Cultural Ecosystem Services Values Research Articles

Estimating the economic value of cultural ecosystem services in an urbanizing area using hedonic pricing

A need exists to increase both knowledge and recognition of the values associated with ecosystem services and amenities. This article explores the use of hedonic pricing as a tool for eliciting these values. We take a case study approach, valuing several services provided by ecosystems, namely aesthetic quality (views), access to outdoor recreation, and the benefits provided by tree cover in Dakota County, Minnesota, USA. Our results indicate that these services are valued by local residents and that hedonic pricing can be used to elicit at least a portion of this value. We find that many aspects of the aesthetic environment significantly impact home sale prices. Total view area as well as the areas of some land-cover types (water and lawn) in views positively influenced home sale prices while views of impervious surfaces generally negatively influenced home sale price. Access to outdoor recreation areas significantly and positively influenced home sale prices as did tree cover in the neighborhood surrounding a home. These results illustrate the ability of hedonic pricing to identify partial values for ecosystem services and amenities in a manner that is highly relevant to local and regional planning. These values could be used to increase policy-maker and public awareness of ecosystem services and could improve their consideration in planning and policy decisions.

Cultural ecosystem services in the context of offshore wind farming: A case study from the west coast of Schleswig-Holstein

Although frequently referred to in the literature, the concept of cultural ecosystem services (CES) has so far been limited in its application. Difficulties arise when specifying the nature of intangible values, but more significantly when it comes to relating intangible values to ecosystem functions. After setting out some conceptual issues, this paper uses a case study on the German North Sea coast to illustrate ways of operationalising the concept in a marine context. Based on a survey of local residents, we first identify current CES in the sea and the intangible values associated with them. Seascape and place emerge as useful conceptual bridges linking ecosystem functioning outcomes to key CES values. We then relate this to offshore wind farming, which some residents perceive as a significant threat to certain CES. Although the approach presented increases the visibility of intangible ecosystem values, the problem remains that such assessments are temporal, in need of added calibration and do not automatically put intangibles on a par with market ecosystem value.

The use of fluoroelastomers in the processing of LLDPE

Crowdsourced social media data has become popular for assessing cultural ecosystem services (CES). Nevertheless, social media data analyses in the context of CES can be time consuming and costly, particularly when based on the manual classification of images or texts shared by people. The potential of deep learning for automating the analysis of crowdsourced social media content is still being explored in CES research. Here, we use freely available deep learning models, i.e., Convolutional Neural Networks, for automating the classification of natural and human (e.g., species and human structures) elements relevant to CES from Flickr and Wikiloc images. Our approach is developed for Peneda-Gerês (Portugal) and then applied to Sierra Nevada (Spain). For Peneda-Gerês, image classification showed promising results (F1-score ca. 80%), highlighting a preference for aesthetics appreciation by social media users. In Sierra Nevada, even though model performance decreased, it was still satisfactory (F1-score ca. 60%), indicating a predominance of people’s pursuit for cultural heritage and spiritual enrichment. Our study shows great potential from deep learning to assist in the automated classification of human-nature interactions and elements from social media content and, by extension, for supporting researchers and stakeholders to decode CES distributions, benefits, and values.