数字乡村建设对土地约束下农业技术选择的影响 基于中国28省面板数据的证据

1. Introduction

Cultivated land is the basic carrier of agricultural production, but it is also one of the most rigidly constrained production factors. With rapid urbanization, ecological protection requirements, and the need to ensure food security, agricultural development increasingly faces the challenge of maintaining production capacity under limited land resources [1,2]. For countries with large populations and scarce per capita farmland, this issue is particularly important. In this context, agricultural transformation cannot rely mainly on the continuous expansion of land input. A more realistic path is to improve how capital, labor, technology, and services support production on existing farmland [3]. With the rapid development of rural digitalization, digital technologies are becoming an important means of reorganizing agricultural production factors and improving the use of existing farmland. Therefore, understanding agricultural technological choice under land constraints requires attention not only to land scarcity itself, but also to how digital transformation reshapes the relationship among capital, labor, and land.

Digital rural construction provides a new perspective for understanding this issue. It can be understood as the integration of digital infrastructure, smart agricultural technologies, digital finance, digital platforms, and data-based services into rural production and rural development. These technologies include broadband networks, smartphones, digital payment systems, e-commerce platforms, agricultural machinery service platforms, drones, sensors, GPS, remote sensing, smart irrigation systems, and data-driven decision-support tools [4]. They can improve information access, reduce transaction costs, support machinery and service matching, and strengthen production management in rural areas. However, the adoption of these technologies is not automatic. Farmers and agricultural producers may face barriers such as high initial investment costs, limited digital skills, insufficient rural infrastructure, weak agricultural service systems, fragmented land management, and uncertainty about the economic returns of digital technologies. Compared with traditional agricultural technologies, digital technologies are not only production tools; they also reshape the matching relationship among capital, labor, and land. Under land constraints, this may change the direction of agricultural technological progress and encourage agricultural production to rely more on the effective use of non-land factors rather than additional land inputs [5].

Existing studies have provided important insights into the relationship between digitalization and agriculture. One strand of literature focuses on the effects of digital rural construction, digital finance, and digital infrastructure on agricultural productivity, farmers’ income, agricultural modernization, and rural development. These studies generally find that digitalization can reduce information costs, improve resource allocation, and promote agricultural transformation [6,7]. Another strand of literature examines agricultural land use, land productivity, cultivated land protection, and food security, emphasizing that land scarcity and land-use constraints have become central issues in sustainable agricultural development [8,9]. A third strand of literature studies focus on biased technological progress, showing that technological progress may favor certain production factors and thereby change the relative efficiency of capital, labor, and land [10,11,12,13].

However, the existing literature still leaves several issues insufficiently addressed. First, many studies examine whether digitalization improves agricultural output, total factor productivity, or green efficiency, but pay less attention to how digitalization changes the direction of agricultural technological choice under land constraints [14,15]. Second, studies on land and agriculture often focus on land productivity, land transfer, or land-use efficiency, but rarely analyze how digital technologies enable capital and labor to support production on existing farmland [14,16]. Third, although biased technological progress provides a useful framework for understanding factor-oriented technological change, few studies apply this framework to examine the land-related effects of digital rural construction. As a result, we still know relatively little about whether digital rural construction promotes capital–land and labor–land biased technological progress, and through which mechanisms this influence occurs.

This paper addresses these gaps by examining the impact of digital rural construction on agricultural technological choice under land constraints. The core research question is: Does digital rural construction reshape agricultural technological choice by changing the relative role of capital and labor in relation to land? To answer this question, this study focuses on two land-related dimensions of biased technological progress: the capital–land biased technological progress index and the labor–land biased technological progress index. These two indicators allow us to examine whether digital rural construction promotes a technological path in which capital and labor better support agricultural production on existing farmland.

The theoretical logic of this paper is as follows. Under land constraints, agricultural production cannot rely mainly on the expansion of cultivated land. Digital rural construction may promote agricultural technological choice by improving the way non-land factors are combined with land. On the one hand, digital infrastructure, smart equipment, machinery services, digital finance, and agricultural service platforms may strengthen the role of capital-embodied technologies in agricultural production. This may promote capital–land biased technological progress. On the other hand, digital platforms, technical guidance, production information, and service systems may improve farmers’ decision-making and production organization, enabling labor to participate more effectively in land management and agricultural production. This may promote labor–land biased technological progress.

