Spatiotemporal Evolution and Influencing Factors of Municipal Rural Revitalization Development Levels in China

Abstract

This study establishes a municipal-level evaluation system for rural revitalization in China, grounded in the five-sphere integrated framework encompassing “prosperous industries, livable ecology, civilized rural customs, effective governance, and affluent life.” Employing methodologies including the entropy weight-coupling coordination model, LISA spatiotemporal analysis, and multi-scale geographically weighted regression (MGWR), it empirically investigates the evolution and driving mechanisms of rural revitalization development across 282 prefecture-level cities from 2011 to 2023. The findings reveal: (1) Nationwide and regional rural revitalization levels demonstrate a consistent upward trajectory, progressing from a state of “Mild Disorder” to being “On the Verge of Disorder,” with a distinct gradient pattern of “Eastern Region > National Average > Central Region > Western Region.” (2) Significant global spatial correlation is observed, manifesting as polarization typified by “high–high” and “low–low” agglomeration, alongside notable volatility in Northeast and Southwest China. (3) Influencing factors display marked spatiotemporal heterogeneity. Agricultural production efficiency (North China) and technological innovation (nationwide, except the Yangtze River Delta) significantly foster rural revitalization. Conversely, economic development level (Northeast, Central, and Western China), government intervention (Northeast China), and industrial structure upgrading (Northwest China) exhibit constraining effects. The localized positive impacts of urbanization (border areas of Yunnan, Heilongjiang, Sichuan, Jilin, and Tibet) and opening up (border ports) are increasingly evident. Building on these insights, the study proposes recommendations—such as implementing differentiated regional policies, innovating spatial governance models, and activating multidimensional drivers—to overcome the “low-level lock-in” predicament and advance comprehensive rural revitalization. Furthermore, this paper reveals the patterns of multidimensional system coupling and the spatial heterogeneity of driving mechanisms. These findings provide a reference for deepening the understanding of geographical complexity within global sustainable development theory.

1. Introduction

Over the 70 years since the founding of New China, the Chinese government has continuously improved the economic system and advanced economic reforms, leading to relatively favorable development in people’s production and livelihoods [1]. However, while rapid industrialization and urbanization have brought agglomeration benefits, they have also intensified the outflow of human capital from rural areas, undermined rural development momentum, and posed threats to food security, ecological sustainability, and economic efficiency [2,3]. Pursuing a sustainable rural future has thus become one of China’s most critical policy issues.

In 2017, China’s Rural Revitalization Strategy, which places agriculture and rural development at the top of its agenda, is seen as a practical tool for China’s modernization and shared prosperity theory [4]. Rural revitalization is a holistic approach that aims to achieve long-term, sound and coordinated development in all aspects of rural areas, including economic, social, environmental and cultural aspects [5]. It is similar to the European Union’s Common Agricultural Policy [6], the USA’s Farm Bill Conservation Programs [7], the Russian Rural Areas Integrated Development and the National Rural and Northern Growth Strategy of Canada [8], the National Rural and Northern Growth Strategy in Canada [9], the Comprehensive Regional Policies in Japan [10], and the “World Social Report 2021: Reconsidering Rural Development” issued by the United Nations [11], all of which focus on the multidimensional aspects of rural development. Broadly speaking, the Chinese Rural Revitalization Strategy represents, in essence, an indigenous and enhanced approach to the Sustainable Development Agenda of The United Nations 2030 Agenda [12]. It integrates several of the SDGs (SDGs), including economic well-being (SDG8), social equity (SDG10), and ecological livability (SDG15), serving as a comprehensive “Chinese laboratory” for testing and advancing sustainable development theories.

The Rural Revitalization Strategy has been implemented for nine years, and there is an urgent need to understand its developmental status, spatiotemporal variations, and influencing factors. This study is conducted against this backdrop. Beyond assessing the outcomes of rural revitalization in China, its deeper scientific significance lies in employing refined spatiotemporal analysis to uncover the coupling coordination relationships and spatial heterogeneity mechanisms within multidimensional sustainable development processes. The findings are expected to provide empirical evidence from the world’s largest developing country, thereby contributing to the enrichment of spatial and complexity theories in sustainable development research.

This paper selects municipal-level regions as the research subject. Firstly, China is a vast country, and the municipal level serves as a bridging link in the governance system, effectively connecting macro-level strategies with micro-level practices. This makes it easier to observe the actual development of each village as well as the cooperation between the villages. Secondly, the urban scale provides enough space units to support the entropy weight-coupling coordination model, LISA spatiotemporal analysis and multi-scale geographically weighted regression (MGWR). This enables the precise identification of spatial variations in rural revitalization levels, clustering patterns, and the spatiotemporal heterogeneity of driving mechanisms. Thirdly, municipal-level data are relatively complete and strongly correspond to policies. This not only helps uncover the underlying causes of regional disparities, such as those between the eastern, central, and western parts of China, but also provides a precise basis for formulating differentiated regional policies and innovating spatial governance models. It thus helps to address more scientifically the problems of reviving rural areas and supporting integrated rural development.

On the basis of previous studies, this paper (1) constructs a comprehensive assessment system of five spheres which reflects the comprehensive development of rural areas. Based on the data of 282 prefecture-level cities in China from 2011 to 2023, this paper uses the entropy weight-coupling coordination model to measure the level of revitalization in each area. (2) Utilizing the LISA spatiotemporal data analysis method, it dynamically characterizes the differentiation characteristics, agglomeration patterns, and evolutionary pathways of rural revitalization levels across Chinese cities from three aspects: time, space, and spatiotemporal interaction. (3) Using the MGWR model and based on two temporal cross-sections (2011 and 2023), it meticulously delineates the complex spatiotemporal variations in the effects, scopes of influence, and significance of factors such as natural conditions, economic foundations, and institutional innovation. This yields a series of empirical conclusions that are “place-specific” and “time-specific”.

The contributions of this article are mainly reflected in four respects: (1) Unlike prior studies that often assign weights to the five dimensions of rural revitalization as a whole or focus on broad provincial analysis, this research couples the five dimensions into an interactive system and conducts a nationwide analysis at the prefectural-city level. This approach avoids masking structural issues within the system—a limitation of using entropy weighting alone—and overcomes the overly generalized nature of provincial studies and the lack of coherence in case studies. As a result, it offers a more detailed and spatially specific understanding of the dynamics of rural revitalization. (2) While most existing studies rely on traditional statistical methods, this paper introduces a spatial analysis technique from geography—LISA spatiotemporal data analysis. This method visually reveals the dynamic evolution and fluctuation of rural revitalization’s spatial structure. This methodological advancement enables a finer examination of the spatial autocorrelation of rural revitalization and its changes over time, which has been underexplored in earlier research. (3) Departing from conventional regression models, this study employs the multi-scale geographically weighted regression (MGWR) model. It uncovers the spatiotemporal heterogeneity of various influencing factors in terms of scale, direction, and importance. This allows for a more precise identification of region-specific drivers and barriers, moving beyond the “one-size-fits-all” explanations common in the literature. (4) Building on rigorous empirical findings, the paper proposes a differentiated regional policy framework tailored to the diverse factors identified. Unlike the generic recommendations often found in existing studies, our suggestions are grounded in concrete mechanisms and spatial governance innovations. They provide actionable solutions and insights for breaking the “low-level lock-in” dilemma in lagging regions.

