A Framework on Setting Strategies for Enhancing the Efficiency of State Power use in Thailand’s Pursuit of a Green Economy

The objectives of this study are to investigate the efficiency of state power use in governing the country towards a green economy and to examine proactive strategies to enhance the efficiency of state power use. This study employs a mixed-methods research approach, including quantitative research involving the construction of a model, SEM-LCM-VECM, to assess the above efficiency. Additionally, the findings from quantitative research are integrated into qualitative research to formulate proactive strategies for exercising state power to foster sustainable development. The findings indicate that the use of state power for the development of a green economy, in accordance with the 20-Year National Strategic Plan and various development strategies of Thailand, has proven to be inefficient. This inefficiency stems from continuous growth in the economic and social sectors, while the environmental sector has consistently deteriorated. The most significant contributing factor directly impacting the environment is the economic sector, followed by the social sector. Moreover, Thailand's adaptability towards sustainability has been notably slow and falls below the established standards. If the government continues to use state power and pursue policies in a manner similar to the past, it is likely to have severe adverse consequences for the environment. This is due to the fact that reactive measures, including civil measures, administrative measures, and criminal measures, cannot effectively facilitate the development of a green economy. Therefore, the guidelines for addressing and formulating proactive strategies are of paramount importance and highly necessary for achieving sustainability. Research findings suggest that the government must establish reactive measures alongside proactive measures in economic aspects. These measures include 1) taxation and revenue collection; 2) subsidies and tax incentives; 3) financial enforcement incentives; 4) deposit systems and refund mechanisms; and 5) ownership and market creation systems. The study also reveals that countries efficiently implementing these economic measures for sustainability include European nations and Asian countries such as South Korea and Japan. Consequently, Thailand should consider applying the research findings to appropriately and efficiently shape the use of state power before the nation causes further irreparable damage. It is imperative that these proactive measures are pursued diligently and continuously to promote green economy policies and ensure sustainability in both the present and future

Guidelines for Increasing the Effectiveness of Thailand’s Sustainable Development Policy based on Energy Consumption: Enriching the Path-GARCH Model

The objective of this study is to develop a model for forecasting energy consumption and to increase the effectiveness of Thailand's sustainable development policy based on energy consumption by using the best model, the Path Analysis-Generalized Autoregressive Conditional Heteroscedasticity Model (Path-GARCH model). To improve the effectiveness of sustainability policies, the researcher has envisioned the final energy consumption over a 20-year period (AD 2023–2022) by defining a new scenario policy. Comparing the performance of the Path-GARCH model to other previous models, the Path-GARCH model was found to have the lowest mean absolute percentage error (MAPE) and root mean square error (RMSE) values. In addition, the study found that energy consumption continued to rise to 125,055 ktoe by 2042, with a growth rate of 115.05% between 2042 and 2023, which exceeded the carrying capacity limit of 90,000 ktoe. When a new scenario policy is implemented, however, the final energy consumption continues to rise to 74,091 ktoe (2042). Consequently, defining a new scenario policy is a crucial development guideline for enhancing the effectiveness of Thailand's sustainable development policy

