The Coupling Coordination Characteristics and Graded Control Measures of Cultivated Land Quality and Economic Development in the Northern Slope Economic Belt of the Tianshan Mountains Based on Future Scenarios

Abstract

This paper addresses the dual challenges of food security and sustainable development by examining the balance between arable land quality and economic development. Coordinating and optimizing development models is essential for achieving sustainable agricultural and economic progress. The North Slope Economic Belt of Tianshan Mountain (UANST), a semi-arid agriculturalpastoral transition zone in northwest China, exemplifies a coupled human environment system where global sustainability targets confront regional development imperatives. Focusing on seven cities and counties within the UANST, this study employs information sensitivity indicators to quantitatively select evaluation metrics. It provides a comprehensive analysis of the current state of the coupling and coordination degree (CCD) between arable land quality and economic development in the region. Using a system dynamics model (SDM), four scenario models were developed to predict and analyze the interaction between cultivated land quality and economic development on the North Slope of Tianshan. The study proposes a model to improve coordination between cultivated land quality and economic development. The key findings are as follows: (1) “preliminary screening + information sensitivity analysis” method identified 12 arable land quality evaluation indicators and 11 economic development evaluation indicators for the North Slope Economic Belt of Tianshan. (2) The coupling coordination between arable land quality and economic development in the seven counties and cities improved from 0.469 to 0.663, reflecting a transition from “marginal imbalance” to “primary coordination”. By 2021, all regions had reached the initial stage of coordinated development. (3) Among the development models analyzed, the coordinated development model achieved the highest coupling coordination score (0.9136). This model also demonstrated lower carbon dioxide emissions and reduced water resource consumption, alleviating environmental pressures and offering an optimal solution for regional coordinated development.

1. Introduction

Amidst the dual challenges of global food security and sustainable development, it is crucial to explore a balanced and coordinated development model that integrates arable land quality with economic growth. This approach is essential for achieving sustainable agricultural and economic progress [1,2]. As the most fundamental means of production, the quality of arable land significantly influences agricultural productivity and sustainability. This, in turn, has a profound impact on social and economic stability and growth [3,4,5] The United Nations’ 2023 Global Sustainable Development Report (GSDR) emphasizes the necessity of integrating sustainable practices to future development, highlighting the importance of balancing environmental protection with economic progress [6,7]. Therefore, in the agricultural sector, the critical issue is how to effectively promote regional economic sustainable development while safeguarding the quality and health of arable land. Addressing this issue is an urgent priority and provides a significant scientific basis and reference value for formulating and adjusting policies for regional sustainable development.

The interaction between arable land and sustainable economic development is highly complex. Domestic and international scholars have employed various models, such as the geographical detector model [8], the Kuznets curve [9], the gravity center model [10], and the Tapio model [11] Yang et al. utilized the geographical detector model to investigate the relationship between cropland pressure (CLP) and economic development levels at national, regional, and urban scales. Their study revealed the spatiotemporal coupling characteristics of the CLP index and economic development, providing insights into how different aspects of arable land and economic growth interact across various geographic levels [12] Pang et al. conducted a comprehensive examination of the spatiotemporal coordination between cropland investment scale and the geopolitical economic system. They utilized the coupling coordination degree (CCD) and spatial autocorrelation models to analyze the intricate relationships and coordination dynamics over time and across different regions [13] Lei et al. used provincial data to develop a coupling coordination degree (CCD) model and employed the Theil index to investigate how spatiotemporal changes in the non-grain use of arable land impact economic growth. Their study provided insights into the effects of these changes on economic development across different provinces [14] Kazi et al. utilized the least squares growth rate model and regression analysis model to examine the relationship between trends in arable land change and the growth of socioeconomic factors [15]. Other scholars have explored the relationship between different aspects of arable land and the economy from perspectives such as intensive arable land use [16], land use efficiency [17], ecological degradation of arable land [18], virtual land resources [19], land use transformation [20], and ecological security pressure of arable land [21], providing rich theoretical and empirical insights. However, several aspects still require further in-depth research: current studies rarely explore the coupling coordination relationship between arable land quality and economic development. Additionally, existing research mainly focuses on the spatiotemporal characteristics of the coupling coordination degree, with few multi-scenario simulations of the interactive development trends between arable land quality and economic development, providing limited insights into strategies for regional coordination.

