Evaluation and Impact Mechanism of High-Quality Development in China’s Coastal Provinces
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Areas and Data Source
2.2. Construction of Evaluation Index System
2.3. Research Methods
3. Evaluation of High-Quality Development Level in China’s Coastal Provinces
3.1. Temporal Evolution of the Level of Quality Development in the Study Area
3.1.1. Temporal Evolution of Overall High-Quality Development Level
3.1.2. Temporal Evolution of the Four Dimensions of High-Quality Development Level
3.1.3. Temporal Evolution of High-Quality Development Level by Provinces and Cities
3.2. Spatial Evolution of the Level of Quality Development in the Study Area
3.2.1. Spatial Evolution of Overall High-Quality Development Level
3.2.2. Spatial Evolution of the Four Dimensions of High-Quality Development Level
3.2.3. Spatial Evolution of High-Quality Development Level by Province
4. Factor Analysis of Obstacles to High-Quality Development
4.1. Analysis of Overall Obstacle Factors in Eastern Coastal Provinces
4.1.1. Obstacle Analysis of Four Dimensions of High-Quality Development Level
4.1.2. Analysis of Main Obstacle Factors of High-Quality Development Level
4.2. Obstacle Factor Analysis of Provinces in the Eastern Coastal Provinces
4.2.1. Analysis of Obstacles in Four Dimensions of High-Quality Development Level in Provinces and Cities
4.2.2. Analysis of Main Obstacle Factors of High-Quality Development Level in Various Provinces
5. Conclusions
5.1. Major Findings
- (1)
- While GDP growth slowed down, the quality of development gradually improved in the eastern coastal provinces, as shown by the gradual increase from 3.212 in 2010 to 3.977 in 2012, then decreasing to 3.864 in 2014, and then gradually increasing to 4.979 in 2020, showing a “rise-decline-rise” upward fluctuation. In terms of the development of the four dimensions, the level of development of all four dimensions had been increasing, and the development level of the residents’ living standards dimension was the highest, at 2.440 in 2020, which was much higher than the development level of the other dimensions, while the level of development of the dimension of innovation efficiency had the fastest growth rate, at 66.1%. The level of high-quality development of the provinces showed different degrees of growth, while the coefficient of variation decreased from 0.331 to 0.271, gradually tending toward balanced development among the provinces. In Shanghai, Jiangsu Province, and Zhejiang Province, the high-quality development level was high and in a dominant position, while in Liaoning Province, the development level growth rate was 88.4%, the largest growth rate.
- (2)
- The spatial distribution pattern of the eastern coastal provinces in China has been relatively stable, showing a pattern of “high in the east and low in the west, high in the north and low in the south”, and the spatial differences in the north–south and east–west directions have been obviously increasing. From the spatial pattern of the four dimensions, the spatial evolution of each dimension was relatively stable, and generally showed a spatial distribution pattern with high-value areas as the core, among which the bipolar spatial effect of the innovation efficiency dimension was becoming more prominent, while the regional synergistic development effect of the resident living standards dimension was more obvious. From the spatial pattern of each province, there were significant differences among the provinces, showing the spatial distribution characteristics of “center–periphery” in general, in which the high-value areas were mainly Shanghai and Jiangsu, and the surrounding provinces belonged to the peripheral areas of high-quality development, and there was a CCE in the evolution of high-quality development in various provinces, which resulted in the high-quality development in various regions being affected by the return or diffusion effect for surrounding regions.
- (3)
- The greatest barriers to residents’ living standards and economic vitality were found in China’s eastern coastal provinces as a whole. The barriers to residents’ living standards decreased from 40.62% to 39.61%, but the barriers to economic vitality increased from 30.48% to 33.49%. In terms of the development of each barrier factor, the barrier factors with a barrier degree greater than 10% changed from three barrier factors (X32 number of patent applications per 10,000 people, X12 industrial structure advancement, X26 public library stock per capita) to two barrier factors (X14 proportion of total export–import volume in GDP, X26 public library stock per capita), which indicated that the significant obstacle factors gradually changed from insufficient output in innovation and unreasonable industrial structure to reduction in the scale of foreign trade and the problems and shortages of per capita public cultural resources.
