Costs of
mitigating eutrophication
Background Paper
Preface
BalticSTERN (Systems Tools and Ecological-economic evaluation – a Research Network) is an international research network with partners in all countries around the Baltic Sea. The research focuses on costs and benefits of mitigating eutrophication and meeting environmental targets of the HELCOM Baltic Sea Action Plan. Case studies regarding fisheries manage-ment, oil spills and invasive species have also been made, as have long-term scenarios regarding the development of the Baltic Sea ecosystem.
The BalticSTERN Secretariat at the Stockholm Resilience Centre has the task to coordinate the network, communicate the results and to write a final report targeted at Governments, Parliaments and other decision makers. This report should also discuss the need for policy instruments and could be based also on results from other available and relevant research.
The final report “The Baltic Sea – Our Common Treasure. Economics of Saving the Sea” was published in March 2013. This Background Paper, Costs of miti-gating eutrophication, is one of eight Background Papers (BG) where methods and results from BalticSTERN research are described more in detail. In some of the papers the BalticSTERN case studies are discussed in a wider perspec-tive based on other relevant research.
CONTENTS
1. Introduction ... 4
2. Loads, targets and objectives ... 5
3. Models used ... 8
3.1 The Ahlvik et al. model (2012) ...8
3.2 BALTCOST – Baltic Sea Catchment model of Hasler et al. (2012) ...10
3.3. Comparison of the main characteristics of the two models ....12
4. Abatement measures ... 14
Capacities and costs of measures ... 15
Fertilizer reduction ...17
Catch crops ...19
Wetland restoration ... 20
Constructing sedimentation ponds ...22
Livestock reduction ...23
Wastewater treatment plants ... 24
Banning of phosphorus in detergents ...25
Summary ... 26
5. Total cost results and cost-effective allocations ... 27
Reduction to basins and by countries ...27
Total costs and allocation of costs ... 28
Allocation of measures ... 30
6. Generalizations & assumptions ... 33
6.1 Uncertainties and asymmetric information ...34
6.2 Cost-effectiveness ...36
7. Conclusions... 38
References ... 40
1. Introduction
A Cost-Benefit Analysis (CBA) is undertaken by comparing the total cost of measures reaching the eutrophication objectives of the Baltic Sea Action Plan (BSAP) with total benefits obtained from this reduction. The costs arise due to the resources that have to be used in order to implement the measures, required in order to meet the target. A cost-effective solution reaches the BSAP targets (either load reduction or nutrient concentration in sea-basins) with as low cost as possible for society.
This paper starts by describing present nutrient loads to the Baltic Sea and the BSAP nutrient targets, as well as some alternative approaches to reach these targets. Thereafter, the models used to estimate the total costs of abate-ment measures are described, followed by a description of the different abatement measures used for estimating the total cost of reaching the BSAP, with regard to their effect, capacity and costs,. The costs of fulfilling the targets are then given for three different approaches using two different models. The corresponding country reduction allocation is presented, as isthe allocation between the different measures. The generalizations and uncertain-ties behind the cost estimates are thereafter discussed.
The two different models used are BALTCOST and a model developed by Ahl vik et al. The two models and the cost functions are described more in detail in Hasler et al., (2012) and Ahlvik et al. (2012) respectively.
2. Loads, targets and objectives
Nutrients originate from various natural sources and human activities, for example,from airborne emissions, discharges from point sources along the coast (e.g. industry and water treatment plants) or from diffuse input sources (e.g. agriculture or scattered settlements).The major source for nitrogen to the Baltic Sea originates from diffuse inputs (71% of the total load) with agri-culture alone contributing with 80 per cent to these loads. The largest phos-phorus loads originate from point sources (56%) with municipalities as the main source (90% of the total point source discharges). About 75 per cent of the nitrogen and at least 95 per cent of the phosphorous arrives via rivers or as direct discharges to the Baltic Sea. (HELCOM, 2009)
The HELCOM Baltic Sea Action Plan (BSAP) aims to stimulate goal-orien-ted multilateral co-operation across the Baltic Sea region (HELCOM, 2007a), and to achieve ambitious targets for restoring good ecological status to the common Baltic marine environment by 2021 in a way that is “fair and accept-able to all HELCOM contracting parties” (HELCOM, 2007a). The BSAP was adopted by all nine littoral Baltic Sea countries at the HELCOM Ministerial meeting in Krakow, Poland, in 2007.
According to HELCOM, one of the most serious problems of the Baltic Sea is the continuing eutrophication. Some scientists have stressed the importance of a sea-basin-specific assessment instead of a sea-wide ecologi-cal target (Rönnberg & Bonsdorff, 2004) and, therefore, the action plan approaches eutrophication reduction in the Baltic sea-regions by setting targets for nitrogen and phosphorus loads for each basin. First, it sets maxi-mum allowable loads of nitrogen and phosphorus to each of the seven sea-basins of the Baltic (Bothnian Bay (BB), Bothnian Sea (BS), Baltic Proper (BP), Gulf of Finland (GoF), Gulf of Riga (GoR), Danish Straits (DS) and Kattegat (KT)) derived from the ecological targets. In BSAP the good status is measured sea-basin by sea-basin with water transparency, indicated by the Secchi depth, being the primary parameter (HELCOM, 2007b). A sea-basin is considered to have a good ecological status, if the Secchi depth does not exceed the level of a pristine sea by more than 25 per cent (HELCOM, 2007a). Thereafter, it sets country-wise provisional nutrient reduction requirements that will generate these load targets for each sea-basin.
Since loads have changed from the 1997-2003 level, for which the BSAP targets are based, the required nutrient load to different sea-basins from different countries have also changed. In Ahlvik et al. (2012), the most recent available data on loads are used. Thus the mean of flow-normalized loads between 2004-2008 are the load levels used in this study. Because of changes in agricultural sectors, population, precipitation, and the recent nutrient abatement activities, the difference between the 2004-2008 level, and the maximum allowable loads deviates from the calculations made for reductions in the BSAP agreement. This implies that in order to meet the required BSAP load targets for the different sea-basins some countries will have to do less and some countries more, in comparison to the original quotas, with regard to nitrogen and phosphorus abatement.Ahlvik et al. (2012) have, therefore,
recalculated the BSAP reduction targets taking this new data into considera-tion (see table 2.1 and 2.2 below).
Table 2.1 BSAP Reduction targets for the different sea-basins.
1997-2003 2004-2008
Sea-basin Nitrogen Phosphorus Nitrogen Phosphorus
BB 0 0 2 404 0 BS 0 0 0 0 BP 94 000 12 500 57 880 8 043 GoF 6 000 2 000 10 191 1 407 GoR 0 750 7 741 1 105 DS 15 000 0 11 417 0 KT 20 000 0 12 991 0 Total 135 000 15 250 102 624 10 555
To find the cost-effective combination of abatement measures to reach the objectives set by the Baltic Sea Action Plan (BSAP) is the focus of the two models Ahlvik (Ahlvik et al., 2012) and BALTCOST (Hasler et al., 2012).
Table 2.2 Reduction quotas (tons) for the different countries, original BSAP quotas and updated
1997-2003 2004-2008 (Ahlvik et al. 2012)
Country Nitrogen Phosphorus Nitrogen Phosphorus
Denmark 17 210 16 8 607 0 Estonia 900 220 1 490 201 Finland 1 200 150 1 768 224 Germany 5 620 240 4 856 0 Latvia 2 560 300 1 782 1 681 Lithuania 11 750 880 13 263 1 656 Poland 62 400 8 760 40 638 6 828 Russia 6 970 2 500 5 326 1 354 Sweden 20 780 290 16 656 180 Total 129 390 13 356 94 386 12 124
The targets of the BSAP are solved in Ahlvik et al. (2012) (1,2 and 3) and Hasler et al. (2012) (2) under the following three different objectives:
1. Solve both the country- and sea-basin-specific targets in a cost-effective way. 2. Solve the sea-basin specific target in a cost-effective way.
