The Estimation of the Correlation between GHG and the Technical Efficiency of Korean Short-Sea Ports

Abstract

Short-sea ports in Korea are classified as national or local according to their management and operation entities. Korea has 29 ports, of which 11 are nationally managed and 18 are locally managed. Meanwhile, according to Korea’s Harbor Act, short-sea ports are designed to support the promotion of benefits such as handling cargo required for local industries, transportation of passengers, and revitalization of tourism. However, even though they are designated as short-sea ports, there are cases in which the initial designation purpose was not achieved because of the minor traffic volume and number of passengers. Consequently, this study evaluated the operational efficiency of 29 Korean short-sea ports employing the data envelopment analysis (DEA) and presented the operational efficiency of 29 short-sea ports. Moreover, the study measured the correlation between greenhouse gas (GHG) emissions and the port operation efficiency of 29 Korean short-sea ports. The analysis results of this study are as follows: First, the correlation between port-operation efficiency and sulfur dioxide was −0.41707. Second, the correlation between efficiency and carbon monoxide was −0.39952. Third, the correlation between efficiency and nitrogen dioxide was −0.30888. In summary, this study concludes that the higher the port-operational efficiency, the lower the GHG emissions. Improving the operational efficiency of ports can reduce GHG emissions, which can positively (+) affect port sustainability.

1. Introduction

Since 2003, the International Maritime Organization (IMO), a United Nations specialized agency, has been making efforts to develop the mechanisms required to reduce and limit shipping-related greenhouse gas (GHG) emissions. This began to be regulated in earnest from The Initial IMO Strategy on Reduction of GHG Emissions from Ships in 2018. In addition, the IMO adopted the 2023 IMO GHG Strategy for GHG emissions from ships (the 2023 IMO GHG Strategy) in July 2023, which could further drive the reduction of GHG emissions from shipping. The levels of ambition of the 2023 IMO GHG Strategy were set to reach net zero GHG emissions from international shipping by 2050. Ports should also be supported when eco-friendly technologies for the decarbonization of shipping accelerate because of international organizations.

The requirement to improve the efficiency and eco-friendliness of port operations continues to grow. Wanke and Barros [1] explained that port efficiency should be measured to identify ports with low efficiency and perform transportation activities between ports more smoothly by implementing policies to improve efficiency as a follow-up measure. Xu et al. [2] demonstrated that the Chinese government promotes the local economy by improving the operational efficiency of its short-sea ports and suggested that the operational efficiency of short-sea ports must be enhanced.

Research methods to measure port efficiency include field surveys and interviews, quantitative comparisons of actual processing performance with port capacity, and data envelopment analysis (DEA). Zhou et al. [3] and Tovar and Wall [4] conducted onsite surveys and interviews and identified port efficiency by collecting the opinions of port officials. Jose et al. [5] and Nguyen et al. [6] measured the absolute efficiency by quantitatively comparing port capacity and processing performance. Conversely, similar to the current study, there are studies that analyzed the relative efficiency of ports using DEA, which are discussed in detail in Section 2.

Winnes et al. [7] explained that climate change has recently received increasing attention in the shipping sector. This is mainly due to the growing demand for reduced air emissions and because the shipping industry is one of the fastest growing sectors in terms of GHG emissions. Simultaneously, ports have begun introducing programs and policies to address these emissions. According to their research, ports should implement operational efficiency improvement efforts to improve air quality. GHG emissions can be reduced from port areas by fast-unloading work and avoiding unnecessary ship congestion. In short, rapid cargo loading and unloading can improve port efficiency, which can contribute to the establishment of eco-friendly industries [7]. Alamoush et al. [8] suggested that ports can play a significant role in reducing greenhouse-gas emissions. Their study was conducted through a systematic literature review (112 studies) and informed by a four-dimensional conceptual framework: port policymakers, port polluters, uptake of GHG emission reduction technical and operational measures, and the implementation schemes. Their research showed that timely government policies can improve the operational efficiency of ports, which will ultimately reduce GHG emissions from global shipping [8]. In addition, Mocerino et al. [9] analyzed the positive (+) effect of port efficiency on reducing GHG emissions. According to their research, the introduction of digital or smart devices in ports reduces the time required for loading and unloading cargo, which can eventually reduce the GHG emitted by vessels entering and leaving the port [9]. Previous studies have shown that improving port efficiency can improve the environment.

According to the Korean Harbor Law [10], Korea’s short seaports are located on the coast and carry cargo from other ports in the country. The country’s short-sea ports are classified as nationally and locally managed according to their management and operation entities. There are currently 29 short-sea ports—11 nationally managed and 18 locally managed. The national management of short-sea ports, such as national security or territorial sea management, focuses on the evacuation of ships during emergencies. This includes the Yonggi, Yeonpyeong, Sangwangdeungdo, Heuksando, Gageohangri, Chuja, Geomundo, Hwasun, Kukdo, Hupo, and Ulleung ports. The local management short-sea port focuses on convenience, such as handling cargo and transporting passengers for local industries, and supporting tourism activation, including Daecheon Port, Beein Port, Songgong port, Hongdo Port, Jindo Port, Ttangkkeut Port (Galdu Port), Hwahheungpo Port, Sinma Port, Nokdong New Port, Narodo Port, Chunghwa Port, Busan Nam Port, Guryongpo Port, Ganggu Port, Jumunjin Port, Aewol Port, Hanlim Port, and Seongsanpo Port.

