Spatial Characteristics of Coupling Development of Ecological Protection and Agricultural Economy in China

Abstract

[Purpose] To explore the coupling coordination evolution law of China’s agricultural ecological environment and economic development, and provide reference and a decision-making basis for coordinating the relationship between the ecological environment and agricultural economy in the process of China’s agricultural economic development. [Method] Based on the coupling coordination mechanism between ecological protection and agricultural economy, this paper constructs an evaluation index system for agricultural ecological environment and economic growth, and explores the spatiotemporal evolution law of the coupling coordination between the agricultural ecological economic system in China’s 31 province administrative regions from 2010 to 2020. [Result] From 2010 to 2020, the overall development level of China’s agricultural ecology and economy was gradually improving; the level of coupling coordination increased significantly before 2015, but then the growth remained stable; the global Moran’s I index of coordination is “first descending and then ascending”; and the LISA cluster diagram of the local Moran’s I index was “east high and west low” before 2015, but then “west high and east low”. [Conclusions] The level of coupling and coordination development of agricultural ecological economic systems in various provinces has steadily increased, but the development level between regions is uneven. It is necessary to strengthen natural disaster management, increase investment in agricultural development, and enhance regional cooperation between regions to promote high-quality and green sustainable development of agricultural ecological economic systems. [Innovation] A new evaluation index system was constructed, the indicator data are complete and comprehensive (including data from 31 different province administrative regions from 2010 to 2020), and the analysis perspective is rich and diverse (through the comparison and changes of the time and space perspectives, the results from different perspectives were comprehensively analyzed). This paper finds that the coordination degree shows significant periodicity with 2015 as the boundary; the growth rate is “west fast and east slow”.

1. Introduction

Agriculture is the basis of the national economy, and ecology is the largest growth merit of the countryside. Keeping the ecological environment livable and suitable for business is the cornerstone of ensuring sustainable economic development, and a high-quality economic development model is the material basis for creating a green ecology. The latest Central Document No. 1 released in 2022 emphasizes that in order to promote the steady and healthy development of China’s economy and society, we must focus on the major strategic needs of the country, to stabilize the basic agricultural market and do a good job in the work of the “three rural”, such as further promoting the green growth of agriculture and rural regions, actively promoting the evaluation and research of the green and sustainable growth of agriculture, and boosting the stable and healthy growth of the agricultural ecological economy [1]. This profoundly demonstrates the importance of the “three rural issues” in the long-term social and economic development of China; correctly understanding and promoting the “three rural” work has important practical guiding significance for comprehensively driving rural revitalization, boosting the achievement of agricultural and rural modernization, and establishing a well-coordinated and efficient agricultural ecological economic system. However, with the rapid development of the agricultural economy, people pursue high yield and high efficiency excessively, continuously apply pesticides and fertilizers on the already-overloaded land, and use agricultural plastic films on a large scale, which cause increasingly serious problems such as damage to the agricultural ecological environment, and a shortage of arable land and freshwater resources. Therefore, how to coordinate the relationship between the ecological environment and agricultural economy in the process of agricultural economic development has become a focus of theoretical attention, public attention, and government attention.

Based on the above background, this paper hypothesizes that due to the dual pressures of ecological destruction and resource scarcity, China’s agricultural ecological environment problems are very serious and have become an important factor restricting the coordinated development of China’s agricultural ecology and economy. Based on this hypothesis, this paper constructs an evaluation index system for agricultural ecological environment and economic growth based on the coupling coordination mechanism between ecological protection and agricultural economy. It selects the agricultural ecological environment and economic development indicators and data of China’s 31 province administrative regions from 2010 to 2020, through using the coupling coordination model and spatial autocorrelation theory to study the system’s coupling coordination relationship and spatial agglomeration characteristics from two dimensions of time and space. The aim is to study the spatiotemporal evolution of China’s agricultural ecological economic system’s coupling coordination, and to provide a basis for formulating scientific and reasonable strategies and suggestions for high-quality agricultural development in China’s various regions. The innovation lies in constructing a new evaluation index system, the indicator data is complete and comprehensive (including data from 31 different provinces from 2010 to 2020), and the analysis perspective is rich and diverse (through the comparison and changes of the time and space perspectives, we comprehensively analyze the results from different perspectives).

2. Literature Review

2.1. Literature Overview of Agricultural Ecological Economic Systems

In the early 1980s, international scholars focused on the definition and research characteristics of sustainable development of agricultural ecological economic systems. The definition of sustainable development was clearly put forward in “Our Common Future” for the first time [2] (1987). SS Batie (1989) proposed that sustainable development means that the contemporary people ‘s development cannot impair the capacity of future generations to satisfy material demands and have a healthy environment, and we should pursue economic development and people’s material satisfaction under the premise of protecting the ecological environment to the greatest extent [3]. David R. Kanter (2018) believed that a dynamic, resilient, and high-yield agricultural economic system is the basis for achieving sustainable development goals, and the interaction between ecology and economy created the potential for synergies and trade-offs in agricultural ecological economic systems [4]. Ram Sigdel (2019) applied the dynamic data coupling theory of social ecology to analyze the methods of sustainable development of agricultural ecological economic systems [5]. China’s scholar Lu (1995) believed that the sustainable development of the agricultural ecological economic system is the foundation of sustainable social and economic growth, which includes two layers of connotation: the sustainable growth of the ecological economy, and the sustainable growth of resources and the environment in ecological economic development [6]. Ren (1999) proposed to use the system coupling theory to analyze the coupling and synergistic connection between the agricultural ecological environment system and the sustainable growth of the rural economic growth system, and constructed a large agricultural system structure [7]. This paper summarizes the above research, from the perspective of the interaction, mutual promotion, and balance between the agricultural ecological environment and the sustainable development of the agricultural economy, to jointly establish a structure of the agricultural ecological economic development system.

