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Measurement and Spatiotemporal Dynamic Evolution of China's High-Quality Economic Development

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12 April 2024

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15 April 2024

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Abstract
China's economic development has shifted from high speed to high quality. This study constructs an evaluation index for China’s high-quality development utilizing a new development concept. The entropy method and cluster analysis were used to assess the level of high-quality development in China between 2015 and 2021. Kernel density estimation method was employed to explore spatiotemporal dynamics. The results reveal significant differences in the level of economic development among provinces in China; the eastern region is significantly better than the central, northeastern, and western regions. From the perspective of the sub-item evaluation, challenges with uneven, uncoordinated, and insufficient economic development exist in various Chinese provinces.
Keywords: 
Subject: Business, Economics and Management  -   Econometrics and Statistics

1. Introduction

Since the reform and opening up of the country, China has witnessed a rapid increase in its level of economic development, creating a “Chinese economic miracle,” which has shocked the world. However, ecological and environmental problems caused by over-exploitation, inefficient utilization, and wasteful consumption of resources are increasingly prominent [1,2]. Therefore, China’s future development necessitates a shift in the mode of economic development and an improvement in its quality [3]. The Fifth Plenary Session of the 18th Central Committee of the Communist Party of China established, for the first time, the new concepts of “innovation, coordination, greenness, openness, and sharing” for economic and social development. The report of the 19th National Congress of the Communist Party of China stated that “socialism with Chinese characteristics has entered a new era, with the economy shifting from a high-speed growth to high-quality development” [4]. Some scholars argue that high-quality economic development entails pursuing both quantity and quality simultaneously, which represents coordinated development [5]. However, high-quality economic development is a concept that appears simple yet difficult to grasp, posing new and complex challenges in statistical accounting. This study aims to measure the level of high-quality economic development and its spatiotemporal dynamic evolution in each province from 2015 to 2021. Investigating these claims presents crucial practical significance in promoting high-quality development in China.
Existing studies have had different understandings of high-quality development since their proposal. Some literature focuses on a single evaluation index, such as total factor productivity, green total factor productivity [6,7,8], labor productivity [9], GDP per capita [10], economic growth rate [11], and labor productivity [12,13,14] to quantify high-quality development. However, one indicator hardly suffices to capture the entirety of high-quality economic development. While others constructed a multi-dimensional evaluation index system of high-quality development [15,16], Ma [17] (2022) designed an evaluation index system of high-quality economic development employing three dimensions of “economic development, social life, ecological environment.” Moreover, Huang et al. [18] established the evaluation index system of China’s high-quality development from five aspects such as innovation development, urban-rural coordination, ecological environment, opening to the outside world, and individuals’ livelihood.
Current research is mostly guided by the five principles of development and constructs a high-quality economic development evaluation system from the five dimensions of innovation, coordination, greenness, openness, and sharing [19,20,21,22,23,24]. Chen et al. [25] used principal component analysis to measure China’s high-quality economic development in each province and explored the main factors influencing high-quality economic development. Chen et al. [26] used the Entropy weight Topsis model to evaluate the urban ecological level. Xiao et al. [27] employed evaluation systems, the entropy-weighted method, and the Gini coefficient method to explore high-quality development levels and evolving trends from 2015 to 2020. Li et al. [28] explored the effects of environmental regulation, foreign direct investment, and their interaction term on high-quality economic development, while Yuan et al. [29] analyzed the influencing factors of high-quality coordinated development and identified problem areas.
Various studies on economic development revolve around spatial correlation patterns [30,31,32]. Chen et al. [33] explored the spatial correlation mechanism of high-quality development by using social network analysis. Zhou et al. [34] examined the coupling coordination and spatial correlation effects of green finance and high-quality economic development in 30 Chinese provinces, while others used the Dagum Gini coefficient method and the kernel density function to describe the dynamic evolution of high-quality development [35,36,37].
Zhang et al. [38] explored the spatiotemporal patterns and evolution characteristics of the coupling coordination degree (CCD) between urban resilience and high-quality development and further analyzed the influence factors with the spatial econometric models. Liu and Zhou [39] examine the spatiotemporal evolution dynamics associated with high-quality urban development trends in the region. Hu et al. [40] investigated the spatial-temporal pattern of the coupling and coordinated development of the three-dimensional economic, social, and ecological environments system in 21 prefecture-level cities. Wang et al. [41] explored the characteristics of spatial-temporal variations in the high-quality development level from 2010 to 2020 and use the obstacle degree model to explore the factors that are obstacles to high-quality development. Wan et al. [42] evaluated the CCD between the social economy and ecological environment, analyzed the changing laws and characteristics of the CCD from the perspective of time and space, and used regression analysis to determine the key factors affecting it.
To summarize, scholars have conducted in-depth research on the connotation and evaluation system of high-quality economic development, which exploration of this study. First, it is essential to clarify the connotation of high-quality economic development and select measurement indicators purposefully. Second, compared to a single indicator, comprehensive indicators can provide a comprehensive and accurate evaluation of high-quality economic development. Third, while the subjective empowerment method cannot emphasize objective and effective information, various objective empowerment evaluation methods exist to choose from. Fourth, the spatiotemporal dynamic evolution of high-quality development is conducive to grasping the situation of economic and social development. Therefore, based on these principles of development, this study employs the entropy method to measure the level of high-quality economic development. Cluster analysis and kernel density estimation were utilized to examine the spatiotemporal dynamic evolution of high-quality economic development, providing support and a theoretical foundation for achieving high-quality economic development in various regions.
The structure of this paper is as follows. Section 2 introduces the methodology of this study. Section 3 presents the measurement and analysis of the overall index and various subdimensions of high-quality economic development. Section 4 describes the dynamic spatiotemporal evolution of high-quality economic development. Finally, Section 5 presents conclusions and suggestions.

