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1 INTRODUCTION
The shipping sector presents the backbone of global
trade, as maritime transport makes up over 50% of the
value of trade and 70% of the volume of international
trade [1]. Due to the possibility of transporting large
quantities of goods over long distances, at a reasonable
price, maritime transport takes the leading place in
global transport, compared to land and air, and a
moderate growth in maritime trade volume is expected
by 2028 [2]. The problems arise, both in an
environmental and economic sense, when the fact that
the maritime fleet is almost entirely powered by fossil
fuels, is considered. As stated by van Leeuwen and
Monios [3], 79% of maritime transport is powered by
Heavy Fuel oil (HFO), while the rest is powered by
marine diesel oil, marine gas oil or Liquified Natural
Gas (LNG). Consequently, the level of Greenhouse
Gases (GHGs) caused by maritime fleets is assessed at
3% of global GHG emissions caused by human activity,
with an increase of 20% over the last decade [2].
From an environmental viewpoint, the use of fossil
fuels tends to be reduced as much as possible and in
the shortest possible time. Decarbonization measures
set by the Paris Agreement [4] aim to limit the global
average temperature to well below 2°C above pre-
industrial levels, with efforts to keep it to 1.5°C. On its
basis, the International Maritime Organization (IMO)
advocated for the reduction of GHG emissions in
international shipping, applying various technical,
operational and market measures. Starting with
technical measures such as the Energy Efficiency
Design Index (EEDI) mandatory for new ships, and
operational the Ship Energy Efficiency Management
Plan (SEEMP), IMO continues to develop new
Towards Green Ferry Corridors in the Adriatic Sea –
Integrating Monte Carlo Simulations into Life Cycle
Costing Scheme for Methanol
M. Koričan
1
, M. Sjerić
1
, D. Nikolić
2
, A. Fan
3
& N. Vladimir
1
1
University of Zagreb, Zagreb, Croatia
2
University of Montenegro, Kotor, Montenegro
3
Wuhan University of Technology, Wuhan, China
ABSTRACT: The maritime transport sector is essential to global trade, handling a large share of international
trade volume, but its reliance on fossil fuels raises significant environmental and economic concerns. The
transition to greener options is slow, with 98.8% of the fleet still using fossil fuel, while the research into economic
performance of this transition often overlooks fuel price volatility, a critical factor influenced by global events
which have caused significant fuel price fluctuations. This paper introduces a model which incorporates fuel price
volatility into lifecycle cost assessments (LCCA) on an example of a ferry connecting the Croatian and Italian
shores of the Adriatic Sea. A comparison of diesel- and methanol-powered systems is provided, using Monte
Carlo simulations to evaluate economic sustainability under volatile fuel prices. Diesel prices showed a
consistently symmetrical and normal distribution across all simulations, indicating a stable price range, while
methanol prices demonstrated more volatility. The LCCA results, which included the simulated fuel prices,
showed that methanol-powered systems despite greater price volatility, show lower overall costs through
different carbon taxation scenarios.
http://www.transnav.eu
the International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 19
Number 4
December 2025
DOI: 10.12716/1001.19.04.28
1296
strategies and expand them to an ever-increasing share
of the fleet [5]. An example is the Energy Efficiency
Existing Ship Index (EEXI) and the annual operational
Carbon Intensity Indicator (CII), which entered into
force in January 2023 and are mandatory for ships of
5000 GT and above [6]. Next to technical and
operational measures, marked-based ones such as
carbon taxation, proved to be an effective means of
controlling emissions [7], [8] and policy changes are
recommended for emerging countries working on
supporting environmentally friendly, sustainable
economic growth [9], [10].
1.1 Transition to cleaner fuels
Decarbonization policies and climate regulations are
significantly transforming the marine fuel landscape
and have led to increased investment in alternative
fuels, such as methanol, ammonia, LNG, biofuels,
hydrogen, and electro-fuels, and in cleaner propulsion
technologies, including fuel cells and hybrid-electric
systems [11], [12], [13]. The introduction of carbon
pricing, efficiency measures (like EEXI and CII), and
fuel-specific emission limits has made conventional
fossil fuels more expensive, while simultaneously
encouraging the development of low-carbon
alternatives [14], [15].
