Research on Evaluation of Meteorological Disaster Governance Capabilities in Mainland China Based on Generalized λ-Shapley Choquet Integral
Abstract
:1. Introduction
2. Meteorological Disaster Governance Capacity and Index System Construction
2.1. Analysis of Meteorological Disaster Governance Capacity
2.2. Rating Index System Construction
3. Models and Computational Methods
3.1. Fuzzy Measure and Choquet Integral
- (1)
- ;
- (2)
- If and .
3.1.1. Determination of Attribute Weights
3.1.2. Generalized λ-Shapley Choquet Integral
3.2. Meteorological Disaster Governance Capability Evaluation Method Based on λ-Shapley Choquet Integral
4. Evaluation Value Calculation and Method Comparison
4.1. Evaluation of Provincial Meteorological Disaster Governance Capacity in Mainland China
4.2. Comparison of Methods
4.2.1. Comparison with Entropy Method TOPSIS Model
4.2.2. Comparison of Maximum Sequence Differences
4.2.3. Comparison of Weights
5. Conclusion and Policy Recommendations
5.1. Conclusions
5.2. Policy Recommendations
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Primary Indicator | Secondary Indicator | Tertiary Indicator |
---|---|---|
Law and building defense capacity c1 | Legal system building capacity c11 | Legal local meteorological disaster standard cumulative count c111 |
Meteorological disaster defense planning provincial, municipal, and county coverage rate—city c112 | ||
Completion rate of meteorological disaster emergency plans c113 | ||
Defense mechanism construction capacity c12 | Number of comprehensive disaster reduction demonstration communities c121 | |
Disaster defense engineering construction capacity c13 | Total storage capacity c131 | |
Information-processing capacity c2 | meteorological disaster monitoring capacity c21 | Number of operational weather radar observation stations (pcs) c211 |
Number of automatic weather stations (one) c212 | ||
Number of operational stations for lightning-positioning monitoring (pcs) c213 | ||
Satellite data receiving stations (pcs) c214 | ||
Meteorological disaster prediction capacity c22 | Storm warning accuracy rate c221 | |
Early warning of strong convective weather c222 | ||
The accuracy rate of 24-h clear rain forecast c223 | ||
Early warning information transmission capacity c23 | The coverage rate of social units of meteorological warning information c231 | |
The coverage rate of meteorological early warning information broadcast media c232 | ||
The coverage rate of meteorological early warning information social institutions c233 | ||
Disaster-related information acquisition capacity c24 | Two-way sharing rate of meteorological disaster information departments c241 | |
