Are disproportionate costs of the WFD an issue?

A screening of catchments in Denmark

C.L.JensenI, A. DubgaardI, B. H.JacobsenI, S. B. OlsenI, B. HaslerII

I) Institute of Food and Resource Economics, University of Copenhagen, Rolighedsvej 25, DK-1958 Frederiksberg C, Denmark

II) Aarhus University, Department of Environmental Science - Policy Analysis. Frederiksborgvej 399, DK-4000 Roskilde, Denmark.

Abstract

EU’s Water Framework Directive (WFD) is implemented as an instrument to obtain good ecological status in water bodies of Europe. The directive recognizes the need to accommodate social and economic considerations to obtain cost-effective implementation of the Directive. In particular, EU member states can.apply for various exemptions from the objectives if costs can be considered disproportionate, e.g. compared to potential benefits. This paper addresses the costs and benefits of achieving good ecological status and demonstrates a methodology designed to investigate disproportionate costs at the national level. Specifically, we propose to use a screening procedure based on a relatively conservative Cost-Benefit Analysis (CBA) as a first step to identifying areas where costs could be disproportionate. We provide an empirical example by applying the proposed screening procedure to a total of 23 water catchment areas in Denmark where costs and benefits are estimated for each of the areas. The results suggest that costs could be disproportionate in several Danish water catchment areas. The sensitivity analysis further helps to pinpoint two or three catchments where we suggest that more detailed and precise CBAs are needed in order to properly ascertain whether costs are indeed disproportionate.

Keywords: Screening procedure, Disproportionate costs, Water Framework Directive, Cost-Benefit Analysis, Benefit transfer

JEL: q25, q28

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1. Introduction

The European Union’s Water Framework Directive (WFD) seeks to achieve Good Ecological Status (GES) of water bodies in the European Union preferably by 2015 and no later than 2027.1 However, article 4 of the WFD opens for exemptions from the GES target, extended deadlines, or less stringent environmental objectives in cases where achieving GES can be deemed disproportionately costly. While the concept of disproportional costs is only vaguely defined in the WFD, the typical interpretation is that costs are disproportional when costs exceed the environmental benefits. It is explicitly suggested in the guidelines to the WFD that judgment of disproportional cost could be based on an economic analysis of the costs and benefits of achieving GES (EU commission 2009, WATECO 2003)2. It is furthermore suggested that the costs need to be somewhat higher than the benefits – exactly how much is a delicate and somewhat arbitrary decision which remains a political decision.

General guidelines on how to perform the disproportionate cost analysis are available (Wateco, 2003; European Commission, 2009) but these guidelines are not very detailed and they do not suggest a practical procedure by which a country can carry out this analysis. What is missing is an approach to the CBA that on one hand can cover a whole country while on the other hand being affordable and practically implementable. Extending on the surprisingly few investigations on the use of CBA for disproportionate cost assessment available in the literature (e.g. Bateman et al. 2006, Hanley and Black 2006, De Nocker et al. 2007, Lago et al. 2010, Molinos-Senante et al. 2011, Kinnel et al. 2012, Vinten et al. 2012), in this paper we propose a novel CBA-based, water catchment level screening procedure as a first step towards identifying and narrowing down the number of catchments in a country where disproportionate costs are likely to occur – areas where more comprehensive and costly CBAs would seem worthwhile undertaking in order to properly assess whether costs can be considered disproportionate. The aim of the paper is also is to exemplify the suggested approach on a relevant case, by providing an initial screening in order to identify if there are water catchment areas in Denmark, where the disproportionate cost clause of the WFD could potentially be invoked. As the EU member states are obviously different in a plethora of aspects, the key issue in the paper is to suggest a road map for an initial nation-wide disproportional cost screening of the WFD implementation which should be adaptable for use in all member states, based on Denmark as a case example. Given the uncertainty that is inherent in estimates of costs and benefits, the WFD guidelines emphasize that disproportionality should not begin at the point where measured costs simply exceed quantifiable benefits. Rather, a precautionary approach should be used, where particular uncertainties in either estimating cost or benefits should be taking into account. Thus, we propose a cautious approach in the CBA using a baseline for the analysis based on values from the lower and the upper end, respectively, of the spectrum of likely values for benefits and costs. In other words, care is taken not to underestimate costs and not to overestimate benefits by assuming a sort of worst-case pessimistic outcome for the cost and benefits that are inherently uncertain. This ensures that we get a relatively conservative estimate of the bottom-line welfare gain in the CBA. Through this approach it will be possible identify the catchments, if any, where additional assessments should be made to provide satisfactory decision support for the application of exemption possibilities in the

1 OJ L 327, 22.12.2000 Directive 2002/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy.2 Another interpretation suggested in the WFD is that costs or expenditures are disproportionate when the costs are not affordable by the polluters, e.g. agriculture, the municipalities or the countries and societies as a whole (EU Commission 2009). This interpretation can however only lead to postponement and not exemption from the GES target. We do not pursue this interpretation any further in the current paper as the focus here is on investigating the use of CBA in the screening of disproportionate costs.

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WFD. Thus, if the welfare gain is clearly positive for a given area in the baseline CBA, it will most likely not be possible to claim that the costs associated with adhering to the WFD requirements are disproportionate. However, if the welfare gain for an area is not clearly positive there is a potential for disproportionate costs which should be further investigated. The screening provided by the baseline CBA will thus identify the areas where further considerations and more detailed CBAs could be necessary in order to properly establish if there are disproportionate costs and at which spatial scale. If the welfare gain is clearly negative it is likely that the area or part of the area would qualify for the exemption.

Results from our case example suggest that the proposed procedure may indeed serve as a first step towards assessing whether implementation of the WFD can be considered disproportionally costly in some water catchment areas. Using water catchments as the geographical scale for the analysis, for each of the 23 water catchment areas in Denmark current average water quality status for the main categories of water bodies is identified and the costs and benefits of achieving GES relative to the current situation are assessed. Based on the conservative baseline welfare gain estimates accompanied by a range of less conservative sensitivity analyses, we are able to identify three water catchments where the costs of fulfilling the WFD GES target are markedly higher than benefits, potentially to an extent that may be deemed disproportionate. For these three areas, where we would expect the highest probability of obtaining disproportionate costs, we recommend Danish policy makers to prioritize these areas and initiate more detailed CBA analyses in order to obtain more solid results – results that may serve as useful ‘evidence’ if the Danish state wants to apply the EU Commission for derogations from the WFD requirements. Furthermore, we identify nine catchments where costs and benefits are more or less at the same level. For these areas, we recommend considering doing further analysis since costs could turn out to be disproportionate in more detailed analyses. However, priority should be given to the three areas mentioned previously. Finally, for the remaining 11 water bodies, benefits seem to clearly outweigh the costs, suggesting that disproportionate costs are highly unlikely in these areas.

While the proposed procedure is of course relatively data demanding, all the data used in the Danish case example was already available and accessible as a results of previous and on-going work related to the national implementation of the WFD. Hence, new data requirements were limited, and the main task was to merge it all. Many other European countries similarly have built up extensive bodies of data related to WFD implementation. We thus believe that the screening procedure proposed here offers a novel and practically useful tool that can be relatively easily implemented in most European countries at reasonable efforts and cost.

The paper is structured as follows: Section 2 provides a short description of the proposed screening procedure. Section 3 presents a thorough empirical exemplification of the proposed screening procedure on the case of Denmark. Based on this, we conclude with final remarks in section 4.

2. A screening procedure for identification of areas where costs of fulfilling the WFD may be disproportionate

As already mentioned, CBA has been suggested as one way of providing economic information to decision makers concerning disproportionate costs associated with the implementation of the WFD. Hence, the central steps in the proposed screening procedure outlined below are largely similar to the typical steps in any CBA. However, since one of the main goals with the screening procedure is to make it practically useful for instance by as far as possible avoiding the need for collecting extensive amounts of new data, some digressions from the ‘ideal’ textbook CBA may be necessary. This is one of the reasons why we refer to the procedure merely as an initial screening. In order to make the approach practical in real life, we may make some simplifying assumptions, compromises and short-cuts that could reduce the accuracy of the results,

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potentially to an extent that is inadequate for policy decisions but still suitable for an initial screening (Pearce et al. 2006). The proposed procedure follows the steps outlined here:

1. Defining the geographical scale of analysis Ideally at the individual water body level, though availability and accessibility of national

WFD related data may require a more aggregate scale such as catchment level or regional level.

2. Identification of the current ecological status of the water bodies For different types of water bodies the current ecological status should be assessed

according to the biophysical definitions used in the WFD. Depending on the chosen scale of analysis this may be at individual water body scale or at some aggregate level.

This step implies a physical quantification of impacts associated with changing from current status to GES

3. Assessment of benefits from achieving GES Since benefits will mainly be non-marketed, the values associated with moving from the

current status to the GES should be assessed using primary valuation studies for each area if available. If unavailable, benefit transfer method may be used to avoid costly and time consuming primary valuation studies, though this is subject to the availability of at least one useful study site where primary valuation has been done. The benefit assessment should as far as possible cover both users’ as well as non-users’ benefits.

4. Assessment of costs associated with achieving GES A decision has to be made regarding which measures to apply to achieve GES in each area.

The measures chosen have to be cost effective as stated in the WFD. Several European countries have already worked extensively on this in the RBMPs. Typically the RBMPs assess the resource costs associated with implementing the required measures. Such figures which are based on factor prices need to be adjusted to consumer prices in order to assess the welfare economic costs necessary for the CBA. Furthermore, the marginal cost of public funding, i.e. tax deadweight loss, should also be adjusted for according to official national guidelines.

