03 Hermouche Toufik Sabri

51

Journal of Korea TradeVol. 17, No. 4. November. 2013, 51-67

Measuring “Energy Use Efficiency”of Container Terminals

262728Hermouche Toufik Sabria, Myung‐shin Hab, Ga‐hyunKimc

Abstract

When planning energy use reduction policies, one of the crucial elements that might provide a solid basis for the decision makers is an energy efficiency measurement. In fact, decision makers are reluctant to act if there is no strong and accurate data and results to support the policy position they may take. The purpose of this study is to propose an alternative method to measure energy use efficiency (Energy Efficiency) in the port industry, and more precisely, in the container terminals sector. The approach is based on the Data Envelopment Analysis (DEA) introduced by Farrell in 1957. The objective is to reduce the amount of energy inputs. As the first step, the input variables are converted into their energy equivalents, then the energy efficiency is measured as an overall indicator using a constant return to scale model, and decomposed into technical and scale efficiencies in order to diagnose the origin of inefficiency in energy use for each unit in the selected sample. This allows us to provide more accurate results, and gives a more solid basis to decision makers in order to establish their policies. As the final step, a DEA Super Efficiency analysis is performed to achieve a full ranking in terms of energy efficiency for the units under study.

Keywords: Data Envelopment Analysis, energy efficiency, Container Terminals

a Main Author: Department of International Commerce and Logistics, College of Business Administration, PuKyong National University, Korea, [email protected]

b Corresponding Author: Department of International Commerce, College of Business Administration, Professor, PuKyong National University, Korea, [email protected]

c Third Author: Department of International Commerce and Logistics, College of Business Administration, PuKyong National University, Korea, [email protected]

Journal of Korea Trade Vol. 17, No. 4, November. 201352

I. Introduction

In any economic sector, goods production and services delivery are functions of many factors such as human labor, capital, infrastructure, and particularly technology where energy has been used intensively, leading to a considerably decreasing amount of natural resources and an increasing amount of contaminants.

To lower the environmental adverse effects that are related to technology, we need to increase the energy use efficiency by developing scientific forecasts and analyses of energy consumption to allow us to plan energy strategies and policies.

In the globalization era, two main factors have had a great effect on the port industry. The first one is the continuous growth of international trade. In 2011, the seaborne trade (the world’s throughput including containerized goods, liquid cargo, bulk cargo, and general cargo, etc.) reached a record of 87 billion tons (UNCTAD, Review of Maritime Transport 2012), resulting in an intensification of port activities. This refers to the considerable increase in the number of ship’s calls. From 2009 to 2011 and only considering the world’s containerized port traffic, the tonnage moved expanded from 472,273,661 to 572,834,421 (TEU) representing an increase of 20.5% (UNCTAD, Review of Maritime Transport 2012). The second factor is the containerization that transformed ports into a highly mechanized sector by introducing a wide range of machinery and equipment. These two factors dramatically increased the port’s energy consumption in both electricity and fossil fuels, which is extremely critical for ports given their considerable environmental impact that sometimes affects entire regions (Button, 1993).

According to Shin and Cheong in their assessment of greenhouse gas (GHG) emissions from the ports of Busan in South Korea in 2008, about 14.3% of the total emissions were estimated to be directly generated by cargo handling activities (Shin and Cheong, 2011). Merk, et al., concluded that 17% of NOx and 10% of SO2 emissions during the year of 2005 in the agglomeration of Le Havre in the north of France were connected to maritime and inland river transportation, where a very large share of these emissions (approximately 70% for CO2 and NOx as well as 85% of SO2) are associated with the energy‐related sectors in the port area (Merk, et al., 2011). Also based on the 2001~2002 year, emissions inventories for both the Port of Los Angeles and Port of Long Beach in the United States of America, the contribution of emissions by cargo handling equipment was estimated to be 14% (San Pedro Bay Ports Clean Air Action Plan, November 2006). These three examples, from different regions in the world, clearly illustrate the global effects that the ports’ cargo handling activities might have on the environment.

In this study, we sought to bring a contribution to reduce the environmental adverse effects of the port’s activities, through establishing effective environmental‐friendly strategies for energy use by providing relevant recommendations following a thorough analysis of energy use efficiency in container terminals.

