The Future Environmental Impact in the Food Industry Sector of Thailand and China as a Result of Economic and Social Growth based on Sustainable Development Policy
Keywords:
sustainable development, population growth, GDP growth, income per capita, greenhouse gas, carrying capacity, clean technology, plan and policy, sustainabilityAbstract
The objective of this study is to forecast the effects of economic growth along with population growth on Greenhouse gas emission in Food Industry sector of Thailand and China due to Economic and Social growth under Sustainable Development policy for the year 2018 to 2045. The study has found that Thailand's economic growth rate steadily rises to 23.45 percent in 2045 and the population has an increase by 7.24 percent. In the meanwhile, the greenhouse gas from the consumption in the food industry sectors would have been increased by 39.2 percent. To China's economic growth rate, there is also a continuous rise by 45.9 percent while its population grows by 5.32 percent. At the same time, its greenhouse gas from the consumption in the food industry sector has increased by 12.75 percent. However, based on the research investigated in the food industry sector from 2018 to 2045, the environmental impact of Thailand has continuously increased and its impact is much higher than in China. The main reason is that China has a strict policy and seriously implements the carrying capacity policy. In addition to this, China has succeeded at promoting the consumption of clean technology. Therefore, as far as Thailand’s sustainable development is concerned, Thailand shall take a serious action to implement the rigorous policy of carrying capacity.
References
References
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Sutthichaimethee, P. & Ariyasajjakorn, D. (2017b). Forecasting Energy Consumption in Short-Term and Long-Term Period by using Arimax Model in the Construction and Materials Sector in Thailand. Journal of Ecological Engineering, 18(4), 52-59.
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Sutthichaimethee, P. & Sawangdee, Y. (2016a). Model of Environmental Impact of Service Sectors to Promote Sustainable Development of Thailand. Ethics in Science and Environmental Politics, 16(1), 11-17.
Sutthichaimethee, P. & Sawangdee, Y. (2016b). Indicator of Environmental Problems Priority Arising from the use of Environmental and Natural Resources in Machinery Sectors of Thailand. Environmental and Climate Technologies, 17(1), 18-29.
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Translated Thai Reference
Srieakbungrod, V., & Sutthichaimethee, P. (2018). Leadership, Corporate Culture, Organizational Commitment and Worker Characteristics affecting the Organizational Efficiency in Gemopolis Industrial Estate. Local Administration Journal, 11(1), 78 – 94. (In Thai)
วริศนันท์ ศรีเอกบุญรอด และพฤทธ์สรรค์ สุทธิไชยเมธี. (2561). ภาวะผู้นำ วัฒนธรรมองค์กร ความผูกพันต่อองค์กร และคุณลักษณะผู้ปฏิบัติงานที่มีผลต่อประสิทธิภาพขององค์กรในนิคมอุตสาหกรรมอัญธานี. วารสารการบริหารท้องถิ่น, 11(1), 78–94.
Acaravci, A. & Ozturk, I. (2010). On the relationship between energy consumption, CO2 emissions and economic growth in Europe. Energy. 35(12), 5412–5420.
Akpan, G.E. & Akpan, U.F. (2012). Electricity consumption, carbon emissions and economic growth in Nigeria. International Journal of Energy Economics and Policy, 2(4), 292–306.
Alkhathlan, K. & Javid, M. (2013). Energy consumption, carbon emissions and economic growth in Saudi Arabia: An aggregate and disaggregate analysis. Energy Policy, 62(C), 1525–1532.
Arouri, M.E.H., Youssef, A.B., M’henni, H. & Rault, C. (2012). Energy consumption, economic growth and CO2 emissions in Middle East and North African countries. Energy Policy, 45, 342–349.
Asian Development Bank: ADB. (2014). Environment, Climate Change, and Disaster Risk Management (Research report). Manila: Asian Development Bank.
Cryer, J. D. & Chan, K. (2008). Time Series Analysis with Applications in R. (2nd ed.). New York: Springer-Verlag.
Dai, S., Niu, D. & Han, Y. (2018). Forecasting of Energy-Related CO2 Emission in China Based on GM (1,1) and Least Squared Support Leaping Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability. Sustainability, 10(4), 958.
Dickey, D.A. & Fuller, W.A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057–1072.
Dong, B., Coa, C., & Lee, S.E. (2015). Applying support vector machines to predict building energy consumption in tropical region. Energy Build, 37(5), 545-553.
Enders, W. (2010). Applied Econometrics Time Series. Wiley Series in Probability and Statistics, University of Alabama: Tuscaloosa, AL, USA.
Hao, J., Liu, D., Li, Z., Chen, Z., & Kong, L. (2012). Power system load forecasting based on fuzzy clustering and gray target theory. Energy Procedia, 16, 1852–1859.
Harvey, A.C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press.
Hsiao-Tien Pao, N. & Chung-Ming, Tsai. (2010). CO2 emissions, energy consumption and economic growth in BRIC countries. Energy Policy, 38(12), 7850–7860.
Lee, Y-S. & Tong, L-I. (2012). Forecasting nonlinear time series of energy consumption using a hybrid dynamic model. Applied Energy, 94, 251–256.
