Open AccessArticle

The Impact of Land Transfer on Sustainable Agricultural Development from the Perspective of Green Total Factor Productivity

Yangchenhao Wu

and

Wang Zhang

School of Economics and Management, Northwest University, Xi’an 710127, China

Author to whom correspondence should be addressed.

Sustainability 2024, 16(16), 7076; https://doi.org/10.3390/su16167076

Submission received: 9 July 2024 / Revised: 31 July 2024 / Accepted: 15 August 2024 / Published: 18 August 2024

(This article belongs to the Special Issue Impacts of Land Use and Land Cover on Regional Sustainable Development)

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Abstract

China’s agricultural sector is transitioning from extensive management to intensive management, and land transfer brings about changes in land use and management methods, which may encourage the agricultural sector to enter a sustainable development track, but this mechanism has not been effectively proven. Using the SBM-GML index to construct a green total factor productivity index to measure the level of sustainable agricultural development in each province (or autonomous region or municipality directly under the central government) and provincial panel data from 2010 to 2022, we applied a panel interactive fixed-effects model to empirically test the impact of land transfer on sustainable agricultural development, with a focus on analyzing the heterogeneity and related mechanisms of this impact. The results indicate that (1) land transfer significantly promotes sustainable agricultural development, and this conclusion still held true after robustness tests such as controlling for regional omitted variables, replacing dependent variables, changing the sample size, IV estimation, and GMM estimation. (2) The mechanism testing found that land transfer mainly promotes sustainable agricultural development by increasing the desirable output, and has no significant effect on reducing non-point source pollution. At the same time, land transfer mainly improves the desirable output through factor allocation effects rather than scale operation effects, thereby promoting sustainable agricultural development. (3) The heterogeneity analysis found that the higher the quantile of agricultural development level is, the weaker the role of land transfer in promoting sustainable agricultural development, indicating that land transfer has a greater impact on areas with poor agricultural development foundations, and areas with poor agricultural development foundations are more likely to obtain sustainable development space through land transfer. The impact of different land transfer methods and land transfer objects on sustainable agricultural development was heterogeneous. Compared with non-market transfer methods such as exchange and transfer, market-oriented transfer methods such as leasing and equity had a more significant impact on sustainable agricultural development. Compared to transferring land to ordinary farmers, transferring land to new business entities such as family farms, professional cooperatives, and enterprises can significantly promote sustainable agricultural development.

Keywords:

land transfer; sustainable development of agriculture; green total factor productivity

1. Introduction

Sustainable agricultural development is an important aspect of achieving sustainable development of the national economy. Land is widely recognized as the most important agricultural production factor, and the effective allocation of land resources has a significant impact on farmers’ employment, agricultural production, and macroeconomic growth. However, for a long time, small-scale farmers have accounted for the vast majority of farmers in China, and the problem of land fragmentation is very serious. The highly fragmented management model of land per household makes it difficult to meet the development requirements of productivity and has become the main obstacle to agricultural development. Therefore, how to break the small-scale and decentralized management pattern and promote moderate-scale operations in agriculture has become the key to promoting the sustainable development of Chinese agriculture. Therefore, land transfer has become a key policy direction for the country. Under the promotion of policies, the transfer of rural land in China has developed rapidly. According to data from the Ministry of Agriculture and Rural Affairs, as of 2022, the total area of household-contracted arable land transfer in China reached 604 million mu (1 mu ≈ 667 m²), accounting for one-third of the total arable land area in the country. At the same time, since the reform and opening up, China has made significant achievements in agricultural development. However, the rapid growth of agriculture comes at the cost of increasingly depleted resources and deteriorating ecological environments, leading to a growing conflict between agricultural development and ecological environment governance. In fact, agricultural development has reached a critical period and the development model of “exchanging the environment for growth” must be changed [1]. Therefore, there is an urgent need to promote the transformation of agriculture from quantitative expansion to quality improvement [2]. So, can the large-scale transfer of land play a role in promoting sustainable agricultural development? If possible, what is its internal mechanism of action? Is there any difference in its effectiveness using different land transfer distributions, land transfer methods, and land transfer objects? The answers to the above questions are of great significance for further improving land transfer policies and optimizing institutional pathways to promote sustainable agricultural development.

In terms of sustainable agricultural development, the existing literature mainly includes the construction of indicator systems and measurement methods for sustainable agricultural development. In terms of constructing an indicator system for sustainable agricultural development, due to significant differences in natural climate, economic development, infrastructure, and other factors among the different regions in China, the focus of constructing an indicator system for sustainable agricultural development may also vary [3,4,5]. For example, Tan et al. evaluated the sustainable agricultural development of Hunan Province from three aspects: agricultural resources and environment, agricultural production and economy, and agricultural population and society. They found that, overall, Hunan Province presents an unbalanced development state, with the Changsha–Zhuzhou–Xiangtan urban agglomeration as the core, and the level of urbanization decreasing from east to west [6]. Some scholars have also attempted to construct a national evaluation index system for sustainable agricultural development and carried out dynamic evaluations of sustainable agricultural development [7,8]. In terms of evaluation methods for sustainable agricultural development, they mainly include subjective weighting evaluation methods and objective weighting evaluation methods. The former is subjectively judged by experts based on experience to obtain the weights. Common methods include the Analytic Hierarchy Process [9], Fuzzy Comprehensive Evaluation Method [10], etc. These methods can better reflect the multidimensionality and richness of relevant concepts, but their disadvantages are their strong subjectivity and randomness, high data requirements, and no exact economic meaning, which is not conducive to horizontal comparisons of evaluation results. Objective weighting evaluation methods determine the weights based on the coefficient of variation or correlation of each indicator; these methods include grey relational analysis [11], TOPSIS comprehensive evaluation [12], principal component analysis [13], the entropy method [14], Data Envelopment Analysis [15], etc. Compared to subjective weighting evaluation methods, objective weighting evaluation methods determine the weight based on the contribution rate of each factor, overcoming the subjective uncertainty and making the evaluation results more objective and reasonable.

In recent years, agricultural green total factor productivity has been widely used as a proxy variable for agricultural sustainable development to examine the effectiveness of agricultural green development [16,17,18]. However, there are certain differences among scholars in the selection of measurement methods and indicators when measuring agricultural green total factor productivity. In terms of measurement methods, the existing research mainly adopted Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) methods [19]. DEA has advantages in processing data with multiple inputs and outputs, and does not require specific production function forms, which can reduce subjective controversies in research; it has become the most commonly method for measuring agricultural green total factor productivity used by domestic and foreign scholars. However, the traditional DEA model exhibits a proportional decrease or increase in output depending on the input, and does not take into account slack and is unable to distinguish between good and bad outputs, resulting in measurement errors. Therefore, scholars have improved this method by using radial distance functions and mixed distance functions derived from relaxing or angle-correcting linear programming constraints based on DEA to calculate TFP, i.e., the SBM model. However, this type of method only considers the desirable output using the factor inputs [20] and ignores the true contribution of undesirable outputs in the economic system. In view of this, Chung et al. [21]. proposed Malmquist Luenberger (ML) index based on the directional distance function and combined it with an SBM model to form the SBM-ML index. This index was rapidly applied in various industries in the economy and society, with a focus on undesirable outputs. In recent years, it has been introduced into the agricultural field and has become a widely adopted method in related research [22]. Subsequently, Tone [23] constructed a non-radial and non-angular DEA-SBM (Slack-Based Measure) standard efficiency model that considers undesirable outputs, further incorporating slack variables into the objective function, effectively overcoming the measurement bias caused by the radial and angular selection of traditional DEA models. However, when there are multiple valid decision units, the SBM standard efficiency model cannot further distinguish them [24]. Moreover, the efficiency values calculated using the SBM standard efficiency model have non-negative truncation characteristics. When analyzing its influencing factors, a Tobit regression model specifically designed to handle the restricted dependent variable is required [25]. With the development of economics, due to the problems of unsolvable linear programming and lack of transitivity, ordinary ML indices can no longer meet research needs. In order to solve the above problems, Oh [26] constructed the Global Malmquist Luenberger (GML) productivity index by combining the directional distance function with the Global Malmquist index. Some scholars began to use DEA methods to globally measure the SBM-ML index to characterize agricultural green total factor productivity [27,28], namely the SBM-GML index. In terms of index application, Chen et al. [29] used Super SBM and GML indices to measure green total factor productivity, and examined policy effects and spillover effects. Liao et al. [30] used the SBM model to calculate the impact of technical efficiency and pure technical efficiency in terms of energy efficiency in 37 industrial sectors in Guangdong Province. Wang et al. [31] used the DEA–Global-Malmquist model to evaluate the green efficiency and total factor productivity of 30 provinces in China and studied their influencing factors. Zhao et al. [32] comprehensively used Super SBM and the Malmquist index to discover significant differences in agricultural green total factor productivity in ethnic regions from the perspective of environmental regulation. Zhu et al. [33] used the SBM-GML model index and found that, from the perspectives of carbon sink and carbon emissions, the improvement in green total factor productivity in Yunnan’s agriculture mainly relied on technological progress. Da [34] used the SBM-ML model to calculate the green total factor productivity of agriculture in Hebei Province. Han et al. [35] used the SBM model and GML index to measure the green total factor productivity of agriculture in the middle reaches of the Yangtze River urban agglomeration. Guo et al. [36] used the global GML index to calculate the growth rate of agricultural green total factor productivity, and analyzed the convergence characteristics from a dynamic perspective. In addition to different measurement methods, there are also differences in the indicator systems that were constructed in previous studies, especially the significant differences in the undesirable agricultural output indicators [37,38,39,40,41]. Some studies incorporated agricultural non-point source pollution as an undesirable output into the measurement of agricultural green total factor productivity, while others measured agricultural green total factor productivity by considering carbon emissions during the agricultural production process as an undesirable output [42,43,44]. However, overall, due to the bias of the existing research in selecting unexpected outputs, the measurement results for agricultural green total factor productivity also differ [45].

