Spatiotemporal Pattern Differentiation and Influencing Factors of Cultivated Land Use Efficiency in Hubei Province under Carbon Emission Constraints

Abstract

The rapid conversion of cultivated land resources has posed a severe danger to national food security, where the primary concerns are the quantity as well as the quality of the land being cultivated. Cultivated land use efficiency (CLUE) reflects the rational allocation and utilization level of cultivated land, labor, capital, and other factors so as to maximize output and minimize cost. In this study, carbon emissions were included as an unexpected output into the measurement framework of CLUE. The super SBM undesirable model, Spatial analysis model, and Tobit model were comprehensively used to measure the difference pattern and influencing factors of CLUE in 72 counties of Hubei Province from 2005 to 2020. The results show the following: the CLUE in Hubei Province showed significant regional differences and temporal variation characteristics. During the observation period of 2005 to 2020, the overall CLUE in Hubei Province increased, reaching 0.7475 by 2020, but was still at a low level (less than 1). Due to the limitation of topographic factors, this does not show obvious spatial agglomeration characteristics on the whole. In general, the CLUE value formed after considering the carbon emission index in most counties decreased by different ranges compared to the value formed without considering the carbon emission index. From the perspective of transverse terrain comparison, the measured results of the super SBM undesirable model showed that the cultivated land utilization efficiency of the mountain and hill was higher than that of the plain. From the vertical time comparison, the CLUE of different regions showed an upward trend, indicating obvious regional differences in the study period. The results of the Tobit model show that farmers’ income level can actively promote the improvement of CLUE in Hubei Province. Urbanization level, rural power consumption, per capita cultivated land scale, and agricultural mechanization level have an impact on cultivated land use efficiency as a whole, but the impact has topographic heterogeneity. This study can provide theoretical and technical reference for the improvement of regional cultivated land efficiency and the formulation of cultivated land protection strategies and policies.

1. Introduction

Cultivated land is a strategic natural resource related to economic development and social stability [1]. It is also the basis of rural revitalization and overall urban and rural development [2]. The dynamic changes in its quantity, quality, and cultivated land intensive efficiency will have a far-reaching impact on China’s grain production pattern and urbanization [3]. Cultivated land use efficiency (CLUE) reflects the rational allocation and utilization level of cultivated land, labor, capital, and other factors so as to maximize output and minimize cost [4]. Cultivated land, as the main type of agricultural land, not only contributes to the production of agricultural products and increases agricultural output values, but it also emits a significant amount of CO₂ during actions pertaining to the production of agriculture [5], negatively impacting the regional ecological environment. Some studies have shown that every increase of 1 km² in cultivated land will result in 0.0422 t of carbon emissions [6]. The extensive use of agricultural means of production such as pesticides, chemical fertilizers, and fossil energy, along with the unreasonable development and utilization of cultivated land, will not only easily accelerate the loss of the inert carbon pool in deep soil [7], but will also weaken the capacity of the soil carbon storage and vegetation carbon fixation [8,9], thus destroying the virtuous cycle of a cultivated land system and changing the intensity and pattern of carbon emissions in this region [10]. Amidst the realistic background of a non-agricultural use of cultivated land and insufficient reserve resources of cultivated land, China must change the traditional concept of cultivated land use to avoid following the past extensive cultivated land use model [11]. Relying on modern management and scientific and technological progress; changing the current high-intensity and extensive mode of cultivated land use; controlling the scale of carbon emissions while improving the output quality of cultivated land use; and realizing the harmonious progress of cultivated land use, food production, and ecological protection have become the main issues in the management and control of cultivated land use at different spatial scales in China. The answers to these problems are to comprehensively grasp the interaction degree and efficiency pattern of various positive and negative information flows in the cultivated land use system.

Regarding research content, researchers have focused on the construction of an index system and the influencing factors of cultivated land use efficiency (CLUE) [12]. Most studies have mainly measured the CLUE at the national [13,14,15], provincial [16,17,18], and municipal [19,20] levels. From the research perspective, early research has mainly selected indicators related to the agricultural production process and calculated the CLUE by constructing the input–output index system [21]. In recent years, the research perspective has gradually expanded and deepened, gradually shifting from the macroeconomic elements of input and output to more micro landscape fragmentation [22,23,24,25,26], cultivated land fragmentation [24,27], farmers’ land awareness [28,29], farmers’ livelihood differentiation and family division of labor [30,31,32], labor age structure [33,34], labor price [35,36], and different land types [37]. In terms of research methods, quantitative research methods such as mathematical models have been widely used in the calculation of CLUE, such as the super DEA (Data envelopment analysis) [38,39], the constant return to scale model (CRS) [40], the stochastic frontier approach (SFA) [41,42], the SBM-undesirable model [43,44,45], and the enhanced vegetation index (EVI) [46]. In addition, some scholars have also used the global autocorrelation index and local autocorrelation index of GIS (Geographic Information System) and Geoda software to analyze the spatial distribution characteristics of CLUE [3,47]. The research theme of foreign scholars has rarely been CLUE, but they have contributed by measuring the efficiency in the process of multi-scale and multi-regional agricultural production. The relevant research results mainly cover the calculations of agricultural or crop production efficiency [48,49,50,51,52,53,54,55] and the identification of influencing factors [56,57,58,59,60]. In terms of methods, the DEA, SFA, and other mathematical models have mostly been used to calculate the CLUE, but spatial autocorrelation analysis has rarely been used to explore the spatial differentiation features of CLUE [61,62].

Based on the above literature, there are two main deficiencies in the current research on CLUE. First, relevant research focuses on the national, provincial, and municipal levels, while there is a lack of discussion on the CLUE at the county or town level [63,64]. Second, the traditional DEA model (including the CCR and BCC models) only calculates the CLUE from the two aspects of input and output, without considering the negative external effects of cultivated land use on the ecological environment, resulting in a deviation from the actual situation [10,63,65,66]. Therefore, the main contribution of this study is to build a comprehensive measurement system of CLUE by considering the impact of carbon emissions in the process of cultivated land utilization, and to explore its regional difference and influencing factors to increase the research depth of CLUE. Protecting cultivated land and the environment is the internal requirement of economic development and social progress in Hubei Province, a large agricultural province. Based on this, in this study, we integrated the carbon emissions from cultivated land use into the measurement framework of CLUE. Based on the relevant data of Hubei Province from 2005 to 2020, this study used the super SBM undesirable model containing an unexpected output to measure the CLUE. The global Moran’s I index of CLUE in Hubei Province was calculated based on the Geoda platform so as to analyze the spatially dominant morphological characteristics of CLUE in Hubei Province from 2005 to 2020. Finally, the influencing factors of CLUE were analyzed with the help of the Tobit model, and specific low-carbon strategies and the efficient utilization of cultivated land resources were determined. The discussion and discovery of this paper have important practical guiding significance for effectively reducing carbon emissions in the process of cultivated land use and building a resource-saving and environmentally friendly society.

