Spatial and Temporal Evolution of Multi-Scale Regional Quality Development and the Influencing Factors

Abstract

In recent years, environmental pollution and massive consumption of resources in the traditional development model have posed significant challenges to the environment and society. In this study, we discuss the influencing factors of high-quality development. High-quality development is increasingly important to exploring the current state of quality development in China. Using the evaluation data of the major nodes of China’s provincial administrative regions from 2007 to 2019, the entropy value method was applied to calculate the comprehensive index of high-quality regional development, explain the spatial and temporal evolution pattern of China’s high-quality regional development, and reveal the internal driving factors of spatial and temporal evolution. The study concludes that the level of high-quality regional development in the east is higher than that in the western region, and the level of high-quality regional development in the southern region is higher than that in the north. Investment influence, industrial development, and urban–rural development have positive effects on high-quality regional development. However, location and transportation negatively affect high-quality regional development.

1. Introduction

High-quality development is essential in the field of regional development and economy, and there is considerable literature on it [1,2,3], especially on high-quality economic development, high-quality industrial development, and high-quality agricultural development. High-quality development models have emerged with the problems of high consumption, high pollution, and unsustainability in the traditional development model, attracting widespread attention from scholars worldwide. Most of the existing research focuses on economic, agricultural, and high-quality industrial development. However, research on spatial and temporal changes in high-quality regional development and comparative research on high-quality regional development is lacking. The construction of a high-quality regional development index system is an important basis for measuring the level of high-quality development. In recent years, many scholars have tried to build an evaluation index system for high-quality development, but there are no unified standards; moreover, the index system is often not comprehensive and cannot fully reflect the regional development status. In this study, we construct an evaluation system using the five new development concepts—innovation, coordination, green, openness, and sharing—to evaluate the level of high-quality regional development in China from 2007 to 2019. On this basis, we analyzed the characteristics of the spatial and temporal changes in China’s high-quality development and the influencing factors. This study can provide an intuitive overview of China’s high-quality development level and a basis for policy formulation to improve the regional high-quality development level.

1.1. Connotation of High-Quality Development

The connotation of high-quality development has been interpreted from multiple perspectives. Based on Marxist economics, Ren (2018) proposed that a theory of high-quality development with Chinese characteristics should be established based on the national conditions in China [4]. Jin (2018) interpreted high-quality development from the perspective of economic theory, arguing that high-quality development should be able to resolve the current major social contradictions and meet people’s needs, and proposed a new concept of development; that is, the development concept of innovation, coordination, green, openness, and sharing [5]. Fan et al. (2020) defined the connotation of high-quality regional development from the perspective of spatial positioning of geographical units. He pointed out that high-quality regional development is the simultaneous development of economic, social, and ecological benefits, by giving full play to regional advantages, based on the differentiated functional positioning, and targeting the required equivalence of comprehensive benefits between regions, taking into account optimal long-term and comprehensive short-term benefits [6]. Regional high-quality development has received attention from many scholars. According to Koo (2018), the core of high-quality regional development is innovation-driven, and technological breakthroughs are the cornerstone of high-quality development [7]. State Council of the PRC (2018) defined high-quality development as development that meets the growing needs of people that can reflect the concept of innovative development, innovation as the leading driver, coordination as the endogenous feature, green as the universal form, openness as the essential path, and sharing as the fundamental purpose.

Ren (2022) combined various discussions on high-quality development in academic circles and summarized high-quality development as follows: when the total amount and scale of development reach a certain stage, it will inevitably lead to changes in the mode of development, structural optimization, the transformation of old and new driving forces and the coordinated development of society, as well as the narrowing of the gap between people’s lives and the continuous improvement of the level [8].

All these views highlight the common features of high-quality development: green and efficient economic development, improved living standards of the people, and simultaneous economic, social, and ecological development.

