Can Digital Finance Improve China’s Agricultural Green Total Factor Productivity?

Abstract

The improvement of agricultural green total factor productivity (AGTFP) is crucial to achieve sustainable agricultural development. By matching China’s provincial digital financial inclusion index and agricultural production data from 2011 to 2020, and on the basis of using the DEA–Malmquist productivity index to measure AGTFP, the fixed effect model and Mesomeric effect model are used to empirically test the impact and mechanism of digital financial development on China’s AGTFP. Our research found that from 2011 to 2020, China experienced consistent improvement in AGTFP, which was largely attributed to advancements in technology. Interestingly, the AGTFP in non-major grain-producing areas surpassed that in major grain producing areas. Additionally, digital finance has proven to be an effective tool in boosting China’s AGTFP, the coverage subdimension, the depth of use subdimension and the digitization subdimension all significantly promote the AGTFP. Digital finance can significantly promote the efficiency and progress of agricultural green technology, which shows that digital finance promotes AGTFP in a “dual wheel” driven manner. However, the impact of digital finance on agricultural green technology efficiency and progress is more pronounced in major grain-producing areas than in non-major areas. The impact mechanism demonstrates that digital finance has the potential to stimulate AGTFP in two key ways. First, it can improve the mismatch of agricultural production resources; second, it can promote agricultural technology innovation. Therefore, it is necessary to further promote the rapid development of digital finance, optimize the rational allocation of financial resources, and formulate tailored digital finance development strategies to promote green development of agriculture.

1. Introduction

Green production is the inherent requirement of sustainable economic development. In 2020, China proposed the goal of “striving to achieve carbon peak by 2030 and carbon neutrality by 2060”. In recent years, China’s ecological and environmental pollution in agricultural production has been very serious. In 2020, China’s total carbon emissions reached 9.9 billion tons, of which total agricultural greenhouse gas emissions accounted for approximately 17% of China’s total carbon emissions. The problem of agricultural pollution has become increasingly prominent, and agricultural nonpoint source pollution has become a bottleneck, restricting the green development of agriculture [1]. In recent years, China’s agricultural development has been constrained by rigid resource endowment. Therefore, more attention should be given to the green development of agriculture and the reduction of agricultural carbon emissions to promote the implementation of the “double carbon” strategy [2]. According to neoclassical economic growth theory, increasing factor input and improving productivity level are very important for long-term economic growth. Therefore, to realize the sustainable development of agriculture, we must take the path of AGTFP growth [3,4,5].

Finance plays a policy-oriented role in promoting the development of green economic transformation [6] and is regarded as an important regulatory tool for environmental governance. Finance, as a scarce resource, is an important factor affecting the development of agricultural green economy [7]. However, there are obvious differences in the development level of financial resources in different regions of China, especially in the agricultural sector, which faces serious financial exclusion and suppression [8]. In recent years, with the continuous penetration of digital information technology in the financial field, digital finance (DF) with inclusive characteristics has come into being. The characteristics of DF are replicable, storable, and liquid, which are not possessed by traditional financial models [9]. With the wide popularization of the Internet in rural areas, the level of agricultural and rural digitization has gradually improved. Financial resources and services have gradually extended to agriculture and rural areas, improving the demand upgrading and supply transformation of agriculture and rural areas, stimulating the green development potential of agriculture and bringing opportunities for the development of China’s agricultural modernization. According to the evaluation report on the development level of county agricultural and rural informatization in 2021, the overall level of county agricultural and rural informatization development in China in 2020 was 37.9%, and the level of agricultural production informatization was 22.5%. From this, the research question of this paper arises: With the gradual extension of digital financial services to agriculture and rural areas, what is the impact mechanism and effect of DF on AGTFP?

Digital finance (DF) mainly refers to the integration of digital information technology with traditional finance based on digitalization and big data [10], and has penetrated into all aspects of economic development and can effectively promote the transformation of the economic growth mode from being production-factor-driven to innovation-driven. Existing studies show that the impact of DF on economic development is multidimensional [11,12]. At the macro level, DF has the characteristics of low cost and easy access, and has stronger financial support for economic development [13]. It helps to co-ordinate the economic activities of various production entities [14], enhances the resilience of economic development, and promotes economic and social efficiency reform. At the meso level, relevant studies believe that DF can help promote the renewal and iteration of green technology, thereby reducing energy consumption and improving green productivity [15]. In addition, DF can also provide new channels for the market to promote green financing and effectively promote sustainable economic development [16]. At the micro level, relevant studies suggest that DF has changed the business model of enterprises [17], and has a more significant effect on the green technology innovation of enterprises, which can effectively improve their productivity [18]. Current research generally affirms the role of DF in promoting the development of the real economy and enterprise innovation. In the context of the gradual extension of digital financial services to agriculture and rural areas, existing studies also show that DF has a catalytic effect on AGTFP by promoting land circulation [19] and optimizing industrial structure [20,21]. However, the existing research on the internal mechanism of DF and AGTFP has not yet formed a complete and unified analytical framework, and there is a lack of empirical research on the internal impact mechanism from the perspective of improving the mismatch of agricultural resources and promoting agricultural technology innovation.

Agricultural total factor productivity reflects the contribution of agricultural technological progress to agricultural economic growth [22]. With the aggravation of environmental pollution, scholars began to include agricultural carbon emissions in the accounting framework of agricultural total factor productivity [23,24], that is, AGTFP. Current research shows that China’s AGTFP is lower than the traditional agricultural total factor productivity [25,26]. Among them, agricultural green technology progress is the main source of promoting China’s agricultural total factor productivity growth [27], and the contribution of the green technology efficiency improvement is relatively low. At present, the measurement methods of AGTFP are mainly divided into parametric methods and nonparametric methods. The parametric method is mainly based on the stochastic frontier production function (SFA) [28], which can fully consider the influence of stochastic factors on the frontier. Data envelopment analysis (DEA) is the main nonparametric method that obtains the production boundary through linear programming and calculates the productivity of each Decision making unit (DMU). Due to the special advantages of DEA models in measuring multiple output variables, the DEA–Malmquist productivity index method is widely used in measuring AGTFP [29]. From the perspective of factors affecting AGTFP, the current research mainly focuses on technical factors, economic factors, environmental factors, etc., including technological innovation, rural per capita income, agricultural structure, rural human capital, urbanization, agricultural policies, Carbon emissions trading pilot policy [30,31,32,33]. However, the current research still lacks evidence of the impact of DF on AGTFP. This paper will further clarify the effect and mechanism of DF on AGTFP through theoretical combing and empirical analysis.