2. Materials and Methods

2.1. Study Area

Hubei is a province situated in Central China, with the coordinates of 29°01′53″~33°6′47″ N to 108°21′42″~116°07′50″ E (Figure 1). It is bordered on the east by Anhui, on the south by Jiangxi and Hunan, on the west by Chongqing, on the northwest by Shaanxi, and on the north by Henan. From east to west, it stretches for 740 km. From north to south, it stretches for approximately 470 km. The total area of the province is 185,900 square kilometers. According to its altitude and morphological characteristics, the landform of the whole province can be divided into three types: mountains, hills, and plains. According to the summary of the preliminary data of the 2018 land change survey, the cultivated land in Hubei Province makes up 78.5859 million mu, accounting for 28.18% of the total area; the per capita cultivated land makes up 1.3 mu, which is lower than the national average of 1.52 mu. The contradiction of insufficient cultivated land reserve resources, with more people and less land, is very prominent. According to the data released by the government, the area of acidified soil in Hubei Province makes up more than 17 million mu, accounting for 36.13% of the total cultivated land area of the province, of which the area of acidified soil (pH ≤ 6.50) is more than 15.768 million mu, and the area of severely acidified soil (pH ≤ 4.50) is 1.2432 million mu, which is mainly distributed in the Debbie Mountains in eastern Hubei, 46 counties (cities and districts) in the Walling Mountain Area in Western Hubei, and the Mafi mountain area in southern Hubei. Biodiversity, water conservation capacity, and ecosystem services in some areas of Hubei Province have decreased significantly, and the sustainable development of the cultivated land ecosystem is facing severe challenges.

2.2. Research Methods

2.2.1. Super SBM Undesirable Model

In the process of agricultural production, the input of labor, capital, energy, and other factors also produce CO₂, which has detrimental external impacts on the environment while producing agricultural products. There are multiple decision-making units with 100% efficiency in most efficiency evaluation studies; that is, there are more efficiency values of 1. It is now difficult to effectively evaluate and rank the decision-making units. As it considers the unexpected output in the production process, the SBM model, initially developed by Tone, is better in accordance with the actual scenario [67]. As a result, it is commonly utilized in the calculation and research of carbon emission performance [68], ecological efficiency [69], and energy efficiency [70]. Compared to the traditional data envelopment analysis (DEA), the SBM model, considering unexpected output, can handle the problem of input-output relaxation, as well as the problem of efficiency analysis in the presence of unexpected output [71].

Therefore, in order to ensure that the efficiency analysis produces a more reasonable and scientific efficiency evaluation value, and in combination with the research of Tone [67], in this study, we selected the super-efficiency SBM undesirable model to calculate the CLUE in Hubei Province. The model assumes a production system with R decision-making units. Each decision-making unit is composed of three input-output vectors: input, expected output, and unexpected output. Using M inputs can produce $R_1$ expected output and $R_2$ unexpected outputs [68,72]. The specific expression of the model is as follows:

$$\begin{gathered} min\rho =\frac{1-\frac{\left(\frac{1}{M}\right){{\displaystyle \sum }}_{i=1}^M{w}_i^{-}}{x_{ik}}}{\frac{1}{\left(R_1+R_2\right)\left(\frac{{{\displaystyle \sum }}_{s=1}^{R_1}{w}_s^d}{y_{sk}^d}+\frac{{{\displaystyle \sum }}_{q=1}^{R_2}{w}_q^u}{y_{qk}^u}\right)}} \\ \mathrm{s}.\mathrm{t}. x_{ik}={\displaystyle \sum }_{j=1}^n{x}_{ij}{\lambda }_j+w_i^{-} \\ {y}_{sk}^d={\displaystyle \sum }_{j=1}^n{x}_{sj}^d{\lambda }_j-w_s^d \\ {y}_{qk}^d={\displaystyle \sum }_{j=1}^n{x}_{qj}^u{\lambda }_j+w_q^u \\ {\lambda }_j>0;w_i^{-}⩾0;w_s^d⩾0;w_q^u⩾0;j=1,2,\cdots ,n; \\ i=1,2,\cdots ,M;s=1,2,\cdots ,R_1;q=1,2,\cdots ,R_2 \end{gathered}$$

(1)

where: ρ is the value of cultivated lane efficiency, and $w_i^{-}$, $w_s^d,$ and $w_q^u$represent the relaxation of input redundancy, expected redundancy, and undesired redundancy, respectively. When the efficiency value $\rho >1$, it shows that the decision-making unit is efficient; when the efficiency value $\rho <1$, it indicates that the decision-making unit is invalid. The above models are based on the assumption of constant return to scale.

2.2.2. Kernel Density Estimating

The measurement results of the super-efficiency SBM undesirable model can reflect the change pattern of relative differences in CLUE but cannot reveal the distribution dynamics and evolution law of absolute differences in CLUE [70]. In this study, we used kernel density estimation (KDE) to further reveal the distribution dynamics and evolution law of CLUE in Hubei Province.

Kernel density estimation is a representative method for analyzing the differences in specific geographical aspects or phenomena. This method can effectively avoid the subjectivity of function setting in parameter estimation and capture the objective reality of data distribution [73,74]. The basic idea of this is to regard the distribution pattern of the investigated object as a certain probability distribution and use the continuous density curve to describe the distribution pattern of the object [75]. In this study, the kernel density estimation method was used to obtain the probability distribution curve of CLUE in Hubei Province so as to analyze the distribution characteristics and change trend of CLUE in Hubei Province. The formula is as follows:

$$f\left(x\right)=\frac{1}{nh}{\displaystyle \sum }_{i=1}^{n}k\left(\frac{x-x_i}{h}\right)$$

(2)

where: $k\left(\frac{x-x_i}{h}\right)$represents the kernel function; $h$ represents the broadband; $N$ represents the number of samples. According to different expression forms, the kernel function is divided into the Gaussian kernel, triangular kernel, quartic kernel, and other types. The authors of this paper mainly selected the widely used Gaussian kernel function and analyzed it with the help of Stata 16.0 software. Specifically, with the passage of time, if the distribution curve of CLUE moves to the right (left) as a whole, it indicates that the level of CLUE is improving (decreasing) as a whole. When the peak height of the kernel density estimation curve becomes steeper or flattened, it shows that the difference in CLUE is narrowing or expanding. When the shape of the kernel density estimation curve and the number of peaks changes to a single peak, double peak, or multi-peak, it shows that the value of CLUE has the evolution trend of convergence, polarization, or multi-polarization.

