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Ferries are crucial for the transportation of passengers in coastal areas, offering multi-route, high-frequency, and point-to-point services at a lower cost than bridges or tunnels. Recently, many countries have been advocating for autonomous ferries due to their eco-friendliness and potential to mitigate accidents that often occur on traditional manned ferries due to human error. However, the public's acceptance of autonomous transport systems is frequently influenced by scientific evidence that supports their practical applications. This study endeavours to scrutinise the social acceptance of autonomous ferries from the passenger's perspective, employing the perceived value theory. A total of 151 responses have been collated from our questionnaire, aiming to investigate the public's acceptance of autonomous ferries, and to obtain the users' opinions on key influential dimensions such as the perceived price, quality, value, risk, and willingness to use an autonomous ferry. Structural equation modelling (SEM) has been employed to examine the relationship among these dimensions. The findings reveal that the perceived price and quality have a positive effect on the perceived value, which, in turn, has an indirect effect on the willingness to use autonomous ferries. The conjecture that the perceived risk influences the perceived value and willingness to use autonomous ferries is fully supported by our findings. Building on the findings of this research, this study offers specific recommendations to industry operators and government authorities to guide policy formulation and operational decision-making in the development of autonomous ferry. • Explores passenger willingness to use autonomous ferries. • Identifies key social factors influencing the acceptance of autonomous ferry services. • Provides insights into the public perception of autonomous ferry transportation. • Contributes to the understanding of technology acceptance in urban transport systems. • Offers recommendations for enhancing the public's trust in autonomous ferry services. [ABSTRACT FROM AUTHOR]

Maritime Autonomous Surface Ships (MASS) face regulatory challenges, with some suggesting that the existing Collision Regulations (COLREG) present linguistic barriers for autonomous vessels' development and implementation. While academic research has focused on developing autonomous collision avoidance (CA), it is producing inconsistent results compared to conventional navigation practices. This study aims to identify trends and weaknesses in recent studies on CA for MASS by conducting a systematic review and analysis of the most relevant literature. The Conventional-Collision-Avoidance-Process (CCAP), which benchmarks manned modern ships' capacity for CA compliance under COLREG and industry requirements, is used to break down a ship's collision avoidance process into 53 CA functions under eight main categories. A total of 32 papers were chosen through filtering based on keywords, publication period, language, and relevance. The content of the recent academic literature was then grouped under appropriate CCAP codes. Statistical and graphical interpretations were generated using the collected literature content data and evaluated statistics of the existing digital contribution of CCAP. The study uncovers significant trends, inconsistencies, and weaknesses that could guide future academic research towards comprehensive CA solutions for MASS. • A systematic literature review in the context of Autonomous Collision Avoidance. • Identification of 53 Conventional-Collision-Avoidance-Process (CCAP) functions under eight main categories. • Illustration of the flow map of all the CCAP functions for collision avoidance assurance. • Analysis of the trends and gaps of collision avoidance for maritime autonomous surface ships (MASS). [ABSTRACT FROM AUTHOR]

Cybersecurity risks present a growing concern in the maritime industry, especially due to the fast development of digitalised technologies, also vis-à-vis autonomous shipping. Research on maritime cybersecurity is receiving increased attention. This paper aims to assess the cybersecurity risks in the maritime sector and improve safety at sea and in coastal areas. First, we identify all the concerned cyber threats in the sector based on literature review and expert opinion. A novel risk assessment framework of maritime cyber threats, which combines Failure Mode and Effects Analysis (FMEA) with a Rule-based Bayesian Network (RBN), is proposed and used to evaluate the risk levels of the identified threats and to better understand the threats that contribute the most to the overall maritime cybersecurity risk. The results can inform stakeholders about the most vulnerable parts in their cyber operations and stimulate the development of risk-based control measures. More specifically, the next step in managing cyber threats is to tackle the threats that are associated with unacceptable risk levels and identify cost-effective measures to manage them. To that extent, our findings provide a list of top threats – that is the areas where efforts should be focused on. As a result, this work can help the whole community to grow its resilience to cyber-attacks and improve the security of shipping operations. • Maritime cyber threats are identified and verified. • A novel maritime cybersecurity risk analysis method under high uncertainty is proposed. • Failure Mode and Effects Analysis (FMEA) with a Rule-based Bayesian Network (RBN) and a sensitivity analysis are utilised. • Quantitative analysis and prioritisation of the risk levels of maritime cyber threats are presented. • New solutions to enhancing maritime cybersecurity are discussed. [ABSTRACT FROM AUTHOR]

