Benefits of meeting nutrient reduction targets for the Baltic Sea – a contingent valuation study in the nine coastal states

Abstract

This paper presents the results of an internationally coordinated contingent valuation study on the benefits of reducing marine eutrophication in the Baltic Sea according to current policy targets. With over 10,500 respondents from the nine coastal states around the sea, we examine public willingness to pay (WTP) for reduced eutrophication and its determinants. There are considerable differences in mean WTP between countries, with Swedes being willing to pay the most and Latvians the least. The aggregate annual WTP is approximately €3600 million. In addition, we find that countries are heterogeneous in terms of the effects of income, attitudes and familiarity on WTP. Income elasticities of WTP are below 1 for all countries, ranging between 0.1 and 0.5. Attitudes and personal experience of eutrophication are important determinants of WTP, but the specific effects differ between countries. The findings can be used in economic analyses for the European Union (EU) Marine Strategy Framework Directive and to justify additional eutrophication reduction measures in the Baltic Sea.

Keywords:

• the Baltic Sea
• Baltic Sea Action Plan
• contingent valuation
• eutrophication
• water quality
• willingness to pay

Acknowledgements

The authors are grateful for the funding and support provided by the following projects/organisations: the research project Protection of the Baltic Sea: benefits, costs and policy instruments (PROBAPS), funded by four Finnish Ministries (Ministry of Agriculture and Forestry, Ministry of Environment, Ministry of Transport and Communications and Ministry of Finance); the research project 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 (Formas); the research alliance Integrated management of agriculture, fishery, environment and economy (IMAGE), 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 (UBA, Fkz 3710 25 202); and the Swedish Environmental Protection Agency. The third author gratefully acknowledges the support of the Polish Ministry of Science and Higher Education and the Foundation for Polish Science.

Notes

1. HELCOM (Baltic Marine Environment Protection Commission – Helsinki Commission) is the governing body of the Helsinki Convention (HELCOM 1974, 1992) that aims to protect the Baltic Sea environment.

2. That is, we used careful pre-testing to evaluate the questionnaire, made an effort to ensure a logical question ordering and grouped related questions together, and sent multiple contacts to potential web survey respondents and varied the content of the contacts to increase their effectiveness.

3. The full questionnaire can be found in Ahtiainen et al. (2012).

4. As the results of the more modest scenario associated with partial implementation of the BSAP are consistent with our findings here, we focus on the more ambitious nutrient reduction scenario in this paper.

5. More information on the ecological modelling and the description of eutrophication can be found in Ahtiainen et al. (2012).

6. The BSAP specifies in total a 42% reduction in phosphorus loads and 18% in nitrogen loads compared to loads in 1997–2003 (HELCOM 2007).

7. Individual rather than household valuation was preferred because the unitary model of household consumption is conceptually difficult and income pooling is empirically rejected; for discussion on collective consumption models, see e.g. Cherchye, De Rock, and Vermeulen (2009), and on implications for stated preference exercises, see e.g. Bateman and Munro (2009). The questionnaire emphasised that the payment would be collected from each individual, and all questions – regarding attitudes, knowledge, experience of eutrophication, etc. – were directed to the individual respondent and not the household. Hence, the questionnaire was designed to reflect the methodological argument that individuals, not households, have preferences. Attitudinal and other explanatory variables were used consistently as determinants for WTP. We wanted to allow for welfare analyses at the individual level as this approach also enables alternative assumptions about collective decision-making on WTP in households, e.g. weighting by gender.

8. In pre-testing and pilots, 20- and 40-year time horizons were also compared, but a commitment to a lifetime payment was perceived the most realistic. In practice, a time horizon of 40 years involves a lifetime commitment for adult population of an average age of about 50 years.

9. In the Russian survey, a 4 × 4 bid matrix was employed (due to technical problems).

10. The design of the payment cards may have influenced the observed WTPs, although we cannot test this effect with our data.

11. Baltic coastal areas included the Leningrad and Kaliningrad regions and St. Petersburg, and the remainder of the country was represented by respondents from the Khabarovsk, Novosibirsk, Samara, Stavropol, Sverdlovsk, Rostov and Voronezh regions. All of these, except Khabarovsk, are located in Western Russia, i.e. the area that lies within Europe.

12. The so-called protest responses were not excluded from the sample as there is evidence that most protest zeros tend to be similar to low WTP responses (Carson and Hanemann 2005). Therefore, protest respondents were retained in the sample and treated similarly to ‘true’ zeros (Jorgensen and Syme 2000; Meyerhoff and Liebe 2006), a decision that may lower the WTP estimates.

13. Using the ‘market’ exchange rates for the purpose of comparing WTPs across economies can lead to greater distortions inter-temporally than using PPP rates, which are relatively stable over time and are also relevant to non-traded goods and, therefore, are regarded as a better measure of well-being (Callen 2007).

14. The bid ranges from the lowest positive bid to the highest (in PPP-corrected 2011 euros) were: Denmark: [4.4, 440]; Estonia: [2.8, 283]; Finland: [4.1, 818]; Germany: [4.8, 459]; Latvia: [1.1, 169]; Lithuania: [2.4, 189]; Poland: [2.0, 164]; Russia: [1.3, 255]; and Sweden: [6.0, 1290].

15. Distance in hundreds of kilometres from the geometrical centre point of the municipality or postal code area of residence to the sea (Denmark, Finland, Germany, Latvia, Lithuania, Poland and Sweden), or the nearest point to the sea of the home municipality (Estonia). For Russia, we used a binary variable Russian coast indicating whether the respondent lives in the coastal area (Leningrad or Kaliningrad regions or St. Petersburg).

16. Models were estimated using Stata 12.1.

17. Coefficient estimates, their signs and significance are similar in the restricted OLS models (available upon request), so here we report only the extensive OLS models for comparison with the extensive interval regression models.

18. These inferences are supported by respondents’ answers to debriefing questions in which 92% of respondents agreed that they had read the questions thoroughly, 87% of respondents agreed that they understood the questions completely and 84% of respondents agreed that it was easy to understand the maps which depicted eutrophication level.

19. Using a similar approach to Turner et al. (1999), i.e. GDP per capita adjustment for BT from Sweden and Poland to other countries, the transferred mean WTPs are 1.7–3.4 times higher than the original estimates for Denmark, Finland and Germany, and 1.5–1.7 times higher for Latvia, Lithuania and Russia. For Estonia, the mean WTP in our study is about two times higher than the transferred estimate.

20. Figure 4 is based on the extensive interval regression models (Table 5). The results are similar for the OLS model (available upon request).

21. National PPP-corrected aggregate WTP values can be converted to aggregate national WTP values in 2011 local currencies via Eurostat (2013b) and OCED (2013). This conversion could be appropriate for subsequent cost-benefit analysis (CBA) with the estimated national costs of eutrophication reduction.