Applying SBM-GPA Model to Explore Urban Land Use Efficiency Considering Ecological Development in China
Abstract
:1. Introduction
2. Current State of the Art on Describing and Measuring ULUE
3. Data and Methods
3.1. Data
3.2. Methods
3.2.1. SBM Model
3.2.2. Geospatial Analysis Methods
Spatial Autocorrelation Model
Spatial Gravity Center Model
4. Results
4.1. Evaluation of Provincial Urban Land Use Efficiency
4.2. Geospatial Analysis of Provincial Urban Land Use Efficiency
4.2.1. Spatial Correlation Analysis
4.2.2. Spatial Feature Analysis
4.2.3. Gravity Center Analysis
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Methods | Advantages | Disadvantages | Sources |
---|---|---|---|
Coupling Degree Model | Reflects the harmonious degree and benign coordination relationship of system components. | Only reflects the relationship between elements; cannot reflect the internal relationship of elements. | [24] |
Cobb–Douglas Production Function | Effectively analyzes the impact of input on output and explores the relationship between the number of various production factors. | Cannot distinguish the difference between input factors, and the calculated results may not be consistent with the actual situation. | [25] |
Data Envelopment Analysis (DEA) | A popular non-parametric method for evaluating the relative efficiency of decision-making units (DMUs) with multiple inputs and outputs. | Lacks input–output relaxation variables and neglects data measurement errors. | [26] |
Stochastic Frontier Analysis (SFA) | A popular parametric method for considering the influence of random factors on output. | Cannot be used for multiple outputs, but can only be used for a single output. | [27] |
Directional Distance Function (DDF) | Widely used to process efficiency evaluation and obtains unbiased dynamic efficiency evaluation results. | Lacks theoretical basis and does not discuss the choice of direction. | [28] |
Theil Index | Reflects the overall difference and reveals the source of regional difference. | Does not consider how subsamples within a subpopulation are distributed. | [29] |
Gini Coefficient | Quantitatively describes the characteristics of spatial differences and reveals the composition and source of spatial differences. | Reflects a general time point change, and cannot reflect the overall change process and situation. | [19] |
Criteria Layer | Indicator Layer | Reference | ||
---|---|---|---|---|
Input indicators | Capital investment | Fixed asset investment. | [39] | |
Land investment | Areas of urban land. | [40,41] | ||
Labor input | Employees in the secondary and tertiary industries. | [40,41] | ||
Output indicators | Desired output | Economic benefits | Added value of the secondary and tertiary industries. | [19,41,42,43] |
Social benefits | Annual per capita disposable income of urban households. | [35] | ||
Ecological benefits | Green coverage within built-up area. | [33,39,41] | ||
Undesired output | Negative ecological effects | Industrial sulfur dioxide emissions. | [35,40,42] |
Province | 2008 | 2011 | 2014 | 2017 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
TE | PTE | SE | TE | PTE | SE | TE | PTE | SE | TE | PTE | SE | |
Beijing | 0.9107 | 0.9291 | 0.9802 | 0.9651 | 0.9937 | 0.9713 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
Tianjin | 0.9774 | 1.0000 | 0.9774 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
Hebei | 0.9638 | 0.9651 | 0.9987 | 0.8844 | 0.9587 | 0.9225 | 0.7656 | 0.3950 | 1.9381 | 0.8460 | 0.9485 | 0.8920 |
Shanxi | 0.8579 | 0.8752 | 0.9802 | 0.8261 | 0.8285 | 0.9972 | 0.6172 | 0.4137 | 1.4919 | 0.7618 | 0.7800 | 0.9766 |
Inner Mongolia | 0.8137 | 0.8296 | 0.9808 | 0.8765 | 0.9102 | 0.9630 | 1.0000 | 1.0000 | 1.0000 | 0.7549 | 0.7614 | 0.9915 |
Liaoning | 0.6315 | 0.6340 | 0.9960 | 0.6716 | 0.6756 | 0.9941 | 0.7526 | 0.5164 | 1.4574 | 0.8417 | 0.8492 | 0.9912 |