2.2. Literature Review on the Index Evaluation System of Agricultural Ecological Economic Systems

Lin (2020), based on the concept of the environmental safety boundary and the “doughnut” theory, constructed an ecological and economic index evaluation system with four dimensions, including “environmental carrying capacity, structural robustness, public demand, and technical efficiency”, and used multi-criteria analysis to measure China’s ecological economic development level of 30 province administrative regions in 2010 and 2015 [8]. Li (2020) took northwest China as an example to construct ecology and agriculture layers of ecological and agricultural systems, which include eight indicator layers, and construct the economic structure and urbanization level of the economic system, which include ten index layers; the research shows that, in this region, the coordination degree of the agricultural ecological economic system has significant diversities and there is a large room for improvement, so should improve the development status of the agricultural ecological economic system according to local conditions [9]. Yu (2020) established an index system of the coupling association between ecological security and economic growth on the basis of the DFESAR (Driving-Presser-Exposure-Sensitivity-Adaptation-Response) framework, and used the adjustment of state control variables to explore the optimal control model for the development of agricultural ecological economic systems; he proposed the suggestions on strengthening the implementation of the government’s green economic development policies for the green growth of agriculture in the Liaoning Province [10]. Huang (2014) took the Poyang Lake Ecological Economic Zone as an example, and constructed an ecological economic index evaluation system consisting of five dimensions: economy, ecology, resources, social development, and environmental protection, and the results show that the Poyang Lake Ecological Economic Zone has a low score, and there are obvious differences in the ecological economic index of each county, city, and district [11]. This paper comprehensively compares the above research, based on the coupling coordination mechanism between ecological protection and agricultural economy, using the DFSR model and from the perspective of the mutually progressive relationship between the system’s driving force state response structure to establish a comprehensive evaluation index system for economic growth and the agricultural ecological environment.

2.3. Literature Review on Agricultural Ecological Environment and Rural Economy

Ren (2021), by constructing an assessment index system for natural ecosystems and social and economic systems in the Gansu Province, conducted an empirical analysis of the coupling coordination degree model in the Gansu Province from 2007 to 2017; the research shows that the evolution process of the rate of change of the two systems in the Gansu Province has changed from having a large difference but no obvious restriction into developing simultaneously [12]. Tang (2020), based on the gravity model, studied the coupling coordination of economic growth and ecological environment in the Shaanxi Province, and he found that the overall coupling coordination is good, but the coupling coordination development is not balanced, and there are obvious differences [13]. Li (2016), based on the energy theory, calculated the development status of agricultural green GDP in China and various provincial units from 2003 to 2014; the result shows that China’s per capita agricultural green GDP is growing slowly, the spatial agglomeration of agricultural green economic growth is significant, and the agglomeration effect is gradually increasing [14]. Zhang (2017) used the scissors diversity analysis approach to study the coordination status of the agricultural ecological economic systems in the Anhui Province from 2005 to 2015, and he found that the coupling coordination degree changed from low-level coordination into whole coordination [15]. Xue (2022) used the entropy method, coupling coordination model and relative development degree model to analyze the spatiotemporal pattern of the coupling coordination degree between economic development and ecological environment in the Yellow River Basin from 2005 to 2018; he found that the coordination degree fluctuates and also decreases, that it is generally is in the stage of mild imbalance and near imbalance, and the spatial differences is obvious, so the level of coordination degree needs to be improved [16]. Zheng (2022), based on the perspective of green development, used the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) entropy method to calculate the coupling coordination dispatch of high-quality economic development and environmental protection in western China from 2004 to 2018, by analyzing the characteristics of regional differentiation and dynamic evolution trends, and he found that its coordination degree shows a slight increase in fluctuations, but the gap between the southwest regions gradually increases, and the convergence in the northwest region tends to strengthen [17]. Based on the above research conclusions, it is found that the coupling coordination level of agricultural ecological environment and economic development in China has gradually improved in recent years, but regional differences have become increasingly significant and spatial aggregation responses have gradually strengthened; this provides a reference for the conclusions for this paper.

In summary, based on different methods and perspectives, existing studies have analyzed the relationship between economic development and the ecological environment, but they are mostly concentrated in partial regions or individual provinces such as the Yangtze River Delta, northwest China, Beijing, Tianjin and Hebei, and relatively few studies have been conducted nationwide. In view of this, based on the coupling coordination degree model and spatial autocorrelation model, this paper conducts an empirical analysis of the spatial agglomeration development characteristics of the ecological economic coupling relationship and coordination degree in China’s 31 province administrative regions, comprehensively elaborating their own development coordination and regional relevance from both horizontal and vertical dimensions of time and space, proposing scientific countermeasures and suggestions, which has important reference significance for the implementation of sustainable development strategies in China.

3. Indicator Constructions, Research Methods, and Data Sources

3.1. The Theoretical Mechanism of Coupling Agricultural Ecology and Agricultural Economy

The agricultural ecological economic system is an environmental and social system, which formed by the coupling of the agricultural ecological environmental system and the agricultural economic system. It not only includes the material cycle and energy flow of the natural system, but also is affected by human economic and social activities, which reflect the information transmission and value flow between and within the system. The connection between the agricultural ecological environment system and the agricultural economic system is based on human economic and social activities, and the production goal is high-yield and high-quality agricultural products. Its conditions include agricultural biology, agricultural environment, economic society, and science and technology. Its factors include population, resources, environment, technology, material and capital (Figure 1).

3.1.1. The Agricultural Ecological Environment System Is the Foundation of the Agricultural Economic Development System

Agricultural economic activities take agricultural organisms as the object, and based on their natural life movement process, it carry out production activities according to the growth laws of biological organisms. The reproduction and growth of agricultural organisms cannot be separated from a specific ecological environment, requiring continuous material and energy exchange with the environment. The beginning of agricultural production is to use agricultural biological life activities to obtain various agricultural products to meet people’s material needs. In agricultural production practice, it is necessary to not only understand, master and utilize natural laws to engage in agricultural production, but also fully recognize the fundamental position of the agricultural ecological environment and the relationship between the agricultural economy and agricultural ecological environment, so as to better achieve the united benefits of economy, society and ecology.

