## 4 Performance Analysis  
In order to ensure the validity and generalization ability of the proposed model, we divided the collected dataset in the ratio of 8:2. Specifically, 80% of the data were randomly selected as the training set for model training and optimization, while the remaining 20% was used as the test set for independent validation of the model's prediction performance. This division strategy aims to validate the generalization ability of the model by ensuring that the model's performance on unseen data matches that of the training through the validation results of the independent test set. Our proposed model was compared and analyzed against support vector machine (SVM), K-nearest neighbors (KNN), random forest, multilayer perceptron (MLP), LightGBM, and XGBoost, and their versions optimized using Optuna. To optimize the comparative analysis of indicators while preventing the large variance in target values, namely unit emissions, from adversely affecting distance-based machine learning models such as KNN and SVM models, and to avoid potential gradient vanishing or explosion during the model fusion stage using MLP, we adopted a strategy of converting the unit of unit emissions into \"ten thousand tons\" for calculation. This approach not only makes the scale of data more uniform, facilitating model processing, but also enhances the convenience of analysis, ensuring the efficiency of the data processing workflow and the stability of the model training process. Furthermore, using this unit conversion, we avoid the step of denormalizing when outputting model prediction results, simplifying the post-processing. Thus, we can directly display clear and intuitive prediction results in \"ten thousand tons\", making the application of the model and the interpretation of results more direct and convenient. Model comparisons are based on the following metrics: mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2), as shown in Table 5.  
In a comprehensive performance comparison, our Optuna-LightGBM-XGBoost model, with its superior performance, significantly outperforms traditional SVM, KNN, random forest, MLP, LightGBM, and XGBoost models alone, including their Optuna-optimized versions. Specifically, our model achieves 0.652 in mean absolute error (MAE), which is an improvement of about 27% over the closest Optuna-LightGBM and Optuna-XGBoost models at 0.891, and significantly outperforms the other models with 0.939 in mean squared error (MSE), which is significantly lower than that of Optuna-LightGBM's 1.772, i.e., a reduction of about 47%. In addition, the mean absolute percentage error (MAPE) is only 0.136, which is an improvement of about 16.6% compared to 0.163 for Optuna-LightGBM. In terms of the coefficient of determination (R2), our model achieves 0.994, which is about 0.6 percentage points better than the 0.988 of the second-best model, Optuna-LightGBM. The improvement is even more significant when compared to conventional models; for example, compared to the original SVM model, our MAE and MSE are improved by 63.8% and 90.5%, respectively, and MAPE and R2 are also significantly improved. This leapfrog performance improvement highlights the overall advantages of our model in terms of accuracy, error control, and ability to account for data variability. Compared to models such as KNN and random forest, our model also demonstrates a clear lead, both in terms of accuracy and in explaining complex data structures. By combining the strengths of LightGBM and XGBoost and using Optuna for hyperparameter fine-tuning, our model not only improves prediction accuracy and reduces errors, but also significantly improves the ability to explain data variability, demonstrating the great potential of integrated learning and parameter optimization in improving the performance of machine learning tasks.