2. Measurement Methods and Indicator System Construction

2.1. Entropy Method

The entropy value method was used to compute the weight of urban resilience and high-quality development indicators to ensure reasonable and scientifically sound weighting results [43]. The calculation steps are as follows:
Step 1. We standardized the initial data using the range method to remove dimensional connections between indicators and improve comparability:
y i j = x i j − min ⁡ x i j max ⁡ x i j − min ⁡ x i j ，   x i j       r e p r e s e n t s   a   p o s i t i v e   i n d i c a t o r
y i j = m a x x i j − x i j max ⁡ x i j − min ⁡ x i j ， x i j   d e n o t e s   a   n e g a t i v e   i n d i c a t o r
Step 2. We calculated the information entropy using the formula:
  e j = 1 ln ⁡ n ∑ i = 1 n p i j ln ⁡ p i j , p i j = y i j ∑ i = 1 n y i j , i = 1 , . . . n , j = 1 , . . . m
where n represents the total number of provinces surveyed.
Step 3. We calculated the weight of indicators using the formula:
w j = 1 − e j ∑ j = 1 m 1 − e j , j = 1 , . . . m
Step 4. We calculated the final result based on the weight.

2.2. The Construction of Evaluation Index System

The connotations of high-quality economic development are significant. Using a single indicator to measure it may not accurately express its meaning. Therefore, it is essential to construct an indicator system to define high-quality economic development. In 2020, the Chinese government introduced the “Proposal of the Central Committee of the Communist Party of China on Formulating the Fourteenth Five-Year Plan for National Economic and Social Development and the Visionary Goals for 2035,” which identified five principles of development: innovation, coordination, greenness, openness, and sharing. According to these principles, the evaluation index system for China’s high-quality economic development level, constructed in this study, includes five primary indicators, 11 secondary indicators, and 21 tertiary indicators.
1) Innovation. Innovation has a high efficiency value. According to the principles of economic development, when the growth rate of investment reaches a plateau and fails to meet effective demand, innovation becomes the driving force for sustainable economic development. Innovative activities encompass processes that begin with inputs, including financial and human resources, and culminate in the production of innovative results. Therefore, innovation development can be expressed in terms of innovation and output.
2) Coordination. Coordination development mainly considers the integrity, systematics, and equilibrium of the socialist market economy’s development to address developmental imbalances. Under the concept of new development, the level of coordinated development can be expressed regarding urban-rural coordination and economic and social coordination.
3) Greenness. Greenness emphasizes sustainability, drawing upon the rich ecological wisdom found in traditional Chinese culture. Concepts such as the unity of heaven and man, the law of nature, and the equality of all living beings in Confucianism, Taoism, and Buddhism reflect a deep-rooted awareness of the interconnectedness between man and nature. Green development is primarily assessed through three aspects: resource consumption, ecological environment, and green life.
4) Openness. The concept of openness prioritizes the value of mutual benefit and a win-win situation, reflecting a commitment to the shared destiny of humanity. Openness entails embracing the principles of mutual benefit and common prosperity, advancing with time. Openness development is comprehensively reflected in two aspects: trade and investment openness.
5) Sharing. Sharing emphasizes the value of justice and stands as the ultimate goal of economic and social development, serving as the driving force for building a moderately prosperous society. In China, shared development comprises efficiency and fairness embodies the socialist goal of common prosperity. The concept of shared development can be expressed in both economic and social outcomes. The evaluation indicators at the various levels are listed in Table 1.

2.3. Data Sources and Pre-Processing

We collected data from 30 provinces of China (excluding Tibet, Hong Kong, Macao, and Taiwan). The study period spanned from 2015 to 2021. The index data and related variables utilized in this study were mainly obtained from sources such as the “China Statistical Yearbook,” the “China Urban Statistical Yearbook,” and the “China Energy Statistical Yearbook.” During the period from 2016 to 2021, the total output value of large and medium-sized industrial enterprises was missing. In such cases, we substituted it with the profit of large and medium-sized industrial enterprises. For certain years, the urban sewage treatment rate data were missing for Beijing, Tianjin, and Shanghai. In these cases, we calculated the average of similar years.

