A Relational Analysis Model of the Causal Factors Influencing CO2 in Thailand’s Industrial Sector under a Sustainability Policy Adapting the VARIMAX-ECM Model

Abstract

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 CO 2 emissions below 20.8%, or not exceeding 115 Mt CO 2 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 CO 2 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 CO 2 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 CO 2 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 CO 2 emissions. With the VARIMAX-ECM model employed to forecast CO 2 emissions for the period of 2018 to 2029, the results show that CO 2 emissions continue to increase steadily by 14.68%, or 289.58 Mt CO 2 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.

A Forecasting Model on Carrying Capacity for Government's Controlling Measure Under Environmental Law in Thailand: Adapting Non-Recursive Autoregression Based on the Var-X Model

“…As for its validity, the model was assessed by white noise testing and goodness of fit. The testing results confirm that the model falls within the accepted standards, availing itself to be the most appropriate model for the study (Sutthichaimethee and Kubaha, 2018a;Sutthichaimethee and Ariyasajjakorn, 2018). The research flow is explained below and is shown in Figure 1.…”

Regional sustainable development: The relationship between natural capital utilization and economic development

“…Thus, although research on economic development and natural capital utilization has become important and widespread, present studies emphasize the conflicting relationship between the natural ecosystem and economic development but neglect their interactive relationship. This omission could then be impacting the expected effects of related public policies (Sutthichaimethee & Kubaha, ). Our research objective looks to answer the following questions: If natural capital utilization and economic development are integrated in the relevant models, will the economic conclusions be different?…”

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].…”