Evolutionary Characteristics, Regional Differences and Spatial Convergence of China’s Sustainable Agricultural Development Level

Abstract

Sustainable agricultural development is a fundamental requirement and a crucial goal of modern agriculture. It is also a significant means of enabling farmers to increase their incomes. This paper analyses the evolutionary characteristics, regional differences and spatial convergence of the level of sustainable agricultural development using kernel density estimation, Dagum’s Gini coefficient and the spatial convergence model based on panel data from 30 provinces in China from 2012 to 2021.The results show that: (1) At the level of development, the level of sustainable agricultural development at the national level and in the three major regions has shown an upward trend with fluctuations, with the average level of development in the eastern and central parts of the country higher than the national average, and in the western part of the country lower than the national average; however, the growth rate in the western part of the country is the highest among the three major regions. (2) In terms of evolutionary characteristics, the level of sustainable agricultural development in the country and the three major regions is characterised by spatial agglomeration, with varying degrees of polarisation. (3) In terms of regional differences, the Gini coefficients for the country as a whole, within the three major regions and between regions, generally show a downward trend, with interregional differences remaining the main source of overall differences. (4) In terms of spatial convergence, there is σ-convergence and β-convergence in the level of sustainable agricultural development across the country and the three major regions, and there is a positive spatial spillover effect. The conditional β convergence results show that the eastern region has the fastest rate of convergence. The above findings provide a scientific basis for the formulation of policies related to sustainable agricultural development in China.

1. Introduction

Agriculture is considered the foundation of the national economy and plays a crucial role in reducing poverty and ensuring food security [1]. China has solved the feeding problem of nearly 20% of the world’s population, contributing over 70% to global poverty reduction with only 9% of the world’s arable land [2]. This demonstrates China’s significant contribution to the promotion of global sustainable development. Since the 18th CPC National Congress, China has achieved comprehensive economic and social development. The level of agricultural modernisation has significantly improved, resulting in stable and guaranteed production and increased income for farmers. From 2012 to 2021, China’s grain output increased from 612.226 million tons to 682.847 million tons, and the per capita disposable income of rural residents increased from 8389 yuan to 18,931 yuan [3]. Sustainable agricultural development promotes sustainable national and social development. However, in some areas, there is a serious over-exploitation of groundwater, an excessive use of pesticides and chemical fertilizers in exchange for production capacity, and soil erosion. These factors have led to a decline in the quantity and quality of agricultural arable land, increasingly severe hard constraints on energy resources, and the continued deterioration of the ecological environment [4]. This poses a serious threat to the sustainable development of agriculture. Against this background, China’s Ministry of Agriculture and Rural Development has repeatedly emphasised the promotion of sustainable agricultural development and has made guidance plans. In May 2015, the National Plan for Sustainable Agricultural Development (2015–2030) was officially released, which proposes optimising the development layout, stabilising and increasing agricultural production capacity, protecting arable land resources, promoting the permanent use of farmland, maintaining high-efficiency water use, ensuring agricultural water safety, treating environmental pollution, improving the agricultural and rural environment, restoring the agro-ecological system and improving ecological function, as well as other key priorities for promoting the sustainable development of agricultural tasks. In 2018, the Opinions on the Implementation of the Rural Revitalisation Strategy emphasised adhering to a green orientation, developing resource-saving, environmentally friendly green agricultural production methods, reducing pressure on agricultural resources and the environment, reducing agricultural land pollution, and improving the level of sustainable agricultural development. In 2021, the implementation of the deployment of key agricultural and rural work for the in-depth promotion of the green development of agriculture continues to improve the agro-ecological environment and other issues proposed to promote the reduction in fertilizers and pesticides, the depth of water conservation and control of water, strengthen the quality of arable land construction and a series of tasks aimed at building a support system for the green development of agriculture, and accelerate the construction of agricultural modernisation. The relevant policy documents mentioned above provide guidance for sustainable agricultural development. Given this background, it is of great theoretical and practical significance to conduct a comprehensive and in-depth study of the evolutionary characteristics, regional differences, and the spatial convergence of China’s sustainable agricultural development. This will help clarify the current situation of sustainable agricultural development, optimise the main direction of the allocation of agricultural resources and factors, and promote the process of agricultural modernisation.

The connotation of sustainable development dates back to 1987, when it was first defined by the World Commission on Sustainable Development as development that meets the needs of the present without jeopardizing the ability of future generations to meet their own needs [5]. Since then, sustainable development, as a socio-economic development model continuously pursued and explored by all mankind, has become a topic of focus for policy makers and researchers [6,7,8], and has begun to appear in studies in different industries [9,10,11], enterprises [12,13,14] and regions [15,16,17].

In the field of agricultural economics, the issue of sustainable agricultural development has been a hot topic of concern for scholars. Agriculture as a complex system [18,19], its development is no longer an isolated and intermittent issue. Factors such as social, economic, and environmental interactions [20,21], population [22], water carrying capacity [23,24], and quality of arable land [25] all influence the level of sustainable agricultural development. Therefore, scholars have begun to try to construct a system of indicators for evaluating the level of sustainable development of agriculture from the aspects of population, society, economy, environment and resources [26,27], and different methods such as a data envelopment analysis model [28], an improved entropy weight method [29], structural modelling [30], and the environmental accounting method [31] are also used to explore the accounting methods and change the trends in the level of sustainable development of agriculture in the different scales of the regions, such as the national level [32] and the provincial level [33]. Most studies have shown that the level of sustainable agricultural development is on an upward trend, but there are large differences between different geographic regions, which are mainly constrained by population number, the amount of resources, and the level of science and technology [34], as well as facing challenges in terms of food security, the wide gap between the rich and the poor, and environmental factors [35]. In addition to quantitatively studying the trends of changes in the level of sustainable agricultural development, some studies have summarised the initiatives to promote sustainable agricultural development by qualitatively analysing their typical patterns [36] and strategic ideas [37].

The above research shows that the majority of scholars have achieved certain results in defining the connotation of sustainable agricultural development, constructing the evaluation index system of the level of sustainable agricultural development, and measuring and analysing the current status of its development by using different measurement methods, which provides ideas for this paper to draw on. However, there are still aspects that deserve to be explored in greater depth: (1) With regard to the construction of the indicator system, the literature has constructed an evaluation indicator system for the sustainable development of agriculture in five dimensions: population, society, economy, environment and resources. However, a review of the history of agricultural development shows that changes in agricultural technology have taken shape on the basis of a full understanding of the laws of agricultural development and the establishment of a harmonious symbiotic relationship between human beings and nature. Therefore, agricultural technology plays an important role in realizing sustainable agricultural development [38,39]. In order to reflect the level of sustainable agricultural development more comprehensively, this paper introduces a technology system based on the existing literature. (2) In terms of research content, the existing literature is more inclined to describe the changes and trends in the level of sustainable agricultural development from the time and space dimensions, while there is little discussion on its evolutionary characteristics, regional differences and spatial convergence, which provides space for this paper to continue to explore in depth. Therefore, this paper adopts the entropy weight method to measure the level of China’s sustainable agricultural development based on the panel data of 30 provinces in China from 2012 to 2021, and applies the kernel density estimation method, Dagum’s Gini coefficient decomposition method, and the convergence model to analyse the evolutionary characteristics, regional differences, and the spatial convergence of sustainable agricultural development, so as to provide empirical evidence for comprehensively grasping the current situation of China’s sustainable agricultural development and formulating relevant policies.

2. Theoretical Analysis Framework

Agriculture is the cornerstone of the development of human civilisation, while sustainable agricultural development is an important way to achieve food security, rural economic prosperity and ecological and environmental protection. In the face of the challenges of global demographic and environmental change, sustainable agricultural development has become a global concern. In the current period, there is an unprecedented historical opportunity to promote sustainable agricultural development. First, a broad consensus has been reached on sustainable agricultural development. Guided by the scientific outlook on development, the construction of an agro-ecological civilisation is the layout for development, pointing out the direction for sustainable agricultural development. The functions of agriculture in many aspects, such as economic, social, ecological and cultural, have received great attention, and its level of development has a bearing on the immediate interests of the general public, prompting a re-examination of agriculture, and the issue of the sustainable development of agriculture has become a focus of the community, as well as a major scientific issue to be resolved urgently. Secondly, the material basis for sustainable agricultural development is strong. The central and local governments at all levels have continued to increase diversified inputs of financial funds for agricultural infrastructure construction, agricultural science and technology, and rural public services in a variety of forms, providing a favourable environment and conditions for agricultural structural adjustment and industrial upgrading, and promoting more sustainable agricultural development. Thirdly, the system for sustainable agricultural development is becoming increasingly sophisticated. As rural reform and the reform of the ecological civilisation system are steadily advancing, the development of the agricultural industry has shifted from the production sphere to one that places equal emphasis on production and ecology, with the implementation and improvement of policies on protecting and upgrading the quality of arable land, as well as on the ecological restoration and protection of agricultural resources, and with the focus of agricultural support policies tilted in the direction of advancing the sustainable development of agriculture.

