Research on the Spatial Differences and Convergence Mechanism of Urban Land Use Efficiency under the Background of Regional Integration: A Case Study of the Yangtze River Economic Zone, China
Abstract
:1. Introduction
2. Theoretical Hypothesis: ULUE Space Converges under the Background of Regional Integration
2.1. Macro Convergence Mechanism
2.2. Meso Convergence Mechanism
2.3. Micro Convergence Mechanism
3. Study Area Overview
4. Model Setting and Variable Description
4.1. Model Setting
4.1.1. The Super Efficiency SBM Model
4.1.2. Exploratory Spatial Data Analysis
4.1.3. Spatial Convergence Model
4.2. Variable Description
4.2.1. Explained Variable: Urban Land Use Efficiency (ULUE)
4.2.2. Control Variables
4.2.3. Variable Statistics
4.3. Data Source
5. Empirical Analysis
5.1. Spatio-Temporal Evolution of ULUE under the Background of Regional Integration
5.1.1. Timing Series Evolution
5.1.2. Spatial Pattern
5.2. Spatial Differences of ULUE
5.2.1. Global Spatial Association
5.2.2. Local Spatial Correlation
5.3. Spatial Convergence Analysis of ULUE
6. Conclusions and Discussion
- (1)
- Actively promoting the integration of the Yangtze River Economic Zone. It is necessary to break the administrative barriers, establish a regional coordinated development mechanism, strengthen the guidance of ecological priority policies, and establish a development committee of the Yangtze River Economic Zone led by national authorities and joined by provinces along the Yangtze River. At the same time, it is necessary to incorporate “regional cooperation” into the assessment system of party and government leading cadres and promote regional integration development by examining the role of “baton”, thus maximizing its role in promoting the green utilization of urban land; additionally, we should focus on building a regional collaborative development mechanism, giving full play to the spatial spillover effect of integrated development, focusing on optimizing the spatial distribution channels of elements, and improving the ability of cross-regional resource allocation and element spatial integration. Moreover, it is necessary to reduce the scale of regional pollution emissions through industrial spatial redistribution, thus realizing the green transformation of land use.
- (2)
- According to the spatial pattern of ULUE, formulating the regional development strategy according to local conditions [46]. HH agglomeration areas should not only give priority to cultivating green high-tech industrial community and accelerate the formation of land green intensification effect with industrial green development but also give full play to its diffusion effect and strengthen the radiation driving effect on the surrounding areas. HL agglomeration and LH agglomeration areas, as transitional plates, should fully rely on geographical proximity to connect spatial elements transfer corridors and ultimately achieve ULUE collaborative promotion. In contrast, the LL agglomeration areas should focus on the establishment of regional internal sharing and external linkage mechanisms, actively promote the process of open interaction patterns between ULUE high-value areas and low-value areas, and actively absorb advanced technologies and development concepts in high-value areas.
- (3)