3. Solve the nutrient loads leading to the BSAP good ecological status in a cost-effective way.
First, the cost-effective way to fulfil both the basin-load target and the country quotas is solved (objective 1). The model of Ahlvik et al. (2012) finds the set of abatement measures in each sub-catchment such that the total cost is mini-mized under the sea-basin and country quotas constraints.
Thereafter, the total cost is minimized under the constraint that the sea-basin targets are reached, allowing for a cost-effective allocation of measures between countries, not forcing them to fulfil the BSAP country quotas (objective 2). If the country quotas are not cost-effective, objective 2 will generate an allocation of measures that differs from these quotas, implying a lower total cost of reaching the targets compared to objective 1.
The BALTCOST model version (BALTCOST 8.0) used in Hasler et al. (2012) only allow for basin-wise targets, as this model version is not yet set up to model country-wise targets. The results from BALTCOST (documented in Hasler et al. (2012)), used for the recalculated BSAP targets in this report, therefore only gives results on the cost-effective set of measures under the sea-basin quota constraint (i.e. objective 2).
To reach objective 3 Ahlvik et al. (2012) recalculated the maximum allowa-ble loads to each sea-basins in an economically effective way, such that nitro-gen and phosphorus concentration, as well as biomasses of algae and cyano-bacteria, are at least as low as with the original maximum allowable load levels. This was done by combining the catchment model and a basin-scale ma-rine model. In other words, it provides an alternative way to reach the good ecological status (GES) as defined in the BSAP agreement.
3. Models used
Ahlvik et al. (2012) and Hasler et al. (2012) have both used different data sources and several models in order to obtain the costs of a reduced eutrophi-cation in the Baltic Sea. Models are used to capture the linkages between drivers, pressures, state and impact with regard to different environmental problems, in our case eutrophication. In that way, the full effect of different abatement measures on the load, and thereby the state of the Baltic Sea can be estimated. Further information about how these data and models are used in each of the models can be found in Ahlvik et al. (2012) and Hasler et al. (2012).
3.1 The Ahlvik et al. model (2012)
The model of Ahlvik et al. (2012) also includes modelling of interactions between abatement measures. Taking into account the interactions one ends up with another combination of measures, as the effect of one measure may be influenced by the use of another. Another strength of the model of Ahlvik is its phosphorus dynamics, which helps to identify the most cost-effective phosphorus measures in the long run.
The models used for the calculations can be divided into:
• catchment models estimating the effect of different measures applied on land within a catchment with the objective to reduce the nutrient load from the same, and
• marine models estimating one or several effects in the marine waters caused by the nutrient load it receives.
In order to calculate the economically effective measures for reaching the three different objectives a catchment model, including the different abate-ment measures as well as retention, was used. The catchabate-ment model estimates the contribution to the nutrient load from different sources within the catch-ment, by combining information regarding discharges, emissions and leakage with data regarding the retention. The nutrient retentions in soil, ground-water and rivers that are used are derived from the statistical model MESAW (Stålnacke et al., 2012).
The catchment model also estimates the costs and effects of the abatement measures that could be implemented to reach the different objectives. A non-linear optimization problem is solved to derive the cost-effective allocation of abatement measures such that the objectives described above are fulfilled.
Figure 3.1. Division of the Baltic Sea into different sea-basins and drainage basins.
The catchment model of Ahvik et al. (2012) divides the Baltic Sea drainage area into 23 sub-catchments such that each combination of a country and a sea-basin forms a single unit (see Figure 3.1). The catchment model was then combined with a marine model to solve the effect of measures on the state of the Baltic Sea. The marine model, linking the nutrient load with the effects on the Baltic Sea, was also described in the BG Paper Benefits of mitigation. The model framework has a twofold aim: it includes the biophysical processes of the ecosystem in detail sufficient to produce credible large-scale and long-term projections of nutrient and phytoplankton quantities when combined with the marine model. Yet, it is simple enough to be extended to identify econom-ically optimal nutrient abatement measures, perform economic optimization and search for cost-effective combinations of nutrient abatement measures.
The Ahlvik et al. (2012) model attempts to take into account two important features of the nutrient reduction problem: interdependency of abatement
measures and lag in load reduction. Interdependency refers to the relation-ships between abatement measures; the use of one measure can affect the efficiency and cost of other measures implemented in the same catchment. For example, the effect of wetlands depends on inflow from fields and it becomes less effective if using less fertilizer reduces this inflow. This, in turn, will increase the marginal cost of wetlands. Also, the efficiency of reducing phosphorus in detergents depends strongly on the current level of wastewater treatment. Furthermore, lags in abatement measures is caused by accumula-tion of nutrients in soil of fields. This effect is significant for phosphorus which, unlike nitrogen, discharges only via leaching. Therefore, the full effect of phosphorus fertilizer reduction is only seen after decades, which further-more affects efficiency of related measures such as catch crops, wetlands and phosphorus ponds. Retention is modelled as a time-independent constant coefficient. Thus, accumulation of nutrients in streams and lakes, and the lag it causes to nutrient abatement, is excluded from the analysis.
3.2 BALTCOST – Baltic Sea Catchment model of Hasler et al. (2012)
The BALTCOST model (Hasler et al., 2012) is also a non-linear optimization model for the Baltic Sea catchments, and aims to model cost-minimizing so-lutions to obtain nutrient load reductions to the Baltic Sea regions. The mod-el is static, and the results give information regarding the cost-effective com-bination of measures between catchments to obtain nutrient load reductions at the river mouth to the seven Baltic sea-basins.
As mentioned, this model version only address nutrient load reductions to the sea-basins and not according to the country-wise nutrient load targets. The model results thus provide information about the costs per country from delivering the sea-basin nutrient load target.
The focus of the development of BALTCOST #8.0, used in Hasler et al. (2012), has been to utilize detailed geographical data from in all 117 sub-catch-ments around the Baltic (see Figure 3.2.) on e.g.
• crop distribution and soil types,
• initial fertilizer application to these crops, • livestock production,
• waste water treatment and effectiveness including information on house-holds not connected to WWTP, and
• retention.
These data were used to estimate costs of measures, capacities of these mea-sures in terms of restrictions on their use (e.g. how large is the potential for wetland restoration in each of the catchments around the Baltic Sea), as well as the effects on nutrient load reductions, including retention. The strength of the model is the spatially disaggregated information on crop distribution, retention etc., that influences the cost-effective distribution of measures between the catchments. The catchment data on crops and livestock, as well as effects on nutrient load reductions, are retrieved from cooperation within the Baltic Nest Institute and the BONUS project RECOCA. Most of the catchment data are described in Andersen et al. (2011).
Figure 3.2. Division of the Baltic Sea land area into 117 catchments.
The nutrient retentions in soil, groundwater and rivers were derived from the statistical model MESAW (Stålnacke et al., 2012). Hasler et al. (2012b) also used an additional set of retention estimates that were produced by Hägg us-ing the hydraulic load (HL-BO) method (Behrendt et al., 1999; Hägg, 2010). By using these different sets of retentions, as well as no retention, a sensitivity analysis of the effects of the retention on the cost-effective solution was per-formed (Hasler et al., 2012). The sensitivity analysis indicated that the model-ling of retention, and the differences between and within catchments, have huge impacts on the cost-effective distribution of measures and the total costs of achieving the load reduction targets to the sea basins.