Measuring “Energy Use Efficiency”of Container Terminals 53

Among the alternative approaches for measuring, and decomposing energy efficiency, we list the non‐parametric estimation technique of Data Envelopment Analysis (DEA). This technique is especially powerful in evaluating the relative performance of different Decision Making Units (DMUs) in transforming a given amount of inputs (Resources) into Outputs (Goods or Services).

Initiated by Farrell in 1957 and developed by Charnes, et al. in 1978, DEA involves the use of linear programming methods to construct a non‐parametric frontier. The best practices located on the frontier form the benchmark against which the potential energy saving for those that are not on the frontier can be calculated. Therefore, by comparing the practice of different ports or terminals, we can identify the amount of energy savings likely to be possible.

As stated by Zhou and Ang, and Zhou, et al., DEA has gained in popularity in energy efficiency analysis (Zhou and Ang, 2008) and (Zhou, et al., 2008). Among the reviewed literature, excluding port industry related research, various papers deal with the topic of energy efficiency by using a DEA framework. Nassiri and Singh, and Heidari, et al., determined the amount and efficiency of energy consumption for paddy, and horticultural greenhouses production in Iran by using basic DEA methods (Nassiri and Singh, 2009; Heidari, et al., 2012). Lee used the multiple linear regression method and DEA to examine the efficiency of energy management in government buildings in Taiwan (Lee, 2007). Onut and Soner used a DEA approach to assess energy efficiency for the Antalya Region hotels in Turkey (Onut and Soner, 2005). Also Wu, et al. used a DEA model with undesirable output to assess industrial energy efficiency with CO₂emission in China (Wu et al., 2012).

In this study the energy efficiency (EE) of ten container terminals located in the metropolitan city of Busan in Korea has been analyzed using a DEA approach. As the first step, we assessed the technical EE of each unit in the data set by running a CCR model. In a further step, the Technical EE was decomposed into Pure Technical EE, and Scale EE, using a BCC model to determine the origin of the overall inefficiency of each inefficient unit. As a final step, a full ranking of the container terminals in terms of best practices in energy use, is provided by an additional analysis using the DEA Super‐Efficiency model.

Ⅱ. Data and Methodology

1. Variables and Data selection

In this study, all data was collected during the month of August 2013, primarily via direct interviews with container terminals’ managers and with e‐mails and telephone calls when additional information was needed. The study covers 10 container terminals (DMUs) located in the two ports of Busan Metropolitan city in the south of Korea.


Namely “Busan New Port” and “Busan North Port”, both under the jurisdiction of the Busan Port Authority (BPA). Busan ports are the main gateway to the Korean Peninsula and reputed to be among the 5 busiest ports in the world. Busan New Port is a new Semi‐Automated Container Terminal and has been operational since 2005. It includes five container terminal operators (Hanjin Shipping terminal, Hyundai terminal, PNIT, BNCT, and PNC), while Busan North port contains the five other terminals (Hutchison, Uam, Singamman, Shinsundae, Gamman). The sample represents several homogeneous characteristics. All terminals are located in the same geographical area, serving the same hinterland, using the same logistics infrastructure and inland transportation, sharing the same cargo to handle, and dealing with the same regulations and port authorities, which may enhance the reliability of our model.

As outputs we considered the annual container throughput for the year of 2012 as expressed in TEU, and the main consumer of energy in a container terminal; Ship to Shore (STS) Cranes, and yard equipment that includes Transfer Cranes (T/C) and yard vehicles. The major energy sources in a container terminal are electricity for cranes, and diesel fuel for yard vehicles. For the same year, we selected the annual consumption of electricity and diesel as energy inputs (for more details please refer to the Appendix).

In order to ensure a full homogeneity in our data set, we have to overcome two main heterogeneity factors. The first one is that (as have been mentioned previously) Busan New Port is a semi‐automated port, where terminals exclusively use Rail Mounted Gantry (RMG) cranes operated by electric motors, while Busan North Port contains conventional terminals that mostly use Rubber Tired Gantry (RTG) cranes operated by internal combustion diesel engines1, which may be misleading in our general analysis of energy use efficiency. To overcome this issue, we will convert inputs into a common energy unit (the Joule) by multiplying the energy content of each variable2 through the annual consumed quantity, then use the sum of the total consumed quantities of electricity and diesel as a common single input.