In terms of land transfer, the existing research mainly focused on the relationship between land transfer and economic and environmental variables, such as the scale of agricultural operations [46], agricultural production efficiency [47,48,49,50,51,52] and production costs [53], non-point source pollution [54], poverty vulnerability [55], allocation efficiency of agricultural land [56], use of green production methods [57], use of mechanical technology [58], farmers’ financing demands [59], farmers’ income [60,61,62], and environmental performance [63,64]. Specifically, in terms of the relationship between land transfer and sustainable agricultural development, Hu Yiqin proposed that innovative land transfer and its related systems and mechanisms are important directions for improving the efficiency of land element allocation, promoting the sustainable use of land resources, and achieving sustainable agricultural development [65]. Hu et al. [66], Feng [67], Rao [68], Li et al. [69,70], Wang [71], and others all believe that poor land circulation is an important reason for the low efficiency of land resource allocation. In the process of land circulation, improving the intensification, scale, mechanization, organization, and socialization of land management is conducive to improving agricultural labor productivity, increasing farmers’ income levels, and promoting sustainable agricultural development. Guo et al. [72] studied the influencing factors of land transfer from the perspective of agricultural sustainability using the logistic model. Jiang Renliang believes that in the process of coordinated development of urbanization, industrialization, and agricultural modernization in China, rural land transfer promotes the reconfiguration of agricultural resources; affects the scale, structure, and technology of agricultural production; affects the rural ecological environment; and changes the capacity of the entire ecosystem, thereby affecting the sustainability of agricultural development [73]. In addition, some scholars believe that the non-standard transfer of land among farmers can easily lead to long-term degradation of farmland fertility, which is not conducive to maintaining the sustainable production capacity of the soil and thus not conducive to the sustainable development of agriculture [74,75,76]. Xia et al. [77] used the translog model to examine the impact of changes in the agricultural land transfer system on sustainable agricultural development. They believe that the rural land transfer system should be conducive to realizing a market-oriented transfer mechanism for land resources, fully activating the assets and capital attributes of land resources, and promoting the establishment of a transfer system that various market entities can participate in; this system includes land rights capitalization, diversified management, and modernized governance. Some scholars focused on the relationship between land transfer and total factor productivity (TFP) in agriculture. Adamopoulos and Restuccia [78] found that land transfer can increase agricultural TFP through promoting capital-intensive technologies. Zhu et al. [79] pointed out that in areas with highly developed land transfer markets, land transfer significantly improved agricultural TFP. Liu et al. [80] found that land transfer can effectively improve agricultural technical efficiency and then increase agricultural TFP. Helfand and Taylor [81] argued that the scale expansion brought about by land transfer reduces agricultural TFP. Kuang et al. [82] found a significant “inverted U-shaped” relationship between land transfer and agricultural TFP.

In summary, there have been many achievements in the research on the relationship between land transfer and sustainable agricultural development, providing important references for this study. However, firstly, there is still relatively limited literature on the study of sustainable agricultural development from the perspective of land transfer. Even though a few studies have investigated the relationship between land and agricultural development, they have not directly established a relationship between land transfer and sustainable agricultural development. There is still a gap in the literature on the impact of land transfer on GTFP, and the conclusions of different studies vary greatly [83,84]. At the same time, there is a lack of research on its mechanism. Secondly, the existing research mainly analyzed the impact of land transfer on agricultural producers or households at the micro level [85], and there is less research on the relationship between land transfer and agricultural development at the macro level. The research of Kuang et al. [86] and Mo et al. [87] are some of the few studies that tested the effectiveness of land transfer in terms of total quantity. Finally, although the research has preliminarily confirmed the promoting effect of land transfer on sustainable agricultural development, the differences in the impact of different transfer distributions, transfer methods, and transfer objects on sustainable agricultural development have not been explored. Therefore, it is necessary to further clarify the impact and mechanism of land transfer on sustainable agricultural development from the perspective of green total factor productivity. In view of this, this study incorporated agricultural non-point source pollution emissions into the total factor productivity framework as an undesirable output, constructed green total factor productivity indicators, and calculated the level of sustainable agricultural development in each province. Furthermore, the effects, transmission mechanisms, and heterogeneity of the effects of land transfer on sustainable agricultural development were examined at the provincial level.

The research contribution of this study is mainly in the following aspects: Firstly, previous studies have mainly focused on the economic impact of land transfer, with only a few studies investigating the environmental impact of land transfer. This study found a direct link between land transfer and green total factor productivity, considering the comprehensive economic and environmental impacts of land transfer, which enriches the knowledge in this research field. Secondly, most of the existing research was conducted at the individual and household levels of agricultural producers, neglecting the policy implementation effects of land transfer at the macro level. This study conducted empirical research on panel data from 30 provinces in China, enriching the research in this field. Finally, the previous research mainly focused on the direct impact of land transfer on environmental pollution, with little attention paid to exploring its specific mechanisms, as well as the differences in the impact of different transfer distributions, transfer methods, and transfer objects on sustainable agricultural development. This study incorporated agricultural non-point source pollution emissions into the total factor productivity framework as an undesirable output, revealing how land transfer affects GTFP, and conducted mechanism testing and heterogeneity analyses, which helps us to understand how land transfer affects sustainable agricultural development.

2. Theoretical Analysis and Research Hypotheses

From the perspective of green total factor productivity, the connotation of sustainable agricultural development includes two aspects, development and sustainability, which means minimizing undesirable outputs while ensuring stable or sustained growth of desirable outputs. Therefore, the role of land transfer in promoting sustainable agricultural development can be analyzed from three aspects: its impact on the total output, its impact on desirable outputs, and its impact on undesirable outputs.

2.1. The Overall Impact of Land Transfer on Sustainable Agricultural Development

In 2010, the Central Committee of the Communist Party of China issued, in the No. 1 central document, the “Several Opinions of the Central Committee of the Communist Party of China and the State Council on Strengthening the Overall Urban and Rural Development and Further Consolidating the Foundation of Agricultural and Rural Development”, which clearly pointed out that we should promote the use of standard text for land transfer contracts, stabilize and improve the basic rural management system, strengthen the management and services of the transfer of rural land contractual management rights, improve the transfer market, and develop various forms of moderate-scale operations on the basis of voluntary and paid transfers according to law. Specifically, it is necessary to accelerate the cultivation of land transfer service organizations, coordinate grassroots land contract management departments, improve the land transfer service system, and provide services such as the provision of information, policy consultations, contract signing, and price evaluations to promote the process of land transfer. The role of land transfer in improving agricultural output benefits, agricultural environmental benefits, and the agricultural technology level has been confirmed by previous research. However, it remains to be verified whether the role of land transfer in improving agricultural output benefits, agricultural environmental benefits, and agricultural technology level has an overall effect on promoting sustainable agricultural development. Based on this, this study proposed the following hypotheses:

Hypothesis 1.

Land transfer has a positive driving effect on sustainable agricultural development.

2.2. The Impact of Land Transfer on the Desirable Output of Sustainable Agricultural Development

The impact of land transfer on the desirable output of sustainable agricultural development is the output effect of land transfer. The output effect includes two aspects; one of these aspects is the factor allocation effect. Land transfer reconfigures the land among entities with different production capacities. On the one hand, it helps to concentrate land in the hands of business entities with a high agricultural production capacity, promote the matching of land and production capacity, achieve optimized allocation of land factors, and thereby improve land use efficiency and agricultural output. On the other hand, it is also induces farmers to reasonably allocate labor factors according to their comparative advantages and promote the specialized division of labor, thereby improving efficiency. The second output effect is the scale operation effect. Land transfer enables the concentration of land from scattered small-scale farmers into new large-scale and specialized management entities represented by large-scale growers, professional farmers, family farms, agricultural enterprises, etc. [88], which helps to achieve moderate-scale operations in agriculture. Due to the richer and more advanced agricultural professional knowledge mastered by new agricultural management entities compared to ordinary farmers, they are more inclined to adopt modern agricultural production technologies and scientific management methods. Moreover, the expansion of the land management scale also provides the conditions for the promotion and application of large- and medium-sized agricultural machinery, advanced agricultural production technology, and management models, which is conducive to improving the organic capital composition of the agricultural sector, promoting the division of agricultural labor and specialization, reducing agricultural production costs [89], and achieving economies of scale, and thus greatly improving agricultural production efficiency [47,90] and promoting an increase in desirable agricultural outputs. Therefore, Hypothesis 2 was proposed.

