Forecasting CO₂ Emissions with Machine Learning Methods: Türkiye Example and Future Trends

Year 2024, Volume: 5 Issue: 2, 82 - 87, 30.12.2024

İbrahim Ayaz

https://doi.org/10.46572/naturengs.1595329

Abstract

Climate change is a critical problem that causes global environmental and social issues due to increased greenhouse gas emissions caused by human activities. Carbon dioxide (CO₂) emissions, in particular, are one of the main elements of global warming and have devastating effects on ecosystems. Keeping track of carbon dioxide emissions resulting from human activities like burning fossil fuels, clearing forests, and farming, as well as forecasting their future patterns, is essential for creating effective sustainable environmental strategies. The study utilized machine-learning models to evaluate CO₂ emissions per individual, using the dataset from the Global Carbon Atlas that has released in 2023.

In the study, the traditional ARIMA model and deep learning-based LSTM networks were comparatively discussed. The models were trained with the aim of predicting Türkiye's future CO₂ emission levels by learning from past data, and their performances were evaluated with MAE, MSE, RMSE, and R² metrics. The LSTM model achieved an R² score of 90.4%, while the ARIMA model achieved an R² score of 94.3%. The findings show that machine learning techniques are a powerful tool in the fight against climate change and provide valuable insights for policymakers. The findings of the study guide more effective monitoring of CO₂ emissions and determination of strategies for sustainable development goals.

Keywords

Carbon Emission, Machine Learning, Time Series, Forecasting

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