Trend Analysis of CO2 Emissions in the Transport Sector Using Data Mining Algorithms
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Abstract
Analyzing trends in carbon dioxide (CO2) emissions in the transportation sector is a critical issue that impacts both the environment and human health. This research focuses on the application of data mining techniques— amely clustering, linear regression analysis, and random forest—to group and predict future CO2 emission trends. Historical fuel consumption data was used as the primary input for analysis. The performance of each technique was evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R2). The results revealed that the random forest technique provided the highest accuracy, achieving an R2 of 97.14 %. In comparison, linear regression and clustering yielded R2 values of 86.58 % and 51.14 %. These findings highlight the potential of the random forest algorithm as an effective tool for forecasting carbon emissions to support greenhouse gas reduction planning efforts.
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