Research on the Spatiotemporal Evolution and Driving Factors of the Coordinated Development of the Rural Economy–Ecology–Society–Innovation System in Guangdong Province

Liu lianhua1

Zhenglifen2✉Emailzhenglifen2014@126.com

Macau Polytechnic University

Guangzhou Huashang College

Liu lianhua ¹², Zheng lifen²

1. Macau Polytechnic University 2. Guangzhou Huashang College

Corresponding authors is Zheng lifen, E-mail:zhenglifen2014@126.com

Abstract

The implementation of China’s “dual-carbon” strategy and the Rural Revitalization Strategy has placed rural development in Guangdong Province under multiple and overlapping pressures, including economic upgrading, ecological constraints, social equity, and innovation. However, uneven regional growth, ecological vulnerabilities, and insufficient innovation capacity remain persistent challenges.This study examines 21 prefecture-level cities in Guangdong from 2017 to 2023. The entropy weight method is applied to evaluate the development levels of the rural economy, ecology, society, and innovation subsystems. A coupling coordination degree model is then used to assess the spatiotemporal trajectory of coordinated development, while a geographic detector identifies dominant driving forces and interactive effects.The findings show that the provincial rural coordination index rose steadily from 0.356 to 0.380, displaying a gradient diffusion pattern—high in the Pearl River Delta and low in peripheral regions. Guangzhou and Shenzhen achieved primary coordination, whereas Shanwei and Yunfu remained in mild to moderate imbalance. Among the influencing factors, science and technology expenditure, patent authorizations, and social security coverage (Q > 0.74) emerged as the strongest drivers. Interaction analysis further reveals that the joint effects of innovation, social security, and ecological responses generate the most significant nonlinear enhancement of coordination.These results provide direct implications for Guangdong’s rural governance, suggesting that policies linking innovation, social security, and ecology can foster more balanced development. The study also offers a replicable framework for other regions seeking integrated rural transformation.

Keywords:

coupling coordination

innovation-driven development

ecological response

spatial heterogeneity

1. Introduction

In the new era, the Rural Revitalization Strategy has become a key initiative for China to achieve the comprehensive modernization of its society. The report of the 20th National Congress of the Communist Party of China emphasized the need to comprehensively advance rural revitalization, prioritize agricultural and rural development, consolidate the achievements of poverty alleviation, accelerate the building of an agricultural power, and promote the revitalization of rural industries, talent, culture, ecology, and governance. This provides a clear direction for rural development in China.

As the frontier of China’s economic growth, Guangdong Province plays a demonstrative role in advancing the integrated development of the rural economy, society, and ecology. Yet, its rural regions face significant challenges, particularly in balancing development across different areas. Although Guangdong’s rural economic structure has been gradually optimized, stark disparities persist between the Pearl River Delta and the eastern and northwestern regions. These disparities are evident in economic performance, infrastructure provision, and the accessibility of social services. In some areas, economic growth remains sluggish, social services such as education, healthcare, and cultural facilities are underdeveloped, and local governance systems are incomplete.

Environmental pressures further complicate rural sustainability. Rapid economic growth in certain areas has led to excessive exploitation of land, water pollution, and forest degradation, undermining ecological security. These realities highlight the urgent need for Guangdong’s rural areas to pursue innovative, coordinated strategies that reconcile economic, social, and ecological development.

This study focuses on the innovation-driven, coordinated development of Guangdong’s rural economy, society, and ecology. By systematically analyzing the spatiotemporal evolution and underlying driving mechanisms, the research aims to provide both theoretical insights and practical guidance for sustainable rural transformation. Building on four dimensions—economy, society, ecology, and innovation—the study examines the spatial dynamics of coordinated development across different stages and reveals its internal mechanisms. Through the construction of a comprehensive analytical framework, the study contributes to the theoretical discourse on regional coordination, introduces new perspectives and methodological approaches, and offers policy-relevant evidence. The findings will not only inform Guangdong’s rural governance and development strategies but also provide comparative lessons for rural revitalization efforts in other regions. Ultimately, this research aligns with the objectives of the national Rural Revitalization Strategy and addresses the imperative of achieving high-quality rural development in Guangdong Province.2. Literature review

2.1 Theoretical research

The coordinated development of ecology, economy, and society has long been a central focus of academic inquiry. As early as 1966, Cumberland applied the input–output model to measure the correlation between economic production and ecological conditions [1]. Later, Grossman and colleagues (1995) proposed the well-known Environmental Kuznets Curve (EKC), which suggests that the relationship between environmental pollution and per capita income follows an inverted “U” shape: rising at first and then declining. This model highlighted a shift in the relationship between economic growth and environmental resources from conflict and exclusion to complementarity and mutual benefit [2]. Building on this logic, a series of subsequent studies employed quantitative methods such as the ecological footprint and data envelopment analysis [3–5]. These approaches have established a foundational framework for analyzing the coupling and coordination of ecological, economic, and social systems.

Theoretical research has primarily advanced through the refinement of conceptual frameworks. The notion of coupling coordination has been widely adopted, with most scholars applying systems theory to treat the ESE system as a complex structure composed of interrelated subsystems, such as resources, environment, economy, and society [6–7]. Zhou (2016), for instance, systematically examined the contradictions and countermeasures in China’s rural reforms, stressing the importance of balancing economic development and environmental protection [8]. Ma et al. (2011) outlined ecological, economic, and social strategies for the transformation of resource-based cities [9], while Shi (2016) proposed an interval intuitionistic fuzzy decision-making model to evaluate ecological performance in small towns, emphasizing the necessity of integrated ESE development [10]. In measuring the performance of complex systems, the degree of coupling and coordination remains the most common metric.

The framework has since been expanded. Zou (2025) introduced the “two mountains” theory to embed ecological value realization into the economic system [11]. Erokhin (2024) proposed the “five-capital” model to construct integrated enterprise reports across financial, natural, human, social, and intellectual dimensions [12]. Liu (2025) developed a dual-index system—green total factor productivity (GTFP) and green space ecological service value (GSESV)—to capture synergies between high-quality growth and ecological benefits [13]. This model reveals two transmission pathways: innovation-driven industrial upgrading with green development and factor allocation with spatial spillover and policy intervention.

2.2 Empirical Research

2.2.1 Research Content

Empirical studies have progressed at both macro and micro levels. At the macro level, scholars focus on three main areas: (1) spatiotemporal evolution of coupling coordination, which often illustrates the transition of ESE systems from maladjustment to primary and then advanced coordination [14–19]; (2) identification of driving and obstacle factors such as industrial structure, technological innovation, population density, financial investment, openness, energy structure, and the digital economy [20–24]; and (3) policy evaluation and scenario simulation, which assess the effects of initiatives such as the Yangtze River Delta expansion, ecological protection of the Yellow River Basin, rural revitalization, and green infrastructure investment [25–27]. At the micro level, research highlights enterprise practices, including integrated reporting in agricultural firms and the role of ecological capital investment in promoting circular and green economies [13, 28].

2.2.2 Research Perspectives

Research perspectives are commonly categorized as macro, meso, and micro. The macro perspective emphasizes provinces, watersheds, and urban agglomerations, focusing on spatial disparities and policy zoning. The meso perspective addresses industries and sectors, such as resource-based cities, manufacturing–logistics integration, agriculture–tourism linkages, and digital villages. The micro perspective concentrates on firms, particularly listed agricultural companies and resource-intensive enterprises. Interdisciplinary approaches further enrich this field by integrating population structure (e.g., aging, talent density), industrial synergy, agglomeration effects, and innovation-driven growth under digital transformation. For example, Liu et al. (2025) highlighted the role of green technology innovation and sustainable consumption in advancing the circular economy [29]. Zhang et al. (2023) empirically confirmed the positive impact of the digital economy on integrating rural industries, mediated by technological innovation and human capital [30]. Luo et al. (2024) analyzed the nonlinear relationship between land use intensification and agricultural green innovation [31].