- (4)
- Barrier factors in the eastern coastal provinces of China varied with the level of economic and quality development. For provinces with a high level of high-quality development and a relatively developed economy, the biggest obstacle factors were economic vitality and residents’ living standards, such as in Jiangsu, Zhejiang, Shanghai, and Guangdong provinces. For provinces with low levels of high-quality development and relatively backward economies, the biggest obstacle factors of obstacle degree were green development and innovation efficiency, such as in Hebei, Guangxi, Liaoning, and Hainan provinces. From the development of each obstacle factor, there were six common obstacle factors for high-quality development in the provinces of the eastern coastal region, which reflected common development problems in the region, mainly including the reduction in the scale of foreign trade, shortages of public cultural resources per capita, unreasonable industrial structure, large development gaps between urban and rural communities, insufficient investment in innovation and low output of scientific and technological innovation achievements. However, different provinces in the stage of high-quality development had different obstacle factors, such as Guangdong, which also had an obstacle factor with a large obstacle degree, X24 (number of health institutions per 10,000 people).
5.2. Planning Implications
5.3. Limitations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Dimensions | Indicators | Company | Index Attribute |
---|---|---|---|
Economic vitality X1 (0.270) | Per-capita GDP, X11 (0.050) | CNY | + |
Industrial structure advancement, X12 (0.088) | p.c | + | |
Total retail sales of consumer goods per capita, X13 (0.082) | CNY | + | |
Proportion of total export–import volume in GDP, X14 (0.050) | P.c | + | |
Residents’ living standards X2 (0.387) | Engel’s coefficient of residents, X21 (0.026) | P.c | - |
Coverage rate of endowment insurance, X22 (0.038) | P.c | + | |
Per capita road area, X23 (0.059) | m2 | + | |
Number of health institutions per 10,000 people, X24 (0.088) | - | + | |
Number of full-time teachers per 100 general high school students, X25 (0.008) | - | + | |
Public library stock per capita, X26 (0.051) | - | + | |
Urban water penetration rate, X27 (0.046) | P.c | + | |
Ratio of urban to rural per capita disposable income, X28 (0.071) | - | - | |
Innovation efficiency X3 (0.213) | FTE of R&D personnel per 10,000 people, X31 (0.035) | year | + |
Number of patent applications accepted per 10,000 people, X32 (0.078) | - | + | |
Proportion of science and education expenditure in general budget expenditure, X33 (0.100) | P.c | + | |
Green development X4 (0.130) | Treatment rate for urban sewage, X41 (0.018) | P.c | + |
SO2 emissions per CNY 10,000 of industrial output value, X42 (0.009) | Tons of standard coal/CNY 10,000 | - | |
Power consumption per CNY 10,000 of GDP, X43 (0.010) | Tons/CNY | - | |
Greening coverage rate of built-up area, X44 (0.020) | P.c | + | |
Utilization rate of general industrial solid waste, X45 (0.051) | P.c | + | |
Treatment rate for MSW, X46 (0.022) | P.c | + |
2010 | 2012 | 2014 | 2016 | 2018 | 2020 | |
---|---|---|---|---|---|---|
CV | 0.331 | 0.245 | 0.288 | 0.291 | 0.289 | 0.271 |
Range | 0.334 | 0.281 | 0.304 | 0.328 | 0.340 | 0.382 |
Year | 2010 | 2012 | 2014 | 2016 | 2018 | 2020 |
---|---|---|---|---|---|---|
higher area | 0 | 0 | 0 | 1 | 1 | 2 |
high area | 1 | 1 | 1 | 2 | 3 | 3 |
median area | 0 | 4 | 4 | 2 | 2 | 3 |
