Artificial Intelligence and Machine Learning for Green Shipping: Navigating towards Sustainable Maritime Practices

Hoang Phuong Nguyen - Academy of Politics Region II, Ho Chi Minh City, 700000, Viet Nam
Cao Thao Uyen Nguyen - Faculty of International Trade, College of Foreign Economic Relation, Ho Chi Minh city, 700000, Viet Nam
Thi Men Tran - Faculty of International Trade, College of Foreign Economic Relation, Ho Chi Minh city, 700000, Viet Nam
Quoc Hai Dang - Institute of Postgraduate Education, Ho Chi Minh City University of Transport, Ho Chi Minh City, 700000, Viet Nam
Nguyen Dang Khoa Pham - PATET Research Group, Ho Chi Minh City University of Transport, Ho Chi Minh City, 700000, Viet Nam

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This paper aims to investigate the role that artificial intelligence (AI) plays in promoting sustainability in the marine industry. The report demonstrates the potential of AI-driven technology to improve vessel operations, decrease emissions, and promote environmental stewardship. This potential is shown by detailed examination of existing trends, problems, and possibilities. Several vital studies highlight the significance of policy interventions that encourage the use of artificial intelligence. These interventions include financial incentives, legal frameworks, and programs to increase capability. Throughout this work, the importance of the role that artificial intelligence plays in driving efficiency, safety, and sustainability is emphasized. This work also highlights the urgent need for action to address climate change and environmental degradation in the marine sector. The marine industry can lessen its carbon footprint, decrease pollution, and improve ecosystem health if it shifts to various alternative fuels, renewable energy sources, and technologies powered by artificial intelligence. At the end of this work, an appeal is made to policymakers, industry stakeholders, and technology providers, urging them to prioritize investments in artificial intelligence research and development and to create collaboration to speed up the transition to a marine sector that is more sustainable and resilient.


Green shipping; sustainability; renewable energy; artificial intelligence; machine learning

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X. P. Nguyen, “Solutions for navigated safety of super-tankers operating on Dinh River traffic lanes and PTSC port,” 2020, p. 020017. doi: 10.1063/5.0000141.

A. T. Hoang et al., “Technological solutions for boosting hydrogen role in decarbonization strategies and net-zero goals of world shipping: Challenges and perspectives,” Renew. Sustain. Energy Rev., vol. 188, p. 113790, Dec. 2023, doi: 10.1016/j.rser.2023.113790.

A. T. Hoang et al., “Energy-related approach for reduction of CO2 emissions: A critical strategy on the port-to-ship pathway,” J. Clean. Prod., vol. 355, p. 131772, Jun. 2022, doi:10.1016/j.jclepro.2022.131772.

J. Kowalski and W. Tarelko, “NOx emission from a two-stroke ship engine. Part 1: Modeling aspect,” Appl. Therm. Eng., vol. 29, no. 11–12, pp. 2153–2159, Aug. 2009, doi:10.1016/j.applthermaleng.2008.06.032.

A. Podsiadlo and W. Tarelko, “Modelling and developing a decision-making process of hazard zone identification in ship power plants,” Int. J. Press. Vessel. Pip., vol. 83, no. 4, pp. 287–298, Apr. 2006, doi:10.1016/j.ijpvp.2006.02.017.

N. D. K. Pham, G. H. Dinh, H. T. Pham, J. Kozak, and H. P. Nguyen, “Role of Green Logistics in the Construction of Sustainable Supply Chains,” Polish Marit. Res., vol. 30, no. 3, pp. 191–211, Sep. 2023, doi: 10.2478/pomr-2023-0052.

M. D. Nguyen, K. T. Yeon, K. Rudzki, H. P. Nguyen, and N. D. K. Pham, “Strategies for Developing Logistics Centres: Technological Trends and Policy Implications,” Polish Marit. Res., vol. 30, no. 4, pp. 129–147, 2023, doi: 10.2478/pomr-2023-0066.

A. Al-Enazi, E. C. Okonkwo, Y. Bicer, and T. Al-Ansari, “A review of cleaner alternative fuels for maritime transportation,” Energy Reports, vol. 7, pp. 1962–1985, Nov. 2021, doi:10.1016/j.egyr.2021.03.036.

O. Soner, E. Akyuz, and M. Celik, “Use of tree-based methods in ship performance monitoring under operating conditions,” Ocean Eng., vol. 166, pp. 302–310, Oct. 2018, doi:10.1016/j.oceaneng.2018.07.061.

S. Vakili, A. I. Ölçer, A. Schönborn, F. Ballini, and A. T. Hoang, “Energy‐related clean and green framework for shipbuilding community towards zero‐emissions: A strategic analysis from concept to case study,” Int. J. Energy Res., vol. 46, no. 14, pp. 20624–20649, Nov. 2022, doi: 10.1002/er.7649.

B. Hu, “Application of Evaluation Algorithm for Port Logistics Park Based on Pca-Svm Model,” Polish Marit. Res., vol. 25, no. s3, pp. 29–35, Dec. 2018, doi: 10.2478/pomr-2018-0109.

Z. Domachowski, “Minimizing Greenhouse Gas Emissions From Ships Using a Pareto Multi-Objective Optimization Approach,” Polish Marit. Res., vol. 28, no. 2, pp. 96–101, Jun. 2021, doi:10.2478/pomr-2021-0026.

M. I. Lamas, R. C.G., T. J., and R. J.D., “Numerical Analysis of Emissions from Marine Engines Using Alternative Fuels,” Polish Marit. Res., vol. 22, no. 4, pp. 48–52, Dec. 2015, doi: 10.1515/pomr-2015-0070.

