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Slow steaming of liner trade: its economic and environmental impacts a

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Jingbo Yin , Lixian Fan , Zhongzhen Yang & Kevin X. Li

d

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Department of International Shipping, School of Naval Architecture, Ocean and Civil Engineering , Shanghai Jiaotong University , Shanghai , China b

School of Management , Shanghai University , Shanghai , China

c

Transportation Management College, Dalian Maritime University , Dalian , China d

Department of International Logistics, College of Business & Economics , Chung-Ang University , Seoul , Republic of Korea Published online: 28 Aug 2013.

To cite this article: Jingbo Yin , Lixian Fan , Zhongzhen Yang & Kevin X. Li , Maritime Policy & Management (2013): Slow steaming of liner trade: its economic and environmental impacts, Maritime Policy & Management: The flagship journal of international shipping and port research, DOI: 10.1080/03088839.2013.821210 To link to this article: http://dx.doi.org/10.1080/03088839.2013.821210

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Maritime Policy & Management, 2013 http://dx.doi.org/10.1080/03088839.2013.821210

Slow steaming of liner trade: its economic and environmental impacts JINGBO YIN†, LIXIAN FAN‡, ZHONGZHEN YANG¶ and KEVIN X. LI§*


†

Department of International Shipping, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiaotong University, Shanghai, China ‡ School of Management, Shanghai University, Shanghai, China ¶ Transportation Management College, Dalian Maritime University, Dalian, China § Department of International Logistics, College of Business & Economics, Chung-Ang University, Seoul, Republic of Korea From 2000s, there have been three forces provoking slow steaming practice in the liner industry: (1) oversupply of shipping capacity, (2) increase of bunker price and (3) environmental pressure. This paper analyses the background and the recent application of slow steaming in liner shipping. The research looks into the questions of how slow steaming can save bunker consumption and bring benefits to the environment. On the other hand, solutions are also examined to the adverse side of slow steaming practice, i.e., how it affects the container transit time. For which, a cost model is developed to demonstrate the impact of slow steaming on the revenue change, with application to the North Europe—Far East Trade as a case study. The final result shows that the optimal speed for the shipowner is correlated with the designed speed, bunker price and the price of CO2. With the increase of the bunker price and the price of CO2, the optimal speed will also increase, which means that slow steaming practice has a positive impact on the environmental protection.

1. Introduction Oversupply of shipping capacity has put the freight rate at unsustainable levels. Idle tonnage becomes the source for loss rather than for profit for shipowners. To make things worse, the steady increase of bunker price, consequently, poses a major challenge for container shipowners who have already struggled to control rising operating costs. In addition to these, there has been growing pressure around the world to protect the environment and to reduce emissions of CO2, NOX and SOX. The above three issues have been the critical issues for contemporary liner trade operations. With this background, slow steaming has emerged as a major operation strategy for liner shipping. Based on the cubic rule that the bunker consumption is proportional to the cube of the speed (V 3), the practice of slow steaming will directly reduce fuel consumption and hence CO2 emission. Furthermore, on the condition of service frequency unchanged, slow steaming strategy can also absorb idle tonnage, which may otherwise be laid up and result in considerable lay-up cost. However, the side effect of slow steaming is longer transit time at sea. *To whom correspondence should be addressed. E-mail: [email protected]

© 2013 Taylor & Francis

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According to Alphaliner, 45% of container liner capacity has been using slow steaming (Cariou 2011). Shipowners of larger vessels are more likely to perform slow steaming than those with small vessels (Cariou 2011). In this paper, a cost model is developed for shipowners to determine the optimal speed at the lowest cost, with CO2 reduction economically quantified. The current paper looks into the questions of how slow steaming can save bunker consumption and bring benefits to the environment while reducing interruption to the global supply chain.

