[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124829-en":3,"doc-seo-124829-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124829,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","The ESG Determinants of Mental Health Index Across Italian Regions - A Machine Learning Approach","The article analyses the relationship between the mental health index and Environment, Social, and Governance (ESG) variables across Italian regions from 2004 to 2023. It proposes a static analysis to identify trends and regional gaps, then applies k-means clustering to group patterns. The study compares 11 machine-learning algorithms for predicting mental health index performance. Results are discussed in light of the scientific literature, followed by economic policy suggestions.","Munich Personal RePEc Archive  \nThe ESG Determinants of Mental Health Index Across Italian Regions: A Machine Learning Approach  \nResta, Emanuela and Logroscino, Giancarlo and Tafuri, Silvio and Peter, Preethymol and Noviello, Chiara and Costantiello, Alberto and Leogrande, Angelo  \nUniversity of Foggia, University of Bari Aldo Moro, University of Bari Aldo Moro, University of Bari Aldo Moro, University of Bari Aldo Moro, Lum University Giuseppe Degennaro, Lum University Giuseppe Degennaro  \n14 June 2024  \nOnline at [https://mpra. ub. uni-muenchen. de/121204/](https://mpra. ub. uni-muenchen. de/121204/)  \n[MPRA Paper No. 121204](MPRA Paper No. 121204) , [posted 21 Jun 2024 06:44 UTC](posted 21 Jun 2024 06:44 UTC)  \nResta Emanuelaଵ , Logroscino Giancarloଶ , Tafuri Silvioଷ , Peter Preethymolସ , Noviello Chiara ହ ,  \nCostantiello Alberto଺ , Leogrande Angelo ଻  \n1 University of Foggia, [restaemanuela@gmail.com](restaemanuela@gmail.com)  \n2,3,4,5 University of Bari “Aldo Moro” [giancarlo.logroscino@uniba.it](giancarlo.logroscino@uniba.it); [silvio.tafuri@uniba.it](silvio.tafuri@uniba.it); [p.peter@studenti.uniba.it](p.peter@studenti.uniba.it), [chiaranoviello@icloud.com](chiaranoviello@icloud.com)[ ](chiaranoviello@icloud.com)6,7 Lum University Giuseppe Degennaro, [costantiello@lum.it](costantiello@lum.it), [leogrande.cultore@lum.it](leogrande.cultore@lum.it)  \nThe ESG Determinants of Mental Health Index Across Italian Regions: A Machine Learning Approach  \nAbstract  \nThe 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.  \nKeywords: Mental Health Index, Machine Learning, ESG, Regional Inequalities.  \nJEL CODE: I11, I12, I13, I14, I15, I18 .  \n1. Introduction  \nUnderstanding the relationship between the mental health index and ESG-Environmental, Social, and Governance factors in Italian regions is crucial for several reasons. This complex approach provides a comprehensive view of the factors influencing mental health, which is essential for developing effective public health strategies and policies. This detailed analysis can lead to targeted interventions, improved healthcare systems, and ultimately, better mental health outcomes for the population. The importance of this relationship is examined through various lenses: the environmental impact on mental health, social determinants of health, and the role of governance in health outcomes. The environment significantly impacts mental health, influencing both direct and indirect pathways. Clean air, green spaces, and low levels of pollution are directly associated with improved mental health outcomes. In contrast, high pollution levels, poor air quality, and limited access to green spaces can exacerbate mental health issues such as anxiety and depression. Analysing the mental health index in relation to environmental factors allows policymakers to identify specific environmental risks and develop strategies to mitigate these risks. For instance, regions with higher pollution levels can be targeted for environmental clean-up initiatives, while urban areas lacking green spaces can be prioritized for the development of parks and recreational areas. Moreover, climate change and its associated phenomena, such as extreme weather events, have been linked to mental health issues. In Italy, regions prone to natural disasters like floods and landslides often experience heightened stress a","cbCaijr5QxZ3HN0o","https://ap.wps.com/l/cbCaijr5QxZ3HN0o","pdf",1640276,1,35,"English","en",105,"# Abstract\n# Introduction\n## Environment and mental health\n## Social determinants and mental health\n## Governance and policy implications","[{\"question\":\"What is the main goal of the article?\",\"answer\":\"To analyze how the mental health index relates to ESG (Environmental, Social, and Governance) variables across Italian regions from 2004 to 2023.\"},{\"question\":\"What methods are used to study regional differences?\",\"answer\":\"The article uses a static analysis to identify trends and regional gaps, followed by k-means clustering to structure patterns in the data.\"},{\"question\":\"How is prediction of the mental health index performed?\",\"answer\":\"It compares 11 machine-learning algorithms to predict mental health index performance and evaluates the results critically in relation to the literature.\"}]","The ESG Determinants of Mental Health Index Across Italian Regions - A Machine Learning Approach | PDF",1785894866,88,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"the-esg-determinants-of-mental-health-index-across-italian-regions-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-esg-determinants-of-mental-health-index-across-italian-regions-a-machine-learning-approach/124829/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the article?","Question",{"text":75,"@type":76},"To analyze how the mental health index relates to ESG (Environmental, Social, and Governance) variables across Italian regions from 2004 to 2023.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What methods are used to study regional differences?",{"text":80,"@type":76},"The article uses a static analysis to identify trends and regional gaps, followed by k-means clustering to structure patterns in the data.",{"name":82,"@type":73,"acceptedAnswer":83},"How is prediction of the mental health index performed?",{"text":84,"@type":76},"It compares 11 machine-learning algorithms to predict mental health index performance and evaluates the results critically in relation to the literature.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]