The core idea of the theory of sustainable development is the integrated development of economic development, social progress, resource development and utilisation, and ecological and environmental protection, advocating attention to long-term interests and the interests of the human race on Earth and of future generations as a whole [5]. Following the 1992 United Nations Conference on Environment and Development, China has earnestly implemented its sustainable development strategy, emphasising the scientific concept of development and building a resource-saving and environmentally friendly society. According to the theory of sustainable development and the current status of the implementation of China’s sustainable development strategy, agriculture, as an important part of China’s national economic development, also needs to be upgraded to a level of sustainable agricultural development in order to ensure that the current social situation requires agricultural production and to meet the further needs of future generations for agricultural resources.

This paper focuses on the issue of sustainable agricultural development and defines it in the field of cultivation. Agriculture is the main productive activity carried out by farmers in the countryside, and the social capital and human resources in rural areas are also an important support for sustainable agricultural development, i.e., the development of agriculture cannot be separated from the issue of rural development and rural livelihoods. Among them, farmers, as the main body engaged in agricultural production activities, provide an important element of labour for agriculture. Rural areas are the main sites of agricultural economic activity, and the sustainable development of all types of production factors and resources, and the environment in rural areas, is an important guarantee of sustainable agricultural development. The central idea of the systems theory is to emphasise the holistic view of the system, where the elements of the system do not exist in isolation, and where each element plays a specific role in the system [40]. From a systems theory perspective, sustainable agricultural development is viewed as a holistic system consisting of six subsystems: demographic, social, economic, resource, environmental and technological. The population subsystem reflects the composition of people as producers and consumers, whose quantity and level of quality are prerequisites for agricultural production, and are likewise an indicator of the level of economic development; the continuity of the social subsystem ensures that the social conditions for agricultural development are favourable, both in terms of the rural living environment and in terms of macro-urbanisation; the economic subsystem reflects the level of inputs and outputs for sustainable agricultural development and is the core system for sustainable agricultural development; the resource subsystem is the material resources on which agricultural production and agricultural economic activities depend, and the rational development and use of agricultural resources is the key to resolving the contradiction between the supply of and demand for agricultural resources in the process of sustainable agricultural development; the environmental subsystem characterisation effectively maintains ecological health and is an important safeguard for sustainable agricultural development; and the technological subsystem recombines the factors of production so as to transform the mode of agricultural production and enhance the efficiency of agricultural production, which is an important supportive driving force for the sustainable development of agriculture. The consideration of the above six subsystems, and their indicator components as a whole, is conducive to measuring the evolutionary characteristics of the level of sustainable agricultural development, regional differences and their spatial convergence.

According to the theory of regional differences in economic geography [41], the level of sustainable agricultural development shows regional differences under the influence of natural factors, such as climate and geomorphology, the level of regional macroeconomic development, and agricultural development strategies. Specifically, natural factors, such as light and precipitation, determine to a large extent the regional distribution of crops, which not only constrains crop yields, but also relates to the type of crop, farming system and cultivation methods. Unpredictable natural disasters, such as droughts and floods, can also affect the inputs of the agricultural factors of production and ultimately have a negative impact on the agricultural economy [42]. Regional differences in natural factors have resulted in regional variations in the level of sustainable agricultural development. The level of regional macroeconomic development affects the ability of governments at all levels to diversify their financial support for agriculture, such as agricultural infrastructure development, agricultural science and technology, and rural public services. The level of regional macroeconomic development affects the ability of governments at all levels to diversify their financial support for agriculture, such as agricultural infrastructure development, agricultural science and technology, and rural public services. In the context of the national development strategy, the promotion of the rural revitalisation strategy, the implementation of the agricultural production strategy, and the ecological environmental protection strategy in various regions have a guiding effect on agricultural production methods, the use of agricultural resources and the management of agricultural surface pollution, which further affects the level of sustainable development of agriculture, and the differences in the way the implementation of the agricultural development strategy in different regions has led to regional disparities in the level of the sustainable development of agriculture.

The first law of geography emphasises the interrelationships in the spatial distribution of geographic things or attributes [41], i.e., the existence of spatial correlations between things or phenomena in different regions [43]. Therefore, the level of sustainable agricultural development is not only affected by factors within the region, but may also be affected by the level of sustainable agricultural development in the surrounding areas, i.e., there is a spatial correlation in the level of sustainable agricultural development. Specifically, on the one hand, sustainable agricultural development has a regional diffusion effect. The natural conditions, agricultural industry structure and production patterns of neighbouring regions are more similar, and the adaptability of agricultural technology is also stronger, coupled with the fact that the transport in neighbouring regions is more convenient, which provides conditions for the flow of agricultural factors and the diffusion of technology, thus generating a spatial spillover effect on the sustainable development of agriculture in neighbouring regions. On the other hand, sustainable agricultural development has a regional mimicry effect. As interregional economic activities become increasingly close, regions with advanced concepts of sustainable agricultural development will also have a demonstrative effect on the neighbouring regions, and the neighbouring regions will be able to improve the level of sustainable agricultural development in their own regions by imitating and learning from the agricultural management modes, the business practices and the use of technology in regions with a higher level of sustainable agricultural development. Taken together, sustainable agricultural development has externalities due to the diffusion and imitation effects, but it is still subject to the influence of natural, economic, social and intraregional linkages, and the externality has different strengths and weaknesses, which leads to differences in the role played by spatial spillover effects.

3. Materials and Methods

3.1. Study Area

Considering the availability of data, the study area of this paper includes 30 provinces, autonomous regions, and municipalities directly under the central government in China (excluding Tibet, Hong Kong, Macao and Taiwan). At the same time, the study area was divided into the eastern, central and western regions, based on the National Statistical Office’s classification of the “three regions”. An overall overview of the study area is shown in Figure 1. The eastern region is situated on the coast, with gentle terrain, relatively obvious transport, market, economic and labour advantages, strong technological capabilities and relatively high agricultural labour and land productivity. Agricultural development trends in the eastern region are moving in the direction of a modern, efficient and export-oriented agricultural model. The central region, with its plateau in the north, hills in the south and numerous plains, has favourable conditions for agricultural production and natural resources, and is China’s main production base for agricultural products, known as China’s “breadbasket”. Agricultural development in the central region has insisted on ensuring that the area under food cultivation and production remain stable, promoting the construction of functional food production zones, protected areas for important agricultural products, and zones of superiority for special agricultural products. The western region is vast, with complex terrain and diverse climates, and the overall farming efficiency is lower than in the central and eastern regions. The western region relies on the region’s unique agricultural resource advantages to develop speciality agriculture and form a modern agricultural development model with speciality products.

3.2. Study Methods

3.2.1. Entropy Weight Method

The entropy weight method is mainly based on data information, and determines the weight of each indicator according to the amount of information it conveys to the decision maker, which is highly accurate and more objective [44], and it can circumvent human factors such as subjective preference and the empirical limitations of subjective assignment method. The entropy weight method, as a multi-indicator comprehensive evaluation method, is now widely used in socio-economic and other fields [45,46,47].

The entropy weight method for each variable is mainly based on the entropy principle, based on the raw data of the objective environment, where the weights are determined by analysing the degree of correlation between the indicators and the amount of information provided by each indicator. If the information entropy of the indicator is smaller, it indicates that its degree of dispersion is larger, i.e., the more information the indicator contains in the comprehensive evaluation model, the greater the weight of the indicator, and vice versa [45]. Its principle for the integration of variables in the process is mainly reflected in the assumption that there are n evaluation objects, m evaluation indicators, then the original data to form a matrix for X = (x_ij)n × m, so the probability of the jth indicator of the ith evaluation object is: $p_{ij}=\frac{x_{ij}}{{\displaystyle \sum _{i=1}^m{x}_{ij}}}$, and the entropy of the jth indicator is defined as: $e_j=-\frac{1}{lnn}{\displaystyle \sum _{j=1}^n{p}_{ij}}ln{p}_{ij}$.