H. P. Nguyen, P. Q. P. Nguyen, D. K. P. Nguyen, V. D. Bui, and D. T. Nguyen, “Application of IoT Technologies in Seaport Management,” JOIV Int. J. Informatics Vis., vol. 7, no. 1, pp. 228–240, Mar. 2023, doi: 10.30630/joiv.7.1.1697.

H. P. Nguyen, N. D. K. Pham, and V. D. Bui, “Technical-Environmental Assessment of Energy Management Systems in Smart Ports,” Int. J. Renew. Energy Dev., vol. 11, no. 4, pp. 889–901, Nov. 2022, doi: 10.14710/ijred.2022.46300.

C. Wang, J. Shen, P. Vijayakumar, and B. B. Gupta, “Attribute-Based Secure Data Aggregation for Isolated IoT-Enabled Maritime Transportation Systems,” IEEE Trans. Intell. Transp. Syst., pp. 1–10, 2021, doi: 10.1109/TITS.2021.3127436.

C. D. Paternina-Arboleda, D. Agudelo-Castañeda, S. Voß, and S. Das, “Towards Cleaner Ports: Predictive Modeling of Sulfur Dioxide Shipping Emissions in Maritime Facilities Using Machine Learning,” Sustainability, vol. 15, no. 16, p. 12171, Aug. 2023, doi:10.3390/su151612171.

V. G. Nguyen et al., “Using Artificial Neural Networks for Predicting Ship Fuel Consumption,” Polish Marit. Res., vol. 30, no. 2, pp. 39–60, Jun. 2023, doi: 10.2478/pomr-2023-0020.

V. D. Bui and H. P. Nguyen, “Role of Inland Container Depot System in Developing the Sustainable Transport System,” Int. J. Knowledge-Based Dev., vol. 12, no. 3/4, p. 1, 2022, doi:10.1504/IJKBD.2022.10053121.

H. Geerlings and R. Van Duin, “A new method for assessing CO2-emissions from container terminals: a promising approach applied in Rotterdam,” J. Clean. Prod., vol. 19, no. 6–7, pp. 657–666, 2011.

M. Fitzmaurice, “The International Convention for the Prevention of Pollution from Ships (MARPOL),” in Research Handbook on Ocean Governance Law, Edward Elgar Publishing, 2023, pp. 91–108. doi:10.4337/9781839107696.00019.

P. Brodie, “International Convention for the Prevention of Pollution from Ships (MARPOL),” in Commercial Shipping Handbook, Informa Law from Routledge, 2014, pp. 219–221. doi:10.4324/9781315774695-85.

X. P. Nguyen and D. K. Pham Nguyen, “Experimental Research on the Impact of Anchor-Cable Tensions in Mooring Ship at Vung Tau Anchorage Area,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 6, pp. 1892–1899, 2019.

X. P. Nguyen, “A simulation study on the effects of hull form on aerodynamic performances of the ships,” 2020, p. 020015. doi:10.1063/5.0000140.

D. T. Nguyen and H. C. Le, “Potential of jute fiber-reinforced composites in the manufacture of components and equipment used on ships and hulls,” J. Emerg. Sci. Eng., vol. 1, no. 1, pp. 14–21, Sep. 2023, doi: 10.61435/jese.2023.3.

V. D. Bui and H. P. Nguyen, “Sustainable development of Vietnam’s transportation from analysis of car freight management,” Int. J. Knowledge-Based Dev., vol. 12, no. 2, p. 77, 2021, doi:10.1504/IJKBD.2021.10045882.

H. P. Nguyen, P. Q. P. Nguyen, and T. P. Nguyen, “Green Port Strategies in Developed Coastal Countries as Useful Lessons for the Path of Sustainable Development: A case study in Vietnam,” Int. J. Renew. Energy Dev., vol. 11, no. 4, pp. 950–962, Nov. 2022, doi:10.14710/ijred.2022.46539.

K. Rudzki, P. Gomulka, and A. T. Hoang, “Optimization Model to Manage Ship Fuel Consumption and Navigation Time,” Polish Marit. Res., vol. 29, no. 3, pp. 141–153, Sep. 2022, doi: 10.2478/pomr-2022-0034.

T. Chu Van, J. Ramirez, T. Rainey, Z. Ristovski, and R. J. Brown, “Global impacts of recent IMO regulations on marine fuel oil refining processes and ship emissions,” Transp. Res. Part D Transp. Environ., vol. 70, pp. 123–134, May 2019, doi:10.1016/j.trd.2019.04.001.

P. Sharma and A. K. Sharma, “AI-Based Prognostic Modeling and Performance Optimization of CI Engine Using Biodiesel-Diesel Blends,” Int. J. Renew. Energy Res., no. v11i2, 2021, doi:10.20508/ijrer.v11i2.11854.g8191.

S. Serbin, B. Diasamidze, V. Gorbov, and J. Kowalski, “Investigations of the Emission Characteristics of a Dual-Fuel Gas Turbine Combustion Chamber Operating Simultaneously on Liquid and Gaseous Fuels,” Polish Marit. Res., vol. 28, no. 2, pp. 85–95, Jun. 2021, doi: 10.2478/pomr-2021-0025.

Z. Stelmasiak, J. Larisch, J. Pielecha, and D. Pietras, “Particulate Matter Emission from Dual Fuel Diesel Engine Fuelled with Natural Gas,” Polish Marit. Res., vol. 24, no. 2, pp. 96–104, Jun. 2017, doi:10.1515/pomr-2017-0055.

