The Efficiency of Long-Term Forecasting Model on Final Energy Consumption in Thailand’s Petroleum Industries Sector: Enriching the LT-ARIMAXS Model under a Sustainability Policy

Abstract

Presently, Thailand runs various sustainable development-based policies to boost the growth in economy, society, and environment. In this study, the economic and social growth was found to continuously increase and negatively deteriorate the environment at the same time due to a more massive final energy consumption in the petroleum industries sector than any other sectors. Therefore, it is necessary to establish national planning and it requires an effective forecasting model to support Thailand’s policy-making. This study aimed to construct a forecasting model for a final energy consumption prediction in Thailand’s petroleum industry sector for a longer-term (2018–2037) at a maximum efficiency from a certain class of methods. The Long Term-Autoregressive Integrated Moving Average with Exogeneous variables and Error Correction Mechanism model (LT-ARIMAXS model) (p, d, q, Xi, ECT(t−1)) was adapted from the autoregressive and moving average model incorporating influential variables together in both long-term relationships to produce the best model for prediction performance. All relevant variables in the model are stationary at Level I(0) or Level I(1). In terms of the extraneous variables, they consist of per capita GDP, population growth, oil price, energy intensity, urbanization rate, industrial structure, and net exports. The study found that the variables used are the causal factors and stationary at the first difference as well as co-integrated. With such features, it reflects that the variables are influential over the final energy consumption. The LT-ARIMAXS model (2,1,2) determined a proper period (t − i) through a white noise process with the Q test statistical method. It shows that the LT-ARIMAXS model (2,1,2) does not generate the issues of heteroskedasticity, multicollinearity, and autocorrelation. The performance of LT-ARIMAXS model (2,1,2) was tested based on the mean absolute percentage error (MAPE) and the root mean square error (RMSE). The LT-ARIMAXS model (2,1,2) can predict the final energy consumption based on the Sustainable Development Plan for the 20 years from 2018 to 2037. The results showed that the final energy consumption continues to increase steadily by 121,461 ktoe in 2037. Furthermore, the findings present that the growth rate (2037/2017) increases by 109.8%, which is not in line with Thailand’s reduction policy. In this study, the MAPE was valued at 0.97% and RMSE was valued at 2.12% when compared to the other old models. Therefore, the LT-ARIMAXS model (2,1,2) can be useful and appropriate for policy-making to achieve sustainability.

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

“…In addition, this should agree with the regression equation, in which there is no problem of multicollinearity, no problem of heteroscedasticity, no problem on autocorrelation, and no problem of non-normality. The formative model differs from the reflective model as follows [4,62]:…”

“…These include measures in the industries of transportation, textiles, iron and steel, and other industrial products that are exclusively outside of the agricultural sector. In addition, with respect to the long run, the government has implemented a number of different strategies, such as promoting exports and diversifying products for export, encouraging foreign direct investment, developing the tourism industry, improving labor skills for heavy industry, expanding local markets for foreign investors, adjusting the tax basis, and developing large industries of the government among many others [4].…”

The Efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand: Adapting the SEM-VARIMAX Model

“…In addition to this, the authors conducted other studies to support the forecasting models with different indicators. Those studies are titled "The efficiency of long-term forecasting model on final energy consumption in Thailand's petroleum industries sector: enriching the LT-ARIMAXS Model under a sustainability policy" [91] and "A relational analysis model of the causal factors influencing CO 2 in Thailand's industrial sector under a sustainability policy adapting the VARIMAX-ECM Model" [92]. The mentioned studies used a stationary process, while adapting the concept of a co-integration and error correction mechanism in order to analyze Energies 2019, 12, 3092 8 of 21 the real impact of the indicators on the dependent variable.…”

“…It can be observed that the number of parameters in the VAR(1) model is less than the SEM-VARIMAX(1) model's. Even though all the parameters can be estimated in the VAR(1) model, we still cannot use the relationship between the parameters of both models to find the parameter estimator in the SEM-VARIMAX(1) model [91,92].…”

Modeling and Simulation of Carbon Emission-Related Issues

“…Sutthichaimethee and Kubaha [30] applied LT-ARIMAXS (the Long Term-Autoregressive Integrated Moving Average with Exogeneous variables and Error Correction Mechanism model) to conduct energy consumption long-term forecasting for the petroleum industry in Thailand.…”