Journal of Resources and Ecology ›› 2021, Vol. 12 ›› Issue (6): 849-868.DOI: 10.5814/j.issn.1674-764x.2021.06.013
• Typical Ecological Restoration Modes and Their Ecological Effects • Previous Articles Next Articles
REN Guoping1,2(), LIU Liming3,*(
), LI Hongqing4, SUN Qian1,2, YIN Gang1,2, WAN Beiqi5
Received:
2021-01-29
Accepted:
2021-05-18
Online:
2021-11-30
Published:
2022-01-30
Contact:
LIU Liming
About author:
REN Guoping, E-mail: renguoping82@163.com
Supported by:
REN Guoping, LIU Liming, LI Hongqing, SUN Qian, YIN Gang, WAN Beiqi. Geographical Impact and Ecological Restoration Modes of the Spatial Differentiation of Rural Social-Ecosystem Vulnerability: Evidence from Qingpu District in Shanghai[J]. Journal of Resources and Ecology, 2021, 12(6): 849-868.
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URL: http://www.jorae.cn/EN/10.5814/j.issn.1674-764x.2021.06.013
Target | Criteria | Indicator | Indicator description and data source |
---|---|---|---|
Economic vulnerability | Pressure (Input) | Gross income of region (yuan) | The overall regional economic difference, using economic statistics data |
Growth rate of secondary and tertiary industries (%) | The non-agriculture development difference, using economic statistics data | ||
Total value of farm output (yuan) | The agriculture development difference, using rural statistics data | ||
State (Output) | Per capita income (yuan person-1) | Individual resistance to vulnerability, using the household survey data | |
Household income (yuan) | Family resilience to vulnerability, using the household survey data | ||
Net household income (yuan) | Family resilience to vulnerability, using the household survey data | ||
Response (Output) | Per capita disposable income (yuan person-1) | Family environment improvement capacity, using economic statistics data | |
Road density (%) | Rural external communication capacity, using the land use vector data | ||
Social vulnerability | Pressure (Input) | Urbanization rate (%) | Regional non-agricultural population pressure, using economic statistics data |
Total population (person) | Regional population carrying pressure, using economic statistics data | ||
Density of population (person km-2) | The per unit area population carrying pressure, using economic statistics data | ||
State (Output) | Net population outflow (person) | State of social identity change, using the rural survey data | |
Ratio of settled and farmland (%) | State ofhousehold livelihood capital, using the land use vector data | ||
Number of agricultural workers (person) | Rural social employment, using the rural survey data | ||
Deserted farmland area (ha) | State of agricultural behavior, using rural statistics data | ||
Grain yield (t) | State of regional food security, using economic statistics data | ||
Response (Output) | Fixed investments (yuan) | Resilience in social services, using economic statistics data | |
Household education expenditure (yuan) | Family resilience to vulnerability, using the household survey data | ||
Medical insurance coverage (%) | Resilience in public services, using economic statistics data | ||
Ecological vulnerability | Pressure (Input) | Fertilizer usage (t) | Environmental pollution pressure of agriculture, using the rural survey data |
Plastic film application (t) | Environmental pollution pressure of agriculture, using the rural survey data | ||
Pollution emissions (t) | Industrial pollution pressure, using the environmental survey data | ||
State (Output) | Vegetation coverage (%) | State of green land, using the land use vector data | |
