3066-280XISSN (Online)
Volume 4 , Issue 1 , PP: 31-40, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Omnia M. Osama 1 * , Marwa M. Eid 2 , El-Sayed M. El Rabaie 3
Doi: https://doi.org/10.54216/MOR.040104
Artificial intelligence systems are revolutionizing how industries reduce carbon dioxide emissions in numerous business fields. This study combines research on how artificial intelligence merges with carbon reduction methods, specifically in industrial procedures and electric vehicle manufacturing, with an environmental sustainability focus. Multiple empirical studies and advanced AI models provide insight into sustainability effects caused by AI systems and emission decrease processes. AI technology performs three essential functions to enhance energy optimization pro, mote eco-friendly research, and improve environmental prediction accuracy. The identified information provides essential guidance to policymakers and industrial leaders about AI applications for achieving zero emissions and sustainability targets. The review presents evidence that AI technology can redefine sustainability throughout vehicle production while managing transportation and other fields thus helping solve escalating climate issues and drive eco-friendly developments.
AI , Carbon emission reduction , Industrial processes , Electric vehicles , Sustainability , Energy efficiency
[1] G. M. I. Alam et al., “Deep learning model based prediction of vehicle CO2 emissions with eXplainable AI integration for sustainable environment,” Sci Rep, vol. 15, no. 1, p. 3655, Dec. 2025, doi: 10.1038/S41598-025-87233-Y.
[2] “Why more AI firms should invest in carbon removal solutions | World Economic Forum.” Accessed: Apr. 05, 2025. [Online]. Available: https://www.weforum.org/stories/2025/02/ai-s-energy-problem-why-carbon-removal-can-t-wait/?utm_source=chatgpt.com.
[3] X. Niu, C. Lin, S. He, and Y. Yang, “Artificial intelligence and enterprise pollutioemissions: From the perspective of energy transition,” Energy Econ, vol. 144, p. 108349, Apr. 2025, doi: 10.1016/J.ENECO.2025.108349.
[4] L. Chen et al., “Artificial intelligence-based solutions for climate change: a review,” Environ Chem Lett, vol. 21, no. 5, pp. 2525–2557, Oct. 2023, doi: 10.1007/S10311-023-01617-Y.
[5] A. H. Al-Nefaie and T. H. H. Aldhyani, “Predicting CO2 emissions from traffic vehicles using a hybrid machine learning approach,” J. Clean Prod., vol. 340, p. 130645, Jan. 2022, doi: 10.1016/j.jclepro.2021.130645.
[6] A. S. Devi et al., “Internet-of-Vehicles Network for CO2 Emission Estimation and Reinforcement Learning-Based Emission Reduction,” IEEE Access, vol. 12, pp. 20345–20356, 2024, doi: 10.1109/ACCESS.2024.3441949.
[7] J. Liu, H. Shen, J. Chen, X. Jiang, and A. W. Siyal, “Artificial Intelligence and Carbon Emissions: Mediating Role of Energy Efficiency, Factor Market Allocation and Industrial Structure,” Energies, vol. 18, no. 5, p. 1102, Feb. 2025, doi: 10.3390/EN18051102.
[8] W. Liu, C.-H. Wu, Y. Chen, and S. Jin, “Artificial Intelligence and Carbon Emissions in Manufacturing Firms: The Moderating Role of Green Innovation,” Processes, vol. 11, no. 9, p. 2705, Sep. 2023, doi: 10.3390/PR11092705.
[9] T. Mi and T. Li, “Industrial Intelligence and Carbon Emission Reduction: Evidence from China’s Manufacturing Industry,” Sustainability, vol. 16, no. 15, p. 6573, Jul. 2024, doi: 10.3390/SU16156573.
[10] Q. Cao, C. Chi, and J. Shan, “Can artificial intelligence technology reduce carbon emissions? A global perspective,” Energy Econ, vol. 143, p. 108285, Mar. 2025, doi: 10.1016/J.ENECO.2025.108285.
