Modelling and forecasting non-renewable energy consumption and carbon dioxide emissions in China using a PSO...

In China, the consumption of non-renewable energy increases not only in general economic growth but also in large amounts of carbon dioxide (CO₂) emissions which cause disasters and catastrophic damages to the environment. To alleviate environmental pressure, it is neccessary to forecast and model the relationship between energy consumption and CO₂ emissions. In this study, a fractional non-linear grey Bernoulli (FANGBM(1,1)) model based on particle swarm optimization is proposed to forecast and model non-renewable energy consumption and CO₂ emissions in China. Firstly, based on the FANGBM(1,1) model, non-renewable energy consumption in China is predicted. The comparison results of several competitive models show that the FANGBM(1,1) model has the best predictive performance. Then, the relationship between non-renewable energy consumption and CO₂ emissions is modeled. On this basis, China's future CO₂ emissions are effectively predicted based on the established model. The forecast results show that the growth trend of China's CO₂ emissions will continue to grow to 2035, while the prediction results in different scenarios also show that that the different growth rates of renewable energy share lead to different times to peak CO₂ emissions. In the end, relevant suggestions are proposed to support China's dual carbon goals.