Y. Li, B. Li, F. Deng, Q. Yang, and B. Zhang, “Research on the Application of Cold Energy of Largescale Lng-Powered Container Ships to Refrigerated Containers,” Polish Marit. Res., vol. 28, no. 4, pp. 107–121, Jan. 2022, doi: 10.2478/pomr-2021-0053.

V. N. Nguyen et al., “Understanding fuel saving and clean fuel strategies towards green maritime,” Polish Marit. Res., vol. 30, no. 2, pp. 146–164, 2023, doi: 10.2478/pomr-2023-0030.

S. reza seyyedi, E. Kowsari, S. Ramakrishna, M. Gheibi, and A. Chinnappan, “Marine plastics, circular economy, and artificial intelligence: A comprehensive review of challenges, solutions, and policies,” J. Environ. Manage., vol. 345, p. 118591, Nov. 2023, doi:10.1016/j.jenvman.2023.118591.

F. P. García Márquez, M. Papaelias, and S. Marini, “Artificial Intelligence in Marine Science and Engineering,” J. Mar. Sci. Eng., vol. 10, no. 6, p. 711, May 2022, doi: 10.3390/jmse10060711.

E. M. Ditria, C. A. Buelow, M. Gonzalez-Rivero, and R. M. Connolly, “Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective,” Front. Mar. Sci., vol. 9, Jul. 2022, doi: 10.3389/fmars.2022.918104.

V. H. Dong and P. Sharma, “Optimized conversion of waste vegetable oil to biofuel with Meta heuristic methods and design of experiments,” J. Emerg. Sci. Eng., vol. 1, no. 1, pp. 22–28, Sep. 2023, doi: 10.61435/jese.2023.4.

S. Zhu et al., “A Triboelectric Nanogenerator Based on a Pendulum-Plate Wave Energy Converter,” Polish Marit. Res., vol. 29, no. 4, pp. 155–161, Dec. 2022, doi: 10.2478/pomr-2022-0053.

P. Geng, X. Xu, and T. Tarasiuk, “State of Charge Estimation Method for Lithium-Ion Batteries in All-Electric Ships Based on LSTM Neural Network,” Polish Marit. Res., vol. 27, no. 3, pp. 100–108, Sep. 2020, doi: 10.2478/pomr-2020-0051.

J. Ugwu, K. C. Odo, L. O. Oluka, and K. O. Salami, “A Systematic Review on the Renewable Energy Development, Policies and Challenges in Nigeria with an International Perspective and Public Opinions,” Int. J. Renew. Energy Dev., vol. 11, no. 1, pp. 287–308, Feb. 2022, doi: 10.14710/ijred.2022.40359.

D. D. S. Garcia-Marquez, I. Andrade-Gonzalez, A.-M. Chavez-Rodriguez, M. I. Montero-Cortes, and V. S. Farias-Cervantes, “Prototype of a Solar Collector with the Recirculation of Nanofluids for a Convective Dryer,” Int. J. Renew. Energy Dev., vol. 11, no. 4, pp. 1124–1133, Nov. 2022, doi: 10.14710/ijred.2022.44221.

O. K. Ahmed, R. W. Daoud, S. M. Bawa, and A. H. Ahmed, “Optimization of PV/T Solar Water Collector based on Fuzzy Logic Control.,” Int. J. Renew. Energy Dev., vol. 9, no. 2, pp. 303–310, 2020.

A. Y. Kian and S. C. Lim, “On the Potential of Solar Energy for Chemical and Metal Manufacturing Plants in Malaysia,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 5, pp. 1898–1904, 2023, doi:10.18517/ijaseit.13.5.19052.

I. Ismail, A. H. Ismail, and G. H. N. Nur Rahayu, “Wind Energy Feasibility Study of Seven Potential Locations in Indonesia,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 5, pp. 1970–1978, 2020, doi:10.18517/ijaseit.10.5.10389.

D. H. Barus and R. Dalimi, “Multi-Stage Statistical Approach to Wind Power Forecast Errors Evaluation: A Southern Sulawesi Case Study,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 11, no. 2, pp. 633–641, 2021, doi: 10.18517/ijaseit.11.2.12385.

H. Zhang, Y. Hu, and J. He, “Wind Tunnel Experiment of Multi-Mode ARC Sail Device,” Polish Marit. Res., vol. 28, no. 4, pp. 20–29, Jan. 2022, doi: 10.2478/pomr-2021-0046.

Y. Shi and W. Luo, “Application of Solar Photovoltaic Power Generation System in Maritime Vessels and Development of Maritime Tourism,” Polish Marit. Res., vol. 25, no. s2, pp. 176–181, Aug. 2018, doi: 10.2478/pomr-2018-0090.

A. A. Salem and I. S. Seddiek, “Techno-Economic Approach to Solar Energy Systems Onboard Marine Vehicles,” Polish Marit. Res., vol. 23, no. 3, pp. 64–71, Sep. 2016, doi: 10.1515/pomr-2016-0033.

X. P. Nguyen and V. H. Dong, “A study on traction control system for solar panel on vessels,” in International Conference on Emerging Applications in Material Science and Technology, ICEAMST 2020, 2020, p. 020016. doi: 10.1063/5.0007708.

