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The Revised Input-Output Table to Determine Total Energy Content and Total Greenhouse Gas Emission Factors in Thailand

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A full energy chain analysis (FENCH) or a life cycle analysis (LCA) is indeed essential in making any decision on both minimal greenhouse gas (GHG) emissions and the energy content in various commodities. In this article, the energy Input-Output Analysis (IOA) approach is investigated to determine the factors for the total greenhouse gas emission and total energy content, and it deems the elimination of the boundary constraints existing in the Process Chain Analysis (PCA) approach to be practical to. This study, aims to identify the factors in embedded energy and embedded greenhouse gas (GHG) total values derived from the total Thai economic sectors of 180 in various commodities productions. The previous outdated IOA is enhanced in the study by revising the elements of sectoral energy consumption in the power sector, which is later found to be influential and significant to all other economic sectors. In addition, the 2005 sectoral energy consumption is used to show individual energy consumption, whereas the 2010 Input-Output (I-O) table, most timely data, is used to show the economic structure. Furthermore, the study uses a report of Thai electric power to revise the data of 2005 fuel mix in the power sector in order to obtain the 2010 and 2015 fuel mix. The reason of such revision is that the changes of fuel mix in the power sector are influential towards the factors in both total energy content and total greenhouse (GHG) emission. Hence, the 2015 electricity-fuel mix is taken to present the above-mentioned factors.
Rocznik
Strony
166--170
Opis fizyczny
Bibliogr. 34 poz., tab.
Twórcy
  • Faculty of Economics, Chulalongkorn University, Wang Mai, Khet Pathum Wan, Krung Thep Maha Nakhon, Bangkok, Thailand, pruethsan@gmail.com
  • Faculty of Economics, Chulalongkorn University, Wang Mai, Khet Pathum Wan, Krung Thep Maha Nakhon, Bangkok, Thailand, Danupon.A@Chula.ac.th
Bibliografia
  • 1. Asian Development Bank (ADB). 2014. Environment, Climate Change, and Disaster Risk Management. Manila. Asian Development Bank.
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  • 6. Ciabattoni L., Grisostomi M., Ippoliti G., Longhi S. 2014. Fuzzy logic home energy consumption modeling for residential photovoltaic plant sizing in the new Italian scenario. Energy, 74, 359-67.
  • 7. Chienwattanasook Krisada, Sutthichaimethee Pruethsan. (2012). Trend of Thailand Jewelry Export to the USA Market. International Academy of Business and Economics, 12(3).
  • 8. Dong B., Coa C., Lee S.E. 2015. Applying support vector machines to predict building energy consumption in tropical region. Energy Build, 37, 545-553.
  • 9. Ekonomou L. 2010. Greek long-term energy consumption prediction using artificial neural networks. Energy, 35, 512-7.
  • 10. Hao J., Liu D., Li Z., Chen Z., Kong L. 2012. Power system load forecasting based on fuzzy clustering and gray target theory. Energy Proc, 16, 1852-9.
  • 11. Jovanovic RZ., Sretenovic ́ AA. , Z ivkovic ́ BD. 2015. Ensemble of various neural networks for prediction of heating energy consumption. Energy Build, 94, 189-99.
  • 12. Lee Y-S., Tong L-I. 2011. Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Convers Manage, 52, 147-52.
  • 13. Lee Y-S., Tong L-I. 2012. Forecasting nonlinear time series of energy consumption using a hybrid dynamic model. Appl Energy, 94, 251-6.
  • 14. Leontief W.W. 1986. Input-Output Economics (2nd ed.). New York, Oxford University Press.
  • 15. Mamlook R., Badran O., Abdulhadi E. 2009. A fuzzy inference model for short-term load forecasting. Energy Policy, 37, 1239-48.
  • 16. Office of the National Economic and Social Development Board. 2015. National Income of Thailand. Bangkok: NESDB.
  • 17. 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, 301-7.
  • 18. Pappas SS., Ekonomou L., Karamousantas DC., Chatzarakis G., Katsikas S., Liatsis P. 2008. Electricity demand loads modeling using Auto Regressive Moving Average (ARMA) models. Energy, 33, 1353-60.
  • 19. Pruethsan Sutthichaimethee. (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.
  • 20. Pruethsan Sutthichaimethee , et al. (2015). Environmental problems indicator under environmental modeling toward sustainable development. Global J. Environ. Sci. Manage., 14(1), 325-332.
  • 21. Pruethsan Sutthichaimethee, Wanvicechanee Tanoamchard. (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.
  • 22. Pruethsan Sutthichaimethee, et al. (2016). Model of Environmental Problems Priority Arising From the Use of Environmental and Natural Resources in Construction Material Sectors of Thailand. Advanced Engineering Forum, 14, 76–85.
  • 23. Pruethsan Sutthichaimethee. (2016). Modeling Environmental Impact of Machinery Sectors to Promote Sustainable Development of Thailand. , Journal of Ecological Engineering, 17 (1).
  • 24. Pruethsan Sutthichaimethee, Yothin Sawangdee. (2016). Indicator of Environmental Problems of Agricultural Sectors under the Environmental Modeling. Journal of Ecological Engineering, 17 (2).
  • 25. Pruethsan Sutthichaimethee , Danupon Ariyasajjakorn. 2017. Forecasting Model of GHG Emission in Manufacturing Sectors of Thailand. Journal of Ecological Engineering, 18 (1), 18-24.
  • 26. Pruethsan Sutthichaimethee , Danupon Ariyasajjakorn. 2017. 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.
  • 27. Suganthi L., Iniyan S., Samuel AA. 2015. Applications of fuzzy logic in renewable energy systems – a review. Renew Sustain Energy Rev, 48, 585-607.
  • 28. Suganthi L., Samuel AA. 2012. Energy models for demand forecasting – a review. Renew Sustain Energy Rev, 16, 1223-40.
  • 29. Sutthichaimethee Pruethsan, Sawangdee Yothin. (2016). Model of Environmental Impact of Service Sectors to Promote Sustainable Development of Thailand. Ethics Sci Environ Polit, 16(1).
  • 30. Sutthichaimethee Pruethsan and Sawangdee Yothin. (2016). 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.
  • 31. Thailand Development Research Institute (TDRI). 2007. Prioritizing Environmental Problems with Environmental Costs. Final report prepared the Thailand Health Fund. Bangkok.
  • 32. Xie N-m., Yuan C-q., Yang Y-j. 2015. Forecasting China’s energy demand and self sufficiency rate by grey forecasting model and Markov model. Int J Electr Power Energy Syst, 66, 1-8.
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Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4e709638-a710-4da5-a897-63bbfe287ed9
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