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“This is an Accepted Manuscript of an article published by Taylor &
Francis in Maritime Policy on 07 Mar 2018 , available online:
https://www.tandfonline.com/doi/full/10.1080/03088839.2018.1443226”
The impact of ice class on the economics of wet and dry bulk shipping
in the Arctic waters
Tomi Solakivi*, Tuomas Kiiski and Lauri Ojala
Department of Marketing, University of Turku, Turku School of Economics, Finland
*) Corresponding author: 20014 University of Turku,
tomi.solakivi@utu.fi
Shipping in Arctic waters is subject to technical requirements posed by harsh
ambient conditions. Vessels operating in ice-infested waters need reinforced hulls
and more powerful engines, for example. These requirements are reflected in the
vessel’s ice class, which has specific implications not only for operational issues
but also for shipping economics. This paper examines the extent to which the
shipping costs of wet and dry bulk vessels compliant with the IACS Polar Class
rules exceed those of vessels without an ice class.
Descriptive statistics and multiple regression analysis were used to estimate
differentials in capital and fuel costs, as well as in cargo-carrying capacity. The
applied dataset, obtained from two major shipping registries, includes technical
details of 21,352 bulk vessels, of which 711 possessed a PC, and the remaining
20,641 comprised the reference category.
The results show that Polar Class compliant vessels could be up to three times
more expensive to build than vessels without an ice class. Moreover, the
respective increase in fuel costs may account for up to 150 per cent given the
additional engine power and the increased hull weight. Finally, the reduction in
cargo-carrying capacity amounts to 20 per cent compared to the reference
category.
Keywords: Arctic shipping, ice class, bulk shipping, cost structure, cost
differential
Funding: This work was supported by Jenny and Antti Wihuri Foundation
Introduction
Recent and projected climatological changes appear to be making Arctic waters more
accessible and relevant to commercial cargo shipping (Khon et al. 2017). As pointed out
in a body of academic literature, however, shipping potential in Arctic waters is still
overshadowed by uncertainties connected with climate, economic and political
concerns, for example (Arbo et al. 2013; Kiiski 2017). These concerns are in contrast to
the over-optimistic visions recurrently presented in the media concerning the rapid
growth of Arctic shipping. Hence, there is an evident need for reliable information to
convey realistic perceptions about shipping in Arctic waters (Marken et al. 2015).
There have been several studies exploring the feasibility of Arctic shipping from
different perspectives, including i) maritime infrastructure capacity (Kiiski et al. 2016),
ii) techno-environmental aspects (Larsen et al. 2016) and iii) economic viability (Xu et
al. 2011). Findings about economic viability vary, and tend to favour bulk shipping
linked with the transport of natural resources (Lasserre 2014). Compared to shipping
markets elsewhere, however, the market potential in the Arctic is limited by the higher
operational and commercial risks due to the harsh conditions, the scarcity of cargoes
and the higher costs associated with the inherent requirements affiliated with operating
in Arctic waters (Lee and Kim 2015; Lasserre et al. 2016; Beveridge et al. 2016).
Vessels operating in ice-infested waters in general, and specifically in the Arctic,
need specialized design in terms of reinforced hulls and powerful engines, for example
(Riska 2010). These requirements are reflected in the vessel’s ice class, which refers to
certification provided by a Maritime Administration or a classification society in
conformance to a specific set of ice-class rules. Finnish-Swedish Ice Class Rules
(FSICR), the Russian Maritime Register of Ships (RS) and the Polar Class (PC)
developed by the International Association of Classification Societies (IACS) constitute
traditional standards depending on the geographical area and subsequent ice conditions.
A vessel’s ice class has implications not only on the operational level but also for
shipping economics (Meng et al. 2017). Its influence extends beyond shipping costs to
the earnings potential of a vessel (Veenstra and Ludema 2006).
Expensively built and operated vessels are the primary reasons behind the
impaired economics of ice-class fleets relative to conventional fleets (Lasserre and
Pelletier 2011). More specifically, increased fuel consumption and reduced cargo-
carrying capacity add to the burden (Laulajainen 2009; Erikstad and Ehlers 2012). The
cost differentials widen in tandem with the vessel’s ice-navigation capability, which
reflects its operational profile in Arctic waters (von Bock und Polach et al. 2014).
