@article{Sutthichaimethee_Ariyasajjakorn_2020, title={A Forecasting Model on Carrying Capacity for Government’s Controlling Measure under Environmental Law in Thailand: Adapting Non-Recursive Autoregression based on the Var-X Model}, volume={10}, url={https://econjournals.com/index.php/ijeep/article/view/9439}, abstractNote={<p>This research aimed to analyze the relationship of causal factors and forecast CO<sub>2</sub> emissions for a 15 years period from 2020 to 2034 by applying a non-recursive autoregression vector autoregression with an exogeneous model (Non-Recursive Var-X model). The Non-Recursive Var-X model has been made available for use in long-term forecasting (2020-2034), particularly in regards to the implementation of the ‘Industry 4.0’ policy of the Thai government. The study found that the results of the Thai government’s efforts or ‘governmental power’ (GP) will likely lead to levels of CO<sub>2</sub> emissions that exceed the country’s carrying capacity as determined under its national strategic plan. The findings of this study show that CO<sub>2</sub> emissions are expected to have a growth rate of 27.23 percent (2020-2034), reaching 95.88 Mt CO<sub>2</sub> Eq by 2034. The Non-Recursive Var-X model provides a mean absolute percentage error (MAPE) of 1.12% and a root mean square error (RMSE) of 1.25%. In this research, the Non-Recursive Var-X model was used and CO<sub>2</sub> emissions were forecasted to rise continuously over the established period. This rise exceeds the carrying capacity of Thailand according to the criteria set by the Thai government.</p><p class="MDPI18keywords"><strong>Keywords</strong><strong>:</strong><strong> </strong>Non-recursive model, Sustainability policy, Carrying capacity, Energy consumption<strong></strong></p><p class="MDPI19classification"><strong>JEL Classifications:</strong> P28, Q42, Q43, Q47, Q48</p><p class="MDPI19classification">DOI: <a href="https://doi.org/10.32479/ijeep.9439">https://doi.org/10.32479/ijeep.9439</a></p>}, number={6}, journal={International Journal of Energy Economics and Policy}, author={Sutthichaimethee, Pruethsan and Ariyasajjakorn, Danupon}, year={2020}, month={Oct.}, pages={645–655} }