Forecasting Carbon Dioxide Emissions (CO2) from Industry Sector in Thailand
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Abstract
The purposes of this research were to examine and compare the forecasting methods for carbon dioxide emissions (CO2) from the industry sector in Thailand. The data were gathered from Energy Policy and Planning Office, Ministry of Energy between January 2017 and October 2020, and 46 values were used and separated into two groups. The first group contained 36 values between January 2017 and December 2019 for studying and comparing the most appropriate forecasting methods by (1) Moving Average Method, (2) Trend Analysis Method, (3) Single Exponential Smoothing Method, (4) Double Exponential Smoothing Method, (5) Triple Exponential Smoothing Method, and (6) Decomposition Method. The suitable forecasting method was chosen by considering the smallest value of Mean Absolute Percent Error and Mean Absolute Deviation. Then the selected suitable method was used to determine the most suitable forecasting period by the second group which contained 10 values from January 2020 to October 2020 for finding the most suitable predictive timing. The result indicated that Double Exponential Smoothing Method was the best and most suitable forecasting 3 months in advance.
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References
Ampawa, P. and Dansawad, N. 2020. A Comparative Forecasting Model of Monthly Rainfall in Pathum Thani Province, pp. 1120-1129. In National Conference on Innovation Management: Circular Economy with The King’s Philosophy for Sustainable Development 5th. College of Innovative Management, Valaya Alongkorn Rajabhat University under the Royal Patronage, Pathum Thani. (in Thai)
Bermúdez, J.D., Segura, J.V. and Vercher, E. 2006. Improving Demand Forecasting Accuracy using Non-Linear Programming Software. Journal of the Operational Research Society 57: 94-100.
Chummee, W. 2011. Climate Change Closer and Scary than We Thought!. Available Source: https://www.scbeic.com/th/detail/product/703, November 21, 2020. (in Thai)
Jitrat, S. 2015. Accuracy Comparison on Stocks of the Information Technology and Communication Sector in the Stock Exchange of Thailand Forecasting Between ARIMA Model and E-GARCH Model. Master Thesis of Economics, Khon Kaen University. (in Thai)
Kawinpas, M., Payakkapong, P. and Chomtee, B. 2015. A Comparison of Forecasting Methods between Bayesian Network and Exponential Smoothing for the Stock Price Index of Property and Construction Groups in Thailand. Thammasat Journal 23(2): 203-211. (in Thai)
Ngamrabiab, C. 2020. Thailand Reduces Greenhouse Gas Emissions by 45.68 million tons of Carbon Dioxide Equivalent (in 2019). Available Source: https://www.bltbangkok.com/news/12495/, November 21, 2020. (in Thai)
Limsakul, A. and Jungoth, R. 2016. The Paris Agreement: An Important Turning Point in Global Action on Climate Change. Green Research 13(34): 3-11. (in Thai)
Poonsuan, W. 2007. A Forecasting System for the Household Manufacturer A Case Study S.B. Furniture Co., Ltd. Master Thesis of Engineering (Industrial Engineering), King Mongkut’s Institute of Technology North Bangkok. (in Thai)
Taengphukieo, R. and Issarapong, N. 2019. Analysis of Comparing Forecasting Methods for Production Planning: Case Study of Beef Companies, Nakhon Phanom Province. EAU Heritage Journal: Science and Technology 13(3): 222-232. (in Thai)
Taesombut, S. 2006. Quantitative Forecasting. 1st ed. Kasetsart University, Bangkok. (in Thai)
Thailand Greenhouse Gas Management Organization (Public Organization). 2018. Low Carbon City. Greenhouse Gas Emissions Reporting for Local Government. Available Source: http://www.tgo.or.th/2020/index.php/en, March 3, 2020. (in Thai)
The Office of SMEs Promotion. 2020. Gross Domestic Product of Small and Medium Enterprises of 2019. SME WHITE PAPER 2020. Available Source: https://www.sme.go.th/upload/mod_download/download-20201005123037.pdf, March 3, 2020. (in Thai)
Tojumsil, J. and Pimsakul, S. 2018. Forecasting Model for Advanced Purchasing Planning by Exponential Smoothing. Ladkrabang Engineering Journal 35(2): 22-32. (in Thai)