Land degradation index | State of regional land quality, using the land degradation classification data | ||
Land patch density (piece ha-1) | State of land fragmentation, using the land use vector data | ||
Agricultural landscape fractal dimension | State of agricultural shape, using the land use vector data | ||
Response (Output) | Land consolidation area (ha) | Regional ecological improvement, using economic statistics data | |
Environmental protection input (yuan) | Regional environmental protection efforts, using economic statistics data | ||
Grain production per unit area (kg ha-1) | Regionalfood productivity, using economic statistics data |
Table 1 Evaluation index system of social-ecosystem vulnerability in each administrative village in 2018
Target | Criteria | Indicator | Indicator description and data source |
---|---|---|---|
Economic vulnerability | Pressure (Input) | Gross income of region (yuan) | The overall regional economic difference, using economic statistics data |
Growth rate of secondary and tertiary industries (%) | The non-agriculture development difference, using economic statistics data | ||
Total value of farm output (yuan) | The agriculture development difference, using rural statistics data | ||
State (Output) | Per capita income (yuan person-1) | Individual resistance to vulnerability, using the household survey data | |
Household income (yuan) | Family resilience to vulnerability, using the household survey data | ||
Net household income (yuan) | Family resilience to vulnerability, using the household survey data | ||
Response (Output) | Per capita disposable income (yuan person-1) | Family environment improvement capacity, using economic statistics data | |
Road density (%) | Rural external communication capacity, using the land use vector data | ||
Social vulnerability | Pressure (Input) | Urbanization rate (%) | Regional non-agricultural population pressure, using economic statistics data |
Total population (person) | Regional population carrying pressure, using economic statistics data | ||
Density of population (person km-2) | The per unit area population carrying pressure, using economic statistics data | ||
State (Output) | Net population outflow (person) | State of social identity change, using the rural survey data | |
Ratio of settled and farmland (%) | State ofhousehold livelihood capital, using the land use vector data | ||
Number of agricultural workers (person) | Rural social employment, using the rural survey data | ||
Deserted farmland area (ha) | State of agricultural behavior, using rural statistics data | ||
Grain yield (t) | State of regional food security, using economic statistics data | ||
Response (Output) | Fixed investments (yuan) | Resilience in social services, using economic statistics data | |
Household education expenditure (yuan) | Family resilience to vulnerability, using the household survey data | ||
Medical insurance coverage (%) | Resilience in public services, using economic statistics data | ||
Ecological vulnerability | Pressure (Input) | Fertilizer usage (t) | Environmental pollution pressure of agriculture, using the rural survey data |
Plastic film application (t) | Environmental pollution pressure of agriculture, using the rural survey data | ||
Pollution emissions (t) | Industrial pollution pressure, using the environmental survey data | ||
State (Output) | Vegetation coverage (%) | State of green land, using the land use vector data | |
Land degradation index | State of regional land quality, using the land degradation classification data | ||
Land patch density (piece ha-1) | State of land fragmentation, using the land use vector data | ||
