Empirical Investigation of Cultivated Land Green Use Efﬁciency and Inﬂuencing Factors in China, 2000–2020

: The rapid industrialization and urbanization promote socioeconomic development, but also pose a certain threat to food and ecological security. Cultivated land green use efﬁciency (CLGUE) is an important indictor to comprehensively reﬂect the coordinated relationship between cultivated land utilization and ecological protection. Therefore, it is of great practical signiﬁcance to explore CLGUE to guarantee efﬁcient and sustainable utilization of cultivated land resources. This paper thus conducts an empirical investigation of 31 provinces in mainland China during 2000–2020, aiming to measure the CLGUE level using the Super-SBM model and explore its inﬂuencing factors based on panel regression model. The data, which were mainly derived from various statistical yearbooks, together with the reference dataset, were all accurate. The results show that the average CLGUE value in China exhibited a ﬂuctuating upward development trend, with the highest efﬁciency value of 0.957 in 2020 and the lowest one of 0.853 in 2003. Northeastern China had the highest efﬁciency value, while Central China had the lowest efﬁciency value. The overall ranking of CLGUE in the four major regions from high to low is Northeastern, Eastern, Western, and Central China. Spatially, there are signiﬁcant diversities in CLGUE across China, which means that differentiated measures need to be taken to improve the efﬁciency based on regional natural conditions and the socioeconomic level. The regression model indicated that the crop diversity index, GDP per capita, urbanization level, effective irrigation rate, and ﬁscal support for agriculture positively inﬂuenced the CLGUE, while the proportion of natural disaster area had a negative impact. The ﬁndings had important implications for improving the CLGUE and achieving sustainable agricultural development.


Introduction
Cultivated land is the essence of land resources, and plays a vital role in ensuring food production, socioeconomic development, and ecological security [1,2]. The world's cultivated land area is about 1.6 billion hectares, supporting nearly 8 billion people. It contributes an important role in alleviating the food crisis, maintaining social stability and ensuring sustainable development in the world. Since the reform and opening up in 1978, China's agriculture has witnessed rapid development, achieving abundant harvests in both grain output and agricultural output value [3]. Grain output increased from 304.7 million tons in 1978 to 669.49 million tons in 2020, with an average growth rate of 2.85%. The total agricultural output value has also risen, from 111.75 billion yuan in 1978 to 16,690 billion yuan in 2020, with an average growth rate of 353.22%. However, the issue of food security in China cannot be ignored. The country's cultivated land accounts for approximately 7.5% of the world's total, but it needs to feed around 23% of the global population [4,5]. Furthermore, with the accelerated process of industrialization and urbanization, urban construction continues to encroach upon cultivated land, leading to a continuous decrease In this paper, we aim to use the Super-SBM model to calculate the CLGUE values based on the panel data of 31 provinces in mainland China and analyze its spatio-temporal characteristics from 2000 to 2020 by using mathematical statistics and GIS visualization methods. Then, the panel regression model is applied to explore how factors influence the CLGUE across China and its different regions from the perspective of natural conditions, socioeconomic development level, cultivated land use conditions, and agricultural policies. Finally, some policy implications are proposed to improve the CLGUE and promote sustainable agricultural development.

Study Area
The total number of administrative divisions in China is 34 (including provinces, autonomous regions and municipalities). The country has a vast territory, which leads to a significant difference among its administrative divisions in terms of natural geography, economic development, and resource endowment [39]. Based on data availability, the research objects of this paper were selected as 31 provinces in mainland China from 2000 to 2020, excluding Hong Kong, Macao and Taiwan. In addition, according to relevant studies [40,41]  significance in promoting the coordinated development of cultivated land utilization, agricultural economy growth, and ecological environment protection.
In this paper, we aim to use the Super-SBM model to calculate the CLGUE values based on the panel data of 31 provinces in mainland China and analyze its spatio-temporal characteristics from 2000 to 2020 by using mathematical statistics and GIS visualization methods. Then, the panel regression model is applied to explore how factors influence the CLGUE across China and its different regions from the perspective of natural conditions, socioeconomic development level, cultivated land use conditions, and agricultural policies. Finally, some policy implications are proposed to improve the CLGUE and promote sustainable agricultural development.

