Digital Financial Inclusion, Land Circulation and High-Quality Development of Agriculture

Abstract

With the deep integration of digital technology and inclusive finance, digital inclusive finance has provided a new opportunity for agricultural high-quality development through “overtaking on curves”. This article empirically examines the impact of digital inclusive finance on agricultural high-quality development and the dynamic mechanism of land circulation in its transmission process, utilizing panel data from various provinces in China from 2011 to 2021. The research indicates that digital inclusive finance has a significant improvement effect on agricultural high-quality development, and this conclusion remains valid after a series of endogenous treatments and robustness tests. Meanwhile, intelligent manufacturing has a more pronounced role in promoting agricultural high-quality development in China’s eastern regions, regions with sound infrastructure, and regions with high environmental regulation intensity. Further research reveals that digital inclusive finance can promote agricultural high-quality development through the mechanism of promoting land circulation. The research conclusions provide important empirical evidence and policy implications for achieving coordinated development of agricultural economic growth and environmental protection, thereby realizing the beautiful vision of comprehensive rural revitalization.

1. Introduction

Since the reform and opening-up, China has made remarkable achievements in economic construction. However, rapid economic growth comes at the cost of an increasing depletion of resources and deterioration of the ecological environment. Facing constraints such as resource shortages, industrial policies, and institutional barriers, the contradictory relationship between agricultural development and ecological environment governance has not been systematically resolved. There is an urgent need to promote quality-oriented agriculture [1]. Furthermore, agriculture, as an inherent ecological carbon sequestration system, manifests unique industrial attributes amidst the impact of climate change. Nevertheless, the present carbon sequestration capacity falls short of balancing the greenhouse gases emitted during production, thereby posing a significant threat to global food security, human health, and the sustainability of economic development [2]. Indeed, agricultural development has reached a crucial period where the development model of “trading the environment for growth” must be changed. Amidst the pursuit of fostering quality development, the report of the 20th National Congress of the Communist Party of China underscores the urgent need to drive agricultural transformation from quantitative expansion towards qualitative enhancement. Specifically, it emphasizes that the entire Party and society must endeavor to comprehensively bolster rural revitalization, expedite agricultural and rural modernization, incessantly enhance agricultural innovation and competitiveness, and resonate with the central theme of “quality agriculture, green agriculture, and brand-building agriculture”. Hence, the transformation of agriculture’s quantitative growth model, reliant on input factors, can be achieved through the integration of modern agricultural machinery and equipment, expedited technological innovations, and stringent environmental regulations. This approach will enhance the soil quality and carbon sequestration capabilities of farmland, thereby significantly augmenting the level of green production and resource utilization efficiency. Ultimately, it will foster a harmonious alignment of economic, social, and ecological benefits [3]. Additionally, this is imperative for safeguarding China’s food security, societal stability, and attaining sustainable economic and environmental development.

Constrained by the marginality and aggregation of rural geographical spaces, weak infrastructure construction, and obstacles such as the “voluntary financial exclusion” of vulnerable groups, traditional inclusive finance is unwilling to penetrate into rural hinterlands, and financial services are difficult to extend to the “long-tail” population [4]. Drawing inspiration from the concept of “internal hematopoiesis” and aligned with the principles of sustainable development, digital inclusive finance maximizes its “multiplier effect” in resource allocation, enabling intelligent analysis and precise delivery of financial services tailored to vulnerable groups, thereby substantially elevating the efficiency of capital matching [5,6]. This enables farmers who have long been excluded by traditional financial services to obtain formal financial support, further contributing to the modernization of traditional agricultural industries, the facilitation of rural public services, the intelligentization of rural governance, the transparency of rural credit systems, and the greening of agricultural industries. Concurrently, while significantly contributing to the traditional financial service system, digital inclusive finance profoundly transforms and revolutionizes agricultural production and economic modalities. In terms of its green attributes and positive environmental externalities, digital inclusive finance consistently assumes the role of green finance, offering an efficacious means to elevate the quality of agricultural development in China [7]. The jointly issued “Key Points for the Development of Digital Villages in 2023” by the Cyberspace Administration of China, the Ministry of Agriculture and Rural Affairs, and four other departments underscores that digital inclusive finance, as an enabler of high-quality agricultural development, serves as the cornerstone for achieving supply-side structural reform and rural revitalization under the “dual carbon” target. Furthermore, it constitutes a fundamental component in China’s pursuit of high-quality economic development.

Given this, this article utilizes panel data from various provinces in China from 2011 to 2021 to focus on exploring the impact mechanism of digital inclusive finance on high-quality agricultural development, and analyzes whether land transfer can help tap into the technological and structural dividends of digital inclusive finance in terms of agricultural green and low-carbon transformation, aiming to provide theoretical and empirical evidence for cultivating new competitive advantages and developing new momentum in agriculture.

The research contributions of this article are mainly reflected in the following aspects: Firstly, it focuses on the ecological function of agriculture, establishing a theoretical analysis framework for digital inclusive finance and high-quality agricultural development, and quantitatively assessing their impact effects based on provincial-level data in China. Secondly, unlike heterogeneity analysis based on geographical location, this article divides the samples based on factors such as the degree of digital infrastructure improvement, environmental regulation intensity, and urban location characteristics, identifying the heterogeneous impact of digital inclusive finance on high-quality agricultural development. The conclusions obtained are more conducive to administrative departments and financial institution managers to take precise measures to address deficiencies. At the same time, this article does not simply use the mean of sample data for sample differentiation, but rather relies on the actual economic development status of the region and relevant policy documents, indirectly evaluating the effectiveness of green financial reform pilot zones and national-level big data comprehensive experimental zones in terms of high-quality agricultural development. Thirdly, this article innovatively explores the role of land transfer in the process of digital inclusive finance influencing high-quality agricultural development, thereby expanding the research perspective and content of digital inclusive finance supporting green agricultural development.

The subsequent sections of this scholarly article are structured as follows: The second chapter offers a comprehensive literature review encompassing the essence, evaluation metrics, and contributing factors of high-quality agricultural progression. Additionally, it delves into the mechanisms and avenues through which digital inclusive finance influences this development. The third chapter examines the impact trajectory of digital inclusive finance on high-quality agricultural development, highlighting the pivotal role played by land transfer in this intricate process. This analysis culminates in the formulation of this article’s research hypotheses. The fourth chapter introduces the methodological framework and data sources utilized in this article. The fifth chapter employs a range of mathematical and statistical models to validate the stimulatory effect of digital inclusive finance on agricultural advancement, as well as the mediating role of land transfer. The sixth chapter summarizes the key findings, recommends pertinent policies, and discusses the limitations of the present study while outlining potential directions for future research. A visual representation of the research trajectory is provided in Figure 1.

