Impact of Reducing Waiting Time at Port Berths on CII Rating: Case Study of Korean-Flagged Container Ships Calling at Busan New Port

Abstract

This study investigates the impact of reducing waiting times for port berth on improving the Carbon Intensity Indicator (CII) ratings of Korean-flagged container ships. As the International Maritime Organization (IMO)’s CII regulation mandates corrective actions for poorly rated ships for Greenhouse Gas (GHG) reduction in international shipping, the analysis focuses on container ships with projected D or E ratings by 2035. Using Automatic Identification System (AIS) data from ships, this study identifies annual waiting times and simulates changes in CII ratings under scenarios of reduced waiting times (30%, 50%, 70%, and 100%). The relationship between ship speed and fuel consumption was established by analyzing the recent literature, and the CII improvement was evaluated based on IMO Data Collection System (DCS) 2022 data. The results show that a 30% reduction in waiting time can lower CO₂ emissions by 12.18% and improve the CII rating by one or two levels for approximately half of the sample ships. However, a 50% reduction or more is required to maintain improved ratings beyond 2030. The findings highlight the significance of just-in-time (JIT) practices in minimizing latency and enhancing regulatory compliance. The policy recommendations advocate for prioritizing port call optimization and recommend the adoption of JIT as a measure to achieve the IMO’s GHG reduction targets.

1. Introduction

According to the Fourth IMO Greenhouse Gas (GHG) Study 2020 [1], published by the International Maritime Organization (IMO), total CO₂ emissions from shipping in 2018 reached 1.056 billion tons, representing 2.89% of global emissions. Of this total, international shipping was estimated to account for 740 million tons. Furthermore, it is projected that by 2050, under a long-term business-as-usual (BAU) scenario, CO₂ emissions from ships could increase to between 90% and 130% of the 2018 levels.

To mitigate the impact of shipping on the climate crisis, the 72nd Marine Environment Protection Committee (MEPC) adopted the 2018 IMO Initial Strategy to Reduce GHG Emissions from Ships (initial strategy) [2]. This strategy outlines specific numerical targets and periodic measures, serving as a catalyst for reducing GHG emissions from international shipping. Based on the initial strategy, IMO member states began developing technical and operational measures to enhance energy efficiency in the short term (short-term measures). They agreed to implement technical measures such as the Energy Efficiency Existing Ship Index (EEXI) and operational measures based on an annual Carbon Intensity Indicator (CII) and rating. On 1 November 2022, Annex VI of the International Convention for the Prevention of Pollution from Ships (MARPOL) was amended to incorporate these measures. As a result, 105 states, representing 96.81% of the world’s merchant fleet, are obligated to comply with the regulations related to the short-term measures, which came into force on 1 January 2023 [3].

The short-term measures, the EEXI and CII, serve the same purpose of enhancing energy efficiency. However, the EEXI takes into account the ship’s specifications and technologies, including its volume, speed, and CO₂ emissions from the main engine, auxiliary engine, and shaft generator, similar to the Energy Efficiency Design Index (EEDI). It is challenging to implement radical changes [4]. On the other hand, the CII directly regulates the operation of a ship, as it is determined by assessing the vessel’s annual distance traveled and fuel oil consumption [5]. Due to the characteristics of the CII, compliance is highly flexible based on annual operating results, and each shipping company should actively manage the CII values for its ships and develop strategies for improvement.

The Ministry of Oceans and Fisheries of the Korean government has issued guidelines to facilitate the effective implementation of regulations for shipping companies. In 2020, the Ministry conducted an investigation into the national flag fleet, including ships under Bareboat Charter Hire Purchase (BBCHP). It calculated the CII for the national flag fleet based on operational data from 2020 to derive ratings. The findings revealed that 34.2% of the ships were rated as D or E [6], highlighting the urgent need for the industry to develop appropriate responses [7,8]. Additionally, the Ministry initiated a consulting project aimed at improving the CII ratings for national flag carriers [9]. This project provides operational and technical measures to enhance the ratings of ships expected to be rated as D or E by 2025, in accordance with their specifications and operation characteristics [10]. According to the consulting outcomes [10,11], the main barriers to rating improvement were identified as follows: (1) long waiting times for ships due to port congestion, (2) difficulties in applying CII correction factors, (3) insufficient technical and policy measures to manage CII ratings, and (4) a lack of communication and cooperation with shippers regarding CII regulations [12,13,14,15,16,17,18,19].

Factors 2, 3, and 4 arise from the implementation process of the CII and can be improved through collaboration among member states. However, although waiting times can arise for various reasons, they represent periods during which a ship remains stationary—neither sailing nor processing cargo—despite having previously traveled at speeds sufficient to arrive at the port on schedule. By reallocating this waiting time to sailing time, the ship can operate at a lower speed. This speed reduction can make waiting times significantly more economical, particularly when just-in-time (JIT) arrivals are achieved. Therefore, to effectively minimize waiting times, it is essential to quantitatively assess how much this reduction enhances the implementation of the CII. This study aims to assess the impact of reducing waiting times at port berths on the CII, focusing on Korean-flagged container ships.

2. Literature Review

According to the objectives of this study, a literature review was conducted by searching for keywords such as “short-term measures”, “CII”, “waiting time”, and “JIT arrival”. Distinctions of this study were also confirmed.