M. F. Fazri, Lintang Bayu Kusuma, Risa Burhani Rahmawan, Hardiana Nur Fauji, and Castarica Camille, “Implementing Artificial Intelligence to Reduce Marine Ecosystem Pollution,” IAIC Trans. Sustain. Digit. Innov., vol. 4, no. 2, pp. 101–108, Feb. 2023, doi:10.34306/itsdi.v4i2.579.

K. Bakker, “Smart Oceans: Artificial intelligence and marine protected area governance,” Earth Syst. Gov., vol. 13, p. 100141, Aug. 2022, doi: 10.1016/j.esg.2022.100141.

Ni. Agarwala, “Managing Marine Environmental Pollution using Artificial Intelligence,” Marit. Technol. Res., vol. 3, no. 2, p. Manuscript, Jan. 2021, doi: 10.33175/mtr.2021.248053.

A. Kheiter, S. Souag, A. Chaouch, A. Boukortt, B. Bekkouche, and M. Guezgouz, “Energy Management Strategy Based on Marine Predators Algorithm for Grid-Connected Microgrid,” Int. J. Renew. Energy Dev., vol. 11, no. 3, pp. 751–765, Aug. 2022, doi:10.14710/ijred.2022.42797.

Y. Nain and O. Chi, “Application of Nonlinear Autoregressive Neural Network to Model and Forecast Time Series Global Price of Bananas,” Int. J. Data Sci., vol. 2, no. 1, pp. 19–37, 2021.

B. V. Duc and H. P. Nguyen, “A Comprehensive Review on Big Data-Based Potential Applications in Marine Shipping Management,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 11, no. 3, pp. 1067–1077, 2021.

T. T. Le et al., “Management strategy for seaports aspiring to green logistical goals of IMO: Technology and policy solutions,” Polish Marit. Res., vol. 30, no. 2, pp. 165–187, 2023, doi: 10.2478/pomr-2023-0031.

H. P. Nguyen, P. Q. P. Nguyen, and V. D. Bui, “Applications of Big Data Analytics in Traffic Management in Intelligent Transportation Systems,” JOIV Int. J. Informatics Vis., vol. 6, no. 1–2, pp. 177–187, May 2022, doi: 10.30630/joiv.6.1-2.882.

B. Aprilia, M. Marzuki, I. Taufiq, and F. Renggono, “Development of a Method for Classifying Convective and Stratiform Rains from Micro Rain Radar ( MRR ) Observation Data Using Artificial Neural Network,” Int. J. Data Sci., vol. 3, no. 2, pp. 71–79, 2022.

Y. Antonisfia, Y. Silvia, and M. Botto-tobar, “E-Nose Application for Detecting Banana Fruit Ripe Levels Using Artificial Neural Network Backpropagation Method,” Int. J. Data Sci., vol. 3, no. 1, pp. 11–18, 2022.

B. Aruwa, A. Taye, and O. Adegoke, “Adaptive Android APKs Reverse Engineering for Features Processing in Machine Learning Malware Detection,” Int. J. Data Sci., vol. 4, no. 1, pp. 10–25, 2023.

V. G. Nguyen et al., “An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO 2 emission,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 45, no. 3, pp. 9149–9177, Aug. 2023, doi:10.1080/15567036.2023.2231898.

R. V. N. Seutche, M. Sawadogo, and F. N. Ngassam, “Valuation of CO2 Emissions Reduction from Renewable Energy and Energy Efficiency Projects in Africa: A Case Study of Burkina Faso,” Int. J. Renew. Energy Dev., vol. 10, no. 4, pp. 713–729, Nov. 2021, doi:10.14710/ijred.2021.34566.

H. W. Asrini, G. W. Wicaksono, and B. Budiono, “Curriculum Management Systems for Blended Learning Support,” JOIV Int. J. Informatics Vis., vol. 7, no. 4, pp. 2189–2197, 2023.

R. Kesavan, N. Palanichamy, and T. Thirumurugan, “IoT and Deep Learning Enabled Smart Solutions for Assisting Menstrual Health Management for Rural Women in India: A Review,” JOIV Int. J. Informatics Vis., vol. 7, no. 4, pp. 2198–2205, 2023.

A. H. Rangkuti, V. H. Athala, F. H. Indallah, E. Tanuar, and J. M. Kerta, “Optimization of Historic Buildings Recognition: CNN Model and Supported by Pre-processing Methods,” JOIV Int. J. Informatics Vis., vol. 7, no. 4, pp. 2230–2239, 2023.

I. Huda, A. A. Suhendra, and M. A. Bijaksana, “Design of Prediction Model using Data Mining for Segmentation and Classification Customer Churn in E-Commerce Mall in Mall,” JOIV Int. J. Informatics Vis., vol. 7, no. 4, pp. 2280–2289, 2023.

A. E. K. Gunawan and A. Wibowo, “Stock Price Movement Classification Using Ensembled Model of Long Short-Term Memory (LSTM) and Random Forest (RF),” JOIV Int. J. Informatics Vis., vol. 7, no. 4, pp. 2255–2262, 2023.

V. D. Bui and H. P. Nguyen, “A Comprehensive Review on Big Data-Based Potential Applications in Marine Shipping Management,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 11, no. 3, pp. 1067–1077, Jun. 2021, doi: 10.18517/ijaseit.11.3.15350.

M. Drzewiecki and J. Guziński, “Design of an Autonomous IoT Node Powered by a Perovskite-Based Wave Energy Converter,” Polish Marit. Res., vol. 30, no. 3, pp. 142–152, Sep. 2023, doi:10.2478/pomr-2023-0047.