1.2. Research on High-Quality Development from Different Perspectives

Time and space are essential dimensions for the study of regional development. The evolutionary trend of regional development can be analyzed from the time dimension, and the spatial dimension can be used to understand the relative position of regional development. (1) Time perspective: based on the research period of the regional development changes, some studies took reform and opening up as the starting point. Zhu (2020) explored the evolution pattern of China’s high-quality regional development concept since its reform and opening up [9]. Li (2021) considered 1998 as starting point for analyzing changes in the level of high-quality development in China [10]. These studies reflect some conditions of regional development at that time; however, for a long time, China’s industry has experienced a large-scale and rough development stage, with more emphasis on development quantity and speed. A part of the past industrial era emphasizes innovation and sustainability, which can provide an experience reference for high-quality regional development nowadays. (2) Spatial perspective: research results on regional high-quality development are abundant, and the scale of regional selection varies, such as the county, provincial, basin, and national scales. Zhou et al. (2014) studied the spatial and temporal evolution patterns of high-quality development in the counties [11]. Ren et al. (2019) studied the regional high-quality development of the Yellow River Basin [12]. Shi et al. (2022) focus on the disadvantages of the traditional disease analysis model and propose a network-constrained ring-shaped hotspot detection method based on the classical spatial scan statistic model [13]. (3) Spatiotemporal perspective: the perspective of combining the two dimensions, time and space, to observe the changes in high-quality regional development can describe the trends of regional high-quality development from horizontal and vertical perspectives. Yang et al. (2021) measured the level of high-quality development in Chinese regions using five development concepts, reflecting the spatial and temporal changes from 2013–2018 [14]. Shi et al.’s (2022) research on traffic flow has taken a new perspective. The traffic congestion pattern is explained more accurately based on the combination of time and space. It provides a basis for dynamic route planning [15]. Utilizing data from 1978, 2010, and 2019, Ning et al. (2021) analyzed the level of high-quality development in China’s provincial capitals and comprehensively reflected the spatial and temporal trends of high-quality development in the provincial capitals [16]. The spatiotemporal perspective can describe both the changes in the high-quality development of a single region and the trends in the development of a single region relative to other areas.

1.3. Analysis of the Factors Influencing High-Quality Development

Through a literature review, it is evident that most scholars have conducted research on the evaluation of China’s high-quality economic development, but there are relatively few quantitative analyses of the driving factors under the phenomenon of regional high-quality development. Zhao et al. (2019) constructed an SEM (structural equation modeling) model of science and technology innovation capability and high-quality economic development, and quantitatively analyzed the influence path of the components of science and technology innovation capability on high-quality economic development [17]. Fu et al. (2021) quantitatively explored the mechanism of the role of the aging population on high-quality economic development in China [18]. Some scholars also qualitatively explored the factors influencing high-quality development based on the evaluation of the high-quality development of China’s municipal economy [19]. Hua et al. (2021) pointed out that the influencing factors of high-quality coordinated development have obvious geographical characteristics, and the intensity of their effects decreases in the following order: economic strength, agglomeration capacity, government financial support, and infrastructure [20]. Because different decision-makers at different stages have differences in high-quality development, their connotations are dynamic. The factors that influence high-quality development are naturally not static and difficult to comprehensively exhaust, which leads to diversity in research findings. It is more relevant to find the main influencing factors of high-quality regional development that are needed to adapt to a stage.

In summary, most of the literature research on high-quality development mainly focused on the definition of connotation, evaluation measurement, etc., and high-quality development in the economic field. There are studies on high-quality development from the spatial and temporal dimensions, which provide ideas and a basis for regional high-quality development research. However, this literature reflects more high-quality conceptual and evaluative analyses and lacks quantitative studies of multi-scale temporality, spatial, and spatiotemporal evolution of China’s high-quality development level. The impact factors of high-quality regional development still need to be examined. To comprehensively and deeply explore high-quality regional development, we select high-quality evaluation indicators that are result-oriented, combine temporal and spatial perspectives, and comprehensively measure China’s high-quality regional development levels. This study mainly discusses the regional high-quality development from the provincial level and the large regional level, the purposes of this article are as follows: the first is to establish a regional high-quality evaluation index system; the second is to use the above-mentioned indicator system for comprehensive measurement to understand the evolution of regional high-quality development levels in spatiotemporal evolution. The third is to explore the influencing factors and quantitative relationship of regional high-quality development. We also quantitatively explore the influencing factors which cause such changes and put forward relevant policy recommendations accordingly.

