## 5 Conclusions  
In this study, we propose a non-experimental approach to predicting CO2 exhaust emissions from vessel propulsion engines during operation, addressing a globally significant issue. We explored TCN-LSTM hybrid model, integrating TCNs and LSTM networks, to analyze complex time-series data. The models were trained using engine operation data and exhaust emission measurements, and based on various validation processes, we derived the following conclusions:  
1. Among the CNN, LSTM, and TCN single architectures, the CNN model demonstrated the best predictive performance. This is likely due to its ability to recognize local features and extract patterns from noisy data, which are key strengths of CNNs. By extending CNN architecture--traditionally used for image processing--we experimentally verified its effectiveness in estimating engine exhaust emissions. 2. We developed a TCN-LSTM hybrid model by integrating multiple architectures, resulting in more complex neural network structures. These hybrid models exhibited faster convergence and more stable training compared to single architectures. Moreover, they proved to be more robust, achieving higher prediction accuracy and significantly lower residual distributions. 3. The TCN-LSTM hybrid model achieved the best overall performance, significantly improving accuracy metrics. It outperformed the TCN single model by 24.9% in MAE, 19.8% in RMSE, and 48.8% in MAPE, and the LSTM single model by 21.1% in MAE, 19% in RMSE, and 45.7% in MAPE, confirming its superior predictive capability. 4. All models exhibited deviations and reduced prediction accuracy in sections with rapid engine-operating changes. This is likely due to limited data in these sections and inherent correlations between the model structure and input data. These findings suggest the need for further analysis, potential structural refinements, and data augmentation to enhance model performance in these areas. 5. The hybrid model demonstrated significantly higher predictive performance than single architectures. This suggests that a predictive model can effectively capture engine combustion characteristics and exhaust emissions under varying conditions. 6. Future research will focus on analyzing the complex correlations between model structure and data to address observed inconsistencies. Additional measures- such as structural refinements and enhanced data processing-will be explored to further improve prediction performance. Moreover, data from the vessel's external environment (e.g., ocean currents, wind direction, speed, etc.) will be incorporated into the training data to achieve a higher goodness-of-fit in sections with frequent time-series variations. 7. The proposed hybrid prediction model has strong potential for integration into real-time engine monitoring systems and emission control platforms onboard vessels. It provides valuable insights into emission levels under various engine operating conditions and may also be applied to assess emissions associated with engine aging, components degradation, or system faults. Furthermore, the results of this study can serve as a foundation for future research by offering data-driven insights for the development of emission inventories and carbon reduction strategies. * The hybrid model developed in this study was validated on the Hyundai-MAN B&W 6540 ME engine. Its general applicability to other engine types remains to be verified. Future research may involve retraining the model using data from different engine types, optimizing hyperparameters, and conducting comparative performance evaluations to assess the generalization capability of the proposed model. Conceptualization, J.O. ; Software, S.L. ; Validation, J.O. ; Formal analysis, S.L. ; Investigation, S.L. ; Data curation, S.L. ; Writing--original draft, S.L. ; Writing--review & editing, J.O. ; Visualization, S.L. ; Supervision, J.O. ; Project administration, J.O. ; Funding acquisition, J.O. All authors have read and agreed to the published version of the manuscript.