2. Literature review Slow steaming has become an increasingly common practice in container liner shipping as the amount and unit size of available vessel capacity rises and the price of fuel increases. Slow steaming can help to absorb vessel overcapacity and is proven to be an effective way to save fuel costs (Notteboom and Cariou 2011). Also, slow steaming can reduce environmental emissions (Kollamthodi et al. 2008; Buhaug et al. 2009; Corbett, Wang, and Winebrake 2009; Faber et al. 2010; Cariou 2011). Relevant research in areas such as vessel deployment, vessel lay-up and technical principles behind slow steaming has been conducted thoroughly (Notteboom 2006; Power 2008; Chen, Fu, and Sun 2005; Notteboom and Vernimmen 2009). Notteboom (2006) investigated the design of a liner service schedule and studied the characteristics of liner schedules in practices. Power (2008) analysed ship preparation and protection during lay-up and explained the cost involved in laying up a vessel. Chen, Fu, and Sun (2005) analysed the category of vessel speed and how slow steaming can cut bunker consumption. Notteboom and Vernimmen (2009) proposed several methods to tackle the booming bunker price, one of which is slow steaming. A thorough analysis is made using integrated approaches, including shifting in bunker fuel grades, changing vessel design and optimising vessel speed and vessel scale. Cariou (2011) examined the bunker break-even price for multi-trade services and suggested that slow steaming would be viable at the break-even point. However, in practice, applying slow steaming is not a multi-trade services’ decision but is based on separate services. Besides, the model holds the assumption that adding vessels to the service would increase cost in the form of charter rent. However, when the market is gloomy, the shipowner has to lay up the vessel. Thus, adding a vessel into the existing service does not increase cost but decreases lay-up cost. Corbett, Wang, and Winebrake (2009) established a model to estimate route-specific, economically efficient vessel speeds using profit-maximisation equation. Then, they analysed the policy impacts of fuel tax and speed limit on CO2 emissions. Ronen (1982) analysed the trade-off between fuel saving, through slow steaming, and the loss of revenue, due to the resulting voyage extension, and presented three models for the explicit determination of the optimal speed of a ship. He analysed how slow steaming leads to the reduction of CO2 emission. Kotovas and Psaraftis (2011) identified the relationship between fuel consumption and vessel speed for container vessels using regression analysis. Eide et al. (2011) used a new integrated approach combining fleet projections with activity-based CO2 emission and projected development of measures for CO2 emission reduction. However, few studies on slow steaming can be found in literature, especially the research from the perspective of shipowners’ perspective.

Slow steaming of liner trade: its economic and environmental impacts

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3. Methodology 3.1. The relationship between speed and main engine fuel consumption When the ship is sailing at sea, there are two resistances, i.e., from both air and water. For a civil ship, the resistance to the body of the ship R is proportional to the square of its speed (V2). Therefore, the effective power to overcome the sailing resistance is PE ¼ RV ¼ AR V 3 ;

(1)


where AR is the resistance coefficient related to the shape of the ship body, displacement, towing, navigation lane and sea condition. To show the relationship between main engine power and vessel effective power, we have Pe ¼ PE = ηp ηt ηs ;

(2)

where Pe is the main engine power, ηp is stern propeller efficiency, ηt is shaft transmission efficiency and ηs is the hull efficiency. To a certain vessel, when the speed changes, ηt and ηs can be viewed as unchanged, so Pe V 3 = ηp : The bunker consumption of the engine G is the result of engine power times bunker consumption rate and is given as, G ¼ Pe ge 103 ;

(3)

where Pe is the efficient power of the engine in kW; ge is engine power multiplied by bunker consumption rate in kg/kWh. Combining Equations (1)–(3), the following equation is obtained G ¼ AR V 3 ge 103 =ηp ηt ηs t=h ¼ KV 3 =ηp ;

(4)

where K is a coefficient and stands for AR · ge · 10−3/ηt · ηs. To simplify the model, ηp, the stern propeller efficiency, is assumed to be constant. Therefore, the fuel consumption is roughly proportional to the cube of the speed, V3. From the above, we know that when the ship slows down, the engine power reduces significantly, and therefore bunker consumption is reduced (Chen, Fu, and Sun 2005). 3.2. The relationship between fuel consumption and CO2 emission The effect of slow steaming on CO2 emissions reduction can be expressed as below for a single vessel on a single voyage. ΔCO2; Vds !Vss ¼ 3:17 MEsea;Vds Dsea;Vds MEsea;Vss Dsea;Vss ;

(5)

MEsea ¼ SFOCELkWh;

(6)

with

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where the emission factor in kilograms of CO2 emitted per tonne of fuel burnt by the main engine is 3.17, Dsea, Vds and Dsea, Vss are the number of days at sea when the vessel is sailing at the design speed and slower speed, respectively. MEsea,Vds and MEsea,Vss are the daily fuel consumption of the main engine at the design speed and slower speed, respectively. MEsea is the product of specific fuel oil consumption (SFOC), engine load (EL) and engine power (kWh) (Corbett, Wang, and Winebrake 2009). As discussed in Equation 4 that the fuel consumption is roughly proportional to the cube of the speed, V3, MEsea,Vds and MEsea,Vss are the functions of speed, MEsea;Vds ¼ K0 Vds3

