[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125210-en":3,"doc-seo-125210-105":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},125210,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Logistics Performance and ESG Outcomes - An Empirical Exploration Using IV Panel Models and Machine Learning","This study investigates the relationship between logistics performance and Environmental, Social, and Governance (ESG) performance using a multi-method framework that combines econometric techniques with modern machine learning. Using instrumental-variable panel regressions (2SLS and G2SLS) on a balanced sample of 163 countries from 2007 to 2023, the analysis links the Logistics Performance Index (LPI) to multiple ESG indicators. Machine learning and clustering methods uncover latent structures and predict LPI dynamics. Results associate logistics improvements with environmental, social, and governance developments, while indicating environmental trade-offs and potential social imbalances.","Munich Personal RePEc Archive  \nLogistics Performance and ESG Outcomes: An Empirical Exploration Using IV Panel Models and Machine Learning  \nMagaletti, Nicola and Notarnicola, Valeria and Di Molfetta, Mauro and Mariani, Stefano and Leogrande, Angelo  \nLUM Enterprise s.r.l. , LUM Enterprise s.r.l. , LUM Enterprise s.r.l. ,  \nLUM Enterprise s.r.l. , LUM Enterprise s.r.l.  \n14 May 2025  \nOnline at [https://mpra. ub. uni-muenchen. de/124746/](https://mpra. ub. uni-muenchen. de/124746/)  \n[MPRA Paper No. 124746](MPRA Paper No. 124746) , [posted 16 May 2025 10:43 UTC](posted 16 May 2025 10:43 UTC)  \nLogistics Performance and ESG Outcomes: An Empirical Exploration Using IV Panel Models and Machine Learning  \nNicola Magaletti, LUM Enterprise s.r.l., Casamassima, Italy, [magaletti@lumenterprise.it](magaletti@lumenterprise.it)[ ](magaletti@lumenterprise.it)Valeria Notarnicola, LUM Enterprise s.r.l., Casamassima, Italy, [notarnicola@lumenterprise.it](notarnicola@lumenterprise.it)[ ](notarnicola@lumenterprise.it)Mauro Di Molfetta, LUM Enterprise s.r.l., Casamassima, Italy, [dimolfetta@lumenterprise.it](dimolfetta@lumenterprise.it)[ ](dimolfetta@lumenterprise.it)Stefano Mariani, LUM Enteprise s.r.l., Casamassima, Italy, [s.mariani@lumenterprise.it](s.mariani@lumenterprise.it)[ ](s.mariani@lumenterprise.it)Angelo Leogrande, LUM Enterprise s.r.l., Casamassima, Italy, [leogrande.cultore@lum.it](leogrande.cultore@lum.it)  \nAbstract  \nThis study investigates the complex relationship between the performance of logistics and Environmental, Social, and Governance (ESG) performance drawing upon the multi-methodological framework of combining econometric with state-of-the-art machine learning approaches. Employing IV panel data regressions, viz. 2SLS and G2SLS, with data from a balanced panel of 163 countries covering the period from 2007 to 2023, the research thoroughly investigates how the performance of the Logistics Performance Index (LPI) is correlated with a variety of ESG indicators. To enrich the analysis, machine learning models—models based upon regression, viz. Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosting Regression, Decision Tree Regression, and Linear Regressions, and clustering, viz. Density-Based, Neighborhood-Based, and Hierarchical clustering, Fuzzy c-Means, Model Based, and Random Forest—were applied to uncover unknown structures and predict the behaviour of LPI. Empirical evidence suggests that higher improvements in the performance of logistics are systematically correlated with nascent developments in all three dimensions of the environment (E), the social (S), and the governance (G) . The evidence from econometrics suggests that higher LPI goes with environmental trade-offs such as higher emissions of greenhouse gases but cleaner air and usage of resources. On the S dimension, better performance in terms of logistics is correlated with better education performance and reducing child labour, but also demonstrates potential problems such as social imbalances. For G, better governance of logistics goes with better governance, voice and public participation, science productivity, and rule of law. Through both regression and cluster methods, each of the respective parts of ESG were analyzed in isolation, allowing to study in-depth how the infrastructure of logistics is interacting with sustainability research goals. Overall, the study emphasizes that while modernization is facilitated by the performance of the infrastructure of logistics, this must go hand in hand with policy intervention to make it socially inclusive, environmentally friendly, and institutionally robust.  \nKeywords: Logistics Performance Index (LPI), Environmental Social and Governance (ESG) Indicators, Panel Data Analysis, Instrumental Variables (IV) Approach, Sustainable Economic Development.  \nJEL Codes: C33, F14, O18, Q56, M14 .  \n1. Introduction  \nIn the globalized world of today, logistics systems' productivity and resilience are ess","cbCaitaHAOl3tvLk","https://ap.wps.com/l/cbCaitaHAOl3tvLk","pdf",3763087,1,68,"English","en",105,"# Introduction\n## Research gap and motivation\n## Study objective and approach","[{\"question\":\"What is the main research question of the study?\",\"answer\":\"The study examines how interactions between logistics performance and each ESG pillar vary by country, and how disaggregated ESG indicators relate to logistics performance.\"},{\"question\":\"Which empirical methods are used to analyze the link between logistics performance and ESG outcomes?\",\"answer\":\"It uses instrumental-variable panel regressions, including 2SLS and G2SLS, based on a balanced panel of 163 countries from 2007 to 2023.\"},{\"question\":\"How does machine learning contribute to the analysis?\",\"answer\":\"Machine learning regression and clustering models are applied to uncover unknown structures and to predict the behavior of the Logistics Performance Index.\"}]","Logistics Performance and ESG Outcomes - An Empirical Exploration Using IV Panel Models and Machine Learning | PDF",1785897450,171,{"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},"logistics-performance-and-esg-outcomes-an-empirical-exploration-using-iv-panel-models-and-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/logistics-performance-and-esg-outcomes-an-empirical-exploration-using-iv-panel-models-and-machine-learning/125210/",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 research question of the study?","Question",{"text":75,"@type":76},"The study examines how interactions between logistics performance and each ESG pillar vary by country, and how disaggregated ESG indicators relate to logistics performance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which empirical methods are used to analyze the link between logistics performance and ESG outcomes?",{"text":80,"@type":76},"It uses instrumental-variable panel regressions, including 2SLS and G2SLS, based on a balanced panel of 163 countries from 2007 to 2023.",{"name":82,"@type":73,"acceptedAnswer":83},"How does machine learning contribute to the analysis?",{"text":84,"@type":76},"Machine learning regression and clustering models are applied to uncover unknown structures and to predict the behavior of the Logistics Performance Index.","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"]