A scalable digital twin employing machine-learning to discover actionable insights to reduce emissions/resource consumption utilising shared portside data. (Portunus)

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90% of everything we consume is moved by sea. However, the shipping industry remains a laggard in terms of digitalisation and the development of disruptive, data-driven, real-time analytics to improve and streamline operations.The shipping industry is responsible for around 940mt of CO2 annually, at least 2.5% of the world's total CO2 emissions (UKRI, 2021).Ports are well-positioned to catalyse a reduction in shipping emissions.**This project will introduce Portunus**, a digital twin designed to enable just-in-time (JIT) arrivals through real-time data sharing across ports and optimize port resources (cranes/forklift/trucks etc.). We will utilise ML models and delivering actionable insights to reduce emissions/resource consumption utilising shared portside data.**Environmental impact -- Portunus:*** Information on JIT at least 12 hours before a vessel arrives at port can **reduce total journey emissions by 4%** (IMO,2022).* **2% reduction in total emissions** from a vessel for every hour saved in/around port.UK shipping emissions for 2019=14.3 MtCO2e/year. If all UK ports adopt Portunus, EA anticipate approximately **1 million tonnes reduction** **in total GHG emissions within shipping industry.**