Varimax Model to Forecast the Emission of Carbon Dioxide from Energy Consumption in Rubber and Petroleum Industries Sectors in Thailand

• Autorzy: Sutthichaimethee P.

Identyfikatory

DOI

10.12911/22998993/70200

• Języki publikacji: EN

Abstrakty

EN

This study aims to analyze the forecasting of CO₂ emission from the energy consumption in the Rubber, Chemical and Petroleum Industries sectors in Thailand. The scope of research employed the input-output table of Thailand from the year 2000 to 2015. It was used to create the model of CO₂ emission, population, GDP growth and predict ten years and thirty years in advance. The model used was the VARIMAX Model which was divided into two models. The results show that from the first model by using which predicted the duration of ten years (2016–2025) by using VARIMAX Model (2,1,2), On average, Thailand has 17.65% higher quantity of CO₂ emission than the energy consumption sector (in 2025). The second model predicted the duration of 30 years (2016–2045) by using VARIMAX Model (2,1,3) shows that Thailand has average 39.68% higher quantity of CO₂ emission than the energy consumption sector (in 2025). From the analyses, it shows that Thailand has continuously higher quantity of CO₂ emission from the energy consumption. This negatively affects the environmental system and economical system of the country incessantly. This effect can lead to unsustainable development.

Słowa kluczowe

EN

Rubber, Chemical and Petroleum Industries sectors; population; forecasting model; energy consumption; CO2 emission; GDP growth

• Wydawca: Polskie Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology
• Czasopismo: Journal of Ecological Engineering
• Rocznik: 2017
• Tom: Vol. 18, nr 3
• Strony: 112--117
• Opis fizyczny: Bibliogr. 20 poz., rys., tab.

Twórcy

autor

Sutthichaimethee P.

• Faculty of Economics, Chulalongkorn University, Thailand

Bibliografia

• 1. Alizadeh M., Jolai F., Aminnayeri. M, Rada R. 2012. Comparison of different input selection algorithms in neuro-fuzzy modeling. Expert Syst Appl, 39, 1536–44.
• 2. Asian Development Bank (ADB). 2014. Environment, Climate Change, and Disaster Risk Management. Manila. Asian Development Bank.
• 3. Assaad M., Boné R., Cardot H. 2008. A new boosting algorithm for improved time-series forecasting with recurrent neural networks. Inform Fusion, 9, 41–55.
• 4. Azadeh A., Asadzadeh S., Saberi M., Nadimi V., Tajvidi A., Sheikalishahi M. 2011. A neuro-fuzzystochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: the cases of Bahrain, Saudi Arabia, Syria, and UAE. Appl Energy, 88, 3850–59.
• 5. Azadeh A., Saberi M., Seraj O. 2010. An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: a case study of Iran. Energy, 35, 2351–66.
• 6. Barak S., Dahooie JH., Tichy´ T. 2015. Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick. Expert Syst Appl, 42, 9221–35.
• 7. 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, 359–367.
• 8. Ekonomou L. 2010. Greek long-term energy consumption prediction using artificial neural networks. Energy, 35, 512–517.
• 9. 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.
• 10. Jovanovic´ RZ., Sretenovic´ AA. , Zivkovic´ BD. 2015. Ensemble of various neural networks for prediction of heating energy consumption. Energy Build, 94, 189–199.
• 11. Lee Y-S., Tong L-I. 2012. Forecasting nonlinear time series of energy consumption using a hybrid dynamic model. Appl Energy, 94, 251–256.
• 12. Leontief W.W. 1986. Input-Output Economics (2nd ed.). New York, Oxford University Press.
• 13. Mamlook R., Badran O., Abdulhadi E. 2009. A fuzzy inference model for short-term load forecasting. Energy Policy, 37, 1239–48.
• 14. Office of the National Economic and Social Development Board. 2015. National Income of Thailand. Bangkok: NESDB.
• 15. 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, 301–307.
• 16. 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.
• 17. Suganthi L., Iniyan S., Samuel AA. 2015. Applications of fuzzy logic in renewable energy systems – a review. Renew Sustain Energy Rev, 48, 585–607.
• 18. Suganthi L., Samuel AA. 2012. Energy models for demand forecasting–a review. Renew Sustain Energy Rev, 16, 1223–40.
• 19. Thailand Development Research Institute (TDRI). 2007. Prioritizing Environmental Problems with Environmental Costs. Final report prepared the Thailand Health Fund. Bangkok.
• 20. Zhao H., Magoulès F. 2012. A review on the prediction of building energy consumption. Renewable Sustainable Energy Rev, 16, 3586–3592.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).