Logistics Performance and ESG Outcomes: An Empirical Exploration Using IV Panel Models and Machine Learning

Logistics Performance and ESG Outcomes: An Empirical Exploration Using IV Panel Models and Machine Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Logistics Performance and ESG Outcomes: An Empirical Exploration Using IV Panel Models and Machine Learning Nicola Magaletti, Valeria Notarnicola, Mauro Di Molfetta, Stefano Mariani, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6664928/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This 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. JEL Codes: C33, F14, O18, Q56, M14. Logistics Performance Index (LPI) Environmental Social and Governance (ESG) Indicators Panel Data Analysis Instrumental Variables (IV) Approach Sustainable Economic Development. Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6664928","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456592123,"identity":"cd2f5e11-835f-4697-9ad7-835db047f2e5","order_by":0,"name":"Nicola Magaletti","email":"","orcid":"","institution":"LUM Enterprise s.r.l.","correspondingAuthor":false,"prefix":"","firstName":"Nicola","middleName":"","lastName":"Magaletti","suffix":""},{"id":456592124,"identity":"2164cdef-eb85-4d87-9685-b63d87920f5f","order_by":1,"name":"Valeria Notarnicola","email":"","orcid":"","institution":"LUM Enterprise 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