TX_1~AT/TX_2~AT International Journal of Energy Economics and Policy ISSN: 2146-4553 available at http: www.econjournals.com International Journal of Energy Economics and Policy, 2021, 11(5), 600-608. International Journal of Energy Economics and Policy | Vol 11 • Issue 5 • 2021600 Indicators of Environmental and Economic Problems Priority Arising from Energy Use in Food Manufacturing Sector in Realizing Sustainable Development Policy under Thai Environmental Law Framework Pruethsan Sutthichaimethee1*, Danupon Ariyasajjakorn1, Apinyar Chatchorfa2, Boonton Dockthaisong2, Sthianrapab Naluang3, Sirapatsorn Wongthongdee4, Nachatchaya Thongjan3 1Faculty of Economics, Chulalongkorn University, Wang Mai, Khet Pathum Wan, Bangkok 10330, Thailand, 2Department of Political Sciences, Faculty of Social Sciences, Mahachulalongkornrajavidyalaya University, Phahon Yothin Road, Kilometer 55 Lam Sai, Wang Noi, Phra Nakhon Si Ayutthaya 13170, Thailand, 3School of Law, Assumption University, 592/3 Ramkhamhaeng 24, Hua Mak, Bangkok 10240; Thailand, 4Faculty of Public Administration, Dhurakij Pundit University, 110/1-4 Prachachuen Road, Laksi District, Bangkok, Thailand. *Email: pruethsan.s@chula.ac.th Received: 15 April 2021 Accepted: 27 July 2021 DOI: https://doi.org/10.32479/ijeep.11515 ABSTRACT This research was conducted to examine the true benefit of energy consumption within the scope of energy cost, as well as model a forecasting tool for energy cost in food manufacturing industry. It was limited to the analysis of true benefit of the consumption, energy cost, forward-and-backward relationship, and prediction of future energy cost during the next 10 years ranging from 2021 to 2030, and 20 years ranging from 2021 to 2040. The analysis was made possible via an application of ARIMAX model optimizing the input-output table of Thailand. As for the result, it reveals that the product of tobacco is found with the highest true value of benefit. While candy and sweets, sugar, breweries, corn, distilled spirit, slaughtering, milled rice, coffee and tea, and canned meat are respectively detected. In taking forward-and-backward relationship into account, a close monitoring is required for the sector of canned meat and milled rice, respectively. Since the developed model is confirmed for its validity, an optimization of RMSE, MAE, and MAPE measurement for 10 years (2021-2030) and 20 years (2021-2040) prediction of energy cost would result in the following outcomes; (1) a gradual increase of 41.86 percent is estimated for the energy cost by 2030 compared to 2021 per illustration in Model 1, and (2) energy cost is calculated at a steadily increased 70.79% by 2040 in comparison with 2021 per presentation in Model 2. Keywords: Economic Problem, Environmental Law, Sustainability, Food Manufacturing Sector, Energy Cost JEL Classifications: P28, Q42, Q43, Q47, Q48 1. INTRODUCTION Global warming is one of many global concerns contributing negative effect and challenges to the world. Since mid-century to date, a rise of average temperature has been observed by the Intergovernmental Panel on Climate Change or known as IPCC (The World Bank: Energy Use (Kg of Oil Equivalent Per Capita) Home Page, 2021; Office of the National Economic and Social Development Council: NESDC, 2021; National Statistic Office Ministry of Information and Communication Technology, 2021). Human activities are among many key causes sparking tenses in greenhouse gas (GHG) effect (NESDC, 2021). During 21st century (2001-2100), an increase of 1.1 up to 6.4 degree Celsius of average temperature surface is estimated to happen. In most cases, This Journal is licensed under a Creative Commons Attribution 4.0 International License Sutthichaimethee, et al.: Indicators of Environmental and Economic Problems Priority Arising from Energy Use in Food Manufacturing Sector in Realizing Sustainable Development Policy under Thai Environmental Law Framework International Journal of Energy Economics and Policy | Vol 11 • Issue 5 • 2021 601 developed country Annex 1 group has the ability to reduce such a challenge (Department of Alternative Energy Development and Efficiency, 2021; Thailand greenhouse gas management organization (public organization), 2021). Unfortunately, it is not the case for developing countries, including Thailand and most countries in ASEAN community, in coping and dealing with the issue (NESDC, 2021; United Nations Framework Convention on Climate Change, UNFCCC, Bonn, Germany, 2016). Thailand has rapidly and continuously developed proper economic system while GHG emission is found to increase day by day at the same time (NESDC, 2021; Pollution Control Department Ministry of Natural Resources and Environment. Enhancement and Conservation of National Environmental Quality Act, B.E. 2535, 2021); Pollution Control Department Ministry of Natural Resources and Environment. Navigation of Thai Waterways Act, B.E. 2546, 2021; Pollution Control Department Ministry of Natural Resources and Environment. Principle 4: In order to achieve sustainable development, environmental protection shall constitute an integral part of the development process and cannot be considered in isolation from it, 2021). The data retrieved from the Office of Natural Resources and Environmental Policy and Planning presents the