(7)

MEsea;Vss ¼ K0 Vss3 :

(8)

MEsea;Vss ¼ ðVss =Vds Þ3 MEsea;Vds :

(9)


and

So,

4. Model There is a trade-off for slow steaming policy. The advantage of slow steaming is that it reduces bunker cost and gas emission. It can improve the real supply/demand balance and partially offset the huge order book. Thus, slow steaming could help cut down the lay-up cost if there is idle capacity. However, if there is no sufficient idle capacity, slow steaming could extend a ship’s voyage time. The disadvantage of slow steaming is that the lengthened voyage leads to higher operation cost. In this model, operation cost, excluding main engine bunker cost, includes diesel oil used for auxiliary engine, crew wage, supplies, fresh water, etc. In addition, long voyage time means that the containers are needed to be loaded on a ship for a long time. Since this additional cost is too trivial compared with the total operation cost and is impractical to collect the data, the additional cost is purposely neglected in this model. The costs of operating a ship may be divided into three types: (1) a fixed daily cost, which consists of the costs of the crew, supplies, insurance, maintenance and fuel for auxiliary engines, (2) costs of bunker fuel for the main engines, which depends on the cruising speed and (3) port charges, which are constant for a specified voyage (Ronen 1982). As some costs stay constant whether there is slow steaming or not, it is reasonable to only consider the variable costs in slow steaming model. (1) Main Engine Bunker Cost: BC For a single vessel on a single voyage, the main engine bunker cost can be expressed as ΔBCVds !Vss ¼ BP MEsea;Vds Dsea; Vds MEsea;Vss Dsea; Vss ;

(10)

where ΔBCVds→Vss is the bunker cost change from design speed to slow steaming speed and BP is the bunker price.


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(2) Fixed Operation Cost: OC On the other hand, fixed operation cost includes auxiliary engine fuel, crew wages, supplies, fresh water etc. The operation cost for a voyage is proportional to the number of days at sea and at port. No matter whether the vessel sails at design speed or slower speed, the time at port can be viewed as unchanged, so the change of operation cost for a voyage is ΔOCVds !Vss ¼ Rdoc Dsea;Vss Dsea;Vds ;

(11)


where Rdoc is daily operation cost. (3) Inventory cost With slow steaming, the transit time for a voyage is extended and the containers on board will be occupied for a longer time, producing the inventory cost: ΔICVds !Vss ¼ Pc iQL Dsea;Vss Dsea;Vds =365;

(12)

where Pc is the average value of a container; i is the interest rate, Q is the capacity of the vessel, L is the load factor. (4) Lay-up cost When there’s a reduction in the vessel speed, to keep the frequency of the liner service or the schedule, more vessels need to be added into the service. For example, there is a service whose maximum allowable round-trip time is 56 days, and the frequency is once a week, i.e., F = 1, so the number of vessels deployed is eight. If, somehow, through slow steaming, the maximum allowable round-trip time increases to 63 days, with frequency unchanged, one more vessel is needed to add into the service (Notteboom &Vernimmen 2009). In the case of gloomy liner market, a vessel can be added into the service, otherwise it may be laid up. When vessels become idle, considerable lay-up cost is generated. Depending on the lay-up condition, such as hot or cold, daily lay-up cost can be varied. To calculate the saved lay-up cost spread on a fleet of vessels, we have ΔLCVds !Vss ¼ Rdlc Dsea; V ss þ Dport; V ss n=ðN þ nÞ;

(13)

where Rdlc is the daily lay-up cost, n is the number of added vessels and N is the number of vessels deployed. (5) Carbon trade ΔCO2Vds !Vss ¼ 3:17 MEk;sea; Vds Dk;sea; Vds MEk;sea; Vss Dk;sea; Vss ; ΔCCVds !Vss ¼ PCO2 3:17 MEk;sea;Vds Dk;sea; Vds MEk;sea; Vss Dk;sea; Vss :

(14) (15)

Therefore, the changed revenue, ΔR, can be expressed as following based on the above calculations

