Comparative Analysis of the Factors Influencing Land Use Change for Emerging Industry and Traditional Industry: A Case Study of Shenzhen City, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.1.1. Overview of Shenzhen City
2.1.2. Types of Emerging Industry Land in Shenzhen
2.1.3. Spatial Distribution of Emerging Industry Land in Shenzhen
2.2. Data Sources
2.3. Methods
3. Results and Analysis
3.1. Results for Emerging Industry Land
3.2. Results for Industrial Land
3.3. Comparative Analysis
- (i)
- Natural conditions. The two explanatory variables, elevation and slope, were not included in the regression model of emerging industry land use, which shows that natural conditions have minimal effects on the land use change of emerging industry, perhaps because emerging industry land is mainly from newly transferred land, innovative industrial parks, and cultural and creative parks, which are similar in their natural conditions. The land slope is included in the regression model of industrial land use, probably because flat terrain is more likely to be chosen when selecting industrial land sites. The terrain in Shenzhen is mostly low hilly land with gentle slopes, and there are no obvious changes in altitude; therefore, areas with low slopes are more likely to be chosen for industrial land.
- (ii)
- Traffic locations. The two explanatory variables, the number of railway stations and the distance from airports, were included in the regression model of emerging industry land use rather than industrial land, probably because the regional locations and aviation economy produce large differences in the exports of emerging industries. Since part of emerging industry land has been transformed from traditional industry land, more mature industry development and more convenient transportation increase the likelihood that emerging industry regions are formed. The distance from ports was included in the regression model of industrial land use, indicating that foreign capital and technology have major influences on the change in industrial land use and that ports play an important role.
- (iii)
- Population. The size of the population was included the land use regression model of traditional industry rather than that of emerging industry. The quality of the population is exactly the opposite, mainly because during the industrialization process in Shenzhen, most of the industry has been labor-intensive and thus has relied strongly on labor availability. With advances in technology, abundance of capital, and shortage of land resources, some industrial enterprises have been updated from the original “three types of processing plus compensation trades” [60]. Before 2008, there was no emerging industry land in Shenzhen. This land did not appear until 2009 in Nanshan District, where science and technology have reached very high levels, and in Longgang District, where there is a relatively large industrial area. Innovative industry strongly depends on talent, so population quality has become an explanatory variable of emerging industry land use.
- (iv)
- Economic development. The two explanatory variables, proportion of secondary industry and proportion of tertiary industry, were included in both the land use regression models of emerging industry and traditional industry, mainly because the development of secondary and tertiary industries has motivated land use change. Industrial development increases the chance of agricultural land being converted into emerging industry land or industrial land. From the differences between the two, industrial land is more affected by fixed asset investments, mainly because the change in the amount of fixed asset investments directly affects the area of industrial land, thus affecting industrial land change. Emerging industry lies between secondary industry and tertiary industry, and it is more likely to appear in the regions where tertiary industry is well-developed.
- (v)
- Innovation drive. Four influencing factors, the number of regional patent applications; the number of regional universities and science and research institutions; the number of regional primary and middle schools; and the number of regional libraries, exhibition halls, and museums, were all included in the land use regression model of emerging industry rather than that of traditional industry, which shows that innovation drive has a marked impact on the land use change of emerging industry but a relatively small impact on industrial land use change. As shown in the distribution of the emerging industry land in Shenzhen, emerging industry land is close to science and research institutions and libraries, and the educational facilities around it are relatively mature. These factors work together to attract emerging industry. In contrast, industrial land is more dependent on roads, rail transportation, and other municipal facilities.