2.2.3. ESDA Correlation Model

Spatial autocorrelation analysis can be divided into global spatial autocorrelation analysis and local spatial autocorrelation analysis. Global spatial autocorrelation analysis mainly uses Moran’s I index to reveal the spatial agglomeration of attribute variables in the whole study area [76]. The expression is as follows:

$$I=\frac{n{{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j=1}^n{w}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{\left({{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j=1}^n{w}_{ij}\right){{\displaystyle \sum }}_{i=1}^n{(x_i-\overline{x})}^2}$$

(3)

where: $n$ represents the value space of the research object; $x_i$ represents the attribute value in the $i$th area; $x_j$ represents the attribute value in the john area; $\overline{x}$represents the average value of attribute values in the study area; $w_{ij}$represents the spatial weight matrix, which is used to express the proximity relationship of spatial regions at $n$ locations. It is generally a symmetric matrix, and its expression is as follows:

$$w=\left(\begin{array}{ccc}{w}_{11}& \dots & w_{1n}\\ ⋮& \ddots & ⋮\\ w_{n1}& \cdots & w_{nn}\end{array}\right)$$

(4)

where: $w_{ij}$ represents the proximity relationship between regions $i$and $j$, which can be measured according to the adjacency standard and distance.

The simplest binary adjacency matrix commonly used is as follows:

$$w_{ij}=\left(\begin{array}{c}1, \mathrm{when} \mathrm{regions} i \mathrm{and} j \mathrm{are} \mathrm{adjacent}\\ 0, \mathrm{otherwise}\end{array}\right)$$

(5)

The commonly used binary space weight matrix based on distance is as follows:

$$w_{ij}=\left(\begin{array}{c}1, \mathrm{when} \mathrm{the} \mathrm{distance} \mathrm{between} \mathrm{regions} i \mathrm{and} j \mathrm{is} \mathrm{less} \mathrm{than} d\\ 0, \mathrm{otherwise}\end{array}\right)$$

(6)

The statistic of I can be regarded as the ratio of covariance to variance considering the spatial location relationship, that is, the spatial autocorrelation coefficient. Its ratio reflects the relationship between the covariance and variation between adjacent elements. If the adjacent elements have the aggregation trend of “high-high, low-low”, the adjacent cross-product result is larger and positive, so the ratio of covariance to variance is greater than 0, showing a positive spatial correlation. If the adjacent elements show the mutually exclusive distribution trend of “high-low, low-high”, the cross-product result of the adjacent elements will tend to be negative, so the ratio of covariance to variance is less than 0, which shows a negative spatial correlation. If there is no obvious agglomeration of adjacent elements, the positive and negative values in the cross-product results of adjacent elements offset each other, the covariance is close to 0, and the ratio of covariance to variance is close to 0, which shows a spatially random distribution. The value range of Moran’s I coefficient is [−1, 1]. When its value is greater than 0, it indicates that there is positive spatial correlation in the study area, and the closer it is to 1, the stronger the spatial autocorrelation is. The value of the study object presents the characteristics of aggregation distribution. When the value is less than 0, it indicates that there is negative spatial correlation in the study area, and the closer the value is to −1, the stronger the negative spatial correlation is, and the value of the study object presents discrete characteristics.

Local spatial autocorrelation analysis mainly uses the local Moran’s I index to reveal the local spatial agglomeration of attribute variables within the region [77]. The expression is as follows:

$$I_k=Z_k^{\prime }{\displaystyle \sum }_{l=1}^n{W}_{kl}{Z}_l^{\prime }$$

(7)

where: $Z_k^{\prime }$and $Z_l^{\prime }$denote the standardization of the observed values in areas $k$ and $l$, respectively; $W_{kl}$ is the spatial weight, where ${\displaystyle \sum }_l^n{W}_{kl}=1$. When $I_k>0$, it indicates that the difference between the CLUE of region K and the surrounding areas is less significant. When $I_k<0$, it shows that there is a significant difference between the CLUE of region K and the surrounding areas.

2.2.4. Tobit Regression Analysis

After evaluating and analyzing the CLUE and its spatial correlation of counties in Hubei Province, in order to further explore the influencing factors of the change in CLUE and determine specific low-carbon strategies and efficient utilization of cultivated land resources, we further analyzed the influencing factors of CLUE in Hubei Province. Since the efficiency value calculated by the super-efficiency SBM method is greater than 0, which belongs to the “restricted dependent variable”, the estimation results will be biased using the traditional ordinary least square method. Therefore, in this study, we used a method in the existing research [78] and empirically revealed the influencing factors of regional differences in CLUE under carbon emission constraints by using the Tobit model [79]. The Tobit model [77], also known as the sample selection model or restricted dependent variable model, was first proposed by Tobin. It is applicable to cases where the dependent variable is a cut or fragmented value. The Tobit model is also applicable to the estimation of some samples in the whole sample, meeting the needs of analyzing the influencing factors of CLUE based on terrain in this study. The basic structure of the model is as follows:

$$y_{it}=\left\{\begin{array}{cc}{\beta }^{\top }{x}_{it}+{\epsilon }_{it}& \left({\beta }^{\top }{x}_{it}+{\epsilon }_{it}>0\right)\\ 0& \mathrm{otherwise}\end{array}\right.$$

(8)

where: $y_{it}$ is the observed dependent variable, $x_{it}$is an argument, and ${\beta }^T$ is the parameter vector to be estimated.

2.3. Indicator System and Data Source

2.3.1. Index System of CLUE

As per the results of existing research [80,81,82], the input factors of cultivated land mainly include land, labor force, and various material means of production. Among them, the planting areas of crops ($I_1$) were selected for the land input and the quantity of employees in the primary industry ($I_2$) was selected for the labor input. The input of the material means of production included agricultural machinery, chemical fertilizer, pesticide, and plastic film, which were measured via the total power of agricultural machinery ($I_3$), the application amount of agricultural chemical fertilizer ($I_4$), the application amount of pesticide ($I_5$), the application amount of agricultural plastic film ($I_6$), and effective irrigation area ($I_7$). Output indicators mainly included the expected and unexpected output. Expected output refers to the economic and social effects produced by the use of cultivated land, which are expressed by the total output value of the agricultural planting industry $O_1$ and the total grain output $O_2$, respectively. Unexpected output refers to the total carbon emissions in the process of cultivated land use $O_3$. The index system of CLUE constrained by carbon emissions is shown in

Table 1. The index system of CLUE.

