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The Relationship of Causal Factors Affecting the Future Equilibrium Change of Total Final Energy Consumption in Thailand’s Construction Sector under a Sustainable Development Goal: Enriching the SE-VAR_{X} Model

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## Abstract

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_{X}model). This model was developed to fill research gaps and differs from those of previous studies. In the selection of variables, the study focused on Sustainable Development (SD)-based variables available through the lens of Thailand. The exogenous variables included real GDP, population growth, urbanization rate, industrial structure, oil price, foreign direct investment, international tourist arrivals, and total exports and imports. Every variable had a co-integration at level (1) and was used to structure the SE-VAR

_{X}model. This particular model can effectively analyze the influence of the direct relationship and meet the criteria of goodness of fit without spuriousness. This SE-VAR

_{X}model allowed us to discover that every variable in the model had an influence on the equilibrium change, where the real GDP is the fastest variable to adjust to the equilibrium while the total final energy consumption has the slowest adjustment ability. The SE-VAR

_{X}model can be used to project the total final energy consumption, as verified by the performance test result. The test was measured based on the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE), and their results were 1.09% and 1.01%, respectively. This performance result had the highest value compared to other models in the past. Thus, the SE-VAR

_{X}model is suitable for forecasting over the next 10 years (2019–2038). The results of this study reveal that the total final energy consumption in the construction sector of Thailand will exhibit a continuously increasing growth rate from 2019 to 2028, amounting to about 144.29% or equivalent to 364.01 ktoe. In addition, the study also found that future government plans may be difficult to achieve as planned. Therefore, the introduced model should be integrated into national development planning and strategies to achieve sustainable development in the future and to enable its application to other sectors.

## 1. Introduction

_{2}emissions keep increasing, so does the greenhouse effect at the same time [3]. However, although many countries have implemented various policies and collaborated between one another to reduce greenhouse emissions, the issue remains unresolved and adds no value in terms of sustainability. As for Thailand, it is one of many other countries willing to cooperate and take initiative to address the problem. One such action is the country’s participation and involvement in the United Nations Framework Convention on Climate Change, Kyoto Protocol, and Paris Agreement [1,3] in order to provide a better framework for achieving sustainable development.

_{2}emissions from the national energy consumption increased by 1.5% in 2017 compared to 2016 [1,5]. In particular, the construction sector emits a high volume of CO

_{2}emissions. This reflects that the ecological system is affected, and greenhouse gas emissions continue to increase, mainly contributed by the construction sector. In addition, methane (CH

_{4}) is ranked as having the second highest emission levels [3,5]. This impact deteriorates the environmental system, though the government has tried to implement measures and strategies to minimize the impact on the environment. In the context of Thailand, the government has undertaken many initiatives in terms of supportive policies to reduce energy consumption, be it through promotional or penalty policies. However, energy consumption and CO

_{2}emissions continue to increase regardless, especially energy consumption in the construction sector on what appears to be a daily basis. This reflects the historical absence in Thailand of an important and effective tool and measure to formulate accurate and effective policies. Based on the relevant studies available in Thailand, spurious models continue to be applied, and such applications result in the failing of the policies implemented in all application terms. Concerning the sustainable development goal of 1 to 5 years, to support the medium term of 6 to 10 years and the long term of 11 to 20 year in Thailand, the efforts to meet this goal seem unsuccessful due to limited implementation tools for effective forecasting that can be universally applied to different sectors. Thus, this study aims to fill this gap, so Thailand can perform better in national strategic planning. Therefore, we attempted to develop a Structure Equilibrium-Vector Autoregressive with Exogenous Variables (SE-VAR

_{X}) model for such planning. After reviewing various relevant studies, we found that the SE-VAR

_{X}model is a model that differs from other models in the past, and it is an ideal model to be deployed in different contexts and sectors.

_{2}emissions from commuting workers in Beijing by proposing an integrated analytical framework. The study’s results illustrated the great impact of such a change in terms of increased transport CO

_{2}emissions. In addition, the change relative to a more decentralized urban area has a greater impact in the above matter, resulting in a large rise in CO

_{2}emissions.