The BALTCOST model is static and the current version includes six different measures. The Baltic Sea drainage area is separated into 22 drainage basins, which are further divided into 117 watersheds, each of the drainage basins comprises between 1 and 16 separate watersheds (cf Figure 3.2.). Unlike the model of Ahlvik et al. (2012), the interdependency between measures, as described above, is not modelled in BALTCOST #8.0.
The current version of the model (Version 8.0) does not account for transport of nutrients between sea regions, as this transport was taken into account when HELCOM set the load reduction targets in the BSAP. The model therefore minimizes costs for delivering the required load reduction targets for each sea-basin separately, rather than for the Baltic Sea as a whole.
The model receives separate load reduction targets for nitrogen and phosphorus for the seven Baltic Sea sea-basins (see Figure 3.1). Abatement cost minimisation is carried out separately for each of these seven sea-basins in turn to produce a cost-effective solution for the Baltic as a whole, given the nitrogen and phosphorus load reduction targets assigned to the separate Baltic Sea sea-basin.
3.3 Comparison of the main characteristics of the two models Table 3.3 Characteristics of used models
Ahlvik et al. (2012) Hasler et al. (2012)
Temporal framework Dynamic Static
Number of measures 9 6
Drainage basins 23 22 and 117 watersheds Treatment of N & P Interlinked Separate load reduction targets
for N & P as set in the BSAP (HELCOM, 2007)
Data Aggregate data for the 23
catch-ments. Prices are in 2008 Euros. Data on crop distribution, fertilizer application, wastewater treatment for households and retention within 117 catchments, based on aggregation of data from 10x10 km grid cells. Price and produc-tion data from 2005.
Contribution Combines existing data into one catchment model and models the interactions carefully
Provides completely new infor-mation on effects and costs of abatement measures Account for interdependency
between measures Yes No
Linkage to a marine model Yes No
The separation of drainage basins in the two models differs for Denmark, Lithuania and Russia, while it is the same for the rest of the Baltic Sea countries. While Ahlvik et al. (2012) have divided the drainage area from Russia into three catchments (Baltic Proper, Gulf of Finland and Gulf of Riga) and Lithuania into two (Baltic Proper and Gulf of Riga) Hasler et al. (2012) only divided the Russian drainage basin into two catchments (Baltic Proper and Gulf of Finland) and the Lithuanian drainage basin into one catchment (Baltic Proper). However, Hasler et al. (2012) divided the Danish drainage basin into three catchments (Baltic proper, Danish Straits, and
Kattegat), while Ahlvik et al. (2012) divided this drainage basin into two catchments (Danish Straits and Kattegat). As usually assumed in the litera-ture, the non-littoral countries (i.e. Belarus, Ukraine, Norway, Czech republic and Slovakia) are excluded from the studies because of their less significant importance for nutrient loads to the Baltic Sea, and the lack of data (see e.g. Gren et al., 1997; Turner et al., 1999).
4. Abatement measures
A major part of the measures described below, targets the nutrient load from the agricultural sector as this sector stands for the largest contribution of ni-trogen and phosphorus to the Baltic Sea. Measures aimed at improving wastewater treatment also have a great potential in most of the surrounding countries, and such measures are therefore also included in the studies.
The following measures to reduce the effects of eutrophication were included in the cost estimates study of Ahlvik et al. (2012):
• Reduced fertilization (N & P abatement) • Catch crops (N abatement)
• Reduction in cattle numbers (N & P abatement) • Reduction in number of poultry (N & P abatement) • Reduction in number of pigs (N & P abatement) • Restoring wetlands (N & P abatement)
• Constructing phosphorus ponds (P abatement) • Improving wastewater treatment (N & P abatement) • Banning phosphorus in detergents (P abatement)
In the study of Hasler et al. (2012) the following measures were included: • Reductions in fertilizer applications to arable crops (N abatement) • Catch crops under spring-sown cereals (N abatement)
• Reduction in cattle numbers (N & P abatement)
• Reduction in number of pigs (poultry and pigs) (N and P abatement) • Restoring wetlands on agricultural soils (N & P abatement)
• Improving wastewater treatment (WWT), including treatment or connec-tion of households not connected (N & P abatement)
For some of the measures that are included in both studies the same data regarding cost, effect or capacity have been used as these have been retrieved from the BALTCOST model (Hasler et al., 2012). This is the case for waste-water treatment for which the cost functions are the same. Whereas regarding, for example, fertilizer reduction the measures differ between the two studies and model setups. In Ahlvik et al. (2012), the phosphorus reduction of this measure is modelled and taken into account.
Hasler et al. (2012) use nitrogen yield functions to estimate cost-functions for nitrogen fertilizer reductions for all 14 crops. The fertilizer input includes both mineral (inorganic) fertilizers and animal manure (organic fertilizer), and the cost-functions are calibrated to the yield and price level of each of the nine countries. The initial fertilization level for each of the 14 crops is obtained from data at 10x10 km grid cell level (Andersen et al., 2011). Similar data and yield functions were not available for phosphorus, and since it was empha-sised to build the model on as reliable, detailed and realistic catchment data as possible from catchment modellers, it was decided to not include phos-phorus fertilizer reduction in the current version of the model. The number of measures in BALTCOST (Hasler et al., 2012) was restricted by both cost data availability and the certainty by which the catchment modellers would deliver parameters for the nutrient load reduction effects with scientific
certainty. The effects on nutrient load have been estimated by the RECOCA project team (cf Hasler et al., 2012; Andersen et al., 2011; Humborg et al., 2012; Stålnacke et al., 2011).
Capacities and costs of measures
In order to determine the cost-effectiveness of any measure, information is required regarding the nutrient load reduction effect, the reduction capacity and the cost of that measure. The cost of a measure is determined by its run-ning, opportunity and investment costs, as well as its effect (e.g. kg nitrogen abated). In Ahlvik et al. (2012) any investment cost was transformed into an-nual cost by using the lifespan of the investment and discounted with a dis-count rate of 3.5 per cent, a figure based on recommendation by Boardman et al. (2006).
No discount rate was used in BALTCOST, as annual costs were estimated using the opportunity cost approach for each agricultural measure. For WWT discounting would have been important if investment costs of new WWT plants were included, but because of lack of data the assumption was made that there is sufficient unused capacity in existing WWTPs within the watershed. This includes currently established plants in Russia and Poland. The costs of upgrading this capacity were measured as annual costs including labour costs and other operating costs. The capacity is therefore assumed sufficient to accommodate wastewater treatment for households (measured as Person Equivalents (PE)) that are currently not connected, as well as upgrading of PEs without having to build completely new WWTPs. The cost data for the cost-function are average WWT operating costs (Euros/PE) for each of the 117 watersheds. Further information about the assumptions and the methods can be found in Hasler et al. (2012). The prices in the Ahlvik et al. (2012) model are expressed in year 2008 Euros, while the prices in Hasler et al. (2012) are expressed in year 2005 Euros.
For the measures fertilizer reductions and wastewater treatment non-linear cost- functions are estimated and applied. The other cost functions are linear. This means that for abatement levels where only these measures are applied the marginal costs are constant, while they are increasing with increasing abatement levels when the non-linear abatement measures are used.
The marginal cost denotes the cost of reducing one more unit of the nutrient. The marginal cost curve illustrated in Figure 4.1 indicates how the cost of abating an additional unit of the nutrient changes with increased reduction.
The vertical axis shows the cost, while the horizontal axis represents the total load reduction. This marginal cost increases with reduction, as it is initially possible to implement very cheap measures to bring about a reduc-tion in load, while increasingly expensive measures have to be taken when the volume of reduction increases. It is necessary to establish a target to make it at all possible to decide whether a measure is cost-effective or not. Such a target is illustrated by the dotted vertical line in Figure 4.1. Based on the marginal cost curve and the target it is possible to see that the measures to the left of the target are cost-effective, while those to the right are not.
