Analysis of the Driving Mechanism of Land Comprehensive Carrying Capacity from the Perspective of Urban Renewal
Abstract
:1. Introduction
2. Materials and Methods
2.1. Overview of the Study Area
2.2. Establishment of the Land Comprehensive Carrying Capacity Driving Mechanism Analysis Model
2.2.1. Concept and Development of Land Carrying Capacity
2.2.2. Basics of Model Construction
- Socio-economic growth orientation: Economic growth is a development goal for governments at all levels, and accelerating industrial capital aggregation and urban construction are fundamental driving mechanisms for the current spatial expansion of cities in our country. Policies aimed at attracting external investments to expand the city’s scale have resulted in the generation of many measures to increase land supply. There is a certain similarity between the trends in land development and GDP growth in our country. Therefore, economic growth serves as the fundamental driving force for the growth of urban land construction activities and the spatial expansion of cities;
- Infrastructure construction: The proportion of public infrastructure investment in China has been increasing year by year, which is closely related to the trend of increasing urban land development. Infrastructure investment and construction serve as tools to drive economic growth and act as a link for coordinated urban and rural development [23]. With the increasing demand for road facilities, the government has intensified the supply of transportation infrastructure. The gradual improvement in infrastructure enables urban land to accommodate more construction activities and larger development scales;
- Resource and ecological environment: The ecological environment plays a constraining role in the transformation of urban land use patterns, and is essential for ensuring the sustainable development of cities [24]. Guiding the orderly expansion of cities and determining the distribution of various functional areas within a city have a significant impact on improving the urban environment and enhancing livability [25]. Additionally, in the process of urban renewal, it is important to consider the resilience of cities in social and natural disaster conditions.
2.2.3. Research Variables and Hypotheses
- The dimension of urban development scale: Urban scale is an important manifestation of urban renewal and a concrete dimension of socioeconomic growth. Land carrying capacity is closely related to urban scale. Specific indicators within this dimension include per capita residential land, road density, per capita fixed asset investments of the entire society, and value-added indicators of the main industry utilizing land resources, such as the construction industry. These indicators directly reflect the current state of the land carrying capacity;
- The dimension of social economy: This dimension directly reflects the orientation of socio-economic growth. It is manifested by indicators such as economic density, urbanization level, per capita GDP, and the Engel coefficient. This dimension represents the integration of qualitative and quantitative analyses. The indicators within this dimension reflect the correlation between the land carrying capacity and the level of urban economic development;
- The dimension of urban renewal: This dimension focuses on the improvement and optimization of spatial form and functions in urban built-up areas, transforming the scarcity function of urban land. It primarily reflects the effectiveness of urban infrastructure development. The indicators within this dimension include urban building density, floor area ratio, urban redevelopment cost, and resident satisfaction. These indicators establish the relationship between the perception of residents and the effectiveness of urban transformation, providing an assessment of the level of land carrying capacity;
- The dimension of urban ecology: This dimension captures the carrying capacity generated by the absorption capacity of urban land and environmental greening, reflecting the connection between land resources and urban ecological conditions. Considering the close relationship between land resources and urban ecology, and the fact that the urban ecological level is also an evaluation indicator for urban renewal, specific indicators within this dimension include per capita urban green space area, built-up area greening coverage rate, and centralized treatment rate of domestic wastewater. These indicators comprehensively reflect the level of urban ecological governance from the perspectives of residents and the effects of urban transformation;
- The dimension of urban infrastructure development: Urban land serves as the foundation for urban infrastructure development, and urban land resources are crucial carriers for providing various basic needs of urban residents, in terms of living and working. Therefore, indicators within this dimension include transportation accessibility, water supply capacity, power supply capacity, and infrastructure investment;
- The dimension of urban disaster resilience: This dimension focuses on the ability of cities to reduce potential risks and minimize disasters, both natural and man-made, by implementing effective measures and utilizing land resources rationally to enhance urban safety. The concentration of the population and the expansion of urban scale impose higher requirements on urban resilience. Specific indicators within this dimension include building disaster resistance rate, per capita road area, and stability of the drainage system, which are directly related to the land carrying capacity.
2.2.4. Construction of Conceptual Model
2.3. Structural Equation Modeling
2.3.1. The Principle of Mechanism Analysis in Driving Factors
- (1)
- Measurement model.