Although previous research has produced various cost estimates, the following
issues complicate any evaluation of the validity and consequent applicability of
different parameters in economic-viability analyses of Arctic bulk shipping (Lasserre
2014; Meng et al. 2017; Pruyn 2016; Solakivi et al. 2017): the data is scattered; the
values are highly variable; industry averages are commonplace; and many of the
estimates are initially based on containerships. Studies providing cost parameters
specifically for bulk shipping, including dry bulkers (Schøyen and Bråthen 2011; Pruyn
2016) and tankers (Zhang et al. 2016a), are not focused on Arctic-specific ice-class
categories, in other words on the Polar Class rules (PC) of the IACS.
With the aim of tackling this knowledge gap, this paper contributes with its
statistical analyses of the shipping costs of PC-equivalent ice-classed wet and dry
bulkers. The applied dataset comprises the worldwide database of all cargo vessels
containing extensive technical details from Clarkson’s World Fleet Register (CRSL
2017). With specific regard to vessels flying the Russian flag, technical details were
supplemented with data obtained from the Russian Maritime Register of Shipping
(RMRS 2017). Altogether, the analysed dataset included technical details on 21,352
bulk vessels, of which 711 possessed a PC, and the remaining 20,641 without a PC
comprised the reference category. Table 1 gives a more detailed division of vessels into
PC:s and the reference category.
The focus in this paper is on the extent to which the shipping costs of PC-
compliant wet and dry bulk vessels exceed those of vessels without a PC. LNG carriers
are excluded, however, given their unique cost structure.
Descriptive statistics and multiple regression analysis are used to estimate the
economics of shipping of existing ice-class wet and dry bulkers in the world, and to
compare them with similar vessels without any ice class. The results are further
validated with shipping companies that operate fleets of this kind.
The main contribution is the provision of a statistical and evidence-based
account of the cost structures and cost differentials of PC and conventional bulk vessels
by size, type and level of ice class. To our knowledge, this paper represents the first
attempt systematically to analyse the cost differentials of Arctic vessels and open water
vessels from a comprehensive statistical perspective.
The article continues as follows. The principles of Arctic vessel design are
discussed in the following section, after which the previous literature on the
implications of the ice class on the economics of Arctic bulk shipping is reviewed. The
next two sections describe the research methodology and data sources, and present the
empirical results. The final section sets out and discusses the conclusions.
The design of vessels navigating in Arctic waters
Vessels operating in Arctic waters are designed in line with their intended purpose and
the requirements of the trading area, which largely refers to ice-navigation capability.
The functional specifications are based on either the ice-class rules or the predefined
conditions of the area in question (Riska 2010). The higher the ice capability, the more
dependent the productivity of the vessel is on autonomous operations in ice-infested
waters (von Bock und Polach et al. 2014).
Table 1 shows the approximated equivalences between the FSCIR, the RS and
the PC ice-class regimes based on the work of Zhang et al. (2016b). The Polar Code,
which was developed by the International Maritime Organization and comprises
mandatory regulations for vessels operating in polar waters, came into force on 1
January 2017 (IMO 2017). Vessels operating in the Arctic must carry a Polar Water
Operational Manual and obtain a Polar Ship Certificate that classifies them in one of
three categories (A, B, C) that are roughly equivalent to the PC categories.
Table 1. Approximate equivalencies between FSICR, RS and PC ice class
regimes (Zhang et al. 2016b) and number of vessels in the analysis.
The ice-navigation design influences all parts of the vessel (Riska 2010). The
hull needs to be fitted with steel to ensure adequate structural strength to cope with ice
loads. This increases the vessel’s weight: by between four and 16 per cent for FSICR
ice classes from IC to IAS (Schneekluth and Bertram 1998). In the case of tankers the
increase is between four and 10 per cent depending on the ice class (Magelssen 2005).
Winterization arrangements, which aim to mitigate the risk of ice and low temperatures
on vessels, are likely to increase the weight further (Yang et al. 2013; Braithwaite and
Khan 2014).
Vessels need to have enough engine output to ensure an adequate level of
operational capacity in various conditions. The power requirement relies on an
estimation of the hydrodynamic and physical resistances they encounter. Open-water
resistance includes frictional, residual and air resistance (MAN Diesel & Turbo 2011).
