Dynamic evolution and obstacle factor analysis of green development in China's agriculture and rural areas based on entropy-based TOPSIS model

Abstract

Green development of agriculture and rural areas (GDARA) is an essential part of rural revitalization and high-quality development. Based on 2011 to 2020 provincial panel data from China, the entropy-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) model is constructed to measure the level of GDARA, finding that the overall level during the sample period was positive. Then the regional differences mainly from intra-regional were revealed by the methods of Dagum Gini coefficient and Kernel density estimation and were shrinking overall. Besides, the results of Markov chain transfer matrix show the dynamic characteristics clearly, which the low-level regions shift to the middle and high levels with a greater probability. The results of the spatial autocorrelation test display that GDARA has a spatial clustering effect. Finally, the industrial upgrading and the business income of leisure agriculture are respectively proven to hinder GDARA at most by the Obstacle degree model empirically. Based on a series of empirical tests, a few relevant policy recommendations are proposed to promote the road to strong agriculture and rural areas in China.

1. Introduction

Green and sustainable development provides a foundation for rural revitalization. Since the speedy development of the rural economy, the achievement of development in China's agricultural and rural economy has been remarkable, but the rough management of agricultural inputs has led to kinds of problems, such as the deterioration of the rural habitat environment, serious agricultural surface pollution, degradation of ecosystem functions and so on. To some extent, this has become the outstanding shortcomings of GDARA and even sustainable modernization construction.

China has issued a series of top-level design documents to address the outstanding problems mentioned above since the 18th Party Congress (2012). The attention index of GDARA has been found risen to more than 15% based on the text data mining of a large number of relevant policy documents, which means that the 14th Five-Year Plan (2021–2025) is at a critical stage of comprehensively promoting the modernization of GDARA. "Opinions on Accelerating Agricultural and Rural Modernization by Comprehensively Promoting Rural Revitalization" issued by the State Council in 2021 has pointed out that accelerating the green development of agriculture and completing the improvement of rural habitat environment should be finished in five years. Based on this background, a reasonable measurement of GDARA is conductive to understand the regional differences clearly. Furthermore, analysis of the specific obstacles faced will reveal its shortcomings, which is of great significance to comprehensively promote rural modernization [1] and break the ecological obstacles [2].

In addition, many studies have focused actively on the related topics of GDARA. On the one hand, the Food and Agriculture Organization of the United Nations released the report titled "The State of Food and Agriculture 2022" mentioned that there was an imbalance in the countryside between environmentally, socially and economically sustainable development, with environmental sustainability being given priority [3]. Therefore, it has a profound impact on promoting the sustainable development of GDARA [4], which means to break the resource constraints, ecological degradation and other bottlenecks [5]. Under the background of structural imbalance and low synergy of policy, the level of GDARA still varies greatly between regions, which seriously hampers the progress of coordinated development. Therefore, the main contents [6], existing problems [7], directions and paths of [8] GDARA have been further analyzed. In terms of content, GDARA is a complex system that encompasses agricultural production, people's lives and ecological environment [9]. Cui and Wang (2021) [10] pointed out that existing problems mainly lie in the weak ecological awareness among farmers and sloppy development of agricultural economy. Therefore, the paths of GDARA may include ecological conservation, green production, healthy living and urban-rural integration, which has been analyzed mainly in the directions of the environmental, economic and social dimensions [11].

On the other hand, the entropy-based TOPSIS [9], policy text analysis [12], the combination of hierarchical analysis [13] and kinds of improved entropy methods [14] have been constructed to research GDARA in empirical studies [15]. Besides, to explore the spatial and temporal characteristics of GDARA, the models of Dagum Gini coefficient, the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution [16] and the Moran's I have been mainly used [[17], [18], [19], [20]]. Meanwhile, the methods of multiple-criteria decision-making [16,21] fuzzy analytical hierarchy process [22,23] and PROMETHEE II [24] have been used to assess the potential for rural ecological development. In addition, the Obstacle degree model used to find the main obstacle factors has been recognized by most studies [25,26].

Based on existing studies, the connotation of GDARA is constantly enriched and iterated with the evolution of the times. However, most of studies are based on the concept of sustainable development and agricultural production itself. As a result, there is little literature on updating the content of GDARA in the light of the "14th Five-Year" National Green Agricultural Development Plan and the context of "carbon peak and carbon neutral". Secondly, the measurement framework of GDARA has to be enriched, and also the selection of indicators and measurement methods needs to be improved. Thirdly, existing research lacks further analysis of the characteristics of its regional differences, temporal changes, state shifts, and obstacle factors. As a result, it is necessary to analyze the current situation in terms of multiple perspectives. This paper collected the panel data in 31 provinces (municipalities) from 2011 to 2020, calculating the index of GDARA by the entropy-based TOPSIS model. Meanwhile, its dynamic evolution characteristics and obstacle factors in each dimension and region are also explored by the methods of Dagum Gini coefficient, Kernel density estimation, Markov chain transfer matrix, Spatial autocorrelation test and Obstacle degree model.

The possible marginal contributions are as follow:

Firstly, in terms of the indicator evaluation system, different from existing studies [[27], [28], [29], [30]] this paper broadly defines the content of the GDARA based on the conceptual connotation, data constraints and policy targets firstly. Further the indicator system has been constructed to measure GDARA from the five dimensions as follow: resource conservation, environmental friendliness, ecological conservation, industrial upgrading, and life improvement. The above studies may enrich the evaluation of GDARA and also serve as a reference for other developing countries.

Secondly, in terms of empirical analysis, the study measured the level of GDARA in China based on entropy-based TOPSIS method, and utilized various statistical tools to characterize the facts, such as Dagum Gini coefficient, Kernel density estimation, and so on. According to these methods, the time-series characteristics and regional synergistic development of GDARA have been revealed clearly. Besides, the Obstacle degree model used analyze deeply the current obstacles facing the GDARA. In a word, the study provides a quantitative basis for comprehensively revealing the current situation of regional differences.

Thirdly, in the analysis of regional differences, the unbalanced development of GDARA in different regions has been ignored by most of the existing studies. Different with dividing by geographic location [28], this paper innovatively divides the sample based on the level of GDARA. Then, the coordinated relationship and spatial agglomeration of different regions at multiple levels are described by the methods of Dagum Gini coefficient and spatial autocorrelation test. The results demonstrate that the level of GDARA has increased gradually and kept no bipolar or multipolar polarization in the whole country and regions with overall reduction in differences. Thus, this study may fill the gap how to promote the green and coordinated development of agriculture and rural areas in China.

The rest of our study will be organized as follows: Section 2 constructs an indicator evaluation system based on the definition of GDARA. Section 3 carries out a number of empirical tests such as the methods of Dagum Gini coefficient, kernel density estimation, spatial autocorrelation tests, and Markov chain. Section 4 analyzes the main obstacle factors currently facing GDARA by constructing the Obstacle degree model. Section 5 summarizes the research in this paper and makes corresponding recommendations.