Hypothesis 2.

Land transfer increases desirable agricultural outputs through factor allocation effects and scale management effects, thereby affecting sustainable agricultural development.

2.3. The Impact of Land Transfer on Undesirable Outputs of Sustainable Agricultural Development

One undesirable output of land transfer for sustainable agricultural development is the environmental effects. Firstly, compared to the fragmentation before the transfer, the expansion of the land management scale makes the allocation of input factors more scientific, especially the reduction in and efficiency improvement of polluting factors such as fertilizers, thereby reducing agricultural non-point source pollution emissions [91]. Secondly, a larger cultivated area of land can lower the threshold for mechanical operations, making it easier to use intelligent agricultural machinery such as drones and fertilizing machines for precise fertilization [92], improving the efficiency of chemical use and avoiding fertilizer waste and pollution to the ecological environment caused by improper application. Once again, with the expansion of the land management scale, farmers’ production methods will also undergo changes, such as shifting from the original “small and comprehensive” diversified planting to more efficient specialized planting. This transformation is conducive to increasing farmers’ knowledge on reducing fertilizer usage and improving fertilizer utilization efficiency [93]. Finally, the expansion of the land management scale can also reduce the unit area use cost for agricultural green technology, effectively incentivizing farmers to use green technology to replace polluting factors such as fertilizers and pesticides [94,95], thereby directly reducing agricultural non-point source pollution emissions and reducing undesirable agricultural output. Therefore, Hypothesis 3 was proposed.

Hypothesis 3.

Land transfer reduces agricultural non-point source pollution and decreases undesirable agricultural outputs, and thus affects sustainable agricultural development.

3. Research Methods and Data Sources

3.1. Empirical Model Setting

To test whether land transfer can promote sustainable agricultural development, this study constructed the following benchmark regression model:

{I n P T F P}_{i t} = α + β {T r a n s f e r}_{i t} + X_{i t}^{'} θ + δ_{i} + η_{t} + φ_{i}^{'} F_{t} + ε_{i t}

(1)

In the formula, the subscripts

i

and

t

represent provinces (or autonomous regions or municipalities directly under the central government) and years, respectively. The dependent variable

I n P T F P

represents the logarithmic value of the agricultural green total factor productivity in the

t

-th year of province (or autonomous region or municipality directly under the central government)

i

, which is used to measure the level of regional agricultural sustainable development.

{T r a n s f e r}_{i t}

represents land transfer, which is the core explanatory variable of interest in this study.

X_{i t}^{'}

represents a series of control variables, where

δ_{i}

and

η_{t}

represent individual fixed effects and year fixed effects in the province (or autonomous region or municipality directly under the central government), respectively.

F_{t}

is the common factor,

φ_{i}^{'}

is the factor load, and

φ_{i}^{'} F_{t}

is the interactive fixed effect, which can be regarded as the product of multidimensional individual effects and multidimensional time effects.

α

β

, and

θ

are the parameters to be estimated, and

ε_{i t}

is the random error term.

The advantage of the interactive fixed-effects model (In-FE) defined by Equation (1) is that it controls the heterogeneity of provinces (or autonomous regions or municipalities directly under the central government) that do not change over time through individual fixed effects, the common impact that changes over time through time fixed effects, and the unobservable factors that change both over time and with individuals through the interaction term between individual effects and time effects. This can effectively alleviate the endogeneity of model estimation and better fit panel data in practical problems to improve the goodness of fit of the model.

3.2. Measurement of Green Total Factor Productivity in Chinese Agriculture

3.2.1. Research Methods

This study used Data Envelopment Analysis (DEA) to measure the green total factor productivity of agriculture. This method was first proposed by Charnel et al. [96] and was expanded by Banker et al. [97] to form a series of efficiency evaluation models. The early literature used angle and radial DEA models for efficiency evaluation, which required selecting the input or output angle of the model and required proportional changes in the input or output, which did not match the actual production situation [98]. Considering the non-angular and non-radial features of the DEA model, Tone [99] proposed the SBM standard efficiency model. However, when there are two or more effective units in the same period, the SBM standard efficiency model cannot rank them. For this reason, Tone [100] proposed the SBM super efficiency model, but this model also failed to consider the undesirable outputs. Based on the above shortcomings, this study referred to Tone’s [23] research and selected the SBM super efficiency model that includes unexpected outputs to measure the green total factor productivity of agriculture.

Assuming that the

k

-th decision unit

(j = 1,2, . . ., n)

has input vector

x \in R^{M}

, the desirable output vector

y^{g} \in R^{s_{1}}

and the undesirable output vector

y^{b} \in R^{s_{2}}

. Meanwhile, the matrix X is defined as

[x_{1}, x_{2}, . . ., x_{n}] \in R^{m} \times n, Y^{g} = [y_{1}^{g}, y_{2}^{g}, . . . y_{n}^{g}] \in R^{s_{1}} \times n

, and

Y^{b} = [y_{1}^{b}, y_{2}^{b}, . . . y_{n}^{b}] \in R^{s_{2}} \times n

. To determine the decision unit

k

, Equation (2) is used:

m i n ρ = \frac{1 + \frac{1}{m} \sum_{i = 1}^{m} \frac{s_{i}^{-}}{x_{i k}}}{1 - \frac{1}{s_{1 +} s_{2}} (\sum_{r = 1}^{s_{1}} \frac{s_{r}^{g}}{y_{r k}^{g}} + \sum_{t = 1}^{s_{2}} \frac{s_{t}^{b}}{y_{t k}^{b}})}

(2)

s . t . \sum_{j = 1, j \neq k}^{n} x_{i j} λ_{j} - s_{i}^{-} ≪ x_{i k}

\sum_{j = 1, j \neq k}^{n} y_{r j} λ_{j} + s_{r}^{g} ≫ y_{r k}^{g}

\sum_{j = 1, j \neq k}^{n} y_{t j} λ_{j} - s_{t}^{b} ≪ y_{t k}^{b}

λ ≫ 0, s^{g} ≫ 0, s^{b} ≫ 0, s^{-} ≫ 0

where

λ

is the weight vector, and

s_{i}^{-}, s_{r}^{g}

, and

s_{t}^{b}

are relaxation variables.

\frac{1}{m} \sum_{i = 1}^{m} \frac{s_{i}^{-}}{x_{i k}}

represents the degree of inefficiency of the inputs, and

\frac{1}{s_{1 +} s_{2}} (\sum_{r = 1}^{s_{1}} \frac{s_{r}^{g}}{y_{r k}^{g}} + \sum_{t = 1}^{s_{2}} \frac{s_{t}^{b}}{y_{t k}^{b}})

represents the average degree of inefficiency of the outputs.

ρ

is the efficiency value of the decision-making unit and should be greater than 1 so that effective decision-making units can be distinguished. The efficiency level of each evaluated unit under certain technical conditions can be calculated using Equation (1), but the technical efficiency at this time is a static analysis and cannot directly reflect the role of productivity changes in agricultural production and development. Therefore, some scholars have referred to the Malmquist productivity index proposed by Diewert et al. [101] and Fare et al. [102] to form ML [21] and GML [26] indices that consider resource consumption and environmental pollution. However, the total factor productivity measured by the ML index lacks cyclic and cumulative characteristics, and can only make short-term judgments near the production period, and is unable to measure the long-term trend of the growth level. The GML productivity index, constructed based on the global technology set, can effectively avoid the problem of no feasible solutions in linear programming and allow for global comparisons of production frontiers. At the same time, continuous production frontiers can avoid inward shifts, that is, technological regression, and thus avoid the problem of passive improvements in total factor productivity. Referring to Oh’s [26] results, this study defined the GML index as

{G M L}_{t}^{t + 1} = \frac{1 + \vec{D_{0}^{G}} {(x}^{t}, y^{t}, b^{t}; y^{t}, - b^{t})}{1 + \vec{D_{0}^{G}} {(x}^{t + 1}, y^{t + 1}, b^{t + 1}; y^{t + 1}, - b^{t + 1})}

(3)

{G M L}_{t}^{t + 1} < 1

, it indicates a decrease in the expected output and an increase in the unexpected output, and the agricultural green total factor productivity is lower than the previous period level. On the contrary,

{G M L}_{t}^{t + 1} > 1

indicates an increase in agricultural green total factor productivity. The GML index can be further decomposed into pure technical efficiency change (PECH), scale efficiency change (SECH), and technological progress (TECH). The formula and decomposition results are as follows:

{G M L}_{t}^{t + 1} = \frac{1 + \vec{D_{0}^{G}} {(x}^{t}, y^{t}, b^{t}; y^{t}, - b^{t})}{1 + \vec{D_{0}^{G}} {(x}^{t + 1}, y^{t + 1}, b^{t + 1}; y^{t + 1}, - b^{t + 1})} = \frac{1 + \vec{D_{v}^{t}} {(x}^{t}, y^{t}, b^{t}; y^{t}, - b^{t})}{1 + \vec{D_{v}^{t + 1}} {(x}^{t + 1}, y^{t + 1}, b^{t + 1}; y^{t + 1}, - b^{t + 1})} \times \frac{\frac{1 + \vec{D_{c}^{G}} {(x}^{t}, y^{t}, b^{t}; y^{t}, - b^{t})}{1 + \vec{D_{v}^{t}} {(x}^{t}, y^{t}, b^{t}; y^{t}, - b^{t})}}{\frac{1 + \vec{D_{c}^{G}} {(x}^{t + 1}, y^{t + 1}, b^{t + 1}; y^{t + 1}, - b^{t + 1})}{1 + \vec{D_{v}^{t}} {(x}^{t + 1}, y^{t + 1}, b^{t + 1}; y^{t + 1}, - b^{t + 1})}} \times \frac{\frac{1 + \vec{D_{v}^{G}} {(x}^{t}, y^{t}, b^{t}; y^{t}, - b^{t})}{1 + \vec{D_{v}^{t}} {(x}^{t}, y^{t}, b^{t}; y^{t}, - b^{t})}}{\frac{1 + \vec{D_{c}^{G}} {(x}^{t + 1}, y^{t + 1}, b^{t + 1}; y^{t + 1}, - b^{t + 1})}{1 + \vec{D_{v}^{t + 1}} {(x}^{t + 1}, y^{t + 1}, b^{t + 1}; y^{t + 1}, - b^{t + 1})}} = {P E C H}_{t}^{t + 1} \times {S E C H}_{t}^{t + 1} \times {T E C H}_{t}^{t + 1}

(4)

Among them, the values of

{G M L}_{t}^{t + 1}, {P E C H}_{t}^{t + 1}, {S E C H}_{t}^{t + 1}, a n d {T E C H}_{t}^{t + 1}

are all greater than 0. A

{G M L}_{t}^{t + 1}

value greater than 1 indicates an increase in green total factor productivity, while a value less than 1 indicates a decrease in green total factor productivity.

{P E C H}_{t}^{t + 1}, {S E C H}_{t}^{t + 1}, a n d {T E C H}_{t}^{t + 1}

values greater than 1 indicate efficiency improvement, scale efficiency improvement, and technological progress, while values less than 1 indicate efficiency deterioration, scale efficiency decline, and technological regression. They respectively reflect the level of institutional management, economies of scale, and technological changes in the region [103].

3.2.2. Indicator Selection and Processing

This study took the agricultural green total factor productivity of 30 provincial units in the Chinese mainland from 2010 to 2022 as the research object. In terms of input variables, this study referred to the practices of Ma et al. [104] and Luo et al. [105] and categorized them as land input, labor input, machinery input, fertilizer input, and water resource input. Land input was calculated based on the planting area of crops; labor input was calculated based on the number of agricultural laborers; mechanical input was expressed in terms of the total power of the agricultural machinery; fertilizer input was calculated based on the net application rate of agricultural fertilizers; and water resource input was calculated based on agricultural water consumption.

The desirable output was a high total output value of agriculture (narrow definition agriculture, i.e., planting industry), while the undesirable output was the emission of agricultural non-point source pollutants [40,41], mainly including fertilizer pollution and solid waste pollution in farmlands. The unit survey and evaluation method that was commonly used in previous studies was used for accounting [106,107].

Meanwhile, due to the fact that the agricultural total factor productivity measured based on the SBM-GML index is a dynamic relative value, in order to avoid the problem of insignificant measurements caused by the inability to directly regress dynamic indicators and small changes around 0, and referring to the research of Zhang [108] and others, 2010 was selected as the base year, and the TFP of that year was set to 1. The cumulative ATFP of 2011 was represented by the product of the ATFP of 2010 and the Malmquist index of 2011. Thus, the cumulative ATFP for each region and year was calculated, the dynamic agricultural green total factor productivity was converted into the cumulative agricultural green total factor productivity, and it was logarithmized before inputting it into the regression model.

3.2.3. Measurement Results of Agricultural Green Total Factor Productivity

This study was based on input–output data from 30 provincial-level units in China from 2010 to 2022, and it calculated the growth rate of China’s agricultural green total factor productivity and its decomposition terms based on the SBM-GML index (see Table 1). As a comparison, the table also reports the growth of traditional total factor productivity without considering environmental pollution. The results show that, overall, after taking into account environmental factors, the average annual growth rate of China’s agricultural green total factor productivity from 2010 to 2022 was 3.029%, far lower than the average annual growth rate of 4.684% of traditional total factor productivity without considering environmental pollution. This means that China’s agricultural growth has to some extent come at the cost of sacrificing the environment and has not yet achieved sustainable development. From the perspective of growth rate, although there were occasional fluctuations between 2010 and 2022, the overall trend showed a slow upward trend. Among them, technological progress was the main driving force behind the growth of China’s agricultural green total factor productivity, with an average annual growth rate of 2.041%. However, pure technical efficiency and scale efficiency did not make significant progress and have not made effective contributions, indicating that China’s agriculture has not yet formed obvious economies of scale and scale effects. The sustainable development of agriculture is currently mainly relying on the single-wheel drive of green technological progress.

3.3. Variable Selection and Measurement

3.3.1. Explained Variable

The dependent variable in this study was the level of sustainable agricultural development, and agricultural green total factor productivity was selected as the measurement indicator. As shown in the results of Section 3.2 above, the cumulative form of agricultural green total factor productivity obtained through transformation was included in the regression model and logarithmically processed.

3.3.2. Core Explanatory Variables

The core explanatory variable in this study was the land transfer rate, which was measured as the ratio of the area of land involved in contract management rights transfers (including transfer and exchange areas) in each province (or autonomous region or municipality directly under the central government) to the total area of household contracted farmland [109]. Generally speaking, the land transfer rate should consider both the area of land transferred in and out. However, for a certain region, the transfer of land by some farmers is equivalent to the transfer of land by another group of farmers, meaning that the amount of land transferred in and out is equal in quantity. With the development of various new agricultural management entities, farmers are the only entities transferring land out but not the only entities transferring land in, which leads to certain noise in the land transfer indicators. Therefore, this study mainly calculated the land transfer rate from the perspective of transfers out.

3.3.3. Control Variables

To improve the external validity of the research conclusions, in addition to land transfer, it is also necessary to control the impact of other factors on sustainable agricultural development. In theory, sustainable agricultural development is closely related to production factor quality, economic structure, government policies, and the natural environment [110,111]. Therefore, the control variables in this article include the following:

(1) Quality of production factors. As the fundamental elements of agricultural production, the quality of land and labor directly determines the level of sustainable development of agriculture. The quality of land directly determines the agricultural output, and the irrigation rate of arable land is used as a proxy indicator to characterize land quality [112], which is expressed as the ratio of irrigated arable land area to total crop sowing area. In terms of labor force, following the approach of Thomas et al. [113], the quality of the labor force was characterized by the average years of education. Generally speaking, the higher the education level of agricultural producers, the easier it is for them to master production skills and use chemical elements reasonably, which theoretically has a positive impact on agricultural green total factor productivity.

(2) Agricultural economic structure. The agricultural economic structure is an important factor restricting the sustainable development of agriculture. Generally speaking, the more advanced the agricultural economic structure is, the more likely it is to have a positive impact on agricultural green total factor productivity [104]. This is usually reflected by economic indicators such as agricultural output composition, crop sowing area composition, and the proportion of labor use [114]. This study used both the proportion of grain crop sowing area to total crop sowing area and the proportion of migrant labor force to total rural labor force to reflect the regional agricultural economic structure.

(3) Regional economic situation. The regional economy affects the sustainable development level of agriculture in the region. The higher the level of urbanization, the more high-end the regional industrial structure is, the higher the level of the agriculture technology and machinery, and the higher the possibility of adopting green production methods is. The ratio of urban population to rural population was selected to reflect the level of urbanization, and the proportion of the total output value of the secondary and tertiary industries to the output value of the primary industry reflected the regional industrial structure. The per capita investment in village road and bridge construction was used to measure the level of rural transportation construction.

(4) Agricultural policies. Government policies can have a direct or indirect impact on sustainable agricultural development through macroeconomic regulation and the supply of public goods. This study focused on two policies: foreign trade of agricultural products [104] and fiscal agricultural investment. The former was measured as the ratio of the total import and export of agricultural products to the total output value of agriculture, forestry, animal husbandry, and fisheries, while the latter was expressed as the proportion of agricultural, forestry, and water expenditures to local general public budget expenditures.

(5) Natural environmental factors. Agriculture is an inherently weak industry, and natural environmental changes have a significant impact on sustainable agricultural development. The proportion of affected areas to total crop planting area was used to reflect the impact of uncontrollable natural environmental factors [104], and the annual average temperature of each province was used to measure climate conditions.