This study evaluated the operational efficiency of 29 Korean short-sea ports employing the DEA method and presented the operational efficiency of 29 short-sea ports. Subsequently, the correlation between GHG emissions and the port operation efficiency of 29 Korean Short-Sea ports were measured. This study attempts to confirm whether the higher the port operational efficiency, the lower the GHG emissions. Improving the operational efficiency of ports can reduce GHG emissions, which can have a positive (+) effect on port sustainability.

Previous studies have measured the efficiency of Korean ports; however, most have limited the analysis of targets to large cargo-oriented ports, such as trade ports. Moreover, this study estimates the operational efficiency of Korean short-sea ports using the latest data as of 2022, and measures passenger and cargo operational efficiencies. Against the backdrop of continuously strengthening international environmental regulations, there is a growing requirement to calculate port efficiency. If the port operational efficiency is high, the cargo entering and leaving the port can be processed more quickly, which reduces the port’s GHG emissions. This study conducted an efficiency analysis of Korea’s short-sea ports and quantitatively presented the efficiency of individual ports. Low-efficiency ports are required to improve the overall efficiency by reinforcing the berth, dredging, recruiting a workforce, and introducing ecofriendly unloading devices.

The remainder of this paper is organized as follows: Section 2 reviews prior studies that estimated port efficiency using the DEA methodology or analyzed short-sea shipping. Section 3 describes the data used to evaluate the operational efficiency of short-sea ports (29 locations) in Korea. Section 4 explains the DEA estimation results for Korea’s short-sea ports and presents their implications. Section 4 shows the relationship between port efficiency and GHG emissions. Section 5 summarizes the study and proposes policy implications based on the findings.

2. Review of Prior Studies

The methods for measuring efficiency include parametric and nonparametric methods. The parametric method estimates efficiency using a function consisting of variables. Among non-parametric techniques, DEA has recently been increasingly used. DEA assumes an efficient frontier and compares the relative efficiency by calculating the distance between the element and the frontier. The DEA measures efficiency by comparing input and output factors; therefore, it has the advantage of being able to measure the efficiency of an analysis object relatively simply. This study considers a previous study that measured port efficiency using DEA in Section 2.

In addition, this study considers previous studies that have analyzed short-sea shipping. Seven previous studies explored the following:

• Efficient transportation of cargo in Northern Europe using the short-sea roll-on–roll-off (RoRo) service.
• Tradeoffs between mineral mining in short-sea and pollution.
• Short-sea shipping sustainability in the Baltic area.
• The importance of communication among short-sea ports in Cyprus and the East Mediterranean.
• The sustainable growth measures for short-sea shipping, particularly in South Africa.
• The slow-steaming strategy of RoRo and mixed freight passengers (RoPax) in short-sea areas responds to ecofriendly regulations.
• The transportation in short-sea shipping among Mediterranean countries and improvement of transport efficiency.

This study aims to measure the efficiency of each major port in the Korean Short Sea. South Korea has North Korea to the north, the sea surrounds the east, west, and south, and trade with other countries has developed; however, the importance of short-sea shipping is relatively less emphasized. However, less effort has been made to improve the sustainability and efficiency of Korean short-sea shipping. These eight studies analyzed the sustainable growth of short-sea shipping and suggested reasonable implications. This study seeks inspiration for improving short-sea port efficiency and sustainable development by considering seven previous studies on short-sea shipping.

2.1. Preliminary Study on Port Efficiency Measurement Using DEA

Nikolaou and Dimitriou [11] estimated port efficiency using DEA. This study compares and analyzes the efficiencies of the top 50 international container ports using a stochastic frontier model. This study showed that as competition between container terminals heightens, the force to optimize the operational efficiency level of container terminals increases, and that it is necessary to place ports with high operating efficiency by measuring the efficiency of the main ports. In this study, a service of Twenty-foot Equivalent Units (TEUs) was used as the output variable during the DEA analysis. The number of cranes, terminal space, and queue length are the input variables.

In addition, many studies have used DEA to measure port operational efficiency. Zarbi et al. [12] evaluated the relative efficiency of Iranian ports, employing data for seven years (2012–2018), and selected throughput (TEU) as the output variable, and quay wall length, number of berths, number of gantry cranes, and yard space as the input variables. According to their study, four ports—Khorramshahr, Bushehr, Bandar Imam Khomeni, and Chabahar—have presented increased port-average efficiency, and only Shahid Rajaei has shown a decrease [12].

