The Spatial-Temporal Transition and Influencing Factors of Green and Low-Carbon Utilization Efficiency of Urban Land in China under the Goal of Carbon Neutralization

Abstract

Urban-land development and utilization is one of the main sources of carbon emissions. Improving the green and low-carbon utilization efficiency of urban land (GLUEUL) under the goal of carbon neutrality is crucial to the low-carbon transition and green development of China’s economy. Combining the concept of green and low-carbon development in urban land use, carbon emissions and industrial-pollution emissions are incorporated into the unexpected outputs of the GLUEUL evaluation system. The super-efficient slacks-based measure (SBM) model, Exploratory Spatial-Temporal Data Analysis (ESTDA) method and Geographically and Temporally Weighted Regression (GTWR) model were used to analyze the spatial-temporal transition and the influencing factors of GLUEUL in 282 cities in China from 2005 to 2020. The result shows that: (1) From 2005 to 2020, the green and low-carbon land-utilization efficiency of Chinese cities shows an increasing temporal-evolution trend, but the gap between cities is gradually widening. (2) From the spatial-temporal dynamic characteristics of Local Indicators of Spatial Association (LISA), regions with the highest GLUEUL have strong dynamics and instability, while cities at the lowest level have a relatively stable spatial structure. On the whole, the local-spatial-transfer direction of GLUEUL of each city is stable, with certain path-dependent characteristics. (3) There are differences in the degree of influence and direction of action of different factors on GLUEUL. The economic development level, industrial-structure upgrading, financial support, wealth level, and green-technology-innovation ability have positive effects on overall GLUEUL, with industrial-structure upgrading promoting GLUEUL the most, while urban population size, foreign-investment scale, and financial-development level play a negative role. This study can provide some empirical and theoretical references for the improvement of GLUEUL.

Exploring Spatial-Temporal Coupling and Its Driving Factors of Green and Low-Carbon Urban Land Use Efficiency and High-Quality Economic Development in China

“…On the other hand, as the cornerstone of urban economic activity, the core of GLUUL is that the social, economic, and ecological gains should be as large as possible, while the environmental losses should be as small as possible in the urban land system under the input of certain production factors [42]. HED, moreover, stresses more efficient and ecological economic development.…”

Horizontal CO2 Compensation in the Yangtze River Delta Based on CO2 Footprints and CO2 Emissions Efficiency

“…It avoids the influence of human subjective factors and broadens the applicable constraints of the model. In addition, it can overcome the previous drawback of not being able to compare the efficiency values of multiple municipalities when they are 1 [53,54]. Based on these characteristics, we applied the super-SBM model to measure the municipal CO 2 emissions efficiency (ρ it ) (Equation ( 4)) [55].…”

Quota and Space Allocations of New Urban Land Supported by Urban Growth Simulations: A Case Study of Guangzhou City, China

“…This result may indicate that the new urban land quota has more synergy with the potential urban growth under a constrained scenario, which implies that the quota allocation is more reasonable in a constrained scenario. We introduced two tools that could more intuitively display the difference in the new urban land quota allocation, the gravity center and standard deviation ellipse, which are widely used to characterize allocating geographical elements [58]. The concept and calculation method for the gravity center and standard deviation ellipse can be found in Polajzer's research [59].…”

“…The Land Space Planning of Guangzhou City proposes an eastward and southward development strategy. Huangpu is the future industrial center of Guangzhou City, and Nansha will be built as the sub-center of We introduced two tools that could more intuitively display the difference in the new urban land quota allocation, the gravity center and standard deviation ellipse, which are widely used to characterize allocating geographical elements [58]. The concept and calculation method for the gravity center and standard deviation ellipse can be found in Polajzer's research [59].…”