metadata: authors: 0: author: Navrud,Stale
1: author: Strand,Jon
count: Latin America
docna: 0: docna: Valuing global public goods : a European
            Delphi stated preference survey of population willingness to
            pay for Amazon rainforest preservation
docty: Policy Research Working Paper
subtopic: Forestry,Global Environment,Biodiversity,Global Environment Facility,Energy and Mining,Energy Demand,Energy and Environment,Artisans,Culture in Sustainable Development,Culture and Cultural Practice,Arts & Music,Literature & Folklore,Cultural Policy
teratopic: Rural Development,Environment,Energy,Culture and Development
abstracts: cdata!: The Amazon Rainforest is a global public
            good. As such, and given that 15 percent of the original
            Amazon forest area has already been lost, households
            worldwide might be willing to pay to reduce or avoid
            additional losses. A full elicitation of global preferences
            for valuing preservation of the current forest, using
            stated-preference population surveys, would be costly and
            time consuming. Alternatively, this paper uses a Delphi
            stated-preference technique in which 48 European
            environmental valuation experts were asked to provide
            "best guesses" on the possible outcomes of
            population surveys in their own countries and Europe as a
            whole. The expert judgments indicate willingness to pay in
            Europe for preserving the current Amazon Rainforest of about
            28 Euro per household per year on average; a slightly lower
            value is inferred for a plan that allows a 10 percent future
            reduction from the current rainforest area. The income
            elasticity of experts' stated willingness to pay with
            respect to per-capita income in their own countries is in
            the range 0.5-0.8. These findings indicate that Delphi
            studies might be used more widely as a tool for global
            benefit transfer.
repnme: repnme: Valuing global public goods : a European
            Delphi stated preference survey of population willingness to
            pay for Amazon rainforest preservation
display_title: Valuing global public goods : a
            European Delphi stated preference survey of population
            willingness to pay for Amazon rainforest preservation
listing_relative_url: /research/2013/10/18347722/valuing-global-public-goods-european-delphi-stated-preference-survey-population-willingness-pay-amazon-rainforest-preservation
topicv3: Biodiversity m1326214 2084,Energy and Extractives m1326595 507,Environment, Natural Resources and Blue
            Economy m1327391 566,Cultural Heritage and Sustainable Tourism
            m1327654 1765,Rain forest Conservation m1583151 4725
publishtoextweb_dt: 2013-10-01T00:00:00Z
historic_topic: Rural Development,Environment,Energy,Culture and Development
no_of_pages: 51
author_keys: 0: author_key: 1187993
1: author_key: 1008099
model_extractions: WPS6637 Policy Research Working Paper 6637

# Valuing Global Public Goods

A European Delphi Stated Preference Survey of Population Willingness to Pay for Amazon Rainforest Preservation _Ståle Navrud Jon Strand_ The World Bank Development Research Group Environment and Energy Team October 2013
Policy Research Working Paper 6637

# **Abstract**

The Amazon Rainforest is a global public good. As such, and given that 15 percent of the original Amazon forest area has already been lost, households worldwide might be willing to pay to reduce or avoid additional losses. A full elicitation of global preferences for valuing preservation of the current forest, using stated-preference population surveys, would be costly and time consuming. Alternatively, this paper uses a Delphi stated-preference technique in which 48 European environmental valuation experts were asked to provide “best guesses” on the possible outcomes of population surveys in their own countries and Europe as a whole. The expert judgments indicate willingness to pay in Europe for preserving the current Amazon Rainforest of about 28 € per household per year on average; a slightly lower value is inferred for a plan that allows a 10 percent future reduction from the current rainforest area. The income elasticity of experts’ stated willingness to pay with respect to per-capita income in their own countries is in the range 0.5–0.8. These findings indicate that Delphi studies might be used more widely as a tool for global benefit transfer.

This paper is a product of the Environment and Energy Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jstrand1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Valuing Global Public Goods: A European Delphi Stated Preference Survey of Population Willingness to Pay for Amazon Rainforest Preservation**

Ståle Navrud and Jon Strand<sup>1</sup> Key words: Delphi expert survey; stated preferences; contingent valuation; global public goods; Amazon rainforest.

JEL classification: Q23; Q51; Q57.

Sector board: Environment 1 Navrud: School of Economics and Business, Norwegian University of Life Scienes, 1432 Ås, Norway; E-mail: stale.navrud@umb.no. Strand: Development Research Group, Environment and Energy Team, the World Bank, Washington D.C. 20433, USA; E-mail: jstrand1@worldbank.org. We thank Ariel Ortiz-Bobea, Michael Toman and Jeff Vincent, and participants at a World Bank seminar, for helpful comments. This research has been supported by a grant from the Bank’s Knowledge for Change Program. The conclusions and viewpoints in this paper are those of the authors and not necessarily the World Bank, including its management, directors and staff.
# **1. Introduction**

In addition to the 15 % already lost, the effects of further significant forest losses in the Amazon could be dramatic. A significant fraction of trees in the Amazon basin could dry out and die; and large swaths of land could be converted from forest to savannah. The entire ecosystem of the Amazon basin, which today supplies the Earth with more than 20 percent of both its oxygen and fresh water, might change dramatically. The climate in the entire South American region, and beyond, will likely be affected. Numerous species might disappear, probably including many yet unknown.

More reliable and robust measures of the economic loss to the global community associated with forest loss in the Amazon region, for populations outside of South America, are called for, due to several reasons.<sup>2</sup> One is the significance and uniqueness of the Amazon as a global natural resource – perhaps _the_ canonical global public good. Secondly, while substantial efforts have been put into the mapping of more direct impacts of the Amazon forest losses for the local population and region<sup>3</sup> , little is known about the non-use value of protecting rainforests, in general and for the Amazon in particular.<sup>4</sup> Most of the benefits to individuals outside the Amazon region are likely to be non-use (“passive use”; “existence”; “bequest”) values as most would never have visited, nor plan to visit, the Amazon rainforest. Even if non-use values could be smaller per household than (the combined use and non–use) values among the population in the Amazon region, the global population is both much larger, and also to a large extent wealthier, so that the globally aggregate non-use value could easily constitute a substantial or even decisive fraction of the Total Economic Value (TEV) of conserving or avoiding further losses to Amazon rainforest. Obtaining analytically sound estimates of the global non-use values, even if imprecise, thus could be important for mobilizing expenditure of additional economic resources to reduce or avoid future Amazon forest losses. Economic resources must likely be mobilized both locally and regionally (throughout South America), and by the international donor community, given the public-good nature of the resource. For a diverse set of policy makers to agree to such actions, a scientifically based and objective assessment of how the global economic benefits of protection compare to the protection cost could be quite useful.