release of real GHG emission at 379.61 MtCO2e in 2020 (Pollution Control Department Ministry of Natural Resources and Environment. Principle 4: In order to achieve sustainable development, environmental protection shall constitute an integral part of the development process and cannot be considered in isolation from it, 2021). The energy consumption sector was found as the major contributor to this emission discharging about 272.49 MtCO2e or 75.15 percent of entire greenhouse gas in Thailand. In comparison to the year 2000, the emission growth is progressed more than 65% as illustration in Figure 1 (Department of Alternative Energy Development and Efficiency, 2021; Thailand greenhouse gas management organization (public organization), 2021). With the information given, it implies that our world is gradually falling to a warmer state (Thailand greenhouse gas management organization (public organization), 2021). The later consequence due to climate change leads to various massive natural disasters, including floods, droughts, and many more. These events have a direct impact on consumers in relation to food security. Eventually, it causes economic growth to shrink (NESDC, 2021). Thailand has made a number of drafting laying out plans and policies designed to improve manufacturing structure and business productivity. Unfortunately, energy plans and policies are observed incomplete and interrupted. Thailand needs energy input-output analysis (IOA) by acquiring most recent data and energy input-output information. In addition to this analysis, a forecasting model is required to predict greenhouse gas emission in energy consumption, and it must be fit and valid in order to manage and administer energy and environment for a better sustainability. Thus, this research is developed to explain the development of prediction model for optimal national use in energy sector. The research extends on ARIMA model integrating exogenous X variable. The analyses are systematically carried out in line with research methodology and statistical procedure in order to obtain research results with least residuals. The research also depicts no studies focusing on a forecasting model development with an integration of exogenous variables and the aforementioned model. Hence, this research is believed beneficial and practical for policy and plan formulation, and expected to benefit other countries as well. 2. MATERIALS AND METHODS 2.1. ARIMA Model The model ARIMA comprises of three core components: Auto Regressive (AR), Integrated (I), and Moving Average (MA). These components can be explained and detailed out below (Zhang and Broadstock, 2016; Zhang and Xu, 2012). 1. Auto Regressive (AR) is composed of general characteristics of order p are as shown below Yt=α+β1Yt–1+β2Yt–2+…+βpYt–p+εt (1) Where; β1…βp are parameters; α is a content, εt is the random variable (white noise) (Yalta and Cakar, 2012) 2. Integrated (I) means to retrieve the difference of variables. Since the ARIMA is non-stationary, it is essential to define the differences of order p so that stationary data can be obtained from non-stationary data (Zhang and Lin, 2012; Lu, 2017) 3. Moving Average (MA) is to estimate the error term from forecasting by the differences in actual variables (Y Actual) with dependent variables (Y Forecast) or εt =Yat–Yft with prediction of needed variables in the future as formulated below (Xu et al., 2014; Ren et al., 2014). Yt=δ+εt–y1εt–1–y2εt–2–…–yqεt–q (2) Where Moving Average of Order q or MA(q) by q indicates the last order of error value applied. The model development form of ARIMA can be explained as ARIMA (p,d,q). That is Order of AR=p, I=d, and MA=q, respectively. 2.2. ARIMAX Model The ARIMAX Model is a newly-introduced model developed from ARIMA Model. When GHG emission is given to be dependent variable, various factors consisting of population, CO2, CH4, and energy, 79.15%, 79% waste, 1.03%, 1% AFOLU, 13.06%, 13% IPPU, 6.76%, 7% Emission in 2020 by energy sector (%) energy waste AFOLU IPPU Figure 1: Ratios of greenhouse gas discharged in Thailand given by different sectors as of 2020 Sutthichaimethee, et al.: Indicators of Environmental and Economic Problems Priority Arising from Energy Use in Food Manufacturing Sector in Realizing Sustainable Development Policy under Thai Environmental Law Framework International Journal of Energy Economics and Policy | Vol 11 • Issue 5 • 2021602 N2O emission becomes independent variables (Dai et al., 2018; Liu, 2019). In fact, the ARIMAX model is expected to provide accurate and effective outcomes in the event of predicting future GHG emission due to following details (Ma, 2018). 