionContingent valuation (CV) is the most widespread technique among stated-preference methods.CV is a survey-based approach to valuing environmental goods and services. The approachentails the construction of a hypothetical (or simulated) market via a questionnaire whererespondents are asked either to answer if they would pay a specified amount for a describedhypothetical commodity (referendum, or 'discrete choice' format) or state their highest WTP forit (open-ended format)25. The hypothetical commodity is defined as a bundle of different cha-racteristics (quality, quantity, different services, etc.) and CV seeks to elicit willingness to payfor the entirety of the bundle26.

Valuation contextCV, as all other stated-preference techniques, is able to estimate the total economic value of anenvironmental good or service, i.e. both use value and non-use value components. It measuresbenefit-based preferences and it includes the consumers' surplus.


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CV is said to be an appropriate economic valuation method for environmental goods that haveno indirect effects on other goods. It is therefore suited for the valuation of amenities or othereasy-to-perceive aspects of nature, but not for the valuation of natural processes, such as clima-te regulation, where effects on human welfare are difficult to grasp. However, CV does not pro-duce valid measurements when it concerns goods that people are not familiar with. Nor does itwork when people reject responsibility for the good in question (property right issue)27.

LimitationsValidity may be a problem, since it is very difficult to describe a natural good in such a waythat all its attributes are accounted for. In CV surveys one can encounter various sources ofbias, such as samples which are not representative, strategic behaviour of respondents or confu-sion about the size of the good that is to be valued (part-whole bias), especially if they are notcarefully done28. The result of the debates about the reliability of CV studies has been an infusi-on of methods and theory, particularly from the disciplines of psychology and survey research,to enhance questionnaire design to mitigate these concerns (Krosnick, 1991; Fischhoff, 1997).In addition, the US National Oceanic and Atmospheric Administration (NOAA) convened apanel of well-known economists to review and evaluate the methodology in 1993. The paneldevised a set of 'best practices' recommendations for the method, particularly as it relates tonatural resource damage assessments (NOAA, 1993).

Several authors argue that CV works best where it is least needed. It does not provide validestimates when people are unfamiliar and inexperienced with the good. Freeman (1986) notesthat CV works best for those goods resembling ordinary commodities. This means that it is bestsuited for valuing consumption goods that people consume more of as their income increases(and the price decreases). When goods are not easily commoditised, as in choices concerningentire ecosystems, CV results are doubtful. Supporters of this notion argue that an environmen-tal good has several attributes and that compressing the values of these attributes into a singlemetric (such as willingness to pay) leads to an information loss. The same argument is, howe-ver, also relevant to private goods, but in that case people seem to accept their prices as theirtrue value. The idea behind this is that people have experience in valuing and making trade-offsfor attributes of private goods, whereas they do not have any experience in valuing environ-mental goods. In fact, they may not even be aware of all attributes. This situation makes peopleliable to construct their values heuristically on the basis of the information provided by the eli-citation setting (Vatn and Bromley, 1994). Taking account of the restrictions of valuation expe-rience and commodity resemblance, it seems that CV is most accurate for the valuation ofpublicly-managed goods with private characteristics such as natural sites.

Although CV studies are known to have large data requirements29, they do not require seconda-ry data, since one can gather all necessary data, i.e. the willingness to pay and its explainingvariables such as income and attitude towards nature, by means of one survey. Since CV isonly suited for 'easy to perceive aspects' of the natural environment, one may conclude that themethod does not allow us to determine the total economic value of nature. However, CV can beused to estimate the non-use value components of total economic value.

27 If people are asked, for example, about their willingness to pay for clean soil, they may state that it is zero,because they feel the polluter should pay. This does not mean that they do not appreciate clean soil.

28 In particular, the concern that CV surveys do not require respondents to make actual payments has led cri-tics to argue that responses to CV surveys are biased because of the hypothetical nature of the good.Reliability tests on the data that conform to expectations from both economic and psychological theory canenhance the credibility of a CV survey. Surveys without these tests should be suspect and the ones whoseresults fail the tests may be disregarded.

29 This is true when using single- and double-bounded discrete choice formats, not necessarily when usingopen ended formats.


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Multi-attribute Valuation

Multi-attribute valuation techniques is a family of survey-based methodologies for modelingpreferences for goods, where goods are described in terms of their attributes and the levels thatthese take30. Two different types of multi-attribute techniques have been suggested (Merino-Castellò, 2003): (i) preference-based approaches which require the individual to rate or rankeach alternative product and (ii) choice-based approaches which make the consumer to chooseone among several alternative products. The former is a research technique in which consumersare asked to evaluate a series of hypothetical and real products, defined in terms of their featu-res. The latter differs in that consumers are asked to view a series of competing products andselect one or, in some cases, more than one31. Attribute valuation approaches allow a moredirect route to the valuation of the characteristics or attributes of a good and of marginalchanges in these characteristics32.

These methods are a variation on stated-preference methods that aim to evaluate marginaltrade-offs rather than the total value for a described change that is evaluated in CV studies(Johnson et al., 1994). Choice-based approaches originate from the economics discipline andhave been widely used for valuing a diverse range of goods and services. On the contrary, pre-ference-based approaches have their origins in the marketing literature and are mainly focusedon gaining an insight into consumer preferences rather than estimating economic values(Louviere, 1988).

These methods have recently received the favour of many researchers which high-light theadvantages of these methods compare to contingent valuation method (Boxall et al., 1996;Hanley et al., 1998; Hanley et al., 2001). The multi-attributes approach is based on the notionthat attributes of an environmental good can be used to understand the general trade-offs whichan individual is willing to make.

Individual preferences can be elicited by asking respondents to rank the options presented tothem, to score them or to choose their most preferred. These different ways of measuring prefe-rences correspond to different variants of conjoint analysis and choice modeling. There are fourmain variants according to the measurement scale for the dependent variable: contingent rating,paired comparison, choice experiments and contingent ranking (Figure 1). These techniquesdiffer in the quality of information they generate, in their degree of complexity and also in theirability to produce WTP estimates that can be shown to be consistent with the usual measures ofwelfare (Bateman et al., 2002).

30 The conceptual microeconomic framework for multi-attribute valuation lies in Lancaster's (1966) characteri-stics theory of value which assumes that consumers' utilities for goods can be decomposed into utilities forcomposing characteristics.

31 In this regard, choice-based approaches are based on a more realistic task that consumers perform everyday, the task of choosing a product from among a group of competitors while preference-based approachesdo not require respondents to make a commitment to select a particular option. In particular, the preference-based approach is purely mathematical and deals with the behaviour of numerical systems, not the behavio-ur of humans or human preferences (Louviere, 2000). This is one of the reasons why choice-basedapproaches are better than or, at least, more preferred to preference-based approaches.

32 Contingent valuation can, of course, be used to value such changes, but the number of scenarios that can beconsidered is limited. There will be a presumption, therefore, that multi-attribute valuation ap-proaches will bepreferred over contingent valuation approaches in contexts where it is important to value several attributes.


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Preference-based Approaches: Conjoint Analysis (CA)DefinitionBoth contingent rating and paired comparison belong to the family of conjoint analysis (CA),which is a purely mathematical theory dealing with the behaviour of number systems not thebehaviour of humans, and for this reasons it is generally inappropriate for economic evaluation. The two variants – contingent rating and paired comparison – differ in the measurement scalefor the dependent variable. In a contingent rating exercise, respondents are presented with anumber of scenarios one at a time and are asked to rate each one individually on a semantic ornumeric scale. This variant does not, therefore, involve a direct comparison of alternative choi-ces. In a paired comparison exercise, respondents are asked to choose their preferred alternativeout of a set of two choices and to indicate the strength of their preference in a numeric orsemantic scale.

Valuation contextBoth variants can be used whenever goods and services can be described in terms of characteri-stics (or 'attributes') and the levels that these characteristics take. If properly designed, they areable to estimate the total economic value of an environmental good or service.

LimitationsIn contingent rating, ratings must be transformed into a utility scale. The indirect utility functi-on is assumed to be related to individual's ratings via a transformation function. These data aretypically analysed using ordinary least square (OLS) regression techniques which imply astrong assumption about the cardinality of the ratings scale. These assumptions relate either tothe cardinality of rating scales or to the implicit assumption of comparability of ratings acrossindividuals: both are inconsistent with consumer theory. Hence, contingent rating does not pro-duce welfare consistent value estimates.

Paired comparison combines elements of choice experiment (choosing the most preferred alter-native) and rating exercises (rating the strength of preference). Also in this case, the utilityfunction is estimated using ordinary least squares. And in this case also the obtained welfaremeasures are inconsistent with consumer theory.

Choice-based Approaches: Choice Modelling (CM)DefinitionChoice modeling methods are well rooted in the random utility theory based on the behaviour ofhumans and it can be considered as a multi-attribute version of the milestone of contingentvaluation (Louviere, 2000). They are based around the notion that goods and services can bedescribed in terms of characteristics (or 'attributes') and the levels that these characteristics take. Choice experiment (CE) approach, also called contingent choice or conjoint choice, consists ofseveral choice sets, each containing two or more options also said bundles of (environmental)goods. Participants are shown the choice sets in turn and are asked which option they prefer.Each option is described by a set of attributes and each attribute can take one of several levels.One of these attributes is usually the price and a baseline alternative, corresponding to the sta-tus quo, is included in each choice set33. As each combination has a 'price' attached, subsequentanalysis of respondents' choices. Contingent ranking (CR) experiment is implemented much in the same way of choice experi-ment. However, with this method, respondents are required to rank a set of alternative optionsfrom the most to the least preferred. Each alternative is characterised by a number of attributes,which are offered at different levels across options. Respondents are then asked to rank the opti-ons according to their preferences. Therefore ranking data provide more statistical informationthan choice experiment but the degree of complexity for respondents and researchers is higher.

33 Therefore as Hanley et al. (1998) states 'CE technique is an application of the characteristics theory of value(Lancaster, 1966), combined with random utility theory (Thurstone, 1927; Manski, 1977)'.


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Valuation contextAs with CV, choice modeling is able to estimate the total economic value of an environmentalgood or service, i.e. both use value and non-use value components (and values held by bothusers and non-users). Choice modeling is more flexible than standard CV in estimating indivi-dual values for different ecosystem services as many more potential combinations of environ-mental change can be presented. This allows for a better incorporation of uncertainty surroun-ding environmental impacts than can be afforded by contingent valuation. Moreover, choicemodeling provides more information with small sample size as compared to CV34.CE is well rooted in the random utility theory and gives welfare consistent estimates for fourreasons. First, they force the respondents to trade-off changes in attribute levels against the costof making these changes. Secondly, the respondents can opt for the status quo (provided thisoption is included in the choice set: this is required). Thirdly, we can represent the econometrictechnique used in a way which is exactly parallel to the theory of rational and probabilisticchoice. Fourthly, we can derive estimates of compensating and equivalent surplus35. CR may also give welfare consistent estimates, provided that one of the options is always inthe individual's currently feasible choice set. This is because, if a status quo is not included inthe choice set, respondents are effectively being forced to choose one of the alternatives pre-sented, which they may not desire at all.

LimitationsThe practical limitations are similar to those of contingent valuation. However, ethical protestresponses (Hanley et al., 2001), yeah-saying and other distortions problem criticized to CVmethod can be resolved with CM (Bateman et al., 2002).More complex choice modeling designs may cause problems for respondents leading to anincreased degree of random error in responses. Therefore it should be expected that as the num-ber of attributes (or rankings) increase the likelihood of inconsistent responses will also increasedue to limits in cognitive ability unless sample sizes are increased to reduce number of choiceseach respondent is asked to make. However, for the respondent the cognitive burden of choice incontingent ranking is higher compared to choice experiment (Foster and Mourato, 2002).Finally, for researchers econometric models are more sophisticated (Layton, 2000): the basicspecification model of the choice-based approaches is the multinomial logit model and the esti-mation procedure is the maximum likelihood (MLE). Due to the differences in the measure-ment scale, the model specification for the choice experiments is McFadden's conditional logitwhile the model specification for the contingent ranking is the rank ordered logit or explodedlogit of Beggs et al. (1981).

