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Improving the Analysis of CO 2 $${ }_{2}$$ Emissions with a Filter and Imputation-Based Processing Method

Author

Listed:
  • Amrita Das Tipu

    (Hajee Mohammad Danesh Science and Technology University
    Dhaka International University)

  • Priyanka Roy

    (Hajee Mohammad Danesh Science and Technology University
    Sylhet International University)

  • Md Palash Uddin

    (Hajee Mohammad Danesh Science and Technology University
    Deakin University)

  • Mahmudul Hasan

    (Hajee Mohammad Danesh Science and Technology University
    Deakin University)

Abstract

The increasing greenhouse gas emissions are the major contributors to global warming and climate change. CO 2 $$_2$$ is a greenhouse gas, and effective reduction of CO 2 $$_2$$ emission is required for sustainable development. To decrease carbon emissions and achieve several sustainable development goals, efficient and accurate forecasting is necessary before forming any preventive measures. The proposed study examines the current state of emissions and its related variables. For this purpose, the proposed FIDP method is applied to create and preprocess the dataset. After investigating various properties of the dataset and relations between variables, six machine learning models are evaluated using the cross-validation technique. The proposed ensemble model predicts carbon emissions with the highest R 2 $$^2$$ score of 97.92% and MAE, MSE, and RMSE values of near zero. The lower standard deviations across folds indicate the robustness of the proposed model. The result of this study paves the way for further optimizing emission preventive measures.

Suggested Citation

  • Amrita Das Tipu & Priyanka Roy & Md Palash Uddin & Mahmudul Hasan, 2025. "Improving the Analysis of CO 2 $${ }_{2}$$ Emissions with a Filter and Imputation-Based Processing Method," International Series in Operations Research & Management Science,, Springer.
    • Handle: RePEc:spr:isochp:978-3-031-95099-5_5
    DOI: 10.1007/978-3-031-95099-5_5
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