National Quality and Sustainable Development: An Empirical Analysis Based on China’s Provincial Panel Data

Abstract

Based on panel data of Chinese provinces from 2011 to 2020, this paper presents empirical research on the quantitative relationship between China’s national quality and sustainable development. Moreover, this research is implemented under the index system of national quality competitiveness. Firstly, a system for assessing national quality level is established, and the factor analysis method is applied to comprehensively analyze each province’s national quality; this produces a national quality score for each province. Secondly, an Index of Sustainable Economic Welfare (ISEW) and calculation methods are created. Thirdly, the Pearson Correlation Coefficient is employed to explore the relationship between the national quality of Chinese provinces and the sustainability of their economic development. Finally, the test of Granger causality (panel data) is applied to further analyze the interplay between national quality indexes and sustainable economic development. As the research results indicate, national quality can comprehensively impact sustainable economic development at any economic level, as well as its sustainable capability. National quality can still comprehensively affect the sustainable development of the economy. Nevertheless, sustainable economic growth does not affect all elements of national quality, especially at the level of morality. Additionally, the positive influence of sustainable economic development on national quality is limited and lags behind. Based on the results of our empirical analysis, several policy recommendations are given to improve China’s national quality and sustainable economic development.

1. Introduction

In China, the lack of self-cultivation in its population, food safety problems, and official corruption and bribery are obvious problems. Although recent years have witnessed a rise in the Chinese economy, the national quality in this country has been widely criticized. There are a series of severe problems facing Chinese citizens, including a lack of virtue, food safety, and official corruption and bribery. According to data from the World Competitiveness Yearbook released by the International Institute for Management Development (IMD) in Lausanne, Switzerland, China’s international competitiveness reached a global peak in 2007, ranking 15th. However, it dropped to 17th place in 2022. In 2007, China’s economic strength was identified as its core source of international competitiveness, ranking the third in the world. In 2022, it remained almost unchanged, ranking fourth. However, China’s national quality failed to achieve a good ranking, ranging from 15 to 20th. There was a large gap between the world ranking of China’s national quality and its economic strength, especially between 2007 and 2014, during which the ranking of China’s national quality declined despite its growing economic strength [1].

National quality is a basic quality that can shape the national economy [2]. Regional national quality reflects the long-term sustainable development and growth of human resources in each region within a country, and includes various aspects of human resources such as physical, scientific, cultural, and psychological qualities. Based on the system for assessing national quality competitiveness, the “National Quality Competitiveness in China Regions” research group puts forward five indexes of national quality, including population characteristics, labor force features, employment conditions, national education, and life quality [3]. National quality is reflected not only in the micro-national foundation of a country, but also in other meso- and macro-aspects. This way, quality indicators can also be used by enterprises and governments, enabling them to control their performance. Citizens are identified as the micro-foundation of enterprises and governments. Essentially, the quality of enterprise managers, employees, and government officials determines a country’s national quality. China’s economic development focuses on many aspects, such as the improvement of economic growth, environmental sustainability, social welfare, etc. [4]. However, there is dialectical interplay between national quality and sustainable economic growth. Meanwhile, due to the different levels of regional economic development, national quality will inevitably be demonstrated at different levels [5]. National quality plays a decisive role in the capacity for long-term economic growth in a country [6]. In the meantime, economic growth and development should primarily aim to improve people’s spiritual quality. Likewise, good development in any individual should be based on institutional progress, the continuous and effective growth of material wealth, and the improvement of economic welfare [7].

Is there interplay between national quality and the sustainability of economic development? If yes, what is the specific influential mechanism? Determining the answer to this question would be of great practical significance, because it would help promote the improvement of national quality and enhance the capacity for sustainable economic development. If improving national quality has a great influence on promoting the sustainable development of an economy, China must prioritize this. Conversely, if sustainable economic development plays a bigger role in improving national quality, priority should be given to sustainable economic development. If there is mutual interplay between national quality and sustainable economic development, more attention should be paid to the robust development of both national quality and sustainable economic growth. For this reason, this paper first establishes an index system for national quality competitiveness in China, and then, selects panel data from 31 provinces from 2011 to 2020. Thus, the national quality index of each province can be measured effectively. Secondly, an accounting system and an index for the sustainable development of economic welfare are established, and the ISEW of 30 provinces is calculated according to the available data, which measures each province’s competitiveness regarding sustainable economic development. Finally, through factor analysis, a correlation test, and a Test of Granger Causality, quantitative analysis and comparative research are conducted to analyze the relationships among national quality, the indexes of national quality, and sustainable economic development. Compared with existing research, this research shows innovation from the following three points of view. First, this paper expands the meaning of national quality in a broad sense, assuming that national quality will be simultaneously reflected in three dimensions: micro, meso, and macro (i.e., citizens, enterprises, and governments). Secondly, in this paper, national quality is re-measured, and the spiritual index of morality is added, which expands the scope of the national quality index. Third, the relationship between the national quality index and sustainable economic development is quantitatively measured by means of factor analysis (panel data) and the test of Granger causality, which supplement the insufficient quantitative analysis in existing literature.

2. Literature Review

The level of national quality is a critical factor in a country’s economic, social, cultural, and ecological development. People are the most decisive force in productivity, and sustainable development ultimately depends on meeting human needs, both material and spiritual. Improving the material aspect can be achieved by enhancing science and culture to improve living environments, physical health, and more. Meanwhile, enhancing the spiritual aspect involves continuously improving people’s ideological and moral quality to promote spiritual civilization. The combination of these two aspects can maximize human potential, boost productivity, and provide essential support for sustainable economic and social development [8]. As science and technology have advanced and productivity has improved, material civilization has greatly progressed [9]. However, in the future, people’s needs will shift from the material to the spiritual level, and accumulating intellectual capital will be crucial to fulfilling these needs and promoting sustainable societal development [10]. National quality is mainly represented by demographic characteristics, including labor force, employment conditions, national education, and life quality.

