Forecasting carbon emissions using MGM(1,m|λ,γ) model with the similar meteorological condition

Area of Science:

• Environmental Science
• Climate Change Modeling
• Econometrics

Background:

• Carbon emissions pose a significant global challenge, necessitating accurate forecasting for effective reduction strategies.
• The inherent instability of carbon emission trends requires models that prioritize new data for trend correction.

Purpose of the Study:

• To develop an improved grey forecasting model (MGM(1,m)) for carbon emission prediction.
• To enhance the model's responsiveness to new information using priority operators and nonlinear parameters.
• To validate the model's efficacy across various geographical scales and emission patterns.

Main Methods:

• Modification of the traditional MGM(1,m) model by incorporating the new information priority operator (λ).
• Introduction of a nonlinear parameter (γ) to further refine the model's dynamic adjustment capabilities.
• Construction and comparison of three novel models: MGM(1,m|λ), MGM(1,m|γ), and MGM(1,m|λ,γ).
• Application of the developed models to predict carbon emissions in diverse regions and for different trend types.

Main Results:

• The enhanced grey forecasting models demonstrated superior prediction accuracy compared to the traditional approach.
• The integration of dynamic parameters (λ and γ) significantly improved the model's forecasting performance.
• The refined model proved effective for predicting carbon emissions across cities, countries, and continents with fluctuating, rising, or declining trends.

Conclusions:

• The novel MGM(1,m) models with dynamic parameters offer a scientifically sound and practical advancement in forecasting carbon emissions.
• Accurate prediction of carbon emission trends is crucial for informing policy and implementing effective emission reduction measures.
• The study provides insights into the current state and future trajectory of global carbon emissions, supporting evidence-based recommendations.