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Ecosystem Service Valuation from Floodplain Restoration in the Danube River
Basin: An International Choice Experiment Application
Conference Paper · June 2009
DOI: 10.13140/2.1.2884.7044
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Ecosystem Service Valuation from Floodplain
Restoration in the Danube River Basin: An
International Choice Experiment Application
Roy Brouwer1, Markus Bliem2, Zsuzsanna Flachner3, Michael Getzner4, Sandor
Kerekes5, Simon Milton5, Teodora Palarie6, Zsuzsanna Szerényi5, Angheluta
Vadineanu6, Alfred Wagtendonk1
1
Institute for Environmental Studies (IVM), VU University, Amsterdam, The Netherlands. 2 Institute for Advanced
3
4
5
Studies Carinthia (IHSK), Austria. RISSAC, Budapest, Hungary. University of Klagenfurt, Austria. Corvinus
6
University Budapest, Hungary. University of Bucharest, Romania.
Abstract
The objective of this paper is to estimate the welfare impacts of ecological floodplain restoration and test their
transferability across the second largest river basin in Europe. Floodplain restoration reduces flood risk and
improves water quality, the welfare impacts of which are measured in an identical choice experiment conducted
in three countries. The estimated choice models differ in preference and scale parameters due to preference
heterogeneity. Although transfer errors are reduced by up to almost 75% if preference heterogeneity is controlled
for, the estimated welfare measures are not transferable when imposing a 20% error tolerance level. Similarly,
substantial errors are found if average values are aggregated without controlling for preference heterogeneity and
socio-economic conditions that are unevenly distributed in space.
Key words: Choice Experiment, Ecological Restoration, Water Quality, Flood Risk, Benefits
Transfer, GIS, Water Framework Directive
1
1. Introduction
Integrated river basin management and the economic valuation of water resources have been
at the forefront of the European environmental economics research agenda since the
introduction of the European Water Framework Directive (2000/60/EC). In particular,
payments for watershed ecosystem services have received a lot of attention (e.g. Porras et al.,
2008). This paper addresses the economic valuation of the ecosystem services associated with
floodplain restoration. More specifically, the non-market benefits of ecological restoration of
heavily modified river stretches in three different countries in the international Danube river
basin are estimated. With a length of 2,780 km the Danube is the second largest river in
Europe. The river has been subject to anthropogenic modification and environmental
pressures over the past centuries, including canalization, construction of embankments,
navigation, use of hydropower and pollution (ICPDR, 2007). The shape of the river has
changed and large parts of the floodplains have been drained for agricultural purposes. The
hydrological connectivity of the river and its tributaries to the surrounding floodplains has
been reduced substantially (e.g. Hohensinner et al., 2004). Only in small areas, such as the
Danube National Park in Austria, are the original Danube ecosystem and adjacent wetlands
still intact. Restoring the river to a ‘good ecological status’, as required by the European
Water Framework Directive (WFD), is only feasible if parts of the river are transformed back
as close as possible to their original state with regard to natural hydro-morphological
conditions.
Through the application of a common choice experiment design, the economic values of river
restoration in three river basin countries (Austria, Hungary and Romania) are compared. The
main objective of the study presented here is to estimate public willingness to pay (WTP) for
the ecosystem services associated with river restoration in terms of reduced flood risk and
2
water quality improvements and to test the transferability of the non-market benefits of river
restoration policy scenarios across the three river basin countries, whilst accounting for
preference heterogeneity. Geographical information systems (GIS) are used to aggregate
economic welfare implications of these policy scenarios across the relevant population of
beneficiaries in the three countries, controlling for spatially correlated choices through
distance-decay functions and differences in income distribution across the international river
basin. Distance-decay refers to the expected negative relationship between WTP for river
restoration and distance of the respondent’s place of residence from the Danube river (e.g.
Bateman et al., 2006). Respondents living closer to the river are expected to assign more
value to the benefits of river restoration than respondents living further away.
Estimated non-market values can be used, together with potential market benefits such as the
avoided costs of flood damage or water purification, to justify investments in Danube river
restoration projects to achieve the ecological objectives of the WFD based on economic
welfare considerations. These projects are expected to create a double dividend from the
necessary investments to transform river stretches back in their natural conditions, as they
restore the natural floodwater storage capacity of the river (hence reduce flood risk) and also
improve water quality at the same time (e.g. Schiemer et al., 1999). In view of the public
nature of the ecosystem services provided by the river restoration projects, stated preference
methods, such as choice experiments, are considered important tools to inform policy and
decision-making regarding these projects (Brouwer, 2008). Choice models are considered
superior to contingent valuation methods for the purpose of value transfer because of their
ability to value marginal changes in good and site characteristics (Morrison and Bergland,
2006). However, the application of choice models to water quality improvements in the
context of the WFD is limited (e.g. Hanley et al., 2006). Although various economic valuation
3
methods and techniques, including stated preference methods such as contingent valuation,
have been applied over the past decades to value the non-priced public goods and services
provided by wetlands and floodplains (e.g. Brouwer et al., 1999; Brander et al., 2006), a
common valuation design for international comparison is missing.
2. Choice experiment design
A common choice experiment (CE) was developed to assess the non-market value of the
ecosystem services associated with river restoration. The design consists of two exclusive
categories of benefits: the impact of river restoration on floodwater storage and a
corresponding reduction in flood risk, and the river’s nutrient retention capacity and hence
water quality. These benefits make up two of the three attributes used to evaluate policy
alternatives in the CE. A monetary cost price was included as a third attribute to enable
monetization of the benefits of different river restoration projects.
