Measurement and Spatial-Temporal Characteristics of Inclusive Green Growth in China

Performance evaluation of inclusive green growth in China: Dynamic evolution, regional differences, and spatial correlation

Inclusive green growth is an essential way to achieve sustainable development. We construct an index system for inclusive green growth performance levels (IGGPLs) in Chinese cities and measure the IGGPLs of 271 cities in the Chinese mainland from 2006 to 2020 based on the vertical and horizontal scatter degree method. We employ the Kernel density method, Dagum Gini coefficient method, Moran index method, and Markov chain method to investigate distribution evolution, regional disparities, spatial correlations, and state transition of IGGPLs at the city level, respectively. The research results reveal that: (1) China's IGGPL has improved rapidly, but regions with lower IGGPLs still predominate. The eastern region is far ahead, followed by the northeastern region, with the western and center regions trailing; (2) The development trends of IGGPLs in the eastern and central regions are positive, with no signs of polarization. Although polarizations are obvious in the western and northeastern but have been improved significantly; (3) Regional differences exist, but are gradually narrowing over time. By decomposing regional differences, we find that regional differences are the main cause of total differences; (4) The IGGPLs of cities have significant spatial correlations, presenting the spatial agglomeration characteristics of "high-high" and "low-low".

Fostering inclusive green growth in China: Identifying the impact of the regional integration strategy of Yangtze River Economic Belt

Inclusive green growth has garnered significant attention from governments and international organizations worldwide. Utilizing data from 282 cities in China spanning from 2010 to 2020, this study employs the dual machine learning model and the chain mediated effect model to delve into the effects and internal mechanisms of the regional integration of Yangtze River Economic Belt on inclusive green growth. Our findings indicate that the regional integration of Yangtze River Economic Belt has a substantial influence on inclusive green growth, particularly in cities with a higher degree of marketization, non-industrial cities, and cities lacking natural resource advantages. Mechanistic analysis reveals that the regional integration of Yangtze River Economic Belt exerts its influence on inclusive green growth through three parallel development paths including enhancing urban innovation levels, fostering mass entrepreneurship, and promoting the advancement of digital financial inclusion. Furthermore, the chain mediated effect is supported. Additionally, the spatial spillover effect of target policy is observed. These findings offer empirical evidence regarding the impact of the regional integration of Yangtze River Economic Belt on inclusive green growth, and provide valuable insights for optimizing and enhancing inclusive green growth strategies in China and other emerging economies.

Impact of Artificial Intelligence on Regional Green Development under China's Environmental Decentralization System-Based on Spatial Durbin Model and Threshold Effect

Artificial intelligence (AI) is the core technology of digital economy, which leads the transition to a sustainable economic growth approach under the Chinese-style environmentally decentralized system. In this paper, we first measured the green total factor productivity (GTFP) of 30 Chinese provinces from 2011 to 2020 using the super-efficiency slacks-based measure (SBM) model, analyzed the mechanism of the effect of AI on GTFP under the environmental decentralization regime, and secondly, empirically investigated the spatial evolution characteristics and the constraining effect of the impact of AI on GTFP using the spatial Durbin model (SDM) and the threshold regression model. The findings reveal: a U shape of the correlation of AI with GTFP; environmental decentralization acts as a positive moderator linking AI and GTFP; the Moran index demonstrates the spatial correlation of GTFP; under the constraint of technological innovation and regional absorptive capacity as threshold variables, the effect of AI over GTFP is U-shaped. This paper provides a useful reference for China to accelerate the formation of a digital-driven green economy development model.