2. Study design

2.1. Components of green development in agriculture and rural areas

The 14th Five-Year Plan points out that agriculture should be upgraded in five aspects, including resource utilization, pollution prevention and control, ecological restoration, emission reduction and carbon sequestration, and green innovation. Based on this, the study believes that the connotation of GDARA should reflect the five elements of resources, environment, ecology, industry and life, so as to ultimately achieve the goals of resource conservation, environmental friendliness, ecological conservation, industrial upgrading and life improvement.

Resource conservation. Resources are a kind of essential element in agricultural production and rural life. The agricultural production is strongly dependent on resources, but resources are limited, then intensive use of resource are the basic feature of green development and modernization of agriculture and rural areas [31]. Specifically, in agricultural production, arable land and irrigation water are irreplaceable means of production, and their stock and quality determine the yield and quality of agricultural products. In rural life, the use of energy such as electricity and diesel fuel determine the efficiency of agricultural and rural production. Therefore, the study measures the level of resource conservation in agriculture and rural areas from three aspects: arable land protection, water conservation and energy use efficiency.

Environmental friendliness. The unreasonable use and non-appropriate disposal of relevant waste have led to the large amounts of agricultural film residues and serious soil damage, resulting in non-negligible surface source pollution. Consequently, controlling the intensity of agricultural material use and reducing the emission of greenhouse gases and malodorous gases are effective ways to alleviate agricultural surface source pollution. In this paper, the level of environmental friendliness is measured in three aspects: agricultural use intensity, water pollution and air pollution.

Ecological Conservation. Ecosystem is the cornerstone of normal operation of agriculture and rural areas. According to the idea of co-management, agricultural ecosystems including mountains, water, forests, fields, lakes and grasses should be comprehensively protected and restored. In addition, the balance of the ecosystem should be maintained. Consequently, the level of ecological conservation is examined from three aspects: mountain and forest protection, wetland restoration and species diversity.

Industrial upgrading. The improvement of green product supply quality is an important manifestation of industrial upgrading, which refers to continuously deepening the supply-side reform, starting from production, researching green and healthy and high-quality agricultural products, continuously deepening the supply-side reform, and exploring new modes of business supply. In addition, it is essential to reduce the waste of non-essential resources, improve the efficiency of input and output of agricultural production factors. Therefore, the efficiency of agricultural production, food output, green production of agricultural products and the supply of new forms of business will be used to measure the level of industrial upgrading.

Life improvement. The rise of farmers' income and the improvement of living environment are significant guarantees to enhance the quality of life. On the one hand, increasing farmers' income is the key to achieving common prosperity. Efforts to promote the increase of farmers' income effectively improve the living quality of rural residents. On the other hand, improving the efficiency of domestic waste disposal is conducive to fostering the concept of green living in rural areas, which in turn effectively enhances the quality of the rural living environment. Only by improving level of domestic waste treatment and effectively implementing domestic waste classification, the concept of green living and quality of rural living environment significantly will be cultivated and improved respectively. In summary, the level of life improvement will be measured in farmers' income and rural habitat environment.

2.2. Indicator system construction

A review of existing research has mostly measured the level of GDARA in terms of indicators such as percentage of cultivated land area [32], fertilizer application intensity [33] and farmland plastic film coverage [34]. In addition, there are some studies to construct a comprehensive indicator system of total factor productivity in agriculture to measure GDARA, and the indicators they use are mainly agricultural carbon emissions [35], pesticide usage [19], consumption of ecological water resource [36], total rural domestic energy consumption [27] and number of patents and technology achievements in agriculture, forestry, animal husbandry, and fishery [37]. Based on the above elaboration of the connotation and components of GDARA, and following the three principles of system, science and operability, the index system of GDARA is constructed, in which the five first-level indicators are resource conservation, environmental friendliness, ecological conservation, industrial upgrading and life improvement, containing a total of 21 specific indicators (Table 1).

Table 1.

The indicator system of China's GDARA level.

System	Indicator	Unit	Attribute
Resource Conservation System (RCS)	Cultivated land replanting index (RCS1)	%	–
	Agricultural water intensity (RCS2)	m3/million CNY	–
	Percentage of water-saving irrigation area (RCS3)	%	+
	Agriculture, forestry, animal husbandry and fishery unit output value of electricity consumption (RCS4)	kWh/CNY	–
Environmental Friendliness System (EFS)	Intensity of fertilizer application (EFS1)	kg/hm2	–
	Intensity of pesticide application (EFS2)	kg/hm2	–
	Agricultural film application intensity (EFS3)	kg/hm2	–
	Agricultural COD emission intensity (EFS4)	g/m3	–
	Agricultural ammonia nitrogen emission intensity (EFS5)	g/m3	–
	Carbon emission intensity (EFS6)	kg/million CNY	–
Ecological Conservation System (ECS)	Forest coverage (ECS1)	%	+
	Wetland conservation rate (ECS2)	%	+
	Area shares of nature reserves (ECS3)	%	+
Industrial Upgrading System (IUS)	Agricultural labor productivity (IUS1)	CNY/person	+
	Grain production (IUS2)	kg/hm2	+
	Number of Green Food Certificates (IUS3)	unit	+
	Leisure agriculture operating income (IUS4)	Billion CNY	+
Life Improvement System (LIS)	Per capita disposable income of rural residents (LIS1)	CNY/Per	+
	Rural domestic waste treatment rate (LIS2)	%	+
	Rural domestic sewage treatment rate (LIS3)	%	+
	Sanitary toilet penetration rate (LIS4)	%	+

2.3. Method

2.3.1. Entropy-based TOPSIS model

In terms of indicator system construction and weighting, the extant literature mainly uses a variety of methods such as Entropy method, Analytical Hierarchy Process (AHP), Delphi method, Principal Component Analysis (PCA), Factor analysis and the method of Criteria Importance Through Intercriteria Correlation (CRITIC). However, they each suffer from the shortcomings of irrational weight allocation, subjective evaluation results, sensitivity to outliers, and restriction to predefined rules. The benefit of the entropy-based TOPSIS model is to avoid the subjectivity that may exist when weights are given, and thus is often used in the evaluation of indicator systems [38]. In addition, the model is widely applied to evaluate the relative merits of multiple evaluation objects and more suitable for solving scenarios where the raw data are quite sufficient for almost quantitative analysis. Therefore, in this paper, the entropy-based TOPSIS model is used to measure the level of GDARA based on the above index system, and the calculation steps are roughly as follows.

• (1)The units of different indicators vary and are not obviously comparable in unit dimension. Therefore, firstly, each indicator is divided into positive and negative attributes according to its influence on GDARA, and then standardized separately. It not only minimizes the variability of the indicators due to the different units of measurement, but also reduces the impact of the positive and negative indicators.

The formula for normalizing positive indicators is illustrated in Equation (1):

$$X_{ij}=\frac{x_{ij}-\mathit{min}\left\{x_{1j},\cdots ,x_{mj}\right\}}{\mathit{max}\left\{x_{1j},\cdots ,x_{mj}\right\}-\mathit{min}\left\{x_{1j},\cdots ,x_{mj}\right\}}$$ (1)

The formula for normalizing negative indicators is illustrated in Equation (2):

$$X_{ij}=\frac{\mathit{max}\left\{x_{1j},\cdots ,x_{mj}\right\}-x_{ij}}{\mathit{max}\left\{x_{1j},\cdots ,x_{mj}\right\}-\mathit{min}\left\{x_{1j},\cdots ,x_{mj}\right\}}$$ (2)

where $m$ is the number of samples, $k$ refers to the number of indicators, and $x_{ij}$ denotes the data normalized to the value of the $j$ th indicator of the initial $i$ th sample $\left(i=1,2,m;j=1,2,...,k\right)$.