The second factor resides in the type of the quayside equipment used in each terminal. If we assume that the yard equipment is almost similar in both ports though the STS cranes may differ in size, capacity, and technology from one terminal to another, we can also describe these things in terms of energy consumption, e.g., a crane equipped by a Tandem‐Lift spreader will need more energy to be operated than a crane equipped by a Twin‐Lift spreader because the Tandem‐Lift spreader is capable of handling four TEUs in one lift, while a Twin‐Lift spreader can handle only a maximum of two TEUs per lift. To partially overcome this issue, instead of considering each STS crane as one single unit, we will use a representative of its TEU handling capacity per lift3

1 Regardless of the energy efficiency use, the consumption of electricity per TEU will be relatively higher for terminals in the New port. On the other hand, terminals in the North port will represent a relatively higher consumption of diesel fuel per TEU

2 387*10-4 GJ/ liter of diesel, and 36*10-4 GJ/kWh


multiplied by the respective total number of cranes for each terminal to get the total lifting capacity in one handling move per terminal. <Table 1> represents the summary statistics for input and output variables.

<Table 1> Statistics for Input and Output Variables Minimum Maximum Mean Standard DerivationTotal Energy Use "GJ" 76,970.33 368,021.02 189,517.90 94,407.60

Throughput "TEU" 506,526.00 3,353,330.00 1,590,764.20 861,409.52STS Total Box/Lift "TEU" 10.00 68.00 30.80 17.62Yard Equipment "Units" 42.00 204.00 108.70 49.21

To increase the validity of the proposed model, we examined the correlation between input and output factors.4 The results of the correlation matrix, shown in <Table 2>, do not indicate any inappropriate variable among the data set that may affect the robustness of our model.

<Table 2> Correlation among Input and Output FactorsTotal Energy Use "GJ" Throughput "TEU" STS Total Box/Lift "TEU"

Throughput "TEU" 0.96STS Total Box/Lift

"TEU" 0.87 0.91

Yard Equipment 0.91 0.94 0.91

2. Data Envelopment Analysis (DEA) Methods

DEA is a non‐parametric analytic method that measures the relative efficiency of homogenous sets of organizations or Decision Making Units (DMUs) performing the same tasks to transform resources (Inputs) into final products or services (Outputs), i.e. efficiency is the ratio of the sum of weighted outputs to the sum of weighted inputs as shown in Eq. (1).

(1)

The DMU is considered efficient when it achieves a score of 1 or mathematically as (Cooper, Seiford and Tone, 2006):

(2)

3 Four TEUs per lift for tandem‐lift cranes, and two TEUs per lift for twin‐lift cranes4 An increase in input should not result in a decrease in any output (Chien. et al., 2003).


Where ‘x’ and ‘y’ refer respectively to inputs and outputs, ‘v’ and ‘u’ are the respective weights of inputs and outputs, ‘q’ is the number of inputs (q = 1, 2,…, Q), ‘p’ represents the number of outputs (p =1, 2,…, P), and ‘j’ represent DMU.

In our case, the DMUs refer to container terminal operators, the inputs are the electricity and diesel fuel consumed to perform the terminals’ operations, and as outputs, the total annual container throughput and the amount of equipment operated within each terminal has been considered.

In DEA, there are two methods to improve the efficiency of an inefficient DMU. The first method is output oriented. It aims to increase the outputs level while holding the inputs level constant. The second method is input oriented where the outputs level is kept constant while decreasing the level of inputs. In our case, as we aim to decrease the consumption of energy for each inefficient DMU, it is more appropriate to opt for an input oriented method.

DEA has two basic models, the CCR model developed by Charnes, Cooper and Rhodes in 1978, on the assumption of Constant Return to Scale (CRS), and the BCC Model developed by Banker, Charnes and Cooper in 1984 that is based on a Variable Return to Scale (VRS). Thus, the efficiency of DEA is defined according to 3 distinctive forms: Overall Technical Efficiency (TE) under the CCR Model, which can be decomposed into Pure Technical Efficiency (PTE), and Scale Efficiency (SE) under the BCC Model (Cooper et al., 2006) as shown in Eq. (3)

(3)

This unique decomposition depicts the sources of the overall inefficiency whether it is caused by inefficient operation (PTE) or by disadvantageous conditions displayed by the scale efficiency (SE) or both.

1) Technical Efficiency

To assess the overall TE, Charnes et al. developed the CCR Model in 1978, Eq. (4).

(4)


Where is a vector of J elements representing the influence of each grower in determining the technical efficiency of the under study and is the technical efficiency.