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
References
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District | Area (hm2) | Proportion of the Total Area of the City (%) | High-Tech Industrial Parks | Creative INDUSTRIAL Parks | Enterprise Incubators | Corporate Headquarters | Makerspaces | Industrial Buildings |
---|---|---|---|---|---|---|---|---|
Luohu | 69 | 3.79% | 6 | 5 | 2 | 2 | - | - |
Futian | 212 | 11.64% | 7 | 10 | 4 | 12 | - | 2 |
Nanshan | 623 | 34.21% | 14 | 10 | 34 | 35 | 5 | 2 |
Yantian | 9 | 0.49% | - | 1 | 1 | - | - | - |
Baoan | 169 | 9.28% | 26 | 9 | 10 | 3 | 2 | - |
Longgang | 458 | 25.15% | 17 | 14 | 8 | 5 | 1 | - |
Guangming | 115 | 6.32% | 2 | 2 | 2 | 1 | - | - |
Pingshan | 42 | 2.31% | 2 | 1 | 2 | - | - | - |
Longhua | 118 | 6.48% | 3 | 8 | 2 | 2 | - | - |
Dapeng | 6 | 0.33% | 2 | 1 | 2 | - | - | - |
Total | 1821 | 100.00% | 79 | 61 | 67 | - | - | - |
Variable | Factor | Type | Units | |
---|---|---|---|---|
Dependent | land use change in emerging industry (2009–2019) | two-category classification | 0–1 | |
land use change in traditional industry (2000–2019) | two-category classification | 0–1 | ||
Independent | natural conditions | elevation | continuous | m |
land slope | multi-category classification | 1–3 | ||
traffic locations | distance from highways | continuous | km | |
distance from public roads | continuous | km | ||
distance from airports | continuous | km | ||
distance from ports | continuous | km | ||
distance from railway freight stations | continuous | km | ||
number of railway stations | continuous | number | ||
population | change in population size | continuous | 104 persons | |
change in population quality | continuous | % | ||
economic development | GDP change | continuous | 108 yuan | |
change in proportion of secondary industry | continuous | % | ||
change in proportion of tertiary industry | continuous | % | ||
change in fixed assets investment | continuous | 108 yuan | ||
innovation drive | change in number of regional patent applications | continuous | number | |
change in number of regional science and research institutions (universities and science and research institutions) | continuous | number | ||
change in number of regional primary and middle schools | continuous | number | ||
change in number of regional libraries, exhibition halls, and museums | continuous | number |
Variable | Wald x2 Statistics | Significance Level | Exp (B) |
---|---|---|---|
Distance from public roads | 6.15 | * | 0.84 |
Distance from highways | 173.76 | * | 0.29 |
Distance from airports | 25.40 | * | 0.66 |
Distance from railway freight stations | 5.72 | * | 1.22 |
Number of railway stations | 19.57 | * | 1.43 |
Quality of population | 24.39 | * | 0.79 |
Proportion of secondary industry | 53.92 | * | 1.84 |
Proportion of tertiary industry | 66.57 | * | 10.80 |
Number of regional patent applications | 50.80 | * | 2.04 |
Number of regional science and research institutions (universities and science and research institutions) | 45.02 | * | 1.83 |
Number of regional primary and middle schools | 4.41 | * | 0.04 |
Number of regional libraries, exhibition halls, and museums | 16.84 | * | 0.39 |
Constants | 46.02 | * | 0.00 |
ROC | 0.83 |
Variable | Wald x2 Statistics | Significant Level | Exp (B) |
---|---|---|---|
Land slope | 28.70 | * | 1.22 |
Distance from highways | 13.60 | * | 0.86 |
Distance from public roads | 538.65 | * | 0.30 |
Distance from ports | 78.55 | * | 1.76 |
Distance from railway freight stations | 7.87 | * | 1.23 |
Size of population | 36.50 | * | 1.24 |
Proportion of secondary industry | 38.81 | * | 1.32 |
Proportion of tertiary industry | 14.24 | * | 1.77 |
Fixed asset investments | 30.76 | * | 1.14 |
Constant | 53.26 | * | 0.00 |
ROC | 0.81 |
Variable | Factor | Influence on the Land Use Change in Emerging Industry | Influence on the Land Use Change in Traditional Industry |
---|---|---|---|
natural conditions | elevation | - | - |
land slope | - | √ | |
traffic locations | distance from highways | √ | √ |
distance from public roads | √ | √ | |
distance from airports | √ | - | |
distance from ports | - | √ | |
distance from railway freight stations | √ | √ | |
distance from railway stations | √ | - | |
population | size of population | - | √ |
quality of population | √ | - | |
economic development | GDP | - | - |
proportion of secondary industry | √ | √ | |
proportion of tertiary industry | √ | √ | |
fixed asset investments | - | √ | |
innovation drive | number of regional patent applications | √ | - |
number of regional science and research institutions (universities and science and research institutions) | √ | - | |
number of regional primary and middle schools | √ | - | |
number of libraries, exhibition halls, and museums | √ | - |
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Peng, Y.; Yang, F.; Zhu, L.; Li, R.; Wu, C.; Chen, D. Comparative Analysis of the Factors Influencing Land Use Change for Emerging Industry and Traditional Industry: A Case Study of Shenzhen City, China. Land 2021, 10, 575. https://doi.org/10.3390/land10060575
Peng Y, Yang F, Zhu L, Li R, Wu C, Chen D. Comparative Analysis of the Factors Influencing Land Use Change for Emerging Industry and Traditional Industry: A Case Study of Shenzhen City, China. Land. 2021; 10(6):575. https://doi.org/10.3390/land10060575
Chicago/Turabian StylePeng, Yunfei, Fangling Yang, Lingwei Zhu, Ruru Li, Chao Wu, and Deng Chen. 2021. "Comparative Analysis of the Factors Influencing Land Use Change for Emerging Industry and Traditional Industry: A Case Study of Shenzhen City, China" Land 10, no. 6: 575. https://doi.org/10.3390/land10060575