In the evaluation index system of the level of sustainable development of agriculture, due to the different attributes and quantitative outlines of the indicators, a direct calculation using the initial data will lead to the distortion of the evaluation results, therefore, first of all, it is important to differentiate between the positive and negative attributes of the indicators, and carry out the standardisation of the polarity of the indicators with different attributes. Secondly, the information entropy of each index is calculated through the principle of information entropy, and the entropy weight method of each index is obtained after normalisation, and finally, the linear weighting method is used to calculate the index of sustainable development of agriculture in each region in order to form a coherent and robust index. Referring to Hu, Z. et al.’s study [48], the entropy weight method was applied to determine the weights of indicators and calculate the index of sustainable agricultural development.

The specific calculation steps for the entropy weight method to calculate the agricultural sustainability index are as follows:

Step 1: Standardisation of indicators

$$\mathrm{Positive} \mathrm{indicators}: P_{ij}=\frac{x_{ij}-x_{\min }}{x_{\max }-x_{\min }}$$

(1)

$$\mathrm{Negative} \mathrm{indicators}: P_{ij}=\frac{x_{\max }-x_{\mathrm{ij}}}{x_{\max }-x_{\min }}$$

(2)

where P_ij is the indicator value after the standardisation of indicator j in province i, x_ij is the original value of indicator j in province i, and x_max and x_min are the maximum and minimum values of the jth indicator, respectively.

Step 2: Determine the weights of the indicators

Calculate the standardised weight f_ij for each indicator:

$$f_{ij}=\frac{p_{ij}}{{\displaystyle \sum _{i=1}^m{p}_{ij}}}\hspace{1em}\hspace{1em}\mathrm{j}=1,2, \dots \mathrm{m}$$

(3)

Calculate the information entropy of the indicator H_j:

$$H_j=-\frac{1}{lnn}{\displaystyle \sum _{j=1}^n{f}_{ij}}ln{f}_{ij}$$

(4)

Step 3: Calculate the weights of the indicators W_j:

$$W_j=\frac{1-H_j}{m-{\displaystyle \sum _{i=1}^m{H}_j}}\hspace{1em}\mathrm{j}=1,2 \dots \mathrm{m}$$

(5)

Step 4: Calculate the value of the agricultural sustainability index T_i:

$$T_i={\displaystyle \sum _{j=1}^m{W}_j}{P}_{ij}$$

(6)

3.2.2. Kernel Density Estimation Method

Kernel density, as a nonparametric test for estimating the probability density function [49] without any parametric modelling assumptions, is used to estimate the distributional characteristics of the data by applying the kernel function, K(y), to portray the distributional pattern of the random variable with the trend of time evolution [50]. The evolutionary characteristics of the attribute values are reflected based on the centre position of the kernel density curve, the height of the main peak, the number of crests, and the length and thickness of the trailing tail. Its probability density function is:

$$f(\mathrm{y})=\frac{1}{nh}{\displaystyle \sum _{i=1}^{n}K(\frac{{\mathrm{y}}_i-\overline{\mathrm{y}}}{h}})$$

(7)

where K(y) is the kernel function, estimated using the Gaussian kernel function. y_i is the independent and identically distributed observations, $\overline{y}$ is the mean of the observations, n is the sample size; and h is the bandwidth.

3.2.3. Dagum’s Gini Coefficient

The Gini coefficient and its decomposition method take full account of the distribution of the subsamples, thus clarifying the sources of regional differences. The overall Gini coefficient can be decomposed into three components: intraregional variation, interregional variation and hypervariable density. In this paper, the intraregional Gini coefficients represent intraregional differences in the east, central region and the west; the interregional Gini coefficients indicate the differences between the east–central, east–west and central–west regions; the hypervariance density indicates the contribution of the overlap between different regions to the overall difference. The formula is as follows [51]:

$$G=\frac{{\displaystyle \sum _{j=1}^k{\displaystyle \sum _{h=1}^k{\displaystyle \sum _{i=1}^{n_j}{\displaystyle \sum _{r=1}^{n_h}|y_{ij}-y_{hr}|}}}}}{2n^2\overline{y}}$$

(8)

$$G=G_{\mathrm{w}}+G_{\mathrm{nb}}+G_{\mathrm{t}}$$

(9)

where G is the overall Gini coefficient, k is the number of regional divisions, n is the number of provinces, y_ij is the agricultural sustainability index for province j in region i, y_hr is the agricultural sustainability index for province r in region h, $\overline{y}$ is the average of the agricultural sustainability indices, G_w denotes the contribution of intraregional variation, G_nb denotes the contribution of interregional variation, and G_t denotes the contribution of hypervariable density.

3.2.4. Global Spatial Autocorrelation

Global spatial autocorrelation is a statistical description of the spatial correlation and distribution pattern of the research object from a global perspective. It is based on the location and value of the elements, and the degree of regional agglomeration is judged by the positive and negative values and the magnitude of the Moran’s I index. [52]. If Moran’s I index is positive, it is a positive spatial correlation, and the closer the value is to 1, the more pronounced the degree of agglomeration of regions with similar attributes is; if Moran’s I index is negative, it is a negative spatial correlation, and the closer the value is to −1, the more pronounced the degree of agglomeration of regions with dissimilar attributes is; when Moran’s I index is 0, it indicates that there is no spatial correlation in the neighbourhood. The functional form of the global Moran’s I index is [53]:

$$I=\frac{{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{w}_{ij}({\mathrm{y}}_i-\overline{\mathrm{y}}})-({\mathrm{y}}_j-\overline{\mathrm{y}})}}{s^2{\displaystyle \sum _{i=1}^n{\displaystyle \sum _{j=1}^n{w}_{ij}}}}$$

(10)

where I is the global spatial autocorrelation coefficient, n is the number of samples, y_i and y_j are the attribute values of region i and region j, $\overline{y}$ is the mean of the attribute values of the region, s² is the variance, and w_ij is the spatial weight matrix.

3.2.5. Spatial Convergence Model

The spatial convergence model consists mainly of σ-convergence and β-convergence [54]. In this paper, the coefficient of variation is used to measure σ-convergence to determine whether the magnitude of the deviation of the level of agricultural sustainability from the mean shows a decreasing trend over time. The formula is as follows:

$${\sigma }_{ht}=\frac{\sqrt{\raisebox{1ex}{${\displaystyle {\sum }_{i=1}^{N_h}\left(y_{iht}-\overline{y_{ht}}\right)}$}\!\left/ \!\raisebox{-1ex}{$N_h$}\right.}}{\overline{y_{ht}}}$$

(11)

where σ_ht denotes the coefficient of variation for year t in region h, N_h denotes the number of provinces in region h, y_iht denotes the agricultural sustainability index for the ith province of region h in year t, ${\overline{y}}_{ht}$ denotes the average value of the agricultural sustainability index for year t in region h.

The β-convergence model is divided into absolute β-convergence and conditional β- convergence [55]. This paper uses the β-convergence model to describe whether regions with lower levels of sustainable agricultural development have a faster rate of development and are able to catch up with regions with higher levels of sustainable agricultural development, leading to a gradual reduction in the differences between regions. According to existing studies, the level of sustainable agricultural development in each region is spatially correlated [25], so this paper incorporates a spatial convergence model for testing. Equations (6)–(9) are the spatial absolute β-convergence models in the form of traditional panel models, SAR, SEM, and SDM, respectively:

$$\mathrm{OLS}: \ln \left(\frac{y_{i,t+1}}{y_{it}}\right)=\mathsf{\alpha }+\mathsf{\beta }\ln \left(y_{it}\right)+{\mathsf{\mu }}_i+{\mathsf{\theta }}_t+{\mathsf{\epsilon }}_{it}$$

(12)

$$\mathrm{SAR}: \ln \left(\frac{y_{i,t+1}}{y_{it}}\right)=\mathsf{\alpha }+\mathsf{\beta }\ln \left(y_{it}\right)+\mathsf{\rho }{\displaystyle \sum _{j=1}^n{w}_{it}}\ln \left(\frac{y_{i,t+1}}{y_{it}}\right)+{\mathsf{\mu }}_i+{\mathsf{\theta }}_t+{\mathsf{\epsilon }}_{it}$$

(13)

$$\mathrm{SEM}: \ln \left(\frac{y_{i,t+1}}{y_{it}}\right)=\mathsf{\alpha }+\mathsf{\beta }\ln \left(y_{it}\right)+{\mathsf{\mu }}_i+{\mathsf{\theta }}_t+{\mathsf{\epsilon }}_{it}\hspace{1em}{\mathsf{\epsilon }}_{it}=\mathsf{\lambda }{\displaystyle \sum _{j=1}^n{w}_{it}}{\mathsf{\epsilon }}_{it}+{\mathsf{\eta }}_{it}$$

