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Factors of Environmental Sustainability in Italian Industrial Districts: A Composite Environmental Sustainability Index

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Rethinking Clusters

Part of the book series: Sustainable Development Goals Series ((SDGS))

Abstract

This chapter joins the increasing set of studies on place-based factors of environmental strengths and weaknesses, looking in particular at industrial districts (IDs). According to the ID literature, the dynamics characterizing systems of specialized SMEs may embed in local societies and rest on sets of externalities and specific public goods. The work builds on those foundations and aims at progressing on methods for measuring environmental performances in IDs. It introduces a composite environmental sustainability index and applies it to a novel dataset collected in Italy at the municipality level. This application allows comparing IDs and other types of local systems’ environmental sustainability performances.

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Notes

  1. 1.

    Becattini (2015) relates “conscience of place” and environmental sustainability in a chapter illustrating a “metaphor of the lake” (Becattini, 2015, 111–114).

  2. 2.

    Those concepts and views are distributed throughout various chapters of Becattini et al. (2009), see in particular at pages 254–255, 261–262, 271.

  3. 3.

    E.g. Mazzanti and Zoboli (2009); Russo and Tencati (2009); Battaglia et al. (2012); Cainelli et al. (2012); Da Ronch et al. (2013).

  4. 4.

    Johnson and Wichern (2007); Jolliffe (2002). The principal components are the linear combination of the original variables that collects a greater part of the data variance.

  5. 5.

    They were computed using a regression method with Stata 13.0 software (Mehmetoglu & Jakobsen, 2017).

  6. 6.

    We will refer to them as provincial capitals.

  7. 7.

    Note that, in the Italian geography of local systems identified by Istat, each province includes one or more different local systems or parts of local systems (Istat, 2015). The identification to which we refer is built on data extracted from the National Census realized in 2011.

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Authors and Affiliations

  1. Dipartimento di Scienze per l’Economia e l’Impresa, Università di Firenze, Florence, Italy

    Marco Bellandi

  2. Departamento de Economía española e Internacional, Econometría e Historia e Instituciones Económicas, Universidad de Castilla-La Mancha, Ciudad Real, Spain

    Maria J. Ruiz-Fuensanta

  3. Dipartimento di Economia e Management, Università di Trento, Trento, Italy

    Erica Santini

Authors
  1. Marco Bellandi
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  2. Maria J. Ruiz-Fuensanta
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  3. Erica Santini
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Correspondence to Marco Bellandi .

Editor information

Editors and Affiliations

  1. Department of Economics and Management, University of Padova, Padova, Italy

    Silvia Rita Sedita

  2. Department of Economics and Management, University of Padova, Padova, Italy

    Silvia Blasi

Appendix

Appendix

Before applying the Principal Component Analysis (PCA) (see Section “Methodological Premises for a Composite Index of Local Environmental Performance”), we used the Kaiser–Meyer–Olkin (KMO) test of sampling adequacy and the Bartlett’s sphericity test to check whether our sample was appropriate for the analysis. The scores of the KMO test (Table 5) for 2011 and 2014 are 0.658 and 0.588, respectively, which exceed the recommended minimum value of 0.5 (Kaiser, 1974). The Bartlett’s test is also highly significant, thus confirming the suitability of PCA in this case.

Table 5 Tests of sample adequacy and internal reliability of the index
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Table 6 shows the eigenvalues obtained after applying PCA, as well as the proportion of variance explained by each principal component. The first four components account for over 70% of the variability in the data. Nevertheless, since our purpose is not to reduce the dimensionality of the data, but to obtain objective weights to aggregate the information of the set of indicators of environmental performance, we retained all the components obtained from the PCA, as in other studies (Fu, Yu, & Zhang, 2019; Santero-Sanchez, Segovia-Pérez, Castro-Nuñez, Figueroa-Domecq, & Talón-Ballestero, 2015). To evaluate the internal consistency of the index, we have calculated the Cronbach’s alpha. This is equal to 0.750 and 0.731 in 2011 and 2014, respectively, which is considered an acceptable value (Nunnally, 1978).

Table 6 Eigenvalues and percentage of variance explained by principal components
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Bellandi, M., Ruiz-Fuensanta, M.J., Santini, E. (2021). Factors of Environmental Sustainability in Italian Industrial Districts: A Composite Environmental Sustainability Index. In: Sedita, S.R., Blasi, S. (eds) Rethinking Clusters. Sustainable Development Goals Series. Springer, Cham. https://doi.org/10.1007/978-3-030-61923-7_9

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  • DOI: https://doi.org/10.1007/978-3-030-61923-7_9

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