An overview of current and emerging options
aimed at reducing GHG emissions in the marine sector
are given in [13]. Alternative fuels are categorized into
low-carbon, carbon-neutral, zero-carbon, and electro-
fuels, each with distinct characteristics, benefits, and
challenges. Low-carbon fuels, such as natural gas
(CNG/LNG) or methanol, offer lower carbon content
compared to conventional fuels and it can be easily
applied in dual-fuel engines. LNG presents a fossil
fuel-based alternative which is widely adopted,
primarily due to its significant environmental
advantages over conventional marine diesel fuels. It
emits approx. 25% less CO2, produces only trace
amounts of SOₓ and particulate matter, and can reduce
NOₓ emissions by up to 91.4% [16]. Carbon-neutral
fuels, predominantly biofuels like Liquefied Biogas
(LBG) and biodiesel blends, reduce GHG emissions
significantly by offsetting combustion-related CO₂
with biomass absorption. Methanol stands out as a
flexible option due to its liquid state at normal
conditions, compatibility with existing infrastructure,
and relatively straightforward storage and handling.
While it reduces emissions compared to conventional
fuels, its energy density is significantly lower,
requiring larger storage volumes [17]. Its high oxygen
content promotes more efficient combustion in engine
systems, resulting in nearly zero SOX emissions and
significant reductions in CO2. Additionally, methanol
contributes to lower NOX emissions compared to
conventional marine fuels [16]. The potential of
implementing methanol in dual-fuel engine is
demonstrated in [18], which showed that combined
with a slow steaming technique, the configuration can
significantly reduce exhaust emissions by up to 90%.
Zero-carbon fuels, including full electrification,
hydrogen, and ammonia, eliminate CO₂ emissions
during use and are viewed as long-term solutions.
Hydrogen can be classified in several types, depending
on the production method. Currently, most hydrogen
is produced via methane reforming, typically from
natural gas, although oil and coal are also used [16].
According to [19], 99.5% of the LCA emissions of
hydrogen powered systems arises from electricity
usage. Further research found that emissions in case of
non-renewable hydrogen are significantly higher than
compared to renewable. Renewable hydrogen can
reduce GHG emissions by 75.8% compared to diesel-
powered ferries [20]. Electro-fuels produced using
renewable electricity, hydrogen, and captured carbon
(e.g., e-methanol, e-methane), present another
pathway to decarbonization but are currently
expensive and dependent on subsidies and pilot
projects for wider adoption [13].
However, the widespread adoption of these
alternatives remains limited due to high capital costs,
technological uncertainty, retrofitting challenges, and
underdeveloped global bunkering infrastructure [21],
[22]. Life-cycle assessments (LCA) consistently
highlight the emissions-reduction potential of cleaner
fuels [23], [24], while life-cycle cost assessments
(LCCA) often reveal that these options are not yet
economically viable without substantial subsidies or
high carbon taxes [25], [26]. Probabilistic methods such
as Monte Carlo simulations have been increasingly
used to evaluate investment risk under fluctuating fuel
and carbon prices, often showing LNG as a favourable
near-term option due to its balance of cost and
emissions [27], [28]. These challenges are exacerbated
by global disruptions, such as the COVID-19 pandemic
and the Russia–Ukraine conflict, which have
destabilized energy markets, disrupted supply chains,
and caused extreme volatility in fuel prices [22]. These
conditions underscore the need for more resilient,
efficient, and economically feasible marine energy
systems, supported by consistent policy, adaptive
regulation, and integrated techno-economic-
environmental evaluation methods. The variation of
crude oil prices (expressed in $ per barrel) in period
2018-2024 is plotted in Figure 1 where the effects of
COVID-19 pandemic and start of Russia-Ukraine war
are evident.
Figure 1. Fluctuations of crude oil prices 2018-2024 [29]
1.2 Research gap and aim of paper
The literature survey indicates large number of studies
dealing with the economic analysis of alternative fuels
in the maritime sector, but fuel price modelling is done
in a rather simplified manner. While LCCA is widely
used to evaluate the economic feasibility of alternative
fuels and technologies, most existing studies do not
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account for the volatility of fuel prices and, thus, the
long-term economic impacts of transitioning to
alternative fuels under volatile market conditions are
not well understood. The following research gaps are
identified from the literature review:
− Current economic feasibility studies of alternative
fuels in shipping often overlook the impact of fuel
price volatility, which significantly affects
operational costs and investment decisions;
− Many studies fail to consider the effects of global
events, such as the COVID-19 pandemic and the
Russia-Ukraine war, which disrupt energy market
supply and demand, affecting the fuel prices;
− Incorporating probabilistic methods, such as Monte
Carlo simulations, into LCCA can provide a more
detailed understanding of risks and uncertainties
compared to traditional approaches.