In studies on demographic characteristics using China’s provincial-level data during the period 1989–2004, Zheng found that a reduction in China’s dependency ratio helped it achieve rapid economic growth [11]. For Asian countries, dependency ratios do not have a short-term impact on GDP per capita growth, but they have a significant long-term impact and play an important role in sustainable economic development [12]. As for national education, based on provincial data from 1978–2005, Li found that decreasing the student–teacher ratio (primary education) and increasing the percentage of the population aged 6 years and older with an education level of secondary or above had a positive effect on economic growth [13]. Some studies using time series data from the World Bank Indicators for the period of 1997–2020 have shown that increased public spending on education has a positive impact on its GDP increase [14]. Gylfason found that one of the reasons for slow economic growth in countries rich in natural resources was the neglect of education [15]. Dong uses the Engel coefficient to represent regional quality of life, and it has been an important mechanism for explaining economic development and life quality in China [16]. Iwata, based on province-level panel data, used a semiparametric partial linear model to account for potential nonlinearity in the relationship between traffic accidents and economic growth, and found that the relationship exhibited an inverted U-shaped pattern [17]. In addition, many economists argue that poverty and inequality contribute to lower moral standards. However, the increasing crime rates that emerged in industrialized countries after World War II indicate that despite technological advancements and economic growth, social trust and interpersonal relationships are becoming increasingly fragile. This erosion of social bonds may ultimately lead to a decline in moral standards and spiritual quality [18]. National quality is an important part of national basic competitiveness that reflects the development of human capital and the ability of a country to sustain economic development and growth in the long term. The current research focuses on the overall level of national quality, but there is a lack of research on the regional level. By studying the regional level, it is possible to analyze the development status of relevant factors in the region, make regional comparisons to accurately reflect the regional and factor advantages of economic development, and lay the foundation for evaluating the overall national quality competitiveness of the country [3]. While previous studies have examined the relationships between the five dimensions of national quality and economic growth, there is limited literature on their connection to sustainable economic growth. This paper aims to establish a comprehensive evaluation system to better understand the relationship between national quality and sustainable economic growth. The findings of this study will provide guidance for policymakers to achieve sustainable economic development goals.

3. Materials and Methods

3.1. The System for the Assessment of National Quality

Before 2001, IMD evaluated the advantages and disadvantages of each country’s international competitiveness according to the eight-element index system of international competitiveness. The eight-element index includes national economic strengths, internationalization, government management, financial system, infrastructure, enterprise management, scientific technology, and national quality. After 2001, IMD changed the original assessment system and established four international competitiveness index systems, including economic operation, government efficiency, enterprise efficiency, infrastructure, and social system. However, the new system is more capable of meeting the demands of market economies in developed countries [3]. At that time, China was experiencing a period of economic reform and rapid development, and the original eight-element index system was more applicable to the assessment of China’s international competitiveness. Accordingly, in this paper, the index system of national quality is still grounded in the eight-element indexes of international competitiveness established before 2001, wherein the national quality elements consist of 5 sub-elements and 48 indexes. To elaborate, the 5 sub-elements refer to demographic characteristics, labor force features, employment conditions, national education, and life quality. The first three categories are used to depict human resource quantity, structure, and utilization degree, and the remaining two types represent the quality of human resources.

In order to establish a robust index system of national quality and obtain the scores of national quality factors, we preprocessed the 48 indexes under the 5 sub-elements of the original national quality system before conducting an effective quantitative analysis. Index processing was conducted as follows. First, 21 indexes were obtained after dealing with missing indexes and deleting indexes that lacked data integrity. Second, it was necessary to use existing indexes to replace the original unavailable ones. According to the current situation in China and data availability, the index “proportion of women in parliament” that represents equal opportunities was replaced with “proportion of women in village committees and neighborhood committees”. Third, most of the indexes reflecting life quality belong to subjective indexes that are difficult to obtain. For this reason, we added a series of coefficients, such as “Urban Household Engel’s Coefficient” and “Rural Household Engel’s Coefficient”, in order to measure life quality and standard of living. Fourth, to further measure the moral index of national quality, an index that measures morality was added based on the original index system. Although morality is a relatively abstract concept, it is closely associated with people’s obedience to laws and regulations. In this regard, obedience to traffic rules in a certain area can indirectly reflect the moral levels of local citizens. For this reason, we integrated the number of traffic accidents in a certain area with the index system of national quality, creating a new index to measure the morality of national citizens. After preprocessing, there were still 25 indexes left in the system, and these are shown in

Table 1. National quality index after pre-processing.

Sub-Factor	Index	Description	Variable Name
Demographic Characteristics	Proportion of people aged under 15 years		X1
	Proportion of people aged above 65 years		X2
	Dependency ratio	The population excluding people aged 15–64/the population of people aged between 15 and 64	X3
	Average life expectancy		X4
Features of Labor Force	Labor force population	Employment and registered unemployment. Due to the lack of rural data, we only measured the urban features of labor force.	X5
	The ratio of the labor force population		X6
	The growth rate of the labor force		X7
	The proportion of the female labor force		X8
Employment Conditions	Employed population	Due to the lack of rural data, we only measured the urban employment conditions.	X9
	Employment rate		X10
	The growth rate of employment		X11
	The proportion of agriculture employment		X12
	The proportion of industrial employment		X13
	The proportion of service industry employment		X14
	Unemployment rate		X15
National Education	The proportion of people with higher education		X16
	Student–teacher ratio (primary education)	Number of pupils per teacher (primary education, including regular primary schools)	X17
	Student–teacher ratio (secondary education)	Number of pupils per teacher (secondary education, including regular high schools, secondary vocational education, and regular junior high school)	X18
	The weights of public education expenditure in GDP		X19
	Illiteracy rate	The proportion of illiterate adults (over 15 years old)	X20
Life quality	The proportion of the urban population		X21
	Engel’s Coefficient of Urban Households	The ratio of expenditure on food per urban household	X22
	Engel’s Coefficient of Rural Households	The ratio of expenditure on food per rural household	X23
	The proportion of women in village committees and neighborhood committees	The proportion of women in village committees and neighborhood committees refers to a proxy variable for the proportion of women in Congress	X24
	Frequency of traffic accidents	The index of morality	X25

below.

The set of data presented above stems from the China Statistical Yearbook, the China Labor Statistical Yearbook, the China Social Statistical Yearbook, the China Health and Family Planning Statistical Yearbook, and the Provincial Statistical Yearbooks. Moreover, the 25 types of index data employed in this paper cover 31 provinces, autonomous regions, and municipalities in China from 2011 to 2020.

3.2. The Calculation of Sustainable Economic Development

There are many index systems used to measure sustainable economic development, and the Index of Sustainable Economic Welfare (ISEW) is currently widely used. The ISEW was put forward by Daly and Cobb [19] in 1989, and afterwards, it was modified by Cobb [20]. The ISEW integrates gross domestic product, which measures the performance of the macroeconomy, with certain indexes that represent social and environmental factors. The main contribution lies in the fact that it not only considers the social and environmental costs related to economic growth, but supplements and regulates the traditional single economic index [21]. However, the social and environmental costs are important standards for measuring national development sustainability. This paper mainly refers to the ISEW framework proposed by Clarke and Islam [22], and incorporates method of measuring China’s sustainable economic welfare proposed by Zhu et al. [23]. On the basis of data availability, a set of sustainable economic welfare indexes can be established, and these include: a. adjusted personal consumption expenditure; b. household labor value; c. the income of durable products and services; d. public expenditure; and e. social cost and environmental pollution cost. The calculation method for the ISEW is illustrated in Equation (1).

Table 2. ISEW system construction and data sources.