(14)

$$\mathrm{SDM}: \ln \left(\frac{y_{i,t+1}}{y_{it}}\right)=\mathsf{\alpha }+\mathsf{\beta }\ln \left(y_{it}\right)+\mathsf{\rho }{\displaystyle \sum _{j=1}^n{w}_{it}}\ln \left(\frac{{\mathrm{y}}_{\mathrm{i},\mathrm{t}+1}}{y_{it}}\right)+\mathsf{\gamma }{\displaystyle \sum _{j=1}^n{w}_{it}}\ln \left(y_{it}\right)+{\mathsf{\mu }}_i+{\mathsf{\theta }}_t+{\mathsf{\epsilon }}_{it}$$

(15)

Conditional convergence is the addition of other control variables that have an impact on the level of agricultural sustainability to absolute convergence. In this paper, the level of regional economic development, the level of financial support for agriculture, the structure of agricultural cultivation, and the level of regional financial deepening are used as control variables, and the conditional convergence model is applied to test the convergence of the level of sustainable agricultural development. Equation (16) is a traditional panel model and Equations (17)–(19) are spatial econometric models.

$$\mathrm{OLS}: \ln \left(\frac{y_{i,t+1}}{y_{it}}\right)=\mathsf{\alpha }+\mathsf{\beta }\ln \left(y_{it}\right)+\mathsf{\delta }\ln \left(x_{i,t+1}\right)+{\mathsf{\mu }}_i+{\mathsf{\theta }}_t+{\mathsf{\epsilon }}_{it}$$

(16)

$$\mathrm{SAR}: \ln \left(\frac{y_{i,t+1}}{y_{it}}\right)=\mathsf{\alpha }+\mathsf{\beta }\ln \left(y_{it}\right)+\mathsf{\rho }{\displaystyle \sum _{j=1}^n{w}_{it}}\ln \left(\frac{{\mathrm{y}}_{\mathrm{i},\mathrm{t}+1}}{y_{it}}\right)+\mathsf{\delta }\ln \left(x_{i,t+1}\right)+{\mathsf{\mu }}_i+{\mathsf{\theta }}_t+{\mathsf{\epsilon }}_{it}$$

(17)

$$\mathrm{SEM}: \ln \left(\frac{y_{i,t+1}}{y_{it}}\right)=\mathsf{\alpha }+\mathsf{\beta }\ln \left(y_{it}\right)+\mathsf{\delta }\ln \left(x_{i,t+1}\right)+{\mathsf{\mu }}_i+{\mathsf{\theta }}_t+{\mathsf{\epsilon }}_{it}\hspace{1em}{\mathsf{\epsilon }}_{it}=\mathsf{\lambda }{\displaystyle \sum _{j=1}^n{w}_{it}}{\mathsf{\epsilon }}_{it}+{\mathsf{\eta }}_{it}$$

(18)

$$\mathrm{SDM}: \ln \left(\frac{y_{i,t+1}}{y_{it}}\right)=\mathsf{\alpha }+\mathsf{\beta }\ln \left(y_{it}\right)+\mathsf{\rho }{\displaystyle \sum _{j=1}^n{w}_{it}}\ln \left(\frac{{\mathrm{y}}_{\mathrm{i},\mathrm{t}+1}}{y_{it}}\right)+\mathsf{\gamma }{\displaystyle \sum _{j=1}^n{w}_{it}}\ln \left(y_{it}\right)+\mathsf{\delta }\ln \left(x_{i,t+1}\right)+\mathsf{\tau }{\displaystyle \sum _{j=1}^n{w}_{it}}\ln \left(x_{i,t+1}\right)+{\mathsf{\mu }}_i+{\mathsf{\theta }}_t+{\mathsf{\epsilon }}_{it}$$

(19)

where y_i,t+1 and y_i,t denote the level of the sustainable agricultural development of province i in years t + 1 and t, respectively, x_i,t are the control variables, β is the convergence coefficient, ρ is the spatial autocorrelation coefficient, λ indicates the spatial error term coefficient, w_it is the spatial weight matrix, μ_i denotes individual fixed effects, θ_t denotes time fixed effects and ε_it denotes the random perturbation term. The speed of convergence is given by:

$$b=-\ln (1+\mathsf{\beta })/T$$

(20)

3.3. Variable Selection

3.3.1. Construction of the Indicator System

The crude development of traditional agriculture focuses only on production, which puts enormous pressure on resources and the environment. From the perspective of sustainable development, modern agriculture should achieve ecological, production and economic, and social development sustainability while maintaining stable agricultural productivity, thus achieving sustainable agricultural development [56]. This paper draws on some of the literature for the selection of indicators for the demographic, social, economic, resource and environmental subsystems [26,57]. At the same time, the National Plan for Sustainable Agricultural Development and the 14th Five-Year Plan for Promoting Agricultural and Rural Modernisation not only explain the contents of the above five subsystems, but also emphasise the scientific and technological work of improving the application of agricultural machinery and equipment, as well as water-saving irrigation to strengthen the sustainable development of agriculture, so this paper adds a technological system to improve the indicator system under the guiding requirements of China for the sustainable development of agriculture.

Table 1. System of indicators for evaluating sustainable agricultural development.

Target Layer	Criterion Layer	Indicator Layer	Indicator Types
Level of sustainable agricultural development	Population system	Natural population growth rate	−
		Regional population density	−
		The proportion of rural education	+
	Social system	Urbanisation rate	+
		Urban–rural income gap	−
		Rural Engel coefficient	−
		Industrial structure	+
	Economic system	Gross agricultural product per capita	+
		Per capita disposable income of rural residents	+
		Per capita investment in agricultural fixed assets	+
		Consumption expenditure per rural inhabitant	+
	Resource system	Crops sown per capita	+
		Percentage of employees in the primary sector	−
		Agricultural water use per unit of sown area	+
		Rural electricity consumption per capita	+
	Environmental system	Intensity of fertilizer use	−
		Intensity of pesticide use	−
		Intensity of use of mulch	−
		Agricultural disaster rate	−
	Technical system	Effective irrigation rate	+
		Total mechanical power per unit of sown area	+
		Level of agricultural R&D inputs	+
		Level of contribution of agricultural science and technology	+

demonstrates the results of the selection of indicators for the level of sustainable agricultural development.

Among them, the proportion of rural educated population is calculated as 1-the proportion of illiterate population in the population aged 15 years and above; the industrial structure is calculated as 1-(agricultural output value/gross output value of agriculture, forestry, animal husbandry and fishery); the level of agricultural R&D investment and the level of the contribution of agricultural scientific and technological progress are calculated by referring to the studies of Li Qiang [58] and Tao Qunshan [59], respectively.

It is worth noting that the negative attributes of the indicators of the natural population growth rate and population density are due to the following: In terms of agricultural development, China’s per capita arable land is about one third of the world average, and the contradiction between man and land is prominent. During the study period, China’s population was on the rise, and higher population growth rates and population densities would cause construction land to take up arable land area, leading to a more prominent contradiction between more people and less land. At the same time, this paper also refers to the way of measuring the demographic system and the attributes of the indicators in the system of indicators of sustainable agricultural development in part of the literature, and identifies the two indicators of natural population growth rate and population density as negative indicators [26,57].

3.3.2. Control Variable

In this paper, the following control variables are selected:

(1)

Level of economic development (x1), measured using GDP per capita. Driven by economic development, the increasingly sound construction of agricultural infrastructure and the ability to attract a concentration of scientific and technological talent and promote the flow of agricultural factors has a positive effect on the sustainable development of agriculture, i.e., the higher the level of economic development, the more favourable it is to the sustainable development of agriculture.

(2)

Level of financial support for agriculture (x2): measured by expenditure on agriculture, forestry and water per unit of sown area. Financial support for agriculture can provide financial support for agricultural production materials, subsidies for the purchase of agricultural machinery, and the introduction of advanced agricultural technology, thereby promoting the modernisation of agriculture and enhancing the level of sustainable agricultural development.

(3)

Agricultural cropping structure (x3): Measured by the share of area sown to grain in the total area sown to crops. By adjusting the agricultural planting structure, it is possible to optimise the allocation of resources, improve the ecological environment and achieve an overall increase in economic and ecological benefits, i.e., a scientific agricultural planting structure can promote the sustainable development of agriculture.

(4)

Regional financial deepening (x4): measured by the ratio of financial institutions’ loan balances to GDP at the end of the year. The level of financial deepening is an important factor in agricultural production and operations. Finance maximises the value of agriculture by reducing the cost of agricultural operations, mitigating the risk of technological innovation, and providing financial protection against natural disaster risk and market price risk, thereby enhancing sustainable agricultural development.

Severe skewing of the above control variables can lead to a reduction in the level of sustainable agricultural development.

