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.

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 CO₂ emissions below 20.8%, or not exceeding 115 Mt CO₂ 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 CO₂ 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 CO₂ 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 CO₂ 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 CO₂ emissions. With the VARIMAX-ECM model employed to forecast CO₂ emissions for the period of 2018 to 2029, the results show that CO₂ emissions continue to increase steadily by 14.68%, or 289.58 Mt CO₂ 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.

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’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’s findings reveal that the LS-ARIMAXi $\left(2,1,1,X_{t-1}\right)$ -ECM model is a forecasting model with an appropriate time period (t − 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 $\left(2,1,1,X_{t-1}\right)$ -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.

Developing universities to become green and sustainable universities is important. This is in line with the world’s sustainable development guidelines. In developing a university towards being green and sustainable, different sustainability assessment criteria have been used, including the UI GreenMetric, GRI, STARS, AUN, THE’S Impact Ranking, and AISHE. Each criterion is designed in a broad-spectrum manner that is not specific. Therefore, this research aims to develop sustainable and green university assessment indicators that suit the national context in Thailand, both in terms of the size and location of universities. Based on the criteria from the UI GreenMetric, there are six categories, totaling 51 indicators. Thus, this study aims to analyze the weaknesses and strengths of each indicator using a questionnaire distributed by the Sustainable University Network of Thailand to all 36 universities and to organize a meeting with sustainability experts (focus group) in each area. To obtain appropriate indicators and to prioritize each indicator, the analytic hierarchy process (AHP) and weighted arithmetic mean (WAM) method were used to develop an index of sustainability assessment criteria (GU SI) suitable for the Thai context. The results of this study show that the newly developed GU SI comprises seven aspects, including a total of 27 indicators. These are criteria that can be used to assess the sustainability of small, medium, and large universities. They are not complex, are straightforward to use, and more importantly reflect the Thai context. By applying these new sustainability assessment criteria, it was found that the universities sampled in this study yielded significantly high scores. This study is limited by its application of sustainability assessments to only universities in Thailand. Though there are different contexts across different Thai universities, the proposed indicators can still be used to evaluate the sustainability of universities in Thailand.

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 increasing rate of population growth and urban expansion has led to a higher demand for fossil fuels, which, in turn, directly generate greenhouse gas emissions into the atmosphere. These emissions contribute to environmental problems such as global warming and climate change. This study aims to present the total life-cycle energy analysis (LCEA) of a single-family detached house designed with an energy conservation approach. Using a cradle-to-grave scope, this study quantifies the embodied energy in six stages of the building’s life cycle, i.e., initial, transportation, construction, operational, recurrent, and demolition. An input–output (IO)-based method was employed to construct a Thailand-specific embodied energy coefficient for 36 key building materials. This coefficient was then used to quantify both the initial embodied energy and the recurrent embodied energy in this study. The case-study house was broken down into 13 building materials. Concrete was the most consumed material, followed by fiber–cement, steel, and timber, in that order. However, the results of the embodied energy distribution for these materials revealed that fiber–cement ranked first, accounting for 29%. Steel was next, at 21%, followed by concrete at 18%, and, finally, aluminum at 12%. The case-study house had an initial embodied energy of 7.99 GJ/m² and a total life-cycle energy consumption of 0.66 GJ/m²/year. This study provides valuable information on LCEA for residential buildings, fostering public understanding of energy conservation in the Thai context. Furthermore, this study’s results can be applied to establish energy conservation guidelines for residential buildings. These guidelines can help reduce energy resource depletion, carbon emissions, and environmental problems, ultimately contributing to Thailand’s goal of achieving carbon neutrality by 2050.