Index	Calculation Method	Data Resource
Weighted Residential Consumption, CISEW	CISEW = Per Capita Residential Consumption Expenditure × Inequality Index of Income Distribution	China Statistical Yearbook
Services of Household Labor, GISEW	GISEW = GDP × 30%	China Statistical Yearbook
Services from durable consumer goods, IISEW	IISEW = Per Capita Residential Consumption Expenditure on Consumer Durables × 10%	Statistical Yearbook of Provinces
Public expenditure cost, W	W = 75% × Infrastructure Spending + 50% × Public Expenditure on Health and Education	CSMAR
Social expenditure cost, D	D = Cost of Commuting + Cost of Car Accidents + Urbanization Cost	Statistical Yearbook of Provinces
Environmental expenditure cost, E	E = Cost of Water Pollution + Cost of Air Pollution + Cost of Long-term Environmental Damage	China Energy Statistical Yearbook, Ministry of Ecology and Environment of the People’s Republic of China, National Bureau of Statistics

indicates the specific indexes and calculation methods of the ISEW system construction in each province. Due to the lack of data, the calculation of the ISEW index in Tibet is excluded in this paper.

$$ISEW=C_{ISEW}+G_{ISEW}+I_{ISEW}+W-D-E$$

(1)

3.2.1. Weighted Residential Consumption, C_ISEW

Residents’ economic welfare cannot be truly reflected if the expenditure of residents’ consumption (rc) is directly incorporated into ISEW accounting. Hence, it must be readjusted in order to reflect the real level. Because there is significant income inequality between households and individuals, consumption expenditure can be readjusted using income inequality [24]. Thereby, we employed the Gini coefficient (Gini) to calculate the index of income distribution inequality (ie) [25], and the formula of weighted readjusted household Consumption (C_ISEW) is as follows:

$$C_{ISEW}=rc\times ie=rc\times \frac{1}{Gini+1}=\frac{rc}{Gini+1}\begin{array}{ccc}& & \end{array}$$

(2)

3.2.2. Services of Household Labor, G_ISEW

The household benefits created by performing housework cannot be commercially evaluated, and the real economic value of unpaid housework cannot be precisely measured. Likewise, the value of non-market labor cannot be calculated, while the value of household labor plays an important role in the index of sustainable welfare economy. The mainstream accounting methods include opportunity cost, industry substitution, and comprehensive substitution. The value of China’s household labor force accounts for about 1/3 of the GDP [26]. On this basis, we chose to use 30% of the GDP to estimate unpaid household labor.

3.2.3. Services from Durable Consumer Goods, I_ISEW

This index aims to reflect the real value of durable consumer goods rather than their purchase value. In simpler terms, this index reflects the service benefits durable goods bring to residents. Thus, we chose 10% of residents’ per capita consumption of durable goods to reflect this index [27].

3.2.4. Public Expenditure Cost, W

Public expenditure cost represents a non-defensive public expense, and includes spending on public infrastructure, public health, and education. Since there is no direct payment when people use infrastructure, the service value of this hard-to-calculate consumer expenditure was included in this study. The welfare benefits created by government spending in developed countries are much larger than those in their developing counterparts. As a developing country, China’s circumstances only allowed us to choose 75% of its infrastructure expenses as the weight for infrastructure spending [23]. Pulselli et al. note that only half of the expenses in public education and public health are preventive; for this reason, this spending (50%) was incorporated into public expenditure cost [28].

3.2.5. Social Expenditure Cost, D

Social expenditure cost represents a defensive public expense, and includes commuting cost, car accident cost, and urbanization cost. We employed traffic expenditure, a portion of residents’ per capita consumption expenditure (rct), to calculate their commuting costs. Specifically, commuting cost = 0.3 (rct − 0.3 rct), where 0.3 rct represents the estimated cost of private car depreciation [4]. Car accident cost is defined as the direct financial losses caused by traffic accidents, and the cost of urbanization accounts for 18% of urban residents’ income [22].

3.2.6. Environmental Expenditure Cost, E

Environmental cost refers to the spending of funds on environmental protection in a certain area, which indicates the sustainable development of the area. It includes water pollution costs, air pollution costs, and long-term environmental damage costs. The cost of water pollution was obtained by multiplying the shadow price of water pollution by the total amount of sewage treatment. We standardized the shadow price of water pollution as 0.77 CNY/ton [29]. The air pollution cost included the cost of gas emissions, such as nitrogen oxides, sulfur dioxide, particulate matter, and carbon dioxide [23], each of which was multiplied by its respective shadow price. The shadow prices of these pollutants are as follows: 904 EUR/ton (nitrogen oxides), 2324 EUR/ton (sulfur dioxide), 130 EUR/ton (particulate matter), and 7.28 USD/ton (carbon dioxide) [28,30]. The long-term environmental losses are depicted by the carbon emission costs of three nonrenewable resources, such as coal, oil, and natural gas. The carbon emission cost of each resource was obtained by multiplying the carbon emission amount converted by each resource and the shadow price of carbon emission. The shadow price of carbon emissions was standardized as 717.27 CNY/ton [29]. The shadow prices above are revised according to the residential price index on a yearly basis.

4. Empirical Analysis and Results

4.1. Factor Score and Classification of National Quality

The measurement of national quality plays an important role in its research. At present, the domestic and foreign measurement method involves calculating the competitiveness indexes of national quality, and then, comparing and analyzing the national quality levels of various countries. Under the national quality index system, in this paper, we used 25 indexes of 31 provinces from 2011 to 2020 to conduct factor analysis. In doing so, we obtained comprehensive factor scores to implement continuous measurements and comparisons of the national quality of various provinces in China.

There are both negative and positive components of the selected indexes. Specifically, the negative indexes consist of the population aged above 65, the dependency ratio, agricultural employment, industrial employment, the unemployment rate, the student–teacher ratio (primary education), the student–teacher ratio (secondary education), the illiteracy rate, the Engel coefficient of urban households, the Engel coefficient of rural households, and frequency of traffic accidents. We positively processed the data of the negative indexes, among which the positive processing method of the negative indexes’ percentages involved taking the reciprocal. Conversely, the positive processing method of the negative indexes’ absolute values involved taking the negative number [31].

We conducted factor analysis 10 times on each cross-section dataset between 2011 and 2020, followed by the calculation of factor scores and comprehensive scores. Due to the limits of this paper, we can only specify the calculation process of factor scores in 2011.

Table 3. Component score matrix, 2011.

