Does New Urbanization Support the Rural Inclusive Green Development under Domestic Circulation in China?

Abstract

New urbanization is an endogenous driving force to enhance domestic circulation. Driving the development of rural industries with urbanization to achieve interactive symbiosis has become an important topic to promote the coordinated development of urban and rural green. Based on the panel data of 30 provinces in China from 2009 to 2021, this paper constructs an evaluation index system for new urbanization and rural inclusive green development, and uses principal component analysis and panel regression model to analyze the impact of new-type urbanization on inclusive green development in rural areas. The results of the study show the following: (1) Rural inclusive green development and new urbanization have been significantly improved during the study period, but there are significant regional differences. (2) The construction of the new urbanization significantly promotes rural inclusive green development, but there is significant spatial heterogeneity. This effect is more significant in the Eastern and Central regions. (3) Population urbanization, land urbanization, social urbanization, and environmental urbanization can effectively promote rural inclusive green development, but economic urbanization will have a negative impact on green development in the countryside during the study period. Therefore, it is necessary to further strengthen the leading role of central cities and urban agglomerations, to promote the countryside with the city and at the same time to combat environmental pollution and to create ecologically livable towns and villages. In addition, the government should strengthen top-level design, provide industrial support to backward areas, improve the spatial layout of urbanization, and promote the deepening of new urbanization.

1. Introduction

Since the reform and opening up, with the rapid development of China’s economy, a large amount of surplus labor in rural areas has been flowing and gathering in urban areas, and urbanization has been developing continuously. However, after 2010, the degree of aging in China has been deepening, the “demographic dividend” has gradually disappeared, and China’s economy has entered the era of “structural deceleration”, so it is urgent to improve the quality of urbanization. In view of this, the Chinese government put forward the concept of “New Four Modernizations “ for the first time, which includes the new urbanization [1]. The outline of the 14th Five-Year Plan (2021–2025) for National Economic and Social Development and Vision 2035 of the People’s Republic of China and the National Plan on New Urbanization (2021–2035) explicitly mention the need to insist on taking the people-oriented road of new urbanization with Chinese characteristics. New urbanization is people-centered urbanization. Promoting new urbanization and high-quality development is an essential way to realize Chinese modernization. Under the continuous efforts of the Party and the people, the construction of new urbanization has achieved great results, with the ratio of disposable income per capita of urban and rural residents dropping from 2.88:1 to 2.5:1 from 2012 to 2021, and the resident population of China’s cities and towns reaching 920.71 million by the end of 2022, with the rate of urbanization climbing from 10.6% at the beginning of the founding of New China to 65.2% (Figure 1). The construction of new urbanization that integrates urban and rural areas, integrates cities and towns, interacts with industries, and saves and intensifies resources should be ecologically livable and developed in a harmonious manner. It not only helps to improve people’s well-being and has social benefits but also helps to promote the transformation of the economy from a crude to an intensive one.

In the current process of China’s economic development, the unbalanced development of urban and rural areas is a problem that cannot be ignored, and urban–rural interaction and associated development is an effective way to harmonize the relationship between urban and rural areas and to promote the common development of urban and rural areas [2]. The rapid development of China’s cities and villages has inevitably brought negative impacts on the ecological environment, and the Chinese government has made a series of strategic plans to set up and practice the viewpoint that “ Lucid waters and lush mountains are invaluable assets” and put forward the concept of “Chinese modernization”. Chinese modernization is the modernization of harmonious coexistence between human beings and nature, and the viewpoint of leading rural revitalization with green development coincides with the viewpoint of the United Nations Environment Assembly to “nature-based”, integrating green recovery and sustainable development into inter-country cooperation and the global governance process, which China intends to carry out. According to the United Nations Development Programme, inclusive green development includes three pillars: economy, socialization and environment. Rural inclusive green development is an innovative development model that combines the two concepts of inclusive development and green development. Its essence is to seek synergies between the quality of economic growth, social equity and environmental stability. Therefore, this paper focuses on the effect of urbanization on rural inclusive green development, which is particularly important for China in the new normal economic development stage characterized by the “three-phase overlap” [3]. In this context, an in-depth exploration of the mechanisms of new urbanization on rural inclusive green development can help provide a reference for the Chinese government to formulate policies that help green development in the countryside, promote balanced urban–rural development and enhance the resilience of urban–rural ecosystems.

2. Literature Review

Rural development has become a hot issue for governments and an important topic for academics. Starting with the theory of rural sociology, Li et al. (2022) used the theory of back-flow to study the influencing factors of labor return under the background of rural revitalization strategy and the logical relationship and response mechanism between the two [4]. Zhao et al. (2023) examined the level of inclusive green rural development at the provincial level in China from three dimensions: inclusive green ecology in rural areas, inclusive green production in agriculture, and inclusive green life for farmers [5]. Malik et al. (2022) argued that the use of digital technology can help to build a smart countryside, thereby improving rural life and promoting the social and economic development of the countryside [6]. Ahmad et al. (2021) also argued that it is possible for the government to support the development of rural agricultural science and technology to make more efficient use of resources and realize the economic independence of rural areas [7]. Gao et al. (2017) analyzed that the development of rural tourism is not only conducive to the preservation of the history and culture of traditional villages but also contributes to the revitalization and sustainable development of villages [8]. Lin et al. (2023) found that rural social governance can promote the sustainable development of rural villages by taking the famous Bonsai village of Guangzhou as a typical case [9]. Cui et al. (2023) explored the evolution characteristics and driving mechanism of regional functions in the agricultural and pastoral intertwined zone based on the “element–structure–function” framework, and realized the coordinated development of rural regional functions by promoting the coupling of population, land and industry [10]. Cheng et al. (2013) used the rural areas in Northeast China’s counties as an entry point and found that the improvement in the conditions of agricultural production, the economic development and industrial structure adjustment are important factors affecting the unbalanced development of rural areas [11]. Some other scholars have conducted research at the micro-enterprise level, arguing that rural enterprises play a positive role in the economic and social development of rural areas. Charman (2017) found that the government can revitalize the township economy by channeling resources to small and micro-enterprises and creating opportunities to support them [12,13].