3.3.3. Data Sources

Considering the data availability, this paper takes 30 provinces, autonomous regions and municipalities directly under the central government in China (excluding Tibet, Hong Kong, Macao and Taiwan) as the research object, and the time span is 2012–2021. The basic data for each indicator are mainly obtained from the China Statistical Yearbook of Population and Employment, the China Rural Statistical Yearbook, the China Statistical Yearbook, the China Environmental Statistical Yearbook, the China Water Resource Bulletin, the China Agricultural Machinery Industry Yearbook, and the EPS database, and some of the indicators are obtained through secondary calculations, while individual missing values are filled in by interpolation. The convergence analysis section was treated as logarithmic for all variables in order to eliminate the effect of data heteroscedasticity.

4. Results

4.1. Measurement and Trend of Sustainable Development Level in China’s Agriculture

4.1.1. Trends in the National Level of Sustainable Agricultural Development

In this study, the entropy weight method is used to measure the national agricultural sustainable development index from 2012 to 2021. It is shown in Figure 2: From the point of view of spatial distribution, over time, China’s agricultural sustainable development index has always shown the spatial distribution characteristics of high in the east and low in the west. In 2012, Heilongjiang, Jilin, Liaoning and Jiangsu were in the first tier, with high levels of sustainable agricultural development; Hainan, Gansu, Guizhou and Yunnan were in the sixth echelon, and their level of sustainable agricultural development is low. In 2021, the layout of the level of sustainable agricultural development changes, with Jilin, Tianjin, Jiangxi, and Qinghai in the first tier, and Hainan, Gansu, and Shaanxi in the sixth tier. In terms of the distribution of the three major regions, all the provinces in the eastern region are in the top three echelons, except for four provinces, namely Beijing, Shanghai, Guangdong and Hainan; The central region is in the top three echelons, with the exception of two provinces, Shanxi and Henan; in contrast, the development of the western region is more uneven, and the distribution of the level of sustainable agricultural development among the provinces in the region is more dispersed, but still improved compared with 2012.

As a whole (Figure 3), China’s agricultural sustainable development index shows an upward trend in fluctuation, and its index is raised from 0.2966 in 2012 to 0.3513 in 2021, with an increase of 18.44%, and the level of sustainable development of agriculture has been significantly improved. The reason for this is that during the past 10 years, the Chinese government has continued to provide agricultural technology and related policy support, and has taken multiple initiatives to promote sustainable agricultural development. The mode of agricultural development has changed from crude traditional agriculture to intensive, efficient and environmentally friendly agriculture, and the level of sustainable agricultural development has continued to improve.

From the differences in the annual agricultural sustainable development level index, a negative growth in 2015–2016 can be seen, maintaining a relatively stable growth trend in the remaining years. The reason may be that 2015–2016 is the nodal point between the 12th Five-Year Plan and the 13th Five-Year Plan period. As the requirements for sustainable agricultural development change in each period, the strategic focuses for each subsystem of sustainable agricultural development are different. As a result, during the period of policy adjustment to the different objectives of sustainable development in agriculture, its development index showed a downward trend of some magnitude, but this did not affect the overall trend change in the sustainable development in agriculture. In addition, the index of sustainable agricultural development increased the most in 2019–2020, reaching 0.0118. The reason for this is the publication of the National Plan for Sustainable Agricultural Development (2015–2030), which sets out requirements for 2020 in such areas as the contribution rate of scientific and technological progress in agriculture, water use for agricultural irrigation, and zero growth in chemical fertilizers and pesticides, and provides important guidance for key tasks involving sustainable agricultural development, such as the upgrading of agricultural production capacity, the safety of water use in agriculture, and the management of environmental pollution.

4.1.2. Trends in the Level of Sustainable Agricultural Development in the Three Major Regions

The trends in the national and three major regional agricultural sustainability indices from 2012 to 2021 are shown in Figure 4. From the point of view of the time change trend, the bar charts as a whole all show an upward trend, indicating that the level of sustainable development of agriculture at the national level and in the three major regions has increased to a greater extent during the sample period.

In terms of spatial trends, the eastern region saw a decrease in the agricultural sustainability index during the period 2015–2016, while maintaining an upward trend throughout the rest of the years, with a level of development slightly higher than that of the country as a whole. The eastern region has a better economic foundation and an advantage in terms of labour and technological resources, which is an important guarantee for sustainable agricultural development. The level of sustainable agricultural development in the central region has been steadily increasing, with a level of development higher than the national average, and in 2014 it surpassed that of the eastern region and was in a leading position. The central region, mostly a major grain-producing area, is an important agricultural base in China, with a large total endowment of agricultural resources and superior production conditions, laying a solid foundation for sustainable agricultural development. The level of sustainable agricultural development in the western region shows an upward trend, but it is far below the national average. The western region is limited by geographical location, and there is still much room for improvement in the level of sustainable agricultural development.

In terms of the rate of change, the 2021 index in the eastern, central and western regions increased by 14.31%, 19.59% and 22.12%, respectively, compared with 2012. It shows that although the level of sustainable agricultural development in the western region is low, it can maintain a high growth rate and its development potential is large by virtue of its unique advantages in terms of geographical, environment and resources, and relevant national policy support.

4.2. Characteristics of the Dynamic Evolution of the Level of Sustainable Agricultural Development in China

Using the kernel density estimation method, the dynamic evolution characteristics of the level of sustainable agricultural development in China and the three major regions were analysed separately. The results are shown in Figure 3.

4.2.1. Kernel Density Estimation of the Level of Sustainable Development of Agriculture at the National Level

Figure 5a depicts the dynamic evolution of the level of agricultural sustainability from 2012–2021 at the national level.

From the distribution position of the curve, the centre of the kernel density curve as a whole shows a shift to the right, indicating that the level of sustainable agricultural development in China during the sample period has shown an upward trend, and the current situation of agricultural development has improved significantly, which is consistent with the results of the analysis above.

From the shape of the curve, the height of the main peak of the kernel density curve maintains an upward trend, and the width of the main peak increases, reflecting that the distribution of China’s level of sustainable agricultural development during the sample period exhibits a trend of agglomeration, but that the overall absolute difference between agglomerated areas has widened. The possible reasons for this are the similarity of the level of economic development and the characteristics of factor endowment in the neighbouring regions, the basic consistency of agricultural business activities, and the agglomeration of the level of sustainable development of agriculture, but it is still limited by the backwardness of the level of agricultural development in some provinces, which has led to a widening of the absolute differences between some of the provinces within the scope of the agglomeration area.

In terms of distribution polarisation, there is a clear “double peak” in the 2013–2017 kernel density curve, with a “single main peak” in the remaining years. It shows that the level of sustainable agricultural development in the country during the sample period was polarised between 2013 and 2017, and then the phenomenon weakened. This indicates that the polarisation feature within the level of sustainable agricultural development in China has tended to weaken in recent years, and the degree of intraregional differentiation has decreased.

4.2.2. Kernel Density Estimates of the Level of Sustainable Agricultural Development in Three Major Regions

Figure 5b–d characterise the dynamic evolution of the level of sustainable agricultural development in the eastern, central and western regions of China over the period 2012–2021, respectively.

In terms of the distribution position of the curves, the eastern, central and western curves fluctuate, but the overall performance is shifted to the right, and the level of sustainable agricultural development has increased. This is consistent with the national trend in the level of sustainable agricultural development.

In terms of the distribution pattern of the wave peaks, there is a clear upward trend in the height of the main peaks of the nuclear density curves in the three major regions and a narrowing trend in the width of the main peaks. This means that the level of concentration of sustainable agricultural development has increased in all regions, that there is a “catching up” phenomenon between provinces, and that the differences in the level of development of the concentration areas have been reduced.

In terms of distributional polarisation, the kernel density curve in the eastern region has evolved from a “double-peak” to a “single-peak”, and the polarisation in the eastern region has eased; in the central region, most of the years are characterised by the “double-peak” feature, i.e., the central region as a whole is polarised. The reason for this is that the central region’s fierce competition for high-quality agricultural factors such as capital, talent and technology has led to its polarisation. In the western region, there was a “single peak” in 2012 and 2013 and a “double peak” in 2014–2016, after which the curve gradually became smooth. This indicates that there was polarisation in the western region in 2014–2016, after which the polarisation phenomenon weakened.

4.3. Regional Differences in the Level of Sustainable Agricultural Development in China

In order to further prove the spatial differences of agricultural sustainable development level in China and the sources of differences, this paper uses Dagum’s Gini coefficient to measure the overall differences, intra-regional differences, interregional differences and the sources of differences in the agricultural sustainable development level of the country and the three major regions from 2012 to 2021. The results are shown in

Table 2. Dagum’s Gini coefficient for the level of sustainable agricultural development in China.