3.4. Data Source and Description

This study used the balanced panel data of 30 provinces (or autonomous regions or municipalities directly under the Central Government) in China (Xizang, Hong Kong, Macao, and Taiwan were excluded due to a lack of data) from 2010 to 2022 to conduct an empirical study. The sources of data for each indicator were as follows:

The relevant data for measuring the level of sustainable development in agriculture came from the website of the National Bureau of Statistics and the EPS database, the China Population and Employment Statistical Yearbook, the China Environmental Statistical Yearbook, and the China Urban Rural Construction Database. The scale of land transfer and the area of household contracted cultivated land were derived from National Rural Economic Statistics and the China Rural Management Statistics Annual Report. The area of irrigated farmland and the area of sown crops date were from the China Water Resources Bulletin and China Statistical Yearbook. The years of education of the rural population data were from the China Population and Employment Statistical Yearbook. The number of migrant workers and the total rural labor force data were from the China Population and Employment Statistical Yearbook. The relevant data on the level of financial support for agriculture, regional industrial structure, and urbanization level were from the National Bureau of Statistics. The climate data came from the China Meteorological Data Network. The total import and export volumes of agricultural products came from the China Agricultural Yearbook. All other indicator data were sourced from the National Bureau of Statistics database (https://data.stats.gov.cn). The moving average method was used to fill in individual missing data points. Table 2 reports the descriptive statistical information of the variables used in this study.

4. Results

4.1. Benchmark Regression Results

Table 3 reports the benchmark regression results of this study. From columns (1) to (5), it can be observed that in the process of introducing control variables, province fixed effects, year fixed effects, and province–year interaction fixed effects in sequence, the estimated coefficient of the core explanatory variable land transfer rate remained significantly positive at the 1% significance level, reflecting the robustness of the model estimation results of this study. According to the estimation results of the panel interaction fixed-effects (In-FE) model (column (5)), for every 1 percentage point increase in the land transfer rate, the level of sustainable agricultural development, as measured by green total factor productivity, increased by 18.5%, indicating that land transfer can effectively promote sustainable agricultural development. Hypothesis 1 of this study was thus preliminarily validated. Furthermore, comparing the regression results of columns (3) to (5), it was found that the more fixed effects that were considered, the smaller the estimated coefficient of land transfer rate was. This indicates that sustainable agricultural development is influenced by both region, time, and their interaction effects. Therefore, using a panel interaction fixed-effects model for estimation is more appropriate.

4.2. Endogenous Treatment

The accuracy of the benchmark regression results may face endogeneity challenges. This endogeneity mainly comes from the omission of important variables, reverse causality, measurement errors, and sample selection issues. Regarding these four sources, Table 4 discusses them using five methods and robustness tests were conducted.

(1) Controlling regional omitted variables. In the benchmark regression, this study controlled for province fixed effects, year fixed effects, and province–year interaction fixed effects to eliminate the influence of omitted variables on the regression results. However, there may still be some regional omitted variables that were not considered, which can lead to endogeneity issues. Therefore, we considered adding interaction terms between the eastern, central, and western regions and years to control for the influence from time-varying regional differences or regional policy differences on the regression results. From the results in column (1) of Table 4, it can be observed that the land transfer rate remained significantly positive at the 1% level, indicating that the conclusion that land transfer promotes sustainable agricultural development still held true after controlling for relevant omitted variables that changed over time at the regional level.

(2) Dependent variable replacement. Based on the indicator system constructed in the previous sections, the principal component analysis method was also used to measure the level of sustainable agricultural development. The preliminary analysis showed that the correlation coefficient between the indicators was generally above 0.3, and therefore, the use of the principal component analysis method is feasible. Furthermore, the recalculated level of sustainable agricultural development was used as the dependent variable, and the results are shown in column (4) of Table 4. The table shows that the impact coefficient of land transfer on the recalculated sustainable development of agriculture was 0.081 and reached a significance level of 1%, indicating the reliability of the benchmark estimation results.

(3) Changing the sample size. The transfer of rural land in China is a government-led process, and policy changes largely determine the scale and trend of land transfer, especially the proposal of the “Opinions on Guiding the Orderly Transfer of Rural Land Management Rights and Developing Moderate Scale Agricultural Operations” in 2014. This policy document clearly proposes the separation of the three rights of rural land, guiding rural land transfer, and promoting large-scale agricultural operations. Since then, the speed of land transfer in China has significantly accelerated. Therefore, after removing sample data before 2014 and conducting the regression analysis again, the results shown in column (5) were obtained. Among them, the impact coefficient of land transfer on sustainable agricultural development was 0.216 and reached a significance level of 1%, indicating that the introduction of the “Opinions” strengthened the positive correlation between land transfer and sustainable agricultural development. This suggests that after changing the sample size, land transfer still had a significant promoting effect on sustainable agricultural development, further confirming the robustness of the regression results from this study.

(4) IV estimation. The benchmark regression results may also have endogeneity issues caused by reverse causality between land transfer and sustainable agricultural development. This study used the instrumental variable method to overcome this problem. Based on expert advice, we selected terrain undulation as the instrumental variable for panel two-stage least squares (2SLS) regression. On the one hand, terrain undulation is related to land turnover rate. Generally speaking, land with smaller terrain undulations is easier to cultivate and has correspondingly higher market demand, making it easier to circulate. Land with significant terrain undulations is the opposite. On the other hand, there was no direct correlation between terrain undulations and the level of sustainable agricultural development, namely agricultural green total factor productivity, thus meeting the criteria for selecting instrumental variables. The data on terrain undulation were obtained according to the methods used in Feng’s [115] research. Column (4) in Table 4 shows the regression results of the second stage of 2SLS. It can be seen that after using the instrumental variable method to address potential endogeneity issues, land transfer still showed a significant positive promotion effect on sustainable agricultural development, confirming the robustness of the conclusions in this paper. In addition, the test results for the instrumental variables in column (4) show that the K-P LM statistic was significant, rejecting the null hypothesis of “insufficient recognition”. The K-P F statistic was greater than the critical value of the Stock–Yogo test at the 10% level, rejecting the null hypothesis of “weak instrumental variables” and further indicating that the instrumental variables were sufficiently correlated with the endogenous variables. In the overdiagnosis test, the p value from the Hansen J test was greater than 0.1, indicating that the instrumental variable also had good exogeneity and is an effective instrumental variable.

(5) GMM estimation. Due to the widespread heteroscedasticity and serial autocorrelation in macroeconomic variables and in order to improve the reliability of estimation results, this study adopted a two-step optimal GMM regression method. Meanwhile, as sustainable agricultural development is a dynamic process, green total factor productivity in agriculture may have economic inertia, meaning that the current and future level of sustainable agricultural development may be influenced by past values. In view of this, this study also introduced a first-order lag term of the dependent variable in the model, extended it into a dynamic panel model, and used a two-step system GMM for regression estimation. Thus, it can not only control the inertia adjustment force of sustainable agricultural development itself, but also alleviate the endogenous bias caused by omitted variables that change over time. These results are reported in columns (5) and (6) of Table 4, respectively. The results showed that after adding the first-order lagged term of the dependent variable to the regression model as a proxy variable for omitted variables, the positive impact of land transfer on sustainable agricultural development remained significant at the 5% level. In addition, according to the results in column (5) of Table 4, both the Kleiberen Paap rk LM statistic and the Kleiberen Paap rk Wald F statistic indicated that the model did not have any issues with unidentifiable variables or weak instrumental variables. According to the results in column (6) of Table 4, if AR (1) is less than 0.01 and AR (2) is greater than 0.1, it indicates that there is first-order autocorrelation in the perturbation term difference, but no second-order autocorrelation, and the null hypothesis of “no autocorrelation in the perturbation term” can be accepted. In the over-identification test, the p value from the Hansen J test was greater than 0.1, and thus the null hypothesis that “all instrumental variables are valid” cannot be rejected, meeting the conditions for using system GMM estimation.

4.3. Impact Mechanism Testing

The previous analysis showed that land transfer has a significant promoting effect on sustainable agricultural development. The theoretical part suggested that land transfer has an impact path on both desirable and undesirable agricultural outputs, thereby promoting sustainable agricultural development. The impact on the desirable output can be further divided into two sub-paths: the factor allocation effect and scale operation effect. We examined its mechanism in this regard.

4.3.1. Output Effect or Environmental Effect

In the theoretical analysis Section 2, we divided the impact of land transfer on sustainable agricultural development into two aspects: the impact on the desirable output (output effects) and the impact on the undesirable output (environmental effects). So, was the promotion effect of land transfer on sustainable agricultural development that was confirmed by benchmark regression caused by changes in desirable agricultural outputs, undesirable changes in agricultural non-point source pollution, or both? We examined the above mechanism by directly testing the relationship between policy variables and mechanism variables [116]. The dependent variables were replaced with the logarithm of the agricultural total output value and the logarithm of agricultural non-point source pollutant emissions, and then regression analysis on the land transfer rate was performed. The results are shown in columns (1) and (2) of Table 5. In the regression analysis with the logarithm of the total agricultural output value as the dependent variable (column (1)), the coefficient of land transfer rate was positive and passed the significance test at the 1% level. In the regression analysis with the logarithm of agricultural non-point source pollutant emissions as the dependent variable (column (2)), the land transfer rate did not pass the significance test. This indicates that at present, land transfer mainly promotes sustainable agricultural development through increasing outputs rather than reducing pollution. Hypotheses 2 and 3 were proven.