2.2.4 Benefit Transfer

DefinitionBenefit transfer can be a feasible alternative to using one of the primary stated- or revealed-preference research methods described in previous sections. Rather than collecting primarydata, the benefit transfer approach relies on information from existing studies that have appliedother methods (Boyle and Bergstrom, 1992; Desvousges et al., 1998). The advantages to benefit transfer are clear. Original studies are time consuming and expensi-ve; benefit transfer can reduce both the time and financial resources needed to develop benefitsestimates of a proposed policy. Given the demands of the regulatory process, these considerati-ons may be extremely important. Additionally, while the quality of primary research isunknown in advance, the analyst performing a benefit transfer is able to gauge the quality ofexisting studies prior to conducting the transfer exercise.

34 During the last twenty years researchers have been using discrete choice models extensively in a variety offields, from modeling future transportation choices (Train, 1986) to proposed tenant regulation and policies(Walker et al., 2002).

35 In this case, we estimate a McFadden's conditional logit model using the maximum likelihood procedure.


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Valuation contextIn the terminology of benefits transfer, monetary estimates of the value of an environmentalgood or service are transferred from a 'study' good or site to a 'policy' good or site36.Considering that, in reality, the policy good and the study good are unlikely to be identical, abenefit transfer study can be carried out only if information are available that make possible toadjust the study good WTP estimate in some way to account for the difference with the policygood (Bateman et al., 2000) such as: socio-economic characteristics, physical characteristics,valuation context (i.e. proposed changes in the quality and/or quantity of policy and studygoods that are valued), and availability of substitutes at each site.

LimitationsWhere values are transferred from CV studies, benefits transfer exercises can enable the totaleconomic value (TEV) of an environmental good or service to be valued (i.e. use value andnon-use value). If results are transferred from revealed preference studies then benefits transferenables an assessment of use value to be made.The most important limitation of a benefit transfer study is the availability of relevant, high-quality existing studies and the degree to which additional primary research would reduce theuncertainty of the current benefits estimate.Another issue is estimate accuracy. In fact, estimates derived using benefit transfer techniquesare unlikely to be as accurate as primary research tailored specifically to the new policy case.Of concern to the analyst is whether more accurate benefits information makes a difference inthe decision-making process. There are many situations in which a benefit transfer may provideadequate information. For example, if the entire range of benefits estimates from the transferexercise falls well above or below the costs of the policy being considered, more accurate esti-mates will probably not alter the efficiency conclusion.

2.2.5 Some Concluding Remarks on Economic Valuation Methods

From the discussion above it is self-evident that revealed-preference techniques rely on theinfluence of the availability or quality of the resource on individuals' purchases of marketgoods, while stated-preference techniques do not depend on this condition. The restriction of alinkage between public or semi-public goods and the private goods market candidates therevealed-preference techniques to the evaluation of such components of TEV (cf. Annex 2)characterised by weak complementarity or substitutability between the resource and the privategood, i.e. use values (Mäler, 1974). On the contrary, there are no limitations to the use ofstated-preference techniques in estimating all TEV components. Furthermore, recalling thepublic good nature of many LUF (cf., for example, the human health and recreation, cultural,support and provision of habitat, and maintenance of ecosystem functions in Annex 1), thestated-preference methods are the only techniques that in principle can be applied to provide amonetary assessment of policy-induced changes in LUF.

Within this family of techniques there are some that are better suited for estimating thoseimpacts. In fact, the multidimensional environmental, social and economic impacts of manypolicies require estimation of multidimensional trade-off surfaces. Although contingent valuationcan be modified through careful survey design to include controlled changes in several characte-ristics, this requires complex experimental set-ups involving large number of splits in the surveysampling framework. Reliable estimation of multidimensional trade-off surfaces will require alarge number of respondents in the sample. The actual experimental design for valuation of mul-tidimensional change can draw upon the available methods used in conjoint analysis. Buildingupon the strengths of contingent valuation and conjoint analysis makes choice experiments andcontingent ranking viable alternatives for valuation of multidimensional change.

36 The study good refers to the asset that is the subject of an existing valuation study, whilst the policy good isthat asset for which a valuation is required.


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Contingent valuation and choice experiments can both generate results that are consistent withwelfare theory (Table 2). Contingent ranking can also generate welfare theory-consistentresults, if do-nothing is included as an option so that the respondents are not forced to rankother options. On the other hand, contingent rating is not widely used in economic valuationmainly due to the dubious assumptions that need to be made in order to transform ratings intoutilities; however, due to their simplicity, conjoint analysis variants have been frequently usedin marketing fields.

In conclusion, choice modeling approaches (i.e. choice experiments and contingent ranking) arethe most suitable economic valuation approach for the monetary assessment of LUF changes,offering the possibility to estimate welfare-consistent measures multi-attributes changes withaffordable survey cost.

Table 2 Characteristics of Stated Preference Approaches (from Merino-Castellò, 2003)

2.3 Participation (or Deliberative) ApproachesSeveral participatory (or deliberative) approaches are available and used in environmentalvaluation and decision support (EFTEC, 2006; DEFRA, 2007). They are quite disparate innature and objectives. Some of them are designed for problem-specific participatory democracy(Beierle, 1998); some may be used for supporting economic valuation techniques (e.g. use offocus groups in stated-preference studies, cf. Mitchell and Carson, 1989); some may be combi-ned with decision support methods such as multi-criteria assessment (Banville et al., 1998) oreven cost-benefit analysis. Moreover, several of these methods can be used for more than oneof these functions.

While there is some disagreement amongst experts and practitioners regarding precisely whatthe proper utilisation of any given method is (and even regarding the inclusion or not of agiven approach in thus category of methods), a common feature of these approaches is thatthey apply more of a qualitative approach rather than focusing solely on assigning monetaryvalues. Therefore, they do not, in general, easily fit into the more formal process of economicappraisal that aims to capture the total economic value. They rather tend to explore how opini-ons are formed or preferences expressed in units other than money. These can elicit valuesoften by asking people to explain or discuss why they behave in a particular way or hold a


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particular view. The focus can be on what people think society should do, rather than on theirpersonal behaviour (Schmeer, 1999; van Asselt and Rijkens-Klomp, 2002). Participatory methods obviously have a role to play in understanding people's preferences andthe process of decision-making and may therefore influence policy choices. A non-exhaustivelist of such approaches includes (Table 3): qualitative semi-structured surveys, focus groups,citizens' juries, Q-methodology, and Delphi surveys.

Table 3 Main Participatory (or Deliberative) Valuation Techniques

Method Suitable for Ability to getmonetaryestimates

Use /non-usevalue

Data requirements Limitations

Qualitativesemi-structuredsurveys

Gathering baseline infor-mation about public attitu-des, views and behaviorabout any natural resour-ce or policy issue

No Both No backgrounddata required

No scope for deepexploration of issu-es; No internet ormail surveys

Focus / in-depthgroups

Discovering the positionsof participants regarding,and/or explore how parti-cipants interact when dis-cussing, a pre-definedissue or set of relatedissues (e.g. choice bet-ween conflicting objecti-ves)

Generallynot, but itmay be if pro-perly imple-mented (e.g.,group valuati-on)

Both Some backgrounddata required

Lack of statisticalrepresentativeness,lack of inference;Need of personalinteraction (i.e. face-to-face)

Citizens'juries

Citizens' decision makingintegrated into deliberati-ve processes

Generallynot, but impli-citly it maylead to mone-tary estima-tes throughdeliberativechoice

Both Heavy backgroundinformation/datarequired (fromexperts or otherstakeholders)

Very labour intensiveand data deman-ding; Lack of statisti-cal representative-ness, Need of perso-nal interaction (i.e.face-to-face)

Q-methodology

Identifying 'typical' waysin which people under-stand, think and feelabout any issue, discove-ring attitudes, shared per-ceptions, developing 'typi-cal' sets of views

No Both No backgrounddata required

Very labour intensiveand time consuming

Delphi surveys /systematicreviews

Discussing/reviewingcomplex and/or speciali-sed issues, wheneverthere is a need to drawtogether and summarisesa wide range of literatureor expert opinion

Generallynot, but itmay be if pro-perly imple-mented (e.g.,benefit trans-fer)

Both Heavy backgroundinformation/datarequired (from lite-rature and/orexperts)

Very labour intensi-ve, time consumingand data deman-ding; It may be per-ceived as a threat toother deliberation


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DefinitionQualitative semi-structured surveys and interviews are very useful for eliciting broad, baselineinformation about public attitudes, views and (reported) behaviour and the reasons behindthese. They do not give scope for discussion and deep exploration of issues, but do allow forlarge, statistically significant samples.

Valuation contextThis approach can be applied to cover any natural resource or policy issue. However, the tech-nique is best suited to 'taking the pulse' of existing attitudes on environmental issues and beha-viours, rather than engaging the public in complex reflection on new and challenging topics.Questions can also be directed at estimating policy impacts/effectiveness, for example trying tofind out how respondents might alter their behaviour under a particular policy (however, thereis always a problem here going from stated intentions to actual responses).

LimitationsA limitation of qualitative surveys is the low ability of making inference from the sampledinformation, although if those information stem out from large size samples there is the possi-bility to analyse correlations between demographic and attitudinal factors. Of course, these sur-veys are for their very nature restricted to face-to-face or telephone interviews (i.e. they canonly very hardly be administered through the internet or standard mail).

2.3.2 Focus Groups and In-depth Groups

DefinitionFocus groups and in-depth groups are research methods which can be integrated into deliberati-ve processes. Focus groups aim at discovering the positions of participants regarding, and/orexplore how participants interact when discussing, a pre-defined issue or set of related issues,for example revealing how people think and how groups develop a discourse on complex envi-ronmental issues and trade-offs. In-depth groups are similar in some respects, but they maymeet on several occasions, and are much less closely facilitated, with the greater emphasisbeing on how the group creates discourse on the topic.

Valuation contextConceptually there are no limits to the issues that may be covered in focus groups and in-depthgroups. In theory focus groups can consider any concept of value, although monetary valuationof an environmental good or service is generally not the objective of the exercise. More likely,the group will focus on how to choose between conflicting objectives, or on what decisionshould be made in a particular circumstance, or on the reasons underlying particular behaviorsor responses to policy.

LimitationsThe most severe limitation of focus groups and in-depth groups is the lack of statistical signifi-cance of the views expressed by participants. The gain in terms of flexibility of this technique hasa pay-off in terms of inference. In fact, unlike the economic valuation approaches, focus groupsdo not allow any statistically significant extrapolation of results to the relevant population.


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2.2.3 Citizens' JuriesDefinitionThe main aim of a citizens' jury is to obtain informed citizens input on policy decisions. It isbased on the rationale that, given adequate information (from experts and other stakeholders)and opportunity to discuss, such a jury can be trusted to take decisions regarded as legitimateand fair on behalf of the community. Participants are charged with acting in what they perceiveto be the public interest.

Valuation contextPotentially citizens' juries can capture all values in a framework of deliberative choice. Generallythe emphasis is on citizen values rather than private values, although values are not expressedquantitatively or directly. The citizens' jury process can be applied to any resources, but they areparticularly suitable where there are fundamental and/or complex social choices at stake.

LimitationsTypically citizens' juries are very labour intensive. For researchers, facilitators, jurors and wit-nesses expenses and compensation for time must be allowed for as well as the costs of meetingfacilities and support. Ensuring representativeness of the affected population (if desired) couldalso be a limiting factor.Data and information required for citizens' juries is not trivial typically including basic back-ground research and briefings. As Lenaghan et al. (1996) point out, the difficulty is ensuringthat this information is neutral, at least on balance.