Study Area
The total number of administrative divisions in China is 34 (including provinces, autonomous regions and municipalities). The country has a vast territory, which leads to a significant difference among its administrative divisions in terms of natural geography, economic development, and resource endowment [39]. Based on data availability, the research objects of this paper were selected as 31 provinces in mainland China from 2000 to 2020, excluding Hong Kong, Macao and Taiwan. In addition, according to relevant studies [40,41], we divided the administrative divisions of China into four regions: Northeastern, Eastern, Central and Western China (

Data Sources
In this paper, we used the input-output data of cultivated land utilization in mainland China during 2000-2020. The data were mainly derived from various statistical yearbooks, including the China Statistical Yearbook, China Environmental Statistical Yearbook, China Energy Statistical Yearbook, China Water Resource Bulletin, and China Rural Statistical Yearbook. Some missing data were obtained using interpolation or trend extrapolation. The above basic data were collated and summarized in Excel 2019. Finally, a panel dataset on CLGUE measurement and its influencing factors was formed.

Variable Selection of Measuring the CLGUE
Essentially, the CLGUE means to obtain as large a desired output and as small an undesirable output as possible with as little resource input as possible, which comprehensively reflects the coordinated relationship between cultivated land utilization and ecological protection [17,23]. Based on existing research [5,15,17,19] and considering data availability, we constructed a measurement indicator system of CLGUE (Table 1). The input variables included three parts, i.e., land, labor, and the production materials of cultivated land utilization, such as the research of Xie et al. (2018) [15] and Yang et al. (2021) [17]. Among them, land and labor were characterized by the sown area of cultivated land and the number of employees in the primary industry, respectively. The production materials were represented by the total power of agricultural machinery, and the usage of agricultural films, pesticides, and chemical fertilizers, respectively. The expected outputs included three aspects: economic output, social output, and environmental output. The total value of agricultural output reflects the economic benefits brought by various crops in the cultivated land utilization process, providing an important basic support for the economy and industrial development. So, it was used as the economic output of the CLGUE. The grain output reflects the supply capacity of cultivated land utilization for the staple food of local residents, which is of great significance for ensuring food security and social stability. Therefore, it can be used as the social output. Crops planted on cultivated land can absorb carbon dioxide through photosynthesis, serving as a carbon sink [34,42]. Thus, the total carbon sinks were selected as the environmental output. The formula was as follows: where Ca represents the total carbon sink of crops grown on cultivated land, Ca n represents the carbon absorption of the nth crop, m is the type of crops planted, including wheat, corn, rice, sorghum, soybean, millet, and potatoes. δ n is the carbon content of the nth crop, Q n is the yield of the nth crop, ε n represents the moisture coefficient of the nth crop, and σ n is the economic coefficient of the nth crop. The economic coefficient, water coefficient and carbon absorption rate are seen as in Table 2 [43,44].  The agricultural non-point source pollution (NPSP) and total carbon emission (TCE) were selected as the unexpected outputs of CLGUE. The calculation method of the NPSP was as follows. Firstly, the total nitrogen (TN), total phosphorous (TP), and chemical oxygen demand (COD) outputs generated by pesticides, fertilizers, and agricultural waste in farmland use were calculated. Then, the standard emissions of NPSP can be obtained by multiplying the output of each type of pollutant and their respective emission assessment coefficients. According to existing research [45,46], the emission coefficients of COD, TN, and TP were set at 20 mg/L, 1.0 mg/L, and 0.2 mg/L, respectively. The TCE were calculated based on an integrated assessment method for agricultural carbon emission coefficients. The formula was as follows: where Ce is the total carbon emissions, E i is the total amount of various carbon emission sources, and θ i is the emission coefficient for each carbon emission source. The main carbon emission sources include fertilizers, plastic mulch film, diesel fuel consumption in agricultural machinery use, tillage, and agricultural irrigation. The carbon emission coefficients are shown in Table 3 [43,47].