Frictional resistance in bulkers and tankers usually accounts for 70–90 per cent of the
total resistance, whereas residual and air resistance account for 10-25 and around two
per cent, respectively (Babicz 2015). In the case of ice navigation, account is also taken
of ice resistance in terms of icebreaking, buoyancy and clearing (Cho and Lee 2015).
Icebreaking resistance may account for up to 50 per cent of the total resistance (Riska
2010) of vessels sailing in ice-infested waters.
Ice-navigating vessels are equipped with several technical solutions that make
them different from conventional vessels. Specialized bow shapes, with sub-optimal
performance in open water, have replaced the bulbous bows (von Bock und Polach et al.
2014). Diesel-electric propulsion allows maximum output at very low speeds, whereas
controllable-pitch design and azimuth thrusters provide increased manoeuvrability and
ice-damage endurance (Magelssen 2005; Babicz 2015).
The economic implications of the ice class for bulk shipping in the Arctic
The economic viability of Arctic shipping has been a topical subject for research in
recent years, although inherently included in diverse models, cost estimates and
conclusions (Lasserre 2014). Nevertheless, bulk shipping including, for example,
tankers (Cariou and Faury 2016; Zhang et al. 2016a) and dry bulkers (Schøyen and
Bråthen 2011; Faury and Cariou 2015) is considered to have the highest - although still
reasonably limited - potential.
Shipping companies have reported that one of the reasons behind the restricted
prospects relate to the higher costs associated with operating in Arctic waters, namely
because ice-class vessels are more expensive to build and to operate (Lasserre and
Pelletier 2011; Lee and Kim 2015; Lasserre et al. 2016). Such statements underline the
role of the ice class in determining costs and its consequent impact on economic
viability (von Bock und Polach et al. 2014; Meng et al. 2017).
It has been reported in the research literature that the ice class directly influences
capital and voyage costs, whereas in the case of operating costs the effect is more
dependent on the ambient environment (Erikstad and Ehlers 2012; Lasserre 2014). The
actual level of these implications depends on the level of specialization and the ice class
relative to conventional vessels.
There are a reasonable number of studies indicating the cost differentials in
various shipping-cost categories (capital, voyage and operating costs). Capital-cost
increments vary between six and 100 per cent (Laulajainen 2009; Erikstad and Ehlers
2012). The effect of the ice class is more complex when calculating voyage costs, which
comprise fuel and icebreaking costs among other things, because of a trade-off between
the vessel’s icebreaking capability and its open-water performance (Riska 2010). A
higher icebreaking capability usually entails lower icebreaking fees (Gritsenko and
Kiiski 2016) but, in turn, leads to increased fuel consumption in open water. Between
about five and 15 per cent of this increase is attributable to several factors relating to the
altered bow shape, and the increased weight and draft (Erikstad and Ehlers 2012;
Lasserre 2014; Pruyn 2016). In contrast, a vessel with a low ice class requires more
icebreaking assistance and is subjected to higher fees, but at the same time achieves
higher fuel efficiency.
There are no clear indications that the ice class directly affects operating
expenses comprising crew, maintenance and insurance costs. However, more
challenging operational conditions may incur increased crew and maintenance costs
(Lasserre 2014), whereas a higher ice class could arguably reduce risks and thus
decrease insurance costs (Meng et al. 2017). Estimating Arctic insurance costs per se is
a complex issue, which is subject to, inter alia, ship specifications, crew capacity and
maritime infrastructure, as well as the ambient conditions at a given time (Giguère et al.
2017).
The influence of the ice class extends beyond costs to the earnings potential of
the vessel in that the increased weight impairs cargo-carrying capacity (Veenstra and
Ludema 2006; Erikstad and Ehlers 2012). The loss of carrying capacity in Arctic
vessels may be up to 25 per cent compared to open-water vessels (Laulajainen 2009).
It is fair to argue that analyses of the economic viability of Arctic shipping
ventures are only as good as the parameters. The availability of extensive cost estimates,
which tend to be compiled from various sources, may lead to misleading conclusions, as
Lasserre (2014) notes. In addition, Pruyn (2016) and Solakivi et al. (2017) underline the
difficulty of evaluating the validity of industry estimates. A further limitation is that a
large proportion of the estimates are, arguably, initially based on containerships
(Lasserre 2014; Meng et al. 2017). A commonly adopted practice, if no true cost
parameters are available, is to derive cost estimates from previous literature. This
approach is certainly reasonable in most cases, although it may be subject to bias if the
parameters are used interchangeably between different ship types (such as bulkers and
containerships). Moreover, empirical evidence on the economics of larger-sized
containerships is scarce given that the capacity of the world’s largest PC-equivalent ship
in September 2017 was 2,800 TEU (CRSL 2017).