Spatial units of analysis range from watersheds (e.g., the Yellow River Basin [6, 26], Yangtze River Economic Belt [24]) to urban agglomerations (Yangtze River Delta [14, 25], Beijing–Tianjin–Hebei [10], Guanzhong Plain, and Jinnan urban clusters [13]) and provinces (31 provinces in China [16, 28], Shaanxi [13, 20], the North China Plain [17], and the three northeastern provinces [15]). Studies have also focused on cities and counties (e.g., northern Anhui [21], western Hubei [32], and 30 cities in five northwestern provinces [23]), as well as international comparisons, such as Russia’s agricultural ESE system and innovation networks [12, 33].

2.2.3 Research Methods

Methodologically, entropy-weight TOPSIS, Delphi, and DPSIR frameworks are used to minimize subjective weighting bias [20, 34]. The coupling coordination degree (CCD) model remains the most widely adopted for evaluating ESE system development [6, 13, 16, 18, 19, 21]. Spatial econometric models, such as the spatial Durbin model (SDM), spatial lag model (SLM), and geographically weighted regression (GWR), capture spatial heterogeneity [14, 17, 18, 22]. Super-SBM and the Malmquist–Luenberger index have been applied to analyze the role of green innovation efficiency [21]. Policy effects and nonlinearities are often tested using methods such as multi-period difference-in-differences (DID), panel vector autoregression (PVAR), and threshold regression [25, 35].

2.3 Summary of Literature

Overall, existing research has formed a relatively complete loop from “concept–index–model” to “policy–mechanism–path.” Nevertheless, significant gaps remain. Few studies address the spatiotemporal evolution of rural ESE coordination mechanisms under the context of China’s Rural Revitalization Strategy. Research is also limited on emerging topics such as the digital economy and the mechanisms of innovation-driven rural transformation. In particular, there is a lack of systematic investigation into the rural ESE system in Guangdong Province—China’s economic frontier—where new technologies and innovation play a pivotal role. This paper therefore focuses on Guangdong, examining the economy–society–ecology–innovation (ESEI) system. By incorporating innovation as a new driving force, this study aims to expand the research paradigm from ESE to ESEI, explore a four-dimensional model of coordinated rural development, and contribute theoretical and empirical insights to support the dual-carbon goals, high-quality regional growth, and improved rural livability.

3. Research methods and data sources

3.1 Index system construction

(1) Evaluation index system

With the advancement of scientific and technological innovation, rural revitalization can be understood as an open and dynamic geospatial system aimed at fostering the internal coordination of the economy, resources and environment, and society. Drawing on systems synergy theory, this study develops an evaluation framework for the coupling and coordinated development of rural areas in Guangdong Province across four dimensions: economy, ecology, society, and innovation. The conceptual model is illustrated in Fig. 1.

Fig. 1

Rural Economy- Ecology-Society-Innovation Coupling Model

From the perspective of system construction, the four subsystems interact through the flows of human, financial, material, informational, and energy resources within both internal and external environments, thereby shaping the direction and structural pattern of coordinated evolution. The economic subsystem serves as the material foundation of regional development, providing financial and material support for social, ecological, and innovation progress. The social subsystem reflects a people-centered orientation, offering services and a security framework to sustain economic, ecological, and innovation development. The ecological subsystem acts as the fundamental constraint of regional development, ensuring ecological security for the economic, social, and innovation systems. The innovation subsystem represents a new driving force, supplying scientific and technological momentum to advance economic, social, and ecological development.

Based on the connotation of the coupling coordination mechanism, and with reference to prior studies [10, 17, 21], this paper constructs an evaluation index system for the coordinated development of the rural economy, ecology, society, and innovation in Guangdong Province. In selecting indicators, the principles of scientific rigor, comparability, and data availability were fully considered. A total of 29 representative indicators were identified. The economic subsystem reflects the comprehensive input–output benefits of agricultural production. The ecological subsystem is structured on the Pressure–State–Response (PSR) conceptual model, encompassing three dimensions: ecological pressure, ecological state, and ecological response. The social subsystem is described in terms of the foundation of social development and the level of social security. The innovation subsystem is captured from two dimensions: innovation inputs and innovation outputs. The detailed indicators are presented in Table 1.

Table 1
Evaluation index of rural economy ecology society innovation development
Target layer	Rule layer	Index laye	Variable (unit)
Evaluation index system of economic development level	Agricultural production level	Average grain yield	Ton(X1)
		Average vegetable yield	Kilogram(X2)
		Average fruit yield	Ton(X3)
		Pork production	Ton(X4)
		Degree of Agricultural Mechanization	Million Kilowatts(X5)
	Agricultural income level	Output value of agriculture, forestry, animal husbandry and fishery	100 million Yuan(X6)
	Agricultural income level	Rural per capita disposable income	Yuan(X7)
Evaluation index system of ecological development level	Ecological environment foundation	cultivated area	Hectare(X8)
		Population density	People/ km²(X9)
		per capita green are	Square meter(X10)
	Ecological environment status	10-year average concentration of particulate matter	µg/m3(X11)
		Annual average concentration of particulate matter PM2.5	µg/m3(X12)
		Average amount of agricultural chemical fertilizer applied on land	Ton(X13)
		Average use of agricultural plastic film	Ton(X14)
		Average pesticide use	Ton(X15)
	Ecological environmental response	Proportion of energy conservation and environmental protection expenditure in GDP	%(X16)
		Newly increased comprehensive control area of water and soil loss	Thousands Hectare(X17)
		Drainage area of each city	Thousands Hectare(X18)
Evaluation index system of social development level	Social foundation	Number of people on duty in agriculture, forestry, animal husbandry and fishery	Ten thousand people(X19)
		Highway density	Km/km²(X20)
		Urban and rural community expenditure	100 million Yuan(X21)
		Total retail sales of consumer goods in rural areas	100 million Yuan(X22)
		Student teacher ratio in primary and secondary schools	Ratio of students to teachers(X23)
		Number of beds in health institutions	Medical beds per 10,000 people(X24)
	Social security	Number of urban and rural residents covered by basic endowment insurance	Ten thousand people(X25)
	Social security	Number of urban and rural residents covered by basic medical insurance	Ten thousand people(X26)
Evaluation index system of innovation development level	innovation infrastructure	Science and technology expenditure	Ten thousand Yuan(X27)
	innovation infrastructure	Number of on-the-job employees in scientific research and technical services at the end of the year	Ten thousand people(X28)
	Innovative achievements	Number of patents authorized	Piece(X29)

3.2 Research methods

This study first applies the entropy weight method to evaluate the development levels of the rural economy, ecology, society, and innovation in Guangdong Province. Second, the coupling analysis method is employed to measure the degree of four-dimensional coordinated development across 21 prefecture-level cities. Third, based on the coupling data of these cities, geographic analysis tools are used to examine spatial correlations. Finally, the GeoDetector model is applied to identify the driving factors underlying the spatial evolution of rural four-dimensional coordinated development in Guangdong. The specific research methods are as follows:

3.2.1 entropy weight method

Entropy weight method can be used to evaluate the index objectively and avoid the randomness of subjective evaluation. The entropy method needs to set the index matrix, (i = 1,2,3,…m, j = 1,2,3,…n)。 The stronger the data dispersion in the matrix, the more information provided, the smaller the information entropy, the greater the weight of the index, and the greater the impact on the comprehensive evaluation; On the contrary, the weaker the data dispersion, the less information provided, the greater the information entropy, the smaller the index weight, and the smaller the impact on the comprehensive evaluation.

