In the light of the modernization theory, the present paper proposes an empirical assessment of the population-economy-environment nexus in Italy by investigating the historical trends (1862-2009) in 58 variables describing five themes (environment, demography, education, trade, agriculture). Socioeconomic variables representing changes in population structure and demographic dynamics compared with indicators of forest expansion allowed verifying the temporal coherence between demographic and forest transitions at the country scale. The study develops an exploratory data analysis framework based on principal component analysis, hierarchical and non-hierarchical clustering and identifies four homogeneous time intervals (1862-1899, 1900-1930, 1931-1970, 1971-2009) in socioeconomic and environmental attributes. Different trends (positive linear, negative linear and non-linear) in the studied variables were identified through hierarchical clustering.

Beyond the Modernization Theory: Socio-demographic Changes, Economic Structure and Forest Transition in a multi-dimensional time-series analysis for Italy

FERRARA, Agostino Maria Silvio;SALVATI, LUCA;
2014

Abstract

In the light of the modernization theory, the present paper proposes an empirical assessment of the population-economy-environment nexus in Italy by investigating the historical trends (1862-2009) in 58 variables describing five themes (environment, demography, education, trade, agriculture). Socioeconomic variables representing changes in population structure and demographic dynamics compared with indicators of forest expansion allowed verifying the temporal coherence between demographic and forest transitions at the country scale. The study develops an exploratory data analysis framework based on principal component analysis, hierarchical and non-hierarchical clustering and identifies four homogeneous time intervals (1862-1899, 1900-1930, 1931-1970, 1971-2009) in socioeconomic and environmental attributes. Different trends (positive linear, negative linear and non-linear) in the studied variables were identified through hierarchical clustering.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11563/95494
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