Evaluation of Urban Flood Governance Efficiency Based on the Data Envelopment Analysis Model and Malmquist Index: Evidence from 30 Provincial Capitals in China
Abstract
:1. Introduction
2. Methods and Data
2.1. DEA Model
2.2. Malmquist Index
2.3. Evaluation Indicators
2.4. Data Source
3. Empirical Results and Analysis
3.1. Static Analysis of the DEA Model
3.1.1. Comprehensive Technical Efficiency Analysis
3.1.2. Pure Technical Efficiency Analysis
3.1.3. Scale Efficiency Analysis
3.2. Dynamic Analysis of the Malmquist Index
3.2.1. Period Analysis
3.2.2. Regional Analysis
3.3. Empirical Results
4. Discussion
5. Conclusions
- (1)
- Among the 30 provincial capitals we selected, only approximately 25% had high comprehensive technical efficiency. Most cities were affected by scale efficiency and had low efficiency in urban flood disaster governance. Therefore, China’s urban flooding disaster governance has not been at the optimal input–output scale for a long time. The efficiency of urban flooding governance through increased human, material and financial investment by government departments has been very limited.
- (2)
- From 2012 to 2021, the efficiency of urban flooding disaster governance in China’s provincial capitals showed a decreasing trend year by year, but it was largely influenced by national policies. In 2017, the government strongly supported the construction of sponge cities, and accordingly, various government departments were able to receive greater policy support, financial support and technical support in the process of urban flooding disaster governance. Based on this, the efficiency of technological progress in urban flooding disaster governance in China continued to be high after 2017.
- (3)
- China’s urban flooding disaster governance efficiency has obvious regional features, and the promotion of urban flooding disaster governance is closely related to the local annual precipitation characteristics. The greater the annual precipitation in the capital city is, the more efficient the urban flooding governance. Government departments tend to learn from previous years’ experiences in managing urban flooding, which makes it difficult to withstand the huge impact of extreme precipitation events on cities in the long run.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Main Notation | Specific Meaning |
---|---|
DEA | Data envelopment analysis |
DMU | Decision-making unit |
CRS | Constant returns to scale |
VRS | Variable returns to scale |
TE | Comprehensive technical efficiency |
PTE | Pure technical efficiency |
SE | Scale efficiency |
TFP | Total factor productivity |
EC | Comprehensive technical efficiency change |
TC | Technological progress efficiency change |
Primary Indicators | Secondary Indicators | Tertiary Indicators | Indicator Descriptions |
---|---|---|---|
Input Indicators | Human Inputs | Number of people in flood control | Number of urban nonprivate sector staff involved in water resources and public facilities management, operation, maintenance and rescue (people) |
Material Input | Medical and health security | Number of beds in medical and health institutions (sheets) | |
Financial Input | Financial expenditure on flood control | City’s financial spending for flood control (CNY 10,000) | |
