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

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

Relationships between Causal Factors Affecting Future Carbon Dioxide Output from Thailand’s Transportation Sector under the Government’s Sustainability Policy: Expanding the SEM-VECM Model

This research aims to analyze the relationships between causal factors likely to affect future CO2 emissions from the Thai transportation sector by developing the Structural Equation Modeling-Vector Autoregressive Error Correction Mechanism Model (SEM-VECM Model). This model was created to fill information gaps of older models. In addition, the model provides the unique feature of viable model application for different sectors in various contexts. The model revealed all exogenous variables that have direct and indirect influences over changes in CO2 emissions. The variables show a direct effect at a confidence interval of 99%, including per capita GDP ( Δ ln ( GDP ) t − 1 ), labor growth ( Δ ln ( L ) t − 1 ), urbanization rate factor ( Δ ln ( U R T ) t − 1 ), industrial structure ( Δ ln ( I S ) t − 1 ), energy consumption ( Δ ln ( E C ) t − 1 ), foreign direct investment ( Δ ln ( F D I ) t − 1 ), oil price ( Δ ln ( O P ) t − 1 ), and net exports ( Δ ln ( X − E ) t − 1 ). In addition, it was found that every variable in the SEM-VECM model has an indirect effect on changes in CO2 emissions at a confidence interval of 99%. The SEM-VECM model has the ability to adjust to the equilibrium equivalent to 39%. However, it also helps to identify the degree of direct effect that each causal factor has on the others. Specifically, labor growth ( Δ ln ( L ) t − 1 ) had a direct effect on per capita GDP ( Δ ln ( GDP ) t − 1 ) and energy consumption ( Δ ln ( E C ) t − 1 ) at a confidence interval of 99%, while urbanization rate ( Δ ln ( U R T ) t − 1 ) had a direct effect on per capita GDP ( Δ ln ( GDP ) t − 1 ), labor growth ( Δ ln ( L ) t − 1 ), and net exports ( Δ ln ( X − E ) t − 1 ) at a confidence interval of 99%. Furthermore, industrial structure ( Δ ln ( I S ) t − 1 ) had a direct effect on per capita GDP ( Δ ln ( GDP ) t − 1 ) at a confidence interval of 99%, whereas energy consumption ( Δ ln ( E C ) t − 1 ) had a direct effect on per capita GDP ( Δ ln ( GDP ) t − 1 ) at a confidence interval of 99%. Foreign direct investment ( Δ ln ( F D I ) t − 1 ) had a direct effect on per capita GDP ( Δ ln ( GDP ) t − 1 ) at a confidence interval of 99%, while oil price ( Δ ln ( O P ) t − 1 ) had a direct effect on industrial structure ( Δ ln ( I S ) t − 1 ), energy consumption ( Δ ln ( E C ) t − 1 ), and net exports ( Δ ln ( X − E ) t − 1 ) at a confidence interval of 99%. Lastly, net exports ( Δ ln ( X − E ) t − 1 ) had a direct effect on per capita GDP ( Δ ln ( GDP ) t − 1 ) at a confidence interval of 99%. The model eliminates the problem of heteroskedasticity, multicollinearity, and autocorrelation. In addition, it was found that the model is white noise. When the SEM-VECM Model was used for 30-year forecasting (2018⁻2047), it projected that CO2 emissions would increase steadily by 67.04% (2047/2018) or 123.90 Mt CO2 Eq. by 2047. The performance of the SEM-VECM Model was assessed and produced a mean absolute percentage error (MAPE) of 1.21% and root mean square error (RMSE) of 1.02%. When comparing the performance value with the values of other, older models, the SEM-VECM Model was found to be more effective and useful for future research and policy planning for Thailand’s sustainability goals

The Revised Input-Output Table to Determine Total Energy Content and Total Greenhouse Gas Emission Factors in Thailand

A full energy chain analysis (FENCH) or a life cycle analysis (LCA) is indeed essential in making any decision on both minimal greenhouse gas (GHG) emissions and the energy content in various commodities. In this article, the energy Input-Output Analysis (IOA) approach is investigated to determine the factors for the total greenhouse gas emission and total energy content, and it deems the elimination of the boundary constraints existing in the Process Chain Analysis (PCA) approach to be practical to. This study, aims to identify the factors in embedded energy and embedded greenhouse gas (GHG) total values derived from the total Thai economic sectors of 180 in various commodities productions. The previous outdated IOA is enhanced in the study by revising the elements of sectoral energy consumption in the power sector, which is later found to be influential and significant to all other economic sectors. In addition, the 2005 sectoral energy consumption is used to show individual energy consumption, whereas the 2010 Input-Output (I-O) table, most timely data, is used to show the economic structure. Furthermore, the study uses a report of Thai electric power to revise the data of 2005 fuel mix in the power sector in order to obtain the 2010 and 2015 fuel mix. The reason of such revision is that the changes of fuel mix in the power sector are influential towards the factors in both total energy content and total greenhouse (GHG) emission. Hence, the 2015 electricity-fuel mix is taken to present the above-mentioned factors

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

Evolution of ASEAN financial integration in the comparative perspective

Since the Asian financial crisis of 1997-1998, the Association of Southeast Asian Nations (ASEAN) has continuously worked on the context of financial integration and put tremendous effort into ensuring financial stability in the region. Two ASEAN Economic Community (AEC) blueprints have been endorsed with elements of financial integration and liberalization, toward its goal of achieving regional financial integration and the complement of financial inclusion and financial stability. Though the effort seems ambitious, regional initiatives have been agreed and partly implemented in the areas of banking, insurance, capital accounts, capital markets, payment and settlement systems, taxation, financial inclusion, financial stability, financial resilience, and sustainable finance. Given the long-standing process of financial integration of the European Union (EU), and the proven tangible economic benefits associated with a significant degree of financial integration, ASEAN could learn from the EU's experience, particularly under the current global challenges. This paper reviews ASEAN's process, which includes financial integration and financial stability, in comparison with that of the EU. The paper examines the effectiveness of the initiatives agreed and implemented by ASEAN against the EU's success story as an institutional benchmark. A set of conclusions and policy recommendations are derived at the end of this paper

ASEAN FTA, distribution of income, and globalization

This paper examines the impact of Free Trade Agreements on income distribution within the Association of Southeast Asian Nations (ASEAN) and between the members and their trading partners outside ASEAN. The study uses a Computable General Equilibrium model, a modified version of the 57 sector, 87 country, Global Trade Analysis Project (GTAP) integrated model of national input-output tables, version-6.2 (2001 database) with its reserve matrix facility, to simulate income distribution results as an outcome of certain parameter changes that appear in intra and inter regional trade. Within ASEAN, trade liberalization will stimulate the output of each country within the region according to their comparative advantage. Since trade liberalization tends to increase output of capital-intensive goods more than labor-intensive goods, the less-developed countries within the region tend to get smaller benefits compared to other member countries. In addition, the physical means of production tend to gain more relative to the gains of labor from the FTAs. This tends to widen the income gap between high-income and low-income households within ASEAN. Comparing ASEAN and the developed non-ASEAN countries, an FTA within ASEAN tends to reduce the returns to labor of the developed non-member countries and narrow the income gap between ASEAN, as a whole, and those rich countries since capital-intensive products of developing countries are often labor-intensive goods of developed countries.ASEAN Income distribution Regional integration CGE model