Calculation steps:

(1)Data standardization

$\:{\text{X}}_{\text{ij}}\text{=}\frac{{\text{a}}_{\text{ij}}\text{-min{}{\text{a}}_{\text{ij}}\text{}}}{\text{max{}{\text{a}}_{\text{ij}}\text{}-min{}{\text{a}}_{\text{ij}}\text{}}}$

(2)Calculate the proportion of index J in year I.

$\:{\text{p}}_{\text{ij}}\text{=}\frac{{\text{X}}_{\text{ij}}}{\sum\:_{\text{i}\text{=1}}^{\text{m}}{\text{X}}_{\text{ij}}}$

(3)Calculate the entropy of index J, and the formula is as follows.

$\:{\text{e}}_{\text{j}}\text{=-}\frac{\text{1}}{\text{lnm}}\sum\:_{\text{i}\text{=1}}^{\text{m}}\text{（}{\text{p}}_{\text{ij}}\text{ln}{\text{p}}_{\text{ij}}\text{），}{\text{e}}_{\text{j}}\text{=[0,1]}$

(4)The difference coefficient is calculated as follows.

$\:{\text{g}}_{\text{i}}\text{=1-}{\text{e}}_{\text{j}}$

(5) Calculate the weight of index item, and the formula is as follows.

$\:{\text{w}}_{\text{j}}\text{=}\frac{{\text{g}}_{\text{i}}}{\sum\:_{\text{j}\text{=1}}^{\text{n}}{\text{g}}_{\text{i}}}$

(6)Calculate the comprehensive score of each sample, calculate the rural development level of 21 cities in Guangdong Province by entropy method, and show the rural development level of 21 cities in Guangdong Province.

$\:{\text{D}}_{\text{i}}\text{=}\sum\:_{\text{j}\text{=1}}^{\text{m}}{\text{w}}_{\text{j}}\text{*}{\text{p}}_{\text{ij}}\text{，}\text{i}\text{=1,2,…,}\text{n}$

3.2.2 Coupling Coordination Cegree Model

In this study, the coupling coordination degree (CCD) model is employed to calculate the coupling level of four-dimensional coordinated rural development in Guangdong Province. The essence of such four-dimensional integration lies within the scope of industrial linkage, and the CCD model provides an effective approach for evaluating the degree of coordinated development among subsystems within the coupling system. To analyze the four subsystems, three key indicators are introduced to capture the coupling relationships.

The first indicator is the coupling degree, denoted as C, with a value range of [0,1]. The coupling degree reflects the strength of interconnection among the four subsystems. A higher value indicates stronger linkages and greater mutual influence. Its mathematical expression is presented in Eq. (7):

(7)

U1, U2, U3 and U4 represent the comprehensive development levels of the economic, ecological, social, and innovation subsystems of rural areas in Guangdong Province, respectively. The weights derived from the entropy method, together with the corresponding indicator data, are aggregated using a weighted average approach.

In some regions, the levels of economic, ecological, social, and innovation development remain relatively low, while the degree of coupling appears high. To mitigate the potential bias arising from this situation, a coordination index, denoted as T, is introduced. This index serves as a consistency measure of the system’s development level, reflecting its absolute state. A larger value of T indicates a higher level of system development. Its mathematical formulation is presented in Eq. (8)..

$\:\text{T=α}{\text{U}}_{\text{1}}\text{+β}{\text{U}}_{\text{2}}\text{+γ}{\text{U}}_{\text{3}}\text{+δ}{\text{U}}_{\text{4}}$

(8)

$\:\text{α}$

、

$\:\text{β}$

、γ、δ represents the weight of the four subsystems, which is determined by the relative importance of the four subsystems. In this study, economic, ecological, social and innovative development are equally important, so this paper takes the weight of each of the four systems as 0.25.

Finally, the coupling coordination degree is calculated. This measure integrates both the coupling degree and the coordination index. It not only captures the strength of interconnections among the four subsystems but also reflects their absolute levels of development. Thus, it provides a comprehensive representation of the degree of coordinated development among the subsystems. In this study, it serves as a key index for assessing the overall level of coordinated rural development. Denoted as D, its mathematical formulation is presented in Eq. (9):

(9)

Combining with relevant literature research, the coordination degree is divided into 10 levels, as shown in Table 2.

Table 2
Classification of Coordination Degree Levels
Coupling coordination degree D	coordination state coupling level	coordination degree range	Coupling coordination degree D	coordination state coupling level	coordination degree range
(0.0 0.1)	extreme maladjustment	1	[ 0.5 0.6)	reluctantly coordinated	6
[0.1 0.2)	severe maladjustment	2	[0.6 0.7 )	primary coordination	7
[ 0.2 0.3)	moderate maladjustment	3	[0.7 0.8 )	intermediate coordination	8
[0.3 0.4)	mild maladjustment	4	[0.8 0.9)	good coordination	9
[ 0.4 0.5)	close to maladjustment	5	[0.9 1.0)	high quality coordination	10

3.2.3 Geo detector

Geographic detector is an effective tool to study the characteristics of regional spatial differentiation. Through single factor detection, the dominant factors of influencing factors can be effectively identified. Through the interactive detection function, the interaction between two factors can be identified. This paper uses the geographic carbon measurement function to explore the driving factors of the evolution of coupling coordination degree of four-dimensional rural development in Guangdong Province. The model formula is shown in Eq. 10.

$\:\text{q=1-}\frac{\text{1}}{\text{M}{\text{∂}}^{\text{2}}}\sum\:_{\text{k=1}}^{\text{N}}{\text{M}}_{\text{k}}{\text{∂}}_{\text{k}}^{\text{2}}$

(10)

Q is the interpretation degree of the impact factor on the coupling coordination degree. The value range is

$\:\left[\text{0,1}\right]$

. The greater the value, the greater the impact of the factor on the coordination degree. It indicates the total amount and variance of the research samples. And

$\:\text{\:M、∂}$

are the sample size and variance of the k-th layer respectively. N is the number of classification layers. In this paper, the natural breakpoint method is used to take 6 according to the number of samples

3.3 Data sources

This study focuses on the four-dimensional coordinated and coupled development of rural areas in 21 prefecture-level cities of Guangdong Province. A total of 29 indicators were selected from the four dimensions—economy, ecology, society, and innovation—covering both cities and their rural areas, and used as the basic dataset. The data were obtained from the Guangdong Provincial Macroeconomic Database (http://stats.gd.gov.cn/gdtjnj/), with missing values supplemented through interpolation. The analysis aims to evaluate the four-dimensional development levels of rural areas across the 21 cities, examine the spatial characteristics and driving factors of their evolutionary process, and propose targeted recommendations to enhance the coordinated development of rural areas in Guangdong Province.

4. Spatio temporal evolution trend of rural economy ecology society innovation development in Guangdong Province

4.1 Evolution of comprehensive development level of rural economy, ecology, society and innovation in Guangdong Province

Time-series evolution of rural economic–ecological–social–innovation development in Guangdong Province. Overall, the comprehensive development level of the rural economy, society, ecology, and innovation in Guangdong Province has exhibited a steady upward trend. As illustrated in Fig. 2, the average composite evaluation score increased from 0.2169 in 2017 to 0.2526 in 2023, representing a growth rate of 16.44%. Among this, the growth rate from 2017 to 2020 was 13.68%, while that from 2020 to 2023 was 2.76%, indicating a marked slowdown in growth momentum.

