Projections in Various Scenarios and the Impact of Economy, Population, and Technology for Regional Emission Peak and Carbon Neutrality in China

A multi-factor combination prediction model of carbon emissions based on improved CEEMDAN

As the global greenhouse effect intensifies, carbon emissions are gradually becoming a hot topic of discussion. Accurate carbon emissions prediction is an important foundation to realize carbon neutrality and peak carbon dioxide emissions. To accurately predict carbon emissions, a multi-factor combination prediction model based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), bidirectional long short-term memory optimized by lemurs optimizer (LOBiLSTM) and least squares support vector machine optimized by lemurs optimizer (LOLSSVM), named ICEEMDAN-LOBiLSTM-LOLSSVM, is proposed. Firstly, the influencing factors of carbon emissions are selected by Spearman correlation coefficient, and carbon emissions are decomposed into intrinsic mode functions (IMFs) by ICEEMDAN. Secondly, the influencing factors and IMFs are input into LOBiLSTM and LOLSSVM respectively for prediction. Then, the point prediction results are obtained by weighting the prediction results of LOBiLSTM and LOLSSVM. Finally, probability density function of point prediction error is calculated by kernel density estimation, and the interval prediction results are calculated according to different confidence intervals. Carbon emissions of China and Germany are selected to verify the superiority of ICEEMDAN-LOBiLSTM-LOLSSVM. The experiment shows that RMSE, MAE, MAPE, and R2 of the proposed model are 0.4468, 0.3612, 0.0120, and 0.9839 respectively for China, which is the best among the nine models, as well as for Germany.

Research on the community electric carbon emission prediction considering the dynamic emission coefficient of power system

Based on the counted power system emission factors of North China Power Grid, a community carbon emissions sample database is constructed. The support vector regression (SVR) model is trained to forecast the power carbon emissions, which is optimized by genetic algorithm (GA). A community carbon emission warning system is designed according the results. The dynamic emission coefficient curve of the power system is obtained by fitting the annual carbon emission coefficients. The time series SVR carbon emission prediction model is constructed, while the GA is improved to optimize its parameters. Taking Beijing Caochang Community as an example, a carbon emission sample database is generated based on the electricity consumption and emission coefficient curve to train and test the SVR model. The results show that the GA-SVR model fits well with the training set and the testing set, and the prediction accuracy of the testing set reaches 86%. In view of the training model in this paper, the carbon emission trend of community electricity consumption in the next month is predicted. The carbon emission warning system of the community is designed, and the specific strategy of community carbon emission reduction is proposed.

Dual Carbon Goal-Based Quadrilateral Evolutionary Game: Study on the New Energy Vehicle Industry in China

In an effort to tackle climate change, the "Dual Carbon" target raised by the Chinese government aims to reach peak carbon dioxide emissions by 2030 and to achieve carbon neutrality by 2060. Accordingly, policy incentives have accelerated the new energy vehicle (NEV) sector. Whilst previous studies have focused on the bilateral game between governments and manufacturers, NEV development has witnessed interaction among multiple players. In this paper, we construct a quadrilateral evolutionary game model, considering the impact of government policies, manufacturers' R&D investments, dealers' support, and consumer choice on the evolutionary stabilization strategy (ESS) in the context of China. The results show that: (1) in the absence of government incentives, there is no motivation for manufacturers, dealers and consumers to consider the development of NEVs; (2) government incentives affect manufacturers and consumers on the evolutionary paths in the short term. In the long term, benefit- and utility-based limited rationality has a dominant role in the ESS. This study contributes to the understanding of the multilateral dynamics of NEV innovation and provides important implications to practitioners and policy makers.