• (2)The entropy value of each indicator is calculated for 31 provinces (municipalities) and the weights are determined. (3) Based on the obtained weights, the comprehensive index of GDARA of 31 provinces (municipalities) is calculated. Due to the limitation of space, the relevant calculation formulae can be found in some studies, which are not repeated here [14,39].

2.3.2. Markov chain model

Markov chain model is used to predict the probability of the occurrence of time, which deduces the trend of change by studying the initial probability of different states and the frequency of the transition between states, and then the process of the change of the level of GDARA is called the Markov process [40]. The core of the Markov chain model is the state transfer matrix. The general form of the Markov transfer probability matrix is shown in Equation (3):

$$S_{\left(t+1\right)}=S_{\left(t\right)}{P}_{ij}$$ (3)

In Equation (1), $S_{\left(t\right)}$ and $S_{\left(t+1\right)}$ are the system states at moments $t$ and $t+1$, $P_{ij}$ is the dynamic transfer probability matrix.

The state transfer probability matrix is the key to studying the level of GDARA, and the general expression of the mathematical formula for determining the state transfer matrix is indicated in Equation (4):

$$P_{ij}=\left[\begin{array}{c}{P}_{11}{P}_{12}{P}_{13}\cdots P_{1n}\\ P_{21}{P}_{22}{P}_{23}\cdots P_{2n}\\ P_{31}{P}_{32}{P}_{33}\cdots P_{3n}\\ \cdots \cdots \cdots \cdots \cdots \\ P_{n1}{P}_{n2}{P}_{n3}\cdots p_{nn}\end{array}\right]$$ (4)

In Equation (2), $n$ denotes the number of GDARA class types.

2.3.3. The method of Dagum Gini coefficient

Regional differences and causes are measured by Dagum Gini coefficient [41]. It is used to measure the regional differences in the level of GDARA, and decomposes it into intra-regional differences, inter-regional differences and hypervariable density. The specific decomposition is shown in Equations (5), (6), (7), (8), (9), (10), (11), (12)

$$G=\frac{\sum _{j=1}^k\sum _{h=1}^k\sum _{i=1}^{n_j}\sum _{r=1}^{n_h}\left|y_{ji}-y_{hr}\right|}{2n^2\bar{y}}$$ (5)

$$G_{jj}=\frac{\sum _{i=1}^{n_j}\sum _{r=1}^{n_j}\left|y_{ji}-y_{jr}\right|}{2n_j^2\overline{y_j}}$$ (6)

$$G_{jh}=\frac{\sum _{i=1}^{n_j}\sum _{r=1}^{n_h}\left|y_{ji}-y_{hr}\right|}{n_j{n}_h\left(\overline{y_j}+\overline{y_h}\right)}$$ (7)

$$G_w=\sum _{j=1}^k{G}_{jj}{p}_j{s}_j$$ (8)

$$G_{nb}=\sum _{j=2}^k\sum _{h=1}^{j-1}{G}_{jh}\left(p_j{s}_h+p_h{s}_j\right)D_{jh}$$ (9)

$$G_t=\sum _{j=2}^k\sum _{h=1}^{j-1}{G}_{jh}\left(p_j{s}_h+p_h{s}_j\right)\left(1-D_{jh}\right)$$ (10)

$$d_{jh}={\int }_0^{\infty }d{F}_j\left(y\right){\int }_0^y\left(y-x\right)d{F}_h\left(x\right)$$ (11)

$$p_{jh}={\int }_0^{\infty }d{F}_h\left(y\right){\int }_0^y\left(y-x\right)d{F}_j\left(x\right)$$ (12)

Where $G$ is the overall Dagum Gini coefficient, the value of which indicates the magnitude of the difference between the level of GDARA among the 31 provinces (municipalities). Similarly, the $G_{jj}$ denotes the Dagum Gini coefficient for the $j$ region. $G_{jh}$ denotes the Dagum Gini coefficient for the $j$ and $h$. And $G_w$, $G_{nb}$, $G_t$ represent the size of the contribution of the gap within a region, the size of the contribution of the net worth gaps between regions, and the size of the contribution of the hyper variance density, respectively. $F_j$ denotes the cumulative density distribution function for the first region. $k$ is 3, representing Optimized Development Zone (ODZ), Moderate Development Zone (MDZ) and Protected Development Zone (PDZ), respectively. $n$ represents 31 provinces (municipalities), $j$, $h$ denotes the inner set of the region, $i$, $r$ denotes different provinces, $y_{ji}$ and $y_{hr}$ denotes the level of GDARA in the $i$ province in the $j$ region and the $r$ province in the $h$ region, respectively. $d_{jh}$ denotes the difference in the level of GDARA between the $j$ and $h$ regions. That is, the mathematical expectation of the sum of all sample values greater than 0 when $y_{ji}-y_{hr}>0$; $p_{jh}$ denotes the mathematical expectation of the sum of all samples greater than 0 when $y_{ji}-y_{hr}>0$ in the $j$ and $h$ regions.

2.3.4. The method of kernel density estimation

Kernel density estimation is used to estimate the density function of the sample location change in this paper, so as to analyze the dynamic evolution of the distribution of the level of GDARA in the three regions, Optimized Development Zone, Moderate Development Zone and Protected Development Zone. The advantage of it is that it does not make any assumptions about the data distribution, and only examines the distribution characteristics from the sample data, which is more robust [42]. The specific calculation method is described in Equations (13), (14).

Firstly, the density function of the random variable is set:

$$f\left(x\right)=\frac{1}{Nh}\sum _{i=1}^{N}K\left(\frac{X_i-\bar{x}}{h}\right)$$ (13)

$$k\left(x\right)=\frac{1}{\sqrt{2\pi }}{e}^{-\frac{x^2}{2}}$$ (14)

Where $N$ is 31, denotes the number of regional provinces; $X_i$ denotes independent identically distributed observations; $x$ denotes the mean; $K(\bullet )$ denotes the kernel density, and $h$ denotes the bandwidth.

2.4. Data

The original data of the indicators involved in this paper were obtained from China Statistical Yearbook, China Animal Husbandry and Veterinary Statistics Yearbook, China Agricultural Statistics Yearbook, China Environmental Statistics Yearbook, China Environmental Yearbook, etc. The sample observation period spans from 2011 to 2020, and a few missing values are filled in by interpolation. On the purpose of eliminating the interference of price factors, the starting year of the sample is used as the base period, and the indicators involving prices are deflated. Except for the cropland replanting index, other indicators were logarithmized because of the large deviations.

