The Impact of High-Standard Farmland Construction (HSFC) Policy on Green Agricultural Development (GAD): Evidence from China

Abstract

Studying the policy effectiveness and impact process of the high-standard farmland construction (HSFC) on the green agricultural development (GAD) provides reference for sustainable development of agriculture. Based on the quasi experimental conditions for the sub regional promotion of China’ s HSFC policy and making use of the balanced panel data from 2004 to 2022 in China, this article diagnoses the level and evolution characteristics of GAD in China, empirically tests the effects of the China’s HSFC policy on the GAD level by the continuous difference-in-differences (DID) model, and then further analyzes the mediating roles of horizontal agricultural production division and land management scale efficiency. The research results indicate that (1) the GAD level of China continues to improve form 2004 to 2022; (2) the HSFC policy has been positively influencing the GAD level, and has gone through a significance level test of 0.01; (3) further study reveals that the HSFC policy promotes the GAD level primarily through the agricultural green technology progress (AGTP) and the agricultural green efficiency change (AGEC), with the AGTP being the main contributor; and (4) the HSFC policy positively influences the GAD level by enhancing horizontal agricultural production division and land management scale efficiency. To improve the level of GAD, it is essential to continuously optimize policy for the HSFC, promote the AGTP and the improvement of the AGEC, and effectively improve the horizontal agricultural production division level and land management scale efficiency.

1. Introduction

Green agricultural development (GAD) is a modern agricultural development mode guided by the concept of green low-carbon (GLC) development, aimed at providing green food and ecological products, supported by GLC technology, GLC inputs, GLC production, GLC machining, and GLC logistics, and safeguarded by the GLC culture and GLC institutions [1,2,3,4]. The GAD is a way of agricultural development that balances production, livelihood, and ecology. The GAD is not only important for optimizing the development mode of modern agriculture, but is also an important way to promote comprehensive rural revitalization and achieve high-quality agricultural development [5,6,7]. China’s 14th Five-Year Plan to Promote Agricultural and Rural Modernization clearly states that the GAD is a focal point for rural revitalization, and the No.1 Central Document of 2024 in China further emphasizes that promoting the GAD is a critical task of comprehensive rural revitalization. In light of the China GAD Report 2023, the GAD level in China has steadily increased, with significant improvements in the supply capacity of the GLC agricultural products, agricultural production environment protection and governance, and the level of resource utilization of agricultural waste [8]. The role of GAD in improving the agricultural environment, ensuring food safety, and increasing the farmers’ incomes has become increasingly prominent, gradually establishing itself as an important engine driving the modernization of agriculture and rural areas in China, promoting comprehensive rural revitalization and enhancing the level of livable, business-friendly, and beautiful rural construction [9,10,11]. The implementation of the high-standard farmland construction (HSFC) policy produces a very important opportunity for improving the GAD [12,13,14]. Ecological economic theory emphasizes the unity of ecological and economic benefits to ensure the sustainability of agricultural development. The continuous promotion of the HSFC can not only ensure food security but also maintain the overall stability of the ecosystem [13,14,15]. The HSFC refers to the construction activities of cultivated land resource smoothing, soil amelioration, irrigation and drainage, field roads, farmland protection and ecological environment preservation, and other engineering reconstruction to build high-standard farmland, ameliorate and clear up the main restrictive factors, and comprehensively improve the quality of cultivated land resources, as well as ensure its efficient use [12,15]. Through measures like land consolidation and water-saving irrigation, the policy improves farmland infrastructure, thereby creating concentrated, ecologically friendly, and healthy farmland. The HSFC policy greatly enhances arable land quality, safeguards food security, and improves the agricultural production environment [15,16,17]. The No.1 Central Document of 2024 in China emphasizes strengthening the supervision and management of all lifecycles of the HSFC project to effectively improve the effectiveness of the HSFC project and encourages rural collective economic organizations, new agricultural management entities, farmers, and others to directly participate in the post-management and maintenance of the HSFC project. Therefore, studying the policy effectiveness and impact process of the HSFC on the GAD not only helps theoretically explain the significance of implementing the policy but also provides practical guidance for advancing the GAD through HSFC.

Since 1998, China has explored medium- and low-yield field improvement and HSFC. China first described the content “carry outing the HSFC to effectively enhance the level of food security” in the No.1 Central Document of 2004 and formally implemented the HSFC policy in 2011 [15,18]. Since then, experts and scholars have examined various aspects of HSFC, including comprehensive benefits [19,20,21], performance evaluation [22,23,24,25], farmer participation [26,27,28], problem diagnosis [29,30,31], implementation paths [32,33,34], and policy effects [13,35,36,37,38,39]. The HSFC policy has the following policy effects: effectively increases the relative return on grain cultivation and promotes a grain-oriented planting structure through the expansion of grain sown area [40]; facilitates land leveling, agricultural environmental protection, and agricultural infrastructure improvements for yield enhancement [41,42]; reduces farmland fragmentation and expands plot scale management to reduce fertilizer use [43,44]; improves agricultural management efficiency and reduces agricultural operating risks to encourage farmland transfer [45]; and promotes agricultural machinery services to increase farmer incomes [46] and advances the agricultural green technology progress (AGTP) to effectively diminish the agricultural carbon emissions [47,48].

The research results of experts and scholars provide a solid foundation for analyzing the effects of the HSFC policy on the GAD level of China; yet, there remains scope for further exploration. First, the research results of experts and scholars have mainly focused on the effects of the HSFC policy on the agricultural carbon reduction effect, the optimizing the agricultural production environment, and the reduction in fertilizer use, with few studies directly examining the effects of the HSFC policy on the GAD level. Second, the existing studies have not further decomposed the GAD level, leaving the direct contributions of the HSFC policy to the GAD level unclear. Third, the previous studies have not conducted an in-depth analysis of the influence mechanism of the HSFC policy on the GAD level, with mediating variables yet to be fully explored, and few studies have incorporated horizontal agricultural production division and land management scale efficiency within the same analytical framework.

Therefore, based on the quasi experimental conditions for the sub regional promotion of China’ s HSFC policy, and making use of the balanced panel data in China, this article applies a continuous difference-in-differences (DID) model to assess the influence of the HSFC policy on the GAD level of China. By using the horizontal agricultural production division and land management scale efficiency as mediating variables, this article analyses the internal mechanisms behind these effects and deeply dissects the influence mechanism and transmission mechanism of the HSFC policy on the GAD level, thereby offering a scientific basis for precise policy implementation to achieve the goal of promoting the GAD level.

The structure of the paper is arranged as follows: Section 2 discusses the theoretical analysis used on the effects of the HSFC policy on the GAD level, Section 3 describes materials and methods used, Section 4 discusses the results of the effects of the HSFC policy on the GAD level in China, Section 5 discusses the effects of the HSFC policy on the GAD level in China, and Section 6 offers conclusions and future prospects. According to theoretical analysis, the dependent variable of this article is the GAD level of China, the core independent variable is the HSFC policy of China, and the mediating variables of this article are the horizontal agricultural production division level (HDit) and the land management scale efficiency (LandTransferit).

