Assessment and Decomposition of Regional Land Use Efficiency of the Service Sector in China
<p>Visual distribution map of four regions in China.</p> "> Figure 2
<p>Spatial and temporal distribution and evolution trend of SLUE in China.</p> "> Figure 3
<p>Distribution of SLUE density by region.</p> "> Figure 4
<p>Spatial differences and contribution rates in SLUE in China. (<b>a</b>) Variation trend in the Theil index in China; (<b>b</b>) Contribution rates of <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>w</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>b</mi> </msub> </mrow> </semantics></math>; (<b>c</b>) Contribution rates of regional differences.</p> "> Figure 5
<p>Drivers of SLUE growth by region.</p> ">
Abstract
:1. Introduction
2. Methods and Data
2.1. Efficiency Assessment Model
2.1.1. The Global SBM-Undesirable Model
2.1.2. Malmquist Productivity Index (MPI)
2.2. Regional Difference Analysis
2.3. Dataset and Variables
2.3.1. Dataset
2.3.2. Input and Output Variables
3. Results
3.1. Temporal and Spatial Pattern of Regional Differences in SLUE
3.2. The “Beggar-Thy-Neighbor” Situation in the SLUE Regional Differences
3.3. Dynamic Trend and Efficiency Decomposition of SLUE in China
4. Discussion
4.1. Spatio-Temporal Distribution of SLUE
4.2. Regional Differences of SLUE
4.3. Dynamic Trend of SLUE
5. Research Implications and Research Contributions
5.1. Research Implications
5.2. Research Contributions
6. Conclusions and Limitations
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Region | Provinces, Municipalities, and Autonomous Regions |
---|---|
Eastern | Beijing, Tianjin, Shanghai, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, Hainan |
Central | Henan, Shanxi, Anhui, Hubei, Hunan, Jiangxi |
Western | Gansu, Guizhou, Ningxia, Qinghai, Shaanxi, Yunnan, Xinjiang, Sichuan, Chongqing, Inner Mongolia, Guangxi, Tibet |
Northeastern | Liaoning, Heilongjiang, Jilin |
Category | Indicator | Specific Indicator | Unit |
---|---|---|---|
Inputs | Land input | Area of built districts | Square kilometers |
Capital input | Fixed capital stock | 100 million yuan | |
Workforce input | Employment | 10 thousand persons | |
Desirable outputs | Economic output | Value-added of tertiary industry | 100 million yuan |
Social output | Average wage of employed persons | Yuan | |
Undesirable outputs | Environmental output | CO2 emissions | Million tons |
Region | Province | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | Average |
---|---|---|---|---|---|---|---|---|---|
Eastern | Shanghai | 1.0000 | 1.0000 | 1.0000 | 0.9441 | 1.0000 | 1.0000 | 1.0000 | 0.9920 |
Beijing | 0.7977 | 0.8166 | 0.8633 | 0.8823 | 0.9216 | 0.9428 | 1.0000 | 0.8892 | |
Tianjin | 0.6708 | 0.7204 | 0.8884 | 0.9019 | 0.9176 | 0.9413 | 1.0000 | 0.8629 | |
Jiangsu | 0.5164 | 0.5213 | 0.5616 | 0.5788 | 0.6548 | 0.8484 | 1.0000 | 0.6688 | |
Hainan | 0.5722 | 0.6034 | 0.6049 | 0.6586 | 0.5871 | 0.6829 | 0.7244 | 0.6334 | |
Zhejiang | 0.3381 | 0.3524 | 0.3768 | 0.4013 | 0.4328 | 0.4892 | 0.5666 | 0.4224 | |
Fujian | 0.3142 | 0.3397 | 0.3947 | 0.4081 | 0.4229 | 0.4481 | 0.4717 | 0.3999 | |
Guangdong | 0.2476 | 0.2592 | 0.2759 | 0.2653 | 0.2512 | 0.2456 | 0.2589 | 0.2577 | |
Hebei | 0.1907 | 0.2071 | 0.2221 | 0.2520 | 0.2731 | 0.2722 | 0.3168 | 0.2477 | |
Shandong | 0.0941 | 0.0964 | 0.1843 | 0.1987 | 0.2164 | 0.2368 | 0.2320 | 0.1798 | |
Average | 0.4742 | 0.4916 | 0.5372 | 0.5491 | 0.5678 | 0.6107 | 0.6570 | 0.5554 | |
Central | Jiangxi | 0.3546 | 0.3602 | 0.3378 | 0.3492 | 0.3354 | 0.3565 | 0.3617 | 0.3508 |
Shanxi | 0.2765 | 0.2811 | 0.2925 | 0.2992 | 0.2926 | 0.2878 | 0.3060 | 0.2908 | |
Anhui | 0.2704 | 0.2355 | 0.2344 | 0.2366 | 0.2455 | 0.2617 | 0.2709 | 0.2507 | |