5. Calculation of baseline welfare gain Flows of costs and benefits should be converted to a bottom-line welfare gain according to

official national guidelines. This could be reported either as a net present value or as a net annuity.

6. Sensitivity analyses Should be based on less conservative/pessimistic estimates of in the costs and benefit. Hence

the sensitivity analysis would generally lead to higher welfare gains with the baseline analysis serving as a lower bound.

7. Final recommendations on the indication of areas where costs may potentially be disproportionate and where more comprehensive and precise spatial analyses and CBAs are warranted.

While this list of steps may suggest a unidirectional progression, it is important to recognize that the procedure is iterative and it could be relevant to backtrack and reconsider earlier steps. For instance, the definition of the scale of analysis in step 1 is very much dependent on data availability for steps 2, 3 and 4. Furthermore, it is important to realize that there will potentially be a lot of difference in the application of the procedure across countries. For instance, the issue of cost-effectiveness in relation to choosing which measures should be used to reach GES is far from agreed upon in the EU member states (step 4). Other issues where official national recommendations differ across member countries are which discount rate to

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use (step 5), which, if any, standard conversion factor to use (step 4), and how to incorporate tax deadweight loss (step 4). We deliberately do no go further into the discussion of these issues as they are beyond the scope of the current paper. Instead, we provide an example of how the procedure could be applied.

3. Case example: Denmark

In the following we apply the outlined screening procedure above to the case of Denmark in order to exemplify the usefulness of the approach as well as the limitations and compromises that can be necessary in an applied analysis. Section numbers correspond to the step numbers.

3.1 Defining the geographical scale of analysis (Step 1)

GES is defined by different indicators in different water bodies and there are large variations in the current conditions of the water bodies. Therefore, the costs and benefits of achieving GES will vary between water bodies and river basins as a whole and the disproportionality of costs must be evaluated on the basis of a regional assessment taking spatial variability into consideration. Bateman et al. (2006) and Refsgaard et al. (2007) and others show how spatial separation of costs and benefits can be accomplished through the use of GIS techniques in the modeling of agricultural land use, the integration of land use and hydrological models, and the estimation, aggregation and transfer of the economic value of the benefits.

Another approach could be to use some type of relevant physical, geographical or political delineation for the spatial disaggregation, for instance by looking at water catchment areas. It is costly and time consuming to conduct CBAs. If an individual CBA was to be conducted for every single water body in Denmark, the task would be immense. Nevertheless, the guidance documents from WATECO (2003) and the European Commission (2009) suggest that disproportionate cost assessments should take place at the water body level (Martin-Ortega 2012). Considering the costs of doing CBA, this may explain why there are relatively few CBA studies of WFD implementation available in the literature (Bateman et al. 2006, Hanley and Black 2006, De Nocker et al. 2007, Lago et al. 2010, Molinos-Senante et al. 2011, Kinnel et al. 2012, Vinten et al. 2012). However, others have argued that more aggregate levels of analysis may be suitable (Stemplewski et al. 2008, Vinten et al. 2012). Since the geographical scale on which the derogation might be given is yet unclear, we have chosen to base our CBA analysis on the water catchment level for reasons of data availability and accessibility. This is in line with e.g. Bateman et al. (2006), Hanley and Black (2006) and Molinos-Senante et al. (2011). The majority of previous work relevant for an assessment of cost and benefits associated with the implementation of the WFD in Denmark, i.e. the RBMPs, operate at the aggregate water catchment level. Furthermore, the primary valuation study serving as the central basis for our benefit assessment was conducted at the catchment level.

3.1.1 The geographical location of catchments sites

The division of Denmark into 23 catchment areas is illustrated in figure 1.

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I, 1 Kattegat and SkagerrakI, 2 LimfjordenI, 3 Mariager fjordI, 4 Nissum fjordI, 5 Randers fjordI, 6 DjurslandI, 7 The Bay of AarhusI, 8 Ringkøbing fjordI, 9 Horsens fjordI, 10 Wadden seaI, 11 The Belt sea (Lillebelt, Jutland)I, 12 The Belt Sea (Lillebelt, Funen)I, 13 Odense fjordI, 14 The Belt sea (Storebelt, Funen)I, 15 The Sea south of Funen (Sydfynske ø-hav)II, 1 KalundborgII, 2 Isefjord and Roskilde fjordII, 3 OresundII, 4 The Bay of KogeII, 5 The sea south-west of Zealand (Smålandsfarvandet)II, 6 The Baltic Sea (Baltic Proper)III, 1 BornholmIV, 1 Kruså/vidå

Figure 1. The 23 catchments sites in Denmark

Denmark is surrounded by water and there is a coastal zone of 7,314 km which includes four sea areas: The North Sea (to the west), Skagerrak (to the north), Kattegat (to the east) and the Baltic Sea (in the south and east) and a number of smaller waters: The Sound (Oresund), the Belts (Lillebelt and Storebelt) and waters between the islands. Denmark consist of four water districts as indicated by the roman numerals in figure 3.1. Catchment IV.1. is the only border catchment as it continues into Germany to the south. The six catchments in the western part of Jutland (I.1, I.2, I.4, I.8 and I.10 and IV.1) are connected to the North Sea. The other catchments in the eastern part of Jutland and the islands of Funen and Sealand are connected to inland seas (Kattegat, Oresund and the Belts). Moreover, the catchment of Bornholm, which is an island, is positioned in the Baltic Proper (Baltic Sea). In total all catchments cover about 26,700 km of streams, 349 km2 of lakes and 3,415km2 of fjords. In addition, coastal waters within the 12 sea miles limit cover about 23,400 km2.

3.2 Identification of the current ecological status of the water bodies (Step 2)

Information on the ecological status and characteristics of waters bodies in the catchments is available in the Danish RBMPs (RBMP, 2011) which contain detailed plans for the Danish implementation of the WFD. In the RBMPs the assessments of water quality is divided between streams, lakes, fjords and other coastal waters. In the RBMPs the ecological status is presented as it appeared in 2007-2009. The observed ecological status is a result of measures implemented under a number of directives, e.g. the Nitrate Directive, the Bathing Water Directive and the Drinking Water Directive (Mikkelsen et al., 2010). The Danish Aquatic Action Plan III has been implemented to comply with both the Nitrate directive and partly with the WFD (Børgesen et al., 2009).

In the Danish RBMPs the ecological status is classified based on the distribution of water bodies on a five point scale: 1) bad quality, 2) poor quality, 3) moderate quality, 4) good quality, and 5) high quality. This information is used to form an indicator of average and aggregated status of water quality in each catchment. We have transformed the five point scale in RBMPs into a four point scale by aggregating the classes of bad quality and poor quality into one class. We have done this to ensure comparability with the ‘water quality ladder’ used in the AquaMoney study (Hasler et al. 2009) from where we obtain our benefit estimates in section 3.3. The water quality ladder uses a definition of water quality in four levels based on chemical, physical, flora and fauna characteristics as well as use characteristics (Hime et al, 2010)..The constructed water quality indicators provide an overall assessment of each type of water body at the catchment level.

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This entails that if the average water body in a catchment is classified as good, this average value holds no information about the variance in the underlying distribution, i.e. whether it is more or less dispersed. In other words, even though the average is good, there may be some specific water bodies that are below good quality while others may be well above the good quality level. Nevertheless, an overall indication of the state of water bodies seems adequate for our purpose as a certain level of spatial aggregation is necessary in a national level screening procedure as this. It may be relevant to look at a less aggregated spatial level, e.g. specific water bodies, in more detailed CBAs for water catchment areas where costs may be disproportionate as identified through our screening procedure.

The assessment of the average water quality status at the catchment level incorporates the size of the individual waters bodies.3 For streams the average status is based on water quality weighted by the length (km) of the individual streams. For lakes, fjords and coastal waters the average indicators are based on weighting according to the size of the individual water bodies (km2). Unclassified water bodies in the RBMPs are not included in the aggregated water quality indicator.

Table 1.The current average ecological status in the 23 catchments

Catchments Streams Lakes Fjords Coastal watersKattegat and Skagerrak Moderate Good No Fjord PoorLimfjorden Moderate Poor Poor PoorMariager fjord Good Poor Poor No coastal waterc

Nissum fjord Good Good Poor PoorRanders fjord Good Moderate Poor No coastal waterc

Djursland Good Moderate No Fjord PoorThe Bay of Aarhus Moderate Poor Poor PoorRingkøbing fjord Good Good Poor PoorHorsens fjord Good Poor Poor No coastal waterc

Wadden sea Moderate Moderate No Fjord PoorThe Belt sea (Lillebelt, Jutland) Good Moderate

PoorPoor

The Belt Sea (Lillebelt, Funen) Moderate Poor Poor PoorOdense fjord Moderate Poor Poor PoorThe Belt sea (Storebelt, Funen) Moderate Poor Poor PoorThe Sea south of Funen Moderate Moderate Moderatea ModerateKalundborg Moderate Poor Poor PoorIsefjord and Roskilde fjord Moderate Poor Moderate No coastal waterc

Oresund Moderate Good No Fjord PoorThe Bay of Koge Moderate Moderate No Fjord ModerateThe sea south-west of Zealand Moderate Moderate Poora ModerateThe Baltic Sea (Baltic Proper) Moderate Moderate Moderate ModerateBornholm Good Good No Fjord Moderate b

Kruså/Vidå Good Moderate No Fjord Poora The status is not classified in the final RBMP. Instead we use the status from the hearing version of the RBMP. b The status of water quality of coastal waters is not classified in either the final or the hearing version of the RBMP. Instead we have used the status of the Baltic Sea catchment to indicate the water quality of coastal waters.c In these catchments the water exchange between the fjords and the surrounding coastal waters is particularly weak and therefore coastal waters has not been included in these catchments. While water does flow from fjord to sea it is assessed that sea quality impacts will mainly occur further from the coastline than what is covered by the WFD. Furthermore, these catchments have very limited direct coastline were benefits can be found.

