# Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities
## 3 The Role of AI in Sustainable Shipping
### Energy Efficiency  
AI-driven route optimization is revolutionizing energy efficiency in maritime operations by leveraging advanced algorithms to determine the most efficient routes for vessels. Traditional route planning often relies on static and less dynamic methods, which can lead to suboptimal fuel usage and increased energy consumption. In contrast, AI algorithms can process a multitude of real-time data points, such as weather conditions, ocean currents, wave heights, and traffic patterns, to continuously update and optimize routes [86; 87; 88; 89]. The choice of the most profitable pace strategy is the key to optimizing a ship's route. To begin with, the average main engine (ME) power for the voyage duration is estimated as a percentage of MCR (Maximum Continuous Rating). Two speed strategies--constant power and dual speed--are available when planning a voyage. For constant power strategy, it should be ensured that ME power remains steady as much as possible. On the other hand, dual-speed strategy means using low power for one part of journey and high power for another. Indeed, making a choice among these two port pairs can be very challenging in terms of efficiency and optimization. Estimating the average ME power is usually not direct because there are multiple internal and external factors involved. These include fouling on propeller and hulls, condition and design limitations inherent to ME operations, sea state, wind strength, ocean currents, speed restricted zones and ship draft. They are crucial in selecting the best route and speed towards an intended port destination. Tools like ECO-Voyage are available to facilitate decision making. It was developed by Maersk Maritime Technology, and based solely on user submitted inputs, it can forecast ocean currents and depths, wind, and waves over a planned journey. This program comes up with an optimum ME power, engine RPM, and estimated speed, which would reach the destination in time considering the weather conditions. In ECO-Voyage, after calculating the average vessel power for the voyage, the user can select one from two types of speed strategies: constant power or dual speed. The tool determines total fuel consumption for the specified speed strategy. After comparing projected fuel consumption of both approaches, the user will be able to choose the most cost- and time-effective travel schedule. Furthermore, it helps to see how one might execute such a trip at different velocities and power levels. Nevertheless, although there are many advantages associated with ECO-Voyage, as mentioned above, it is simply a supportive decision-making tool that does not consider real-time variations in factors influencing energy performance; other than that, input by users may entail mistakes due to observation errors. Still, when properly, used ECO-Voyage can significantly reduce costs in addition to mitigating environmental impact [90]. ML models can predict and adapt to changing conditions, ensuring that vessels take the most energy-efficient routes. For instance, AI systems can suggest minor course adjustments to avoid adverse weather or heavy seas, which can significantly reduce fuel consumption. These models can also incorporate historical data to learn from past voyages, improving the accuracy and efficiency of future route planning [91; 92; 93]. Additionally, AI-driven route optimization can integrate with shipboard systems to provide real-time recommendations to the crew or automate navigational adjustments directly. By doing so, vessels can maintain optimal speeds and operational conditions that