2.1. CII Regulations

Taskar et al. (2023) predicted the changes in CII ratings, by considering ships, performance, resistance, routes, and meteorological conditions, simulating the voyage per ship speed, and deriving energy-saving changes according to seasons and ship speed, in order to study the effects of low-speed operation for securing a high CII rating [20]. Farkas et al. (2023) analyzed the benefits of low-speed operation by targeting one Post-Panamax-class container ship and four container ships under actual sailing conditions [21]. Bayraktar and Yuksel (2023) analyzed the changes in the EEXI and CII according to the main engine size and optimum ship speed through scenario analysis for five ship types, bulk carriers, gas carriers, tankers, general cargo ships, and container ships [22].

Kim et al. (2023) calculated the CII ratings by applying the cargo transport volume data of EU-MRV per ship type, analyzed the change in CII ratings until 2030 [23], and conducted a comparative analysis on the difference in the CII using the deadweight tonnage and actual cargo transport volume.

Rauca and Batrinca (2023) analyzed the annual routes of four ships to evaluate the CII and analyzed the operational factors affecting CII [24]. Yuan et al. (2023) optimized a ship’s schedule by considering factors such as ship and route characteristics and operational constraints and derived a short-term operational efficiency optimization plan for the existing fleet of ships [25]. Tsai and Lin (2023) analyzed the CII improvement effect of northeastern route development through correlation analysis of ship-related factors and a CII prediction model [26].

Garbatov et al. (2023) analyzed the costs of LNG conversion of very-low-sulfur fuel oil (VLSFO) propulsion systems [27], such as taxes, insurance, maintenance, interest rates, depreciation, resale, and opportunity costs, as well as changes in the CII, in order to derive the optimal ship age, risks, and challenges for the conversion. Gallo et al. (2023) analyzed the environmental performance of various power systems per fuel for ships (e.g., generators, battery energy storage systems, fuel cells, hydrogen storage systems) and evaluated the CII based on performance indicators such as CO₂ emissions, equipment weight, and volume [28].

2.2. JIT Arrival in Terms of Reducing Waiting Times

Kim and Chang (2008) examined berthing priorities and berth operation methods per ship, compared berth occupancy rates, and analyzed the changes in the port time and berth occupancy rates in line with the higher distribution rates of large ships [29].

Cho et al. (2023) predicted the moving path of ships and the scheduled and actual port arrival time and suggested a measure to improve JIT at ports [30]. Furthermore, Senss et al. (2023) identified obstacles to JIT, analyzed the effects of JIT mechanisms in the medium and long term, and compared the application of JIT with the ship arrival system (VAS) of the First-Come–First-Served (FCFS) approach [31]. Yoon et al. (2023) determined the optimal parameters to select the error for the minimum actual arrival time of the ship by inputting AIS data, developed an algorithm to improve the accuracy of the arrival time between the terminal operation system and the ship, and demonstrated the effect of improving the error [32]. Dewan and Godina (2023) targeted seafarers, ship managers, and operation managers from 46 shipping companies around the world, identified their roles and challenges in improving shipping energy efficiency, and recommended the roles of seafarers and tasks regarding operational measures [33].

Additionally, Dewan and Godina (2024) analyzed 58 articles from a period of 10 years, identified the main influencing factors such as knowledge, awareness, education, rewards, social factors, technology, and barriers to participation, and emphasized financial incentives and regulatory framework development through cooperation among stakeholders [34]. Arjona Aroca et al. (2020) figured out why JIT should be implemented through stakeholder collaboration by analyzing the impact of AIS-based JIT of 33 ships and evaluating the potential savings from the solution [35].

Previous studies related to CII regulation have utilized actual ship data to examine various implementation measures, including the ship speed, engine, tonnage, route, and fuel conversion. In addition, JIT arrival studies aimed at minimizing waiting times have proposed strategies that can be applied at various stages for both ports and ships to reduce port congestion, utilizing Automatic Identification Systems (AISs) in the process. In addition, several studies have explored cooperation plans from the perspectives of various stakeholders, including crews, ship managers, and operations managers.

However, there is a lack of sufficient research to quantitatively demonstrate the impact of waiting time, as well as to clarify the necessity of JIT arrivals in the context of implementing the CII regulation.

3. Materials and Methods

Based on the background and objectives of the study, this research begins with the following question: “What is the impact of changes in ship speed through the reduction in waiting time for berthing on CII ratings?” The aim of this study is to assess the quantitative impact of reducing berth waiting times on the CII for Korean-flagged container ships entering Busan New Port, which are anticipated to fail to meet CII regulations by 2035. Accordingly, the study was conducted in a total of six steps, as shown in Figure 1.

3.1. Step 1: CII Rating Prediction

This study calculated the CII reference value by applying the calculation formula according to the guidelines of the CII regulation to apply the reduction rate by year, based on the data of the achieved CII value in 2022 for the national flag fleet of ships. Compared to the achieved CII values, the CII ratings by 2035 were predicted.

3.1.1. Calculation of Attained CII

The CII guidelines [12,13,14,15,16,17,18,19] define the operational CII as the average CO₂ emissions per unit of transport work performed by a ship. The annual operational CII for an individual ship is determined by calculating the ratio of total CO₂ emissions (M) to total transport work (W) over a calendar year [12,16]. The total CO₂ emissions (M) represent the sum of CO₂ emissions generated from the fuel oil consumed on board a ship during the year. This value is expressed as the product of the total fuel consumption (FC_j) for each type of fuel (j) reported to the IMO DCS within that year and the corresponding CO₂ conversion factor (C_Fj). The CO₂ conversion factor for each fuel type is applied following the same methodology outlined in the guidelines for calculating the EEDI for new ships [4]. For fuel oils not covered in the guidelines, the conversion factor must be supplied in writing by the oil refining industry.