2. Theoretical Analysis for the Effects of the HSFC Policy on the GAD Level

The HSFC effectively enhances the quality of the cultivated land resources in China through a series of important measures, such as implementing the farmland protection system, enhancing farmland infrastructure construction and ecological restoration, intensifying agricultural land consolidation, refining the arable land quality evaluation system, and establishing regular updates and dynamic monitoring for farmland quality grading. As cultivated land resources are a very crucial resource for agricultural production, the improvement in the quality of the cultivated land resources aligns deeply with the fundamental characteristics of efficient resource utilization in the GAD in China [49]. It serves as a vital source of sustainable growth for the GAD [50] and promotes it through the following pathways.

2.1. The HSFC Policy and the GAD Level

First, the farmland ecosystem is a significant carbon sink, with soil carbon sequestration constituting a key component. Improving the quality of farmland can effectively increase the soil’s carbon storage and accumulation [51], thereby enhancing its carbon sequestration function and promoting the GAD. Second, the General Principles of the HSFC highlight that land consolidation projects can alleviate the current fragmented and scattered farmland issues [52], enabling synchronous expansion of the plot size and management scale [53]. The management scale expansion reduces the per-unit area cost of adopting green production technology, thus encouraging its adoption. Moreover, it facilitates centralized training and guidance on green agricultural technology and aids in promoting and disseminating green agricultural technology, thereby reducing the intensive use of polluting agricultural inputs, lowering undesirable outputs, and improving the GAD level. Lastly, large pieces of continuous flat land improve conditions for mechanized operations, such as fertilizer and pesticide application, thereby reducing dependency on inputs like pesticides and fertilizers in agricultural production and promoting the GAD. Therefore, this leads to the first hypothesis:

H1: 

The HSFC policy contributes to promoting GAD.

2.2. The Agricultural Green Efficiency Change (AGEC), the AGTP, and the GAD Level

The agricultural green efficiency change (AGEC) and the AGTP are the main drivers for promoting the agricultural green total factor productivity (AGTFP). They directly or indirectly affect how the HSFC policy promotes the GAD. The AGEC refers to maximizing output through scientifically allocated input factors while considering environmental factors [53]. This process is constrained by the current production technology frontier, encompassing improvements in resource allocation efficiency and management practices, organizational form optimization, and conceptual updates—collectively regarded as “soft technology” [54]. The AGTP refers to innovation processes that enhance resource efficiency and environmental performance by developing new agricultural varieties, production technologies, or materials compared to existing ones [55]. This advancement constantly moves the production technology frontier forward and includes improvements in machinery, plant protection, cultivation, and biochemical technologies—collectively regarded as “hard technology”.

On the one hand, the HSFC policy can directly promote soft technology improvements in three main ways: (1) by diminishing the input of polluting production factors, thereby promoting the allocation efficiency of intermediate inputs [54]; (2) by accelerating farmland transfer, thereby enhancing land resource allocation efficiency; and (3) by raising the level of agricultural mechanization, allowing machinery to replace labor, thereby optimizing labor resource allocation [55]. On the other hand, the HSFC policy can indirectly promote improvements in hard technology. Although the policy does not directly involve certain green production technologies, it enhances the willingness and extent of green production technology adoption among agricultural producers. Under the promotion of the HSFC policy, the dispersed management structure of smallholders struggles to cope with the expanding scale of land management. As cooperative management organizations emerge, the demand for commercial agricultural services among agricultural producers also grows increasingly stronger [56]. Against this backdrop, commercial agricultural services have developed rapidly, effectively integrating green agricultural technology into agricultural production. This development has broadened the exposure of various agricultural producers to green agricultural technology, significantly increasing both their willingness and extent of adoption. Therefore, this leads to the second hypothesis:

H2: 

The HSFC policy improves the level of GAD by enhancing AGTP and AGEC.

2.3. The HSFC Policy, the Horizontal Agricultural Production Division, and the GAD Level

The HSFC policy promotes the GAD by enhancing the horizontal agricultural production division level. Horizontal agricultural production division refers to the reduction in the number of production projects undertaken by agricultural producers or the expansion of cultivation and livestock breeding scales [57], that is, improved specialization. The HSFC policy significantly improves farmland infrastructure by consolidating land, improving irrigation and drainage, constructing field roads, and upgrading farmland power distribution systems [58]. It also enhances soil productivity through three major projects: soil improvement, removal of obstructive soil layers, and soil fertilization [59], thereby increasing the feasibility of large-scale cultivation of specific crops or livestock breeding in the same area and raising the specialization level, that is, the horizontal agricultural production division level. Therefore, the HSFC policy can accelerate horizontal agricultural production division.

Further deepening horizontal agricultural division can achieve a more advanced level of industry clustering within the cultivation sector. This not only enables the efficient use of capital, talent, and technology within an area but also further decreases the application costs of green agricultural technologies to facilitate the free circulation of green production technology within the area [60], thereby promoting the GAD. Therefore, this leads to the third hypothesis:

H3: 

The HSFC policy promotes GAD by fostering a horizontal agricultural production division.

2.4. The HSFC Policy, the Land Management Scale Efficiency, and the GAD Level

The HSFC policy promotes the GAD by increasing the land management scale efficiency. An important indicator of improved land management scale efficiency is the improvement of the farmland transfer rate. On the one hand, the HSFC can increase farmers’ agricultural income, thereby accelerating farmland transfer [45]. Compared to ordinary farmland, the improved and consolidated soil in high-standard farmland is more fertile, which reduces production costs while increasing yields and income. As a result, the unit rental price for farmland transfer is higher, thus speeding up the farmland transfer process.

On the other hand, the HSFC can reduce production and management risks, thereby encouraging farmers to participate in land transfer. Dispersed farming requires individual risk-bearing in agricultural operations, whereas the HSFC facilitates a contiguous land management structure, thereby spreading risks and attracting more farmers to transfer land. Therefore, implementing the HSFC policy enhances the land management scale efficiency. Improved land management efficiency facilitates economies of scale [61], with agricultural producers expanding investment in new elements and technology and increasing the use of agricultural machinery, thereby promoting the GAD. Therefore, this leads to the fourth hypothesis:

H4: 

The HSFC policy promotes the GAD level by the improvement of land management scale efficiency.

3. Materials and Methods

3.1. The Data Sources of the Regression Model and the Descriptive Statistical Results of Variables

This study makes use of balanced panel data in China for model analysis and dissects the influence mechanism and transmission mechanism of the HSFC policy on the GAD level in China. The relevant data are sourced from China Statistical Yearbook (2005–2023), China Rural Statistical Yearbook (2005–2023), China Financial Yearbook (2005–2023), China Trade and External Economic Statistical Yearbook (2005–2023), China Urban Statistical Yearbook (2005–2023), China Transport Yearbook (2005–2023), Annual Statistical Report on Agricultural Management in Rural China, Annual Statistical Report on Policies and Reforms in Rural China, and the statistical yearbooks of various provinces in China. For a small portion of missing data, this study uses linear interpolation to complete it [62]. In addition, all variables related to prices in the study are adjusted, using 2004 as the base year.