2.3.4 Q-MethodologyDefinitionQ-methodology aims at identifying typical ways in which people think about environmental (orother) issues. The key difference of the Q-methodology from standard survey analysis is thatrather than focusing on why individuals hold certain attitudes (e.g. as affected by age, gender,race, income), Q focus on patterns of attitudes, or discourses, shared perceptions, developing'typical' sets of views (which need not exactly represent the views of any specific individual).

Valuation contextWhilst Q-methodology can potentially capture any kind of value, the process is not explicitlyfocused on 'quantifying' or distilling these values. Instead it is concerned with how individualsunderstand, think and feel about environmental problems and their possible solutions. The Q-methodology approach is useful for initial analysis flagging up common issues or viewsamongst the public. This can help establish which policies are likely to meet with wide supportor resistance.

LimitationsImplementing Q-methodology is typically quite labour intensive and requires a reasonably longtime frame.

2.3.5 Delphi Surveys and Systematic ReviewsDefinitionThe intention of Delphi surveys and systematic reviews is to produce summaries of expert opi-nion or scientific evidence relating to particular questions. Delphi surveys are essentially ameans of synthesising expert opinion rather than a way of uncovering public values or attitu-des. Systematic reviews are a technique for robust and systematic literature search and reviewof evidence, pioneered in health research and more recently applied in the environmental field.


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Valuation contextBoth Delphi and systematic reviews can be applied to any type of values for a given type ofissue. Delphi and systematic reviews may be particularly useful for situations in which the issu-es are complex and/or specialised, and where there is a perceived need to draw together andsummarise a wide range of literature or expert opinion. Where an issue is uncertain and conte-sted, these methods can help decision-makers to understand why the scientific community isdivided, and how the current balance of opinion appears to lie.

LimitationsBoth Delphi and systematic reviews are likely to be time consuming. Delphi may require seve-ral weeks to months depending on how quickly experts can reply and how many iterations arerequired. Another disadvantage is if this process is seen as a threat to other deliberation (becau-se scientific results are presented as being 'the right answer') or if it is interpreted as havingmore statistical weight than is true (see discussion above). However, if research is conducted,presented and used in light of 'best-practice' these concerns should be minimal.

2.4 Which Technique for the LUF Valuation in the Sensor Project?The review carried out so far shows that there are two groups of methods to valuing LUFchanges: the 'economic valuation methods' and the 'participatory (or deliberative) methods'(EFTEC, 2006). The main differences between the two groups of methods are the following:a) economic valuation methods are based on economic theory and aim to quantify all or

some components of the total economic value. These methods assume that individualshave preferences for or against environmental, social and economic changes as pre-exi-sting and generally stable constructs (which is not to say they may not change overtime given experience, education and information concerning a particular issue). It isalso assumed that individuals can trade-off both between different environmental, soci-al and economic changes and between those changes and monetary amounts, whichleads to the possibility of a monetary assessment of those changes. Essentially, econo-mic methods study choices or trade-off decisions, and from this the idea of preferencesand values consistent with those decisions;

b) participatory (or deliberative) methods focus on what people think society should do –not on their personal actions, motivations or values37. Those methods consider that pre-ferences about complex environmental, social and economic matters are only formedthrough a process of deliberation and focus on the processes of decision-making andmanagement in terms of procedures. Those methods may also examine the valuesunderlying decisions, but do this by asking people to explain or discuss why they beha-ve in a particular way, or hold a particular view. Very rarely they allow for a monetaryevaluation of environmental change38.

We are now in the position to answer the first research question raised in the introduction: onthe basis of the evidence reported in the literature, is there any technique which is suited for theanalysis of stakeholder targets and preferences with specific reference to the land use functi-ons? In principle, both economic valuation and participatory approaches can be used for LUFassessment within the SENSOR project. However, the choice is not a case of either economicor non-economic valuation methods but of using a combination of both, as required by the con-text of the decision. In other words, the two groups of methods are not alternatives, rather com-plements.

37 In this sense they can be (but are not necessarily) very different from economic methods, which focus on theindividual level, and apply external value judgments about how individual values should be aggregated toreach a social welfare assessment.

38 Although there is the possibility of doing so, as in the case of the so called 'group valuation' discussed insection 3 below.


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In fact, LUF valuation intervenes at different levels within the SENSOR project framework: atan earlier stage the 'stakeholder analysis', providing some insights to the SIAT about the sustai-nability space with specific reference to LUF change in a given regional context; at a laterstage the 'policy impact assessment', when alternative policy options have been set by the poli-cy maker and the relevant impacts have been forecast through SIAT simulations.

If we keep in mind the differences between these two valuation exercises we are able to con-clude that participatory approaches are better suited to carry out what in the SENSOR projectjargon is properly called 'stakeholder analysis' aiming at identifying stakeholders' targets,understanding how do they form their own views and preferences, and assessing stakeholders'acceptability of the implied LUF change (i.e. exploring the sustainability space comparing theimplied changes and the sustainability limits as well as analysing the trade-offs between theimplied changes).

Vice versa, in the overall assessment of a given policy impacts – a 'policy case' assessment,according to the SENSOR project jargon – people’s preferences towards policy-induced econo-mic, social and environmental changes are assumed as preexisting constructs and the focus ison selecting the most preferred policy option or on ranking the various policy options at handaccording to a given criterion. This is where economic valuation methods work better. In parti-cular, given the inherent complexity and multidimensionality of the implied changes (cf. secti-on 2.2.5) the best suited approaches are choice modeling techniques (choice experiments andcontingent rankings).

Therefore, in the following sections the focus will be on how to implement participatoryapproaches for stakeholder analysis, with specific reference to the so-called 'group valuation' andits application to the Malta case study, and on internet-based policy assessment, with specificreference to the bio-energy policy case and its implementation through a cross-European survey.


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3 How to Implement Stakeholder Analysis: Group Valuation The objective of the SENSOR project is to produce Sustainability Impact Assessment Tools(SIAT) for land use European policy. A clear understanding of the impacts of land use policy isnecessary in order to stimulate efficient and sustainable policy choices. SIAT aims at facilita-ting this process. In this context stakeholder consultation plays a central role as stakeholderopinions, values and preferences need to be taken into account when making policy choices.In order to stimulate communication and an exchange of views, opinions and values among sta-keholders it is necessary to bring them together and let them participate in policy choices.Participatory processes create a social and political space – 'forums' for exchange that areorganized for the purposes of facilitating communication between stakeholders such as govern-ment, citizens, stakeholders/interest groups, and businesses regarding a specific decision pro-blem (Renn et al., 1993).

This section deals with 'group valuation', a suitable participatory approach that pursues threemain objectives in the stakeholder analysis within the SENSOR project framework:a) assessing stakeholders' targets, views and preferences about LUF changes,b) validating previous work on regional sustainability problems done within SENSOR,

andc) attempting a monetary estimation of LUF changes.

This section is organized as follows. First a brief background on stakeholder analysis and parti-cipation is provided, explaining the rationale and methodological implementation of groupvaluation. In the second part, details about how group valuation should be practically imple-mented are reported, with specific reference to one of the SENSOR case study, i.e. Malta.

3.1 Who is a Stakeholder?A stakeholder has a declared or conceivable interest or stake in a policy concern (Schmeer,1999). Stakeholders can be of different form, size and capacity including individuals, organisa-tions, or unorganised groups. In most cases, stakeholders fall into one or more of the followingcategories: international actors (e.g. donors), national or political actors (e.g. legislators, gover-nors), public sector agencies (e.g. MDAs), interest groups (e.g. unions, medical associations),commercial/private for-profit, nonprofit organisations (NGOs, foundations), civil society mem-bers, and users/consumers. Government institutions are stakeholders for resources in their juris-diction, and citizens of other countries may be stakeholders when they derive welfare from thelong-term indirect-benefits from land use functions such as carbon sequestration, tourism andnature conservation.

A broadly used stakeholder classification is according to the way they have a vested interest ina decision problem or alternatively from the degree that they affect and/or are affected by thedecision and valuation process (Grimble and Wellard, 1997; Banville, 1998; de Groot et al.,2006). Basically, three groups are distinguished: a) one category of stakeholders concerns the degree that they can influence the decision

making (the way the decision problem is formulated or solved) and valuation process,b) a second category concerns the degree that stakeholders would be affected by the out-

come of the decision making process,c) finally, there is the last category of stakeholders that can influence the decision making

process but are also affected by the outcome.

Furthermore, stakeholders can also be categorised according to their institutional organisationand scale: stakeholders can have a vested interest at a global, national, regional or local level(Grimble and Wellard, 1997).


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Stakeholders have four main attributes regarding their interests in LUF (de Groot et al., 2006): a) the type of function use practiced by the stakeholders and their impact on the function, b) the level of influence (power) they hold, c) their degree of dependency on the land use functions (availability of alternatives)

and/or the way they are affected by function loss, and d) the group/coalition to which they belong.

These attributes can be identified through various data collection methods, including interviewswith country experts knowledgeable about stakeholders or with the actual stakeholders directly.It is clear that the stakeholders deriving benefits from an ecosystem may be just as diverse asthe land use functions themselves. Nevertheless, it is crucial to consider the differences in sta-keholders when analysing LUF, as stakeholder interests and access rights will determine theinterests and motivations of stakeholders in managing the resource, and management plansneed to be fine-tuned with these interests in order to obtain stakeholder collaboration at diffe-rent levels.

3.2 What Group Valuation Is About?Group valuation processes are examples of participatory or deliberative approaches (with stake-holders at the center of the process, cf. section 2.3) that can be used to support environmental-social-economic and land use decision making. They are a mix between a participatory techni-que, which takes into account elements of the various processes described in Table 2, and mostclosely resembling focus groups, and an economic valuation technique. More precisely, groupvaluation combines analytical methods (i.e. a valuation technique) with a process-based delibe-rative approach, where group processes are a key mechanism to elicit information.

An important difference between group valuation and economic valuation techniques is that thelatter always seek for information on individual preferences. However, for many land use functi-ons, such as the provision of an attractive land use and amenity functions, there is no market andindividuals are not used to attributing value judgments on these aspects. Therefore many stake-holders will find difficult to express, as individuals, their preferences with regard to land usechoices. Furthermore various studies have shown that value estimates for such functions varyvery widely between stakeholders, and it may not be easy to come to a reliable value estimate.

With group valuation, it is attempted to stimulate a discussion and to modulate stakeholders toreach a consensus on the value of certain land use functions and preferences of certain land usechoices. Consensus however is not forced and when necessary differences should be respected.The specific advantage is that, because of the group and deliberative processes, stakeholdersare encouraged to debate on their value statements, share opinions and thus increase awarenessof their own values, which is assumed to lead to more reliable indications of certain prefe-rences for stakeholders.

Hence, group valuation is a deliberative, discourse-based valuation approach, which seeks toelicit rational value statements based, when feasible, on group consensus. While not specifical-ly targeted to economic values, Wilson and Howarth (2002) argue that value statements fromdiscursive deliberative methods may be meaningfully reported in monetary terms39. The mone-tary values derived using these methods would be thought as estimates of 'social' willingness topay and social trade offs rather than 'individual' willingness to pay.

39 In this way, they can be used to be compared or to complement results from economic valuation methods.For example, Macmillan et al. (2002) compared contingent valuation with group based approaches, andfound that group valuation offers important advantages over the survey interview approach especially forunfamiliar and/or complex environmental goods such as land use functions.