3.1.2. The Agricultural Economic Development System Is the Leading Role of the Agricultural Ecological Environment System

The fundamental goal of agricultural production is through the effects of human activities on agricultural organisms to achieve expected agricultural results. As the main body of the agricultural ecological economic system, human beings not only have the ability to adapt to nature, but also can consciously use objective conditions to transform nature, affect the environment and obtain more agricultural products. With the development of human beings, agriculture has been evolving from low level to high level, and the agricultural economic connotation has also been constantly enriched. The agricultural economic leading role in the ecological environment has also become increasingly significant. Therefore, as a product of transforming and utilizing nature, the dominant agricultural economic system not only undertakes the function of maximizing economic benefits, but also has the function of protecting, maintaining and improving the balance for the agricultural ecological environment system. This requires the agricultural economic system to dominate the ecological environment system and continuously strengthen the fundamental role of the agricultural ecological environment system, so as to adapt to the needs of agricultural economic development.

In summary, the agricultural ecological environment system, through three aspects, “opulation, resources and environment”, affects the development level of the rural economy, while the rural economic development system, through three aspects, ”technology, materials and funds”, transforms the development trend of agricultural ecology; thereby, promoting high-quality development of the agricultural ecological economy.

3.2. Indicator Constructions

The DFSR index system evaluation model is the Driving Force-State-Response model, which is based on the PSR (Press-State-Response) model, and further expands the single-type evaluation index of the environmental category into a multi-category evaluation index including economic and social categories. In the method of systematic evaluation of indicators, “driving force” refers to the motive force for human beings to adapt to the social environment; “state” refers to the process of acquiring social and environmental resources, the changed state of social resource reserves, and environmental quality; and “response” refers to responding to state changes [18]. According to the relevant principles of the research data, based on the DFSR model, this paper from the three dimensions of driving force, state, and response set up a comprehensive assessment index system of agricultural ecological environment and rural economy. Finally, this paper selects 12 indicators such as pesticide usage, to reflect the agricultural ecological environment status of 31 province administrative regions, and select 11 indicators such as the total power of agricultural machinery, to reflect the agricultural economic development status, as shown in

Table 1. Evaluation indicators of agricultural ecological economic system.

	Indicator Classification	Index	Nature of Indicators	Weights
Agricultural ecological subsystem	Driving force	Pesticide usage (10,000 tons)	Negative	0.1072
		agricultural chemical fertilizer usage (10,000 tons)	Negative	0.0771
		Agricultural plastic film (10,000 tons)	Negative	0.1072
	State	Rural population (10,000 person)	Positive	0.0619
		Forest coverage (%)	Positive	0.1976
		Water resources per capita (m3/person)	Positive	0.0527
		Disaster area (1000 hectares)	Negative	0.0518
	Response	Efficient irrigation region (1000 hectares)	Positive	0.0591
		Drainage area (1000 hectares)	Positive	0.0803
		Water and soil loss control area (1000 hectares)	Positive	0.0814
		Total afforestation area (1000 hectares)	Positive	0.0695
		Total sown area of crops (1000 hectares)	Positive	0.0542
Agricultural Economic Subsystem	Driving force	Total power of agricultural machinery (10,000 kW)	Positive	0.0871
		Supporting agricultural tools for large- and medium-sized tractors (Department)	Positive	0.1067
		Number of combine harvesters (set)	Positive	0.0928
		Agricultural diesel consumption (10,000 tons)	Positive	0.082
	State	Grain output (10,000 tons)	Positive	0.0809
		Meat output (10,000 tons)	Positive	0.0994
		Disposable income of rural residents	Positive	0.0955
		Total agricultural output value (100 million yuan)	Positive	0.0902
		Per capita GDP (yuan/person)	Positive	0.0943
	Response	Added value of agriculture, forestry, animal husbandry and fishery (100 million yuan)	Positive	0.0886
		Incidence of poverty (%)	Negative	0.0825

. The data of every index mainly originate from the 2010–2020 “China Rural Statistical Yearbook”, “China Statistical Yearbook”, and “China Rural Poverty Monitoring Report”. The data were “interpolated” using the method of mean value of adjacent years and the method of backward calculation of growth ratio [19].

3.3. Coupling Coordination Model

Coupling means the result of balances between multiple systems, and it describes the tendency of a particular system to change its relationship at a certain point in time [20]; for example, the interaction between various components in a mechanical system, the interaction between electronic components in a circuit, and the dependency relationship between modules in a software system are all manifestations of coupling, which is akin to a philosopher saying “three monks have no water to drink, three Stooges are better than one Zhuge Liang”. The former is a combination of all elements in a system, but the total result is that it is difficult to form a purposeful system due to the lack of endogenous power, while the latter is the correlation and coupling of all elements within a system, which gives the system an overall purpose and forms an organic dynamic and collaborative system with “1 + 1” larger than 2. This concept was first applied to physics, and was gradually applied to the field of economics with continuous interdisciplinary development. Nevertheless, the coupling extent cannot effectively show the overall coordination of the two subsystems of the agricultural ecosystem and the rural economic system, so it is necessary to analyze the coupling coordination extent [21]. This paper selects the coupling degree (C) model to research the difference between the agricultural ecologic system and agricultural economic system, and selects the coupling coordination degree (D) to study their coordination state.

For the evaluation indexes of the two subsystems we constructed a spatial matrix, where m represents the evaluation index and n represents the year, obtaining the original data matrix X:

$$X={\left(\begin{array}{cccc}{x}_{11}& x_{12}& \cdots & x_{1n}\\ x_{21}& x_{22}& \cdots & x_{2n}\\ ⋮& ⋮& \cdots & ⋮\\ x_{m1}& x_{m2}& \cdots & x_{mn}\end{array}\right)}_{m\times n}$$

Nondimensionalization: In order to eliminate the influence of unit, size and positive and negative indicators among each index, the original data of each evaluation index are subjected to dimensionless processing, and a standard matrix is obtained. The formula is as follows:

If the indicator is a positive indicator (such as the pesticide usage):

$$y_{ij}=\frac{x_{ij}-min\left(x_i\right)}{max\left(x_i\right)-min\left(x_i\right)}$$

(1)

If the indicator is a negative indicator (such as the forest coverage):

$$y_{ij}=\frac{max\left(x_i\right)-x_{ij}}{max\left(x_i\right)-min\left(x_i\right)}$$

(2)

In Formulas (1) and (2):

x_ij is the original data and y_ij is the processed data, max(x_i) represents the maximum values of the data for the i-th indicator and min(x_i) represent the minimum values.

