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1 INTRODUCTION

Maritime shipping is a mainstay of the global economy

and acts as a lifeline between global trade networks.

Yet this ubiquitous activity generates enormous

amounts of greenhouse gas emissions, accounting for

around 3% of total global emissions. Growing concern

about climate change has encouraged global action to

reduce greenhouse gas emissions from all sources,

including shipping. International bodies such as the

International Maritime Organisation (IMO) are leading

efforts to make the shipping industry carbon neutral by

2050 [1]. Greenhouse gas emissions are to be

significantly reduced by 2030 and 2040, which is a

necessary step on the way to complete

decarbonization. Greenhouse gas emissions from the

maritime sector come largely from the combustion of

fossil fuels by ships (mainly HFO and LSFO), which

release carbon dioxide, methane and nitrous oxide as

well as sulphur and nitrogen oxides, which have an

impact on public health. While ports are also a source

of these emissions, they are one of the main drivers of

decarbonization, both by curbing emissions from ships

in port and by spearheading the decarbonization of the

various maritime actors.

Ports are part of the global supply chain and act as

hubs between shipping and land transport. Due to

rising energy prices and the need to reduce their

overall emissions, ports are taking measures to reduce

their energy needs, leading to stricter environmental

measures to limit pollutants and greenhouse gas

emissions from energy consumption [2]. The

integration of new technologies into port operations

brings many difficulties, such as problems with traffic

congestion, harmonisation of surrounding residential

areas with the port and the calculation and reduction

of CO2 emissions, as well as general energy transition

planning [3]. Ports have far-reaching impacts that

usually involve rail, road and inland waterway

System Dynamics Approach to Global Shipping

Emissions

M. Hero, P. Vidmar & M. Perkovič

University of Ljubljana, Ljubljana, Slovenia

ABSTRACT: This paper presents an improved System Dynamics (SD) model to assess the greenhouse gas (GHG)

emission reduction measures between 2023 and 2050 at the container terminal of the Port of Koper. The new

model builds on previous models and includes several subsystems that take into account many decarbonisation

instruments such as the electrification of facilities, the introduction of alternative fuels (ammonia, methanol,

hydrogen, biofuels) and onshore power supply (OPS). The simulation results show that emissions can be reduced

by 88.6% by 2050, mainly through harbour equipment. The model follows the dynamics of the S-curve in the

introduction and estimation of throughput growth in reality. Decarbonisation has not been fully completed but

can be advanced with future technologies such as carbon capture for additional emission reductions. The study

proves that dynamic modelling is efficient in long-term sustainability planning and provides a scalable model

that can be used by other ports wishing to meet the IMO 2050 targets.

http://www.transnav.eu

the International Journal

on Marine Navigation

and Safety of Sea Transportation

Volume 20

Number 1

March 2026

DOI: 10.12716/1001.20.01.17

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networks, so it is important to consider the

environmental issues associated with these interlinked

modes of transport [4]. The sustainability of maritime

transport consists of environmental, economic and

social aspects, with the environment taking centre

stage in terms of emissions from ships and port

machinery [5]. Ports must not only decarbonise, but

also combat the effects of climate change, such as rising

sea levels and increasing storms. To effectively reduce

port emissions, including emissions from docking

ships, the contributions of different emission sources

need to be assessed.

This quantification enables targeted policies and

measures to reduce emissions [6]. Technological

solutions such as exhaust gas cleaning systems and

alternative fuels are at the centre of reducing air

pollution and climate change from ships. However, a

rigorous assessment of overall sustainability must be

made when selecting measures to reduce air pollution.

The same applies to ports. Maritime players are

increasingly forced to change their business and

strategies due to new, ever-changing environmental

regulations and consumer pressure for sustainability.

This type of transition generally means the

introduction of multimodal transport chains,

alternative fuels and the imposition of sustainability

requirements on their partners [7].

