Unveiling the Spatio-Temporal Evolution and Key Drivers for Urban Green High-Quality Development: A Comparative Analysis of China’s Five Major Urban Agglomerations

Abstract

Faced with the dual challenges of ecological degradation and economic deceleration, promoting urban green high-quality development (UGHQD) is pivotal for achieving economic transformation, ecological restoration, and regional sustainable development. While the existing literature has delved into the theoretical dimensions of UGHQD, there remains a notable dearth of empirical studies that quantitatively assess its developmental levels, spatio-temporal evolution, and driving factors. This study examines 107 cities of China’s five major urban agglomerations from 2003 to 2020, constructing a comprehensive evaluation indicator system for UGHQD. By employing methodologies, including the Dagum Gini coefficient, Kernel density estimation, Markov chain, and geographical detector, this study extensively assesses the spatial difference, dynamic evolution, and underlying driving forces of UGHQD in these urban agglomerations. The findings indicate: (1) The UGHQD level of the five major urban agglomerations has witnessed a consistent year-over-year growth trend, with coastal agglomerations like the Pearl River Delta (PRD) and Yangtze River Delta (YRD) outperforming others. (2) Pronounced regional differences exist in UGHQD levels across the urban agglomerations, with inter-regional differences primarily contributing to these differences. (3) The dynamic evolution of UGHQD distribution generally transitions from a centralized to a decentralized pattern, with a marked “club convergence” characteristic hindering cross-type leaps. (4) While a range of factors drive UGHQD in these agglomerations, technological innovation stands out as the principal factor inducing spatial differentiation. The comprehensive analysis and findings presented in this research not only contribute to academic knowledge but also hold practical implications for policymakers and practitioners striving for environmentally conscious land use planning and urban management.

From efficiency to resilience: unraveling the dynamic coupling of land use economic efficiency and urban ecological resilience in Yellow River Basin

“…In order to explore the underlying causes behind the fluctuations in the CCD between LUEE and UER in the YRB, we designate the CCD as the dependent variable. Utilizing the “Jenks natural breakpoint” method for categorizing factor variables 63 , 64 , this paper, in line with existing research 65 – 68 and considering the region’s actual developmental context, selects indicators such as topographic relief ( X 1), population density ( X 2), distance to the provincial capital city ( X 3), industrial structure upgrading ( X 4), government fiscal budget expenditure ( X 5), and urbanization level ( X 6) as key detect factors. This approach aims to reveal the drivers influencing the CCD in the YRB.…”

“…This study employs ArcGIS 10.8 to create a spatial visualization vector map of LUEE in the YRB, aiming to delve into the spatial distribution traits and the progression of spatial configurations in different urban areas within the basin. Utilizing the “Jenks natural breakpoint” 68 , 69 , the study classifies the 56 cities in the YRB into four distinct LUEE categories: low, medium–low, medium–high, and high, as depicted in Fig. 5 .…”

Measurement and Spatio-Temporal Characteristics of High-Quality Development Efficiency in Metropolitan Areas: A Case Study of the Changchun Metropolitan Area

“…Qiu et al analyzed the spatialtemporal heterogeneity of green development efficiency and its influencing factors in the growing Xuzhou Metropolitan Area [50]. However, the majority of studies concentrate on the economy and green development of urban agglomerations [51,52], with limited research dedicated specifically to the high-quality development of urban agglomerations and metropolitan areas, and even fewer evaluations of efficiency. Additionally, when assessing the development level of both metropolitan areas and urban agglomerations, the entropy weight method is predominantly used, while other methods are rarely employed.…”