Positive translation: there is a situation in the standard matrix (when y_ij = 0, ln z_ij is undefined). For meeting the validity of the data, all y_ij will be standardized by adding 0.0001:

$$p_{ij}=y_{ij}+0.0001$$

(3)

Normalized. The specific formula is:

$$z_{ij}=\frac{p_{ij}}{{{\displaystyle \sum }}_{j=1}^n{p}_{ij}},\left(i=1,2,\cdots ,m\right)$$

(4)

After the above processing of the data, the weight of every indicator is determined by the entropy weight approach, and the objective weight is determined according to the variability of the index. The given steps are shown below:

Step 1: Calculate the entropy value.

$$H\left(x_i\right)=-k{\displaystyle \sum }_{j=1}^n{z}_{ij}ln{z}_{ij},\left(i=1,2,\cdots ,m\right)$$

(5)

H(x_i) is the entropy value of the i-th index, Adjustment factor k = 1/ln n.

Step 2: Calculate the weight of every index.

$$d_i=\frac{1-H\left(x_i\right)}{m-{{\displaystyle \sum }}_{i=1}^{m}H\left(x_i\right)},\left(i=1,2,\cdots ,m\right)$$

(6)

d_i is the weight of the i-th indicator, ${\displaystyle \sum }_{i=1}^m{d}_i=1$

Step 3: After obtaining the weights of the indicators of the two subsystems, calculate the integrated assessment value of the two subsystem.

$$U_j={\displaystyle \sum }_{i=1}^m{d}_i{y}_{ij},j=\left(1,2,\cdots ,n\right)$$

(7)

Step 4: Based on the coupling model, calculate the coupling degree C between the two subsystems.

$$C={\left[\frac{\left(U_{environment}\times U_{economics}\right)}{{\left(U_{environment}+U_{economics}\right)}^2}\right]}^{\frac{1}{2}}$$

(8)

Among them, U is the comprehensive evaluation value, and the value of C varies from 0 to 1. When C is larger, the coupling of the two subsystems is better.

Step 5: Based on the coupling coordination model, calculate the coupling coordination degree D between the two subsystems.

$$D={\left(C\times T\right)}^{\frac{1}{2}}$$

(9)

$$T=\alpha U_{environment}+\beta U_{economics}$$

(10)

The value scope of D is between 0 and 1. when D is larger, the coordination of the two subsystems is better. T is the comprehensive score between the two systems, where α and β are undetermined parameters. Considering that agricultural ecology and economy have the same degree of influence on the system, both are set to 0.5. Based on the previous research results, the calculated coordination degree value is used to judge the type of coordination degree, which shown in

Table 2. Discrimination criteria and division types of coupling coordination degree.

Level	D Value	Coordination Type	Level	D Value	Coordination Type
1	[0, 0.1)	Extreme maladjustment	6	[0.5, 0.6)	Grudging coordination
2	[0.1, 0.2)	Severe maladjustment	7	[0.6, 0.7)	Primary coordination
3	[0.2, 0.3)	Moderate maladjustment	8	[0.7, 0.8)	Intermediate coordination
4	[0.3, 0.4)	Mild maladjustment	9	[0.8, 0.9)	Good coordination
5	[0.4, 0.5)	verge of maladjustment	10	[0.9, 1)	High-quality coordination

:

3.4. Spatial Autocorrelation Analysis

Spatial autocorrelation analysis is a spatial statistical method, which is typically used to measure the regional structural morphology of neighboring spatial variables. The value scope of Moran’s indicator is [−1, 1]. If Moran’s index value is greater than 0, it displays that the variation tendency of the attribute value of a unit is the same as the variation tendency of its nearby spatial units, which means that there is agglomeration in the spatial phenomena; when the value is closer to 1, the spatial correlation will be stronger; if the index is less than 0, it displays that the variation tendency of the attribute value of a unit is opposite to the variation tendency of its nearby spatial units, which means that there is heterogeneity in the spatial phenomenon; when the value is closer to −1, the spatial heterogeneity will be greater; if the value is equal to 0, it displays no spatial correlation [22].

3.4.1. Global Spatial Autocorrelation

The global Moran’s I index describes that in the entire spatial area, a certain spatial attribute value has a spatial dependence and agglomeration effect. Its calculation formula is shown below:

$$\begin{gathered} I=\frac{n{{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j=1}^n{W}_{ij}\left(Y_i-\overline{Y}\right)\left(Y_j-\overline{Y}\right)}{{{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j=1}^n{W}_{ij}{{\displaystyle \sum }}_{i=1}^n{(Y_i-\overline{Y})}^2} \\ =\frac{{{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j=1}^n{W}_{ij}\left(Y_i-\overline{Y}\right)\left(Y_j-\overline{Y}\right)}{S^2{{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j\ne i}^n{W}_{ij}} \end{gathered}$$

(11)

$\overline{Y}$ is meaning$: \overline{Y}=\frac{{{\displaystyle \sum }}_{i=1}^n{Y}_i}{n}$,$\mathrm{and} S^2 \mathrm{is}$sample variance$: S^2=\frac{{{\displaystyle \sum }}_{i=1}^n{\left(Y_i-\overline{Y}\right)}^2}{n}$.

Among them, n is the total number of provinces, Y_i and Y_j is the coordination degree, and, based on the proximity relationship between spatial units i and j, W_ij is the spatial weight matrix which is established. If i and j are proximal, w_ij is 1, otherwise w_ij is 0. The Hainan Province is on Hainan Island, so this paper assumes that it is not proximal to any province.

3.4.2. Local Spatial Autocorrelation

The local Moran’s I index describes that in the local spatial area, a certain region’s spatial attribute value has a spatial correlation. In other words, a local sub-region has spatial agglomeration or heterogeneity. The calculation formula is as follows:

$$I_i=\frac{Y_i-\overline{Y}}{S^2}{\displaystyle \sum }_{j=1}^n{W}_{ij}\left(Y_j-\overline{Y}\right)$$

(12)

Formula’s variables have the same meaning as global variables.