P. J. B. Sanchez et al., “Use of UIoT for Offshore Surveys Through Autonomous Vehicles,” Polish Marit. Res., vol. 28, no. 3, pp. 175–189, Sep. 2021, doi: 10.2478/pomr-2021-0044.

A. Al-Othman et al., “Artificial intelligence and numerical models in hybrid renewable energy systems with fuel cells: Advances and prospects,” Energy Convers. Manag., vol. 253, p. 115154, 2022.

T. T. Le, J. C. Priya, H. C. Le, N. V. L. Le, T. B. N. Nguyen, and D. N. Cao, “Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability,” Int. J. Renew. Energy Dev., vol. 13, no. 2, 2024, doi:10.61435/ijred.2024.60119.

A. B. Kanase-Patil, A. P. Kaldate, S. D. Lokhande, H. Panchal, M. Suresh, and V. Priya, “A review of artificial intelligence-based optimization techniques for the sizing of integrated renewable energy systems in smart cities,” Environ. Technol. Rev., vol. 9, no. 1, pp. 111–136, 2020.

T. Uyanık, Ç. Karatuğ, and Y. Arslanoğlu, “Machine learning based visibility estimation to ensure safer navigation in strait of Istanbul,” Appl. Ocean Res., vol. 112, p. 102693, Jul. 2021, doi:10.1016/j.apor.2021.102693.

R. Sultanbekov, I. Beloglazov, S. Islamov, and M. Ong, “Exploring of the Incompatibility of Marine Residual Fuel: A Case Study Using Machine Learning Methods,” Energies, vol. 14, no. 24, p. 8422, Dec. 2021, doi: 10.3390/en14248422.

T. Song et al., “A review of artificial intelligence in marine science,” Front. Earth Sci., vol. 11, Feb. 2023, doi:10.3389/feart.2023.1090185.

Z. Islam Rony et al., “Alternative fuels to reduce greenhouse gas emissions from marine transport and promote UN sustainable development goals,” Fuel, vol. 338, p. 127220, Apr. 2023, doi:10.1016/j.fuel.2022.127220.

C. D. Mantoju, “Analysis of MARPOL implementation based on port state control statistics,” J. Int. Marit. Safety, Environ. Aff. Shipp., vol. 5, no. 3, pp. 132–145, Jul. 2021, doi:10.1080/25725084.2021.1965281.

N. Hussain, A. Khan, Shumaila, and S. Memon, “Addressing Marine Pollution: An Analysis of MARPOL 73/78 Regulations and Global Implementation Efforts,” J. Soc. Sci. Rev., vol. 3, no. 1, pp. 572–589, Mar. 2023, doi: 10.54183/jssr.v3i1.193.

M. Issa, A. Ilinca, and F. Martini, “Ship Energy Efficiency and Maritime Sector Initiatives to Reduce Carbon Emissions,” Energies, vol. 15, no. 21, p. 7910, Oct. 2022, doi: 10.3390/en15217910.

M. H. Moradi, M. Brutsche, M. Wenig, U. Wagner, and T. Koch, “Marine route optimization using reinforcement learning approach to reduce fuel consumption and consequently minimize CO2 emissions,” Ocean Eng., vol. 259, p. 111882, Sep. 2022, doi:10.1016/j.oceaneng.2022.111882.

Z. Korczewski, “Energy and Emission Quality Ranking of Newly Produced Low-Sulphur Marine Fuels,” Polish Marit. Res., vol. 29, no. 4, pp. 77–87, Dec. 2022, doi: 10.2478/pomr-2022-0045.

P.-C. Wu and C.-Y. Lin, “Cost-Benefit Evaluation on Promising Strategies in Compliance with Low Sulfur Policy of IMO,” J. Mar. Sci. Eng., vol. 9, no. 1, p. 3, Dec. 2020, doi: 10.3390/jmse9010003.

O. Schinas and C. N. Stefanakos, “Selecting technologies towards compliance with MARPOL Annex VI: The perspective of operators,” Transp. Res. Part D Transp. Environ., vol. 28, pp. 28–40, May 2014, doi: 10.1016/j.trd.2013.12.006.

IMO, “Guidelines for Consistent Implementation of the 0.50% Sulphur Limit Under MARPOL Annex VI,” 2019.

J. Chen, Y. Fei, and Z. Wan, “The relationship between the development of global maritime fleets and GHG emission from shipping,” J. Environ. Manage., vol. 242, pp. 31–39, Jul. 2019, doi:10.1016/j.jenvman.2019.03.136.

K. Aalbu and T. Longva, “From Progress to Delay: The Quest for Data in the Negotiations on Greenhouse Gases in the International Maritime Organization,” Glob. Environ. Polit., vol. 22, no. 2, pp. 136–155, May 2022, doi: 10.1162/glep_a_00653.

P. Serra and G. Fancello, “Towards the IMO’s GHG Goals: A Critical Overview of the Perspectives and Challenges of the Main Options for Decarbonizing International Shipping,” Sustainability, vol. 12, no. 8, p. 3220, Apr. 2020, doi: 10.3390/su12083220.

T.-H. Joung, S.-G. Kang, J.-K. Lee, and J. Ahn, “The IMO initial strategy for reducing Greenhouse Gas(GHG) emissions, and its follow-up actions towards 2050,” J. Int. Marit. Safety, Environ. Aff. Shipp., vol. 4, no. 1, pp. 1–7, Jan. 2020, doi:10.1080/25725084.2019.1707938.

A. Chircop, “The IMO Initial Strategy for the Reduction of GHGs from International Shipping: A Commentary,” Int. J. Mar. Coast. Law, vol. 34, no. 3, pp. 482–512, Aug. 2019, doi: 10.1163/15718085-13431093.

