| Literature DB >> 36011630 |
Daxue Kan1, Xinya Ye2, Lianju Lyu1, Weichiao Huang3.
Abstract
With the rapid development of urbanization, problems such as the degradation of water ecological environment have emerged. How to improve the water ecological environment in the process of urbanization has become one of the urgent problems facing policy makers. This paper studies the coupling coordination relationship between new-type urbanization and water ecological environment, with the purpose of using insights gained from the study to help improve the quality of water ecological environment and promote sustainable development of water ecological environment. We take 11 cities in China's Jiangxi Province as the research object, and construct the coupling coordination evaluation indicator system of new-type urbanization and water ecological environment, then using the coupling coordination degree model to examine the state of coupling coordination between new-type urbanization and water ecological environment from 2009 to 2019. We further explore its driving factors employing random effect panel Tobit model. The results show that: (1) The level of new-type urbanization in Jiangxi Province shows a steady upward trend, and the water ecological environment level tends to rise steadily and slowly, although the comprehensive score of water ecological environment in most cities is lower than 0.1, indicating that the situation of water ecological environment is not optimal yet and there is room for improvement. (2) In 2009, 2014 and 2019, the coupling coordination development level between new-type urbanization and water ecological environment in Jiangxi Province showed an upward trend, from moderate maladjustment recession to mild maladjustment recession, and from low coupling coordination to moderate coupling coordination, although the overall coupling coordination degree was low. (3) The investment in scientific and technological innovation, degree of opening-up and government capacity are positively correlated with the coupling coordination degree, while economic development level, resource agglomeration ability, education level and industrialization level are negatively correlated with the coupling coordination degree. These results can provide insights to support new-type urbanization and water ecological environment in the future, and hold great significance for urban sustainable development.Entities:
Keywords: Tobit model; coupling coordination degree model; entropy method; new-type urbanization; water ecological environment
Mesh:
Substances:
Year: 2022 PMID: 36011630 PMCID: PMC9408538 DOI: 10.3390/ijerph19169998
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
Figure 1Map of Jiangxi Province.
Indicator system and weight of coupling coordination.
| System Layer | Criterion Layer | Indicator Layer | Weight | Indicator Efficacy |
|---|---|---|---|---|
| New-type urbanization system | Population urbanization | Number of urban registered unemployed (person) | 0.0091 | − |
| Economic urbanization | Per capita GDP (Yuan) | 0.0585 | + | |
| Social urbanization | Total retail sales of social consumer goods (104 Yuan) | 0.0820 | + | |
| Spatial urbanization | Proportion of urban construction land in urban area (%) | 0.1068 | + | |
| Water ecological environment system | Water ecological environment pressure | Sewage discharge (104 m3) | 0.0130 | − |
| Water ecological environment status | Total water resources (108 m3) | 0.0861 | + | |
| Water ecological environment response | Urban sewage treatment rate (%) | 0.0061 | + |
Classification of coupling coordination degree types.
| Serial Number | Coupling Coordination Degree | Coupling Coordination Type | Coordination Category |
|---|---|---|---|
| 1 | 0.00~0.09 | Extreme maladjustment recession | Low coupling coordination |
| 2 | 0.10~0.19 | Severe maladjustment recession | |
| 3 | 0.20~0.29 | Moderate maladjustment recession | |
| 4 | 0.30~0.39 | Mild maladjustment recession | |
| 5 | 0.40~0.49 | On the verge of maladjustment recession | Moderate coupling coordination |
| 6 | 0.50~0.59 | Barely maladjustment recession | |
| 7 | 0.60~0.69 | Primary coupling coordination | High coupling coordination |
| 8 | 0.70~0.79 | Intermediate coupling coordination | |
| 9 | 0.80~0.89 | Good coupling coordination | Optimal coupling coordination |
| 10 | 0.90~1.00 | Advanced coupling coordination |
Driving factors of coupling coordination degree.
| Variable Type | Variable Name | Variable Symbol | Variable Description | Unit |
|---|---|---|---|---|
| Dependent variable | Coupling coordination degree |
| Calculation results of coupling coordination degree model | — |
| Independent variable | Economic development level |
| Per capita GDP | USD/person |
| Resource agglomeration ability |
| Population density | Person/km2 | |
| Investment in scientific and technological innovation |
| Proportion of science and technology expenditure in financial expenditure | % | |
| Educational level |
| Proportion of education expenditure in financial expenditure | % | |
| Opening-up degree |
| Actual amount of foreign capital utilized per capita | USD/person | |
| Government capacity |
| Proportion of regional fiscal expenditure in GDP | % | |
| Industrialization level |
| Proportion of added value of secondary industry in GDP | % |
Figure 2New-type urbanization level of Jiangxi Province and cities.
Figure 3Water ecological environment level of Jiangxi Province and cities.