2. Literature Review

In the past, the research of Chinese rural revitalization has mainly concentrated on three aspects: the level measure, the path of development, and the social and economic impact. In terms of measuring the level of revitalization in rural areas, most of the secondary indicators are built on the basis of the “20 Character Policy Guideline”, namely, “Prosperous Industry, Liveable Ecology, Civilized Countryside”, effective governance serves, and affluent life [13,14]. The selection of tertiary indicators varies based on research objectives and data availability [15,16]. For example, Zhang et al. [17] constructed 22 tertiary indicators, Jia et al. [18] constructed 35 tertiary indicators, Tao and Wu [19] constructed 36 tertiary indicators, and Guo and Hu [20] constructed 55 tertiary indicators. As regards measuring methods, the entropy weighting approach is generally applied [13,21], and a combination of TOPSIS [22,23] and entropy weight (AHP) [24,25]. Some scholars also employ factor analysis [26,27]. Spatial coverage is often concentrated on the macro-province analysis [28,29], the macro-urban conurbation [30,31], or the micro-village case studies [32,33]. The analysis covers the application of the Dagum Gini coefficient to investigate spatial differences [34,35], kernel density estimation [36,37], or Markov chain analysis [38,39] for the investigation of dynamic development.

Regarding how to revitalize rural areas, different scholars have proposed various factors influencing rural revitalization from different perspectives. In summary, the main factors affecting rural revitalization include the level of economic development, agricultural modernization, rural education, fiscal support for agriculture, and the quality of the rural population [39,40,41,42,43]. With the development of the digital economy, some specific areas have begun to focus on the role of the digital economy in rural development, such as the activating effect of digital inclusive finance on the rural economy [44], the solid support of digital infrastructure construction for rural development [45], the cultivation and development pathways of digital industries in rural areas [46], and the long-term impact of digital talent on rural areas [47].

With respect to the economic impact of reviving rural areas, some scholars have studied how to effectively link poverty reduction with rural revitalization [48,49], in order to build a long-term mechanism to prevent families from falling back into poverty. Others focus on studying how rural revitalization can narrow the urban–rural gap [50,51] and explore pathways for sharing development outcomes and achieving common prosperity [52]. Additionally, some scholars are concerned with maintaining ecological balance and sustainability during rural construction and development, seeking to achieve positive interaction between economic growth and ecological protection [53,54].

Regarding rural revitalization and sustainable development, there are many similarities between the two in theory [55,56]. The classic theory of sustainable development emphasizes the balance among the three pillars of economy, society, and environment [57], and the five-sphere framework of rural revitalization is the specificization and expansion of this balance concept in the rural field of China. Specifically, this framework translates the abstract tripartite model into actionable rural development dimensions: prosperous industries aligns with the economic pillar; affluent life, effective governance, and civilized rural customs correspond to the social pillar; and livable ecology directly embodies the environmental pillar. However, the Chinese practice extends beyond a mere one-to-one mapping. It introduces governance as a distinct [40], enabling dimension and integrates culture as a core developmental asset [58], thereby enriching the classical sustainability paradigm with institutionally and culturally embedded elements. This characteristic adaptation has addressed rural-specific issues such as spatial equity, the outflow of human capital, and the resilience of social–ecological systems.

Currently, the research framework for rural revitalization continues to expand, yet the research units, content, and methodologies still require further development and innovation. First, rural revitalization encompasses five major systems, but previous comprehensive evaluation methods have overlooked the intrinsic logic and interconnections among these systems. Second, the core of rural revitalization lies in rural areas, and smaller research units can better reflect the actual conditions on the ground. Previous studies, often conducted at the provincial level, tend to be too macro in scale, lacking systematic exploration of the critical “bridging” role played by municipal-level units. Therefore, it is hard to reveal the internal differences in the development of rural areas. Thirdly, it is necessary to study the dynamic transformation of the rural revitalization and the multi-scale impact of the influential factors from the spatial angle. This article will deal with these aspects in the study.

3. Evaluation System and Methodology

3.1. Rural Revitalization

The comprehensive and coordinated development is the basic core of promoting the development of the countryside. The use of multidimensional and multi-indicator systems to evaluate the revitalization of rural areas has become a trend. Based on the Rural Revitalization Strategic Plan (2018–2022), the Comprehensive Rural Revitalization Plan (2024–2027) and related documents on the establishment of the Rural Revitalization Indicator System [31], and taking into account the features of the prefecture-level data, 30 indicators are selected from the five dimensions: prosperous industries, livable ecology, civilized countryside customs, efficient government service, and rich life, to assess the level of revitalization in rural areas (As shown in

Table 1. The measurement index system of rural sustainable development.

Target Variable	Primary Indicators	Secondary Indicators	Specific Indicators	Influence Direction
Rural Sustainable Development	Prosperous Industries	Agricultural Production Capacity Foundation	Total farm machinery power per person	+
			Integrated grain production	+
		Agricultural Production Efficiency	Productivity of agricultural labor (primary industrial output/per head of GDP)	+
		Level of Industrial Integration	Main revenue of large-scale agricultural products processing enterprises	+
	Livable Ecology	Green Development in Agriculture	Pesticide and fertilizer application intensity	−
			Utilization ratio of animal and poultry manure	+
		Rural Living Environments	Percentage of administrative villages treated with domestic wastewater	+
			Percentage of administrative villages treated with household waste	+
			Rural sanitation toilet coverage	+
		Rural Ecological Conservation	Green cover rate in rural areas	+
	Civilized Rural Customs	Educational Attainment of Farmers	Share of household spending in education, culture and recreation in rural areas	+
			Proportion of qualified teachers in rural compulsory education schools with bachelor’s degrees or above	+
			Average years of schooling among rural residents	+
		Transmission of Traditional Culture	Comprehensive population coverage of television programming (while acknowledging that in the digital era, information channels have diversified, this indicator retains critical significance in the context of rural revitalization. First, it represents the baseline infrastructure for the accessibility of public cultural services and mainstream discourse in rural areas, ensuring that even populations affected by the digital divide (such as the elderly) are included. Second, in China’s policy framework, the television network has evolved beyond mere entertainment. It functions as a crucial platform for disseminating agricultural technology, policy announcements, emergency broadcasts, and culturally enriching content, directly supporting the construction of rural spiritual civilization. Thus, it captures the pervasiveness and equity of foundational cultural and information services, a core component of “Civilized Rural Customs.”)	+
			Proportion of administrative villages with internet broadband access	+
		Rural Public Cultural Development	Number of rural cultural stations	+
	Effective Governance	Governance Capabilities	Proportion of villages where the same person holds village chief and party secretary positions (this institutional arrangement, mandated by national policies such as the Regulations on the Work of Rural Grassroots Organizations of the Communist Party of China, is not merely an organizational characteristic. It fundamentally aims to reduce internal friction within the village “two committees”, enhance decision-making and execution efficiency, and ensure the coherent implementation of national strategies like rural revitalization at the grassroots level.)	+
		Governance Initiatives	Proportion of administrative villages with completed village planning	+
			Proportion of administrative villages where village revitalization projects have been implemented	+
	Affluent Life	Farmers’ Income Levels	Farmers’ per capita net income	+
			Per capita income growth rate in rural areas	+
			Urban–rural income ratio	−
			Rural poverty rate	−
		Farmers’ Consumption Structure	Rural Engel coefficient	−
		Farmers’ Living Conditions	Number of vehicles per 100 households	+
			Per capita residential area of rural residents	+
		Infrastructure Development Level	Village road hardening rate	+
			Per capita road area	+
		Basic Public Service Coverage Level	Number of health technicians per 1000 rural residents	+
			The popularization rate of safe drinking water	+

).