This research aims to provide a statistical basis on which to determine ice-class-
related cost parameters. This is potentially valuable in terms of improving the validity
of future economic-viability analyses related to Arctic shipping in general and bulk
shipping in particular. More detailed cost information would be especially relevant in
the case of bulk shipping, given the low per-unit value of transported cargoes where
shipping costs comprise a substantial part of the total landed cost (e.g. Schøyen and
Bråthen 2011).
Methodology and data
This paper focuses on the requirements of the PC and their economic implications for
vessels operating in the Arctic. Although Arctic conditions are also likely to cause
additional costs in other categories, capital costs and voyage costs (including fuel and
icebreaking services) are the two that are considered in the analysis. These form the
bulk of the cost differential and can be systematically analysed, whereas operating costs
depend on the type of vessel and ambient conditions: the operating costs of
containerships usually comprise less than 15 per cent of the total costs (see e.g. Lasserre
2014), whereas the level for bulkers is around 25 per cent (see e.g. Zhang et al. 2016).
The estimated shipping cost (SC) function could therefore be written as:
SC= FC+ K+IA, (1,
where FC refers to fuel costs, K to capital costs and IA to icebreaking costs.
Daily fuel costs were calculated following the example of Cullinane and Khanna
(1999): estimated fuel-oil consumption was multiplied by the unit price per tonne of the
respective fuel. The following equation is used for the daily consumption of fuel oil
(FO):
= ∗ (( ()∗ ∗ (80%))+
( ()∗ ∗ (50%)) ∗
,,)(2
, where SFOC refers to Specific Fuel Oil Consumption. Regression analysis was
used in this research to estimate the vessel-size elasticity of installed kW (Tran and
Haasis 2015; Solakivi et al. 2017). The starting model for the installed power was:
ln()= + ln()+∑
+ , (3
where PolarClass refers to a categorical variable based on the PC regime. For
this purpose, the data, including both the FSICR and RS were transformed into PC
categories by using the approximate equivalence table of ice-class regimes previously
employed by Zhang et al. (2016b). Table 1 shows the equivalences used in this analysis,
together with numbers of observations in each category.
Data concerning the installed power of wet and dry bulkers from Clarkson’s
World Fleet Register (CRSL 2017) was used to estimate engine power in kW. The main
engine SFOC was also calculated from data obtained from the same source.
A somewhat problematic issue relating to the calculation of fuel consumption is
the variance in required engine power between open-water and ice conditions. The
power need is low in open waters, and (very) high in ice conditions. This challenge can
be tackled in modern diesel-electric engines by altering the engine load according to the
conditions (Man Diesel & Turbo 2013), whereas with the conventional diesel engines
currently installed in most of the vessels the engine load cannot be altered during the
voyage. Thus, it was assumed in the calculations that the main engine load was a
constant 80 per cent, and the auxiliary-engine load a constant 50 per cent, as previously
used by Corbett et al. (2009).
To minimize the effect of the volatility in the fuel price, the calculations were
based on a five-year average (2012–2017) of two grades of Intermediate Fuel Oil (IFO):
IFO180 and IFO380 (Bunker Index 2017). The price of IFO180 varied between USD
230 and USD 752 per tonne during the period, with a five-year average of USD 506,
whereas the corresponding figures for IFO380 were USD 204 and USD 786 with a five-
year average of USD 520 per tonne.
Capital cost K was calculated as the sum of the cash price of the ship and
interest payments, less the salvable value of the ship, which is assumed to be 25 per cent
of the purchase price (see also: Wijnolst and Wergeland 2009). Interest was calculated
in accordance with the Capital Recovery Factor method (CRF; see e.g. Zhang et al.
2016a):
=∗()
() (4
, where r is the interest rate for the adequate time period and n is the number of
instalments.
Data from Clarkson’s World Fleet Register (CRSL 2017), limited to the
newbuilding price details of wet and dry bulkers, was used to estimate the vessel price
in accordance with the following equation:
ln()= + ln()+∑
+.(5
Cullinane and Khanna (1999) further calculated the annuity value of a vessel by
assuming a life of 20 years, an interest rate of 10 per cent and a residual value of zero.