2.2.1. Steps for modeling and forecasting 1. Analyze the data for stationary property by testing the Unit Root guided by Augment Dickey and Fuller theory Stationary stochastic process also known as stationary in short is time series data which presents together mean or expected value, constant overtime, variance, and covariance (Qin et al., 2019). This type of data does not lie within time but distance or lag. By assuming Yt as stochastic time series and stationary in the same time, three key properties must exist, and they can be expressed below (Dickey and Fuller, 1981; MacKinnon, 1991). EYt=EYt+k=µ (3) VAR(Yt)=E(Yt–µ) 2=E(Yt–k–µ) 2=σ2 (4) E(Yt–µ)(Yt+k–µ)=γk (5) Upon observing equation (3), (4), and (5), γk is seen as covariance between Yt and Yt+k, where distance exists between two values of Y. However, probability distribution remains unchanged but not expected value and constant variance due to εt lacks of property of white noise (Johansen and Juselius, 1990; Johansen, 1995). Under this phenomenon, autocorrelation is detected with high correlations or higher order autoregressive process. Therefore, a test of augmented Dickey Fuller (ADF) form is required (Enders, 2010; Harvey, 1989). This requirement is due to its addition of lagged variables at a higher level so that autocorrelation of residual, heteroskedasticity, and multicollinearity, can be eliminated as per discussion below (Sims, 1980). � � �Y Y Yt t i p i t i t� � � � � �� � �1 2 1 (6) � � �Y Y Yt t i p i t i t� � � �� � � �� � � �1 1 1 2 1 (7) � � �Y T Y Yt t i p i t i t� � � � �� � � �� � � � �1 2 1 2 1 (8) With the above equations, the p value is explained to be lagged value of first difference variable, and that can be tested by using Unit Root testing with the Augmented Dickey Fuller method. The later equation can be written as follows (Byrne, 2009; Sutthichaimethee, 2018). � � �Y T Y Yt t i p i t i t� � � � �� � � �� � � � �1 2 1 2 1 (9) The equation (9) lays out a fact that three key issues are considered, particularly autocorrelation of εt is set to experience White Noise. When a tau-statistic of the co-efficient δ is found in the absolute term, more critical values are expected to list in the ADF table. However, this expectation fails to hold in core hypotheses. Yet, this fact implies that time series variables are stationary explaining ∆Yt Integrated Number d is represented by ∆Yt∼ I(d) (Sutthichaimethee, 2016; 2017). 2. Analyze all the stationary data at the same level both dependent variables and independent variables (at level of 1st moment and/or 2nd moment only) for the long-term relationship or examine co-integration when variables of the model are relevant in the long term but the same level (Sutthichaimethee and Ariyasajjakorn, 2017). In order to ensure the best model in property, Vector Error-Correction model (ECM) must be applied (Sutthichaimethee and Ariyasajjakorn, 2018) As for this research, the co-integrated relationship is presented based on the Full Information Maximum Likelihood (FIML) Approach as introduced by Johansen and Juselius (1990) due to the followings. 3. The model is applicable to apply with two variables or more 4. Number of co-integrated vectors can be examined without a need of variables specification whether they are exogenous or endogenous. According to Johansen and Juselius method, it lies within the form of Multivariate Co-integration by denoting to Vector Autoregressive (VAR) Model, and that can be exchanged below (Sutthichaimethee and Ariyasajjakorn, 2017; Sutthichaimethee and Dockthaisong, 2018) � � � � � �X X X X ut i k i t t t k t� � � � � � � �� 1 1 1 , (10) From Johansen and Juselius approach point of view, it is interested to seek Co-integrating Vectors of variables Xt in VAR Model. It is also essential to get the right Lag to activate the VAR Model by considering the Likelihood Ratio Test of Sims (1980) or the Minimum Final Prediction Error Test Akaike approach. The test is composed with the following stages (Sutthichaimethee and Kubaha, 2018). • Prepare a needed equation for testing based on Vector Autoregressive Model (VAR) • Conduct the test to obtain numbers of Lag fitting for the above equation • Compute Co-integrating Vectors between model variables and retrieve a metric π rank, in which it is independent of π equal to Rows or Columns • Apply two statistical testing tools to obtain numbers of Co-integrating Vectors (r) inside the model, and they can Trace Test and Maximum Eigenvalue Test, for instance. The both tools often function simultaneously to determine result accuracy (Sutthichaimethee et al., 2019) • Estimating the Best Model: True impact of independent variables on dependent variable must be detected. This impact can be observed tau-statistic values with different significance level of 5%, 10%, and 15% • Testing the Best Model for three core issues as follows • Autocorrelation is tested by deploying Lagrangian Multiplier Test or known as LM test (Valipour et al., 2019; Pacheco, 2013). Sutthichaimethee, et al.: Indicators of Environmental and Economic Problems Priority Arising from Energy Use in Food Manufacturing Sector in Realizing Sustainable Development Policy under Thai Environmental Law Framework International Journal of Energy Economics and Policy | Vol 11 • Issue 5 • 2021 603 The LM Test can be taken when lagged dependent variables in an equation become independent variables. In fact, the LM test can be used to detect Error Terms in relation to autocorrelation in high level. The following equation presents the test’s method Yt=α0+α1Xt+β1Ut–1+β2Ut–2+…+βpUt–p (11) Process the equation Yt=α1Xt+Ut to obtain Residual; major hypothesis can be H1:β1=β2=…=βp=0 and the statistical test is expressed as nR2∼χ2p, while F-Test= n k m R� � � 2 2 1( R ) If χ2p and Fm,n-k – Test statistic is found greater than critical χ 2 value and F critical value at a selected level of significance, it means that the major hypothesis is rejected, and at least one β has a value difference from 0, presenting autocorrelation problem in the model (Sutthichaimethee, 2017). • Heteroskedasticity is tested by applying ARCH Test. The ARCH Testing is taken to examine Heteroskedasticity in time series data. When the Residual is observed, lagged variables of the residual is evaluated by accounting F and nR2 value with Chi-Square distribution. If the χ2p statistical test value is greater than critical value of χ2p at a chosen significance level, that means the hypothesis is rejected due to the presence of Heteroskedasticity problem (Sutthichaimethee, 2018) • Multicollinearity is tested by deploying correlation test as to check on responses from the Correlogram value in comparison to Chi-square value (Sutthichaimethee, 2016). 