## 4 Results
### Results of Performance Evaluation for the Path-GMM-Nearest-Neighbor Model  
In this research, the performance of the Path-GMM-Nearest-Neighbor model was evaluated by comparing it with several previous models, including the Regression model, ANN model, BP model, Fuzzy model, and GM-ARIMA model. The analysis and validity of the models were tested using data and performance metrics to clearly demonstrate their validity. The statistical measures used for evaluation were MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error). The results of this evaluation are presented in Table 4.  
Table 4 shows that the Path-GMM-Nearest-Neighbor model has the highest performance, with the lowest MAPE and RMSE values, which are 1.09% and 1.21%, respectively. The model with the second-best performance is the GM-ARIMA model, with MAPE and RMSE values of 3.99% and 5.05%, respectively. The Fuzzy model follows with MAPE and RMSE values of 7.25% and 6.59%, respectively. The BP model has MAPE and RMSE values of 7.99% and 8.44%, respectively. The ANN model shows MAPE and RMSE values of 9.89% and 9.42%, respectively, while the Regression model has the highest MAPE and RMSE values of 19.05% and 18.70%, respectively. Furthermore, when comparing the performance of the Path-GMM-Nearest-Neighbor model with those of models studied in other countries, such as the LSTM-GRU-BiLSTM model [44], it was found that the latter had MAPE and RMSE values of 4.12 and 0.21, respectively. From Table 4, a quality assessment of various models was conducted to ensure their suitability for long-term forecasting. This was performed through cross-validation using the Self Consistency Test, a key tool for evaluating model reliability. The results indicate that the Path-GMM-Nearest-Neighbor model achieved the highest self-consistency test score at 96%, followed by the GM-ARIMA model (89%), the Fuzzy model (81%), the ANN model (78%), the BP model (72%), and the Regression model (65%). These findings demonstrate that the Path-GMM-Nearest-Neighbor model is the most appropriate for long-term forecasting and can be effectively applied in future predictive analyses. Thus, the Path-GMM-Nearest-Neighbor model demonstrates superior performance, significantly outperforming the other models from the past. This makes it highly suitable for use in future forecasting tasks and as a decision-making tool for the government. Agricultural Waste and Greenhouse Gas Emissions Forecasting Model Using the Path-GMM-Nearest-Neighbor Model  
From the analysis, it was found that the Path-GMM-Nearest-Neighbor model is suitable for long-term forecasting from 2025 to 2044. This is because the model has been \begin{table}
\begin{tabular}{l l l} \hline
**Forecasting Model** & **MAPE (\%)** & **RMSE (\%)** \\ \hline Regression model & 19.05 & 18.70 \\ ANN model & 9.89 & 9.42 \\ BP model & 7.99 & 8.44 \\ Fuzzy model & 7.25 & 6.59 \\ GM-ARIMA model & 3.99 & 5.05 \\ Path-GMM-Nearest-Neighbor model & 1.09 & 1.21 \\ \hline \end{tabular} Source: Author’s estimate (2024).  
\end{table} 
Table 4: Performance evaluation results of the Path-GMM-Nearest-Neighbor Model. validated and does not exhibit spurious results. It was confirmed that the model has no issues with heteroscedasticity, multicollinearity, or autocorrelation. The goodness of fit was tested, and the model met all the statistical testing criteria. Additionally, the performance evaluation using MAPE and RMSE statistics showed that this model achieved the highest performance. Therefore, the researcher developed a new scenario policy, which includes green technology and biomass energy with minimum percentages of 52.5% and 50.50%, respectively (these criteria were set in the country's long-term management plan but have not yet been implemented in practice). Thus, the researcher used the Path-GMM-Nearest-Neighbor model to forecast agricultural waste volumes for the next 20 years (2025-2044) and to forecast greenhouse gas emissions over the same period.