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 Angelo Leogrande

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Logistics Performance Index (LPI); Environmental Social and Governance (ESG) Indicators; Panel Data Analysis; Instrumental Variables (IV) Approach; Sustainable Economic Development. (search for similar items in EconPapers)
JEL-codes: C33 F14 M14 O18 Q56 (search for similar items in EconPapers)
Date: 2025-05-14
New Economics Papers: this item is included in nep-cmp
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Related works:
Working Paper: Logistics Performance and ESG Outcomes: An Empirical Exploration Using IV Panel Models and Machine Learning (2025)
Working Paper: Logistics Performance and ESG Outcomes: An Empirical Exploration Using IV Panel Models and Machine Learning (2025)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:124746

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