EVALUATING THE PERFORMANCE OF GREEN LOGISTICS VS. TRADITIONAL LOGISTICS IN EFFICIENCY AND SUSTAINABILITY ACROSS AFRICAN COUNTRIES: AN SEM ANALYSIS

Abstract

Purpose- The paper analyzes green logistics versus traditional logistics in African countries, with a bias for efficiency (delivery time) and sustainability (CO2 emissions and energy consumption). Using SEM and Random Forest analyses, it studies the logistics firms' data across Africa. Results indicate that green logistics is superior to the traditional kind in terms of efficiency and sustainability, while technology adoption and infrastructure quality take the role of mediators in the relationship formed. The study therefore portrays green logistics as a potential panacea to Africa's logistical and environmental problems, with some policy implications. Methodology- Data from 200 logistics companies from 15 African countries were gathered through surveys from 2022 to 2024 and were complemented by secondary sources, such as the Logistics Performance Index of the World Bank and data from the International Energy Agency. The study relied on Structural Equation Modeling (SEM) to verify the relationships between logistics practices, efficiency, and sustainability, while considering technology adoption and infrastructure quality as mediators. Control variables were considered, comprising economic development, regulatory environment, and population density. Random Forest was applied so as to predict sustainability outcomes, which would also increase the reliability of the results. Findings- SEM analysis reveals green logistics significantly enhances efficiency and sustainability, reducing delivery time (β = −0.22, p < 0.01), CO₂ emissions (β = −0.28, p < 0.01), and energy consumption (β = −0.23, p < 0.05). Mediation effects show technology adoption influencing delivery time (β = −0.16, p < 0.01), CO₂ emissions (β = −0.18, p < 0.01), and energy consumption (β = −0.15, p < 0.05), while infrastructure quality also mediates these outcomes (β = −0.15, −0.12, −0.08, respectively). The Random Forest model confirms high predictive accuracy (R² = 0.88 for CO₂ emissions, 0.85 for energy consumption). Conclusion- The study confirms green logistics outperforms traditional logistics in efficiency and sustainability across Africa, driven by technology adoption and infrastructure quality. These findings underscore the need for a shift to sustainable logistics to tackle inefficiencies and environmental challenges. Policy recommendations include establishing green logistics corridors, improving infrastructure.

Keywords

• Green logistics
• traditional logistics
• Africa
• sustainability
• efficiency

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