The rate of meteorological information personnel in place in villages (communities) c242 | ||
Township (street) meteorological coordinators in place c243 | ||
Disaster relief capability c3 | Social resource mobilization capacity c31 | Insurance premium income c311 |
Welfare Lottery Fund Expenditure c312 | ||
Proactive disaster risk reduction capacity c32 | Available rockets c321 | |
Available anti-aircraft guns c322 | ||
Disaster relief security capacity c33 | Number of beds in medical institutions per 10,000 people c331 | |
Local financial expenditure on medical and health care c332 | ||
Local general public service expenditures per capita (10,000 yuan) c333 |
Primary Indicator | Weight Range | Secondary Indicator | Weight Range | Tertiary Indicator | Weight Range |
---|---|---|---|---|---|
c1 | [0.3, 0.5] | c11 | [0.3, 0.6] | c111 | [0.5, 0.7] |
c112 | [0.25, 0.4] | ||||
c113 | [0.25, 0.4] | ||||
c12 | [0.5, 0.7] | c121 | [0.5, 1] | ||
c13 | [0.2, 0.3] | c131 | [0.5, 1] | ||
c2 | [0.4, 0.6] | c21 | [0.3, 0.5] | c211 | [0.25, 0.4] |
c212 | [0.25, 0.4] | ||||
c213 | [0.25, 0.4] | ||||
c214 | [0.25, 0.4] | ||||
c22 | [0.4, 0.6] | c221 | [0.4, 0.6] | ||
c222 | [0.4, 0.6] | ||||
c223 | [0.2, 0.4] | ||||
c23 | [0.1, 0.2] | c231 | [0.3, 0.5] | ||
c232 | [0.4, 0.5] | ||||
c233 | [0.3, 0.5] | ||||
c24 | [0.2, 0.3] | c241 | [0.25, 0.4] | ||
c242 | [0.5, 0.7] | ||||
c243 | [0.25, 0.4] | ||||
c3 | [0.3, 0.5] | c31 | [0.5, 0.7] | c311 | [0.5, 0.7] |
c312 | [0.5, 0.7] | ||||
c32 | [0.3, 0.6] | c321 | [0.5, 0.7] | ||
c322 | [0.5, 0.7] | ||||
c33 | [0.2, 0.5] | c331 | [0.25, 0.4] | ||
c332 | [0.5, 0.7] | ||||
c333 | [0.25, 0.4] |
0.4 | 0.25 | 0.25 | 0.25 | 0.6 | 0.4 | 0.2 | |
0.354 | 0.215 | 0.282 | 0.215 | 0.517 | 0.327 | 0.156 | |
0.5 | 0.4 | 0.3 | 0.25 | 0.5 | 0.4 | 0.5 | |
0.425 | 0.332 | 0.243 | 0.211 | 0.442 | 0.347 | 0.4 | |
0.7 | 0.7 | 0.5 | 0.4 | 0.5 | 0.25 | ||
0.6 | 0.6 | 0.4 | 0.347 | 0.442 | 0.211 |
Province | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Beijing | 0.030 | 0.038 | 0.006 | 0.006 | 0.036 | 0.040 | 0.035 | 0.055 | 0.010 | 0.031 |
Tianjin | 0.023 | 0.014 | 0.003 | 0.004 | 0.030 | 0.033 | 0.033 | 0.011 | 0.010 | 0.019 |
Hebei | 0.031 | 0.042 | 0.025 | 0.033 | 0.102 | 0.030 | 0.030 | 0.040 | 0.033 | 0.039 |
Shanxi | 0.040 | 0.015 | 0.008 | 0.031 | 0.029 | 0.023 | 0.030 | 0.018 | 0.022 | 0.026 |
Inner Mongolia | 0.023 | 0.023 | 0.012 | 0.038 | 0.030 | 0.040 | 0.034 | 0.020 | 0.055 | 0.027 |
Liaoning | 0.079 | 0.043 | 0.044 | 0.034 | 0.075 | 0.037 | 0.035 | 0.031 | 0.031 | 0.035 |
Jilin | 0.026 | 0.027 | 0.040 | 0.022 | 0.032 | 0.029 | 0.026 | 0.010 | 0.034 | 0.025 |
Heilongjiang | 0.032 | 0.030 | 0.032 | 0.037 | 0.028 | 0.034 | 0.030 | 0.019 | 0.096 | 0.027 |