The transport workload (W) is determined using demand-based calculations, which rely on the actual or estimated amount of cargo loaded onto the ship, or supply-based calculations, which substitute the cargo volume with the ship’s capacity. For this particular case, the supply-based transport workload was used due to insufficient data on the actual transport workload. According to CII guideline G1 [12,16], supply-based transport work is calculated as the product of a ship’s capacity (C) and the voyage distance (D_t) over a calendar year. However, the measurement of ship capacity varies based on ship type, such as deadweight tonnage (DWT) or gross tonnage (GT). The voyage distance is expressed in nautical miles, as reported in the DCS.

The following equation calculates the attained CII value for an individual ship:

$$\mathrm{A}ttained {CII}_{ship}=M\times W={\displaystyle \frac{{FC}_j\times C_{Fj}}{C\times D_t}}$$

(1)

The study was carried out based on the assumption that the 2022 attained CII would remain unchanged due to numerous assumptions and variables that affect the prediction of the national ship’s future attained CII value and raise uncertainty—such as changes in routes, fuel types, and the scrapping or sale of ships.

3.1.2. Calculation of CII Reference and Required CII

A vessel has a CII reference value determined using parameters and c based on its capacity, which can be compared with the attained CII value determined in accordance with the G1 guideline [12,16]. Variables a and c are parameters obtained through median regression analysis, based on the capacity and attained CII values of individual ships, which were calculated using the 2019 IMO DCS data. The CII guideline G2 specifies particular values for parameters [13,17].

The following equation calculates the CII reference value for an individual ship:

$${CII}_{ref}={aCapacity}^{-c}$$

(2)

And the annual required CII for each ship can only be determined by applying the annual reduction factor (Z) outlined in the CII guideline G3 [14]. Z is the annual CII reduction factor relative to the 2019 reference value and is applied uniformly across all ships, regardless of their type or size. In other words, it begins at 1% in 2020, increases to 2% in 2021 and 3% in 2022, and then rises by 2% each year until 2026. The reduction rates for 2027 to 2030 will be adjusted based on further enhancements and developments, considering the review outcomes of short-term measures from IMO member states.

This study aims to predict the CII rating up to 2035 to identify sample subjects among the national ships and analyze the variations in the CII. Therefore, the study assumed that the reduction rate will increase by 2% after 2026. Thus, the required CII value for each ship can be calculated by applying Equation (3), which takes into account the ratio of the reduction (Z) subtracted from the total 100% to the CII reference value.

The following equation calculates the required CII value for an individual ship:

$$required{CII}_{ship}=(1-Z/100)\times {CII}_{ref}$$

(3)

3.1.3. Calculation of CII Rating

CII ratings are categorized into five levels, A (major superior), B (minor superior), C (moderate), D (minor inferior), and E (inferior), based on a comparison between ships’ annual attained CII values and their reference values. The rating boundaries are determined by applying the dd vector to the CII reference value and are distributed as follows: Rating A (15%), Rating B (20%), Rating C (30%), Rating D (20%), and Rating E (15%). The four boundaries generate dd vectors (d1, d2, d3, d4), which are derived through the exponential transformation of the CII reference values. The CII guideline G4 provides specific dd vector values that define the CII rating boundary for each ship type [15,18].

The following equations calculate the CII rating boundaries for an individual ship:

Superior boundary = exp(d1)·Required CII
Lower boundary = exp(d2)·Required CII
Upper boundary = exp(d3)·Required CII
Inferior boundary = exp(d4)·Required CII

(4)

3.2. Step 2: Extraction of Ships with CII Rating Prediction

To comply with the CII regulations effective from 2023, ships are required to calculate their annual attained CII and submit it to the Administration by the end of March of the following year. Additionally, a Statement of Compliance (SoC) issued by the Administration must be kept on board by the end of May. Ships rated D for three consecutive years or E for one year must develop a corrective action plan to meet the required standards within one month of submitting their attained CII values to the Administration. This plan must be incorporated into the Ship Energy Efficiency Management Plan (SEEMP). Accordingly, in Step 2, ships that were rated D for three consecutive years or E for one year were extracted based on the predictions made in Step 1.

3.3. Step 3: Selection of Ships Entering Busan New Port from the Extracted Ships

Among the ships extracted in Step 2, general liners, particularly container ships, were selected as the focus of this study. Ships entering and leaving Busan New Port, which handles the largest volume of container cargo in the Republic of Korea, were selected. This selection was made because only regular ships operating on specific routes can facilitate a comparison of differences based on those routes, as well as provide insights into the current status of Busan New Port. To achieve this, the terminal berth schedule of Busan New Port was reviewed. Busan New Port consists of seven terminals (PNIT, PNC, HJNC, HPNT, BNCT, BCT, and DGT) [36], and the berth schedule and allocation status for each terminal can be accessed through their respective websites [37,38,39,40,41,42,43].

3.4. Step 4: Analysis of the Annual Waiting Time for Ships

In Step 4, we analyzed the waiting time per port and calculated the total waiting time for research subjects using annual AIS data from the Global Ship Tracking Intelligence of Marine Traffic platform [44]. Since the CII rating is based on annual operational data, the analysis utilized the AIS information to assess the impact of reduced waiting time on improving the CII rating, which is the primary objective of this study. Given that the CII rating was predicted using 2022 data, the study aimed to extract the annual waiting time. In this context, waiting time was defined as the period during which the ship’s speed was less than 10 knots, the main engine was inactive, and the ship was located Outside Port Limit (OPL) before berthing. In the AIS data, this event can be represented as the duration from “waiting OPL” to “waiting OPL end.”

