Which cultural ecosystem services is more important? A best-worst scaling approach

ABSTRACT

Identifying relatively important ecosystem services beforehand is essential for efficient and effective assessment. Using a best-worst scaling (BWS) method, we investigated the relative importance of cultural ecosystem services (CES) in Japan, where the second phase of national ecosystem service assessment is under consideration. Classifying CES into seven distinct categories (i.e. spiritual and religious values, recreation and tourism, aesthetic values, education and inspiration, social cohesion and sense of place, cultural diversity, and existence and bequest values), we administered a questionnaire survey at the nation-wide scale and collected 28,854 valid BWS responses from 4122 individuals. As a result, BWS successfully elicited the Japanese preferences for CES with completely distinguishable orders, which the conventional rating approach was unable to achieve. Our analysis proposed that future CES assessments in Japan should put more emphasis on aesthetic values as well as existence and bequest values. As we could not find large differences in preferences for these two services across individuals, groups and regions in relative terms, such prioritization could gain broader understanding and supports from wider audiences.

KEYWORDS:

• Best-worst scaling
• cultural ecosystem services
• preference heterogeneity
• rating approach
• spatial clustering

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 For example, the number of shrines was used to indicate the level of spiritual and religious values of nature; however, no theoretical argument or empirical evidence to justify the use of such an indicator was provided.

2 The information criteria are computed as $\text{C}=-2\text{ln}L+K\lambda$, where $\text{ln}L$ is the log-likelihood of the model at convergence, $K$ is the number of parameters, and $\lambda$ is a variety of penalty constant. For $\lambda =2$, we obtain the AIC; for $\lambda =3$, we obtain the AIC3; for $\lambda =\text{ln}\left(N\right)$, we obtain the BIC; and finally for $\lambda =2+2\left(K+1\right)\left(K+2\right)/\left(N-K-2\right)$, we obtain the crAIC.

3 Such discrepancies in preference shares between the MNL model and the RPL model were also reported by Lusk and Briggeman (2009).

4 An index value near 1 indicates clustering while an index value near −1 indicates dispersion.

Additional information

Funding

This research was supported by the Environment Research and Technology Development Fund (S-15 Predicting and Assessing Natural Capital and Ecosystem Services (PANCES)) of the Ministry of the Environment, Japan.