Forecasting Economic, Social and Environmental Growth in the Sanitary and Service Sector Based on Thailand's Sustainable Development Policy

Sutthichaimethee, Pruethsan

doi:10.12911/22998993/79407

Cited by 5 publications

(7 citation statements)

References 14 publications

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“…Figure 2 indicates the casual factor relationship of the Modern Path Analysis-based on VARIMA-OV i model (Sutthichaimethee, 2018) From the above diagram, it can be seen that variable 1 and 2 are exogenous variables, because their variation is not caused by any other factors in the path. In another word, variable 1 and 2 are to affect other variables, which are variable 3 and 4.…”

Section: The Materials and Methodsmentioning

confidence: 99%

“…This is because variable 3 and 4 are separately affected by external variables (variable 1 and 2). The endogenous variable (variable 3) and the other two exogenous variables (variable 1 and 2) are both independently correlated, and that is known as correlated causes (Sims, 1980Byrne, 2009Sutthichaimethee, 2018.…”

Section: The Materials and Methodsmentioning

confidence: 99%

“…Assuming there are two time series, and both are I (0), affecting each other in the following form (Sutthichaimethee, 2018 (2)…”

Section: The Varima -Ov I Model At Level 1: Varima -Ov (1)mentioning

confidence: 99%

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A Forecasting Model in Managing Future Scenarios to Achieve the Sustainable Development Goals of Thailand’s Environmental Law: Enriching the Path Analysis-Varima-Ovi Model

Sutthichaimethee

Wahab²

2021

IJEEP

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The objective of this study is to develop a forecasting model for causal factors management in the future in to order to achieve sustainable development goals. This study applies a validity-based concept and the best model called "Path analysis based on vector autoregressive integrated moving average with observed variables" (Path Analysis-VARIMA-OV i Model). The main distinguishing feature of the proposed model is the highly efficient coverage capacity for different contexts and sectors. The model is developed to serve long-term forecasting (2020)(2021)(2022)(2023)(2024)(2025)(2026)(2027)(2028)(2029)(2030)(2031)(2032)(2033)(2034). The results of this study show that all three latent variables (economic growth, social growth, and environmental growth) are causally related. Based on the Path Analysis-VARIMA-OV i Model, the best linear unbiased estimator (BLUE) is detected when the government stipulates a new scenario policy. This model presents the findings that if the government remains at the current future energy consumption levels during 2020-2034, constant with the smallest error correction mechanism, the future CO 2 emission growth rate during 2020-2034 is found to increase at the reduced rate of 8.62% (2020/2034) or equivalent to 78.12 Mt CO 2 Eq. (2020/2034), which is lower than a carrying capacity not exceeding 90.5 Mt CO 2 Eq. (2020-2034). This outcome differs clearly when there is no stipulation of the above scenario. Future CO 2 emission during 2020-2034 will increase at a rate of 40.32% or by 100.92 Mt CO 2 Eq. (2020/2034). However, when applying the Path Analysis-VARIMA-OV i Model to assess the performance, the mean absolute percentage error (MAPE) is estimated at 1.09%, and the root mean square error (RMSE) is estimated at 1.55%. In comparison with other models, namely multiple regression model (MR model), artificial neural network model (ANN model), back-propagation neural network model (BP model), fuzzy analysis network process model (FANAP model), gray model (GM model), and gray-autoregressive integrated moving average model (GM-ARIMA model), the Path Analysis-VARIMA-OV i model is found to be the most suitable tool for a policy management and planning to achieve a sustainability for Thailand.

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Section: The Materials and Methodsmentioning

confidence: 99%

Section: The Materials and Methodsmentioning

confidence: 99%

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A Forecasting Model in Managing Future Scenarios to Achieve the Sustainable Development Goals of Thailand’s Environmental Law: Enriching the Path Analysis-Varima-Ovi Model

Sutthichaimethee

Wahab²

2021

IJEEP

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“…When the two models are completely constructed, they may be presented in the form of equation or path diagram for better comprehension and validation. Under this step, Model Identification is extended as another significant consideration lying out important properties as follows (Sutthichaimethee, 2018).…”

Section: Model Specificationmentioning

confidence: 99%

Analyzing the Impact of Causal Factors on Political Management to Determine Sustainability Policy under Environmental Law: Enriching the Covariance-based SEMxi Model