> 2 Direct local and regional impacts of Amazon forest losses within South America are being addressed in separate parts of the overall research program for economic valuation of Amazon forest losses, of which this study is part.

> 3See May, Soares-Filho and Strand (2013).

> 4 Among existing studies to be mentioned, see in particular Kramer and Mercer (1997), Horton et al (2002), and Carson et al (2011).

2
A central challenge in assessing the non-use value among populations outside of the region itself, for conserving or avoiding further forest loss in the Amazon, is the lack of relevant willingnessto-pay (WTP) data. While the value of e.g. reduced agricultural yield from degradation of soil fertility, or reduced illness-related expenditures associated with lower air pollution levels, can be estimated using market data, the nature of non-use values precludes this. The only viable way of eliciting such preferences is via survey-based Stated Preference (SP) methods.

Traditionally SP elicitation has involved random population surveys using contingent valuation (CV).<sup>5</sup> Choice experiments (CE) also have become widely applied for environmental valuation over the last 10 years.<sup>6</sup> The CE approach has however already a long history in other fields including transportation analysis, marketing, and psychological research.<sup>7</sup> Our ultimate aim is to apply both CV and CE for elicitation of preferences for Amazon rainforest preservation, in all countries outside of the Amazon region. Ideally, (at least) the entire OECD area plus all higher- and middle-income countries in Asia, the Middle East, Eastern Europe, and Latin America apart from the Amazon region, would then need to be surveyed. Our realistic aim, given our available resources, is to conduct random sample surveys among national populations in only a few selected European countries, North America, and Japan. If possible, we also aim to do such surveys in some key Amazon region countries (Brazil, Peru and Colombia).

5See Mitchell and Carson (1989), Carson, Flores and Meade (2001), and Carson (2011), for presentations of the CV method. The latter reference contains a comprehensive bibliography. See Carson (1998) for a more specific discussion of CV in the context of rainforest valuation.

> 6 For representative CE studies see Adamowicz et al (1998), Hanley, Wright and Adamowicz (1998), and Louviere, Hensher and Swait (2000). Among prominent, more specific and recent, applications to environmental and resource economics, are Bennett and Blamey (2001), Bateman et al (2002), Holmes and Adamowicz (2004), Rolfe and Bennett (2006), and Bennett and Birol (2010).

> 7 The CE and CV approaches have both relative strengths and weaknesses when used to value environmental goods predominantly non-use values. A main strength of CV is its directness. WTP is typically elicited from respondents through direct valuation questions (either showing a payment card with a range of amounts and asking respondents for their maximum WTP; or through dichotomous choice whereby respondents are asked once or with follow-ups about their maximum WTP, or whether or not they are willing to pay a given stated amount). CE is less direct as maximum WTP is typically found from repeated pairwise choices between alternative programs with different characteristics to respondents including different costs (which usually include a “status quo” program involving zero additional cost).  The choices involve trade-offs between the additional cost,  the  principal good to be valued (in our case the area of rain forest lost); and one or more attributes of the public good in question (or the management of the good) deemed relevant for or related to the choice situation in question. A fundamental tradeoff facing respondents will then involve a monetary payment traded off against the set of other attributes. Each respondent would, during a CE session, face several (4-8) different choice sets where the values for each attribute of the program vary; and from respondents ́ choices, their trade-off and implicit WTP for levels of change in each attribute can be derived.

3
Given that we do not have the resources to conduct population surveys in all countries necessary to provide a reasonably complete global picture of WTP for preserving the Amazon rainforest, in order to supplement our planned population survey work, we also are conducting regional “Delphi surveys.” In this approach, experts in environmental valuation and in SP in particular are surveyed concerning their beliefs about how random population samples, in their respective countries, would respond to CV surveys were they to be undertaken. These Delphi surveys can provide not only an order of magnitude estimate of global non-use values for a wider set of countries than those covered by our planned population surveys, but also preliminary estimates of WTP in the countries selected for the combined CV/CE surveys. For countries where both types of surveys are carried out, our results will permit comparisons, and calibrations, of the Delphi results in relation to the population surveys.

This paper reports the results of the first Delphi expert survey under this project umbrella, conducted in the spring and summer of 2012.<sup>8</sup> Average WTP among European households for particular programs to protect Amazon rainforest were assessed by asking experts on environmental valuation, from 20 different European countries, to provide their best subjective assessments of such values, for their own countries and for Europe as a whole.

In section 2 we first present, and offer a justification for, the Delphi CV method. Section 3 describes our European Delphi expert survey. Section 4 reports the WTP results from two rounds in the European survey, income elasticities of these WTP estimates, and the factors that were found to most significantly affect experts ́ WTP estimates. Section 5 presents and discusses aggregated WTP results for Europe, and offers concluding. The Round 1 and 2 Delphi CV instruments are reproduced in Appendices 1 and 2, respectively; and a theoretical note on the income elasticity of WTP can be found in Appendix 3.

# **2. The Delphi Method**

The background, history, and justification for the Delphi method are different from those for the SP method. The Delphi method was developed largely as a management decision-making tool, to ensure mutual consistency of preferences among a set of participants (managers), and to elicit experts ́ preferences rather than individual preferences that the SP methods seeks to elicit.<sup>9</sup> Underlying the method is a belief that these experts have considerable useful knowledge about such population values that is not embodied in already available valuation studies. In our case, a sample of European environmental economists acquainted with SP survey methods, were asked to predict

> 8 Delphi expert surveys also are being done for North America, Australia and New Zealand, and for a range of Asian countries.

> 9 See e g Sackman (1975).

4
the outcome of a CV survey in terms of mean and median WTP per household among households in their own country and among all European households for avoiding a specified future forest loss in the Amazon. Delphi studies tend to differ according to whether they ask for the experts ́ recommended course of action, or to predict an outcome if some action was taken. Our study falls into this latter category, consistent with the “Oracle of Delphi” as we ask our experts for their best guess about average individual (national, and European) preferences for a public good, were a CV survey to be carried out.

