Can Land Transfer Promote Agricultural Green Transformation? The Empirical Evidence from China

Abstract

As an important means of farmland policy, whether land transfer can promote agricultural green transformation is worthy of further study; however, related research is relatively rare. Based on the inter-provincial panel data from 2005 to 2020, this paper examines the influence of land transfer on agricultural green transformation and its underlying mechanism by using a two-way fixed effect model and an intermediary effect model. This study reveals significant findings as follows: (1) Land transfer substantially promotes agricultural green transformation. (2) Energy consumption is a major contributor to the growth of agricultural carbon emissions; however, land transfer can mitigate this by reducing energy consumption. (3) Land transfer can promote agricultural green transformation by fostering agricultural technology progress. (4) Further analysis reveals that land transfer in economically developed areas and the southeastern side of the “Hu-Huanyong Line” significantly enhances agricultural green transformation. Based on these findings, this paper suggests promoting land transfer while considering regional differences. Additionally, attention should be directed towards reducing energy consumption and encouraging agricultural technology’s progress.

1. Introduction

Global warming poses a significant threat to sustainable economic development, becoming a shared challenge for all nations [1]. Consequently, reducing carbon emissions has garnered substantial attention worldwide [2]. Among the contributors to global carbon emissions, the agricultural sector alone accounts for 14% [3]. To reduce the negative impact of greenhouse gases, it is necessary to reduce carbon emissions from the agricultural sector. In countries such as China, agricultural carbon emission reduction has witnessed heightened attention due to the detrimental impacts of the traditional extensive production model, characterized by high investment and pollution emissions, on the agricultural environment [4]. Since 2005, the Chinese government has introduced land transfer policies and measures, the main purpose of which is to promote large-scale and intensive agricultural production, optimize the allocation of agricultural factors, and improve the agricultural production environment. In addition, land transfers have also been highly valued by other countries. For example, the governments of France and Vietnam use land transfers to improve agricultural management and the utilization efficiency of elements [5,6]. Based on the “Opinions on Innovating Institutional Mechanisms to Promote the Green Development of Agriculture”, the “National Strategic Plan for Quality Agriculture (2018-2022)”, the “14th Five-Year National Agricultural Green Development Plan”, and other relevant documents, it can be believed that the core of agricultural green transformation lies in energy conservation and emission reduction; therefore, the specific performance can be investigated from two dimensions: the agricultural energy consumption and the agricultural carbon emissions. Theoretically, land transfer can reduce the fragmentation of the land and improve the efficiency of mechanical utilization and the use of fossil energy [7], thereby promoting agricultural green transformation. The average annual growth rate of the ratio of land transfer area to total cultivated land area in China from 2005 to 2021 is 13.7% (data sources: National Rural Economic Situation Statistics, China’s Rural Management Statistical Annual Report and 2019 Statistical Annual Report on China’s Rural Policies and Reforms), while energy consumption and carbon emissions decreased by 7.06% and 7.00%, respectively (data sources: China Energy Statistical Yearbook and China Rural Statistical Yearbook). By reducing agricultural carbon emissions and enhancing the agricultural production environment [8], land transfer becomes a critical driver for China’s agricultural green transformation [9].

Land transfer can enhance farmers’ work efficiency and factory utilization through moderate-scale operations, thereby promoting agricultural green transformation [10]. However, it is important to note that land transfer does not necessarily guarantee the agricultural green transformation [11]. This is due to the increasing opportunity cost of agricultural production compared to the benefits of farming, leading agricultural producers to pursue non-agricultural industries with higher returns for their own or household income [12]. In situations where labor is insufficient, some agricultural households with unproductive land may choose to abandon their farmland [13], while others may compensate for the lower workforce by increasing chemical inputs and utilizing agricultural machinery and equipment [14]. These practices, although ensuring farmland productivity, can hinder agricultural green transformation [15]. Therefore, it is essential to examine the impact of China’s land transfer policy, which the government has implemented and intends to continue implementing for an extended period, on the requirements of the era of agricultural green transformation.

Existing literature primarily focuses on the influence of agricultural production agglomeration [16] and agricultural insurance [17] on agricultural green transformation. Surprisingly, there is limited investigation into the impact of land transfer on agricultural green transformation. Some scholars have pointed out that an imperfect land transfer system and an underdeveloped market contribute to the shift of land from a “carbon sink” to a “carbon source,” leading to high carbonization in agricultural development [18]. Additionally, an increased scale of land transfer may hinder the carbon reduction effect associated with expanding land operation scale [19]. Conversely, other scholars argue that land transfer promotes moderate land scales and facilitates agricultural green transformation through scale production and knowledge spillover effects [20]. For example, Hu et al. (2023) and Wang et al. (2023) found that land transfer can significantly inhibit agricultural carbon emissions [5,21]. Gao et al. (2023) analyzed the data of 46 prefectures in Japan based on a structural equation model and found that land transfer could effectively improve the efficiency of land resource allocation, inhibit land abandonment, and contribute to sustainable land development [22]. In fact, land transfer is not only a focal point in agricultural green transformation but also a key driver for agricultural technology progress [23]. Some scholars pointed out that land transfer can promote agricultural technology progress through agricultural organization reform and management mode transformation [24]. For example, Rada et al. (2018) found that land transfer facilitates the adjustment of regional industrial structures and enables efficient land management, thereby promoting agricultural technology progress [25]. Some scholars found that agricultural technology progress can improve energy efficiency [26] and resource utilization rate, which can significantly promote agricultural green transformation [27,28] For example, Yang and Li (2017) found that agricultural technology progress influences the marginal replacement rate between different factors, leading to improvements in mechanized operations, work efficiency, and energy factor utilization, ultimately impacting agricultural green transformation [29]. In addition, there will be regional heterogeneity in the impact of land transfer on agricultural green transformation. For example, Luo et al. (2020) found that the establishment of major grain-producing areas was conducive to reducing agricultural carbon emissions [30]. Using the data of G7 countries, Ibrahim et al. (2023) found that economic growth would intensify carbon emissions [31].