	Components
	1	2	3	4	5	6
Proportion of population aged below 15 years	−0.091	0.041	0.044	−0.175	0.185	0.100
Proportion of population aged 65 years and over	−0.006	0.000	0.046	−0.018	0.387	0.028
Dependency ratio	0.110	−0.035	0.007	0.129	−0.054	−0.059
Average life expectancy	0.105	−0.012	−0.051	−0.149	−0.008	0.003
Labor force population	−0.058	0.052	−0.287	0.191	0.017	0.125
Labor force ratio	0.005	0.204	−0.006	0.081	0.067	0.093
Labor force growth rate	−0.010	0.185	0.035	−0.056	0.024	−0.076
Proportion of female labor force	0.019	−0.169	−0.036	0.019	0.074	0.041
Employed population	−0.059	0.054	−0.287	0.192	0.021	0.129
Employment rate	0.005	0.204	−0.009	0.075	0.070	0.106
Employment growth rate	−0.010	0.185	0.035	−0.059	0.028	−0.075
Ratio of population employed in agriculture	0.087	0.044	0.111	−0.171	0.188	−0.399
Ratio of population employed in industry	−0.029	0.036	0.182	0.010	−0.174	0.291
Ratio of population employed in service industry	0.104	0.012	0.002	−0.017	0.067	0.258
Unemployment rate	0.077	0.000	0.026	−0.194	0.072	0.493
Proportion of people with higher education	0.125	0.014	0.062	−0.037	−0.059	0.137
Student–teacher ratio (primary education)	0.069	−0.024	0.080	0.279	−0.168	−0.092
Student–teacher ratio (secondary education)	0.084	0.028	−0.044	0.203	0.084	0.027
Percentage of public education expenditure in GDP	−0.047	0.055	0.116	0.189	0.089	0.099
Illiteracy rate	0.125	−0.029	0.063	−0.132	−0.048	−0.003
Proportion of urban population	0.129	−0.009	−0.006	−0.150	0.074	−0.038
Urban Engel coefficient	−0.031	0.007	−0.076	0.559	0.017	−0.035
Rural Engel coefficient	0.006	−0.031	0.105	0.059	−0.646	0.080
Proportion of women in village committees and neighborhood committees	0.126	0.042	0.062	−0.056	0.066	−0.073
Frequency of traffic accidents	0.029	0.108	0.261	0.141	−0.171	−0.040

indicates the component score matrix of each index in 2011. The score for each variable is positively associated with its relationship with the common factor; this means the higher the score, the more a common factor is able to represent this variable.

According to the component score matrix, the score for each factor in each province can be calculated, and below is the equation for each factor score:

$$\begin{array}{l} F_1=-0.091X_1-0.006X_2+0.110X_3+0.105X_4-0.058X_5+0.005X_6-0.010X_7+\\ 0.019X_8-0.059X_9+0.005X_{10}-0.010X_{11}+0.087X_{12}-0.029X_{13}+0.104X_{14}+0.077X_{15}+\\ 0.125X_{16}+0.069X_{17}+0.084X_{18}-0.047X_{19}+0.125X_{20}+0.129X_{21}-0.031X_{22}+\\ 0.006X_{23}+0.126X_{24}+0.029X_{25}\end{array}$$

(3)

In the same way, the factor scores of F₂, F₃, F₄, F₅, and F₆ can be obtained. As

Table 4. Common Factor Contribution Rate and Cumulative Variance Contribution Rate, 2011.

Factor	Initial Eigenvalue			Extract Loading Sum of Squares			Rotational Loading Sum of Squares
	Total	Variance, %	Cumulative, %	Total	Variance, %	Cumulative, %	Total	Variance, %	Cumulative, %
1	8.383	33.531	33.531	8.383	33.531	33.531	7.762	31.050	31.050
2	5.600	22.402	55.932	5.600	22.402	55.932	5.142	20.570	51.619
3	3.386	13.544	69.476	3.386	13.544	69.476	3.786	15.142	66.762
4	1.461	5.845	75.322	1.461	5.845	75.322	1.562	6.250	73.011
5	1.389	5.557	80.879	1.389	5.557	80.879	1.533	6.131	79.142
6	1.068	4.273	85.153	1.068	4.273	85.153	1.503	6.011	85.153

indicates, it is clear that the first six common factors’ contribution rates of cumulative variance reach 85.153%, which reflects more than 85% of the comprehensive information on each variable. The other years’ data analysis results also demonstrate that the first six or seven common factors can explain about 85% of the original variables. Accordingly, we can extract the first to sixth common factors as calculation factors to represent the level of development of national quality in China, so that we can analyze and assess the comprehensive level of national quality in each province. Regarding the variance contribution rate of the common factor as the weight, the variance contribution rates of the six common factors amount to 31.050%, 20.570%, 15.142%, 6.250%, 6.131%, and 6.011%, respectively. Below is Equation (4), which shows the comprehensive score for national quality in each province:

$$zF=0.3105F_1+0.2057F_2+0.15142F_3+0.0625F_4+0.06131F_5+0.06011F_6$$

(4)

As shown below,

Table 5. National quality composite score, 2011–2020, 31 provinces.

	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Beijing	1.277	1.223	1.19	1.209	1.242	1.2	1.24	1.143	1.321	1.297
Tianjin	0.922	0.969	0.163	0.225	0.953	0.252	0.761	0.933	0.472	0.165
Hebei	−0.234	−0.202	−0.268	−0.315	−0.343	−0.155	−0.347	−0.28	−0.239	−0.015
Shanxi	−0.035	0.012	0.184	0.322	0.219	0.245	0.218	0.182	0.218	0.077
Inner Mongolia	0.065	0.009	0.351	0.449	0.127	0.073	0.107	0.164	0.615	0.15
Liaoning	0.182	0.229	0.21	0.266	−0.111	0.095	0.168	0.133	0.211	0.243
Jilin	−0.103	−0.045	0.154	0.129	0.068	−0.032	0.067	0.161	0.287	0.019
Heilongjiang	0.295	0.179	0.268	0.388	0.116	0.281	0.33	0.317	0.182	0.138
Shanghai	0.825	0.545	0.539	0.419	0.592	0.605	0.692	0.51	0.608	0.825
Jiangsu	−0.041	−0.274	−0.29	−0.263	−0.221	0.49	−0.2	−0.319	−0.17	0.056
Zhejiang	−0.443	0.08	−0.45	−0.301	−0.356	−0.095	−0.232	−0.147	−0.258	0.018
Anhui	−0.297	−0.145	−0.369	−0.311	−0.302	−0.074	−0.33	−0.383	−0.372	−0.254
Fujian	−0.291	−0.272	−0.439	−0.38	−0.271	−0.326	−0.354	−0.389	−0.301	−0.398
Jiangxi	−0.038	−0.125	−0.192	−0.159	−0.119	−0.127	−0.267	−0.229	−0.133	−0.325
Shandong	−0.076	−0.35	−0.268	−0.343	−0.343	0.377	−0.394	−0.517	−0.306	−0.153
Henan	−0.433	−0.334	−0.35	−0.104	−0.238	0.019	−0.421	−0.394	−0.367	−0.325
Hubei	−0.368	−0.35	−0.213	−0.165	−0.136	−0.438	0.372	0.332	−0.184	−0.321
Hunan	−0.088	−0.281	−0.256	−0.188	−0.281	0.036	−0.231	−0.263	−0.148	−0.27
Guangdong	−0.626	−0.481	−0.155	−0.614	−0.502	0.131	−0.352	−0.387	−0.392	−0.031
Guangxi	0.034	−0.088	−0.037	−0.199	−0.136	−0.092	−0.24	−0.026	0.024	−0.473
Hainan	0.261	0.573	0.298	0.484	0.263	−0.179	0.234	0.16	−0.037	0.193
Chongqing	0.047	0.084	−0.104	−0.014	0.016	0.06	−0.026	−0.101	−0.062	−0.2
Sichuan	−0.409	−0.583	−0.471	−0.453	−0.359	−0.131	−0.341	−0.399	−0.354	−0.075
Guizhou	−0.167	−0.145	−0.087	−0.056	−0.065	−0.237	−0.235	−0.295	−0.338	−0.477
Yunnan	−0.519	−0.319	−0.398	−0.466	−0.464	−0.57	−0.367	−0.148	−0.504	−0.269
Xizang	−0.007	0.127	0.335	0.06	0.137	−0.702	−0.078	0.133	0.095	0.791
Shaanxi	−0.287	−0.222	−0.003	−0.164	−0.178	−0.208	−0.216	−0.1	−0.081	−0.311
Gansu	0.021	0.098	0.202	0.289	0.346	0.06	0.211	0.076	0.164	−0.083
Qinghai	0.172	0.112	0.293	0.154	0.219	−0.204	0.034	0.115	0.014	0.104
Ningxia	0.287	0.016	0.112	0.063	0.184	−0.1	0.175	0.101	0.045	−0.082
Xinjiang	0.073	−0.041	0.051	0.038	−0.056	−0.254	0.026	−0.085	−0.01	−0.016

