Publicidad y promoción

In performing large scale cost analysis, it is necessary to make some generali- zations and assumptions regarding the cost, effect and capacity of the different measures. This can be explained by the difficulties in obtaining location spe- cific data for such a large geographic area, within which certain parameters affecting the cost and capacity of a measure can vary a lot even on a small scale. For example, the nutrient reduction capacity of a wetland depends on location specific parameters, such as topography, soil type, nutrient inflow, precipitation etc. The limited capacity of the computer programs running the models to handle a vast amount of information is another reason behind the need for generalizations.

Due to lack of data regarding either cost, effect or capacity of certain measures (e.g. wetlands), cost functions/estimates made in one or a couple of the countries have been transferred to countries for which such data could not be obtained. For example, the assumed abatement effect of wetlands (150 Kg N/ha) used in Hasler et al. (2012) used for all countries is based on studies made in Sweden and Denmark. Since it is well known that this effect is a function of several variables that differs geographically, any cost estima- tion made will be subject to a large degree of uncertainty. Even if we had precise information regarding the cost, effect and capacity for each possible measure, in each location, the optimization programs would not been able to handle all that information. In summary, some assumptions and generaliza- tions have to be made due to limited data and the capacity of the optimiza- tion programs running the models.

Apart from the measures fertilizer reduction and wastewater treatment, the average abatement cost is used as an approximation for the marginal abate- ment cost. This implies that the marginal cost for those measures is assumed to be constant over the abatement, which is not very likely. For example, in reality the first wetland can be implemented at a location with large effect, and low construction and opportunity costs in order to get the highest possible reduction for every Euro spent, while the cost will increase when implementing more of this measures in locations with higher construction and land opportunity cost and lower effect on the abatement. But due to the uncertainties and variations of cost and effect of wetlands, generalizations of some of these parameters have been made and an average cost have been used in the estimates.

Furthermore, assuming a constant marginal cost, which is what is done when average cost is used as a proxy for marginal cost, implies that a certain measure at a certain place is either fully implemented or not implemented at all. For example, if the abatement cost for catch crops is 40 Euros per kg nitrogen, then, for a specific location, this measure will either not be imple- mented at all, or implemented to its full extent. Whereas abatement by reduced fertilization, for which a marginal cost function is used, can, be implemented to different degrees at each location. Assuming constant marginal abatement cost probably leads to an overestimation of the abatement cost when the implementation of a specific measure is relatively low, and an overestimation of the abatement cost when the implementation of a specific measures is relatively high (i.e. close to its capacity constraint).

6.1 Uncertainties and asymmetric information

As mentioned above, several of the abatement measures described in this study are characterised by some kind of uncertainty regarding their effects and/or costs. Uncertainties can be divided into the following three categories:

• Natural uncertainty regarding, for example, the actual retention between source and recipient or the correlation between a certain activity and its e ffect on the environment.

• Economic uncertainty regarding the actual cost of a specific measure. • Technological uncertainty regarding the actual abatement capacity of a

specific technology.

Natural uncertainty relates to the difficulties to establish the connection

between the discharges of different sources and their impact on the recipient of concern. For example, the natural uncertainty concerning the estimation of the nutrient load is caused by temporal and spatial variations of the bio- chemical and physical processes that drives and affects the flow and retention of nutrients. The effect of a specific abatement measure can, therefore, vary on a daily basis due to factors such as precipitation and temperature. The proba- bility to achieve a certain nutrient load reduction will therefore differ between different abatement measures, due to these variations. Apart from the challenges of creating a reliable model for estimating the actual effect of abatement measures, the uncertainties will, therefore, also affect the likeli- hood of reaching a specific target, which in turn have implications for the total cost of reaching the target (see Gren et al., 2000; Elofsson, 2003).

If there is no need to consider the temporal variations of the nutrient load, the difficulties of determining the optimal allocation of measures will be less. But if this variation is relevant, the complexity will increase since several measures (e.g. wetlands) will influence such variation. For example, apart from abating nutrients, wetlands might also have an effect of the variations on the water flow, usually by slowing it down and thereby lowering the peaks in the nutrient load to the sea. As shown by Gren et al. (2000) this effect can in itself motivate the construction of wetlands.

Uncertainty regarding real abatement costs can to some extent be explained by asymmetric information between the regulated and the regulator. The problem occurs due to the fact that those who will implement the measures (e.g. farmers and industry) usually have better information regarding the abatement cost, and that their incentive to state actual costs (as opposed to under- or overstating the costs) differs between policy instruments. If, for example, there is the possibility to receive a grant for wetland construction, there might be in the interest of the applying part to overstate the costs for wetland construction.

The difficulty with non-point sources, such as agriculture, is in measuring discharges at the source and establishing their effects on the receiving water body at reasonable cost. In addition, the discharges from non-point sources vary over time depending on weather conditions. This implies that a great deal of information is required to select the cheapest measures at non-point sources, information which in addition is often characterised by a high degree of uncertainty regarding the effect as well as the cost of the measure.

Certain measures’ technological capacity to abate nutrients are characteri- sed by large uncertainties, especially for new and untested abatement techno- logies. The technological uncertainty diminishes as research, environmental analysis, and information regarding geographical circumstances improves.

