Systematic Measurement and Evolution Situation of Coupling Coordination Level between Intensive Cultivated Land Utilization and New-Type Urbanization

Abstract

This study uses the entropy method to measure the comprehensive evaluation index of 30 provincial samples, with the exception of the Tibet, Hong Kong, Macao, and Taiwan regions, of farmland intensive utilization and new-type urbanization in China. This study also builds a coupling coordination model to measure the coupling degree, coordination degree, and relative development degree. The kernel density estimation method and Theil index describe its evolution situation and spatial differences. The results showed that the coupling degree, coordination degree, and relative development degree of cultivated land intensive use and new-type urbanization were highly coupled, barely coordinated, and that cultivated land intensive use lagged, respectively. Kernel density estimation shows that the coupling degree, coordination degree, and relative development degree of cultivated land intensive use and new-type urbanization decrease as a whole and the regional differences increase. Theil index analysis shows that the differences of coupling degree, coordination degree, and relative development degree in eastern, central, and western regions are mainly caused by intragroup differences. Therefore, according to the economic situation and resource endowment conditions of each province and city, the benign interaction between the intensive use of cultivated land and new-type urbanization should be promoted according to local conditions. Food security is an important basis of national security. Cultivated land is not only a key element to ensure food security, but also a strong driving force for promoting high-quality, green, and sustainable development of agriculture and coordinated urban–rural development. By improving agricultural infrastructure and production conditions, China could choose to establish a strict arable land protection system and other measures to improve the level of intensive utilization of cultivated land.

1. Introduction

As the world’s largest developing country, with a population of nearly 1.4 billion, China must face the relatively insufficient endowment of arable land resources for its modernization and construction. With the continuous advancement of urbanization and industrialization, the cultivated land, which is already relatively scarce, has been seriously "squeezed", and the "red line" of 1.8 billion mu of arable land to ensure food security is becoming more and more challenging. At present and for a long time to come, on the one hand, in order to accelerate the process of modernization, China’s urbanization construction should also be steadily advanced; on the other hand, in order to ensure food security, under the premise of keeping the red line of cultivated land, the level or efficiency of cultivated land intensive use should also be greatly improved. Therefore, it is particularly important to promote urbanization and cultivated land intensive use in a coordinated manner, and to improve the level of coupling and coordination.

In recent years, the relationship between cultivated land intensive use and urbanization in China has attracted great attention from the academic community, but the relevant research results are still not rich enough. At present, scholars’ research on intensive use of cultivated land mainly involves the following aspects: measurement of the level of intensive use of cultivated land [1,2,3,4,5], spatiotemporal characteristics, driving or influencing mechanisms [6,7,8,9,10], and coordinated development with other factors [11,12,13]; the research on (new-type) urbanization mainly involves the following: the level of new-type urbanization, influencing factors [14,15,16,17,18,19,20,21,22], temporal and spatial characteristics [23,24], urban–rural coordination [25,26,27,28], and research on the coordinated development of new-type urbanization and other factors [29,30,31,32,33,34,35,36,37]. For the research on the intensive use of cultivated land and new-type urbanization, the existing research mainly explores the coupling and coordinated development of one or some provinces or cities (new-type) urbanization and the intensive use cultivated land in a specific region. For example, based on panel data from 2010 to 2019 in 14 cities (prefectures) in Gansu Province, Juanjuan Bao and Jianping Lv (2022) empirically analyzed the coupling coordination level and dynamic evolution characteristics of new-type urbanization and cultivated land intensive use [38]. Zirui Li et al. (2021) measured the coordination level of new-type urbanization and intensive use of cultivated land in the three provinces of Northeast China from 2008 to 2018. It predicted the coordinated development level of new-type urbanization and intensive use of cultivated land in the region from 2019 to 2030 based on a RBF neural network model [39]. Similar work in the literature includes Dan Li et al. (2021), based on Heilongjiang Province [40], Runmiao Zhu and Songlin Chen (2021), based on Fujian province [41], Jueraiti Wubuli et al. (2019), based on Aksu City of Xinjiang [42], Chuxiong Deng and Jian Shi (2018), based on Xiangxi Prefecture in Hunan Province [43], Chunyan Cao (2018), based on Jiangsu Province [44], Qingqing Ye and Juanjuan Cao (2017), based on the Jianghan Plain [45], Yanglu Lu et al. (2016), based on the study of Guangdong [46], and so on. However, based on provincial panel data, there are few analyses of the evolution situation and spatial differences in the coupling coordination level between China’s cultivated land intensive use and new-type urbanization from comprehensive, regional, and provincial levels.

The existing relevant literature provides theoretical and methodological reference for this study. Based on the detailed analysis of specific areas, it can also provide certain decision-making support and a practical basis for the coordinated development of new-type urbanization and cultivated land intensive use in relevant areas. However, as the largest developing country, China’s regional or provincial economic development is "unbalanced and insufficient". This is not only China’s actual national situations, but also the main manifestation of China’s current social contradiction. Systematically studying the relationship between cultivated land intensive use and new-type urbanization from multiple levels at the national, regional, and provincial levels, and carefully depicting the coupling and coordination level, evolution trend, and regional differences between the two from the two dimensions of time and space, can not only provide a new perspective for understanding or interpreting the "unbalanced and insufficient" problem of China’s development, but can also provide richer policy implications for the coordinated promotion of cultivated land intensive use and new-type urbanization at the national level. Based on this, this study constructs a measurement framework and index system for the intensive utilization of cultivated land and new-type urbanization. Then, it uses the entropy weight method to calculate the level of the intensive use of cultivated land and the new-type urbanization of 30 provincial-level sample data in China from 2008 to 2020, except Tibet, Hong Kong, Macao, and Taiwan. Based on the inherent logic relation of the two systems, this study uses a coupling coordination model system to evaluate the coupling degree and coordination degree of the two, the relative development degree of the two, and depicts their evolution characteristics and regional differences from multiple levels, such as national, regional, and provincial levels. Furthermore, the study applies kernel density estimation and the Theil index to carefully analyze their evolution and spatial differences from two dimensions, namely time and space. This study will help to further enrich the research results of cultivated land intensive use and urbanization. It will help to provide a realistic basis for promoting the coupling and coordinated development of China’s cultivated land intensive use and new-type urbanization.

2. Index System, Model and Method

2.1. Index System

A thorough understanding of the connotations of cultivated land intensive use and new-type urbanization is the premise of constructing respective index systems. Cultivated land intensive use is relative to the extensive use of cultivated land. It refers to the guidance of sustainable development and new development concepts. Under the constraints of a certain scale of cultivated land, and with the help of modern science and technology and management methods, cultivated land intensive use can optimize the allocation of input factors, such as physical capital, human capital, land, and technology, promote the appropriate scale operation of land, adjust and optimize the production and operation functions of cultivated land and the structure of agricultural industry, tap the potential of cultivated land utilization, and improve the level of sustainable utilization under the conditions of established arable land resource endowments. The new-type urbanization is not only the natural process of rural population concentration to cities and towns, industrial factors to cities and towns, and the gradual spillover of urban physical space, but also contains the improvement of the quality of urban economic development, the soundness and equalization of public service systems, and the improvement of the ecological environment. The core of new-type urbanization is people-oriented urbanization [47].

Based on the above connotations, this study refers to the existing relevant literature [14,16,35,38,39,40,41,42,43,44,45,46,47,48,49,50,51] by following the principles of multiple dimensions, data availability, scientific indexes, and consistent caliber, and establishes an evaluation index system for cultivated land intensive use and new-type urbanization. At the target layer of cultivated land intensive use, the index system covers six criterion surfaces and twenty indexes, such as arable land input intensity, cultivated land utilization intensity, cultivated land output level, sustainable use of cultivated land, labor intensity, and agricultural structure. At the target level of new-type urbanization, the index system covers six criterion layers and twenty-five indexes, such as economic urbanization, population urbanization, land urbanization, social urbanization, ecological environment urbanization, and innovation and research and development. The index system of cultivated land intensive use and new-type urbanization is shown in Table 1 below.

This researched used the relevant data of the Statistical Yearbook, the relevant survey data carried out by the national unified deployment, the published communiques, and so on. This paper collects and organizes the panel data of 30 sample provinces from 2008 to 2020, aiming at the index data of certain provinces and certain years. It reasonably fills the missing data by a linear interpolation method and uses a weighted average method for the missing index data of certain provinces and years.

Table 1. Comprehensive evaluation index system for cultivated land intensive use and new-type urbanization.

