Prediction of CO2 emissions using machine learning
| dc.contributor.author | Bussaban, Kanyarat | |
| dc.contributor.author | Kularbphettong, Kunyanuth | |
| dc.contributor.author | Raksuntorn, Nareenart | |
| dc.contributor.author | Boonseng, Chongrag | |
| dc.date.accessioned | 2026-08-06T10:43:58Z | |
| dc.date.available | 2026-08-06T10:43:58Z | |
| dc.date.issued | 2024-01-01 | |
| dc.description.abstract | 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. | |
| dc.identifier.citation | Edelweiss Applied Science and Technology, 8(4), 1-11, 2024 | |
| dc.identifier.doi | 10.55214/25768484.v8i4.1097 | |
| dc.identifier.issn | 25768484 | |
| dc.identifier.other | 2-s2.0-85202912970 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/15079 | |
| dc.source | Edelweiss Applied Science and Technology | |
| dc.subject | Carbon dioxide (CO2) | |
| dc.subject | CO2 emissions | |
| dc.subject | Multiple linear regression | |
| dc.subject | Random forest | |
| dc.subject | Support vector machine | |
| dc.title | Prediction of CO2 emissions using machine learning | |
| dc.type | Article |