Index	Indicators	Symbol	Variable	Unit	Data Source
Input	Land input	$I_1$	Sown area of farm crops	103 hm2	Hubei Rural Statistical Yearbook
	Labor input	$I_2$	Rural population 1	104 people	Hubei Rural Statistical Yearbook
	Capital input	$I_3$	Total power of agricultural machinery 2	104 kw	Hubei Rural Statistical Yearbook
	Material input	$I_4$	Consumption of chemical fertilizers	104 t	Municipal Statistical Yearbook
		$I_5$	Consumption of pesticides	104 t	Municipal Statistical Yearbook
		$I_6$	Consumption of plastic film for farm use	104 t	Municipal Statistical Yearbook
		$I_7$	Effective irrigation area	103 hm2	Hubei Rural Statistical Yearbook
Output	Desirable output	$O_1$	Gross agricultural production	104 CNY	Hubei Rural Statistical Yearbook
		$O_2$	Output of grain	104 t	Hubei Rural Statistical Yearbook
	Undesirable output	$O_3$	Carbon emissions	104 t	Municipal Statistical Yearbook

¹ Employees in the primary industry (agricultural output value/total output value of agriculture, forestry, animal husbandry, and fishery). ² Mechanical power not only generated by electrical energy consumption, but also by energy consumption such as diesel and gasoline.

.

Agricultural tillage, mechanized operation, chemical fertilizer, pesticide, agricultural film use, and farmland irrigation are examples of how the usage of cultivated land can cause the emission of carbon [83,84]. Referring to the current research methods, the carbon emissions of cultivated land use in this study were calculated using six indicators: the usage of agricultural chemical fertilizer, the use of pesticide, the use of agricultural film, the amount of mechanical input, the effective irrigation area, and the planting areas of crops. The calculation formula of carbon emissions caused by cultivated land use is as follows:

$$E=\sum E_i=\sum T_i\cdot {\delta }_i$$

(9)

where: $E$ denotes the total carbon emissions from cultivated land use; $T_i$and ${\delta }_i$ represent the original amount and the carbon emission coefficient of each carbon emission source, respectively.

Table 2. Main carbon emission sources of cultivated land use.

Category	Carbon Emission Coefficient	Unit	Reference	Data Sources
Chemical fertilizer	0.896	kg/kg	West et al. [85]	Municipal Statistical Yearbook
Pesticides	4.934	kg/kg	Post et al. [86]	Municipal Statistical Yearbook
Agricultural film	5.180	kg/kg	Bo, L. et al. [87]	Municipal Statistical Yearbook
Total mechanical power	0.180	kg/kW	West et al. [85]	Hubei Rural Statistical Yearbook
Effective irrigation area	20.5	kg/hm2	Bo, L. et al. [87]	Hubei Rural Statistical Yearbook
Ploughing (sown area of crops)	3.126	kg/hm2	Bo, L. et al. [87]	Hubei Rural Statistical Yearbook

shows the carbon emission sources, carbon emission coefficients, and relevant data sources.

2.3.2. Influencing Factor Index System

Many factors influence the CLUE, including nature, society, economy, and planting technology [38]. Analyzing the main factors affecting the spatiotemporal evolution of CLUE is conducive to the sustainable and efficient use of cultivated land. Referring to the previous research [79,88,89], and combined with the actual situation of Hubei Province, seven indicators, namely, the farmers’ income level, the urbanization level, the agricultural industrial structure, rural power consumption, the cultivated land scale, the mechanization level, and the urban–rural income gap, were selected. The calculation and symbols of specific indicators are given in

Table 3. Index system of influencing factors of CLUE.

Index	Index Calculation
Farmers’ income level	Per capita disposable income of farmers (CNY 10,000)
Urbanization level	Urban population/total population (10,000 people/10,000 people)
Agricultural industrial structure	Total output value of planting industry/total output value of agriculture, forestry, animal husbandry and fishery (CNY 10,000/CNY 10,000)
Rural power consumption	Rural power consumption/rural population (10,000 kwh/10,000 people)
Scale of arable land per capita	Planting area/rural population (Ha/person)
Mechanization level	Total power of agricultural machinery/planting area (10,000 kW/HA)
Urban-rural income gap	Per capita disposable income of urban residents/average annual disposable income of rural residents (CNY 10,000/CNY 10,000)

.

2.3.3. Data Source

The Hubei Rural Statistical Yearbook, the Hubei Statistical Yearbook, the statistical yearbooks of various cities and prefectures, and the websites of various cities and prefectures provide all the data in this research. The data of the total agricultural output value, the total output value of the planting industry, and the total output value of agriculture, forestry, animal husbandry, and fishery were reduced to eliminate the interference of price factors, and the corresponding values of each year were converted into the actual values calculated at the comparable prices in 2005.

Affected by the change in administrative division, the statistical data of some counties for many years could not be obtained. We excluded them from the scope of this investigation. Since the counties of Suizhou City, Shiyan City, and Ezhou city did not document statistics on the data of “pesticides and agricultural film” in some years, in order to ensure the integrity and continuity of regional data during the observation period, we combined the data of each county under the jurisdiction of Suizhou City, Shiyan City, and Ezhou City, and replaced them with the data of the overall prefecture-level cities of Suizhou City, Shiyan City, and Ezhou city. Finally, 72 county- (or city-) level observation units were selected as the investigation object, covering all areas of Hubei, which is still very representative.

In 2022, Hubei Province is composed of 103 county-level units, and the economic development, resource endowment, and geographical environments of each region are quite different. In order to obtain more abundant and rigorous research findings, based on the analysis and evaluation of CLUE of counties in Hubei Province, and further considering the topographic factors, the research area was divided into three types: the plain county, the hilly county, and the mountainous county. The specific division standard is from the China county (city) socio-economic statistical yearbook. It should be noted that the division results do not conflict with the results of sample merging.

3. Results

3.1. Time Variation of CLUE

By using MaxDEA software, we computed the CLUE of counties in Hubei Province under the constraint of carbon emissions from 2005 to 2020. At the same time, as a comparison, we further used the super SBM model to measure the efficiency of carbon-free emissions. As shown in

Table 4. Changes in CLUE in three regions of Hubei Province.