Shanghai | 0.026 | 0.021 | 0.001 | 0.011 | 0.032 | 0.040 | 0.035 | 0.037 | 0.005 | 0.027 |
Jiangsu | 0.029 | 0.061 | 0.004 | 0.029 | 0.030 | 0.035 | 0.036 | 0.064 | 0.008 | 0.056 |
Zhejiang | 0.029 | 0.084 | 0.053 | 0.052 | 0.031 | 0.034 | 0.027 | 0.061 | 0.000 | 0.040 |
Anhui | 0.045 | 0.031 | 0.039 | 0.031 | 0.032 | 0.035 | 0.033 | 0.035 | 0.012 | 0.035 |
Fujian | 0.044 | 0.032 | 0.024 | 0.036 | 0.030 | 0.032 | 0.035 | 0.027 | 0.000 | 0.028 |
Jiangxi | 0.027 | 0.040 | 0.036 | 0.033 | 0.029 | 0.026 | 0.028 | 0.029 | 0.068 | 0.031 |
Shandong | 0.032 | 0.061 | 0.026 | 0.040 | 0.031 | 0.035 | 0.031 | 0.066 | 0.004 | 0.054 |
Henan | 0.030 | 0.037 | 0.050 | 0.057 | 0.056 | 0.030 | 0.034 | 0.047 | 0.052 | 0.053 |
Hubei | 0.052 | 0.051 | 0.149 | 0.057 | 0.031 | 0.035 | 0.031 | 0.039 | 0.030 | 0.042 |
Hunan | 0.035 | 0.038 | 0.059 | 0.042 | 0.033 | 0.035 | 0.033 | 0.036 | 0.029 | 0.041 |
Guangdong | 0.023 | 0.084 | 0.053 | 0.034 | 0.035 | 0.036 | 0.035 | 0.083 | 0.006 | 0.067 |
Guangxi | 0.034 | 0.019 | 0.080 | 0.038 | 0.028 | 0.033 | 0.035 | 0.021 | 0.018 | 0.033 |
Hainan | 0.029 | 0.009 | 0.013 | 0.013 | 0.039 | 0.036 | 0.029 | 0.005 | 0.002 | 0.015 |
Chongqing | 0.046 | 0.019 | 0.014 | 0.021 | 0.028 | 0.032 | 0.031 | 0.027 | 0.019 | 0.028 |
Sichuan | 0.069 | 0.050 | 0.043 | 0.062 | 0.028 | 0.031 | 0.032 | 0.048 | 0.062 | 0.049 |
Guizhou | 0.040 | 0.023 | 0.052 | 0.042 | 0.029 | 0.026 | 0.033 | 0.016 | 0.056 | 0.034 |
Yunnan | 0.024 | 0.015 | 0.045 | 0.058 | 0.029 | 0.040 | 0.025 | 0.024 | 0.107 | 0.033 |
Tibet | 0.019 | 0.000 | 0.004 | 0.028 | 0.028 | 0.027 | 0.035 | 0.013 | 0.029 | 0.016 |
Shaanxi | 0.055 | 0.023 | 0.010 | 0.040 | 0.052 | 0.039 | 0.032 | 0.025 | 0.047 | 0.032 |
Gansu | 0.045 | 0.023 | 0.013 | 0.035 | 0.031 | 0.027 | 0.034 | 0.016 | 0.037 | 0.026 |
Qinghai | 0.027 | 0.005 | 0.038 | 0.025 | 0.028 | 0.043 | 0.027 | 0.006 | 0.025 | 0.018 |
Ningxia | 0.030 | 0.008 | 0.003 | 0.014 | 0.031 | 0.031 | 0.034 | 0.004 | 0.014 | 0.015 |
Xinjiang | 0.033 | 0.034 | 0.022 | 0.068 | 0.052 | 0.032 | 0.025 | 0.028 | 0.142 | 0.030 |
0.3 | 0.7 | 0.2 | 0.5 | 0.4 | 0.2 | 0.3 | 0.5 | 0.3 | 0.5 | |
0.234 | 0.614 | 0.152 | 0.371 | 0.287 | 0.218 | 0.208 | 0.390 | 0.210 | 0.390 |
Province | CEV | |||
---|---|---|---|---|
Beijing | 0.027 | 0.030 | 0.033 | 0.029 |
Tianjin | 0.014 | 0.026 | 0.013 | 0.017 |
Hebei | 0.031 | 0.053 | 0.037 | 0.038 |
Shanxi | 0.019 | 0.028 | 0.022 | 0.022 |
Inner Mongolia | 0.020 | 0.035 | 0.034 | 0.028 |
Liaoning | 0.050 | 0.046 | 0.032 | 0.041 |
Jilin | 0.029 | 0.026 | 0.022 | 0.025 |
Heilongjiang | 0.031 | 0.032 | 0.049 | 0.036 |
Shanghai | 0.018 | 0.030 | 0.022 | 0.023 |
Jiangsu | 0.031 | 0.032 | 0.041 | 0.034 |
Zhejiang | 0.056 | 0.037 | 0.034 | 0.041 |