- (2)
- Structural model
2.3.2. Modeling Steps
- (1)
- Theoretical assumptions: Review relevant literature and summarize theoretical assumptions. Establish directed relationships between latent variables and corresponding observed variables, and set up an initial theoretical model;
- (2)
- Model construction: Determine the relationships between different variable combinations and select a model that provides a simple explanation for a greater number of variables. Express the measurement model and structural model through a system of equations or a path diagram;
- (3)
- Model fitting: Estimate the parameters of the variables using collected data and information. The better the fit between the covariance matrix and the sample covariance matrix in SEM, the better the model fit. Common fit indices include chi-square value, goodness-of-fit index (GFI), and root mean square error of approximation (RMSEA), as shown in Table 4;
- (4)
- Model evaluation: Determine if the output indicator values meet the predefined fitness criteria of the model. This evaluation includes overall model evaluation and structural fit evaluation. The former assesses the fit between the sample data and the theoretical model, i.e., whether the observed variables effectively reflect the latent variables. The latter tests the causal relationships proposed by the hypotheses. The model should meet the criteria of the measurement equation errors having a mean of zero, the structural equation residuals having a mean of zero, and the errors being uncorrelated with the factors;
- (5)
- Model modification: If the fit indices indicate poor model fit, model modification is required to improve the fitness. Simultaneously, the adequacy of the modified model is assessed by connecting theoretical results with practical significance;
- (6)
- Model interpretation: interpret the meaning of the relevant data and validate the previously proposed hypothesis relationships.
Category | Indicator | Standard |
---|---|---|
Relative Fit Indices | Goodness-of-Fit Chi-Square Test (Γ) | Acceptance range (2, 5), Fit is considered good when the value is less than 2 |
Comparative Fit Index (CFI) | Acceptance range (0, 1), Closer to 1 indicates better fit | |
Incremental Fit Index (IFI) | ||
Absolute Fit Indices | Root Mean Square Error of Approximation (RMSEA) | For RMSEA, a value of ≤0.05 indicates acceptable fit |
Goodness-of-Fit Index (GFI) | Acceptance range (0, 1), Closer to 1 indicates better fit | |
Parsimonious Fit Index | Parsimonious Goodness-of-Fit Index (PGFI) | Acceptance range (0.5, 1), Closer to 1 indicates better fit. |
2.4. Survey Questionnaire Design and Analysis
2.4.1. Questionnaire Design
2.4.2. Data Collection and Analysis
- (1)
- Procedural remedies
- (2)
- Methodological control
- (3)
- Statistical remedies
3. Results Analysis
3.1. Reliability and Validity Tests
3.2. Model Goodness-of-Fit Test
3.2.1. The Measurement Model
3.2.2. The Structural Model
3.3. Hypothesis Testing
3.3.1. Results of the Path Coefficient Test
3.3.2. Intermediation Effects
3.3.3. Correlation Analysis of Indicators
3.4. Analysis of Model Results
- Urban development (UD)
- 2.
- Social economy (SE)
- 3.
- Urban renewal (UR)
- 4.
- Urban ecology (UE)
- 5.
- Urban disaster prevention and mitigation capacity (DPMC)
- 6.