Agricultural landscape fractal dimension | State of agricultural shape, using the land use vector data | ||
Response (Output) | Land consolidation area (ha) | Regional ecological improvement, using economic statistics data | |
Environmental protection input (yuan) | Regional environmental protection efforts, using economic statistics data | ||
Grain production per unit area (kg ha-1) | Regionalfood productivity, using economic statistics data |
Category | Indicator | Calculation method |
---|---|---|
Natural Geographical Attributes | Y1 Slope (%) | Slope data (resolution 30 m). Using DEM data extraction and re-classification: 0°-1°, 1°-2° and 2°-3° assigned to 1, 2, 3 |
Y2 Elevation (m) | Elevation data (resolution 30 m). Using DEM data extraction | |
Y3 Agricultural acreage (ha) | Extraction of arable land areas from land vector maps | |
Y4 Water area (ha) | Extraction of water areas from land vector maps | |
Y5 Distance from nearest river (km) | Using the river grid data extraction statistics. Grid vector layer (30 m*30 m), overlay administrative village committee and river grid layer, and use grid calculator statistics | |
Y6 Distance from Dianshan Lake (km) | The calculation process was the same as above (Y5). Statistics from Village Committee to Dianshan Lake Center | |
Economic Geographical Attributes | Y7 Distance from nearest main road (km) | The calculation process was the same as above (Y5). Statistics from Village Committee to the nearest main road |
Y8 Distance from township center (km) | The calculation process was the same as above (Y5). Statistics from Village Committee to the nearest township center | |
Y9 Distance from center of Qingpu District (km) | The calculation process was the same as above (Y5). Statistics from Village Committee to the Government of Qingpu District | |
Y10 Distance from center of Shanghai City (km) | The calculation process was the same as above (Y5). Statistics from Village Committee to the Government of Shanghai City | |
Y11 Space accessibility among village | The ‘grid calculator’ was used to calculate the minimum time cost of each grid after the superposition of the road grid layer and the ground grid layer The average value of all time costs associated with an administrative village were used as the index value of spatial accessibility of the village area |
Table 2 Geographical factors influencing social-ecosystem vulnerability of the administrative villages
Category | Indicator | Calculation method |
---|---|---|
Natural Geographical Attributes | Y1 Slope (%) | Slope data (resolution 30 m). Using DEM data extraction and re-classification: 0°-1°, 1°-2° and 2°-3° assigned to 1, 2, 3 |
Y2 Elevation (m) | Elevation data (resolution 30 m). Using DEM data extraction | |
Y3 Agricultural acreage (ha) | Extraction of arable land areas from land vector maps | |
Y4 Water area (ha) | Extraction of water areas from land vector maps | |
Y5 Distance from nearest river (km) | Using the river grid data extraction statistics. Grid vector layer (30 m*30 m), overlay administrative village committee and river grid layer, and use grid calculator statistics | |
Y6 Distance from Dianshan Lake (km) | The calculation process was the same as above (Y5). Statistics from Village Committee to Dianshan Lake Center | |
Economic Geographical Attributes | Y7 Distance from nearest main road (km) | The calculation process was the same as above (Y5). Statistics from Village Committee to the nearest main road |
Y8 Distance from township center (km) | The calculation process was the same as above (Y5). Statistics from Village Committee to the nearest township center | |