At present, academics pay great attention to topics related to new urbanization. Much of the existing literature has analyzed the impact of new urbanization and its influencing factors from the macro level but has not yet reached a consistent research conclusion. Han et al. (2023) explored the impact mechanism of new urbanization on common wealth and came up with positive conclusions, while Ma et al. (2023) studied the development quality of new urbanization based on 77 prefecture-level cities in the Western region, and concluded that the development quality of local new urbanization makes a positive impact on the development quality of new urbanization in the neighboring regions [14,15]. In addition, some other scholars conducted empirical tests from the dimensions of the Yangtze River Economic Belt and the Yellow River Basin, respectively, and concluded that new urbanization generally improves the quality of life of urban residents and is conducive to social development [16,17].

Wang et al. (2015) argued that many problems have emerged during the development of China’s urbanization and new urbanization has not been effective in mitigating big city disease [18]. In the general context, Azarnert (2019) considers the effect of congestion diseconomies in more urbanized areas, while Azarnert (2023) suggests that some net immigration of the lower-skilled individuals from the more densely populated cities to the periphery may be considered as a growth-enhancing strategy [19,20]. There is also part of the literature that explores the core influencing factors of new urbanization from five perspectives: economic foundation, technological innovation, government guidance, industrial structure and foreign trade, using Guangdong Province as a starting point [21]. Shi et al. (2020) started from the panel data on the level of green urbanization in nine provinces in the Yellow River Basin and found that the factors that have a significant positive impact on the level of green urbanization including the level of economic development, the level of scientific and technological innovation, and the size of the city. Industrial structure, foreign direct investment (FDI) and education level play an opposite role to the level of green urbanization [22]. As the problems of environmental pollution in cities and villages come to the forefront, many scholars have begun to explore the coupling relationship between urbanization and environmental systems at the macro level, and the degree of coupling and coordination between rural revitalization and new urbanization, and believe that urbanization and rural development should be complementary and mutually reinforcing. And China’s urbanization can be realized through the entry of rural enterprises into the cities, and that the increasingly serious “country disease” and “city disease” put forward higher requirements for urban–rural integration and rural revitalization [23,24,25,26,27,28,29].

There are many international articles on sustainable development and green development from a global perspective, which provide references for us to study China’s urbanization and rural development [30,31,32,33]. The new urbanization and rural inclusive green development are important driving forces for achieving urban–rural integration, reducing the imbalance between regions and promoting high-quality development. There is a coupling relationship between the two that promotes each other and complements each other.

Most of the studies at home and abroad mainly take new urbanization or rural development as the research entry and lack sufficient attention to the mechanism of the role between new urbanization and rural inclusive green development. In order to make up for the shortcomings of the existing literature, this paper utilizes the panel data of 30 provinces in China from 2009 to 2021 and adopts the principal component analysis method to estimate the impact of new urbanization on rural inclusive green development. The possible research contributions of this paper are as follows: ① This paper constructs the evaluation index system of new urbanization and rural inclusive green development, respectively, in which the new urbanization explores the impact of the data synthesized by the 18 indexes on rural inclusive green development as a whole, as well as exploring the impact of their impacts on rural inclusive green development from the five dimensions of demographic, economic, environmental, social, and land urbanization, respectively. In addition, the evaluation index system of rural inclusive green development is constructed from three aspects: prosperous production, affluent living and ecological livability, with a view to conducting a more comprehensive study. ② The heterogeneity test is carried out. In order to explore whether there are differences in the impact of new urbanization on rural inclusive green development in different regions, the whole sample is divided into four sub-samples in Eastern, Central, Western and Northeastern provinces for regression analysis. This is of great significance for identifying the key factors affecting regional differences and better promoting the regional coordinated development of rural inclusive green development. ③ Few scholars have studied the impact mechanism of new urbanization on rural inclusive green development. This study enriches and expands the research literature on the influencing factors of new urbanization and rural inclusive green development, provides new research perspectives for understanding new urbanization, and provides references for the Chinese government to formulate land policies to solve the “pain points” in the process of rural development. It is hoped that the construction of high-quality new urbanization will provide valuable insights for the revitalization of the countryside and the rural inclusive green development.

3. Model Introduction

3.1. Evaluation Indicator System

3.1.1. Principal Component Analysis

Principal component analysis (PCA) is a mathematical algorithm in which observations are described by several interrelated quantitative dependent variables. Its goal is to extract important information from a table, represent as a new set of orthogonal variables called principal components, and display the pattern of similarity between the observations and variables as points in a diagram [34]. It transforms multiple indicators into a few composite indicators (principal components) by reducing the dimensionality of the data, each of which is a linear combination of the original variables and each of which is uncorrelated with the other. Much of the literature uses the entropy weighting method, combination weighting method, time series weighted average arithmetic method and spatial statistical analysis method to measure the level of new urbanization. This paper applies the principal component analysis method on the basis of constructing the two evaluation index systems of new urbanization and the rural inclusive green development to carry out the scientific measurement of the two, i.e., synthesize the sub-indicators of the two into a single indicator, and call the comprehensive indicator as new urbanization level and rural inclusive green development. So, the integrated data can be processed and transformed into several representative integrated indexes on the basis of less loss of data information, and the explanatory power of the integrated indexes is greater than that of the single indexes [35,36].

We used Stata 16 software for principal component analysis. First, the p-value and kmo value were tested. In the rural inclusive green development evaluation system, the kmo value must be >0.6, while the expenditure on the rural minimum subsistence index was 0.584. This variable was excluded. Then, we carried out the principal component analysis, making the eigenvalue > 1, and obtained 6 principal components in the evaluation system of rural inclusive green development and 5 principal components in the evaluation system of new urbanization, respectively. Finally, we obtained the comprehensive score through the following formula: comprehensive score = (contribution rate of principal component 1 × f1 + contribution rate of principal component 2 × f2 +......)/cumulative contribution rate.

3.1.2. Rural Inclusive Green Development Evaluation System

Under the guidance of the sustainable development theory that takes common, coordinated, equitable, efficient and multidimensional development as the ultimate goal, this paper constructs the evaluation system of rural inclusive green development by comprehensively selecting 17 indicators from the three perspectives of prosperous production, affluent living, and ecological livability. We also take into account the concept and connotation of rural inclusive green development and the availability and quantification of indicator data. The strength of agricultural plastic film, the intensity of fertilizer use in rural areas, the intensity of pesticide use, rural diesel use and rural electricity consumption per capita are negative indicators. They are harmful to the ecological environment to some extent. The level of rural inclusive green development of each province (Ru) was synthesized by principal component analysis (see

Table 1. Rural inclusive green development evaluation indicator system.