Jilin | 0.5541 | 0.5679 | 0.9758 | 0.6463 | 0.6633 | 0.9743 | 0.7131 | 0.3262 | 2.1861 | 0.6612 | 0.6791 | 0.9736 |
Heilongjiang | 0.6365 | 0.6614 | 0.9623 | 0.5458 | 0.5545 | 0.9844 | 0.6273 | 0.3330 | 1.8836 | 0.5339 | 0.5427 | 0.9838 |
Shanghai | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
Jiangsu | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.8649 | 1.0000 | 0.8649 | 0.9777 | 1.0000 | 0.9777 |
Zhejiang | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.8851 | 1.0000 | 0.8851 | 0.9706 | 0.9858 | 0.9846 |
Anhui | 0.5747 | 0.5826 | 0.9864 | 0.6270 | 0.6284 | 0.9978 | 0.6697 | 0.5325 | 1.2578 | 0.6941 | 0.7737 | 0.8971 |
Fujian | 1.0000 | 1.0000 | 1.0000 | 0.9703 | 1.0000 | 0.9703 | 0.9245 | 1.0000 | 0.9245 | 1.0000 | 1.0000 | 1.0000 |
Jiangxi | 0.6273 | 0.6362 | 0.9860 | 0.6949 | 0.7079 | 0.9816 | 0.6328 | 0.4159 | 1.5214 | 0.6792 | 0.6858 | 0.9903 |
Shandong | 0.9146 | 0.9557 | 0.9570 | 0.8354 | 0.8811 | 0.9482 | 0.8413 | 0.6606 | 1.2736 | 0.8334 | 1.0000 | 0.8334 |
Henan | 0.8397 | 0.8735 | 0.9613 | 0.7716 | 0.8480 | 0.9099 | 0.6985 | 1.0000 | 0.6985 | 0.7889 | 0.8331 | 0.9469 |
Hubei | 0.6488 | 0.6551 | 0.9903 | 0.6446 | 0.6453 | 0.9989 | 0.6772 | 0.3878 | 1.7463 | 0.6893 | 0.7804 | 0.8833 |
Hunan | 0.7448 | 0.7519 | 0.9906 | 0.7870 | 0.8162 | 0.9642 | 0.8182 | 0.5736 | 1.4264 | 0.9825 | 0.9958 | 0.9866 |
Guangdong | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.8865 | 1.0000 | 0.8865 |
Guangxi | 0.6175 | 0.6312 | 0.9784 | 0.6987 | 0.7013 | 0.9963 | 0.6369 | 0.3382 | 1.8833 | 0.6108 | 0.6232 | 0.9802 |
Hainan | 0.4667 | 0.6441 | 0.7246 | 0.5227 | 0.5597 | 0.9339 | 0.5534 | 1.0000 | 0.5534 | 0.6916 | 0.8765 | 0.7891 |
Chongqing | 0.6362 | 0.6492 | 0.9801 | 0.6709 | 0.6716 | 0.9990 | 0.6842 | 1.0000 | 0.6842 | 0.7824 | 0.7852 | 0.9965 |
Sichuan | 0.7032 | 0.7040 | 0.9988 | 0.7090 | 0.7503 | 0.9450 | 0.6597 | 1.0000 | 0.6597 | 0.6578 | 0.7130 | 0.9225 |
Guizhou | 0.6114 | 0.6497 | 0.9411 | 0.6493 | 0.6574 | 0.9877 | 0.6366 | 1.0000 | 0.6366 | 0.6373 | 0.6442 | 0.9893 |
Yunnan | 0.6252 | 0.6383 | 0.9795 | 0.6046 | 0.6081 | 0.9941 | 0.6184 | 1.0000 | 0.6184 | 0.6565 | 0.6616 | 0.9923 |
Tibet | 0.4687 | 1.0000 | 0.4687 | 0.8293 | 1.0000 | 0.8293 | 0.5016 | 1.0000 | 0.5016 | 0.5232 | 1.0000 | 0.5232 |
Shaanxi | 0.7435 | 0.7503 | 0.9910 | 1.0000 | 1.0000 | 1.0000 | 0.8634 | 1.0000 | 0.8634 | 0.8225 | 0.8257 | 0.9961 |
Gansu | 0.5666 | 0.6043 | 0.9376 | 0.5229 | 0.5330 | 0.9809 | 0.4643 | 0.3399 | 1.3659 | 0.4589 | 0.4817 | 0.9525 |
Qinghai | 0.7346 | 1.0000 | 0.7346 | 0.7857 | 0.8269 | 0.9502 | 0.6759 | 1.0000 | 0.6759 | 0.6914 | 1.0000 | 0.6914 |
Ningxia | 0.4713 | 0.5703 | 0.8265 | 0.6054 | 0.7352 | 0.8234 | 0.6549 | 1.0000 | 0.6549 | 0.6585 | 0.8105 | 0.8124 |
Xinjiang | 0.5172 | 0.5440 | 0.9508 | 0.4674 | 0.4747 | 0.9846 | 0.4990 | 0.5815 | 0.8581 | 0.4325 | 0.4401 | 0.9826 |
2008 | 2011 | 2014 | 2017 | |
---|---|---|---|---|
Moran’s I | 0.195 | 0.073 | 0.262 | 0.327 |
p value | 0.006 | 0.083 | 0.002 | 0.001 |
Z scores | 2.9321 | 1.335 | 3.6201 | 4.4723 |
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Cui, X.; Huang, S.; Liu, C.; Zhou, T.; Shan, L.; Zhang, F.; Chen, M.; Li, F.; de Vries, W.T. Applying SBM-GPA Model to Explore Urban Land Use Efficiency Considering Ecological Development in China. Land 2021, 10, 912. https://doi.org/10.3390/land10090912
Cui X, Huang S, Liu C, Zhou T, Shan L, Zhang F, Chen M, Li F, de Vries WT. Applying SBM-GPA Model to Explore Urban Land Use Efficiency Considering Ecological Development in China. Land. 2021; 10(9):912. https://doi.org/10.3390/land10090912
Chicago/Turabian StyleCui, Xufeng, Sisi Huang, Cuicui Liu, Tingting Zhou, Ling Shan, Fengyuan Zhang, Min Chen, Fei Li, and Walter T. de Vries. 2021. "Applying SBM-GPA Model to Explore Urban Land Use Efficiency Considering Ecological Development in China" Land 10, no. 9: 912. https://doi.org/10.3390/land10090912