3For a more comprehensive presentation of measures of water quality see Søndergård et al (2003).

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The indicators of average water quality are presented in table 1. The table shows that among the four types of water bodies the ecological status is best generally in the streams. In waters bodies where the ecological status is currently good the GES target is fulfilled and no further measures are required. In addition, the table shows that there is diversity with respect to the type of water bodies between catchments. Streams and lakes are present in all catchments, but there are 7 catchments without fjords. Moreover, in four catchments coastal waters are not included as the impacts of the RBMPs are not expected to affect these.

The Danish approach in the RBMPs has been to focus on the main parameter which needed to be changed to achieve GES. The target for the coastal waters and fjords has therefore been translated into a required reduction in nitrogen. For the lakes the required reduction has been stated with respect to phosphorus. For the streams the focus has been on physical water quality. It is these reduction requirements which form the basis for the identification of cost-effective measures and their associated costs assessed in section 3.4.

3.3 Assessment of benefits from achieving GES (Step 3)

Since the major benefits associated with achieving GES are non-marketed, assessment of increased water quality is often accomplished using stated preference surveys (e.g. Bateman et al. 2011, Colombo and Hanley 2008, Hanley et al. 2006a, Vesterinen et al. 2010). In a similar vein, we base our benefit assessment on stated preference based economic valuation methods. These methods are designed to measure the welfare economic values of the expected changes in non-market goods and services. While a significant component of the total values is most likely recreation values, i.e. use values, there is clearly also a significant non-use component related to e.g. increased biodiversity. While stated preference methods would seem a relevant approach here, it would be exceedingly costly and time consuming to conduct full-scale primary stated preference surveys for all 23 catchment areas4. Accordingly, we have decided to use benefit transfer (BT) in the catchments where primary valuation studies are not available..

3.3.1 Benefit Transfer

BT is inevitably more imprecise than conducting a primary valuation study. However, the level of accuracy needed for the benefit assessment depends on the specific context. According to among others Desvousges et al. (1998) a continuum of required accuracy can be specified based on the purpose of the BT and the importance/impact of the decision context. If very high accuracy is required, as would for instance be the case in litigations or in identifying externality costs for setting a Pigouvian tax, BT may not be sufficient. In a test of BT concerning benefits of WFD-related water quality improvements Hanley et al. (2006a) find results that disfavour the use of BT in this context since the precision is far below the conventional levels of statistical significance. Nevertheless, they also conclude that “…it is hard to see how it [the WFD] can be fully implemented in Europe without such a benefits transfer system being set up….it may well be that policy-makers will view much lower levels of accuracy as acceptable in practice. The question is: how close is close enough?” (Hanley et al. 2006, p.192). Desvousges et al. (1998) note that BT may be a suitable tool for screening purposes where less precision is acceptable. In our case the CBA for the 23 catchment areas may indeed be considered an initial screening for potential disproportionate costs.

4In a Danish context a full-scale primary valuation study typically costs around € 100.000 or more. Therefore, reuse of estimates from previous studies present an opportunity to save a significant amount of money and time.

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3.3.2 Identifying appropriate study site benefit estimates

While a number of slightly different definitions of BT has been offered in the literature, Pearce et al. (2006, p. 255) sum up the type of BT most commonly applied in CBA as “the use of values of a good estimated in one site (the “study site”) as a proxy for values of the (same) good in another site (the “policy site”)” . Among the first steps in any BT is the identification of potential study sites. While a considerable body of literature exists focusing on value assessment of water quality improvements (e.g. Brouwer et al. (1999), van Houtven et al. (2007), Johnston (2010), and Brander et al. (2011)) very few are appropriate candidates for assessing the values associated with the changes brought about by the WFD implementation in Denmark. However, one large-scale European project, namely the so-called AquaMoney project, has focused specifically on assessing the values associated with the improvement of European water bodies to GES as required by the WFD.5 Related to this, a primary valuation stated preference survey was conducted for the Odense Fjord catchment (Hasler et al 2012). Furthermore, the Danish AquaMoney case study was part of a wider Danish stated preference study concerning the benefits of similar WFD related improvements of Odense river, the 10 largest lakes on Funen, and Odense Fjord, using the same questionnaire and valuation design as in the Odense river catchment case study, though only applying the choice experiment method. 6,7 Bateman et al. (2011) concluded that the design of the Odense catchment study could be recommendable for benefit transfer. Lindhjem and Navrud (2008) conclude that international BTs do not generally perform better than domestic BTs, suggesting that choosing a national study should be preferred if possible. Furthermore, the fact that the sample area used in the Odense catchment study has a relatively low average household income8 should further help to ensure conservative WTP estimates. Against this backdrop, we have chosen to use the Odense catchment survey from 2009 as our primary study site for the BT. More specifically, we use the value estimates obtained from the part of the study using the choice experiment technique, where the attributes were defined as 3 groups of lakes (the 10 largest lakes in the area were grouped in three groups where the lakes in each group were close to each other), rivers and streams (the main river is Odense River) and fjords (the only fjord is Odense Fjord) in the Odense catchment. The attribute levels were specified using the before mentioned water quality ladder described in more detail in Hime et al. (2009). The application of the water quality ladder in the Danish context is described in more detail in Hasler et al. (2009) and Kataria et al. (2012). Table 2 provides a description of the recreational opportunities associated with the different water quality levels.

5 See also www.aquamoney.org/6 This concurrent project was named “Costs and benefits of nutrient reductions to Danish water bodies”.7 The Odense catchment case study applied both Contingent Valuation and Choice Experiments.8 Of the 23 catchment areas, the Odense catchment has the second lowest average household income according to Statistics Denmark.

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Table 2. Description of water quality levels

Ecological status Colour DescriptionPoor Red This indicates a stream where the water is not suitable for boating, swimming

and angling. The presence of bird and plants are very limited, and there is few or no fish.

Moderate Yellow

This indicates a stream where the water is suitable for boating, but the possibilities for swimming and angling are more limited. Pollution sensitive fish species are present as they are artificially planted out. The presence of fish, birds and plant species is limited.

Good Green This indicates a stream where the water is suitable for boating, swimming and angling, even though the most pollution sensitive fish can be absent under these conditions. The diversity of birds and plants is somewhat lower than compared to the highest water quality.

Very good Blue This indicates a stream in its highest quality. The water is suitable for boating, angling and swimming. The water is suitable for fish, plant and bird species being present under natural stream conditions.

This information was shown to respondents along with an illustration associated with each water quality level.9 A color code was assigned to each water quality level with red corresponding to the lowest quality and blue corresponding to highest quality. The green color corresponds to the GES required by the WFD. This color coding was used to indicate the levels in the actual choice sets where the alternatives were presented using maps. Figure 2 provides an example of a choice set used in the survey. As can be seen in the figure, the status quo situation for the Odense catchment was found to fit with the description of poor ecological status (red color) for the fjord and the lakes while it was moderate ecological status (yellow color) for the Odense catchment. The two experimentally designed alternatives in each choice set offered improvements to this status quo situation. The experimental design corresponds to the common design used in the AquaMoney case studies (see e.g. Bateman et al. (2011) and Hasler et al. (2009).

9 See Hime et al. (2009) for a color reproduction of the illustrations shown to respondents.

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Figure 2. Example of choice set used in the CE survey

The CE survey was conducted sampling respondents from an online internet panel consisting of approximately 35,000 individuals. This survey mode has previously been shown to be suitable for CE surveys in Denmark (Olsen 2009). In total 520 panelists living in the area were initially contacted, and out of these 219 returned useful responses for the CE analysis.

For the econometric analysis of choices, a random parameter logit model was employed. All quality attributes were specified as normally distributed random parameters to allow for preference heterogeneity. The price parameter was treated as fixed and an alternative specific constant was specified for the status quo alternative. The likelihood of the econometric model was maximized using simulations based on 300 Halton draws, and all estimations were conducted using Nlogit 4.0 software. Table 3 reports selected results from the model.10 First of all, the adjusted pseudo-R2 value of 0.252 indicates that the model fits the data well. While the table only reports mean WTP estimates and associated standard errors obtained using the Krinsky-Robb procedure with 10000 draws, it should also be noted that all attribute mean parameter estimates were found significant and substantial preference heterogeneity was found for all quality attributes. An alternative specific constant associated with the status quo alternative was found to be insignificant, suggesting that no status quo effect was present.

Table 3. Relevant WTP estimates obtained from random parameter logit model using 300 Halton draws

Attribute Mean WTP St.err.Lakes are improved from red to yellow 57.2 10.3Lakes are improved from red to green 71.2 10.5Stream is improved from yellow to green 67.9 11.710 Full model outputs are available from the authors on request.

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Fjord is improved from red to yellow 71.9 10.4Fjord is improved from red to green 96.5 11.3Model details:Adj. Pseudo-R-square 0.252# Respondents 219# Choices 2190Note: The blue level WTPs are omitted since the CBA only concerns ensuring a green level as prescribed by the WFD. Standard errors are obtained using Krinsky-Robb approach with 10000 replications.