Our Delphi expert survey serves at least three purposes. The first is as an independent check of level and plausibility of numbers obtained in the respective population surveys. Secondly, the Delphi surveys will allow us to make valuation assessments for a much larger set of countries than the individual surveys; and thus hopefully provide a basis for calibrating relative population values across countries (including many which will not be surveyed directly). Thirdly, the Delphi surveys include questions the answers to which can be useful for guiding the future random population sample SP surveys.

Even though the wider project, involving SP surveys of random population samples, will use both CV and CE, only the CV format is used in our Delphi surveys, including the current one. This is because Delphi surveys by their nature need to be “direct” (seeking a direct WTP estimate) as is the nature of CV and not of CE. The overall project will then provide two separate types of calibration: between CV estimates for population samples and SP experts; and between CV and CE for the population samples.

We are aware of only one previous similar Delphi CV survey, Carson et al (2012), published recently but conducted in 1997. European SP experts were in that survey asked to provide their best guess for the mean and median WTP for households in their country and in Europe overall, given that a CV survey, asking households about their WTP for restoring the World Cultural Heritage Site of Fes Medina in Morocco, would then have been conducted. This Delphi CV survey was part of a larger effort (including CV surveys of Moroccan households as well as foreign tourists) to estimate the global economic benefits of a World Bank financed restoration program for this World Heritage site. The results from this survey showed that SP experts were willing and comfortable in providing their best guesses of the outcome of a national and a European CV survey of this global public good. The Delphi survey found a positive median and mean WTP for European households. No actual SP survey of the non-use values outside of Morocco was however conducted, to make it possible to validate and calibrate of the Fes Medina Delphi CV survey.

Turning now to this survey, the categories of global welfare gains, accruing to the global population outside of South America, that may result from reduced Amazon rainforest losses, include the following ecosystem services: 5
1. Carbon storage value of the preserved Amazon rainforest

2. Recreational value (of “eco-tourism”) for international tourists 3. Non-use value in the international context, including the biodiversity value of the forest.<sup>10</sup> The carbon storage (item 1) is to be assessed separately from our preference elicitation studies, based on estimates of the Social Costs of Carbon (SCC) in terms of global carbon emissions costs. SP experts were told that they should not consider the carbon storage value when stating their WTP. Thus, the Delphi CV survey and the planned SP studies values items 2 and 3 (but mostly 3 since very few households in a random population sample would actually have visited the Amazon Rainforest).

# **3. The European Delphi Contingent Valuation (CV) Survey**

The Delphi CV survey comprised 48 European environmental valuation experts from 20 different countries in Europe. The survey, further detailed in Appendix 1 and Appendix 2 below, proceeded in two steps. In step 1, each expert answered to a set of questions so as to provide his or her “best guess” about the mean and median WTP, among the population of their own country and among the entire European population, in terms of annual per-household **WTP to avoid further forest losses** up to 2050 in the Amazon rainforest region.

We define a “business-as-usual” **(BAU) alternative of 25% further loss** of forest area by 2050, on top of the 15 loss that already has occurred since the 1970s: There are **two avoidance alternatives:**

- **i) Plan A** - **_No further_ forest losses by 2050, beyond what has already been lost (15% relative to the pristine state).**

- **ii) Plan B** - Some further forest losses, so that, by 2050, **25% will have been lost relative to the pristine state.** Alternative i) is clearly the better and more valuable, and more challenging, outcome to be achieved.

58 experts were contacted during May and June 2012, and 48 of them (83 %) replied in Round 1. Two reminders were issues. 10 did not reply; out of which 2 refused to answer, 1 was on

> 10 In addition to these international components of the Total Economic Value (TEV) of avoiding further loss of the Amazon rainforest; global quasi option context; there is also the quasi-option value (QOV); which can be viewed as a correction factor to TEV. The QOV is the economic value of increased information about the biodiversity value of the Amazon rainforest for medicinal purposes, food security etc that can be gained from _not_ undertaking projects with irreversible effects like extinction of species.

6
paternity leave, and the remaining 7 decided not to answer; mainly due to lack of time. In Round 2 in June and July 2012 all of the original 48 experts replied, after two reminders.<sup>11</sup>

# **4. Results**

# **4.1. Willingness-to-Pay (WTP) Results from Rounds 1 and 2**

The distribution of the 49 experts (where one expert answered in Round 2 only) from European countries is shown in Table 1, and reflects to some extent the countries that have the largest numbers of environmental valuation practitioners. We see that 29 of 49 experts come from Northern and Central Europe, 12 from Southern Europe and 8 for Eastern Europe. The table indicates the level and differences in WTP by country (for one of our questions, on mean WTP for Plan A in the first questioning round; see below). The table also indicates average income levels in the four indicated regions, both regular (non-adjusted) incomes and purchasing power parity (PPP)-adjusted incomes; where the latter are less dispersed than the former.

**Table 1: Geographical distribution of the environmental valuation experts (N = 49) that participated in the European Delphi survey (and the countries they assessed specific WTP estimates for).**

|**Country group**|**Country (number of**<br>**experts)**|**Stated Mean WTP for**<br>**Plan**<br>**A,**<br>**Round**<br>**1;**<br>**euro/household/year**|<br> <br>**Mean Group GDP**<br>**(PPP-adjusted GDP),**<br>**euro/capita/year**|
|---|---|---|---|
||Denmark (2)|36.0||
|Nordic countries|Finland (3)|28.7|51,100|
|(total number of<br>experts = 11)|Norway (3)|65.0|(32,500)|
||Sweden (3)|31.7||
||Austria (2)|57.5||
||Belgium (1)|45.0*||
|Northern and Central<br>E|Germany (3)|88.3|36,200|
|urope<br>(total number of|Ireland (2)|34.0|(29,500)|
||Netherlands (3)|38.3||

> 11 One additional expert, who answered only Round 2 of the survey, is also included in Table 1.

7
|experts = 18)|Switzerland (2)|25.0||
|---|---|---|---|
||United Kingdom (5)|27.4||
||France (3)|44.3||
||Greece (2)|10.5||
|Southern Europe|Italy (4)|15.0|26,800|
|(total number of<br>t 12|Portugal (1)|20.0|(23,400)|
|expers = )|Spain (2)|26.0||
||Croatia (2)|3.2||
|Eastern Europe|Czech Republic (2)|42.5|11,600|
|(total number of<br>8|Hungary (1)|4.0|(15,800)|
|experts = )|Poland (2)|25.0||
||Romania (1)|30.0||

*”Round 2” figure: no figure was given for Round 1

All participating experts except five had the nationality of the country in which they lived and worked. Some of the latter group were asked to state their estimate for the WTP for the country of origin and in which they had conducted studies (if there were few valuation practitioners in their country), or state the WTP for the country they now lived in if that country had few valuation studies. All respondents were also asked to assess the mean and median WTP for all European households; the measure we are aiming for. The Delphi CV survey instruments for Round 1 and 2, including the CV scenario description, are reproduced in appendices 1 and 2, respectively.

