Unveiling the Impact of Digital Financial Inclusion on Low-Carbon Green Utilization of Farmland: The Roles of Farmland Transfer and Management Scale

Abstract

Low-carbon green utilization of farmland, which is a significant driver of high-quality development of agriculture, has aroused wide concern in the recent years. In practice, the expansion of digital financial inclusion seems to provide valuable opportunities for the development of low-carbon green utilization of farmland. In these conditions, using Chinese provincial panel data from 2011 to 2020 and structural equation model (SEM) analysis in STATA 16.0, this paper empirically verified that: (1) digital financial inclusion is positively related to low-carbon green utilization of farmland; (2) farmland transfer mediates the relationship between digital financial inclusion and low-carbon green utilization of farmland; (3) farmland management scale positively moderates the relationship between farmland transfer and low-carbon green utilization of farmland and it is in support of moderated mediating effects. This paper attempts to investigate whether, how, and when digital financial inclusion can affect low-carbon green utilization of farmland, which provides new empirical evidence for the improvement of farmland green utilization.

1. Introduction

Farmland utilization not only affects food security, but also social stability of a country [1]. China is a large agricultural country that is feeding a fifth of the global population [2]; hence, farmland utilization has always been an important concern of the government. In the recent years, with the rapid growth of human population, and the booming development of urbanization and industrialization, the shortage and damage of farmland are worsening continuously [3,4,5,6]. According to the State Statistical Bureau of China, both the world’s and China’s arable land area showed a downward trend from 2011 to 2017 (Figure 1a). Furthermore, high intensity farmland utilization activities in China release a lot of carbon dioxide [7] and seriously pollute the ecological environment around the farmland [8]. Referring to the data from the Food and Agriculture Organization of the United Nations in 2021, the utilization of chemical fertilizers and pesticides per unit area in China is much higher than the world average, resulting in a dramatic decline in the quality of farmland (Figure 1b). Hence, the Chinese government has released a series of policies and laws related to farmland green utilization, such as the Land Administration Law of the People’s Republic of China and Regulations on Farmland Protection, so as to improve farmland green utilization efficiency. Low-carbon green utilization efficiency of farmland is an efficiency measurement concept that takes the output of economic and social dimensions as the desirable output and the environmental pollution as the undesirable output, and its goal is to promote the maximization of economic and social outputs and the minimization of environmental pollution through scientific evaluation [9]. The improvement of low-carbon green utilization of farmland has become the top priority of Chinese agricultural development [10].

Digital financial inclusion, characterized by digitalization and inclusiveness [11], has been generally recognized as a significant promoter of efficiency, effectiveness and sustainability of agricultural production [12,13,14]. Digital financial inclusion is defined as a financial system that can provide effective and comprehensive services to all sectors and groups of society. Its purpose is to emphasize the continuous improvement of financial infrastructure and the availability of financial services, so as to provide convenient financial services to people from all walks of life, especially those in underdeveloped areas and low-income society, at a relatively low cost [15]. According to the Peking University Digital Financial Inclusion Index of China, the coverage breadth, the usage depth, and the digitalized level of inclusive finance increased dramatically from 2011 to 2020 in China [16]. The extant literature has identified that digital financial inclusion can enhance the development of the agricultural supply chain [14], agricultural industrial structure optimization and green total factor productivity [17], agricultural high-quality development [18], agricultural production for rural households [19], etc. It seems that the rapid development of digital financial inclusion brings opportunities for the green development of agricultural production. Nevertheless, research on the relationship between digital financial inclusion and low-carbon green utilization of farmland is scarce.

Accordingly, this paper attempts to investigate whether (directing effects), how (mediating effects) and when (moderating effects) digital financial inclusion can promote low-carbon green utilization of farmland. On the basis of the existing literature, digital financial inclusion can facilitate the development of farmland transfer [18,20], which refers to farmers having the rights to transfer their land use rights to other farmers or economic organizations who are capable and willing to manage it [21]. Further, the existing literature also identified that farmland transfer can enhance low-carbon green utilization of farmland [22,23]. Hence, farmland transfer seems to be a mediator in the relationship between digital financial inclusion and low-carbon green utilization of farmland, while the extant literature ignores the mediating effect mechanism. Moreover, the existing literature verified that farmland management scale seems to be a significant factor that influences low-carbon green utilization of farmland [24,25,26], and it also has close connection with farmland transfer [23,27], while the extant literature ignores exploring their in-depth relationships.

To address these gaps, this paper attempts to investigate the following questions:

• Can digital financial inclusion improve low-carbon green utilization of farmland directly?

• How can farmland transfer mediate the relationship between digital financial inclusion and low-carbon green utilization of farmland indirectly?

• How can farmland management scale moderate the relationship between farmland transfer and low-carbon green utilization of farmland?

In order to deal with these research questions, this paper collects Chinese provincial panel data from 2011 to 2020 and conducts equation model analyses in STATA 16.0. Our research contributes to the extant literature in the following aspects. To our knowledge, it is one of the first to theoretically and empirically identify that digital financial inclusion can dramatically improve low-carbon green utilization of farmland. Additionally, we integrate digital financial inclusion, farmland transfer, and farmland management scale in the analytical framework of low-carbon green utilization of farmland. This study can provide some practical guidance for governments, financial institutions, and farmland households. Based on the study, governments are suggested to increase the investments in the research and development of digital financial technologies, improve farmland transfer regulations and rules, regulate and control farmland transfer procedures, and increase the farmland transfer subsidy to improve low-carbon green utilization of farmland. The financial institutions are recommended to accelerate the establishment of the rural digital financial credit system and optimize agricultural digital financial insurance services. In addition, the farmland households can strengthen the study of Internet knowledge and expand the utilization of the Internet platform to promote low-carbon green utilization of farmland.

The remainder of the paper is structured as follows. The second section focuses on the literature review and hypothesis development. In the third section, we discuss the methodology. In the fourth section, the results and analyses are reported. Then, we demonstrate our conclusions, contributions, and future research directions in the fifth section. Finally, we make a conclusion.