The daily capital costs were evaluated by dividing the annuity value of the vessel by
360. Given that current interest rates are significantly lower, the calculations of this
research follow the recommendation of Wijnolst and Wergeland (2009) to use the Libor
(London Interbank Offered Rates) rate plus a margin of 1.5 per cent.
To avoid interest-rate calculations were based on a five-year average (2012-
2017) of the 12-month Libor rate from the Federal Reserve Bank of St. Louis. In
addition, given that the data consisted of nominal prices from a long time period and in
multiple currencies, the prices were transformed into a single currency (USD) in
accordance with the exchange rates of the respective year. The historical purchasing
prices were discounted to current prices in line with the relevant global inflation rate.
The regression models were estimated with General Linear Model (McCulloch
et al. 2001) with backward elimination, the p-value being used as a criterion. Backward
elimination involves dropping off the variables one by one based on their p-value, until
only significant variables are left in the model. There were a limited number of
variables in the starting model in this research, however, and all of them turned out to
be statistically significant leading to the “full model approach” suggested by Burnham
and Anderson (2002), thus avoiding the problems of model selection often discussed in
the context of stepwise regression analysis.
Various technical specifications (Chen et al. 2010) influence a vessel’s earnings
potential, determined by its speed, flexibility and cargo-carrying capacity, especially in
bulk shipping (Veenstra and Ludema 2006). Vessel performance was estimated by
calculating the admiralty coefficient and analysing the connection between the PC and
the respective coefficient. The admiralty coefficient was calculated as:
=/∗
(6
, where D refers to displacement in tonnes, V refers to (design) speed in knots
and P refers to shaft power in kW.
Given that vessel size is limited in terms of draft and width in some parts of the
Arctic (Kiiski 2017), the earnings potential of the vessels with different PC categories
was compared by calculating a simple ratio of cargo-carrying capacity (DWT) and
vessel size measured as displacement. Scale economies in cargo-carrying capacity
(Chen et al. 2010) were considered in this analysis in a log-log regression analysis that
compared the DWT/displacement ratios (see e.g. Gujarati and Porter 2009).
Results
Table 2 presents the estimates of the propulsion power and purchasing price per vessel
size in DWT. The PC vessels in different categories turned out to be statistically
significantly different both in the engine power per DWT and the purchasing price
DWT ratio. The analysis also revealed that vessels with a higher ice class had more
powerful engines and thus higher fuel consumption than those with a lower ice class,
which is logical. The same applied to the purchase price: vessels with a higher ice class
were more expensive, and thus incurred higher capital costs, than those with a lower or
no ice class. It appears that the engine output of PC 5 and PC 6 vessels is over 30-per-
cent higher, and their purchasing price is around 30-per-cent higher than that of vessels
in the reference category, in other words vessels without a PC. Similarly, PC 3 vessels
have twice as much power and a price tag that is over double that of conventional
vessels in the reference category.
Table 2. Coefficients of the main engine, auxiliary engine and purchasing
price regression analyses.
Figure 1 presents the estimated sums of fuel costs and capital costs per day for
the vessels in the three categories. For the purpose of illustration, vessel size is
presented as high as 180,000 DWT, even though currently the largest vessel with a PC
notation (PC 4) is 106,000 DWT. The results indicate a clear difference between the
different PC classes and the reference category. Overall, it would seem that the daily
fuel and capital costs of bulk vessels with a PC 6 classification are around 38-per-cent
higher than the costs of vessels without an ice class. The PC 3 vessels are at the other
end of the spectrum with combined fuel and capital costs of around 250-per-cent higher
than in the reference category. Even as the fuel price was utilized in the calculation by
using a five year average to control the effects of volatility in the fuel price, the effects
of fuel price changes should still be discussed. In fact, it would seem that the relative
competitiveness of vessels in higher PC categories would improve in case of higher fuel
price, due to their relatively higher share of capital costs and, consequently, lower share
of fuel costs.
The results further indicate that there is a clear positive connection between the
PC and the power of both the main engine and the auxiliary engine: the higher the PC,
the stronger the power of both engines. In the case of the main engine the connection is
rather evident in that additional power is needed to manoeuver for ice navigation. The
additional power in the auxiliary engine reflects the needs of winterization related to
heating the vessel, for example.