3.4.3. LISA Cluster Map

The LISA (Local Indicators of Spatial Association) cluster map is a regional agglomeration state map that Aselin based on the result of the local Moran’s I model to establish [23]. It is mainly divided into four different spatial layout results: H-H (high-high) type, H-L (high-low) type, L-L (low-low) type and L-H (low-high) type. This paper use Geoda spatial data analysis software to calculate the spatial Moran’s I index and its spatial distribution characteristics.

4. Results

According to the entropy weight method, this paper calculates the entropy weight comprehensive evaluation value, and coupling coordination degrees of the agricultural ecosystem and economic system in China’s 31 province administrative regions.

Table 3. The evaluation value of Chinese agricultural ecological system and economic system.

Region	Environmental Evaluation Value				Economic Evaluation Value
	2010	2015	2020	Mean	2010	2015	2020	Mean
China	0.19	0.5	0.9	0.48	0.1	0.72	0.69	0.55
Beijing	0.42	0.5	0.55	0.51	0.54	0.58	0.37	0.54
Tianjin	0.4	0.5	0.59	0.5	0.37	0.69	0.48	0.55
Hebei	0.41	0.4	0.62	0.47	0.24	0.65	0.58	0.53
Shanxi	0.34	0.42	0.73	0.5	0.26	0.65	0.63	0.54
Neimenggu	0.43	0.5	0.65	0.51	0.17	0.65	0.8	0.55
Liaoning	0.48	0.45	0.7	0.51	0.23	0.74	0.59	0.54
Jilin	0.51	0.54	0.69	0.54	0.13	0.68	0.69	0.55
Heilongjiang	0.38	0.52	0.91	0.52	0.12	0.67	0.77	0.57
Shanghai	0.36	0.72	0.65	0.55	0.35	0.72	0.42	0.56
Jiangsu	0.57	0.28	0.47	0.42	0.16	0.72	0.78	0.59
Zhejiang	0.46	0.55	0.62	0.49	0.33	0.59	0.53	0.55
Anhui	0.38	0.59	0.66	0.49	0.09	0.69	0.81	0.56
Fujian	0.3	0.51	0.73	0.48	0.07	0.58	0.75	0.5
Jiangxi	0.28	0.61	0.78	0.52	0.17	0.55	0.71	0.51
Shandong	0.2	0.56	0.85	0.51	0.27	0.67	0.57	0.54
Henan	0.45	0.56	0.81	0.51	0.2	0.67	0.58	0.5
Hubei	0.19	0.56	0.81	0.5	0.12	0.7	0.67	0.56
Hunan	0.4	0.55	0.78	0.49	0.09	0.63	0.77	0.54
Guangdong	0.46	0.38	0.69	0.44	0.16	0.54	0.69	0.48
Guangxi	0.39	0.63	0.69	0.53	0.09	0.68	0.63	0.52
Hainan	0.42	0.52	0.72	0.53	0.14	0.57	0.66	0.52
Chongqing	0.27	0.43	0.68	0.42	0.17	0.57	0.81	0.55
Sichuan	0.21	0.49	0.87	0.48	0.16	0.65	0.73	0.56
Guizhou	0.32	0.6	0.81	0.53	0.11	0.68	0.83	0.56
Yunnan	0.38	0.52	0.79	0.51	0.14	0.64	0.67	0.53
Xizang	0.27	0.46	0.66	0.44	0.13	0.58	0.63	0.47
Shaanxi	0.59	0.47	0.64	0.51	0.15	0.65	0.7	0.54
Gansu	0.39	0.38	0.85	0.49	0.13	0.62	0.73	0.49
Qinghai	0.33	0.44	0.8	0.49	0.11	0.57	0.84	0.51
Ningxia	0.38	0.41	0.69	0.46	0.16	0.6	0.72	0.51
Xinjiang	0.56	0.48	0.52	0.49	0.06	0.61	0.79	0.51

and

Table 4. The coupling and coordination of China’s agricultural ecological economic system.

Region	Coupling Degree				Coordination Degree
	2010	2015	2020	Mean	2010	2015	2020	Mean
Country	0.57	0.49	0.5	0.49	0.26	0.55	0.63	0.51
Beijing	0.5	0.5	0.49	0.5	0.49	0.52	0.48	0.51
Tianjin	0.5	0.49	0.5	0.5	0.44	0.54	0.52	0.51
Hebei	0.48	0.48	0.5	0.5	0.4	0.5	0.55	0.5
Shanxi	0.5	0.49	0.5	0.5	0.39	0.51	0.58	0.51
Neimenggu	0.45	0.5	0.5	0.5	0.37	0.53	0.59	0.52
Liaoning	0.47	0.49	0.5	0.5	0.41	0.54	0.57	0.51
Jilin	0.4	0.5	0.5	0.5	0.36	0.55	0.59	0.52
Heilongjiang	0.42	0.5	0.5	0.5	0.32	0.54	0.65	0.52
Shanghai	0.5	0.5	0.49	0.5	0.42	0.6	0.51	0.53
Jiangsu	0.41	0.45	0.48	0.49	0.39	0.47	0.55	0.5
Zhejiang	0.49	0.5	0.5	0.5	0.44	0.54	0.53	0.51
Anhui	0.39	0.5	0.5	0.5	0.3	0.57	0.6	0.51
Fujian	0.39	0.5	0.5	0.5	0.27	0.52	0.61	0.49
Jiangxi	0.48	0.5	0.5	0.5	0.33	0.54	0.61	0.51
Shandong	0.49	0.5	0.49	0.5	0.34	0.55	0.59	0.51
Henan	0.46	0.5	0.49	0.5	0.39	0.55	0.59	0.5
Hubei	0.49	0.5	0.5	0.5	0.27	0.56	0.61	0.51
Hunan	0.39	0.5	0.5	0.5	0.31	0.54	0.62	0.51
Guangdong	0.44	0.49	0.5	0.5	0.37	0.48	0.59	0.48
Guangxi	0.4	0.5	0.5	0.5	0.31	0.57	0.58	0.51
Hainan	0.43	0.5	0.5	0.5	0.35	0.52	0.59	0.51
Chongqing	0.49	0.49	0.5	0.5	0.33	0.49	0.61	0.49
Sichuan	0.5	0.5	0.5	0.5	0.3	0.53	0.63	0.51
Guizhou	0.44	0.5	0.5	0.5	0.31	0.57	0.64	0.52
Yunnan	0.44	0.5	0.5	0.5	0.34	0.54	0.6	0.51
Xizang	0.47	0.5	0.5	0.5	0.31	0.51	0.57	0.48
Shaanxi	0.4	0.49	0.5	0.5	0.39	0.53	0.58	0.51
Gansu	0.43	0.49	0.5	0.5	0.33	0.49	0.63	0.49
Qinghai	0.43	0.5	0.5	0.5	0.31	0.49	0.64	0.5
Ningxia	0.45	0.49	0.5	0.5	0.35	0.49	0.59	0.49
Xinjiang	0.3	0.5	0.49	0.5	0.31	0.52	0.57	0.5