3. Analysis of the current situation of GDARA

3.1. Analysis of indicator calculation results

3.1.1. Total index measurement results

This paper analyzes the GDARA in 31 provinces (municipalities) from 2011 to 2020, and the results are displayed in Fig. 1. In terms of the overall national situation, the exponential average of GDARA in the country during the sample period is 0.5797, displaying a growth trend. From the perspective of individual provinces, the highest exponential average of GDARA is Sichuan, which is as high as 0.6679. Besides, provinces including Qinghai, Zhejiang, Heilongjiang, Jiangsu, Xizang, Liaoning and Jilin also have indices of 0.65 or higher. The three lowest provinces are Ningxia, Gansu and Shanxi, whose exponential average is below 0.5. In terms of growth rate, Gansu ranks first with a growth rate of 4.8237%. The result could be interpreted by the fact that Gansu has the potential to promote the upgrading of GDARA by utilizing the advantages of its geographical characteristics. While the slowest growth rate is Guangdong, in the past ten years, the growth rate is only 0.1682%, which reflects the relatively weak foundation of agricultural and rural development. At the same time, the accelerated industrialization has also led to the emergence and intensification of environmental pollution in Guangdong. Among them, Shanghai is the only region with a negative growth rate, with an index growth rate of −0.0739%, but its average value is in the 21st place with 0.5632. It might attribute to the reason that there is a huge consumer market and consumption capacity for green agricultural products in Shanghai, and the level of GDARA has taken a leading position nationwide.

Fig. 1.

Average value and growth rate of the index of GDARA (%).

3.1.2. Results of each dimensional sub-index

Furthermore, the sub-index of GDARA from 2011 to 2020 was calculated from five dimensions, and the results are indicated in Fig. 2. Both the composite score and the scores of each dimension have improved during the sample period. Among them, the total score improved from 0.4947 to 0.6143, with an increase rate of 2.44%, which will significantly improve the quality of GDARA. Among results of the sub-index, the highest growth rate is the Life Improvement Index, which is 4.98% over the ten-year period, followed by the Industrial Upgrading sub-index also outpacing the growth rate of the composite score. The lowest is the Resource Conservation Index, with a growth rate of only 0.03%. The explanation may be that the government is constantly improving the completeness of rural infrastructure, which has led to the development of new industries and new forms of business in the countryside. It has not only accelerated the optimization and upgrading of the rural industrial structure, but also broadened farmers' income-generating channels.

Fig. 2.

Comprehensive score and sub-index of each system.

3.1.3. Sub-regional index measurement results

There are often large development gaps between regions in China because of differences in economic level, natural resources and policy orientation. Therefore, 31 provinces (municipalities) were divided into three major development zones for further analysis. Unlike the established literature dividing the country into east, middle and west, this paper divides the country into Optimized Development Zone (ODZ), Moderate Development Zone (MDZ) and Protected Development Zone (PDZ) based on the National Plan for Sustainable Agricultural Development (2015–2030).1 It considers factors such as the carrying capacity of agricultural resources, ecological types and development bases [43]. Macao, Hong Kong, and Taiwan were not considered in the sample in view of the availability of data.

According to the above grouping, the overall index of GDARA and the sub-indexes of the five dimensions of the three major development zones were measured separately. The results are shown in Fig. 3. In 2011, the overall scores were ranked as follows: PDZ > ODZ > MDZ, and during the sample period, the MDZ had the highest growth rate of 3.0580% in the index of GDARA, while the ODZ had the lowest growth rate of 2.0232%. The PDZ still had the highest score in 2020, but the MDZ scored 0.6076, slightly higher than the 0.6061 of the ODZ, which demonstrates that there is a catch-up effect in MDZ.

Fig. 3.

Index of the three major development regions.

In five dimensions, in terms of resource conservation index, after the development of the sample period, the score of PDZ increased from lower than ODZ in 2011 to 0.0853, which is higher than ODZ. This reveals that PDZ focuses on the integrated conservation and rational development of natural resources, which has made remarkable achievements in resource protection. In terms of environmental friendliness, ecological conservation, industrial upgrading, and life improvement, the ranking of environmental friendliness index is: PDZ > MDZ > ODZ, the ranking of ecological conservation index is: PDZ > ODZ > MDZ, and the ranking of industrial upgrading index and life improvement index are both: ODZ > MDZ > PDZ. Possible reasons for this are the high level of marketization and digital technology development in the ODZ, which provides a positive environment for upgrading the industrial structure and increasing farmers' income.

The growth rate of each region is indicated in Fig. 4, and the growth rate of the other two regions is positive, except for ODZ, which is −0.31%. The growth rates of ODZ and MDZ are ranked in the same order, both of which are: Life Improvement > Industrial Upgrading > Ecological Conservation > Environmental Friendliness > Resource Conservation, and their growth rates in both industrial upgrading and life improvement are greater than the overall index growth rates. The top three growth rates of PDZ are the same as the other two regions, but the growth rate of resource conservation is greater than that of environmental friendliness, where the growth rates of the three aspects of life improvement, industrial upgrading and ecological conservation are greater than the overall growth rate. This is further confirmation that the PDZ has achieved significant achievement in resource conservation.

Fig. 4.

Growth rate of sub-index of the three development regions (%).

3.2. Analysis of regional differences

3.2.1. Intra-regional differences and evolution

In this paper, the national Dagum Gini coefficient (Fig. 5) and the intra-regional Dagum Gini coefficient (Fig. 6) were calculated separately to show the evolution of the differences in GDARA in the country as a whole and within each region from 2011 to 2020 [44].

Fig. 5.

The national Dagum Gini coefficient of GDARA index.

Fig. 6.

The intra-regional Dagum Gini coefficient of GDARA index.

At the national level, the Dagum Gini coefficient displays an overall decreasing trend during the sample period, indicating that the differences are decreasing nationwide. Specifically, the Dagum Gini coefficient declined from 0.0636 in 2011 to 0.0557 in 2020, a decline of more than 14%. However, it is worth noting that the Dagum Gini coefficient has rebounded after 2018, with an increase of 17.6% in 2020 compared to 2019. It reflects the trend of another gradual widening of the gap between 2018 and 2020.

At the regional level, the Dagum Gini coefficient of ODZ indicates a trend of rising then falling and then rising again, therefore, the overall fluctuation is not significant. Specifically, the Dagum Gini coefficient of ODZ has increased from 0.0518 in 2011 to 0.0523 in 2020. It might be because that ODZ continues to have an advantage in terms of its development base and resource endowment, with less overall volatility in the level of GDARA in the region.

The coefficient changes in MDZ increased in the two years at the beginning of the sample period, and then declined steadily from 2013 to 2018, indicating that the differences in MDZ displays an overall decreasing trend. To some extent, it reveals the truth that greening of agriculture is gradually being emphasized in MDZ, and green synergistic development of agriculture and rural areas is becoming more effective. The Dagum Gini coefficient of PDZ fluctuates more during the sample period, remaining at a level below 0.01 between 2011 and 2015, rising to a level of about 0.034 between 2016 and 2017, then decreasing to a low level of 0.0066 in 2020. This indicates that the difference within it widens in the middle period after a small gap in the early period but returns to a smaller gap level later. It could be because PDZ is fully able to absorb the "spillover effects" of neighboring high-level regions.