First Indicator	Second Indicator	Methodology for Calculating Indicators	Unit	Causality
Prosperous production	Primary sector’s contribution to GDP growth	Primary sector’s contribution to GDP growth	%	+
	Value of agriculture, forestry, animal husbandry and fisheries output per labor force	Gross output value of agriculture, forestry, animal husbandry and fisheries/labor force in primary sector	Hundred million yuan per 10,000 people	+
	Total food production per capita	Total food production/rural population	Ten thousand tons per 10,000 people	+
	Irrigation coefficient	Effective rural irrigated area/cultivated area	%	+
	Use of total power of agricultural machinery	Gross power of agricultural machinery/value added of primary industry	Million kWh/billion yuan	+
Affluent living	Per capita disposable income of rural residents	Per capita disposable income of rural residents	Yuan	+
	Number of beds in village health centers per capita	Number of beds in village health centers/population of villages	Sheets per 10,000 persons	+
	Number of elderly care institutions per capita	Number of elderly care institutions/population of villages	Per 10,000 people	+
	Expenditure on rural minimum subsistence security	Expenditure on rural minimum subsistence security	Billion yuan	+
Ecologically livable	Strength of agricultural plastic film	Agricultural plastic film use/cultivated land area	Tons/ha	-
	Intensity of fertilizer use in rural areas	Rural fertilizer use/cultivated land area	Tons/ha	-
	Intensity of pesticide use	Pesticide use/cultivated land area	Tons/ha	-
	Rural diesel use	Agricultural diesel fuel use/value added in primary sector	Million tons/billion yuan	-
	Rural electricity consumption per capita	Rural electricity consumption/rural population	Billion kWh/million people	-
	Solar energy utilization per capita in rural areas	Total area of rural solar water heaters/population of villages	10,000 square meters per 10,000 people	+
	Forest cover	Forest cover	%	+
	Share of area of nature reserves	Share of nature reserves in area under jurisdiction	%	+

). Among them, some indicators of prosperous production and affluent living are referred to in the research of Sun et al. (2023) [37]. Some indicators of ecological livability refer to the research of Cheng et al. (2020) and Xu et al. (2021) [38,39]. In addition, we select some relevant indicators from the Chinese Statistical Yearbook.

3.1.3. Evaluation System for New Urbanization Construction

The construction of new urbanization is a dynamic and systematic process, and it is crucial to accurately portray the level of new urbanization. Existing studies have mainly constructed evaluation systems from four aspects, namely population urbanization, economic urbanization, social urbanization, and land urbanization, or from four perspectives, namely economic, demographic, spatial, and sustainable development. Some scholars also construct the evaluation index system from seven aspects: urbanization level, basic public services, infrastructure, resources and environment, population quality, lifestyle, employment and residence. This paper integrates and refines the previous studies and constructs the evaluation index system of new urbanization from five dimensions: demographic, economy, land, society, and environment (see

Table 2. New urbanization evaluation index system.

Dimension	Norm	Unit	Causality
Population urbanization	Share of urban population	%	+
	Urban population density	Persons/km2	+
	Share of secondary and tertiary employment in total employment	%	+
Economic urbanization	Value added of secondary and tertiary industries as a share of GDP	%	+
	GDP per capita	Ten thousand yuan	+
	Consumption expenditure per capita	Ten thousand yuan	+
	Disposable income per capita	Ten thousand yuan	+
Land urbanization	Built-up area as a proportion of urban area	%	+
	Road area per capita	Square meter	+
Social urbanization	Share of pensioners in total population	%	+
	Number of beds in health-care facilities per 10,000 population	Sheet of bed	+
	Per capita number of performing arts venues (per 10,000 people)	Classifier for individual things or people, general, catch-all classifier	+
	Library holdings per capita	Classifier for volumes of books	+
	Buses per 10,000 people in cities	Classifier for wheeled vehicles	+
Environmental urbanization	Greening coverage in built-up areas	%	+
	Green space per capita in parks	Square meter	+
	Non-hazardous treatment rate of domestic waste	%	+
	Public toilets per 10,000 people in towns and cities	Collars	+

), and synthesizes them by using principal component analysis to obtain the evaluation index system of new urbanization level (Ur) for each province. Among them, some indicators of population urbanization, economic urbanization, land urbanization and social urbanization are referred to in the research of Lu et al. (2023), while some indicators of environmental urbanization are referred to in the research of Zhang et al. (2014) [40,41]. In addition, we select some relevant indicators from the Chinese Statistical Yearbook.

3.2. Panel Regression Model

3.2.1. Introduction to the Model

Panel data refers to data that follow the same set of individuals over a period of time. The use of panel data in this paper can solve the problem of omitted variables, provide more information about the dynamic behavior of individuals, and improve the accuracy of estimation because of the large sample capacity. In order to study the impact of new urbanization on rural inclusive green development, this paper establishes three panel models with reference to the research of Chen (2014), Lin S (2017), Beck (2010) and other scholars [42,43,44]. Equation (1) is a mixed regression model, i.e., OLS regression-like dealing with cross-sectional data, with the basic assumption that there are no individual and temporal unobservable factors. Equation (2) is a fixed effects model, which is divided into the individual fixed effects model and the time fixed effects model. The individual fixed effects model addresses the problem of omitted variables that do not vary over time but vary with individuals, while the time fixed effects model addresses the problem of omitted variables that do not vary with individuals but vary over time. Equation (3) is a random effects model, which treats the regression coefficients of the fixed effects model as random variables, with the basic assumption that none of the explanatory variables are correlated.

$$\begin{array}{}\mathrm{(1)}& \begin{array}{l}{Ru}_{it}=\alpha +{\beta }_1{Ur}_{it}+{\beta }_2{Open}_{it}+{\beta }_3{Rely}_{it}+{\beta }_4{Inno}_{it}{+\beta }_5{Traf}_{it}+{\beta }_6{Eco}_{it}+{\beta }_7{Edu}_{it}+\\ {\beta }_8{Res}_{it}+{\beta }_9{Fin}_{it}+{\beta }_{10}{Regu}_{it}+{\epsilon }_{it}\end{array}\mathrm{(2)}& \begin{array}{l}{Ru}_{it}=\alpha +\beta {Ur}_{it}+\delta X_{it}+{\mu }_i+{\lambda }_t+{\epsilon }_{it}\end{array}\mathrm{(3)}& \begin{array}{l}{Ru}_{it}=\alpha +\beta {Ur}_{it}+\delta X_{it}+{\mu }_i+{\epsilon }_{it}\end{array}\end{array}$$

