We estimate the Landscape and Cultural Heritage among Italian regions in the period 2004-2019 using data from ISTAT-BES. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS, Dynamic Panel. We found that the Landscape and Cultural Heritage is negatively associated with “Dissatisfaction with the landscape of the place of life”, “Illegal building”, “Density and relevance of the museum heritage”, “Internal material consumption”, “Erosion of the rural space due to abandonment”, “Availability of urban green”, and positively associated with “Pressure from mining activities”, “Erosion of the rural space by urban dispersion”, “Concern about the deterioration of the landscape”, “Diffusion of agritourism farms”, “Current expenditure of the Municipalities for culture”. Secondly, we have realized a cluster analysis with the k-Means algorithm optimized with the Silhouette Coefficient and we found two clusters in the sense of “Concern about the deterioration of the landscape”. Finally, we use eight different machine learning algorithms to predict the level of “Concern about the deterioration of the landscape” and we found that the Tree Ensemble Regression is the best predictor.

The Determinants of Landscape and Cultural Heritage among Italian Regions in the Period 2004-2019

Costantiello, Alberto
;
Laureti, Lucio
;
2021

Abstract

We estimate the Landscape and Cultural Heritage among Italian regions in the period 2004-2019 using data from ISTAT-BES. We use Panel Data with Fixed Effects, Panel Data with Random Effects, Pooled OLS, WLS, Dynamic Panel. We found that the Landscape and Cultural Heritage is negatively associated with “Dissatisfaction with the landscape of the place of life”, “Illegal building”, “Density and relevance of the museum heritage”, “Internal material consumption”, “Erosion of the rural space due to abandonment”, “Availability of urban green”, and positively associated with “Pressure from mining activities”, “Erosion of the rural space by urban dispersion”, “Concern about the deterioration of the landscape”, “Diffusion of agritourism farms”, “Current expenditure of the Municipalities for culture”. Secondly, we have realized a cluster analysis with the k-Means algorithm optimized with the Silhouette Coefficient and we found two clusters in the sense of “Concern about the deterioration of the landscape”. Finally, we use eight different machine learning algorithms to predict the level of “Concern about the deterioration of the landscape” and we found that the Tree Ensemble Regression is the best predictor.
9788866290216
Environmental Economics; Valuation of Environmental Effects; Pollution Control Adoption and Costs; Sustainability; Government Policy.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.12572/7382
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