Year	Model	Plain Area	Hilly Area	Mountain Area	Total Average
2005	Super SBM-U	0.4534	0.5459	0.6358	0.5474
	Super SBM	0.4956	0.5729	0.6785	0.5860
2010	Super SBM-U	0.4847	0.5948	0.5523	0.5385
	Super SBM	0.5141	0.5978	0.5813	0.5611
2015	Super SBM-U	0.5474	0.6723	0.6001	0.5992
	Super SBM	0.5714	0.6631	0.6836	0.6379
2020	Super SBM-U	0.7230	0.7355	0.7780	0.7475
	Super SBM	0.7400	0.7419	0.8337	0.7769
2005–2010	Super SBM-U	0.0313	0.0489	−0.0835	—
	Super SBM	0.0185	0.0249	−0.0972
2010–2015	Super SBM-U	0.0627	0.0775	0.0478	—
	Super SBM	0.0573	0.0653	0.1023
2015–2020	Super SBM-U	0.1756	0.0632	0.1778	—
	Super SBM	0.1686	0.0788	0.1501
2005–2020	Super SBM-U	0.2696	0.1896	0.1421	—
	Super SBM	0.2444	0.1690	0.1552
Average (2005–2020)	Super SBM-U	0.5521	0.6371	0.6415	0.6103
	Super SBM	0.5803	0.6439	0.6943	0.6395

, the carbon emission index had a certain positive effect on the utilization efficiency of cultivated land. After considering carbon emissions, the results of the super SBM-U model of CLUE in most areas were lower than those of the super SBM model, which shows that if the negative effect of carbon emissions is ignored, the calculation results of CLUE will be overestimated. Therefore, we applied the super SBM-U model to estimate the CLUE in Hubei Province. The rationale behind using this model is that is can avoid erroneous results.

Table 4 reflects the average values and changes in the CLUE of Hubei Province as a whole and in different topographic regions from 2005 to 2020. The results show that although the CLUE of Hubei Province increased by varying degrees during the observation period, the CLUE was still low (less than 0.9). From the perspective of the subregions, the mean value of CLUE in the plain county increased from 0.4534 in 2005 to 0.7230 in 2020, with the largest change (0.2696), followed by the hilly county and the mountainous county, with changes of 0.1896 and 0.1421, respectively. In terms of stage, the biggest change range was from 2015 to 2020. The CLUE of the three regions was the largest at the time, with changes of 0.1756, 0.0632, and 0.1778, respectively.

The CLUE in Hubei Province had obvious differences among the plain, hilly, and mountainous terrains, as shown in the above image. The difference continued to narrow over time, and the CLUE increased. The CLUE in plain areas has always been at the lowest level.

Table 5. Changes in municipal CLUE in Hubei Province.

City	2005		2010		2015		2020		2005–2020		Rank
	Super SBM	Super SBM-U	Super SBM	Super SBM-U	Super SBM	Super SBM-U	Super SBM	Super SBM-U	Super SBM	Super SBM-U
Wuhan	0.4242	0.3622	0.4417	0.3885	0.7895	0.7434	0.7086	0.6917	0.2844	0.3295	4
Shiyan	0.3369	0.3150	0.4197	0.4032	0.5610	0.4958	0.4600	0.4069	0.1231	0.0919	11
Huangshi	0.5030	0.4652	0.3899	0.3945	0.5355	0.4967	0.6869	0.6595	0.1839	0.1943	8
Jingzhou	0.6413	0.6316	0.5517	0.5489	0.4486	0.4558	0.7912	0.7812	0.1499	0.1496	9
Yichang	0.5144	0.4529	0.4983	0.4756	0.6446	0.6042	0.7178	0.6717	0.2035	0.2188	5
Xiangyang	0.5083	0.4613	0.5419	0.5307	0.7729	0.7241	1.0351	1.0138	0.5268	0.5524	1
Ezhou	0.5331	0.4135	0.6295	0.4968	0.7191	0.5948	0.4968	0.3957	−0.0363	−0.0178	14
Jingmen	0.6631	0.6157	0.8583	0.8256	0.8386	0.8336	0.9968	0.7225	0.3337	0.1068	10
Xiaogan	0.6132	0.5782	0.6632	0.6341	0.6670	0.6415	1.0009	0.9588	0.3877	0.3805	2
Huanggang	0.5719	0.5447	0.4276	0.4138	0.5401	0.5057	0.6071	0.5829	0.0352	0.0382	12
Xianning	0.6849	0.6441	0.7503	0.7303	0.8571	0.7930	1.0396	0.9966	0.3546	0.3525	3
Suizhou	0.5430	0.4931	1.0126	1.0126	0.5005	0.4685	0.4735	0.4504	−0.0695	−0.0427	15
Enshi	0.8280	0.7989	0.6685	0.6236	0.5677	0.4799	0.7679	0.7179	−0.0601	−0.0810	16
Xiantao	0.2655	0.2856	0.2375	0.2729	0.2936	0.3321	0.2703	0.3234	0.0048	0.0378	13
Tianmen	0.3545	0.3233	0.3751	0.3852	0.4888	0.4817	0.5674	0.5384	0.2128	0.2151	7
Qianjiang	0.3154	0.2881	0.2703	0.2712	0.3553	0.3833	0.4763	0.5057	0.1609	0.2176	6
Average	0.5172	0.4787	0.5460	0.5255	0.5987	0.5646	0.7091	0.6673	—	—	—

reflects the changes in CLUE in various prefecture-level cities in Hubei Province. On the whole, the CLUE of each prefecture-level city showed an increasing trend. In terms of the change range, the top five changes were in Xiangyang, Huangshi, Xianning, Xiaogan, and Wuhan. Suizhou, Xiantao, and Ezhou ranked as the last three in terms of change. It should be noted that, for the convenience of analysis, we only present the CLUE results of prefecture-level cities here. Meanwhile, we placed the detailed calculation results of CLUE of counties in Appendix A.

From 2005 to 2010, the average value of the CLUE in Hubei Province decreased from 0.5474 to 0.5385, as shown in Figure 2 and Figure 3. The kernel density curve of CLUE moved to the left, the peak value of the curve increased, the waveform width decreased, and the curve shape changed from a single peak to a double peak, which reveals that the overall efficiency of cultivated land use in Hubei Province increased and the regional differences decreased, but that there was a trend of polarization within the area. The average value of CLUE in Hubei Province increased from 0.5385 in 2010 to 0.7475 in 2020. The nuclear density curve of CLUE moved to the right, the peak value of the curve wave decreased, and the waveform width expanded. The tailing phenomenon on the right side was gradually obvious, and the curve shape gradually changed from a double peak to a slow double peak. This shows that during this period, the CLUE in Hubei Province increased, the regional differences gradually expanded, and an increasing number of counties had high CLUE. This trend of a decline first and then a rise may be related to the deterioration of cultivated land ecology from 2005 to 2010 and the macro policies in 2010. For example, the central economic work conference in 2010 required all parts of the country to “focus on improving the quality of cultivated land”. In 2011, Hubei Province, as a pilot province, carried out the pilot work of cultivated land quality grade evaluation.