2. Literature Review and Hypothesis Development

2.1. Literature Review

There have been many studies that focus on the measurements and determinants of low-carbon green utilization efficiency of farmland, which, to some extent, provide the theoretical and empirical foundations for our research. Based on the data from 30 major Chinese provinces and the super-efficiency SBM model, Zhou et al. [23] verified that farmland transfer can promote low-carbon green utilization efficiency of farmland through the mediation of farmland management scale. Qu et al. [28] collected a questionnaire of 952 farming households in Shandong Province and used the super-efficiency EBM model to measure low-carbon green utilization efficiency of farmland. It was found that a farming household’s per capita income is positively related to low-carbon green utilization efficiency of farmland, while agricultural insurance, agricultural subsidies, farmland scale, farmland fragmentation, and regional economic level are negatively related to low-carbon green utilization efficiency of farmland. Referring to Chinese provincial panel data, using a super-efficient SBM-VRS model, Chen and Xie [29] measured the cultivated land green utilization efficiency of China’s major provinces and identified that farmers’ dependence on farmland and agricultural added value can improve Chinese cultivated land green utilization efficiency, while farmers’ occupational differentiation, agricultural machinery density, and agricultural disaster rate adversely influence Chinese cultivated land green utilization efficiency. These studies are conductive to our research on the measurements and influencing factors of low-carbon green utilization of farmland. Although the extant literature ignores the research on the relationship to digital financial inclusion, there have been some studies verifying that high-quality financial services [30,31,32] and digital technology progress [33,34] can dramatically promote farmland green utilization, which also provides support for our studies.

Furthermore, there is an increasing number of researchers paying attention to the impact of digital financial inclusion on agricultural development. According to the extant literature, digital financial inclusion can promote the development of the agricultural supply chain [14], facilitate agricultural industrial structure optimization and green total factor productivity [17], improve agricultural high-quality development [18], promote agricultural production for rural households [19], and enhance agricultural eco-efficiency through influencing the agricultural scientific and technological investment [35]. Moreover, scholars have empirically verified that digital financial inclusion can promote environmental conservation. For instance, digital financial inclusion can enhance the green innovation of heavily polluting firms [36], improve the level of regional green innovation through industrial structure optimization [37], improve green economic efficiency through the strengthening of the credit constraints on high-polluting firms [38], and reduce carbon dioxide emission through the improvement of financial resources and financial services [39]. In general, digital financial inclusion provides guarantees for the green development of agriculture, industry, and business, which is conducive to our research.

In addition, more and more scholars focus on research on the antecedent variables and consequence variables of farmland transfer, which can provide some guidance for this paper. In terms of the antecedent variables, Fan et al. [40] pointed out that the proportion of migrant workers and large-scale farm enterprises are positively related to farmland transfer; however, the proportion of local nonfarm workers is negatively related to farmland transfer. Cao et al. [27] verified that at a single time point the land tenure fragmentation can promote the farmland transferring decision of households, while in the time series, the land tenure fragmentation does not affect the farmland transferring decision of households. Chen et al. [41] demonstrated that farm households moving out to urban areas for employment is a significant driver for farmland transfer, and simultaneously high family income and flat terrain are helpful for the sale of farmland as well. Digital financial inclusion, as is mentioned, can also facilitate the development of farmland transfer [18,20]. These studies mainly focused on the effects of transfer of rural labor and land tenure fragmentation on farmland transfer. In terms of the consequence variables, Lu et al. [21] pointed out that the appropriately expanding plot size through farmland transfer will improve farmland protection techniques and sustainable development of agriculture. Zhou et al. [10] and Zhou et al. [23] empirically verified farmland transfer can directly facilitate low-carbon green utilization efficiency of farmland and indirectly improve low-carbon green utilization efficiency of farmland through farmland management scale. Farmland transfer seems to be a vital driver of the effectiveness, efficiency, and sustainable development of farmland production.

Accordingly, the extant literature has measured low-carbon green utilization efficiency of farmland using super-efficiency SBM model, super-efficient SBM-VRS model, etc., which provides the measurement method and indicators for our research. The extant literature has also verified that farmland transfer, farmland management scale [23], the farming households’ per capita income [28], and farmers’ dependence on farmland and agricultural added value [29] can dramatically improve low-carbon green utilization of farmland, and ignored to explore the relationship between digital financial inclusion and low-carbon green utilization efficiency of farmland. Nevertheless, digital financial inclusion has been verified to be the promoter of agricultural development [14,17,18,19,35] and environmental protection [36,37,38,39], which, to some extent, provided the research basis for our research. In addition, digital financial inclusion has been verified to be the diver of farmland transfer [18,20], and further farmland transfer has been verified to be the promoter of low-carbon green utilization efficiency of farmland [10,23]. Hence, farmland transfer seems to be the mediator in the relationship between digital financial inclusion and low-carbon green utilization efficiency of farmland.

2.2. Hypothesis Development

2.2.1. Digital Finance Inclusion and Low-Carbon Green Utilization of Farmland

With the development of digital financial inclusion, accompanied by the expansion of coverage breadth, usage depth, and digitalized levels of financial services [16], low-carbon green utilization of farmland will grow up gradually. Specifically, first, the expansion of coverage breadth can increase the farmers’ availability of local financial resources and services [42]. With the development of modern agriculture, the costs of agricultural production factor inputs are rising. Farmers’ own incomes are difficult to support the funding demands of large-scale agricultural machinery and equipment inputs [43]. Digital financial inclusion decreases the loan restrictions and standards and increases the availability of low-cost financial lending services, enabling farmers to introduce advanced agricultural technologies and purchase large-scale agricultural machinery and equipment. Numerous empirical evidences support that the introduction of agricultural technology and agricultural machinery is conducive to the improvement of low-carbon green utilization of farmland [44,45,46]. Thereby, the expansion of coverage breadth of digital finance can facilitate low-carbon green utilization of farmland through efficient financing supports and the extension of large-scale agricultural modernization.

Second, the reinforcement of usage depth of digital financial inclusion can expand agricultural markets and pull farmland outputs through changing the traditional payment models and trading channels [14], which, in turn, improves low-carbon green utilization of farmland. During the traditional transactions, the payment patterns used by farmers are mainly offline real currency transactions. The sales channels of agricultural products are single, the sales volume is limited, and the conventional banking is probably not cost-effective for low-ticket-size transactions [47]. On the contrary, the payment function of digital financial inclusion is fast, convenient, and efficient, which can effectively promote the e-commerce of agricultural products [48]. For instance, farmers in remote areas can directly sell agricultural products through the Internet, smart phones, and other platforms; hence, the transaction of agricultural products is no longer limited by geographical scope, the transaction costs will go down, and farmland outputs can be boosted. Consequently, it is helpful for the improvement of utilization efficiency of cultivated land.