Thriving industries are the key drivers of rural economy development, and the three secondary indicators are the main support for implementing them. The foundation of “Agricultural Production Capability” emphasizes on the actual factors, such as the power of agricultural machinery, the comprehensive capacity of grain production, and the establishment of the base of industry development. The “agricultural productivity efficiency” promotes the transformation of the agricultural production from the large-scale increase in output to the enhancement of the quality, which is in line with the development pace of the modern agriculture. The “Level of Industry Integration” moves the agricultural sector from a single production to a comprehensive development of primary, secondary, and tertiary sectors, thus stimulating the diversification of income generation potential in the rural economy.

Livable ecology embodies the developmental goal of harmonious coexistence between humans and nature in rural areas. The corresponding three secondary indicators precisely cover the entire scenario of “production-living-ecology” in the rural ecosystem. “Green development in agriculture” constrains the negative environmental externalities of agricultural production, achieving greening of the production process. The “rural living environments” directly improves the spatial quality of villagers’ daily lives, translating the concept of “livability” into tangible living experiences. “Rural ecological conservation” restores the natural ecological foundation of the countryside, preserving the ecological advantages that distinguish rural from urban areas.

Civilized rural customs represent the core of rural spiritual and cultural development. Its three secondary indicators establish a comprehensive cultivation pathway spanning from subject quality to cultural carriers and then to service provision. The “educational attainment of farmers” enhances the cultural literacy of the rural population, laying the human foundation for civilized rural customs. “Transmission of traditional culture” opens channels for cultural dissemination, ensuring the continuity of rural cultural roots. “Rural public cultural development” addresses gaps in the supply of public cultural services, enriching the spiritual and cultural lives of villagers.

Effective governance serves as the institutional guarantee for the orderly development of rural areas. Its two secondary indicators form a governance loop of capacity building to outcome implementation. “Governance capabilities” optimizes the organizational coordination efficiency of grassroots governance, addressing the effectiveness of subjects in terms of “who governs and how to govern.” “Governance initiatives” directly measure the practical implementation outcomes of governance policies, avoiding formalism characterized by emphasizing deployment while neglecting implementation.

Affluent life represents the livelihood endpoint of rural revitalization. Its five corresponding secondary indicators establish a complete livelihood chain from economic foundation to quality of life and then to public safeguards. The “farmers’ income levels” constitutes the economic foundation of affluent life. The “farmers’ consumption structure” reflects the shift of “affluence” from having enough to eat and wear to quality enhancement. “Farmers’ living conditions” concretizes affluence as lived experience. The “infrastructure development level” and “basic public service coverage level” provide the hardware support and public safeguards for affluent living.

3.2. Methodology

3.2.1. Entropy Weight-Coupling Coordination Model

Rural revitalization is a systematic endeavor. Its five dimensions must be advanced in a coordinated manner to form an organic whole, thereby achieving sustainable and high-quality rural development [40,59]. Guided by the concept of coordinated development, coupling coordination is one of the most objective and effective methods for analyzing and managing the equitable relationships among the five systems of rural revitalization. Existing research has predominantly applied the coupling coordination model to analyzing the relationships between rural revitalization and external systems, such as urbanization [60] and infrastructure development [61], with relatively less focus on the internal coupling coordination relationships within the system itself. Therefore, this paper employs the coupling coordination method to study the internal coordination mechanisms of the rural revitalization system at the municipal-level scale. This approach not only addresses a gap in existing research but also provides a theoretical basis and practical reference for formulating differentiated rural development policies.

First of all, this paper uses the entropy weight method to evaluate the development situation of every subsystem in the process of rural revitalization. Taking into account that the measuring units of the parameters are not uniform for each subsystem, the parameters shall be standardized before calculation using Equation (1)

$$x_{ij}^{\prime }=\left\{\begin{array}{c}{x}_{ij}={\displaystyle \frac{X_{ij}-\min \left(X_{ij}\right)}{\max \left(X_{ij}\right)-\min \left(X_{ij}\right)}} if the influence direction of x_{ij} is positive \\ x_{ij}={\displaystyle \frac{\max \left(X_{ij}\right)-X_{ij}}{\max \left(X_{ij}\right)-\min \left(X_{ij}\right)}} if the influence direction of x_{ij} is negative \end{array}\right.$$

(1)

where $x_{ij}^{\prime }$ is denoted as $x_{ij}$, and $x_{ij}$ is the value of the jth indicator for the ith region after treatment, where $i=1,2,3\cdots \cdots 5$ and $j=1,2,3\cdots \cdots n$. Calculate the proportion of the jth indicator in the ith region using Equation (2).

$$p_{ij}={\displaystyle \frac{x_{ij}^{\prime }}{{\displaystyle \sum _{i=1}^m{x}_{ij}^{\prime }}}}$$

(2)

Calculate the entropy of the jth indicator with Equation (3).

$$e_j=-{\displaystyle \frac{1}{\ln K}}{\displaystyle \sum _{i=1}^m{p}_{ij}\ln p_{ij}}$$

(3)

Calculate the coefficient of variation for indicator j with Equation (4).

$$d_j=1-e_j$$

(4)

Calculate the weight of the jth indicator in relation to all indicators with Equation (5).

$$w_j={\displaystyle \frac{d_j}{{\displaystyle \sum _{i=1}^m{d}_j}}}$$

(5)

Calculate the comprehensive scores of each subsystem in each region according to Equation (6).

$$U_i={\displaystyle \sum _{j=1}^n{w}_j\times x_{ij}^{\prime }}$$

(6)

By using the entropy weight method, we can get the scores of the five subsystems of the rural revitalization.

Secondly, based on the coupling degree model, the evaluation formula of the interaction intensity between subsystems is given as follows:

$$C_i=\theta \times {[{\displaystyle \frac{U_{i1}\times U_{i2}\times \cdots \times U_{i\theta }}{{(U_{i1}+U_{i2}+\cdots +U_{i\theta })}^{\theta }}}]}^{{\displaystyle \frac{1}{\theta }}}$$

(7)

where $\theta$ is the number of subsystems (rural revitalization comprises 5 subsystems). The value range of the coupling degree $C$ is $[0,1]$, where 0 indicates that the subsystems are independent of each other and moving towards disorder, while 1 indicates that the subsystems have achieved benign resonant coupling and are evolving toward a new ordered structure. While $C$ can reflect the resonance relationship among subsystems, it sometimes fails to adequately capture the overall “efficacy” and “synergistic effects” between them. Therefore, the multi-system coupling coordination degree $D$ is further introduced to represent the level of coordinated coupling in rural revitalization development:

$$\left\{\begin{array}{c}{D}_i=\sqrt{C_i\times T_i}\\ T_i={\partial }_1\times U_{i1}+{\partial }_2\times U_{i2}+\dots +{\partial }_{\theta }\times U_{i\theta }\end{array}\right.$$

(8)

where $T$ is the comprehensive coordination index, and ${\partial }_1,{\partial }_2,\cdots \cdots ,{\partial }_{\theta }$ represent the contribution coefficients of different systems. Under the principle that each subsystem is considered equally important, their values are both set to 0.2. The value range of $D$ is $[0,1]$; a higher value indicates a stronger benign coupling relationship within the system. The classification of the coupling coordination level for rural revitalization development is shown in the

Table 2. Coupling coordination degree classification criteria.

	The Value Range of $\mathit{D}$	Subtype	The Value Range of $\mathit{D}$	Subtype
Acceptable Interval	[0.9, 1]	High-Quality Coordination	[0.7, 0.8)	Intermediate Coordination
	[0.8, 0.9)	Good Coordination
Transitional Interval	[0.6, 0.7)	Primary Coordination	[0.5, 0.6)	Barely Coordination
Unacceptable Interval	[0.4, 0.5)	On the Verge of Disorder	[0.1, 0.2)	Severe Disorder
	[0.3, 0.4)	Mild Disorder	[0, 0.1)	Extreme Disorder
	[0.2, 0.3)	Moderate Disorder

below.