Year	Overall Gini Coefficient	Intraregional Gini Coefficient			Interregional Gini Coefficient
		East	Central	West	East–Central	East–West	Central–West
2012	0.0588	0.0467	0.0393	0.0624	0.0464	0.0737	0.0651
2013	0.0616	0.0516	0.0445	0.0657	0.0524	0.0747	0.0641
2014	0.0575	0.0389	0.0467	0.0609	0.0442	0.0669	0.0699
2015	0.0521	0.0364	0.0451	0.0526	0.0416	0.0602	0.0621
2016	0.0589	0.0329	0.0665	0.0551	0.0533	0.0598	0.0677
2017	0.0476	0.0348	0.0526	0.0402	0.0458	0.0487	0.0526
2018	0.0438	0.0275	0.0405	0.0456	0.0358	0.0479	0.0523
2019	0.0448	0.0338	0.0402	0.0459	0.0389	0.0490	0.0511
2020	0.0406	0.0238	0.0377	0.0455	0.0321	0.0452	0.0474
2021	0.0426	0.0277	0.0379	0.0447	0.0349	0.0504	0.0482
Average value	0.0508	0.0354	0.0451	0.0519	0.0426	0.0577	0.0580

and

Table 3. Sources and contributions of differences in the level of sustainable agricultural development in China.

Year	Source			Contribution Rate
	Intraregional Difference	Interregional Difference	Hypervariable Density	Intraregional Difference	Interregional Difference	Hypervariable Density
2012	0.0173	0.0261	0.0155	29.38%	44.33%	26.29%
2013	0.0188	0.0244	0.0185	30.49%	39.56%	29.95%
2014	0.0170	0.0248	0.0156	29.65%	43.19%	27.16%
2015	0.0156	0.0231	0.0135	29.91%	44.29%	25.81%
2016	0.0185	0.0222	0.0182	31.49%	37.65%	30.86%
2017	0.0149	0.0177	0.0150	31.35%	37.20%	31.45%
2018	0.0135	0.0161	0.0143	30.68%	36.72%	32.60%
2019	0.0139	0.0151	0.0158	31.02%	33.67%	35.31%
2020	0.0128	0.0134	0.0145	31.49%	32.88%	35.63%
2021	0.0130	0.0165	0.0131	30.44%	38.79%	30.78%
Average value	0.0155	0.0199	0.0154	30.59%	38.82%	30.58%

.

4.3.1. Analysis of Regional Differences in the Level of Sustainable Agricultural Development in China

With regard to overall differences, the overall Gini coefficient for the level of sustainable agricultural development in China declined from 0.0588 in 2012 to 0.0426 in 2021, a decrease of 27.55%, generally showing a downward trend in fluctuations. It shows that with the adjustment of policies related to the sustainable development of agriculture in China, the regional differences have been reduced as a whole.

With regard to intraregional differences, the average value of the intraregional Gini coefficient shows, in descending order, the western region (0.0519), the central region (0.0451) and the eastern region (0.0354), with the average value of the intraregional Gini coefficient in the west being greater than the national average. The evolution of the Gini coefficient in the three major regions shows a downward trend in all regions. Specifically, the Gini coefficient within the eastern region declined from 0.0467 in 2012 to 0.0277 in 2021, a decrease of 40.69%, the Gini coefficient within the central region declined from 0.0393 in 2012 to 0.0379 in 2021, a decrease of 3.56%, and the Gini coefficient within the western region declined from 0.0624 in 2012 to 0.0447 in 2021, a decrease of 28.37%. The decreases were, in descending order, in the eastern, western and central regions. It is worth noting that the central region reaches the highest intraregional Gini coefficient of 0.0665 in 2016 and then declines to 0.0379 in 2021. This may be due to the fact that the central region, which is mostly a major food-producing region, is better off than other regions in terms of resources and policy support, but may have neglected to pay attention to environmental protection due to production goals such as preserving yields, leading to an increase in the Gini coefficient within its region. Since then, the issuance of the National Land Planning Programme (2016–2030) has emphasised the vigorous construction of major grain-producing areas, the implementation of actions to protect the quality of arable land, the strict observance of the red line of arable land, and the enhancement of the quality of agricultural arable land, thus lowering the Gini coefficient of the level of sustainable development of agriculture within the central region.

In terms of interregional differences, the average of the interregional Gini coefficients is, in descending order, central–west (0.0580), east–west (0.0577) and east–central (0.0426). The evolution of the Gini coefficients among the three major regions shows a downward trend in interregional differences. Specifically, the Gini coefficient between the east–central region declines from 0.0464 in 2012 to 0.0349 in 2021, a decrease of 24.78%; the Gini coefficient between the east–west region declines from 0.0737 in 2012 to 0.0504 in 2021, a decrease of 31.61%; the Gini coefficient between the central–west region declines from 0.0651 in 2012 to 0.0482 in 2021, a decrease of 25.96%. The decreases were, in descending order, in the east–west, central–west and east–central regions. It can be seen that interregional differences are decreasing, especially between the western and eastern regions and the central region, where the decrease in differences is relatively more significant. The western region has been able to leverage its land, climate, and other conditions to accelerate the development of specialty agriculture, narrowing the gap in sustainable agricultural development between the western region and the eastern and central regions.

4.3.2. Sources and Contributions of Regional Differences in the Level of Sustainable Agricultural Development in China

The results of the sources and contributions of regional differences in the level of sustainable agricultural development in China are shown in Table 3 and Figure 6. In general, during the sample period, the average values of intraregional difference, interregional difference and hypervariance density are 0.0155, 0.0199 and 0.0154, and the average contribution rates are 30.59%, 38.82% and 30.58%, respectively.

From the perspective of the size of the source of the difference, during the sample period, the intraregional difference is between 0.0128 and 0.0188, the interregional difference is between 0.0134 and 0.0261, and the hypervariable density is between 0.0131 and 0.0185. Overall, it appears that the effect of the size of the source of the difference on the overall difference can be categorised into 4 stages: in 2012–2016, interregional difference > intraregional difference > hypervariable density, in 2017–2018, interregional difference > hypervariable density > intraregional difference, in 2019–2020, hypervariable density > interregional difference > intraregional difference, and in 2021, interregional difference > hypervariable density > intraregional difference.

In terms of the trend in the changes in difference, the intraregional difference contribution rate shows an upward trend in fluctuation during the sample period, increasing from 29.38% in 2012 to 30.44% in 2021, an increase of 1.06%. Interregional difference shows a downward trend in fluctuations, with the contribution of interregional difference decreasing from 44.33% in 2012 to 38.79% in 2021, a decrease of 5.54%, and the hypervariable density difference contribution increases from 26.29% in 2012 to 30.78% in 2021, an increase of 4.49%.

The combination of the size of the sources of difference and the trend in difference shows that interregional difference is still the first source of overall difference, but interregional difference has shown a downward trend. It shows that attention has been paid to the impact of interregional differences on the overall differences, and that in the coming period, narrowing the interregional differences in the level of China’s sustainable agricultural development and reducing the contribution of interregional differences will be an important measure to enhance the level of China’s sustainable agricultural development and to realise the high-quality development of agriculture.

4.4. Convergence Analysis of the Level of Sustainable Agricultural Development in China

4.4.1. σ-Convergence Test for the Level of Sustainable Agricultural Development in China

Figure 7 illustrates the σ-convergence results for the level of agricultural sustainability for the country and the three major regions. From the national σ-convergence results, the coefficient of variation of the national level of agricultural sustainability during the sample period is located in the range of 0.0727–0.1104, and the overall performance of a downward trend, from 0.1052 in 2012 to 0.0759 in 2021, with a decrease of 27.85%. It indicates that the degree of the deviation of the national level of sustainable agricultural development from the overall average has a decreasing trend over time, i.e., the national level of sustainable agricultural development exhibits a state of σ-convergence.

According to the σ-convergence results of the three major regions, the overall coefficient of variation of the three major regions showed a downward trend during the sample period, demonstrating the σ-convergence characteristics. Among them, the coefficient of variation of the agricultural sustainable development level in the western region decreased the most, from 0.1126 in 2012 to 0.0798 in 2021, with a decrease of 29.13%, followed by the central region, from 0.0728 in 2012 to 0.0529 in 2021, with a decrease of 27.34%, and the eastern region decreased from 0.0854 in 2012 to 0.0706 in 2021, with a decrease of 17.33%, the smallest decrease. Under the guidance and support of relevant national policies, the dispersion degree within the region gradually declines.

