Evaluation of Green Agricultural Development and Its Influencing Factors under the Framework of Sustainable Development Goals: Case Study of Lincang City, an Underdeveloped Mountainous Region of China

Abstract

This study aims to assess the current status of green agricultural development and its influencing factors in Lincang City, a national innovation demonstration zone for sustainable development; it also seeks to enhance the potential and competitiveness of green agricultural development in underdeveloped border areas. To achieve this, an evaluation index system is constructed encompassing six dimensions. Using a coupled coordination and obstacle degree approach, this study explores the spatiotemporal differences in the level of green agricultural and sustainable development, as well as the power, coupled coordination degree, and factors that negatively impact green agricultural development in Lincang City from 2010 to 2019. The Liang-Kleeman information flow method is applied to uncover the key information flow factors that influence the coupled coordination degree in each county and district of Lincang City. The results reveal several insights: First, the comprehensive score of sustainable green agricultural development increased from 0.4405 to 0.5975 during the study period. Second, the coupling coordination degree of green agricultural development was relatively low, fluctuating between 0.1821 and 0.2816. Overall, the development has shifted from severe imbalance to mild imbalance. Third, the obstacle degree increased by 3.75%. From a systemic perspective, the “resource conservation” layer had the highest barrier level, with the maximum value being observed in Yun County at 25.5%. Further analysis of the indicators reveals that the use of outdated water-saving irrigation techniques has resulted in low irrigation efficiency and excessive water resource waste. This is the main cause of the high barrier levels in terms of water-saving irrigation intensity and effective irrigation area. Moreover, the excessive use of chemical pesticides to enhance vegetable production has contributed to high barrier levels for achieving yields of pollution-free vegetable production per unit area. Finally, the information flow values of the factors influencing the coordinated and harmonious development of green agriculture exhibit significant regional heterogeneity among counties and districts. The highest information flow value for the area of drought- and flood-resistant crop cultivation is in Zhengkang County at 1.86. Based on these results, local government departments and decision-makers should focus on promoting comprehensive improvements in the level of green agricultural development. It is crucial to tailor measures to the specific needs of each county to address the shortcomings in green agricultural development. Additionally, efforts should be made to strengthen the innovation-driven chain of green agricultural development, including production, processing and sales. Enhancing the green agricultural development system is essential for long-term progress.

1. Introduction

Agriculture is a vital component of human society and the cornerstone of human survival, providing sustenance and serving as a key driver of economic development and social progress [1]. With the global population surpassing 7 billion people, agriculture has undergone rapid expansion to utilize all available resources and meet the growing demand for food [2,3]. Though playing a crucial role in socioeconomic development [4], agriculture is also a major driver of public health issues, biodiversity loss, and natural resource degradation [5,6]. To address these issues, green development has emerged as an important strategy for achieving high-quality and environmentally friendly agriculture [7]. Sustainable agriculture directly aligns with Goal 2 (SDG2), one of the 17 Sustainable Development Goals, and is interconnected with the other 16 goals, making it pivotal for overall development [8,9,10].

Environmental degradation has become a pressing global issue in recent years [11]. Since its reform and opening-up period, China has experienced tremendous development as well as significant ecological and environmental issues [12]. The extensive growth of Chinese agriculture has resulted in severe environmental pollution [13]. According to the Second National Pollution Census Report [14], as of 2017, agricultural emissions in China, including chemical oxygen demand, total nitrogen, and total phosphorus, reached 10.67 million tons, 1.41 million tons, and 0.212 million tons, respectively, accounting for 49.77%, 46.52%, and 67.21% of the country’s total emissions. Given this context, it is crucial to explore strategies for optimizing resource allocation, enhancing agricultural development efficiency, addressing resource limitations, mitigating environmental constraints, and ultimately achieving green agricultural development through the utilization of existing resources and scientific and technological innovations [15,16,17,18]. Green agricultural development is an important approach to address climate change, build sustainable ecological communities, and achieve common goals globally [19]; it is also a fundamental solution to transform development patterns and restore the natural ecological environment [20]. Consequently, the government has placed increasing emphasis on the advancement of green agriculture [21]. Green development in the context of agriculture is an approach that takes resources and environmental-carrying capacity as a prerequisite, relies on the utilization of various modern technologies, and emphasizes output efficiency, circular development, and resource conservation. It encompasses multiple categories of green development, including industry, layout, production resources, products, and consumption [7,22]. Ecological agriculture, organic agriculture, circular agriculture, natural agriculture, and sustainable agriculture are commonly categorized under the research domain of green agriculture [23,24].

Green agricultural development is one of the focal issues of concern in disciplines such as agronomy, environmental science, and geography. In recent years, the connotation of green development in agriculture, the construction of evaluation indicator systems, and the measurement of development level have attracted widespread attention from scholars [7,22]. However, overall, green agricultural green development still faces numerous difficulties and challenges, such as insufficient supply of green and high-quality agricultural products and the lack of a sound mechanism to incentivize and constrain green development. Vigorous efforts are needed to promote a comprehensive green transformation in agricultural development [25]. The concept of “Green growth” provides important insights and imparts relatively specific characteristics to green development, namely, creating more value at the cost of lower resource consumption and environmental pollution [26].

The construction of an indicator system to measure and evaluate green agricultural development is important for assessing its effectiveness [10,27,28]. Scholars have attempted to build comprehensive evaluation indicator systems to measure the level of green agricultural development [29] and regional disparities in said development [30]. Existing research on green agricultural green development spans various scales, including national [10,19,31,32], provincial [33,34], and specific regional scales focusing on—for example—major grain-producing areas [28,35,36]. These studies can be categorized into five main areas: The conceptualization of green agricultural green development, pathways for achieving green agricultural green development, factors influencing green development, assessment of the current status of green agricultural development, and investigations of ecological benefits. Many scholars have used methods, such as comprehensive evaluation [29,35,37,38], Analytic Hierarchy Process [39], Ecological Footprint [40], Stochastic Frontier Analysis [41], Data Envelopment Analysis [42], Coupling Coordination models [10], Nutrient Flows in Food Chain, Environment and Resource Use models [28], system dynamics models [43], and spatial correlation network structures [27]. However, there is currently a lack of research from an information flow perspective to reveal the key driving factors of green agricultural development.

This study aims to assess the current status of green agricultural development and its influencing factors in Lincang City, a national innovation demonstration zone for sustainable development; it also seeks to enhance the potential and competitiveness of green agricultural development in underdeveloped border areas. This study addresses two key questions: (1) At which coupling stage is the green agricultural green development in Lincang City, a national sustainable development demonstration zone? (2) What are the influencing factors of green agricultural development in Lincang City, which is a national development demonstration zone?