show the results. Limited by the layout and data representation, it only lists the results of 2010, 2015, and 2020 in the table, where the mean represents the average results from 2010 to 2020:

4.1. Evolution Characteristics of Comprehensive Evaluation Value of the Ecological Environment

In Table 3, the comprehensive evaluation degree of the agricultural ecological environment in China and 31 provinces’ administrative regions displayed an increasing trend in general from 2010 to 2020, but the annual changes vary greatly from place to place.

(1) The year when many provinces’ comprehensive evaluation values were decreased. In 2013, except for the Fujian, Hubei, Gansu, Hubei, and Sichuan Provinces, the comprehensive evaluation value of other provinces showed a downward trend. The main reason is that in 2013, there were floods in the south and there was drought with the high temperatures in the north; there were many earthquake disasters in the southwest, and severe haze weather in the north; and the strong winds and hail were frequent and relatively serious in China.

(2) There are obvious diversities in development between areas; the early result is “east high and west low”, but later on, the growth rate is “west fast and east slow”. Among the eastern provinces, Jiangsu Province has been at a relatively low level after 2014. The main reason is that Jiangsu is located in the eastern part of China, with a large population and a lot of development, there were relatively many industrial enterprises, and the industrial polluting and exhaust gas discharging were relatively large, so the development of the ecological environment was relatively poor. The comprehensive environmental evaluation value of Hebei fluctuates up and down, because there are many heavy industries such as steel production in the Hebei Province, although it has taken many positive measures to address environmental pollution issues, but the effectiveness is still limited and the governance effect is not very good. The overall ecological evaluation level of northeast China is higher than that of other regions in China, and the Heilongjiang Province is at a relatively high level among northeast, because the northeast has good environmental resource advantages; especially the Heilongjiang Province, which is a vast territory with a sparse population, having vast black land and it is free from heavy industry pollution and human pollution. Among the central provinces, Henan and Hubei have better development levels, mainly because the ecological agriculture industry in this region is relatively mature compared to other regions, the level of intensification and mechanization is higher, and so it has a high agricultural yield and a lower agricultural pollution level. Among the western provinces, Sichuan, Gansu, Qinghai, and Guizhou have better development trends, mainly because the development prospects of agricultural ecological tourism in this region have been better in recent years, which has promoted the restoration and protection of the local ecological environment.

4.2. The Evolution Characteristics of the Comprehensive Evaluation Value of Economic Development

In China’s 31 province administrative regions, the overall evaluation level of agricultural economic growth displays an increasing trend from 2010 to 2020, but the annual development of different regions varies greatly.

(1) The year when the comprehensive evaluation value was decreased. In 2018, except for the four provinces of Anhui, Guangdong, Hainan, and Jiangxi, the comprehensive evaluation values have decreased slightly in other provinces, and Beijing has gradually decreased since 2014. The main reason is that there occurred serious international trade frictions in 2018, which led to a huge impact on China’s agricultural export trade, and the development of the agricultural economy was generally restricted to a certain extent, resulting in the economic evaluation values generally declining.

(2) In Table 3, the east has a higher starting point, but the annual growth rate of economic evaluation values in the west is higher than that in the east. From the time span, it can be seen that while the agricultural economy is steadily rising, the economic development barycenter has gradually shifted from the east to the west. Especially the three provinces of Guizhou, Qinghai, and Chongqing, although their economic foundation is low, but the development is rapid, so the economic evaluation values of these three provinces ranked from the bottom of the development ranking in 2010 to the top three in 2020 among the west. The main reason is that, in recent years, driven by the growth strategy of the China’s Western Development, the western provinces have made full use of their own resources’ potential and advantages, vigorously developing featured product agriculture, agricultural cultural tourism, green agriculture, and other new types of agriculture, so they gradually stood out from others provinces. Although the east has made a certain progress in agriculture, but preliminary development maturity around 2010, so the development reached a relatively bottleneck period and received certain constraints, the industry has a development trend of shifting to the west.

4.3. Evolution Characteristics of Coupling Coordination Degree between the Agricultural Ecological Environment and Rural Economic Development

Based on the coupling coordination model, this paper calculates the coupling coordination level of the agriculture ecological economic system in 31 province administrative regions, Table 4 shows the results.

(1) The increasing trend of coupling coordination is evident. During the research period, the 31 province administrative regions’ coupling coordination degrees were between 0.27 and 0.49 in 2010, all of which were in an imbalance stage; in 2015, the coupling coordination degrees were between 0.47 and 0.6, with only six provinces in a state of imbalance, which were Jiangsu, Guangdong, Chongqing, Gansu, Qinghai, and Ningxia. In 2020, the coupling coordination degrees were between 0.48 and 0.65, except for Beijing; all 30 provinces achieved coordination, but none of them reached the stage of high-quality coordination.

(2) From a regional perspective, there are certain differences in the level of development among different regions. Some developed provinces, such as Beijing, Shanghai, and Tianjin, have relatively small fluctuations, and the coordination degrees have slightly decreased in recent years. Other provinces, such as Fujian, Heilongjiang, Guizhou, and Qinghai, have significantly increased their coordination degrees, from a very low level in 2010 to a high level in 2020.