3.2.2. LISA Spatiotemporal Data Analysis

To reveal the local spatial structural differences and spatiotemporal synergistic changes in rural revitalization, LISA spatiotemporal data analysis is introduced. This method incorporates a temporal dimension into traditional static spatial pattern analysis by quantifying the movement trajectory of the study object’s coordinates and its spatial lag term in the Moran’s I scatter plot. The geometric characteristics are reflected through relative length and path curvature. Here, a larger relative length $L{L}_i$ indicates a more dynamic local spatial structure of the study object $i$; a larger path curvature $L{C}_i$ indicates a more curved and volatile spatial dependency process of the study object $i$.

$$L{L}_i={\displaystyle \frac{N{\displaystyle \sum _{t=1}^{T-1}d(L_{i,t},L_{i,t+1})}}{{\displaystyle \sum _{i=1}^N{\displaystyle \sum _{t=1}^{T-1}d(L_{i,t},L_{i,t+1})}}}}$$

(9)

$$L{C}_i={\displaystyle \frac{{\displaystyle \sum _{t=1}^{T-1}d(L_{i,t},L_{i,t+1})}}{d(L_{i,t},L_{i,t+1})}}$$

(10)

In the formula, $N$ is the number of study objects, $T$ is the year interval, $L_{i,t}$ represents the position of object $i$ in the scatter plot in year $t$ and $d(L_{i,t},L_{i,t+1})$ denotes the moving distance from year $t$ to year $t+1$.

3.2.3. Multi-Scale Geographically Weighted Regression Model

The multi-scale geographically weighted regression (MGWR) model is used to describe the factors that affect the development level of rural revitalization in different times and geographical locations [57]. As a spatially varying parameter regression model, it overcomes the limitation of a “uniform bandwidth” in traditional geographically weighted regression (GWR). MGWR allows independent bandwidth optimization for different explanatory variables across varying spatial scales, thereby identifying scale differences in the spatial heterogeneity of variable effects [62]. The model is specified as:

$$D_i={\beta }_0(u_i,{\nu }_i)+{\displaystyle \sum _k{\beta }_{bwk}(u_i,{\nu }_i)X_{ik}}+{\epsilon }_i$$

(11)

In the formula, $D_i$ represents the dependent variable (rural revitalization level), $X_{ik}$ denotes a series of explanatory variables, $(u_i,{\nu }_i)$ are the spatial coordinates of the study object $i$, and ${\beta }_{bwk}(u_i,{\nu }_i)$ is the regression coefficient for the k-th variable under bandwidth $bw$.

3.3. Control Variables and Data Sources

Drawing on existing research and considering data availability, seven indicators are selected as influencing factors for changes in the development level of rural revitalization [28,31]: agricultural production efficiency ($NY$ logarithm of per capita output value of agriculture, forestry, animal husbandry, and fishery), economic development level ($JJ$ logarithm of per capita GDP), urbanization level ($CZ$ urbanization rate), level of openness to the external world ($DW$ actual utilized foreign direct investment/gross domestic product), level of government intervention ($ZF$ general budgetary fiscal expenditure/gross domestic product), overall industrial structure upgrading level ($CY$ value-added share of primary industry × 1 + value-added share of secondary industry × 2 + value-added share of tertiary industry × 3), and technological innovation level ($JS$ logarithm of the number of patents granted). Among these, efficient agriculture directly increases farmers’ operational income and accumulates wealth for rural areas, serving as an internal engine for rural revitalization. While the growth of GDP provides the economic basis for returning to the countryside, if it is not distributed equally, it could worsen the disparity between urban and rural. Urbanization brings vigor to the countryside through the transfer of labor and the expansion of the market. However, it can also bring about the problem of empty countryside. Opening up to the outside world opens up the international market of farm products and brings in advanced ideas, but at the same time exposes the countryside to outside risks. While government intervention is the key to solving market failures and providing public goods, excessive interference may inhibit endogenous incentives. Industrial upgrading can drive rural industrial integration and green employment, but it must avoid becoming disconnected from local contexts. Technological innovation is a fundamental force for breaking traditional development bottlenecks in rural areas and achieving leapfrog development.

Rural revitalization was proposed in 2017. Considering the temporal symmetry of the research period, data completeness, and timeliness, this paper selects panel data from 282 prefecture-level cities in China from 2011 to 2023 to measure the level of rural revitalization. The data utilized in this study are drawn from a broad range of credible sources. Specifically, indicators for the rural sustainable development framework are primarily obtained from the China Urban Statistical Yearbook, China Rural Statistical Yearbook, China Population and Employment Statistical Yearbook, China Education Statistical Yearbook, China Urban–Rural Construction Statistical Yearbook, China Environmental Statistical Yearbook, and China Agricultural Machinery Industry Yearbook. Control variables are largely derived from the China Statistical Yearbook. Additional data are sourced from statistical yearbooks and official bulletins of prefecture-level cities and autonomous regions, the EPS database, and the China Economic and Social Big Data Research Platform. In cases of missing data, estimates are generated based on the rate of change from the preceding year, under the assumption of a consistent trend.

4. Spatiotemporal Distribution Characteristics of Rural Revitalization Development Level

4.1. Temporal Characteristic Analysis

The calculation results of the development level of rural revitalization from 2011 to 2023 are shown in

Table 3. Statistical analysis of the development level of rural revitalization.

Year	Minimum Value	Maximum Value	Mean Value	Year	Minimum Value	Maximum Value	Mean Value
2011	0.114 (Hohhot)	0.611 (Tangshan)	0.374	2018	0.167 (Chifeng)	0.695 (Tangshan)	0.417
2012	0.151 (Yuxi)	0.616 (Tangshan)	0.381	2019	0.170 (Xining)	0.695 (Tangshan)	0.423
2013	0.132 (Urumqi)	0.642 (Tangshan)	0.386	2020	0.173 (Xining)	0.715 (Tangshan)	0.428
2014	0.143 (Shizuishan)	0.618 (Tangshan)	0.393	2021	0.170 (Tongliao)	0.710 (Tangshan)	0.432
2015	0.149 (Ulanqab)	0.650 (Tangshan)	0.400	2022	0.182 (Yuxi)	0.737 (Tangshan)	0.439
2016	0.160 (Karamay)	0.655 (Tangshan)	0.407	2023	0.172 (Tongliao)	0.718 (Tangshan)	0.435
2017	0.157 (Lhasa)	0.611 (Tangshan)	0.410

. The national average level of rural revitalization experienced a slight decline of 0.91% in 2023 but maintained an overall upward trend, showing a growth of 16.31% compared to 2011. The period from 2011 to 2014 was characterized by the Mild Disorder Recession stage, transitioning into the On the Verge of Disorder stage from 2015 to 2023. The minimum values consistently appeared in frontier regions such as Qinghai, Inner Mongolia, and Tibet, with some cities remaining in a state of Severe Disorder for extended periods, primarily constrained by harsh environments and resource scarcity. The maximum value was consistently held by Tangshan City, which progressed steadily from Primary Coordination to Intermediate Coordination. This achievement is closely linked to its practices of building a “national model” for rural revitalization, possessing multiple national-level demonstration sites, and hosting five-star leisure agriculture enterprises.