3. Measurement and Analysis of High-Quality Economic Development

3.1. Analysis of the Overall Index and Sub-Dimension Index

1)
Analysis of the overall index of high-quality economic development
This section calculates the comprehensive evaluation values and rankings of high-quality economic development in 30 provinces from 2015 to 2021 using the entropy method (Table 1), and the evaluation results range from 0 to 1. From a comprehensive perspective, Guangdong, Jiangsu, Beijing, Zhejiang, Shanghai, and Shandong consistently ranked among the top six. Among them, Guangdong and Jiangsu were in the top two, with occasional changes in the positions of Beijing, Shanghai, and Zhejiang. Shandong has always ranked sixth, indicating that these six provinces and cities have the strongest overall strengths in terms of innovation, coordination, greenness, openness, and sharing. Second, Tianjin, Fujian, Liaoning, Hubei, Hunan, Hainan, and Anhui also had high comprehensive scores, and their high-quality development levels alternated with the top ten. However, Xinjiang, Qinghai, Gansu, Ningxia, and other provinces and cities had lower comprehensive scores, reflecting their overall weaker strengths.
2)
Analysis of the Subdimensional
The entropy method was used to calculate the sub-dimensional index and ranking of high-quality economic development in 30 provinces in 2021 (Table 2), and the evaluation results ranged from 0 to 1. From the perspective of the various dimensional indices, there were significant differences in the results of the five-dimensional indices. In terms of innovation, the top five were Guangdong, Jiangsu, Zhejiang, Shandong, and Beijing (all in the eastern region), followed by Fujian, Anhui, Shanghai, Hubei, and Henan. Fujian and Shanghai belong to the eastern region, whereas Anhui, Hubei, and Henan belong to the central region. The last five were Heilongjiang, Xinjiang, Gansu, Ningxia, Hainan, and Qinghai. Heilongjiang is in the northeastern region, whereas Xinjiang, Gansu, Ningxia, and Qinghai are in the western region. Hainan Province has a low level of innovative development in the eastern region.
Regarding coordination, the top five regions were Heilongjiang, Qinghai, Liaoning, Anhui, and Chongqing. Heilongjiang and Liaoning belong to the northeastern region, whereas Anhui and Chongqing belong to the western region. Jilin, Shanxi, Guangxi, Hubei, and Sichuan followed. Jilin belongs to the northeast region, Shanxi and Hubei belong to the central region, and Guangxi and Sichuan belong to the western region, indicating that the urban–rural gap in these areas is not significant. The last five were Guangdong, Zhejiang, Fujian, Beijing, and Guizhou, with the eastern region accounting for the four economically developed regions. Guizhou belongs to the western region, indicating a significant urban–rural development gap in these areas.
For greenness, the top five cities are Beijing, Shandong, Jiangsu, Zhejiang, and Hainan, all of which are economically developed and belong to the eastern region. The effect of emphasizing the quality of economic development is significant, indicating that these regions have achieved good results in controlling the total energy consumption and carbon dioxide emissions. Hunan, Fujian, Liaoning, Tianjin, and Anhui scored relatively high. Fujian and Tianjin belong to the eastern region, Anhui and Hunan belong to the central region, and Liaoning belongs to the western region, indicating that these provinces and cities have worked well on green environmental protection. The other five cities were Xinjiang, Ningxia, Guizhou, Inner Mongolia, and Qinghai. Except for Inner Mongolia, all these regions belong to the western region.
Regarding openness, the top five were Shanghai, Guangdong, Shanghai, Hainan, and Jiangsu, all of which belong to the eastern region, followed by Shandong, Beijing, Zhejiang, Tianjin, and Fujian, all of which belong to the eastern region, indicating a high level of open development in the eastern region. The last five cities are Inner Mongolia, Guizhou, Gansu, Ningxia, and Qinghai. Except for Inner Mongolia, which belongs to the central region, the remainder belongs to the western region.
Regarding sharing, the top five were Beijing, Shanghai, Jiangsu, Zhejiang, and Guangdong, all of which belong to the eastern region. Tianjin, Shandong, Shaanxi, Fujian, and Hubei followed. Except for Shaanxi, which belongs to the western region, and Hubei, which belongs to the central region, the rest are in the eastern region, indicating that these provinces and cities have relatively good development in the sharing economy. The bottom five provinces are Ningxia, Liaoning, Guangxi, Gansu, and Heilongjiang, with Liaoning and Heilongjiang belonging to the northeast and the remaining provinces in the western region.

