Comparing land-use alternatives: Using the ecosystem services concept to define a multi-criteria decision analysis

- Ecosystem services (ESs) are excellent criteria in MCDA to compare land-use types.
- The presented 4-step framework is flexible and helps to address land use questions.
- It combines the ecological quantification of ES and weights assigned by stakeholders.
- The case study compares a traditional agro-forestry system with meadow and forest.
- The framework helps to structure the decision-making process in landscape planning.

In landscape planning, land-use types need to be compared including the ecosystem services they provide. With multi-criteria decision analysis (MCDA), ecological economics offers a useful tool for environmental questions but mostly case-specific criteria are applied. This, however, makes it difficult to compare findings. Therefore, we present a systematic framework that includes the ecosystem services as criteria into MCDA. The ecological quantification of the provided ecosystem services is combined with the assigned importance of the single ecosystem services. In a case study from the central Alps, we compared three land-use alternatives resulting from land-use change caused by socio-economic pressures: traditional larch (Larix decidua) meadow, spruce forest (abandonment) and intensive meadow (intensification).Criteria for the MCDA model were selected by experts, criteria importance was ranked by stakeholders and criteria values were assessed with qualitative and quantitative indicators. Eventually spruce forest was ranked as the best land-use alternative followed by traditional larch meadow and intensive meadow. The combined approach of MCDA using ecosystem services as criteria showed how criteria weightings and criteria indicator values influence land-use alternatives' performance. The MCDA-model visualizes the consequences of land-use change for ecosystem service provision, facilitating landscape planning by structuring environmental problems and providing data for decisions.