In Equation (1), $i$ and $t$ denote province and year, respectively; ${Ru}_{it}$ is the rural inclusive green development index of province i in year t; ${Ur}_{it}$ is the new urbanization level of province $i$ in year $t$; the control variables at the inter-provincial level include the level of openness to the outside world (Open), the degree of external dependence (Rely), the level of technological innovation (Inno), the degree of transportation access (Traf), the level of economic development (Eco), the per capita number of years of education (Edu), the endowment of resources (Res), the degree of financial activeness (Fin), and the degree of environmental regulation (Regu); ${\epsilon }_{it}$ is the random disturbance term. If ${\beta }_1$ the coefficient is significantly positive then new urbanization supports rural inclusive green development, and vice versa, it inhibits rural inclusive green development. In Equation (2), $X_{it}$ indicates a range of control variables; ${\mu }_i$ denotes area fixed effects; ${\lambda }_t$ denotes year-fixed effects. In this paper, the mixed effects model, two-way fixed effects model, and random effects model are used to regress the impact of new urbanization on rural inclusive green development, respectively.

3.2.2. Selection of Indicators

In this paper, the level of development of new urbanization is the explanatory variable, and the level of rural inclusive green development is the explanatory variable. Referring to the previous research literature [45], the following series of control variables are selected: (1) Openness to the outside world (OPEN), which indicates the degree of market openness through the share of foreign registered investment in GDP. (2) Degree of external dependence (Rely) through the total amount of import and export accounted for the proportion of GDP to reflect the degree of dependence of each province on the international market so as to measure the degree of its openness to the outside world. (3) Technological innovation level (Inno), using the ratio of R&D expenditure to GDP as a proxy variable for R&D intensity and technological innovation level. (4) Transportation accessibility (Traf), using highway mileage as a percentage of the area of each province to approximate the characterization of whether regional transportation is convenient and the efficiency of commuting. (5) Economic development level (Eco), using GDP per capita to reflect the economic development of each province. (6) Per capita number of years of education (Edu), the formula for which is as follows: per capita number of years of education = proportion of elementary school population $\times$ 6 + proportion of junior high school population $\times$ 9 + proportion of senior high school and secondary school population $\times$ 12 + proportion of college and post-secondary school population $\times$ 16. (7) Resource endowment (Res), in which water resources per capita are chosen to measure the resource endowment of each province. (8) Financial activity (Fin), where the financial development level is measured by the proportion of financial value added to GDP. (9) Environmental regulation (Regu), where the use of industrial pollution control investment and the ratio of industrial value added to measure the level of environmental regulation will affect the manufacturing industry’s living space, thus affecting the productivity and quality of life of residents.

3.3. Sample Selection and Data Sources

Because data collection is difficult in some areas, the research sample selected in this paper is the panel data of 30 provinces in China from 2009 to 2021 (no longer considering Tibet, Hong Kong, Macau and Taiwan data). The data are mainly obtained from the China Statistical Yearbook, China Rural Statistical Yearbook, China Industrial Statistical Yearbook, China Environmental Statistical Yearbook and provincial statistical yearbooks, and a small amount of missing data are filled in using linear interpolation or ARIMA.

Table 3. Descriptive statistics of variables.

Variable	Sample Size	Average Value	Standard Deviation	Minimum Value	Maximum Value
Ru	390	0.8000	0.466	0.065	2.885
Ur	390	1.300	0.658	0.001	3.576
Open	390	0.634	2.864	0.048	45.089
Rely	390	0.268	0.277	0.007	1.399
Inno	390	0.011	0.006	0.001	0.032
Traf	390	0.943	0.509	0.083	2.237
Eco	390	54,414.186	29,088.281	10,309	183,980
Edu	390	9.027	1.163	4.222	12.978
Res	390	2195.067	2613.275	51.9	17,107.4
Fin	390	0.069	0.031	0.022	0.196
Regu	390	0.003	0.003	0	0.031

shows the descriptive statistics of the main variables.

4. Measurement Results

4.1. Results of the New Urbanization

Based on the evaluation index system of new urbanization selected in the previous section, the development level of new urbanization in 30 provinces is measured by principal component analysis, and the two time nodes of 2009 and 2021 are selected for comparative study (Figure 2). From the trend of change, China’s new urbanization development level shows a rapid upward trend from 2009 to 2021, with the fastest growth rate in Guizhou, followed by Gansu; comparing the development levels, only Beijing, Shanghai, Tianjin, Jiangsu and other regions had high levels of new urbanization in 2009, and by 2021, the range of high-level regions is further expanded to include Guangdong, Fujian and other regions, while Guizhou, Yunnan, Gansu and other Western regions have a faster level of development. But because of unfavorable conditions such as economic backwardness and poor natural conditions, the level of new urbanization in these provinces is low. During the sample period, there is a spatial distribution pattern of “Eastern > Central > Northeast > West” in the new urbanization level of 30 provinces. The Eastern region has given birth to many central cities and city clusters because of its superior geographical location, developed economy, policy support and other factors, which have gathered many high-precision industries, and thus can gather more population and factors of production, and has a great influence on the development of new towns. More population and production factors have a siphon effect and radiation effect on the surrounding areas, driving the development of other regions. From a comprehensive point of view, although the construction of new urbanization in China has been effective since 2012, and the gap between provinces has been narrowing, there is still a great imbalance, but the regional gap is obvious, and the spatial pattern of Beijing–Tianjin–Hebei region, the Yangtze River Delta, and Guangdong–Hong Kong–Macao Greater Bay Area as the “highland” is still difficult to change in the short term.

4.2. Results of Rural Inclusive Green Development

The level of rural inclusive green development shows obvious spatial differentiation characteristics, and this paper uses ArcGIS 10.8 software to spatially visualize the results of rural inclusive green development. Because data collection is difficult in some areas, the research sample selected in this paper is the panel data of 30 provinces in China (no longer considering Tibet, Hong Kong, Macau and Taiwan data). The study period is 2009, 2012, 2017 and 2021 (Figure 3).

4.3. Results of Benchmark Regression

In this paper, we conducted regression analysis with the random effects model, fixed effects model, and mixed effects model, respectively, and chose the model with better results (

Table 4. New urbanization and the rural inclusive green development.