The area under the marginal cost curve gives the total economic cost of a particular reduction. It is clear that in order to attain the target at minimum cost all mea sures to the left of the Target should be implemented while those to the right of this Target should not be implemented.
Figure 4.1. Marginal cost and cost-effectiveness
However, for several of the measures included in this study a marginal cost function could not be obtained, so the average cost was used as a proxy for the marginal cost implying a constant marginal cost of these measures.
Since the objective of the BSAP is to improve the state of Baltic Sea it is the marginal cost of a specific measure to the recipient (i.e. the Baltic Sea) that is of interest. Due to retention, only a share of the nitrogen and phosphorus applied in excess of harvest removal or discharge from wastewater in a drainage basin ends up in the Sea. The effect, and thereby the costs, of the abatement measures in terms of nutrient load reduction to the Sea is, there-fore, to some extent determined by the retention between the location of the measure and the Baltic Sea.
This implies that knowledge regarding the retention between the source of the nutrients and the Baltic Sea is required for making estimations of costs, since it determines the marginal cost to the recipient of a measure at a specific location. Even though the marginal cost at source can be the same for a certain measure it might differ for the recipient. For example, the marginal cost of reducing fertilization increases with the retention between the farmed land and the Baltic Sea, favouring down stream measures over up stream measures, everything else equal. To obtain the marginal cost to the recipient, the marginal cost at source/location of the measure is divided with the proportion of one kilogram of the nutrient that reaches the Baltic Sea (i.e. 1 subtracted by the retention). So, the larger the retention is, the larger the abatement cost to the Baltic Sea will be.
Most of the measures can generate positive or negative synergy effects on other environmental targets. These synergy effects were not monetized but the tables below take them into account by assigning whether they have potential to be either positive (+ sign) or negative (- sign). Positive synergy effects of nutrient abatement measures (e.g. recreation benefits from wet-lands) would imply that the costs of reduction for the measure in question would be lower, while the opposite holds for negative synergy effects (e.g. emission of greenhouse gases from wetlands).
In Tables 4.1 to 4.7 the assumptions behind the abatement cost estimates are given.
Fertilizer reduction
It is assumed that fertilization can be reduced at arable lands, as well as at crops grown outside rotation (Hasler et al., 2012). In Ahlvik et al. (2012) both nitrogen and phosphorus reduction can be achieved by reducing the
application of fertilizers, while only nitrogen is reduced in Hasler et al. (2012), In Ahlvik et al. it is assumed that the costs and effects of reducing fertilizers for all crops are equal to reducing the fertilization of spring barley, that is it is assumed that all crops respond equally to nutrient reductions.
Ahlvik et al. (2012) estimate costs of agricultural abatement measures in each of the subareas by considering a representative farm. That farm cultivates a representative crop, which is chosen to be barley, and there is certain leaching from each hectare of cultivated land. Reducing the use of inorganic fertilization or manure reduces nutrient leaching from fields, but has a cost in form of decreased crop yields. Total fertilization consists of inorganic fertilizers and animal manure, the latter assumed to be fully and evenly spread on the fields.
Reduction in applied amount of fertilization results in nitrogen root-zone loss and also reduced yields. The fundamental equations in the cost calcula-tions for fertilizer reduccalcula-tions are, therefore, the crop and drainage basin specific yield functions describing the dose-response relationship between nitrogen fertilizer application and yield functions. The yield functions are decreasing functions of applied amount of fertilizers indicating that the more fertilizers is used, the less the increase in yield will be. It is assumed that the initial level of fertilization is the economically optimal level in terms of the constructed profit function, and any reduction below this point is associated with a cost and this cost will increase exponentially with further reductions.
Hasler et al. (2012) estimate the fertilizer reduction using yield functions for in all 14 crops grown in the 117 catchments. Yield functions are estimated using Danish experimental data for the dose-response of nitrogen to the 14 crops at clay and sandy soils, and these functions are calibrated to yields and fertilizer application levels in the eight other countries. Data for crops grown and nitrogen application in commercial fertilizers and animal manure are withdrawn from Andersen et al. (2011) for the 117 catchments. An effective nitrogen utilization in animal manure of 70 per cent is assumed in Finland, Sweden and Denmark, and 50 per cent in the other countries, because of differences in manure handling, timing and storage. This approach utilizes the very detailed data sampled in the RECOCA project on crops grown, fertilizer application and livestock production.
A calibrated profit function for each type of crop is constructed from the 14 relevant yield functions (Hasler et al., 2012). The initial level of fertilization is assumed to be the economically optimal fertilization in terms of the constructed profit functions. This assumption sets the initial fertilization to the top point of the profit polynomial, which is determined by the relative country specific prices of the nitrogen input and the crop output, and the slope and curvature of the country specific yield function.
The reduction in profit, which results from a reduction in nitrogen fertili-zation, can be calculated as the difference between the profit arising at the reduced level of fertilizer application and the profit arising at an initial, profit maximising, level of fertilizer application. The costs are obtained by estima-ting foregone profits due to reduced yields and subtract the savings in expen-ditures on fertilizers (estimated by multiplying the price of fertilizers with the reduced amount). For this abatement measure a marginal cost curve can, therefore be obtained, where the cost of this measure at a specific location increases the more the application of fertilizers is reduced. Data required in order to estimate these cost functions comprise: prices of the crops in ques-tion, price of nitrogen fertilizers, the country-wise yield functions described above and the nitrogen input to each crop, for which data is utilised on 10 x 10 km grid cell level, and then aggregated to 117 and 22 drainage basins. As mentioned, this approach utilizes the detailed information available from the catchment models (Andersen et al., 2011).
Table 4.1 provides a comparison between the two models regarding the costs and effects of fertilizer reduction. The cost per reduced kg of nitrogen (N) or phosphorus (P) is given at sources as well as to the Baltic Sea. Due to the retention the cost span is larger for the latter. While Ahlvik et al. assumes that fertilizer application can be reduced by 80 per cent, Hasler et al. assumes a reduction of only 20 per cent. The reason for choosing this limitation in BALTCOST is that reduction outside this range is likely to influence the parameters of the yield functions of this model. Increasing this limitation to above 20 per cent can thus lead to faulty results as the shape of the yield functions will change due to depletion of the nitrogen stock in the soil.
These assumptions have implications on the total load reduction capacity (denoted Max N and P reduction in Table 4.1) of this measure, which is larger in Ahlvik et al. (2012) than in BALTCOST (Hasler et al., 2012).
Table 4.1 Cost and effects of fertilizer reduction
Costs
Ahlvik et al. (2012) Hasler et al. (2012)
Effect Estimated by crop yield function using barley as a representative cereal. Specific per catchment, N application and soil phosphorus stock
Estimated by crop yield function, leaching function. Specific per crop, catchment and N appli-cation
Cost € Kg/N at source 1-125 €/kg 0.07-5.3 €/kg Cost € Kg/P at source 0-350 €/kg Not included Cost € Kg/N to sea 2-158 €/kg 0.5-8 €/kg Cost € Kg/P to sea 0-463 €/kg Not included Relevant areas Barley used as a representative
cereal, all areas
All crops, all areas Capacity 80% of initial application 20% of initial application Max N reduction 118 684 tons/year 72 875 tons/year Max P reduction 1 672 tons/year Not included
It can be seen that the cost interval of nitrogen fertilizer reduction in Ahlvik is much higher than in the BALTCOST results. The difference between the models in both the range of cost estimates and the maximum nitrogen reduc-tion can be explained by their respective capacity constraint, which means that the fertilizer application reductions allowed in BALTCOST (i.e. 20% of initial application) is much lower than the constraint in Ahlvik (i.e. 80% of initial application). The difference in costs can also be explained by the as-sumptions of crop distribution, where fertilizer reductions on spring barley, as assumed by Ahlvik et al. (2012) is more costly than fertilizer reduction on the mix of crops modelled in BALTCOST.
