The aim of this research is to forecast CO₂emissions from consumption of energy in Industry sectors in Thailand. To study, input-output tables based on Thailand for the years 2000 to 2015 are deployed to estimate CO₂emissions, population growth and GDP growth. Moreover, those are also used to anticipate the energy consumption for fifteen years and thirty years ahead. The ARIMAX Model is applied to two sub-models, and the result indicates that Thailand will have 14.3541 % on average higher in CO₂emissions in a fifteen-year period (2016-2030), and 31.1536 % in a thirty-year period (2016-2045). This study hopes to be useful in shaping future national policies and more effective planning. The researcher uses a statistical model called the ARIMAX Model, which is a stationary data model, and is a model that eliminates the problems of autocorrelations, heteroskedasticity, and multicollinearity. Thus, the forecasts will be made with minor error.

This research aims to forecast future economic and environmental growth for the next 16 years (2020–2035) according to the government’s strategic framework by applying the second order autoregressive-structural equation model (second order autoregressive-SEM). The model is validated by various measures, fits with the best model standards, meets all criteria of the goodness of fit, and is absent from any issues of heteroskedasticity, multicollinearity, autocorrelation, and non-normality. The proposed model is very distinct from other alternatives in that it produces the optimal outcome. Its mean absolute percentage error (MAPE) is 1.02% while the root mean square error (RMSE) is 1.51%. A comparison of the above results is carried out to compare the same values from other models, namely the regression linear model (ML model), back propagation neural network (BP model), artificial neural natural model (ANN model), gray model, and the autoregressive integrated moving average model (ARIMA model). The second order autoregressive-SEM is a model that is appropriate for long-term forecasting (2020–2035), and accounts for the specifics of the Thai government strategy set under the Industry 4.0 policy framework. The results of the long-term analysis indicate that the current political policy $(Politi)$ will result in continuous economic growth, where the gross national product (GNP) growth rate will climb up to 6.45% per annum by 2035, while the environment is being negatively affected. The study predicts that CO₂ emissions will rise up to 97.52 Mt CO₂ Eq. (2035). The forecasting model also reflects that the economy factor has an adjustment ability to equilibrium stronger than that of the environment factor; further, it shows that the relationship between the factors is causal. In addition, the political policy $(Politi)$ , economy $(Econ)$ , and environment $(Environ)$ factors are found to have both direct and indirect effects. As to the results, this study illustrates that the Industry 4.0 policy is still inefficient, as the carbon dioxide emissions are projected to be higher than the threshold for environment hazards and disasters which set to the limit of 80 Mt CO₂ Eq. by 2035. The effect of such policy will put the environment at risk, and the government must take immediate action to respond to this urgency. Thus, the second order autoregressive-SEM model remains a significant model embedded with the adjustment ability to equilibrium and the applicability for various contexts in different sectors. This introduced model is a vital tool for assisting the national government to create policy that is effective and sustainable, and lead to positive development of the nation. This second order autoregressive-SEM model can be used as a resource for the management of both public policy and private enterprise.

Sustainable development is part and parcel of development policy for Thailand, in order to promote growth along with economic growth, social advancement, and environmental security. Thailand has, therefore, established a national target to reduce CO₂ emissions below 20.8%, or not exceeding 115 Mt CO₂ Equivalent (Eq.) by 2029 within industries so as to achieve the country’s sustainable development target. Hence, it is necessary to have a certain measure to promote effective policies; in this case, a forecast of future CO₂ emissions in both the short and long run is used to optimize the forecasted result and to formulate correct and effective policies. The main purpose of this study is to develop a forecasting model, the so-called VARIMAX-ECM model, to forecast CO₂ emissions in Thailand, by deploying an analysis of the co-integration and error correction model. The VARIMAX-ECM model is adapted from the vector autoregressive model, incorporating influential variables in both short- and long-term relationships so as to produce the best model for better prediction performance. With this model, we attempt to fill the gaps of other existing models. In the model, only causal and influential factors are selected to establish the model. In addition, the factors must only be stationary at the first difference, while unnecessary variables will be discarded. This VARIMAX-ECM model fills the existing gap by deploying an analysis of a co-integration and error correction model in order to determine the efficiency of the model, and that creates an efficiency and effectiveness in prediction. This study finds that both short- and long-term causal factors affecting CO₂ emissions include per capita GDP, urbanization rate, industrial structure, and net exports. These variables can be employed to formulate the VARIMAX-ECM model through a performance test based on the mean absolute percentage error (MAPE) value. This illustrates that the VARIMAX-ECM model is one of the best models suitable for the future forecasting of CO₂ emissions. With the VARIMAX-ECM model employed to forecast CO₂ emissions for the period of 2018 to 2029, the results show that CO₂ emissions continue to increase steadily by 14.68%, or 289.58 Mt CO₂ Eq. by 2029, which is not in line with Thailand’s reduction policy. The MAPE is valued at 1.1% compared to the other old models. This finding indicates that the future sustainable development policy must devote attention to the real causal factors and ignore unnecessary factors that have no relationships to, or influences on, the policy. Thus, we can determine the right direction for better and effective development.

The aim of this research is to study the influence of factors affecting the efficiency of resource consumption under the sustainability policy based on using the DSEM-ARIMA (Dyadic Structural Equation Modeling based on the Autoregressive Integrated Moving Average) model. The study is performed using the Thailand experience. The research findings indicate that continuous economic growth aligns with the country’s objectives, directly contributing to continuous social growth. This aligns with the country’s efficient planning. It demonstrates that the management aligns with the goal of achieving Thailand 5.0. Furthermore, considering the environmental aspect, it is found that economic and social growth directly impacts the ecological aspect due to the significant influence of resource consumption in the construction. The resource consumption in construction shows a growth rate increase of 264.59% (2043/2024), reaching 401.05 ktoe (2043), which exceeds the carrying capacity limit set at 250.25 ktoe, resulting in significant long-term environmental degradation. Additionally, considering the political aspect, it is found to have the greatest influence on the environment, exacerbating environmental damage beyond current levels. Therefore, the DSEM-ARIMA model establishes a new scenario policy, indicating that resource consumption in construction leads to environmental degradation reduced to 215.45 ktoe (2043), which does not exceed the carrying capacity. Thus, if this model is utilized, it can serve as a vital tool in formulating policies to steer the country’s growth toward Thailand 5.0 effectively.

This study aims to analyze the forecasting of energy consumption in the Construction and Materials sectors. The scope of the study covers the forecasting periods of energy consumption for the next 10 years, 2017-2026, 20 years, 2017-2036, and 30 years, 2017-2046, by using ARIMAX Model. The...