The ESG Determinants of Mental Health Index Across Italian Regions: A Machine Learning Approach

Emanuela Resta, Giancarlo Logroscino (), Silvio Tafuri, Peter Preethymol, Chiara Noviello (), Alberto Costantiello () and Angelo Leogrande
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Emanuela Resta: Unifg - Università degli Studi di Foggia = University of Foggia
Giancarlo Logroscino: UNIBA - Università degli studi di Bari Aldo Moro = University of Bari Aldo Moro

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Abstract: The following article analyses the relationship between the mental health index and the variables of the Environment, Social and Governance-ESG model in the Italian regions between 2004 and 2023. First of all, a static analysis is proposed aimed at identifying trends relating to mental health in the Italian regions with indication of the regional gaps. Subsequently, a clustering with k-Means algorithm is proposed. Below is a comparison of 11 machine learning algorithms for predicting the performance of the mental health index. Finally, the article offers some economic policy suggestions. The results are critically discussed in light of the scientific literature.

Keywords: Mental Health Index Machine Learning ESG Regional Inequalities. JEL CODE: I11 I12 I13 I14 I15 I18; Mental Health Index; Machine Learning; ESG; Regional Inequalities. JEL CODE: I11; I12; I13; I14; I15; I18 (search for similar items in EconPapers)
Date: 2024-06-15
Note: View the original document on HAL open archive server: https://hal.science/hal-04612979v1
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Working Paper: The ESG Determinants of Mental Health Index Across Italian Regions: A Machine Learning Approach (2024)
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