Spatial–Temporal Evolution and Influencing Factors of Arable Land Green and Low-Carbon Utilization in the Yangtze River Delta from the Perspective of Carbon Neutrality

Abstract

Arable land green and low-carbon utilization (ALGLU) is an important pathway to safeguard food safety and achieve the green transformation and progress of agriculture, playing a crucial role in promoting agricultural ecological protection and economic sustainability. This study takes the Yangtze River Delta region (YRD), where rapid urbanization is most typical, as the study area. On the basis of fully considering the carbon sink function of arable land, the study measures the green and low-carbon utilization level of arable land in the region using the Super-slack and based measure (Super-SBM) model, and analyzes its spatial and temporal evolution using the spatial autocorrelation model, the center of gravity, and the standard ellipsoid model, and then analyzes its impact with the help of the geographic detector and the geographically weighted regression model. We analyzed the multifactor interaction and spatial heterogeneity of the factors with the help of the geodetector and geographically weighted regression model. Results: (1) The ALGLU in the YRD has shown a fluctuating upward tendency, increasing from 0.7307 in 2012 to 0.8604 in 2022, with a growth rate of 17.75%. The phased changes correspond to national agricultural development policies and the stages of socio-economic development. (2) There are significant spatial differences in the level of ALGLU in the YRD, with high levels distributed in the southwest of Jiangsu, northern Zhejiang, and northwest Anhui, while low levels are distributed in the southwest of the YRD. Positive spatial autocorrelation exists in the level of ALGLU in the YRD. The spatial transfer trends of the gravity and standard deviation ellipses essentially align with changes in the spatial pattern. (3) The level of ALGLU in the YRD is affected by many factors, with the intensity of interaction effects far exceeding that of individual factors. When considering single-factor effects, precipitation, topography, and farmers’ income levels are important factors influencing the level of ALGLU. In scenarios involving multiple-factor interactions, agricultural policies become the primary focus of interaction effects. Furthermore, the driving effects of influencing factors exhibit spatial heterogeneity, with significant differences in the direction and extent of driving effects of each factor in different cities. This study can provide valuable insights for future ALGLU in the YRD and regional sustainable development.

1. Introduction

Agriculture is one of the major sources of greenhouse gas emissions; reducing emissions in agricultural systems and promoting green agricultural development are critical global concerns and key pathways to achieving sustainable development [1,2]. As the world’s largest agricultural producer, China supports nearly 20% of the global populace with only 7% of its arable land, making significant contributions to global food safety and sustainable agricultural progress [3]. However, alongside the achievements in agricultural development, issues such as the wastage of arable land resources, agriculture pollution, and carbon emissions from farming have become increasingly prominent [4,5]. The development of ALGLU has become an urgent task [6]. In light of this, the Chinese government has called for the integration of green concepts pollution control and carbon sequestration into arable land utilization to address the new challenges of transitioning to ALGLU development. The YRD is a typical area in China undergoing rapid urbanization and agricultural green production transformation, with multiple national strategies overlapping and significant conflicts between human activities and land, particularly acute ecological issues related to arable land [7,8,9]. Therefore, studying the ALGLU in this region has become an unavoidable reality. Conducting research on ALGLU is crucial for scientifically assessing the level of ALGLU and understanding its spatiotemporal patterns and influencing mechanisms, thereby improving the efficiency of ALGLU, promoting the green transformation of agriculture, and contributing to the achievement of the “peak carbon” and “carbon neutrality” (“dual carbon”) goals [10,11].

2. Literature Review

Research on ALGLU is a focal topic in the fields of agricultural production and efficient resource utilization, and scholars both domestically and internationally have conducted extensive research on aspects such as measuring the efficiency of arable land utilization under environmental constraints [12,13,14], its spatiotemporal characteristics [15,16], and its influencing factors [17,18]. In terms of measurement system design, scholars typically select indicators from two main aspects: inputs and outputs. Inputs mainly include factors such as crop planting area, agricultural workforce, effective irrigation area, and total agricultural machinery power, while outputs mainly consist of total grain output, agricultural output value, and agricultural financial input, among others. With the deepening implementation of ecological civilization construction, non-expected outputs have been considered by scholars, including aspects like carbon emissions and agricultural pollution emissions, and expected outputs have also included ecosystem service values [19,20,21,22]. Regarding measurement methods, scholars have utilized various approaches for efficiency measurement, such as stochastic frontier production function analysis, random forest models, the DEA–Tobit method, energy analysis, and the Malmquist–Luenberger production index [23,24,25,26]. In recent years, the DEA model has been widely applied. However, traditional DEA models overlook the existence of non-expected outputs, leading to distorted measurement results [27]. Furthermore, scholars have further explored spatiotemporal changes based on efficiency calculations. They have examined spatiotemporal changes using various spatial scales such as nationwide, provincial, municipal, and major grain-producing areas [28,29], employing methods like nonparametric kernel density estimation, spatial autocorrelation analysis, and the Markov transition matrix, among others [25,26,30].

