The Spatial–Temporal Characteristics and Driving Forces of the Coupled and Coordinated Development between New Urbanization and Rural Revitalization

Abstract

In the 21st century’s global push for sustainable development, strategies for new urbanization and rural revitalization in China have transitioned from traditional geographic expansion to a focus on high-quality integration across ecological, social, and economic dimensions. Employing advanced methods such as the entropy weight TOPSIS, coupling coordination model, kernel density estimation, Markov chain, and geographic detector, this study comprehensively explores the spatiotemporal dynamics and driving mechanisms of urban–rural integration in China from 2001 to 2022. Key findings reveal increasing coupling coordination degrees in each province, with significant spatial variations. Notably, during the 15th Five-Year Plan, all regions, including eastern, central, and western areas, exhibited low-level coupling coordination. However, a decreasing ladder-like distribution emerged during the 13th and 14th Five-Year Plans, forming a development pattern centered on eastern coastal regions and spreading inland. The central regions experienced significant changes in development kernel density, while the national eastern and western regions remained relatively stable. Looking ahead, highly coupled regions are expected to maintain leadership, positively influencing neighboring areas and propelling overall urban–rural development towards sustainable goals. Conversely, low-level coupled regions require deeper reforms for leap-frog development. The core driving forces behind spatiotemporal differences in coupling coordination degrees involve innovation within the environment, government capabilities, openness to the outside world, and population agglomeration. Secondary roles are played by factors like non-agricultural industrialization, per capita GDP, government investment, and market conditions, while education, healthcare, transportation, and natural resource levels act as bridges in spatiotemporal differentiation. Overall, this study provides a concise spatiotemporal interpretation and strategic recommendations for urban–rural sustainable integration development, advancing towards a more harmonious, green, and just future in alignment with the core principles of sustainable development.

1. Introduction

Urbanization is a crucial driving force for the modernization of a country or region [1]. In China, the urbanization rate surged from 17.92% to 65.22% by the end of 2022, showcasing a noteworthy accomplishment in the global urbanization landscape. However, the rapid urbanization accompanying economic growth has imposed significant environmental and resource pressures. This has, in turn, hindered sustainable development between urban and rural areas, manifesting in stark disparities in infrastructure, living conditions, and public service facilities.

The resulting urban–rural contradictions have intensified, leading to a widening gap and a host of “rural issues”, including village depopulation, environmental degradation, agricultural decline, farmland conversion, and rural resource depletion, exacerbating rural poverty concerns. In response to these challenges, the 19th National Congress of China introduced the “Rural Revitalization Strategy”, emphasizing the establishment and improvement of an institutional and policy system for integrated urban–rural development. Subsequently, the 20th National Congress underscored the importance of high-quality development in urban–rural relations, advocating for “urban leading rural, rural promoting urban, urban-rural interaction, and urban-rural integration.”

Notably, scholars widely recognize the mutually reinforcing and complementary coupling relationship between rural revitalization and new urbanization [2]. The Rural Revitalization Strategy inherently integrates multiple elements, including urbanization industries, ecology, and culture, while the sustainable development of new urbanization relies on key rural factors such as labor, capital, and land. This underscores the inevitability of integrating these two national strategies.

However, it is crucial to broaden our perspective on urbanization’s impact, particularly on environmental elements such as air quality [3]. The intricate interplay between urbanization and air quality, especially in low- and mid-income countries, has garnered global attention due to concerns over air pollution in urban areas.

Moreover, the size of cities holds significance in regional economic integration (REI) within the framework of sustainable development [4]. The continuous expansion of cities has introduced challenges such as housing shortages, inefficient land use, and environmental degradation. Understanding the optimal city size for efficient REI is paramount for promoting sustainable urbanization.

The focus of contemporary academic research and policy formulation is on the intricate relationship between new urbanization and rural revitalization. In light of the evolving landscape of contributions, particularly within the Chinese literature, it is imperative to position our discourse within the broader international context. Early perspectives on urban–rural relations, such as Lewis A. Lewis’s “Dualistic Economic Structure Theory” and Lipton M. Friedman’s “Core-Periphery Theory”, initially viewed cities as dominating rural areas. However, a shift has occurred over time, emphasizing the importance and agency of rural areas in development [5]. In the evolution of urban–rural relations, Lipton M. Friedman’s “Core-Periphery Theory” emphasized the economic influence of cities (core) on rural areas (periphery). However, scholars have challenged this urban-centric perspective, advocating for a “bottom-up” development strategy that positions rural areas as active participants rather than passive recipients [6]. This shift is encapsulated in Friedman J. and Douglass M.’s “Rural-Urban Development Strategy”, emphasizing the centrality of rural development with poverty reduction and basic needs fulfillment as primary goals [7].

The core idea of this strategy is that rural areas should be the center of development, with poverty reduction and meeting basic needs as the main goals. Overall, Western scholars’ research on urban–rural relations has shifted from the initial urban-centric perspective to a more balanced and inclusive view, emphasizing the importance and agency of rural areas. Current research starts from the perspective of “urban-rural linkages” [8], seeking patterns of balanced urban–rural development and their influencing factors [9]. Internationally, scholars have moved beyond a city-centric perspective, recognizing the crucial role rural areas play in sustainable development [10]. The shift towards a “bottom-up” development strategy and the Rural–Urban Development Strategy reflects a global awareness of the importance of rural areas as active participants in the development process [11]. The focus on “urban-rural linkages” in current research indicates a growing interest in understanding and promoting balanced development between urban and rural areas, with an emphasis on factors influencing this equilibrium. This is in sharp contrast to previous studies that focused on urban–rural segmentation, and opens a new chapter in the study of urban–rural relations. This international perspective contributes valuable insights to the ongoing discourse on urban–rural relations, providing a more comprehensive understanding of the dynamics involved in achieving sustainable and inclusive development.

In China, research on urban–rural relations has evolved into a multi-dimensional system. Yang X. et al. quantitatively assessed rural revitalization in western China, contributing a comprehensive framework for policymakers [12]. Research in China is categorized into three main types. Firstly, scholars measured urban–rural development levels, shifting from single indicators to multidimensional evaluation systems [13]. However, as research deepened, modern scholars tended to use multidimensional and multi-indicator evaluation systems to measure urban–rural development [14]. They are no longer satisfied with simple ratio methods or Theil index methods, and have turned to more comprehensive methods such as the entropy method [15], analytic hierarchy process [16], and coupling models [17]. For example, Wei C. et al. (2021) used the entropy method to assess the coupling coordination development of urban–rural areas, revealing spatial–temporal differentiation patterns [18]. Qiao G. et al. (2023) analyzed the coupling coordination degree between rural revitalization and new urbanization, highlighting significant spatial disparities [19]. Tan B. et al. (2021) identified spatial differences in the synergistic effect between county-level economic development and rural transformation in Xinjiang [20].

Secondly, research explores influencing factors and driving modes of coordinated development. Fang L. et al. (2023) demonstrated the role of the cultural and tourism industry in promoting new urbanization and rural revitalization [21]. Cheng M. et al. (2020) emphasized the impact of road transportation infrastructure on urban–rural development [22]. Yang R. et al. (2020), focusing on the Pearl River Delta region, systematically explored the influencing factors of urban–rural spatial differences through regression models. The results revealed that the formation of urban–rural spatial differences was not attributable to a single factor, but was the result of the combined effects of regional growth dynamics, market drivers, and government controls [23]. Li Z. et al. (2022) highlighted the role of the digital economy in promoting urban–rural integration [24]. Additionally, scholars have also delved into theoretical considerations on the path of urban–rural integration. Chen (2018) believed that leveraging the advantages of the socialist system with Chinese characteristics can help promote integrated urban–rural development [25]. Liu J (2020) emphasized the crucial role of rural human resource development in advancing urban–rural integration [26].

Thirdly, scholars offer theoretical reflections on coordinated development. Xurui Z (2022) explored China’s agricultural economic informatization in the context of “Internet+” [27]. Meanwhile, other scholars have analyzed the changes in China’s urban–rural development since the reform and opening up, providing specific recommendations for future urban–rural co-governance development [28].