In general, the difference of GDARA of PDZ is smaller, while MDZ maintains the largest difference during the sample period but shows a steady decreasing trend overall. The overall level of ODZ displays a medium level among the three areas, and shows a relatively stable declining trend before 2019, but rises steeply to 0.0523 in 2020, even exceeding 0.0518 in 2011. Therefore, the regional differences within it after experiencing a gradual reduction, widen again and the differences are more serious.

3.2.2. Inter-regional differences and evolution

Fig. 7 reflects the differences in GDARA between the three protected areas during the sample period. During the period 2011–2020, the Dagum Gini coefficient between ODZ-PDZ shows a stably decreasing trend, so the differences have been gradually reduced. The overall change trend of differences in GDARA between ODZ-PDZ and MDZ-PDZ is relatively similar, both display an increasing trend at first, then a linear decrease between 2013 and 2016, and then a rebound between 2017 and 2020, i.e., the differences between these two areas display a trend of increasing, then declining and finally increasing again. However, the gap between MDZ-PDZ still shrinks in 2020 compared to 2010, but the change of gap in ODZ-PDZ widens.

Fig. 7.

The inter-regional Dagum Gini coefficient of GDARA index.

In terms of the overall difference, between 2010 and 2013, the difference between MDZ-PDZ is the largest, and the difference between ODZ-PDZ and ODZ-MDZ is basically not very different. Between 2013 and 2019, the difference between ODZ-PDZ is the smallest, and the two differences between the three regions are obvious. In the last two years, the Dagum Gini coefficient of the ODZ-PDZ rises straight up to the ODZ-MDZ with the largest difference. There are inter-regional differences in the level of GDARA in China, of which the difference between MDZ-PDZ is still enlarging.

Fig. 8 reveals the decomposition results of the sources of differences in GDARA among the three major regions. During the sample period, the overall difference mainly comes from intra-regional contribution, whose average contribution rate is up to about 40%. Followed by inter-regional contribution, but the overall trend is decreasing, with the average rate of contribution decreasing from the highest 44.6%–21.8% in 2020. The average rate of contribution of intensity of trans variation is steadily increasing from 17.3% in 2010 to 36.8% in 2020. This reveals that the solution to the problem of overall disparities in GDARA should focus on the reduction of regional disparities and the rapid and efficient promotion of the coordinated, green and high-quality development of GDARA.

Fig. 8.

The sources of difference of GDARA index.

3.3. Dynamic distribution and evolution analysis

To examine the dynamic evolution of GDARA, the estimation was further carried out using the method of three-dimensional Kernel density, and the result are reported in Fig. 9. The result displays that GDARA presents the following characteristics: firstly, the center of the overall distribution position is moving to the right all the time, and the moving position changes significantly, according to which it is inferred that GDARA keeps improving. Secondly, the height of the wave peak displays a trend of rising, indicating that the absolute gap of GDARA in each province tends to expand. Thirdly, the number of main peaks is always only one, illustrating that the current GDARA has not polarized or converged, i.e., the levels of GDARA in the provinces have achieved some degree of synergy.

Fig. 9.

Evolution of dynamic distribution of GDARA.

3.4. Analysis of spatial aggregation

3.4.1. Set the spatial weight matrix

Most previous studies used the inverse of geographic distance to construct the spatial weight matrix [45]. However, differences in geographic location simply reflect the fact that GDARA is subject to geographic correlation. In addition to geographic characteristics, economic characteristics are also key factors influencing GDARA [46]. Established studies have used GDP per capita as a characterizing variable of economic characteristics [47] to construct spatial weighting matrices. Based on it, we integrate the two most prominent geographical attributes and economic attributes among provinces to construct a spatial weight matrix, so that it can more accurately portray the linkage among the spatial effects of different regions. The elements of the economic geography matrix ($W$) are defined in Equations (15), (16):

$$W_1=\{\begin{array}{c}\frac{1}{\left|Y_i-Y_j\right|},i\ne j\\ 0,i=j\end{array}$$ (15)

$$W_2=\{\begin{array}{c}\frac{1}{d^2},i\ne j\\ 0,i=j\end{array}$$ (16)

Where $d$ denotes the distance between two regional geographic centers, $Y_i$ denotes the annual GDP per capita of province $i$ during the sample period, and $Y_j$ denotes the annual GDP per capita of province $j$, the economic geography matrix is: $W=W_1\times W_2$.

3.4.2. Spatial autocorrelation test

Spatial sequences have complex autocorrelation scenarios that not only correlate in multiple directions but can also interact with each other. The Moran's I is the most popular measure of spatial autocorrelation because it examines whether there is spatial dependence in the data.

On the one hand, the global Moran's I is used to examine the spatial aggregation of the entire spatial sequence and is given by Equation (17):

$${Moran}^{\prime }I=\frac{{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{ij}\left(x_i-\bar{x}\right)\left(x_j-\bar{x}\right)}{S^2{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{ij}}$$ (17)

where $w_{ij}$ is the spatial weight matrix, $S^2$ is the sample variance, and $\bar{x}$ is the sample mean. Considering that agricultural production activities are greatly affected by geographic distance; This paper chooses the binary adjacency spatial weight matrix ($W_1$) for research. The results are displayed in Table 2. It can be found that the Moran's I is significantly positive in most of years, indicating that the regional distribution of GDARA shows a significant spatial aggregation effect.

Table 2.

Results of the global Moran's I for GDARA.

Year	Moran's I	Z	P
2011	0.271	2.650	0.004
2012	0.280	2.721	0.003
2013	0.249	2.444	0.007
2014	0.257	2.525	0.006
2015	0.240	2.379	0.009
2016	0.133	1.438	0.075
2017	0.089	1.057	0.145
2018	0.054	0.761	0.223
2019	0.074	0.934	0.175
2020	0.313	3.026	0.001

The local Moran's I, on the other hand, is used to examine the spatial aggregation in the vicinity of a region and is given by Equation (18):

$$I_i=\frac{\left(X_i-\bar{X}\right)}{S^2}{\sum }_{j=i}^n{w}_{ij}\left(X_i-\bar{X}\right)$$ (18)

There is a significant spatial autocorrelation for GDARA, which is shown in Table 2. The local Moran's I was further calculated and scatterplots of Moran's I were plotted (Fig. 10a–d). The results illustrate that most of provinces are distributed in the first and third quadrants, showing either "high-high" or "low-low" aggregation characteristics. It provides a strong demonstration that the GDARA findings are polarized and that the levels of the lagging regions are in urgent need of upgrading.

Fig. 10.

Scatterplots of the Moran's I of GDARA.