Existing studies on AGTFP and DF have provided a theoretical basis for this paper [34,35]. At present, the average index of China’s digital financial inclusion development has risen nearly eight times, from 40 in 2011 to 341.22 in 2020. At the same time, the emergence of a series of digital financial inclusion innovation models, such as the supply chain model based on e-commerce platform, the industrial chain model based on agricultural leading enterprises, and the comprehensive model based on a large-scale financial technology platform, has added an impetus to green development of agriculture. In general, improving resource mismatch is the endogenous power to improve green total factor productivity, and promoting technological innovation is the internal power to improve AGTFP. Therefore, based on resource allocation theory and technology innovation theory, this paper takes the panel data of 29 provinces in China from 2011 to 2020 as a sample, uses the DEA–Malmquist productivity index to measure China’s AGTFP, and uses the fixed effect model and Mesomeric effect model to empirically test the direct effect and impact mechanism of DF on AGTFP.

The contributions of this study are as follows: (1) On the basis of measuring the agricultural unexpected output of multi factor pollution emissions, the DEA–Malmquist productivity index is used to measure and analyze the development trend of AGTFP in various regions of China, which helps to deepen the understanding of China’s agricultural green development level. (2) It is verified that DF development can effectively improve China’s AGTFP by incorporating DF and AGTFP into the same theoretical analysis framework to analyze the impact mechanism of DF on AGTFP multi-dimensionally, which has important practical significance for improving China’s DF level and AGTFP. (3) This paper verifies the impact of DF on AGTFP from the two dimensions of promoting agricultural green technology progress and improving agricultural green technology efficiency and deepens the understanding of the internal relationship between DF and green technology progress, green technology efficiency, and AGTFP. (4) The impact mechanism of DF on AGTFP was verified from two aspects: optimizing agricultural resource allocation and promoting agricultural technological innovation, which deepened the understanding of the impact mechanism of DF and provided empirical evidence for DF to promote agricultural green development.

2. Theoretical Analysis and Research Hypothesis

Existing research shows that the application of digital technology can effectively improve total factor productivity [36,37]. At present, digital finance (DF) based on digital technology has gradually extended to agriculture and rural areas, providing new opportunities for the green development of agriculture [38,39]. This section mainly analyses the impact mechanism of DF on AGTFP from four aspects, and on this basis, proposes a research hypotheses. The impact mechanism is shown in Figure 1.

(1)

Direct impact of the DF on AGTFP

① The development of DF helps to reduce the cost of agricultural production. The development of DF has broken information asymmetry in the process of traditional agricultural production, enriched access to information [9], and improved the flow speed of information, thus contributing to the combination of information and agricultural production. In addition, the development of DF can alleviate the problem of the poor flow of market factors and reduce the cost of agricultural production. At the same time, digital financial services are conducive to agricultural informatization and digital construction to provide agricultural production technology consulting and services for agricultural producers and make agricultural production develop towards standardization and efficiency [40].

② DF helps to improve the scale economic benefits of agriculture, overcome the disadvantages of traditional small-scale agricultural production, and realize the specialization of agricultural production. Digital financial services relying on digital technology improve the transaction efficiency of the market [41], effectively play its convenient credit function, shorten the review time of financial institutions for agricultural production loans, help guide social funds into agricultural production, improve the purchasing power of agricultural producers, and expand the scale of agricultural production to reduce production costs, improving the efficiency of the specialized division of labor and improving ATFP.

③ DF helps to improve the rural governance level. First, with the development of the Internet, the digitization of agriculture in production, governance, and other aspects has been improved, which has led to the popularization of DF in the field of agriculture and strengthened the matching degree of rural digital infrastructure, public services, and agricultural production and management to promote the application of a new generation of information technology in agricultural development, which helps to improve agricultural productivity [42]. Second, DF is an important channel for information penetration, technology and means of production into the various processes of agricultural production, transportation and sales, which not only helps to improve the efficiency of agricultural production but also helps to alleviate the waste of resources and environmental pollution in the process of agricultural production [43], reduce production costs, and ultimately achieve the improvement of AGTFP.

H1. 

DF helps to improve AGTFP.

(2)

The impact of DF on agricultural technology progress and technical efficiency

DF can improve AGTFP by improving agricultural technological progress and technical efficiency. ① DF helps to realize the progress of agricultural technology [44]. DF reduces the information search cost of agricultural producers, expands the scope of agricultural knowledge dissemination, improves the market participation of agricultural producers and the convenience of obtaining knowledge and technology, promotes agricultural production from the traditional closed internal market to a more open external space, and improves the efficiency of the use of advanced agricultural production technology and resources. It has improved the agricultural production mode and conditions and promoted the progress of agricultural technology and production mode innovation [45]. At the same time, digital financial services have also accelerated the penetration of information technology into the field of agricultural production, promoted the exchange of agricultural production and market information, and provided an opportunity for the intensive and refined development of agriculture.

② DF helps to improve the efficiency of agricultural technology. The development of DF has broken the information barriers existing in traditional agricultural and rural areas, expanded access to agricultural information, reduced information asymmetry, improved the degree of agricultural informatization, expanded the scale of the market to a certain extent, alleviated the friction of the external agricultural market, and improved market transaction efficiency [46]. In addition, digital financial services have high convenience and accuracy. Farmers can understand market demand in a timely manner to make more rational production decisions so that capital [47], labor, land, and other factors can be more reasonably allocated, thus improving the efficiency of agricultural technology.

H2. 

DF improves AGTFP by promoting agricultural technological progress and improving agricultural technological efficiency.

(3)

The impact of DF on AGTFP by improving the mismatch of agricultural production resources

Improving the mismatch of agricultural resources refers to adjusting the input structure of agricultural production factors, reducing the degree of factor distortion and improving the allocation efficiency of factors. Improving the mismatch of resources can effectively improve agricultural productivity [48]. ① In the information age of the rapid development of DF, the traditional advantages of agricultural development have gradually disappeared. The application of digital technology has changed the allocation structure of agricultural production resources [49], improved the allocation efficiency of agricultural production resources, paid more attention to the application of modern information technology in the production process, improved the distortion effect of agricultural resources, and accelerated the transformation of the agricultural production mode.