Model simulations have been run with BALTCOST to assess the sensitivity of the assumption that all crops behave as spring barley, that is, the current crop distribution in BALTCOST was changed to spring barley. The total costs of obtaining the BSAP targets increased from 1 400 Million Euros to 1 790 Million Euros, annually, and these results indicate that the differences between Ahlvik et al. (2012) and BALTCOST in the modelling of the crop distribution in the catchments can explain some of the differences in the costs of fertilizer reductions between the models.
Catch crops
By cultivating catch crops the nutrients are tied up during the winter season in order to be utilised with spring preparation. The catch crops are either under-sown in the main crop, or sown after the main crop. For the modelling of catch crops it has been assumed that the catch crops are under-sown in spring crops, and that the catch crop is ray grass. The costs arise due to addi-tional labour and fuel for tillage.
In Ahvik et al. the cost per hectare for catch crops ranges from 29-38 Euro, while in Hasler et al. this cost range from 2 Euros in Latvia to 58 Euros per hectare in Denmark (Table 4.2). The effect of this measure on nitrogen leakage depends on several factors, such as soil type and weather conditions. It has the largest effect in sandy soils, while the effect is lower in clay rich
soils. The net reducing effect on nitrogen leaching by introducing catch crops in spring sown crops (i.e. barley and oats) is estimated to be 35 per cent of the initial leaching from the crop (Hasler et al. 2012) and 30 per cent of the initial leaching in Ahlvik et al. (2012). The effect on phosphorus reduction is very small. In Hasler et al. (2012) there is no effect on phosphorus leaching, whereas in Ahlvik et al. (2012) the effect is 5 per cent. The capacity of the catch crop-measure is calculated as the area grown with barley and oats in BALTCOST (Hasler et al., 2012), and the acreage with barley and oats are measured in the baseline data for the 117 sub-catchments. This determines the maximum nitrogen reduction in BALTCOST; together with the assumption that 35 per cent of the initial leaching is reduced. In Ahlvik et al. (2012) the maximum capacity is 33 per cent of the total arable land.
Table 4.2 Cost and effects of catch crops
Costs
Ahlvik et al. (2012) Hasler et al. (2012)
Cost €/ hectare 29-38 €
Cost of seeds, equipment, and sowing
2-58€
Costs of seeds, sowing. No yield loss or yield increase
Effect P 5 % of P leaching Not included
Effect N 30% of the initial N leaching 35% of the initial N leaching Cost € Kg/N at source 2-72 0,2-2.8
Cost € Kg/P at source 148-2240 Not included Cost € Kg/N at sea 4-133 0.3-9,7 Cost € Kg/P at sea 433-3670 Not included
Relevant areas 33 % of the arable land Area grown with spring cereals Max N reduction 17 429 tons/year 38 440 tons/year
Max P reduction 199 tons/year Not included
The range of the cost estimates is much larger in Ahlvik et al. (2012) than in BALTCOST.
In BALCOST a linear cost-function is applied, and no inter-dependencies between the measures are modelled. This means that for example fertilizers and catch crops can be implemented at the same location at the same time. This has been done since Hasler et al. (2012) don’t have data to estimate how reduction of the N leaching might be influenced if both fertilizer inputs were reduced and catch crops implemented.
Ahlvik et al. (2012) estimates the effect as a percentage of the total flow. The effect of catch crops decreases linearly if the total fertilization is reduced, since that reduced the total flow. The high end of the marginal cost represents a situation where the use of inorganic fertilization is reduced to a minimum. This reduces the effectiveness of catch crops and the marginal cost becomes very high.
Wetland restoration
A wetland is defined as an area with a gradient from occasionally wet grass-lands to lakes. But for simplicity it is in this report assumed that the nutrient load reduction effect is the same per hectare, regardless of the type of wetland
that is restored. Wetland restoration is defined as a wetland that is restored at agricultural land that has been wet in the past.
The maximum nitrogen and phosphorus reductions are at the same level in the two models (see Figure 4.3), but the model approach differs between the models as Ahlvik et al. models the effect of nutrient reductions as a func tion of the inflow, while Hasler et al. (2012) models a constant effect of 150 kg nitrogen/ha and 0,7 kg phosphorus/ha (Hoffman et al., 2006). This effect is the estimate provided by the catchment modellers in the RECOCA project (Andersen et al., 2011). If this is an overestimate it influences the costs per kg nitrogen and phosphorus, and a lower effect will increase the costs per kg N/P reduction.
The cost of wetlands is in Ahlvik et al. (2012) based on construction costs, management cost of the wetland, as well as the opportunity cost of the arable land (i.e. foregone profits from yield), while Hasler et al. (2012) only considers the opportunity cost by estimating the lost land rent from agricultural land converted to wetlands. Hasler et al. do not consider maintenance cost, since it is assumed that restored wetlands are implemented by discarding drainage, or by implementing other hydrological changes on agricultural land. In Hasler et al. (2012) the capacity of this measure is determined by the land classes indicating agricultural use and for the total drainage basin of the Baltic Sea 1.69 per cent of that area has been estimated to have a potential for this measure. This potential is unevenly distributed over the drainage basins, ranging from 0.01 to 15.67 per cent of the drainage basin-specific agricultural area. Ahlvik et al. (2012) assumes a potential of 5 per cent of agricultural land in all countries. Wetlands might in some cases also have positive synergy effects on biodiversity and recreation.
Table 4.3 Cost and effects of restored wetlands
Costs
Ahlvik et al. (2012) Hasler et al. (2012)
Cost Based on construction, opportunity
and management cost Based only on opportunity cost of land Construction cost 2000-12000 € / hectare Not included
Annual construction cost 70-420 € /hectare Not included
Opportunity cost 186-904 € /hectare
Opportunity and management cost (annual)
47-463 €/hectare
Overall annual cost 117-883 €/hectare 186-904 € / hectare Nitrogen reduction 62 % of the inflow 150 kg per hectare Phosphorus reduction 17 % of the inflow 0.7 kg per hectare Cost € Kg/N at source 1-131 1.2-5.8
Cost € Kg/P at source 115-2350 1.4-1048 Cost € Kg/N at sea 2-332 1,6 – 93 Cost € Kg/P at sea 239-3105 1,6-1647 Relevant areas Agricultural land, organic soils.
The ratio between area of wetland and its catchment is one to ten.
Agricultural land, organic soils
Capacity 5% of the arable land 1.69% of total area (0-8.84% depending on capacity (organics soils) in drainage area
Max N reduction 75 521 78 803
Max P reduction 907 959
Synergy effects
Biodiversity +++
Recreation +
Again the cost ranges are larger in Ahlvik et al’s model, and the explanations are the same as for some of the other measures where the BALTCOST cost-function is linear. As seen from the table the annual costs per ha are not lower in BALTCOST compared to Ahlvik et al. (2012), even though the latter addresses the construction costs as well as the opportunity and management costs.
Constructing sedimentation ponds
As the primary purpose of restoring wetlands is reducing nitrogen loads, an alternative concentrating on phosphorus load reduction is sedimentation ponds. It is a small surface flow pond that aims at retaining phosphorus. Ponds are usually dug, and hence their construction cost is much larger than that of wetlands. This measure is only included in Ahlvik et al. (2012). The ef-fect of these ponds is determined as a proportion of the inflow, in this case 30 percent. The capacity of the ponds is set at 0.04 per cent of the arable land.
