Journals / Turkish Journal of Maritime and Marine Sciences / 2025 / Volume: 11 Issue: 4

Artificial Intelligence in Port Environmental Management: A Strategic Analysis

Liman Çevre Yönetiminde Yapay Zeka: Stratejik Bir Analiz

Esma Önal(ARTVIN CORUH UNIVERSITY)0000-0002-7635-525X
Ali Tehci(ORDU ÜNİVERSİTESİ)0000-0001-9949-2794
Pages
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Publication Date
2025-12-01
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

AI and its subfields has the potential to become a strategic tool for achieving seaport sustainability goals that align with environmental priorities.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Maritime activities play a crucial role in global trade.
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Key Findings & Data:Nevertheless, existing studies in literature often focus on a limited scope and fail to incorporate environmental priorities in seaport operations.
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Conclusion & Impact:AI and its subfields has the potential to become a strategic tool for achieving seaport sustainability goals that align with environmental priorities.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Artificial Intelligence · Machine Learning · Deep Learning

Abstract

Maritime activities play a crucial role in global trade. However, seaports cause various environmental problems, particularly pollution in coastal and urban areas. Artificial intelligence (AI) and its subfields, machine learning and deep learning have emerged as promising tools for addressing these problems, garnering increasing interest within the maritime sector. Nevertheless, existing studies in literature often focus on a limited scope and fail to incorporate environmental priorities in seaport operations. This study explored the potential of AI and its subfields to enhance resilience to environmental problems posed by operational activities in seaports. Ten port environmental priorities from the ESPO’s report were included as environmental indicators. The study was conducted in two phases. The first phase involved a systematic literature review of 117 sources from the Web of Science and Scopus databases. In line with the systematic analysis, the second phase was evaluated using a SWOT analysis. Thereafter, a series of strategic recommendations were formulated on the based on an analysis of both internal and external factors. The study provided twelve strategic recommendations for enhancing current practices. AI and its subfields has the potential to become a strategic tool for achieving seaport sustainability goals that align with environmental priorities.

Özet

Denizcilik faaliyetleri küresel ticarette çok önemli bir rol oynamaktadır. Ancak limanlar, özellikle kıyı ve kentsel alanlarda kirlilik olmak üzere çeşitli çevre sorunlarına neden olmaktadır. Yapay zekâ (YZ) ve alt alanları olan makine öğrenimi ve derin öğrenme, bu sorunları çözmek için umut vaat eden araçlar olarak ortaya çıkmış ve denizcilik sektöründe giderek artan bir ilgi görmüştür. Bununla birlikte, literatürdeki mevcut çalışmalar genellikle sınırlı bir alana odaklanmakta ve liman operasyonlarında çevresel öncelikleri dikkate almamaktadır. Bu çalışma, deniz limanlarındaki operasyonel faaliyetlerin yol açtığı çevresel sorunlara karşı dayanıklılığı artırmak için YZ ve alt alanlarının potansiyelini araştırmıştır. ESPO raporundaki on liman çevresel önceliği çevresel göstergeler olarak dahil edilmiştir. Çalışma iki aşamada gerçekleştirilmiştir. İlk aşamada, Web of Science ve Scopus veritabanlarından 117 kaynağın sistematik bir literatür taraması yapılmıştır. Sistematik analizle uyumlu olarak, ikinci aşama SWOT analizi kullanılarak değerlendirilmiştir. Ardından, iç ve dış faktörlerin analizine dayalı olarak bir dizi stratejik öneri formüle edilmiştir. Çalışma, mevcut uygulamaları geliştirmek için on iki stratejik öneri sağlamıştır. YZ ve alt alanları, çevresel önceliklerle uyumlu liman sürdürülebilirlik hedeflerine ulaşmak için stratejik bir araç olma potansiyeline sahiptir.

