Benefit Transfer Guidelines

SIXTH FRAMEWORK PROGRAMME

Project no: 502687

NEEDS

New Energy Externalities Developments for Sustainability

INTEGRATED PROJECT

Priority 6.1: Sustainable Energy Systems and, more specifically,

Sub-priority 6.1.3.2.5: Socio-economic tools and concepts for energy strategy.

Deliverable n° 2.1 - RS 3a

Value Transfer Techniques

and Expected Uncertainties

Due date of deliverable: August 2008

Actual submission date: March 2009

Start date of project: 1 September 2004 Duration: 48 months

Organisation name for this technical paper: SWECO

Authors: Ståle NAVRUD

ABSTRACT

When estimating generalized external costs per tonne of emission, or per kWh of electricity production causing the emission, by applying the Impact Pathway Approah (IPA) e.g in the software tool Ecosense, we predict and transfer the physical impacts the emissions cause to public health, environment/ecosystem services, agriculture, materials etc. and then value the impacts using value transfer techniques. We provide an 8-step procedure for value transfer based on the adjusted unit value transfer technique, with optional input from existing meta analyses of valuation studies. Based on a review of validity tests of value transfer exercises, we recommend using an average error bound of +20-40 %, and +100% for complex environmental goods like biodiversity and ecosystem services. Note however, that this uncertainty have to added to the uncertainty in predicting and transferring the physical impacts. Delivarable 3.1 of NEEDS 3a (Spadaro and Rabl 2008) use this asessment of uncertainty in value transfer toghether with an asessment of uncertainty in the other steps of the IPA to show that the overall uncertainty of the resulting generalized external costs estimate can be 1/3 to 3 times the mean extimate for a 68% confidence interval, and 1/9 to 9 times the mean estimate for a 95 % confidence interval.

1 INTRODUCTION

The objective of WP2 in NEEDS RS 3a is to develop valid transfer units and procedures, which can be applied in cost-benefit analysis, green accounting, energy modelling and assessment/rankings of energy technologies.

The aims of this deliverable are therefore:

i)  to evaluate value transfer techniques for EU conditions, and their applicability for these policy purposes,

ii)  to evaluate empirical tests for the validity of transfers for use and non-use values, both within and between countries;

iii)  to identify a step-by-step procedure for value transfer; including the determination of the most appropriate unit to be transferred, (i.e. euro per tonne pollutant or an area-based unit), and how to take account of differences in population density and other relevant aspects when performing transfers.

iv)  to assess the uncertainty in value transfers and discuss acceptable transfer errors for different policy use.

We starts by introducing value transfer as a necessary last step of the Impact Pathway Approach (IPA; see figure 1) for assessing external effects (both damage costs and benefits) of energy technologies, when we don´t have the time or resources to perform new primary valuation studies of impacts on environmental and health impacts. Figure 1 clearly shows the need to calculate the impacts in units that can directly assessed economically, or at least create a linkage between the units of the endpoints of exposure-response functions (or sometimes units of expert assessment of impacts), and the units the economic valuation techniqies provide values for.

We discuss the need for accuracy for different policy uses (chapter 2) , provides a review of value transfer techniques (chapter 3), and a step-by-step guide on how to perform value transfers within the EU and associated transfer errors (chapter 4); Finally, we review validity tests for transfers, assess the uncertainty of value transfer (chapter 5), and discuss acceptable transfer errors for different policy uses (chapter 6).

Figure 1. The Impact Pathway Approach (IPA)

2. Value transfer and policy use

When performing valuation studies[1] for external costs, there is often lack of resources and time to do new primary valuation studies for each policy application. Increased demand for valuation studies has increased the demand for transfer of economic estimates. Values from the primary valuation study site are transferred to the policy site in question, both in terms of space and time. “Benefit transfer” is the term that have stuck in the literature for these procedures (see e.g. Navrud 2004), but since damage also can be transferred, “value transfer” is a more genereal term (Navrud and Ready 2007).

There are four main types of policy use of valuation studies, for which value transfer can be used:

1. Cost Benefit Analysis (CBA).

2. Externality costing to map marginal environmental and health damage costs;

to be used energy modelling and assessment/rankings of energy technologies

3. Environmental accounting (Green National Accounts)

4. Natural resource and damage assessment (NRDA) after pollution incidents

The need for accuracy increases as we move down the list from CBA to NRDA; hence the applicability of value transfer decreases. Due to the added uncertainty inherent in value transfers, one should try to avoid value transfer when the need for accuracy is large. This is the case when the estimatesd values have direct impacts on a stakeholder/interest group, e.g. as in NRDAs where the polluter could end up paying the estimated value in compensation to those affected by the pollution incident. However, the cost of conducting a new primary study should always be compared with the loss associated with making a wrong decision based on transferred values, and the need for accuracy in the application should be assessed prior to every new study (Navrud, 2004).

3. VALUE TRANSFER TECHNIQUES

There are two main approaches to benefit transfer:

i)  Unit Value Transfer

a)  Simple unit value transfer

b)  Unit value transfer with adjustment for income differences

ii)  Function Transfer

a)  Benefit function transfer

b)  Meta Analysis

In approach (i) the unit value at the study site is assumed to be representative for the policy site; either without (a) or with (b) adjustment for differences in income levels between the two sites (using GDP per capita) and/or differences in the costs of living (using Purchase Power Parity (PPP) indices). In approach (ii) a benefit function is estimated at the study site and transferred to the policy site (a), or a benefit function is estimated from several study sites using meta-analysis (b).

Simple unit transfer is the easiest approach to transferring benefit estimates from one site to another. This approach assumes that the wellbeing experienced by an average individual at the study site is the same as will be experienced by the average individual at the policy site. Thus, we can directly transfer the benefit estimate, often expressed as mean willingnes-to-pay (WTP)/household/year, from the study site to the policy site.

