Applying SBM-GPA Model to Explore Urban Land Use Efficiency Considering Ecological Development in China

Cui, Xufeng; Huang, Sisi; Liu, Cuicui; Ting-ting, Zhou; Shan, Ling; Zhang, Fengyuan; Chen, Min; Li, Fēi; Vries, Walter Timo de

doi:10.3390/land10090912

Cited by 17 publications

(7 citation statements)

References 42 publications

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“…This study uses the global autocorrelation model to determine the spatial distribution pattern of ecosystem service value. The Global Moran’s I index is usually used for this calculation, and the formula is as follows [ 66 , 67 ]:

where I is Moran index.

and

are the observed values of the i th evaluation unit and the j th ESV evaluation unit, respectively.…”

Section: Methodsmentioning

confidence: 99%

Spatial-Temporal Responses of Ecosystem Services to Land Use Transformation Driven by Rapid Urbanization: A Case Study of Hubei Province, China

Cui

Liu

Shan

et al. 2021

IJERPH

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Exploring the changes of ecosystem services value caused by land use transformation driven by urbanization is crucial for ensuring the safety of the regional ecological environment and for enhancing the value of ecosystem services. Based on the land use remote sensing data during the rapid urbanization development period of Hubei Province from 1995 to 2015, this study analyzed the characteristics of land use/land cover change and land use transformation. The spatial–temporal response characteristics and evolution of ecosystem services value (ESV) to land use transformation driven by urbanization were measured by equivalent factor method, spatial autocorrelation analysis, hot spot analysis and gravity model. We found that: (1) Driven by urbanization, the most significant feature of land use transformation in Hubei Province was the expansion of the built-up land and the significant reduction of cropland and forest, among which 90% of the new built-up land was converted from cropland and forest. (2) This land use transformation became the main source of ESV losses. Especially, the sharp increase of the built-up land from 2010 to 2015, occupying cropland and forest, resulted in ESV losses of nearly USD 320 million. The service capacity of climate regulation, soil conservation, gas regulation and food production undertaken by cropland and forest decreased. (3) The ecosystem services value in the study area showed spatial distribution characteristics of high in the west and low in the middle and east regions. The center of gravity of ESV shifted from northwest to southeast. Due to the sharp increase of the built-up land from 2010 to 2015, the center of gravity shift rebounded. This study can help policymakers better understand the trade−offs between land use transformation and ecosystem services driven by urbanization.

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where I is Moran index.

and

are the observed values of the i th evaluation unit and the j th ESV evaluation unit, respectively.…”

Section: Methodsmentioning

confidence: 99%

Spatial-Temporal Responses of Ecosystem Services to Land Use Transformation Driven by Rapid Urbanization: A Case Study of Hubei Province, China

Cui

Liu

Shan

et al. 2021

IJERPH

Self Cite

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“…The traditional DEA model, however, does not aptly address the different environments in which different decision units are located and the situation of uncontrollable factors. Therefore, Fried (2002) proposed a three-stage DEA model that takes into account both environmental factors and random errors, which is a model that uses Stochastic Frontier Analysis (SFA) to remove environmental factors and random errors from the traditional DEA model [36][37][38] The results are again placed in the traditional DEA model with the original input factors. The results are then again placed in the traditional DEA model with the original input factors so that different decision units are placed under the same environmental factors and luck conditions, the model mechanism is shown in Figure 2.…”

Section: Three-stage Dea Modelmentioning

confidence: 99%

Spatial-Temporal Evolution and Driving Mechanism of Urban Land Use Efficiency Based on T-DEA Model: A Case Study of Anhui Province, China

