Grouping and ranking the EU27 countries by their sustainability performance measured by
the Eurostat sustainability indicators
Francesca Allievi and Juha Panula-OnttoFinland Futures Research Centre, University of Turku
www.tse.fi/tutu
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• 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 indicators2. 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
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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
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EU27 case study: indicators and weights used 1/3• Social dimension
Weight4 2 4 4 4
Ranking logic
Reversed Normal Reversed Reversed Reversed
IndicatorTotal long-term
unemployment rate (%)
Life expectancy at age 65 for males
Suicide death rate (crude death rate
per 300 000 persons)
Persons with low educational
attainment (%)
Early school-leavers (%)
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EU27 case study: indicators and weights used 1/3
• Environmental DimensionWeight
2,5 4 2,5 2,5 3
Ranking logicReversed Normal Reversed Reversed Reversed
IndicatorFinal 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)
Weight1,5 1,5 2,5 1,5
Ranking logicReversed Reversed Reversed Normal
IndicatorEmissions 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)
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EU27 case study: indicators and weights used 3/3
• Economic dimensionWeight
2 3 3 2 3
Ranking logic
Normal Reversed Normal Reversed Normal
IndicatorTotal R&D
expenditure (%of GDP)
General government gross debt
GDP per capita in Purchasing Power Standards (PPS)
(EU-27 = 100)
Energy dependencyTotal employment
rate (%)
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Clustering results – overview for 2005
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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
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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
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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
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Ranking results – social dimension (1997-2005)
0 2 4 6 8 10 12 14 16
Portugal
Malta
Bulgaria
Hungary
Lithuania
Slovakia
Estonia
Latvia
Spain
Romania
Poland
Italy
France
Germany
Slovenia
Greece
Czech Republic
Belgium
Luxembourg
Austria
Netherlands
Finland
Ireland
Cyprus
Denmark
United Kingdom
Sw eden
Coun
try
Score0 2 4 6 8 10 12 14
Hungary
Bulgaria
Lithuania
Latvia
Spain
Malta
Slovakia
Estonia
Italy
Portugal
Ireland
Belgium
Slovenia
Romania
France
Poland
Finland
Greece
Luxembourg
Czech Republic
Germany
United Kingdom
Austria
Netherlands
Denmark
Sw eden
Cyprus
Coun
try
Score
12
Ranking results – environmental dimension (1997 -2005)
0 2 4 6 8 10 12 14 16 18 20
Cyprus
Luxembourg
Ireland
Spain
Belgium
Finland
Denmark
Estonia
Malta
Slovenia
Bulgaria
United Kingdom
France
Netherlands
Germany
Greece
Hungary
Italy
Portugal
Austria
Lithuania
Czech Republic
Poland
Sw eden
Slovakia
Romania
Latvia
Coun
try
Score
0 2 4 6 8 10 12 14 16
Luxembourg
Cyprus
Denmark
United Kingdom
Slovenia
Germany
Ireland
Finland
Belgium
Italy
France
Estonia
Spain
Netherlands
Czech Republic
Malta
Bulgaria
Hungary
Greece
Sw eden
Poland
Lithuania
Austria
Portugal
Slovakia
Romania
Latvia
Coun
try
Score
13
Ranking results – economic dimension (1997-2005)
0 1 2 3 4 5 6 7 8 9 10
Malta
Italy
Greece
Hungary
Poland
Bulgaria
Slovakia
Belgium
Cyprus
Portugal
Romania
Spain
Lithuania
Latvia
France
Germany
Czech Republic
Slovenia
Austria
Estonia
Ireland
Netherlands
Finland
United Kingdom
Luxembourg
Sweden
Denmark
Cou
ntry
Score0 1 2 3 4 5 6 7 8 9 10
Bulgaria
Greece
Italy
Malta
Hungary
Spain
Belgium
Slovakia
Cyprus
Latvia
Ireland
Lithuania
Portugal
Romania
Poland
Estonia
Slovenia
France
Austria
Germany
Finland
Czech Republic
Netherlands
Sweden
Luxembourg
Denmark
United Kingdom
Cou
ntry
Score
14
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.