Table 4.4 Cost and effects of constructing sedimentation ponds (only in Ahlvik et al. (2012)
Costs
Construction cost 3-18 € / square meter Annual construction cost 1000-6300 € / hectare Overall annual cost 103-862 € / hectare Phosphorus reduction 30 % of the inflow Cost € Kg/P at source 7-529 €/kg Cost € Kg/P at sea 18-867 €/kg
Capacity 0.04 % of the arable land Max P reduction 1 773 tons/year Livestock reduction
One way to reduce nitrogen and phosphorus root-zone loss is by reducing the application of manure by reducing the number of livestock in the Baltic Sea drainage basin. In Hasler et al. (2012) it is assumed that farmers will sub-stitute the reduction in manure fertilization with mineral fertilization. The effective reduced amount of nitrogen and phosphorus, from this measure, is therefore the difference of the utilisation rates, which is assumed to be 70 per cent for Denmark, Finland and Sweden and 50 per cent for the other countries. In Ahlvik et al. (2012) reducing manure causes a loss of crop. The effect on phosphorus is very uncertain since there are large differences in soil biding capacity of this nutrient, so any livestock reductions motivated by phosphorus load reductions must be treated with caution.
The different types of livestock are separated into cattle, pigs or poultry in Ahlvik et al. (2012), while Hasler et al. (2012) separates it into the two cate-gories cattle and pigs (in which poultry is included). However, one unit of livestock from either of these groups is a heterogeneous unit. The cost of livestock reduction is composed of two parts, establishing the cost of a unit reduction of each livestock class and type, and employing the different weights for livestock classes to aggregate these costs to a unit reduction of a livestock type (Hasler et al., 2012). This means that the cost is based on the opportunity cost of the livestock.
Table 4.5 Cost and effects of livestock reduction
Costs
Ahlvik et al. (2012) Hasler et al. (2012)
Cost Opportunity cost of cattle, pigs and poultry plus the effect of reduced manure on crop yield
Effect Reduced total fertilization
Cost € Kg/N at source 6-405 €/kg 0- 322 €/kg Cost € Kg/P at source 247-~110000 €/kg 0-287 €/kg Cost € Kg/N at sea 16-512 €/kg 0-328 €/kg Cost € Kg/P at sea 950-~150000 €/kg 0-14688 €/kg Capacity 50 % of the initial holding 20% of initial holding Max N reduction Cattle: 32 986 tons/year
Pigs: 13 938 tons/year Poultry: 6 402 tons/year
Cattle: 35 765 tons/year Pigs: 6489 tons /year
Max P reduction Cattle: 472 tons/year Pigs: 369 tons/year Poultry: 108 tons/year
Cattle: 1031 tons/year Pigs: 373 tons /year
Synergy effects
Recreation
-Once again, the capacity constraint is more restrictive in BALTCOST com-pared to Ahlvik et al. as it is anticipated that a reduction of up to 20 per cent can be implemented without large capital costs. This capacity constraint pre-vents implementation of the most costly livestock reductions in BALTCOST, and therefore the capacity restriction also influences the range
of the costs per kg nitrogen and phosphorus. The maximum nitrogen and phosphorus reductions are however more equal.
Wastewater treatment plants
The level of abatement at wastewater treatment plants is typically classified, in order of increasing cleaning capability; as primary, secondary or tertiary level treatment. The reduction obtained by improving wastewater treatment plants (WWTP’s) is regarded as the connection of an additional individual (a ‘person equivalent’ (PE) pollution load) to a higher level of WWT than that to which they are currently connected, thereby reducing nitrogen and phosphorus con-centrations in the effluent discharged from a plant.
Since the combined requirements of the Urban Wastewater Treatment Directive (UWWTD, 91/271/EEC) and the Water Framework Directive (WFD, 2000/60/EC) effectively imply that wastewater treatment among its Member States ultimately have to be upgraded to tertiary level, three poten-tial upgrades are considered in this study:
• from none to tertiary treatment,
• from primary to tertiary treatment , and • from secondary to tertiary treatment.
Dependent on the inclusion of specific nutrient removal process within the treatment system, when upgrading the treatment level, the plants ability to reduce nitrogen and phosphorus concentrations in discharge effluents typi-cally increases as treatment level increases. Nitrogen is typitypi-cally removed by aerobic biological nitrification followed by anoxic biological de-nitrification assisted by introduction of methanol or an input stream of raw wastewater (US EPA, 2004). Even though phosphorus can also be removed biologically, it is more common that it is removed by chemical precipitation following the addition of ferric chloride, aluminium or lime to assist coagulation and sedimentation as a separate ‘chemical’ flocculation stage in the treatment (US EPA, 2004).
The cost of this measure is determined by the required investment costs, as well as operating and maintenance expenditures, such as labour and energy costs. The WWTP cost function for tertiary improvement were in both Ahlvik et al. (2012) and Hasler et al. (2012) based on Danish data and applied to the different watersheds around the Baltic in order to obtain a local estimate of the total cost and average cost of this improvement. Local data for the scale of tertiary WWTPs, electricity and labour price, as well as the capacity for expanding tertiary plants, were obtained primarily from HELCOM and OECD sources in combination with GIS modelling as described in Hasler et al. The resulting estimated average annual cost for this measure is reported in Hasler et al. (2012).
Table 4.6 Cost and effects of wastewater treatment plant reduction
Costs
Ahlvik et al. (2012) Hasler et al. (2012)
Cost Cost functions from Hasler et al. 2012
Nitrogen reduction 25-80 % of inflow 33.7 to 80 %, of inflow Phosphorus reduction 35-85 % of inflow 42.9 to 85% of inflow Cost € Kg/N at source 1-519 €/kg 8,7-6949 €/kg Cost € Kg/P at source 7-1865 €/kg 57-405 €/kg Cost € Kg/N at sea 2-642 €/ kg 14.6 - 13898 €/kg Cost € Kg/P at sea 10-2772 € / kg 57-537 €/kg Capacity Improved wastewater treatment
for 31.9 million people Improved wastewater treatment for 31.9 million people (PE) Max N reduction 42 926 tons/year 50 245 tons/year
Max P reduction 9 772 tons/year 16 693 tons /year
Synergy effects
Sanitary/health +++
Banning of phosphorus in detergents
Another way of reducing the phosphorus loads from the wastewaters sector is by reducing the amounts of phosphorus-containing detergents and substi-tute them by, for example, zeolite based detergents. This measure is only included in Ahlvik et al. (2012), and only has an effect in countries where the phosphorus in detergents is not yet banned.
Table 4.7 Cost and effects of ban of phosphorus in detergents
Costs
Cost Change in consumer prices 11 €/kg
Effect Reduced inflow to wastewater treatment plants Cost € Kg/P at source 15-251 €/kg
Cost € Kg/P at sea 22-373 €/kg
Relevant areas Countries where phosphate in detergents is not yet banned (Russia, Estonia, Latvia, Lithuania, Poland and Denmark),
Capacity 100 % of the initial use Max P reduction 3 324 tons/year
Synergy effects
Imperfect substitutes -Summary
The maximum implementation capacities for the various abatement measures and nutrient retentions in each drainage basin are modelled in more detail than previously by utilising catchment specific data from the BONUS project RECOCA (Humborg et al., 2012).
