This thesis presents a data-driven design approach for emission reduction using bunker delivery notes (BDNs) to help support the revised IMO strategy to achieve net-zero greenhouse gas emissions by international shipping close to 2050. This research supports the Horizon Europe’s Digital Twin for Green Shipping (DT4GS) project which focuses on the development of digital twins (DTs). Part of this project involves the development of a DT-supported method for the design and retrofit of ships. DTs are a promising approach for supporting maritime decarbonization efforts due to their simulation and big data handling capability. Despite the abundance of shipping data and growing digitalization, the potential of using ship operational data for decarbonization efforts remains not fully exploited. A data-driven method such as a DT could fill this gap. However, as DTs, by definition, require real-time connection between a physical entity and the digital representation, developing a true DT for new-build alternatively fueled ship designs remains a challenge. This research thus starts by looking into retrofitting using data from existing ships. A design framework is proposed to construct digital models to support a DT for retrofitting purpose. The proposed framework is tested on a case-study using a 300-meter bulk carrier. Since January 2019, operational ship data is collected through BDNs, a mandatory data collection method for ships of 5000 GT and above, adopted by the IMO. Constructing a DT based on BDNs is considered to be convenient as it provides a solid source of operational data in the future. First, the available data from the BDNs is preprocessed using an adopted framework based on data science literature. The resulting 5,678 data points are used for the construction of a model representing the bulk carrier and a model representing the green ship technologies part. A fuel consumption model is constructed to represent the bulk carrier. It utilizes a gray-box modeling approach, consisting of a white-box resistance model and a black-box artificial neural network. Both models incorporate environmental-dependent inputs. The investigated green ship technologies for the potential retrofit are represented by various wind-assisted ship propulsion (WASP) systems, namely a towing kite, a DynaRig sail, and a Flettner rotor. These systems are modeled using a white-box modeling approach, together with available wind data. Using an adopted integration framework, based on the propeller-engine matching procedure, both representations are combined into one green ship digital model. An environmental assessment is performed using the IMO's EEXI and CII assessment tools, respectively evaluating the design and operational aspects of the potential retrofit. Additionally, a financial assessment is conducted using the payback period. Results showed the design implications and emissions reduction potential of implementing such systems which will guide the retrofit decision by the ship's owner.