The Urban Agglomeration on the Tianshan Mountains (UANST) serves as a significant growth pole in China’s strategic core hub of the “Silk Road Economic Belt” [22]. In 2020, the total production value of the Northern Slope Economic Belt of the Tianshan Mountains (UANST) reached 613.492 billion CNY, accounting for 27.2% of Xinjiang’s total production value. Recent years have seen rapid economic growth and urbanization in the UANST. However, this rapid urbanization has led to the occupation of a significant amount of high-quality arable land, resulting in both a reduction in quantity and a decline in quality. This paper uses the Northern Slope Economic Belt of the Tianshan Mountains as a case study to explore how policy measures can promote the coordinated development of arable land quality and the economy in this region. The core objectives of this study are: (1) to clarify the current status of the coupling coordination between arable land quality and economic development in the Northern Slope Economic Belt of the Tianshan Mountains; (2) to identify the main factors affecting the coupling coordination development of arable land quality and economic development in this region; and (3) to predict and analyze the coupling coordination status of arable land quality and economic development under different scenarios.

This study presents two major innovations and contributions: (1) in the most previous studies that primarily used qualitative methods to select evaluation indicators, this research constructs a comprehensive evaluation framework for the coupling coordination relationship between arable land quality and economic development in the Northern Slope Economic Belt of the Tianshan Mountains using the (CCDM). The primary indicators influencing the coupling coordination level were identified using a model based on information sensitivity indicators. (2) Construction of a (SDM): model was used to simulate the trends in arable land quality and economic development under multiple scenarios in the Northern Slope Economic Belt of the Tianshan Mountains. Comparative analysis of the evolution trends of the coupling coordination degree under different scenarios was conducted, providing decision-making support for promoting the coordinated development of arable land quality and economic growth in the region.

2. Materials and Methods

2.1. Overview of the Study Area

The North Slope Economic Belt of Tianshan is located in the central part of the north of Tianshan and the south of the Junggar Basin in Xinjiang Uygur Autonomous Region (42°55′ N~46°13′ N, 83°25′ E~88°58′ E). It is one of the 19 urban agglomerations in China [23] proposed in the National 13th Five-Year Plan. The research area belongs to the temperate continental climate with a shortage of water resources. Due to its unique geographical location, it has gradually become an important economic corridor connecting the interior of China and Central Asia and the core hub of the “Silk Road Economic Belt” in the national western development strategy, and is the core region of Xinjiang’s economic development [24]. Based on the growth-level theory, the research selected Urumqi City, Changji City, Wusu City, Shawan City, Kuitun City, Manas County, and Hutubi County as research areas (Figure 1).

2.2. Data Sources

The data used in this study primarily come from the following sources: Xinjiang Statistical Yearbook (2015—2021) (https://tjj.xinjiang.gov.cn/tjj/zhhvgh/list_nj1.shtml), China Statistical Yearbook, China Urban Construction Statistical Yearbook (https://www.stats.gov.cn/sj/ndsj/), EPS database (https://www.epsnet.com.cn/index.html#/Index), and statistical yearbooks of cities and counties. Additional data, DEM, and other geospatial data were obtained from the Geospatial Data Cloud platform of the Computer Network Information Center of the Chinese Academy of Sciences (https://www.gscloud.cn). Rainfall and temperature data were sourced from the China Meteorological Data Platform (https://data.cma.cn/metadata/#/layerType). The quality data of arable land were provided by the Soil and Fertilizer Station of the Agricultural and Rural Department of Xinjiang Uygur Autonomous Region (https://nynct.xinjiang.gov.cn/xjnynct/c113635/list.shtml). This study was conducted on a grid scale. To obtain detailed socioeconomic and meteorological grid data, we employed the linear interpolation method. This method is favored in environmental science for its computational efficiency and accuracy in terms of data fitting.

2.3. Indicator Screening and Weight Evaluation

This study identified a total of 30 evaluation indicators. To ensure a realistic and reliable indicator system, we applied the information sensitivity indicator screening model alongside the entropy weight method to effectively select and weight these indicators. The process involved two main steps: initially, we screened the evaluation indicators both qualitatively and quantitatively, and, subsequently, we assigned weights to the selected indicators. The detailed calculation formulas are provided in Equations (1)–(12). The resulting evaluation indicator system is presented in

Table 1. UANST evaluation indicators after quantitative screening.

System	Secondary Indicator	Indicator	Unit	Indicator Property	Weights
Economic development	Economic growth	Total labor productivity (x1)	%	+	0.0450
		Primary industry output (x2)	Yuan	+	0.0642
	Innovation drive	Total power of agricultural machinery (x3)	Kilowatt	+	0.0515
		GDP per capita (x4)	Yuan	+	0.0379
	People’s well-being	Per capita net income for rural residents (x5)	Yuan	+	0.0544
		Per capita disposable income of urban residents (x6)	Yuan	+	0.0359
	Green ecology	Number of industrial enterprises above designated size (x7)	Size	+	0.2578
		Carbon dioxide emission (x8)	Ton	−	0.0204
		Greening coverage of urban built-up areas (x9)	%	+	0.0293
		Gross grain output (x10)	Ton	+	0.3066
		Comprehensive production capacity (x11)	m3	+	0.0970
Cropland quality	Profile character	Effective soil thickness (y1)	cm	+	0.0242
		Tillage thickness (y2)	cm	+	0.0137
	Physical and chemical properties	Soil organic matter (y3)	g/kg	+	0.0269
		Soil pH (y4)	-	+	0.2367
		Soil texture (y5)	-	+	0.0551
	Nutrient profile	Effective phosphorus (y6)	mg/kg−1	+	0.0541
		Quick-acting potassium (y7)	mg/kg−1	+	0.0406
	Agricultural production conditions	Irrigation guarantee rate (y8)	%	+	0.0416
		Field road accessibility (y9)	°	+	0.2866
		Quantity of rainfall (y10)	mm	+	0.0530
		Drainage condition (y11)	-	+	0.1103
		topographic part (y12)	-	+	0.0572