Fig. 2

The level of coordinated development of rural economy-society-ecology-innovation in Guangdong Province

The coordinated development subsystems of the rural economy, society, ecology, and innovation in Guangdong Province display divergent trajectories. As shown in Fig. 1, the economic, social, and innovation subsystems exhibit a clear upward trend, whereas the ecological subsystem demonstrates a tendency toward convergence, with its development level showing a persistent decline. Among the four subsystems, social development made the largest contribution to rural coordinated development, with a growth rate of 30.73%, followed by innovation at 18.57%, and economic development at 9.17%. In contrast, ecological development lags significantly behind the other three subsystems, exerting a constraining effect on overall progress, with a negative growth rate of–8.05%.

The development trends of the rural economy–society–ecology–innovation system also vary across cities in Guangdong Province. Based on their trajectories, cities can be categorized into four types: sustained growth, sustained decline, rise-then-decline, and decline-then-rise, as illustrated in Fig. 3. The sustained growth type includes 12 cities: Shenzhen, Shaoguan, Heyuan, Meizhou, Huizhou, Shanwei, Jiangmen, Yangjiang, Maoming, Zhaoqing, Jieyang, and Yunfu. Foshan and Dongguan fall into the sustained decline type. Zhuhai, Shantou, Zhanjiang, Qingyuan, and Chaozhou exhibit rise-then-decline patterns, while Guangzhou and Zhongshan show decline-then-rise patterns. In terms of overall development level, Guangzhou, Shenzhen, Zhanjiang, and Maoming remained above the provincial average in 2017, 2020, and 2023, whereas the other cities consistently fell below the average, high lighting significant regional disparities in the coordinated development of the rural economy, society, ecology, and innovation.

Fig. 3

The coordinated development level of rural economy-society-ecology-innovation in Guangdong Province's cities

Spatial evolution characteristics of the rural economy–ecology–society–innovation system in Guangdong Province. Based on the evaluation results of the economy–ecology–society–innovation system, the natural breaks method in ArcGIS 10.2 was applied to classify and adjust spatial development types into five levels: very high, high, moderate, low, and very low, as illustrated in Fig. 4.

Fig. 4

Regional distribution of rural economic social ecological innovation development level in Guangdong Province

The spatial differentiation of the rural economy–society–ecology–innovation subsystems in Guangdong Province is pronounced. From the perspective of the economic subsystem, between 2017 and 2023, the rural economic development pattern was characterized by high-value areas in Zhaoqing, Zhanjiang, Maoming, and Foshan. For the social development subsystem, the high-value regions were concentrated in Guangzhou, Shenzhen, Maoming, and Zhanjiang. In terms of the ecological subsystem, high values in 2017 were observed in Shaoguan, Meizhou, and Zhanjiang; in 2020, they were mainly concentrated in Zhaoqing, Zhanjiang, and Meizhou; and by 2023, the high-value regions shifted to Zhaoqing and Meizhou, suggesting a decline in Zhanjiang’s ecological development level. Regarding the innovation subsystem, the overall level remained relatively low, with high-value areas concentrated in the Pearl River Delta, showing a clear centralization around Guangzhou and Shenzhen.

4.2 Spatio temporal evolution trend of rural economy ecology society innovation coupling development in Guangdong Province

4.2.1Evolution trend of economic social ecological innovation coupling coordination

The C value, representing the overall coupling degree of the rural economy, society, ecology, and innovation in Guangdong Province, exhibited an upward trend, though notable regional disparities persisted. As shown in Table 3, the overall coordination degree increased from 0.5819 in 2017 to 0.5911 in 2020 and further to 0.6075 in 2023. However, substantial differences were observed across cities. Guangzhou consistently recorded the highest coupling degree and remained within the high-quality coordination category, while Shanwei ranked last among the 21 cities throughout the study period. Specifically, in 2017, the maximum coupling degree was 0.9866 in Guangzhou and the minimum was 0.1771 in Shanwei, with a range of 0.8095. In 2020, the maximum was 0.9820 in Guangzhou and the minimum was 0.3655 in Shanwei, yielding a range of 0.6165. By 2023, the maximum reached 0.9693 in Guangzhou and the minimum 0.3278 in Shanwei, with a range of 0.6415. These results indicate that although regional disparities in the coupling degree of the rural economy–society–ecology–innovation system in Guangdong remain evident, the overall gap has shown a gradual narrowing trend.

Table 3
Coordination index of rural economy society ecology innovation coupling in Guangdong Province
Region	C Value			T Value			D Value
Region	Y2017	Y2020	Y2023	Y2017	Y2020	Y2023	Y2017	Y2020	Y2023
Guangzhou	0.9866	0.9820	0.9693	0.3952	0.4056	0.4290	0.6244	0.6311	0.6449
Shenzhen	0.9049	0.9340	0.8975	0.2960	0.3050	0.3277	0.5175	0.5337	0.5424
Zhuhai	0.7995	0.8355	0.8119	0.1498	0.1622	0.1614	0.3461	0.3682	0.3620
Shantou	0.6378	0.6362	0.6494	0.1913	0.2155	0.1951	0.3493	0.3703	0.3560
Foshan	0.8037	0.8841	0.8673	0.2504	0.2242	0.2306	0.4486	0.4452	0.4472
Shaoguan	0.4964	0.4479	0.3968	0.2297	0.2456	0.2712	0.3377	0.3317	0.3280
Heyuan	0.4577	0.4997	0.4122	0.1685	0.1859	0.2118	0.2777	0.3048	0.2955
Meizhou	0.4881	0.4435	0.5373	0.2371	0.2663	0.2830	0.3402	0.3437	0.3899
Huizhou	0.6606	0.6772	0.6618	0.2136	0.2173	0.2315	0.3756	0.3836	0.3914
Shanwei	0.1771	0.3655	0.3278	0.1302	0.1409	0.1570	0.1519	0.2269	0.2269
Dongguan	0.8516	0.9160	0.9292	0.2094	0.2001	0.1982	0.4222	0.4281	0.4291
Zhongshan	0.6427	0.6819	0.7278	0.2148	0.1730	0.2112	0.3716	0.3435	0.3921
Jiangmen	0.5956	0.6126	0.6220	0.2203	0.2489	0.2549	0.3622	0.3905	0.3982
Yangjiang	0.4587	0.4948	0.5300	0.1722	0.1917	0.2016	0.2810	0.3080	0.3269
Zhanjiang	0.5684	0.5591	0.5465	0.3779	0.3890	0.3401	0.4635	0.4664	0.4311
Maoming	0.4965	0.4341	0.5281	0.3193	0.3235	0.3295	0.3981	0.3748	0.4171
Zhaoqing	0.4391	0.4346	0.5503	0.2609	0.2977	0.3219	0.3385	0.3597	0.4209
Qingyuan	0.4535	0.4906	0.4894	0.2119	0.2190	0.2176	0.3100	0.3278	0.3263
Chaozhou	0.4833	0.4796	0.5296	0.1351	0.1536	0.1448	0.2555	0.2715	0.2770
Jieyang	0.4970	0.4186	0.5205	0.1658	0.2066	0.2242	0.2871	0.2940	0.3416
Yunfu	0.3213	0.1861	0.2530	0.1489	0.1948	0.2180	0.2187	0.2384	0.2349
Average	0.5819	0.5911	0.6075	0.2237	0.2365	0.2457	0.3561	0.3687	0.3800

The T value, representing the overall coordination degree of the rural economy-society-ecology-innovation system in Guangdong Province, exhibited an upward trend with relatively small regional disparities. The average provincial T value increased from 0.2237 in 2017 to 0.2365 in 2020 and 0.2457 in 2023, reflecting steady growth though at a relatively slow pace. The interregional variation was modest, with a range of 0.260 in 2017, 0.252 in 2020, and 0.284 in 2023.