Target Layer	Criterion Layer	Weights	Index Layer	Weights
Arable land collection utilization subsystem	Intensity of arable land input	0.3414	The average agricultural machinery input	0.0434
			Fertilizer input is averaged in the ground	0.0361
			The average agricultural electricity input is provided	0.2355
			Land average manpower input	0.0264
	Intensity of arable land utilization	0.1082	Multiple cropping index	0.0341
			Reclamation Index	0.0255
			Irrigation index	0.0486
	The level of arable land output	0.1104	Average ground yield	0.0463
			Labor average production	0.0383
			Food yields	0.0258
	Sustainable use of arable land	0.1478	Average land share of water resources	0.0857
			Arable land balance index	0.0130
			Food security index	0.0491
	Degree of labor intensification	0.1021	Arable land safety factor	0.0539
			Labor Force Index	0.0198
			Rural population density	0.0284
	Agricultural structure	0.1901	Proportion of animal husbandry	0.0256
			Proportion of forestry	0.0618
			Proportion of agriculture	0.0261
			Proportion of fisheries	0.0766
Urbanization subsystem	Economic urbanization	0.3014	Per capita disposable income of urban residents	0.0794
			The average wage of urban workers on the job	0.0658
			The added value of the secondary and tertiary industries accounts for the proportion of GDP	0.0143
			General public budget revenue as a share of GDP	0.0390
			GDP per capita	0.0542
			Investment in fixed assets in the whole society	0.0487
	Population urbanization	0.1852	Urban unemployment registration rate	0.0341
			Coverage of basic medical insurance in cities and towns	0.0429
			Proportion of urban population	0.0364
			Urban population density	0.0368
			Urban basic old-age insurance coverage	0.0350
	Land urbanization	0.1086	Fixed asset investment on land for construction purposes	0.0235
			Proportion of built-up areas	0.0334
			Per capita area of urban construction land	0.0517
	Social urbanization	0.1830	Number of students enrolled in institutions of higher learning	0.0339
			Number of announced cars per 10,000 people	0.0393
			Education revenue as a share of GDP	0.0422
			Number of beds in healthcare facilities per 1000 people	0.0332
			Water supply penetration	0.0209
			Gas penetration rate	0.0135
	Ecological environment urbanization	0.0460	The harmless treatment rate of domestic garbage	0.0133
			Municipal sewage treatment rate	0.0127
			Green coverage of built-up areas	0.0200
	Innovation and R&D	0.1758	R&D funding intensity	0.0601
			Number of patent applications granted per 10,000 people	0.1157

Table 1. Comprehensive evaluation index system for cultivated land intensive use and new-type urbanization.

Target Layer	Criterion Layer	Weights	Index Layer	Weights
Arable land collection utilization subsystem	Intensity of arable land input	0.3414	The average agricultural machinery input	0.0434
			Fertilizer input is averaged in the ground	0.0361
			The average agricultural electricity input is provided	0.2355
			Land average manpower input	0.0264
	Intensity of arable land utilization	0.1082	Multiple cropping index	0.0341
			Reclamation Index	0.0255
			Irrigation index	0.0486
	The level of arable land output	0.1104	Average ground yield	0.0463
			Labor average production	0.0383
			Food yields	0.0258
	Sustainable use of arable land	0.1478	Average land share of water resources	0.0857
			Arable land balance index	0.0130
			Food security index	0.0491
	Degree of labor intensification	0.1021	Arable land safety factor	0.0539
			Labor Force Index	0.0198
			Rural population density	0.0284
	Agricultural structure	0.1901	Proportion of animal husbandry	0.0256
			Proportion of forestry	0.0618
			Proportion of agriculture	0.0261
			Proportion of fisheries	0.0766
Urbanization subsystem	Economic urbanization	0.3014	Per capita disposable income of urban residents	0.0794
			The average wage of urban workers on the job	0.0658
			The added value of the secondary and tertiary industries accounts for the proportion of GDP	0.0143
			General public budget revenue as a share of GDP	0.0390
			GDP per capita	0.0542
			Investment in fixed assets in the whole society	0.0487
	Population urbanization	0.1852	Urban unemployment registration rate	0.0341
			Coverage of basic medical insurance in cities and towns	0.0429
			Proportion of urban population	0.0364
			Urban population density	0.0368
			Urban basic old-age insurance coverage	0.0350
	Land urbanization	0.1086	Fixed asset investment on land for construction purposes	0.0235
			Proportion of built-up areas	0.0334
			Per capita area of urban construction land	0.0517
	Social urbanization	0.1830	Number of students enrolled in institutions of higher learning	0.0339
			Number of announced cars per 10,000 people	0.0393
			Education revenue as a share of GDP	0.0422
			Number of beds in healthcare facilities per 1000 people	0.0332
			Water supply penetration	0.0209
			Gas penetration rate	0.0135
	Ecological environment urbanization	0.0460	The harmless treatment rate of domestic garbage	0.0133
			Municipal sewage treatment rate	0.0127
			Green coverage of built-up areas	0.0200
	Innovation and R&D	0.1758	R&D funding intensity	0.0601
			Number of patent applications granted per 10,000 people	0.1157

2.2. Models and Methods

2.2.1. Entropy Method

Based on the index system in Table 1 and the relevant data collected and collated, this study used the entropy method to calculate the intensive utilization level of arable land and the level of new-type urbanization in 30 sample provinces from 2008 to 2020. Due to the different statistical units of each index, the index weighting is particularly important for the evaluation of the cultivated land intensive use and the level of new-type urbanization, and the subjective weighting method has a certain arbitrariness. Therefore, the objective weighting method is adopted to standardize the indicators according to the actual data, and the weights of the index layer is determined. The weights of the criterion layer are the sum of the weights of the corresponding index layers, so as to minimize the error caused by human subjective factors and to make the results more objective [52]. The specific calculation process is as follows:

(1)

Data standardization processing is as follows:

$$\mathrm{Positive} \mathrm{Index}: z_{ij}=\frac{x_{ij}-x_{min}}{x_{max}-x_{min}}$$

(1)

$$\mathrm{Negative} \mathrm{Index}: z_{ij}=\frac{x_{max}-x_{ij}}{x_{max}-x_{min}}$$

(2)

In Equation (1) and Equation (2), $z_{ij}$ is the standard value of the index, $x_{ij}$ is the actual index value, $x_{max}$ is the actual maximum of the index, and $x_{min}$ is the actual minimum value of the index.

(2)

Calculate the proportion of the value of the jth index in the ith year, as follows:

$$Y_{ij}=\frac{z_{ij}}{{{\displaystyle \sum }}_{i=1}^m{z}_{ij}}$$

(3)

(3)

Calculate the entropy $E_j$ of index information, as follows:

$$E_j=\raisebox{1ex}{$-1$}\!\left/ \!\raisebox{-1ex}{$\mathit{ln}m$}\right.{{\displaystyle \sum }}_{i=1}^m(Y_{ij}\times \mathit{ln}{Y}_{ij})$$

(4)

(4)

Calculate the redundancy of information entropy, as follows:

$$D_j=1-E_j$$

(5)

(5)

Calculate the weight of each index $W_j$, as follows:

$$W_j=\frac{D_j}{{{\displaystyle \sum }}_{j=1}^n{D}_j}$$

(6)

(6)

Calculate the comprehensive evaluation index, as follows:

$$U_{\mathit{ij}}={\displaystyle \sum }_{j=1}^n(W_j\times Z_{\mathit{ij}})$$

(7)

where $U_{ij}$ is the comprehensive evaluation index of cultivated land intensive use and new-type urbanization, $W_j$ are the weights of each sub-index, and $Z_{ij}$ are the normalized values of each index data.

2.2.2. Coupled Coordinated Development Degree Model

There is an inherent logical relationship between the two subsystems of cultivated land intensive use and new-type urbanization. The improvement of the level of cultivated land intensive use can provide more and better agricultural and sideline products or raw materials for the development of new-type urbanization through the increase in the quantity and quality of agricultural and sideline products. The release of the surplus labor force and accumulated capital will guarantee the supply of factors for the development of new-type urbanization. The development of new-type urbanization helps to absorb the transfer of agricultural surplus labor, promote the circulation of agricultural farmland management rights, and promote the specialization and moderate scale operation of cultivated land. Through the spillover effects of knowledge, technology, and management, the new-type urbanization has improved the progress of agricultural technology and the efficiency of the allocation of production factors and improved the level of arable land utilization and output performance. This shows that there is an inherent logical relationship between the cultivated land intensive use and the new-type urbanization but, at the empirical level, whether there is a coupling and coordination relationship between the two needs to be tested empirically.