Third, the development of digital levels of digital financial inclusion can further promote the low-carbon green utilization of farmland. Apart from the characteristics of inclusiveness, digital financial inclusion also has the characteristics of digitalization [11]. Thanks to the development of digitalization, the IoT-based irrigation and fertilization systems can improve the efficiency of irrigation processes and minimize water and fertilizer losses in agricultural fields. The blockchain-based electronic agriculture and solutions based on drones and robotics can reduce herbicide and pesticide utilization [49]. Additionally, with the expansion of the coverage breadth of digital financial inclusion to a wider population, which usually strengthens the credit constraints on high-polluting agricultural loans and provides efficiency for green finance [50], low-carbon green utilization of farmland can be dramatically improved. Accordingly, we propose the following hypothesis:

Hypothesis 1:

Digital financial inclusion is positively related to low-carbon green utilization of farmland.

2.2.2. Digital Finance Inclusion and Farmland Transfer

The development of the coverage breadth, usage depth, and digitization level of digital financial inclusion can effectively promote farmland transfer in the following aspects. First of all, the expansion of the coverage breadth greatly increases the area and population covered by digital financial services [42]. The expansion of digital financial services can ease the problems of information asymmetry, reduce the transaction costs, and optimize the capital allocation [51], which, in turn, promotes farmland transfer. Specifically, thanks to the development of digital financial inclusion, the asset information, land information, and credit information about transferees and transferees are more transparent. It dramatically shortens the distance between the transferees and transferees, strengthens their communication and cooperation, and subsequently promotes the farmland transferring behaviors. Research carried out in Nicaragua indicated that an incomplete credit market would resist the formation of land markets [52], supporting it from another perspective. Additionally, the transaction costs of farmland transferring are declining with the convenient financial services; hence, the willingness of conducting farmland transferring transactions of both parties will increase [53]. In the whole, the coverage breadth of digital financial inclusion can enhance farmland transfer through easing the problems of information asymmetry, reducing the transaction costs, and optimizing the capital allocation.

Furthermore, with the extension of the usage depth of digital financial inclusion, regional economic development levels and non-agricultural incomes will increase, which further promotes farmland transfer. Specifically, with the reinforcement of usage depth of digital finance, non-agricultural entrepreneurial and employment opportunities are created [54]. On the one hand, digital financial inclusion can help farmers to obtain online loans at reasonable interest rates without collateral assets, which efficiently supports farmers’ entrepreneurial activities [55]. On the other hand, digital financial inclusion can promote regional economic growth, especially the development of small and medium enterprises; hence, many employment opportunities are provided to farmers [56]. Since a large number of farmers are engaged in non-agricultural sectors, more farmland management rights will be transferred to professional operators and organizations. Additionally, according to Peng, et al. [57], not only can digital financial inclusion increase non-agricultural employment opportunities, but it also can increase the profits of land investment-related behaviors, thus jointly promoting farmland transfer from the two aspects of farmland transfer-out and transfer-in. The further deepening of digital financial inclusion is conducive to the optimization of agricultural structure, so as to improve agricultural incomes, which, to some extent, attracts some farmers to buy farmland to expand farmland operating scale [17]. Consequently, the coverage breadth of digital financial inclusion can promote farmland transfer through enhancing regional economic development levels, non-agricultural incomes, and agricultural incomes.

In addition, the improvement of the digitalized levels of financial inclusion can expand the scale of agricultural mechanization, further facilitating farmland transfer. In the modern agriculture, the smart farm mechanization consists of application of sensors, controllers, IoT, AI and robotics [58]. Obviously, digital technologies provide the technical support and digital finance provides funding guarantee for smart farm mechanization. Further, it has been verified that agricultural mechanization levels play an important role in promoting farmland flow intensity [59]. Hence, the digitalized levels of financial inclusion can enhance farmland transfer through agricultural mechanization. Based on the above analysis, the following hypothesis is proposed:

Hypothesis 2:

Digital financial inclusion is positively related to farmland transfer.

2.2.3. Farmland Transfer and Low-Carbon Green Utilization of Farmland

The essence of farmland transfer is the transferring of farmland management rights from some individual farmers to more professional farmers or organizations. In the process, it is usually accompanied by the transfer of managing rights of farmland from low-productivity managers to high-productivity managers. The process mitigates farmland resources misallocation and effectively promotes the development of low-carbon green utilization of farmland [60]. Specifically, first, operators with high efficiency own technological and cultural advantages, which drives them to control energy inputs and reduce the carbon and pollutant emissions [61]. Moreover, long-term and stable management rights can increase operators’ farmland protection intention [21]. According to Zang et al. [62], land transfer can not only improve the possibility of trusteeship farmers with green production intention to engage in green production, but also introduce green production factors to trusteeship farmers with productive service needs but no clear green production intention. On the whole, farmland transfer can promote low-carbon green utilization of farmland through transferring management rights to more professional operators and improving farmers’ green intention.

Second, farmland transfer can facilitate low-carbon green utilization of farmland through the adjustment of grain planting structure. According to Lu et al. [21], under the provisions of non-grain conversion of farmland in the process of farmland transfer, farmland cannot be used for cash crops or animal husbandry, and the farmers can only grow grain in their transfer-in land. Since the fertilizer and pesticide usages of food crops are significantly lower than that of other cash crops, low-carbon green utilization of farmland can be improved. Further, the Chinese government encourages farmland transfer with the aim to ensure the grain supply and food security [63], which further increases the proportion of grain crops and maintains the reduction of pollution. Accordingly, farmland transfer optimizes grain planting structure and consequently improves low-carbon green utilization of farmland.

