Spatial and Temporal Evolution of Multi-Scale Regional Quality Development and the Influencing Factors
Abstract
:1. Introduction
1.1. Connotation of High-Quality Development
1.2. Research on High-Quality Development from Different Perspectives
1.3. Analysis of the Factors Influencing High-Quality Development
2. Regional High-Quality Development Evaluation Index System Construction
2.1. Economic Vitality
2.2. Green Development
2.3. Innovation Ability
2.4. Coordinated Development
2.5. Development for Global Progress
2.6. Shared Development
3. Research Methodology and Comprehensive Evaluation Results
3.1. Data Selection
3.2. Measurement Method of Regional High-Quality Development
- Standardized evaluation matrix construction:
- Objective empowerment based on information entropy:
- The regional quality development indices are calculated as in Equation (7):
3.3. Results of High-Quality Comprehensive Evaluation of China’s Provincial Regions
4. Analysis of the Spatial and Temporal Evolution of High-Quality Development in China’s Large Regions
4.1. Analysis of the Spatial and Temporal Evolution of Large Regions
- An obvious polarization effect is evident. Whether in 2007 or 2019, the regional high-quality development index generally showed a distribution trend in which the East was larger than the West. It shows a more concentrated trend; the regions with higher quality development indices are concentrated in the coastal areas, and the regions with lower quality development indices are concentrated in Qinghai-Tibet, Northwest, and Southwest regions;
- The level of high-quality development in the Northeast region has declined. In 2007 and 2013, the high-quality development level in the Northeast region was in the middle position. In recent years, the declining trend of high-quality development in the entire Northeast region is obvious, and the development problems in the Northeast region have drawn the most attention from the government. It is imperative to promote the revitalization of the northeast region vigorously;
- The high-quality development level of the Qinghai-Tibet region, Southwest region, and Xinjiang region is low. In both 2007 and 2019, the high-quality development of the Qinghai-Tibet region and Southwest region was low, and the ability to break upward was not strong. Preferential industrial policies should be formulated based on regional resources to encourage the development of resource-related industrial chains or industrial clusters to promote high-quality development in those regions;
- The high-quality development index in Northwest China is highly variable. Due to geographical location and transportation constraints, the level of high-quality development in Northwest China is mostly in the middle and lower levels. Xinjiang is the main node of “One Belt and One Road,” an important platform for China’s westward expansion to Central Asia, which is rich in natural resources and energy. The quality development index of this region ranked 27th nationwide in 2007, which was in the middle to lower level, but the subsequent years showed a decline. The region’s quality development index ranked 27th in 2007, which is in the middle and lower levels, but declined in the subsequent years.
4.2. Evolution of Development Differences in Large Regions
5. Analysis of the Factors Influencing Regional High-Quality Development
5.1. Factors Influencing Regional High-Quality Development
5.2. Model Building and Solving
5.2.1. Statistical Testing and Selection of the Model
5.2.2. Regression Analysis
- (1)
- Investment in science and technology has a positive impact on regional high-quality development. With other variables kept constant, every 1 unit increase in investment in scientific research will increase the high-quality regional development index by 0.04 units. With China’s gradual development, investment has shifted from the traditional emphasis on quantity to quality. This signifies a new understanding of regional development in China, in which the development concept of traditional drivers is replaced with that of innovation-led drivers. Innovation-driven development also makes regional development more efficient and the regional development structure more stable, which can lead to benign development;
- (2)
- Industrial development has a significant and positive impact on high-quality regional development. With other variables kept constant, each increase of 1 unit in technology contract turnover will increase the regional high-quality development index by 0.33 units. The turnover of technology contracts can significantly reflect the level of regional industrial development, not only the scale of the industrial development level but also the development quality. Only by relying on the improvement of the industrial development level can we realize the upgrade of industrial structure, improve the efficiency of resource utilization, and then realize regional high-quality development. Therefore, improving the level of industrial development is an effective way to enhance high-quality regional development;
- (3)
- Urban–rural development has a positive impact on high-quality regional development. For every 1 unit increase in the urbanization rate, the level of regional high-quality development can be increased by 0.04 units. Since China’s reform and opening up, the level of urbanization has gradually increased, but there is still room for development. Urbanization can absorb a large rural surplus population and gradually change the industrial structure from that of primary industries to secondary and tertiary industries. It also helps to improve residents’ living environment and quality of life, which can promote high-quality regional development;
- (4)
- Aging degree suppresses high-quality development. Every 1 unit increase in aging index will reduce the level of high-quality regional development by 0.03 units. In recent years, due to the increase in the proportion of the elderly population in China and the young population going out to work, aging has become a problem that society must face; hence, in Northeast China, Central China, and Sichuan Chongqing, where aging is prominent, it is necessary to attract population return and promote fertility.