Mustafa et al. [13] explained that the significance of South Asian and Middle Eastern ports was energized by the introduction of the China–Pakistan Economic Corridor (CPEC). Their study investigated the operational efficiency of 15 ports in Asia by employing several kinds of DEA models. The results show that among the South and Middle Asian ports, only one port, each from India and the South and Middle Asian ports, was found to be efficient in the CCR model, with several ports in the BCC model increasing by 47%. In East Asia, two ports in Republic of Korea and one in China were found to be efficient in the CCR model, with a 33% increase in the BCC model. The output variable in their study was container throughput (TEU), and the input variables were the number of cranes, berth length, and draught length [13].

Kuo et al. [14] intermixed context-dependent DEA and forecasting models to achieve an efficient measurement of 53 Vietnamese ports and forecast upcoming performance in the port sector. By hiring a technique to evaluate performance, their study created port benchmark-learning mechanisms for inefficient ports to gradually improve. The attractiveness and improvement scores help ports situate themselves. Furthermore, evaluating port performance results supports decision-makers with more precise information to make more advantageous decisions regarding their strategies and assets. The output variables of this study are throughput(tons) and number of ship calls, and the input variables are the size of the total terminal area, terminal length, and number of pieces of equipment [14].

Sun et al. [15] suggested a non-radial DEA preference model based on the directional distance function (DDF) and variable returns to scale (VRS). The proposed model is employed to estimate and investigate the efficiency of Chinese listed port enterprises. The efficiency results revealed that the average efficiency of every port was low when environmental factors were considered. The regression results show that port assets, geographical location, and berth quantity can significantly influence the environmental performance of Chinese port companies. The output variables of their study were cargo throughput (ten throughput and tons), operating cost (100 million RMB), fixed assets (100 million RMB), net profit (100 million RMB), and NOx (tons), and the input variables were staff number (persons) [15].

Wanke et al. [16] explained that port efficiency had been widely analyzed using classic DEA models and their variations. These models do not account for the inner structure, which is comparable to the estimates characterizing port operation performance. They measured the efficiency of Brazilian ports using a two-stage process. A network DEA centralized efficiency model was used to optimize both stages simultaneously. The results demonstrated that private administration positively affects physical infrastructure efficiency. Simultaneously, the operation of both cargo and hinterland size positively impacts shipment consolidation efficiency. The output variables of their study were the solid bulk throughput (tons/year) and container throughput (TEU/year), and the input variables were the number of berths, size of the yard area (sqm), and warehousing area (sqm) [16].

Wang et al. [17] compared and estimated the efficiency of 11 main container ports in China using the DEA model and suggested a future strategy to improve the efficiency of the main Chinese ports. The empirical results explain that (1) port cooperation can increase the overall expected outcome but will lose its advantage with the improvement of particulate matter (PM) emission standards, (2) ports in eastern China (Shanghai, Nanjing, and Ningbo) have higher port efficiency, and (3) ports follow the same direction of output loss despite profitable decisions. The output variables of their study were standard container throughput, and the input variables were labor, terminal length, number of berths, and total assets [17].

2.2. Preliminary Study on Short Sea Shipping

Christodoulou et al. [18] found that RoRo shipping represents a maritime element that can quickly become part of an intermodal transport system, as cargo must not be lifted in ports. They examined the operation of RoRo shipping services in Europe, focusing on short-sea transportation services chartered by a major shipper whose demand significantly impacts service design, potentially impacting the frequency of departures, and even stipulating the usage of specific vessels. The case of cooperation between shippers and shipping companies in Northern Europe is analyzed, providing insight into ways in which these RoRo services work and integrate successfully into sustainable intermodal transport chains. Despite initiatives taken by various stakeholders, the level of integration of shipping in short-sea logistics chains has been relatively slow. The results of this study could contribute to identifying barriers that prevent RoRo shipping from being a possible option for road transport for specific transportation routes and assist in finding policies and incentives that could guide the development of sustainable short-sea logistic chains [18].

Michaelides et al. [19] explained that the sustainability of short-sea shipping (SSS) is prominent in a clean, safe, and efficient European Union (EU) transport system. They reported critical challenges for increasing reliability, quality, and safety and withdrawing disproportionate costs and delays at SSS hubs, focusing on Cyprus and the Eastern Mediterranean. They primarily evaluated the effect of port-2-port (P2P) communication on port efficiency by quantitatively and qualitatively examining the elements affecting different waiting times at the Port of Limassol. The quantitative estimation depends on data from over 8000 port calls during 2017–2018, which were examined with respect to port of origin, ship type, and shipping agent. Qualitative results were established based on the views of key stakeholders in the port call process. The estimated key performance indicators (KPIs) include arrival punctuality, berth waiting, and utilization. The analysis indicated a substantial variation in agent performance regarding KPIs, implying a lack of attention to the social aspect of a port’s sociotechnical technique. They proposed measures for enhancing agent performance based on port collaborative decision-making (PCDM) principles, including P2P communication, data sharing, and transparency among everyone concerned in the port call process, including agents and dissemination of agent-specific KPIs [19].