2. Literature Review

As the product of the integration and innovation of traditional inclusive finance and modern information technology, digital inclusive finance has become a hot topic of concern for governments and scholars in recent years as they explore how to fully unleash its boosting power for high-quality agricultural development, enhance the efficient allocation of production resources, and ensure widespread access to financial digital dividends. A review of the existing literature reveals that research pertaining to this topic can be broadly categorized into the following dimensions:

Firstly, there is the analysis and measurement evaluation of the connotation of high-quality agricultural development. Regarding the conceptual elaboration of high-quality agricultural development, the existing studies generally believe that a sound agricultural production and management system, a diversified supply system of agricultural products, high-level agricultural production efficiency, and high-quality international competitiveness in agriculture are the inherent requirements of high-quality agricultural development [8,9]. Wang et al. (2022) proposed that the essence of high-quality agricultural development is the process of improving agricultural quality and efficiency, upgrading, and modernizing agricultural and rural areas, starting from the power transformation led by advanced technology, focusing on the transformation of agricultural economic efficiency, and aiming to achieve the transformation of agricultural development quality [10]. In terms of scientifically measuring the level of high-quality agricultural development, the academic community has not yet reached a unified standard. Some scholars use multiple indicator-weighting methods such as the multi-objective linear weighted function method, entropy weight method, and fuzzy comprehensive evaluation method to measure it. For example, Huang et al. (2023) constructed a comprehensive evaluation system for high-quality agricultural development from four dimensions: agricultural economic efficiency and stability, optimization of agricultural economic structure, leadership in agricultural green production, and healthy and sustainable agricultural development [11]. They also used the generalized Bonferroni curve to depict its spatial and temporal evolution and convergence. Yang et al. (2024) used a three-stage DEA dynamic analysis model based on spatial heterogeneity and found that high-quality agricultural development level showed a geographic “blockchain” convergence trend with obvious radiation and driving effects [12]. Additionally, a few scholars have used various methods such as Dagum’s Gini coefficient decomposition method, kernel density estimation, Markov chain, and spatial Markov chain to analyze the high-quality development status of agriculture in various regions of China. They found that the overall trend is upward, and the regional gap is continuously narrowing [13,14].

The second aspect concerns research on the optimization paths and influencing factors of high-quality agricultural development. In discussing how to promote high-quality agricultural development, many scholars have proposed targeted paths from the perspectives of the industrial system, production system, and management system. For instance, leveraging digital technology to promote deep integration of industries and optimize agricultural industrial structure, strengthening agricultural technological innovation and promoting low-carbon agricultural development, improving the efficiency of land resource allocation, and cultivating new agricultural business entities [15,16,17]. In terms of exploring the factors influencing high-quality agricultural development, most of the existing literature employs methods such as the Logarithmic Mean Divisia Index (LMDI) decomposition method and the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. These studies have concluded that technological innovation capability, industrial capital intensity, industrial structure, innovative human capital, and environmental regulations can effectively impact high-quality agricultural development, though the research conclusions revealed vary [18]. As exemplified by Li et al. (2021), the process of household registration urbanization and permanent urbanization has been found to enhance the quality of agricultural development by facilitating more efficient resource allocation, deeper capital investment, and wider technological dissemination [19]. Han et al. (2022) put forth the argument that challenges including the “small-scale, fragmented, and disorganized” nature of agricultural enterprises, the significant loss of rural labor, agricultural environmental pollution, and the low agricultural total factor productivity have posed considerable impediments to achieving high-quality agricultural development, thereby limiting the competitiveness of China’s agricultural products and sector [20].

The third dimension entails innovative inquiries into the underlying mechanisms and pathways that digital inclusive finance exerts its influence on through the attainment of high-quality agricultural development. As the concept of digital inclusive finance is relatively new, and its connotations are constantly enriching and evolving, there are few studies that delve deeply into the relationship between digital inclusive finance and high-quality agricultural development. The existing literature mainly focuses on whether digital inclusive finance, relying on the Internet, 5G, and big data technology, can promote high-quality agricultural development by correcting factor misallocation, alleviating credit constraints, and exerting energy-saving and carbon reduction effects. However, there are still some differences in the research conclusions that have been formed. Guo et al. (2022) and Shen et al. (2023) both pointed out that digital inclusive finance can promote the decentralization of the financial industry through financial deepening, digitization, and intelligence, breaking through the spatiotemporal limitations of traditional financial services and the “financial exclusion” of agricultural business entities, thereby alleviating financing constraints faced by agricultural development [21,22]. Such advancements facilitate farmers’ adoption of agricultural machinery and innovative agricultural technologies, thereby enhancing agricultural production efficiency, minimizing resource consumption and environmental degradation, and ultimately advancing the intensification, industrialization, and modernization of agricultural production. Moreover, Hong et al. (2022) noted that as a promoter of information dissemination and sharing, digital inclusive finance enriches and improves the new agricultural socialized service system, driving the development of agriculture-related industries and facilitating the transfer of rural labor in various stages of agricultural production, circulation, sales, and services [23]. This alleviates the phenomenon of “excessive concentration” of labor in agricultural production and supports the efficient and green transformation of agriculture.

In summary, while numerous scholars have conducted extensive and impactful research on the nexus between digital inclusive finance and high-quality agricultural development, offering numerous insightful conclusions that provide both conceptual provocation and empirical insights for this study, there remain certain research gaps. The existing studies rarely combine digital inclusive finance and high-quality agricultural development within the same analytical framework, and there is an even greater lack of exploration of the intrinsic mechanism from the perspective of land circulation. Therefore, this article explores the impact of digital inclusive finance on high-quality agricultural development and its mechanism of action based on provincial panel data from 2011 to 2021 in China, providing valuable practical references for the construction of an agricultural powerhouse and the implementation of the rural revitalization strategy.

3. Theoretical Analysis and Research Hypothesis

3.1. Direct Influence of Digital Inclusive Finance on High-Quality Development of Agriculture

High-quality agricultural development refers to the joint participation of basic production factors such as labor, capital, and land. By expanding agricultural production scales, enhancing production efficiency, and optimizing the allocation of production factors, high-quality agricultural development aims to mitigate the negative externalities of agricultural production and achieve high-quality, vibrant, and low-pollution agricultural development. However, substantial capital investment is imperative for such development. Obstacles like fragmented agricultural capital demand and challenges in gathering farmers’ credit information hinder direct support from traditional financial institutions. Amidst such significant capital requirements and pressure for green transformation, digital inclusive finance emerges as an inevitable trend. Leveraging digital technology, digital inclusive finance can significantly enhance capital matching efficiency and financial service accessibility in the agricultural sector at a low cost, reduce information asymmetry, and implement targeted risk mitigation, thereby providing robust financial support for agricultural production and innovation activities, ultimately fostering high-quality agricultural development [24]. Specifically, digital inclusive finance harnesses information technologies encompassing the internet, mobile communication networks, and vast data analytics to transcend geographical limitations, broaden the scope of rural financial services, and establish agricultural big-data-driven credit systems. This facilitates financial institutions in identifying potential capital seekers efficiently and precisely, thereby mitigating financial exclusion in remote rural regions and augmenting farmers’ access to productive credit support [25]. The reduction in agricultural loan thresholds favorably contributes to encouraging the adoption of agricultural machinery and equipment, improved crop varieties, and innovative agricultural technologies among smallholder farmers, large-scale growers, and agricultural enterprises. This augmentation not only elevates agricultural production efficiency but also mitigates environmental degradation stemming from over-cultivation and excessive fertilizer usage, thereby fostering the intensification, industrialization, and modernization of agricultural production [26,27]. At the same time, digital inclusive finance innovates agricultural financial models, such as agricultural crowdfunding, internet wealth management, Internet of Things insurance, and supply chain finance. These models can meet the capital needs of agricultural development while reducing agricultural production risks (such as crop failures, natural disasters, and sluggish sales of agricultural products), ensuring stable agricultural development [28]. Furthermore, due to its targeted marketing capabilities and inherent green attributes, digital inclusive finance can reduce moral hazards and adverse selection issues caused by information asymmetry. It possesses the capability to precisely and conditionally allocate adequate funds to environmentally sustainable business activities in domains such as plant cultivation, breeding, animal husbandry, and rural tourism. This facilitates the influx of high-quality production factors into green agriculture and other environmental protection projects, thus promoting the green transformation of traditional agricultural practices [29,30]. Furthermore, alongside the advancement of underlying financial technologies, new media platforms encompassing mobile payments, online lending, and mobile banking have gained widespread adoption in rural areas. Gradually integrating green and low-carbon concepts, environmental protection projects such as “Ant Forest” have been launched. Through online virtual energy collection and afforestation, these projects are ultimately transformed into real-world afforestation projects, directly promoting high-quality and green agricultural development [31].