As there were only a handful of participating experts from any one country (1-5 ; see table 1), we only reproduce the experts ́ guesstimate of mean and median WTP /household/year, related to a hypothetical survey of a random sample of European households, in their own country, and in all of Europe. Tables 2-3 report the main results from Round 1 of this expert survey for Plan A, and Plan B; respectively. The tables report mean, median, standard deviation and lowest and highest amount stated as experts’ “best guesses” for mean and median household WTP per year; in their own country, and in Europe as a whole.

We see that experts’ mean expressed WTP in terms of their assessed mean WTP of preserving the current Amazon Rainforest among their own national population (Scenario A: 85 of original forest % protected) is about 34 euros, higher than the about 29 euros for Scenario B (75 % of 8
original forest protected). The stated “no-policy” alternative with no measures (i.e. the BAU alternative) is assumed to retain only 60 % of original forest protected by 2050. The mean estimates of median WTP are lower than for mean WTP for Scenarios A and B, 19 and 14 euros, respectively.<sup>12</sup> Thus, the distribution is skewed to the right. All experts reported equal or higher WTP for the most comprehensive preservation Plan A than for the less comprehensive Plan B. Guesstimates for both mean and median WTP vary from zero to 180 euros.

Another aspect of the answers is that experts’ averages for mean WTP in Europe as a whole are somewhat lower than their average answers for own country mean WTP (with an average differences of 2-3 euros). Experts thus on the whole appear to be slightly more pessimistic regarding WTP for Europe as a whole, compared to their own-country assessments.<sup>13</sup> In Round 2, each of the experts was shown the overall distribution of experts ́ Round 1 estimates for mean and median WTP for Europe (see appendix 2), and asked to reconsider their own estimate in the light of this new information. They were also asked to reconsider their estimates for mean and median WTP for their own country. Tables 4-5 show the main results of experts’ answers in Round 2. We see that overall mean values are lowered somewhat in Round 2; by almost 3 euros on the average for mean WTP in their own country for Plan A, and closer to 5 euros for Plan B; with smaller differences for median WTP assessments. For Europe overall mean WTP is reduced by about 4 and 5 euros for Plan A and B, respectively. This constitutes a reduction in mean WTP of about 13 and 18 % for Plan A and B, respectively.

> 12 None of these differences are statistically different from zero, due mainly to the low number of data points.

> 13 These differences should not be over-emphasized; they might in part follow from differences in methods of (implicit and explicit) aggregation across European countries, in our data and as done by experts themselves, and the fact that countries with high overall WTP levels may be overrepresented among the sample of experts.

9
# **Tables 2-3: Mean and median WTP figures expressed by experts, Round 1.**

# **All figures in euros/hh/y**

**Table 2: WTP Plan A**

|**Value concept**|**Mean value**|**Median**|**St dev**|**Min**|**Max**|
|---|---|---|---|---|---|
|**Mean WTP in own**<br>**country**|34.5|25|34.4|0|180|
|**Median WTP in own**<br>**country**|18.9|15|20.4|0|120|
|**Mean WTP in Europe**|32.2|20|25.2|0|110|
|**Median WTP in Europe**|19.1|11|22.3|0|100|

**Table 3: WTP Plan B**

|**Value concept**|**Mean value**|**Median**|**St dev**|**Min**|**Max**|
|---|---|---|---|---|---|
|**Mean WTP in own**<br>**country**|28.9|20|39.1|0|250|
|**Median WTP in own**<br>**country**|14.4|10|15.7|0|80|
|**Mean WTP in Europe**|26.2|18|20.6|0.5|90|
|**Median WTP in Europe**|15.1|10|16.8|1|80|

10
# **Tables 4-5: Mean and median WTP figures expressed by experts, Round 2.**

# **All figures in euros/hh/y**

**Table 4: WTP Plan A**

|**Value concept**|**Mean value**|**St dev**|**Min**|**Max**|
|---|---|---|---|---|
|**Mean WTP in own**<br>**country**|31.7|33.2|1|180|
|**Median WTP in own**<br>**country**|16.0|14.5|0|70|
|**Mean WTP in Europe**|28.1|14.7|2|80|
|**Median WTP in Europe**|16.1|9.3|1|40|

**Table 5: WTP Plan B**

|**Value concept**|**Mean value**|**St dev**|**Min**|**Max**|
|---|---|---|---|---|
|**Mean WTP in own**<br>**country**|24.0|23.5|1|130|
|**Median WTP in own**<br>**country**|12.2|11.0|0|45|
|**Mean WTP in Europe**|21.4|11.2|2|60|
|**Median WTP in Europe**|12.3|8.2|0|40|

Figures 1 and 2 show the frequency distributions of mean and median WTP for Europe, respectively; for Plan A and B and after Round 1 and 2. For the country estimates; see figures 3 and 4. Both for Europe and experts ́ own country WTP estimates the WTP distributions narrow from Round 1 to 2, as we would expect.