The figures above indicate that: (1) High-High Aggregation Zone (Quadrant 1), including provinces such as Xizang, Qinghai, and Heilongjiang, etc. which have a significant diffusion effect. They can boost the level of GDARA in their own regions while also driving the common development of neighboring regions with higher levels of GDARA. (2) The Low-High Agglomeration Zone (Quadrant II), which includes provinces such as Yunnan, Xinjiang, and Anhui, etc. These Provinces do not have a high level of GDARA on their own, but neighboring provinces have a high level of GDARA, which can take care of driving the development of these provinces. (3) Low-Low Aggregation Zone (Quadrant III), including provinces such as Shaanxi, Shanxi, and Ningxia, etc. They and neighboring regions do not have high levels of GDARA. Therefore, turning regional resources into an advantage to raise the level of GDARA is essential. (4) High-Low Aggregation Area (Quadrant 4), provinces such as Sichuan in this area have a high level of GDARA, but the adjacent areas have a low level. Consequently, it is the key for provinces in high level of GDARA to play an active role in radiating and driving the surrounding areas. Finally, it is of great significance to realize synergistic development among regions.

3.5. Analysis of state transfer

According to the previous paper, it is known that GDARA has different dynamic characteristics over time. Consequently, Markov chain transfer matrix is used to explore them. Firstly, the quartile method is applied to divide each region into four levels according to the level of GDARA, which are recorded as level 1, level 2, level 3 and level 4, where the higher the level, the higher the level of GDARA indicated. Then, the sample period was divided into three stages: the whole sample period, 2011–2014 and 2015–2020, then the average transfer probability in each stage and the maximum likelihood estimation matrix of its probability were obtained, whose results are shown in Table 3.

Table 3.

Markov transfer probability matrix of GDARA.

Spatial Lag	Level	1	2	3	4
2011–2020	1	0.0000	0.2249	0.4142	0.3609
	2	0.0000	0.8617	0.0521	0.0862
	3	0.0000	0.0227	0.9424	0.0349
	4	0.0000	0.1123	0.0290	0.8586
2011–2014	1	0.8879	0.0561	0.0561	0.0000
	2	0.0000	1.0000	0.0000	0.0000
	3	0.0000	0.0000	1.0000	0.0000
	4	0.0000	0.0000	0.0000	1.0000
2015–2020	1	0.0000	0.1436	0.2723	0.5842
	2	0.0000	0.7730	0.0882	0.1389
	3	0.0000	0.0405	0.9003	0.0592
	4	0.0000	0.1796	0.0524	0.7680

To Specifically, the diagonal probabilities of each rank are above 80% during the sample period, except for the main diagonal element of rank 1 which is 0. It demonstrates the truth that the regions with lower levels of GDARA will move to higher levels after the whole sample period. Therefore, this suggests that areas lagging behind in GDARA have greater potential for upgrading. In terms of stability magnitude, areas with lower levels have more upward mobility, and less stable. It is conducive to narrowing the gap of GDARA between regions. During the period 2011–2014, the probabilities on the diagonal of the transfer matrix are greater than those on the off diagonal, which suggests that the GDARA has a steady state nature and a strong "club convergence" phenomenon. Only areas at level 1 had a higher probability of state shifts, while none of the other three levels had shifts. The possible reason is that GDARA in Level 1 regions can further develop new industries such as leisure and tourism agriculture, green agriculture and eco-agriculture on the basis of the existing foundation, thus enhancing the potential of GDARA. During the sample period of 2015–2020, regions with low GDARA shifted with nearly 100% probability, and the minimum value of the probability on the diagonal of the other rank matrices was as high as 0.7680, which were all greater than the probability on the non-diagonal, again indicating that regions with low GDARA had a greater probability of leveling up. Overall, throughout the sample period, low level (rank 1) areas have a 22.49%, 41.42%, and 36.09% probability of shifting to rank 2–4 areas, respectively. Low and medium level (rank 2) areas have a 5.21% and 8.62% probability of shifting to high level and will not move closer to low level areas. Medium and high level (rank 3) areas maintain a greater stability, with only a 2.27% are likely to drop one level; High level (level 4) areas are more stable, with a transfer probability of less than 15%. Although the "Matthew effect" has been revealed in regions with different levels of GDARA, the greater potential for upward shifts in the low-level. As a result, GDARA in China will realize coordinated development at high level.

The whole probability shift matrix shows that the low-level areas have a greater level improvement throughout the sample period, and the other three levels are less likely to shift, which suggests that GDARA has the potential to leapfrog in low-level areas. The improvement of GDARA in the low-level areas mainly occurs between 2015 and 2020, which indicates that the effect of improvement is not obvious in the early part of the whole sample period, and only after 2015 does it gradually start to take effect, making the green development level of low-level regions to improve. This is probably because that the government accelerated rural environmental management and agricultural resource protection in2015,2 steadily promoting sustainable development in agriculture and rural areas. However, the overall transfer probability of the three levels other than level 1 is low. The possible reason for this is that the areas in the other three ranks themselves have excellent ecological environment, well-developed agricultural infrastructures, rich natural resources and other conductive conditions to the development of the GDARA, and therefore, to a certain extent, these advantages are able to guarantee that the area will maintain its own level.

4. Analysis of obstacles factor

4.1. Obstacle degree model

The Obstacle degree model is adopted to specifically analyze the obstacles faced in GDARA. The contribution of the factors of the tertiary indicators to the level of GDARA is taken as the factor contribution degree, and the gap between the secondary indicators and the level of GDARA is taken as the factor deviation degree. Due to the differences in the properties of different indicators, they usually have different quantitative outlines. In order to eliminate the impact of different outlines and ensure the reliability of the results, the method of polar deviation in Equation (19) is adopted to standardize the indicator values so that they lie between 0 and 1. In Equation (20), the degree of deviation (${\mathrm{I}}_{\text{ij}}$), which indicates the gap between a single indicator and the overall goal of GDARA, can be obtained by subtracting the standardized data for each indicator from 1, which shows the gap between a single indicator and the overall goal of GDARA. After standardizing the secondary indicators, the difference between them and 1 is calculated. The specific calculation formula is as follows:

$${X_{ij}}^{\prime }=\frac{x_{ij}-\mathit{min}\left(x_{ij}\right)}{\mathit{max}\left(x_{ij}\right)-\mathit{min}\left(x_{ij}\right)}$$ (19)

$$I_{ij}=1-{x_{ij}}^{\prime }$$ (20)

$$O_{ij}=\frac{W_{ij}{I}_{ij}}{\sum _{i=1}^{31}{W}_{ij}{I}_{ij}}\times 100\%$$ (21)

$$U_{ij}=\sum O_{ij}$$ (22)

Where $X_{ij}$ denotes the weight of secondary indicators and $I_{ij}$ denotes the gap between indicator $j$ in tier $i$ and the optimal value. $W_{ij}$ denotes the factor contribution rate, and in this paper is the weight of each tertiary indicator. In Equation (21), $O_{ij}$ denotes the obstacle degree of indicator $\mathrm{i}$ to GDARA, and the larger the value, the stronger the obstacle of the indicator. In Equation (22), $U_{ij}$ indicates the degree of obstruction at the guideline level.

4.2. Obstacle degree of guideline level

In this paper, the Obstacle degree model is used to estimate the change of the obstacle degree of five first-level indicators between 2011 and 2020, and the results are displayed in Fig. 11 [48].

Fig. 11.

Obstacle degree of GDARA index.