Variable	Random Effect Model	Individual Fixed Effect Model	Time Fixed Effect Model	Two-Factor Fixation Effect Model
Ur	0.1806378 *** (4.57)	0.2402229 *** (5.72)	0.3085729 *** (8.15)	0.2402229 *** (3.54)
Open	0.0141279 *** (5.77)	0.0134745 *** (5.57)	0.0323045 *** (4.51)	0.134745 *** (15.97)
Rely	−0.1893588 * (−1.87)	−0.2949277 *** (−2.76)	0.7121415 *** (5.86)	−0.2949277 *** (−1.95)
Inno	10.36344 *** (2.83)	11.34392 *** (3.13)	−3.685483 * (−0.52)	11.34392 ** (1.97)
Traf	−0.1410168 * (−1.72)	−0.1621286 * (−1.84)	−0.1051691 * (−1.73)	−0.162186 (−1.28)
Eco	$5.82 \times {10}^{-7}$ (0.75)	$3.08 \times {10}^{-7}$ (−0.39)	$9.99 \times {10}^{-7}$ (0.60)	$-3.08 \times {10}^{-7}$ (−0.17)
Edu	0.0193484 (0.77)	0.016584 * (0.64)	−0.0345174 * (−1.70)	0.016584 * (0.66)
Res	$8.32 \times {10}^{-6}$ (0.93)	$6.84 \times {10}^{-6}$ * (0.75)	0.000014 (1.60)	$6.84 \times {10}^{-6}$ (1.06)
Fin	−0.7599771 (−1.10)	−1.209813 * (−1.74)	−3.020758 ** (−2.45)	−1.209813 ** (−0.81)
Regu	−5.081224 * (−1.96)	−3.842454 *** (−1.50)	−0.2550257 * (−0.04)	−3.842454 * (−1.33)

Note: *, **, and *** represent the significance of the estimates at the 10%, 5%, and 1% levels, respectively, and the t or z values of the regression coefficients are given in parentheses.

) to explore the impact of new urbanization on rural inclusive green development.

In order to further explore the influence mechanism of new urbanization and rural inclusive green development, this paper selects one core indicator from each of the five dimensions of the new urbanization evaluation index system: demographic, economic, land, social, and environmental. The proportion of the urban population, the proportion of added value of the secondary and tertiary industries in the GDP, the proportion of the built-up area in the urban area, the proportion of pensioners in the total population, and the greening coverage rate of the built-up area are used to represent population urbanization, economic urbanization, land urbanization, social urbanization, and environmental urbanization are represented as ${Ur}_1,{Ur}_2,{ Ur}_3,{Ur}_4,{Ur}_5$, respectively, and regression analysis is performed.

5. Discussion

5.1. Analysis Results of Rural Inclusive Green Development

According to the results in Figure 2, from a temporal point of view, the level of rural inclusive green development shows an overall upward trend, although the level of green development was lower before and after 2009, in which the index of the best-developed provinces, such as Shandong and Shanghai, even reached almost 80%. From a spatial point of view, the Eastern region has the highest level of rural inclusive green development, which may be attributed to the following reasons: (1) The open-door policy. After 1984, China further opened up 14 coastal cities, which laid a solid foundation for the future development of the Eastern coastal region. With the continuous expansion of urbanization, many large, medium and small cities gathered together, and city clusters came into being, resulting in the “1 + 1 > 2” effect of “city clusters” and the radiation effect of the city clusters to drive the development of the surrounding countryside. (2) Strong scientific and technological innovation capacity. The Eastern region’s earlier economic and urbanization development attracted a large amount of talent inflow, the establishment of a dense knowledge network and the early transformation and upgrading of industries, eliminating excess capacity, leading the intensive, green and efficient development of industry, providing good conditions for the green development of the countryside. (3) Advantageous geographical location. The Eastern region is mainly plain and has abundant water resources, which is suitable for agricultural cultivation and provides favorable geographical conditions for rural development. In addition, the Yangtze River Delta and the Pearl River Delta have prominent advantages, and their leading role is constantly emphasized, leading to the coordinated development of the countryside [46]. The rural inclusive green development in the Central region comes in second. According to the gradient transfer theory in regional economic theory, developed regions transfer industries to more developed regions first, followed by less developed regions to drive the overall economic development, so the Central region, which is more developed around the Eastern region, contracts a large number of transferred industries. Being driven by the radiation of the Central cities in the Eastern region, the Central region carries more industries and population, and thus urbanization develops rapidly, thus boosting the economic development of the countryside.

The level of the rural inclusive green development in the Western region is relatively backward, because the poor natural and geographical conditions are not conducive to the growth of crops and make the industry withered, and the backward transportation infrastructure construction has blocked the link between the city and the countryside. So, the positive externality of the agglomeration effect of the construction of the new urbanization cannot be effectively diffused to the outside world, and therefore it is difficult to achieve a breakthrough in the development of the city, and the backward urbanization is also weak in the countryside to pull the development [14]. On the one hand, the Northeast region supplies the whole country with steel, iron, food, etc. On the other hand, high pollution, and high energy consumption of heavy industry compared to the environment has brought great pollution, so the level of rural inclusive green development lagged behind the Eastern region. Northeast China’s industrial transformation and upgrading of high-energy-consuming industries urgently need to realize cleaner production. In addition, in order to promote the revitalization of the Northeast and realize rural inclusive green development, we must pay great attention to the innovation capacity of the Northeast’s old industrial base, based on agriculture and industry, and the development of tertiary industries such as the financial industry, i.e., the formation of a virtuous circle of synergistic development of the strategic emerging industries and traditional industries. Comprehensive analysis shows that the regional spatial differentiation of China’s rural inclusive green development is obvious, and the level of rural inclusive green development in the Eastern–Central region is obviously higher than that in the Western region. China’s overall rural green development is good, but there is still a long way to go to realize regional coordinated development.

