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

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

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

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

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

MODELING ENVIRONMENTAL IMPACT OF MACHINERY SECTORS TO PROMOTE SUSTAINABLE DEVELOPMENT OF THAILAND

The objective of this research is to propose an indicator to evaluate environmental impacts from the machinery sectors of Thailand, leading to more sustainable consumption and production in this sector of the economy. The factors used to calculate the forward linkage, backward linkage and real benefit included the total environmental costs. The highest total environmental cost was railway equipment need to be resolved immediately because it uses natural resources in carrying capacity, higher than standard environmental cost, and contribute to low real benefit. Electric accumulator & battery, secondary special industrial machinery, motorcycle, bicycle & other carriages, and engines and turbines need to monitor closely because they are able to link to other production sectors more than other production sector do and they have high environmental cost. In order to decide the sustainable development strategy of the country, there is a need to use this research to support decision-making

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

Analyzing the Impact of Causal Factors on Political Management to Determine Sustainability Policy under Environmental Law: Enriching the Covariance-based SEMxi Model

This research aims to develop a causal relationship model on political management for sustainability policy formation under Thai environmental law by applying the best and valid model with a non-spurious property called the Covariance-based on Structural Equation Model with exogenous variables (Covariance-based SEMxi Model). This newly-developed model is in distinction with any past models as it is made effectively applicable to any sectors across areas. The model can also be utilized to design a long-term forecasting model with the ability to determine appropriate future scenarios. When assessing the covariance-based SEMxi model performance, the mean absolute percentage error (MAPE) and the root mean square error (RMSE) are estimated at 1.19% and 1.30%, respectively, in comparison of other models, including Gray-Autoregressive Integrated Moving Average Model (GM-ARIMA), Gray Model (GM), Back Propagation Neural Network (BP), Artificial Neural Natural Model (ANN), and Multiple Regression Model (MR). As for the results, this research reveals a direct impact of economic factors on environmental and social factors. In the meanwhile, social factors have a direct impact on environmental and economic factors. The research also indicates a direct effect on the environment with a maximal magnitude of 67%. Whereas a direct effect of social factors on the environment is detected at the magnitude of 55%. These effects are perceived to exceed the specified carrying capacity set by Thailand. In addition, a causal relationship is observed between economic and social factors, where the environment is found with the lowest error correction capability of only 5 percent. At the same time, economic and social factors are noticed with greater correction capability of 59% and 31%, respectively. This finding implies that the ecosystem will experience slow recovery whenever it deteriorates. Hence, the government must place a higher concentration on the environment, while different measures on environmental legislation should be closely controlled to contain any future damage. Besides, energy consumption must be managed not to exceed the established carrying capacity by simultaneously implementing both proactive and reactive measures. This process can be strengthened by optimizing the newly-introduced model produced by this work for a scenario design in policy management to attain sustainability

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

INDICATOR OF ENVIRONMENTAL PROBLEMS OF AGRICULTURAL SECTORS UNDER THE ENVIRONMENTAL MODELING

The objective of this research is to propose an indicator to deal with environmental problems for agricultural sectors caused by goods and services production. The aspects to calculate the real benefit of agricultural sectors and environmental cost for analyzing are natural resources materials, energy and transportation, fertilizer and pesticides, and sanitary and similar service. From the research it was found that the highest environmental cost of natural resources materials was 026: charcoal and fire-wood, while the lowest was 010 coconut. The highest environmental cost for energy and transportation was 024: agricultural services, while the highest environmental cost for fertilizer and pesticides was 011: palm oil. lastly, 017: other agricultural products was found as the highest environmental cost for sanitary and similar service. As a result, 010: coconut gained the highest real benefit, while 024: agricultural services presented as the lowest read benefit for the company. If Thailand using environmental problem indicator, especially with the agricultural sector, it can help to formulate efficient policies and strategies for the country in 3 development areas, which are social, economic, and environmental development