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A Spatial Composite Indicator for Human and Ecosystem Well-Being in the Italian Urban Areas

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Abstract

The concept of well-being has evolved over the last decades and a multidisciplinary literature has acknowledged the multidimensional nature of this phenomenon that encompasses different key dimensions. To give concise measure of well-being, methodologies based on composite indices assume relevance, for their capability to summarize the multidimensional issues, rank the units and provide interesting analysis tools. This paper intends to make a contribution to the efforts of assessing human and ecosystem well-being in the Italian urban areas, by appreciating the spatial dimension of the elementary indicators involved in the building process of the composite indicator. To this end, we derive a set of local weights reflecting the spatial variability of data through the Geographically Weighted PCA. Then, the analysis proceeds by employing a unitary-input DEA model, also known as Benefit of Doubt approach, as a benchmarking tool for constructing a spatial composite indicator to evaluate the well-being in the Italian urban areas. In such way, we can take local peculiarities into account and identify the best performing cities to follow as examples of good administrative practices for promoting urban well-being. The approach followed in this specific study is applied empirically with data from the Urban ‘Equitable and Sustainable Well-Being” (Ur-Bes) project, proposed by ISTAT.

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Notes

  1. We adopted a min–max transformation in a continuous scale from 70 (minimum) to 130 (maximum), which represents the range of each indicator over the given time period.

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Authors and Affiliations

  1. Department of Economics, University “G.d’Annunzio”, Chieti-Pescara, Italy

    Annalina Sarra & Eugenia Nissi

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  1. Annalina Sarra
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  2. Eugenia Nissi
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Appendix

Appendix

See Tables 2, 3 and 4 and Fig. 3.

Table 2 List of elementary indicators for each domain and their expected effect on well-being
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Table 3 Results of global PCA analysis by retaining the first two components
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Fig. 3
figure 3figure 3

Spatial distribution of first component. (Blue points positive values-red points negative values)

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Table 4 Results of Moran I test for the first two components scores
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Sarra, A., Nissi, E. A Spatial Composite Indicator for Human and Ecosystem Well-Being in the Italian Urban Areas. Soc Indic Res 148, 353–377 (2020). https://doi.org/10.1007/s11205-019-02203-y

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