As mentioned, the status quo levels for lakes and fjord are red (poor level), while it is yellow (moderate level) for the stream. Consequently, there are WTP estimates available for red-to-yellow (poor-to-moderate) and red-to-green (poor-to-good) level changes for lakes and fjord, and the WTP for a yellow-to-green (moderate-to-good) level change is the difference between these, assuming a linearly relationship. However, the only WTP estimate available for the stream is for a yellow-to-green (moderate-to-good) level change. Hence, we have no directly applicable value estimates for a red-to-green (poor-to-good) improvement of streams. Furthermore, the benefits associated with water quality improvements to coastal waters as a consequence of the WFD were unfortunately not included in the CE survey, so also here we lack some value estimates.

Searching the literature, we found a significant number of value estimates related to coastal water quality improvements as well as stream water quality improvements. However, only few estimates were related to the water quality ladder and the WFD, and none of them were highly relevant as BT study sites for the Danish context in the current CBA. Hence, also keeping the conservative approach according to the screening purpose of our CBA in mind, we have decided to conduct our baseline benefit assessment assuming a zero value for the benefits to coastal waters. This is not to say that improvements to coastal waters in poor or moderate condition will not entail a benefit in real life. However, in relation to our purpose of screening for potentially negative bottom-line welfare gain, this assumption will not affect our overall conclusion in cases where the CBA returns a positive welfare gain – taking these values into account would only make it more positive. Of course, the other side of the coin is that if the CBA does return a negative welfare gain, such neglected benefits clearly need to be taken into account before making any final conclusions concerning disproportionate costs. As for the missing estimate for the red-to-green (poor-to-good) improvement to streams, there is no point in assuming a zero value since theory would predict it to be at least as high as the yellow-to-green (moderate-to-good) value. Hence, maintaining the conservative approach, for the BT we assume the same value for a red-to-green (poor-to-good) improvement as estimated for a yellow-to-green (moderate-to-good) improvement to streams. Table 4 provides a summary of the study site estimates that will be transferred in order to assess the benefits in the remaining 22 catchment areas.

Table 4. Study site mean WTP estimates Type of water body Red-to-green

(from poor to good status)Yellow-to-green

(from moderate to good status)Streams 68 (missing) 68Lakes 71 14Fjords 97 25Coastal waters 0 (Missing) 0 (Missing)Note: All values are in € per household per year

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3.3.3. Transferring benefit estimates

There are, broadly speaking, two types of benefit transfer, namely simple mean value transfer and value function transfer (Navrud and Ready 2007). Function transfers, although superior to simple value transfer in theory, have not generally been proven to outperform simple value transfer in empirical surveys (see e.g. Loomis (1992), Kirchhoff et al. (1997), Brouwer and Spaninks (1999), Bateman et al. (2000), Brouwer (2000), Barton (2002), Brouwer and Bateman (2005), Navrud and Ready (2007)). Bateman et al. (2011) note that especially for sites that are relatively similar, function transfers may be more imprecise than the simple univariate transfer. Related specifically to our case Källstrøm et al. (2010) test the reliability of BT when transferring CVM estimates from Odense Fjord to another Danish fjord, namely Roskilde Fjord – and from Roskilde Fjord to Odense Fjord. They find that simple transfer of mean WTP estimates yield markedly lower transfer errors than when using function transfer, and they conclude that simple value transfer is to be preferred at least when the sites are as similar as is the case when transferring between two Danish sites. There are of course differences in the 23 catchment areas in Denmark. Notwithstanding this, we argue that they may be considered relatively similar when comparing to value transfers conducted across borders or to value transfers where the decision context or the proposed policy change is not the same at the study site and the policy site. Hence, based on the findings of Källstrøm et al (2010) and Bateman et al (2011) and with the screening purpose of the current survey in mind, we have chosen to use the simple value transfer approach to transfer benefit estimates from the study site at the Odense Fjord catchment to the remaining 22 water catchments in Denmark.

Combining the fact that most people in Denmark live relatively close to water bodies with the fact that the CBA of the WFD essentially considers improving all Danish water bodies to GES, we find it reasonable to make the simplifying assumption that the benefits of improving water bodies in a specific catchment area to GES are mainly of value to people in that specific area. The two main arguments for doing so are related to spatial issues. The first argument is the distance decay effect, which implies that willingness to pay for the improvements is expected to be a decreasing function of the distance from respondents’ place of residence to the water body (Sutherland and Walsh, 1985). Hence, the closer the individual lives to the good in question, the higher the value. This effect has been found in several empirical surveys (see e.g. Bateman et al., (2006), Bateman and Langford (1997), Brouwer et al. (2010), Hanley et al. (2003), Johnston et al. (2005), Kniivilä (2006), Moore et al. (2011), Whitehead et al. (1995)). While economic theory would predict distance decay effects in use values, it has no similar prediction regarding non-use values – a major value component when considering the benefits of water quality improvements. Nevertheless, both Bateman et al. (2006) and Hanley et al. (2003) find significant distance decay effects among non-users in CVM studies focusing on demand for stream water quality improvements. Furthermore, Schaafsma (2011) finds that non-users’ preferences are even more sensitive towards the distance to the good compared to users and that the non-users’ WTP declines faster with the distance to the resource. Of particular relevance to the current CBA, similar results are found by Brodersen et al. (2011) in a study using data from the Danish AquaMoney case study. While this may suggest the use of such distance decay functions to establish the relevant geographical boundaries for assessing the aggregated value of specific water bodies, we stick to the water catchment delineation for two reasons. First of all, while the presence of distance decay has been proven, the actual relationships are not yet well established and it has yet to be shown to what extent distance decay relationships can be generalized for BT in Denmark. Secondly, we believe it is beyond the scope of the initial screening procedure to use such advanced approaches to define the geographical unit for aggregation. This may be relevant in more detailed and precision-requiring follow-up CBAs in catchment areas identified as potentially having disproportionate costs in this initial screening. Another argument for only considering local people as the relevant target population for the BT is the substitution effect which would imply that the

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value placed on a water body depends on the availability of substitute water bodies (Sutherland and Walsh, 1985). Economic theory would predict decreasing value with increasing substitution possibilities. In relation to water quality improvements this effect has been empirically substantiated by Brouwer et al. (2010), Bateman et al. (2011), Schaafsma (2011) as well as Brodersen et al. (2011). All these studies further suggest that distance decay and substitution effects are highly interlinked effects that together constitute a complex spatial relationship. Hence, substitution effects are likely to add to the distance decay effects in the sense that individuals value water quality improvements higher when they occur close by and when substitution possibilities are low. Finally, by assuming that only people living inside a catchment area benefit from quality improvements of water bodies in that area, and hence ignoring visitors, tourists and potential non-use values that people living outside the catchment area might hold, we aim to ensure a relatively conservative benefit estimate that will not inflate the welfare gain.

The outcome of the baseline benefit assessment is outlined in Table 5. The baseline benefit assessment is obtained by multiplying the WTP study site estimates (conditional on the current ecological status in table 1 and the specific type of water body) with the number of households in each catchment. The number of households is available for municipalities from Statistics Denmark. Since the catchment sites do not completely follow municipality borders there are a number of incidences where it is necessary to split municipalities between catchments. For municipalities that are included in more than one catchment, the number of households in municipality is split equally between catchments. The baseline benefit assessment is expressed as the annual payment that the beneficiaries are willing to pay per year for obtaining GES of the waters bodies in the catchment. For simplicity, it is assumed in these calculations that the improvements to GES occur immediately after the measures to improve water quality are effectuated.

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Table 5 Baseline assessment for benefits in the 23 catchment sites in Denmark

Catchment Number ofHouseholds

Streams (€/house-hold/year)

Lakes (€/house-hold/year)

Fjords (€/house-hold/year)

Baselinebenefit

(€ Mill./year)Kattegat and Skagerrak 69,430 68 0 --- 4.712Limfjorden 240,156 68 71 97 56.593Mariager fjord 19,048 0 71 97 3.196Nissum fjord 52,143 0 0 97 5.035Randers fjord 145,511 0 14 97 16.101Djursland 26,856 0 14 --- 0.378The Bay of Aarhus 191,164 68 71 97 45.048Ringkøbing fjord 60,743 0 0 97 5.866Horsens fjord 57,007 0 71 97 9.565Wadden sea 116,776 68 14 --- 9.570The Belt sea (Lillebelt, Jutland) 137,787 0 14 97 15.246The Belt Sea (Lillebelt, Funen) 41,697 68 71 97 9.826Odense fjord 115,803 68 71 97 27.289The Belt sea (Storebelt, Funen) 19,921 68 71 97 4.694The Sea south of Funen 49,922 68 14 25 5.323Kalundborg 33,712 68 71 97 7.944Isefjord and Roskilde fjord 202,584 68 71 25 33.175Oresund 398,456 68 0 --- 32.653The Bay of Koge 336,455 68 14 --- 22.833The sea south-west of Zealand 151,128 68 14 97 26.978The Baltic sea (Baltic Proper) 37,227 68 14 25 3.969Bornholm 20,547 0 0 --- 0.000Kruså/Vidå 49,309 0 14 --- 0.694National 2,573,376 346.688