2) Pure Technical Efficiency

In 1984, Bankers et al. proposed the BCC Model based on VRS, Eq. (5).

(5)

The equation

is a convexity constraint which specifies the VRS framework

(Mostafa, 2009,Heidari, et al., 2012). Without this convexity constraint, the BCC model will be a CCR model (Heidari, et al., 2012) like in Eq. (4).

3) Scale Efficiency

Scale efficiency measures can be obtained for each terminal by conducting both CCR and BCC DEA models, then decomposing the TE score obtained from the CCR DEA into two components. One component is due to scale inefficiency, and the other is due to Pure Technical inefficiency. If there is a diffrence in the CCR and BCC scores for a particular terminal, this indicates that the firm has scale inefficiency (Timothy, et al., 2005), Eq. (3)

Eq. (3) can be also defined by:

(6)


4) Super Efficiency

The CCR and BCC models dichotomize container ports into inefficient and efficient units. However, it is not possible to differentiate between the efficient terminals, since all efficient units receive the same efficiency score of one (1). For example in our case, we can differentiate between the most inefficient terminals and the least inefficient terminals according to their location from the efficiency frontier, but we cannot differentiate between the most efficient terminals and the least efficient terminals, since all of them are located on the efficient frontier5.

To overcome this limitation, Andersen and Petersen proposed the Super‐Efficiency ranking method for only efficient DMUs. The Super‐Efficiency measures how much inputs can be increased (or the outputs decreased) while not becoming inefficient (So, et al., 2007).

The super‐efficiency model is identical to the DEA model previously described, but a DMU under evaluation (k) is excluded from the reference set. The formulation for the super‐efficient model follows Equation (4), but is evaluated without unit k (for i=1,...,n, i ≠ k). For an efficient unit, its exclusion from the reference set will alter the frontier and allow the unit to be located above the efficient frontier that is to be super‐efficient.

Ⅲ. Results and Discussion

1. Results with CRS

As a first step, we used DEA with Constant Returns to Scale (CCR). The results are as shown in <Figure1>.

<Figure 1> Efficiency Scores under CRS Efficiency Graph √

Hyundai 83.9 % 84% Hanjin Shipping 75.6 % 76%

PNIT 100 % 100% √PNC 86 % 86%

BNCT 76.6 % 77% SBTC 52.2 % 52% KBCT 73.8 % 74% DPCT 100 % 100% √UTC 68.4 % 68%

Hutchison 100 % 100% √√ : Efficient * : Weak Efficient

5 The efficient frontier is formed by all (efficient) units that have score 1.


The results show that the EE scores varied from 0.52 for the most inefficient to 1 for the most efficient6, with an overall average EE of 81.65%. Indicating that, on the average, the terminals could minimize their energy consumption by 18.35% to be 100% efficient.

Among the ten terminals, only three are relatively efficient (PNIT, DPCT, Hutchinson), while the seven other terminals are inefficient, with two terminals representing scores above the average, and five terminals having scores below the average. As a first deduction, we can conclude that in Busan’s ports, 70% of the terminals represent waste in energy use. <Table3> shows the possible reduction in energy consumption for each inefficient terminal.

In order to improve the practices in terms of energy use, for each inefficient terminal, an efficient terminal or a set of efficient terminals (Benchmarks) with a corresponding intensity (λ) are selected7, and used as efficiency improvement reference(s), as shown in <Table4>.

<Table 3> Possible Reduction in Energy Consumption for Inefficient UnitsTotal Energy Use "GJ"

Hyundai 236951.88 to 198706.218Hanjin Shipping 302380.77 to 228731.528

PNIT 134690.7 to 134690.7PNC 368021.02 to 316567.149

BNCT 99351.45 to 76097.847SBTC 167565.75 to 87490.607KBCT 274827.93 to 202896.137DPCT 105875.48 to 105875.48UTC 76970.33 to 52668.798

Hutchison 128543.66 to 128543.66

<Table 4> Inefficient Terminals & Corresponding BenchmarksPNIT DPCT Hutchison

Hyundai 0.108 0 1.433Hanjin Shipping 0 0.201 1.614

PNIT 1 0 0PNC 0 0.106 2.376

BNCT 0 0 0.592SBTC 0 0.558 0.221KBCT 0 1.151 0.63DPCT 0 1 0UTC 0 0.162 0.276

Hutchison 0 0 1

6 Score 1 means 100% efficient, and score 0.52 means efficient at 52% or a lack of 48% in efficiency.

7 enchmarks are selected on the basis of their comparable inputs and outputs to the inefficient DMUs.