Hunan | 0.2126 | 0.2494 | 0.2244 | 0.2295 | 0.2338 | 0.2451 | 0.2609 | 0.2365 | |
Henan | 0.1905 | 0.1907 | 0.1789 | 0.1836 | 0.1836 | 0.1978 | 0.2153 | 0.1915 | |
Hubei | 0.1471 | 0.1570 | 0.1812 | 0.1795 | 0.1996 | 0.1940 | 0.1983 | 0.1795 | |
Average | 0.2419 | 0.2457 | 0.2415 | 0.2463 | 0.2484 | 0.2572 | 0.2688 | 0.2500 | |
Western | Qinghai | 0.8159 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 1.0000 | 0.9737 |
Ningxia | 0.8185 | 0.8933 | 1.0000 | 1.0000 | 0.9929 | 0.9795 | 1.0000 | 0.9549 | |
Gansu | 0.4457 | 0.4560 | 0.4119 | 0.4087 | 0.4154 | 0.4167 | 0.4191 | 0.4248 | |
Yunnan | 0.3239 | 0.3370 | 0.3999 | 0.4051 | 0.4514 | 0.4812 | 0.5343 | 0.4190 | |
Xinjiang | 0.4196 | 0.4246 | 0.3918 | 0.3877 | 0.3658 | 0.3555 | 0.3473 | 0.3846 | |
Chongqing | 0.3324 | 0.3426 | 0.3540 | 0.3970 | 0.3893 | 0.4047 | 0.4218 | 0.3774 | |
Guizhou | 0.3557 | 0.3628 | 0.3582 | 0.3644 | 0.3639 | 0.3636 | 0.3643 | 0.3619 | |
Shaanxi | 0.2556 | 0.3156 | 0.3641 | 0.3502 | 0.3482 | 0.3881 | 0.3974 | 0.3456 | |
Inner Mongolia | 0.2722 | 0.2866 | 0.2866 | 0.2716 | 0.3035 | 0.3541 | 0.4128 | 0.3125 | |
Guangxi | 0.2341 | 0.2327 | 0.2971 | 0.2789 | 0.2840 | 0.2897 | 0.2966 | 0.2733 | |
Sichuan | 0.1704 | 0.1790 | 0.1782 | 0.1904 | 0.2025 | 0.1826 | 0.1856 | 0.1841 | |
Average | 0.4040 | 0.4391 | 0.4584 | 0.4594 | 0.4652 | 0.4741 | 0.4890 | 0.4556 | |
Northeastern | Jilin | 0.2874 | 0.2974 | 0.3220 | 0.3523 | 0.3262 | 0.3398 | 0.3699 | 0.3279 |
Heilongjiang | 0.2131 | 0.1995 | 0.1987 | 0.1998 | 0.1980 | 0.2003 | 0.2184 | 0.2040 | |
Liaoning | 0.1496 | 0.1544 | 0.1712 | 0.1682 | 0.1825 | 0.1917 | 0.2104 | 0.1754 | |
Average | 0.2167 | 0.2171 | 0.2306 | 0.2401 | 0.2356 | 0.2439 | 0.2662 | 0.2357 | |
Nationwide | Average | 0.3763 | 0.3957 | 0.4185 | 0.4248 | 0.4331 | 0.4533 | 0.4787 | 0.4258 |
Province | MPI | TC | PTE | SE | Province | MPI | TC | PTE | SE |
---|---|---|---|---|---|---|---|---|---|
Guangxi | 14.3 | 3.8 | 9.9 | 0.2 | Sichuan | 1.5 | 2.8 | 0.5 | −1.7 |
Jilin | 12.6 | 3.3 | 10.1 | −0.9 | Chongqing | 0.6 | 4.2 | −2.1 | −1.4 |
Hunan | 11.7 | 3.7 | 7.4 | 0.2 | Henan | 0.5 | 2.7 | 2.0 | −4.0 |
Shanxi | 10.6 | 4.4 | 3.8 | 2.1 | Shandong | 0.1 | 3.8 | −4.8 | 1.3 |
Gansu | 9.3 | 5.1 | 6.0 | −1.8 | Anhui | 0.0 | 2.3 | −3.5 | 1.2 |
Liaoning | 8.7 | 3.4 | 4.0 | 1.0 | Inner Mongolia | 0.0 | 3.1 | 0.9 | −3.9 |
Jiangxi | 8.2 | 3.1 | 4.8 | 0.2 | Hainan | −0.4 | 3.4 | −5.4 | 1.8 |
Zhejiang | 6.4 | 4.7 | 1.5 | 0.1 | Heilongjiang | −0.5 | 4.0 | −0.2 | −4.1 |
Ningxia | 4.0 | 4.0 | 0.0 | 0.0 | Xinjiang | −0.6 | 3.9 | −2.4 | −1.9 |
Guizhou | 3.8 | 2.6 | 0.1 | 1.1 | Shaanxi | −0.7 | 3.9 | −3.8 | −0.7 |
Tianjin | 3.1 | 3.1 | 0.0 | 0.0 | Jiangsu | −1.4 | 1.5 | −1.6 | −1.2 |
Qinghai | 3.1 | 3.1 | 0.0 | 0.0 | Shanghai | −2.1 | 2.9 | −3.5 | −1.4 |
Hubei | 2.6 | 3.3 | 5.2 | −5.6 | Hebei | −2.1 | 2.2 | −4.1 | −0.2 |
Yunnan | 2.0 | 3.1 | 0.2 | −1.3 | Beijing | −3.5 | 5.0 | −8.0 | −0.1 |
Fujian | 1.9 | 3.2 | −1.9 | 0.6 | Guangdong | −8.5 | 2.7 | −9.5 | −1.5 |
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Zhang, M.; Liu, H.; Su, Y.; Zhou, X.; Li, Z.; Chen, C. Assessment and Decomposition of Regional Land Use Efficiency of the Service Sector in China. Land 2022, 11, 1911. https://doi.org/10.3390/land11111911
Zhang M, Liu H, Su Y, Zhou X, Li Z, Chen C. Assessment and Decomposition of Regional Land Use Efficiency of the Service Sector in China. Land. 2022; 11(11):1911. https://doi.org/10.3390/land11111911
Chicago/Turabian StyleZhang, Mingzhi, Hongyu Liu, Yangyue Su, Xiangyu Zhou, Zhaocheng Li, and Chao Chen. 2022. "Assessment and Decomposition of Regional Land Use Efficiency of the Service Sector in China" Land 11, no. 11: 1911. https://doi.org/10.3390/land11111911