- Email: [email protected]
An operational activity-based method to estimate CO2 emissions from container shipping considering empty container repositioning
Transportation Research Part D 17 (2012) 91–96
Contents lists available at ScienceDirect
Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
An operational activity-based method to estimate CO2 emissions from container shipping considering empty container repositioning Dong-Ping Song ⇑, Jingjing Xu International Shipping and Logistics Group, Business School, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
a r t i c l e
i n f o
Keywords: Container shipping emissions Shipping service routes Maritime fuel consumption Empty maritime container repositioning
a b s t r a c t This paper develops an operational activity-based method to estimate CO2 emissions from container shipping in contrasts to the traditional aggregated activity-based method. Two case studies investigate the impacts of empty container repositioning policies and port handling capacity on CO2 emission index. The results show that the aggregated method could well overestimate CO2 emissions and the operational activity-based method is more appropriate. The paper also demonstrates that high port-handling capacity and efficient empty container repositioning could reduce CO2 emissions in seaborne container transportation. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction World container carrying capacity has grew to 14.7 million TEUs (20-foot equivalent unit) in 2009 from 4.7 million in 1999. This has resulted in significant increases in CO2 emissions. One way to estimate CO2 emissions from is the activitybased approach. It is based on the aggregated activity data for different ship sizes and types. A fixed ship speed and a constant load factor are often assumed for the same category of ships or for the entire journey of a ship in these calculations. Container shipping, however, has some unique characteristics compared to other shipping that may significantly affect the calculation of its CO2 emissions. For example, containerships are often deployed in a specific service route with a regular service frequency but these routes may have different service activities. A service route may also consist of a number of ports with a fixed sequence of port-of-calls. The load factor usually differs by legs and this affects the fuel consumption of the ship’s main engine. Additionally, a ship’s sailing speed varies by leg depending on its published schedule, port traffic, and the physical distance of each leg, affecting its fuel consumption. Usually both laden and empty containers are lifting on/off ships at each port due to trade imbalance and dynamic operations. The movements of empty containers will undoubtedly affect ship’s service activity and utilization, in particular the ship’s berth times at ports as the lifting times for empty containers are similar to that of laden containers. Empty container repositioning policy determines the empty container movements and it is also an important operational factor in estimating CO2 emissions. Finally, sailing directions and weather conditions may affect the vessel speed and fuel consumption. Thus apart from ship sizes and types, other factors need to be taken into account in estimating containership CO2 emissions; the majority related to operational activities. It is therefore reasonable to analyze the CO2 emissions of containerships by considering more detailed operational activities rather than the aggregated activities.1
⇑ Corresponding author. E-mail address: [email protected] (D.-P. Song). As containership’s capacity and activities are usually measured in TEUs, a natural performance index to measure the CO2 efficiency is this CO2 emission per TEU per kilo-metre. The key performance index (KPI) of CO2 emission for a containership used here is KPI = gram CO2/(laden TEUs transport distance). 1
1361-9209/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.trd.2011.06.007
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D.-P. Song, J. Xu / Transportation Research Part D 17 (2012) 91–96
2. CO2 emission estimation methods and empty container repositioning policies Consider a given shipping service route with a round-trip journey time T, we let N be the number of port-of-calls in a single round-trip of the service route; p(i) be the physical port that the index i refers to, where i 2 {0, 1,2, . . . , N 1} and index 0 refers to the first port-of-call in the journey; the home port. The distance in nautical miles from port p(i) to p(i + 1) is denoted as di. The laden (empty) containers in TEUs from port index i to j carried by the ship is yij (xij). Let t ai be the ship arrival time at port p(i) in the schedule. The ship’s sailing speed and sailing time between ports p(i) and p(i + 1) are represented by si and T si . The ship’s berth time at port p(i) is denoted as T pi and Ri is the container handling rate at port p(i) in TEUs per hour. C is the ship’s maximum carrying capacity in TEUs. The ship load factor from port p(i) to p(i + 1) is defined as the ratio of the number of laden containers on board to the ship’s capacity and is denoted as wi. As a ship travels along a service route, it unloads and loads laden and empty containers at ports. The fuel consumption (FC) over a round-trip is the sum of fuel consumption in each leg and at each port, i.e.
FC ¼
X
F m ðsi ; C; wi ; T si Þ þ
i
X
F a ðC; T si ; T pi Þ
ð1Þ
i
where Fm is the bunker fuel consumption of the main engine, itself a function of the ship’s sailing speed, its carrying capacity in TEUs, the load factor, the sailing time, and other ship related data (e.g. shaft power factor); Fa is the fuel consumption of the auxiliary engine, which depends on the ship carrying capacity in TEUs, the sailing time at sea, and the berth time at ports. The fuel consumption in the next leg from port i to port i + 1 is included in Eq. (1).2 Apart from a ship’s carrying capacity, the other parameters in Eq. (1) depend on the operational activities along the shipping route including the berth time at a port that depends on the handling time for both laden and empty containers, and the castoff and moor time; Eq. (2). The ship sailing time depends on the departure time at the current port and the scheduled arrival time at the next port; Eq. (3). The ship sailing speed is determined by the sailing distance between two ports and the sailing time; as Eq. (4). The load factor depends on the number of laden containers on board given in Eq. (5); if a port index exceeds N in 5, it should take the mod with N.