3.2. Model Selection for the Effects of the HSFC Policy on the GAD Level in China

3.2.1. Baseline Regression Model for the Effects of the HSFC Policy on the GAD Level in China

The HSFC policy was implemented nationwide in China in 2011. However, due to differences in natural and agricultural production conditions across provinces, a single dummy variable indicating whether the policy was implemented cannot fully capture its impact. Thus, a continuous DID model is used, which preserves sample heterogeneity and avoids overlooking policy impact variations that the traditional DID approach might miss [63,64,65].

In this study, 2011 is regarded as the policy implementation point, based on the quasi-experimental conditions for the sub-regional promotion of China’s HSFC policy, evaluating the effects of the HSFC policy on the GAD level in China using the continuous DID model. Here, the ratio of the combined area of improved medium- and low-yield farmland and the HSFC to the arable land resources area—referred to as the land consolidation area ratio—is used as a continuous variable to replace the dummy variable in the traditional DID model.

The continuous DID model for the effects of the HSFC policy on the GAD level in China is as follows:

$$Y_{it}=\alpha +{\beta High}_i\times {{\displaystyle I}}_t^{\mathrm{post}}+\delta X_{it}+{\omega }_i+{\gamma }_t+{\epsilon }_{\mathrm{it}}$$

(1)

In Equation (1), Y_it represents the GAD level of i province in t years, respectively; High_i represents the proportion of land consolidation area; ${{\displaystyle I}}_t^{\mathrm{post}}$ represents a policy treatment variable, taking the value of 1 if $\mathrm{t}\ge 2011$; otherwise, it is 0; $\delta$ represents a parameter to be estimated, indicating the net effect for the influences of the HSFC policy on the GAD level in China; ${\omega }_i$ represents the province fixed effect for the influences of the HSFC policy on the GAD level in China; ${\gamma }_t$ represents the year fixed effect for the influences of the HSFC policy on the GAD level in China; ${\epsilon }_{\mathrm{it}}$ represents the random error term of the baseline regression model.

3.2.2. Parallel Trend Test for the Effects of the HSFC Policy on the GAD Level in China

A key precondition for making use of the continuous DID model is to pass the parallel trend test. That is to say that the GAD levels across provinces exhibit similar trends over time before carrying out the HSFC policy [65,66,67].

The parallel trend test for the effects of the HSFC policy on the GAD level in China is as follows:

$$AGTF{P}_{\mathrm{it}}=a+{\displaystyle \sum _{t=2004}^{2017}{\beta }_t(Hig{h}_i\times {\mathrm{year}}_t)+\eta X_{it}+{\omega }_{\mathrm{i}}+{\gamma }_t+{\epsilon }_{it}}$$

(2)

In Equation (2), year_t is represents a year dummy variable for the effects of the HSFC policy on the GAD level in China, ${\beta }_{\mathrm{t}}$ is used to estimate the dynamic changes in the effects of the HSFC policy on the GAD level in China.

3.2.3. Mediation Effect Model for the Effects of the HSFC Policy on the GAD Level in China

A three-step method is applied to examine the roles of horizontal agricultural production division and land management scale efficiency in how the HSFC policy influences the GAD [46,68].

The mediation effect model for the effects of the HSFC policy on the GAD level in China is as follows:

$${\mathrm{AGTFP}}_{it}={\beta }_0+{\beta }_{1}H{\mathrm{igh}}_i\times {{\displaystyle I}}_t^{post}+\gamma X_{it}+{\omega }_i+{\gamma }_i+{\epsilon }_{it}$$

(3)

$$H{\mathrm{d}}_{it}={\alpha }_0+{\alpha }_{1}H{\mathrm{igh}}_i\times {{\displaystyle I}}_t^{post}+{\alpha }_2{X}_{it}+{\omega }_i+{\gamma }_i+{\epsilon }_{it}$$

(4)

$${\mathrm{AGTFP}}_{it}={\beta }_0+{\beta }_{1}H{\mathrm{igh}}_i\times {{\displaystyle I}}_t^{post}+{\beta }_{2}H{D}_{\mathrm{it}}+\gamma X_{it}+{\omega }_i+{\gamma }_i+{\epsilon }_{it}$$

(5)

$$LandTransfe{r}_{it}={\alpha }_0+{\alpha }_{1}H{\mathrm{igh}}_i\times {{\displaystyle I}}_t^{post}+{\alpha }_2{X}_{it}+{\omega }_i+{\gamma }_i+{\epsilon }_{it}$$

(6)

$${\mathrm{AGTFP}}_{it}={\beta }_0+{\beta }_{1}H{\mathrm{igh}}_i\times {{\displaystyle I}}_t^{post}+{\beta }_{2}LandTransfe{r}_{it}+\gamma X_{it}+{\omega }_i+{\gamma }_i+{\epsilon }_{it}$$

(7)

In Equations (3) and (4), HD_it represents the horizontal agricultural production division level; In Equations (6) and (7), LandTransfer_it represents the land management scale efficiency.

3.3. Variable Selection for the Effects of the HSFC Policy on the GAD Level in China

3.3.1. The Dependent Variable of the Regression Model

According to theoretical analysis, the dependent variable of this article is the GAD level of China, which is measured by the agricultural green total factor productivity (AGTFP). The AGTFP is calculated using a super-efficiency SBM model with undesirable outputs [69,70,71,72], considering both input indicators and output indicators. The article describes the specific content of input and output indicators (

Table 1. Indicator for measuring the AGTFP.

Indicator Type	Variable Name	Variable Meaning	Unit	Abbreviation
The input indicators	The land resource input	The area of crop planting	Thousand hectares	I1
	The mechanical input	The total power of agricultural machinery	Million kilowatts	I2
	The irrigation input	The effective irrigation area	Thousand hectares	I3
	The fertilizer input	The reduction in application amount of agricultural chemical fertilizers.	Ten thousand tons	I4
	The pesticide input	The usage of pesticide	Ten thousand tons	I5
	The agricultural plastic film input	The usage of agricultural plastic film	Ton	I6
	The diesel input	The usage of agricultural diesel fuel	Ten thousand tons	I7
	The draught animal input	The number of large livestock at the end of the year	ten Thousand heads	I8
	The plantation labor input	(The added value of planting industry /the added value of agriculture, forestry, animal husbandry and fishery) * Headcount in primary industry	Ten thousand people	I9
The output indicators	The expected output	The actual agricultural output value based on 2004	Trillion yuan	O1
	The undesirable output	The total agricultural carbon emissions	Ten thousand tons	U1

). Undesirable outputs in agriculture, represented by various forms of environmental pollution, are expressed as the total carbon emissions from agriculture, including six sources: agricultural plowing, agricultural irrigation, fertilizers, pesticides, agricultural plastic film, and agricultural diesel [73,74]. This article makes use of the IPCC carbon emission coefficient method to compute the total carbon emissions in the field of agriculture [75,76], and the specific calculation formula is as follows:

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

(8)

In Equation (8), E represents the total carbon emissions in the field of agriculture, E_i represents the emissions from the six carbon sources, T_i represents the quantity, and $\delta$_i represents the coefficient for the carbon emissions from agriculture, as specified in

Table 2. Carbon emission coefficients and sources for the six types of agricultural carbon sources.

