Application of grey model in influencing factors analysis and trend prediction of carbon emission in Shanxi Province

In recent years, global warming has attracted extensive attention. The main cause of global warming is the emission of greenhouse gases, known as carbon emissions. Therefore, it is of great significance to explore the influencing factors of carbon emissions and accurately predict carbon emissions for reducing carbon emissions and slowing down climate warming. This paper takes the carbon emissions of Shanxi Province in China as the research object. Firstly, the emission factor method is used to calculate the carbon emissions, and then the grey correlation model is used to screen out the factors that have a greater impact on carbon emissions (per capita GDP, urbanization rate, resident population, energy consumption, expenditure on R&D projects). Then, an improved grey multi-variable convolution integral model (AGMC(1, N)) is used to accurately predict carbon emissions. The results show that the application of the AGMC(1,N) model to carbon emission prediction has a good prediction effect. In addition, the carbon emissions of Shanxi Province will increase with the growth rate of per capita GDP, energy consumption, resident population, and expenditure on R&D projects, while the carbon emissions will gradually decrease with the increase of urbanization level. The prediction results provide the direction for carbon emission reduction in Shanxi Province. At the same time, theAGMC(1,N) model can also be applied to the prediction of carbon emissions in other provinces or other fields.

Automated summary

This study focuses on carbon emissions in Shanxi Province, China. By using the emission factor method and grey correlation model, the influencing factors of carbon emissions are identified. An improved grey multi-variable convolution integral model (AGMC(1,N)) is then employed to accurately predict carbon emissions. The results demonstrate that the AGMC(1,N) model is effective in predicting carbon emissions. Additionally, the study reveals that carbon emissions in Shanxi Province will increase with the growth rate of per capita GDP, energy consumption, resident population, and expenditure on R&D projects, but will gradually decrease with the increase of urbanization level. This study provides valuable insights for carbon emission reduction in Shanxi Province and suggests that the AGMC(1,N) model can be applied to carbon emission prediction in other provinces or fields.