② The development of DF weakens the boundary of agricultural production, facilitates the free flow of agricultural production factors, widens the flow range of agricultural production factors, and effectively alleviates the configuration distortion of agricultural production factors. At the same time, digital financial services can fully mine and collect information, which helps to accurately match the supply side and demand side of agricultural production resources [50], and enhance the agricultural resource allocation efficiency to promote the improvement of AGTFP.

③ Agricultural products are characterized by regional production, seasonal supply, and perishable storage, and it is difficult to match supply and demand, high transportation losses, and high transaction costs. Relying on strong financial market service characteristics [51], DF reduces the transaction costs of agricultural suppliers and demanders, enhances the transaction efficiency, promotes the balance between the supply and demand, enhances the factor distortion effect, and effectively reduces agricultural carbon emissions.

H3. 

DF promotes AGTFP by improving the mismatch of agricultural production resources.

(4)

The impact of DF on AGTFP by promoting agricultural technology innovation

Promoting agricultural technology innovation means improving agricultural production efficiency by increasing investment in agricultural science and technology research and development, the research and development of new agricultural varieties and new technologies, and technology transformation and application. ① Agricultural RD institutions accelerate agricultural technology innovation and promote the transformation of the agricultural production mode by increasing RD investment in new technologies [52]. The development of DF helps alleviate the financing pressure of agricultural RD institutions [53,54] and broadens the financing channels of agricultural RD institutions in the market to promote agricultural technology innovation [55].

② As a new green driving force [56], DF can realize the breakthrough innovation of key agricultural technologies with digital technology as the carrier [57]. While accelerating the deep integration of finance and science and technology, it can drive the digital and intelligent transformation of traditional production technologies [58], so as to help reduce the consumption of agricultural resources and pollution emissions, and promote agricultural technology progress, thus helping the green development of the agricultural sector [59].

③ DF development has led to a variety of new agricultural formats, such as ecommerce and Internet, which help alleviate the sales difficulties of agricultural products [60]. At the same time, the development of DF optimizes the matching of supply and demand and helps to improve the sales price of agricultural products [61]. In addition, the popularization of DF has improved the agricultural digital information system and further improved the accuracy of financial services.

H4. 

DF promotes AGTFP by promoting agricultural technology innovation.

3. Materials and Methods

3.1. Data Sources

By matching China’s provincial digital finance (DF) development index and agricultural production data from 2011 to 2020, this paper empirically studies the impact and mechanism of DF development level on China’s AGTFP. Considering that the integration and development of digital technology and the financial industry in China mainly occurred after 2010, and based on the availability of DF indexes, the research period is determined to be from 2011 to 2020. The DF development index is sourced from the DF Research Center of Peking University. The measurement data of AGTFP comes from official statistical data such as the China Rural Statistical Yearbook, the National Agricultural Science and Technology Statistics, and the National Compilation of Cost Benefit Data of Agricultural Products. Due to the serious lack of agricultural green production data (such as agricultural irrigation, fertilizer, agricultural film, and other indicators) in Tibet and Xinjiang provinces, the remaining 29 provincial samples in China were selected for the study, and the relevant data will be processed based on 2010.

3.2. Study Setting

Assuming that the level of DF affects agricultural output through external factors, the following agricultural production function is established:

$$F\left(Y\right)=A(D,t) f(L,K)$$

(1)

where D refers to the level of DF, L refers to the input of agricultural labor force, K refers to the input of agricultural production factors, and Y refers to the level of comprehensive agricultural output.

The $A(D,t)$ setting form is:

$$A(D,t)=A{D}^{\beta }{e}^{\lambda t}$$

(2)

where $\beta$ is the influence parameter of DF level on agricultural productivity and $\lambda$ represents the effect parameter of technological progress. We substitute Equation (2) into Equation (1) to calculate AGTFP and obtain:

$$AGTFP=F\left(Y\right)/f(L,K)=A{D}^{\beta }{e}^{\lambda t}$$

(3)

Take the logarithm of Equation (3) to obtain:

$$LnAGTFP=LnA+\beta LnD+\lambda t$$

(4)

Based on this, a fixed effect model is constructed to test the direct impact of DF on AGTFP, aiming to solve the heterogeneity problem in Panel data. Since there are obvious heterogeneity differences among regions in China, it may have an impact on the model estimation results to make the results inaccurate. By setting the regional fixed effect to control the heterogeneity between regions, the time fixed effect is set to control the influencing factors that do not change with the region, so as to improve the accuracy of the model estimation results:

$$AGTF{P}_{it}=\alpha +\beta Di{e}_{it}+\lambda X_{it}+{\mu }_i+{\phi }_t+e_{it}$$

(5)

where $AGTF{P}_{it}$ is the explained variable of AGTFP in various regions of China, $Di{e}_{it}$ is the core explanatory variable of the digital financial development index and its three sub-dimension indexes (Coverage, Depth of Use, Degree of Digitization), $X_{it}$ is the control variable, ${\mu }_i$ indicates the regional fixed effect, ${\phi }_t$ indicates the time fixed effect, and $e_{it}$ is a random disturbance term.

In addition, AGTFP is decomposed into green technological efficiency (GEC) and green technological progress rate (GTC), and the impact of DF level on green technological efficiency and green technological progress rate is tested:

$$GE{C}_{it}=\alpha +\beta Di{e}_{it}+\lambda X_{it}+{\mu }_i+{\phi }_t+e_{it}$$

(6)

$$GT{C}_{it}=\alpha +\beta Di{e}_{it}+\lambda X_{it}+{\mu }_i+{\phi }_t+e_{it}$$

(7)

Further, we use the three-stage mediating effect model to test the impact mechanism of DF on improving AGTFP:

$$Pos{e}_{it}=\alpha +\beta Di{e}_{it}+\lambda X_{it}+{\epsilon }_i+{\phi }_t+e_{it}$$

(8)

$$AGTF{P}_{it}=\alpha +\beta Di{e}_{it}+\gamma Pos{e}_{it}+\lambda X_{it}+{\epsilon }_i+{\phi }_t+e_{it}$$

(9)

$$R{D}_{it}=\alpha +\beta Di{e}_{it}+\lambda X_{it}+{\epsilon }_i+{\phi }_t+e_{it}$$

(10)

$$AGTF{P}_{it}=\alpha +\beta Di{e}_{it}+\gamma R{D}_{it}+\lambda X_{it}+{\epsilon }_i+{\phi }_t+e_{it}$$

(11)

where $Pos{e}_{it}$ represents the allocation index of agricultural production factors, and $R{D}_{it}$ represents the level of agricultural technology innovation. Based on the analysis of DF on optimizing the allocation of agricultural resources and promoting agricultural technology innovation, this paper tests the impact mechanism of DF on AGTFP.