In addition to the above perspectives, it is essential to consider the latest trends in rural revitalization and digital development. The impact of COVID-19 has accelerated the adoption of digitalization as the latest model of rural revitalization, giving rise to the construction of smart villages as a new global trend. One study provides valuable insights into the evolution model and factors influencing digital villages, emphasizing the key role of digitalization in sustainable village development [29].

Considering these perspectives, our study aims to deepen the understanding of three key areas:

Mechanisms Driving Integration: Our study explores cooperative mechanisms between new urbanization objectives and the “Twenty-word policy” of rural revitalization, constructing a complementary cooperation framework.

Spatial–Temporal Differentiation: We conduct empirical analysis at the provincial level from 2001 to 2022, dissecting the development of new urbanization and rural revitalization into five phases for a nuanced understanding of dynamic evolution.

Dynamic Evolution of Driving Forces: Utilizing a geographic detector, our study identifies dominant factors influencing the spatial pattern of coupled development, offering a dynamic perspective on shaping the urban–rural integration landscape.

In conclusion, our study contributes to a nuanced understanding of urban–rural integration in China, offering valuable insights for coordinated development policymaking.

2. The Coupled Mechanism of New Urbanization and Rural Revitalization

The integration development path of new urbanization and rural revitalization should focus on the integration of space, economy, and elements, starting from the deeper meaning of urban–rural integration. This path aims to promote strategic cooperation and policy coherence between urban and rural areas in key areas such as industry building, ecological protection, culture exchange, social management, and people’s livelihoods [30]. The interplay between new urbanization and rural revitalization can be analyzed specifically in the following five aspects:

(1)

Economic urbanization links to industrial prosperity, accelerating the full integration of urban and rural industries. New urbanization, by optimizing the industrial layout and utilizing its radiation effect, can reasonably transfer some industries to rural areas, bringing more resources to rural industrial development. At the same time, the Rural Revitalization Strategy aims to integrate and optimize agricultural resources, aligning them with urban industrial systems to create a multidimensional model for industrial development [28].

(2)

Green urbanization links to ecological livability, achieving integrated urban and rural environmental protection. Green urbanization emphasizes ecological preservation and sustainable development in the urbanization process. This concept aligns with the goal of ecological livability in rural areas, jointly promoting overall environmental improvement and sustainability in both urban and rural areas [31].

(3)

Service urbanization links to rural civilization, promoting cultural complementarity and integration between urban and rural areas. Under the impetus of new urbanization, the population movement brings modernization to rural areas, enriching the leisure and recreational activities of rural residents. This further enhances the spiritual life and cultural experiences of rural residents. Additionally, it opens up new avenues for rural governance and helps rural areas discard outdated traditions and customs, facilitating the development of rural civilization [32].

(4)

Social urbanization coupled with effective governance is essential for the integration of urban and rural social governance. As new urbanization progresses, the social governance models and experiences developed in urban areas are gradually influencing rural areas, offering new ideas and approaches to rural governance. For example, advanced experiences and governance methods from urban community governance can serve as valuable references for rural areas. This can include establishing well-organized community structures and utilizing technology to enhance governance efficiency [33].

Simultaneously, as agricultural and rural modernization transformations take place, there are profound changes in the social and industrial structures of rural areas. Rural industries become more specialized, and settlement patterns evolve into new rural communities. In this context, rural governance needs to be more tailored and timely.

Furthermore, considering the similarities in social governance between urban and rural areas implies that there are many commonalities in governance goals, methods, and principles between urban and rural areas. This provides new perspectives and directions for rural governance.

(5)

Population urbanization links to economic well-being, promoting the integration of urban and rural public resources. Cities offer extensive employment and development opportunities for residents, while rural areas become preferred destinations for urban residents seeking leisure and vacations. This linkage promotes the sharing and integration of urban and rural public resources, enhancing people’s quality of life [34].

These interconnections contribute to achieving a profound level of urban–rural integration, fostering cooperation and coordination between cities and rural areas in various domains, further promoting the organic integration of the strategies of new urbanization and rural revitalization.

The interconnection mechanism between new urbanization and rural revitalization is characterized by the interconnection and mutual support of various elements. Specifically, new urbanization not only activates urban economic vitality but also serves as a bridge for the flow of rural–urban elements and economic integration with the creation of locally distinctive towns. This bridging effect facilitates the transfer of core development elements such as talent, capital, and technology to rural areas, providing sustained impetus for rural revitalization. The introduction and application of technology play a crucial role in rural revitalization, particularly in improving agricultural production efficiency.

As production efficiency improves, a significant surplus of labor in rural areas is released. Some of this labor chooses to remain in rural areas, driving the development of secondary and tertiary industries in rural areas, and bringing new vitality to the rural economy. Others migrate to urban areas, meeting the ongoing demand for labor in the urbanization process. Furthermore, the rational transfer of rural land provides space for the development of modern agricultural parks, characteristic towns, rural tourism, and other new industry forms, while also ensuring urban expansion and industrial layout. This flow of rural–urban elements is essentially a win-win process, promoting rural revitalization while satisfying the continuous development needs of urbanization.

Based on the characteristics and requirements of rural–urban element flow in new urbanization and rural revitalization, as well as the linkage mechanism, a pathway diagram for the linkage mechanism between new urbanization and rural revitalization is proposed (Figure 1).

However, it is essential to acknowledge that, as the urbanization and rural revitalization processes unfold, there may be associated costs in terms of the potential loss of rural characteristics, lifestyle, and culture. The assumed benefits of changes in the nature of rural areas, such as advances in governance, need to be weighed against these potential long-term costs. The preservation of rural characteristics, lifestyle, and culture is crucial for maintaining the unique identity of rural areas and ensuring the well-being of their residents. Future research should delve into the nuanced dynamics between urbanization, rural revitalization, and the preservation of cultural heritage, considering the long-term sustainability and overall harmony of the urban–rural landscape.

3. Research Design

3.1. Index Construction

Quantitative research on new urbanization has developed a relatively comprehensive framework. Following the research direction in academia, a set of evaluation indices for new urbanization has been established. These indices are based on five dimensions: green, economic, social, service, and population [35]. By incorporating the concept of linkage mechanisms, this evaluation index system provides a comprehensive framework for assessing new urbanization [36].

The “Twenty-word policy”, as outlined by the Party Central Committee of the Communist Party of China, serves as the overarching requirement for rural revitalization. It serves as a comprehensive standard for measuring China’s rural economic, ecological, cultural, political, and social “five-in-one” construction during the rural revitalization process. Based on a deep understanding of the essence of the Rural Revitalization Strategy, an evaluation index system for rural revitalization has been constructed [28].

Furthermore, following principles such as systematization and data availability, this study has established a collaborative development evaluation index system for new urbanization and rural revitalization. This system comprises 42 specific indicators, as detailed in

Table 1. Evaluation index system for the coordinated development of new urbanization and rural revitalization.