2.3. Measurement of the Forecasting Performance Accuracy checking is assessed to see prediction capability. This forecasting accuracy is evaluated by comparing three different evaluation statistics, namely Root Mean Square Error (RMSE), Mean Absolute (MAE), and Mean Absolute Percentage Error (MAPE). These statistics are written as follows (Enders, 2010; Harvey, 1989): RMSE = ( ) /F A ni i i n � � � 2 1 (12) MAE = F A ni i i n � � � 1 / (13) MAPE = F A A ni i i i n �� � � � � / / 1 100 (14) From the above equations, Fi and Ai are the forecasted value and actual value, respectively, while n is the total number of forecasting. As for this research, it aims to retrieve results with the least error, and that reason accounts models with MAPE values of less than 30%. The expansion of the GHG emission model developed in this research is made possible with the use of the ARIMAX model as per illustration below (Sutthichaimethee, 2018). Where GHGt represents greenhouse gas at time t, GHGt–i denotes dependent variables in the duration t–i, Populationt–i expresses of population number, Techt–i denotes technology, (CO2)t–i in the duration t–i, refers to Carbon Dioxide, (CH4)t–I represents Methane in the duration t–i, (N2O)t–i signifies Nitrous oxide in the duration t–i , ECM embodies Error Correction Model, MAi refers to Moving Average at time i, εt signifies residual at time I, ∆ refers to first difference operator, and ln is explained as natural logarithm. 3. EMPIRICAL ANALYSIS 3.1. Indicator Analysis Findings on Environmental and Economic Problems Priority Arising from Energy Use Cost in Food Manufacturing Sector The results of the real benefit, energy costs, Forward Linkage and Backward Linkage are classified by each category of the production. This research can be summarized as following: Table 1 lists the top ten food manufacturing sectors in terms of Forward Linkage, Backward Linkage real benefit, and energy cost. In this study, “Forward Linkage” presents the relationships among each production line. It can show where input are sent from. It also signifies that the higher level of the forward linkage, the higher relationships among the involved sectors. real benefit is the revenue for a sector, minus the environmental costs. The average real benefit was 0.96. If the real benefit for a given industry is lower than the average, it can be considered to represent a loss, while values higher than the average represent profit. Besides, Table 1 has further explained the analysis outcome by highlighting key features as follows: 3.1.1. Highlights from the findings include the following 1. The highest real benefit in the Food manufacturing sectors was tobacco products, while the lowest real benefit was canning and preserving of meat. The lowest real benefit could signify loss in profit (Figure 2) 2. The highest Forward Linkage in the food manufacturing sectors was for canning and preserving of meat, while the lowest Forward Linkage was rice milling 3. The highest Backward Linkage in the food manufacturing sectors was for ice, while the lowest Forward Linkage was canning and preserving of meat. Analyzing the indicator of environmental impacts from the food manufacturing sectors of Thailand. The average carrying capacity value for energy cost was 0.071, if the cost for a particular industry � � �ln( ) ln( ) lnGHG GHG Populationt i t i i n i t i i n � � � �� � � � � �� � � � 1 1 2 1 33 1 4 2 1 5 4 1 i t i i n i t i i n i t i i Tech CO CH � � � ln( ) ln( ) ln( ) � � � � � � � �� �� � nn i t i i n i i m t N O MA ECM � � � � � � � � � � � � � � 5 2 1 6 1 7 �ln( ) Sutthichaimethee, et al.: Indicators of Environmental and Economic Problems Priority Arising from Energy Use in Food Manufacturing Sector in Realizing Sustainable Development Policy under Thai Environmental Law Framework International Journal of Energy Economics and Policy | Vol 11 • Issue 5 • 2021604 is lower than the average carrying capacity value, there is further capacity for production. environmental cost values that are higher than the average carrying capacity value signify that there is no further capacity for production. 3.1.2. Highlights from the findings include the following The food manufacturing sectors with the highest environmental cost in terms of energy cost was Ice. The cost indicator was above the average carrying capacity value, signifying that this sector does not have capacity for further production (Figure 3). 