Effects of container ship speed on supply chains’ CO2 emission
Dr. Nguyen Khoi Tran
Maritime Institute, Nanyang Technological University, Singapore.
Email: [email protected]
Prof. Dr. Jasmine Siu Lee Lam
Corresponding author
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore.
Email: [email protected]
Abstract
This paper studies ship speed beyond the conventional sea scope to explore how it influences cargo
lead time and CO2 emission in supply chains. A model is proposed to simulate thousands of container
flows on the Trans-Atlantic trade via a shipping service. According to the simulation results, sailing
speed determines half of the total transit time and more than 70% of the carbon footprint. On the one
hand, slow steaming brings about fuel saving as well as less CO2 emission. On the other hand, it extends
the transit time of goods and raises inventory burden. An optimal speed is fundamentally a trade-off
between fuel cost of ship operators and inventory carrying cost of shippers.
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1. Introduction
Container liner shipping plays an important role in international trade. In 2014, it transported 1.63b
tonnes (171m TEUs), equivalent to 15% of world seaborne trade (UNCTAD, 2015). Its share in the total
value was approximately 60% (Statista, 2016). Shipping operation is carried out on route systems, so
route design influences greatly on the efficiency and effectiveness of this mode as well as of intercontinental supply chains.
This research aims to measure effects of sailing speed on cargo lead time and CO2 emission. It is
conducted by simulating the movement of container flows between Europe and the USA through a
Trans-Atlantic service under different speed levels. The first innovative point is that ship speed is
investigated not only in the context of seaside, but also in a wider scope of global supply chains. This
approach helps to examine its effects comprehensively. The second point is that the simulation is done
with thousands of container shipments, so it is suitable with the practice that a liner service often
carries containers for many customers between different points, unlike tramp or industrial shipping
with merely a few customers.
The rest of this paper is organised as follows. Previous studies are reviewed in Section 2. Section 3
describes the research framework. Simulation results and analyses are demonstrated in Section 4.
Some conclusions and future perspectives are mentioned in the final section.
2. Literature review
In terms of operation, ship speed regulates time at sea of goods. This time indicator is a decisive factor
of a service’s competitive advantage and related to freight movements from loading to unloading
ports. Seaside transit time contributes a significant portion to global logistics pipelines. It takes around
3 weeks to carry containers between Hong Kong and Rotterdam, 2 weeks between Hong Kong and Los
Angeles or between Rotterdam and New York.
Saldanha et al. (2009) test the importance of transit time in logistics chains and suggest that it should
be a priority of shippers in choosing appropriate carriers. Fast transit time could be seen as a valuable
marketing asset for service providers (Pearson and Fossey, 1983). Some studies have indicated market
segmentations of small, urgent or high-value freight, for which carriers can provide sprint and reliable
services in exchange of premium charges (Alderton, 2008; De Langen, 1999; Stopford, 2001; Visser and
Braam, 2001).
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In terms of finance, ship speed determines bunker cost, one of the major shipping expenses. According
to a model of Drewry (2009) with a 10,000 TEU vessel and fuel price of $500 per tonne, increasing
speed from 20 to 25 knots will lower Europe/Asia round voyage time by 8.8 days but raise a cost of
$0.71m. Notteboom and Vernimmen (2009) point out that high bunker price gives carriers more
incentive to lower steaming or to add more ports in loops.
Being one of the most important transport modes of international trade, container shipping has
generated a great amount of greenhouse gases. Cutting ship emission through speed reduction has
become a major concern in the industry. Corbett et al. (2009) explore fuel tax levels to incentivize
operators to reduce speed and CO2 emission. Lindstad et al. (2011) investigate the decrease of gas
emission and shipping expense at lower speeds. Orsic and Faltinsen (2012) estimate speed loss and
expected CO2 emission on a North Atlantic route. Khan et al. (2012) measure the decline of greenhouse
gases and criteria pollutants when speed goes down on the trips between the ports of San Pedro and
Oakland. Doudnikoff and Lacoste (2014) discuss the impact of low-sulphur fuel requirement on sailing
speed. Based on ship call data, Zis et al. (2014) evaluate how speed reduction and cold-ironing
influence on ship emission near and at ports.
