A Forecasting Model for Economic Growth and CO2 Emission Based on Industry 4.0 Political Policy under the Government Power: Adapting a Second-Order Autoregressive-SEM

摘要: This research aims to forecast future economic and environmental growth for the next 16 years (2020–2035) according government’s strategic framework by applying second order autoregressive-structural equation model (second autoregressive-SEM). The is validated various measures, fits with best standards, meets all criteria of goodness fit, absent from any issues heteroskedasticity, multicollinearity, autocorrelation, non-normality. proposed very distinct other alternatives in that it produces optimal outcome. Its mean absolute percentage error (MAPE) 1.02% while root square (RMSE) 1.51%. A comparison above results carried out compare same values models, namely regression linear (ML model), back propagation neural network (BP artificial natural (ANN gray model, autoregressive integrated moving average (ARIMA model). autoregressive-SEM a appropriate long-term forecasting (2020–2035), accounts specifics Thai government strategy set under Industry 4.0 policy framework. analysis indicate current political ( P o l i t ) will result continuous growth, where gross national product (GNP) rate climb up 6.45% per annum 2035, environment being negatively affected. study predicts CO2 emissions rise 97.52 Mt Eq. (2035). also reflects economy factor has an adjustment ability equilibrium stronger than factor; further, shows relationship between factors causal. In addition, , E c n v r are found have both direct indirect effects. As results, this illustrates still inefficient, as carbon dioxide projected be higher threshold hazards disasters which limit 80 2035. effect such put at risk, must take immediate action respond urgency. Thus, remains significant embedded applicability contexts different sectors. introduced vital tool assisting create effective sustainable, lead positive development nation. can used resource management public private enterprise.