Green growth, a new growth mode to tackle resource and environment crises, is imperative in light of current environmental crises and resource depletion. Most evaluation strategies of green growth, while emphasizing the time dimension, ignore the spatial association and diffusion. This study provides a measurement framework of green growth with which to select a set of 18 indicators and evaluates the efficiency of green growth in different regions. The coefficient of variation and the regional Gini coefficient were applied to analyze the spatial variation of green growth. The Exploratory Spatial Data Analysis (ESDA) was used to identify the evolution of the geographical agglomeration in 30 administrative regions in mainland China. The results show that China's green growth capacity is constantly improving, and the gap between regions in this respect is shrinking. The spatial evolution trend of green growth is expanding horizontally from the East to the West and vertically from the central to the southwest and the northwest regions. Green growth in the eastern and central regions is active but poor in the northeast region. Compared with the continuous stability of the 22 provinces, eight provinces exhibited spatial activity and growth spillover, which affected the adjacent regions. Promoting the outflow of capital and technology is key to increasing green growth in the eastern and central region, while increasing investment and introducing technology through policy advantages to promote industrial transformation is an urgent task for the northeast region.

The manufacturing industry has created a rapid evolution of the economy, but it has also negatively impacted the ecosystem. A better understanding of the manufacturing industry in green growth is crucial to achieving the sustainability goals in China’s high-quality development stage and is better for identifying the impact of scale effect or technological effect in EKC. In this research, a super-efficiency slacks-based measure model is proposed to evaluate the green growth efficiency of 27 manufacturing industries, and a Luenberger index method is adopted to interpret the driving forces of efficiency. The results demonstrate that green growth efficiency in the manufacturing industry shows a fluctuating upward trend, and more than 60% of the industries are in a gray growth state. The growth of green growth efficiency mainly depends on the pulling effect of technological dividends brought by technological progress, rather than the improvement of technical efficiency. As the industry heterogeneity is analyzed, technology-intensive industries still dominate in the process of manufacturing industry and have shown a significant upward trend. Finally, some suggestions are proposed from the perspective of the government and enterprises.