This work makes three main contributions to the literature: First, an evaluation index system is constructed for green agricultural development in Lincang City, a national sustainable development demonstration zone, encompassing six dimensions: green production, resource conservation, environmental friendliness, ecological protection, economic growth, and social development. Second, it complements the comprehensive assessment of county-level green agricultural development Liang-Kleeman information flow to clarify non-linear relationships between independent and dependent variables, thus revealing key factors influencing green agricultural development. Third, the challenges faced by green agricultural development in Lincang City are identified and corresponding recommendations and measures are outlined.

The rest of the paper is organized as follows. Section 2 comprises an introduction to the study area, the methodology used, and the data sources supporting this research. Section 3 presents the main results. Section 4 contains an in-depth discussion on the main results and the study’s limitations. Last, Section 5 provides a brief summary and concluding remarks.

2. Materials and Methodology

2.1. Study Area

Lincang City is located in the southwestern part of Yunnan Province, China (Figure 1). In 2019, it was approved as a National Sustainable Development Innovation Demonstration Area with “borderland, multi-ethnic, and underdeveloped” thematic designations. It is situated between Lincang River and Nu River and has abundant water resources. The annual average temperature is 17.3 °C, and the annual average rainfall ranges from 920 to 1750 mm (https://www.lincang.gov.cn/zjlc/fyxj.htm?eqid=d31d6519000003d100000006643f7e4b accessed on 29 July 2023). Its unique topography and climatic conditions have shaped a long history of agricultural development in the region and a distinctive planting structure. Key food crops cultivated in the area include rice, corn, wheat, and soybeans. In 2019, the total agricultural output value reached CNY 20.065 billion, contributing to 62.11% of the total output value of the agriculture, forestry, animal husbandry, and fishery sectors.

2.2. Materials

County-level education and socioeconomic data for Lincang City from 2010 to 2019 were sourced from Lincang Statistical Yearbook, Lincang Leadership Handbook, Lincang Environmental Bulletin, Lincang Agricultural Comprehensive Annual Report, and provisions from the Ecological Environment Bureau, Housing and Construction Bureau, Education and Sports Bureau of Lincang City, and its respective counties/districts.

2.3. Methodology

2.3.1. Indicator System for Green Agricultural Development

When conducting relevant research, scholars typically consider resources, environment, and ecology as factors in green agricultural development [7,27,28,30,44]. A green development indicator system for Lincang City was constructed in this study after thoroughly analyzing its production resources, ecological environment, and economic conditions relevant to green agricultural development. The system was designed following the principles of scientificity, systematicity, comprehensiveness, and authenticity, while also considering the availability of data. The specific indicators are listed in

Table 1. Evaluation index system of green agricultural development level in Lincang City.

Target	Indicators	Indicator Number	Unit	Indicator Type	Source of Indicators	Indicator Meaning	Corresponding SDG Indicators	Weight (%)
Green production	Application intensity of nitrogen, phosphorus, and potassium fertilizers ^	A1	Tons/hectare	Negative	[27,45]	Fertilization amount of nitrogen, phosphorus, and potassium fertilizers/planting area of crops	Zero hunger (SDG2.4.1)	0.76
	Pesticide application intensity ^	A2	Tons/hectare	Negative	[27,44,46]	Pesticide application rate/crop planting area	Zero hunger (SDG2.4.1)	0.93
	Use strength of agricultural plastic film ^	A3	Tons/hectare	Negative	[27,44]	Agricultural plastic film usage/crop planting area	Zero hunger (SDG2.4.1)	0.73
	Yield of pollution-free vegetables per unit area ^	A4	Tons/hectare	Positive		Total yield of pollution-free vegetables/area of pollution-free vegetables	Zero hunger (SDG2.4.1)	8.92
Resource conservation	Farmland Multiple Cropping Index ^	B1	%	Negative	[27,44]	Annual crop planting area/cultivated land area * 100%	Zero hunger (SDG2.4.1)	1.47
	Water saving irrigation intensity ^	B2	-	Positive	[27]	Effective irrigation area/crop planting area	Zero hunger (SDG2.4.1)	15.05
	Annual average growth rate of cultivated land quantity ^	B3	%	Positive		(Area of arable land in the following year—Area of arable land in the previous year)/Area of arable land in the previous year * 100%	Zero hunger (SDG2.4.1)	0.4
	Rural electricity consumption ^	B4	10,000 kWh	Negative	[10]	Rural power consumption	Zero hunger (SDG2.4.1)	0.77
Environmental friendliness	Agricultural COD emissions ^	C1	Ton	Negative		Agricultural COD emissions	Clean water and sanitation (SDG6.5.1)	0.89
	Consumption of ecological water resource ^	C2	100 million m3	Negative		Ecological environment water consumption	Clean water and sanitation (SDG6.5.1)	0.16
	The proportion of days with good ambient air quality status a, b #	C3	%	Positive		Proportion of days with good ambient air quality in a year	Sustainable cities and communities (SDG11.6.2)	1.42
	Direct economic losses caused by disasters *	C4	CNY 10,000	Negative		Direct economic losses caused by disasters	Sustainable cities and communities (SDG11.5.2)	0.25
Ecological protection	Drought and flood protection area ^	D1	Hectare	Positive		Drought and flood protection area	Zero hunger (SDG2.4.1)	8.05
	Forest coverage a, b *	D2	%	Positive	[34,44,45,46]	Forest coverage	Life on land (SDG15.1.1)	0.84
	Straw returning area ^	D3	Hectare	Positive		Straw returning area	Zero hunger (SDG2.4.1)	3.76
Economic growth	Per capita disposable income of rural permanent residents ^	E1	CNY	Positive	[27,28]	Per capita disposable income of rural permanent residents	Zero hunger (SDG2.1.1)	2.95
	Agricultural population ^	E2	person	Positive	[10,27]	Agricultural population	Zero hunger (SDG2.4.1)	3.87
	The proportion of agricultural workers in rural population ^	E3	%	Negative		Agricultural employed population/total rural population × 100%	Zero hunger (SDG2.4.1)	2.33
	The proportion of agricultural workers in rural labor force ^	E4	%	Negative		Agricultural employed population/rural labor force population × 100%	Zero hunger (SDG2.4.1)	1.46
	Per capita possession of food ^	E5	kg	Positive		Annual grain production/total population	Zero hunger (SDG2.1.2)	1.53
	Tea Garden Unit Yield ^	E6	Tons/hectare	Positive		Total tea production at the end of the year/actual tea plantation area at the end of the year	Zero hunger (SDG2.4.1)	1.98
	Engel’s coefficient of rural residents ^	E7	%	Negative		Engel’s coefficient of rural residents	Zero hunger (SDG2.1.1)	3.02
	Value added of the primary industry #	E8	CNY 10,000	Positive		Current year’s unit output value of the primary industry—previous year’s unit output value of the primary industry	Zero hunger (SDG2.3.1)	2.97
	The proportion of the total output value of the primary industry to the regional GDP #	E9	%	Negative	[34]	Gross output value of the primary industry/Gross regional product × 100%	Zero hunger (SDG2.3.1)	0.66
	Per capita GDP ^	E10	CNY 10,000/person	Positive	[28]	Gross Domestic Product/Total Population	Decent work and economic growth (SDG8.4.1)	1.81
	Poverty alleviation rate ^	E11	%	Positive		Poverty alleviation population/total population	No poverty (SDG1.1.1)	5.71
Social development	Effective irrigation area ^	F1	Hectare	Positive	[10,28]	Irrigated Area	Zero hunger (SDG2.4.1)	15.55
	Average water consumption per mu for farmland irrigation ^	F2	m3/hectare	Negative		Average water consumption per mu for farmland irrigation	Zero hunger (SDG2.4.1)	1.07
	Promotion area of water-saving irrigation technology in farmland ^	F3	hectare	Positive		Promotion area of water-saving irrigation technology in farmland	Zero hunger (SDG2.4.1)	7.62
	Urbanization level ^	F4	%	Negative	[28]	Urban resident population/total population × 100%	Sustainable cities and communities (SDG11.a.1)	0.9
	Urban-rural income gap ^	F5	-	Negative	[46]	Per capita disposable income of urban permanent residents/per capita disposable income of rural permanent residents	Sustainable cities and communities (SDG11.a.1)	1.22
	Proportion of villages benefiting from tap water ^	F6	%	Positive		Number of villages benefiting from tap water/number of village committees	Sustainable cities and communities (SDG11.a.1)	0.32
	Proportion of Tongchi Village ^	F7	%	Positive		Number of Tongchi Village/Number of Village Committees	Sustainable cities and communities (SDG11.a.1)	0.32
	Proportion of Telephone Villages ^	F8	%	Positive		Number of villages with phone calls/number of village committees	Sustainable cities and communities (SDG11.a.1)	0.31