The alternatives describe different end states created through river restoration measures. The
link between these end states and river restoration projects is explained in the introduction to
the CE. Respondents are told that river restoration measures will affect both flood risk and
water quality as a result of an improvement of the river’s floodwater storage and nutrient
retention capacity. Variations in end states are caused by different degrees of river restoration
and corresponding scale effects. In this way, respondents are not asked to value the river
restoration measures per se, but rather their outcomes in order to avoid correlation due to
causality. To increase the realism of the presented alternatives, respondents were shown
existing river restoration plans on a map. The same map was used in the three case study
applications: a 2000 CORINE land cover map at 1:100,000 scale, displaying the main level 3
4
ecosystem types (human settlements, agriculture, forests and meadows, wetlands and
freshwater ecosystems).
A main effects fractional factorial design was used to generate 32 different choice sets
blocked in 8 different versions of 4 cards. Each respondent was thus shown 4 cards. The
allocation of card sets across respondents was random. Each choice card consists of two
unlabelled restoration alternatives (A or B) and a description of the status quo. Respondents
were asked to choose between the two river restoration alternatives or the status quo situation
(see the example card in Figure 1). The trade-off here is the price respondents pay as a private
household for the presented public river restoration benefits on top of their current annual
water bill. Hence, the estimated welfare measure is individual WTP to secure the river
restoration benefits (compensating surplus). If respondents choose the current situation, they
obviously forego restoration benefits and the cost price is zero. An overview of the attributes
utilised and their corresponding levels is presented in Table 1.
Table 1: Overview of attribute levels
Level
Flood return period
Water quality
Cost price
(€/household/year)
Baseline
Once every 5 years
Moderate
0
1
Once every 25 years
Good
3
2
Once every 50 years
Very good
10
3
Once every 100 years
30
4
50
5
Figure 1: Example of a choice card
Flood frequency
Option A
Option B
Current
situation
Once every
25 years
Once every
25 years
Once every
5 years
Good
Very good
Moderate
€3
€ 10
€0
Water quality
Increase in
water bill
I prefer:
Option A
Option B
Neither
Flood risk is defined as the flood return period. Currently, the study areas face severe highimpact flooding approximately once every 5 years (as opposed to regular low-impact annual
floods). As a result of river restoration, this return period can be reduced to once every 100
years. Water quality is described in categorical terms and explained with the help of the water
quality ladder created by Resources for the Future (RFF) in the United States (Carson and
Mitchell, 1993). The ladder categorises water quality into recreational uses such as
‘swimmable’, ‘boatable’ and ‘fishable’ on the one hand and illustrates levels of biological
diversity of aquatic life on the other. Coloured pictograms were used to visualise the water
quality levels (where red reflects poor water quality conditions, yellow moderate, blue good
and green very good water quality conditions). Based on expert consultation, moderate water
quality levels were chosen as the baseline category (i.e. the current situation). The monetary
6
attribute was specified as an increase in the household water bill. Payment levels varied from
3 to 50 Euros per year (with the corresponding monthly amounts also presented between
brackets). In the case of Hungary and Romania these values were converted into national
currencies (Hungarian Forint and Romanian Lei).
3. Econometric model
The choice model used here has its roots in random utility theory (e.g. Ben-Akiva and
Lerman, 1985). The Multinomial Logit Model (MNL) is the most commonly used structure
for choice models. The MNL model assumes that the random components of the utility of the
alternatives are independently and identically (i.i.d.) Gumbel distributed with a type I extreme
value (EV) distribution (Train, 2003). The random parameters or mixed logit model is more
flexible than the MNL model and relaxes the assumption of independence of irrelevant
alternatives (IIA) as a result of the iid property, and allows – among other aspects – for
(random) preference heterogeneity. Any random utility model can be approximated by a
mixed logit model (McFadden and Train, 2000). The model is defined based on the functional
form for its choice probabilities. The standard indirect utility function underlying the mixed
logit model is:
(1)
U ij = Vij + ε ij = β i X ij + ε ij
where Uij refers to the utility of individual i obtained from choice alternative j, Vij is the
measurable component of utility measured through a vector of utility coefficients β associated
with a vector of observed attribute and individual characteristics Xij, and εij captures the
unobserved influences on an individual’s choice with an iid extreme value distribution. The
utility coefficients β vary according to individual (hence βi) with density f(β). This density
7
can be a function of any set of parameters, and represents in this case the mean and
covariance of β in the sample population:
(2)
U ij = β X ij + f ( β ) X ij + ε ij
In equation 2, β is a vector of fixed parameters and f(β) the vector of standard deviation
parameters, reflecting the deviation from mean β. Thus instead of being fixed across
individuals (as in the MNL model), β represents a random variable. Based on the choice
design used in this case study, equation (1) can be rewritten as (ignoring the notation of
random terms for the sake of simplicity):
Vijl = β 0l + β 1l Flood ijl + β 2l Quality ijl + β 3l Pr iceijl + ε ijl
(3)
where β0 is the alternative specific constant (ASC) and β1 to β3 refer to the vector of
coefficients related to the attributes flood return period (Flood), water quality (Quality) and
cost price (Price). The subscript l represents the specific country in which the survey was
conducted (l=1,2,3). The null hypothesis tested here is that the part-worth utilities associated
with the different attributes are uniform across the three different river basins.
Choice behaviour is expected to be related negatively to the flood return variable (i.e. the
lower the flood return period, the higher the probability of choosing the river restoration
alternative), and positively to the water quality variable (the higher the quality level, the
higher the probability of choosing the river restoration alternative). The cost price is expected
to have a negative effect on choice behaviour (the higher the price, the lower the probability
of choosing the river restoration alternative).
8
The conditional choice probability L that individual i prefers choice alternative j (if ε is i.i.d.