indicates the composite score for national quality in 31 provinces from 2011 to 2020.

Based on Table 5, we can calculate the average score for the national quality factor from 2011 to 2020, and then, sort and categorize the 31 provinces according to their national quality. In order to further analyze the regional gap in national quality development, this paper divides the 31 provinces into two groups depending on their national quality rankings. The first group consists of the 1st–15th places, and represents higher-level development areas or developed areas. The remainder belong to the second group, which represents areas with a lower level of development or that are underdeveloped [32]. See

Table 6. Ranking and classification of national quality, 2011–2020, 31 provinces.

Province	Average Score	National Quality Ranking	National Quality Level Classification
Beijing	1.2342	1	Provinces with High Level of National Quality
Shanghai	0.616	2
Tianjin	0.5815	3
Heilongjiang	0.2494	4
Hainan	0.225	5
Inner Mongolia	0.211	6
Shanxi	0.1642	7
Liaoning	0.1626	8
Gansu	0.1384	9
Qinghai	0.1013	10
Xizang	0.0891	11
Ningxia	0.0801	12
Jilin	0.0705	13
Xinjiang	−0.0274	14
Chongqing	−0.03	15
Jiangsu	−0.1232	16	Provinces with Low Level of National Quality
Guangxi	−0.1233	17
Hubei	−0.1471	18
Jiangxi	−0.1714	19
Shaanxi	−0.177	20
Hunan	−0.197	21
Guizhou	−0.2102	22
Zhejiang	−0.2184	23
Shandong	−0.2373	24
Hebei	−0.2398	25
Anhui	−0.2837	26
Henan	−0.2947	27
Guangdong	−0.3409	28
Fujian	−0.3421	29
Sichuan	−0.3575	30
Yunnan	−0.4024	31

for details.

As shown in Table 5, according to our national quality evaluation system, the national quality of each province does not improve year by year, and fluctuates up and down during this decade. Compared with 2011, the national quality level of each province in 2020 does not improve significantly, and the number of provinces with positive scores changes from 13 to 12. Fortunately, the provinces with the lowest scores in 2011 (Guangdong, with −0.626) and in 2020 (Guangxi, with −0.473) improved by 24.4%. Beijing, the province with the highest score for national quality, only increased by 1.566% during that decade. From Table 6, it can be seen that more than half of the provinces in China have negative scores, and the central region ranks lower. In addition, the difference between the highest and lowest national quality score is as high as 1.6366. The above results show that there is room to improve the national quality in China, and there is a large gap among provinces, while it is more difficult to improve the national quality in regions with higher scores, but relatively easy in regions with lower scores.

4.2. The Level of Sustainable Economic Development in Each Province

According to the index and calculation methods outlined in Section 3.2, the indexes of sustainable economic welfare development in 30 provinces from 2011 to 2020 are shown in

Table 7. ISEW per capita (unit: 10,000 CNY), 2011–2020, 30 provinces.

	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
Beijing	3.644	3.957	4.482	4.727	5.149	5.447	6.180	6.784	6.416	5.627
Tianjin	2.363	2.638	2.725	3.116	3.171	3.414	3.772	5.158	4.345	3.385
Hebei	1.013	1.166	1.046	1.155	1.205	1.352	1.568	3.239	1.788	2.138
Shanxi	0.338	0.377	0.159	0.094	−0.150	−0.087	0.124	1.229	0.396	0.384
Inner Mongolia	0.341	0.349	0.284	0.307	0.419	0.606	0.620	1.287	0.035	−0.394
Liaoning	1.422	1.589	1.495	1.579	1.764	1.914	2.042	2.655	2.320	1.932
Jilin	1.192	1.318	1.236	1.396	1.550	1.707	1.789	1.846	2.538	2.230
Heilongjiang	1.119	1.235	1.206	1.271	1.309	1.364	1.439	1.615	2.196	1.315
Shanghai	3.611	3.791	4.184	4.385	4.822	5.490	5.998	4.626	4.576	4.637
Jiangsu	2.411	2.574	2.726	2.927	3.230	3.460	3.952	3.544	3.902	3.714
Zhejiang	2.589	2.649	2.776	3.041	3.210	3.380	3.646	3.135	3.417	3.077
Anhui	1.317	1.574	1.269	1.502	1.572	1.800	1.976	2.160	2.452	2.282
Fujian	2.079	2.333	2.302	2.607	2.914	3.098	3.517	3.601	3.889	3.656
Jiangxi	1.310	1.506	1.386	1.546	1.685	1.842	2.058	2.585	2.641	2.065
Shandong	1.535	1.640	1.584	1.738	1.886	2.084	2.243	2.445	2.235	2.005
Henan	1.221	1.437	1.212	1.381	1.552	1.640	1.890	2.494	2.276	2.160
Hubei	1.469	1.765	1.774	1.936	2.138	2.399	2.706	3.259	2.979	2.903
Hunan	1.500	1.703	1.554	1.811	1.995	2.137	2.289	2.627	2.671	2.595
Guangdong	2.384	2.610	2.316	2.623	2.979	3.313	3.499	3.119	3.542	3.195
Guangxi	1.271	1.464	1.143	1.329	1.520	1.599	1.767	2.160	2.293	1.943
Hainan	1.517	1.671	1.592	1.772	1.867	2.038	2.215	2.502	2.694	2.527
Chongqing	1.687	1.868	1.783	1.982	2.218	2.459	2.752	2.813	3.194	2.963
Sichuan	1.514	1.616	1.491	1.612	1.830	1.976	2.288	2.543	2.782	2.577
Guizhou	0.803	0.942	0.732	0.958	1.116	1.317	1.476	1.992	2.665	2.021
Yunnan	1.131	1.353	1.120	1.289	1.432	1.586	1.851	4.258	3.205	2.528
Shaanxi	1.456	1.580	1.351	1.427	1.561	1.655	1.816	4.008	2.010	2.179
Gansu	0.956	1.192	0.947	1.067	1.127	1.325	1.437	2.294	1.799	1.787
Qinghai	1.095	1.235	1.225	1.415	1.589	1.724	1.925	2.868	2.210	2.281
Ningxia	0.010	0.137	−0.116	−0.006	0.094	0.253	−0.028	0.489	−0.362	−0.899
Xinjiang	1.073	1.247	0.952	0.922	0.960	1.048	1.187	2.252	1.523	1.169