" T pi ¼
X X ðyji þ xji Þ þ ðyij þ xij Þ j
#, Ri þ castoffMoorTime
ð2Þ
j
T si ¼ t aiþ1 ðt ai þ T pi Þ
ð3Þ
si ¼ di =T si
ð4Þ
wi ¼
j1 iþN X X
!, yjk
C
ð5Þ
j¼iþ2 k¼iþ1
From the fuel consumption in Eq. (1), we estimate CO2 emissions using an emission factor of 3.17 regardless of the type of fuel.3 To calculate the CO2 emission KPI, we compute the TEU-km in a round-trip for the ship,
TEU-km ¼
X ðC wi di 1:852Þ
ð6Þ
i
where 1.852 is used to convert nautical miles into kilo-metres. The CO2 emission KPI in g/TEU km is
KPI ¼ 3:17 FC 1; 000; 000=TEU-km
ð7Þ
The operational activity-based method can be illustrated in a flow chart in Fig. 1 that consists of port operational, service operational activity, and ship static data. Studies on estimation of ship fuel consumptions are often based on aggregated activity data for ship sizes and types with for each category of ship, a fixed speed and a fixed load factor being assumed. In most cases, a universal load factor is used for a journey or an entire category of ships. We now consider the aggregated method for containerships in which the distance the ship travels is assumed known, but the detailed port lifting on/off activities are not considered. The sailing and ship’s berth times are derived from the fixed speed and the distance. Together with the attributes of the ship and aggregated service activities, the fuel consumption and the CO2 emission KPI are calculated as:
FC ¼ F m ðs; C; w; D=sÞ þ F a ðC; D=s; T D=sÞ
ð8Þ
KPI ¼ 3:17 FC 1; 000; 000=ðC w D 1:852Þ
ð9Þ
2 The formulas to calculate bunker fuels consumption of the main engine and of the auxiliary engine in equation1 are based on the discussions with commercial consultants (Song et al., 2005). 3 The emission factor is a mean value often used in CO2 emission calculations based on fuel consumption (Psaraftis and Kontovas, 2009).
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Port operational data Container handling rates Castoff and moor times
Service operational activity data
Ship static data
Port to port laden containers Empty repositioning policy
Port to port empty containers
Ship berth time at each port
Ship schedule with arrival times
Ship sailing time at sea at each leg
Auxiliary engine fuel consumption
Sailing distance in each leg
Ship sailing speed at each leg
Carrying capacity in TEUs Maximum sailing speed Shaft power factor
Load factor at each leg in the route
Main engine fuel consumption
Total CO2 emission in tons CO2 emission KPI in g/TEU-km Fig. 1. Operational activity-based method.
where s is the fixed sailing speed, C is the ship’s maximum carrying capacity in TEUs, w is the load factor, D is the distance that the ship has travelled, and T is the journey time of the ship including sailing and berth times. When the physical distance that a ship has travelled is not known, an alternative aggregated method can be based on the aggregated sailing times at sea and the berth time at ports obtainable from published schedules. Many activity-based methods are generally in this line, although the sailing and berth times may be estimated separately. Moreover, s is often assumed to be the maximum sailing speed or very close to it, although in reality this may be simplistic because ship’s sailing speeds could vary over individual legs and deviate from the maximum speed significantly. For example, with the adoption of slow steaming strategy in 2008, a significant number of containerships are sailing at 17–19 knots instead of full speed of 23–25 knots, and some have adopted ‘‘super slow steaming’’, running ships as slow as 14–16 knots on some segments of long backhaul routes from Europe to Asia (Bonney, 2010). Further, the aggregated method does not consider the operational activities such as container handling at ports and shipping companies’ empty repositioning policies, distorting overall CO2 emissions estimates. Empty container repositioning is a complicated because of trade imbalances, we therefore use two static flow balancingbased repositioning policies (Song and Dong, 2011) as examples. The first is a point-to-point policy, in which empty container movements are determined by balancing the container flows between any port-pairs. For any port-pair (p, q), if there are more laden containers from port p to q than that in the opposite direction, then the difference is the empty containers repositioned from port q to p. There may be multiple port indexes corresponding to the same port, and thus, the index-pair with the shortest distance from port q to p should be allocated, i.e. allocate the amount to the variable xlk such that p(l) = q Pk1 di is the shortest path from port q to p in the service route. Under this repositioning policy, the and p(k) = p, and dlk :¼ i¼l shipping line essentially treats each port-pair separately and as such is operationally simplistic with minimum data requirements. The second policy, the coordinated repositioning policy, in when empty containers are repositioned by balancing container flows among all ports in the service route. For any port p, we define its net demands (flow-in minus flow-out) as, P P P DDp := qyqp qypq; where ypq := { yij|p(i) = p and p(j) = q for i, j = 0, 1, . . . , N 1}. If DDp > 0, port p is a surplus port; otherwise it is a deficit port. The coordinated repositioning policy will reposition empty containers from surplus to deficit ports heuristically in a sequence from the shortest distance port-pair first. 3. Two case studies Two case studies involving a Trans-Atlantic and the second an Asia–Europe route are used to calculate the KPI of CO2 emissions for a containership and make a comparison under different scenarios. The Asia–Europe route has a higher degree of trade imbalance. Empty container repositioning policy, both point-to-point policy (P2P) and coordinated repositioning policy (CRP), is examined in the studies. Second, the port-handling rate, that depends on factors including efficiency of equipment, numbers of gantry cranes, buffer capacity, and combination of 20-foot and 40-foot boxes that directly affects the ship berth times at port, is included. In practice, three or four cranes are normally be used to serve a deep-sea ship simultaneously; crane can handle 30–60 TEUs/h. To simplify data collection, we assume all ports have similar handling rates, but vary the port-handling rate to examine its impacts on the emission KPI.