Target	Criterion Layer	Indicator Layer	Indicator Explanation/Unit	Indicator Direction	Index Weights
New urbanization	Population urbanization	Urbanization rate	Urbanization rate of resident population (%)	+	0.031
		Population density	Urban population density (person/km2)	+	0.041
		Employment status	Urban registered unemployment rate (%)	−	0.008
		Employment structure	Proportion of employed persons in secondary and tertiary industries (%)	+	0.012
		Education level	Proportion of the population of general higher education (%)	+	0.052
	Economic urbanization	Economic development level	Per capita GDP (yuan)	+	0.074
		Economic structure	Proportion of the added value of the tertiary industry in GDP (%)	+	0.047
		Financial revenue	General public budget revenue (ten thousand yuan)	+	0.124
		Investment level	Investment in fixed assets (billion yuan)	+	0.124
		Residents’ income	Disposable income of urban residents (yuan)	+	0.077
	Social urbanization	Infrastructure	Every 10,000 people have access to a bus (car)	+	0.033
			Per capita urban road area (square meters)	+	0.037
		Quality of life	Internet penetration (%)	+	0.036
			Consumer price index (%)	−	0.057
	Green urbanization	Garbage disposal	Domestic waste harmless treatment rate (%)	+	0.019
			Road cleaning area (hectare)	+	0.098
		Ecological basis	Per capita park green area (square meters/person)	+	0.018
			So2 emission (million tons)	−	0.019
		Water quality	Industrial wastewater discharge (million tons)	−	0.017
	Service urbanization	Public cultural service	Number of public cultural facilities (units)	+	0.017
		Public services	Education expenditure (million yuan)	+	0.031
			Number of health technicians per thousand people (person)	+	0.028
Rural revitalization	Industrial revitalization	Agricultural production capacity basis	Per capita total power of agricultural machinery (KW)	+	0.036
			Comprehensive grain production capacity (million tons)	+	0.038
		Agricultural production efficiency	Agricultural labor productivity (yuan/person)	+	0.036
		Industrial convergence	Main business income of agricultural products processing enterprises above designated size (billion yuan)	+	0.038
	Ecological livability	Rural living environment governance	Proportion of administrative villages that treat domestic sewage (%)	+	0.036
			Proportion of administrative villages that dispose of domestic waste (%)	+	0.035
			Popularity rate of sanitary toilets (%)	+	0.037
			Rural greening rate (%)	+	0.036
	Rural civilization	Rural public culture construction	Number of rural cultural stations (number)	+	0.036
			Cable TV coverage (%)	+	0.036
		Education level of farmers	Proportion of rural residents ’ education and cultural entertainment expenditure (%)	+	0.036
			Average years of education of rural residents (%)	+	0.050
	Effective governance	Control measures	Proportion of administrative villages that have compiled village plans (%)	+	0.036
			Proportion of administrative villages that have carried out village renovation (%)	+	0.036
		Governance capacity	Proportion of “one shoulder” of village directors and secretaries (%)	+	0.342
	Economic well-being	Farmer’s income level	Per capita net income of farmers (yuan)	+	0.036
			Urban–rural income ratio (%)	-	0.015
		Living conditions of farmers	Engel coefficient of rural residents (%)	−	0.014
			Rural residents per capita housing area (square meters)	+	0.036
			Percentage of safe drinking water (%)	+	0.036

.

3.2. Data Sources and Processing

This investigation centers on China’s provincial-level administrative units, encompassing 31 provinces, autonomous regions, and municipalities. Owing to constraints in data availability, the regions of Hong Kong, Macao, and Taiwan are temporarily excluded from consideration. The raw data for the designated indicators have been meticulously curated from a spectrum of reputable publications, notably including the “China Statistical Yearbook”, “China Population and Employment Statistics Yearbook”, “China Agricultural Statistics Yearbook”, “China Environmental Statistics Yearbook”, “China’s Tertiary Industry Statistical Yearbook”, Rural Statistical Yearbook, provincial statistical yearbooks, and authoritative ecological environment journals. In instances of missing data for select indicators, interpolation methods have been judiciously applied.

China is stratified into three distinct regions: the eastern, central, and western regions, delineated in accordance with the nation’s three major economic zones. The eastern region comprises 11 provinces, namely Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan. The central region encompasses 10 provinces, including Shanxi, Inner Mongolia, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan, and Guangxi. The western region encompasses 9 provinces, namely Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. This regional categorization forms a foundational framework for the subsequent analysis of urban–rural integration dynamics and underscores the nuanced heterogeneity present across China’s diverse geographical landscape.

3.3. Research Methods

3.3.1. Coupling Coordination Model

The term “coupling” originates from systems theory, describing the interrelationships and interactions among factors within a system or between multiple systems, leading to the phenomenon of synergistic development resulting from the interrelation and interaction of various factors within a system or among multiple systems [37]. Given the close and complementary connection between new urbanization and rural revitalization, the development of the two is inseparable and mutually reinforcing. Therefore, this study introduces a coupling degree measurement model to quantitatively analyze the coordinated development relationship between the two. Initially, the comprehensive levels of new urbanization and rural revitalization are calculated using the entropy weight method. The specific calculation process and steps are referenced from the approach used by BMDS A et al. (2019) [38]. Subsequently, the coupling coordination degree, reflecting the degree of mutual coordination between new urbanization and rural revitalization, is calculated using Equation (1).

$$C=2\times {\left[\frac{U_1\times U_2}{U_1+U_2}\right]}^{\frac{1}{2}}$$

(1)

Here, C represents the coupling coefficient of the indicators of new urbanization and rural revitalization, with a value range of [0, 1]. $U_1$ and $U_2$ are the evaluation indices of the two. As the numerical value of C approaches 1, the coupling level increases, and vice versa.

However, relying solely on coupling degree cannot fully express the coordinated development level of new urbanization and rural revitalization. To address this, the study introduces a coupling coordination model to more accurately describe the coordinated development level of the two. The coupling coordination degree ($D$) is calculated using Equations (2) and (3) as follows:

$$D=\sqrt{C\times T}$$

(2)

$$T=\alpha U_1+\beta U_2$$

(3)

Here, $D$ represents the coupling coordination degree of new urbanization and rural revitalization, with a value range of [0, 1]. $T$ is the comprehensive evaluation index of the two, and $\alpha$ and $\beta$ are the coefficients indicating their relative importance. Considering that urban development has a slightly stronger driving effect on rural areas than the reverse, referring to the research by Sun, J. et al. (2023), $\alpha$ is set to 0.6, and $\beta$ is set to 0.4 [39]. Referring to the research by Chun Y. et al. (2020) on coupling coordination and relative development coefficient delineation, this study classifies coupling coordination into five types: lower, low, moderate, high, and extreme coordination, using 0.2, 0.4, 0.6, and 0.8 as the dividing points. A larger coupling coordination degree indicates a stronger synergy between the two [40], as shown in

Table 2. Classification criteria and types of coupling coordination degree.

Coupling Coordination Degree D Value	Coupling Coordination Degree Type
(0, 0.2]	Lower
(0.2, 0.4]	Low
(0.4, 0.6]	Moderate
(0.6, 0.8]	High
(0.8, 1]	Extreme

.

3.3.2. Geographic Detector

The geographic detector, a statistical method deployed in this study, serves the purpose of elucidating the underlying causes for spatial distribution disparities observed in the coupled and coordinated development between new urbanization and rural revitalization. This method provides valuable insights into the driving forces contributing to these spatial variations and finds applications across diverse disciplines, including economics [41], society, and environmental studies [42]. Within the context of this model, the factor detector specifically scrutinizes whether particular factors contribute to spatial distribution differences in key indicators [43]. Given the pronounced spatial heterogeneity characterizing the coupling coordination of China’s new urbanization and rural revitalization, our investigation employs factor detection and interactive detection facilitated by the geographic detector model. The objective is to discern the predominant factors shaping the coordination and integration dynamics between new urbanization and rural revitalization.

Equation (4), presented below, captures the essence of this approach:

$$q=1-\frac{1}{n{\sigma }^2}{\displaystyle \sum _{h=1}^L{n}_h{\sigma }_h^2}$$

(4)

where $L$ represents the number of types of factors; $n$ and $n_h$ are the sample sizes in the study area and within type $h$, respectively; and ${\sigma }^2$ and ${\sigma }_h^2$ denote the dispersion variances of sample sizes within the study area and type $h$, respectively. The q-value ranges from [0, 1] and serves as an indicator, with higher q-values signifying the greater explanatory power of the influencing factors on the coordination and integration between new urbanization and rural revitalization. This analytical framework enables a nuanced exploration of the spatial relationships and factors influencing the coupled development of these critical dimensions, contributing to a more comprehensive understanding of the intricate dynamics at play.