Since the second half of the 2000s, slow steaming (SS) has grown in popularity to confront the surge
of fuel price and over-supply of shipping capacity. Economic and environmental aspects of this strategy
have been examined by Wo and Moon (2013), Yin et al. (2014) and Psaraftis and Kontovas (2010). Tai
and Lin (2013) validate its effects on the Europe/Far East route. Maloni et al. (2013) clarify cost and
benefit of SS by simulating container flows to/from Asia through the port of Los Angeles. Notteboom
and Cariou (2013) study the impact of SS on fuel surcharge. Lee et al. (2015) study its effect not only
on fuel consumption but also on delivery reliability. Chang and Wang (2014) explore the dependence
of speed reduction on fuel cost and charter rate. Ferrari et al. (2015) take the link between SS and
service patterns into consideration.
Maritime transportation has been growingly integrated into global supply chains. Ports have been no
longer stops in end-to-end transport chains, but intermediaries between production and consumption.
In the light of such trend, some researchers have attempted to quantify the impact of shipping
operation on supply chains. Vernimmen et al. (2007) measure how schedule unreliability affects safety
stock levels of shippers. Saldanha et al. (2009) estimate the relationships between transit time and
transit time variability, and logistics costs. Tran (2011) and Tran et al. (2016) analyse the role of logistics
factors on route design. Harrison and Fichtinger (2013) simulate the effects of transit time, time
variability and service frequency on inventory and service levels. Zhang and Lam (2014, 2015) examine
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Daily Maersk’s impact on shippers’ supply chain inventory. Hassel et al. (2016) take accounts of the
link between ship scale and generalised chain cost.
The issue of ship speed in container shipping has been handled in many works, but is often restricted
in the context of maritime transport. As shipping operation has been viewed as a non-detachable part
of global supply chains, ship speed is necessarily studied in a broader scope. Our research is influenced
by this trend. It approaches the literature gap to study different aspects of ship speed from the whole
supply chain perspective.
3. Research framework
3.1. Description
The key research question is how transit speed affects supply chain performance. To answer it, we
simulate the movement of container flows between the USA and Europe (France, Germany, Belgium,
Netherland and the UK) through a shipping service. A monthly seaborne trade data, provided by Port
Import Export Reporting Service (PIERS), has been classified into 3,920 freight flows from Europe to
the USA and 2,594 vice versa (Table 1). Each flow determines the number of boxes, TEUs and cargo
value between two hinterland areas.
Table 1: Cargo flows
Europe to US
US to Europe
Number of flows
3,920
2,594
Total TEUs
97,578
74,348
Total boxes
59,154
42,318
Total value
$5,517,965,376
$4,013,047,458
The simulation process is carried out in six steps (Table 2). Based on the simulation, total lead time and
carbon footprint of goods can be estimated. Container movement on the supply chain is divided into
five stages: (i) transporting from the original point to the loading port, (ii) dwelling in the loading port,
(iii) shipping between loading and unloading ports, (iv) dwelling in the unloading port, and (v)
transporting to the destination. CO2 emission is investigated during inland transportation, port
operation and sea transportation.
Table 2: Simulation process
Step 1: Inputting data
Freight (origin/destination, number of TEUs and containers, total value)
Route (ports of call, order of ports)
Ship (capacity, speed)
Port-to-port nautical distance
Inland distance between hinterlands and ports
Step 2: Establishing operational models
Fuel consumption of main, auxiliary and boiler engines
Step 3: Simulating flow movement
4
Selecting loading/unloading ports for each cargo flow
Combining flows through ports, and port-to-port connections
Step 4: Calculating time indicators
Ship time at sea/in ports
Door-to-door time of each shipment
Step 5: Calculating CO2 emission
Landside operation
Handling operation in ports
Ship operation in ports and at sea
3.2. Input data
3.2.1. Freight
,
: the number of TEUs from hinterland
to hinterland
per month;
=
,
,
: the number of
TEUs transported per trip, : the number of trips per month
,
: the number of containers from hinterland
to hinterland
per month;
=
,
,
: the number
of containers transported per trip
,
: total cargo value from hinterland
to hinterland
per month;
,
=
,
: total cargo value
per trip (USD)
= ( , ) : set of container flows from Europe to the US
= ( , ) : set of container flows from the US to Europe
3.2.2. Ship
: Ship capacity (TEU)
! ": Speed (knot)
: the number of trips per month. It is calculated by dividing the monthly shipping demand on the main
haul by ship capacity.