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3.1. A Trans-Atlantic service route We use a weekly service operated by Maersk Line in 2008 to illustrate a typical Trans-Atlantic container shipping service route. The ship calls at six ports in sequence following Charleston, Houston, Norfolk, Rotterdam, Felixstowe, Bremerhaven, and back to Charleston. Five ships, with and average capacity is 6000 TEUs, are deployed to provide weekly service with a round-trip time of 35 days. The maximum sailing speed is 25 knots per hour and the detailed sailing schedule (arrival time in days from the home port) and port distances to the next port in nautical miles, based on Containerisation International, are given in Table 1. We take one ship sailing in a round-trip as an example and assume the castoff and moor time is 3 h for each port. The trade demands are moderately imbalanced with load factors for laden containers on board 0.701, 0.626, 0.604, 0.650, 0.678, and 0.855 for sea legs from Charleston, Houston, Norfolk, Rotterdam, Felixstowe, and Bremerhaven. Fig. 2 shows the CO2 emission KPI corresponding to handling rates from 90 to 240 TEU/h under the P2P and CRP empty repositioning policies using the operational activity-based method in Eqs. (1)–(7). From the figure, we see that the emission KPI decreases as the port-handling rate increases because higher handling rates lead to a reduction of the ship berth time, and enables reduced sailing speeds at sea. The emission KPI also decreases at a diminishing rate as the handling rate increases by the same amount, e.g. increasing handling rate by 30 TEUs per hour from 90, 120, 150, 180, 210 TEU/h, it can reduce the emission KPI by 13.7%, 7.5%, 4.8%, 3.3%, 2.5% under the P2P policy, and by 12.7%, 7.1%, 4.6%, 3.2%, 2.3% under the CRP policy. In addition, empty container repositioning policy affects CO2, although its impacts decrease as the handling rate improves. For example, the CRP policy reduces the CO2 emission KPI from the P2P policy by 2.6%, 1.5%, 1.1%, 0.9%, 0.8%, 0.6% with port handling rates of 90, 120, 150, 180, 210, 240 TEU/h. For the aggregated activity-based method, the physical distance D can be obtained from Table 1. Because the method assumes fixed load factors and ship speeds, we vary these parameters to examine performance sensitivity. The CO2 emission KPI under load factors 0.6, 0.7, 0.8 and seven levels of fixed speeds from 19 to 25 knots is shown in Fig. 3. It can be seen that the CO2 emission KPI is sensitive to both parameters. The KPI rises as either the ship speed or the load factor increases. More specifically, if speed is reduced from 25 knots to 19 knots, the CO2 emission KPI falls by 40.7%, 41.3%, 41.6% for the load factors of 0.6, 0.7 and 0.8. On the other hand, if the ship load factor decreases from 0.8 to 0.7, the CO2 emission KPI falls by 22.2%, 22.3%, 22.4%, 22.4%, 22.5%, 22.6%, 22.6% for ship speeds of 19, 20, 21, 22, 23, 24, 25 knots. The selection of the fixed ship’s speed and load factor has a substantial impact on the CO2 KPI estimation. Many studies have used a fixed load factor 0.7 and the ship’s maximum speed that would lead to a CO2 emission KPI of 112.89 g/TEU km that is significantly higher than those calculated using the operational activity-based method that range from 83.20 to 59.66 g/TEU km under P2P reposition policy or 81.01 to 59.27 g/TEU km under CRP repositioning policy for port handling rates between 90 and 240 TEU/h. 3.2. An Asia–Europe service route The second case is Maersk AE7 – an Asia–Europe service route in which nine ships are deployed to provide weekly services with a round-trip time of 63 days. The sequence of port-of-calls is: Xiamen, Shanghai, Ningbo, Hong Kong, Yantian, Table 1 Schedule and nautical miles in Case 1. Port
Arrival time
Distance
Charleston Houston Norfolk Rotterdam Felixstowe Bremerhaven Charleston
0 4.7 9.6 19.4 22.2 24.3 35.0
1379 1705 3547 121 311 4014
g CO2/TEU*km P2P CRP
handling rate
Fig. 2. CO2 emission KPI under various handling rates for Case 1.