3.3. Variable Selection

(1) AGTFP: This paper uses the nonparametric DEA–Malmquist productivity index to measure the AGTFP. First, the distance function is defined. Set Production Collection $p_t(x,y,b)=\{(x,y,b)|$t, x can produce y and b}, then the output distance function is:

$$D_t(x,y,b)=mi{n}_{\theta }\{\theta :(x,y/\theta ,b)\in p_t(x,y,b),\theta >0\}$$

(12)

In the production process, if the decision-making unit has m inputs of production factors $x=(x_1,x_2\dots \dots x_m)\in R^m$, n expected outputs $y=(y_1,y_2\dots \dots y_n)\in R^n$, and Z unexpected outputs $b=(b_1,b_2\dots \dots b_z)\in R^z$, we set the vector as $x\in R_n, y\in R_m, b\in R_z$. We define the production possibility set as:

$$\left\{\begin{array}{l}{\displaystyle \sum _{N=1}^n{\lambda }_k^{\lambda }}{y}_{kn}^t\ge y_{kn}^t, n=1,2,3\dots N\\ {\displaystyle \sum _{z=1}^z{\lambda }_k^{\lambda }}{b}_{kz}^t\le b_{kz}^t, m=1,2,3\dots Z\\ {\displaystyle \sum _{M=1}^m{\lambda }_k^{\lambda }}{x}_{km}^t\le x_{km}^t, m=1,2,3\dots M\\ {\displaystyle \sum _{z=1}^z{\lambda }_k^{\lambda }=1},{\lambda }_k^{\lambda }\ge 0, k=1,2,3\dots z\end{array}\right.$$

(13)

where $x=(x_1,x_2\dots \dots x_m)\in R^m$ represents the input vector, $y=(y_1,y_2\dots \dots y_n)\in R^n$ represents the expected output vector, $b=(b_1,b_2\dots \dots b_z)\in R^z$ represents the unexpected output vector, is the efficiency of the decision-making unit, and is the weight vector of each observation value.

For $D_t(x,y,b)$, the closer its value is to 1, the higher the production efficiency of the production unit. Under the technical conditions of period T, the change in TFP from period t to period t + 1 can be expressed as the DEA–Malmquist productivity index:

$$GTF{P}_t=D_t(x_{t+1},y_{t+1},b_{t+1})/D_t(x_t,y_t,b)$$

(14)

Similarly, under the technical conditions of period t + 1, the DEA–Malmquist productivity index from period t to period t + 1 is:

$$GTF{P}_{t+1}=D_{t+1}(x_{t+1},y_{t+1},b_{t+1})/D_{t+1}(x_t,y_t,b_t)$$

(15)

To avoid possible differences caused by the randomness of period selection, the Malmquist productivity index is defined as the geometric average of the two periods, which can be expressed as the Malmquist productivity index with the unchanged DMU, and the index is decomposed into green technological efficiency (GEC) and green technological progress rate (GTC):

$$\begin{array}{l}GTFP(x_t,y_t,b_t)={[\frac{D_{{}^t}(x_{t+1},y_{t+1},b_{t+1})}{D_{{}^t}(x_t,y_t,b_t)}\times \frac{D_{{}^{t+1}}(x_{t+1},y_{t+1},b_{t+1})}{D_{{}^{t+1}}(x_t,y_t,b_t)}]}^{1/2}\\ =\frac{D_{t+1}(x_{t+1},y_{t+1},b_{t+1})}{D_t(x_t,y_t,b_t)}\times {[\frac{D_t(x_{t+1},y_{t+1},b_{t+1})}{D_{t+1}(x_{t+1},y_{t+1},b_{t+1})}\times \frac{D_t(x_t,y_t,b_t)}{D_{t+1}(x_t,y_t,b_t)}]}^{1/2}\\ =GEC\times GTC \end{array}$$

(16)

where $x_t$ is the input of production factors, $y_t$ is the expected output, and $b_t$ is the unexpected output. Greater than, equal to, and less than 1 represent the increase, unchanged, and decrease of GTFP, respectively. The green technological efficiency (GEC) indicates the degree of advance from the production DMU to the optimal production possibility boundary from period T to period (t + 1). The ability to obtain the maximum output under the given input is called the “catch-up effect”. The green technological progress rate (GTC) indicates the extrapolation and movement of the production front from period T to period (t + 1), causing the outwards movement of the production front, known as the “growth effect”.

Considering the selection of indicators in other studies [62,63,64], this paper selects agricultural green input and output indicators, as shown in

Table 1. Agricultural green input output indicators.

Index	Element	Definition	Unit
Input Index	Labor Input	Number of employees in rural agriculture, forestry, animal husbandry and fishery	ten thousand people
	Fertilizer Input	Net amount of agricultural chemical fertilizer application	10,000 tons
	Mechanical Input	Agricultural machinery power	10,000 kw
	Land Input	Crop sown area	hectares
	Agricultural Irrigation	Effective irrigation area	hectares
Output Indicators	Output	Total output value of agriculture, forestry, animal husbandry and fishery	100 million CNY
	Undesirable Output	Using the sum of carbon emissions from agricultural production	10,000 tons

Note: the number of employees and the number of days a person works, is converted into one day of multiple workers, that is, labor input.

:

Undesirable Output mainly refers to various non-point source pollution produced by agricultural production. There is no unified accounting standard for undesirable agricultural output in the literature. For the unexpected output of agriculture, different scholars use different indicators to measure the unexpected output and comprehensively compare the availability of various accounting methods and data. In this paper, the pollution emissions of pesticides, fertilizers, machinery, agricultural film, and irrigation are regarded as unexpected outputs.

The calculation formula of agricultural pollutant emissions is:

$$E={\displaystyle \sum P{U}_{ij}}{C}_{ij}(P{U}_{ij},S)$$

(17)

where E is the emission of agricultural pollutants, Pu is the base of pollutant generation, and C is the pollution emission coefficient, which is determined by the output characteristics of different pollutants S. Referring to relevant studies [33,65], the pollutant emission coefficient C is shown in

Table 2. Influencing parameters of agricultural output unit intensity.

Pollutant	Chemical Fertilizer	Pesticide	Agricultural Film	Mechanical	Irrigation
Parameters	0.8965 kgC kg−1	4.9341 kgC kg−1	5.18 kgC kg−1	0.5927 kgC·kw	25 kgC ha−1

. Its pollutants mainly include chemical fertilizer, agricultural film, machinery, irrigation, etc.