EV distributed) can be expressed in terms of the following logistic distribution (McFadden,
1974):
Lij =
e
λβ i X ij
∑e
(4)
λβ i X j
j∈C
Because βi is not observed, the unconditional choice probability P equals the integral of
equation 4 over all possible variables βi (Train, 2003):
 λβ X
 e i ij
Pij = ∫ 
λβ X
 ∑ e i j
 j∈C


 f ( β ) dβ


(5)
In equations 4 and 5, λ is a scale parameter, typically assumed to be 1, implying constant
error variance, and C is the choice set. For the purpose of facilitating international comparison
of the estimated choice models, we explicitly test the equality of scale parameters across
countries using a Full Information Maximum Likelihood (FIML) estimation procedure
(Hensher, 1986). The scale factor and model parameters are confounded (Vij=λ[β0+ β0X1 +
...+βnXn]); that is, the model estimates equal β/σε , making it impossible to attribute model
differences to differences in parameter estimates or scale (Swait and Louviere, 1993). Even if
the model parameters from the three countries are truly identical, this may be obscured by
variance inequality, and if the scale parameter varies by respondent and/or country, this may
significantly affect predicted probabilities (Louviere et al., 2002).
9
The inclusion of a monetary attribute in the choice model allows for the estimation of
monetary Hicksian welfare measures for different river restoration policy scenarios and
changes in individual components of these scenarios (e.g. Hensher et al., 2005). These
monetary welfare measures form the basis for the international comparison and transferability
tests (e.g. Morrison et al., 2002). The marginal rate of substitution (MRS) for a change in one
of the river restoration attributes, for example flood return period, is calculated as the negative
ratio of the first derivatives of the flood return period (β1 in equation 3) to the cost price (β3 in
equation3). The price attribute in the denominator is interpreted here as the marginal utility of
income. The MRS hence equals in this example marginal WTP for a reduction in the flood
return period. Besides testing the equality of these marginal (implicit) prices across river basin
countries, we also compare and test the equality of the estimated compensating surplus (CS)
of a number of policy scenarios, i.e. foreseen discrete changes in flood risk and water quality
as a result of river restoration in Austria, Hungary and Romania, accounting for demographic
and socio-economic factors.
4. Case study description
The Danube is the second largest river in Europe. It originates in the Black Forest in Germany
and flows through 10 Central and Eastern European counties before emptying into the Black
Sea. The river has a length of 2,780 km, and a catchment area of more than 800,000 km2.
Although some parts of the river still are in a near-natural state, most river stretches have been
classified as heavily modified due to embankment and regulation works, and intensive
navigation. The shape of the river has been drastically changed and large parts of the
associated formerly-waterlogged area have been drained for agricultural purposes, reducing
the connectivity between the Danube and the surrounding area and its tributaries to small
patches. The structure and state of the riparian zone directly influence the biological and
10
hydro-morphological quality elements of the river. Various river restoration projects have
been identified in the Danube river basin. Three of them were included in this study (Figure
2): the Donau-Auen National Park in Austria, Által-ér in Hungary, and the Islands of Braila in
Romania. These case studies will be briefly described below.
Figure 2: Location of the Danube river restoration projects
The Austrian Danube National Park is located east of Vienna. It is a green ribbon floodplain
area with a length of 35 km, linking Vienna and Bratislava. The national park covers an area
of 93 km2 and is a complex ecosystem with an enormous diversity of habitats, plants and
animals. In some parts, the floodplain is still to a high degree ecologically intact, displaying
the characteristics of a large stream. Within and around the floodplain, the main economic
activities include agriculture, forestry and fishing. Overall, water quality can be classified as
moderate to good. The main source of pollution is the wastewater treatment system of
11
municipalities upstream (notably the City of Vienna) although the latter has been improved
substantially over the past decades. Parts of this stretch of the Danube are classified as heavily
modified water bodies, especially since the river bed was canalized and the construction of the
hydropower station of Freudenau (within the city limits of Vienna downstream to the east)
further changed the free-flowing character of the river. Achieving the WFD objectives along
the Danube and in particular around Vienna is uncertain due primarily to non-point source
pollution (Institute for Water Quality, 2008).
The Hungarian Által-ér is a tributary of the Danube which has been partly disconnected from
the main river over the past centuries. The watershed covers an area of 520 km2 and the Általér river has a length of approximately 50 km. The river’s watercourses are highly regulated and
run in artificial riverbeds. A series of dams were constructed in the past to assure water supply.
The main sources of diffuse pollution are wastewater from small-scale domestic animal husbandry
and manure and fertilizer runoff from arable land. The most serious water quality problems are
found in and around the industrialized cities of Tatabánya, Vértesszőlős and Tata due to emissions
of heavy metals and nutrients. Most of the (often illegal) municipal landfills located in this area
also contain dangerous substances, which leak into surface and groundwater bodies. Although
monitoring data in the watershed are inadequate to assess current water quality levels, overall
water quality in the watershed is rated as ‘moderate’ based on expert judgment.
Finally, the Romanian Braila Islands are part of the former Inland Danube Delta. Braila City is the
most important urban centre in this area and has approximately 215 thousand inhabitants. Land
use is predominantly agricultural (approximately three quarters of the total area), while industry is
concentrated in Braila City. The Danube river has been classified in this section (195 km in
length) as heavily modified due to hydro-technical works and dredging of more than 20 percent of
the river bed for the purpose of navigation. The wetlands from the Small Island of Braila Natural
12
Park (210 km2) and the floodplains between the riverbanks and dikes are the main remnants of the
natural floodplains. Almost 80 percent of the whole area has been drained for agricultural
purposes. As a consequence, connectivity between the Danube and the floodplains is very limited.
Water quality in this stretch of the Danube river is rated as moderate. The main pollution sources
are agriculture, industry, navigation, and domestic households. On the other hand, the Small
Island of Braila is especially rich in bird species: 136 different species have been identified, of
which 47 are listed in the annex of the EU-Bird Directive. Together with the coastal Danube
Delta, the wetland system is an important stepping stone for bird migration routes in SouthEastern Europe.