3.2. Temporal Evolution of High-Quality Economic Development

Using the entropy method, the high-quality economic development levels of 30 provinces in China were measured from 2015 to 2021, and the evaluation results ranged from 0 to 1. From the average values for each year, the region with the highest economic high-quality development index was Guangdong (0.7647), and the province with the lowest was Gansu (0.1032). The high-quality development of the Chinese economy has not continued to increase as it has grown, which also indicates the necessity of shifting towards high-quality development at this stage. From a regional perspective, the level of high-quality economic development in China varies with decreasing trends in the eastern, central, northeastern, and western regions. The high-quality economic development of the eastern provinces is above the national average, whereas that of the central, northeastern, and western regions is lower than the national average (see Figure 1).

3.3. Spatial Analysis of High-Quality Economic Development

To obtain the spatial distribution of high-quality economic development in China, K-means clustering analysis was used to obtain five aspects of economic development in each province and city. The economic and social development levels of 30 provinces and cities were divided into four categories. The results are summarized in Table 2.
Table 4. The 2021 high-quality development cluster analysis results.
Table 4. The 2021 high-quality development cluster analysis results.
Index Innovation Coordination Greenness Openness Sharing High-Quality
Development
Category
High level Jiangsu
Guangdong		 Heilongjiang Beijing Guangdong Beijing Guangdong
Jiangsu
Middle high level Zhejiang
Shandong		 Shanxi, Liaoning
Jilin, Anhui
Guangxi, Qinghai
Chongqing		 Jiangsu, Zhejiang
Fujian, Shandong
Hunan, Hainan		 Shanghai, Hainan Tianjin, Shanghai
Jiangsu, Zhejiang
Guangdong		 Beijing, Shanghai Zhejiang, Shandong
Middle level Beijing, Hebei
Shanghai, Anhui
Fujian, Henan Hubei, Hunan Sichuan, Shaanxi		 Beijing, Jiangsu Zhejiang, Guangdong Fujian, Guizhou
Yunnan, Gansu		 Tianjin, Hebei Shanxi, Liaoning
Jilin, Heilongjiang
Anhui, Henan Hubei, Guangdong
Chongqing, Yunnan
Shanxi		 Beijing, Tianjin
Jiangsu, Zhejiang
Fujian, Shandong		 Inner Mongolia
Fujian, Shandong
Hubei, Hunan
Chongqing, Sichuan Shaanxi, Xinjiang		 Tianjin, Hebei
Liaoning, Anhui
Fujian, Henan Hubei, Hunan Chongqing, Sichuan
Hainan, Shaanxi
Low level Tianjin, Liaoning, Shanxi, Jilin
Inner Mongolia Heilongjiang,
Jiangxi, Guangxi Hainan, Guizhou
Yunnan, Gansu Qinghai, Ningxia, Xinjiang, Chongqing		 Tianjin, Hebei,
Inner Mongolia Shanghai, Hunan
Sichuan, Jiangxi
Shandong, Henan
Hubei, HainanShaanxi, Ningxia,
Xinjiang		 Inner Mongolia, Shanghai
Sichuan, Guizhou
Gansu, Jiangxi
Guangxi, Qinghai
Ningxia, Xinjiang		 Hunan, Sichuan
Shaanxi, Hebei
Shanxi, Inner Mongolia
Liaoning, Jilin Heilongjiang, Anhui
Jiangxi Henan, Hubei
Guangxi, Guizhou Chongqing, Yunnan
Gansu, Qinghai
Ningxia, Xinjiang		 Hebei, Henan
Hainan, Liaoning Jilin,Heilongjiang
Anhui, Jiangxi
Guizhou, Yunnan Shanxi, Gansu
Qinghai, Ningxia		 Shanxi, Inner Mongolia
Jilin, HeilongjiangJiangxi, Gu
Guangxi, Yunnan Gansu, Qinghai Ningxia, Xinjiang
This study utilized the k-means clustering analysis method to conduct spatial classification research on high-quality economic development levels of various regions in 2021. The analysis categorized the level of high-quality economic development into four, indicating a spatial imbalance. The results indicated that Guangdong and Jiangsu provinces are at the highest level of high-quality economic development. Additionally, Beijing, Shanghai, Zhejiang, and Shandong, all located in the eastern region, attained a higher level, with most of these provinces also demonstrating high levels of innovation, greenness, openness, and sharing.
A low level of coordination indicated a significant gap between urban and rural development. Exceptions to the following situations, Beijing and Shanghai are at a middle level of innovation development. Beijing, Jiangsu, Zhejiang, and Shandong are at a middle level in openness development and Shanghai is at a low level in greenness development. The provinces with a middle level of high-quality economic development include Tianjin, Hebei, Liaoning, Anhui, Fujian, Henan, Hubei, Hunan, Chongqing, Sichuan, Hainan, and Shaanxi, with the majority being in the eastern and central provinces. Tianjin, Liaoning, Chongqing, and Hainan are at a low level of innovation development. Sichuan had a low level of greenness development, Shanxi had a low level of openness development, Hainan had a middle-high level of openness development, and Fujian had a middle-high level of greenness development. Provinces with low levels of high-quality economic development, particularly in the western region, include Shanxi, Inner Mongolia, Jilin, Heilongjiang, Jiangxi, Guizhou, Guangxi, Yunnan, Gansu, Qinghai, Ningxia, and Xinjiang. Most of these provinces also have a low level of innovation, greenness, openness, and sharing, whereas Heilongjiang and Qinghai have a high level of coordinated development.