X. Guo, J. Li, and S. Huang, “Study on Trade Effects of Green Maritime Transport Efficiency: An Empirical Test for China Based on Trade Decision Model,” Sustainability, vol. 15, no. 16, p. 12327, Aug. 2023, doi: 10.3390/su151612327.

A. T. Hoang and V. D. Tran, “Experimental Analysis on the Ultrasound-based Mixing Technique Applied to Ultra-low Sulphur Diesel and Bio-oils,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 1, p. 307, Feb. 2019, doi: 10.18517/ijaseit.9.1.7890.

V. D. Tran, A. T. Le, and A. T. Hoang, “An Experimental Study on the Performance Characteristics of a Diesel Engine Fueled with ULSD-Biodiesel Blends,” Int. J. Renew. Energy Dev., vol. 10, no. 2, pp. 183–190, May 2021, doi: 10.14710/ijred.2021.34022.

V. V. Pham and A. T. Hoang, “Aalyzing and selecting the typical propulsion systems for ocean supply vessels,” in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), IEEE, Mar. 2020, pp. 1349–1357. doi: 10.1109/ICACCS48705.2020.9074276.

V. V. Pham, A. T. Hoang, and H. C. Do, “Analysis and evaluation of database for the selection of propulsion systems for tankers,” p. 020034, 2020, doi: 10.1063/5.0007655.

O. Kanifolskyi, “General Strength, Energy Efficiency (EEDI), and Energy Wave Criterion (EWC) of Deadrise Hulls for Transitional Mode,” Polish Marit. Res., vol. 29, no. 3, pp. 4–10, Sep. 2022, doi:10.2478/pomr-2022-0021.

T. Vidović, J. Šimunović, G. Radica, and Ž. Penga, “Systematic Overview of Newly Available Technologies in the Green Maritime Sector,” Energies, vol. 16, no. 2, p. 641, Jan. 2023, doi:10.3390/en16020641.

K. Cullinane and R. Bergqvist, “Emission control areas and their impact on maritime transport,” Transp. Res. Part D Transp. Environ., vol. 28, pp. 1–5, May 2014, doi: 10.1016/j.trd.2013.12.004.

T. Brewer, “A Maritime Emission Control Area for the Mediterranean Sea? Technological Solutions and Policy Options for a ‘Med ECA,’” Euro-Mediterranean J. Environ. Integr., vol. 5, no. 1, p. 15, Apr. 2020, doi: 10.1007/s41207-020-00155-1.

S. Karamperidis, C. Kapalidis, and T. Watson, “Maritime Cyber Security: A Global Challenge Tackled through Distinct Regional Approaches,” J. Mar. Sci. Eng., vol. 9, no. 12, p. 1323, Nov. 2021, doi: 10.3390/jmse9121323.

R. Hopcraft and K. M. Martin, “Effective maritime cybersecurity regulation – the case for a cyber code,” J. Indian Ocean Reg., vol. 14, no. 3, pp. 354–366, Sep. 2018, doi: 10.1080/19480881.2018.1519056.

P. Louvros, F. Stefanidis, E. Boulougouris, A. Komianos, and D. Vassalos, “Machine Learning and Case-Based Reasoning for Real-Time Onboard Prediction of the Survivability of Ships,” J. Mar. Sci. Eng., vol. 11, no. 5, p. 890, Apr. 2023, doi: 10.3390/jmse11050890.

H. Lan, J. Gao, Y.-Y. Hong, and H. Yin, “Interval forecasting of photovoltaic power generation on green ship under Multi-factors coupling,” Sustain. Energy Technol. Assessments, vol. 56, p. 103088, Mar. 2023, doi: 10.1016/j.seta.2023.103088.

P. Gupta, A. Rasheed, and S. Steen, “Ship performance monitoring using machine-learning,” Ocean Eng., vol. 254, p. 111094, Jun. 2022, doi: 10.1016/j.oceaneng.2022.111094.

H. Tu, K. Xia, E. Zhao, L. Mu, and J. Sun, “Optimum trim prediction for container ships based on machine learning,” Ocean Eng., vol. 277, p. 111322, Jun. 2023, doi: 10.1016/j.oceaneng.2022.111322.

X. Lang, D. Wu, and W. Mao, “Physics-informed machine learning models for ship speed prediction,” Expert Syst. Appl., vol. 238, p. 121877, Mar. 2024, doi: 10.1016/j.eswa.2023.121877.

Y. E. Senol and A. Seyhan, “A novel machine-learning based prediction model for ship manoeuvring emissions by using bridge simulator,” Ocean Eng., vol. 291, p. 116411, Jan. 2024, doi: 10.1016/j.oceaneng.2023.116411.

M. Zhang, P. Kujala, M. Musharraf, J. Zhang, and S. Hirdaris, “A machine learning method for the prediction of ship motion trajectories in real operational conditions,” Ocean Eng., vol. 283, p. 114905, Sep. 2023, doi: 10.1016/j.oceaneng.2023.114905.

L. P. Perera, K. Belibassakis, E. Filippas, and M. Premasiri, “Advanced Data Analytics Based Hybrid Engine-Propeller Combinator Diagram for Green Ship Operations,” in Volume 5A: Ocean Engineering, American Society of Mechanical Engineers, Jun. 2022. doi: 10.1115/OMAE2022-79490.