4. Results

4.1. Analysis of the Spatial Evolution Characteristics of Coupling Coordination between New Urbanization and Rural Revitalization

To visually depict the current status of the coupling coordination between new urbanization and rural revitalization, this study first divided the coupling coordination values from 2001 to 2022 into five intervals based on the criteria in Table 2. Then, according to China’s 10th (2001~2005), 11th (2006~2010), 12th (2011~2015), 13th (2016~2020), and 14th (2021~2022) Five-Year Plans, they were categorized into five periods. Subsequently, a spatial distribution map of the coupling coordination between new urbanization and rural revitalization was created using ArcGIS 10.8 software, as shown in Figure 2.

(1)

Extreme Coupling Coordination: During the study period, only Guangdong reached a high level of coordination, evolving from low to medium to medium to high to extreme degrees of coordination from the 10th to the 14th Five-Year Plan. Guangdong, located in southern China and adjacent to Hong Kong and Macao, has been a pioneer in reform and opening-up. It possesses abundant resources and an advantageous geographical location, serving as an important gateway connecting China to the world. During the 10th Five-Year Plan period, Guangdong’s level of coupling coordination between new urbanization and rural revitalization ranked third (0.3888), and it was in the low degree of coupling coordination stage. Shanghai (0.4717) and Beijing (0.4500) ranked first and second, respectively. As Guangdong’s economy developed rapidly, many areas that were originally engaged in agriculture also joined industrial production, and the construction of urbanization and the development of rural revitalization progressed rapidly. Both new urbanization and rural revitalization developed rapidly, with new urbanization providing infrastructure and workforce support for rural revitalization, and rural revitalization having a positive feedback effect on new urbanization. Therefore, during the 11th and 12th Five-Year Plan periods, Guangdong’s coupling coordination between new urbanization and rural revitalization increased to the medium degree of the coupling coordination stage.

During the 11th Five-Year Plan period, Guangdong Province still ranked third, with a coupling coordination degree of 0.4679, while Shanghai (0.5275) and Beijing (0.4972) remained in the first and second positions, respectively. During the 12th Five-Year Plan period, Guangdong surpassed Beijing and ranked second, with a coupling coordination degree of 0.5509, while Shanghai (0.5798) remained in the first position, and Beijing (0.5482) dropped to third. Due to the implementation of policies and measures such as poverty alleviation, new urbanization, and the modernization of agriculture, significant development was achieved in agriculture in Guangdong Province during the 13th Five-Year Plan period. The total output value of agricultural products increased from 1.59 trillion yuan in 2016 to 2.01 trillion yuan in 2020, with an average annual growth rate of 5.9%. Rural infrastructure construction continued to improve, and the living environment and quality of life for farmers also continued to improve. The development level of rural revitalization saw significant growth during this period, and the corresponding construction of new urbanization also developed substantially, achieving coordinated development between the two. During the 13th Five-Year Plan period, Guangdong’s coupling coordination between new urbanization and rural revitalization increased to the high degree of coupling coordination stage, ranking second, with a coupling coordination degree of 0.6945. Shanghai (0.7008) ranked first, and Jiangsu (0.6716) also surpassed Beijing, ranking third.

With the issuance of the “New Urbanization Planning of Guangdong Province” in 2021, Guangdong’s new urbanization construction ushered in more orderly and efficient development. The development level of rural revitalization also continued to rise. During the 14th Five-Year Plan period, Guangdong’s coupling coordination between new urbanization and rural revitalization reached the extreme degree of coupling coordination stage, ranking first with a coupling coordination degree of 0.8116. Shanghai (0.7675) ranked second, and Jiangsu (0.7463) ranked third.

(2)

High Degree of Coupling Coordination: Before 2015, no province or municipality had reached a high degree of coupling coordination between new urbanization and rural revitalization. The first regions to achieve a high degree of coupling coordination were concentrated in the eastern coastal and southern coastal areas. During the 13th Five-Year Plan period, Shanghai, Guangdong, Jiangsu, Beijing, Shandong, and Zhejiang were at this level. From the 10th to the 14th Five-Year Plan, Beijing evolved from medium to high degree, while other provinces evolved from low to medium to high degree.

During the 14th Five-Year Plan period, high coupling coordination areas gradually expanded from the Yangtze River Basin and Yellow River Basin to inland areas. In addition to the provinces mentioned above, 16 provinces including Hunan, Hubei, Fujian, Hebei, Shaanxi, Heilongjiang, Chongqing, and Gansu reached a high degree of coupling coordination between new urbanization and rural revitalization during the 14th Five-Year Plan period. These regions experienced rapid development due to the spillover of technology, funds, and talent from the eastern coastal areas. National policies such as the “Rise of the Eastern Region” strategy and the “Northeast Revitalization” plan also provided strong support for new urbanization and rural revitalization, leading to a positive trend of coordinated development between the two.

(3)

Moderate Coupling Coordination: Beijing was in the moderate coupling coordination stage during the 10th Five-Year Plan. During the 11th Five-Year Plan, Heilongjiang, Shaanxi, Liaoning, and some eastern coastal provinces reached a moderate level of coupling coordination. During the 12th Five-Year Plan, provinces along the Yangtze River and Yellow River reached a moderate level of coupling coordination. During the 13th Five-Year Plan, except for some eastern coastal areas that were in the high degree of the coupling coordination stage and Qinghai and Tibet, which were in the low degree of the coupling coordination stage, the rest of the provinces were in the moderate coupling coordination stage. During the 14th Five-Year Plan, only 8 provinces including Xinjiang, Gansu, Tibet, and Inner Mongolia were still in the moderate coupling stage.

Shaanxi province needs special attention as its characteristics evolved from low to moderate. Shaanxi province initially led in the early stages of new urbanization and rural revitalization, but it is a cause for concern that its coupling coordination remained at a moderate level from 2006 to 2022. Therefore, it is an important research topic to explore measures to improve the quality of life for the people of Shaanxi, achieve high-quality social development, and promote coordinated development between urban and rural areas.

(4)

Low Coupling Coordination: During the 10th and 11th Five-Year Plans, most provinces were in a low degree of coupling coordination. However, by the 14th Five-Year Plan, all provinces had moved away from the low degree of coupling coordination stage. This indicates that regions are placing greater emphasis on the integrated development of urban and rural areas in their development strategies. They are achieving organic links and coordinated development between urban and rural economies and societies by raising the level of urbanization and improving the rural development environment. Moving away from the low coupling coordination stage signifies that the development goals of new urbanization and rural revitalization have been effectively integrated. Provinces are no longer pursuing urbanization or rural revitalization separately, but are organically combining the two, creating a development model where they mutually reinforce each other. This helps optimize the allocation of resources, talents, and funds, promoting comprehensive economic and social development in both urban and rural areas.

(5)

Lower Coupled Coordination: From 2001 to 2022, no province was in the weakly coupled coordination stage, indicating that the Chinese government’s policies in the urban–rural domain have yielded positive results. The coupling coordination of new urbanization and rural revitalization in all provinces has shown an upward trend, but with significant spatial variations. During the 10th Five-Year Plan, the eastern, central, and western regions were all in the low coupling coordination stage. However, by the 13th and 14th Five-Year Plans, coupling coordination displayed a stepped distribution resembling the Chinese character “Chuan”, gradually forming a development pattern centered around the eastern coastal regions and spreading to inland areas.

4.2. Analysis of the Spatial Evolution Characteristics of Coupling Coordination between New Urbanization and Rural Revitalization

Kernel density estimation is an important nonparametric method often used to describe the dynamic evolution of variables [44]. In order to delve deeper into the dynamic evolution trend of coupling coordination between new urbanization and rural revitalization over time and reveal its distribution characteristics, this paper has drawn kernel density curves for the years 2001, 2006, 2011, 2016, and 2022 for the entire country, as well as the eastern, central, and western regions, as shown in Figure 3.

Figure 3 shows that, from 2001 to 2022, the distribution centers of coupling coordination between new urbanization and rural revitalization in the whole country, as well as in the eastern and western regions, remained stable, with the main peak heights increasing year by year and the distribution curves gradually widening. This indicates an improvement in the coupling coordination in these regions but also suggests that internal differences are gradually increasing. Additionally, looking at the peaks, there is a trend from a single peak to a primary and secondary peak, indicating an intensifying trend of polarization.