Y9 Distance from center of Qingpu District (km) | The calculation process was the same as above (Y5). Statistics from Village Committee to the Government of Qingpu District | |
Y10 Distance from center of Shanghai City (km) | The calculation process was the same as above (Y5). Statistics from Village Committee to the Government of Shanghai City | |
Y11 Space accessibility among village | The ‘grid calculator’ was used to calculate the minimum time cost of each grid after the superposition of the road grid layer and the ground grid layer The average value of all time costs associated with an administrative village were used as the index value of spatial accessibility of the village area |
Region | CCR-DEA model | ACE-DEA model | EW-DEA model | |||
---|---|---|---|---|---|---|
Average of RVS-E | RVS-E | Average of RVS-E | RVS-E | Average of RVS-E | RVS-E | |
Qingpu District | 0.623 | 0.699 | 0.583 | |||
Eastern part | 0.689 | 0.756 | 0.625 | |||
Central part | 0.542 | 0.643 | 0.554 | |||
Western part | 0.638 | 0.699 | 0.609 | |||
Guanglian Village in Xujing | 0.598 | 0.665 | 0.662 | |||
Songshan Village in Huaxin | 7.125 | 7.193 | 7.194 | |||
Chuiyao Village in Zhaoxiang | 0.629 | 0.698 | 0.703 | |||
Zhongxin Village in Chonggu | 1.000 | 0.924 | 0.648 | |||
Tangyu Village in Xiayang | 0.554 | 0.655 | 0.592 | |||
Heqiao Village in Yingpu | 0.665 | 0.777 | 0.706 | |||
Jinxing Village in Xianghuaqiao | 1.000 | 0.964 | 0.614 | |||
Wangjing Village in Baihe | 1.000 | 0.972 | 0.729 | |||
Wanlong Village in Zhujiajiao | 0.551 | 0.632 | 0.580 | |||
Beidai Village in Liantang | 1.000 | 0.985 | 0.732 | |||
Tianshanzhuag Village in Jinze | 0.582 | 0.676 | 0.626 |
Table 3 The comparison of vulnerability assessment for administrative villages in Qingpu District of 2018
Region | CCR-DEA model | ACE-DEA model | EW-DEA model | |||
---|---|---|---|---|---|---|
Average of RVS-E | RVS-E | Average of RVS-E | RVS-E | Average of RVS-E | RVS-E | |
Qingpu District | 0.623 | 0.699 | 0.583 | |||
Eastern part | 0.689 | 0.756 | 0.625 | |||
Central part | 0.542 | 0.643 | 0.554 | |||
Western part | 0.638 | 0.699 | 0.609 | |||
Guanglian Village in Xujing | 0.598 | 0.665 | 0.662 | |||
Songshan Village in Huaxin | 7.125 | 7.193 | 7.194 | |||
Chuiyao Village in Zhaoxiang | 0.629 | 0.698 | 0.703 | |||
Zhongxin Village in Chonggu | 1.000 | 0.924 | 0.648 | |||
Tangyu Village in Xiayang | 0.554 | 0.655 | 0.592 | |||
Heqiao Village in Yingpu | 0.665 | 0.777 | 0.706 | |||
Jinxing Village in Xianghuaqiao | 1.000 | 0.964 | 0.614 | |||
Wangjing Village in Baihe | 1.000 | 0.972 | 0.729 | |||
Wanlong Village in Zhujiajiao | 0.551 | 0.632 | 0.580 | |||
Beidai Village in Liantang | 1.000 | 0.985 | 0.732 | |||
Tianshanzhuag Village in Jinze | 0.582 | 0.676 | 0.626 |
Matching type | Y9 | Y6 | Y10 | Y4 | Y11 | Y7 | Y3 |
---|---|---|---|---|---|---|---|
-4 | 6.63 | 10.25 | 8.68 | 4.55 | 0.85 | 5.66 | 1.67 |
-3 | 15.57 | 19.54 | 16.52 | 16.68 | 9.66 | 12.68 | 10.57 |
-2 | 29.68 | 28.54 | 25.35 | 22.58 | 18.57 | 19.54 | 16.68 |
-1 | 37.51 | 35.44 | 29.87 | 26.64 | 26.64 | 24.65 | 19.54 |
0 | 43.56 | 41.25 | 34.89 | 33.45 | 32.24 | 31.55 | 25.67 |
1 | 8.51 | 9.56 | 5.65 | 5.65 | 3.55 | 2.51 | 3.24 |
2 | 19.65 | 18.64 | 16.66 | 14.68 | 16.54 | 14.66 | 15.63 |
3 | 34.57 | 25.62 | 26.58 | 26.44 | 27.66 | 22.28 | 21.58 |
4 | 49.68 | 34.58 | 40.11 | 39.54 | 41.42 | 37.65 | 32.54 |
Table 4 The coupling results of vulnerability grades and influencing factors
Matching type | Y9 | Y6 | Y10 | Y4 | Y11 | Y7 | Y3 |
---|---|---|---|---|---|---|---|
-4 | 6.63 | 10.25 | 8.68 | 4.55 | 0.85 | 5.66 | 1.67 |