3.2. Formation of Analysis Modeling with the ARIMAX Model The results of the Forecasting model of energy costs is classified by each category of the production. This research can be summarized as following: 1. Unit root test: with the augmented Dickey-Fuller test is shown in Table 2 Table 2: The ADF Test Statistic at level of all variables has a variable unit root component or non stationary i.e. the value Table 1: Indicator of environmental and economic problems priority arising from the use of energy cost in food manufacturing sectors Forward linkage Backward linkage Real benefit Energy cost Value Sectors Value Sectors Value Sectors Value Sectors 0.7855 Canning and preserving of meat 0.6576 Ice 0.9212 Tobacco products 0.4893 Ice 0.7607 Monosodium glutamate g 0.6117 Monosodium glutamate 0.8053 Confectionery 0.1569 Monosodium glutamate 0.7292 Tapioca milling 0.5119 Tapioca milling 0.7931 Sugar 0.1444 Tapioca milling 0.7213 Coconut and palm oil 0.3958 Coconut and palm oil 0.7712 Breweries 0.1436 Coconut and palm oil 0.6853 Noodles and similar products 0.3796 Dairy products 0.7490 Grinding of maize 0.1297 Noodles and similar products 0.6827 Coffee and tea 0.3664 Animal oil, animal fat, vegetable oil and by- products 0.7433 Distilling and spirits blending 0.1207 Confectionery 0.6460 Slaughtering 0.3231 Canning and preservation of fish and other sea foods 0.7413 Slaughtering 0.1184 Canning and preserving of meat 0.6392 Grinding of maize 0.3098 Canning and preservation of fruit and vegetables 0.7219 Rice milling 0.1173 Sugar 0.6332 Confectionery 0.3025 Grinding of maize 0.7065 Coffee and tea 0.1138 Canning and preservation of fruit and vegetables 0.6308 Rice milling 0.3019 Canning and preserving of meat 0.6996 Canning and preserving of meat 0.1107 Canning and preservation of fish and other sea foods 0 0.2 0.4 0.6 0.8 1 1.2 To ba cc o pr od uc ts C on fe ct io ne ry S ug ar B re w er ie s G rin di ng o f m ai ze D is til lin g an d sp iri ts bl en di ng S la ug ht er in g R ic e m ill in g C of fe e an d te a C an ni ng a nd p re se rv in g of m ea ta ve ra ge c ar ry in g ca pa ci ty v al ue sectors average carrying capacity value Real Benefit Linear (average carrying capacity value) Figure 2: Real benefit Sutthichaimethee, et al.: Indicators of Environmental and Economic Problems Priority Arising from Energy Use in Food Manufacturing Sector in Realizing Sustainable Development Policy under Thai Environmental Law Framework International Journal of Energy Economics and Policy | Vol 11 • Issue 5 • 2021 605 calculated from the ADF, all lower than the critical value. From the table at the significance level of 1%, 5% and 10%, so that it must be to qualify as Stationary by the difference moment. This research found that all variables Stationary at the first differencing included energy consumption (EC), population (Population), technology (Tech), and GDP per capita (GDP). The value of the test based on the “Tau-test” is greater than the all “Tau-critical” at the first difference, results in Table 3. 2. Result of the co-integration test The result in Table 4 brings all variables are stationary at the first difference to test co-integration by using the method of “Jansen Juselius” shown in Table 3. Table 3 as the results, “co-integration test” showed that model is a co-integration because of the Trace Test is 178.28, which is higher than the critical value at significance level of 1% and 5%, the Maximum Eigen value test at 127.75 which is higher than the critical value significance level of 1% and 5%. 3. The result of ARIMAX model 1. ARIMAX Model 1 (2,1,1) ∆In(EC)t=–0.031+2.61∆In(EC)*t–1+2.95∆In(EC)*t–2+4.16∆In (Population)**t–1–7.12 ∆In(Tech)**t–1+5.78∆In(GDP)**t–2+2.02 MA*1+5.23ECM*** Where ** is significance α = 0.01, * is significance α = 0.05, R-squared is 0.95, Adjusted R-squared is 0.93, Durbin-Watson stat is 2.02, F-statistic is 154.75 (Probability is 0.00), ARCH-test is 43.05 (Probability is 0.11), LM – test is 1.63 (Probability is 0.10) and response test (χ2>critical) is significance. 2. ARIMAX Model 1 (2,1,2) ∆In(EC)t=–0.073+4.12∆In(EC)**t–1+3.78∆In(EC)**t–2+6.53∆In (Population)**t–1–6.97 ∆In(Tech)**t–1+5.05∆In(GDP)**t–1+2.12 MA*1+2.78MA**1+2.10ECM* Where ** is significance α = 0.01, * is significance α = 0.05, R-squared is 0.92, Adjusted R-squared is 0.89, Durbin-Watson stat is 2.17, F-statistic is 165.05 (Probability is 0.00), ARCH-test is 33.05 (Probability is 0.12), LM – test is 2.71 (Probability is 0.11) and response test (χ2>critical) is significance. 3.3. The Results of Forecasting Model When the modeling ARIMAX Model 1 (2,1,1) and ARIMAX Model 2 (2,1,2), which is the best model that was used to predict 2 models. The first, 10 years forecast (year 2021-2030), the second, 20 years forecast (2021-2040) the forecast results shown in Figures 4 and 5. The results forecasts found that the model 1 (year 2021-2030) energy cost volume increased steadily and average rising up to 41.86% in 2030, and the model 2 (year 2021-2040) energy cost volume increased steadily as well and average rising to 70.79% in 2040. However, that model 1 and model 2 were tested the effectiveness of the model compared with Actual value found that both models are highly effective with the low deviation can be used to decision making that shown in Table 5. This study, the first of its kind in Thailand, creates the forecasting model of energy cost using ARIMAX model and