Figure 1 shows the trend of the average development level of rural revitalization nationwide and in the eastern, central, and western regions. From the beginning to the end of the period, the increases in the eastern, central, and western regions were 15.55%, 15.99%, and 18.39%, respectively, reflecting the steady advancement of rural revitalization. The western region showed particularly significant improvement due to policy support and enhanced development momentum. In 2023, all regions experienced a slight decline, with decreases of 0.61%, 0.71%, and 1.29% in the eastern, central, and western regions, respectively. This indicates a weakening of development momentum affected by structural shortcomings and external shocks. The larger decline in the western region is also related to relatively severe natural disasters. Overall, a gradient pattern of “Eastern > National > Central > Western” is observed, aligning with the declining trend of economic and agricultural levels from east to west across regions. The trends for the national, eastern, and central regions are generally consistent, while the western region exhibits stronger heterogeneity and volatility due to differences in geographical and socio-cultural conditions.

4.2. Spatial Characteristic Analysis

To observe the spatial status of rural revitalization development levels more clearly, the years 2011, 2015, 2019, and 2023 were selected for visualization, as shown in Figure 2. Before and shortly after the proposal of the Rural Revitalization Strategy in 2017, specifically during the periods 2011–2015 and 2015–2019, there were no significant changes in the spatial characteristics of national rural revitalization levels or in the development levels across different regions. The pattern exhibited a “high at both ends, low in the middle” configuration (higher in coastal and northeastern regions, lower in the central hinterland). From 2019 to 2023, Tangshan reached the Intermediate Coordination stage, and various regions saw cities with notable leaps in rural revitalization levels, yet the overall pattern did not change significantly.

In line with the “Two Horizontal and Three Vertical” urbanization strategic framework, rural revitalization levels in the coastal, Beijing–Harbin, and Baotou–Kunming corridors are high, ranging from 0.3 to 0.7. Levels along the Beijing–Guangzhou corridor are in the 0.2–0.3 range, while some cities in Northeast China, Inner Mongolia, Yunnan, Xinjiang, and Tibet fall within the 0.1–0.2 range.

Specifically examining the 19 urban clusters proposed in the 13th Five-Year Plan, the Dianzhong Urban Agglomeration and parts of Inner Mongolia are the most lagging in rural revitalization, remaining in the Severe or Moderate Disorder stages for extended periods. The Jinzhong Urban Agglomeration, Central Plains Urban Agglomeration, and Middle Yangtze River Urban Agglomeration follow, consistently in the Mild Disorder stage throughout the study period. The development levels of other urban clusters are largely comparable. After prolonged stability, the Harbin–Changchun, Beijing–Tianjin–Hebei, Hohhot–Baotou–Ordos–Yulin, and Pearl River Delta urban clusters saw some cities improve their rural revitalization status in 2023.

This pattern corresponds to the agricultural resource characteristics of the three vertical economic belts and the respective urban clusters. Notably, the Central Shanxi Urban Agglomeration in the lower central region suffers from resource scarcity and constraints in geography, climate, soil, and other factors, which are unfavorable for agricultural development. Although the Central Plains and Middle Yangtze River urban clusters have favorable planting environments, their agricultural structure is relatively homogeneous and has not formed an enabling advantage.

4.3. Spatiotemporal Transition Analysis

To analyze the spatiotemporal synergistic changes in rural revitalization development levels, two time intervals, 2011–2017 and 2018–2023, were selected to examine the relative length and curvature of LISA time paths. The calculated results were normalized using thresholds set at 50%, 100%, and 150% of the mean relative length (path curvature) of the time paths for both intervals. Accordingly, the relative length (path curvature) of the time paths was categorized from low to high into four types: low relative length (low path curvature), lowish relative length (lowish path curvature), highish relative length (highish path curvature), and high relative length (high path curvature). These results are visually represented in Figure 3.

From the perspective of relative length, during 2011–2017, the numbers of regions categorized from high to low relative length of LISA time paths were 34 (12.06%), 66 (23.40%), 158 (56.03%), and 24 (8.51%), respectively. High values were concentrated in provinces such as Heilongjiang, Inner Mongolia, Ningxia, and Yunnan. Among these, cities like Karamay, Urumqi, Lhasa, and Ulanqab far exceeded the national average, indicating dynamic local spatial structural changes in these areas. During 2018–2023, the numbers of regions from high to low relative length were 32 (11.35%), 85 (30.14%), 131 (46.45%), and 34 (12.06%), respectively. Although the number of high-relative-length regions slightly decreased, they remained concentrated in Inner Mongolia, Ningxia, Yunnan, and other provinces. This may be related to the complex interactions among natural conditions, economic structures, and policy adaptability in these regions. For instance, Inner Mongolia’s significant geographical heterogeneity—with grasslands, deserts, and agro-pastoral ecotones coexisting—leads to varied effectiveness in rural revitalization policy implementation. In Yunnan, mountainous terrain fragments inter-regional transportation and economic connections, causing frequent shifts in local spatial dependency directions. Ningxia exhibits a prominent urban–rural dual structure between the Yellow River Basin Ecological Economic Zone and its southern mountainous areas, and its small-scale economies, such as specialized agriculture and tourism, are susceptible to market fluctuations, contributing to spatial structural volatility. Furthermore, the cultural distinctiveness of ethnic minority settlements may affect the synergistic outcomes of rural revitalization policies. Compared to 2011–2017, the 2018–2023 period saw an increase of 10 low-value regions and a decrease of 27 relatively low-value regions. Their distribution is relatively scattered but shows a trend of spreading from east to west. Additionally, regions such as Shanghai, Beijing, Tianjin, and Chongqing were significantly below the national average, indicating highly stable local spatial structures in these areas. This stability primarily stems from their multifaceted advantages in policy, technology, and resources during rural revitalization development, which have ensured structural stability. However, these regions still need to strengthen technological innovation and shoulder greater responsibility in leading rural revitalization efforts. Overall, northeastern and southwestern China exhibit higher relative length (indicating turbulence), coastal areas show lower relative length (indicating stability), and since the introduction of the Rural Revitalization Strategy, central China has experienced an expansion of areas with higher relative length, reflecting increased volatility.

From the perspective of path curvature, during 2011–2017, there were 50 (17.73%) and 29 (10.28%) regions categorized as having high and highish curvature, respectively. These were primarily concentrated in Anhui, Hebei, Jiangsu, Shandong, and Gansu provinces, including cities like Bengbu, Shijiazhuang, Suzhou, Rizhao, and Zhangye. This indicates significant spatial dependency fluctuations in these areas, likely due to multiple dynamic factors encountered during their rural revitalization processes. For example, Bengbu, as a major agricultural city in northern Anhui, has accelerated its modern agricultural development but still faces an overall low development level and numerous challenges in deep processing of agricultural products and the comprehensive integrated development of primary, secondary, and tertiary industries in rural areas. Shijiazhuang experiences limited improvement in agricultural quality and yield due to the insufficiently widespread application and promotion of modern agricultural technology, coupled with issues like talent outflow to the Beijing–Tianjin region. Suzhou grapples with the problem of “many people, little land,” small-scale leading agricultural enterprises, and incomplete industrial chains for specialty agricultural products. Rural areas in Rizhao still rely predominantly on traditional agriculture, with generally low levels of scale and industrialization in specialty agriculture. In Zhangye, specialty industries have not achieved scale, and the situation of selling raw materials and primary products has not fundamentally changed. During the same period, there were 129 (45.74%) and 74 (26.24%) regions categorized as having low and lowish curvature, respectively. These were primarily located in provinces including Beijing, Tianjin, Shanghai, and Henan. This suggests that the spatial dependency of rural revitalization development levels in these areas is relatively stable, with less fluctuation and influence from neighboring regions, which is related to factors such as stronger economies or larger, more mature agricultural scales in these regions. In 2018–2023, the number of regions categorized by curvature from high to low was 41 (14.54%), 29 (10.28%), 73 (25.89%), and 139 (49.29%), respectively. Compared to 2011–2017, the numbers of regions with highish and lowish curvature were largely similar. However, high path curvature regions decreased by 9, while low path curvature regions increased by 10. Provinces such as Heilongjiang and Gansu showed more shifts from high to low categories, whereas Guangdong exhibited more shifts from low to high categories. This indicates that after the implementation of the Rural Revitalization Strategy, fluctuations in cities within remote areas have shown a decreasing trend due to policy support and other measures, leading to a tendency toward more stable spatial dependency relationships. In contrast, coastal areas have experienced greater impacts from international trade in recent years, resulting in fluctuations in their rural revitalization development levels. Overall, although the distribution of path curvature is scattered, there is a trend of low path curvature regions exerting pressure on high path curvature ones. The overall spatial dependency direction across the country is tending toward stability, reflecting the emerging effectiveness of various rural revitalization measures.