4.3.2. Factor Allocation Effect or Scale Operation Effect

According to Hypothesis 2, land transfer can increase the desired agricultural output, but is it due to the “factor allocation effect” generated by promoting the optimization of factors such as land and labor, or is it the result of the “scale operation effect” formed by the continuous concentration of land? Based on the above two possibilities, this study replaced the dependent variables with the following variables for regression: (1) the factor allocation effect, represented by land productivity and labor productivity, where land productivity is the ratio of total agricultural output to crop sowing area, and labor productivity is the ratio of total agricultural output to the number of agricultural laborers engaged in agriculture. (2) The scale operation effect, which is expressed as the proportion of farmers with a cultivated land area of more than 50 acres. The regression results are shown in columns (3) to (5) of Table 5. Among them, in the regression analysis with land productivity and labor productivity as the dependent variables (columns (3) and (4)), the coefficients before the land transfer rate were significantly positive at different levels of significance, indicating that land transfer can significantly improve the efficiency of land and labor factor allocation, that is, the factor allocation effect is an important channel for land transfer to promote sustainable agricultural development. In the regression analysis with the proportion of large-scale operating farmers as the dependent variable (column (5)), the estimated coefficient of land transfer rate was significantly negative at the 5% level, indicating that active land transfer has not led to a significant increase in the number of large-scale operating farmers. Column (6) also shows the impact of land transfer on changes in agricultural scale efficiency, with the explained variable being the Green Scale Efficiency Index derived from the decomposition of the Green Total Factor Productivity Index. The results showed that the impact of land transfer on the change of scale efficiency was negative but not significant, which also indicates that at present, land transfer does not have the nature of large-scale operations. Instead, it may inhibit the improvement of agricultural green scale efficiency due to diseconomies of scale, which may be one of the reasons why the environmental effects of land transfer were not significant.

4.4. Heterogeneity Analysis

4.4.1. Heterogeneity of Different Land Transfer Distributions

Considering the different development foundations of agricultural production regions, the impact of land transfer on their level of sustainable agricultural development may vary. The panel quantile model was utilized to further examine the impact of land transfer on the level of sustainable agricultural development at different quantiles; the results are shown in Table 6. Overall, the marginal effect of land transfer on sustainable agricultural development was significant at the 1% level at the 0.1, 0.25, 0.5, and 0.75 percentiles. However, starting from the 0.5 percentile, this marginal effect gradually decreased and was not significant at the 0.9 percentile. This indicates that the impact of land transfer on the level of sustainable agricultural development is heterogeneous due to differences in local agricultural development foundations. In areas with poor agricultural development foundations (below the 0.5 percentile), there was a greater marginal impact of land transfer on their level of sustainable agricultural development. Beyond the 0.5 percentile level, that is, in areas with a good foundation for agricultural development, the marginal impact of land transfer on their level of sustainable agricultural development was relatively small. The reason may be that compared to areas with a better foundation for agricultural development, the allocation of land resources is already close to the optimal level, so the space for land transfer to improve the sustainable development level of local agriculture is limited. In areas with poor agricultural development foundations, due to their low level of agricultural development, the efficiency of land resource allocation is relatively poor. Land transfer has a wider space for improving the sustainable development level of local agriculture, and it is easier to obtain sustainable development space through land transfer.

4.4.2. Heterogeneity of Land Transfer Methods

There are various forms of rural land transfer in China, including exchange, transfer, leasing (subcontracting), and equity participation. Different land transfer methods reflect differences in the marketization level and transfer scope. Among them, the scope of transfer and exchange is limited to the collective economic organization: the spontaneous and primitive transfer of land by farmers. In contrast, leasing and equity have broken through the limitations of village collectives, expanding the scope of land transactions from within villages to outside villages, and expanding the trading players from ordinary farmers to new business entities such as family farms, professional cooperatives, and enterprises. The scope of circulation is wider and the degree of marketization is higher [117]. The impact of different circulation scopes and marketization degrees on sustainable agricultural development is naturally not completely the same.

Based on this and according to the degree of marketization, this study categorized land transfer into two types: non-market transfers (including exchange and transfer) and market transfers (including leasing, equity participation, and others). Then, a regression analysis was conducted using the proportion of the corresponding transfer area to the household contracted farmland area as the explanatory variable to test whether there are differences in the impact of different land transfer methods on sustainable agricultural development. The results are shown in Table 7. Column (1) and column (2) show the regression results considering only non-market circulation and market circulation, respectively, while column (3) shows the regression result considering both transfer methods simultaneously. The results of the two regression analyses were consistent, showing that market-oriented transfer significantly promotes sustainable agricultural development at the 1% level, while non-market-oriented circulation had no significant impact on sustainable agricultural development. The reason for this is that under the form of transfer and exchange, land is limited to circulation within village groups or among members of village collectives, with a low degree of marketization. Moreover, due to the fact that this circulation is often for the purpose of facilitating farming and human relationships, it is difficult to ensure the transfer of land to high-efficiency management entities [118]. The low-level homogenization of small-scale farmers has led to inefficient land circulation [119], resulting in a development dilemma where scale expansion and efficiency decline coexist, making it difficult to contribute to sustainable agricultural development. Leasing and investing can help break the fragmented and decentralized land management pattern of small-scale farmers and promote the flow of land to efficient management entities [120], thus amplifying the positive effects of factor allocation and scale management on sustainable agricultural development on a larger scale.

4.4.3. Heterogeneity of Land Transfer Objects

Depending on the different lessees, land transfer can be divided into transfers to ordinary farmers and transfers to new business entities such as family farms and professional cooperative enterprises. The different circulation objects mean different levels of productivity [121], which in turn have different impacts on the sustainable development of agriculture. Therefore, in order to examine the differences in the impact of different land transfer destinations on sustainable agricultural development, this study divided the total land transfer area into two parts: the area that flows to ordinary farmers and the area that flows to new business entities. Then, a regression analysis was conducted with the proportions of household contracted cultivated land area as the explanatory variable. From Table 8, it can be seen that transferring land to new business entities such as family farms, professional cooperatives, and enterprises significantly promoted agricultural sustainable development at least at the 5% level, while the impact of transferring land to ordinary farmers on agricultural sustainable development was not significant.

The reason for this may be that the transfer of land cultivation rights to ordinary farmers is more of a transfer between small-scale farmers, which is a “small-scale farmer replication” from small-scale farmers to small-scale farmers, and does not fundamentally change the fragmented and decentralized land management pattern. Instead, it solidifies this situation, which is not conducive to improving agricultural production efficiency [122]. At the same time, the problem with small-scale farmers is that, due to their own capacity limitations, most farmers are unable to introduce new production factors and bring about significant changes in their production and management methods after land transfer. In addition, the land transfers between farmers are mainly informal transfers through verbal agreements, with short transfer periods and high uncertainty, which makes it difficult to incentivize the land transferee to invest in the land [75], thus making it difficult to have a substantial promoting effect on sustainable agricultural development. The transfer to new business entities such as family farms, professional cooperatives, and enterprises usually adopts a large-scale, centralized, and contiguous transfer method, with a long transfer period and stable contract relationships. This not only facilitates the large-scale cultivation of the land and obtains economic benefits and higher productivity, but it also promotes long-term investment in agriculture [123], thereby promoting sustainable agricultural development.

5. Discussion and Conclusions

5.1. Discussion

Land transfer is an important means of rational allocation of agricultural resources. Previous studies mainly discussed the economic impact of land transfer. For example, Qiu et al. [124] believe that land transfer can promote improvements in agricultural production efficiency, while Chen et al. [125] believe that the formation of a land transfer market will improve the efficiency of land transfer and bring about an increase in the scale of agricultural operations, and thus improve agricultural productivity. But, there are few studies discussing the impact of land transfer on the environment. The research of Li et al. [126] and Zaehringer et al. [127] showed that due to the expansion of land transfer, the input factors tend to be more scientific, especially the reduction and efficiency improvements in polluting factors such as fertilizers, thereby reducing agricultural non-point source pollution emissions. However, the reality is that land transfer not only has economic effects, but also environmental effects. Therefore, only by considering both the economic and environmental impacts of land transfer can the research results be unbiased. Based on this, this study simultaneously considered the economic effects (desirable output) and environmental effects (undesirable output) of land transfer, and incorporated them into the analytical framework of agricultural green total factor productivity (AGTFP).

At the same time, we noticed that previous studies mainly used the SBM standard efficiency model and ML index to measure the outputs. The problem with this method is that when there are two or more effective units in the same period, the SBM standard efficiency model cannot rank them, while the total factor productivity measured by the ML index lacks cyclic and cumulative characteristics, and it can only make short-term judgments near the production period and is unable to measure the long-term trend of the growth level. To avoid these issues, we used the SBM super efficiency model and the GML productivity index, which were constructed based on the global technology set, for measuring agricultural green total factor productivity. This not only solves the problem of sorting effective decision-making units, but it also allows for global comparisons of production frontiers. At the same time, continuous production frontiers can avoid inward shifts, that is, technological regression, thereby avoiding the problem of passive increases in total factor productivity and improving future research.