Carbon Source	Carbon Emission Coefficient	Reference Source
The agricultural plowing	312.6 kg/hm2	College of Biology and Technology, China Agricultural University [75]
The agricultural irrigation	266.48 kg/hm2	Cao Xiaojuan and Jin Ting (2024) [77]
The fertilizers	0.8956 kg/kg−1	Oak Ridge National Laboratory [78]
The pesticides	4.9341 kg/kg−1	Oak Ridge National Laboratory [78]
The agricultural plastic film	5.1800 kg/kg−1	Institute of Resources and Ecological Environment, Nanjing Agricultural University [79]
The agricultural diesel	0.5927 kg/kg−1	IPCC [80]

.

The Malmquist Productivity Index (ML Index) is introduced in this article to measure the AGTFP. This article decomposes further the ML Index into the pure technical change (PTC) and the pure efficiency change (PEC), that is, $ML=EC\times TC$. Setting the AGTFP in the base year 2004 as 1, a multiplicative method is employed to obtain the AGTFP for each province from 2005 to 2022. The AGTP and AGEC are processed similarly [72].

3.3.2. The Core Independent Variable of the Regression Model

According to theoretical analysis, the core independent variable is the HSFC policy of China, represented by the interaction between the land consolidation area ratio and the HSFC policy implementation point [81,82]. The land consolidation area ratio is the ratio of the combined area of improved medium- and low-yield farmland and HSFC to the arable land resources area, that is, ${High}_i\times I_t^{post}$.

In the equations, High_i represents the proportion of the land consolidation area; ${{\displaystyle I}}_t^{\mathrm{post}}$ represents a policy treatment variable, taking the value of 1 if $\mathrm{t}\ge 2011$; otherwise, it is 0.

3.3.3. The Mediating Variables of the Regression Model

According to theoretical analysis, the mediating variables of this article are the horizontal agricultural production division level (HD_it) and the land management scale efficiency (LandTransfer_it). The horizontal agricultural production division level is measured using the Herfindahl–Hirschman Index (HHI) [83].

The calculation formula for the intermediary variable of the HD_it is as follows:

$$H{\mathrm{d}}_{it}={\displaystyle \sum \begin{array}{l}N\\ n=1\end{array}}(a_{i\mathrm{t}n})2$$

(9)

In Equation (9), HD_it represents the horizontal agricultural production division level, based on a value between 0 and 1. The research results indicate that the larger the value of the HD_it, the higher the level of horizontal agricultural production division. N represents the number of crop species, in this article, N = 7, including the grains, oilseeds, sugar, tobacco, vegetables, fruits, and cotton [60]. a_itn represents the ratio of the sowing area of the n-th crop in the i-th province in the t-th year to the total sowing area of the crops.

The land management scale efficiency is represented by the land transfer rate of each province, that is, the ratio of the transferred family-contracted farmland area to the total family-contracted farmland area in that province (autonomous region, municipality directly under the central government) [61,84].

3.3.4. The Control Variables of the Regression Model

This article, drawing on related research results [85,86,87,88,89], selects the eight variables as control variables of the regression model. The variables are specifically divided into the Internet development level, the road transport level, the disaster severity, the urbanization level, the urban-rural income gap, the financial support for agriculture level, the degree of openness to the outside world, and the industrialization level (

Table 3. The descriptive statistics results of variables.

Variable Type	Variable Name	Variable Code	Observations	Mean Value	Standard Deviation
The dependent variables	The agricultural green total factor productivity	AGTFP	434	1.368	0.879
	The agricultural green efficiency change	AGEC	434	0.945	0.226
	The agricultural green technology progress	AGTP	434	1.463	0.819
The core independent variables	The land consolidation area ratio	High	434	0.018	0.016
	The land consolidation area ratio * Policy implementation point dummy variable	$Hig{h}_i\times {{\displaystyle I}}_t^{\mathrm{post}}$	434	0.0918	0.157
The mediating variables	The horizontal agricultural production division level	HDit	434	0.503	0.145
	The land management scale efficiency	LandTransferit	434	2.1615	1.687
The control variables	The rural-urban income gap	income	434	3.029	0.658
	The industrialization level	industry	434	0.429	0.085
	The urbanization level	urban	434	5.297	6.978
	The disaster severity	disaster	434	0.253	0.167
	The trade level	trade	434	0.315	0.394
	The agricultural support expenditure	support	434	0.036	0.008
	The road transport level	road	434	0.777	0.719
	The internet development level	internet	434	1.435	1.380

). The Internet development level (Internet) is reflected by the number of Internet users. An increase in Internet usage overcomes the limitations of spreading green agricultural technology solely “on foot”, thereby reducing the labor and time costs of technology dissemination [90], improving agricultural production technology, and promoting the GAD. The road transport level (Road) is reflected by the ratio of highway freight volume to highway mileage. The disaster severity (Disaster) is reflected by the ratio of the disaster-affected area of a region to the total area of regional arable land resources, with a higher disaster severity corresponding to a lower level of the GAD. The urbanization level (Urban) is reflected by the ratio of the urban population of a region to the total permanent population of the region. The urban–rural income gap (Income) is reflected by the ratio of the per-capital disposable income of urban residents to the per-capital disposable income of rural residents. A greater income gap has a more negative psychological impact on farmers, influencing their production decisions [90,91]. The financial support for agriculture level (Support) is reflected by the ratio of the financial expenditure on agriculture in a region to the regional total fiscal expenditure. As a vulnerable sector, agriculture depends on government fiscal support for industrial upgrading, which is essential for the GAD. When the total fiscal expenditure is fixed, a higher proportion of agricultural support expenditure is more conducive to the GAD. The degree of openness to the outside world (Trade) is reflected by the ratio of the total import and export volume in a region to the regional Gross Domestic Product. As the trade level increases, farmers’ access to diverse domestic and international resources for developing green agriculture improves significantly, which is conducive to raising the GAD level in China [92]. The industrialization level (Industry) is reflected by the ratio of value added of the secondary industry in a region to the regional Gross Domestic Product [93].