:

As shown in

Table 8. ISEW Average Province Ranking, 2011–2020, 30 provinces.

Province	Average ISEW	Ranking
Beijing	5.2413	1
Shanghai	4.612	2
Tianjin	3.4087	3
Jiangsu	3.244	4
Zhejiang	3.092	5
Fujian	2.9996	6
Guangdong	2.958	7
Chongqing	2.3719	8
Hubei	2.3328	9
Hunan	2.0882	10
Hainan	2.0395	11
Sichuan	2.0229	12
Yunnan	1.9753	13
Shandong	1.9395	14
Shaanxi	1.9043	15
Liaoning	1.8712	16
Jiangxi	1.8624	17
Anhui	1.7904	18
Qinghai	1.7567	19
Henan	1.7263	20
Jilin	1.6802	21
Guangxi	1.6489	22
Hebei	1.567	23
Heilongjiang	1.4069	24
Guizhou	1.4022	25
Gansu	1.3931	26
Xinjiang	1.2333	27
Inner Mongolia	0.3854	28
Shanxi	0.2864	29
Ningxia	−0.0428	30

, we categorized the average ISEW values of the 30 provinces and determined the capabilities and rankings for sustainable economic development in each province.

As shown in Table 7, the sustainable economic development capacity of most provinces improves year by year from 2011 to 2019, but declines in 2020. In 2011, the ISEW of Shanxi, Inner Mongolia, Guizhou, Gansu, and Ningxia is significantly lower than that of other provinces. By 2020, the ISEW of Guizhou and Gansu has increased, but that of Shanxi, Inner Mongolia, and Ningxia has decreased. From Table 8, the mean value of the ISEW is only negative for Ningxia, and it has a large difference of 5.2851 from the highest value in Beijing. In this decade, the sustainable economic development capacity of municipalities that are directly under the central government and of coastal provinces is higher, and the gap is obvious. The central region is at the middle level and the capacities of each of its regions are close, while the capacity of the northeast and northwest regions is generally poor.

4.3. Analysis of the Correlation between National Quality and Sustainable Economic Development

To test the correlation between national quality development and sustainable economic development, a Pearson Correlation Coefficient test was conducted.

Table 9. Pearson Correlation Coefficient of national quality and ISEW from 2011 to 2020.

Years	Pearson Correlation Coefficient of National Quality and ISEW	Significance Level
2011	0.393 **	0.031
2012	0.438 **	0.016
2013	0.294	0.115
2014	0.200	0.289
2015	0.360 *	0.051
2016	0.570 ***	0.001
2017	0.417 **	0.022
2018	0.454 **	0.012
2019	0.231	0.220
2020	0.410 **	0.024

Note: ***, **, and * indicate rejection of the null hypothesis at 1%, 5%, and 10% significance levels, respectively.

lists the correlation coefficient and significance level between the average national quality factor score and the ISEW from 2011 to 2020. As shown in Table 9, in 7 out of 10 years, China’s national quality level is positively associated with sustainable economic development. In other words, the higher the national quality level, the stronger the capability for regional economic sustainable development, and the greater the interplay between them.

In order to explicitly compare the correlation between national quality and sustainable economic development, this paper adopted the method of 0–1 standardization to readjust the average national quality factor score and ISEW to within a range of 0–1. In doing so, a 2011–2020 Comparison Chart of National Quality and ISEW could be produced. Only the 2011 comparison chart is shown here. Based on Figure 1, was found that there is a strong correlation between national quality and ISEW in 2011. Areas with a higher level of national quality, such as Beijing and Shanghai, reflect stronger capabilities for sustainable economic development. By contrast, provinces with a lower level, such as Hebei and Jilin, demonstrate weaker capabilities.

4.4. Granger Causality Test of National Quality Indexes and Sustainable Economic Development

The national quality index system contains 25 indexes, and we extracted 6 common factors after conducting dimensionality reduction using factor analysis. Furthermore, after calculating the score for national quality, correlation analysis was conducted with sustainable economic development, which led to the finding that there is a strong correlation between national quality and sustainable economic development. In order to figure out which indexes of national quality have a mutual influence on sustainable economic development, we conducted a Granger causality test on the panel data of each index (among 25 indexes) of national quality and the ISEW. As shown in Equations (5) and (6), the panel model required by the Granger causality test was established in this paper.

$$ISE{W}_{it}={\displaystyle {\sum }_{p=1}^s{\alpha }_i}{x}_{i,t-p}+{\displaystyle {\sum }_{p=1}^s{\beta }_i}ISE{W}_{i,t-p}+{\epsilon }_{1it}$$

(5)

$$x_{it}={\displaystyle {\sum }_{p=1}^s{\lambda }_i}{x}_{i,t-p}+{\displaystyle {\sum }_{p=1}^s{\delta }_i}ISE{W}_{i,t-p}+{\epsilon }_{2it}$$

(6)

In these equations, x represents the national quality index of each province, and α, β, γ, and δ, respectively, refer to the ISEW and x’s estimated coefficients in the model. p is the lag order, and assuming that ε_1it and ε_2it are white noise and they are mutually uncorrelated, i = 1, 2, …, 30, t = 1, 2, …, 10. As α_i is generally not equal to 0 at a significant level, national quality will be a Granger cause of sustainable economic development. Similarly, when λ_i is not equal to 0 at a significant level, sustainable economic development will become a Granger cause of national quality.