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

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</p

Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model

The Thailand Development Policy focuses on the simultaneous growth of the economy, society, and environment. Long-term goals have been set to improve economic and social well-being. At the same time, these aim to reduce the emission of CO2 in the future, especially in the construction sector, which is deemed important in terms of national development and is a high generator of greenhouse gas. In order to achieve national sustainable development, policy formulation and planning is becoming necessary and requires a tool to undertake such a formulation. The tool is none other than the forecasting of CO2 emissions in long-term energy consumption to produce a complete and accurate formulation. This research aims to study and forecast energy-related carbon dioxide emissions in Thailand&rsquo;s construction sector by applying a model incorporating the long- and short-term auto-regressive (AR), integrated (I), moving average (MA) with exogenous variables (Xi) and the error correction mechanism (LS-ARIMAXi-ECM) model. This model is established and attempts to fill the gaps left by the old models. In fact, the model is constructed based on factors that are causal and influential for changes in CO2 emissions. Both independent variables and dependent variables must be stationary at the same level. In addition, the LS-ARIMAXi-ECM model deploys a co-integration analysis and error correction mechanism (ECM) in its modeling. The study&rsquo;s findings reveal that the LS-ARIMAXi ( 2 , 1 , 1 , X t &minus; 1 ) -ECM model is a forecasting model with an appropriate time period (t &minus; i), as justified by the Q-test statistic and is not a spurious model. Therefore, it is used to forecast CO2 emissions for the next 20 years (2019 to 2038). From the study, the results show that CO2 emissions in the construction sector will increase by 37.88% or 61.09 Mt CO2 Eq. in 2038. Also, the LS-ARIMAXi ( 2 , 1 , 1 , X t &minus; 1 ) -ECM model has been evaluated regarding its performance, and it produces a mean absolute percentage error (MAPE) of 1.01% and root mean square error (RMSE) of 0.93% as compared to the old models. Overall, the results indicate that determining future national sustainable development policies requires an appropriate forecasting model, which is built upon causal and contextual factors according to relevant sectors, to serve as an important tool for future sustainable planning

Forecasting Economic, Social and Environmental Growth in the Sanitary and Service Sector Based on Thailand's Sustainable Development Policy

The purpose of this study is to forecast the long run implementation of Thailand’s sustainable development policy in three main aspects, including economic, social and environmental aspect for the the sanitary and service sectors from 2016 until 2045. According to the national data for the years 2000–2015, based on the ARIMAX model, it has been found that Thai economy system is potentially changed and growing rapidly by 25.76%, the population has grown by 7.15%, and the Greenhouse gas emissions will gradually increase by 49.65%, in the year 2045. However, based on the analysis above, if Thailand fails to run the afore-mentioned policy properly, it will be difficulto successfully implement sustainable development, because the increased emission is moving in the same direction with economy and social aspect of Thailand

Forecasting Energy Consumption in Short-Term and Long-Term Period by Using Arimax Model in the Construction and Materials Sector in Thailand

This study aims to analyze the forecasting of energy consumption in the Construction and Materials sectors. The scope of the study covers the forecasting periods of energy consumption for the next 10 years, 2017–2026, 20 years, 2017–2036, and 30 years, 2017–2046, by using ARIMAX Model. The prediction results show that these models are effective in the forecast measured by RMSE, MAE, and MAPE. The results show that from the first model (2,1,1), which predicted the duration of 10 years, 2017–2026, indicates that Thailand has increased an energy consumption rate with the average of 18.09%, while the second model (2,1,2) with the prediction of 20 years, 2017–2036, Thailand arises its energy consumption up to 37.32%. In addition, the third model (2,1,3) predicted the duration of 30 years from 2017 to 2046, and it has found that Thailand increases its energy consumption up to 49.72%

FORECASTING MODEL OF GHG EMISSION IN MANUFACTURING SECTORS OF THAILAND

This study aims to analyze the modeling and forecasting the GHG emission of energy consumption in manufacturing sectors. The scope of the study is to analysis energy consumption and forecasting GHG emission of energy consumption for the next 10 years (2016-2025) and 25 years (2016-2040) by using ARIMAX model from the Input-output table of Thailand. The result shows that iron and steel has the highest value of energy consumption and followed by cement, fluorite, air transport, road freight transport, hotels and places of loading, coal and lignite, petrochemical products, other manufacturing, road passenger transport, respectively. The prediction results show that these models are effective in forecasting by measured by using RMSE, MAE, and MAPE. The results forecast of each model is as follows: 1) Model 1(2,1,1) shows that GHG emission will be increasing steadily and increasing at 25.17% by the year 2025 in comparison to 2016. 2) Model 2 (2,1,2) shows that GHG emission will be rising steadily and increasing at 41.51% by the year 2040 in comparison to 2016