4. Analysis of the Dynamic Evolution Distribution of High-Quality Economic Development

4.1. Principle of Kernel Density Estimation

Non-parametric kernel density estimation is a method adopted to estimate probability density functions, providing continuous density curves that depict the distributional pattern of random variables. Compared to histograms, this method offers improved continuity of the estimation results. Therefore, the study utilizes the kernel density estimation method to analyze the spatiotemporal evolution characteristics of high-quality economic development, exploring distribution position, main peak distribution trend, polarization trend, and extensibility. The formula for kernel density estimation used is as follows:
Preprints 103761 i001
where N is the number of observations, Xi represents independent and identically distributed observations, x is the mean of all observations, K(·) represents the kernel density function, and h denotes the bandwidth. Generally, a smaller bandwidth leads to higher estimation accuracy. f(x) is the density function of high-quality Chinese development. We used the Gaussian kernel density function to estimate the distribution and dynamic evolution results of the national high-quality economic development index and various dimensional indices.

4.2. Analysis of the Distribution and Dynamic Evolution Characteristics of High-Quality Economic Development in China

Figure 2 presents the evolution of the national high-quality development index and its various dimensions from 2015 to 2021. Figure 2 (a) shows the three-dimensional kernel density distribution dynamics of the national high-quality development index, while Figure 2b-f presents the three-dimensional kernel density distribution dynamics of the innovation, greenness, openness, sharing, and coordination development indices. Based on the peak shift, Figure 2 (a) reveals that the main peak position of the distribution curve shifts to the right continuously, indicating that the level of high-quality economic development across the country is improving constantly. Observing the peak shift in Figure 2(a), we note a continuous rightward movement of the main peak position of the distribution curve. This indicates that the gap in high-quality economic development among different regions across the country shows an increasing trend. Based on the extensibility of distribution, the left-tailed feature of the kernel density curve is significantly weakened, while the right-tailed phenomenon is further enhanced. This indicates that provinces with lower levels of high-quality economic development nationwide have a trend towards approaching the mean, while provinces with higher levels of development still retain their “sample power.” For example, Guangdong, Jiangsu, Beijing, Zhejiang, Shanghai, and Shandong are at the forefront of high-quality development.
Figure 2 b-f illustrates a rightward shift in the main peak positions of each dimension index, indicating a continual improvement in development levels across dimensions. The innovation index distribution exhibits a slight right tail, indicating that certain provinces, such as Guangdong, Jiangsu, Beijing, Zhejiang, Shanghai, and Shandong in the eastern region, possess significant advantages in innovation development. The presence of multiple peaks in the polarization phenomenon indicates varying levels of polarization. The widening distribution of the coordination index signifies a significant gap in coordination development among provinces, with provinces exhibiting higher coordination development differing from those with higher levels of high-quality and innovation development. Provinces such as Heilongjiang, Chongqing, Jilin, Liaoning, and Tianjin demonstrate leading levels of coordination development.
The widening distribution of the green development index indicates a significant gap in green development levels among provinces. While the polarization phenomenon has weakened, provinces with high levels of high-quality economic development continue to lead in green development. Similarly, the narrowing distribution of the openness development index indicates significant developmental gaps among provinces. While the polarization phenomenon persists, the number of peaks has decreased over time, indicating a weakening polarization trend. Provinces with advanced economic development still exhibit higher openness development. The distribution of sharing development levels has narrowed, indicating a gradual reduction in the gap between provinces with lower levels of sharing development. Provinces with lower levels of shared development are gradually approaching the mean, while those with higher levels of high-quality economic development still lead. Despite some polarization, indicating differing development levels among provinces, the overall trend suggests a narrowing gap in sharing development.
Preprints 103761 g002aPreprints 103761 g002b
Figure 2.
Figure 2.
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Table 5. Distribution and dynamic evolution characteristics.
Table 5. Distribution and dynamic evolution characteristics.
Categories Distribution Location Main Peak Distribution Pattern Distribution Extensibility Polarization Phenomenon
High-quality development index right-shift Peak rise,
The width narrows		 Right trailing, Extension widen Polarization, with three peaks observed in most years
Innovation right-shift The peak value first decreases,
then increases and then decreases,
The width narrows		 Right trailing, Extension narrows Polarization, with three peaks observed in most years
Coordination right-shift The peak value first rises, then falls,
then rises and then falls again,
The width narrows		 Right trailing, Extension widen No polarization phenomenon
Greenness right-shift The peak value first decreases then rises, then decreases, and then rises again,
The width narrows		 Right trailing, Extension widen Polarization phenomenon weakened
Openness right-shift The peak value first rises, then decreases, and then rises again,
The width narrows		 Right trailing, Extension widen Polarization, with three peaks observed in most years
Sharing right-shift The peak first value rises, then decreases, and then rises again,
The width narrows		 Right trailing, Extension narrows Polarization, with three peaks observed in most years

