1. Copernicus Marine Service.https://marine.copernicus.eu

2. Engine Volvo Penta tier13 A2022 8398.https://www.volvopenta.com/about-us/news-page/2022/jun/imo-tier-iii-range-expands-with-new-d13-solutions/

3. Shap package.https://github.com/slundberg/shap

4. StormGlass API.https://stormglass.io

5. Marine Traffic (2022).https://www.marinetraffic.com/en/ais/details/ships/shipid:1088282/mmsi:265513810/imo:8602713/vessel:BURO

6. Ampah, J.D., Yusuf, A.A., Afrane, S., Jin, C., Liu, H.: Reviewing two decades of cleaner alternative marine fuels: towards IMO’s decarbonization of the maritime transport sector. J. Clean. Prod.320, 128871 (2021)

   Article  Google Scholar 

7. Arrieta, A.B., et al.: Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion58, 82–115 (2020)

   Article  Google Scholar 

8. Bank, W.: Accelerating Digitalization: Critical Actions to Strengthen the Resilience of the Maritime Supply Chain. World Bank, Washington (2020)

   Google Scholar 

9. Bellingmo, P.R., Pobitzer, A., Jørgensen, U., Berge, S.P.: Energy efficient and safe ship routing using machine learning techniques on operational and weather data. In: 20th International Conference on Computer Applications and Information Technology in the Maritime Industries (2021)

   Google Scholar 

10. Donner, P., Johansson, T.: Sulphur directive, short sea shipping and corporate social responsibility in a EU context. In: Corporate Social Responsibility in the Maritime Industry, pp. 149–166 (2018)

    Google Scholar 

11. Eurostat: Short sea shipping - country level - gross weight of goods transported to/from main ports (2023).https://ec.europa.eu/eurostat/databrowser/view/mar_sg_am_cw/default/table?lang=en

12. Haranen, M., Myöhänen, S., Cristea, D.S.: The role of accurate now-cast data in shi p efficiency analysis. In: 2nd Hull Performance & Insight Conference, pp. 25–38 (2017)

    Google Scholar 

13. Fourth IMO GHG study 2020 (2020)

    Google Scholar 

14. Jørgensen, U., Belingmo, P.R., Murray, B., Berge, S.P., Pobitzer, A.: Ship route optimization using hybrid physics-guided machine learning. In: Journal of Physics: Conference Series, vol. 2311, p. 012037. IOP Publishing (2022)

    Google Scholar 

15. Kim, D., Antariksa, G., Handayani, M.P., Lee, S., Lee, J.: Explainable anomaly detection framework for maritime main engine sensor data. Sensors21(15), 5200 (2021)

    Article  Google Scholar 

16. Lang, X., Wu, D., Mao, W.: Comparison of supervised machine learning methods to predict ship propulsion power at sea. Ocean Eng.245, 110387 (2022)

    Article  Google Scholar 

17. Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

18. Medda, F., Trujillo, L.: Short-sea shipping: an analysis of its determinants. Maritime Policy Manag.37(3), 285–303 (2010)

    Article  Google Scholar 

19. Sinnott, R.W.: Virtues of the haversine. Sky Telescope68(2), 158 (1984)

    MathSciNet  Google Scholar 

20. Sugimoto, K.: Digital twin for monitoring remaining fatigue life of critical hull structures

    Google Scholar 

21. Veerappa, M., Anneken, M., Burkart, N., Huber, M.F.: Validation of XAI explanations for multivariate time series classification in the maritime domain. J. Comput. Sci.58, 101539 (2022)

    Article  Google Scholar 

22. Zakaria, A., Md Arof, A., Khabir, A.: Instruments utilized in short sea shipping research: a review. In: Ismail, A., Dahalan, W.M., Öchsner, A. (eds.) Design in Maritime Engineering, pp. 83–108. Springer, Cham (2022).https://doi.org/10.1007/978-3-030-89988-2_7

    Chapter  Google Scholar 

23. Zis, T.P., Psaraftis, H.N., Ding, L.: Ship weather routing: a taxonomy and survey. Ocean Eng.213, 107697 (2020)

    Article  Google Scholar 

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