Figure 1. Fuel costs and capital costs per day for vessels in different Polar Classes
and the reference group.
Previous results refer to the cost differential in open-water conditions, thus
ignoring the cost of the icebreaking that is usually needed while operating in Arctic
waters. We therefore included the cost of icebreaker assistance in the cost function. We
obtained icebreaking fees for the Northern Sea Route (NSRIO 2017), and extrapolated
them to cover all size and PC categories. We used the summer tariffs in this analysis on
the assumption that navigation in the Arctic during the winter season could be
considered exceptional. In line with the logic applied in Russian icebreaking tariffs, the
assumption was also made that PC 3, PC 4 and PC 5 vessels are able to operate
independently, and thus avoid the costs associated with icebreaking. Figure 2 presents
the shipping costs per day.
Figure 2. Shipping costs (USD/day) for vessels in different Polar Classes
and the reference group in Arctic summer conditions (icebreaking fees from
Northern Sea Route).
It seems that the need for icebreaker assistance reverses the cost differential
between the conventional vessels in the reference category and the PC vessels, the
former being the most expensive. Another finding is that the classes with the lowest
total costs in (Arctic) summer conditions are PC 5 and PC 4, whereas PC 3 vessels are
still more expensive to operate. The cost effectiveness of the former could relate to the
exclusion of icebreaking costs. Furthermore, a positive relationship between the PC and
shipping costs also emerged when icebreaking winter tariffs were considered: despite
the winter tariffs, PC 5 turned out to be the cheapest to operate, and PC 3 the most
expensive (see Appendix 1). Not surprisingly, it would seem that PC 4 and PC 3 vessels
in particular are designed for ice navigation, with less emphasis on costs.
Generally, there would seem to be a negative connection between the PC and the
admiralty coefficient of the vessels. We further tested this connection by means of both
ANOVA and linear regression analysis. According to the ANOVA results, the
differences between the PC categories were statistically significant. Furthermore, the
regression coefficient (-50,1) was significant, confirming the linear connection between
the admiralty coefficient and the PC of the vessel. Figure 3. presents the admiralty
coefficients of vessels in PC 3-6 and the reference category.
Figure 3. Admiralty coefficient (mean) of vessels in different Polar Classes and the
reference group.
Table 3 shows the results of the regression analysis. Overall, it seems that
vessels in all the PC categories have significantly lower cargo-carrying capacity than
conventional vessels. Further, as one might assume intuitively, there seems to be a
negative connection between the cargo-carrying capacity and the PC of the vessel.
Table 3. DWT/displacement –ratio of vessels with a Polar Class and the
reference group.
In practice, the coefficients in the regression analysis could be interpreted to
imply that PC 5 and PC 6 vessels have approximately five-per-cent lower cargo-
carrying capacity than vessels in the reference group, and that in the highest PC 3
category, the cargo-carrying capacity is as much as 20-per-cent lower.
Discussion and conclusions
This research analysed the costs and earnings potential of bulk vessels compliant with
IACS Polar Class rules. The respective costs were derived by calculating and comparing
the fuel costs and capital costs of vessels with a PC rating against the reference group.
The possible costs of icebreaking were taken into account in the analysis. Loss of
earnings potential was analysed by comparing the relative cargo-carrying capacity of
the vessels in the different PC categories against the reference group.
As expected, fuel costs and capital costs both turned out to be connected with
the PC of the vessels. More precisely, the higher the PC, the larger were the main
engine and the auxiliary engine (and hence the fuel consumption) of the vessel. The
higher power of the main engine reflects the need also to operate in icy conditions,
whereas the higher power of the auxiliary engine is probably attributable to the
additional power needs of winterization, in other words the additional heating and other
utilities needed in cold and icy conditions.
Overall, the increase in fuel costs should be considered the upper limit, given
that the methodology behind the calculation is somewhat simplistic:, namely, the fixed-
engine load we used does not apply to modern diesel-electric engines, which tend to be
installed in vessels with high ice-navigation capability (referring especially to PC 3 and
PC 4). Ice-classed vessels with a basic diesel-engine configuration are not suitable for
changing the engine load, however, and are burdened by their excess engine power and
fuel consumption in open-water conditions. This also means that they are not suitable
for slow steaming, which has often been claimed in previous literature to produce
higher fuel-cost savings (Meng et al. 2017). At the same time, ice-classed vessels have
an approximately 10-15-per-cent higher design speed than the non-ice classed because
of their additional power, which partly compensates for the additional costs. Increased
weight between two and 12 per cent (for PC 6–PC 3) is in line with the previous
estimates by Schneekluth and Bertram (1998) and Magelssen (2005), while also
showing that there exists no significant difference between the PC and the FSCIR
regimes.