Li, J., Shi, J. & Li, J. (2016). Exploring Reduction Potential of Carbon Intensity Based on Back Propagation Neural Network and Scenario Analysis: A Case of Beijing, China. Energies, 9(8), 615.
Liang, Q.M., Fan, Y., & Wei, Y.M. (2007). Multi-regional input–output model for regional energy requirements and CO2 emissions in China. Energy Policy, 35(3), 1685–1700.
Lotfalipour, M.R., Falahi, M.A., & Bastam, M. (2013). Prediction of CO2 Emissions in Iran using Grey and ARIMA Models. International Journal of Energy Economics and Policy, 3(3), 229–237.
MacKinnon, J. (1991). Critical Values for Cointegration Test. In Long-Run Economic Relationships. Oxford: Oxford University Press.
Manu, S.B. & Sulaiman, C. (2017). Environmental Kuznets curve and the relationship between energy consumption, Economic growth and CO2 emissions in Malaysia. Journal of Economics and Sustainable Development, 8(16), 142–148.
Menyah, K. & Wolde-Rufael, Y. (2010). Energy consumption, pollutant emissions and economic growth in South Africa. Energy Economics, 32(6), 1374–1382.
Mulugeta, S.K., Nondo, C., Schaeffer, P.V., & Gebremedhin, T.G. (2012). Income level and the energy consumption–GDP nexus: Evidence from Sub-Saharan Africa. Energy Economics, 34(3), 739–746.
Office of the National Economic and Social Development Board. (2015). National Income of Thailand (Research Report). Bangkok: NESDB.
Osorio, G., Matias, J., & Catalão, J. (2015). Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information. Renew Energy, 75(C), 301–307.
Sulaiman, C. & Abdul-Rahim, A.S. (2017). The relationship between CO2 emission, energy consumption and economic growth in Malaysia: A three-way linkage approach. Environmental Science and Pollution Research, 24(32), 25204–25220.
Sulaiman, C. (2014). The causality between energy consumption, CO2 emissions and economic growth in Nigeria: An application of Toda and Yamamoto Procedure. Advances in Natural and Applied Sciences, 8(8), 75–81.
Sutthichaimethee, P. (2018). An Analysis of Future Energy Consumption with A Relational Model Based on Economic, Social and Environmental Sector under Thailand’s Sustainable Development Policy by Adapting a GM-ARIMAX with HP Filter. Local Administration Journal, 11(4), 144-161.
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. & Tanoamchard, W. (2015). Carrying Capacity Model of Food Manufacturing Sectors for Sustainable Development from using Environmental and Natural Resources of Thailand. Journal of Ecological Engineering, 16(5), 1-8.
Sutthichaimethee, P. (2016). Modeling Environmental Impact of Machinery Sectors to Promote Sustainable Development of Thailand. Journal of Ecological Engineering, 17(1), 18-25.
Sutthichaimethee, P. & Sawangdee, Y. (2016). Indicator of Environmental Problems of Agricultural Sectors under the Environmental Modeling. Journal of Ecological Engineering, 17(2), 12-18.
Sutthichaimethee, P. & Ariyasajjakorn, D. (2017a). Forecasting Model of GHG Emission in Manufacturing Sectors of Thailand. Journal of Ecological Engineering, 18(1), 18–24.
Sutthichaimethee, P. & Ariyasajjakorn, D. (2017b). Forecasting Energy Consumption in Short-Term and Long-Term Period by using Arimax Model in the Construction and Materials Sector in Thailand. Journal of Ecological Engineering, 18(4), 52-59.
Sutthichaimethee, P. & Ariyasajjakorn, D. (2017c). The Revised Input-Output Table to Determine Total Energy Content and Total Greenhouse Gas Emission Factors in Thailand. Journal of Ecological Engineering, 18(5), 166-170.
Sutthichaimethee, P. & Sawangdee, Y. (2016a). Model of Environmental Impact of Service Sectors to Promote Sustainable Development of Thailand. Ethics in Science and Environmental Politics, 16(1), 11-17.
Sutthichaimethee, P. & Sawangdee, Y. (2016b). Indicator of Environmental Problems Priority Arising from the use of Environmental and Natural Resources in Machinery Sectors of Thailand. Environmental and Climate Technologies, 17(1), 18-29.
Thailand Development Research Institute: TDRI. (2007). Prioritizing Environmental Problems with Environmental Cost. Final report prepared the Thailand Health Fund (Research Report). Bangkok. Thailand Development Research Institute.
Zhao, H., Huang, G. & Yan, N. (2018). Forecasting Energy-Related CO2 Emissions Employing a Novel SSA-LSSVM Model: Considering Structural Factors in China. Energies, 11(4), 781.
Translated Thai Reference
Srieakbungrod, V., & Sutthichaimethee, P. (2018). Leadership, Corporate Culture, Organizational Commitment and Worker Characteristics affecting the Organizational Efficiency in Gemopolis Industrial Estate. Local Administration Journal, 11(1), 78 – 94. (In Thai)
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2019-06-25
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Sutthichaimethee, P. (2019). The Future Environmental Impact in the Food Industry Sector of Thailand and China as a Result of Economic and Social Growth based on Sustainable Development Policy. Local Administration Journal, 12(2), 185–200. Retrieved from https://so04.tci-thaijo.org/index.php/colakkujournals/article/view/197687
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