5.2. Analysis Results of Benchmark Regression

From Table 4, we found that the regression coefficients of Ur did not change significantly, and they were all significantly positive at the 1% level, which indicates that the construction of new urbanization can generally support rural inclusive green development in a better way. Among them, column (3) is the result of controlling for district-fixed effects, and column (4) further controls for year-fixed effects. According to the two-factor fixed effects model, in terms of economic significance, every 1% increase in the level of new urbanization increases the degree of rural inclusive green development by an average of 24%, possibly for three reasons. First, the construction of new urbanization has laid a solid foundation for breaking down urban–rural barriers and promoting the two-way flow of residents and factors of production between urban and rural areas, thus unclogging the domestic circulation. On the one hand, the countryside provides sufficient labor for the cities while releasing huge consumption potential, and the countryside also guarantees national food security, keeping food banks at a high level and food prices stable. On the other hand, cities provide capital, technology, talent and other innovative elements for the countryside to help rural industrial transformation and upgrading and cultivate new kinetic energy for urban–rural integration. Second, the construction of new urbanization can promote the green development of the countryside. Zhao et al. (2023) found that the increase in urbanization can lead to the improvement of rural education and technology level, which makes the rural economic development change to become more economical and moderate, green and low-carbon and civilized and healthy [47]. Thirdly, the construction of new urbanization can help promote the integrated development of urban and rural areas and form a new pattern of rural inclusive development. With the increasing connection between urban and rural areas, the government has formulated an integrated urban and rural development strategy to make efficient and economical use of land resources and optimize the spatial layout of towns and cities, thus breaking down the urban–rural dichotomy barrier. However, while urbanization is developing rapidly, it is also necessary to prevent excessive rural population loss and avoid such phenomena as the hollowing out of rural industries and the aging of the population [48].

In terms of control variables, the degree of openness to the outside world (Open) is positively correlated with the level of rural inclusive green development at the 1% significance level. According to the 2021 Statistical Bulletin of China’s Outward Foreign Direct Investment released by the Ministry of Commerce, investment in agriculture, forestry, animal husbandry and fisheries amounted to 930 million dollars, a year-on-year increase of 173.5% compared with 340 million dollars in 2009, accounting for 0.5% of the total flow. This shows that with the deepening degree of opening up to the outside world, the rural economy continues to develop to help the transformation and upgrading of traditional agriculture, laying a solid foundation for rural inclusive green development. But at the same time, it is important to be wary of the higher degree of external dependence (Rely) and pay great attention to the issue of food security to build a strong agricultural country. A lack of social innovation is often one of the strongest constraints on rural vitality and further development, and an increase in the level of technological innovation (Inno) can effectively pull rural development [49]. The negative regression coefficient of transportation access (Traf) indicates that although the development of transportation and logistics facilitates the travel of rural residents, it will bring certain environmental pollution to the countryside, which is not conducive to the green development of the countryside. So, China should build, manage and maintain rural roads with high quality and high efficiency, and try its best to minimize pollution. The higher the level of economic development (Eco), per capita number of years of education (Edu), and resource endowment (Res), the better the inclusive green development of the countryside, but it is not statistically significant. Financial activity (Fin) in the process of new urbanization construction on the rural inclusive green development has a negative effect, maybe because the traditional financial industry in the countryside bringing investment opportunities also causes a certain degree of environmental pollution. Therefore, the Chinese government should vigorously develop green finance to help high-carbon and high-pollution industries in their green transformation and win the battle against pollution. The negative regression coefficient of environmental regulation (Regu) is due to the declining trend of investment in industrial pollution control, which accounts for a smaller proportion of industrial value added, reflecting the concept of end-to-end management, the importance of source management and the effectiveness of industrial pollution control, which is conducive to the rural inclusive green development.

As can be seen from

Table 5. Different aspects of new urbanization and inclusive green development in the countryside.

Variable	Population Urbanization	Economic Urbanization	Land Urbanization	Social Urbanization	Environmental Urbanization	New Urbanization
${Ur}_1$	0.0063474 ** (2.54)					0.0064641 ** (1.07)
${Ur}_2$		−0.036858 ** (−0.07)				−0.1216795 * (−0.11)
${Ur}_3$			0.0406009 * (0.30)			0.0467532 * (0.19)
${Ur}_4$				0.0075521 *** (1.54)		2.625623 *** (1.21)
${Ur}_5$					0.0075521 *** (1.54)	0.0081129 *** (0.93)
Open	0.0301133 *** (10.44)	0.014182 *** (12)	0.0303135 *** (11.79)	0.0315404 *** (11.20)	0.0149511 *** (11.57)	0.0155808 *** (11.88)
Rely	0.7567389 *** (9.86)	−0.2648986 * (−1.33)	0.8667748 *** (14.21)	0.8359575 *** (13.62)	−0.317066 * (−1.79)	−0.3699285 * (−2.24)
Inno	−22.50995 ** (−2.92)	8.873619 (1.40)	−17.31574 ** (−2.27)	−19.18642 ** (−2.53)	8.955139 (1.48)	10.60993 (1.36)
Traf	−0.1106711 * (−2.18)	−0.0415244 (−0.31)	−0.184113 *** (−4.12)	−0.1209788 ** (−2.55)	−0.0361252 (−0.27)	−0.1871724 * (−1.22)
Eco	$4.13 \times {10}^{-6}$ *** (6.09)	$1.47 \times {10}^{-6}$ (0.87)	$4.78 \times {10}^{-6}$ *** (4.88)	$5.47 \times {10}^{-6}$ *** (5.59)	$1.06 \times {10}^{-6}$ (0.65)	$1.14 \times {10}^{-6}$ ** (0.10)
Edu	0.056022 *** (−6.93)	0.0761224 ** (2.27)	0.063039 *** (−8.02)	0.0630066 *** (−7.94)	0.0419603 * (1.71)	0.0032679 * (−0.14)
Res	0.000018 *** (6.90)	0.000013 * (1.88)	0.0000112 *** (4.00)	0.0000177 *** (6.06)	$3.76\times {10}^{-6}$ (0.47)	$-2.51\times {10}^{-6}$ ** (−0.30)
Fin	−7.074062 *** (−8.23)	−0.2499057 (−0.16)	−5.927342 *** (−7.76)	−6.437938 *** (−7.93)	−0.3961417 (−0.27)	−1.758869 * (−1.10)
Regu	−0.4559464 (−0.08)	−9.347649 ** (−2.63)	−5.335357 (−0.87)	−0.8690271 (−0.14)	−7.178002 ** (−2.50)	−5.228926 * (−2.19)

Note: *, **, and *** represent the significance of the estimates at the 10%, 5%, and 1% levels, respectively, and the t or z values of the regression coefficients are given in parentheses.