(2) Digital finance Index: Taking the digital inclusive finance index of provinces in China developed by the DF research center of Peking University as the core explanatory variable, it is recorded as die. Its three sub-dimensions cover breadth, depth of use, and degree of digitization. The coverage mainly reflects the regional financial environment, which is represented by the number of electronic accounts in the region. The depth of use reflects the ability of regional financial services, which is reflected in the degree of regional use of digital financial services. The degree of digitalization mainly reflects the transaction cost and efficiency of the region, focusing on the convenience and efficiency of regional DF. It is recorded as the logarithm of the DF index and the three sub-dimension indexes.

(3) Mechanism Variable:

① Agricultural technology innovation level (RD): agriculture is a basic industry; currently, China’s agricultural technology innovation investment is generally dominated by government innovation investment [66], supplemented by enterprise innovation investment. The number of agricultural authorized patents in each region is used to measure the level of agricultural technology innovation more accurately.

② The allocation index of agricultural production factors (Pose): Based on the research of Hsieh and Peter [67], this paper first calculates the factor mismatch index of agricultural production capital and labor force ${\tau }_{Ki},{\tau }_{Li}$:

$${\tau }_{Ki}=1/{\gamma }_{Ki}-1,{\tau }_{Li}=1/{\gamma }_{Li}-1$$

(18)

where ${\gamma }_i$ is the absolute distortion coefficient of the elements, which is usually impossible to measure; in practice, it is usually replaced by the relative distortion coefficient ${\widehat{\gamma }}_i$:

$${\widehat{\gamma }}_{Ki}=(\frac{K_i}{K})/(\frac{s_i{\beta }_{Ki}}{{\beta }_K}), {\widehat{\gamma }}_{Li}=(\frac{L_i}{L})/(\frac{s_i{\beta }_{Li}}{{\beta }_L})$$

(19)

where $s_i$ is the proportion of agricultural output in total economic output in different regions and ${\beta }_K, {\beta }_L$ are the contribution rates of agricultural capital and labor weighted by output weight, respectively. ${\widehat{\gamma }}_i$ reflects the deviation degree of actual use and effective allocation.

The specific calculation process adopts the method of Bai and Liu [68] and takes into account the substitution between capital and labor factors to build the allocation index of agricultural production factors:

$$Pose=-\left|{\widehat{\gamma }}_{Ki}/{\widehat{\gamma }}_{Li}\right|$$

(20)

When the regression coefficient of this variable is positive, it indicates that the allocation of agricultural resources has been optimized. On the contrary, it indicates that the mismatch of agricultural resources is intensifying.

(4) Control Variables:

① Human capital (Human), the proportion of nonurban population in the total population in each region as the weight, multiplied by the number of years of education per capita in the region, is used as an alternative variable for the quality of agricultural human capital. ② Environmental governance investment (Environ), measured by the logarithm of environmental pollution governance investment in each region. ③ Marketization degree (Market) is measured by the logarithm of the marketization index of each region. ④ Agricultural market demand (Demand): the growth rate of agricultural GDP (%) in each region is used to measure the growth degree of agricultural market demand. ⑤ Non-agricultural income (Revenue), measured by the logarithm of per capita non-agricultural income in each region. ⑥ Infrastructure (Infract), represented by the logarithm of the mileage of traffic road facilities in each region.

The descriptive analysis results of the above variables are shown in

Table 3. Descriptive statistics of variables.

	Symbol	Concept	Mean	Std	Min	Max
Explained variable	AGTFP	Agricultural Green Total Factor Productivity	1.02	0.58	0.97	1.09
	GEC	Green technology efficiency	0.97	0.43	0.98	1.05
	GTC	Green technology progress	1.01	0.41	0.96	1.09
Explanatory	Die	Digital finance index	7.21	3.65	2.33	11.91
variable	Scop	Coverage breadth	5.93	2.54	2.68	10.13
	Deep	Depth of use	4.59	2.47	1.09	8.33
	Dig	Degree of Digitalization	4.02	1.54	2.74	6.58
Mediating variable	Pose	Allocation of agricultural production factors	1.93	2.16	0.45	6.12
	RD	Promoting innovation	4.19	1.78	2.23	9.63
Control	Human	human capital	2.54	2.19	1.45	6.88
variable	Environ	Environmental governance investment	5.12	3.28	4.26	9.89
	Market	Marketization degree	3.24	2.47	2.14	7.68
	Demand	Agricultural market demand	1.42	1.52	0.04	3.03
	Revenue	Non-agricultural income	7.32	1.43	2.89	10.78
	Infract	Infrastructure	5.97	0.75	3.06	13.42

Note: N = 290.

:

(5) Variables Inspection: In order to improve the interpretability of the empirical results and prevent the “pseudo regression” phenomenon in the model, a root of unity test was carried out on the model variables to analyze the stability among the variables, and Stata was used for measurement. The measurement results are shown in

Table 4. Unit Root Test.

ADF	GTFP	GEC	GTC	Pose	RD	Die	Scop	Deep	Dig
Original value	−199.92	−187.74	−152.78	−179.34	−162.68	−329.10	−268.44	−277.62	−187.08
p-value	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
Conclusion	Stable	Stable	Stable	Stable	Stable	Stable	Stable	Stable	Stable
First order difference	−208.18	−249.62	−182.39	−315.48	−217.42	−230.73	−313.84	−324.16	−230.59
p-value	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
Conclusion	Stable	Stable	Stable	Stable	Stable	Stable	Stable	Stable	Stable

. According to the ADF method to test the stationarity between variables, the calculation results show that the original values and first-order differences of GTFP, GEC, GTC, Pose, RD, Die, Scop, Deep, and Dig are all stationary, indicating that the model does not have the phenomenon of “pseudo regression”.

4. Results and Discussion

4.1. Trend Analysis of China’s AGTFP

According to

Table 5. Change trend of China’s AGTFP from 2011 to 2020.

	2012	2013	2014	2015	2016	2017	2018	2019	2020	Mean
AGTFP	1.047	1.004	1.019	1.003	1.013	1.021	0.991	1.018	1.044	1.018
GEC	1.019	1.009	1.002	1.019	0.976	1.027	0.974	1.007	1.015	1.005
GTC	1.028	0.996	1.017	0.985	1.038	0.995	1.018	1.011	1.029	1.013

Note: 2012 in the table refers to the growth rate of AGTFP from 2011 to 2012.