Based on previous studies, it was found that the existing literature mainly has the following shortcomings: (1) Fail to consider the effect of land transfer on agricultural green transformation; (2) Fail to consider the mediating effects of energy consumption and agricultural technology progress; (3) Regional heterogeneity is not taken into account. Theoretically, the agricultural technology progress resulting from land transfer holds several positive externalities that contribute to the agricultural green transformation [32]. Therefore, incorporating land transfer, agricultural technology progress, and agricultural green transformation into the same theoretical framework is essential for further exploring the internal mechanisms by which land transfer impacts agricultural green transformation. In light of this, the primary focus of this paper is on the relationship between land transfer and agricultural green transformation. The paper aims to achieve the following: (1) From the perspective of land transfer, using a two-way fixed effect model to explore its impact on agricultural green transformation; (2) Using the mediation effect model to explore the internal mechanism of land transfer affecting agricultural green transformation; (3) Discussing the Heterogeneity of the impact of land transfer on the agricultural green transformation on both sides of the “Hu-Huanyong line” and economically differentiated regions.

The rest of this paper is structured as follows: Section 2 introduces the Theoretical analysis and research hypothesis; Section 3 presents the Empirical strategy and variable selection; Section 4 reports the Empirical results; Section 5 presents the Discussion and Section 6 presents the Conclusions and policy recommendations.

2. Theoretical Analysis and Research Hypothesis

Land transfer plays a crucial role in transitioning from a small-scale peasant economy to large-scale and intensive modern agriculture, directly impacting agricultural green transformation [21]. Firstly, land transfer reduces land fragmentation, leading to improved efficiency by reducing work loss on small plots [33]. This also facilitates mechanization and increases the adoption and energy utilization rates of agricultural machinery [34], reducing energy consumption. Additionally, it enhances factor utilization efficiency through knowledge spillover effects [5] thereby boosting the potential for land carbon sequestration and reducing agricultural carbon emissions. Secondly, land transfer promotes the large-scale management of cultivated land [7], optimizing resource allocation. Specifically, it encourages some farmers to transition away from the agriculture sector, enabling land consolidation and agglomeration management [35]. This enhances the bargaining power and economic efficiency of land operators, leading to decreased pesticide and fertilizer applications and the efficient use of machinery [36].

Finally, land transfer plays a role in lowering the threshold for adopting green production methods [37] It enhances the utilization efficiency of chemical factors and encourages agricultural producers to embrace green production practices, promoting the agricultural green transformation [38]. One reason for this is that continuous planting, resulting from land transfer, reduces the average cost and increases the economic efficiency of farmers adopting green production methods [8]. Moreover, the land market directs land toward larger agricultural workers with comparative advantages in capital, technology, or labor force [39] This, in turn, promotes their medium- and long-term investments in the agricultural sector [40], facilitating the purchase of new agricultural machinery and equipment, which can reduce energy consumption [41]. Furthermore, continuous improvement in land transfer accelerates the development of agricultural infrastructure and boosts farmers’ enthusiasm for participating in agricultural technology training [41]. This, in turn, enhances farmers’ environmental awareness and encourages the adoption of low-energy and low-emission input elements, further promoting agricultural green transformation. Based on these points, we propose Hypothesis 1:

Hypothesis 1. 

Land transfer can improve machinery utilization efficiency and factory utilization efficiency through large-scale production, thus promoting agricultural green transformation.

Land transfer plays a crucial role in reducing carbon emissions by directly decreasing energy consumption through large-scale agricultural production and the adoption of green production technology [24]. Specifically, land transfer changes the fragmented production and management mode to a large-scale production mode, which is conducive to the efficient utilization of fossil energy. Mechanized production on contiguous plots, for instance, reduces energy consumption per unit of land, which can reduce agricultural carbon emissions [42]. Furthermore, land transfer encourages the adoption of green technology among large-scale farmers [43]. This enables them to utilize new agricultural machinery and equipment with high energy efficiency, thereby lowering energy consumption [44]. The application of green production technology and agricultural green production modes serves as a demonstration effect, further encouraging other farmers to adopt this high-efficiency, high-profit production approach [43], leading to the reduction of carbon emissions [12]. Based on these points, we propose Hypothesis 2:

Hypothesis 2. 

Land transfers will reduce agricultural carbon emissions by reducing energy consumption.

Land transfer can also have an impact on agricultural transformation through agricultural technology progress [45]. When farmers transition to non-agricultural industries and transfer their land, the issue of farmland fragmentation is addressed, which can facilitate large-scale land management [46]. As a result, efficient agricultural mechanized operations and land management become achievable, subsequently promoting agricultural technology progress. Moreover, as land gradually shifts to large-scale agricultural operators, they are more likely to access agricultural loans and government support [47]. This, in turn, reduces financing constraints and loan difficulties for these operators, encouraging them to adopt new production technology and further promoting agricultural technology progress [48].