4. Results for the Effects of the HSFC Policy on the GAD Level in China

4.1. Analysis of the GAD Level in China

The super-efficiency SBM model with a non-expected output and the Malmquist index were used to estimate the GAD level of China from 2004 to 2022. On the whole, China’s AGTFP shows an upward trend, and the GAD level of China continues to improve. In terms of stages, the first stage is a slow growth stage from 2004 to 2011, and the average velocity of increase for the GAD level in China is relatively low. The second stage is the rapid growth stage from 2012 to 2015, and the growth rate of the GAD level increased. The third stage is the rapid growth stage from 2016 to 2022, and the growth rate of the GAD level increased significantly. The policy of the HSFC in China had just begun to be implemented in 2011. Although the goals and requirements of the HSFC are clearly defined, the effect of the HSFC policy is not immediately realized. The growth rate of the GAD level in the third stage should be affected by the lagging effect of the HSFC policy.

On this basis, this study further decomposes the level of the GAD. The analysis results indicate that both the AGTP and the AGEC show an upward trend during the study period. The AGEC growth is less obvious, and the AGTP shows the same growth trend as the GAD level. From 2004 to 2005, the change trend of the AGTP and the AGEC is basically the same. After 2005, the growth rate of the AGTP far exceeded that of the AGEC and became the main contributor to the growth of the GAD level, while the AGEC first experienced a low-speed reduction period from 2005 to 2014 before ushering in a tortuous growth period after 2014.

4.2. The Regression Results for the Effects of the HSFC Policy on the GAD Level in China

This article describes the regression results for the effects of the HSFC policy on the GAD level in China (

Table 4. Benchmark regression results for the impact of the HSFC policy on the GAD.

	(1)	(2)	(3)	(4)	(5)	(6)
Variable	AGTFP	AGEC	AGTP	AGTFP	AGEC	AGTP
$Hig{h}_i\times {{\displaystyle I}}_t^{\mathrm{post}}$	0.885 **	0.0943 **	0.716 **	1.061 ***	0.230 ***	0.872 ***
	(0.354)	(0.0466)	(0.288)	(0.362)	(0.0505)	(0.303)
internet				6.72 * 10−5	0.000102 ***	4.63 * 10−5
				(9.99 * 10−5)	(2.05*10−5)	(0.000104)
road				0.0600	0.00345	0.0344
				(0.0600)	(0.0114)	(0.0507)
disaster				−0.408 **	−0.147 ***	−0.290
				(0.184)	(0.0464)	(0.184)
urban				−0.0864 *	−0.0298	−0.0230
				(0.0516)	(0.0196)	(0.0551)
income				−0.134	0.138	0.220
				(0.788)	(0.160)	(0.814)
support				820.6 ***	139.7	563.1 **
				(234.3)	(90.32)	(239.6)
trade				0.808	0.387 **	0.790
				(0.906)	(0.174)	(0.900)
industry				−109.7 ***	−18.93	−72.24 **
				(34.34)	(13.25)	(35.27)
Constant	0.693 ***	1.159 ***	0.537 ***	12.21 **	2.598 *	6.101
	(0.182)	(0.0387)	(0.158)	(5.778)	(1.506)	(5.920)
Year fixed effect	Controlled
Province fixed effect	Controlled
Observations	434
R-squared	0.603	0.571	0.554	0.613	0.641	0.558

Note: ***, **, * are on behalf of the regression coefficients pass the significance test respectively at the levels of 0.01, 0.05, and 0.1; The numbers in parentheses are on behalf of standard errors; The following table is the same.

). The model analysis results display that the HSFC policy has had a positive effect on the GAD level of China when including control variables. The regression coefficient for the effects of the HSFC policy on the GAD level in China reaches 1.061, and the regression coefficients of the model get past the significance level test of 0.01. From this, we know that the effect of the HSFC policy can actively promote the improvement of the AGTFP and effectively improve the GAD level of China. Therefore, the hypothesis H1 of this article is corroborated. The results in Columns 5 and 6 display that the effect of the HSFC policy has had a positive influence on the AGEC and the AGTP, and the regression coefficients pass the significance level test of 0.01. Therefore, the hypothesis H2 of this article is corroborated. Further analysis shows that the regression coefficient of the AGTP (${{\displaystyle \beta }}_{AGTC}=0.872$) is much larger than that of the AGEC (${{\displaystyle \beta }}_{AGEC}=0.230$). Hence, it can be inferred that the HSFC policy promotes the GAD level in China, mainly through the AGTP.

4.3. Parallel Trend Test for the HSFC Policy in China and the Dynamic Influence of the HSFC Policy

In the parallel trend test for the HSFC policy in China, the first year (2004) of the study period was used as the benchmark year to verify the consistency of the variation trend of the GAD level among different provincial regions before the HSFC policy implementation. This article illustrates the changing trend of estimation coefficients for the HSFC policy at a 95% confidence level in Figure 1.

The negative numbers on the x-axis in Figure 1 represent the year before the HSFC policy was enacted in China, 0 represents the starting year of the HSFC policy in China, and the positive numbers on the x-axis represent the year after the HSFC policy was enacted in China.

From this figure, it can be seen that before the HSFC policy was put into effect in China, the confidence intervals for the estimating coefficients all contained 0. Therefore, we can accept the hypothesis that the estimated coefficient values were not significantly different before the HSFC policy was enacted in China. Since the fourth year (2015) after the HSFC policy in China, none of the confidence intervals of the estimated coefficient values contained 0. Therefore, we can reject the hypothesis that the estimated coefficient values are not significantly different after the HSFC policy was put into effect in China. Thus it can be seen that the hypothesis of parallel trends is confirmed.

This article describes the estimated results for the dynamic effects of the HSFC policy on the GAD level in China in

Table 5. Estimated results for dynamic effects of the HSFC policy on the GAD level.

Interaction Term	Estimated Coefficient	Standard Error	p-Value
High_2005	−3.234	24.811	0.897
High_2006	12.441	25.965	0.635
High_2007	20.184	24.193	0.411
High_2008	23.809	21.431	0.275
High_2009	8.164	16.388	0.622
High_2010	13.450	24.741	0.591
High_2011	11.040	19.149	0.569
High_2012	8.434	13.982	0.551
High_2013	7.688	11.952	0.525
High_2014	6.173	6.064	0.317
High_2015	25.279	11.463	0.035
High_2016	24.763	7.837	0.004
High_2017	23.011	5.231	0.000
Constant	1.130	0.229	0.000
Control variable	Controlled
Year fixed effect	Controlled
Province fixed effect	Controlled
Observations	434
R-squared	0.7308

. From this, we know that the estimated coefficient values of the HSFC policy began to pass the significance test in the fourth year (2015) after the HSFC policy was enacted in China. The results of the dynamic effect analysis indicate that the HSFC policy causes a lagging effect on the positive promotion of the GAD level in China.

