文章基本信息

标题：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
本地全文：下载
作者：Pruethsan Sutthichaimethee ; Harlida Abdul Wahab
期刊名称：International Journal of Energy Economics and Policy
电子版ISSN：2146-4553
出版年度：2021
卷号：11
期号：4
页码：398-411
DOI：10.32479/ijeep.9693
出版社：EconJournals
摘要：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-OV i 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-OV i 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 CO 2 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 CO 2 Eq. (2020/2034), which is lower than a carrying capacity not exceeding 90.5 Mt CO 2 Eq. (2020-2034). This outcome differs clearly when there is no stipulation of the above scenario. Future CO 2 emission during 2020 to 2034 will increase at a rate of 40.32% or by 100.92 Mt CO 2 Eq. (2020/2034). However, when applying the Path Analysis-VARIMA-OV i 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-OV i model is found to be the most suitable tool for a policy management and planning to achieve a sustainability for Thailand.
其他摘要：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-OV i 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-OV i 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 CO 2 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 CO 2 Eq. (2020/2034), which is lower than a carrying capacity not exceeding 90.5 Mt CO 2 Eq. (2020-2034). This outcome differs clearly when there is no stipulation of the above scenario. Future CO 2 emission during 2020 to 2034 will increase at a rate of 40.32% or by 100.92 Mt CO 2 Eq. (2020/2034). However, when applying the Path Analysis-VARIMA-OV i 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-OV i model is found to be the most suitable tool for a policy management and planning to achieve a sustainability for Thailand.
其他关键词：Sustainable Development, Energy Consumption, Managing Future Scenarios, Forecasting Model, Carrying Capacity