- Infrastructure development
4. Discussion and Conclusions
4.1. Discussion
- The research results indicate that all six dimensions in the conceptual model have a direct positive impact on the land carrying capacity. In terms of direct effects, the influencing factors are ranked in descending order of magnitude as follows: urban development, urban disaster prevention and mitigation capacity, infrastructure development, urban renewal, social economy, and urban ecology. In terms of overall effects, factors are ranked in descending order of magnitude as follows: urban development, social economy, urban ecology, urban renewal, urban disaster prevention and mitigation capacity, and infrastructure development;
- According to the conceptual model, the paths with significant correlations with the integrated land carrying capacity are determined as UD → LCCC, UD → SE → LCCC, UD → ID → LCCC, SE → LCCC, SE → UE → LCCC, SE → ID → LCCC, UR → LCCC, UR → ID → LCCC, UR → DPMC → LCCC, UE → LCCC, DPMC → LCCC, and ID → LCCC. The paths of influence relationships between the drivers of each dimension are UD → SE, UD → ID, SE → UE, SE → ID, UR → ID, and UE → DPMC;
- The United States, Canada, Australia, Malaysia, and other countries have conducted studies on the comprehensive land carrying capacity with their resource endowments and social development patterns. They have identified the mechanisms through which factors such as population, economy, and natural resources influence the land carrying capacity. This paper aligns with the theoretical framework of the AEZ method. It integrates existing indicator systems based on different countries, and combines them with China’s economic development characteristics and urban renewal conditions to investigate the regional land carrying capacity. The findings are consistent with the quantitative evaluation results of the AEZ method, and also reveal the driving mechanisms of land carrying capacity under China’s urban development characteristics. Therefore, this paper has broad applicability in the field, and provides a unique perspective within the context of China’s economic development. It has regional relevance, and enriches the existing research outcomes in this field;
- Through the findings of this paper, it can be observed that, from the perspective of urban renewal, multiple dimensions have a positive impact on the land carrying capacity, and there are interactions and mediating effects among the factors. These conclusions align with the research conducted by Tian G et al. [65] and Irwin E G et al. [66], which indicate that urban renewal activities have a promoting effect on land carrying capacity. Through the above analyses, the ultimate goal of this paper is to study the driving mechanism of comprehensive land carrying capacity from the perspective of urban renewal, taking a specific region in China as the research object, and to provide theoretical and technical support with practicality and operability for various aspects of urban construction, urban resource allocation, and land resource conservation.
4.2. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Urban Land Development and Construction Activities | Dimensions of Comprehensive Land Carrying Capacity Evaluation |
---|---|
Social and Economic Growth Orientation [29,30] | Social Economy |
Urban Development Scale | |
Infrastructure Construction [31,32] | Urban Infrastructure Development |
Urban Renewal | |
Resource and Ecological Environment [33] (including Urban Resilience) | Urban Ecology |
Urban Disaster Resilience |
Dimensions | Indicator | Code | Assumption |
---|---|---|---|