Note: In the table, “a” represents the indicators for building a beautiful China, and “b” represents the indicators for Yunnan’s 14th Five Year Plan. Source of indicators: * represents SDG directly selected indicators, ^ represents SDG localization indicators, and # represents SDG extension indicators. Specific indicators are selected according to the column marked “references”. Some indicators without “references” are improved and deepened on the basis of existing references. Remaining selections are based on data availability regarding the resources, environment, and ecological characteristics of the case.

. At the same time, the calculation of the weight of each of these indicators was performed using the entropy weights method according to Formulas (4)–(6).

2.3.2. Panel Grey Correlation Model

The original data were first standardized using the range normalization method to eliminate dimensionality.

$$\begin{array}{l}\mathrm{Positive} :X{\prime }_{ij}=(X_{ij}-\min \{X_{ij}\}/(\max \left\{X_{ij}\right\}-\min \left\{X_{ij}\right\})\\ \mathrm{Negative}:X{\prime }_{ij}=\left(\max \right\{X_{ij}\}-X_i{}_j/(\max \left\{X_{ij}\right\}-\min \left\{X_{ij}\right\})\end{array}$$

(1)

In the formula, $X{\prime }_{ij}$ is the standardized value; $X_{ij}$ is the original value of the j-th indicator in the i-th year; and $\max \left\{X_{ij}\right\}$ and $\min \left\{X_{ij}\right\}$ are the maximum and minimum values of the corresponding indicators, respectively.

Based on previous work by Dang et al. [47], an exponential grey incidence model was constructed to extract the developmental degree and direction information across both time and object dimensions in the panel data. The “incremental difference” and “deviation difference” between indicators were transformed into absolute values to assess the similarity of curve shapes. The correlation strength, whether positive or negative, was determined based on the direction difference, resulting in correlation coefficients for both time and object dimensions.

Assume that the panel data of index $i$ and index $j$ are $X_i$ and $X_j$, where $\left(i,j=1,2,\dots \cdot N \mathrm{and} i\ne j\right)$, so:

$${\gamma }_{ij}^T\left(m,t\right)=\mathrm{sgn}\left(T_{ij}\left(m,t\right)\right)e^{-{\gamma }_{ij}^T\left(m,t\right)}$$

(2)

The correlation coefficient between the $i$-th and $j$-th indicators for the $m$-th object during the [t, t + 1] period, or the correlation coefficient between the $i$-th and $j$-th indicators at time t with the $m$-th object, can be obtained using the same principle:

$${\gamma }_{ij}^M\left(m,t\right)=\mathrm{sgn}\left(M_{ij}\left(m,t\right)\right)e^{-{\gamma }_{ij}^M\left(m,t\right)}$$

(3)

Based on the grey correlation coefficient classification method proposed by Deng [48], the correlation coefficients were divided into five categories: <0, 0–0.35, 0.35–0.65, 0.65–0.85, and 0.85–1. A larger value indicates a stronger correlation between the two indicators, while a negative value indicates a negative correlation [49].

2.3.3. Comprehensive Evaluation Model for Green Agricultural Development Sustainability

The entropy weight method was used to determine the weights of 34 indicators (Column ‘Weight’ in Table 1) for 6 target layers (green production, resource conservation, environmental friendliness, ecological protection, economic growth, and social development). The specific steps and formulas are as follows [1]:

The entropy weight method [50] calculates the weight of evaluation indicators by analyzing the correlation between indicators and the information load of each indicator. It is an objective weighting method that, to some extent, avoids bias caused by subjective factors. The calculation steps are as follows:

First, index proportion transformation:

$$S_{it}=\frac{X_{it}}{{\displaystyle \sum _{i=1}^n{X}_{it}}}$$

(4)

In the formula, $X_{it}$ is the numerical value of the $l$-th indicator in the $i$ -th year and $n$ is the number of statistical years.