4.4.2. β-Convergence Test for the Level of Sustainable Agricultural Development in China

(1)

Spatial correlation test

Moran′s I index was applied to test whether the level of sustainable agricultural development in China is spatially correlated, and the results are shown in

Table 4. Moran′s I index of the level of agricultural sustainability. *, **, *** indicate significance at the 10%, 5% and 1% levels, respectively.

Year	Moran Index	Z-Statistic	p-Value
2012	0.315 ***	2.871	0.002
2013	0.289 ***	2.649	0.004
2014	0.279 ***	2.598	0.005
2015	0.299 ***	2.763	0.003
2016	0.233 **	2.243	0.012
2017	0.267 ***	2.489	0.006
2018	0.192 **	1.863	0.031
2019	0.206 **	1.954	0.025
2020	0.048	0.670	0.251
2021	0.126 *	1.300	0.097

. In the case of selecting the adjacency weight matrix, except for 2020, in most years Moran′s I index is significantly positive at the 10% statistical level, and it can be initially judged that the level of sustainable agricultural development in China has a significant positive spatial correlation, showing a spatial clustering state.

(2)

Absolute β-convergence test

From the above analysis, it can be seen that there is a significant spatial positive correlation in the level of sustainable agricultural development in China. Therefore, based on the spatial adjacency weight matrix, this paper adopts the spatial β-convergence model to analyse the convergence of the level of sustainable agricultural development in the whole country and the three major regions. Since there may be different spatial effects on the level of sustainable agricultural development in different regions, the LM test was first used to determine its spatial autocorrelation, and then the optimal spatial model was selected based on the results of the spatial lag LM test, the robust spatial lag LM test, the spatial error LM test, and the robust spatial error LM test.

Table 5. Absolute β-convergence results for the level of sustainable agricultural development. *, **, *** indicate significance at the 10%, 5% and 1% levels, respectively.

Regional Scope	Countrywide	East	Central	West
Model type	SEM	SAR	SEM	SEM
β	−0.456 ***	−0.224 ***	−0.184 **	−0.242 **
	(0.060)	(0.074)	(0.084)	(0.100)
Convergence rate	6.09%	2.54%	2.03%	2.77%
ρ	-	0.276 ***	-	-
		(0.093)
λ	0.718 ***	-	0.499 ***	0.499 ***
	(0.047)		(0.088)	(0.126)
LM Spatial error	47.280 ***	6.259 **	18.790 ***	10.419 ***
	0.000	0.012	0.000	0.001
Robust LM Spatial error	4.299 **	1.195	2.248	2.798 *
	0.038	0.274	0.134	0.094
LM Spatial lag	44.003 ***	5.730 **	18.015 ***	8.900 ***
	0.000	0.017	0.000	0.003
Robust LM Spatial lag	1.021	0.667	1.473	1.279
	0.312	0.414	0.225	0.258
Individual fixation	YES	YES	YES	YES
Fixed time	YES	YES	YES	YES
R2	0.038	0.043	0.018	0.028
Log-likelihood	578.937	183.512	170.042	212.685
Observed value	270	99	72	99

shows the results of the absolute β-convergence test for the level of sustainable agricultural development in the country and the three major regions. In terms of the state of convergence, the convergence coefficient β is significantly negative for the whole country and the three major regions during the sample period, indicating that there is absolute β-convergence in all of them, i.e., the levels of the sustainable development of agriculture in the whole country and the three major regions converge to their respective steady state levels without considering the influence of other factors.

In terms of the rate of convergence, there are differences in the rate of convergence of the level of sustainable agricultural development, with the fastest rate of convergence at 6.09% for the country as a whole, and 2.54%, 2.03% and 2.77% for the eastern, central and western regions, respectively.

In terms of the performance of spatial effects, the level of sustainable agricultural development in the country and the three major regions passed the LM test, which means that the spatial convergence model can be selected for analysis. The application of the SEM model to the national region, the central region and the western region indicates that the level of sustainable agricultural development in the regional provinces has not yet generated spatial spillovers to other provinces, which are caused by random shocks. The SAR model was applied to the eastern region, and the spatial correlation coefficient β was significantly positive, indicating that the level of sustainable agricultural development in the eastern region is subject to positive spatial spillover effects from neighbouring provinces.

(3)

Condition β-convergence test

After controlling for the influencing factors that may affect the level of sustainable agricultural development, such as the level of economic development, the level of financial support for agriculture, the structure of agricultural cultivation and the level of financial deepening in the region, the conditional β-convergence model is applied to test the spatial convergence, and the results are shown in

Table 6. Conditional β-convergence results for the level of sustainable agricultural development. **, *** indicate significance at the 5% and 1% levels, respectively.

Regional Scope	Countrywide	East	Central	West
Model type	SAR	SAR	SAR	SEM
β	−0.437 ***	−0.560 ***	−0.501 ***	−0.510 ***
	(0.049)	(0.090)	(0.104)	(0.094)
Convergence rate	5.74%	8.21%	6.95%	7.13%
Lnx1	0.094 ***	0.127 ***	0.088 ***	0.105 ***
	(0.018)	(0.035)	(0.025)	(0.038)
lnx2	−0.012	−0.031	0.009	0.015
	(0.011)	(0.019)	(0.026)	(0.030)
lnx3	−0.043	−0.052	0.019	−0.011
	(0.035)	(0.056)	(0.071)	(0.075)
lnx4	0.070 ***	0.105 ***	0.048 **	0.080 **
	(0.015)	(0.028)	(0.021)	(0.036)
ρ	0.043 ***	0.234 ***	0.395 **	-
	(0.063)	0.028	(0.082)
λ	-	-	-	0.416 ***
				(0.104)
LM Spatial error	41.855 ***	5.617 **	15.203 ***	9.211 ***
	0.000	0.018	0.000	0.002
Robust LM Spatial error	0.102	0.081	0.023	0.129
	0.749	0.776	0.878	0.720
LM Spatial lag	43.631 ***	5.629 **	15.731 ***	9.112 ***
	0.000	0.018	0.000	0.003
Robust LM Spatial lag	1.878	0.093	0.551	0.030
	0.171	0.760	0.458	0.862
Individual fixation	YES	YES	YES	YES
Fixed time	YES	YES	YES	YES
R2	0.2325	0.2959	0.3063	0.2017
Log-likelihood	587.7185	196.6484	178.5319	222.3839
Observed value	270	99	72	99

.

In terms of the state of convergence, the coefficients of convergence β for the country as a whole and for the three major regions during the sample period are significantly negative at −0.437, −0.560, −0.501, and −0.510, respectively, suggesting that they still converge to their respective steady state levels, taking into account the effects of the control variables.

In terms of the speed of convergence, the conditional β convergence speed differs from the absolute β convergence speed, which is 5.74%, 8.21%, 6.95% and 7.13% for the national region, the eastern region, the central region and the western region, respectively. The eastern region has the fastest rate of convergence, which may be due to the fact that the level of sustainable agricultural development in the eastern region has a large regional difference at the beginning of the sample, and the provinces and municipalities have improved the efficiency of agricultural factor allocation through the spatial spillover effect among each other, so as to maintain a high rate of convergence.

In terms of the performance of spatial effects, the whole country and the three major regions passed the LM test, and the SAR model was applied to the whole country, the east and the central part of the country, and the spatial correlation coefficient ρ was significantly positive, and the level of sustainable agricultural development in the region was subjected to a positive spatial spillover effect from neighbouring provinces. The western region is suitable for the SEM model, which does not have the spatial lags of the explanatory variables, i.e., the level of sustainable agricultural development in the western region has not yet generated spatial spillovers to other provinces. It shows that the overall level of agricultural development in the western region still needs to be improved, that it has not yet established the radiation-driven role of high-level areas, and the spatial spillover effect is not significant.

In terms of control variables, the level of economic development of the country as a whole and of the three major regions has a significantly positive impact on the level of sustainable agricultural development at the 1% statistical level; the impact of the level of financial deepening on the level of sustainable agricultural development is significantly positive at the 1% statistical level in the national and eastern regions, and at the 5% statistical level in the central and western regions; while the effect of the level of financial support to agriculture and the structure of agricultural cultivation showed no significant effect. This means that at the present stage, the country and the three major regions still need the support of economic development, which can strengthen technological progress, attract relevant talents and increase interregional exchanges and cooperation, so as to provide conditions for the sustainable development of agriculture. Finance can likewise broaden the level of financing in rural areas and play an important role in promoting agricultural development.