Based on the above analysis, this paper puts forward research Hypothesis 1:

Hypothesis 1.

Digital inclusive finance can empower high-quality agricultural development by easing credit constraints, sharing agricultural production risks and promoting the development of green agriculture.

3.2. Heterogeneity of Digital Inclusive Finance Affecting High-Quality Development of Agriculture

Due to differences in factors such as digital infrastructure construction, environmental regulation intensity, and financial development level, the efficiency of digital inclusive finance cannot truly benefit remote areas, resulting in significant heterogeneity in the impact of digital inclusive finance on the high-quality development of agriculture. Specifically, first, digital infrastructure construction, as a key material basis for breaking the problems of small-scale and extensive agricultural development, can effectively guarantee the conduct of agricultural production and business activities, enhance the resilience of the agricultural economy, and provide good support for accelerating the development of facility agriculture and the construction of high-standard farmland in rural areas. It is an important historical opportunity for achieving high-quality and green agricultural development. In regions where digital infrastructure is more comprehensive, the capabilities for information collection, transmission, sharing, and data processing are substantially bolstered. This aids digital inclusive finance in maximizing the long-tail effect of the digital economy, enabling real-time feedback on agricultural production data, refining crop production processes, and attaining precise allocation of pesticides, fertilizers, and water resources. This can promote the flow and sharing of factor resources throughout the agricultural industry chain, improve production models, and reduce agricultural carbon emissions [32,33,34]. At the same time, the development of digital platforms facilitates farmers’ direct access to high-quality online education through internet channels, improving their professional abilities and overall quality, enabling them to adapt to employment situations at different stages and meet the needs of different industrial developments, thereby supporting the modernization of the agricultural industry. In addition, the “siphon effect” released by the improvement in digital infrastructure attracts more and more rural surplus labor to transfer to non-agricultural sectors in cities and towns, accelerating the alleviation of the problem of “excessive concentration” of agricultural labor, thereby driving the effective circulation of land resources and improving the efficiency of green agricultural production.

Moreover, in accordance with the “compliance cost” effect of environmental regulation, an escalation in the rigor of environmental regulations will subsequently increase pollution control costs for market participants. These costs include the recuperation expenses for agricultural waste, the procurement costs of clean production factors, and the acquisition or upgrade expenses for agricultural machinery and equipment, as well as the costs associated with environmental restoration. This crowds out the productive investments of agricultural producers, especially the research and development investments in clean production technologies that require large upfront investments and long cycles, leading to the preference of “two highs and one surplus” industries for areas with looser environmental regulations to achieve cost savings [18,35]. In areas with stronger environmental regulation, agricultural producers will reflect on issues such as low factor utilization and high pollution emissions in their production processes, prompting them to adopt green production technologies to optimize factor allocation, reduce pollution emissions, and increase product added value, thereby alleviating or offsetting the energy-saving hard constraints brought by environmental regulation policies. At the same time, as the government pays increasing attention to environmental protection issues, financial products such as green securities, green bonds, and green funds are constantly being enriched, guiding capital elements to flow towards rural new formats such as rural tourism, rural e-commerce, circular agriculture, and smart agriculture, meeting the funding needs of eco-friendly agricultural industries, promoting the green upgrading of the agricultural industrial structure, and thus driving the high-quality development of agriculture [36,37].

Drawing upon the aforementioned analysis, this paper advances research Hypothesis 2:

Hypothesis 2.

Due to differences in digital infrastructure construction, environmental regulation intensity, and location factors, there exists heterogeneity in the impact of digital inclusive finance on the high-quality development of agriculture.

3.3. Channel Mechanism of Land Circulation

Land, being the focal point of agricultural development research, serves as an indispensable means of production in agricultural production activities. Given the gradually tightening constraints of resources and the environment, the key to promoting high-quality agricultural development lies in overcoming the obstacles of small-scale fragmented operations among smallholder farmers. With the deepening implementation of land transfer policies, farmers have expanded their business scale by transferring land, optimized factor allocation, and altered factor output elasticity, thereby promoting high-quality agricultural development to a certain extent [38,39]. Specifically, first, the expansion of land transfer scale helps overcome the long-standing practical challenges faced by the domestic agricultural production sector, such as land fragmentation, high cost depletion, and low economic efficiency. This transformation guides agricultural production from “survival ethics” to “profit maximization”, providing the possibility for contiguous land management and large-scale planting and breeding [40,41]. Additionally, under stable land ownership, land transfer stimulates the integration of agricultural machinery technology applications and advanced management techniques, maximizing factor combination productivity, thereby reducing agricultural non-point source pollution and enhancing agricultural environmental efficiency. Furthermore, land transfer facilitates the redistribution of land property rights among entities with varying behavioral capabilities and preferences for operational decision-making, thereby enabling market participants to engage in industrial specialization activities grounded in their comparative advantages [42]. For instance, farmers possessing non-agricultural comparative advantages can enhance the congruency between their remaining land and other production factors by transferring surplus land, thereby facilitating more efficient planning and consolidation of fragmented land parcels. Moreover, land transfer is not a simple process of transferring in or out. It promotes the integration of land parcels and forms a policy “combination punch” with agricultural infrastructure construction projects, contributing to the rational planning and layout of various aspects such as fields, roads, mountains, water, and villages, ultimately forming a modern agricultural operation mode characterized by “interconnected fields”, “integrated field and water systems”, and “integrated rural villages”. Lastly, as the country places increasing emphasis on farmer training and strongly advocates for agricultural professionals to engage in the agricultural sector, agricultural human capital continues to sink into rural areas, giving rise to a new breed of agricultural operators such as large-scale grain farmers and professional farmers. After rural land is transferred to these new agricultural operators, they are more inclined to adopt modern agricultural production techniques and scientific management models due to their more comprehensive agricultural knowledge structure [43]. This not only promotes agricultural specialization and professionalization but also contributes to the promotion of appropriate land-scale operations, enhancing agricultural “increment, quality, and efficiency”, ultimately achieving high-quality agricultural development [44].

However, land transfer, as a contractual process between land transferors and transferees, faces challenges in rural remote areas due to limited information exchange and high transaction costs, significantly hindering the improvement in rural land transfer systems. Digital inclusive finance, as an information dissemination carrier, alleviates information asymmetry between land supply and demand subjects and provides effective solutions to credit constraints such as “difficult and expensive financing” and “where to acquire the money” that limit land transfer and agricultural expansion. This not only effectively supports the development of land transfer and promotes the rational allocation of land but also avoids resource waste caused by fragmented planting, thereby contributing to high-quality agricultural development.

Based on the above analysis, this paper puts forward research Hypothesis 3:

Hypothesis 3.

Land circulation serves as a pivotal intermediary in the process where digital inclusive finance fosters high-quality agricultural development.

4. Research Design and Data Sources

4.1. Variable Selection

4.1.1. Explained Variable

High-quality agricultural development (HAD) pertains to the attainment of two significant objectives: “quality enhancement” in terms of environmental improvement and reduction in resource consumption, alongside “efficiency augmentation” through the application of novel technologies and seamless trade, all while fulfilling the “incremental” aspirations such as agricultural production growth and income augmentation. In other words, it aims to comprehensively achieve the triple goals of “increment, quality improvement, and efficiency enhancement” in agriculture. In light of this, adhering to the overarching principles of high-quality economic development and the blueprint of rural revitalization strategic planning, this article adopts the research framework of Tang et al. (2022) and Wen et al. (2023) to devise an evaluation index system for high-quality agricultural development encompassing five key dimensions: innovation, coordination, greenness, openness, and sharing (as detailed in Table 1) [45,46]. Specifically, innovation serves as the core driving force for high-quality agricultural development, able to accelerate the breakthrough of agricultural “bottleneck” technologies through innovative inputs such as agricultural fiscal investment and agricultural machinery applications, thereby driving the improvement in agricultural product quality. Coordination is a characteristic of high-quality agricultural development, which requires the coordinated development of the primary, secondary, and tertiary industries in rural areas. Greenness, as the form of high-quality agricultural development, demands that agriculture must follow a low-carbon, energy-saving, and environmentally friendly development path. Openness is the trend of achieving high-quality agricultural development. The long-term high-quality development of agriculture cannot be separated from the utilization and optimization of domestic and foreign resources and markets. It should actively participate in the international market of agricultural products, innovate agricultural trade strategies, and promote diversification of exports and imports. Sharing is the goal of high-quality agricultural development, which aims to satisfy the pursuit of farmers for increased income and a better life and achieve shared benefits.