5. Analysis of Influencing Factors on Rural Revitalization Development Level

5.1. Overall Analysis

This section selects the two time points of 2011 and 2023 and employs the multi-scale geographically weighted regression (MGWR) model to explore the spatial scale of effects of the aforementioned factors on rural revitalization development level and to compare the heterogeneity of their impacts. From the model estimation results, show in

Table 4. Statistics of the optimal bandwidth and significance proportion of variables.

Variable	2011		2023		2011	2023	2011	2023
	GWR	MGWR	GWR	MGWR	Optimal Bandwidth (Units)		Significance Ratio (%)
$R^2$	0.654	0.731	0.562	0.777
Adjusted $R^2$	0.511	0.651	0.430	0.677
AICc	673.807	600.974	695.232	634.931
${\sigma }^2$	0.489	0.349	0.570	0.323
Sigma-Squared MLE	0.347	0.270	0.438	0.223
NY					223	55	2.48	20.92
JJ					282	63	100	21.28
CZ					67	61	6.38	10.64
DW					245	10	18.79	6.74
ZF					282	126	100	18.44
CY					98	30	29.43	7.09
JS					30	268	8.51	92.55

, the MGWR model demonstrates higher explanatory power and stronger robustness, enabling it to more accurately capture the characteristics of multi-scale spatial variations.

The scales of influence of the various factors show significant differences. At the beginning of the study period, the bandwidths for agricultural production efficiency, economic development level, level of openness to the external world, and overall industrial structure upgrading level were relatively large, with some approaching a global scale. By the end of the study period, their bandwidths decreased to 55, 63, 10, and 30, respectively, indicating enhanced spatial heterogeneity. The bandwidth for urbanization level remained consistently small and stable, suggesting persistently significant spatial differences. The bandwidth for level of government intervention decreased from a global scale to 126. Although still relatively large, regional differences gradually became apparent. Conversely, the bandwidth for technological innovation level expanded from 30 to nearly a global scale, reflecting a tendency toward spatial homogenization of its influence and a weakening of regional disparities.

The significance levels of each variable for rural revitalization show a diverging trend. The proportion of significance for agricultural production efficiency, urbanization level, and technological innovation level continuously increased, indicating the growing role of agricultural scaling and mechanization, deepening urbanization, and the multiplier effect of technology in rural development. Among these, technological innovation level stands out with nearly 100% significance, highlighting its crucial role and aligning with China’s current strategic focus on developing the digital economy and fostering new quality productive forces. On the other hand, the level of openness to the external world, the level of government intervention, and the overall industrial structure upgrading level declined. The influence of openness remained consistently weak, reflecting that its benefits have largely concentrated on urban manufacturing and service sectors, with limited penetration into low-value-added rural areas. The significance proportions for economic development and government intervention dropped from nearly 100% at the beginning to approximately 20%, suggesting that as economic growth and structural transformation progress, their driving force for rural revitalization has somewhat weakened, urgently necessitating the construction of deeper-level linkage mechanisms.

5.2. Factor-Specific Analysis

MGWR results reveal complex spatiotemporal heterogeneity, suggesting that the influence of individual factors on rural revitalization is neither purely promotional nor inhibitory. To deepen the understanding of the underlying mechanisms, this paper constructs an analytical framework of “driving potential–transformation conditions–net effect.” Its core argument is that each factor inherently contains a “theoretical potential” for driving rural revitalization. However, whether this potential can be translated into an actual “positive net effect” depends on whether the region possesses the corresponding “transformation conditions.” These conditions include the development level of factor markets such as capital and labor, the strength of feedback from secondary and tertiary industries to the primary sector (e.g., whether secondary and tertiary industries can support agriculture), the effectiveness of local governance, among others. If the transformation conditions are favorable, the driving potential is released; if they are lacking, the driving potential may be weakened, offset, or even reversed into an inhibitory effect. The following analysis of the seven factors will be integrated based on this framework.

The regression coefficients of each influencing factor were visualized, and their spatial distribution was obtained using the natural breaks classification method, as shown in Figure 4.

The impact of agricultural production efficiency on rural revitalization has shown a significant strengthening trend. In the earlier period, its positively significant areas were sporadically distributed in the transitional zone between North China and the middle and lower reaches of the Yangtze River Plain, such as Chuzhou and Bengbu in Anhui Province, and Nanjing in Jiangsu Province. By 2023, the scope of positively significant areas had notably expanded, forming a concentrated coverage over traditional major agricultural provinces in North China like Shandong and Henan. However, it remained insignificant in Northeast China, which has higher levels of land scale and mechanization. This contrast is a typical manifestation of the “driving-transformation” framework. The “driving potential” of improving agricultural production efficiency is the direct increase in farmers’ income. In North China, the dominant household-based operation ensures that the benefits of efficiency gains can be widely distributed among smallholder farmers and quickly realized through the market. This effectively translates into rural consumption and investment capacity, creating favorable “transformation conditions.” In contrast, in Northeast China, the dividends of large-scale agriculture are more concentrated in distribution. Furthermore, shortcomings in the industrial chain, which is primarily oriented towards the output of unprocessed grain, result in relatively weak “transformation conditions” for multidimensional progress in rural revitalization. Consequently, the “positive net effect” is not evident.

The impact of economic development level on rural revitalization exhibits phased changes. In 2011, it showed a significantly negative effect on all regions, particularly prominent in areas such as Karamay and Urumqi in Xinjiang. By 2023, the spatial scope of the negative effect had somewhat narrowed, mainly concentrating in the northeast and central-western regions, but the degree of the impact had further intensified. Some areas in the southeastern coastal region began to show a positive trend, although not reaching statistical significance, which aligns with the research findings of Du and Xu [63]. The early widespread inhibitory effect stemmed from the “urban-biased” pattern of economic growth, where factors and benefits concentrated in cities and failed to effectively benefit rural areas under the urban–rural dual system, reflecting a systemic lack of “transformation conditions.” The later spatial differentiation arises from disparities in regional “transformation conditions.” In less developed regions such as the northeast and central-western areas, economic growth remains tied to traditional industries with low linkage to rural sectors, resulting in a weak “trickle-down effect” on rural areas, thereby sustaining the inhibitory effect. In contrast, in more developed regions like the southeastern coast, higher levels of urban–rural market integration, more diversified industrial structures, and stronger fiscal feedback capacity have begun to provide some channels for directing the fruits of economic growth towards rural areas, indicating signs of an effect shift.

