The Temporal-Spatial Evolution and Driving Mechanism of Rural Green Development in China

Abstract

Green development is the inherent requirement and important support of comprehensive rural revitalization in China. This study constructs a rural green development index system drawing on the framework of "green growth-green wealth-green welfare" and elaborates the temporal-spatial evolution and driving mechanism of rural green development in 30 provinces in China based on the entropy method, spatial autocorrelation analysis and Geodetector. The results indicate that the rural green development index (RGDI) showed an upward trend from 2004 to 2019, with an average annual growth rate of 7%. Nevertheless, the overall level of rural green development is relatively low. The RGDI presented the spatial differentiation characteristic of high in the eastern region and low in the central and western regions. In particular, the urbanization level, economic scale, industrial structure, and public facility investment are the primary factors driving rural green development. The research proposes relevant policy recommendations from the perspectives of four major regions to improve the rural green development level in China, including green agricultural production, ecological protection, public services and infrastructure investment, and industrial integration.

Zoning management framework for comprehensive land consolidation in oasis rural in arid area: Case study of the Ugan‐Kuqa River Delta Oasis in Xinjiang, China

“…This data was obtained from the local land administration and we converted it to raster data with a resolution of 10 m for use. Referring to the classification principles and methods in related literature (A. Wang et al, 2022; J. Wang et al, 2023) and combining with the land use cover characteristics of dryland areas and the main purpose of this study, it was classified into three primary classifications of production space, living space and ecological space and seven secondary classifications (Table S1). (2) Spatial data for geographical elements for administrative boundaries at all scales, traffic network, and river systems in the study area were extracted from the National Geomatics Center of China (http://www.ngcc.cn/ngcc/).…”

Spatio–Temporal Evolutionary Features and Drivers of Green Competitiveness of Cities Surrounding the Yellow River

“…Other scholars have evaluated the development level of the green competitiveness of municipalities based on four dimensions [12], namely economic green development, social green development, resource green development, and environmental green development, or three dimensions [13], namely green economy, green investment, green utilization, and green security. In addition, Wang et al (2022) constructed a comprehensive evaluation index system for green competitiveness in rural China based on the "green growth-green wealth-green welfare" framework [14]. (3) On the spatio-temporal patterns of green competitiveness.…”

“…Studies show that China's regional green competitiveness continues to improve, with the green competitiveness of the eastern region being much higher than that of the other regions, and the regional differences are characterized first by an increase and then a decrease; the coupling coordination degree of China's regional green competitiveness system exhibits significant spatial dependence, forming a high-coupling coordination degree agglomeration with Shanghai as the center spreading to the south and a low-coupling coordination degree agglomeration with Qinghai as the center spreading to the east [11]. Wang et al (2022) analyzed the spatial and temporal characteristics and drivers of China's rural green competitiveness through spatial autocorrelation (SA) analysis and GeoDetector and found that China's rural green development presents a spatial differentiation feature that is high in the eastern region and low in the central and western regions [14]. Ma et al (2023) analyzed regional differences in China's green competitiveness through methods such as coefficient of variation analysis and found that the coefficient of variation in green competitiveness of China's 30 provinces and the eastern, central, and western regions show an inverted U-shape trend of first increasing and then decreasing [19].…”

“…The development of a city's green competitiveness is affected by multiple factors. On the basis of extant studies' findings [14], the reality of the development of green competitiveness of cities surrounding the YR, and the indicator weights in the previous index system, the level of urbanization X1 (urbanization rate), industrial structure upgrading X2 (proportion of tertiary industry output value), S&T innovation X3 (S&T spending as a percentage of total fiscal spending), government regulation and control ability X4 (financial expenditure per capita), the level of city infrastructure construction X5 (length of city drainage pipes), and financial support X6 (the balances of deposit and loan held by financial institutions at the end of the year as a percentage of GDP) were selected as the six indicators of green competitiveness drivers for cities surrounding the YR.…”

The vulnerability assessment and obstacle factor analysis of urban agglomeration along the Yellow River in China from the perspective of production-living-ecological space

“…Therefore, we aim to develop a comprehensive evaluation system for urban vulnerability to serve as a reference for assessing the overall quality of urban development and promoting sustainable development for both people and the environment. In recent years, with the gradual popularization of the "three pillars" concept in the international community, academics have increasingly focused on the study of territorial space from a production-living-ecological perspective [23,24]. The integrated spatial layout of production-livingecological space plays a crucial role in harmonizing the relationship between people and the land and provides a methodology for improving the quality of urban development and reducing urban vulnerability.…”