Liu

Wang

et al. 2023

Sustainability

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As China’s urbanization has shifted from high-speed to high-quality development, Urban Land Use Efficiency (ULUE) has become an important scale for evaluating urban connotative development. However, existing research has paid less attention to errors caused by different urban environmental factors and random disturbances in ULUE. Therefore, the purpose of this study is to eliminate the impact of environmental factors and random disturbances on ULUE measurement results by placing different cities under the same environmental conditions. First, a three-stage DEA envelopment analysis (T-DEA) model is introduced to calculate the ULUE of 16 prefecture-level cities in Anhui Province from 2001 to 2020. On this basis, the kernel density estimation model, gravity center model, and geographic detector models are used to study the spatial and temporal evolution and driving factors. The results show that (1) the ULUE increases nonlinearly with time, with an increase of 12.74%; (2) the overall peak of ULUE is on the rise, and changes from a single peak to a multi-peak, indicating that ULUE is constantly improving and that there is multi-level differentiation between different cities; (3) during the study period, the center of gravity of the ULUE value moved 22.66 km to the northwest; the overall moving distance was small, and the moving rate was slow; and (4) the influence of the interaction of double driving factors on ULUE is significantly greater than that of a single driving factor, and the factors of urban built-up area and degree of openness, as the key driving factors affecting ULUE, also have a degree of duality. In addition, to achieve efficient urban land use and to coordinate the environmental differences faced by different cities, the government must formulate systematic policies and development strategies considering the spatial characteristics of urban land use efficiency and the complexity of the driving factors.

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“…Global autocorrelation model is mainly used to explore the spatial correlation degree of the whole region, which is generally expressed by Global Moran's I; Local autocorrelation model is mainly used to explore the degree of spatial heterogeneity between local regions, which is generally expressed by Local Moran's I [29], and their formulas are as follows.…”

Section: Spatial Autocorrelation Modelmentioning

confidence: 99%

Spatial Patterns, Drivers and Heterogeneous Effects of PM2.5: Experience from China

Cui¹,

Huang²,

Deng³

et al. 2022

Pol. J. Environ. Stud.

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PM2.5 not only affects air visibility, but also can enter the lungs and blood through the respiratory tract, causing important damage to the human respiratory system, cardiovascular and cerebrovascular system. Infants, children, the elderly, patients with cardiovascular disease and chronic lung disease have become more sensitive to it, and has become an important factor endangering public health. Identifying PM2.5 spatial and temporal characteristics and influencing factors can provide key information for urban atmospheric environmental governance and public health improvement. Previous studies have only explored the influencing factors of PM2.5, while ignoring which is the more important factors. Firstly, this study explores the spatial-temporal evolution characteristics and spatial correlation characteristics of PM2.5 distribution in 285 cities in China. Then, a selection model---Bayesian model average method is applied to identify which variables are more likely to affect PM2.5 in China. We find that (1) From 2000 to 2018, the high-value concentration areas of PM2.5 distribution in China were mainly distributed in the central and eastern regions, and showed the trend of "moving eastward". (2) Among all variables in this study, population density, utilization rate of common industrial solid wastes, per capita Gross Domestic Product (GDP), proportion of secondary industry to GDP, centralized treatment rate of sewage treatment plant and industrial emission of sulfur dioxide are the most important drivers to predict PM2.5 in China. In addition, we found that the relationship between the selected variables and PM2.5 tends to change over time. In addition, we also show that the influence of selected variables on PM2.5 depends on the distribution of PM2.5, that is, there is a heterogeneous effect.

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Applying SBM-GPA Model to Explore Urban Land Use Efficiency Considering Ecological Development in China

Cited by 17 publications

References 42 publications

Spatial-Temporal Responses of Ecosystem Services to Land Use Transformation Driven by Rapid Urbanization: A Case Study of Hubei Province, China

Spatial-Temporal Responses of Ecosystem Services to Land Use Transformation Driven by Rapid Urbanization: A Case Study of Hubei Province, China

Spatial-Temporal Evolution and Driving Mechanism of Urban Land Use Efficiency Based on T-DEA Model: A Case Study of Anhui Province, China

Spatial Patterns, Drivers and Heterogeneous Effects of PM2.5: Experience from China

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