Measurement, spatial-temporal pattern and dynamic evolution of inclusive green growth in Chinese cities

This study takes 279 prefecture-level and above cities in China from 2006 to 2020 as the research objects. It employs the entropy weight method to measure the inclusive green growth levels of different cities. Additionally, it utilizes the Dagum Gini coefficient and its decomposition methods, stochastic kernel density estimation, and Markov chain analysis to investigate the spatiotemporal patterns and dynamic evolution of inclusive green growth. The results indicate that the inclusive green growth levels of Chinese cities exhibit an overall upward trend with fluctuations. Cities in the coastal economic zones have relatively higher inclusive green growth levels compared to those in other economic zones. Significant regional disparities in inclusive green growth levels exist among the eight major comprehensive economic zones, with interregional differences being the primary source. The stochastic kernel density estimation and Markov chain analysis reveal that, without considering spatial factors, the inclusive green growth levels of Chinese cities tend to be highly persistent and less mobile. However, when spatial factors are taken into account, cities with high levels of inclusive green growth can exert a radiating and driving effect on neighboring cities with medium and low development levels. This research enriches the measurement and analytical framework of urban inclusive green growth. The findings provide theoretical and practical insights for accelerating the construction of an innovation and green dual-driven development model, narrowing regional disparities in inclusive green growth, and steadily enhancing the inclusive green growth levels of cities.