11
<!-- Start of picture text --> &0<br>70<br>£2<br>g 60<br>§ —— Mean WTP for PlanA for<br>1<br>2 30 Europé in Round<br>ce<br>5 40 —— lean WTP for PlanA for<br>v Europé in Round 2<br>s 30<br>€ —— Mean WTP for Plan B for<br>: 20 Europé in Round 1<br>a<br>——=Vlean WTP for Plan B for<br>10 Européin Round 2<br>0<br>0 50 100 150<br>WTP (EUROS)<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>90<br>g &0<br>c<br>3 70<br>S ——ledian WTP for PlanA for<br>2 60 Europé in Round 1<br>ce<br>= 50 ——=|Viedian WTP for PlanA for<br>wv~ =40 Europe pin Round2<br>€ —— Median WTP for Plan B for<br>= 30 Europé in Round 1<br>a 20 ——|Vledian WTP for Plan B for<br>10 Europé in Round 2<br>0<br>0 50 100 150<br>WTP (Euros)<br><!-- End of picture text -->
<!-- Start of picture text --> &0<br>70<br>wi<br>= 60<br>3<br>5 50 —— Mean WTP for PlanA for<br>2uv Countryin Round 1<br>S 40 —— Mean WTP for PlanA for<br>Y Country in Round 2<br>£ 30<br>5 —— Mean WTP for Plan B for<br>a0 Country .in Round 1<br>5<br>—— Mean WTP for Plan B for<br>10 Countryin Round 2<br>0<br>0 50 100 150<br>WTP (Euros)<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>90<br>&0<br>70 —— Median WTP for PlanA for<br>60 Countryin Round 1<br>—— Median WTP for Plan A for<br>50 Countryin Round 2<br>40 —— Median WTP for Plan B for<br>Countryin Round 1<br>30<br>—— Median . WTP for Plan B for<br>20 Countryin Round 2<br>10<br>0<br>0 50 100 150<br><!-- End of picture text -->
Table 6 reports the percentage shares of experts that changed their WTP estimates for Europe for Plan A and B, from Round 1 to Round 2. We see that more experts retained their original mean estimate for Plan B than for (the more extensive) Plan A; 59 and 33 % respectively. On the other hand, changes were greater on average for Plan B than for Plan A. More experts lowered than increased their mean WTP estimates for both plans. For Plan A, 27 % increased their mean WTP while only 8 % did so for Plan B. Those one-third and one-half of experts who did _not_ change their estimates from Round 1 to 2 have likely felt little pressure toward a consensus. Those that did change their stated values seemed overall to be affected by the information given about Round 1 results. A hypothesis, that experts more experienced in conducting SP surveys had a higher probability of changing their WTP estimate, was rejected .Other background variables for the experts could also not explain why some changed their WTP estimate while others did not.

Table 6 also shows the average adjustments (up or down) for those that changed their estimates, which were around 8 euros for Plan A answers, and much higher (15 euros) for Plan B answers.

# **Table 6: Percent of experts who lowered, increased or stated the same WTP estimates  for Europe in Round 2.**

|**Plan**|**WTP**<br>**estimate**<br>**sought**|**Lower**|**Same**|**Higher**|**Average**<br>**change, all**<br>**(euros/hh/y)**|**Average**<br>**change,**<br>**changers**<br>**(euros/hh/y)**|
|---|---|---|---|---|---|---|
|**Plan A**|**Mean**|40|33|27|5.7|8.5|
||**Median**|27|42|31|4.8|8.3|
|**Plan B**|**Mean**|33|59|8|6.2|15.1|
||**Median**|35|53|12|2.7|5.7|

# **4.2 Income Elasticities of WTP**

Tables 7 and 8 present elasticities of expert-assessed average own-country WTP, with respect to own-country income, from Rounds 1 and 2 for the European WTP estimates for Plan A and B; for two measures of income: regular GDP per capita, and Purchasing Power (PPP) corrected 14
GDP per capita. Income levels are there the average per-capita incomes in the home countries of the experts asked to provide their “best guesses” for average WTP per household/year for plans A and B for their own country and for Europe overall.<sup>14</sup> These relations are then estimated on log-linear form; see Appendix 3 for a more formal presentation. This elasticity is of some interest, as it should (under various additional assumptions) make it possible to perform a “calibration”, or “benefit transfer”, of overall value estimates from countries where population studies are made, to countries in Europe where such studies are not carried out. The idea pursued is that experts may, collectively, have an idea about the relative values across these countries, even when they do not know the actual values. Income elasticity estimates are then particularly useful as they will permit us to infer approximate average population values for countries where full population surveys are not carried out, using unit value transfer with income adjustments as the benefit transfer method (see e.g .Navrud 2004; Navrud and Ready 2007).

In Appendix 3 we also discuss some prior expected hypotheses about the relations between these elasticities. We argue that the elasticity estimates with respect to PPP-adjusted per-capita income are likely to exceed those for un-adjusted income; this is a result we recover in strong form from our data. We there also argue that it is more appropriate to base our assessment of cross-country WTP on the income elasticities which are calculated from the non-corrected GDP/capita as the income measure. The reason is that such valuation is for international payments that require (non-corrected) foreign exchange. We there also discuss the expected relations between elasticities from Rounds 1 and 2, and argue that (under certain assumptions) Round 2 figures could be more “compressed” thus yielding lower elasticities. But this may not hold if Round 2 figures are lower on the average. This in reflected in our data: elasticities are generally higher in Round 2 than in Round 1.

> 14 As country GDP per-capita is a more clearcut predictor of own country WTP rather than overall Europe WTP  we focus on results for the experts ́own country WTP estimates, but presents both; see tables 7-8 and 9-10.

15
**Table 7: Elasticities of WTP for own country with respect to average GDP per-capita level in expert’s country, with income as the only variable (raw) and with background variables included , see table 12 (corrected)**

||Rou|nd 1|Roun|d 2|
|---|---|---|---|---|
||Coefficient|t statistic|Coefficient|t statistic|
|WTP plan A (raw variable)|0.587|2.00|0.760|2.82|
|WTP plan A (corrected)|0.453|1.57|0.679|2.65|
|WTP plan B (raw)|0.688|2.35|0.781|2.98|
|WTP plan B (corrected)|0.627|2.08|0.794|3.12|

**Table 8: Elasticities of WTP for own country with respect to average Purchasing Power Parity (PPP) corrected GDP per-capita level in expert’s country; with income as the only variable (raw) and with background variables included , see Table 13 (corrected)**

||Rou|nd 1|Roun|d 2|
|---|---|---|---|---|
||Coefficient|t statistic|Coefficient|t statistic|
|WTP plan A (raw variable)|1.30|2.50|1.54|3.20|
|WTP plan A (corrected)|0.98|1.96|1.28|2.82|
|WTP plan B (raw)|1.44|2.76|1.57|3.37|
|WTP plan B (corrected)|1.28|2.43|1.50|3.35|

The number of data points in our survey (48 experts were included) is too small to yield precisely estimated coefficients for the effects of average national income on average national WTP for Amazon forest preservation. We see, however, that for estimations with respect to noncorrected GDP, in Table 8, virtually all estimated elasticities are in the range 0.5 -0.8.<sup>15</sup> It thus appears as relatively safe to assume this as our main conclusion. Elasticities with respect to PPP-

> 15 Note that we are presenting both “raw” (with only per-capita national income included) and “corrected” calculations (which include a number of relevant background variables some of which are significant; see here also Table 12 below).