= max ∑( , )∈()
*+,
,
;
∑( , )∈./
*+,
,
3.2.3. Route
List of visited ports:
0, 1, 2 … 4
(5: the number of visited ports)
The sequence of port calls on round voyages:
0 , 1 , 2 … 4 , 460
5
=
0 , 460
=
1 , 462
=
2…
"7!,8,,9 : voyage distance between ports
:
and
;
(mile)
"7!,8,,8<= is directly extracted from the port-to-port distances of Dataloy website
(http://www.dataloy.com)
;C0
46;C0
> > 7 + 1: "7!,8,,9 = ∑DE: "7!,B ,,B<= ; > < 7: "7!,8,,9 = ∑DE:
G,8,,9 : set of visited ports between ports
> > 7:G,8,,9 = H : ,
:60 … ; I;
:
and
"7!,B,,B<=
;
> < 7:G,8,,9 = G,8,,9<J
3.3. Operational models
3.3.1. Landside operation
K"
,
, LK"
,
: the loading and unloading ports of containers from hinterland
to hinterland . The
closest ports to original and final points of each flow will be chosen as loading and discharging ports.
K"7!
,M : inland distance between hinterland
and port N (km), measured through the ROUTENPLANER
function of Google maps (https://maps.google.com).
Inland speed is assumed to be 50 km per hour. C02 emission of landside transport between hinterlands
and ports are adapted from Tran et al. (2016a) with a unit amount 0.54 kg per TEU.km in Europe and
0.45 kg in the USA.
3.3.2. Port operation
Dwell time before/after ship operation of import/export containers: 3 days. This indicator is adapted
from practical surveys of Rankine (2003).
CO2 emission in port operation: 17.29 kg per TEU (based on APM Terminals, 2010).
Handling productivity: 180 boxes per hour (it is assumed that all ports deploy 6 gantry cranes with an
hourly productivity of 30 moves).
3.3.3. Ship operation
Ship operation is classified into three modes: transit at sea, manoeuvring, and hoteling at berths. Fuel
is burned for the working of main engines (MEs), auxiliary engines (AEs) and boiler engines (BEs). AEs
are used in all operational modes, MEs in transit and manoeuvring, and BEs in two latter modes. MEs,
AEs and BEs are assumed to use both heavy fuel oil (HFO) and marine diesel oil (MDO). Their usage
6
portions (87% and 13%) are determined using the global sale data of marine bunkers between 2007
and 2011 (published in IMO, 2014). The fuel consumption of AEs and BEs is calculated by multiplying
their engine power (Kwh) and specific fuel oil consumption (SFOC – g/Kwh) (Table 3), whereas that of
MEs needs considering sailing speed as the following formula: OLPK Q !LR S7Q =
]+:D: ^],__` 2
a .
],__`
P T7 P QUPV ∗ XYZ[ ∗ \ `_]:^
Table 3: Specific fuel oil consumptions (SFOC – g/Kwh)
HFO (87%)
MDO (13%)
Average
Main engine
215
205
213
Auxiliary engine
227
217
226
Boiler
305
300
304
Source: SFOCs and the shares of HFO (87%) and MDO (13%) are extracted from IMO (2014).
Obc , Obb : fuel consumption per hour of main engines (ME) in transit and manoeuvring (tonne)
Obc = 0.0004202703 ∗ cap + 2.765805R1 = 0.98
Obb = 0.0000071994 ∗ cap + 0.08646522R1 = 0.96
Specifications of ME power (Kwh) and design speed (knot) of various ship sizes are provided by Man
Diesel and Turbo (2013). The consumption is calculated at an operational speed of 20 knots and SFOC
of 213 g/Kwh.