We also found that the previous research on land transfer was mainly conducted at the individual or household level of agricultural production. For example, Liu et al. [128] believe that land transfer has significantly reduced the multidimensional relative poverty situation of farmers. Li et al. [129] found that land transfer can reduce the poverty vulnerability of rural households, and the larger the transfer area, the stronger the improvement effect on household poverty vulnerability. There is little literature discussing the impact of land transfer at a macro level, and most studies focused on the relationship between land transfer, agricultural production costs, and agricultural economic growth. For example, Xu et al. [130] believe that non-grain land transfer significantly and negatively affects the land cost and transfer land rent for grain production, while grain land transfer significantly and positively affects the land cost and transfer land rent for grain production. Fei et al. [56] believe that land transfer is beneficial for improving the efficiency of farmland allocation, thereby increasing agricultural output. This study empirically tested the impact of land transfer on sustainable agricultural development (green total factor productivity) at a macro level based on provincial panel data from China from 2010 to 2022, which expands the scope of the current research. At the same time, we also discussed the mechanism by which land transfer affects sustainable agricultural development from the perspectives of “output effects or environmental effects” and “factor allocation effects or scale operation effect”, as well as the differences in the impact of different transfer distributions, transfer methods, and transfer objects on sustainable agricultural development. This provides a new perspective for further improving land transfer policies and optimizing institutional pathways to promote sustainable agricultural development.

5.2. Conclusions

This study was conducted from the perspective of green total factor productivity, based on provincial panel data from 30 provinces (or autonomous regions or municipalities directly under the central government) in China from 2010 to 2022. Using a panel interaction fixed-effects model, we empirically tested the impact of land transfer on sustainable agricultural development, and deeply analyzed the underlying mechanisms and heterogeneity. The following conclusions were drawn:

(1) Land transfer can significantly promote sustainable agricultural development. After conducting robustness tests using five methods, including controlling regional omitted variables, replacing dependent variables, changing the sample size, IV estimation, and GMM estimation, this conclusion still held true.

(2) Mechanism testing found that land transfer mainly promotes sustainable agricultural development by increasing the desirable output, and has no significant effect on reducing non-point source pollution. At the same time, land transfer mainly improves the desirable output through factor allocation effects rather than scale operation effects, thereby promoting sustainable agricultural development.

(3) The heterogeneity analysis found that the higher the quantile of agricultural sustainable development level is, the weaker the role of land transfer in promoting agricultural sustainable development, indicating that land transfer has a greater impact in areas with poor agricultural development foundation, and areas with poor agricultural development foundation are more likely to obtain sustainable development space through land transfer. The impact of different forms of land transfer and different land transfer objects on sustainable agricultural development was heterogeneous. Compared with non-market transfer forms such as exchange and transfer, market-oriented transfer forms such as leasing and equity had a more significant impact on sustainable agricultural development. Compared to transferring land to ordinary farmers, transferring land to new business entities such as family farms, professional cooperatives, and enterprises can significantly promote sustainable agricultural development.

Based on this, we propose the following policy recommendations:

(1) We should continue to implement land transfer policies, deepen the separation of rural land rights, stabilize agricultural land contracting rights, gradually relax agricultural land management rights, adopt household contract management methods, and promote multiple management methods such as enterprise management and cooperative management. We should establish a sound system for the registration and confirmation of agricultural land transfer management rights, strictly regulate agricultural land transfer behaviors, strengthen the management and service efficiency of the entire process of agricultural land transfer, and achieve the goal of driving sustainable agricultural development through land transfer.

(2) As the current land transfer system has not effectively promoted larger-scale agricultural operations, this means that small-scale farmers are still the main force for agricultural production. Therefore, it is necessary to encourage and promote moderate-scale operations in agriculture, and improve the efficiency of agricultural operations. Specifically, we need to vigorously introduce and train new types of agricultural management entities such as professional farmers and large-scale planters, and drive the process of agricultural scaling. We should give full play to the link between urban and rural employment departments to solve the worries of idle rural labor forces in land transfer households. At the same time, in order to change the situation of decentralized agricultural management in China, in addition to developing land transfer, it is also necessary to simultaneously promote the construction of a socialized agricultural service system, accelerate the cultivation of new types of agricultural service entities; guide various agricultural service organizations to carry out socialized services such as contract farming and planting, entrusted collection, and full process entrusted management; and make up for the shortcomings of land transfer operations with the expansion of the service scale.

(3) Due to the current land transfer system mainly promoting sustainable agricultural development by increasing desirable outputs, its role in reducing agricultural non-point source pollution is not yet evident. We believe that this is mainly due to the current land transfer system not effectively promoting large-scale agricultural operations, which has affected the scientific and intelligent allocation of input factors, for example, the expansion of land scale, the use of agricultural machinery, green production methods and technology, and other mechanisms, resulting in a less significant effect on reducing agricultural non-point source pollution emissions. The corresponding countermeasures and suggestions are mainly to promote the large-scale operation of agriculture, which is consistent with the previous one.

(4) There was a greater promotion effect of land transfer on the sustainable development of agriculture in underdeveloped areas. Therefore, it is necessary to flexibly formulate and adjust land transfer policies based on the actual situation. Not only should we take into account the overall situation, but we should also pay attention to the implementation of land transfer policies in underdeveloped agricultural areas and explore suitable land transfer policy models for different regions during the policy implementation process. It is also important to emphasize dynamic management and timely feedback on issues during policy implementation in order to strengthen the promotion of sustainable agricultural development through land policy implementation.

(5) Due to the heterogeneity of the impact of different forms and objects of land transfer on agricultural sustainability and in the current context of the slowing down or even declining growth rate of land transfer, improving the marketization level of land transfer and guiding and promoting the rational concentration of land towards new agricultural management entities should be the focus of further explorations of the role of land transfer in promoting agricultural sustainable development. On the one hand, it is necessary to strengthen the construction of intermediary institutions and service systems for land transfer and provide intermediary services for farmers to transfer land through the establishment of regional land transfer information platforms, “land banks”, and other forms to promote the orderly, market-oriented, and organized transfer of land from the scattered spontaneous flow of “farmers to farmers” to the orderly, market-oriented, and organized transfer of “farmers to intermediary service organizations to new business entities”. On the other hand, it is necessary to reasonably use village collectives to coordinate land transfer and promote the centralized transfer of rural land to new agricultural management entities through various methods such as land sharing cooperation, land trusteeships, and joint farming and planting.

The limitations of this article and future research directions are as follows. Firstly, due to the lack of a large amount of annual data for county-level cities, we were unable to use data at the city and county levels for the analysis. In the next study, we will attempt to collect panel data at the prefecture level to analyze the impact of land transfer. Secondly, although we considered the main aspects that cause agricultural pollution, including agricultural fertilizer pollution and agricultural solid waste pollution, when calculating agricultural green total factor productivity, this does not fully cover all types of agricultural pollution emissions. Therefore, further research is needed to expand the selection of agricultural pollution sources in order to more appropriately reflect the undesirable outputs of agriculture. Finally, this study used provincial data to discuss the impact of land transfer in China on sustainable agricultural development from 2010 to 2022. This relatively short time period may not capture long-term trends or structural changes in the agricultural sector. Therefore, future research can collect data over a longer period to more comprehensively and accurately reflect the long-term impact of land transfer on sustainable agricultural development.

Author Contributions

Y.W. mainly provided the overall idea for the study, determined the structure of the article, and wrote the manuscript. W.Z. was responsible for collecting and organizing the data, as well as writing the empirical part of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Social Science Foundation Program of China (No. 22CJY040).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used in this study mainly came from statistical yearbooks and EPS databases of the various years (https://www.epsnet.com.cn/index.html#/Index) (accessed on 30 June 2024) and from the National Bureau of Statistics database (https://data.stats.gov.cn).

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. China’s agricultural green TFP index growth rate and its components during 2010–2022 (%).

Year	Green TFP				Traditional TFP
Year	TFP	PECH	TECH	SECH	TFP	PECH	TECH	SECH
2011	2.897	0.861	1.353	0.685	3.451	0.422	2.509	0.518
2012	2.932	0.724	1.417	0.807	3.114	0.815	1.607	0.690
2013	2.373	0.478	1.511	0.400	3.390	0.486	2.163	0.744
2014	2.308	0.532	1.465	0.308	3.379	0.250	2.940	0.192
2015	2.674	0.297	1.672	0.718	3.502	0.171	2.514	0.816
2016	3.443	0.675	1.923	0.848	4.575	0.449	3.659	0.469
2017	3.190	0.370	2.342	0.458	5.129	0.469	3.901	0.762
2018	3.142	0.257	2.224	0.662	6.369	0.792	4.916	0.663
2019	3.758	0.179	2.632	0.953	4.925	0.893	3.503	0.529
2020	3.127	0.397	2.486	0.248	6.255	0.448	4.983	0.823
2021	3.339	0.116	2.750	0.475	6.890	0.956	5.148	0.787
2022	3.118	0.216	2.714	0.190	5.217	0.377	4.445	0.388
Mean	3.029	0.426	2.041	0.563	4.684	0.546	3.523	0.617

Table 2. Variable definition and descriptive statistics.

