Ανάλυση δεδομένων σε θέματα περιβαλλοντικής βιωσιμότητας και ενέργειας στον τομέα της ναυτιλίας

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Keywords
Environmental sustainability ; Machine learning ; Energy efficiency ; MARPOL ; IMOAbstract
This thesis addresses environmental sustainability in shipping, focusing on the reduction of emissions and the improvement of energy efficiency. Shipping plays a crucial role in international trade, transporting over 86% of global goods, yet it significantly contributes to global greenhouse gas emissions, accounting for 3%.
The study analyzes the challenges faced by the shipping industry in achieving sustainable development, examining the technical, regulatory, and economic requirements involved. The need for the adoption of innovative technologies, such as artificial intelligence and machine learning, is highlighted, as these technologies assist in data analysis and fuel consumption optimization, while also promoting energy efficiency and reducing emissions.
The institutional framework, including the MARPOL Convention and European regulations for monitoring and reducing emissions, is thoroughly examined. Optimization models are proposed to minimize fuel consumption and emissions. Special emphasis is placed on the use of real-time monitoring systems and data-driven decision-making, aiming to enhance sustainable practices within the shipping sector.