The Thailand Development Policy focuses on the simultaneous growth of the economy, society, and environment. Long-term goals have been set to improve economic and social well-being. At the same time, these aim to reduce the emission of CO₂ in the future, especially in the construction sector, which is deemed important in terms of national development and is a high generator of greenhouse gas. In order to achieve national sustainable development, policy formulation and planning is becoming necessary and requires a tool to undertake such a formulation. The tool is none other than the forecasting of CO₂ emissions in long-term energy consumption to produce a complete and accurate formulation. This research aims to study and forecast energy-related carbon dioxide emissions in Thailand’s construction sector by applying a model incorporating the long- and short-term auto-regressive (AR), integrated (I), moving average (MA) with exogenous variables (Xi) and the error correction mechanism (LS-ARIMAXi-ECM) model. This model is established and attempts to fill the gaps left by the old models. In fact, the model is constructed based on factors that are causal and influential for changes in CO₂ emissions. Both independent variables and dependent variables must be stationary at the same level. In addition, the LS-ARIMAXi-ECM model deploys a co-integration analysis and error correction mechanism (ECM) in its modeling. The study’s findings reveal that the LS-ARIMAXi $\left(2,1,1,X_{t-1}\right)$ -ECM model is a forecasting model with an appropriate time period (t − i), as justified by the Q-test statistic and is not a spurious model. Therefore, it is used to forecast CO₂ emissions for the next 20 years (2019 to 2038). From the study, the results show that CO₂ emissions in the construction sector will increase by 37.88% or 61.09 Mt CO₂ Eq. in 2038. Also, the LS-ARIMAXi $\left(2,1,1,X_{t-1}\right)$ -ECM model has been evaluated regarding its performance, and it produces a mean absolute percentage error (MAPE) of 1.01% and root mean square error (RMSE) of 0.93% as compared to the old models. Overall, the results indicate that determining future national sustainable development policies requires an appropriate forecasting model, which is built upon causal and contextual factors according to relevant sectors, to serve as an important tool for future sustainable planning.

This study aims to analyze the influence of the relationship between causal factors that affect the future equilibrium of the total final energy consumption in the construction sector of Thailand under the sustainable development policy for the period of 10 years (2019–2028). This analysis was achieved with the application of the Structure Equilibrium-Vector Autoregressive with Exogenous Variables model (SE-VAR_X model). This model was developed to fill research gaps and differs from those of previous studies. In the selection of variables, the study focused on Sustainable Development (SD)-based variables available through the lens of Thailand. The exogenous variables included real GDP, population growth, urbanization rate, industrial structure, oil price, foreign direct investment, international tourist arrivals, and total exports and imports. Every variable had a co-integration at level (1) and was used to structure the SE-VAR_X model. This particular model can effectively analyze the influence of the direct relationship and meet the criteria of goodness of fit without spuriousness. This SE-VAR_X model allowed us to discover that every variable in the model had an influence on the equilibrium change, where the real GDP is the fastest variable to adjust to the equilibrium while the total final energy consumption has the slowest adjustment ability. The SE-VAR_X model can be used to project the total final energy consumption, as verified by the performance test result. The test was measured based on the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), and their results were 1.09% and 1.01%, respectively. This performance result had the highest value compared to other models in the past. Thus, the SE-VAR_X model is suitable for forecasting over the next 10 years (2019–2038). The results of this study reveal that the total final energy consumption in the construction sector of Thailand will exhibit a continuously increasing growth rate from 2019 to 2028, amounting to about 144.29% or equivalent to 364.01 ktoe. In addition, the study also found that future government plans may be difficult to achieve as planned. Therefore, the introduced model should be integrated into national development planning and strategies to achieve sustainable development in the future and to enable its application to other sectors.