D.-P. Song, J. Xu / Transportation Research Part D 17 (2012) 91–96
95
g CO2/TEU*km load factor = 0.8 load factor = 0.7 load factor = 0.6 speed
Fig. 3. CO2 emission KPI using the aggregated method with different fixed speeds for Case 1.
g CO2/TEU*km
P2P CRP handling rate
Fig. 4. CO2 emission KPI under various handling rates for Case 2.
Algeciras, Rotterdam, Bremerhaven, Algeciras, Tanjung Pelepas, Hong Kong, and Xiamen with an average ship capacity of 12,118 TEU and maximum sailing speed of 26 knots per hour. As with Case 1, we trace a ship over a round-trip with castoff and moor time of 3 h for each port. A reasonable trade demand scenario is assumed yielding a load factor for laden containers on board of 0.878 for the west-borne journey from Asia to Europe and 0.578 for the east-borne journey; trade is thus more imbalanced than Case 1. Fig. 4 shows the CO2 emission KPI corresponding to levels of handling rate under the two empty container repositioning policies using the operational activity-based method. The results confirm the findings of Case 1 in terms of impacts of port handling rate and empty container repositioning policy on CO2 emission KPI. For the aggregated activity-based method, D Table 2 Schedule and nautical miles in Case 2. Port
Arrival time
Distance
Xiamen Shanghai Ningbo Hong Kong Yantian Algeciras Rotterdam Bremerhaven Algeciras Tanjung Pelepas Hong Kong Xiamen
0 2.6 6.0 8.9 10.8 26.2 30.0 35.3 42.4 55.5 60.5 63.0
509 101 704 46 8365 1355 215 1534 6907 1431 287
g CO2/TEU*km
load factor = 0.8 load factor = 0.7
load factor = 0.6
speed
Fig. 5. CO2 emission KPI under the aggregated method with different fixed speeds for Case 2.
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D.-P. Song, J. Xu / Transportation Research Part D 17 (2012) 91–96
can be obtained from Table 2. The CO2 emission KPI using this method with load factors of 0.6, 0.7, 0.8 and seven levels of fixed speeds from 20 knots to 26 knots are seen in Fig. 5. This confirms the results of sensitivity of the CO2 emission KPI with respect to the ship speed and the load factor found for Case 1. Case 2 deploys ships with capacity of 12118 TEUs, which are among the largest containerships in the current fleet. They require more sophisticated gantry cranes to handle the lift on/off activities at ports with handling rates usually more than 210 TEU/h, implying that the CO2 emission KPI is less than 79 g/TEU km; Fig. 4. As with the aggregated method, if a ship’s speed is 25 or 26 knots and there is a fixed load factor 0.7 the CO2 emission KPI is over 85 g/TEU km (Fig. 5), showing that the aggregated method could significantly overestimate the emission KPI. Alternatively, if the fixed speed and the load factor are too small, it would underestimate the emission KPI. The main challenge for the method is in choosing appropriate values for the fixed speed and the load factor, and this is difficult without utilizing the detailed operational data. 4. Conclusions This paper considers the CO2 emission problems in relation to container shipping. By taking into account the characteristics of container shipping, an operational activity-based method is used to estimate the CO2 emission index of ships. It is demonstrated that improving port-handling rate and adopting more efficient empty repositioning policies are two important measures to reduce the CO2 emission KPI. The former has implications on requiring additional investment or CO2 emissions on the port side, whereas the latter is more preferable as it can lead to both economic and environmental benefits. The operational activity-based method is compared with the traditional aggregated activity-based method in two case studies. The results show that the aggregated method can overestimate the CO2 emission KPI compared to the detailed operational activity-based method. The findings also suggest that to make a more accurate estimation of CO2 emissions, the detailed operational activity-based method should be used, but if the aggregated method is used, appropriate ship speed and load factor need be selected. References Bonney, J., 2010. Carriers move full speed into slow steaming. Journal of Commerce Online – News Story (January 12). Psaraftis, H.N., Kontovas, C.A., 2009. CO2 emission statistics for the world commercial fleet. WMU Journal of Maritime Affairs 8, 1–25. Song, D.P., Dong, J.X., 2011. Flow balancing-based empty container repositioning in typical shipping service routes. Maritime Economics & Logistics 13, 61– 77. Song, D.P., Zhang, J., Carter, J., Field, T., Marshall, M., Polak, J., Schumacher, K., Sinha-Ray, P., Woods, J., 2005. On cost-efficiency of the global container shipping network. Maritime Policy and Management 32, 15–30.
