Identifying Trade-Offs and Synergies of Production–Living–Ecological Functions and Their Drivers: The Case of Yangtze River Urban Agglomerations in China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. Methods
2.3.1. An Indicator System for Assessing PLE Functions
2.3.2. PLE Function Evaluation
2.3.3. Trade-Offs and Synergies among PLE Functions
2.3.4. Analysis of the Driving Mechanism
- (1)
- Selection of driving factors
- (2)
- Geographically Weighted Regression
3. Results
3.1. Spatiotemporal Characteristics of PLE Functions
3.2. Spatial Patterns of Trade-Offs and Synergies among PLE Functions
3.2.1. Temporal Change of Trade-Offs and Synergies
3.2.2. Spatial Distribution Characteristics of Trade-Offs and Synergies
3.3. Driving Forces of the Trade-Offs and Synergies in PLE Functions
4. Discussion
4.1. Spatiotemporal Changes of Trade-Offs and Synergies among PLE Functions
4.2. Identifying Driving Factors Affecting Trade-Offs and Synergies among PLE Functions
4.3. Policy Implications
4.4. Limitations and Future Research
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Functions | Sub-Functions | Indicator | Unit | Weight | Reference |
---|---|---|---|---|---|
Production function | Agricultural production function (P1) | Proportion of cultivated land (P11) | % | 0.049 | [39] |
Total output values of agriculture, forestry, animal husbandry, and fishery (P12) | 108 CNY | 0.048 | [22] | ||
Non- agricultural production function (P2) | Real estate development investment (P21) | 108 CNY | 0.053 | [17] | |
The proportion of the added value of the tertiary industry in GDP (P22) | % | 0.051 | [39] | ||
Number of employees in the tertiary industry (P23) | \ | 0.045 | [22] | ||
Water used for production and operations (P24) | m3 | 0.053 | [16] | ||
Per capita GDP (P25) | CNY/ person | 0.048 | [4] | ||
Living function | Basic living security function (L1) | Per capita daily domestic water consumption (L11) | L | 0.052 | [17] |
Proportion of construction land (L12) | % | 0.045 | [17] | ||
Total retail sales of consumer goods (L13) | 108 CNY | 0.041 | [16] | ||
Social welfare function (L2) | Education expenditure/government expenditure (L21) | % | 0.052 | ||
Number of hospitals and health institutions (L22) | \ | 0.047 | [22] | ||
Living quality function (L3) | Population density (L31) | person/ km2 | 0.053 | [39] | |
Harmless disposal rate of household garbage (L32) | % | 0.052 | [4] | ||
Ecological function | Environmental pollution function (E1) | Industrial wastewater discharge (E11) | 104 t | 0.052 | [17] |
Industrial sulfur dioxide emissions (E12) | t | 0.053 | [4] | ||
Environmental governance function (E2) | Comprehensive utilization rate of industrial solid waste (E21) | % | 0.052 | [4] | |
Centralized treatment rate of sewage treatment plant (E22) | % | 0.052 | [4] | ||
Environmental beautification function (E3) | Greenery coverage in built-up areas (E31) | % | 0.052 | [4] | |
Per capita park green space (E32) | m2/ person | 0.051 | [4] |
Types | Classification Criteria | ||
---|---|---|---|
Positive/Negative Coefficients | Significance | ||
Synergies | Extremely significant synergy | + | p ≤ 0.01 |
Significant synergy | + | 0.01 < p ≤ 0.05 | |
Insignificant synergy | + | p > 0.05 | |
Trade-offs | Extremely significant trade-off | − | p ≤ 0.01 |
Significant trade-off | − | 0.01 < p ≤ 0.05 | |
Insignificant trade-off | − | p > 0.05 |
Factors Type | Indicator | Description | Calculation | Reference |
---|---|---|---|---|
Natural | Elevation (X1) | Elevation (m) | Extract from DEM | [42] |
NPP (X2) | Net primary productivity | ArcGIS raster statistics | [42] | |
Precipitation (X3) | Annual mean precipitation (mm) | ArcGIS raster statistics | [43] | |
Dis2river (X4) | Distance to river (m) | ArcGIS raster statistics and Euclidean distance | [43] | |
Socio-economic | Dis2city (X5) | Distance to the provincial capital city (m) | ArcGIS raster statistics and Euclidean distance | [30] |
Dis2road (X6) | Distance to road (m) | ArcGIS raster statistics and Euclidean distance | [30] | |
ED (X7) | Economic density (104 CNY/km2) | GDP/total land area | [41] | |
LUD (X8) | Land use degree (\) | L is the land use degree, Ai is the grade index of the i-th land use type, and Ci is the percentage of the i-th land use type in the total area | [43] |
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Zheng, L.; Wang, Y.; Yang, H.; Bi, Y.; Xu, L.; Wang, Y. Identifying Trade-Offs and Synergies of Production–Living–Ecological Functions and Their Drivers: The Case of Yangtze River Urban Agglomerations in China. Land 2024, 13, 1322. https://doi.org/10.3390/land13081322
Zheng L, Wang Y, Yang H, Bi Y, Xu L, Wang Y. Identifying Trade-Offs and Synergies of Production–Living–Ecological Functions and Their Drivers: The Case of Yangtze River Urban Agglomerations in China. Land. 2024; 13(8):1322. https://doi.org/10.3390/land13081322
Chicago/Turabian StyleZheng, Liang, Yajing Wang, Hui Yang, Yuzhe Bi, Lei Xu, and Ying Wang. 2024. "Identifying Trade-Offs and Synergies of Production–Living–Ecological Functions and Their Drivers: The Case of Yangtze River Urban Agglomerations in China" Land 13, no. 8: 1322. https://doi.org/10.3390/land13081322