3.5. Step 5: Impact Verification of CII Rating Improvement for Each Scenario of Waiting Time Reduction

As indicated by previous studies on JIT arrival, there is a possibility that ships may be prevented from waiting OPL. However, achieving a complete 100% reduction in waiting time for JIT arrivals can be challenging due to the potential for unforeseen circumstances in the logistics processes involving ships and ports. Consequently, this study developed scenarios aimed at reducing waiting times by 30%, 50%, 70%, and 100%, as illustrated in

Table 1. Definition of optimal ship speed scenarios.

Scenario Symbol	Definition
ShipBase	Average ship speed based on actual navigation data
Ship30%	Optimal ship speed achievable with a 30% reduction in the ship’s annual waiting time
Ship50%	Optimal ship speed achievable with a 50% reduction in the ship’s annual waiting time
Ship70%	Optimal ship speed achievable with a 70% reduction in the ship’s annual waiting time
Ship100%	Optimal ship speed achievable with a 100% reduction in the ship’s annual waiting time

.

The IMO DCS is highly reliable from a bottom–up perspective, as shipping companies directly report fuel consumption by type. DCS data includes information such as the ship type, gross tonnage, deadweight tonnage, voyage distance, voyage time, and fuel oil consumption categorized by fuel type. This study determined the average voyage speed of the vessels under investigation by analyzing their voyage distance and time using the 2022 DCS data. Reassigning each vessel’s waiting time to “voyage” instead of “waiting” allows for a further reduction in vessel speed. Consequently, the annual average waiting time obtained from AIS data was incorporated into the voyage time to calculate vessel speeds for each scenario.

Meanwhile, the specifications provided by the main engine manufacturer to the shipowner include information on fuel oil consumption (FOC) based on engine output. However, since this information is maintained by the shipowner, there are limitations in verifying it for this study. The previous literature primarily established daily FOC using the cubic law (β = 3), which posits a proportional relationship with linear velocity (FOC ∝ V^β) [45,46,47]. Nonetheless, empirical evidence suggests that the application of this law is effective only near the design speed. Studies indicate that β ranges from 1.5 to 2.2 when derived from actual operational data [48,49]. Consequently, this study adopts a value of β = 2 based on these findings. Furthermore, there is a direct 1:1 relationship between FOC and carbon emissions [49]. Therefore, a reduction in linear velocity results in decreased FOC, which in turn leads to lower carbon emissions, thereby contributing to environmental improvement. Subsequently, the fuel consumption, CO₂ emissions, and CII rating were re-calculated using the method from Step One, based on the adjusted ship speed and the resulting reduction in waiting time. This was conducted to verify the improved CII rating and its quantitative impact.

3.6. Step 6: Policy Recommendations and Suggestions

In Step 6, this study made policy recommendations based on the implications of this study and proposed future tasks related to waiting time.

4. Results

4.1. CII Rating Forecast for Korean-Flagged Ships

The number of national ships rated as D and E among 353 ships is projected to rise by about 3.3 times, from 81 in 2023 to 268 in 2035. Conversely, the number of ships rated as A, B, and C is forecasted to decrease by 68.75%, from 272 in 2023 to 85 in 2035. Since the CII regulation mandates corrective action for ships that are rated as D three consecutive times or as E, the number of ships requiring corrective action plans is projected to be 81 by 2025, which is three years after the regulation’s enforcement in 2023, 153 by 2030, and 243 by 2035, when these conditions are taken into account. If no changes are made and operations continue as they were in 2022, around 68.8% of national ocean-going ships will need corrective action by 2035.

This study forecasted the changes in the CII ratings of 397 BBCHP ships from 2022 to 2035. Among the 397 BBCHP ships, the number of ships rated as D and E is projected to rise by approximately 3.09 times, from 115 in 2023 to 356 in 2035, while those rated as A, B, and C are expected to decline by about 85.46%, reaching 41 by 2035. Consequently, the number of ships requiring corrective action plans is anticipated to be 115 by 2025, 217 by 2030, and 326 by 2035, representing approximately 82.11% of the total ships in Figure 2.

4.2. Selection of Research Subjects

As outlined in Section 3.3, the subjects of this research were container ships entering and leaving Busan New Port. To facilitate this, the terminal plan for Busan New Port was reviewed, and data on the ships entering and leaving the port were collected. Consequently, a final selection of 31 ships, which were rated D for three consecutive years or E for one year, was chosen for analysis, as presented in

Table 2. Information on the container ships chosen as research subjects.