Second, index entropy calculation:

$$e_l=-k{\displaystyle \sum _{i=1}^n{S}_{il}}\ln S_{il};$$

(5)

In the formula, $k=\frac{1}{\ln n}$, $k>0$, $0\le e_l\le 1$;

Third, calculate the weight of indicator $X_l$:

$$w_t=\frac{d_l}{{\displaystyle \sum _{i=1}^n{d}_l}}$$

(6)

In the formula, $d_l=1-e_l$, $d_l$ is the coefficient of difference of indicator $X_l$.

Next, the research results of Liao et al. [51] and Wang et al. [52] were combined with practical situations to subsequently calculate the sustainable development level of each target layer. The specific steps and formulas are as follows:

$$S_{\left(\mathrm{GP}\right)}={\displaystyle \sum _{i=1}^n{a}_i{w}_i}; S_{\left(\mathrm{RS}\right)}={\displaystyle \sum _{i=1}^n{a}_i{w}_i}; S_{\left(\mathrm{EF}\right)}={\displaystyle \sum _{i=1}^n{a}_i{w}_i}; S_{\left(\mathrm{EC}\right)}={\displaystyle \sum _{i=1}^n{a}_i{w}_i}; S_{\left(\mathrm{EG}\right)}={\displaystyle \sum _{i=1}^n{a}_i{w}_i}; S_{\left(\mathrm{SD}\right)}={\displaystyle \sum _{i=1}^n{a}_i{w}_i};$$

(7)

where $w_i$ represents the weights of evaluation indicators for each target layer; $a_i$ represents the dimensionless values of the evaluation indicators for each subsystem; and $S_{\left(\mathrm{GP}\right)},S_{\left(\mathrm{RS}\right)},S_{\left(\mathrm{EF}\right)},S_{\left(\mathrm{EC}\right)},S_{\left(\mathrm{EG}\right)},S_{\left(\mathrm{SD}\right)}$ represent the sustainable development capabilities of the six target layers, respectively; the value range of S is [0, 1]. A larger S value indicates a stronger comprehensive level for green and sustainable agricultural development in Lincang City.

2.3.4. Coupling Coordination Model

The coupling degree should be calculated based on the concept of capacity coupling and the capacity coupling coefficient model in physics. In this study, based on the research results of Liao et al. [51] and Wang et al. [52], a coupling degree model was constructed for six target layers of sustainable green agricultural development (green production, resource conservation, environmental friendliness, ecological protection, economic growth, and social development):

$$C={}^6\sqrt{\frac{S_{\left(\mathrm{GP}\right)}{S}_{(\mathrm{RS})}{S}_{\left(\mathrm{EF}\right)}{S}_{\left(\mathrm{EC}\right)}{S}_{\left(\mathrm{EG}\right)}{S}_{\left(\mathrm{SD}\right)}}{(\frac{S_{\left(\mathrm{GP}\right)}+S_{(\mathrm{RS})}+S_{\left(\mathrm{EF}\right)}+S_{\left(\mathrm{EC}\right)}+S_{\left(\mathrm{EG}\right)}+S_{\left(\mathrm{SD}\right)}}{6}{)}^{{{\displaystyle }}^{{}^6}}}}$$

(8)

where C represents the degree of coupling, C ∈ [0, 1]. When C = 0, the subsystems are independent and the development direction and structure are disordered. As C approaches 1, the degree of coupling between subsystems improves. When C = 1, the subsystems have achieved a positive resonant coupling and are developing in an ordered direction.

According to Liao et al. [51] and Wang et al. [52] regarding the coupling development law for green agricultural development in the study area, the degree of coupling was divided into four levels: Low-level, 0 < C $\le$ 0.3; antagonistic state, 0.3 < C $\le$ 0.5; coordinated state, 0.5 < C $\le$ 0.8; and high-level, 0.8 < C $\le$ 1. The degree of coupling alone can only reflect the strength of interaction among the six systems; it does not reflect the level of interactive coordination. To resolve this, a coupling coordination model is introduced. Coupling coordination models are typically used to measure the coupling coordination development status within a system and can determine the extent of positive coupling within the system. The coupling coordination model used in this study is as follows:

$$\left\{\begin{array}{l}D=\sqrt{CT}\\ T=a{S}_{\left(\mathrm{GP}\right)}+b{S}_{(\mathrm{RS})}+c{S}_{\left(\mathrm{EF}\right)}+d{S}_{\left(\mathrm{EC}\right)}+e{S}_{\left(\mathrm{EG}\right)}+f{S}_{\left(\mathrm{SD}\right)}\end{array}\right.$$

(9)

where D represents the degree of coupling coordination; C represents the degree of coupling; T is the comprehensive evaluation index of green agricultural development, reflecting the overall synergistic effect among subsystems; and a, b, c, and d, are undetermined coefficients.

The four subsystems are considered to have equal importance here, which is supported by research results of Pereira [53]. Accordingly, a = b = c = d = e = f = 1/6, $D\in [0,1]$. The types and criteria for coupling coordination were determined with reference to Sun et al. [54]: Severe imbalance, 0 < $D$ $\le$ 0.2; mild imbalance, 0.2 < $D$ $\le$ 0.4; general coordination, 0.4 < $D$ $\le$ 0.6; good coordination, 0.6 < $D$ $\le$ 0.8; and excellent coordination 0.8 < $D$ $\le$ 1.

2.3.5. Obstacle Model

An obstacle degree model was formulated with the comprehensive evaluation model of sustainable development level for green agricultural development and the coupling coordination model as a foundation [55,56]. This model is used to diagnose the factors hindering green agricultural green development through mathematical modeling. By calculating the obstacle degree of each indicator to the sustainable development of green agricultural development in each county and district, it enables the provision of targeted recommendations for green agricultural development in Lincang City.

$$O_j=\frac{C{o}_j\times D{e}_j}{{\displaystyle \sum _{j=1}^{n}C{o}_j\times D{e}_j}}\times 100\%=\frac{W_j\left(\max \left(X\right)-x_{ij}\right)}{{\displaystyle \sum _{j=1}^n(W_j\times \left(\max \left(X\right)-x_{ij}\right)}}$$

(10)

where $O_j$ is the obstacle degree of the j-th index; $C{o}_j$ is the factor contribution, represented by a variable weight $W_j$; and $D{e}_j$ is the degree of deviation, expressed as the difference between the ideal value and the index value.

2.3.6. Liang-Kleeman Information Flow Method

The Liang-Kleeman information flow method [57], which was rigorously derived by Liang based on information flow theory, quantitatively characterizes the causal relationship between two time-series, X₁ and X₂, by utilizing the information transfer between them within a unit time without any prior knowledge. The connection between Liang-Kleeman information flow and causality follows a theorem: If the development and evolution of X₁ are independent of X₂, that is, there is no causal relationship from X₁ to X₂, then the information flow T_1→2 from X₁ to X₂ is 0. The specific information flow calculation is shown in Formula (7).