Contents lists available at ScienceDirect
Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
An operational activity-based method to estimate CO2 emissions from container shipping considering empty container repositioning Dong-Ping Song ⇑, Jingjing Xu International Shipping and Logistics Group, Business School, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
a r t i c l e
i n f o
Keywords: Container shipping emissions Shipping service routes Maritime fuel consumption Empty maritime container repositioning
a b s t r a c t This paper develops an operational activity-based method to estimate CO2 emissions from container shipping in contrasts to the traditional aggregated activity-based method. Two case studies investigate the impacts of empty container repositioning policies and port handling capacity on CO2 emission index. The results show that the aggregated method could well overestimate CO2 emissions and the operational activity-based method is more appropriate. The paper also demonstrates that high port-handling capacity and efficient empty container repositioning could reduce CO2 emissions in seaborne container transportation. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction World container carrying capacity has grew to 14.7 million TEUs (20-foot equivalent unit) in 2009 from 4.7 million in 1999. This has resulted in significant increases in CO2 emissions. One way to estimate CO2 emissions from is the activitybased approach. It is based on the aggregated activity data for different ship sizes and types. A fixed ship speed and a constant load factor are often assumed for the same category of ships or for the entire journey of a ship in these calculations. Container shipping, however, has some unique characteristics compared to other shipping that may significantly affect the calculation of its CO2 emissions. For example, containerships are often deployed in a specific service route with a regular service frequency but these routes may have different service activities. A service route may also consist of a number of ports with a fixed sequence of port-of-calls. The load factor usually differs by legs and this affects the fuel consumption of the ship’s main engine. Additionally, a ship’s sailing speed varies by leg depending on its published schedule, port traffic, and the physical distance of each leg, affecting its fuel consumption. Usually both laden and empty containers are lifting on/off ships at each port due to trade imbalance and dynamic operations. The movements of empty containers will undoubtedly affect ship’s service activity and utilization, in particular the ship’s berth times at ports as the lifting times for empty containers are similar to that of laden containers. Empty container repositioning policy determines the empty container movements and it is also an important operational factor in estimating CO2 emissions. Finally, sailing directions and weather conditions may affect the vessel speed and fuel consumption. Thus apart from ship sizes and types, other factors need to be taken into account in estimating containership CO2 emissions; the majority related to operational activities. It is therefore reasonable to analyze the CO2 emissions of containerships by considering more detailed operational activities rather than the aggregated activities.1
⇑ Corresponding author. E-mail address: [email protected] (D.-P. Song). As containership’s capacity and activities are usually measured in TEUs, a natural performance index to measure the CO2 efficiency is this CO2 emission per TEU per kilo-metre. The key performance index (KPI) of CO2 emission for a containership used here is KPI = gram CO2/(laden TEUs transport distance). 1
1361-9209/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.trd.2011.06.007
92
D.-P. Song, J. Xu / Transportation Research Part D 17 (2012) 91–96
2. CO2 emission estimation methods and empty container repositioning policies Consider a given shipping service route with a round-trip journey time T, we let N be the number of port-of-calls in a single round-trip of the service route; p(i) be the physical port that the index i refers to, where i 2 {0, 1,2, . . . , N 1} and index 0 refers to the first port-of-call in the journey; the home port. The distance in nautical miles from port p(i) to p(i + 1) is denoted as di. The laden (empty) containers in TEUs from port index i to j carried by the ship is yij (xij). Let t ai be the ship arrival time at port p(i) in the schedule. The ship’s sailing speed and sailing time between ports p(i) and p(i + 1) are represented by si and T si . The ship’s berth time at port p(i) is denoted as T pi and Ri is the container handling rate at port p(i) in TEUs per hour. C is the ship’s maximum carrying capacity in TEUs. The ship load factor from port p(i) to p(i + 1) is defined as the ratio of the number of laden containers on board to the ship’s capacity and is denoted as wi. As a ship travels along a service route, it unloads and loads laden and empty containers at ports. The fuel consumption (FC) over a round-trip is the sum of fuel consumption in each leg and at each port, i.e.
FC ¼
X
F m ðsi ; C; wi ; T si Þ þ
i
X
F a ðC; T si ; T pi Þ
ð1Þ
i
where Fm is the bunker fuel consumption of the main engine, itself a function of the ship’s sailing speed, its carrying capacity in TEUs, the load factor, the sailing time, and other ship related data (e.g. shaft power factor); Fa is the fuel consumption of the auxiliary engine, which depends on the ship carrying capacity in TEUs, the sailing time at sea, and the berth time at ports. The fuel consumption in the next leg from port i to port i + 1 is included in Eq. (1).2 Apart from a ship’s carrying capacity, the other parameters in Eq. (1) depend on the operational activities along the shipping route including the berth time at a port that depends on the handling time for both laden and empty containers, and the castoff and moor time; Eq. (2). The ship sailing time depends on the departure time at the current port and the scheduled arrival time at the next port; Eq. (3). The ship sailing speed is determined by the sailing distance between two ports and the sailing time; as Eq. (4). The load factor depends on the number of laden containers on board given in Eq. (5); if a port index exceeds N in 5, it should take the mod with N.
