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Beyond efficiency: unpacking AI’s dual role in driving sustainable and energy-conscious logistics in North Africa

Muath Alsheikh et al.

Technological Sustainability2025https://doi.org/10.1108/techs-05-2025-0101article
ABDC C
Weight
0.37

What the paper says

Purpose The current study examines the impact of artificial intelligence (AI) adoption on logistics sustainability and the energy efficiency of AI systems in North African nations. It also examines the role played by renewable energy infrastructure and the moderating effect exerted by the quality of institutions, government support and company size. Design/methodology/approach Based on partial least squares structural equation modeling (PLS-SEM) and multi-group analysis (MGA), this study examines secondary data from 2022 to 2024 in Morocco, Algeria, Tunisia and Egypt. Data are drawn from the World Bank’s GovTech Index, IEA, IRENA, and logistics performance indicators. Findings Results confirm that AI adoption has a strong impact on logistics sustainability (β = 0.48) and a modest impact on energy efficiency (β = 0.36). Renewable energy plays an important mediation role (indirect effect = 0.19), and these effects are moderated by institutional quality, governmental support and size of firm, and are significant. MGA reveals stronger AI–sustainability links in Morocco (Δβ = 0.27, p = 0.015) and greater benefits among large firms (Δβ = 0.22, p = 0.032). Practical implications The study emphasizes the significance of matching AI implementation with digital governance and renewable energy infrastructure. Low-carbon computing, energy monitoring, and policies catered to infrastructural gaps and corporate capacity take the stage. Originality/value The study makes an input in the area of energy management by positioning AI both as an optimizer of logistics and an energy user. It presents an innovative computational sustainability approach suited to developing economies with energy-constrained systems.

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https://doi.org/https://doi.org/10.1108/techs-05-2025-0101

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@article{muath2025,
  title        = {{Beyond efficiency: unpacking AI’s dual role in driving sustainable and energy-conscious logistics in North Africa}},
  author       = {Muath Alsheikh et al.},
  journal      = {Technological Sustainability},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1108/techs-05-2025-0101},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.