Super-SBM with Undesired Output
As for measuring the cultivated land use efficiency, the traditional data envelopment analysis (DEA) model and slack-based measure (SBM) model are currently the most widely used methods. However, the efficiency measured by the DEA model only includes proportionate improvements, which cannot solve the issue of inefficiency caused by the input-output relaxation and may lead to bias in the efficiency evaluation of decisionmaking units [48]. To solve this problem, Tone (2002) [49] proposed the Super-SBM model, which not only includes non-desirable outputs, but also can directly incorporate relaxation Land 2023, 12, 1589 6 of 17 variables into the objective function. Thus, we applied this model to measure the CLGUE values. The Super-SBM model with undesired output was as follows.
where ρ represents the CLGUE under constant returns to scale, and 0 ≤ ρ ≤ 1; K is the amount of unit; N, M, and I represent the number of inputs, expected outputs, and unexpected outputs, respectively; s x n , s y m , and s b i represent slack variables for inputs, expected outputs, and unexpected outputs, respectively; x n0 , y m0 , and b i0 are the output value of inputs, expected outputs, and unexpected outputs for each unit; λ k is the weight vector.

Panel Regression Model
In this paper, the dynamic panel regression model was applied to analyze the influencing factors of CLGUE in China and its four regions. The model was as follows.
where Y it represents the dependent variable, x it is the independent variable, β 0 is the intercept, β k represents the regression coefficients, and ε it is the error term. Existing research has shown that natural conditions, socioeconomic development level, cultivated land use conditions, and agricultural policies can influence cultivated land use efficiency [17,19,33,50]. Based on the existing research and cultivated land condition of China, we selected the variables that characterize influencing factors of the CLGUE (Table 4). They were the crop diversity index, GDP per capita, urbanization level, effective irrigation rate, proportion of natural disaster area, and fiscal support for agriculture, respectively. Therefore, the specific model investigating the influencing factors of CLGUE can be set as follows.     Figure 4 shows the temporal trend of CLGUE from 2000 to 2020 in China. During the study period, the average CLGUE value exhibited a fluctuating upward development trend, indicating that some provinces with lower efficiency tended to converge towards the high levels. The highest efficiency value was 0.957 in 2020, and the lowest one was 0.853 in 2003, with an average value of 0.905. Overall, the CLGUE in China was at a relatively high level, and the gaps among different provinces were gradually narrowing. Due   Figure 4 shows the temporal trend of CLGUE from 2000 to 2020 in China. During the study period, the average CLGUE value exhibited a fluctuating upward development trend, indicating that some provinces with lower efficiency tended to converge towards the high levels. The highest efficiency value was 0.957 in 2020, and the lowest one was 0.853 in 2003, with an average value of 0.905. Overall, the CLGUE in China was at a relatively high level, and the gaps among different provinces were gradually narrowing. Due to regional development imbalances, there were regional disparities in the endowment of factors for agricultural land production. As a result, there were variations in CLGUE among different regions in China.

ER REVIEW
10 of 17    a certain degree of decline, indicating there is need for further improvement in the futur Central China had the lowest CLGUE value, with an average efficiency of 0.657, and e hibited a characteristic of wave-like change. Therefore, there was significant room for im provement in efficiency in the future.  Based on the results of the CLGUE measurement in China, the efficiency level was divided into four categories from low to high: low level (<0.50), low-medium level [0.50-0.80), medium-high level [0.80-1.00), high level (≥1.00). In order to visually represent the spatial pattern of CLGUE in China, we imported the results of provincial-level measurements into the ArcGIS 10.2 software and created a spatiotemporal differentiation map of CLGUE ( Figure 6). From the overall perspective, there were significant spatial differentiation characteristics in CLGUE across China, with some differences in efficiency between the 31 provinces. Most provinces in China experienced a slight increase in CLGUE overall during 2000-2020. Spatially, the provinces of Heilongjiang, Jilin, Shanghai, Guizhou, Tianjin, Guangdong, and Hainan generally had higher CLGUE values, while Gansu, Shanxi, Yunnan, Guangxi, Hubei, and Hunan had lower ones. To this end, the provinces with the lower efficiency should pay more attention to cultivated land conservation in the process of cultivated land utilization. Efforts should be made to minimize the negative impacts, while improving agricultural output and grain production, e.g., reducing the use of pesticides, chemical fertilizer, and other chemical productions, and controlling agricultural non-point source pollution and carbon emissions.  Table 5 shows the influencing factors of the spatiotemporal disparities of CLGUE in China and its four regions. Diversity has a positive impact on CLGUE. The greater the  Table 5 shows the influencing factors of the spatiotemporal disparities of CLGUE in China and its four regions. Diversity has a positive impact on CLGUE. The greater the Diversity, the higher the level of CLGUE. For every 1% increase in Diversity, the CLGUE in China will improve by 0.074%. From a regional perspective, except for Central China, Diversity elicits positive effects on the CLGUE in Northeastern, Eastern, and Western China. Generally, a higher crop diversity index corresponds to a higher land use intensity, resulting in higher expected outputs such as grain production and agricultural value per unit area. This is beneficial for improving the CLGUE to some extent. However, as the Diversity increases, there is also an increasing investment in agricultural machinery, as well as in the usage of fertilizers, pesticides, and plastic films. This may result in excessive carbon emissions and agricultural non-point source pollution, which have some negative impacts on CLGUE, for example the Central China. Note: *, **, and *** denote the significance at the 10%, 5%, and 1% levels, respectively.