4. Survey design and implementation
The questionnaire was developed during several meetings of the international project group
over a six month time period and subsequently pre-tested in each of the three river basin
countries. After each pre-test, the results were shared to improve the common valuation
design. Although the questionnaire was identical in all three countries, survey administration
differed. Interviewing took place face-to-face in Hungary and Romania, while a web-based
survey was conducted in Austria. For the pre-test in Austria, 526 people were recruited
randomly in Vienna, of whom 109 completed the web-based questionnaire (a response rate of
21%). In addition, 15 questionnaires were sent to water experts with whom face-to-face
interviews were conducted to test the structure and wording of the questionnaire. In Hungary,
the pre-test consisted of 32 face-to-face interviews in Tata, one of the largest towns in the
Által-ér catchment, where approximately 20 percent of the catchment population live. The
questionnaire was pre-tested in two rounds in Romania, targeting a total of almost 100 local
residents living in different villages throughout the case study area and the city of Braila.
After each pre-test, minor changes were introduced to the structure and wording of the
13
questionnaire. Special attention was paid to respondents’ understanding of the choice
experiment and the credibility of combination of alternatives.
The final questionnaire consists of 37 questions, most of which are close-ended (multiple
choice), and is divided into three main parts. The first part of the questionnaire contains
questions about respondents’ general perception of water related issues, including water
quality and flood experience. People were also asked about their recreational activities, e.g.
how often they visit the case study area and whether they would visit more often if water
quality was improved. The second part consists of the CE, while the third part of the
questionnaire is designed to collect information on standard respondent demographic and
socio-economic characteristics.
The main survey was carried out simultaneously in all three countries in November 2007. In
Austria, the main survey targeted a random sample of 1,977 households from a representative
household market panel in Vienna and Lower Austria. The response rate was 26 percent
(n=506). Stratified sampling procedures were followed in Hungary and Romania, based on
gender, age and representative shares of the rural and urban population living along both sides
of the Danube river. In Hungary, 892 people were asked to participate in the survey, of whom
471 agreed (a response rate of 53%), while in Romania 519 of the 850 respondents who were
asked to participate completed the questionnaire (a response rate of 61%).
14
5. Results
5.1. Sample characteristics
Sample characteristics across the three subsamples are summarized in Table 2. Generally, the
samples are fairly representative compared to the underlying national population from which
they were drawn. Men are slightly overrepresented in Hungary (the share of the male
population at national level is 47%), but overall an equal number of men and women are
included. The average age is also more or less the same across countries (41 in Austria, 43 in
Hungary and 44 in Romania), although minor differences are found in age structure and
distribution. Some degree of self-selection may have played a role in Hungary and Romania
in view of the relatively high share of respondents with higher (secondary and higher)
education in these two sub-samples (26%) when compared to Austria (10%).
As expected, pronounced differences are found when comparing disposable household
income between the three sub-samples. Mean annual household income in the Austrian
sample (€22,025) is two and a half times higher than in Hungary (€8,925) and four times
higher than in Romania (€5,590). As can be seen from Table 2, most Romanian households
earn less than 500 Euros per month, while around 30 percent of respondents in Hungary and
less than 10 percent in Austria fall into the lowest income category. Most Hungarian
respondents earn between 500 and 1,000 Euros per month, and most Austrian respondents
between one and two thousand Euros per month. Average household income in the Austrian
sample is slightly higher than the national average, while average household income in
Hungary and Romania conforms more or less to the national average in these respective
countries (Eurostat, 2008). Almost 10 percent of the Austrian sample are members or donate
15
to an environmental organization. This share is substantially lower in Hungary (5%) and
Romania (<1%).
Table 2: Respondent demographic and socio-economic characteristics
Relative distribution (%)
Austria
Hungary
Romania
48
55
51
=< 19 year
10
6
3
20-29
18
20
16
30-39
21
19
22
40-49
22
17
21
50-59
18
20
21
>= 60 year
11
18
17
€0-500/month
8
29
68
€500-1000
13
46
25
€1000-2000
38
23
6
€2000-3000
24
1
1
> €3000/month
17
1
0
Share male respondents
Age groups
Income groups
Most respondents visited the study area where the river restoration measures are to take place.
This share is lowest in Romania (47%) and highest in Hungary (88%). The average distance
respondents live from the Danube river is lowest in the Hungarian sample (4 km), and highest
in the Austrian sample (47 km). Romanian respondents live, on average, about 15 km from
the Danube. In all three samples, a majority of respondents (80%) walk regularly along the
16
river. Fewer people fish or swim in open waters. The share of respondents who swim in open
waters is highest in Austria (69%) and lowest in Hungary (22%). On the other hand, the share
of respondents fishing in the Danube is lowest in Austria (9%), while respectively 23 to 40
percent of the Hungarian and Romanian sample fish in the river.
Investigating flood experiences and water quality perception, Romanian respondents have
least experience with floods. Less than 10 percent have ever experienced a flood during their
lifetimes. This share is higher in Austria and Hungary (respectively 16 and 19%). Significant
differences are found when examining the public perception of water quality (Figure 3).
Figure 3: Public perception of Danube water quality in the three samples
Based on the WFD water quality classification, almost three quarters of all Austrian
respondents believe that current water quality is good to very good. Only 5 percent perceive
water quality to be poor. In Hungary and Romania almost half of the sample classify current
water quality as poor. Just over 15 percent think it is good and less than one percent rate it as
very good. Hence, in the latter two countries a discrepancy exists between the expert
17
classification of current water quality (and thus the corresponding status quo description of
water quality in the CE) and public perception. Around a quarter (Austria) to a third (Hungary
and Romania) of the respondents perceive current water quality to be at the level it is
described in the CE. We now turn to the CE results.
5.2. Marginal WTP for floodplain restoration and tests of international transferability
Out of the more than 17,000 choice occasions in all three samples (6,000 in Austria, 5,445 in
Hungary and 5,988 in Romania), the status quo was chosen in 22 percent of all cases. This
percentage was lowest in Austria (17%) and highest in Romania (28%), presumably due to a
lack of monetary resources to pay for the restoration benefits. No major differences were
found between the three countries in terms of choices for option A or B. Option A was chosen
in 36 percent of all the choice occasions and option B in 42 percent.