Based on the logical relationship between the cultivated land intensive use and the new-type urbanization, this study draws on the concept of physical coupling [53] to establish a coupling coordination model for the intensive use of cultivated land and new-type urbanization. The specific formula is as follows:

$$C={\left\{\frac{f\left(x\right)·g\left(y\right)}{{\left(\frac{f\left(x\right)+g\left(y\right)}{2}\right)}^2}\right\}}^k$$

(8)

$$T=\alpha f\left(x\right)+\beta g\left(y\right)$$

(9)

$$D=\sqrt{C·T}$$

(10)

$$E=f\left(x\right)/g\left(y\right)$$

(11)

In Formula (8), $C$ is the coupling degree of the two subsystems of cultivated land intensive use and new-type urbanization, the value range is [0, 1], and the larger the $C$ value, the higher the degree of interaction and mutual influence between the two, $k$ is the adjustment coefficient, and the general value is 2 ≤$k$≤ 5, according to the relevant research results and, thus, the study selected $k$= 5. In Formula (9), $T$ is the comprehensive coordination index of cultivated land intensive use and new-type urbanization, $\alpha$ and $\beta$ are the coefficients to be determined, and $\alpha +\beta =1$. In this study, it is believed that the cultivated land intensive use and new-type urbanization are equally important to economic and social development. Thus, take $\alpha =\beta =0.5$. Here, $D$ in Equation (10) is the degree of coordination, which measures the degree of coordination achieved in the interaction between the two. Meanwhile, $E$ in Equation (10) is the relative degree of development, which measures the relative level of development between cultivated land intensive use and new-type urbanization. In this study, with reference to the relevant research of Yanglu Lu [46] and Runmiao Zhu [41], the criteria for determining the coupling, coordination, and relative development degree of cultivated land intensive use and new-type urbanization were determined (

Table 2. Classification of coupling and coordination development levels and types.

Project	Classification Criteria	Stage or Type
Coupling degree	[0, 0.2]	Highly uncoupled
	(0.2, 0.4]	Not coupled
	(0.4, 0.6]	Low degree of coupling
	(0.6, 0.8]	Moderate coupling
	(0.8, 1]	Highly coupled
Coordination degree	[0, 0.3]	Severely dysfunctional recession
	(0.3, 0.4]	Moderate dysregulation decline
	(0.4, 0.5]	Mild dysregulation decline
	(0.5, 0.6]	Reluctantly coordinated development
	(0.6, 0.7]	Primary coordinated development
	(0.7, 0.8]	Moderately coordinated development
	(0.8, 1]	Well coordinated development
Relative development degree	[0, 0.8]	Cultivated land intensive use lag type
	(0.8, 1.2]	Synchronous development type
	(1.2, θ)	The new-type urbanization lag type

).

2.2.3. Kernel Density Estimation

The kernel density estimation method is a nonparametric estimation method that can accurately describe the distribution of random variables and is widely used in the study of spatial distribution imbalance [54]. The specific formula is as follows:

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

(12)

In Equation (12), $N$ is the number of observations, $h$ represents bandwidth, $K(·)$ is a kernel function, $X_i$ represent observations that are independent of the same distribution, and $x$ is the mean. This paper uses the Gaussian kernel function, which can effectively characterize the cultivated land intensive use and the coupling and coordinated development of new-type urbanization by the location, peak height, quantity, width, shape, and ductility of the kernel density map.

2.2.4. Theil Index Method

In 1976, the Dutch economist Theil applied the entropy theory to the study of income disparities and proposed the Theil index, which can break down overall inequality into interregional and intraregional differences; the smaller the Theil index, the smaller the difference between regions, and vice versa. Referring to the practice of Yan Li, Zongjun Ke et al. [55,56], this study uses the Theil index to describe the regional differences and sources of the coupled and coordinated development of cultivated land intensive use and new-type urbanization. The specific calculation and decomposition methods from (13) to (17) give the following:

$$T=\frac{1}{n}{\displaystyle \sum }_{i=1}^n\frac{z_i}{u}ln\frac{z_i}{u}$$

(13)

$$T=T_W+T_B$$

(14)

$$S_k=\frac{n_k}{n}\ast \frac{u_k}{u}$$

(15)

$$T_W={\displaystyle \sum }_{k=1}^k{s}_k{\ast T}_{Z_k}$$

(16)

$$T_B={\displaystyle \sum }_{k=1}^k{s}_{k}ln\frac{u_k}{u}$$

(17)

where $n$ is the sample size, $z_i$ represents the measure index of coupling degree, coordination degree and relative development degree of the ith province, and $u$ is the average of the measure index of coupling degree, coordination degree, and relative development degree of all provinces. Here, $k$ is the number of groups, $n_k$ refers to the number of provinces in group $k$, $u_k$ is the average of the correlation index of the sample of group $k$, and $T_{\left(Z_k\right)}$ is the Theil index of group $k$. Finally, $T_W$ represent intragroup differences, and $T_B$ represent intergroup differences.

3. Results

3.1. Evolutionary Characteristics of the Level of Cultivated Land Intensive Use and New-Type Urbanization

This study estimates the comprehensive level of cultivated land intensive use and new-type urbanization in 30 sample provinces in China from 2008 to 2020. Due to space limitations, it only reports the beginning (2008), end of the period (2020), and the average values between 2008 to 2020 in the sample period, as shown in Table 3.

It can be seen that the five provinces with the highest level (mean) of cultivated land intensive use during the sample period were as follows from high to low: Shanghai (0.5633), Fujian (0.4460), Guangdong (0.4112), Zhejiang (0.3870), and Jiangsu (0.3759), all of which belonged to the eastern region. The lowest five provinces, from lowest to highest, were as follows: Gansu (0.1238), Shanxi (0.1543), Ningxia (0.1731), Guizhou (0.1747), and Shaanxi (0.1897), all of which were western regions except Shanxi. The five provinces with the highest level (mean) of new-type urbanization during the sample period were as follows from high to low: Beijing (0.6394), Shanghai (0.5612), Jiangsu (0.5224), Guangdong (0.5053), and Zhejiang (0.5018), all of which belonged to the eastern region; the lowest five provinces from low to high were as follows: Guangxi (0.2040), Guizhou (0.2119), Gansu (0.2245), Yunnan (0.2256), and Jilin (0.2350), all of which were western regions except Jilin.

At the end of the period (2020) compared with the beginning of the period (2008), there are three provinces with an increase in the level of cultivated land intensive use, namely Heilongjiang, Guizhou, and Shaanxi, which are all in the central and western regions; there are twenty-seven provinces where the level of cultivated land intensive use has declined, of which eleven are in the east, seven are in the central region, andnine are in the west. There are twenty-two provinces with improved levels of new-type urbanization, including five in the east, seven in the central region, and ten in the west. There are eight provinces which have declined, of which six are in the east, one in the central region, and one in the west. It shows that the vast majority of provinces with cultivated land intensive use during the sample period have declined, while the vast majority of provinces with new-type urbanization levels have increased, and the two show a relationship as “one falls, and the other rises”.

Table 3. Comprehensive evaluation index of cultivated land intensive use and new-type urbanization.