The D value, representing the overall coupling coordination degree, also increased but revealed more pronounced regional differences. The average D value rose from 0.3561 in 2017 to 0.3687 in 2020 and 0.3800 in 2023, showing a clear growth trend though with considerable room for improvement. From 2017 to 2023, Guangzhou consistently recorded the highest coordination level, remaining within the category of primary coordination. Shanwei, by contrast, lagged behind and fell within the range from severe imbalance to moderate imbalance, although its coordination level improved steadily over time. The ranges of D were 0.473 in 2017, 0.404 in 2020, and 0.418 in 2023, suggesting that regional disparities, while significant, tended to narrow over time.

From the perspective of score changes, the coupling coordination degree of 21 cities in Guangdong showed an overall upward trajectory between 2017 and 2023, although growth rates varied considerably across cities, and some lagged behind. Shanwei recorded the largest increase at 49.39%. Zhaoqing, Jieyang, Yangjiang, and Meizhou also achieved growth rates of 24.37%, 19.00%, 16.31%, and 14.61%, respectively, indicating stronger attention to ecological and environmental protection in parallel with innovation-driven economic and social development. By contrast, Guangzhou, Shenzhen, Zhuhai, Shantou, Heyuan, Huizhou, Dongguan, Zhongshan, Jiangmen, Maoming, Qingyuan, Chaozhou, and Yunfu registered increases between 0% and 10%, suggesting the need to further strengthen ecological protection and promote coordinated development across innovation, the economy, society, and ecology. Three cities—Foshan (–0.31%), Shantou (–2.87%), and Zhanjiang (–6.98%)—showed negative growth, implying that ecological coordination was neglected in their development processes and that a shift toward more sustainable development models is urgently required.

With respect to changes in coordination categories, the overall level type of rural areas in Guangdong remained largely unchanged, falling within the category of mild imbalance. This indicates that the coordination level in most cities has yet to shift significantly. Notable changes were observed in Shanwei, Yangjiang, Jieyang, Zhaoqing, and Maoming. Shanwei moved from severe imbalance to moderate imbalance; Yangjiang and Jieyang progressed from moderate imbalance to mild imbalance; while Zhaoqing and Maoming improved from mild imbalance to near coordination. Guangzhou consistently ranked first, remaining in the stage of primary coordination, followed by Shenzhen in the barely coordinated stage. All other cities remained in maladjusted states, underscoring the generally low coupling coordination level of rural Guangdong and the substantial potential for further improvement. These findings highlight the urgent need to explore new economic and social development models that prioritize innovation-driven growth and place stronger emphasis on ecological sustainability.

4.2.2 Spatial variation characteristics of coupling coordination degree

Based on the time-series analysis of the coupling coordination degree, the spatial dynamics of rural economy–society–ecology–innovation coordination in Guangdong Province were examined using the visualization functions of ArcGIS 10.2. The results are presented in Fig. 5.

Fig. 5

Spatial distribution of rural economy society ecology innovation coupling coordination in Guangdong Province

From 2017 to 2023, the coupling coordination degree of the rural economy-society-ecology-innovation system in Guangdong Province demonstrated notable improvement, accompanied by significant spatial disparities. Overall, the pattern revealed a diffusion trend radiating from the Pearl River Delta toward eastern, northern, and western Guangdong. Guangzhou consistently maintained the highest coordination level, followed by Shenzhen, and then Foshan, Dongguan, and Zhanjiang.

In 2017, Shanwei, Jieyang, and Shantou in eastern Guangdong, together with Yunfu and Yangjiang in western Guangdong, were at the stage of moderate imbalance. By 2020, Heyuan and Yangjiang had advanced from moderate imbalance to mild coordination. However, in 2023, Heyuan regressed to moderate imbalance, while Jieyang progressed from moderate imbalance to mild coordination. By 2023, the coordination level in eastern Guangdong, alongside the Pearl River Delta, occupied the leading position within the province. The spatial distribution of coupling coordination thus displayed a diffusion pattern from the central core to the surrounding cities.

5. Driving mechanism of ecological economic social innovation coupling coordination

5.1 Driving Factors

Using the geographical detector method, as shown in Eq. (10), the explanatory power of each variable on the coupling coordination degree was calculated, with the results presented in Table 4. The analysis indicates that 19 out of the 29 factors passed the 5% significance test, demonstrating their significant driving effects on the coupling coordination degree. Among them, factors with a significance level less than or equal to 5% and an influence value (Q) greater than 0.5 were identified as the leading drivers of coordinated development in the rural economy–society–ecology–innovation system of Guangdong Province. These leading factors were further categorized for subsequent analysis..

Table 4
detection results of driving factors of geo detector in 2017–2023
Factors	q	P	Factors	q	P	Factors	q	P
X1	0.5150	0.0030	X11	0.5338	0.0606	X21	0.6774	0.0000
X2	0.4652	0.6981	X12	0.525B	0.0003	X22	0.4852	0.0344
X3	0.5497	0.0003	X13	0.4678	0.0076	X23	0.4183	0.0839
X4	0.3626	0.1737	X14	0.4735	0.0073	X24	0.2620	0.2682
X5	0.3809	0.0394	X15	0.5137	0.0036	X25	0.6195	0.0001
26	0.4222	0.0296	X16	0.2756	0.7922	X26	0.6434	0.0000
X7	0.5270	0.0376	X17	0.4869	0.1341	X27	0.8780	0.0000
X8	0.2525	0.0505	X18	0.6855	0.0049	X28	0.8509	0.0000
x9	0.4540	0.0347	X19	0.4627	0.8338	X29	0.7435	0.0004
X10	0.6496	0.0388	X20	0.4089	0.0475

From Table 4 and Fig. 6, The agricultural production level (x1, x3) is the main factor affecting the rural economy society ecology innovation coupling coordination in Guangdong Province.

Fig. 6

P value and Q statistical value of driving factor

The internal driving force of the economic foundation is evident, with regions exhibiting higher coupling coordination values generally concentrated in areas of stronger rural economic development. The level of social development (X25, X26) is primarily driven by social security, which plays a key role in regulating regional coupling and coordination. The ecological environment—represented by ecological foundation (X10), ecological status (X12), and ecological response (X18)—acts as a baseline constraint on regional coordinated development, exerting both promotional and restrictive effects on the economic and social systems, thereby influencing overall system coordination. Within the innovation subsystem, three factors (X27, X28, and X29) emerge as the dominant drivers of coupling coordination. These findings underscore that transforming the development model and establishing a green, innovation-oriented growth path constitute the essential driving forces for promoting coordinated development of the rural economy, society, ecology, and innovation in Guangdong Province.

5.2 Interaction of impact factors

The interaction detection results of the influencing factors of rural economy–society–ecology–innovation coupling coordination in Guangdong Province reveal the relationship between the combined effect of two factors on the system’s coupling coordination degree and the independent effects of each factor considered separately, as illustrated in Fig. 7..

Fig. 7

Interaction detection diagram of the impact factors of rural economy society ecology innovation coupling coordination in Guangdong Province

The The interaction effects of any two influencing factors on the coupling coordination degree of the rural economy–society–ecology–innovation system in Guangdong Province are greater than the effects of any single variable on the spatial distribution of coordination. Both two-factor enhancement and non-linear enhancement were identified, indicating that the comprehensive and coordinated development of the system is the outcome of multiple interacting factors. Based on the analysis of three cross-sections and panel data from 2017, 2020, and 2023, the three indicators from the innovation subsystem (X27, X28, and X29) consistently ranked among the top three single factors, all meeting the 1% significance threshold. These factors exerted the strongest combined effects when interacting with others, suggesting that innovation-related drivers play a decisive role in improving coupling coordination.