6. Conclusions and Policy Recommendations
6.1. Accelerate the Development of Provincial Differences and Synergy
6.2. Development of a Large Regional Strategy
6.3. Key Influencing Factors and the Path to High-Quality Development
6.4. Focus on Economic Development
6.5. Policy Formulation with a Long-Term Vision
7. Shortcomings and Prospects
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Tier 1 Indicators | Tier 2 Indicators | Explanation of Indicators | Indicator Properties |
---|---|---|---|
Green Development | Energy consumption reduction rate per unit of GDP Green space per capita Sulfur dioxide emissions Harmless disposal rate of domestic waste | Energy consumed per unit of GDP this year/energy consumed last year Total area of parkland/resident population Sulfur dioxide emissions Non-hazardous domestic waste/total domestic waste | Negative |
Positive Negative Positive | |||
Innovation Capability | Number of students in higher education per 100,000 people Number of valid invention patents of industrial enterprises above the scale Full-time equivalent of R&D personnel in industrial enterprises above scale Proportion of employed persons not receiving education | Number of students in higher education/total population Number of invention patents of industrial enterprises above the scale R&D personnel/total employees of industrial enterprises above the scale Number of employed persons with no education/total employed persons | Positive Positive Positive Negative |
Coordinated Development | Value added of the tertiary industry as a proportion of GDP The proportion of per capita disposable income of urban and rural households Social security and employment expenditure as a proportion of fiscal expenditure The proportion of local financial expenditure on science and technology GDP per capita Resident consumption level Engel’s coefficient of consumption of urban residents | Tertiary sector value added/GDP The proportion of disposable income per capita of urban and rural households Social security and employment expenditures/total fiscal expenditures Local science and technology expenditure/total fiscal expenditure GDP/total regional population CPI spent on food/Total amount spent | Positive Positive Positive Positive Positive Positive Negative |
Open Development | Total imports and exports as a percentage of GDP Foreign invested enterprises accounted for the total number of enterprises | Total imports and exports/GDP Number of foreign and Taiwanese enterprises/total number of enterprises | Positive Positive |
Shared Development | Library collections per 10,000 people Number of health technicians per 10,000 people Road area per capita Number of Internet port accesses | Library collection/resident population (10,000 people) Number of health technicians/resident population (10,000) Total road area/resident population Number of Internet port access | Positive Positive Positive Positive |
Region Year | 2007 | 2010 | 2013 | 2016 | 2019 | |||||
---|---|---|---|---|---|---|---|---|---|---|
Score | Rank | Score | Rank | Score | Rank | Score | Rank | Score | Rank | |
Beijing | 0.5525 | 4 | 0.4488 | 5 | 0.6748 | 4 | 0.4827 | 5 | 0.4706 | 1 |
Tianjin | 0.5129 | 6 | 0.3874 | 7 | 0.4000 | 7 | 0.3701 | 7 | 0.3253 | 5 |
Hebei | 0.1212 | 12 | 0.1202 | 14 | 0.1026 | 18 | 0.1094 | 17 | 0.1962 | 12 |
Shanxi | 0.0739 | 23 | 0.0851 | 24 | 0.0852 | 22 | 0.0860 | 21 | 0.1848 | 18 |
Inner Mongolia | 0.0880 | 20 | 0.0941 | 22 | 0.1143 | 15 | 0.1043 | 19 | 0.1520 | 27 |
Liaoning | 0.2355 | 9 | 0.2401 | 9 | 0.2299 | 9 | 0.2048 | 9 | 0.2076 | 11 |
Jilin | 0.1292 | 11 | 0.1432 | 11 | 0.1282 | 13 | 0.1207 | 15 | 0.1758 | 22 |
Heilongjiang | 0.0917 | 19 | 0.0989 | 21 | 0.0741 | 23 | 0.0708 | 25 | 0.1703 | 24 |
Shanghai | 0.8886 | 2 | 0.7749 | 1 | 0.7479 | 2 | 0.7545 | 3 | 0.4567 | 2 |
Jiangsu | 0.6776 | 3 | 0.6774 | 2 | 0.6995 | 3 | 0.7692 | 2 | 0.3568 | 4 |
Zhejiang | 0.4970 | 7 | 0.4301 | 6 | 0.4260 | 6 | 0.4291 | 6 | 0.3244 | 6 |
Anhui | 0.1007 | 14 | 0.1141 | 15 | 0.1167 | 14 | 0.1455 | 12 | 0.1885 | 16 |
