In the following article, the “Satisfaction with the Environmental Condition” in the 20 Italian regions between 2004 and 2020 was estimated using ISTAT-BES data. The data were analyzed using the following econometric techniques, namely: Panel Data with Random Effects, Panel Data with Fixed Effects, Dynamic Panel, Pooled OLS, WLS. The results show that satisfaction with the environmental situation is positively associated with the following variables “People with at least high school diploma”, “Satisfaction with leisure time”, “Concern for the deterioration of the landscape” and negatively associated with “Gross disposable income per capita”, “Dissatisfaction with the landscape of the place of life”, “Perception of the risk of crime”. A cluster analysis was then carried out using the unsupervised k-Means algorithm optimized through the Silhouette coefficient and 3 clusters were found. A comparative analysis was then carried out between eight different machine learning algorithms to predict the trend of satisfaction by environmental situation. The analysis showed that the Tree Ensemble Regression algorithm is the best predictor and estimates a reduction of the variable of 0.05%. Subsequently, using augmented data, a further prediction was made with an estimated result equal to -1.93%.
Satisfaction with the Environmental Condition in the Italian Regions between 2004 and 2020.
Lucio Laureti
;Alberto Costantiello
;
2022-01-01
Abstract
In the following article, the “Satisfaction with the Environmental Condition” in the 20 Italian regions between 2004 and 2020 was estimated using ISTAT-BES data. The data were analyzed using the following econometric techniques, namely: Panel Data with Random Effects, Panel Data with Fixed Effects, Dynamic Panel, Pooled OLS, WLS. The results show that satisfaction with the environmental situation is positively associated with the following variables “People with at least high school diploma”, “Satisfaction with leisure time”, “Concern for the deterioration of the landscape” and negatively associated with “Gross disposable income per capita”, “Dissatisfaction with the landscape of the place of life”, “Perception of the risk of crime”. A cluster analysis was then carried out using the unsupervised k-Means algorithm optimized through the Silhouette coefficient and 3 clusters were found. A comparative analysis was then carried out between eight different machine learning algorithms to predict the trend of satisfaction by environmental situation. The analysis showed that the Tree Ensemble Regression algorithm is the best predictor and estimates a reduction of the variable of 0.05%. Subsequently, using augmented data, a further prediction was made with an estimated result equal to -1.93%.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