The overall capacity of a certain measure is also subject to a great deal of uncertainty. For example, how much of the arable land in a country can be used for wetland construction? Or how much can the application of fertili- zers be reduced? The overall capacity is usually based on some kind of assumption regarding what is realistic from, for example, an economic or a technical aspect. In theory, it might be possible to convert all agricultural land to wetlands or to connect all households to wastewater treatment plants, but in practice it does not seem very realistic.

There will always be some uncertainty regarding the cost of different measures. Figure 6.1 shows the effect of uncertainties and lack of information with regard to actual marginal abatement costs of different measures. Since the degree of uncertainty differs between different measures, the span of confidence will vary. Cost-effective measure A, in the figure, is characterised by a relatively small uncertainty with regard to the abatement cost, while the non-cost effective measure D is characterized by a relatively large degree of uncertainty. The uncertainty related to the cost of abatement measure A is not of such a great concern, due to the fact that it will probably still be a cost- effective measure even if the uncertainty have caused an underestimation of the cost. It is more important to analyse the uncertainties related to the abatement cost of measures B and C, since the great span indicates that the ranking might change if the cost of measure B is underestimated while the cost of measure C is overestimated.

In general, large-scale studies like this one are usually subject to a larger de- gree of uncertainty compared to small-scale studies (e.g. those focusing on reducing the nutrient load from a single catchment) since the latter often can base its estimates on more precise data (with regard to, for example, soil type, retention, land use) and with a smaller degree of generalisation.

Even if the uncertainties related to certain measures, and thereby policy instruments, can not be fully eliminated, there exists a number of ways to handle these in a methodological way in the search of a cost-effective solu- tion (see e.g. Charnes & Cooper, 1963; Elofsson, 2003; Fishelson, 1976; Adar & Griffin, 1976; Papakyriazis & Papakyriazis, 1998; Gren et al., 2000).

One source of uncertainty is the discount rate used for the optimization. As costs and benefits are reported as annual values, discounting only affects investment costs of abatement measures. Large share of measures, especially the ones related to agriculture, inflict high annual costs and their cost esti- mates are thus independent of the choice of discount rate. The abatement measures, which are sensitive to the choice of interest rate, are constructing wetlands and wastewater treatment plants. Their share of the optimal set of measures (Table 4.1 and 4.7) decreases when the discount rate increases and vice versa. For example, if the interest rate of the economy increases, the annuity of a possible loan to fund the investment increases and the annual- ized cost becomes larger. This in turn decreases the amount of measures with high investment cost in the optimal solution.

Table 6.1 shows the total costs of reaching the different objectives for three different discount rates. By using the higher discount rate of 5 per cent, total costs increases between 15 to 24 per cent compared with the discount rate of 3.5 per cent used in the estimates above. Using a lower discount rate of 2 percent implies lower total costs in about the same range (i.e. 15 -24%). Table 6.1 Total costs of Ahlvik et al (2012) under different discounts rates

Discount rate Obj.1 Obj.2 Obj.3

2 % 2 379 1 893 1 143

3.5 % 2 802 2 336 1 487

5 % 3 273 2 698 1 846

6.2 Cost-effectiveness

This study only considers the damage caused by the nutrients on one recipient, the Baltic Sea, ignoring any damage on upstream water bodies (lakes and rivers within the catchments). If upstream benefits of implementing the different abatement measures also were taken into consideration, the cost-effective allocation between regions would probably look different compared to the one described above. That is, even if a cost-effective allocation of measures with regard to the Baltic Sea could be implemented, it might not be in the in- terest of the nations since some might want to obtain improvements in cer- tain coastal zones or upstream recipients and thereby choose a different amount and geographical allocation of measures than described in this re- port. Such upstream benefits might therefore imply a more ambitious nutri- ent reduction target in some drainage basins compared to the cost-effective

benefits.

(For studies regarding multiple receptors see e.g. Atkinson & Tietenberg, 1982; Krupnick et al., 1983; Bohm & Russell, 1985; Krupnick, 1986; Ermoliev et al., 1996; Zylicz, 2003)

Another aspect to cost-effectiveness relates to the policy instruments that will be needed in order to get the abatement measures described above implemented. All policy instruments generate some kind of transaction costs, for example, administrative costs, legal costs, and monitoring costs (see BG Paper Management Frameworks for a more thorough discussion of transac- tion costs). These transaction costs where not included in the cost-estimates of Ahlvik et al. (2012) and Hasler et al. (2012) since they depend on the type of policy instrument used (e.g. tax, legislation, tradable permit, information etc.) and thereby difficult to address before a final concretisation and design of policy instruments have been made. In the end, the inclusion of transaction costs would to some extent increase the total costs and might very well also alter the ranking between the measures.

Furthermore, apart from the actual abatement and transaction costs of a measure, its implementation might cause effects and costs on other markets, especially when the use of a measure is significant. For example, a significant reduction of livestock in a certain country may cause profit losses to business sectors involved in processing, transporting and selling meat products. These, so called, “general-equilibrium” or “spill-over” effects of measures are not included in the estimations of this study. If these effects are not marginal, the costs of reaching the BSAP targets will be larger than indicated above.