A new multiregional carbon emissions forecasting model based on a multivariable information fusion mechanism and hybrid spatiotemporal graph convolution network

Developing scientific and effective carbon emissions reduction policies relies heavily on precise carbon emission trend prediction. The existing complex spatiotemporal correlation and diverse range of influencing factors associated with multi-regional carbon emissions pose significant challenges to accurately modeling these trends. Under this constraint, this study is inspired by graph learning to establish a hybrid dynamic and static graph-based regional carbon emission network framework, which introduces a novel research standpoint for investigating short-term carbon emissions prediction (CEP). Specifically, a parallel framework of attribute-augmented dynamic multi-modal graph convolutional neural networks (ADMGCN) and temporal convolutional networks with adaptive fusion multi-scale receptive fields (AFMRFTCN) is proposed. The proposed model is evaluated against nineteen state-of-the-art models using daily carbon emission data from 30 regions in China, demonstrating its effectiveness in accurately predicting the trends of multi-regional carbon emissions. Conclusions are drawn as follows: First, especially in regions with marked periodicity, compared with the best baseline model, the mean absolute percentage error (MAPE) of our model is reduced by 20.19%. Second, incorporating graph convolutional neural networks (GCNs) with dynamic and static graphs is advantageous in extracting the spatial features of China's carbon emission network, which are influenced by geographical, economic, and industrial factors. Third, the parallel ADMGCN-AFMRFTCNs framework effectively captures the influence of external information on carbon emissions while mitigating the issue of low prediction accuracy resulting from univariate information. Fourth, the analysis reveals significant differences in the short-term (30-day) growth rate of carbon emissions among different regions. For example, Henan exhibits the highest growth rate (37.38%), while Guizhou has the lowest growth rate (−7.46%). It is valuable for policymakers and stakeholders seeking to identify regions with distinct emission patterns and prioritize mitigation efforts accordingly.