, the average annual growth rate of China’s AGTFP from 2011 to 2020 was 1.8%, the average annual growth rate of technological progress was 1.3%, and the average annual growth rate of technical efficiency was 0.5%. This indicates that China’s AGTFP is driven by a “dual wheel” growth model of technological progress and technological efficiency, and the contribution of technological progress is greater than the contribution of technological efficiency. From the time trend analysis, the agricultural green technology progress rate shows an increasing trend in most years and is still the main driving force of AGTFP growth, while the change in technical efficiency is relatively stable.

To further analyse the regional differences in China’s AGTFP, according to the guidance on the establishment of grain production functional zones and important agricultural product production protection zones issued by the State Council of China, the research sample is divided into two groups, namely major grain-producing areas and non-major grain-producing areas, and the differences in their AGTFP are compared, as shown in

Table 6. Trends in AGTFP in China’s provinces, major grain-producing areas and non-major grain-producing areas from 2011 to 2020.

	AGTFP	GEC	GTC		AGTFP	GEC	GTC
Main Area	1.015	1.003	1.012	Non-Main Area	1.020	1.006	1.014
Anhui	0.972	0.986	0.985	Beijing	1.09	1.001	1.088
Heilongjiang	0.977	1	0.977	Tianjin	1.079	1.045	1.032
Hebei	1.041	0.992	1.049	Shanxi	0.996	0.995	1.001
Henan	1.006	1.017	0.989	Shanghai	1.029	1	1.029
Hubei	1.01	1.006	1.003	Guangdong	1.041	1.011	1.029
Hunan	1.006	0.994	1.012	Guangxi	0.971	1	0.971
Jilin	1.005	1.028	0.977	Hainan	0.984	1	0.984
Jiangsu	1.039	0.997	1.042	Chongqing	1.022	1	1.022
Jiangxi	1.012	0.993	1.019	Zhejiang	1.048	1.009	1.038
Liaoning	1.031	0.993	1.038	Fujian	1.047	1	1.047
Nei Mongol	0.999	1.032	0.968	Guizhou	1.016	1	1.016
Shandong	1.055	1.001	1.053	Yunnan	0.983	1	0.983
Sichuan	1.041	1	1.041	Shaanxi	1.032	1.013	1.018
				Gansu	0.999	1.004	0.995
				Qinghai	0.997	1	0.997
				Ningxia	0.985	1.01	0.975

.

From the perspective of AGTFP, the changes in AGTFP in China’s provinces are significant. The average annual growth rate of AGTFP in most provinces is positive. The average annual growth rate of 13 provinces is greater than the national average growth rate (1.8%), which is mainly located in the eastern region. China’s AGTFP is “high in the eastern region—low in the central and western regions”. According to the decomposition results, technological progress has a promoting effect on AGTFP in most provinces, while the promoting effect of technical efficiency is relatively low. Among the provinces with a negative annual growth rate of AGTFP, technological retrogression is also the main reason for the decline in productivity in these provinces. From the perspective of AGTFP in major grain producing areas and non-major grain producing areas in China, the average annual growth rate of AGTFP in major grain producing areas is 1.5%, the average annual growth rate of technological progress is 1.2%, and the average annual growth rate of technical efficiency is 0.3%. The average annual growth rate of AGTFP in non-major grain producing areas is 2%, the average annual growth rate of technological progress is 1.4%, and the average annual growth rate of technical efficiency is 0.6%, both of which are reflected in the growth mode driven by “two wheels”.

4.2. The impact of Digital Finance (DF) on AGTFP

To verify the direct impact of DF development level on AGTFP, GTE, and GTC, empirical tests were conducted based on Equations (5)–(7) to verify the research hypotheses 1 and 2 in this article. The test results are shown in

Table 7. Impact test of DF on AGTFP.

	AGTFP		GEC		GTC
Die	0.1974 * (0.1081)	0.1935 ** (0.0934)	0.1696 ** (0.0838)	0.1683 * (0.0912)	0.1762 ** (0.0873)	0.1706 * (0.0964)
Human		0.1145 * (0.0659)		0.1048 * (0.0572)		0.1183 * (0.0677)
Environ		0.0592 * (0.0309)		0.0622 * (0.0365)		0.0753 * (0.0416)
Market		0.0286 * (0.0164)		0.0353 * (0.0201)		0.0303 * (0.0176)
Demand		0.0328 (0.0212)		0.0316 (0.0217)		0.0286 (0.0179)
Revenue		−0.1106 * (0.0607)		−0.1087 (0.0682)		−0.1137 (0.0738)
Infract		0.0595 * (0.0312)		0.0615 * (0.0324)		0.0597 * (0.0306)
R2	0.279	0.284	0.274	0.306	0.307	0.314

Note: the brackets indicate the standard deviation. **, * represent the significance levels of 5% and 10%, respectively.

. According to the test results in Table 7, the impact coefficient of DF on AGTFP is significantly positive. After considering the fixed effect and control variables, the impact coefficient of DF is 0.1935, and the 5% significance test shows that the improvement of the level of DF has a significant role in promoting China’s AGTFP, which verifies research hypothesis 1. The development of DF has effectively reduced the production cost of agriculture and the RD cost of agricultural technology, helped to form a wider range of long tail effects, was conducive to the dissemination of agricultural knowledge and technology, and improved the spillover effect of agricultural technology, thus improving AGTFP. Further analysis of the impact of DF on green technology efficiency and green technology progress shows that DF has a significant role in promoting green technology efficiency and green technology progress. After considering the control variables, the role of DF in promoting GTC is greater than that of GEC (0.1706 > 0.1683), which shows that the improvement of AGTFP by DF is mainly to promote the progress of green technology, supplemented by improving the efficiency of green technology, and to drive the improvement of AGTFP by “two wheels”, which verifies research hypothesis 2 of this paper.

We further test the impact of the three sub-dimensions of DF on AGTFP. According to the test results in

Table 8. Impact test of sub-dimensions of DF on AGTFP.

	Scop		Deep		Dig
Die	0.1575 * (0.0824)	0.1426 * (0.0785)	0.1319 ** (0.0657)	0.1298 * (0.0675)	0.1493 * (0.0798)	0.1412 * (0.0763)
Control variable	No	Yes	No	Yes	No	Yes
R2	0.263	0.281	0.256	0.274	0.264	0.309

Note: the brackets indicate the standard deviation. **, * represent the significance levels of 5% and 10%, respectively.