Province	Evaluation Index	In 2008	In 2020	Mean	Province	Evaluation Index	In 2008	In 2020	Mean
Beijing	CL	0.3902	0.3063	0.2956	Beijing	NU	0.6973	0.5758	0.6394
Tianjin	CL	0.3311	0.2196	0.2543	Tianjin	NU	0.4637	0.3862	0.4271
Hebei	CL	0.3177	0.2136	0.2569	Hebei	NU	0.2798	0.3332	0.2789
Shanxi	CL	0.1760	0.1385	0.1543	Shanxi	NU	0.2542	0.2733	0.2550
Inner Mongolia	CL	0.2103	0.1920	0.1954	Inner Mongolia	NU	0.2245	0.2470	0.2457
Liaoning	CL	0.2656	0.1873	0.2113	Liaoning	NU	0.3804	0.2749	0.3193
Jilin	CL	0.2398	0.2027	0.2032	Jilin	NU	0.2479	0.2401	0.2350
Heilongjiang	CL	0.2240	0.2614	0.2410	Heilongjiang	NU	0.2509	0.2839	0.2591
Shanghai	CL	0.5774	0.4946	0.5633	Shanghai	NU	0.6528	0.4877	0.5612
Jiangsu	CL	0.4294	0.3215	0.3759	Jiangsu	NU	0.5306	0.5284	0.5224
Zhejiang	CL	0.4883	0.3642	0.3870	Zhejiang	NU	0.5244	0.4877	0.5018
Anhui	CL	0.3119	0.2478	0.2810	Anhui	NU	0.2483	0.3468	0.2826
Fujian	CL	0.5178	0.3970	0.4460	Fujian	NU	0.3083	0.3688	0.3184
Jiangxi	CL	0.3690	0.2578	0.3096	Jiangxi	NU	0.2924	0.3423	0.2970
Shandong	CL	0.3932	0.2752	0.3150	Shandong	NU	0.4081	0.4162	0.3994
Henan	CL	0.3643	0.2643	0.3027	Henan	NU	0.2940	0.3792	0.3008
Hubei	CL	0.3165	0.2501	0.2734	Hubei	NU	0.2673	0.3174	0.2920
Hunan	CL	0.3814	0.2912	0.3265	Hunan	NU	0.2569	0.3800	0.2836
Guangdong	CL	0.4941	0.3722	0.4112	Guangdong	NU	0.5039	0.5477	0.5053
Guangxi	CL	0.3137	0.2743	0.2838	Guangxi	NU	0.1886	0.2641	0.2040
Hainan	CL	0.3669	0.3154	0.3137	Hainan	NU	0.2124	0.2985	0.2527
Chongqing	CL	0.2546	0.2254	0.2207	Chongqing	NU	0.2537	0.2794	0.2959
Sichuan	CL	0.2665	0.2358	0.2320	Sichuan	NU	0.2659	0.3427	0.2912
Guizhou	CL	0.1859	0.1944	0.1747	Guizhou	NU	0.1638	0.2714	0.2119
Yunnan	CL	0.2302	0.1857	0.2016	Yunnan	NU	0.2243	0.2787	0.2256
Shaanxi	CL	0.1942	0.1987	0.1897	Shaanxi	NU	0.3261	0.3583	0.3309
Gansu	CL	0.1460	0.1342	0.1238	Gansu	NU	0.2120	0.2528	0.2245
Qinghai	CL	0.2305	0.1926	0.2143	Qinghai	NU	0.2439	0.2749	0.2511
Ningxia	CL	0.2048	0.1610	0.1731	Ningxia	NU	0.2356	0.2748	0.2587
Xinjiang	CL	0.2587	0.1938	0.2387	Xinjiang	NU	0.3280	0.3021	0.3199
mean		0.3150	0.2523		mean		0.3247	0.3471

Here, CL stands for the comprehensive evaluation index of cultivated land intensive use, and NU stands for the comprehensive evaluation index of new-type urbanization.

Table 3. Comprehensive evaluation index of cultivated land intensive use and new-type urbanization.

Province	Evaluation Index	In 2008	In 2020	Mean	Province	Evaluation Index	In 2008	In 2020	Mean
Beijing	CL	0.3902	0.3063	0.2956	Beijing	NU	0.6973	0.5758	0.6394
Tianjin	CL	0.3311	0.2196	0.2543	Tianjin	NU	0.4637	0.3862	0.4271
Hebei	CL	0.3177	0.2136	0.2569	Hebei	NU	0.2798	0.3332	0.2789
Shanxi	CL	0.1760	0.1385	0.1543	Shanxi	NU	0.2542	0.2733	0.2550
Inner Mongolia	CL	0.2103	0.1920	0.1954	Inner Mongolia	NU	0.2245	0.2470	0.2457
Liaoning	CL	0.2656	0.1873	0.2113	Liaoning	NU	0.3804	0.2749	0.3193
Jilin	CL	0.2398	0.2027	0.2032	Jilin	NU	0.2479	0.2401	0.2350
Heilongjiang	CL	0.2240	0.2614	0.2410	Heilongjiang	NU	0.2509	0.2839	0.2591
Shanghai	CL	0.5774	0.4946	0.5633	Shanghai	NU	0.6528	0.4877	0.5612
Jiangsu	CL	0.4294	0.3215	0.3759	Jiangsu	NU	0.5306	0.5284	0.5224
Zhejiang	CL	0.4883	0.3642	0.3870	Zhejiang	NU	0.5244	0.4877	0.5018
Anhui	CL	0.3119	0.2478	0.2810	Anhui	NU	0.2483	0.3468	0.2826
Fujian	CL	0.5178	0.3970	0.4460	Fujian	NU	0.3083	0.3688	0.3184
Jiangxi	CL	0.3690	0.2578	0.3096	Jiangxi	NU	0.2924	0.3423	0.2970
Shandong	CL	0.3932	0.2752	0.3150	Shandong	NU	0.4081	0.4162	0.3994
Henan	CL	0.3643	0.2643	0.3027	Henan	NU	0.2940	0.3792	0.3008
Hubei	CL	0.3165	0.2501	0.2734	Hubei	NU	0.2673	0.3174	0.2920
Hunan	CL	0.3814	0.2912	0.3265	Hunan	NU	0.2569	0.3800	0.2836
Guangdong	CL	0.4941	0.3722	0.4112	Guangdong	NU	0.5039	0.5477	0.5053
Guangxi	CL	0.3137	0.2743	0.2838	Guangxi	NU	0.1886	0.2641	0.2040
Hainan	CL	0.3669	0.3154	0.3137	Hainan	NU	0.2124	0.2985	0.2527
Chongqing	CL	0.2546	0.2254	0.2207	Chongqing	NU	0.2537	0.2794	0.2959
Sichuan	CL	0.2665	0.2358	0.2320	Sichuan	NU	0.2659	0.3427	0.2912
Guizhou	CL	0.1859	0.1944	0.1747	Guizhou	NU	0.1638	0.2714	0.2119
Yunnan	CL	0.2302	0.1857	0.2016	Yunnan	NU	0.2243	0.2787	0.2256
Shaanxi	CL	0.1942	0.1987	0.1897	Shaanxi	NU	0.3261	0.3583	0.3309
Gansu	CL	0.1460	0.1342	0.1238	Gansu	NU	0.2120	0.2528	0.2245
Qinghai	CL	0.2305	0.1926	0.2143	Qinghai	NU	0.2439	0.2749	0.2511
Ningxia	CL	0.2048	0.1610	0.1731	Ningxia	NU	0.2356	0.2748	0.2587
Xinjiang	CL	0.2587	0.1938	0.2387	Xinjiang	NU	0.3280	0.3021	0.3199
mean		0.3150	0.2523		mean		0.3247	0.3471

Here, CL stands for the comprehensive evaluation index of cultivated land intensive use, and NU stands for the comprehensive evaluation index of new-type urbanization.

3.2. The Evolution of the Coupling and Coordination Level of Cultivated Land Intensive Use and New-Type Urbanization

Based on the comprehensive evaluation index of cultivated land intensive use and new-type urbanization in 30 sample provinces in China from 2008 to 2020 calculated by the entropy method above, the coupling and coordinated development model was used to calculate the coupling degree, coordination degree, and relative development degree of the 30 sample provinces from 2008 to 2020. In order to better analyze and study, this paper will analyze the coupling degree, coordination degree and relative development degree from the following three dimensions: national level, regional level, and provincial level.

3.2.1. Overall National Level

Figure 1 reports the evolution trend of coupling degree, coordination degree and relative development degree between cultivated land intensive use and new-type urbanization in China from 2008 to 2020. Based on the above coupling degree determination benchmark, the coupling degree of the two fluctuated in the range of 0.8370–0.9088, the average value was 0.8711, and the whole was in the highly coupled stage. However, the overall trend of decline during the sample period showed a gentle decline, indicating that the coupling relationship between the two tended to deteriorate during the sample period, and the interaction correlation level between new-type urbanization and cultivated land intensive use decreased. Based on the above coordination degree judgment benchmark, the coordination degree is between 0.4884–0.5302, with an average value of 0.5027, which shows a slow downward trend, indicating that the coordination degree of cultivated land intensive use and new-type urbanization has retreated from barely coordinated development to a mild imbalance recession, indicating that the coordination relationship has gradually weakened. Based on the above relative development degree judgment benchmark, the relative development degree is between 0.7321–1.0239, and the average value is 0.8689, which has undergone a retreat from synchronous development type to cultivated land intensive use lag type. This shows that the coupling degree, coordination degree, and relative development degree between the cultivated land intensive use and the new-type urbanization during the sample period showed a downward trend to different degrees.