" T pi ¼
X X ðyji þ xji Þ þ ðyij þ xij Þ j
#, Ri þ castoffMoorTime
ð2Þ
j
T si ¼ t aiþ1 ðt ai þ T pi Þ
ð3Þ
si ¼ di =T si
ð4Þ
wi ¼
j1 iþN X X
!, yjk
C
ð5Þ
j¼iþ2 k¼iþ1
From the fuel consumption in Eq. (1), we estimate CO2 emissions using an emission factor of 3.17 regardless of the type of fuel.3 To calculate the CO2 emission KPI, we compute the TEU-km in a round-trip for the ship,
TEU-km ¼
X ðC wi di 1:852Þ
ð6Þ
i
where 1.852 is used to convert nautical miles into kilo-metres. The CO2 emission KPI in g/TEU km is
KPI ¼ 3:17 FC 1; 000; 000=TEU-km
ð7Þ
The operational activity-based method can be illustrated in a flow chart in Fig. 1 that consists of port operational, service operational activity, and ship static data. Studies on estimation of ship fuel consumptions are often based on aggregated activity data for ship sizes and types with for each category of ship, a fixed speed and a fixed load factor being assumed. In most cases, a universal load factor is used for a journey or an entire category of ships. We now consider the aggregated method for containerships in which the distance the ship travels is assumed known, but the detailed port lifting on/off activities are not considered. The sailing and ship’s berth times are derived from the fixed speed and the distance. Together with the attributes of the ship and aggregated service activities, the fuel consumption and the CO2 emission KPI are calculated as:
FC ¼ F m ðs; C; w; D=sÞ þ F a ðC; D=s; T D=sÞ
ð8Þ
KPI ¼ 3:17 FC 1; 000; 000=ðC w D 1:852Þ
ð9Þ
2 The formulas to calculate bunker fuels consumption of the main engine and of the auxiliary engine in equation1 are based on the discussions with commercial consultants (Song et al., 2005). 3 The emission factor is a mean value often used in CO2 emission calculations based on fuel consumption (Psaraftis and Kontovas, 2009).
93
D.-P. Song, J. Xu / Transportation Research Part D 17 (2012) 91–96
Port operational data Container handling rates Castoff and moor times
Service operational activity data
Ship static data
Port to port laden containers Empty repositioning policy
Port to port empty containers
Ship berth time at each port
Ship schedule with arrival times
Ship sailing time at sea at each leg
Auxiliary engine fuel consumption
Sailing distance in each leg
Ship sailing speed at each leg
Carrying capacity in TEUs Maximum sailing speed Shaft power factor
Load factor at each leg in the route
Main engine fuel consumption
Total CO2 emission in tons CO2 emission KPI in g/TEU-km Fig. 1. Operational activity-based method.
where s is the fixed sailing speed, C is the ship’s maximum carrying capacity in TEUs, w is the load factor, D is the distance that the ship has travelled, and T is the journey time of the ship including sailing and berth times. When the physical distance that a ship has travelled is not known, an alternative aggregated method can be based on the aggregated sailing times at sea and the berth time at ports obtainable from published schedules. Many activity-based methods are generally in this line, although the sailing and berth times may be estimated separately. Moreover, s is often assumed to be the maximum sailing speed or very close to it, although in reality this may be simplistic because ship’s sailing speeds could vary over individual legs and deviate from the maximum speed significantly. For example, with the adoption of slow steaming strategy in 2008, a significant number of containerships are sailing at 17–19 knots instead of full speed of 23–25 knots, and some have adopted ‘‘super slow steaming’’, running ships as slow as 14–16 knots on some segments of long backhaul routes from Europe to Asia (Bonney, 2010). Further, the aggregated method does not consider the operational activities such as container handling at ports and shipping companies’ empty repositioning policies, distorting overall CO2 emissions estimates. Empty container repositioning is a complicated because of trade imbalances, we therefore use two static flow balancingbased repositioning policies (Song and Dong, 2011) as examples. The first is a point-to-point policy, in which empty container movements are determined by balancing the container flows between any port-pairs. For any port-pair (p, q), if there are more laden containers from port p to q than that in the opposite direction, then the difference is the empty containers repositioned from port q to p. There may be multiple port indexes corresponding to the same port, and thus, the index-pair with the shortest distance from port q to p should be allocated, i.e. allocate the amount to the variable xlk such that p(l) = q Pk1 di is the shortest path from port q to p in the service route. Under this repositioning policy, the and p(k) = p, and dlk :¼ i¼l shipping line essentially treats each port-pair separately and as such is operationally simplistic with minimum data requirements. The second policy, the coordinated repositioning policy, in when empty containers are repositioned by balancing container flows among all ports in the service route. For any port p, we define its net demands (flow-in minus flow-out) as, P P P DDp := qyqp qypq; where ypq := { yij|p(i) = p and p(j) = q for i, j = 0, 1, . . . , N 1}. If DDp > 0, port p is a surplus port; otherwise it is a deficit port. The coordinated repositioning policy will reposition empty containers from surplus to deficit ports heuristically in a sequence from the shortest distance port-pair first. 3. Two case studies Two case studies involving a Trans-Atlantic and the second an Asia–Europe route are used to calculate the KPI of CO2 emissions for a containership and make a comparison under different scenarios. The Asia–Europe route has a higher degree of trade imbalance. Empty container repositioning policy, both point-to-point policy (P2P) and coordinated repositioning policy (CRP), is examined in the studies. Second, the port-handling rate, that depends on factors including efficiency of equipment, numbers of gantry cranes, buffer capacity, and combination of 20-foot and 40-foot boxes that directly affects the ship berth times at port, is included. In practice, three or four cranes are normally be used to serve a deep-sea ship simultaneously; crane can handle 30–60 TEUs/h. To simplify data collection, we assume all ports have similar handling rates, but vary the port-handling rate to examine its impacts on the emission KPI.