2 LITERATURE REVIEW

Ports around the world are trying to find the most

appropriate strategies for their decarbonization, from

electrification to the introduction of alternative fuels

and energy efficiency measures [8]. Not only are most

port activities leading to decarbonization, but the

sustainability of ports in general is also being driven by

the inclusion of new green infrastructure [9].

Synergies and standardisation between

environmental and energy management as part of the

"Green Port" concept can improve the competitiveness

of Mediterranean ports [10]. By standardising

environmental improvement measures and working

together, ports can reduce their greenhouse gas

emissions more effectively and improve their overall

sustainability performance. In this way, advanced

technologies can be implemented and ports can be

made more environmentally friendly by shifting from

carbon-intensive to low-carbon operations [2]. This

will involve the introduction of new fuels, renewable

energy and new technologies. Recent studies indicate

that incentives need to be created to accelerate the

introduction of electromobility in river and maritime

shipping and reduce ship emissions [11]. Collaboration

between shipowners, terminal operators and policy

makers is required for the effective introduction of new

technologies [12]. Improving optimisation and further

developing new technologies, including alternative

fuels and renewable energies, are basic prerequisites

for efficiently reducing emissions [13]. The integration

of renewable energy such as solar and wind power

could also be feasible in some ports.

Ports play a dual role in decarbonization, firstly by

reducing their own emissions and secondly by

promoting decarbonization in the maritime sector [14].

To reduce port emissions even further, alternative

fuels, renewable energy and other technologies should

be explored to accelerate emission reductions [15]. For

these solutions to materialise, infrastructure needs to

be upgraded for the introduction of alternative fuels,

which is currently not the case for the majority of

measures [16]. The standardisation and high cost of

infrastructure in ports and on ships for the widespread

introduction of various measures could also become a

challenge [17]. The appropriate introduction of

alternative fuels will also consider their environmental

impact on a life cycle basis [18]. The largest emitters in

harbours are ships, more specifically their diesel

generators. For this reason, an important measure for

ports is the introduction of On-shore Power Supply

(OPS), which significantly reduces emissions from

ships while in port [12]. This also includes pollutants

such as noise [19]. If OPS is not possible from an

economic point of view, alternative methods must be

further researched [20]. OPS will in most cases require

electricity from the local grid, which would require

costly investments in the local grid or local power

generation.

To translate all these measures into a workable

model (including the emphasis on OPS), SD Modelling

can be used. To date, there are not many studies that

have used SD modelling to estimate GHG emissions

from shipping. In one case [21], a SD model was

developed to reduce GHG emissions from container

ships travelling from Shanghai to LA. In another SD

model [22], emissions from ships on the Northern Sea

Route (NSR) were assessed. In another study [23],

emissions from the Qingdao container port were

reduced in a SD model. In one more study [24], authors

have finalised the decarbonization model for the Port

of Koper container terminal by 2050 using SD. Since

this study deals with a similar topic in relation to the

Port of Koper, the resulting research question is of

course: Is it possible to improve the existing model and

how? To answer this research question, our aim was to

further upgrade the existing model.

3 MATERIALS AND METHODS

This study applies a SD approach to the reduction of

greenhouse gas emissions from the container terminal

in the Port of Koper between 2023 and 2050 with

annual time steps. In this way, it is harmonised with

the IMO targets in order to achieve the neutrality

targets. The base year 2023 is the starting year, as the

most recent data was available for this year. SD is an

excellent tool for analysing complex relationships

between different variables such as economic,

technological, technical and political measures,

especially over time. This makes it ideal for long-term

planning. The study employs a quantitative

methodology that uses different emission sources,

technology deployment curves and policy measures to

provide a comprehensive scenario analysis for

decarbonization strategies.

The system boundaries include the following GHG-

emitting activities: direct emissions from diesel-

powered port equipment and ships, indirect emissions

from electricity consumption in the container terminal

from the Slovenian power grid and infrastructure

emissions from the energy consumption of the

container terminal buildings.