from review of literature of many of sources such as Qin et al. (2019) constructed Autoregressive (AR) model and Long Short-Term Memory (LSTM) model in Python language based on the TensorFlow framework aimed at simulating and predicting the hydrological time series. As of their study’s result, the feasibility of the models is captured for the prediction of the hydrological time series. Mosavi et al. (2018) revisited the existing literature and studies to illustrate the state of the art of Machine Learning (ML) models in flood prediction and to investigate the most suitable models. By taking ML models as a benchmark, hybridization, data decomposition, 0 0.1 0.2 0.3 0.4 0.5 0.6 Ic e M on os od iu m g lu ta m at e T ap io ca m ill in g C oc on ut a nd p al m o il N oo dl es a nd s im ila r pr od uc ts C on fe ct io ne ry C an ni ng a nd p re se rv in g of m ea t S ug ar C an ni ng a nd p re se rv at io n of fr ui t a nd v eg et ab le s C an ni ng a nd p re se rv at io n of fi sh a nd o th er s ea fo od s av er ag e ca rr yi ng c ap ac ity v al ue sectors average carrying capacity value Energy Cost Linear (average carrying capacity value) Figure 3: Energy cost Table 2: Unit root test at level Variables Lag ADF test MacKinnon Critical Value Status 1% 5% 10% In(EC) 1 –2.71 –4.31 –3.45 –3.25 I(0) In(Population) 1 –2.47 –4.31 –3.45 –3.25 I(0) In(Tech) 1 –3.08 –4.31 –3.45 –3.25 I(0) In(GDP) 1 –2.74 –4.31 –3.45 –3.25 I(0) Table 3: Co-integration test by Johansen Juselius Variables Hypothesized No. of CE(S) Trace statistic test MacKinnon critical value Max-Eigen statistic test MacKinnon critical value Status 1% 5% 1% 5% ∆In(EC), ∆In(Population), ∆In(Tech), ∆In(GDP) None** 178.25 21.16 15.41 127.75 17.78 15.21 I(1) At Most 1** 70.05 4.75 3.25 78.03 4.75 3.25 I(1) Sutthichaimethee, et al.: Indicators of Environmental and Economic Problems Priority Arising from Energy Use in Food Manufacturing Sector in Realizing Sustainable Development Policy under Thai Environmental Law Framework International Journal of Energy Economics and Policy | Vol 11 • Issue 5 • 2021606 algorithm ensemble, and model optimization are found as the most effective strategies in improving the quality of the flood prediction models. While Lohani et al. (2014) proposed Peak Percent Threshold Statistic (PPTS) as a new model performance criterion to examine the performance of a flood forecasting model using hourly rainfall and discharge data as a sample. They also compared the result of the proposed model with artificial neural networks (ANN), Self-Organizing Map (SOM) based ANN model and subtractive clustering-based Takagi Sugeno fuzzy model (SC-T-S fuzzy model). As of their analysis, the SC-T-S fuzzy model is shown with reasonably accurate forecast coupled with sufficient lead-time. To Shrestha et al. (2013) examined the quality of precipitation forecasts from four Numerical Weather Prediction (NWP) models, namely ACCESS-G 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km, based on the Australian Community Climate Earth-System Simulator (ACCESS). As part of their findings, it presents that the systematic biases in rainfall forecasts has to be removed before using the rainfall forecasts for streamflow forecasting. Jabbari et al. (2020) deployed a numerical weather prediction and a rainfall-runoff model to assess the precipitation and flood forecast for the Imjin River (South and North Korea). As a result, they no result, they notice that the Weather Research and Forecasting (WRF) model underestimates precipitation in point and catchment assessment. However, there has not been any study done at all. Therefore, this study is a guide for the studying and applying in other countries in the future. 4. CONCLUSION AND DISCUSSION In conclusion, this research indicates that tobacco products come with the highest value of true benefit. Whereas confectionery, sugar, breweries, maize, distilled spirit, slaughtering, milled rice, coffee and tea, and preserved meat, are found respectively. By taking the relationship value of forward-and-backward linkage into account, preserved meat and milled rice segments are shown - 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14 20 15 20 16 20 17 20 18 20 19 20 20 20 21 20 22 20 23 20 24 20 25 20 26 20 27 20 28 20 29 20 30 pe rc en ta te o f e ne rg y co st Year Actual ARIMAX Model1 Figure 4: Forecasting from ARIMAX model 1 (2,1,1) - 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 20 00 20 02 20 04 20 06 20 08 20 10 20 12 20 14 20 16 20 18 20 20 20 22 20 24 20 26 20 28 20 30 20 32 20 34 20 36 20 38 20 40 pe rc en ta te o f e ne rg y co st Year Actual ARIMAX Model2 Figure 5: Forecasting from ARIMAX model 2 (2,1,2) Table 5: The performance monitoring of forecasting model Forecast of energy cost RMSE MAE MAPE Model 1: ARIMAX Model (2,1,1) (2021-2030) 0.013 0.040 1.05 Model 2: ARIMAX Model (2,1,2) (2021-2040) 0.058 0.077 1.02 Table 4: Unit root test at the first difference Variables Lag ADF test MacKinnon critical value Status 1% 5% 10% In(EC) 1 –4.37 –4.31 –3.45 –3.25 I(1) In(Population) 1 –5.57 –4.31 –3.45 –3.25 I(1) In(Tech) 1 –4.75 –4.31 –3.45 –3.25 I(1) In(GDP) 1 –6.47 –4.31 –3.45 –3.25 I(1) Sutthichaimethee, et al.: Indicators of Environmental and Economic Problems Priority Arising from Energy Use in Food Manufacturing Sector in