-3 | 15.57 | 19.54 | 16.52 | 16.68 | 9.66 | 12.68 | 10.57 |
-2 | 29.68 | 28.54 | 25.35 | 22.58 | 18.57 | 19.54 | 16.68 |
-1 | 37.51 | 35.44 | 29.87 | 26.64 | 26.64 | 24.65 | 19.54 |
0 | 43.56 | 41.25 | 34.89 | 33.45 | 32.24 | 31.55 | 25.67 |
1 | 8.51 | 9.56 | 5.65 | 5.65 | 3.55 | 2.51 | 3.24 |
2 | 19.65 | 18.64 | 16.66 | 14.68 | 16.54 | 14.66 | 15.63 |
3 | 34.57 | 25.62 | 26.58 | 26.44 | 27.66 | 22.28 | 21.58 |
4 | 49.68 | 34.58 | 40.11 | 39.54 | 41.42 | 37.65 | 32.54 |
Region | Township | Y6 | Y7 | Y3 | Y11 | Y9 | Y4 | Y10 |
---|---|---|---|---|---|---|---|---|
Eastern part | Xujing | 0.446 | 0.269 | 0.115 | 0.329 | 0.584 | 0.326 | 0.358 |
Zhaoxiang | 0.459 | 0.258 | 0.126 | 0.308 | 0.572 | 0.338 | 0.369 | |
Huaxin | 0.449 | 0.246 | 0.175 | 0.215 | 0.569 | 0.345 | 0.372 | |
Chonggu | 0.455 | 0.281 | 0.205 | 0.229 | 0.557 | 0.366 | 0.395 | |
Baihe | 0.385 | 0.314 | 0.459 | 0.217 | 0.507 | 0.397 | 0.337 | |
Central part | Xiayang | 0.315 | 0.210 | 0.511 | 0.262 | 0.451 | 0.401 | 0.468 |
Xianghuaqiao | 0.324 | 0.235 | 0.469 | 0.259 | 0.449 | 0.386 | 0.459 | |
Yingpu | 0.339 | 0.219 | 0.485 | 0.253 | 0.432 | 0.412 | 0.447 | |
Western part | Zhujiajiao | 0.512 | 0.345 | 0.411 | 0.401 | 0.495 | 0.450 | 0.455 |
Liantang | 0.499 | 0.348 | 0.109 | 0.354 | 0.502 | 0.259 | 0.451 | |
Jinze | 0.642 | 0.195 | 0.516 | 0.358 | 0.265 | 0.491 | 0.311 | |
Qingpu District | 0.475 | 0.304 | 0.284 | 0.331 | 0.532 | 0.394 | 0.428 |
Table 5 Coupling analysis of vulnerability grades and influencing factors
Region | Township | Y6 | Y7 | Y3 | Y11 | Y9 | Y4 | Y10 |
---|---|---|---|---|---|---|---|---|
Eastern part | Xujing | 0.446 | 0.269 | 0.115 | 0.329 | 0.584 | 0.326 | 0.358 |
Zhaoxiang | 0.459 | 0.258 | 0.126 | 0.308 | 0.572 | 0.338 | 0.369 | |
Huaxin | 0.449 | 0.246 | 0.175 | 0.215 | 0.569 | 0.345 | 0.372 | |
Chonggu | 0.455 | 0.281 | 0.205 | 0.229 | 0.557 | 0.366 | 0.395 | |
Baihe | 0.385 | 0.314 | 0.459 | 0.217 | 0.507 | 0.397 | 0.337 | |
Central part | Xiayang | 0.315 | 0.210 | 0.511 | 0.262 | 0.451 | 0.401 | 0.468 |
Xianghuaqiao | 0.324 | 0.235 | 0.469 | 0.259 | 0.449 | 0.386 | 0.459 | |
Yingpu | 0.339 | 0.219 | 0.485 | 0.253 | 0.432 | 0.412 | 0.447 | |
Western part | Zhujiajiao | 0.512 | 0.345 | 0.411 | 0.401 | 0.495 | 0.450 | 0.455 |
Liantang | 0.499 | 0.348 | 0.109 | 0.354 | 0.502 | 0.259 | 0.451 | |
Jinze | 0.642 | 0.195 | 0.516 | 0.358 | 0.265 | 0.491 | 0.311 | |
Qingpu District | 0.475 | 0.304 | 0.284 | 0.331 | 0.532 | 0.394 | 0.428 |
Region | Subsystem | Y6 | Y7 | Y3 | Y11 | Y9 | Y4 | Y10 |
---|---|---|---|---|---|---|---|---|
Eastern part | Social system | 0.387 | 0.259 | 0.188 | 0.169 | 0.511 | 0.115 | 0.438 |
Economic system | 0.212 | 0.208 | 0.109 | 0.161 | 0.586 | 0.074 | 0.354 | |
Ecological system | 0.145 | 0.186 | 0.125 | 0.175 | 0.689 | 0.106 | 0.341 | |
Central part | Social system | 0.336 | 0.393 | 0.274 | 0.311 | 0.497 | 0.325 | 0.498 |
Economic system | 0.301 | 0.328 | 0.227 | 0.264 | 0.457 | 0.247 | 0.468 | |
Ecological system | 0.299 | 0.337 | 0.241 | 0.301 | 0.448 | 0.295 | 0.457 | |
Western part | Social system | 0.454 | 0.232 | 0.438 | 0.194 | 0.396 | 0.428 | 0.404 |
Economic system | 0.421 | 0.228 | 0.425 | 0.148 | 0.336 | 0.417 | 0.333 | |
Ecological system | 0.409 | 0.214 | 0.412 | 0.186 | 0.298 | 0.398 | 0.347 | |
Qingpu District | Social system | 0.396 | 0.279 | 0.267 | 0.235 | 0.435 | 0.279 | 0.413 |
Economic system | 0.365 | 0.255 | 0.254 | 0.191 | 0.460 | 0.246 | 0.385 | |
Ecological system | 0.381 | 0.246 | 0.259 | 0.221 | 0.478 | 0.266 | 0.382 |
Table 6 The coupling analysis of vulnerability grades and influencing factors