. The information sensitivity method enhances the principal component analysis (PCA) by evaluating the sensitivity of each indicator. This approach highlights the significance of each indicator within the original system, allowing for the removal of those with minimal impact on the evaluation outcomes. By refining the indicator system in this way, the method increases the overall sensitivity and accuracy of the assessment. This method offers a novel approach to developing regional evaluation indicator systems within the CQED framework and introduces fresh perspectives for promoting regional sustainable development. The indicator selection process and the model based on indicator information sensitivity are detailed below [25].

The eigenvalues of the correlation coefficient matrix X^TX = (r_ij) n × n are calculated using Equation (1).

$$\left|X^{T}X-{\lambda }_i{{\rm E}}_n\right|=0$$

(1)

E_n—an n-order identity matrix.

Calculate the variance contribution rate of the principal components ω_i.

$${\omega }_i={\lambda }_i/\sum _{i=1}^n{\lambda }_i$$

(2)

${\omega }_i$—the proportion of the total variance of all n original indicators explained by the i principal component Zi, reflecting the proportion of the information content of the i principal component Zi to the total information content of all original indicators.

Determination of the number of principal components k to retain.

$${\Omega }_k=\sum _{i=1}^k{\omega }_i$$

(3)

${\Omega }_k$—variance contribution rate, which calculates the proportion of the total information content of the first k principal components to the total information content of all original indicators. The first k principal components are selected so that the cumulative variance contribution rate is not less than 90%.

From Equation (4), the fundamental solution set of the system of equations is obtained, yielding the orthonormal eigenvector u_i^T of X^TX.

$$\left(X^{T}X-{\lambda }_i{E}_n\right)x=0$$

(4)

According to Equation (1), calculate the sensitivity of the i principal component to changes in the size of the j indicator.

$$b_{ij}=\left|\partial Z_i/\partial X_j\right|=\left|u_{ij}\right|$$

(5)

$\left|\partial Z_i/\partial X_j\right|$ indicates the extent to which an infinitesimal change in the size of the j indicator X_j, while keeping other indicators constant, causes a change in the information of the i principal component Z_i.

Calculation of the sensitivity β_j, which reflects how the information of all original indicators, represented solely by the i principal component, is affected by changes in the j indicator.

$${\beta }_{ij}={\omega }_i|\partial Z_i/\partial X_j|={\omega }_i\left|u_{ij}\right|$$

(6)

Calculation of the information sensitivity β_j of the j indicator.

$${\beta }_j=\sum _{i=1}^k{\omega }_i\left|\partial Z_i/\partial X_j\right|=\sum _{i=1}^k{\omega }_i\left|u_{ij}\right|$$

(7)

β_j—represents the extent to which the indicator X_j affects the evaluation results. Therefore, the larger the β_j, the more important the j indicator; conversely, the smaller the β_j, the less important the j indicator.

Eliminate indicators with low information sensitivity. τ_m is calculated by Equation (8):

$${\tau }_m=\sum _{j=1}^m{\beta }_j^{*}/\sum _{j=1}^n{\beta }_j\ge 90\%$$

(8)

2.4. Entropy Weight Method

The entropy weight method calculates the weight of indicators by evaluating the degree of dispersion between the values of the indicators. It is a more objective and precise method for comprehensive weight assessment [26]. This method performs both vertical and horizontal weight analyses of the evaluation indicators to minimize the influence of subjective factors. The specific steps for calculating the indicator weights are as follows:

(1)

Standardize the original data:

$$\left\{\begin{array}{c}{\displaystyle \frac{x_{ij}-\mathit{min}\left(x_{ij}\right)}{\mathit{max}\left(x_{ij}\right)-\mathit{min}\left(x_{ij}\right)}}, \mathrm{Positive} \mathrm{indicators}\\ {\displaystyle \frac{\mathit{max}\left(x_{ij}\right)-{\mathit{minx}}_{ij}}{\mathit{max}\left(x_{ij}\right)-\mathit{min}\left(x_{ij}\right)}}, \mathrm{Negative} \mathrm{indicators}\end{array}\right.$$

(9)

where i is the index number, j is the year $x_{ij}$ is the original data value of the indicator, ${\gamma }_{ij}$ is the standardized value and $\mathit{max}\left(x_{ij}\right)$ and $\mathit{min}\left(x_{ij}\right)$ are the maximum and minimum values of the $i$ indicator, respectively.