Variable	Definition	Max	Min	Mean	SD
Desirable Output	2010 fixed price agricultural total output value (100 million CNY)(+)	5126.231	104.730	986.536	671.392
Undesirable Output	Agricultural non-point source pollutant emissions (ten thousand tons) (−)	51.290	18.241	27.396	16.019
Land Input	Total sowing area of crops (thousand hm²) (+)	15,209.413	143.813	6252.495	3806.764
Labor Input	Number of agricultural laborers/ten thousand people (+)	1364.827	42.107	348.457	274.206
Mechanical Investment	Total power of agricultural machinery (ten thousand kW) (+)	5474.301	917.285	1964.727	1634.487
Fertilizer Input	Agricultural fertilizer conversion to pure application amount (ten thousand tons) (−)	492.820	27.864	158.442	126.416
Water Resource Investment	Agricultural water consumption (ten thousand tons) (−)	82.554	34.350	51.690	46.908
Agricultural Sustainable Development	Agricultural green total factor productivity, calculated based on SBM-GML index (+)	1.119	0.894	1.027	0.168
Land Transfer Rate	Transfer area of land contract management rights/household contract management cultivated land area (+)	0.611	0.107	0.293	0.155
Irrigation Rate of Cultivated Land	Irrigation area of farmland/total sowing area of crops (+)	0.973	0.198	0.576	0.127
Rural Human Capital	The average education years of rural population (years) (+)	9.685	3.218	8.127	0.572
Agricultural Planting Structure	Sowing area of grain crops/total sowing area of crops (+)	0.823	0.314	0.643	0.154
Rural Laborers	Migrant labor force/total rural labor force (−)	0.513	0.225	0.417	0.170
Urbanization Level	Urban population/rural population (+)	11.054	1.721	1.924	2.745
Regional Industrial Structure	Total output value of the secondary and tertiary industries/output value of the primary industry (+)	427.281	14.374	24.289	36.521
Traffic Level	Per capita investment in village road and bridge construction (thousand CNY ) (+)	0.295	0.046	0.173	0.116
The Level of Agricultural Opening Up to the Outside World	Total import and export of agricultural products/total output value of agriculture, forestry, animal husbandry, and fisheries (+)	8.410	0.053	0.242	0.868
Financial Support for Agriculture	Agriculture, forestry, and water expenditure/local general public budget expenditure (+)	0.239	0.063	0.121	0.048
Natural Disaster Rate	Affected area of crops/total sowing area of crops (−)	0.459	0.004	0.138	0.187
Climatic Conditions	Annual average temperature in each province (°C) (+)	20.664	5.484	14.038	4.122

+: Indicating a positive impact. −: Indicates negative impact.

Table 3. Benchmark regression results.

Explanatory Variable	(1) Pooled OLS	(2) Pooled OLS	(3) FE	(4) Tw-FE	(5) In-FE
Land transfer rate	0.363 *** (0.014)	0.258 *** (0.055)	0.386 *** (0.053)	0.135 *** (0.076)	0.185 *** (0.021)
Control variable	-	Yes	Yes	Yes	Yes
Provincial fixed effects	-	-	Yes	Yes	Yes
Year fixed effects	-	-	-	Yes	Yes
Fixed effects of province–year interaction	-	-	-	-	Yes
Sample size	442	442	442	442	442

Note: The robust standard error is indicated in parentheses, and *** represents the 1% significance level. Due to space limitations, the results of the controlling variables are not listed; the same is true in the tables below.

Table 4. Endogeneity test results.

Explanatory Variable	(1) In-FE	(2) Dependent Variable Replacement	(3) Change Sample Size	(4) 2SLS	(5) 2GMM	(6) SYS-GMM
TFP	-	-	-	-	-	0.695 *** (0.127)
Land transfer rate	0.169 *** (0.056)	0.081 *** (0.069)	0.216 *** (0.087)	0.182 *** (0.065)	0.173 *** (0.053)	0.074 ** (0.033)
Control variable	Yes	Yes	Yes	Yes	Yes	Yes
Provincial fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Province–year interaction fixed effect	Yes	Yes	Yes	-	-	-
Region–year fixed effect	Yes	-	-	-	-	-
Kleibergen Paap rk LM	-	-	-	183.097 [0.000]	183.097 [0.000]
Kleibergen Paap rk Wald F	-	-	-	59.392 {13.43}	59.392 {13.43}	-
Hansen J	-	-	-	0.876 [0.348]	0.876 [0.348]	5.328 [0.581]
AR (1)	-	-	-	-	-	0.000
AR (2)	-	-	-	-	-	0.274
Sample size	442	422	304	417	417	419

Note: *** Indicates the 1% significance level, ** Indicates a significance level of 5%. The value inside [] is the p-value of the corresponding statistic; The value within {} is the critical value for the Stock–Yogo test at the 10% level. The same is true in the tables below.

Table 5. Impact mechanism test results.

Explanatory Variable	(1) In (Total Agricultural Output Value)	(2) In (Agricultural Non-Point Source Pollution Emissions)	(3) In (Land Productivity)	(4) In (Labor Productivity)	(5) The Proportion of Large-Scale Operating Farmers	(6) In (Scale Efficiency Index)
Land transfer rate	0.314 *** (0.036)	0.059 (0.087)	0.028 ** (0.075)	0.401 *** (0.013)	−0.021 ** (0.019)	−0.015 (0.064)
Control variable	Yes	Yes	Yes	Yes	Yes	Yes
Provincial fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Province–year interaction fixed effect	Yes	Yes	Yes	Yes	Yes	Yes
Sample size	442	442	442	442	413	442

*** represents the 1% significance level, ** indicates a significance level of 5%.

Table 6. Land transfer results at different quantiles.

Explanatory Variable	(1) 0.1 Percentile	(2) 0.25 Percentile	(3) 0.5 Percentile	(4) 0.75 Percentile	(5) 0.9th Percentile
Land transfer rate	0.215 *** (0.052)	0.231 *** (0.043)	0.229 *** (0.036)	0.174 *** (0.041)	0.102 (0.142)
Control variable	Yes	Yes	Yes	Yes	Yes
Provincial fixed effects	Yes	Yes	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes	Yes	Yes
Province–year interaction fixed effect	Yes	Yes	Yes	Yes	Yes
Sample size	442	442	413	442	442

*** represents the 1% significance level.

Table 7. Regression results of different land transfer forms.

Explanatory Variable	(1)	(2)	(3)
Non-marketization transfer	0.217 (0.024)	-	0.273 (0.041)
Marketization transfer	-	0.226 *** (0.013)	0.230 *** (0.012)
Control variable	Yes	Yes	Yes
Provincial fixed effects	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes
Province-year interaction fixed effect	Yes	Yes	Yes
Sample size	442	442	442

*** represents the 1% significance level.

Table 8. Regression results of different land transfer objects.

Explanatory Variable	(1)	(2)	(3)
Transferred to ordinary farmers	0.042 (0.016)	-	0.045 (0.084)
Transferred to new business entities	-	0.231 ** (0.037)	0.206 *** (0.052)
Control variable	Yes	Yes	Yes
Provincial fixed effects	Yes	Yes	Yes
Year fixed effect	Yes	Yes	Yes
Province–year interaction fixed effect	Yes	Yes	Yes
Sample size	417	417	417

*** represents the 1% significance level, ** indicates a significance level of 5%.

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MDPI and ACS Style

Wu, Y.; Zhang, W. The Impact of Land Transfer on Sustainable Agricultural Development from the Perspective of Green Total Factor Productivity. Sustainability 2024, 16, 7076. https://doi.org/10.3390/su16167076

AMA Style

Wu Y, Zhang W. The Impact of Land Transfer on Sustainable Agricultural Development from the Perspective of Green Total Factor Productivity. Sustainability. 2024; 16(16):7076. https://doi.org/10.3390/su16167076

Chicago/Turabian Style

Wu, Yangchenhao, and Wang Zhang. 2024. "The Impact of Land Transfer on Sustainable Agricultural Development from the Perspective of Green Total Factor Productivity" Sustainability 16, no. 16: 7076. https://doi.org/10.3390/su16167076

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The Impact of Land Transfer on Sustainable Agricultural Development from the Perspective of Green Total Factor Productivity

Abstract

1. Introduction

2. Theoretical Analysis and Research Hypotheses

2.1. The Overall Impact of Land Transfer on Sustainable Agricultural Development

2.2. The Impact of Land Transfer on the Desirable Output of Sustainable Agricultural Development

2.3. The Impact of Land Transfer on Undesirable Outputs of Sustainable Agricultural Development

3. Research Methods and Data Sources

3.1. Empirical Model Setting

3.2. Measurement of Green Total Factor Productivity in Chinese Agriculture

3.2.1. Research Methods

3.2.2. Indicator Selection and Processing

3.2.3. Measurement Results of Agricultural Green Total Factor Productivity

3.3. Variable Selection and Measurement

3.3.1. Explained Variable

3.3.2. Core Explanatory Variables

3.3.3. Control Variables

3.4. Data Source and Description

4. Results

4.1. Benchmark Regression Results

4.2. Endogenous Treatment

4.3. Impact Mechanism Testing

4.3.1. Output Effect or Environmental Effect

4.3.2. Factor Allocation Effect or Scale Operation Effect

4.4. Heterogeneity Analysis

4.4.1. Heterogeneity of Different Land Transfer Distributions

4.4.2. Heterogeneity of Land Transfer Methods

4.4.3. Heterogeneity of Land Transfer Objects

5. Discussion and Conclusions

5.1. Discussion

5.2. Conclusions

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

References

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