Agricultural technology progress will promote agricultural green transformation through ecological production methods [49]. Firstly, agricultural technology progress can improve the efficiency of land management and resource utilization, which help farmers reduce energy consumption while maintaining their original output, thereby reducing carbon emissions [50]. Secondly, agricultural technology progress can improve the allocation of elements in the agricultural industry, increasing the marginal productivity of pesticides, fertilizers, and agricultural film, thereby reducing carbon emissions [51]. Finally, agricultural technology progress can also promote the accumulation of farmers’ experience and knowledge, which is conducive to reducing agricultural production costs and ultimately realizing the “green transformation“ [52]. Thus, we propose Hypothesis 3:

Hypothesis 3. 

Land transfer can lower the barriers to using new technology, promote the application of green technology, and promote agricultural green transformation through this agricultural technology progress.

3. Empirical Strategy and Variable Selection

Considering that the Chinese government issued the Administrative Measures for the Transfer of Contractual Rural Land Management Rights in 2005, it clearly stipulated the basic principles and methods of land transfer. Therefore, this paper uses the panel data of 30 provincial regions in China from 2005 to 2020 and uses a two-way fixed effect model to verify the above three research assumptions: First, test Hypothesis 1, whether the land transfer can promote the agricultural green transformation; second, test Hypothesis 2, that is, whether the land transfer can affect the agricultural carbon emission through energy consumption; and finally, test Hypothesis 3, that is, whether the agricultural technology progress acts as the intermediary variable that the land transfer affects the agricultural green transformation.

3.1. Measurement Model Construction

In order to explore the possible impact of land transfer on the agricultural green transformation, this paper takes agricultural energy consumption and agricultural carbon emission as explained variables and adopts the two-way fixed effect model for analysis. The measurement model is constructed as follows:

$$e{i}_{it}={\alpha }_0+{\alpha }_{1}f{t}_{it}+{\alpha }_2{X}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

(1)

$$a{c}_{it}={\alpha }_0+{\alpha }_{1}f{t}_{it}+{\alpha }_2{X}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

(2)

where I stands for province, t represents year; $e{i}_{it}$ represents agricultural energy consumption; $a{c}_{it}$ represents agricultural carbon emissions; ei and ac are explained variables in this paper; $f{t}_{it}$ represents land transfer and is the core explanatory variable, $X_{it}$ indicates a series of control variables, ${\mu }_i$ indicates regional fixed effect, $v_t$ indicates time fixed effect, and ${\epsilon }_{it}$ indicates random error items. Considering that the use of fossil energy will also produce agricultural carbon emissions, model (3) is constructed to test the carbon emission effect of energy consumption, as follows:

$$a{c}_{it}={\beta }_0+{\beta }_{1}e{i}_{it}+{\beta }_{2}f{t}_{it}+{\beta }_3{X}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

(3)

where, ${\beta }_i$ (i = 1, 2 … 3) is the parameter to be estimated, and the other variables are the same as above. Theoretical analysis also shows that agricultural technology progress has a significant intermediary effect in the process of land transfer to promote agricultural green transformation; therefore, this paper builds model (4), model (5), and model (6) to investigate the intermediary role of agricultural technology progress:

$$t{c}_{it}={\gamma }_0+{\theta }_{1}f{t}_{it}+{\theta }_2{X}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

(4)

$$e{i}_{it}={\gamma }_0+{\theta }_{1}t{c}_{it}+{\theta }_{2}f{t}_{it}+{\theta }_3{X}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

(5)

$$a{c}_{it}={\gamma }_0+{\theta }_{1}t{c}_{it}+{\theta }_{2}f{t}_{it}+{\theta }_3{X}_{it}+{\mu }_i+v_t+{\epsilon }_{it}$$

(6)

Among them, $t{c}_{it}$ represents the progress of agricultural technology and is the mediating variable, and the other variables are the same as above.

3.2. Variable Definitions

3.2.1. Explained Variable

Agricultural green transformation: The key to green transformation lies in energy conservation and emission reduction. Therefore, this paper takes two indicators of agricultural energy consumption and agricultural carbon emissions as alternative variables of agricultural green transformation. The specific calculation method is described as follows:

Agricultural energy consumption (ei): This paper, in accordance with the classification of the China energy statistical yearbook (http://www.zgtjnj.org/navibooklist-n3022013309-1.html, accessed on 1 June 2023), selects the annual report of 17 kinds of fossil energy and, using the standard coal conversion coefficient (

Table 1. Energy type and standard coal conversion coefficient.

Energy Type	Conversion Coefficient	Energy Type	Conversion Coefficient	Energy Type	Conversion Coefficient
Raw coal	0.7143	Other gas	3.5701	Other coking products	1.3000
Cleaned coal	0.9000	Fuel oil	1.4286	Liquefied petroleum gas	1.7143
Briquettes	0.6000	Crude oil	1.4286	Other washed coal	0.2850
Refinery gas	1.5714	Gasoline	1.4714	Other petroleum products	1.2000
Coke	0.9714	Kerosene	1.4714	Natural gas	13.3000
Coke oven gas	6.1430	Diesel oil	1.4571

), sums the agricultural energy consumption [52]. At the same time, the measured agricultural energy consumption index is reciprocal; the greater the value, the lower the agricultural energy consumption is.