5. Conclusions and Recommendations

This study utilizes factor and cluster analyses to conduct a sub-evaluation and comprehensive evaluation of the HQED of China’s provincial economy. The findings reveal that China’s HQED in the five aspects of innovation, coordination, greenness, openness, and sharing has achieved certain results. However, the economic development of the provinces is still uneven, uncoordinated, and insufficient. Significant differences were observed in the level of economic development among the provinces, with the eastern region outperforming the central, western, and northeastern regions. To promote HQED in China, the following policy recommendations are based on the characteristics of high-quality economic development in each province.
1) Improved level of innovation development
The empirical analysis results indicate that the level of innovation development in various provinces of China is uneven, with Guangdong Province ranking first with the highest level of innovation, while Henan Province ranks fifth with significantly lower scores. Other provinces also scored below Henan’s 0.63 points on the innovation common factor. China’s transition from rapid high-quality development has increased its dependence on external factors, including rising labor costs. First, it is crucial to fully leverage the leading role of technological innovation centers, such as Guangdong and Jiangsu. Second, increasing innovation investment in innovation, particularly in strategic emerging industries represented by big data such as artificial intelligence, the Internet of Things, and cloud computing, is essential. Third, efforts should be made to cultivate multilevel innovative talent and optimize the human capital allocation system. This can be achieved by implementing measures to attract talent and integrating multilevel talent resources across different levels and regions. Fourth, active promotion of technological innovation is vital, fostering effective cooperation among government agencies, enterprises, and universities to ensure the coordinated development of production factors and innovative resources.
2) Strengthening the coordinated development of urban and rural areas
Coordinated development is essential for sustainable and healthy economic development. To achieve this: First, the coordinated development of urbanization with a multilayered structure must be implemented. Additionally, a multilayered urban system with regional central cities, local central cities, and small towns at its core should be established according to the situation of each region. Second, through the spatial agglomeration effect of innovative and new industries, the region’s economic development must be promoted, and the economic growth of other regions must be radiated. The radiation and diffusion of multiple regions with a high level of high-quality regional development positively influence the mode of production, production behavior, and production layout of the surrounding regions. Third, the barriers between urban and rural areas must be broken. According to the different functions of urban and rural areas, a proper division of labor must be carried out, forming a regional community of interconnected, mutually supportive, and dependent urban and rural areas.
3) Enhancing China’s economic sustainability and driving China’s economic growth with green total factor productivity
Green development is crucial for promoting individuals’ well-being and improving living conditions. First, the concepts of green development and technology were applied to support every aspect of social production and reproduction. These include green design and production. Second, to enhance the level of comprehensive utilization of resources, the mode of economic development from rough to intensive, and the mode of production from high pollution and high consumption to low emission and low pollution clean. In addition, the resource recycling technology can be comprehensively utilized to realize green production and form a high-quality supply. Third, environmental protections must be strengthened by implementing measures such as bolstering nature reserves, and urban forests, and restoring wetlands with ecological functions.
4) Enhancing reform and opening up and establishing a new situation of comprehensive reform and opening-up
The promotion of regional economic prosperity and development through openness involves several key strategies: First, provinces should constantly strengthen their cooperation with neighboring developed countries and provinces to facilitate the convergence of international and domestic markets. This includes reducing barriers to market integration and fostering organic integration between the global and Chinese economies. Second, all provinces should actively engage in the construction of international and regional areas along the “One Belt, One Road” route. By participating in initiatives such as the construction of free trade zones, provinces can seize opportunities for integration into the global economy and capitalize on economic globalization. Third, provinces should proactively expand their domestic and overseas market channels to promote economic globalization. By strengthening the construction of open platforms such as bonded zones, free trade zones, export processing zones, and ports, they train and import high-level domestic and foreign talent.
5) Improving the system of ensuring individuals’ livelihoods and achieving shared development
The ultimate goals of economic and social development are aligned to achieve shared prosperity. First, efforts should be made to narrow the income gap between urban and rural areas, reducing spatial differences in regional development, establishing a fair and reasonable distribution system, and reflecting on the superiority of socialist systems. Second, we should concentrate our efforts on carrying out basic, inclusive, and bottom-line livelihood projects, providing services in areas such as healthcare, education, and public service supply, and consolidating the system to improve individual livelihoods. Third, the government must promote poverty alleviation, social security, common prosperity, and inclusive development among all provinces.