_{2}emissions from 2007 to 2014 in five West African countries. They also proposed a non-assumption driven forecasting technique by using a bidirectional long short-term memory (BiLSTM) sequential algorithm formulation to forecast the long-term total CO

_{2}emissions from 2015 to 2020. Their results indicated the presence of a unidirectional relationship between GDP and CO

_{2}emissions. Also, they were able to suggest sustainable policy targets based on CO

_{2}emissions projections. Zhixin and Xin [15] investigated the causal link between energy consumption and economic growth of Shandong, China, from 1980 to 2008, by implementing a Generalized Least Square (GLS) method, which concluded that a long-term relationship between these two factors exists, implying that the economic growth of the studied area is dependent on energy consumption. Du et al. [16] applied the Bernoulli–Nash Social Welfare Function (BNSWF) to examine the effects of the emission cap on emission-dependent manufacturers’ decision-making. The study shows that the system-wide and manufacturer’s profits are enhanced with such an emission cap, while the permit supplier is found to decline. However, it is suggested that the manufacturer and permit supplier can increase the profit through coordination in the supply chain.

_{2}emissions by conducting a panel data analysis in 30 major nuclear energy-based countries from 1990 to 2010. The study concluded that there is a positive long-run effect of nuclear energy consumption on GDP growth, but not on CO

_{2}emissions. Furthermore, Cutlip and Fath [20] investigated causality between carbon emissions and economic development through a study of case studies in six countries. This investigation showed a connection between those variables.

_{2}emissions on the development of economy and finance, Al-mulali and Che Sab [23] selected 19 countries with an application of a panel model covering the period of 1980 to 2008. The assessment provides evidence of energy consumption contributing to the studied development. However, the study found that the high levels of said development have increased CO

_{2}emissions.

_{X}model to fill the above gap as well as to provide maximum efficiency and effectiveness in application. The SE-VAR

_{X}model was developed for medium- and long-term forecasting, yet was found to be the best model. We used the time series data for the period 1990–2017, and applied linear structural relations (LISREL, version 9.2, Chicago, IL, USA) software in later estimations. The research process is shown below.

- Determine all variables for the SE-VAR
_{X}model so as to analyze the influence of the relationship between causal factors affecting the equilibrium adjustment of the total final energy consumption based on the sustainable development policy for 10 years (2019–2028). This process applies Augmented Dickey–Fuller theory [50,51] along with the concept of walk drift and time trend at the first difference using data from 1990 to 2017. - Construct the SE-VAR
_{X}model to analyze the influence of the relationship in both the long term and short term. - Testify the performance of the SE-VAR
_{X}model based on the value of the Mean Absolute Percentage Error (RMSE) and Root Mean Square Error (RMSE) [54,55], and compare them with the same sort of values in Multiple Linear Regression model (ML model), Artificial Neural Network model (ANN model), Back Propagation neural network model (BP model), Grey model, and Autoregressive Integrated and Moving Average model (ARIMA model). - Forecast the total final energy consumption by using the SE-VAR
_{X}model for the years 2019–2028, totaling 10 years. The flowchart of the SE-VAR_{X}model is shown in Figure 1.

## 2. Materials and Methods

#### 2.1. Stationary Features

_{X}model have to be selective and identified as stationary. This selection is illustrated below.

#### 2.2. Co-Integration Test

#### 2.3. A Forecasting Model Using the SE-VAR_{X} Model

_{X}model employs stationary variables at the first difference with a co-integration characteristic based on Johansen’s concept in order to analyze both the short- and long-term relationships of each variable in the model. Here, each causal factor has a direct effect, and is later used to construct the SE-VAR

_{X}model as explained below.

_{X}model applicable to forecasting the total final energy consumption in the construction sector as demonstrated below.

#### 2.4. Measurement of the Forecasting Performance

## 3. Empirical Analysis

#### 3.1. Screening of Influencing Factors for Model Input

_{X}model, we selected variables relevant to the sustainable development policy in the construction sector by optimizing the time series data of the period of 1990 to 2017. In this study, nine variables were taken for analysis: the total final energy consumption ($FEC$), real GDP ($GDP$), population growth (${P}_{g}$), urbanization rate (${U}_{r}$), industrial structure (${I}_{s}$), oil price (${O}_{p}$), foreign direct investment (${F}_{i}$), international tourist arrivals (${I}_{t}$), and total exports and imports (${X}_{m}$). Each of those variables was stationary at the first difference, as verified by the Augmented Dickey–Fuller unit root test shown in Table 1.

#### 3.2. Analysis of Co-Integration

_{X}model as illustrated below.

#### 3.3. Formation of Analysis Modeling with the SE-VAR_{X} Model

_{X}model, we used all the variables mentioned above to build the model, and tested them for goodness of fit. It was found that the model met all criteria of the test. In addition, a test on the spuriousness of the model gave normal results. The fulfillment of those tests enabled the model to analyze the magnitude of the direct effect the adjustment ability toward the equilibrium, the results of which can be seen in Table 3.