Realizing Sustainable Development Policy under Thai Environmental Law Framework International Journal of Energy Economics and Policy | Vol 11 • Issue 5 • 2021 607 with a need of close control and administration, respectively. This research also predicts energy cost in 10 years (2021-2030) and 20 years (2021-2040) by measuring RMSE, MAE, and MAPE. The prediction results can be summarized as per presentation in two sub-models; Model 1 and Model 2 have been estimated with a gradual rise in energy cost by 41.86% and 70.79%, respectively. This research has made an assurance of sustainable development through policy making and planning by carefully controlling specified sectors suggested by this research finding. Some sectors have been observed with higher energy cost, yet they are lower in real benefit. The sectors cover various fields across consumer production lines, including ice, monosodium glutamate, milled tapioca, palm oil and coconut, noodles and similar products, preserved food in meat, fruits, vegetables, fish and other sea foods. In the meanwhile, some sectors have also found with high value of forward-and-backward connection. This finding signifies an urgency alarm for the government and policy makers to take a serious caution and control over these sectors. In relation to the prediction of energy cost in both terms, 10 years (2021-2030) and 20 years (2021-2040), this research indicates that Thailand has witnessed an increment in rates making it experience in higher energy cost as per confirmation by this study. This phenomenon implies an impact on economy and society, especially environmental damage due to higher predicted ratio than Thailand’s carrying capacity to handle. With this effect, it spreads a rise of greenhouse gas worldwide. Nevertheless, findings from this research can be used as a noble guideline for effective and efficient national policy and planning, enabling Thailand and other countries in application to achieve sustainable development. 5. ACKNOWLEDGMENTS This research is supported by Rachadapisek Sompote Fund for Postdoctoral Fellowship, Chulalongkorn University. REFERENCES Byrne, B.M. (2009), Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. London: Routledge. Dai, S., Niu, D., Li, Y. (2018), Forecasting of energy consumption in china based on ensemble empirical mode decomposition and least squares support vector machine optimized by improved shuffled frog leaping algorithm. Applied Sciences, 8(5), 678. Department of Alternative Energy Development and Efficiency. (2021), Available from: http://www.dede.go.th/ewtadmin/ewt/dede_web/ ewt_news.php?nid=47140. [Last accessed on 2021 Jan 20]. Dickey, D.A., Fuller, W.A. (1981), Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057-1072. Enders, W. (2010), Applied Econometrics Time Series, Wiley Series in Probability and Statistics. Tuscaloosa: University of Alabama. Harvey, A.C. (1989), Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge: Cambridge University Press. Jabbari, A., So, J.M., Bae, D.H. (2020), Precipitation forecast contribution assessment in the coupled meteo-hydrological models. Atmosphere, 11(1), 34. Johansen, S. (1995), Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. New York: Oxford University Press. Johansen, S., Juselius, K. (1990), Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52, 169-210. Liu, X., Lin, J., Hu, J., Lu, H., Cai, J. (2019), Economic transition, technology change, and energy. Energies, 12(13), 2581. Lohani, A.K., Goel, N.K., Bhatia, K.K.S. (2014). Improving real time flood forecasting using fuzzy inference system. Journal of Hydrology, 509, 25-41. Lu, W.C. (2017), Electricity consumption and economic growth: Evidence from 17 Taiwanese industries. Sustainability, 9(1), 50. Ma, J., Oppong, A., Acheampong, K.N., Abruquah, L.A. (2018), Forecasting renewable energy consumption under zero assumptions. Sustainability, 10(3), 576. MacKinnon, J. (1991), Critical values for cointegration test. In: Engle, R., Granger, C., editors. Long-Run Economic Relationships. Oxford: Oxford University Press. Mosavi, A., Ozturk, P., Chau, K.W. (2018), Flood prediction using machine learning models: Literature review. Water, 10(11), 1536. National Statistic Office Ministry of Information and Communication Technology. (2021), Available from: http://www.web.nso.go.th/ index.htm. [Last accessed on 2021 Jan 25]. NESDC. (2021), Office of the National Economic and Social Development Council. Available from: http://www.nesdb.go.th/ nesdb_en/more_news.php?cid=154 and filename=index. [Last accessed on 2021 Jan 10]. Pacheco, F.A.L., Fernandes, L.F.S. (2013), The multivariate statistical structure of DRASTIC model. Journal of Hydrology, 476, 442-459. Pollution Control Department Ministry of Natural Resources and Environment. (2021), Enhancement and Conservation of National Environmental Quality Act, B.E. 2535. Available from: http://www. pcd.go.th/info_serv/reg_envi.html. [Last accessed on 2021 Feb 05]. Pollution Control Department Ministry of Natural Resources and Environment. (2021), Navigation of Thai Waterways Act, B.E. 2546. Available from: http://www.pcd.go.th/info_serv/reg_envi.html. [Last accessed on 2021 Feb 05]. Pollution Control Department Ministry of Natural Resources and Environment. (2021), Principle 4: In Order to Achieve