From 2005 to 2020 (Figure 4), the average value of CLUE in Plain County of Hubei Province increased from 0.4534 to 0.7230. The nuclear density curve of CLUE gradually moved to the right, the peak value of the curve wave decreased, the width of the waveform expanded, and the tailing phenomenon on the right was gradually more obvious. This shows that during this period, the CLUE in the plain area of Hubei Province increased and the differences among the regions gradually expanded, but the polarization phenomenon gradually disappeared and there were an increasing number of plain counties with high CLUE.

From 2005 to 2020, the average value of CLUE in hilly counties of Hubei Province increased from 0.5459 to 0.7355, but the median of CLUE first decreased and then increased, the moving direction of the CLUE kernel density curve showed a trend of “left–right–left”, the peak value of the curve decreased, and the phenomenon of tailing to the right was obvious. In the period of 2005–2020, the CLUE of hilly counties in Hubei Province showed a development trend of “decline–rise–decline”. The differences in cultivated land use among regions gradually expanded, but the phenomenon of polarization gradually disappeared and an increasing hilly county had high CLUE.

In 2005, 2010, 2015, and 2020, the mean value of CLUE in the mountainous counties of Hubei Province were 0.6358, 0.5523, 0.6001, and 0.7780, respectively. The nuclear density curve of CLUE in the mountainous counties had a moving trend of “left to right”, the peak value of the curve first increased and then decreased, and the phenomenon of tailing to the right was obvious. The curve kurtosis had a changing trend of “single peak–double peak–return double peak”. This shows that, from 2005 to 2020, the CLUE of mountainous counties in Hubei Province had a development trend of “decline–rise”, the differences among the regions had trends of “narrowing expansion” and polarization, and an increasing number of mountainous counties had high CLUE.

3.2. Spatial Distribution of CLUE

The natural breakpoint method was used to divide the CLUE into five levels and ArcGIS 10.7 was used to create a geographical display. Figure 5 visually depicts the spatial distribution pattern of CLUE in Hubei Province from 2005 to 2020. There were considerable significant regional differences and temporal variation characteristics in the CLUE within Hubei Province. They were only found in a limited area and did not have any obvious spatial agglomeration characteristics on the whole. From 2005 to 2020, Chibi City, Tongshan County, Jiayu County, Luotian County, and Macheng City had a high level of CLUE. However, they had no direct spatial connection and appeared to have isolated distribution. Qianjiang City, Xiantao City, Tianmen City, and Shiyan City were at a low level. Among them, Qianjiang City, Xiantao City, and Tianmen City were spatially adjacent and had similar geographical environments. It should be noted that the Jianghan Plain, as a major grain production base in China, has a superior geographical position and climate compared to other places. However, the findings show that throughout the observation period, the level of CLUE in this area was low, which may be related to population and natural factors. On the one hand, the regional agricultural production endowment was excellent, the population was concentrated, and the per capita cultivated land area was small, which did not have a scale effect on the cultivated land. On the other hand, the development of mountainous and hilly areas, especially mountainous areas, emphasizes ecological benefits and environmental protection, so the mode of agricultural development is more intensive and has low carbon emissions. Farmers in plain areas have more non-agricultural livelihood choices. In the process of rapid urbanization, they pay less attention to high-quality agricultural development, cultivated land protection, and intensive utilization, and have poor awareness of agricultural sustainable development. Therefore, Jianghan Plain should accelerate the transfer of rural surplus labor force, promote the large-scale management of agricultural land, and improve the quality of cultivated land so as to improve the utilization level of cultivated land.

3.3. Spatial Autocorrelation Analysis of CLUE

3.3.1. Global Spatial Differentiation Pattern of CLUE

The Moran’s I index was used to calculate the spatial autocorrelation degree of CLUE from 2005 to 2020 to analyze the global correlation of CLUE in geographical space. As shown in

Table 6. Global Moran’s I index of CLUE.

Year	Moran’s I	Z Value	p-Value
2000	0.1775	3.7561	0.0001
2005	0.0982	2.2630	0.0236
2010	0.1578	3.4028	0.0006
2015	0.0582	1.4200	0.1555
2020	0.0582	1.4259	0.1538

, according to the calculation results of each year, it was found that the Moran’s I index of CLUE was significant and positive in 2000, 2005, and 2010 and not significant in 2015 and 2020, indicating that there was a spatial correlation between CLUE in Hubei Province in 2000, 2005, and 2010, which shows the spatial characteristics of agglomeration distribution.

3.3.2. Spatial Differentiation Efficiency of Cultivated Land

Using the ArcGIS 10.7 platform and LISA theoretical formula, the LISA agglomeration map of CLUE from 2005 to 2020 was calculated (as shown in Figure 6). It was divided into five basic types: H-H (high-high), which refers to the high level of CLUE of the city itself and adjacent cities; the H-L (high-low) type refers to high CLUE in cities, while it is low in the adjacent cities; the L-H (low-high) type refers to low CLUE in cities, while it is high in the adjacent cities; the L-L (low-low) type refers to the low CLUE in the city itself and the adjacent cities.

The H-H type was mainly concentrated in southwest Hubei in 2005, north Hubei in 2010, central and southeast Hubei in 2015, and north and southeast Hubei in 2020. Since 2010, the H-H areas have gradually decreased, and the spatial distribution shows the agglomeration characteristics of “polarization and dynamic contraction”. The L-L type was mainly distributed in northwest and central Hubei in 2005, in northwest and central Hubei in 2010, in northwest and central Hubei in 2015, and in Ezhou and Tuanfeng County in northwest and central Hubei in 2020. The overall spatial pattern shows the characteristic transformation from a two-core agglomeration to a single-core contraction agglomeration. Among them, Shiyan was categorized as the L-L type throughout the study period. This is because the landform of this area is complex and the cultivated land resources are relatively scarce, which leads to the low CLUE in this area. The H-L type was only distributed in Huangshi District in eastern Hubei in 2020. The L-H type was mainly distributed in Yicheng city in 2010. This type is mainly matched with the change characteristics of CLUE in high-value areas. In 2015, it was mainly distributed in Honghu City and Chongyang County of the Jianghan plain.

Generally speaking, the CLUE in Hubei Province is concentrated in a small range and does not show obvious spatial agglomeration characteristics on the whole. This may be related to the topographic factors of Hubei Province. The continuous agglomeration distribution characteristics of plains, hills, and mountains lead to the overall performance of CLUE as a small-scale agglomeration.

3.4. Analysis of Influencing Factors of CLUE

Stata 16.0 software was utilized to perform a Tobit regression analysis of CLUE in Hubei Province. The variables in the model were normalized to prevent the issue of the estimation coefficient being too large or too small due to different dimensions. The estimation results of the Tobit model, as can be seen in

Table 7. Influencing factors of CLUE under carbon emission constraints.