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ΔR ¼ FΔR ðV Þ ¼ ΔBCVds !Vss þ ΔLCVds !Vss ΔOCVds !Vss ΔICVds !Vss þ ΔCCVds !Vss ; (16) where ΔBCVds !Vss ¼ BP MEk;sea;Vds Dsea;Vds MEk;sea;Vss Dsea;Vss ; ΔLCVds !Vss ¼ Rdlc Dsea;Vss þ Dport;Vss n=ðN þ nÞ; ΔOCVds !Vss ¼ Rdoc Dsea;V ss Dsea;V ds ; ΔICVds !Vss ¼ Pc iQL Dsea;Vss Dsea;Vds =365;

(17) (18) (19) (20)

ΔCCVds !Vss ¼ PCO2 3:17 MEsea;Vds Dsea;Vds MEsea;Vss Dsea;Vss :

(21)


The number of days of transit time on the sea without slow steaming is Dsea;Vds ¼ S=Vds : The number of days of transit time on the sea with slow steaming is Dsea;Vss ¼ S=Vss ; where S is the distance. Therefore, S S Dsea;V ss Dsea;V ds ¼ 24V 24V : ss ds

Given these assumptions, Equation (16) generates the profits difference on a single voyage for a single vessel. ðBP þ 3:17PCO2 ÞMEsea;V ds S 1 Vss2 S n FΔR ðV Þ ¼ ΔR ¼ 3 þ Rdlc þ Dport 24 Vds Vds 24Vss nþN PC iQL S S Rdoc þ : 365 24Vss 24Vds The objective is to maximise the profits difference: max fΔRg ; subject to : Vm Vss Vds where only Vss is a decision variable. Setting dΔR ¼0 dVss gives the cubic equation

Slow steaming of liner trade: its economic and environmental impacts dΔR 2ðBP þ 3:17PCO2 ÞMEsea;Vds S 1 3 Vss ¼ dVss 24 Vds S n 1 2 þ Rdlc 24 n þ N Vss PC i Q L S 1 2 : Rdoc þ 365 24 Vss

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ð22Þ

Then,


Vss

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 n Rdlc nþN 3 Rdoc þ PC i Q L 365 ¼ ; 3 2ðBP þ 3:17PCO2 ÞMEsea;Vds Vds

* is the optimal speed for the shipowner, Rdoc is daily operation cost, Rdlc is where Vss the daily laid cost, MEk,sea,Vds is the daily main engine fuel consumption at the design speed.

d

dΔR 2ðBP þ 3:17PCO2 ÞMEsea;Vds S 1 PC i Q L S dVss 3 þ Rdoc þ ¼ 24 365 24 dVss Vds S n 1 Rdlc 2 3 : 24 n þ N Vss

dðdΔR=dVss Þ is always less than 0; hence, F is a decreasing function. dVss Therefore, when Vss 0, F increases. When Vss >Vss , F′(V) < 0, F decreases. So, when Vss ¼ Vss , F reaches its maximum.

So,

5. Model test To test the model, we selected the typical liner service of AE1 of COSCO Container Lines as an example. AE1 is a route on the North Europe–East Asia trade calling nine ports in Northeast China, Southeast Asia and North Europe. Since COSCO uses container vessels of 8468 TEU on this route, this type of container vessel is used to test the model. The total scheduled round-trip time for the AE1 service is 55.69 days, of which over 9 days are port time (Notteboom and Vernimmen 2009). The maximum allowable round-trip time is 56 days, with eight vessels deployed at a frequency of one call per week. The vessel opted in this model testing is of 8468 TEU, with a load factor of 70%. With 9.18 days of port time for eight vessels, the liner services can only be operated when vessels’ speed exceeds 22 knots with a total sailing time of 44 days (Notteboom and Vernimmen 2009). To apply slow steaming, the shipping line would decide to have nine vessels instead of eight in the loop, then the maximum allowable round-trip time increases from 56 to 63 days and speed is reduced to 19–20 knots. Thus, there are plenty of time buffers to cope with delays and disruptions. So N = 8, n = 1, Dsea,Vds = 47, Dport,Vss = 9.