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Cite This Article

Önal, E., Toygar, A., Tehci, A. (2025). Artificial Intelligence in Port Environmental Management: A Strategic Analysis. Turkish Journal of Maritime and Marine Sciences, Volume: 11 Issue: 4. https://doi.org/10.52998/trjmms.1707732

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References (90)

  1. Abbass, H. A. (2019). Social integration of artificial intelligence: functions, automation allocation logic and human-autonomy trust. Cognitive Computation, 11(2), 159–171. doi: 10.1007/s12559-018-9619-0
  2. Abdellaoui, B., Ech-Cheikh, H., Sadik, M., Rachid, A., Lissane Elhaq, S., & Mounadel, A. (2023). A review on ship-generated oily waste management at ports: current practices, challenges and future directions. Environment, Development and Sustainability. doi: 10.1007/s10668-023-04226-5
  3. Abonamah, A. A., Tariq, M. U., & Shilbayeh, S. (2021). On the commoditization of artificial intelligence. Frontiers in Psychology, 12(696346). doi: 10.3389/fpsyg.2021.696346
  4. Acciaro, M., Ghiara, H., & Cusano, M. I. (2014). Energy management in seaports: A new role for port authorities. Energy Policy, 71, 4–12.
  5. Agostinelli, S., Neshat, M., Majidi Nezhad, M., Piras, G., & Astiaso Garcia, D. (2022). Integrating renewable energy sources in Italian port areas towards renewable energy communities. Sustainability, 14(21), 13720. doi: 10.3390/su142113720
  6. Agrawal, A., Gans, J. S., & Goldfarb, A. (2019). Exploring the impact of artificial intelligence: Prediction versus judgment. Prediction versus Judgment. Information Economics and Policy, 47, 1–6. doi: 10.1016/j.infoecopol.2019.05.001
  7. Andrei, N., & Scarlat, C. (2024). Marine Applications: The Future of Autonomous Maritime Transportation and Logistics. IntechOpen. doi: 10.5772/intechopen.1004275
  8. Arel, I., Rose, D. C., & Karnowski, T. P. (2010). Deep machine learning-a new frontier in artificial intelligence research [research frontier]. IEEE Computational Intelligence Magazine, 5(4), 13–18. doi: 10.1109/MCI.2010.938364
  9. Barraza, M. T., Junior, L. U. D. T., Alexandre, J., de Castro Xavier, G., Carneiro, J. C., da Silva, L. G. C. H., & de Azevedo, A. R. (2024). Characterization of port dredging waste for potential used as incorporation on materials for civil construction: A case study in Brazil. Journal of Materials Research and Technology, 32, 4379–4386. doi: 10.1016/j.jmrt.2024.09.023
  10. Borowiec, M. L., Dikow, R. B., Frandsen, P. B., McKeeken, A., Valentini, G., & White, A. E. (2022). Deep learning as a tool for ecology and evolution. Methods in Ecology and Evolution, 13(8), 1640–1660. doi: 10.1111/2041-210X.13901
  11. Butt, N. (2007). The impact of cruise ship generated waste on home ports and ports of call: A study of Southampton. Marine Policy, 31(5), 591–598. doi: 10.1016/j.marpol.2007.03.002
  12. Camliyurt, G., Park, Y., Kim, D., Kang, W. S., & Park, S. (2023). Machine Learning with Multi-Source Data to Predict and Explain Marine Pilot Occupational Accidents. Journal of Marine Science and Technology, 31(4), 2. doi: 10.51400/2709-6998.2709
  13. Cecchi, G., Cutroneo, L., Di Piazza, S., Rosa, E., Zotti, M., & Capello, M. (2023). Myco-Barriers as Sustainable Tool for Port Seawater Decontamination from Metals. Journal of Marine Science and Engineering, 11(6), 1117. doi: 10.3390/jmse11061117
