A novel integrated optimization model for carbon emission prediction: A case study on the group of 20.
J Environ Manage
; 344: 118422, 2023 Oct 15.
Article
in En
| MEDLINE
| ID: mdl-37384985
ABSTRACT
Carbon emission is a central factor in the study of the greenhouse effect and a crucial consideration in environmental policy making. Therefore, it is essential to establish carbon emission prediction models to provide scientific guidance for leaders in implementing effective carbon reduction policies. However, existing research lacks comprehensive roadmaps that integrate both time series prediction and analysis of influencing factors. This study combines the environmental Kuznets curve (EKC) theory to classify and qualitatively analyzes research subjects based on national development patterns and levels. Considering the autocorrelated characteristics of carbon emissions and their correlation with other influencing factors, we propose an integrated carbon emission prediction model named SSA-FAGM-SVR. This model optimizes the fractional accumulation grey model (FAGM) and support vector regression (SVR) using the sparrow search algorithm (SSA), considering both time series and influencing factors. The model is subsequently applied to predict the carbon emissions of the G20 for the next 10 years. The results demonstrate that this model significantly improves prediction accuracy compared to other mainstream prediction algorithms, exhibiting strong adaptability and high accuracy.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Carbon
/
Carbon Dioxide
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Country/Region as subject:
Asia
Language:
En
Journal:
J Environ Manage
Year:
2023
Document type:
Article
Affiliation country:
China