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To assess the cost, we only need to focus on inventory cost, fuel cost, operation cost and lay-up cost, as the other costs remain nearly the same. As for the daily operating cost (fuel cost excluded) of 8468 TEU,

Half of the total operating costs of modern vessels are fuel costs (Notteboom 2006). Baird (2001) identified two ways to estimate fuel costs: (1) the specific fuel oil consumption (SFOC) in grams per hp hour, multiplied by the normal utilisation of power to achieve desired service speed and (2) the stated daily fuel consumption in tonnes per day at desired service speed. Figure 1 provides the estimation of fuel cost based on tonnes per day. So, in this model testing, fuel consumption can be drawn from the graph; when the vessel sails at 22 knots, daily fuel consumption is about 180 tonnes per day. So, MEsea, Vds = 180 tonnes per day (Table 1). To determine the inventory cost for the container, this paper relies on the estimate provided by Eefsen and Cerup-Simonsen (2010) of an average of $2300 per TEU, an annual interest rate of 12%, with 70% of full containers, i.e., L = 70%. That is to say, daily cost for a DC20 is $ 0.75 Rdlc = $1970/day. From the above assumption, the equation of ΔR can be simplified as following: ffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 n Rdlc nþN 3 Rdoc þ PC i Q L 365 Vss ¼ 2ðBP þ 3:17PCO2 ÞMEsea ;Vds Vds3 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 0:7 1 1970 1þ8 3 16712 þ 2300 12% 8468 365 ¼ : 2ðBP þ 3:17PCO2 Þ180 223 Finally, we get Vss ¼ 85

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 3

1 BPþ3:17PCO2 :

18 17

Vss

16

Vss(Pco2 = 0)

15

Vss(Pco2 = 1)

14

Vss(Pco2 = 10)

13 12 11 10 9

130 150 170 190 210 230 250 270 290 310 330 350 370 390 410 430 450 470 490 510 530 550 570 590 610 630 650 670 690


Rdoc ¼ ð4975 þ 7225Þ 103 =2 365 USD16712 per day:

Bunker price

Figure 1.

The relationship among bunker price per tonne, price of CO2 and the optimal speed.


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Table 1. Operating cost of panama and mega-post-panamax ships (per thousand USD).

Manning Repair and maintenance Insurance Stores and lupes Administration Fuel Port charges Total operating costs per annum Total operating costs per annum (fuel cost excluded)

Panamax 4000 TEU

Maga-post-panamax 10 000 TEU

850 900 800 250 175 4284 2000 9259 4975

850 1150 1700 350 175 7269 3000 14 494 7225


Source: Notteboom (2006).

So, we can get that the optimal speed for the shipowner is correlated with the design speed, bunker price and the price of CO2. With the increment of the bunker price and the price of CO2, the optimal speeds will decrease. Figure 1 provides more details on the relationship among bunker price per tonne, price of CO2 and the optimal speed for the shipowner. The figure reveals that if the price of CO2 is zero and the fuel price is USD 130 per tonne, the optimal speed will be around 16.8 knots on this route. If the fuel price increases to USD 650 per tonne, the optimal speed decreases to 9.8 knots on same route. The price of CO2 is difficult to measure now; so, we use this data as an example to show the relationship only. If the CO2 is charged 1 unit, the optimal speed will be decreased to 16.6 knots when the fuel price is USD 130 per tonne. The optimal speed will be decreased to 15.6 knots when the CO2 is charged 10 units. With the increase of the fuel price, the effect of the CO2 price on the optimal speed is depressed. When the bunker price increases to around USD 650 per tonne, the optimal speed is 9.56 knots if the CO2 is charged 1 unit compared to 9.43 knots when the CO2 is charged 10 units.

6. Conclusions Oversupply of fleet capacity, increase of bunker price and environmental pressure to reduce CO2 emissions are the three factors that drive the practice of slow steaming in liner services. There is prevalent application of slow steaming in liner industry, especially in Asia–Europe trades and for vessels about 4000 TUE. The industry’s practice on ship deployment and port rotation show the principle which can also be applied to explain why slow steaming can digest idle capacity and save idling cost. From a technical view, Cube Rule tells why and to what extent slow steaming can save bunker cost and the probable damage to the main engine. This paper digs out the industry practice of vessel deployment and looks into the questions of how slow steaming can reduce bunker consumption and environment emission. Since slow steaming will add to transit time, a cost model is developed to demonstrate the impact of slow steaming on the revenue change, with application to the North Europe—Far East Trade as a case study. The final result shows that the optimal speed for

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the shipowner is correlated with the designed speed, bunker price and the price of CO2. With the increase of the bunker price and the price of CO2, the optimal speed will also increase, which means that slow steaming practice has a positive impact on the environmental protection.

Acknowledgements This research was supported by IMC Maritime Centre at The Hong Kong Polytechnic University. Thanks to Miss Xuli Shen for her assistance in preparation of this research project.


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