Urban Development (UD) | Per Capita Residential Land | UD1 | H1. The expansion of urban development scale has a positive impact on the comprehensive carrying capacity of land. H2. The expansion of urban development scale has a positive impact on socioeconomic development. H3. The expansion of urban development scale has a positive impact on urban infrastructure construction. |
Road Density | UD2 | ||
Per Capita Fixed Asset Investment in the Whole Society | UD3 | ||
Value Added of the Construction Industry | UD4 | ||
Social Economy (SE) | Economic Density | SE1 | H4. Socioeconomic development has a positive impact on the comprehensive carrying capacity of land. H5. Socioeconomic development has a positive impact on urban ecology. H6. Socioeconomic development has a positive impact on urban infrastructure construction. |
Urbanization Level | SE2 | ||
Per Capita GDP | SE3 | ||
Engel’s Coefficient | SE4 | ||
Urban Renewal (UR) | Building Density | UR1 | H7. Urban renewal activities have a positive impact on the comprehensive carrying capacity of land. H8. Urban renewal activities have a positive impact on urban infrastructure construction. |
Plot Ratio | UR2 | ||
Renovation Cost | UR3 | ||
Resident Satisfaction | UR4 | ||
Urban Ecology (UE) | Per Capita Urban Green Space Area | UE1 | H9. Urban ecology has a positive impact on the comprehensive carrying capacity of land. H10. Urban ecology has a positive impact on urban infrastructure construction. H11. Urban ecology has a positive impact on urban disaster prevention and mitigation capability. |
Green Coverage Rate of Built-up Area | UE2 | ||
Concentration Rate of Domestic Wastewater Treatment | UE3 | ||
Urban Infrastructure Development (ID) | Transportation Accessibility | ID1 | H12. Urban infrastructure construction has a positive impact on the comprehensive carrying capacity of land. |
Water Supply Capacity | ID2 | ||
Power Supply Capacity | ID3 | ||
Infrastructure Investment | ID4 | ||
Urban Disaster Prevention and Mitigation Capability (DPMC) | Building Disaster Resistance Rate | DPMC1 | H13. Urban disaster prevention and mitigation capability has a positive impact on the comprehensive carrying capacity of land. |
Per Capita Road Area | DPMC2 | ||
Stability of Water Supply and Drainage System | DPMC3 |
Components | Significance |
---|---|
Latent Variables | Unobservable variables that need to be measured using observed variables |
Observed Variables | Quantifiable variables used to measure latent variables, obtained through direct observation or objective measurement based on the actual situation |
Error Variables | Unmeasured variables representing the errors in latent variables and observed variables, as well as the random variation errors in the model |
Exogenous Variables | Independent variables that influence other variables without being influenced by other variables |
Endogenous Variables | Dependent variables that are influenced by other variables |
Measurement Model | A model that represents the relationship between latent variables and observed variables |
Structural Model | A model that represents the structural relationships among latent variables |
Path Coefficients | Coefficients representing the relationships between latent variables |
Respondent Basic Information | Frequency | Percentage (%) | |
---|---|---|---|
Occupation Category | University Experts | 79 | 27.62 |
Researchers from Institutes and Related Fields | 75 | 26.22 | |
Land Resource Planners | 37 | 12.93 | |
Land Development Technicians | 68 | 23.78 | |
Urban Planners | 27 | 9.44 | |
Education Level | Doctorate and above | 46 | 16.08 |