_{X}model was found to have both short-term and long-term relationships, with a direct effect for independent variables. This changed for dependent variables, including $\Delta \mathrm{ln}(FEC)$, $\Delta \mathrm{ln}(GDP)$, $\Delta \mathrm{ln}({P}_{g})$, $\Delta \mathrm{ln}({U}_{r})$, $\Delta \mathrm{ln}({I}_{s})$, $\Delta \mathrm{ln}({O}_{p})$, $\Delta \mathrm{ln}({F}_{i})$, $\Delta \mathrm{ln}({I}_{t})$, and $\Delta \mathrm{ln}({X}_{m})$. However, this study disclosed that when there is a change in the real GDP ($\Delta \mathrm{ln}(GDP)$) by 1%, it will cause the total final energy consumption ($\Delta \mathrm{ln}(FEC)$) to change by 6.54% at a significance level of 1%. When the population growth ($\Delta \mathrm{ln}({P}_{g})$) changes by 1%, it will then cause the total final energy consumption ($\Delta \mathrm{ln}(FEC)$) to change by 4.02% at a significance level of 1%. Also, when the urbanization rate ($\Delta \mathrm{ln}({U}_{r})$) changes by 1%, the total final energy consumption ($\Delta \mathrm{ln}(FEC)$) will change by 5.33% at a significance level of 1%. When the industrial structure ($\Delta \mathrm{ln}({I}_{s})$) changes by 1%, the total final energy consumption ($\Delta \mathrm{ln}(FEC)$) will change by 5.74% at a significance level of 1%. While the oil price ($\Delta \mathrm{ln}({O}_{p})$) changes by 1%, the total final energy consumption ($\Delta \mathrm{ln}(FEC)$) will change by 5.13% at a significance level of 1%. When the foreign direct investment ($\Delta \mathrm{ln}({F}_{i})$) changes by 1%, the total final energy consumption ($\Delta \mathrm{ln}(FEC)$) will change by 4.91% at a significance level of 1%. Furthermore, when the international tourist arrivals ($\Delta \mathrm{ln}({I}_{t})$) rate changes by 1%, the total final energy consumption ($\Delta \mathrm{ln}(FEC)$) will change by 5.65% at a significance level of 1%. Lastly, when the total exports and imports ($\Delta \mathrm{ln}({X}_{m})$) rate changes by 1%, the total final energy consumption ($\Delta \mathrm{ln}(FEC)$) will change by 3.91% at a significance level of 1%. Table 3 tells us that when each of the independent variables changes, there will be an effect of change observed in the dependent variables, including $\Delta \mathrm{ln}(GDP)$, $\Delta \mathrm{ln}({P}_{g})$, $\Delta \mathrm{ln}({U}_{r})$, $\Delta \mathrm{ln}({I}_{s})$, $\Delta \mathrm{ln}({O}_{p})$, $\Delta \mathrm{ln}({F}_{i})$, $\Delta \mathrm{ln}({I}_{t})$, and $\Delta \mathrm{ln}({X}_{m})$, which is shown in the relevant parameters.

_{X}model, it indicates an adjustment ability toward the equilibrium of the dependent variables, and can be presented as $EC{T}_{t-1}$. Here, the analysis verifies the variables with such ability to be the following. Firstly, the real GDP ($\Delta \mathrm{ln}(GDP)$) is equivalent to 54.5% at a significance level of 1%. Consequently, the next variables are foreign direct investment ($\Delta \mathrm{ln}({F}_{i})$), industrial structure ($\Delta \mathrm{ln}({I}_{s})$), urbanization rate ($\Delta \mathrm{ln}({U}_{r})$), total exports and imports ($\Delta \mathrm{ln}({X}_{m})$), international tourist arrivals ($\Delta \mathrm{ln}({I}_{t})$), oil price ($\Delta \mathrm{ln}({O}_{p})$), population growth ($\Delta \mathrm{ln}({P}_{g})$), and total final energy consumption ($\Delta \mathrm{ln}(FEC)$), with the ability sizes of 52.2%, 52.1%, 41.9%, 40.7%, 33.9%, 31.1%, 30.7%, and 29.1%, respectively. Those results are produced at a significance level of 1%, except for population growth and total final energy consumption which are produced at a significance level of 5%.

_{X}model with that of other models based on MAPE and RMSE. The other models included the ML model, ANN model, BP model, Grey model, and ARIMA model.