O+c , O+b , O+q : fuel consumption per hour of auxiliary engines (AE) in transit, manoeuvring and hoteling
(tonne)
O+c = 0.004467568 ∗ capr.st12 R1 = 0.7
O+b = 0.05714862 ∗ capr.1u2s R1 = 0.41
O+q = 0.012890814 ∗ capr.21tv R1 = 0.51
Owb , Owq : fuel consumption per hour of boiler engines (BE) in manoeuvring and hoteling (tonne)
Owb = 0.009096288 ∗ capr.21v1 R1 = 0.83
Owq = 0.009096288 ∗ capr.21v1 R1 = 0.83
The consumptions of AEs and BEs are estimated using the statistics of the Port of Los Angeles
(Starcrest, 2016) recording vessel capacity and requested power (Kw) of AEs and BEs during the
operating modes, and SFOCs of 226 g/Kwh and 304 g/Kwh, correspondingly (Table 3).
3.4. Estimation of performance indicators
3.4.1. Time indicators
Inland transport time between area
and port N S D ,M =
7
D`:] ,x
vr
(hour)
Ship hoteling time in port N: the quotient of total loaded/unloaded boxes in the port and handling
productivity (180 moves per hour) SMq =
∑ w ,y
∀
0zr
, : (K"
,
= N) ∪ (LK"
,
= N) (hour)
Total ship time in port N: manoeuvring time (3 hours per arrival/departure) and hoteling time
,
SM = 3 + SMq + 3 (hour)
Voyage time from port N to port }: cruising time at sea and total ship time in ports
•
SM,~
=
•`:]x,€
],`
+ ∑•∈‚x,€ S•q (hour)
Total transit time of freight from area
to area : (i) transport time to the loading port; (ii) dwelling
time before ship operation; (iii) voyage time from the loading to unloading ports; (iv) dwelling time
after ship operation; (v) transport time to the final point.
Sc , = S
,D` ,
•
+ 72 + SD`
Total ship time at sea „X =
,ƒD` ,
,
+ 72 + S
•`:]…=,…J 6•`:]…J ,…=
],`
Total ship time in manoeuvring „
,ƒD` ,
(hour)
(hour)
= 6 ∗ 5 (hour)
,
Total ship time at berths „ = ∑4:E0 S,8 (hour)
3.4.2. CO2 emission
CO2 emission rate: 3.126 tonnes per tonne of fuel (based on emission rates of 3.114 for HFO and 3.206
for MDO published by IMO, 2014).
In transit time (ME and AE): P]c = „X ∗ (Obc + O+c ) ∗ 3.126 (tonne)
In manoeuvring time (all engines): P]b = „
∗ (Obb + O+b + Owb ) ∗ 3.126 (tonne)
In berth time (AE and BE): P]q = „ ∗ (O+q + Owq ) ∗ 3.126 (tonne)
,
In port operation (17.29 kg per TEU): P† = (∑
Landside operation: P†D = ∑(
∑(
, )∈Š‰
,
∗ (7K"
,D` ,
, )∈ˆ‰
,
∗ 0.54 + 7K"
,
) ∗ 17.29/1000 ∗ 2 (tonne)
∗ (7K"
,D` ,
,ƒD` ,
∗ 0.45)/1000 (tonne)
4. Results and analyses
8
∗ 0.45 + 7K"
,ƒD` ,
∗ 0.54)/1000 +
4.1. Shipping operation in the supply chain
At a speed of 20 knots, a 6,000 TEU ship spends 593 hours at sea and 141 hours in ports to carry 6,239
containers (10,572 TEUs) between Europe and the USA. It follows an end-to-end journey embracing
12 ports (Le Havre -> Felixstowe -> Bremerhaven -> Hamburg -> Rotterdam -> Antwerp -> New York ->
Norfolk -> Baltimore -> Charleston -> Savannah -> Houston -> Le Havre). On each trip, 3,414 tonnes of
fuel are consumed, 97.4% of which is burnt at sea. Especially, the majority of bunker (3,135 tonnes) is
used by main engines for ship propulsion (Figure 1).
