Re-evaluating the contingent valuation method as a standard method for monetary valuation of ecosystem services : a case study of the Vitberget forest region in Skellefteå in Sweden

University essay from SLU/Dept. of Ecology

Abstract: Ecosystems and their services are key to humans. One if not the main methods to obtain a monetary value of cultural ecosystem services is the contingent valuation method. This thesis sets out to contest predominant assumptions of how to design contingent valuation study surveys intended for monetary valuation of non-good values by using the Vitberget forest region in Skellefteå in Sweden as a case study. Specifically, I tested the assumption that survey design and length is influencing the outcome of the estimated monetary value of the ecosystem services. The thesis is presented in six chapters: the first chapter introduces relevant concepts and methods. The focus is placed upon the concept of ecosystem services and the contingent valuation method. The second chapter presents the applied methodology used in the case study as well as it focuses on the experiment design and data analysis – person to person interviews in the field were held in order to execute the contingent valuation exercise. The third chapter presents the results, which show that there is no linear relationship between survey design and monetary evaluations. Longer or more complex survey versions don’t produce better results than shorter, less complex survey versions. The total estimated value for cultural ecosystem services of the study region is 21.969.738 SEK per year. The fourth chapter forms the discussion, which lead to the conclusion that predominant opinions about survey design regarding contingent valuation studies can be contested. In the last chapter an outlook for future research is presented as well as recommendations for practitioners are made. Future research should build on the good base of this study regarding survey design and contingent valuation. Practitioners should aim for simplified surveys with maximized sample size to improve value for money.

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