Emission calculations

There are two conceptual approaches for estimating ship emissions inventories in the maritime industry: fuel-based (top-down) and activity-based (bottom-up). The top-down approach collects total fuel consumption based on bunker sales data and through emission factors that amount is converted to CO₂ emissions. It is commonly used in estimating emission inventories at the national and global levels, especially when detailed traffic data is not available, however, it is not as detailed for not accounting for the real movement of ships. In contrast, the bottom-up approach calculates GHG and air pollutants emitted by a ship in a specific spatial context and aggregates them over time and fleet for total emission estimation. Recently, the bottom-up approach has become more widely used, especially in combination with ship Automatic Identification System (AIS) data, which can provide more accurate results.

There have been several studies using AIS data along with ship-specific information to apply a spatial distribution bottom-up approach to the emissions from the marine sector (Smith et al. (2014), Olmer et al. (2017), Johansson et al. (2017), Faber et al. (2020), Kramel et al. (2021)). Those models enabled estimations of emissions in maritime transport, helping to provide insights into technical and operational energy efficiency.

However, due to constraints of fleet-level analysis including a lack of detailed information about ships, the uncertainty of the collected data, and the calculation complexity, their methods for predicting power or energy consumption sometimes rely on simple calculations or empirical methods. For instance, the IMO studies use the Admiralty Formula that gives ship’s power in calm water by using the cubic rule, and the weather effect used as a particular percentage of sea margin. Although more complex methods have recently been used in some studies, it is necessary to develop a comprehensive powering prediction method that can be used for a bottom-up approach by supplementing existing studies.

Resistance models

The MariTeam model strives to incorporate different ship resistance model in order to increase the coffidence in predicting evermore accurate ship power demand and the emissions associated with it.

Calm water resistance

Well-established empirical methods such as Holtrop and Mennen (1982), Hollenbach (1998), Guldhammer and Harvald (1974), and Kristensen and Lützen (2012) were used to estimate calm water resistance. An internal alogrithm handles the choice of the most appropriate ship resistance model depending on ship type and the performance of the resitance model as compared to the ship resistance at service speed. For example, at service speed, the calm water resistance needs to be within a give margin of the total install power at the main engine. Below, a summary of the calm water resistance in the main global shipping emission models.

Weather effect

Weather can result in an increase in emissions of as much as 15-20% in some weather prone areas with heavy ship traffic, such as the North Atlantic (see more). In order to improve accuracy in the MariTeam model, the weather effect is subdivided into wind and waves, and methods for estimating added resistance due to wind such as Blendermann (1996), ITTC (2017b), and Fujiwara (2006), and waves such as ITTC (2017b).

The effect of weather

We can also assess the regions where weather conditions contribute most to increased emissions. Traditional emission inventories often apply a fixed sea margin, such as a 12.5% increase in fuel consumption when operating in water, to account for environmental resistance. However, our approach is more refined, incorporating real-time wave height and wind speed data to better estimate the additional energy demand. By integrating these dynamic weather factors, we can more accurately capture how adverse conditions impact fuel consumption and emissions, leading to a more precise understanding of regional variations in shipping emissions. For more information, you can read the paper by Young-Rong Kim (2023) “Modelling of ship resistance and power consumption for the global fleet”.

Different ship types experience varying shares of resistance from calm water, wind, and waves due to differences in hull shape, size, and operational profiles. In calm water, frictional and residual resistance dominate, with slender, high-speed vessels like container ships experiencing relatively higher wave-making resistance than bulk carriers or tankers, which have fuller hull forms optimized for efficiency at lower speeds. Wind resistance is more significant for vessels with large above-water profiles, such as cruise ships and car carriers, compared to low-profile tankers or bulkers. Wave resistance varies with sea conditions and hull design, with larger ships generally handling waves better due to their length and displacement, while smaller vessels or those with lower freeboard, like some ferries or offshore supply vessels, experience proportionally greater resistance in rough seas. These factors influence fuel consumption and emissions, making hydrodynamic optimization and weather routing critical for efficiency across ship types.

Emission curves

Emissions are the result of the combustion inside the engine chamber and are directly dependent of the engine configuration and fuel properties. Whereas carbon dioxide (CO2) and water are originated as result of the hydrocarbon structure of fossil fuels, other emissions occur due to non-ideal circumstances during combustion, such as incomplete combustion of fuel due to inadequate air/fuel mixture.

Fuel consumption is calculated based on the load conditions at the engine and the specific fuel consumption (SFOC). Whenever operating outside its optimal range, fuel consumption will increase accordingly, reaching up to 62% increase in some cases. Emissions of other pollutants that are not strictly related to the fuel chemical composition, but rather vary depending on optimal combustion conditions are calculated differently.

After an extensive review of the literature, we have developed specfic emission curves for the main emissions stemming from conventional fuel engines, see below.