4.4. The Robustness Test for the Effects of the HSFC Policy on the GAD Level in China

4.4.1. Placebo Test for the Effects of the HSFC Policy on the GAD Level in China

The placebo test for the effects of the HSFC policy on the GAD level in China is usually conducted using two methods: a fictitious treatment group and changing the time of the put into effect of the HSFC policy in China. In the first method, if the interaction coefficient between the HSFC policy and the dummy variable of the regression model is still significant, it is because the regression results of the effects of the HSFC policy on the GAD level in China are likely to be biased. In this placebo test for the effects of the HSFC policy on the GAD level in China, the distribution of the coefficient values and p-values of the interaction term was determined by repeating the regression analysis 1000 times, which is shown in Figure 2. In this figure, the vertical axis represents the p-values and density values of the regression model, the horizontal axis represents the variable regression coefficient, the curves represent the kernel density distribution for the variable regression coefficient, and the round dots represent the p-values for the variable regression coefficient. The regression model analysis results show that the estimated coefficients of random sampling are mainly distributed around 0 (the black vertical dashed line in Figure 2), which is much lower than the benchmark regression coefficient (the benchmark regression model regression coefficient is 1.061). From the distribution pattern of the round dots, it can be seen that the p-values corresponding to the regression coefficients mostly exceed 0.1, which indicates that most of the regression coefficients do not pass the significance test. From this, we know that excluding the possibility that the benchmark regression results are caused by factors other than the HSFC policy (i.e., the regional differences in the GAD level are due to the effect of the HSFC policy in China).

In order to further conduct a robustness test for the effects of the HSFC policy on the GAD level in China, we eliminated the sample data after the HSFC policy was put into effect and conducted a placebo test using 2009 and 2010 as the time points of when the HSFC policy was enacted, respectively. The paper describes the model test results (

Table 6. Robustness test for the impact of the HSFC policy on the GAD.

Variable	The Timing for Changing Policies		Other Policies
	The Year 2009	The Year 2010	Zero Growth Action Policy for Fertilizers and Pesticides
$Hig{h}_i\times {{\displaystyle I}}_t^{\mathrm{post}}$	4.129	5.343202	0.609 *
	(3.312)	(5.933387)	(0.369)
Constant	8.980 **	9.436 **	15.89 ***
	(4.480)	(3.903)	(4.451)
Control variable	Controlled Controlled Controlled
Year fixed effect
Province fixed effect
Observations	217		372
R-squared	0.643	0.645	0.639

Note: ***, **, * are on behalf of the regression coefficients pass the significance test respectively at the levels of 0.01, 0.05, and 0.1.

). From this, we know that the regression coefficient values of $Hig{h}_i\times {{\displaystyle I}}_t^{\mathrm{post}}$ are positive but do not pass the significance test, with the rest representing no policy effects of the HSFC policy existing before it was enacted in China.

4.4.2. Eliminating the Effects of Other Policies

In 2015 (within the put into effect of the HSFC policy period), the Ministry of Agriculture and Rural Affairs of China put forward two major action plans—the “Action Plan for Zero Growth of Fertilizer Use by 2020” and the “Action Plan for Zero Growth of Pesticide Use by 2020”—to effectively promote the reduction in usage and raise the efficiency of chemical fertilizers, and greatly promote the control of pesticides. Both two major action plans propose the concept of “yield-increasing, cost-effective, and environmentally-friendly fertilization” and advocate a road of high-yield, high-efficiency, high-quality, environmentally friendly, and sustainable agricultural development. This will inevitably have a positive impact on the GAD level, thereby interfering with our policy effect analysis of the HSFC policies. To eliminate the interference between the two action plans, sample data after 2015 were eliminated and a new regression analysis for the effects of the HSFC policy on the GAD level in China was carried out. The regression analysis results for the effects of the HSFC policy on the GAD level in China are shown in Table 6. According to Table 6, it can be observed that the HSFC policy can still effectively promote the GAD level when the interference of the two action plans is excluded. The regression coefficient of the model reaches 0.609, which passes the significance level test of 0.1 and shows a decrease compared to the original regression coefficient of 1.061 (in Table 4 and Table 6). This suggests that the policies for the zero-growth action of fertilizers and pesticides can also promote the GAD level.

4.5. Further Mechanism Analysis for the Effects of the HSFC Policy on the GAD Level in China

The regression results for the effects of the HSFC policy on the GAD level in China reveal that the HSFC policy effectively promotes the GAD level in China. The theoretical analysis results indicate that the HSFC policy can effectively enhance the GAD level in China by deepening the horizontal agricultural production division level and improving the land management scale efficiency. Making use of a three-step method [46,48], this study further analyzes the internal mechanism underlying how the HSFC policy promotes the GAD level in China as follows: (1) by analyzing whether the effects of the explanatory variable on the dependent variable exceed through the significance test; (2) by analyzing whether the effects of the explanatory variable on the mediating variables exceed the significance test; and (3) by analyzing whether the effects of the mediating variables on the dependent variable exceed the significance test under the premise of controlling the explanatory variable. The mediating variables produce a partial mediating effect if the results of the three steps all get through the significance test, and the direction of the regression coefficient is in line with expectations. By contrast, the mediating variables produce a complete mediating effect if two conditions are met: (1) the results of the first and second steps pass the significance test; however, the regression coefficient of the explanatory variable in the third step results does not pass the significance test, and (2) the regression coefficient of the mediating variable passes the significance test.

According to

Table 7. Regression results of impact mechanism analysis.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
	HDit	LandTransferit	AGTFP
$Hig{h}_i\times {{\displaystyle I}}_t^{\mathrm{post}}$	0.0584 **	5.266 *	1.061 ***	0.920 ***	1.003 ***	0.897 ***
	(0.0274)	(2.821)	(0.362)	(0.306)	(0.342)	(0.299)
HDit				2.422 ***		2.195 ***
				(0.838)		(0.811)
LandTransferit					0.0110 *	0.00679
					(0.00624)	(0.00591)
Constant	2.843 ***	271.5 ***	12.21 **	5.326	9.215	4.128
	(0.384)	(41.96)	(5.778)	(5.907)	(5.842)	(6.069)
Control variable	Controlled
Year fixed effect	Controlled
Province fixed effect	Controlled
Observations	434
R-squared	0.943	0.928	0.613	0.622	0.616	0.623

Note: ***, **, * are on behalf of the regression coefficients pass the significance test respectively at the levels of 0.01, 0.05, and 0.1.

, it can be observed that in the absence of mediating variables (i.e., the horizontal agricultural production division and the land management scale efficiency), the HSFC policy produces a positive effect on the GAD level in China and passes the significance level test of 0.01. The research results indicate that the HSFC policy has a positive effect on the horizontal agricultural production division and passes the significance level test of 0.05. The research results of this article indicate that the coefficients of the HSFC policy and the horizontal agricultural production division are both positive and have got through the significance test, and the regression coefficient of the policy treatment variable decreases from 1.061 in Table 4 to 0.920, indicating that the horizontal agricultural production division level produces a partial mediating role in the effects of the HSFC policy on the GAD level in China. Therefore, Hypothesis 3 is corroborated.