The eastern region continues to exhibit a single peak distribution, reflecting a certain level of consistency in its strategies for new urbanization and rural revitalization. Although the development speed and patterns may vary across regions, they generally follow some common principles or directions.

In contrast, the western region shows a primary and secondary peak distribution, indicating a continuous trend of polarization in this region.

Figure 3c demonstrates that the central region experienced significant changes in the kernel density curve of coupling coordination between new urbanization and rural revitalization from 2001 to 2022. During 2001–2006, the region exhibited a flat and multi-peak distribution, suggesting a lack of a clear and unified development trend in coupling coordination during this period. Different regions within the central region may have had their own development priorities and strategies, leading to various coexisting development patterns.

From 2006 to 2011, the peak value of the kernel density distribution curve sharply increased, and the distribution center shifted to higher values, exhibiting a bimodal distribution with significant differences between the two peaks. This indicates that, during this period, the central region made significant progress and development in coupling coordination. The significant difference between the two peaks may suggest that some cities or areas within the central region made significant progress during this time, while others lagged.

From 2011 to 2022, compared to 2006–2011, the distribution curve of the central region showed a continuous decrease in the main peak height, gradually transitioning to a bimodal distribution, with the peak values gradually becoming similar. This suggests that the previously significant development patterns or strategies in the central region gradually balanced out during this period. The central region exhibited a slowdown in development speed, a more balanced development pattern, integrated strategies, and a stable development trend in coupling coordination from 2011 to 2022.

4.3. Analysis of the Spatiotemporal Transition Characteristics of Coupling Coordination between New Urbanization and Rural Revitalization

While kernel density estimation provides a good depiction of the overall shape of the development changes in coupling coordination between new urbanization and rural revitalization, it may not intuitively reflect the changes in relative positions during the evolution of this coordination in different regions and future development trends. Therefore, we further analyze the dynamic evolution patterns of coupling coordination using traditional Markov chains and spatial Markov chains. This analysis helps identify the intrinsic correlations, internal mobility, and stability among neighboring regions [45].

We divided the coupling coordination into four discrete categories using quartiles: low-degree coordination (L), moderately low degree coordination (ML), moderately high degree coordination (MH), and high-degree coordination (H). We calculated both the traditional Markov transition probability matrix and the spatial Markov transition probability matrix for coupling coordination in the study area.

By applying the principles of traditional Markov chains, we obtained the traditional Markov chain transition matrix for coupling coordination between new urbanization and rural revitalization from 2001 to 2022, as shown in

Table 3. Degree of traditional Markov transition probability matrix.

t/t + 1	L	ML	MH	H
L	77.38%	22.62%	0%	0%
ML	0%	69.64%	30.36%	0%
MH	0%	0%	46.43%	53.57%
H	0%	0%	0%	100%

. In this matrix, the values on the main diagonal represent the probabilities of coupling coordination remaining in its current state, while the off-diagonal values indicate the probabilities of changes in coupling coordination, either moving up or down.

First, we will look at the stability of the overall development trend. The transition probabilities on the diagonal are higher than those off the diagonal, indicating that most regions at different development levels are likely to maintain their current states shortly. Specifically, regions at the “L” level have a 77.38% probability of staying at the low-level coupling and coordination development, regions at the “ML” level have a 69.64% probability of maintaining their current status, regions at the “MH” level have a 46.43% probability of maintaining their status, and regions at the “H” level are completely stable, with a 100% probability of maintaining their high-level coupling and coordination development. This suggests that the development patterns of various regions will remain stable, and regions with a high degree of coupling and coordination will not experience a significant decline in the short term, while regions with lower quality also exhibit a certain degree of path dependence, making it difficult to achieve significant improvements in the short term.

Second, we will look at fluctuations in the coupling and coordination development levels of some regions. Looking at the transition probabilities off the diagonal, it is evident that some regions exhibit fluctuations in their coupling and coordination development levels. Specifically, the transition probability from “L” to “ML” is 22.62%, indicating that some low-level coupling and coordination regions may improve to a slightly higher level. The transition probability from “ML” to “MH” is 30.36%, and the transition probability from “MH” to “H” is 53.57%. Both of these probabilities indicate an upward trend, suggesting that these regions have an unstable coupling and coordination development level, which may fluctuate in the future.

While the coordinated development of urban and rural areas continues to improve in various regions of the country, the regional linkage effects are becoming increasingly significant. Incorporating spatial factors into the Markov chain model to investigate the evolution trajectory of coupling and coordination is detailed in

Table 4. Spatial Markov transition probability matrix.

Neighborhood	Type (t/t + 1)	L	ML	MH	H
L	L	4.17%	95.83%	0%	0%
	ML	0%	52.94%	47.06%	0%
	MH	0%	0%	46.15%	53.85%
	H	0%	0%	0%	100%
ML	L	95.45%	4.55%	0%	0%
	ML	0%	72.00%	28.00%	0%
	MH	0%	0%	54.55%	45.45%
	H	0%	0%	0%	0%
MH	L	68.57%	31.43%	0%	0%
	ML	0%	92.59%	7.41%	0%
	MH	0%	0%	55.81%	44.19%
	H	0%	0%	0%	100%
H	L	100%	0%	0%	0%
	ML	0%	61.90%	38.10%	0%
	MH	0%	0%	0%	100%
	H	0%	0%	0%	100%

.

First, the spatial transition probabilities of regional coupling coordination are closely related to neighborhood relationships. When a region’s coupling coordination is “L”, if its neighbors are also “L”, then the probability of the region maintaining the “L” status in the future is 4.17%, while the probability of upgrading to “ML” is as high as 95.83%. Similarly, when a region’s coupling coordination is “MH” and its neighbors are “H”, the region will completely maintain the “H” status. This further confirms the influence of neighboring areas.

Second, spatial spillover effects differ between different levels of coupling coordination. For regions in the “ML” state, when their neighbors are in the “MH” state, there is a 7.41% probability of upgrading to the “MH” state, whereas when the neighbors are in the “H” state, there is a 38.10% probability of upgrading to the “MH” state.

Third, there is a significant emphasis on mutual influence between regions. When a region’s coupling coordination is “ML” and its neighbors are “L”, the probability of downward transition is 0%, but when its neighbors are “MH”, the probability of upward transition is 7.41%. Similar results apply to other types, indicating that regions adjacent to those with low coupling coordination will not be negatively affected, while regions adjacent to those with relatively high coupling coordination will experience positive effects.

5. Analysis of the Spatiotemporal Differentiation Driving Mechanisms of the Coupling Coordination of New Urbanization and Rural Revitalization

5.1. Model Construction for Detecting Driving Factors

This section primarily analyzes the spatiotemporal dynamics of the coordination and coupling between new urbanization and rural revitalization without delving into the reasons for its evolution. Therefore, this section aims to analyze the factors causing the spatiotemporal variations in coordination and coupling. Since some variables had significant data gaps before 2011, this section mainly focuses on the years 2011 to 2022 to examine how various factors influenced the spatiotemporal differentiation of coordination and coupling between new urbanization and rural revitalization.

The formation and shaping of coordination and coupling between new urbanization and rural revitalization are influenced by numerous factors within the complex economic and social systems. Drawing on the research of relevant scholars, this study selects a comprehensive set of influencing factors, including economic factors, social factors, market factors, degree of openness, innovation environment, resource endowment, and others (as shown in

Table 5. Spatiotemporal differentiation influencing factors of coordination and coupling between new urbanization and rural revitalization.

