## 4 Results
### Analysis of the Influence of Relationships in the Path-GMM-Nearest-Neighbor Model  
The analysis of the causal relationship influence in this research includes three factors: economic, civil politics, and environmental, as follows:  
From Table 3, it was found that the Path-GMM-Nearest-Neighbor model was validated and no issues with heteroskedasticity, multicollinearity, or autocorrelation were observed. Additionally, upon checking the goodness of fit, all values met the specified criteria: \(\chi^{2}/df\) is 1.00, \(RMR\) is 0.000, \(RMSEA\) is 0.01, \(GFI\) is 0.70, \(AGFI\) is 0.95, R-squared is 0.91, the F-statistic is 75.05 (probability is 0.00), the ARCH test is 15.05 (probability is 0.00), and the LM test is 1.82 (probability is 0.10). Therefore, the results of the relationship influence analysis can be summarized as follows: The civil politics sector has the greatest direct effect on the environmental sector at 0.73%, the economic sector has a direct effect on the environmental sector at 0.65%, the civil politics sector has a direct effect on the economic sector at 0.51%, and the economic sector has a direct effect on the civil politics sector at 0.21%. These findings indicate that changes in the civil politics sector by the government will have the greatest and most rapid impact on the environment. Based on these results, the researcher performed sensitivity analysis and examined the responsiveness to equilibrium, which is shown in the following diagram. Figure 3 presents the results of the analysis on the ability to adapt to equilibrium using the Path-GMM-Nearest-Neighbor Model. Under Thailand's sustainability policy, it was found that the economic sector had an error correction mechanism (ECM) value of 0.54, indicating that it can adjust to equilibrium at a rate of 0.54%, making it the fastest sector to regain stability. Following this, the civil politics sector had an ECM value of 0.31, showing an adjustment rate of 0.31% toward equilibrium. Lastly, the environmental sector exhibited the slowest adaptation, with an ECM value of 0.03, meaning it adjusts at a rate of only 0.03%. This finding highlights the severe difficulty in restoring environmental balance once damage occurs. For example, if an ecosystem is destroyed, causing significant biodiversity loss, recovery could take an exceptionally long time or, in some cases, may not be possible at all. This aligns with the Office of Natural Resources and Environmental Policy and Planning (ONEP), which defines systems with an ECM \(\leq\) 0.09, as requiring at least 1000 \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Dependent Variables**} & \multirow{2}{*}{**Type of Effect**} & \multicolumn{2}{l}{**Independent Variables**} & \multirow{2}{*}{**Environmental**} & \multirow{2}{*}{**Error Correction Mechanism \((\)_Ecm\()\)**} \\  & & & & & \\  & & & & & \\ \hline \multirow{3}{*}{Economic Civil politics} & DE & - & 0.51 *** & - & \(-\)0.54 *** \\  & IE & - & - & - & - \\ \cline{1-1}  & DE & 0.21 *** & - & - & \(-\)0.31 *** \\ \cline{1-1}  & IE & - & - & - & - \\ \cline{1-1}  & DE & 0.65 *** & 0.73 *** & - & \(-\)0.03 *** \\ \cline{1-1}  & IE & 0.11 *** & 0.19 *** & - & - \\ \hline \hline \end{tabular} Note: *** denotes significance \(\alpha\) = 0.01, DE is the direct effect and IE is the indirect effect.  
\end{table} 
Table 3: Results of the direct and indirect effect analysis from the Path-GMM-Nearest-Neighbor model. to 2000 years to return to equilibrium. In contrast, the economic sector can recover much more quickly, meaning that, when a shock occurs within the economic system, it has the ability to stabilize within a relatively short period. The new scenario policy is determined by conducting sensitivity analyses with all indicators to assess their appropriateness for use in shaping the new scenario policy. The analysis revealed that the factors with statistical significance at the 99% confidence level, both in the long and short term, include green technology and biomass energy.