Sustainable Development, Environmental Protection Shall Constitute an Integral Part of the Development Process and Cannot be Considered in Isolation from it. Available from: http://www.infofile.pcd. go.th/law/Environmental%20law55_1.pdf?CFID=1741861 and CFTOKEN=32274043. [Last accessed on 2021 Feb 05]. Qin, J., Liang, J., Chen, T., Lei, X., Kang, A. (2019), Simulating and predicting of hydrological time. Polish Journal of Environmental Studies, 28(2), 795-802. Ren, S., Yin, H., Chen, X.H. (2014), Using LMDI to analyze the decoupling of carbon dioxide emissions by China’s manufacturing industry. Environmental Development, 9, 61-75. Shrestha, D., Robertson, D., Wang, Q.J., Pagano, T.C., Hapuarachchi, P. (2013), Evaluation of numerical weather prediction model precipitation forecasts for short-term streamflow forecasting purpose. Hydrology and Earth System Sciences, 17(5), 1913-1931. Sims, C.A. (1980), Macroeconomics and reality. Econometrica, 48, 1-48. Sutthichaimethee, P. (2008), Forecasting economic, social and environmental growth in the sanitary and service sector based on Thailand’s sustainable development policy. Journal of Ecological Engineering, 19(1), 205-210. Sutthichaimethee, P. (2016), Model of environmental problems priority arising from the use of environmental and natural resources Sutthichaimethee, et al.: Indicators of Environmental and Economic Problems Priority Arising from Energy Use in Food Manufacturing Sector in Realizing Sustainable Development Policy under Thai Environmental Law Framework International Journal of Energy Economics and Policy | Vol 11 • Issue 5 • 2021608 in machinery sectors of Thailand. Environmental and Climate Technologies, 17(1), 18-29. Sutthichaimethee, P. (2017), Varimax model to forecast the emission of carbon dioxide from energy consumption in rubber and petroleum industries sectors in Thailand. Journal of Ecological Engineering, 18(3), 112-117. Sutthichaimethee, P., Ariyasajjakorn, D. (2017), Forecasting model of GHG emission in manufacturing sectors of Thailand. Journal of Ecological Engineering, 18(1), 18-24. Sutthichaimethee, P., Ariyasajjakorn, D. (2017), The revised input-output table to determine total energy content and total greenhouse gas emission factors in Thailand. Journal of Ecological Engineering, 18(6), 166-170. Sutthichaimethee, P., Ariyasajjakorn, D. (2018), Forecast of carbon dioxide emissions from energy consumption in industry sectors in Thailand. Environmental and Climate Technologies, 22(1), 107-117. Sutthichaimethee, P., Ariyasajjakorn, D. (2020), A forecasting model on carrying capacity for government’s controlling measure under environmental law in Thailand: Adapting non-recursive autoregression based on the Var-X model. International Journal of Energy Economics and Policy, 10(6), 645-655. Sutthichaimethee, P., Chatchorfa, A., Suyaprom, S. (2019), A forecasting model for economic growth and CO2 emission based on industry 4.0 political policy under the government power: Adapting a second- order autoregressive-SEM. Journal of Open Innovation: Technology, Market, and Complexity, 5, 69. Sutthichaimethee, P., Dockthaisong, B. (2018), A relationship of causal factors in the economic, social, and environmental aspects affecting the implementation of sustainability policy in Thailand: Enriching the path analysis based on a GMM model. Resources, 7(4), 87. Sutthichaimethee, P., Kubaha, K. (2018). A Relational Analysis Model of the Causal Factors Influencing CO2 in Thailand’s Industrial Sector under a Sustainability Policy Adapting the VARIMAX-ECM Model. Energies, 11(7), 1704. Sutthichaimethee, P., Naluang, S. (2019), The efficiency of the sustainable development policy for energy consumption under environmental law in Thailand: Adapting the SEM-VARIMAX model. Energies, 12, 3092. Thailand Greenhouse Gas Management Organization. (2021). Available from: http://www.tgo.or.th/2015/thai/content.php?s1=7 and s2=16 and sub3=sub3. [Last accessed on 2021 Feb 16]. The World Bank. (2020), Energy Use (Kg of Oil Equivalent Per Capita) Home Page. Available from: https://www.data.worldbank.org/ indicator/EG.USE.PCAP.KG.OE. [Last accessed on 2020 10 Jan ]. United Nations Framework Convention on Climate Change, UNFCCC, Bonn, Germany. (2016), Global progress in environmental law. Environmental Policy and Law, 46(1), 23-27. Valipour, M., Banihabib, M.E., Behbahani, S.M.R. (2013), Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. Journal of Hydrology, 476, 433-441. Xu, S.C., He, Z.X., Long, R.Y. (2014), Factors that influence carbon emissions due to energy consumption in China: Decomposition analysis using LMDI. Applied Energy, 127(C), 182-193. Yalta, A.T., Cakar, H. (2012), Energy consumption and economic growth in China: A reconciliation. Energy Policy, 41(C), 666-675. Zhang, C., Lin, Y. (2012), Panel estimation for urbanization, energy consumption and CO2 emissions: A regional analysis in China. Energy Policy, 49(C), 488-498. Zhang, C., Xu, J. (2012), Retesting the causality between energy consumption and GDP in China: Evidence from sectoral and regional analyses using dynamic panel data. Energy Economics, 34(6), 1782-1789. Zhang, J., Broadstock, D. (2016) The causality between energy consumption and economic growth for china in a time-varying framework. The Energy Journal, 37(01), 29-53.