Y. Han, W. Ma, and D. Ma, “Green maritime: An improved quantum genetic algorithm-based ship speed optimization method considering various emission reduction regulations and strategies,” J. Clean. Prod., vol. 385, p. 135814, Jan. 2023, doi:10.1016/j.jclepro.2022.135814.

D. Ma, W. Ma, S. Hao, S. Jin, and F. Qu, “Ship’s response to low-sulfur regulations: From the perspective of route, speed and refueling strategy,” Comput. Ind. Eng., vol. 155, p. 107140, May 2021, doi:10.1016/j.cie.2021.107140.

A. De, A. Choudhary, and M. K. Tiwari, “Multiobjective Approach for Sustainable Ship Routing and Scheduling With Draft Restrictions,” IEEE Trans. Eng. Manag., vol. 66, no. 1, pp. 35–51, Feb. 2019, doi: 10.1109/TEM.2017.2766443.

Y. Yao, M. Wei, and B. Bai, “Descriptive statistical analysis of experimental data for wettability alteration with surfactants in carbonate reservoirs,” Fuel, vol. 310, p. 122110, Feb. 2022, doi:10.1016/j.fuel.2021.122110.

Y. Wang, X. Shao, C. Liu, G. Cai, L. Kou, and Z. Wu, “Analysis of wind farm output characteristics based on descriptive statistical analysis and envelope domain,” Energy, vol. 170, pp. 580–591, Mar. 2019, doi: 10.1016/

Z. Said, P. Sharma, B. J. Bora, and A. K. Pandey, “Sonication impact on thermal conductivity of f-MWCNT nanofluids using XGBoost and Gaussian process regression,” J. Taiwan Inst. Chem. Eng., vol. 145, p. 104818, Apr. 2023, doi: 10.1016/j.jtice.2023.104818.

T. Chen and C. Guestrin, “XGBoost,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA: ACM, Aug. 2016, pp. 785–794. doi: 10.1145/2939672.2939785.

A. T. Hoang and V. V. Pham, “A Review on Fuels Used for Marine Diesel Engines,” J. Mech. Eng. Res. Dev., vol. 41, no. 4, pp. 22–32, Nov. 2018, doi: 10.26480/jmerd.04.2018.22.32.

V. V. Pham and A. T. Hoang, “Technological perspective for reducing emissions from marine engines,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 6, pp. 1989–2000, 2019, doi: 10.18517/ijaseit.9.6.10429.

M. Rodriguez Valido, J. Perez Marrero, A. Mauro González, P. Fabiani Bendicho, and C. Efrem Mora, “Evaluation of the Potential of Sentinel-5P TROPOMI and AIS Marine Traffic Data for the Monitoring of Anthropogenic Activity and Maritime Transport NOx-Emissions in Canary Islands Waters,” Sustainability, vol. 15, no. 5, p. 4632, Mar. 2023, doi: 10.3390/su15054632.

R. Li, Y. Liu, and Q. Wang, “Emissions in maritime transport: A decomposition analysis from the perspective of production-based and consumption-based emissions,” Mar. Policy, vol. 143, p. 105125, Sep. 2022, doi: 10.1016/j.marpol.2022.105125.

C. Sui, P. de Vos, D. Stapersma, K. Visser, and Y. Ding, “Fuel Consumption and Emissions of Ocean-Going Cargo Ship with Hybrid Propulsion and Different Fuels over Voyage,” J. Mar. Sci. Eng., vol. 8, no. 8, p. 588, Aug. 2020, doi: 10.3390/jmse8080588.

D. Huang, Y. Wang, and C. Yin, “Selection of CO2 Emission Reduction Measures Affecting the Maximum Annual Income of a Container Ship,” J. Mar. Sci. Eng., vol. 11, no. 3, p. 534, Mar. 2023, doi: 10.3390/jmse11030534.

Z.-M. Yao, Z.-Q. Qian, R. Li, and E. Hu, “Energy efficiency analysis of marine high-powered medium-speed diesel engine base on energy balance and exergy,” Energy, vol. 176, pp. 991–1006, Jun. 2019, doi:10.1016/

T. D. Eddy et al., “Energy Flow Through Marine Ecosystems: Confronting Transfer Efficiency,” Trends Ecol. Evol., vol. 36, no. 1, pp. 76–86, Jan. 2021, doi: 10.1016/j.tree.2020.09.006.

L. Wang, J. Yao, H. Zhang, Q. Pang, and M. Fang, “A sustainable shipping management framework in the marine environment: Institutional pressure, eco-design, and cross-functional perspectives,” Front. Mar. Sci., vol. 9, Jan. 2023, doi: 10.3389/fmars.2022.1070078.

O. Oloruntobi, K. Mokhtar, A. Gohari, S. Asif, and L. F. Chuah, “Sustainable transition towards greener and cleaner seaborne shipping industry: Challenges and opportunities,” Clean. Eng. Technol., vol. 13, p. 100628, Apr. 2023, doi:10.1016/j.clet.2023.100628.

A. A. Yusuf, F. L. Inambao, and J. D. Ampah, “Evaluation of biodiesel on speciated PM2.5, organic compound, ultrafine particle and gaseous emissions from a low-speed EPA Tier II marine diesel engine coupled with DPF, DEP and SCR filter at various loads,” Energy, vol. 239, p. 121837, Jan. 2022, doi:10.1016/

C. W. Mohd Noor, M. M. Noor, and R. Mamat, “Biodiesel as alternative fuel for marine diesel engine applications: A review,” Renew. Sustain. Energy Rev., vol. 94, pp. 127–142, Oct. 2018, doi:10.1016/j.rser.2018.05.031.

