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

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-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)(2021)(2022)(2023)(2024)(2025)(2026)(2027)(2028)(2029)(2030)(2031)(2032)(2033)(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 2020-2034, constant with the smallest error correction mechanism, the future CO 2 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 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 2020-2034 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.

A hybrid model for forecasting the consumption of electrical energy in a smart grid

“…[30] proposed a combination of data mining techniques such as K‐Means, K‐Nearest Neighbours (KNN) and Autoregressive Integrated Moving Average (ARIMA) for Predicting electrical load. Other authors have also worked on conventional models for time series such as ARIMA model [31], VARIMA model [32] and Dynamic Approach [33]. Various works have been proposed with the aim of energy forecasting regarding the ARIMA model [34], Vector Autoregressive (VAR) model [35], variational mode decomposition and support vector machine based on quantum‐behaved particle swarm optimization [36], and Grey Model (GM) [37].…”

Demand-Supply Forecasting based on Deep Learning for Electricity Balance in Cameroon

“…Predicting electrical load by data mining techniques has also been studied by (Abubaker, 2021) who proposed a combination of these data mining techniques such as K-Means, K-Nearest Neighbours (KNN) and autoregressive integrated moving average (ARIMA). Other authors have also worked on conventional models for time series (Dritsaki1 et al, 2021;Sutthichaimethee and Wahab, 2021;Billah et al, 2021). Regarding the ARIMA, vector autoregressive (VAR) and Grey Models (GM), various works have been proposed with the aim of energy forecasting (Xu et al, 2015;Chaoqing et al, 2016;Feng et al, 2020;Yuan et al, 2016).…”