A data-driven rule-base approach for carbon emission trend forecast with environmental regulation and efficiency improvement

Greenhouse gas emissions are widely recognized as the primary cause of global warming, leading to a growing attention on carbon emission management. However, the existing studies still failed to propose a feasible approach to directly forecast carbon emission trends and also did not take into account both environmental regulation and efficiency improvement. Hence, this study aims to propose a novel carbon emission trend forecast model based on data-driven rule-base with considering the intensity coefficient of environmental regulation and the management efficiency of carbon emissions. Carbon emission data of 30 Chinese provinces are collected to illustrate the effectiveness of the proposed model. Results indicated that: 1) the data-driven rule-base model is able to directly forecast carbon emission trends within range from -18.54 % to 19.18 %; 2) by integrating regulation intensity, the predicted results of the model have smaller carbon emission tends, e.g., decrease of average changing rate from 0.4100 to 0.2762; 3) by further integrating efficiency improvement, the predicted results align more with the expected objectives of policy makers, i.e., the average carbon emission efficiency approximates 0.8920 and the number of provinces being effective efficiency is increased to 8. These findings also highlighted the importance of carbon emission tend forecast with environmental regulation and efficiency improvement. The proposed carbon emission trend forecast model could serve as an alternative tool for achieving dual carbon goals in the context of China.