Dimension	Sequence	Variable	Index
Economic factors	X1	Regional economic strength	Per capita gdp (yuan)
	X2	Non-agricultural industrialization	Proportion of the added value of the secondary and tertiary industries in GDP (%)
	X3	Urban development level	Proportion of urban population (%)
Social factors	X4	Government capacity	Proportion of government expenditure in GDP (%)
	X5	Social investment level	Total fixed asset investment as a percentage of GDP (%)
	X6	Medical level	Number of beds in medical and health institutions per unit of population (sheets/1000 persons)
	X7	Educational level	Public education expenditure as a percentage of GDP (%)
	X8	Transport infrastructure level	Highway mileage (km)
Market factors	X9	Urban residents’ income	Per capita disposable income of urban residents (yuan)
	X10	Rural residents’ income	Rural residents per capita disposable income (yuan)
	X11	Consumption level	Total retail sales of social consumer goods as a percentage of GDP (%)
Opening degree	X12	Level of opening up	Total imports and exports as a percentage of GDP (%)
	X13	Degree of dependence on foreign capital	Proportion of FDI in GDP (%)
Innovation environment	X14	Technical level	Patent grants (item)
	X15	Innovation capital	R&D expenditure (yuan)
	X16	Innovative talents	Number of R&D personnel (number)
Resource endowment	X17	Population concentration	Population density (persons/km2)
	X18	Natural carrying capacity	Per capita water resources (cubic meters/person)

). Using geographic detectors, the study aims to explore these influencing factors and their interactions with the coordination and coupling of new urbanization and rural revitalization in the research area.

5.2. Identification of Dominant Driving Factors

The explanatory variables were introduced into the geographic detector model to determine the influence (q-values) of each explanatory variable on the spatiotemporal differentiation of coordination and coupling. The results, as shown in

Table 6. Geographic detector results.

Independent Variable	p-Value	Significance	q-Value	Ranking of Explanatory Power	2011	2016	2022
X1	0.05	0.05	0.407	14	0.490	0.444	0.614
X2	0.05	0.05	0.494	12	0.442	0.453	0.529
X3	0.05	0.05	0.502	11	0.569	0.458	0.398
X4	0.00	0.05	0.663	6	0.720	0.566	0.661
X5	0.42	0.05	0.534	8	0.687	0.624	0.286
X6	1.00	0.05	0.187	17	0.261	0.051	0.300
X7	0.27	0.05	0.275	16	0.161	0.119	0.280
X8	0.95	0.05	0.160	18	0.094	0.089	0.142
X9	0.01	0.05	0.595	7	0.630	0.612	0.681
X10	0.72	0.05	0.527	9	0.609	0.624	0.560
X11	0.05	0.05	0.472	13	0.373	0.375	0.562
X12	0.00	0.05	0.688	5	0.672	0.616	0.620
X13	0.34	0.05	0.516	10	0.523	0.462	0.519
X14	0.00	0.05	0.773	1	0.753	0.749	0.778
X15	0.00	0.05	0.760	3	0.785	0.764	0.738
X16	0.00	0.05	0.773	2	0.706	0.775	0.763
X17	0.00	0.05	0.698	4	0.659	0.696	0.696
X18	0.05	0.05	0.286	15	0.321	0.253	0.245

, indicate that the 18 indicators across six dimensions exhibited different impact characteristics at the overall average level and in different years.

From the perspective of the overall average level from 2011 to 2022, among the driving factors influencing coordination and coupling between new urbanization and rural revitalization, 12 indicators, including technological level, innovation funding, and innovative talent, passed the significance test. The top three factors in terms of influence were technological level, innovative talent, and innovation funding, with corresponding q-values of 0.773, 0.773, and 0.760, respectively. This suggests that these three indicators explain changes in coordination and coupling between new urbanization and rural revitalization to a considerable extent, reaching 77.3%, 77.3%, and 76.0%, respectively. This result also highlights the dominant role of factors related to the innovation environment in the development of coordination and coupling between new urbanization and rural revitalization. However, while indicators related to social investment level, rural resident income level, and foreign capital dependence exhibited high q-values, they did not pass the significance test. Among the social factors, the factors related to medical level, education level, and transportation infrastructure level had lower contribution rates and did not pass the significance test, indicating that regions adjacent to low coordination and coupling areas are not negatively influenced by them, while regions adjacent to areas with relatively higher coordination and coupling levels are positively influenced.

To explore the impact of q-values on the spatiotemporal differentiation of coordination and coupling between new urbanization and rural revitalization in 2011, 2016, and 2022, it was observed that the influence of resource endowment and openness level on the spatiotemporal differentiation of coordination and coupling weakened, while the influence of economic factors, social factors, market factors, and innovation environment gradually increased. Among the innovation environment factors, technological level, innovation funding, and innovative talent consistently ranked high in influence. The influence of the economic environment also expanded year by year, with all factors passing the significance test. This illustrates the significant regional disparities in China’s economic development, resulting in differences in the development of coordination and coupling between urbanization and rural areas. The influence of urban resident income levels and consumption levels increased year by year and was significant. However, while rural resident income levels exhibited a high q-value, the impact was not significant. This suggests that encouraging rural labor migration to urban areas, which can effectively increase income levels, thereby promoting regional consumption levels, plays a crucial role in promoting the coordinated development of urbanization and rural revitalization. Due to differences in resources, geographical location, and economic policies, China has a significant spatial disparity in openness levels, which contributes to the current spatiotemporal differentiation of coordination and coupling. A higher level of openness in a region indicates more resource inflow and expanded market opportunities, which positively contributes to the coordinated development of urbanization and rural revitalization. However, the influence of openness level slightly decreased from 2011 to 2022, possibly due to changes in the international situation. Natural carrying capacity and population density play important roles in the coordinated development of new urbanization and rural revitalization. Population density increased year by year, indicating that the flow of population from areas or sectors with low productivity to those with higher productivity promotes coordinated development between urban and rural areas. Therefore, encouraging rural residents to work in cities can effectively increase income levels, thereby promoting regional consumption levels and further advancing the coordinated development of urbanization and rural revitalization. While the influence of natural carrying capacity decreased year by year, it remained significant. This suggests that, as urbanization accelerates, the concentration of urban populations increases, leading to a strong demand for water resources. However, China has effectively addressed the issue of water scarcity through extensive water diversion projects and water resource management, resulting in a decreased impact of regional water resources on the development of new urbanization and rural revitalization. Education levels and medical levels exhibited non-significant impacts, possibly due to the long and complex pathways through which they affect coordination and coupling between new urbanization and rural revitalization. The transportation infrastructure level consistently ranked last in influence and was non-significant. This may be because this study used the specific indicator of road mileage, and by 2011, China’s road infrastructure had become quite developed. Furthermore, during the period from 2011 to 2022, road mileage did not experience significant changes. Additionally, the increasing popularity of alternative transportation modes such as subways, high-speed railways, and airplanes reduced the impact of road mileage on the spatiotemporal differentiation of coordination and coupling between new urbanization and rural revitalization. However, this should not negate the importance of transportation infrastructure development, as improving transportation conditions enhances regional accessibility and accelerates the flow of factors between regions and within regions, and thus promotes the development of urbanization and rural revitalization.

5.3. Interactive Detection Results

Through an analysis of the interactive detection results of influencing factors (Figure 4), it is evident that there are interactions among the various influencing factors in the spatiotemporal differentiation of the coupling coordination between new urbanization and rural revitalization. These interactions between different factors significantly enhance their explanatory power, and the results show varying degrees of bivariate enhancement or non-linear enhancement. However, education level, medical level, and natural-resource-carrying capacity have relatively weak interactions, and they exhibit weakened and independent relationships with each other. This indicates that the spatiotemporal differentiation of the coupling coordination between new urbanization and rural revitalization is the result of the interaction of multiple factors, such as economic, social, market, innovation environment, openness, and resource endowment.

The technical level has the strongest interaction with other factors, with an explanatory power exceeding 76%. Next is the interaction between government capability and other factors, with an explanatory power of 75%. The interaction of innovation funds and innovation talents with other factors is similar and also has a high explanatory power. This indicates that the spatiotemporal differentiation of the coupling coordination between new urbanization and rural revitalization is mainly influenced by factors such as technical level, innovation funds, innovation talents, and government capability working together.