We first conducted unit root tests on the ISEW and 25 national quality indexes from 2011 to 2020, respectively. If a unit root was found, we obtained a stationary sequence by calculating the difference. The unit root tests of the panel data mainly included the LLC test, HT test, Breitung test, IPS test, and Fisher test. The limitation of the LLC, HT, and Breitung tests lies in the fact that they require each individual’s autoregressive coefficients to be equal, so the IPS test and Fisher test were selected in this paper, and the results are shown in

Table 10. Unit root test results of panel data, 30 provinces.

Data Format	Index		Test Methods
			IPS		ADF-Fisher
	Index	Variable Name	IPS Value	p Value	ADF-Fisher Value	p Value
Date of levels	ISEW	Y	−2.0471	0.0203	11.2975	0.0000
	Proportion of population aged below 15 years	X1	−3.8881	0.0001	13.8603	0.0000
	Proportion of population aged 65 years and over	X2	−4.5382	0.0000	14.7418	0.0000
	Dependency ratio	X3	−2.4808	0.0066	11.7212	0.0000
	Average life expectancy	X4	5.2176	1.0000	1.8704	0.0307
	Labor force population	X5	5.8119	1.0000	2.3287	0.0099
	Percentage of labor force population	X6	0.8515	0.8027	7.5536	0.0000
	Growth rate of labor force	X7	−16.8142	0.0000	28.9487	0.0000
	Proportion of female labor force	X8	0.7196	0.7641	8.2669	0.0000
	Employed population	X9	5.7663	1.0000	2.2934	0.0109
	Employment rate	X10	0.7973	0.7874	7.7260	0.0000
	Employment growth rate	X11	−16.7056	0.0000	28.8722	0.0000
	Proportion of agriculture employment	X12	−13.9603	0.0000	28.2504	0.0000
	Proportion of industrial employment	X13	1.2498	0.8943	8.2154	0.0000
	Proportion of service industry employment	X14	3.4912	0.9998	5.6663	0.0000
	Unemployment rate	X15	0.9910	0.8392	7.7260	0.0000
	Percentage of population with higher education degrees	X16	−4.3521	0.0000	14.3846	0.0000
	Student–teacher ratio (primary education)	X17	−0.9393	0.1738	10.3537	0.0000
	Student–teacher ratio (secondary education)	X18	−4.5286	0.0000	13.4477	0.0000
	Weights of public education expenditure in GDP	X19	−1.7657	0.0387	10.8515	0.0000
	Illiteracy rate	X20	−1.9751	0.0241	11.1441	0.0000
	Urban population	X21	4.0235	1.0000	3.9462	0.0000
	Engel’s Coefficient of Urban Households	X22	−6.0242	0.0000	15.8059	0.0000
	Engel’s Coefficient of Rural Households	X23	−7.1633	0.0000	15.4370	0.0000
	Proportion of women in village committees and neighborhood committees	X24	1.7580	0.0394	10.7478	0.0000
	Frequency of traffic accidents	X25	2.7072	0.9966	5.2165	0.0000
	Average life expectancy	X4	−27.4869	0.0000	13.4422	0.0000
	Labor force population	X5	−1.5905	0.0559	3.4194	0.0003
	Ratio of labor force population	X6	−3.5966	0.0002	4.3397	0.0000
	The proportion of the female labor force	X8	−1.7041	0.0442	10.9323	0.0000
	Employed population	X9	−1.8704	0.0307	6.0871	0.0000
	Employment rate	X10	−4.7746	0.0000	4.4357	0.0000
	Proportion of industrial employment	X13	−2.9655	0.0015	11.9595	0.0000
	Proportion of service industry employment	X14	−3.9369	0.0000	13.0636	0.0000
	Unemployment rate	X15	−6.0203	0.0000	15.9677	0.0000
	Student–teacher ratio (primary education)	X17	−5.9289	0.0000	15.4195	0.0000
	Urban population	X21	−5.6559	0.0000	15.5085	0.0000
	Frequency of traffic accidents	X25	−8.9034	0.0000	19.5053	0.0000

.

Since the first-order difference in the population’s average life expectancy holds no economic significance, the economic significance of the first-order difference between the labor force and employed populations refers to the growth rate of the labor force and of employment. These two indexes were included in the original 25 indexes. Therefore, in this paper, we directly conducted the Granger causality test on the remaining 22 national quality variables and the ISEW, and only four null hypotheses had to be rejected at the 5% significance level. These four null hypotheses are shown in

Table 11. The Granger causality test results in the form of panel data (4 rejected null hypotheses).

Null Hypothesis	Z-Statistic	p Value
The weights of public education expenditure in GDP are not a Granger cause of ISEW.	−0.4762	0.6340
ISEW is not a Granger cause of the weights of public education expenditure in GDP.	0.8453	0.3980
ISEW is not a Granger cause of the urban household Engel’s coefficient.	−0.7231	0.4696
ISEW is not a Granger cause of traffic accident frequency.	0.7316	0.4644

Note: Regarding the selection of the i-order lag item, since the panel data selected in this paper contain 10-year data, the empirical model only employes first-order lag.

.

Table 11 indicates that there is no significant causality between the proportion of public education expenditure in GDP and sustainable economic development. Additionally, all elements of national quality level relate to the ISEW factors, which proves that sustainable economic development is fully affected by national quality level. On the other hand, the ISEW is not a Granger cause of the urban household Engel coefficient or the frequency of traffic accidents, which signals that sustainable economic development fails to significantly improve the life quality of urban households and traffic safety in different areas.

The improvement of national quality has a comprehensive impact on sustainable economic development from the perspective of five subcomponents: demographic characteristics, labor force features, employment conditions, national education level, and life quality. The improvement of these elements can significantly accelerate the sustainable development of the regional economy, while continuous optimization of the population structure, labor force, and employment situation lays a foundation for the long-term development of the regional economy. The level of national public education and life quality can provide a sustainable driving force for regional economic development. Only the ratio of public education expenditure has a non-significant effect on sustainable economic development, indicating that increasing public education expenditure cannot significantly affect the level of national education or improve the sustainability of the economy.

A different image can be observed in the improvement of national quality based on sustainable economic development. From the perspective of population characteristics, sustainable economic development can have an influence on the proportion of the population aged below 15 years, the population aged over 65 years, and the dependency ratio. This indicates that sustainable economic development can optimize the population structure of the area. From the perspective of labor force and employment conditions, sustainable economic development can boost the growth of the labor force and increase the number of employees in various industries. In terms of national education, due to sustainable economic development, the proportions of people with higher education will increase, and the teacher–student ratios in secondary and primary education will grow as well. Naturally, the illiteracy rate will drop, but the figure for public education expenditure in the GDP will not show an upward trend. In this case, increasing spending on public education does not necessarily rely only on economic growth, but to a larger extent, on national educational guidelines and policies. Regarding life quality, sustainable economic development will promote the process of urbanization and enhance the life quality of rural households, but will not improve urban households’ living standards. Additionally, the influence of sustainable economic development on residents’ moral indexes appears very insignificant. It cannot reduce the number of traffic accidents, but it can somewhat increase the proportion of women in village and neighborhood committees, which has an impact on the improvement of women’s discourse.