In the baseline benefit estimates presented in table 5, it is evident that a zero benefit is obtained for the catchment of Bornholm since GES is already obtaining in Bornholm for streams and lakes, there are no fjords on this island, and, as explained, in the baseline assessment we do not incorporate coastal water improvements. Unsurprisingly, the highest benefits are found in catchments with many households and where waters bodies are currently of poor ecological status. In the benefit assessments, there are different sources of uncertainty. The most obvious uncertainty is related to coastal waters for which there were no primary valuation WTP estimates considered to be valid, and, hence, this benefit was left out in the baseline. This type of uncertainty of course only relates to catchments where coastal waters are present which is not the case in four of the catchments as indicated in table 1. Another source of uncertainty in the benefit assessment is related the use of simple mean value transfer as such, i.e. using Odense catchment as a benchmark for estimating WTPs in fjords, lakes and streams and then transferring these mean WTP estimates directly to the other catchments. Theoretically one would expect that a relatively large scope (size) of a water body in a catchment, ceteris paribus, would increase utility (the assumption of non-satiation, i.e. more-is-better). In applying the benefit transfer, it therefore makes sense to look at to which extent the water bodies in each of the other catchments diverge in scope relative to the scope of water bodies in the Odense catchment. One indicator of scope of the water bodies is to look at the share of water body (in km 2) relative to the land (in km2) in the catchment. Substantial differences between the Odense catchment and other catchments could to some extent question the direct transfer of mean value estimates. An overview of the scope of the water bodies in each catchment is available in appendix A. The appendix reveals that the

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Odense catchment has a lower share of coastal waters, fjords and lakes, but a larger share of streams compared to the national average. This could imply that using the Odense catchment WTP estimates in this national screening potentially generally underestimates the average WTP for fjords and lakes (and indirectly also the coastal waters in the sensitivity analysis) while underestimating it for streams. However, the non-satiation assumption has often been shown to be violated in economic valuations studies concerning environmental goods. In particular, the Aquamoney case study was designed to test to the so-called scope effects, i.e. when a respondent states the same WTP regardless of the scope (size) of the good offered (e.g. Bateman et al. 2004). Tests for scope in several of the Aquamoney studies did indeed show that scope effects were present, implying that the non-satiation assumption was violated (see e.g. Bateman et al. 2008; Hasler et al. 2009). Against this backdrop, it would seem that the simple mean value transfer method is justifiable for the screening procedure.

3.4 Assessment of costs associated with achieving GES (Step 4)

The costs assessment should be based on welfare economic costs which include the resource costs associated with implementing the required measures. The compliance costs associated with WFD typically emerge due to necessary emission reduction requirements from agriculture and urban sources (see e.g. Balana et al. 2011, Fezzi et al. 2008, Hutchins et al. 2009). In relation to the cost assessment the EU Member States must decide on which measures to apply to achieve GES of water bodies. As stated in the WFD the measures chosen have to be the cost effective, i.e. the measures that achieve the GES target at minimum costs. However, carrying out the ranking of measures with multiple objectives is not always straightforward when the objectives are not quantifiable (Interwies et al. (2004) and Jacobsen (2007). The issue of cost-effectiveness has been discussed in working groups in relation to the implementation of the WFD and it is clear that there are large differences between Member States on the approach chosen. Recently, Balana et al (2011) have shown that European studies on cost assessment related to implementation of the WFD mainly focus on direct costs, e.g. investment and operating costs.11 However, from a social point of view it is important also to include welfare costs, e.g. distortion costs following from increased taxation required to finance the implementation of the WFD. Furthermore the value of side-effects of measures such as increased (decreased) CO2 and NH3 emissions should also be included (as a negative cost). These costs should be assessed for each catchment and correspond to a complete fulfilment of the targets of the WFD. In Denmark, the RBMPs for each catchment contain assessments of investment and operating costs for measures that have been identified though cost effectiveness analysis. To use these in the current CBA-based screening procedure, it is necessary to adjust the cost assessments from the RBMPs in two ways. Firstly, we need to supplement the cost assessment with additional measures because the RBMPs do not completely fulfil the requirement of the WFD. Secondly, we have to convert the costs to reflect consumer prices as well as supplement the calculations with the distortion effects of taxation in order to obtain cost estimates that are consistent with the welfare economic theory underlying CBA.

3.4.1 The cost of measures in the Danish RBMPs

The Danish implementation of the WFD is based on a comprehensive approach where the measures are also related to reductions of Green House Gas and ammonia emissions and increased biodiversity in the Green Growth Plan (Government, 2009). In the Danish RBMPs the cost assessments are based on a manual that

11 Jacobsen (2007) shows that the costs included in national recommendations vary significantly across countries and not all costs are always included in the WFD cost analysis. This might change the costs by a factor 2 including the conversion to welfare economic costs which several countries advocate, but fewer actually use.

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follows the guidelines from WATECO.12,13 (Jacobsen, 2007). A stepwise guideline procedure is used that transforms objectives into levels of emissions and cost of measures (Environmental Protection Agency, 2006). The first step involves defining GES in terms of requirements for limiting emissions of nitrogen and phosphorus, or in terms of physical standards of water bodies. The next step is to find the most cost effective measures.

The Danish RBMPs build on measures that reduce the emissions from urban wastewater and non-point agricultural pollution. Urban wastewater pollution is reduced through investments in wastewater treatment plants, sanitation treatment and establishment of delay pools for rain water discharges. The measures aimed at reducing non-point agricultural pollution include catch crops, riparian zones along streams, establishment of wetlands and reduced cutting of water weeds in streams. Other measures include investments in reopening of culverted watercourses, removal of stream obstructions, and stream restoration.

The cost assessment is conducted by integrated environmental models describing abatement costs divided between non-point pollution (agriculture) and urban wastewater. Schou et al., (2007) and Jensen et al., (2009) document the cost analyses of measures that reduce non-point agricultural pollution. Jørgensen et al. (2008) and Environmental protection Agency (2006) document the cost analyses of measures that reduce point sources of pollution. The analyses provide estimates of the costs of reducing emissions of nitrogen and phosphorous – either per unit of reduced emissions or in terms of implementation costs per hectare. The costs have been re-assessed based on their actual implementation in RBMP (Jacobsen, 2012a).

The RBMPs provide a solid cost assessment to be included in the CBA, but it is a limitation that the RBMPs are only made for 9000 tonnes nitrogen reduction out of the 19,000 tonnes required to achieve the requirement of GES in the WFD (Government, 2009). It is furthermore a problem that no specific measures have been included in 7 catchments as it was found that the data quality in these catchments should be improved before the required nutrient reductions could be calculated. These areas constitute 1/3 of the agricultural area and they are in the RBMP from 2011 referred to as V3 areas14.

3.4.2 The cost of additional measures in the Danish RBMPs

In order to include costs related to achieving GES in all catchments, it was decided to include preliminary cost analysis of the additional measures necessary to reduce N-losses with a further 10,000 tonnes in order to reach the target of a 19,000 tonnes nitrogen reduction. (Andersen et al., 2012 and Jacobsen, 2012b)The additional measures include more catch crops, afforestation of agricultural land, higher utilization of animal manure, energy crops or simply taking land out of production and other measures (Andersen et al., 2012). In that analysis the potential of each measure is divided into 5 retention groups for each catchment. The implication is that the effect of the measures does not only vary with soil type and livestock density, but also the local retention. In general a high retention from the root zone to the coastal water would imply that the effect of e.g. catch crops would be limited.

12In Denmark the WFD cost assessments are related to three previous Danish Aquatic Environment Plans, which have so far reduced N-leaching from the root zone by 50 % compared to the mid 1980´ties (Mikkelsen et al., 2010). The focus of Aquatic Environment Plan I was on non-point pollution from agriculture as well as point source emissions from urban areas and industry. The efforts in Aquatic Plan II and III, on the other hand, have been directed towards non-point pollution from Agriculture. Since the introduction of Aquatic Plan II in 1987 the cost-effectiveness analyses applied have gradually become more comprehensive (Jacobsen, 2004 and Jacobsen, 2010). 13 For the guidelines for the CEA procedure used in the Danish RBMPs, see Environmental protection Agency (2007).14 The V3 areas are Kattegat, Djursland, Wadden sea, Øresund, The Bay of Køge, Bornholm and Kruså/vidå.

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The analysis of the last 10000 tonnes has therefore been conducted based on two different assumptions. The first analysis is based on a site specific location of the measures based on livestock density, soil type and retention. This is called the SMART analysis. The other approach is the average approach, which is based on the use of the average retention for the whole catchment. For some measures (like utilization of manure) it seems unlikely that the requirement can be set according to the retention of the land, whereas set-aside could be targeted areas with low retention. The analyses show that the costs are on average 20% higher if the measures cannot be geographically targeted (Jacobsen, 2012b). In some catchments the difference is limited, whereas the costs in some catchments are 40% higher when using the average approach as opposed to the spatially targeted SMART approach. In line with the deliberately conservative approach we take in our screening procedure, we have chosen to use estimates based on the average approach in our baseline cost assessment. The SMART based costs are incorporated in the subsequent sensitivity analysis. The cost-effectiveness analysis based on the average approach shows a variation in the costs between 4 and 14 € per kg N in the SMART analysis, whereas the costs in the average analysis are between 4 and 20 € for the different catchments.

In the V3 areas only general measures are included in the RBMPs due to lack of data. For this paper it is assumed that the reduction requirement will be 5 kg N/ha in the V3 areas, which is lower than the national average of 7 kg N/ha. We make this assumption as it could be argued that the water bodies in the V3 areas will probably require lower reductions as the ecological status in these catchments, e.g. Bornholm catchment, is already relatively good and there is no fjord in these catchments. These further requirements in V3 areas increase the additional nitrogen reduction requirements mentioned above from 10,000 ton to 12,000 ton. It should be noted that the measures required to reach this additional 12,000 ton N reduction needed to fully realize the objectives of the WFD have not yet been officially proposed.