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The main design characteristics of a deliberative discourse-based approach to valuation are(Sagoff, 1998): a) the analysis should be based on small groups (e.g., citizens, stakeholders) rather than

individuals (as is the case in conventional economic valuation techniques). Smallgroups considered generally to involve more than two and less than twenty participants,

b) all participants should be allowed to participate and express their preferences, viewsand values on the land use services and products, and

c) all participants should strive towards consensus based building workshops with theassistance of facilitators.

The conceptual principles of the group valuation approach are rooted in behaviour and decisionanalysis. The main focus and scope of this approach is to provide the means and the process toparticipants to construct their preferences and values rather than simply expressing them. Inthis analysis stakeholder preferences, views and values will be elicited through multi-attributeutility analysis.

3.2.1 Multi-attribute Utility Analysis as Basis of Group Valuation

Multi-attribute utility analysis (MAUA) is a careful preference elicitation process where thevaluation of environmental resources is based on the in depth knowledge of the issue from therespondents/stakeholders and their values are rather constructed through the process than revea-led as a "true" values. Multi-attribute approach involves structural steps and concepts conside-red as appropriate stages for a constructive preference and value elicitation process (Gregory etal., 1993; Gregory and Slovic, 1997). Based on this and considering the specific land use poli-cy context of the SENSOR project, the following steps are proposed: a) problem identification and problem structuring, b) selection of land use functions, criteria and indicators, c) impact assessment and limits, d) elicitation of stakeholder preferences, e) revision of stakeholders preferences and search for group consensus, f) calculation of total aggregated value (TAV).

It is important to point out that this operational framework has been developed in close colla-boration with SENSOR Module 7 partners who are also working with stakeholder participationin policy choices. Steps from a) to c) are exactly the same used also in Module 7 tasks40.However the last steps of group valuation method presented here (i.e. form the elicitation ofpreferences on) slightly differ from the work carried out in Module 7. Major differencesdepend on the fact that in implementing the group valuation method:a) multi-attribute utility analysis is used as basis for group valuation, b) stakeholders express their preferences by knowing the performance of each land use

function for each selected scenarios, c) stakeholders are invited to think in terms of trade-off when expressing their prefe-

rences,d) monetary valuation is included in the analysis.

40 The interested reader is invited to refer to the work done in Module 7 for a thorough analysis of common ele-ments and differences between the stakeholder analysis as carried out in Module 3 (i.e. group valuation) andthe one carried out in Module 7.


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3.2.2 Operationalising Multi-attribute Utility Analysis

Problem Identification and Problem Structuring

This first step is to identify the issue under consideration, to agree on the focus and the scopeof the analysis. At this stage all elements (state of the art, conflicts, future plans, etc.) concer-ning a specific land use policy under analysis (e.g. implementation of biodiversity policy)should be identified. Stakeholders with an interest or stake in the policy case should beselected41. In the presence of multiple stakeholders a common understanding of the policy caseshould be achieved through discussion of the background information, ideas and the sharing ofconcerns. Main issues, constraints, strengths, feasibility related to the selected policy caseshould be openly discussed. The next step is to structure the decision problem by defining possible alternatives (scenarios)that should be taken into account in order to implement a certain policy. For example if biodiver-sity is the policy case under analysis one could think of three alternatives, let's say scenario A 'lowbiodiversity protection', Scenario B 'medium biodiversity protection' and Scenario C 'high biodi-versity protection'. Furthermore, a set of policy variables should be selected, through which detailhow to implement the possible alternatives. For example under the alternative 'low biodiversityprotection' it can be foreseen an increase in the area of protected areas; whereas for a 'mediumprotection alternative' it can be foreseen a moderate increase in protected areas, and so forth). Aspecific and detailed analysis of policy alternatives and policy variables is recommended.

Selection of Land Use Functions and Indicators

Once the problem has been structured it is possible to select the LUF that are relevant for thepolicy case and the area under study. Land and ecosystems have a variety of functions that to-gether with human skills can contribute to the well being and survival of humanity (MEA, 2005;de Groot, 1992; de Groot et al., 2002).Within the context of SENSOR project the LUF are defi-ned as the potentiality of human and natural resources in the region, the use and non-use valuesof these resources (i.e. the performance), the economic and social construction of space. NineLUF have been selected (cf. Annex 1), based on the social, economic and environmental pillars :a) social: provision of work, human health and recreation, cultural aspects, b) economic: residential and non-land-based industrial and services, land based production, infrastructure/mobility, c) environmental: provision of abiotic resources, support and provision of habitat, maintenance of ecosystem processes.

Considering that each LUF has a quite broad meaning, in order to identify which aspect of theLUF is taken into account, a specific criterion is selected for each LUF. The criterion selectedrepresents thus a specific aspect of the LUF considered in the analysis. Furthermore each soci-al, ecological and economic LUF and corresponding criterion is linked to specific indicators.For example, within the social dimension and for the LUF 'cultural aspects' stakeholders mayselect the criterion 'cultural heritage and national identity value' and may select a correspondingindicator, say the 'number of designated and managed heritage sites'.41 In this selection the main elements discussed in section 3.1. should be taken into account.

42 A more detailed description of the meaning of each LUF can be found in the SENSOR website(http://www.sensor-ip.org/) or more specifically in SENSOR Deliverable 3.2.1.


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Impact Assessment and LimitsThe aim of this step is to obtain the impact of each land use policy alternatives on the LUF andcorresponding indicators and to estimate limits. In order to estimate the impacts, the indicatorsshould be measured with a qualitative, quantitative, monetary scale mainly according to feasi-bility and availability of data. However, at least for one indicator the impact should be expres-sed in monetary terms. Furthermore the limits of impacts will be estimated. These are the limitsbeyond which the losses in functionality cannot be accepted (because they may compromisethe current and future availability and use of environmental, social, economic resources).Information about the qualitative/quantitative/monetary impacts and limits should be based onanalysis of existing data or data gathered from field work or (mainly) through experts'judgments and/or estimations.

Elicitation of Stakeholder Preferences This task aims at capturing the stakeholders' preferences on indicators, criteria and correspon-ding land use functions. Preferences cannot be considered as definite and a priori fixed, butthey are adapted during the decision making process by means of specific learning techniquesenabling decision makers to become aware of their preferences and values. Not all functionsand associated indicators are equally important. Here the concepts of 'weights' and 'trade offs'are introduced as operational elements of MAUA. In MAUA, weights play the role of scalingfactors in the sense that they relate scores among the different indicators and functions43. Byassigning weights denoting relative importance, stakeholders implicitly express how much ofone indicator/function they are willing to give up (trade off) in order to improve the perfor-mance of another function/indicator by one unit. The elicitation of preferences is based on theso-called 'swing method' where monetary equivalents of land use functions could be obtainedin the presence of at least one indicator expressed in monetary terms44.

Revision of Stakeholder Preferences and Search for Group ConsensusAfter stakeholders have expressed their own preferences, the results in terms of weights andvalues will be discussed. Every stakeholder can argue and/or reconsider his initial preferences.Thus, each single stakeholder may realise ambiguities or false expression of his own values andpossibly ask for a reconsideration of his initial judgments. A structured discussion, supportedby specific computational techniques, aiming at discovering main sources of divergence and atjustifying or rejecting judgments is usually of great help in enabling the revision of preferentialaspects. This will lead to an interactive and informative discussion within the group on the dif-ferent views and values of LUF and the importance given to different environmental, social andeconomic aspects. Thus, it is possible to gradually arrive at a commonly accepted solution thathas more chances to be adopted by all stakeholders and successfully implemented. Furthermorefor each land use alternative the impacts on LUF will be identified. Then these impacts will becompared to the limits of loss of functionality (cf. section 3.2.2.3). Results could be represented in various ways. Both quantitative and qualitative informationresulting from the group valuation workshops need to be conveyed through maps, diagrams,tables as illustration for SIAT and also fact and information sheets. An example of how to pre-sent some of the results for a hypothetical Scenario A is depicted in Figure 2, where the impor-tance (0 = not important; 10 = very important) given to LUF by stakeholders, the (monetised)positive and negative impacts on functions and the acceptable limits in loss of functionality arerepresented.

43 Weights play in MAUA the same role as shadow prices in traditrional cost-benefit analysis.

44 See Annex 3 for an explanatory note about how weights and monetary values are calculated.


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Fig 2 Example of results for a hypothetical Scenario

If the negative impacts are beyond the relevant limits, the implementation of that specific scenario should be seriously reconsidered, especially if the LUF taken into account has a highimportance (weight). Through this visual representation it is showed to which extent the impacton LUF in a given scenario can be acceptable. In the example depicted in Figure 2, the impacton the function 'human health and recreation' is more negative than what is acceptable (limits),whereas for 'land based production' the impact can be considered sustainable because it is morepositive than the acceptable limit, etc. This information could be useful in order to think aboutadjustment that could be made in terms of policy variables before implementing a policy whichwill result in a given scenario and about rooms for maneuver that are available.


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Calculation of Total Aggregated ValueIn this step the monetary equivalents of all impacts on the indicators and corresponding criteriaand land use function will be estimated. Consequently the total aggregated value (TAV) forevery land use policy alternative can be calculated. Although the calculation of the TAV is not akey objective within the SENSOR project, this could be seen as an added value which can pro-vide useful information to policy makers and other stakeholders when making plans for theimplementation of policy alternatives.

As mentioned above, if at least one indicator is expressed in monetary terms, it is possible to cal-culate the monetary equivalents of all other qualitative or quantitative impacts (Annex 3). Withthe swing method the preferences of stakeholders are expressed as relative values vi, with vi = 10for the most important function/indicator (the first swing from worst to best performance) and vj< 10 assigned in a decreasing order to the other functions/indicators from the second-most impor-tant to the least important. This relative value rank vi is then transformed to normalised weightswi. Indirect monetisation is based on the assumed equivalence of the stakeholders' relative prefe-rences, relating the weight calculated for each physical impact to the weight of the function/indi-cator in monetary terms, both expressed on the corresponding impact scale.

The calculation of utility or value for each land use policy alternative implies the use of anaggregation formula together with the weighting factors, or their monetary equivalents, provi-ded by the stakeholders. The simplest and most commonly used aggregation model is additive:

where, U(a) is the total value associated with each policy alternative a, and wi is the weightattached to each indicator i by the stakeholders. Finally, policy alternatives can be rankedaccording to U(a) from the highest to lowest value.

3.3 The Implementation of Group Valuation: The Malta Case StudyThis section describes the practical set-up of the group valuation process which will first betested in Malta. The policy case selected for the Malta case study is 'biodiversity'. The mainobjective of the workshops is to test how a participatory approach can be used at a regionallevel to analyse the implications of different land use policy alternatives for economic, ecologi-cal and social values of LUF in a multifunctional land use context. Two workshops have beenplanned in Malta in 2007. The first workshop (with experts) will mainly focus on problemidentification and problem structuring, selection of land use functions and indicators, impactassessment and limits. The second workshop (with the main stakeholders), a few months later,will focus on the remaining steps focused on elicitation and revision of preferences, search ofgroup consensus, calculation of the TAV and final results.

A key methodological aspect of the workshops is that they allow for a deliberative processthrough which stakeholders can come to a common understanding of complex values, includingvalues for amenity aspects (e.g. landscape beauty and effects on health) and other 'public services'such as influence on air quality, water availability and conservation values. A number of activitieshave to be undertaken to ensure the maximum potential outcome from the workshops, in both thepreparation, implementation (of the workshop) and evaluation phases, as described below.

∑ ⋅=i

i auwaU i )()(


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3.3.1 Preparation Phase In the preparation phase, both organisational matters and a number of content-aspects have tobe undertaken. In terms of organisation, a suitable workshop facility needs to be reserved, par-ticipants have to be selected (according to the criteria described in a previous section 3.1) andinvited, catering has to be organised, and it should be decided how the interest of potential par-ticipants is ensured, and what level of compensation they should receive (for instance travelcosts and a meal). For the success of the workshop, it is crucial that the participants receive aclear insight into the objectives and approach of the workshop as well as the relevant back-ground information to ensure efficiency of the workshop discussions.