_{X}model produced an MAPE value of 1.09%, while its RMSE value was 1.01%. This further indicates that its performance peak is better compared to other studied models. In other lines of models, the ARIMA model had an MAPE value equivalent to 3.22% and its RMSE value was 3.04%. The Grey model presented an MAPE value of 5.37% and an RMSE value of 4.41%, while the BP model had an MAPE value of 11.59% and an RMSE value of 11.26%. In addition to this, the ANN model had an MAPE value of 12.64% and an RMSE value of 12.49%. Lastly, the ML model was found to have an MAPE value of 19.16% and an RMSE value of 17.52%. Hence, through this evidence, the researchers chose the SE-VARx model for a projection of the total final energy consumption over the next 10 years (2019–2028).

#### 3.4. Total Final Energy Consumption Forecasting Based on the SE-VAR_{X} Model

_{X}model was found to be suitable for such forecasting applications, and it was not spurious. Hence, it produces effective outcome with fewer errors, as shown in Figure 2.

## 4. Conclusions and Discussion

_{X}model. This model is shown to have good capacity for effective forecasting and can be used as a significant tool in policy-making to achieve sustainable development goals. In the study, each variable of the model is stationary at the first difference, yet co-integrated. The variables consist of total final energy consumption (FEC), real GDP (GDP), population growth (P

_{g}), urbanization rate (U

_{r}), industrial structure (I

_{s}), oil price (O

_{p}), foreign direct investment (F

_{i}), international tourist arrivals (${I}_{t}$), and total exports and imports (${X}_{m}$). In addition, all those variables have both short- and long-term direct effects, and they are influential in the equilibrium change. Moreover, the SE-VAR

_{X}model tells us that all dependent variables are adjustable to the equilibrium at different magnitudes. The real GDP ($GDP$) is the fastest variable to be adjusted the equilibrium. Meanwhile, foreign direct investment ($\Delta \mathrm{ln}({F}_{i})$), industrial structure ($\Delta \mathrm{ln}({I}_{s})$), urbanization rate ($\Delta \mathrm{ln}({U}_{r})$), total exports and imports ($\Delta \mathrm{ln}({X}_{m})$), international tourist arrivals ($\Delta \mathrm{ln}({I}_{t})$), oil price ($\Delta \mathrm{ln}({O}_{p})$), population growth ($\Delta \mathrm{ln}({P}_{g})$), and total final energy consumption ($\Delta \mathrm{ln}(FEC)$) were found to adjust to the equilibrium at slower pace. When the SE-VAR

_{X}model was applied to predict a medium-term forecast of 10 years (2019–2028), the total final energy consumption was found to continuously increase with a growing rate, thus affecting the implementation of sustainable development policies in Thailand.

_{X}model has been used in policy planning to support the sustainable development goals of Thailand in the past. Most models previously used are spurious, and this has caused errors and inaccuracies as they did not meet the criterion of the “Best Model”. This also has a negative impact upon the policy implementation and strategic planning of the country [5]. Thus, the SE-VAR

_{X}model was made to close the research gap left by other previous research. Through a review of relevant studies, it was shown that the research flow of this model may be similar to others, yet it is different in terms of the direct effect analysis of each causal factor. Furthermore, it provides evidence of adjustment ability to the equilibrium, thus making it a suitable model for forecasting applications. Most importantly, the SE-VAR

_{X}model is unique in its ability to be applied to different sectors and contexts. Also, it effectively supports the national policy-making and planning efforts and can be used as a guideline for knowledge discovery. In the construction of the SE-VAR

_{X}model, we employed advanced statistics and verified that it was the best model by a comparison with other previously reported models. The SE-VAR

_{X}model is an ideal model for medium-term forecasting (6–10 years) [4,5], which differs greatly from short-term models, especially in the field of policies, as these are extremely changeable over time. This factor affects future forecasts, mostly medium- and long-term forecasting [5]. Hence, it is crucial to have the right tool, such as the proposed SE-VAR

_{X}model, for the implementation of policy and strategic planning in Thailand.

_{X}model. As it has been mentioned, only stationary causal factors at the same level were used, and the co-integration test was employed along the analysis of direct effect, which separates this model from those investigating indirect effect. No other research along these lines has been done before. Since the SE-VAR

_{X}model is a non-spurious model, it allowed us to attain a more accurate magnitude of the influence and confirmed that this model could significantly contribute to the national policy-making endeavors. It is important to confirm that the SE-VAR

_{X}model is suitable for forecasting, thus enabling Thailand to formulate proper policies in the future.