In the construction of the agricultural high-quality development index, the disparity in weight allocation among various sub-indicators critically impacts the reliability and precision of the evaluation outcomes. Therefore, this article employs the fixed-base range entropy weight method to assess and analyze the level of high-quality agricultural development. This approach integrates the entropy weight method with the fixed-base range method, which not only mitigates the subjectivity of weight assignment but also establishes a universally applicable reference framework through fixed-base methodology, thus reflecting the trends of change in both spatial and temporal dimensions. The specific computational procedures are outlined below:

Step 1: The above-mentioned positive and negative indicators in the agricultural high-quality development indicator system should be subject to non-dimensionalization. The treatment method is as follows:

(1)$Y_{ij}^t=\{\begin{array}{c}\frac{X_{ij}^t-\min \left(X_{ij}^t\right)}{\max \left(X_{ij}^t\right)-\min \left(X_{ij}^t\right)},X_j^t is a positive indicator\\ \frac{\max \left(X_{ij}^t\right)-X_{ij}^t}{\max \left(X_{ij}^t\right)-\min \left(X_{ij}^t\right)},X_j^t is a negative indicator.\end{array}$

Among them, $X_{ij}^t$ represents the original data of the j index of the i province in the t year; and $Y_{ij}^t$ is the data after dimensionless processing using the range method. For positive indicators, the greater the numerical value, the greater its contribution to the index and the better its performance, whereas for negative indicators, the smaller the numerical value, the greater its contribution to the index and the better its performance.

Step 2: Calculate the proportion of the indicator value in the t-th year under the j-th indicator.

(2)$P_{ij}^t=\frac{Y_{ij}^t}{{{\displaystyle \sum }}_{i=1}^n{Y}_{ij}^t},i\in \left[1,n\right],j\in \left[1,m\right]$

where $P_{ij}^t$ represents the proportion of the i-th province under the j-th indicator in year t compared to that indicator. If the specific gravity value $P_{ij}^t=0$, $li{m}_{Y_{ij}^t\to 0}{P}_{ij}^t\times \ln \left(P_{ij}^t\right)=0$ is defined.

Step 3: Compute the index information entropy (E).

(3)$E_j^t=-{[\ln \left(n\right)]}^{-1}\times {\displaystyle \sum }_{i=1}^n\left[P_{ij}^t\times \ln \left(P_{ij}^t\right)\right]$

where $E_j^t$ denotes the information entropy of the (j)-th index in the (t)-th year. A smaller index information entropy signifies a higher degree of data dispersion, indicating a greater amount of information provided and thus a higher index weight. Conversely, a larger index information entropy leads to a smaller index weight.

Step 4: Calculate the weight of the j-th indicator.

(4)$W_j^t=\frac{\left(1-E_j^t\right)}{{{\displaystyle \sum }}_{j=1}^m\left(1-E_j^t\right)}$

where $W_j^t$ is the weight of the j-th index. The greater the weight of an indicator, the greater its contribution to the measurement result.

Step 5: Utilize the fixed-base range method to process the original data.

(5)$Z_j^t=\frac{X_j^t-\min \left(X_{j,min}^{2011}\right)}{\max \left(X_{j,max}^{2011}\right)-\min \left(X_{j,min}^{2011}\right)}$

Here, $Z_j^t$ represents the dimensionless index value of the (j)-th index in the (t)-th year after being processed using the fixed-base range method. $X_j^t$ denotes the original data, while $X_{j,min}^t$ and $X_{j,max}^t$ signify the minimum and maximum values of the (j)-th index in the original data of all cities during the base year, respectively. This article designates 2011, the initial year of the sample, as the base year.

Step 6: Compute the comprehensive index. This paper utilizes the index weight determined by the entropy weight method to weight the dimensionless index value processed using the fixed-base range method, resulting in the derivation of the comprehensive index for agricultural high-quality development in each province:

(6)$S_j^t={\displaystyle \sum }_{j=1}^m\left(W_j^t\times Z_j^t\right)$

4.1.2. Explanatory Variable

The original intention of digital inclusive finance (DFI) is to improve the availability of financial-related services through the continuous popularization and strengthening of digital financial basic services, and to provide more convenient financial and credit services to all sectors of society, especially low-income groups such as rural underdeveloped areas and farmers, at low cost. In view of this, this paper refers to the practices of Ge et al. (2022) and Fu et al. (2024), and selects the digital inclusive finance index compiled by the Digital Finance Research Center of Peking University to measure the development level of digital finance [47,48]. The index is constructed based on extensive real-world transaction data from Ant Financial, comprehensively analyzing financial development in terms of coverage, usage depth, and the extent of digital support services. This approach aims to capture the convenience and inclusiveness fostered by the development of digital inclusive finance.

4.1.3. Mediating Variable

Land transfer (LT) refers to the process of activating the operation and use rights of rural land and promoting the orderly flow of rural land management rights. Driven by relevant policies of land transfer, the scale of domestic agricultural land transfer continues to expand. However, due to the widespread existence of problems such as “difficult and expensive loans” in rural areas, the scale of land transfer has not yet reached the ideal state. Most of the existing studies on the measurement of the probability of agricultural land transfer focus on micro data such as questionnaires, and there is no systematic research on the measurement of agricultural land transfer at the provincial level. In view of this, this paper refers to the practices of Wang et al. (2023) and Yang et al. (2023) and selects the total area of cultivated land transfer contracted by rural households (in ten thousand mu) as a measure of land transfer level [49,50]. This indicator includes the area of lease (subcontracting), transfer, exchange, joint-stock cooperation, and other forms of land transfer.

4.1.4. Control Variable

Given the multitude of macro and micro factors influencing inclusive growth, this article aims to minimize the bias stemming from omitted variables in model causal inference [25,51,52,53]. Consequently, it selects the following control variables based on the research perspectives found in existing literature: (1) rural education investment, quantified by the average years of education attained by rural residents; (2) internet popularization, measured by the number of rural broadband subscribers per 10,000 households in each province; (3) industrial structure upgrading, assessed by the ratio of the output value of the secondary industry to the tertiary industry; (4) urbanization level, represented by the proportion of urban to rural population in each province; and (5) fiscal environmental expenditure, calculated as the ratio of local fiscal expenditure on environmental protection to the local fiscal general budget expenditure.

4.2. Model Setting

To validate the direct influence of digital inclusive finance on the high-quality development of agriculture, combined with research Hypothesis 1 and the availability of data, this study constructs the following panel econometric model:

(7)$HA{D}_{it}={\alpha }_0+{\alpha }_{1}DF{I}_{it}+{\alpha }_{2}Contro{l}_{it}+{\nu }_t+{\lambda }_i+{\epsilon }_{it}$

where ${\alpha }_0$ represents a constant term; ${\alpha }_1$ and ${\alpha }_2$, respectively, represent the regression coefficients to be fitted and calculated; subscripts i and t represent individuals and time, respectively; $Control$ represents a series of control variables selected above; and ${\lambda }_i$ and ${\nu }_t$ represent individual fixation effect and time fixation effect, respectively. Individual-fixed effects can reflect the intrinsic differences among different individuals (which do not change over time), while time-fixed effects reflect the trend differences among cross-sections in different years (which do not change with individuals); and ${\epsilon }_{it}$ is a random disturbance term that obeys the white noise process.

