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The International Maritime Organization (IMO) aims to reduce greenhouse gas (GHG) emissions by 40% by 2030 compared with 2008. The carbon intensity indicator (CII) calculates the annual reduction factor required to continuously improve a ship's operational carbon intensity at a specific rating level. Verification and documentation of the achieved annual operational CII against the prescribed target are necessary to establish the operational carbon intensity rating. This study focuses on the intricate process of data collection for CII within the oil shipping industry, targeting engineering departments and shipboard management teams. Against the backdrop of the industry's substantial carbon dioxide emissions, the IMO has mandated the calculation of CII values for ships exceeding 5000 gross tons to promote sustainability and reduce environmental impact. We have collected emission data of 20 oil tankers over a period of 2 years using our ship maintenance and operating system (SMOS) and analyzed the data to compare the CII ratings. Our results indicate that a staggering ~63% of the vessels had the lowest CII rating of category E. It is therefore crucial to properly collect, organize, and evaluate data for CII calculation and take necessary measures to improve rating. This paper provides a deeper insight into the evolving CII calculation methodology, emphasizing the incorporation of correction factors and exclusions, and delineates the essential data collection practices needed to facilitate accurate CII calculations. [ABSTRACT FROM AUTHOR]

Computer vision (CV) techniques have been widely studied and applied in the shipping industry and maritime research. The existing literature has primarily focused on enhancing image recognition accuracy and precision for water surface targets by refining CV models themselves. This paper introduces innovative methods to further improve the accuracy of detection and recognition using CV models, including using ensemble learning and integrating shipping domain knowledge. Additionally, we present a novel application of CV techniques in the maritime domain, expanding the research perspective beyond the traditional focus on the accurate detection and recognition of water surface targets. Specifically, a novel solution integrating a CV model and the transfer learning method is proposed in this paper to address the challenge of relatively low-speed and high-charge internet services on ocean-going vessels, aiming to improve the online video viewing experience while conserving network resources. This paper is of importance for advancing further research and application of CV techniques in the shipping industry. [ABSTRACT FROM AUTHOR]

Growing environmental concerns have prompted the shipping industry to adopt stringent measures to address greenhouse gas emissions, with fuel-powered ships being the primary source of such emissions. Additionally, alternative forms of ship propulsion, such as internal combustion engine hybridization, low-carbon fuels, and zero-carbon fuels, face significant challenges either in terms of cost or emission-reduction capability at present. In order to decarbonize navigation, countries are focusing the maritime industry's transition towards low-carbon alternatives on transforming energy consumption, with widespread attention on the electrification of ships. Therefore, this paper provides a comprehensive review of the feasibility of fully electrifying ships, covering aspects such as technological prospects, economic viability, and emission-reduction capabilities. Firstly, the current state of research on ship electrification technology is summarized; the applicability of different battery types to electric ship technology is compared. Subsequently, the economic viability and emission-reduction capabilities of five different electric ship lifecycles are discussed separately. The results indicate that ship electrification is a key pathway to achieving zero-emission shipping, with lithium-ion batteries being the most suitable battery technology for maritime use currently. Short-to-medium-range electric ship types have demonstrated economic advantages over traditional diesel ships. As battery costs continue to decline and energy density keeps improving, the economic feasibility of ship electrification is expected to expand. [ABSTRACT FROM AUTHOR]

The shipping industry transports nearly 80% of the goods worldwide and requires large funding. The shipping industry shifted from debt to equity as the source of funding in the last decade. Because most shipping companies already had their initial public offering before 2013, these companies tend to engage in follow-on equity offerings (FPO). However, the challenge faced by the shipping companies is the lack of knowledge on successful FPO. The purpose of this study is to identify the most influential factors affecting shipping companies’ FPO from the investor perspective. This research applies a hybrid multiple-criteria decision-making model integrating the fuzzy-Delphi method and Decision-Making Trial and Evaluation Laboratory, processing survey responses covering four dimensions and 16 criteria from 33 investment experts. The results show that financial indicator is the primary cause affecting offering condition, technical indicators. An increase in earnings per share would help the financial performance of the shipping companies to appear most attractive to investors. [ABSTRACT FROM AUTHOR]