M. Lashgari, A. A. Akbari, and S. Nasersarraf, “A new model for simultaneously optimizing ship route, sailing speed, and fuel consumption in a shipping problem under different price scenarios,” Appl. Ocean Res., vol. 113, p. 102725, Aug. 2021, doi:10.1016/j.apor.2021.102725.

B. Rolf, I. Jackson, M. Müller, S. Lang, T. Reggelin, and D. Ivanov, “A review on reinforcement learning algorithms and applications in supply chain management,” Int. J. Prod. Res., vol. 61, no. 20, pp. 7151–7179, Oct. 2023, doi: 10.1080/00207543.2022.2140221.

S. El Mekkaoui, L. Benabbou, S. Caron, and A. Berrado, “Deep Learning-Based Ship Speed Prediction for Intelligent Maritime Traffic Management,” J. Mar. Sci. Eng., vol. 11, no. 1, p. 191, Jan. 2023, doi: 10.3390/jmse11010191.

W. Peng, X. Bai, D. Yang, K. F. Yuen, and J. Wu, “A deep learning approach for port congestion estimation and prediction,” Marit. Policy Manag., vol. 50, no. 7, pp. 835–860, Oct. 2023, doi:10.1080/03088839.2022.2057608.

J. Jin, M. Ma, H. Jin, T. Cui, and R. Bai, “Container terminal daily gate in and gate out forecasting using machine learning methods,” Transp. Policy, vol. 132, pp. 163–174, Mar. 2023, doi:10.1016/j.tranpol.2022.11.010.

N. P. Ventikos and K. Louzis, “Developing next generation marine risk analysis for ships: Bio-inspiration for building immunity,” Proc. Inst. Mech. Eng. Part O J. Risk Reliab., vol. 237, no. 2, pp. 405–424, Apr. 2023, doi: 10.1177/1748006X221087501.

A. Rawson and M. Brito, “A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis,” Transp. Rev., vol. 43, no. 1, pp. 108–130, Jan. 2023, doi: 10.1080/01441647.2022.2036864.

S. Filom, A. M. Amiri, and S. Razavi, “Applications of machine learning methods in port operations – A systematic literature review,” Transp. Res. Part E Logist. Transp. Rev., vol. 161, p. 102722, May 2022, doi: 10.1016/j.tre.2022.102722.

L. Barua, B. Zou, and Y. Zhou, “Machine learning for international freight transportation management: A comprehensive review,” Res. Transp. Bus. Manag., vol. 34, p. 100453, Mar. 2020, doi: 10.1016/j.rtbm.2020.100453.

J. P. Panda, “Machine learning for naval architecture, ocean and marine engineering,” J. Mar. Sci. Technol., vol. 28, no. 1, pp. 1–26, Mar. 2023, doi: 10.1007/s00773-022-00914-5.

Z. Li and Y. Liu, “An Improved Model for Marine Energy Price and Efficiency Assessment,” J. Coast. Res., vol. 94, no. sp1, p. 640, Sep. 2019, doi: 10.2112/SI94-128.1.

A. T. Hoang, A. R. Al-Tawaha, Lan Anh Vu, Van Viet Pham, A. M. Qaisi, and J. Křeček, “Integrating Environmental Protection Education in the Curriculum: A Measure to Form Awareness of Environmental Protection for the Community,” in Environmental Sustainability Education for a Changing World, Cham: Springer International Publishing, 2021, pp. 191–207. doi: 10.1007/978-3-030-66384-1_12.

T. A. T. Do, Q. T. Le, and T. N. D. Hoang, “Integration training information in Vietnam maritime university based on the Conceive-Design-Implement-Operate,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 3, pp. 1017–1024, 2019, doi: 10.18517/ijaseit.9.3.8260.

T. Q. Le and T. T. A. Do, “Active teaching techniques for engineering students to ensure the learning outcomes of training programs by CDIO Approach,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 1, pp. 266–273, 2019.

T. Q. Le, “Approaching CDIO to innovate the training program for seafarers to meet the requirements of the industrial revolution 4.0,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 2, pp. 648–655, 2019.

N. Assani, P. Matić, N. Kaštelan, and I. R. Ćavka, “A review of artificial neural networks applications in maritime industry,” IEEE access, 2023.

S. Poongavanam, D. Rajesh, K. Viswanathan, and S. R. Banu, “Role and Challenges of Artificial Intelligence in the Maritime Industry,” J. Surv. Fish. Sci., vol. 10, no. 3S, pp. 6313–6317, 2023.

H. P. Nguyen, “Core Orientations for 4.0 Technology Application on the Development Strategy of Intelligent Transportation System in Vietnam,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 10, no. 2, pp. 520–528, Mar. 2020, doi: 10.18517/ijaseit.10.2.11129.

V. T. Pham, “Critical information for vietnamese economy aiming at a strategic breakthrough as approaching the industry 4.0,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 9, no. 3, pp. 1008–1016, 2019.

A. Sharafi, H. Iranmanesh, M. S. Amalnick, and M. Abdollahzade, “Financial management of Public Private Partnership projects using artificial intelligence and fuzzy model,” Int. J. energy Stat., vol. 4, no. 02, p. 1650007, 2016.

J. Baldoni, E. Begoli, D. F. Kusnezov, and J. MacWilliams, “Solving hard problems with AI: dramatically accelerating drug discovery through a unique public-private partnership,” J. Commer. Biotechnol., vol. 25, no. 4, 2020.