Agricultural carbon emissions (ac): Agricultural carbon emissions are mainly carbon emissions caused by agricultural production and operation activities, including the use of chemical factors, as well as carbon emissions caused by production behaviors [53]. These data are extracted from the “China Rural Statistical Yearbook” (China Rural Statistical Yearbook: https://www.yearbookchina.com/naviBooklist-YMCTJ-0.html, accessed on 1 June 2023) from 2006 to 2021. This paper uses the carbon emission coefficient method (

Table 2. Types of carbon sources and carbon emission coefficient.

Carbon Source	Carbon Emission Coefficient	Reference Source
Chemical fertilizer	0.8956 kg C·kg−1	Oak Ridge National Laboratory
Pesticide	4.9341 kg C·kg−1	Oak Ridge National Laboratory
Agricultural film	5.1800 kg C·kg−1	Institute of Resource, Ecosystem, and Environment of Agriculture
Diesel	0.5927 kg C·kg−1	Intergovernmental Panel on Climate Change
Land tilling	312.60 kg C·hm−2	College of Agronomy and Biotechnology, China Agricultural University
Irrigation	266.48 kg C·hm−2	He et al. (2022) [54]

) to calculate agricultural carbon emissions. In order to facilitate the follow-up analysis, the measured agricultural carbon emission index is counted down; the larger the value, the lower the agricultural carbon emission.

3.2.2. Core Explanatory Variable

Land transfer (ft): the ratio of the total area of household contracted land transfer to the area of household contracted land is measured [55]. Land transfer can promote the continuous production of fragmented land and moderate-scale operations, which shows the green transformation reduction effect on agricultural production.

3.2.3. Mediating Variable

Agricultural technology progress (tc): the capital-labor ratio was used to measure agricultural technology progress. According to the above theoretical analysis, agricultural technology progress is an important variable affecting land transfer and agricultural green transformation. Referring to the practice of Xu et al. (2023), this paper adopts the degree of agricultural capital deepening to measure agricultural technology progress [56] and the perpetual inventory method to estimate the capital stock, whose depreciation rate is 5.42%.

3.2.4. Control Variable

According to the existing research [57,58,59], this paper selects the following control variables. ① Urbanization (urb): Measured by the proportion of urban population to total population. ② Trade dependency (tra): Characterized as the proportion of total imports and exports of agricultural products in the total agricultural product of the region. ③ Educational attainment (edu): Measured by the average years of schooling of the agricultural labor force. ④ Industrial structure adjustment (ins): Measured by the proportion of the sown area of food crops to the total sown area of crops. Descriptive statistics for each variable are shown in

Table 3. Descriptive statistics of variables.

Variable Type	Variable Name	Code	N	Mean	Sd	Min	Max
Explained variable	Agricultural energy consumption	ei	480	0.028	0.049	0.003	0.380
	Agricultural carbon emission	ac	480	0.008	0.012	0.001	0.070
Core explanatory variable	Land transfer	ft	480	0.236	0.179	0.014	0.911
Mediating variable	Agricultural technology progress	tc	480	3.938	6.676	0.036	78.068
Control variable	Urbanization	urb	480	0.549	0.141	0.195	0.896
	Trade dependency	tra	480	0.300	0.360	0.016	1.696
	Educational attainment	edu	480	7.678	0.652	5.459	9.838
	Industrial structure adjustment	ins	480	0.654	0.132	0.328	0.971

.

Simultaneously, to ensure more robust estimation results, the paper addresses the endogeneity among variables, and the findings are presented in

Table 4. Coefficient of correlation between variables.

	ei	ac	ft	tc	urb	tra	edu	ins
ei	1.000
ac	0.836	1.000
ft	0.039	0.213	1.000
tc	0.349	0.452	0.433	1.000
urb	0.142	0.393	0.693	0.500	1.000
tra	0.096	0.357	0.364	0.080	0.673	1.000
edu	−0.110	0.068	0.506	0.429	0.668	0.375	1.000
ins	−0.231	−0.261	−0.050	0.099	−0.044	−0.276	0.073	1.000

. The correlation coefficient’s maximum value among the variables is 0.836, indicating the absence of multicollinearity issues among them.

3.3. Data Sources

This paper examines data from 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) spanning from 2005 to 2020. The variables considered in the research include agricultural energy consumption, agricultural carbon emissions, and agricultural technology progress, which were primarily calculated by the author. Data on land transfer is sourced from “National Rural Economic Situation Statistics” spanning from 2006 to 2021, as well as “China’s Rural Management Statistical Annual Report” and “2019 Statistical Annual Report on China’s Rural Policies and Reforms in 2006.”Data on urbanization is obtained from the “China Statistical Yearbook” (China Statistical Yearbook: http://www.stats.gov.cn/sj/ndsj/, accessed on 1 June 2023) from 2006 to 2021. Data on trade dependency is sourced from the “China Agricultural Yearbook” (http://www.zgtjnj.org/navibooklist-n3022050503-1.html, accessed on 1 June 2023) and the “China Agricultural Trade Development Report” from 2006 to 2021. Data on educational attainment is extracted from the “China Population and Employment Statistical Yearbook” (China Population and Employment Statistical Yearbook: https://www.yearbookchina.com/navibooklist-n3022013208-1.html, accessed on 1 June 2023) from 2006 to 2021. Data on industrial structure adjustment is extracted from the “China Rural Statistical Yearbook” (China Rural Statistical Yearbook: https://www.yearbookchina.com/naviBooklist-YMCTJ-0.html, accessed on 1 June 2023) from 2006 to 2021.