Furthermore, to examine the channel through which digital inclusive finance influences the high-quality development of agriculture, particularly the mediating role of land circulation in this process, this paper, in line with research Hypothesis 3, employs the mediation effect model for fitting and computation. The specific model is detailed as follows:

(8)$L{T}_{it}={\beta }_0+{\beta }_{1}DF{I}_{it}+{\beta }_{2}Contro{l}_{it}+{\nu }_t+{\lambda }_i+{\epsilon }_{it}$

(9)$HA{D}_{it}={\gamma }_0+{\gamma }_{1}DF{I}_{it}+{\gamma }_{2}L{T}_{it}+{\gamma }_{3}Contro{l}_{it}+{\nu }_t+{\lambda }_i+{\epsilon }_{it}$

Equations (1)–(3) together form the test equations of land circulation. According to the research ideas of Moon et al. (2015) and Ye et al. (2023), the precondition for verifying the mediation effect of land transfer is that parameter ${\alpha }_1$ is significant [54,55]. Furthermore, if the sign of the indirect effect is consistent with that of the direct effect, it shows that the intermediary effect is established; otherwise, there is a masking effect.

4.3. Data Source

Adhering to the principles of data availability and consistency in statistical standards, this article selects panel data spanning from 2011 to 2021 for 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) as the research sample. The data sources and descriptive statistical analysis of each variable are shown in Table 2. For a few missing values, the Lagrange interpolation method is used to fill them in.

5. Empirical Result Analysis

5.1. Analysis of Benchmark Regression Results

The p values obtained from the likelihood ratio test and Hausman test both reject the null hypothesis at a significance level of 1%, thus prompting the adoption of the two-way fixed effects model as the benchmark for empirical testing in this paper. Additionally, to mitigate the potential influence of heteroscedasticity on the accuracy of cross-sectional data fitting, the Driscoll–Kraay (DK) estimation method is employed to process subsequent fitting results unless otherwise specified. The detailed benchmark regression results are presented in Table 3.

For the sake of model robustness, a fitting approach was adopted by gradually incorporating control variables. As shown in Table 1, the results in column (5), which included all control variables, indicated that the estimated parameter for digital inclusive finance was 0.098 and significant at the 1% significance level. This suggests that digital inclusive finance has a significant promotional effect on high-quality agricultural development, initially confirming research Hypothesis 1. Potential explanations for this phenomenon lie in the utilization of digital inclusive finance’s technological strengths, including artificial intelligence, blockchain, cloud computing, and big data. These technologies effectively diminish the reliance of traditional financial institutions on physical branches, thereby enhancing farmers’ access to financial products such as internet insurance and investment management. This, in turn, facilitates the dispersal of financial resources and satisfies the productive capital requirements of agricultural development in less developed or “tail” regions. After obtaining sufficient funds, ordinary farmers and rural micro-enterprises will increase their application of agricultural machinery and equipment, agricultural varieties, and new agricultural technology equipment, reduce the use intensity of agricultural inputs such as fertilizer and pesticides, accelerate the realization of production scale and intensification, and thus provide new momentum for high-quality agricultural development. Concurrently, as green consumption gains prominence and the government tightens agricultural environmental regulations, digital inclusive finance not only mitigates financial exclusion in rural areas but also underscores the green credentials of agricultural output. To secure the backing of digital inclusive finance, agricultural production and operation entities are inclined to prioritize green investments in their operational activities. This approach aims to enhance pollution prevention and control capabilities in agricultural production processes, minimize agricultural carbon emissions and non-point source pollution, ultimately fostering the green transformation of traditional agriculture [56].

5.2. Robustness Test

The benchmark regression results confirm that digital inclusive finance can significantly promote high-quality agricultural development. To validate the robustness of the conclusions drawn, this paper employs three distinct methods for demonstration and interpretation, as evidenced in the specific estimation results presented in Table 4.

• (1). Winsorization. Considering that agricultural production is extremely vulnerable to force majeure or major natural disasters, various agricultural production and operation entities will face significant survival risks, difficulties in cashing out agricultural products, extended financing periods for green investment projects, and market oversaturation, leading to outliers in the sample data and thus biasing the final regression results. Therefore, this paper performs a 1% winsorization on all continuous variables and then re-estimates using a two-way fixed effects model.

• (2). Changing the sample size. Due to heterogeneity in factors such as digital infrastructure construction, innovation capabilities, resource endowments, and the scale of agricultural composite talents between China’s municipalities (Beijing, Tianjin, Shanghai, and Chongqing) and other regions, as well as significant financial policy biases, this article re-estimates the model after excluding these samples.

• (3). Replacing proxy indicators for explanatory variables. Referring to the research ideas of Lin et al. (2022), this paper replaces the overall digital inclusive finance index with the coverage breadth index and re-estimates using a two-way fixed effects model [57]. The coverage breadth embodies the financial broadening concept of digital finance, emphasizing the inclusion of more groups into the financial coverage and adjusting the flow of social funds through the rational allocation of financial resources. It is worth noting that according to the Peking University Digital Financial Inclusion Index, the coverage breadth index measures the usage of digital inclusive financial services from the perspective of account coverage, reflecting the outreach of digital financial services.

• (4). Replace the model. When there are intra-group correlation, inter-group correlation, and contemporaneous correlation in the random disturbance term, the estimation results of the two-way fixed effects model may be biased. Moreover, in the baseline regression analysis mentioned above, to address potential issues of heteroscedasticity, autocorrelation, and cross-sectional dependence, the Driscoll–Kraay standard errors were utilized. However, apart from this standard error correction, the feasible generalized least squares (FGLS) method can also handle the three main threats posed by short panel data. Given the relatively small number of cross-sections, we allow each individual to have the same autoregressive coefficient during the estimation process, and employ the specific AR(1) autocorrelation structure that is characteristic of panel data.

Table 4

Robustness test and endogenous processing results.

Variable	Robustness				Endogenous Treatment
	Tail Shrinking Treatment	Change the Sample Size	Replace Explanatory Variable	Replace the Model	IV2SLS	GMM
$lnDFI$	0.099 ***(7.83)	0.065 ***(4.35)	0.094 ***(5.19)	0.089 ***(4.71)	0.189 ***(2.83)	0.168 ***(3.30)
Control variable	Control	Control	Control	Control	Control	Control
Weak instrumental variable test					62.524
Unidentifiable test					92.308 ***
${\mathrm{R}}^2$	0.8030	0.6884	0.7616		0.7842	0.7917

Note: *** is significant at 1% significance levels.

Based on the empirical findings presented in Table 4, it is evident that the estimation coefficient associated with digital inclusive finance remains consistently positive in promoting agricultural high-quality development, while the level of statistical significance remains largely unchanged. This consistency serves as a robust validation of the reliability and robustness of the aforementioned benchmark regression analysis.