Understanding the influencing factors is a crucial prerequisite for improving the level of ALGLU [31]. Currently, scholars both domestically and internationally categorize the influencing factors of ALGLU into natural factors, socio-economic factors, and ecological environmental factors [3,32]. With the gradual improvement of relevant research in recent years, additional factors have emerged, including agricultural technological levels, resource endowments, agricultural production conditions, and technological intensity. There are other studies that probe the effects of factors such as arable land landscape patterns, agricultural population migration, and multi-objective policies on ALGLU [3,33,34]. In terms of research methods for studying influencing factors, they can be classified into two main categories based on the different research purposes and data used [20,24,26,28]. The first category is non-spatial regression models, which analyze the average impact and direction of influencing factors from a global perspective, such as the Pearson correlation model, stepwise regression model, and Tobit model, among others. The second category is spatial regression models, which allow for exploring the relationships among factors of ALGLU from a local perspective, such as geographic detectors, the spatial Durbin model, the geographic weighted regression model, etc. In reality, the level of ALGLU is affected by multiple factors such as agricultural production conditions, economic development level, etc. [14]. Moreover, the intensity of the impacts caused by the interaction of single factors with multiple factors varies across different regions and time frames [25]. Therefore, spatial models are more capable of accurately reflecting the driving mechanisms of farmland utilization compared to non-spatial models.

In conclusion, the existing research has systematically explored the relevant aspects of ALGLU, providing important theoretical references and practical support. However, unfortunately, most of the existing studies focus on traditional agricultural development areas, and there are fewer studies on rapidly urbanizing areas where the contradiction between the green and low-carbon development of agriculture is more prominent. In addition, the carbon sequestration function of farmland is often ignored when measuring ALGLU, leading to biased evaluation results. Finally, in the analysis of the influencing factors of ALGLU, there is a lack of exploration of its multifactor interactions and spatial heterogeneity.

Based on this, this paper attempts to answer the following research questions: (1) How does the level of ALGLU change in the YRD, which is the most typical region for rapid urbanization development? (2) What is the spatial evolution mechanism of ALGLU in the region? (3) What are the driving mechanisms of each factor on ALGLU in the region? To address the above questions, this paper uses the Super-SBM model to measure the ALGLU in the YRD from 2012–2022, uses the spatial autocorrelation model and gravity and standard ellipse difference model to reveal its spatial and temporal evolution characteristics, and uses the geodetector and geographically weighted regression model to analyze the driving mechanism of the influencing factors. Specifically, the marginal contributions of this paper are mainly reflected in the following aspects.

(1) The YRD, where human–land conflicts are most prominent, is selected as the study area, which improves the research case of ALGLU in rapidly urbanizing regions and provides a reference for ALGLU in other rapidly urbanizing and developing regions in the future. (2) The concepts of “green” and “low-carbon” development are incorporated into the evaluation system when measuring the ALGLU level, and the carbon sequestration function of arable land is taken into account, which makes the measurement of ALGLU more comprehensive and accurate. (3) The interactions between the influencing factors and the spatial heterogeneity of the effects of the factors are discussed in depth, which helps to provide more detailed and specific guidance for regional ALGLU.

3. Methods and Data

3.1. Indicator Selection and Data Description

3.1.1. Measurement Indicators of ALGLU

The core of the “dual carbon” goals is to promote the transformation of development towards green and low-carbon utilization [35,36]. Under the realistic requirements of the “dual carbon” goals, this study integrates the concept of green and low-carbon development into the field of arable land utilization, comprehensively and systematically discussing the ALGLU in the YRD [37]. Arable land utilization is a dynamic process of “input + output”, and ALGLU refers to maximizing the expected outputs of arable land utilization, such as grain yield, agricultural output value, and carbon sequestration, while minimizing the non-expected outputs like carbon emissions and pollution emissions, under the multiple constraints of food safety, ecological sustainability, and the “dual carbon” goals. Referring to existing achievements [20,25,28,31,38,39,40] and the basic requirements of agriculture during the process of farmland green and low-carbon utilization, this paper comprehensively constructs a measurement index system for ALGLU (Figure 1).

In terms of input indicators, this study selects land, labor, and production materials as indicators. Specifically, land input is measured by arable land area, labor input is expressed by the number of primary industry employees, and production material input includes the usage of agricultural plastic film, fertilizer, pesticides, and the total power of agricultural machinery. For expected output indicators, the agricultural total output value can effectively reflect the economic benefits of various crops, thus serving as a measure of economic output; grain yield reflects the capacity of arable land to supply human food, making it a measure of social output; and arable land carbon sequestration refers to the ability of crops planted in arable land to absorb carbon dioxide from the atmosphere through photosynthesis, representing environmental output.

$$Ca={\displaystyle \sum _{n=1}^{m}C{a}_n={\displaystyle \sum _{n=1}^m\left\{\left[{\delta }_n\times Q_n\times (1-{\epsilon }_n)\right]/{\sigma }_n\right\}}}$$

(1)

where $Ca$ represents the total amount of carbon sequestration; $C{a}_n$ represents the carbon absorption amount of the $n$ crop; $m$ denotes the type of crops (including wheat, corn, rice, legumes, and tubers); ${\delta }_n$ represents the carbon absorption rate of the $n$ crop; $Q_n$ represents the yield of the $n$ crop; ${\epsilon }_n$ represents the water coefficient of the $n$ crop; and ${\sigma }_n$ represents the economic coefficient of the $n$ crop. The setting of the carbon absorption rate, water coefficient, and economic coefficient refers to achievements that have been made [26], as detailed in

Table 1. Relevant coefficients of major crop carbon sequestration.

Type	Carbon Absorption Rate	Water Coefficient	Economic Coefficient
wheat	0.485	0.12	0.40
corn	0.471	0.13	0.40
rice	0.414	0.12	0.45
legumes	0.450	0.13	0.34
tubers	0.423	0.70	0.70

.