16
adjusted income, in Table 9, are (as expected) larger, all in excess of unity (although these estimates will play less of a role overall).

Which elasticities are “more correct” to use; those with respect to regular GDP or those with respect to PPP-adjusted GDP? As a measure of “true” WTP, both are “equally correct”: the regular GDP measure captures the “relative price” effects between low- and high-price level countries; while the adjusted GDP measure captures “real income” effects and their impact on WTP. But in our context, since everything, in the end, is measured in international purchasing power of the actual WTP, the non-corrected measure may appear as the more relevant one. This measure is roughly in the range 0.5 – 0.8 with our data.

**Table 9: Income elasticities of mean WTP/hh/y for Europe as functions of own-country average income; with income as the only variable (raw) and with background variables included, see table 12 (corrected)**

||Rou|nd 1|Roun|d 2|
|---|---|---|---|---|
||Coefficient|t statistic|Coefficient|t statistic|
|WTP plan A (raw variable)|-0.571|-2.11|-0.128|-0.72|
|WTP plan A (corrected)|-0.643|-2.22|-0.185|-0.95|
|WTP plan B (raw)|-0.500|-1.78|-0.165|-0.94|
|WTP plan B (corrected)|-0.534|-1.74|-0.181|-0.95|

Tables 9 and 10 represent estimates of experts’ assessments of mean European WTP overall, as function of per-capita average GDP levels. We find, in Table 9, all elasticities for these relations to be negative (so that experts in countries with relatively high incomes provided relatively low estimates of the average European WTP). This tendency is however far less pronounced for round 2 answers than for round 1. This issue is difficult to fully account for. It could, however, be that answers in round 2 are more qualified and rational than those in round 1 (although they could be biased by the information given between the two rounds). Overall these numbers, however, indicate a certain amount of “pessimism” with respect to average WTP among experts in high-income countries; or “optimism” among experts in low-income countries. We have little basis for assessing which of these two hypotheses is more precise.

Table 10 gives us numbers regarding absolute effects equivalents to the numbers in Table 10. We there see that, for round 1 answers, an decrease in WTP per household of about 1 – 2.5 euros per 10,000 increase in own-country per-capita GDP for round 1, and lower numbers (0.5 – 1 euros) for round 2.

17
**Table 10: Estimate of average WTP in Europe, relationship to own-country average income. US$ increase in WTP per US$10,000 average own-country per-capita income; with income as the only variable (raw) and with background variables included , see table 12 (corrected)**

||Roun|d 1|Roun|d 2|
|---|---|---|---|---|
||WTP change<br>(per 10,000<br>euros, own<br>country<br>income)|t statistic|WTP change<br>(per 10,000<br>euros, own<br>country<br>income)|t statistic|
|WTP plan A (raw variable)|-1.99|-1.21|-0.66|-0.65|
|WTP plan A (corrected)|-2.97|-1.59|-1.21|-1.11|
|WTP plan B (raw)|-1.55|-1.10|-0.58|-0.74|
|WTP plan B (corrected)|-2.47|-1.61|-0.85|-1.06|

# **4.3 Which Factors Affect Experts’ WTP Estimates, and How?**

Data on experts ́ experience in conducting SP studies (both CV and CE) , multi-country studies, benefit transfer exercises and their assessment of how close the Delphi estimate would be an actual CV study were collected in Round 1 ( see appendix 1 for the questionnaire), and regressed on their stated WTP amounts in Round 1 and Round 2. The variables are described in table 11, and the results presented in tables 12 and 13.

18
**Table 11. Description of variables used to explain variation in WTP/hh/y**

|**Variable**<br>(Question number; according to appendix 1);<br>Description, and messurement unit|**Mean**|**Std. dev**|
|---|---|---|
|(J 1)<br>How close is Delphi estimate to true value<br>(Likert scale 1-10, where 10 is very close)|6.3|1.6|
|(J 2)<br>How difficult to implement CV study on Amazon<br>(Likert scale 1-10, where 10 is very difficult)|5.6|2.5|
|(J 3)<br>How difficult to implement CV study on domestic<br>biodiversity<br>(Likert scale 1-10, where 10 is very difficult)|5.4|2.4|
|(B1)<br>Familiarity with CV literature.<br>(Likert scale 1-10, where 10 is very familiar)|8.4|1.5|
|(B2)<br>Familiarity with CE literature<br>(Likert scale 1-10, where 10 is very familiar)|7.4|2.0|
|(B5)<br>Number of CV and CE surveys on biodiversity done|5.6|5.8|
|(B10)<br>Number of papers on CV and CE published, last 5 years|6.6|6.4|
|(B11)<br>Number of papers on CV and CE reviewed, last 5 years|12.1|18.5|
|(B 8)<br>Fraction of research time spent on CV and CE, ( %)|47.9|25.0|

19
|(B9, alt 5)|0.73|0.45|
|---|---|---|
|Read JEEM regularly (dummy, 1if read and 0 if not)|||
|(B9, alt. 1)|0.20|0.41|
|Read AJAE regularly (dummy, 1if read and 0 if not)|||
|(B7)|3.7|5.5|
|Number of benefit transfer exercises conducted|||
|(B 12)|1.9|2.2|
|Number of multi-country(i.e. 2 or more countries) CV or CE<br>studies conducted in Europe|||

20
# **Table 12: Potentially important background variables and their partial effects on mean WTP in the expert ́s own country for implementing plan A in rounds 1 and 2.**

# **(* = significant at level 15%, ** at level 10%, *** at level 5%)**

|Variable|Rou|nd 1|Rou|nd 2|
|---|---|---|---|---|
||Log-linear|Linear|Log-linear|Linear|
|How close is Delphi estimate<br>to true value (1-10)|0.040|-2.24|0.063|-0.76|
|How difficult to implement<br>CV study on Amazon (1-10)|-0.111 (*)|-1.42|-0.088|-0.63|
|How difficult to implement<br>CV study on domestic<br>biodiversity (1-10)|-0.030|-4.29 (*)|-0.038|-4.13 (*)|
|Familiarity with CV<br>literature. (1-10)|-0.137|-1.96|-0.197 (**)|-1.76|
|Familiarity with CE literature<br>(1-10)|<br>0.064|-1.68|0.066|-2.99|
|Number of CV and CE<br>surveys on biodiversity done|-0.037|-0.78|-0.017|-0.53|
|Number of papers on CV and<br>CE published, last 5 years|0.007|1.26|-0.015|1.40|
|Number of papers on CV and<br>CE reviewed, last 5 years|-0.006|-0.26|-0.020|-0.27|
|Fraction of research time<br>spent on CV and CE, %|0.013(**)|0.20|0.016 (***)|0.15|
|Read JEEM regularly<br>(dummy)|1.00 (***)|34.5 (***)|0.903 (***)|30.8 (***)|
|Read AJAE regularly<br>(dummy)|-1.14 (***)|-31.2 (***)|-1.03 (***)|-24.0 (***)|