  14. Chaibi, M., & Daghrir, J. (2023). Artificial Intelligence for Predictive Maintenance of Port Equipment: A Revolution in Progress. International Conference Design and Modeling of Mechanical Systems, 332–340. doi: 10.1007/978-3-031-67152-4_35
  15. Chen, W., Men, Y., Fuster, N., Osorio, C., & Juan, A. A. (2024). Artificial intelligence in logistics optimization with sustainable criteria: A review. Sustainability, 16(21), 9145. doi: 10.3390/su16219145
  16. D’Amico, G., Szopik-Depczyńska, K., Dembińska, I., & Ioppolo, G. (2021). Smart and sustainable logistics of Port cities: A framework for comprehending enabling factors, domains and goals. Sustainable Cities and Society, 69(102801). doi: 10.1016/j.scs.2021.102801
  17. Deng, T., Chau, K. W., & Duan, H. F. (2021). Machine learning based marine water quality prediction for coastal hydro-environment management. Journal of Environmental Management, 284(112051). doi: 10.1016/j.jenvman.2021.112051
  18. Deng, Y., & Han, J. (2024). Energy Management of Green Port Multi-Energy Microgrid Based on Fuzzy Logic Control. Energies, 17(14), 3601. doi: 10.3390/en17143601
  19. Di Vaio, A., Varriale, L., & Alvino, F. (2018). Key performance indicators for developing environmentally sustainable and energy efficient ports: Evidence from Italy. Energy Policy, 122, 229–240. doi: 10.1016/j.enpol.2018.07.046
  20. Dinh, G. H., Pham, H. T., Nguyen, L. C., Dang, H. Q., & Pham, N. D. K. (2024). Leveraging artificial intelligence to enhance port operation efficiency. Polish Maritime Research, 31(2), 140–155. doi: 10.2478/pomr-2024-0030
  21. Drungilas, D., Kurmis, M., Senulis, A., Lukosius, Z., Andziulis, A., Januteniene, J., & Voznak, M. (2023). Deep reinforcement learning based optimization of automated guided vehicle time and energy consumption in a container terminal. Alexandria Engineering Journal, 67, 397–407. doi: 10.1016/j.aej.2022.12.057
  22. Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63–71. doi: 10.1016/j.ijinfomgt.2019.01.021
  23. Durlik, I., Miller, T., Kostecka, E., Łobodzińska, A., & Kostecki, T. (2024). Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities. Applied Sciences, 14(14), 5994. doi: 10.3390/app14145994
  24. Ejjami, R., & Boussalham, K. (2024). Resilient supply chains in Industry 5.0: Leveraging AI for predictive maintenance and risk mitigation. International Journal For Multidisciplinary Research, 6(4). ESPO. (2024). EcoPorts inSights 2024: Environmental report. European Sea Ports Organisation. https://www.espo.be/media/ESPO Environmental Report 2024.pdf
  25. Fallon, C. K., & Blaha, L. M. (2018). Improving automation transparency: Addressing some of machine learning’s unique challenges. Augmented Cognition: Intelligent Technologies: 12th International Conference, AC 2018, Held as Part of HCI International 2018, 245–254. doi: 10.1007/978-3-319-91470-1_21
  26. Fredianelli, L., Bolognese, M., Fidecaro, F., & Licitra, G. (2021). Classification of noise sources for port area noise mapping. Environments, 8(2), 12. doi: 10.3390/environments8020012