Ship	Flag State	GT	DWT	2022 Attained CII	Forecasted CII Rating
					23	24	25	26	27	28	29	30	31	32	33	34	35
A	Panama	54,519	65,023	8.998474399	D	D	D	D	D	E	E	E	E	E	E	E	E
B	Marshall Islands	72,597	72,982	9.156594586	D	D	E	E	E	E	E	E	E	E	E	E	E
C	Liberia	40,029	50,162	9.860797273	C	C	D	D	D	D	E	E	E	E	E	E	E
D	Marshall Islands	9520	12,839	17.65685185	C	C	C	C	C	C	D	D	D	D	E	E	E
E	Panama	9742	11,891	20.29537416	C	D	D	D	D	D	E	E	E	E	E	E	E
F	Panama	9599	11,820	20.46985566	C	D	D	D	D	E	E	E	E	E	E	E	E
G	Liberia	152,003	160,927	4.599427675	B	B	B	B	B	C	C	C	C	C	D	D	D
H	Marshall Islands	9944	13,288	15.86696838	B	B	B	B	C	C	C	C	C	D	D	D	D
I	Republic of Korea	72,597	26,918	23.94673696	E	E	E	E	E	E	E	E	E	E	E	E	E
J	Republic of Korea	72,634	72,982	9.091027093	D	D	E	E	E	E	E	E	E	E	E	E	E
K	Republic of Korea	7406	9618.27	19.31363263	B	B	C	C	C	C	C	C	D	D	D	D	E
L	Republic of Korea	74,962	80,855	8.204372198	D	D	D	D	E	E	E	E	E	E	E	E	E
M	Republic of Korea	40,741	52,316	8.262909861	B	B	B	C	C	C	C	C	C	D	D	D	E
N	Republic of Korea	40,839	51,314	9.279412664	C	C	C	C	D	D	D	D	E	E	E	E	E
O	Republic of Korea	9955	12,402	17.16187087	B	B	C	C	C	C	C	D	D	D	D	D	E
P	Republic of Korea	28,927	39,276	9.389112939	B	B	B	B	C	C	C	C	C	D	D	D	D
Q	Republic of Korea	40,898	51,752.8	9.07376826	C	C	C	C	C	D	D	D	D	E	E	E	E
R	Republic of Korea	40,898	51,701.1	8.64896082	B	C	C	C	C	C	C	D	D	D	D	E	E
S	Republic of Korea	53,100	31,099	16.16775973	E	E	E	E	E	E	E	E	E	E	E	E	E
T	Republic of Korea	53,100	63,071	7.374232651	B	B	B	B	C	C	C	C	C	D	D	D	D
U	Republic of Korea	142,620	146,046	5.306167401	C	C	C	C	C	C	D	D	D	D	E	E	E
V	Republic of Korea	142,620	146,046	5.150066496	B	B	C	C	C	C	C	D	D	D	D	E	E
W	Republic of Korea	7447	8213	23.35423702	C	C	C	D	D	D	D	E	E	E	E	E	E
X	Republic of Korea	40,898	51,750	9.000006739	C	C	C	C	C	D	D	D	D	D	E	E	E
Y	Republic of Korea	18,606	21,810	12.69580517	B	B	B	C	C	C	C	C	D	D	D	D	E
Z	Republic of Korea	73,780	85,523	6.525108818	B	B	B	C	C	C	C	C	D	D	D	D	E
AA	Republic of Korea	7447	8218	22.94580721	C	C	C	C	D	D	D	D	E	E	E	E	E
AB	Republic of Korea	7447	8283	21.69293784	C	C	C	C	C	C	D	D	D	D	E	E	E
AC	Republic of Korea	6490	8662	19.19370435	B	B	B	B	B	C	C	C	C	C	D	D	D
AD	Republic of Korea	7683	10,728	17.44449658	B	B	B	B	C	C	C	C	C	C	D	D	D
AE	Republic of Korea	75,488	80,855	8.382434343	D	D	D	E	E	E	E	E	E	E	E	E	E

.

4.3. Calculation of the Annual Berth Waiting Time

As defined in Section 3.4, the annual waiting times for the ships were extracted and are presented in

Table 3. Identification of route, major annual ports, and annual waiting time for ships.