Figure 1: Breakdown of fuel consumption. Source: visualized by the authors
To transport a 20-foot container, it takes 515.3 hours from the origin to destination, generates 1,280
kg CO2 (Table 4). Inventory carrying cost is often missed in models of route design (Tran and Haasis,
2015b). Nevertheless, a large volume of goods staying in the logistics pipeline means that shippers
must pay a lot for this hidden cost.
Table 4: Breakdown of CO2 emission and lead time in the supply chain
Landside
In ports
CO2 emission (kg per TEU)
Inland transport
251
Handling operation
17.29
Ship operation
23.3
Time (hour)
10.6
229.8
Seaside
Total
850
281.53
251
17.29
873.3
515.31
Source: calculated by the authors
Sailing speed determines seaside operation, which represents a significant part of the total supply
chain cost. Moreover, over half of containers’ lead time is at sea (281.5 hours). Consequently, 52.3%
of inventory carrying cost, and 77% of CO2 emission (980 kg) arise in this stage. Our research is done
9
on the Trans-Atlantic corridor, which is much shorter than Trans-Pacific and Europe/Asia ones. If it is
put in the two latter cases, ship speed’s effect on supply chains may be much more considerable.
4.2. Speed variation and the viability of slow steaming
On the one hand, slower speed results in longer supply chain pipeline. This leads to higher capital and
operational ship costs, and especially more inventory volume. These factors go up at the same
decreasing rate of ship speed. On the other hand, fuel consumption and CO2 emission substantially go
down. It is generally accepted that unit fuel consumption (per day or hour) decreases in cube with the
pace of speed change. However, extra voyage time also incurs with lower speed. Therefore, the
relationship between total fuel consumption in a voyage and speed actually follows a square function.
In the latter 2000s, the shipping industry was hit by the financial crisis, upswing of oil price and overcapacity. Liner carriers have applied slow steaming to face with such a poor market. Fuel is one of the
key attentions of operators. Following our base scenario, at a price of $274 per tonne, fuel expense
will be $86.22 per TEU. Additionally, its variation with speed is also larger than the variations of capital
and operating costs. If the speed changes from 20 to 15 knots, unit fuel cost will move down by 44.75%
($33.89 per TEU). Fuel saving of 1,308 tonnes per trip simultaneously converts into less CO2 emission
from 13,514 to 9,427 tonnes (Figure 2). Cutting carbon footprint is a striking advantage of slow
steaming, which has become a key argument of liner operators to stick with this strategy even when
fuel price is cheaper.
Extended voyage is an outcome of slow steaming. Average voyage time of Trans-Atlantic routes
increased from 28 days in 2005 to 32 days in 2011, of Trans-Pacific routes from 34 to 37 days, of Far
East/Europe from 57 to 70 days (Tran and Haasis, 2016b). Consequently, additional 1 or 2 vessels
needed deploying per loop. In 2010, 47 vessels, equivalent to 2.4% of global fleet, were absorbed by
slow steaming (Bonney and Leach, 2010), which narrowed down the supply/demand gap. Although
carriers must pay extra capital and operating costs, the benefit from slow steaming are more
overwhelming.
Slow steaming seems to bring positive effects for operators as well as environment. In exchange,
shippers must keep more inventory to feed longer supply chain. From 20 to 15 knots, average lead
time extends by 94 hours, equivalent to an additional inventory cost of $148.63 per TEU, which is far
from the saving of fuel cost.
10
2500
2500
Average lead time (hour) - Left axis
Co2 emission per TEU (kg) - Right axis
2000
2000
1500
1500
1000
1000
500
500
0
0
12
14
16
18
20
22
Ship speed (knot)
24
26
28
30
Figure 2: Speed effects on lead time and CO2 emission. Source: visualized by the authors
5. Conclusions and future outlooks
In this article, ship speed is examined in a whole supply chain based on the simulation model and data
retrieved from practical and reliable sources. The effect of ship speed may be not big in terms of supply
chain cost, but considerable in terms of lead time and carbon footprint. It has different impacts on
operators and shippers. Optimal speed should be a balance between the costs of operators and
shippers. Slow steaming benefits operators and environment, but it is necessary to examine
customers’ losses concerning extended lead time and higher inventory burden.