Forecast of Carbon Dioxide Emissions from Energy Consumption in Industry Sectors in Thailand

The aim of this research is to forecast CO2emissions from consumption of energy in Industry sectors in Thailand. To study, input-output tables based on Thailand for the years 2000 to 2015 are deployed to estimate CO2emissions, population growth and GDP growth. Moreover, those are also used to anticipate the energy consumption for fifteen years and thirty years ahead. The ARIMAX Model is applied to two sub-models, and the result indicates that Thailand will have 14.3541 % on average higher in CO2emissions in a fifteen-year period (2016-2030), and 31.1536 % in a thirty-year period (2016-2045). This study hopes to be useful in shaping future national policies and more effective planning. The researcher uses a statistical model called the ARIMAX Model, which is a stationary data model, and is a model that eliminates the problems of autocorrelations, heteroskedasticity, and multicollinearity. Thus, the forecasts will be made with minor error

The Relationship of Causal Factors Affecting the Future Equilibrium Change of Total Final Energy Consumption in Thailand’s Construction Sector under a Sustainable Development Goal: Enriching the SE-VAR_X Model

This study aims to analyze the influence of the relationship between causal factors that affect the future equilibrium of the total final energy consumption in the construction sector of Thailand under the sustainable development policy for the period of 10 years (2019&#8315;2028). This analysis was achieved with the application of the Structure Equilibrium-Vector Autoregressive with Exogenous Variables model (SE-VARX model). This model was developed to fill research gaps and differs from those of previous studies. In the selection of variables, the study focused on Sustainable Development (SD)-based variables available through the lens of Thailand. The exogenous variables included real GDP, population growth, urbanization rate, industrial structure, oil price, foreign direct investment, international tourist arrivals, and total exports and imports. Every variable had a co-integration at level (1) and was used to structure the SE-VARX model. This particular model can effectively analyze the influence of the direct relationship and meet the criteria of goodness of fit without spuriousness. This SE-VARX model allowed us to discover that every variable in the model had an influence on the equilibrium change, where the real GDP is the fastest variable to adjust to the equilibrium while the total final energy consumption has the slowest adjustment ability. The SE-VARX model can be used to project the total final energy consumption, as verified by the performance test result. The test was measured based on the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), and their results were 1.09% and 1.01%, respectively. This performance result had the highest value compared to other models in the past. Thus, the SE-VARX model is suitable for forecasting over the next 10 years (2019&#8315;2038). The results of this study reveal that the total final energy consumption in the construction sector of Thailand will exhibit a continuously increasing growth rate from 2019 to 2028, amounting to about 144.29% or equivalent to 364.01 ktoe. In addition, the study also found that future government plans may be difficult to achieve as planned. Therefore, the introduced model should be integrated into national development planning and strategies to achieve sustainable development in the future and to enable its application to other sectors

VARIMAX MODEL TO FORECAST THE EMISSION OF CARBON DIOXIDE FROM ENERGY CONSUMPTION IN RUBBER AND PETROLEUM INDUSTRIES SECTORS IN THAILAND

This study aims to analyze the forecasting of CO2 emission from the energy consumption in the Rubber, Chemical and Petroleum Industries sectors in Thailand. The scope of research employed the input-output table of Thailand from the year 2000 to 2015. It was used to create the model of CO₂ emission, population, GDP growth and predict ten years and thirty years in advance. The model used was the VARIMAX Model which was divided into two models. The results show that from the first model by using which predicted the duration of ten years (2016-2025) by using VARIMAX Model (2,1,2), On average, Thailand has 17.65% higher quantity of CO₂ emission than the energy consumption sector (in 2025). The second model predicted the duration of 30 years (2016-2045) by using VARIMAX Model (2,1,3) shows that Thailand has average 39.68% higher quantity of CO2 emission than the energy consumption sector (in 2025). From the analyses, it shows that Thailand has continuously higher quantity of CO₂ emission from the energy consumption. This negatively affects the environmental system and economical system of the country incessantly. This effect can lead to unsustainable development