2. Regional High-Quality Development Evaluation Index System Construction

High-quality development is an abstract concept, and specifically, different evaluation standards should be constructed in different fields based on their reality. The development of a high-quality regional evaluation index system is a prerequisite for high-quality regional development measurement, and the design of the index system should reflect the connotation of high-quality development. Therefore, it is suitable and feasible to sort out high-quality development evaluation indices from the connotation. As previously pointed out, many studies have explored the connotation of high-quality development. Moreover, Guo et al. (2020) established a high-quality development evaluation index system in terms of wealth indicators; specifically, they measured high-quality development in three aspects: human wealth, material wealth, and spiritual wealth [21]. Zhang (2018) constructed regional high-quality development indicators using the new development concept, mainly using the five dimensions of innovation drive, industrial upgrading, economic vitality, green development, and shared development [22]. Yang et al. (2019) constructed a high-quality development indicator system from the dimensions of economy, innovation, green, and people’s lives [23]. Cheng et al. (2022) established an evaluation index system from three dimensions: ecology, internal and external circulation, and economic development support, considering ecological environmental protection and focusing on economic stability and innovation drive [24]. In addition, the regional Sustainable Development Indicators of the European Union, the Green Growth Index of the Netherlands, and the New Economy Index of the United States are all measured in terms of green development, economic vitality, and people’s lives. Based on this, we believe that a high-quality regional development evaluation index system should include six dimensions: economic vitality, green development, innovation capability, coordinated development, open development, and shared development. These dimensions can reflect whether regional development is dynamic, whether the structure is coherent, whether development is green and sustainable, and consider the level of regional social benefits.

2.1. Economic Vitality

The connotation of high-quality regional development requires the regional economy to not only achieve scale and speed growth but also to emphasize high-quality growth. High-quality regional development should first focus on the level of regional economic development because, without a degree of economic foundation, high-quality development is impossible to discuss. Ren (2012) defined the connotation of economic growth quality, including economic growth structure, economic development stability, welfare changes, outcome distribution, resource use, and environmental costs [25]. Wei et al. argued that the quality of economic growth is measured at several levels, such as the transformation of dynamic mechanisms and structural optimization [26]. High-quality regional development should not only consider the level of regional economic development but also pay attention to economic development vitality. Economic vitality is mainly expressed as the richness and sustainability of economic development factors, which is an important reflection of the results of high-quality regional development. The indicators of the economic vitality dimension are mainly GDP per capita, the consumption level of residents, and the Engel coefficient of urban residents’ consumption. These indicators can objectively and accurately reflect the level and quality of a region’s economic development (seen in

Table 1. Regional high-quality development evaluation index system table.

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

).

2.2. Green Development

Green development requires not damaging the natural environment to achieve the goal of environment-friendly and sustainable development. Wu and Zhang (2008) explored the spatial correspondence between environmental and economic development from a coupling perspective [27]. Many existing research results have emphasized the need for a high correlation between the economy and the environment, drawing theoretical and empirical relationships between them. Green development is an important dimension of high-quality development, and it can be argued that any development at the expense of the environment is not high-quality. The green development dimension selects the reduction rate of energy consumption per unit of GDP, per capita park area, sulfur dioxide emission, and harmless domestic waste treatment rate to reflect the degree of regional green development.

2.3. Innovation Ability

Unlike economic vitality, the innovation capacity mainly reflects the endogenous development ability of the region. When measuring the high-quality development of the Yellow River Basin, Xu et al. (2020) used indicators such as the intensity of R&D expenditure investment and the number of invention patents per 10,000 people to measure the regional innovation capacity [28]. Apparently, increasing innovation investment helps regions improve their independent innovation capacity, which helps industrial upgrading, thus promoting regional development. Innovation capacity is an important sign of regional development strength that effectively reflects the efficiency and sustainability of high-quality development. Based on this, in this study, we selected four indicators to characterize regional innovation capacity: the number of students enrolled in higher education per 100,000 people, the number of effective invention patents in industrial enterprises above the scale, the full-time equivalent of R&D personnel in industrial enterprises above the scale, and the proportion of employed persons with no education.