, the impacts of population, economic, land, social and environmental urbanization on inclusive green development in the countryside are all relatively significant, except for economic urbanization, but the other four aspects can effectively promote the rural inclusive green development. According to the results of the Seventh National Population Census, the proportion of the urban population in the national population is 63.89%, which is an increase of 14.21 percentage points compared with the results of the Sixth National Population Census in 2010. Therefore, while promoting the rapid growth in the proportion of the urban population, the state should also take into account the quality of population urbanization and promote the healthy development of urbanization in China [50]. The economy is the lifeblood of the country and the basis of people’s livelihood. At present, China’s economy has entered a period of transition pains and resource and environmental constraints are becoming stronger and stronger, and the traditional high-input, high-energy-consumption, rough economic development mode will have a negative impact on the sustainable development of the countryside. Promoting the transformation and upgrading of countryside industries is conducive to improving the resilience of the countryside economy, so as to enable the economy to transform into a refined and green development [51]. The proportion of built-up area to urban area is an important indicator of land urbanization. By the end of the 13th Five-Year Plan period, the number of cities in China had reached 684, and the total built-up area had reached 60,300 square kilometers, which indicates that China’s housing and urban–rural construction has made great achievements. However, the Chinese government should be more vigilant about the disorderly development that occurs during the rapid development of urbanization, especially the blind expansion of the edges of large and medium-sized cities, which has led to the degradation of soil and water resources and the destruction of the ecological environment [52]. At the level of social urbanization, the upward trend in the proportion of pensioners in the total population is a manifestation of the results of improving the pension insurance system and raising the level of pension security, and it is also an important aspect of promoting social urbanization and thus promoting rural development. Although environmental urbanization promotes the green development of the countryside, due to the current concept of green development in the countryside being weak, the countryside’s ecological environment problems are highlighted, the green transformation of agricultural production methods is lagging behind, the countryside green development system is not sound and there are other realities of the dilemma, restricting the advancement of green development in the countryside [53]. For this reason, promoting the green and sustainable development of the countryside through ecological revitalization and building ecologically livable villages and towns are the top priorities in China’s development process. From a comprehensive point of view, it is necessary to promote the coupling and interaction among population urbanization, economic urbanization, land urbanization, social urbanization and environmental urbanization to promote the coordinated development of large, medium-sized and small cities and towns based on city clusters and metropolitan areas, and to build a coordinated urban and rural development system.

6. Robustness and Heterogeneity Tests

6.1. Robustness Tests

6.1.1. Lag of 1 to 2 Periods

The impact of new urbanization construction on rural inclusive green development may have a time lag, and this paper considers the long-term effect of new urbanization policies and examines the impact effect with a lag of one to two years. The regression results of columns (1) and (2) in

Table 6. Robustness test.

Variable	(1)	(2)	(3)
	One-Period Lag	Two-Period Lag	Shortening the Time Window
Ur	0.2557037 *** (5.54)	0.2708201 *** (5.86)	0.2217744 *** (3.34)
Open	0.0137133 *** (5.75)	0.0137645 *** (5.94)	0.0138447 *** (12.41)
Rely	−0.2033418 * (−1.85)	−0.2280849 * (−1.93)	−0.2336023 (−1.35)
Inno	10.6766 *** (2.89)	10.55959 *** (2.84)	10.58933 * (1.96)
Traf	−0.1542406 * (−1.69)	−0.1590667 * (−1.71)	−0.1315131 (−0.93)
Eco	$2.12 \times {10}^{-7}$ (0.27)	$-1.86 \times {10}^{-7}$ (−0.23)	$-8.64 \times {10}^{-8}$ (−0.04)
Edu	0.0135333 (0.50)	0.0515445 (1.50)	0.0625868 (1.19)
Res	$7.41 \times {10}^{-6}$ (0.79)	$6.90 \times {10}^{-6}$ (0.74)	$3.54 \times {10}^{-6}$ (0.40)
Fin	−0.7939672 (−1.07)	−0.4998359 (−0.64)	0.3509936 (0.31)
Regu	−3.7438 (−1.47)	−3.934808 (−1.59)	−0.7298547 (−0.31)
Year-fixed effects	Yes	Yes	Yes
Regional fixed effect	Yes	Yes	Yes
N	360	330	300

Note: * and *** represent the significance of the estimates at the 10% and 1% levels, respectively, and the t or z values of the regression coefficients are given in parentheses.

show that the regression coefficients of the variables with a lag of one period and two periods are significantly positive, indicating that new urbanization has a significant and sustained promotional effect on the rural inclusive green development and that the promotional effect is gradually increasing. This paper adopts a panel two-way fixed-effects model for benchmark regression, which reduces the endogeneity problem caused by omitted variables to a certain extent; in addition, subjective avoidance is made in the selection of indicators for explanatory variables (Ur) and explained variables (Ru), which reduces the endogeneity problem caused by two-way causation.

6.1.2. Shortening the Time Window

China formally proposed the new urbanization strategy in 2012. In order to unify the research of this paper in the context of the new urbanization strategy and exclude the influence of other possible policies, column (3) in Table 6 only retains the 30 inter-provincial panel data samples from 2012 to 2021, and the regression coefficients are still significantly positive and even larger than those before excluding some data. It shows that the promotion of inclusive green rural development by new urbanization is not only robust but also more significant after excluding the policy factors. All these results confirm the robustness of the benchmark regression results.

6.2. Heterogeneity Test

The previous paper studied the impact of new urbanization on rural inclusive green development at the national level. Considering that this paper selects inter-provincial panel data and that development policies, economic conditions, resource endowments, and ecological environments vary greatly from region to region, there may be heterogeneity in the impact of new urbanization on rural inclusive green development in different regions. In view of this, this paper divides the whole sample into four sub-samples in the Eastern, Central, Western and Northeastern regions to conduct regression analyses to analyze the possible heterogeneity in depth, which is of great significance for identifying the key elements affecting the regional differences and better promoting the regional synergistic development of the rural inclusive green development.

Table 7. Regional heterogeneity test.