If all the measures included in Ahlvik et al. (2012) were implemented to their full capacity, the annual amount of load reduction to the Baltic Sea would be 248 377 tons of nitrogen and 16 731 tons of phosphorus. This
amount is a function of the effect of the measure (e.g. how much nitrogen can be reduced from a hectare of wetland) and the capacity assumed (e.g. how many hectares of land that can be converted into wetlands). If all of the measures in BALTCOST were implemented to their full capacity, the annual load reduction to the Baltic Sea would be 214 292 tons of nitrogen and 12 500 tons of phosphorus. The difference can to some extent be explained by the fact that capacity of the measures wetland-, fertilizer- and livestock reduction, as well as for catch crops, is somewhat lower in BALTCOST compared to the model of Ahlvik et al. (2012).
Although all measures are capable of reducing the nutrient load to the Baltic, some of these measures can be regarded as passive measures as they do not really remove the nutrient from the cycle, but rather parks the nutrient in the environment or deliver it into the atmosphere. Wetlands and to some extent abatement at wastewater treatment plants can be regarded as passive measures. Measures, such as catch crops and phosphorus ponds, are of a recycling type since they create the possibility to actually reduce the inputs of the nutrients. Reduction of fertilizers and livestock, as well as a ban of
phosphorus in detergents, are measures reducing the input of nutrients to the cycle.
5. Total cost results and cost-effective allocations
This section will describe the estimated cost of meeting the BSAP targets for the three different objectives, as well as the corresponding allocation of measures.
A comparison with results from previous studies is made towards the end of the chapter. It should, however, be emphasised that the results of Ahlvik et al. (2012) and Hasler et al. (2012) in Table 5.1-5.4 are not directly comparable with former results presented in Table 5.5 (e.g. Schou et al., 2006 and Gren et al., 2008) because the cost data used in the new estimations are more recent than those used in former models.
Reduction to basins and by countries
Tables 5.1 and 5.2 show nutrient reductions and the costs of the cost-effective-ness calculations for the three different objectives and for each sea-basin (Table 5.1) and country (Table 5.2).
Table 5.1 Reduction of nutrients to the different sea-basins for the different objectives (ton/year)
BSAP Obj. 1 Obj. 2 BALTCOST
obj. 2 Obj. 3 Sea-basin N P N P N P N P N P BB 2404 0 2404 57 2404 57 2404 0 2923 68 BS 0 0 652 13 652 13 0 0 1949 19 BP 57880 8043 63560 8460 57880 8043 57880 8043 53590 8332 GoF 10191 1407 10191 1407 10191 1408 10191 1407 6576 1968 GoR 7741 1104 14312 1104 13314 1104 22314 1104 7743 1035 DS 11417 0 11417 587 11417 587 11417 280 9423 556 KT 12991 0 12991 190 12991 198 12991 3 11615 266 Total 102624 10555 115528 11818 108849 11409 117198 10557 93819 12244 As indicated in Table 5.1 the reduction exceeds the reduction quotas for some sea-basins. This is because for some sea-basins, BSAP sets reduction obliga-tions for only one of the nutrients. In the cost-effective solution, countries reach the targets by using measures that affect both nutrients. For example, increasing wastewater treatment capacity to satisfy a phosphorus load reduc-tion target will also generate nitrogen load reducreduc-tions, which seems to be the case for Estonia, Latvia, and Lithuania for nitrogen, and Germany and Russia with regard to phosphorus.
Furthermore, in Table 5.2 it can be seen that the phosphorus load targets for Latvia and Lithuania are too strict, and only 52 and 51 per cent of the targets, respectively, can be reached for objective one and with the measures included in the modelling exercise. This is because of increased loads from these countries, which is, at least partly, explained by increased loads from non-littoral upstream countries.
Table 5.2 Country allocations of reductions to sea-basins where the reductions are needed according to the BSAP (ton/year). Green values denote non-binding constraints BSAP Obj. 1 Country N P N P Denmark 8 607 0 10 257 0 Estonia 1 490 201 3 577 201 Finland 1 768 224 1 768 224 Germany 4 856 0 4 856 131 Latvia 1 782 1 681 1 819 8741 Lithuania 13 263 1 656 15 460 9762 Poland 40 638 6 828 40 638 6 828 Russia 5 326 1 354 5 326 1 557 Sweden 16 656 180 16 656 180 Total 94 386 12 124 115 528 11 818
1 Target could not be reached; constraint was relaxed by 48 % 2 Target could not be reached; the constraint is relaxed by 49 % Total costs and allocation of costs
The total cost for the Baltic Sea countries of meeting the BSAP ranges, for the different objectives, from 1 500 to 2 800 million Euros a year (see Table 5.3).
The total cost of reaching the country quotas (objective 1) is 2 800 million Euros a year, while the cost of reaching the basin targets (objective 2) is 2 300 million Euros a year since the bordering countries carry out the reduction to each basin cost-effectively instead of being constrained by their BSAP quotas. In comparison, the total cost for objective 2 using the BALTCOST model amounted to only 1 400 million Euros a year. The cost of reaching the good ecological status (objective 3) is 1 500 million Euros a year, because, under this objective, all the countries can cooperate and plan maximum allowable loads in a truly cost-effective way.
To put these cost numbers in perspective it might be worth knowing that in 2008 the Baltic Sea EU member countries received 12 600 million Euros in farm subsidies from EU, ranging from an average of € 1 065 per farm in Poland to € 25 968 per farm in Denmark.1
Table 5.3 Cost allocations between countries (Million €/year)
Country Obj.1 Obj.2 BALTCOST
(obj.2) Obj.3 Denmark 619.8 629.9 122.5 267.1 Estonia 35.5 77.5 69.4 35.9 Finland 48.8 23.4 15.6 51.5 Germany 650.9 480.1 112.8 99.3 Latvia 122.7 85.4 230.7 54.6 Lithuania 134 101.4 140.9 82.6 Poland 752.5 544.1 372.9 579.7 Russia 112.8 104.5 276.9 105.8 Sweden 325.9 289.7 84.4 210.8 Total 2 802.9 2 336.0 1 426.5 1 487.3
Objective 3 generates the lowest costs for all countries except for Estonia for which objective 1 would mean the lowest costs, and Finland, Poland and Russia, for which objective 2 generates the lowest costs. The cost-effective solution of meeting the basin load targets (objective 2) with BALTCOST, has significantly lower total costs compared to Ahlvik et al. (2012).In Ahlvik et al., as well as for the BALTCOST, the targets can be fulfilled in all the cases except for objective 1, and thus there is less need for the drastic high-cost measures required in objective 1.
When relaxing the country-wise reduction quotas, but still requiring the maximum allowable loads to be satisfied (objective 2), the total cost decreases significantly. This is because countries with high country-specific quota can replace some of their quotas with more low-cost measures in other countries. In such a solution the total cost of BSAP is reduced by almost 500 Million Euro a year.
Recalculating the maximum allowable loads in the sea basins (objective 3) can further reduce this cost. BSAP requires that only one nutrient is reduced for some sea areas, for example nitrogen in the Danish Straits or phosphorus in the Gulf of Riga. Firstly, it is economically effective to implement the low-cost abatement measures in all the catchments, because they also have an effect in further sea-basins due to water exchange. For example, the nitrogen reduction quotas for Danish Straits and Kattegat can be partly replaced by reduction in other sea-basins. Secondly, the results suggest implementing some measures in catchments without reduction obligation in the original agreement, Bothnian Bay and Bothnian Sea, because those reductions contribute to improvements in the Baltic Proper as well. Thirdly, the marine model takes into account the biophysical processes of the Sea. For example, controlling phosphorus loads can reduce the external nitrogen input caused by nitrogen-fixing cyanobacteria. The economically effective maximum allowable loads are shown in Table 5.4 below.