2.4. Coordinated Development

Coordinated development not only reflects economic sustainability but also indirectly reflects the driving forces of the economy. Hua et al. (2021) used Zhejiang counties as an example to construct a high-quality development indicator system; the coordinated development indicators that they constructed focused on urban–rural coordination, industrial coordination, and financial coordination [29]. In this study, the ratio of per capita disposable income of urban and rural households was selected to reflect urban–rural coordination and the proportion of tertiary industry-added value to GDP, the proportion of local financial science and technology expenditure was selected to reflect industrial coordination social security, and the proportion of employment expenditures to total local government expenditure was selected to reflect social coordination.

2.5. Development for Global Progress

Cheng et al. (2022) and Tang et al. (2022) used the total import and export amounts as indicators to measure the degree of regional openness when constructing the index system of regional high-quality development [24,30]. In this study, the proportion of total import and export amount to GDP and the proportion of foreign-, Hong Kong-, Macao-, and Taiwan-invested enterprises to the total number of enterprises were selected as indicators to measure the degree of regional openness.

2.6. Shared Development

The aim of high-quality development is to realize the pursuit of a happy life for the general public. The 19th National Congress pointed out that the fundamental purpose of high-quality development is for the sake of social livelihood. Wang et al. (2021) used the registered unemployment rate of the urban population and a new commodity residential price index to measure the living conditions of regional people [31]. Combined with existing research results, this study measured the shared development dimension mainly through the library collection per 10,000 people, the number of health technicians per 10,000 people, the road area per capita, the number of Internet port accesses, and 4 secondary indicators.

3. Research Methodology and Comprehensive Evaluation Results

3.1. Data Selection

Considering the periodicity and long-term nature of regional development, the annual evaluation data of China’s provincial administrative regions in 2007, 2010, 2013, 2016, and 2019 (excluding Hong Kong, Macao, and Taiwan) were first selected, and the data were processed using the entropy value method to obtain a comprehensive index of China’s regional high-quality development by assembling evaluation information. The overall situation of China’s regional high-quality development was then examined in time and space dimensions according to the ranking of the index, from which the spatial and temporal evolution patterns of regional high-quality development were obtained. The data were obtained from the China Statistical Yearbook, China Population, and Employment Statistical Yearbook, and China Regional Statistical Yearbook, as well as provincial and municipal statistical yearbooks, and some missing data were made up by the interpolation method.

3.2. Measurement Method of Regional High-Quality Development

There are many methods for the comprehensive evaluation of indicators, which can be divided into subjective and objective assignment methods, among which the entropy method is an objective assignment method that can prevent the interference of subjective factors in the results; it is based on the size of the information provided by the observation of each indicator to determine the indicator weights. Many studies have been conducted to calculate the weights based on the entropy method and then calculate the composite score. Huang (2022) used the entropy value method to measure the development level of China’s digital economy [32]. Li (2022) used the entropy value method to measure the level of new urbanization and rural industrial revitalization in the Beijing–Tianjin–Hebei regions [33]. The entropy method was used to objectively calculate the weights of each index by highlighting local differences to determine the comprehensive measure of regional high-quality development. The specific calculation steps are as follows:

• Standardized evaluation matrix construction:

Because the units of different indicators are different, direct comparison and calculation were not possible; therefore, the evaluation value of each indicator was first standardized. The main standardization methods are vector specification, standardization, and extreme value methods. In this study, the extreme value method was used to transform all the indicators into values between 0 and 1.

For positive indicators, the normalization is as in Equation (1):

$$C_{ij}=\frac{x_{ij}-\min x_j}{\max x_j-\min x_j}$$

(1)

where $c_{ij}$ denotes the standard value of indicator $j$ in province $i$;$X_{ij}$ is the initial value of the indicator for the province; $\max x_j$ is the maximum value of the indicator; $\min x_j$ is the minimum value of the indicator.