Anhui | 0.038 | 0.032 | 0.026 | 0.031 |
Fujian | 0.033 | 0.032 | 0.017 | 0.026 |
Jiangxi | 0.035 | 0.028 | 0.043 | 0.034 |
Shandong | 0.036 | 0.034 | 0.040 | 0.035 |
Henan | 0.039 | 0.045 | 0.050 | 0.043 |
Hubei | 0.073 | 0.040 | 0.036 | 0.048 |
Hunan | 0.042 | 0.035 | 0.034 | 0.036 |
Guangdong | 0.056 | 0.034 | 0.050 | 0.045 |
Guangxi | 0.041 | 0.033 | 0.024 | 0.031 |
Hainan | 0.015 | 0.029 | 0.008 | 0.016 |
Chongqing | 0.023 | 0.028 | 0.024 | 0.024 |
Sichuan | 0.052 | 0.040 | 0.052 | 0.046 |
Guizhou | 0.038 | 0.033 | 0.034 | 0.034 |
Yunnan | 0.026 | 0.040 | 0.057 | 0.039 |
Tibet | 0.006 | 0.029 | 0.019 | 0.017 |
Shaanxi | 0.027 | 0.040 | 0.034 | 0.032 |
Gansu | 0.025 | 0.031 | 0.026 | 0.027 |
Qinghai | 0.025 | 0.032 | 0.015 | 0.023 |
Ningxia | 0.012 | 0.028 | 0.010 | 0.016 |
Xinjiang | 0.029 | 0.046 | 0.070 | 0.046 |
Province | 2014 | 2015 | 2016 | 2017 | 2018 |
---|---|---|---|---|---|
Beijing | 0.029 | 0.027 | 0.028 | 0.027 | 0.027 |
Tianjin | 0.017 | 0.018 | 0.017 | 0.016 | 0.017 |
Hebei | 0.038 | 0.033 | 0.032 | 0.031 | 0.031 |
Shanxi | 0.022 | 0.023 | 0.022 | 0.022 | 0.026 |
Inner Mongolia | 0.028 | 0.030 | 0.033 | 0.030 | 0.026 |
Liaoning | 0.041 | 0.040 | 0.034 | 0.033 | 0.031 |
Jilin | 0.025 | 0.027 | 0.031 | 0.031 | 0.030 |
Heilongjiang | 0.036 | 0.035 | 0.035 | 0.036 | 0.034 |
Shanghai | 0.023 | 0.024 | 0.028 | 0.025 | 0.022 |
Jiangsu | 0.034 | 0.034 | 0.034 | 0.036 | 0.039 |
Zhejiang | 0.041 | 0.042 | 0.041 | 0.042 | 0.043 |
Anhui | 0.031 | 0.031 | 0.031 | 0.031 | 0.029 |
Fujian | 0.026 | 0.027 | 0.029 | 0.029 | 0.027 |
Jiangxi | 0.034 | 0.029 | 0.029 | 0.030 | 0.029 |
Shandong | 0.035 | 0.042 | 0.040 | 0.041 | 0.041 |
Henan | 0.043 | 0.041 | 0.037 | 0.040 | 0.040 |
Hubei | 0.048 | 0.046 | 0.044 | 0.043 | 0.047 |
Hunan | 0.036 | 0.039 | 0.038 | 0.039 | 0.036 |
Guangdong | 0.045 | 0.045 | 0.051 | 0.049 | 0.052 |
Guangxi | 0.031 | 0.031 | 0.031 | 0.032 | 0.035 |
Hainan | 0.016 | 0.015 | 0.016 | 0.016 | 0.017 |
Chongqing | 0.024 | 0.027 | 0.024 | 0.024 | 0.024 |
Sichuan | 0.046 | 0.046 | 0.045 | 0.043 | 0.041 |
Guizhou | 0.034 | 0.033 | 0.035 | 0.036 | 0.036 |
Yunnan | 0.039 | 0.041 | 0.043 | 0.044 | 0.047 |
Tibet | 0.017 | 0.016 | 0.016 | 0.017 | 0.017 |
Shaanxi | 0.032 | 0.031 | 0.032 | 0.033 | 0.033 |
Gansu | 0.027 | 0.027 | 0.029 | 0.029 | 0.030 |
Qinghai | 0.023 | 0.023 | 0.025 | 0.028 | 0.026 |
Ningxia | 0.016 | 0.017 | 0.016 | 0.016 | 0.017 |
Xinjiang | 0.046 | 0.045 | 0.044 | 0.042 | 0.040 |
Tertiary Indicators | 2014 | 2015 | 2016 | 2017 | 2018 |
---|---|---|---|---|---|
c111 | 0.1236 | 0.1311 | 0.1169 | 0.1051 | 0.0682 |
c112 | 0.0100 | 0.0078 | 0.0074 | 0.0039 | 0.0038 |
c113 | 0.0001 | 0.0001 | 0.0000 | 0.0000 | 0.0000 |