Table 5.4 Cost-effective maximum allowable loads to each sea-basin
Nitrogen load (ton/year) Phosphorus load (ton / year)
1997-2003
level BSAP Cost-effec-tive 1997-2003 level BSAP Cost-effec-tive BB 51 440 51 440 50 920 2 580 2 580 2 267 BS 56 790 56 790 53 450 2 460 2 460 2 167 BP 327 260 237 0101 241 290 19 250 7 9561 7 666 GoF 112 680 106 680 110 300 6 860 4 860 4 299 GoR 78 400 78 400 78 400 2 180 1 8801 1 950 DS 45 890 30 890 32 880 1 410 1 410 829 KT 64 260 44 260 45 640 1 570 1 570 1 297
1 The load reduction obligations for Belarus and Ukraine are excluded from this analysis
The studies of Ahlvik et al. (2012) and Hasler et al. (2012) are not the first esti-mating the costs of reducing nutrients to the Baltic Sea. In Table 5.5 the main cost results of earlier studies, dating back to 1997, are shown. Since costs, to a large extent depends on the targeted reduction of nitrogen and phosphorus, and these differ between the studies, the first two columns indicate for what reduction target these costs have been estimated. Differences in total costs are also to a large extent dependent on the measures included in the studies and the underlying assumptions regarding these measures. For a more thorough description of these studies and their results see the Swedish EPA report The costs of environmental improvements in the Baltic Sea and Skagerrak (2008).
Table 5.5 Comparison costs of other studies (Source: Swedish EPA, 2008)
Study Reduction tons/year Cost Billion €/year Number of
measures
N P N P
Gren et al. (1997) 360 000 18 500 1.6 0.35 15
Elofsson (1999) 130 000 1.4 11
Elofsson (2003) 257 000 7 000 1.2 (N&P) Gren & Wulff (2003)
Ollikainen & Honkatukia
(2001) 429 000 35 000 15.2 0.9
Schou et al. (2006) 160 000 0.804 10
COWI (2007) 106 000 13 000 3 (N&P) 16
Gren (2008) 50% 50% 2.8 1.8 14(N) 7(P)
Allocation of measures
The cost-effective set of abatement measures for each of the three objectives is shown in Table 5.6. In Ahlvik et al. (2012b), the most important measure for nitrogen reduction is reduction of total fertilization, while the next most important nitrogen abatement measures are wastewater treatment and intro-duction of wetlands. This ranking corresponds well to previous studies (Gren et al., 1997; COWI, 2007; Gren, 2008). Catch crops have a significant role only in objective 1. In line with previous studies (Elofsson, 1999; Schou et al., 2006) reduction of livestocks came out as one of the most expensive measures for reducing the nutrient load.
With regard to reaching the phosphorus targets, improved wastewater treatment alone is responsible for about 60 per cent of phosphorus abatement for all the three objectives in Ahlviket al. (2012). Reduced fertilization accounts for around a third of the nitrogen load reduction and around 10 per cent of the phosphorus reduction. Wetlands stand for almost 30 per cent of the nitrogen reduction but only 2.6 to 3.9 per cent of the phosphorus reduction. Banning phosphorus-containing detergents is a low-cost measure, and it accounts for between 10 to 20 per cent of the reduction depending on the objective. The fact that banning phosphorus in detergents stands for a smaller share of the reduction for objective 2, than for objective 1 and objective 3, is explained by its interdependency with reductions by wastewater plants.
Table 5.6 Measures proportion of total load reduction (in %) for the different objectives in Ahlvik et al. (2012)
Objective 1 Objective 2 Objective 3
N P N P N P Fertilization 34.5 12.4 38.1 9.7 32.3 10.1 Catch crops 3.5 0.4 2.5 0.3 1.4 0.2 P-ponds 8.6 0 7.9 10.2 Wetlands 29.3 3.9 26.5 3.2 27 2.6 Detergents 20.2 0 11.2 18.5 WWTP 32.7 54.6 33 67.7 39.3 58.5
In BALTCOST calculations 92 per cent of the phosphorus reduction is delivered by sewage treatment, 3 per cent by wetlands, 1 per cent by cattle and 4 per cent by pig production reductions. For nitrogen the load reductions are more evenly distributed; 25 per cent from wetlands, 34 per cent from fertilizers, 10 per cent from catch-crops, 4 per cent from cattle, 1 per cent from pigs and 26 per cent from sewage.
Abatement according to country quotas (objective 1, Ahlvik) requires drastic reductions for some catchments, and thereby abatement measures with higher marginal costs have to be used to a larger extent, most notably reduction of cattle, pigs and poultry. For objective 3, measures with lower marginal cost can be implemented.
Table 5.7 illustrates each measures’ proportion of phosphorus and nitrogen load reduction for each country under the cost-effective solution of objective 2. It can be seen that the role of a specific measures varies between countries. In Finland (FI), Germany (DE) and Russia (RU) the phosphorus load reduc-tion is dominated by reducreduc-tion of fertilizers, while wastewater treatment plants stands for the largest proportion of phosphorus load reduction in Poland (PL), Denmark (DK), Sweden (SE), Lithuania (LT) and Latvia (LV). Ban of phosphorus in detergents is an important measure in Estonia (EE), Russia and Lithuania.
Reduced fertilization is the dominating measure for reducing the nitrogen load for all countries except Latvia and Poland. The reason behind this is that it makes more sense in Poland to “capture” the nutrients by wetlands instead of reducing fertilization. This result comes from the leaching function, shapes
of yield functions and prices. Wetland construction is an important measure for reducing the nitrogen load in Latvia, Estonia, Poland, Lithuania and Denmark.
Table 5.7 Measures proportion of load reduction within countries (in %) for objective 2 in Ahlvik et al. (2012)
DK EE FI DE LV LT PL RU SE Phosphorus Fertilization 22.8 94.4 63.4 28.3 13.3 <0.1 39.3 27.3 Catch crops 4.8 1.8 0.01 2.3 <0.1 0.3 0 <0.1 1.1 P-ponds 3.7 1.9 0 0 5.0 6.5 10.3 2.2 7.8 Wetlands 24.9 9.2 1.1 12.1 5.3 3.8 1.6 0.2 5.5 Detergents 0 48.3 0 0 2.3 32.9 0 36.0 0 WWTP 71.8 16 4.4 22.2 38.1 43.2 88.1 22.3 58.3 Nitrogen Fertilization 67.1 53.8 98.5 80.7 21.8 60.7 1.3 63.0 80.4 Catch crops 5.9 10.3 0.02 3.3 1.0 3.1 0 <0.1 2.7 Wetlands 18.3 32.5 0.04 10.3 61.3 24.0 26.7 3.0 8.5 WWTP 8.7 3.4 7.9 5.7 15.9 12.2 72 34 8.4
Since the final effect of certain measures (e.g. reduced fertilization, catch crops) on the Baltic Sea might take some time, due to for example transporta-tion time between source and recipient, it might be ideal to implement the different measures at different points in time. (See Ahlvik & Kulmala, 2012)
The main report focused on the costs obtained in Ahlvik et al. (2012), with a reference to the costs obtained by using the BALTCOST model (Hasler et al., 2012). The main reason behind focusing on the former is that the model used for those estimates considered the interdependency between measures, as well as the dynamic aspects of the phosphorus stock. As shown in this Background Paper, Ahlvik et al. (2012) estimated the cost for 3 different objectives, but only the costs for two of these objectives were presented in the main report. The third objective was based on a different marine model then the one used for establishing the BSAP nutrient reduction targets, and that was the main reason for leaving these estimates out of the report, even though they were significantly lower than the costs of the two other objectives. To go with the more conservative cost estimates in a CBA also makes the results more robust.
6. Generalizations & assumptions
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 impleimple-mented 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).
