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

Abstract

In the context of the widely recognized concept of inclusive green growth, it is of great practical significance to study the measurement of inclusive green growth, its temporal characteristics and its spatial heterogeneity. However, it should be noted that most existing studies only consider the situation of China when constructing inclusive green growth indices, and such studies lack international comparators. The index selection dimension is relatively single, and the temporal characteristics and spatial heterogeneity are rarely studied simultaneously. In this study, a spatial correlation analysis method is introduced, and the time series characteristics and spatial heterogeneity of inclusive green growth are deeply analyzed by means of a Moran’ I and LISA agglomeration diagram. The results show that: (1) Economic development, fair opportunity, green production and consumption and ecological environment protection are important factors affecting the level of inclusive green growth, but their development is not balanced; (2) Inclusive green growth has obvious time series characteristics, but there are great differences between provinces and regions; (3) The inclusive green growth has significant spatial heterogeneity. From 2010 to 2019, the inclusive green growth level has a dynamic evolution process. Moran’s I Index Global Spatial Test results show that inclusive green growth has obvious spatial autocorrelation, that is, it has great spatial differences. Local spatial test results also show that the inclusive green growth in China has obvious spatial aggregation characteristics. Therefore, it is important to coordinate the development of inclusive green growth between provinces and regions and improve the overall level of inclusive green growth in the future.

Toward inclusive green growth for sustainable development: A new perspective of labor market distortion

“…To eliminate the influence of different measurement units and make the results more comparable, the original indicators are normalized by the min–max method. Consistent with Zhou and Wu (2018) and Gu et al (2021), the fixed base range entropy weight method is employed to calculate the weights of indicators, and the detailed calculation is described in the Appendix. The calculation results of inclusive green growth are shown in the following figure (see Figure 3).…”

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

“…), which found that Internet development significantly contributed towards the enhancement of total factor carbon productivity [ 45 ]. Zhao’ s study (2022) is concerned with inclusive economic growth and focuses mainly on the analysis of the intrinsic drivers of inclusive economic growth [ 46 ]. Li and Song’s study (2022) constructs a green evaluation system based on three dimensions: economic, environmental, and social and investigates its intrinsic mechanisms [ 47 ].…”

Digital Inclusive Finance and Regional Economic Development Level——Empirical Evidence based on Provincial Panel Data from 2011-2020

“…At the micro level, Zhang Xun et al ( 2020) conducted an empirical study using micro-household data and found that the development of DIFI contributes to inclusive growth in China by increasing household income, especially for rural low-income groups. Xie, Gai-Li et al (2018) studied the impact of DIFI on entrepreneurship and found that the total digital finance index as well as the three first-order dimension indicators significantly promoted entrepreneurship [7][8].…”