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3.1. A Trans-Atlantic service route We use a weekly service operated by Maersk Line in 2008 to illustrate a typical Trans-Atlantic container shipping service route. The ship calls at six ports in sequence following Charleston, Houston, Norfolk, Rotterdam, Felixstowe, Bremerhaven, and back to Charleston. Five ships, with and average capacity is 6000 TEUs, are deployed to provide weekly service with a round-trip time of 35 days. The maximum sailing speed is 25 knots per hour and the detailed sailing schedule (arrival time in days from the home port) and port distances to the next port in nautical miles, based on Containerisation International, are given in Table 1. We take one ship sailing in a round-trip as an example and assume the castoff and moor time is 3 h for each port. The trade demands are moderately imbalanced with load factors for laden containers on board 0.701, 0.626, 0.604, 0.650, 0.678, and 0.855 for sea legs from Charleston, Houston, Norfolk, Rotterdam, Felixstowe, and Bremerhaven. Fig. 2 shows the CO2 emission KPI corresponding to handling rates from 90 to 240 TEU/h under the P2P and CRP empty repositioning policies using the operational activity-based method in Eqs. (1)–(7). From the figure, we see that the emission KPI decreases as the port-handling rate increases because higher handling rates lead to a reduction of the ship berth time, and enables reduced sailing speeds at sea. The emission KPI also decreases at a diminishing rate as the handling rate increases by the same amount, e.g. increasing handling rate by 30 TEUs per hour from 90, 120, 150, 180, 210 TEU/h, it can reduce the emission KPI by 13.7%, 7.5%, 4.8%, 3.3%, 2.5% under the P2P policy, and by 12.7%, 7.1%, 4.6%, 3.2%, 2.3% under the CRP policy. In addition, empty container repositioning policy affects CO2, although its impacts decrease as the handling rate improves. For example, the CRP policy reduces the CO2 emission KPI from the P2P policy by 2.6%, 1.5%, 1.1%, 0.9%, 0.8%, 0.6% with port handling rates of 90, 120, 150, 180, 210, 240 TEU/h. For the aggregated activity-based method, the physical distance D can be obtained from Table 1. Because the method assumes fixed load factors and ship speeds, we vary these parameters to examine performance sensitivity. The CO2 emission KPI under load factors 0.6, 0.7, 0.8 and seven levels of fixed speeds from 19 to 25 knots is shown in Fig. 3. It can be seen that the CO2 emission KPI is sensitive to both parameters. The KPI rises as either the ship speed or the load factor increases. More specifically, if speed is reduced from 25 knots to 19 knots, the CO2 emission KPI falls by 40.7%, 41.3%, 41.6% for the load factors of 0.6, 0.7 and 0.8. On the other hand, if the ship load factor decreases from 0.8 to 0.7, the CO2 emission KPI falls by 22.2%, 22.3%, 22.4%, 22.4%, 22.5%, 22.6%, 22.6% for ship speeds of 19, 20, 21, 22, 23, 24, 25 knots. The selection of the fixed ship’s speed and load factor has a substantial impact on the CO2 KPI estimation. Many studies have used a fixed load factor 0.7 and the ship’s maximum speed that would lead to a CO2 emission KPI of 112.89 g/TEU km that is significantly higher than those calculated using the operational activity-based method that range from 83.20 to 59.66 g/TEU km under P2P reposition policy or 81.01 to 59.27 g/TEU km under CRP repositioning policy for port handling rates between 90 and 240 TEU/h. 3.2. An Asia–Europe service route The second case is Maersk AE7 – an Asia–Europe service route in which nine ships are deployed to provide weekly services with a round-trip time of 63 days. The sequence of port-of-calls is: Xiamen, Shanghai, Ningbo, Hong Kong, Yantian, Table 1 Schedule and nautical miles in Case 1. Port
Arrival time
Distance
Charleston Houston Norfolk Rotterdam Felixstowe Bremerhaven Charleston
0 4.7 9.6 19.4 22.2 24.3 35.0
1379 1705 3547 121 311 4014
g CO2/TEU*km P2P CRP
handling rate
Fig. 2. CO2 emission KPI under various handling rates for Case 1.
