# Resilience Assessment for the Northern Sea Route Based on a Fuzzy Bayesian Network
## 5 Conclusions  
The present work attempts to propose a comprehensive framework by which the safety level of the NSR can be improved. The safety level of the NSR is a common concern for mariners, authorities, and related organizations. Although huge endeavors have been  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} **Capacity Type \((Y_{i})\)** \\ \end{tabular} & \begin{tabular}{c} **Conditional Entropy** \\ \(H(X|Y_{i})\) \\ \end{tabular} & \begin{tabular}{c} **Mutual Information** \\ \(I(X,Y_{i})\) \\ \end{tabular} & \begin{tabular}{c} **Implication of Mutual Information** \\ \end{tabular} \\ \hline Absorptive capacity \((Y_{1})\) & 0.421 & 0.322 & \begin{tabular}{c} The uncertainty of system resilience can be \\ reduced by 32.2\% in the case of a good \\ knowledge of absorptive capacity \\ \end{tabular} \\ \hline Adaptive capacity \((Y_{2})\) & 0.440 & 0.303 & \
\begin{tabular}{c} The uncertainty of system resilience can be \\ reduced by 30.3\% in the case of a good \\ knowledge of adaptive capacity \\ \end{tabular} \\ \hline Restorative capacity \((Y_{3})\) & 0.542 & 0.201 &
\begin{tabular}{c} The uncertainty of system resilience can be \\ reduced by 20.1\% in the case of a good \\ knowledge of restorative capacity \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 15: Summary of the information entropy analysis.  
made, the safety level of the NSR is still far from being satisfied. In the present study, system resilience is introduced to analyze the NSR safeguard capacity. Based on the existing studies about system resilience, the resilience capacity of the NSR is decomposed into three capacities: absorptive capacity, adaptive capacity, and restorative capacity. Then, we identify the contributing factors pertaining to these capacities. Moreover, the disruptions to the NSR are also identified. Next, all the identified disruptions, contributing factors, and capacities are regarded as nodes in the BN and, as a result, a BN model is developed. The prior probabilities for the nodes without parents and the CPT for the network inference are obtained by the fuzzy methodology proposed by [34]. Finally, the established BN model is simulated with GeNle 2.3 software, and a sensitivity analysis and an information entropy analysis are conducted with regard to the results obtained from the simulation. Overall, the present study provides a resilience perspective to understand and evaluate the NSR safety issue, which can be seen as the main innovation of this work. The analysis framework proposed in the present study can improve our understanding of the system resilience of the NSR and aid in mitigating disruptions to the NSR.  
Although the framework proposed in the present study is demonstrated to effectively quantify and interpret the resilience of the NSR, the structure of the proposed BN can be optimized based on additional information from experts and stakeholders. In addition, the resilience level of the system is generally regarded as a variable that changes over time; therefore, in the future, potential work can be devoted to improving the performance of resilience modelling for the NSR based on the framework given in the present study. The work associated with the abovementioned issues remains in progress.  
Conceptualization and project administration, X.M.; Methodology, W.Q.; investigation, Y.L.; Writing, H.L. All authors have read and agreed to the published version of the manuscript. This research was funded by National Social Science Foundation of China, grant numbers are 19 BZZ104 and 20 VHQ012. The APC was funded by Fundamental Research Funds for the Central Universities (grant no. 3132019190).  
Not applicable.  
The authors declare no conflict of interest.