The Influencing Factors of the CLGUE
GPC and Urbanization have positive impacts on CLGUE. For every 1% increase in GPC and Urbanization, the CLGUE in China will improve by 0.016% and 0.008%, respectively. From a regional perspective, the two variables both have positive impacts on CLGUE in Northeastern and Western China, while they have certain negative impacts on CLGUE in Eastern and Central China. With rapid industrialization and urbanization in China, a large number of rural populations are continuously leaving the countryside and flocking to urban areas. Consequently, this leads to the abandonment of substantial amounts of cultivated land, resulting in a negative impact on CLGUE to some extent, for example, in Eastern and Central China. Meanwhile, Urbanization can also have an impact on cultivated land utilization by promoting a shift from extensive farming to a more intensive and efficient utilization mode. This, in turn, may have a positive effect on CLGUE by promoting more sustainable and efficient cultivated land-use practices.
EIR has a positive impact on CLGUE. For every 1% increase in EIR, the CLGUE will rise by 0.026% in China. It directly affects the growth condition and yield of crops, improving soil health and protecting ecological environment [51,52]. The higher the EIR, the higher the CLGUE level. From the regional perspective, EIR has a positive impact on the CLGUE in Eastern and Central China, while it tends to have negative impacts in Northeastern and Western China. This may be because there are abundant water resources and better agricultural irrigation infrastructure in Eastern and Central China, which causes EIR to have a positive impact on CLGUE. On the other hand, Northeastern and Western China have relatively poor agricultural infrastructure and face water scarcity, resulting in lower EIR, which limits the CLGUE level to some extent.
Disaster has a negative impact on the CLGUE in China and its four regions. For every 1% increase in Disaster, CLGUE will decrease by 0.046%. Natural disasters, such as floods, droughts, landslides, and mudslides, pose a threat to regional land use and agriculture production, severely limiting the increase in grain production and agricultural output. It also negatively impacts the production and living environment of rural areas, which is detrimental to the sustainable use of regional cultivated land.
FSA has a positive impact on the CLGUE in China and its four regions. The higher the FSA, the higher the level of CLGUE. For every 1% increase in it, the CLGUE will improve by 0.387%. Some FSA policies such as direct subsidies for grain production, agricultural tax reforms, and subsidies for the purchase of agricultural machinery are beneficial for controlling the abandonment of cultivated land and alleviating the financial burden on farmers in terms of agricultural production. They can stimulate farmers' enthusiasm for production, and play a positive role in increasing investment and improving expected outputs. In addition, FSA can help to promote agricultural science and technology development and rural environmental governance, which could have a positive effect on the green and low-carbon production of cultivated land.