low area | 4 | 3 | 2 | 3 | 4 | 3 |
lower area | 6 | 3 | 4 | 3 | 1 | 0 |
Year | Dimension | TJ | LN | JS | SH | GD | HN | SD | ZJ | FJ | GX | HB | SUM | CV |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2010 | Economic vitality | 0.098 | 0.055 | 0.093 | 0.173 | 0.118 | 0.073 | 0.055 | 0.087 | 0.069 | 0.030 | 0.033 | 0.884 | 0.508 |
Residents’ living standards | 0.134 | 0.130 | 0.134 | 0.189 | 0.116 | 0.118 | 0.150 | 0.138 | 0.127 | 0.133 | 0.136 | 1.504 | 0.143 | |
Innovation efficiency | 0.104 | 0.051 | 0.113 | 0.133 | 0.087 | 0.039 | 0.068 | 0.109 | 0.067 | 0.044 | 0.046 | 0.860 | 0.418 | |
Green development | 0.113 | 0.054 | 0.117 | 0.106 | 0.097 | 0.083 | 0.116 | 0.110 | 0.103 | 0.074 | 0.060 | 1.032 | 0.242 | |
2012 | Economic vitality | 0.109 | 0.064 | 0.099 | 0.182 | 0.123 | 0.080 | 0.066 | 0.095 | 0.077 | 0.036 | 0.038 | 0.971 | 0.468 |
Residents’ living standards | 0.165 | 0.186 | 0.204 | 0.225 | 0.161 | 0.186 | 0.221 | 0.193 | 0.192 | 0.179 | 0.195 | 2.106 | 0.105 | |
Innovation efficiency | 0.122 | 0.056 | 0.164 | 0.121 | 0.118 | 0.039 | 0.089 | 0.152 | 0.087 | 0.049 | 0.056 | 1.054 | 0.446 | |
Green development | 0.122 | 0.069 | 0.117 | 0.112 | 0.101 | 0.091 | 0.122 | 0.113 | 0.115 | 0.089 | 0.055 | 1.106 | 0.222 | |
2014 | Economic vitality | 0.118 | 0.075 | 0.107 | 0.194 | 0.128 | 0.097 | 0.077 | 0.107 | 0.083 | 0.044 | 0.045 | 1.074 | 0.429 |
Residents’ living standards | 0.145 | 0.179 | 0.198 | 0.213 | 0.138 | 0.138 | 0.201 | 0.175 | 0.166 | 0.152 | 0.179 | 1.883 | 0.152 | |
Innovation efficiency | 0.149 | 0.045 | 0.163 | 0.118 | 0.120 | 0.036 | 0.089 | 0.163 | 0.091 | 0.053 | 0.052 | 1.080 | 0.487 | |
Green development | 0.121 | 0.069 | 0.126 | 0.125 | 0.107 | 0.083 | 0.128 | 0.120 | 0.118 | 0.088 | 0.065 | 1.151 | 0.227 | |
2016 | Economic vitality | 0.128 | 0.092 | 0.119 | 0.206 | 0.134 | 0.108 | 0.087 | 0.118 | 0.087 | 0.050 | 0.055 | 1.185 | 0.396 |
Residents’ living standards | 0.157 | 0.193 | 0.215 | 0.226 | 0.150 | 0.145 | 0.208 | 0.188 | 0.171 | 0.163 | 0.185 | 2.003 | 0.150 | |
Innovation efficiency | 0.164 | 0.049 | 0.182 | 0.132 | 0.142 | 0.035 | 0.098 | 0.191 | 0.107 | 0.057 | 0.058 | 1.217 | 0.505 | |
Green development | 0.123 | 0.064 | 0.125 | 0.126 | 0.119 | 0.092 | 0.115 | 0.120 | 0.107 | 0.089 | 0.084 | 1.165 | 0.194 | |
2018 | Economic vitality | 0.141 | 0.100 | 0.134 | 0.221 | 0.142 | 0.119 | 0.099 | 0.136 | 0.101 | 0.065 | 0.065 | 1.323 | 0.361 |
Residents’ living standards | 0.160 | 0.210 | 0.229 | 0.237 | 0.163 | 0.157 | 0.224 | 0.204 | 0.193 | 0.179 | 0.199 | 2.154 | 0.144 | |
Innovation efficiency | 0.144 | 0.050 | 0.194 | 0.139 | 0.193 | 0.035 | 0.097 | 0.215 | 0.126 | 0.048 | 0.057 | 1.297 | 0.554 | |
Green development | 0.125 | 0.074 | 0.129 | 0.123 | 0.122 | 0.078 | 0.111 | 0.123 | 0.105 | 0.081 | 0.087 | 1.158 | 0.201 | |
2020 | Economic vitality | 0.144 | 0.078 | 0.135 | 0.241 | 0.137 | 0.140 | 0.101 | 0.132 | 0.113 | 0.080 | 0.074 | 1.375 | 0.374 |
Residents’ living standards | 0.195 | 0.245 | 0.249 | 0.281 | 0.198 | 0.189 | 0.230 | 0.239 | 0.208 | 0.220 | 0.186 | 2.440 | 0.135 | |
Innovation efficiency | 0.158 | 0.058 | 0.214 | 0.168 | 0.205 | 0.045 | 0.113 | 0.220 | 0.129 | 0.050 | 0.069 | 1.428 | 0.522 | |
Green development | 0.126 | 0.080 | 0.127 | 0.126 | 0.120 | 0.104 | 0.108 | 0.130 | 0.110 | 0.082 | 0.088 | 1.204 | 0.171 |
Year | Economic Vitality | Residents’ Living Standards | Innovation Efficiency | Green Development |
---|---|---|---|---|
2010 | 30.48% | 40.62% | 22.12% | 6.79% |
2012 | 33.47% | 37.50% | 22.37% | 6.66% |