Output Indicators | Life Safety | Affected population | Number of people affected by urban flooding disasters (10,000 people) |
Property Safety | Direct economic loss | Property loss caused by urban flooding disaster (CNY 10,000) |
Cities | 2012 | 2021 | ||||||
---|---|---|---|---|---|---|---|---|
TE | PTE | SE | Scale Return | TE | PTE | SE | Scale Return | |
Beijing | 0.099 | 0.876 | 0.113 | drs | 0.137 | 0.993 | 0.138 | drs |
Tianjin | 0.205 | 0.971 | 0.211 | drs | 0.261 | 0.999 | 0.262 | drs |
Shijiazhuang | 0.342 | 0.949 | 0.361 | drs | 0.282 | 0.985 | 0.286 | drs |
Taiyuan | 0.835 | 1.000 | 0.835 | drs | 0.416 | 0.966 | 0.430 | drs |
Hohhot | 1.000 | 1.000 | 1.000 | - | 1.000 | 1.000 | 1.000 | - |
Shenyang | 0.212 | 0.954 | 0.222 | drs | 0.231 | 0.997 | 0.232 | drs |
Changchun | 0.379 | 1.000 | 0.379 | drs | 0.271 | 0.998 | 0.272 | drs |
Harbin | 0.208 | 0.993 | 0.210 | drs | 0.264 | 0.984 | 0.268 | drs |
Shanghai | 0.109 | 0.994 | 0.110 | drs | 0.107 | 1.000 | 0.107 | drs |
Nanjing | 0.320 | 0.991 | 0.323 | drs | 0.272 | 1.000 | 0.272 | drs |
Hangzhou | 0.307 | 0.939 | 0.327 | drs | 0.528 | 0.997 | 0.530 | drs |
Hefei | 0.358 | 0.990 | 0.361 | drs | 0.792 | 0.997 | 0.794 | drs |
Fuzhou | 0.431 | 0.994 | 0.434 | drs | 0.418 | 0.996 | 0.419 | drs |
Nanchang | 0.507 | 0.978 | 0.519 | drs | 0.674 | 0.995 | 0.677 | drs |
Jinan | 0.393 | 0.980 | 0.401 | drs | 0.266 | 1.000 | 0.266 | drs |
Zhengzhou | 0.234 | 1.000 | 0.234 | drs | 0.159 | 0.738 | 0.216 | drs |
Wuhan | 0.214 | 0.977 | 0.220 | drs | 0.158 | 0.971 | 0.163 | drs |
Changsha | 0.473 | 0.968 | 0.489 | drs | 0.339 | 0.980 | 0.346 | drs |
Guangzhou | 0.173 | 0.984 | 0.176 | drs | 0.169 | 0.999 | 0.169 | drs |
Nanning | 0.404 | 0.992 | 0.407 | drs | 0.298 | 0.997 | 0.299 | drs |
Haikou | 1.000 | 1.000 | 1.000 | - | 0.869 | 1.000 | 0.869 | drs |
Chongqing | 0.109 | 0.948 | 0.116 | drs | 0.136 | 0.972 | 0.140 | drs |
Chengdu | 0.297 | 0.876 | 0.340 | drs | 0.138 | 0.920 | 0.150 | drs |
Guiyang | 0.730 | 0.993 | 0.735 | drs | 0.434 | 0.995 | 0.436 | drs |
Kunming | 0.408 | 0.988 | 0.412 | drs | 0.282 | 0.994 | 0.283 | drs |
Xi’an | 0.514 | 0.988 | 0.520 | drs | 0.207 | 0.915 | 0.226 | drs |
Lanzhou | 0.780 | 0.969 | 0.805 | drs | 0.534 | 0.991 | 0.539 | drs |
Xining | 1.000 | 1.000 | 1.000 | - | 1.000 | 1.000 | 1.000 | - |
Yinchuan | 1.000 | 1.000 | 1.000 | - | 1.000 | 1.000 | 1.000 | - |
Urumqi | 0.863 | 1.000 | 0.863 | drs | 0.599 | 1.000 | 0.599 | drs |
Average | 0.464 | 0.976 | 0.471 | 0.408 | 0.979 | 0.413 |
Year | Comprehensive Technical Efficiency | Technological Progress Efficiency | Pure Technical Efficiency | Scale Efficiency | Total Factor Productivity |
---|---|---|---|---|---|
2012–2013 | 1.024 | 0.913 | 0.996 | 1.028 | 0.935 |
2013–2014 | 1.068 | 0.969 | 1.015 | 1.053 | 1.035 |
2014–2015 | 0.981 | 0.912 | 1.000 | 0.982 | 0.895 |
2015–2016 | 0.960 | 0.992 | 0.983 | 0.977 | 0.953 |
2016–2017 | 1.055 | 0.960 | 1.015 | 1.039 | 1.013 |
2017–2018 | 0.988 | 1.014 | 1.004 | 0.984 | 1.002 |
2018–2019 | 0.849 | 1.189 | 0.996 | 0.852 | 1.009 |
2019–2020 | 0.907 | 1.052 | 0.986 | 0.920 | 0.954 |
2020–2021 | 1.062 | 0.936 | 1.008 | 1.054 | 0.993 |
Average | 0.986 | 0.990 | 1.000 | 0.985 | 0.976 |
