Grey optimization Verhulst model and its application in forecasting coal-related CO₂ emissions

Area of Science:

• Environmental Science
• Energy Economics
• Computational Modeling

Background:

• Global focus on energy saving and emission reduction.
• Accurate carbon emission prediction is vital for environmental policy.
• Traditional grey Verhulst model has error limitations.

Purpose of the Study:

• To improve carbon emission prediction accuracy.
• To optimize the grey Verhulst model for better performance.
• To provide early warnings for environmental strategy implementation.

Main Methods:

• Analysis of grey Verhulst model errors.
• Optimization of background value using extrapolation.
• Particle swarm optimization for parameter tuning.
• Comparison with existing grey models using statistical indicators.

Main Results:

• The enhanced grey Verhulst model demonstrates superior predictive performance.
• Carbon dioxide emissions from raw coal slowed between 2016-2020.
• Emissions from clean and washed coal increased by 12.7% and 19.2% respectively.

Conclusions:

• The optimized model offers improved accuracy for carbon emission forecasting.
• China faces a potential rebound in carbon emissions, particularly from cleaner coal types.
• Urgent need for enhanced energy saving and emission reduction strategies, focusing on coal consumption.