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Is the value of environmental goods sensitive to the public funding scheme? Evidence from a marine restoration programme in the Black Sea


In this paper, we conduct choice experiments in Turkey and Ukraine on the valuation of a marine restoration programme in the Black Sea, to examine whether the value of environmental goods is sensitive to the source of public financing. We contribute to the debate on the optimal composition of public expenditure, an issue that can be controversial in times of financial crises. We discriminate between two funding regimes through the reallocation of public spending. The first proposes financing the marine restoration programme by reducing public expenditure for investments in renewable energy, and the second by reducing public expenditure for civil servants’ training. The results reveal that the marginal value of public money depends on the funding source. Furthermore, attribute values are sensitive to the trade-off implied by the funding scheme. The magnitude of the results differs in the two countries considered, because of their heterogeneity in political and cultural dimensions.

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  1. The World Bank classification of countries according to gross national income (GNI) per capita is available at Based on its GNI per capita, every country is classified as low-income, middle-income (subdivided into lower-middle and upper-middle), or high-income. Furthermore, according to the United Nations country grouping, Turkey is a developing economy (sub-grouping: Asia and the Pacific) and Ukraine is an economy in transition from centrally planned to market economy (sub-grouping: Commonwealth of Independent States) (United Nations Statistics Division 2010).

  2. In particular, the survey was developed in collaboration with scientists working in the Sesame FP6 project, a research project assessing and predicting changes in the Mediterranean and Black Sea ecosystems due to climate change. Marine biologists also reviewed the final survey information to ensure accuracy.

  3. In Turkey, training is delivered through the Ministry of National Education and the ‘Public Administration Institute for Turkey and the Middle East’. Furthermore, many public institutions have their own ‘In-service Training Departments’ that are in charge of providing in-service training. Acar and Ozgur (2004) provide an overview of the main policies and institutions associated with training for civil servants in Turkey and discuss challenges in developing effective training policies and practices. In Ukraine, training for civil servants is mainly provided by the National Academy of Public Administration along with its four regional offices and 27 local centres, and a number of Higher Education Institutes.

  4. According to the 2010 Corruption Perceptions Index prepared by Transparency International (2010a), Turkey ranks 56st with a score of 4.4 (on an index from 0 to 10, with 10 corresponding to no corruption at all and 0 to full corruption) and Ukraine 134th with a score of 2.4. Furthermore, Transparency International’s (2010b) 2009 Global Corruption Barometer study reports a perceived corruption rate for public officers/civil servants of 3.6 in Turkey and 4.5 in Ukraine on a scale of 1–5 (where 1 corresponds to not at all corrupt and 5 to extremely corrupt). In both countries, respondents named public officials and civil servants as those most affected by corruption compared to other sectors.


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Authors would like to deeply thank Duygu Avcı (Boğaziçi University, Turkey), Dr. Olga Diukanova (Fondazione ENI Enrico Mattei, Venice) and Dr. Liliya Salomatina (Institute of Industrial Economics of National Academy of Sciences of Ukraine) for their assistance in questionnaires translation and surveys implementation in Turkey and Ukraine. We are also grateful to Prof Michalis Skourtos, Dr. Areti Kontogianni (Aegean University, Greece) and Dr. Olivia Langmead (University of Plymouth, UK) for valuable comments and input into the questionnaires design. Anna Dermitzaki assisted greatly in the development of the visual materials used in the surveys. Funding for the research presented in this paper has been provided by the SESAME FP 6 project (Southern European Seas: Assessing and Modelling the Changes in Ecosystems) and is gratefully acknowledged. We are also grateful to the editor and two anonymous referees for valuable comments and suggestions.

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Correspondence to Kyriaki Remoundou.



The script depicting budget reductions in the renewable energy projects read as follows:

To cover the cost of the marine restoration programme described above, funds will be raised from the government purse in Ukraine [Turkey]. In this case no new taxes will be introduced. Money will be reallocated to the marine programme through a reduction in the 2010 public budget on renewable energy without any further taxation. Therefore, this money will no longer be available for financing renewable energy projects that would contribute to the increase of the share of renewable energy in the total energy mix in the countries of the Western Black Sea.

Respectively, the script explaining that part of the tax revenues currently being spent on training projects for public servants would be used to finance the marine programme read as follows:

To cover the cost of the marine restoration programme described above, funds will be raised from the government purse in Ukraine [Turkey]. In this case no new taxes will be introduced. Money will be reallocated to the marine programme through a reduction in the 2010 public budget on civil servants’ training expenses without any further taxation. Therefore, this money will no longer be available for financing training projects aiming at improving civil servants’ skills and productivity and at making them work more efficiently and able to support citizens better.

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Remoundou, K., Adaman, F., Koundouri, P. et al. Is the value of environmental goods sensitive to the public funding scheme? Evidence from a marine restoration programme in the Black Sea. Empir Econ 47, 1173–1192 (2014).

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  • Black Sea
  • Marine resources
  • Public goods
  • Stated choice experiment
  • Tax revenues reallocation

JEL Classification

  • H41
  • H50
  • Q51
  • Q57