Ship	Route	Major Ports Along the Route	Annual Waiting Time [dd:hh:mm]
A	Far East–Indian Mediterranean	Busan New Port–Gwangyang–Shanghai–Ningbo–Shekou–Klang–Nhava Sheva–Mundra–Karachi	30:04:38
B	Far East–Eastern Latin America	Busan New Port–Shanghai–Ningbo–Shenzhen–Singapore–Kattupalli–Santos–Paranagua–Itapoa–Navegantes–Buenos Aires–Montevideo–Singapore–Hong Kong	53:13:30
C	Asia–Western United States	Busan New Port–Yantian–Ningbo–Shanghai–Seattle–Fraser River	25:04:02
D	Northeast Asia (Korea–China–Japan)	Busan New Port–Ulsan–Gwangyang–Tianjin–Dalian–Nishi–Tomakomai–Muroran–Qingdao–Sakata–Akita–Hakata–Hitachinaka–Sendai–Hachinohe	9:19:37
E	Northeast Asia (Korea–China–Japan)	Busan New Port–Tianjin–Dalian–Pohang–Naoetsu–Toyama New Port–Niigata–Ulsan–Naoetsu	23:15:42
F	Northeast Asia (Korea–Japan)	Busan New Port–Ishikari–Tomakomai–Akita–Ishikari–Tsuruga–Otaru	26:06:12
G	Asia–Northern Europe	Busan New Port–Shanghai–Yentian–Colombo–Algeciras–Rotterdam Maasvlakte–Hamburg–Antwerp–Thames	21:21:44
H	Northeast Asia (Korea–Japan)	Busan New Port–Shimizu–Kashima–Hitachinaka–Sendai–Hachinohe–Tomakomai–Sakaiminato–Sakata–Maizuru–Gwangyang–Yokohama–Nagoya–Gwangyang	15:04:53
I	Far East–Indian Mediterranean	Busan New Port–Gwangyang–Shanghai–Ningbo–Kaohsiung–Shekou–Singapore–Klang–Kattupalli–Nhava Sheva–Hazira–Mundra–Karachi–Shenzhen–Da Chan Bay–Colombo–Santos	20:00:38
J	Asia–Oceania	Busan New Port–Qingdao–Shanghai–Ningbo–Yantian–Botany–Melbourne–Brisbane	23:07:44
K	Northeast Asia (Korea–Japan)	Busan New Port–Gwangyang–Osaka–Kobe–Ulsan	16:08:11
L	Asia–Western United States	Busan New Port–Gwangyang–Qingdao–Shanghai–Ningbo–Long Beach–Portland	24:15:32
M	Asia–Western United States	Busan New Port–Yantian–Ningbo–Shanghai–Fraser River–Seattle–Steveston	30:12:37
N	Asia–Western United States	Busan New Port–Yantian–Ningbo–Shanghai–Fraser River–Seattle	34:11:46
O	Northeast Asia (Korea–Japan)	Busan New Port–Hakata–Kanazawa–Nishi–Tomakomai–Muroran–Ishikari–Akita–Tomakomai	34:19:32
P	Far East–Indian Mediterranean	Busan New Port–Incheon–Qingdao–Shanghai–Hong Kong–Port Klang–Pasir Gudang–Hong Kong–Chiwan	28:06:17
Q	Far East–Indian Mediterranean	Busan New Port–Incheon–Tianjin–Dalian–Singapore–Jakarta	11:08:21
R	Far East–Indian Mediterranean	Busan New Port–Hong Kong–Singapore–Jakarta–Surabaya –Gwangyang–Ulsan	08:16:01
S	Far East–Indian Mediterranean	Busan New Port–Gwangyang–Shanghai–Ningbo–Shenzhen –Shekou–Singapore–Port Klang–Kattupalli–Nhava Sheva –Hazira–Mundra–Karachi	43:06:54
T	Far East–Indian Mediterranean	Busan New Port–Shanghai–Ningbo–Shenzhen–Singapore–Kattupalli–Santos–Paranaguá–Navegantes–Buenos Aires–Montevideo	34:14:04
U	Asia–Eastern America–Eastern Latin America	Busan New Port–Panama Canal–Manzanillo–New York–Norfolk–Charleston–Savannah–Manzanillo–Kaohsiung –Yangtian–Shanghai–Ningbo–Singapore–Piraeus	22:00:43
V	Asia–Eastern America–Eastern Latin America	Busan New Port–Panama Canal–Manzanillo–New York–Norfolk–Charleston–Savannah–Manzanillo–Kaohsiung –Yangtian–Shanghai–Ningbo–Yangtian–Xiamen	42:05:15
W	Northeast Asia (Korea–Japan)	Busan New Port–Gwangyang–Hakata–Moji–Oita–Shibushi–Kochi– Kitakyushu–Gwangyang–Satsumasendai–Nagasaki– Kumamoto–Yatsushiro–Imari	48:00:01
X	Far East–Indian Mediterranean	Busan New Port–Hong Kong–Singapore–Jakarta–Surabaya –Gwangyang–Ulsan	13:06:44
Y	Far East	Busan New Port–Shanghai–Ningbo–Ho Chi Minh–Sriracha–Incheon	11:15:48
Z	Far East–Indian Mediterranean	Busan New Port–Ningbo–Qianwan–Singapore–Port Klang –Nhava Sheva–Mundra–Karachi–Port Klang–Hong Kong –Qingdao–Gwangyang	46:22:31
AA	Northeast Asia (Korea–Japan)	Busan New Port–Mishima–Takamatsu–Hiroshima –Iwakuni–Hososhima–Oita	63:12:29
AB	Northeast Asia (Korea–Japan)	Busan New Port–Sakaiminato–Nakata–Kanazawa–Maizuru–Okgye	38:08:21
AC	Northeast Asia (Korea–Japan)	Busan New Port–Shibushi–Nagasaki–Kumamoto– Yatsushiro–Satsumasendai–Imari	64:08:58
AD	Northeast Asia (Korea–Japan)	Busan New Port–Hakata–Tokuyama Kudamatsu–Moji–Kitakyushu–Moji	33:04:09
AE	Far East–Indian Mediterranean	Busan New Port–Ningbo–Qianwan–Singapore–Port Klang–Nhava Sheva–Mundra–Karachi–Hong Kong–Qingdao–Gwangyang	36:06:55

. As container ships, they exhibited the operational characteristic of repeatedly visiting designated ports of call. The routes included numerous intra-Asia connections, including those between Northeast Asia and Southeast Asia, as well as links within Northeast Asia that connected Korea, China, and Japan. Additionally, there were routes linking Northeast Asia with the United States and Latin America.

Depending on the ship’s route, the annual waiting time varied from approximately 10 to 60 days, with an average of 30 days. Ships operating over shorter distances between ports experienced significantly longer waiting times due to their higher frequency of arrivals and departures. Additionally, this study found that waiting times could range from a few hours to a maximum of 10 days, depending on the congestion levels at individual ports.

4.4. Fuel Consumption and CO₂ Emissions Based on Adjusted Speed, Considering Waiting Times

As the speed of the ship was adjusted based on the waiting time derived in Section 4.3, fuel consumption was calculated for each scenario outlined in Section 3.5. This calculation allows for the updating of CO₂ emissions in

Table 4. CO₂ emissions and reduction rates of each scenario.