This paper addresses these gaps by developing a
model that incorporates fuel price volatility into
economic assessments, providing a clearer picture of
the financial risks and benefits associated with
different decarbonization strategies. Using historical
fuel price databases, Monte Carlo simulations are
conducted, and the probability of price changes is
given. The values obtained are further used in the
LCCA to provide insight into the impact of fuel prices
on the total operating costs. Furthermore, few analyses
have been adapted to the operational characteristics of
ferries, which differ significantly from deep-sea
shipping in terms of voyage duration, port frequency
and fuel consumption. In addition, most models do not
include life-cycle emission costs or variables related to
fuel production and use. This results in a limited
understanding of the long-term economic viability of
alternative fuels under changing future conditions.
Addressing these shortcomings through
comprehensive Monte Carlo frameworks, that
consider cost variability over time and ferry-specific
operational parameters, is essential to guide
investment and regulatory decision-making in the
transition to low-carbon shipping.
2 METHODOLOGY
2.1 Monte Carlo Simulations
Monte Carlo simulation is a widely used statistical
technique for analysing systems influenced by
uncertainty, enabling more robust and informed
decision-making. By running a large number of
simulations, the probability distribution of possible
outcomes in processes driven by random variables is
estimated. The inputs are selected from chosen
probability distributions (normal, uniform, binominal)
which describe the probability of different outcomes
[30]. Monte Carlo simulation is widely used in various
fields, and they are commonly performed using
software such as MATLAB, Minitab, @Risk, etc. The
process begins with defining a mathematical model of
the process that clearly defines the relationships
between inputs and outputs, along with the
appropriate probability distributions. Random
samples are then generated using random number
generators, and the simulation is executed repeatedly
to produce a range of possible outcomes. These
outcomes are analysed to extract key statistical
indicators such as means, variances, and percentiles,
and are often visualized using histograms or
cumulative probability curves. This method is
especially valuable in fields like finance, engineering,
and energy systems, where decision-making under
uncertainty is critical.
In this paper, the normal distribution is chosen due
to its characteristic symmetry where most data cluster
towards the middle of the range. This distribution
effectively captures the behaviour of fuel prices [31] in
the maritime sector, where fluctuations generally
remain around a central value, and larger deviations
become less frequent. Therefore, the assumption of
normality serves as a reasonable approximation for
modelling price volatility.
In mathematical form, the normal distribution
N(μ,σ
2
) can be expressed as:
( )
2
1
2
2
2
1
, ,
2
x
Ne
−
−
=
(1)
where μ marks the arithmetic mean and σ marks the
standard deviation. The arithmetic mean is the
mathematical average of a data set, found by dividing
the sum of all values x by the number of values, while
standard deviation describes how spread-out values
are from its mean value [32]. In a normal distribution,
68.2% of results fall within the first standard deviation,
95.4% within two standard deviations and 99.7%
within three standard deviations.
Even though Monte Carlo simulation offers several
advantages, such as handling complex problems and
providing a range of possible outcomes and their
probabilities, it can be computationally intensive,
especially considering multiple simulations. Moreover,
the results depend on the quality of the input data and
the accuracy of the probability distributions, thus the
interpretation and validation of results require
expertise [32]. Monte Carlo simulations are often used
in maritime research to account for uncertainty in
emissions reductions, fuel life-cycle analysis, and
regulatory compliance costs. For instance, in [33]
Monte Carlo is used to model the cumulative
distributions of the Net Present Value (NPV) for
different fuel cases—ammonia, LNG, and low sulfur
fuel oil —under two scenarios of Emissions Trading
Systems (ETS) from the EU and the IMO. In the
evaluation of alternative marine fuels, Taljegard et al.
[34] used Monte Carlo to analyse the robustness of the
results which included parameters such the availability
of primary energy resources, the adoption of carbon
capture and storage (CCS) technologies, and the costs
associated with different technologies and fuels. In a
similar research [25], Monte Carlo has been used to
model the variability in electricity prices by employing
a uniform distribution, along with other production
costs. In this paper, the Monte Carlo simulation is
conducted to model fuel price volatility. For each fuel
type, a number of simulations of fuel prices are
conducted, all with the same statistical properties
(mean and standard deviation). The historical data on
fuel prices were used to calculate the mean, standard
deviation, variance, and skewness.
