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    Prediction of CO2 emissions using machine learning
    (2024-01-01)
    Bussaban, Kanyarat
    ;
    Kularbphettong, Kunyanuth
    ;
    Raksuntorn, Nareenart
    ;
    Boonseng, Chongrag
    Carbon dioxide (CO<inf>2</inf>) contributes significantly to climate change as a greenhouse gas. The Earth's atmosphere is naturally kept warm enough to support life by greenhouse gases which trap heat in the atmosphere. However, human activity has significantly increased the amount of CO2 in the atmosphere because of deforestation and the use of fossil fuels. One of the key concerns with human evolution that fuels global climate change is carbon dioxide (CO<inf>2</inf>). It is released as fuels burn and as a result, people worldwide are gradually becoming more conscious of environmental issues. Effective policy formulation requires an investigation of the factors influencing CO<inf>2</inf> emissions, yet tiny datasets and traditional research methodologies have hampered prior investigations. This research uses three prediction models to estimate CO<inf>2</inf> trapping efficiency among CO<inf>2</inf> emissions, energy use and GDP: Multiple Linear Regression (MLR), Support Vector Machine (SVM) and Random Forest (RF). The machine learning (ML) techniques used in this work have demonstrated strong performance with multiple linear regressions, support vector machines and random forest models with mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). The investigation has proposed a technique for approximating CO<inf>2</inf> emissions and the results indicate that Support Vector Machine (SVM) can attain the highest degree of precision. The outcome could be a useful model for the decision support system to enhance an appropriate course of action for reducing CO<inf>2</inf> emissions worldwide.
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    Energy demand modeling for low carbon cities in Thailand: A case study of Nakhon Ratchasima province
    (2023-07-01)
    Tippichai, Atit
    ;
    Teungchai, Kattreeya
    ;
    Fukuda, Atsushi
    Nakhon Ratchasima is one of the northeastern cities which has been promoted as one of the low-carbon cities in Thailand. The study aims to evaluate policies and measures on greenhouse gas (GHG) emissions mitigation to meet the target at the provincial level. The Low Emissions Analysis Platform (LEAP) is used as a modeling tool to simulate energy demand for each economic sector. The 2019 data is set as a base year, using top-down and bottom-up approaches depending on the availability of data for the analysis. The model consists of two scenarios: (1) Business-as-usual (BAU) scenario and Low carbon scenario (LCS). Transport and industry sectors are the most energy-consuming and CO2-emitting sectors in Nakhon Ratchasima Province. In the LCS case, the final energy demand and CO2 emissions in 2050 will be reduced by about 40% compared to the BAU case. In addition, CO2 emissions in Nakhon Ratchasima Province will peak around 2038, this is not the case with BAU. The study could predict future energy demand and propose a way forward to reducing GHG emissions at the provincial level.