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Policy Goals for Improved Water Quality in the Baltic Sea: When do the Benefits Outweigh the Costs?

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Abstract

This paper develops and applies a spatially explicit bioeconomic model to study transboundary nutrient pollution of the Baltic Sea. We combine catchment, marine and economic models covering the entire Baltic Sea region to weigh the costs of nutrient abatement and the benefits of improved water quality and solve for the optimal level of water protection. The overall benefits of the Baltic Sea Action Plan, the present convention on nutrient abatement, clearly outweigh the costs. Nevertheless, the total cost could be almost halved if the mix of measures and the regional targets were planned in a spatially cost–effective manner and if the consequent reductions of nitrogen and phosphorus, the two nutrients causing eutrophication, were better balanced. Policy optimizations, however, suggest that the optimal level of nutrient abatement is somewhat lower than the more ambitious level envisaged by the convention. The welfare gains from cost sharing that makes the optimal level of nutrient abatement worthwhile for all littoral countries would be 170 million euros annually.

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Notes

  1. Since the first Helsinki Convention in 1974, the littoral countries have agreed to reduce their loads of phosphorus and nitrogen, the main nutrients contributing to plant growth in aquatic ecosystems. The original target of reducing both loads by 50 per cent, defined in the Ministerial Declarations of 1988 and 1990, was never met and was replaced by a revised set of targets in the Baltic Sea Action Plan (BSAP) in 2007.

  2. Biogeochemical marine models combine chemical (e.g. sedimentation of phosphorus), physical (e.g. water exchange) and biological (e.g. nitrogen fixation) processes of the sea in a coherent framework to understand and to make projections of the future evolution of the marine ecosystem.

  3. We focus on the model components that are critical for the purpose of cost–benefit analysis here. More details on the catchment model, cost function and the marine model can be found in Ahlvik et al. (2014) and on the survey eliciting citizens’ WTP for reduced eutrophication in Ahtiainen et al. (2014).

  4. According to the baseline scenario, waterborne nutrient loads will slightly increase during the next 40 years (1 and 4 % of N and P loads, respectively) if the present infrastructure is maintained and no new investments in water protection are made. Nitrogen deposition follows the decreasing trend estimated in EMEP data (BNI 2010); and phosphorus deposition is assumed to be fully correlated to N-deposition and to be 1 % of its value. The time period is assumed to be short enough for climate change not to have a significant effect on nutrient loads.

  5. The analysis can be extended to probabilistic load projections, including seasonal weather-driven variations in the loads. However, yearly variations tend to cancel each other out, making them relatively unimportant over a time horizon of several decades.

  6. Phosphorus in marine sediments is not included as a state variable, but sediment phosphorus release, modeled as a function of phytoplankton biomass, is part of the nutrient accumulation process.

  7. The choice of survey mode was made to ensure representative samples from each country. In Ahtiainen et al. (2012) the Polish data revealed lower WTP in face-to-face interviews than in Internet surveys. However, this observation cannot be generalized to other countries without further study. For example, Lindhjem and Navrud (2011) did not find contingent-valuation-method-based WTP from Internet panel responses to be “less reliable” or “significantly different” from face-to-face responses in Norway.

  8. The survey questions on WTP fulfil the requirements of consequentiality (Carson and Groves 2007). The survey was motivated to the respondents by saying that their “answers would help governments around the Baltic Sea to develop appropriate water quality improvement programs”, implying that their responses would affect whether these programmes would be implemented. In addition, the majority of the respondents cared about reducing eutrophication, and all effects of eutrophication were seen, on average, as problematic.

  9. Two biogeochemical marine models with high temporal and spatial resolution were used: EIA-SYKE (Kiirikki et al. 2006) and ERGOM (Maar et al. 2011).

  10. No reference was made in-text or on the water quality maps to the possible indirect effects in inland waters. This would have blurred our focus on the Baltic Sea and the BSAP as an international agreement. Grey was used for inland areas in the maps. We found the focus well-placed in survey pre-testing.