Marginal WTP values for the attributes can be derived from the estimated choice model (see
section 3). For this, a simple model including the design attributes only is estimated. The null
hypothesis of equal model parameters across the three river basin countries, whilst permitting
scale factors to differ between countries, is tested by pooling the datasets and jointly
estimating the model and scale parameters in a nested logit model (using the FIML procedure
in NLOGIT 3.0). The Likelihood Ratio (LR) test rejects the null hypothesis of equality of
model parameters at the one percent confidence level (chi-squared (5) = 151.382). Following
Swait and Louviere (1993), this also implies that the hypothesis of equality of scale
parameters is rejected. So, we expect the marginal WTP values to be different due to the fact
that both model parameters and variances differ between the three country specific models.
The marginal WTP values and their standard errors, estimated using the delta method
(Greene, 2003), are presented in Table 3. For the purpose of international comparison, the
18
values in Hungary and Romania were adjusted using the purchasing power of the Euro in
Austria (e.g. Ready and Navrud, 2006)1.
The flood risk attribute is only significantly different from zero in Austria, and indicates what
a household is willing to pay, ceteris paribus, for a reduction of the flood return period by one
year. The values for good and very good water quality equal what households are willing to
pay for a change in water quality from moderate to good and very good conditions. In order to
test for possible non-linearity, dummy coding is used for the categorical water quality levels
(moderate water quality being the baseline category). Equality of dummy variables for the
different water quality levels (within each country) was tested with the help of the Wald test.
The null hypothesis of equal parameter estimates is convincingly rejected at the one percent
confidence level in each country, reflecting sensitivity to scope2.
Table 3: Attribute implicit prices in the three samples (€/household/year)
Marginal change in:
Austria
Hungary
Romania
0.20
0
0
(0.05)
(not significant)
(not significant)
44.5
21.2
23.0
(6.5)
(3.1)
(10.7)
75.3
42.5
36.8
(8.4)
(4.2)
(14.1)
Flood risk
Water quality conditions
moderate → good
moderate → very good
Standard errors between brackets.
1
Purchasing power parities (PPP) were taken from the World Bank 2008 World Development Indicators.
2
Chi-square values (1 degree of freedom) are respectively 66.62 (p<0.001), 108.87 (p<0.001) and 6.25
(p<0.012) for Austria, Hungary and Romania.
19
Using the two one-sided t-test (Kristofersson and Navrud, 2005) to test whether the marginal
values are equivalent between river basin countries at a significance level of α=0.05 and a
transfer error tolerance level of 20%3, the null hypothesis of inequality is rejected for Hungary
and Romania (test results are available from the authors). So, the PPP adjusted estimated
marginal WTP values for both good and very good water quality are transferable between
Hungary and Romania, but not between Austria and Hungary and Austria and Romania. In
the latter case, the transfer errors range (depending on transfer between countries and the
extent of the water quality improvement) between 44 and 110 percent.
5.3. Modelling preference heterogeneity for floodplain restoration
Preference heterogeneity is picked up in the fixed and stochastic part of the estimated utility
functions. The statistically significant results for the country specific random parameters logit
models are presented in Table 4. For efficiency purposes, the model is estimated (in NLOGIT
3.0) using the Halton sequence in a quasi-Monte Carlo maximum likelihood simulation
method (Bhat, 2001). Random effects were detected for the highest water quality attribute in
each country. The standard deviation is highly significant in all three samples, suggesting that
preferences are indeed heterogeneous and taste variations are partly random. The flood risk
attribute is (as before) only statistically significant in Austria. Flood risk was included in this
case as number of years (flood return period). The expected positive coefficient hence implies
that an alternative is more likely to be chosen if the flood return period is lower (e.g. 100
instead of 50 years). The water quality attributes are statistically significant and positive in all
countries (the impact of very good quality again being significantly higher than that of good
quality based on the Wald test), except in Romania, where only very good water quality is
3
Following Kristofersson and Navrud (2005). Other levels are also possible. The tolerance level is the transfer
error the researcher or policymaker is willing to accept.
20
significant. As expected, the cost attribute has a significant negative impact on choices in all
the three countries.
Table 4: Estimated random parameters choice models for the three samples
Austria
Hungary
Romania
Variable
Coef. est.
S.e.
Coef. est.
S.e.
Coef. est.
S.e.
***
ASC
0.010
0.140
-0.685 0.202
0.234
0.097**
Design attributes
FLOODRISK
0.005 0.002***
-0.0004
0.002
0.009
0.006
***
***
GOOD QUALITY
1.353 0.171
0.837 0.191
0.198
0.148
VERY GOOD QUALITY
1.814 0.180***
2.209 0.184***
0.457 0.162***
COST
-0.035 0.006***
-0.067 0.007***
-0.046 0.005***
Preference heterogeneity
HIGHEDUC x ASC
0.484 0.178***
*
VISITOR x ASC
1.184
0.627
0.905 0.195***
INCOME x COST
0.002
0.001*
0.010 0.001***
0.014 0.002***
***
***
QPERCEP x GQ
-0.011 0.003
-0.011 0.004
QPERCEP x VGQ
-0.010
0.003**
-0.006
0.003*
-0.009 0.003***
FUTVISIT x GQ
0.792 0.307***
0.325
0.197*
0.503 0.170***
***
FUTVISIT x VGQ
1.321 0.320
0.565 0.183***
DIST x GQ
-0.005 0.002***
DIST x VISITOR
-0.006
0.003*
DIST x FLOODAFFECT
-0.047
0.020**
Standard deviation
VERY GOOD QUALITY
1.211 0.437***
1.591 0.325***
1.623 0.334***
VISITOR
2.134 0.817***
Model fit
LL
-1884.105
-1499.563
-1801.288
LR TEST
238.172 p<0.001
613.111 p<0.001
626.2394 p<0.001
ADJ R2
0.139
0.167
0.059
N
2000
1815
1996
***
1% significance
**
*
5% significance 10% significance
Preference heterogeneity was accounted for in the fixed part of the estimated utility functions
through interactions with the ASC and the design attributes. In the former case, whether or
not a respondent had ever visited the area where the river restoration is planned (VISITOR)
and whether or not a respondent has a higher secondary education degree (HIGHEDUC) have
a significant positive impact on choices in Austria and Hungary. These results show which
21
characteristics of respondents increase the likelihood of being in favour of river restoration
compared to maintenance of the status quo or no restoration policy. VISITOR also appeared
to have a significant standard deviation, suggesting random taste variation around this
variable.