This research also raises some further issues. Firstly, sailing speed is fixed in the whole shipping route,
but different levels may be taken into account in a later work. Secondly, the next simulation can be
done on the Europe/Asia and Trans-Pacific corridors, which are longer than the Trans-Atlantic one. In
these cases, the role of sailing speed can be more considerable.
Acknowledgement
This research is supported by the Singapore Maritime Institute under the project SMI-2015-MA-16.
Reference
Alderton, P., 2008. Port Management and Operations. London: Informa.
11
APM Terminals, 2010. APM Terminals Reduces CO2 Emissions per TEU by 8% in 2009. Retrieved October 06
2016 from the World Wide Web:
http://www.apmterminals.com/~/media/Files/Corporate/News/Press%20Releases/2012/12/219%20100226%20A
PM26_02_09_Terminals_Reduces_CO2_Emissions_by_8_in_2009.ashx.
Bonney, J and Leach, P.T., 2010. Slow boat from China. The Journal of Commerce, February, 2010, 10-14.
CMA-CGM., 2015. CMA CGM Bougainville, vessel with exceptional dimensions. Retrieved October 06 2016
from the World Wide Web: http://www.cma-cgm-blog.com/cma-cgm-bougainville/
Chang, C.C and Wang, C.M., 2014. Evaluating the effects of speed reduce for shipping costs and CO2 emission.
Transportation Research Part D, 31, 110–115.
Corbett, J.J. Wang, H. and Winebrake, J.J., 2009. The effectiveness and costs of speed reductions on emissions
from international shipping. Transportation Research Part D, 14, 593–598.
De Langen, P.W., 1999. Time Centrality in Transport. International journal of maritime economics 1(2), 41–55.
Drewry., 2009. Capacity Management - surviving the container crisis. London: Drewry Shipping Consultants.
Doudnikoff, M and Lacoste, R., 2014. Effect of a speed reduction of containerships in response to higher energy
costs in Sulphur Emission Control Areas. Transportation Research Part D 28, 51–61.
Ferrari, C. Parola, F and Tei, A., 2015. Determinants of slow steaming and implications on service patterns.
Maritime Policy & Management, 42(7), 636-652.
Harrison, A and Fichtinger, J., 2013. Managing variability in ocean shipping. The International Journal of
Logistics Management, 24(1), 7 – 21.
Hassel, E.V. Meersman, H. de Voorde, E.V and Vanelslander, T., 2016. Impact of scale increase of container
ships on the generalised chain cost. Maritime Policy & Management, 43(2), 192-208.
IMO., 2014. Third IMO Greenhouse Gas Study 2014. Suffolk: Micropress Printers.
Khan, M.Y. Agrawal,H. Ranganathan, S. Welch, W.A. Miller, J.W and Cocker, D.R., 2012. Greenhouse Gas
and Criteria Emission Benefits through Reduction of Vessel Speed at Sea. Environment Science Technology, 46,
12600−12607.
Lee, C.Y. Lee, H.L and Zhang, J., 2015. The impact of slow ocean steaming on delivery reliability and fuel
consumption. Transportation Research Part E, 76, 176–190.
Lindstad, H. Asbjørnslett, B.E and Strømman, A.H., 2011. Reductions in greenhouse gas emissions and cost by
shipping at lower speeds. Energy Policy, 39, 3456–3464.
Maloni, M. Paul, J.A and Gligor, M.G., 2013. Slow steaming impacts on ocean carriers and shippers. Maritime
Economics & Logistics, 15, 151–171.
Man Diesel and Turbo., 2013. Propulsion Trends in Container Vessels. Copenhagen: Man Diesel and Turbo.
Man Diesel and Turbo., 2014. How to influence CO2. Copenhagen: Man Diesel and Turbo.
Notteboom, T and Cariou, P., 2013. Slow steaming in container liner shipping: is there any impact on fuel
surcharge practices? The International Journal of Logistics Management, 24(1), 73 – 86.
12
Notteboom, T and Vernimmen, B., 2009.The effect of high fuel costs on liner service configuration in container
shipping. Journal of transport geography, 17(5), 325-337.