Education level (0.275), medical level (0.187), transportation infrastructure level (0.160), and natural-resource-carrying capacity (0.286) have relatively weaker impacts in single-factor detection. However, after the interaction, the impacts of education level with technical level, medical level with innovation funds, transportation infrastructure level with innovation funds, and natural-resource-carrying capacity with government capability become nonlinearly enhanced, with interaction-enhanced q-values of 0.905, 0.875, 0.867, and 0.835, respectively. This suggests that the impacts of medical level, education level, transportation infrastructure level, and natural-resource-carrying capacity on the spatiotemporal differentiation of the coupling coordination between new urbanization and rural revitalization need to be fully manifested through the combined action of factors such as innovation funds and technical level.

Natural-resource-carrying capacity, as previously analyzed, requires coordinated government management to exert its maximum impact. The interaction between consumption level and population density has the highest explanatory power, reaching 95%. This indicates a strong interaction between consumption level and population density in the development process of new urbanization and rural revitalization. Specifically, areas with high consumption levels often have higher population densities, while areas with low consumption levels tend to have lower population densities. Regions with high consumption levels usually have more developed economic and social resources, attracting a large influx of population, and leading to an increase in population density. This, in turn, stimulates the rise in consumption levels, creating a positive feedback loop.

5.4. The Mechanism behind the Spatiotemporal Differentiation of Coupling Coordination between New Urbanization and Rural Revitalization

Based on the identification of dominant influencing factors and their interaction detection, further construction of the spatiotemporal formation mechanism of coupling coordination between new urbanization and rural revitalization (Figure 5) is conducted. Specifically, within the regional innovation environment, factors such as technological level, innovation funds, and innovative talents exhibit strong single-factor explanatory power, all exceeding 0.75. Their interactions with other indicators are also significant, highlighting the direct and leading impact of the innovation environment in the spatiotemporal differentiation of coupling coordination between new urbanization and rural revitalization. Government capacity, the level of openness to the outside world, and population density show significant single-factor explanatory power and prominent interactions with most indicators. This indicates that:

• The government plays a critical role in regulating and guiding the coordinated development of new urbanization and rural revitalization.
• Opening up, especially via international market connections, is a crucial driving force for promoting new urbanization and rural revitalization.
• Population concentration is the foundation and prerequisite for new urbanization and rural revitalization.

Factors related to industrial non-agricultural levels, per capita GDP, government investment levels, and market elements exhibit relatively weak self-explanatory power. However, their interactions with other factors are significant and display a bilinear enhancement, suggesting that these factors indirectly influence the spatiotemporal differentiation of coupling coordination between new urbanization and rural revitalization.

Factors such as education level, medical level, natural-resource-carrying capacity, and transportation infrastructure level have the weakest single-factor explanatory power. Nevertheless, their interactions with the innovation environment, government capacity, degree of openness, etc., are highly significant and exhibit nonlinear enhancement. This indicates that education level, medical level, transportation infrastructure level, and natural-resource-carrying capacity indirectly impact the spatiotemporal differentiation of coupling coordination between new urbanization and rural revitalization in the research area.

In summary, the spatiotemporal differentiation of coupling coordination between new urbanization and rural revitalization is the result of the dominant and direct effects of the innovation environment, government capacity, openness to the outside world, and population concentration. This is further influenced by secondary drivers and effects of factors like industrial non-agricultural levels, per capita GDP, government investment levels, market elements, education level, medical level, transportation infrastructure level, and natural-resource-carrying capacity, which have indirect effects through interactions.

6. Discussion

6.1. The Connotations and Importance of the Research Results

This study delves into the spatiotemporal evolution and driving mechanisms of the coupling and coordination between new urbanization and rural revitalization, revealing a series of profound implications and development trends.

• Significant and Complex Spatial Differentiation: Despite the general trend of improvement in coupling coordination among provinces, the significance of spatial differentiation cannot be ignored. This phenomenon highlights the complexity of regional integration in China and serves as a warning to policymakers. When constructing future strategies for urban–rural coordinated development, regional disparities must be fully considered and integrated into the policy-making process.
• Increasing Internal Disparities: While the peak values of coupling coordination at the national and regional levels are on the rise, the widening distribution curve indicates a gradual increase in internal disparities. This may reflect resource imbalances and differences in development strategies and practices among different regions. Addressing these internal disparities poses a challenge for policymakers in identifying and supporting relatively lagging areas more accurately.
• Duality of Spatial Effects: This study further reveals the closeness of spatial transfer probabilities of coupling coordination to neighborhood relationships. This implies that regions with highly coordinated development may have positive spillover effects on their neighboring areas, while regions with low coordination may not negatively affect their adjacent regions. This provides a new perspective and approach to regional cooperation.
• Central Role of Innovation Environment: Factors related to the innovation environment play a significant role in the spatiotemporal differentiation of coupling coordination between new urbanization and rural revitalization. This underscores the central importance of innovation in driving coordinated development between urban and rural areas. Additionally, factors such as education, healthcare, transportation, and natural resources, while individually having weaker explanatory power, exhibit significant interactions with other factors, further highlighting the indirect but indispensable role of these infrastructures and social services in urban–rural coordinated development.

In summary, this study not only provides new insights and in-depth analyses for understanding the coupled and coordinated development of new urbanization and rural revitalization, but also offers valuable references for future policy formulation and implementation. In future research and policymaking, addressing the balance and coordination of development among different regions, reducing internal disparities, harnessing spatial effects, and further leveraging the central role of the innovation environment will be essential topics worth exploring.

6.2. Strategies

6.2.1. Strengthen Regional Collaborative Development

Given the significant differences in coupling coordination among various regions, it is imperative to enhance cooperation and communication between the eastern, central, and western regions. To achieve this, a comprehensive approach to strengthening regional collaborative development is recommended. Prioritize optimizing resource allocation to promote the development of regions with lower coupling coordination, contributing to a more balanced and coordinated development framework. Regional collaboration platforms should be established to facilitate the exchange of best practices, knowledge, and resources among different regions.

6.2.2. Optimize the Innovation Environment

The innovation environment plays a crucial role in the coupling coordination of new urbanization and rural revitalization. To further enhance this environment, increased government investment in research and development is essential, as well as reinforcing talent attraction and development initiatives to foster a skilled workforce capable of driving innovation; stimulating innovation within regions by creating supportive ecosystems, such as innovation hubs and research clusters; and promoting comprehensive economic and social development by integrating innovation into various aspects of urban and rural planning.

6.2.3. Advance Comprehensive Openness and Regional Cooperation

Deepening the synergy between new urbanization and rural revitalization in terms of openness and cooperation is vital. It is recommended to strengthen international and regional exchanges to achieve mutual benefits and sustainable development; foster partnerships between urban and rural areas, encouraging collaborative projects that leverage the strengths of both; and develop policies that facilitate the flow of ideas, technologies, and investments between regions, promoting a more interconnected and interdependent urban–rural relationship.

6.2.4. Develop Tailored Strategies

To address regional disparities, it is recommended to formulate highly adaptable strategies for new urbanization and rural revitalization. These strategies should be customized to meet the specific needs of each region, considering their unique social, economic, and environmental contexts. This would involve encouraging local governments to actively engage with communities to identify and prioritize development goals and implementing policies that take into account the cultural and historical aspects of each region, ensuring that the strategies resonate with the local population and contribute to sustainable development.

These recommendations aim to provide valuable guidance to policymakers in addressing the challenges of new urbanization and rural revitalization, with the ultimate goal of fostering a more balanced and sustainable urban–rural development landscape.

6.3. Relevance to Existing Research

Currently, in the field of coordinated urban–rural development, numerous studies have provided multidimensional perspectives and rich research findings. While many review articles have covered specific aspects, disciplines, or research areas, there remains a need for more quantitative research and data to address existing issues from a scientific perspective.

For instance, Ye C. et al. (2019) delved into the logic behind the coordinated development of urban and rural areas, summarizing paths and directions for rural transformation into new urbanization [45]. Similarly, Yang Z. (2019) analyzed the theories, logic, and mechanisms of urban–rural integration development, emphasizing synergistic effects on the economy, society, and ecological environment [46]. These studies offer theoretical perspectives, yet there is a gap in quantitative research and data to support this.