Table 6 shows the estimated annual direct costs (investment and operating costs) of implementing the measures required in the 23 Danish catchment areas. The costs are divided between cost calculated in the RBMPs (second column), the additional costs required to fully realize the objectives of the WFD (third column) based on the average approach, and the total direct costs (fourth column). While current ecological status of water bodies, natural physical conditions like soil and weather, and the size15 of the catchment areas naturally give rise to differences in total costs across catchments, the variations in implementation costs are also very much caused by differences related to groundwater protection and sewage cleaning capacity required to deal with overflow.

15 Appendix A underlines that the catchments differ a lot not only in size but also with regard to share of water bodies.

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Table 6 Baseline assessment of direct costs (investment and operating cost)

Catchment Direct costscalculated in RBMP

€ (Mill)/year

Additional direct costsnot included in the RBMP

€ (Mill) /year

BaselineTotal direct costs

€ (Mill) /yearKattegat and Skagerrak 2.108 3.155 5.263Limfjorden 13.042 52.048 65.089Mariager fjord 0.918 2.195 3.113Nissum fjord 1.535 3.721 5.256Randers fjord 3.396 18.003 21.399Djursland 0.571 2.664 3.235The Bay of Aarhus 0.590 1.953 2.543Ringkøbing fjord 3.757 2.273 6.030Horsens fjord 0.991 3.704 4.695Wadden sea 4.770 3.264 8.034The Belt sea (Lillebelt, Jutland) 3.864 18.829 22.693The Belt Sea (Lillebelt, Funen) 1.246 0.806 2.052Odense fjord 2.300 3.323 5.623The Belt sea (Storebelt, Funen) 0.795 0.087 0.882The Sea south of Funen 1.415 3.862 5.277Kalundborg 0.873 0.742 1.615Isefjord and Roskilde fjord 4.355 1.407 5.762Oresund 0.550 2.664 3.213The Bay of Koge 2.347 1.444 3.791The sea south-west of Zealand 4.675 11.041 15.716The Baltic sea (Baltic Proper) 1.266 0.522 1.788Bornholm 0.449 0.611 1.060Kruså/Vidå 0.806 5.568 6.374National 56.619 143.885 200.504Note: The costs are calculated in 2008 prices using producer prices (exclusive of VAT and consumer commodity-specific taxes) and based on a discount rate of 6 %. The overall cost assessment uses a consistent aggregation into discounted annual costs as used in the RBMPs that takes into account the flow of investment costs, investment horizon, operating costs including land rent, and scale of the measures employed. The additional direct costs are calculated in the same way.

3.4.3 Adjusting cost estimates according to the principles of welfare economic analysis

The cost-benefit analysis of the WFD implementation in Danish catchment areas is based on the principles of welfare economics. Welfare economic costs express the changes in consumption possibilities for the Danish society which the implementation of, for example, pollution abatement measures would result in.

Achieving an efficient allocation of a resource in a market economy requires that producers and consumers are faced with the same set of relative prices. However, taxes drive a wedge between producer prices and the market prices facing consumers. Producers’ optimization efforts are based on producer prices generally without commodity taxes, whereas consumers’ optimization of their consumption bundles is based on market prices including commodity taxes such as VAT etc. The need to account for this discrepancy is accentuated by the fact that economic valuation of environmental benefits typically renders value estimates

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at the consumer price level,16 whereas projects costs are typically in producer prices. Johansson (1993, p. 82) establishes the following price adjustment criteria for a CBA: a) produced inputs should be valued at consumer prices, i.e. inclusive of VAT and other commodity-specific taxes; b) labour should be valued at market wages, plus social fees, plus VAT and other commodity-specific taxes. Accordingly, costs in factor or producer prices should be converted to the consumer price level. In the present CBA costs measured at factor prices are converted to the consumer price level using a standard conversion factor. The standard conversion factor specified by the Danish Ministry of Finance is equal to 1.35 (Danish Energy Council 2009).

It is a general principle of CBA that subsidies and taxes should not be included since they represent transfer payments rather than the use of resources. This is due to the fact that transfers between members of a society net out in the aggregate. However, this does not hold if transfers take place across the boundaries of a society. In the present CBA society is delineated as the nation state, Denmark. Accordingly, the CBA does not include intra-societal transfer payments (subsidies and taxes) from/to the Danish state. Subsidies from the EU, on the other hand, represent welfare gains for the Danish society and as such they are included as benefits (or negative costs) in the CBA.

The welfare economic costs in the CBA also include distortion effects associated with tax financing of implementation costs. The WFD related costs of the public sector (municipalities and state) are assumed to be financed through increases in the income tax which creates distortion effects that should be accounted for in welfare economic cost assessments. The adjustment for tax distortion effects for the part of the costs that is supposed to be publically funded follows the national guidelines which state that the average deadweight loss from taxations is 20% of the tax revenue (Danish Ministry of the Environment, 2010). Hence, these costs are multiplied by a marginal cost of public funding factor of 1.2. Table 7 shows implementation costs distributed on national sectors and the baseline net welfare economic costs for each catchment. All costs are presented in consumer prices and discounted to present value and subsequently annuitized using a discount rate of 6 per cent per annum (Danish Ministry of Finance 1999).

16 This is due to the fact that the estimated willingness to pay for a non-market benefit reflects the trade-off between the non-market good in question and market goods (Freeman, 1993). Consumers’ optimization of their consumption bundles is based on market prices including commodity taxes. Accordingly, the WTP values estimated will be in consumer prices.

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Table 7.Implementation costs and Baseline net welfare costs

Implementation costsBaseline

Net welfare costs€ (Mill) /year

Catchment Agriculturenet costs,

€ (Mill)/year

Municipalitiesnet costs,

€ (Mill) /year

Statenet costs,

€ (Mill) /yearKattegat and Skagerrak 1.337 1.180 1.373 5.941Limfjorden 24.670 3.405 18.507 68.802Mariager fjord 0.380 0.165 1.284 2.860Nissum fjord 3.442 0.428 0.693 6.462Randers fjord 6.210 0.727 7.231 21.276Djursland 1.766 0.205 0.632 3.739The Bay of Aarhus 1.256 0.133 0.577 2.846Ringkøbing fjord 2.389 0.772 1.435 6.800Horsens fjord 1.672 0.229 1.397 4.891Wadden sea 2.597 2.436 1.501 9.883The Belt sea (Lillebelt, Jutland) 5.017 1.204 8.236 22.065The Belt Sea (Lillebelt, Funen) 0.828 0.443 0.391 2.468Odense fjord 2.577 1.098 0.974 6.836The Belt sea (Storebelt, Funen) 0.121 0.447 0.157 1.141The Sea south of Funen 1.728 0.437 1.556 5.562Kalundborg 0.183 0.497 0.467 1.809Isefjord and Roskilde fjord 0.488 3.048 1.113 7.400Oresund 0.546 0.451 1.109 3.262The Bay of Koge 1.427 2.013 0.175 5.472The sea south-west of Zealand 7.561 1.016 3.569 17.636The Baltic sea (Baltic Proper) 0.652 0.358 0.389 2.090Bornholm 0.578 0.208 0.137 1.340Kruså/Vidå 2.985 0.794 1.298 7.418National 70.409 21.694 54.200 218.001Note: The costs are expressed in consumer prices including VAT and consumption taxes and after compensation from EU. The baseline net welfare costs are adjusted for tax deadweight loss caused by tax funding.

The welfare economic cost calculations are based on the assumption that the agricultural sector has to cover all cost related to the reductions in non-point agricultural pollution. However, it is assumed that the agricultural sector is fully compensated for the costs associated with the establishment of riparian zones, wetlands and other types of land conversion. Under this support program the costs will be shared equally by the Danish state and the EU. As explained above, the transfer of subsidies from the EU is considered a benefit in the national CBA. This benefit is assumed to exactly equal the costs that the EU subsidy is aimed at covering, hence they essentially cancel each other out in the CBA. Therefore, only half of the costs associated with the implementation of this type of measures are included in the CBA, namely the costs covered by the Danish state.

The municipalities are assumed to cover all the cost related to reductions of urban water pollution and costs associated with reopening culverted watercourses, removal of stream obstructions and stream restoration. The state incurs expenses associated with the payment of compensation to the agricultural sector and the municipalities. It is assumed that the municipalities and state expenses are financed through an increase in income taxes. Hence, tax distortion effects are adjusted for in the net welfare cost.

3.5 Calculation of baseline welfare gain (Step 5)

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Since cost flows over time in the RBMPs were discounted and subsequently annuitized to annual values, and the benefit estimates were also reported as per year annuities, for reasons of simplicity we have chosen to compare these annual values directly without further discounting to net present values. Whether presenting the present value of an annuity or simply presenting the annuity itself, is just a matter of scaling the bottom-line welfare gain with the chosen discount rate. The first columns in table 8 presents the annual welfare gain as well as benefit-cost (B/C) ratios obtained for each catchment area under the baseline calculations of costs and benefits as outlined in sections 3.3 and 3.4. In the table, the catchments are listed in ascending order of welfare gains and B/C ratios, which corresponds to descending probability of disproportionate costs. Furthermore, based on the B/C ratios we have divided the catchments into three categories in the table; catchments where 1) costs are clearly higher than benefits, 2) cost and benefits are more or less at the same level, and 3) benefits are clearly higher than costs. It is evident from the table that three catchments appear to be in category 1, nine are in category 2 and only the eleven catchments in category 3 are, according to our results, very far from showing disproportionate costs. Since the present analysis is regarded as a rough screening for disproportionate cost and the baseline is purposefully pessimistic about the welfare gains, it is evident that these baseline results need to be accompanied by sensitivity analyses before making any recommendations.