International freight transportation experienced significant disruptions during the COVID-19 pandemic. The impact of the pandemic and related government restriction measures on international freight transportation is worth analysing for the development of transportation policies and practices in the post-pandemic period, but has received limited attention. To fill the gap, this study applies structural equation models to analyze the impact of the COVID-19 pandemic on the international transportation market and the relationships among the pandemic, government restriction measures, and international transportation market. The impact is also differentiated for different modes of transportation. Results confirm that both demand and supply of international transportation services have been negatively affected by the first wave of the pandemic, with sea transportation being more affected by the reduction of demand and air transportation more by the supply volatility. The government restriction measures are found to play a mediation role, in the way that the impact of the pandemic on the supply of transportation service is suppressed through the government restriction measures. Our findings provide important guidance for transportation industry players and governments in their decision-making process facing with global market shocks such as a pandemic. [ABSTRACT FROM AUTHOR]

Highlights: [•] Identify risk factors in container shipping causing safety and security damage. [•] Conduct empirical research to measure risk likelihood, consequence, and risk scale. [•] Propose stochastic dominance method to analyse relative importance of risk factors. [•] Apply risk-mapping technique to identify response strategies to risk factors. [ABSTRACT FROM AUTHOR]

In a 2019 European Commission report, the Blue Economy (BE) within the United Kingdom (UK) represented 22% of the European Union's (EU) BE Gross Value Added (GVA) at approximately €39 billion. Coupled with the clear value of the BE to the UK, there is an urgent need to innovate and develop technologies to decarbonise and advance the sector. A deeper understanding of the current position for multiple stakeholders must be considered before any major governmental or long-term strategy decisions can be made. This paper presents the perspective of academic, industrial and governmental stakeholders analysis of how the UK can move forward with developing innovations within the BE. Utilising a questionnaire and round table discussions, specialists from all stakeholders gave their opinions on industry-academia-governmental working relationships and technology transfer readiness. Reasonably high satisfaction was found with key aspects that enable a successful collaborative project between academia and industry towards technology commercialisation; however, there is still room for improvement. This paper offers an analysis of how to further enhance and foster technology development within the UK BE. A collaborative approach is proposed to ensure best practices, and a 'triple helix' (TH) collaboration strategy to be used as a tool for those engaging in these types of working relationships. Future directions on enhancing technology transfer innovation within the UK BE are also suggested. [ABSTRACT FROM AUTHOR]

• Identify the hazards in maritime autonomous surface ships (MASS) development and operations. • Introduce a BN model to assess the identified hazards in maritime autonomous surface ships. • Analyse and rank the impacts of hazards on maritime autonomous surface ships operations. • Propose solutions for dealing with hazards in maritime autonomous surface ships. Maritime Autonomous Surface Ships (MASS) are attracting increasing attention in the maritime industry. Despite the expected benefits in reducing human error and significantly increasing the overall safety level, the development of autonomous ships would undoubtedly introduce new risks. The overall goal of this work is to develop an approach to evaluate the risk level of major hazards associated with MASS. To that extent, a Failure Modes and Effects Analysis (FMEA) method is used in conjunction with Evidential Reasoning (ER) and Rule-based Bayesian Network (RBN) to quantify the risk levels of the identified hazards. The results show that 'interaction with manned vessels and detection of objects' contributes the most to the overall risk of MASS operations, followed by 'cyber-attacks', 'human error' and 'equipment failure'. The findings provide useful insights on the major hazards and can aid the overall safety assurance of MASS. [ABSTRACT FROM AUTHOR]