To provide systematic empirical evidence on this relationship, this study uses provincial panel data from China and constructs a multidimensional index of digital rural construction. Based on the theoretical framework, this paper further builds empirical models to examine both the baseline effect and the mechanism effect of digital rural construction on agricultural technological choice under land constraints. Specifically, the baseline model is used to estimate the impact of digital rural construction on capital–land and labor–land biased technological progress. The mechanism model further examines whether digital rural construction affects agricultural technological choice through the scale effect and the price effect. This research design links the theoretical mechanism with empirical testing and helps clarify how digital rural construction reshapes the relationship among capital, labor, and land in agricultural production.

This paper makes several contributions. First, it introduces a land-constraint perspective into the study of digital rural construction. Instead of only asking whether digitalization improves agricultural productivity, this paper examines how digitalization reshapes agricultural technological choice when land resources are limited. Second, it links digital rural construction with land-related biased technological progress by distinguishing between capital–land and labor–land dimensions. This helps clarify how digital technologies change the relationship between non-land factors and existing farmland. Third, it examines the scale effect and price effect as mechanisms, providing a more detailed explanation of how digital rural construction influences agricultural technological choice. This study provides evidence from China, a major agricultural country facing strong land constraints, thereby offering useful implications for agricultural transformation, cultivated land protection, and land-related digital governance.

2. Theoretical Analysis and Research Hypotheses

2.1. Digital Rural Construction and Agricultural Technological Choice Under Land Constraints

The theory of biased technological progress suggests that technological progress does not improve the productivity of all production factors equally. Instead, it may improve the efficiency of one factor more rapidly than that of another factor [17,18]. In agriculture, biased technological progress reflects changes in the relative productivity of capital, labor, and land. The direction of technological progress is shaped not only by factor endowments, but also by market conditions, institutional arrangements, and the diffusion of new technologies. Under the constraints of cultivated land protection and agricultural security, land has become a relatively rigid factor in agricultural production. Compared with capital and labor, land is more difficult to expand in the short term. Therefore, agricultural development increasingly depends on whether capital, labor, and digital technologies can better support production on existing farmland.

Digital rural construction provides new technological conditions for agricultural production under land constraints. Through the development of rural digital infrastructure, digital production systems, digital finance, digital transactions, and digital public services, digital rural construction changes the way agricultural producers obtain information, allocate resources, adopt technologies, and organize production [19]. Rural networks, smart terminals, agricultural meteorological information, and digital platforms reduce the cost of information acquisition and transmission [20]. Digital finance and online transactions improve the accessibility of agricultural capital and production services [21]. Smart agricultural equipment, machinery services, sensors, irrigation systems, drones, and digital management platforms further promote the integration of digital technologies with agricultural production [22].

From the perspective of land constraints, the key role of digital rural construction is not to expand land resources, but to reshape how non-land factors interact with land. Digital technologies can improve the ability of capital to support production on existing farmland. For example, smart machinery, digital equipment, agricultural machinery services, and platform-based production services allow capital to be more effectively embedded in land-related production processes [22,23]. This may lead agricultural technological progress to become more oriented toward capital relative to land. In this sense, capital-related technological progress reflects a pathway in which agricultural production responds to land constraints by strengthening the role of capital-embodied and digitally enabled technologies.

At the same time, digital rural construction may also improve the role of labor in agricultural production under land constraints. Digital platforms, technical guidance, market information, weather information, and production management tools can improve farmers’ information access and decision-making capacity [24]. Labor is not only a traditional production input, but also an important carrier of digital information, production coordination, and land management. When farmers and agricultural operators are better connected with digital services and agricultural service systems, labor can more effectively support agricultural production on existing farmland. Therefore, digital rural construction may also promote labor–land biased technological progress.

Based on the above analysis, this study proposes the following hypotheses:

Hypothesis****1a.

Under land constraints, digital rural construction promotes capital–land biased technological progress in agriculture.

Hypothesis****1b.

Under land constraints, digital rural construction promotes labor–land biased technological progress in agriculture.

2.2. Scale Effect, Price Effect, and Biased Agricultural Technological Progress

Digital rural construction may affect agricultural technological choice under land constraints through changes in factor allocation and relative factor returns. In agricultural production, capital, labor, and land are not independent of each other. The effect of digital technologies depends on how they change the relationship among these factors. Since cultivated land is difficult to expand, the adjustment of capital and labor around existing farmland becomes an important way for agriculture to respond to land constraints. In this process, the scale effect and the price effect are two important channels through which digital rural construction may influence biased technological progress.