Variable	(1)	(2)	(3)	(4)
	Eastern Part	Central Section	Western Part	Northwest
Ur	0.1884651 *** (2.08)	0.0604478 *** (0.68)	0.013025 (0.18)	0.158846 (1.02)
Open	0.0090764 *** (4.72)	0.158872 (1.06)	0.0725284 *** (1.72)	0.2737604 * (2.05)
Rely	0.2428662 *** (1.07)	−0.9283174 * (−1.68)	−0.5083494 * (−2.23)	0.8465178 (0.70)
Inno	20.1875 *** (3.61)	−4.83716 (−0.88)	−38.94908 (−5.45)	−9.968308 (−0.91)
Traf	0.3957214 (1.53)	0.0865234 (0.80)	0.3887934 *** (5.87)	−0.6194699 (−0.90)
Eco	$-2.06 \times {10}^{-6}$ (−1.41)	$8.07 \times {10}^{-6}$ *** (3.63)	$1.89 \times {10}^{-6}$ (0.91)	$3.22 \times {10}^{-6}$ (0.96)
Edu	0.0523058 (0.67)	0.1091765 (1.32)	0.037482 (1.67)	0.091993 (0.90)
Res	−0.000014 (−0.48)	0.0000325 (1.46)	$3.62 \times {10}^{-7}$ (0.04)	0.0000602 (1.69)
Fin	−9.313602 *** (−5.40)	−0.4476254 (−0.24)	4.167663 *** (4.39)	7.74961 *** (2.91)
Regu	−10.48159 (−1.54)	−17.47846 * (−1.99)	−3.484587 *** (−1.83)	38.7981 * (1.75)

Note: * and *** represent the significance of the estimates at the 10% and 1% levels, respectively, and the t or z values of the regression coefficients are given in parentheses.

shows the results of the regional heterogeneity test.

The results of column (1) show that the new urbanization in the Eastern region has a significant role in promoting the level of rural inclusive green development and has a greater role compared with the other three regions, and the Central region is affected by the radiation-driven role of the Eastern region and the rise of Central China’s policy of the development of a good situation. The Central region should actively undertake the transfer of emerging industries in the Eastern region and adhere to green development. The promotion effect of new urbanization in the Western region on rural inclusive green development is positive but not significant. The poor natural conditions and location disadvantages make the Western region lag far behind the Eastern region. In order to break out of this predicament, it is of great significance to strengthen opening up, improve transportation infrastructure, and strengthen environmental pollution control to achieve high-quality urbanization in the Western region and thus promote rural inclusive green development. In addition, the government should also increase policy support for the Western region and promote east–west collaboration and targeted assistance in an orderly manner. The new urbanization construction in the Northeastern region has a positive but equally insignificant role in promoting rural inclusive green development in which financial activity plays a significant role in promoting the rural inclusive green development. In order to promote the comprehensive revitalization and development of the Northeastern region, we must pay great attention to the innovation ability of the old industrial base in the Northeast, adjust the industrial structure to enact industrial transformation and upgrading, enhance the core competitiveness of industry, and promote urbanization and rural development [54,55].

The results of the regional heterogeneity test show that the effect of new urbanization construction on the rural inclusive green development in each region is different, and although it promotes the development of the countryside as a whole, it is not all significant. The city and the countryside are an organic whole that promotes and advances each other, and it is necessary to break the dilemma of the urban–rural dual structure and the unbalanced and uncoordinated urban–rural development and to realize the synergistic development of urban and rural areas in the Eastern, Central, Western and Northeastern region.

7. Conclusions

With China’s economy shifting from the stage of high-speed growth to the stage of high-quality development, urbanization is an important driving force to promote the economic society and the countryside to achieve high-quality green development. Exploring the influence mechanism of new urbanization and rural inclusive green development has certain insights for the Chinese government to formulate the urban and rural development plan. This paper adopts panel data from 30 provinces in China from 2009 to 2021, establishes a panel fixed effects model, conducts a heterogeneity test, and systematically examines the mechanism of the impact of new urbanization on rural inclusive green development. The results of the study show the following: (1) New urbanization significantly promotes rural inclusive green development, and it has become an important path to promoting common wealth in the new era. The conclusion still holds after a series of robustness tests. (2) Population urbanization, land urbanization, social urbanization and environmental urbanization can effectively promote rural inclusive green development, while the regression coefficient of economic urbanization is negative in the study period. From a long-term perspective, accelerating the construction of a new development pattern dominated by domestic circulation and mutually reinforcing domestic–international dual circulation is conducive to easing the pain of economic downturn, so economic urbanization may have a positive impact on rural inclusive green development in the future. (3) The results of regional heterogeneity analysis show that the promotion effect of new urbanization construction on rural inclusive green development is more significant in the Eastern and Central regions. While it is not significant in the Western region, increasing the opening up to the outside world, improving transportation facilities, and combating environmental pollution can effectively pull the rural inclusive green development. For the Northeastern region, improving financial activity and realizing industrial transformation and upgrading are the top priorities.

In view of the above research conclusions, this paper puts forward the following policy recommendations: First, play a leading role in central cities and urban agglomerations, promote the free flow of factors between urban and rural areas in a reasonable and orderly manner, promote the countryside with the city, and create an organic community for rural development. Second, promote the balanced development of the five aspects of population, economic, land, social and environmental urbanization, improve the resilience of rural economies while promoting the high quality of urbanization and create ecologically livable towns and villages, thereby improving the residents’ sense of well-being and accessibility. Third, the government should strengthen top-level design, support the improvement of infrastructure construction in areas lagging behind in new urbanization and rural development through precise policies, and promote the establishment of provincial, city, and county pairing and sectoral collaboration, from industrial assistance and industrial collaboration to market-oriented development of characteristic industries. Fourth, the government should insist on green and low-carbon development as the fundamental policy, form a spatial pattern, industrial structure, mode of production and lifestyle that saves resources and protects the environment through the treatment of agricultural and rural pollution and the development of an ecological economy, and build beautiful villages that are ecologically pleasant to live in. Fifth, it is necessary to construct a scientifically reasonable macro-layout of urbanization based on the carrying capacity of the resources and the environment, optimize the spatial layout of towns and cities and improve the living environment of the inhabitants, enhance the quality of new urbanization construction, and push forward the continuous development of new urbanization to a deeper and deeper level.

Our study provides interesting findings and policy implications, but it also has some limitations. One limitation is that it only estimated the model based on the panel data for the period 2009–2021. Future studies could be based on wider data sets. Another limitation of this research is in the variables measuring new urbanization and rural inclusive green development. In future studies, other variables could be added to measure urbanization, such as the number of Internet broadband access users and days with good air quality. Other variables can also be included to measure rural inclusive green development, such as carbon emission per unit of cultivated land and the number of geographical indications of agricultural product brands.