The distinctive features of maritime infrastructures present significant challenges in terms of security. Disruptions to the normal functioning of any part of maritime transportation can have wide-ranging consequences at both national and international levels, making it an attractive target for malicious attacks. Within this context, the integration of digitalization and technological advancements in seaports, vessels and other maritime elements exposes them to cyber threats. In response to this critical challenge, this paper aims to formulate a novel cybersecurity risk analysis method for ensuring maritime security. This approach is based on a data-driven Bayesian network, utilizing recorded cyber incidents spanning the past two decades. The findings contribute to the identification of highly significant contributing factors, a meticulous examination of their nature, the revelation of their interdependencies, and the estimation of their probabilities of occurrence. Rigorous validation of the developed model ensures its robustness for both diagnostic and prognostic purposes. The implications drawn from this study offer valuable insights for stakeholders and governmental bodies, enhancing their understanding of how to address cyber threats affecting the maritime industry. This knowledge aids in the implementation of necessary preventive measures. • Analyzing two decades of maritime cyber-attacks from the MCAD Database. • Developing a data-driven Bayesian Network for maritime cyber threat analysis. • Model validation through data splitting, measure assessment, and sensitivity analysis. • Extracting insights to enhance maritime industry resilience against cyber threats. [ABSTRACT FROM AUTHOR]

• SO 2 emissions in China have been found to exhibit spatial heterogeneity. • A random effect eigenvector spatial filtering (RE-ESF) approach is suggested to deal with spatial dependence in empirical studies. • The global and local relationships between factors and SO 2 emissions can be captured. • Centralized and province-specific policies for reducing SO 2 emissions can be developed. Sulfur dioxide (SO 2) emissions have been a great challenge in China over the last few decades due to their serious impact on the environment and human health. In this paper, a random effect eigenvector spatial filtering (RE-ESF) approach without and with non-spatially varying coefficients (SNVC) is identified to examine spatial heterogeneity and economic driving factors of SO 2 emissions in China from 2011 to 2017. Using the Moran eigenvectors to extract information on spatial dependence, the main findings of the RE-ESF approach are as follows: First, after comparing different approaches for dealing with spatial dependence, it is found that the RE-ESF approach demonstrates the best fit to the dataset. Second, the global investigation shows that SO 2 emissions are negatively determined by economic growth and government expenditure for environment protection, but are positively determined by road freight transport, coal consumption and oil consumption. Third, the local investigation indicates that the spatially varying coefficients of economic growth and coal consumption range from 0.1401 to 0.2732 with the median value of 0.2478 and from 0.2406 to 0.3611 with the median value of 0.3210, respectively, revealing significant spatial heterogeneity of SO 2 emissions driven by economic growth and coal consumption. These findings provide meaningful insights into centralized and province-specific policies for reducing SO 2 emissions. [ABSTRACT FROM AUTHOR]

• Propose a novel hybrid approach to develop a multi-data-driven BN risk model based on AIS data and subjective judgements. • Introduce a target-free data learning approach to train data-driven BNs using AIS data. • Apply the hybrid approach in an emerging research topic of vessels and offshore wind farm collision. • Establish an effective risk model to support offshore wind farm collision avoidance and decision-making. • Demonstrate advantages of the new risk model through real case analysis. Offshore wind farms (OWFs) are relatively new installations at sea. Accident records related to vessel collisions with OWFs are insufficient to support a full quantitative risk analysis using traditional probabilistic approaches. This paper aims to develop a semi-qualitative risk model to assess the vessel-turbine collision risks by incorporating Bayesian networks (BN) with evidential reasoning (ER) approaches. First, a BN is trained based on Automatic Identification Systems (AIS) data to characterise real vessel traffic flows, including the detailed information and relationships between traffic flow parameters. Secondly, through synthesising expert judgements by ER, five risk factors influencing the probability and consequence of vessel-turbine collisions are identified (incl. the associated conditional probabilities) in the established BN. Finally, the updated BN with ER input is tested through ten real scenarios and validated by processing a validity framework. This paper pioneers the use of multi-data-driven BNs to characterise traffic flows and assess vessel-turbine collision risk for navigational safety assurance near OWFs. The research findings provide empirical evidence of using ER to supplement BN subjective data to advance its applications in risk analysis. [ABSTRACT FROM AUTHOR]