In terms of non-expected output indicators, this study selects arable land pollution and carbon emissions for characterization. Pollution is mainly caused by agricultural plastic film, residual fertilizer, etc. According to previous studies, the residual rate of fertilizer is calculated as 65% of the fertilizer input, and the residual rate of plastic film is calculated as 10% of the plastic film input [20]. Carbon emissions are generated by activities such as plowing, fertilization, agricultural plastic film usage, diesel fuel consumption for agricultural machinery, and agricultural irrigation in the process of arable land utilization. The carbon emission coefficients are shown in

Table 2. Carbon emission coefficients.

Carbon Source	Plowing (kg·kg−1)	Fertilization (kg·kg−1)	Agricultural Plastic Film Usage (kg·kg−1)	Diesel (kg·km−2)	Agricultural Irrigation (kg·hm−2)
Coefficients	25	0.8956	5.18	0.5927	25

.

$$C_s={\displaystyle \sum _{}^n{U}_r\times K_r}$$

(2)

$$C_e={\displaystyle \sum \left(E_i\times {\theta }_i\right)}$$

(3)

where $C_s$ represents the emissions of pollution; $K_r$ denotes the residual rate; $U_r$ represents the input amount; $C_e$ represents carbon emissions; $E_i$ represents the carbon emission sources; and ${\theta }_i$ represents the emission coefficient of various carbon emission sources [20]. $r$ represents the $r$-th source of surface pollution. $i$ represents the $i$-th carbon source category.

3.1.2. Influencing Factors of ALGLU

Based on the actual state of ALGLU in the YRD and referring to relevant research results [14,25,26,28,40], this study selects indicators from four dimensions: the natural environment, socio-economics, agricultural modernization, and agricultural policies, exploring their mechanisms of influence on the level of ALGLU. The factors are set as shown in

Table 3. Influencing factors of ALGLU.

Dimension	Influencing Factor	Characterization Indicator	Code
natural environment	Terrain	Slope	X1
	Precipitation	Annual precipitation	X2
socio-economics	Level of urbanization	Urbanization rate	X3
	Farmers’ income	Farmers’ disposable income	X4
agricultural modernization	Agricultural irrigation level	Effective irrigation area	X5
	Level of agricultural machinery	Total power of agricultural machinery	X6
agricultural policy	Government subsidies	Expenditure on agricultural, forestry, and water affairs in fiscal expenditure	X7

. In terms of the natural environment, the slope and annual precipitation are selected as indicative factors. An appropriate natural environment is a key for agricultural development, as the slope and annual precipitation directly affect the conduct of arable land utilization and greatly influence arable land output [41]. In terms of socio-economics, the urbanization rate and disposable income of farmers are chosen as indicative factors. The development of urbanization drives population movement between urban and rural areas, creating conditions for the transformation of arable land management practices [20,42]. Farmers’ disposable income influences their willingness to engage in the green and low-carbon utilization and protection of arable land, ultimately reflecting in their actions in arable land utilization [43]. In terms of agricultural modernization, the level of agricultural mechanization and the area of effective irrigation are selected as measures. A higher level of agricultural mechanization and broader coverage of agricultural irrigation indicate a higher level of agricultural modernization, which can effectively improve the production conditions of arable land and enhance production efficiency. However, the significant inputs of pesticides, diesel, and other resources will inevitably lead to an increase in non-expected outputs, impacting the ALGLU [28]. In terms of agricultural policies, government subsidies play a role in promoting farmers’ production enthusiasm. However, most agricultural financial support policies tend to focus on resources such as pesticides and fertilizers, potentially increasing the risk of pollution in arable land, ultimately affecting ALGLU [34].

3.1.3. Data Sources

The arable land area data are sourced from the Zenodo data management platform (https://zenodo.org/record/8176941/ accessed on 1 March 2024); precipitation data are from the National Earth System Science Data Center (https://www.geodata.cn/main/#/ accessed on 1 March 2024); and DEM data are from the Geographic Spatial Data Cloud (https://www.gscloud.cn/ accessed on 1 March 2024); spatial data are allocated to various cities using zoning statistics in ArcGIS 10.8 software. Other socio-economic data, such as the number of agricultural employees, agricultural machinery input, total agricultural output value, grain production, etc., are sourced from the statistical yearbooks of provinces (municipalities) within the region and the National Economic and Social Development Statistics Bulletin (2012–2022). Some missing data are interpolated.

3.2. Study Area

The YRD is located at the intersection of the East Asian geographical center and the East Asia route in the Western Pacific Ocean. As shown in Figure 2, the region includes four provinces (municipalities): Shanghai, Zhejiang, Jiangsu, and Anhui, totaling 41 cities. The terrain is higher in the south and lower in the north, with the north primarily consisting of arable land and construction land, while the south is predominantly forested. The region has a subtropical monsoon climate with ample sunlight, moderate temperatures, and abundant rainfall. Benefiting from its geographical advantages, the YRD has created favorable conditions for agricultural production and serves as a key modern agricultural production base in the country, playing a crucial role in ensuring national food safety [44]. However, the region is also an important ecological functional area, responsible for functions such as soil and water conservation, climate regulation, and pollutant degradation. In recent years, the rapid expansion of construction land has led to its encroachment on arable land, resulting in agricultural production relying on excessive land exploitation and overuse of chemical fertilizers. The trends of the non-agriculturalization of arable land, pollution, and carbon emissions have intensified, leading to tighter constraints on the environment of arable land [14]. Research on ALGLU in the YRD is of great practical importance and urgency [45].