  27. Gobbi, G. P., Di Liberto, L., & Barnaba, F. (2020). Impact of port emissions on EU-regulated and non-regulated air quality indicators: The case of Civitavecchia (Italy). Science of the Total Environment, 719(134984). doi: 10.1016/j.scitotenv.2019.134984
  28. Guo, J., Jiang, Z., Chu, X., & Wang, W. (2024). Parallel intelligent monitoring system of port water quality based on the ACP method. Journal of Marine Science and Engineering, 12(2), 218. doi: 10.3390/jmse12020218
  29. Iris, Ç., & Lam, J. S. L. (2019). A review of energy efficiency in ports: Operational strategies, technologies and energy management systems. Renewable and Sustainable Energy Reviews, 112, 170–182. doi: 10.1016/j.rser.2019.04.069
  30. ISO. (2024). Deep learning: The mechanics of magic. International Organization for Standardization. https://www.iso.org/artificial-intelligence/deep-learning
  31. Jeevan, J., Mohd Salleh, N. H., Abdul Karim, N. H., & Cullinane, K. (2023). An environmental management system in seaports: evidence from Malaysia. Maritime Policy & Management, 50(8), 1118–1135. doi: 10.1080/03088839.2022.2047814
  32. Jiang, C., Zheng, S., Ng, A. K., Ge, Y. E., & Fu, X. (2020). The climate change strategies of seaports: Mitigation vs. adaptation. Transportation Research Part D: Transport and Environment, 89(102603). doi: 10.1016/j.trd.2020.102603
  33. Kimera, D., & Nangolo, F. N. (2020). Predictive maintenance for ballast pumps on ship repair yards via machine learning. Transportation Engineering, 2, 100020. doi: 10.1016/j.treng.2020.100020
  34. Kok, J. N., Boers, E. J., Kosters, W. A., Van der Putten, P., & Poel, M. (2009). Artificial intelligence: definition, trends, techniques, and cases. Artificial Intelligence, 1(270–299), 51.
  35. Kotsiopoulos, T., Sarigiannidis, P., Ioannidis, D., & Tzovaras, D. (2021). Machine learning and deep learning in smart manufacturing: The smart grid paradigm. Computer Science Review, 40, 100341. doi: 10.1016/j.cosrev.2020.100341
  36. Kovalishin, P., Nikitakos, N., Svilicic, B., Zhang, J., Nikishin, A., Dalaklis, D., & Stefanakou, A. A. (2023). Using Artificial Intelligence (AI) methods for effectively responding to climate change at marine ports. Journal of International Maritime Safety, Environmental Affairs, and Shipping, 7(1), 2186589. doi: 10.1080/25725084.2023.2186589
  37. Kshetri, N. (2024). The Environmental Impact of Artificial Intelligence. IT Professional, 26(3), 9–13. doi: 10.1109/MITP.2024.3399471
  38. Kusumawati, E. D., Karjono, K., & Karmanis, K. (2023). Review of Port Management Integrated Digitization System: A Pathway to Efficient and Sustainable Port Operations. Maritime Park Journal of Maritime Technology and Society, 55–60. doi: 10.62012/mp.v2i2.26531
  39. Lam, J. S. L., & Notteboom, T. (2014). The greening of ports: a comparison of port management tools used by leading ports in Asia and Europe. Transport Reviews, 34(2), 169–189.
  40. Lee, H., Chatterjee, I., & Cho, G. (2023). AI-powered intelligent seaport mobility: enhancing container drayage efficiency through computer vision and deep learning. Applied Sciences, 13(22), 12214. doi: 10.3390/app132212214
  41. Lee, H. T., Lee, J. S., Yang, H., & Cho, I. S. (2021). An AIS data-driven approach to analyze the pattern of ship trajectories in ports using the DBSCAN algorithm. Applied Sciences, 11(2), 799. doi: 10.3390/app11020799