Master’s degree | 72 | 25.17 | |
Bachelor’s degree | 143 | 50 | |
Associate degree and below | 25 | 8.75 | |
Years of Experience in the Construction Industry | 10 years and above | 62 | 21.68 |
6–10 years | 85 | 29.72 | |
3–5 years | 88 | 30.8 | |
Less than 3 years | 51 | 17.8 | |
Age | Below 25 years old | 69 | 24.13 |
26–35 years old | 98 | 34.27 | |
36–45 years old | 62 | 21.68 | |
46–55 years old | 37 | 12.94 | |
Over 55 years old | 20 | 6.98 | |
Participation in Project Scale | Large-scale projects | 98 | 34.27 |
Medium-scale projects | 109 | 38.11 | |
Small-scale projects | 79 | 27.62 |
Latent Variable | Indicator | Indicator Loadings | t-Value | CA | CR | AVE | KMO |
---|---|---|---|---|---|---|---|
UD | UD1 | 0.907 | 63.631 ** | 0.907 | 0.935 | 0.782 | 0.836 |
UD2 | 0.935 | 97.418 ** | |||||
UD3 | 0.928 | 89.036 ** | |||||
UD4 | 0.877 | 39.037 ** | |||||
SE | SE1 | 0.802 | 30.670 ** | 0.806 | 0.873 | 0.631 | 0.766 |
SE2 | 0.777 | 23.898 ** | |||||
SE3 | 0.787 | 22.196 ** | |||||
SE4 | 0.812 | 42.966 ** | |||||
UR | UR1 | 0.850 | 43.435 ** | 0.846 | 0.896 | 0.684 | 0.808 |
UR2 | 0.815 | 34.701 ** | |||||
UR3 | 0.821 | 34.456 ** | |||||
UR4 | 0.823 | 34.464 ** | |||||
UE | UE1 | 0.877 | 46.237 ** | 0.833 | 0.900 | 0.750 | 0.706 |
UE2 | 0.821 | 29.156 ** | |||||
UE3 | 0.897 | 68.934 ** | |||||
ID | ID1 | 0.831 | 29.614 ** | 0.841 | 0.893 | 0.677 | 0.799 |
ID2 | 0.791 | 25.449 ** | |||||
ID3 | 0.806 | 24.381 ** | |||||
ID4 | 0.862 | 39.419 ** | |||||
DPMC | DPMC1 | 0.826 | 31.025 ** | 0.856 | 0.913 | 0.778 | 0.719 |
DPMC2 | 0.910 | 60.956 ** | |||||
DPMC3 | 0.907 | 52.645 ** | |||||
LCCC | LCCC1 | 0.911 | 65.903 ** | 0.903 | 0.939 | 0.837 | 0.753 |
LCCC2 | 0.921 | 84.523 ** | |||||
LCCC3 | 0.913 | 74.088 ** |
UD | SE | UE | ID | UR | DPMC | LCCC | |
---|---|---|---|---|---|---|---|
UD | 0.884 | ||||||
SE | 0.535 | 0.795 | |||||
UR | 0.483 | 0.588 | 0.827 | ||||
ID | 0.471 | 0.539 | 0.539 | 0.823 | |||
UE | 0.234 | 0.363 | 0.370 | 0.305 | 0.866 | ||
DPMC | 0.435 | 0.528 | 0.560 | 0.443 | 0.321 | 0.882 | |
LCCC | 0.545 | 0.570 | 0.567 | 0.529 | 0.385 | 0.555 | 0.915 |
UD | SE | UE | UR | ID | DPMC | LCCC | |
---|---|---|---|---|---|---|---|
UD1 | 0.879 | 0.484 | 0.415 | 0.190 | 0.419 | 0.400 | 0.433 |
UD2 | 0.872 | 0.433 | 0.437 | 0.198 | 0.418 | 0.411 | 0.513 |
UD3 | 0.903 | 0.498 | 0.427 | 0.238 | 0.423 | 0.364 | 0.454 |
UD4 | 0.882 | 0.478 | 0.431 | 0.203 | 0.408 | 0.364 | 0.523 |
SE1 | 0.418 | 0.802 | 0.536 | 0.324 | 0.452 | 0.463 | 0.448 |
SE2 | 0.354 | 0.777 | 0.511 | 0.283 | 0.415 | 0.430 | 0.430 |
SE3 | 0.366 | 0.787 | 0.408 | 0.267 | 0.401 | 0.407 | 0.450 |
SE4 | 0.543 | 0.812 | 0.420 | 0.280 | 0.442 | 0.383 | 0.481 |
UR1 | 0.420 | 0.481 | 0.850 | 0.321 | 0.445 | 0.508 | 0.512 |
UR2 | 0.389 | 0.503 | 0.815 | 0.304 | 0.455 | 0.389 | 0.469 |
UR3 | 0.395 | 0.499 | 0.821 | 0.313 | 0.440 | 0.480 | 0.477 |
UR4 | 0.395 | 0.464 | 0.823 | 0.285 | 0.444 | 0.475 | 0.411 |
UE1 | 0.204 | 0.268 | 0.310 | 0.877 | 0.256 | 0.267 | 0.330 |
UE2 | 0.202 | 0.335 | 0.310 | 0.821 | 0.259 | 0.226 | 0.294 |
UE3 | 0.204 | 0.338 | 0.340 | 0.897 | 0.275 | 0.332 | 0.370 |
ID1 | 0.392 | 0.395 | 0.403 | 0.179 | 0.831 | 0.349 | 0.416 |
ID2 | 0.387 | 0.465 | 0.427 | 0.247 | 0.791 | 0.340 | 0.451 |
ID3 | 0.372 | 0.441 | 0.477 | 0.331 | 0.806 | 0.395 | 0.437 |
ID4 | 0.400 | 0.467 | 0.462 | 0.238 | 0.862 | 0.372 | 0.434 |
DPMC1 | 0.338 | 0.436 | 0.434 | 0.232 | 0.349 | 0.826 | 0.479 |
DPMC2 | 0.403 | 0.479 | 0.527 | 0.301 | 0.412 | 0.910 | 0.478 |
DPMC3 | 0.405 | 0.480 | 0.517 | 0.312 | 0.409 | 0.907 | 0.510 |
LCCC1 | 0.478 | 0.472 | 0.522 | 0.390 | 0.504 | 0.514 | 0.911 |
LCCC2 | 0.507 | 0.539 | 0.511 | 0.329 | 0.460 | 0.489 | 0.921 |