Variable	Total Sample	Plain County	Hilly County	Mountain County
	Model (1)	Model (2)	Model (3)	Model (4)
Farmers’ income level	0.2135 ***	0.1792 ***	0.1330 *	0.2770 ***
	(0.0370)	(0.0605)	(0.0794)	(0.0737)
Urbanization level	−0.2534 *	0.0060	0.1090	−0.6410 **
	(0.1324)	(0.2140)	(0.2710)	(0.2445)
Agricultural industrial structure	−0.0060	−0.0080	−0.5387	0.2327
	(0.0097)	(0.0103)	(0.3778)	(0.3169)
Rural power consumption	−0.0867 ***	−0.0544	−0.0959 *	−0.0639
	(0.0265)	(0.0483)	(0.0494)	(0.0444)
Per capita cultivated land scale	1.1381 ***	1.1391 **	1.2720 *	1.0295
	(0.3480)	(0.5473)	(0.6517)	(0.6633)
Mechanization level	−0.0177 **	−0.0138 *	−0.0141	−0.0579 **
	(0.0069)	(0.0076)	(0.0593)	(0.0224)
Income gap between urban and rural areas	0.0228	0.0389	−0.0542	−0.0225
	(0.0313)	(0.0963)	(0.0703)	(0.0490)
Intercept	0.0762 ***	−0.0092	0.0185	0.0977 **
β	(0.0278)	(0.0655)	(0.0731)	(0.0474)
N	288	104	72	112

*, **, and *** denote statistical significance at 10%, 5%, and 1%, respectively.

, show that in the total sample, except for the agricultural industrial structure and the urban-rural income gap, the impact of the other variables on CLUE was significant, at a level of 10%. Specifically, for every unit of increase in farmers’ income level, under the condition that other variables remained unchanged, the CLUE of Hubei Province increased by 0.2135 units. It can be seen that the improvement in farmers’ income level plays a positive role in the improvement of CLUE. This may be because the higher the income level, the more farmers invest in means of production and technology, and the more capable they are of improving the agricultural production conditions so as to promote the improvement of CLUE.

The expansion of the per capita cultivated land scale plays a positive role in improving CLUE. For each unit of cultivated land scale increase, under the condition that other variables remained unchanged, the CLUE of Hubei Province increased by 1.1381 units. This is because a certain amount of cultivated land resources not only directly affects the “input-output” efficiency of various factors but also affects the carbon emissions in agricultural production. A larger scale of cultivated land per capita is conducive to the formation of the “land regulation effect” and the saving and full utilization of input factors in agricultural production. At this time, the input of various production factors in agricultural production is more reasonable. However, the current situation of “farmland fragmentation” in Hubei Province hinders the exertion of the scale effect.

Urbanization hinders the improvement of CLUE. For every unit of CLUE, under the condition that other variables remained unchanged, the CLUE of Hubei Province decreased by 0.2534 units. This is because the acceleration of urbanization inevitably means an increase in construction land, which will lead to a reduction in the agricultural land scale to a certain extent, which is not conducive to the exertion of the land scale effect. On the other hand, the current development level of county urbanization in Hubei Province is low, and the development process is too extensive. In the process of promoting county urbanization, more human and material resources are invested in cities and towns, ignoring the sustainable development of agriculture, failing to realize the intensive and efficient utilization of rural cultivated land resources, and even leading to the widespread abandonment of cultivated land.

An increase in rural power consumption is not conducive to the improvement in CLUE. For every unit of power consumption increase, the CLUE decreased by 0.0867 units under the condition that other variables remained unchanged. This may be because agricultural power consumption is strongly related to rural power consumption. The more rural per capita power consumption, the more carbon emissions there are from agricultural production and the lower the utilization efficiency of the cultivated land is. Therefore, we should promote the intensive use of energy in agricultural production and rural life. The level of agricultural mechanization hinders the improvement of CLUE. For every unit of mechanization, the CLUE in Hubei Province decreased by 0.0177 units under the condition that other variables remained unchanged. This may be due to the redundancy of mechanical input per unit of cultivated land area in Hubei Province. The excessive input of mechanical factors not only increases the cost of agricultural production and reduces the efficiency of mechanical farming, but it also increases the carbon emissions of agricultural production.

The regression results show that the terrain heterogeneity has an obvious impact on the CLUE in Hubei Province. Specifically, the effect of rural economic development level on the improvement of CLUE has obvious topographic differences; that is, the effect of rural economic development level on the improvement of CLUE is the largest in the mountainous counties, followed by the plain counties and hilly counties. The negative effect of the urbanization level on CLUE exists only in the mountainous counties, but not in the plain and hilly counties. The negative effect of farmers’ living standards on CLUE exists only in the hilly counties. The positive effect of cultivated land scale on CLUE exists only in the plain and hilly counties. The negative effect of the mechanization level on CLUE exists only in the plain and mountainous counties. In the study of Hubei Province, we should consider not only the complexity of the terrain and social operation but also the specific impact of different terrains on the efficiency of cultivated land.

4. Discussion

4.1. Limitations and Future Directions

This research only studies the spatiotemporal pattern differentiation of CLUE in Hubei Province since 2005. The time scale is relatively short, and a longer time series can better explain the laws and trends of the research object. For example, some scholars [90,91] have summarized the research on the intensive use of cultivated land in China in the last 30 years and believed that the analysis of long time series data has obvious advantages in analyzing the change laws and trends of the intensive use of cultivated land, especially for carrying out the coupling analysis on the double scales of time and space, as it is easier to obtain guiding results. Therefore, the future research still needs to further study the spatiotemporal differentiation characteristics of CLUE in the region on a long-time scale [92]. From the perspective of the data availability and operability, the construction of the evaluation index system of CLUE is still not comprehensive. After consulting the existing literature on CLUE [93,94], it is found that most of the existing studies are analyzed from the perspective of traditional indicators such as land, labor, and capital, resulting in some deficiencies in the construction of the index system [95]. For example, the calculation of the DEA cannot reflect the initial differences in cultivated land fertility, cultivated land productivity, natural conditions, and socio-economic basis, resulting in biased calculations of CLUE [96]. Due to the lack of data on soil pollution, this study cannot reflect the loss of CLUE caused by environmental pollution [97]. Moreover, in future research, micro input indicators, including crop improvement and field farming technology, can be considered to be included in the evaluation index system.