Significant interactions were also detected between X1 and other factors, confirming that the economic subsystem, as the material foundation, can effectively promote the development of both the social and innovation subsystems. By contrast, the single-factor effects of X4, X8, and X16 were relatively weak. Among them, X8 and X16, which belong to ecological indicators, failed to pass the 5% significance test, indicating limited independent contributions to coupling coordination. However, their explanatory capacity improved substantially when interacting with other factors. This demonstrates that economic, social, and innovation development rooted in ecological conditions has become a critical prerequisite for advancing coupling coordination in Guangdong Province.

6 Conclusions and recommendations

6.1 Conclusions

The coupling coordination degree model was applied to reveal the evolutionary trajectory of the four-dimensional system, and the driving factors of spatial differentiation were further identified using geographical detectors. The main conclusions are as follows:

Overall development trend. The comprehensive four-dimensional development level of rural areas in Guangdong Province exhibited a steady upward trajectory, though the growth rate slowed markedly. From 2017 to 2023, the average comprehensive evaluation score increased from 0.2169 to 0.2526, representing a growth of 16.44%. Growth was concentrated in the period from 2017 to 2020 with 13.68%, but slowed to only 2.76% from 2020 to 2023. By subsystem, the social dimension contributed the most with 30.73%, followed by innovation with 18.57% and the economy with 9.17%, while ecology declined by 8.05%, becoming the principal constraint on overall progress.

Regional disparities. Significant spatial heterogeneity was observed, with a “center–periphery” diffusion pattern. Guangzhou, Shenzhen, Zhanjiang, and Maoming consistently remained above the provincial average. Among them, Guangzhou, Shenzhen, Foshan, and Dongguan, as the core cities of the Pearl River Delta, took the lead in coupling coordination, while most cities in eastern, western, and northern Guangdong remained at mild imbalance or below. In 2023, only Guangzhou advanced into the stage of primary coordination, while Shanwei remained at moderate imbalance, with the maximum regional gap reaching 0.418.

Coupling coordination dynamics. The provincial average D value rose from 0.3561 in 2017 to 0.3800 in 2023, with an average annual growth rate of less than 1%, leaving the overall system at a level of mild imbalance. Cities such as Shanwei, Yangjiang, Jieyang, Zhaoqing, and Maoming showed improvement, while Foshan, Shantou, and Zhanjiang experienced negative growth, largely due to ecological degradation.

Driving mechanisms. The results reveal a dual mechanism of economic and innovation leadership supported by social and ecological foundations. The primary driving factors identified include R&D expenditure, number of researchers, and patent authorizations. Additional significant variables include average fruit yield, social security coverage in pension and medical insurance, and ecological response indicators. Interaction analysis revealed nonlinear enhancement, with the combined effects of innovation, agricultural productivity, and social security exerting the strongest influence. This suggests that innovation-driven green rural revitalization represents the key pathway to overcoming the current low level of coordination.

6.2 Recommendations

First, ecological priority should be strengthened to address the shortcomings of green development. Given the negative drag of the ecological subsystem, investments in energy conservation, environmental protection, and soil and water conservation must be increased. The use of chemical fertilizers and pesticides should be strictly controlled, while an ecological product value realization mechanism centered on carbon sinks and watershed compensation should be established, thereby achieving the transformation from “lucid waters and lush mountains” to “gold and silver mountains.”

Second, differentiated zoning policies should be implemented to overcome the “core–periphery” dilemma. In the core Pearl River Delta region, emphasis should be placed on promoting high-end industries such as digital agriculture and smart logistics, so as to prevent ecological overload from industrial concentration. In eastern and northwestern Guangdong, fiscal transfers, land security, and talent inflow should be leveraged to establish agro-processing parks, cold-chain logistics nodes, and county-level e-commerce centers, thereby enhancing the economic density and logistics efficiency of peripheral regions. An innovation–industry–security linkage mechanism should also be constructed to reinforce regional resilience.

Third, innovation-driven development should be prioritized by strengthening a trinity innovation system integrating research platforms, leading enterprises, and cooperatives. At the same time, expanding pension and healthcare insurance coverage is essential to narrowing urban–rural disparities in social security, enhancing rural demographic vitality, and forming a virtuous cycle of innovation investment, industrial efficiency, social protection, and talent return.

Fourth, a dynamic monitoring and iterative policy system should be established. Drawing on Guangdong’s macroeconomic database, the four-dimensional rural coordination index should be updated biennially to provide early warnings for cities experiencing regression in coupling coordination. In conjunction with the “High-Quality Development Project of 100 Counties, 1000 Towns, and 10,000 Villages,” a dynamic “one city, one policy” assessment mechanism should be implemented to ensure timely and accurate governance responses.

6.3 Contributions

This study advances theoretical, methodological, and policy dimensions of research on rural regional development. At the theoretical level, it extends coupling coordination theory to rural systems by constructing a four-dimensional analytical framework encompassing economy, ecology, society, and innovation. This framework addresses the limitations of existing single- or three-dimensional approaches and enriches the theoretical connotation of regional coordinated development.

At the methodological level, the study integrates the entropy weight method, the coupling coordination degree model, GIS natural breaks classification, and geographical detector interaction analysis to establish a “coupling spatiotemporal driving” paradigm. This integrated approach not only ensures objective weighting of indicators and enables spatial visualization, but also quantitatively reveals the interaction effects of multiple factors, thereby offering a replicable and scalable technical route for future research.

At the policy level, using data from 21 cities in Guangdong between 2017 and 2023, the study conducted a rolling diagnosis that identified two key regional types: “system coupling with lagging levels” and “system imbalance with high potential.” The findings directly support Guangdong’s “High-Quality Development Project of 100 Counties, 1000 Towns, and 10,000 Villages,” and propose an innovation–ecology dual-drive strategy combined with zoning-based implementation. This provides an actionable path for peripheral cities in the Guangdong–Hong Kong–Macao Greater Bay Area to absorb innovation spillovers from the core while safeguarding ecological bottom lines.

6.4 Limitations and Prospects

Despite its contributions, this research has two main limitations. First, the timeliness of data is restricted, with the latest year being 2023, and the absence of county-level microdata limits the ability to capture recent developments following the full rollout of the “Hundred-Million Project.” Second, the indicator system is constrained by the scope of official statistics. Although variables such as R&D funding and patent authorizations are representative, they fail to encompass emerging innovation carriers such as digital inclusive finance and rural e-commerce livestreaming.

Future research should incorporate multi-source, real-time microdata such as points of interest (POI) and nighttime light data to improve spatiotemporal resolution. Simulation techniques could also be employed to compare the long-term income and cost distribution of strategies such as ecological priority, industrial upgrading, and social security expansion. These efforts would provide more forward-looking and evidence-based decision support for local governments in promoting rural revitalization and sustainable development.

Data Availability

The data used to support the findings of the Research on the spatiotemporal evolution and driving factors of coordinated development of rural Economy-Ecology-Society-Innovation in Guangdong Province are available from all the authors upon request.

Conflicts of Interest

The authors declare there are no conflicts of interest regarding the publication of this paper.

Funding

This research was supported by Regular topics of the "14th five year plan" for the development of philosophy and Social Sciences in Guangzhou in 2025 (2025GZGJ264);Guangzhou Huashang College Logistics Management Applied Demonstration Program, (HS2024SFZY10)༛Guangzhou Huashang College 2024 school-level quality engineering project 'Sanfu' industry-education integration practice teaching base, (HS2024ZLGC16);Zhaoqing's philosophy and Social Sciences discipline co construction project (25GJ-365).

Electronic Supplementary Material

Below is the link to the electronic supplementary material

Supplementary Material 1

Author Contribution

Liu Lianhua wrote the main manuscript text and Zhen lifen prepared figures 1-7 and date analysis . All authors reviewed the manuscript."

