The Impact of Causal Factors Relationship over the Changes in Future Scenario Management under the Sustainability Policy of Thailand

Abstract

The objective of this research is to analyze the impact of causal factors relationship over the changes in future scenario management under the sustainability policy of Thailand by creating a model with validity called “Partial Least Square Path Modeling based on Autoregressive Integrated Moving Average with Observed Variables (PLS Path Modeling-ARIMAx).” The results showed that the three latent variables (economic, social, and environmental) were found to be causal related. From the PLS Path Modeling-ARIMAx (1,1,1), it is characterized as the best linear unbiased estimator (BLUE) with highest performance, where mean absolute percentage error (MAPE) equals to 1.55%, and root mean square error (RMSE) equals to 1.97% upon comparing them to other models. If the government implements this model to define a new scenario policy by stipulating future total energy consumption (2020-2039) below the national carrying capacity, with minimal error correction mechanism and great impact on model relationship, the future CO2 emission (2020-2039) is expected to drop a growth rate continuously. When a new scenario policy is determined, CO2 emission was found to increase at a growth rate of only 8.95% (2020/2039) or by 78.99 Mt CO2 Eq. (from 2020 to 2039) going below carrying capacity set off at 90.05 Mt CO2 Eq. (from 2020 to 2039). The result is clearly different in the absence of the new scenario policy.