Sustainable development is part and parcel of development policy for Thailand, in order to promote growth along with economic growth, social advancement, and environmental security. Thailand has, therefore, established a national target to reduce CO 2 emissions below 20.8%, or not exceeding 115 Mt CO 2 Equivalent (Eq.) by 2029 within industries so as to achieve the country's sustainable development target. Hence, it is necessary to have a certain measure to promote effective policies; in this case, a forecast of future CO 2 emissions in both the short and long run is used to optimize the forecasted result and to formulate correct and effective policies. The main purpose of this study is to develop a forecasting model, the so-called VARIMAX-ECM model, to forecast CO 2 emissions in Thailand, by deploying an analysis of the co-integration and error correction model. The VARIMAX-ECM model is adapted from the vector autoregressive model, incorporating influential variables in both short-and long-term relationships so as to produce the best model for better prediction performance. With this model, we attempt to fill the gaps of other existing models. In the model, only causal and influential factors are selected to establish the model. In addition, the factors must only be stationary at the first difference, while unnecessary variables will be discarded. This VARIMAX-ECM model fills the existing gap by deploying an analysis of a co-integration and error correction model in order to determine the efficiency of the model, and that creates an efficiency and effectiveness in prediction. This study finds that both short-and long-term causal factors affecting CO 2 emissions include per capita GDP, urbanization rate, industrial structure, and net exports. These variables can be employed to formulate the VARIMAX-ECM model through a performance test based on the mean absolute percentage error (MAPE) value. This illustrates that the VARIMAX-ECM model is one of the best models suitable for the future forecasting of CO 2 emissions. With the VARIMAX-ECM model employed to forecast CO 2 emissions for the period of 2018 to 2029, the results show that CO 2 emissions continue to increase steadily by 14.68%, or 289.58 Mt CO 2 Eq. by 2029, which is not in line with Thailand's reduction policy. The MAPE is valued at 1.1% compared to the other old models. This finding indicates that the future sustainable development policy must devote attention to the real causal factors and ignore unnecessary factors that have no relationships to, or influences on, the policy. Thus, we can determine the right direction for better and effective development.
Enhancing power generation using waste energy promotes sustainability. Biowaste from palm oil mills (POMs) has been used as a renewable energy (RE) source to produce steam and electricity in Thai POMs for decades. Because the amount of generated bio-waste normally exceeds the amount that can be used internally by the POMs, energy-efficient practices in the cogeneration plant, which includes waste heat emitted by sterilization steam venting and the biogas engine in the power plant, are disregarded. However, a government policy that allows operators to sell electricity back to the grid has been enacted, and plant owner's attitudes toward cogeneration have since changed. Increased attention has been focused on fuel conservation and energy-efficiency practices, and this study is a product of that increased interest. This work aims to increase power generation from POMs by integrating waste energy through an improved cogeneration system. A standard 45-t/h fresh fruit bunch (FFB), POM in southern Thailand was selected as a case study. The steam consumption and boiler and turbine efficiency were measured and analyzed along with the data on the engine waste heat, and a power generation model for the POMs was then proposed. The analysis results showed that the surplus electricity can reach 2.834 MW and 4.223 MW with and without crude palm oil (CPO) production, respectively. The operational rate of the cogeneration plant is also suggested to be extended to 7,500 continuous hours per year instead of the 4351 intermittent hours implemented at present. 155
Presently, Thailand runs various sustainable development-based policies to boost the growth in economy, society, and environment. In this study, the economic and social growth was found to continuously increase and negatively deteriorate the environment at the same time due to a more massive final energy consumption in the petroleum industries sector than any other sectors. Therefore, it is necessary to establish national planning and it requires an effective forecasting model to support Thailand’s policy-making. This study aimed to construct a forecasting model for a final energy consumption prediction in Thailand’s petroleum industry sector for a longer-term (2018–2037) at a maximum efficiency from a certain class of methods. The Long Term-Autoregressive Integrated Moving Average with Exogeneous variables and Error Correction Mechanism model (LT-ARIMAXS model) (p, d, q, Xi, ECT(t−1)) was adapted from the autoregressive and moving average model incorporating influential variables together in both long-term relationships to produce the best model for prediction performance. All relevant variables in the model are stationary at Level I(0) or Level I(1). In terms of the extraneous variables, they consist of per capita GDP, population growth, oil price, energy intensity, urbanization rate, industrial structure, and net exports. The study found that the variables used are the causal factors and stationary at the first difference as well as co-integrated. With such features, it reflects that the variables are influential over the final energy consumption. The LT-ARIMAXS model (2,1,2) determined a proper period (t − i) through a white noise process with the Q test statistical method. It shows that the LT-ARIMAXS model (2,1,2) does not generate the issues of heteroskedasticity, multicollinearity, and autocorrelation. The performance of LT-ARIMAXS model (2,1,2) was tested based on the mean absolute percentage error (MAPE) and the root mean square error (RMSE). The LT-ARIMAXS model (2,1,2) can predict the final energy consumption based on the Sustainable Development Plan for the 20 years from 2018 to 2037. The results showed that the final energy consumption continues to increase steadily by 121,461 ktoe in 2037. Furthermore, the findings present that the growth rate (2037/2017) increases by 109.8%, which is not in line with Thailand’s reduction policy. In this study, the MAPE was valued at 0.97% and RMSE was valued at 2.12% when compared to the other old models. Therefore, the LT-ARIMAXS model (2,1,2) can be useful and appropriate for policy-making to achieve sustainability.
The ISO 50001 energy management system (EnMS) standard was published in June 2011 and has been widely adopted by organizations from around the world, including Thailand. From 2014–2017, there was a continuous increase in the number of ISO 50001-certified companies in the East Asia and Pacific regions and, more broadly, the world, although this is not consistent with the number of companies that emerged during this period in Thailand. This information shows that the implementation of energy management in some companies may not be sustainable. This research offers a novel method for assessing the quality of energy management in the form of an energy management system sustainability index (EnMS SI) framework, presenting the economic, organizational, energy performance, and environmental aspects of sustainable energy management. Data collection, from a literature review of related research and the EnMS good practices, was implemented in order to select sustainability indicators and further develop a sustainability index for energy management. The analytic hierarchy process (AHP) and weighted arithmetic mean (WAM) were used to establish an EnMS SI. The study results were then assessed and validated using 31 ISO 50001-certified companies in Thailand. Direct interviews and questionnaires were used to obtain responses from energy management representatives. The studied data indicated that an EnMS SI framework can be used in qualitative analyses to effectively determine the sustainability of an EnMS. Significant sustainability indicators, consisting of continuous benefits, top management commitment, and long-term strategic planning, were found. The results also revealed that the EnMS in Thailand has been significantly economically weak. The EnMS SI framework is a tool for assessing energy management sustainability, which allows for the determination of an organization’s actual strengths and weaknesses. The benefits of this framework include the possibility of determining guidelines for correcting and improving the EnMS to achieve sustainability.
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