Third, cultivating land transfer can greatly improve farmland green utilization through large-scale agricultural modernization. According to Chen et al. [41], farmland transfer plays significant roles in dealing with the conflicts between the farmland fragmentation of landholders and large-scale agricultural modernization. Large-scale agricultural modernization is a significant enhancer of the green utilization of cultivated land. The introduction of agricultural green technologies has certain requirements on the operating scale, for the reason that it is difficult for small-scale individuals to conduct the time-consuming, high-investing, and high-technical activities [64]. Moreover, the governments and public welfare departments always focus on the training and guidance on agricultural green technology for large-scale operating organizations, which further enhances low-carbon green utilization of farmland [65]. Therefore, we propose the following hypothesis:

Hypothesis 3:

Digital financial inclusion is positively related to farmland transfer.

Combining Hypothesis 1, Hypothesis 2, and Hypothesis 3, we further propose the following hypothesis:

Hypothesis 4:

Farmland transfer mediates the relationship between digital financial inclusion and low-carbon green utilization of farmland.

2.2.4. The Moderated Mediating Effects of Farmland Management Scale

As is mentioned above, farmland transfer can facilitate low-carbon green utilization of farmland through transferring management rights to more professional operators, and driving them to introduce more green technologies and activities. When the management scale (size per agricultural labor) increases, the positive effects will be strengthened. According to Paul et al. [66], small-scale agricultural production is both scale and technically inefficient. Observed production patterns in the U.S. agricultural sector indicate that the technological and structural changes are usually associated with economies from both scale of production and output composition, so that larger and more diversified farms are increasingly more productive or efficient than small farms [67]. Both farmland transfer and the expansion of management scale aim at promoting the transformation of management from small operating entities into large ones. The combined effects of farmland transfer and management scale can efficiently achieve large-scale agricultural production and subsequently promote low-carbon green utilization of farmland.

Additionally, it is demonstrated that farmland transfer can optimize grain planting structure, which, in turn, enhances low-carbon green utilization of farmland. In the process, the expansion of management scale can reinforce the effects of farmland transfer on grain planting structure adjustment. As we have mentioned, increasing the planting of food crops can control the inputs of fertilizer and pesticide to cultivated land. Compared with non-food crops, growing food crops (rice, wheat, and corn) depends more on the management scale of farmland [24]. The extant literature has verified that farmland management scale is positively related to the reduction of fertilizer and pesticide [68], supporting it from the empirical results. The combined effects of farmland transfer and management scale can optimize grain planting structure and consequently facilitate low-carbon green utilization of farmland.

Hypothesis 5:

Farmland management scale positively moderates the relationship between farmland transfer and low-carbon green utilization of farmland.

Based on the above analyses, we build up the following conceptual model (Figure 2):

3. Materials and Methods

3.1. Sample Selection and Data Sources

In order to test the proposed hypotheses, we use the sample of Chinese provincial panel data. There are 34 provincial administrative units in China. Limited by the availability of data, Hong Kong, Macao, Taiwan, and Tibet are excluded from empirical research. Hence, the research objects of this paper are the 30 provinces in mainland China, including Beijing, Tianjin, Chongqing, Hebei, Shanxi, Henan, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Guangxi, Shanghai, Jiangsu, Guizhou, Zhejiang, Anhui, Qinghai, Fujian, Jiangxi, Guangdong, Shandong, Hubei, Hunan, Ningxia, Hainan, Sichuan, Yunnan, Tibet, Shaanxi, Gansu, and Xinjiang.

This paper uses the “China Statistical Yearbook”, “China Land and Resources Statistical Yearbook”, “China Rural Management Statistical Yearbook”, “China Rural Statistical Yearbook”, “China Environmental Statistical Yearbook”, and the website of the National Bureau of Statistics to collect the data indicators of low-carbon green utilization efficiency of farmland, farmland transfer, and farmland management scale. Meanwhile, this paper uses the interpolation method to fill the gaps caused by missing data in each year. The data source of digital financial inclusion is collected from the Peking University China digital financial inclusion index. Due to the statistical data of the digital financial inclusion index from 2011, the observation period is selected from 2011 to 2020.

3.2. Measurements of Variables

3.2.1. Measurements of Explained Variable

The explained variable in the research is low-carbon green utilization of farmland. Referring to Xie et al. [69] and Ke et al. [22], the measurement of low-carbon green utilization efficiency of farmland consists of three significant components: input indicators, desirable output indicators, and undesirable output indicators. Wherein input indicators involve labor inputs, land inputs, and capital inputs; desirable output indicators include economic outputs, social outputs, and environmental outputs; undesirable outputs refer to pollutant emissions and carbon emissions. The specific measurement variates and descriptions are presented (Table 1). In terms of the measurement method, the SBM-Undesirable-VRS model was used (Formulas (1) and (2)). We suppose that the number of decision-making units (DMUs) in farmland utilization is n, the number of input factor types is m, the number of desirable output types and undesirable output types are S₁ and S₂, respectively, and the three sets of vectors $x\in R^m$, $y^g\in R^{s_1}$, $y^b\in R^{s_2}$ represent inputs, desirable outputs, and undesirable outputs, respectively; we define $X=\left[x_1,\dots , x_n\right]\in R^{m\times n}$, $Y^g=\left[y_1^g,\dots , y_n^g\right]\in R^{s_1\times n}$, $Y^b=\left[y_1^b,\dots , y_n^b\right]\in R^{s_2\times n}$.

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

(2)$s.t. \left\{\begin{array}{c}{x}_{ik}\ge {\displaystyle \sum }_{j=1, j\ne h}^n{\lambda }_j{x}_{ij}-D_i^{-},i=1, \dots , m\\ y_{rh}^g\ge {\displaystyle \sum }_{j=1, j\ne h}^n{\lambda }_j{y}_{rj}^g+D_i^g,r=1, \dots , S_1\\ y_{kh}^b\ge {\displaystyle \sum }_{j=1, j\ne h}^n{\lambda }_j{y}_{kj}^b-D_k^b,k=1, \dots , S_2\\ \begin{array}{c}1-\frac{1}{s_1+s_2}\left({\displaystyle \sum }_{r=1}^{s_1}\frac{D_r^g}{y_{rh}^g}+{\displaystyle \sum }_{k=1}^{s_2}\frac{D_k^b}{y_{kh}^b}\right)>0\end{array}\\ D^{-}\ge 0,D^g\ge 0,D^b\ge 0\end{array}\right.$

In Formulas (1) and (2), D⁻, D^g, and D^b represent the slack variables of inputs, desirable outputs, and undesirable outputs, λ is the weight vector, and ${\rho }^{\ast }$ represents the index of low-carbon green utilization efficiency of farmland.