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2.2 LCCA
Life-cycle Cost Assessment (LCCA) is an economic
mechanism for predicting the total costs of a product
and simplifying the improvement of its cost-
effectiveness [35]. The method includes total costs that
occurred during the product's lifetime, from the
investment to operative costs up to the recycling stage.
In recent years, LCCA often includes the cost of
externalities associated with directives, such as carbon
taxation [36]. It is widely adopted in the industrial
sector, and it has gained significant attention in
maritime research for assessing various economic
options. This paper considers the investment, fuel and
maintenance costs of two different power systems
(diesel and methanol) over the selected vessel's lifetime
(20 years).
The investment costs LCCAinv refer to the
installation of the power system and the calculation
multiplies the power of the engine Peng with its cost
PReng [37]. The investment costs are calculated equally
for the diesel-power system as well as the methanol
one, with a difference in engine price of 250 €/kW for
the diesel engine [13]. The retrofitting costs for
methanol dual-fuel engines range from approximately
250–350 up to 650 €/kW, and in this paper an average
cost of 450 €/kW is considered [38].
The operating costs LCCAop include the fuel and
maintenance costs. In the case of a diesel-powered
system, the costs can be calculated as:
,_
.
op D D D D main
LCCA FC PR t EC PR t= +
(2)
Input on fuel consumption FCD, in kg, depends on
the vessel type and is further detailed in the following
section presenting the case study. The price of diesel
fuel, PRD (€/kg), is collected from statistical databases
[39], based on which Monte Carlo simulations are
performed and possible future values are determined.
The equation also includes the number of years t for
which the costs are calculated, in this case 20 years. The
maintenance costs of the vessel are calculated by
multiplying the annual EC with a conversion factor of
0.014 €/kWh [13], both in the case of a diesel- and
methanol-diesel dual-fuel driven vessel. The operating
costs of a methanol-diesel dual-fuel driven vessel are
calculated similarly:
, _
.
op M M M P D D M main
LCCA FC PR t FC PR t EC PR t
−
= + +
(3)
The main difference compared to a diesel-powered
system is the use of a dual-fuel engine which requires
a combination of fuel - 5% diesel as pilot fuel and 95%
of methanol [40]. Thus, the fuel consumption of
methanol FCM and pilot fuel FCP-D are calculated as
0.95
MM
FC EC SFC=
(4)
0.05
D P D
FC EC SFC
−
=
(5)
with a specific fuel consumption of methanol SFCM of
327.2 g/kWh and a specific consumption of pilot fuel
SFCP-D of 10.1 g/kWh [13]. The price of methanol is
gathered from [41] and Monte Carlo simulations are
performed the same as in the case of diesel.
2.3 Carbon taxation
Carbon taxation is a marked-based measure designed
to mitigate carbon dioxide (CO2) emissions, a primary
GHG contributing significantly to global warming. The
main objective of this measure is to impose a tax on the
carbon content of fossil fuel, thereby incentivizing the
reduction of fossil fuel use and encouraging a shift
towards greener alternatives [42]. Currently, the
carbon tax is estimated at 85 €/t CO2 and it is projected
to rise to as much as 238 €/t CO2 [43]. There are various
scenarios for carbon taxation that progressively tighten
regulations to accelerate the achievement of net-zero
emissions. This paper focuses on the Net Zero
Emission Scenario (NZES), the strictest scenario with
the goal of reaching net-zero emissions in advanced
economies by 2050 at the latest [44].
The carbon tax is calculated by multiplying the CO2
emissions released due to fuel combustion TTW, in kg
CO2, with the considered carbon allowance CA, in €/kg
CO2, of a particular year. The tailpipe emissions TTW
are calculated as follows:
TTW EF FC=
(6)
where EF marks the emission factor in kg gas/kg fuel.
The EF of CO2 emissions for marine diesel equals 3.206
kg gas/kg fuel and 1.375 kg gas/kg fuel for methanol
[5].
The value fluctuations according to the given
scenarios are shown in Figure 2, and the carbon taxes
for the following years were obtained by interpolation.
