## 5 Discussion  
For this research, a model was developed with high validity and suitability for use as a decision-making tool, called the Path-GMM-Nearest-Neighbor model. This model effectively addresses the gaps of previous models and was specifically developed for long-term forecasting. Additionally, the model was tested for white noise to prevent spurious results. It was found that the model fits well with all evaluation criteria, which demonstrates that the relationship influences obtained from this research can be highly trusted and accurate. For this research, the researcher gained knowledge that allows for the analysis of the ability to adapt to equilibrium across different sectors. The economic sector was found to be the most responsive to adapting to equilibrium, followed by the civil politics sector, while the environmental sector adapted the slowest. Therefore, any changes affecting these sectors will reveal that the environmental sector will have the hardest time returning to normal conditions, or it may not be able to adapt at all, or it would require thousands of years to do so. This finding suggests that the government must strategically prioritize indicators for the new scenario policy. Through sensitivity analysis, it was found that green technology and biomass energy are the most valid and appropriate indicators for creating a new scenario policy for long-term national governance. Furthermore, this research aligns with previous studies such as those by [PERSON] and [PERSON] [20], [PERSON] et al. [49], and [PERSON] [54]. The research shows that when using the Path-GMM-Nearest-Neighbor model and implementing the new scenario policy, agricultural waste growth is projected to increase by 50.58% (2044/2025), which is below the carrying capacity threshold. This indicates that the indicators used in this model are suitable for forming long-term national strategies. Additionally, the growth rate of greenhouse gas emissions increased only by 43.68% (2044/2025), remaining below the carrying capacity. These results confirm the hypothesis and generate knowledge that will be highly beneficial for developing policies and long-term plans for Thailand. Furthermore, this research aligns with previous studies such as those by [PERSON] et al. [50], [PERSON] et al. [51], [PERSON] et al. [54], [PERSON] et al. [63], and [PERSON] and [PERSON] [64]. As for the recommendations, the research developed the Path-GMM-Nearest-Neighbor model and established a new scenario policy for improving agricultural efficiency. The government can apply this model to other sectors as well, but it must consider the indicators used in setting the new scenario policy. Accurate and appropriate indicator selection can address issues effectively. Therefore, when applying this model, it is crucial to consider the context and involve stakeholders to better understand the problems in-depth. Furthermore, Thailand must establish a comprehensive roadmap to ensure effective implementation and progress toward a green economy and the Thailand's Net Zero Roadmap in the agricultural sector. This research emphasizes the importance of applying the proposed model in long-term policy formulation to support decision-making processes. Urgent action is required, along with parallel efforts in effective livestock waste management, agricultural waste management, improved rice cultivation practices, efficient water resource management, smart agriculture implementation, intelligent renewable energy utilization, the promotion of organic fertilizers, and enhancing energy efficiency in agriculture. These measures must be implemented immediately and monitored rigorously to ensure sustainable progress toward achieving the Thailand's Net Zero Roadmap by 2065.