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The global transport sector, a significant contributor to energy consumption and greenhouse gas (GHG) emissions, requires innovative solutions to meet sustainability goals. Artificial intelligence (AI) has emerged as a transformative technology, offering opportunities to enhance energy efficiency and reduce GHG emissions in transport systems. This study provides a comprehensive review of AI's role in optimizing vehicle energy management, traffic flow, and alternative fuel technologies, such as hydrogen fuel cells and biofuels. It explores AI's potential to drive advancements in electric and autonomous vehicles, shared mobility, and smart transportation systems. The economic analysis demonstrates the viability of AI-enhanced transport, considering Total Cost of Ownership (TCO) and cost-benefit outcomes. However, challenges such as data quality, computational demands, system integration, and ethical concerns must be addressed to fully harness AI's potential. The study also highlights the policy implications of AI adoption, underscoring the need for supportive regulatory frameworks and energy policies that promote innovation while ensuring safety and fairness. [ABSTRACT FROM AUTHOR]

The maritime industry, central to global trade, faces critical challenges related to energy efficiency and environmental sustainability due to significant energy loss from waste heat in marine engines. This review investigates the potential of waste heat recovery (WHR) technologies to enhance operational efficiency and reduce emissions in marine systems. By analyzing major WHR methods, such as heat exchangers, Organic Rankine Cycle (ORC) systems, thermoelectric generators, and combined heat and power (CHP) systems, this work highlights the specific advantages, limitations, and practical considerations of each approach. Unique to this review is an examination of WHR performance in confined marine spaces and compatibility with existing ship components, providing essential insights for practical implementation. Findings emphasize WHR as a viable strategy to reduce fuel consumption and meet environmental regulations, contributing to a more sustainable maritime industry. [ABSTRACT FROM AUTHOR]

The maritime industry, responsible for moving approximately 90% of the world's goods, significantly contributes to environmental pollution, accounting for around 2.5% of global greenhouse gas emissions. This review explores the integration of artificial intelligence (AI) in promoting sustainability within the maritime sector, focusing on shipping and port operations. By addressing emissions, optimizing energy use, and enhancing operational efficiency, AI offers transformative potential for reducing the industry's environmental impact. This review highlights the application of AI in fuel optimization, predictive maintenance, route planning, and smart energy management, alongside its role in autonomous shipping and logistics management. Case studies from Maersk Line and the Port of Rotterdam illustrate successful AI implementations, demonstrating significant improvements in fuel efficiency, emission reduction, and environmental monitoring. Despite challenges such as high implementation costs, data privacy concerns, and regulatory complexities, the prospects for AI in the maritime industry are promising. Continued advancements in AI technologies, supported by collaborative efforts and public–private partnerships, can drive substantial progress towards a more sustainable and efficient maritime industry. [ABSTRACT FROM AUTHOR]

Urban centers, replete with diverse amenities and opportunities, simultaneously grapple with the challenges brought on by rapid urbanization, notably in the realms of transport and logistics. A pivotal move towards energy- efficient and sustainable systems is essential to mitigate these challenges. In this landscape, machine learning (ML), and particularly recurrent neural networks (RNNs), emerge as powerful tools for effectively addressing these urban complexities. This comprehensive review zeroes in on the deployment of RNNs within sustainable urban transportation and logistics, highlighting their adeptness in processing sequential data, a critical component in various forecasting and optimization tasks. We commence with a foundational understanding of RNNs, segueing into their successful applications in urban transport and logistics. This review also critically examines the constraints of current methodologies and potential avenues for enhancement. We scrutinize the application of RNNs across several areas, encompassing the energy shift in both passenger and freight transport, logistics management, integration of low- and zero-emission vehicles, and the energy dynamics of transport and logistics. Additionally, the role of RNNs in traffic and infrastructure planning is explored, particularly in forecasting traffic flow, congestion patterns, and optimizing energy usage. The crux of this review is to amalgamate and present the existing knowledge on the instrumental role of RNNs in facilitating the transition to energy-efficient urban transportation and logistics. Our goal is to highlight effective strategies, pinpoint challenges, and map out avenues for future research in this domain. [ABSTRACT FROM AUTHOR]