[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127388-en":3,"doc-seo-127388-105":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},127388,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Determinants of Uneven Progress in Sustainable Development in the EU - Predictive Approaches Based on Machine Learning","This paper investigates the relative importance of distributional versus structural socioeconomic variables in predicting Sustainable Development Goals (SDG) performance across European Union member states. Using a comprehensive panel dataset and advanced machine learning methods, it finds that distributional indicators such as income inequality and poverty thresholds provide stronger, more consistent predictive power than traditional macroeconomic measures. The study also reveals a non-linear, threshold effect of inequality and shows that regional location remains a significant predictor after controlling for economic and social factors.","DETERMINANTS OF UNEVEN PROGRESS IN SUSTAINABLE DEVELOPMENT IN THE EU: PREDICTIVE APPROACHES BASED ON MACHINE LEARNING  \n| Please cite this article as:\u003Cbr>Bușu, M., Gheorghe, M., Staicu, G., Prinz, E. and Fonseca, L.M., 2025. Determinants of Uneven Progress in Sustainable Development in the EU: Predictive Approaches Based on Machine Learning. Amfiteatru Economic, 27(Special Issue No. 19), pp. 1272-1291.\u003Cbr>DOI: [https://doi.org/10.24818/EA/2025/S19/1272](https://doi.org/10.24818/EA/2025/S19/1272) . | Article History\u003Cbr>Received: 16 August 2025\u003Cbr>Revised: 8 September 2025\u003Cbr>Accepted: 26 September 2025 |\n| --- | --- |\n\nAbstract  \nThis paper investigates the relative importance of distributional versus structural socioeconomic variables in predicting the performance of Sustainable Development Goals (SDG) across European Union member states. Using a comprehensive panel dataset and advanced machine learning techniques, the study demonstrates that distributional indicators such as income inequality and poverty thresholds have stronger and more consistent predictive power than traditional macroeconomic measures. Moreover, the research uncoversa non-linear relationship between inequality and SDG outcomes, revealing a threshold effect where reductions from high to moderate inequality levels yield greater improvements than further reductions. Finally, regional location within Europe is found to be a significant predictor of SDG performance even after controlling for economic and social factors, highlighting persistent structural differences between Southern and Northern/Western European regions. These findings offer robust insights for policymakers aiming to design more targeted and equitable sustainability strategies.  \nKeywords: SDGs, income inequality, European Union, regional development, Random Forest, poverty threshold, machine learning  \nJEL Classification: O15, Q01, R11, C53  \n􀀍 Corresponding author, Mihail Bușu – e-mail: [mihail.busu@fabiz.ase.ro](mihail.busu@fabiz.ase.ro)  \nThis is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2025 The Author(s).  \n1272 Amfiteatru Economic  \nIntroduction  \nThe implementation of the United Nations Sustainable Development Goals has become a central policy priority for the European Union (EU) over the past decade, with EU member states seeking to balance economic, social, and environmental objectives in increasingly complex political and economic environments. Yet, despite a shared EU policy framework, progress toward these goals remains highly uneven across countries and regions within the EU. A growing number of researchers has aimed to identify the national characteristics that most reliably predict SDG performance, with particular attention to the impact of traditional economic indicators (Asadikia et al., 2022; Dobre, 2025), institutional quality (Barra and Falcone, 2024), and distributional socioeconomic factors (Filho et al., 2021) .  \nMost of the existing research on sustainable development determinants has emphasised structural economic indicators such as GDP per capita, inflation rates, and foreign direct investment (FDI) intensity (Swart et al., 2023) . These metrics remain fundamental to governance frameworks and continue to guide policy priorities, especially in areas related to industrial development, infrastructure, and macroeconomic stability (Nauman et al., 2024) . However, recent findings highlighted the limitations of focusing on aggregate economic measures to explain variations in sustainable development outcomes, particularly in highly developed regions such as the EU (D ’Adamo et al., 2022) .  \nRecent literature emphasises the significant role of distributional socioeconomic factors such as income inequality, poverty rates, and access to social services - in reaching SDG outcomes (van Niekerk, 2020). ","cbCainHzTGZSVUXW","https://ap.wps.com/l/cbCainHzTGZSVUXW","pdf",1072342,1,20,"English","en",105,"# Abstract\n# Introduction\n## Policy context and uneven SDG progress in the EU\n## Structural economic determinants in prior research\n## Distributional socioeconomic factors and institutional quality\n## Geography and regional clustering in SDG outcomes\n# Study contribution and scope","[{\"question\":\"Which variables matter more for predicting SDG performance in EU member states: distributional or structural socioeconomic factors?\",\"answer\":\"Distributional indicators, including income inequality and poverty thresholds, show stronger and more consistent predictive power than traditional macroeconomic measures.\"},{\"question\":\"How does income inequality relate to SDG outcomes in the study?\",\"answer\":\"Inequality has a non-linear relationship with SDG results, featuring a threshold effect where reducing inequality from high to moderate levels improves outcomes more than further reductions.\"},{\"question\":\"Does regional location within Europe influence SDG performance after accounting for other factors?\",\"answer\":\"Yes. Regional location remains a significant predictor even after controlling for economic and social variables, reflecting persistent structural differences between regions.\"}]","Determinants of Uneven Progress in Sustainable Development in the EU - Predictive Approaches Based on Machine Learning | PDF",1785938627,50,{"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},"determinants-of-uneven-progress-in-sustainable-development-in-the-eu-predictive-approaches-based-on-machine-learning","",{"@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/determinants-of-uneven-progress-in-sustainable-development-in-the-eu-predictive-approaches-based-on-machine-learning/127388/",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},"Which variables matter more for predicting SDG performance in EU member states: distributional or structural socioeconomic factors?","Question",{"text":75,"@type":76},"Distributional indicators, including income inequality and poverty thresholds, show stronger and more consistent predictive power than traditional macroeconomic measures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does income inequality relate to SDG outcomes in the study?",{"text":80,"@type":76},"Inequality has a non-linear relationship with SDG results, featuring a threshold effect where reducing inequality from high to moderate levels improves outcomes more than further reductions.",{"name":82,"@type":73,"acceptedAnswer":83},"Does regional location within Europe influence SDG performance after accounting for other factors?",{"text":84,"@type":76},"Yes. Regional location remains a significant predictor even after controlling for economic and social variables, reflecting persistent structural differences between regions.","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},[93,97,101,105,110,114,119,122,126,129,133],{"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":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]