To better illustrate our results, let’s expose for instance the case of the “UTC” terminal from North Port that represents an inefficient score of 68.4% <Figure1>. To reach a score of 100%8, this terminal should reduce its energy consumption by 31.6% which corresponds to 24,301.532 GJ <Table 3>, while keeping operating the same amount of equipment, and the same handling throughput. To find an appropriate improvement pattern to that, UTC terminal has a reference composite DMUs formed by the two efficient terminals, DPCT (0.162), and Hutchison (0.276)9. While the values between brackets indicate the respective weights (λ) of each reference terminal, that show their respective contribution amounts in evaluating the UTC terminal as inefficient <Table 4>, i.e., UTC Terminal’s decision makers may establish their energy consumption policy based on the practices performed by the two Terminals, DPCT and Hutchison, that are evaluated to be the most suitable references, in terms of energy use efficiency. It allows reducing about 31.6% of its total energy use as compared to the consumption of the year 2012, while using the same amount of inputs to produce the same amount of outputs.

2. Results with VRS

As a second step, in order to identify whether the origin of the waste in energy is due to the PTE (managerial deficit), SE (operating at an incorrect RTS), or both, we used a BCC model to evaluate the PTE and SE of each terminal. The results are as shown in <Table 5>.

<Table 5> Scores under the BCC ModelCCR BCC Scale

Hyundai 83.9 % 92.1 % 91 %Hanjin Shipping 75.6 % 85 % 89 %

PNIT 100 % 100 % 100 %PNC 86 % 100 % 86 %

BNCT 76.6 % 97.5 % 78.6 %SBTC 52.2 % 58 % 90.1 %KBCT 73.8 % 84.7 % 87.2 %DPCT 100 % 100 % 100 %UTC 68.4 % 100 % 68.4 %

Hutchison 100 % 100 % 100 %

The decomposition of the overall technical efficiency into its components, PTE and SE, shows that the overall inefficient terminals also have Pure Technical Efficiency

8 100% is score 1 expressed in a percentage.9 The UTC terminal is closer to the efficient frontier segment formed by these two terminals

on the efficient frontier.


representing a constant return to scale, and among the terminals estimated inefficient under CCR model, two terminals have Pure Technical Efficiency (UTC, and PNC), indicating that the origin of their energy waste is due to unfavorable returns to scale conditions, and the other terminals represent both scale and Pure Technical inefficiencies. But two of them (Hyundai, and BNCT) have their PTE score bigger than the SE score. This suggests that their overall inefficiency is mostly due to operating at incorrect scale, while the other three terminals (Hanjin Shipping, KBCT, and SBTC) have their SE score bigger than their PTE score which indicates that the most dominant reason for the overall inefficiency is probably due to managerial failure. For example, in their initiatives to improve energy efficiency, decision makers of inefficient units that represent a higher SE score than PTE score, should concentrate their efforts on developing a better use of inputs to reduce their energy consumption. On the other hand, for those representing a PTE score that is higher than their SE score, decision makers should concentrate their actions on upgrading their inputs themselves to reach an optimal return to scale and reduce their energy consumption.

To better illustrate our results under VRS, let’s expose, for instance, the case of the “BNCT” terminal from the New Port, which represents both Pure Technical and Scale inefficiencies with the respective scores of 97.5 %, and 78.6 %. Even though the most dominant reason of its overall inefficiency is probably SE10, we cannot omit the contribution of technical factors in the waste of energy especially when the difference between the two scores is small.

As the first step, we checked the container yard side, where we could see that all the terminals in the New Port use a similar mix of inputs in terms of technology, capacity, and type of vehicles. Thus, we can assume that on the yard side, if there is any waste in energy by BNCT, it is rather due to an inefficient management. Effectively if we compare the consumption of diesel used for 2012 at the New Port, we can see that the consumption of diesel by Liter/TEU at BNCT is evaluated at 163% more than the average consumption made by its pair of terminals in the New Port <Table 6>.

