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A Relational Analysis Model of the Causal Factors Influencing CO 2 in Thailand’s Industrial Sector under a Sustainability Policy Adapting the VARIMAX-ECM Model

Author

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  • Pruethsan Sutthichaimethee

    (Division of Energy Management Technology, School of Energy, Environment and Materials, King Mongkut’s University of Technology Thonburi, 126 Pracha Uthit Rd., Bang Mod, Thung Khru, Bangkok 10140, Thailand)

  • Kuskana Kubaha

    (Division of Energy Management Technology, School of Energy, Environment and Materials, King Mongkut’s University of Technology Thonburi, 126 Pracha Uthit Rd., Bang Mod, Thung Khru, Bangkok 10140, Thailand)

Abstract

Sustainable development is part and parcel of development policy for Thailand, in order to promote growth along with economic growth, social advancement, and environmental security. Thailand has, therefore, established a national target to reduce CO 2 emissions below 20.8%, or not exceeding 115 Mt CO 2 Equivalent (Eq.) by 2029 within industries so as to achieve the country’s sustainable development target. Hence, it is necessary to have a certain measure to promote effective policies; in this case, a forecast of future CO 2 emissions in both the short and long run is used to optimize the forecasted result and to formulate correct and effective policies. The main purpose of this study is to develop a forecasting model, the so-called VARIMAX-ECM model, to forecast CO 2 emissions in Thailand, by deploying an analysis of the co-integration and error correction model. The VARIMAX-ECM model is adapted from the vector autoregressive model, incorporating influential variables in both short- and long-term relationships so as to produce the best model for better prediction performance. With this model, we attempt to fill the gaps of other existing models. In the model, only causal and influential factors are selected to establish the model. In addition, the factors must only be stationary at the first difference, while unnecessary variables will be discarded. This VARIMAX-ECM model fills the existing gap by deploying an analysis of a co-integration and error correction model in order to determine the efficiency of the model, and that creates an efficiency and effectiveness in prediction. This study finds that both short- and long-term causal factors affecting CO 2 emissions include per capita GDP, urbanization rate, industrial structure, and net exports. These variables can be employed to formulate the VARIMAX-ECM model through a performance test based on the mean absolute percentage error (MAPE) value. This illustrates that the VARIMAX-ECM model is one of the best models suitable for the future forecasting of CO 2 emissions. With the VARIMAX-ECM model employed to forecast CO 2 emissions for the period of 2018 to 2029, the results show that CO 2 emissions continue to increase steadily by 14.68%, or 289.58 Mt CO 2 Eq. by 2029, which is not in line with Thailand’s reduction policy. The MAPE is valued at 1.1% compared to the other old models. This finding indicates that the future sustainable development policy must devote attention to the real causal factors and ignore unnecessary factors that have no relationships to, or influences on, the policy. Thus, we can determine the right direction for better and effective development.

Suggested Citation

  • Pruethsan Sutthichaimethee & Kuskana Kubaha, 2018. "A Relational Analysis Model of the Causal Factors Influencing CO 2 in Thailand’s Industrial Sector under a Sustainability Policy Adapting the VARIMAX-ECM Model," Energies, MDPI, vol. 11(7), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1704-:d:155503
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    Cited by:

    1. Pruethsan Sutthichaimethee & Chanintorn Jittawiriyanukoon, 2022. "Analyzing the Impact of Causal Factors on Political Management to Determine Sustainability Policy under Environmental Law: Enriching the Covariance-based SEMxi Model," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 282-293, July.
    2. Pruethsan Sutthichaimethee & Harlida Abdul Wahab, 2021. "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," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 398-411.
    3. Pruethsan Sutthichaimethee & Danupon Ariyasajjakorn, 2021. "The Management Efficiency of the Sustainable Development Policy under Thailand s Energy Law: Enriching the SEM-based on the ARIMAXi model," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 472-482.
    4. Pruethsan Sutthichaimethee & Jindamas Sutthichaimethee & Chittinan Vutikorn & Danupon Ariyasajjakorn & Sirapatsorn Wongthongdee & Srochinee Siriwattana & Apinyar Chatchorfa & Borworn Khomchunsri, 2023. "Guidelines for Increasing the Effectiveness of Thailand s Sustainable Development Policy based on Energy Consumption: Enriching the Path-GARCH Model," International Journal of Energy Economics and Policy, Econjournals, vol. 13(1), pages 67-74, January.
    5. Pruethsan Sutthichaimethee, 2024. "A Framework on Setting Strategies for Enhancing the Efficiency of State Power use in Thailand’s Pursuit of a Green Economy," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 108-120, January.
    6. Pruethsan Sutthichaimethee & Chanintorn Jittawiriyanukoon, 2022. "The Impact of Causal Factors Relationship over the Changes in Future Scenario Management under the Sustainability Policy of Thailand," International Journal of Energy Economics and Policy, Econjournals, vol. 12(5), pages 36-46, September.

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