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 were the Total Environmental Costs. The highest total environmental cost was Railway Equipment which needs to be resolved immediately because it uses natural resources more than its carrying capacity, higher environmental cost than standard, and contributes low real benefit. Electric Accumulator & Battery, Secondary Special Industrial Machinery, Motorcycle, Bicycle & Other Carriages, and Engines and Turbines need to be monitored closely because they are able to link to other production sectors more than any other production sectors do, and they have high environmental cost. To decide a sustainable development strategy of the country, therefore, results of this research must be used to support decision-making.
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.
This study aims to analyze the forecasting of CO 2 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 CO 2 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 CO 2 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 CO 2 emission than the energy consumption sector (in 2025). From the analyses, it shows that Thailand has continuously higher quantity of CO 2 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.
This research aimed to analyze the influence of the direct and indirect relationships of economic, social, and environmental factors as well as predict their future effects by applying a path analysis of a generalized method of moments model (path analysis–GMM model). The model is believed to be the most effective in relationship analysis, as it is capable of accurate prediction compared to the original models. Most importantly, the model can be applied to different contexts, benefiting the development areas of those contexts. Furthermore, the model has also been found to be the best linear unbiased estimation (BLUE), which is suitable for long-term forecasting. However, the study’s results reflect that the three latent variables of economic, social, and environmental factors have direct and indirect effects. In addition, both economic and social factors were found to have causal relationships. The availability of the path analysis–GMM model enables us to forecast the social and economic changes over the next 20 years (2019–2038), and predict the change in energy-related CO2 emissions for the next 20 years (2019–2038). Thus, the study was able to discern the economic and social growth of Thailand. Studies have shown that the economic and social growth of Thailand has increased by 7.85%, based on various indicators. The economic indicators include per capita gross domestic product ( G D P ) , urbanization rate ( U R E ) , industrial structure ( I S E ) , net exports ( X − E ) , and indirect foreign investment ( I F I ) , while the social indicators include employment ( E M S ) , health and illness ( H I S ) , social security ( SSS ) , and consumer protection ( CPS ) . However, the environment has continuously deteriorated, as understood via environmental indicators such as energy consumption ( E C E ) , energy intensity ( E I E ) , and carbon dioxide emissions ( C O 2 ) . This is due to the increment of CO2 emissions in energy consumption of 39.37% (2038/2019) or 103.37 Mt CO2 eq. by 2038. However, by using the path analysis–GMM model to test for performance, it produced the mean absolute percentage error (MAPE) of 1.01% and a root mean square error (RMSE) of 1.25%. A comparison of the above results with other models, including the multiple regression model, grey model, artificial neural natural model (ANN model), back propagation neural network (BP model), and the autoregressive integrated moving average model (ARIMA model) provided evidence that the path analysis–GMM model was the most suitable in forecasting and contextual application to support the formulation of the national strategy in the future.
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