3.6 Sensitivity analyses (Step 6)

The baseline assessment of both benefits and costs are obviously associated with different sources of uncertainty that deserve to be investigated more closely. While numerous sensitivity analyses could be undertaken in this regard, we have chosen only to present re-calculations pertaining to what we see as two of the most central simplifications we make in our baseline; one relating to the benefit side and one relating to the cost side. Results from each of these re-calculations are presented in the middle columns in table 8, scenario1 and scenario 2, respectively. A combination of these two is presented in the last column in table 8, labelled scenario 3. Additionally, we conduct a less stringent sensitivity analysis assessing the expected directional impact of a range of other assumptions and simplifications on welfare gains. These are reported in table 9.

3.6.1 Including the value of improved water quality in coastal waters (Scenario 1)

The baseline benefit assessment is undertaken without including other coastal areas than what is included in the classification of fjords, essentially assuming that the benefits of coastal water improvements are zero. The implication of this conservative assumption is that we are potentially under-estimating the total benefit, particular in Denmark, where coastal waters are present in 19 of out the 23 catchments 17. As mentioned, we have used this as the baseline since we found no suitable study site candidate for BT since no studies have considered WTP for GES in Danish coastal waters. One possibility would be to transfer values from other potentially relevant study sites in Scandinavia such as e.g. Eggert and Olsson (2008) or Vesterinen et al (2010). We have however chosen not to use international benefit transfer since comparisons of the results from Bateman et al. (2011) and Källstrøm et al. (2010) show that, in the context of the Aquamoney project, a national benefit transfer performs better than an international benefit transfer in terms of markedly lower transfer errors. While it may be argued that the lack of a national study site to transfer from would justify using international BT as a second-best alternative, for this sensitivity analysis we have instead chosen to use the fjord benefit estimate obtained in the Odense catchment CE as a proxy value for the benefit of coastal waters. We combine the WTP estimates on fjord with ecological status of coastal waters in each catchment

17 Considering the spatial fact that no matter where you go in Denmark there will not be more than 52 km to a coast, the impact of omitting benefits of improved coastal water quality in the CBA is potentially large.

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to constitute sensitivity analysis scenario 1. As expected, for the catchments were coastal water is present scenario 1 results in an increase in welfare gainss compared to the baseline assessment. Nevertheless, it would seem that the main conclusion regarding the three category 1 catchments has not changed since the B/C ratios are still below one, though clearly not as much as in the baseline. For the majority of the category 2 catchments, however, we see a large increase in welfare gains raising the B/C ratios above well above one for many of them.

3.6.2 Using the SMART analysis to assess costs (Scenario 2)

The baseline cost assessment contains the cost on measures from the RBMPs as well as additional costs related to extra measures needed to fulfil the targets of the WFD. A major element of uncertainty in the costs assessment relates to these extra agricultural measures. In the baseline assessment, the additional cost assessment is based on the average retention in the area. However, Abildtrup et. al. (2004), Refsgaard et al. (2007) and Hasler et al. (2012) show that the costs of measures vary a lot according to retention in Denmark, as well as in other catchments and countries. In accordance with these findings, the Nica-project 18 shows how the data needed for this cost efficient implementation can be provided. The key issue is to find areas where the net effect of measures is the highest, when measured in water bodies. This is areas where the effect down to the root zone is large and where the retention is low. Andersen et al. (2011) has made an assessment of the effects and the potential of the measures based on the local retention condition in Denmark – the so-called SMART analysis mentioned in section 3.4.2. In sensitivity analysis scenario 2, instead of using the baseline average retention based approach, we assess the additional costs concerning the extra agricultural measures necessary to meet the requirements of the WFD based on the SMART analysis (Jacobsen, 2012b). As expected, using this approach generally increases net average values since costs are generally reduced compared to the baseline19. It is however evident that the impact on overall conclusions is fairly limited for the majority of the catchments.

3.6.3 Combining scenarios 1 and 2 (Scenario 3)

In the last columns of table 8 we have combined the two previous scenarios, i.e. incorporating coastal water improvements on the benefit side and using the SMART analysis to assess additional costs. Since this third scenario obviously is our least conservative/pessimistic, it is not surprising that this is where we find the highest welfare gains and B/C ratios. It is however interesting to note that here our conclusions change quite a lot, especially for two of the category 1 catchments. While the welfare gains remained negative for Kruså/Vidå and Djursland throughout the Baseline and the two first sensitivity analysis scenarios, being less pessimistic on both the cost and the benefit side at the same time results in positive welfare gains. This suggests that it is maybe only in the Bornholm catchment that we would expect to find disproportionate costs. However, also the Randers fjord catchment in category 2 remains negative throughout.

18The NICA-project is a research project on nitrate reduction in geologically heterogeneous catchments (http://www.nitrat.dk/about_us_uk/main.html).19 Baseline benefits assessment is used in scenario 2.

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Table 8. Annual welfare gain and Benefit-Cost ratios (B/C) under baseline and sensitivity analysis scenarios.

Catchment BaselineBaseline benefit-

Baseline cost€ (Mill)

B/C

Scenario 1Alternate benefit

Baseline cost€ (Mill)

B/C

Scenario 2Baseline benefit

Alternate cost€ (Mill)

B/C

Scenario 3Alternate benefit

Alternate cost€ (Mill)

B/CCategory 1: Costs are higher than benefitsBornholm -1.340 0.0 -0.833 0.4 -1.308 0.0 -0.801 0.4Kruså/Vidå -6.723 0.1 -1.962 0.7 -4.576 0.1 0.186 1.0Djursland -3.361 0.1 -0.768 0.8 -2.147 0.1 0.446 1.2Category 2: Cost and benefits are at the same levelThe Belt sea (Lillebelt, Jutland) -6.819 0.7 6.487 1.3 -5.885 0.7 7.421 1.4Kattegat and Skagerrak -1.229 0.8 5.476 1.9 -0.514 0.9 6.190 2.2Limfjorden -12.210 0.8 10.981 1.2 1.783 1.0 24.975 1.5Nissum fjord -1.427 0.8 3.608 1.6 0.506 1.1 5.541 2.2Randers fjord -5.175 0.8 -5.175 0.8 -3.153 0.8 -3.153 0.8Ringkøbing fjord -0.934 0.9 4.931 1.7 -0.966 0.9 4.900 1.7Wadden sea -0.314 1.0 10.963 2.1 -0.150 1.0 11.127 2.1Mariager fjord 0.336 1.1 0.336 1.1 0.666 1.3 0.666 1.3The Sea south of Funen -0.239 1.0 0.993 1.2 -0.189 1.0 1.043 1.2Category 3: Benefits are higher than costsThe sea south-west of Zealand 9.342 1.5 13.072 1.7 10.737 1.7 14.466 1.9The Baltic sea (Baltic Proper) 1.879 1.9 2.798 2.3 1.874 1.9 2.793 2.3Horsens fjord 4.674 2.0 4.674 2.0 4.979 2.1 4.979 2.1The Belt Sea (Lillebelt, Funen) 7.358 4.0 11.385 5.6 7.489 4.2 11.515 5.9Odense fjord 20.453 4.0 31.636 5.6 21.377 4.6 32.559 6.5The Belt sea (Storebelt, Funen) 3.553 4.1 5.476 5.8 3.558 4.1 5.482 5.8The Bay of Koge 17.361 4.2 25.664 5.7 17.380 4.2 25.684 5.7Kalundborg 6.135 4.4 9.391 6.2 6.453 5.3 9.708 7.5Isefjordand Roskilde fjord 25.776 4.5 25.776 4.5 25.936 4.6 25.936 4.6Oresund 29.390 10.0 67.581 21.8 31.103 21.1 69.581 45.9The Bay of Aarhus 42.201 15.8 60.661 22.3 43.053 22.6 61.513 31.8National 128.687 1.6 293.438 2.3 158.006 1.8 322.756 2.7

3.6.4 Expected directional impacts of further central assumptions on welfare gains

In table 9 we provide another component of the sensitivity analysis in terms of assessing what could be expected if we loosen or change other central assumptions and simplifications we have made in the Baseline. We do this in a less stringent manner than above by simply indicating the expected directional impacts on welfare gain estimates rather than actually re-calculating the whole thing. The purpose of this is two-fold; first of all to show what could be done if for instance data is missing or insufficient, or there is (scientific or politic) disagreement on for instance what measures should be used to reach the target. Second, this part of the sensitivity analysis seeks to illustrate what the impact of using naïve value transfer may be in our case. While full re-calculations would of course provide more information, we argue that this simpler approach can provide valuable information when related to the numbers presented in table 8.

In some of the catchment areas there is an on-going debate about which measures are needed to reach GES. It has been claimed that a reduction in the number of animal units may be necessary in some areas. It is difficult in absolute monetary terms – and also beyond the purpose of this paper – to assess what the costs would be if farmers had to reduce the number of animal units, but it is largely agreed that this would be a relatively costly measure to use. Hence in the first column in table 9 we indicate with a negative sign for these catchments that the welfare gains might actually be reduced if this measure is brought into use. A zero

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indicates that for this particular issue there is no expected directional impact on the welfare gain estimated for the catchment, whereas a positive sign indicates that the welfare gains might actually be higher than what we have estimated in table 8.