_{X}model were determined and selected carefully in consideration of the Nationall Strategic Framework of Sustainable Development from 1990 until the present. This is also based on the Thai context, with some factors potentially differing from previous studies. Thus, results may differ if we were to use the same factors from those studies for the SE-VAR

_{X}model. Hence, researchers should select the appropriate factors for the appropriate context and sector, so as to produce the most accurate analytical results for effective policy making for national governance in the future. In addition, due to the rapid economic and social growth in Thailand resulting from the promotional policies in place, such growth inevitably leads to a continuous negative impact on the environment. Although the government is trying to tackle the issue of environmental damages, it still cannot solve such issues completely. This is because certain factors do not work in line with the market mechanism, including the factor of government intervention in the price of oil, and this will result in the distortion of real market trends. In addition, the government has been involved in the promotion of exporting energy to foreign countries for the purposes of national revenue generation. Yet, this forces Thailand to import certain energy types from abroad. Thus, this activity may influence the total exports and imports in the long term. Therefore, it is essential to consider the context of the whole country so that sustainable development can be achieved efficiently.

_{X}model in real-world applications. By enhancing the model, forecasting at maximal efficiency can be realized. Using a method of factor analysis along with path analysis, this approach will facilitate the accurate measurement of variable selection for a given context and enable us to effectively deploy latent variables and observed variables to forecast the changes of government policies in the future.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**The flowchart of the Structure Equilibrium-Vector Autoregressive with Exogenous Variables Model (SE-VARx model). Sources: own research.

**Figure 2.**The forecasting results of the total final energy consumption in the construction sector of Thailand from 2019 to 2028. Sources: own research.

Tau Test at First Difference I(1) | MacKinnon Critical Value | |||
---|---|---|---|---|

Variables | Value | 1% | 5% | 10% |

$\Delta \mathrm{ln}(FEC)$ | −5.96 *** | −5.05 | −4.75 | −3.50 |

$\Delta \mathrm{ln}(GDP)$ | −5.84 *** | −5.05 | −4.75 | −3.50 |

$\Delta \mathrm{ln}({P}_{g})$ | −5.25 *** | −5.05 | −4.75 | −3.50 |

$\Delta \mathrm{ln}({U}_{r})$ | −5.77 *** | −5.05 | −4.75 | −3.50 |

$\Delta \mathrm{ln}({I}_{s})$ | −6.05 *** | −5.05 | −4.75 | −3.50 |

$\Delta \mathrm{ln}({O}_{p})$ | −5.39 *** | −5.05 | −4.75 | −3.50 |

$\Delta \mathrm{ln}({F}_{i})$ | −5.71 *** | −5.05 | −4.75 | −3.50 |

$\Delta \mathrm{ln}({I}_{t})$ | −5.44 *** | −5.05 | −4.75 | −3.50 |

$\Delta \mathrm{ln}({X}_{m})$ | −5.70 *** | −5.05 | −4.75 | −3.50 |

Variables | Hypothesized Number of CE(S) | Trace Statistic Test | Max-Eigen Statistic Test | MacKinnon Critical Value | |
---|---|---|---|---|---|

1% | 5% | ||||

$\Delta \mathrm{ln}(FEC)$, $\Delta \mathrm{ln}(GDP)$, $\Delta \mathrm{ln}({P}_{g})$, $\Delta \mathrm{ln}({U}_{r})$, $\Delta \mathrm{ln}({I}_{s})$, $\Delta \mathrm{ln}({O}_{p})$, $\Delta \mathrm{ln}({F}_{i})$, $\Delta \mathrm{ln}({I}_{t})$, $\Delta \mathrm{ln}({X}_{m})$ | None *** | 225.11 | 132.70 | 15.05 | 12.50 |

At Most 1 *** | 70.09 | 49.02 | 5.05 | 3.75 |

Dependent Variables | Direct Effect | |||||||||
---|---|---|---|---|---|---|---|---|---|---|