This information should be sent to participants a couple of weeks before the workshop.Furthermore this information will be condensed into a presentation to be held at the beginningof the workshop. It is important to focus and narrow down the objective of the discussions inview of the limited time that will be available during the workshop. For the first workshop therelevant background information includes: general issues about the selected policy case; poten-tial policy options and a list of possible variables that could be considered; definition of landuse policy alternatives; a first draft assessment of the LUF, possible criteria and indicators (andways to measure them) which will be used later for the impact assessment; a first assessment oflimits to impacts conducted on the basis of inputs from various local and national experts orfrom available data, studies, or from field work; a preliminary identification of stakeholdersaffected by a given policy alternative.

3.3.2 Implementation Phase

A key issue during the workshops is that all participants get an equal opportunity to expresstheir views and contribute to the deliberative process. This requires that moderators/facilitatorsare present at each session. These people should be experienced with facilitation in order toensure that they are not influencing the contents of the discussions, but rather focus on the pro-cedures. It is conceivable that widely varying views exist among stakeholders, e.g. with respectto the values of different functions. Where possible, a consensus view needs to be found, butprovisions need to be made for reporting heterogeneous views. As mentioned above, it is envi-saged to organise two workshops.

Workshop 1 (background information and land use impact assessment)Workshop 1 will take place in Malta during Spring 2007. Only experts will be invited to theworkshop. The aim of this workshop is to structure information and issues about the biodiversi-ty policy case in Malta, perform a land use impact assessment and define limits for selectedindicators. Several experts from different backgrounds are invited to provide their views onbiodiversity issues in the area. Workshop 1 is divided in three sessions as follows.

Session 1 (objectives, policy case, land use alternatives)During the first part of this session background information are presented with power point.Here the structure of the workshop is presented (i.e. division in sessions, breaks, lunch). Thenthe general objectives of the project are highlighted (e.g. description of the project, partners,aims, objectives). It follows a general description of the study area, the policy case selected,policy land use alternatives, policy options. In the second part, participants are asked to providegeneral comments on the project and specific feedback on proposed policy alternatives andoptions (e.g. participants are asked to express their opinions about the applicability and impli-cations of the alternatives and options presented and in case to identify additional ones andjustify their choice).

Session 2 (impact assessment and limits)In the first part, specific information about the ecological, socio-cultural and economic LUF,


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and potential corresponding criteria and indicators are briefly presented with power point.These may be physical indicators (e.g. number of species or amount of agricultural product) orsocial and economic indicators (e.g. employment, income). The second part will deal with par-ticipants' reactions. Participants are asked to provide their opinion/feedback on the indicatorsselected, the way to measure them, their relation to land use functions. Additionally, expertswill provide their opinion on potential impacts, provide a qualitative assessment of the impactson land use functions, discuss limits of the impacts on land use functions, and suggest quantita-tive and/or monetary impact assessment studies, dealing with a similar issue that can provideuseful information in this analysis.

Session 3 (types of stakeholders, conclusion, recommendation)During this last session a list of types of stakeholders affected by the selected policy optionswill be presented. This list is very important also in order to identify the stakeholders that needto be invited to workshop 2. The identification and selection of stakeholders will be doneaccording to criteria discussed in section 3.1. Participants can provide feedback on the list ofstakeholders presented. Conclusion and final recommendations will complete the session.

Workshop 2 (elicitation of preferences and results)Workshop 2 will take place in Malta about six months after workshop 1. Various stakeholderswill be invited. The central aim of this workshop is to stimulate discussion among stakeholdersabout their preferences with regard to land use functions and land use choices. Furthermore thisworkshop aims at identifying stakeholder preferences about the importance of selected indica-tors and functions keeping in mind the concept of trade off, at discussing about how sustainableand acceptable are the impacts of land use alternatives, at identifying most sustainable land usealternative, etc. this workshop can be broken down into two sessions as follows.

Session 1 (introduction and elicitation of preferences)During this session, the whole valuation exercise will be explained to participants in details.First of all background information and results of workshop 1 will be briefly summarised.Participants may be divided into groups, according to the types and the number of stakeholders,and each group will be required to express their preferences on the relative importance of landuse functions. The elicitation of their preferences will be based on the swing method where fac-tors of relative importance and monetary equivalents of indicators/functions can be obtained inthe presence of at least one indicator expressed in monetary terms. Weights derived from swingmethod play the role of scaling factors. This means that by assigning weights of relative impor-tance, stakeholders implicitly determine how many units in one function they are willing togive up (trade off), in order to improve the performance of another function by one unit. So, ifthe weight of function a is double as much the weight of function b, then if the stakeholderassigns a value of 5 units to function a, function b should be valued 10 units.

Session 2 (reaching consensus and final results)The preferences expressed in session 1 will be presented to all participants. Participants canreact on this and express their agreement and/or disagreement. The divergence of preferences,weights and monetary values of stakeholders on different type of indicators/LUF will arise anin depth and informative dialogue between stakeholders, yielding important information aboutsocial trade-offs and monetary estimates for the social value of land use functions. Stakeholderswill always have the opportunity to reformulate their initial judgments and thus to convergeand agree after a thorough discussion on the final selection and agreement on functions'weights and their monetary equivalents. This process will eventually lead to the computation ofTAV and the possibility to rank the alternatives (with and without considering the limits forland use functions). Furthermore this analysis will stimulate a discussion on sustainability issu-es, on limits to impacts and the sustainability of land use alternatives under analysis.


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3.3.3 Evaluation Phase

In the evaluation phase, both the process and the outcomes have to be evaluated. The processneeds to be evaluated by the facilitators also taking into account any positive/negative com-ments pointed out by participants as well as suggestions. Based on the lessons learnt, the proce-dures for future group valuation processes in other areas can be enhanced. The outcome needsto be evaluated on the basis of the inputs given by experts and stakeholders during workshops1 and 2 and also taking into account the preliminary work done when consulting literature ordoing fieldwork.


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4 How to Implement a Policy Impact Valuation: Internet-based Choice Experiment

The main conclusion of section 2 is that participatory valuation methods are better than econo-mic valuation techniques in identifying stakeholders' targets, understanding how they form theirown views and preferences, and assessing stakeholders' acceptability of the implied LUF change(i.e. stakeholder analysis in section 2.1). In particular, this will be carried out in the SENSORproject module 3.3.4 with the so-called group valuation. However, it has also been stressed thatthe gain in terms of flexibility of participatory approaches has a pay-off in terms of inference. Infact, unlike the economic techniques, participatory approaches (group valuation included) do notallow any statistically significant extrapolation of results to the relevant population.

Therefore, there is a need to find out a way to complement group valuation in order to providea more robust inference basis to the stakeholder analysis. Considering the importance givenwithin the SENSOR project to the information and computer technologies, a natural candidateis the use of the internet as a medium for doing this. However, this is not costless since theadvantages in terms of reduced costs and larger sample size ensured by an internet-based sur-vey come at cost of a lack of flexibility in the interaction within the surveyed sample whicheventually prevents to explore the richness of different stakeholders' targets and preferences aswell as how a consensus can be reached. Therefore, internet-based surveys can be used onlywhen clearly defined, simple questions/issues are presented to the respondents. As such, onlygeneral questions like, for instance, the relative importance of LUF can be explored throughthis medium.

On the other hand, in a properly designed valuation exercise, internet represents a verypowerful tool for a quick and vast assessment of competing policy alternatives. This is precise-ly the second typology of valuation exercises included in the SENSOR project framework (i.e.the so-called 'policy impact assessment' in section 2.1), that is the one to be carried out whenalternative policy options have been set by the policy maker and the relevant impacts havebeen forecast through SIAT simulations. This is precisely what is asked for by the SENSORproject implementation plan in module 3.3.3 with specific reference to the bio-energy policycase45. Therefore, in the following sections we will address the main issues related to the inter-net-based survey, detailing the different steps we have to go through in order to deliver. We willstart with a brief introduction to internet-based surveys, summarising their advantages andaddressing some implementation issues (i.e. equipment requirements and software develop-ment). Then we will deal with survey design issues and we conclude focusing on the most critical task, that is the design of questionnaire.

4.1 Why an Internet-based Survey?Internet-based surveys can offer several advantages vis-à-vis traditional surveys (such as perso-nal, telephone or postal interviews), namely:a) a faster data gathering as compared to traditional surveying techniques (Kehoe and

Pitkow, 1996; Smith, 1997; McCullough, 1998). For example, studies have shown thatseveral hundred responses can be generated over the course of a single weekend. Thistime factor alone suggests huge benefits in terms of being able to collect and quicklyanalyse data and implement decisions based on the findings;

b) reduced costs because both data collection and analysis can be minimised by the use of

45 The third Annual Report - New Implementation Plan for months 37-54 specifies the activities to carried out inmodule 3.3.3 as follows: 'Carrying out an internet-based valuation study on the bio-energy policy case in nineEU27 countries aiming at assessing public acceptance/preferences among alternative policy packages provi-ded by SIAT simulation (M4). This analysis will be complementary to the group valuation (WP3.3.4)'. Bio-energy was chosen as a case study because it has been the first policy case for which the results of SIATsimulations has been made available.


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internet-based surveys (McCullough, 1998). Over and above high start-up costs forequipment and web-page design, the actual implementation of a survey can be almostfree of charge, with no costs for paper or postage;

c) simpler data analysis: direct transfer from the web-page form to the analysis softwarewill eliminate any data entry activity and limited data cleaning would be necessary(McCullough, 1998);

d) higher response rates because of the anonymity in replies, respondents may be morewilling to respond without fear that their answers may be identified (Kiesler andSproull, 1986);

e) minimised interviewer error because respondents fill their answers directly in a form ona web-page. Therefore, survey responses will be free from errors caused by interview-ers, resulting in cleaner data (Schillewaert et al., 1998; McCullough, 1998);

f) minimised interviewer bias because the lack of an interviewer eliminates any potentialfor bias that the interviewer may imply (McCullough, 1998).

However, all these advantages should be contrasted with some issues that relate to the requiredequipments and software development. The realisation of an internet-based survey can beaccomplished either contracting out or developing everything internally. The first option ismore expensive because the cost related to software development and sampling must be incur-red. Furthermore, contracting out means that the overall research process can be monitored byresearchers only to a limited extent. Therefore, in SENSOR we will adopt the second option.However, in this case there is a need to have a web server to host the survey and to develop thesoftware necessary to launch the survey. The development of the web-site requires the use of HTML and internet language program-ming (Java Script, PHP and/or ASP.NET) necessary to satisfy particular research needs and tolink data coming from the web to the database (Figure 3). In the development of the software itis also necessary to standardise the survey for the most important browsers (Internet Explorerand Netscape) in order not to lose potential respondents46.

46 The software development was started after the delivery of feedbacks by all involved SENSOR partners onthe a preliminary draft questionnaire (September 2007). The website has been eventually developed and thequestionnaires are currently being uploaded on the server at the Department of Agricultural and ResourceEconomics at the University of Florence (www.sensor.unifi.it)

At the moment of delivering this deliverable, the Italy questionnaire has been already uploaded, while theUK, Malta, Germany, Austria, the Netherlands and France questionnaires are under the piloting phase andwill be uploaded soon. The Spain, Poland and Estonia questionnaires are currently under translation.

Fig 3 IT Necessary for Developing the SENSOR Internet Survey


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4.2 The implementation of an Internet-based Policy Assessment: bioenergy in the EU

4.2.1 Survey Design Issues

The issues to tackle in designing an internet-based survey refer essentially to drawing a repre-sentative sample of individuals to whom the survey should be administered. The analysis ofthese issues aims at making decisions on the target population, sampling frame, samplingmethod, and sample size.

