Advanced Time Series Forecasting for CO₂ Emissions: Insights for Sustainable Climate Policies

Authors

  • P. M. Hrithik

    Department of Statistics, Lovely Professional University, Phagwara 144411, India
  • Mohammed Osman Eltigani

    Department of Management, Dhofar University, Salalah 211, Sultanate of Oman
  • Mohammad Shahfaraz Khan

    Department of Business Administration , University of Technology and Applied Sciences, Muscat 133, Sultanate of Oman
  • Imran Azad

    Department of Business Administration , University of Technology and Applied Sciences, Muscat 133, Sultanate of Oman
  • Amir Ahmad Dar

    Department of Statistics, Lovely Professional University, Phagwara 144411, India
  • Saqib Ul Sabha

    Department of Computer Science, Lovely Professional University, Phagwara 144411, India

DOI:

https://doi.org/10.30564/jees.v7i5.8783
Received: 18 February 2025 | Revised: 10 March 2025 | Accepted: 12 March 2025 | Published Online: 9 May 2025

Abstract

To address the global issue of climate change and create focused mitigation plans, accurate CO2 emissions forecasting is essential. Using CO2 emissions data from 1990 to 2023, this study assesses the predicting performance of five sophisticated models: Random Forest (RF), XGBoost, Support Vector Regression (SVR), Long Short-Term Memory networks (LSTM), and ARIMA To give a thorough evaluation of the models’ performance, measures including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are used. To guarantee dependable model implementation, preprocessing procedures are carried out, such as feature engineering and stationarity tests. Machine learning models outperform ARIMA in identifying complex patterns and long-term associations, but ARIMA does better with data that exhibits strong linear trends. These results provide important information about how well the model fits various forecasting scenarios, which helps develop data-driven carbon reduction programs. Predictive modeling should be incorporated into sustainable climate policy to encourage the adoption of low-carbon technologies and proactive decision-making. Achieving long-term environmental sustainability requires strengthening carbon trading systems, encouraging clean energy investments, and enacting stronger emission laws. In line with international climate goals, suggestions for lowering CO2 emissions include switching to renewable energy, increasing energy efficiency, and putting afforestation initiatives into action.

Keywords:

CO₂ Emissions; Time Series Forecasting; Climate Change; Machine Learning Models; ARIMA; Sustainability

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How to Cite

P. M. Hrithik, Eltigani, M. O., Khan, M. S., Azad, I., Dar, A. A., & Saqib Ul Sabha. (2025). Advanced Time Series Forecasting for CO₂ Emissions: Insights for Sustainable Climate Policies. Journal of Environmental & Earth Sciences, 7(5), 360–371. https://doi.org/10.30564/jees.v7i5.8783

Issue

Vol. 7 , Iss. 5 (May 2025)

Article Type

Article

License

Copyright © 2025 P. M. Hrithik, Mohammed Osman Eltigani, Mohammad Shahfaraz Khan, Imran Azad, Amir Ahmad Dar, Saqib Ul Sabha

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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