Forecasting Model of GHG Emission in Manufacturing Sectors of Thailand

Sutthichaimethee, Pruethsan; Ariyasajjakorn, Danupon

doi:10.12911/22998993/64566

Cited by 8 publications

(10 citation statements)

References 19 publications

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“…There are four parts in the model ARIMAX; Auto Regressive (AR), Moving Average (MA), Exogenous Variable and Integrated (I) [4], [5], [7]. The model comes with the following details.…”

Section: Arimax Modelmentioning

confidence: 99%

“…In order to manipulate the model to become more accurate and good in forecasting the energy consumption in the future, the researcher, thus, decides to use the Autocorrelation Integrated Moving Average model (ARIMAX Model) adapted from the ARIMA model (p, d, q) [7], [8], as follows:…”

Section: Arimax Modelmentioning

confidence: 99%

“…At the same time, consumption has the potential to increase too, especially in energy consumption. Thus, this high consumption in energy may affect the surroundings and destruct the environment [5]- [7]. Sutthichaimethee et al [4], [5] have found that, with economic growth, the population has increased their consumption, and the environmental damage is seen to rise.…”

Section: Introductionmentioning

confidence: 99%

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Forecast of Carbon Dioxide Emissions from Energy Consumption in Industry Sectors in Thailand

Sutthichaimethee

Ariyasajjakorn

2018

Environmental and Climate Technologies

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The aim of this research is to forecast CO2emissions from consumption of energy in Industry sectors in Thailand. To study, input-output tables based on Thailand for the years 2000 to 2015 are deployed to estimate CO2emissions, population growth and GDP growth. Moreover, those are also used to anticipate the energy consumption for fifteen years and thirty years ahead. The ARIMAX Model is applied to two sub-models, and the result indicates that Thailand will have 14.3541 % on average higher in CO2emissions in a fifteen-year period (2016-2030), and 31.1536 % in a thirty-year period (2016-2045). This study hopes to be useful in shaping future national policies and more effective planning. The researcher uses a statistical model called the ARIMAX Model, which is a stationary data model, and is a model that eliminates the problems of autocorrelations, heteroskedasticity, and multicollinearity. Thus, the forecasts will be made with minor error.

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“…There are four parts in the model ARIMAX; Auto Regressive (AR), Moving Average (MA), Exogenous Variable and Integrated (I) [4], [5], [7]. The model comes with the following details.…”

Section: Arimax Modelmentioning

confidence: 99%

Section: Arimax Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecast of Carbon Dioxide Emissions from Energy Consumption in Industry Sectors in Thailand

Sutthichaimethee

Ariyasajjakorn

2018

Environmental and Climate Technologies

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“…To know the right correlation of indicators and latent variables, the relationship of latent variables should be studied in advance. The form of specifications can be identified as follows [61]:…”

Section: Second Order Autoregressive-semmentioning

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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“…The authors, therefore, developed a new model called the SEM-VARIMAX model, which effectively incorporates various exogenous variables in different contexts or various sectors. The details of the SEM-VARIMAX model are explained as follows [107].…”

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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Forecasting Model of GHG Emission in Manufacturing Sectors of Thailand

Cited by 8 publications

References 19 publications

Forecast of Carbon Dioxide Emissions from Energy Consumption in Industry Sectors in Thailand

Forecast of Carbon Dioxide Emissions from Energy Consumption in Industry Sectors in Thailand

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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