(2)

Calculate the information entropy of indicator j:

$$P_{ij}=y_{ij}/\sum _{i=1}^n{y}_{ij}$$

(10)

(3)

Calculate the redundancy of the information entropy of indicator j:

$$E_j=-{\ln \left(n\right)}^{-1}\sum _{i=1}^m{p}_{ij}·\ln p_{ij}$$

(11)

(4)

Calculate the weight of indicator j:

$$W_j={\displaystyle \frac{1-E_j}{{\sum }_{j=1}^m\left(1-E_j\right)}}$$

(12)

In the formula, $W_j$ represents the final weight of the j indicator

Taking into account the actual situation the North Slope Economic Belt of Tianshan, and the Fourteenth Five-Year Plan for the Xinjiang Uygur Autonomous Region, an evaluation index system for the quality of cultivated land and economic development has been established. The evaluation indicators for economic development include a total of 15 indicators across five dimensions: economic growth, innovation-driven development, people’s well-being, green ecology, and security [27,28]. The arable land quality evaluation index system encompasses 15 indicators across five dimensions: profile characteristics, physical and chemical properties, nutrient status, agricultural production conditions, and terrain conditions. The conceptual evaluation index of cultivated land quality is integrated with the degree of affiliation to determine the evaluation value [29,30].

2.5. Coupling Coordination Model

CCDM is an analytical method used to examine the relationship and proximity between systems or elements [31]. This method offers several distinct advantages, making it suitable for this study to thoroughly investigate the coordination effect of CQED subsystems within UANST. Based on the coordination degree and coupling coefficient, coupling coordination can be categorized into various states, as detailed in

Table 2. Coupled coordination level and state type.

C	Class	Coupling Degree	Coupling Coordination Degree
0 ≤ C < 0.4	Low level coupling	0 ≤ D < 0.1	Extreme disorder
		0.1 ≤ D < 0.2	Serious disorder
		0.2 ≤ D < 0.3	Moderate disorder
		0.3 ≤ D < 0.4	Low disorder
0.4 ≤ C < 0.7	Medium level coupling	0.4 ≤ D < 0.5	Marginal disorder
		0.5 ≤ D < 0.6	Marginal coordination
		0.6 ≤ D < 0.7	Primary coordination
0.7 ≤ C < 1.0	High level coupling	0.7 ≤ D < 0.8	Moderate coordination
		0.8 ≤ D < 0.9	Good coordination
		0.9 ≤ D < 1.0	High coordination

. The specific calculation formulas for CCDM are provided in Equations (13)–(15).

$$\mathrm{C}=\sqrt{{\displaystyle \frac{U_1{U}_2}{{\left(\frac{U_{1 }+{ U}_2}{2}\right)}^2}}}$$

(13)

$$T=\alpha U_1+\beta U_2$$

(14)

$$D=\sqrt{C\times T}$$

(15)

The coupling coordination degree (D) is determined by the comprehensive coordination score (T) of two systems. In this context, α and β are undetermined coefficients whose sum equals 1. Given the equal importance of cultivated land quality and economic development, we assign values of 0.5 to both α and β. The classification standard for the coupling coordination degree is detailed in Table 2

2.6. SD Model Construction

System dynamics (SDM) is an effective method for analyzing and simulating complex systems. A scientific framework is essential for accurate modeling and prediction of the development of regional systems [32]. Building on previous research and considering the geographical and economic characteristics of the North Slope Economic Belt of Tianshan, this study establishes a comprehensive CQED system framework. Through Vensim PLE software (10.1.3), we analyze the positive and negative feedback relationships among the elements of each subsystem. Based on these causal relationships and after repeated testing and adjustment, we construct the structural flow chart of the farmland quality and economic development system of the North Slope of Tianshan. GDP, total power of agricultural machinery, carbon dioxide emissions, and soil organic matter are selected as primary variables, while other factors serve as auxiliary variables.

There is a complex interaction between the quality of cultivated land and economic development. On the negative side, economic growth leads to population increases and land expansion, which can result in the occupation and degradation of high-quality arable land. On the positive side, economic growth drives advancements in agricultural science and technology, increases agricultural capital investment, and provides a solid foundation for improving cultivated land quality. The quality of arable land directly influences crop yield and quality. High-quality arable land supports higher crop yields and better quality, which, in turn, fosters diversified agricultural development. These superior agricultural products can extend the agricultural industrial chain, promote the development of related industries, and create new driving forces for economic growth. To investigate the interaction between arable land quality and economic development, we established a multi-dimensional evaluation framework and utilized the (CCD) model to assess the degree of coordination (Figure 2).

