A Relationship of Causal Factors in the Economic, Social, and Environmental Aspects Affecting the Implementation of Sustainability Policy in Thailand: Enriching the Path Analysis Based on a GMM Model

Abstract

This research aimed to analyze the influence of the direct and indirect relationships of economic, social, and environmental factors as well as predict their future effects by applying a path analysis of a generalized method of moments model (path analysis–GMM model). The model is believed to be the most effective in relationship analysis, as it is capable of accurate prediction compared to the original models. Most importantly, the model can be applied to different contexts, benefiting the development areas of those contexts. Furthermore, the model has also been found to be the best linear unbiased estimation (BLUE), which is suitable for long-term forecasting. However, the study’s results reflect that the three latent variables of economic, social, and environmental factors have direct and indirect effects. In addition, both economic and social factors were found to have causal relationships. The availability of the path analysis–GMM model enables us to forecast the social and economic changes over the next 20 years (2019–2038), and predict the change in energy-related CO2 emissions for the next 20 years (2019–2038). Thus, the study was able to discern the economic and social growth of Thailand. Studies have shown that the economic and social growth of Thailand has increased by 7.85%, based on various indicators. The economic indicators include per capita gross domestic product ( G D P ) , urbanization rate ( U R E ) , industrial structure ( I S E ) , net exports ( X − E ) , and indirect foreign investment ( I F I ) , while the social indicators include employment ( E M S ) , health and illness ( H I S ) , social security ( SSS ) , and consumer protection ( CPS ) . However, the environment has continuously deteriorated, as understood via environmental indicators such as energy consumption ( E C E ) , energy intensity ( E I E ) , and carbon dioxide emissions ( C O 2 ) . This is due to the increment of CO2 emissions in energy consumption of 39.37% (2038/2019) or 103.37 Mt CO2 eq. by 2038. However, by using the path analysis–GMM model to test for performance, it produced the mean absolute percentage error (MAPE) of 1.01% and a root mean square error (RMSE) of 1.25%. A comparison of the above results with other models, including the multiple regression model, grey model, artificial neural natural model (ANN model), back propagation neural network (BP model), and the autoregressive integrated moving average model (ARIMA model) provided evidence that the path analysis–GMM model was the most suitable in forecasting and contextual application to support the formulation of the national strategy in the future.

Analysis of Inclusive Economic Development of Environmental Quality in Indonesia by Examining Environmental Kuznets Curve

“…Economic indicators include the gross domestic product (GDP) per capita, urbanization rate, industrial structure, net exports, and indirect foreign investment. In contrast, social indicators include employment, health and disease, social security, and consumer protection, negative and significant effects on the environment (Sutthichaimethee and Dockthaisong, 2018). GDP per capita and Exports increase CO2 emissions per capita (Cheng et al, 2019).…”

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

“…The concept can be described as a Non-Recursive Vector autoregression based on an exogeneous model or "Non-Recursive Var-X" model. The research has utilized the linear structural relations (LISREL) model (Sutthichaimethee and Dockthaisong, 2018). As for its validity, the model was assessed by white noise testing and goodness of fit.…”

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

“…As a whole, this construct is called "Structural Equation Modeling-Vector Autoregressive with Exogeneous Variables Model" (SEM-VARIMAX Model). The research also utilizes Linear Structural Relations (LISREL) [93] software along with Econometric Views (EVIEWS) [94,95].…”

“…The research flow is explained below, as shown in Figure 1. [93] software along with Econometric Views (EVIEWS) [94,95]. The above-mentioned model is assessed in term of its Model Validity and Best Modelling, as well as "Best Linear Unbiased Estimated (BLUE) assessment.…”