, the coverage, depth of use, and degree of digitization have significant promoting effects on AGTFP. With the improvement of the coverage breadth of DF, the threshold of financial services has been lowered and the original restrictions of financial market have been broken, and digital financial services have brought more long-tail effects to agricultural development, which has effectively promoted the green development of agriculture. As the use depth of DF increases, it indicates that the availability of services such as investment, credit, and evaluation in DF has improved, which helps to improve the risk resilience in agricultural production and agricultural product sales and can effectively improve the economic benefits of agricultural production. The deepening of the degree of digitization helps to reduce agricultural transaction costs and improve the efficiency of agricultural production. After considering the control variables, the coverage had the greatest promoting effect on AGTFP, the promotion effect of the digitization degree was second, and the promotion effect of depth of use was the lowest.

4.3. Effect mechanism of DF on AGTFP

According to theoretical analysis and research hypotheses 3 and 4, DF has a positive impact on AGTFP by improving the mismatch of agricultural resources and promoting agricultural technological innovation. This impact mechanism is tested based on the Mesomeric effect model Formula (8)–(11). The test results are shown in

Table 9. Test on the mechanism of improving the mismatch of agricultural resources.

	Pose	AGTFP	GEC	GTC
Die	0.2039 * (0.1186)	0.1483 * (0.0873)	0.1364 * (0.0738)	0.1342 ** (0.0689)
Pose		0.2217 * (0.1296)	0.1564 * (0.0812)	0.1785 * (0.0918)
Control variable	Yes	Yes	Yes	Yes
R2	0.311	0.307	0.287	0.273

Note: the brackets indicate the standard deviation. **, * represent the significance levels of 5% and 10%, respectively.

and

Table 10. Test of the intermediary effect of promoting agricultural technology innovation.

	RD	AGTFP	GEC	GTC
Die	0.1927 * (0.1082)	0.1524 ** (0.0762)	0.1372 * (0.0723)	0.1368 * (0.0771)
RD		0.2133 * (0.1129)	0.1614 * (0.0883)	0.1754 * (0.0978)
Control variable	Yes	Yes	Yes	Yes
R2	0.281	0.293	0.305	0.273

Note: the brackets indicate the standard deviation. **, * represent the significance levels of 5% and 10%, respectively.

.

According to the test results in Table 9, DF has a significant positive impact on the allocation index of agricultural production factors. After considering the control variables, the impact coefficient is 0.2039, and the 10% significance test shows that the development of DF has a significant improvement effect on the mismatch of agricultural production resources. At the same time, DF has a significant role in promoting AGTFP, and the allocation index of agricultural production factors has a significant positive impact on AGTFP, indicating that improving the mismatch of agricultural production resources plays a partial mediating effect in improving AGTFP by DF; that is, there is a mechanism of “DF—improving the mismatch of agricultural production resources—improving AGTFP”, which verifies research hypothesis 3 of this paper. Improving the mismatch of agricultural production resources is the endogenous driving force of DF to improve AGTFP. In addition, improving the mismatch of agricultural production resources also plays an intermediary effect in the process of DF affecting the progress of agricultural green technology and the efficiency of agricultural green technology.

According to the test results in Table 10, DF has a significant positive impact on the level of agricultural technology innovation. After considering the control variables, the impact coefficient is 0.1927 through the 10% significance test. This shows that the development of DF helps to improve the level of agricultural technology innovation. At the same time, DF has a significant role in promoting AGTFP, and the level of agricultural technology innovation has a significant positive impact on AGTFP, indicating that agricultural technology innovation plays a partial mediating effect in the impact of DF on AGTFP. That is, there is a mechanism of “DF—agricultural technology innovation—improving AGTFP”, which verifies research hypothesis 4 of this paper. Promoting agricultural technology innovation is the internal driving force of DF to improve AGTFP. In addition, agricultural technology innovation also plays a mediating role in the process of DF affecting agricultural green technology progress and agricultural green technology efficiency.

4.4. Regional Heterogeneity of DF on Agricultural Green Productivity

We further analyze the regional differences in the impact of DF on AGTFP. According to the regional characteristics of China’s agricultural development, the research sample is divided into two subsamples: major grain-producing areas and non-major grain-producing areas. The heterogeneity test results are shown in

Table 11. Regional heterogeneity of DF on AGTFP.

	Major Grain-Producing Areas			Non-Major Production Area
	AGTFP	GEC	GTC	AGTFP	GEC	GTC
Die	0.2059 ** (0.1026)	0.1625 ** (0.0812)	0.1744 * (0.0983)	0.1626 * (0.0937)	0.1472 * (0.0833)	0.1582 * (0.0836)
Control variable	Yes	Yes	Yes	Yes	Yes	Yes
R2	0.280	0.293	0.271	0.264	0.288	0.249

Note: the brackets indicate the standard deviation. **, * represent the significance levels of 5% and 10%, respectively.

. Analysing the regional heterogeneity of DF on AGTFP will help each region formulate the development strategy of DF according to local conditions.

According to the test results in Table 11, there are obvious regional differences in the impact of DF on AGTFP. Specifically, the role of DF in promoting AGTFP in major grain-producing areas is significantly greater than that in non-major grain-producing areas (0.2059 > 0.1626). The possible reason for this is that the level of agricultural development in major grain-producing areas is relatively high, and with the continuous integration and development of DF and the agricultural industry, it provides an impetus for agricultural technology innovation. To a certain extent, it has improved the level of agricultural technology innovation, provided a new impetus for improving the mismatch of agricultural resources, and made it easier to achieve the improvement of AGTFP. However, the agricultural output value in non-major grain-producing areas is relatively low, and DF mainly serves the real economy of the market, which leads to the low promotion effect of DF on AGTFP in non-major grain-producing areas. In addition, the role of DF in promoting GTC and GEC in major grain-producing areas is also significantly greater than that in non-major grain-producing areas.

4.5. Robust Test

To reduce the impact of endogeneity and improve the robustness of research conclusions, this paper uses a differential GMM model for robustness testing. According to the inspection results in

Table 12. Robust Test.

	Die	Scop	Deep	Dig
Die	0.1628 * (0.0816)	0.1257 ** (0.0703)	0.1024 * (0.0574)	0.1323 * (0.0716)
AGTFPt−1	0.1023 * (0.0552)	0.0827 ** (0.0413)	0.0793 * (0.0421)	0.0802 * (0.0429)
Control variable	Yes	Yes	Yes	Yes
R2	0.204	0.223	0.216	0.196

Note: the brackets indicate the standard deviation. **, * represent the significance levels of 5% and 10%, respectively.