(3) Overall, the center of the coordinated growth point is moving from the east to the west. In 2010, the coordination levels of eastern provinces (such as Beijing, Tianjin, Hebei, Shanghai, Zhejiang) were significantly higher than those of western provinces (such as Qinghai, Guizhou, Sichuan, Gansu, Chongqing), but in 2015 and 2020, western provinces gradually overtook eastern provinces, which indicates that the coordination level’s regional differences are gradually shifting from “ east high and west low” to “ west high and east low “.

Based on the agricultural ecological economic system’s coordination degree in China’s 31 province administrative regions in 2010, 2015 and 2020, through comparing the values in Table 4 with the discrimination criteria and division types in Table 2, this paper draws a comparative map of the distribution characteristics, as shown in Figure 2.

In Figure 2, It can be seen that in 2010, all of China’s 31 province administrative regions were in a state of imbalance. By 2020, the coupling coordination levels of all 31 province administrative regions had achieved “upgrading”. It indicates that under the influence of the coordinated development concept, the production process in China’s agricultural economy has become more scientific, efficient and environmentally friendly, and agricultural production methods have become more intensive. It reflects that the good trend of green development in China’s agriculture has driven its economic development and improved its coupling coordination. Especially in the Heilongjiang Province, most of the western areas and most of the southern areas, the development speed is relatively fast and has reached a primary coordination level by 2020. This figure can also indicate that the coordination level’s regional differences are gradually shifting from “east high and west low” to “west high and east low”.

4.4. Analysis of Spatial Autocorrelation Results

According to the spatial autocorrelation theoretical model, using Geoda software, this paper calculates the global Moran’s I index of agricultural ecological economic system coordination in China’s 31 province administrative regions from 2010 to 2020, as shown in Figure 3.

(1) The changing trend of the global Moran’s I index showed significant stages, which generally is “first descending and then ascending” and forms a low-lying area from 2013–2017. From the analysis of the overall Moran’s I index, the spatial autocorrelation of China’s agricultural ecological economic system can be divided into two stages: in the first stage, from 2010 to 2015, the Moran’s I index changed from a strong positive correlation to a weak negative correlation year by year; in the second stage, from 2015 to 2020, it transformed from a weak negative correlation into strong positive correlation year by year. The first reason is that the 2008 economic crisis broke the general tendency of China’s agricultural economic growth and hit the confidence in the steady growth of China’s agricultural economic development. So from 2010 to 2015, each province hoped to seek breakthroughs and industrial transformation, and the east was striving to gradually eliminate low-end agricultural production models, while the west also wanted to break through traditional inefficient agriculture, so the Moran’s I index was decreasing. The second reason is that from 2015 to 2020, China formulated a series of guidelines and policies to boost economic development, which advocate the coordinated growth of regional ecology and economy, and the east has gradually eliminated “high pollution and low efficiency” agricultural production models, while the west has also utilized the advantages of characteristic products to develop electronic commerce agriculture and agricultural ecological tourism. The agricultural ecological economic system has gradually shifted from a transitional decline period to a recovery development period, and achieved the agricultural green development and development gathering, which increases the spatial agglomeration level, so the Moran’s I index is increasing.

(2) The local Moran’s I index varies greatly among regions, the early result is “east high and west low”, but later is “west high and east low”, and the growth rate is “west fast and east slow”. From the analysis of the LISA cluster map results (Figure 4), in 2010, the Beijing–Tianjin–Hebei region was a high-high agglomeration area, Guangdong was a high-low agglomeration area, southwest and west were low-low agglomeration areas, and the aggregation was significant. In 2015, the agglomeration was weakened, Jiangsu was a low-high agglomeration area, Xinjiang was a low-low agglomeration area, and other regions did not have significant agglomerating characteristics. By 2020, the agglomeration was significantly increasing, the Beijing–Tianjin–Hebei area was a low-low agglomeration area, the west was a high-high agglomeration area. Comparing to the 2010 results, the eastern coast was a high-high agglomeration area in 2010, but this was the western inland in 2020; and the western inland area was a low-low agglomeration area in 2010, but this was the eastern coastal area in 2020; the results were exactly the opposite before and after, which shows a significant mutual transfer between the high area and low area.

Based on the above analysis results, and comparing with the results of Lin (2020) [8], Liu (2021) [24], Huang (2022) [25], Zhang (2022) [26], and others, they all proposed that in recent years, the comprehensive evaluation values of agricultural ecological economic systems in China’s 31 province administrative regions have steadily increased. The coordination degree has gradually transitioned from being mainly at the mild maladjustment to being mainly at the primary coordination, and there are significant differences in development levels between regions, with the east starting early as the good foundation. Although the west has a low foundation, its growth rate is higher than that of the east, so the western development advantages are gradually emerging, and the development barycenter is transferring from east to west. This coincides with the results of this paper, so it provides strong data and result confirmation for this paper’s conclusions.

5. Conclusions and Policy Recommendations

5.1. Conclusions

This paper establishes an indicator assessment system based on the DFSR model, adopts the entropy weight method to decide the weight of every indicator, and through the coupling coordination degree model to comprehensively evaluate the coupling coordination status of agricultural ecological economic systems in China’s 31 province administrative regions from 2010 to 2020, finally using the spatial autocorrelation model to analyze the spatial agglomeration feature of each province‘s coordination degree. The conclusions are as follows:

5.1.1. Conclusions

(1) The comprehensive evaluation level of the ecological environment and economic development have grown steadily, but the regional variation characteristics are significantly different. First, from 2010 to 2020, most provinces’ agricultural ecological evaluation values were growing stably; the early result was “east high and west low”, but later on it was “west high and east low”. Second, the 31 provinces’ agriculture economic evaluation values have generally developed relatively well, but the annual growth rate in the west is higher than that in the east. Overall, The results of agricultural ecology and economic evaluation shifted from the early “east high and west low” to the later “west high and east low” in 2010–2020.