Sutthichaimethee

Jittawiriyanukoon

2022

IJEEP

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This research aims to develop a causal relationship model on political management for sustainability policy formation under Thai environmental law by applying the best and valid model with a non-spurious property called the Covariance-based on Structural Equation Model with exogenous variables (Covariance-based SEMxi Model). This newly-developed model is in distinction with any past models as it is made effectively applicable to any sectors across areas. The model can also be utilized to design a long-term forecasting model with the ability to determine appropriate future scenarios. When assessing the covariance-based SEMxi model performance, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) are estimated at 1.19% and 1.30%, respectively, in comparison of other models, including Gray-Autoregressive Integrated Moving Average Model (GM-ARIMA), Gray Model (GM), Back Propagation Neural Network (BP), Artificial Neural Natural Model (ANN), and Multiple Regression Model (MR). As for the results, this research reveals a direct impact of economic factors on environmental and social factors. In the meanwhile, social factors have a direct impact on environmental and economic factors. The research also indicates a direct effect on the environment with a maximal magnitude of 67%. Whereas a direct effect of social factors on the environment is detected at the magnitude of 55%. These effects are perceived to exceed the specified carrying capacity set by Thailand. In addition, a causal relationship is observed between economic and social factors, where the environment is found with the lowest error correction capability of only 5 percent. At the same time, economic and social factors are noticed with greater correction capability of 59% and 31%, respectively. This finding implies that the ecosystem will experience slow recovery whenever it deteriorates. Hence, the government must place a higher concentration on the environment, while different measures on environmental legislation should be closely controlled to contain any future damage. Besides, energy consumption must be managed not to exceed the established carrying capacity by simultaneously implementing both proactive and reactive measures. This process can be strengthened by optimizing the newly-introduced model produced by this work for a scenario design in policy management to attain sustainability.

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“…With the investigation of Sapukotanage et al [35] on the sustainable practices of the manufacturing firms in a developing nation in South Asia, there was evidence of such sustainable practices leading towards sustainable performance. Sutthichaimethee [36] predicted the sustainable development policy implementation in the sanitary and service sectors of Thailand by 2045 with the result of potential growth of Thai economy system by 25.76% along with changes. Gradually, the Greenhouse gas emissions are found to increase by 49.65%.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Sutthichaimethee¹,

Naluang²

2019

Energies

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This research aims to predict the efficiency of the Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand for the next 17 years (2020–2036) and analyze the relationships among causal factors by applying a structural equation modeling/vector autoregressive model with exogenous variables (SEM-VARIMAX Model). This model is effective for analyzing relationships among causal factors and optimizing future forecasting. It can be applied to contexts in different sectors, which distinguishes it from other previous models. Furthermore, this model ensures the absence of heteroskedasticity, multicollinearity, and autocorrelation. In fact, it meets all the standards of goodness of fit. Therefore, it is suitable for use as a tool for decision-making and planning long-term national strategies. With the implementation of the Sustainable Development Policy for Energy Consumption under Environmental Law ( S . D . E L ) , the forecast results derived from the SEM-VARIMAX Model indicate a continuously high change in energy consumption from 2020 to 2036the change exceeds the rate determined by the government. In addition, energy consumption is predicted to have an increased growth rate of up to 185.66% (2036/2020), which is about 397.08 ktoe (2036). The change is primarily influenced by a causal relationship that contains latent variables, namely, the economic factor ( E C O N ) , social factor ( S O C I ) , and environmental factor ( E N V I ) . The performance of the SEM-VARIMAX Model was tested, and the model produced a mean absolute percentage error (MAPE) of 1.06% and a root-mean-square error (RMSE) of 1.19%. A comparison of these results with those of other models, including the multiple linear regression model (MLR), back-propagation neural network (BP model), grey model, artificial neural natural model (ANN model), and the autoregressive integrated moving average model (ARIMA model), indicates that the SEM-VARIMAX model fits and is appropriate for long-term national policy formulation in various contexts in Thailand. This study’s results further indicate the low efficiency of Sustainable Development Policy for Energy Consumption under Environmental Law in Thailand. The predicted result for energy consumption in 2036 is greater than the government-established goal for consumption of no greater than 251.05 ktoe.

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Forecasting Economic, Social and Environmental Growth in the Sanitary and Service Sector Based on Thailand's Sustainable Development Policy

Cited by 5 publications

References 14 publications

A Forecasting Model in Managing Future Scenarios to Achieve the Sustainable Development Goals of Thailand’s Environmental Law: Enriching the Path Analysis-Varima-Ovi Model

A Forecasting Model in Managing Future Scenarios to Achieve the Sustainable Development Goals of Thailand’s Environmental Law: Enriching the Path Analysis-Varima-Ovi Model

Analyzing the Impact of Causal Factors on Political Management to Determine Sustainability Policy under Environmental Law: Enriching the Covariance-based SEMxi Model

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

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