Region | Subsystem | Y6 | Y7 | Y3 | Y11 | Y9 | Y4 | Y10 |
---|---|---|---|---|---|---|---|---|
Eastern part | Social system | 0.387 | 0.259 | 0.188 | 0.169 | 0.511 | 0.115 | 0.438 |
Economic system | 0.212 | 0.208 | 0.109 | 0.161 | 0.586 | 0.074 | 0.354 | |
Ecological system | 0.145 | 0.186 | 0.125 | 0.175 | 0.689 | 0.106 | 0.341 | |
Central part | Social system | 0.336 | 0.393 | 0.274 | 0.311 | 0.497 | 0.325 | 0.498 |
Economic system | 0.301 | 0.328 | 0.227 | 0.264 | 0.457 | 0.247 | 0.468 | |
Ecological system | 0.299 | 0.337 | 0.241 | 0.301 | 0.448 | 0.295 | 0.457 | |
Western part | Social system | 0.454 | 0.232 | 0.438 | 0.194 | 0.396 | 0.428 | 0.404 |
Economic system | 0.421 | 0.228 | 0.425 | 0.148 | 0.336 | 0.417 | 0.333 | |
Ecological system | 0.409 | 0.214 | 0.412 | 0.186 | 0.298 | 0.398 | 0.347 | |
Qingpu District | Social system | 0.396 | 0.279 | 0.267 | 0.235 | 0.435 | 0.279 | 0.413 |
Economic system | 0.365 | 0.255 | 0.254 | 0.191 | 0.460 | 0.246 | 0.385 | |
Ecological system | 0.381 | 0.246 | 0.259 | 0.221 | 0.478 | 0.266 | 0.382 |
Geographical impact types | Vulnerability characteristics | Governance emphases | Governance measures | Governance objects | Restoration modes |
---|---|---|---|---|---|
Economic environment constraint type | High grade of vulnerability High vulnerability of social and ecological systems Rapid loss of the rural population and rural culture dies out | Rural organization and culture | Comprehensive rural social improvement | √ Rural subject raise √ Rural order reconstruction | Embedded mode |
Natural environment constraint type | Low grade of vulnerability High vulnerability of economic system Agricultural productivity and environmental pollution | Land resources | Agricultural land renovation | √ Cultivated land protection, capacity upgrade √ Leisure industry exploited | Consolidation mode |
Public services constraint type | High grade of vulnerability High vulnerability of social and ecological systems Unequal coverage of public services | Social public spaces | Removing and reforming government | √ Multi-subject coordination √ Step-by-step promotion | Renew mode |
Resource abundance constraint type | Medium grade of vulnerability High vulnerability of economic and ecological systems Water resources development and water pollution | Water pollution | Water Environment treatment | √ Node protection √ Sewage treatment | Oriented mode |
Economic and natural environment coupling impact type | High grade of vulnerability High vulnerability of social and ecological systems Dilemma of industrial choice | Industrial types undertaken | Industrial renovation | √ Industrial upgrading √ Industrial chain extension | Cascaded mode |
Natural environment and public services coupling impact type | High grade of vulnerability High vulnerability of social and economic system Public services extension | Public services integration | Infrastructure government | √ Convenient services √ Industrial and city emergent | Equivalent mode |
Public services and resource abundance coupling impact type | High grade of vulnerability Medium vulnerability of all subsystems Public space accessibility and connectivity | Space fragmentation | Ecological remediation | √ Hydrophilic green bank √ Water-green blend | Dredging mode |
Economic-natural environment and public services multiple impact type | High-low grade of vulnerability High social and ecological systems Vacant and wasteful resources | Resources use efficiency | Reduction government | √ Increase & decrease contact √ Intensive use | Displacement mode |
