CO2 emission forecasting based on nonlinear grey Bernoulli and BP neural network combined model

Under the background of a green, low carbon economy, it is significant to accurately estimate the future CO2 emissions of countries with significant CO2 emissions for developing the world's green economy. A new Nonlinear Grey Bernoulli and BP neural network combined model (BP-ONGBM (1,1) model) have been proposed to study the CO2 emissions of China, the USA, the European Union, India and Japan. Firstly, the Particle Swarm Optimization (PSO) algorithm is optimized using the Artificial Fish Swarm Algorithm (AFSA). Then, the background value of the ONGBM (1,1) model is dynamically optimized. Based on the linearization of the model, the time response function is derived. Then, the ONGBM (1,1) model is combined with the BP neural network model. An improved PSO algorithm determines the combined weight and the background value coefficient. Finally, according to the observation data from 2010 to 2021 in the Emissions Database for Global Atmospheric Research 2022, the model is established to calculate the CO2 emissions of the selected countries from 2022 to 2026 and compared with the prediction results provided by multiple competitive models. The empirical application shows that the proposed BP-ONGBM (1,1) model is significantly better than other competitive models.