This research evaluated variations in Brent oil prices and the interrelation with maritime traffic density in the Turkish Straits during the COVID-19 pandemic. The number of commercial ships that made non-stop over passage through the Turkish Straits in the last 5 years, covering the COVID-19 -and post-pandemic periods with economic instabilities was investigated along with variables of vessel characteristics such as; gross tonnage, size and type of vessel loads. Results of the present study reveal that the maritime traffic density between 2019 and 2023, was influenced by the pandemic crisis, when harsh quarantine measures of lockdown and curfews in the first shock wave. In the aftermath, conflicts between Ukraine and Russia led to economic recession or upheaval with instabilities in Brent oil prices. For the period examined in this study, the number of non-stop over passage vessels and gross tonnages used the Turkish Straits were affected by the pandemic outbreak and Brent oil price variations. The number of vessels decreased by 5.22% from 84,871 to 80,440 during the epidemic in 2020, and by 5.38% from 43.342 to 42.340 during the global recession in 2022. Overall, the number of non-stop over passage vessels using the Turkish Straits between 2019 and 2023 declined by 1.15%, while the gross tonnage and ship length increased by 3.44% and 13.24%, respectively. In total, the number of non-specific tankers (TTA) and those carrying chemicals (TCH) increased by 2.92% and 10.97%, respectively, but a 13.25% decrease was noted for the liquefied petroleum gas (LPG) tankers over the 5 years. Considering that the world trade network is largely dependent on maritime transportation, identifying the changes in maritime transportation with the interrelation of Brent oil during global crises may provide important data for strategy building of best trade management with foresights to world economic crises. [ABSTRACT FROM AUTHOR]

AS THIS ISSUE of the Naval War College Review hits the streets, we look back with pride on our June 2024 graduation ceremony in which nearly eight hundred resident and [...]

Abstract The technology of the Internet of Things (IoT) represents a cornerstone of the fourth industrial revolution. We adopt a machine learning approach to examine the effect of IoT technology on shipping business operations. Text mining and the probabilistic latent Dirichlet allocation are applied for an unsupervised topic modelling analysis of two hundred and twenty-eight academic papers. Our findings reveal the potential of IoT to provide more efficient approaches to business operations and improve the quality of services, highlighting the value of instant and secure information flow among all parties involved. Problematic areas of the new technology are also identified, in reference to issues of standardization and interoperability. Relatively few studies have used machine learning techniques to elicit insights into the holistic effect of emerging IoT technology in the shipping industry. The research findings highlight the potential of IoT technology to transform shipping operations, offering useful and practical implications to academics and professionals.

The technology of the Internet of Things (IoT) represents a cornerstone of the fourth industrial revolution. We adopt a machine learning approach to examine the effect of IoT technology on shipping business operations. Text mining and the probabilistic latent Dirichlet allocation are applied for an unsupervised topic modelling analysis of two hundred and twenty-eight academic papers. Our findings reveal the potential of IoT to provide more efficient approaches to business operations and improve the quality of services, highlighting the value of instant and secure information flow among all parties involved. Problematic areas of the new technology are also identified, in reference to issues of standardization and interoperability. Relatively few studies have used machine learning techniques to elicit insights into the holistic effect of emerging IoT technology in the shipping industry. The research findings highlight the potential of IoT technology to transform shipping operations, offering useful and practical implications to academics and professionals. [ABSTRACT FROM AUTHOR]

The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASSs) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks such as real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASSs, using bibliographic analysis to identify policy and future research directions in this evolving field. We analyze 3363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. The findings reveal a rapidly growing field dominated by image recognition research. Keywords such as "datum", "image", and "detection" suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with "image analysis and classification research" as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind that on other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASSs and informing future policy and research agendas regarding the integration of electric propulsion systems within the maritime industry. [ABSTRACT FROM AUTHOR]