c121 | 0.0474 | 0.0519 | 0.0548 | 0.0599 | 0.0606 |
c131 | 0.0854 | 0.0894 | 0.1049 | 0.1036 | 0.1212 |
c211 | 0.0644 | 0.0688 | 0.0819 | 0.0876 | 0.0861 |
c212 | 0.0390 | 0.0433 | 0.0475 | 0.0470 | 0.0417 |
c213 | 0.0291 | 0.0326 | 0.0362 | 0.0395 | 0.0267 |
c214 | 0.0445 | 0.0471 | 0.0515 | 0.0559 | 0.0628 |
c221 | 0.0539 | 0.0614 | 0.0672 | 0.0729 | 0.0768 |
c222 | 0.0433 | 0.0477 | 0.0522 | 0.0566 | 0.0430 |
c223 | 0.0000 | 0.0054 | 0.0022 | 0.0015 | 0.0018 |
c231 | 0.0983 | 0.0939 | 0.0208 | 0.0029 | 0.0391 |
c232 | 0.0002 | 0.0004 | 0.0003 | 0.0004 | 0.0005 |
c233 | 0.0001 | 0.0003 | 0.0001 | 0.0000 | 0.0000 |
c241 | 0.0064 | 0.0012 | 0.0006 | 0.0005 | 0.0001 |
c242 | 0.0251 | 0.0113 | 0.0040 | 0.0037 | 0.0029 |
c243 | 0.1278 | 0.1304 | 0.1528 | 0.1540 | 0.1518 |
c311 | 0.1206 | 0.0952 | 0.1088 | 0.1043 | 0.1094 |
c312 | 0.0106 | 0.0044 | 0.0028 | 0.0021 | 0.0011 |
c321 | 0.0001 | 0.0001 | 0.0000 | 0.0000 | 0.0000 |
c322 | 0.0003 | 0.0000 | 0.0002 | 0.0002 | 0.0000 |
c331 | 0.0020 | 0.0020 | 0.0023 | 0.0026 | 0.0029 |
c332 | 0.0337 | 0.0368 | 0.0432 | 0.0478 | 0.0488 |
c333 | 0.0341 | 0.0375 | 0.0413 | 0.0481 | 0.0506 |
Year | Methods | c1 | c2 | c3 |
---|---|---|---|---|
2014 | Interaction method | 0.304 | 0.304 | 0.392 |
Entropy weight TOPSIS method | 0.267 | 0.312 | 0.422 | |
2015 | Interaction method | 0.304 | 0.304 | 0.392 |
Entropy weight TOPSIS method | 0.280 | 0.306 | 0.414 | |
2016 | Interaction method | 0.240 | 0.519 | 0.240 |
Entropy weight TOPSIS method | 0.284 | 0.238 | 0.478 | |
2017 | Interaction method | 0.240 | 0.519 | 0.240 |
Entropy weight TOPSIS method | 0.272 | 0.236 | 0.491 | |
2018 | Interaction method | 0.240 | 0.519 | 0.240 |
Entropy weight TOPSIS method | 0.254 | 0.255 | 0.491 |
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Wang, Y.; Xiao, F.; Zhang, L.; Gong, Z. Research on Evaluation of Meteorological Disaster Governance Capabilities in Mainland China Based on Generalized λ-Shapley Choquet Integral. Int. J. Environ. Res. Public Health 2021, 18, 4015. https://doi.org/10.3390/ijerph18084015
Wang Y, Xiao F, Zhang L, Gong Z. Research on Evaluation of Meteorological Disaster Governance Capabilities in Mainland China Based on Generalized λ-Shapley Choquet Integral. International Journal of Environmental Research and Public Health. 2021; 18(8):4015. https://doi.org/10.3390/ijerph18084015
Chicago/Turabian StyleWang, Yajun, Fang Xiao, Lijie Zhang, and Zaiwu Gong. 2021. "Research on Evaluation of Meteorological Disaster Governance Capabilities in Mainland China Based on Generalized λ-Shapley Choquet Integral" International Journal of Environmental Research and Public Health 18, no. 8: 4015. https://doi.org/10.3390/ijerph18084015