With the land management scale efficiency as a mediating variable, the results of the three-step method also reveal that the scale efficiency of land operation also produces a partial mediating effect (the regression coefficient of the HSFC policy decreases from 1.061 in Table 4 to 1.003). Therefore, Hypothesis 4 is corroborated. Column 6 in Table 7 describes the estimated results when both the horizontal agricultural production division and the land management scale efficiency are introduced into the regression model as the mediating variables. The research results of this article indicate that the coefficient values of the horizontal agricultural production division and the land management scale efficiency are both positive, further indicating that the HSFC policy effectively raises the GAD level in China by deepening the horizontal agricultural production division and improving the land management scale efficiency.

5. Discussion of the Effects of the HSFC Policy on the GAD Level in China

In recent years, the GAD has become not only an important matter in the field of agriculture but also a research hotspot in academia. Strengthening the studies of the GAD has very important value for alleviating the shortage of agricultural resources, the decline in the quality of the agricultural production environment, the degradation of agricultural ecosystems, and the short supply of green and ecological agricultural products in China. Ecological economic theory emphasizes the unity of the ecological and economic benefits to ensure the sustainability of agricultural development. The continuous promotion of the HSFC can not only ensure food security but also maintain the overall stability of the ecosystem [13,14,15,94]. The effect of the HSFC policy has produced comprehensive benefits and offers an effective means to promote the GAD level in China and agricultural sustainability. The HSFC policy has the following policy effects: effectively increases the relative return on grain cultivation and promotes a grain-oriented planting structure through the expansion of grain sown area [40]; reduces farmland fragmentation and expands plot scale management to reduce fertilizer use [43,44]; enhances the efficiency of grain production by promoting large-scale land management and improving the level of socialized agricultural services [95], significantly improving the sustainable production capacity of food through land empowerment, facility empowerment, and financial empowerment [96]; and advances the AGTP to effectively diminish the agricultural carbon emissions [47,48].

This study concluded that the HSFC policy positively affects the AGTFP, which is consistent with the research findings of scholars such as Zhang [70] and Li [81], indicating that the HSFC policy can improve the level of the GAD. This may be because multiple mandatory provisions and requirements in the HSFC policy have promoted a dramatic improvement in farmland quality, certain enhancements in the carbon sink and capacity in the soil, and an effective solution to farmland fragmentation. This promotes the GAD level by facilitating the contiguous use of green agricultural production technology and effectively reducing the frequency of using polluting production factors.

Further studies have shown that the HSFC policy promotes the GAD level in China mainly through the AGTP. Scientific and technological progress has always been an important source of economic and social development and the AGTP plays an indispensable role in raising the GAD level in China. Although the technology of the GAD is not directly mentioned in the HSFC policy, it supplies a good environment and suitable soil for the promotion and application of GAD technology, thus promoting the GAD level. Regarding the first aspect, the HSFC policy has dramatically raised resource allocation efficiency (e.g., intermediate inputs, land, and labor) through farmland infrastructure construction and field remediation, thus creating a prerequisite for the promotion and application of GAD technology. The second aspect, the HSFC policy, has made land transfer and land operation scale expansion a common occurrence in agricultural production and has promoted the rapid development of agricultural social services, boosting the promotion and application of GAD technology in agricultural production. From this, we know that the new type of agricultural management entity (such as the family farmers, the large-scale planters, the agricultural enterprises, and the farmer professional cooperatives) are more motivated and willing to adopt the technology of the GAD, thus promoting the GAD level.

Owing to the lagged effect, the HSFC policy’s positive effect on the GAD level did not emerge until the fourth year after the HSFC policy was put into effect. At the early stage of the HSFC policy, it was not yet perfect and faced various problems (e.g., insufficiency of funds before construction, lax supervision during construction, and inadequate management and maintenance after the HSFC), which led to non-uniformity of construction standards, disparity in construction quality, and severe aging or damage. Hence, the HSFC policy failed to achieve the expected goal. As the HSFC policy continues to deepen, its effectiveness in promoting the GAD level emerges along with gradual improvement in supporting institutions and measures and advancement in technological and engineering means.

According to the theory of agricultural economics, the higher the horizontal agricultural production division level, the higher the degree of circulation of capital, talents, and technologies within a region. The HSFC policy enables an increased number of production and operation items per unit area and expansion of farming scale. Increased specialization in agricultural production serves to promote the promotion and application of the technology of the GAD in the region, thus improving its GAD level. Therefore, the horizontal specialization in agriculture is a mediating variable to be reckoned with.

The land management scale efficiency also plays an important role when the HSFC policy affects the GAD level. The new type of agricultural management entity (such as the family farmers, the large-scale planters, the agricultural enterprises, and the farmer professional cooperatives) tends to make rational agricultural production decisions based on economic benefits and risks. Compared with the ordinary cultivated land resources, the cultivated land resources after the HSFC are characterized by higher economic outputs but lower production and operation risk. Therefore, the HSFC policy motivates agricultural production and operation entities to transfer their cultivated land resources in hand, thus accelerating the transfer of cultivated land resources and effectively enhancing land management scale efficiency. This can accelerate the formation of scale economies and stimulate a new type of agricultural management entity (such as the family farmers, the large-scale planters, the agricultural enterprises, and the farmer professional cooperatives) to make use of the new production factors and technologies (e.g., the green agricultural machinery and the technology of the GAD), thus promoting the GAD level.

Compared with existing studies, this study made two novel contributions. First, this study introduces the AGTFP to characterize the GAD level, deconstructing it into the AGTP and the AGEC, and distinguishes their degree of contribution to the GAD level, thoroughly analyzing the influence mechanism of the HSFC policy on the GAD level. The purpose was to ensure that the HSFC policy was formulated and implemented in a targeted manner. Second, based on the theories of specialized division of labor and scale economy, this article explored the internal mechanism of how the HSFC policy affects the GAD level from two perspectives (i.e., the horizontal agricultural production division and land management scale efficiency), thus ascertaining the mechanism underlying the promotion of the GAD level through the HSFC policy and enriching the related studies.

6. Conclusions and Prospects for the Effects of the HSFC Policy on the GAD Level in China

6.1. Research Conclusions for the Effects of the HSFC Policy on the GAD Level in China

This article introduces the AGTFP to characterize the GAD level, and the research results indicate that China’s AGTFP is showing an upward trend and the GAD level of China continues to improve, while both the AGTP and the AGEC showed a growth trend from 2004 to 2022.

The HSFC policy has a promoting effect on raising the GAD level in China, but the most positive effect is the hysteresis quality. The HSFC policy positively influences the GAD level by enhancing horizontal agricultural production division and land management scale efficiency.