For negative indicators, the normalization is as in Equation (2):

$$C_{ij}=\frac{\max x_j-x_{ij}}{\max x_j-\min x_j};$$

(2)

• Objective empowerment based on information entropy:

$p_{ij}$ represents the share of $i$ under indicator $j$ and the share of the province, calculated as in Equation (3):

$$p_{ij}=\frac{c_{ij}}{{\sum }_{i=1}^n{c}_{ij}}$$

(3)

which denotes the entropy value of the first indicator, where $0\le e_j\le 1$ and is calculated as in Equation (4):

$$e_j=-\frac{1}{\ln n}{\displaystyle \sum _{i=1}^n{p}_{ij}\ln (p_{ij})}$$

(4)

The discrepancy coefficient was then calculated. $d_j$ denotes the information entropy redundancy of the ith indicator, where $0\le d_j\le 1$, and is calculated as in Equation (5):

$$d_j=1-e_j.$$

(5)

For the weighting factor determination, $0\le w_j\le 1$, and is calculated as in Equation (6):

$$W_j=\frac{d_j}{{\displaystyle \sum _{j=1}^m{d}_j}}$$

(6)

To avoid the logarithmic nonsense problem in the entropy solving process, the normalized data were panned;

• The regional quality development indices are calculated as in Equation (7):

$$s={\displaystyle \sum _{j=1}^m{w}_j\times p_{ij}}.$$

(7)

3.3. Results of High-Quality Comprehensive Evaluation of China’s Provincial Regions

The literature studies the evolution of habitat quality based on land use change from 2000 to 2020. The period of the paper is 2000–2020, which selects three time points: 2000, 2010, and 2020. Same as the model of reference [34], we have selected five-time nodes to measure the high-quality development Index, taking into account factors such as excessive data and insignificant changes in short intervals. Using the method described in Section 3.2, we calculated the weights of each indicator and then used the weighting method to calculate the comprehensive index of high-quality development of China’s provincial regions and ranked them according to the comprehensive index.

Table 2. China’s regional quality development score and ranking table.

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

presents the results.

ArcGIS software was used to map the changes in regional high-quality development in China from 2007 to 2019 according to the comprehensive index of high-quality development in China’s provinces. The details are shown in Figure 1 (no data for Hong Kong, Macao, or Taiwan).

From the results and the ranking of the comprehensive index measurement of high-quality development in China’s provinces, we observe the following.

The rankings of some provincial administrative regions changed significantly. The northeastern provinces saw the greatest decline in ranking, whereas Henan, Shaanxi, Hainan, and Henan saw an increase in ranking. The ranking of Heilongjiang Province decreased from 19 in 2007 to 24 in 2019. Jilin Province dropped from 11th place in 2007 to 22nd place in 2019, and Liaoning Province also dropped 2 places in the ranking. Provinces that rose faster in the ranking included Henan, Shaanxi, and Hainan. The above results show that Jilin, Heilongjiang, and Liaoning’s overall index of high-quality development is gradually decreasing, mainly because the imbalanced economic and industrial structure of northeast China is difficult to adapt to the new economic status with the increasing pressure on the environment, and the loss of talents and labor force, which makes the level of high-quality development decline. The gradual improvement of Henan and Shaanxi’s high-quality development level can not be separated from the support of policies such as “Western Development” and “Belt and Road” and the vigorous development of the real economy.

Chinese provincial high-quality development has varied widely. Guangdong, Shanghai, Beijing, and Jiangsu were at the top of China’s high-quality development ranging from 2007 to 2019; Yunnan, Tibet, and Guizhou were at the bottom for a long time. After standardization, the difference between the extreme values of the high-quality development index reached nearly 10, which shows that China’s provincial high-quality development varies greatly, and regionally balanced development needs to be further strengthened.

To better analyze the spatial and temporal evolution of regional high-quality development in China, the ArcGIS natural breakpoint method was used to grade the regional high-quality development level. Based on the comprehensive index of high-quality regional development, China’s provincial administrative regions were classified into five levels: high-level, higher-level, middle-level, lower-level, and low-level regions. Details are presented in

Table 3. High-quality development index grading and regional distribution for 2019.

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

.