3. Results

3.1. Spatiotemporal Distribution of the Synergy Between CQ and ED

The coupling coordination degree model is a crucial indicator for assessing the development level of coupling coordination between subsystems. During the study period, the coupling degree between cultivated land quality and economic development exceeded 0.9, indicating a high degree of coupling. This suggests a complex and close relationship between the factors, with significant interaction. From 2015 to 2021, the coupling coordination degree in the Tianshan North Slope Economic Zone increased from 0.469 to 0.663, showing an overall upward trend. This shift marks a transformation from “marginal disharmony” to “primary coordination” (Figure 3).

In 2015, most cities and counties in the Tianshan North Slope Economic Zone were on the verge of disharmony, except for Urumqi, which had reached the barely developed stage. This was primarily due to Urumqi’s advantageous geographical location, dense population, and high level of economic development. In 2017, with the implementation of the 13th Five-Year Plan, the nation began to focus on the development of central and western regions. Supported by relevant policies, these areas made progress in scientific research innovation, urbanization, agricultural green development, ecological civilization, and expanding openness. Urumqi, Kuitun, and Shawan reached the barely coordinated stage by this time. However, Changji, Wusu, Manas County, and Hutubi County remained on the verge of disharmony. The primary challenges were harsh natural environments, outdated infrastructure, and inconvenient transportation, which hindered economic development. Additionally, the lack of a suitable economic transformation model weakened their economic momentum. The fragile ecological environment and substantial ecological protection responsibilities also contributed to the low coupling and coordination levels between the systems. By 2019, most regions had reached the barely coordinated stage, though Manas County was still on the verge of disharmony. By 2021, Urumqi, Kuitun, and Shawan had a coupling coordination degree exceeding 0.7, reaching the intermediate coupling coordination stage, while the other cities and counties achieved the primary coordination stage. Overall, the coupling coordination level of the seven cities and counties in the Tianshan North Slope Economic Zone improved significantly, and the regional differences in coupling coordination were markedly reduced (

Table 3. UANST Coupling Coordination Degree.

Year	Urumqi	Changji	Wusu	Kuitun	Shawan	Hutubi	Manas
2015	0.5395	0.4665	0.3862	0.4941	0.4944	0.4776	0.4252
2016	0.5603	0.4786	0.4278	0.5266	0.5583	0.4868	0.4310
2017	0.5539	0.4560	0.4614	0.5119	0.5740	0.4681	0.4200
2018	0.5466	0.5837	0.5730	0.5220	0.5642	0.5202	0.4733
2019	0.6762	0.5974	0.5908	0.6287	0.5414	0.5168	0.4301
2020	0.6717	0.6129	0.5994	0.6375	0.7099	0.6076	0.4549
2021	0.7119	0.6547	0.6100	0.7033	0.7230	0.6192	0.6187

).

3.2. Correlation Analysis of UANST Indicators

The absolute value of the correlation coefficient provides a clear measure of the strength of the linear relationship between variables. Specifically, values below 0.3 suggest no linear correlation, 0.3 to 0.5 indicate a low correlation, 0.5 to 0.8 reflect a moderate correlation, and values above 0.8 represent a strong correlation.

Figure 4 illustrates the correlation coefficients between GDP, total power of agricultural machinery, CO₂ emissions, soil organic matter, and other indicators. As depicted in the, GDP shows a strong positive correlation with CO₂ emissions, indicating that as GDP increases, CO₂ emissions also rise. This likely reflects the industrial activities associated with economic growth. Additionally, GDP has a moderate positive correlation with the total power of agricultural machinery, suggesting that as GDP grows, there is an increase in social investment in fixed assets, including agricultural machinery. The enhancement of agricultural mechanization, in turn, can boost total grain output, further supporting GDP growth (Figure 5).

3.3. UANST Coupled Coordination Scenario Setting Analysis

According to the North Slope Economic Belt of Tianshan Mountain, the Fourteenth Five-Year Plan for the Xinjiang Uygur Autonomous Region, the relevant plans, several key variables have been selected as control variables. These include labor productivity of all employees, output value of the tertiary industry, total power of agricultural machinery, investment in social fixed assets, carbon dioxide emissions, and comprehensive production capacity. By adjusting different combinations of these control variables, four simulation schemes for various scenarios have been established.

The specific regulatory factors for the North Slope of Tianshan from 2022 to 2030 are detailed in

Table 4. The value of the control variable in plan 1–plan 4.