The impact of urbanization level on rural revitalization exhibits distinct spatiotemporal evolution. In 2011, it showed a significant inhibitory effect only in less developed northwestern regions such as Inner Mongolia, Ningxia, Gansu, and Xinjiang. This was primarily due to substantial urban–rural disparities and scarce rural resources, where urbanization led to a sharp outflow of resources to cities, negatively impacting rural areas. By 2023, urbanization in most parts of Heilongjiang and Yunnan, as well as in some areas of Sichuan, Jilin, and Tibet, had shifted to a positive influence, with the effect being particularly pronounced in Yunnan. In earlier periods in regions like the northwest, weak rural foundations meant that urbanization mainly resulted in a net loss of human capital, thereby restraining rural development. In later stages, in border areas such as Yunnan and Heilongjiang, the urbanization process integrated with local characteristic resources (e.g., port economies and specialty agriculture), creating stable markets for rural products and even attracting the return of factors. This formed unique, localized “transformation conditions” capable of converting the “driving potential” of urbanization into momentum for rural development.

In 2011, the overall impact of level of openness to the external world was negative, particularly pronounced in central and western regions, reflecting that the dividends of openness failed to effectively benefit rural areas and that the opening up process somewhat squeezed rural development space. By 2023, some border areas such as Heilongjiang, Inner Mongolia, and Yunnan shifted to showing significant positive effects, indicating that port trade has brought new opportunities for agricultural product sales and agricultural development. Simultaneously, although not reaching significant levels in central and western regions, a diffusion trend of positive effects has emerged, while the negative impact in eastern regions has become insignificant. This change may stem from the following: in most inland areas, foreign investment has weak linkages with the rural economy, making it difficult for dividends to penetrate. In contrast, in border port areas, cross-border trade has directly activated the export of local agricultural products and supporting industrial chains, forming an effective “transformation interface” and thereby releasing a positive marginal breakthrough effect.

Level of government intervention has consistently shown a negative impact on rural revitalization, indicating that excessive intervention can hinder its progress. In 2011, regions with significant negative effects were concentrated in central and northern China. By 2023, they were primarily focused in northeastern China. That is, significant negative impacts are mainly observed in the northern regions of China, while the effects in the southern regions are not significant. This serves as a warning that we should not only focus on the intensity of intervention but must also examine the compatibility between the “intervention mode” and the local “institutional environment.” In old industrial bases like northeastern China, government intervention may exhibit strong “path dependence,” tending to maintain traditional structures rather than cultivate new growth drivers. The expenditure structure may also be biased toward cities and industries. When this intervention mode combines with local “constraints” such as rigid factor markets and weakened rural actors, it not only struggles to correct the urban–rural imbalance brought about by “economic development” but may even further suppress rural endogenous vitality due to distorted resource allocation. Its negative effect is precisely the result of an inefficient “intervention mode” and an imbalanced “growth model” becoming locked together under a specific institutional environment.

The impact of overall industrial structure upgrading level on rural revitalization exhibits distinct spatiotemporal differentiation. In 2011, it showed a significant positive effect in southern coastal and southwestern regions, primarily benefiting from industrial integration and cluster development and effectively boosting rural employment and income growth. By 2023, the impact shifted to predominantly negative, with significant effects concentrated in northwestern regions and areas like Yunnan. This highlights that the “structural dividends” of industrial upgrading do not automatically translate into “rural dividends.” When industrial upgrading manifests as a disconnect—technologically, in terms of factors, and spatially—between high value-added industries and traditional rural sectors, it may instead exacerbate the marginalization of rural industries. In regions like the northwest, fragile ecological foundations and weak rural industrial bases make it more difficult to absorb or integrate into regional industrial upgrading processes. This may even divert resources originally intended to support rural development, resulting in a negative “net effect.”

Technological innovation level has consistently played a significantly positive promoting role in rural revitalization. Its areas of significant impact have expanded from parts of Northeast China to most regions across the country except the Yangtze River Delta, indicating that the application of advanced technologies is a key support for comprehensively promoting rural revitalization. Technological innovation can enhance resource utilization efficiency and market connectivity. This “empowering-driven nature” is relatively less dependent on traditional “transformation conditions” and possesses stronger penetrating power. Even in areas with weak foundations, the promotion and application of a single suitable agricultural technology can directly lead to increased production and income. The lack of significant impact in the Yangtze River Delta region may be because its rural development has entered a more advanced stage, where the marginal improvement effect of technological innovation is relatively less apparent, or because more complex organizational and business model innovations are required to drive the next phase of development. This, in turn, highlights the nonlinear characteristic of the driving effect of technological innovation as it evolves across different development stages.

Analysis based on the “driving potential–transformation conditions–net effect” framework reveals that the influencing mechanisms of rural revitalization at the municipal level in China form a system of complex interactions among factors under regional heterogeneity: there are logical connections between factors. For instance, the dual inhibitory effects of “economic development level” and “level of government intervention” in Northeast China reveal a “growth-intervention” lock-in, while the misalignment of effects between North China and Northeast China regarding “agricultural production efficiency” and “ overall industrial structure upgrading level” exposes a disconnect between production-side efficiency and industrial-end value. Therefore, policies need to shift from “imposing drivers” to “nurturing the soil,” focusing on improving regional “transformation conditions”: breaking the “growth-intervention” lock-in in the northeast and shifting toward human capital and business environment development; establishing linkages between rural industries and the regional economic system in the northwest; and nationwide, strengthening enabling drivers while encouraging local exploration of distinctive “transformation interfaces,” so as to synergistically activate various driving potentials and promote comprehensive revitalization.

6. Discussion

The empirical findings presented above reveal the complex spatiotemporal patterns and heterogeneous driving mechanisms within China’s rural revitalization process. This discussion section aims to elucidate the broader theoretical implications of these findings and reflect on their potential value for policymaking and future research.

6.1. Theoretical Implications: The “Driving Potential–Transformation Conditions” Framework and Sustainable Development

A key contribution of this study is the articulation and validation of the “driving potential–transformation conditions–net effect” analytical framework. The results from the multi-scale geographically weighted regression (MGWR) model clearly demonstrate that the same factor—such as economic growth, industrial upgrading, or urbanization—can exert diametrically opposite effects on rural revitalization in different regions. This compellingly challenges the prevalent assumption of uniform causality in the existing literature [64,65]. This framework enriches sustainability and regional development theory by providing a dynamic, place-based lens through which to understand socio-ecological transitions. It moves beyond merely identifying static “drivers” or “barriers” to focus on the mediating processes that translate global potentials into local outcomes. This aligns with and advances calls within sustainability science for greater attention to context-dependency and transformative pathways [66,67].

6.2. Policy and Governance Implications: From Uniformity to Differentiated Spatial Governance

The persistent spatial gradient (“Eastern > Central > Western”) and the observed co-existence of volatile and stable LISA spatiotemporal paths have direct and critical implications for governance. They debunk the notion of a one-size-fits-all approach to rural revitalization or sustainable development. The high stability observed in coastal core regions like the Yangtze River Delta suggests these areas may be transitioning into a phase requiring policies focused on quality upgrading, institutional innovation diffusion, and managing the complexities of deep urban–rural integration. In contrast, the volatility observed in Southwest and Northeast China signals a need for robust risk-buffering mechanisms, targeted capacity-building, and policies that enhance regional resilience. Our findings strongly advocate for the adoption of differentiated spatial governance models. This entails: (1) recognizing regions not only by their outcome levels (e.g., high/low) but also by their dynamic states (stable/volatile) and underlying “transformation conditions”; (2) designing spatially targeted policy bundles, for example, breaking the “growth-intervention” lock-in in Northeast China, building connective infrastructure and niche markets in Northwest China, and fostering “enabling-driven” innovation ecosystems in the east; and (3) leveraging spatial interdependence by utilizing high-value agglomerations as demonstration and diffusion nodes while providing coordinated support for low-value locked-in regions to prevent their peripheralization.