  11. WTP was also modeled using interval regression, which produced similar parameter and WTP estimates for all countries. The mean WTPs used here are robust to controls for the recreational use of the Baltic Sea (see Ahtiainen et al. 2014).

  12. For complete documentation of the study, see Ahtiainen et al. (2014).

  13. Disaggregation of national benefit estimates was based on a debriefing question that asked “Did you consider the whole Baltic Sea or a certain area of the Baltic Sea when answering how much you were willing to pay?”. The respondents who did not consider the entire sea were then asked to specify: “Which area(s) of the Baltic Sea did you have in mind when answering how much you were willing to pay? You may choose one or several areas.”

  14. The choice of interest rate is often decisive when evaluating long-term environmental investments, with a lower rate tending to favour measures with long lags (Nordhaus 2007). Here, a 3.5 per cent real rate of interest was chosen for discounting to accord with the rates typically used for evaluating public projects (e.g. Treasury 2003)

  15. The respondents were asked “Which of the effects of eutrophication did you have in mind when answering how much you were willing to pay? You may choose one or several alternatives. (i) Water turbidity, (ii) blue-green algal blooms, (iii) underwater meadows loss, (iv) fish species composition changes, (v) lack of oxygen in deep sea bottom areas”.

  16. For example, the upper limits imposed on the reductions in the numbers of production animals ensured that national food security requirements would be fulfilled.

  17. Nutrient abatement improves the quality of inland waters and some of the measures, such as establishing wetlands, may enhance the biodiversity of agricultural lands and the scenic value of the landscape.

  18. Table 1a presents the initial loads, baseline development and targets for the five policy goals.

  19. In our model, primary production in all the sea basins except for the Bothnian Bay are nitrogen-limited.

  20. Note that identification of new low-cost abatement measures depends on the choice of policy instruments and incentives created for farmers and industries to improve nutrient abatement technologies.

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Acknowledgments

This research was conducted as part of the project “Protection of the Baltic Sea: Benefits, Costs and Policy Instruments”, financed by the Finnish Advisory Board of Sectoral Research, and as part of the BalticSTERN research network. We are grateful to the following organizations and projects for collaboration: “Managing Baltic nutrients in relation to cyanobacterial blooms: what should we aim for?”, funded by the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning; “Integrated management of agriculture, fishery, environment and economy,” funded by the Danish Strategic Research Council; The Baltic Nest Institute—Aarhus University; the BalticSTERN Secretariat at the Stockholm Resilience Centre, Stockholm University; the German Federal Environment Agency; and the Swedish Environmental Protection Agency. We are grateful to Mike Elliot, Vivi Fleming-Lehtinen, Risto Lignell and Kerry Turner for helpful discussions and comments and Richard Foley for the English language revision.

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Authors and Affiliations

  1. Department of Economics and Management, University of Helsinki, P.O. Box 27, 00014 , Helsinki, Finland

    Kari Hyytiäinen

  2. MTT Agrifood Research Finland, Latokartanonkaari 9, 00790 , Helsinki, Finland

    Lassi Ahlvik, Heini Ahtiainen & Janne Artell

  3. Government Institute for Economic Research VATT, P.O. Box 1279, 00101 , Helsinki, Finland

    Anni Huhtala

  4. Finnish Environment Institute SYKE, P.O. Box 140, 00251 , Helsinki, Finland

    Kim Dahlbo

Authors
  1. Kari Hyytiäinen
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  2. Lassi Ahlvik
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  3. Heini Ahtiainen
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  4. Janne Artell
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  5. Anni Huhtala
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  6. Kim Dahlbo
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Corresponding author

Correspondence to Kari Hyytiäinen.

Appendix

Appendix

See Table 5.

Table 5 Lower and upper confidence intervals (95 %) for the benefit estimates and Policy Goals I–V
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Hyytiäinen, K., Ahlvik, L., Ahtiainen, H. et al. Policy Goals for Improved Water Quality in the Baltic Sea: When do the Benefits Outweigh the Costs?. Environ Resource Econ 61, 217–241 (2015). https://doi.org/10.1007/s10640-014-9790-z

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