Coupling degree, coordination degree and coupling coordination degree.
| Region | 2009 | 2014 | 2019 | ||||||
|---|---|---|---|---|---|---|---|---|---|
| C | T | D | C | T | D | C | T | D | |
| Jiangxi Province | 0.98 | 0.06 | 0.24 | 1.00 | 0.09 | 0.29 | 1.00 | 0.13 | 0.36 |
| Nanchang | 0.76 | 0.11 | 0.28 | 1.00 | 0.07 | 0.27 | 0.99 | 0.13 | 0.36 |
| Jingdezhen | 0.97 | 0.07 | 0.26 | 0.97 | 0.05 | 0.23 | 1.00 | 0.19 | 0.44 |
| Pingxiang | 0.94 | 0.11 | 0.10 | 1.00 | 0.07 | 0.07 | 1.00 | 0.13 | 0.09 |
| Jiujiang | 0.86 | 0.06 | 0.23 | 0.98 | 0.08 | 0.27 | 0.97 | 0.21 | 0.45 |
| Xinyu | 0.66 | 0.09 | 0.24 | 0.99 | 0.07 | 0.27 | 0.92 | 0.10 | 0.30 |
| Yingtan | 0.98 | 0.06 | 0.06 | 0.98 | 0.06 | 0.06 | 1.00 | 0.14 | 0.14 |
| Ganzhou | 0.75 | 0.06 | 0.21 | 1.00 | 0.06 | 0.24 | 0.99 | 0.16 | 0.40 |
| Ji’an | 0.99 | 0.06 | 0.24 | 0.99 | 0.07 | 0.27 | 0.99 | 0.11 | 0.32 |
| Yichun | 0.94 | 0.07 | 0.25 | 1.00 | 0.07 | 0.26 | 0.96 | 0.16 | 0.40 |
| Fuzhou | 0.99 | 0.13 | 0.36 | 1.00 | 0.07 | 0.26 | 0.99 | 0.14 | 0.37 |
| Shangrao | 0.73 | 0.06 | 0.22 | 0.99 | 0.08 | 0.27 | 0.97 | 0.13 | 0.36 |
| Mean value | 0.96 | 0.08 | 0.24 | 0.99 | 0.07 | 0.23 | 0.98 | 0.14 | 0.33 |
Regression results.
| Variable | Coefficient | Standard Deviation | Adjust-R2 | D.W | F | ||
|---|---|---|---|---|---|---|---|
|
| 0.5214 | 0.0654 | 7.9770 | 0.0000 *** | 0.4959 | 0.9699 | 17.8655 |
|
| −4.2301 | 3.3803 | −1.2502 | 0.2138 | |||
|
| −3.6003 | 3.0901 | −1.1646 | 0.2466 | |||
|
| 0.0119 | 0.0053 | 2.2411 | 0.0270 ** | |||
|
| −0.0033 | 0.0019 | −1.7908 | 0.0760 * | |||
|
| 0.0001 | 6.3302 | 1.9792 | 0.0502 ** | |||
|
| 0.0003 | 0.0001 | 1.8214 | 0.0712 * | |||
|
| −0.0043 | 0.0009 | −4.8338 | 0.0000 *** |
Note: *, ** and *** indicate that the variable is significant at the level of 10%, 5% and 1% respectively.
Results of robustness test (1).
| Variable | Coefficient | Standard Deviation | ||
|---|---|---|---|---|
|
| 0.4678 | 0.0607 | 2.1632 | 0.0351 ** |
|
| −4.1263 | 3.3739 | −1.2817 | 0.2034 |
|
| −3.5170 | 3.0961 | −1.2085 | 0.2309 |
|
| 0.0192 | 0.0043 | 2.2651 | 0.0263 ** |
|
| −0.0037 | 0.0018 | −1.8104 | 0.0821 * |
|
| 0.0008 | 6.3273 | 1.9812 | 0.0487 ** |
|
| 0.0024 | 0.0006 | 1.8338 | 0.0680 * |
|
| −0.0045 | 0.0004 | −3.6560 | 0.0000 *** |
Note: *, ** and *** indicate that the variable is significant at the level of 10%, 5% and 1% respectively.
Results of robustness test (2).
| Variable | Coefficient | |
|---|---|---|
|
| 0.5024 | 0.0463 ** |
|
| −3.9007 | 0.1192 |
|
| −3.0143 | 0.1248 |
|
| 0.0159 | 0.0320 ** |
|
| −0.0032 | 0.0271 ** |
|
| 0.0045 | 0.0737 * |
|
| 0.0068 | 0.0056 *** |
|
| −0.0037 | 0.0129 ** |
| Wald test | 1045.6541 | |
| Sargan test | 0.2320 | |
| Arellano-Bond AR (1) | 0.0052 | |
| Arellano-Bond AR(2) | 0.2356 | |
Note: *, ** and *** indicate that the variable is significant at the level of 10%, 5% and 1% respectively.