For time-series X₁ and X₂, the unit time information flow from X₂ to X₁ is:

$$T_{2-1}=\frac{C_{11}{C}_{12}{C}_{2,d1}-C_{12}^2{C}_1{}_{,d1}}{C_{11}^2{C}_{22}-C_1{}_1{C}_{12}^2}$$

(11)

Among which, $C_{i,j}=\frac{{\displaystyle \sum _{n=1}^N(X_i-{\overline{X}}_{i,n}(X_{j,n}-{\overline{X}}_{j,n})}}{n}$ is the co-variance of $X_i$ and $X_j$;$C_{i,dj}$ is the co-$X_i$ and j; and ${\stackrel{·}{X}}_J=\frac{X_j{{}_{,}}_{N+1}-X_j{}_{,N}}{\Delta t}$ represents the time step.

If T_2→1 equals 0, then the development and evolution of X₂ are independent of X₁, meaning that X₂ is not a cause of X₁. If T_2→1 is not 0, then X₂ is a cause of X₁. Unlike traditional climate research, which uses leading or lagging correlation analyses to infer causal relationships between time-series, the Liang-Kleeman information flow method can differentiate the directionality of correlation analysis. It has been widely applied in the study of time-series causal relationships.

3. Results

3.1. Panel Grey Correlation Results

In this study, the comprehensive score of sustainable green agricultural development level in Lincang City was taken as the mother sequence, while the evaluation indicators of green agricultural development level were considered a sub-sequence. The panel grey relational analysis model was applied to measure the grey relational degree between the mother sequence and sub-sequence across both temporal and spatial dimensions. The resulting grey relational degree scores and rankings of the influencing factors of sustainable green agricultural development in Lincang City from 2010 to 2019 were organized; the occurrence frequency of the top-ten ranked indicators was statistically analyzed, as shown in Figure 2. It appears that reducing the use of agricultural plastic film, reducing ecological environment water consumption, minimizing direct economic losses caused by disasters, improving infrastructure coverage, and enhancing resident well-being are key factors in promoting sustainable green agricultural development in Lincang City.

Figure 3a shows that the average correlation intensity between the comprehensive score of green agricultural development and various indicators of green agricultural development in Lincang City is 0.60. There is a “reverse V”-shaped fluctuation trend during the study period, increasing in intensity before 2017–2019 and reaching its highest value in 2017 (0.63). A possible explanation for this is the gradual abolition of agricultural taxes in China in 2006, coupled with the implementation of policies promoting green agricultural development, which motivated farmers to participate. However, insufficient awareness and investment in green production technologies created a bottleneck in 2017, leading to a decline in the comprehensive score and a subsequent decrease in the correlation coefficient.

Figure 3b provides insights from a spatial perspective, where there is regional heterogeneity in the highest correlation intensity indicators between the comprehensive scores of sustainable green agricultural development and various indicators in each county and district. Among them, Gengma and Shuangjiang counties have the highest correlation coefficients, followed by Fengqing and Yun counties; Lincang District, Yongde, Zhenkang, and Cangyuan counties have the lowest correlation coefficients.

3.2. Capability for Green and Sustainable Development in Agriculture

As shown in

Table 2. Scores of Green and Sustainable Development Level of Agriculture in Lincang City from 2010 to 2019.

Year	Green Production	Resource Conservation	Environmental Friendliness	Ecological Protection	Economic Growth	Social Development	Sustainable Development Level Score
2010	0.0377	0.0180	0.0107	0.0214	0.3227	0.0300	0.4405
2011	0.0181	0.0176	0.0178	0.0226	0.3342	0.0309	0.4413
2012	0.0165	0.0179	0.0203	0.0229	0.3357	0.0333	0.4467
2013	0.0171	0.0263	0.0210	0.0225	0.3836	0.0438	0.5143
2014	0.0359	0.0171	0.0181	0.0232	0.2980	0.0418	0.4341
2015	0.0353	0.0170	0.0223	0.0199	0.3004	0.0474	0.4422
2016	0.0469	0.0179	0.0186	0.0392	0.2401	0.0398	0.4026
2017	0.0442	0.1226	0.0247	0.0658	0.1965	0.1436	0.5975
2018	0.0445	0.0178	0.0183	0.0243	0.2401	0.0527	0.3978
2019	0.0205	0.0172	0.0226	0.0260	0.2925	0.0568	0.4355

, the annual sustainable development level score encompasses six components: green production, resource conservation, environmental friendliness, ecological protection, economic growth, and social development. According to the analysis of sustainable development (Table 2), the annual average comprehensive score for sustainable green agricultural development in Lincang City is 0.4552. The economic growth dimension has a dominant role in the sustainable development level score, primarily due to the consistently high score of Engel’s coefficient of rural residents (E7) during the study period. This indicates that the integrated development of urban and rural areas has been effectively implemented in Lincang City, leading to a gradual reduction in the income gap between urban and rural residents and an increase in the consumption level of rural residents. Consequently, there is a higher score for the rural residents’ Engel coefficient.

The green agricultural development level in Lincang City can be divided into two stages. The first is characterized by a gradual increase, with some fluctuations, from 2010 to 2017. The difference between the highest and lowest scores during this period is 0.1997. In 2016–2017, there was a phase of development where the comprehensive score for sustainable green agricultural development increased from 0.4405 to 0.5975 (by 0.1570). At this point, Lincang City actively responded to the concept of green development proposed in the 18th Fifth Plenary Session of the Chinese Communist Party in 2016, resulting in a rapid improvement in the level of green agricultural green development. From 2017 to 2019, there was a period of fluctuation and decline. This may be attributable to a significant decrease in water-saving irrigation intensity, many days of poor air quality, as well as a reduction in drought- and flood-resistant crops and effective irrigation areas in 2018 compared to 2017—resulting in a substantial decrease in the scores for the four objectives of resource conservation, environmental friendliness, ecological protection, and social development. As a result, the comprehensive score for green agricultural sustainable development decreased.