Economic-natural environment and resource abundance multiple impact type | High-low grade of vulnerability High-low economic system Industrial structure rigid | Space quality | Environmental Improvement | √ Outskirt planning √ Ecotourism | Outskirt mode |
Integrated impact type | Low grade of vulnerability Low vulnerability of all subsystems Coordinated development | Resources overall planning | Integrated territory consolidation | √ Urban and rural city emergent √ Integration | Aggregative model |
Table 7 The geographical impact types and ecological restoration modes of the social-ecosystem vulnerability in Qingpu District
Geographical impact types | Vulnerability characteristics | Governance emphases | Governance measures | Governance objects | Restoration modes |
---|---|---|---|---|---|
Economic environment constraint type | High grade of vulnerability High vulnerability of social and ecological systems Rapid loss of the rural population and rural culture dies out | Rural organization and culture | Comprehensive rural social improvement | √ Rural subject raise √ Rural order reconstruction | Embedded mode |
Natural environment constraint type | Low grade of vulnerability High vulnerability of economic system Agricultural productivity and environmental pollution | Land resources | Agricultural land renovation | √ Cultivated land protection, capacity upgrade √ Leisure industry exploited | Consolidation mode |
Public services constraint type | High grade of vulnerability High vulnerability of social and ecological systems Unequal coverage of public services | Social public spaces | Removing and reforming government | √ Multi-subject coordination √ Step-by-step promotion | Renew mode |
Resource abundance constraint type | Medium grade of vulnerability High vulnerability of economic and ecological systems Water resources development and water pollution | Water pollution | Water Environment treatment | √ Node protection √ Sewage treatment | Oriented mode |
Economic and natural environment coupling impact type | High grade of vulnerability High vulnerability of social and ecological systems Dilemma of industrial choice | Industrial types undertaken | Industrial renovation | √ Industrial upgrading √ Industrial chain extension | Cascaded mode |
Natural environment and public services coupling impact type | High grade of vulnerability High vulnerability of social and economic system Public services extension | Public services integration | Infrastructure government | √ Convenient services √ Industrial and city emergent | Equivalent mode |
Public services and resource abundance coupling impact type | High grade of vulnerability Medium vulnerability of all subsystems Public space accessibility and connectivity | Space fragmentation | Ecological remediation | √ Hydrophilic green bank √ Water-green blend | Dredging mode |
Economic-natural environment and public services multiple impact type | High-low grade of vulnerability High social and ecological systems Vacant and wasteful resources | Resources use efficiency | Reduction government | √ Increase & decrease contact √ Intensive use | Displacement mode |
Economic-natural environment and resource abundance multiple impact type | High-low grade of vulnerability High-low economic system Industrial structure rigid | Space quality | Environmental Improvement | √ Outskirt planning √ Ecotourism | Outskirt mode |
Integrated impact type | Low grade of vulnerability Low vulnerability of all subsystems Coordinated development | Resources overall planning | Integrated territory consolidation | √ Urban and rural city emergent √ Integration | Aggregative model |
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