The high-quality development of China’s provincial quality development is stepwise in nature. The regions with higher development indices were mainly Shanghai, Beijing, Jiangsu, Zhejiang, Guangdong, and Tianjin, which were all concentrated in the eastern coastal region. The regions with lower development indices were Yunnan, Guizhou, Tibet, Guangxi, and Qinghai, all in the western inland region of China.

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

In this paper, following the principle of zoning related to geographical nodes and integrating historical and ethnic dimensions, China is divided into seven major regions: North China, Northeast China, East China, Central China, South China, Southwest China, and Northwest China. Among them, North China includes Beijing, Tianjin, Hebei, Shanxi, and Inner Mongolia Autonomous Region; Northeast China includes Liaoning, Jilin, and Heilongjiang provinces; East China includes Shanghai Jiangsu Province, Zhejiang Province, Anhui Province, Fujian Province, Jiangxi Province, Shandong Province; Central China includes Henan Province, Hubei Province, Hunan Province, and Hunan Province.

The Southern region includes Guangdong Province, Guangxi Zhuang Autonomous Region, and Hainan Province; the Southwest region includes Chongqing Municipality, Sichuan Province, Guizhou Province, Yunnan Province, and Tibet Autonomous Region; the Northwest region consists of Shaanxi Province, Gansu Province, Qinghai Province, Ningxia Province, Gansu Province, Qinghai Province, Ningxia Hui Autonomous Region, Xinjiang Uygur Autonomous Region, and Hong Kong Special Administrative Region. No data are available for the Macao Special Administrative Region and Taiwan Province. By analyzing the changes in the level of high-quality development in the seven regions of China from 2007 to 2019, the absolute values of provincial indicators within the region were taken and summed up, and the distribution of the major regional high-quality development indices was obtained after standardization as shown in Figure 2.

As shown in Figure 2, the high-quality development of China’s seven regions shows the following pattern:

• 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

The coefficient of variation can effectively reflect the degree of regional development differences; the larger the value, the greater the development differences within the region. In this study, the coefficient of variation was used to analyze high-quality regional development differences from two aspects, analyzing the trend of the degree of high-quality development differences in China over time from a global perspective, while China was divided into seven regions for zonal studies (Figure 3 and Figure 4).

From a macro perspective, the national coefficient of variation shows a fluctuating downward trend, fluctuating from 0.83 in 2007 to 0.45 in 2019, and the gap in the development level of the region-wide high-quality development index was further narrowed. From 2016 to 2019, both the region-wide coefficient of variation and the sub-regional coefficient of variation declined faster, indicating that in the 2016–2019 period, the development difference in China has been rapidly narrowing. Among them, the largest decreases were 0.77 and 0.43 in South China and Southwest China, respectively, indicating that the above-mentioned regions have made effective progress in collaborative development during 2016–2019.

5. Analysis of the Factors Influencing Regional High-Quality Development

A comprehensive index of China’s regional quality development helps better capture the regional development results. It is obviously of great practical importance to explore the factors influencing high-quality regional development and find countermeasures. In this study, five influencing factors of high-quality regional development were initially selected to test the regression step-by-step.

5.1. Factors Influencing Regional High-Quality Development

Regional high-quality development cannot be separated from the influence of production factors and the environment, and different factors have different driving roles and degrees of influence. Under the constraints of limited resources, exploring more sustainable, stable, and efficient influencing factors is conducive to understanding the main factors and making precise efforts, to promote high-quality development. Through a literature review, combined with existing research results, five regional quality development factors were selected, with the development process as the guide, as shown in

Table 4. Regional high-quality impact factor table.

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

.

5.2. Model Building and Solving

5.2.1. Statistical Testing and Selection of the Model

To avoid the pseudo-regression phenomenon, a unit root test is required before the regression of panel data to test whether the data are smooth. This study used LLC and ADF for the test. As seen in

Table 5. Unit root test for panel data.

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

Note: *** denotes variables that are significant at the 1% level.

, the p-values of all variables were less than 0.05; hence, the results were smooth, no cointegration test was needed, and regression analysis was conducted directly.

