Abstract
This study investigates how Industry 4.0 technologies can optimize transportation efficiency and contribute to global sustainability goals by reducing CO2 emissions. In response to the pressing climate emergency, the research examines the role of the Internet of Things (IoT), Artificial Intelligence (AI), and predictive analytics in enhancing operational performance and aligning transportation systems with Sustainable Development Goals (SDGs), particularly Goal 13 (climate action) and Goal 9 (industry, innovation, and infrastructure). Using a qualitative research approach, semi-structured interviews and focus groups were conducted with industry experts, and the data were analyzed using thematic analysis and qualitative network mapping in NVivo software. The findings reveal that IoT enhances real-time monitoring, AI enables dynamic route optimization, and predictive analytics supports proactive maintenance, collectively achieving an average emission reductions of 30%. However, adoption is hindered by infrastructure gaps, high implementation costs, skill shortages, and fragmented regulatory frameworks. This study integrates the Technology–Organization–Environment (TOE) framework and Sustainable Corporate Theory to provide a structured analysis of digital transformation in transportation. The findings offer strategic insights for policymakers and industry stakeholders, highlighting the need for stronger regulatory support, targeted incentives, and digital infrastructure investments.
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Divisions: | Leeds Business School School of Built Environment, Engineering and Computing |
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Identification Number: | https://doi.org/10.3390/futuretransp5020034 |
Status: | Published |
Refereed: | Yes |
Publisher: | MDPI AG |
Additional Information: | © 2025 by the authors |
Uncontrolled Keywords: | 3509 Transportation, logistics and supply chains; 4005 Civil engineering |
SWORD Depositor: | Symplectic |
Depositing User (symplectic) | Deposited by Mann, Elizabeth |
Date Deposited: | 16 Apr 2025 10:12 |
Last Modified: | 19 Apr 2025 06:12 |
Item Type: | Article |
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Read more research from the author(s):
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H Fatorachian
ORCID: 0000-0002-2569-7882
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H Kazemi
ORCID: 0000-0001-5982-2605
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K Pawar
ORCID: 0000-0001-8830-1024