Target Population

The target population for the analysis of the bio-energy policy case can be represented by thepublic at large because all individuals in the surveyed countries can be potentially interested inthe validation of sustainability targets. However, since many individuals still do not have accessto internet this raises problems in drawing a representative sample because some of them maynot be recruited. So, a first issue to discuss is whether going online is still convenient. It proba-bly is because the share of internet users in EU25 was already one half the total population in2005 (Table 4). Furthermore, if we take into account that the surveyed countries are the oneswhere the internet is more developed within the EU2547, this will probably mean that the shareof internet users in the total population is likely to be higher than the reported percentage.Finally, taking into account the very fast rate of growth of internet users (Table 4), it would notbe surprising if in some countries the number of internet users could account for more than 60-70% of the total population when the SENSOR survey will be launched (September 2008).

47 The countries covered by the internet survey are the following: Austria, Estonia, France, Germany, Italy,Malta, the Netherlands, Poland, Spain, and UK.

World Regions Population Penetration index (% of population)

User growth rate (2000-2005)

Africa 915,210,928 2.5% 403%Asia 3,667,774,066 9.9% 218%EU (25) 462,371,237 49.8% 147%EU candidate countries 108,157,395 17.3% 444%Rest of Europe 236,760,388 3.6% 386%Middle East 190,084,161 2.9% 454%North America 331,473,276 68.1% 108%Latin America/Caribbean 553,908,632 14.3% 337%Oceania/Australia 33,956,977 52.9% 132%World 6,499,697,060 15.7% 182%

Table 4 Adoption of Internet in World Region Areas, 2005

Another point to be acknowledged is that the backgrounds of internet users and non-users maybe different. In addressing the issue of non-user under-representativeness a 'mixed modeapproach', using a sample quota, could be adopted. For example, a quota of the total samplesize (10%-20%) in selected countries could be administered interviewing people using a com-puter aided personal interview (CAPI) survey. The CAPI survey could be conducted using thesame software developed for the internet-based survey. However, administering a CAPI surveyis not cheap. Therefore, a decision on whether to go for it and, if so, how much of the totalsample size to administer as CAPI will be made only if some money will be left out of the bud-get used for the internet survey. If the mixed mode approach fits the project budget, then it hasto be decided where the CAPI survey should be administered. For example, computer labs

Source: Internet World Statistics (www.internetworldstats.com)


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offered by universities or research institutions involved in the project could be a good solutionto allow recruited people to take part in the survey. These people can be recruited randomly viatelephone and must be non-user of internet.

Sampling Frame and Sampling Method

A sampling frame is a representation of the elements of the target population that consists of aset of directions for identifying the target population (Malhotra, 1996). In the case of internetsurveys, sample control is considerably more difficult than for conventional communicationmedia because people cannot be easily identified and contacted as in most traditional surveyingtechniques (e.g. telephone, mail, etc.). When people use internet-connected PCs, IP addressescan easily change time after time even if a PC is located inside a public network. As a result,specific computers or their users cannot be located in advance because they may change conti-nuously. However, since internet is arranged primarily around its services, the specific procedu-re for isolating individuals or enhance sampling methods depends upon the nature of the givenservice, such as mailing lists, email and web surfing (Best and Krueger, 2004).

According to Couper (2000) samples from internet-based surveys can be either non-probabili-stic (entertainment surveys, self-selected web-surveys and surveys made up of volunteer panelsof internet users) or probabilistic (intercept surveys, surveys that obtain respondents from an e-mail request, mixed-mode surveys where one of the options is a web-survey and, pre-recruitedpanels of a particular population as a probability sample)48. In the bio-energy survey, a probabi-listic sampling method will be used to invite respondents to fill in the questionnaire. Invitationthrough e-mail seems the best method because it gives researchers the possibility of contactingpotential respondents according to certain socio-economic and demographic characteristics. In fact, the main gateways in the EU can stratify the potential sample according to certain cha-racteristics (e.g., age, sex, location) which could fit those of the target population. An alternati-ve approach would be selecting one important gateway for each involved country which willsend out invitations to take part in the survey. The costs of contacting people using severalnational portals is surely higher but it should be considered if a single European gateway is notable to cover all the countries involved in the survey. In the bio-energy survey we choose thefirst option (a single EU gateways for all countries), except for the case of Estonia, where alocal gateway has been identified by the Estonian partner.

Sample Size

The size of the sample has to be decided upon certain factors such as the population size, sam-pling error and of course budget availability. Among these factors financial availability alwaysdeserves particular attention. In the case of the SENSOR project going online in so many coun-tries is undoubtedly cheaper and quicker than other traditional methods such as face-to-face,mail survey, CATI and CAPI surveys. In a cross-national study like this, going online makespossible to reach a sample size such that, even without previous knowledge of population stati-stics, will yield statistically significant results both at aggregate (i.e. European) and individualcountry level adopting a probabilistic sampling drawn randomly from the list of internet users.Table 5 shows per each involved country, the total population, the expected sample size, thetarget population of internet users (i.e. the internet users who will receive the invitation letter)and the expected sampling response rate. The sampling response rates have been computedaccording to the usual response rates in similar internet surveys and ranges from around 20‰for larger countries, to 30‰ for medium population countries (Austria, the Netherlands) andare much higher for smaller countries (Estonia and Malta). It is self-evident that the countrieswith the lowest population size are over-sampled in order to guarantee a minimum sample sizeof individuals to be interviewed. As a result, all countries will have at least 300 questionnaires,which should be able to yield a statistical significant sampling even at country level.

48 For a detailed discussion on the different ways of web sampling, see Schonlau et al. (2002).


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Country Population (million)

Sample size Target population Sampling response rate(per 1,000)

Austria 8.1 300 10,000 30.0Estonia 1.4 300 5,000 60.0France 61.1 800 40,000 20.0Germany 82.5 1,000 55,000 18.2Italy 57.3 800 40,000 20.0Malta 0.4 300 5,000 60.0 The Netherlands 16.2 400 15,000 26.7Poland 38.2 300 15,000 20.0Spain 45.1 600 32,000 18.8United Kingdom 59.3 800 40,000 20.0Total 369.6 5,600 250,000

Table 5 Sample Size for the SENSOR Internet-based Survey

4.2.2 Questionnaire Design Issues

Any questionnaire-based survey needs to pay attention in designing properly the vehicle usedto gather information/data, that is the questionnaire. Usually, the criteria upon which the questi-onnaire development is informed are:a) understandability, that is the ability to convey the relevant information to the respon-

dents and the clarity of the raised questions such that even lay people will be able tounderstand what is the issue at stake and respond accordingly;

b) friendliness, which partly depends on how understandable the questionnaire is, but isalso some more, namely having a reasonable length (e.g. requiring no more than 15-20minutes for filling it in), being consistent within and across sections, and beingappealing in order to catch and keep the attention of the respondents for the time requi-red to fill the questionnaire in.

Furthermore, considering that this study has to be conducted in several European countries itwill be necessary to pay attention to cross-cultural issues, that is ensuring the semantic equiva-lence of the questionnaires in any adopted language. Those criteria have been adopted in desi-gning the bio-energy questionnaire. Drafting the final questionnaire was quite a long lastingprocess made by several rounds of revisions as a result of the reactions/suggestions of all invol-ved SENSOR partners, both remotely and on ad hoc sessions (e.g. Vienna and Cottbus SEN-SOR meetings), as well as specific focus group sessions to pre-test the questionnaire. Cross-cultural equivalence has been tested using the expertise of SENSOR partners in all involvedcountries, asking them to check the translation from the English draft (i.e. the 'base' question-naire), and using the expertise of lecturers at the University of Florence Language Center forthe back-translation to English of the national languages questionnaires. Finally, the online que-stionnaires need to be piloted to be sure that online format and software will work properly.

The final result is a questionnaire that in its English version is reported in Annex 4. It is madeup by six sections built according to a 'zooming' criterion, which is starting with very generalquestions to move to a more detailed assessment of the bio-energy issue:I. sources of energy,II. energy and the environment,III. road transport habits,IV. policies promoting bio-fuel use,V. land use functions,VI. socio-demographics.


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The sections that are more closely related to the objectives of SENSOR module 3.3.3 are secti-on IV, involving the choice experiment elicitation questions, and section V, where questionsabout the importance of LUF are reported.

Referring to the elicitation section it should be noticed that when faced with a choice situation,as in the case of bio-energy alternatives, consumers usually choose one of the available alterna-tives or rank them according to their own preferences, rather than being able to evaluate eachone of them. In other words, the process is most likely approximate by choice modeling techni-ques (Train, 1986). The advantage of those approaches lies in the fact that the investigator cantest many hypothetical alternatives at once thus enabling policy makers to efficiently estimateconsumer response to different but related policies. In the bio-energy survey we decided toadopt a choice experiment modeling because of its less-demanding computational burden ascompared to contingent ranking (cf. section 2.2.3.2.2).

In order to develop a choice model for letting consumers decide the policy alternatives mustmeet five criteria (Bennett and Blamey, 2001; Train, 1986):a) the number of alternatives should be finite;b) the set of alternatives should be exhaustive, that is they need to represent all possible

choices available to the consumer;c) the alternatives should be mutually exclusive;d) each of the alternatives can be described by a list of characteristics;e) each alternative can be distinguished from another based on this list.The preparation of a choice experiment exercise for the bio-energy internet-based survey requi-res first of all the identification of variables (i.e. indicators) and levels (i.e. thresholds) charac-terising the policy scenarios (i.e. alternatives) under study. In doing this, it is useful to refer tothe SENSOR deliverable report 2.1.2 'Policy cases' de-livered on July 11th 2006, which reliesin turn on the Impact Assessment of the EU Biomass Plan of Action results of December 2005(EC, 2005). Information contained in these policy scenarios should be integrated with SIAT data for landuse change and country-specific (or, better, NUTS x-specific) differences should be taken intoaccount, that is the policy scenarios presented to respondents will be different in each country.In fact, it could be interesting to explore consumer choice towards European environmentalpolicy scenarios taking into account the socio-economic and agro-environmental-specific coun-try contexts, i.e. countries differences for the various attributes and levels highlighted in thealternative policy scenarios. A tentative list of available variables and levels for the bioenergycase are reported in Table 649.

As we can see from Table 6, a scenario is described by two types of variables: a) policy variables are those that will be used as instruments by the policy-makers, namely: mandatory percentage of biofuel at a given year, budget allocation in terms of R&D and production subsidies, level of import restrictions;b) impact variables describe the impacts of the hypothesized policies on the economy, the

society and the environment, as they result from the SIAT simu-lations, namely: price of fuels, employment, balance of payment, CO2 emissions, land use change.

49 As provided by M2 modelers in the SENSOR project. Policy impacts are different per each country: this iswhy the relevant cells are left blank in Table 6.


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Table 6 List of Possible Variables and Levels for the Bio-energy Policy Case

Pre-testing the questionnaire through focus groups showed that the computational burden of ascenario description like the one reported in Table 6 was too heavy for the average respondent.As a result of the Cottbus meeting it was decided to simplify the choice experiment showing tothe respondent only some impact variables, leaving the detailed description of the policy varia-bles to a pop-up menu that would be prompted by clicking on the scenario verbatim description(cf. Q19 in the questionnaire, Annex 4).

The elicitation of information/data about the LUF (section V of the questionnaire) is mucheasier. It is a simple translation of the LUF definitions into questions about the relative impor-tance of each LUF to the respondent, based on a five-point Likert scale.

4.2.3 Data Elaboration Issues

The assumptions about the number of attributes, the elicitation format and the measurementscale determine the model specification and the estimation procedure for each of the variants.The specification model of the choice-based approaches is the multinomial logit model and theestimation procedure is the maximum likelihood estimator. Due to the characteristics of theadopted measurement scale, the model specification for the choice experiment is McFadden'sconditional logit.