The International Maritime Organization (IMO) has set targets to reduce carbon emissions from shipping by 40% by 2030 (IMO2030) and 70% by 2040 (IMO2050). Within the framework of decarbonising the shipping industry, liquefied natural gas (LNG) fuel and carbon capture technologies are envisioned as a transitional option toward a pathway for clean energy fuels. The aim of the complex experimental and computational studies performed was to evaluate the CO2 capture potential through the utilisation of LNG cold potential on the FSR-type vessel within a dual-fuel propulsion system. Based on the experimental studies focused on actual FSRU-type vessel performance, the energy efficiency indicators of the heat exchanging machinery were determined to fluctuate at a 0.78–0.99 ratio. The data obtained were used to perform an algorithm-based systematic comparison of energy balances between LNG regasification and fuel combustion cycles on an FSRU-type vessel. In the due course of research, it was determined that LNG fuel combustion requires 18,254 kJ/kg energy to separate and capture CO2 in the liquid phase to form exhaust gas; meanwhile, low sulfur marine diesel oil (LSMDO) requires 13,889 kJ/kg of energy. According to the performed calculations, the regasification of 1 kg LNG requires 1018 kJ/kg energy, achieving a cryogenic carbon capture ratio of 5–6% using LNG as a fuel and 7–8% using LSMDO as a fuel. The field of carbon capture in the maritime industry is currently in its pioneering stage, and the results achieved through research establish an informative foundation that is crucial for the constructive development and practical implementation of cryogenic carbon capture technology on dual-fuel ships. [ABSTRACT FROM AUTHOR]

The contemporary cyber threat landscape presents a complex tapestry of challenges, featuring diverse actors ranging from non-state entities like organized crime syndicates and terrorist organizations to state-sponsored operatives affiliated with nations such as China, Russia, and Iran. Motivations driving these actors span from financial gain to ideological pursuits and geopolitical objectives, resulting in a wide range of cyber operations aimed at individuals, businesses, and governments worldwide. These highprofile incidents underscore the disruptive potential, and strategic implications need of a comprehensive Shipping Industry's Cyber Counterintelligence approach, to safeguard critical assets and operations in an increasingly digitized environment. [ABSTRACT FROM AUTHOR]

The shipping industry transports nearly 80% of the goods worldwide and requires large funding. The shipping industry shifted from debt to equity as the source of funding in the last decade. Because most shipping companies already had their initial public offering before 2013, these companies tend to engage in follow-on equity offerings (FPO). However, the challenge faced by the shipping companies is the lack of knowledge on successful FPO. The purpose of this study is to identify the most influential factors affecting shipping companies’ FPO from the investor perspective. This research applies a hybrid multiple-criteria decision-making model integrating the fuzzy-Delphi method and Decision-Making Trial and Evaluation Laboratory, processing survey responses covering four dimensions and 16 criteria from 33 investment experts. The results show that financial indicator is the primary cause affecting offering condition, technical indicators. An increase in earnings per share would help the financial performance of the shipping companies to appear most attractive to investors.

The Green Economy Initiative aims to achieve economic development while minimizing carbon emissions by implementing a low-carbon economy across all sectors. It is noteworthy that ships play a significant role in global commodity transportation, accounting for approximately 80–90 percent. However, this also leads to a surge in fossil fuel consumption and alarming pollution levels. The data utilized in this article spans from 2010 to 2022 and specifically focuses on the shipping industry, drawing from information collected in 20 different provinces of China. Multiple panel regression models were constructed to analyze the influence of fiscal policies on facilitating the transition toward a low-carbon economy. In addition, a vector autoregression model was employed to examine the interconnected dynamics between low-carbon transition and budgetary guidelines. The findings indicate that tax-based policies demonstrate an inverted U-shaped relationship with low-carbon transition, whereas transfer payment policies exhibit an N-shaped pattern. The shipping sector is actively embracing low-carbon practices, largely due to incorporating digital technologies that mitigate the adverse impacts of fiscal regulations.

Computer vision (CV) techniques have been widely studied and applied in the shipping industry and maritime research. The existing literature has primarily focused on enhancing image recognition accuracy and precision for water surface targets by refining CV models themselves. This paper introduces innovative methods to further improve the accuracy of detection and recognition using CV models, including using ensemble learning and integrating shipping domain knowledge. Additionally, we present a novel application of CV techniques in the maritime domain, expanding the research perspective beyond the traditional focus on the accurate detection and recognition of water surface targets. Specifically, a novel solution integrating a CV model and the transfer learning method is proposed in this paper to address the challenge of relatively low-speed and high-charge internet services on ocean-going vessels, aiming to improve the online video viewing experience while conserving network resources. This paper is of importance for advancing further research and application of CV techniques in the shipping industry.