References:

Cumberland, J. H. A regional interindustry model for a- nalysis of development objectives [J]. Papers Reg. Sci. Association. 17 (1), 65–94 (1966).

Grossman, G. M. & Krueger, A. B. Economic growth and the environment[J]. Q. J. Econ. 110 (2), 353–377 (1995 ).

Stern, D. I., Common, M. S. & Barbier, E. B. Economic growth and environmental degradation : the environmental Kuznets curve and sustainabl development [J]. World Development, 24(7) : 1151–1160 (1996).

Reidsma, P. et al. Methods and tools for integrated assessment of land use policies on sus- tainable development in developing countries[J]. Land. Use Policy. 28 (3), 604 (2011 ).

Charfeddine, L. & Mrabet, Z. The impact of economic de- velopment and social-political factors on ecological footprint : a panel data analysis for 15 MENA coun- tries[J]. Renew. Sustain. Energy Rev., 2017, 76 : 138 .

Wei, W. et al. The coordination evaluation of ecological environment-economic development-carbon emission and spatiotemporal evolution in the important ecological functional area —A case study in the Yellow River Basin[J].Environment, Development and Sustainability,2025,1–26.

Xuesong, S. & Zaisheng, Z.. Coupling and Coordination Level of the Population, Land, Economy, Ecology and Society in the Process of Urbanization. Meas. Spat. Differentiation[J] Sustain. 2021, 13(6):3171–3171 .

Li, Z. & Chinese Rural Comprehensive Reform. Theoretical Innovation, Key Contradictions and Countermeasures[J]. J. Northeast Agricultural Univ. 2016, 23(3):79–89 .

Jun, H. S., Pei, Z. & ,Jing, Z. Study on Transformation Path and Planning Countermeasure of Resource-Based City Guided by New Development View[J]. Adv. Mater. Res. 368–373 (368–373), 1776–1779 (2011).

10.

Shi, S. Performance evaluation of urban ecological environment construction with interval-valued intuitionistic fuzzy information[J]. J. Intell. Fuzzy Syst. 32 (1), 1119–1127 (2016).

11.

Zou, S. & Zhou, S.. A Study on the Pathways for Realizing the Ecological Value in Xiamen under the Two Mountains Theory[J]. Acad. J. Bus. Manage. 2025, 7(4).

12.

Erokhin, V. B. A. et al. Determining the Composition of Integrated Reporting Indicators Under Uncertainty: The Innovation-Based Convergence of Economic, Managerial, Social, and Environmental Contexts[J].Journal of the Knowledge Economy,2024,1–36.

13.

Liu, Z. et al. Synergistic Evolution Characteristics and Driving Factors of High-Quality Economic Development and Green Space Ecological Benefits at Multiple Spatial Scales: Evidence from Shanxi Province. China[J] Land. 2025, 14(4):819–819 .

14.

]Rui, N., Feier, W. & ,Jie, Y.. Spatiotemporal changes in sustainable development and its driving force in the Yangtze River Delta region, China[J].Journal of Cleaner Production,2022,379(P1).

15.

Yige, S. & Qingshan, Y.. Study on Spatial–Temporal Evolution Characteristics and Restrictive Factors of Urban–Rural Integration in Northeast China from 2000 to 2019[J].Land,2022,11(8):1195–1195 .

16.

Xiaoyu, L. & Yun, X.. Exploring the characteristics and driving factors of coupling coordination of regional sustainable development: evidence from China's 31 provinces.[J].Environmental science and pollution research international,2022,29(47):71075–71099 .

17.

Li, Y. & Murzin, A. Spatial-temporal evolution of the coupling state of agricultural socio-ecologo-economic system in North China Plain[J]. Int. J. Sustainable Agricultural Manage. Inf. 2022, 8(4):367–384 .

18.

Chen, R. et al. Interplay of Urbanization and Ecological Environment: Coordinated Development and Drivers[J].Land,2023,12(7):1459-.

19.

Xie, M., Wang, J. & Chen, K. etal.Coordinated Development Analysis of the Resources-Environment-Ecology-Economy-SocietyComplex System in China [J].Sustainability,2016,8(6):582 .

20.

Cheng, Z., Ding, B. & ,Ma, L.. Assessing the synergies of the water–economy–ecology nexus in resource-based cities: a case study of Shaanxi, China[J].Frontiers in Sustainable Cities,2025,71586642 – 1586642.

21.

Fan, L. et al. An investigation of the coupling coordination of a regional agricultural economics-ecology-society composite based on a data-driven approach[J].Ecological Indicators,2022,143.

22.

Zhu Yue,Du,等.Does industrial collaborative agglomeration improve environmental efficiency? Insights from China’s population structure[J]1–20 (Environmental Science and Pollution Research, 2021).

23.

LiJuan, S. et al. Urban Green Development towards Sustainability in Northwest China: Efficiency Assessment, Spatial-Temporal Differentiation Characters, and Influencing Factors[J].COMPLEXITY,2021,2021.

24.

Ye, T. P. H. & ,Xu, Z.. Spatial analysis, coupling coordination, and efficiency evaluation of green innovation: A case study of the Yangtze River Economic Belt.[J].PloS one,2020,15(12):e0243459–e0243459 .

25.

Chen, S., Du, Y. & ,Liu, Y.. Regional Integration and Urban Green and Low-Carbon Development: A Quasi-Natural Experiment Based on the Expansion of the Yangtze River Delta Urban Agglomeration[J].Sustainability,2025,17(8):3621–3621 .

26.

Zhao, A. et al. Research on the Evolutionary Path of Eco-Conservation and High-Quality Development in the Yellow River Basin Based on an. Agent-Based Model[J] Syst. 10 (4), 105 (2022).

27.

Feng, Z., Xintian, W. & ,Xiaojie, L.. Research on Functional Value Estimation and Development Mode of Green Infrastructure Based on Multi-Dimensional Evaluation Model: A Case Study of China[J].Land,2022,11(9):1603–1603 .

28.

Fanqi, Z. & Tinghui, L.. The Impact of Agricultural Ecological Capital Investment on the Development of Green Circular Economy[J].Agriculture,2022,12(4):461–461 .

29.

Liu, L. et al. Strategies for building a resource-saving and environmentally friendly society from the perspective of circular economy[J]. Int. J. Sustainable Dev. 2025, 28(2–3):185–197 .

30.

Zhang, Z., Sun, C. & ,Wang, J.. How Can the Digital Economy Promote the Integration of Rural Industries—Taking. China as Example[J] Agric. 2023, 13(10):2023 -.

31.

Luo, T., Cheng, Z. & ,Ma, H.. Impact of Land-Use Intensification on the Development of Sustainable Agricultural Green Innovation Technology[J]. J. Knowl. Econ. 2024, 15(4):1–35 .

32.

Jun, L., Jundong, H. & ,Yang, L.. Analysis of Rural Ecological Environment Governance in the Two-oriented Society Construction: A Case Study of Xiantao City in Hubei Province[J].Procedia Environmental Sciences,2011,111278–111284 .

33.

Ladykova, T. & Vasilieva, I.. Imitation of the impact of external factors in the modeling of innovation-oriented regional socio-economic systems[J].E3S Web of Conferences,2020,21013035-.

34.

Mingxia, X., Jiayao, W. & ,Ke, C. The coordinating evaluation and spatial correlation analysis of CSGC: A case study of Henan province. China [J] PloS one. 12 (6), e0174543 (2017).

35.

Ma, D., Sun, D. & ,Wang, Z.. Exploring the Rural Revitalization Effect under the Interaction of Agro-Tourism Integration and Tourism-Driven Poverty Reduction: Empirical Evidence for China[J].Land,2024,13(1).