As with engine power and fuel costs, the purchasing price and thus the capital
costs were also connected with the PC; the higher the ice-class notation, the higher were
the purchasing price and capital costs of the vessel. The increase in capital costs appears
to be consistent with findings reported by both Erikstad and Ehlers (2012) and Solakivi
et al. (2017). This suggests that ship type is not a decisive factor when capital cost
increase is considered. Overall, it would seem that PC vessels fall roughly into two
groups: PC 5 and PC 6 vessels are level in terms of engine power and purchasing price,
whereas PC 3 and PC 4 vessels clearly differ, incurring considerably higher fuel and
capital costs. This, as such, is natural given that PC 4 and PC 3 vessels are designed
specifically for Arctic conditions, with the capability of independent operation in the
area.
It was for this reason that we included icebreaking costs in the comparison. In
this case it would seem that the cost differences between the PC and the reference
categories are at least partly reversed. PC 4 and PC 5 vessels turned out to be the most
cost-efficient in terms of operating in the Arctic during the summer season, which is
arguably attributable to the assumption of exempted icebreaking fees. PC 3 vessels, on
the other hand, are still considerably more expensive to operate. This result underlines
the functional specification of PC 3 vessels in terms of having superior ice-navigation
capabilities at the cost of open-water efficiency.
In terms of operational efficiency and earnings potential, both the admiralty
coefficient and the cargo-carrying-capacity ratio gave similar results. The higher-PC
vessels had a lower admiral coefficient, indicating less efficiency or alternatively higher
power requirements to reach the desired speed, as well as a lower cargo-carrying
capacity in relation to their displacement. At the extreme, cargo-carrying capacity of the
PC 3 vessels was around 20-per-cent less than that of the vessels in the reference
category, which is well in line with previous estimates produced by Laulajainen (2009).
The findings also underline the complexity of finding an economically optimal
ice class on an annual basis (Erikstad and Ehlers 2012; von Bock und Polach et al.
2014; Bergström et al. 2016). The process takes into account the days of operation in
particular conditions (open-water and ice) and operational profile (icebreaker-escorted
and independent navigation), after which compromises in ship design are needed in
order to achieve a suitable level of performance.
This research contributes to the existing literature on shipping in Arctic waters,
as well as on a managerial level. Its novelty lies in estimating the additional costs and
limited earnings potential of vessels capable of operating in Arctic waters via a
statistical analysis based on a large fleet database. For further reliability, multiple data
sources were used to ensure the inclusion of all existing vessels in the analysis. As such,
the results give a more realistic and robust estimate of the true costs and earnings
potential of Arctic shipping. From the managerial perspective, the findings could be
used to estimate the viability of Arctic Sea routes. With some limitations, they could
also be used to analyse the viability and costs of shipping in seasonally ice-infested
waters, such as in the Baltic Sea during the winter.
In addition to addressing economic issues, the results, especially those related to
the engine power and fuel consumption of PC vessels, contribute to the discussion on
the environmental impact of shipping in the Arctic, the effects of which appear to be
adverse (Lindstad et al. 2016). They could be used, for example, to analyse the impact
of the proposed ban on heavy fuel oil in the Arctic that may drive changes in engine
technology towards the use of more sustainable fuel grades.
The main limitations of the results and the discussion are as follows. The
analysis concerned the voyage and capital costs of PC vessels but excluded operating
costs, which usually comprise up to 25 per cent of total shipping costs given their
dependence on the ambient operational environment. The variables in the analysis
include the size of the vessel measured in terms of both DWT and displacement, and the
PC. However, design features of the vessel such as the shape of the bow were not
included. For this reason, the results are not accurate on the level of the individual
vessel, but function rather on a more general level. A certain bow shape may be optimal
in icy conditions, for example, whereas the vessel’s fuel efficiency may suffer in open
water. The Double Acting Ship concept could provide a trade-off in this respect,
enabling the vessel to have high ice performance aft and high open-water performance
in the bow.
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Appendices