Growing environmental concerns have prompted the shipping industry to adopt stringent measures to address greenhouse gas emissions, with fuel-powered ships being the primary source of such emissions. Additionally, alternative forms of ship propulsion, such as internal combustion engine hybridization, low-carbon fuels, and zero-carbon fuels, face significant challenges either in terms of cost or emission-reduction capability at present. In order to decarbonize navigation, countries are focusing the maritime industry’s transition towards low-carbon alternatives on transforming energy consumption, with widespread attention on the electrification of ships. Therefore, this paper provides a comprehensive review of the feasibility of fully electrifying ships, covering aspects such as technological prospects, economic viability, and emission-reduction capabilities. Firstly, the current state of research on ship electrification technology is summarized; the applicability of different battery types to electric ship technology is compared. Subsequently, the economic viability and emission-reduction capabilities of five different electric ship lifecycles are discussed separately. The results indicate that ship electrification is a key pathway to achieving zero-emission shipping, with lithium-ion batteries being the most suitable battery technology for maritime use currently. Short-to-medium-range electric ship types have demonstrated economic advantages over traditional diesel ships. As battery costs continue to decline and energy density keeps improving, the economic feasibility of ship electrification is expected to expand.

Abstract Today, several research/initiatives exist in AI technology at the port operation. They mainly focus on solution development in a particular port and shipping industry domain. This scattered implementation leads to an unstructured overview of the port sector regarding AI innovation in use. Equally, this study aims to structure this overview for aligning stakeholders and AI providers toward implementation. In doing so, the first step would be collecting the port stakeholders' insight regarding AI implementation. This study develops a structured framework to collect the port stakeholders’ insight through both desk and empirical research. Besides, an online survey is built based on the framework and sent to a target group of port stakeholders to collect their insight. The survey results show that a huge effort is needed to establish a structure for data exchange to increase the quality of data. Additionally, by highlighting the importance of data from specific stakeholders, developers can effectively convey the value proposition to these stakeholders. They can emphasize how utilizing this vital data can result in operational enhancements, cost reductions, improved decision-making, and competitive advantages. As an implication, alignment between port stakeholders and AI providers enhances the maturity level of the market in AI solutions by fostering collaboration, addressing industry-specific challenges, tailoring solutions, and garnering support for implementation.

Objective: competition and cooperation occur in different areas of the market. The shipping industry is one of the areas characterized by strong competition and operational cooperation with the objective of operational improvement. Through mergers and acquisitions and joint ventures, the shipping industry has advanced with its business and sought better results. Methods: we analyzed coopetition strategies between global actors of shipping industry in the Brazilian market. We used the multiple case study method with the four main shipping lines in the world based on semi-structured interviews and data from official documents. Results: the results indicate that the shipping lines often employ the strategy of joint services and slot service in partnership with other shipping companies. Market coverage, customer needs, and reduced operating costs were identified as the main motivations for coopetition in the shipping industry. Conclusions: we particularly reveal the coopetition strategies of the shipping companies evaluated in the Brazilian market and their plans to continue operating in this strategic region for the shipping industry.

The aim of this study is to construct monthly coincident and leading composite indicators for the shipping industry in Korea. The coincident and the leading indicators are computed using the generalized dynamic factor model. We use the production index of the water transport industry as the reference variable and compute the coincident indicator based on the common component of eight economic indicators. The analysis shows that the Korean shipping industry went through four business cycles during the sample period from 2007M1 to 2021M5. The leading indicator provides early signals of turning points in business cycles with a high correlation. The results suggest that the business cycle indicators present in this study may be useful diagnostic tools for understanding the timely and frequent economic state of the Korean shipping industry and its likely development in the near future. • A composite business cycle indicator of Korea's shipping industry was developed. • The indicator provides meaningful information on the current status of the industry. • The indicator helps in predicting the industry's business cycle. • The generalized dynamic factor model was used to develop the composite business cycle indicator. [ABSTRACT FROM AUTHOR]

Coastal regions worldwide face increasing air pollution due to maritime activities. This technical note focuses on assessing the air pollution in the Daesan port area, Republic of Korea, using hourly emission measurements. Leveraging Automatic Identification System (AIS) data, we estimate vessel-induced air pollutant emissions and correlate them with real-time measurements. Vessel navigational statuses are categorized from the AIS data, enabling an estimation of fuel oil consumption. Random Forest models predict specific fuel oil consumption and maximum continuous ratings for vessels with unknown engine details. Using emission factors, we calculate the emissions (CO2, NO2, SO2, PM-10, and PM-2.5) from vessels visiting the port. These estimates are compared with actual air pollutant concentrations, revealing a qualitative relationship with an average correlation coefficient of approximately 0.33. [ABSTRACT FROM AUTHOR]