Significant interaction terms with the design attributes include disposable household income
(INCOME) and cost price. As expected, in all countries higher income groups are more likely
to choose one of the two river restoration alternatives at a higher price than lower income
groups. Other interaction terms are respondent perception of current water quality
(QPERCEP) and the value they attach to water quality improvements (good quality GQ and
very good quality VGQ) and whether a respondent would visit the case study sites more often
in the future (FUTVISIT) if water quality were improved. In the first case, the negative sign
indicates that respondents who already perceive water quality as good value a water quality
improvement less. In the latter case, the positive sign tells us that those respondents who said
they would visit the area more often if water quality were improved are more likely to pay for
river restoration than respondents who said their visiting frequency would not change as a
result of any water quality changes.
Additionally, in all three samples significant distance-decay (DIST) effects were found4,
albeit through different interaction terms. In Austria, a significant distance-decay effect is
found if water quality is improved to good status (GQ), while distance-decay is only
significant in Romania for users (VISITOR), not for non-users. In Hungary, distance-decay
4
Distance is defined as the one-way distance reported by respondents from their home to the Danube River due
to lack of sufficient variation in the available postal codes in the surveyed rural areas in this case study needed to
calculate the distance from a respondent’s home to the river using GIS).
22
plays a significant role for respondents who have been affected by flooding
(FLOODAFFECT). Those affected by flooding, but living further away from the river are less
likely to choose one of the river restoration alternatives. The distance-decay functions
demonstrate spatial correlation in choice behaviour. We return to these spatial relationships in
section 5.5 when aggregating the welfare effects of the policy scenarios presented in the next
section across the population of beneficiaries using GIS.
5.4. Compensating surplus for floodplain restoration and tests of international transferability
Based on the statistically best fit models presented in the previous section, a number of policy
scenarios were simulated and their welfare implications estimated, changing flood frequency
and water quality simultaneously. The compensating surplus (CS) is estimated using the
standard Hanemann formula (e.g. Bennett and Blamey, 2001): -1/βcost (V0-V1) where V0 and
V1 are linear combinations of attribute levels in the estimated utility functions in the current
situation and a new policy scenario respectively. To what extent these CS measures are
transferable across the three river basin countries is tested again using the two one-sided t-test
(α=0.05; tolerance level=20%).
The estimated welfare measures for five different policy scenarios in each river basin country
are presented in Table 5. As before, standard errors needed to calculate the 95 percent
confidence intervals are estimated using the delta method and the values are adjusted for
differences in purchasing power. Two policy scenarios involve the improvement of water
quality to a good ecological status, with flood risk variations of once every 25 and 50 years,
and three policy scenarios involve water quality improvements up to very good ecological
status with flood risk reductions varying from once every 25 years to once every 100 years.
Average values were used for respondent characteristics, including income and distance,
23
assuming that the samples are more or less representative for the entire river basin in each
country.
Table 5: Compensating surplus welfare measures for different policy scenarios (€/household/year)1
Policy scenario
1
2
3
4
5
1
CS
Flood risk
Water quality
Once every 25 yrs
Good
Once every 50 yrs
Once every 25 yrs
Once every 50 yrs
Once every 100 yrs
Austria
Hungary
Romania
69.6
20.2
4.8
(37.5-101.8)
(15.2-25.3)
(4.0-5.5)
73.5
20.2
4.8
(40.8-106.2)
(15.2-25.3)
(4.0-5.5)
85.0
32.1
9.5
(50.2-119.8)
(26.3-37.9)
(6.8-12.2)
88.8
32.1
9.5
(53.4-124.2)
(26.3-37.9)
(6.8-12.2)
96.4
32.1
9.5
(59.6-133.3)
(26.3-37.9)
(6.8-12.2)
Good
Very good
Very good
Very good
95% confidence intervals between brackets.
In Austria, the CS welfare measures for the policy scenarios increase gradually, reflecting
sensitivity to scope. Keeping water quality constant, a reduction in the flood return period
yields a higher CS. As expected based on the results shown in Table 4, the same applies when
keeping flood risk constant: the CS is significantly higher for a higher water quality level. The
results for Hungary and Romania only differ due to water quality changes (higher quality
yields, as expected, a higher value) in view of the fact that flood risk is not a significant
24
determinant of choice behavior. The estimated CS therefore are the same when keeping water
quality levels constant and varying flood risk.
The CS in Austria are significantly higher than the CS for the same policy scenario in
Hungary, while the CS in Hungary are significantly higher than the CS in Romania for each
policy scenario. Confidence intervals are also largest for Austria. Based on the standard error
(σ), the variation coefficient underlying the estimated CS (σ/CS) is, on average, twice as high
in Austria compared to Hungary and Romania. Generally, a higher inaccuracy of estimation
results is expected to result in more problematic transfers. Based on the results in Table 5,
transfer errors vary between 62 and 263 percent for transfers between Austria and Hungary
(average 138%), 89 and 1442 percent for transfers between Austria and Romania (average
580%), and 70 and 325 percent for transfers between Hungary and Romania (average 172%)
(see Table 6).
