Prediction of carbon dioxide emissions in China using a novel grey model with multi-parameter combination optimization. (10th June 2023) - Yin, Fengfeng; Bo, Zeng; Yu, Lean; Wang, Jianzhou
Abstract: With the rapid development of industrialization and urbanization in China, the carbon dioxide emissions keep increasing for a long time. It is of great significance to reasonably predict the carbon dioxide emissions to realize "carbon peaking and carbon neutrality" goals in China. To this end, based on the new structure grey model, the variable orders are defined and optimized differentially, and the smoothing generation operator is introduced to variously design and optimize the driving term. Thus, a new multivariable grey prediction model with the combination optimization of multi-parameter is constructed. The new model is applied to predict the carbon dioxide emissions in China, and its comprehensive error is only 0.085%, which is far superior to the traditional similar grey prediction models (3.274%, 7.713%). This study provides a new modeling method for carbon dioxide emissions prediction, and has positive significance for enriching and improving the structural and methodological systems of grey prediction models. Highlights: A new multivariable grey model is proposed to predict Chinese CO2 emissions. The order of dependent/independent variable is designed and optimized differently. The idea of combination optimization is adopted to improve the modeling ability. The research can realize the dynamic analysis and monitoring of CO2 emissions. Relevant suggestions are put forward according to the prediction results.