(2) The coupling coordination degree has grown steadily, but the level has not reached the high-quality coordination level. The coordination degree has generally risen steadily; by 2020 it gradually reached a medium-to-upper level of about 0.5–0.6. The gap between the ecological environment and economic development was gradually narrowing, and the coordination degree development continued to improve. Especially, the central, western, and northeastern provinces’ coordination degrees have steadily improved, although some eastern provinces (such as Beijing, Tianjin, Shanghai, and Zhejiang), had a relatively high starting point for coordination degree, but fluctuated significantly, with the growth rate lower than other provinces. Finally, The agricultural ecological economic system’s coordination in each province has not reached the level of high-quality coordination.

(3) The coupling coordination degree has significant aggregation change characteristics, and regional discrepant characteristics are obvious. In the spatial distribution, before 2015, the east had high-high aggregation, and the west had low-low aggregation. However, the global Moran’s I decreased year by year, and the correlation gradually weakened. After 2015, the low-low aggregation in the eastern provinces was more significant, and the high-high aggregation in the western provinces was more significant, and the correlation was gradually strengthened. Overall, comparing before 2015 and after 2015, the spatial autocorrelation gradually weakened before 2015, and gradually strengthened after 2015, which formed a low-lying area from 2013–2017. The early result is “east high and west low”, but later is “west high and east low”, and it shows significant aggregation and the aggregation feature is exactly opposite transformation before period and after period.

5.1.2. Summary

Based on the above conclusions, the summary is drawn as follows:

(1) China’s comprehensive evaluation level of the agricultural ecological environment and economic development has developed well, but the east has the problem of the development speed lagging. The eastern development level was surpassed by the western, because the east started early and relied heavily on international and external factors, but it encountered development bottlenecks in the later stage. Although there was some growth, the development pressure increased and the speed lagged behind.

(2) The coordination degree of China’s agricultural ecological economy system is gradually increasing, but it has not reached a high-quality coordination level. Mainly due to the population growing rapidly, environment polluting is serious and resource wasting is huge, which seriously restricts the coordinated development; China is still in an extensive development model.

(3) The spatial aggregation of China’s agricultural ecological economic system’s coordination degree is “first descending and then ascending”, but the east changed from a high-high agglomeration area to a low-low agglomeration area. “First descending and then ascending” shows the process of the agricultural industrial transformation and upgrading; the west has transformed from a low region to a high region, because it has utilized its regional advantages to form the new types of agriculture such as feature product agriculture, electronic commerce agriculture, ecological tourism agriculture, and highly mechanized agriculture. However, the eastern new agriculture has not yet matured, so the task of realizing the coordinated growth is still very arduous.

5.2. Policy Recommendations and Significance

Recently, China’s agricultural ecological economic system’s comprehensive evaluation levels and coupling coordination degrees in different provinces have risen steadily, but the overall development level is unbalanced. Therefore, the following recommendations are made:

(1)

Optimizing agricultural industrial structure, promoting agricultural technological innovation

The government actively guides, and the enterprises actively take the initiative, strengthening investment in agricultural economic development and industrial transformation upgrading. China’s traditional agricultural development model also belongs to an extensive development model, which has caused serious problems such as resource wasting, ecological environment degradation and environmental pollution. Essentially, this is a high investment, but low efficiency and an unsustainable development model. China’s governments and enterprises should pay more attention to increasing technological investment, vigorously promote agricultural technological innovation, firmly grasping the current emerging digital technology opportunities, empowering agriculture with digital technology to achieve high-quality development. Especially the east provinces (such as Beijing, Tianjin, Shanghai, and Jiangsu) should draw on the experience of the western new agricultural development such as feature product agriculture, electronic commerce agriculture, ecological tourism agriculture and highly mechanized agriculture, to ultimately improve the development level of agricultural modernization, and achieve overall high-quality agricultural development.

(2)

Strengthen ecological environment protection, promote low-carbon economic development

Empirical research shows that the factors that significantly affect the level of agricultural green development are mainly reflected in the resource environment, production efficiency, and environmental protection. Therefore, the Beijing–Tianjin–Hebei region should focus on the ecological environment, gradually restoring the destroyed ecological areas and decreasing the application of pesticides and fertilizers, such as promoting organic fertilizers to replace ordinary fertilizers; this paper encourages regions to strengthen the digitization of agricultural water conservancy facilities and use water-saving facilities to reduce agricultural water consumption, so as to develop low-carbon industries, explore innovative development paths for agricultural ecological economy.

(3)

Improve regional coordination and cooperation, assist in the revitalization of the agricultural economy

First, the eastern early development advantages are obvious, but in the later stage the ecological environment development is relatively fragile. Therefore, it is necessary to strengthen ecological environment protection, eliminate “high energy consumption and high pollution” enterprises, promote the upgrading of enterprise industrial chains and promote cross-regional economic development. Second, the western early development is relatively backward, while the later development is relatively good. Therefore, it is necessary to continue accelerating economic construction, but not follow the development model of “developing while polluting, and treating pollution afterwards”, so as to promote the upgrading of the agricultural industrial technological innovation and achieve sustainable development. Therefore, each region should fully leverage its regional advantages, enhance regional cooperation, fill development gaps, and narrow regional disparities, so as to accelerate the revitalization of the agricultural economy, promote the development of new agriculture and form a good situation for regional coordinated development.

5.3. Limitations and Future Research

The coupling system of the agricultural ecological environment and agricultural economic development is a relatively complex comprehensive system, covering a very wide range. This paper attempts from the geographical space–time perspectives to study China’s agricultural ecological economic system coordinated development, and analyze it from a relatively complete perspective.

However, this paper still has limitations and can be improved in the future. Firstly, the research on the coupling coordinated development of agricultural ecological economic systems in this paper is mainly based on the analysis and research of system coupling. If integrating other theories, it will be more able to quantitatively reflect the development level of the agricultural ecological economy, such as the energy theory. Second, this paper has not predicted the coupling trend of the Chinese agricultural ecological economic system, so further research can focus on how to choose appropriate methods to predict and determine the coupling trend of the system, such as the introduction of linear regression models.