The maritime industry is a major carbon emission contributor. Therefore, the global maritime industry puts every effort into reducing carbon emissions in the shipping chain, which includes vessel fleets, ports, terminals, and hinterland transportation. A representative example is the carbon emission reduction standard mandated by the International Maritime Organization for international sailing ships to reduce carbon emissions this year. Among the decarbonization tools, the most immediate solution for reducing carbon emissions is to reduce vessel waiting time near ports and increase operational efficiency. The operation efficiency improvement in maritime stakeholders’ port operations can be achieved using data. This data collection and operational efficiency improvement can be realized using a digital twin. This study develops a digital twin that measures and reduces carbon emissions using the collaborative operation of maritime stakeholders. In this study, the authors propose a data structure and backbone scheduling algorithm for a port digital twin. The interactive scheduling between a port and its vessels is investigated using the digital twin. The digital twin’s interactive scheduling for the proposed model improved predictions of vessel arrival time and voyage carbon emissions. The result of the proposed digital twin model is compared to an actual operation case from the Busan New Port in September 2022, which shows that the proposed model saves over 75 % of the carbon emissions compared with the case.