An application of panel VAR technique to explore a link between commitments to energy projects in public–private partnerships and green logistic management

Purpose Our study used a Panel Vector Autoregressive (PVAR) model to examine the relationship between public–private partnerships (PPPs) in energy (PPPE) and green logistics management (GLM) across 38 countries from 2010 to 2022. Design/methodology/approach The globe is facing major problems environmental, social and economic issues. Under this circumstance, green logistics activities aim to reduce carbon emissions and enhance the ecological impacts of supply chain activities on the path toward the green economy transition. Public–private partnerships (PPPs) in energy are increasingly recognized as critical to achieving sustainable logistics, particularly in balancing environmental sustainability with trade. This study investigates the dynamic relationship between PPPs in energy and green logistics. Findings Our findings show that green logistics has a significant positive impact on PPP investments in energy in the short term, enhancing the efficiency of energy infrastructure projects. However, this effect diminishes over time as long-term infrastructural and logistical challenges, particularly in developing countries, limit the sustained impact of green logistics. The study emphasizes the need for comprehensive policies that promote green logistics while addressing these long-term challenges to ensure sustainable energy transitions. Originality/value Determining whether PPPE is directly or indirectly responsible for GLM activities could lead to various theoretical advancements. It makes obvious if the way GLM frameworks are assessed has an impact on the contradictory and ambiguous practical findings of earlier investigations. Our paper contributes to the literature by providing a theoretical link and empirical evidence on the PPPE–GLM nexus. In this work, we apply a variety of statistical techniques to a global database covering the years 2006–2022. In this study, tests confirming the cross-sectional dependency of our dataset are regularly followed by the use of the panel vector autoregression (PVAR) model.