  42. Lehmacher, W., Lind, M., Poikonen, J., Meseguer, J., & Cárcel Cervera, J. L. (2022). Reducing port city congestion through data analysis, simulation, and artificial intelligence to improve the well-being of citizens. Journal of Mega Infrastructure & Sustainable Development, 2(sup1), 65–82. doi: 10.1080/24724718.2022.2133524
  43. Lemley, J., Bazrafkan, S., & Corcoran, P. (2017). Deep learning for consumer devices and services: pushing the limits for machine learning, artificial intelligence, and computer vision. IEEE Consumer Electronics Magazine, 6(2), 48–56. doi: 10.1109/MCE.2016.2640698
  44. Lim, S., Pettit, S., Abouarghoub, W., & Beresford, A. (2019). Port sustainability and performance: A systematic literature review. Transportation Research Part D: Transport and Environment, 72, 47–64. doi: 10.1016/j.trd.2019.04.009
  45. Mansoursamaei, M., Moradi, M., González-Ramírez, R. G., & Lalla-Ruiz, E. (2023). Machine learning for promoting environmental sustainability in ports. Journal of Advanced Transportation, 1, 2144733. doi: 10.1155/2023/2144733
  46. Martínez-Moya, J., Vazquez-Paja, B., & Maldonado, J. A. G. (2019). Energy efficiency and CO2 emissions of port container terminal equipment: Evidence from the Port of Valencia. Energy Policy, 131, 312–319. doi: 10.1016/j.enpol.2019.04.044
  47. Martínez, R., García, J. A., & Felis, I. (2023). AI-Driven Estimation of Vessel Sailing Times and Underwater Acoustic Pressure for Optimizing Maritime Logistics. Engineering Proceedings, 58(1), 26. doi: 10.3390/ecsa-10-16091
  48. Munim, Z. H., & Schramm, H. J. (2018). The impacts of port infrastructure and logistics performance on economic growth: the mediating role of seaborne trade. Journal of Shipping and Trade, 3(1), 1–19. doi: 10.1186/s41072-018-0027-0
  49. NASA. (2024). What is Artificial Intelligence? https://www.nasa.gov/what-is-artificial-intelligence/
  50. NHL Stenden University. (2024). MCAD Maritime Cyber Attack Database. MCAD Maritime Cyber Attack Database. https://maritimecybersecurity.nl/
  51. Nogué-Algueró, B. (2020). Growth in the docks: Ports, metabolic flows and socio-environmental impacts. Sustainability Science, 15(1), 11–30. doi: 10.1007/s11625-019-00764-y
  52. Norén, A., Fedje, K. K., Strömvall, A. M., Rauch, S., & Andersson-Sköld, Y. (2020). Integrated assessment of management strategies for metal-contaminated dredged sediments–What are the best approaches for ports, marinas and waterways? Science of the Total Environment, 716, 135510. doi: 10.1016/j.scitotenv.2019.135510
  53. Notteboom, T., van der Lugt, L., van Saase, N., Sel, S., & Neyens, K. (2020). The role of seaports in green supply chain management: Initiatives, attitudes, and perspectives in Rotterdam, Antwerp, North Sea Port, and Zeebrugge. Sustainability, 12(4), 1688. doi: 10.3390/su12041688
  54. Owusu-Mfum, S., Hudson, M. D., Osborne, P. E., Roberts, T. J., Zapata-Restrepo, L. M., & Williams, I. D. (2023). Atmospheric pollution in port cities. Atmosphere, 14(7), 1135. doi: 10.3390/atmos14071135
  55. Özbay, İ., Aksoy, C., Özbay, B., & Sayın, F. E. (2024). Port waste reception facilities in iron-steel industry: A case study from Türkiye. Sigma Journal of Engineering and Natural Sciences, 42(3), 845–853.