Fujian | 0.5469 | 5 | 0.5202 | 4 | 0.6275 | 5 | 0.6444 | 4 | 0.2637 | 7 |
Jiangxi | 0.0700 | 24 | 0.0886 | 23 | 0.0675 | 25 | 0.0800 | 23 | 0.1769 | 21 |
Shandong | 0.3411 | 8 | 0.3202 | 8 | 0.3174 | 8 | 0.3302 | 8 | 0.2506 | 8 |
Hebei | 0.0989 | 15 | 0.1041 | 18 | 0.1083 | 16 | 0.1340 | 14 | 0.2364 | 9 |
Hubei | 0.1403 | 10 | 0.1637 | 10 | 0.1439 | 10 | 0.1575 | 11 | 0.2121 | 10 |
Hunan | 0.1026 | 13 | 0.1229 | 13 | 0.1070 | 17 | 0.1096 | 16 | 0.1839 | 19 |
Guangdong | 0.9052 | 1 | 0.6700 | 3 | 0.9009 | 1 | 0.9327 | 1 | 0.3955 | 3 |
Guangxi | 0.0564 | 26 | 0.0733 | 26 | 0.0740 | 24 | 0.0752 | 24 | 0.1753 | 23 |
Hainan | 0.0796 | 21 | 0.1036 | 19 | 0.1009 | 19 | 0.0951 | 20 | 0.1928 | 13 |
Chongqing | 0.0939 | 18 | 0.1069 | 17 | 0.1333 | 11 | 0.1586 | 10 | 0.1818 | 20 |
Sichuan | 0.0969 | 16 | 0.1137 | 16 | 0.1309 | 12 | 0.1381 | 13 | 0.1920 | 14 |
Guizhou | 0.0075 | 31 | 0.0162 | 31 | 0.0087 | 30 | 0.0215 | 30 | 0.1275 | 30 |
Yunnan | 0.0393 | 28 | 0.0505 | 29 | 0.0264 | 28 | 0.0253 | 29 | 0.1335 | 28 |
Tibet | 0.0077 | 30 | 0.0371 | 30 | 0.0085 | 31 | 0.0080 | 31 | 0.1120 | 31 |
Shaanxi | 0.0778 | 22 | 0.1018 | 20 | 0.0939 | 20 | 0.1059 | 18 | 0.1904 | 15 |
Gansu | 0.0240 | 29 | 0.0506 | 28 | 0.0198 | 29 | 0.0254 | 28 | 0.1612 | 25 |
Qinghai | 0.0569 | 25 | 0.1417 | 12 | 0.0510 | 27 | 0.0469 | 27 | 0.1600 | 26 |
Ningxia | 0.0963 | 17 | 0.0806 | 25 | 0.0883 | 21 | 0.0848 | 22 | 0.1861 | 17 |
Xinjiang | 0.0443 | 27 | 0.0605 | 27 | 0.0627 | 26 | 0.0499 | 26 | 0.1296 | 29 |
Classification | High-Quality Development Index | Provincial Districts |
---|---|---|
High level | 0.053–0.062 | Beijing, Shanghai |
Higher level | 0.037–0.053 | Tianjin, Guangdong, Zhejiang, Jiangsu |
Medium level | 0.031–0.037 | Shandong, Fujian, Henan |
Lower middle Level | 0.027–0.031 | Hainan, Liaoning, Shanxi, Shaanxi Ningxia Hebei, Hubei, Hunan, Anhui, Sichuan, Chongqing, Ningxia |
Lower level | 0.022–0.027 | Qinghai, Gansu, Heilongjiang, Inner Mongolia Jilin, Jiangxi, Guangxi |
Low level | 0.001–0.022 | Yunnan, Guizhou, Tibet, Xinjiang |
Tier 1 Indicators | Tier 2 Indicators |
---|---|
Technology investment | Investment in scientific research C1 |
Administrative environment | Public finance expenditure per capita C2 |
Industrial development | Technology contract turnover C3 |
Urban and rural development | Rate urbanization C4 |
Aging degree | The aging population accounts for the total population C5 |
Variables | ADF | Conclusion |
---|---|---|
Investment in scientific research | 135.98 *** (0.0000) | Stable |
Public finance expenditure per capita | 219.64 *** (0.0000) | Stable |
Technology Contract Turnover | 227.56 *** (0.0000) | Stable |
Rate urbanization | 1064.26 *** (0.0000) | Stable |
Aging degree | 524.21 *** (0.0000) | Stable |
Variables | Regression Coefficient | Standard Error | Z | P |
---|---|---|---|---|
Investment in scientific research | 0.0439507 | 0.0102196 | −5.87 | 0.000 *** |
Public finance expenditure per capita | 0.0011781 | 0.0015152 | 0.78 | 0.440 |
Technology contract turnover | 0.0334987 | 0.0161556 | 2.07 | 0.040 ** |
Rate urbanization | 0.0439507 | 0.011570 | 3.80 | 0.000 *** |
Aging degree | −0.0297654 | 0.0169342 | 3.80 | 0.171 * |
C | 0.0194788 | 0.0041288 | 4.72 | 0.000 *** |
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Du, L.; Zhou, X.; Yang, R.; Cheng, P.; Cheng, S. Spatial and Temporal Evolution of Multi-Scale Regional Quality Development and the Influencing Factors. Sustainability 2023, 15, 6046. https://doi.org/10.3390/su15076046
Du L, Zhou X, Yang R, Cheng P, Cheng S. Spatial and Temporal Evolution of Multi-Scale Regional Quality Development and the Influencing Factors. Sustainability. 2023; 15(7):6046. https://doi.org/10.3390/su15076046
Chicago/Turabian StyleDu, Liping, Xianghong Zhou, Ruting Yang, Pengfei Cheng, and Sijie Cheng. 2023. "Spatial and Temporal Evolution of Multi-Scale Regional Quality Development and the Influencing Factors" Sustainability 15, no. 7: 6046. https://doi.org/10.3390/su15076046