Table 6: Performance of the two main value transfer methods for different policy scenarios
(transfer errors in %)
Policy
AU to HU
HU to AU
AU to RO
RO to AU
HU to RO
RO to HU
scenario
UAT
AVT
UAT
AVT
UAT
AVT
UAT
AVT
UAT
AVT
UAT
AVT
1
244
125
71
55
1362
482
93
66
325
52
76
52
2
263
136
72
58
1442
519
94
67
325
52
76
52
3
165
76
62
39
792
276
89
60
237
39
70
49
4
177
83
64
42
832
294
89
62
237
39
70
49
5
200
97
67
47
912
331
90
65
237
39
70
49
210
103
67
48
1068
380
91
64
272
44
73
50
Average
AU: Austria; HU: Hungary; RO: Romania; UAT: Unadjusted Value Transfer; AVT: Adjusted Value Transfer
25
Transfer errors are reduced substantially when transferring the entire choice model from one
river basin country to another instead of the predicted unadjusted average values in Table 5.
The average respondent characteristics in the river basin country to which the estimated
model is transferred are now used to predict the CS for each policy scenario: CS 1 = βˆ 2 X 1
(where β̂ is a vector with coefficient estimates, X the vector with relevant design attributes
and respondent characteristics; 1 is the country to which the model is transferred, 2 the
country in which the model was originally estimated). The errors associated with this ‘value
function approach’ (from country 1 to country 2 and the other way around) are also presented
in Table 6 (labeled as adjusted value transfer or AVT). On average, errors are reduced by 45
percent based on AVT as compared to unadjusted value transfer (UAT) for transfers between
Austria and Hungary, 62 percent for transfers between Austria and Romania and 73 percent
for transfers between Hungary and Romania. However, the predicted values remain nontransferable based on the outcome of the two one-side t-test if an error tolerance level of 20
percent is used (test results are available from the authors). Errors are lowest when
transferring the choice models between Hungary and Romania (39-52%), and highest when
transferring the models between Austria and Romania (60-519%). To what extent these errors
are acceptable in a cost-benefit policy evaluation will depend on policy-maker demand for
accurate estimates and the stage of the policy and decision-making cycle for which the
estimated CS are used.
5.6. Welfare aggregation using GIS
A final step in the welfare estimation procedure is the aggregation of the estimated CS across
the population benefiting from the welfare gains associated with the river restoration policy
scenarios. This step is often critical to arrive at a valid and reliable (and hence credible)
estimation of total economic value (TEV). Most studies simply use the number of people
26
living in an administrative unit or geographic jurisdiction (e.g. county, province, state or
country), and average values are transferred unconditionally (i.e. uncorrected) across the
population living within the boundaries of this geographical unit. Depending on area size and
population density, aggregated TEV can differ enormously (Bateman et al., 2006). Strict
guidelines for welfare aggregation do not exist, making the estimation procedure vulnerable
to manipulation. In view of the fact that the population from which the samples in this study
were drawn and their characteristics are unevenly distributed over space, the aggregation
procedure is carried out using GIS.
To this end, GIS data about Europe’s major rivers including the Danube from the 2008 ESRI
database were combined with (1) the JRC 100 by100 meter population density grid (Gallego,
2008) and (2) NUTS-3 level information about per capita income disaggregated from NUTS3 regions to 100 x 100 m grid cells5. Euclidian distances were calculated per 100 by 100m
grid cell to the Danube river in meters. A TEV is calculated for two policy scenarios:
improvement of water quality in the Danube river to (1) good and (2) very good conditions
(keeping flood conditions in both cases constant). The importance of accounting for
preference heterogeneity in welfare aggregation procedures, in this case income and distancedecay for which secondary GIS data were available and additional calculations could be
made, will be illustrated by comparing the outcomes of two different TEV approaches: TEV
calculated by aggregating the unadjusted average values across the whole population living
within the boundaries of the administrative units surveyed in this case study (TEV1)6 and TEV
5
The Nomenclature of Territorial Units for Statistics (NUTS) is a breakdown of territorial units to harmonize
regional European statistics. NUTS-3 is the lowest aggregation level, and usually follows a European member
state’s own regional administrative structure.
6
The administrative units surveyed in this case study were the states of Lower Austria and Vienna in Austria,
Komárom-Esztergom County in Hungary and the counties Braila, Constanta, Ialomita and Tulcea in Romania.
27
adjusted for the estimated income and distance-decay effects (TEV2). In the former case, the
sample population and the estimated CS per capita are assumed to represent the population at
large in the administrative units. The steps in the latter aggregation procedure are summarized
below.
First, information about population density was converted to number of inhabitants per
100x100m grid cell. Second, the average CS for reaching good and very good water quality
with the help of river restoration were converted from per household to per capita values
based on each sample’s average household size, and multiplied with the number of people in
each 100 by 100m grid cell. For each grid cell also average per capita income was determined
based on the specific geographical NUTS-3 area to which a grid cell belonged. This average
per capita income was subtracted from mean per capita income in the sample and multiplied
on a cell by cell basis with the estimated income coefficient for each country and the number
of people living in each cell7. In this way, the economic value per grid cell was modified
upwards or downwards depending on the income difference.
The distance of each grid cell to the Danube river was used to correct the economic value per
capita per grid cell for the distance-decay effects detected in each country8. The estimated
distance-decay factor was multiplied by the calculated distance of each grid cell from the river
in kilometres and multiplied by the number of people (illustrated for good water quality in
Austria in Figure 4). In a final step, the income and distance adjusted values (in the case of
Hungary only distance adjusted) are added up to estimate the TEV of river restoration to good
7
Except in Hungary. The area surveyed in Hungary falls within the boundaries of a single NUTS-3 region, so no
income variation can be found within this area based on the available GIS data.
8
Modified for the percentage of respondents who visited the case study area in Romania and the percentage of
flood affected households in Hungary (see Table 4).
28
and very good water quality (illustrated for very good water quality in Romania in Figure 5).