5. Discussion

With the acceleration of the process of modernisation of agriculture and rural areas, China’s comprehensive agricultural production capacity has achieved great results, but it is still constrained by land, resources, the environment and other factors, and for a long period of time it has not established a sound institutional mechanism for sustainable agricultural development [60]. The purpose of this study is to systematically explore the evolutionary characteristics, regional differences and the spatial convergence of the sustainable development of agriculture in China and the three major regions, so as to lay an important foundation for upgrading the current status of sustainable development of agriculture and constructing a perfect institutional mechanism for sustainable development of agriculture. Distinguished from previous studies [28,57,61,62], this paper provides a deeper exploration in terms of indicator system construction and research content. With regard to the construction of the indicator system, a sustainable agricultural development indicator system covering six subsystems, namely, demographic, social, economic, resource, environmental and technological, was established based on the definition of sustainable development and the current situation of agricultural development, which supplements and expands the research results of the existing literature, and more comprehensively and systematically measures the current situation of the level of sustainable development of agriculture in China and the three major regions. In terms of research content, unlike the existing research that only explores the temporal and spatial changes in the level of sustainable agricultural development, this paper starts from a multi-method and multi-perspective approach, and comprehensively applies the entropy weight method, the kernel density estimation method, Dagum’s Gini coefficient, the global spatial autocorrelation, and the spatial convergence model, etc., to measure the index of sustainable agricultural development in China and the three major regions, and analyse the characteristics of their evolutions, as well as the regional differences and the spatial convergence, so as to clearly identify the current status of the sustainable development of China’s agriculture in recent years, its trend of change, the regional differences and their origins, and the characteristics of the spatial convergence. This paper finds that at present, the level of sustainable agricultural development in China is generally on an upward trend, but there are still significant differences in the level of sustainable agricultural development in various regions; the kernel density estimation map shows that the level of sustainable agricultural development in the country and in the regions exhibits varying degrees of polarisation; the results of Dagum’s Gini coefficient show that there are interregional differences in the level of sustainable agricultural development in China, and that interregional differences remain the first source of overall differences; and the results of the convergence analysis show that the level of sustainable agricultural development in China and the three major regions exhibits obvious spatial spillover effects.

The results of the above studies reveal the problems of unbalanced regional development and polarisation in the level of sustainable agricultural development in China, but corresponding measures can still be formulated based on the current status of its development, regional differences and spatial convergence, and other characteristics. First, optimise the layout of development and enhance the level of sustainable agricultural development. The government should introduce appropriate policies and measures to promote agricultural business models such as cleaner production, waste resourcing, ecological business models and intensive and efficient technology. At the same time, it has improved its policies for strengthening, supporting and benefiting agriculture, reduced the risks of agricultural production and management, improved the efficiency of land production, and achieved the goals of sustainable agricultural development, including stable production, the sustainable use of resources, and ecological and environmental friendliness. Secondly, the potential for sustainable agricultural development should be stimulated in accordance with local conditions. This involves developing differentiated measures, taking into account the realities of each region, such as the basis for agricultural development and the carrying capacity of resources and the environment. The eastern region has played a leading role in strengthening agricultural science and technology innovation by giving full play to its advantages in terms of capital, technology and human resources; the central region, based on its factor endowment advantages, has introduced advanced agricultural technologies and continued to improve the quality and greening of agriculture; the western region has strengthened its infrastructure, broken through regional geographical constraints, and promoted factor mobility and efficient agglomeration. Thirdly, regional collaboration should be strengthened and intraregional polarisation weakened. In the face of the phenomenon of polarisation in the process of the sustainable development of agriculture in various regions, a coordinating role should be given full play to strengthen the linkage mechanism between regions with a higher level of sustainable agricultural development and less developed regions, promoting interconnection and information sharing among various regions, and establishing a sound mechanism for providing assistance. It is also essential to promote the concepts of ecological farming and arable land protection, share and provide production and management experience, and weaken polarisation between regions. Fourthly, the spatial spillover effect should be brought into play to narrow the interregional gap in sustainable agricultural development. There are similarities between neighbouring regions in terms of climatic conditions, soil types and cropping structures, and the experience of agricultural development is indicative. Therefore, the positive spatial spillover effect and demonstration effect of regions with a higher level of sustainable agricultural development can be used to promote the flow of factors such as capital, talent, technology and resources, as well as multi-level and systematic agricultural cooperation, in order to address the problem of imbalance and insufficiency in the sustainable development of agriculture.

In addition, the path to promoting sustainable agricultural development varies for different modes of agricultural development. The eastern region is mostly a coastal area with special location advantages and ample employment opportunities. The massive transfer of agricultural labour has prompted the eastern region to regulate the land transfer system and accelerate the realisation of large-scale agricultural operations. At the same time, we are taking advantage of the good external conditions of rapid industrialisation and urbanisation to create modern agriculture and build large-scale agricultural production bases, thereby realising the ideal situation of industry feeding agriculture and cities feeding the countryside, and continuing to develop modern, highly efficient and outward-looking modes of agriculture in accordance with local conditions and conditions, so as to promote the sustainable development of agriculture. The central region has rich agricultural factor resources and better conditions for agricultural production, and most of the provinces in the central region are major food-producing areas. It should be based on the multifunctional attributes of agriculture, such as production, society and ecology, and accelerate the upgrading of agricultural technology to enhance the level of agricultural modernisation. While guaranteeing food security, the process of green development in agriculture is being promoted, so as to improve the quality of agriculture and increase its efficiency, and to guarantee the sustainable development of agriculture. The western region is on a synergistic and resonant economic belt with Eurasian countries, with a strong market, policy and human environment. In addition, the western region is rich in arable land resources and has a large potential for development. Therefore, the western region should increase its investment in agricultural science and technology, accelerate the transformation and upgrading of agriculture, and at the same time, relying on the advantages of agricultural resources, accelerate the development of pollution-free and ecological speciality agriculture, and enhance the level of sustainable agricultural development.

This paper enriches the research content in the field of sustainable agricultural development to a certain extent by constructing an indicator system for sustainable agricultural development and analysing the evolutionary characteristics, regional differences and spatial convergence of the sustainable agricultural development in China and the three major regions during the period of 2012–2021 on this basis. However, there are still some limitations to be addressed. First of all, this paper mainly collects relevant indicator data by consulting macro databases; due to the availability of data, a total of 23 evaluation indicators have been selected for this study, and there may be other indicators that have not yet been covered, and therefore do not comprehensively reflect the level of China’s sustainable agricultural development. In future research, micro data can be obtained through research and other ways, using a combination of macro and micro ways, to build a better indicator system and put it to the test using the current situation of China’s agricultural sustainable development; secondly, this paper explores the evolutionary characteristics, regional differences and spatial convergence of sustainable agricultural development mainly through quantitative means, and in the future, a combination of qualitative and quantitative methods can be used to refine the research, with a view to formulating more detailed policy recommendations.

6. Conclusions

With regard to the level of development, there is an upward trend in the level of sustainable agricultural development at the national level and in the three regions. The average level of agricultural sustainability is higher than the national average level of development in the eastern and central regions and lower than the national average level of development in the western region, but the western region had the highest rate of growth in the level of agricultural sustainability during the sample period, followed by the central and eastern regions. Taken together, the level of sustainable agricultural development among the three major regions as a whole shows a favourable trend, but there are still differences in the level of regional development, with the lowest level of development in the west, but with a higher rate of growth, which shows good potential for development.

With regard to evolutionary features, firstly, the evolutionary trend in the level of sustainable agricultural development at the national level and in the three major regions, although fluctuating, has remained consistent overall, with an upward trend. Second, the level of sustainable agricultural development at the national level and in the three major regions shows a trend of agglomeration, but the overall absolute difference between agglomerated areas has increased at the national level, and the overall absolute difference between agglomerated areas within the three major regions has decreased. Finally, the polarisation of sustainable agricultural development tends to weaken in the national region, the eastern region and the western region, while the polarisation in the central region remains. Overall, while maintaining an increased level of sustainable agricultural development, the absolute differences between the three major regions and the polarisation between regions remain to be addressed.

With regard to regional variations, the national, intraregional and interregional variations show a downward trend. The intraregional differences are, in descending order, between the western, central and eastern regions. The interregional differences are, in descending order, central–west, east–west, and east–central. In terms of variance contribution, interregional variance contributes the most, followed by intraregional variance, and hypervariable density. The combination of changes in regional variances and variance contributions leads to the conclusion that national and intra- and interregional variances have decreased, but interregional variances are still the first source of overall variance.

In terms of spatial convergence, the coefficients of variation of the level of sustainable agricultural development in the country as a whole and in the three regions show a downward trend, i.e., there is σ-convergence, which indicates that the degree of dispersion within the region is decreasing. Second, there is also absolute β-convergence and conditional β-convergence, indicating that the level of sustainable agricultural development in the country and in the three regions converges to their respective steady-state levels, with the fastest rate of convergence in the eastern region, taking into account the effects of the control variables.