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Port performance measurement (PPM) and comparison research, presenting a multiple criteria decision making (MCDM) issue in nature, has been intensively conducted by researchers from both decision science on modelling and port studies from empirical perspectives. Assigning an appropriate weight to each defined port performance indicator (PPI) is essential for rational decision and precise performance measurement. However, PPIs are often presented in a hierarchy, having the interdependency among them ignored. It causes concerns on the accuracy of PPIs’ weight allocation and arguments on the performance measurement results, revealing a significant research gap to be addressed. As far as MCDM modelling is concerned, the importance of criteria has been studied utilising either absolute or relative comparisons, while the calculation of their importance also takes into account both independency and interdependency factors. However, there is lack of empirical studies in the literature to provide supporting evidence to distinguish the different impacts of the two factors. This study aims to compare the analysis of PPIs importance when taking into account their independent relationship using an analytic hierarchy process (AHP) and their interdependent relationship using a decision making trial and evaluation laboratory (DEMATEL) incorporating an analytic network process (ANP), respectively. The same domain experts are invited to evaluate the importance of the defined PPIs based on both approaches. The results demonstrate that a similar variance of relative importance across the PPIs but a clear difference on their importance scores and ranking. As a result, the results make contributions to fulfil the research gap on consideration of interdependency among PPIs in PPM and on the provision of convincing empirical evidence to highlight the impact of interdependency of criteria on MCDM modelling. Another practical significance draw from this study is that use of DEMATEL can aid port stakeholders to make more rational decision as to whether the interdependency among PPIs should be taken into account in PPM and/or port choice. [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]

• Elaborate all the indicators influencing airport climate resilience. • Develop a climate resilience evaluation method using evidential reasoning. • Use big data to quantify the airport climate resilience. • Conduct an empirical study to analyse the climate resilience of the UK airports. Due to increased extreme weather events, climate adaptation has become an essential issue to be addressed by all transport infrastructures, including airports. This paper aims to develop a Climate Resilience Indicator (CRI) framework for assessing airport climate resilience, which for the first time, considers: climate exposure, vulnerability and adaptive capacity simultaneously and advances the development of climate risk analysis of airports to a point where their adaptation and resilience can be quantified under uncertainty in data. Climate-related data was collected from multiple sources to evaluate an airport's performance against each indicator. An evidential reasoning (ER) approach is used to evaluate each airport by integrating all the indicators to derive its final CRI score. The findings provide valuable insights into how urgently an airport needs to deal with climate change and reveal information to help with resource allocation for different airports nationally through proactive adaptation planning. [ABSTRACT FROM AUTHOR]

• Major hazards influencing cycling safety are identified from various resources. • A novel BN model is developed to enable the prediction of cycling accident severity. • The influence of major hazards on cycling accident severity are evaluated. • New findings of useful insights for improving cycling safety are derived. • Safety suggestions are provided to reduce accident severity and ensure cycling safety. Cycling helps reduce traffic congestion, environmental pollution and promote a healthy lifestyle for the general public. However, it could also expose cyclists to dangerous environments, resulting in severe consequences and even death. Transport authorities are seeing growing accidents in city regions with increasing cycling population, requiring the development of new risk informed cycling safety policies. This paper aims to develop a new conceptual risk analysis approach based on a Bayesian network (BN) technique to enable the analysis and prediction of the severity of cycling accidents. To identify the risk factors influencing accident severity, 2,269 cycling accident reports from the UK city region were manually collected, where primary data was extracted and analysed. An advanced data training method (i.e. Tree Augmented Naïve Bayes (TAN)) for BN development was applied to investigate their correlation and their individual and combined contributions to cycling accident severity. As a result, the risk factors influencing accident severity are prioritised in terms of their risk contribution. The risk levels of accident severity can be predicted in dynamic situations based on the data from simulated and/or real cycling environments. The findings can provide useful insights for making rational cycling safety policies in proportion to different risk levels. [ABSTRACT FROM AUTHOR]

Climate change is the most threating environmental issue and the biggest challenge that humanity has ever faced. While acting as the key nodes of globalisation and international business, seaports are exposed to the vulnerability of climate impacts, mainly because of their locations, including low-lying areas, coastal zones, and deltas. The paper is to develop a Climate Change Risk Indicator (CCRI) framework for climate risk assessment of seaports, enabling research-informed policymaking on such a demanding topic. Due to the increasing number of extreme weather events (EWEs), climate change adaptation is becoming an essential and necessary issue to be addressed by seaport stakeholders. Climate risk analysis aids rational adaptation planning. Many climate assessments have been done for measuring climate vulnerabilities, and various climate adaptation measures have been proposed for reducing climate risks. However, few of them used quantitative approaches for climate risk evaluations in seaports and fewer on the provisions of CCRIs for comparing climate risks of different locations and timeframes to guide rational policy making. Furthermore , climate change is a dynamic issue, requiring big objective data to support the analysis (e.g. monthly climate data on CCRIs) of climate threats and vulnerabilities. In this paper, Evidence Reasoning (ER) is employed to evaluate the climate risks in seaports by tackling the incomplete data. The findings reveal the quantitative measures of climate change risks in different locations and in different months. Furthermore, the risk levels of seaports in the future are assessed for observing the changes and informing policy making. The main contributions of this study include the visualisation of the comprehensive climate risk levels and provision of a new climate risk analysis framework through the comparison of climate change risks with respect to different locations and timeframes. Suitable climate adaptation measures can be chosen to implement, and seaports can cooperate on climate resilience issues (e.g. seaport network service and pre-disaster relief logistics). • Propose an Evidential reasoning (ER) approach to integrate all Climate Change Risk Indicators (CCRI) and establish a CCRI framework for seaports. • Assess twelve seaports in the United Kingdom (UK) as a demonstration. • Observe the monthly and seasonal climate changes. • Forecast the future climate risks for the seaports in the UK. • Comparing the climate risks with respect to location and timeframe for informing policymaking. [ABSTRACT FROM AUTHOR]

