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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

Author

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  • Pruethsan Sutthichaimethee

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

  • Harlida Abdul Wahab

    (School of Law, Government and International Studies, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia.)

Abstract

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-OVi 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 (2020-2034). 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-VARIMAOVi 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 CO2 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 CO2 Eq. (2020/2034), which is lower than a carrying capacity not exceeding 90.5 Mt CO2 Eq. (2020-2034). This outcome differs clearly when there is no stipulation of the above scenario. Future CO2 emission during 2020-2034 will increase at a rate of 40.32% or by 100.92 Mt CO2 Eq. (2020/2034). However, when applying the Path Analysis-VARIMA-OVi 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-OVi model is found to be the most suitable tool for a policy management and planning to achieve a sustainability for Thailand.

Suggested Citation

  • 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.
    • Handle: RePEc:eco:journ2:2021-04-46
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    More about this item

    Keywords

    Sustainable Development; energy consumption; Managing Future Scenarios; Forecasting Model; 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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