The ranking of the obstacle degree of the five level indicators during the sample period is: industrial upgrading > ecological conservation > life improvement > resource conservation > environmental friendliness. This means that the rural ecological environment has continued to improve and the green transformation of agriculture has achieved initial results. The obstacle degree of life improvement shows a steady declining trend, the obstacle degree of industrial upgrading rises first and then falls. Ecological conservation, on the contrary of industrial upgrading, displays a trend of falling first and then rising. This may be because of China's vast territory and complex natural condition. Consequently, each region should promote ecological civilization in accordance with local conditions. The obstacle degree of resource conservation and environmental friendliness is stable but rising. This means that GDARA should focus on green environmental protection and recovery in the future.

Specifically, the obstacle degree of industrial upgrading declined from 45.23% in 2011 to 39.21% in 2020, but rose during 2011–2013, and thereafter declined steadily to 46.38% in 2017, with large fluctuations throughout the period. The obstacle degree of ecological conservation dropped from 25.21% to 15.48% in the first four years, and continued to rise for three years after 2014, and rose to a maximum of 29.05% during the sample period immediately after a brief decline, with more frequent and larger overall fluctuations. The obstacle degree of life improvement maintains a steady downward trend, and only showed a small rebound in 2013 and 2014. The obstacle degree of resource conservation displays an overall rising trend, from 4.52% in 2011 to 14.06% at the end of the sample period, which has increased three times during the whole period. The obstacle degree of environmental friendliness indicates a three-step change, gradually rising from 2.7% in 2011 to 3.96% in 2015, followed by a steady decline to 2.38% in 2019, and then a steep three-fold increase to 12.78% in 2020.

4.3. Obstacle degree of indicator level

Then the top 10 obstacle factors for the degree of GDARA during the sample period were selected for analysis. The result reveals that the cumulative obstacle degree of them exceeded 85% during the sample period, and even the cumulative obstacle degree exceeded 90% in all years except for 2020. The specific results are indicated in Table 4.

Table 4.

Main obstacle factors for GDARA from 2011 to 2020 (%).

Year	Rank
	1	2	3	4	5	6	7	8	9	10
2011	${IUS}_4$ 25.58	${ECS}_3$ 12.18	${LIS}_3$ 11.18	${IUS}_3$ 9.62	${IUS}_1$ 8.30	${ECS}_2$ 7.67	${LIS}_1$ 5.69	${ECS}_1$ 5.36	${LIS}_2$ 4.01	${RCS}_3$ 3.32
2012	${IUS}_4$ 27.27	${LIS}_3$ 11.57	${ECS}_3$ 10.50	${IUS}_3$ 9.57	${IUS}_1$ 8.36	${ECS}_2$ 8.17	${ECS}_1$ 5.72	${LIS}_1$ 5.32	${LIS}_2$ 3.77	${RCS}_3$ 2.94
2013	${IUS}_4$ 30.37	${LIS}_3$ 12.85	${ECS}_3$ 10.53	${IUS}_3$ 10.24	${IUS}_1$ 8.92	${LIS}_1$ 5.21	${RCS}_3$ 4.92	${LIS}_2$ 3.66	${ECS}_1$ 3.22	${ECS}_2$ 2.43
2014	${IUS}_4$ 30.72	${LIS}_3$ 13.96	${IUS}_3$ 9.72	${ECS}_3$ 9.29	${IUS}_1$ 9.01	${LIS}_1$ 5.05	${RCS}_3$ 4.58	${ECS}_1$ 3.55	${LIS}_2$ 3.37	${ECS}_2$ 2.64
2015	${IUS}_4$ 28.70	${LIS}_3$ 14.52	${IUS}_3$ 11.64	${ECS}_3$ 10.10	${IUS}_1$ 9.26	${LIS}_1$ 4.93	${RCS}_3$ 4.45	${ECS}_1$ 3.87	${ECS}_2$ 2.89	${LIS}_2$ 1.72
2016	${IUS}_4$ 20.81	${LIS}_3$ 14.00	${IUS}_3$ 13.07	${ECS}_3$ 12.46	${IUS}_1$ 11.29	${LIS}_1$ 5.41	${ECS}_1$ 4.91	${RCS}_3$ 4.90	${ECS}_2$ 3.69	${LIS}_2$ 1.83
2017	${IUS}_4$ 19.35	${ECS}_3$ 15.01	${IUS}_3$ 13.77	${IUS}_1$ 11.51	${LIS}_3$ 11.07	${ECS}_1$ 5.77	${LIS}_1$ 5.42	${RCS}_3$ 4.47	${ECS}_2$ 4.33	${IUS}_2$ 1.76
2018	${IUS}_4$ 28.73	${IUS}_1$ 13.81	${IUS}_3$ 12.15	${ECS}_3$ 9.80	${LIS}_3$ 8.34	${ECS}_2$ 6.17	${LIS}_1$ 5.46	${RCS}_3$ 3.57	${RCS}_1$ 2.76	${IUS}_2$ 1.89
2019	${IUS}_4$ 31.50	${IUS}_1$ 14.79	${IUS}_3$ 12.48	${ECS}_3$ 11.73	${ECS}_2$ 7.69	${RCS}_1$ 5.15	${RCS}_3$ 3.94	${LIS}_1$ 3.31	${LIS}_3$ 1.88	${ECS}_1$ 1.22
2020	${IUS}_4$ 28.09	${ECS}_3$ 16.44	${ECS}_1$ 10.38	${RCS}_1$ 8.91	${IUS}_3$ 7.10	${EFS}_4$ 3.91	${RCS}_3$ 3.21	${EFS}_3$ 3.18	${LIS}_3$ 2.66	${IUS}_1$ 2.47

Among the five dimensions, the evaluation indicators affecting the GDARA are mainly industrial upgrading, ecological conservation and life improvement. Specifically, the evaluation indicator of resource conservation has no more than 13% influence, while the environmental friendliness has been ranked after 10 during the period of 2011–2019. More specifically, the obstacle degree of leisure agriculture business income has been in the first place during the sample period, reaching a minimum of 19.35% (2017) and a maximum of 31.5% (2019) during the decade. In the context of leading China's economic construction with a new development philosophy, the level of GDARA is still unsatisfactory. Environmental friendliness has indeed been improved with remarkable results. However, the upgrading of industries and improvement of life are still relatively slow, which is the main obstacle to the GDARA. The possible reason is that the problems of single industrial structure, brain drain and aging population in rural areas will be difficult to achieve upgrading of industrial structure. As a result, the development of new industries will be inhibited, and it is not conducive to the increase of income from leisure agriculture.

4.3.1. Factor analysis of obstacle degree by guideline level

In the period 2011–2018, in the second to fifth place are ECS₃(area shares of nature reserves, LIS₃(rural domestic sewage treatment rate), IUS₃(number of green food certificates), and IUS₁(agricultural labor productivity), which are always in the range of 8.3%–15.01%, and ECS₂(wetland conservation rate) with 7.69% replacing LIS₃(rural domestic sewage treatment rate) in 2019 as the No.5, and RCS₁(cultivated land replanting index) with 8.91% replacing IUS₁(agricultural labor productivity) in 2020 ranked fourth. Among them, LIS₃(rural domestic sewage treatment rate) increased from 11.18% (2011) to 14.52% (2015) and then gradually decreased to 2.66% (2020). The results are consistent with the regional development dynamics: along with the continuing poor rural environment, the government provided 6 billion yuan in funding for the treatment of domestic sewage in rural areas in 2015. At the same time, pilot demonstration work on rural domestic sewage treatment has been gradually rolled out across the country, accelerating the process of rural domestic sewage control and then promoting GDARA. In addition, ECS₃(area shares of nature reserves) and IUS₃(number of green food certificates) always fluctuates within the range of 9%–16.5% and 7%–14%.

