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Within the framework of economic globalisation, ports serve as critical junctures in international trade and play a vital role. However, as infrastructure is closely linked to the natural environment, ports are highly susceptible to the impacts of meteorological disasters. Therefore, a comprehensive assessment of the risks posed by meteorological hazards to ports, establishing corresponding early warning mechanisms, and adopting reasonable response and recovery strategies, is paramount in ensuring the safe operation of ports and maintaining the stability of international trade. This study has comprehensively analysed historical data and identified the pre-established loss stratification system, improving the theoretical construct of "expected loss". Additionally, this research has innovatively integrated the idea of preventative factors aligned with risk indicators. A quantitative algorithm was used to factor in the preventative factors within the computational procedure, deriving the weights pertinent to each risk indicator. This research aimed to reduce the subjectivity inherent in the weighting assignment process through such an approach, thereby enhancing disaster risk assessment's scientific rigour and reliability. Moreover, it underscores the critical role of adaptive urban planning in enhancing the resilience of crucial economic nodes like ports, thereby contributing to the broader objectives of sustainable urban development. [ABSTRACT FROM AUTHOR]

Climate change has threatened the infrastructure, operation, policymaking, and other pivotal aspects of transport systems with the accelerating pace of extreme weather events. While a considerable amount of research and best practices have been conducted for transport adaptation to climate change impacts, there is still a wide gap in the systematic assessment of climate risks on all-round transport modes (i.e., road, rail, sea, and air) with a comprehensive review and a quantitative scientific framework. This study aimed to critically review studies on how the transport sector has adapted to the impacts posed by climate change since the dawn of the 21st century. To support climate risk assessment in comprehensive transport systems, we developed a Climate Change Risk Indicator (CCRI) framework and applied it to the case of the British transport network. Focusing on a multi-modal transport system, this offers researchers and practitioners an invaluable overview of climate adaptation research with the latest tendency and empirical insights. Meanwhile, the developed CCRI framework elaborates a referable tool that enables decision-makers to employ objective data to realise quantitative risk analysis for rational transport adaptation planning. [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]