  56. Palomares, I., Martínez-Cámara, E., Montes, R., García-Moral, P., Chiachio, M., Chiachio, J., & Herrera, F. (2021). A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects. Applied Intelligence, 51, 6497–6527. doi: 10.1007/s10489-021-02264-y
  57. Peng, Y., Liu, H., Li, X., Huang, J., & Wang, W. (2020). Machine learning method for energy consumption prediction of ships in port considering green ports. Journal of Cleaner Production, 264, 121564. doi: 10.1016/j.jclepro.2020.121564
  58. Priya, A. K., Devarajan, B., Alagumalai, A., & Song, H. (2023). Artificial intelligence enabled carbon capture: A review. Science of The Total Environment, 886, 163913. doi: 10.1016/j.scitotenv.2023.163913
  59. Puig, M., Cirera, A., Wooldridge, C., Sakellariadou, F., & Darbra, R. M. (2024). Mega Ports’ Mitigation Response and Adaptation to Climate Change. Journal of Marine Science and Engineering, 12(7), 1112. doi: 10.3390/jmse12071112
  60. Puig, M., & Darbra, R. M. (2024). Innovations and insights in environmental monitoring and assessment in port areas. Current Opinion in Environmental Sustainability, 70, 101472. doi: 10.1016/j.cosust.2024.101472
  61. Quintana, C. G., Olea, P. M., Abdallah, P. R., & Quintana, A. C. (2016). Port environmental management: Innovations in a Brazilian public port. RAI Revista de Administração e Inovação, 13(4), 261–273. doi: 10.1016/j.rai.2016.09.001
  62. Raj, N., & Brown, J. (2023). Prediction of Mean Sea Level with GNSS-VLM Correction Using a Hybrid Deep Learning Model in Australia. Remote Sensing, 15(11), 2881. doi: 10.3390/rs15112881
  63. Ren, S., & Wierman, A. (2024). The uneven distribution of AI’s environmental impacts. Harvard Business Review. https://hbr.org/2024/07/the-uneven-distribution-of-ais-environmental-impacts
  64. Saafi, S., Vikhrova, O., Fodor, G., Hosek, J., & Andreev, S. (2022). AI-aided integrated terrestrial and non-terrestrial 6G solutions for sustainable maritime networking. IEEE Network, 36(3), 183–190. doi: 10.1109/MNET.104.210035
  65. Safdie, S. (2024). What is the environmental impact of AI? https://greenly.earth/en-gb/blog/ecology-news/what-is-the-environmental-impact-of-ai
  66. Safuan, S., & Syafira, A. (2024). Artificial Intelligence in Indonesian Ports: Opportunities and Challenges. Transactions on Maritime Science, 13(2). doi: 10.7225/toms.v13.n02.w07
  67. Schenone, C., Pittaluga, I., Repetto, S., & Borelli, D. (2014). Noise pollution management in ports: a brief review and the EU MESP project experience. Proceedings of the 21st International Congress on Sound and Vibration, 13–17.
  68. Schipper, C. A., Vreugdenhil, H., & De Jong, M. P. C. (2017). A sustainability assessment of ports and port-city plans: Comparing ambitions with achievements. Transportation Research Part D: Transport and Environment, 57, 84–111. doi: 10.1016/j.trd.2017.08.017
  69. Shu, Y., Wang, X., Huang, Z., Song, L., Fei, Z., Gan, L., & Yin, J. (2022). Estimating spatiotemporal distribution of wastewater generated by ships in coastal areas. Ocean & Coastal Management, 222, 106133. doi: 10.1016/j.ocecoaman.2022.106133
  70. Sogut, M. Z., & Erdoğan, O. (2022). An investigation on a holistic framework of green port transition based on energy and environmental sustainability. Ocean Engineering, 266, 112671. doi: 10.1016/j.oceaneng.2022.112671
  71. Sugrue, D., & Adriaens, P. (2022). Maritime transport efficiency to inform demand-driven user fees for harbor infrastructure. Journal of Waterway, Port, Coastal, and Ocean Engineering, 148(1), 04021049. doi: 10.1061/(ASCE)WW.1943-5460.0000695
  72. Sun, Z., Sandoval, L., Crystal-Ornelas, R., Mousavi, S. M., Wang, J., Lin, C., & John, A. (2022). A review of earth artificial intelligence. Computers & Geosciences, 159, 105034. doi: 10.1016/j.cageo.2022.105034