Ship	ShipBase [tCO2]	Ship30% [tCO2]	Ship50% [tCO2]	Ship70% [tCO2]	Ship100% [tCO2]	(ShipBase-Ship30%)/ ShipBase	(ShipBase- Ship50%)/ ShipBase	(ShipBase-Ship70%)/ ShipBase	(ShipBase-Ship100%)/ ShipBase
A	57,058.2	53,679.8	51,591.5	49,622.7	46,874.4	5.92%	9.58%	13.03%	17.85%
B	58,054.3	51,060.9	47,078.0	43,543.5	38,943.9	12.05%	18.91%	25.00%	32.92%
C	47,476.8	44,971.8	43,410.4	41,928.8	39,844.8	5.28%	8.57%	11.69%	16.08%
D	9752.7	9340.3	9079.8	8830.0	8474.2	4.23%	6.90%	9.46%	13.11%
E	13,948.8	12,932.2	12,315.1	11,741.0	10,952.4	7.29%	11.71%	15.83%	21.48%
F	15,035.7	13,958.4	13,303.2	12,693.1	11,853.7	7.17%	11.52%	15.58%	21.16%
G	58,659.4	55,544.7	53,604.0	51,763.3	49,175.0	5.31%	8.62%	11.76%	16.17%
H	13,145.6	12,681.2	12,385.1	12,099.3	11,688.8	3.53%	5.78%	7.96%	11.08%
I	59,976.2	57,249.6	55,533.9	53,894.2	51,567.5	4.55%	7.41%	10.14%	14.02%
J	64,790.3	61,182.0	58,941.4	56,821.6	53,850.1	5.57%	9.03%	12.30%	16.89%
K	12,678.1	12,145.4	11,808.7	11,485.8	11,025.7	4.20%	6.86%	9.40%	13.03%
L	49,596.4	45,878.8	43,627.9	41,538.6	38,675.3	7.50%	12.03%	16.25%	22.02%
M	43,249.8	40,365.7	38,600.3	36,948.3	34,662.2	6.67%	10.75%	14.57%	19.86%
N	47,232.6	44,240.3	42,400.6	40,673.4	38,273.9	6.34%	10.23%	13.89%	18.97%
O	13,766.3	12,385.6	11,577.4	10,845.7	9871.8	10.03%	15.90%	21.22%	28.29%
P	32,865.8	30,604.6	29,224.3	27,935.3	26,156.1	6.88%	11.08%	15.00%	20.42%
Q	31,481.3	30,630.7	30,082.6	29,549.2	28,775.2	2.70%	4.44%	6.14%	8.60%
R	36,683.2	35,801.0	35,230.4	34,673.4	33,862.3	2.40%	3.96%	5.48%	7.69%
S	51,554.3	46,851.0	44,064.2	41,518.9	38,095.3	9.12%	14.53%	19.47%	26.11%
T	55,618.8	51,577.2	49,122.8	46,839.4	43,701.7	7.27%	11.68%	15.78%	21.43%
U	65,854.0	62,212.6	59,950.3	57,809.1	54,806.2	5.53%	8.96%	12.22%	16.78%
V	62,950.3	56,528.5	52,778.1	49,389.0	44,886.2	10.20%	16.16%	21.54%	28.70%
W	10,555.9	9056.1	8227.1	7507.0	6591.9	14.21%	22.06%	28.88%	37.55%
X	41,692.3	40,307.5	39,422.3	38,566.0	37,332.8	3.32%	5.44%	7.50%	10.46%
Y	25,441.3	24,723.9	24,262.3	23,813.5	23,163.4	2.82%	4.63%	6.40%	8.95%
Z	56,077.2	50,390.7	47,067.1	44,061.8	40,066.1	10.14%	16.07%	21.43%	28.55%
AA	9979.7	8133.6	7173.6	6374.1	5403.9	18.50%	28.12%	36.13%	45.85%
AB	11,033.1	9846.4	9158.5	8540.3	7724.1	10.76%	16.99%	22.59%	29.99%
AC	8597.2	6639.2	5683.5	4920.2	4034.2	22.77%	33.89%	42.77%	53.07%
AD	8724.2	7816.6	7288.0	6811.2	6179.2	10.40%	16.46%	21.93%	29.17%
AE	65,126.7	61,338.6	58,993.9	56,781.1	53,688.2	5.82%	9.42%	12.81%	17.56%

. According to the estimation results, a 30% reduction in existing waiting times can lead to an average decrease in CO₂ emissions of 7.69%. Similarly, a 50% reduction can achieve a decrease of 12.18%, while a 70% reduction can result in a decrease of 16.26%. Furthermore, completely eliminating waiting time can lead to an average reduction of 21.74%.

4.5. Impact on CII Rating Improvement Under Reduced Waiting Time Scenarios

Table 5. CII rating of scenarios based on 2022 DCS data (2025, 2030, 2035).