Orsic, J.P and Faltinsen, O.M., 2012. Estimation ofshipspeedlossandassociatedCO2 emissions inaseaway. Ocean
Engineering, 44, 1–10.
Psaraftis, H.N and Kontovas, C.A., 2010. Balancing the economic and environmental performance of maritime
transportation. Transportation Research Part D, 15, 458-462.
Pearson, R and Fossey, J., 1983. World deep-sea container shipping. Aldershot: Gower.
Rankine, G., 2003. Benchmarking container terminal performance. In Proceedings of Container Port Conference,
Rotterdam, Netherlands.
Saldanha, J., Tyworth, J.E. Swan, P. F and Russell,D., 2009. Cutting Logistics Costs with Ocean Carrier
Selection. Journal of Business Logistics, 30(2), 175-195.
Saldanha, J. P. Russell, D. M. and Tyworth, J. E., 2006. A Disaggregate Analysis of Ocean Carriers' Transit
Time Performance. Transportation Journal, 45(2), 39-60.
Starcrest., 2016. Port of Los Angeles - Inventory of air emission 2015. Technical report
Statista., 2016. Statistics and Facts about Container Shipping. . Retrieved October 06 2016 from the World Wide
Web: https://www.statista.com/topics/1367/container-shipping/
Stopford, M., 2001. A new revolution. Containerisation International, January, 2001, 46-48.
Tai, H.H and Lin, D.Y., 2013. Comparing the unit emissions of daily frequency and slow steaming strategies on
trunk route deployment in international container shipping. Transportation Research Part D: Transport and
Environment, 21, 26–31.
Tran, N. K., 2010. Studying port selection on liner routes: An approach from logistics perspective. Research in
Transportation Economics (2011), 32, 39 – 53.
Tran, N.K and Haasis, H.D., 2014. Empirical analysis of the container liner shipping network on the East-West
corridor (1995-2011). Netnomics, 15(3), 121-153.
Tran, N.K and Haasis, H.D., 2015a. An empirical study of fleet expansion and growth of ship size in container
liner shipping. International Journal of Production Economics, 159, 241-253.
Tran, N.K and Haasis, H.D., 2015b. Literature survey of network optimisation in container liner shipping.
Flexible Services and Manufacturing Journal, 27, 139-179.
Tran, N.K, Haasis, H.D, Buer, T., 2016a. Container shipping route design incorporating the costs of shipping,
inland/feeder transport, inventory and CO2 emission. Maritime Economics and Logistics, forthcoming.
Tran, N.K and Haasis, H.D., 2016b. A research on operational patterns in container liner shipping. Transport
Journal, forthcoming.
Vernimmen, B. Dullaert, W and Engelen. S., 2007. Schedule Unreliability in Liner Shipping: Origins and
Consequences for the Hinterland Supply Chain. Maritime Economics & Logistics, 9, 193–213.
Visser, D and Braam, T.B.C., 2001. Liner Trade 2020 – Vision of a Huge Global Operation. In Dynamar (Ed.),
Liner Shipping 2020 (pp 69-82). Alkmaar: Dynamar.
13
Woo, J.K and Moon, D.S.H., 2014. The effects of slow steaming on the environmental performance in liner
shipping. Maritime Policy & Management,41(2), 176-191.
Yin, J. Fan, L. Yang, Z and Li, K.X., 2014. Slow steaming of liner trade: its economic and environmental
impacts. Maritime Policy & Management, 41(2), 149-158.
Zhang, A and Lam, J.S.L., 2014. Impacts of Schedule Reliability and Sailing Frequency on the Liner Shipping
and Port Industry. Transportation Journal, 53 (2), 235-253.
Zhang, A and Lam, J.S.L., 2015. Daily Maersk’s impacts on shipper’s supply chain inventories and implications
for the liner shipping industry. Maritime Policy & Management, 42:3, 246-262.
Zis, T. North, R.J. Angeloudis, P. Ochieng, W.Y and Bell, M.G.H., 2014. Evaluation of cold ironing and speed
reduction policies to reduce ship emissions near and at ports. Maritime Economics & Logistics, 16, 371–398.
UNCTAD., 2015. Review of Maritime Transport. United Nations: New York and Geneva.
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