2014 | 31.74% | 40.46% | 21.84% | 5.96% |
2016 | 31.90% | 41.00% | 21.02% | 6.09% |
2018 | 31.53% | 40.95% | 20.95% | 6.57% |
2020 | 33.49% | 39.61% | 20.54% | 6.35% |
Year | Ranking | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|---|
2010 | Obstacle factor | X32 | X12 | X26 | X22 | X14 | X31 | X25 | X11 | X13 | X28 |
Obstacle degree (%) | 12.08 | 10.72 | 10.29 | 8.06 | 7.87 | 7.66 | 6.72 | 6.05 | 5.84 | 5.50 | |
2012 | Obstacle factor | X32 | X12 | X26 | X14 | X31 | X25 | X11 | X13 | X22 | X24 |
Obstacle degree (%) | 12.21 | 11.96 | 10.83 | 9.32 | 8.58 | 7.38 | 6.17 | 6.02 | 5.76 | 5.41 | |
2014 | Obstacle factor | X32 | X12 | X26 | X14 | X31 | X28 | X25 | X22 | X11 | X13 |
Obstacle degree (%) | 11.73 | 11.13 | 10.96 | 9.76 | 7.78 | 7.54 | 6.82 | 5.72 | 5.56 | 5.29 | |
2016 | Obstacle factor | X26 | X14 | X12 | X32 | X31 | X28 | X25 | X22 | X11 | X13 |
Obstacle degree (%) | 11.11 | 11.10 | 10.50 | 9.98 | 8.21 | 8.10 | 6.85 | 6.05 | 5.38 | 4.91 | |
2018 | Obstacle factor | X14 | X26 | X12 | X32 | X28 | X31 | X25 | X22 | X11 | X13 |
Obstacle degree (%) | 11.36 | 11.21 | 10.52 | 9.24 | 8.68 | 8.55 | 7.01 | 6.35 | 4.97 | 4.68 | |
2020 | Obstacle factor | X14 | X26 | X28 | X12 | X31 | X32 | X11 | X13 | X25 | X22 |
Obstacle degree (%) | 13.06 | 11.67 | 9.90 | 9.66 | 8.94 | 8.29 | 5.45 | 5.31 | 5.18 | 4.95 |
Region | Ranking | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|---|
TJ | Obstacle factor | X28 | X14 | X25 | X26 | X31 |
Obstacle degree (%) | 12.43 | 11.79 | 11.79 | 10.27 | 9.50 | |
LN | Obstacle factor | X32 | X14 | X31 | X26 | X12 |
Obstacle degree (%) | 12.07 | 11.43 | 10.22 | 9.97 | 9.16 | |
JS | Obstacle factor | X12 | X14 | X26 | X28 | X25 |
Obstacle degree (%) | 17.56 | 17.43 | 15.64 | 13.82 | 7.30 | |
SH | Obstacle factor | X28 | X14 | X31 | X25 | X23 |
Obstacle degree (%) | 18.25 | 14.93 | 14.88 | 13.67 | 13.08 | |
GD | Obstacle factor | X26 | X12 | X14 | X28 | X24 |
Obstacle degree (%) | 15.37 | 13.25 | 11.00 | 10.26 | 6.72 | |
HN | Obstacle factor | X32 | X14 | X31 | X26 | X28 |
Obstacle degree (%) | 13.61 | 12.87 | 12.64 | 12.13 | 8.03 | |
SD | Obstacle factor | X26 | X14 | X32 | X12 | X31 |
Obstacle degree (%) | 13.59 | 13.25 | 11.28 | 11.07 | 9.75 | |
ZJ | Obstacle factor | X14 | X12 | X28 | X26 | X25 |
Obstacle degree (%) | 16.67 | 16.06 | 15.01 | 13.24 | 7.17 | |
FJ | Obstacle factor | X14 | X12 | X26 | X32 | X28 |
Obstacle degree (%) | 14.15 | 13.40 | 11.56 | 9.50 | 9.34 | |
GX | Obstacle factor | X32 | X14 | X26 | X31 | X12 |
Obstacle degree (%) | 13.35 | 11.96 | 11.54 | 11.42 | 7.83 | |
HB | Obstacle factor | X32 | X26 | X14 | X31 | X12 |
Obstacle degree (%) | 11.91 | 11.88 | 11.84 | 9.97 | 8.85 |
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Wang, X.; Han, R.; Zhao, M. Evaluation and Impact Mechanism of High-Quality Development in China’s Coastal Provinces. Int. J. Environ. Res. Public Health 2023, 20, 1336. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph20021336
Wang X, Han R, Zhao M. Evaluation and Impact Mechanism of High-Quality Development in China’s Coastal Provinces. International Journal of Environmental Research and Public Health. 2023; 20(2):1336. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph20021336
Chicago/Turabian StyleWang, Xiaojie, Rongqing Han, and Minghua Zhao. 2023. "Evaluation and Impact Mechanism of High-Quality Development in China’s Coastal Provinces" International Journal of Environmental Research and Public Health 20, no. 2: 1336. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph20021336