5.3. Endogenous Treatment

Although this article controls for a large number of observable confounding factors when analyzing the relationship between digital inclusive finance and high-quality agricultural development, it still faces the endogenous problem of potential mutual causality between the two. For instance, high-quality agricultural development signifies a rationalized agricultural production paradigm and optimized resource allocation. This facilitates agricultural-oriented enterprises to actively raise and utilize capital, attaining intensification of operations and enhancing utilization efficiency, thereby bolstering the advancement of digital inclusive finance. Consequently, in adherence to the methodologies of causal inference within econometrics, this study employs a two-stage least squares approach to mitigate the endogeneity issue, effectively disentangling the underlying endogenous relationship between the two phenomena. Specifically, drawing on the research methods of Zhang et al. (2021) and Gao et al. (2022), we construct an interaction term between the number of fixed-line telephones per 100 people, the number of post offices per million people, and the number of internet users in the previous year as an instrumental variable for digital inclusive finance for model estimation [7,58]. The instrumental variable needs to be uncorrelated with the disturbance term while highly correlated with the explanatory variable. The number of fixed-line telephones and post offices can reflect the historical information transmission volume in a region to a certain extent. Digital inclusive finance transmits information through electronic computers and the internet, and digital inclusive finance in regions with high numbers of fixed-line telephones and post offices is more likely to thrive and develop. At the same time, considering that there are heteroscedasticity and series autocorrelation problems in macroeconomic variables, systematic GMM estimation has the advantages of solving the problems of unrecognized individual differences, the influence of variables that are not considered, and the correlation between variables and random terms. Therefore, referring to the practice of Teng et al. (2020), the systematic GMM model is introduced for estimation so as to obtain more accurate parameter estimation results [59].

As can be seen from Table 3, the models passed the tests of non-identifiability, weak instrumental variables, and over-identification. The test results indicate that the endogenous problem caused by missing important variables or bidirectional causality has no significant impact on the core conclusions, and even the estimation coefficients increased slightly.

5.4. Endogenous Treatment

5.4.1. Heterogeneity Analysis

• (1). Location factor. Considering differences in agricultural policy support, resource endowment, and agricultural development status among regions, it is easy to cause mismatch of high-quality production factors, affecting the development level of digital inclusive finance and the agricultural ecological environment in various regions. Concurrently, amidst the incessant surge in urbanization, spatial interconnectedness emerges in agricultural carbon emissions and energy utilization among adjacent urban agglomerations, thereby exerting an influence on the agriculture’s pursuit of high-quality green development [60]. In view of this, this paper divides the samples according to the basic characteristics of the three major economic zones of east, central, and west formed from China’s coast to the interior, and further examines the differences in the impact of digital inclusive finance on the high-quality development of agriculture. As can be seen from Table 5, the marginal contribution of digital inclusive finance to the high-quality development of agriculture in the eastern region is the highest, followed by the western region, while the empirical results in the central region have not passed the significance test. Research Hypothesis 3 has been partially demonstrated. Meanwhile, this conclusion may be related to regional layout planning and the development stage it is in. Most cities in the eastern region have strong economic strength and abundant technological resources, providing good hardware support and growth environment for the development of digital inclusive finance. This facilitates the precise and swift delivery of financial services to the agricultural sector, thereby satisfying the diverse requirements for high-quality agricultural development. At the same time, financial institutions in the eastern region have high innovation capabilities and service awareness in the field of digital inclusive finance, and can actively use digital technology to develop financial products and services suitable for agricultural development, such as agricultural supply chain finance and rural e-commerce finance, effectively improving the coverage and penetration of financial services.

• (2). Digital infrastructure construction. The improvement in digital infrastructure construction creates a favorable technical environment for the development of digital inclusive finance, which not only improves the network coverage and communication quality in rural and remote areas but also provides strong support for the availability and coverage of financial services. Concurrently, to execute the “Action Plan for Promoting Big Data Development” promulgated by the State Council, the National Development and Reform Commission initiated pilot policies for comprehensive big data experimentation in eight regions, encompassing Guangdong, Shanghai, and Beijing, in 2016. These policies aim to lead the flow of technology, materials, funds, and talents through data flow, and promote the sharing, integration, collaboration, and efficient utilization of social production factors. Therefore, these regions themselves have better digital infrastructure, and their digital financial systems and product supply are also more developed and mature. Given this, this article takes the national comprehensive big data pilot policy as the sample classification standard to explore whether different levels of digital infrastructure will cause deviations in the practice of digital inclusive finance. As can be seen from Table 5, the regression coefficient of digital inclusive finance in the pilot cities of the national comprehensive big data pilot zone is 0.120, higher than that in non-pilot cities, and both have passed the 1% significance test. Research Hypothesis 3 has been partially demonstrated. This result indicates that digital inclusive finance has a more significant role in promoting the high-quality development of agriculture in areas with better digital infrastructure, which is in line with expectations. The reason for this phenomenon lies in the fact that improved digital infrastructure construction helps to create a favorable financial ecological environment, promote cooperation and competition among financial institutions, and promote the formation of a financial infrastructure system with reasonable layout, effective governance, advanced reliability, interconnectivity, and flexibility [61]. This helps agricultural operators to more conveniently manage funds, make payments, and obtain loans, effectively addressing the issue of traditional financial services being unable to fully cover rural areas. At the same time, with the implementation of the Broadband China and Digital Rural Strategies, the ability to collect, transmit, and process information in various stages of agricultural production has been significantly enhanced. Through mobile applications, farmers can obtain real-time information on market prices, weather forecasts, and advanced agricultural technologies, enabling them to make better decisions and plan agricultural production. This helps to improve the efficiency and quality of resource utilization in agricultural development, effectively breaking through resource and environmental bottlenecks, and achieving an intensive utilization of agricultural development resources [62].

• (3). Green finance. Amidst the constraints of limited endogenous financing and an indirect financing-dominated financial structure in China, bank credit emerges as a crucial source of capital for corporate innovation activities. In 2017, the Chinese government implemented green finance pilot policies across ten regions, including Zhejiang, Guangxi, Guizhou, and Xinjiang. These policies seek to diversify financing avenues for green funds, establish robust mechanisms for disclosing corporate environmental responsibility information, steer social capital towards active participation in green project investments, and ultimately foster the green transformation of traditional industries and sustainable economic and societal development. This also implies that market entities are facing stronger environmental regulations, forcing them to focus more on proactive prevention rather than end-of-pipe emission reduction. Given this context, this article uses the green finance reform pilot policy as a sample classification standard to explore whether digital inclusive finance has a differentiated impact on the high-quality development of agriculture in regions with stronger environmental regulations and more sophisticated green financial systems. As shown in Table 5, the impact coefficient of digital inclusive finance on the high-quality development of agriculture is higher in the green finance policy pilot regions, with a value of 0.584, and it passed the 1% significance test. Research Hypothesis 3 has been partially demonstrated. This result indicates that digital inclusive finance plays a more significant role in promoting the high-quality development of agriculture in regions with stronger environmental regulations, which is consistent with expectations. Green finance, as a new financial strategy, focuses on financial institutions as the mainstay, actively leveraging various channels to incentivize market entities to transition towards cleaner and low-carbon production activities, thereby effectively achieving pollution control and environmentally friendly production [63]. Driven by the pilot policies, the establishment of a sound green financial standard system and the implementation of incentive policies, combined with digital technology and the core concepts of inclusive finance, provided agricultural operators with a series of green financial products and continuously innovated the financial service system. At the same time, through the establishment of fiscal special funds, direct financial support can be provided to agricultural green and low-carbon projects, facilitating their rapid development. Furthermore, the synergistic interplay between the digital governance paradigm of digital inclusive finance and green finance synergistically drives the reconfiguration of low-carbon civilization, thereby augmenting farmers’ low-carbon literacy. This, in turn, provides ample endogenous impetus for advancing digital governance in agricultural and rural regions and fostering a novel form of green civilization. Ultimately, these concerted efforts jointly expedite the green and low-carbon transformation of green rural areas, green agriculture, and small- and micro-enterprises, laying a solid foundation for the high-quality and sustainable development of agriculture [64].

Table 5

Heterogeneity analysis results.