Comi and Polimeni [20] explored short-sea shipping services as a competitive and sustainable transport system that can react to economic, social, and environmental requirements. An estimation methodology is suggested that considers an aggregate discrete choice model that affects the split between competitive transport options in the Mediterranean Basin. The proposed method assesses the potential of SSS and the net advantages derived from lower external costs in the northwestern Mediterranean basin. Two forthcoming scenarios were evaluated: introducing new SSS services as envisaged by current EU schemes and strategies, new SSS routes, and an upsurge in existing service frequencies. Notable results have been obtained regarding the shifting freight traffic from road networks and external benefits [20].

2.3. Input/Output Variables in Previous Studies When Measuring the Port Efficiency

The following table summarizes the input and output variables used in prior studies on DEA analysis for port efficiency measurements. Several studies have adopted berths as representative variables that affect port efficiency. Factors related to the berth include the berth length and the number of berths. Nikolaou and Dimitriou [11], Zarbi et al. [12], and Mustafa et al. [13] used the berth length as input variables. In contrast, Zarbi et al. [12], Wang et al. [17], Mustafa et al. [13], and Wanke et al. [16] used the number of berths as input variables.

Several previous studies have included factors related to terminals in the input variables. Nikolaou and Dimitriou [11], Zarbi et al. [12], and Kuo et al. [14] used the size of the terminal area as the input variable, whereas Wang et al. [17] and Kuo et al. [14] used terminal length as the input variable. The yard generally occupied the largest area among the factors constituting the terminal. If the yard was narrow, the number of layers in the container box increased, which delayed smooth circulation of cargo, thereby degrading port efficiency. Studies using yard area as input variables include those by Zarbi et al. [12] and Wanke et al. [16].

The crane has a significant effect on the reduction in the unloading time. The larger the number of cranes, the greater is the amount of cargo that can be handled per hour. Many previous studies have included the number of cranes as the input variable, including those by Nikolaou and Dimitriou [11], Zarbi et al. [12], Wang et al. [17], and Mustafa et al. [13]. In addition, the input variables selected by previous studies to measure port efficiency included draught length, number of workers, total assets, number of pieces of equipment, and warehouse area.

After comprehensively reviewing prior studies, we selected variables (input and output) to measure the operational efficiency of Korean short-sea ports. First, in this study, the factors related to berths commonly adopted by most previous studies were selected as input variables because port efficiency is easier to measure when using the factors verified and adopted in many previous studies. In this study, considering the characteristics of short-sea ports that handle not only cargo but also passengers, the length of the berth was reclassified as the length of the cargo ship berth and the length of the passenger ship berth. In addition, the draught length, which was selected as the input variable in several previous studies, was chosen as an input variable, and the breakwater length was added as an input variable. The stable entry and departure of ships is expected to be possible if sufficient water depth and breakwater length are secured.

However, in the case of output variables, all previous studies adopted cargo handling. In this study, we included the amount of cargo and number of passengers in the output variable, considering the characteristics of short-sea ports handling cargo and passengers.

Table 1. Key Input/Output Variables for Port Efficiency Measurement.

	Factors	Nikolaou and Dimitriou [11]	Zarbi et al. [12]	Wang et al. [17]	Mustafa et al. [13]	Kuo et al. [14]	Sun et al. [15]	Wanke et al. [16]	Current Study
Input Variables	Berth	V	V	V	V			V	V
	Terminal	V	V	V		V
	Yard		V					V
	Crane number	V	V	V	V
	Draught length	V	V		V
	Staff Number			V			V
	Others			asset		equipment		warehouse
Output Variables	Cargo Amount	V	V	V	V	V	V	V	V
	Others								passengers

Notes: V indicates that the corresponding factor is used as a variable in this study.

presents the input and output variables selected in previous and current studies for measuring port efficiency.

3. DEA Port Efficiency Measurement

Based on the results of a previous study, this study adopted five input variables: cargo ship berth length, passenger ship berth length, breakwater length, number of berths, and draught length, which can be easily used to measure port efficiency. Short-sea cargo and passengers were selected as the output variables to reflect the characteristics of short-sea ports that handle passengers and cargo. The input/output data of the short seaports used in this study are presented in the

Table 2. Input/Output variables by port (national management).

