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<p>The existing literature on environmental sustainability rankings, obtained through a summary measure of territorial performance, often fails to consider the complexity of the underlying phenomena. Units with very high aggregate scores may hide critical situations in other areas, just as cities positioned low in the rankings may exceed in some specific fields. In this paper, we have adopted a two-step methodology to overcome this issue. First, the Pena Distance (DP2) method was used to construct an aggregate multidimensional index of urban sustainability. Secondly, cluster analysis was used to identify common patterns across cities. As can be seen from the results of this study, environmental sustainability challenges are common to both high scoring cities (e.g. Bologna and Florence) and low scoring cities. Consequently, cities with average scores demonstrate a recommendable capacity for resource management. The cluster analysis facilitates the transcendence of the hierarchical structure of cities that has emerged from the ranking, thereby enabling a deeper comprehension of the shared dynamics. Consequently, this approach offers a more efficient interpretation for the formulation of targeted and differentiated environmental policies.</p>

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Beyond the rankings: What multidimensional index and clustering reveal about environmental sustainability

  • 期刊论文

  • 发表日期：

    2025年6月20日

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  • Carolina Bruzzi,
  • Enrico Musso,
  • Pietro Stanislao Parisi,
  • Tiziano Pavanini

摘要

The existing literature on environmental sustainability rankings, obtained through a summary measure of territorial performance, often fails to consider the complexity of the underlying phenomena. Units with very high aggregate scores may hide critical situations in other areas, just as cities positioned low in the rankings may exceed in some specific fields. In this paper, we have adopted a two-step methodology to overcome this issue. First, the Pena Distance (DP2) method was used to construct an aggregate multidimensional index of urban sustainability. Secondly, cluster analysis was used to identify common patterns across cities. As can be seen from the results of this study, environmental sustainability challenges are common to both high scoring cities (e.g. Bologna and Florence) and low scoring cities. Consequently, cities with average scores demonstrate a recommendable capacity for resource management. The cluster analysis facilitates the transcendence of the hierarchical structure of cities that has emerged from the ranking, thereby enabling a deeper comprehension of the shared dynamics. Consequently, this approach offers a more efficient interpretation for the formulation of targeted and differentiated environmental policies.