<Table 6> NP Diesel Consumption in 2012DMUs

Variables

2012 Diesel use "Liter"

2012 Throughput "TEU"

Diesel Use "Liter/TEU"

Average Liter/TEU Ratio

Hyundai 2,319,757 2,078,010 1.12

1.20 1Hanjin Shipping 3,161,931 2,432,255 1.30

PNIT 1,353,687 1,220,000 1.11

PNC 4,260,000 3,353,330 1.27

BNCT 994,093 506,526 1.96 1.96 1.63

10 Because SE score is smaller than PTE score.


After adjusting its yard operation management and energy use policy, at the beginning of 2013, BNCT could reduce its diesel fuel consumption by about 37% to an average of 1.24Liter/TEU instead of an average consumption of 1.96 Liter/TEU in the year of 2012 <Table 7>. It may confirm the assumption that, at the yard level, the failure in energy use is mostly due to technical failure.

<Table 7> BNCT Average Diesel Consumption in 2012 & 2013DMUs

Variables

2012 Diesel Use

"Liter"

2012 Throughput

"TEU"

2013 (Jan,Feb)

Diesel Use "Liter"

2013 (Jan,Feb)

Throughput "TEU"

2012 Diesel Use

"Liter/TEU"

2013 (Jan,Feb)

Diesel Use "Liter/TEU"

BNCT 994,093 506,526 222,441 179,920 1.96 1.24

At the quay side, managerial performance cannot have a big influence on the technical performance of the STS cranes since all kinds of cranes are operated by the same way within a limited amplitude of movement. Thus, we can suppose that energy waste is caused by operating at an incorrect return to scale conditions, probably due to a technological disadvantage.

In general for container terminals, most yards’ equipment represents big similarities, but STS container cranes may vary in type (high/low profile), size (Panamax, Post panamax, and Super‐Post panamax), Safe Working Load (SWL) capacity, volume of TEUs handled per hour, type of trolleys and spreaders, etc. This variety in equipment can affect the energy consumption for operating the STS crane. According to the collected data, we can see that the average electricity amount consumed per TEU by BNCT to operate its STS cranes is about 178% higher than its pair of terminals in the New Port <Table 8>.

<Table 8> NP Electricity Consumption in 2012DMUs

Variables

2012 Electricity use "Kwh"

2012 Throughput

(TEU)Kwh/TEU Average

Kwh/TEU Ratio

Hyundai 40,882,580 2,078,010 19.67

18.75 1HanjinShipping 50,003,899 2,432,255 20.56

PNIT 22,987,582 1,220,000 18.84

PNC 56,433,062 3,353,330 16.83

BNCT 16,911,124 506,526 33.39 33.39 1.78


After checking the type of STS cranes used in the New Port11, we found that all terminals’ STS cranes represent big similarities in term of size (all of them are Super‐Post Panamax) and type (all of them have high profile). However, a main difference resides in the type of spreaders, where BNCT is the only terminal in New Port that is equipped with Twin‐Lift spreaders for the integral of its cranes while all other terminals use Tandem‐lift spreaders. In fact, operating a Twin‐Lift spreader consumes less electricity per move than a Tandem‐Lift spreader (as mentioned previously). Nevertheless, it always needs to make two times more moves to handle the same volume of TEUs which may considerably affect the energy consumption, especially in time of busy container traffic. Thus we can estimate that BNCT decision makers may be able to reduce its energy consumption by upgrading the terminal’s quayside equipment (e.g., equipping its STS cranes with Tandem‐Lift spreaders rather than Twin‐Lift spreaders).

3. Ranking Analysis by Super‐Efficiency Model

In order to obtain a full ranking of the terminals in our sample, we applied the DEA Super‐Efficiency model that allows the efficient units under evaluation to be excluded from the reference set, and thus be able to get a score that is bigger than (1). This method will allow us to rank the terminals in the sample under study from the relatively most efficient to the relatively most inefficient, which allows a full comparison of the performance of the terminals and identifies the terminal representing the best practices among the selected sample. <Figure2> represents the result obtained with the Super‐Efficiency model.

<Figure 2> Efficiency Scores by Super‐Efficiency Efficiency Graph √

Hyundai 83.9 % 84% Hanjin Shipping 75.6 % 76%

PNIT 122.7 % 123% √PNC 86 % 86%

BNCT 76.6 % 77% SBTC 52.2 % 52% KBCT 73.8 % 74% DPCT 106.7 % 107% √UTC 68.4 % 68%

Hutchison 142.7 % 143% √√ : Super‐Efficiency *: Weak Efficiency

11 The analysis was limited only to the New Port terminals because the terminals at the North Port use a different type of T/C.


