The first global uncertainty maps of monetary valued goods and services provided by nature for human well-being (ecosystem services) have been developed by a group of researchers of the Helmholtz Centre for Environmental Research (UFZ). Monetary values were estimated by transferring values already developed in other studies of a similar area (benefit transfer) under consideration of different sources of uncertainty. Major patterns of uncertainties could be identified and discussed for 12 ecosystem services. The scientists Schmidt, Manceur and Seppelt have recently published their results in the journal PLoS ONE.
Growing demand of resources increases pressure on ES and biodiversity. Monetary valuation of ES is frequently seen as a decision-support tool by providing explicit values for unconsidered, non-market goods and services, e.g. Mangroves value for storm protection and flood prevention. Valuation methods, such as benefit transfer, are an attractive option for researcher and policy-makers facing resource constraints (information, time and money) and are more frequently used. The researcher from the UFZ developed global benefit transfer models for 12 ES and estimated uncertainties that need to be taken into account. Major sources of uncertainties include errors resulting from the input data for the benefit transfer model, the model characteristics and the transfer error due to generalization. The latter, for instance, arises when values are transferred to other areas under the assumption that particular socio-environmental conditions, spatial or temporal scale are constant. For the ES food provisioning it is often assumed that agricultural productivity is the same for all croplands, regardless natural composition and anthropogenic management effects. Schmidt, Manceur and Seppelt showed that although socio-economic indicators are often discussed for benefit transfer applications, the consideration of biophysical heterogeneity in ecosystems is most important.
Figure: Overview of input data and characteristics of benefit transfer model for 12 ES. The table shows for each ES the number of case studies and monetary values (2nd column). In the 3rd column pie charts reflect which indicator are important to consider in order to avoid transfer error from generalization, expressed in percentage values for indicator groups (relative influence of indicator groups on the model) and size of the pie slide. The number in brackets represents the number of indicator per group. The bluish bar charts in column 4 illustrate the model quality based on percentage of variance explained by the model (R-squared). Additionally, column 4 shows the percentage area of terrestrial earth surface covered accordingly to uncertainty classes (low, medium, high).
For 6 out of 12 ES, with the highest variance in monetary values, the most influential indicators have been discussed in
detail. These include food and water provision, climate and extreme events regulation as well as recreation and habitat
service. Monetary values for extreme events regulation, for instance, are mostly explained by inherent conditions of
ecosystems and social vulnerability. In degraded ecosystems and societies with strong risk awareness the prevention of
weather extremes and natural hazards is high valued.
The analysis of Schmidt, Manceur and Seppelt represents a stepping stone to establish a standardized integration of and reporting on uncertainties for reliable and valid benefit transfer as an important component for decision support.
This study is a product of the scientific coordination project GLUES (Global Assessment of Land Use Dynamics, Greenhouse Gas Emissions and Ecosystem Services), which is a part of the Sustainable Land Management research program funded by the German Federal Ministry of Education and Research. In this program, international research teams investigate land use changes in twelve regions worldwide. The GLUES team not only links the regional projects and communicates their results to the public and the scientific community, but it also conducts research in the area of scientific synthesis as in the current study.
The bivariate maps show the extrapolated relative monetary values (yellow to green) and uncertainties (yellow to
red) of the benefit transfer functions for the ES: A) food provision, B) water provision, C) climate regulation, D) extreme
event regulation, E) recreation service and F) habitat service, G) raw material provision, H) provision of medicinal resources,
I) waste treatment, J) erosion regulation, K) soil fertility regulation and L) maintenance of genetic diversity. Monetary values
and uncertainties are grouped into different classes from low to high (in the legend the first number represents uncertainty
and the second monetary values, e.g. 11= both low, 12= uncertainty low and monetary value medium, 32= uncertainty high and
monetary value medium etc.). The classes were defined by equal interval distances for each ES separately. Accordingly, classes
between ES contain different ranges of values.
|Detailled information are available in the following publication:
Schmidt S, Manceur AM, Seppelt R.: Uncertainty of monetary valued ecosystem services - value transfer functions for global mapping. PLoS One. 2016; http://dx.doi.org/10.1371/journal.pone.0148524.
|Analysis results are available as WMS service. Detailled information about the Uncertainties in Monetary Valuation of Nature web service is available in the GLUES Metadata catalog.|