Recently, decarbonizing the maritime industry, which accounts for 2.8% of world emissions, has become essential. However, as a crucial component of maritime transportation, container shipping also carries substantial significance. In this context, the International Maritime Organization endeavors to endorse several projects and methods to mitigate maritime transport emissions. As a result, this research assesses frameworks, infrastructure, training, and other critical factors to analyze multiple operational and technological possibilities for predicted decarbonization solutions in container shipping using the multi-criteria decision-making (MCDM) approach to assess ship owners’ and stakeholders’ desires. It employs a comprehensive methodology that begins with a systematic literature review using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method to prepare questionaries, followed by an MCDM analysis using the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) methods to examine the results. This research contributes to the scholarly discourse on reducing the emissions of maritime transportation. According to the findings, operational alternatives, such as ship speed, trim, and maritime route optimizations, are considerably more appealing than design and technology solutions, such as technically advanced ship hulls or machinery reforms. The pragmatic advantages of the operational alternatives, such as lower costs and shorter implementation schedules, stimulate their adoption. In contrast, design and technological solutions can influence emission reductions in the long term. It is possible to find operational alternatives for short-term decarbonization, while technological and design advancements can aid in long-term emission reductions in container shipping.

This research develops a dual physics-based machine learning system to forecast fuel consumption and CO₂ emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte Carlo simulations provides a solid foundation for training machine learning models, particularly in cases where dataset restrictions are present. The XGBoost model demonstrated superior performance compared to Support Vector Regression, Gaussian Process Regression, Random Forest, and Shallow Neural Network models, achieving near-zero prediction errors that closely matched physics-based calculations. The physics-based analysis demonstrated that the Combined scenario, which combines hull coatings with bulbous bow modifications, produced the largest fuel consumption reduction (5.37% at 15 knots), followed by the Advanced Propeller scenario. The results demonstrate that user inputs (e.g., engine power: 870 kW, speed: 12.7 knots) match the Advanced Propeller scenario, followed by Paint, which indicates that advanced propellers or hull coatings would optimize efficiency. The obtained insights help ship operators modify their operational parameters and designers select essential modifications for sustainable operations. The model maintains its strength at low speeds, where fuel consumption is minimal, making it applicable to other oil tankers. The hybrid approach provides a new tool for maritime efficiency analysis, yielding interpretable results that support International Maritime Organization objectives, despite starting with a limited dataset. The model requires additional research to enhance its predictive accuracy using larger datasets and real-time data collection, which will aid in achieving global environmental stewardship.

This literature review critically examines the development and optimization of sustainable energy and exergy analysis software specifically designed for offshore wind farms, emphasizing the transformative role of machine learning (ML) in overcoming operational challenges. Offshore wind energy represents a cornerstone in the global transition to low-carbon economies due to its scalability and superior energy yields; however, its complex operational environment, characterized by harsh marine conditions and logistical constraints, necessitates innovative analytical tools. Traditional deterministic methods often fail to capture the dynamic interactions within wind farms, thereby underscoring the need for ML-integrated approaches that enhance precision in energy forecasting, fault detection, and exergy analysis. This PRISMA-ScR review synthesizes recent advancements in ML techniques, including Random Forest, Long Short-Term Memory networks, and hybrid models, demonstrating significant improvements in predictive accuracy and operational efficiency. In addition, it critically identifies current gaps in existing software tools, such as inadequate real-time data processing and limited user interface design, which hinder the practical implementation of ML solutions. By integrating theoretical insights with empirical evidence, this study proposes a unified framework that leverages ML algorithms to optimize turbine performance, reduce maintenance costs, and minimize environmental impacts. Emerging trends, such as incorporating digital twins and Internet of Things (IoT) technologies, further enhance the potential for real-time system monitoring and adaptive control. Overall, this review provides a comprehensive roadmap for the next generation of software tools to revolutionize offshore wind farm management, thereby aligning technological innovation with global renewable energy targets and sustainable development goals.

The energy industry is undergoing rapid change. As a result of efforts to reduce greenhouse gas emissions and safeguard the planet for future generations, electricity generation and consumption are changing. Renewable energy sources, such as wind and solar, are expected to contribute nearly 70% of global electricity production by 2050. Digital technology is facilitating these dramatic changes to the energy system. An essential digital technology in the energy sector is remote sensing. Remote sensing is used for energy management and information collection. For instance, remote sensing can be used to determine the optimal location for the construction of hydroelectric, solar, and wind power plants. Asset planning, management, and risk management can be supported by using remote sensing technologies. Using this technology reduces the need for human inspections and facilitates advanced decision support and risk management. In conjunction with geospatial analytics, satellite-based remote sensing can supplement or replace traditional methods that require expensive and labour-intensive. In addition, several types of assets can be managed, including power plants, transmission and distribution grids, pipeline infrastructure, wind farms, power lines, pipeline infrastructure, and asset condition monitoring. In this paper, a variety of energy management applications are examined using remote sensing technologies.

Sustainability is a concept for achieving integration and balance in development to preserve and use resources properly by providing new solutions to overcome structural, social and economic misconceptions to prevent further degradation and waste of natural resources and create a better future for human societies. Sustainable development can be defined as a strategy for the correct use of resources, by modifying and redefining consumption patterns and avoiding one-sided development approaches. Therefore, sustainable development is defined as a long-term and unlimited solution for aligning different concepts (social, economic, and environmental), seemingly independent but related and influencing each other. This paper examines the challenges and capacities of the Oman Maritime Community and Maritime Energy Management's role in terms of sustainable development. To analyze this issue, the role of key players in Oman's maritime industry, such as the Oman Fisheries Company and the Oman Shipping Company, has been studied with the five main indicators of sustainable development goals, including people, the planet, peace, welfare, and partnership.