3.3. Methods

3.3.1. Super-SBM Model

ALGLU serves as a comprehensive indicator considering economic growth, resource consumption, and greenhouse gas emissions. When introducing economic output variables, it is essential to also consider non-expected outputs. Traditional DEA models overlook the slack variables of input–output, which may compromise the accuracy of efficiency evaluation. In 2001, Tone proposed the SBM model, incorporating slack variables to address the issues of CCR and BCC models requiring proportional improvements. Subsequently, the Super-SBM model was developed, enabling the effective comparison of decision-making units and optimal selection [27]. With the continuous promotion of ecological civilization construction, the non-desired output in the process of cropland utilization has been valued, which is in line with the idea of the model, so the model has been widely used in cropland utilization efficiency measurement [46,47]. Therefore, this study selects the Super-SBM model for calculations. The basic principle is as follows: assuming there are $n$ decision-making units in the arable land utilization process, $m$ input factors, $s_1$ expected outputs, and $s_2$ non-expected outputs, with three sets of vectors $x\in R^m$, $y^g\in R^{s_1}$, and $y^b\in R^{s_2}$, representing input, expected output, and non-expected output, and defining a matrix, $X=\left[x_1,\cdots ,x_n\right]\in R^{m\times n}$, $Y^g=\left[y_1^g,\cdots ,y_n^g\right]\in R^{s_1\times n}$, $Y^b=\left[y_1^b,\cdots ,y_n^b\right]\in R^{s_2\times n}$, the constructed Super-SBM model is as follows:

$${\rho }^{*}=\min {\displaystyle \frac{1+\frac{1}{m}{\displaystyle \sum _{i=1}^m\frac{D_i^{-}}{x_{ih}}}}{1-\frac{1}{s_1+s_2}({\displaystyle \sum _{r=1}^{s_1}\frac{D_r^g}{y_{rh}^g}}+{\displaystyle \sum _{k=1}^{s_2}\frac{D_k^b}{y_{kh}^b}})}}$$

(4)

$$s.t.\left\{\begin{array}{l}{x}_{ik}\ge {\displaystyle \sum _{i=1,j\ne h}^n{\lambda }_j{x}_{ij}-D_i^{-},}\\ y_{rh}^g\ge {\displaystyle \sum _{j=1,j\ne h}^n{\lambda }_j{y}_{rj}^g+D_r^g,}\\ y_{kh}^b\ge {\displaystyle \sum _{j=1,j\ne h}^n{\lambda }_j{y}_{kj}^b+D_k^b,}\\ 1-{\displaystyle \frac{1}{s_1+s_2}}({\displaystyle \sum _{r=1}^{s_1}\frac{D_r^g}{y_{rh}^g}}+{\displaystyle \sum _{k=1}^{s_2}\frac{D_k^b}{y_{kh}^b}})>0\\ D^{-}\ge 0,D^g\ge 0,D^b\ge 0\end{array}\right.$$

(5)

where $p^{*}$ represents the efficiency index of ALGLU, $D^{-}$, $D^g$, and $D^b$ are slack variables for input, expected output, and non-expected output, and $\lambda$ is the weight vector. $j$ and $h$ represent the $j$-th year and the $h$-th region, respectively. $i$ represents the $i$-th type of input. $r$ represents the $r$-th type of expected output. $k$ represents the $k$-th type of unexpected output.

3.3.2. Spatial Autocorrelation Analysis

Spatial autocorrelation analysis is used to investigate whether there is significant correlation between geographic units within the region and their neighboring units. Global spatial autocorrelation analysis is utilized to determine whether a particular attribute overall exhibits spatial correlation or spatial clustering, typically described using Moran’s I index, with the range of [−1, 1]. Positive and negative values indicate the positive and negative spatial autocorrelation of attribute distribution, respectively, with the presence of spatial clustering. A zero value suggests no spatial autocorrelation in attribute distribution. Local spatial autocorrelation can reveal local clustering characteristics spatially, categorizing them into four types. As individual cities are not completely closed, the level of green and low-carbon utilization of cultivated land in the region during the process of cultivated land utilization is often affected by the surrounding cities, and the use of spatial autocorrelation analysis can better reveal this phenomenon [48,49].

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

(6)

where $n$ represents the total number of cells, $w_{ij}$ is the spatial weight matrix, and $x_i$, $x_y$ is the value of attribute $x$, $y$ in the cell. $i$ and $j$ represent the $i$-th and $j$-th municipalities, respectively.

3.3.3. Geographic Detector

The level of green and low-carbon utilization of arable land is the result of a combination of factors, and the geographic detector is a suitable tool to reveal their driving effects [50,51]. It not only quantitatively assesses the impact of each factor but also detects whether there are interactions between two factors, along with the nature and extent of these interactions. The influence degree is represented using the $q$ value in the range of [0, 1], where a higher value indicates a greater impact of the factor.

$$q=1-{\displaystyle \frac{{\displaystyle \sum {}_{h=1}^L{N}_h{\sigma }_h^2}}{N_{{\sigma }^2}}}$$

(7)

where $h$ represents the hierarchy of variable $Y$ or factor $X$; $N_h$ represents the number of units in layer $h$; $N$ represents the total number of units in the entire region; ${\sigma }_h^2$ represents the variance of $Y$ values in layer $h$; and ${\sigma }^2$ represents the variance of $Y$ values in the entire region.