For the past few decades this procedure has routinely been used in the United States to estimate the recreational benefits associated with multipurpose reservoir developments and forest management (USDA Forest Service). The selection of these unit values could be based on estimates from only one or a few valuation studies considered to be close to the policy site (both geographically and in terms of the good valued), or based on an average WTP estimate from literature reviews of many studies (in terms of meta analysis)..

The obvious problem with this transfer of unit values for recreational activities is that individuals at the policy site may not value recreational activities the same as the average individual at the study sites. There are two principal reasons for this difference. First, people at the policy site might be different from individuals at the study sites in terms of income, education, religion, ethnic group or other socio-economic characteristics that affect their demand for recreation. Second, even if individuals´ preferences for recreation at the policy and study sites were the same, the recreational opportunities (i.e., substtute sites and activities) might not be.

Unit values for non-use values of e.g. ecosystems from CV studies might be even more difficult to transfer than recreational (use) values for at least two reasons. First, the unit of transfer is more difficult to define. While the obvious choice of unit for use values are consumer surplus (CS) per activity day, there is greater variability in reporting non-use values from CV surveys, both in terms of WTP for whom, and for what time period. WTP is reported both per household or per individual, and as a one-time payment, annually for a limited time period, annually for an indefinite time, or even monthly payments. Second, the WTP is reported for one or more specified discrete changes in environmental quality, and not on a marginal basis.

The simple unit value transfer approach should not be used for transfer between countries with different income levels and costs of living. Therefore, unit transfer with income adjustments has been applied. The adjusted WTP estimate Bp' at the policy site can be calculated as

WTPp' = WTPs (Yp / Ys)ß (3.1)

where WTPs is the original WTP estimate from the study site, Ys and Yp are the income levels at the study and policy site, respectively, and ß is the income elasticity of demand for the environmental good in question. Income elasticity of WTP ß for different environmental goods are typically smaller than 1, and often in the 0.4 - 0.7 range. (Note that this is the income elasticity of WTP, and not of demand; and that there is no simple relationship between the two measures). A recent multi-country CV study of Value of a Life Year (VOLY), conducted within WP6 of NEEDS RS 1b, found the income elasticity to be about 0.4 for the overall sample, and about 0.2 and 0.5 for the EU-15 and the New Member Countries, respectively (Desaigues et al 2007). When we lack data on the income levels of the affected populations at the policy and study sites, Gross Domestic Product (GDP) per capita figures have been used as proxies for income in international benefit transfers. However, this approach could give wrong results in international benefit transfers when income levels at the local study and/or policy site deviates from the average income level in the countries.

Using the official exchange rates to convert transferred estimates in U.S. dollars to the national currencies does not reflect the true purchasing power of currencies, since the official exchange rates reflect political and macroeconomic risk factors. If a currency is weak on the international market (partly because it is not fully convertible), people tend to buy domestically produced goods and services that are readily available locally. This enhances the purchasing powers of such currencies on local markets. To reflect the true underlying purchasing power of international currencies, the U.S. International Comparison Program (ICP) has developed measures of real GDP on an internationally comparable scale. The transformation factors are called Purchasing Power Parities (PPPs).

Even if PPP adjusted GDP figures and exchange rates can be used to adjust for differences in income and cost of living in different countries, it will not be able to correct for differences in individual preferences, initial environmental quality, substitute sites and goods, and cultural and institutional conditions between countries (or even within different parts of a country).

Transferring the entire benefit function is conceptually more appealing than just transferring unit values because more information is effectively taken into account in the transfer. The benefit relationship to be transferred from the study site(s) to the policy site could be estimated using either revealed preference (RP) approaches like TC and HP methods or stated preferences (SP) approaches like the CV method and Choice Experiments (CE). For a CV study, the benefit function can be written as:

WTPij = b0 + b1Gj + b2 Hij + e (3.2)
where WTPij = the willingness-to-pay of household i at site j, Gj = the set of characteristics of the environmental good at site j, and Hij = the set of characteristics of household i at site j, and b0 , b1 and b2 are sets of parameters and e is the random error.

To implement this approach the analyst would have to find a study in the existing literature with estimates of the constant b0 and the sets of parameters, b1 and b2. Then the analyst would have to collect data on the two groups of independent variables, G and H, at the policy site, insert them in equation (1), and calculate households´ WTP at the policy site.

The main problem with the benefit function approach is due to the exclusion of relevant variables in the WTP (or bid) function estimated in a single study. When the estimation is based on observations from a single study of one or a small number of recreational sites or a particular change in environmental quality, a lack of variation in some of the independent variables usually prohibits inclusion of these variables. For domestic benefit transfers researchers tackle this problem by choosing the study site to be as similar as possible to the policy site.

Instead of transferring the benefit function from one selected valuation study, results from several valuation studies could be combined in a meta-analysis to estimate one common benefit function. Meta-analysis has been used to synthesize research findings and improve the quality of literature reviews of valuation studies in order to come up with adjusted unit values. In a meta-analysis, several original studies are analysed as a group, where the result from each study is treated as a single observation in a regression analysis. If multiple results from each study are used, various meta-regression specifications can be used to account for such panel effects.

The meta analysis allows us to evaluate the influence of a wider range in characteristics of the environmental good, the features of the samples used in each analysis (including characteristics of the population affected by the change in environmental quality), and the modelling assumptions. The resulting regression equations explaining variations in unit values can then be used together with data collected on the independent variables in the model that describes the policy site to construct an adjusted unit value. The regression from a meta-analysis would look similar to equation (3.2), but with one added independent variable; Cs = characteristics of the study s (and the dependent variable would be WTPs = mean willingness-to-pay from study s).