	Variables	Unit	Yonggi Port (West)	Yeonpyeong Port (West)	Sangwang- Deungdo Port (West)	Heuksando Port (West)
Input	Cargo Berth Length	Meter	50	125	80	1481
	Passenger Berth Length	Meter	180	50	6	260
	Breakwater Length	Meter	734	183	165	1438
	Berth Numbers	Numbers	10	12	11.5	12
	Draught Length	Meter	2	1	2	3
Output	Cargo Amount	Thousand Tons	144	0.4	5.5	58
	Passenger Amount	Thousand Tons	401	108	0.385	323
	Variables	Unit	Gageohangri Port (West)	Chuja Port (South)	Geomundo Port (South)	Hwasun Port (South)
Input	Cargo Berth Length	Meter	-	970	655	200
	Passenger Berth Length	Meter	20	50	85	-
	Breakwater Length	Meter	90	305	805	1185
	Berth Numbers	Numbers	9.5	10	11	13
	Draught Length	Meter	1	2	3	1
Output	Cargo Amount	Thousand Tons	1.1	10.2	63	828
	Passenger Amount	Thousand Tons	-	86	171	-
	Variables	Unit	Kukdo Port (South)	Hupo Port (East)	Ulleung Port (East)
Input	Cargo Berth Length	Meter	110	1185	448
	Passenger Berth Length	Meter	-	-	317
	Breakwater Length	Meter	113	1312	1575
	Berth Numbers	Numbers	10	12.5	11.5
	Draught Length	Meter	1	1	5
Output	Cargo Amount	Thousand Tons	1.5	133	201
	Passenger Amount	Thousand Tons	6	114	754

Notes: The above 11 ports are classified as nationally managed short-sea ports. Source: 4th (‘21~’30) Korean National Short-Sea Port Revised Basic Plan (search date: June 2023) [21].

and

Table 3. Input/Output variables by port (local management).

	Variables	Unit	Daecheon Port (West)	Beein Port (West)	Songgong Port (West)	Hongdo Port (West)
Input	Cargo Berth Length	Meter	1462	424	114	53
	Passenger Berth Length	Meter	70	55	67	150
	Breakwater Length	Meter	1230	1310	188	250
	Berth Numbers	Numbers	12	9	10.5	11.5
	Draught Length	Meter	4	3	3	3
Output	Cargo Amount	Thousand Tons	112	-	263	-
	Passenger Amount	Thousand Tons	340	-	204	282
	Variables	Unit	Jindo Port (South)	Ttangkkeut Port (South)	Hwahheungpo Port (South)	Sinma Port (South)
Input	Cargo Berth Length	Meter	70	109	200	110
	Passenger Berth Length	Meter	120	55	150	-
	Breakwater Length	Meter	135	144	111	55
	Berth Numbers	Numbers	10.5	9.5	11	10
	Draught Length	Meter	3	2	3	2
Output	Cargo Amount	Thousand Tons	112	603	439	-
	Passenger Amount	Thousand Tons	234	637	547	-
	Variables	Unit	Nokdong New Port (South)	Narodo Port (South)	Chunghwa Port (East)	Busan Nam Port (East)
Input	Cargo Berth Length	Meter	330	604	153	4015
	Passenger Berth Length	Meter	412	174	230	-
	Breakwater Length	Meter	350	350	370	558
	Berth Numbers	Numbers	11	10	10.5	9.5
	Draught Length	Meter	4	6	3	1
Output	Cargo Amount	Thousand Tons	1048	-	398	2107
	Passenger Amount	Thousand Tons	310	27	542	-
	Variables	Unit	Guryonpo Port (East)	Ganggu Port (East)	Jumunjin Port (East)	Aewol Port (South)
Input	Cargo Berth Length	Meter	2008	967	1287	418
	Passenger Berth Length	Meter	-	-	-	-
	Breakwater Length	Meter	1090	641	1130	440
	Berth Numbers	Numbers	12.5	11	10.5	10
	Draught Length	Meter	1	1	1	1
Output	Cargo Amount	Thousand Tons	51	42	57	1612
	Passenger Amount	Thousand Tons	-	-	-	-
	Variables	Unit	Hanlim Port (South)	Seongsanpo Port (South)
Input	Cargo Berth Length	Meter	1500	1080
	Passenger Berth Length	Meter	-	20
	Breakwater Length	Meter	2248	2144
	Berth Numbers	Numbers	13	12
	Draught Length	Meter	1	2
Output	Cargo Amount	Thousand Tons	985	630
	Passenger Amount	Thousand Tons	84	1638

Notes: The above 18 ports are classified as locally managed short-sea ports. Source: 4th (‘21~’30) Korean National Short-Sea Port Revised Basic Plan (search date: June 2023) [21].

.

4. Correlation between GHG and Efficiency of Korean Short-Sea Ports

4.1. DEA

DEA is a methodology that objectively evaluates the efficiency of similar types of individual factors; that is, DEA is designed to measure the relative efficiency of factors based on inputs and outputs, which has the advantage of being modeled even if there is no assumption of any prior functional relationship between inputs and outputs.

When the input and output variables were selected, an analysis model was established. Applying this model to a factor yielded an efficiency indicator. Excel or several other software packages are used for the DEA analysis, such as Frontier Analysis, DEA Frontier, and DEAP.

4.2. Efficiency of Korean Short-Sea Ports

As a result of this study’s DEA analysis, the efficiency distribution by the port is shown in the following

Table 4. Efficiency of Korean Short-Sea Ports.