Short Term | Long Term | |||||||||

$\sum \mathsf{\Delta}\mathbf{ln}(\mathit{F}\mathit{E}\mathit{C})$ | $\sum \mathsf{\Delta}\mathbf{ln}(\mathit{G}\mathit{D}\mathit{P})$ | $\sum \mathsf{\Delta}\mathbf{ln}({\mathit{P}}_{\mathit{g}})$ | $\sum \mathsf{\Delta}\mathbf{ln}({\mathit{U}}_{\mathit{r}})$ | $\sum \mathsf{\Delta}\mathbf{ln}({\mathit{I}}_{\mathit{s}})$ | $\sum \mathsf{\Delta}\mathbf{ln}({\mathit{O}}_{\mathit{p}})$ | $\sum \mathsf{\Delta}\mathbf{ln}({\mathit{F}}_{\mathit{i}})$ | $\sum \mathsf{\Delta}\mathbf{ln}({\mathit{I}}_{\mathit{t}})$ | $\sum \mathsf{\Delta}\mathbf{ln}({\mathit{X}}_{\mathit{m}})$ | $\mathit{E}\mathit{C}{\mathit{T}}_{\mathit{t}-1}$ | |

$\Delta \mathrm{ln}(FEC)$ | - | 6.54 ** | 4.02 ** | 5.33 ** | 5.74 ** | 5.13 ** | 4.91 ** | 5.65 ** | 3.91 ** | −2.01 * |

$\Delta \mathrm{ln}(GDP)$ | 4.64 ** | - | 4.11 ** | 5.49 ** | 6.42 ** | 6.18 ** | 6.28 ** | 6.77 ** | 6.45 ** | −4.45 ** |

$\Delta \mathrm{ln}({P}_{g})$ | 2.17 * | 3.11 ** | - | 2.11 * | 3.69 ** | 2.19 * | 2.01 * | 2.92 * | 2.13 * | −3.07 * |

$\Delta \mathrm{ln}({U}_{r})$ | 4.07 ** | 5.61 ** | 2.15 * | - | 5.97 ** | 6.55 ** | 5.89 ** | 4.68 ** | 6.51 ** | −5.19 ** |

$\Delta \mathrm{ln}({I}_{s})$ | 3.99 ** | 4.94 ** | 2.04 * | 5.09 ** | - | 6.19 ** | 6.10 ** | 4.01 ** | 5.73 ** | −5.21 ** |

$\Delta \mathrm{ln}({O}_{p})$ | 3.19 ** | 2.04 * | 4.60 ** | 5.74 ** | 4.67 ** | - | 2.01 * | 2.19 * | 4.34 ** | −3.11 ** |

$\Delta \mathrm{ln}({F}_{i})$ | 4.17 ** | 2.19 * | 3.95 ** | 6.02 ** | 5.81 ** | 4.90 ** | - | 2.04 * | 3.97 ** | −5.22 ** |

$\Delta \mathrm{ln}({I}_{t})$ | 2.03 * | 2.24 * | 2.60 * | 5.94 ** | 2.91 * | 4.19 ** | 2.01 * | - | 2.23 * | −3.39 ** |

$\Delta \mathrm{ln}({X}_{m})$ | 4.95 ** | 2.21 * | 3.99 ** | 4.71 ** | 5.84 ** | 6.05 ** | 4.87 ** | 3.93 ** | - | −4.07 ** |

Forecasting Model | Mean Absolute Percentage Error (MAPE) (%) | Root Mean Square Error (RMSE) (%) |
---|---|---|

ML model | 19.16 | 17.52 |

ANN model | 12.64 | 12.49 |

BP model | 11.59 | 11.26 |

Grey model | 5.37 | 4.41 |

ARIMA Model | 3.22 | 3.04 |

SE-VAR_{X} Model | 1.09 | 1.01 |

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## Share and Cite

**MDPI and ACS Style**

Sutthichaimethee, J.; Kubaha, K. The Relationship of Causal Factors Affecting the Future Equilibrium Change of Total Final Energy Consumption in Thailand’s Construction Sector under a Sustainable Development Goal: Enriching the SE-VAR_{X} Model. *Resources* **2019**, *8*, 1.
https://doi.org/10.3390/resources8010001

**AMA Style**

Sutthichaimethee J, Kubaha K. The Relationship of Causal Factors Affecting the Future Equilibrium Change of Total Final Energy Consumption in Thailand’s Construction Sector under a Sustainable Development Goal: Enriching the SE-VAR_{X} Model. *Resources*. 2019; 8(1):1.
https://doi.org/10.3390/resources8010001

**Chicago/Turabian Style**

Sutthichaimethee, Jindamas, and Kuskana Kubaha. 2019. "The Relationship of Causal Factors Affecting the Future Equilibrium Change of Total Final Energy Consumption in Thailand’s Construction Sector under a Sustainable Development Goal: Enriching the SE-VAR_{X} Model" *Resources* 8, no. 1: 1.
https://doi.org/10.3390/resources8010001