On another note, Xu M. et al. (2021) focused on the Beijing–Tianjin–Hebei region, exploring driving mechanisms of rural land transformation on rural economic development [47]. Xiong W. (2023) concentrated on the willingness of rural migrant workers to become urban citizens in Guangdong Province, revealing the connection between policy design and willingness matching [48]. Although these studies contribute significantly to our understanding of the coordinated development of urban and rural areas from specific research perspectives and regions, they overlook the systematic nature of the research field, the overall scope of research, and the continuity of research over time. This oversight makes it challenging to establish a unified logical relationship between different research directions, comprehend comprehensive issues in China as a whole, and discern the evolving process of problems, thereby influencing the formulation of national policies by the Chinese government.

In response to these gaps, our study makes innovative attempts from various angles, including the research time span, research area scope, and research methods. We employed a series of methods, including the entropy-weighted TOPSIS method, kernel density estimation, Markov chain, and geographic detector, to comprehensively assess and analyze the coupling relationship in urban–rural development. This integrated application not only enriches the depth and breadth of the research, but also provides a diversified analysis framework for future studies.

Our research findings reveal a step-like pattern of “higher in the southeast and lower in the northwest” in the spatial distribution of the coupling coordination degree between new urbanization and rural revitalization. This pattern aligns with the results of Cao J. et al. (2021) [49], further confirming the close relationship between the coupling coordination degree of new urbanization and rural revitalization and regional population, economy, and resources. Additionally, this study further explored several factors influencing the coupling coordination degree, among which the factor of innovation environment played a dominant role. This conclusion aligns with the viewpoint of LLJ Cheng (2020) that China’s economic development has shifted from being factor-driven to innovation-driven, with innovation becoming the primary driver of economic development [50]. However, it is worth noting that, unlike LLJ Cheng’s focus on China’s overall development, this study’s conclusion is more focused on the development of the “three rural” areas, helping to change the traditional notion that agriculture and rural development are unrelated to innovation.

In summary, our study not only contributes to the existing literature by addressing the gap in quantitative research, but also provides a holistic and systematic understanding of the coordinated development of new urbanization and rural revitalization, offering valuable insights for the formulation of national policies by the Chinese government.

6.4. Limitations of the Study and Future Directions

The coupled and coordinated development of new urbanization and rural revitalization constitutes a complex and multidimensional research process. Although this paper proposed a theoretical mechanism for their linkage, empirical research is limited to the quantitative study of overall coupling coordination. It does not delve into the linkage levels of each dimension. This limitation hinders our ability to identify which dimension’s linkage significantly contributes to overall coupling coordination and which aspects are relatively weak, thus preventing us from fully leveraging strengths and addressing weaknesses. Therefore, future research can focus on quantitatively analyzing the development levels of various dimensions of linkage, identifying their strengths and weaknesses, and providing a more scientific basis for policy formulation.

Secondly, due to data availability constraints, some conclusions may deviate from expectations. For example, this study uses road mileage as a representative indicator of the level of transportation infrastructure, and by 2011, China’s road infrastructure construction was relatively developed, with no significant changes in road mileage between 2011 and 2022. Meanwhile, the emergence of new transportation tools such as airplanes, high-speed trains, and ships may have led to the continued low ranking and insufficient significance of the impact of transportation infrastructure levels. Future research may consider selecting indicators more in line with the current development context, strengthening communication with relevant departments to obtain more suitable data, and reducing the impact of data issues on results.

In addition, although this paper has drawn the important conclusion that innovation has the dominant influence on the coupling coordination of new urbanization and rural revitalization, it has not further explored its influencing mechanism. Future research can use methods from various disciplines such as econometrics, geography, and management to conduct a more comprehensive and in-depth exploration, thereby further unleashing the role of innovation in urban–rural integration development.

Finally, although this paper conducted an extensive literature review, it did not deeply explore the potential contradictions between the two systems, namely, new urbanization and rural revitalization. According to Marxist contradiction theory, everything is a combination of contradictions and unity. Therefore, future research can start from the contradictions between the two and conduct a more in-depth exploration to enrich the research in the field of urban–rural development. Based on this, future research directions can be further expanded to the multidimensional and multilevel coupling coordination mechanisms of new urbanization and rural revitalization, as well as the differences and specificities in different development stages and regional contexts. This will provide deeper theoretical support and empirical evidence for formulating more precise and efficient policies.

7. Conclusions

After conducting an in-depth analysis of the evolution characteristics and driving factors of the coupled coordination of new urbanization and rural revitalization in 31 provinces and cities in China from 2001 to 2022, this paper draws the following main conclusions:

• The coupled coordination of new urbanization and rural revitalization in each province has shown an upward trend, but there is significant spatial differentiation. During the 15th Five-Year Plan period, the eastern, central, and western regions were all in a low degree of coupled coordination. However, by the 13th Five-Year Plan and the 14th Five-Year Plan periods, the coupled coordination has exhibited a “川”-shaped step-wise distribution, gradually forming a development pattern centered on the eastern coastal areas and spreading to the inland regions.

Policy Recommendation 1: Prioritize Targeted Interventions

Acknowledge the spatial disparities and focus on targeted interventions during the implementation of development policies. Tailor strategies to the specific needs and challenges of each region, with an emphasis on addressing low-coupling-coordination areas during planning and resource allocation.

2.

The kernel density curves of coupled coordination in the central region show significant changes. In comparison, the distribution centers of the kernel density curves of coupled coordination for the whole country, eastern, and western regions have remained stable, while the peak heights continue to rise, and the distribution curves gradually widen, with each region exhibiting unique evolution characteristics.

Policy Recommendation 2: Strengthen the Central Region’s Resilience

Recognize the unique challenges faced by the central region and implement policies that specifically enhance the resilience of these areas. This may involve targeted investments in infrastructure, innovation, and education to support the evolving development patterns identified in the central region.

3.

The development patterns of each region will remain stable, and highly coupled and coordinated development regions will not experience a significant decline in the short term, while low-quality regions will find it difficult to achieve a substantial improvement in the short term. The spatial transfer probability of regional coupled coordination is closely related to neighboring relationships. Regions adjacent to areas with low coupled coordination will not be negatively affected, while regions adjacent to areas with higher coupled coordination will be positively influenced.

Policy Recommendation 3: Strengthen Regional Collaborations

Encourage collaboration and resource sharing between regions, particularly focusing on areas with high coupled coordination to positively influence neighboring regions. Facilitate knowledge transfer and joint initiatives to enhance the overall coupled coordination across adjacent areas.

4.

Factors related to the innovation environment play a dominant role in the coupled coordination development of new urbanization and rural revitalization. The spatial–temporal differentiation of coupled coordination between new urbanization and rural revitalization is the result of the primary driving and direct effects of the innovation environment, government capabilities, openness to the outside world, and population agglomeration. Additionally, factors such as industrial non-agricultural level, per capita GDP, government investment level, and market factors play secondary roles, and factors like education level, medical level, transportation infrastructure level, and natural-resource-carrying capacity have indirect effects.

Policy Recommendation 4: Foster Innovation and Government Capacities

Prioritize policies that foster innovation and enhance government capacities, recognizing their dominant role in coupled coordination development. Invest in creating an innovation-friendly environment and strengthening government capabilities to ensure sustainable urban–rural development.

In conclusion, China has exhibited a complex and unique development pattern in the coupling and coordination process of new urbanization and rural revitalization. From significant spatial variations to differences in the kernel density curves across regions, and the establishment of a stable regional development pattern, each aspect reveals the diverse efforts and achievements of different regions in China. The innovation environment and various socio-economic factors have played a decisive role. This provides us with an important insight: in future development strategies, we should place greater emphasis on integrating and optimizing various resources. We should further promote the organic integration of new urbanization and rural revitalization, ensuring the resilience and long-term sustainability of urban and rural development, and address the environmental and social challenges brought about by rapid urban–rural transformation.