• Analyse the impact of climate change on road transport systems. • Develop a risk prediction tool to reduce climate impact on roads. • Conduct economic analysis of various measures to reduce climate impact on roads. • Conduct an empirical study to provide useful insights for rational risk based climate adaptation. This paper aims to analyse the impacts of climate change to the current and predicted future situations of road transportation in the UK and evaluate the corresponding adaptation plans to cope with them. A conceptual framework of long-term adaptation planning for climate change in road systems is proposed to ensure the resilience and sustainability of road transport systems under various climate risks such as flooding and increased temperature. To do so, an advanced Fuzzy Bayesian Reasoning (FBR) model is first employed to evaluate the climate risks in the UK road transport networks. This modelling approach can tackle the high uncertainty in risk data and thus facilitate the development of the climate adaptation framework and its application in the UK road sector. To examine the feasibility of this model, a nationwide survey is conducted among the stakeholders to analyse the climate risks, in terms of the timeframe of climate threats, the likelihood of occurrence, the severity of consequences, and infrastructure resilience. From the modelling perspective, this work brings novelty by expanding the risk attribute "the severity of consequence" into three sub-attributes including economic loss, damage to the environment, and injuries and/or loss of life. It advances the-state-of-the-art technique in the current relevant literature from a single to multiple tier climate risk modelling structure. Secondly, an Evidential Reasoning (ER) approach is used to prioritise the best adaptation measure(s) by considering both the risk analysis results from the FBR and the implementation costs simultaneously. The main new contributions of this part lie in the rich raw data collected from the real world to provide useful practical insights for achieving road resilience when facing increasing climate risk challenges. During this process, a qualitative analysis of several national reports regarding the impacts posed by climate change, risk assessment and adaptation measures in the UK road sector is conducted for the relevant decision data (i.e. risk and cost). It is also supplemented by an in-depth interview with a senior planner from Highways England. The findings provide road planners and decision makers with useful insights on identification and prioritisation of climate threats as well as selection of cost-effective climate adaptation measures to rationalise adaptation planning. [ABSTRACT FROM AUTHOR]

• Analyse the impact of climate change on rail transport systems. • Explore the uncertainties in climate risk data. • Develop new climate risk analysis methods using advanced uncertainty modelling. • Develop a risk prediction tool to reduce climate impact on rails. • Conduct a UK national wide empirical study to provide useful insights for rail network adaptation to climate change. Climate change poses critical challenges for rail infrastructure and operations. However, the systematic analysis of climate risks and the associated costs of tackling them, particularly from a quantitative perspective, is still at an embryonic phase due to the kaleidoscopic nature of climate change impacts and lack of precise climatic data. To cope with such challenges, an advanced Fuzzy Bayesian Reasoning (FBR) model is applied in this paper to understand climate threats of the railway system. This model ranks climate risks under high uncertainty in data and comprehensively evaluates these risks by taking account of infrastructure resilience and specific aspects of severity of consequence. Through conducting a nationwide survey on the British railway system, it dissects the status quo of primary climate risks. The survey implies that the top potential climate threats are heavy precipitation and floods. The primary risks caused by the climate threats are bridges collapsing and bridge foundation damage due to flooding and landslips. The findings can aid transport planners to prioritise climate risks and develop rational adaptation measures and strategies. [ABSTRACT FROM AUTHOR]

• Analyse the key risk factors influencing cargo theft from freight supply chains. • Develop a data-driven Bayesian Network model to realise cargo theft risk prediction and diagnosis. • Evaluate the critical risk factors to predict the occurrence of cargo theft accidents. • Analyse the real accidents to verify the model and provide implications for cargo theft prevention. Cargo theft has been among the most concerning risks influencing global freight supply chains, which causes serious supply chain disruptions, injuries/deaths, economic loss, and environmental damage. However, there are very few studies on the risk analysis of cargo theft, particularly in a quantitative manner, and fewer on the relevant risk factors affecting theft-related accidents in the current literature. This paper aims to analyse the risk influential factors (RIFs) of cargo theft and predict the occurrence likelihood of different types of cargo theft accidents. The historical data of 9316 cargo theft accidents that happened in the UK from 2009 to 2021 were first collected from the TAPA IIS database, and then purified and trained to construct a Bayesian network (BN) based cargo theft risk analysis model. The data-driven BN interprets the interdependency of RIFs and their combined effects on the occurrence of different types of cargo theft accidents. Compared with the previous studies, this paper makes new contributions, including that (1) The cargo theft RIFs are identified from the literature and accident records. (2) A data-driven BN is proposed to construct the model with uncertainty to realise cargo theft risk prediction and diagnosis. (3) The critical RIFs contributing to cargo theft are evaluated and prioritised to predict the occurrence of possible cargo theft accidents. (4) The real accidents are investigated to verify the model and draw useful insights for cargo theft prevention. The findings show that the most influential RIFs for the occurrence of cargo theft accidents are product category, year, location type, modus operandi (MO), and region. The findings also reveal the combined risk contributions of the RIFs, hence providing useful insights for cost-effective theft risk control in practice. [ABSTRACT FROM AUTHOR]