Varimax Model to Forecast the Emission of Carbon Dioxide 
From Energy Consumption in Rubber and Petroleum Industries Sectors in Thailand

Sutthichaimethee, Pruethsan

doi:10.12911/22998993/70200

Cited by 14 publications

(10 citation statements)

References 16 publications

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“…Given that A 0 =B -1 Γ 0 , A 1 =B -1 Γ 1 and u t= B -1 ε t , then Equation ( 7) can be written as follows (Sutthichaimethee, 2017).…”

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

confidence: 99%

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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“…Given that A 0 =B -1 Γ 0 , A 1 =B -1 Γ 1 and u t= B -1 ε t , then Equation ( 7) can be written as follows (Sutthichaimethee, 2017).…”

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

confidence: 99%

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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“…In considering parameter γ t it is found that the increasing information of T will increase parameter value of γ t too. In case of time series X t at 2 time interval have correlation (or γ t 0) time series X t is memory of the process [60].…”

Section: Stationarymentioning

confidence: 99%

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

Sutthichaimethee

Chatchorfa

Suyaprom

2019

Journal of Open Innovation: Technology, Market, and Complexity

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This research aims to forecast future economic and environmental growth for the next 16 years (2020–2035) according to the government’s strategic framework by applying the second order autoregressive-structural equation model (second order autoregressive-SEM). The model is validated by various measures, fits with the best model standards, meets all criteria of the goodness of fit, and is absent from any issues of heteroskedasticity, multicollinearity, autocorrelation, and non-normality. The proposed model is very distinct from other alternatives in that it produces the optimal outcome. Its mean absolute percentage error (MAPE) is 1.02% while the root mean square error (RMSE) is 1.51%. A comparison of the above results is carried out to compare the same values from other models, namely the regression linear model (ML model), back propagation neural network (BP model), artificial neural natural model (ANN model), gray model, and the autoregressive integrated moving average model (ARIMA model). The second order autoregressive-SEM is a model that is appropriate for long-term forecasting (2020–2035), and accounts for the specifics of the Thai government strategy set under the Industry 4.0 policy framework. The results of the long-term analysis indicate that the current political policy will result in continuous economic growth, where the gross national product (GNP) growth rate will climb up to 6.45% per annum by 2035, while the environment is being negatively affected. The study predicts that CO2 emissions will rise up to 97.52 Mt CO2 Eq. (2035). The forecasting model also reflects that the economy factor has an adjustment ability to equilibrium stronger than that of the environment factor; further, it shows that the relationship between the factors is causal. In addition, the political policy , economy , and environment factors are found to have both direct and indirect effects. As to the results, this study illustrates that the Industry 4.0 policy is still inefficient, as the carbon dioxide emissions are projected to be higher than the threshold for environment hazards and disasters which set to the limit of 80 Mt CO2 Eq. by 2035. The effect of such policy will put the environment at risk, and the government must take immediate action to respond to this urgency. Thus, the second order autoregressive-SEM model remains a significant model embedded with the adjustment ability to equilibrium and the applicability for various contexts in different sectors. This introduced model is a vital tool for assisting the national government to create policy that is effective and sustainable, and lead to positive development of the nation. This second order autoregressive-SEM model can be used as a resource for the management of both public policy and private enterprise.

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“…There are 2 series of time series, which are Y t and Z t . They can be written in the VAR(p) model as shown below [105,106].…”

Section: Sem-varimax Modelmentioning

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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Varimax Model to Forecast the Emission of Carbon Dioxide From Energy Consumption in Rubber and Petroleum Industries Sectors in Thailand

Cited by 14 publications

References 16 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

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

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

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