Efficiency Range/Numbers		Technical Efficiency
1.0	7 (24.1%)	Ulleung Port (1.00), Songgong port (1.00), Jindo Port (1.00), Ttangkkeut Port (1.00), Hwahungpo Port (1.00), Nokdong New Port (1.00), Chunghwa Port (1.00)
0.8~	7 (24.1%)	Yonggi Port (0.91), Heuksando Port (0.85), Geomundo Port (0.82), Hupo Port (0.80), Daecheon Port (0.89), Hanlim Port (0.91), Seongsanpo Port (0.92)
0.5~	5 (17.2%)	Chuja Port (0.74), Hwasun Port (0.54), Hongdo Port (0.60), Busan Nam Port (0.70), Aewol Port (0.78)
0.4~	4 (13.8%)	Yeonpyeongdo Port (0.46), Sangwangdeungdo Port (0.48), Kukdo Port (0.44), Ganggu Port (0.42)
0.3~	3 (10.3%)	Gageohangri Port (0.32), Guryongpo Port (0.31), Jumunjin Port (0.31)
0.0~	3 (10.3%)	Beein Port (0.00), Sinma Port (0.00), Narodo Port (0.21)

. This study used DEAP, which is DEA efficiency-analysis software.

Seven of the 29 short-sea ports were measured to be efficient, recording efficiency one (1.0): Ulleung Port, Songgong Port, Jindo Port, Ttangkkeut Port, Hwahheungpo Port, Nokdong New Port, and Chunghwa Port. These highly efficient ports recorded relatively high cargo and passenger performances compared with five input variables: cargo ship berth length, passenger ship berth length, breakwater length, number of berths, and draught length.

Among the 29 short-sea ports, seven ports were measured to have high efficiency by recording efficiency of 0.8 or more and less than one, including Yonggi Port, Heuksando Port, Geomundo Port, Hupo Port, Daecheon Port, Hanlim Port, Seongsanpo Port. Among the seven ports measured to be highly efficient with an efficiency of 0.8 or more and less than one, there were ports with high cargo and passenger performance, but the reason they were not measured as efficient (1.0) is that they may have excessive cargo or passenger handling capacity. For example, Seongsanpo Port recorded an offshore cargo performance of 630,000 tons in 2020; however, its cargo-handling capacity was excessive, as the length of the cargo ship reached 1080 m.

In addition, among the 29 short-sea ports, five ports have normal efficiency, with an efficiency of 0.5 or more and less than 0.8, including the Chuja, Hwasun, Hongdo, Busan Nam, and Aewol ports. The five ports with an efficiency of 0.5 or more and less than 0.8, are observed as ports that show an imbalance in cargo and passenger performance, owing to the high performance of only one cargo or passenger. For example, the cargo performance of Hwasun Port in 2020 was 828,000 tons, but the passenger performance was zero.

Among the 29 short-sea ports, ten ports were measured to be less than 0.5 in efficiency, including Yeonpyeongdo Port, Sangwangdeungdo Port, Gageohangri Port, Kukdo Port, Beein, Sinma, Narodo, Guryongpo, Ganggu, and Jumunjin Ports. Most of the ten ports, measured as less efficient with efficiencies less than 0.5, are ports with low performance in both cargo and passenger performance.

4.3. Efficiency of Korean Short-Sea Ports by Location

The efficiency analysis by location using the DEA shows the results of the efficiency distribution by location in

Table 5. Efficiency of Korean Short-Sea Ports by Location.

Location	Efficiency Estimates
West Coast	0.61
South Coast	0.72
East Coast	0.65

. When 29 short-sea ports were classified into the west coast (Nine Ports: Yonggi, Yeonpyeongdo, Sangwangdeungdo, Heuksando, Gageohangri, Daecheon, Beein, Songgong, and Hongdo Ports); south coast (Thirteen ports: Chuja, Geomundo, Hwasun, Kukdo, Jindo, Ttangkkeut, Hwahheungpo, Sinma, Narodo, Aewol, Hanlim, Seongsanpo Ports, and Nokdong New Port); and east coast (Seven Ports: Hupo, Ulleung, Chunghwa, Busan Nam, Guryongpo, Ganggu, Jumunjin Ports), the current study measured them as 0.61 on the west coast, 0.72 on the south coast, and 0.65 on the east coast. In short, as a result of measuring efficiency by location, it was found that efficiency was high in the order of ‘South Coast > East Coast > West Coast’.

The current study measured the efficiency of the southern coast as the highest because the cargo and passenger performances of the south coast port were much higher than others. Among the ports located on the south coast, those measured as efficient (1.0) due to their high cargo and passenger performance are Jindo Port, Ttangkkeut Port, Hwahheungpo Port, and Nokdong New Port. Many ports on the southern coast use port facilities effectively; therefore, efficiency is estimated to be high.