Sightseeing	Existing Continuity Type	Economic Priority Development Type	Cropland Protection Type	Harmonized Development Type
Total labor productivity	1.00	1.1	0.95	1.02
Tertiary Industry Output	1.00	1.1	1.0	1.05
Total power of agricultural machinery	1.00	1.0	1.1	1.05
Investment in social fixed assets	1.00	1.0	0.95	1.02
Carbon dioxide emission	1.00	1.05	0.95	0.95
Comprehensive production capacity	1.00	1.05	0.95	1.05
Change in soil organic matter	1.00	1.0	1.1	1.1
Gross food production	1.00	1.1	0.95	1.05

:

3.4. Analysis of Future Coordination Coupling Based on Scenario Setting

Scheme 1: simulation results of Existing Continuity Type indicate that, based on current trends, GDP will rise from 7681.56 billion CNY in 2022 to 10,616.40 billion CNY by 2030, with an average annual growth rate of 4.13%. The per capita disposable income for urban residents and the per capita net income for rural residents are projected to reach 49,032.1 CNY and 30,940.6 CNY, respectively. Despite these economic gains, environmental concerns are significant. Carbon dioxide emissions are expected to increase from 16.7219 million tons in 2022 to 60.0024 million tons by 2030. Additionally, agricultural mechanization is projected to grow to 637,200 kilowatts, and total grain output is forecasted to rise from 920,252 tons in 2022 to 962,294 tons in 2030. These figures underscore a dual reality: economic growth is accompanied by escalating environmental pollution. This trend necessitates further research and the development of policies aimed at controlling environmental degradation while promoting sustainable development alongside economic expansion (Figure 6).

Economic Priority Development Type: this plan, economic development is projected to sustain a high growth rate. By 2030, GDP is expected to reach 111.293 billion CNY, with urban residents’ per capita disposable income at 52,130.7 CNY and rural residents’ per capita net income at 32,983.4 CNY—the highest among the four plans. However, this rapid economic and social development comes with substantial environmental costs. Carbon dioxide emissions are anticipated to rise to 72.3359 million tons by 2030, which is 12.3335 million tons more than in Option 1. Additionally, comprehensive production capacity will increase to 137,900 cubic meters, exacerbating the water shortage crisis. While Option 2 achieves rapid economic growth, it does so at the expense of the environment and involbes significant resource consumption. This approach is unsuitable for the sustainable and healthy development of the Tianshan North Slope Economic Zone (Figure 7).

Cropland Protection Type: this plan, environmental pollution is reduced, leading to improved environmental quality. Carbon dioxide emissions are projected to decrease to 54.5726 million tons, and soil organic matter content will be the highest among the four plans, reaching 7.51 g/kg by 2030. Economically, the GDP is expected to reach 105.084 billion CNY in 2030. The per capita disposable income of urban residents will be 47,982.7 CNY, while rural residents’ per capita disposable income will be 30,248.7 CNY. Total grain output is projected to be 914,179.3 tons. Although the direct impact of enhanced farmland quality on economic development might be less pronounced, the economic benefits remain substantial in the context of significant improvements in environmental quality (Figure 8).

Harmonized Development Type: an in-depth analysis of the advantages and disadvantages of the first three options, we further adjusted the model parameters to design a coordinated development plan. Under this plan, GDP is projected to reach 10,696.6 billion CNY by 2030. The per capita disposable income for urban residents is expected to be 49,576.4 CNY, and the per capita net income for rural residents is projected to be 31,299.4 CNY. Environmental benefits are significant under this plan, with carbon dioxide emissions expected to decrease to 60.3438 million tons by 2035, which is substantially lower than the emission levels projected in Plan II for the same period. The total grain output is estimated to reach 1,010,408 tons, and the comprehensive production capacity is forecasted to be 137,800 cubic meters, alleviating the water shortage issue. Considering all factors, Plan IV emerges as an ideal model for the coordinated development of cultivated land quality and economic growth in the Tianshan North Slope Economic Zone. This plan not only promotes sustained economic growth but also effectively controls environmental pollution, achieving a win-win situation for both the economy and the environment (Figure 9).

According to the data in

Table 5. Degree of coupling coordination (2022–2030).

Year	Existing Continuity Type	Economic Priority Development Type	Cropland Protection Type	Harmonized Development Type
2022	0.4250	0.4276	0.4129	0.4115
2023	0.4817	0.4840	0.4780	0.4652
2024	0.5671	0.5724	0.5654	0.5427
2025	0.5568	0.5516	0.5601	0.5338
2026	0.7221	0.7022	0.7190	0.6992
2027	0.6571	0.6332	0.6547	0.6234
2028	0.7037	0.6738	0.7073	0.6687
2029	0.6679	0.6410	0.6673	0.6877
2030	0.8651	0.8471	0.8641	0.9136

, the coupling coordination degree between cultivated land quality and economic development in the economic zone on the northern slope of the Tianshan Mountains shows an overall upward trend under different scenarios. Generally, Harmonized Development Type achieves the highest coupling coordination degree. The coupling coordination degree of Economic Priority Development Type initially peaks but then declines. Cropland Protection Type (cultivated land protection) starts behind other schemes but demonstrates a continuous upward trend, eventually surpassing Existing Continuity Type and Economic Priority Development Type in the middle half. This indicates that while immediate economic gains can be achieved through economic prioritization, long-term sustainability and higher coordination can be better achieved through more balanced and environmentally focused strategies.