Cities | Comprehensive Technical Efficiency | Technological Progress Efficiency | Pure Technical Efficiency | Scale Efficiency | Total Factor Productivity |
---|---|---|---|---|---|
Beijing | 1.037 | 0.945 | 1.014 | 1.023 | 0.980 |
Tianjin | 1.027 | 0.951 | 1.003 | 1.024 | 0.977 |
Shijiazhuang | 0.979 | 0.987 | 1.004 | 0.975 | 0.966 |
Taiyuan | 0.925 | 1.058 | 0.996 | 0.929 | 0.979 |
Hohhot | 1.000 | 1.005 | 1.000 | 1.000 | 1.005 |
Shenyang | 1.010 | 1.004 | 1.005 | 1.005 | 1.013 |
Changchun | 0.964 | 1.008 | 1.000 | 0.964 | 0.971 |
Harbin | 1.027 | 1.006 | 0.999 | 1.028 | 1.032 |
Shanghai | 0.998 | 0.953 | 1.001 | 0.997 | 0.950 |
Nanjing | 0.982 | 0.997 | 1.001 | 0.981 | 0.979 |
Hangzhou | 1.062 | 1.076 | 1.007 | 1.055 | 1.143 |
Hefei | 1.092 | 0.972 | 1.001 | 1.091 | 1.061 |
Fuzhou | 0.996 | 0.965 | 1.000 | 0.996 | 0.962 |
Nanchang | 1.032 | 0.976 | 1.002 | 1.030 | 1.007 |
Jinan | 0.958 | 0.989 | 1.002 | 0.956 | 0.948 |
Zhengzhou | 0.958 | 1.004 | 0.967 | 0.991 | 0.962 |
Wuhan | 0.967 | 0.964 | 0.999 | 0.967 | 0.931 |
Changsha | 0.964 | 0.986 | 1.001 | 0.963 | 0.951 |
Guangzhou | 0.997 | 0.950 | 1.002 | 0.995 | 0.947 |
Nanning | 0.967 | 0.999 | 1.001 | 0.966 | 0.965 |
Haikou | 0.985 | 0.961 | 1.000 | 0.985 | 0.946 |
Chongqing | 1.025 | 0.993 | 1.003 | 1.022 | 1.017 |
Chengdu | 0.918 | 1.048 | 1.005 | 0.913 | 0.963 |
Guiyang | 0.944 | 0.998 | 1.000 | 0.944 | 0.942 |
Kunming | 0.960 | 0.990 | 1.001 | 0.959 | 0.950 |
Xi’an | 0.904 | 0.971 | 0.991 | 0.912 | 0.878 |
Lanzhou | 0.959 | 0.984 | 1.003 | 0.957 | 0.944 |
Xining | 1.000 | 0.982 | 1.000 | 1.000 | 0.982 |
Yinchuan | 1.000 | 0.946 | 1.000 | 1.000 | 0.946 |
Urumqi | 0.961 | 1.044 | 1.000 | 0.961 | 1.003 |
Average | 0.986 | 0.990 | 1.000 | 0.985 | 0.976 |
Annual Precipitation | Comprehensive Technical Efficiency | Technological Progress Efficiency | Pure Technical Efficiency | Scale Efficiency | Total Factor Productivity |
---|---|---|---|---|---|
<400 mm | 0.973 | 0.991 | 1.001 | 0.973 | 0.964 |
400–800 mm | 0.982 | 0.993 | 0.998 | 0.984 | 0.974 |
>800 mm | 0.993 | 0.989 | 1.002 | 0.991 | 0.981 |
Average | 0.986 | 0.990 | 1.000 | 0.985 | 0.976 |
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Guo, B.; Hu, X.; Li, J.; Zhang, W. Evaluation of Urban Flood Governance Efficiency Based on the Data Envelopment Analysis Model and Malmquist Index: Evidence from 30 Provincial Capitals in China. Water 2023, 15, 2513. https://doi.org/10.3390/w15142513
Guo B, Hu X, Li J, Zhang W. Evaluation of Urban Flood Governance Efficiency Based on the Data Envelopment Analysis Model and Malmquist Index: Evidence from 30 Provincial Capitals in China. Water. 2023; 15(14):2513. https://doi.org/10.3390/w15142513
Chicago/Turabian StyleGuo, Bin, Xinmiao Hu, Jianna Li, and Wen Zhang. 2023. "Evaluation of Urban Flood Governance Efficiency Based on the Data Envelopment Analysis Model and Malmquist Index: Evidence from 30 Provincial Capitals in China" Water 15, no. 14: 2513. https://doi.org/10.3390/w15142513
APA StyleGuo, B., Hu, X., Li, J., & Zhang, W. (2023). Evaluation of Urban Flood Governance Efficiency Based on the Data Envelopment Analysis Model and Malmquist Index: Evidence from 30 Provincial Capitals in China. Water, 15(14), 2513. https://doi.org/10.3390/w15142513