Planning Resilient and Sustainable Cities: Identifying and Targeting Social Vulnerability to Climate Change
Abstract
:1. Introduction
1.1. Vulnerability Concepts
1.2. Conceptual Frameworks and Methods
1.3. Research in China
2. Materials and Methods
2.1. Selection of Indicators
2.2. Case Study: The Coastal Region in China
2.3. Research Method: the Projetion Pursuit Cluster (PPC) Model
3. Results
3.1. Weighting Values of Indicators
3.2. Spatial Pattern of Social Vulnerability
3.3. Urban Development and Social Vulnerability
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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No. | Indicator | Factor | Dimension of SVI | Impact to SVI | Included |
---|---|---|---|---|---|
1 | Population density | People exposure | Exposure | + | ✓ |
2 | Rate of natural increase (RNI) | + | ✓ | ||
3 | Employees in primary industry | + | ✓ | ||
4 | GDP in primary sector | Economic exposure | + | ✓ | |
5 | GDP density | + | ✓ | ||
6 | Houses with no bath facilities | House exposure | + | ✓ | |
7 | Houses with no lavatory | + | ✓ | ||
8 | Houses with no tap water | + | ✓ | ||
9 | Houses with no kitchen | + | ✓ | ||
10 | Children | People sensitivity | Sensitivity | + | ✓ |
11 | Elderly | + | ✓ | ||
12 | Household size | Family sensitivity | + | × | |
13 | Single parent households | + | ✓ | ||
14 | Female | Vulnerable group | + | ✓ | |
15 | Illiterate | + | ✓ | ||
16 | Unemployed | + | ✓ | ||
17 | Renter | + | ✓ | ||
18 | Ethnic minorities | + | ✓ | ||
19 | Immigration from other counties | Immigration | + | ✓ | |
20 | Immigration from other cities | + | ✓ | ||
21 | Immigration from other provinces | + | × | ||
22 | GDP per capita | Economic adaptability | Adaptability | − | ✓ |
23 | Higher education graduate | Individual adaptability | − | ✓ | |
24 | Educated year | − | × | ||
25 | Urban residents | − | × | ||
26 | Beds in hospital per 1000 people | Health care infrastructures | − | ✓ | |
27 | Physicians in hospital per 1000 people | − | ✓ | ||
28 | Employees in management sector | Management services | − | ✓ |
No. | Indicator | Weighting Values | Dimension | No. | Indicator | Weighting Values | Dimension |
---|---|---|---|---|---|---|---|
1 | House with no lavatory | 0.37528 | E | 13 | Beds in hospital | 0.12294 | A |
2 | House with no bath facilities | 0.36641 | E | 14 | Ethnic minorities | 0.12271 | S |
3 | Employees in primary industry | 0.33666 | E | 15 | GDP per capita | 0.12145 | A |
4 | Houses with no tap water | 0.32098 | E | 16 | Elderly | 0.11635 | S |
5 | GDP in primary sector | 0.29614 | E | 17 | Single parent households | 0.10807 | S |
6 | Young | 0.29186 | S | 18 | Unemployed | 0.10239 | S |
7 | House with no kitchen | 0.23247 | E | 19 | Female | 0.09319 | S |
8 | Rate of natural increase (RNI) | 0.21444 | E | 20 | Immigration from other counties | 0.07198 | S |
9 | Employees in management sector | 0.20641 | A | 21 | Illiterate | 0.06463 | S |
