Fig 1.
The two conceptual frameworks used for the analysis of Nature-based Solutions (NbS): (1) the values-rules-knowledge framework that defines the decision-context, and (2) the transformative characteristics of the implemented NbS.
The variables used to code the interviews are also displayed. Adapted from [8, 60].
Fig 2.
Map of the twenty studied Nature-based Solutions (NbS), coloured according to the clustering analysis based on the levers and barriers mentioned by the NbS managers during semi-structured interviews and the transformative characteristics of the NbS.
Elevation data is publicly available for academic use by Worldclim (https://worldclim.org/). Country borders and the perimeter of the Alpine Convention Space are publicly available for academic use by the Permanent Secretariat of the Alpine Convention (https://www.atlas.alpconv.org).
Table 1.
Definition of each element of the decision-making context from the values-rules-knowledge framework, their related indicators based on the literature, and the elements used to code the interviews.
Table 2.
Definition of each transformative characteristic and their relative indicators identified in the literature, the variable, and its modalities.
Fig 3.
Barplot of the number of interviewees during which the levers and barriers to implementing their Nature-based Solutions were mentioned, plotted according to the decision-making context cluster, and for the subset of the levers and barriers mentioned by more than five interviewees.
Significance level of the difference of occurrence between clusters for each lever or barrier: * p-value < 0.1; ** p-value < 0.05; ***p-value < 0.01.
Fig 4.
Violin boxplot of the level of the transformative characteristics of each Nature-based Solutions (NbS).
Within each transformative characteristic, the dots represent individual NbS, coloured according to the decision-making context cluster they belong to.
Fig 5.
Clustering analysis of the levers and barriers identified in decision-making contexts for Nature-based Solutions (NbS) implementation.
They show the clusters displayed on the first and the second axes of the Multiple Correspondence Analysis (MCA) used to compute the clustering algorithm. For each axis, the percentage of variance explained by each dimension of the MCA is indicated. Each NbS code corresponds to the ID in S1 Table in the supplementary information).
Fig 6.
The decision-making context clusters of the implemented Nature-based Solutions shown through vrk (values-rules-knowledge) flowers, plotted according to the Multiple Correspondence Analysis (MCA) of their levers (inside the related petals), their barriers (around the related petals) and their transformative characteristics.
Indicated levers and barriers are those that contributed the most to the clustering analysis and that are well represented in the MCA. Numbers indicate the percentage variance explained by each axis of the MCA. Symbols transformative characteristics associated with each axis, with increasing levels for these characteristics for clusters with higher scores the axis.
Fig 7.
Barplot of the number of interviews during which the levers and barriers to future amplification of similar Nature-based Solutions were mentioned, plotted according to the decision-making context clusters, and for the subset of levers and barriers mentioned by more than four interviewees.
Significance level of the difference of occurrence between clusters for each lever or barrier: * p-value < 0.1; ** p-value < 0.05; ***p-value < 0.01.