3.2.2. Measurements of Explanatory Variable

In the research, the explanatory variable is digital financial inclusion. We measure it using the digital financial inclusion index. The digital financial inclusion index consists of three dimensions: coverage breadth, usage depth, and digitalized level. The specific measurement indicators are presented in Table 2. Detailed information about the measurement of this index can be found at https://idf.pku.edu.cn/docs/20210421101507614920.pdf (accessed on 1 August 2022).

3.2.3. Measurements of Mediating Variable

The mediating variable of this research is farmland transfer. Referring to Liu and Liu [70] and Kuang and Peng [71], farmland transfer is measured by the proportion of total area of transferred farmland to total area of contracted farmland of farm households.

3.2.4. Measurements of Moderating Variable

The moderating variable of this research is farmland management scale. Referring to Zhou et al. [23] and Chen and Wang [72], farmland management scale is measured by the ratio of total planting area to total rural population (size per rural labor).

3.3. Model Construction

3.3.1. Models of Main Effects

The construction of structural equation models can not only deal with explicit variables and latent variables, but also analyze the relationship between multiple explanatory variables, multiple explained variables, and multiple mediation variables [73]. Referring to the relationships between explanatory variable and explained variable, this paper constructs the following path model of the main effects (Formula (3)):

(3)$lcgu{f}_{i,t}=c_{1}df{i}_{i,t}+e_{i,t}$

In Formula (3), lcguf_i,t represents the low-carbon green utilization efficiency of farmland of the province i in the year t, dif_i,t represents the digital financial inclusion index of province i in the year t, e_i,t represents the error term, and c₁ is the path coefficient of digital financial inclusion influencing low-carbon green utilization efficiency of farmland. If the path coefficient c₁ is significantly positive, H1 is verified.

3.3.2. Models of Mediating Effects

According to the relationships among explanatory variable, mediating variable, and explained variable, this paper constructs the following path model of the mediating effects (Formula (4)).

(4)$\left\{\begin{array}{c}fl{t}_{i,t}=a_{1}df{i}_{i,t}+e_{i,t}\\ lcgu{f}_{i,t}=b_{1}fl{t}_{i,t}+c_1^{`}df{i}_{i,t}+e_{i,t}\end{array}\right.$

In Formula (4), flt_i,t represents farmland transfer ratio of the province i in the year t, a₁ is the path coefficient of digital financial inclusion affecting farmland transfer, and b₁ and $c_1^{`}$ represent the path coefficients of farmland transfer influencing low-carbon green utilization efficiency of farmland and digital financial inclusion affecting low-carbon green utilization efficiency of farmland, respectively. If the path coefficient a₁ is significantly positive, H2 is verified. If the path coefficient b₁ is significantly positive, H3 is supported. If the mediating path coefficient a₁ × b_{1 (}dfi→flt→lcguf) is significantly positive, H4 is verified.

3.3.3. Models of Moderated Mediating Effects

Based on the relationships among explanatory variable, mediating variable, moderating variable and explained variable, this paper constructs the following path model of the moderated mediating effects (Formula (5)).

(5)$\left\{\begin{array}{c}fl{t}_{i,t}=a_{2}df{i}_{i,t}+e_{i,t}\\ lcgu{f}_{i,t}=b_{2}fl{t}_{i,t}+c_2^{`}df{i}_{i,t}+b_{3}flm{s}_{i,t}+b_{4}dfl{t}_{i,t}\times dflm{s}_{i,t}+e_{i,t}\end{array}\right.$

In Formula (5), flms_i,t represents the farmland management scale of the province i in the year t, dflt_i,t and dflms_i,t represent the decentration value of farmland transfer and farmland management scale, respectively, and b₄ represents the coefficient of the interaction term of farmland transfer and farmland management scale. If the coefficient b₄ is significantly positive, H5 is verified.

4. Results

4.1. Results of Descriptive Statistics

The results of the descriptive statistics of the variables are illustrated in Table 3. First, in terms of digital financial inclusion, the mean value of the three dimensions are 196.700, 211.100, and 290.100, respectively; the standard deviation of the three dimensions are 96.560, 98.190, and 117.300, respectively. It indicates that the data of the coverage breath, usage depth, and digitalized level of digital financial inclusion vary dramatically. Moreover, the minimum values are 1.960, 6.760, and 7.580, respectively, indicating that the levels of digital financial inclusion of some provinces in some years are much lower than the average level. Second, in terms of farmland transfer, the mean value and standard deviation are 0.316 and 0.163, respectively; the minimum and maximum value are 0.0034 and 0.9111, respectively. Accordingly, the data of the ratio of farmland transfer vary slightly and the ratio of farmland transfer in most provinces is at a relatively low level. Third, in terms of farmland management scale, the mean value and standard deviation are 2.878 and 1.843, respectively; the minimum value and maximum value are 0.307 and 13.620, respectively. The data of the farmland management scale vary slightly. Finally, as for low-carbon green utilization of farmland, the mean value of is 0.704, indicating that low-carbon green utilization of farmland in most provinces is kept at relatively high levels. However, the minimum value is 0.315, which indicates that the low-carbon green utilization of farmland in some provinces in some years is quite lower than the average level. The standard deviation is 0.198, indicating that the entire data varies slightly.

4.2. Structural Equation Model Results of the Main Effects

The structural equation model results of the main effects are presented in Figure 3 and Table 4. The RMSEA value is 0.037 (less than 0.05), the SRMR value is 0.014 (less than 0.08), and CFI and TLT are 0.999 and 0.998, respectively (close to 1). They indicate good goodness of fit of the main effect model [74]. The factor loadings of coverage breath, usage depth, and digitalized level on digital financial inclusion are 0.979, 0.957, and 0.836, respectively. Furthermore, the path coefficient of digital financial inclusion on low-carbon green utilization of farmland is 0.438, significant at the 1% level. Hypothesis 1 is supported.