4. Empirical Results

4.1. Investigating the Impact of Land Transfer on the Agricultural Green Transformation: An Empirical Test of Hypothesis 1

This paper employs Stata 15 to perform regressions for (1) and (2). Additionally, the Hausman test results show 41.520 (p = 0.000) and 87.200 (p = 0.000), significantly rejecting the null hypothesis. As a result, it is feasible to use a two-way fixed effect model to examine the impact of land transfer on agricultural green transformation, with the outcomes presented in

Table 5. The effect of land transfer on agricultural green transformation.

	ei		ac
	Coef.	Std. Err.	Coef.	Std. Err.
ft	0.106 ***	0.022	0.013 ***	0.002
urb	−0.026	0.036	−0.009 **	0.004
tra	−0.049 ***	0.012	−0.012 ***	0.001
edu	−0.002	0.008	0.001 *	0.001
ins	0.106 ***	0.066	0.015 **	0.007
_cons	0.193 ***	0.022	0.013 ***	0.002
Time effect	YES		YES
Regional effect	YES		YES
N	480		480
R-sq	0.182		0.385

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

. The estimated coefficients of land transfer on agricultural green transformation are all significantly positive, confirming the validity of Hypothesis 1. Specifically, the impact coefficient of land transfer on energy consumption is 0.106, which is significantly positive at the 1% level, indicating that every 1% increase in land transfer rate can save energy by 0.106%. This is because land transfer promotes contiguous and large-scale land operations, improves agricultural machinery utilization and energy efficiency, and thus reduces energy consumption per unit of land. Additionally, the continuous implementation of the land transfer policy encourages agricultural entities to adopt clean production technology and advanced machinery, further reducing energy consumption. Likewise, the estimated coefficient of land transfer on agricultural carbon emissions is 0.013, suggesting that every 1% increase in land transfer rate will reduce carbon emissions by 0.103%. Land transfer transfers arable land from low-productivity farmers to high-productivity farmers or agricultural organizations, reducing the cost of production per unit area of land. This prompts agricultural entities to allocate saved costs towards the purchase and application of green production technology, curbing agricultural carbon emissions. Furthermore, this shift enables the transformation from fragmented to large-scale cultivation modes, thereby increasing the adoption rate of agricultural machinery and enhancing land resource allocation efficiency through economies of scale, leading to reduced agricultural carbon emissions.

4.2. Investigating the Role of Energy Consumption in the Impact of Land Transfer on Agricultural Carbon Emissions: An Empirical Test of Hypothesis 2

Theoretical analysis suggests that land transfer can reduce agricultural carbon emissions by curbing agricultural energy consumption. This section aims to empirically test this mechanism. The testing process consists of two steps: first, examining the effect of land transfer on agricultural energy consumption with the regression results presented in Table 5; and second, investigating the combined effect of land transfer and agricultural energy consumption on agricultural carbon emissions. If the coefficient estimates for energy consumption are statistically significant, it indicates that energy consumption acts as an intermediary variable in the relationship between land transfer and agricultural carbon emissions. The results of this test are displayed in

Table 6. The role of energy consumption in land transfer affecting agricultural carbon emissions.

	Coef.	Std. Err.
ft	0.005 ***	0.001
ei	0.082 ***	0.003
urb	−0.007 ***	0.002
tra	−0.008 ***	0.001
edu	0.002 ***	0.000
ins	0.003 *	0.002
_cons	−0.001	0.004
Time effect	YES
Regional effect	YES
N	480
R-sq	0.806

Note: * p < 0.1, *** p < 0.01.

. The estimated impact coefficient of energy consumption on agricultural carbon emissions is 0.005, which is significantly positive at the 1% level. This indicates that energy consumption acts as an intermediary variable in the relationship between land transfer and agricultural carbon emissions. Thus, land transfer can effectively reduce agricultural carbon emissions by suppressing energy consumption, thereby confirming Hypothesis 2.

4.3. Mechanism Test of Agricultural Technology Progress in Land Transfer Affecting Agricultural Green Transformation: An Empirical Test of Hypothesis 3

To explore the role of agricultural technology progress in the impact of land transfer on agricultural green transformation, this paper considers agricultural technology progress as an intermediary variable. The specific testing process involves two steps: first, examine the effect of land transfer on agricultural technology progress. The significance of the land transfer coefficient indicates its influence on the intermediary variable. Second, assess the joint effect of land transfer and agricultural technology progress on agricultural green transformation. If the estimated coefficients of land transfer and technology progress are significant, it suggests that technological progress acts as an intermediary in the process of agricultural green transformation. The results are presented in

Table 7. An examination of the mechanism of agricultural technology progress in the process of land transfer affecting agricultural green transformation.

	Regression1		Regression2		Regression3
	tc		ei		ac
	Coef.	Std. Err.	Coef.	Std. Err.	Coef.	Std. Err.
ft	7.234 **	3.444	0.092 ***	0.021	0.011 ***	0.002
tc			0.002 ***	0.000	0.003 ***	0.000
urb	−7.913	5.515	−0.011	0.034	−0.006 **	0.003
tra	−12.869 ***	1.906	−0.025 **	0.012	−0.008 ***	0.001
edu	0.769	1.242	−0.003	0.008	0.001 *	0.001
ins	7.911 *	4.781	−0.203 ***	0.030	−0.015 ***	0.003
_cons	−2.079	10.189	0.197 ***	0.063	0.015 ***	0.006
Time effect	YES		YES		YES
Regional effect	YES		YES		YES
N	480		480		480
R-sq	0.553		0.250		0.545

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

.