4.3.2. Factor analysis of obstacle degree by indicator level

Those in the sixth to tenth place during 2011–2016 have been ECS₂(wetland conservationn rate), LI(per capita disposable income of rural residents), ECS₁(forest coverage), LI(rural domestic waste treatment rate), and RCS₃(percentage of water-saving irrigation area), which fluctuated in the ranges of 2.64%–8.47%, 4.93%–5.69%, 3.22%–5.72%, 1.72%–4.01%, and 2.94%–4.92%, and RCS₃(percentage of water-saving irrigation area) was in the 7th-10th place throughout the sample period. IUS₂(grain production) was in the 10th place in 2017 and 2018, and RCS₁(cultivated land replanting index) was in the 9th and 6th place with 2.71% and 5.15% obstacles in 2018 and 2019. Besides, EFS₄(agricultural COD emission intensity), EFS₃(agricultural film application intensity), and IUS₁(agricultural labor productivity) appear briefly in the sixth, eighth, and tenth positions in 2020. This is owing in large part to China's active implementation of its sustainable development strategy. It not only includes protection of the rural environment and ecology, but also involves promotion of energy conservation and emission reduction, rural wetland restoration and afforestation.

5. Discussion

According to various analyses of regional differences, distributional dynamics and obstacles, the GDARA has been improving from 2011 to 2020 by 31 provinces (municipalities), and the overall differences among regions have been narrowing. The findings above are consistent with those of Zhang et al. (2023) [27] and Liu et al. (2020) [49], revealing a pattern of China's rural areas. The economic development is usually focused on in the early stages, then shift to achieve the balance between environment and economic growth, finally prioritize green environmental protection and restoration. The results of obstacle factor analysis also suggest a rising trend in the level of obstacles to resource conservation and environmental friendliness.

In addition, the industrial upgrading inhibits the spatial agglomeration of GDARA to some extent, which in turn exhibits high-high and low-low agglomeration areas. However, the findings reached by Liu et al. (2020) [49] is reverse. The proportion of output value of secondary and tertiary industries was to positively affect the green development of agriculture. This could be because that industrial upgrading is a dynamic process of adjustment and optimization. A reasonable industrial structure will accelerate GDARA but weaken by the opposite. Besides, the degree of income obstacles to leisure agribusinesses revealed in this paper ranked first and severely inhibited the development of GDARA. In contrast, the rapid development of leisure agriculture is believed to have contributed to a significant increase in the level of GDARA [28]. The possible reason is that the problems such as single industrial structure, brain drain and aging population have made it difficult to upgrade the industrial structure in the current rural areas. Furthermore, this is not conducive to the development of new industries and the increase of leisure agriculture income.

6. Conclusions and recommendations

Green development in agriculture and rural areas is crucial to the revitalization and construction of a strong agricultural country, and its unbalanced development is a matter of great concern and needs to be solved. (1) The paper constructs the index system of China's GDARA, then measures the level of it, finally draws a conclusion that the overall level during the sample period is improving. The growth rates of sub-indices of MDZ and PDZ in five dimensions are positive, and PDZ has the highest overall score but the smallest growth rate. (2) The results of the Dagum Gini coefficient reveal that the overall difference nationwide displays a trend of shrinking. The differences between the ODZ-MDZ keep shrinking, and the Gini coefficients of two and two between other regions display a fluctuating trend, and the differences mainly come from within their respective regions. The results of Kernel density estimation indicate that the level of GDARA shows a continuous rightward shift in the center, an increase in the height of the wave, a main peak, and a constant improvement in the level of it. The results of the spatial autocorrelation test illustrate a significant spatial clustering effect at the level of GDARA. The matrix results of Markov chain transfer probability reveal that there is a trend of transferring the lower level of GDARA to the higher level, and it mainly occurs in the period of 2015–2020. (3) The results of the Obstacle degree model indicate that the obstacle degree of industrial upgrading is the largest, the environmental friendliness is the smallest, and only the obstacle degree of life improvement displays a downward trend. In terms of specific indicators, the obstacle degree of leisure agriculture business income has always been in the first place.

In view of the above findings, this paper puts forward the following policy recommendations:

Firstly, the concept of green production should be integrated into the agricultural production process. On the one hand, it is highly important to protect the existing weak foundation of agriculture, and actively innovate diversified green technologies [50] for preventing and controlling agricultural pests. On the other hand, based on the deep understanding of resource endowment of each region, taking carbon peaking and carbon neutral as the target orientation [51] and actively exploring new low-carbon development mode. It is the effective way to promote the protection of agricultural soil by technical means.

Secondly, the measures revitalizing the beautiful countryside should be taken by the government. For one thing, the goal of improving the green development in agriculture and rural areas must be fall into the assessment system of local governments [52]. Only in this way the rural residents' mindset will be changed through institutional innovation. For the existing chaotic layout of rural areas, unified planning and construction should be carried out. For the role of government, it is essential to promote the modernization of rural areas through making full use of the dividends of the digital era and vigorously promoting digital technology in the countryside [53].

Last but not least, it is crucial to provide the necessary guidance to the green lifestyles of rural residents. On the one hand, the concept of life of rural residents should be green and popularize the modern rural life such as using clean energy based on understanding the essence of green life. On the other hand, the principle of "who protects, who benefits" and "who benefits, who pays" has not been fully implemented, which causes the "free rider" behavior. Consequently, only by establishing a market-based compensation mechanism, can the "free-rider" behavior decline. Finally, if the green development of agriculture and rural areas and the interests of farmers are linked, then the community will work in the same direction.

Additionally, this paper only focuses on the provincial level, and fails to go down to the city, county and rural level for a more detailed examination. In the future, big data technology can be used to obtain data from multiple sources to refine the granularity of the data. Moreover, despite the reference to previous literature based on the reality of "peak carbon and carbon neutral", the selection of indicators for GDARA is still relatively partial. Considering the limitation of data availability, many more representative indicators could not be added to the evaluation system.

Data availability statement

The original data of the indicators involved in this paper were obtained from China Statistical Yearbook, China Animal Husbandry and Veterinary Statistics Yearbook, China Agricultural Statistics Yearbook, China Environmental Statistics Yearbook, China Environmental Yearbook, etc. Data will be made available on request.

Fund project

This research was funded by the Graduate Innovation Program of Northwest University, grant number CX2023042.

CRediT authorship contribution statement

Li Wang: Validation, Supervision, Resources, Project administration, Investigation, Conceptualization. Nan Li: Writing – review & editing, Visualization, Validation, Funding acquisition, Data curation. Qian Xie: Writing – original draft, Visualization, Software, Methodology, Formal analysis, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.