3.3.4. Geographically Weighted Regression Model

In reality, the natural conditions, socio-economic conditions, and other factors of each city are different, so the direction and form of their influencing factors also have spatial heterogeneity [51]. The geographically weighted regression model takes into account the local effects of the spatial objects, and based on the nonparametric methods of curve fitting and smoothing in locally weighted regression, the geographic location information of the data is embedded into the regression parameters, so that an accurate estimation of the local spatial changes are accurately estimated [52].

$${\mathrm{y}}_i={\beta }_0\left(U_i,V_i\right)+{\displaystyle {\sum }_{k=1}^n{\beta }_k\left(U_i,V_i\right)}xk\left(U_i,V_i\right)+{\epsilon }_i$$

(8)

where $\left(U_i,V_i\right)$ are the spatial coordinates of $i$, $k$ is the number of independent variables, ${\beta }_0\left(U_i,V_i\right)$ is the geographically weighted regression intercept of $\left(U_i,V_i\right)$, ${\beta }_k\left(U_i,V_i\right)$ and $xk\left(U_i,V_i\right)$ are the geographically weighted regression coefficients and variable values of $k$ in $\left(U_i,V_i\right)$, respectively, and ${\epsilon }_i$ is the algorithm residual.

4. Results

4.1. Temporal Changes in ALGLU

As shown in Figure 3, the ALGLU exhibits a fluctuating upward trend, with significant increases in regions with above-average levels of ALGLU, and regional disparities are narrowing. The average ALGLU index increased from 0.7307 in 2012 to 0.8604 in 2022, representing a growth rate of 17.75%. The overall level of ALGLU has improved, with many areas that were previously at low or moderate levels transitioning to moderate and above levels. Looking at specific regions, Shanghai has the highest ALGLU level, reaching 0.9416, while Zhejiang has the lowest at 0.6597. In terms of growth rate changes, the ALGLU mainly shows an increasing trend, with growth rates in descending order being represented by Anhui, Zhejiang, Jiangsu, and Shanghai at 31.34%, 12.92%, 9.96%, and 2.82%, respectively. Additionally, there is a trend of narrowing regional disparities in ALGLU over the study period, with the regional difference decreasing from 0.3790 in 2012 to 0.3009 in 2022.

The changes in ALGLU exhibit phased characteristics. From 2012 to 2013, there was a decline phase, when the introduction of Central Document No. 1 in 2012 emphasized accelerating agricultural technological innovation. The YRD was designated as a national pilot demonstration area, with large agricultural machinery widely used in land utilization processes. This led to a significant increase in input factors and improved agricultural output. However, in 2013, the region experienced a higher number of typhoons and frequent extreme weather events, resulting in a noticeable decrease in expected output, thereby affecting the level of ALGLU for that year. From 2013 to 2018, there was a phase of fluctuating growth. This period coincided with the deepening of new industrialization and urbanization, where land was still being occupied, and the incentive mechanisms for land protection were not yet robust. With the implementation of supportive agricultural policies, farmer initiatives increased, boosting grain output and agricultural value. However, excessive input of agricultural materials and machinery led to severe pollution and carbon emissions, causing fluctuations in ALGLU levels. From 2018 to 2022, there was a phase of stable growth. In 2018, China established the Ministry of Natural Resources to oversee the development and protection of arable land, providing guarantees for the integrated protection of land quantity, quality, and ecology. Under the dual effects of stringent land protection measures and rapid agricultural technological development, the grain output and agricultural value increased effectively, while pollution and carbon emissions were controlled, leading to a stable increase in ALGLU levels.

4.2. Spatial Characteristics of ALGLU

Based on the results of ALGLU analysis in the YRD, the area was divided into four levels using the natural breakpoint method from low to high: low level (0, 0.444], relatively low level (0.444, 0.660], moderate level (0.660, 0.859], relatively high level (0.859, 1.024], and high level (1.024, 1.218]. To visually display the spatial characteristics of ALGLU levels in the YRD, a spatiotemporal differentiation map was created using ArcGIS software (Figure 4). In 2012, there were 11 cities with a high level of ALGLU, distributed in the southwest of Jiangsu and the north of Zhejiang. By 2017, cities with high levels of ALGLU increased to 15, with additional growth in the northwest of Anhui compared to 2012. In 2022, there were still 15 cities with a high level of ALGLU, but the distribution range had expanded. Cities with relatively high and moderate levels of ALGLU reached 14 in 2022, an increase of 3 compared to 2012, mainly in the northeastern part of the YRD. Cities with relatively low and low levels of ALGLU decreased from 19 in 2012 to 12 in 2022, primarily located in the southeastern part of the YRD.

As shown in Figure 5, using GeoDa 1.12 and ArcGIS 10.8 software, the Moran’s I of ALGLU in the YRD from 2012 to 2022 was calculated. The results indicate that the Moran’s I was statistically significant and positive in all years, suggesting a positive spatial autocorrelation of ALGLU in the region. The Moran’s I of ALGLU exhibited a fluctuating decreasing trend, decreasing from 0.283 in 2012 to 0.143 in 2022, indicating fluctuating agglomeration changes in ALGLU across different cities as they developed. To further explore the local agglomeration status of ALGLU in the YRD, a LISA agglomeration map and LISA significance map were created. As shown in Figure 6, the high–high and low–low agglomeration cities decreased from 7 in 2012 to 3 in 2022, with high–high agglomeration located in Taizhou, Yangzhou, Zhenjiang, and Changzhou, and low–low agglomeration areas mainly in Quzhou, Lishui, and Wenzhou. The low–high agglomeration cities also decreased annually, primarily distributed in Jiaxing, Nantong, and Yancheng, while high–low agglomeration cities remained constant, located in Hangzhou, Chizhou, and Taizhou.

