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Environmental problems that seriously affect both natural systems and social development of human beings have drawn extensive attention from governing authorities all around the world, and become an urgent issue to be addressed. Ports play a significant role in the international shipping which inevitably influence the global environment. Thus, the concept of green port is developed to mitigate the negative impacts of inappropriate port operations on environment. This paper analyzes the current status of green port development worldwide. An evaluation model for quantitative measurement of green port development is established based on the Drivers, Pressures, States, Impacts and Responses (DPSIR) framework. The weight of each index composing the evaluation model is calculated through an analytical hierarchy process method, and the evaluation results of the investigated ports with respect to each index are aggregated using an evidential reasoning approach. The evaluation model is further demonstrated through a comparative analysis of five major ports in China. The novel model developed along with the methods applied in this paper can provide significant insights for the comparative evaluation on the development of green ports in other countries and/or regions, as well as a powerful tool to conduct self-assessment of green port development. [ABSTRACT FROM AUTHOR]

Modelling the interdependencies among the factors influencing human error (e.g. the common performance conditions (CPCs) in Cognitive Reliability Error Analysis Method (CREAM)) stimulates the use of Bayesian Networks (BNs) in Human Reliability Analysis (HRA). However, subjective probability elicitation for a BN is often a daunting and complex task. To create conditional probability values for each given variable in a BN requires a high degree of knowledge and engineering effort, often from a group of domain experts. This paper presents a novel hybrid approach for incorporating the evidential reasoning (ER) approach with BNs to facilitate HRA under incomplete data. The kernel of this approach is to develop the best and the worst possible conditional subjective probabilities of the nodes representing the factors influencing HRA when using BNs in human error probability (HEP). The proposed hybrid approach is demonstrated by using CREAM to estimate HEP in the maritime area. The findings from the hybrid ER-BN model can effectively facilitate HEP analysis in specific and decision-making under uncertainty in general. • Identify the shortcomings of traditional human reliability analysis methods in maritime applications. • Develop a quantitative human reliability analysis method using fuzzy Bayesian evidential reasoning. • Rationalise subjective probability distribution in BNs to realise marine engineers' failures under uncertainty. • Demonstrate advantages of the new risk model through a real case analysis. [ABSTRACT FROM AUTHOR]