The evaluation index system of CLUE, which is constructed in this study, only considers the carbon emissions generated by cultivated land use activities but does not consider the carbon absorption (i.e., carbon sink) in the process of crop growth [98,99]. In addition, carbon emissions are only included in the ecological efficiency evaluation index system of cultivated land use as an unexpected output, and the problem of non-point source pollution caused by cultivated land use activities is not considered [100,101]. Therefore, in the future, we can improve the unexpected output indicators in the evaluation index system of CLUE from two aspects—net carbon emissions (carbon emissions vs. carbon absorption) and non-point source pollution—so as to perform a more scientific and accurate calculation of the ecological efficiency of cultivated land use.

Different scholars have studied the factors affecting the improvement of CLUE from different aspects. For instance, Yang analyzed the impact of aging on farmers’ CLUE, and found that aging is not necessarily the cause of the decline in farmers’ CLUE [34]. If farmers are in good health, even if they are old, their CLUE may also be higher than that of young and middle-aged farmers [34]. Some scholars have used econometric models to test the impact of fragmentation on CLUE [102]. Existing studies have only analyzed the influencing factors of CLUE in China from the national level, and there is a lack of analysis of the regional differences in influencing factors of CLUE [64]. Due to the great differences in natural resource conditions and the socio-economic development levels among regions, the influencing factors of CLUE are also quite different [76]. Only by analyzing the regional differences in the influencing factors of CLUE can we determine the leading factors affecting CLUE in each region, which is conducive to China’s selective and regional differentiated agricultural development support policies according to the local conditions, promoting the improvement of CLUE in each region [100]. This is the first time we have included topography as a vital factor in the study of CLUE. Our research shows that topography is an important factor affecting the CLUE and there are differences in the CLUE among different terrains. On the whole, the CLUE under carbon emission constraints of mountain and hill is higher than that of plain. Therefore, it is necessary to consider the impact of topography in future studies and take it as an important aspect affecting the efficiency of cultivated land use.

4.2. Policy Enlightenment

The input–output analysis of CLUE shows that there are obvious regional and topographic differences in the input–output of the different regions of Hubei Province. In view of this, Hubei Province should pay full attention to the actual situations of the different regions (natural environment, economic development level, social development, etc.) and take differentiated and targeted measures to improve efficiency. In the process of comprehensively promoting rural revitalization, we should vigorously develop rural non-agricultural industries, provide increasingly convenient non-agricultural jobs for small-scale farmers, and ensure that the agricultural labor force liberated from productive services has diversified non-agricultural employment opportunities [103,104,105]. For areas with low efficiency of agricultural machinery, we should speed up the transfer of employees in the primary agricultural industry so as to accelerate the speed of land transfer and realize land scale management to further improve the mechanical utilization efficiency of Hubei Province [106,107,108]. We can strengthen the construction of farmland infrastructure, promote land improvement and the construction of high-standard farmland, and provide good production conditions for farmers. In view of the destruction of the ecological environment of cultivated land caused by the massive use of chemical substances in cultivated land utilization, including chemical fertilizers and pesticides, we should actively use science and technology to realize the transformation of the cultivated land utilization mode [109]. Specifically, we should move away from the traditional cultivated land utilization mode relying on the input of chemical fertilizers and pesticides and towards the modern cultivated land utilization mode, which reduces fertilizer and pesticides in favor of harmless livestock and poultry manure and a cleaning ecosystem [110,111,112].

Hubei Province can also establish a guaranteed system and mechanism for agricultural non-point source pollution utilization [113] and establish a reasonable compensation and relief mechanism for cultivated land environmental protection [114]. Hubei Province can also promote land circulation by standardizing the land circulation market, using local finance to subsidize farmers who flow out of the land, and encouraging land circulation and financial reform so as to enable farmers to realize large-scale operation and intensive production [115]. Hubei Province can also improve the operation conditions of large agricultural machinery by integrating scattered small plots into large plots and changing irregular shapes into regular shapes through land remediation so as to improve the CLUE [116]. Hubei Province needs to build a diversified agricultural technology extension organization led by the government and improve the management system and operation mechanism of the agricultural technology extension system so as to realize the separation of public welfare functions and operational functions [117]. It should be noted that “large country and small farmers” are still China’s basic national conditions, and urbanization is predicted to be the development trend of China for a long time in the future [118]. However, at present, the urban problems of big cities are prominent. If urbanization is still promoted in an extensive way, it may lead to low efficiency of land use and the destruction of soil ecology. Therefore, we should take the county as the core, gather production factors, and actively promote intensive and efficient county urbanization. Benefits lie in the following two aspects: first, this strategy can develop the county economy, promote the integrated development of rural primary, secondary, and tertiary industries; optimize the rural industrial structure; and realize the diversification of farmers’ income. Second, it can promote the nearby and local transfer of farmers to promote agricultural scale management, enable farmers to consider the two types of activities of farming and working during busy agricultural periods, improve the CLUE resources, and reduce the possibility of abandoned farmland.

5. Conclusions

In this study, the super SBM undesirable model based on unexpected output was used to measure the CLUE in Hubei Province from 2005 to 2020. With the help of the ESDA analysis method, the spatiotemporal evolution characteristics of CLUE in Hubei Province were described, and the influencing factors were investigated. The following findings were made:

The spatiotemporal change pattern of CLUE is the outcome of the combined action of the cultivated land use system itself and a variety of external factors. The CLUE in Hubei Province shows significant regional differences and temporal variation characteristics. During the observation period of 2005 to 2020, the overall CLUE in Hubei Province increased, reaching 0.7475 by 2020, but was still at a low level (less than 1). Due to the limitation of topographic factors, this does not show obvious spatial agglomeration characteristics on the whole. Through the comparison of different landforms, it was found that the CLUE in the plain county of Hubei Province was the lowest, whilst it was higher in the mountainous and hilly areas. There were obvious differences in the CLUE of the plain, hilly, and mountainous terrains, and the difference decreased with time.

The carbon emission index has a certain positive effect on CLUE. The results of the super SBM-U model of CLUE in most areas of Hubei Province are lower than those of the super SBM model after considering the carbon emissions of cultivated land use, indicating that if the negative effect of carbon emissions is ignored, the calculation result of CLUE will be overestimated.

The kernel density curve of CLUE moved to the left, the peak value of the curve increased, the waveform width decreased, and the curve shape changed from a single peak to a double peak, which reveals the overall efficiency of cultivated land use in Hubei Province increased and the regional differences decreased, but there was a trend of polarization within the area.

The Tobit model’s estimated findings revealed that farmers’ income levels may actively promote the improvement of CLUE in Hubei Province. The urbanization level, rural power consumption, per capita cultivated land scale, and agricultural mechanization level have an impact on CLUE as a whole, but the impact has topographic heterogeneity. In different terrains, the direction and degree of the impact of the urbanization level, rural power consumption, per capita cultivated land scale, and agricultural mechanization level on CLUE are also different.