This study also investigated the changes in the centroid trajectory and standard deviation ellipse position of ALGLU, as shown in Figure 7. From 2012 to 2022, there was only a slight variation in the rotation angle θ of 2.09°, indicating a relatively stable distribution pattern of ALGLU levels. The ellipse area in 2022 increased compared to 2012, indicating an expanded distribution range of ALGLU, consistent with the conclusion of an increased number of cities with moderate and above levels of ALGLU. The flattening ratio of the standard deviation ellipse decreased, suggesting a weakening directionality of ALGLU. The standard deviation ellipse predominantly displayed a “northwest–southeast” distribution, with the centroid mainly located in Nanjing at all time points and generally moving in a northwest direction. This indicates that areas with high levels of ALGLU are concentrated around Nanjing and exhibit a trend towards northwest development, corresponding to the spatial distribution characteristics of ALGLU levels.

4.3. Factors Influencing ALGLU

4.3.1. Detection Results of Single-Factor and Multifactor Interactions

Figure 8 presents the results of the geographic detector analysis of the influencing factors during the study period. The explanatory power of factors influencing ALGLU ranges from high to low as follows: natural environment > socio-economic factors > agricultural policies > agricultural modernization. Within the natural environment factors, the average impact strengths of topography (X1) and annual precipitation (X2) from 2012 to 2022 were 0.2940 and 0.4398, respectively, indicating the critical influence of natural environmental factors on the status of ALGLU. Most areas in the YRD have flat terrain and abundant arable land resources, conducive to mechanized farming and large-scale operations, promoting economies of scale and agglomeration economies, thereby enhancing the level of ALGLU. However, mountainous and hilly areas in the southwest restrict agricultural development, affecting the level of ALGLU. Precipitation is a fundamental factor in grain production, with the YRD experiencing abundant rainfall, favorable for high crop yields. This facilitates an increase in ALGLU levels by boosting expected outputs. However, the region is prone to extreme climate disasters such as typhoons and floods, posing a threat to the improvement of grain output and agricultural value. According to data from the “China Statistical Yearbook”, the average area of crop damage caused by floods, typhoons, and other disasters in the YRD from 2012 to 2022 reached 1610 thousand hectares. Particularly, in 2013, the area of crop damage exceeded 3612 thousand hectares, 2.24 times the average for the study period, resulting in direct economic losses of RMB 93.87 billion, which is detrimental to the region’s green and sustainable land use.

In the aspect of socio-economic factors, the factor influence indices of the urbanization level (X3) and disposable income of farmers (X4) in 2012 were 0.2051 and 0.4308, respectively, ranking second and fifth in distribution. By 2022, the influence index of the urbanization level had risen to 0.2431, while the influence index of disposable income had decreased to 0.1449. The YRD, as a typical representative of rapid urbanization, has experienced rapid urbanization construction, leading to a disorderly expansion of construction land and soil environmental degradation, negatively impacting both the quantity and quality of arable land resources. Additionally, urbanization construction has accelerated the migration of resources such as people, finances, and materials within the region, resulting in a large amount of abandoned farmland, which has a detrimental effect on ALGLU. The scarcity of agricultural labor forces the region to actively transform its previous extensive mode of arable land development and utilization. By utilizing large-scale agricultural machinery and transferring farmland, the potential utilization space and marginal efficiency of arable land are explored, creating conditions for the transformation of arable land utilization towards intensification and efficiency, which may have a positive effect on ALGLU. Arable land operations are the main source of income for farmers, and disposable income directly affects farmers’ perception and acceptance of green land use and ecologically sustainable protection. When disposable income is low, farmers may increase the input of production materials to pursue economic benefits at the expense of ecological benefits, leading to a decline in arable land quality and a reduction in the level of ALGLU. Higher disposable income indicates higher rural economic development and living standards, which are conducive to farmers’ investment in green production practices, positively influencing the level of ALGLU.

Regarding agricultural modernization factors, the factor influence indices of the effective irrigation area (X5) and agricultural mechanization level (X6) in 2012 were 0.1409 and 0.1227, respectively. Over time, these indices fluctuated and increased, reaching 0.1492 and 0.1606 in 2020, indicating a continuous enhancement of the influence of agricultural modernization on ALGLU. The improvement in the agricultural mechanization level and effective irrigation area contributes to improving regional grain production conditions, reducing production costs for the utilization of arable land, and enhancing the efficiency and economic benefits of arable land resources. However, while providing convenience, it also leads to an excessive input of petroleum resources such as diesel, resulting in carbon emissions and pollution. Thereby, the ALGU is inhibited.

In terms of agricultural policy factors, the factor influence index of financial support for agriculture (X7) in 2005 was 0.2443, and it decreased to 0.1904 by 2020, indicating that financial support has had a certain effect on ALGLU, with the degree of this impact decreasing over time. On one hand, government support through agricultural policies such as agricultural tax reforms can stimulate farmers’ production enthusiasm. Additionally, investments in arable land environmental governance within agricultural support expenditures can effectively alleviate issues such as arable land pollution during utilization, helping to improve arable land ecological quality and playing a positive role in increasing arable land input and enhancing expected outputs. On the other hand, financial support policies have tended to focus on subsidies for elements such as pesticides, fertilizers, and agricultural films. Excessive use of agricultural chemical products can lead to pollution, negatively impacting ALGLU.