- Coordinating regional green development strategy and implementing policies for different cities based on different driving factors. Government management and technological innovation are the main driving forces for ULUE convergence. Based on increasing the investment of the joint pollution control fund, the government should give full play to the correlation effect of infrastructure and promote the coordinated promotion of ULUE by accelerating inter-regional cooperation to achieve a win–win situation. The downstream areas should make full use of the economic system to realize the effective connection between capital and technology and accelerate the cultivation of green management talents and incubation of green technology. More importantly, the downstream areas should drive the development of the ULUE around by releasing spillover effects of the green kinetic energy; the midstream and upstream areas should increase government technical support and at the same time introduce supporting policies for talent introduction. The midstream and upstream areas need to achieve steady improvement and leapfrog development of ULUE through policy advantages. In addition, the optimization and upgrading of industrial structures can accelerate the convergence of ULUE. The region should actively construct the linkage and sharing mechanism of industrial development and form a cooperative and progressive pattern of industrial dislocation, complementary advantages, and division of labor. The key for technological innovation to promote ULUE growth is to guide enterprises to produce “efficiency compensation” to offset the “compliance cost” of industrial environmental pollution control and ultimately realize the win–win situation between the economy and the environment of regional land use.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Type | Variable | Symbol | N | Maximum | Minimum | Mean | Standard Error of Mean |
---|---|---|---|---|---|---|---|
explained variable | urban land use efficiency | ULUE | 1819 | 1.7146 | 0.1672 | 0.6171 | 0.2865 |
control variable | level of government intervention | Govern | 1819 | 64.8833 | 2.4790 | 14.8268 | 7.1067 |
level of infrastructure investment | Infrastructure | 1819 | 67.2118 | 0.3764 | 7.3605 | 6.5291 | |
upgrade of industry structure | Upgrade | 1819 | 9.0569 | 0.2393 | 0.9769 | 0.5759 | |
rationalization of industrial structure | Rational | 1819 | 1.1871 | 0.0000 | 0.2089 | 0.2034 | |
technology innovation level | Technology | 1819 | 2.9825 | 0.0008 | 0.2555 | 0.2940 |
Year | 2003 | 2004 | 2005 | 2006 | 2007 | 2008 | 2009 | 2010 | 2011 | |
Index | ||||||||||
Global Moran’s I | 0.1732 *** | 0.1418 ** | 0.1601 ** | 0.1924 *** | 0.1873 *** | 0.1912 *** | 0.1787 ** | 0.1854 *** | 0.2196 ** | |
P | 0.0060 | 0.0170 | 0.0130 | 0.0060 | 0.0050 | 0.0030 | 0.0130 | 0.0080 | 0.0020 | |
Z(I) | 2.8206 | 2.2858 | 2.5899 | 3.0259 | 2.9287 | 2.9755 | 2.7526 | 2.7921 | 3.6524 | |
Year | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | ||
Index | ||||||||||
Global Moran’s I | 0.2217 *** | 0.2424 *** | 0.3414 *** | 0.3615 *** | 0.4480 *** | 0.4507 *** | 0.4513 *** | 0.4702 *** | ||
P | 0.0010 | 0.0020 | 0.0010 | 0.0010 | 0.0010 | 0.0010 | 0.0010 | 0.0010 | ||
Z(I) | 3.5266 | 3.6499 | 5.1338 | 5.5886 | 6.5327 | 6.8070 | 6.7150 | 6.9872 |
Variable | The Yangtze River Economic Zone | The Upstream Areas | The Midstream Areas | The Downstream Areas | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
OLS | SEM | SLM | OLS | SEM | SLM | OLS | SEM | SLM | OLS | SEM | SLM | |
β | −0.0769 *** (−9.2300) | −0.5599 *** (−24.2215) | −0.4030 *** (−20.4593) | −0.0377 *** (−2.8986) | −0.4762 *** (−11.0950) | −0.3756 *** (−10.5089) | −0.0815 *** (−5.7188) | −0.6574 *** (−17.2210) | −0.5703 *** (−16.0932) | −0.1083 *** (−7.0555) | −0.5116 *** (−13.3342) | −0.4440 *** (−13.4126) |
λ | 0.3160 *** (10.0879) | 0.1840 *** (3.4196) | 0.1878 *** (2.6557) | 0.4101 *** (5.8158) | ||||||||
ρ | 0.1799 *** (6.1925) | 0.0660 ** (1.9657) | 0.1869 *** (3.9364) | 0.4024 *** (8.4036) | ||||||||
s | 0.0047 | 0.0586 | 0.0303 | 0.0023 | 0.0462 | 0.0277 | 0.0052 | 0.0765 | 0.0497 | 0.0067 | 0.0512 | 0.0345 |