The result shows that the Super‐Efficiency scores of the terminals. Hutchison, DPCT and PNIT, of which all efficiency indices are equal to 1 under CRS, are respectively about 1.43, 1.07, and 1.23.12

Therefore, the Hutchison terminal is evaluated to be the most efficient unit and both the practice performed and the results obtained by the Hutchinson Terminal in terms of energy use may be respectively used as a sound basis and realistic target in establishing a comprehensive energy policy and issuing more rational regulations by decision makers (e.g., Busan Port Authority) in the ports of Busan.

Ⅳ. Conclusion

Ports have always been considered as important logistics platforms for international commerce, as they are the unique interface between sea and land transportation modes.

Merchandise trade and seaborne shipments continue to move in tandem (UNCTAD, Review of Maritime Transport 2012) and any increase in the international commercial exchanges will have a direct impact on the port industry through the intensification of port commercial activities. As a consequence, a port’s energy consumption will increase exponentially, because the port industry always reacted to the increase in the international trade volume by increasing the amount of the port’s equipment or using more advanced technologies that consume more energy, With respect to these circumstances, in this study the degree of performance in terms of energy use efficiency of ten terminals located in Busan’s ports in Korea had been investigated. Our approach described an in‐depth application of input‐oriented Data Envelopment Analysis that not only allows the determination of terminals representing bad practices and the origin of their inefficiency, but also the way to improve their performance using the terminals representing the best practices as references. It may be useful for the policy makers to design their policies differently depending on the type of inefficiency that characterizes each terminal. Also the super‐efficiency results allow the Port Authority to establish a more realistic energy policy and effective regulation that permits terminal operators to reach a maximal energy saving and emissions reduction for both ports, at general level based on the practices and results of the best performing unit in the sample.

The results also indicate that, in general for container terminals, there is no dominant cause of inefficiency and the lack of energy performance can be driven either by mismanagement of inputs (terminal’s layout, horizontal transport that connect the terminal yard to the quay, stacking criteria, etc.) or by unfavorable scale conditions mostly related to technological issues. It means that size of terminals doesn’t have

12 The Super‐Efficiency scores of the inefficient container terminals are the same as the efficiency scores indicated in CCR model.


a big influence in term of energy efficiency.Finally within the framework of our sample on an average, a policy designed in

order to induce Busan Port terminals to move towards less intensive‐energy terminals will save up to 18% of energy, resulting not only in energy cost reduction, but also in generating less emissions from the port sector, and consolidating sustainability by using more environmentally friendly practices.

Ⅴ. Study limitation and Future research

The DEA method allows researchers the ability to distinguish the efficient units from the inefficient ones. It also can designate a benchmark(s) for each inefficient unit in order to get the best use of its inputs. Furthermore, it dichotomizes the technical inefficiency into pure‐technical and scale inefficiencies, allowing us to determine whether the lack in efficiency is due to managerial underperformance or related to technological issues. It is the ultimate limit of the DEA method, i.e., DEA doesn’t precisely indicate among the various factors affecting the PTE which one needs to be focused on as waste generators, or which part of the inputs is representing a technological disadvantage that might lead to scale inefficiency and need to be upgraded.

For the above mentioned reason, based on DEA results, future research using regression analysis needs to be conducted to precisely determine the respective causes of energy waste and to reach a more accurate analysis and provide substantial recommendations for strategy planning in terms of energy use, for each inefficient terminal.

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<Appendix> Initial Terminals’ DATA for the year 2012DMUs

Variables

Throughput "TEU"

Diesel "liter"

Electricity "kwh"

Nº STS/C“Units”

Nº T/C“Units”

Nº Yard Vehicles“Units”

STS Total Box/Lift"TEU"

Total Yard Equipment

"Units"

Hyundai 2,078,010 2319757 40882580 11 38 97 44 135Hanjin

Shipping 2,432,255 3161931 50003899 12 42 109 48 151

PNIT 1,220,000 1342000 22987582 9 28 57 36 85

PNC 3353330 4260000 56433062 17 58 146 68 204

BNCT 506526 994093 16911124 8 38 36 16 74

SBTC 966341 3279082 11295910 7 21 40 14 61

KBCT 2230306 4410068 28932862 15 42 107 30 149

DPCT 1193690 1517623 13095408 7 19 42 14 61

UTC 568753 1479095 5480376 5 13 29 10 42

Hutchison 1358431 1810844 16240000 14 33 92 28 125

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