Vig Viktor and Kozma Tímea (2026) Smart green logistics solutions: the potential role of artificial intelligence in optimising the use of electric and gas-powered truck fleets. Prosperitas, early access. pp. 1-17. ISSN 2786-4359 (online)
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Abstract
Artificial intelligence and digital technologies are reshaping sustainable freight transport. As road freight transport remains a major source of global carbon dioxide emissions, the transition to green logistics is essential. This paper examines how artificial intelligence and Internet of Things technologies can improve the operational efficiency of electric and gas-powered truck fleets through energy optimisation, emission reduction and data-driven decision support. The study applies an integrative qualitative multi-source research design combining a structured literature review, policy document analysis and documentary corporate case analysis. The analysis further examines how European Union policy initiatives, including the European Green Deal, the Fit for 55 package and the Sustainable and Smart Mobility Strategy, shape innovation in the logistics sector. The findings suggest that AI-supported logistics optimisation can improve fleet utilisation, reduce empty runs and support the operational integration of low-emission trucks. At the same time, organisational, technological and infrastructural barriers continue to constrain large-scale implementation. The study contributes an integrated analytical framework linking technological innovation, logistics operations and European transport policy within the concept of smart green logistics.
Tudományterület / tudományág
társadalomtudományok > gazdálkodás- és szervezéstudományok
Kar
Institution
Budapesti Gazdaságtudományi Egyetem
| Item Type: | Article | |||||||||
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| Uncontrolled Keywords: | Artificial Intelligence, green logistics, digitalisation, sustainable transport, European Green Deal | |||||||||
| Depositing User: | Kinga Eszenyi-Bakos | |||||||||
| DOI azonosító: | https://doi.org/10.31570/prosp_2026_0185 | |||||||||
| Date Deposited: | 2026. Jul. 23. 09:35 | |||||||||
| Last Modified: | 2026. Jul. 23. 09:35 | |||||||||
| URI: | https://publikaciotar.uni-bge.hu/id/eprint/2676 |
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