3.2.2. Regional Level

This section will be divided into the following three major regions: the east, the central region, and the west, and it will analyze the change trend of coupling degree, coordination degree, and relative development degree from 2008 to 2020. The coupling degree in the eastern region is between 0.7925–0.8952 and the mean value is 0.8439; the coupling degree in the central region is between 0.8740–0.9485 and the mean is 0.9287; the coupling degree in the western region is between 0.8015–0.9225 and the mean is 0.8563. From the average point of view, the three regions are generally in the high coupling stage. From the downward trend in the fluctuation, indicating that the interaction relationship and internal relationship between the cultivated land intensive use and the new-type urbanization in the three major regions tended to weaken with the evolution of time. In comparison, except for in 2019 and 2020, the coupling degree in the rest of the sample period was higher in the central part than in the western part, and slightly higher in the western part than in the eastern part, indicating that the interaction relationship and internal correlation between the cultivated land intensive use and the new-type urbanization in the central region were relatively strong, followed by the western region, and then by the eastern region (Figure 2).

In terms of coordination, the coordination degree of the eastern region fluctuates in the range of 0.5306–0.6128, with an average value of 0.5653; the coordination degree of the central region is between 0.4831–0.5092, with an average value is 0.4982; the coordination degree of the western region is between 0.4325–0.4628, with an average of 0.4435. It can be seen that the eastern region has retreated from the primary coordinated development stage to the reluctant coordinated development, the central region has fluctuated back and forth across the boundary line of the reluctantly coordinated development and mild imbalance recession stage, and the western region has been in the stage of a mild imbalance recession. The coordination degree of cultivated land intensive use and new-type urbanization in the three major regions is still in the downward trend of fluctuation in the time trend and, in the spatial dimension, it is characterized by the gradient characteristics of the east being higher than the central region and the central part being higher than the west, which is highly consistent with the level of new-type urbanization and cultivated land intensive use in the three major regions (Figure 3).

In terms of relative development, the relative development degree of the eastern region fluctuates in the range of 0.7461–1.0076, with an average of 0.8592; the relative development degree of the central region is between 0.7489–1.1224, with an average of 0.9552; the relative development degree of the western region is between 0.6943–0.9686, with an average of 0.8159. According to the above-mentioned benchmark for judging the relative development degree (Table 2), the relative development degree of the three major regions has gradually developed from a synchronous development type to a lagging type of cultivated land intensive use. Moreover, the relative development degree of the three major regions in terms of time trend shows a downward trend of fluctuation, indicating that the lagging characteristics of cultivated land intensive use are becoming more and more obvious compared with new-type urbanization (Figure 4).

Figure 2. The coupling degree of the three major regions of the east, central region, and west.

Figure 2. The coupling degree of the three major regions of the east, central region, and west.

Figure 3. The coordination degree of the three major regions of the east, central region, and west.

Figure 3. The coordination degree of the three major regions of the east, central region, and west.

Figure 4. The relative development degree of the three major regions of the east, central region, and west.

Figure 4. The relative development degree of the three major regions of the east, central region, and west.

3.2.3. Provincial Level

Based on the national and regional analysis of the above, this study further describes the coupling, coordination, and relative development degree between the cultivated land intensive use and the new-type urbanization from the provincial level of 30 samples (

Table 4. Stages of coordinated development of the provinces (mean) coupling.

Project	Classification Criteria	Stage or Type	Province
Coupling degree	[0, 0.2]	Highly uncoupled
	(0.2, 0.4]	Not coupled
	(0.4, 0.6]	Low degree of coupling	Beijing (0.4798)
	(0.6, 0.8]	Moderate coupling	Tianjin (0.7073), Shanxi (0.7213), Shaanxi (0.6814), Gansu (0.6456)
	(0.8, 1]	Highly coupled	Hebei (0.9289), Inner Mongolia (0.9293), Liaoning (0.8077), Jilin (0.9692), Heilongjiang (0.9869), Shanghai (0.9968), Jiangsu (0.8654), Zhejiang (0.8980), Anhui (0.9638), Fujian (0.8506), Jiangxi (0.9638), Shandong (0.9076), Henan (0.9436), Hubei (0.9590), Hunan (0.9218), Guangdong (0.9216), Guangxi (0.8326), Hainan (0.9194), Chongqing (0.8752), Sichuan (0.9225), Guizhou (0.9320), Yunnan (0.9548), Qinghai (0.9512), Ningxia (0.8051), Xinjiang (0.8897)
Coordination degree	[0, 0.3]	Severely dysfunctional recession
	(0.3, 0.4]	Moderate dysregulation decline	Shanxi (0.3836), Gansu (0.3332)
	(0.4, 0.5]	Mild dysregulation decline	Beijing (0.4691), Tianjin (0.4906), Hebei (0.4980), Inner Mongolia (0.4524), Liaoning (0.4615), Jilin (0.4605), Heilongjiang (0.4967), Guangxi (0.4500), Chongqing (0.4741), Sichuan (0.4906), Guizhou (0.4225), Yunnan (0.4508), Shaanxi (0.4209), Qinghai (0.4703), Ningxia (0.4154) Xinjiang (0.4982)
	(0.5, 0.6]	Reluctantly coordinated development	Anhui (0.5209), Fujian (0.5685), Jiangxi (0.5403), Shandong (0.5689), Henan (0.5331), Hubei (0.5204), Hunan (0.5299), Hainan (0.5094)
	(0.6, 0.7]	Primary coordinated development	Jiangsu (0.6233), Zhejiang (0.6315), Guangdong (0.6496)
	(0.7, 0.8]	Moderately coordinated development	Shanghai (0.7482)
	(0.8, 1]	Well coordinated development
Relative Development degree	[0, 0.8]	Cultivated land intensive use lag type	Beijing (0.4618), Tianjin (0.5935), Shanxi (0.6059), Inner Mongolia (0.7993), Liaoning (0.6649), Jiangsu (0.7187), Zhejiang (0.7727), Shandong (0.7936), Chongqing (0.7600), Shaanxi (0.5736), Gansu (0.5572), Ningxia (0.6778), Xinjiang (0.7476)
	(0.8, 1.2]	Synchronous development type	Hebei (0.9454), Jilin (0.8670), Heilongjiang (0.9323), Shanghai (1.0066), Anhui (1.0123), Jiangxi (1.0522), Henan (1.0332), Hubei (0.9528), Hunan (1.1859), Guangdong (0.8176), Sichuan (0.8058), Guizhou (0.8492), Yunnan (0.9109), Qinghai (0.8582)
	(1.2, θ)	The new-type urbanization lag type	Fujian (1.4146), Guangxi (1.4351), Hainan (1.2615)

).

According to the level and type of coupling coordinated development (Table 2), combined with the level of coupling and coordinated development of each province, it can be seen that the coupling degree (mean) during the sample period defines only Beijing as being in the low coupling stage, with Tianjin, Shanxi, Shaanxi, and Gansu in the moderate coupling stages, and with the remaining twenty-five provinces in the high coupling stage. In terms of coordination degree (mean), the highest degree of coordination is Shanghai, which is in the stage of moderate coordinated development, the lowest two provinces are Gansu and Shanxi, and the remaining sixteen provinces are in the stage of mild dysregulation decline, with eight provinces in the barely coordinated development stage, and three provinces in the primary coordinated development stage. From the perspective of relative development degree (average), thirteen provinces are lag type in cultivated land intensive use, fourteen provinces are synchronous development, and there are three provinces with new-type urbanization lag.

The provinces with an increase in coupling level at the end of the period compared to the beginning of the period (table omitted) are as follows: Heilongjiang, Shanghai, Fujian, Hunan, Guangxi, and Hainan. Among these provinces, three are in the eastern region, two are in the central region, and one are in the western region. The declining provinces are as follows: Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Jiangsu, Zhejiang, Anhui, Jiangxi, Shandong, Henan, Hubei, Guangdong, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. Among these provinces, eight are in the eastern region, six are in the central region, and ten are in the western region. The provinces with improved coordination level are as follows: Heilongjiang, Fujian, Hunan, Guangxi, Hainan, and Guizhou. Among these provinces, two are in the eastern region, two are in the central region, and two are in the western region. The declining provinces are as follows: Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Shandong, Henan, Hubei, Guangdong, Chongqing, Sichuan, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. Nine of these provinces are in the eastern region, six are in the central region, and nine are in the western region. The provinces with improved relative development level are as follows: Heilongjiang and Shanghai. One is located in the east region, one in the central region, and there are no improved relative development level provinces in the west region. The provinces that declined were as follows: Beijing, Tianjin, Hebei, Shanxi, Inner Mongolia, Liaoning, Jilin, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangdong, Guangxi, Hainan, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang, with ten in the east region, seven in the central region, and eleven in the west region.