, after replacing the inspection model, the level of DF has a significant role in promoting AGTFP, and the coverage, depth of use, and digital degree have a significant role in promoting AGTFP.

5. Discussion

Firstly, from 2011 to 2020, China’s AGTFP continued to improve, mainly driven by technological progress. In terms of regional distribution, the AGTFP in non-major grain-producing areas is significantly higher than that in major grain-producing areas. Although China’s AGTFP shows a “two wheel” growth mode driven by technological progress and technical efficiency, technological regression is the main reason for the decline in AGTFP in some provinces. This confirms the views of Wang et al. (2022) [69] and Jin et al. (2023) [70] to some extent.

Secondly, digital finance (DF) has effectively improved the AGTFP of China’s provinces. This is consistent with the research conclusions of Shen et al. (2023) [20] and Hong et al. (2022) [19]. On the premise of continuous progress of digital financial inclusion, more and more financial resources have been able to enter agricultural development, and the financing difficulties of farmers and small and micro enterprises in rural areas have been improved to a certain extent. DF can achieve economies of scale and improve rural governance, thereby increasing AGTFP, which is difficult to achieve with traditional finance.

Thirdly, the three sub-dimensions of DF (coverage, depth of use, and degree of digitization) have promoted AGTFP. With the improvement of the coverage breadth of DF, the threshold of financial services has been lowered and the original restrictions of the financial market have been broken, and digital financial services have brought more long-tail effects to agricultural development, which has effectively promoted the green development of agriculture. As the use depth of DF increases, it indicates that the availability of services such as investment, credit, and evaluation in DF has improved, which helps to improve the risk resilience in agricultural production and agricultural product sales and can effectively improve the economic benefits of agricultural production. The deepening of the degree of digitization helps to reduce agricultural transaction costs and improve agricultural production efficiency. The coverage had the greatest promoting effect on AGTFP, the promotion effect of digitization degree was second, and the promotion effect of depth of use was the lowest. However, Tang et al. (2022) [71] found in the empirical analysis of the impact of digital financial inclusion development on ATFP that the strongest role in promoting ATFP is the depth of use of digital financial inclusion products and services, followed by the degree of digitalization, and finally the breadth of coverage. The inconsistency of conclusions may be related to the selection of sample cross-sections and different indicator-processing methods.

Fourthly, we discuss the impact mechanism of digital financial inclusion on AGTFP from four aspects: the direct impact of DF on AGTFP, the impact of DF on agricultural technology progress and technical efficiency, the impact of DF on AGTFP by improving the mismatch of agricultural production resources, and the impact of DF on AGTFP by promoting agricultural technology innovation. Unlike existing research [19,20,21], we opened the “black box” of DF to improve AGTFP, analyzed the impact of digital financial inclusion on AGTFP and its internal mechanism more systematically and comprehensively, and provided empirical evidence for DF’s role in promoting agricultural green development.

6. Conclusions

This study matched the China digital finance (DF) index from 2011 to 2020 with data on China’s agricultural production, measured the agricultural green total factor productivity (AGTFP) of China’s provinces, and investigated the impact mechanism of DF on China’s AGTFP.

6.1. Research Conclusion

First, from 2011 to 2020, China’s AGTFP continued to improve, mainly driven by technological progress. In terms of regional distribution, the AGTFP in non-major grain-producing areas is significantly higher than that in major grain-producing areas. Although China’s AGTFP shows a “two wheel” growth mode driven by technological progress and technical efficiency, technological regression is the main reason for the decline in AGTFP in some provinces.

Second, DF has effectively improved China’s AGTFP, and the three subdimensions of coverage, depth of use, and digital degree have promoted AGTFP. This conclusion is still valid after robustness testing; the regional heterogeneity test shows that it has significantly promoted the green total factor productivity of agriculture in the main grain-producing areas.

Third, DF has a significant role in promoting green technology efficiency and green technology progress, and has a greater role in promoting green technology progress, which indicates that DF drives the improvement of the green total factor productivity of agriculture with two wheels; meanwhile, in major grain-producing areas, the impact of DF on the GTC and GEC is more pronounced.

Fourth, the impact mechanism shows that DF can promote AGTFP, green technology progress, and green technology efficiency by improving the mismatch of agricultural resources and promoting agricultural technology innovation.

6.2. Policy Implications

First, in the context of vigorously developing digital technology, we should deepen the integration of digital information technology with traditional finance, pay attention to the coordinated development of urban and rural areas, further promote the coverage of DF in agriculture and rural areas, improve the level of DF in various regions, strengthen its functions in credit and financial management, accelerate the “construction of digital countryside”, focus on protecting the rights and interests of rural, agricultural, farmers, and other groups in the financial field, make the DF policy more accurately allocated to the weak link of the green transformation of agriculture, improve the availability of digital financial services in agricultural and rural areas, and give full play to the service characteristics and financial attributes of DF.

Second, we should optimize the allocation of financial resources, promote agricultural technology innovation, and improve the mismatch of agricultural resources relying on digital financial policies. Considering the differences in regional economic development, we should further optimize the regional allocation of financial resources, vigorously support the development of DF in backwards areas, accelerate the construction of financial infrastructure, improve the efficiency of the use of regional financial resources, and actively use digital technology to guide the flow of digital financial services to agricultural and rural areas to promote the overall improvement of AGTFP.

Third, we should formulate the development strategy of DF according to local conditions and further improve the construction of digital infrastructure. DF policy should favor relatively backwards areas such as non-major grain-producing areas, rely on the inclusive characteristics of DF to improve the level of agricultural green production in the region, accelerate the intensive construction of DF infrastructure, promote the inclusive development of DF, improve the efficiency of digital financial services to the real industry, and promote the growth of AGTFP in non-major grain-producing areas by improving the mismatch of agricultural resources and promoting agricultural technology innovation.

6.3. Under Research

This paper also has some shortcomings: First, due to the limitation of data access, this paper empirically tests the impact and mechanism of DF development on AGTFP based on 29 provincial samples in China from 2011 to 2020. However, there is a large gap in the development level of China’s provinces, which may underestimate the impact of DF on AGTFP. Future research can analyze the relationship between the two more accurately at the urban level. Second, this paper mainly analyzes the impact mechanism of DF on AGTFP from two aspects: improving agricultural resource mismatch and promoting agricultural technology progress. Future research can start from the regulatory effect, and other impact mechanisms of DF need to be analyzed further.