τ | 147.4781 | 11.8285 | 22.8761 | 301.3683 | 15.0032 | 25.0233 | 133.2975 | 9.0607 | 13.9467 | 103.4548 | 13,5380 | 20.0912 |
R2 | 0.0416 | 0.2723 | 0.2558 | 0.0163 | 0.2380 | 0.2225 | 0.0473 | 0.3916 | 0.3661 | 0.0552 | 0.2129 | 0.1590 |
Log(L) | −27.4357 | 154.4103 | 180.4458 | 60.9026 | 90.0146 | 117.9425 | −17.8858 | 80.6979 | 96.6282 | −52.7359 | −8.5234 | 18.9436 |
Test | Absolute β-Convergence | Conditional β-Convergence | ||
---|---|---|---|---|
t-Statistic | p-Values | t-Statistic | p-Values | |
Hausman test of spatial error model | 143.0404 | 0.0000 | 146.0059 | 0.0000 |
Hausman test of spatial lag model | 18.2321 | 0.0000 | 29.2753 | 0.0001 |
LM (lag) | 39.2015 | 0.0000 | 37.9432 | 0.0000 |
Robust-LM (lag) | 96.9212 | 0.0000 | 52.9370 | 0.0000 |
LM (error) | 120.5283 | 0.0000 | 95.7544 | 0.0000 |
Robust-LM (error) | 178.2479 | 0.0000 | 110.7482 | 0.0000 |
Variable | The Yangtze River Economic Zone | The Upstream Areas | The Midstream Areas | The Downstream Areas | ||||
---|---|---|---|---|---|---|---|---|
Ordinary Least Squares | Spatial Error Model | Ordinary Least Squares | Spatial Error Model | Ordinary Least Squares | Spatial Error Model | Ordinary Least Squares | Spatial Error Model | |
β | −0.5041 *** (−23.1664) | −0.5282 *** (−23.0323) | −0.4542 *** (−11.1850) | −0.5216 *** (−11.3815) | −0.6598 *** (−17.6315) | −0.7272 *** (−18.6605) | −0.4723 *** (−13.0524) | −0.5213 *** (−13.7073) |
λ | 0.1460 *** (4.7308) | 0.0950 * (1.8732) | 0.1418 ** (1.9670) | 0.1701 ** (2.3489) | ||||
Govern | 0.0061 ** (2.3006) | 0.0106 ** (2.3184) | 0.0152 * (1.6023) | 0.0289 * (−1.2997) | 0.0173 * (0.2906) | 0.0126 * (1.6496) | 0.0066 (1.6057) | 0.0038 * (0.4945) |
Infrastructure | −0.0229 * (−1.2919) | −0.0623 * (−1.7483) | −0.0212 (−1.0708) | −0.0652 * (−1.7217) | −0.0022 (−0.2660) | −0.0016 * (−0.0608) | −0.2043 ** (−2.5395) | −0.6230 *** (−3.6308) |
Upgrade | 0.0557 *** (3.1482) | 0.1424 *** (4.1034) | 0.0033 (0.1280) | 0.0933 * (1.7763) | 0.0702 ** (2.4184) | 0.2696 *** (4.2889) | 0.1436 *** (3.6011) | 0.3033 *** (4.2133) |
Rational | 0.0210 ** (2.3221) | 0.0205 * (2.2104) | 0.0053 (0.8311) | 0.0432 (2.2644) | 0.0108 (1.0400 | 0.013 5(1.3369) | 0.0057 (1.3423) | 0.0524 * (1.9097) |
Technology | −0.0610 ** (−3.1067) | −0.0581 *** (−2.8815) | −0.0364 (−1.6664) | −0.0829 ** (−2.5606) | −0.0959 *** (−2.9750) | −0.0930 *** (−2.8302) | −0.0013 (−1.5400) | 0.0051 ** (2.0041) |
s | 0.0501 | 0.0537 | 0.0433 | 0.0527 | 0.0770 | 0.0928 | 0.0457 | 0.0526 |
τ | 13.8353 | 12.9078 | 16.0080 | 13.1527 | 9.0019 | 7.4693 | 15.1673 | 13.1778 |
R2 | 0.2662 | 0.3219 | 0.2284 | 0.2844 | 0.3868 | 0.4522 | 0.2377 | 0.3005 |
Log(L) | 120.3042 | 153.8936 | 87.2214 | 97.0128 | 81.8231 | 103.2177 | −12.4631 | 4.5342 |
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Ge, K.; Zou, S.; Chen, D.; Lu, X.; Ke, S. Research on the Spatial Differences and Convergence Mechanism of Urban Land Use Efficiency under the Background of Regional Integration: A Case Study of the Yangtze River Economic Zone, China. Land 2021, 10, 1100. https://doi.org/10.3390/land10101100
Ge K, Zou S, Chen D, Lu X, Ke S. Research on the Spatial Differences and Convergence Mechanism of Urban Land Use Efficiency under the Background of Regional Integration: A Case Study of the Yangtze River Economic Zone, China. Land. 2021; 10(10):1100. https://doi.org/10.3390/land10101100
Chicago/Turabian StyleGe, Kun, Shan Zou, Danling Chen, Xinhai Lu, and Shangan Ke. 2021. "Research on the Spatial Differences and Convergence Mechanism of Urban Land Use Efficiency under the Background of Regional Integration: A Case Study of the Yangtze River Economic Zone, China" Land 10, no. 10: 1100. https://doi.org/10.3390/land10101100