Comparison with Previous Studies
In this study, we conducted an  [50] took the Yangtze River Economic Belt of China as a case study, and discussed how urbanization influenced the eco-efficiency of cultivated land utilization. They demonstrated that driving and feedback effects had positive impacts on efficiency, while agglomeration and barrier effects showed negative impacts. Summarily, different indicator evaluation systems and models of the CLGUE will lead to different measurement results. For instance, some studies regard agricultural non-point source pollution as an undesirable output, emphasizing the negative impacts of environmental pollution on the CLGUE [3,15,53]. Some studies regard agricultural carbon emissions as an undesirable output, which focus on the low-carbon utilization and clean production of cultivated land [5,17,19,21]. Our study comprehensively considers the environmental effects as the outputs, i.e., carbon emissions, carbon sinks and agricultural non-point source pollution, which is conducive to more accurately evaluating the CLGUE level.

Policy Implications
Some policy implications were proposed based on this study. Firstly, the government should strengthen the formulation and implementation of relevant policies to promote the green utilization of cultivated land. We should increase the publicity of cultivated land green utilization, improve incentive mechanisms for ecological utilization modes, and encourage the development of circular agriculture and biofertilizers. Only in this way, can a low-carbon-emission and green assessment system of cultivated land be established and provide policy supports for the CLGUE. Secondly, we should explore regional differentiation paths for enhancing the CLGUE in China. There are significant differences in resource endowments and economic development levels among different regions in China. For the economically developed and resource-abundant regions, such as Central and Eastern China, it is crucial to vigorously promote land circulation and large-scale operations, develop modern agriculture, implement clean production, and promote cultivated land green utilization. For the relatively economically underdeveloped regions, such as Northeastern and Western China, it is important to actively formulate pro-agriculture policies related to cultivated land utilization. This includes continuously absorbing more agricultural labor force while promoting the optimization of cultivated land functions. Furthermore, efforts should be made to enhance the capacity of cultivated land to reduce carbon emissions and increase carbon sequestration. Thirdly, the government needs to increase financial investment in agriculture. It is of significance to continuously strengthen financial investment in agriculture, with a particular focus on controlling non-point source pollution from agriculture. This includes strict control over the use of chemical fertilizers, pesticides, and plastic films. Additionally, through financial subsidies, we can encourage farmers to adopt green and low-carbon practices in cultivated land utilization. This will not only can lead to a demonstration effect but also drive the comprehensive improvement in the CLGUE level.

Limitations
This paper also has some limitations. First, we mainly focused on the estimation of CLGUE at the provincial level in China. The research scale was large and there was no detailed analysis on CLGUE at the city level. Second, this study explored China's CLGUE change, but included a lack of global changes on cultivated land and its use efficiency. In future research, we will adopt prefecture-level cities as the research units to estimate the CLGUE and investigate the underlying impact mechanism on agricultural economic growth. Furthermore, our study will investigate global changes on cultivated land utilization and analyze the differences in CLGUE in different countries in the world.

Conclusions
This paper used the Super-SBM model to measure the CLGUE values of 31 provinces in mainland China during 2000-2020 and analyze its influencing factors based on a panel regression model. We found that the average CLGUE in China exhibited a fluctuating upward development trend, with the highest efficiency value of 0.957 in 2020 and the lowest one of 0.853 in 2003. Northeastern China had the highest efficiency value, with an average of 1.129, while Central China had the lowest efficiency value, with an average efficiency of 0.657. Spatially, there are significant differentiations in CLGUE across China. Heilongjiang, Jilin, Shanghai, Guizhou, Tianjin, Guangdong, and Hainan generally had higher CLGUE values, while Gansu, Shanxi, Yunnan, Guangxi, Hubei, and Hunan had lower ones. The regression model indicated that Diversity, GPC, Urbanization, EIR, and FSA positively influenced the CLGUE, while Disaster had negative impacts. In addition to improving the CLGUE and achieving the sustainable utilization of cultivated land resources, some attention should be paid to establishing a low-carbon emission and green assessment system of cultivated land and continuously strengthening financial investment in agriculture, with a particular focus on controlling agricultural carbon emissions and non-point source pollutions.
Our study customized an empirical investigation into the CLGUE in China. Through our research, we offered valuable policy recommendations that can effectively promote sustainable agricultural development. To further improve the depth of CLGUE, future research should adopt prefecture-level cities as the research units to estimate the CLGUE level in China and investigate global changes on cultivated land utilization and analyze the differences of CLGUE of different countries in the world.