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 - Pruethsan Sutthichaimethee; Boonton Dockthaisong (Resources, 2018)
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 <semantics> ( G D P ) </semantics> , urbanization rate <semantics> ( U R E ) </semantics> , industrial structure <semantics> ( I S E ) </semantics> , net exports <semantics> ( X − E ) </semantics> , and indirect foreign investment <semantics> ( I F I ) </semantics> , while the social indicators include employment <semantics> ( E M S ) </semantics> , health and illness <semantics> ( H I S ) </semantics> , social security <semantics> ( SSS ) </semantics> , and consumer protection <semantics> ( CPS ) </semantics> . However, the environment has continuously deteriorated, as understood via environmental indicators such as energy consumption <semantics> ( E C E ) </semantics> , energy intensity <semantics> ( E I E ) </semantics> , and carbon dioxide emissions <semantics> ( C O 2 ) </semantics> . 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.