Forecasting carbon peaking in China using data-driven rule-base model : an in-depth analysis across regional and economic scenarios

Abstract

At the 2020 United Nations Climate Summit, China officially announced the goal to achieve carbon peaking by 2030. Exploring whether it is possible to reach the peak of carbon emissions earlier necessitates an urgent and imperative need for precise long-term forecasting of China's carbon emissions dynamics. However, the current carbon peaking predictions mostly depend on mechanical or mathematical models, which failed to consider the interdependence between carbon emissions and the time series-based patterns existed in carbon emission data. Therefore, this study presents a novel carbon peaking prediction method based on the data-driven rule-base model, which is implemented by the adaption of the extended belief rule base (EBRB) model for time series forecasting (TSF), and thus the proposed method is referred to as TSF-EBRB model. The TSF-EBRB model not only captures and measures the temporal correlations within the data throughout the processes of modeling and inference, but also consists of a novel parameter optimization model based on the temporal correlations. The study collected carbon emission data from 30 provinces in China for empirical analysis. It computed and predicted the carbon peaking trajectories of each province under three different scenarios from 2022 to 2030, validating the effectiveness and superiority of the TSF-EBRB model better than other existing carbon peaking prediction methods. The results indicated that, with policy interventions, the majority of provinces are projected to reach carbon peaking before 2030.