4.3. Structural Equation Model Results of the Mediating Effects

The structural equation model results of the mediating effects are illustrated in Figure 4 and Table 5. The RMSEA value is 0.156 (larger than 0.05, unsatisfactory), and the SRMR value is 0.033 (smaller than 0.08). CFI and TLT are 0.975 and 0.938, respectively (close to 1). They indicate accepted goodness of fit of the mediating effect model [74]. The factor loadings of coverage breath, usage depth, and digitalized level on digital financial inclusion are 0.937, 0.964, and 0.835, respectively. Further, the path coefficient of digital financial inclusion on farmland transfer is 0.497, passing the test at the 1% significant level. Hypothesis 2 is supported. Meanwhile, the path coefficient of farmland transfer on low-carbon green utilization of farmland is 0.273, significant at the 1% level; hence, Hypothesis 3 is verified. Furthermore, based on the results of the significance test of indirect effect (Table 6), the indirect path coefficient a₁ × b₁ (dfi→flt→lcguf) is 0.132, significant at the 1% level; H4 is verified. The path coefficient of digital financial inclusion on low-carbon green utilization of farmland is 0.310, passing the test at the 1% significant level. Farmland transfer plays partial mediating effects on the relationship between digital financial inclusion and low-carbon green utilization of farmland.

4.4. Structural Equation Model Results of the Moderated Mediating Effects

The structural equation model results of the moderated mediating effects are illustrated in Figure 5 and Table 7. The RMSEA value is 0.134 (larger than 0.05, unsatisfactory), the SRMR value is 0.031 (smaller than 0.08). CFI and TLT are 0.956 and 0.912, respectively (close to 1). They indicate accepted goodness of fit of the mediating effect model [74]. The factor loadings of coverage breath, usage depth, and digitalized level on digital financial inclusion are 0.977, 0.960, and 0.836, respectively. Furthermore, the path coefficient of the interaction term of farmland transfer and farmland management scale on low-carbon green utilization of farmland is 0.182, significant at the 1% significant level. The moderating effects of farmland management scale on the relationship between farmland transfer and low-carbon green utilization of farmland is verified. Further, the test of moderated mediating effects of farmland management scale is passed, with the path coefficient of 0.330, significant at the 1% level (Table 8). Hence, Hypothesis 5 is supported.

5. Discussion and Implications

5.1. Discussion

With the rapid growth of human population and the booming development of urbanization and industrialization, the shortage and damage of farmland are worsening continuously. The high intensity farmland utilization activities in China release a lot of carbon dioxide and seriously pollute the ecological environment around the farmland. Given the development of digital financial inclusion, the associated economic, social, and environmental benefits in agricultural development have attracted extensive attention. In this case, this paper attempts to explore whether, how, and when digital financial inclusion can affect low-carbon green utilization of farmland. First, the results of the structural equation model of the main effects indicate that digital financial inclusion is positively related to low-carbon green utilization of farmland (path coefficient is 0.438, significant at 1% level). Accordingly, digital financial inclusion can directly facilitate low-carbon green utilization of farmland. Specifically, with the expansion of coverage breadth, usage depth, and digitalized level of financial services, farm householders can obtain efficient financing support, achieve large-scale agricultural modernization, improve agricultural product sales, reduce transaction costs, and introduce digital technologies and green finance, which consequently facilitates the low-carbon green utilization of farmland.

According to the results of the structural equation model of the mediating effects, digital financial inclusion is positively related to farmland transfer (path coefficient is 0.497, significant at 1% level), farmland transfer is positively related to low-carbon green utilization of farmland (path coefficient is 0.273, significant at 1% level), and the mediating effect test of farmland transfer is passed (path coefficient is 0.136, significant at 1% level). Hence, digital financial inclusion can indirectly enhance low-carbon green utilization of farmland through the increasing of farmland transfer. To be specific, with the development of the coverage breadth, usage depth, and digitalized level of digital financial inclusion, information asymmetry problems can be eased, transaction costs can be reduced, agricultural and non-agricultural incomes will be increased, and scale of agricultural mechanization can be expanded, which are significant drivers of farmland transfer. Further, farmland transfer facilitates low-carbon green utilization of farmland through transferring management rights from low-efficiency operators to high-efficiency operators, reducing carbon and other pollutant emissions and adjusting grain planting structure.

Based on the results of the structural equation model of the moderated mediating effects, the path coefficient of the interaction terms of farmland transfer and farmland management scale is 0.182, passing the significant tests at the 1% level. Moreover, the moderated mediating effect test of farmland management scale is passed (path coefficient is 0.330, significant at 1% level). It indicates that farmland management scale positively moderates the relationship between farmland transfer and low-carbon green utilization of farmland. Farmland management scale plays moderated mediating effects. With the increase in farmland management scale, the positive effects of farmland transfer on low-carbon green utilization of farmland will grow. When the management scale of household increases, operators are more likely to introduce green technologies and activities and optimize the grain planting structure. The combined effects of farmland transfer and management scales can achieve large-scale agricultural production and the reduction of fertilizer and pesticide utilization, which subsequently improves the low-carbon green utilization of farmland.

5.2. Theoretical Implications

The results of the research provide various insights into the relationship between digital financial inclusion and low-carbon green utilization of farmland. The extant literature has identified that digital financial inclusion is positively related to agricultural supply chain [14], agricultural industrial structure optimization and green total factor productivity [17], agricultural production for rural households [18], agricultural high-quality development [19], etc. Nevertheless, the research on the relationship between digital financial inclusion and low-carbon green utilization of farmland is scarce. Through empirical tests, this paper identified that digital financial inclusion can efficiently enhance low-carbon green utilization of farmland. Our findings provide new evidence on the relationship between digital financial inclusion and low-carbon green utilization of farmland.

Another contribution of this study is that it provides a deeper understanding of mechanisms of how digital financial inclusion can improve low-carbon green utilization of farmland through the mediator of farmland transfer. In the extant literature, scholars have verified that digital financial inclusion is positively related to farmland transfer [19,20]. There are also some studies verifying that farmland transfer is positively related to low-carbon green utilization of farmland [22,23]. However, scholars ignored the mediating effects played by farmland transfer in the relationship between digital financial inclusion and low-carbon green utilization of farmland. This paper identified that digital financial inclusion can promote low-carbon green utilization of farmland through the positive mediating effects of farmland transfer. Our research provides a deeper understanding of mediating mechanisms between digital financial inclusion and low-carbon green utilization of farmland.