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g CO2/TEU*km load factor = 0.8 load factor = 0.7 load factor = 0.6 speed
Fig. 3. CO2 emission KPI using the aggregated method with different fixed speeds for Case 1.
g CO2/TEU*km
P2P CRP handling rate
Fig. 4. CO2 emission KPI under various handling rates for Case 2.
Algeciras, Rotterdam, Bremerhaven, Algeciras, Tanjung Pelepas, Hong Kong, and Xiamen with an average ship capacity of 12,118 TEU and maximum sailing speed of 26 knots per hour. As with Case 1, we trace a ship over a round-trip with castoff and moor time of 3 h for each port. A reasonable trade demand scenario is assumed yielding a load factor for laden containers on board of 0.878 for the west-borne journey from Asia to Europe and 0.578 for the east-borne journey; trade is thus more imbalanced than Case 1. Fig. 4 shows the CO2 emission KPI corresponding to levels of handling rate under the two empty container repositioning policies using the operational activity-based method. The results confirm the findings of Case 1 in terms of impacts of port handling rate and empty container repositioning policy on CO2 emission KPI. For the aggregated activity-based method, D Table 2 Schedule and nautical miles in Case 2. Port
Arrival time
Distance
Xiamen Shanghai Ningbo Hong Kong Yantian Algeciras Rotterdam Bremerhaven Algeciras Tanjung Pelepas Hong Kong Xiamen
0 2.6 6.0 8.9 10.8 26.2 30.0 35.3 42.4 55.5 60.5 63.0
509 101 704 46 8365 1355 215 1534 6907 1431 287
g CO2/TEU*km
load factor = 0.8 load factor = 0.7
load factor = 0.6
speed
Fig. 5. CO2 emission KPI under the aggregated method with different fixed speeds for Case 2.
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can be obtained from Table 2. The CO2 emission KPI using this method with load factors of 0.6, 0.7, 0.8 and seven levels of fixed speeds from 20 knots to 26 knots are seen in Fig. 5. This confirms the results of sensitivity of the CO2 emission KPI with respect to the ship speed and the load factor found for Case 1. Case 2 deploys ships with capacity of 12118 TEUs, which are among the largest containerships in the current fleet. They require more sophisticated gantry cranes to handle the lift on/off activities at ports with handling rates usually more than 210 TEU/h, implying that the CO2 emission KPI is less than 79 g/TEU km; Fig. 4. As with the aggregated method, if a ship’s speed is 25 or 26 knots and there is a fixed load factor 0.7 the CO2 emission KPI is over 85 g/TEU km (Fig. 5), showing that the aggregated method could significantly overestimate the emission KPI. Alternatively, if the fixed speed and the load factor are too small, it would underestimate the emission KPI. The main challenge for the method is in choosing appropriate values for the fixed speed and the load factor, and this is difficult without utilizing the detailed operational data. 4. Conclusions This paper considers the CO2 emission problems in relation to container shipping. By taking into account the characteristics of container shipping, an operational activity-based method is used to estimate the CO2 emission index of ships. It is demonstrated that improving port-handling rate and adopting more efficient empty repositioning policies are two important measures to reduce the CO2 emission KPI. The former has implications on requiring additional investment or CO2 emissions on the port side, whereas the latter is more preferable as it can lead to both economic and environmental benefits. The operational activity-based method is compared with the traditional aggregated activity-based method in two case studies. The results show that the aggregated method can overestimate the CO2 emission KPI compared to the detailed operational activity-based method. The findings also suggest that to make a more accurate estimation of CO2 emissions, the detailed operational activity-based method should be used, but if the aggregated method is used, appropriate ship speed and load factor need be selected. References Bonney, J., 2010. Carriers move full speed into slow steaming. Journal of Commerce Online – News Story (January 12). Psaraftis, H.N., Kontovas, C.A., 2009. CO2 emission statistics for the world commercial fleet. WMU Journal of Maritime Affairs 8, 1–25. Song, D.P., Dong, J.X., 2011. Flow balancing-based empty container repositioning in typical shipping service routes. Maritime Economics & Logistics 13, 61– 77. Song, D.P., Zhang, J., Carter, J., Field, T., Marshall, M., Polak, J., Schumacher, K., Sinha-Ray, P., Woods, J., 2005. On cost-efficiency of the global container shipping network. Maritime Policy and Management 32, 15–30.