10 | Highly educated | 0.19688 | A | 22 | GDP density | 0.05134 | E |
11 | Physicians in hospital | 0.16131 | A | 23 | Renter | 0.03627 | S |
12 | Population density | 0.15618 | E | 24 | Immigration from other cities | 0.03507 | S |
Level | SVI | Dimension 1 | Dimension 2 | Dimension 3 |
---|---|---|---|---|
Exposure | Sensitivity | Adaptability | ||
High | 7.53% | 4.79% | 7.88% | 4.11% |
High-medium | 15.75% | 25.34% | 16.44% | 10.62% |
Medium | 23.38% | 38.01% | 21.92% | 17.12% |
Medium-low | 39.4% | 23.97% | 32.53% | 38.01% |
Low | 14.04% | 7.88% | 21.23% | 30.14% |
Urbanization (%) | District | County | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Count | EI | SI | AI | SVI | Count | EI | SI | AI | SVI | |
0~10 | 8 | 0.37 | 0.69 | 0.06 | 1.80 | 21 | 0.36 | 1.04 | 0.04 | 2.17 |
10~20 | 21 | 0.31 | 0.59 | 0.08 | 1.62 | 111 | 0.38 | 0.78 | 0.07 | 1.90 |
20~30 | 27 | 0.31 | 0.50 | 0.11 | 1.51 | 58 | 0.36 | 0.67 | 0.08 | 1.76 |
30~40 | 24 | 0.28 | 0.42 | 0.17 | 1.35 | 15 | 0.33 | 0.56 | 0.12 | 1.58 |
40~50 | 22 | 0.31 | 0.53 | 0.15 | 1.49 | 1 | 0.42 | 0.73 | 0.05 | 1.91 |
50~60 | 25 | 0.29 | 0.36 | 0.18 | 1.28 | 1 | 0.31 | 0.87 | 0.05 | 1.94 |
60~70 | 21 | 0.30 | 0.33 | 0.23 | 1.20 | 0 | 0 | 0 | 0 | 0 |
70~80 | 16 | 0.32 | 0.31 | 0.23 | 1.22 | 1 | 0.35 | 1.08 | 0.13 | 2.11 |
80~90 | 26 | 0.34 | 0.26 | 0.30 | 1.12 | 0 | 0 | 0 | 0 | 0 |
90~100 | 8 | 0.37 | 0.21 | 0.27 | 1.11 | 1 | 0.27 | 1.10 | 0.11 | 2.08 |
Types | EI | SI | AI | SVI | Difference between Urban and County | City Name | |
---|---|---|---|---|---|---|---|
District | |||||||
0 | − | − | + | − | optimal | / | |
1 | County | + | + | − | + | Large | Tianjin, Qinhuangdao, Cangzhou, Dandong, Jinzhou, Huludao, Shanghai, Nantong, Lianyungang, Yancheng, Ningbo, Wenzhou, Shaoxing, Taizhou, Fuzhou, Putian, Quanzhou, Zhangzhou, Ningde, Qingdao, Dongying, Yantai, Weifang, Weihai, Rizhao, Binzhou, Guangzhou, Shantou, Jiangmen, Zhenjiang, Maoming, Huizhou, Shanwei, Yangjiang, Chaozhou, Jieyang, Beihai, Fangchenggang, and Qinzhou |
2 | − | + | − | + | Large-medium | Tanshan, Dalian, Yingkou, Panjin, and Zhoushan | |
3 | + | + | + | + | Hangzhou | ||
4 | + | − | − | − | Medium | Jiaxing | |
5 | − | − | − | − | Small | Suzhou |
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Ge, Y.; Dou, W.; Liu, N. Planning Resilient and Sustainable Cities: Identifying and Targeting Social Vulnerability to Climate Change. Sustainability 2017, 9, 1394. https://doi.org/10.3390/su9081394
Ge Y, Dou W, Liu N. Planning Resilient and Sustainable Cities: Identifying and Targeting Social Vulnerability to Climate Change. Sustainability. 2017; 9(8):1394. https://doi.org/10.3390/su9081394
Chicago/Turabian StyleGe, Yi, Wen Dou, and Ning Liu. 2017. "Planning Resilient and Sustainable Cities: Identifying and Targeting Social Vulnerability to Climate Change" Sustainability 9, no. 8: 1394. https://doi.org/10.3390/su9081394