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Existing literature ignores to consider multiple types of pollutants when analyzing energy efficiency and its convergence. Under this background, using SuperSBM-GML model, the improved entropy method, and convergence model, this paper calculates the green total-factor energy efficiency and its evolution trend of 35 China's industrial sub-sectors considering multiple pollutants, and analyzes its convergence. The results indicate that the average score of industrial green total-factor energy efficiency is low, and there are significant differences among sub-sectors: "Utilization of waste resources" is the highest, while "Manufacture of paper and paper products" is the lowest. The green total-factor energy efficiency shows an overall upward trend from 2006 to 2021, the main driving force comes from technological progress, but numerous sub-sectors have not sufficiently caught up with existing cutting-edge technologies. Specifically, the growth rate of green total-factor energy efficiency in high-energy-consumption sub-sectors is higher than low-to-medium-energy-consumption sub-sectors. There are both σ-convergence and β-convergence in low-to-medium-energy-consumption group, indicating that development of sub-sectors is stable and outstanding. Nevertheless, the high-energy-consumption group only exhibits conditional β-convergence, revealing an imbalance in energy efficiency development. Consequently, formulating the benchmark level of energy efficiency and developing energy efficiency "leader" system are suggested for low-to-medium and high-energy-consumption sub-sectors, respectively. [ABSTRACT FROM AUTHOR]

After the strategic concept of global energy Internet constructed, it has produced a strong response in the world. In recent years, countries around the world are actively promoting energy transformation and have developed clean energy development goals and plans. This paper constructs a comprehensive evaluation index system for the level of clean energy development by considering policies and regulations, energy supply, environmental impact, energy consumption, technology, economy and so on. At the same time, China, Germany, the United States and other countries are selected as evaluation objects. On the basis of searching, processing and analyzing a large number of data, firstly, the research uses the principal component analysis method to carry out the correlation cluster analysis of the index and then uses the rough set method to assign the weight of the extracted principal components. Finally, the weight of each index in the index system is calculated. The results of the comprehensive evaluation of each country are compared and analyzed, which shows that the weight determination method based on rough set theory and principal component analysis is more reasonable and objective. At the end of the paper, some suggestions are proposed to promote the development of clean energy. [ABSTRACT FROM AUTHOR]