LCCC3 | 0.509 | 0.552 | 0.522 | 0.338 | 0.488 | 0.518 | 0.913 |
Fitting Index | Test Value | Whether It Meets the Standard |
---|---|---|
Γ | 1.313 | Yes |
CFI | 0.981 | Yes |
IFI | 0.924 | Yes |
RMSEA | 0.033 | Yes |
GFI | 0.916 | Yes |
PGFI | 0.716 | Yes |
Path | f2 | Path | f2 |
---|---|---|---|
UD → LCCC | 0.060 | UE → LCCC | 0.025 |
UD → SE | 0.401 | UE → ID | 0.020 |
SE → LCCC | 0.020 | UE → DPMC | 0.115 |
SE → UE | 0.152 | ID → LCCC | 0.028 |
SE → ID | 0.058 | UD → ID | 0.039 |
UR → LCCC | 0.021 | DPMC → LCCC | 0.052 |
UR → ID | 0.073 |
Path | Path Coefficient | t-Values | p-Values | Result |
---|---|---|---|---|
H1: UD → LCCC | 0.214 | 4.001 | *** | Support |
H2: UD → SE | 0.535 | 10.346 | *** | Support |
H3: UD → ID | 0.188 | 3.485 | *** | Support |
H4: SE → LCCC | 0.140 | 2.590 | ** | Support |
H5: SE → UE | 0.363 | 5.367 | *** | Support |
H6: SE → ID | 0.252 | 3.463 | *** | Support |
H7: UR → LCCC | 0.141 | 2.542 | ** | Support |
H8: UR → ID | 0.275 | 4.201 | *** | Support |
H9: UE → LCCC | 0.121 | 2.610 | ** | Support |
H10: UE → ID | 0.067 | 0.943 | Non-significant | No Support |
H11: UE → DPMC | 0.321 | 5.367 | *** | Support |
H12: ID → LCCC | 0.149 | 2.974 | ** | Support |
H13: DPMC → LCCC | 0.203 | 3.593 | *** | Support |
NO. | Path | Indirect Effect | t-Values | Confidence Interval | Results |
---|---|---|---|---|---|
1 | UR → DPMC → LCCC | 0.065 | 3.009 | [0.027, 0.111] | Accept |
2 | SE → UE → LCCC | 0.044 | 2.325 | [0.009, 0.084] | Accept |
3 | UD → SE → LCCC | 0.075 | 2.523 | [0.019, 0.136] | Accept |
4 | UD → ID → LCCC | 0.028 | 2.312 | [0.007, 0.054] | Accept |
5 | SE → ID → LCCC | 0.038 | 2.168 | [0.009, 0.076] | Accept |
6 | UR → ID → LCCC | 0.041 | 2.308 | [0.011, 0.080] | Accept |
UD | SE | UR | UE | ID | DPMC | |
---|---|---|---|---|---|---|
UD | 1 | |||||
SE | 0.623 ** | 1 | ||||
UR | 0.493 ** | 0.478 ** | 1 | |||
UE | 0.556 ** | 0.562 ** | 0.575 ** | 1 | ||
ID | 0.529 ** | 0.527 ** | 0.440 ** | 0.513 ** | 1 | |
DPMC | 0.325 ** | 0.351 ** | 0.314 ** | 0.303 ** | 0.339 ** | 1 |
Mean value | 3.35 | 3.20 | 3.18 | 3.46 | 3.78 | 3.71 |
Standard deviation | 0.88 | 0.89 | 1.07 | 0.94 | 0.82 | 0.91 |
Path | Direct Effect | Indirect Effect | Total Effect | |
---|---|---|---|---|
UD | → LCCC | 0.214 | 1.096 | |
→ SE → LCCC | 0.131 | |||
→ ID→ LCCC | 0.028 | |||
→ SE | 0.535 | |||
→ ID | 0.188 | |||
SE | → LCCC | 0.140 | 0.861 | |
→ UE → LCCC | 0.068 | |||
→ ID → LCCC | 0.038 | |||
→ UE | 0.363 | |||
→ ID | 0.252 | |||
UR | → LCCC | 0.141 | 0.522 | |
→ ID → LCCC | 0.041 | |||
→ DPMC → LCCC → ID | 0.275 | 0.065 | ||
UE | → LCCC | 0.121 | 0.442 | |
→ DPMC | 0.321 | |||
DPMC | → LCCC | 0.203 | 0.203 | |
ID | → LCCC | 0.149 | 0.149 |
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Tang, Y.; Yuan, Y.; Tian, B. Analysis of the Driving Mechanism of Land Comprehensive Carrying Capacity from the Perspective of Urban Renewal. Land 2023, 12, 1377. https://doi.org/10.3390/land12071377
Tang Y, Yuan Y, Tian B. Analysis of the Driving Mechanism of Land Comprehensive Carrying Capacity from the Perspective of Urban Renewal. Land. 2023; 12(7):1377. https://doi.org/10.3390/land12071377
Chicago/Turabian StyleTang, Yang, Yongbo Yuan, and Boquan Tian. 2023. "Analysis of the Driving Mechanism of Land Comprehensive Carrying Capacity from the Perspective of Urban Renewal" Land 12, no. 7: 1377. https://doi.org/10.3390/land12071377
APA StyleTang, Y., Yuan, Y., & Tian, B. (2023). Analysis of the Driving Mechanism of Land Comprehensive Carrying Capacity from the Perspective of Urban Renewal. Land, 12(7), 1377. https://doi.org/10.3390/land12071377