6.3. Methodological Reflection and Future Research Directions

This study demonstrates the value of integrating the entropy weight-coupling coordination model, LISA spatiotemporal analysis, and MGWR. This multi-method approach enabled us to measure systemic synergy, visualize its dynamic spatial structure, and dissect the underlying spatially varying mechanisms, offering a more holistic diagnosis than any single method could provide. Furthermore, it points to fruitful avenues for future research. First, while our indicator system is comprehensive, incorporating more direct metrics, such as villager satisfaction surveys, granular environmental quality data, or digital literacy rates, could more precisely capture emerging dimensions of rural vitality and governance quality. Second, while the MGWR model excels at revealing correlation and spatial heterogeneity, establishing definitive causal pathways behind the identified “transformation conditions” would benefit from complementary methods, such as qualitative case studies or quasi-experimental designs. Finally, our municipal-level analysis effectively bridges macro and micro scales. Future research could drill down further by utilizing county or village-level data to uncover the intra-regional disparities.

7. Conclusions and Recommendations

7.1. Conclusions

This study, based on a temporal and spatial perspective, analyzed the large-scale and comprehensive sustainable development practice of “rural revitalization” in China. The conclusions drawn below not only summarize the empirical model but also provide important insights into the core issues in sustainable development spatial equity, system resilience, and the regional nature of transformation paths.

(1)

Overall Trend and Spatial Pattern. The rural revitalization level across the nation and its eastern, central, and western regions shows an overall upward trend. The national average exhibited sustained growth, except for a slight decrease of 0.91% in 2023. The period 2011–2014 was characterized by the Mild Disorder stage, transitioning into the On the Verge of Disorder stage from 2015 to 2023. The eastern region consistently exceeded the national average, with no significant narrowing of its internal disparity. The central and western regions were successively below the national average. Within the central region, areas with high values gradually converged toward the average, while the western region was dragged down by its low-value areas. The spatial pattern closely aligns with the “Two Vertical and Three Horizontal” regional framework and has remained stable over the long term. This reflects that the Rural Revitalization Strategy has not exacerbated regional imbalances and has played a demonstrative role in the development of typical areas.

(2)

Spatiotemporal Synergistic Evolution. LISA spatiotemporal path analysis reveals significant regional differences in the dynamics of local spatial structures for rural revitalization. Northeastern and southwestern China exhibit higher relative lengths and path curvature, indicating more volatile and turbulent spatial structural changes. In contrast, developed coastal regions like Beijing–Tianjin–Hebei and the Yangtze River Delta display high stability. Following the implementation of the Rural Revitalization Strategy, the overall spatial dependency relationship across the country has tended towards stability, indicating the initial manifestation of policy effects.

(3)

The impact of various factors on rural revitalization exhibits distinct spatiotemporal heterogeneity. The 2023 results indicate that agricultural production efficiency has a significant promoting effect in North China. Economic development level shows a significant inhibitory effect in Northeast and Central-Western China. Urbanization level has a significant promoting effect in areas like Heilongjiang and Yunnan. The level of openness to the external world has a significant promoting effect in some border regions of Inner Mongolia and Yunnan. The level of government intervention shows a significant inhibitory effect in Northeast China. Overall industrial structure upgrading level has a significant inhibitory effect in areas like Gansu, Ningxia, and Xinjiang. Technological innovation level has a significant promoting effect in most regions except the Yangtze River Delta.

7.2. Recommendations

(1)

Formulate differentiated regional policies to synergistically advance rural revitalization. In regions with “institutional lock-in,” such as Northeast China, the focus should be on breaking the negative “growth-intervention” cycle: Shift fiscal expenditures from maintaining traditional structures towards investments in rural human capital, business environment optimization, and market system development. Simultaneously, deepen market-oriented reforms of production factors to promote equitable exchange of resources between urban and rural areas. In regions with “weak transformation conditions,” such as Northwest China, the policy emphasis should be placed on building organic linkages between rural industries and the regional economy: support the development of characteristic industrial chains suited to local ecological resources and ethnic cultures, and improve connective infrastructure like port logistics and digital networks, rather than merely pursuing metrics of industrial structure sophistication. In eastern and other advantaged regions, their function of cultivating “enabling-driven” forces and “transformation interfaces” should be strengthened: On one hand, increase the R&D, promotion, and application of core agricultural technologies and inclusive digital platforms. On the other hand, encourage the exploration of “transformation” models that integrate urban and rural areas and link domestic and international markets (e.g., smart agricultural parks and cross-border e-commerce empowerment bases), forming replicable institutional experiences.

(2)

Innovate spatial governance mechanisms to promote the dynamic alignment between driving potential and transformation conditions. Attention should be paid to the spatial interconnections and structural fluctuations in rural revitalization, leveraging the radiating and driving role of high-value areas. In high-value agglomeration areas (such as the southeastern coast), establish demonstration zones for systematic innovation in rural revitalization, focusing on exploring synergy mechanisms among driving factors and models for sharing dividends, while preventing internal siphoning effects. For low-value locked-in areas (such as some contiguous regions in the southwest and northwest), implement a combined strategy of “targeted assistance and capacity building,” introducing external resources through cross-regional partnerships and enclave economies, while strengthening the cultivation of local grassroots organizations and leaders. For volatile active areas (such as parts of the northeast and southwest), establish risk early-warning and adaptive intervention mechanisms to proactively address external shocks and internal transformation risks. Ultimately, through the innovation of spatial governance mechanisms, promote a higher-level dynamic alignment between the “transformation conditions” of various regions and their different “driving potentials,” systematically breaking the dilemma of “low-level lock-in” in rural revitalization.

(3)

Implement targeted policies to unleash the spatial effects of influencing factors. For “enabling-driven” factors (such as technological innovation level), consistent, foundational, and nationwide investment should be maintained. Establish long-term support funds to continuously promote the research, development, and application of suitable technologies in grassroots scenarios. For “structural drivers” (such as agricultural production efficiency, economic development level, urbanization level, level of openness to the external world, and overall industrial structure upgrading level), the key to policy lies in establishing incentive mechanisms that foster urban–rural industrial linkages and factor mobility. Through measures like tax incentives, joint park development, and talent return programs, guide the effective connection of urban capital, technology, and talent with rural industries. For “institutional drivers” (such as level of government intervention), the core is to promote a transformation in governance models. Building on a negative list management approach, widely implement a “performance-based funding allocation” evaluation system to incentivize local governments to shift from being “administrators” to “service providers” and “enablers”.

(4)

Theoretical and Practical Contributions to Global Sustainable Development. The core findings of this study extend beyond China’s context, offering substantive contributions to sustainability science. First, it provides a robust methodological template, coupling multidimensional evaluation (entropy weight-coupling model) with spatially explicit mechanism analysis (MGWR), for diagnosing the synergy and trade-offs within sustainable development systems elsewhere. Second, it delivers critical empirical evidence that the universality of sustainability drivers (e.g., technology and economic growth) is a myth; their efficacy is contingent on local “transformation conditions.” This empirically grounds the imperative for place-based solutions in global SDG implementation. Finally, China’s experience underscores a governance insight: navigating the tension between top-level strategic design and local empowerment is paramount for translating sustainability potentials into outcomes. This offers a vital reference for global efforts in governing large-scale, complex sustainability transitions.