As shown in Figure 9, the results of the geographic detector’s interaction detection indicate that the influence of each factor on the level of ALGLU is not independent but a result of joint action, mainly manifested as nonlinear enhancement and dual-factor enhancement, with nonlinear enhancement having a greater explanatory power for the level of ALGLU. The nonlinear enhancement interaction between urbanization level and agricultural policy has the greatest explanatory power for the level of ALGLU, with a q value of 0.9442. Additionally, the interactions of precipitation with agricultural policy, disposable income with topography, and disposable income with agricultural policy also have strong explanatory power for the level of ALGLU, with q values of 0.9112, 0.9048, and 0.8838, respectively.

4.3.2. Spatial Heterogeneity of Influencing Factors

As shown in Figure 10, in terms of natural factors, the topography has a significant negative effect on the level of ALGLU in the southwest part of the YRD. This is mainly due to the mountainous terrain in the southwest, which makes it difficult to carry out large-scale farmland operations. In the northeast part, the topographical effect is positive, as this area is mostly plains with flat terrain, fertile soil, abundant farmland resources, and easy farmland operations. Precipitation overall has a negative impact on the level of ALGLU, with the degree of impact gradually increasing from north to south. This is because the YRD experiences frequent typhoons and flooding disasters in the summer, which coincide with the critical period of crop growth. Excessive precipitation leads to reduced crop yields, affecting the level of ALGLU. Cities closer to the southeastern sea area suffer greater damage from disasters, intensifying this negative impact. In terms of socio-economic factors, the level of urbanization has a generally positive effect on the level of ALGLU, gradually strengthening from west to east. This is because the eastern part of the YRD has a relatively high level of urbanization, with a slower growth rate and a more mature awareness of green and low-carbon development in the process of development. Therefore, the increase in urbanization level has a certain positive impact on the level of ALGLU. However, some cities exhibit negative effects, particularly in the western part of the YRD, where urbanization is accelerating rapidly. The rapid expansion of these cities threatens both the quantity and quality of farmland, hindering ALGLU. The impact of rural residents’ income is generally consistent with the level of urbanization. Farmers in the western cities of the YRD have lower incomes, forcing them to increase inputs such as fertilizers and pesticides to pursue higher economic benefits, leading to increased carbon emissions and non-desired outputs such as pollution. In contrast, farmers in the eastern region have higher incomes and stronger awareness of farmland protection. In terms of agricultural modernization factors, the continuous improvement in mechanization levels and effective irrigation areas has a positive impact in the southwest region. Due to environmental constraints, mechanized farming areas in this region are relatively small, and irrigation is often carried out through small-scale water conservancy projects. The limited use of agricultural machinery and irrigation facilities helps reduce production costs and increase farmland output, thus promoting ALGLU to some extent. In the central and northern regions, although the favorable farmland environment facilitates the use of agricultural machinery and large-scale irrigation facilities, excessive investment in agricultural machinery leads to the overuse of resources such as diesel, resulting in farmland environmental pollution and inhibiting the improvement in ALGLU. In terms of agricultural policy factors, government agricultural policies have a clear positive correlation with the driving impact on the level of ALGLU, with the degree of impact increasing in a circular manner from west to east. Regions with higher levels of economic development often provide greater support, leading to a stronger impact on the level of ALGLU.

5. Discussion

pAs an important part of the ecosystem, arable land has the mission of guaranteeing food security, sequestering carbon, and increasing sinks [53]. Under the circumstances of global climate change and the continuous utilization of arable land, the level of ALGLU directly affects the realization of social, economic, and ecological benefits of arable land utilization, and has a profound impact on the sustainable development of the region and the maintenance of human well-being [54,55]. The results of this study show that the ALGLU level in the YRD has improved in recent years. This is mainly attributed to the national protection and development of arable land resources [56]. In recent years, the YRD has consistently implemented a strict cropland protection system. Through measures such as crop rotation and fallow, high-standard basic farmland construction, safe utilization of cropland, and risk control, the basic conditions of cropland have been continuously enhanced [57,58]. In terms of spatial distribution, the areas with high levels of ALGLU are mainly concentrated in southwest Jiangsu, north Zhejiang, and northwest Anhui, and there is a significant spatial correlation of ALGLU among different cities. The spatial and temporal characteristics of ALGLU are consistent with the findings of Hou et al. [50,59]. In terms of influencing factors, the level of ALGLU is the result of a combination of factors, but the forms of influence of different factors differ to some extent [20,26]. Overall, the explanatory power of the factors on the level of agricultural modernization is shown as natural environment factors > socio-economic factors > agricultural policy factors > agricultural modernization factors. And there is a significant interaction between the influencing factors, which is mainly manifested as nonlinear enhancement and two-factor reinforcement. This study also found that there is spatial heterogeneity in the influence of the natural environment, socio-economic factors, agricultural modernization, and agricultural policies on the ALGLU in the YRD. Zhang and other scholars, in their study of arable land use, also found that the arable land use efficiency shows spatial heterogeneity that is affected by both natural and anthropogenic factors, which is in line with the conclusions of this study [50,52].