In the simple value transfer used to assess the benefits in 22 of the catchments, we have disregarded scope differences between these catchments and the Odense catchment. For instance, Appendix A reveals that the Horsens catchment has a much larger relative share of fjord-to-land than the Odense catchment (but lower relative share of lakes-to-land). Thus, it may be reasonable to suspect that the benefits of reaching GES in Horsens fjord would be larger than in Odense (but lower for lakes). Due to scope effects as well as decreasing marginal utility it is however difficult to say exactly how much. Nevertheless, it is possible to conjecture that based on such reasoning we might expect the actual benefits of improving Horsens fjord to GES to be higher than what is estimated in our analysis – as indicated with a plus in table 9. Similarly, in catchments where the scope is markedly higher than the scope in the Odense catchment (an arbitrary 10 % cutoff level is used here), we indicate with a positive sign that the welfare gain might actually be higher than estimated in table 8. Likewise, when the scope of coastal water is markedly smaller (10 % lower) than in the Odense catchment, a negative sign indicates that the welfare gain should probably be reduced.

For illustrational purposes we have included baseline and scenario 3 annual welfare gain estimates in table 9. Relating the expected directional impacts to these values proves informative, especially where a unidirectional impact is indicated across the five columns for a given catchment. For instance, for the Bornholm catchment which until now has been one of our most obvious candidates for disproportionate costs, we see nothing in this additional sensitivity analysis suggesting that we could have underestimated the welfare gains. Rather the negative signs for Lakes and Streams might indicate that estimated welfare gains for Bornholm are actually over-estimated. Of course, for other catchments where both negative and positive signs are entered, it is more difficult to make any conclusions. Nevertheless, this might serve as an indicator of where particular focus should be directed in subsequent more detailed CBA analysis in the catchments pointed out here as potentially showing disproportionate costs.

Table 9. Expected directional impacts on annual welfare gain associated with certain assumptions made in main analysis Location Uncertainty relating to…

reduction of animal

units needed?

the use of simple value transfer for…

Baseline€ (Mill)

Scenario 3€ (Mill)

coastalwaters

fjords lakes streams

Category 1: Costs are higher than benefitsBornholm 0 0 0 - - -1.340 -0.801Kruså/Vidå - + 0 - - -6.723 0.186Djursland 0 + 0 - - -3.361 0.446Category 2: Cost and benefits are at the same level

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The Belt sea (Lillebelt, Jutland) -- + + - - -6.819 7.421Kattegat and Skagerrak - + 0 - - -1.229 6.190Limfjorden -- - + + - -12.210 24.975Nissum fjord -/0 0 - 0 - -1.427 5.541Randers fjord -/0 0 - + - -5.175 -3.153Ringkøbing fjord 0 0 + - - -0.934 4.900Wadden sea 0 - 0 - - -0.314 11.127Mariager fjord - 0 + - - 0.336 0.666The Sea south of Funen 0 + - 0 - -0.239 1.043Category 3: Benefits are higher than costsThe sea south-west of Zealand 0 + - - - 9.342 14.466The Baltic sea (Baltic Proper) 0 + - - 0 1.879 2.793Horsens fjord -/0 0 + - - 4.674 4.979The Belt Sea (Lillebelt, Funen) 0 + - - 0 7.358 11.515Odense fjord 0 0 0 0 0 20.453 32.559The Belt sea (Storebelt, Funen) 0 + - - - 3.553 5.482The Bay of Koge 0 + 0 0 - 17.361 25.684Kalundborg 0 + + + - 6.135 9.708Isefjord and Roskilde fjord 0 0 + + - 25.776 25.936Oresund 0 + 0 + - 29.390 31.103The Bay of Aarhus 0 + - - - 42.201 61.513National -- 128.687 284.279Note: (--) indicates costs that are likely to be higher than €2 million annually.

3.7 Final recommendations (Step 7)

The overall goal of the previous six steps has been to identify the catchments in Denmark where disproportionate costs are most likely to appear. Based on estimates of welfare gains and B/C ratios we conclude that in particular the catchments of Bornholm, Kruså/Vidå and Djursland are candidates for further more detailed analyses since we find that costs are likely to be so much larger than the benefits that they may be disproportionate. Furthermore, a few less obvious candidates may also be subject to more detailed investigations of disproportionate cost. Especially, the Randers fjord catchment would seem to incur costs that might substantially outweigh benefits. It is also worth noting that among the catchments where costs and benefits are at the same level (category 2) there is considerable variation among the catchments. While some catchments have numerically low costs and benefits, others have much higher numbers. For instance, while Limfjorden and Ringkøbing catchments show very similar B/C ratios just below one, the fact that the numerical values of costs and benefits are about ten times higher in Limfjorden catchment also implies that the numerical difference between costs and benefits are ten times here in the baseline. Considering the many uncertainties inherent in this type of analysis as well as the fact that it is still not entirely clear how much larger than benefits the costs have to be in order to be deemed disproportionate, it would seem relevant to also consider the Limfjorden catchment in additional analyses

While the sensitivity analyses generally increase welfare gain estimates and, thus, reduce the number of catchments areas with negative welfare gains, the overall conclusion remains quite stable, namely that there are at least three to five catchment areas in Denmark where costs could be disproportionate, and where further analyses are warranted. The sensitivity analyses furthermore suggests that especially the benefit assessments should be investigated more closely as assumptions made in this part of the baseline analysis seems to affect results more than assumptions made on the cost side.

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4. Concluding remarks

Disproportionate cost is a central issue in the implementation of the WFD as it opens for extensions and possibly exemptions from the target of GES in all European water bodies by 2015. In practice, CBA on catchments is not straightforward to conduct because it requires detailed information on characteristics of catchments (water bodies, ecological status, and retention), implementation and welfare costs, as well as benefit assessments.

In this paper we suggest that a 7-step CBA screening procedure can provide valuable policy input in terms of providing an overview of the catchments in which disproportional cost are most likely to appear. The screening process should be founded on a “conservative” assessment to minimize the risk of over-assessing the incidence of potential disproportionate cost, i.e. ensuring that costs are not underestimated and benefits are not overestimated. The screening procedure should contain elements of assessing ecological status in waters bodies, welfare economic costs and benefits, and it should include a sensitivity analysis in which the elements associated with high uncertainty are identified. To this end it is important to reveal the direction of the uncertainties and whether these are likely to affect the conclusion on disproportional costs. In the paper, we provide an empirical example showing how to employ the screening procedure on the implementation of the WFD in Denmark. Our results reveal 3-5 water catchment areas in Denmark where costs are more likely to be disproportionate than in other catchment areas. We argue that more comprehensive CBAs may be necessary in these areas in order to provide firm conclusion on whether or not the costs do actually outweigh benefits in these catchments.

While our results could serve as valuable input to Danish policymakers considering how to meet the WFD requirements and where to target further analytical efforts, we believe that an equally important contribution of our paper is to provide fellow researchers with a methodological tool that could be used to provide an initial screening for disproportionate costs of reaching GES of water bodies in EU. We believe that the procedure presented in this paper can be conducted in most European countries as the data requirements are relatively limited. The procedure should be seen as a rough estimate analysis identifying areas where more detailed and elaborate CBAs may be targeted.

Of course, the economic analysis should only be seen as providing information and guidance to the policy- and decision-makers. There are still a lot of unanswered questions relating to the exemption clause in the WFD and what disproportionate costs actually are. With this paper it is also our hope to encourage policy-planners and –makers in the process towards providing a clearer description of what is required for the member states to actively use the disproportionate costs clause in the directive. Hopefully in a manner that is founded in environmental economics and adopted to local data availability.

Acknowledgements:This study was funded by the Danish Research Programme "Animal Husbandry, The neighbours and the environment", the Danish Ministry of Food, Agriculture and Fisheries.

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Appendix A Summary statistics on the 23 catchment areas in Denmark

Location Total catchment area (km2)

Agricul-tural area

(km2)

Share of coastal waters (km2) to land

(km2) %

Share offjords (km2)to land (km2)

%

Share oflakes (km2)

to land (km2)%

Streams in km per km2

land(km/km2)

Kattegat and Skagerrak 2,672 1,495 116 0.0 0.4 0.67Limfjorden 7,590 5,246 8 17.0 1.5 0.54Mariager fjord 573 375 0 8.0 0.0 0.56Nissum fjord 1,633 1,027 34 3.9 0.5 0.63Randers fjord 3,255 2,028 0 0.8 2.1 0.75Djursland 1,011 577 399 0.0 0.1 0.65The Bay of Aarhus 775 450 220 2.0 0.1 0.72Ringkøbing fjord 3,485 2,182 32 8.4 0.1 0.57Horsens fjord 794 552 0 69.3 0.0 0.68Wadden sea 4,438 3,042 14 0.0 0.0 0.50The Belt sea (Lillebelt, Jutland) 2,370 1,559 48 16.0 0.1 0.49The Belt Sea (Lillebelt, Funen) 990 687 92 1.1 0.3 0.85Odense fjord 1,194 768 34 5.2 0.5 0.93The Belt sea (Storebelt, Funen) 538 372 53 2.2 0.4 0.75The Sea south of Funen 764 518 121 2.1 0.5 0.69Kalundborg 977 612 170 7.6 3.3 0.45Isefjord and Roskilde fjord 1,952 1,141 0 22.5 2.5 0.40Oresund 812 201 107 0.0 4.8 0.54The Bay of Koge 996 502 73 0.0 0.5 0.56The sea south-west of Zealand 3,445 2,396 52 3.3 0.0 0.83The Baltic sea (Baltic Proper) 1,083 784 133 1.9 0.0 1.00Bornholm 590 351 39 0.0 0.0 0.65Kruså/Vidå 1,102 827 116 0.0 0.1 0.43National 43,038 27,692 57 7.9 0.8 0.62

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