A Path Analysis—Generalized Method of Moments Based on a Nearest-Neighbor with Observed Variable Model for Developing New Scenario Policies to Reduce Greenhouse Gas Emissions from Agricultural Waste Towards Sustainability

This research aims to identify effective strategies for reducing greenhouse gas emissions from agricultural waste. It employs a quantitative research approach using an advanced model, the Path Analysis—Generalized Method of Moments Based on a Nearest-Neighbor with Observed Variable Model (Path-GMM-Nearest-Neighbor Model). This model incorporates white noise and addresses gaps in previous models, ensuring minimal forecasting errors. The findings highlight the need for the government to implement the most suitable policy scenario to achieve sustained reductions in agricultural waste over the next two decades (2025–2044). Additionally, we found that the Path-GMM-Nearest-Neighbor Model demonstrated the highest performance, exhibiting the lowest Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). Following in performance, in descending order, were the GM-ARIMA Model, Fuzzy Model, BP Model, ANN Model, and Regression Model. The optimal indices identified are green technology and biomass energy. Implementing these indices in national administration is projected to reduce agricultural waste growth to a rate of only 50.58% (2044/2025) while continuously decreasing greenhouse gas emissions, with an expansion rate limited to 43.68% (2044/2025). These measures ensure that emissions remain below Thailand’s carrying capacity threshold of 1560 Gg CO2e. Thus, adopting this strategy as a national policy will enable Thailand to sustainably advance toward a green economy in the future.