4. Re-Examination Based on Kernel Density Estimation and Theil Index

4.1. Kernel Density Estimation

Figure 5, Figure 6 and Figure 7 describe the evolution trend of the dynamic distribution of the kernel density curve of the coupled coordinated development level of cultivated land intensive use and new-type urbanization at the national level and the three regional levels of the eastern, central, and western regions during the sample observation period. From Figure 5, it can be seen that, at the national level, from the perspective of position characteristics, the kernel density curve of the coupling degree level shows a trend of shifting to the left, and the overall level is reduced. From the perspective of peak characteristics, the peak degree as a whole shows the transformation phenomenon of sharp peak → wide peaks, the peak is shown as weak bimodal or weak multi-peak, and the left tail phenomenon is first strengthened and then weakened, indicating that the regional difference is expanding, the degree of dispersion has generally assumed a certain upward trend, accompanied by a certain polarization phenomenon, and the difference between high-level areas and low-level areas has experienced a trend of first expanding and then shrinking. As far as the eastern region is concerned, from the perspective of position characteristics, the kernel density curve of coupling degree shows a left-shifting trend, indicating that the coupling degree level shows a downward trend. From the perspective of peak characteristics, the peak degree gradually widens, and gradually shows obvious bimodal or multi-peak distribution, indicating that regional differences are expanding, the concentration is generally declining, and the polarization phenomenon is increasing. The left tail of 2020 is significantly shorter than that of 2016, indicating that the areas with low level values are decreasing, and the difference between high-level areas and low-level areas has been reduced to a certain extent. As far as the central region is concerned, from the perspective of positional characteristics, the kernel density curve of the coupling degree shows a change by first shifting to the right and then to the left, indicating that the coupling level first rises and then declines. From the perspective of peak characteristics, the kurtosis is first narrowed and then widened, and the bimodal distribution is presented. In addition, the left trailing phenomenon intensified in 2016 and 2020, and the phenomenon of a "warping tail" appeared, indicating that regional differences were expanding, the degree of concentration experienced changes by first rising and then decreasing, and the areas with low-level values further increased and concentrated, the coupling degree between high-level areas and low-level regions was significantly different, and multi-level or polarization had a tendency to deepen. As far as the western region is concerned, from the perspective of position characteristics, the kernel density curve of the coupling degree shifts to the left, indicating that the coupling degree level shows a downward trend. From the perspective of peak characteristics, the kurtosis shows the change in sharp peak → wide peak, accompanied by bimodal or multi-peak distribution, and the left trailing phenomenon is obvious first and then alleviated, indicating that the regional differences are gradually expanding, the concentration is gradually decreasing, the polarization phenomenon still exists, and the proportion of low coupling values is still high.

From Figure 6, it can be seen that, at the national level, from the perspective of location characteristics, the kernel density curve of the coordination degree shows a left shift change, indicating that the level of coordinated development is in a downward trend. From the perspective of peak characteristics, the kurtosis shows the phenomenon of narrowing and then widening, reflecting that the concentration of the regional level rises first and then declines, and the regional difference decreases first and then becomes larger. The evolution of the crests generally shows a weak bimodal or weak multi-peak and moves to the left, indicating that the coordinated development level of the region has declined, and there is a trend of bipolar or multi-level differentiation; The phenomenon of right trailing persists but has a certain degree of shortening, indicating that the area of high levels of values is decreasing. As far as the eastern region is concerned, from the perspective of location characteristics, the kernel density curve of the coordination degree shows a change by first moving sharply to the left and then moving slightly to the right, indicating that the coordinated development level of the region has experienced a certain decline and then has rebounded. From the perspective of peak characteristics, the kurtosis change is not large, but it shows the distribution of weak multi-peak → unipolar → weak multi-peak, indicating that regional differences persist and there is a certain polarization phenomenon. In the case of the central region, from the perspective of position characteristics, the kernel density curve of the degree of coordination did not move significantly; From the perspective of peak characteristics, the kurtosis mainly shows convergence changes, and the distribution of weak multi-peaks appears, and the left tail phenomenon continues to exist and expands, and there is also an obvious left warping tail phenomenon in 2020. Overall, the degree of concentration in the central region has increased, but the areas with low level values have also further increased and are more concentrated, and the absolute difference in coordination has a certain tendency to expand. As far as the western region is concerned, from the perspective of location characteristics, the kernel density curve of the coordination degree generally shows a trend of shifting to the left, indicating that the level of coordinated development in the region is declining. From the perspective of peak characteristics, the kurtosis is first narrowed and then broadened, and the overall change is of wide peak → sharp peak → wide peak. There are also weak bimodal or weak multi-peak distributions and, in 2016, there was also a left tail and warping tail phenomenon, while the left tail in 2020 has ameliorated but still exists. This shows that the concentration of coordination in the western region is generally at a declining level, regional differences have expanded to a certain extent, and there is a weak polarization phenomenon.

From Figure 7, it can be seen that, at the national level, from the perspective of location characteristics, the kernel density curve of the relative degree of development generally shows a trend of shifting to the left, indicating that the relative level of development tends to decline. From the perspective of peak characteristics, the kurtosis shows a wide peak → sharp peak change, indicating that the concentration of the region is gradually increasing, and the regional difference is gradually narrowing. The wave peaks showed the evolution of weak bimodal to unimodal to bimodal, the right tail phenomenon gradually weakened in 2008, 2012, and 2016, and the warping tail phenomenon appeared in 2020, indicating that the area of high-level values was gradually decreasing, and there was a certain degree of polarization. As far as the eastern region is concerned, from the perspective of position characteristics, the kernel density curve of the relative degree of development shows a slight shift to the right and then a significant left shift, indicating that the relative development level of the region first rose slightly and then decreased rapidly. From the perspective of peak characteristics, the peak degree generally shows the change in wide peak → sharp peak, and the right tail phenomenon exists but gradually weakens, accompanied by the warping tail phenomenon, indicating that the regional difference is decreasing, the degree of concentration is increasing, the area with high level value is decreasing and gradually concentrating, and there is a certain polarization phenomenon. As far as the central region is concerned, from the perspective of location characteristics, the kernel density curve of the relative degree of development gradually shifts to the left, indicating that the relative level of development in the region is gradually decreasing. From the perspective of peak characteristics, the curve gradually changes from flat to towering, showing the change in wide peaks → sharp peak, and there is also a left warping tail phenomenon in 2020, indicating that the regional differences in 2008 and 2012 are large, the regional differences in 2016 and 2020 are gradually decreasing, and the degree of concentration increases, but the areas with low level values are increasing and concentrated, and there is a certain polarization tendency. As far as the western region is concerned, from the perspective of location characteristics, the kernel density curve of the relative development degree has undergone a significant left shift tendency, indicating that the relative development degree of the region has shown a downward trend. From the perspective of peak characteristics, the kurtosis gradually narrows, accompanied by weak bimodal or weak multi-peaks, and the phenomenon of right tail and right warping tail gradually weakens, indicating that regional differences are decreasing, the degree of concentration is increasing, and the areas with high-level values are gradually decreasing and becoming concentrated, but there is still a polarization phenomenon.

In general, the kernel density curves of coupling degree, coordination degree, and relative development degree all show a tendency to shift to the left, indicating that the level of regional coordinated development is declining, and most of the kernel density curves have a tailing phenomenon, weak bimodal or weak multi-peak, indicating that the regional differences in various regions are large, and that there is a polarization tendency or phenomenon.

Figure 5. Kernel density estimation of coupling degree. (a) Kernel density estimation of coupling degree at the national level; (b) kernel density estimation of coupling degree in the eastern region; (c) kernel density estimation of coupling degree in the central region; (d) kernel density estimation of coupling degree in the western region.

Figure 5. Kernel density estimation of coupling degree. (a) Kernel density estimation of coupling degree at the national level; (b) kernel density estimation of coupling degree in the eastern region; (c) kernel density estimation of coupling degree in the central region; (d) kernel density estimation of coupling degree in the western region.