4.4. Efficiency of Korean Short-Sea Ports by Management Type

This study assumes that the efficiency of ports managed and operated by local governments (local port offices) would be higher before analyzing the efficiency of Korean short seaports by management type. The local port authority of Korea is an organization that manages and operates ports and port hinterlands and generally tends to manage and operate ports considering the balance between cargo and passengers. In short, it is assumed that the port’s efficiency will be higher than that managed by the central government because the local port authority has expertise as a port management and operation organization, and systematically manages the port while considering its unique characteristics.

Table 6. Efficiency of Korean short-sea ports by location.

Division	Ports		Estimates
West Coast	National	Yonggi Port, Yeonpyeongdo Port, Sangwangdeungdo Port, Heuksando Port, Gageohang Port	0.60
	Local	Daecheon Port, Beein Port, Songgong Port, Hongdo Port	0.62
South Coast	National	Chuja Port, Geomundo Port, Hwasun Port, Kukdo Port	0.63
	Local	Jindo Port, Ttangkkeut Port, Hwahheungpo Port, Sinma Port, Nokdong New Port, Narodo Port, Aewol Port, Hanlim Port, Seongsanpo Port	0.76
East Coast	National	Hupo Port, Ulleung Port	0.90
	Local	Chunghwa Port, Busan Nam Port, Guryongpo Port, Ganggu Port, Jumunjin Port	0.55

shows the efficiency of Korean shot-sea ports by location. As a result of analyzing the efficiency of each management type using DEA, the west coast showed 0.60 in the case of national management ports and 0.62 in the case of local management ports. The southern coast was measured as a central government-managed port (0.63 and a local management port (0.76). Third, the east coast was estimated to be a central government management port (0.90 and a local management port (0.55). As such, there is little difference in efficiency between central-government-managed and local-managed ports on the west coast, whereas local-managed ports on the south coast are more efficient than central-government-managed ports. On the east coast, central government management was more efficient than local management. These results suggest no significant difference in the management and operational efficiency of ports, regardless of whether the port manager of the Korean short-sea port was the central or local government. There is no need for port governance to be owned by the central or local governments to improve efficiency.

4.5. Correlation between GHG and Technical Efficiency of Korean Short-Sea Ports

This study measured the correlation between the GHG emitted by ports and the port operation efficiency of 29 Korean Short-Sea ports, as described in

Table 7. GHG Data of 29 Korean Short-Sea Ports. Unit: ppm.

Port Name	Technical Efficiency	Sulfur Dioxide	Carbon Monoxide	Nitrogen Dioxide
Ulleung	1.00	0.002	0.239	0.018
Songgong	1.00	0.000	0.188	0.022
Jindo	1.00	0.003	0.264	0.010
Ttangkkeut	1.00	0.000	0.225	0.006
Hwahungpo	1.00	0.003	0.082	0.007
Nokdong	1.00	0.002	0.223	0.012
Chunghwa	1.00	0.003	0.223	0.012
Yonggi	0.91	0.003	0.348	0.025
Heuksando	0.85	0.002	0.150	0.007
Geomundo	0.82	0.003	0.148	0.010
Hupo	0.80	0.005	0.665	0.023
Daecheon	0.89	0.002	0.232	0.004
Hanlim	0.91	0.003	0.242	0.053
Seongsanpo	0.92	0.003	0.348	0.015
Chuja	0.74	0.004	0.794	0.015
Hwasun	0.54	0.004	0.490	0.031
Hongdo	0.60	0.003	0.288	0.016
Busan Nam	0.70	0.003	0.148	0.022
Aewol	0.78	0.003	0.288	0.016
Yeonpyeongdo	0.46	0.003	0.547	0.024
Sangwangdeungdo	0.48	0.002	0.142	0.037
Kukdo	0.44	0.002	0.298	0.008
Ganggu	0.42	0.003	0.284	0.016
Gageohangri	0.32	0.003	0.284	0.028
Guryongpo	0.31	0.005	0.529	0.020
Jumunjin	0.31	0.002	0.319	0.025
Beein	0.00	0.003	0.284	0.011
Sinma	0.00	0.004	0.813	0.032
Narodo	0.21	0.005	0.304	0.029

Source: Marine Environment Information Portal (Ministry of Oceans and Fisheries), https://meis.go.kr/mei/observe/airMntrnInfo.do (accessed on 25 June 2023) [22].

.

The GHG selected in this study were sulfur dioxide, carbon monoxide, and nitrogen dioxide, and we used daily average emissions data from the port for one year (1 January 2022 to 31 December 2022). The data used in this study are presented in the Table 7.

The analysis results of this study in

Table 8. Correlation between GHG and Technical Efficiency of Ports.

Estimated Correlation	Sulfur Dioxide	Carbon Monoxide	Nitrogen Dioxide
Technical Efficiency	−0.41707	−0.39952	−0.30888

are as follows. First, the correlation between port operation efficiency and sulfur dioxide was −0.41707. This suggests that sulfur dioxide gas decreases by approximately 41.7% when port operational efficiency increases by one unit (1.00), indicating that sulfur dioxide emissions decrease as the operational efficiency of Korean short-sea ports increases.