6. Patents

 

Author Contributions

L.Y.: Methodology, Literature review, Writing—Original draft preparation, Investigation. X.B: Conceptualization, Software, Policies, Supervision, Validation, Writing—Reviewing and Editing. X.S.: Visualization, Data curation. All authors have read and agreed to the published version of the manuscript.

Funding

This paper was supported in part by the Social Science Foundation of Liaoning Province under Grant (L21BJY009).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets used during the current study are available from the corresponding or first author on reasonable request.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Preprints 103761 g001
Figure 1. Mean of High-Quality Economic Development.
Figure 1. Mean of High-Quality Economic Development.
Preprints 103761 g001
Table 1. The indicator system of high-quality economic development in China.
Table 1. The indicator system of high-quality economic development in China.
Primary
Indicators		 Secondary
Indicators		 Tertiary Indicators Unit Indicator Attributes
Innovation Innovationinput Number of R&D person A1 person +
R&D expenditure A2 million yuan +
Innovationoutput The gross output value of large and medium-sized industrial enterprises A3 billion yuan +
Number of patent applications received A4 Individual +
Coordination Urban-rural coordination The difference in per capita disposable income between urban and rural areas A5 - -
The difference between urban and rural per capita living consumption expenditure A6 - -
Economic and Social Coordination The proportion of social security and employment expenditure to fiscal expenditure A7 - +
Urban registered unemployment rate A8 % -
Greenness Resource and energy consumption Energy consumption per unit of GDP A19 tons of standard coal per ten thousand Yuan -
Electricity consumption per unit of GDP A10 million hours/yuan -
Ecological environment Greening coverage rate of built-up area A11 % +
Green Life Public vehicles per 10,000 people A12 Standard Vehicles per 10,000 individuals +
Urban sewage treatment rate A13 % +
Openness Trade Openness Total import/export/GDP A14 - +
Openness of Investment Number of foreign-invested enterprises at the end of the year A15 individual +
Foreign-invested enterprises’ total investment of A16 million dollars +
Sharing Economic Achievement GDP per capita A17 % +
General public service expenditure A18 million yuan +
Per capita education expenditure A19 million yuan per individual +
Social Achievements Tertiary education per 100,000 individuals A20 per 100,000 individuals +
Health technicians per 10,000 individuals A21 per 10,000 individuals +
Table 2. Results of high-quality development.
Table 2. Results of high-quality development.
2015 2016 2017 2018 2019 2020 2021
Cities Score Rank Score Rank Score Rank Score Rank Score Rank Score Rank Score Rank
Beijing 0.405 6 0.5564 3 0.5152 3 0.4995 3 0.5075 3 0.4714 4 0.4537 3
Tian jin 0.3192 7 0.3401 7 0.2958 7 0.2717 7 0.2443 8 0.232 11 0.2638 8
Hebei 0.1974 17 0.1884 17 0.1888 17 0.1897 17 0.1869 17 0.2012 18 0.1942 18
Shanxi 0.1368 24 0.1271 25 0.1369 24 0.1366 24 0.1356 25 0.1455 24 0.1702 22
Inner Mongolia 0.1517 20 0.1582 20 0.1497 21 0.138 23 0.1336 26 0.1379 25 0.1433 25
Liaoning 0.2501 9 0.2242 11 0.2435 9 0.2295 9 0.2152 14 0.2069 15 0.2157 13
Jinlin 0.1546 19 0.1572 21 0.1464 22 0.1434 21 0.1371 23 0.1482 23 0.158 23
Heilongjiang 0.1397 23 0.1452 23 0.1617 20 0.1482 20 0.1565 19 0.1585 20 0.1778 19
Shanghai 0.5102 3 0.5558 4 0.5095 4 0.4582 4 0.4433 5 0.4257 5 0.4103 5
