Strategies for improving the industrial carbon emission efficiency in China: an approach based on trend prediction and regional learning mechanism

It is an important way for mining the characteristics of regional carbon emission efficiency and exploring regional similarity to learn from intergovernmental learning mechanisms and regional industrial low-carbon development experiences. This study proposes a regional learning mechanism of industrial carbon emission efficiency (ICEE) prediction and regional similarity analysis to explore strategies for carbon emission reduction. We first calculated the industrial carbon emission efficiency of 30 provinces in China from 2000 to 2021 using the super-SBM model. Secondly, the spatiotemporal characteristics of industrial carbon emission efficiency were explored through the space-time cube model, time series clustering method, and local outlier analysis. Finally, the screening of regions with low efficiency levels and the search for learning objects were realized by forest regression prediction and regional similarity calculation. The results of the study were as follows: (1) There were significant differences in industrial carbon emission efficiency among different provinces. (2) Based on the time series clustering results, we found that there were similar change characteristics of industrial carbon emission efficiency in different provinces. (3) The industrial carbon emission efficiency of most provinces had significant correlation in space and time, mainly in high-high clustering. (4) The industrial carbon emission efficiency of most regions will maintain a high efficiency level in the next 10 years, but the six provinces of Xinjiang, Qinghai, Gansu, Ningxia, Liaoning, and Heilongjiang will always be at a low efficiency level. It is possible to set appropriate learning targets for each region and to find lessons to be learned from the regions with high similarity by calculating the similarity between each province and the six provinces.