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5 ConclusionsThe general objective of module 3.3.1 of the SENSOR project is the critical review of differentvaluation techniques for the assessment of stakeholders' targets and preferences. In pursuanceof this objective, this deliverable has addressed the following three research questions:a) on the basis of the evidence reported in the literature, is there any technique which is


b) if so, how such a technique can be implemented in the specific context of the SENSORproject?

c) considering the importance given within the SENSOR project to the ICT, what are thepotential for using the internet as a medium for carrying out the stakeholder analysisand/or any other valuation which is relevant to validate SIAT result?

Now we are in the position to answer these questions. Referring to the first one, section 2reported a critical assessment of existing valuation methodologies, summarizing the pros andcons of each technique belonging either to the so-called 'economic valuation approaches' (i.e.the ones aiming at a monetary valuation of policy induced impacts as developed in the environ-mental economics literature) or to the so-called 'participatory (or deliberative) approaches' (i.e.the ones that tend to explore how opinions are formed or how individuals' preferences areexpressed in units other than money).

In principle, both economic valuation and participatory approaches can be used for LUF assess-ment within the SENSOR project. However, the choice is not a case of either economic or non-economic valuation methods but of using a combination of both, as required by the context ofthe decision. In other words, the two groups of methods are not alternatives, rather comple-ments. In fact, LUF valuation intervenes at different levels within the SENSOR project frame-work: at an earlier stage the 'stakeholder analysis', providing some insights to the SIAT aboutthe sustainability space with specific reference to LUF change in a given regional context; at alater stage the 'policy impact assessment', when alternative policy options have been set by thepolicy maker and the relevant impacts have been forecast through SIAT simulations.

If we keep in mind the differences between these two valuation exercises we are able to con-clude that participatory approaches are better suited to carry out what in the SENSOR projectjargon is properly called 'stakeholder analysis' aiming at identifying stakeholders' targets,understanding how do they form their own views and preferences, and assessing stakeholders'acceptability of the implied LUF change (i.e. exploring the sustainability space comparing theimplied changes and the sustainability limits as well as analyzing the trade-offs between theimplied changes).

Vice versa, in the overall assessment of a given policy impacts – a 'policy case' assessment,according to the SENSOR project jargon – people preferences towards policy-induced econo-mic, social and environmental changes are assumed as pre-existing constructs and the focus ison selecting the most preferred policy option or on ranking the various policy options at handaccording to a given criterion. This is where economic valuation methods work better. In parti-cular, given the inherent complexity and multidimensionality of the implied policy changes thebest suited approaches are choice modeling techniques.

Section 3 showed that in order to stimulate communication and an exchange of views, opinionsand values among stakeholders it is necessary to bring them together and let them participate inpolicy choices. Participatory processes create a social and political space-'forums' for exchangethat are organised for the purposes of facilitating communication between stakeholders such asgovernment, citizens, stakeholders/interest groups, and businesses regarding a specific


48

decision problem. A suitable participatory approach is the so-called 'group valuation', which isable to pursue three specific objectives in the stakeholder analysis:a) assessing stakeholders' targets, views and preferences about LUF changes,b) validating previous work on regional sustainability problems done within the SENSOR

project, andc) attempting a monetary estimation of LUF changes.

This is why this approach will be used in the specific application of stakeholder analysis withinmodule 3.3.4 of the SENSOR project.

In the light of the considerations made in section 4, we can conclude that internet is a researchtool that can properly fit the scope and the needs of SENSOR project module 3.3, if coupledwith group valuation exercises. This is because the advantages in terms of reduced costs andlarger sample size ensured by an internet-based survey come at cost of a lack of flexibility inthe interaction within the surveyed sample which eventually prevents to explore the richness ofdifferent stakeholders' targets and preferences as well as the way such preferences are formedand a consensus can be reached.

Therefore, internet-based surveys can be used only when clearly defined, simple questions/issu-es are presented to the respondents. As such, only general questions like, for instance, the rela-tive importance of land use functions can be explored through this medium. On the other hand,it is acknowledged that, in a properly designed valuation exercise, internet represents a verypowerful tool for a quick and vast assessment of competing policy alternatives, which is indeedthe way it will be used within the module 3.3.3 of the SENSOR project with specific referenceto the bio-energy policy case. Therefore, the internet-based survey will address both issues.More specifically, the elicitation section of the questionnaire (section IV) is built as a choiceexperiment exercise, considering that when faced with a choice situation, as in the case of bio-energy alternatives, consumers usually choose one of the available alternatives (or rank themaccording to their own preferences), rather than being able to evaluate each one of them.Moreover, this technique is less demanding in terms of computational burden as compared to,say, contingent ranking (i.e. the other suitable technique). The elicitation of information/dataabout the LUF (section V of the ques-tionnaire) is much easier. It is a simple translation of theLUF definitions (Annex 1) into questions about the relative importance of each LUF to therespondent, based on a five-point Likert scale.


49

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54

Annex 1 Land Use Functions DefinitionThe nine key Land Use Functions (cf. SENSOR Deliverable 3.2.2.a) are presented in Table A.1and their definitions are summarised below.

Table A.1 Key Land Use Functions

SOCIAL ECONOMIC ENVIRONMENTAL

Provision of work Residential and non land basedindustrial and services

Provision of abiotic resources

Human health & Recreation (spiritual & physical)

Land based production Support & Provision of habitat(biodiversity, genetic pool)

Cultural (Landscape identity(scenery & cultural heritage)

Infrastructure Maintenance of ecosystem pro-cesses

Social functions

(1) Provision of work: refers tothe employment provision forall, according to activities inrelation with natural resources;quality of jobs, lack of jobsecurity, localisation of jobs(con-straints/commuting)

(2) Human health &Recreation (spiritual & physi-cal): refers to access to healthand recreational services andfactors that influence servicesquality.

(3) Cultural (Landscape identi-ty, scenery & cultural herita-ge): refers to factors influen-cing the appreciation of land-scape aesthetics quality andlocal culture valorization

Economic functions

(4) Residential and non-landindustry and services: refers tothe space where residential,social and productive humanactivities take place in a con-centrated mode. The utilisati-on of the space is mainly irre-versible due to the high con-centrations of the buildings.

(5) Land based production:refers to human productiveactivities that determinechanges which are mainlyreversible (agric, for, naturalenergy sources, land basedindustry-mining).

(6) Infrastructure: refers to thespace used for infrastructuresthat determine changes whichare irreversible.

Environmental functions

(7) Provision of abioticresources: refers to the capaci-ty of the land to provide suffi-cient quantity and quality ofair, water and soil.

(8) Support & Provision ofhabitat: refers to factors affec-ting the capacity of the land toprovide biodiversity, from thegenetic diversity of organismsto a diversity of habitat in thelandscape that are in suitableecological condition.

(9) Maintenance of eco-system processes: refers to thecapacity and factors affectingto vital processes such aswater purification, nutrientcycling, etc…)


55

Annex 2 Total Economic ValueEconomists typically classify ecosystem goods and services according to how they are used.The main framework used is the so-called Total Economic Value (TEV) approach. According tothe TEV paradigm several components of value can be singled out (Table A.2).

Table A.2 Total Economic Value (TEV) Components (from Albani and Romano, 1998)

SYNOPSIS

Definition Characteristics

Directusevalues

Indirectusevalues

- Consumptive UV

- Non-consumptive UV

- Vicarious consumption

Optionvalue

Quasi-Optionvalue

Bequestvalue

Intrinsicvalue

EX-POST

EX-ANTE

On-site, weakcomplementarityOn-site, weakcomplementarity

Off-site, weakcomplementarity

Static, risk aversion,soft uncertaintiy

Dynamic, preference forflexibility, hard uncertaintiy,learning by doing

Intergenerational alt ruism

RandallandStoll

(1983)

Fisher andRaucher(1984)

Boyle andBishop(1987)

USEVALUES

EXISTENCEVALUES

USEVALUES

NON-USEVALUES

CURRENTUSE

BENEFITS

INTRINSICBENEFITS

Directuse

Indirectuse

Potentialuse

Nouse

USEVALUES

NON-USEVALUES

Freeman(1993)

Interpersonal altruism,benevolence toward people,sympathy toward animals,environmental responsability

The breakdown and terminology vary slightly among authors, but generally include the following:a) direct use value,b) indirect use value,c) option value, and d) non-use value.

The first three are generally referred to together as 'use values'. Direct use values refer to eco-system goods and services that are used directly by human beings. They include (i) the value ofconsumptive uses such as harvesting of food products, timber for fuel or construction, andmedicinal products and hunting of animals for consumption; and (ii) the value of non-con-sumptive uses such as the enjoyment of recreational and cultural activities that do not requireharvesting of products. Direct use values are most often enjoyed by people visiting or residingin the ecosystem itself.

Indirect use values are derived from ecosystem services that provide benefits out-side the eco-system itself. Examples include natural water filtration which often benefits people far down-stream, the storm protection function of mangrove forests which benefits costal properties andinfrastructure, and carbon sequestration which benefits the entire global community by abatingclimate change.

Option values are derived from preserving the option to use in the future ecosystem goods andservices that may not be used at present by oneself. Provisioning, regulating, and cultural ser-vices may all form part of option value to the extent that they are not used now but may beused in the future.


56

Non-use values (or, sometimes, passive use value) refer to the enjoyment people may experi-ence simply by knowing that a resource exists even if they never expect to use that resourcedirectly themselves.

There are three main components: 'bequest value', where individuals attach value from the fact that the ecosystem resour-

ce will be passed on to future generations; 'altruistic value', where individuals attach values to the availability of the ecosystem

resource to others in the current generation; 'existence value', derived from the existence of an ecosystem resource, even though an

individual has no actual or planned use of it.


57

Annex 3 The Swing MethodPreferences of stakeholders in the swing method are expressed as relative values vi, with vi = 10 given to the most important criterion/impact (the first swing from worst to best performance) and vj < 10 assigned in a decreasing order to the other criteria/impacts from the second most important to the least important criterion/impact. This relative value scale vi is then expressed to normalized weights wi:

Relating the weight calculated for each criterion with the weight of the criterion expressed inmonetary terms, while both are reduced to the corresponding impact range (IR), we can yieldan indirect monetization, assuming equivalence of stakeholder's relative importance prefe-rences:

where IRi and IRc denote the impact range (i.e. the maximum potential gain) of the impact iand of the criterion c expressed in monetary terms, respectively. The underlying assumption isthat the value functions for all criteria (impacts) are linear, e.g. improvements in the impactlevel are valued the same, irrespective of the absolute impact level. Hence, the per unit moneta-ry value of the i-th impact, mi, is calculated as:

For any pair i/c the higher the ratio of weights and/or the lower the ratio of impact ranges, thehigher is the per unit monetary value implicitly assigned to the impact i.

∑=

i

ii v

vw

c

c

i

i

IRw

IRw =

ci

ici wIR

wIRm⋅⋅=

A-5

Annex 4. Internet-based Questionnaire

European Union 6th Framework Pro-gramme for Research

SENSOR Project

Cross-European Survey on Bio-energy and Bio-fuel

Dear Recipient, We are carrying out a European research project on a highly topical issue, the use of al-ternative sources of energy. More specifically we are interested in knowing your opin-ion on alternative sources of energy for road transport. Our research covers ten European Union countries and involves thousands of respon-dents across Europe. This research project is carried out by Universities and non-profit Research Centres that have no economic interest in products derived from alternative sources of energy. We would like you to fill in the following questionnaire, which should not take more than fifteen minutes. Your contribution is very important to us and we would like to stress that we greatly appreciate your willingness to contribute to our research and to give us your time. We would like to stress there are no “right” or “wrong” answers to the questions. We are just interested in knowing your opinion. Answering truthfully is what matters to us. The information you supply with your responses will be treated anonymously in accor-dance with the EU legislation on privacy. If any problem should arise, please do not hesitate to contact the research coordinator, Professor Donato Romano of the University of Florence, Italy. To contribute to this survey please click on questionnaire.