Jiangshu 0.7074 2 0.7128 2 0.6312 2 0.5982 2 0.5868 2 0.5788 2 0.5713 2
Zhejiang 0.4287 5 0.4729 5 0.4423 5 0.4521 5 0.4676 4 0.484 3 0.4503 4
Anhui 0.23 12 0.2177 12 0.215 12 0.2179 12 0.2274 11 0.2472 8 0.2411 10
Fujian 0.2757 8 0.2813 8 0.2581 8 0.2693 8 0.2729 7 0.2737 7 0.2677 7
Jiangxi 0.16 18 0.1686 18 0.1666 18 0.1645 18 0.1733 18 0.1811 19 0.176 20
Shandong 0.451 4 0.4393 6 0.4044 6 0.3695 6 0.355 6 0.3995 6 0.4081 6
Henan 0.2418 10 0.24 10 0.2304 11 0.2181 11 0.2156 13 0.2262 13 0.2099 16
Hubei 0.2409 11 0.2543 9 0.2352 10 0.2273 10 0.229 10 0.2332 10 0.2371 11
Hunan 0.2069 15 0.1912 16 0.1989 16 0.2068 13 0.2321 9 0.2438 9 0.2289 12
Guangdong 0.7588 1 0.7595 1 0.7695 1 0.7927 1 0.7864 1 0.7661 1 0.7202 1
Guangxi 0.1398 22 0.1599 19 0.1617 19 0.1545 19 0.1447 20 0.1569 21 0.136 21
Hainan 0.143 21 0.1523 22 0.1387 23 0.1346 25 0.14 22 0.2301 12 0.1705 9
Chongqing 0.207 14 0.2066 14 0.2056 14 0.1997 16 0.2048 16 0.2016 17 0.2632 15
Sichuan 0.2085 13 0.1994 15 0.2052 15 0.2042 14 0.2234 12 0.2215 14 0.2128 14
Guizhou 0.1021 29 0.1154 27 0.1076 28 0.1156 27 0.1233 27 0.1255 27 0.2129 29
Yunnan 0.1102 25 0.1208 26 0.1282 26 0.1322 26 0.1419 21 0.1499 22 0.1077 24
Shanxi 0.2004 16 0.2129 13 0.2079 13 0.2022 15 0.2057 15 0.2064 16 0.1447 17
Gansu 0.1028 28 0.1113 28 0.0993 29 0.1004 30 0.1046 30 0.1042 30 0.2029 30
Qinghai 0.0843 30 0.1006 30 0.0974 30 0.1052 29 0.1179 28 0.1113 29 0.1057 27
Ningxia 0.1053 27 0.1072 29 0.1156 27 0.1085 28 0.1094 29 0.1118 28 0.1147 28
Xinjiang 0.1097 26 0.1392 24 0.1321 25 0.1411 22 0.1363 24 0.1365 26 0.1128 26
Table 3. Results of the 2021 high-quality development.
Table 3. Results of the 2021 high-quality development.
Innovation Coordination Greenness Openness Sharing
Cities score rank score rank score rank score rank score rank
Beijing 0.322 5 0.2595 29 0.9167 1 0.2886 6 0.8409 1
Tian jin 0.0795 19 0.461 13 0.5872 9 0.2279 8 0.4229 6
Hebei 0.1366 13 0.453 15 0.5658 13 0.0663 17 0.2021 23
Shanxi 0.087 18 0.5505 7 0.5127 16 0.0317 24 0.204 22
Inner Mongolia 0.0636 20 0.4278 18 0.3426 29 0.0203 26 0.2672 11
Liaoning 0.1105 17 0.6134 3 0.5877 8 0.1293 11 0.1805 27
Jinlin 0.0411 23 0.5564 6 0.4744 19 0.0406 23 0.2282 17
Heilongjiang 0.0327 25 0.8699 1 0.5775 11 0.051 21 0.1342 30
Shanghai 0.2278 8 0.4316 17 0.4384 21 0.5143 2 0.5714 2
Jiangshu 0.8069 2 0.3636 23 0.6808 3 0.3574 4 0.5062 3
Zhejiang 0.5847 3 0.2878 27 0.6765 4 0.2726 7 0.441 4
Anhui 0.2304 7 0.6037 4 0.5819 10 0.0754 14 0.1864 25
Fujian 0.2366 6 0.2862 28 0.6174 7 0.1757 9 0.2884 9
Jiangxi 0.1174 16 0.458 14 0.4161 23 0.0588 19 0.2271 18
Shandong 0.457 4 0.3796 22 0.7374 2 0.291 5 0.3459 7
Henan 0.2031 10 0.4029 20 0.5124 17 0.0565 20 0.2286 16
Hubei 0.224 9 0.4997 9 0.507 18 0.067 16 0.2808 10
Hunan 0.1927 11 0.4651 12 0.6297 6 0.0689 15 0.2435 15
Guangdong 1 1 0.3067 26 0.4607 20 0.7571 1 0.4387 5
Guangxi 0.0477 22 0.5306 8 0.4016 24 0.137 10 0.1746 28
Hainan 0.002 29 0.4129 19 0.6697 5 0.441 3 0.1904 24
Chongqing 0.1178 15 0.6034 5 0.5659 12 0.0809 13 0.2482 14
Sichuan 0.1855 12 0.4678 10 0.4177 22 0.0857 12 0.2511 13
Guizhou 0.041 24 0.224 30 0.3487 28 0.0184 27 0.2067 21
Yunnan 0.0505 21 0.3258 25 0.517 14 0.0471 22 0.2107 20
Shanxi 0.1355 14 0.3841 21 0.5128 15 0.0609 18 0.3247 8
Gansu 0.0187 27 0.3472 24 0.3854 25 0.0143 28 0.1676 29
Qinghai 0.0014 30 0.6231 2 0.2485 30 0 30 0.2189 19
Ningxia 0.0128 28 0.4658 11 0.3598 27 0.0096 29 0.1849 26
Xinjiang 0.0316 26 0.4513 16 0.3681 26 0.0248 25 0.2563 12
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