China is currently in a crucial stage of deepening urbanization, as well as a period of tackling challenges to promote the coordinated reduction in pollution and carbon emissions, and the transformation of agricultural green development [60]. Conducting research on the ALGLU in the YRD, a typical area experiencing rapid urbanization, holds significant practical significance [61]. This research comprehensively considers both the expected and unexpected outputs generated during the land use process, measures the efficiency level of ALGLU in the YRD, and reveals the patterns of its spatial and temporal evolution, as well as the mechanisms of influencing factors. This research has reference value for the protection of arable land and the sustainable utilization of arable land resources in China. However, there is still room for the research to be strengthened. Firstly, due to limitations in statistical data, this study focuses on the city scale in terms of spatial resolution. Future research can refine the spatial scale through in-depth investigations, down to the level of individual counties or even grid cells. Secondly, the carbon emissions from arable land use are not only influenced by the factors of arable land use activities mentioned in this article, but also by key agricultural policies such as the “farmland red line” and “afforestation” [62,63]. Therefore, in subsequent research, the indicators for calculating carbon emissions from arable land use can be further enriched. Finally, as the selection of influencing factors is limited, further exploration is needed to delve into the driving mechanisms. Future research can expand the selection of driving factors, incorporating factors from more dimensions to explore the driving mechanisms of ALGLU.

6. Conclusions and Management Implications

Based on economic statistical data and spatial data from 2012 to 2022, this study utilized the Super-SBM model to build an evaluation system to measure the ALGLU in the YRD. Using spatial autocorrelation, the gravity center, and standard ellipse difference, we explored the spatiotemporal pattern changes in ALGLU levels. Additionally, the study employed a geographic detector and geographically weighted regression model to explore the driving mechanisms of influencing factors. The results are as follows. (1) Overall, the ALGLU in the YRD has shown a fluctuating upward trend, increasing from 0.7307 in 2012 to 0.8604 in 2022, with a growth rate of 17.75%. In terms of subregions, there are large differences in the average ALGLU across regions, with Shanghai (0.9416) > Jiangsu (0.9126) > Anhui (0.7090) > Zhejiang (0.6597), but in recent years, these inter-regional differences have been gradually narrowing, decreasing from 0.3790 in 2012 to 0.3009 in 2022. Changes in the ALGLU show obvious stage characteristics, which is very consistent with the national agricultural development policies and the stages of socio-economic development. (2) There are significant spatial differences in the level of ALGLU in the YRD, with high-level areas distributed in the southwest of Jiangsu, the northern part of Zhejiang, and the northwest of Anhui, while low-level areas are predominantly found in the southwest of the region. The areas of high and higher levels have expanded in recent years. There is a significant spatial autocorrelation of ALGLU levels among the cities in the region, with a high–high agglomeration in Taizhou, Yangzhou, Zhenjiang, and Changzhou, a low–low agglomeration in Quzhou, Lishui, and Wenzhou, a low–high agglomeration in Jiaxing, Nantong, and Yancheng, and a high–low agglomeration in Hangzhou, Chizhou, and Taizhou. The standard deviation ellipse shows a “northwest–southeast” distribution, generally moving in the northwest direction, corresponding to the spatial distribution characteristics of the ALGLU level. (3) The level of ALGLU in the YRD is influenced by multiple factors, with the intensity of factor interactions far exceeding that of individual factors. Precipitation, topography, and farmers’ income are important factors when considering individual effects, while agricultural policies become the main focus in terms of factor interactions. Furthermore, there is spatial heterogeneity in the driving effects of these factors: topography has a positive effect in the southwest region and a negative effect in the northern region, while precipitation has a negative effect overall, increasing in impact from northwest to southeast. Social environmental factors have a negative effect in the west and a positive effect in the east; agricultural modernization factors have a negative effect in the north and a positive effect in the south; and agricultural policy factors have a positive effect overall, with the impact increasing in a circular pattern from west to east.

The following policy recommendations have been proposed. (1) Optimize relevant policy pathways. On one hand, strengthen the construction of farmland protection systems to avoid unauthorized occupation of farmland due to land expansion. Efforts should be made to prevent the non-agricultural use of farmland and ensure the proper management of existing farmland to fulfill the responsibility of food production. On the other hand, agricultural support policies should reform traditional methods such as fertilizer and pesticide subsidies, and provide diversified assistance to farmers, such as increasing food subsidies and offering agricultural technology training. Additionally, integrating farmland carbon sequestration into carbon trading markets can highlight the economic value of low-carbon farmland use, thereby increasing farmers’ willingness to engage in green and low-carbon farmland practices. (2) Implement dual efforts to reduce emissions and increase carbon sequestration. On one hand, it is important to control the intensity of inputs in farmland production by promoting the use of green and energy-efficient agricultural machinery to reduce the consumption of fossil fuels in agricultural production. Additionally, practices such as conservation tillage and improved irrigation methods can help reduce carbon emissions and non-desirable outputs like surface water pollution while maintaining expected outputs. On the other hand, measures like increasing the use of organic fertilizers, returning crop straws to the soil, implementing crop rotation, and enhancing the construction of high-standard farmland can enhance the organic carbon content in the soil and improve farmland carbon sequestration capabilities. (3) Strengthen agricultural science and technology empowerment. On one hand, efforts should be made to accelerate the transformation and application of agricultural green and low-carbon development technologies. This includes innovating clean energy agricultural machinery and irrigation equipment, as well as cultivating excellent varieties adapted to regional climate change and characterized by water and fertilizer efficiency, high yield, and quality. On the other hand, promoting the digital transformation of farmland utilization, operation, and management is essential. Initiatives such as smart farmland monitoring, farmland quality protection, efficient water-saving irrigation, green agricultural production, and intelligent farm machinery management should be implemented to provide technological support for achieving green and low-carbon farmland utilization.