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Assessing AI-based eco-driving solutions for reducing GHG emissions in green transportation systems

Author

Listed:
  • Alyamani, Rakan
  • Solangi, Yasir Ahmed
  • Iqbal, Muddesar
  • Almakhles, Dhafer
  • Magazzino, Cosimo

Abstract

The transportation sector in the Kingdom of Saudi Arabia (KSA) is a major contributor to greenhouse gas (GHG) emissions, driven by the country's heavy reliance on oil and fossil fuels. Transitioning to a green and sustainable transport system is critical for reducing emissions and aligning with Saudi Arabia's Vision 2030 goals of diversifying its economy and promoting environmental sustainability. Thus, this research examined the adoption of a green sustainable transport system to reduce GHG emissions and reduce dependence on fossil fuels for sustainable development in the KSA. The study evaluates various factors and Artificial Intelligence (AI)-based eco-driving solutions to systematically implement green transportation systems. In this study, the Fuzzy Analytical Hierarchy Process (FAHP) method is applied to evaluate the five factors and eighteen sub-factors crucial for developing a green transportation system in the country. Next, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) method is used to prioritize the most significant AI-based eco-driving solutions for the implementation of smart and green transportation in KSA. The findings of the FAHP show that environmental impact (33 %) is the most crucial factor, followed by regulatory compliance (21.3 %) and economic viability (16.9 %). The FTOPSIS indicates that the smart navigation system (CCi = 0.682) is the most critical AI-based eco-driving solution because this can help reduce GHG emissions and increase the efficiency of traffic regulation in the country. The electric and hybrid vehicle integration (CCi = 0.585) and carbon footprint tracking systems (CCi = 0.355) are the next most significant solutions. This study is helpful in reducing GHG emissions, supporting sustainable development, and guiding policymakers toward effective green transport initiatives.

Suggested Citation

  • Alyamani, Rakan & Solangi, Yasir Ahmed & Iqbal, Muddesar & Almakhles, Dhafer & Magazzino, Cosimo, 2025. "Assessing AI-based eco-driving solutions for reducing GHG emissions in green transportation systems," Research in Transportation Economics, Elsevier, vol. 113(C).
    • Handle: RePEc:eee:retrec:v:113:y:2025:i:c:s0739885925001155
    DOI: 10.1016/j.retrec.2025.101632
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    1. Khurshid, Adnan & Huang, Yupei & Cifuentes-Faura, Javier & Khan, Khalid, 2024. "Beyond borders: Assessing the transboundary effects of environmental regulation on technological development in Europe," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    2. Henner Gimpel & Sebastian Heger & Moritz Wöhl, 2022. "Sustainable behavior in motion: designing mobile eco-driving feedback information systems," Information Technology and Management, Springer, vol. 23(4), pages 299-314, December.
    3. Ye Li & Lei Bao & Wenxiang Li & Haopeng Deng, 2016. "Inventory and Policy Reduction Potential of Greenhouse Gas and Pollutant Emissions of Road Transportation Industry in China," Sustainability, MDPI, vol. 8(12), pages 1-19, November.
    4. Rusul Abduljabbar & Hussein Dia & Sohani Liyanage & Saeed Asadi Bagloee, 2019. "Applications of Artificial Intelligence in Transport: An Overview," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
    5. George Aniegbunem & Andrea Kraj, 2023. "Economic Analysis of Sustainable Transportation Transitions: Case Study of the University of Saskatchewan Ground Services Fleet," Sustainability, MDPI, vol. 15(7), pages 1-19, March.
    6. Bhatti, Ghanishtha & Mohan, Harshit & Raja Singh, R., 2021. "Towards the future of smart electric vehicles: Digital twin technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    7. Li, Jie & Fotouhi, Abbas & Liu, Yonggang & Zhang, Yuanjian & Chen, Zheng, 2024. "Review on eco-driving control for connected and automated vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    8. Bai, Shengxi & Liu, Chunhua, 2021. "Overview of energy harvesting and emission reduction technologies in hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    9. Michał Adamczak & Adrianna Toboła & Jadwiga Fijałkowska & Piotr Cyplik & Maciej Tórz, 2020. "Analysis of Incentives to Eco-Driving for Car Rental Companies’ Customers," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    10. Laimon, Mohamd & Mai, Thanh & Goh, Steven & Yusaf, Talal, 2022. "System dynamics modelling to assess the impact of renewable energy systems and energy efficiency on the performance of the energy sector," Renewable Energy, Elsevier, vol. 193(C), pages 1041-1048.
    11. Sarbast Moslem & Omid Ghorbanzadeh & Thomas Blaschke & Szabolcs Duleba, 2019. "Analysing Stakeholder Consensus for a Sustainable Transport Development Decision by the Fuzzy AHP and Interval AHP," Sustainability, MDPI, vol. 11(12), pages 1-22, June.
    12. Alyamani, Rakan & Solangi, Yasir Ahmed & Almakhles, Dhafer & Alyami, Hadi H., 2024. "Analysis of solution strategies for the transition to renewable energy in Saudi Arabia," Renewable Energy, Elsevier, vol. 235(C).
    13. Sa'd Shannak & Jeyhun Mikayilov & Rubal Dua, 2022. "How to Mitigate Transportation Emissions in Saudi Arabia? The Role of Energy Price Governance," Discussion Papers ks--2022-dp04, King Abdullah Petroleum Studies and Research Center.
    14. Nassereddine, M. & Eskandari, H., 2017. "An integrated MCDM approach to evaluate public transportation systems in Tehran," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 427-439.
    15. Svetlana Maslova, 2020. "Achieving sustainable development goals through public private partnership: critical review and prospects," International Journal of Innovation and Sustainable Development, Inderscience Enterprises Ltd, vol. 14(3), pages 288-312.
    16. Ran Tu & Junshi Xu & Tiezhu Li & Haibo Chen, 2022. "Effective and Acceptable Eco-Driving Guidance for Human-Driving Vehicles: A Review," IJERPH, MDPI, vol. 19(12), pages 1-14, June.
    17. Huang, Yuhan & Ng, Elvin C.Y. & Zhou, John L. & Surawski, Nic C. & Chan, Edward F.C. & Hong, Guang, 2018. "Eco-driving technology for sustainable road transport: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 596-609.
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    JEL classification:

    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General

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