Figure 2. Carbon taxation scenarios [44]
3 CASE STUDY
The case study focuses on a Croatian ferry operating on
the international route between Ancona, Italy, and
Split (Spalato), Croatia. This route covers a distance of
approximately 245 kilometres, with each crossing
taking around 9.5 hours to complete. The ferry
performs a total of 116 trips annually, serving as a vital
connection across the Adriatic Sea [45]. Regarding
technical characteristics, the vessel is equipped with
four main engines, which provide the primary
propulsion power, complemented by four auxiliary
engines that support onboard electrical and
operational systems. The configuration is typical for a
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medium-sized ferry, making it an appropriate subject
for analysing fuel consumption, emissions, and the
economic viability of alternative fuels under real
operational conditions. More ferry details are provided
in Figure 4.
Figure 3. Technical characteristics of ferry “Marko Polo” [45]
In real-world operation, a vessel’s service speed
often deviates from its design speed due to factors such
as meteorological conditions, weather routing, fixed
timetables, and other operational limitations. Thus, at
the design operating speed, the average power
demand of the main engines PME,ave (kW) is estimated as
90% of the engine’s maximum design output PME (kW):
,
0.9
ME ave ME
PP=
(7)
The auxiliary engines are assumed to operate at an
average load of 50% and the total average ship power
Pave (kW), is obtained by summing the average main
engine power and average auxiliary engine power. For
a diesel-powered ferry, the energy consumption per
unit distance, EC (kWh/km), is determined using the
following equation:
ave
ave
P
EC
v
=
. (8)
The average speed vave (kn) is calculated by dividing
the total route length by the average trip duration. In
this study, it is assumed that all alternative power
systems have the same energy requirements as the
reference diesel-powered ferry.
There is a wide selection of alternative fuels that
provide the possibility of reducing GHG emissions
while at the same time ensuring that operational
requirements are met. The literature provides
countless studies on different fuels, and methanol has
proven to be the optimal solution in terms of both
environmental friendliness and economic
sustainability [46], [47]. Despite the advantages, the
current production methods for methanol, including
natural gas reforming and coal gasification, are carbon-
intensive, limiting its potential as a truly low-carbon
fuel. Although alternative feedstocks are being
explored, the production of methanol still relies
heavily on fossil-based sources. Additionally,
methanol's toxicity, volatility, and flammability pose
safety concerns, and the infrastructure for large-scale
production and distribution remains under
development [23]. Methanol fuel prices experience
considerable fluctuations driven by various factors,
much like other energy commodities, as seen in Figure
4. They are influenced by multiple, interconnected
factors, from global supply and demand dynamics to
crude oil prices, regulatory changes, and geopolitical
events. The total cost of methanol is affected by its
production process. Since methanol is mainly derived
from natural gas, the changes in natural gas prices have
a direct impact on methanol production costs and,
therefore, on methanol prices [48], [49].
Chronologically speaking, before the COVID-19
pandemic methanol prices were relatively stable with
fluctuations mainly driven by changes in production
prices and demand dynamics, but during the
pandemic a significant drop is visible due to reduced
demand across various industries. After the pandemic,
there was a rapid increase in prices similar to crude oil,
but there was volatility influenced by the Russia-
Ukraine war [41], [50]. Currently, the methanol prices
experience fluctuations influenced by regulatory
changes, incentives for the application of green
technologies and varying production costs. For
instance, FuelEU Maritime Regulation aims to achieve
a more sustainable, low-carbon maritime sector by
encouraging the use of cleaner fuels. Thus, the
initiative provides support for the development and
deployment of innovative technologies and fuels that
can lower the carbon footprint of shipping, such as
ammonia, hydrogen, and methanol [51]. Since its
application is more and more recommended in the
shipping sector, price volatility must be considered as
it affects the cost-effectiveness of methanol as a cleaner
alternative to traditional marine fuels.
Figure 4. Methanol price fluctuations, 2014-2024 [41]
4 RESULTS
After collecting the necessary data and creating a
database on marine diesel and methanol prices, several
Monte Carlo simulations of fuel prices were carried
out. For each fuel separately, each histogram illustrates
the distribution of 10 000 simulated fuel prices. These
simulations are based on random number generation,
ensuring that each run provides a different set of fuel
prices, yet all share the same statistical properties, such
as mean and standard deviation, derived from the
historical data, as presented in Table 1. Figures 6 and 7
show the histograms of 6 such simulations. Thus, the
key difference between the six scenarios lies in the
random number generation process, which allows for
the modelling of fuel price volatility under varying
conditions. This offers the possibility to assess the
potential future costs for each fuel type across different
time periods (2030, 2040, and 2050), and to compare the
economic impact of using diesel versus methanol in the
LCCA.