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The model contains the stock and flow structure of

SD models by including the following elements: state

variables (equipment size, technology adoption rate,

and emission accumulation rate), rate variables

(technology adoption rates, efficiency improvements,

and emission generation rates), auxiliary variables

(throughput growth and regulatory factors), and

constants (equipment specifications, operating

parameters, and emission factors).

The model integrates five subsystems: The

economic subsystem, which accounts for the increase

in container throughput; the technological subsystem,

which includes equipment electrification rates, the

introduction of alternative fuels and efficiency

improvements; the regulatory subsystem, which

includes IMO regulations and emission reduction

targets; the operational subsystem, which includes

equipment utilisation along with maintenance and

replacement cycles; and the environmental subsystem

with its emission calculations and decarbonization of

the network.

The data for port equipment, such as emission

factors and working hours, were mainly taken from the

article [24]. The ship data was provided to us by the

harbour authorities of the Port of Koper. In terms of

maritime regulations and targets, the primary source

was the IMO [25].

Technology adoption rates follow logistic S-curve

functions to represent realistic technology penetration

patterns:

( )

k t x

Adoption Rate

−−

(1)

k = adoption rate parameter (technology-specific)

t = time index

x₀ = inflection point (50% adoption time)

The model was programmed using the Python

programming language. NumPy was used for

numerical calculations and array operations. We used

ScyPy to interpolate functions for parameter

trajectories. Matplotlib visualised the data and

presented the results. Pandas completed the

management and analysis of the data structures.

The simulation uses discrete time integration with

annual time steps and calculates the emissions

generated by the equipment based on emission factors

and working hours, the introduction of technologies

with S-curve functions, the transition from

conventional fuels to alternative fuels and the

development of emission values.

Figure 1. The Basic Flowchart of Model Variables

4 RESULTS

Based on the assumed parameters and the data

provided by the port authorities, we have estimated

the total GHG emissions of the container terminal in

the Port of Koper in 2023 at approximately 25,047

tonnes CO2EQ. The emissions are distributed as

follows: 69.2% of total emissions are from port

equipment, 30.0% from vessels and 0.8% from

infrastructure systems. Port equipment emissions are

divided into the following categories: Straddle carriers

produce 5,581 tonnes of CO2EQ (22.3% of total

emissions), terminal trucks contribute 5,644 tonnes of

CO2EQ (22.5% of total emissions), RTG cranes 4,725

tonnes of CO2EQ (18.9% of total emissions), STS cranes

320 tonnes of CO2EQ (1.3% of total emissions). Other

port equipment contributes 1,040 tonnes CO2EQ (4.2%

of all emissions). STS cranes have low total emissions

as they are fully electrified. We have estimated ship

emissions at the Port of Koper container terminal at

around 7,500 tonnes CO2EQ.

This model shows significant reductions in total

emissions, from 25,047 tonnes CO2EQ in 2023 to 2,847

tonnes CO2EQ in 2050, which corresponds to a

reduction in total emissions of 88.6. The emission

reductions follow a non-linear pattern. By 2030, total

emissions will fall to 22,156 tonnes CO2EQ (11.5% less

than in 2023), by 2040 total emissions will amount to

9,823 tonnes CO2EQ (60.78% less than in 2023) and by

2050 they will reach 2,847 tonnes CO2EQ (88.63% less

than in 2023), thus coming very close to

decarbonization.

The Equipment emission reductions show the

highest reduction level, from 17,310 tonnes CO2EQ in

2023 to 1,205 tonnes CO2EQ in 2050 (93% reduction).

This is primarily due to electrification (78%

electrification of diesel appliances by 2050),

decarbonisation of the grid, which reaches 0.01 tonnes

CO2EQ /MWh [26], and efficiency improvements of

30% [27].

