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

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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.