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Each histogram is overlaid with a red line
representing a normal distribution curve with the same
mean and standard deviation, providing a visual
reference for how closely the simulated data
approximates a normal distribution. The choice of
normal distribution helps the study to model fuel price
volatility in a way that is both practical for real-life
application and simplifies the calculation [31]. Then,
the LCCA is conducted using the fuel prices obtained
by Monte Carlo simulations, as presented in Figures 8,
9 and 10. LCCA results were compared for diesel and
methanol in order to gain insight not only into the
difference in total costs but also into price movements
and their stability. By adding carbon taxes, the trends
of total costs for 2030, 2040 and 2050 were compared.
Considering that the validity of the investment is to be
considered in the worst case (i.e. when the total costs
are the highest), carbon taxes are calculated for the
most expensive scenario, the Net Zero Emission
scenario.
Table 1. Statistical results of diesel and methanol prices
Mean,
€/kg
Variance,
€/kg
Standard deviation,
€/kg
Skewness,
€/kg
Diesel (D)
0.86
0.003
0.06
0.48
Methanol
(M)
0.34
0.009
0.09
0.38
The results of Monte Carlo simulations for diesel
prices, Figure 5, are calculated for a mean of 0.86 €/kg
and a standard deviation of 0.06 €/kg. The x-axis
represents the diesel price, ranging from 0.66 to 1.08
€/kg, while the y-axis indicates the frequency, showing
how often each diesel price range appeared in the
simulations. The standard deviation of 0.06 €/kg
indicates a relatively narrow spread around the mean.
In D-1, the distribution is symmetrical and centred
around the mean, with the highest frequency bar
observed between 0.84 and 0.88 €/kg. D-2 exhibits a
similar centred distribution, with a more pronounced
peak occurring in the same range, indicating a higher
concentration of diesel prices near the mean. Graph D-
3 also demonstrates a well-centred distribution closely
following the normal curve, with limited deviations at
the tails. D-4 shows a slightly wider spread compared
to the others, with minor flattening around the mean,
indicating slightly lower peak frequencies and a
marginal increase in tail occurrences. Histogram D-5
demonstrates the sharpest peak among the six,
concentrated around 0.84 to 0.88 €/kg, while D-6
displays a balanced distribution, symmetrical and
well-aligned with the expected normal pattern.
Overall, all histograms indicate that the simulated
diesel prices are predominantly centred around 0.86
€/kg, with frequencies decreasing gradually as values
of the prices deviate from the mean. Extreme price
values are rare, and the overall pattern remains
consistent.
Figure 5. Monte Carlo simulations of diesel prices
The results of Monte Carlo simulations for
methanol prices, Figure 6, are calculated for a mean of
0.34 €/kg and a standard deviation of 0.09 €/kg. The
higher standard deviation compared to diesel indicates
moderate variability, causing the prices to occasionally
deviate more significantly from the mean. The x-axis
represents the methanol price, ranging from 0 to 0.75
€/kg, and the y-axis shows the frequency. The graph M-
1 displays a roughly symmetrical distribution centred
around the mean, with the highest frequency observed
between 0.33 and 0.39 €/kg. M-2 follows a similar
pattern but with a slightly sharper peak at the mean,
indicating a stronger central clustering of prices. Minor
deviations are observed in the tails of both histograms,
reflecting slight skewness. In M-3 there is a noticeable
peak around 0.33 to 0.37 €/kg, but also a minor bimodal
tendency is observed. Although the results of skewness
based on previous methanol price data showed that
fairly symmetrical values can be expected, variations
due to the random generation process are obvious in
the histograms. Graph M-4 presents a broader peak
with more pronounced tails, indicating a higher
frequency of extreme price values. As the prices cluster
into two distinct ranges rather than forming a single
dominant peak, this suggests that methanol prices may
experience variations influenced by market
fluctuations or other external factors. Histogram M-5
shows similar results as M-4, while histogram M-6
maintains a symmetrical shape with frequencies
gradually declining towards both tails. Overall, the
methanol price distributions show a noticeable
difference in skewness, tail thickness, and spread,
particularly in M-4 and M-5. These deviations suggest
that while methanol prices remain relatively stable
around the mean of 0.34 €/kg, occasional wider
fluctuations and market-driven subgroups may
emerge.