Emissions from shipping are gradually decreasing,

from 7,500 tonnes CO2EQ in 2023 to 1,598 tonnes

CO2EQ (78.7% reduction). This is primarily due to the

introduction of alternative fuels (85% adoption rate),

the introduction of OPS (95% adoption rate) and the

improvement of ship efficiency (up to 30%

improvement) [28]. The adoption of alternative fuels is

divided into an adoption rate of 30% for ammonia, 25%

for methanol, 15% for biofuels and 15% for hydrogen.

Infrastructure emissions fall from 237 tonnes

CO2EQ in 2023 to 44 tonnes CO2EQ in 2050 (81.4%

reduction). This is achieved primarily through the

decarbonization of the grid.

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Figure 2. Modelled trajectory versus IMO targets

Figure 3. Contribution of total emissions by each subsystem

through timeline in the model

Figure 4. Adoption rate of main decarbonization measures

throughout the timeline

5 DISCUSSION

This SD model is in some respects an improvement on

Model 1 and Model 2 [24], which were produced in

another study. The new study offers a more complex

approach in which emission sources and mitigation

measures operate in a more interconnected system and

to a lesser extent as independent variables.

The S-curves of the adoption functions of the

current SD model are more realistic than the linear

adoption rates in the earlier Model 1 and Model 2.

These logistic curves depict the slow initial adoption,

rapid scaling phase, and eventual market saturation

typical of real-world technology adoption. The same

path is followed by the total emission reductions of the

current model (gradual decline from 2023 to 2030,

accelerated reduction from 2040 to 2050 and strong

decarbonization from 2040 to 2050).

In this model, fuel transition is also more diverse. In

addition to ammonia, hydrogen and methanol, we

have also added biofuels as a potential, albeit only

partial, substitute for fossil fuels.

We have also included the integration of

throughput growth in the model, with an annual

increase in container throughput of 2.5%, which is

more realistic compared to static throughput. For an

accurate assessment of the increase in container

throughput, we would need more data from the port

authorities.

However, the new, improved model has some

limitations. The model requires real and not estimated

input data, which must be constantly updated over

time. The sophisticated interactions of the model are

more sensitive to parameter calibration errors. Future

models could be improved by waste heat utilisation,

material recycling and circular economy projections.

Despite its limitations, the model can be transferred

to other ports by adapting the framework and

variables, preserving the structural relationships and

providing a template that can be effectively customised

and extended. The best thing about this model is that it

can be constantly updated with new insights and new

technologies that are not yet on the market, with new

variables in real time.

6 CONCLUSION

The SD model developed for the container terminal of

the Port of Koper shows that large emission reductions

can be achieved by coordinating different SD variables,

such as technology adoption and policy adaptation.

While the approximate emission reduction of 88.6% by

2050 is not a complete decarbonization, being slightly

short of it, it represents a realistic path to a net-zero

target when ambitions and operational requirements

are taken into account.

The most important thing about this model is the

fact that the integrated technology assessment and

regulatory compliance provide a basis for planning

future strategies and decisions that could bring the Port

of Koper to the forefront of sustainable port operations.

The integrated approach is far more effective

compared to traditional linear models as it allows for

more accurate forecasts.

Although achieving full decarbonization will

require further improvements and perhaps even the

addition of new variables by 2050, the model is an

excellent template for a future roadmap towards this

goal. The 2,847 tonnes of CO2EQ remaining in 2050

could be an opportunity for further research and

additional measures such as carbon capture and faster

integration of nuclear energy into the grid.

This study shows that ports can lead the way in

decarbonising maritime transport through sheer

planning, new technologies and collaboration. The Port

of Koper’s science-based decarbonization planning,

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underpinned by sophisticated modelling, could

contribute to global emissions reduction in the port

industry.

The transition to a carbon-neutral port operation is

also a strategic opportunity, not only for the

environment, but also for further efficiency gains,

improved competitiveness through innovation and

collaboration. In this way, the Port of Koper can

achieve its decarbonization goals while remaining an

important port of entry for Central Europe.

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