Ship	CII Rating
	2025					2030					2035
	ShipBase	Ship30%	Ship50%	Ship70%	Ship100%	ShipBase	Ship30%	Ship50%	Ship70%	Ship100%	ShipBase	Ship30%	Ship50%	Ship70%	Ship100%
A	D	C	C	C	B	E	D	D	D	C	E	E	E	E	D
B	E	C	C	B	A	E	E	D	C	B	E	E	E	D	C
C	D	C	C	C	B	E	D	D	D	C	E	E	E	E	D
D	C	C	B	B	B	D	D	C	C	C	E	E	E	D	D
E	D	C	C	B	B	E	D	D	C	C	E	E	E	E	D
F	D	C	C	B	B	E	D	D	C	C	E	E	E	E	D
G	B	B	A	A	A	C	C	B	B	B	D	D	C	C	C
H	B	B	B	B	A	C	C	C	C	B	D	D	D	D	C
I	E	E	E	E	E	E	E	E	E	E	E	E	E	E	E
J	E	E	E	D	D	E	E	E	E	E	E	E	E	E	E
K	C	B	B	B	B	D	C	C	C	C	E	E	D	D	D
L	E	E	D	D	C	E	E	E	E	D	E	E	E	E	E
M	C	B	B	B	A	D	C	C	C	B	E	E	D	D	C
N	D	C	C	B	B	E	D	D	C	C	E	E	E	D	D
O	C	B	A	A	A	D	C	B	B	A	E	D	C	C	B
P	C	C	B	B	A	D	D	C	C	B	E	E	D	D	C
Q	B	B	A	A	A	C	C	B	B	B	D	D	C	C	C
R	C	C	C	C	C	D	D	D	D	D	E	E	E	E	E
S	E	E	E	E	D	E	E	E	E	E	E	E	E	E	E
T	C	C	B	B	A	D	D	C	C	B	E	E	D	D	C
U	C	B	B	B	A	D	C	C	C	B	E	D	D	D	C
V	B	B	A	A	A	C	C	B	A	A	E	D	C	B	B
W	E	D	C	B	C	E	E	D	C	B	E	E	E	D	C
X	C	C	C	C	C	E	D	D	D	C	E	E	E	E	E
Y	C	C	B	B	B	D	C	C	C	C	E	E	E	D	D
Z	C	C	B	A	A	D	C	C	B	B	E	E	D	C	C
AA	E	C	B	A	A	E	D	C	B	A	E	E	D	C	B
AB	D	C	C	B	A	E	D	D	C	B	E	E	E	D	C
AC	D	B	A	A	A	E	C	B	A	A	E	D	C	A	A
AD	C	B	A	A	A	D	C	B	B	A	E	D	C	C	B
AE	E	D	D	D	C	E	E	E	E	D	E	E	E	E	E

presents the results of comparing the CII rating change of target ships across different scenarios for the years 2025, 2030, and 2035, based on the 2022 DCS data. Out of the 31 ships analyzed, 16 exhibited an improvement of one to two grades (indicated by green and yellow cells) in the scenario involving a 30% reduction in waiting times in 2025 compared to the Ship_Base scenario. However, in the same scenario for 2035, only five ships showed improvement. In 2035, we confirmed that the ratings of 13 ships improved compared to the base scenario when waiting times were reduced by 50%.

Our findings indicate that the reduction in latency positively impacted the CII ratings, as shown in Table 5. However, we established that while a reduction in latency can lead to short-term improvements in CII ratings due to decreased fuel consumption, a minimum reduction of 50% in latency is necessary in the mid-to-long term to achieve sustained improvements. As presented in Table 5, the degree of improvement in the CII rating relative to the base scenario (Ship_Base) is visually represented through color coding: green indicates a one-level improvement, yellow indicates two levels, and orange represents improvements of three levels or more.

4.6. Policy Recommendations and Suggestions

According to the results presented in Section 4.5, the waiting time for ships at port berths must be reduced by 30% in the short term and by at least 50% in the long term to improve the CII rating. This indicates a correlation between the energy efficiency of ships and their waiting time at port. In light of the 2023 IMO GHG reduction strategy’s net-zero goal for 2050 [2], one critical factor that eco-friendly shipping must consider is the realization of JIT arrival. Therefore, it is essential to identify the obstacles to achieving JIT arrival, despite efforts to reduce fuel consumption, lower GHG emissions, and implement environmental regulations. Prioritizing the identified obstacles is necessary to facilitate effective policy development. If proposed in detail, the reduction in waiting time should be accompanied by the establishment of a robust information-sharing system related to cargo processing, port congestion, and ship arrival information. Furthermore, successful implementation will depend on the proficiency of various stakeholders in utilizing the system and ensuring smooth cooperation.

5. Conclusions

Research on the CII regulation and JIT arrival in relation to waiting times is often approached separately at the ship and port levels. Specifically, previous studies on the CII focus on the vessels required to implement this regulation, as compliance is mandatory. In contrast, JIT arrival primarily examines how ports manage congestion and establish the JIT mechanism. We would like to emphasize that the CII regulation and JIT arrival are interconnected within the context of marine environmental protection. Improving the energy efficiency of ships should not only enhance the vessel’s energy management but also take into account the conditions at the port to which the ship is headed. In this regard, this study can be viewed as a unique investigation that links the CII regulation for ships with waiting times at ports.

This study conducted a case analysis of Korean-flagged container ships entering Busan New Port and quantitatively demonstrated the relationship between changes in the CII rating and the reduction in berth waiting time. Decreasing the waiting time is effective in enhancing the CII rating, and a higher reduction rate can lead to more significant positive results in the short term. However, the number of ships showing improvement from a mid-to-long-term perspective remains limited. Therefore, a reduction of at least 50% is necessary.

Excessive delay at port berths negatively impacts the energy efficiency of ships. However, overcoming this challenge is difficult when ships and ports operate independently. Collaboration between ships and ports is essential for enhancing energy efficiency and promoting the adoption of eco-friendly fuels. Such cooperation can play a pivotal role in achieving objectives related to addressing the climate crisis. Therefore, based on this study, a policy review aimed at implementing JIT arrival practices should be conducted both domestically and internationally.

In addition, this study had limitations in quantitatively analyzing the correlation between the CII rating and factors such as the age, type, or route of the ship. Therefore, further research should be conducted to explore the reasons for receiving a low CII rating, even when the ship is not aged, as well as to identify the types of ships or operational routes that may negatively impact the CII rating.