The limitation of this model can be observed in
some deviations, even though the majority of
histograms follow a normal distribution curve. The
presence of this bimodal shape and slight skewness
suggest that the assumption of a normal distribution
may not fully capture the underlying price behaviour.
The deviations may reflect the randomness inherent in
Monte Carlo simulations or incorrect assumption of
distribution in the model. If the fuel price distribution
is indeed bimodal or influenced by different market
conditions or price regimes, it may lead to incorrect
cost projections. Thus, future research, with a greater
number of simulations, is necessary to understand the
1301
reasons for the bimodal distribution and to determine
whether it is a result of specific market events, pricing
models, or other external factors.
Figure 6. Monte Carlo simulations of methanol prices
The LCCA is usually calculated based on a fixed
fuel value, which is why the results for a vessel (or
product in general) with the same technical and
operational characteristics can vary significantly
depending on the trend in the observed fuel prices. In
Figure 8, the LCCA costs, calculated based on the mean
values of diesel and methanol fuel, for a ferry lifetime
of 20 years, are presented to show the breakdown of
costs. Diesel power-system shows higher total costs
than the methanol one, primarily due to higher fuel
costs and carbon taxes based on NZES scenario.
Although the investment cost for methanol is
approximately 1.8 times higher than that of diesel,
maintenance costs remain comparable for both fuels.
The most significant cost differences arise from fuel
expenses, where diesel results in substantially higher
expenditures due to its fuel price, around 1.5 times
higher than methanol dual-fuel engine.
Figure 7. LCCA results based on mean values of fuel prices
To get a deeper insight into lifecycle costs, carbon
tax fluctuations are taken into account in this
calculation, which gives a direct insight into the LCCA
trends, assuming a lifetime of 20 years. The carbon tax
prices, according to the NZES scenario, are obtained
according to Figure 3. The results are given in Figures
9 and 10. Figure 9 shows the influence of carbon
taxation over time and compares it in the case of a
diesel- and methanol-diesel dual fuel driven ferry.
Both fuels show an increase in costs over time,
reflecting the anticipated rise in carbon taxation.
However, diesel consistently shows higher values
compared to methanol over the years, indicating
higher expected costs under the given conditions. Even
during a cost increase and variable cost projections,
methanol remains to show lower expenses than diesel,
making it a more cost-effective alternative under
stringent carbon tax regulations.
Figure 8. Carbon taxation cost for NZES scenarios
In Figure 10, the histograms illustrate the LCCA for
both diesel-powered and methanol-powered ferry
under three carbon tax NZES scenarios: CA 2030, CA
2040, and CA 2050. The x-axis, ranging from 0 to 180
million EUR, is kept constant across all scenarios to
clearly visualize the shift in costs as carbon taxes
increase through the years. The y-axis represents the
frequency of occurrence for each cost range.
For the CA 2030 scenario, the diesel-powered ferry
shows a narrow and sharply peaked distribution
cantered around approximately 160 mill. EUR,
indicating low variability and high predictability of
costs. In contrast, the methanol-powered ferry displays
a broader distribution centred around 115–140 million
EUR, reflecting slightly higher variability but overall
lower costs compared to diesel. In the CA 2040
scenario, there is a discrete shift to the right in both
cases, reflecting higher expected costs due to increased
carbon taxation. Diesel centres around 180–195 mill.
EUR with a narrow peak, while methanol ranges from
roughly 135 to 160 mill. EUR with a wider spread. By
CA 2050, the rightward shift becomes more
pronounced for both fuels. Diesel is centred around 202
million. EUR, showing the highest expected costs
among the scenarios but still maintaining a relatively
narrow distribution. Methanol, on the other hand,
clusters around 158 million EUR with a wider spread,
indicating increased uncertainty in long-term cost
projections. As seen before, the graphs demonstrate a
clear upward trend in LCCA for both fuels as carbon
taxes increase over time. Methanol consistently offers
lower average costs compared to diesel but exhibits
greater variability. Diesel remains more predictable in
terms of distribution shape, but at the expense of
higher projected costs.
