Spatial-Temporal Evolution and Influencing Factors of Urban Green and Smart Development Level in China: Evidence from 232 Prefecture-Level Cities

Abstract

Green and smart city is an optimal choice for cities to realize their modernization of governance capacity and sustainable development. As such, it is necessary to clarify the evolutionary characteristics and driving mechanism of urban green and smart development level (GSDL) systematically. From the perspective of green total factor productivity (GTFP), this study adopted the SBM-GML (slack-based model & global Malmquist–Luenberger) method to measure the urban GSDL considering smart input-output elements. Based on the panel data of China’s 232 prefecture-level cities from 2005 to 2018, the spatial and temporal evolution characteristics of urban GSDL were explored, and the factors and structural mutation points affecting urban GSDL were analyzed with quantile regression tests and threshold regression tests. The findings of this paper showed that (1) there is an upward trend in the volatility of urban GSDL from 2005 to 2018, in which the eastern region was highest, followed by the central and western regions, and the differentiation showed no converge among regions; (2) the effect of technical progress and technical efficiency improvement on the urban GSDL was demonstrated with a fluctuating “Two-Wheel-Drive” trend on the whole; (3) the urban GSDL was promoted by the opening-up level and urban scale significantly, while inhibited by the level of economic development and government size. Additionally, the effects of industrial structure, financial development level, and human capital level on the urban GSDL were distinctive at different loci; (4) the threshold effects of economic and financial development level on improving the positive effects of industrial structure and opening-up level on urban GSDL were significant. These findings may enrich the research literature on the evolutionary heterogeneity of green and smart cities and provide theoretical and practical exploration for the construction of green and smart cities.

Spatial differences, distributional dynamics, and driving factors of green total factor productivity in China

“…This is consistent with the result of Huang C et al (2022). The eastern region, with its advantageous geographical location, high level of green technology, and good business environment, can effectively promote the development of GTFP (Xu et al, 2022). The average annual growth rate of the GTFP was positive in Beijing (5.9%), Tianjin (0.1%), Shanghai (2.2%), Jiangsu (1.7%), Zhejiang (1.5%), Anhui (0.1%), Hubei (0.9%), and Chongqing (1%) provinces.…”

Study on the Evolution of Spatial and Temporal Patterns of Carbon Emissions and Influencing Factors in China

“…The significant economic changes in Region N in 2020, driven by the introduction of new industries, underscore the profound environmental impacts of economic shifts. Additionally, the dynamic interplay between urban planning and demographic changes, particularly in the CS region as discussed by Xu et al [73], calls for a reevaluation of urbanization strategies to align with sustainable development goals. Moreover, the importance of region-specific approaches, such as those adopted in the Southwest to enhance ecological protection and land use efficiency [74], illustrates the need for adaptive strategies that respect local economic, political, and climatic conditions.…”

The Influencing Effect of Tourism Economy on Green Development Efficiency in the Yangtze River Delta

“…The green development efficiency was mainly measured from the perspective of input-output based on green total factor productivity, which effectively integrated the benefits of economic growth and environmental protection [42,43]. Among them, capital, labor, and technology were selected as non-resource inputs, and energy and land resource consumption was selected as resource input.…”