RESUMO
Global warming and climate change are gaining traction in recent years. As a major cause of global warming, carbon emissions were centered to China's climate change policy initiatives. Nevertheless, the existing policy discourse has yet reached a consensus on the optimal modeling method for carbon emissions prediction that is well-informed of both policy goals and the time-series pattern of carbon emissions. This paper fills the gap by promoting a novel data-driven decision model for carbon emissions prediction that is based on the extended belief rule base (EBRB) inference model. The new decision model consists of three components: 1) an indicator integration method, which aims to generate a few group indicators from a large number of statistical indicators; 2) a new EBRB construction method, which aims to consider the management policy goals for constructing EBRB; 3) a new ER-based inference method, which aims to predict carbon emissions based on time series change of relevant factors. The effectiveness of the proposed decision model has been tested against carbon emissions management data from 30 provinces in China. Experimental results demonstrate that the model will offer powerful reference value in the policy decision-making process, which will help to meet policy requirements for carbon emissions.