5. Discussion and Conclusions

This paper employs factor analysis, Pearson correlation analysis, and the test of Granger causality to explore the interplay between national quality and sustainable economic development, based on the national quality index system of international competitiveness and the system of sustainable economy and welfare growth.

In growth economics, most economists explore the mechanistic influences of various factors on the quantity of economic growth and the quality of economic development. These factors include population structure, human capital, morality, and democratic equality. However, there are few methods that can be used for the overall evaluation of national quality. Thomas et al. mainly focus on some factors’ contributions to the quality of economic growth, including human capital, physical capital, and natural capital [33]. By contrast, Ziberi et al. believe that education sets the foundation for sustainable economic development and, importantly, that primary education has the greatest impact on economic growth [14]. They found that the national quality level in all Chinese provinces failed to increase significantly from 2011 to 2020, and the scores of all provinces fluctuated from time to time, with low growth rates in general. Some provinces even showed a downward trend, which indicates that the improvement of national quality is a long-term process. Surprisingly, in areas with lower scores for national quality, it improved more dramatically.

Economic growth itself should not be independent goal of policy, and the qualities of economic growth, such as fair income distribution, the degree of civilization, medical security, and the ecological environment, are all important factors affecting sustainable economic development [34]. The quality of economic growth needs to be examined from both material and moral points of view. Morality is an important indicator of national quality, and the sustainability of economic development serves as a significant performance of economic growth quality [18]. The last decade has witnessed dramatic growth in the abovementioned provinces’ sustainable economic growth. Nevertheless, The ISEW of all provinces dropped in 2020, which may have been caused by recessions during the COVID-19 pandemic period; this included an increasing unemployment rate, declining labor force quality, a lower rate of international exchange, and a significant reduction in the sustainable development of the regional economy [35].

Some phenomena can be found in the correlation between national quality level and the capability for sustainable economic development. First, regardless of the periods or regions where economic sustainable development capability is high or low, there is a strong correlation between national quality and sustainable economic development capability. This indicates that even in some regions with relatively higher economic levels and stronger long-term development capabilities, national quality can still effectively promote sustainable economic development. There are often more opportunities in cities with a higher economic level, and those cities usually have a higher tolerance for diversity and a greater insistence on fairness and democracy. These improvements in morality and national quality contribute to higher-level social development in the long run [18]. Second, although there is a significantly positive interplay between national quality and the capability for sustainable economic development, the increasing speed of each province’s capacities for sustainable economic development is much faster than that of the national quality level; this indicates that the positive influence of sustainable economic development on national quality is limited and lags behind. Zhao assumes that sustainable economic development is based on advances in national quality, and that increasing national quality cannot be attributed to economic development [36]. This idea of one-way causality is not completely consistent with the results of this paper, with this paper stresses that the hysteresis influences of sustainable economic development on national quality may lead to different degrees of differences depending on the calculation methods. Hu believes that economic growth determines the basic level of national quality, and national quality, in turn, influences the quality of economic growth [37]. This two-way relationship is basically in line with the conclusion of the current paper.

Based on the results of the test of Granger causality, we can discuss the causality between sustainable economic development and each sub-element.

First, sustainable economic development in China can be attributed to a multitude of factors, such as demographic structure, the labor force situation, employment structure, and life quality. Furthermore, physical capital serves as an important basis for economic growth, human capital guarantees sustainable economic growth, and the level of national education and the student–teacher ratio both determine the quality of human capital [38]. Nonetheless, this paper finds that the proportion of public education expenditure is not the cause of sustainable economic development, which is not consistent with the research findings of Ziberi et al. [14]. They assume that even a 1% increase in public educational expenditure will have a positive impact on sustainable economic growth. This is the reason why this paper uses the proportion of public education expenditure as an indicator for measuring national education level. Public educational expenditure cannot further influence the capability of sustainable economic development by affecting the national education level. As a result, such an indirect influential mechanism will weaken the influences of public educational expenditure on sustainable economic development.

Second, and in contrast, sustainable economic development can promote optimization of the population structure and the employment situation, and increase the labor force. However, the growing labor force does not necessarily signify improvement in the labor force structure. The increase in the employed population in various industries does not represent the optimization of industrial structure. In short, the interplay cannot be proved yet among sustainable economic development, the labor force, and industrial structure. Notably, sustainable economic growth leads to a growing female labor force. Especially in developing countries, there is a U-shaped relationship between the female labor force and economic growth, and sustainable economic growth will provide the female population with more employment opportunities in the long run [39].

Third, sustainable economic growth promotes the development of secondary education and higher education and decreases the illiteracy rate, but has no impact on the proportion of public education expenditure in the GDP. A possible reason for this could be that public education spending is an indirect effect.

Finally, from a life quality perspective, sustainable economic development fails to improve the life quality of urban residents. In a period of low economic development, sustainable economic development can significantly improve the life quality of urban residents. However, with the rapid rise in its economy, China’s economic strengths gradually keep pace with those of other countries with higher levels of national quality. As a result, the life quality of urban households improves and gradually attains a relatively stable level. Nevertheless, sustainable economic growth seems to have no positive influence on urban residents’ life quality. Friedman argues that economic growth can create extensive social benefits because it alters the psychological balance between jealousness and altruism, making individuals more generous and more concerned about others’ well-being [18]. However, Easterlin believes that a country’s well-being will not increase along with the average income growth [40]. Economic growth is positively associated with economic inequality, while economic inequality is negatively associated with people’s desire for government legislation. This means that economic growth may increase economic inequality and reduce the awareness of democracy and equality [41,42]. This paper finds that in China, sustainable economic development results in improvement in the power of women’s discourse, but it has no connection with improvement in morality. However, this conclusion cannot be further explored due to a lack of moral indexes. Simultaneously, it also suggests that in periods of rapid economic development, little attention is paid to the improvement in residents’ morality, and there is a lack of corresponding guidance and education, such as a comprehensive legal system or effective moral information. Mishan points out that sustainable and rapid economic growth will inevitably invite extreme liberalism, which degenerates moral civilization [34]. Therefore, the formation and development of national quality should be regulated and restricted by the social system [43].

Additionally, the test of Granger causality itself has certain limitations. Devlin and Hansen reveal that this test is based on a two-variable model, which may ignore relevant variables from a theoretical perspective [44]. Meanwhile, the Granger test provides a discussion direction for future research that aims to explore the relationship between national quality and sustainable economic development.

At present, based on the long-term achievement of rapid economic development, it is necessary for China to seize this opportunity, learn from developed countries, and thoroughly promote the transformation and improvement of national quality. This way, sustainable development of the national economy will be guaranteed.