Figure 6. Kernel density estimation of coordination degree. (a) Kernel density estimation of coordination degree at the national level; (b) kernel density estimation of coordination degree in the eastern region; (c) kernel density estimation of coordination degree in the central region; (d) kernel density estimation of coordination degree in the western region.

Figure 6. Kernel density estimation of coordination degree. (a) Kernel density estimation of coordination degree at the national level; (b) kernel density estimation of coordination degree in the eastern region; (c) kernel density estimation of coordination degree in the central region; (d) kernel density estimation of coordination degree in the western region.

Figure 7. Kernel density estimation of relative development degree. (a) Kernel density estimation of relative development degree at the national level; (b) kernel density estimation of relative development degree in the eastern region; (c) kernel density estimation of relative development degree in the central region; (d) kernel density estimation of relative development degree in the western region.

Figure 7. Kernel density estimation of relative development degree. (a) Kernel density estimation of relative development degree at the national level; (b) kernel density estimation of relative development degree in the eastern region; (c) kernel density estimation of relative development degree in the central region; (d) kernel density estimation of relative development degree in the western region.

4.2. Theil Index

In order to further reveal the formation source of the spatial difference between the coupling and coordinated development of cultivated land intensive use and new-type urbanization, this paper uses the Theil index to decompose the coupling degree, coordination degree, and relative development degree of the two major systems, and measures the intraregional differences (within the group) and the interregional differences (between the groups) of the three major regions (eastern, central, and western), respectively, as shown in Figure 8. The respective contribution results are shown in Table 5.

According to the decomposition of the Theil index of the coupling degree of the three major regions, the main source of coupling degree difference is the intragroup difference. The average difference within the group is 0.0128, the average difference between the group is 0.0010, and the intragroup difference is higher than the intergroup difference. From the perspective of the contribution rate of the intragroup difference and the intergroup difference, the average contribution rate of the intragroup difference is 93.43%, the maximum contribution rate is 97.17%, and the minimum contribution rate is 87.16%. The average contribution rate of the difference between the groups was 6.57%, the maximum contribution rate was 12.84%, and the minimum contribution rate was 2.83%. From the perspective of time series, the differences within the group and between groups in 2008–2012 showed a slight downward trend after a slight increase, and the differences within the group and between groups in 2012–2016 showed a significant upward trend, and gradually declined after 2016, indicating that the coupling degree of the three regions in the region and between regions showed a trend of expansion in the previous period, and the gap gradually narrowed in the later period, but the differences within the group as a whole showed an enlarging trend.

According to the decomposition of the Theil index of the three major regional coordination degrees, the main source of the coordination degree difference is still the intragroup difference. The average intragroup difference is 0.0090, the average intergroup difference is 0.0060, and the intragroup difference is higher than the intergroup difference. From the perspective of the contribution rate of intragroup difference and intergroup difference, the average contribution rate of intragroup difference is 62.05%, the maximum contribution rate is 79.62%, and the minimum contribution rate is 47.83%. The average contribution rate of the difference between the groups was 37.95%, the maximum contribution rate was 52.17%, and the minimum contribution rate was 20.38%. From the perspective of time series, intragroup differences and intergroup differences as a whole show different development trends, as follows: intragroup differences as a whole show an expanding trend, of which the fluctuations from 2008 to 2012 declined, rose sharply from 2012 to 2017, and gradually declined after 2017. The fluctuations between the groups increased slightly from 2008 to 2010, decreased from 2010 to 2018, and rose again in 2018, but the overall trend is a downward trend, which indicates that the differences between the coordination areas are gradually narrowing in the process of dynamic adjustment.

According to the decomposition of the Theil index of the relative development degree of the three major regions, the main source of the difference in relative development degree is still the intragroup difference, as the average difference within the group is 0.0370, the average difference between the group is 0.0020, and the intragroup difference is higher than the intergroup difference. From the perspective of the contribution rate of the intragroup difference and the intergroup difference, the average contribution rate of the intragroup difference is 94.40%, the maximum contribution rate is 97.49%, and the minimum contribution rate is 90.36%. The average contribution rate of the difference between the groups was 5.60%, the maximum contribution rate was 9.64%, and the minimum contribution rate was 2.51%. From the perspective of time series, intragroup differences and intergroup differences as a whole show different development trends, as follows: intragroup differences decreased in fluctuations from 2008 to 2012, rose from 2012 to 2016, and fluctuated downwards after 2016 and, as a whole, they were in a downward trend, indicating that the differences in the relative development degree region showed a dynamic adjustment process of synergy–differentiation–convergence; the difference between the groups from 2008 to 2016 showed a slight fluctuation and increased, and then gradually declined after 2016 but, on the whole, the difference between the years was not large, and the overall trend was relatively flat, indicating that the overall difference in relative development degree between regions does not change much.

Based on the above results, it can be seen that the differences in the coordinated development of coupling in the three major regions are mainly caused by the differences within the group, and the differences within and between the groups in general show a downward trend of fluctuations, indicating that the differences in the coordinated development of coupling and coordination within the region and between regions are gradually narrowing, but the differences within the region are still relatively significant. Therefore, paying attention to the differences within the region has rich policy implications for the coordinated development of the region.

Figure 8. Results of the Theil index for the three regions. (a) Results of the Theil index for coupling degree; (b) results of the Theil index for coordination degree; (c) results of the Theil index for relative development degree.

Figure 8. Results of the Theil index for the three regions. (a) Results of the Theil index for coupling degree; (b) results of the Theil index for coordination degree; (c) results of the Theil index for relative development degree.

Table 5. Contribution rate of the Theil index of the coupling and coordinated development of the three major regions.

Coupling Degree			Coordination Degree			Relative Development Degree
Year	Within the Group	Between Groups	Year	Within the Group	Between Groups	Year	Within the Group	Between Groups
In 2008	0.9599	0.0401	In 2008	0.4832	0.5168	In 2008	0.9575	0.0425
In 2009	0.9464	0.0536	In 2009	0.5405	0.4595	In 2009	0.9679	0.0321
In 2010	0.9717	0.0283	In 2010	0.5108	0.4892	In 2010	0.9036	0.0964
In 2011	0.9561	0.0439	In 2011	0.4888	0.5112	In 2011	0.9450	0.0550
In 2012	0.9714	0.0286	In 2012	0.4783	0.5217	In 2012	0.9190	0.0810
In 2013	0.8879	0.1121	In 2013	0.6133	0.3867	In 2013	0.9585	0.0415
In 2014	0.8934	0.1066	In 2014	0.6590	0.3410	In 2014	0.9619	0.0381
In 2015	0.9130	0.0870	In 2015	0.6664	0.3336	In 2015	0.9548	0.0452
In 2016	0.8716	0.1284	In 2016	0.7165	0.2835	In 2016	0.9114	0.0886
In 2017	0.9200	0.0800	In 2017	0.7778	0.2222	In 2017	0.9214	0.0786
In 2018	0.9375	0.0625	In 2018	0.7962	0.2038	In 2018	0.9480	0.0520
In 2019	0.9483	0.0517	In 2019	0.6820	0.3180	In 2019	0.9480	0.0520
In 2020	0.9685	0.0315	In 2020	0.6542	0.3458	In 2020	0.9749	0.0251
Mean	0.9343	0.0657	Mean	0.6205	0.3795	Mean	0.9440	0.0560

Table 5. Contribution rate of the Theil index of the coupling and coordinated development of the three major regions.