A-6

SECTION I: SOURCES OF ENERGY Energy is very important because it allows us to carry out a lot of daily tasks at home, at work and during our leisure activities. In order to generate power we can use several sources of en-ergy. Some of these sources of energy are renewable, i.e. they will not be run out if used appro-priately, and others are non-renewable, i.e. when they are finished they cannot be replenished very quickly in the foreseeable future. Please, indicate which of the following sources of energy you think are renewable or non-renewable:

Q1 Sources of energy Renewable Non-renewable Don’t know

Energy derived from oil Solar energy Energy derived from natural gas Hydropower Wind power Energy derived from coal Geothermal energy Nuclear energy Bio-energy

Bio-energy is a source of energy which can be obtained from biomass. Please, indicate whether you think that bio-energy can be produced from the following sources:

Q2 Sources of bioenergy Yes No Don’t know

Oil Natural gas Wind Wood Municipal solid waste Manure Corn Sugar cane Other crops Animal fats

A-7

SECTION II: ENERGY & ENVIRONMENT The way in which we use energy affects the quality of the environment in which we and future generations have to live. Please express your degree of agreement on the statements below re-garding the environment and the use of energy. Q3

Energy savings help to reduce global warming. Completely

agree Agree Neither agree nor disagree Disagree Completely

disagree

Q4

The fact that non-renewable energy sources are running out is a problem. Completely


disagree

Q5

If we use less energy environmental quality will improve. Completely


disagree

Q6

Using less energy is the best way to combat global warming. Completely


disagree

Q7

I feel jointly responsible for non-renewable energy sources running out. Completely


disagree

Q8

I feel jointly responsible for global warming. Completely


disagree

Q9

Not only are the government and industry responsible for high energy consumption levels, but so am I.

Completely agree Agree Neither agree

nor disagree Disagree Completely disagree

A-8

Q10 My household cannot be blamed for environmental problems caused by energy production and use.



Q11

I feel guilty when I waste energy. Completely


disagree

Q12

If I were to buy a new household appliance (e.g. washing machine, refrigerator, freezer, etc.) I would feel morally obliged to buy an energy efficient one.



Q13

I do feel guilty when I buy products that, coming from distant countries, imply more energy consumption for their transport.



Q14

I worry about saving energy only when it helps to lower my utility bills. Completely


disagree

A-9

SECTION III: ROAD TRANSPORT HABITS In this section we are interested in knowing your road transport habits and behaviour. Q15. How far is your working place from your home? [Round trip distance] ___________ km Q16. What is the usual means of transport you use to commute to work? [You can choose more than one item]

Train 1 Bus/tram 2 Underground 3 Private car (used individually) 4 Car pooling/car sharing 5 Motorbike/scooter 6 Bicycle 7

On foot 8

Other (specify): _________________9 Q17. How many motor vehicles do you own in your household? ________ Q18. For each motor vehicle, please indicate type (motorbike, car, van, truck, etc.), fuel (petrol, diesel, gas, electricity, other), and average cost per week.

Description Vehicle no. 1 Vehicle no. 2 Vehicle no. 3 Vehicle no. 4 Type Motorbike Car Van Truck Other: _______________ Fuel Petrol Diesel Gas Ethanol Electricity Average weekly expendi-ture _______ £/week _______ £/week _______ £/week _______ £/week

A-10

SECTION IV: POLICIES PROMOTING BIO-FUEL USE The European Commission is considering the opportunity to implement policies aimed at pro-moting the use of bio-fuel50 for road transport. Bio-fuel use support can be pursued by means of several policy options51 each one will result in different economic, social and environmental im-pacts52. Below we present five policy options (identified with letter A to E in the table below) offering incentives for bio-fuel use plus the business-as-usual option (option F) which implies not modi-fying the current policy. The five policy options below (A to E) are characterized by different levels of bio-fuel used for road transport as well as different degrees of support to the production of bio-fuel by means of subsidies to bio-fuel production, investment in bio-fuel research and development, and protec-tion on bio-fuel imports. All options in the table below (A to F) can have both positive and negative impacts. Positive impacts are a decrease in carbon dioxide (CO2) emissions53 and the increase of employment. On the other hand, as bio-fuel is more costly, the most important negative impact is the increase in yearly household expenditure for fuel, which means that you will have less money available to buy other goods and services. So we would like you to consider each policy option in the table below very carefully, taking into account the impacts that each of them will have in the year 2025. The impacts are reported as differences vis-à-vis the situation you would face in the year 2025 should no changes occur in the current policy (option F). If there are no changes in the current policy, the impacts you will be facing in year 2025 are the following: - the yearly fuel expenditure of your household will be … pounds; - the yearly emissions of carbon dioxide (CO2) will be 529 million tons; - the average number of people employed in UK will be 31.3 million. Please, think as carefully as you can about the next question and select your most preferred op-tion clicking on the relevant choice button.

50 Biofuel can be broadly defined as fuel consisting of, or derived from biomass that is from recently living organisms (i.e. plants, animals and their by-products, for example, manure, garden waste and crop residues). 51 A policy option is an action the Government can undertake in pursuing its own objectives. It is defined by a given mix of policy instruments such as: - a given share of bio-fuel that should be compulsory used in petrol or diesel, - a given level of investment in bio-fuel research and development in the bio-fuel industry, - a given level of subsidies to bio-fuel production, and - a given level of protection on bio-fuel imports (in order to stimulate the domestic production of bio-fuel). 52 Policy impacts are the consequences at social (e.g. employment), economic (e.g. price of fuel) and environmental (e.g. CO2 emissions) levels of the implementation of a given policy option. 53 Carbon dioxide is one of the most important “greenhouse gases” responsible for global warming and climate change.

A-11

Q19. Which of the following options do you prefer, considering the impacts each of them will have in year 2025? [If you click on policy options you will get a detailed description of each policy option]

Policy impacts in year 2025 (as difference vis-à-vis option F)

Yearly household fuel expen-diture increase

Employment increase

Reduction of CO2 emissions

Policy options

£/year % change Thousands units % change Million

ton/year % change

Choice

A Low support with strong protection of

European producers 0.15% 9.9 0.03% -22,575.2 -4.27%

B High support with strong protection of

European producers 0.78% 58.5 0.19% -55,014.7 -10.40%

C Low support without protection of Euro-

pean producers 0.08% 2.0 0.01% -22,575.2 -4.27%

D High support without protection of Euro-

pean producers 0.78% 32.4 0.10% -55,014.7 -10.40%

E Medium support with moderate protec-

tion of European producers 0.30% 18.5 0.06% -34,626.4 -6.55%

F No changes in current policy (none of the options above)

NO CHANGE FROM ACTUAL LEVEL

A-12

SECTION V: LAND USE FUNCTIONS Land can be used for many purposes. Below we list different land uses. We are interested in knowing how important each of the following land uses is for you. Please express how impor-tant the following land uses are for you. Q20 Land used for residential (i.e. buildings) purposes

Not at all important Not really important Somewhat important Quite important Very important

Q21 Land used for industrial (i.e. factories) purposes


Q22 Land used for primary sector (i.e. agriculture, forestry) purposes


Q23 Land used for infrastructural (i.e. roads, railroads, airports, etc.) purposes


Q24 Land used for recreation purposes (i.e. hiking)


Q25 Land used for cultural (i.e. landscape, scenery, cultural heritage) purposes


Q26 Land used as a source of employment (provision of jobs in agriculture, forestry, etc.)


Q27 Land as a source of water and soil


Q28 Land as a source of biodiversity (i.e. diversity of any living forms)


Q29 Land used for maintaining natural processes (e.g. carbon storage, keeping air and water clean, etc.)


A-13

SECTION VI: SOCIO-DEMOGRAPHICS Q30. Are you male or female? male female

Q31. In which year were you born? __________

Q32. Where do you live?

North-east England (Tees Valley, Durham, Northumberland, Tyne and Wear) North-west England (Cumbria, Cheshire, Greater Manchester, Lancashire, Merseyside) Yorkshire and the Humber (North, East, South and West Yorkshire, Northern Lincolnshire) East Midlands (Derbyshire, Nottinghamshire, Leicestershire, Rutland, Northamptonshire, Lincolnshire) West Midlands (Herefordshire, Worcestershire, Warwickshire, Shropshire, Staffordshire, West Midlands) East of England (East Anglia, Bedfordshire, Hertfordshire, Essex) London (Inner and Outer London) South-east England (Berkshire, Buckinghamshire, Oxfordshire, Surrey, East and West Sussex, Hampshire, Isle of Wight, Kent) South-west England (Gloucestershire, Wiltshire, Bristol/Bath area, Dorset, Somerset, Cornwall, Isles of Scilly, Devon) Wales Scotland Northern Ireland

Q33. Number of inhabitants in your city/town/village?

Less than 5,000 Between 5,000 and 9,999 Between 10,000 and 24,999 Between 25,000 and 49,999 Between 50,000 and 99,999 Between 100,000 and 249,999 Between 250,000 and 499,999 Between 500,000 and 999,999 1,000,000 or more Q34. Have you got any children? Yes No

Q35. What is your highest level of education?

Primary Junior high school High school University Postgraduate

A-14

Q36. What is your current occupational status?

Employed Unemployed [go to Q38] Looking for first employment [go to Q38] Housewife [go to Q38] Student [go to Q38] Retired [go to Q38] Other: __________________ [go to Q38] Q37. You are currently employed as

Dependant of a company/public body as: Top manager Manager Employee Worker Apprentice

Self-employed as:

Entrepreneur Professional (e.g. physician, etc.) Self-employee (e.g. shopper, etc.) Member of a cooperative

Q38. You are currently employed in the following sector

Agriculture/Fishery Industry Building Commerce, public services, transports, communications Credit, insurance, other services to industry and/or commerce Social services (e.g., health care, NGOs) International organizations Education Other (specify) ____________________________

Q39. How large is your household? ________ members Q40. How would you describe the income situation of your household?

Difficult Modest Reasonably well We are well off

A-15

Q41. Please tell us your monthly household income (after taxes)

Less than € 500 From € 500 to 999 From € 1,000 to 1,499 From € 1,500 to 1,999 From € 2,000 to 2,499 From € 2,500 to 2,999 From € 3,000 to 3,499 From € 3,500 to 3,999 From € 4,000 to 4,999 From € 5,000 to 5,999 From € 6,000 to 6,999 More than € 6,999

A-16

Policy options Policy instruments

Subsidies to bioenergy production (biogas &

wood)

Bioenergy R&D budget ID Description

Compulsory biofuel share in

total fuel for transport

in million €

Restrictions on imports

A Low level of support with strong protection of European producers

2% 500 500

Same as today for biofuels; for wood biomass such as to restrict imports to the amount needed to supplement do-mestic production potential

B High level of support with strong protection of European producers

10% 5,000 5,000 As in policy option A

C Low level of support without protection of European producers

2% 500 500

No tariffs on ethanol, vegetable oils or wood biomass; biodiesel standards such that palm oil and other oils can be used

D High level of support without protection of European producers

10% 5,000 5,000 As in policy option C

E

Medium level of sup-port with moderate protection of European producers

5% 1,500 1,500 Such that 50% of ethanol and 25% of biodiesel is imported

F No changes in the cur-rent policy (none of the options above)

0% none none Same as today

Main partners involved in this publications are:University of Florence, ITESA Group, Wageningen University, NL

This report was edited by theLeibniz-Centre for Agricultural Landscape Research Contact: [email protected]

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