Overall, this study yielded some valuable discoveries. The HSFC policy, the AGEC, the AGTP, the horizontal agricultural production division, and the land management scale efficiency can significantly improve the GAD level. We can continuously optimize policy for the HSFC, promote the AGTP and the improvement of the AGEC, improve the horizontal agricultural production division level and land management scale efficiency, and effectively promote the GAD. The research conclusions can be used in other countries around the world.

6.2. Policy Suggestions

6.2.1. Continuously Optimizing Policy for the HSFC

It is necessary to continuously optimize the HSFC policy, and further leverage the positive promotion effect of the HSFC policy on the GAD. Regarding capital input, social capital should be introduced while fiscal input is dominant; the cooperation between governmental capital and social capital should be optimized; and a multi-capital input pattern (i.e., expanding the sources of funds) should be developed. Construction planning must regularly formulate and update national HSFC plans, serving as program documents to guide the countrywide HSFC work. Provincial regions should also formulate differentiated HSFC plans suited to their actual farmland conditions. Regarding quality evaluation, provincial regions should establish specific quality evaluation systems based on topographical conditions and related national standards, to provide acceptability criteria for the HSFC. Regarding supervision, it is necessary to establish perfect supervision mechanisms, and form third-party supervision teams at the provincial level and city or county level, to supervise the appropriateness of fund use, quality of farmland construction, and post-construction farmland management and operation. Governments should also actively seek farmers’ comments and feedback during the HSFC, so that farmers are willing to support the implementation of farmland construction and satisfied with farmland construction results. To ensure fairness, provincial regions with low HSFC levels or GAD levels should receive priority in terms of favorable policies, funds, technologies, and human resources to gradually reduce the regional gap and achieve the goal of balanced development.

6.2.2. Promoting the AGTP and the Improvement of the AGEC

The GAD may be promoted through two channels, the AGTP and the AGEC; hence, the two aspects should be given priority. Promoting the AGTP requires increasing input for the technology of the CAD and accelerating the promotion and application of the technology of the CAD. The input for the technology of the CAD includes a fund input (e.g., governments at different levels should set up special funds for the technology of the CAD, and use them exclusively to encourage and support it) and a human resource input (e.g., the government strengthens the cultivation of innovative talents for the CAD, increases incentives for such talents, and enhances their research and development enthusiasm). To accelerate the promotion and application of the technology of the CAD, it is advisable to form green agricultural machinery or technology extension teams, which may adopt the demonstration mode (i.e., large farmers, family farms, and leading agricultural enterprises take the lead in applying the technology of the CAD, thus producing a demonstration effect on small farmers) or the ‘one-to-one’ mode (i.e., experts demonstrate the use of the technology of the CAD in-person to farmers and provide regular query resolutions). It is important to seek farmers’ feedback on using CAD technology, thus facilitating the iterative upgrading of CAD technology and enabling it to better serve farmers.

Improving the AGEC requires taking the following measures: (1) innovation in the agricultural operation mechanism, fostering various new types of agricultural operation entities, and encouraging land transfer, thus facilitating promotion and application for the technology of the CAD; (2) encouraging the establishment of various agricultural social service organizations, and improving agricultural social services and the coordination of different production links; and (3) establishing unified agricultural trading platforms (e.g., agricultural material markets, and agricultural output and selling price websites) to effectively increase the efficiency of agricultural production information circulation.

6.2.3. Improving the Horizontal Agricultural Production Division Level and Land Management Scale Efficiency

The improvement in horizontal agricultural production division and land management scale efficiency enables the HSFC policy to significantly promote the GAD level.

The horizontal agricultural production division may be improved in three ways (i.e., optimizing the allocation of agricultural production resources, developing characteristic agriculture based on comparative advantages, and establishing perfect risk response mechanisms). Regarding the first aspect, namely, to raise the land resources allocation efficiency, the transfer of cultivated land resources may be encouraged to expand the scale of land operation, thus enabling the continuous operation of a particular crop in the same geographical area and improving the specialization in agricultural production. For example, the existence of rural surplus labor greatly inhibits the specialization in agriculture. Governments should, therefore, encourage the transfer of rural surplus labor by creating more non-agricultural employment opportunities. The second aspect is to develop characteristic agriculture; governments should encourage different regions to exert comparative advantages based on their economic and geographical locations, explore characteristic agriculture development models suited to local conditions, deepen the horizontal agricultural production division, promote the development of regional agricultural industry agglomeration, and, thus, form the regional aggregation effect of agriculture. Regarding the third aspect, with the advance in agricultural regional specialization level, the continuous expansion of agricultural operating areas, and the gradual simplification of agricultural product types, agricultural production and operation entities have faced increasing market risks; therefore, it is imperative to establish perfect risk prevention systems such as the agricultural price protection system and agricultural insurance system.

Land operation scale efficiency can be improved by accelerating the transfer of arable land in different ways. The first aspect, governments should encourage farmers’ non-agricultural employment by taking appropriate measures and promoting the transfer of arable land resources. The government should reasonably enhance the non-agricultural employment level of farmers by providing vocational training to enhance their comprehensive abilities, creating an open, flexible, and fair employment market, improving relevant laws and regulations on the employment of migrant workers, and establishing a sound social security system for migrant workers, in order to accelerate the process of the transfer of the arable land resources. For the second aspect, governments should establish open, transparent, and well-informed Internet-based markets for the transfer of arable land resources, release the related information, and supervise the entire process of signing and implementing land transfer agreements. This would provide a solid third-party guarantee for the transfer of the arable land resources. For the third aspect, the government should innovate the method for the transfer of the arable land resources. Governments should encourage new types of agricultural management entities (such as family farmers, large-scale planters, agricultural enterprises, and farmer professional cooperatives) to take part in the transfer of arable land resources in different ways (e.g., trusteeship, shareholding, and leasing) and to maximally stimulate the vitality of the arable land resources transfer markets. For the fourth aspect, it is necessary to further focus on the improvement and popularization of laws and regulations on the transfer of arable land resources. The government should further enhance legal construction in this field of the arable land resources transfer, reduce disputes over the transfer of arable land resources, and provide normative full-process legal consultancy services (e.g., services for legally binding cultivated land transfer contracts) for both contracting parties, as well as hold legal publicity activities regularly in the countryside.

6.3. Research Shortcomings and Prospects

Our study provides new evidence on how the HSFC policy promotes the GAD level. However, the paper still has some shortcomings. Based on the quasi-experimental conditions for the sub-regional promotion of China’s HSFC policy and the use of the balanced panel data in China, this article makes use of the continuous DID model to assess the effects of the HSFC policy on the GAD level in China. However, it cannot depict the differences between the county level and the micro-farmer level, and it cannot provide much reference for policy formulation and decision-making behavior guidance at the farmer level. Therefore, in the future, on the one hand, more attention should be paid to the role of the HSFC policies at the micro level, and, on the other hand, relevant data at the county level should be collected to analyze the effects of the HSFC policies.