Regression 1 reveals that the estimated impact coefficient of land transfer on agricultural technology progress is 7.234, which is significantly positive at the 5% level, indicating that every 1% increase in land transfer rate will increase agricultural technology progress by 7.234%. This is because land transfer facilitates contiguous and large-scale agricultural land operations, which, in turn, enhance farmers’ adoption of mechanized operation methods and new green technologies, thereby driving agricultural technology progress. Furthermore, the findings from Regression 2 and Regression 3 show that the estimated impact coefficients of agricultural technology progress on energy consumption and agricultural carbon emissions are 0.092 and 0.011, respectively. This implies that agricultural technology progress serves as an intermediary variable in the relationship between land transfer and agricultural green transformation, verifying Hypothesis 3. The reason behind this is that agricultural technology plays a pivotal role in agricultural transformation. On the one hand, improved agricultural technology directly boosts agricultural output, effectively promoting agricultural green transformation per unit of land. On the other hand, the knowledge spillover effect resulting from advancements in agricultural technology enhances energy use efficiency, promoting agricultural green transformation.

4.4. Robustness Test

To ensure the robustness of the empirical findings regarding the significant role of land transfer in promoting agricultural green transformation, this study employs three methods for robustness testing. Firstly, a shrinking treatment (0.05 tail shrinkage level) approach is applied to mitigate the impact of outliers on the research results by trimming the tails of continuous variables. Secondly, to account for the significant impact of the “Administrative Measures for the Transfer of Rural Land Contractual Management Rights” implemented in 2005, the No. 1 Central document in 2010 requiring the promotion of the land transfer contracts, and the reform of the “separation of three rights” of rural land in 2014, which has greatly accelerated the land transfer and promoted the agricultural green transformation, Sample data from 2005, 2010, and 2014 are eliminated to test the robustness of the previous regression. Additionally, to address any potential endogeneity issues arising from causality problems, where changes in agricultural green transformation may affect the promulgation and implementation of land transfer policies, the explanatory variables are lagged by one period. The results are presented in

Table 8. Robustness test of land transfer affecting agricultural green transformation.

	Winsorize Treatment		Partial Sample Rejection		The Independent Variable Lags One Stage
	ei	ac	ei	ac	ei	ac
ft	0.106 ***	0.013 ***	0.098 ***	0.012 ***	0.112 ***	0.013 ***
	(0.022)	(0.002)	(0.025)	(0.002)	(0.022)	(0.002)
urb	−0.026	−0.009 **	−0.317 ***	−0.057 ***	−0.031	−0.007 **
	(0.036)	(0.004)	(0.075)	(0.007)	(0.035)	(0.003)
tra	−0.049 ***	−0.012 ***	−0.012	−0.007 ***	−0.060 ***	−0.013 ***
	(0.012)	(0.001)	(0.016)	(0.001)	(0.012)	(0.001)
edu	−0.002	0.001 *	0.001	0.002 *	0.002	0.002 **
	(0.008)	(0.001)	(0.009)	(0.001)	(0.008)	(0.001)
ins	−0.188 ***	−0.012 ***	−0.198 ***	−0.013 ***	−0.213 ***	−0.012 ***
	(0.031)	(0.003)	(0.033)	(0.003)	(0.031)	(0.003)
_cons	0.193 ***	0.015 **	0.311 ***	0.035 ***	0.184 ***	0.013 **
	(0.066)	(0.007)	(0.080)	(0.007)	(0.064)	(0.006)
Time effect	YES	YES	YES	YES	YES	YES
Regional effect	YES	YES	YES	YES	YES	YES
N	480	480	390	390	450	450
R-sq	0.182	0.385	0.232	0.495	0.230	0.417

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

. It is evident that, except for some differences in coefficient size, the significance and sign of the core explanatory variables remain consistent with the regression model’s results in Table 5. This rigorous confirmation reinforces the previous empirical conclusions that land transfer significantly promotes agricultural green transformation.

4.5. Regional Heterogeneity Test

The preceding analysis demonstrates that land transfer significantly promotes agricultural green transformation. However, notable disparities exist in land resource endowments and economic development levels in China’s rural regions. To delve into this heterogeneity, this paper further divides the research samples into groups based on resource endowments and economic development gaps to explore the varying impact of land transfer on agricultural green transformation.

4.5.1. Effect of Land Transfer on Both Sides of the “Hu-Huanyong Line” on Agricultural Green Transformation

In 1935, geographer Hu Huanyong established an oblique line at a 45-degree angle from Tengchong, Yunnan, to Heihe, Heilongjiang, as a dividing line for population density. This division created two regions: the densely populated southeast, which comprises 43% of the country’s land but accommodates 94% of its population, and the sparsely populated northwest. The “Hu-Huanyong line” not only highlights the uneven distribution of China’s population but also serves as a significant demarcation for land resource disparities between the southeast and northwest. In the southeast, overpopulation and limited land result in serious land fragmentation, making it an area with relatively strong implementation of land transfer policies. On the other hand, the complex terrain and landforms in the northwest hinder the effective implementation of land transfer policies. Thus, the energy-saving and emission-reduction effects of land transfer may vary across regions.

We employ the “Hu-Huanyong line” as the basis for dividing the research samples and conducting regression analysis. The results are presented in

Table 9. The difference in the effect of land transfer on agricultural green transformation on both sides of the Hu-Huanyong Line.