21
# **Table 13: Potentially important background variables and their partial effects on mean WTP in the expert ́s own country for implementing plan A in rounds 1 and 2.**

# **(* = significant at level 15%, ** at level 10%, *** at level 5%)**

|Variable|Rou|nd 1|Rou|nd 2|
|---|---|---|---|---|
||Log-linear|Linear|Log-linear|Linear|
|How close is Delphi estimate<br>to true value (1-10)|0.040|-2.24|0.063|-0.76|
|How difficult to implement<br>CV study on Amazon (1-10)|-0.111 (*)|-1.42|-0.088|-0.63|
|How difficult to implement<br>CV study on domestic<br>biodiversity (1-10)|-0.030|-4.29 (*)|-0.038|-4.13 (*)|
|Familiarity with CV<br>literature. (1-10)|-0.137|-1.96|-0.197 (**)|-1.76|
|Familiarity with CE literature<br>(1-10)|<br>0.064|-1.68|0.066|-2.99|
|Number of CV and CE<br>surveys on biodiversity done|-0.037|-0.78|-0.017|-0.53|
|Number of papers on CV and<br>CE published, last 5 years|0.007|1.26|-0.015|1.40|
|Number of papers on CV and<br>CE reviewed, last 5 years|-0.006|-0.26|-0.020|-0.27|
|Fraction of research time<br>spent on CV and CE, %|0.013(**)|0.20|0.016 (***)|0.15|
|Read JEEM regularly<br>(dummy)|1.00 (***)|34.5 (***)|0.903 (***)|30.8 (***)|
|Read AJAE regularly<br>(dummy)|-1.14 (***)|-31.2 (***)|-1.03 (***)|-24.0 (***)|

22
Table 13 provides some partial results with respect to “background variables” that might have affected experts’ expected WTP answers. Few of these effects turn up as significant; not surprising considering that only 49 experts are involved in the survey. The most striking effect is related to the regular reading of and familiarity with two central journals, the Journal of Environmental Economics and Management (JEEM), and the American Journal of Agricultural Economics (AJAE). Readership of JEEM strongly and significantly increases average WTP, while readership of AJAE has the opposite effect. It is unclear how this should be interpreted. We know that JEEM is the primary publication outlet for environmental CV/CE studies, while AJAE readers typically are more conservative and more skeptical toward SP methods (and may tend to place lower values on the environment). High stated WTP guesses among the JEEM readership could represent a combination of being better informed, and bias in the direction of preferring SP methods over other methods (and, perhaps, believing that the true environmental values among the respective populations are high).

Higher fractions of experts’ research time spent working on CV and CE methods also affect stated WTP assessments positively, and significantly given a log-linear specification. Experts who perceive a greater difficulty in implementing a CV study on biodiversity in their own country were found to provide significantly lower WTP assessments under a linear specification. The experts’ familiarity with the CV literature was found to be more important for expressed average WTP values, than having done many SP studies. On the other hand, more time spent on CE and CV research significantly increases stated WTP. These results could be interpreted “positively” (those who know SP methods well understand better how environmental goods are to be valued), or “negatively” (the sample of experts who are particularly familiar with SP methods is self-selected and contains individuals with a propensity to value the environment highly; or tend to think that the population values it highly).

There are few differences between the WTP results from Rounds 1 and 2. Familiarity with CV studies has however a significantly negative effect on WTP in Round 2 (in the log-linear specification), but not Round 1. This could indicate that after experts were presented with the WTP answers from other experts, and allowed to adjust their responses, the pattern that those that were more familiar with CV studies stated lower mean WTP for their own country became clearer. Note also that if these experts thought that the Amazon CV survey would be difficult to implement in their own country, they stated significantly lower than otherwise WTP (in the loglinear specification) in Round 1, whereas this effect disappeared in Round 2 when they saw that other experts had also managed to provide their estimates.

23
# **5.  Aggregate WTP for Europe, and Concluding Remarks**

This study reports on average assessments among European environmental valuation experts, of mean population WTP for protecting the Amazon rainforest in their own countries and in Europe as a whole. The answers were provided through two rounds of a Delphi expert survey. In the first round, experts gave independent WTP estimates, which could be changed by experts in the second round after having observed the overall Round 1 results from the entire survey. Mean WTP estimates across all the European experts was approximately 34 €/hh/y, and 29 €/hh/y, respectively, in Round 1 for preserving by 2050, respectively 85 % (Plan A), and 75 % (Plan B), of the original Amazon by 2050; and where the “no-policy” (or business-as-usual) alternative was presumed to imply that only 60% of the original forest will be preserved by 2050. Plan A corresponds to _no further forest loss_ , whereas Plan B implies an additional _10% loss_ of the original forest. In Round 2, in which experts could adjust their initial value answers after having seen the summary statistics from Round 1, mean answers were approximately 32 €/hh/y, and 24 €/hh/y, for Plans A and B respectively.

Experts were also asked to provide similar valuation assessments for an equivalent Europe-wide CV survey. Average answers were then slightly lower than the averages given for their individual countries.

We have also analyzed the relationships between WTP and average national income, in the form of both regular GDP per capita, and PPP-adjusted GDP per capita. Income elasticities were found to lie in the range 0.5–0.8 with respect to regular GDP per capita (from Table 7). This implies that mean WTP increase with regular per-capita GDP of the respective experts’ countries, but at a rate somewhat less than proportional to income. When considering PPPadjusted per-capita GDP, by contrast, income elasticities are overall slightly above unity (mostly in the range 1-1.5, as seen from Table 8). While these results are interesting and plausible, they can however only be considered as suggestive due to the limited number of participants in our survey (with 48 participating experts).