A comparison of the coupled coordination degrees of the four scenarios in Table 5 shows that neither a single bias toward the economy (Scenario 2) nor arable land protection (Scenario 3) is sustainable, with the former leading to a sharp decline in coordination in the later period due to environmental degradation (CCD 0.8471 → 0.6738 in 2030), and the latter restricting coordination in the long term due to economic lags (peak 0.8641). The continuation of the status quo (Scenario 1), while feasible in the short term (CCD 0.4250–0.8651), is inferior to the equilibrium path of Scenario 4 (CCD 0.9136 in 2030). Scenario 4 systematically coordinates land conservation and economic growth through comprehensive measures such as dynamic zoning planning, precision agriculture subsidies, and circular economy incentives, providing a replicable model for solving the conservationdevelopment conflict in arid zones, and emphasizing that cross-sectoral governance and long-term institutional design are the core of sustainable development.

In the existing continuation development scenario under Option 1, no adjustments are made to the cultivated land quality and economic development system, resulting in a coupling coordination degree ranging from 0.4250 to 0.8651. During the simulation period, this range is higher than that for Scenario 2 and Scenario 3 but lower than that for Scenario 4. This indicates that although the existing continued development scenario can promote the economic zone’s development on the northern slope of the Tianshan Mountains, it is not the optimal solution for achieving coordination between farmland quality and economic development. In the economic priority development scenario in Option 2, the coupling coordination value of cultivated land quality and economic development ranges from 0.4276 to 0.8471. While this scenario leads initially, the coupling and coordination degree significantly decline in the later stages. This decline reveals the unsustainability of a development model dominated by rapid economic growth, which exacerbates the deterioration of the cultivated land environment and disrupts its alignment with economic development, thereby hindering the coordinated development of farmland quality and economic growth in the Tianshan North Slope Economic Zone. In the cultivated land protection scenario in Cropland Protection Type, the coupling coordination value between cultivated land quality and economic development ranges from 0.4129 to 0.8641. The low economic development level of this plan is not conducive to the long-term coordinated progress of farmland quality and economic development. In the coordinated development scenario in Harmonized Development Type, the value of the control variable is balanced between Economic Priority Development Type and Cropland Protection Type. The forecast for 2030 shows that the coupling coordination degree value of Option 4 ranges from 0.4115 to 0.9136, achieving a high-quality coordination level. This highlights that only Option 4, which pays attention to both farmland quality and economic development, can promote the harmony and sustainable development of both systems.

In summary, the coupling coordination degree between cultivated land quality and economic development on the northern slope of the Tianshan Mountains varies under different development scenarios. Harmonized Development Type performs the best in terms of coupling coordination, underscoring the importance of coordinated development strategies in promoting regional sustainable development. This scheme balances economic growth with environmental preservation, demonstrating that sustainable and harmonious development can be achieved through thoughtful planning and strategic adjustments.

4. Discussion

4.1. The Coupling Coordination Characteristics at Different Stages Are Obvious

Between 2015 and 2021, the Tianshan North Slope Economic Zone exhibited a trend toward increased coordination between economic development and cultivated land quality. In Urumqi, Changji, Kuitun, and Shawan, there was a significant temporal and spatial coupling of these two factors. This positive development was largely driven by the strategic direction provided by the 13th Five-Year Plan. The plan’s emphasis on balancing economic growth with environmental protection fostered a mutually beneficial dynamic, leading to concurrent improvements in both economic structure and the quality of cultivated land [33]. In the future, these regions should focus on several key development strategies to ensure sustainable growth. Priorities should include protecting cultivated land, advancing agricultural modernization, optimizing industrial structures, enhancing environmental protection, and embracing green development principles. By doing so, they can promote sustainable development and ensure long-term regional prosperity [34,35].

The coupling coordination degree in some regions remains relatively low. For instance, Wusu, Hutubi, and Manas have lower degrees of coupling coordination due to regional development disparities, challenging natural conditions, and outdated agricultural technology. Moving forward, it is essential to focus on adjusting and upgrading the industrial structure in these areas, increasing investment in agricultural infrastructure, and promoting advanced agricultural technologies. These efforts will help enhance their coupling coordination and drive sustainable development [36,37] The coordinated development level of cultivated land quality and economic development is expected to improve further.