The Hausman test value is 23.74 and the p-value is equal to 0.0000, which is less than 0.05, indicating that the hypothesis that the random effect is better than the fixed effect model is rejected; therefore, the fixed effect model is chosen.

5.2.2. Regression Analysis

The regression model was constructed by taking five factors as independent variables and the regional high-quality development index as the dependent variable:

$$s_{it}={\beta }_0+{\beta }_1{X}_{1it}+{\beta }_2{X}_{2it}+{\beta }_3{X}_{3it}+{\beta }_4{X}_{4it}+{\beta }_5{X}_{5it}+{\epsilon }_{it}.$$

This is the overall index of regional quality development—${\beta }_1,{\beta }_2,{\beta }_3,{\beta }_4,{\beta }_5$—are the regression coefficients explaining each variable; $X_{1it},X_{2it}$, $X_{3it}$, $X_{4it}$, $X_{5it}$ are factors affecting the quality development of the region; ${\beta }_0$ is the random perturbation term; ${\epsilon }_{it}$ is the random error term.

According to the regression results, the Rho value is 0.935, indicating that 93.5% of the total variance is contributed by the sample data, and the fit is good. The regression results are shown in

Table 6. Panel data regression results.

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 ***

Note: ***, **, and * denote variables that are significant at the 1%, 5%, and 10% levels, respectively.

.

The regression equation according to Table 6 is as follows:

$$s=0.195+0.044X_{1it}+0.001X_{2it}+0.033X_{3it}+0.044X_{4it}-0.030X_{5it}+{\epsilon }_{it}.$$

According to the regression results in Table 6, the analyses are as follows:

(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

To reveal the spatial and temporal evolution patterns of high-quality regional development in China, we selected the regional development evaluation data of five-time nodes, namely 2007, 2010, 2013, 2016, and 2019. The following conclusions can be obtained through the comprehensive evaluation of high-quality development: (1) The level of high-quality development has been relatively low in the Qinghai-Tibet region, Northwest region, and Western region. The development level is higher in Coastal regions. Among them, Guizhou, Yunnan, and Tibet Autonomous Regions are at the bottom of China’s high-quality development provinces, and the high-quality development level of these provinces needs to be improved urgently. (2) The level of development is uneven. Uneven high-quality regional development in China has been a major problem, and this general trend has not fundamentally changed from 2007 to 2019. In general, the level of high-quality regional development in China is higher in the Southern region than in the Northern region, and the Yangtze River Basin has a higher-high-quality development index than the Yellow River Basin. Development in the Eastern region is higher than the Western region, and the level of development decreases from the Southeast coast to the Northwest inland region. There are large differences within and between the regions. (3) The high-quality development index in some regions has declined. From 2007 to 2019, Heilongjiang Province and Jilin Province in the Northeast region dropped by 5 and 11 places, respectively, with a significant downward trend. (4) Shanxi, Hebei, Hainan, Sichuan, Shaanxi, and other provinces showed an upward trend in the high-quality development index. This indicates that the level of high-quality development in the region improved faster. (5) From 2007 to 2019, the differences in the regional high-quality development index were 0.8977, 0.7587, 0.8924, 0.9247, and 0.3586, respectively, and the overall trend shows a decrease. This indicates that the gap between China’s provincial high-quality development levels decreased from 2007 to 2019, but a gap still existed.

Based on the study’s findings, the following policy recommendations are proposed to promote regional high-quality development in China, taking into account the actual high-quality regional development in China.

6.1. Accelerate the Development of Provincial Differences and Synergy

Different provinces establish leading industries based on their respective conditions in line with their advantages to avoid excessive homogeneous competition. First, it is necessary to follow the differences in the endowment of provincial resources, adhere to the principle of regional differentiated development, and establish leading regional industries in line with their own advantages to avoid excessive industrial homogenization. The development goals for different regions are tailored to local conditions; ecologically fragile areas focus on protecting the ecological environment, and traffic-unchanged areas focus on improving traffic conditions. Second, it is essential to constantly improve the evaluation index of high-quality development. High-quality regional development should be combined with the local reality based on natural conditions and human conditions; high-quality development cannot just entail economic development but needs more comprehensive evaluation indicators to ensure that a scientific and reasonable evaluation can be conducted. According to the evaluation results, the development countermeasures for each region should be adjusted optimally. Third, the focus should be on the regional inclusive green development capacity. Inclusive green development is an inherent requirement for achieving long-term sustainable development and is a fundamental expression of high-quality development. For regional high-quality development, it is necessary to consider not only economic and social equity but also a long-term sustainable development concept. Particularly in ecologically fragile regions, a green and sustainable development model should be selected to achieve synergistic economic and ecological development.