1302
Figure 9. Comparison of LCCA simulations of a diesel- and
methanol-powered ferry under different carbon taxes
Both fuels show increasing total costs over time,
reflecting the impact of rising carbon taxes. Diesel
consistently has higher mean costs with less variability,
exhibiting tightly grouped distributions that align well
with normal curves, indicating low uncertainty and
predictable costs. In contrast, methanol shows lower
average costs but with broader spreads and greater
variability, suggesting higher uncertainty and more
dispersed results. Overall, while diesel presents a
stable and predictable cost profile, methanol offers
potential cost reductions but with increased long-term
unpredictability.
5 CONCLUSIONS
The shipping sector, despite its important role in global
trade and transportation, faces significant challenges
related to environmental friendliness and economic
sustainability. Since the sector is a major consumer of
fossil fuels, it highly contributes to global GHG
emissions and an increase in the share is expected.
Efforts to reduce the environmental impact of shipping
are based on the decarbonization goals set by the Paris
Agreement, and technical, operational and market
measures are set to reduce the use of fossil fuels and
improve its efficiency. These measures, while crucial
for environmental protection, have also influenced the
economic aspect of marine fuels, influencing the
volatility of their prices. Innovations in the maritime
sector are crucial for improving energy efficiency and
reducing dependence on fossil fuels. While research
into alternative fuels and electrification shows
promise, the economic feasibility of these technologies
is heavily influenced by fluctuating fuel prices.
The LCCA and Monte Carlo simulations have been
conducted to provide insights into the financial risks
and benefits associated with different decarbonization
strategies, such as alternative fuels and carbon
taxation. In the case of a ferry operating in the Adriatic
Sea, the transition from a diesel-powered to a
methanol-diesel dual-fuel drive is presented. The
Monte Carlo simulations for diesel and methanol
prices reveal crucial insights into their price volatility
and the economic impact on the observed vessel. The
simulations show the following:
− Diesel prices, despite some variability, tend to
cluster around the mean value of 0.86 €/kg, with a
relatively narrow range of fluctuations;
− The resulting stability is crucial for forecasting long-
term operational costs for vessels relying on diesel
fuel;
− Methanol price simulations, centred around a mean
price of 0.34 €/kg, showed a higher standard
deviation, reflecting more significant variability
compared to diesel;
− The bimodal characteristics and wider spread
suggest that methanol prices are more susceptible
to market, political and/or other influences,
affecting its cost-effectiveness as a marine fuel over
time;
− The LCCA results show that diesel configuration
result in 1.5 times higher costs due to high fuel costs
and carbon taxation;
− With the increase in carbon taxes, the difference in
costs could reach 31% by 2050;
− When including carbon taxation, the variability in
methanol becomes lower than in the diesel-
powered case, suggesting that methanol could be
more appropriate for long-term planning and
investment.
The LCCA results indicate that the methanol-diesel
dual-fuel driven system, despite higher initial
investment and more significant fuel price volatility,
presents a slightly lower total LCCA compared to the
traditional diesel-powered system. Lower emissions
and the resulting reduced carbon taxation achieved by
using methanol fuel, have an important impact in the
overall cost. The economic assessment shows that
methanol is a viable alternative despite its price
volatility. The reduced carbon footprint aligns with
global decarbonization efforts, and the lower total
costs, when considering carbon taxation, recognize
methanol an economically sustainable option.
Innovations in the maritime sector are essential for
enhancing energy efficiency and reducing the use of
fossil fuels. Research on alternative fuels, such as
methanol, show promise but are economically
1303
sensitive to fluctuating fuel prices. This study
highlights the importance of considering price
volatility in economic assessments of alternative fuels.
Future research should expand this approach to other
alternative fuels and technologies and research policy
frameworks and incentives that could stabilize fuel
prices and support the adoption of cleaner
technologies.
ACKNOWLEDGEMENT
This research was conducted within the project Green
Corridors for Carbon-Neutral Cruise and Ferry Shipping in
the ADRION Region (GREENROUTES), which is co-funded
by the European Union through the Interreg IPA ADRION
programme. Views and opinions expressed are however
those of the author(s) only and do not necessarily reflect
those of the European Union. Neither the European Union
nor the granting authority can be held responsible for them.
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