Variable	Heterogeneity Analysis
	Location Factor			Digital Infrastructure Construction		Green Finance
	Eastern Region	Central Region	Western Region	Pilot Area	Non-Pilot Area	Pilot Area	Non-Pilot Area
$lnDFI$	0.101 ***(5.27)	0.014(0.29)	0.072 ***(4.39)	0.120 ***(6.30)	0.048 ***(2.06)	0.584 ***(3.08)	0.107 ***(5.17)
Control variable	Control	Control	Control	Control	Control	Control	Control
Regional effect	Control	Control	Control	Control	Control	Control	Control
Time effect	Control	Control	Control	Control	Control	Control	Control
${\mathrm{R}}^2$	0.7929	0.6361	0.9296	0.8217	0.6930	0.9117	0.6371

Note: *** is significant at 1% significance levels.

5.4.2. Mechanism Analysis

To validate the mechanism of land circulation in the facilitation of high-quality agricultural development through digital inclusive finance, according to the recursive equations of intermediary effect, the results in Table 6 are obtained by fitting.

According to Table 6, the results of the third segment of the mediation effect equation group indicate that the fitting coefficient between digital inclusive finance and land circulation for the high-quality development of agriculture is significantly positive, indicating that both variables can promote the high-quality development of agriculture. The results of the second segment equation show that digital inclusive finance can significantly promote the development of land circulation. Based on the magnitude, significance, and signs of the coefficients in the benchmark regression and mediation equation group, it is evident that the partial mediation effect of land circulation is valid, accounting for 78.13% of the total effect, thus validating Hypothesis 3. Digital inclusive finance truly realized short-cycle digital financial services such as “land mortgage cloud loans”, “agricultural credit cloud loans”, and “borrow and repay anytime” by establishing shared credit data for farmers and focusing on data-based credit enhancement, forming a financing model of “agricultural big data + finance” to meet the financing needs of agricultural operators for scale operations and promote agricultural-related projects such as land contracting and farmland transfer [65]. Concurrently, digital inclusive finance also exerts a positive influence on land circulation in aspects of digital securities and digital insurance. The securitized transfer of farmland facilitated by digital technology not only fosters market-based pricing of land resources but also aids in steering social capital towards rural areas, thereby enhancing profitability in the rural factor market. Additionally, digital agricultural insurance effectively utilizes risk compensation mechanisms to safeguard the expedited transfer of rural land and revitalize land and other factor resources. When farmland is transferred to new agricultural operators, such as large-scale farmers, they are inclined to adopt modern agricultural production technologies and scientific management practices due to their more comprehensive agricultural knowledge base compared to individual farmers. This process not only promotes agricultural specialization and division of labor but also helps achieve a scientific allocation of agricultural factor inputs, thereby improving agricultural production efficiency, operational efficiency, and environmental efficiency [66].

6. Research Conclusions and Policy Recommendations

6.1. Research Conclusions

The evolution of digital inclusive finance, centered on data elements, has emerged as a pivotal catalyst for revolutionizing agricultural development methods and attaining sustainable green growth. Utilizing panel data spanning from 2011 to 2021 across 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan), this study validates the direct influence of digital inclusive finance on the advancement of agricultural quality. Furthermore, employing a mediation effect model, we delve into the indirect impact mechanism mediated by land circulation. Our findings yield several insights: Primarily, digital inclusive finance is a significant enhancer of agricultural quality development, a conclusion upheld by rigorous robustness tests and endogeneity adjustments. Secondly, a heterogeneous analysis reveals that digital inclusive finance notably propels agricultural quality in China’s eastern regions, areas with robust digital infrastructure, and financially advanced regions. Thirdly, through channel analysis, we discover that land circulation serves as a positive mediator in the process of digital inclusive finance bolstering agricultural quality development.

6.2. Policy Recommendations

The above research conclusions provide important theoretical and empirical evidence for China to deepen the application of digital inclusive finance, promote the transformation and upgrading of traditional agriculture, and cultivate new competitive advantages and new momentum for agricultural development. In view of this, this article proposes the following policy recommendations:

Firstly, the digital technology hardware infrastructure construction in the less developed regions of central and western China should be strengthened to empower the implementation of digital inclusive finance. Specifically, the central and local governments should establish special funds to support the construction of digital technology hardware infrastructure in less developed regions of central and western China, including but not limited to network coverage, data center construction, and cloud computing platform establishment. This will optimize the layout of digital technology hardware, ensure coverage of various financial service demand points in urban and rural areas, and improve the availability of financial services. Concurrently, government departments should actively encourage private capital to collaborate with the government, adopting a Public–Private Partnership (PPP) model to jointly invest in digital infrastructure construction, and establish reasonable mechanisms for revenue distribution and risk sharing. On the one hand, the investment and construction of the industrial internet system built by 5G, cloud computing, big data, and artificial intelligence should be strengthened to lay the foundation for the application of digital inclusive finance. On the other hand, the construction of rural digital inclusive finance sharing platforms should be strengthened to support the development of digital industries such as “Internet +” agriculture, rural e-commerce, and smart agriculture, and enhance the modernization level of the agricultural industry chain.

Secondly, rural land transfer policies should be improved and moderate scale operation in agriculture promoted. Specifically, the first priority is to improve the detailed rules and regulations governing the management of agricultural land management rights transfer, standardize the procedures and contract content of agricultural land transfer, including but not limited to critical elements such as the duration, area, price, payment method, and breach of contract responsibilities, thereby ensuring the transparency and simplicity of the agricultural land transfer process and safeguarding the legitimate rights and interests of both parties involved in the transfer. The second step is to increase subsidies for farmland transfer and promote the subsidy schemes for farmland transfer, encouraging farmers to learn from and imitate the experience of a small number of farmers who have benefited from farmland transfer, and urging farmers to voluntarily “wash their feet and go to the fields” to achieve real “de-involution” in agriculture and promote the scaled operation of agricultural production and management. The third step is to strengthen the connection between urban and rural employment departments during the process of farmers’ professional non-agriculturalization, promote the transfer of rural redundant labor, and enable rural labor to better integrate into urban life. The fourth step is to rely on digital inclusive finance service platforms to achieve smooth financing for both the transferor and transferee of farmland, better leveraging the driving role of digital inclusive finance and farmland transfer in the high-quality development of agriculture. Meanwhile, enterprises are encouraged and supported to conduct recruitment activities in rural areas, providing rural laborers with more employment opportunities and job choices. Fourthly, on the existing digital inclusive financial service platforms, a module for agricultural land transfer financing services should be added to provide convenient and efficient financing services to both parties involved in the transfer. Additionally, cooperation with financial institutions should be strengthened to explore financial product innovations such as agricultural land transfer mortgage loans, aiming to reduce the financing costs and risks for farmers.

Thirdly, coordination and cooperation among multiple entities should be actively sought and the connection between government functional institutions and market financial institutions should be strengthened. On the one hand, environmental regulation policies must effectively curb environmental pollution during agricultural production to protect rural ecological environments while avoiding being overly stringent to prevent imposing excessive economic burdens on farmers and agricultural enterprises. The government needs to formulate reasonable environmental regulation intervals to ensure that policies achieve both environmental protection goals and promote sustainable development in agricultural production. On the other hand, the market must fully leverage digital inclusive finance as a financial tool to exert its growth, inclusiveness, and poverty reduction effects on agriculture, rural areas, and farmers. In practice, it is necessary to balance the relationship between economic growth and green development, embarking on a new path of agricultural green and high-quality development where government regulation and market regulation complement each other. The government should strengthen supervision and guidance for agricultural green development to ensure the effective implementation of policies and the orderly operation of the market. At the same time, the government should respect market rules, giving full play to the decisive role of market mechanisms in resource allocation, and promoting the marketization, industrialization, and socialization of agricultural green development.

