Petroleum storage and transport systems necessitate robust safety measures to mitigate oil spill risks threatening marine ecosystems and sustainable development through ecological and socioeconomic safeguards. We aimed to gain a deeper understanding of the evolution patterns of accidents and effectively mitigate risks. An improved risk assessment method that combines the Bow-Tie (BT) theory and Dynamic Bayesian theory was applied to evaluate the safety risks of petroleum storage and transportation facilities. Additionally, a scenario modeling approach was utilized to construct a model of the event chain resulting from accidents, facilitating quantitative analysis and risk prediction. By constructing an accident chain based on fault trees, the BT model was converted into a Bayesian Network (BN) model. A Dynamic Bayesian Network (DBN) model was established by incorporating time series parameters into the static Bayesian model, enabling the dynamic risk assessment of an oil storage and transportation base in the Zhoushan archipelago. This study quantitatively analyzes the dynamic risk propagation process of storage tank leakage, establishing time-dependent risk probability profiles. The results demonstrate an initial leakage probability of 0.015, with risk magnitude doubling for the temporal progression and concurrent probabilistic escalation of secondary hazards, including fire or explosion scenarios. A novel risk transition framework for the consequences of petrochemical leaks has been developed, providing a predictive paradigm for risk evolution trajectories and offering critical theoretical and practical references for emergency response optimization.

The large-scale storage and stable supply of oil products are essential for national energy security and economic development. As the economy expands and energy demands rise, centralized storage and supply systems become increasingly vital for ensuring the efficiency and reliability of oil product distribution. However, large oil storage depots present substantial safety risks. In the event of fires, explosions, or other accidents, emergency response efforts face stringent demands and challenges. To enhance the emergency response capabilities of oil storage and transportation bases (OSTBs), this paper proposes an innovative approach that integrates the improved analytic hierarchy process (IAHP) with the Entropy Weight Method (EMW) to determine the combined weights of various indices. This approach reduces the subjective bias associated with the traditional analytic hierarchy process (AHP). The emergency response capabilities of OSTBs are subsequently evaluated through fuzzy comprehensive analysis. An empirical study conducted on an OSTB in the Zhoushan archipelago quantitatively assesses its emergency preparedness. The results show that the base excels in pre-incident prevention, demonstrates robust preparedness and response capabilities, and exhibits moderate recovery abilities after incidents. These findings provide a theoretical foundation for reducing the likelihood of accidents, enhancing emergency response efficiency, and mitigating the severity of consequences. Practical recommendations are also offered based on the results.