Volume 7 • Issue 2 • PP: 18–22 • 2024
CO2 Emissions Forecasting Using Time Series Analysis and Metaheuristic Optimization for Environmental Sustainability
Open Access & Copyright
© 2024 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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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 such as genetic algorithms optimize forecasting accuracy. Challenges include data quality, model complexity, and computational demands. Advanced machine learning has the potential to make CO2 forecasting more accurate and efficient. Composite models that combine approaches show promise, alongside real-time data integration and advanced machine learning. Future research should prioritize comprehensive databases and interdisciplinary collaboration to refine models. Improvements in forecasting can aid policy decisions and climate-change mitigation, highlighting the growing need for accurate CO2 predictions and advanced analytical techniques.
Keywords
References
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