  73. Susman, R., Gütte, A. M., & Weith, T. (2021). Drivers of land use conflicts in infrastructural mega projects in coastal areas: A case study of patimban seaport, indonesia. Land, 10(6), 615. doi: 10.3390/land10060615
  74. Taye, M. M. (2023). Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers, 12(5), 91. doi: 10.3390/computers12050091
  75. Toygar, A. (2024). Sustainability in the Maritime Industry: Integration of Digital Twin and Autonomous Control. In Strategic Innovations for Dynamic Supply Chains (pp. 31–49). IGI Global. doi: 10.4018/979-8-3693-3575-8.ch002
  76. Tsolakis, N., Zissis, D., Papaefthimiou, S., & Korfiatis, N. (2022). Towards AI driven environmental sustainability: an application of automated logistics in container port terminals. International Journal of Production Research, 60(14), 4508–4528. doi: 10.1080/00207543.2021.1914355
  77. Twrdy, E., & Zanne, M. (2020). Improvement of the sustainability of ports logistics by the development of innovative green infrastructure solutions. Transportation Research Procedia, 45, 539–546. doi: 10.1016/j.trpro.2020.03.059
  78. UNCTAD. (2024). Review of marine transport 2024. https://unctad.org/publication/review-maritime-transport-2024
  79. Verschuur, J., Koks, E. E., & Hall, J. W. (2020). Port disruptions due to natural disasters: Insights into port and logistics resilience. Transportation Research Part D: Transport and Environment, 85, 102393. doi: 10.1016/j.trd.2020.102393
  80. Wang, M., Guo, X., She, Y., Zhou, Y., Liang, M., & Chen, Z. S. (2024). Advancements in Deep Learning Techniques for Time Series Forecasting in Maritime Applications: A Comprehensive Review. Information, 15(8), 507. doi: 10.3390/info15080507
  81. Woo, J. K., Moon, D. S., & Lam, J. S. L. (2018). The impact of environmental policy on ports and the associated economic opportunities. Transportation Research Part A: Policy and Practice, 110, 234–242. doi: 10.1016/j.tra.2017.09.001
  82. Xiao, G., Yang, D., Xu, L., Li, J., & Jiang, Z. (2024). The application of artificial intelligence technology in shipping: A bibliometric review. Journal of Marine Science and Engineering, 12(4), 624. doi: 10.3390/jmse12040624
  83. Yang, J., Xiao, W., Jiang, C., Hossain, M. S., Muhammad, G., & Amin, S. U. (2018). Ai-powered green cloud and data center. IEEE Access, 7, 4195–4203. doi: 10.1109/ACCESS.2018.2888976
  84. Yang, Y. C., & Chen, S. L. (2016). Determinants of global logistics hub ports: Comparison of the port development policies of Taiwan, Korea, and Japan. Transport Policy, 45, 179–189. doi: 10.1016/j.tranpol.2015.10.005
  85. Yao, H., Wang, D., Su, M., & Qi, Y. (2021). Application of digital twins in port system. Journal of Physics: Conference Series, 1846(1), 012008. doi: 10.1088/1742-6596/1846/1/012008
  86. Zare, A., Ablakimova, N., Kaliyev, A. A., Mussin, N. M., Tanideh, N., Rahmanifar, F., & Tamadon, A. (2024). An update for various applications of Artificial Intelligence (AI) for detection and identification of marine environmental pollutions: A bibliometric analysis and systematic review. Marine Pollution Bulletin, 206, 116751. doi: 10.1016/j.marpolbul.2024.116751
  87. Zheng, Y., Zhao, J., & Shao, G. (2020). Port city sustainability: A review of its research trends. Sustainability, 12(20), 8355. doi: 10.3390/su12208355
  88. Zhou, K., Yuan, X., Guo, Z., Wu, J., & Li, R. (2024). Research on Sustainable Port: Evaluation of Green Port Policies on China’s Coasts. Sustainability, 16(10), 4017. doi: 10.3390/su16104017
  89. Zhuk, A. (2023). Artificial Intelligence Impact on the Environment: Hidden Ecological Costs and Ethical-Legal Issues. Journal of Digital Technologies and Law, 1(4), 932–954. doi: 10.21202/jdtl.2023.40
  90. Zuin, S., Belac, E., & Marzi, B. (2009). Life cycle assessment of ship-generated waste management of Luka Koper. Waste Management, 29(12), 3036–3046. doi: 10.1016/j.wasman.2009.06.025