Finally, our study is one of the first to empirically verify that the effect of farmland transfer is of increased relevance to low-carbon green utilization of farmland in the conditions of large farmland management scale. The extant literature pointed out that farmland management scale plays mediating effects on the relationship between farmland transfer and farmland green utilization efficiency [23]. There is also some evidence identifying farmland management as positively related to the reduction of fertilizer and pesticide [73,74]. It ignored the possibility of moderating effects played by farmland management scale in the relationship among digital financial inclusion, farmland transfer, and low-carbon green utilization of farmland. Farmland scale management represents an inevitable trend toward global modern agriculture. Our research is one of the first to identify the positive moderating effects of farmland management scale played in the relationship between farmland transfer and low-carbon green utilization of farmland.

5.3. Practical Implications

Our findings also provide some practical insights to governments, financial institutions, and farm households. In terms of governments, first, they are suggested to increase the investments in the research and development of digital financial technologies, so as to reduce the cost of financing transactions, and continuously extend digital financial inclusion services to the wider population. Second, it is necessary for governments to improve farmland transfer regulations and rules, and regulate and control farmland transfer procedures, in order to ensure the processes of farmland transfer are more transparent and simpler. Third, governments are suggested to increase the subsidy for farmland transfer, widely publicizing the subsidy scheme for farmland transfer to encourage the farmland transferring actions. With the improvement of digital finance systems and the extension of farmland transfer, low-carbon green utilization of farmland can be efficiently achieved.

In terms of financial institutions, they are recommended to accelerate the establishment of the rural digital financial credit system and optimize agricultural digital financial insurance services. Specifically, first, they are suggested to use the digital platform to establish internal links between agricultural insurance and agricultural credit, forming an organic combination of online and offline agricultural digital financial models. Second, financial institutions are encouraged to develop big data technologies to ease the problems of information asymmetry between farmers and digital financing platforms, and effectively promote the process of high-quality agricultural development. Relying on digital inclusive financial platforms, financing for both transfer-out and transfer-in of farmland transfer will be smoother, which would jointly drive low-carbon green utilization of farmland.

As for farmland households, they are encouraged to strengthen the study of Internet knowledge and expand the utilization of the Internet platform. Hence, Internet knowledge will be gradually popularized and applied in rural areas, the digital divide between regions will narrow, and equalization of access to digital inclusive financial services in rural areas will be achieved. Consequently, the low-carbon green utilization of farmland can be efficiently achieved.

5.4. Limitations and Future Research Directions

Despite the contributions this research makes to the existing literature, it still has several limitations. First, we only used the data collected from China. It is difficult to generalize our findings to other settings. Second, due to time and resource constraints, other relevant mediating and moderating variables were not introduced into the conceptual model, the variables that can influence the mediating and moderating variables were not discussed, and the antecedent variables of the independent variable were not explored as well.

However, these limitations also create the opportunity for future studies. First, it would be of necessity to test the conceptual model using a sample from other countries and regions to expand its applicability. Second, it is interesting to explore the antecedents of digital financial inclusion and some other factors that may matter in the relationship between digital financial inclusion, farmland transfer, farmland management scale, and low-carbon green utilization of farmland.

6. Conclusions

Digital financial inclusion has been gradually regarded as a significant promoter of the efficiency, effectiveness, and sustainability of agricultural production. However, the literature focusing on the relationship between digital financial inclusion and low-carbon green utilization of farmland is scarce. Accordingly, this paper attempts to explore whether, how, and when digital financial inclusion can affect low-carbon green utilization of farmland. Using a sample of Chinese provincial panel data from 2011 to 2020 and SEM analyses in STATA 16.0, this paper draws the following conclusions. First, digital financial inclusion can directly facilitate low-carbon green utilization of farmland; second, digital financial inclusion can indirectly improve low-carbon green utilization of farmland through the mediator of farmland transfer; third, farmland management scale positively moderates the relationship between farmland transfer and low-carbon green utilization of farmland. Farmland management scale played moderated mediating effects on the relationship among digital financial inclusion, farmland transfer, and low-carbon green utilization of farmland. In theory, our findings provide new empirical evidence for the research on the relationship between digital financial inclusion and low-carbon green utilization of farmland. We also provide a deeper understanding of mechanisms of farmland transfer and the role of farmland management scale in the relationship between digital financial inclusion and low-carbon green utilization of farmland. In practice, governments are suggested to increase the investments in the research and development of digital financial technologies, improve farmland transfer regulations and rules, regulate and control farmland transfer procedures, and increase the farmland transfer subsidy. Financial institutions are recommended to accelerated the establishment of the rural digital financial credit system and optimize agricultural digital financial insurance services. Farmland households are encouraged to strengthen the study of Internet knowledge and expand the utilization of the Internet platform. With the cooperation of governments, financial institutions, and farmland households, the low-carbon green utilization efficiency of farmland can be dramatically improved.

Author Contributions

Conceptualization, H.Z., Q.Z. and M.A.; methodology, H.Z. and Q.Z.; software, H.Z. and Q.Z.; validation, H.Z. and M.A.; formal analysis, H.Z. and Q.Z.; investigation, H.Z. and Q.Z.; resources, H.Z., Z.Z. and N.H.; data curation, H.Z., Z.Z. and N.H.; writing—original draft preparation, H.Z., Z.Z. and N.H.; writing—review and editing, H.Z., M.A., Z.Z. and Q.Z. visualization, H.Z., Z.Z. and N.H.; supervision, H.Z. and M.A.; project administration, H.Z. and Q.Z.; funding acquisition, H.Z. and Q.Z. All authors have read and agreed to the published version of the manuscript.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available from authors upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. (a). Farmland per capita in the world and in China from 2011–2017; (b) fertilizer use (kg/ha) and pesticide use (kg/ha) by country in 2021.

Figure 2. Conceptual model.

Figure 3. Path diagram and empirical results of the main effects. (Note: *** represents that it is significant at the 1% level).

Figure 4. Path diagram and empirical results of the mediating effects. (Note: *** represents that it is significant at the 1% level).