Figure 4 provides a spatial analysis (eight counties and districts) of the study period focusing on three time points: 2010, 2014, and 2019. The results reveal the sustainable green agricultural development level of different counties. In 2010, Fengqing, Shuangjiang, and Yun counties had relatively strong sustainable green agricultural development capacities, all exceeding 0.4649. In 2014, Fengqing County had the highest score of 0.5547. In 2019, Gengma County had the highest score of 0.5193. Throughout the study period, Cangyuan County consistently had a weaker sustainable green agricultural development level than other counties. This is mainly due to its location on the border, limiting spillover effects from Linxiang District and resulting in lagging economic development and lower comprehensive scores for the “economic growth” objective of sustainable green agricultural development. Although Linxiang District has a location advantage, the pollution caused by economic development creates obstacles to achieving the “environmental friendliness” objective, leading to a relatively low comprehensive score.

3.3. Coupling Coordination Analysis

From a temporal perspective, two stages in the overall coupling coordination degree of green agricultural development in Lincang City were observed (

Table 3. Types of Coupling and Coordinated Development of Green Agricultural Development in Lincang City from 2010 to 2019.

Year	Comprehensive Evaluation Index (T)	Coupling Degree (C)	Coupling Coordination Degree (D)	Coupling Coordination Type
2010	0.0092	0.0577	0.1840	Severe imbalance
2011	0.0735	0.4507	0.1821	Severe imbalance
2012	0.0744	0.4563	0.1843	Severe imbalance
2013	0.0857	0.4558	0.1977	Severe imbalance
2014	0.0724	0.5310	0.1960	Severe imbalance
2015	0.0737	0.5358	0.1987	Severe imbalance
2016	0.0671	0.6327	0.2060	Mild imbalance.
2017	0.0996	0.7965	0.2816	Mild imbalance.
2018	0.0663	0.6121	0.2014	Mild imbalance.
2019	0.0726	0.5349	0.1970	Severe imbalance

). From 2010 to 2017, there was a slight and gradual upward trend with minor fluctuations. From 2017 to 2019, there was a decrease in fluctuations. Despite these changes, the coupling coordination degree of green agricultural development in Lincang City’s counties and districts was relatively poor, with a small range of fluctuation between 0.1821 and 0.2816. Although there was an improvement from severe imbalance to mild imbalance, significant room for improvement remains in the coupling coordination degree of green agricultural development in Lincang City’s counties and districts.

From a spatial perspective (Figure 5), the coupling coordination degree of green agricultural development in each county and district is generally low, indicating poor coupling coordination effectiveness. Although there are small regional variations, spatial differences in the coupling coordination degree persisted across different years of the study period. In 2010, Fengqing County had the highest coupling coordination degree of 0.1963, while Linxiang District had the lowest at 0.1562. The spatial range of the coupling coordination degree was 0.0401, indicating a state of severe imbalance across all counties and districts. By 2019, Shuangjiang County had the highest coupling coordination degree of 0.2154, while Linxiang District had the lowest at 0.1756. The spatial range of the coupling coordination degree remained at 0.0398. Except for Shuangjiang and Gengma counties, which were in a state of mild imbalance, all counties and districts were still in a state of severe imbalance. There was a slight improvement in the coupling coordination degree from 2010 to 2019, and the spatial differences in the coupling coordination degree were relatively slight. The year 2017 marks a crucial turning point, as there was an increase in fluctuations before 2017 and a decrease in fluctuations afterward.

3.4. Obstacle Analysis

As shown in Figure 6, the overall obstacle level of the six target layers exhibits slight increases during the study period, reaching a maximum obstacle level of 21.75% in 2010 and 25.50% in 2019 (with a difference of only 3.75%). The obstacle factors affecting the green development of agriculture vary significantly for different years across different counties and regions, but overall, the obstacle level of the “environmental friendliness” target layer was lowest during the study period, while the obstacle level of the “social development” target layer was consistently high. The main obstacle factor for the “social development” target layer during the study period is the effective irrigation area (F1), with obstacle degrees of 12.21% in 2010, 13.24% in 2014, and 14.46% in 2019.

By comparing the three time points, the obstacle degree for the “green production and resource conservation” layer appears to have significantly increased over time. Specifically, the obstacle level for the yield of pollution-free vegetables per unit area (A4) in the “green production” layer increased from 13.73% in 2010 to 14.83% in 2014 and 19.83% in 2019. The obstacle factor hindering the green development of agriculture in the “resource conservation” layer was the intensity of water-saving irrigation, with obstacle levels of 18.52%, 20.15%, and 21.91% in 2010, 2014, and 2019, respectively.

Barriers to “green production and resource conservation” were also found to have significantly increased, indicating that economic development may come at the cost of extensive production and resource consumption. Conversely, barriers to “environmental friendliness” have consistently remained low, suggesting effective attention given to environmental governance. The barriers to “economic growth” have significantly decreased, indicating the significant impact of economic growth initiatives implemented by policymakers.

3.5. Information Transmission between Coupling Coordination Degree and Green Agricultural Development Indicators

To quantitatively analyze the causal relationship between the coupling coordination degree and green agricultural development indicators in Lincang City, the Liang-Kleeman information flow method was employed to calculate the information transmission between green agricultural development and the coupling coordination degree in the study area from 2010 to 2019. The results for each county and district are shown in

Table 4. Information flow of coupling coordination degree and green agricultural development indicators among counties and districts.

	Linxiang	Fengqing	Yun	Yongde	Zhenkang	Shuangjiang	Gengma	Cangyuan
A1	-	-	-	-	-	-	-	-
A2	0.28	-	-	-	-	-	-	-
A3	-	-	0.12	-	-	-	-	0.31
A4	-	-		0.39	-	-	-	0.07
B1	0.08	-	0.12	-	-	-	-	-
B2	0.54	-	1.19	-	-	-	-	-
B3	-	-	-	-	-	-	-	-
B4	0.48	-	0.32	-	-	0.46	0.22	-
C1	-	-	-	0.17	0.36	0.29	0.13	-
C2	-	-	-	-	-	0.56	-	0.10
C3	-	-	-	0.22	-	0.21	0.20
C4	-	-	-	-	-	-	0.09	0.14
D1	0.75	-	-	-	1.86	-	-	-
D2	0.13	-	0.11	-	0.01	0.21	0.13	0.07
D3	-	-	1.29	-	0.10	-	-	-
E1	-	-	0.33	-	0.42	0.46	0.26	-
E2	-	-	0.07	-	-	0.33	-	-
E3	-	-	-	-	-	-	-	-
E4	-	-	0.09	0.33	0.02	-	0.15	-
E5	-	-	0.22	-	0.31	0.32	-	-
E6	0.16	-	0.20	-	-	0.15	-	-
E7	-	-	-	-	-	-	-	0.06
E8	-	-	-	-	-	0.30	-	-
E9	-	-	-	-	-		-	-
E10	-	-	-	-	-	0.08	-	-
E11	0.90	-	1.62	-	-	-	-	-
F1	0.55	-	1.22	-	-	-	-	-
F2	-	-	-	-	-	0.21	0.21	-
F3	-	-	-	-	0.41	0.40	0.53	-
F4	-	-	-	-	-	0.47	0.27	-
F5	0.33	-	0.26	0.18	0.28	0.34	0.19	-
F6	-	-	-	-	0.02	-	-	-
F7	-	-	-	-	-	-	-	-
F8	-	-	-	-	-	-	-	-

Note: The information flow values in the table have all passed the 95% significance level test. Bold numbers represent the top three information flow values of the county.