4.2. Differences in Dominant Factors of UANST Coupling Coordination in Future Scenarios

Like many other studies, we found that the coordinated changes in the two systems are influenced by multiple factors. Natural factors, such as the rugged terrain of the Tianshan Mountains, water scarcity, and severe soil salinization, play a significant role. Additionally, human policies, including the “14th Five-Year Plan”, and the “One Belt and One Road Initiative”, have a direct impact on the future coordination of CQED. Our analysis indicates that population, economic, and social development indicators have a more substantial effect on coupling coordination than indicators related to surface environment and climate characteristics. Notably, these factors also influence social and economic development. Furthermore, regional GDP directly impacts the living standards of local people, which, in turn, affects the coupled and coordinated development of the UANST’s farmland and economy [36].

4.3. Policy Recommendations

By analyzing the current status of the Tianshan North Slope Economic Zone from 2015 to 2021 and the changing trend of the coupling coordination degree between cultivated land quality and economic development under the simulated conditions from 2022 to 2030, and based on existing research, the current status of the Tianshan North Slope Economic Zone, and the 14th Five-Year Plan for the Xinjiang Uygur Autonomous Region and this paper puts forward the following policy recommendations on cultivated land quality and economic development from the perspective of regional coordinated development:

First, Enhancing laws and regulations regarding cultivated land protection in the Tianshan North Slope Economic Zone is essential to prevent illegal occupation and pollution. Accelerating the implementation of high-standard farmland construction and actions to protect and improve cultivated land quality is crucial. Establishing an effective water resources management system will enhance comprehensive production capacity, ensure rational water resource allocation, and prevent over-exploitation and waste. Promoting water-saving irrigation technologies, such as drip and sprinkler irrigation, can significantly improve water resource efficiency. Providing regular training for farmers on sustainable agricultural science and technology will improve their skills and environmental, encouraging practices like increasing agricultural mechanization, reducing chemical pesticide and fertilizer use, and enhancing soil organic matter content. Additionally, establishing a cultivated land quality monitoring system to regularly assess soil fertility and pollution will help take timely measures to boost grain production and meet “14th Five-Year Plan” targets.

Second, Introducing modern industrial development concepts and organizational methods into agriculture is essential for maximizing the natural resources of the Tianshan North Slope Economic Zone. By establishing modern agricultural industrial parks tailored to local conditions, the region can significantly increase per capita disposable income. Additionally, the development of a modern agricultural system will enhance the output value of the primary industry and promote greater coupling and coordination within the UANST region. These initiatives will drive economic growth while ensuring sustainable agricultural practices.

Third, it is crucial to prioritize resources and environmental constraints. Protecting arable land and the environment must be a top priority. This involves clearly defining the sustainable development boundaries for arable land protection areas, managing soil pollution risks, and enhancing soil risk management to ensure the safe use of both construction and agricultural land. In urban areas, efforts should focus on increasing the greening coverage rate within built-up zones, controlling the expansion of industrial enterprises, and keeping carbon dioxide emissions within the limits set by the “14th Five-Year Plan.” These measures are essential for fostering green development that harmonizes economic growth with environmental preservation [36].

4.4. Policy Recommendations

The coupling coordination system of cultivated land quality and economic is inherently complex and multifaceted. This paper focuses on the Tianshan North Slope Economic Zone, conducting targeted research that has yielded certain results. However, there are notable limitations in this study, such as constraints in the quality and statistical data cultivated land, the short duration the research data, and the need for improved accuracy in the prediction models. Future research should incorporate longer time series and more comprehensive indicators of cultivated land quality and economic development. This approach will enhance our understanding of the interplay between these factors, enabling more in-depth analysis and addressing current research limitations. Expanding the scope and depth of the research will improve its applicability and assist in the development of more scientifically sound policies for the region.

5. Conclusions

This study investigates the economic zone on the northern slope of the Tianshan Mountains, focusing on the coupling coordination between cultivated land quality and economic development. To address this complex interaction, a combination of qualitative methods and principal component analysis (PCA) based on information sensitivity was used to select key evaluation indicators. Building on this analytical foundation, the coupling coordination degree model measured the interaction between these factors, while a system dynamics (SD) model simulated and predicted future trends under four different scenarios. The empirical results reveal two distinct phases: 2015 to 2021, the coupling coordination degree between cultivated land quality and economic development in this region improved from 0.469 to 0.663, marking a shift from “on the verge of imbalance” to “primary coordination”, accompanied regional disparities in coordination patterns. Looking forward, the SD model’s scenarios (2022–2030) revealed that among all tested approaches, the coordinated development scheme—integrating the strengths of all four scenarios—achieved the highest coupling coordination degree of 0.9136. Notably, this scheme demonstrated multidimensional advantages: beyond enhancing landeconomic interactions, it significantly reduced carbon dioxide emissions and water resource consumption, thereby establishing an environmental pressure mitigation mechanism. These findings collectively suggest that the coordinated development scheme represents the optimal path for sustainable regional development, where agricultural productivity and economic progress are balanced through strategic environmental impact management.