The Spatiotemporal Non-Stationary Effect of Industrial Agglomeration on Urban Land Use Efficiency: A Case Study of Yangtze River Delta, China
Abstract
:1. Introduction
2. Mechanism of Industrial Agglomeration Externalities on ULUE
3. Methods and Data
3.1. Study Area
3.2. GTWR Model
3.3. Variable Selection and Data Processing
3.3.1. ULUE Evaluation
3.3.2. Agglomeration Externalities
- (1)
- Specialization agglomeration externalities (MAR)
- (2)
- Diversity agglomeration externalities (Jacobs)
- (3)
- Competition agglomeration externalities (Porter)
3.3.3. Control Variables
4. Results
4.1. Spatiotemporal Distribution of ULUE
4.2. Spatiotemporal Distribution of Three Agglomeration Externalities
4.2.1. Spatiotemporal Distribution of MAR
4.2.2. Spatiotemporal Distribution of UV
4.2.3. Spatiotemporal Distribution of RV
4.2.4. Spatiotemporal Distribution of Porter
4.3. Spatial Autocorrelation Analysis
4.4. Estimation Results of OLS and GTWR Model
4.5. Spatiotemporal Impacts of Three Agglomeration Externalities on ULUE
4.6. Impacts of the Control Variables on ULUE
5. Discussion
- (1)
- Selectively cultivating agglomeration externalities according to the status quo of local industries. Although agglomeration externalities have been proven by many scholars to be beneficial to urban development, not all agglomeration externalities can be beneficial to local economic activities. Specialization, diversity or competition externalities may not affect or even hinder the development of the city. For cities with incomplete infrastructure, it is still recommended to focus on cultivating some related industries, rather than advocating a balanced development of each industry.
- (2)
- Following the laws of industry cycle and giving support to enterprises in industrial transformation. Industrial transformation is still a trend within the Yangtze River Delta urban agglomeration, but in the process of industrial transformation, the environment faced by each economic subject will undergo drastic changes, and the agglomeration externalities that previously played an important role in urban development may become an obstacle to development. To this end, local governments need to provide subsidies to enterprises within their capacity to promote the smooth transformation of local enterprises.
- (3)
- Regulating market order and achieving healthy competition. Local governments should pay attention to the internal development of local enterprises and impose severe sanctions against monopolies, vicious competition and other behaviors that destroy the market environment, so as to guide the free flow of production factors and cultivate the international competitiveness of enterprises.
- (4)
- Looking to the future and purposefully cultivate diversified industries. Diversified industries help cities develop in the long run, but many cities do not have the resources to do so now. To this end, local governments should take a progressive reform approach on the basis of respecting basic economic laws and develop diversified industries with the purpose to lay a foundation for long-term urban development.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Composition of Indicator | Description | Unit | Sources | N | Mean | Max | Min | SD | |
---|---|---|---|---|---|---|---|---|---|
Inputs | Land | The area of urban built-up area | km2 | 492 | 191.6 | 2916 | 13 | 402.5 | |
Capital | The gross investment in fixed assets | 10,000 yuan | 492 | 14,800,000.00 | 67,500,000.00 | 756,084.00 | 14,200,000.00 | ||
Labor | The number of jobholders in secondary and tertiary sectors | 10,000 people | 492 | 68.98 | 724.9 | 6.24 | 90.46 | ||
Outputs | Output value | Output value of secondary and tertiary sectors | 100 million yuan | 492 | 2458 | 28,069 | 84.55 | 3351 | |
Road | The per capita urban road area | m2 | CHINA CITY STATISTICAL YEARBOOK | 492 | 12.78 | 37.95 | 1.43 | 5.957 | |
Green area | The green ratio of a built-up area | % | China Urban Construction Statistical Yearbook | 492 | 39.67 | 49.78 | 14.18 | 5.176 | |
Undesirable outputs | Wastewater | The volume of discharged industrial wastewater | 10,000 tons | 492 | 13,105 | 85,735 | 626 | 15,396 | |
soot | The volume of discharged industrial soot emissions | tons | CHINA CITY STATISTICAL YEARBOOK | 492 | 55,654 | 496,377 | 1925 | 55,725 | |
