In order to prevent safety risks, control marine accidents and improve the overall safety of marine navigation, this study established a marine accident prediction model. The influences of management characteristics, environmental characteristics, personnel characteristics, ship characteristics, pilotage characteristics, wharf characteristics and other factors on the safety risk of maritime navigation are discussed. Based on the official data of Zhejiang Maritime Bureau, the extreme gradient boosting (XGBoost) algorithm was used to construct a maritime accident classification prediction model, and the explainable machine learning framework SHAP was used to analyze the causal factors of accident risk and the contribution of each feature to the occurrence of maritime accidents. The results show that the XGBoost algorithm can accurately predict the accident types of maritime accidents with an accuracy, precision and recall rate of 97.14%. The crew factor is an important factor affecting the safety risk of maritime navigation, whereas maintaining the equipment and facilities in good condition and improving the management level of shipping companies have positive effects on improving maritime safety. By explaining the correlation between maritime accident characteristics and maritime accidents, this study can provide scientific guidance for maritime management departments and ship companies regarding the control or management of maritime accident prevention.
China is the world’s primary energy consumer. In order to address global warming, China has proposed a strategic goal of “reaching peak carbon and carbon neutrality”, which is related to a balance between human and natural life and has vital strategic significance for accelerating the construction of a sustainable society and achieving high-quality development. The energy sector is the main battlefield upon which the country will strive to achieve the “double carbon” goal, and power systems take the hierarchical first place in the current carbon emissions structure in China. Thermal power enterprises are facing severe challenges, such as low-carbon development, transformation, and upgrading. Therefore, it is crucial to study the thermal power industry’s carbon footprint. A scenario prediction method for estimating the carbon footprint of the thermal power industry in Zhejiang Province based on stacking integrated learning—i.e., the STIRPAT model—is proposed in this study. Using this model, to identify the main influencing factors, one can take the coefficient of determination (R2) and mean absolute percentage error (MAPE) as evaluation indicators, building a fusion advantage model to predict the carbon footprint. Four carbon peak action scenarios are set up to determine the thermal power industry’s carbon peak in 2021–2035, taking Zhejiang Province as an example. The findings indicate that the proposed method can accurately predict the carbon footprint of the thermal power industry, with the prediction coefficient (R2) being higher than 0.98 and the error (MAPE) being lower than 0.01. The carbon emission peaks of the thermal power industry under different carbon peak action scenarios are calculated, verifying that Zhejiang Province can reach the goal of a carbon peak; however, the low-carbon development model is too extreme and needs to be revised in combination with more reasonable improvement methods. Therefore, Zhejiang Province must be restructured industrially, the construction of high-tech industries must be encouraged, the energy consumption structure must be optimized, energy efficiency must be boosted, and energy use must be reduced. Relevant research offers a theoretical foundation and benchmark for China’s thermal power industry to promote industrial restructuring and low-carbon transformation by means of comprehensive governance.
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