	Southeast		Northwest
	ei	ac	ei	ac
ft	0.132 ***	0.015 ***	−0.057	−0.003
	(0.025)	(0.003)	(0.054)	(0.004)
urb	−0.039	−0.011 ***	0.022	0.003
	(0.039)	(0.004)	(0.084)	(0.007)
tra	−0.041 ***	−0.011 ***	0.076	0.006
	(0.012)	(0.001)	(0.079)	(0.006)
edu	0.002	0.002 *	−0.022	−0.001
	(0.009)	(0.001)	(0.016)	(0.001)
ins	−0.257 ***	−0.017 ***	0.084	0.010 *
	(0.035)	(0.004)	(0.065)	(0.005)
_cons	0.200 **	0.016 *	0.136	0.009
	(0.080)	(0.009)	(0.120)	(0.010)
Time effect	YES	YES	YES	YES
Regional effect	YES	YES	YES	YES
N	320	320	160	160
R-sq	0.276	0.463	0.204	0.331

Note: * p < 0.1, ** p < 0.05, *** p < 0.01.

. In the southeast, the estimated coefficients of land transfer on energy consumption and agricultural carbon emissions are 0.132 and 0.015, respectively. This implies that every 1% increase in the land transfer rate will save energy and reduce emissions by 0.132% and 0.015%, respectively. The high land fragmentation in these areas benefits from an increased land transfer rate, which promotes contiguous and centralized production, thereby improving the utilization efficiency of chemical elements and mechanical equipment, reducing energy consumption and carbon emissions in agricultural production, and ultimately promoting agricultural green transformation. Conversely, the sparsely populated northwest region experiences restrictions in implementing land transfer policies due to its large land area. Additionally, the complex terrain and landforms in this region impede the use of machinery and equipment, limiting the adoption of new green technology. Thus, the impact of land transfer on agricultural green transformation in this region is insignificant.

4.5.2. The Impact of Land Transfer on Agricultural Green Transformation in Economically Differentiated Regions

The imbalance of economic structure will aggravate the change in the regional environment [60]. In addition, in more economically developed areas, more capable farmers carry out large-scale land [61] transfers. Therefore, regional economic development differences may have an impact on the effect of land transfer on agricultural green transformation. To explore these differences, this paper divides the research samples into three categories based on economic development: economically developed regions, economically less-developed areas, and economically underdeveloped regions. The goal is to investigate how land transfer impacts agricultural green transformation in regions with varying economic development levels. The results are presented in

Table 10. The difference of the effect of land circulation on agricultural green transformation in economic differentiation area.

	Economically Developed Areas		Economically Less-Developed Areas		Economically Underdeveloped Areas
	ei	ac	ei	ac	ei	ac
ft	0.136 **	0.018 ***	0.005	0.001 ***	−0.007	0.002
	(0.058)	(0.006)	(0.010)	(0.000)	(0.060)	(0.005)
urb	−0.065	−0.023 **	0.033	0.001	−0.002	−0.001
	(0.095)	(0.010)	(0.022)	(0.001)	(0.059)	(0.005)
tra	−0.000	−0.011 ***	−0.009	−0.000	0.057	0.003
	(0.034)	(0.004)	(0.009)	(0.000)	(0.093)	(0.007)
edu	0.004	0.004	0.001	0.000	−0.012	0.000
	(0.024)	(0.003)	(0.004)	(0.000)	(0.016)	(0.001)
ins	−0.483 ***	−0.026 ***	−0.006	0.001 ***	−0.010	0.002
	(0.082)	(0.009)	(0.015)	(0.000)	(0.069)	(0.005)
_cons	0.291	0.025	0.001	0.002 **	0.150	0.009
	(0.208)	(0.023)	(0.030)	(0.001)	(0.122)	(0.010)
Time effect	YES	YES	YES	YES	YES	YES
Regional effect	YES	YES	YES	YES	YES	YES
N	112	112	208	208	160	160
R-sq	0.443	0.585	0.057	0.583	0.192	0.384

Note: ** p < 0.05, *** p < 0.01.

. In economically developed areas, the estimated coefficients of land transfer on energy consumption and agricultural carbon emissions are 0.136 and 0.018, respectively. In these regions, fragmented land is prevalent, and land subcontracting, leasing, and swapping with high turnover rates facilitate contiguous and large-scale land planting. This leads to improved utilization efficiency of fossil energy and mechanical equipment, ultimately promoting agricultural “green transformation.” Additionally, the high level of economic development in these areas encourages the adoption of new green technology by farmers, further supporting agricultural green transformation.

Conversely, in economically less developed areas, the estimated coefficient of land transfer on energy consumption is not significant, and the estimated coefficient on agricultural carbon emissions is 0.001. It primarily demonstrates emission reduction effects with limited energy-saving effects. As large producing provinces with extensive land areas, these regions witness improved agricultural production scale through land transfer, promoting mechanized production, and increasing the efficiency of mechanical equipment usage, thereby reducing agricultural carbon emissions. However, overall fossil energy consumption increases due to the expanded production. Economically underdeveloped areas also face challenges in implementing land transfer policies, leading to low land transfer rates. This situation fails to address issues related to fragmented cultivated land and excessive use of chemical elements. Moreover, many of these areas, primarily located in economically backward western regions, employ traditional production methods with limited use of mechanical equipment and new technology. Consequently, the estimated coefficient of land transfer on agricultural green transformation is not significant in these areas.