Assuming that our study permits us to add up such values across all European households, and assuming that experts represent their respective populations correctly, our results indicate that the annual WTP among European households for execution of Plan A (after both rounds) could be close to 28 €/hh/y x 300 mill hh, or approximately 8.4 billion €/y, given that experts are correct in their assessments of this population value.<sup>16</sup> This indicates that benefits to distant

> 16 This result is provided under the reservation that experts’ answers need to be weighted with their respective population sizes, which they are generally not. Weighting might bring overall population WTP numbers down somewhat as experts in high income, and high WTP, regions (such as Scandinavia) seem to be over-represented in our sample of experts.

24
beneficiaries (primarily non-users) could be substantial for the change in provision of this global public good. We however stress that this Delphi CV estimate is highly preliminary; it will need to be validated and calibrated by planned actual CV surveys in Europe.

We note also that the mean WTP estimate derived from this Delphi CV survey is close that found by Horton et al (2002, 2003), who asked random samples of households in the UK and Italy about their WTP to preserve rainforest areas in the Amazon. However, both of the preservation scenarios used in that study were different from those in our Delphi CV survey; the population covered was different; and that study was carried out more than 10 years prior to our Delphi Survey.

Our European Delphi CV survey indicates a European willingness to spend at least 8.4 million € annually to avoid any further forest loss in the Amazon rainforest. Drawing such a strong conclusion from a survey of 48 European environmental valuation experts may however be premature. The results need to be controlled, and corroborated, through population surveys in at least some of these European countries, which we intend to do later as part of the overall research program of which this study is part. Hopefully, our study may at least provide some useful information about the relative values across countries on the European continent, and how these co-vary with average national incomes.

The present European Delphi survey is also just a part of more comprehensive set of such surveys related to the global economic value of these preservation plans for the Amazon Rainforest. Delphi surveys (CV-based) are also being executed for experts from North America, Australia/New Zealand, and several countries in Asia; while random population surveys are planned for at least three European countries, Japan, and at least two countries in South America. We view the present study as a promising start of this research program.

25
# **References:**

Adamowicz, W., P. Boxall, M. Williams, and J. Louviere (1998), “Stated Preference Approaches for Measuring Passive Use Values: Choice Experiments and Contingent Valuation.” _American Journal of Agricultural Economics_ 80:64-75.

Bateman, I. et al. (2002) _Economic Valuation with Stated Preference Techniques: A Manual_ . Edward Elgar, Cheltenham, U.K..

Bennett, J. and E. Birol (eds.) (2010), _Choice Experiments in Developing Countries: Implementation, Challenges and Policy Implications_ (Edward Elgar, 2011).

Bennett, J. and R. Blamey (2001), _The Choice Modelling Approach to Environmental Valuation_ . Cheltenham: Edward Elgar.

Carson, R. T. (1998), Valuation of Tropical Rainforests: Philosophical and Practical Issues in the Use of Contingent Valuation. _Ecological Economics_ , 24, 15-29.

Carson, R. T. (2011), _Contingent Valuation. A Comprehensive Bibliography and History_ . Northampton, MA: Edward Elgar Publishers.

Carson, R. T., Flores, N. E. and Meade, N. F. (2001), Contingent Valuation: Controversies and Evidence. _Environmental and Resource Economics_ , 19, 173-210.

Carson, R. T. et al (2011), Valuing the Protection of Belum-Temengor to the Malaysian Public. Mimeo, December 2011.

Carson, R. T., M. B. Conaway, and S. Navrud (2013), Preliminary Valuation of a Cultural Heritage Site of Global Significance: A Delphi Contingent Valuation Study. Forthcoming in Rizzo, I and A. Mignosa (eds.) 2013: _Handbook on the Economics of Cultural Heritage_ . Edward Elgar Publishing, Cheltenham, UK and Northampton MA, USA.

Hanley, N., R. E. Wright, and V. Adamovicz (1998), Using Choice Experiments to Value the Environment. _Environmental and Resource Economics_ , 11, 413-428.

Holmes, T. P., and W. L. Adamowicz (2003), Attributed-based methods. Ch. 6 in P. A. Champ, K. J. Boyle, and T. C. Brown, eds., _A Primer on Nonmarket Valuation_ . Boston: Kluwer Academic Publishers.

Horton, B., G. Colarullo, I. Bateman, and C. Peres (2002), Evaluating Non-Users’ Willingness to Pay for the Implementation of a Proposed National Parks Program in Amazonia: A UK/Italian Contingent Valuation Study. CSERGE Working Paper ECM 02-01.

26
Horton, B., G. Colarullo, I. Bateman, and C. Peres (2003), Evaluating Non-User Willingness to Pay for a Large-Scale Conservation Programme in Amazonia: a UK/Italian Contingent Valuation Study, _Environmental Conservation 30 (2); 139-146._ Kramer, R.A. and D.E. Mercer (1997), Valuing a Global Environmental Good: US residents' Willingness to Pay to Protect Tropical Rain Forests, _Land Economics_ , **73** , 196–210.

Lindhjem, H. and S. Navrud (2011a), Using Internet in Stated Preference Surveys: A review and comparison of survey modes. _International Review of Environmental and Resource Economics_ , 5 (4); 309–351.

Lindhjem, H. and S. Navrud (2011b), Are Internet Surveys an Alternative to Face-to-Face Interviews in Contingent Valuation? _Ecological Economics_ 70 (9); 1628-1637.

Louviere, J., D. Hensher, and J. Swait (2000 _) Stated Choice Methods: Analysis and Applications_ . Cambridge University Press. Cambridge, UK.

May, P., B. Soares-Filho, and J. Strand (2013), How Much is the Amazon Worth? Upublished working paper, Development Research Group, World Bank.

Mitchell, R. C. and R. T. Carson (1989), _Using Surveys to Value Public Goods: The Contingent Valuation Method_ . Baltimore, MD: Johns Hopkins Press.

Navrud, S. (2004): Value transfer and environmental policy. Chapter 5 (pp. 189-217) in Tietenberg, T. and H. Folmer (eds.) 2004: _The International Yearbook of Environmental and Resource Economics 2004/2005. A survey of Current Issues_ . Edward Elgar Publishing, Cheltenham, UK and Northampton, MA, USA Navrud, S and R. Ready (eds.) (2007): _Environmental Value Transfer: Issues and Methods_ . Springer, Dordrect, The Netherlands.

Rolfe, J. and J. Bennett (eds.) (2006), _Choice Modeling and the Transfer of Environmental Values_ (Edward Elgar, 2006).

Sackman, H. (1975), _Delphi Critique: Expert Opinion, Forecasting and Group Process_ , Lexington, MA: D.C. Heath.

27