Coupling Degree			Coordination Degree			Relative Development Degree
Year	Within the Group	Between Groups	Year	Within the Group	Between Groups	Year	Within the Group	Between Groups
In 2008	0.9599	0.0401	In 2008	0.4832	0.5168	In 2008	0.9575	0.0425
In 2009	0.9464	0.0536	In 2009	0.5405	0.4595	In 2009	0.9679	0.0321
In 2010	0.9717	0.0283	In 2010	0.5108	0.4892	In 2010	0.9036	0.0964
In 2011	0.9561	0.0439	In 2011	0.4888	0.5112	In 2011	0.9450	0.0550
In 2012	0.9714	0.0286	In 2012	0.4783	0.5217	In 2012	0.9190	0.0810
In 2013	0.8879	0.1121	In 2013	0.6133	0.3867	In 2013	0.9585	0.0415
In 2014	0.8934	0.1066	In 2014	0.6590	0.3410	In 2014	0.9619	0.0381
In 2015	0.9130	0.0870	In 2015	0.6664	0.3336	In 2015	0.9548	0.0452
In 2016	0.8716	0.1284	In 2016	0.7165	0.2835	In 2016	0.9114	0.0886
In 2017	0.9200	0.0800	In 2017	0.7778	0.2222	In 2017	0.9214	0.0786
In 2018	0.9375	0.0625	In 2018	0.7962	0.2038	In 2018	0.9480	0.0520
In 2019	0.9483	0.0517	In 2019	0.6820	0.3180	In 2019	0.9480	0.0520
In 2020	0.9685	0.0315	In 2020	0.6542	0.3458	In 2020	0.9749	0.0251
Mean	0.9343	0.0657	Mean	0.6205	0.3795	Mean	0.9440	0.0560

5. Conclusions, Implications and Prospects

5.1. Conclusions

This study used the entropy method, the coupled coordinated development degree model, the kernel density estimation, the Theil index, and other methods, based on the data of 30 provinces and cities of China from 2008 to 2020, to analyze the comprehensive level of cultivated land intensive use and new-type urbanization, the spatiotemporal distribution characteristics, and regional differences of the coupled coordinated development. The following conclusions were drawn:

(1) Through the analysis of evolution characteristics of the level of the cultivated land intensive use and new-type urbanization, it can be seen that the eastern part is higher than the central region, and the central part is higher than the western region in the sample period; moreover, most provinces with cultivated land intensive use showed a downward trend, while the vast majority of provinces with new-type urbanization levels showed an upward trend, and the relationship between them was as “one falls, and the other rises”;

(2) From the evolution of the coupling and coordination level of cultivated land intensive use and new-type urbanization, it can be seen that the coupling degree, coordination degree, and relative development degree between the cultivated land intensive use and the new-type urbanization at the national level showed a downward trend of different degrees during the sample period. Overall, the coupling degree was in the highly coupled stage, the coordination degree was in the stage of barely coordinated development, and the relative development degree was mainly manifested as the lag in the cultivated land intensive use;

(3) The coupling degrees of the three major regions are all in a downward trend of fluctuations in time series and, with the evolution of time, the interaction and internal correlation of the two systems tend to weaken, but it is generally in the highly coupled stage. The overall performance of the central region is higher than the western region, and the western region is slightly higher than the eastern, indicating that the interaction relationship and internal relationship between the cultivated land intensive use and the new-type urbanization in the central region are relatively strong, followed by the western region and the eastern region. The coordination degree of the three major regions is also in a fluctuating decline. The eastern part has regressed from the initial stage of coordinated development to reluctantly coordinated development. The central part has fluctuated back and forth between the stage of barely coordinated development and the stage of mild unbalanced recession, and the west has been in the stage of mild unbalanced recession. In the spatial dimension, it is characterized by a gradient characteristic of the east being higher than the central region and the central part being higher than the west, which is highly consistent with the new-type urbanization and cultivated land intensive use in the three major regions. The relative development degrees of the three major regions have experienced a gradual development from a synchronous development type to a lagging type of cultivated land intensive use and, in the time trend, the relative development degrees of the three regions all reflect a fluctuating downward trend, indicating that, compared to the new-type urbanization, the lag characteristics of cultivated land intensive use are becoming more and more obvious;

(4) From the analysis of the coupling coordination degree at the provincial level, it can be seen that most provinces are in the highly coupled stage from the mean point of view, but the coupling level at the end of the period has decreased compared with the beginning of the period. The coordination degree of 30 provinces showed an "inverted u-shaped" distribution from the average point of view, and the coordination degree at the end of the period compared with the beginning of the period was mainly decreasing. Taking relative development degree from the perspective of the average value as a view, the lag in the cultivated land intensive use is very serious, and compared with the beginning of the period, the relative development degree at the end of the period is still mainly declining;

(5) Through the analysis of the kernel density curve of the coupling and coordinated development degree at the national level and in the three major regions, the kernel density curve shows a left shift tendency, indicating that the level of regional coupling coordinated development is declining. Most of the kernel density curves have a tailing phenomenon, with weak bimodal or weak multi-peak, indicating that the regional differences are large, and there is a polarization tendency or phenomenon;

(6) From the measurement of the Theil index of the three major regions, it can be seen that the difference in the coupling and coordinated development in the three major regions is mainly caused by the difference within the group, and the differences within and between groups on the whole shows a downward trend of fluctuation, indicating that the difference of coupling and coordinated development within the region and between the region is gradually narrowing, but the difference within the region is still relatively significant.

5.2. Implications

Due to the location factors, economic development conditions, natural resources, and other factors of various provinces, the level of cultivated land intensive use and the level of new-type urbanization between provinces are uneven. The cultivated land intensive use shows a development trend that lags behind that of new-type urbanization. The coordinated development of urban and rural areas should further promote a new-type urbanization based on economic factors, population, land, social factors, ecological and environmental factors, innovation and research and development synergy, and sustainable development. Furthermore, food security is an important foundation for national security, and cultivated land is not only a key element to ensure food security, but also a strong driving force for promoting high-quality agriculture, green sustainable development, and coordinated development of urban and rural areas. It is also very important to improve the level of cultivated land intensive use. According to local conditions, the economic situation of various provinces, resource endowment conditions, etc., this study gives some suggestions, as follows. Firstly, the local government of each province should strengthen inter-provincial cooperation and exchanges, promote complementary advantages, and deepen resource sharing. On the other hand, further promotion of the coordinated development of cultivated land intensive use and new-type urbanization is needed, encouraging the industrial chain of large and medium-sized cities to extend to the county, promoting urbanization with county towns as an important carrier, smoothing the urban and rural economic cycle, promoting the coordinated development of urban and rural areas, and improving the coordinated development of rural areas through the industrial model of the urban belt and rural areas, advancing rural economic development, and strengthening the construction of talent teams. Secondly, to establish the talents attraction policies, it is important to promote the positive interaction between the cultivated land intensive use and new-type urbanization. Thirdly, in accordance with the endowment conditions of cultivated land resources in various provinces, it is important to improve agricultural infrastructure and production conditions, increasing the input of agricultural machinery, the quality and ability of the labor force, and the intensity of agricultural scientific and technological innovation to promote the intensive utilization and sustainable development of cultivated land, and improve the utilization and output efficiency of cultivated land. Fourthly, it is important to establish a strict farmland protection system, and to take farmland protection as the premise and foundation for ensuring food security and high-quality development of urbanization. The related organization should not only protect the quantity of cultivated land, but also improve the quality of cultivated land resources, and increase investment in agricultural development, in order to pay more attention to the sustainable development and utilization of cultivated land, to reduce the pollution and destruction of cultivated land, to promote the construction of high-standard farmland, and to improve the level of cultivated land intensive use, so as to obtain well-coordinated development of cultivated land intensive use and new-type urbanization.

5.3. Prospects

In the process of urbanization and industrialization in all countries of the world, especially in developing countries, the coupling and coordinated development of new-type urbanization and the intensive use of cultivated land is an inevitable choice to build a new type of urban–rural relationship and realize the coordinated development of the urban and rural economy. Taking China as an example, this study measures the coupling degree, coordination degree, and relative development degree of cultivated land intensive utilization and new-type urbanization by constructing a coupling coordination model, an in-depth description of its temporal and spatial evolution characteristics and regional differences, and puts forward the intensive utilization of cultivated land and the coordinated development of the new-type urbanization coupled policy implications. It not only enriches the research perspective of urban–rural relations in theory, but also has important reference significance for other developing countries to realize the coordinated development of urban and rural areas in practice. However, there is room for further expansion in this study. Specifically, this study only measures the level of coupling and coordination between the intensive utilization of cultivated land and new-type urbanization in 30 provincial-level regions in China, and then analyzes its evolution and regional differences. The mechanism of its influence has not been further revealed, which weakens its research value to a certain extent. In the subsequent research, we will theoretically further explain the formation mechanism of the coupling and coordinated development of the intensive utilization of cultivated land and the new-type urbanization, and empirically test it with the econometric model. For example, with the gradual connection and improvement of China’s urban and rural transportation infrastructure and digital infrastructure, it will surely exert an important impact on the coupling and coordinated development of China’s new-type urbanization and the intensive use of cultivated land. Of course, China is quite different from other developing countries in the world in terms of political system, economic development level, industrialization, and urbanization process. Thus, this study can only provide a perspective or approach, or reference to other developing countries, and the resulting research conclusions and policy implications are not completely applicable to other developing countries. Therefore, we advocate more researchers or research teams to conduct more in-depth research with other developing countries other than China as the research object, so as to further enrich the research results of the global urban–rural economic relationship.