Second, the correlation between port operational efficiency and carbon monoxide is −0.39952, which suggests that sulfur dioxide decreases by approximately 39.9% when port operational efficiency increases by 1 unit (1.00). Third, the correlation between port operation efficiency and nitrogen dioxide is −0.30888, showing that sulfur dioxide decreases by approximately 30.8% when port operation efficiency increases by 1 unit (1.00).

In short, this study concludes that the higher the port operation efficiency, the lower the GHG emissions and suggests that port efficiency should be improved to reduce GHG emissions in a situation where regulations on GHG emissions, such as the IMO, are strengthened. Improving the operational efficiency of ports can reduce GHG emissions, which can positively (+) affect their sustainability.

5. Conclusions

Smooth support for international logistics is essential for the sustainable development of the national economy. In terms of maritime transportation in charge of global logistics, one of the main policy tasks is to maintain and strengthen international competitiveness as competition with neighboring ports intensifies.

Conversely, to secure the international competitiveness of ports, it is necessary first to analyze domestic ports where cargo is transported between major domestic regions and analyze the efficiency of each port. After cargo transportation between major domestic ports occurs, cargo transportation between international ports begins. Through port efficiency measurements, it is possible to select ports for which the maritime government should implement policies to improve efficiency.

This study attempts to analyze and evaluate the factors that are the source of competitiveness for Korean short-sea ports and to measure the efficiency of each port. This study quantitatively measured port efficiency using DEA and compared and presented the estimated port-specific efficiency. All 29 short-sea ports in Korea were selected as analysis targets, and their efficiencies were analyzed after collecting and securing the latest data for 2022.

The main DEA results of this study are as follows. First, when the 29 short-sea ports were classified into west coast, south coast, and east coast, the current study measured them as 0.61 on the west coast, 0.72 on the south coast, and 0.65 on the east coast, respectively. In short, as a result of measuring efficiency by location, it was found that efficiency was high in the order of ‘South Coast > East Coast > West Coast’. We can conclude that the efficiency of the southern coastal port was the highest because the cargo and passenger performances of the southern coastal port were much higher. Among the ports located on the south coast, those measured as efficient (1) due to their high cargo and passenger performance are Jindo Port, Ttangkkeut Port, Hwahheungpo Port, and Nokdong New Port. Many ports on the southern coast use port facilities effectively; therefore, efficiency is estimated to be high.

Second, analyzing efficiency by management type using DEA, the west coast showed 0.60 in the case of national management ports and 0.62 in the case of local management ports. The southern coast was measured as a national management port at 0.63 and a local management port at 0.76; thirdly, the east coast was measured as a national management port at 0.90 and a local management port at 0.55, respectively. As such, there is little difference in efficiency between the national and local management ports on the west coast, and the local management ports on the south coast are more efficient than the national management ports. On the east coast, central government management was more efficient than local management. These results suggest no significant difference in the management and operational efficiency of ports, regardless of whether the port manager of the Korean short-sea port was the central or local government. We conclude that, to improve efficiency, there is no need for port governance to be owned by the central or local governments.

Third, the correlation between port operational efficiency and sulfur dioxide was −0.41707. The correlation between the efficiency and carbon monoxide was −0.39952, and the correlation between efficiency and nitrogen dioxide was −0.30888. In short, it is expected that the higher the port operation efficiency, the lower are the GHG emissions. Improving the operational efficiency of ports can reduce GHG emissions, which can positively (+) affect port sustainability.

This study presents three measures for improving port efficiency. First, sufficient manpower is secured. As the port industry is recognized as a 3D (Dangerous, Dirty, and Different) industry, the phenomenon of avoiding employment in the port industry is spreading, and securing manpower can ultimately improve efficiency by enhancing port capacity. Second, sufficient port equipment must be secured. Extension of berths, dredging, etc., through port development are required, and a sufficient number of cranes can reduce the time required for cargo loading and unloading. The third is the promotion of the digitalization and decarbonization of the port industry. Thus, it is expected that the sustainable growth of port efficiency can be promoted and customer satisfaction can be improved.

In addition, this study proposed a leap into smart ports as a policy recommendation. Smart ports are fourth-generation ports that autonomously optimize logistics flows through fourth-generation industrial revolution technologies, such as artificial intelligence (AI), Internet of Things (IoT), and Information and Communications Technology (ICT). Smart ports can streamline their overall operations through big data analyses and predictions.

This study presents a correlation between the operational efficiency of major short-sea ports in Korea and GHG emissions, but there is a limitation in that overseas short-sea ports were not included in the analysis. In future studies, we intend to expand the scope of analysis to the EU, China, and Japan. It is also necessary to conduct follow-up studies to track the changes in port efficiency over time. This longitudinal approach, which extends the analysis over many years, is expected to provide insights into the dynamic flow of port efficiency.