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Guidelines for Increasing the Effectiveness of Thailand s Sustainable Development Policy based on Energy Consumption: Enriching the Path-GARCH Model

Author

Listed:
  • Pruethsan Sutthichaimethee

    (Institute for Population and Social Research, Mahidol University, Salaya, Thailand,)

  • Jindamas Sutthichaimethee

    (Ministry of Higher Education, Science, Research and Innovation, Bangkok 10400, Thailand,)

  • Chittinan Vutikorn

    (Faculty of Management Science, Phranakhon Rajabhat University, Bangkhen, Bangkok 10220, Thailand,)

  • Danupon Ariyasajjakorn

    (Faculty of Economics, Chulalongkorn University, Wang Mai, Khet Pathum Wan, Bangkok 10330, Thailand,)

  • Sirapatsorn Wongthongdee

    (Faculty of Public Administration, Dhurakij Pundit University, 110/1-4 Prachachuen Road, Laksi District, Bangkok 10210, Thailand,)

  • Srochinee Siriwattana

    (Suan Sunandha Rajabhat University, U-Thong nok Road, Dusit, Bangkok 10300, Thailand,)

  • Apinyar Chatchorfa

    (Department of Political Sciences, Faculty of Social Sciences, Mahachulalongkornrajavidyalaya University, Phahon Yothin Rd., Kilometer 55 Lam Sai, Wang Noi, Phra Nakhon Si Ayutthaya 13170, Thailand.)

  • Borworn Khomchunsri

    (Department of Political Sciences, Faculty of Social Sciences, Mahachulalongkornrajavidyalaya University, Phahon Yothin Rd., Kilometer 55 Lam Sai, Wang Noi, Phra Nakhon Si Ayutthaya 13170, Thailand.)

Abstract

The objective of this study is to develop a model for forecasting energy consumption and to increase the effectiveness of Thailand's sustainable development policy based on energy consumption by using the best model, the Path Analysis-Generalized Autoregressive Conditional Heteroscedasticity Model (Path-GARCH model). To improve the effectiveness of sustainability policies, the researcher has envisioned the final energy consumption over a 20-year period (AD 2023 2022) by defining a new scenario policy. Comparing the performance of the Path-GARCH model to other previous models, the Path-GARCH model was found to have the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE) values. In addition, the study found that energy consumption continued to rise to 125,055 ktoe by 2042, with a growth rate of 115.05% between 2042 and 2023, which exceeded the carrying capacity limit of 90,000 ktoe. When a new scenario policy is implemented, however, the final energy consumption continues to rise to 74,091 ktoe (2042). Consequently, defining a new scenario policy is a crucial development guideline for enhancing the effectiveness of Thailand's sustainable development policy.

Suggested Citation

  • 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.
  • Handle: RePEc:eco:journ2:2023-01-10
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    References listed on IDEAS

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    1. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    2. Pruethsan Sutthichaimethee & Kuskana Kubaha, 2018. "The Efficiency of Long-Term Forecasting Model on Final Energy Consumption in Thailand’s Petroleum Industries Sector: Enriching the LT-ARIMAXS Model under a Sustainability Policy," Energies, MDPI, vol. 11(8), pages 1-18, August.
    3. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
    4. Wang, Qiang & Li, Rongrong, 2017. "Decline in China's coal consumption: An evidence of peak coal or a temporary blip?," Energy Policy, Elsevier, vol. 108(C), pages 696-701.
    5. Shuyu Li & Rongrong Li, 2017. "Comparison of Forecasting Energy Consumption in Shandong, China Using the ARIMA Model, GM Model, and ARIMA-GM Model," Sustainability, MDPI, vol. 9(7), pages 1-19, July.
    6. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    7. Pruethsan Sutthichaimethee & Boonton Dockthaisong, 2018. "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," Resources, MDPI, vol. 7(4), pages 1-26, December.
    8. 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.
    9. Jialing Zou & Weidong Liu & Zhipeng Tang, 2017. "Analysis of Factors Contributing to Changes in Energy Consumption in Tangshan City between 2007 and 2012," Sustainability, MDPI, vol. 9(3), pages 1-14, March.
    10. Xiong, Ping-ping & Dang, Yao-guo & Yao, Tian-xiang & Wang, Zheng-xin, 2014. "Optimal modeling and forecasting of the energy consumption and production in China," Energy, Elsevier, vol. 77(C), pages 623-634.
    11. Jie Zhao & Nguyen Xuan Thinh & Cheng Li, 2017. "Investigation of the Impacts of Urban Land Use Patterns on Energy Consumption in China: A Case Study of 20 Provincial Capital Cities," Sustainability, MDPI, vol. 9(8), pages 1-22, August.
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    More about this item

    Keywords

    Sustainability policy; new scenarios policy; energy consumption; forecasting; carrying capacity;
    All these keywords.

    JEL classification:

    • P28 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - Natural Resources; Environment
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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