The volume of discharged industrial SO2 emissions | tons | 492 | 25,335 | 131,433 | 971 | 20,369 |
Industry Categories | Specific Industries | |
---|---|---|
Primary industry | Agriculture, forestry, animal husbandry and fishery | |
Secondary industry | Mining, manufacturing, electricity, gas and water production and supply and construction | |
Tertiary industry | Producer Services | Finance, real estate, leasing and business services |
Consumer Services | Accommodation and catering, residential and other services, culture, sports and entertainment | |
Circulation Services | Transportation, warehousing and postal services, information transmission, computer services and software, wholesale and Retail | |
Social Services | Scientific research and technical services, qualified exploration, water conservancy environment and public facilities management, education, health and social security and social welfare, public administration and social organizations |
Layer | Layer 2 | Variable | N | Mean | Max | Min | SD |
---|---|---|---|---|---|---|---|
Dependent variables | ULUE | 492 | 0.88 | 1.534 | 0.314 | 0.264 | |
Independent variables | MAR | 492 | 4.526 | 46.06 | 1.255 | 5.958 | |
UV | 492 | 1.199 | 1.558 | 0.667 | 0.181 | ||
RV | 492 | 0.923 | 1.168 | 0.377 | 0.142 | ||
Porter | 492 | 1.275 | 2.707 | 0.25 | 0.545 | ||
Control variables | Globalization | FDI | 492 | 6.232 | 9.951 | 2.52 | 1.034 |
Marketization | LF | 492 | 0.136 | 1.841 | 0.0022 | 0.206 | |
Government behavior | DEC | 492 | 0.664 | 1.174 | 0.0686 | 0.245 | |
Urbanization | POP | 492 | 7.694 | 9.149 | 5.226 | 0.535 | |
IS | 492 | 0.831 | 2.339 | 0.313 | 0.254 | ||
TP | 492 | 7.573 | 21.05 | 0.43 | 4.386 | ||
ED | 492 | 10.54 | 11.7 | 9.318 | 0.461 | ||
EDU | 492 | 10.84 | 13.63 | 7.445 | 1.102 |
Year | Moran’s I | z | p-Value * |
---|---|---|---|
2005 | −0.044 | −0.247 | 0.403 |
2006 | −0.034 | −0.115 | 0.454 |
2007 | −0.019 | 0.081 | 0.468 |
2008 | 0.066 | 1.193 | 0.116 |
2009 | −0.077 | −0.679 | 0.248 |
2010 | 0.090 * | 1.502 | 0.067 |
2011 | 0.183 ** | 2.703 | 0.003 |
2012 | 0.271 *** | 3.838 | 0.000 |
2013 | 0.076 * | 1.298 | 0.097 |
2014 | 0.348 *** | 4.833 | 0.000 |
2015 | 0.355 *** | 4.906 | 0.000 |
2016 | 0.257 *** | 3.638 | 0.000 |
VARIABLES | OLS | GTWR | ||
---|---|---|---|---|
Coef. | Max | Min | Mean | |
MAR | −0.012 *** | 0.1508711132 | −0.0607905963 | −0.0052732571 |
UV | 0.227 ** | 2.1743913471 | −1.6965369279 | −0.2644367352 |
RV | −0.179 | 3.6012901471 | −1.3257455998 | 0.4657644234 |
Porter | −0.168 *** | 0.4097858984 | −0.4741365000 | −0.0058395606 |
FDI | 0.047 *** | 0.3582414797 | −0.0915614750 | 0.0560940909 |
LF | −0.037 | 2.0845341573 | −2.2141543222 | −0.0683669281 |
DEC | −0.222 * | 2.1450094485 | −2.0230748214 | 0.0766327641 |
POP | 0.043 | 0.4790694941 | −0.4903563348 | −0.0364431313 |
IS | 0.296 *** | 1.4901585525 | −1.0841017332 | 0.1370764120 |
TP | 0.002 | 0.0473856253 | −0.0255634035 | 0.0011525221 |
ED | −0.101 *** | 0.2626034279 | −0.4263408446 | −0.0544773004 |
EDU | −0.054 | 0.2802125440 | −0.3129669496 | −0.0019328456 |
R2 | 0.186 | 0.804 | ||
Bandwidth | - | 0.111311 | ||
CV | - | 23.5291 |
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Hu, H.; Pan, L.; Jing, X.; Li, G.; Zhuo, Y.; Xu, Z.; Chen, Y.; Wang, X. The Spatiotemporal Non-Stationary Effect of Industrial Agglomeration on Urban Land Use Efficiency: A Case Study of Yangtze River Delta, China. Land 2022, 11, 755. https://doi.org/10.3390/land11050755
Hu H, Pan L, Jing X, Li G, Zhuo Y, Xu Z, Chen Y, Wang X. The Spatiotemporal Non-Stationary Effect of Industrial Agglomeration on Urban Land Use Efficiency: A Case Study of Yangtze River Delta, China. Land. 2022; 11(5):755. https://doi.org/10.3390/land11050755
Chicago/Turabian StyleHu, Hangang, Lisha Pan, Xin Jing, Guan Li, Yuefei Zhuo, Zhongguo Xu, Yang Chen, and Xueqi Wang. 2022. "The Spatiotemporal Non-Stationary Effect of Industrial Agglomeration on Urban Land Use Efficiency: A Case Study of Yangtze River Delta, China" Land 11, no. 5: 755. https://doi.org/10.3390/land11050755
APA StyleHu, H., Pan, L., Jing, X., Li, G., Zhuo, Y., Xu, Z., Chen, Y., & Wang, X. (2022). The Spatiotemporal Non-Stationary Effect of Industrial Agglomeration on Urban Land Use Efficiency: A Case Study of Yangtze River Delta, China. Land, 11(5), 755. https://doi.org/10.3390/land11050755