6.2. Development of a Large Regional Strategy

It is necessary to integrate China’s Western development strategy, Central rising strategy, Northeast revitalization strategy, and other regional development strategies. First, we should further improve Western developmental strategies. The “Belt and Road” development initiative gives full play to the advantages of western resource endowments, enhances cooperation and exchanges with partners along the “Belt and Road” in multiple fields, and strengthens the regional synergy with high-quality Eastern development. Relying on the advantages of the Eurasian Continental Bridge, Western resources advance economic development advantages and promote the Western region to leapfrog in development. Second, we should enhance the ability of the Central region to develop in a synergistic manner. The Central region should make full use of its superior geographical position, give full play to the advantages of major transportation routes, optimize the ability to develop the Yellow River ecological and economic belt and Yangtze River economic belt synergistically, and promote high-quality development of the region’s leading industries. We should strengthen cooperation with the Southeast coastal region, determine the development model that meets our needs, and clarify the development benchmark and development path. This is possible by drawing on the experience of other countries and regions in the rise of development. Third, we should clarify the industrial positioning of the Northeast region and implement multiple measures to promote the implementation of the Northeast revitalization strategy. In the Northeast region, government functions should be changed to better support industrial development. While recognizing the population outflow in the Northeast region, it is more important to strengthen technological innovation and promote scale and high-quality development. Measures such as increasing infrastructure construction, developing new types of agriculture, and intelligent high-tech manufacturing will reinvent the advantages of the old industrial bases in the Northeast and accelerate its high-quality development.

6.3. Key Influencing Factors and the Path to High-Quality Development

Investment in science and technology, industrial development, and urban and rural development play a positive role in the high-quality development of the region. Investment should be increased in science and technology in each region, and high-quality science and technology investment should replace the scale investment model. Moreover, investment in science and technology in ecologically fragile areas should be increased to protect the ecological environment and develop tourism. Second, it is necessary to strengthen industrial development in each region, enhance technological innovation in the industry, strengthen the pulling power of industry on the economy and society, and accelerate the elimination of backward industries to make their industrial structure more reasonable and efficient. Finally, there should be a promotion of urban and rural development. The urbanization process should be accelerated and high-quality development center cities in the Western and Qinghai-Tibet regions should be established to lead the development and avoid population loss.

6.4. Focus on Economic Development

Whether it is to increase investment in science and technology or to protect the ecological environment, it is not feasible without the support of economic strength. Not only should the central government focus on investing in areas with weak levels of high-quality development, but local governments should also actively take measures to develop their economies. It is necessary to create special industries and brands, develop tourism, attract talent, and retain the population. Only the strengthening of economic strength can enhance investment in science and technology, improve the level of urbanization, and achieve high-quality regional development.

6.5. Policy Formulation with a Long-Term Vision

Policies that are detrimental to future development should not be pursued for short-term gain. Reasonable use of local mineral and tourism resources is necessary, not blind development. It is not possible to pursue industrial development at the expense of the environment. Priority should be given to long-term development and sustainability.

7. Shortcomings and Prospects

Despite the fact that this study has made some useful explorations, it still has some deficiencies. As a first point, the division of large regions in this paper is limited to existing geographical partitions, and no clustering is performed based on multiple indicators. Second, the high-quality indicator values are all from statistical data, which are all numerical, without considering the ambiguity and uncertainty of evaluation information. Third, the entropy weight method is used to calculate the objective weights while ignoring the subjective weights. These limitations can be discussed in future studies with consideration. It will be necessary to take these factors into account in the future to gain a deeper understanding of the regional quality development in China.