Abstract:
The rapid industrial production and increased use of fossil fuels such as coal and petroleum have led to a significant rise in CO2 emissions, contributing to global warming. Nations are now actively monitoring CO2 emissions and developing long-term plans to mitigate their impact. In this project, we propose the use of random forest and support vector machine techniques to predict CO2 emissions in Turkey. While time is a crucial factor in forecasting, other attributes such as population and fuel use can also play a role. Our study demonstrates that the support vector machine method produces superior forecasting results compared to other techniques.
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