The results of the two aggregation procedures are presented in Table 7. The values are
adjusted for differences in purchasing power between the three countries.
Figure 4: Illustration of distance-decay effects for good water quality in Austria
The most striking observation from Table 7 is the considerable difference between TEV1, calculated
by simply multiplying the number of inhabitants living inside the administrative units, and TEV2
where the CS is adjusted for (a) distance-decay only and (b) distance-decay and income differences.
On average, accounting for distance-decay yields a 30 percent lower TEV than the unadjusted TEV,
while the additional income correction reduces TEV by another 10 percent. These differences are
most pronounced for Romania. Accounting for distance-decay results in Romania in a 35 to 50
percent lower TEV (for very good and good water quality respectively). To put the statement the
29
other way around, not accounting for distance-decay overestimates the TEV by a factor of 1.5 to 2.
Based on the available NUTS-3 information about average income levels in the study area (which
were lower than the sample average possibly due to the fact that the sample consisted of a relatively
high share of more higher educated respondents), TEV is reduced by an additional 9 to 12 percent
(for very good and good water quality respectively).
Figure 5: Illustration of TEV for very good water quality in Romania adjusted for distance-decay
In Hungary, unadjusted TEV is 42 percent higher than TEV adjusted for distance-decay in the case of
good water quality, and 17 percent in the case of very good water quality. No income correction
30
could be carried out because the survey area falls completely within one NUTS-3 region, and so there
was no variation in income levels within that area.
Table 7: TEV for good and very good water quality based on two aggregation procedures (€106/year)
TEV1
TEV2
unadjusted aggregation
GIS adjusted aggregation
Distance-decay
Distance-decay and
correction
income correction
Austria
Good water quality
54.0
46.3
41.9
Very good water quality
73.8
na
69.4a
Good water quality
2.0
1.4
na
Very good water quality
3.9
3.3
na
Good water quality
10.2
5.0
4.4
Very good water quality
15.3
9.9
9.0
Hungary
Romania
a
Income correction only. na: not available.
In Austria, accounting for distance-decay produces a TEV for good water quality that is 14 percent
lower than the unadjusted TEV. Because the distance-decay effect was only significant when
included as an interaction term with good water quality (see Table 4), no adjusted TEV for very good
water quality is presented for Austria in Table 7. Adjusting TEV also for the spatial variation in
income levels across the survey area lowers TEV by another 10 percent. The sample income average
was somewhat higher than the average in the whole survey area, possibly due to the
overrepresentation of respondents from Vienna where average income levels are relatively high
31
compared to the rest of the country. The income adjusted TEV for very good water quality is 6
percent lower than unadjusted TEV.
6. Discussion and conclusions
The main objective of this paper was to estimate the welfare impacts of the joint benefits
generated by ecological restoration of heavily modified river stretches (i.e. flood risk
reduction and water quality improvement) and test their transferability across the second
largest river basin in Europe. The welfare impacts were measured through a stated choice
experiment, carried out in three different countries, using an identical research format. The
common experimental design was developed to enable direct comparison of the non-market
benefits of WFD implementation. Recommendations to develop and adopt a common
approach for water quality valuation go back to the 1992 special issue on value transfer in
Water Resources Research (Desvousges et al., 1992), but their use is rare and in stated
preference research mainly limited to contingent valuation (e.g. Brouwer and Bateman, 2005).
One of the most important challenges faced during the development of the common valuation
design was to keep the design generally applicable to all three countries. This meant
compromising on the inclusion of case study specific detail while keeping the design at the
same time sufficiently meaningful to policy makers and lay public in each country. This may
also have been a reason why flood risk, often a poorly understood concept in valuation
research (e.g. Loomis and duVair, 1993), was only a significant determinant of public choice
in one of the three river basin countries.
The estimated choice models in the three countries are significantly different, both in terms of
model parameters and variances, indicating that their potential for transferability is limited.
Observed and unobserved preference heterogeneity plays a significant role in the three
32
countries, and is an important reason for the non-transferability of the results. The PPP
adjusted marginal values for water quality improvements were the same in Hungary and
Romania, but not the PPP adjusted CS estimates for different floodplain restoration policy
scenarios. None of the estimated CS measures were transferable at the 20 percent error
tolerance level imposed. When controlling for preference heterogeneity, most of the transfers
also exceeded the 50 percent tolerance level (transfer errors were lower than 50% in only 30%
of the cases), even through transfer errors were reduced by up to almost 75 percent. Hence,
the ‘function approach’ to value transfer clearly outperformed the ‘unadjusted value
approach’, but transfer errors remained relatively high, varying on average between 48 and
380 percent for different floodplain restoration scenarios. Accounting for preference
heterogeneity due to distance-decay and income effects in the welfare aggregation procedure
of the CS estimates across the population of beneficiaries also consistently resulted in lower
TEV across the three countries. Compared to unconditional aggregation of average CS
estimates over the population living within the boundaries of the administrative units from
which the survey samples were drawn (common practice in many value transfer studies), the
calculated TEV turned out to be 10 to 50 percent lower in the more sophisticated GIS-based
aggregation procedure presented in this paper.
In conclusion, the welfare impacts of floodplain restoration in the context of the WFD differ
significantly throughout the Danube river basin, i.e. within and between the countries through
which the river flows. Errors are substantial when using country-specific values and models to
predict values elsewhere, even if one takes preference heterogeneity and different socioeconomic conditions in the three countries that were part of this study, such as purchasing
power, into account. Similarly, substantial error may be created if average values are
aggregated in the same country without controlling for preference heterogeneity and socio-
33
economic conditions that are unevenly distributed in space. As such, both sources of error will
affect the outcome of the policy analysis for which the values are to be used.
Acknowledgements
This study was carried out as part of the EU DG Research funded project AquaMoney (SSPI022723) (www.aquamoney.org). The useful comments of the project group on the study
design and results presented at several project meetings are gratefully acknowledged.
34
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