.

Significant regional heterogeneity was observed in the maximum information flow factor influencing the coupling and coordination level of green agricultural development in each county, with a confidence level of 95%. The most influential information flow factors in each county and district are: Linxiang District, Yunxian County E11 (poverty alleviation rate), Yongde County A4 (pollution-free vegetable yield per unit area), Zhenkang County D1 (area with guaranteed income due to drought and waterlogging), Shuangjiang County C2 (ecological water consumption), Gengma County F3 (area of agricultural water-saving irrigation technology promotion), and Cangyuan County A3 (agricultural plastic film use intensity).

By comparing the top three information flow value factors (bold numbers in Table 4) of each county, some counties were found to have the same information flow factor. For example, Shuangjiang and Cangyuan share C2 (ecological environment water consumption); Linxiang District and Zhenkang County share D1 (areas with guaranteed income due to drought and waterlogging); Zhenkang, Shuangjiang, and Gengma counties share E1 (per capita disposable income of rural permanent residents); E11 (poverty alleviation rate) and F1 (effective irrigation area) are shared across Linxiang District and Yunxian County; and Shuangjiang and Gengma counties share F4 (urbanization level). Accordingly, these are key influencing factors affecting the coupling coordination degree of each county and district, and targeted measures should be taken to regulate them.

In Linxiang District, efforts should be made to develop distinctive industries and enhance the level of industrial development to consolidate and improve the poverty alleviation rate (E11). Additionally, it is necessary to strengthen flood control and disaster resistance capabilities by constructing reservoirs and river networks to increase the amount of drought and flood protection area (D1). Yun County also needs to enhance its capacity to develop distinctive industries and improve the level of industrial development, which would consolidate and improve the poverty alleviation rate (E11). In Yongde County, while paying attention to the selection of appropriate fertilizers, it is also important to avoid pollution and waste caused by excessive fertilizer application in order to increase the yield of pollution-free vegetables per unit area (A4).

Furthermore, in Zhenkang County, flood control and disaster resistance capabilities could be strengthened while encouraging farmers to cultivate high-quality grains and increasing agricultural investment to improve the drought and flood protection area (D1), as well as the per capita disposable income of rural permanent residents (E1). In Shuangjiang County, while focusing on increasing the utilization rate of water resources and agricultural investment in grains, it is necessary to account for the urban–rural income gap in order to improve the consumption of ecological water resources (C2), the per capita disposable income of rural permanent residents (E1), and the urbanization level (F4). In Gengma County, while focusing on the urban–rural income gap, it is necessary to increase agricultural investment in grains and optimize agricultural irrigation techniques to improve the urbanization level (F4), the per capita disposable income of rural permanent residents (E1), and the promotion area of water-saving irrigation technology in farmland (F3). In Cangyuan County, in the process of crop protection, efforts should be made to select environmentally friendly and degradable materials while improving the efficiency of water resource utilization in order to reduce the usage strength of agricultural plastic film (A3) and the water consumed from the ecological environment (C2).

4. Discussion

4.1. Temporal and Spatial Changes

Green agricultural development is a multifaceted concept that extends beyond food security and encompasses health and environmental considerations, playing a crucial role in sustainable development [10,19]. The attainment of coordinated development between sustainable green agricultural development and food security is vital for promoting national food security and ensuring agricultural sustainability [10]. The results of this study indicate that there is an inevitable connection between green agricultural development and economic growth, which is consistent with the findings of the Zhang et al. [19] Economic benefits play a significant role in promoting green agricultural development, so the various counties and districts in Lincang City should increase their investment in green production to maximize economic benefits. However, at the same time, an increase in economic benefits may lead to environmental pollution and resource waste, thereby hindering green agricultural development. Accordingly, the green agricultural development requires collaborative efforts beyond individual counties or districts.

Collaborative governance among counties and districts is also essential for fostering coordinated development of various elements in the six target layers across different areas, which would enhance the overall level of green agricultural development. Chen et al. [30], Zhang et al. [58], Lee et al. [59], Karim et al. [60], and Fang et al. [61] reached similar conclusions. An important finding of the present study is that the “economic growth” layer plays a dominant role in the green development of agriculture in Lincang City, while the “resource conservation” layer significantly constrains the green development of agriculture in the city. In the future, resource utilization efficiency must be improved while emphasizing economic growth. Furthermore, as the sole national sustainable development innovation demonstration zone in Yunnan Province, Lincang City serves as an exemplary and representative area that can drive progress in surrounding regions.

The sustainable development level and coupling coordination level of Lincang City appeared to fluctuate and increase from 2010 to 2017. This suggests that increasing attention from government departments towards agriculture, enhanced awareness of sustainable green agricultural development, improved transportation convenience in various counties and districts, and the strengthening of advanced agricultural technologies have contributed to varying degrees of improvement in the level of green agricultural development across the study area. These results align with research conducted by Zuo et al. [62].

During the study period, the coupling coordination level of Lincang City changed from a serious imbalance to a mild imbalance. In general, the application intensity of nitrogen, phosphorus, and potassium fertilizers (A1) and the water-saving irrigation intensity (B2) in the “green production” layer decreased year by year, while the per capita GDP (E10) in the “economic growth” layer increased steadily, thus improving the level of sustainable green development for the six-layer integrated agricultural system.

From 2017 to 2019, there was a decrease in fluctuations. Both the score for the sustainable green agricultural development level and the coupling coordination degree reached their peaks in 2017, which marks an important turning point. It appears that Lincang City actively responded to the concept of green development proposed in the Fifth Plenary Session of the 18th Central Committee in 2016, leading to effective implementation and a rapid improvement in green agricultural green development. However, after 2017, these scores did not continue to increase and, in fact, declined. The analysis of obstacles suggested that the water-saving irrigation intensity (B2) of the “resource conservation” layer was the main obstacle to the green development of agriculture, which suggests that Lincang City is vigorously developing green agriculture at the expense of excessive water resource consumption and waste. Improving the rationality of agricultural planning, as well as the use of spray irrigation, drip irrigation, and other less water-intensive methods, would be conducive to overcoming this obstacle.