5. Discussion

Existing studies have focused on the impact of other factors on agricultural green transformation. For instance, Zhang et al. (2022) discovered a nonlinear relationship between agricultural production agglomeration and agricultural green transformation [62]. Wong et al. (2020) found that agricultural insurance significantly inhibits agricultural green transformation [63]. Similarly, Li et al. (2023) identified a nonlinear relationship between urbanization and agricultural green transformation [3]. However, there is a lack of literature investigating the influence of land transfer on agricultural green transformation. In fact, land transfer, as a crucial tool for facilitating large-scale land management [8] is conducive to improving the economic efficiency of land utilization [64] and promoting agricultural green transformation. One of the key contributions of this paper is examining the relationship between land transfer and agricultural green transformation. This further expands the research of Hu et al. (2023) and Wang et al. (2021) [21,36]. This paper not only focuses on the impact of land transfer on agricultural carbon emissions but also comprehensively considers the energy-saving and emission-reduction effects of land transfer. Consequently, it provides a valuable reference for the advancement of agricultural green transformation.

Subsequent studies have demonstrated the close relationship between agricultural energy consumption and agricultural carbon emissions. For instance, Sun et al. (2022) identified fossil energy as a crucial input in large-scale and mechanized agricultural production, directly contributing to carbon emissions [65]. However, limited literature exists on whether land transfer can mitigate agricultural carbon emissions through the reduction of agricultural energy consumption. Thus, the second contribution of this paper is to assess the role of agricultural energy consumption as a mediating variable in the connection between land transfer and agricultural carbon emissions. This finding holds significant value for the government in formulating environmental goals such as “green transformation.” The results of this paper show that land transfer can reduce agricultural carbon emissions by reducing agricultural energy consumption, which builds on the research results of Sun et al. (2022) [65].

Additionally, agricultural technology progress is intricately linked to land transfer and agricultural green transformation [66]. The third contribution of this paper is to explore the influence of agricultural technology progress on the relationship between land transfer and agricultural green transformation. This enhances our understanding of the mechanisms underlying land transfer for agricultural green transformation, expanding the study of Hu et al. (2023) and Wang et al. (2021) [21,36]. The results of this paper show that land transfer can promote agricultural green transformation by promoting agricultural technology progress, which expands the research conclusion of Ge et al. (2017) [67]. Moreover, this paper further explores the regional heterogeneity of land transfer in agricultural green transformation. This further expanded the studies of Luo Xuan (2020) [30], Geng, and Luo (2022) [68]; however, they mainly focused on the heterogeneity of land transfer on agricultural carbon emissions in different regions of food function. This paper further expands on this by grouping the study samples according to resource endowment and economic development gap. This segmentation provides vital insights for the government to deepen rural land system reforms and facilitate agricultural green transformation.

Exploring the relationship between land transfer and agricultural green transition is of great significance for agricultural green development, and it is important to consider certain limitations when exploring similar topics. Firstly, this paper solely analyzes land transfers at the provincial level. Enhancing the credibility of research conclusions can be achieved by utilizing county-level data to examine the impact of land transfer on agricultural green transformation. Unfortunately, there is a significant lack of available county-level data currently. Therefore, conducting an analysis with panel data at the prefecture level would be meaningful. Secondly, this study focuses on capital deepening as a measure of agricultural technology progress. However, it is important to acknowledge that mechanical and biological technological progress may also play a role in the relationship between land transfer and agricultural green transformation. Therefore, exploring the effects of other types of technological progress in the land transfer process can yield valuable insights.

6. Conclusions and Policy Recommendations

Based on China’s inter-provincial panel data from 2005 to 2020, this paper employs the fixed effect model and the mediation effect model to investigate the impact of land transfer on agricultural green transformation and its internal mechanisms. The findings of the research are as follows: Firstly, land transfer demonstrates an energy-saving effect by reducing energy consumption and an emission reduction effect by lowering agricultural carbon emissions. That is, land transfer plays a significant role in promoting agricultural green transformation. Secondly, land transfer can suppress carbon emissions through the reduction of agricultural energy consumption. Thirdly, agricultural technology progress resulting from land transfer also plays a role in promoting agricultural green transformation. Furthermore, there is heterogeneity in the effect of land transfer on agricultural green transformation. Specifically, land transfer significantly promotes agricultural green transformation on the southeast side of the Hu-Huanyong Line and in economically developed areas. However, on the northwest side of the Hu-Huanyong Line and in economically underdeveloped areas, land transfers do not have a significant impact.

The research conclusions above hold significant policy implications for achieving agricultural green transformation. Firstly, it is essential to optimize the land transfer market. By promoting land transfer, we can achieve intensive and contiguous land operations, thereby boosting the agricultural sector’s contribution to China’s green transformation efforts. Secondly, when formulating environmental goals, the government should prioritize policy measures aimed at reducing agricultural energy consumption. Thirdly, expediting the agricultural technology’s progress is crucial. This can be achieved through the adoption of green production technology, thereby improving energy efficiency and reducing pollution emissions. Lastly, it is imperative to recognize the differentiated layout of land transfers. In economically developed regions, it is essential to encourage farmers to focus on operating efficiency, leading to intensified and conservation-oriented practices to accelerate the agricultural green transformation. On the other hand, in economically underdeveloped areas, it is essential to increase the implementation of land transfers, encouraging farmers to adopt moderate-scale approaches to promote agricultural green transformation.