6.3. Research Limitations

Although this article provides some empirical insights for the government’s decision-making in the field of digital inclusive finance and promoting high-quality green agricultural development, there are still certain limitations. Firstly, regarding data granularity and data collection periodicity, this paper utilizes provincial-level data to discuss the impact of China’s digital inclusive finance development on agricultural high-quality development from 2011 to 2021. The relatively short span of data years may fail to capture long-term trends or structural changes in the agricultural sector. Furthermore, future research could explore how digital inclusive finance influences the production processes of agricultural enterprises, subsequently affecting agricultural high-quality development, by adjusting research methods and perspectives. Secondly, from the perspective of research areas, major grain-producing areas are important regions for China’s grain supply. However, due to the quasi-public nature of grain and the low industry profit rate, the economic strength and financial resources of these areas are relatively weak. Therefore, future research can include the high-quality development of agriculture in China’s 13 major grain-producing areas in the research framework to draw more profound conclusions. Finally, this study belongs to empirical research in statistics and econometrics, and the research conclusions do not provide detailed operational plans. In future research, it would be beneficial to use case studies to deeply analyze the specific measures and experiences of financial institutions in promoting high-quality agricultural development through digital inclusive financial products from specific cases.

Author Contributions

Conceptualization, X.G. and J.Y.; Methodology, Q.X.; Software, Q.X.; Formal Analysis, Q.X.; Investigation, X.G. and J.Y.; Resources, Q.X.; Writing—Original Draft, Q.X.; Writing—Review and Editing, Q.X.; Supervision, Q.X.; Project Administration, X.G. and J.Y. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

No description in this study involves humans.

Data Availability Statement

The datasets used during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflicts 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.

Figure and Tables

Figure 1. General framework of research.

Table 1

Evaluation index system of agricultural high-quality development.

Primary Index	Secondary Index	Three-Level Index	Specific Explanation	Attribute
Innovation	Innovation foundation	Agricultural mechanization level	Directly available data	+
		Proportion of agricultural financial investment	Proportion of fiscal expenditure of agriculture, forestry and water resources to total fiscal expenditure	+
		Proportion of demonstration counties of leisure agriculture	Proportion of leisure agriculture demonstration counties in the total number of counties in the local area	+
		Proportion of typical counties in rural entrepreneurial innovation	Proportion of typical counties of rural entrepreneurial innovation in the total number of counties in the local area	+
	Innovation benefit	Labor productivity	Proportion of total output value of agriculture, forestry, animal husbandry and fishery in the number of employees in the primary industry	+
		Land productivity	Proportion of total agricultural output value to total sown area of crops	+
		Number of green food enterprises	Number of certified units of green food in that year	+
		Grain yield per unit area	Proportion of grain output in the total grain planting area	+
		Effective irrigation area	Directly available data	+
Coordination	Industrial coordination	Agricultural industrial structure adjustment index	1—(Proportion of total agricultural output value to total agricultural output value)	+
	Urban–rural coordination	Binary contrast coefficient	Proportion of comparative labor productivity of primary industry to comparative labor productivity of secondary and tertiary industries	+
Green	Resource consumption	Usage of agricultural film per unit area	Proportion of agricultural film usage in sowing area	−
		Use intensity of agricultural diesel oil	Proportion of agricultural diesel oil in sowing area	−
		Per capita electricity consumption	Proportion of rural electricity consumption to employees in the primary industry	−
	Environmental pollution	Fertilizer application per unit area	Proportion of chemical fertilizer application rate to sowing area	−
		Application amount of pesticide per unit area	Proportion of pesticide application amount to sowing area	−
	Environmental protection	Forest coverage rate	Directly available data	+
Open	Resource optimization	Rural land circulation rate	Proportion of household contracted land transfer to agricultural land	+
		Proportion of investment in agricultural fixed assets	Proportion of fixed assets investment in agriculture, forestry, animal husbandry and fishery to total fixed assets investment	+
		Proportion of foreign direct investment in agricultural investment	Proportion of foreign direct investment in agricultural investment to total agricultural investment	+
	Market optimization	Market quantity of agricultural products	Directly available data	+
		Market turnover ratio of agricultural products	Proportion of agricultural products market turnover in the added value of the primary industry	+
		Dependence on import and export of agricultural products	Proportion of import and export trade volume of agricultural products in the added value of primary industry in China	+
Share	Standard of living	Income level of rural residents	Per capita net income of rural residents	+
		Overall affluence level of rural residents	Engel coefficient in rural areas	−
		Life richness of rural residents	Proportion of per capita education, culture and entertainment expenditure to per capita consumption expenditure	+
		Rural residents’ attention to medical care	Proportion of per capita health care expenditure to per capita consumption expenditure	+
		Proportion of minimum living security for rural residents	Directly available data	−
	Benefit sharing	Income ratio of urban and rural residents	Proportion of disposable income of urban households in rural per capita disposable income	−
		Urban–rural consumption level ratio	Proportion of urban residents’ per capita consumption expenditure to rural residents’ per capita consumption expenditure	−
		Urban–rural consumption gap	Proportion of retail sales of consumer goods in towns and villages in the retail sales of consumer goods in the whole society	+

Table 2

Data sources and descriptive statistics of related variables.

Variable	Definition	Data Source	Obs	Mean	Std. Dev.	Min	Max
lnLT	Land transfer	China Rural Economic Management Statistics Yearbook	330	6.786	1.167	2.801	8.839
lnDFI	Digital financial inclusion	Peking University Digital Inclusive Finance Index (Third Edition)	330	5.556	0.682	2.026	6.136
lnHAD	High-quality agricultural development	China City Statistical Yearbook and various provincial statistical yearbooks	330	6.544	0.41	5.285	8.335
lnREI	Rural education investment		330	2.055	0.079	1.771	2.294
lnIP	The degree of internet popularization		330	3.963	0.272	3.186	4.521
lnISU	Industrial structure upgrading		330	0.126	0.415	−0.658	1.667
lnUR	Urbanization level		330	−0.537	0.198	−1.049	−0.110
lnFEE	Fiscal environmental expenditure		330	1.034	0.309	0.164	1.919

Table 3

Benchmark regression result.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
$lnDFI$	0.238 ***(17.41)	0.181 ***(12.90)	0.108 ***(6.76)	0.105 ***(6.29)	0.956 ***(5.88)	0.098 ***(5.98)
$lnREI$		2.921 ***(8.43)	1.961 ***(5.75)	1.966 ***(5.75)	1.689 ***(5.04)	1.705 ***(5.08)
$lnIP$			0.422 ***(7.62)	0.427 ***(7.65)	0.016(0.16)	0.013(0.12)
$lnISU$				−0.014(−0.73)	−0.028(−1.45)	−0.029(−1.50)
$lnUR$					1.241 ***(4.69)	1.239 ***(4.69)
$lnFEE$						−0.048(−1.18)
$\mathrm{Constant}$	5.221 ***(68.19)	−0.466(−0.69)	0.237(0.38)	0.230(0.37)	3.148 ***(3.62)	3.166 ***(3.65)
N	330	330	330	330	330	330
${\mathrm{R}}^2$	0.6035	0.6992	0.7648	0.7654	0.7886	0.7901

Note: *** is significant at 1% significance levels.

Table 6

Test results of intermediary effect.

Variable	$\mathit{l}\mathit{n}\mathit{H}\mathit{A}\mathit{D}$	$\mathit{l}\mathit{n}\mathit{L}\mathit{T}$	$\mathit{l}\mathit{n}\mathit{H}\mathit{A}\mathit{D}$
$lnDFI$	0.137 ***(3.12)	0.480 ***(3.23)	0.103 **(2.03)
$lnLT$			0.223 ***(16.76)
Control variable	Control	Control	Control
Regional effect	Control	Control	Control
Time effect	Control	Control	Control

Note: ***, and ** are significant at 1% and 5% significance levels.

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