Grouping and ranking the EU27 countries by their sustainability performance measured by the Eurostat sustainability indicators Francesca Allievi and Juha Panula-Ontto Finland Futures Research Centre, University of Turku

2 Aim of this study is to group EU27 countries in terms of their sustainability levels. Developed within the FP7 project SMILE. The grouping of the countries is carried out by applying hierarchical agglomerative clustering: partitions of the data are created by fusing together individuals or groups of individuals that are most similar Clustering on normalized distance matrices: City Block Distance 1. Compute the distances between all indicators 2. Normalize indicator distances (dividing by maximum distance) 3. Assemble distances in a single distance matrix and divide by the number of contributing factors EU27 case study – aims and methods 1/2

EU27 case study – aims and methods 2/2 Countries have also been ranked on the basis of their sustainability performance For each indicator a weight and ranking logic was selected. Weight measures the relative importance of the indicator in respect to the other indicators in the same dimension. Normal ranking logic means higher score for greater value, reversed ranking logic means higher score for smaller value. For each indicator, the best performing country has been given the number of points equal to the weight of the indicator. The worst performing country has been given a score of zero for the indicator and the other countries have received a linearly scaled score according to their relative performance in respect to the best performing country. It is therefore obvious that the analysis presented here gives only the performance of the EU27 countries in relation to each other 3

EU27 case study: indicators and weights used 1/3 Social dimension 4 Weight Ranking logic ReversedNormalReversed Indicator Total long-term unemployment rate (%) Life expectancy at age 65 for males Suicide death rate (crude death rate per persons) Persons with low educational attainment (%) Early school-leavers (%)

EU27 case study: indicators and weights used 1/3 Environmental Dimension 5 Weight 2,54 3 Ranking logic ReversedNormalReversed Indicator Final energy consumption of road transport (TOE/capita) Renewable energy (% gross electricity consumption) Municipal waste generated (kg/capita) Motorization rate (number of cars per 1000 people) Emissions of particulate matter from road transport (kg per capita) Weight 1,5 2,51,5 Ranking logic Reversed Normal Indicator Emissions of acidifying substances (kg per capita) Emissions of ozone precursors (kg of ozone-forming potential / capita) Domestic Material Consumption (tonnes/capita) Area under organic farming (% of utilized agricultural area)

EU27 case study: indicators and weights used 3/3 Economic dimension 6 Weight Ranking logic NormalReversedNormalReversedNormal Indicator Total R&D expenditure (%of GDP) General government gross debt GDP per capita in Purchasing Power Standards (PPS) (EU-27 = 100) Energy dependency Total employment rate (%)

Clustering results – overview for

Clustering results 1/3 Social dimension (2005) Cluster 1: Estonia, Latvia, Hungary, Lithuania Cluster 2: Poland, Slovakia Cluster 3: Czech Republic, Slovenia, Bulgaria, Romania Cluster 4: Denmark, Finland, Sweden, Austria, France, Germany Cluster 5: Ireland, United Kingdom, Luxembourg, Netherlands, Belgium, Greece, Cyprus Cluster 6: Malta, Portugal Cluster 7: Italy, Spain 8

Clustering results 2/3 Environmental dimension (2005) Cluster 1: Estonia, Greece, Czech Republic, Portugal, Slovenia, Spain, Belgium, Italy, Sweden Cluster 2: Hungary, Lithuania, France, United Kingdom, Germany, Netherlands, Malta Cluster 3: Poland, Slovakia, Romania, Bulgaria, Latvia Cluster 4: Cyprus, Ireland Cluster 5: Denmark, Finland, Austria Outlier: Luxembourg 9

Clustering results 3/3 Economic dimension (2005) Cluster 1: Latvia, Lithuania, Estonia, Bulgaria, Romania, Poland, Hungary, Slovakia Cluster 2: Cyprus, Portugal, Greece, Italy, Malta Cluster 3: Czech Republic, Slovenia, Ireland, Spain Cluster 4: Austria, Germany, France, Belgium Cluster 5: Netherlands, United Kingdom, Finland, Sweden Outliers: Denmark, Luxembourg 10

Ranking results – social dimension ( ) 11

Ranking results – environmental dimension ( ) 12

Ranking results – economic dimension ( ) 13

Conclusions This should be considered solely as an example of what can be done to study sustainability in EU27 with the data currently available Data lack was a relevant issue, in some cases indicators had to be left out because of this Further developments could include a more accurate sensitivity analysis and, if forecasted data was available, the creation of future scenarios The final results are heavily dependent on the choices made: in order to see the effects of a different selection, the tool created for this purpose can be used and new results can be obtained rather quickly. 14