Forecast of carbon emissions in China based on time lag MGM(1,m,N|τ) grey model

Global climate issues have been gaining international attention in recent years. As the largest developing country and the prime carbon emitter, the Chinese government has proposed a strategic 'double carbon' target for carbon emissions. To predict carbon emissions more accurately, clarify the future supply situation and optimise resource allocation, based on the grey $$ model, we introduced and applied the particle swarm algorithm to determine the time lag parameter $$ and proposed a new $$ grey model. We give a detailed modelling procedure, including calculation steps and intelligent optimisation algorithms, by fully considering the effect of time lag. In this study, this new model is used to simulate and forecast China's carbon emissions from 2010 to 2019 and compare it with other traditional grey models and their improved time-lagged forms. The results show that the new model has significant advantages in prediction accuracy and validity, plus good prediction performance for carbon emissions, which can be extended to more macro and micro energy consumption prediction problems.