Mining interpretable fuzzy If-Then linguistic rules from energy and economic data to forecast CO2 emissions of regions in China

Abstract

Forecasting CO${}_2$ emission is the one of most important issues for the “$30\cdot 60$” CO${}_2$ emission target in China. Due to unbalanced socio-economic developments of regions in China, exactly forecasting CO${}_2$ emissions of provinces depend on their energy consumptions and economic developments. In the paper, a novel method based on $K$-means clustering method and computing with words is proposed to forecast CO${}_2$ emissions of 30 provinces, which is consisted by (1) $K$-means clustering method is used to respectively cluster energy consumption and economic datasets of provinces and the interpretable fuzzy If-Then linguistic rules of CO${}_2$ emissions are mined from the clusters; (2) computing with words method is utilized to transform fuzzy If-Then linguistic rules into fuzzy If-Then rules with membership functions on the universe of discourse; (3) a fuzzy inference method is adopted to forecast CO${}_2$ emissions of 30 provinces. To show the usefulness and effectiveness of the novel method, energy consumptions and economic datasets of 30 provinces from 1997 to 2021 are employed to forecast CO${}_2$ emissions, metrics of MAE, MAPE, RMSE and the out-of-sample $R_{oos}^2$ are utilized to evaluate CO${}_2$ emission forecasting of 30 provinces, means of them reach 13.304, 15.279, 0.081 and 0.965. By comparative analysis for forecasting CO${}_2$ emissions of 30 provinces, means of MAE, MAPE, RMSE and the out-of-sample $R_{oos}^2$ by the novel method are more than SVM, ANFIS and MLR methods. In addition, four kinds of mechanisms influencing CO${}_2$ are discovered and analyzed by the fuzzy If-Then linguistic rules of 30 provinces, which can help to improve energy consumption and sustainable development of 30 provinces in China.