Figure 1 Rural Economy- Ecology-Society-Innovation Coupling Model

Figure 2 The level of coordinated development of rural economy-society-ecology-innovation in Guangdong Province

Figure 3 The coordinated development level of rural economy-society-ecology-innovation in Guangdong Province's cities

Figure 4 Regional distribution of rural economic social ecological innovation development level in Guangdong Province

Figure 5 Spatial distribution of rural economy society ecology innovation coupling coordination in Guangdong Province

Figure 6 P value and Q statistical value of driving factor

Figure 7 Interaction detection diagram of the impact factors of rural economy society ecology innovation coupling coordination in Guangdong Province

Table 1 Evaluation index of rural economy ecology society innovation development

Target layer	Rule layer	Index laye	Variable (unit)
Evaluation index system of economic development level	Agricultural production level	Average grain yield	Ton(X1)
		Average vegetable yield	Kilogram(X2)
		Average fruit yield	Ton(X3)
		Pork production	Ton(X4)
		Degree of Agricultural Mechanization	Million Kilowatts(X5)
	Agricultural income level	Output value of agriculture, forestry, animal husbandry and fishery	100 million Yuan(X6)
	Agricultural income level	Rural per capita disposable income	Yuan(X7)
Evaluation index system of ecological development level	Ecological environment foundation	cultivated area	Hectare(X8)
		Population density	People/ km²(X9)
		per capita green are	Square meter(X10)
	Ecological environment status	10-year average concentration of particulate matter	µg/m3(X11)
		Annual average concentration of particulate matter PM2.5	µg/m3(X12)
		Average amount of agricultural chemical fertilizer applied on land	Ton(X13)
		Average use of agricultural plastic film	Ton(X14)
		Average pesticide use	Ton(X15)
	Ecological environmental response	Proportion of energy conservation and environmental protection expenditure in GDP	%(X16)
		Newly increased comprehensive control area of water and soil loss	Thousands Hectare(X17)
		Drainage area of each city	Thousands Hectare(X18)
Evaluation index system of social development level	Social foundation	Number of people on duty in agriculture, forestry, animal husbandry and fishery	Ten thousand people(X19)
		Highway density	Km/km²(X20)
		Urban and rural community expenditure	100 million Yuan(X21)
		Total retail sales of consumer goods in rural areas	100 million Yuan(X22)
		Student teacher ratio in primary and secondary schools	Ratio of students to teachers(X23)
		Number of beds in health institutions	Medical beds per 10,000 people(X24)
	Social security	Number of urban and rural residents covered by basic endowment insurance	Ten thousand people(X25)
	Social security	Number of urban and rural residents covered by basic medical insurance	Ten thousand people(X26)
Evaluation index system of innovation development level	innovation infrastructure	Science and technology expenditure	Ten thousand Yuan(X27)
	innovation infrastructure	Number of on-the-job employees in scientific research and technical services at the end of the year	Ten thousand people(X28)
	Innovative achievements	Number of patents authorized	Piece(X29)

Table 2 Classification of Coordination Degree Levels

Coupling coordination degree D	coordination state coupling level	coordination degree range	Coupling coordination degree D	coordination state coupling level	coordination degree range
(0.0 0.1)	extreme maladjustment	1	[ 0.5 0.6)	reluctantly coordinated	6
[0.1 0.2)	severe maladjustment	2	[0.6 0.7 )	primary coordination	7
[ 0.2 0.3)	moderate maladjustment	3	[0.7 0.8 )	intermediate coordination	8
[0.3 0.4)	mild maladjustment	4	[0.8 0.9)	good coordination	9
[ 0.4 0.5)	close to maladjustment	5	[0.9 1.0)	high quality coordination	10

Table 3 Coordination index of rural economy society ecology innovation coupling in Guangdong Province

Region	C Value			T Value			D Value
Region	Y2017	Y2020	Y2023	Y2017	Y2020	Y2023	Y2017	Y2020	Y2023
Guangzhou	0.9866	0.9820	0.9693	0.3952	0.4056	0.4290	0.6244	0.6311	0.6449
Shenzhen	0.9049	0.9340	0.8975	0.2960	0.3050	0.3277	0.5175	0.5337	0.5424
Zhuhai	0.7995	0.8355	0.8119	0.1498	0.1622	0.1614	0.3461	0.3682	0.3620
Shantou	0.6378	0.6362	0.6494	0.1913	0.2155	0.1951	0.3493	0.3703	0.3560
Foshan	0.8037	0.8841	0.8673	0.2504	0.2242	0.2306	0.4486	0.4452	0.4472
Shaoguan	0.4964	0.4479	0.3968	0.2297	0.2456	0.2712	0.3377	0.3317	0.3280
Heyuan	0.4577	0.4997	0.4122	0.1685	0.1859	0.2118	0.2777	0.3048	0.2955
Meizhou	0.4881	0.4435	0.5373	0.2371	0.2663	0.2830	0.3402	0.3437	0.3899
Huizhou	0.6606	0.6772	0.6618	0.2136	0.2173	0.2315	0.3756	0.3836	0.3914
Shanwei	0.1771	0.3655	0.3278	0.1302	0.1409	0.1570	0.1519	0.2269	0.2269
Dongguan	0.8516	0.9160	0.9292	0.2094	0.2001	0.1982	0.4222	0.4281	0.4291
Zhongshan	0.6427	0.6819	0.7278	0.2148	0.1730	0.2112	0.3716	0.3435	0.3921
Jiangmen	0.5956	0.6126	0.6220	0.2203	0.2489	0.2549	0.3622	0.3905	0.3982
Yangjiang	0.4587	0.4948	0.5300	0.1722	0.1917	0.2016	0.2810	0.3080	0.3269
Zhanjiang	0.5684	0.5591	0.5465	0.3779	0.3890	0.3401	0.4635	0.4664	0.4311
Maoming	0.4965	0.4341	0.5281	0.3193	0.3235	0.3295	0.3981	0.3748	0.4171
Zhaoqing	0.4391	0.4346	0.5503	0.2609	0.2977	0.3219	0.3385	0.3597	0.4209
Qingyuan	0.4535	0.4906	0.4894	0.2119	0.2190	0.2176	0.3100	0.3278	0.3263
Chaozhou	0.4833	0.4796	0.5296	0.1351	0.1536	0.1448	0.2555	0.2715	0.2770
Jieyang	0.4970	0.4186	0.5205	0.1658	0.2066	0.2242	0.2871	0.2940	0.3416
Yunfu	0.3213	0.1861	0.2530	0.1489	0.1948	0.2180	0.2187	0.2384	0.2349
Average	0.5819	0.5911	0.6075	0.2237	0.2365	0.2457	0.3561	0.3687	0.3800

Table 4 detection results of driving factors of geo detector in 2017–2023

Factors	q	P	Factors	q	P	Factors	q	P
X1	0.5150	0.0030	X11	0.5338	0.0606	X21	0.6774	0.0000
X2	0.4652	0.6981	X12	0.525B	0.0003	X22	0.4852	0.0344
X3	0.5497	0.0003	X13	0.4678	0.0076	X23	0.4183	0.0839
X4	0.3626	0.1737	X14	0.4735	0.0073	X24	0.2620	0.2682
X5	0.3809	0.0394	X15	0.5137	0.0036	X25	0.6195	0.0001
26	0.4222	0.0296	X16	0.2756	0.7922	X26	0.6434	0.0000
X7	0.5270	0.0376	X17	0.4869	0.1341	X27	0.8780	0.0000
X8	0.2525	0.0505	X18	0.6855	0.0049	X28	0.8509	0.0000
x9	0.4540	0.0347	X19	0.4627	0.8338	X29	0.7435	0.0004
X10	0.6496	0.0388	X20	0.4089	0.0475

Yes