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
| Autor: | Harlida Abdul Wahab, Pruethsan Sutthichaimethee |
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| Rok vydání: | 2021 |
| Předmět: |
Mean squared error
Latent variable Best linear unbiased prediction Energy industries. Energy policy. Fuel trade Moving-average model Environmental sciences General Energy Mean absolute percentage error Statistics Linear regression GE1-350 HD9502-9502.5 Autoregressive integrated moving average Path analysis (statistics) General Economics Econometrics and Finance Mathematics |
| Zdroj: | International Journal of Energy Economics and Policy, Vol 11, Iss 4, Pp 398-411 (2021) |
| ISSN: | 2146-4553 |
| DOI: | 10.32479/ijeep.9693 |
| Popis: | 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-VARIMA-OVi 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 to 2034, constant with the smallest error correction mechanism, the future CO2 emission growth rate during 2020 to 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 to 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. Keywords: Sustainable Development, energy consumption, Managing Future Scenarios, Forecasting Model, Carrying Capacity.JEL Classifications: P28, Q42, Q43, Q47, Q48DOI: https://doi.org/10.32479/ijeep.9693 |
| Databáze: | OpenAIRE |
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