On the other hand, Lincang City experienced rapid regional development as a result of accelerated economic development, optimized industrial structure, and improved resource allocation efficiency driven by national and government policies. The environmental pollution and excessive resource consumption caused by this economic development, however, have significantly increased obstacles, thereby hindering further achievement in green agricultural development, Therefore, while pursuing economic growth, it is still crucial to implement measures to reduce resource consumption and suppress the negative impact of environmental pollution. For example, local governments should adjust measures to local conditions and reasonably intercrop to improve the “resource saving” layer of the Farmland Multiple Cropping Index (B1); efforts to develop new energy sources to reduce agricultural COD emissions in the “environmental friendliness” (C1) and vigorous promotion of ecological environment water resource protection (C2) may also be effective.

4.2. Limitations

The effects of six target layers (green development, resource conservation, environmental friendliness, ecologicalprotection, economic growth, and social development) on green agricultural development were investigated in this study according to comprehensive scores of agricultural green and sustainable development. However, sustainable agricultural development encompasses many other factors; for example, food security is also influenced by factors such as trade (SDG2.b.1), government agricultural expenditure (SDG2.a.1), and the proportion of agricultural workers and agricultural land (SDG5.a.1). In future studies, it is advisable to incorporate these multiple factors into the scope of consideration and conduct assessments from multiple perspectives.

Additionally, in terms of methodological selection, future research could explore more in-depth approaches for the study area, such as system dynamics models, variable coefficient semi-parameter estimation methods, and multi-causal identification methods. It is also important to note that due to challenges in obtaining county-level data, interpolation methods were used to complete some missing data, which may have influenced the results of this work. Nonetheless, the results reported here provide valuable insights for future reference.

Lastly, the study area of this article is an innovative demonstration zone for sustainable development in China. In future research, this space could be expanded to include emerging countries and regions to explore the levels of green agricultural development in different areas and propose optimization pathways, providing more comprehensive recommendations for policymakers.

4.3. Implications and Recommendations

Based on the research results, it is evident that Lincang City exhibits significant variations in its agricultural sustainable development level, with relatively low levels observed across each county and district. The coupling coordination level is also relatively low, and there has been a slight increase in obstacles. Therefore, policymakers should prioritize strengthening the positive correlation among indicators of green agricultural development, enhancing the coordination among different systems, and reducing or eliminating obstacles.

It is necessary to enhance the agricultural sustainable development level and coupling coordination among the six target layers investigated in this study. To improve the coupling coordination among counties and districts, it is necessary to enhance their agricultural sustainable development level. Special attention should be given to the “green development, resource conservation, environmental friendliness, ecological protection, and social development” target layers. While promoting economic growth, it is essential to address issues such as excessive resource consumption and environmental pollution, which hinder the sustainable development of green agricultural practices.

It is also necessary to reduce or eliminate the obstacles hindering sustainable agricultural development in each county and district. Although the increase in obstacles during the study period was not significant, the main obstacles are concentrated in the “social development” target layer. However, there was a significant improvement in the obstacle level of the “economic development” layer by the end of the research period, though the obstacle level in the “resource conservation” layer had noticeably increased.

Finally, promoting the positive development of information flow factors among counties and districts, and tailoring the enhancement of green agricultural development in Lincang City to the specific environmental differences and information transmission conditions in each county and district, would facilitate the transformation of Lincang City’s agricultural sector towards high-quality development. (Specific measures are shown in Figure 7).

5. Conclusions

This research focuses on the Lincang National Sustainable Development Innovation Demonstration Zone, which is located in an underdeveloped border area with multiple ethnic groups. Various statistical methods were employed to analyze the comprehensive score of sustainable green agricultural development, the level of coupling coordination, and the influencing factors in Lincang City. The main results can be summarized as follows:

(1) The comprehensive score of green and sustainable agricultural development in various counties and districts of Lincang City fluctuated slightly, reaching its peak in 2017 and then declining. The increase in this comprehensive score extends from 0.4405 to 0.5975. The average correlation strength between the comprehensive score of sustainable green agricultural development and various indicators of green agricultural development is 0.60, with the highest correlation coefficient observed for the annual growth rate of cultivated land at 0.80. Therefore, each county should vigorously curb the illegal construction of buildings on agricultural land and explore the reserve resources of arable land in order to target the annual average growth rate of cultivated land (B3).

(2) The level of coupling coordination in green agricultural development in Lincang City is relatively low. Overall, it transitioned from severe imbalance to mild imbalance, with a fluctuating decrease after an initial increase before 2017. Coupling coordination in each county and district is poor, fluctuating within the range of 0.1821to 0.2816. Therefore, measures should be taken according to local conditions to improve the coordination and integration among various counties. In Linxiang District, efforts should be made to leverage its location advantage and intensely develop its economy. Fengqing, Yun, and Shuangjiang counties should strive to build a first-class tea culture industry. Yongde, Zhenkang, and Cangyuan counties should utilize their hospitality and cultural resources to bolster their tourism industry.

(3) The “social development” layer has consistently shown relatively high obstacle levels, with significant obstacles related to effective irrigated areas. At the end of the study period, the main obstacles to green agricultural development in each county and district of the study area were mainly observed in the “green production and resource conservation” layer. The overall obstacle level of the six target layers slightly increased, with the maximum obstacle level reaching 21.75% in 2010 and 25.50% in 2019. Local governments should adopt appropriate measures tailored to local conditions, such as implementing rational crop rotation patterns, making efforts to develop new energy sources, and promoting water-saving irrigation techniques to reduce barriers to “resource conservation” and “environmental friendliness”.

(4) The factors with the greatest impact on information flow vary among each county and district. Noteworthy examples include the poverty alleviation rate in Linxiang District and Yun County, pollution-free vegetable yield per unit area in Yongde County, drought- and flood-resistant area in Zhenkang County, ecological environment water consumption in Shuangjiang County, promotion area of water-saving irrigation technology in Gengma County, and intensity of agricultural plastic film usage in Cangyuan County. This highlights significant regional heterogeneity in the factors that affect the coupling coordination in each county and district, which suggests, each county should take differentiated measures to guide increases in the maximum information flow value.