First, digital rural construction may affect agricultural technological progress through the scale effect. The improvement of digital infrastructure, agricultural information services, and digital production systems can reduce information asymmetry and transaction costs in agricultural production. It can also improve the efficiency of land transfer, machinery service matching, and production service coordination. These changes may promote more organized forms of agricultural production and improve the matching between land, capital, labor, and agricultural services [25,26]. Under more organized production conditions, machinery services, digital management, standardized production, and platform-based agricultural services are more likely to be adopted. Therefore, the scale effect does not only refer to the expansion of operation size; it also reflects the reorganization of production factors around land. By improving factor coordination, digital rural construction may change the relative efficiency of capital, labor, and land, thereby affecting the direction of agricultural technological progress.

Second, digital rural construction may affect agricultural technological progress through the price effect. According to the theory of induced technological change, changes in relative factor prices can influence the direction of technological progress [17,18,27]. When the use cost of a factor declines or its relative return increases, technological progress is more likely to improve the efficiency of that factor. Digital rural construction can affect relative factor prices by reducing information costs, transaction costs, financing constraints, and service access costs. For example, digital finance can improve access to agricultural capital; digital platforms can reduce the cost of matching machinery services and production services; and online transactions can improve the efficiency of agricultural input and output markets. These changes may alter the cost–benefit conditions of capital, labor, and land use, thus influencing technological choice.

From the perspective of land constraints, both the scale effect and the price effect are closely related to how agriculture uses existing farmland. The scale effect changes the organizational conditions under which capital and labor are combined with land, while the price effect changes the economic incentives for adopting different technologies. Together, these two channels may guide agricultural technological progress toward different factor directions. Therefore, digital rural construction may influence capital–land and labor–land biased technological progress by changing both factor organization and relative factor returns.

Based on the above analysis, this study proposes the following hypothesis:

Hypothesis****2.

Digital rural construction affects biased agricultural technological progress through both the scale effect and the price effect.

3. Research Design

3.1. Measurement of Biased Agricultural Technological Progress

According to classical economic growth theory, production depends on both factor inputs and technology. Since this study focuses on agricultural production, land is introduced as a key production factor in addition to capital and labor. The general production function can be written as follows:

$$
Y = F \left(\right. \Gamma_{t}^{K} K , \Gamma_{t}^{N} N , \Gamma_{t}^{C} C_{t} \left.\right)
$$

(1)

where $Y$ denotes agricultural output; $K$, $L$, and $C$ represent capital, labor, and land inputs, respectively; $\Gamma_{t}^{K}$, $\Gamma_{t}^{N}$ and $\Gamma_{t}^{C}$ denote capital-augmenting, labor-augmenting, and land-augmenting technological progress, respectively. Equation (1) is a general form that can describe different types of technological progress. When $\Gamma_{t}^{K} \neq \Gamma_{t}^{N} \neq \Gamma_{t}^{C}$, technological progress is neutral. When the growth rates of $\Gamma_{t}^{K}$, $\Gamma_{t}^{N}$ and $\Gamma_{t}^{C}$ differ, technological progress is factor-biased.

To construct an operational measurement of biased technological progress and avoid measurement bias caused by differences in index benchmarks, this study adopts a normalized factor-augmenting CES production function. The specific form is as follows1:

$$
Y_{t} = Y_{0} \left[\alpha \left(\frac{K_{t}}{K_{0}} \frac{\Gamma_{t}^{K}}{\Gamma_{0}^{K}}\right)^{\frac{\sigma - 1}{\sigma}} + \beta \left(\frac{N_{t}}{N_{0}} \frac{\Gamma_{t}^{N}}{\Gamma_{0}^{N}}\right)^{\frac{\sigma - 1}{\sigma}} + \gamma \left(\frac{C_{t}}{C_{0}} \frac{\Gamma_{t}^{C}}{\Gamma_{0}^{C}}\right)^{\frac{\sigma - 1}{\sigma}}\right]^{\frac{\sigma}{\sigma - 1}}
$$

(2)

where the subscript 0 denotes the normalization benchmark; $Y_{0}$, $K_{0}$, $L_{0}$, and $C_{0}$ denote output, capital input, labor input, and land input at the benchmark point, respectively. $\alpha , \beta and \gamma$ are distribution parameters, and $\sigma$ denotes the elasticity of substitution among factors.

Biased technological progress reflects changes in the relative marginal products of