## 4 Empirical Analysis
### Formation of Analysis Modeling with the SEM-VALTM Model
These models include the Artificial Neural Network (ANN model), Multivariate Adaptive Regression Splines (MARP model), Gradient Boosting (GB model), Gray model, and Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX model). Performance was evaluated using statistical measures, such as MAPE and RMSE, as presented in the following Table 4:  
Table 4 presents the results of performance evaluation for the SEM-VALTM model and previously established models, including the ANN model, MARP model, GB model, Gray model, and ARIMAX model. The researcher utilized secondary data spanning from 1990 to 2024 and incorporated the factors outlined above. To ensure the highest effectiveness in performance measurement, all models were constructed using the same controlled factors. The research findings indicate that the SEM-VALTM model achieved the lowest MAPE and RMSE values compared to the other models, with values of 1.09% and 1.71%, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Forecasting Model** & **MAPE (\%)** & **RMSE (\%)** \\ \hline ANN model & 15.55 & 17.50 \\ MARP model & 12.25 & 15.05 \\ GB model & 9.05 & 10.15 \\ Gray model & 6.05 & 8.95 \\ ARIMAX model & 6.01 & 8.25 \\ SEM-VALTM model & 1.09 & 1.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance Measurement Results for Forecasting. Therefore, the researcher selected the SEM-VALTM model to forecast greenhouse gas emissions based on current energy consumption. This was used to compare the ecological damage resulting from greenhouse gas emissions under the new scenario policy, which emphasizes clean technology and renewable capabilities, as detailed below. Forecasting the Results of Total Energy Consumption and Greenhouse Gas Emissions Using the SEM-VALTM Model  
For this forecast, the researcher applied the SEM-VALTM model based on the newly formulated scenario policy to predict total energy consumption. The results of the total energy consumption forecast were then utilized to support the subsequent forecast of greenhouse gas emissions. 1. The total energy consumption forecast using the SEM-VALTM model for the period 2025 to 2037 is shown in Figure 4.  
From Figure 4, it is evident that total energy consumption between 2025 and 2037 shows a dramatic increase. In 2025, energy consumption was recorded at 55,872.90 ktoe, escalating sharply to 210,061.30 ktoe by 2037, with a growth rate (2037/2025) of 275.96%. This rate significantly exceeds the carrying capacity standard of 13,025.50 ktoe. However, when a new scenario policy was implemented within the SEM-VAL model, the energy consumption trend showed a marked reduction in growth. By 2037, the consumption will be limited to 95,027.89 ktoe, with a significantly lower growth rate (2037/2025) of 90.01%. This reduction demonstrates that the adjusted policies help ensure that future energy consumption aligns with sustainability goals. These findings were subsequently utilized to analyze trends in greenhouse gas emissions for the same period (2025 to 2037) to further evaluate the environmental impacts under different scenarios. 2. Forecasting Results of Greenhouse Gas Emissions Using the SEM-VALTM Model (2025-2037)  
From Figure 5, the forecasted trend in greenhouse gas emissions between 2025 and 2037 reveals a significant rise. In 2025, emissions were recorded at 26.05 Mt CO\({}_{2}\) Eq., increasing sharply to 95.05 Mt CO\({}_{2}\) Eq. in 2037, with a dramatic growth rate (2037/2025) of 264.88%. This growth rate far exceeds the carrying capacity threshold of 60.5 Mt CO\({}_{2}\)  
Figure 4: The forecasting results of total energy consumption from 2025 to 2037 in Thailand. Eq., posing a serious risk of ecological damage and surpassing the environment's ability to recover. According to risk management principles, immediate measures must be taken to mitigate greenhouse gas emissions. This research identified energy consumption in the environmental sector as the most influential factor driving greenhouse gas emissions.