# Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities
## 4 AI in Green Ports  
### Port Operations  
AI for Cargo Handling and Terminal Operations  
The integration of AI in cargo handling and terminal operations is revolutionizing the efficiency and sustainability of port activities. AI technologies enhance various aspects of cargo management, from container handling to storage optimization, significantly improving the speed, accuracy, and safety of port operations [157, 158, 159]. AI-powered systems can automate and optimize the placement and retrieval of containers within a terminal. AI containing information on the terminal layout and storage locations, linked to software containing information on the IMDG code and the required methods for segregating and separating dangerous goods carried in packaging, could accurately and efficiently plan the stowage of such cargo at the container terminal. ML algorithms analyze data on container sizes, weights, and destinations to determine the most efficient stacking and storage strategies. This optimization reduces the time and energy required for cranes and other equipment to move containers, leading to faster turnaround times and lower energy consumption [84, 160, 161]. Computer vision, a subset of AI, plays a crucial role in enhancing cargo handling. Advanced image-recognition systems can identify and track containers, inspect them for damages, and verify their contents against shipping manifests. This technology improves the accuracy of inventory management and reduces the risk of human error. Additionally,AI can streamline the process of customs inspections by quickly scanning and analyzing container contents, expediting clearance procedures [5; 159; 162]. Robotic systems, guided by AI, are increasingly being used for automating the physical handling of cargo. Autonomous-guided vehicles (AGVs) and automated stacking cranes (ASCs) can operate continuously and with greater precision than human operators. These systems not only improve efficiency but also enhance safety by reducing the risk of accidents associated with manual handling [163; 164; 165]. AI also contributes to the predictive maintenance of terminal equipment. By analyzing data from sensors embedded in cranes, conveyors, and other machinery, AI can predict when maintenance is needed, preventing unexpected breakdowns and ensuring that equipment operates at peak efficiency. This proactive approach minimizes downtime and extends the lifespan of terminal assets [166; 167]. Automation in Port Logistics  
Automation in port logistics, driven by AI, is transforming the way goods are transported, stored, and managed within port facilities. AI systems enable seamless coordination between different logistical operations, enhancing the overall efficiency and sustainability of port activities [168; 169]. One of the key applications of AI in port logistics is the optimization of supply chain workflows. AI algorithms can analyze data from multiple sources, including shipping schedules, cargo volumes, and transportation routes, to create optimized logistics plans. These plans ensure that cargo is moved through the port as efficiently as possible, minimizing delays and reducing congestion [170; 171]. AI-powered logistics platforms can automate the scheduling and coordination of transport vehicles, such as trucks and trains, within the port. By using real-time data on traffic conditions, vehicle availability, and cargo readiness, AI systems can dynamically adjust schedules to optimize the flow of goods. This optimization reduces waiting times, fuel consumption, and emissions associated with idling vehicles [172; 173; 174]. Smart warehouse management systems, driven by AI, enhance the efficiency of storage and retrieval operations within port warehouses. These systems use ML to predict demand for different types of cargo and optimize storage layouts accordingly. By ensuring that frequently accessed goods are stored in easily accessible locations, AI systems reduce the time and energy required for handling [175; 176].