Figure 5. Path diagram and empirical results of the moderated mediating effects. (Note: *** represents that it is significant at the 1% level).

Table 1

The indicator systems for measuring low-carbon green utilization of farmland.

First Order Indexes	Second Order Indexes	Variates and Descriptions
Inputs	Labor input	AFAHF × (Total agriculture output/TO) (104 people)
	Land input	The total area of crops sowed (103 hectares)
	Capital input	Consumption of chemical manures (104 tons)
		Consumption of pesticide (104 tons)
		Consumption of agriculture film (104 tons)
		Total agriculture machinery power (104 kw · h)
		Valid irrigation area (103 hm2)
Desirable Outputs	Economic output	Total value of agricultural output (104 Yuan)
	Social output	Total grain production (104 tons)
	Environmental output	The total carbon sink (104 tons)
Undesirable Outputs	Pollution emission	The total loss of manure nitrogen (phosphorus), insecticides, and agriculture films (104 tons)
	Carbon emission	The carbon emissions from farmland utilization (104 tons)

Note: AFAHF represents the abbreviation of agricultural, forestry, animal husbandry, and fishery practitioners; TO represents the abbreviation of total output values of agriculture, forestry, animal husbandry, and fisheries.

Table 2

The indicator systems for measuring digital financial inclusion.

First-Order Dimensions	Second-Order Dimensions	Measurement Indicators
Coverage breadth	Alipay accounts coverage	Number of Alipay accounts per 10,000 persons
		Ratio of accounts with credit card linkage
		Average number of linked debit and credit cards per Alipay account
Usage depth	Payment	Frequency of payment per capita
		Amount of payment per capita
		Ratio of high frequency users (use 50 times or more per year)
	Lending	Number of accounts with consumer credits per 10,000 accounts
		Number of loans per capita
		Amount of loans per capita
		Number of accounts with small and micro enterprise credit per 10,000 accounts
		Number of loans per small and micro entrepreneurs
		Amount of loans per small and micro entrepreneurs
	Insurance	Number of accounts with insurance per 10,000 accounts
		Number of insurance policies per capita
		Amount of insurance per capita
	Investment	Number of accounts with investment per 10,000 accounts
		Number of investment per capita
		Amount of investment per capita
Digitalized level	Financial convenience	Ratio of mobile payments in total payments
		Ratio of the amount of mobile payments in total
	Cost of financial service	Average interest rate of loans to small and micro enterprises
		Average interest rate of consumer credits

Table 3

Descriptive statistics of variables.

Variables	Mean	Standard Deviation	Minimum	Maximum
cb	196.700	96.560	1.960	397.000
ud	211.100	98.190	6.760	488.700
dl	290.100	117.300	7.580	462.200
flt	0.316	0.163	0.034	0.911
flms	2.878	1.843	0.307	13.620
lcguf	0.704	0.198	0.315	1.000

Table 4

Structural equation model of main effects.

Effect	Coefficient	Standard Error	Z Value	p Value	95% Confidence Interval
					Lower Limit	Upper Limit
dfi→ lcguf	0.438	0.045	9.690	0.000	0.350	0.527
cb→ dfi	0.979	0.007	134.690	0.000	0.965	0.994
constant	4.669	0.333	14.020	0.000	4.017	5.322
ud→ dfi	0.957	0.008	115.080	0.000	0.941	0.973
constant	6.552	0.398	16.440	0.000	5.771	7.333
dl→ dfi	0.836	0.018	45.720	0.000	0.800	0.872
constant	6.607	0.389	17.000	0.000	5.845	7.369

Table 5

Structural equation model of mediating effects.

Effect	Coefficient	Standard Error	Z Value	p Value	95% Confidence Interval
					Lower Limit	Upper Limit
dfi→ flt	0.497	0.045	11.100	0.000	0.409	0.585
constant	1.937	0.098	19.780	0.000	1.746	2.129
flt→ lcguf	0.273	0.057	4.820	0.000	0.162	0.384
dfi→ lcguf	0.301	0.057	5.270	0.000	0.189	0.413
constant	3.034	0.197	15.390	0.000	2.647	3.421
cb→ dfi	0.973	0.008	126.650	0.000	0.958	0.988
constant	6.198	0.260	23.880	0.000	5.689	6.707
ud→ dfi	0.964	0.008	117.600	0.000	0.948	0.980
constant	8.045	0.333	24.130	0.000	7.392	8.699
dl→ dfi	0.835	0.018	45.350	0.000	0.799	0.871
constant	7.912	0.328	24.110	0.000	7.269	8.555

Table 6

Tests of mediating effects.

Effect	Coefficient	Standard Error	Z Value	p Value	95% Confidence Interval
					Lower Limit	Upper Limit
Mediating test	0.136	0.031	4.390	0.000	0.075	0.196

Table 7

Structural equation model of moderated mediating effects.

Effect	Coefficient	Standard Error	Z Value	p Value	95% Confidence Interval
					Lower Limit	Upper Limit
dfi→ flt	0.493	0.045	10.980	0.000	0.405	0.581
constant	1.937	0.098	19.870	0.000	1.746	2.129
flt→ lcguf	0.268	0.056	4.830	0.000	0.159	0.377
flt×flms→lcguf	0.182	0.066	2.760	0.006	0.053	0.311
flms→ lcguf	−0.003	0.065	−0.040	0.967	−0.129	0.124
dfi→ lcguf	0.349	0.058	6.010	0.000	0.235	0.462
constant	2.995	0.217	13.830	0.000	2.571	3.420
cb→ dfi	0.977	0.007	135.130	0.000	0.963	0.991
constant	6.198	0.258	24.030	0.000	5.692	6.704
ud→ dfi	0.960	0.008	117.850	0.000	0.944	0.975
constant	8.045	0.332	24.240	0.000	7.395	8.696
dl→ dfi	0.836	0.018	45.600	0.000	0.800	0.872
constant	7.912	0.327	24.190	0.000	7.271	8.553

Table 8

Tests of moderated mediating effects.

Effect	Coefficient	Standard Error	Z Value	p Value	95% Confidence Interval
					Lower Limit	Upper Limit
Moderated mediating test	0.330	0.123	2.690	0.007	0.090	0.571

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