CO2 Emissions Forecasting Using Time Series Analysis and Metaheuristic Optimization for Environmental Sustainability

Ahmed El-Sayed Saqr^1,*, El-Sayed M. El-Kenawy^{2, 3}, Mohamed S. Saraya¹

¹Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Egypt

²Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt

³MEU Research Unit, Middle East University, Amman 11831, Jordan

Emails: a7mdsqr@std.mans.edu.eg; skenawy@ieee.org; mohamedsabry83@mans.edu.eg

Abstract

CO2 emission prediction is crucial for environmental policy and climate change mitigation. This review explores time series analysis and metaheuristic optimization in CO2 forecasting, summarizing research findings and methodological insights. Time series analysis uncovers past patterns and future trends, while metaheuristic methods like genetic algorithms optimize forecasting accuracy. Challenges include data quality, model complexity, and computational demands. However, the potential of advanced machine learning is a beacon of hope. It can revolutionize CO2 forecasting, making it more accurate and efficient. Composite models combining approaches show promise alongside real-time data integration and advanced machine learning. Future research should prioritize comprehensive databases and, importantly, stress the need for interdisciplinary collaboration to refine models. Improvements in forecasting can aid policy decisions and combat climate change, highlighting the growing need for accurate CO2 predictions and advanced analytical techniques.

Keywords: CO2 Prediction; Time Series Analysis; Metaheuristic Optimization; Climate Policy; Machine Learning