[11] C. Zhao, Y. Li, Z. Liu, and X. Ma, “Artificial intelligence and carbon emissions inequality: Evidence from industrial robot application,” J. Clean Prod., vol. 438, p. 140817, Jan. 2024, doi: 10.1016/J.JCLEPRO.2024.140817.
[12] Y. Wang, “Intelligent energy management and operation efficiency of electric vehicles based on artificial intelligence algorithms and thermal energy optimization,” Thermal Science and Engineering Progress, vol. 55, p. 102900, Oct. 2024, doi: 10.1016/J.TSEP.2024.102900.
[13] J. Gu, Z. Wu, Y. Song, and A. C. Nicolescu, “A win-win relationship? New evidence on artificial intelligence and new energy vehicles,” Energy Econ, vol. 134, p. 107613, Jun. 2024, doi: 10.1016/J.ENECO.2024.107613.
[14] S. Greene, H. Jia, and G. Rubio-Domingo, “Well-to-tank carbon emissions from crude oil maritime transportation,” Transp Res D Transp Environ, vol. 88, Nov. 2020, doi: 10.1016/J.TRD.2020.102587.
[15] S. Syed, “Zero Carbon Manufacturing in the Automotive Industry: Integrating Predictive Analytics to Achieve Sustainable Production,” Journal of Artificial Intelligence and Big Data, vol. 3, no. 1, pp. 17–28, Nov. 2023, doi: 10.31586/JAIBD.2023.1179.
[16] H. Lv, B. Shi, N. Li, and R. Kang, “Intelligent Manufacturing and Carbon Emissions Reduction: Evidence from the Use of Industrial Robots in China,” Int. J. Environ. Res. Public Health, vol. 19, no. 23, p. 15538, Nov. 2022, doi: 10.3390/IJERPH192315538.
[17] T. Ding, J. Li, X. Shi, X. Li, and Y. Chen, “Is artificial intelligence associated with carbon emissions reduction? Case of China,” Resources Policy, vol. 85, p. 103892, Aug. 2023, doi: 10.1016/J.RESOURPOL.2023.103892.
[18] H. Xu, Z. Cao, and D. Han, “Towards Sustainable Development: Can Industrial Intelligence Promote Carbon Emission Reduction,” Sustainability, vol. 17, no. 1, p. 370, Jan. 2025, doi: 10.3390/SU17010370.
[19] A. H. Al-Nefaie and T. H. H. Aldhyani, “Predicting CO2 Emissions from Traffic Vehicles for Sustainable and Smart Environment Using a Deep Learning Model,” Sustainability 2023, vol. 15, p. 7615, May 2023, doi: 10.3390/SU15097615.
[20] T. Mi and T. Li, “Industrial Intelligence and Carbon Emission Reduction: Evidence from China’s Manufacturing Industry,” Sustainability, vol. 16, no. 15, p. 6573, Jul. 2024, doi: 10.3390/SU16156573.
[21] H. Lv, B. Shi, N. Li, and R. Kang, “Intelligent Manufacturing and Carbon Emissions Reduction: Evidence from the Use of Industrial Robots in China,” Int. J. Environ. Res. Public Health, vol. 19, no. 23, p. 15538, Nov. 2022, doi: 10.3390/IJERPH192315538.
[22] J. Liu, H. Shen, J. Chen, X. Jiang, and A. W. Siyal, “Artificial Intelligence and Carbon Emissions: Mediating Role of Energy Efficiency, Factor Market Allocation and Industrial Structure,” Energies, vol. 18, no. 5, p. 1102, Feb. 2025, doi: 10.3390/EN18051102.
[23] C. Zhao, Y. Li, Z. Liu, and X. Ma, “Artificial intelligence and carbon emissions inequality: Evidence from industrial robot application,” J. Clean Prod., vol. 438, p. 140817, Jan. 2024, doi: 10.1016/J.JCLEPRO.2024.140817.
